summaryrefslogtreecommitdiff
path: root/poster.svg
blob: 91c03d944d38e876efca1e85658d625c6a91216a (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<svg
   xmlns:dc="http://purl.org/dc/elements/1.1/"
   xmlns:cc="http://creativecommons.org/ns#"
   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
   xmlns:svg="http://www.w3.org/2000/svg"
   xmlns="http://www.w3.org/2000/svg"
   xmlns:xlink="http://www.w3.org/1999/xlink"
   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
   width="420.0mm"
   height="297.0mm"
   viewBox="0 0 420.0 297.0"
   version="1.1"
   id="SVGRoot"
   sodipodi:docname="poster.svg"
   inkscape:version="1.0.2 (e86c870879, 2021-01-15)">
  <defs
     id="defs411">
    <rect
       x="206.15167"
       y="75.125"
       width="1.8831659"
       height="8.9166539"
       id="rect2473" />
    <inkscape:path-effect
       effect="skeletal"
       id="path-effect1841"
       is_visible="true"
       lpeversion="1"
       pattern="M 0,5 C 0,2.24 2.24,0 5,0 7.76,0 10,2.24 10,5 10,7.76 7.76,10 5,10 2.24,10 0,7.76 0,5 Z"
       copytype="single_stretched"
       prop_scale="1"
       scale_y_rel="false"
       spacing="0"
       normal_offset="0"
       tang_offset="0"
       prop_units="false"
       vertical_pattern="false"
       hide_knot="false"
       fuse_tolerance="0" />
    <inkscape:path-effect
       effect="spiro"
       id="path-effect1839"
       is_visible="true"
       lpeversion="1" />
    <inkscape:path-effect
       effect="skeletal"
       id="path-effect1835"
       is_visible="true"
       lpeversion="1"
       pattern="M 0,5 C 0,2.24 2.24,0 5,0 7.76,0 10,2.24 10,5 10,7.76 7.76,10 5,10 2.24,10 0,7.76 0,5 Z"
       copytype="single_stretched"
       prop_scale="1"
       scale_y_rel="false"
       spacing="0"
       normal_offset="0"
       tang_offset="0"
       prop_units="false"
       vertical_pattern="false"
       hide_knot="false"
       fuse_tolerance="0" />
    <inkscape:path-effect
       effect="powerstroke"
       id="path-effect1831"
       is_visible="true"
       lpeversion="1"
       offset_points="3,0.1322915"
       sort_points="true"
       interpolator_type="CubicBezierJohan"
       interpolator_beta="0.2"
       start_linecap_type="zerowidth"
       linejoin_type="extrp_arc"
       miter_limit="4"
       scale_width="1"
       end_linecap_type="zerowidth" />
    <inkscape:perspective
       sodipodi:type="inkscape:persp3d"
       inkscape:vp_x="0 : 148.5 : 1"
       inkscape:vp_y="0 : 1000 : 0"
       inkscape:vp_z="420 : 148.5 : 1"
       inkscape:persp3d-origin="210 : 99 : 1"
       id="perspective1745" />
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.95069371,0.5058792,-0.46975126,0.88279882,79.7404,-65.489821)">
      <meshrow
         id="meshrow4494">
        <meshpatch
           id="meshpatch4496">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.02618463,1.0765903,-0.99970436,0.02431462,508.99172,61.850765)">
      <meshrow
         id="meshrow4494-3">
        <meshpatch
           id="meshpatch4496-5">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.48878921,0.4999324,-0.46422915,0.45388176,454.56345,275.93717)">
      <meshrow
         id="meshrow4494-3-0">
        <meshpatch
           id="meshpatch4496-5-6">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.00331697,0.69916829,-0.64923637,-0.00308009,627.38811,407.66199)">
      <meshrow
         id="meshrow4494-3-0-0">
        <meshpatch
           id="meshpatch4496-5-6-2">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.69514014,-0.07501656,0.06965917,-0.6454959,575.08405,648.87215)">
      <meshrow
         id="meshrow4494-3-0-0-9">
        <meshpatch
           id="meshpatch4496-5-6-2-7">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.15520655,0.68173179,-0.63304511,-0.1441223,635.07995,527.28711)">
      <meshrow
         id="meshrow4494-3-0-0-9-9">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-10"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.27267561,-1.4904579,1.154065,-0.3038557,286.31562,761.45209)">
      <meshrow
         id="meshrow5887">
        <meshpatch
           id="meshpatch5889">
          <stop
             path="c 22.6915,0  35.0874,-8.00407  57.7788,-8.00447"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop5891" />
          <stop
             path="c -0.000316933,11.0324  11.6565,-15.0078  18.0345,45.7241"
             style="stop-color:#800080;stop-opacity:1"
             id="stop5893" />
          <stop
             path="c -22.6915,-0.000203963  -49.1926,2.46609  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop5895" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9638"
             style="stop-color:#800080;stop-opacity:1"
             id="stop5897" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.62548702,-0.31243125,0.29011862,-0.58081713,505.99246,765.43632)">
      <meshrow
         id="meshrow4494-3-0-0-9-65">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.44125292,0.54234967,-0.50361713,-0.40974033,604.49526,694.397)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4-9"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.69871756,-0.02531937,0.02351116,-0.64881783,423.67739,838.30531)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4-2">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3-0">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0-6" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7-8" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8-9" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6-2" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-0"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.40327221,-0.57115569,0.53036595,-0.37447205,335.03393,847.77015)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-4">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-8">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-7" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-1" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-7" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-2" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4-9-6"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.23947203,-0.65688693,0.60997459,-0.22236986,132.06284,778.90456)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4-2-1">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3-0-5">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0-6-9" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7-8-4" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8-9-9" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6-2-0" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4-9-6-5"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.17223799,-0.67762922,0.62923556,0.15993741,42.614139,613.55513)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4-2-1-9">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3-0-5-7">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0-6-9-7" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7-8-4-6" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8-9-9-7" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6-2-0-3" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4-9-6-5-3"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.17223799,-0.67762922,0.62923556,0.15993741,54.084243,560.50118)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4-2-1-9-9">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3-0-5-7-4">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0-6-9-7-8" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7-8-4-6-1" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8-9-9-7-2" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6-2-0-3-9" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4-9-6-5-3-8"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.41154494,-0.56522389,0.52485779,0.38215398,52.852823,461.57274)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4-2-1-9-9-8">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3-0-5-7-4-5">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0-6-9-7-8-0" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7-8-4-6-1-9" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8-9-9-7-2-6" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6-2-0-3-9-3" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4-9-6-5-3-8-1"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.46771354,-0.51970311,0.48258793,0.43431124,83.716284,402.05086)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4-2-1-9-9-8-5">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3-0-5-7-4-5-9">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0-6-9-7-8-0-8" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7-8-4-6-1-9-4" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8-9-9-7-2-6-8" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6-2-0-3-9-3-1" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4-9-6-5-3-8-1-4"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.57844098,-0.39275096,0.36470221,0.53713095,128.08035,334.36877)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4-2-1-9-9-8-5-4">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3-0-5-7-4-5-9-4">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0-6-9-7-8-0-8-4" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7-8-4-6-1-9-4-7" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8-9-9-7-2-6-8-6" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6-2-0-3-9-3-1-3" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-0-9"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.37511751,-0.59002894,0.54789134,-0.34832805,285.31258,765.60348)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-4-6">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-8-2">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-7-1" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-1-7" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-7-8" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-2-5" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-0-9-1"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.41692994,-0.5612635,0.52118023,-0.3871544,262.85683,716.86897)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-4-6-8">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-8-2-5">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-7-1-9" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-1-7-7" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-7-8-5" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-2-5-3" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-0-9-1-3"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.3359475,-0.61317744,0.56938667,-0.31195541,201.22668,666.25449)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-4-6-8-1">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-8-2-5-8">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-7-1-9-9" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-1-7-7-6" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-7-8-5-4" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-2-5-3-3" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-0-9-1-3-6"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.2715922,-0.64427088,0.59825954,-0.25219612,165.72825,612.54565)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-4-6-8-1-0">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-8-2-5-8-4">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-7-1-9-9-8" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-1-7-7-6-8" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-7-8-5-4-8" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-2-5-3-3-9" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-9"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.62548702,-0.31243125,0.29011862,-0.58081713,369.88896,684.49706)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-46">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-9">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-2" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-2" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-4" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-7" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-1-4-2"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.2520003,-0.65218337,0.60560694,-0.2340034,303.16578,685.14578)">
      <meshrow
         id="meshrow4494-3-0-0-9-9-4-8">
        <meshpatch
           id="meshpatch4496-5-6-2-7-4-3-9">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-7-0-3" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-8-7-6" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-4-8-8" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-5-6-0" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-9-8"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.15909434,-0.680835,0.63221237,-0.14773242,176.21714,564.08399)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-46-5">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-9-0">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-2-6" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-2-4" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-4-6" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-7-2" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-9-8-2"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(-0.364109,-0.5968852,0.55425795,-0.33810571,277.25422,565.15433)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-46-5-8">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-9-0-4">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-2-6-7" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-2-4-2" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-4-6-4" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-7-2-0" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-9-8-2-9"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.1509364,-0.68268991,0.6339348,0.14015712,171.59075,465.85739)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-46-5-8-0">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-9-0-4-8">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-2-6-7-1" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-2-4-2-3" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-4-6-4-1" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-7-2-0-1" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-9-0"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.01257415,-0.69906308,0.64913867,0.01167617,153.16943,786.07174)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-46-3">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-9-9">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-2-1" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-2-9" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-4-69" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-7-3" />
        </meshpatch>
      </meshrow>
    </meshgradient>
    <meshgradient
       inkscape:collect="always"
       id="meshgradient964-5-6-2-8-7-9-0-0"
       gradientUnits="userSpaceOnUse"
       x="127.30518"
       y="116.33867"
       gradientTransform="matrix(0.01257415,-0.69906308,0.64913867,0.01167617,163.36559,714.28078)">
      <meshrow
         id="meshrow4494-3-0-0-9-65-46-3-0">
        <meshpatch
           id="meshpatch4496-5-6-2-7-6-9-9-4">
          <stop
             path="c 22.6915,0  26.0036,-6.34291  48.6952,-6.34291"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4498-6-2-3-3-9-2-1-6" />
          <stop
             path="c -0.000612527,11.0324  20.7401,-16.6697  27.1181,44.0624"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4500-2-6-7-6-3-2-9-2" />
          <stop
             path="c -22.6915,-0.000203963  -53.0176,-15.7555  -75.709,-15.7558"
             style="stop-color:#ffffff;stop-opacity:1"
             id="stop4502-9-1-5-1-7-4-69-6" />
          <stop
             path="c -8.077,-21.0321  -0.617787,-5.75267  -0.1043,-21.9637"
             style="stop-color:#800080;stop-opacity:1"
             id="stop4504-1-8-9-2-4-7-3-7" />
        </meshpatch>
      </meshrow>
    </meshgradient>
  </defs>
  <sodipodi:namedview
     id="base"
     pagecolor="#ffffff"
     bordercolor="#666666"
     borderopacity="1.0"
     inkscape:pageopacity="0.0"
     inkscape:pageshadow="2"
     inkscape:zoom="0.70710678"
     inkscape:cx="849.98497"
     inkscape:cy="666.56885"
     inkscape:document-units="mm"
     inkscape:current-layer="layer1"
     inkscape:document-rotation="0"
     showgrid="false"
     inkscape:window-width="1920"
     inkscape:window-height="1125"
     inkscape:window-x="2560"
     inkscape:window-y="0"
     inkscape:window-maximized="1" />
  <metadata
     id="metadata414">
    <rdf:RDF>
      <cc:Work
         rdf:about="">
        <dc:format>image/svg+xml</dc:format>
        <dc:type
           rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
        <dc:title />
      </cc:Work>
    </rdf:RDF>
  </metadata>
  <g
     inkscape:label="Layer 1"
     inkscape:groupmode="layer"
     id="layer1">
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-9-9-3"
       transform="translate(81.992951,97.189773)"><tspan
         x="194.47379"
         y="112.34783"><tspan>This pcb holds the color </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>sensors and motor </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>drivers. it also connecs </tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>to the motors. </tspan></tspan></text>
    <path
       style="fill:#7fad18;fill-opacity:1;stroke-width:0.426807"
       d="M 4.7358206e-4,160.5 V 24 H 210.00385 420.0072 V 160.5 297 H 210.00385 4.7358206e-4 Z"
       id="path1263" />
    <path
       style="fill:#7fad18;fill-opacity:1;stroke-width:0.302901"
       d="M 130,99.569782 V 0 L 275.00001,0.36011607 420,0.72023208 V 100.29001 199.85981 L 275.00001,199.49969 130,199.13957 Z"
       id="path1263-3" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:4.23333px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583;"
       x="345.18433"
       y="289.62415"
       id="text1305"><tspan
         sodipodi:role="line"
         id="tspan1303"
         x="345.18433"
         y="289.62415"
         style="font-size:4.23333px;stroke-width:0.264583;fill:#3b4084;fill-opacity:1;">https://github.com/ableTI/robotica/</tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:4.23333px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583;"
       x="289.19458"
       y="295.16605"
       id="text1309"><tspan
         sodipodi:role="line"
         id="tspan1307"
         x="289.19458"
         y="295.16605"
         style="font-size:4.23333px;stroke-width:0.264583;fill:#3b4084;fill-opacity:1;">https://mendelcollege.nl/ontdek-je-talenten/robotica-en-beta/</tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:6.35px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="288.44714"
       y="283.39572"
       id="text1313"><tspan
         sodipodi:role="line"
         id="tspan1311"
         x="288.44714"
         y="283.39572"
         style="font-size:6.35px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">Sites:</tspan></text>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0218117"
       id="rect1315"
       width="1.4912366"
       height="20"
       x="285"
       y="276.88248" />
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0565178"
       id="rect1315-6"
       width="133.5"
       height="1.5"
       x="286.49124"
       y="276.88248" />
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0244578"
       id="rect1315-7"
       width="1.5"
       height="25"
       x="284.99124"
       y="251.88248" />
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0565178"
       id="rect1315-6-5"
       width="133.5"
       height="1.5"
       x="286.49124"
       y="251.88248" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:4.23333px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="286.69916"
       y="274.74313"
       id="text1490"><tspan
         sodipodi:role="line"
         id="tspan1488"
         x="286.69916"
         y="274.74313"
         style="font-size:4.23333px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">Software engineer: Abel Tim </tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:4.23333px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="362.37711"
       y="274.95642"
       id="text1490-3"><tspan
         sodipodi:role="line"
         id="tspan1488-5"
         x="362.37711"
         y="274.95642"
         style="font-size:4.23333px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">10810@mendelcollege.nl</tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:4.23333px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="362.25656"
       y="263.29584"
       id="text1490-6"><tspan
         sodipodi:role="line"
         id="tspan1488-2"
         x="362.25656"
         y="263.29584"
         style="font-size:4.23333px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">10498@mendelcollege.nl</tspan></text>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0244578"
       id="rect1315-7-9"
       width="1.5"
       height="25"
       x="360.7366"
       y="253.095" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:5.64444px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="286.37979"
       y="257.69968"
       id="text1550"><tspan
         sodipodi:role="line"
         id="tspan1548"
         x="286.37979"
         y="257.69968"
         style="font-size:5.64444px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">Team:</tspan></text>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0399851"
       id="rect1315-6-5-1"
       width="133.64091"
       height="0.75"
       x="286.36438"
       y="264" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:4.23333px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="286.82019"
       y="268.68573"
       id="text1490-2"><tspan
         sodipodi:role="line"
         id="tspan1488-7"
         x="286.82019"
         y="268.68573"
         style="font-size:4.23333px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">Hardware engineer: Jorn Schouten </tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:4.23333px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="286.57098"
       y="262.96277"
       id="text1490-2-0"><tspan
         sodipodi:role="line"
         id="tspan1488-7-9"
         x="286.57098"
         y="262.96277"
         style="font-size:4.23333px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">General : Nathanaël den Hertog </tspan></text>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0399715"
       id="rect1315-6-5-1-3"
       width="133.54906"
       height="0.75"
       x="286.46539"
       y="270" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:4.23333px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="362.41626"
       y="268.65585"
       id="text1490-3-6"><tspan
         sodipodi:role="line"
         id="tspan1488-5-0"
         x="362.41626"
         y="268.65585"
         style="font-size:4.23333px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">10832@mendelcollege.nl</tspan></text>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0399851"
       id="rect1315-6-5-1-6"
       width="133.64091"
       height="0.75"
       x="285.46588"
       y="258.81815" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:5.64444px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="362.67593"
       y="257.87668"
       id="text1550-2"><tspan
         sodipodi:role="line"
         id="tspan1548-6"
         x="362.67593"
         y="257.87668"
         style="font-size:5.64444px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">Contact info:</tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583;"
       x="51.957634"
       y="170.70865"
       id="text1743"><tspan
         sodipodi:role="line"
         id="tspan1741"
         x="51.957634"
         y="170.70865"
         style="stroke-width:0.264583;fill:#3b4084;fill-opacity:1;">Software</tspan></text>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.923602;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1"
       id="rect1815"
       width="116.222"
       height="128.51486"
       x="4.9620237"
       y="42.352779" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:7.42307px;line-height:1.25;font-family:sans-serif;fill:#fffff5;fill-opacity:1;stroke:none;stroke-width:0.197717"
       x="43.534771"
       y="53.84491"
       id="text1845"><tspan
         sodipodi:role="line"
         id="tspan1843"
         x="43.534771"
         y="53.84491"
         style="font-size:7.42307px;fill:#fffff5;fill-opacity:1;stroke-width:0.197717">Software:</tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:2.82222px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:74.5713;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="25.996389"
       y="107.72469"
       id="text1849"
       transform="matrix(1.5029828,0,0,1.5029828,-31.24612,-96.621116)"><tspan
         x="25.996389"
         y="107.72469"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">There are two codes for this robot becouse there are </tspan></tspan><tspan
         x="25.996389"
         y="111.25247"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">two microcontrollers on the robot. 
</tspan></tspan><tspan
         x="25.996389"
         y="114.78025"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">-The camera's code is written in python. The code is               </tspan></tspan><tspan
         x="25.996389"
         y="118.30803"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">written so that you can select the goal collor using </tspan></tspan><tspan
         x="25.996389"
         y="121.83581"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">input from a dip switch. After you select the goal </tspan></tspan><tspan
         x="25.996389"
         y="125.36359"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">collor it will detect it and output where the goal is </tspan></tspan><tspan
         x="25.996389"
         y="128.89137"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">reletive to the robot in degrees. This is send to the </tspan></tspan><tspan
         x="25.996389"
         y="132.41914"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">teensy using a uart connection.
</tspan></tspan><tspan
         x="25.996389"
         y="135.94691"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">-The teensy's code is written in loops of a few </tspan></tspan><tspan
         x="25.996389"
         y="139.47468"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">microseconds. At the start of each loop it will </tspan></tspan><tspan
         x="25.996389"
         y="143.00245"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">garther all incoming data first. This is the sensor </tspan></tspan><tspan
         x="25.996389"
         y="146.53022"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">data from IR seekers, compas data and color sensor </tspan></tspan><tspan
         x="25.996389"
         y="150.05799"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">data. It also collects the data from the camera </tspan></tspan><tspan
         x="25.996389"
         y="153.58576"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">about where the goal is located. After it has </tspan></tspan><tspan
         x="25.996389"
         y="157.11353"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">collected the data it will first check the color sensors </tspan></tspan><tspan
         x="25.996389"
         y="160.64131"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">if it is on a line and needs to move back. After that it </tspan></tspan><tspan
         x="25.996389"
         y="164.16908"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">will check if it has the ball using one of the IR </tspan></tspan><tspan
         x="25.996389"
         y="167.69685"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">seekers. If it has then it will go towards the goal </tspan></tspan><tspan
         x="25.996389"
         y="171.22462"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">using the camera data. If it does not have the ball it </tspan></tspan><tspan
         x="25.996389"
         y="174.75239"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">will go towards the ball using the other IR seekers.  </tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="152.955"
       y="100.63959"
       id="text1853"><tspan
         sodipodi:role="line"
         id="tspan1851"
         x="152.955"
         y="100.63959"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">IR seekers</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857"
       transform="translate(-47.533085,-3.120458)"><tspan
         x="194.47379"
         y="112.34783"><tspan
           style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">We use the TSSP 4038 IR </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan
           style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">recievers to locate the </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan
           style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">ball.</tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="159.54375"
       y="148.32965"
       id="text1853-1"><tspan
         sodipodi:role="line"
         id="tspan1851-8"
         x="159.54375"
         y="148.32965"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Camera</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-9"
       transform="translate(-47.656793,47.411533)"><tspan
         x="194.47379"
         y="112.34783"><tspan>We use openmv H7 to </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>find the goal.</tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="242.85855"
       y="99.498093"
       id="text1853-1-2"><tspan
         sodipodi:role="line"
         id="tspan1851-8-0"
         x="242.85855"
         y="99.498093"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Motors</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-2"
       transform="translate(34.371519,-3.3009449)"><tspan
         x="194.47379"
         y="112.34783"><tspan>We are using the pololy </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>metal gearmotors to </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>move the robot.</tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="239.00046"
       y="151.38217"
       id="text1853-1-2-3"><tspan
         sodipodi:role="line"
         id="tspan1851-8-0-7"
         x="239.00046"
         y="151.38217"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Upper PCB</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-2-9"
       transform="translate(37.069932,46.470682)"><tspan
         x="194.47379"
         y="112.34783"><tspan>We designd the upper </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>pcb to hold most of the </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>microchips and the IR </tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>seeksers. </tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="153.38248"
       y="202.24332"
       id="text1853-1-2-3-2"><tspan
         sodipodi:role="line"
         id="tspan1851-8-0-7-2"
         x="153.38248"
         y="202.24332"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Lower PCB</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;stroke-width:0.264583"
       x="189.50746"
       y="215.60909"
       id="text2175"><tspan
         sodipodi:role="line"
         id="tspan2173"
         x="189.50746"
         y="215.60909"
         style="stroke-width:0.264583" /></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-9-9"
       transform="translate(-50.229119,96.881312)"><tspan
         x="194.47379"
         y="112.34783"><tspan>This pcb holds the color </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>sensors and motor </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>drivers. it also connecs </tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>to the motors. </tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="234.62903"
       y="197.36482"
       id="text1853-1-2-3-2-6"><tspan
         sodipodi:role="line"
         id="tspan1851-8-0-7-2-1"
         x="234.62903"
         y="197.36482"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Color sensors</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-2-9-9"
       transform="translate(28.376655,94.980955)"><tspan
         x="194.47379"
         y="112.34783"><tspan>We are using the </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>TCS34725 color sensors </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>to detect the lines on </tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>the ground.</tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="318.35428"
       y="17.431398"
       id="text1853-1-2-3-2-6-3"><tspan
         sodipodi:role="line"
         id="tspan1851-8-0-7-2-1-1"
         x="318.35428"
         y="17.431398"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Compas</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-7"
       transform="translate(109.69945,-83.951729)"><tspan
         x="194.47379"
         y="112.34783"><tspan>We use the MinIMU-9 v6 </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>Gyro as a compas. This </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>is a backup to locate the </tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>goal in case the camere </tspan></tspan><tspan
         x="194.47379"
         y="140.57004"><tspan>does not work.</tspan></tspan></text>
    <text
       xml:space="preserve"
       id="text2471"
       style="line-height:1.25;font-family:sans-serif;font-size:5.64444444px;white-space:pre;shape-inside:url(#rect2473);" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="217.65178"
       y="56.762501"
       id="text1853-1-2-3-2-6-3-0"><tspan
         sodipodi:role="line"
         id="tspan1851-8-0-7-2-1-1-3"
         x="217.65178"
         y="56.762501"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Teensy</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-2-6"
       transform="translate(8.5901481,-45.504323)"><tspan
         x="194.47379"
         y="112.34783"><tspan>We are using the teensy </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>4.1 as main </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>microconrtoller that </tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>directs all components.</tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="109.05659"
       y="245.6022"
       id="text1853-1-2-3-2-1"><tspan
         sodipodi:role="line"
         id="tspan1851-8-0-7-2-0"
         x="109.05659"
         y="245.6022"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Port multiplexer</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-9-9-6"
       transform="translate(-77.146342,145.11477)"><tspan
         x="194.47379"
         y="112.34783"><tspan>We are using the </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>MCP23017 as a IO </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>expander</tspan><tspan> becouse the </tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>teensy does not have </tspan></tspan><tspan
         x="194.47379"
         y="140.57004"><tspan>enough ports. </tspan></tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:10.5833px;line-height:1.25;font-family:sans-serif;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="307.57828"
       y="73.705849"
       id="text1853-1-2-3-2-1-3"><tspan
         sodipodi:role="line"
         id="tspan1851-8-0-7-2-0-2"
         x="307.57828"
         y="73.705849"
         style="fill:#ffffff;fill-opacity:1;stroke-width:0.264583">I2C multiplexer</tspan></text>
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:70.9774;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-9-9-6-0"
       transform="translate(117.19611,-26.669306)"><tspan
         x="194.47379"
         y="112.34783"><tspan>We are using the </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>pca9548a as i2c </tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>multiplexer becouse the </tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>color sensors do not </tspan></tspan><tspan
         x="194.47379"
         y="140.57004"><tspan>have customisable i2c </tspan></tspan><tspan
         x="194.47379"
         y="147.6256"><tspan>addreses and thus need </tspan></tspan><tspan
         x="194.47379"
         y="154.68116"><tspan>to have a device that </tspan></tspan><tspan
         x="194.47379"
         y="161.73671"><tspan>lets us read them all. </tspan></tspan></text>
    <path
       style="fill:#ffff00;fill-opacity:0.99198669;stroke:none;stroke-width:1.00157;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1"
       d="m 238.01223,90.845841 c 5.88175,-0.18543 15.78173,-0.201099 21.99998,-0.03481 6.21824,0.166275 1.40591,0.317991 -10.69408,0.337141 -12.09998,0.01915 -17.18764,-0.116896 -11.3059,-0.302326 z"
       id="path2779"
       transform="scale(0.26458333)" />
    <rect
       style="fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.422124;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1"
       id="rect1815-6"
       width="129.99953"
       height="24"
       x="0.00047358207"
       y="0" />
    <image
       width="119.46521"
       height="17.601458"
       preserveAspectRatio="none"
       style="fill:#7fad18;fill-opacity:1;image-rendering:optimizeSpeed"
       xlink:href="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAT8AAAAvCAYAAACVB82EAAAABHNCSVQICAgIfAhkiAAAGmdJREFU eJztnXtwXNV9x7+/szIStcCbaey9bgNabMm0U7DWcZpiS7bXCcaPQi0hmabYRWtKYSZ2LJk0OHWw 9loGx5ABydRkamjwikJKkZYVQ8YPDPbKsuxA6nglQ6fGMpGcSb2Sm44UJCKB9vz6x91daXfvPrXy g97PjGa0e889j7v3/u75nd/jELJA6dK6YmaxAMwLQPgLgG6NLsNALzG/J4lOCqb3hgc/f+/UKfXT bLRvYGBgkC6U6Yl3LK0rMbFYB3AlQF/OqBLGWyD8e7v38Vcz7YeBgYFBJqQt/EqWPLmMwY8TYTEA 2IoLIo53ne/F4OBw6h0gYAqZfssCezA6ssvrVVM/2cDAwCBDUhZ+8+3ql3NhqifQOgCYqZjxXP06 zFSmxZS96B/AuS4/Tvsu4Hj7R7jo79etcwoJXE854c8ByCEGrz985B+b0h6JgYGBQRqkJPzuWPLE PQL8L0Q0I/Tdg45FeLBqMQCgrf0jvN78frj8PNvNmKmYUVRoQeFsC8519WL/wc4IQXgdCeSNE3zj +QzycP/Al/7y1KlHPs98aAYGBgbxSSj8/syu5pthagDo76KPLS69FTt3VAIASpc+GbeOmYoZ82wF WFQ6B4tK5mD/wU688/YH+K+O3yTs2Cjk/47myFVH3976XkojMTAwMEgDU6KDhdY7HyXQ9/SO9Vz4 Lb46rwCKYkbX+T70XPitbh2Dg8M419WLd4/8J153/wLTv3wDHvn7pbijpAgA8PH5Pt3zBOh6k6Rv zfnTu/adO3d4MK1RGRgYGCQh7sxvof2JxwnYEfqcn5+HwtkWfNVWEPyci3m2AhQVWjA4OIyfuNpw rqsXQ0OasNNDECGfpgAAFpQUoaxiPhjAT18+gU7fBd1zGPyJyKM5+/c/5s94lAYGBgZR6Aq/hfYd KoGcgCb0HqxahPsqv55WxSGjR1dXH37p60HX+V4EPh3FFIiIcnNtN+Phby/F+a4+/PTlE+j1D8TU FSDu+03g97M/9KrGDNDAIE1q3YpKgHN7hT/uZMfpVpiB1roKv/0ydu2KEmNxWLj4icVg3hYSi/dV /nmE4DtwqBOnfRdw0d+P076emArz8/NQVGgBEDR8zDTjoXmLYCsuwMddfejsuIDOjl/j5+1dAIBO 3wVsfLgRZRVfw1PPfgt7nz+Ck+3nIuo0Mc34I8p950PgjqyN3MDA4P81EcKvtFS9WRI3EVF4ehZS cwFg67ZmHDt+NmGFg4PDYaE4XjheL3Iw32bFrNkzcNfy2/HdLavQ6buAk+1dONl+Di3u/8DhQ2dQ W1eO24u/ghd+fDSyo2T6i29+c9fWd9/9/s50B6l6FKuUqNI9yNxfV9m7O/W6ptukNK3WOyaAHrXC 74p7bpNilwJLUm0rXK9Eq7rG79Wt0604JFAQcyCNcdW6FWdEe9kYB7GPQf0mQo9a7u9OpR+6bcUb XxKEQGO8dqPHm6y8wReTCOEnc0yPEsbcWQBg/8EzYUfmT9JwXh6PiQhTINDpu4BO3wW0uP8DgLbu N9d2E9ZVleDE8Y/w5hu/xJZHX0NZxdewZ28Vtnz3NQwNjoTrmcK0rXDlc890Hdg0Eq8tXUZhJQFV 9xgRVI/yZqo3vgyYaoj0BakEWgG44p4rYCcg5sFL2qbAdgDeOG06CDqCiAhqk9IRT2hGFEXktcnK OJhAAJgBp1vpBuAlge3pCpi440vGKFoB6LYVPd5k5Q2+mIRneAvsO/+EQBujC+w/2IGXGo8BADZt WIZ5trRfwjBFrfOFONl+DnufPwLH/Xvxq/OX8OhjK7H5sZU42X4Or758ArV15ZianxsuT6C8gpFP X0m7A8mQXJZqUSLWnfVdjTBx/ZXuQxArAAdL/MrZbPGoHrP5SnfIwECM/SO/jziuLy+52rDmb56H r6MH1RuX4eBb38U/1a9LyQhSVGjBfFsBbi++CavvnY+1D5Tg9uKbwn+zCrWJ5uFDH2DLo6/h3bc/ xKOPrcQdJYVocZ+KEYBTSJQvWPDs9RMd+Hgkw55KOdUz3QbQtfPgEtlUt6XmSncjAqIylnmntWtp YHDlyAGABfYfFgKjaxP5PF/092P3nsOYZyvAvOICnO7o0XVpWVQyB4tKb0VRoQVFhRb4OnowPPg5 zp8PlWXMtd0EAJg924Kp+bmwKNMwdWouzp/vwxnfr/FKYzvy8/NQVjEfU6fm4tHHVmJHbQsAgBim /Os//wmA+7N1EYhoteoxm9Xyfv04vCAyIByUcSoIfZh5AES+RGXEBNQxBpyqx+xKNraJojeOBOqq laU4qnrMt2TSLwZ6kOSaUE5gUsdrcO2TAwACgbuBOLFmUfj9A3iwfhHOdc3BdzaPaaAzFTO2brkb 82wFaGv/CPsa23Ds+FkQgBvEdSl1Zq7tZsyaPQPLlt+GBaVF6PRdwAzLjVhQWIQFJUVhKzCB70p3 oNEw8wARjQUmy7wyJFjnAjQhmbCOTCDyTa57AZllIFcFMLkzwDjjUJtnlEkilUDF0f3iQJ4HwNIM WnPVVfjVTLp5OVE9ZrPk3CVgipjlMsG7415/a8p1BPJWg2AdqwDdwjT85mS/0JJR+4ZldcTYiH11 9/a+mW49ekZEJv017lQMaDH1MfcLE8Ws6wcFHjtSzXFw0d+PnU/9DFu33I19Lz6EtuNnceBQJ3bu WIOZyjQ0ud/H7j2Hx05IY6oUMogAAJ4+gIe/vRQWiyZbHv720rDwM0H84TdXPTPn3f3f/SjlymPx AghfoKDq64pXWPVMt7EcuwEZ3AGifmSyGD+JMLgjWtAQUbXqURquhDVTrexrAdBS26y4YgxFBLva PKMsWOYLg+oxm6XMq2bJNaSzTEKaEQgkcEu830T1mM0s8+pZIlbbIIBlHmrdiirE8O7LLQRrmy3V RFDBUWNjgtNt6Sfm9an8pqpHsXIA+1jCjvGaA7NNgFS9cyRjO/QMVgg+owFTPUvYIy4ZEViiwelW XCSGN4euV87cu340lUdG5qajzu0/2IHTvh7MsxVAUW6EZISzuxw7nrk8mlU4A6vvnY/ZhTMwNDiC Xv8A+np/h6GhkfD64MddWjgcDQc2AtiUaVuC4OVxwi+ZISNa5RWAi4GrxaAwDmphoD9a5eQA9iGz WVZWEKbhGinz7BTltsKaM/0XRvipHrOZA7lHiWBjpkZhCjSo5Zd8448jcJ1dguzby3u79etQrBxg Dwg2ZujUMd0mA0IlIpUDuWWqx7z0cglAp1vZB8DBQKsQgZqIfmkuUA0g4VHdyvqE7lIexcqSTzPR gGBZvj1KWKpuxSGZG4hoGonhLyUbnzY5EUcZTAK0HmK4JXSO6lGsMsA1RFTNgVxb6Hrl3PDZyO2g 9PP6XfT34+LBsf4cONSJlcvnYtOGZXjJdQxt7akLwbm2m3H/AwvR5x/AO29/iPqnD8SUmVWoqcN7 u44AAAjym+n2OQJJPqbxaiuZE81ColVeCGqBvAqFH7NZmGQNS9PpiO+v8CxLLe/vV92KysC+yH6R LZX11msFDuQeZeAWAazfXhn78AfH2YIEAp8D2AciG8Wt45IPQJnqVhxMtE8G8hoAOLI2iDiobksN Aw5mNNZV+mPaU9f4varHbJcy1wvQPtWjeOPNbDmAfQyQMMGulvfFlFEr/C7VM93H0nQ62fi0WbI4 ykCPMI3Yo++lYB9qVLelm4nqpcyrAaAKALNTH358ntz1FnY+9TMMDQ3jh0+swfGjP8C+Fx/C1i13 xz3HokzDtroyrH1gIV748RE8+/SBuDG+H3f1RSRJFaCbJt5rirgBJciuVypombSGPjO446p1iCWy qeWXfMxojD7EJK6ssBbD+g984Dr75e3I5FDrVlQQ2QRBTTTrSYTqVhwg2JnRmKwOtcLvYkYjEaom 23quehQrA04GWvUEX7hceX+/ENIBADIQTz1VrCBtmSnRc6Tdx/xmcq0srwEgsxDSkeglqlb0NjDQ SpoR0JzD4K9kMPHTZf/BDpzr8kOxTMMngyPhJAc3Rhk8LMo03P/AQiwsLcILzx/B4UMfxBzXi/Ed GvwsfEyAbphofwVkC0OE16GCs7sYw4CeyjvRtsNtAkucboXjHc803lKYhmtkILcsyiBjrXUr6pUy Fqjl/f21bqUnWvWVJGxIQ/UlwOnUidIIcaViVAmoYqBne0VvQ6Z1cNB5XJjiOOVHIUxQWaJKBkw1 mMTZn5RwEMgsZPJ+qeWXfLVupVUTyuaaGIE0CisEIMDepA1r64Cr1SbFruewr6nPqGJG4/ZxKng8 gstVSyBzHQLAl5J2IA3OdfXiq/MKsKdhHfa9+BD2vfgQ1j5QgrUPlODhby/Fnr1V2PNCFT4dGoHj /r0Rgs+iTMPmx1Zizwv6kWgfn++DJSJzNE9IauuogFa9N6iuynuVo5b394Mo5iEkcPUVdjLuvoJt Txpqk2KHph24Mq4jqGEwc8oRR2q5vzuV2dFECQn2VCKGgmjlRvNiZ6QktfuPxcSXOoIBCgIytWcy qH1Ihl0wIzU/lDTYvecwVtzzDLZua0Zb+1kwSYAYQ0MjePXlE1jzV89h7/NHIkLX5tpuxp69VVi2 /LZw+Fs0nb4LuL34K+HPdvv2hPkIEyGF5tjMzBGmeRkQjvGfrymVN4q6Cr8a9IkbB5mDaygGWSR0 Pwmp76KRWiUmTVAk8fuMgcgHkHmyVN/gy9KKNF5coesQui4RhISeGOe+k4wc/bbDAQqmz7ypVBOe hRKZcwg0NeUOpMHg4DCOHT+LY8fPJkxZDwCr752PRzZ8A+8c+gCzCmegxX0qpTZGRm6cAmB0Iv0U RC0RVt8oK+lkqryXAyHhYIGILBFEqFKbFFcab3GDZDCsIG3RP9MqJGAlpO/ULiS8LODEqGlyZvSj eTYIgBislxRCD0YCwZYz7ONA7gBAVUjmWwtUMbgjnmUcpLnbSJlXXetWUunaWDcYPJStNb94jCLu khaWLb8Nj2z4BuqfPoDbi2/Cm+5TETPCROTm/m7ie3yI4RbIvDELJJFN9SjW0OxuslVeBncISXEd kCcaqaCu8Xtr3UprjOsLwYmgahJcBL4s/orR632ZwIxGwfEfmisS3UFpzGKSITNcGhBsQ5wEGFmB YCekFgqaiODabwMAZ22zpTpe9iGn21IPwCokJXXR0k1WkYQcAUz6VpEV9/45/s6xGPn5uXj15Xa8 2ngCgCb4Ht7wDWx8pBFDgyNYW1WCZ3XcXOLh9aoTmvUBoR/CEukYrK0jNOg5Nsd9A2UM9U/2DEwI LalAZLOwq27FkalVMhM0l4RYISFYpqnmoVutNGatMTB3T2r1wPZsGcvqKvxqbbNiJaIGp1upYcAV iuoghp00442VgPUpPB/d2yv8t6TbB8Gg/0m752mwdcs9qN64DPnB5ARrHyiBRZmmZXDe8A1sefQ1 fNzVh7KK+TFJTKOx6GyTmQ2iVVkJKgNi1//Ak/hWnUSCi+Ixb1cG119W44cWQhhLius1VzXMPiDo xpEhIXVXd50s8Ylam9kwIFxG6ir9DgJvhibkVMHwCoYXzJuZ0UoCt6T4crZm0n4OEfQ33MgCW7fc g1Ur5sLfO4Dn9hzGL06cx466CpRVzMedd90WFnwAcMfCQtT/6GDC+mbNnoE+/+8AAAHwUNY6GuWw TMASbZYSqfIKk3Rlrc3LjDCNqDKQ64h0fSFz0OHzssA6OQC12fS17+AsCN0MAAFpQ6YWbYluCADM ab2QJLONiICc4fRm0CmirvF7ndp6mj2r9TYpdgY7CUgYDZIQRjcIS8YvVaWKAHA+o0aTcF/l17Fq xVwcONQJx0P/gmPHz2KEAzh86AxW3zsfL/z4SFjwWZRpsCjTcD7OxkchFpQUorPj1wAAJo7NoZ8h arm/O9oqGkwGYA19ZqBHTcGP6GpFLe/vFxS7LkKAMxvrcMkIhkVZo78XiHXHuSYR0gsAEiLl3JDR qGv8Xi1ZRpys43EgotWaF8LkvUS0mHFtUpCN+lSP2cyCPQzaPZGlFxFKgCDTF8zik+tyzzAnsEhk QFGhBZs2LMOBQ514ctdb4cgMyYybZn8Zvf6BCP++WbO1nH6JDB1T83Mxq3BG2PlZAomnienCHGHI IKLqRMevRYIe7novDeuktelRrM5miwc6DricJF3+tYRafsnH4A7NsTdz1VeLOiKz6lYcKbWrlbNO 9kskVH/WNIXRPBs0p2nvhOoRwy3MPKCnVSQjp/Pt7w2V2He8C9Cd6Z5cVGjB1i33oKjQgv0HO7Hz qbeQn5+HnTvWwNfRgyd3vRVzzooVc8OZWkLMKpyetK11VSURa4Kcm/Ncuv1NhDBJF0tTdaLj2Wwv BAEFydwHEu3hkS56ri/ZIHocBDaDYWcJm15mH2YeECaZ0SyJGEuSXrM09+Rggapat5LU4l1X4d8e t01JNSxwdCJJJIQJqgxwGYicqsfckjBcSzMgORnooXihg1lCrfC7at2WGgKqVc/0lglrQTmBfkjT hC3UEZbjNKOXgs535AaQlvDLz8/DP9Wvw+DQCNraP8KqFXPR5H4fi0rnYGhoGN9/vDnmnMWlt6Lt +FnMsxXg9xzA9RTpoxwvrC2U7WX92hcAaFtZHtn/D1lTe4FgSE6zRTc/HwM9qYTOZIg1mZk+0R4e 6RLP9SULRI2D4mZJY+YBwVSW8QOUistF+ntyOFJ0+Ior/NQ1fm9ts2U3EVU7my0eMtHmRCmr9ASb Wu7vVjXr5z4O5B5VPdPX610ntUmxc4DrQbAKluWXIzGEENIhA8ILKY6qTUp5ohdyKHtNvEQa2vOm NIKo3ulWqsE6vxWxjxndwjTSmGh8dRV+tdZtKSOQ0+m2TCMxsj1R+do3LKsFUYeWzDQg3NIUqAco L8HYI9i65W6c7rgQVmsfdCzCg47FmFd8M76z+ZWIJAQhFpXOwf6DnbAVF+BzDkBC4g8oB0ODnwEA 7lz+Z2E3mBCzCmfgqWe+hTffODWm8jJ+mmo/04NaAJ31li+AyjseXdeXywSDO4SJyq6VKJl0qavs ralttoCIqlmy3elWWhjoDi7MWwGAmMtYkg1xXg9qhd+luhUw0T6WptPOZksLj4v6IOYy1tJdDQgg pdx52UAtv+RTPdPtUppaIHDU2WzxMVGLJrjYHHI4Do2PCa2IE7OtJWnl0wBWA2TV95MkOxHAMld1 ui2u7RW9m+P1TYgRuwzkNRBRDctcR/i6E/tCCVeJ2QaCHUxmBLA0BwDa2rZeKrHvaATwSCoXYXHp rfijmWZsrBkTcgcOnsGD/7YYLzUe001vDwCFsy047evBDTdoMjbAjCGM4pcd2iRu7QMlABPeefsD zLDciDuX34Zly2/Dx+f78EpjOwCAgcCo/P33UulnukQnOgh/fw1befVQy/3dtW5leyY7yWUKA60C cG2v6HVdrjavFHWVvTWqR2mQAVIBLieiaePFHBM6hObiERctpZPilQGuAVEZAWVj51MPmHcL04ia yoxPAN3BHfniwkBryF0nYb/KL/lUj9kGmeuQBAcBqja2sQEy8KYAN5IYcenW0aTYWbIHRB3Ecj1M I95449C2LiUHATW1bmUgnlobPN+hNikuSagJX/dx4f9M6ICWH9GlVlzyhWPOWOBHJFMTft/ZsAxb tzVFzO5C21q+5GqLe15o6cfvHxunZMYHH/0Gvo4LsBXfjLVVC7G2amH4+NDQCJ59+kDYGDLKgdfS dm7OCfSzNEX8+LohRKbPvCzzIm8S5v54Ki9H31BJbp5UbsJ458U9yOzj8WtqKdzAACDEcIOUefZ4 dSbrT9JxaDOdbsHSB9Nn3ozdWaLHlyKJojxifrdJIDizdYQ/Z5CzMJSHDhPcgiBoVHIlKpNOFpzg OBqCf2mNLZjE1MOM1rpKf9I131Dfa91KN4GrkWSJKKiKe8fai9+3iLtqoX3HLgJtSVT5qhXFUJQb Y4TcfZVfx6YNy1C69Mm45+5pWIefuNpwX+XX8Y+PN0Ucy8/Pw64nKsN7BAPAmY5fY+84lxgJjBz6 Q+tUNN0XSNRHAwODq5PaZksDEVUnSuGve55bUQlwksTSbBn/IrINfIrArj9AjoMAy/jvN21YhqJC C76z+RWsWnF7HGPGHBw41Jm0waJCC851+WO+HxwcxsaaV1BUaEF+fh78/gFc9IcSMADEBOTwXxuC z8DgGoa09beM13zjZHfJhIjdxH1etZ+J1jKzDH23akUxus73oa39I/zwiTXYf/CMrjHjk8ER5Ocn tpfYiguwcvlcnI6TrRnQ8gGe9vWEBR8AMAMByN1t7zye9s5QBgYG1z7EWMLMA9k0lInoL04e/cG7 IDwW+lxUOAP7D3bg9eb3g/58HTGVzLMV4FyXX1cojmdwaAQ33JCH0750vVT44AnvtssWhmVgYDBJ hGKg3ZaUn2e1eUZZKO19NrsSI/wA4IR32zMMUhcvuhWvN/8CgLYv73hDxXhmKlrES7KZ30uuY7qO z4lgxrEpCJSndZKBgcFViTBRQygio/YNS8Ls06rHbK51K04m4dFcpEbUbPYloRntRZf3X12u4+sA zaCxuHQONta8ElPuQccirFxejCb3+3i9+f2sdY7BP+u7flpl14FNqSX4MzAwuOoJbireEowp7w5a 9Mf8GMFmMNm0jZx4AKAWYRqO3QtkgsRPrwzg7x32v11orztEEP86z1aAX8ZRVwcHRzBTmYa2CezZ Gw0DrhPebeuzVqGBgcFVQTBixao2KXZJcIDICubNoegqBjoA7hag9WQaSRjiNxFScqAqtT9R+sbr m7wvvtRq0lvzm6mYMc9WoLsemBGMH7S3Pr4zO5UZGBgYxJKy9+jzzx8sPHmq57lffXxp5eR1h88G iKt+frT2vclrw8DAwCAN4RfiDvsOuwn0QwB3ZK0XjIsM7Br4n9F//vBD9bOs1WtgYGAQh4x3Lipd WlfMLNYxcDcBf5Lu+cz8vyC8LZl++vPWx9MzARsYGBhMkKxs27Zo0c7pgRy5EMwLAPoawFYC/jic JYb5v0F0gZnPADjJZDp50rv1v7LRtoGBgUEm/B/FQcIN6lut8AAAAABJRU5ErkJggg== "
       id="image434-1"
       x="0.00047358207"
       y="0" />
    <text
       xml:space="preserve"
       style="font-size:7.76111px;line-height:1.25;font-family:sans-serif;fill:#7fad18;fill-opacity:1;stroke-width:0.380094"
       x="48.34219"
       y="23.385881"
       id="text2763-5"><tspan
         sodipodi:role="line"
         id="tspan2761-5"
         x="48.34219"
         y="23.385881"
         style="font-size:7.76111px;fill:#7fad18;fill-opacity:1;stroke-width:0.380094">Netherlands - dutch</tspan></text>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.032772"
       id="rect1315-3"
       width="1.4912366"
       height="45.149899"
       x="216.20222"
       y="251.88248" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:6.35px;line-height:1.25;font-family:sans-serif;fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="218.62785"
       y="259.1395"
       id="text1313-6"><tspan
         sodipodi:role="line"
         id="tspan1311-7"
         x="218.62785"
         y="259.1395"
         style="font-size:6.35px;fill:#3b4084;fill-opacity:1;stroke-width:0.264583">Sponsors::</tspan></text>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke-width:0.0401278"
       id="rect1315-6-53"
       width="67.297775"
       height="1.5"
       x="217.69347"
       y="251.88248" />
    <g
       id="g471"
       transform="matrix(0.07489394,0,0,0.07489394,219.16446,263.48335)">
      <g
         fill="#00aeef"
         class="lines"
         id="g439">
        <path
           d="m 178,62 v 37.2 l 1.4,-0.1 V 62 Z m 118.3,-0.4 v 37.2 l 1.4,-0.1 V 61.6 Z"
           id="path433" />
        <path
           d="m 178.5,109.4 a 6.0074953,6.0074953 0 1 0 0.6,-12 h -0.6 c -0.5,0 -1,0 -1.6,0.2 -6.8,1.8 -5.5,11.8 1.6,11.8 z m 0,-2.6 c -4.5,0 -4.5,-6.8 0,-6.8 4.5,0 4.5,6.8 0,6.8 z M 56.1,100.6 43.2,87.3 H 21.9 v -1 h 21.8 l 12.9,13.2 70,-0.3 v 1.4 H 56.2 Z m 294.3,1.5 V 80.9 l 1.4,2 v 17.8 h 169.3 l -0.2,1.4 z M 21.1,7 H 249.6 V 8.8 H 22.9 v 12.3 l -1.8,0.2 V 7.2 Z m 275.3,3.7 v 39.2 h 1.4 V 12 h 153.3 l -2.2,-1.4 z"
           id="path435" />
        <path
           d="m 131.2,92.3 a 7.6,7.6 0 0 0 -7.4,7.8 c 0,4.2 3.3,7.7 7.4,7.7 4.1,0 7.5,-3.5 7.5,-7.7 0,-4.3 -3.4,-7.8 -7.5,-7.8 z m 0,3.4 c 2.4,0 4.2,2 4.2,4.4 0,2.4 -1.8,4.4 -4.2,4.4 -2.4,0 -4.2,-2 -4.2,-4.4 0,-2.5 2,-4.4 4.2,-4.4 z M 463.1,12 c 0,3.7 -3.3,6.7 -7.2,6.7 -3.9,0 -7,-3 -7,-6.7 0,-3.8 3.1,-6.8 7,-6.8 A 7,7 0 0 1 463,12 Z m -11.2,0 c 0,2 1.8,3.8 4,3.8 a 4,4 0 0 0 4,-3.8 4,4 0 0 0 -8,0 z m 77.2,79.6 a 9.5,9.5 0 1 0 0,19 9.5,9.5 0 0 0 0,-19 z m 0,15 a 5.4,5.4 0 1 1 5.4,-5.5 c 0,3 -2.4,5.4 -5.4,5.4 z M 258.6,0 a 9.5,9.5 0 1 0 0,19 9.5,9.5 0 0 0 0,-19 z m 0,15 A 5.4,5.4 0 1 1 264,9.5 c 0,3 -2.4,5.4 -5.4,5.4 z m 38.1,92.8 a 6.0074953,6.0074953 0 1 0 0.6,-12 h -0.6 c -0.5,0 -1,0 -1.6,0.2 -6.8,1.8 -5.5,11.9 1.6,11.8 z m 0,-2.6 c -4.5,0 -4.5,-6.8 0,-6.8 4.5,0 4.5,6.8 0,6.8 z"
           id="path437" />
      </g>
      <g
         fill="#ee1425"
         class="text open"
         id="g449">
        <path
           d="m 94.8,20.8 h -32 v 68 H 77.9 V 66.7 h 18.2 c 14,-0.8 17.3,-5.5 17.3,-23.1 0,-18.2 -3.7,-22.7 -18.6,-22.7 z M 77.9,35.6 h 13 c 6.8,0 7.5,0.8 7.5,8 0,6.6 -0.7,7.3 -7.5,7.3 h -13 z"
           id="path441" />
        <path
           d="M 90.9,35 H 77.2 v 16.6 h 13.7 c 7.2,0 8.2,-1 8.2,-8 0,-7.6 -1,-8.6 -8.2,-8.6 z m 0,15.2 H 78.6 v -14 h 12.3 c 6.4,0 6.8,0.6 6.8,7.4 0,6.3 -0.4,6.6 -6.8,6.6 z M 90.9,35 H 77.2 v 16.6 h 13.7 c 7.2,0 8.2,-1 8.2,-8 0,-7.6 -1,-8.6 -8.2,-8.6 z m 0,15.2 H 78.6 v -14 h 12.3 c 6.4,0 6.8,0.6 6.8,7.4 0,6.3 -0.4,6.6 -6.8,6.6 z M 90.9,35 H 77.2 v 16.6 h 13.7 c 7.2,0 8.2,-1 8.2,-8 0,-7.6 -1,-8.6 -8.2,-8.6 z m 0,15.2 H 78.6 v -14 h 12.3 c 6.4,0 6.8,0.6 6.8,7.4 0,6.3 -0.4,6.6 -6.8,6.6 z M 90.9,35 H 77.2 v 16.6 h 13.7 c 7.2,0 8.2,-1 8.2,-8 0,-7.6 -1,-8.6 -8.2,-8.6 z m 0,15.2 H 78.6 v -14 h 12.3 c 6.4,0 6.8,0.6 6.8,7.4 0,6.3 -0.4,6.6 -6.8,6.6 z m 4,-29.4 h -32 v 68 h 15 V 66.7 h 18.2 c 14,-0.8 17.3,-5.5 17.3,-23.1 0,-18.2 -3.7,-22.7 -18.6,-22.7 z m 1.2,45 H 77.2 V 88.2 H 63.5 V 21.4 h 31.3 c 14.5,0 18,4.4 18,22 0,17.3 -3.3,21.7 -16.7,22.6 z m -5.2,-31 H 77.2 v 16.8 h 13.7 c 7.2,0 8.2,-1 8.2,-8 0,-7.6 -1,-8.6 -8.2,-8.6 z m 0,15.4 H 78.6 v -14 h 12.3 c 6.4,0 6.8,0.6 6.8,7.4 0,6.3 -0.4,6.6 -6.8,6.6 z M 90.9,35 H 77.2 v 16.6 h 13.7 c 7.2,0 8.2,-1 8.2,-8 0,-7.6 -1,-8.6 -8.2,-8.6 z m 0,15.2 H 78.6 v -14 h 12.3 c 6.4,0 6.8,0.6 6.8,7.4 0,6.3 -0.4,6.6 -6.8,6.6 z M 90.9,35 H 77.2 v 16.6 h 13.7 c 7.2,0 8.2,-1 8.2,-8 0,-7.6 -1,-8.6 -8.2,-8.6 z m 0,15.2 H 78.6 v -14 h 12.3 c 6.4,0 6.8,0.6 6.8,7.4 0,6.3 -0.4,6.6 -6.8,6.6 z m 43.3,23 V 61.8 h 28 V 47 h -28 V 36.5 h 30.9 V 20.8 h -45.8 v 68 h 46 V 73.3 h -31 z"
           id="path443" />
        <path
           d="M 134.2,73.2 V 61.8 h 28 V 47 h -28 V 36.5 h 30.9 V 20.8 h -45.8 v 68 h 46 V 73.3 h -31 z m 30.3,15 H 119.9 V 21.5 h 44.5 v 14.3 h -30.9 v 11.8 h 28 v 13.6 h -28 v 12.6 h 31 z M 211.1,20.8 V 58 h -0.5 L 185.9,20.8 h -14.3 v 68 h 15 v -37 h 0.5 l 24.8,37 h 14.2 v -68 z"
           id="path445" />
        <path
           d="M 211.1,20.8 V 58 h -0.5 L 185.9,20.8 h -14.3 v 68 h 15 v -37 h 0.5 l 24.8,37 h 14.2 v -68 z m 14.3,67.4 h -13.1 l -24.8,-37 H 186 v 37 H 172.3 V 21.5 h 13.2 l 24.5,36.9 0.2,0.2 h 1.5 V 21.5 h 13.7 z M 26.7,20.8 C 5.3,20.8 0,27.6 0,54.8 c 0,27.3 5.3,34 26.7,34 21.3,0 26.6,-6.7 26.6,-34 0,-27.2 -5.3,-34 -26.6,-34 z m 0,53.7 c -11,0 -12.2,-2 -12.2,-19.7 0,-17.7 1.2,-19.6 12.2,-19.6 11,0 12.1,2 12.1,19.6 0,17.8 -1.2,19.7 -12.1,19.7 z"
           id="path447" />
      </g>
      <path
         fill="#333333"
         d="m 275.3,86.7 h 0.1 a 13.8,14.4 0 0 1 -0.1,0 z m -5.5,-26.1 h 14.7 c 0,10.6 -1,17.8 -4.3,22.2 a 13.8,14.4 0 0 1 -4.9,3.8 c -1.4,0.8 -2.9,1.4 -4.2,1.8 h 0.3 l -2.6,0.5 h 0.1 c -2.6,0.5 -4.6,0.5 -4.6,0.5 -1.8,0.2 -4,0.3 -6.1,0.3 -21.3,0 -26.7,-6.8 -26.7,-34.2 0,-27.5 5.4,-34.2 26.7,-34.2 21.2,0 26.4,5.8 26.3,29 h -14.7 c 0.2,-13 -1,-14.5 -11.6,-14.5 -10.8,0 -12,2 -12,19.7 0,17.8 1.2,19.8 12,19.8 10.6,0 11.8,-1.6 11.6,-14.7 z m 28.2,-38 c -2.7,0 -5.5,-0.2 -8.2,0 v 66 h 14 V 22.5 c -1.8,-0.1 -4,0.1 -5.8,0.2 z m 90.8,52.6 c -10.8,0 -12,-2 -12,-19.7 0,-17.7 1.2,-19.7 12,-19.7 10.6,0 11.8,1.4 11.7,14.5 h 14.7 c 0,-23.1 -5.2,-29 -26.5,-29 -21.2,0 -26.5,6.8 -26.5,34.2 0,27.3 5.3,34.2 26.6,34.2 21.3,0 26.5,-5.9 26.3,-29.1 h -14.7 c 0.2,13.1 -1,14.6 -11.7,14.6 z m 63,-52.8 v 39 c 0,12.5 -0.9,13.8 -9,13.8 -8.1,0 -9,-1.4 -9,-13.8 v -39 h -14 v 39 c 0,22.6 4.6,28.3 23,28.3 18.4,0 23,-5.7 23,-28.3 v -39 z m 20,0 v 66.3 h 14 V 22.3 h -14 z m 17.7,0 v 15.3 h 16 v 51 h 13.9 v -51 h 16 V 22.3 H 489.5 Z M 347,59 c 7.4,-1.2 10.5,-6.4 10.5,-17.4 0,-15.4 -3.4,-19.2 -17.3,-19.2 h -29.7 v 66.3 h 14 V 65.3 H 335 c 7.3,0 8,0.7 8,8 v 15.4 h 14.7 V 72.3 C 357.7,63.8 354,59.9 347.1,59 Z m -10.4,-7.3 h -12 v -15 h 12 c 6.3,0 7,0.9 7,7.4 0,6.7 -0.7,7.6 -7,7.6 z"
         class="text circuit"
         id="path451" />
    </g>
    <rect
       style="fill:#3b4084;fill-opacity:1;stroke:none;stroke-width:0.531997;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1"
       id="rect1815-2"
       width="116.222"
       height="42.638527"
       x="158.37491"
       y="3.7035949" />
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:7.42307px;line-height:1.25;font-family:sans-serif;fill:#fffff5;fill-opacity:1;stroke:none;stroke-width:0.197717"
       x="201.41266"
       y="12.828789"
       id="text1845-9"><tspan
         sodipodi:role="line"
         id="tspan1843-1"
         x="201.41266"
         y="12.828789"
         style="font-size:7.42307px;fill:#fffff5;fill-opacity:1;stroke-width:0.197717">Abstract:</tspan></text>
    <text
       xml:space="preserve"
       style="font-style:normal;font-weight:normal;font-size:2.82222px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:74.5713;fill:#ffffff;fill-opacity:1;stroke:none;stroke-width:0.264583"
       x="25.996389"
       y="107.72469"
       id="text1849-2"
       transform="matrix(1.5029828,0,0,1.5029828,122.98727,-144.11947)"><tspan
         x="25.996389"
         y="107.72469"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">We are a team of three members for a school in the </tspan></tspan><tspan
         x="25.996389"
         y="111.25247"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">Netherlands named Mendelcollege. We shared the </tspan></tspan><tspan
         x="25.996389"
         y="114.78025"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">rolles so that one only doing coding en design the </tspan></tspan><tspan
         x="25.996389"
         y="118.30803"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">electricas schems, one is doing the mechanical </tspan></tspan><tspan
         x="25.996389"
         y="121.83581"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">design of the robot, and one is doing a bit of both </tspan></tspan><tspan
         x="25.996389"
         y="125.36359"><tspan
           style="font-size:2.82222px;fill:#ffffff;fill-opacity:1;stroke-width:0.264583">worlds.</tspan></tspan></text>
    <image
       width="46.495819"
       height="9.2438126"
       preserveAspectRatio="none"
       xlink:href="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABpAAAAFOCAYAAACBsF4QAAAABGdBTUEAALGPC/xhBQAACktpQ0NQ c1JHQiBJRUM2MTk2Ni0yLjEAAEiJnVNnVFPpFj333vRCS4iAlEtvUhUIIFJCi4BUaaISkgChhBgS QOyIqMCIoiKCFRkUccDREZCxIoqFQbH3AXkIKOPgKDZU3g/eGn2z5r03b/avvfY5Z53vnH0+AEZg sESahaoBZEoV8ogAHzw2Lh4ndwMKVCCBA4BAmC0LifSPAgDg+/Hw7IgAH/gCBODNbUAAAG7YBIbh OPx/UBfK5AoAJAwApovE2UIApBAAMnIVMgUAMgoA7KR0mQIAJQAAWx4bFw+AagEAO2WSTwMAdtIk 9wIAtihTKgJAowBAJsoUiQDQDgBYl6MUiwCwYAAoypGIcwGwmwBgkqHMlABg7wCAnSkWZAMQGABg ohALUwEI9gDAkEdF8AAIMwEojJSveNJXXCHOUwAA8LJki+WSlFQFbiG0xB1cXbl4oDg3Q6xQ2IQJ hOkCuQjnZWXKBNLFAJMzAwCARnZEgA/O9+M5O7g6O9s42jp8taj/GvyLiI2L/5c/r8IBAQCE0/VF +7O8rBoA7hgAtvGLlrQdoGUNgNb9L5rJHgDVQoDmq1/Nw+H78fBUhULmZmeXm5trKxELbYWpX/X5 nwl/AV/1s+X78fDf14P7ipMFygwFHhHggwuzMrKUcjxbJhCKcZs/HvHfLvzzd0yLECeL5WKpUIxH S8S5EmkKzsuSiiQKSZYUl0j/k4l/s+wPmLxrAGDVfgb2QltQu8oG7JcuILDogCXsAgDkd9+CqdEQ BgAxBoOTdw8AMPmb/x1oGQCg2ZIUHACAFxGFC5XynMkYAQCACDRQBTZogz4YgwXYgCO4gDt4gR/M hlCIgjhYAEJIhUyQQy4shVVQBCWwEbZCFeyGWqiHRjgCLXACzsIFuALX4BY8gF4YgOcwCm9gHEEQ MsJEWIg2YoCYItaII8JFZiF+SDASgcQhiUgKIkWUyFJkNVKClCNVyF6kHvkeOY6cRS4hPcg9pA8Z Rn5DPqAYykDZqB5qhtqhXNQbDUKj0PloCroIzUcL0Q1oJVqDHkKb0bPoFfQW2os+R8cwwOgYBzPE bDAuxsNCsXgsGZNjy7FirAKrwRqxNqwTu4H1YiPYewKJwCLgBBuCOyGQMJcgJCwiLCeUEqoIBwjN hA7CDUIfYZTwmcgk6hKtiW5EPjGWmELMJRYRK4h1xGPE88RbxAHiGxKJxCGZk1xIgaQ4UhppCamU tJPURDpD6iH1k8bIZLI22ZrsQQ4lC8gKchF5O/kQ+TT5OnmA/I5CpxhQHCn+lHiKlFJAqaAcpJyi XKcMUsapalRTqhs1lCqiLqaWUWupbdSr1AHqOE2dZk7zoEXR0miraJW0Rtp52kPaKzqdbkR3pYfT JfSV9Er6YfpFeh/9PUODYcXgMRIYSsYGxn7GGcY9xismk2nG9GLGMxXMDcx65jnmY+Y7FZaKrQpf RaSyQqVapVnlusoLVaqqqaq36gLVfNUK1aOqV1VH1KhqZmo8NYHacrVqteNqd9TG1FnqDuqh6pnq peoH1S+pD2mQNcw0/DREGoUa+zTOafSzMJYxi8cSslazalnnWQNsEtuczWensUvY37G72aOaGpoz NKM18zSrNU9q9nIwjhmHz8nglHGOcG5zPkzRm+I9RTxl/ZTGKdenvNWaquWlJdYq1mrSuqX1QRvX 9tNO196k3aL9SIegY6UTrpOrs0vnvM7IVPZU96nCqcVTj0y9r4vqWulG6C7R3afbpTump68XoCfT 2653Tm9En6PvpZ+mv0X/lP6wActgloHEYIvBaYNnuCbujWfglXgHPmqoaxhoqDTca9htOG5kbjTX qMCoyeiRMc2Ya5xsvMW43XjUxMAkxGSpSYPJfVOqKdc01XSbaafpWzNzsxiztWYtZkPmWuZ883zz BvOHFkwLT4tFFjUWNy1JllzLdMudltesUCsnq1Sraqur1qi1s7XEeqd1zzTiNNdp0mk10+7YMGy8 bXJsGmz6bDm2wbYFti22L+xM7OLtNtl12n22d7LPsK+1f+Cg4TDbocChzeE3RytHoWO1483pzOn+ 01dMb53+cob1DPGMXTPuOrGcQpzWOrU7fXJ2cZY7NzoPu5i4JLrscLnDZXPDuKXci65EVx/XFa4n XN+7Obsp3I64/epu457uftB9aKb5TPHM2pn9HkYeAo+9Hr2z8FmJs/bM6vU09BR41ng+8TL2EnnV eQ16W3qneR/yfuFj7yP3OebzlufGW8Y744v5BvgW+3b7afjN9avye+xv5J/i3+A/GuAUsCTgTCAx MChwU+Advh5fyK/nj852mb1sdkcQIygyqCroSbBVsDy4LQQNmR2yOeThHNM50jktoRDKD90c+ijM PGxR2I/hpPCw8OrwpxEOEUsjOiNZkQsjD0a+ifKJKot6MNdirnJue7RqdEJ0ffTbGN+Y8pjeWLvY ZbFX4nTiJHGt8eT46Pi6+LF5fvO2zhtIcEooSrg933x+3vxLC3QWZCw4uVB1oWDh0URiYkziwcSP glBBjWAsiZ+0I2lUyBNuEz4XeYm2iIbFHuJy8WCyR3J58lCKR8rmlOFUz9SK1BEJT1IleZkWmLY7 7W16aPr+9ImMmIymTEpmYuZxqYY0XdqRpZ+Vl9Ujs5YVyXoXuS3aumhUHiSvy0ay52e3KtgKmaJL aaFco+zLmZVTnfMuNzr3aJ56njSva7HV4vWLB/P9879dQlgiXNK+1HDpqqV9y7yX7V2OLE9a3r7C eEXhioGVASsPrKKtSl/1U4F9QXnB69Uxq9sK9QpXFvavCVjTUKRSJC+6s9Z97e51hHWSdd3rp6/f vv5zsaj4col9SUXJx1Jh6eVvHL6p/GZiQ/KG7jLnsl0bSRulG29v8tx0oFy9PL+8f3PI5uYt+Jbi La+3Ltx6qWJGxe5ttG3Kbb2VwZWt2022b9z+sSq16la1T3XTDt0d63e83SnaeX2X167G3Xq7S3Z/ 2CPZc3dvwN7mGrOain2kfTn7ntZG13Z+y/22vk6nrqTu037p/t4DEQc66l3q6w/qHixrQBuUDcOH Eg5d+873u9ZGm8a9TZymksNwWHn42feJ398+EnSk/Sj3aOMPpj/sOMY6VtyMNC9uHm1JbeltjWvt OT77eHube9uxH21/3H/C8ET1Sc2TZadopwpPTZzOPz12RnZm5GzK2f72he0PzsWeu9kR3tF9Puj8 xQv+F851eneevuhx8cQlt0vHL3Mvt1xxvtLc5dR17Cenn451O3c3X3W52nrN9Vpbz8yeU9c9r5+9 4Xvjwk3+zSu35tzquT339t07CXd674ruDt3LuPfyfs798QcrHxIfFj9Se1TxWPdxzc+WPzf1Ovee 7PPt63oS+eRBv7D/+T+y//FxoPAp82nFoMFg/ZDj0Ilh/+Frz+Y9G3guez4+UvSL+i87Xli8+OFX r1+7RmNHB17KX078VvpK+9X+1zNet4+FjT1+k/lm/G3xO+13B95z33d+iPkwOJ77kfyx8pPlp7bP QZ8fTmROTPwTA5jz/IzFdaUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdw nLpRPAAAAAlwSFlzAAAuIwAALiMBeKU/dgAAU+9JREFUeJzt3c2O21i79vdLD555cx9Bs0camn0E pk+gzQao5J1JBoIgyDuwjQT53Nm2ESCDIIirXyCDBEEszgKQSLMHGWXQ8iSTAGk2kAlHrT6CrX0C 2xloVZdsV4mUSuK9Fvn/AYY/6uvqLpVErnvd95oJAAAAAIzlRRtLig/+6cu/33p6xqdPJEWSNj3e t5H0Lwd/v/2YXbWcN2d8beDR8qJN3R/Tg39+6GchkbSTtH3g7R/d7zvtH+88tgEAwcmLNtH++i7W 3TXjE/dv90l0/PXxPh+/+PtO+9fOW021nO9O+HwY2AOPk2+0fzw8JHYf05zwpRp9fg8hfX7vsa2W 8+0Jn88bM+sAAAAAAMbr4Kbt9vdvdXfzdvtvodm43z/qbiGBBQQ8misUJdr/nCQa9mdk6359dL9v Ql3oAACMh/Fr4yk27vetpD/FRo3BHGxES3V3r5HIz8eJ5B4X7tfv7t82klQt5xuDPEdRQAIAAADw KAHetF3LTvsbwo+6Kypt7eLAd3nRZtp3EiX6vLvIFzvtFzQ+al9QaizDAADGLS/aSPvXw6fu98Qu zcVt3a9G+06VjQLuSrHkioqp7q6hIrs0V7HT/nGy1b4Y2Wj/WGkswswWi/yTxRdG8DZlWT0b4gsF 8Bh9VpbVxjrEqRaL/Ff5eZN666Ysq9fWIawsFnkq6VfrHA+46mN+schjSX9c6/Nf0LosqxfWIXy1 WOSRpH+2ztFDXZbVj9f8AgcXt8Cp1j7eULqOolR3u0BTuzRB2OrzBfitZRjYcsXWTNJzhfmzs5NU S/pF+8fzzjLM2OVFu9L9ozx9UU+5qOh+nlfGMe5VLedvrTOcI4DHvJfXZqFz15ZLja9gdIqN9kWC P7XfgLSxDOMb9xjJdFdYnLJGd4+VjQaYgvD3a35yAHiElaTJFpCmrCyr7WKRN/L/wjGTRAHpYZl1 gJ5+GeBrpJLeDPB1MD4bnTaj/eK+2AWaiBu2c8TaX9esJCkv2q32C/Afq+W8tomEIR0UjZby//qm S6TPH8+1pILH8tXcLqj66omkq27E8Vwsf6/x3loHOJPvj/mNjK/NxuKgaJTJ76LhUFIdPPbzopX2 RYKN9iPOJrcJicfIgxLdXU++kf66v9jo7rHSXPILUkAC4KtoschXZVmtrYPARCH/F1iixSJPQ+xA HMg5h9xbqK0DAD5xBaNMd7v7Yrs0oxVLeiXpVV60O7luDhbgx8eNp7td+BirTFLmHstrST9NbYFr 4rK8aGO+5wD6cNeZK0kvxTVmH4kO1kUOigQfte8A3RlkuqqDzs6leIycItZBR6y7Ltvo7rGyfcwn p4AEwGcvtb8RxfTUkt5bh+jhue4OysTnMusAPdRlWe2sQwDW3I3aS017bIiVSK6bwy0KFGI8TtAm vDgW6a4wupH0jvE7k/FSTI4AcITrJHkpT0c+BiTWXRfwB/d6+0u1nN+YJboQN/b9pcJYRwhBJLfJ R9L7vGgb7deu3p1TePzbxWIBwOUli0WeWIfA8Mqy2iqMzpDMOoCPFos8UxiHWBbWAQBPrLRf+E1M UyDWfgzFH3nR/uxupBGIvGijvGjfan+O43tNq3j0pVTSr3nR/srjeBJW1gEA+Ckv2jQv2l8l/Sae K64hVRgbbx+UF+3KPUZ+Fesr15ToEfd7FJAA+O6ldQCYGeJsmseKKXLe67l1gB52ZVnV1iEA4AGZ WIAPRl60r7QvHL1RGBsohpKKx/EURHnRrqxDAPBHXrRxXrQftC8KpMZx4CFXXPxN0gfxGPEeBSQA vlstFnlkHQImausAPS2tA3gosw7QQ20dAAB6SMUCvLfc4sdtx1FkHMdnqfaP4w9uxB/Gh+thAJIk 141LxxHu5YqLtx1HiXEc9EQBCUAIVtYBMDx3Ns3aOEYfmXUAnzC+DgCuItXdAnxsnGXy3Li699ov fsTGcUKy0n5EY2acA5eXujNOAExUXrSJ6yihGxf3Ohj1m9omwakoIAEIAWPsposxduEJYXzdtiyr jXUIADjDStJvbmQaDLhOsN+0nyOP00WSfnbnfEXGWXBZ3LMBE+WuS34THSW4xxfFRQSIAhKAEMSL RZ5ah8Dw3Bk1O+MYfTC2405mHaCH2joAADxCJOm9G2sXG2eZFLdzlq6jy8i070ZKjXPgcjKKgsC0 uI7cn7Uf5Qp8heLiOFBAAhAKdrRNV20doIfUOoAPXCdWZByjD8bXARiDVPtupJVxjtFzC2S/ip2z lxZpP5rxlXEOXEYkRo8Dk+HGVv6qMDYQYmAUF8eFAhKAUGSLRR5bh4CJn6wD9JDw+JQURidWU5ZV Yx0CAC4kkvQhL9oP1kHGyi2Q/SY2i1zTex7Do8GmP2ACDopHiW0S+Iji4vhQQAIQkpV1AAzPLfZv jWP0kVkH8EBmHaAHuo8AjNEqL9rfGB91WW68GiPrhsFjeBxixhIC4+Y6n39TGJMnMDCKi+NEAQlA SELobsB11NYBepj049ONr4uNY/RRWwcAgCtJtB8HlhjnGAW3QParWCAbUqL9YzgyzoHHmfQ1MTBm 7rWRjlHc66B4FNkmwaVRQAIQknixyFfWIWCCMXb+C2GxYFOW1dY6BABcUSKKSI/GApmpRBSRQrfK iza2DgHgsnhtxDEUj8aNAhKA0ISwSI0Lc4v+jXGMPjLrAIYy6wA9ML4OwBREooh0NhbIvJCIIlLo VtYBAFxOXrSZeG3EAygejR8FJAChSSfe5TFlISz+T7LAyfg6APBOJIpIJ6N45JVEFJFCNslrYmCM 3LUEr424F8WjaaCABCBEb6wDwERtHaCHZLHII+sQBlLrAD3UZVntrEMAwIAiST+zAN8PC2ReSkQR KVSx61gAEDA3jpLiAO7lXp9/Fo+P0aOABCBE2UQX6SfNjbGrjWP0kVkHMBDCLtMQOtgA4NJisQDf 6WD3LPyTiMJeqF5aBwBwPooD6OFXhTGJBI9EAQlAiCJNc5Ee0i/WAXp4bh1gSG6kZGIco8uuLKva OgQAGEkkvbcO4Su3QPZBLJD5LMuLlsdweFLXvQAgTO/l/30ejORF+0E8PiaDAhKAULGjbZpq6wA9 TK1DLrMO0ENtHQAAjK3yon1lHcJTP2scCyBbSRvtX/PeuV+1+7etSaLLeuXOqEJYuGcDAuSeb1fG MeApN6J0ZRwDA/q7dQAAOFOyWORpWVYb6yAYTllWu8UiX8v/i5VM0to4w1AYXwcAYXifF+2mWs4b 6yC+yIv2rcI4x+8+O+0LRL9I2lTL+e7YO7tOq1T7TulMYXZcvc+LtuExHJRVXrTvuh6fAPzhxrrS 9Yl7uc5SRstODAUkACFbar+rEtPyi/wvID3XBApIgYyv21JoBoK31nUKwbHu5rY/0X5BPb3C1/HJ z3nRfs9irpQXbSrpjXWOM2wk/VQt5/UpH+S+57X79cLtLl8qrMd8JOlDXrTPeAwHI9K0NlYBYzDF sa5b9+vjF3/H16bw+NhJarR/DPzp/m1jE+XimnM+iAISgJCtFov8dVlWO+sgGE5ZVvVike/k90VL tljk0QQem5l1gB5q6wAAHu3PajnfDPXF3M7KRNJT7RfXk6G+9gBi7Ysmr41zmDo4GDwkG0nvLvWz UC3na0nrg0JaeonPO4BEPIZD81IUkIAguM7cxDjGEGrti0XNkNeYoXObT1LjGNew1f4666P2Xd1b yzA+ooAEIHQrSTfGGTC8Wv53IWUa/83yc+sAPTC+DsBJ3E3jVq4A7YoNme5Gf4XuVV60xcTHgIW0 e3Yn6bUr+FycWzjbuDOy3iiM/y+v8qL9eGoXFswkedEmE3/OAbznNtCE2Jnbx05u7CuvHedx18Nj Gm241f4xMfVr4l7+Zh0AAB6Jg1mn6SfrAD08tQ5wTYtFHsn/3UdNWVaNdQgAYauW8121nK+r5fxH Sd9Jeqf9QkTIJju73h38nBnH6Gsj6btrFY8OVcv5jaTvFc6Ilg9uMQth4J4N8N8Yrw0aSS+0fy19 QfHoUV4pjE0mXTaSfqyW8++q5fw1xaN+/q5wLhD7iHU3x9xHW41nhmZjHQBw4sUiz8qyqq2DYDhl WTWLRb6V38/5mfYXq2OVWQfoge4jABflupPe5kV7o/2NdKg7dZO8aFdDFCZ84goOoSyQravlfNDr CPf4fpYX7Qf53+kdab8TeszXWmOyyov2NWdXAX5ymytS4xiXtNEFx75O3Ui60xrtO7o3xjmC9Pey rJ5Zh7iUxSJ/K78f0EVZVm+tQwAjtBTnnExRrf3ina+ikRc3QxhfV1sHcNYaz4adRH6PLmg0rnMx GusAuJ9bBH2bF+1a+7N0Ess8Z3qfF209sQXd9wpj9+xr1xFkolrOX+RFK/lfRFq5cYwb6yDoZSVG jwO+8vn6/hRbSS94Xbg4n9fau+y0LybeGOcIGmcgARiDbLHI47KsttZBMKif5HcBSdoXWWrrEJfm xtdlxjG6bHx5Tjg4TyV4bkHRZztuGDEk9/P9vTt0OrSb60j719G3pikGkhdtKv8LIpJ048MiR0BF pPfaj96D/16KAhLgHXcGXmwc4xLeVcv5W+sQY+O6j1bGMc7VaD+ubmucI3icgQRgLJirPTGuONAY x+iSWQe4ksw6QA+MrwMwGLdg8ULhnY30ckLnyIRQ4FtXy7k3XZRuhN7aOkeHxC1+wn+xK+QC8IS7 Bgjh9fGYraTvKR5dTaiPj3W1nH9P8egyKCABGIuVdQCY8L1IEC0WeWYd4goYXwcAX3DnCT1TWEWk SP538z5aXrQr+X+2QzP0mUc9vZb/G3beTKgQGjo2/QF+eaUwRrs+ZKN98agxzjFK7rU1M45xjhee XlMFiwISgLGIFot8ZR0Cg6utA/QQQrGlt0DG19VlWe2sQwCYHreAEdoZs0vrAAPwfffsTtKP1iHu 487I8r27LtIECqEjkblxSACMueJAyEXddbWcP5vYWY5DWym8AuMLt6kLF0QBCcCYTGEBBAfcGLva OEaXzDrAhaXWAXrwvTMNwIi5IlJIux5j16EzSu6/LTaO0eWFzyNW3GP6nXWODlMaxxi6lXUAAJLC LA7cWtNhMojQCowUj66EAhKAMUkXizyxDoHB/WIdoEM0ssel7x1Vu7KsausQAKbN3bz6vuB+KLQF glP43n1UV8t5bR2iS7Wc32g/KshXkfz/XmNvzM83QEhC/VmkeDSAvGgT+b8B59ANxaProYAEYGxC vQjC+WrrAD2MqTsusw7QobYOAACS5A5z3hjH6CtxCwWjEkD30U77M4ZC4fuC3YoupCBEY+56BEIQ wOvjQygeDSekNYxNtZyHdD0VHApIAMYmc2e0YCLcWTdr4xhdMusAl7BY5Jn8H3PA+DoAPvH97JhD IS0U9OV7R8pPPo+u+5LL6nNnXSTOQgrFGJ9vgJCEuPG2UVibLkKXWQfoaSdPz5EcEwpIAMYmUjgv dLgc38fYxSMZY+f7+LptWVYb6xAAcMstuP9knaOnlXWASwpgd/VW0o1xhnPcyO+iKGchhSHNiza2 DgFMkes4ToxjnGon6cdqOd8Z55iEwMbXveBxcX0UkACMke+7TXFh7sybnXGMLmPYaZlZB+hQWwcA gC+5UXZb4xh9RHnRZtYhLsj33dXvQlzwcJl93gEeaWTF0BHjng2w4fvr431+DKljdwQy6wA9bUI4 R3IMKCABGKN4schT6xAYXG0doENmHeAxGF8HAI/i89ivQ753mvaSF20qv3dXb0M+6Nll3xrHOCbE xdEpyugWA4blfuYy4xinuqmW8411iIl5ah2gJ583tIwKBSQAYzWGbg+cxvcRQaGPsfN9UbEpy6qx DgEA93EL7o1xjD4y6wAX4vt1YCgFxWN8/m+IR9ZNN1aRxvOcA4Qik/+bAg811XJOkWB4qXWAHtbV ct5Yh5gKCkgAxmq1WOSxdQgMxxUPtsYxumTWAR4htQ7Qge4jAL7zfaODtB9jl1qHeAy3u3plHOOY oLuPbrn/hp1xjGPoQgoD3ydgWL5vsPgSxaOBBXQdyP3/gCggARizlXUADK62DtDB9y6ee7nOqdg4 RpfaOgAAHBPA2K9bQb5WHVhZB+gQQiGxL5//W9K8aGPrEOiUBLRYCQTNPSemxjFOweg6G6l1gB4a HhvDooAEYMxC212Dx/N5IUOSkkA743z/WdqUZbW1DgEAPYSwWzK1DvBIPnc07CStjTNc0o11gA4r 6wDoxffrTGAsMusAJ9jJ71GpY/bEOkAPvq/7jA4FJABjFi8WeWYdAsNxRYTGOEaXzDrAGTLrAB1C WJAFACmM4kES6sH2edEm8rtjtq6W8511iEtx/y1r4xjHUJgIwyrU5xwgMCE9J74b0+tlYBLrAD3U 1gGmhgISgLHzeRcqrsP3YkJIF+6MrwOAC6qW863CeM5KrQOcyffX2DHumPX5uivOizazDoFeXlkH AMbMja9LjGP0ta2W8xvrEBMWWwfosKG4ODwKSADGLg10ZBjOV1sH6BDaGDvfF+Pqsqx21iEA4AS/ WAfo4al1gDOtrAMcsa2W88Y6xKW5Mwi2xjGOCf1Mr6nw/XoTCF1mHeAEjK4z4jq5fRfCdfToUEAC MAV0IU2IG2NXG8foklkHOEFmHaCDzzufAeA+tXWAHhLrAKdynSaRcYxjxth9dKu2DnBEZh0AvdAt BlxXKMX0bbWcr61DTFhkHaCHxjrAFFFAAjAFK+sAGJzvu1KC2GUZwPi6XVlWtXUIADiFG7uxMY7R JbUOcAbfF8dq6wBX5PNmjojCRDCCuD4GQuPOGEuNY/Tl8+vJFKTWAbq4zmcMjAISgCmIFot8ZR0C g6qtA3RIFos8sg7RQ2odoENtHQAAzuT7RgflRZtaZzhRZh3giMadfzVKbjTf1jjGMb4XF7GXuXNa AFxWZh2gp52kG+MM8NvGOsBUUUACMBWMsZsQdybO2jhGl8w6QA++7wRlhxqAUG2sA/SQWAfoy83s j4xjHDOF16vaOsARmXUA9MY9G3B5oZxrWLsubdh5Yh2gw9Y6wFRRQAIwFYkbx4Xp8H13t9e7YReL PJbfi4fbsqw21iEA4ByuY2NnHKPLt9YBTuD7hofaOsAAfC6SMcYuHCvrAMAIZdYBehrzWYGhiKwD dPjTOsBUUUACMCXsaJsQdzbOzjjGMZnnY+wy6wAdausAAPBIG+sAHRLrACfIrAMcsR3z+LpbAYyx C2UH/tRFedGurEMAYxFAh+6txr2OAMdsrQNMFQUkAFOy8nzBHpdXWwfokFkHOML33dw+73QGgD5+ tw7QIbEO0Ic7MyU2jnFMbR1gQBvrAEdk1gHQm+/XoEBIUusAPXFv54fIOkCHrXWAqaKABGBqVtYB MCjf2+C9HGMXwPi6piyrxjoEADzSxjpAhygv2sg6RA+ZdYAOH60DDMjn8cGx24kP/6V8r4CLCaX7 cm0dAJL8XgOAIQpIAKaGMXYT4ooMW+MYx/g6xi6zDtCBHWoAxqCxDtBDYh2gB68Xx6rlvLbOMKCN dYAOqXUA9MY9G3AZqXWAHupqOd9Zh0AQttYBpooCEoCpiReLPLUOgUHV1gE6ZNYB7uH1Ypz8/54C QCe3WLIzjtEltg7QQ2od4IiNdYAhucd0YxzjGN+vb3AnC6QDEvBWQOcf+dy9Co9M4UxJX1FAAjBF 7GibFt/H2Hm1mOE6ojLjGMdsyrLaWocAgAtprAN0iK0DHJMXbSq/F8emNL7u1sY6wBGZdQD0FonR 48BjpdYBeqqtAwA4jgISgCnK3BkvmABXbGiMYxyTWQf4QmYdoAPj6wCMSWMdoMO31gE6pNYBOmys Axjwumjmio4IA5v+gMd5Yh2gh4bxdYD/KCABmKqVdQAMyueiQ7RY5Jl1iAPPrQN0qK0DAMAF/Yt1 gA6xdYAOXnXxfqlazjfWGQxsrAN0SK0DoLeYgh/wKKl1gB4YXwcEgAISgKlaWgfAoGrrAB28KNoE ML6uLstqZx0CAC5oYx2gQ2QdoENiHeCIjXUAC5yDhAvjng04gztDLDaO0UdtHQDhyIs2ts4wVRSQ AExVvFjkK+sQGIYbY1cbxzgmsw7gZNYBOvjcSQYAY5RYB3hIAIeDN9YBDDXWAY5IrQPgJCsWDIGz JNYBethVy3ljHQJBia0DTBUFJABTxo62afG5Pd6XMXZedEI9YFeWVW0dAgAurLEOELDUOkAHr88C ujKv/9sZixaclXUAIECpdYAeNtYB8JWddQD4iQISgClLF4s8tg6BwdTWATr4ULzJrAMcUVsHAIBL C+HgaI93//t+OHhjHcBQYx2gQ2IdACdh0x9wOt9fIyXPNxtMVGMdoENkHWCqKCABmLo31gEwDHd2 zto4xjGp5Rf3pAPqGMbXARirnXWADrF1gAck1gGO2FXL+dY6hJUARhJxDlJY4rxoM+sQQGAS6wA9 bKwDIDiJdYCpooAEYOqyxSKPrENgMD6PsYsXizwx/Po+dEA9ZFuW1cY6BABcSWMdIFCJdYAjGusA HthYBzgisQ6Ak720DgAEJrYO0IHzj3COb60DTBUFJABTF8nvsV24IHeGzs44xjGWIzoyw6/dpbYO AAATFlkH+FIAZ9gwlsfvIlqcF21kHQInST0epwl4JYDXSMnvTQbwV2wdYKooIAEAO9qmprYOcERm 8UXd+LrI4mv3xPg6ALCTWAe4R2IdoENjHcADv1sH6JBYB8DJuGcD+omtA/Tg+2vEVPm+ASa1DjBV FJAAQEoWizy1DoHB/GQd4AirMXY+j69ryrJqrEMAwBX5frPuI98PB99aB/DA1jpAh9Q6AE62onMM 6CW2DtDDxjoAwpQXbWKdYYooIAHAnuXoMAzIFSO2xjGOsXgsZgZfsy+6jwAAX4qtAxzDuQ5StZxv rDN08L0Iia9F8vuaFfDFU+sAPTTWAXCvnXWAHlLrAFNEAQkA9laLRR5Zh8BgausAR2RDfrEAxtfV 1gEAAN5JrQMcsbEO4JHGOsARsXUAnIUxdkC3yDpAh221nO+sQ+BejXWAHkIokI4OBSQAuLOyDoDB MMbujs8XYJuyrLbWIQAA/siLNrbO0GFrHcAjW+sARyTWAXCWhPFFQKfEOkCHxjoAHrSzDtBDZh1g iiggAcAddrRNhCtKNMYxjkkH/FrZgF/rVIyvAwB731gH+EJsHaDDn9YBPOL1IekUIoLFPRvwgECe 17x+bZiyUEbw5kW7ss4wNRSQAOBO7MZ5YRp8Lk4Mcg6S63SKh/haZ6qtAwAAvNvJnFoH6NBYB/DI 1jpAh9g6AM6yyos2sg4BeCqyDtDDxjoAjtpaB+jhuXWAqaGABACfG2ThHl6orQMckSwWeTzA1/H5 8V6XZbWzDgEAA9hZBwjMt9YBOmytA3hkax2gQ2IdAGdbWQcAPJVYB+hhax0AR22tA/SQBTDSeFQo IAHA57KBFu5hzI2xq41jHJON5Gucy+cOMQC4pMY6QGBi6wDHhDL+ZSCNdYAOvhcj8TDG2AH3i6wD dKmW8611Bhz10TpAT2+sA0wJBSQA+Bo3JNPxi3WAI67aHeT5+LpdWVa1dQgAgJcS6wBH7KwD+KRa znfWGTrE1gFwtjgv2tQ6BOChJ9YBOmysA6DT1jpATyu6kIZDAQkAvrayDoDB1NYBjrj2GDuvx9dZ BwAAeCuyDnBEYx3AQxvrAEck1gHwKGz6A74WWQfosLMOgE6NdYAT0IU0EApIAPC1aLHIV9YhcH3u jJ21cYxjskA/92Mxvg4A8JW8aBPrDB121gFwksg6AB6FMzCAryXWATr8bh0Ax7lRvDvjGH2t6EYd BgUkALifz90ZuCyfx9g9v8YndZ1N8TU+9wVsy7LaWIcAAHgpsg7QgYWxr3l9lgILT8FbWQcAPBNZ B+iwtQ6AXhrrACf4kBdtZB1i7CggAfDNTn6MukjdGTEYOXfWzs44xkPSxSKPrvB5syt8zkuprQMA ALyVWgfosLMOAEwMY+wAJ5COvK11APTi8ybbL8WS3luHGDsKSAB805Rl9Ux+jBXjhmQ6ausAR2RX +Jw+d9gxvg7A1KTWAXAxjXUADzXWATqk1gECtZb0TPaLwVFetCvjDIAvYusAPWytA6CXjXWAE614 LbguCkgAfPWTdQBJ2ZW6P+AfHx5vD7noGDs3vi655Oe8oKYsq8Y6BADAW0+sA+BkO+sAuIo/q+V8 Iz+uoX3eGAXgQLWcb60zoFtg5yDd+pAXbWYdYqwoIAHwkltEboxjRPJ71BcuxD3etsYxHnLpQmZ2 wc91aXQfAYB/fDrDJrIO0KGxDuChrXWADk+tAwRubR1AUhrI6C7g2lLrAB221gFwkto6wBk+5EWb WIcYIwpIAHzmw462N9YBMJjaOsAR2QU/l8+7NGvrAAAAr8XWAY6plvOddQbfsNt83Nxjfm0cQ+Ke DQjB1joAThLSOUi3Ikm/UkS6PApIAHxWy75tNl4s8tQ4A4bhQ8HyIRcZY+f5+LpNWVZb6xAAAK/F 1gGO2FkHwFli6wAj4EMHeZYXbWQdAjD2jXWADjvrAOivWs5rhfk9i0QR6eIoIAHwVllWO/mxo83n jg1ciCteNMYxHnKpMXbZBT7Htfiw+AAAwLka6wAea6wDHBFbBwidOwupMY4Rye/rXGAIiXWADr9b B8DJausAZ4pEEemiKCAB8J0PXSEr17mB8fO5iJFe4HP4POe/tg4AAEa+tQ4QAhYBgrazDoCr8+Ge jTF2AHBZPjy3nysSRaSLoYAEwGuuK2RjHEOSVtYBMIjaOsARjxpj5zqYsoskubzadRwCwBTF1gEC EVkH6LCzDoDz5EWbWmcYgVr2PwMx30vAa411AJymWs4b+bEed65I+yLSyjhH8CggAQiBD7seGGM3 Aa5gWRvHeEhm/PHX5HPnFwBM3cY6QCAYzfOwrXUAXFe1nO/E6HHAWmodoMPOOgDOEvq9eiTpQ160 b41zBI0CEgDvlWVVy/7GM14s8sw4A4bxi3WAB0SPfAw+qoPpinbuZxwAgGNS6wA425/WATAIHzb9 rfKijaxDAMBYVMv5WvbrcZfwJi/an3mNOA8FJACh8GHXw0vrABhEbR3giLOKQL6Pr7MOAADGUusA wMSl1gHGoFrOt/KjY/GVdQAA92qsA+Bs76wDXEgm6TfORTodBSQAoVhbB5CULhZ5bB0C1+XO4lkb x3hINvDHDcGH4jAA4GFb6wCBaKwDAB7w4bqOMXaYnLxoY+sMXdyoSwRoRF1I0v7s0d8YaXcaCkgA guDOplkbx5DoQpqKsY2x83V83bYsq411CACwEsiCz9Y6g/PEOkCHnXUAj22sA2AYniwyxnnRZsYZ gKHF1gEwei+sA1zYm7xofw3hWtwHFJAAhMSHHW0r6wC4Pncmz844xkPOKQallw5xIbV1AAAwFlsH CEhkHQCj5XtxMjQ+3LPRhQT4ZWcdAI9TLecbjW9DSCrpD7qRulFAAhAM16mwNY4RLRb5yjgDhlFb B3hAeso7u46l6BpBLsCHBQYAsBRbB+jQWAcABhBZBxiZtXUASRm7ygGvNNYBcBFj60K69SYv2j/y ok2tg/iKAhKA0PxkHUCMsZsKHx5r94kXizw54f19HV/XlGXVWIcAAGOxdYAOO+sAAdlZBwB84MZe 1sYxJO7ZAOCi3PP7O+scVxJL+pWxdvejgAQgNGvZ36AnJy7gI0CuuLE1jvGQU8ZyZNcK8Uh0HwGA 9K11gA5b6wAHEusAx1TLeWOdwWNb6wAYnA8bsVbWAYABpdYBMA3Vcv5W435dT7Ufa/chL9rIOIs3 KCABCEpZVjuxow3Dqa0DPCDr806ej6+rrQMAgAdi6wAd/rQOcCCyDoDzuB3LPoutA4yNOytjaxwj yot2ZZwBAMZorKPsDq3kzkeikEQBCUCYvNjRtljkkXUIXJ0Pj7X79B1j5+v4uk1ZVlvrEADggdQ6 QIetdQBgALF1gJHy4TqaTX+AHz5aB8DluE0CN8YxhhBJeqN9IWllG8UWBSQAwXGjxTbGMSTGIoye K3I0xjEe0meMXXbtEGdifB2AycuLNrHO0MPWOgCAYK3lwejxQJ5rASAo1XL+Wv6ulVxaJOlDXrST LSRRQAIQKh8WoNnRNg0+PNbukx1742KRp/J33E9tHQAAPBBbB+jidpgCwMmq5XwnP675uGcDgOt4 IfuNAkOKNdFCEgUkAEEqy2ot+xeq2C3SY9xq6wAPiBeLPD7ydl/H19XuLDMAmLqn1gE67KwD3Apg 9vzWOgDgKR/G2GUBPIcAQHCq5byR9No6h4FYEyskUUACELK1dQCxo2303Bi72jjGQ7Iz32bJ144u ABhaYh2gQ2Md4EBiHaDD1joA4CO3uNgYx4jE6HGMn++bUjBS1XK+1jTOQ7pPrIkUkiggAQiZFzva OrpAMA6/WAd4wL3nIC0WeSI/RyPtyrKqrUMAgCdS6wAdOPAal7SxDnBMXrSpdYYR8+GejU1/AHAl 7jykjXUOQ7EOCklj7HqlgAQgWB51hqysA+DqausAD0geKGDeW1jyQG0dAAB8kBdtZp2hh8Y6AIDw ud3pO+MYMUVCwFRjHQBX96P4PseSPkj6Iy/at2MqJFFAAhA6H8Zh+bpYjwtxZ/asjWM8JOv5bz7w 4ecVAHwQwqiZxjoAgNFYWwcQ92yApZ11AFxXtZzvJD0T32tpPzr1jaR/zov2Q160sW2cx6OABCBo bhzW1jhGvFjkK+MMuL4gxth5PL5uW5bVxjoEAHgisw7QYVct51vrEABGw4cxdqsxLOIBgK8oIt1r pX1H0s8hd8JSQAIwBj50NbCjbeRcsXJnHOM+X46x8/WxWFsHAAAfuAXM2DhGl411AADj4QrSG+MY EqPHAeCqquW8EUWk+2SSfs2L9re8aFfGWU5GAQnAGNxYB5CUPnAWDcaltg7wgOzgz6lRhi4+FHoB wAeZdYAePloHADA6PnQh+brRCgBGgyLSUYmkD3nR/hFSIYkCEoDgeXQ+zRvrALg6H2587/NUklwR MzFNcr+mLKvGOgQAeOKldYAeNtYBAIxLtZzX8mD0eEgLdgAQqoMi0tY2ibdi3RWSMuMsnSggARgL H7obssUij6xD4HpcEWRrHOM+t4+9zDjHQ3z4+QQAc3nRJvJ/fN3O3fQDwKX5cE1IFxIADMBdT34v qbFN4rVY0s950f7q8xlJFJAAjEJZVhvZvyhF8ncBH5dTWwd4wD9Lem8d4gG1dQAA8EQI3Ue1dYB7 xNYBAFzEjXUASak7iw4AcGXVcr7TvhOptk3ivVT7M5J+9vE1igISgDHxYbxYCAtDeBwfHmch2ZRl tbUOAQDW8qKNFMYB7j6efxRbBwDweG4hcW0cQ+KeDQAGUy3nu2o5/1F+bCLwXSbpj7xo37p7By9Q QAIwJrXsD+lLFos8Nc6AK3LFkMY4Rkh8GFUCAD54ZR2gp9o6AIBR8+HacOXTwhwATEG1nL+W9KPs 1+1C8EbSb76MtaOABGA0yrLayY9FD+Zqj58PN76hqK0DAIA1t1AZwo73jesQAICrqJbzjezPFI3E 6HEAGFy1nNfaj7RrbJMEIdbdWLvIMggFJABj8846gKTVYpFH1iFwVbV1gEDUrrALAFP3SvsFS9/9 Yh0AwCT4cM8WQlEfAEanWs4b7YtIN7ZJgpFpP9YuswpAAQnAqLjxYhvjGFIYZxzgTO5xVhvHCAGd WgAmzx2EG8pC5do6AIBJqGU/wijJizYxzgAAk+TORWKkXX+RpJ+tupEoIAEYIx8WrUNZKML52KV9 3K4sq9o6BAB44L3C6D6qGV8HYAjuuaY2jiFxzwYAptxIu+/kx2tCCDIZnI1EAQnA6JRltZb9DoZ4 scgz4wy4rto6gOdq6wAAYM2NmsiMY/TFxggAQ/rJOoCklfW5EgAwda4b6UfRjdRXrP3ZSG+H+oIU kACMlQ83JEvrALged7bP2jiGz3zoBAQAM25R8oN1jp521XK+tg4BYDrcGRgb4xgSo8cBwAsH3Uhr 2yTBeJMX7a9DbISggARgrNbWASRli0UeW4fAVbFb+37bsqw21iEAwNivCmN0neTHxhsA0+PDhiPG 2AGAJ1w30gtJzyQ1xnFCkEr649pn+lFAAjBKZVlt5ccILW5IRsyd8bMzjuGj2joAAFjKi/aDpMQ6 xwnW1gE67KwDALg81/m4M44Ru3GjAABPVMv5plrOv5f0WvavE76LtD8XaXWtL0ABCcCY+bCbdmUd AFdXWwfwkA+7SQHAhCseraxznGBdLedb6xAdGusAAK7Gh3s2Ro8DgIeq5fxG+7F274yjhODDtc5F ooAEYLTcCK2tcYxoschXxhlwXT7c9PqkKcuqsQ4BABYCLB5J3JADsLW2DiApy4s2tg4BAPiaG2v3 VpyP1Mcbdz9yURSQAIydD4v77GgbMVcs2RrH8AndRwAmJy/aKNDiUQjdRwBGzD0H1cYxpPCevwFg UqrlfOvOR6KQdNzq0kUkCkgAxm5tHUBSuljkiXUIXFVtHcAjtXUAABiSO7T2V4W5+Ej3EQAf+LAB ibNrASAAXxSSbsQZSfe5aBGJAhKAUSvLaic/ikjckIybD51uPtiUZbW1DgEAQ3Fzxn+TlNgmOcsN 3UcAfFAt57XsO/qjax5ADgC4LFdIeq19Iem17F9HfHOxIhIFJABT4MPifrZY5JF1CFyHK5o0xjF8 4MPuUQC4urxoV3nR/iHpjXWWM+1E9xEAv/hwz8bocQAIjDsj6aZazr+T9ELSxjiSTy5SRKKABGD0 3Bk1jXGMSFJmnAHXRfGE8XUARiwv2jgv2leucPRBUmwc6THeVcv5zjoEABxYWweQlOZFG1uHAEYo sg6AaaiW83W1nD+T9EysT9xaPbbDlgISgKnwYUdbqLuU0U9tHcBY7UZGAsBoHBSNfpb0h6T3Crtw JEmbajm/sQ4BAIdcUXttHEPing24hsQ6AKalWs431XL+o/bj7dbGcXzwIS/a7NwPpoAEYCpq2R+s Fy8WeWqcAVfixtjVxjEs0YEFIGiuWJTmRfs2L9qfXafRbdEos013MTvtR3sAQ0usA3RorANAkh/X k1letJF1CADA47lzkl7orpC0Mw1k60NetMk5H/j3CwcBAC+VZbVbLPK1pFfGUZZiHuuY/aLxLDKe YleWVW0dAsBoPc2L9u0FP9+3+ryLKNF0Rqu8q5bzrXWIM2ytA3SIrAMEILIOcAwjHf1QLeebvGgb 2RYcI+2v59eGGYBTNZJS4wyAt9z174u8aF9rvy74Up5fm1xBpH0R6dmp1z0UkABMyU+yLyCtFov8 netWwfjU2p+LMTW1dQAAo5aKRZFLqEMdXVct59u8aK1jHJNYBwBG5CfZX0+/EQUkhOVfrAMAIXCF k7d50d5omoWkRPvpCidNJGCEHYDJcEWbjXEMSVpZB8B1uDOA1sYxLPgwbgQA8LBGjK4DEIZa9iOG 4rxoU+MMAIArqZbzXbWcv9V+tN072b/uDGmVF+3qlA+ggARgan6yDqD9GDuM1y/WAQa2LctqYx0C APCgnaQXjOgCEAL3XLU2jiFxzwZc0jfWAYD7TLiQ9D4v2rjvO1NAAjAp7pyWrXGMeLHIM+MMuBL3 GNsZxxhSbR0AAHDUs2o5b6xDAB7bWQfAV3zY9LfKizayDgGMRGIdADjmoJD0vfzYxHBtkU4YF0sB CcAU+TBu66V1AFxVbR1gQD78PAEA7veC4hGsBbAI31gHwOfcYecb4xiS/fm5AIABVcv5tlrOX2jf kbQ2jnNtaV60r/q8IwUkAFO0tg4gKV0s8tg6BK7Gh12TQ2jKsmqsQwAA7vWiWs7X1iEuaGMd4JhT xoBMUGIdAEHyYZMSY+wQisY6ADAmB4WkZ/L8GvSR3vTZ6EMBCcDklGW1lR9FJLqQRsoVVbbGMYbg w409AOBrYysehSC2DgCMiXsO2xrHiPOizYwzAH3srAMAY1Qt55tqOX8m6UfZvyZdQyTpfdc7UUAC MFU+LHyvrAPgqmrrAAOorQMAAD6zk/QjxSMAI+HDPRtdSMDjpdYBgMeolvO6Ws6/k/TOOssVrPKi TY+9AwUkAJNUltVG9rsHosUiXxlnwPWMfYzdxnXzAQD8sJP0rFrOa+McQGg+WgfAg9bWASRljKgE AEhStZy/lfS9xjc28s2xN1JAAjBlPizwM8ZupFxxpTGOcU0+7AgFAOw1kr6rlvPGOMc1scgfrsQ6 AMJULedb+dHxzj0bfLezDgBMRbWcN9Vy/r3G1Y2UHutCooAEYMrWsr/QShaLPDHOgOsZc5Gltg4A AJAk3VTL+ffVcr6zDjJxqXUAj0XWARA0Hzb9rawDAMeEsIEkL9rEOgNwSQfdSFvbJBfzYBcSBSQA k1WW1U5+LIKzo228ausAV1K7nx8AgJ2d9ucdvbYOAgSusQ6Ah1XL+Ub2i3NRXrQr4wxA6CLrAMCl ueLt9xrH2s+DXUgUkABMnRc72haLPLIOgctzY+xq4xjXMObOKgAIQa39yLraOMeQGusAONs31gE6 7KwDoJMP92xs+gMAfKVaznfVcv6jxjHS7t7XOgpIACatLKtG0sY4hsRYhDH7xTrAhe3KsqqtQwDA RO207zr6cYIj63bWATp8ax3AY4l1AARvLfvngIQRXPBcYx2gQ2IdALgmN9LuhXWOR8ryoo2//EcK SADgRzcFO9rGq7YOcGG1dQAAmKgbTa/rKCSxdQCcrbEOgONcwbw2jiFxzwa/7awDdIisAwDXVi3n a+1H2u1skzzK6st/oIAEYPLKslrL/sk9Xizy1DgDrsCdFbQ2jnFJPhRcAWBKNpK+r5bz1xPsOvqL OwcFuLgp/1wFxocxdlletJF1CCBQvo8zBS7CnYv0TPbrjOdafvkPFJAAYG9tHUDsaBuzsYyx25Zl tbEOAQATsZH0rFrOn7kbUfgttg7gsdQ6wBE76wDoxz0PNsYxIjF6HP7aWgfokFgHAIYSeBEp/nJk KwUkANjzYkfbYpHH1iFwee7MoJ1xjEuorQMAwARsdFc42hhn8c3OOsARsXUAnKWxDoCT+HDPxqY/ +OpP6wAA7gReRPqsC4kCEgBIKstqKz8Wx1fWAXA1tXWAC2B8HQBc347C0YMa6wA4zX0HMQPncmdL 7IxjxHnRpsYZgBCl1gGAoR0UkUKTHv6FAhIA3PFhcfyrWaMYDR92TD5GU5ZVYx0CACYgY3EyTHzf 7hVbB+jQWAfAydbWAcQ9G/zUWAcA8DVXRHphneNEyeGZfxSQAMBxY8a2xjHixSL/IG5KRscVX7bG MR7DhwIrAEzFG+sAnvpoHQCj8y/WAXAyHzZlreiug4d21gG6sNECU+U6aG+MY5wqu/0DBSQA+JwP i+QrMcpurGrrAI9QWwcAgAlJWWQJUmwdwEOpdYAOjXUAnKZazrfanxVn7Q9Jv1qHAA7srAMAeFi1 nL9WWNcdT27/QAEJAD53Yx0Ao+bDjslzbNw5YQCA4XywDuChxjpAh9g6AE62sw6As4R6TQ1cjRuT 5bvUOgBgLKRRduntHyggAcCBsqx28mOuNkbIFWEa4xjn8KEzDwCmJs6LdmUdwjM76wAdvrUO4KEn 3e9iqrEOgNNVy3mtsEdDA1P1jXUAwJIr9L6zztFTcvsHCkgA8DUWy3FNIT6+ausAADBRnIV0oFrO N9YZOsTWATwUWQc4plrOd9YZcLYQr6mBa9tYB+iQWAcAPHAj/zdFSbo7t4wCEgB8oSyrjdiNiOup rQOcqHadeQCA4cV50b61DoHeYusAHkqsAxyxsQ6AR7mxDgDgZIl1AMCa27wSyijWWKKABAAPCeXJ HIFxY+xq4xinYHcnAGtrSc+u8Gs33H/Co7zMizayDuGRjXWAI2LrAB6KrAMcsbMOgPO5Bbi1cQzA Nx+tA3SIrAMAnlhbB+gplqS/G4cAAF/Vkt6LCxxcxy+SMusQPezKsqqtQwCYvD+vMbosL9qfFMaI uEjSK0lvTVP4Y2cd4Ji8aJNADjK/utuxJx773ToAHq2QtLIOAaC/vGjTAEbSAldVLefbvGhr+b8u 9FSiAwkA7uVGdtXGMTBetXWAnmrrAABwRTfyvBhx4E1etLF1CE/4vugfWQfwSGQdoMPWOgAexy1C b41jAD7ZWAfoIbEOAHjiF+sAfVFAAoCHvbMOgHFyBcq1cYw+GF8HYLQCmz8uhdEtNYTGOkCH1DqA RxLrAB221gFwEdyzAXd21gF6+NY6AOCJ2jpAD6lEAQkAHuTOqtkYx8B4+b7bZFuW1cY6BABc2Y3C WGyRpBVdSJL8X/T/xjqAR55YBziGEUqjUSuc53HgqgIZoZpYBwB84DazbYxj9EIBCQCOowMD17Kz DtBhax0AAK4twC6kD9YBrAWwOJZYB/BIbB3giK11AFyGex6vjWMAPtlaB+iQWgcAPPLROkCXvGgT CkgAcERZVmv5v9APAADOdyP/F1tupXnRptYhPNBYBzgisQ7gkcQ6wBGNdQBcVEgbAYBr21oH6JIX bWKdAfDExjpADxEFJADoxg0JAAAj5Xavh3SGBmch+b04FjFqMIjFwd+tA+ByXGfixjgG4IvGOkAP qXUAwBONdYA+KCABQLe1dQAAAHA91XK+lt9FiUNpXrSZdQhjvi/+x9YBPBBbB+jQWAfAxTF6HNj7 0zpAD16fkQcMxW1k8x0j7ACgS1lWWzFXGwCAsQupC+m9dQBjG+sAHVLrAB5IrAN0aKwD4LLcRoCd cQzAB411gB5S6wCARzbWATowwg4AemKMHQAAIxZYF1KcF+3KOoShxjpAB3ZWS0+tAxyxq5bzrXUI XAX3bID/r5HS/jomsg4BoB8KSADQQ1lWG4WzqAQAAM4TUhfSm6kuvrhxH1vjGMck1gE8kFgHOKKx DoCrWVsHAKwF8Bp5K7UOAHiisQ7QhQISAPTHjjYAAEYstC4kSa+MM1hqrAMcMemd1XnRxpIi4xjH fLQOgOtwnWW1cQzAB1vrAD343KkKDOlfrAN0oYAEAP2trQMAAICrC6kL6eWECxW/WwfokFgHMJRY B+iwsQ6AqyqsAwAeCKFQnlkHANAPBSQA6Kksq50oIgEAMGqBdSFFkt5YhzCysQ7QIbUOYMjrXeXV cr6xzoDrqZbzWuE8hwPX0lgH6CF2HasAPEcBCQBOwxg7AADGL6QupFdTXIAJoAjgdRHlyhLrAEc0 1gEwCO7ZMHWNdYCeUusAALpRQAKAE5Rl1SicizEAAHCGwLqQJLqQfJRYBzCUWgc4YmMdAINYWwcA LLnzwHbGMfp4bh0AQDcKSABwOna0AQAwfq+tA5xglRdtYh3CgM9nPERT/J7kRZtaZ+jg82MGF1It 5ztRRAI21gF6yKwDAOhGAQkATlcrjN08AADgTO4cjY1xjFO8tw5gYGMdoENqHcBAah2gw8Y6AAZT WAcAjP1uHaCPvGgz6wwAjqOABAAnKstqJ3a0AQAwBSGdhZQG0P1xUZyD5CWf/5sb15mCCXDPD41x DMDSxjpAT4yxw9Q9sQ7QhQISAJyHMXYAAIycW4DcGMc4xRTPQtpYBzgitQ5gILUOcMQv1gEwOO7Z MFkBbLK4lVkHAIxF1gG6UEACgDOUZbWV3wsWAADgMkLrQlpZhxiYz2faTOocpAA64GrrABhcLUaP Y9o21gF6iCZ47QIciqwDdNhQQAKA87GjDQCAkaMLyXu1dYAOmXWAAfk8hmhXLeeNdQgMy40sXBvH ACz5vMnikM+vH8C1JdYBulBAAoAzlWVVS9oaxwAAANcXUhdSPKWdvK4osDWOccyUFsVS6wBH1NYB YIZNf5iyjXWAnrK8aGPrEMDQQnncU0ACgMcprAMAAIDrCrAL6X1etJF1iAFtrAMckYSyOPAY7r8x MY5xTCi78HFh1XK+ld/PEcDVuOuXnXGMvlbWAQADsXWALtVyzgg7AHiktXUAAAAwiJC6kCJJr4wz DOkX6wAdUusAA8isA3SorQPAFJv+MGUb6wA9La0DAAZS6wB9UEACgEcoy2orikgAAIxegF1IL6fS hVQt57V1hg5TGGPn88Jf7c7CwURVy/lafo+6BK7J900Wt+K8aDPrEMDAnlgH6LCRKCABwCWwow0A gGkIrQvpvXWIAdXWAY7IxlzMC2B8XSiLp7gu7tkwVRvrACd4aR0AGFhqHaDDTqKABACPVpbVRuxo AwBg9ALsQlpN4fwdx/ciQWYd4Ioy6wAdausA8MLaOgBgwZ0D1hjH6CvNiza1DgEMIS/aRPsNXz77 XaKABACX8pN1AAAAMIgX1gFO9MY6wEBq+X1QuM8j3h7L5/82xtdB0l+L6LVxDMBKSB14U7luATLr AD00EgUkALiUtfxetAAAABfgFiHXxjFOsZrCbl5XJKiNYxyTjrEbzO2eTYxjHON7ZxqGxaY/TNXG OsAJ6ELCVIRwRuZWooAEABdRltVOfi9aAACAywnpLCRpOrt5fS8WjPFsB5+7j3bVcr62DgF/uDGk W+MYwOCq5bxRWI/9qVy3YKIC2IAj7a+jGokCEgBcEjvaAACYgAC7kCaxm7dazmv5vUC2sg5wSXnR RvL7v6m2DgAvcc+GqQrpsZ/mRbuyDgFcUQibiprbP1BAAoALKcuqUVit4QAA4HyhdSG9tw4wkNo6 wBHRyBbEMvl9+HNIi6UYzlqMHsc01dYBTvTebVQARsU9rjPjGH18vP0DBSQAuKyQDqcEAABnCrAL KRlZ8eIhvhcNQthx2pfPI4aa27ErwKEAzksDrsJdtzTGMU4Rye/XGeBcr+T3Bpxbm9s/UEACgAsq y2otdrQBADAVoXUhjX4hxi2QbYxjHJOMYZxgXrSZpNg4xjG+FxJhi8cHpiq0x/6rMbxmArdc91EI m4l27txASRSQAOAa1tYBAADA9QXYhRTnRfvKOsQAfF8gG0Mhz+fFj53oMMERrjutMY4BWKgV3obX D4yyw4i8V2DdRxIFJAC4Bt8XLQAAwOUE14U09oWYajmvJW2NYxyThryj2mVPjWMcs3ZjyoBjuGfD 5AQ6wjGW9ME6BPBY7vppZRyjr18O/0IBCQAurCyrrcK7KAMAAGcIsAsp0n72+tj5vjgccheS7wt5 vn/v4YFqOV8rvE4M4BJCfI7MJtJBjZFym7d8v346VB/+hQISAFxHYR0AAAAMJrQupJd50cbWIa5s Lb8Xh9O8aFfWIU7lMsfGMY5Zu6Iu0MfaOgAwNDfCcWMc4xzv3fl7QIjey+/rp0P1l53cFJAA4ArK sqrl9+gUAABwIW7BOqQiUqSwO2A6uRtf33dZBzVO0GV9b52jQ0g/h7Dn+3MEcC2hbnj9kBdtYh0C OIXrnlsZxzjFV88PFJAA4HpCvSgDAACnu5HfHS9fWk2gC+lGfn9PYoVVyHsjvw9+pvsIJ3GPl41x DGBwboTj1jjGOSJJv1JEQihc57bvm28O7dxZop+hgAQA13NjHQAAAAwjkI6XL4V0Q3uyQL4nr0JY CHMHP78yjtGF7iOcw/fnCOBaQn3OjEQRCQFwxaOQzj2SHnhNpIAEAFdSltVOzNUGAGBKbuR3x8uX MlcYGLMb+f89+dnnUXaBHPxM9xHO4nZab41jAIMLuAtJuisiZcY5gHsFWjySHljDpIAEANfFGDsA ACYikI6XL4U0Qu1kgXxPYvndDfZB/h/8HOpOeviBezZMVcjPnZH2GzBeGecAPpMX7QcFWjx6aDMO BSQAuKKyrDaSGuMYAABgODfyv+PlUDqRLqStcYYuKx8XwfKifSspM47R5R3dR3ikG+sAgIXAu5Bu vc+L1utOXkxDXrRJXrS/SVpZZznTgwVlCkgAcH2+73oFAAAXEkjHy5dC3CXZm/uevLbO0cN7N/LE Cy6L7x1qO7H4j0dyzxFr4xiAlZC7kG5lkv5gpB0s5EUbuQ03v0lKbNOc7egoYApIAHB9tcLaiQwA AB7nRmG99sc+FS6uwZ1zsjGO0ccHH74XAc3uf+0W/4HHYowdJsl1IW2MY1xCpP1Iu1/zoo2Ns2AC XOFoJekP+b/hpsvRQjIFJAC4srKsdtoXkQAAwAQE2oUU+o1vHy+sA/RkWkQKqHi0cQufwKNVy/lG 4Y/yAs41hi6kW6n23Ugf8qJNjLNghPKijV3H0R/aXy9FpoEer3MUMAUkABjGmC7IAABAtxuF14X0 1jrENbmb41CuyT5YfD/yon2vMIpHO4VTEEQ4Qnl+AC7KFVDXxjEubSXpN9eRtDLOgsDddhvlRfuz 7jqOIttUF7FVj1HAFJAAYABlWW01jrZwAADQQ6BdSC/Hfgh1tZy/ldQYx+jrzVCjeA4Ofn517a91 IZ27ZYEz1Aqr8A9c0muN8/Gfar8p459dV1JmnAeBcNdGr1zR6J+132CT2aa6uF6jgP8+QBAAwF6h /cULAACYhhtJS0mxbYzeIu0LCG9NU1zfC0m/Koydo6n2o3jeSbq59Hk/rmD4RuEUjqT96Lob6xAY n2o53+VFW2vfuQBMinv8v5D0s3WWK4m0/9le5UUr7Tf4ftT+NWVjFQp+cOMOY0mJpKfu98gqz0Bq d0ZoJwpIADCQsqzWi0X+XuN/EQIAAPprMeadwhgJdutNXrTrMXd3VMt5474v762znOCN9h1iP0l6 9PfHdTWtJL1UWNemOzG6Dtf1kyggYaKq5bx2RdTMOMoQUvfrjSsobd2vj+7tG/f7rlrOmyGD4XJc YSg6+Kfbv39z8OdkyEye2OqE6ykKSAAwrJ80jUOqAQCApGo5X+dF+0bhdCFJ+2uVUS/SV8v5TV60 TxXWIlmk/ffmjVvg+0X73aO7Ph/suo0ySc8V1n/3oRdjLm7Cniswb8TkCEzXC+0X1GPbGIOL3a/U /f2vdRtXYArFs8d0VOVFm2rfpY1xe3FKVzsFJAAY1loUkAAAmJrQupBWedFO4YyZF7obVxKazP36 kBdto/1O0t/d2xr3e+J+f6JxLAbe9B21AjwSo8cxWa57+kdJv1lnAXAVr08tMv7tSkEAAPcoy2qr /eGsAABgIqrlfK39An9IQip4ncXtvHyh8A8NT7QvJr1xv352v27/nin84tGmWs5fW4fANLjn7J1x DMCMG9k26k5kYKLW55wjSQEJAIb3k3UAAAAwuHfWAU6UujEmo8YiWRAaST9ah8DkcM+GSXOF1LVx DACX00g6azMOBSQAGFhZVhuFtwsZAAA8QqBdSJMYu+vGolFE8tNO0o+nzOkHLmRtHQCwVi3nL3Q3 FhVAuBrtz8fanfPBFJAAwAY72gAAmJ4Qu5Ay6xBDcAW+G+MY+NxO+8WOrXEOTJB73NXGMQAfPBNF JCBkjR5RPJIoIAGAlbV1AAAAMKxAu5DeWwcYijtjZ22dA5LuikeNcQ5MW2EdALDmFp0pIgFhaiS9 eGwnNwUkADBQltVOLFAAADBFoXUhxXnRrqxDDMWN61lb55i4nSgewQNuvOXWOAZgzi0+/6j98zOA MDS60PUUBSQAsMMYOwAAJibQLqQ3edFG1iGGQhHJ1E4Uj+AX7tkA/TXW8TvRiQSEoNEjx9YdooAE AEbKsmrExRcAAFP02jrAiWJJr4wzDMoVkUL7PoVuJ4pH8M/aOgDgC8bZAUFY64LFI4kCEgBYY0cb AAAT48YibYxjnOrllLqQJKlazm8kvbDOMRGNpO8pHsE3bgFubRwD8MZBEWljmwTAPV5Xy/mjzzz6 EgUkALBViznCAABMUWhnIUWS3liHGJobOfi9uF67plr7nbJb4xzAQwrrAIBPquV8Vy3nzyTdWGcB IGk/Hvt7t/np4iggAYChsqx2YkcbAACTUy3nG4W3e/dVXrSxdYihua6Y7xTe9ysE76rl/MdL75QF Lsk9XzfGMQDvVMv5a+07dXfGUYApu9GVu7gpIAGAPcbYAQAwTaF1IUkT7EKSPtttHeL3zEdb7Rc7 3hrnAPring24x0GnbmObBJicrfYd3K+vvRGHAhIAGCvLait2tAIAMDmBdiGt8qJNrENYcQUPFsoe 50acd4Tw1KLLArhXtZxvq+X8e+03WeyM4wBjt9P+rKPv3L3E1VFAAgA/sKMNAIBpCrGj5b11AEvV ct6wUHaWrQbaKQtcmnvMro1jAF472GSxsU0CjNJO+2vP76511tFDKCABgAfKsqq1v6kGAAATEmgX UpoXbWodwtrBQtnaNon3dhp4pyxwJWz6Azq4bqRnkp6JNQ7gEna6Kxy9tdiEQwEJAPxRWAcAAAAm QuxCmuRZSF9yC2UvxI7r++xktFMWuIZqOd+Kn3Ogl2o531TL+XeSXohCEnCOrfY/P2aFo1sUkADA H2vrAAAAYHgBdyGtrEP4wo21eyY6kiQPdsoCV8SmP+AE1XK+doWkHxXetU5oGj2+WLfV/sw32Nhp fx35zHVur324jqKABACeKMtqKxYcAACYKrqQRsAVkl5I+k777+nWNtGgNpJeVMv5P1A4wlhVy/la 0/q5Bi6iWs5rt9HiO0k34gzBS9hpX+x5IekfquX8e9cpeTbXWf2jpH9wn3ctvldDqHXXbfTCt5G/ f7cOAAD4TCFpZR0CAAAMq1rON3nR1pIy4yiniPOiXbkFVRxwCzhvJb3NizaTtFRY39u+ttovevz0 2EUrICCFKKADZ3GvFa8lvXavj8+1f32MzEKFY6t9l9FHSZtqOW+u9YXcJpC1+/UiL9pEUirpifs9 vtbXnoit9htvfqmW89o0SQ9jKyCt5Xc75NY6QKCeWQfo0FgHONNr+fkCvbMOYKksq81ikfOYn4ZG fj+/7awDwEs7+X2t1VgHCNRWfn9ft9YBBuTr9dkxz0UH9VFuYaDOizbSfpHsufaLL5FVpkfaal80 Kq65eAVJ/r6uba0DGFtLemodYqQa6wAddtYBxuT29VH7AkWm/c9VJooT0l2x6Hf3e2O5UcO93je3 f3fXNInuikqJ+L4ds9VAxb9rmFkHAAAAAABgavKiTbVfeHnqfvfVTvsi80dJNZ1GAIBryos21t3r Y+J+jdXW/fp4+2ffxpedwl3bxO7X04M/T8lOd8WiRsbFv0uggAQAAAAAgDG36JLobidvYhRlo4Nd z6HtkgUAjM8Xr5Gx/N54cWinu86dj+73jaTdlF5f3Qi8SPvvYaT99zFS2AWmRvvv70fdfZ+bMZ4B SQEJAAAAAAAPuV3Ysb5ecNHBv51iq7vxY1tJfx782ygXPQAA43QwRu32d+nz18lYlytO7PT1iMWd 9pstbjW6G3PIa+qJXJFQ+vz7KUnf6uvvY6LLjwLe6vMRrVvtr5M+e1vIHWIAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAcK+ZdQAAAAAAAAAAAAAMJy/aSFLywJt31XLe/H24OAAAAAAAAAAAADiUF2165M2x+3Wf b/RwEUjubdHpibSR9IwCEgAAAAAAAAAAmLy8aGM9XKyJdLxY80QPF2u6PtZLFJAAAAAAAAAAAIA3 OsarScc7a77Vw0UgSUpPTzRNFJAAAAAAAAAAAMC98qJN9HCxJtbxYs3TI2/r+lgYo4AEAAAAAAAA AIDnOsarScc7a4515UQKcLwaro8CEgAAAAAAAAAAJ8iLNj3y5kQPd+x8o/NHswGDooAEAAAAAAAA AAhSx3i1SMeLNU+OfGwsxqth4iggAQAAAAAAAAAeJS/aSMeLNemRtx0br9b1sQCuhAISAAAAAAAA AIxIx3i1WMeLNU+PvC0R49WAyaCABAAAAAAAAABXkBdtrIeLNZHOH6/W9bEA8GgUkAAAAAAAAACM Wo/xaokeLtZ80/Gx6emJAMB/FJAAAAAAAAAADCYv2kTnd9YcG68W6/hoNgDACSggAQAAAAAAABPU MV5NOt5Z8+2Rj43EeDUACB4FJAAAAAAAAMBYXrTpkTfHerhY0zVeLdHD3T4AADyIAhIAAAAAAADg PHK82pMjHxuL8WoAgIBQQAIAAAAAAIB3/r2ijXRbrJnd/funT9Kn/R8TPVysOTZeTTo+mg0AAIgC EgAAAAAAADr8B8X/l36a/VW4OSzi6NNsFssVa/52UOn59OnT7duf/uvtv7mPlSTNZpIrAn06+Lh/ PfzzJwEAACMUkAAAAAAAAALxHxf/byxXrPmkmT59ui22/FV0ST/Nvv449z5PJEWf9HkhyIk0myV/ vefssBD02NQAACBEFJAAAAAAAADO8Hr9/6TSQaeNZn8VZjQ7GK922HGzL8x8Iyn55N7w6aDi4/6Y 3n6+vz7dQRGHeg4AABgCBSQAAAAAABC0/+zD/53onrNw3Ni0SLfn6EjSYdfOTJI+Pd3/cXb7Dzqo 58T/+um+c3T+KgQBAACMFgUkAAAAAABwEf/l//Yx0mfFGn3WLvNpNkvv+zjXwPOt9mfp3Oevz/tp djiyjSIOAADAtVBAAgAAAABghP7xf/2/0tvizaevu2Xi/a+vCzCfZrNv9GURSNLB+ya6p9tHsxmj 1QAAAEaEAhIAAAAAAFf0T//z/xlLX45B+6sYE2k2S778mIOzdJ7osFjzeb3Hfd6ZG7+mixRxZuKM HQAAAFBAAgAAAABMxD/9T/9H9OmeYs3eTLrtrJkd/NttJWU2+1ZfFYEOCi2zWTo76PKZMVoNAAAA gaOABAAAAAAY3D/9u/89kRQdllk+3RVu4v0vSTNp9vX4tacH73z4R336NNt/3pk0OygEfaKlBgAA ADgJBSQAAAAAmLD/5n8s4tnMFWu+tC/ApId/+ezNf/vbwXi1r94eSUo+uerObHb4LhR0AAAAAN9R QAIAAAAAT/zjf/+/pNJh58z+LJy9WTKbzaKvPmj/zt9ISu7p1Ll9p9T9ztk2AAAAAHqhgAQAAAAA X/iv/rt/l8x0V6z59HlhJpKUHHbczD4/8ebp3RsOx6zNpP0ZOvHdux58jgeLPwAAAAAwPApIAAAA ALz1X7z7HyJJiaSDzpnZYbElva/u4samfSspnv3t7t/vPm6mT5qlX34cNRwAAAAA2KOABAAAAKCX //S//m/Twy6Z29FqM0mzmeusmd2NSDvoyflGs1miv/794A/7z5dIs+j2TJzZ7It+HgAAAADA4Cgg AQAAAIF5/Z//Y6y/xqAdttdIkiJpltz+5Z6Omif79zn4sLt3imYHhZ4vPzEAAAAAYDooIAEAAABn +rcv/5NIUjI7nJF2a19zSSRFn3XT3L3vt7PbItBs9vn5N/s/p599OmarAQAAAAAGRAEJAAAAo/Af /kf/NtFtZ81BscUVb2LNbjt27i3GPN3/+1fdPJrNZrE0iz/7R3FWDgAAAABg3CggAQAA4KKWqxex pHj2t6+KOHIFmFTSfR03kvRkdjBe7a/3mc2kmSJJyUwz3R6y84kqDgAAAAAAV0EBCQAAYMT+zb/5 99P9n2Z/FWn2tRhJUiLNor/e+bBrZ/a3byQlM33RaXP7OTRL9Fe3z+Gb7xnlBgAAAAAAgkMBCQAA YADPsx8SHXTWHJrNZtFsf1bOZ105s8OunNldoWd29w5yo9XiBz4OAAAAAADgLBSQAADApDzPfojk ijUPSI+87Vvp7hydEz8WAAAAAAAgGBSQAACAmefZD+mRN8c6Xqx5euRtiR7o9gEAAAAAAEA3CkgA AEDPsx9iPVysiXS8Y+eJHi7WdH0sAAAAAAAAPEQBCQAAj/QYr5bo4WLNNx0fm56eCAAAAAAAAFNE AQkAgAc8z35I9HCxJtb549W6PhYAAAAAAAAwRQEJAOC9jvFq0vHOmm+PfGwkxqsBAAAAAAAAX6GA BAA4yfPsh/TImxOdP17t2McCAAAAAAAAGBAFJAAIVMd4tUjHizVPjnxsLMarAQAAAAAAAJNGAQkA Hul59kOk8ztrjo1Xk46PZgMAAAAAAACAq6CABGBUOsarxTperHl65G2JGK8GAAAAAAAAYCIoIAG4 iufZD7HO76w5Nl4t0vFuHwAAAAAAAADAI1FAAkbukePVvun42PT0RAAAAAAAAAAA31FAAgb0PPsh 0fmdNcfGq8U63u0DAAAAAAAAAEBvFJAwSY8cr/btkY+NxHg1AAAAAAAAAEDgKCDB3PPsh/TIm2M9 XKzpGq+W6OFuHwAAAAAAAAAA8AAKSPhLR1dOpOPFmid6uFhz7PMCAAAAAAAAAADPUEDy0PPsh0jn d9YcG68mHR/NBgAAAAAAAAAAQAGpy/Psh0Tnd9Y8PfK2Y58XAAAAAAAAAADATDAFpI7xatLxzppj 49UiHe/2AQAAAAAAAAAAmJSzCkjPsx/SI29O9HCx5hsdL9Yc+7wAAAAAAAAAAAAYwN+lewtCsY53 +5zrXyR9PPL2Y28DAAAAAAAAAADAdW0l6f8HlYJMoBOA1mEAAAAASUVORK5CYII= "
       id="image747"
       x="218.82277"
       y="273.94888" />
    <text
       xml:space="preserve"
       style="font-size:5.64444px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:61.0389;fill:#ffffff;fill-opacity:1;stroke-width:0.264583"
       x="194.47379"
       y="112.34783"
       id="text1857-9-9-67"
       transform="translate(166.70548,61.329057)"><tspan
         x="194.47379"
         y="112.34783"><tspan>The team on the </tspan></tspan><tspan
         x="194.47379"
         y="119.40338"><tspan>dutch championship.
</tspan></tspan><tspan
         x="194.47379"
         y="126.45893"><tspan>Left: Jorn
</tspan></tspan><tspan
         x="194.47379"
         y="133.51448"><tspan>Middel: Nathanaël
</tspan></tspan><tspan
         x="194.47379"
         y="140.57004"><tspan>Right: Abel  </tspan></tspan></text>
    <image
       width="58.969437"
       height="48.542709"
       preserveAspectRatio="none"
       xlink:href="data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAASABIAAD/4QCMRXhpZgAATU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUA AAABAAAASgEbAAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAABIAAAAAQAA AEgAAAABAAOgAQADAAAAAf//AACgAgAEAAAAAQAABVOgAwAEAAAAAQAABGIAAAAA/+0AOFBob3Rv c2hvcCAzLjAAOEJJTQQEAAAAAAAAOEJJTQQlAAAAAAAQ1B2M2Y8AsgTpgAmY7PhCfv/iAihJQ0Nf UFJPRklMRQABAQAAAhhhcHBsBAAAAG1udHJSR0IgWFlaIAfmAAEAAQAAAAAAAGFjc3BBUFBMAAAA AEFQUEwAAAAAAAAAAAAAAAAAAAAAAAD21gABAAAAANMtYXBwbOz9o444hUfDbbS9T3raGC8AAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACmRlc2MAAAD8AAAAMGNwcnQAAAEsAAAAUHd0cHQA AAF8AAAAFHJYWVoAAAGQAAAAFGdYWVoAAAGkAAAAFGJYWVoAAAG4AAAAFHJUUkMAAAHMAAAAIGNo YWQAAAHsAAAALGJUUkMAAAHMAAAAIGdUUkMAAAHMAAAAIG1sdWMAAAAAAAAAAQAAAAxlblVTAAAA FAAAABwARABpAHMAcABsAGEAeQAgAFAAM21sdWMAAAAAAAAAAQAAAAxlblVTAAAANAAAABwAQwBv AHAAeQByAGkAZwBoAHQAIABBAHAAcABsAGUAIABJAG4AYwAuACwAIAAyADAAMgAyWFlaIAAAAAAA APbVAAEAAAAA0yxYWVogAAAAAAAAg98AAD2/////u1hZWiAAAAAAAABKvwAAsTcAAAq5WFlaIAAA AAAAACg4AAARCwAAyLlwYXJhAAAAAAADAAAAAmZmAADypwAADVkAABPQAAAKW3NmMzIAAAAAAAEM QgAABd7///MmAAAHkwAA/ZD///ui///9owAAA9wAAMBu/8AAEQgEYgVTAwEiAAIRAQMRAf/EAB8A AAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMFBQQEAAABfQECAwAEEQUS ITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkqNDU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4 ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/EAB8BAAMBAQEBAQEBAQEA AAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMRBAUhMQYSQVEHYXETIjKB CBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2Rl ZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJ ytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/bAEMAAwMDAwMDBAMDBAYEBAQGCAYGBgYICggI CAgICg0KCgoKCgoNDQ0NDQ0NDQ8PDw8PDxISEhISFBQUFBQUFBQUFP/bAEMBAwMDBQUFCQUFCRUO DA4VFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFf/dAAQA Vv/aAAwDAQACEQMRAD8A+rJ9ftPElkwljaPRDZST3sxjPm4Q7TtC5KPnBTj1rj4tYu49VfTIYYNS h8qOKEmFdjIuCsnzgeXuz84GeR2rqp9LHha6uL+PWcPLEiCO54y2M87BhvU/LxXE/br/AFbRNS1u 4uIZjEuES3k3PEqnarEMu0qfqDXQ5NbnNCJbuD/ZesyTXRe8tb23ltiIwqNDJccxuMkY9Nwxz2PW suCS5tzZ2sc0NtPG0LILiMqJHhbAY5xnpzgZ710Musaf4j0m3tLJI49SkmjSO8lJjdUfuByHUEEc /d7Vz19faXpOqXekOsdzZ3U8AS/beBbsADKIywyHYDgNjpWEqxpGnqa+lMfE2pS6xFb/ANkyQXXm eTCCySTgbQw77fbGK7K8uNX1OxeGw8iLUIJQlxADuOIwCy7T3fqMcc151pesacsEh0C7ka9tb55U xCwUxQLuJlDYwW5HHepbnxb/AMJEhv7YRaLNqMDW090+AYjnKDAU4Y4784rSnXjGFyXDUXxRquqX F/bTM76beo/lkchEZuNhePKAH73XPH0rA8ReLbsyLDcz/bUd1Ed1IqyNAF5CK3ORnnDD/Gs238Ra vbeJHluVMen25aOUIVTeFDBTJtOGYtxn061fvdL0iWzvjp00tg8bAzRhlOfM5DbXCjIDEfLXHUxP OtAVM5fW7mwPiqS9tcTwRMonkkQbWYRYL7R0x1x2/Wu80e8a8m1HxDNeQyXkMSKquytHtRsum8YH TgZHB4Fed3un2FrqGnXdzAt3YBhaXN8kgHmBQAWdIwenr34+lekeDk09dK127URB/srPB9obiSJj jOOcyH7p9MZrkwlSSm7s15dDJ1rw9q8X2XVVshpVhq06GJkn3SkOPneQyFiT0wmTg/w15/f6LdeG 7oW+oSCV593+jqvmyLH0UyKoChtuDwfwr1v4geIrbVfDWhXVnOIr7TZFEdpLnkKSJGHZl4453Z7V ynjLWoZIdD1XToFt7x4zBKJM+adyAbRnrz3PrxxU46EbbhA5CbxBql7oEGk6ldvLpdjM3lqVGY2V icDpyw4Ga1rT4fyX8til1MI0uWiVxKrJ5KvypduATtIx2PvS+HVZJljns1kuNN3gwbNzM5wU3Ifv Y6fQ10B1nWtJvZ1fTzbab4jh2JB5mzcsHy5VnywPsQAP4fWuXCQi/emXKIy38P8Ai66m+y26Jeaf pUgt4yeWt2DbkkKdFDkZyCa7pvFVrrOtaFdaVCkOsWtwd4YqVkklfaY2bHO0A/TisnWxbnTtH1zU p/7N1BLXc9jpas8ssYYGDzbhWAz2+Yk8c1z2pwT2C2nibTW8nSYbuB7lJAu/z2X5m3DH48V6cJW2 MZo6PX9LbxV4xbSEvfscEc0lv58qgC3YjzPJkkUbWHBCjrT9Q8UXFlJBo1lpiRW2hYjGVEvnngPK 0i4O88MF6hRwKg1nxPYah4VfUvDtnFaXWpX32UxB2KRDGFuXDcbz64xmvJdJ1C+tEuDdCW6giunW SQNgPKBiOQEADI7+1Y1sUqbCnTvudz4n8LXVjr8V5Z3K3FtdoJ5LuNlkj8x03bZDtAUn07dPSrvg /TrXw/YQ+KryFr+1cjNssewxxN/GGUjPzHPstcVb6xb/ANlS2Ml9Jbo0yXEkKkHcQNu3Hpmp4fEc UVtb2ZiuSdP3NAvmBPvcOnQhgQBwf8K444yKqc9za142PRbm+Nhcb9FuEuNVMpjuZ4mUqlux+RSe eCO/978a3Cuk6Rc3+qeSz29vEyCc/I8E8mCxYcZkReMDvXlsV3eaYx1SeNjHcjdMqhSy27nLEBtm cgY6fKffirMd2Z757vWtV3W4mluBbhRMvmOMAP05wAN3T3rujjluYOB2GrW9nbhYYZ47Ea24Ebuw lihDACUzyHhpGUfwnAPArDtLO6sHubJJTKtrG8CSbCAkcf8AqlQ/QA81xVubZXm0y/uma0mLZiiO 5Q7ZC4flcr14P0rsPDcR1+5u7e7nm/ty92/Y4JIzKJxjqfuhUCjnvSWKVWegW0O9kvrrTNFtNb1G OOa1mlP2xpEDOcxk7gwzt5HArl/CXi7SblHtdaujvbc3m9CgbgYxwRjHNaM19ovmX+iwzZtZ7ZYN Qhjl2xJNAM74t3IIGQ3r2rx27sNMdIdc0y5dLNSbeVWkDPCNqhQuQMqxJ7cVviMU42cSIo71tEur 2eK9u7Bo49bleQzrNmBbfB8qJlHHIXdz1PSuVstRM2s6faC5mC2WAJJGUbTITgoG42g8/StzWbDX Nd0ma+k1EQ6dZRpDDas4Vnjb5QpVSMgD8/oK4yWGPUG+3aZaC3WO3QOsz5BkhQFzn09O/P4Vw4qX M1JmlPsd7Jpz6OkWrG9k/tHXA5bzflLLCcBy5AADg5HHtWnps26f7XGJZ5nBeUHnYcY2xqRgj3zz 1rjpNbuPF+tJDKsi6YbRIyp+Y24iHRVOMIT79q1rC28QaYUudOEM1pc4TyWJVolzsZkyec47dK66 VVc3ug1uj0m0vZLl5ReOybGLIkgCrtdccjaeorO06wtwJ9PuydkJ85Y2P3YzzyyjgVDpbQXl/a29 gfs9xAzRyQyEus8HUDcRjINR3VprLar/AGaHO2ZJCEGVZl2fdBxyM9q9CdWy5kZwp3kkU7jx14X0 yUxRXLXEa8FY0LMpH91mArn734uWiKf7P0t3bJwZ5fL/ADCZrx35mlCEZVd4I4yMHHasEmYhljBw pJyBnvXlSzutrFH6HlfC+DmlKpqek6z8Std1SFrYLb20TjBEUeSfQ7jj+VY9rqV+Lxrpblll2Aow PGe4OK4yFxJdC286N5hzs3KWAAySQK37e+tJIGaI+bEmVaSPlVYHpmvIxeLqt3bNs1jg8JFU6UUb cerXVjPHfm5aS4dWGHy3IPXnp2rs7Hx7N5P9n6z+9tn2ksB8y8gt1+lecsBNMDjMZjYY91xTmd7i OT5C4QkbVxx3P5Vjh8wqQd7nytClRm3zI+idE8Q6De2sNuNTheZRtHm/uiecDhsdvSrzaxpyXCWx m+d38sMoJXd6DAx+NfLV7OhaMthvkHGM45wAOn6U221K6tLhXtJmhePLAqSNpB7Zr6KjxG4xVz3o cAqvT9rSnufXXr32kg+2DTTgjIrwK0+KHiS1CrdvFfqOvnpliP8AeUD+Vb9x8T7+6jK29hb25dAM szSf+hAV9Bg80hX0hufJZxw1icAuertc9YAJPAJ9sUskcLwOl0FELDawk4U+/wD+qvBpfF+vSqIx evGmMYiCx/y5/WsWa9muCXuJnnOQfndm/mTXc4SZ845roe43On6Elp9gjs5b0XGVB81ti+46ipNM 0Ow0pFMNsqTquzK9FH1/+tXkb6he2V2TaXDwbUQjB4GVGOOldBaeO9WQgXQivFHUsNh/ArWMcPyy ua/WLrlPTXbp+C44J/Cq5IP4f0rG0vxz4fee3bV7K4VC/wC+CMGjMfsfvZ/CtU6poF/cSnSrxBDu PlxykLIq9gc4rdS6CjJAabirRhcchTgdwKjK1fMUyGmkVLgY7Um3jpTuKyGAGlp1JjPalZEyQlMx T8GkNTYkbSY5p204zijBpANzTgaaQc0oHagqw/r0pwpq0/FBIzByaeB0oA5p+DTuWJipB0pAOKUH tTaFceKcKZ6U6lYkG5NNwamA4oxTuBEOBTlp+OlAHNACjA69acO+OlAWndDVgSgcU6kXoKdg0AJT hTadkUALRSUZoAWk7migCgB9FFFACjrUgPB9hgVGOtOxn6+lKXwhF6nCas2oz6/eiF7eCCGZWUup dm+XI44HenKtw06W9zfPLC6MQsSxxAY9gD/Oo9WEg127dpnWJ5U+RSFwNi87quwy+HYzuuJIpJAS B5rmc7MjIwD/AEr5iv8AEz2oPRFSWDTIYJIgys2OF3s5PP1x/wCO1pRSWsKlLazl3nJwkeMg85+Y ACqGn3sNrb4SFv4h+7jX1/vMAOnpWi51Fp0nNrhVTAM79fT7ma5xjIZL63t3lNttUNkGWTOA3H/L PNE0N9M0U0U6B4ydqRxlu2D1/DtT47XU75XieeG3j34/dR5OQM43H/Cse+Rkv44Lq8me0yCSzbCd wy2Cu0/rTuBp2cG83ZvL15I4nw23aiAEcqyqBVWOHTI7a3y6tulBwCZdqduM5HFVI/7FjmuYYpEl WWPeTtMpVgOmDu/U1tW+piGxhUWUgCqqFUTYAwGPvHaP0pSYGlbXluF8m0t5JnQcAJsAHTOWx6Uy JNStbZ28qAEPvVnkJYZPQBQP/Qv8Kggk1MyC8itkLH5FFwx4wenBNZt3c6xfyRq10kCJKwMdrEDv AHQlj/7LRIDW03Tb2PVru4a5+ztEqf8AHvFt5kXJ4diPTtWh5dmJJ4tQuZJFRkI8yUKCCM/w7f5V iwWo/tllu7x2gkgjby5JPLy4HcqVHH0/xrbVPDUK7ysBmGFKr++bdjjnJP6URYFKe40IRxw2Xlzy owYrGhdgB2G0E/rWvHqLNJJb2mnXLkrvYS7YuM8Z3Nn9KgtL62htEYW1w4QZc7RGv0BP5VlaY08d xfanbWgMILxOZZCx/dnJXCDHX0pTdmBNqLa5Z6ZcSNbQJGWZ9+8vsbH0Hp24rLbTSup6dALrfDth uDJEm0KRG3Qf3vu84/rWvPd6jqumag6GGNbdGUxxpkFdvzjce4B/wrmYI45/EIRLh47TTlzG8743 gBYzjpgY4HWuWtrJAd1c21tFeCPULp2WSHcPtM23DBumAwFNkv8AQo45V05YpJcYxBGXbLdOgb/0 L+lSLN4YtZvlkgLtnaVXzH5OcY59R2rLu9Tv4Z5Lm3tLpLQHljHsjKlVUn5sYxjPSuyS5UJs34tV ukt0SOxupAgQkugRR/DjLkd/auWWHULrWzLFb20PkXCo7PIJdrOu5egXP3cda1m1W4vv3NpbQJGz q2d29zghuURT6Vh/2ffWXmPcSCG5txNMsapuV8FZFYHOO57VjOSewza8QwX09tNYXF2XaKe3VFgj RAVdtrMrOzHjvj0rnte097S/mtbjUJGt7KCOSEtJhpImO0qMHquOmenNdNqlkiCzuJ5J9QvruUBG MioF2rnB2qnyge9Zb2einUUE5QmeykIkDNI6SJJwy7sndjjB421jXjpdAN02z8NadeYiVLxSzglA Jm8sAYIwCSeSev0qC1W51G8eFIJrpLdkieMARq4B53q5GDjHTFXdE1I21sLq1Emy3i27/LHyuTli eBzn9KmXUTD9puby2MLTRfaXMjlMySNhSqJ3IA9elZR1siTRur/UtSuke2hh02HTiQ8NxPjcxGwK UjXPHHetK0Gux2FruvIbdCUjAjtzu3MSgJ3ydOOuKda6RdNbPIHSeS6XzJWVCzOThlHzNgfkKzrV r/U9Dha7v5TPGyultGoXdEHwMlRkfnXYUUtF0tdYvZL+9vpbq4tU8lplZok3bipCbccADBwetasl hottezm48hwkcW03DmYjPBI3kn9f8BbudO0KwtbZZQsbJIimKaUyAZ5J2lsDmrKXuiL5cVnH50qn BFrD5hCjn+BP6mtErCuRR6pa7JbbTkLsylF8iHK5xg/dG0fnVuO61hLZY10tkKpgvLKq/U4Xe3pV OHVLhftl5Hp0y22SfMdo4V/djLZ3HPFSx3eoa5ZeZai3SN1KhllaTG4ccYC8/WnzILnOXd/4iudR lkdEtYtO2I+35tpdSUYHjdn5R0xyRUWo22rf8JI9hLel1jijke527G8kozupwDh+oB6dB1p99BcQ XfmSXps/kO6MR+SZXiG0Ku5iGz1BA61r6houh6vrekahfX08+6CXZFcS8ySKVZEk2BQFGWBHrXFL 4guUfE2m2+mvYTywytDLFPJcSMzDpDtTcpIz15wKo6jdaKdSgXTJrRHmtrq3Uw4cMJHiVWZVUnKj cfwxROnhlDf3AgjszNZyy2g2gMMxqrKScnjGW5/wqPSNRhlv7OS5tmeSy0n95LDGTEwYkI43cbTu wWxy3ShzGdf4h16Iadf29rb3N21tbiQiSJ4VClh843DOD24p2najqczI0NokkmouZGeSUnA25CHy wSMAccDpXn2l/b7W51GTUBLFvshA08REpVcjGRhl4bsAMDr3ru7S6nt9MhvL67tbc2rmEEq8nmux I3jOMcHJ5wPpWlKt1JuYXi67vLPy7a+u7eCTUlMFoYYnIjcMoZQ5IwxU8HAHrWnBYzyaoNHv7258 xLS0SeGPokYWVnVioztHGDnGawLC1ufFOpanLdTC4htorm2jAChBNxluVP8AcXBB+laN5Jb6Vrkv 2wbbqC0iEsry7Q5UkOM8bhjChfU1lUq/bQIdqNjowtxqZuPOXU0MLySsS0kmQQSCc/w1rx6ro9pB DfNceTdWUke1IkYAOjMfnwrZLD+HrXN2N1p8Ux1CGziEbatGyQBCZ2VI90a9Oi4ycHv6AV0lpfWd pa3WqxRzXV5cStLkRyNDEWO0DJxlVHGTzRFpq6Eed6Zqs+qfY4YoPsyWx1C+TlRI0DXG4Ku4ff3A gLXSR+I9bvTFcTRQSaTc3X7xG8wSLn5UEzJgADAyvSsrwTJqenxeIddtbKK8a1maygmmlKhiZCzB F+bo7da7TXNJ1OOzsoHuLFGgb7RNDBGwaRc7t25gMgE5NJRduYBlr/a+o/Eq++2z/YV8P6PFBAbW NSrfa5N5xvzxhPSs1tIitvEaaTNd3E0OqCQKUcRlWT9586oAML/CR+NXtLS6vvF/iCUX8pLNptu7 phVz5LMVG3jqf61leIoLDS9etdReQagq3sVnNbTswlhSVHbcpB+6xHUACrbtG7A09V0+28O36XEZ s5ZrwTITPiSO3b/lmzbsHOOvqeayNRvNPv8AUWhWX7KkOkv89tGXV5ywU48vpxGTxx+NJKLaz8V6 VOTbQwXSNMbe1CuVkXlEmdR1YVv3Ez6l4o1pYI5Z1i0y3Em1SBum3PjJAxhTxUt3jZDRwsPiCKHS 9VTy5rprZrWa2ueVFvxglk3g7TjOOvPSul0S11u28C+INXe2tZBq1vcSP5jOScIcbFA9cnluOnau Ju4p7FLmy1KzkRbcFDFJIsbKitkM4/iDbuPYfSus0PQtTXwpqV5De7bUWFxCFJMm9njb5kDYC1wY ed6ln0GzYjt5U8IWUsswb+3WQNbQoqSGOb5yhkbc3yr7+3SraXDX1jqnk3tx9n0+V4Y2gYrC0YVc IduFDDOD7ivLX/0xrHStP1eWGy6S+bgS2zBd07mUDKqowOvcKK9asdPhn8PvLZR3AtFVdhmY/vEC 5dkR2we3LgV2Qr+0lpsK5naL/Y1lrd5FPn7Oyp9lEjDyzHKfvEOc/eIzn+WcZPjW/wAaxYQ2Uxl0 63Idlsl4TGd5IXuQRj17ZrFurzT0toFSFLi7uIC1nHAFEgkUDy87eNu1yW9CKn1fVo72JXt4JI7u 7Q2Mt0cN5kRQxTGBF4by2PAGecc1hWracg33OgtPEEmmyWGpxRT3dtHBJao6AQ4gG3zZsseXMnf+ 7WRHFKLrR9XCeY09zKI4pnCGJ4BkIwI+UHuS2PpXUXNzqGt38Wn2umDS7LR7cCTd5YOI2UKpHOAd udvXGB61xl/etqmtTTb/ACWWN2kllfzFEc7BPlQALuUg4J9ee1TVm4RVyW0dr4l02/vLjT/7c1CO KF5mi82CNi0TGPcH3MWyGwFPHTpWlBa3NzKZb27ne5iZbIJEQqrvII2BR91sEg9ayzbPr11aQXZS OHT7hLeJEYLstthG51ZiMjAHPNLrumR6ZdaRdxTTJscl4mYhJ4IRu3DCgKR1U12U6y+JFRYi2lhP rtrCi+dGjSiSO5meXf5fzMU3ZwQP4cDNTaXrGm2WlxR3BjFsPPUtFFukVt5KYCqccDFOM/h2G8tL i4uLUW19CzgouOZG+WQKcMdnP8j0rC06/traHSoFDXkUGZbxgvG1Jm8vAA/izn0q1LS4zpbWd5NV ZreKee7WNpJ44kKeV5pygbdt7Vuy6heyTFYtOSOWFVYvNIi/e45C7q5K212V/E11qGl21zqDXqKZ iUWEJ5fBDEkZwBwAOldLb/2wtxcXtpaRQrPGq7riUk/Kc8hAT+tdGGmmgEuNO1mS8F9JPaQOI9oj ERlwucjBYx/hkVSha91K5vkbWLlltVQbYFSHfIwGVzsZhge9Zera1qU0N4kk0bNbiJZEt7dgS8jj Co8jHO3qeP6VuWHhuSGAwy300z3C7iYlCAnHQlNvaq5ru0QFOg6Uz75YpLiVAuWubiZ8ovUfewfy qCzl0PTpbxJRbWcyTKo3qpIURg4Gf8ai/sbSxeCO8j88GEczSu3fB4ZyO1WTc+H7FSgezszjkxIm QRz6Z5qwGXniDTXmRYpWurkICi2sbONvoQoJ/HpTIruy1TTvInWSG7tbhZYfMUxXCerx+oAOMVl2 3iPTINcurz99dWtxbRjdHBI6thyHAKKTxxVaQwa5cW9tfW9xaDzkERkUxyRKeki9cEHufxGOaHoO x6lFduivpWuhJISPLhn6RyZOcHH3WwR069a8k1IRaT+0BaRSCae3i8ORJ90ysFaS5AUqM/KD7cCv QF8R2mj2Utv4nnTUCjGRfJKyNOANuwxj/loeDxweue1eR6h4lk0/4lQeLLO3uTYQ6QlobK5eKOVZ 1ebCF+SgCygr16emKlzilcqx69qDTWdut9pMguNLEpCwg52hiHzGe474/Cvzz/a5eC48TeHJLOTa nlXMkbx/IyyHy8gY6Nxx79q+zbDxpbx2ttiKT7QfMa+WJVWB5CMhoz2IHsMmvKviB4R8H/EKGHVf E0EkgRRAmLhLeZI2dfMERAY5fABbZxXLHEQS95hP4ThPh58Qb7xt4EtYPEMWp32q3ARbmWOEAvNG 5MN3ay5KbgF+fc3LZyMHA3LjxZ4S8UTX1zeTy+KFtcWWqCwRobrT4XJiaUBQspX5fmZC2w8dK37W z8L6Np66VoM10YNMWOw0tZLnkoX+8wXYC2M9h+JqvFpvh7T7Y3Nlo9raXMpM8/2W3KztIW5fzhmR X7ZWvlalGnSlKVNtNig+h4t4z+Hsngvx5psngOVrnTricapprI7PNFHKFkeOTOWwNp5/j3YOOK7L SLTVdH07VPEEOgSpePPcXAC/uxDGvzKhz8v3ctg16roOu6XdXE1jeakyLI7S2atO0UmcZlt7kP8A MHwD5cwOD0Nc9bDxhqthqfh1rW1Fneh91wZWluPKJwhfgj5lwOvy1WHxjqw9nJaoconN3HgrWjd6 Vdj7LYefBiUXUoxKxHmK+yEyMgKnjGc9dvNOaws9G/s6S7mh1G4jUtPHMo+zxoxIkiG4bpIZBxJk fIcMAO3Wa+niPR7yG5tbiCJCEsIG2K0isiAgmVjj7oK5CYqO70PxAn2bVtW1I6sHtvPYXdtA0IfG SPLEY42/x53flVqlOvD3DSGjsYLTaRperW+jX73baFqcL2mmNeRbo4mzh7OWSRfKkkUf6lskMuON wJrduPAfg+0vtD0+40gieGzEegNJdv8AY4DAQ/7uMYGWd8yZzuOR2qJtLn0jQ28NeI9L/wCEi8Pa nMDCjyfPvgGcKo2ozqDlBuVpFA6kVnr9h0ySw12LVbm+g0AyeSl5EzS2sVxGIwJs/wAEZAP3OFx9 a5ZydCi4mlWnBO50Oo2s2l2lzZ3EMdrd6tbgSWvyt5yArKjlSDtBYE5rC8Kx6ta6hDpekagulzhZ JfLkQfZZVY7oYyG/eRbssA0RGT14xXZ23iXS9b0qz1XXpz51pGbe5RYEZZ1Y5Vhxjbjpxj9KxZrS y0jw5e32laTeorXCyz2s0Gxfs0pyVg2keVIF+YAdeeBU4GpOpTsZKajeI2/murHVIWW3ZZ5N3mb2 2uCqHKqTgnDBRuAxtqPwbateWGraBaSC6MFyNVs3YAs0N6m5t3TpJvGfbmrNuuNOXWP7WlhsobZZ 4JV8m5uHy6Iskazqw4LEDn5QO4GKgl0rU9E8a20s+pzXQ8TWNxYMQ6+YstkyyqjiGONPmQtzGoU5 qa2Bc6bQoNxZzWraTNfGGz8XX1nHa3Usd0be0yCYraUHE4cDbvbavy8Yz61f8WTDTZYxbxb57IW+ sq+4sW253qB0KmENnHasnRLiz1rWfFHiC9n+0WrXX9kQi3Q7YtPtWJkk8zIA3zMwGBk7K9H1K3l1 Twbf6t4Sjge3DR6ZB5pd0jtpiEmkkBYM0SqclVYbyAOlceGk6P7qRcaT50y34Z8FwaboVnZ6PFD9 hVS0O+CWY4di/wB9Rg8k4x0HHat3/hGbv/njb/8AgHN/hXlvhzU9IudDsG1y5NpqEcKwzxYBAaH9 3kEZGCFyOTwa2vtfhP8A6CR/75rudN9zr5kf/9D6H8S6pJd3ls11AsMNriV3EZPlufvOWwf3cg4H HHtXAaxr2mC5vNK0LSLy4sbuDLQRH/ayrHaM7fT2r0uynGmTXdtNLGt5fOmLZyCIjjKs0hzs9AMe 2K4251q4077TJrOmRM9xNJanUGRleMjhhGFxnYwzWWJk7aERduh5+dUhhngl0pN6wzFTHIfLkZWG GV8/eC/wnjFLBfXmm22oxXUU32TXo1kAXlneI8FXG4ZGOTR/adxcalJaxQx6zeYkWULEMMij724f h069s1Qjluv7NW1vP9Eg0osWK5Ia4bHkx7D/AA84J6YrxZ1WdKVzuvCc880MC3WnkSwQyeRdK/lx zyM2/DXA43fwgHg4re1LT/7KXT01XTZbvUtSuDcxWqTDE0oUHzHRQeNp68ZXArjPAeqBZlsnllis 7sFLiNW3J5chzJGgP94ABf7vPNVp9QvnvLWfRIkhewzEs8shdoUhkwYQMnKoOnOccdOa7adZKlqR KOpp+IItPXRIpZ7aSK6MxLxgKtvHK7Esq5OVVRj5Ru61i2EV/q+oaxrUs6m6RGl+zgjMm5RsUZ42 YBGB83t2rQv4LrUr7U0uGjhG+W4DTMTFI0YDF046tnAA/Diug1I6VH4R0caNdW1reX6ym+ugCflZ MCF+OoJG1h0xWSje8uhLZ5MH+2F7keXZWluBDKkZOxEbIXaCPmwR1q9/Z2raPCLizRo4ZtwjuhuT zI0wX+VuGRv7vHNVNSMt1FBZNsaC2hKxmNdrOi5KY/vDnA44FXNZ8VSS2sNhdRi4lsRC8U4fzY1V z8oVO3Xn3rzY4iKd7lLYn1wJb3MF5qaSST3DJIs9s5dBHnd8i4+UgADHQVftkudejhtJC92l3cPc zPKQfJy22KJj2XLBsrxxVbQ9biur6exv99xYtE0NvuI3xYX5VXov1Gc4rkbSTZJHZWCuqCQ+b1I+ Q8rxjaM9M03iVa+4lCx02k3d9ouvSXdmzTgsYI57b5tkjLtyN3UZG2uq1aCw1LTk02Zr2Geyi+1x Zt2dj82Xy/UjPJ4Hp2rgrW6kjEss9wq211HgWpAYh16EYGflIzwa3PCuo+IdXWSTT3d2ktpIZEyQ r8fMq4GEpYfER2LG6sINK1LR94VrDyEmAtsjzN33iyMerenbr7V01z4p0awvIoNE0m4t9Kg27lJT 5XYBtpOHVup5J6VHpPhXT49B0vV/EtvNHp9xIub2JzL5RjP+raP731wPu9axtYa3Ca1dWGYo5J1a 1gs8NZuvAYy5OR7elbyqyhojLk1IpPENhJr179jtd+jXW4vYmQgFo14ZcAYOfmx0P6VyJ+1RwIpu d6SKWKJwoJPR17fWux1jw3FpNvPaSrHD/aMaS2s75BiRAGm5XOcfwn0/KuO1SQTTuIrZY4LeMBxb 52OAoO7cQMg9a8zE8z1ZvGNjbjvLNLKTTtUhFwgCvb7VAeN8cnf3U+hrPg87T7iG5aJ1tQwliMqk gop5wT1Cn5T9KjguY54oLZ/3Sk5Xd0P+zmres63H5ltZJ920ZSkbE4Ut97aT0B/u881zwtKOoWLV ncW+v6hPLq1+1rbwR7mlPzYDtwiJxgA84qjrWkTWtvBPGGcAt5jx5bLbdylsdiMHHbpUFrayXV+s 84WdJw80oDCNWZR0A6fhVrxDHdW0iWE1zJMkax3WIjhQGA+UkdeAAauFR8rIcDO8+e2iS5hI2yN+ +QL15GfYdeo5r13wxpWq6fqrayLdfs+nR+YguJTGJIjkPtZM5yBtGcfSvI43likEF3GRC0e9oh1A kUDd+gNdFeS+II9KZYrk3tvZoI2dFyYzMvyqSDycdsdORXRl9TlfMzNxMq/u9LS4vBpMnnQySiWY EFUjaXgRbjj+H170zR1sLy5tIpoJ5bNJ90kcThUEUYyQP9rd61LodzpH9kjRb+Mh7udGknY5iUDj aY8fP9SeKdD4btbbXbu3EDXVhpuWAicgtGwU5Tb98DIGOvrWjqSnUUhcltj23UG8MafbxWNuZ1tr 8OVaFt5jYnaFkIByVzzjgY9MV5xrIsLe8h0WzRLCyEvlSSxne0obG6Qnkdu1aEuqWtjrdnqlzA1v ZNEqNDtKtG8bAbwrcZIGPc1zOtQrda3cQWVq6xPt2sF2yGNepbtuPtwPpXrYnEqUUieSx6jpmjaZ HNqNzdMLa1vovOjC5V3SJtpRf9p8fdHbrXR6b4es0eyv9NjM9sw8pZ5uCsk3zAEkjgj2rL8QWWna 5HpKaMTDHBDErQj5N80ifKcDIbG3OcgZ4Ndbp+sR2msN4b1mZruNTEkTRptMqRjHzqBhWU/hjvXp 4eMUrGUk7nJ2Gmate308klu+nf2YrImfm8tx9xyB/Aw9zXTaOf7Rlg1KS/SY6abjbgBQdydD3HJ4 rprPV5tSvr3T7WZLeXy2ig3KJPN288nheM+tcpounrp2rLaFvti3DNI7twZFBwQO3Hb2rriopWEt zyFfhh9oRtUh1SW0mnuHhWJ4VeA7zj7+d36VwWq/ArxB4l+13Wl3K3q6cywzRrc7PL29WKnbwQMj 1r64v9Q3x3umeHLKaebzWjimhTzEtwGBOB0z71yPgLSDrv8AaE15JHDpNlcFLx2O3zpB8rRSjjdu IznPy9BXO8NT5tEd0sxrJW5j4mfwtdeCNG1tZ9MaPXBaiaN7hNoeKT/nnkZ5Aq1YahqD6fYrbQx/ ZmjjkaMDy1XcOuBgn8q+mPj4lhqdzpupW8zSRRKto26EoACMooZsfKRk47dK8QjBUQxeQVyiKuFJ 4U4IXArxcygoPlsQpylq2U5JPKS5Fy/2dfKWUEHBUnuue1RaY7Xlul3bNvuyvlgK2M/xZ5wM4r0n wz8NJPFFvq91cmVYtO/dyIJAkm4LuJOf4F4+vaqEXw+vNEjiu5nidbiA4VMZ/eDcenC4A+8cVwRw E+XmFzWZwusarYQWIubu5himdSq/aF8sbgM7ZGGdvseK5KHxEIljk1LTp4BcRjypLcpNE/H8JyT7 V02s2OrmF1htIb2zkKjyXQb3iZMNHI3O7PBVsDFeBRrrWlzCxuJ50hhRLcwOpCqnbCnjcD3r1sDl 8ZwtI9zA8T4nCq0ZaHsMGv6NeN5FvfRJMNqmOXMb8joVbFdPDeQJHmSaNFTjJYdhk143ZeHJ76CH UHR7meO+hiAI3ho8g9/X6cVv+JbIWnibVoFgW2Cz5SMIFwrIu3j3+levl+AVCtzxHnvE1TMMMqVR bM9K/t/RouGu1fHTywWz+QxVaTxRpxBMEcsvY5AUfrXl0e8c4B9uB+oxWkjZQg8DnOPpXve1dj4t 00ey3Wq+aFu1spmhaONSYcOVwOuAc/pVOLW9HmbYl4iybQuyUGJ89htbFX9DctYxeyjHOMAr3xXY +HbjR447yx1iOKSO4A2rLEJl4Bz2IH5VCrvqTKirHMozqquANrDgjofpikD5ADLwcj8R9B/Wuv0z wN4Mv9Phezg+xXQlK7rGdoSRk4+XcQf++fwpR8N9WOpahaWGtBY7KOOZI9QhBJ3ZyN8flnt/dqo4 iPUy9lIwLXVb+x+azuZIscAAkj6YNdJbeN9UiIW6jhuh3LLsP/fQrl77w94u0qO1e60f7Ul6wSOS wkEh3MMgbH2kce3+NYjanZQ3Ahu2ksZl+Vo7qNocNjODuAHStVKEtmS4yR7Na+MtLl+W5gltyepG HX/x3mty2v8ASb0hba+jdj/Cx2t+RxXh0RLR+YjBlY8MhBB5OPapC/QMAcevb+X86fL2Hzs95Nu4 J+UkDuKaI89q8astWv7Jw1pePHjsWyv0wa6O38canCjNdxRXiq2CWXy264+8tJj9omeh+UTjHOeB R9mbjI5Pb6VlWXizTbiHzrqCSyiDiLzJGBj8w8hQ3qa66yutGv2CWWoQzHHQHBJx0G7GaSmUtTG8 g+n8u1MMeOcYrYLFL17GWB0lRQ5UjgKeh/yafLbYG7GQO4o5kVYwChz04pvlHOcVsm3UjIOfpUPk dgPxqbiuZoXFSKpq6LcEjpzT1gA4q0IprGaVkPpxWmkIwPakeH0FJjuZwX2o8ts9KvCLnpUqw+3S mmOxQET46U7ym9K01iOMAU/yT3ouSZgXAANJszz2rQaE9ccVH5XGKQFIrjilVfUVZMdJtNXoBEB2 pCDnpU20+lG32p3ARAcCpMUKOAMU8fSmA3FGKmCcUuzHamBXwaXFTFD6Umw0tAI8CkA56cVKEJ6C l2EUARUZFPK80bPagBoqRQcgUoXHbindBnv2pPYFueZazb6T/wAJNeS3wBlIi+8Sc5jUAYBqaOW1 8xDaWTOAhA2oI1Hp97296d4inmj8SXUNvZzXG77OT5YGOUTu3FTRS3zyi1MMcB24xLJvAx6gfpXz GI+No9mn8KH+fqN6pSOyCI5PzSMSDg46DjtVyS51F5BA1zBGoTdlYjxtOMAsT/IVFHZalZ2pgW4C MA+NsXGSc/ez7960pNNjij33cruSpxucL8pHbFc7KMqeCSCJ5ZbiTDtuHzqnJ442gVSuF0W2vVLM syNCAWO6Zt/Xb3PStK3uNGje4aUI/lupVmBk+XHpUGpS200KXFjZyNFbuHaURiMbScYHrTaAr2wk eC+itLKb51EsLGLYAFH8QIFbg+3X9rG0UUUcYK/MxOCw6kBcgUx72/u4LqO3tEjAt9wkMpbKH7ow vsDU0VpdtYxP9pSNTGgVo4geTyQd2OewxUiuM36l/aP2P7ZEuV3EQxgsM9AS2PbtWVqNglvNGouG 2tIGk3OOPXgdPzrRWwju7+USzSxxwou93lwzDdgL0GAfrkU1X0eK6uVZ41VH2cZfap9fWlKQxlku hQ3GoSSmFtjL5QwZD9wEEDk1s2jSGaeW1sZSrbCCkaxg7eD1rAivom+12tvDJMnmgSSxYhKx7MDJ x6nFdat9NIgt4bJchMAySA/KOONuaUZIBXl1W7tW2WyQLJyDJJk/LxztHt61V06K5lkljfU0t3eR mYRR7lST0Lk981YeDXrSwkZbiONYY2fbHFuPryTj+VZtpYWcVg9/f6hI6nfORCRHgZyTg9+tRVaG U4LTGh3VwJHllgYKxlkMccpyM4XAHtVDwvBoUT6k2oSQM0aRRRNKuTliZM9SemO1RaC+nmxla42b 0uRteUltse13+73zwaueHZ7caVeTQ6Zc3V3dyeahtrclQAq8b87eMVlFJtCOpi1e3juoJbGznuIx EynyIiq7mYH+Lb/Ok1LVL+SyuZP7PSBIlBd7ucAsGOAq7CSSe3NaDXV/czJBbaXJ5kqmQ/aJlTag ODlQCe9cx4kGoCSS0nNopd7cKY1MigsWCY45ZME9MfpXTiH7mpLOdEuqLdI0shtbhljlhEpLCWPd 91x0Pl4yvOcEDtXaS26vqp+13N1drcQOAy7Y+Nu4r2wu0n5c/jV9PDNre6aIb68uZ/MiRj91V3Dh XxnquM9q4/R5NMglWPURHNeWN24kmuZvMhlRgFDbcgLlT09QcdK5KMXBFG4r6HFqWnRStF5kkf3n kMhXapB4JPX0ql4m1Wxke2k0yDcsMM8BMcZQMZo8DBbHzLgV0NtcaRZa29lp8G5EikulEUJYBnwh AyPug1yesTCS6s7S5spY28/zQikAOr7yR9f5Vdd8tPQDR8PXGoaf4TW1l0wt9tuGihLyr8zFti5A 5/h9K0NJ+36vfG5a4t7SaVBN5bxNLgJ8ijJI4+X/AD1rH0vWLqztbdbdIwulvMzJHmZzubJ3KeRs 38kelXPCkdzJFBaRXJhiljfc0Sxht4PmBNxyed3b/wCtWVCrFtIlnTRJc3MMk95qdwyb3AjgCRK4 THHQ4z9a5TT4NLOkm/kkxGoEcUE82WX94ScYIH6VFIMzPbXcLR5DvAzTt5Tybgp3EFegxkAVv+Hr nQbHTIjKYI5lDo7JGZCdpPI3Z7dK6Pac07BctX+qeHtNtBPoiQG8lPloEg3ZZhjj5cn1qOG61az0 5pfIu7kQwRs1xKCmHVtx4J4GOKkbV7SS6tI7Czvr1bKfziViIG7GBj7uKNV1m9kfyJ7EpHPD5UkN w23fk5DDYW6e+KqqmOxky6tN/ZEmg2QhhkdPL3LOON58wk8Y+Ydjj0rX0SXUINKe0srq2WC0WV2a KLJBB4G0nB45ziucPh69jtv7PWEW9jPdJPsZm8xXHyB9/B2t1C10N2klnZ2+sz3E10HVbO5EYRWI Y4jZMA/d75OStc9OTWshWKvi7Q4A1nNJcy3ktqQ0iTbQPLYhjsCrweOPaoLBtPXUtKupphAYv3Tl 8MsssgJyPTitLUNKik0a2SaUyzzYG0TZbrn5Rleg/KsfTtV0LStJ1hLqdZpCwntXjUksYgDgDBxg ggnPfNOo/e5gsUNcvbSWfWrRwpYPC1iqpnyVkY7xnoQc7j7ED0rTimebxPekWr3E5+y2Qjt9qnYq SMyBCMbWLqSOiis2aG3tNK1OZbZ0F7cWyWc7HIjMZVhvJ7OvH5Vf8NXEg0rWNbuxBAk7xW6y+Yyu PJVD8hA+8Tyf7wHHFYN3kO5v6XpupPZX9xBb6fptvPkedJIT5hVNhY4+VVPocGuH1TU4pDGjvG1z bXXkI6IDDuchVCNnBPqT6YrsNL0i71XTpbYXMNtbh5ZLkqjSNIyHcchzhQTjjArmv7Lm1rV9Og09 mktrWB727RVSGFZcALsZcKnGfxFXVjZKxJ3dp4Z/4Rzw6bTXbuV7iCKR/LSUxouAS2wLjdn1rz0S 28mofaZ41SCLy7zYR53mK8wYqS57Lhj7ity/TRNQkttS1GaSXTHkDNKxYuoZT8pHXBf73H3etJqV 7puqS2U9qfJmd1gb7NFujkVoduQozkGT5RiliEuVRRSRY1LW0sda83SEeC3a9kljiVWVw7KV8wY4 ET84zjiultLS8u9GTTbHTXkN1dLck+aI0ZCQdrEtnB9K8ul1i/uLk6lc20sLwafBYO2zaA8ZY42+ 0WAfrXpWnT3cWj39+iWsPnnzC7zkFNoAXZsz37Ad8Vnh6qvysTRzfgiGfWV1X7NJaWVs+p6gCXy5 +aQR4XOB8u3jirWsXWuarp8+neckVtDMkBulVVVwp24D53Z9h+Ncl4agvrvQrKLTok8h4J7oyZ3H HnZdd/GcnJzXpWq6PLNoEdus3m28aCURSbI41Y4JK/L97ty5raN5xaJbMjwr4fh/4SDxV5807WFh q6qdzsPNdrZcFipHTtgYrlvG1lIuuXMUQisLe5jSSOcN5kkht13bc7gdpPAz39qm8Mz6ZPqupPM2 63m1RnkNzNwUVQoztOOOnX/CtvX7TRRqltf21zHJaSP5QW2i3bVkHlkbskA8/L/tCoqtSpWW4S2K kN/pMt3pU6pLpyxQmC4ihUBmaRG2sxAzgnGSuea5zTLq40+18S6dBb7J5rqGIt0WJYUXaO5LN13c /SqumSXkYguLm3OonSphb3Fw6HJWFsL5m3p8uN1Hgwavf332m0ECxaheXN+WlZygVJPLVBtBz14r zYzba5RQR0Ot6Tc6bBNrGrw21y8t5aGfc5O2FnCkZHYEgt7mnvcTaRoktsl19qh1iyWUGNB+5LHb sODkZV8ocDI47YroNfvE1bT3uHvVtJ72c7rcQFmKiBlfAcqNhKrtPZ8V5ffPqOoaVZxyeeIY4LaG 1OwRI0WQFlkIzx97bz1rqxUow0juzV2SOz8MeGP7ctri5uoJrWC4lVftEbKCuwDag35xwPvYGT16 Vp+Kp9OiF9p0UEfkmJVhIcl5FH3yjbug5HQfStvTJ9EUaU0ERaxud6iz80iV5YgFfeOMc4x7c1yV 1ZafZPEX+wwytNcyXhWRnHlxuHjhi6/xdDwP+A1tRhyUVFEEXiqVE1HRp7edJJ2TzFMEOwRHyjGs atjDPngj0FVnkmiOj/Y7Q3M1sj3+xkwiOkqgup4AD9x67a0tavbC81db/Q9RuryKxlW7/eLxDK7b CpAUDb0PTjt61kJqWqWniu1k0u3i8y208Wxhc58zznLc9jtYAfT8xxzS9rqEmeiLfXlvqeo6xpNs oluLCK4meSQYlwzbAdoIDY4HPauM1i6L6813c3ELXEmk/vIVGTG5m24OeSwxzxwAMVsRzeKJpvEF isdrbTy28Vi5ulkVQ6Ix4bAA3D5lJxzXmlzd3l1eeQkmyGWGGJpUQN5gyz8P1OcYJH41nmVf3EkO Wxt+ffWw07feztbPGv7kmM+eAWdQz4LHgAc12mqafprS2a3iyWsH2a3Esk0zEq0xzsUAgAbOOneu Z02w0/W47aeKeTdPdRQR5bbsgdvLjDL/AAso5+mK3r8aLB4iFvLch4fIZYrm5zJ9l+XHmNn5TtI+ XNLCvljdkplvz/Dmg+J7fVNJjaS1UPNcRZVggVOCpO7jkDb7Ve0zXdHtfD5uZUmu57ySSeSCFGOz JJWNmXC7Vz2rnvCa2uoCyttTuoo4bAyYuIApkuCz7v3oUZXI7Zqvba3c29ismmwxgXqTRFEQlIQs pEhKEklmrvo1Ha5SZ3dlLfRG3votGZJGjczkzRp5rsNwcZyemMCpJdX1YTSqsVpCYoftGJZWb5B/ uryfwqjH4kUW4g0e1kumQ/M9wURUVRhkK54I7e35VHb6f4j1G6ttSIsrOCHeFjnEhZ4y2QQuP0PB 616cKitZFGf4fstV1Z21r7bLbxbnMYEQ+XB+8jHr7ZFbMduL29vYLq/vbtbdAULSmPcTwTtTb/Or 8seqG9hs5r1FSaJ5MwwIgTHGPSpDpNlJva5M08m1RgyFdw6jAXAranHlAb/wj+gRsdkUfmrHl2na R2AB/wBt2qrb6r4csL++ieazttojMfCKGwBkLhc+9Q3EGlR38VuhSGN7Zi483dkhgf4mPpU6a54U tzGiX1uny8CJFkYMORwi8dKqwFG48caJLdSW+nSXOoXC8mO3hd1AOMAnbj9a5K9TUbq/uNQi0y5n 8wlkF0ViwMDIIP8ACOMVvx6y58Spq1tYajdWzWhtHaGApul8zdn58Z9Of/rUkuua3qGoy2Gn+GpT PaQhZReTxxDbJyDhdxpTjdAtDgr3TdbiuJLiS1tLXysSyBW3LEz4Aj+Ucvk8c4xRH4Rv5m2S6pLA g+cLb264Yg/eYyu2T+BrqTZeJ21R9Qu4tPsJDCqiHdNcDnB3FsBenG3FVHg146mbOS/SK1NutyDB DtyrHYUG5jjkcHGKydLQ0uY0vhmOO/gtLu/vJ0nWY7TtjO+Mblx5agYOe5qgdH0axtihtiJCC8ck skkylgOM+ZlRzjIr0aHQoo4Wmna6uLmMHywZMYDcDOwAc1UlsrD+0RbXVoiRG2YssrMy7mYYPzcf p/SsquFjKOiEzzi4uIZLOzthKlvOS3nwxxooZQAQMgcc88c1SsrqCZ5re4E4jeZ1l8lSWZN3mbd5 7An7veun26fpstxoi3cMas32oebjDYUDCMo7HkAdaw9Gv4bzWS0tvM1taSebdrb5LyShPL3YGfk5 FeJ9XtPUUdwjsWu9VnsovC1zdXE0kanzGjiePZ8qZBPAkU5XHQda12g13QY1t77RzcQ/aTAI7eeO eSNEXI3KuWc+uzcK6C31N9Gmsf7F8PXwtry9UPcXDoMTMMAglsgelXtOtNUnBSSw8lrK8+1qEn+0 TqynC+aoCkpzjAPHv0rpeFhCNy+ZHFqYtT8MCZGEMAvYxHdZEy73XgbV2kdOQ3fj2rV1rSr1fBt5 rtp4jvbxUtc+Wyw26I+drRqI0OQT8uCaxNX8K29tql94kttRhstS1GdGnswXEcjo2YmeN8YHY4Gf WpI9b03xFca74Iiu3g1fUU81bKcBRHcRyoxMDD78TqN4xyD2rjwvuSlyAomHdaJo+oaBZ6RLe3C3 /wBqWf7QZWP2KVJPLiZc8FwP4c4K1qyeGtM1K11S0+0/ZtfefdBerIYFinOI9qnOIw45G4FMnbjv W1rmjRXst1Laut0toZFjZiQrxxnyj7GVeeQPuYBxWFb/ANlap4f1Obzo9LeDy1huiole6nQYZZI1 /wCXchtuAOBznIrzcZatNU5BCpZ2Zm6/pE/w90a11Tw3FdanpRne21uwC7jbSr8kc8MLfMrMfmZB lD/B6VtW3i+217StK8PpLLcRaoPLF0iMFLoeUuD/AMs3XHOcdquXY0Xdp3iHSjia93tdywSuz7oC ERMcg7Ov0wemDXA3Ph+bw/crc+CNb+xalKu64F189ldrK5fbcRLhvNZcKX9hXNOnUpXVyp1I/Cai QP4T1+7sLuyzZ6jI8Rw4hSB2VZZ0TPAR9oKL/Cdw9KoeNPEd1b2jeMdFt4EsvDite2RfLSLI8fkj cDjGPM5/3fpW5pPj2Pxfp11oHj7TrfRPEunsFezcjyriAgOl1E/cAAgt271TutFk8QaB4q8KWkFv I+ooLi2RgcuJU8oeWw4+VyG7c8VvgsY17tQi/JNJmPoHhWDw9ouiaaNUbTdS+yw3l3LbuYD5qx4x LIoO7LknDDHP0zo6H4qsvDmn32j6hHdvbXcsM5ubDy2Mc5UsyRpx8snBLY4PFbHg3w5cfEK20LVp Zvslpc6VA17I0f8AEg2Mq9v9Ym7mtC88DeGrhb230fWzqwSUuYAAPunAA28HaxPevPxOKh7U25J/ EZGoeA/hVqt2+oa14qWwv5wpmgmbdIrBQPmPl9SBn2ziqf8AwrP4Lf8AQ6w/n/8Aa61F8Qanpaix lgsVeLqJo4HfLfNyX578Z7e1O/4S/UP+eWmf9+bauv60uxPtEf/R9SivL5/tOuLpUaapFhF2zqVc c4+VwflzySenbtWNdav4nvdUu7vUzBfJYv5i+SfLCyOoL7S2A4A+U8ZxTTqGr6pA97fXIOnRSNOG mjVWJf8AhULyASMLnjFX9VaaztdO/tBJLu0S3I2oMRytcN1DjuePpXnTnzLQq1uhzF7cz6zfNb2O iJY6hMm9fsz7dqjkOMYwOf6VcvNGsrW4NpbPLcWcSRvNdSwhXhY5+WQAnP7zgHHTFdbZ+EotNv5r yC88/wDs+JXxC38WAwTc38WTypP0rmdU16aSfWJLGJbSee1jivIn3FZERhtdBj7+a5KkVGPNIpSI 7LR9QWxexdGmlt5NqfZJEWRWYZB5IyM8cVteG/CN1cSvNqNsZQd8iKuWkkkZcK23juPmHtWXFqKW OoR3xuhOYp1uPNaPZIJPLz5RAx8vB7da6/Qru9iuodYu70WptL0SpGjDa4mGfvLnHyEj/erSlCEt 2NvQ5+98OWPiGC30eC/vLfVjaySES8KZECkOhH8KqOnbj0rj9V15LrToNEuFa3i02ERxvHzuYvuz 0BOTwOK6f+1LSTU55LW5urvUYg09tHaEERuueg+g+bPGKwYEvNaS9vNSdIjdokpMKAtu6qY/UDBz j/CufGT/AJCYrqc3NYfbrWK7sIrgCCLDXI3FQuDggjgDJH5+1WNJWxtJ47ElEhiwHdxlVJHykn+6 CefevRYrzW7Dw6X1BIhbgLa2scyCPdtPyBVGNx+bJz6elYF7qs2lrdxwWVvZ6hFB5E52YSbzG7dQ QpyxPv7VySwfKk2UpXZFd6RGsF9qesSCS+eaMRQxMIw5Y87dvAAGPmzXN27rocs0Utt5s6MFkjLb 1VOG2n+8cn1p1nMk8V1qJhjia2jkbDKWDZ+UADtg8is3TZFmju0vI0cPGzLNkgBumR68c15+IqQX wA2X57kXBkKTpEUIZUWNVBaQ8YHaqa6jqUDxrDLhJT5b7R5eG3dMjjB9eKwGlVyktnL5xL7nHckH 5cA4q8rK6zRmUiSNzJhx/OuD2jvczUzqWuHt5/s8M8scttK8iMkrLGsW0YWIdQx7+tZdzqNzFLDd w74VBL+ZEAwMbY835RgNtOSQf8auaHqenIxubuM3LKpKRoQGY9iD6A81fi1q9uLf7PYxtG7+c020 K5bzBhlGcYG3H413UqzlqzWLM7xFrFzqV6It7XK2mxJJjgKYCcx+X6bhjePwqKXUH1CfyJ7pYrb5 o96j5FwvABGDtPoawIhJPJGQNqWcXlqG4OAfkyP4q3NRlto7RUngkEyxkEJhELNyHIPsRSnV5ty2 Zwns/JtbqPc7wnEhkwMODjIxx9KRrWOW6t7eA75ry5UFpMKUZQWJDHsBUSxl7ZLjjzYVy6t91sHi o9Qk+w3K3y5nMw88g5JjYEFlUe6nH0rCD1Ebtuhs5ba+W6jmulu2iEKZL/KOJUPRlIIratrrUtZu 5dMdC8GnxMW80KA2T5h3Nwcc9PwqpcG3vZ7bWNsq3D8kBVRIowMIqAc424Fc7DJHaGV5JJFRpjI4 TlzuPRc8t06dK6HNR0HaxNeRiZt9kjIlwV2Jk7oxwDndzjuue3Sut1OG7h8NrqGn3PlWtrKkDRMS GW4yv7wqcZJXOOuBnOMisWyuZjrSu6tPbys5ERjyyF+QcD24xnitbxT4okvNXU/2cIEsYFiaGSPy klI/iCD/ANCPP4V14fkjByZmzl7TMrXCm8FvmPdmVBIHfcOjH2zXQw3WzwpDG+pTI8SySxpHkRrI z7HDYOQCMH0rlryW4uBawfZ/IgnLOy7T+9crj8AB2H1q/pmprbm+ivoIZ4Lty/ljjDL0Vf8AZ5FZ QqqLsI6LSWiuWuLnxXPdtY27rhIgDuk+9FyxPy5OTzVG7k26gLyG6e3huHWIndllRsDK/UZ47Vnz XV1fXb2VlGIYXZYoYy3BUcsWzgfjnIFdVf2tptslsJvtAtFjiHybsysrEiPHUD1Nbr32ooTZ6X4N 0+307xhqMd5PJMmj2P8AoCkqwIKEncQcZAbNavgM60+qy3ttt1S5mwFkOCkh/wCWinj92CD3wc1n 6foVvAdOj1GS0t4763i3zK5ZZXmOP3jrnDAcfhXb+FooPAT+IINGmintY3G54QGXzDgYUk+nWvo8 PF6GMyfWdClg1qG0mDXLTNJP9lsusMZwdjHsB2PesTxJqGgab4e067ja6S8spWGXAVEZ+MTY5TaO MGumgj1yy1f/AISWxf7eNQnRY70vsTaOGj2cDy053EnluldTc+HtIvL+OzsPLDF/Mnt5CGkkU/NI XHO5j1XtivRt0RjGxwkPjHwV4L0HUJdG1xZNSnjEsMakyoHZd2xdvTrjmvDNI8X2Wm6RdrDbzTat Ndi686QBomB5ZHBI4PqBmuJ8SRi38Qa5a27kQR3sixk465yDhRxVAt5kOEyob5Xx0yOK+ZxebTjL l7HvYHBUZP3zrvHHxBv/ABfZx6ffRRxKjRzSbTndIqbVABAwOw9q4KyvGhFykRMZgzNDtHAOMEd+ vX8Kgu4SsqREkKNuenUD1qa1VHu5LeJc7O46EMec149bFzqyTbMatBRlaJ6T4S8Sy+H4lhZ5MeWQ jjO7jqrlhnbjpxjNe12WqaN4i0ySxm1VIZLNpJLYtEEZjNG3yswzjB/A9a8lTStHutFt73zESR2i SAtGfMTAxJufoysPujqPyqHU9E07TZLuytr6VYZBG9ukREvEe4ZP17ivqcI5RhaR5tWOpza6Vq97 MRpsInKjYxt+QHA5Nc8dFYSST6xCdpYbZLmLIyFxhS2O4rceG70uSxMtyHg1BxNGv3SgjlCt8oIx 1rVi8ReMdIh1eKCa4hjiiWWEbRLGjeaoxtO8dGrfA4i90jnqWW5hpFappMckswxGIiduDwCP4Vry jx7FH/wl98QCFeO3cHpkFAAcc+levz+JtEmtbS81zQrd76cyIJ1aSFt8Mo42KRHyD/dH+PP+MtL0 G/8AEEl3bx3UAMYikUyKyhogVAXuPfNezQcuczlPS54yqAAE9OmasJswRuB4xgc9vavRYdE0WFiy WxkP+2xPT1Fa0ENrCuIbeKPOcYQcfnXp+xZy+2QmhvstoGk+UCNRhhjOR0z07VvvJZ+aqynySwwC CB/PFdlp2satYaDEbFJJYS6mdY4UkXhWxuUjp9DWnZa34cbUTaahodkzXEcbt5Mf2dx8m7d1I9a5 WuV8poqlzz2KMFQ8MgQg8EZXp3yK2dP8QeI9Lv5zZX0h3woXDHzAwUkDPXNdmmkfDLVrD7Qn2nSp Scb1O4ZI39Vz29qzLn4ZX9zqLnw/4ptpFS2Qqk6hiec8g7SPyrDnW1jXkKUvjy+ng0qG+soStrcx urRbo2OCRjGStbN14r8PapZanaX8Tp5sJ2JNGsqBtpHVcfqK4bWfDfj/AEVrCy1HRF1BZ58QyWTk 5ZcnBVsAce+Kx7rUYLSW4i1myu9KlZQpFxEwAYjjkAj9a05YdAXoejxeDPAOuCwa2igtZpIQHexk Nu4IRdpKgoP0/wAa55fAmo/Y7m7sNcdjDdPAkd9CsilVYgEOuD0x2rAsGsrmK0Npco3yAExsCRgA c4rQtbzVNOS7NpdPEiSsMBsqc9++aceZbGc4pkE+heL7K7urR9IW/Nsiu0lhOG+VjwQkm01h3OoQ xk6fdJcWN2zxt5FxE6MQxyG6Y7V6FD4u1E6pNLcQw3DtbKpCjy2wpJz8tc34z12PWLvSdatpZrC+ sIk8uCX95BM8WZAH56Hp+dOVeS6Gfso7HTW81tqNjDp2lRrLe2+nrcyuqiXy2iaR5ITnHlb2CneR XMzSW33oLf7PDgBIw28qQNpG7jPNYkvieWx8PQ+JddW4it76182/sLcrPhmungYCRijbMDOOcdBx W1qsekRa/wCE9FsZLixi18+SEIWeIAIkg4bBU89q3w0JVNjPEtQUSxYa/qulJbz2F5JA8knlNgk5 AY8HdnP+RXdWnxI1S3haS+tre9VeD8vlv/47xXkN7LJZ+I4vCKGK5nkljaBmcQvL5g3dDxxnFaes ebptgYtQhlsxNhUdgWjZuuN68VpJJPlMk5W0Pb08f6FMh+0WlxaydhkMo9eRXQWl3p2ohPst3A+8 Z2hwG5GRwa+c4p0uYJWgmjlCoQfKZWwRxhh2/HFOV90Ue48BBnAAPC9OMUOHYbnyq7Po+aRILiO2 ljkR5BuAKkbl7EYq15IJzjOe+P8ACvn7SNc1SwS2uLW7kSWJCqFvnATP3RnpXcWnxI1KPAv7S3vR wNwBjb8+nFKzNPaRS1PTViHYZ/WlMI9CKwLHx74duyBdLNYse7r5i+n3lz/KuwtJrG/iM2n3Md1G OD5eOM+1K5acXsZ625HbirSW3c4FaSQdBjFWVtlFFyuUx/I2joKY0QI6c1tPApHXp0qtsIOMU7g4 GKYsDB61EYuelbzwfLnHP4VSaEg9KLk2MwxZphgI7VrLD37VOLfd9KYWMHyKQwkVuta+gqIwkU9B WMby+elTRw5xxWj9nz2qRYMY4piKhgAFRGL2rTMZxTPJoHczRHxThBuPNaIhHTHFT+QBg0riMwQK oqB4+TxWs0Y5qFoc9Kq4GUU5oEfFaotxikNv6Ci4GaFHSl2YB71cEGGxipVh7Yp3VhpanmniRtRt tYuZIIYfs+2DdI7EclQuAB70senX0hjaS7VTHlcRpkggDvT/ABVZ3s/iO5toLloIJo4AVjUEltgI YsTgVlxxWzzhbq6kuFYMS0swCk4x0FfM4n+Iz1oPRGgiLcpILu8Z9spUpK4CsPdRiq7XGmLeR/Yw jYUg7V34wexzVzGjxW0q2cMRdsj5Yizbv97Jq7a3Riht4oLCR2kAUcLGMgc88Vzs0I/7QjljeG1t JnkAKHcgQbmHTJA4xVS5utRWzS3WzRNyJHmViQQfl+UL71eRNWNxcSfZ7eNZAr7ZJCzHyx7VWe2u rpI5bm5VHn2OqQR58tgeACef0pvYCpp8GoxWrSSXi73RYGjjhwyqpI5Jx29qgNq19Z2kTXEryeSJ 0DSLGjLHxxz14/zzVrS5dPl1fUTeSPNuXEbyOF+7lS3HX1HtWZaz2clnHJB80sf7kAIXYtvIBj7f KAc9q55TuKxZ0RrF5mubqIJLI8mYjukwgUKmR3yea6ia8t2t5VgtnkUAxMPLES9PlHI61Dav9is4 30SyuFdj5mXUAyRjk4z/AErOvZJdWZ22rDZRTebMzOfl+Tg8d88cVEpcsBmNpT3sltcQhEiW5kPm DO2UeWNuCy5LDp2rstLtNRs5WtftyedHBvZUi3FVJzwTXHaENRvktm0xERg1wzzN8wjQnCn/AIF9 K7hoZL3UYbO7u5p7Z4Ck84ZYVz3C454rGg7q4GTDeDUSIdRuLqe2lLIpkcQqzE/KuV/hz1rG0+Sx g1LVzNHbNLaRlQuTJE567F+v5Ve1iz062jgWyZAbi5aMx8yOyqPlx6Y6n3rOhubm2urgT2rW8YhF u6xRgRPIzAK49M96mpKRLJtM1WNbb7MY0dXViITGBh0Vl+9joBjiui8LyX1n4eaWOzjEbNK+4yqn KHBXB57VxWoPeWou9wjgVJ3IIO4J9sUBTkej/lXTabDfroFja6jNEseoTTWiNnPDOSCF9+cN0qcP VXPYaZ0V1FeQwy6rJPb2oW0lARAS2G+Y4JxzkDHpn0rE0qFtUkW51Uy2dl8s8R4T5pDljg9AcYX/ AApJbS61/VrSyluXexsQ6TvgKCBztQDuUAVs1ja2dF/txEtHZTEdnzOShYME3Nj/AJZ4wV44retV 1TYWLetavYQaYYdOnkmZWlSLzGLu4wFbbjhcckUt9daLF5J02PZcWcH2W5gaJn+1KnT5hxuH+sjb GMnBOKj0mazuL6C6u3LW9sZWWOCDeUhhfEe9iOTJn0+7XSajPcSazaT6baP508RhLShI/lZT9xPy wT/9ap+JcwyLS9UvJNStJILea6hitZbSYMyqyMGRxIzE5Ctszz64FZWsPNPe2r3FssMwu1JLOfLR m3RkFj0wAOKpLd6lo63ky28FuRPb/a2kct/qJcqxx1XkrJ26Yq1rkOpTpaK+qqHnvtqwiMf89Bhl 7t8rE8iib5oWYGFb6feMLqxs7vdc32s/Zo3iwpw0abpFP91h1B/nXbT6fbwXeoaOwMbW7rLBK8vl xrGU5YA49BxXGLayXPiPUdQe4Ma2kUN5vlwBvkhx5YAx8xOcY/wrqbKLQNN1a01FrlbqPU5JUkWR AxgbAMY6n5eMVzYdK4MDfaNZzWzW0dtNNbHcqupYMWQJkYzyxyR61oP4khj09rC3SWWLDy3apHjZ htwVG453H7o5qL7TZ3UOovptrJLuuY5Fkgg3Rr5C4jk5AHP1GBWPZvrFlZizltDKIbmWaUM+PLB5 TGPX1NdLTi9CDtU1G8txHjTW824kAxLMgy7d8qSaXW7bUreK31KQ2yzbxDHGAZOS2edwAqxNFd3E IuJHt7GLT5RIzDMh453fNjispGutRuYvIvLmdZ3acxhEiO+EDaYxztDZ7/Wum+hZpNZ32qau9rey ySxW6RSuI1CgEj5OnYGsqfTEF3c2czwqsJJjEjkbBj93kZ5YZPasXVrqVv8Aic2cs8e51hkW4mwz 7VzuyPQ8YrrPDz6RbWiTX0yfaHy0zYMhZs9BkdgB0rng1KXKO5k2V5pthYmBrm2Ro50k8x0YSpG5 wVY9v9nHWsfXdSbS9dn03TkmlsRc7oI3VVdzexqrKScfIHwfTmmeIry6fVzFar9ttdTureVYBHsL AMBiNiO2OcnA9uKsahq623jucjTkkgu0iMKTyBvLRv3DKSCcEMGbH8P4CspTV+Ulu4tvpF95EPhS FXjeHWcXJkkBNxFFn5+/yIkYHtxUfyRa5roRN89vdzLGsEf7qJhtt422Hjj5v5+9a3h621aXxTH4 hv7q0iCW99bySOCVDW8u2WcHphs49OBWF4Jjl1nW31NZUP2y9ub4yIDG32dZXXcy9S3HcYocUrWJ PTvDugMumre3N7d/ar5DJchHCqS5x0xx0A9K8+1G3s7pJX0icQLDeNFNM02FSEnyDuHRgAM59Wq5 4q1aC1iktbB2ZpA8mZZWZVhWNpJfukbZTjKL/k6Xhe28PweFrOO9vLMX1xbbr3zju3NKvKkrn7pr epJSjZAjSsdW8LaJpkenymO6AkmtiyJuYqoIJY/7QxiuLtNUuoYEubZZZ20G382cKgGyOKdZF+jb QRyOoqC91OTU7We3jgQwLPHcrNEuY4jGAjxZwP8AeHrgYqNte07Q9N8R6bY2ct5Bf2DCFpFKu23K t5mOdwJ54x39a43VcpqJVx+qwz3oumu5vI3znVdsjYDw3BURKu3PZBn2OKmNzdrGLs2RuLW4ujK1 om7EVyELMMA/6ro/5Vd8U6fq1jHo2p6zHbWqxMluIlZnkWQoAC8hGOAm0IMj05rG0tdQ1D+37iPU VtlsbeWSaONAr7Xj2gqzEcgLz3xUVo8tWyI5jqfAPh65XS9I0qC+cQC3lNxHDlECTZlGwtlsc+lb PiTR7eN9IspLyS3tLify7iSWXcNgUEKc44Pr2rjLyK/sNF8O+WZfst3p6sLgOQyskWdjAcAf7eeO laXhK6i1LVG1K8gtX062KhJpmEaSGXk535Z9vAzjn9K66NdL900HU5Y3UltZ3SRJBLHPe3VnbK6r hcEBd2cfuznIIyd35V2l7qMl5oCaWlrP9qt18mC0CjYsytlZWcADGPu5NclpV7C2n6tdwob1bXWZ ZRbLb70h3MBlZR8ojYHo2OenNbzfbPChS0sGuWivnZytyg37VH+oQt1KsflHXbiuaUXG47HANqV7 BG9nFutZdQiLyxSEBZLiL5JeFzncgGcehNdh4QsNYWwsFs4Y4hpVoq3ZiJCzNL8o3Yz8v05rjNS0 3Vr3S7+4lmLT2cpG2PCzW8qhWLKOw2Owau4svFev6jqEuk6aYbT7YqW22KMbV8oKgfJx8xAwBXJR 9yWpXQqatPfzeJDpFzqLRfZLkxzfZ0yyoI2+RBgnByAp6dTVKOU+Jk8M2sFtMbWS5BmaRyhvfIjJ CxKcAIvfsK0/EWnxmaW20+S51B3sb+e+ljAUKsUah0WQfxYPzc8cBeQQKtr4f06zvfD15LfzXGoT addiMqxWJWEUTRQBP4Qg3Lkcsa0dGTlrsIs+Fo7aCS5gk061mvPLa5Zi6nyvLJygYtjccBU4O7Hp W5Y+KYZ7K9tbK0ghhubK4kxFEY5oyAcM+Q3frjj04xV+O9stL0C1jBgt7u0QXFwVA86Qzg4VcZ5j UgHPT61wk2r2WlTPqME15Dp11bQ2hOG3kMSWiD4G7f8AL8uOc1v8NkJj/E+uafqOnaq9ncGO5nOQ UBUJGpVhIdoyxJTj6YqneTzaVcG3W22w+fGIlilVpCTBt8wYzncfm5IArRn0bVbi6N3qWi3cd/q3 lRQ2scilIEVcxxOh9OSareB79IvC9v4btdOhuNYS4nhlupGbephnMaDkfLxjHOMVyVoOU7hJG48u q3M2q2lyfMgufs8KYdnD3ZRlOWP8Yzkjoo4FWLHTtQ/tfVJLmadZtNuLS0QxKmI4wm+STyxwN+7n r+WDVCx1HUJn1K7eZRCt8Li8iO1XAWLZI6n/AGSOV4yKifxD/YFrqGsadc3Hk3UxmMS43XCEcYXk qvGwenSr9xQ94JPQm1jR5rC0t9WYExw3wuhKMsr2zttTapxzu/KsW4QXbx6dcjGn3RSSUoMsIl+R N56jkHcOnSuo1i40v/hECRZ3qzNNHbWYeclQksjnOPRNuG9wK4eU2aafczyybLi+lMFsvnbwIoz5 bs4H3WZz8o74zWOJinKLiL7J6DfappVl4muZ9NeNYYfJjsxaLvMhCYZ34xHkjHvjNO8F2Fpa+X4g 1i1uW1C6R5lggL+WUEjb5PmwvJ54pjanpdyJ9C0S2lvLue22XUTgxs84GGO9OSo9ulSadf3lnpNr qFxA+om2gNlaxJuWJSXxuZPvkgdsYrvoq+gIrQQ3GtTNf29uEtVuC07XEnlNdSg43svZcY+teged rsSyWDS2lpFFEk2VV5iCx27V3AcfWsJBcTlJJdNEkss6l3lcqqwxnBVUX+HPJPvXVXRvxeNqaXcE kkkDRqsUZb5VydnzYH59q9GjHl1NCjdWDxlbm61W4uJAfKT7NHHH1GdgABP1NZcej29xqLxXcUkw VI3Xz5WOB+GBWPoCarqn9nrLHJBCySyRrC4/d73KtKcZruB4YsWZ3ugZJE+TdLNkcdO+K6YycgM2 TTfDOlKUuI7CBuWLEJlsDpuOTWJpes6JZatqaxzRMHSAwGGIyKnyfOMIK0bj/hHbDWILm6Nlbqts f9cFA3bsZGSc8VpL4s8NqJEspjK0f3ltYiwI9flHeqAonxVaTXZtFtb64LJvwImVjnoy57VlW9x4 hh1rUdVg8PebFexwxxi5nWJkaBSMtjJ2ndVmW61d/EY1my0O/uoRaLB+82xsSHJzlu3TvVtNW8Wa nqNxZ2Gk2sdzaRJI8dzcjCiToP3eR2pgcvLqXjC71RtOli0iwCxfaCQ09xgZ2r0AB/pVhfD2sX+o x6tc+I7jesPkgW9tEiGPJYKd2Tw1S6vYa/YalH4g1DUrOznNs1mEghMkaI5EmG3gZPHB96dHY60+ tf2E3iO7MRtI70CCKKDcJGxwMEgc1HNrYdyudFF54ostJvNVv7iC7sZ7uSMzbV3wugXITHynd2rV bw7oWlW51F7WyiNuCqtdykqw6McyPzx04q9B8O9AtGF9NLeXl267fMmuJDtXAJwRtH4dOK5qfSfC Wl+KNMj1CytEtprS6LC4dXj3IyAM7SMRnB6flVSXQLnLa7f+HdQXS7qCO28qwvm+W2zJ+6EYOfkU 9zXOPqFtb38uoXRae1uZPszNFGyK0O0FCDxk+vocV6o2q+DLe9s5tF1a1tYYZxJPBp6vunRVI2qs alfWuOtfLkWPTLf7VdQWV5d3du1woSXEkYKRsrYzwCn0wRXk4qloxxOV0o+Jbzw3ptpNYkafY6hN Cssr+WszxyblzhiRsTADY28cmvVLLU9aGjWFxZ21rB9tvTYpJOzS7Sx/dyADBfkdc4/SuWtPEGo6 ZoNlotppktjYfaGjhQsrebaz/NLHcO/cjcuB6da62C11vVbODQ47ax0Sz0O4gnso5Wcv5WS8KbMY 2gH1zRhIXXvES3ZznjfwnrFn4cRrnVrX7Z50E7yRQNvaRZsvJvfjfgYCMFAHFM8aeGpNV8PTeIPE 2qX13BLfIllPII1Sxto2BjmfylVtztwMfdz7V0nj3/hMtO8E65qEus22WVcRWdmqhfMkRS4eQ843 cZqLxPod8NLvdI13Vrn7NFLaWdq6HZEzMyYJhUHLOpYYxjv1qquH5L8iNIM56+0rRPDlqNd8R20j 2d8xWO7s90jQMzYRZAvyySSDA3DsOa52K1+zaKmpzNstUu2sY0XYWDHHYr3XqO3SuxvdGi8LaHc3 lpqc0KXzypGolWWGIxvt2TJLhXTGMY+YNWBe3Fzbw21gIbZLDUIvLmWWESAuOBKgQnDIRuVxzjjB FfL4mlKE1VmXo3bqc/Hef2cba80xjJaWVvJDPboAG8tBgyqAB86Nz/tDjtVnSBnVLqaA/botWiTy Z2UExPHjaVPpIrdP/wBVaOr+LbDwuI9F1oNZXsj20UNzaQCUXCygK07zdFBOMhsAcg1n2VkbTxAP C0cy2Taq/wBptBKCiB03KVRwMMGwJUA/hwBSrVFJcwpUm9znvEHhyxudJuZ7uIQXMUU+Hdh9sRGC BZIncgQZKBdzdVJG2ubhfXtO0u3ury9bT9LWVI49Xt8xx72VTtuoRloRuPl+aPk3cnBrudZ0Ke1u biwthBdWFpAW1FXOftEc0vkudpO5lAX8ASfStPwRHLD4Fg1bVtW0+z06+0wrPazxl2lwNmAzjarN jAHfPODzWmHwarRjJijJL4jP8E6p4i8B+CLe01C0htPtGo3U1vHcMdkgkPmhUYfKw+Y45/wrA8J3 t/aRW2i20ptrd7tjc3EUYMoE77XbPUIPXt+dZOl2DwaN4l0i2nurLSrKGDxNodncSiZFtZAyT2zI 2792jqPlPQEVr6TpXiiTTL+SwuESYXUVktssJlhZJbR7pnX+OIIgUEofl64xXK8JGEjerd2aOui0 iyCssup7mR3QEokhKqxC/MTzwBz+VSf2Rp3/AEEh/wB+Y/8AGvKp/E+l27iDULK9trqNEWSO08u5 hDBRnbLu+bPU+hJB6VF/wlnh/wD546t/34j/APiq09n/AHTH2Z//0un1K9uLjwtc6ZewuLqxCWqo MCP5W7kck4+7SHWNKsNF+yQQs10LhQiTSN5MSR/NuiU88n1A+lVvEurQahr0aWrrDbSPiAj5VZSv Bcf38+tQTnS0tniVS95PteVi3ylUJwMdU/Svm62JesYmyiTeG9SW+vZPtcv2KJ52nYyPtDN/ex/s 571JqC6RJqGp+S729nHCNyM25pnPyqyt225VvpmnK0o0CFoLJDmQQyTKhkaWNvlYEfyx2rlb6yfT rlsqzmBzG0R+baoxtPftWVWrJRXMCgen+G7HQ5rO11O6uDJElg0rPKwDiSN9mGXurnOSOali0TQ5 fD+o3d3I8U0NyhW3tmLrEJiCmzu2zO1uOOtT/D7wp4f1q2vNRvi88FlbbpLWNgZJWYcqoHbPI963 7fR7a01WwAleGyfTJLxoCVLAo2FU9t43Bn57V7GGpxlDmMpSs7GUfCmmaR4fi1q21K7tbLWy4CSK qvBGvDMGHOSOFHcV5d58cdxC2myPCiMuzcu3Y+RxhcnZ+FdLqmv6zrljHp39oo9nHbbZIjhOQwby 0H+yAOn/ANaud0qfS9Qni/t+8u0eEYiNv8oG84Yh8cbcdCOa8vFSi6nLA2pqyNnxfr9zqbJDqGmi yuXVWOJjJF+7O3zY1ONpbuah1aXSb3Slijs2XVLtIkNzcuYVidRgyKOdwYdAO1YF1a2+oalLb2F6 nmph4TdMN0i5xkuDgj24FNebUr+7uFmkiBtMq0fDRLtGD5Q+7+R+lc/1qUW+Yza7FDUkFhBbWUTH Mu07gMZVTjc34nNY8+rvNFbrFiGC3lZvKXqUzjHv+Fbb2EcULXepqEtIo4QFk3DcW5BTHXnjHSsy xlitbbUSirm4lkEIcA8A9M9jnoK8jRopx0KW2G3muZBETkFNo+/Hu5BzxUtr9qWxjRzvcllaR1+9 n39qlWdY447i3ZjdZywbB2qwxj37mrEyte6YIhcJYZGBIe69TgeprBR1sQoHO2vnW8sJ2hTAdrMM hdueCcV1lzflZtkUERFqu7K8ZD98jrXOJNNbO9k9wJICq+cWHJjY9a0midLiOG2t2lkXBjYEYCnk OfoOxrR+69B8pUlL2yi4C5WR1KA4GVzz19KtXd295P8AaZ7hpokKq4xysKnLL71d1uZ76P7TeIJJ 5CYw4AQBQOu0cdq5yAumLVkAKFRKBzkEYJ9ORipbNUa9xYmYSLYgQoZDKoLZGzdxupJ7e2vL6ON7 w2saW/neYV3oZI+FQccbugp8LKLeGDPlshZgjY+ZdxAQ1ZuGR4I2ml+xlJhIzxjdwowq7cdv5/hT pv3gehQg1Jy8Ml+ziVCYZRIMYG3gUW8bfNdb12xhkQAHcxb+RApZ2jlgMgzcMTjeV+aQjkSn/aNW dKbzrxLa5GEdhuQYXO4AHn3FXNe8Tc1bjVNXsodPv4beOC1sixTY26SSUI2SR1K7c+1RatfR61cw apqU/n/bFVj/AAbBjqMdlGBip9bn06wLaZplpmc3JjhkL7iseCroO3Q4rEnsnS1cNMjz2TbWGPU4 YMB0OTXVUn7qihpGtrD2VlqNpqOkpPe2EPlRkXeVWZtvzgY+6i5wD3+lTS6cLCObXb8riaYSFdvA d1+RTjjbt9PSjTZ7WWdI7i5+zBWSM5HykcBcnkEY7da05ZbzWL+7022eNLKG4aYWqEDzGUY/co38 W3JweP5VcZJg2WdN8Oz3d7ds08Vv9uRWDwrvWMvyiFjwpc57/d/CvRvA2harf6BdXWmxgPp822OT yhiKNvmZlfsNw5GOlcz4CQppWoW2p2E0ls0xcsFZXjaNiEc4zwB1PQGul8H6zr1tBfeDtLQXLSXj PGoO/ItyWaIY4w4r2MNBRsznqajfE09159pqljdRwJD5Lm1s41McUina5KHtnmtzSrS/sXOs/bYI 7pbhhDbSIkkNxuOWUgZ2g9cmvPorfX52e5e1aeWGY3Toi+bGUztLOowVUAdK9K8MeJ47Wy1K607R Yo7mziKyqjMHcTfxRo/tz7fSvRpy94jWx06XVv4u1cXNzYeRsh8uC0tct5soGCMjCgcZV8AAYHWs uwudR8H2+uag1wrXRSOwt4jh7pZ5iAk0Z/iQ5OR1GOmKl8O6TpnhfSLHXL+LUZrbT7Z4jNauW8sk eYZONrKMfeyDk5wO1cro92PH3iTQrLw/AyaTpiSSS3LxBpZJehlk2/eVf4cV0yqCUTxrxJps2i6l qmn3UqXFzbT7JJU5ErN8zHP1PNc9JIRCuF8tV4wO5NbXiaynsdY1WGeSOdLe5aMyQZMZyeozz+GO KxPIe6uYkif9xESHPVSwFfG42LVRn0UeaPKu5UeKaWeK2VgBG4JJyc8Akela1n5Q1Btr85VSUXIL HkH8qciBYbhQRGqAO7AZYsx/hpjSzRSsbeTa07xoqD5SMDG7p6e9ckNxuEactT0HU/EFteWmm6dY o9pBEkYmeP8AiK9WUEdc/wAqvWB0hr+TyZFlhnjLBrgDdn0ccZPYe1MttQ8NHS9MhvLd4pYYkkMi ksH5zzgdeOnWsCyn02d47dg0BkkKqY2ERbcMoT1+lfVQqcsU7nh10nUbRHPHZXmp2KQ3KxW1qks8 Y+78+4Eoe/8ADn9O1dZdeZYXF2sUcc0MqSyKUUru+YNg5/SuOtNLkt7a4mk3pJJNIAuAeTyR+fHF a8kfiQXt6EkVyYZWghbbwTHuC4PrXXgGrnJUh3KN+IZ7ENcWfmK8soIwHCjCENnsQax7+y0/zL5L UASiYyudxO5yvJ/rUkmvahbafG+q6ayP58wkC5AACow6evNWDqFpd6nPBbjBi8tGDKBndCr5H516 826b5jBWkrI5JV4H5Z6fpUwBA49z/SnuAjsqD5VOBmjBI6e1fQxd1c8xxPUfC/nyaUY4QzHA4Vgu eH68iti1mtp9W0m3vIwU/dK6yKCpzkYyO3HrXCR+Ir3wx4cjv9PjgkeWZYmWfO0jcQMkDjrium0/ V0ll0TUJIfLluIoLgleYwctlVrzK8Xz3O2jZRudM/gzw3em8s7HdawqUlj+zyYUNtKn5T2qkfCep ie01KC/3G7TylikXG1tu0Eso6cZrX07XPD99Lc3N2iRI8ccXHHznI7dK0reOGS00k2lzh0mjGxXD Y5bHyGseWQSmmcRs8daTCI08y4kspQzmCY7V3R4DYbtW7N8Qro3l7HrmmxTQCPlJYCodFAAOR1/M 11Jj1G3bWELxzZjUkSgx8bSMLjimPO0kukfa7I+UsTICQGVxtA6de2elS0uxS9TzP+w/hRq9xbzv pH2YXW6PMMikg7tvy7dh61VPw10prG9l8O+MJ7a4SVhHDegmPaT8qtvx9Ov+Fd3Ppfhi6s7u6mt0 jns7ljASDHj5hjoMCse48J2Qubqz0u9mhE+2bdlZwCGJ27W7HNJu2xcZeR5nceFviJZ3Ur3OlW+p RxRjEts5Xcp53BQSMc+lc5r1tqul29u2u6DeWUWOZPLSSMAocHcnT8hXtseg+I7CW0vLOWOaOeAQ KFLRSF1iIHI7Ej0qGfxB4vgsF0/WYbiWN5DFPHKq3CqPlwrNgEZBOOehq4znsipTs9UfP9/HHe/D UyWUqusFtcwNzgiQ34ZFKtzyG9OntXo7W0moSfD/AFuyRbiG0njadx/yzUxIrsCegBVs+n5V1WtN 8PfFkWp2niXRI9gRLUPbSvBIu2VmXG7AX68+nSszSfB+l6O0Vv4b8SXdpDdx/aJ4r+MTwbimSysP unb1A4Nd+CxMaekzz8dRdRLk6HHeJIE/4WNoV6hHmW9xbxJuA6SKnrVmN7o3vjjRbqWR7SSBryAN JvVHR0UFQfucNWjqnw48eRahB4j0u7s/EFlDqjOG8wJcuEkQg9u3y4HTFHjK9l8J2OsajeeH76w1 DVCtrNcSYktot+GiAlHTtu45Kj3rnxNZSn7p0Yek1TSe42z8IQW/iC3ku3tNTihci+t1QrLbvPEW iyVwG+nPNaFr4I1KfTNJttMMsV1IfPvLxytxDDCxb5G3YAyAu3bnFaej+JtI1E2+oW/iGwewvZba CKzCotysw3K6PJw2Qc4J4qLR4Lhb3wkVMiwGDU42VCfLfYz8EZwQjD5TWSqtfCaRo33PDT4/bTdV u9C1LT0kmsZWhd4JtjFgecRPgD2Ga3LLxr4auXCzXbWDHjF6hiHPo/3fyrwLx0L3UPG2s3dzK8s0 9wGkkfBZiOM5wPTtXu37O/whs/H0+tT61cvDbafHBt2jcWZjjGP8a0hipQ0FVwimzubaaGZPOt5o 50IzmJg+c+4/wr3n4RIk1rrOT/HETjGfSrWifs9/DjRJmnt7ad3fliGEefwWvUNC8LaH4bWSLRrT 7MLgjzGaRpC2DnndTliObZDhQUCX7CFA4wBUbwDsM1u4wB29utQyBcZ7+1LnZfKc48XXiqzQjOa2 ZApyDwaqmMA4HNaKQnEobD0zUbQ7j71peUOS3ygZ56dB/wDXqX7Pt6ev9Kpak2MdbcLyal8tfTAr Q8o59B+VIYuD3p3FYzSox6ioSntzV9kC9RioyqkcVVwsU9o9KXYPpUpXk0zaadyHEbtHbFG0elPC nrinFDii4uQiwo7U4AHilK4FSoo4pByEXkZ7cUptj6cCryR78YHFa9vbfJgjrS5h8pzXk4x/hSfZ yegrqpNMJGU+nYYzUK2T4b5c7ByBzjv/ACo51YfIczOsNpF51ywjQnbn1JHQYqzBbi4gjmjVlWUb lBBBwemQeelWriL+1FsI7N0KNL5gkXDlfL7bf/rVqTajptnd/Y7udLeZUSQhv7rHYCP+BcY/pUKp dD5DwT4gw6fBrbPPKkUz20AjzIVJYeij29qyJ5dNRQlpbnJGTiHJOOM/Niuw+JbG28W27GPzALKF lGwMBywFc7aNqCTRysirINyoJZB/GOhA/SvDr/GelTXukNs97b2eY7I7Yt0gIdV49wM1rK1/d/ZZ XEEGxA4HLHn3/wDrVCHnlSUSywxR5MRKKWLH2JpRBNG0Nvc3TiEr8rAqnAHtWLSLGQteXmo3FjJc BYoVBBh+Vm3CsbW0Wzkjh+1s0fG1jKPlUYLDHrgEDHrU7/Y47tnkmRbYx/KfMJZ2zjBA7+gFZmtS 2i2P2ZLX93+6UsUIzuIVyM9CKxqT0sJs04Lbw/p9q408wC5gXIKRs5EnJCsTn5e1UtHuGk09L+Ky kYRK5hbKhY5J+mPovHSjUFuoIreP7HJb2rywxszOqEAAgFtufvdc1NZ216vg6BiY4omcickFpNpl IBHQcDHSsk1sCZ07z6lY6bFKkEcksQ3mMOSSMDpnpx1rJ0yKa+F3qQvGt3u/MHkogK+Wo7BuM89a szxS6ncW1nFes1uUYu6qEUfL8iDOM8/exWS2nw6eIrfUsK0BHnMspw6Sn5jGo/hFZz+G4zN8NL50 cFg19LYwGRYWO8IQqks4HbJ+v0rqtZl0LToLae1ZJ5I7lWKRsZMIxIJk/Q/yrnvCU2m6ZoeoefHE zRTuLfd3x90AnuaTTo5NTWbVLmwkj8wKgQOsKlySoZ8dV/wrNVLR0Fc6jQQoSTUjBcTt80qPEihQ mcYXPsK57xDJfLrQEoFurssuGXI5ICbyvXiuhsNeaPTL6KO0QSxRbHLSb2dwCuyPp+grkdbvbufU VjUbLlljXavzZMa42sD0x0qa9WPs0SVdRi1fUZmzFHbTaiEUHOY0k8xQu/6cuK19Na9/0eHTpnur yDT9nm5CQW48xnlDDngDIU+prP1UnTVtTdSPMhSC4kCnPmtNuUHI/hj7p7Vv+EZdC0bw2v2sW11f iCKab5mO9W/vY7cH5fSsMK/e1GXtWg03TIBYadtSG12+ZPKxOWY4bp13fe+lYtm1za3VxNFFGZbi 02zJ5eWCS/dPPA+U9aq3t3ALa3vb1SsMrMEtVjwX8kfPN8+PvA/KOmAKs2s8t3qF/wDZIi9zdt5I JIVfKkjDISO3l4OaU5+9YR1emJqV0Lu4tleS3lYQW8xKRfIg2bQPfk1d1q0mu7W1jghtoLtEX7Pc 72diYV3gH8B096zIL2aKOxt7NrSGKzWQ7NxYS7vmDMAPUfhnmumFtdX09tJBfLiObY4jjUKpVcMU PU4yBnFejSa5bFnBX1ncDRmv5Gc2d8r213FEikr5rfMP7x2sR0FZloq3d3oVpr1y32zT76S3uEDD e0MUb4ZfaRe/UE10t5YGw1W6dRc+QtxvkDNnzDkFivYfIMfXntXGahpmlnxMNIW5iWxvfMuZbrBl ljEUPzx8dd6EbeRg1zVPdYmzYt7PTLe31y5S3W4vzJYSWKA7v3J3KBno2MbW9uRWnroh08aZdooa 6sz5FxCkJUB0+5IGYAtgH5RjkYrHt9bhfxPC+l2xitoYIGKwxhmV4kZMAN/A3y4+ldPq7T3mq6Je zWE/2aKEXE/nyBA6ooG4gZ2g5HvWdP3k5Ik6LSDfafpl2DpktxDc/vA0ssceFxyGrL8JWuo3VvqL AwIspEIeT94wQA7cL0wRio9buNWumulElqmm2BU+YHdxbfJ904+8r/TiqegSX6PeSWtyumrdQrKg VQRIoGMI/TjBrs9qm1FDSNiKwvQ7aTcX7LHBcxIBHEq+ZHLnHJPPPbtVwaRY6brVmNz7WtpmlaaU gxumMfN9O35dqnvNGZbSC7u5p7sQhZPKmm2g5HJXpyD0rl9TGh29/FbiJVi1OzcsTIZPKKnJJ55b Aq5KxRBcR2N5dRTW3lQ/Z2EzIfm3vjeE79cdhXptl4lsb7SraW1hLy3UW4pbRkqshXGNxAHX8q86 1+60qzfSotJLxW6AphYmXzGcKwckr1xnHrWtofiCTSbD/hH7bTLq5nsLh4ASyoJFfdImP9pl5/nW dKpGE/eJ5jC1d55bvRrXU7O5jitYTayFJMNJuyeD/CCBhiP6YrP8LQalrXimKW8mtrOWyBuFmuQS s6r8yIw9lfL8c8d+K2rzVrzxFdr5bQaNBZx+bNJMC7ZVSVUFupxwQOawNHkOo2FhA8z263F49qmU G545IyjNn0Mmzb7VzXj7TQF3GXQ1i5A0O6lC+QL+ONYVCbhcXYiUkk9jGxx/dAqzDpsWoWUOoaVO VkjFw0sruQfs0kq/dUAfOGOcfhVPwlp1j4h8VWMM9xNawWseoC9SaX5p3F5hlAXJUJnr78VHoWn2 50vVrh3ic6bcPb28csuVVHnbcVUckBNozg+tNu75gkdfNaeHdH0WxtHa0u7/AFyWJUkJ/wBTGzIZ MkEjO3Oc89ccCt/XdT0d7yxhESyWksiK0VrCSgJYY3ttzuxnA6VmXVxpF1qtreQFLJrDTZLoQLHv j+1S/u442+U8nJx/+qpbjW9OgtrG4ltLp9b09Y7eYouHPRR+7HDbuQGxXTGSjGwjG12eKKRNH8O6 ffxtfyhgJYwP3gDHABx/Dz6isXU9P1HULJfE4kP2O9lgtopZJsrDvcC4LBQMqcFcdua6HUbr/hIN Qn1Ax+Ra6eVk3TykyBoYyRs2/cfkkjrjg1n6eNV1vGhm4WKGbVLdo4yg27Zbhmd1BIzznjtXnSa5 7IroReKb3U7+XT9K1K5S4VJ3vRIGVFldgQj+6qDx71sWuj3Vmi6jBbzQ6Tdwz2shYKLhkMZLOyc/ u1cYz1rH0/TobzxTaadZyXEq21jKMxpt3O0gGCcHGF+YcDmuj1G0tX0aS81mR21CJLmOETOWMaRj DHC+nGexNXh1rKUiLGFpEEOq+HvCtlcLuuVVgzGTcuxVCp3AOemK7a6/sPw+FtNLt7aWa5geRooF 3qvl9GVudjH0AxXlVs+n3Gn211KJAyWqW0MMBBLEKrF0AB54A56c9K9B/wCEg0ezv4Nd1GBpJZ08 i1EMKtHafLj7qY35I6jPNb0ai36jS2OO8GaoI9C8qaS8239xcSQ21qgAdpnbf5jH72MDqflr0C0g 1DxrDb6rPZNKbTdAMyr5ULKMMw77++78q8m8Hwa3ptrpurWsRl+0bnAc43M7tvA9PbH/ANeu11a6 1ywa1ggUWthebrq2Jydz4G6ElfQDPNSp+97yGZni6yvLK2utPvbW2iuQlrLJNHIQXXf5QlDDAJ2K N2e/WjSovIs7CVbx7W6sJJnZmUAqXX5OD96STPA7AVS+KOo3s154ZjUQS3Et1/oEyoVhCxwMZRIO pweDkc8CrNlp1nrEul2GlX82owraLJNM4ETLNglkX32qyqegxhSSK56yXOrFM1TnUtH126+03ds2 m6RdKtj/AKsRNLL+880YH9znnmorvTtL0/UvEumowGoR+HLO4jllbEYmJYusfXazoAAfXnpWv4j0 Oy0211q4DOk0uiWayB5i5eW6uJA4bOC3UHkZ4rnNbh0nSNX8Y62LuINIkcWlrG3m7yY0j8th6Et/ wDFdE2orUg6G+vrWx8JQo935V28HmMvy5jtYzs8psAlnJfdkdSPSuI+1Wss+mxr9pYWSK1hHEn+u uJNxkuZM8ISGAiH90ZHt0TzWt7Dqf2lW1DVtU8oWVnDC3lwysfLlug2R93DYOdobKjpXpemWdr4W 0Yzw6NMjQW/+ksxXzJEjGTjJJwvY8dcVKpOa5kM4rTNY1bVNTl1SFRbjS7Lz72RJeXuFY/KjEnIH 5H161x/hpH0G7lg8nzZNW021lhcnzpP3jkAE8bcHb74Iq/LrVjolzq2qY+zL9ikEMWxgkUrxMixr xjgYYk/Wk1HRpNEvPCep3gMkE1ullOY1x++khjXcp77ZAv4g1io8zsgZhyWVxp+pXLXW8X0U7IyI 5OIeku9Mc7hzzW1b2FzJYWOoCJopkjeWByc+fas5ZwgPv/yz9Bn2rEvH+1a9rNzcW0t5eWRWeVgw jKMiiMl/UbgeO+M12mj22nXTeH9IklNzfRxsCkx2w2bmLbIqn+9g7sdjXDGhzzcWQ0ebeILgaJAf tzNPPp0U6LApLfuOXGc/wjb1HPNegXYtDb6XoFpNFNPJOp+0xW37xbcr5h24Byyfdye2O9cRf40K w0dYma6mWCbzXchlkBBVevJzxx3zXfaHqMljdWmotiG4W2kWPIG0M/zuSCN33RgKBSw94ztIIo7D VNc0LSbuBtOh2G0Z1cNHmRy8ZVcnjdz3rG8KS3Wnf2ZIlrNcz3dllVb5UiJbO9G69xu49hXNaXdx +ZqLXsj6jqOpxyXgCnKQxpnCMnYgHgcHHauwtta1PR9T0OG5hVIZbaOGKOaUEICoOSV7cj5etexR mnK76DRpRJrGqTahetLbILaOW3ZF3Mp3rudscf3RWddanqc7Wb6bqP7q5tRAxhhWIwIWCjdnpknG 7uKdeXepweIIosRRXepW8piswp/fsnyZZTjClTx7Cqun2sdki2lqLi9jljY3zoqw7PKOWYk8BFIw tdU6vWJVzd0jTLW7vLy1i1CfUpNOCRh4pSse1h8yBQB8oouNH0yz1Zre9MKKIklHnMQDhsEHLD8K ztL03TZLnyLeC8w8RkkMrkLwRgApjeSOTjNdRNZ6NaHLxWyoHyqy4ZgAeQCx5/AVvh5XWoXKE954 UsQ2020gj+dQgHG7A+XhuBms4avBaarfX9vBczQXEEUYENsxBkizyduBjtVf7Zog8Q4ub4S26tJ5 aRx5iUkJhGZV6cGuhg8Q6esxtrMXEskgLGNIGIIAxlScccelblFb/hMbyWRLK20W+Mjr5yfapEgD KvBbBJwoP+fSvYWfjFtVvtYt7KxsDcQxoUlmaUBUO4M2wd8nFQ3MOq3WoQa1Z6dK4t7SS2zM6wsD MwIYddwGOlXbTWdeuNQm0GCwsLW5htlnljuJmbCM2wOAoHG/8B0oQFCQ+NL28tdFvtTsLFbmCS63 Q2ol4jdRsy59G/Csz/hHf7Gv4/7Y8QX25IdjSROEZrdSW2bQDt+b36V08Wh+JDepevrqR3EMbxDy 7ZQCs+C2N+PulR71knTrnUPENromo6hqNzDNZvcNHlVKkOqkfKDxt56/4UVOgDdS8M6dLrOhws13 d2c4nlnWW4kO9VUYzgitgaZ4H02OCYWOjW6ysQ3nSI5TB7lyx6fyrNn8MQWMU1/qlgb6SHEdtNcz lzEhOMFcr8pHfFZGq2fgnSPFehRH+zYrKGO7luZMxyDIjxGSPmP+7SjJJXkBf1rxFo0WoaJc6bJB LbWVzI10LOM7VhMbRn7ijoWyMVz+oW2j65Frlo0N3JPdalD/AGfcLGWk4gyEBJG3Ix1NdTB8QPDk mnXsF+fsMDu1vDbwwsJZB0DkIoUKcZx6VzMusxW+p6p4j0uwvHTSZrIsHTaPmttu7BJOemOOlcte pGQ4syLCw1nWLa2sba1lnGnXcJhlaVRGhlXcAecqoCndx97NbPi2x1zS5LPUbkWEhnmtodokkk2X YkzERx0PTaeKwPD+p3PhTXolg0xpJIbh4ZIpLgc/aQrZbBwdjk/QGvSdes/GPiiwudJmbSNHLusi TReZNItxEd8ec4HUdffjipwri72E0Y/xDGtP4Y8QadquqwQJCbSJoorVUE6TXESb1diPuv6Diq/x Z0WbSNPh1C61O9vFup7W1DbwqrdJKXgk8tR0VQwz1rj/AIganreq/Dr+2tT8QQTzi6tVFpHbJHMJ 4bgB1L7jnaw6V03xQ0+CGx0YX2qXurS6nrNgvk3EuYVLE7xsQYXjjrXTJ6XHsWvA+laHD4f0uPxF ZST3muxrItxeOZVRZyRtUHhcfSvN5tN07T9Z16aNo7fT9OgIjWVyI3Ak2L5agk7kPPyA5z6V6DZa BpTeHdKubW3SIW6tcXjSSn7oDKFjRm4/Dt+VeXYi0i10HVFmgCW+oRzzRyYdgmJAwRW65XBUewr5 7M6TnDkQRlrctXgur77DpTeZdXk8EiwmJA4khHQr1R+TgqDlSPpSXPh64022jsrVbh0mhw2nXQML RglWMtnO4IjdXwShyMrnIHFR+Fb+1vo5Rr6m00OeQXcEfls77ZS0e5cY2M7DoCAcetaWgeKZdKGo Wt9f+fpyAbrQRSyNE0jbUOx8iJmP44zxmvmJU8RQg0o3OqMo7tmLpGuapqWuG1uLF/8AhJrDybad Z4wJJrNZ/luFwduHHEgXIzwOCtbUNldJa2nhiBLe50rwzYXN1a3UqfNd5kb9ydoKpLCTtXPVvmPG K43xRpV14i1G4v8ASR5cMsYlFxMGjMMhKxSJ5iY+QnbkYwwbd1Wp/DGr+MvD8VzqeiXi3NhpDS2s mk3jBmt1ZApSOcjMkiN9wsPnXn3r0surTVNSlpYVSCbdhPEeqXGj+MvC9zdQw/2anl6dcKAdj22s JskB/vKkoBFZMlvqd0/iDSbG8mjtbO/uIbyCFmWaVkTy1i3jpEFGGUfMy8e1W75rfx9BqegWQ+zX 09g66fb3KiGRJEXMAIbGT5qDbIuU3dxXY+FBp+q+HbLxdfXM9uLhGaeygQtcy3mwR3PmDI6EdPvb t2BWFOtyOXMTG/Kdr4T8G+GtU8O2F9a6nLFDLGdqLbxOFCsVwCVJ7etdD/wr7Qf+gvN/4Cw//EV5 ZBBo9pEILLxNe6fApO22WyuFWLJyVA2HgEmpt1j/ANDlf/8AgHcf/G6X1uRlzM//09Ww0JLnVorG ZVJmWV1lEoCyIoyhy2AAD361V22Wm62th4kify1Ijd7dgWYEfKVP8SEYpstmlymq2+mRG8toXjZL gpsmRIjzswcH0YelU4xaXElzfsHnEREYljYrhs7V3b+3YccCvj6k05aHQzsdE1zUdOkk0/SwzadN M0YhkCjfu+XG88Dj0qrdwWFpFDI5ivDbFTc2zjEkcrMwMfmj74VcVqa1pWqaI1hFqNmkFherHI8l qdtu8uAAZGycsM9QR0rmrywuLywv9aiijgtoZ1CksArmQAbB33cZH1rpbsrNAS6bq97ocX2DS71r S4u03sVXOFPQKf8AZ461p2za2JZNVv7lryHTPL3x5LLILskkBB94MeHycj6c1c8J/wDCOX1zO+py LYRWiIzyfMcoCCYxweTioNGvLfT9cn8S6otxZm6uC1lEu0hMtgZXudvTIArei3FavQymzL1y6so9 ZtntxbrIAy3DW64QvIASFRsdCdox6VRs7iOBYYdNsVuLwLIWEn3STjblf9kDoK0dQ1Hy9O1PSvIR 47u78wTsu6Vwh3Y3D7pzjOK5lBcG8kNtN9kVn8wsvJjUH5nY9vTFeXianLU5om1PYJ7O1u5LODTy llBDCsYnujtG7PzsvGWXnGKWXT4bedzb363sERyjrGYg4AySE7AD1qeGazCwx25mZnbaZZlD7Yeo G3tg96iurOSO4lhhmkvmkOIiEwzZIHAA6Y7VzzlzR0EkWNU1E3l1b2rxFtieUGHJ65zg4AxXNXL2 6XM6upke4kZumNnOcY9D61rajbSgG4WRokU7TyCoOcfKKz1lSC9UPIIclIXdsKoDDIJK5xXLvI0a KNvHFeXYRJ4RGf4E4ZQBksT6CtWNIG0x042MfkZ/bjv69apaRZ6fLrCQ2dycPEwny+5NoPUNjkY5 Nad1pk1mk1xCYbmC3DY+fcrKOvH+7gD/APXRVp2dyUcnqNhEHEkIWcbC0jA8AdFXtxn/AD0rVW6n gktTGfKuFCoxJ4aM8AVl3c9rLtKxeUMlRADj5egDD8MVcvJVmtIrvyP31nsVo+QXXjKn6VEryFZF m5/tBY7pLqEP88Z3J0IL4wM8dBVMXlta3DAHznMwZM4GeMbfw9Kt6ZK32pxJK8tmwbEB/hDfd5Pf NZV5CtpqIkzstlIiQtyWkPQY/vVKfQSOidfmjjuU4yGUAcg7uM96ofuxdzpcE+Q7nbgFvmUc8D+l Xne1inh+QuWxkA5YOPlOc9hisWwD29/qDxbVS3yFZmy5Yn5mC+goViWy9b2t1ex21rApLSZVASFQ gnOWJxirE/ladqYgiKzOdkTPyVBC/Mq/yzWDfhluRcXT+cgwqlflI7AnHGK0YsPaRq67XBkzOvYZ +X5QfbFbPuIvXNrNBp1pdsqgWcmN24Fm6ZKgd93tS3t/Lcq8ybS027zbgKEJkyCSR05PerFwbG60 25vLi6RDFE8kMCrlpJXAXauPQ1XnvjNF9kgO42VkBMkaDCSKuSp7HPU8/wBKuKUh3Kd1bT+RY2UL ZmKNJKRgqoKjv0zjoffiux02OO+misLOygSe5HyvKwjCMBl5RI5XBxn/AOvXd6H4IW/8E2r6bdQ3 2ra8+2RLeAyNDCoy4Mn8Gwc7MYI715vc2ek/aotJec2ctrMsJdsbbgtN5D+XzsHy8nLYrv8Aqc42 khc6OlsfFM2jaJLosMr29rqDPBPPAxDPhuN5bPyn0HNd5aLo7eHrK88OXsum+KrO4W4WCHCxzuCI SFJ7hW+YdxST/D7R9e8Ut4W0h0jht4dr3kQAglkQfvHIPqF6r0NaSeEodf8ABt1Z6dpkIj0i7muB cJn7RH8u9d7hx8jDpjqoHpXr4elPm12Rk2b/AIcuAJTYNGTe30v2pJbciOWF0JLRuT1i9UxWU7aT bXbarDd/2Rq+jXkkXkR2pLNLIMfaDvwnsOeB2rLtYPE+h6xa/wBk6gNYu5LCNpkMYi+zwTAdWfAD IMAlj713+lr4Z17xDO19b3Uly8y/ZzcN5RmlIyyTKoIZFPMeRzj0ruhZ7GfU5XV7vV9B8HalrD3c LLcspijM28zsQRM+wZzIP7pG2vQ/hjpv9jW9kdMtYbaeWwO6UHfHI5bIMgH+rZvQVxmieE7Dxn42 1VvEUYvtP02IwJOMxI90pwzhB91l+62ODjOO1dH4bv4vD2t3ckEEj2t9CbOG3bBRp7fkFckfKT0N bRjreQ7WPDvjBpslv8RNbSVwhnSCZhHjbuKjIArzK2YxFY0GxIflYcf6xuSTj6123jw61/wk0j61 Kkl9Iq3DMv8AyzUj5Y8ewFefeYEtY41lDSXD4YN1/wD1V8lmc/3jsevSqt01ctTS+a8MUeVO9hIV 6bV5zVKYefeQJHEzwqfMZiQDxjbjp3zWgzJAFjgCvGRsklUjZk9s59KIxIhmXfG6NtAKsOArcCuC C1PTw2F55RU3udpBrEkMFjbrYqYodjIpVvLeQMAxOQOuPWqME9jC7XziOzd90TArkJlztwPbGOK1 /D2o26NN/aFzshAZURGBG6TjHfFZp+wzDUWcGOeaV1UTDKBjhlzn8a96NROMUjwsTCMaskjcmsI5 NKk1R7su87sRFuwFOACwP1qe2sojLHcyTFy8bJhvnw2w4Yfp+X4VHeWFnLLbaPAPI27ZAc/KN8eX 64/iXP41nafpl+2oW8kDlfNif733FZkOOeldyajNWOR6xsPmWe306RXKzBZ9uM7c7ovQ1l3sYOp7 9hjZ4oCDgc5iXJzS3kviGz025KQR3ey5hbA7p5bBgM1Qm1G+nv0eSE2yxWMDNkE5JjXj8OnFerir unzHLTdnYy7hBHPIpGAGxUY56VYuf9aJAABIN2Ac9veoVB54r6fCSUqUZeR5NTSTO003wvJ4x0Ea LBMIJPOWUORlRsfcQfwr0LQLe7s/CWixSRfaEtAsRkQggGKUjOzH6V5emr6honhl9Q0y6azuI50A lUAkBpFUgA8d67HQ/E+tP4a0aOSAXENyrO8mNrEiXHIFcWI+I6qU/dO+uNI8N3uo3UNzZrbRyRb3 +Qx/OHGCCPxrC/4RnT9lhe2l40UocQhd4dSMsvsauQ+N7UX15cahZSW5hjMTKy8H95gH58DpVM3+ k3K2TxFVaV0ljDfL8pcjtxUcr6F3iA0LxRZJd2ljqCzOgUkvmPchUjbyDmnrqviOznsTNYmW3t4w 0DoB8zCMjGQc+vaty38l7q5+xXrL50SHKsH7kYwelEMt/Ha6XKWicK4AVgUYEBl5pJivF7HOp4rs /sVzHfQNFLeznCuobaSBwd2Pw4p8cmmzX8hjYIoiAdkYrhs9DW5NI8ltqi3lkDJuz8u2QL8meN2P amT6D4WvNQitvKNqJ7dzIfmjyQVIPOB+tS7dSrNbFqygnOl6RLHdMQJlAWTDKPvL7GpJ2vI7fXVZ I5d3JMbGPH7vghTXNp4XEdrDeafqMkWJXt1HHTcYw4I5yKy7jRviLpdlc2lpq8OpSQNuuDcjmSEo NiBj/EpB5o0Bzkzu7y30i6vNmp2G2Jrbd+8j4Zg/DEqD2PFczH4V0G7jsZ7Z3t7i8U27mGTouxkD AN3wB2qw/iLWLG8aS/0uTyktysLKpO9ARhv5dqhtPEOlzJpMM6hWWTe+9RkId7YLcGkoyFzIpHwl qVlturXURJDYXQicTIS7ZdW3blxjg026bx5p+n3mmzJJeRiWN5Uhm8z92VIDHzB0JGCuOOua6YS6 XcWV9LaXezddbokRhggCPqrc/pWtKbszavJFPHMGtkJMikcfPwCtIGzyq4vvDEct3/bXhexY24QR yyWf2dgyv8kgaMckZxS+HfA/wvfUYrqa91bTri2ja5tmjvC0drIwDbkQZJyTgoRgivVIpS9zZi7t Fmj+xEBFKtuwq84PpWGNG8M6kunJcQCKS5SSGdyDG5TacfPx6AZ7UaDsz5g8UfCbxVrWs3euWGqa Zqt1eOJZIWJsZCFyNwVlMQ4HYjPXvX1D+zX4S1fw14S1abWrRrW6vbxRyyuGRFypV1LA/gaoJ4Ot nNvLp2pSpGJ3skjZlmi2ljhznBOAcdak0+08Z+HYoYrC8jkgtZzFOEYx7ixBUqnI6f56VHLqaxk0 tT6PlYQwtMR9wDNMBH0OORXhcPxA8VWINpremPKGYRSDaML0IJ257c13HhHxi/irUr23WCKC2tlO Nu7cMHHO4CtecUZJ7HekioHPapBnj8KXYCM9u/boP881Vy7me8W/oORjOOvp0pBb49vUentVLxCu p21gt9p0qwzWjhxFIAPtGR/q134Gcc0211s3GoW8EVsz2t3GI96KFktroctFMnUA/eU96amuorFD xBdPY6Nc6tY3AV9NnjDYwVzv2NG4P3eveqWmeJZLm7NlPZKwW4EIuYGxAN3APz4br8ucYyCKi13Q 7jVfFEZ0+J10uWFotUkscBpZQ2GW4Vso2xOhwT0rz+bUoPDTT6PbXa3OqWk8C2VyrBkZoJAMTIO/ lkn0LflXJWxHLIfIezXV3aWWoW+lTyYurlS0Y9VXP+HFSsv414rJ4ntbTxZpmp6Vcpe2N3I7Tm6B EsBkCtMvI4A3ZTsMGvVE8U6I8gSabyvllxJwYz5TAFRt/jIIOPT6GtqOLjMhxZalU+lVdpJwBW4Y iyq+Mqyhgw5G3AxzTfJCdcA4ziu6LuiLGP5T9xTSnrWsy46j2qEqvpTuIzdvt0o4A54+tXyoweOR VVtqqzkEbQSSOTwM4FO4EQUH6VPDBvbjpVHTL+11W1+12okRVYq6yAqy49c+vWtpJbS1gkvLuVYo Igpd/QNwOn4dqG1a4F23tlBHqK3YIkjUu+FXk5bAAA789KgSIJGsp4DgEZGOvTrXL+IxrD3llY25 S403Uopre6tpCsSKGwFkL43HPQYrlnOyuCiP1CfVNT8QR2unRB7XTxb3EUySAxmRtx/eAYG1h8o5 rI8EzwS6jeahqusxyPrBZls5dqMjgnPAPyps4ANchcmTwx4u05dV1RI9LuVhsoJrQrEUjjU7Y5EI O7Ye/XvXAa5b6Z4Qt9RS3u4tbm1B5BAkbGbyoVYhJppVA+YucqvABx9K8ititTVI9M0nSk0L4hov huz83T9Qw0LNISsagD7TlB05xjPp710McsPiLxE2pzXIi0+0usxIwB+0Rxjy1C+0cm4kV5BrWvx+ Hbm11Twhb3MF1DF5N20+2dJJLlQ7bJFYjeep9DjgU7wXqflLYafpkj6lq91bvqE8M8kqxwzeaI2w QBs6qccA4PelSxL5rXHLyOs+KpS48Vwx2rRnzNPiDFgW2srycAjr9MVy0cN3NJHvuRtkOMLHjGK6 Dx54dk0PV7dlvppbpdPjknlkO55pQ5UFmJ4AAwB6fjXNyLYQsftE5dkOVMso5z3C8dqdTdnXS+Ed d6fbWtlNIZX2oS/7yQIDgdhVSFtHkjSSQh9yD72XOce3tVD7RpxSSNkR3e6Ty1Mb8qMcqTkf56Vv Nc3M8kc9lYyOEYk4Cp2x9f0rKWqKOQt4lF5FdW6SOv2jMIRM/KIztcBvRhirNzM3ie6sBFEhWWUo N77VIDCT5gOjcdKQXOqWlikX2UCVGbe7SZcOcsPm44AwBjvUkOn3Gm6hoizzKkU4ZoTHHkh1wuZf XIc89K89vUixYkXU57+a0SSGL+z4T5xG6QvEF+UYbHQmqE1xfR+F9LhilkTzASkMgGZdpI3D2Jq/ e20EGrXDRX9zqNxa27q8TfJHtPXO0Y/3eee1UdHgszodleT3Mc5V4Ixj53hjA3BFUcjk85/GhJX0 GjprnT9P0TT7SS3ZJEtZELh23O4YYPAP6Ut6llCl3cWMe9jtAUqSJIWA3hd3SpNZktXayt0VXT7V xJDGFYq2Qck4zz+Vaa3t3NYxWr2Dhrb9zMbqRVwTyNhHXitorRplHAaVHLf6bFaRRrcW014xAkUD KtnbnpgZH1rpr7TrjS7a5EFvBJcLbrPPDHIW2pGRt2qfQ9u9ZHgWHVBp1rqMLQyRxTvEQ4cjLufL BDADjrmtfULfVvN16OK6VpxHEuBHs82Fm5K/RqxhC0LsTWhh3Olyab4gtHvpPPF5G8sqRphk+TzF x065HvVK+t9PF3dNaXE0kZkt5oH3Y/1siiZVZeu0mmy2F3Fp9jqkl2XlSSYkNKSsrRcoCMZwOenX pWdNp9uljJc2VwEuorkSbVz82QpzGh4DHGMdDXlzbuZmz4/W0sNRgjspC0MFpIYBGfM3TMpPOOCF 61tSbdRgjtrD7QmnparFIY027pWHJOcYX5cY965DVjbahALjzpUlkuY4TB5RHyBc7SmPvcHH5V6L q817a2dos9q9m4nHkQvKFaYM20mZV7rlSq9Fxk110FdORfQytT1tNZMd/YW628OkxSWlrvYZPmIM yOrHhdq8E96o+G7TUrlL7ULV1H2IDaXJO91h+U5HfbnPbNSXul6rp3h0oz282nOXeCRXy8pl3qzF +56ZBwOMiptJl1DSYNdS4ujb+V5vmon/ACyx2Zv4NxIUfmaxcLzTYWNaC2zb2vk3QMsr3Mc8iIAs UZAZiT7g8fp3qt4d0c6j9n1a6WSCxN15MSO23eCMMcg9Aw/LnpWTcR6dLNNBpkr3UesJEYXlYRpH JIpG444yijhu/Fa2nQaXLHHb/aINht08tn3bwQMSAkcZXqMcnNdEaiTuh3OqsYPDPm34luYWhWeS KNWzIX+Vd2CDz6cV5G0s2m+MBcW7ySQadbteqGiCNJZOfKZAjfd2c885wD7V23hK5hgvDayoZ1iD mJ1j+diWU/dbBzzjFY+ttd694r1yWSCSS2tbD7NJA2I2ERKb1LDoCfeuio1OPMgI9GsJJPFj3lvb NDA1ms0ckaBVOJ9gZgT90jPNXLO/1TU5rprGcwWt2+xZV3NCiQ9fvj+LGcCuc8Nx3+pSNpaXCt9h MelyyBnCpaxsZBjkZLb1HPQ5zXt8VtLZabG97f8A2W3sX3xrHAigmQ8jBJz14zWdCjdaCUGY+q6I 9v4elSy1aIw6gFafKKVmMg+7k45IIwKzotIsotZu7SVH+z24iQNv8tCDtQkY4wetW9TtLaB73Q9T +0XlpmKS0UyhdrOcvwMDA6j0rOA0uzeWPWxab76wuPs5WTp5bgR7ckZyegPNXJJS0DY6zULDw7ZL GsbR7lkTPztIQg7gc8Y5ridTuLdNR1A6XJHd3EEtsdP8uPpufdJhGxnKgDniuhtNY0i18N2kOjSh 7pYsNGkbE+ZjBUEr97/ZpdIsryXVBf8A9jzBrCFGAlZUkM45B49c1rVblsUUfEV1dN4usdUSzn+y vbArE5CqsqqSJeP9Xt7j/wDVWVcavNJNILa0hsLTVmj1CM3LO3Ntw7R9N29Tjr2q9s1EeJLaC2jt raWSd5nLO0jSrMvzRSEZ6EZHHTiud1TTb2xvltJ7+ORNEzhI18z7PHO2ZIV7ZXOcenauSablcV0d Drti9lphuNxuysYbciDYGkVSqov94kgE9Kf9i01vDGkWT3Tya9cMkUFuZFQLJK/TjkbXAPsBTp0j 1Cb+yLXU5Lq/jvFSGMsEBjHl43KOAVcjHPIGa4qOwWXVrphtjneac2iykFnxH8+1s8EuDjOOuKJL l2EX/AQ0u8v/ABILq4R9RntRGhumERt7h5JZOpx8rEd8dOaueEhbS+GNagiuIWuo9Uu5LRvKecMy Nth4xhFjjO3r94HIrJ02LS9C1vWNCvY4ZJ7mysr5FVsoZIPMM6eaeVHl4c/7W6uq+HWrWeg+GNS1 COzu5UE/2nEEJKqTGJHR2yOrMOOvNXTtsNrQs6PqMNnLrVxLbyfbpDHDCpRVjjW0Ujc+fvfOWXP+ zTtPkuFuYtfNrFawyTxTSlA5E8kR+VtnJzjt0/GsHSLC6uWnvW0+W8+yF2kjnk2F7mbc0xxnkb+g 6Vtyyavev/wjVkRbaddFZCS2/NwpTaMDlMjoPQH3qI3ejEZ3iCTV/Dsy/bIIYo9ZklnWCNsrGGiZ XRmPfBz7VBcQWmj3PhMX2oS204gmw8Sb9u5GcN6nkbun8VP8exNJ9m068vI5o7ZC3lQoC8MkpMTA seME+v16USaXLrfibQvD2sySvuEqmWMhNyQJjeicYwV+b1B4rkqWjKyJuWfBVr5/iC/M14xgS1U5 kfy2cNsfgDoB3JNbXi6fRtOjnjigt7tY9Oku3RXw0qA5RS+SFUEBye+3muPOo6Vp+v6hPBpgmktE VT5xAjmjLBGDAHI5BKjqfl7c11Vzb6PpvhnW11E2/wDa2oR3ELzFBuZHjAHlgZGAcBQO1d1C0aPK xmN4flt9FtdHuZkF5dXVsZpTGpLG4lTG9BjIXb6DFdK+o2uqaHdyvpEs11bosdj5YXy4mgTcq+zN y3fNcpZeI7+SaO6tUWK+tbBNPaOOPeFg2bS7M2P3hxj5elaupWU2jmzljSaK28P2kl3PbvNGjtJH E4+cDORhumecVhCV17oGMNU1Gx8AeG5WESpc2jFISfMlKMTvCqOc9ee3610EumeKYPC1hKiJb2ck yy2kwTe1vvG2PzYucpj5WI9qzdJ03U5fB/g7SriO2so4LYSif77t50W9GzjjOdoXFaV9da3rTf2B Z3JnkkItkKhVieDON/mHn5T8v6DpWiVpajMHXrJvFni3Q7bUbr7VBp9rf3E23CvF5YRE+7wpDdMd QKo+HJLa9ktI7ma4hmt4I7cTTKGbdnML/wAPzKT8vqM98V22m2VuPG9nC9hc3N6/hxy9rbsEy6XJ jKu4A2o20gn1ryyKGe8vGR5V09GG2a1MphB+zg4EkhGX284AAzj8K5sbK3KwsTE3Jubq3nltzqxi 0+Vo3lL/ADi8kWTJGfmXOcfw9O1dTrFhpelm/wB+ox3s91dJZKgTDbgY8OHTJdCRjaB9ayb3Vxd+ KbDV9NltrGBFmaORI0VG2BSQc/3zzknls1n/AGu0Z08WWpH2ptdeCxjOWZY4i5+ZQcbcqRkc/pSb 5lcFY7DSNR1O2trnTI4RLrl5IUBjO1IYo0Kqj9Q2Ml1A9a62z1ua+iurFbNrmz06AxyCSTa08nl5 ZGC8OV2k7eOK8tl8RXEevL4iKtbz3sP2tIAuUEagpCyg+5Pf5uMVo2viDVLC2mCbUuIZpZFkmY+b CjffkMYHLndgEisYYzl91mbkrmfqfn3GkT6nbQOY47fzNQlY7oYs7YlYdt7q5WMe3oK3ta1N9e8C W1rNenY04to4lBLxY3KjdOSo8vByK429up59Jj0+S4dYbnVo5Nkm6S3GB57H90CQRtOxcHK54Fa0 UlxbeXosu6CKcyBPJdVido0V/MQn7ob5eO3I7cJTalzJDuULq9bWbvV9XlCxjVhFp6yMSGF0Fiil YLxkebu3KeiGvW/F9pZ+HNMtLbR7a1kLtLFAyHEnzqzPOcZ/hJTJ/iArz/w/Bp0994PlvLdIhFqW tanqXmyboozGrhIuef3mEfJHsKoTXp1S8kvfs5tft04ECRZ+bcBvjjJ6BmK4Pbpjit4zUKbaKZua zqKGLWLG2tjd3X2W2W0SGPcYxbRbywx24AzWVoM+m3FnAb6O8Z7qG0uAu4RjCIpkljZ/vNkgBfT6 Vq+GdRi0CbVrXzV0u4BmVJJF+0SuV+U27M3TdyeO1UfA+iajdaXoVzJHHew2rGffbKc7YgEjjDt/ rA+0fuwP8a4oXmlJbjTO18YW7eHtEtLfStGNlJdu487dGWlkKbWV+uMj3xnpRZeJvJ1C11V7G2ne yhj09X+faZgBmcdAuR2A965jxlc6xcaHa3+pW5tpILhI9Rt1YbmEj7gzcHb5fGADx71LOmsme+8S axBFb/YFSRhEQIpBIoUMmcbzjAztrsr1XGpyols6eS0udG8QaJq2q6sZL7UYHZmK7/LPIiTHPysD jNaPhK11F7DVbTUGJMguDJFFJ/rZc4ZSR2xxtrlpL+/u73SYLgbpUhZUXhpPL6+UT7Doa9CsbO50 +S5bV4zOEeJomiYx7YZ0yGfH3mBABNdeHk5MpFbTI/DpsdOkuYTfXkE6wylmZVikmHOWyBjHFOvJ ND0q/ku8W0yNZ4jSPDgylyqqcE49yBXHS+GIn8T6Do94IZNIjE13JLuVI3ZR5iFwP97bk+lekwar o8M4uTNbQQQkx20cMYk37sAs21f4eg4r0MNK+gijpOoaRa6XBpqzLvtI98oVGG7HBwSBuwTjiqD6 tI2utqFrY39xD9ljiQIgBUq2Tgkjirt5qkF1eWd8ttciJEmX91C4XLEFcdKJPEzktBbaTfPdOu8b tq5UcE87v5V1DuNu/E1zDcR2v9jzI0wZhHLKoyF544OMZ55rltfi8Zw6lZ+KtO0+xivLNPIls3yf tFsxJkjDnAD52t6HYB61sT2viTVtTsrxtPihhtEcCK6k3b1cAnAjA6/SrZPiJNVbTp5bCykuIftC eVGZFYhhj7/Q9T/nFBRz9nrniK/1vS9HTV41t75JJ0NjAF8vAyB8/IwcjHbvW3f+GrNpftmo31/e TwwsikzMjcjLRjYOAcAnNc1f+GL3w7qUWsreySn5jILSERvGzAr50eT/AN9r3GCKks7BbzxRp+lX t3eX+nXNq0jRyzFd8hGMkgj/AD+NTfcdiOTw94TOu6JHMT5t0/mOLp2lRgcjYcsOP84rU12LwXp+ mzWGiX1paXLxNLJbqBMJSowVwoYgYPTjH1rmXtvD9lZ3+j3AjluoS09lO0bMGRefKWTJORnIPQ/p WxNrPh6y1XQLqARaUNO+0JczxoH4aDG7aqtu+Y9K51VTfKybnGR6lpGo6d4f0+wW8u/sf72YGJl3 biN6p0yF7c5xV7xP4gsb668RyWljdyW8d7p4VCyp+88owhH646cc1f8AEPi3R0tRpvh+CC9up3+a UWskIk83hMhtu7JBDKDXJT6hNf3njO20LTbhra1ls7zfcfuZIUt0+WM9vvA+9c1RWkNHY6/oXiDx Fq2qaENMtdPvprkTibzvMYJ5XTcvTn1x6VSt/EesX1oyRyWml3qTx2cyLGzy+bu2Ptc/xL97p06V 1yeI/ENn4h8ReJj4fhSPbYri4uvmiEiMDuA6dPwFcR4o8Panpt7bXGpvbW9hf3MZWW0BdreXkxvv bG5TkjPrx0xV8nL78dgsxnibwNfadr9p4T1CY3mheINXtbiacRiIQyyA73XBP3iqj6/UVu/FPw6N M8MC+tNU1C/uYNTtwGuZiyJ5aykbVGAOg4//AF1yfiW98QRT6edb8RyyAX0qXDRx+WsZSLK793R+ N3l+uOlbWqaLp2r+E428Q6/fWWoC8sxc3DO7QeXdZCTJHwNko+XP8LZFP6xHlcuhSjc1bKw0Pw7p 3hq8/s+zvEvbEFzcKxMc7JuAZgxzuJ6V4zPFZXE9lY3U6m5hgnkWAJuY7jtIRUzggZKbscV7Lrlh 4W8PrqsOmWyfb9Et455PNV2lLlSsZiOSuHC57159p81lb67Z2t6yvcXFgjyuFXzYWuixLBB94oEC 9ehrw6+IVaa5NjNppmzpmtaOmgTaIZb21m+zw2khhgM9qqLIbhCwX5ud/Ue2O9WrSLw9qM91HLq9 ultdXSzzeQJWmnKpgbwFOWJAz2Tnaa6HwT4p0jQLTUorqR2vrq4V2VI8RoscKqqqc4+VcDb1HP1r k7fxNp1h4r1TUNNLRLpz20txGU275Jhhlh/2AOcjj+VVWwFR0lUjM2hOPVHMSWGp6VfanqFvqKT6 Td3K6e9uHeaPayqTEYyBsO9RjHaqcOv22i+KZtb+wTBLURNrNs8ci3L24PlQ3Kxldpa2wCO7R5qH UNXlvxr1xdGK1XUNSn1azjDMCvly/KTjOck9jXcapdeN/ENxcais9zpDxWkVzMTKE88XJEaLA+Mc BfcD8s8ksI3DcSnqUmsvDOr6JDp2tzSatfF3uLedP3P9nRImVlglj5bcmz5ckH2ryzwVd6to+i+I 9FvC6Msqa3aYwGNne5dJUIzsbfjP+0TU2jXdzo0lvokj/bbSWT7Tpsjfuk8xt2LSVxncp5aEHqfl /uitvU9Ll0LUvCXiC8SBLDUYzoN9bq+BBHfNvt1bPZJVPHbpxXnUcO480Z+p0J3Vj2eTQPiZE221 1+S4iIDK4vIsHcMnGRngnFM/sT4qf9BiT/wNh/wrqovCGk63FHqkGpXGlrcIpNrG52xOo2uo46bg T6elSf8ACu9P/wChhu/++/8A61eT9dw5Hsj/1Nq48JyWWk/2u11F9ju3cbVkHm5I6GLnK988fjWb cW9tfulvfamyReWnkFoiBMR8qooPTnj5jUYit45VjnY+dbs0ZiPzKQvAdW6GtqyL6ZqGIp7S8sry AxzRzoSm11+ZCD/dPI6c96+Mco3SOiKIL3UL+PTLnw7rFrJHcxsnlpcEs0JD5JCnrx/9asbUpCbU w2hk+zx+SWC85ZFGZCgz+fXFSkfZ7c30lj9pS1IgW4eQsRtPAkAJIKj/AFTDOOh4qtd3nlN58kzN uYDaAFHPJJI65AxRiK7WzG0ad9q+oX0SRPcGdniEUgiAjjKJ935QOWNZX2a8Q73326M4QuB9xhzt GckNj8K63RrrRY9Iu7u/hWV7iMFTDu8yPOfKXIOwAHqetc3NeTaoBdK0aK4+5GxKJtGzcc5O7HOa 5a83GCbZlciLahNcK0HzJFIvCnc21+rnOPSrMhT7PLE2nTTwzhzBJG4GZAhJc464/unjHvUum2MY 06abev2p/wDVocAmMc56jrnNWZLuK60yx0GC28hQ5Mh3ee7q4ITO/Zs2H+H+KopQ0941TMmy1CKw toN7QXETMY5rd9xYFvlCuQc7f4lwO9W9Nvp0naTz20+K3ljMbbCWQ8KME544pl3pelg/Zbi4NpaW 5MkbzQbZH8v1TonXgAn+lY8F00kdwd3lW7XMZKuQ25Q2c46jitJS5UCepLqUgjPlzAoSC+TjpuPX PHT0rLilgkvR99i5CqqjdvzwRn+Gr3iUyx+ZMEd+NqjBK7GyQ2AM9/SqOnmWzSQztuzvNsScrkR5 yD9e1cfNrdDchj6ctjd20un2otFuws4hJym6QFSV/unA6dzzUtrNFFHHNPA11CifPBK5HmKCf3eR 09f0qTRUjd0hMse5IfnMuNqY5AXgnJ/zisae78q8uTFC5VklnES42oBjnJA5zmtJO4XJcyvZyaJC sTjVgr5mVQYsNkYkJGMcDB5q5eyf2fp01rPPE0jcbwMNiPklfbHGa47XIC+p2jaazzpfxKQx+6pT rnpV3WPNuJrQvF5y+S8eRjaOe34dqLaDOhgn+wRvdSNG4dAzDBywP3cHpWHqN3ZQSx6kqbbqGRPM 2kPu8wAAtjOMf3q0N6QaDYNPlJJYNvl4BY/8BP8AQVykcmfEVpaWttuMypAFUHEjAZ+fNZ04czsQ zs9SMs17YXNsGWP51mYcngdD0zV7TxZRXc6sP3l6pLb8ZwR8oTv9axby6vG1C0lgIhtmYxTQrj7w GcgVZ0y+t0vJ7dWdhMu4McblwOSM8jHoKl05E2M2zE0xSUKGQZ88NnAw2P1IqaINevgOyIr7FiHZ gM9utRoqJHN580ixTNsMhyN2O4wK1tDgW7jn8xRCbZj5z5xlByMHjBI9a0sNou30McmmOjbJI7ZS 4V4zhsAlgMdM/wAxT7ZHuZLfTTPBp0GmpFcXczLsDSnp8nUsynao/ixnvVKZjJeywW+IWMQ8tHXc GVwcj3bHPArT02K20TUkmkun1OaaF+YRjZIybXQpNgh0wOMYxgit8PTEkdZ4fv8AUtJgS4l8230p JRbSfZh5UskMpbKOwOUV89AOT6Aity60TSLqae48P2sf9lfaQbG2nTEksSjL72PC85xkc9vStKyX VLDXY76z1Wxs9N19Vti0rCWMlRjEojGd5JPPXvgYxXqzaZ9k0m30Qzxi+kkivL+F9hi3N83llgd2 PKIICsMd69/CxdrMiSPNJ9Za18QaDq+hNDcXVzZRmaCOQCKKDDDY5B4IA+cnHUewo0fxZFp1zqlz aah5d20lvB9kuDvikbdl4wOCiHOBwTyaw7v7XBYS2+l6ZHaafBdmyjlmDKPMLfujuUfMSP4fTGap +H7jRdJmkhubEallJRMCDJILiLCo0bAZyDnJNVOs41NCWkej6tbeIYLqbWo7VtRsLUyQXkEcm6P7 OuN29l5ZuSo9sVvaNZaRpFneyafcltW1C6ii0eGdmEluZ/uyyZ/gjTnPTrT9Q8aeHrrw1BoTWC6f bLAwc3MrxeY6LkK6D75cfMCTzXLeFtN1/wDtDQrnxVpkeoadd6fOunws5PkWqjez7owWyzH5Rz8t dydmrEM9N8d2ejeFNN0Tw9pDhZppg93IrF5JFcfvJXUY3FuTk10niC28PXsMlnLbCRbSy80S2zFf IhjYEYPaVgTn0rxgx6Bq9vLqU9xJpwhmJ+0R5BjjzjYITklyOueB7GvT9dn01/Bx0yJvO0meykLm BwblHUZG6ReCH6nPbiuvnvuQfKni5YLfVpY4reRvNiiVFeQOzqwO0yMD1K7a4dTOlx9myss0krRl WXBV8Zz04WtXUZ764na30+b7HdnYpkYbthVdrsA3PT7vYUumWWnaVDFDl3aFmLSMS0khP8TN6n8v SvicU+ebn5nvZfTjNx5tDNTTrfTg28LczyLku3Ma5/hWPjb9TzWlZ2pDMZlTkjdnkce47VXEqSyu 5SRi3CgDgBeME+9OsZFhheDb84LMQzcbQev8q5nLU9ytywr8y2Ogjvhb2hhurVfsUzyGMBMZdDjK nrnNS6PeQxXEVxP912PmKcMgKd2yaTW7O+i8Mafqr+TLYxXM6qMkfNO23eO/AXGFrGjsHjW0nLN5 c+1miQA4XPUrzXanKEonyFa/O7nXyXqz+drLzibZOYkgQcFGX73/AAGrPmzSSQvNLHL5qI2yNtvl ALwEXucVfmfT9Ra50+WyaKxkIkikA8sxyLxyeMKw7GsUWGnvc+dbykyIigAA4X5QuAe+BXspJrmu YJ2di6YrkaVMEfaRNE/7xc4+V8glfrXP6nA261DsAXgj5QkAgBzt5+lT3NlqMFvNaWt9tmd1eMlz tITP3h+VUbx9Wb7Cl7Gjx/Y42LLjPmCWQYH4e1e1hbTocvU5cRK07oqyMkzSKsZVYc4J44qqF65O MU9pXa5jRwwaV1GewXoakkUI7LkE5PP869zLalr02eZiFrc6/wAP6BB4n0qbRbiTy4p3++MfKyMh 3VuvaXXhSzsfD7RJLDZyMqTN8rsplUg5H1446VxLpOPB+sNB5iSIhYGPIbHGduPwrd0NdW1PwtY3 EbfaPJmuQwuMmUrG6FcE/jRibN7m9D4Ge1z3Xn6vsv7EtGbeUYG2XK7l7MO3vXKXGn+HpLQytbrb 3EVyRHhSm1Q44z070s+qeKtO1Oe5vtKz5UcrQGMfeQ4PBXPbtVU+LbQ2HkXVq8Ut1O8gMn8J8xRt +fFZr1J5u5KPBthLdT2Wn3skCyRiYFW8wBlkz2qvHpPiGG30+4t7xZrdisYhOV/eAMN2MkVqz6to N9eyyxsqokeJMqU+YvgcjFFlIv2TTUiuXAWRfl3KwxuxkDNLVdBWXcypLvxRp6XkdzprSMzASmPB AQpw3y1sReMLJr5Pt1pJGlnAyvuXcGB24IDACt7N8J9XCTRXDGFR86bf4CMDaT6VlrdyPLpsdxZ+ bGISuAVfcAob7pobHHTqZa6jpFxbxCMpHJJdeZGR8h2iTnoccVrQ3H2v+1Psl4SskS4w4lDDBGKq Lb+Crki2uob23uxNuVo7CXYu5slS6cbfoK5uT4aaBdfa7LSLyaxIUXIaGQk+aQVPVuhHaojy3NZJ o9Einuxf6W37mdvsp2rypA2rwf8A9VZ7ray2ljFeafuYXmxpMBhhpGBXPXv6VhR6X4lgntbixv1k E1ufs6OGG07BjOcjoKW31LxLZLaJeaeJY0u97yRgYEiyk9Vwf0okuwnLyJLzRPD7w6hLbs1ubSUN bqGZcZVSfkYD0qWXwtfQ3d9Bp2pszPGsuSCcLlvlyM+tQP4xtngv/wC0bV4Zbk+WAcEIwQDJ349K 2YL3w9PqV08cojjWDyiw3R5k3Y6jNO4lJXM6zuPFdrPDKkUd8TaHyEUjJUhc8npS23iKe3ewS/06 SJbfzXPBw338rkjHWtywSVJdIFreFme1K8lZAo2DrirMC3yw6eGjjlVLxxjGwk5f1ofmi+W+zMS1 1jw/cxWbgmGaS+MuRxiPI4JXNWZpUZbj+z9QZomuo3UbhKGGB9KS4g0yZB9sscSi/HzABtikqWXI /wAKo3Wh6ETePZXEkH2SdGiUSZzuGMYYL0qdB8srWF12a/N5I8uyRo5I2OwsuUHOMdzXT/DzxJpf irxHf6hpMbxxLG0MqSIFYSL2wvB6d65efQdQs7iaCPUYplR0lInG0srDG0YyBUvwU0S60bxBqdpc mMmZ5ZUMRPC7T1B9KzZpTVj34j5sdMcDtms17q31GK806MvLO8UkSwxkK7nGTsbOPTqR0reeIq/Q cY6DPf8A+tXIteQeH/FhW+ib7HfwPcCbCCNSo2FfmGN2fcVpKVkU0ed31t4j1OPRbS5i+36KtvND vulW0YNGoDTlX+8EH8ODnHFcLoPia98Nw3kkFnJqTjz4XnFuDsiVfkuIifm24J+XB654FL4q1691 bUBo1tc3KxRSPLaGBkaOSGTgOxHR9p8vYSBxzSWNuZr+w0Pw/ci4vreKVLouUtt8QjziVHDMZVzg qp6Y6HivGrV/fKPTvBk8y6fcyXCiHQpNOFxdkKoLSSRksscozvLABlA+6c14XpttavFpmrT51LRL 3UH0+a1clryCaUMY0+XaQ0m1fn4Hfrurp/AdxreqW974ZudQl0qOxEd7BFPAxjtEt5N4ZWOSA4I+ 9xtJ6dKw/Gker20F7eaRBDa+GfFCrIkKkqC1qS0m4D5kYSMX3r6nHGKMRO8Ck9TSmv8AT59Jght7 k6Z4h0CcpD5iBVuTN8kzysARu/2T2HHWvWdN0vZ4PsbyztLW2cTrIIWVHWeG6HzO6OdpdX+eM5yd uMDNfO9+q6FKILO4gkjgnintXB3JIHXDn733DjI3YPOa76HW7TxH4PvtD1KeWC9tYZpLXA2WsXKn DyDlmOFReycYrPBYhN8o5o9N8GLe2er3cF0kFvp9rb+SZI5C6SyxyMzPg58pgMAq1bGreJZbG20/ Vo4llsbuY280ILrIrmVV8wFQRkKfuEjPao/DOiaxaaIl9rMaXU928VxLHuUceWIyZnjyHKhRx3z7 A1g+F9Rk1u41DRH+zXDwuLy3toFKwvsmbbiU8s67fl6Ad/SvYhVcYmPKdTBrNnc3FwvmCC3WRVt5 5PkjmjY4BBbAySDx1/Cr8UsU/mGE7xE5jOAeCPw9K82u2HiW4j/s/wAq9ke4kkjs48lAzkqUdeHX AUjzVwBwfWvVtG8Pf2bpkVskoufL3/vsgs5Bxtfb1dR8rEdwc1tTr3diZRKrp0BIXPHP+NYb63pc MscYmDiSRoiwONjL35xn8K6u8sVKywTRpIjAjY5whzxgsOcHHauH1Szi0TwzdxypHPc6er3llDCm 7ySSEVWPVk6jJ/8Ar1vOpbYnlNPUUt5TJo00/kSXyZiEb7WZhwQMdfb17VBqMkemafYtDAbq2tPN gM5kJMWVwRtxltx4PTtjmue1fVruGzs9eWNbq8sIUu2tLRd0sEU6BceYMqeRwFJYc8Vgt4o0zU/C Gq3q3Xkxx6hauI5A7KZv4TAx28bzy3bH0rGpiFsRc9W8MT3culRQXcbW6oAlsWcs7IRk7i2DuXp0 4rJ1/wAINeWtvb3OtSXEyNLMLRjtN5Av70xSKOuNuAwxivPtN8VWen6S82qXV1qPiGyllu3XaFMx LbCF3A/I3BrZfxlqNlrVtH4905VvUjabT73TpEWRI5cb42OdpAzznHtXI8RDls2aRRH4h8OW/i28 8JPp9ibawubWUta+cYHijVSd6hQx3AgAk9fzrxOfboVtY3tl9otLbWrdppUn8uZQEYeWFZclxld3 HsK9n8S/EXQHuLW6069WPWrOGezjtwDHtM+FUxkDJbb8xKnAx71yvivStXvL/wAL2un3MF74hsVW OGawYN5xScM0pBVVjZfmLg8OQNuea83FUozjeLNIvQ82n1RY9Hm0y3uHt769vQ2rQTxthJIOYiiE LtyjZU7snoe1fR1jrnhzSbrw5G1kFjjszHHIWDSTvKofa5fHyn+DJ6nA9K8f8XeH3j1nzNMun1U6 nC88lzbuzKqQ5EodHG7zSVy3dfYV0/gnwjrEumG9vLi0d/EmmbbK4a4UmwjK5JMDkB3b+Ep/q2Fc eBco1eVimanxTuYT4itY9VgntzBaqR5jFlO5y2QE6gE4PPXpxXHpbWUksTQ2ruR98iM9PUbuPyNd N42jht/7G/s9pJ47zTQUG7zI0VWKjY8mDgnkjqKqRXepv5MUlkEkPycuvUD7wCg8V69Xc66fwjJp r25MUUNhtton27pGVcMV+8NvTirkKaiEgsJEt0bO3cxZipVeuMcjiqSJe2t55JntYVcPOC+WDcBS vbtz074rQihvNSsbe8nuZXlMfmiGBEXGRjGSDj86wbWqHc5CzsbxhefbbtnhNxJFxECokjLZxk/K ORz/APqqrdwwJF/a+qSTPKlqPLQvjbucADH93aAa0dFhW/l1NvtXlO00mA0gBypXIYDhs57f/XrL hXTn12/KpjyVRY0X5lDgGQg9flz7V504t7CZlWJtbi9Dworl7Ut5ozKNx3HGB37AYOOtdV4eufOt 4YLKzYn7OkrhIgMMFK/MT3yK5SBrpNavLaW1kjgynnCHCeXGy/wbsfMGzzjpx1rb8KanNZW8uqtG bgJZTRROzGNSsEm3OGXljk9azotqVmKOh2F3p8Qu47pbX7LMoaGF53GEkIByFGRjIzUN5Nf6hb3V 8kaG3Lxx3DFiSsq9ZEA7DGfxrRnfWE0ZLi9EUj2YWd0iUsSowMEkqOjc1zmsF7ZxZxXzNZLNjEfA SRU3KrHOcOOOoH6V2VnyrQpsl8LXd1F4RgvZ5Z41ilaSKGFEV3zIwiYvglVxxnpRqul3Wm60monC /wBowlwJj5m50G5Q/TIPPPFc7oJtz4J024WRo1hhUXG9sguJG2wEccDqaitr1tZubchWYx4jAVD8 u88sB6Ba5ZYmKjyk8/Q04L/QXtobsWgt0aaVliRWcKNucZIwBnj2/KuctnitLCK15msZLyJGt2XL DzXIEgUDLIccrmrsK3umXNxaMx8iynllZQqqDbyALkAn++c/r0pLfzjpEGrxorx6bdLGnnblYg8g hQDvCDv2NcMlKTFFXOasrnz9R0K8k8tFl1Da6IcqGtEKAZJwF2j5eetej/Yta129iudQzZyX96sK Fnctaq+SCVB4Z8c1xd4j2WvXF3eLGzG6tJYIpFyqM6NllUY+Vzxg9sd69B0C2W4aRL27ubK4WZZk iXKgxmMurSA5wd0ZAPtWlPaxbMqS41S8jh8FPDKl1PfPAq4ATYFZVYEk8Y5wccA1lT2U1vqV7bTX wk+23bxXUgG3E6HYVlU8Heygj607T7qK91mG+lufOjNyh8ts7lEgdMGTjBx36AEVYuZ7L+3rr+xj AhtdSSSNpi0kM3zEOzbA2RtPB/vU4pTTBE+jG30jS49caSKW509BF9mlwV37njIwP7hAP8q3Ipjr siSbWfVIp/NEkVuFRI4NwGFOBzuIP4ehrHjudI1HX9UhuYZItOebDwbgA1x5ZIG7rhSd3vWloes6 ksFrFcgA38q+cC5QyBD+7A44U/rVYfX3QZs2WoHVdKggGl/a76F7qeJi8cXys/3SQGw/5DOK5Gxa eW68TXc0cd1AGW2uDJukEKiQkE9B8pXHvXaQ6ZfJYWF+I7O2uUglUJ5bSPJE0pfvj7rHI4ziuc+G um6jc33i/UrS9ks2tbiOF4kRWy2C7q3JwvTt1NddZNSUYglcm8JWBlh1TUYdR8kSawRLDaxRjBiR FSQ5BIB9K6PVtK1GSSwl8UX05sXczTW5nG2RgMhRtAHp0zWN4asdPt/Deq3d011Bu1mWN8yADMix Fi+3oDmtOyt/DEevyveFI7K2R1so7hxLvVW+8RuODx8uR0xW97RshyZgyxWc+o+fqFv9ggmjd1jS XzWR0XKeacnHsOmKk069tZY4L6Wxhjexl8gN5e5gZfkMu3HG3sM+4rG1/XtPi1nUIrS0LQXbIsK7 gW6c7E7E+pOO1VLy/eeGGeGOdbizLBt3lxqNxBQSbW/h968aVb37EJnQafcrf6y9tptvJLHMw80t +7kVF67dxHJ7tnp+VdbZa3qouBDYWsVjbNcMWWSQyKzQrgxhj82eRxj6Vy+k2mqeG7DTvEbRKzRS BHkWYtDIsp4GGH9/8K3dLhvNZEt5JfQWcVldT3LxxwnBmIOWJPGVx2NejTvbUq5TGoa1aancT2lr Ckj3UBnIAcROBt5OeAM5J6Vqz+F77VfDOqH+05pkeW4ZkXEaTSK+C24DOTj5fyPpWT4d0VprQDUW vki1YSzSC2VFaMOSRI7bd2DgEc4xWqkUVnoMFrELm51KWRbna07LHLGpw7KSVG4HGQO+KqK7knnm gGzn0E+INTup3k+0bRJ5pVi0QDdFwe4z+Nb2nWui6XM73siyXt5frJZuCJVhiQA5YAsf3uSvp34O BWX4T0jRLjVb7TRNG9i+ssbgy5DrFCouVUcnaNxCdOV9q6bXl36xNNZzXF/DbObpxHEwhCMpPyNs AHlnnaT34rKz3QSOQ8djRNZ1jxLrOqadPHp1qtoGZA0Ztt1v5TNsAAKq7xOOeeO2a6PwtFqjapfa HNavNawXMWo36iVYrYxRki2Hy5AMjfO4DEbUA9KL/UU1DR/H9pewTz2WqSn7OXQKuXsIdkkxGW2J +7O0CoPDmp63c6jd3mlxObiVbO0ngWQyIsr25SeIoBg/vIzt54+XtTej5i1qjfgubvQfE1zY3Gn2 zWusf6UY5GLrkB23BxgHftA7Y/Ws/RHS9XUUF/DpcUh+3WrSpv2hH/gz3Qfd+vpmm6hpoEWl6Qkl la3n2aREWHc0kQt5eZWkdiPmbgAgVc1XTbjWLnTbWOeQTW6CG0lAVQoVwjLJhRxnKHCmsakWnclo 86urh9Yvwt3cllR4IbmbBCFnchgAQG4G3sa6jxVpEmn+MfDGtXXmG31dJ1CyNvaOOO2kxFGcjDSd m7e9cj4n0izsYl8qA2tzdeJIbaeFmJIjWbIWTOACwQ5I6rzXf+Ir3wzba34W1GxmtpBqUtx5Pl7p EtpPJJCMgOQnO7novsRnDDrfmFY5DRhZDxXr8l5taNFiE7SOdpWZsI7nq+1STlRyQK1/Fmuy3j6l o9vcJf21hbM8TQhkWQK25FToAUBxnPIGe9dxomp2lj4o16y8JINRleysopLryf8AlsTPI7yKqgdW +7+XSqOm+GNY0Oxe+jg86e1a4W5wQoltJlzKpSQ7hgng9sYronRfLoNoy/DV3JYTxzLbR3FvdRWl 0JcjzpIlj82RQjZwc+tdV4nmv4vCeuXlzHEn22Ca68syZkMM67QAFUD5c84rlvCkGoXOk6a94LSC GxUyEXAbLxKfKjzjqAozg4rovFV1ruufD++muNPtYrW3DDIyXGzb5cqbjt2Nn+XFXQp8tNxEZmmf a9N0a11CTUI3EVsLJA4VnSLy1Vfl9s/K2KtWmlTXlyt1pM11c3unRwRObbbD+6yd/lKo+6vXOfvG uOWSfUfD6afBEbP7HaM9oI0CSSiNgf3rDguCe3au8tdVk8P2uiWi2t0YrtPJnxMYVa8fks0jgELs AOc49KyhUTjdhc5uKGS5+Ket3E11cRQWemWkMDSylzd+YZHC7mI2kt99SeOtYOj6S+u6cs2qW6Pp 14kkRmsyvnWzxAGKUKSTJukAwem3r1zWfpkGoXHirV7y7IjNtd2DTBpFkPKbrkwsRh2C/hz68V7N 4Bm0e3tlOlQ7bO4e7mje3RpYxHJOXRBtHAVOMdq1jD2z1H6HmPhS30zUfGngaw1a2kZ3GpNeQSDf ue3itkiyFH+q3uxX06dqwXn0208L+FNsUrzW3iTUCGTarIomLhcHBLMrjaccHjFeialcyeHPiUNf sIZLtFsry5eHbskihkCtJt3YGz5PlI78V53LBB/ZsrQ2aXN5e6fBOLsSxH7MLq5R7ZQoz+9ZYzz1 /Cpr8sYuKBPoZWqHUmnvUEbRx2JKCAsCyBdzpDuHHBPAB/wrqbLSdQntL7zbmF4rWMSXEoeMM88m QY1kbkyhVweoHYdKpeGdHui02uxwLFZ6WwlvVYmVC207C4HLZ5JKkVta/Z6Zptol60MSWusTICgj BEeRkOCfmHLYO3aQMZ9a8SNBvVmcolzwuLq8a3RN1pY6A93eYnaPfFttfJjaVwpP7nzGU5XkkU/U dd0S+0GKeG2urWezjSKYTu4XzseYsg2gDaRvywHoMcUzwbPbjUr+XV5porgWyW6yDIhbzW3Otxkd JkRfXdjI5NYV9Hpr+CtfGq263Hl6O0dq8bHdBcW4fYrdCRg55/hzXpU3aHKxrU4Syv2VrVU0v7IL DT34mQlpZL/EiNJydx2fKvG7vivZ/Fd5aWljY2VvLGPIe0hia3G7ay/6xicfe3HPHauP8P67aQeJ ItQSNILHw+sFrIgUPJPfw2ZVJG3dEXHLdgBtBxVrWXuNVmsr23uIpRcbTDCRIkbTchmRnCp5TEbE ccnrXJB+6xtFPR1aO3mm1+JWmurSa6luWkCPFKu7cWVsYLKAU9Sa6/4ZzQ+GNO0mW9hna+l0+Obb Iyo0qSxgxKi+3I+UVwuvalpf/CF6lfanaPdWz20sTJG6yQm9bcglaUYJMeMImMV6vdaB4n8R/wBm XOm6YyWtnYR2cYeQeYYBCFdGP3V+YHIBz24rpwUOqHBGf8QILrUbK0vdWe3tZZLyNLcFtxiVekUi gDcT7jim/wBgS3slrpmtrILaXbFbra7UeCVfmaTbJwEPG8McAe/FZnxSv9QvvDek+G47aPS4XnCb Y7co4EOCy7nJ8vIUYPOfU1q6Fp134iE+r6rqh027m3Qw/Z4kaVhGvyg8fKpXGVx+874rWrHmqoG9 SfXYdcgSO58UXknlWJa6huYERRI0Q2PGI8AqdvTsR711t3a6VaDTJN8q2t9pMpDXEjbmcqGi3A+5 xjtXAeJ9PvdPsYrC7nnukVIrmF45CiRqHVZYiucIcdcZA9a1NfbwrHpt7PbW5uY4XM1rG07BmywI Jzn5Aw211U2oIqxn7NIfUba0uJ0iXZHp9+Ax/dNGu9m3dMds9K9GW+0SLULcWSs8EEG2MW8byAM0 mewxXkd3DGuu6Y8WJdOmljOo/KFEZmPmSQp3b5u9elt4lVdZkS3jc6dbMfLFvtUyEIB06Yz6VeDr a2BmpfeJYreZFSw1KeYLxHHEEDc4z8xzj8Ky2u9au7+PWLfTHZ44/LEBYqCrcnc+c/pSN4gkv7qw 1GytHmhWKRWkeaNdu8jsGz8uOlWm1/WpNqWiKnHODww7HNeo2Io3GveJJNRs9MbTYLeW4DMgeRmB 2DHJHIq/H4c8T3Gpx6lcX8NvcBQhMQLbY+BtUH3rKuJvtUwuLt2a6iA8oo+3yyPTHrWfdtqbWySy 3FwZmZ4YJGmfa0nUDanTt14pOSW5Rta1DeJqVnby63dTLOMTONoaPHBGF4wR61kz+HfD2kzS3N7L 9s0uUAHzZUd7YnjKoOZI2/jHVD8y1i2cFvqxEMk7rdIfLaMvJHl0UbwoJz/j2rcGjWmmWbTyzSpJ LKsCRogY5fjcXc9PXtisnOL1LSIPEml+FpL7TP7PtVW0tods5smy4EmOVIOGOACvsc1HpN9o+nT3 F7bacXXTZTsWRdsckTL8vyt03c/j+VRroGoaTFs0x554UJYWzShZFUn/AJYsRjgH7jcemKozwaTd SRJFNcSx3kRjVzuEsVwrArvXs27seoOeRzXHWtfmFyB428QtrFjpjOslpNpl+lwZGI2yQv8AKoVw oT5TgjntXIpNd3N744066uIoX16zspnuFYuFYF3G/wAvIXpnrXc6hBayWNrZzwhZ7W4xueLMexo9 hDH7pGeSDxn7pqnp+k6tFP4gSXT5Le0uILGGCSN18pltY5I32uG4BDD5etc0sRHn94aiZ8ur69qd 94lu1htootUjtb2eSMSBDFG22N49+N3vnAxWxGNW8YRSXWqWW/RbLfmSLbD823YSilu7YK8YHUVx 2lW2n+ba2l6zXD2Vs9isHzZlW3nxGwIPzKqlct29K9Ov5TaanoVuYzbw3HmxEDKoGC5Kse57Lx60 qeIc076EuJ4xFpeoeLdY07wrJqQN4lxfPMzZkSQLAPJuVBA3k9Cf9kgelZXiTQI18NaHMl8b+TTr iLTNXgSRxHbqJQhRQRyHzu7+Wea9O8YG7l8Z+FG8OeWNRvtN1G1WSLbxChVmUMOASMqhP3etcX8Q 49I/4RTxFqnhua8S5MVrbalGN4t7cxSoE4GAZRzkjlhyeDXTypQaQ7G94m1K21rSo9R02Ty59MvB plrd3Mixm5tSWRlaQ4GEdQUPu2OKTwfpmnXGsanp+ryytfOLKCN4iEISRC0mXXOSrP17isXWv7R1 zSoZ7jT/AOy1Zo1sY7hVZZZbT5ziNC2AdvOfU12cdhfPp+reJ9CEbX901veW0UJ3RzQJEEkClgQM kEjbgcetfOU4RoT5hvUzvD7+HrmVYb+zS0RpZJ5LpwWLeSu1EQKTlsj5uMk1Sg1TUNX1PXtEtdLK W0VjA0bKkYuVwP3BZs/umlc8kcdqwvDl41/qMnh/UbKa7muWa8thDgSRpJIru25yFGDtB53FflxW u08t7rU91ok0M0j2FgiRM3kiQC5kYI7HOwRHjbx6da7lWclZGdjFtANO0jV9HuwkCl2SRmVXNpic MWRxngEAv7Z9xW/4h1e2tvDVtc314ZNbsootOgjZj5UjiUrIY9o+UFVBPH8S7etc34kt7GHxJqdk dMgeaTXtrYldHaW6SNiyZ3Bo/m5DcADoCTWneXU2kaKmk2txp0GpWjG2S4mZbhkjxvjieR8r5+wL snIZcJsyGwT59L2lKTi9jXmjynA+MfD9lY3mmxSwmA3NnLNqz2paLz7hixXHXb5e3aOFKgVY+zXn iTwTrmmrcJb3kVpNqMjXG7y74WuBHcWygY+0IQVlAOM/N3rtfGWq2Gqus1nZCdrmOOD53OAUhzl2 PABycnPfmtOcW1j4Qj0mxvF1C405oriG8t0R3t2Rv3ixFenBwR/EgPFOVaEXqaUpaFfwt4x8N6r4 fsLzWdZbTNR8sxXVqQg8qeFjFIuCQfvKTyK3/wDhIPBX/Qzt/wCQ/wD4qvnzxB8KH13W77WIdP1d 1vZTMX0dYxYu7cu0Ac7grNk4PQkjpWN/wpK4/wCgb4p/KD/4quB4TC32OuyP/9WOOI3bLcW0BEEU hWOMsXkGeNvqR3qa8sn0m+exvpIxM0K4XOduTwflP55qO2Nwt4LaxuCuwI8bK23ac8kcVq6usAKS 6mDLc3OUuJBKDJ5i8odvGFxjPqa+ClqdGxj3GpzXt4omtxDNFGIy8KiPco7uOQxx3x0rJj23V7Or MQkWF7KMZzkHnmmTyyQvcK7cTxghgMnYehFLaxokSyKN6OAjbvY9TXNKb0FHU6tbkXWmvBazlrQS +UloWUyNtOFYucAZJ4zVTU7f7FdLprxsk8CYfeVOHI/1bbfwrL0ye3sNYs5jaxXLeed0RJEcqKM4 c/Xbt+laTzTi5vrq9BWZW6MR96Q53HPWrrVlKnqRKOtibXb/AFCWOyjuNphtE+zw4AG0SDLM2Oyj hQfWse3t7W3JlfNxLIFEgkReQDu4zjB9Pfmp7i6a4gUtkktjdxuG1c5A70KLEwHzJJBeuSc/wLGR np13Vgqsi9tjpxqWlpokyNAWt5J28tGIdwAepY46cZ+tc5caFq8ekxamkAMDgus4UbmYH/lmpOSP fHHSsue53ra/fSBomBAA+YEjn9DWhJ4gvdR0Sy0yG5mntdNLCFThUUMd+3tnk98da64VIzT5yNTK vFN7bw3T3Vwk8cixhEfbuLDuBn5c9f8A9VZFpdfbNfDG5dm2MQisPKXajK3y49RW3q9xHBaxzEeZ M4EBjI3AdWLexUDtXM6UDbNDNZyDdeTiN5Tgny2GdvzY+nFcsdUaIsx29rctCu5R9ojDEOxDIyHG M8df5VoRS75vtcrRTTBJbQIeFCtzj5eprAnLRSQxxSfIW2qScZwOo/CtqOAXEclzZBnha43YGD8x G04PH1qpJpCRxRXyp4baYmOWC4aRFZipKgfcHbafrW1qEq3drC8V4tlcHY/H3yvXYo6YII561kah dGW/ku1UoqL5EfmL8xCHDt835fyoj0r+1LlbL70b+SR0GFHJ5OD+ArZw927KudVqUEsc1laRsseI hIu5/kD7uRuPPTtVSytIX1nULp53t7dF27lz8xkIA2n09xWxJaLdzxQxxm4gQy7pByEYJgIf/wBV ULZd8lrZMxedI4yUbAGxXyQR/KuWMmtUDRQ1eHVLTUZb11Z47cxMpVflKKcZHfcAMV01nq2majO6 WQSVZpEmnEoVJo/IQ/Kh4+Vifmx1rjPEt8qanc6ZG/2nbuKqpy21zxg9OPSqXhoXelTwz3kMdxd2 7Fkcfu/KYD5fUNgdVP5V1xl7mozptXuheXtpExUCWbzCkf8AqlVs/Kp9MViw6g00Gr2UW0i4KIJs cYc7ADz/AAtj8M1rS35lnnv77yrZpo0W3ZWJMufvptxjH0qi+mM9ysSRlImMb+YgJDYG47cdQMen FZKQI7HUUsbK70+G28yRLeOMyvkL5pQKmMYBzjpz3FaEOr6ffNLd6vZpf3N0jRQGSUowjiOcjH3z z90/XPanzyafDYSXtria9nckQMm5JYWTd97qvP64qLQtKN7NfT20Swx2FrukhGwhc8SKiuQSF46c 1vTfYcmaCX1vDE80Z/f2DqkMZjUqzY24l/vdeuOma7Hw2l/O6avcXE1o/wBraOKNckFnAVxGQxxk YxkUnhPwifFtomEszfi3YQSSJvbzOgYYIGBHxz61yV3YLpGoLYecz/ZLhMvGTyFIYiMdvTPtjtXf SlOC5nsYtI9ivPEzajsspHt7S3m1WO7PmzskrfZ8rlyQRxge/bpVLTpZLnxCNb/sifWr+ZmMYcCK F7uUsvzbQvysQGHODWo+l6Zb6jqeu6dJb3mn2FnbR7bnEb7rs+ZKqlVJJCoOSe/WuiOr6bp1p4g8 T6ho8KQ30MYsLGQvuM8YEUezd/rDnHQ/LivapU+ZXMZOx5prOsy6rPYeF9f8nT5dCAN/HeP5EkzO T5Nt1ciMcEEDpnOKw9Y1Oe31CDRE0uSyMYXy4YyzOXf5m2FDhkfog4x0xxW94p0GaHRdK8PXulz3 +sy3kd/JqDzq8M3mjaw3DDMEwIwOcKuTg1seH/CX9n+JDfTtJcDTYWn1ELskEDAfuNj5GSOvbH5Z xr81yLGr/wAUyuijULJpYJbH7Ur7nHmxzt/ywkibGfQ8cGua1fxDfaJ4e0u2stXW4N3byWh06ZAj 7nOWUkdUXghs5zx0rgDNceLE1bXrMJu0tgdRiaT5rhppD/pDYwMr0bsPpzVDRJ007U/+Ehktorsa ZuNlb8Fd6gg9z3PPY4rKePjH3WVGm1qzV8R6Ld+FZrDTdq3ExgE126SAsWcbi0ufun0X0xXJQXMs khVlIti33jyeme3+Fdt4u1a3uLTQ0FlE98LLN7dRkN9okkbdvkIXOV6Yzx06VzNgkUSz3LBsumBu PynJwQB2Pp7V4WN+PljsejhpXaIUuLM4a3WSXLdQhIbHbPH0qp5zm4uPstuFV0AGcZ5OSK1IR5Vk r2+FOSqqDwN3fiktYrQGchuECKwX+8e+frXPytvRHu4rEwmoK+iOh1Wy1DUPA2nXC2MiWGl3Ehcu ynbK+AcgH8q1IrmO0Ed2LKRPIeMlRkNtxkDBH3axr3xJfnw7J4Z8uJrWaYyswb52IAUKcduKvDUb u/tUa7cySsiRIx+XaicAcegA617VWrFOCZ4WIUZVZ8mx0N9qGnX2rNKxubO1m2TSYAkO4r0CnGBm uVuJbfO61f8AfMDsLZ+ZenTjFRnXr6e0Edxcs0cI8sDgdeOmP61li8AdWZSSw42gbV55AzXVUlHl TRwcp0aJHFcSBZMF4dwY4KqR1xmoZ3eeKNQDL5EW0AFQSdzENj2zXO3MzzxYxkNIyZPBA49KvWMj h5hH/BHjGRxg4616WBqck7IyqxuhpuoIb1UNlM0iMvAjdiRnA5UY5+tPmuNYeZxZ6JdH52BB8uIf exklyKP7SuI8sVyR354z71bj1qdQMySD2JOP1r1qdZKVznlQILez8aSyC4+x2kSoclJrliWVf4cI jCq0HijUbUYn0y5QoeTazI49xtbb1+ldRb67IQMyIykEEEetYj2FrKGCyFdwPGc4zz7VrKpB7jjT aOrtPiXq1q1vc3Ul1Eka7Y3vLNwmOn30XHQf3v8ACtWD4jWOoWs8JWyvHuHLh4phw+ecKSfSqWna lpkFnDbyeZFJGuwyKMf+gk0S6f4c1AsH8uYnn98it265dSf1pK3RmbktrHQvqPgy+uGN5pktrHIh WQhCMkEFT8jH+QqKLS/CzxWl1Z6o8E9vKFWIvj5d/owB6f7X9BXHnwVZjc1hM1vk5UQTSKv5B9v6 f4VXbw/rUGRFqLvt5xNFG4/MBGrVcy6kJx7Hp9n4W1mD+0YrHWln2W7TRunG/ap+U8tmpLbT/EUm t6dZLbRIs0CeS7kL8xjBIKg7uT7V5Ip8SWErmKOBxt+9byvAzZ6ggqw/8e/Ktex8b+JtOuYrtIr9 Ws28xSUS5UEDA+5k4xUylJlKEDvbLxWLefyr5fFUEjNtdbaSKO3DDggK3O3PrTG8UeETf6j9vf7H 9mCQSi4j2Msu5mBzHkHIHUZFcYPiEXllW6vYd87hylzGYOT6B8VuzeJtI1h5H1TQrW6WZFEnkhSC wPDH9amFl8Q2n0Ovs5LB4dKltL3Mgi5CuDjMfGFJI/SmxS3K2kp81XX7YPvJhjiQHO5cDvjpXHD/ AIQ52tnha60+dY/s8rDJj2BdoIAJAI4HStlfDFzFp8N1pepmSCGQcOv+sV2RgSVHofStXGL2Zk5S W6OgluZX/tcT2gnLRqcKVYLlX5G4Dp9arT6XoF1eILqyMCyWu9yqldzDbg/Ln1rKltPGFkuoQ+XF eTMqk7GGfKw/rirg8T6naTrPf6ZIqQWxERw3zq3l9MjHFCiHOuqIU8OWUp0/7HfSRzXMbQkBgygK pVT27CrNvpviCzFnNb36ywQXDxojDbmQMQG7j0qKy8R6LK2mQ3K7HiRpJCyjJTax7YNXlu9NlhgF nceWHvd0YV8fKX67WpalWja9zKN34htlk+12Pmf6SPPlhwcMCvOR649KuyeJLR/ty6jaPbvPMigO v3duRg7gOvrWyWuZLPUXFyJI/PjPzDr8oPVfpVnUGus6ss1ushCIfkPCjkZwR/Wlzjin0Mi7bQtS uLp7ScW/kbdgGfmdT0weOtUPglreo6t4u1SHUn3eRI8cZAAwuGHNWtQtNDa4m+02n2c+UjK2zGZB 3wmavfDDTdG0zxvv0d0f7Ypafy2zhunKnp+VTJxKjJ3sz6J8rc6dFJ98Yz3H6V8t+KNen8deGLjR r26t49V0q6lLQAMrXVirgNJCByZoVGdvoK+jNcGu6ZqNjq1nM8unwjyp7OMfM8knyowz0VepFeDa jaXWlyHxDJqEM1/Fe3o1CCQKlvLDMBvtxONyLMFHyqOSDXNiJ+6b2OV8aL4Q1HQtF1zw1pdzdWMk EyTTKnluJLZQElOB+LYzuGBWB8OIpfE/ijyriaO63xyFI7qPaLgPGN8ZGNoZlHBBz3q62peOrn4d NqmmQyaRpujys0FvbIfNkSdmVuUDj92P4dvI5ql4FsZ7XUb6NdKk1m/g0+OZI4p0AiSfKPIqqRmc Z/h+nXivEmn7VMtbHqumanBpVnfa3pmlLa2UF8tlqNveSn7MINu0COUxcLEQN6sDxXi3ibxfMbpt JspYXs4Z45jHnzY5VTBhdsnZxkKQuARjIqOWCO41W/8ADGmJemO82JDbT/uphu27LiaR/lVN/DZx yQe9JrF5I9la+INbuI4taN69hcGdDKsX2UDyGmIyNx5A2ja6c+laYms5e6ibWtcoaO6X+tK2qQFb e+l8ye2J8iOXzmwEh+UL1wQFPHQHGK7bSNK1Y6jbaVpru9ql4VvGvogFSHzPL2PGvGDjcvUkANUX gjTFvNctPEUTrNpWkoLi5eFPLWGQOUURYBx6jA/WvR4dAtItXso9F1q8kuopXk1e4FwyyKrIThoj gMPJJG72yOcVOEoS+MdSXRHpk994c8G6WnhPSIJNSurRo1is2BcszuBks2O+c44H5Vwslh4i0+DU tOCpZw3tv9ughnYRSWcEUp8xFeMHKKm0sFJIzW9c6JoPhC8N6tv9qnNpLLY31xtdnul4hVN3zZQA 4OTnf9BXF67qNxE3hKXVL4roty5tH+YSCCaVQJFe5ycrz8wJ56dq9lq6SM7nRwW8Wla54TisrSWa 8lknuZWtSJYygQqIicA7Rv5Izz15zXomg20ujxrcXcnnDVJnmnjhH7mFzlncd1BPB968Qh1/xr4J ubbUkJ1XRz5klzp0Y3XVvEXOUxs+5u+YMWxtx8vc+kaW/iu70fTbHUpbGAyJIxtY0Mk2GJIEmGVR gNyB3/KqU/5QsdDcalqmsS2D+GTbm3nDPcS3DFvLGPlVlXkZH3W6H9K888T69bWckd+7zXos5lDw W0yhmhZwvmRoBlwO6vgZ4rdu9C07w34Wl0K5urqY3w2ObN8XEURJYmMNzsHcZJH8Irxa18fXlzq1 v4f8NWohfRpIoX1FDG032OMYlMhcfMhJ3A9crSlXtoxtFrWF8daJ4gt9FsZbu+0i5ke7S1WF4pJE bmSFkHVgMEKv3ucdK7CW5sLHwpa6/pjWieXdeRqEHR7cSSfNHGrkAyJ/Fuwce9R/FSDTZvDY1SOe 4v7qFM2s4uiD8pzJJbopAd8d06ZNcp4AvNKv7wa7NaQXeptMsdvJkGEXJQAC628Rs4HyE7dx9etZ JpVOUx5D1nwEPA2qPc6jYQxxMjFC93GNzGTlRG0udy8cD6VxHxN8HPLeyavJqkOlQQI5uYzGjzMs sgUh2jACpsHy96pQ+BPGNtqWu694fuIoNPguXnbSrhP3k06jcQFBMS7eDGQDn9Kv3DDXvEmt61p0 4e7n0aGdNNuELRPhd1zHMUwySccD7mTWeIoprYtPQ8O1bUWisLkWF7E48J3a/wBnoYQbiO3myu9p E2iVHYkbP4fvVfh1nxDqniT7N/advHNObS0e4Us6Rxlcqd8R4GSF3L1ruJfDumT6ZdjSPJeHWrOP VrTT4pGBMFsSZlfzOQV5bbuwzfd2YNeNpbW2laVa6+ZTCL27nhtI2GLpVt2AmaUR8tHvysZGMd+a 8arCpT95bFJWO3k0y70PXrbRr/8A4mET3sTG5s5QzPCC2VhkZv3ZJJOeOOozWzZ2enSeGINJimjn 1fUL2GL7BA6h5dOgk3LBHKygRzMEYGQopk6HnmvLNZtrrTlgudQnWzvLyTzfLdsuVjDKCdnCHcwG w4Yeld54c8N38OmWni+HWLHTw8iyWsUqkzxKr+W8pVRuI3HqThRmsMJi5+0a5Ql0JLS71dpbmC+S a3j0VjHHHeyNugiY5SNV5XG3A+9t9OK2jJd362N1BcC3j3mXyxGegAG0k4OenFXfEett4gttP0Zp 47qezt2tJ76CNFhnRvukKxcjaB1bBOc1mW1qseqQu135ljPHE5E0rfKxzlUAbGGIB/yK9Vyvax00 9jTe3m1C5u7e7nRo7OFfLjYbPmcjJU/3hioZW06LT9Ojtbry3mOZMMzlcjdtZVJ7nsuKW4u/Dtvq N7C8SGGRore2eLcS8jhvkXIODx1Jx/KtHRb0w6ZFbLZ3HMrWkYRVQqykjbu45OM5qU9xGHouuaBY zXMS26rrCEvBahQIxnK7skDl8fh7Uh8xLW412CwdEt7hzKNyoktum1XUc53KR6Vkok93qd9bXEfm T7GijklcBYHG9slu4AXnnrWtodvc33hOfzprVY3lacSSfN5geUjMasQATggDvXMqjb5QKFxdX15f XmoW1uXtoXMSlW3EOqJudvWPOOnejSxKfDGhwzYW3t3njYFNzqzMSfM55wRiqOl3FxZXN9ZXEkq2 0lz9hDxqP9UNu2MjGQefm9+B6Ulq1vBbJbWU7OzavP8AO55UGQlcqOjhT0OKyjfdCR6DPdS3ItdO mupJlu42Mqk+WqsMgLx/CevX+lULTTdCtNGuWv1hujcrJEMyktnYRuKjkjgbeMj6c1Jrum6Pp9vF f6QcXUoVXTBmWUkDDMv95T06DtWZe3umi11q7hQQ3Vtp5hQNEAzTIMSuQfu71PGOnbitasrfEWcN pQhbwRYW94V+3CR7cPghlWI8ZxlTnP3jXU6TeanoaX3iKaA3EPli1lLoMBQBwh67gBg8dKwtNt7g 6Ilz9m3xW9/JEC/zLDHgP5ewclT/ABGuysJdb1fT4NHtJLYwzL57ccRy+ZsxgZ6qcdfeuPkvLYhx M2whvJdH13VY4Y5YIrWQMfMbf5LArtwAwbax4yM4x6Ua1cXtt/ZMhjXybmFoxBHlC5t4xvdc9Q52 1u6BaT6bofiyxecW72ULq4aPDENEcEBsd+n59K4rR/t2q65p9w119o0/SjI1q0yGIFVRC0J3YG9M 5Ujg9+wrqkrRQ4IzGnkn8VaVPP58DajdWlvGDtHlh9xYKRyGUevbkV6D4pjtrPWDcTXa3k8ySRzn ftZoE3i3G4k56nnvXA+JbZIpV/syKOf+zL9LwgSD7sAjEwVmI+90U56VtPbLcWE2o3ETS6mk7TSC DzJFMZXZbwovA2qTn1rmt7vKimMs7FhLp90zKFvLwRiIkFzGjjacD+EZPXnt0rp7ueKVp9B0UtBL /aEyTTpGAyW4O8MFyOGc46cfTml1y0TR9FtjBBIs1rCD9quY8FrgMGbYp/hxke2OcHAriJLy68PP rV1bWyyz2JeZSZdhlWWGJgvBwVXGeCePfisWnTXKTc0dEjl1XUHs5IAJdQTzj5shCr5MrAZZclGI wRjPX0rtUutTXTJLu6WG2OnBZ7fejOrrkr8/I64ZOOOPeq3glbi619LGMC1t30+fYXxIshieFHx9 d278areLiLWGTRprvcIjFHJcoET9xMfkQAjO5HX8jXTTXJT50CZ2GpW0t3pemeH2u1lmltlC26hU WNggcDec546HdXBfD9bKSPxIkouo5lnf97FIyod6kndtIzgrjp/jWt/atlp9zteXzb93KB2kUtHH AAFjKjCkn7uR6ccVgeDIrP7Rqs+oXXkGLU/KZWUvuDJIhhxxtxx8/wD+urp1eaamNM6XwXZaJeW2 v/b7mLy01QgyFvMkKtbwsoQc8bv89q6zUZtGu7SG2srV1eOZ42KRbXGFOG5AA5+YeoNcZorxzajq 0fh+3dkEtjLHARnG23aJ3wBjOYx1PNdlqGv6dHbajp1naStNbFbsvLJHGWcuRuBP3gCNuATtxg4r tclGOozzLUJb+TULezu4GnvZLryvNj2rLNGkeMKuOMNjJx1/GsxjcWmpaoLmOPzLeN45I5XBJIbO Bt5ZyDt60+x0298SapJFo8jyOysIZpm+VXb77HHBGPu5HFXNS0+40FbjTb+IfbVa3aOZA85lw2di hiPnwcng+3FeFKk/ac5m1Y9p1W1vdU0a20iSWzsG1KFBb20UTFwAAyNIWzgpiuL0221zVbTw/pgu JJYruOUySEBI3kB/eFdvJQfqeK29ZtotP8PNqt5c3VzvctYzxbSTKEyhdQFAHv8AhgkU3RYdLt7K XV9XCpHpkCWkCSzeZuaUZzwRtyTjgV7ra0KRvXqaLpdxbxS28d1PKnlTyy3PlRrGAOnIyM8YHTpX GabHpNzBfwWnltNBf/ZbSNoln2jO9/Xja2F55HH0k0678KabrN7qEtgrP9nztVmkCPksw3HIGOtU 9K1a7s/E7+JNZguYXuWikysSkKkROFKjDHKkA4XPHpWVSo9kDMHw7eW82peKYktER0neXT4ot7hZ lb7Mcg7S3EeemO1extFBYaK9pb2d3dQ3br5rswi+8G5XBLLzzjnpg4ryvQr9tF16TxLqVlJDFc3u oWMccPBae2kDgcA7VwTkkjJ716h4hj8S3OlfbbNodJC/v5MO7SEdemMcHFXSj7ruByXglL+aHW4b G6trqGaSdZIbpiRNC22D5do+8hUI+Oh2+1cv4HMt1e6jJNfT2t3dQWslzDbxgGWcDZiNh/y0Zlx6 gk+hrS8N3Uy+CdBsJ74xSXsV3qSTw2okkj+0SMZAkm8JkbjvUjpjvWX4AsNauvEN/pljcGP+x4E3 qFZZhEzOyiNmUEnDAq5H15rCctEBueIdFutF1G1S6v18qHy3vmdQAPOdo5MBckqjCPAz2NSvptyZ 11nVZFt2urKS4gubhvlaSI7jE65GGcAuoHepb7SrHU7/AFGzutRa3nvLk207zM0iLvTC5blfMEgA DYAHesK4lt7nRdWhuRai5W1gljnZEEiSNIqMy54OTv3DFROPv6gJ4qs9Bnv/AAZqKXVn5FpK894Y mZpJPLAdd6c/MGJAPPUitrZanX/D8yRNeXubwkRxP5UtsbdlVQhRTvYkbhjA4GeKw7jWdJl1qOK0 jRrTQf7PisWCgiZ5pySzlQAVPlj5e3Ir03V9dtv+E08Nzw293JAltqMgBh2MxKoPk37enf0rSlFS XMwON8Ga9Hper+NEWCUaW0zARsqpcp5aIix7eCMMZMYycVVm1PUbnV5Y7yaaSKO2Vp0UkNIem4sq n92FxnOM+9c6Lq4uXlv0teWu7+8YpJ5bXKCT95AyrkHy+OM46tnFehWwvoNBtLuwls7xb0Qh7O0R 5hHgfdbYTtHp7fnWMpuWkRcxj/D6G5S2MenXEW77fLatEBvWEbCxMnrGfasLxzq3iAeD20maQtps 1rdJuWMBHlV/lj+YfdX+DBq74Oi+zeKdXtYtReyWS9mFsFjUiSSSMM8bc4CYA285znoeKp/E7W40 8KS6Xp+pXF9YRokJtmcGQXKzqFTCrjGD1zyKa/hsNS1p2nWttb3klrPJYRfZlitp5TtMzQQK9wNg O1Pm47Zqz4uuLfxEIvEirbra2toJrSOSVQxkKAb50ftzxj+GsrRl0bTStpewpqM0trK0oIMccU8i fMyKej9s8/SjxHqnh2z8NaHBZws84lhiui675m81NoReMMBXPTa9noUjT+GmtaRZ6lr2pNK3mRea fM8oqsQEKnau1Mh2cZwP4fyr0Pw4kOl6RpqWMV0b+W0jkmgWDyoZto5HzHhlzjePx4rxjw3rDWXg /wAT3wtJjNc3l+8FykXMkspERiJP3Nnaut8W+JNQ1SzsFs7F4YrdPsW+4dAGkwufkEhOCAQPl967 sPVUKYznfFMkmseKbW504z+XLaagd87RmW1kXDSbI/uggEEc9MlfSs21e9m0fXYpltimim0gEjsy qscYWMtEFQKuxnY7iT8zE96qeMfEdzI9u12phXRLI2xXzFYmUHf0T7y4YDoMAdTVXT9N1ex8I3Ls xjv9U097i5MmSW24l2nc2QCwG4nIOOPQcMK3POzITPX7LwT4lhS38Pz6tb2tmkjXMqCI7fnjAfzG ztIRGwNwHJ+tc/Bp1/deGzfmRLi00+a6SKOXZGogDsQ+eEBZmBOTyMAdMUuoXd5ef21qj6qwlvPL ikxsSF/NRAB8oP3/AJBx9az9Z0G607w2ml26yvbTStHIDdSBXZskiFGwGx1+70967JwhyuxXQwtF 1C5tdPu7W5jFzb33lQLNNcOjgQL1KfxKhbA4zV7UYbO38A3c6X1lcajJcR22xMx3a+fOkOY+Tkn8 c5PpisvTrfwt5eom4iubqW4upI7VFwSIeF3hzjHKkDIHHWixj0zVNK0/S7Y/ZrjUPEMUDKXV5hHD NJc7l9GKx7fQZ+leXEmJjm/i8T3eqT6Wphsr66uhchwEcR27JGiHgK3C/M4PfArtbvxPc655rSAa lPZ2Bs5DBGo8sSuGKKRgRHYmdwHA4HrXnWl2tp/YGkJCPskl7PdC3DfMsVt9olKB0PJdOMbvvA81 6JNqr2drLdacFhs7mOHTh5aiKILtELvtH8ZkfkntRQp++09i2jC1aTVJfB1ho0tjJbRPFZW+wfN5 lvczKsatgfeIY/n14r1bUfFl1p63llp2jwNmYlvLDYWQk7S0kmPmA6IvYe1cl4xubK81jw9ZWKf6 KmsRhFaQqrmzVpggX+6p61s6pJFYDdqcgvNTeLEVnBj7LaFnByoz88h7se2dvWu2naKvEdkjkvEX 9q3d5p2naiGhvIriNoQ7fNiXknIGfmPC4JrQtz9jsrC7nvhHapL+8t23BySxA3cswG8enbFZ9i1t avM97DdTD7c93aXkuUjMkLB42mQ/MQMnaowBiu48LeG9PgkudQu2W5k87zUJb924lXf5m0/eMmT8 vQfWsqcnOd4mfLdmLrF5dXmhy6tqCQJvzDGm5YwSSFKjChj/AHjntWNrBn81ZPJ2te3ELPcSbSrl V2YQMMhG48tTjOMj1rf8RSL4Y0zU4JbdLixuJEFruXEds0r4G9fmJUN3/l1rFa1vdaMi6hMk9td3 cNrBIgWLzViBLOVG4ldwwCORitJt3szQ6+30KwitNNuzPK9zHKhkaY/I24klygwFJosLIfbotUSX MEkrPGwJYqg+UDpg/lVCSW9hKeHri73RxMsPnJhVZCw2dOrcYOM8+ldu8MccKQwJhQwAAP3eldeH tfQfKULW1hjluYFCxiSdmUYwBu5zxVkKqXkpRtylQqg5wflGcY96jhOLmY7d375hnsMDvUrKPKMr uAuctjqNo7D6DniuqbtuzSESMW0Ucv2hVG8dyfk/H3rqtJSJhCk23b5wlyRjbgckZ/CuOsHle0mu Lm5VmDMAI1+Taufm3D2FWvD+pwa7ZNKYwRyBExLOUxwT0xnqPQdazUrl/IZKbHXJWuVQxPDOV3sM ETRNjP6/jWHq9hfXLzCe4w8X72zGMJ+7A4RsHDDB3A9c+xxtvpdpbySCGIDzDuPmO+cnJxkEfyzX FXetSaRaazY3z+dqEU0bQQybmjihXy0WSN347ksCQfbrUVXZE7HXaJrZutPsG1Bgk9wH2g4U5RQp V24G/ORkcE1RNpc38V1uHl6vDlZ0EYHmwvJ+6D9PuryCPmUg/SsW3tbC+eW9gk+xTyAqtt/y7w3C DLNGp4x/eAPOQRzWnpPiLTYGu7x7sv5jQW+5cbIym4bc5JPPP41zKon7shMz7vxINTsk0TWYzZaj HPteF2ZlkWJ8K0TkBnVuOAQ3qtZHhjxA1jfa03EFk14oa28xWVi8e0tGMKwIbhlJJq/4/sNJu0s5 bqxWaGeKS4mgcMA6oUCnjafMGQAfvD14rz290K8tvFes6Bopnt0mWyuESadnKw3ELYY3PMqruUdN 9ePj6E5zTXQcUd7pA/sOOyezth9q1a6ube0upED+TNO6jC5/5ZMyc8dq1vEMIvPEZsLWL7Y9sfOi w2ArT4DK/J29Mqfeucm19V0K20e60eTTdetIw1tBIyv/AKRAd4NtcA7Jll5LAHzAScgVtJeq3irV 9QOEsHkjW8AXCb4ow/lJJxnaTj9PauqlXuuWQpRtsaF3YWmleOvC7qUg32GqvJ5Z4T5EACbscACs f4xRSp8Ptbn0dljS8jEl9Gu0xTW6uhd2GDyuRh1q1rVz4fl8WeH7zxGzpY3VpLbRBeFMtzGNyyDj HHSsDxs0q+FPEfhrT91xNBodwlnbsoiPl5AfCHl/LC9sjHNdccbC/suo7F5ZLxdX0y88Pyx313qe lm78gsSqEAAKp2v5TMBndnrjtWNp663/AG+1lYah9ga8IvdOeaIIY7j7lzCY1+TP8eGGCckcVq+F rSJNNsm0e1V4buCC0wrYBlEAPJyC37z04H0rOvPDk1x4k1zxFJfTRy6VJaSwXaSboxf+R5ZjlVc4 jbGOneuCrRXMpoTdjjtVni0bXNRmvbhpLqygtUgkUEmZZgS2dvCY4zzjIzXceHr/AEix/streO4g bQrCf+0LORczz/v0kUliRvI3q2Uzntmqr2Gm61o9pcrYiy1bw9fWlpJG2TvE3yyhgG+bBIcDGfl9 DWd4h0G0i8cadrL4msJLy6jt/wB5uaNbNF8s4znCtEwAOQe3NRRrQlKyFbuQ+Mrhrfxhf3UFvLel dd2CEMQs+21t4/KbglRl9349al8WR6bYXOn2mgaTbQXAshG5nQtFM8atJ5b5yp+UYUtnnBxzXN2/ ivTV8dIH1KHUGTVpzM4njkQl1soorgspK/vDwBnIZSvUVoeMPF2hX+sf2d4c1B9clmaDTIGhV1jT adlzy20MYhzhepFbzg37wrISxFl460nQtMN39ptZriWT7aT5U6RogdlniX5WK5WMDgHsNorp9L0q ysbzUI7UPDp5PmWEnRWynzI7kYwMEDPODXgui65FoGlySW8VxOPOls8yBfLeD7RJFIuSeJmTYVxn G36V1Ul+09lp3hOaK7l0qwhQ332WSSOS6iuC6xQCRh/qpNg85+CoIjXndjxa+Hm56vQrl05kdqPi /eoka6fpOqzWojQRvp4QWpXaP9Tn+AdAe/Wj/hcOs/8AQE8Q/wDkOuKudW0PxRO+t2Wksbaf5Ixa M3kosI8oIuCBlNm1uB8wNQfZdN/6A93/AN9P/wDFVp9Vic/t0f/WqBLePa0hZ3A2sU45HINa1tdR 20tteXCGRmXDAEfdGcfex0PvXN2zSPKn/LNXyuD2xV66azFmHklkRWPlb1xwvrg4/SvzOnNpnW1o MnvItSuN8simbailyMLkHp6dMVSigW2lusqAJVKnJ9ODgdvrVW0s7fTXupVnE8UjIRxkEJ3x2NSS RQItu0JMrocdc8MeKqT1Igh0UIn1SGyQYhVF8xumzHNbGqNcXjRSpguh3Oq/MrIoAGfrVCNWkkkj LoiK5Ulcbs9wa0pZ4NPESwgYkVlAXr8vHJ6dayntYctGZd7KsDwpsKbgWUdSpLYwce1Oe7hWMTMN 0ihVB/u9iDWXdZjkiJcmRirsBzgAe2auqYI1SRWD72TemDVpBEk1MHO2OPONgBzjoMHjtWUkkM9j 9mhcr9nkV2UDB2q+Tn34rV1OKM3TO8saFmBxj+7wB29KitbcRm/ZyYxPEzNGoGdpOc8ZI7f4UxND tXFrFb2ly8PlR3pV2KnIXcB970OBWPaLZ6hqloyqoEU3nqmBtI6DOKnvrCS4JjgRlVpI5UDHk7Yt rBj7E1Xhtpo9QtI50aNmyzFXxtU9fTgZpxlbQdjnNPsbrVdVjUfuoYXlM3/TEJwQB/hXfeHpkmtJ RbpiMeZEpHA2qMs3pWc8KO90bdkICmAAp87KSQ7M45JPQccCpdFNvbIlpuaOHLIkIyTuYe3oKVSY HPvo7Xd1dahfMI7ezi2AbdxcFuM59DWlZ2sj6lJYND9nLAS2tx1hn2jLcD7rflWlYNb3dnM7hpEM hhnjbjlfu/pWlpitbyi0HmASbvJbG7ymYY4z6il7Z2sPQ5u2ttQtIdQuILlkmky6oEXyzjoeD17V El8ttbXPiCaVUa3s8yBvlXzG4QZPv/OtyOBrdJ4bGUybJSp3PhlOPTstUdX06xPh+fSJI0vLeV0k nRzw7FgckjkBccY7flUKp71mhHCaPb/bToWtTEpBMxjnhIJKF2LL0Ge9Y9veyXq6zIkflXUUzwxG LPlsjfddVPO71NdZbOHmkki+/bmOeAJ0U7fLyAOoAPQ1y3hW9muYJdWgCwxu2yNVBRsepkGcY9MV 2xbabEdrP51p9ktpYlaSzhjVdyZBlZfmx7dvl9Kv2F0kolTywLe5dYZISMgbeduMg+tZurGznngF vOJJ7WV5WeMfI/mHAG4nJx7Cte7FxLZQ2pSOzu5PLlcgHq3bPriuZ6AjqtW0q2gZJbB/OtdkirJ9 x1CqWVNvGMHtVS3EizmW3lNqsisJZVVjkkfP+dNsSn2hbWQhlnKKct907WCv9ef1pLiS+Rp7b7O8 B8xn2pzlS2yPI56gc+netl8NyztNB1GDR7O41NLjbPp8EXlW7RBhMWbBXcCMAA5PNZ9t/wATfU77 V7SXyWtA960nl5jIRt7R89B2Wqly3mQ2l5cQtKittKLuVQVGcHAxU/hzVLWbUbaXU7SaewkldpIo SEaRG4VOcBsOoPNdVKu5RUG9DFqx0sso8/S7uFxt1DfeNbPua2F1v2Rny8ghVHGOhrpNb+J+uX3j O2k1Sxtw/hsFIbYKYx9tnjwJOhy3cAcHOR2rymf7TeXMep2IkWETiOOV+DHlt2zH3flHX6V0Whxe I18VafqHkLrmp+KZp5ILAyiK5BgGxJXc/LEgChkbOeCMZr1aGLbfKjGUepT1rxS93ciDUDM62suV klwGVy27GOABuzkelZ0vijU5f7cvLLyrUaksYuI4RtVlZgXCqDwMqOa53xVZa7Z+JLy38UbH1Kym 8q4MeWhDqByDj5uO7AVJDHFHC5titxksd5yMA9FwB+Nedi8ROMrMpLYy11Qrf3UVox8y7jkgLSjc pjON6N044wDWhIRMIoVJgLRswwcc46qB7dPauYeRY2mQyCfYgkYFR17qD6etdZp2ftFpcBFZUjdg OOFA6cmuGUnL4i0tBI2gVN0MXmfZ0zgE4Ibjc2f5VuWMQu9NuraIopUpgsejKQe9cvpsf/H0zGWO QnBVsAYHzewP4V0uny+XbWsq4VLl/MUnHODtpVJ9hxWhl2l75c8iHEavw0a4O1+n61ahdkWeNIJO JFyBjtzn9ajubGCXU2jMkdu7fvg2z/WAdhWhPpVjf2khi1iS2uVbzUJt1kj4/wCWZViCPrV4eUr3 LsZLh9w3RkMh3Ht1/wDr11kNw4t1i2H5M5PoQMDOK8+tI7uB51W8lZ85YR/uhtHcAZBP0rs4Xbyo 5l3hZV3NtC544ycg1eIqPmUmi4NIzZVPnMkbY56cndx9OOKfABt+cY3Z2AnFNuGlafeWEsLBQA6q uNw9V5/SrUEpILC2UrbttAiZkLAHr0wa9WliIdTJohKkQcdFkYjANbWjxNLPdxqAv7ndtxywBwTW BNYyS2k0C6hcW8FxLnzFWESRMQOmcmrVjp0mnyXKy31/NI8TDz5SuVGMgjys/wAq9XCSjN8yMpo1 HhByD07j0xUBiHQ4HofWstNM1+VYX03xGzmRQfKuoI5x+uxx/n2pGfxpakGW207UFBwdjy2zH2AO 9a9WNpbGTTRrLABxgA0eUUJGcH2rKj1u+gLC/wBAu4AoyTDJDMv5ZB/SmnxdoEchjupbjTz3Fzay xL/30V2/rVezkI1N0ybtrtzjr9KnW5uFGMBhiorTVNH1EFdPv7W4Y9o5VJ59s1oi3YqDtJB7jkfm KUroVivHfTROpAMZ9Vb+ldtb+IFyovEO5l++uOccdPwrj2t8bTjqeD9PSk1cBbuyHmLE0kRwCQMj Ppx/KrhVlEiVJSPTpIhNbfaofmGFOVGcjH0/CptPis4dUBuPLjDW5BL7V59814dr+stpKm1B2O8S sv7zDHJ9Mj9K45vEUjnGws3uT1rqVY5/YNbH0xrV7oVxJa2El3CqAMzSAq0aqB93aA3U+1csfD1p c3hSy0nQ7mHy8+fLPLDKW/uskRjwPcbq8IOuXBDHG3ByeT7cdK9SnuJYzGzAMGjU8e4z7VEqqRpG nI6yTwrdwTadfRW8sNu1yq31nb3D3MPk56o8mJMe2RXpLTaCNMmgtj5AR9sCbmTAQKOn1A6//Xrw yDWZ7fAQvEf9hiuPfqas/wBtCaRTcTM5RsqX55xjk49AO1TGpEqVOZ9DyRsdQu1t73erW68NtYDJ bjg1ZtJ76NtM2CKUNbEKOY+qDGeor5+j1ZiZWWchpiCSDjBBPb8a3bbxLdxzF0mddkHlICcjIUDP 44rRSTMmpdUerrFbzpZLdWIcLcSq5QBwylmGOMHnmsOXSfDsqSP5Jt5obzy4VBaPajbW5VvlHX0r H0zxfdD7LBKYnSDdISy7Tv3HjtxWjH4uWW38qa2wbq5VjsbITaV6BvXHrTb8yPd7DpPDUNul0dP1 FwlpIHwxVg+R93jHcegq1d6X4qtPtyQzx3ShVlbI2FoznjnipLrW9DuotRmdPI82RVjDR/xgHn5f YVqyXOmzPf8A2K6IC2wDZbqynBGDVK76C06HL6pe6qqltVsWjTCeQUKkFMDDYyfbiqvwXgnsfiVq izRSQrM6lS67VbK5+U/4V0+uLItnbyXUonRLVSAwHyA4HUVy3h/xnaWfjGSPals1hsR3mJaNlIHQ Jk55+73rGrNLc3pRPo3xd4js7ee1tHVJ4XjkmEoJVIZIed7MGAZQOqY7ZzXzn8X9VWX7HoqJBYae yibapKozSHct35SFUw+T8rDI+vNew6mfDLWN1oa3MksSPBcLDJbM8lnBdjYjbGU+ZHng4wVHB9a8 yvfCGk6tMbOKzv8AVtR8O2qwSRvJGsWoW8LH5TI2fKkKZwu4Yx2rlrXcPdNW0UvCMH2jw/rMPhcN YwWYab7ekm27lkhGU3jkRIzA4+XO37/rXAaYLu0ns/HEOpWFzrV44ma3aVbiN2lk2HzAjBxMj8hQ uzj746V1/hHSrEDWdX8N6pLY2V5YygJKsscq+WdqlJA5HmJINh5Ysv8ADirGr2Gn3GqfbrPR0utY lWKxuIZICVvLqY7oZPOhdViZMfK+AeMsO54ZJWVwUhb611Hxi6waxaWl9qFnbtONRTiYxkbEEkcc mI4o8ZaN+hG779cd8QPD+m+F0Iv5Re3GoyiSz+zq1u90kkeDL5x3bvLlwHRwGJPpXR+Fo/FWmvJf +GVhvNUurybSdRFuwhY3EfzmTz52SRZeAcEPE4znoM+Z/EDxJHqFvBfyeIVmbTWa2uEigRrZJWO4 SwhM4Rwi9gN4qK0Vycw92ejfAW4kudamjljufsLmMSNCBIS/92VzwIUxn7v3uK9M8RNJqM2vXbX4 04vPHpkkyFlkhsY9zrH5e1QUm3gGQsyjuBXI/DnUYdF8AaRdWEd1pWoPqKtNMm5llad9224VwMQM hO0jjcFxXb/FWbSrLxXoF1qlzKNKuk8krbTeTLZSxssgmUHKhHVwr7h+IFdlBWpoc4lxPEVhH4Q0 w60qTwW4mSe0lkjS4SL7gEX3RJ5IPO04x34ArC8fW802m6cz3R1DSntUMCRsI13Pk2jvG3cuv7zn pxXO+K/PfSfEGoX7/wBrRaFqHmW93FPBJHcC4KDbOI/LYHy9i5BPpzxno7XxDJf6Bov2i3tH1TxS gFqCwW3hitWPkxIXBVmc5BTI5OTjIzdOpzOxmenWUp021s7fUXhbxDqkYRkdt1slx5I3bmGAzlVH y5zjABxzVPw7bW/9hWmoxWrCe13ssRIV5Gzt3P3XngL2Hc1ma54f1C00fw9badDBFfpMZHgQOyzN Iv7xVZsoj88FnwO3Fdza2/8AZWmrFaac1ikdm7tOSHljlXDYIBJw3U+9dMZJCPOfHCapGI77VjNa RpPG6CFlED7E83MwZlJCOOife6V5ddfDXTbPxLbeKtn2XSdQuFnEsxmHl/PmUvCpAKuW+QBuPSvX PEujL4rXSfEl9aW11DpMT3LSxlGS5Kqc2zq5Xbk9cjGR1rzPXfFM2v3nhjVYoxYw6pFsJzvhttpA 3Jux5W5MB8rhfu5BrnrJPcepzfjnRPD03xAmsheT2/hiaw+2zRwsxEc8qME8qPGVycYAAbt0rodA W8+H/hS2tboeZ/wlLLaNp0KRtLbkR5eaRivmGVv4oyPociuZ+MiyQX2patbal9nsWlhtkjMbD90B sCZXH7tDlweg9K8nGoalq8DeI9Wvbee202eSW2e4uB5xfbkyRpkv24YDb268V5c8T7OteRDPt74b eJItQ0zXJp53nj0m78ppXB3YijCheQGJGMcjJ+tcBpOvzt4mudZ1SC50O5gnaGSRY0t9PliYZia6 dwTlgflG3Jz8uK888O+PLP4b6vaXE13aaxa+ILFWuY7SR94CthZJPMxiRgTx147Vz994lmvtU1XR NC8R/wBkadoyG5s7i9i/15lBIDyjftwT8hHUccV3vEqUbp6gakXi26ttbex1bTvscdlqE0EdlLvW CzSXHz5b5ihzuKfKjJxxxVu38Spf+ANesdTvoYE8L6rHJbatpqHfKZ5OMRMwKRuG2o27Z8u09Bnl fGdhp95Ppo8L3EuoXF7Z/b9X1Se63NMSo/gwOYm/u5IyFIrjIbax1iby7l/s8rW/2yKRomRriS2Q 7IH3bANx5yAQR6GvNnieS8X1KgM1fw7faM0rap9nj1SOSS8vLZpx9oiZpAEZwcq7srBsIWHrzmug s7HxNq1vpOq6NPb2tu2pSQLMJRE0MrwBpJJvkOIygP8AFzg8Vmaxf3uox3ceu3BuGm0+2vLZYE3N C3/LQp83JUj7mOFxmoILm3tIri0WeVobkI8BL7dqhSC7nggheOm3ofr5NeEITvApo63RbK2trOa8 vJB9leSWO1kDHbJ5XDEJ/vdM/hXb+G9T09YmSK1ZY4YwsgW3Pn8ABT8w4HJ56Yrh9J1TUY7PUNBu tPeH+xg0exlWM75QXJbPX8K2NMj1eW3uI5JYXmjgF4yEne0UeECcdF65+vavSw9TlVkbR0RYtbmZ Jby4jV9Qn+0WzLIrKywkSEZcJkLhcH14rsIU1bVNTgmtljigG+XKEyRtLCdrHttbH59RXNQzzrq+ o3FrCumAiKJo2jLKWZJMhDgKMKNvU810tvpt5YafbRiU20kcyy7FiRC6n5TuznnBNdFMDlminn1J YZbiO1M0kzQyPzHIpb95vyM8jK89MGqmjWc0WkWzPdCFbS1hksy3yrNJgErHwDw3Trz6VoajpWnz WF7rfmiWCK4l2tI2VVV/iMQwX5OcAdsetJod/oNx4a08SRx3ACLtj+YyOFUFSrR/MFToOM1ChaTu K5yukyXE+qGy12JIpLu+DRs8r+WzKM5ZlY8t/F6EVt2ckGg3d7Fb7JIbPXlJlIYqm0K6j5gSeHYc 8+o7Vn29tJf+IjczWlwbRLeVguFyseNn3jtJCnHzBc1e1C9lnh8S29zpbWl1HLZX4O4OqTLHtPA5 fzRhh+XWs6WwWPRPEWrTwaaLhLeXyOrS7EUDdjaf72D24ryfxSkt1p9zrNsPs9w8kdtOWDMdzkKO hA6ADtXdeKl1e50ezfZHHCXMYgRiW2rwPMzjgdsdDXOX0raTYtp1xOyB5Y7sHJ2792cNkdVHbsax xUn7TXYu424vZtJl12OWS3F1FcRLFHCrHc91GAC0ZyeADnjg+nFbXhnSLLTPEMdq9/LbTbNpkiRQ zTP2YEH5MfiMVN/Z2mXviPXdf1N5bs/2baufLKxyNl2j+93BUZOPz5rlLqdYNbjnvbhdl2JEtmkb dho0++CccdFx61U3yNSE3oX9YuJVTUY5jKbpi8cyyucRRom3cXGMjPStfw/BptrqXhqdnF3NI00M kcqNsU+SgIw2AQwH3h6fSjQ7PTLm5mfUDIq6lYyS3UKRM2dmSqkYOFHXPSqbaxb6UfCTtJcFJrea G5gRWMqsYvkaLbk5btx9K1pxv74XOZ8ZXLadour6KIomgTUmlsyqn90pmVlA78c8GuwfWEt7qZLu 1+1WvnrK9sGOWYjC8pyW+UKBXn+rX1jpov73Vt0U91NIBAkxm+V9u0Bm43A5Lc/Lmut+H1zc+ZFq mpMFlZDeKjQPMVT5RHKAPvM+4EY49Oa4ruVS62Edvf6JrfiDR5NcvDsklBuoLeSQqU3LjYf4d3Tg 4wRzXj7slxYxeHpCr3FxfQTFZAN0UMm2TJA++nl/LwePyr3PVRr1rptotldp/Z7SQwwyyQhW8x5P vlDl8cY5K/SvDdNsfsmo6g03mXj6eV04Cc+Yysu8SqhRR8o2xBf9ritsdSs0kO2h1CaxJptx4ck0 +eRr3TWv9PmFtIuQGAbK/KeP3S8HPfHSqEtlYQrGboJcC7d/tPm8L++3sGwMBdvc8c+3FUoptN0f xVpN7cbbuK4uormNWYlSsigDcqgcgsyOPX2wa7XVLrRdT1x7KOzFrY2SKyxxDa7yyZjZScZ+U9ic DGPasVBzXKxI3dCt/DWn6rDZXEdlqFrKPMMlrGZPLzEU7DGVPXnpz7V514IijmurqWG3uZYYbhJ/ LjRvLVhPcj94ScD5doyfTrXoGpanJGdHg0ayzeQQzxTF42LFoiGdsbh9+MEn03CqPw8luo9G161g tliUWNo9xNcSgCHeJpd21QxLfMeOmR+Fd0IpaDIdB8QT+Fte1BY7cTy3un28iSSNtDNDNPjaO4Yt t/CofE1h4klNrcTWcIeCGW5j+yDf5kTHc5kjP90gD19AaqWV5bSeILhECW1w+lCMB2Gd0c+8uN2O MNn5RXQ6sNZ0eSx1i+1Esk0ETT7UCbHKlQFBP3dqktngtg1m2pIlyNDRJL6x1rVtQk1GKyESW9uD GoML+XDuLAdF+aTk4HFUdd0+Yt/bBlF1eX32byLgD9w8o5mVSo2gAfcYr6iud8MzmSG2aceZBIDL JNK6RMsc56KY89sHYTu9BRDp+m2s9nZJqsZEN2LSUOmR5cpZyynecbAOvvxWM6vu8sRNk+rax9r0 C6sZ0H2eZzFZy8nD78sFLH+AE/wj2rQ/tfRo9bsSkAaxsoowkKp5OZkUcFgMknrjbXK6vc6FDphs NP3XBu7n7XHIxOLdQ3yrEOgP98c5GO+asJqmltFDfLMXvUuI5oIyPLVY1bqWXHzrjjrnNYRqyuK/ Q9Otr7+0XgjvtNe3S4eXUZhbwhS8UZ4U7v4D/FnristNauU8WrrcVo8kN42fIlbY7GYCIAHn5Wx1 7fTFZt34s1bTNUn8QxWbJHqkclukcsmQnmnhgqZ+pA4yaq6v4i1HUJFuvs8FlHo9tHDDIsvzuM/e 2gg8Een9a6atZctyuYx49U1bWrfRbNrFY4JdZvrkrJmRlS634RWHykgp3Oa734l6lrOlaLFe3UiW 0xhKpEEVWw5XcwAJyyjnGOw968lttSvoLa50nTrljYxzRarPcDy5G3hkVNrLtCgH3NWfH2sXtzqS 2V3qNxrqxzRwx28rR4BcEyOqhMNsx90/nRDFJ07sND3bwh4U1bTNGs9T8SMzX9pZxmFbNhH5XyZa Mj7vvwRkk5rgfCt/YzeOdYk16SW1smtI5FFxP828Ek+bjG8HICYPH4VQFvNqMzRXTvceYY1D3E0x whOwFFaUj/gOOO3y4qDw94eEfi7Vku5E2WixQwQxKNpG0MrDrnknP+cbKpF2Q7HeavN4OtbGW3gm t01G5aS489NswQE7khfqGL524/HPWuGh1nS00Wz0C4tNl5Z3oQSrEMhXJkct3KLtbaMe3ar155Gr XE9xp0zWdnaRo+8AttkzkjYAOMHB44/CuW1ltPDabq8Q2+XPMLhIh5wSNgBGT1JVHODznk9KyxdZ c1wL+oa1BdIs2n2v2aZNZsbgNcMikSW/lqICI8/JjIXjrz3ro/GHiHVL/WU1MwhLZrW6aBZJ97bZ zErpkcbCg4APNcBELPS7XTb2A/v7vVVvY4jGNxYSCNcsudqZj3c8VqlptfubyymBhg0+xkmEROfm hwsYXuBz15B61xRxEuTlQSiZfh6+n1KaG/024LrpkstzIsSZVlnfAAxlQv8Ae+hFX01rVvC6w2s1 w6rbPdXVpxsW4EpAKuucYRuVxxhiK1Phzf6ZZ+HoZhblbyVDGojQHcsbkBM9yvfI4o1nSzqXiG50 lp/srQRj7Mh5Vbl8tHHK3defmA46VtGEoUk+o407HM+GtDtp9W1Cy1tZINRiZ2NrCxMs00uDuXaQ o28HPAAPNUfFsk2nwXUd7BEk+n3lvC0lugKNbRDLGMyEZLMcHPU8g4rf0TU2PjHXdbuLcxulvEb2 AgAxsoAco49xng4IrI1u7tJtEubvWw7tq88F1DGVIj8jedm3bht5+9tP8qU6ijHlKaNS7GqWJfVJ InnjiaFzCoBYo4+VsbSeAeR+XFWdBjOua3Gjb0s7BkcJ/cc/NFhhgc9MVentNXj0qWa7b7JFBarv lhY7lQjMZPH3j1x2+lc7pGoXdlcQC1hkuLee0ijvPtDBW5+cyRqdoPH/AAFj/EDXFTvCdpbCtYy7 TLeF5LmSdkgn1y6KhI5HeN/tGQVcEYD5xhs/lWxZXH9rzIt6LmXTopd5tuQ77cqWZwMbd+Bs6n86 5Kycz6XoWnJcmKwTUp7gySNgIjO3z4HAJ3jJz+Qr0d7N5b68ube8S3htrMERrvLNBDguocYCtMPm cDJRMdzW7i5u8RNnCeIJLvxHqEklx5NnZzW0Visdu5aQCRizuTzsykewYH1rpbPVtU8y/wBHtIrh pLuylWSSJQ0zQRx427gBtVgeDla41ftkfi+9mtb+NIbV42uVgTYhbyDIoZ8/dhByBklj27V23h1r y3vNTvHLC6vPliYneDCPmwSAoG/IOPwFYxdmSkbnh/RxeeH7S9ZtiTNZf6OqYUMTGVO5iScVjeLN Q1WLU7zTdUvpLWC3IkQS7dyoPnVkyN3AAHygntXReE9We50TRPN2ws95Fb7Adx/cRDaqqcE8KMjt 9Oa8+8S3s+ri8+WJ0m8y3nKoyvumlVflzkuyhcED7oP410YmS5FFMqeiOl0K91K8sZUWRYvsiCKJ ljQGZZy25vu8Mjrk/pTZZ9N0vW4lmbaPLuNVtBKAqgLblJZQygfMJRgAdd1d7HaC9ttJi0m7W1kt Yy0LIpYpECV2Pg/MuMD9c14r42je906C0+ymG/0+0v8ASVQ5AxKUxKzH7sfQ+x9qmScIpsIKyLh3 TeF/CcVpCjXp0Br648qLBR/3ZYu5PON549sVpazd2B0nw7DApgnvpVkmZj/DA2WVUPHLbeTjOfYV 0uuPbxWGu2SiNbfStEsbK3nd8JtQMzncCvBIC8da890iW81jV9Ojs7NRCQHh3Ha3lSyLukVn67vL wPM27V6VE5JP3Sjpbe/uD440gTCMRaBbXaOyExxI9ztiR2bncV5UsBu4xmt23vb+2m1PTYpIvtd4 6rcyqd6o68KsbHueO+R35zWVaJaeINe1Oa0tna2RYLFGhVn2LbF5ZVOBg+azBd3txW6dLuLeKCxt Iyi3G55/KkVjEg5RVXrknOR+eKajO1oolop2S203iDQ7e5UXLmG5+3W7kuUYrhXc9Dt746fhXXeH Vmt9QtNNvGBMEIlBT58mE/uVIXOP3Z/GuP0PTwvjX7ChmmS30uSWU3A+Um4ZU2KOM/ICOCea0tYI 0a+0fUrTzVhKyyCKKJ1wkfADKN3GePwrpoJwjzFRVjR8T2Nzd6P4l0y2H76XTx9mbOSPNmXqG/uk Vn6da6dqviLQbSK2ZksrKaOQyDYvmxgLkLjn13Zx/Kp9Qv4LuVkhvtkErRxOwjAIVTvYscD5eMcD +tZeg6zby64ZNUnzF5E0UQBEQyzbiCWx0AwD3qK2Ii5Cc4lq+shBbxaUkUiJHJHPDeN8x5cqVbaO FyPTNdlpt4jefJO32Q2zLH5DH5QW5EhJ6qf4T+HXivPJNejlSG81C8+z200sT+UMlgscjdgDgdst hT71peKjY208V5ot7LeXFpGjC0nwqXP71ZFTe2Mdz/dHciuilVS1Qe0XQ6OOa7ij1zUpmMBTAjZl OyPA6Ko5O/q2faqfhzxHcasEt4Iis1qTJOZQwWYMu4BsDC/L+FMt/E2garazapHqCLG3mPNAxwoR 12ypMrDHy/38HpxXl1v4rW2Wzv8AVJ0tRFc+ZHKXcooYsvl7AQOdoAzxVVZyc1Yanqey21lPoyf2 c9wt5aPtZJCMvGCcyRnb9/rx6D867SMhGZnKkRgKmOOB06AV8WeMv2n4PAmt6zHYeGYtVtZZUVL6 6mliErKqiQKiIeh9Gz26V5bqX7dXjSWEx6Z4Y0u2LD/WMHkPTjgyEfpXo0qU2a+0R96a3f3WnXMM JddtwS8YRDufaMMh6qM7h3zXRPpq3cH2PUYvtAACyLgYcdDk9eo/WvzBtP2tPit4mvrTQp5LG3XU Z47aNtkUKRySOqq7uY3+VcgnhuM9Kz/i58bvjDoPjO/0e38VX1nZKsbRhbwSgjG1ijxEDG8NjgHH 51Dw8ublZHtNT9FdZ01tPtL22VnuU2LJOjKA0fmswhlHZuR5bEj7uPSsu3ls7aaCa2so7fMqxxrP L/rYhyWOem08sMY5HI6V+ben/EXx3aeHV8VfEO7vde0/Vkkj0hJp23TywNskBcHcIskbs/ewAteo 2fihPC+j+HfHE2p3N7LdEPGiiZbVZJY/LkR7Y8xbBuyyb0kX+FivHnY2hKE+ZCdQ+23+JHhv/hNC 99fQRJY2U1vF5jpJ5ly8yiWNFGRvCrgDtzXneq+KfBF94z8TvpetJdQf2VZwZjLCQSwPL50SJIF+ 5kdOO1eCfE/UNft9RtdZ0RdNsvDF/wD6ZrE00RYwzvMiPKkcrCUNwPL8kBivvXQ+EfEfh/Vmtjfa ZdQCIHTma7tZrZpDdKcCTGAWDBZB87F1JDEcVjXqydK4nVVtT6F16+00aNBL4qnHhyK3tvIt4b5F kkaPbuWWO2BJjk3/AHjIOR05rzbxH8aPDnhL+ydNubW41iW4jkdIb6JLSKX5QHmJziKPPIM2SOpw K4dNIs7e5vdBF1LcaleI895qW4eRBGh3mOLerCP5fvBpOn3am8M+NvDem3dh/bhj8RaJdS3lha3r Dc1oz4Druyd8UgYFQ3p+FeRTcoPmeptGtGUT2XUPHNhqUug6lLpxsrO0CzSQvi6imi8pl2rdWxaN uo67R71i6r441S98JWdtpssN7b6pv05J4YTfSW0E4KqAIt7/AHvkDnhe9cdH4iGlXVjpC3+fEPhk RGaOcRmFkHCttJCY2FQVz0qV4vA9l4judUsdKufD3iTUGjmQaZh41lZctNGYnjmjU/3CNufbipq1 m581tTKMzBuNW/aE0e5msrR9X07Q7DUvsVvY2WnrNfLDtBTUJvMgOLdiGG8spJ6elWv+Ezvntdeu JdRuftRuoBbJcO8sqW8mEuXS1i+/5bsGCShVY8bsACuvubjUoNFk8TQ65r15LJF5kE2jPBbX7bWI kEkU58udEI+RQc5zhc5rmo/inZa/eeTrmvm5ubdkhkvG05Uu02jOyaSz3rcRjjzMMhjI6Z6y8fVr wsktOxbhfW53sFjr3gi/t/HmmeILzxZoE5WHUQ6hZFizuCvBj928eRtlGdw+TjisWPSrLxC8Npp9 gfMeCXTxqCyOkEYRTc5MXZVdQWc9Blc0zwfq80N7cy2N3avazNtu7eS7Esd3bFcFf7wbvHkBuBnv Wdp95pOl/EL/AIRS01gWOrR2R1K2aaEzW00XeJwOdxzj5MN9TxXkU1iHW5UEmnsdFd+H/Dl4fD+o +G9MtrvSI7S3Sxu44TFOl1aTeb9onVRnMz/Ou4YMfA6U3xNpPhHwJr2taD4PsYk17VbmXX9Qn8pJ EhiAj+02tq/JiRGKyYxyrFc1f8HjSNH8R2nhN5preTUZXsIzZlST9qbfFLNvIZhGy7QQu5dx3dQa g8RXOna54TtPEVnBFNeJcDRL8OcyKrv5gRuhOHHzjr+HFehTxFb2ijJ6GUpHhNw82mWuo20ciARz xanCflCfOGbATpu3Z9sCvUbKQeHrzSLa8F3eaWLCdjNId0llcPEkv2qGPG1owzSMolyqsDjBrye3 0LUF0rXmvdA/sh7O+dCXyGEe+ULI2452MPm3AbeeK9btpkfTGsJb5/8Aid2LZS4cNJHtQH90CmRw fl6j+Vd9eaT5TaMrKxh3d3oljd3EEvh+HUXaWSY3dtLfRxTmZjJ5qpA4jXfu3EL3JzzmoP7X0D/o Uz/4E6n/APHKi0/SfiVbWcUb+JtThUgvGkMCbFicl4gPu/wFeCAR3GaufYPiP/0Ner/9+E/xp8q/ mRjyxP/X5/TruOaSW4Z/3duFbKjIIb0rRv7S91GzaGzhLrHvKElFAJ5GckVn6Qv9mWM8w/fMY/l8 z5sbelNvDLcQRbHO6f5pU3YGcdBX5u7KR3X0sOhiuW0opew7Jl5Vl6HHH0qtDN5kQkgA8wA4wP7p 7etS2b3UWnOJ4XjVQSxDblwDxtyfTrVRZGkucKAkaREI/wBzAYZJA47nmnJ6kGxpkTAfa3dXab5w u3PX149a0LoXNuXico8MamUEjoX/AIfWs/TgILO3SRGMscePkI571I07/b3aYmLGzzFPzEgHB4H0 Fc03rYiW5jamfsj7lj2tGwGV7q1XdkUkUPlgALJuIBHzMccLVbW5PtEA5CyOSGGP7o4xV+3tpfLt YFjUFCshz3GFJIq0OJFLbI9z5twqTZ3cspO3aSw/zio7NhJdTjzdhe34z6sB8vFXdcYq1vbxEpuQ ow44IPOSKx4JNt7bxL+7ZJEaQxsPmjAwvBqkymazyyG0H2Z/33nPjcDtIxjtWHNtnuDYRPsXHmTM xJ3sjdC3aJe2Ore1aF0s8GnzBcRyTGUKcltnOSePUcUeH4Lf+z5tQvrUm2jdY2J484KwYIucHk4z WewGpe6RvaVHkaEyZbzjhZAQcEqgPAJ/CsafykuyLf7XcpCm1pHijQF+h2szLXRzSbbxbi8Z0vZl cJGwyp5+834fdFcZrUpn3y3Mj3UbyZgDna3A5JT5cVmmxNm5pyaRb2YeMlbdXZpSWXhunOCRVmyi a2ummjuXkA+cQ5B46cVQtr2X7E9uIYUScIwcAOq9sfpT9P3reRyQ3GxpecEc8d/92m0YM07qSSO3 mnRGgiugVmY/LtA5GD71xlxf3UtlObWLLRsqthsZEZzzntjrXY31gNYsr+3a5IjaFpBtwwWRO/Xj px/kVx2q6S0Rs7yK5Pk3CRv5qqVVlwFzgcdqqDXcqJU07fZSyXQgV1vLWTMTfdBHzjDdu/8A9asL wzYQaffWdvZ7vsd5LNtjbDKj4yR+K9K6CcyJeRRvM0u+OVQr7VJTy2w23PSqXhFWS1eSeMskAM0c mMcOvytzzwDXbT+E0XQx7NdPt9ceOBZZIvtByJT5bRkfNgqufUAcdPxrs9UuL2yszDMiwokm1AQC Gyc8Hr1Jrg9GmQ6g/wDaVyLzVrsL5/lKcAgbQ2cdcDj/APVXe34u9StbW0YxStbqjEnOWC9setKs lzJMR1tlbqZkkWNJZZfJjBT/AGWGPyqHWLm51G8jaGNXmmiKqfuYaJ9rDtneB0xVm3a0t5DCSyTY 8sMmcbgCdqkdCOOelX4LG9v9Li0LZCLa3KzXEyJukWPeXY7xzuxt6DvWeH952HcyTqkUTQwxRSS/ Z5GbZHuO4DO4bQMY689v0pNHuyb5nZRZh1kc7F+7gqUdepz24FW9XvtMitjY6ROtvFHPsDgkOVCb XYj7wyCfl7n61g3Un2S5nbTdjxpH5HzAncGy3yFcgba3dPlehD1NjxLqst9Bb3r3P+twtwuAu0jh TgYGdoxWr4Rn0nSNeaDXnnguLV2hCWsskN1c+auVHnjPlxovYdSa4i+1GzuFs9PjtUjjslXzmfDC SWQkZH0zu5/+tXZ+GdN1W90jXLmBxNbxozSBVG/cif6wnkhPof8A63VhZtVLky0Rzvii2S28Waja 26SW6CZvK+0uzPID90u7nLcd6zJLf7AFun3+XFbs0xUkgk8Dp39K2tSvbfUX0tIphcs9uVJGcrx8 wPB9OKLKX7XZzNJiNJXji45JV8rhvyGK48bWvVuSjz6BJbNJsW/mMY1CoPmLBjnORXQ6Ust5NGsU Z/0eQCdgQBGhGNvb8hXHayJdJtH0iF3gmhuIfMkDAkeY+0AntxXS6CZ7c30k3zxJMsZ3HBHo4A6j pVVo2hzIvY07gzPq4Myl7RANoXvxyMda2NQiJt7Ke3Ah/diJYyeg35BGKSO2S5vZUZdv2S08+MOd pLjuD06dqs6kDNLbQzJuUQrLgZGMgelc0tBqBpSReZplpFcQgXcbEgt97YDuI/WnWup6UkdxHe2Y kKFeeRtXkbuO4446UgSC5gtppWa3jjfLOx3EJtwR39KvabeaRpcdz/aUb3M4BYFT8jAcbW47DHNa YWL5typ7Gdpl3aW8TC5XzJslkQ7drAcjcRyOKzFu5ZIbq/hdk88ABF/gCt2GKgguYGnk+yj7Msj7 ggw+Fz2LYqSZoE1UpCrTNwyheFTCfeYd/YV0Yqpd8pn6FtJENjBM8E6ORltzAbVxnO2pVMDYJ2sk ygxuHO9Wx1ZadeW9u0Utw77p2TaCGxnbzwvSsyztY5bZCsTRhmGQT8wDc5BzROVrFJGgZ4JPLS5c RN95WVST8nUkY/nW7p2syrOJHkwGjcx4VCuxs4+VhyaxoIY0leK4ZQqArED15/pULSW5khiP7okf IAV4x/EPw7VUKri/dFyG1DqsrwQwXeqWsikqvlX1iUIGR8oeE8e3FbUk6RX+oQFbS6tYWby5ILoI 64OPL2sCxHv2rAO6RbBJHEqLcK2SRuYEEc10N5oMDI7tFy2X4HJH3vlr6HD104cyYlB2K9w1lb3Y JsNUWOWCOUvFbLcxrgncC8RPQD0pBNobW0Vw2pw28dwzBBcrJbsSv3h84xxmuWNrI08czyPaRYPk shMfzKOFJU8Z68//AFq3baXxDeaHpzf2g8g5k23I83GSAQ28HPSuzD4xzWjMbPsRX/hHTNXSCf7F Y6nF5qgvH5M2PTp0NZsngGwtm3W0N7pp+7m3mmgAwO2Gx+dXLiwvJNNeG60jTZpftCTE+T9nztGO sJU7vTirGn3K2sOqwxWl/ZTCBI9tteyPh2O8YWbd1Ar0FWl2uTJW6GI+ia3aZ+y+I74KmGZLlIrj 5endFb9a0tQ02x1u2iXWorfUTZxMIpJYVQ7TzwPmx+daFpq8pF15up3n+okZY7+xhmG4AbfnTB/D Fddps2gaksERt7KQOI0ZkaW3Zt8Z3Ahhjk+gwKwr4+NNXlE0hS5tj5o8T2GiPp0F5Zx2xmt5IVjZ NpZVzjA5Y1mxFiw+U8k47+/YCuytPhP4OvH+12un+JNAmTMKzWdxZakgNxJjdtIzjPr06Vw1/wDC 61nmmFp8Tb62Nu43warps9mHTfsI8yJSv5HvXLHPMM99PkaPCTJmOEk3EAbWPJA9efpXvJjSaK1a NlYGFDwQew9K8ZvfgfKy3UFi+matPDklRfmRkKHq63BXjjGK9Wtvh9BJbyrd6Q1jepPK0iWeYQh7 gfZ2UfqRXbRxdCsrQkYSpTj0LT2+GIZCCOOh4xVWSBCQTwvtVWTwjdWjBbHWNStVHRZGWZOv92ZW /nVWKz8YRNMItSsroQvs/wBIgMZOACfmjY+vpWvs/wCUl3W5ofZAfuEkZPr/AEprI0bqFY57Y/wq m1x4ogINxosNwp/itbkc/hMB+WaqjxJbCTzL3TdRskQ7WY2xkUEf7UQIo5X0HqdEs96nfdjoCP5V YW/uE4Zdw/L8BWLb+JfDN1xb6rbhv7sp8t/++WxW0nlS4MUkcuc8oytnH0pa9Rcq7Eq6xIDsIYcD A64I4z+Rq8muIfNV8fvjliRg5Hrisjyf34JG0H1HtSGAFio69+BVKcl1JdNPodzYeJfLkmY7JhcI iFGY7cKO4GD2HeuQ17V7xJb43NvpsI1qKOPzI4tjhomyPLdnJRx+v6Vzj6ro8F09jdSSW8qHGXid V554fbtP1zWbqc97BdtAkiTWtx5ZjDAOAR/dJ6E1w47EcqVxqFtj2TT/AIi6hpOuC+s9ctdXVNOG +eW0EUkibfmhZnGfl4Gcc4rzd/HHiDUZZma6kjku5jIVjKRx75BgkBVB+VMKACa43VbsWSpY3cUU bDLLlRnYwH8Q4PNan2u7ktVuLWONYwMLHlQynO0EV5H1ydrXFbyEu9R8XXWmjRmjZ7e0Je3zckvG c5bjO0K5z1FZHh6bUUv10qVjatNPHciOVmj23CN8km3hW9jyOeK7O21429rqFtkma+hWE/KDk47k jis+fUNQ1Pwy+iOkVxNaTosF1IMyxA4YxxtuUlcjofwNEq8ZW1LivI2PD3jnxDovjK/1zT9WuUNz E6qZkJV9h+WKUMFzsP45J5xxVHxDrv8Ab5lu5tNsUlur2K6ItoM4faRHszhlXPVCT68VyqaoL2a2 0udhFPvK+aj7gwlZdzYf5uvTjgVcvUvbSd0t5mBgAZBER88ZPXPTPNYVKs/hLaR9L6F8RNKstD0/ w7rKvd2kcHmWXlJL5z3URPlrmOQHZH/eYgD8K6KGwtPG3i2Wx1i9srv7NCzNqP2do5UhmOIbYszA SMp6tj7vWvk1bXVNlxfxQM09tGqxTW8pBAPZT7jG7jrXW6bY36WkB1XVDpd/qcH2jTY5GS4WQSHy 5A+FDblcevFe5Qr2glJETZ69p/gTUNBt3u7PVrK3v4ZpdOgsZY/OSWQSeVJLh85G3a8Zbow9KneW bwNapoklzJq2nW822wtZEKhMfMJGn8tQJI2JwFZlPHB4Nc98Mo30/wCIumS+ItQtFa1jaPz2Y+V5 hQkwh2JG8g55O3ivavHXiTwL430w6RcSuIAsiW12sEUjSjEaOttvwVkbdhCCmccZFb00n70TN2KN 7q96Nd0eO81ebT5JI5U2NI0lyGlwwnkt0jEYhJXb2+9WgviCPQILHwv4m1cuJ2e8kvoZNu7zpGyn zK0aqrkxPE2eCGyKwNBjSKJPBOrTX4W1P2ix1C3KpPFAI8nzWzvTauAduAWBGOK5Hxnr0yWl3baB Zi9+2wQ2N0VhjuWmnAY/aJhy0bGOMngryeelW24xJeh6d4X8TyweANUk+y2Uj2j4t7SMRZYOx2F0 yFaUlenFeEWmieJ31PV9HntGNwzS3t5YNOI4mt5AzMQhyh2bg48vOw8bc12HiGTTYPAFjaPoSalN rV/DLYJYzyCVZJoyCy79z4jC7RuHHTgc1yPibTfHujaZY6PrElpaXOl71hu4p91xHHcoWeA7di+X hDnJIrmr1rWYnqavxPt9NudP8MW8V9Dr0081qj6bFtik8kLskO6PA2nALBwzH+92rj/GXh/wz4S+ I2ntrV201nE0QlgtIgY47SReDuA+TyyOExk9TUvhLR7w+H4dduNci02TSrtRA1oqyIi3AKfviypK D0IMbFB/EM5rDvPC2uaj40gudLh/tWGBoIL6S4Tz4nkJ8uSUrIfmBHIx0x6VhUan71hJHPfEHwxZ 6DFYatHrv9rafqEri1PkG3mRS21ZS4wrhum5ckeneorzTZYtC8M+LoLOO4+1NcW1x9mla8SVYeSZ YJAQjD+McIwwdwPFT+MbW80XW9R8O6j9t1PTNCR7O0fcwjWBxuURhxjbGf4c/SsnRLzTdB8D2/iT SbmceNZ7uW3023tZxFmIYPnPAFdiFZcbW27uOo5PFTS9o42Id+geEdbtdF8W6brOp2z31qsswS3X Jl2zpsLgc7njySExjgeldUtrrGreK9Q8P+DdDeC5uktpII7rezRrEdvmuZgPJWT/AGl7jkZFeheG PhR4ibxZe634o1E3N5qunC9SS0/cX9rJdLueVIVVUYtl0kjQklfenah4R1Xwxa2GvaP4ueTWlZrK 8m8yQwiGbIj2q+9oxGYwjRPyDiumhgJctpGkYvcy9M0GTwRcT6b4mO3VJImNxbmOMRn7TBMoMd0G JiCpk4OMuOmKs/DzSdM1DUvC6X0C+IrnUrC4lktbhTBDbywFZYXacZJAAOWVccbcN26SHXdG8e6X 4X0jxLbWuhQalfCHUQUZJ7uGAF4ZkeQL+5ldSjN2LFevFch4EvU0jx34cmitlnl06SXTYwp24tpm ZFTDDgBepPU1o8PCnJXKUjc1rwdd6Jq8nh+O6ilGsOJZLiNnZmeR/mUMT5ny4/iY8e1c9Zrd/wBj 3Ns0vl3tmxSzO0PtXdhtzZBxkfMD/wDWr274nWmkpremWmm3kUMVxK8U8NvIMRDgSzNjkPg4z6ce 9eHhNK1HSbG8DgyW4eN45GIV1Qkv6DpwBms8TRSnaOxqtjYgiudPu00/W5naO7hiu5maTG7JYgL2 Vj6fpW54fu9JfUYr3VrkSXFzD5KrMzMQ6y7huB6ZQZ/T0FUtGuNF1ySY3FsyGSxCgGNuZbeQqGJG 7gZH5/hXM6o2o28j6nboTYvdYM2ww/vDgHBRhj/gXTAzisbuC5hov+JZ4UjvrXS7cCyiuJUdlQ7B 5oUqdyqxGOaq+H2ur/w9oFnp9uE1SwhkUbArI4hJjxuO0jG3riuy0ffNJfaLLYxTteW8d3PJNP5X y7CrHKZ3eo46YrmPDEuraONWii+zRtpzMjxh5JQ1vMN4KleMc9QaTT5uYk3J755dW05pLRtPP9lX qu5l4WNdrFDhCclgcc/SuX1keI7O4vBd5Ya3ohxBJ8zBLXDrIN2CGUdPxHtWlqs8tvrmnzPeLNbQ QIJZY41zFGzjeTlvmVR8ufrUWrXERa7uNQvHlksmigjQTYk+yXqMkhLY+Y9yoqpTvaMSkW7xk1VI 55JWHmosUU8Sqi+ZtWSZQOexyvv+VY3i27tr7Vobc3ktxb2ckcYM7qDuPAcKoAP+1SXK+H7XwdZL azGOb7NBEyjdIY2Em0uFwNr8fMfSql7daDNeJc2YluZZLoyTRKwO4w8eZFkZAxk7SKwxXS4HSO+g 6U8JlWDVru70eSS6jdgVPkTZbj+EbcADPSobySC40WwsbRRJ5Sq7eVDtWN5DuEa4A5VcdyD19a5q 4167TxCltBFHcWt3aaktoXHyqZUTkkEfd9PXp6V1tlqmuWlvYaYbgubItdSSPvIkMi+WoCkHaAv0 NNJVNC5LQz4db1BdO1HTkV7e5M0ii5HytG86cg5A4IH3cYpb251C8tdLu5bVYr1Li0topEcjIgz8 xXHyr8wG7OKra/qOraja3Spbx3eoaTt+1LEo8xlXGWOWJYhc7e/6Vh6qb59L+TmCFVaSIOT5qSSA plWwflPQZzxXNKcouxk7mbqmoapFo/iO/uYIowZDNDERtQ7pVR0XGCRjuT0rqfB01j4fs9Nvmv8A y5o0S2LBlRbaJfmMQQjnBwVxmuc1ePyY7vS9TBuFiEqwt0DMF4X0CjjrUS/2c8f2WwllZo52ZmcZ KOXUlBkYVQM49elS52Gj2XTL3QYdNtpNau3kLvKlpE0ksjW+0ExytkiPJ9QNvOfWuF8GareXWsTa jeWzzyW9/cQxwbAQYc/u8kYy2Dn8j2qxrebCwMNxi6lcIfMUhCMgkYB746r6nirOgaXH/Z2rQXkv 762vZ5GDnmWQEFl3AgkfTp09K7uf2jTY5Ir+PdV0yRJ7fTYPs6NdR3lqZNkZtjJKBcxYHOwvnHoS vbmtXw94kXQ9QvtRu7SCZ7hVj3zTqFZsli2VVtxHeub13SIoo4ta0m3iiuYpLYrHLHhVdpAnzDqO pBYn0OOBV/8As2S90a6voIZUNnfXKyWsjAs/msgYbhuHYqOe1OTUZmkVYNY8X6nNeSX/ANnht4Jb S4dBiQqHjjbLD5RnzF449BVLQbrU7TSLjRwZAz22lhg0SruYQMPnLMR+G1s+1Y/iWye50KGK1Hmo TBGpHzeUsj/Mpx32Mfyrs9Huo7k6hrECRXrvZWjrDKpxHcwpLHt475IG3Gc1jLEcwSgmcan2uLXm RGxNZ20s2FZcJmRWyow3XPRa0fErXN7o00mq3c8017cxoIVdpJFUgFncMMD5Rwufu1Re0sY9SsXB EizW1zE5AJb7SGRwCP7vz8D0xXS+LNMWKWG2t5EWPT9ttMyjb5k7De7cZ/1cIK4/2hUfBCwrWMJo 2e0eea0FrayKoi24V5D0JHOFPYYq9dQ3EdvYSz2n9n2m7y3KsCQepwuT6/eJ/Kur0y2jtEF5daT9 oe/gCHG0qm8YhIyPlJAHP41zesLLaGC1ni+ywWkjzSEYdt2MHczYLY9AMVx7K5DRQvrVI760sbm1 jGXSNwpADAYw3yg48zOa67xHf2ccEcEYW2t4pTKkbRoxMIAUxpsG47f19K4aPzrhSxmVltbaZzJK cOrZ3Zc8dMfL7YFdnoNlb3cOmajHbH7dvgkQXCrtZfvSfNg9c9Diqw65mJF+zttS+zT6lfud9usV xZ28gLIyIcYaPJI4IPXpzWPqey4/tHUltbeCGNBGyRTlULSDlR/vAZwGH616DrsUGqSjTLcbpArM TbEIY9w2jP55+g6VxotLkPqEd1aK0NrZy28rKceX5aDy7gJ37kHriuupSaVrGjicd4bMsFjrVha4 up9Z0cw2jqBEql5WWIeWBgMo4PutbssIvda8PSxEtPeRG+uLoDyyN0e3aduCQRuAz7VWguLmx0Tw 1q2qriZr6fS7oRrtlaE/vY5E6fOrDcPUGszw7DqEOo69cyXguodFtY7VQWzDujmwWIHbDdB+PNZy jZJCse0fZrG71O1gh3eXGmWWP5VViu1R0H3v/r1wmmyWtjq/iLUbNmlupJ4GhUuW2/Z1IOMfwhcZ xW54S1221WxvYrYGJdzu8qDCFFGxJUbsOMqfQGuVgjWxS403ftn0yR0jMZ2mRZZN7K5P44PTBq69 TlSZUnoeleH3+zW2qG+KRSSvG74wojDjHB5GOa8m1K0vNX1bVLSKbNzBDNlVfYSXdc5AGPl2Diuj udRm1G0mvlkMVxZqiPGNpRkONhl3feG7+7ntWQt7qQvNQ020cwwXYRjdjaXeXJMpjx824BueNtc9 espx5SEVxFDLY6LdXFn58EV9ptgWkYiSJ8nMqgclGLAYIrqtdu5R4n1KbTfLNu2mWVrOVUSBVuZn yAo43IPU8YxXISTrdxaJrlkftU9vqVvDFBK4TbNEA6PvH8TsrdtvbNdZo8k2ueKNS1WKNLS3RrSS YEbUeWRDhFHc9SeOv4VtQty2LRg+D0MGhWlrJBcW/wBlkm825hXzJVyxk80uf3fGPu56HHauv069 /tPR5b7SkSW9S8Es3nZUSKh4cHHyrheD0/HNcdDrFwvh3W7HSDzcareRYOV3I7hwq47j5gfYYret UgitLxbKVg0NlHCjFTsbBw6FgM/Kw9BxxVaormObA87WLvT7KUzXOp2Nr5rMW+RC7NsHAzuIGM9O tQ+JlE2iP9vuJBLYajaeeyAH7svlpGme/QH1HPSmaTKuq65ealeCOymkitZkZSCkaW527geMhumO tVvijqeILfW7Hy7YTXVpIzx/Isu1huDqc8jscVnKm2uYhyudv4nvE1wWlpDJJaNe3Xl7UYFWEHyy eYq5+UcYP4V5/pdrPcr4gt/tAg1XS/8AVhm2xuFG9ZWzzhl4CL0J9Oa695NNk1D7VAZbpbFxFIc5 kbaN8jjp8mDs9T1rzzVNSiuJ7qW6zbgOS4hA3RxvL933ZlIWPPHFc2Il1JlINMm1e2vtO0+CP7PM 2ntJDaFROWZmGG98dTnjHA7V67Jf2T6NqNpBefaIYcLb3pQKWlZcXTOBjDBSRj2x6V5naa7pnhXX Td6jqBsppbYQ2KNlpWWT5pElGD8uAuF4wRS33j2HWNYtNDt3drW8ZbhkjtiVFuq5kx90B/4fbvXV hko0biiZ3gfQbjWfEt7m2WW2dZrudFcpHiUiAKCPvlFHQ8j0r3DxZcaPotlDPaWiZtmRxIiAB2H7 sRk56DdnPTivA/C/iXVLa81O6tLKeR9ZumaeaIKI4ELDZnJ259gct2zXS6XefEmR5JtI8FxatJEW hee4v/JRD6LCoYqx64YcDOcdK3oRi1yrcdyaCS5glvINVuWjmspp1t4wnzQcAtJE44TeCVJ/Krmj RTnxRZadcxxuLadjKN7by0Q8wsmRjqQTzk4x7Vzdvo3xIvtbk8OLp2nWOq6tLFMouFM0I3oNxlkH zE4i+UAYJNGl2fiy/wDEV1DHfW8d+WVWuIVAVp5g6gBW442eh/CuWWEfPZkNHr897Na3F7e6LHDb WtwjpKBJueGVgGW5KEHCIcbkGOnFeY+MRf6pq/g/SfDCq+pLcTEzI/lG+jCK7Ft5PaJuDx9Kvat4 T8ZaDr1ld6147vdf3xbZ4LeKK3gjYH5UmZVJxgllG4ZxzgVwunWlvqPjTS1Z/KsYtWTTiz7g63E8 LSTMQuPRMjj5jt55rpxFKzUS4s7ORvOstUlvLiO9gmadbdBHt2mM4Z9hwD0wPp8ozV+W4gtZpmZI DpTIArBgJ40hzzsX7yKGxk4+lQ2/wttdK0FNX0mzkmub6xkvbqNJS1ysauxTyoyfkCDafmA79640 2mralp27TUMN14jvpLewjBw088pjhMoIXO1QHPXAC1y1qLUlyIo7Dwd42h8M6HZWH2i3jvL1575x tdi/mZ2OzKDwi/dXNamk/EXQLK3ZtKtbq4vJg5kumtGhmnd2JDAzbAPau/0rwfYabc29jp6xrFb3 a2cd8AuZZURfNZHcnKg8EYwf0r0e202X+1NdmtPNkt7YRMIYliigclSTu3c5/wB3/wCtXqUITUCW rHzVpPiIpqOo6xZW2o3V6qraPEUjyuxiVjd92FEjnrWnd6h8QdX0+1svsCWD7yjW3mO32ZGGBI7A FWOBjaWH51r6TbSarAfEDQQXC3t5ulhtz5bFA+0I5yM7geSfu4rsrTU7W01G9MT8Wl00ItWlLJGF j3Zy2N3BI689K4nHnvF6CWp4RBd67b3N5HO0DWwRCUEpdnCJtVVbHAHfOOaz/s9w8st3JdSWUDzw JLllJYy8gxrznHHG05/l6AW8Pz+JvEN5ahY7CfUbe1sVdPLU2jqvnCAIpYsjduB9aH1bQdL0y51C 2bzza6oJI3aLG3Z8kUR2rjOeuMkd+9cEsPysylE5afw/rtzLHJps8jzXE4d/NO93ZQMMBjH8O3b/ AA+lLNoFolzcRXc099MkJLQyfuoVeQ/NjHzDoR+tdv4e8RWcXjGx1aKMwWwt7ma6GGaVXwp8rfkb vYbR6ZzTBc297eajLZWVyk2p3MptZFlCybZhlopoySUwAdmCMd+tdFCEeTcLo4G30/TdMmdZbAyz 39rsLeYUgeYk/wCqyNp+ThlJye1em6Z4P8O33hK51WUeZeC5aG3j84/aZHJWMLtXCgbTvG5W656d L/iSw1u80C8t9Bt7OSx0xLa8nlu1WURNbqWVdioGLgLjg555rvvhj4ety9x4g05Db3V5IzlHZlRi pGWaLILbXyE/hGRtrppU3zJFx1Plz9sb4bafp3w50C+sbfyzZRTJITgudz2+HJHVuDlumK/MtdAv GOBDIh77lx+h6Cv3U/aS0/8AtP4cXghWOeaOFpYlYbkZo5I5CCeuG2kZ/Ovye+Pmh6vf+JtS8Xag GjjQ6fat5CfuWC2g/eZ+Upym1cqM17lPExhP2bJqs8L/ALGIJViFHI+bGD+Pp7frX0P8C9F0fWde iOtmHU5tEiZra3YrcEwTgwvA8T5yi796Y5Vua8M8QeBdV8OXcdjribLma3iuI1Vy5kWWMSJgdc84 IwOfwr3vwjomp+Bli0TRfEKT2/ifRo9V+0R4gW3kDurRAFTJuTZhsFcHHGOavHyvTvEyucP4x1bQ bLw7c+EdS091m0+do9O2pIo05POJO/d99p0Qg+5LdhXrujK2q/Dzwpo0QjvZIYCJFhEcn2W5DmSx WWNS+XmjG1fmTkc/MMHlvDcmnRnxbp2t3Uz+DtZlgjF09wk0iX1p81vKFKyvNORKwDRjGMlsr0+j tH8MaF4M0DV5/D8f2y51SwitLuTSkRYZLy1uC8chU4WPzlCnsgbcV614WNrxjSUepaZkQ3uvf8IN c6n4ihtkuWg+16eL4bmjUzsFWTCv5ZtyA6pj2rdso7XQrCXTodVm1RdWb+0J7jUpFM11fsFzJC6y HEbqPu7FZexxVGxll1/QVi1uSezaOXZdQXICrOEbeY2Vc/KDwpHX6V5/qenz2epPqds00Onardld LDQx7csm2YGN2DRxgcZUV5CqXTTGdZZXceoarrWr3uly+Zrd19meHf5ckcQPlmdYidqjK8ED6561 0GgQeFL3xBrWhqlqNKIaOKO6tYlYS+WFFzE7kusgPP7v5Wqi00Wk3KzKbW91aySOO3Wfa8bXAH+s JYD5I87l3Z59qzdNuNbtdH0y71+0ubfUIJJ4kvZo1hM6TN/x8bwMeXv5GO3pXPWTlC9y6fYZfaPr 48X6prWpavFZaHfsHis2jLPJeKixu7EKoG5UDJ8xUk1SuL64Pi7w9d6b5+km/ngmvzEP9fbDKIZX QMIixO7B25HPeu6k1Pw74u0S606Ei21CR4DCJcpsaNSIzGx4lD4J49apQ+H9J1FNa0P7O2n6ylqR BZyts2zxpviKYyX5yQ3QfdHSow2M9p8StbQclY4rU9Znt5bax0SWSCEXtxH5iz/Jsbj5gn+rdGTc OTvyRXK6vfXmoRvNcXTaatnbxRRtp6rEymSQGVljXKEsDukGM9uTXpWn21tZjWfDPiK2l02fUbiO ZL1o96q94i4cKQrkAg/dJ5OSBXPS6FBo95EPh4jTSW9zcRzzXK71hnRd3OFCqz4IDYYfWvQjyRem 44pHYNeeFrDTza+O9D/tbTrxHuIb6zZY7yzthyrPEg2Dk4/hYnvU2p2vhvRbfS9QtfEd7YPZPttk 1nTHltx5w+ZFb7yPt43RydPvZrzzQdRH/CODxDaSXEZ1a+N5eJJcR2oilifC7AIiMbx/rVX2pNYv Z9Xv/s8jRvp+oX4tpXWLM1okg4d4h94Nl8P2Kj615tXBynU51Kxan0PerS10p5o7/Wxb6lbX0kCq ttd+TIsvyrHt+1ZELZwOJhzjjriKWy0rQtVu7jVPtf2PW0Fpe3F7DsU3UA+SVpY8RSeWOhB3E53E V5/4b8Q6SG0681mK0e2trgypNaQs7LLbs0aSr5oOVZgNw5x1rvvFPg2bxHdprvh25YwpcwwXUEH7 ppGuYA8Mu4YRoSTznkFeR0AylPklaehppYyJZorDULlF1C18R6O5VQ9oZJpltypR0aTaEdcZ8v8A iUYXccVz2q2k/h27XxLpKXSaONPk8u4uLVEm3xDDoPlzvAAbBI+WuW8TWzXusERhILuyWGX7RGoM mVlKuvmDGQu7oy9j61btte1Gx8S2UMd9PpMF3C1xcxWGGgMpR0llW2fckm8RD92wwPug1rTw3JL2 q3YJJxuVdZ+MHxP8L350a9tLO6kihglWaS8k3SRXEKTxMRjgmN1O3+Hp2rK/4aB+If8A0DbD/wAC 5P8ACvc4tE0HXYIdV1G68P6xcXESE3cumW6vIiqFjDLIqMpVAF2lRtxjoKk/4Q7wn/z7eG//AAW2 n+NdPtoLTkMbn//QwbeTy4ERh5sc64JUYUdvpTVjENzEykvEQflGM9KtWzCNY4yD5EoYFAB8mDxj 8aWZBHqwiWTEBZug+7x0r8zZ1ljR4GubGVJm2M0nyB89PTjiieyleSSS3t9yoV2OenHUMPTPFRQ4 hhaBSzSvKGQnjIAz9Km+2u6K9xCWijwzGMHLAfwnOKaEwsrma4vljeLG5wpAXAyOoHsFzVK8sbvS rvzDcia6b94+7HKyNwMHHSt22Rrq3u9UKmCBXeNFxhlDKoJP0rAu4JGA1C6MsrnbGhPXaeQazmlc mW5LdZe1UYDSLIAGwOCTxn6Cp4d80qvEOFieJP72DjioGhHlGOKXJA8wgjGWHerlhhUkOctAqA9u W6/5FUVEpSq817cxZVIwvy7uei7qzbFBJOs/lZEp+ct12Y6KR7mpNRuHjupvKUlZMIGPYgHt+PpT JGuVhjjQEE7TkfdHzHj8hTWw7luZJ7gvd2hFvDCq73PMaYJ65/GtTVrq3s44dNWYuJlWXB6Rb/4s e68CpdNsYPttw2Vms/OVTtJ+YgbirKeMHgVR1WKO9vbq+lCI6XHzEdVQ/dG309KwerJuRw+YbuFp N01xDEwTdzg+pJ/EVxd3N9t1L7Q6eXJ5TCTHqBklQK7N5C1zJNAxELRIpXB7dT09K4CLzEmuZHJO 1HjQdsN3z9K0giWrl7SPOkt5IhK2CN+1RjGORXUhJGkhIdUkgQxuvAUxk9Se1c/oa2UqCJVaJxuJ lB4KgfKPxrXjYrfmeSJHT5V2E8dM/NRNGb0Ni6uLazCahBCSoAhYKcLIoHII/wDrVQ1GVItFbMbL GsRaFV+ZQX/5Zn2I4J7UatDEbiFY4VAVhLhmIUBuAAKvGI3b3elNg+aqqARt2kLn5T/n+VY7MSOZ 0LUQsUjlYykNs2LZuWj+T7pz9+PtnNZtte6GZbq+vJUhhfpDHGd6ysAQqjP3FU9s8UzRbS6eXUYp pDCbZCI5iBubepCB0Pp6elVG8OwvZNcXDhBubzp4htVWVQu/Y3/PQtjA7CuyCSN10JfCNs8ctxrd 1II7mZTGJXCkGIA7XOO+Dxx0Aq3a3lxeatNqFqnnXSTMI42G1WiK7VK9OcjP41ztnd2dlBLaaFJD dSeWUjy5Xy9q9RG3XkAVY8DRahc6bb+Uga9bB+Z84JOZXbvx0HpW9SD5ecGj1EJc3WoW88eIgq9C OmRg7h7CtGG5to5o1nLyWkqOhKthvmHJC8Y7VBYxefqd9fWsiyWywjCk/wCyAy/hj0rN+e3YRSyo l5LOTax8FlXAysfXI9c1yQnyiOe1K6WOO4vLsM8sTbVk3AFirYXjjqMdu1UNLu3FvceWrEyoEZtw by9y/ewD94jAFYnjm41I6myqHjEJCttU+a4k4O3suCfrU9hNHYRDTNn2cxW2HduCzIMg5GcsOf8A OK7X8NxpWOh1V7YW/wBotQJmSBFVFbGCuAC49a3NMvoINPBtLe7iubpnjZlmI8wDqioPldABk7iK 426ufNmtPKVU+zpkyBtu0AcCVQOu32rr9KvpLS9tnjhg1mWe3ZY3kdkj4GBgAcEcn9DSpSaepjU3 Ii+mrZXAth5d4ZMicsTvByThcAAAVWhSL7KsKW6lWkj86RQc5X7vA6fWn31rcRmHaYZhZyBcsfnf 5cOAo9eorVXbame8OHV5AYZG+VWcDbyvXA+lcs5psS0Ob8VW9vrGlz20EgeVJYjERlEDRvuw7Y54 4FZWhXg+13FgWc/bcl4+CCRwibj2HtXUX7wz6G486ErJhhtG3c68En2HauN0rRGkvXnMoDJBmTgF eu0FMd66KTvCzNLHXW1zqCtOqqplXClZONh2YPXsa3dTnka7hlfasCW675NvfptJ/lWDPFDDcksJ StxMqlR3VMce+a6DWVdrmSaZPLg+zqAoxhcHGMdN1ZOMmNImZY7nw9diFhgMDkZGOfmwD7VjXerB LaG9SEzZgWQqvRtj7HXPr3rW0sraaVdwrmRSwdt2P4hx1rPayh1LRo47FQk0JaRogD+8DjjAHet8 LT3uW9hqQGCaR4VxCi7kJHO1lyvHXpV/TWnjhluHYbliVRhfmOeENRW1oJ13vJsZVEOAeCByPWrM IaCWUyRq6yREBskY28dKxlCVyIoSY3CxW6QTBGG7CkDjjG01NZW9yE5QKVX94cnbjqQM1eshFIPs lwyBVUsDwSGHJOaqGUxRu0LKSjFAhJO71rSK6FNFFo1iKTAl4jLySOi/z/St210XTjc+Y5jJMO5H kXO3AwO/r2rMgiuXtzsUoWbBEgxjjpXTabJZTrbRoSCuCw4IIxmuij2ZSRXFjA+jLPJFBJJFICHV Sok+Y7+eOSOgFGk+Cbe9v9UtFvtW0ZbYReVFaTAuA4DnO/IYc4746e1aVxpo06GdxOsqzl/LhKjh h1ZR6jPatDRTq0OsX+o25i8qIReZCSd+3BO4Z74GcH/61fRZbThKaTRwV6koHLappes6Tqi251S4 1DTZoRPuubdVaJQSuTjsMZz+VRzXvi6z1DS9F0G207UoLsyITPM9o6ui7yzblK7WHQetd/eLdp4h VLewNxLeaayyxSSCSORFkVsuFzgBcjA71t+H7LTrTU/DGpXCzTyvd3cc0siGSKWMxbkGzoDF93pz +dei8BFYm9tDGGIklc8tute8Q6JEg1/wRfxwzSpCj6fc216DI5+UYRgf/Hazh420TSdW1S58QWWq aBbzeRtN/p80ajy02NlghA596+hvHMHgy70q2e1jtPOTUrPcsf7ph+9+YfKRjAPpmr3j/wAL6I/h DXJbG9ugBDvEa3PmJlJFxwefwya9L+zKf2dAWPd9TwWHxZ4IvpEjtNf09pOQFMoVs47hsflUGprE up6RBp86slxb3XmCCRSC3AXdsI/DOa+iPEXgDTtY0G4a7eC7jksWbF3YwuR+5zwxGc9BXjOmfCXw LqnhrSrmXwvpQujZxfNa+fZzbtv3g0JC5zyeaweWd5G0MZHscLB4X07TppZLSzFozMCxTehZg28H g8flVddK1HTome117UxHe3EdtsknE6x5Jl3RiROvyYweNp9q6nwt8J9Ku/COl38Op+IrG6mhPmT2 9/5wYqxUHy7gS8e3FYmleCfFGq6OlwfHcqSwX0wWG/0qOZA1uzIh8yEq3Knn3rmlk3NukbrGxRHe 2uuXF9eSagNN1BZw6lZrVR9/p8y4Yjj6nirl/cFb6fUZtGiCmRATbzywurNtTnY23r7CjTbL4mX1 td3ccfh7UDa3lxahDPcWUkn2dtocb1kX5vQn8q4DxB8ToNJmu/DPiXR5NLv45VOI7mCaNhDMpLKV 255GOn+NZ0sA6EuaMQlXVTqelavreoJdTzWV1rFuVwI41aGeI7QBsKygHqPWqOqeLLqxviqT2jK0 UUrR3VhIArsgDgyW+R+n+NZtp8UfBGpY23rwyEA4khJA7fwgir914k8J3OmXxsNWtHn8tCiCTaci RG6NjsKfO9mL2PW5q6n4ng0+7FvHY2NxiCOVlg1EQy7mQE/JOB0zwM9Kszahp+l3clvcWGqIsqx3 IeGBblcSRhusLbuOR07VqzaFpOpyNKLeC7RhnKlZP8QK5rXvC1hFa3eoSwFJoEiEZXcoxvVMFk29 AelONR9BKky9qF14S+0/2fqWpWiSsI28u9V1yJFDj/WrgcEcZrMg8CeH7hplS1tWmSVubOfYwB6Y 8tge47Vb1Pw1d3V48tvqt4kiosYj8wPGfLwo+WQt2ArH1/RbmS9Oq3UenTK89vD+/tAXDMQm4NGU IPFaKq72YnBroaP/AAglzbNvtNQ1SyOcfNKWUAD+7KHz/n6V5IvizxVZX01surWd2qXL2xW6tVVh jgE+UVH6CvTJ7fWV8STalFFcW0U1yrn7PezIoUEA5ifehxjgZqJ53k8TG41UXM1ibvrdWlrcjyi5 6NEI5Rnj1rWGIS+JGU4vodNf20z+HJN7bXa3DM4yoPGTjkivLTq0NwVWGzMQjjCqH3fNgDcVBzg1 0wvrZ7ieMuI7N/NVzGLi224JKriXep9K5aFtNWJbNY5czLIxEbCTBIz1O3t6V5GMnCu+wlKSNJvs d5aRWupr9t8zIyARJGCcbQPb16Vzi6bd6fdQLas80bq8ZDBXwqnCtwfvDp0rp7fQN9rc6j9r8hbV U2rcxOm8PwArRgr196zWtbu9s0SxW1aOaUKXjk8vJI4UlsbW/SvOqUmlaOpohHjlmW3kVBL52UEY BWQsOp9O1Z8tzdpD5UexYARKhmOG3AYwuOvNac5u7JYEe3+zTWpICk7h93+JlyBWal5IqO8YjcCT 7pjB3YGMFyePasIx5QZavrcsbrUJbiD7LbkyiQMdwCqFZQgGcfKOnc1p6re20jafd29uIBDbo42D A+f7vOeQV4I6inWltLcxG3t7iPfbRM7RiQR+Yq9uh7HvXKwMyeZY+W5mlz5BVgyRhOPmz/e7Yrop y9x3EekWy3Goac6KCI2w6GQjcpB/g2ckfWoFN99lmQkxyzJ5jmRRkbT92PJ/d8gA7eo5rlbHULPT 7m1uHmbTod+JpipkQIRwCO3cdK19QvftGqLDb3kd1bXQidHDARkscA+o9MV0RrtU0PQ0o5/OMjSq HmSTzGBC/KN2VQKRyje568Y616PP8RLHUXfTfFej3Go2DLBFJE8oRjbxM8hRXj2AfMwARBtAGN2B Xkd2bnTmtL+BjDLIjhsrje4bB3uM5UcFazbjULvUdSjWIOsW0llj+ZhtQoD+J5NbUqzQnG57xq3j rRtYvLS8k8OR6Ro2nwjTw8Dfv4oZuGeNxsDsuPkjc7RyeWNd5/wlHh8Sag3hLVp9Mvo7MeddXUTy SXkiqA25duBKeiyKcKMjA6V8y21vqzziEzQpbuN0kUi5wduQRIPutnitW/17w7pEAmu7uOBU+aco 5+ZgO5bp/Wu2nUk0CpHWHxZrd7FpvmR7J9NnW6t2gjE86mAERoi45JB2sTgcVkXmqeL9e1K51XVd Pnie6bzJF8k5c42ksWxjgYAUYArym4/aK8Km7/s/Rrx7d3/diW3t/mP0kk24/CtW18cm+u1t5PEt 5DK43bbiNULdv3ZchW+gNS490dVOgjsHv50baYndh8pyvIUc4C8Ac89DVeTVpSrM0M6suMEqZNrZ 5YBcVBOkV2Y2udWnuiw+RNpjz/3wazb83MEPl285cA/dmMgGPTkf1oTZ0qnHsareINbOIoTdyo3A +0wxImPoxFKbjV7izFvcxWtmYpvtEN5biGO5jl2eX95WOVx1Ufzrz2TW5rCbF9pcaoR/rIyxH6mt C01XTZiWjsoLgnszHP45x+gocUyJ0o2sfcHwj1nW/FDamIbFLZIbMWzXK3TSKtwMmOSOJslAc5bG PmzWZdaVFpXgXV7Lxjq13o+uXcUrTXsmfKuZI23AvkEEZUBWyGxwDXyDpfim/wDC2pw61okFzocy HJltJGkt5AO0kfPH619QHxjB8V9E/wCEj1bURp2l6bdWcFxa7lmtpWPzOsqoBKu/Hy5O38cgdMZP l0PNq0XHYyNb1O18eweFNfuZ/wCyPPW+0iaO0dMwQLGsium9CNxYcZzngA1zukaE9l4X8Rard3M0 aW19pqrBIUYyx+f5sS7QN0f7twysD/Dj2rnfFNj4auPF/in+y5Liw0ZrKC6s4FXZi6AUiM71zs8w tgdQ2PpVfXdGutC1u+0yzN9JctpXm3en3NwGubaSBPn8wcCSNlIaNx/DgCvOr1Ho2ZRPUvEfiOx8 R+LLhLTToF0WW3k+x3sFps895duWlXHLIRjf7VkTzW0unXEWlaZNHdQyzDzRiNE8yfefkAy3Ax0F Y9xqmnX2r6fqOmKDBLZWdvcokZjj+1CFVlESA8DI5HcnNdRpj6xbWEtxa2C3am9u5NqkBmV5mAVe elYqrz6s6IbHM+Gb1rHWZvKsVuPtqlXhdyEPnnJyo/2kyOnp7VuXa6nm7sdQMGy9Z5JzJGTGyJtk 4Bx9wfdx3607VIRd3S34i+z3MEbRvBIpTCAhh93kjaScjkHpXNW17cand2dvO0n2iwPlgSMCmCfm Bx/e5GTx+Fckq/J7rQmY+nm+k1i1iiunuFW1fy7iJtuYkkBjYejbTsdeakH2zTNdZZWJ3RQbSjbl 2opQI6dQfwwa251ubPWIHNr5VpHcz7CigeSShCxsF7Z6HGDSX2stqGvfZY4XhkaUI8uwBTbzR4PP bMoUDdjaQaJ1LxuXZWM7TLceIdQlEAZop4ZIgxJO+NPn3c4744rdSGDUfDaovN3cpIJMheZrfIcB gNw5GRz34rJ0q6k0HUWhtEiEd1cPMcHBkidCBtP8OB/D7Ve8QsqXclhHJJbvqLLcQqGBVXddpPy9 Ogz0qaVZKm+5KWhzkFzeW+kRW0kUi3UMjhkkdjtZxwPvEHGa1NQtIEt9EknWNbiC6iWVcZb5oth+ VcbgWqpZWtxHqUwhSPdpEiPIImOWimHJ5zu55quzONQ0/wCz5RvOilCyKQNzPtXIPVfxrKNVuyY1 c0fFoEV1odzfgxXbfaLZrePAQIFUgqR1bvx9K62fX7S5sLyG+EasI1jhEI+Z2XggH17kVU+IWnXM uladcXWxJrbVEd5mYfMrKVYJjp24rPspJtQMl5PazMj3CxW6soRg7KC8rL1XCgdh/Stpx5Z2RbLN jpclqpub2DypdYtCPNUlt88XCKUHKkqQPf8AOubm22nhSx1FZi01zF5MzlTw6vnkngY7V6FrF3fX cttqFtd28B0siRWkXdmQD7jAdRjoR0NchpNtDqGgXNlcWhNtpG94llB8zfNMDkg4XAXjr70VILYm 2hmaxYY0sXkq75GlvHmTqGCxnIU+jAcU6zhn8NlLW3UCS9+w6jI7R/Ov2heQQ3/LPIP0PXFRasJH 0OO0hkaRfLlkkJkDMUYeWUA/2QOPSrUE91Z2UWuaizSfbdEWQLwpaKGQRxxd/nIdVx3auHWewkzq Ldh4m8SRNFZs0VkYZppjhSjYPHOOT8pG0EYqfRrSWe7a3uuYbfVL2SSY4zmSVkiBxng4+b8M4qTR t+l2K6PKrfb7p0njlAK75mUfK49EAI9OKzLW8OnnW1idJo5dTu0lj3jMe+YSLKvP3c+navUpRcY3 ZVkW/Ft2LSOS0Mm/zpEMwZeSsM0eDuH8ZOP8808xCPw1b3cspM9/NJLsiY+Y+6RpFbBx9zPP/wBY 1j6lbSLpD35xJJdSlYGkPGxVd2fHsUGOPSut1QWtlpem6K0Ko2qRRw7sEmPKgPKAMnvj2/WpheT5 mF7sxWsoX1DStKiIgsNYu5LweYASPssTM/HGEJ5z/eHvXMWU9u9/eXYPWGC3Lh2RDdBisczsowCV IjB+jHpVm5vkg8Tw26zSTXWiaVeRbGxjeWiihUE8bTHliPzrK0iwMmuXGl/akNhbWivdSyKRHK2G A2AdSOqcflXJXlaSURSlbY6HX5rjRtQ0S9vZInWI3D3EjfvCs9xC+ItqjB5UEfhVHRIb53VLiMST 2s3nXErkrFHcTOAxjG75jHwp7YFVr+DR49F0jSbyeee5S7S/aa5XP7iAEbSB0fbjHt+NWYtR0a20 W4llu/tc98RPcRh0DwZIITKkn5jtHIAzxXQoXBs6ie6S5vJILGeX7HPCPMNzuKlQM/InBRd3A9MV ymr2LalJHaNCY5ba3d7udX3FiCFEfP8ACR2/GqcvjTwhY20Nzq01v5mpKbOSXzSTH/sAJkAhuPrV K98U6fDf3WqalpMsmnXInW1ARj2EWQow25T7Vz1KbQm0X7i0uNU1iGOSOSzD2qhIvLwoDnrJ2xt+ 7n6eleo+HtVtZ9JsrSK0kd7acxR458wxZHPpn3rx2PxHqF3fTancZubxYrdoLd8h2VfkEbbevygY 44rettXurGaS9tLPbPeEzR2s8jBI5oxwzPHk8jjAGeKjD2i9yIyseh6Tq15p06KtnMtoZpEnjkP7 9CgxwuOV7gZ57VjX/iKxN74iurNd73mmi3MTZDOuyRAVGOua5O4vvH+oNa3mt2FvbSx/PA3nkxo2 MD5ZPnbd1HA71g6lovjLRL+2uPG93Yta3ds8Ub2KPCvyHzBC7Fz1OMHaMCu6pKS2L9o+xvXk91Pq FmL24REs0t9TgtgQJVMaiPypXY7ei9f9rFV/D9rcxeIfEzyugRri3P2ZSzp5V+zzfIAOsRI3n0FV J/Dt7GLBdT8R295mOWO7NtH8i+e6uqbgM/u2A/KpBpttNqX9m+IFvZ9MWzmRY7CRo7lrlJlhK5Xv jy8fw4frWManvWYrnfmHQfCt2mmWUckmlXZVQgkdh5MYxIpBA6HBA6Y49a5OGayTU9RIvGitJLjz o2faDJbKMBck9un4V1y/CKxhso7i10uaa2kkiuYYZbhndhGR5sTShyxmZQTtBAO3v35TwTo9pc6r JBZadbz2U9ot3bQyRnDlZJIndk65/dnj1ANViqd9OgpMsz+K/Bs9zbrb3drJbWKiMOrkqYkAPlyY z827MnPasXRtcj1TUG8SWAPkxXd1IDb8/uThUKo3Y7AOmTuNe06Zo2nPaX2tXOmWVtFb2+97loGE FtJaDaZE+6cyHeNo4BFcLollJqsWnyW+LOZ4HeeN41H75ufJK8O8h5KhVJyfaorUbJWM7nntxr+v WsOk2+j+H9SdLLVoL15I7dQqxxyyMImmLD5gH+bArohrfiDT49Q1bRtKa+0u51FFElxJsiR1G1th 5cKCOoByTxWlDNNFoNmlrGwsJ76dZ28xW2MjuGLBuhX92G/x4rY0OawsNM0K2LQ3Vnd31yUtLlsI 42h1Z8Y2xIPm5x2qIO75UUpHC+GrDxDqWsa1aeHhAusyyTToAT9jjAcRnf1O7qOPXnmug0zwp4x1 W+OnXOuaclxdmVpJLdDGrzQhQIuTgE++Mn34rrfhzcaOY0vrMuogtbhplkQjEVxqJMITGCcKoJPJ r0u81eO90/xBHpEUYudVuvItvs6qW3AAg9Nv8OAcEg9cV6FLD+77wtT5f8OeBtV8QX8ek6rql/pL 2JleWe0dUdTESFQEjEYyxJYjrWt8RPhbc+D9Gkdb+8urXzCZby9Y5+WMyHcSBnLd8Cu5GoSD4m2J 0lnkfUrFVuQ0XlsPKb5jOgyGGFOcc8A4GayPjd4knvNANq0TwyaTtLm4i3CTc2xDtzjkt17npSTi qbRSRk2HgSy0yPTNP0qGDU1uYYbmcq7HfFJHvm3MG7HNc7caDa6lYywR2P8AZ4vryIwzAt5IWKRG QgHvg/eJ+lemT6pcW+gahfaZBFZ/YLSO1W7Vd5luAvlugA/vA8LivKdVuL60GixamZoJjcxJHDIh Aki3Z2uo6DAwT2ryakVbmJse3+BdA0u51nxNp0tlHi/uHt4pGUny1t1EUqxsc5YOevGetcbq7afq /ijxUyXW3TbOGLS7aGO3QuVtyRcvFNysfnuduV+bpyKv3HiefRvAVpq2kPdabrkjSi2Bt8xyz6jK XO1j1CRMW9V2jNW7Tw0b3Ttaa7ae3XTlgtYmi/495HQlnPzAAkYJODnNem7ezVNGkTkfCM90/hKx 0e3sFNpeXzPdysfuTKFUAcMxRRzn2/CvojT/AA7JpFvpUmo3bOLm8jV4YnKx7djE8rjczNjkjoa8 1+GH2VNAt9PvFSOTW5bfUhAFCRR28zshUrnJztzmvc8WGl7vtc0Bs1G5DIQxhG4YB9snHtXTgqHL rIR5P4k02M/FSHUo4oobbSfDz3hRX8wzBZJEaJA3Rx5nygnqPwrkPhXcWulX2savBCstvDLHA13s yywoVtT5O4bEDbGfv178V2XxKvrTTdba9tXH2a98N39ms0KcJNJL8hb0HPBHqT2FYvgCax0rw1Ye HNfNzqP2qP7fHZW6t5cbK++MrtPzEojNz0I5GOajESXtOUDJ+IHiWMjxEbeybTdPgkF9cyNDg3Ft ZpiMhskk+Y3LdMNz2rlbeS507wFo2s6tbXKy3GraZqNw4jC2qMZiwxOMlt6HBz/y0PPAFX/FfhW8 urvS4tW81INV1OO+ms3jysmnwusrhJC3zBQMbB94nkba9H+LmoahqHwZe3aO0SG/urFUe0faikX0 KsqqMgcnn0ow8U23McU7m/Nfal4e+F2oXd5pMUMmlaDDHM8TgskRtchlGDnnO5mbPtXjnh+zaz17 wOulNbwr4Y8Px6ldiTJVZ54tsW1CAWmbe2dvHeu0+I3iK61TRvE2hQ3dukOtGy0gyWqNPja+JGZh j5THkH0/Cud+FMeo+L/HXizxNHdC1tbjYmn+Si73jtH+zwiKPps/d5PeipXjNpQKWp3+mas+iWeo QJfQHVI4/tdqAEeXzZPnmhjRlYhYwckpjDdeOTHe6wPFNxqcNmLtESzgvUvLtjGrLAhkd2CgZzgI v+FdReeFvDmnz3uuzXRumuZAGDYHlRsR50fyj7xBGSD7VyXxC8To+ha/4c0mymnmuo1sLRUkYiNG 2xBmC4wSDiNO+K0s0tWS2xPAeiabe+APDWutbPPbaddys6GRjIIrk53MoOA0Zfd1A/Grtx4d8Naf Dfw3zo1nNe/aGnKhroKnX5PlwH6EYz6+taa+DpLO2udHjhgW4vhGLbYcxWvkReXulBJwTgeZx9Bn IrzPxBeNpkfiWwls7ZybWWOK5R45SLgoA8aHA+TPOSBjOO1Y1KkFHVibsZnha6Wzs7/xPaQBZNQh mfT7aQlzFI037tkCn5W8v2rmrfUr2TQ7fS9Ljl1C6e6kmngdA4y02dyseSSzH/aHpgV0K+FL248I WM93Jf6faaRYSb2MIbM2N6tCExtixhSx75qbwpoN28UNuumS39pLe29pDI+YpIQwMrkAEBmfHykn gcmvOqQm5WS0MyHw/YXej28uo6lDdWsdqlzAk3yeXK6ttKpk8kH9a1dC1DSkuEiginu/OcbWL486 SIl8M49T1UdT7Vd8QeJtRi8a6Vf6npPmafpq3L2cflYiMT5i2IASjsuPmPUGub8I21lpmt2l9qNl byxRTLYoZn2xpcznKSNs67cbDzx9KaglJJDsenXPhrWrvQZzY3NrbXmoyrb6kZJX3XBkc5iMW0pG B0Vkw3+1ipvhVrWl2mm/aFhu5dd1KVbCe0ikaTyI7JcGUK+di7gQzZxkjJqrrVzq3irTLG50eS0l xe3Et1FEXBhjsz5auASS+xju4BLdhWp8FvC/iGzj128vNSQzte3FrKsIQo+35kmRl5AJkZivRuM9 K9fl97QcUYnxuvtQ1Hwy1+thc2mi6dptzP5hX5fMfbEsbDgqVw3z9Bur8/5fiDNrerXOj6/DaQ6b qdtbxzO24M6xYaNXkAOSvuAFBLZr9R/iToOm6H8K/E2i2RYQnRtQbY7ly+IwW5OT15xX5taf8PvC 2tasbw2d1eac1vYyzmO48tLQjKzo2RkmT5GTttJX0q66pJ80zOtFnnPxHg8E639u1/w/Ktjc291a R21hbLI6m2kgYOhf5mll85C+/gV1Hhr4Van5FppviyVXkWza+hsxGN0LMjH7O44LuzhGaN/lboCp FdN4N0zQbi58QeFLmOGa1urm7uraxVY2bS7dHAFwrkBipXI2E4wAepNbZ0240rRptU8KanA1tpEk bxFpcyRwvJhfnPDMmWIOT931Irza+K93khsYHC+JtHtfEdh4L0/wdG8dx4stYdguI0RFmt2+ySFW cPtldh83IwqADvV3TtH1DQ7GW21q3MenWFpJYXE0O9ftRSQxxyvtO1Vj5w5I4xXX6hLcRy6TBqZl 1CPVIo2tEtUYJbyCHE80bKPldnZQAq9cv2qvcmLXbGx0Gab7Lp2mwkpNb2vkNOVbcmI2IU71wScn nmuKU+ZWZrFF3QJLSXNo99cCJh50pmwzLJEBshUrkAZ9Tk1P4stNcPhm51GOFEe5+ZmR9wRG4baV 5HuPWuS0jSidQWa21C4XUb26lUOSAZNrBtzPjYHC/eArv9Zlu7bSdXiWQzq8SiQ7QZN5P3hyM/gK 5Z07P3Sr6li98H/2rrWn33h5BNr1rEEhhtWSHzl8kZJDfI4KcbSu8npVTQtfv9el1DwZ490yzeO2 dRNb3FvJFJFAUYrLG24MrIygZb5MY47VieFb+zfR7uTXZhqLCZreLzFaJiYsCPYTgghuSePTmt4e ObtWm0rxCw1fTUk8uOxklZ50RSN0K3A+bHQ4yRUypzjvqaQauebazql7BqEGmaO9taWkFvFcKkkY CzqRkKnA5yD81ezeCPEOrx6rareWceqJqKj+z7nUpAuyLflsMv8Ay1wCIMn2xXK61ZeFfFM0OqWU c+mwWO23WwuCGK73yjLJz8h3bevHfFLoP2m50PWdK8O6NJcQxyGK4uhh1s4YjuVkf5eQw7Ela1lC E4KKWrG1qbPjHQ7e9m1rTtI8QNcW7v8AabaaCLy1QgcfvCxbzASUOBx1IFYOn69qejvJosFkLS/Q Bbo2JeVJR/fkyTmUE79w4yTUXiC4uEGk6tZXP2iC5DR3yQ/u44biaMq0hBPHmYwew/HFPk1mS00y TyniupJ4ljURgny5E5G18A4OMc1hyyS5RvTY6hdEto7mPToymsW09mfOjZliVpHbBVtxBhB7dx1I rkPD2heJ/wC1ni1vQriVJ381yhGFtIm2OwZf9bsLKxA5cc4GDU2lajeXmntqdrMEvFO1h8r4Yjc6 Nlcg8ba7XQbmK5ttR1XR4xBNaoZ5dOkDLvV03SbJwcBgAdqjjqD2rJTlH3TO54vq9za6T4LS8jO7 UNS3R2rwnKQMbiXg9mcgHgdRg17D4Q8Rz6/4b8hJ/seqLEltcGRCkLzQuY9qyJko7IdwGMBa8+8Z T6FqmmjWrC0k8u1jd9820xnyHxbsEXgP1Vu/rXXeEI9D8R6RPfWlsbK68QTYiiUiJpprceYrMufk KurwKR96M5ror0IVaHLbUpS6HIalG9l4kW1+yJPdskkRSFN2cmLDbjyeCWyarrpGrXl3Bc6Zb/a2 053hdEwW/wBIi82HKjBKlsqW6J3xnNeo6DYajcaZ4n8SR6pLdTQRTSQW5jXLmHH2lmfGVZF4THy4 WsvwJa38lzqVtGRJDDp9vqEyqcTTWttPJFPHE3G3zIygPoD9KyhWTXIuh1RjpYkufG02iyLpep+J rnT7u1jjSW2stPilt4jsU7I5Cw3gZxuxg9QSME1/+FlW3/Q5an/4K4f/AIuo5vEXwshlaJvDWsNt wB5c6OoGOFDE5O0YH4VF/wAJN8Kv+hX1v/v5H/jU+0qdjLkif//RoWdwrxx7YCGVcrI3GQeaokfb b6/QXDJ5DshwOCSM4B9jzVfTGuYbYxXarKzTICqNnaGP3V/3etSaeJftV7bO2I4tzhgPvlm5NfmR 1xZeihZmsAqmdoOsmQC3HLEdOa0ZSZbpLNmaOCQhAMDCjrvP8qoWKzn/AEi3+eGNjGw7c9xj+lVj MsUjSxxyPKwIGCSABxmgcmdtE05tZrJ2yqLuGMbGyOfqa5Geado5QchUUbR2x0wPwrZ0e6Q2JuZ2 Z1Q8Y6c96o6jIgtzImCSePQADJqOXUjcxRP5bRQ/Nm6hdAeMLj6UxDKsRS2yfOuoEJ5PCfNwPzqA uc282PLMBZj3B4yP0q3azta2z6h/rHhV2Kj+8VGz+datAkVbm6byhNMhj+0KxjY/wk5/rUVrPKzQ Wy5DtIkb7u+Qo3fTBrQu4o5rCBl/11uMFWOVyT0qrpxl8+e7uELeVbnYOOSzKg4H6UlsN6HQRJE5 NvawSO1vIYlI+Vc9Sx6Z5zWLqN1HvZw32r7OYS24FW6ZAx3xgnmtmwS8jkMUcxgkC7hn+8o6fjWd qFvbIWFxA0czD5puRuMhAAP0HSsbGdiG6vHkhZUiVv8AR/PAzjOTkmuKe+htxMswLI/zPj7wB+Xb j6V0FxDK0d68hKCODyl9gHx/KuLk23Yu1+bAkwGA7Ia2poZ1GjQxwXMFvyYnJ25IyVxnn8Kt3k5a eYxEQzKTGCw+VwB/F/SorCYtqEEpG0xRsMgDBBAANT3cCnVXk3D7wXpkdOamRm2aMMz3htILoRlI YlkIj5GO3zVo3s8+lkSTrkTL+6kTBPPBH48VTMCXaLZA+Wi2/wC7IAQHHOc/44q6sNvLJGWmKyW0 XmHzFyh4x9PTpWMxpFLStMhWAJdqnn/vJQM7nYgcF8/pWPcWS3mjS2eCouDMMxjaxeNMo3/fQxW7 ZW2FvJXlDNNCY45Yx8wBOR19xiqcdwsAtbEQyrIWdWYDk8HLNnoPeqjLW5dz57lGr2Wnfa7bfNLz FFbucENNxwcDoP516J4YlvVvV0q8mVZ49OEQEQ2nzJJlZgMdeO9Yk8YRbcTPsZJ32Mp3YK8KeM57 V2vhXT1glbxU0DXCxp5bKz/dA3SSSHjPbj3r151L07Mrm0O2jnitba/uRtZB5rAITztfaFOPTHPv XOXq/wBn3VrJI4zPJJKjLg7FYAFFY/dBP4Unia9lTR9TsosoDDHeW8seC7bzu8psdT0Oachi1TSN MvL5G+yBIp43QZaIlNxY4GDHkEN+dcMYdQhExNU1JpL77XeTNKVOzCpnBj+U8984rJhRLu2nv7a4 iS0uJQo8+B96KSQ6hh3PY9KZqC3FxrDDC2RIwLYcxhmGWZG7hhz7ZpIU2W7WUKP5UUmEdR9wk5Az 0P1rdvQGx2haTHY6pdN9rS8a4u1nZH3u20KODx6+leoW8NjdaldalPbuhs1HlJ5mVjyNxzgZ7elc lp8NraRmaVne5tx5sJAGBnuW7/7tdBYieKyvpS4P2psuG4JbAPHvxisZu7MZalqxuNNS4jdf7m6W XG5wf88CtKCPTbWzllu5GMZuhNg/xBsKQAemMdqzbKJooJbe2QoHVGdunAUbcfQ5NNcSTPNpTybl 2I7lhuJyOi/zrJ2KRJcx2t0buwsoAkLQGFImxuLE7gV9j/ntWd4dsZYrlYLqFLf5t4Zs/MoH3QPa tXwzCV1Gc53tDC5QtggbTsH4mrNnPBeXCXUcDQQWqTAljudpAdpHoK6KK0NLGfrMciwxIgDnzzIM HDgg4BHQYrsZRa3EBtrnDeZHzu6K5GQRjJrkvtoS/jim8owxFCWOe+crWzp91cW32guQu8MX2hWw QPlH/wCqqlLl0GivDIkemy20reYZQHxH/s8CrumNbyJINot5Y9o3n5csgyoP59uKhuViSzvrwcsL dVAXAAzkDgfhVSIyW8YmjTeXCxuOuc8g/wBKVObv7o2ibTYGhSSGYK1vaESF2/iBJAHH5VptHAs/ lWQ865KeaoQ5XbuIYAmqOn3KSvIjIVkdCm3B2gY8xeD9KilmURwy7ysotTu8oYwnmYb86eq0BEEC x299a3MJZzIXeWORSCpJztwcdxWxBE1nqs6SoSh3bOOGzhjz2IrBtZHtzHdGcyyqcSOwz8rcZA9R j0rpbiGO5st3nSMfs569WLDG7/x2tY07tAiW/vUu4ZGt1SBAVXknJPB3YNU4ZrTT5ba53InyossZ zkZwMcVjaZ50tgLiTDm6iVct/CAetW0heG4X7QVKoQ7BsDIGeB+lRVXvWHc1Reyf8ekdwkqSK0gE g5j39w3riultZ7iw1UXFnZpfpJaxxzrHMAWYjJYBj97tXLXEaTRQ3dsnlfvAE2sMMp7MtdWdI0+a SNJl2SuhLKxXPH4f1r0MvrTjPcxnSUjcOvQQa7Yavc2F9brBZzWzq8e8ndjB3JkGnw+KIkvtGvX1 AxW+j3cREbo0PyKG38EdTnrWFNosSpBDYzzRGEsoAYg/Mf8AHNYrtrcktvYxXbyefPNvE2CQIxn5 cj2r36mLqJN32MJYey2PWPEmuaX4gs71NONneRXGqWt4jjYZAm5d4bKjiuy8Q6XoV7ofjI6baW7A 2yvAYuAGK87dp9favBoob+4sJ38iyuluoVVVZMbdvU5XFVtMkjtb8wzaZHbyLbsf9Gklg3BhtBAy RXpU8zclqc8sHpdH0hPols1kqW9zd2gk0M3O2OVhukEf+0DkYrhPCUV8mleH4Evt8V1YMdrxrndH x8p+tea6Vql/a3VorzarZxJwzNci4Xy9u0j5wPl9hUmleLb+zu4RDrciiJ2jjS7sgeCfugxtgDpW 7x8W7WJjhZI9P8Evf23grQ1tobe5WczQEPuUx7HY7uOP1rlPB1/c2unTK1rvWTVL1AY5FPzGRiw2 tVOx8bX+lldJsjpEltYXMrRKTJASzk5Bz6k9qdbaodKmuLEaXJcwrevfk291GxzL8xVQxXjk1rRx sOpm8O+hqeGJ7S3PiBb+JlRNXulyq+ZhmcYHGf5V5nfeE/DHifX/ABI+q6PZanLFcIlp9uiZxEJr gqzIFZSMemK9Ctdc03Qb3VY7221JDeXi6iWFuxVAyq2zMZPOPauXjv4LjxNrGuabML21cw3k0ZV0 mWBpEkT5ZADWtTGU2txQoSTPDNY+CXhdPGeuaUthFZfZba0eEWE8sMXmSB8nD8gsAOPWvLPEfw3b Q9UlsNM1a/RSkbRpMwcKHUZQ59M+lfXni2w1LVPEWp69pIVIL22sIkEpy37hv3gx24J5/KuV1Dw+ Na8aG6nj/sjTYbWPEuMrIyDYyqO3rk1zutC25t7Oa6HxjHoPjkzSpY3syRxMULRSGP5gOcmPB7iv ov4V+F7rxF4Q8+/8S65FdOI2fyb8EFcnPyTKckFOma7zxz8O9B8CeHf+EwsdefUJL2eL7Rp/lbmj 83cu5XjznG0cVzPwhnH/AAjUdykq+ZbSxhUlXP8Ay0cdOKXuPYblJI14Ln4opcajDYeKIL2HTrr7 Oq6tZox2lFcEujLzzjpU1trnxF120m32ehTG11FEfZcT2rt9mfcvBUqFbcO9S3KSX3ii7QyJHLca gjqir5QaQRp0POPpihIrsyamd3lIupmM/MDGZCi4U/U96j2Sb2KjiJpGvpXi7xDeWJ1F/AmovbrJ LE7WM8E+HhO1yF3B8ccetYFz8QfBd5d6Rq13Z6lYLHK5kkurGZE8orj76gg4cYOentXYeBJbGWxu 4L+WGMGa7MZZSAWzkA7eRn8qi8MW6XXgaONXSO4Ed38vmEMTufaqhuNv4Vj9Tp3vYv6x0Kba98Ov EaKtj4j0ySdBuhJlAIOeMo2Mj8Ktjw/p63Mv9kG21CW6sjcsNyuu4PtcRqhPbnAqvPoWiah8MbSf yLG7le1iiuI5IIXkUnjdkqH7Vo678KfCFp4Ji8RaM9gpjSGBjAs1rMeF3ovlt19wK4K+Gp2LVeNj EtdG0SCN4bjUZFiv1QG2hB4KtncFb+JT1HUelQXfhmWWHVvDv9qzRab9oVY3WJbgfu1zukOCFo1P 4ZjRLLRdd0jxBLp0c1zG1ylreJJJEsoIO3zAzHp81Y8d/dRR/wBn6VqYc3al7iSWFZJXToFypA59 cZFedVkqS5hKrFnFXFvNZSf2RFOIrba0ZkKhYjxkthf6VrDWkW1ks7lVBRY0jSAKV2px5m73IqZE MutsjECJIZMRyt97eu0qoIHSpNJkuIry+hGnNLBboIbWUOg84fedSDyPlyBx1rhjUc9RJEu5L2SC MultbTtEwn8knAbqpK5J6AkUv2SC3lvW067ijSESTLCwIyIh92MY3fMecVNJeaFLJItgl/BBFJhH lwT5e3gMBxuRuMjqtVItUvpNVne4jiSaYEROuFVWALKQCOAcHP1/CsqmmiKsPW/iaO3t5C12gh86 JvLVig3bcHdjav1qNbdriWO2QpZwAyAvPH+7kwfk29McfhS6Rokmt/bIXMcTWscZmaQ7XCHndgYJ A68CtS+t4E3Wdo9w9jFIYBIHwvlDjLKRwOOOeldFCtKPuszcTo/D+iW1zYw6ZqVpPIIHYRxxTgx4 bnHl5Jyx5H1q3d2Vxpi3K2dsllCqkbQP3hAPTPauat4lRYb+1uF3WU0Sq8kCEp8p2Ecjk4/DArX1 G9uL2GBbtlto7OEmd4sgTszE4+bs3evUw9dT6HTTijyfxF4k1KOUR2XmR7V4yOvPUZ4NeHeJLLXt XQwKXU7i+CeueeR7+ley3doNbucwpvAbAGGOwjjA9a7vSPCOnmAQTRl2LZJ/iJ9vSux1OU7adO7s fDB8K6lBJ+9jxsYMNvPIPavaPC+sveRnSNZQecn+raZA8Uqf3XB6HsGr6oh+HmlNn9wHB9e1V734 Z6cYt8NsmRzkf0rCeKR3Qy573PGraxtZd8VvdGyLcNDLmSIjtjngjtzUjwa/o7iU3d1NaBeAT56O B3B5wRXeT+EI4zsaN07Hv+Vc5qljrmiQPPpR+0R5/eRyqSrD0KVEK6kXLByiJa6o3kGS4OE42y7M qOOjr2qNZ7S6D7EijccgxAc+5DbSPwriNE8U3epa/DoYsTpNxdP5aENmF3b7qspzgE8eg/KtrVNa k03LXFv9nngf9/ayBRhhxuiYcn6V0xdzhnGzehoO+pEGK3vJUlXlSoBXpwrZ6/TFZ+ka/rGjasP7 QUSWVwyC6ETlFYIdy+ZHgcofmB/D1rmr3xdDfRsYwszJktGzGNl9wy8g/pXKTeJUmiaNpmVx90t/ rF9m7MPeqa92xzzs9D7QvfHuuwrrt3qGoJqvmWsFgXuIVbNoclBGnAVgRt8zHfd2qDxFqS6zqVte 31xJq9y2J45N+GXzMAh3QDp0VDxXPaXrtlqPhS21hbVZDeaOIbhx9yTLeX8gP3SpbNReH7W31ewt ksLRrC5Nu5AOWw6gBmJON2eCB6+3NeBiqs4po82UbM6zSmtVvZIpw4WwnhjZ0YbpGDkJJx8oIzhy OMe4r2fwyY0smnQ7lE0sSq3AAErDI/rXz9ooSS31EW8skq2ny3MLKFlAVTlsHHHA5HevYdPvIpbN luZHtLaS4nxIjYAlLGRVZevOB0qcvnr7xpTaRd8VX8dhfWuIvllMqygjG+ORPKbLjJG0HI+nFcVp FpFqcly0UrOYCqDchO+NTkZbjqo4PoRV/Vbl9SOoTX0tvA1yrhZOZRGiIu1EIzy7EhiQMYqLTvEe lafcx3kdygguIoBcR5VR0OHC9gOhHsK1qXnUHOzM7xFqD6ZYfbNzvPZX8ToUyMKHVG3Keo2vg554 4qfw5YNqlprdjdXDtcTxlY4wfmkaE7hjvtRl4x1B+tM8T6h4dktL1o7tfMuFadIyQfMQgbWbHTpi sGw1nQ72CR4Sbi9UhpjuaJw7EFFUjlT6juPyrOtG1S3Qhl22k+1WVnqVvBve3RZHBxwx2l4x/unK 1palevb6t/bMC+bAmV2EZ2qMKck4ABzx06Vn2eoQWekwaY2lu8sF3NLK8ZIdvN2jj/pmAcj1rP0/ xBqenJLFpWn/ANqyORZz+a4hjWN37yHg7eGCHGaiMbsq518sZj8TNa2sgmbWdGWeKQDBkNtKMrj1 ZG/SsC9vZpP7O1HlQJRBH0ICRShgv1HSs+4vfFen6rpKTQQwPpZlt9I2MWN0lydriQj+7jJHUZ9M Go7u11rStO1G71wyRW6XuxBEVYeY4Bxtb7vt649c1rWasrFOR0viddZ1BNY1F4HEOjyi4LyghQGk jQoingsMbs+ldHfpr3iTUnttKuVg+zsWnuHkCxFdu4Mij+I9PSuUHhLxjq2kX0zeJPIsnWdhapEH aXKhtjEnGDgA+3SpPDfw+m8V6Hp3iebxZqlumqQxM1tYosUMb/cMSnaTxjFdNHCub1Fc6TRdOsXH l3Tx/ZrUfaBO0rQSLIDhlGR1bg4PBqC11nTLT/hJdN1PUVWLZPLHcggDzg28Kcd8Ef54qG++HEnh 63Z7TVry8IhIkOpXKtJGFO8BFAGM8ds1g3vg3w74wsLlZLcf2jHlEWaTYgGCXBUYV13dee+eKl2h P2YuYxLq/wBMbw7bWk1w0CR3OoLO2V3JK8TiLDf3M/P79qtT+O/CU9t4X+2Ol0+jfNchN5WN44zk hQDnd8rlT0K1BY2+jXKyi5hgfTzqojSEAjy4Ht2REOf4VPIzyPpXS6Vp+njSLG7gXz57lXCRQRBQ 0kUYRE5xkMCCTjls1lTko6IZcn+KOnXespr+k6NdXUFnalVCWkhjdpDnzEZwMkcY4xg1zkurRRW+ q3v9lyR3MWsyzM20KqedtOwqOiYI27sDt14r3Dwx4e1DWNLhkFxGmkzxEsV+Us3bk9Meg9K4ZtLt 4rLxZe6TqMF7Yz36WEImBDtEYFLOV7rvIKt2xXU4ykrCepxmuS+LC6aTf2v9mQZUsCw2+VNtjRlz 3ft2rdlsvHqNfXOqXNvDIjpHaSRLJI0RkHdCRgngEYxmpfFd5o+o2RnWG0juf9B01oyWkdYlMkuw SMcEyH/WEfc4xV/XZLOxult7gSpK1zFDJL8xjkUnLsrDlnj+X7o9K5pNR0Rk1bqcZqMN+l3cwzXb PNLbqpljjwoK8SeuMqTuOSOOKfoXhqLXNd1DSI7u6t7G2tYrqaWE7N0BRkBiVscHkA1tarqcOra3 F/ZqXEVtaWNnBHaooaSQZdZ90n8CjLZ/xNbj6fb2XjDxXBdRMzaf/Z1vaQsQ5+zw2zTKjOCMuFbC kccDNZQpcz5mVzHEf8IVpsA0vQdMtLp7iRiJlvpy4EcuXR9+d2XXB2gjjPFbWqeFdM0a6siLW20m OWJ5pp02mWd4RjY5C5X5cYDHqARWglprNhr/AIV1+5tJ2n1G8vblZJPuvttXB2RdQqcbc9ua6a90 RbbT7+9v5Fu9S03TFkUKwmaTzpsxtN16gdR/LFdEKbTuO4kHh/TtM1W9+yJZzWmkWkiw70LwQPcR nf5ZPMrucblbGGyc44rz9r2xtpY4TIYPsTxiRANu+RV2kBTnA56D0+ldf4oN3ocFvp19JLHd6jIn 2pJIiIypIYsjHAbI4yORXnTXNneeIbHTvtH2XfqDTtO6BTFbKudh3fofpXLianQzOn8D6Ims3+o7 /n1f93cWoYiFYVRTvMoPIGMHjrW14G8NS+IoL62k1C2sNQsPKnto1+dbjbKTKu99ufM5Ax0B9qqe GNc0fTtbkvbiX7PC8iL5+P3nlDP7t89m756Vpy3aavpkVjbWjTak9zdXNtDbIP3MZ5dWxg8jaeM4 xkdaKPJ1QXsdn4vTwlc2VjqWj2aSXU7HacBoSAGjKyHnDDOVBxjHtXKeMtX8P33gdzptwLaXTEsJ IhsUNJvfynbchxkoc9e1csde1bSrHVNRQN/wjGrLJEs80f7r7Vs2MxJ6RuxI/wCmb896ddWun614 Q8M6Y8R0uTzBDdSSr+9neNSXfHuNuOPeuitUurIadzcWyu/Esdjo51Caa6eJ59TeFMRhDtjjklyC zkFV78DiuH1bH/CTWH9iXdzdqk32aWRsEhnjzIhxjZ86KjZ5U8+ldJb6hd6He6b4l06drcxQxl5j NtfcwKywmHkMvTOeBisPxFatpVhFPZXLmCK5m1WRomLDZJOn2pZsDO7ODuPHPvXPBXsx2Z9Labq6 6loNrdkWJubG2hZoGlkRxKTHGJHOMK4eMBT+PTGfE/CsU9v4q0SHxQsiQ6hpspmfBjKA3l3nEeB5 ZjIwQPX0r0XxBY3eoa7LYeFjHd7glxLHGgI8v5Hy/TI+9s/3iO5xynia1N3qHgfW9R1iVW1C61XT 5ZZ4giLIZ/NwUU9wJB19K9Sq7paFNaEus6rqekaDrHgqHUTC+outtHDcDCRLduixhT3ypO73OfSr fhS506LxnZy6lKxmvLtop5BLujMq9CFIBjQkkcgH071FcaPdaDBoGt65gWc0iTMLglZne3jkmjLo T8vzKnbota+o6Pb6sl54u+0QWsum2tteCPAYXVwkP71mz0QjAGPrXM13ISPFNe1Tw7BpNzpjSvFd P4g1AW8gCEJZgynZkZ+/Id3f7v0r1S8sdC1H4Z+EprCOBtUitHuWEKjf5Ajk82XA6/lg7q8Wsbo6 r/aGo3UK2620+hy28CxiQfZJh5Lxt1O1cEs/cnmuzt727h8NNDA8lpa6J4d1DTjeW4GZXmXzFUjH A6fQGpo2TbkUkkeg/DiTQmsdOnZ5bhbTQtPlAh8zEc9wTKY3x/vDHapPC2laprviIXEkN6mnwXN4 rCFthO7/AFTIzH5XQ/h64rAt4/EMUcGg6VDJBFrUWn2U6A73RfK2R7nwORgsOMKoxXs+jyTeFtLb RdItnnuBeSwRSNJuzJ5YYseeBxuz05rtoK+hR4HrcGo+GPGifZJpLg2IN2xVgHmVZcSxyFc84xux 1qf4uXelSHwlfyfLd6ndpcxTA7t0yAKsbK3y7V/gU8d+tb3iPQ4YviFHb3Bjgm13S55JJI1NyIZJ HG4hAeG2jgV5Fr9tfao3gvw9bqHGn6/5cEjx+VLJvO2Eureu3gVy1fdk4PqB6tBod7octxc3t2bI z38FvPaBQwK+Y0xuDH1ATABYDkcD0ry+71/U9S+wRao63EOr6neiS74Ywy+S7ZjXrsK4VQOPxr2/ XLSz1Lx5o51S6ki1DbINQ8seWpaCXy41C88svAboa+UvGqy2EmkxXFr50uuS3lxpkyE+ZG7T+Vkk feCJk5x1rDE0OVJAz3bwhaW2pT6B/wAJQbi10HwnaNG6SuBHd3EiDz5ItnzHqsRVT68jt0Gv6XLa +FCx1Ca6ulF3qNzZC5ISG33+UjRRn/f55NeZx3cVnZHw7YKzrYybbe9yXjg+TfOGDctJwWIAxW14 lvtMFg9pbTQ/23PZxxvcR/Lvied5lJwTywUfL71pDExUbEqZ2+iato3hrwhoOpWliIdUi0y2jbcR L5lvg/udjEEK+SVcdDxR4M1ex1jTtRsNCumvNQjYXGxg0SGEnCBcgg4GdwbGevavNtGbTbibVrKx tW1E2EcMMMjJsjMUL7vK3HBWMjeZDnghcV0ul6haTJfeG/B7ySSa1Fsn1SCAk+U+FWBFP+r+86Fz 1xjjin7Vy2DnOF8YPd63YRWk8l3JNMrQwSuSYghLMcKo+bBJ3HoCteo+FdPlh0yx+xwSPdSLFLAk fyNJb+Wx2q3VVkfbknHGR3rK+JWm3o1zwlp+krNbrC5tiXCRxlj5ax2qRZxGIhndJnLZ713E95pE Hi2fRvECXDzWK28fkw3XkpNI2ZWY7eqqw2AZAA4rOlTfPzSYWObuLn4j+KfE1/YNBHay+CLaGZ2m lCyQzXxMv3xlTthRcgfdUgYrn/iFq97rcnhHw7YWttpN5r2riQptLRySR4lkuCD8vl+bgcdcZxXf aVPe6d8OPEvxK1WOKBvEN1qOpfZN2ZriKb/R4bbJ4JMSKEx615J4X0zxL4i+IOnxJE+ox+HLKayh t5JRD5by2yyTb92DtDTR7j1BHFddWk4q6KSE8UeJpbfxFdaMt0s8mj/art7mziWMwidCCFT7kid1 PPeup+EOl32heHI9Z1EPZ30sV1MqwuvltAQjLhAG3FHkbPPsOleYjy7rSfGOu3jfYNW8RXUekWNu Y0Ijs2njgR1G75VKMzFvTae9ex+DrOfxClmllc3FtpNk8wtzdkfZ/Kklcl3I5zu4Cjjj0riwytIq Jft7/WPE2nRyXQvdSgsZXiu1gRIfPnd1VDGg5A3D5sjp+Ncnrq6nD4q8NaPrenXOlveO5ne4QFyt sDKssEytt8wE4xn5frXuemeGzoMV3r2iXMGpWt1Gp+55edhLGVWyNwOfmXGcivJdVbSPFHxgn1EP nRPDllDjzCyxhrzKPNGCfkUbGzXdVo/akM6vVNQh0ya2tFkf+z75ZJJVYJ9oUbR8xTlnHO0k9Ote Z69CDZJpFtfW1vaW9rJfTzWab5liWVY4Y24B82TJAHoPwrtYr/TdJi1XTLWTzW8wQ2l/dLHIk8aF WQO23zBkEgHPGKraNrkHiDQrqDQbSNJr0m3VbdMvIltNykfHP3dyd1+leRUhGFT2r22HZSQ6+8Wj TvB+p6BFYXl8dZvGt2cbiyRuB5SqrncGwCdvSofh1qjWHiYollJclFeSfE3yiSMeXE3lE4YleG6Y 6CqmoWF5DbeJ2tY74Sw5ItZgrfvZCgdDIp+ZQrAnoV/GmeC01bwxfXWqw6b/AGgLiOW3Maz4Xhdj uzdck9B/CBgVtTlNNa6GO3QwItVtdOv9XuZ1tTrNpG9tCZHdDHNO+dsUKHG3H393OK3NJ8NaldwW ugSy29xYwXa3V1dTcR/vPlkEYAHD9AexHrWJf6k15rkdmkVlprW8UcXlwMz/AGu52rFFIjdTJlyM dwvNfRMmkeJNLtIp7vUYUntIJzIFhEkjWyDMyqjYDZ7A++OeK6cNRU5XEzjfD3h//hF/E934di1+ LT4rW0jsbJ3gVllWU+aYlLY3PGzAeuDnk8V1fwme4soNd1fU7pDGmrX1vIkfTzjckZHt0A6cflXn +jeGr7xH9i1bUNajfyZ7pmtliVmspZAF3hQTkLsOFPTj0r0z4K2ttF4f1aSS5W+ivNbvfKYp8pEb Y3Ads7S3pXqRunZouOxm/HO/iuNEurbLW9slpe21xdSxbreJpYQU3vxw5wuRwO9fDseuaVouoy2v 9oxpHGoV5sbm+QbYEVR/yz3HOcHpx2r9Bfive6knhLU7GxFvKlxY3bNPNgxhEiYt8gBPyqQQcEZx X5e2uq+Hhr9ne36w+fA0Nwsww3zwABVCfx7ycbce9cmNoOZlVR6F4olttZ8ELaaViK8gYyTfZ0Am usuoHzKNxUhtvXAxz3rHv43tdGutN1DT1uLm1gghu2EYiiKydWk8rPMZHTq3pXTSjT9NOj28cbSf a4I7pfKx+4nlDMNm3oJCB83uBgcGvME8V3cB1Af2oNDur4NODJGFWWRfla2lznB6jcOvQV41GF9D nPaPCF+YNH060nuYbj+wpJreaVWU7wcbVBPKkDcD34XvmvLoJ7eTxFJY67fTacs1sZAPLZo/NDAR pIQfkQL/AHe1N8OafJc3UOsJbGwltb2WO7hOGjEzRnkKSOMEHnp0rbv4NP8AG7WTXmnv+6lljht4 3Aa8YKQQzg/ucbckN1Xj0odPlnZmsdiLVbM2dpZXumXY82GNg9rG3zROfk8w9fk45YHO2p9Sghg0 ZdPuJpLR9RKOGH73dOf9UyMuT5Lfw8dKzINMubzQ7bbqUNvBpM4F7BbQ70l8wgbQOqYxt6ng9Otd trCXMDadbXzBPskCuzuwEdvBG37uJVH90cGs3UihXMSz8Lrqj3FxfRGXQ5ZplEoZfM3xoGaRIxyo 8zjnrXCeHV0+0v8AVbWaCX7MrbkRgA4JUKXDDpyOfUV2via2fQNKtrC11D7L9py/2hUPlMbyQmFc 8thB1OMVxfh6DULS4calNMl5aWc8I2oPNc5xuL/3e6ce9bU53g2axnfc6ZfFFto97bPqdvFcaVrC GC6ilVgY1dw0ioQOCq8r9K7v4meGdXkstKHgs/avD+k2V1d3UguI7eBbdZA0cr7mAkmZOij5vzrH bwrqFv4av9XubE+INKtFiUmz/wBJnV1wS1xCuXVVHO4D5qqTaro974O0DR77TBq+n6Hpk6zqVK5k ln3Myc/dcMB0+Xsa5Y1I86cOhr9m5y9jqxtbSSGW38jVhi5f7Yf3UkDEB45lPWNsh89UbkZzXsVt Z6He/bbjULQX0Oi+Wl9Bp1ym0Wk/H2p4gvzlT8rupOGB6c14xa+KtL1jU9P8LWGl2wjtlCSQXyl8 rj5dpyWfoAwcjjHGeK9X8O6vcx+GD4w8O2kdpcaDNMHt0G5Wd2CtbuhA/czcZJyEADjnOXi4uTuR F82hyF2PDNvrU6+F7m4bSrxwtuCVe5hfPVgAB5an7pbB9am0O1W58SQJpLK0t/IpuEjnX7S7byqn yyQBuBG/bwKz1hsLnUNT8UaNZRSi6vfsbafdQPHcwNcAMohlUjEZO6NOOWqt4Vtb/wAH69d6rD4P k1SOSL7PPbO266iAbBw0mG3oykttzxxUNQta+opRsdJpmnwLY3thq8MgsLLVLmdgMLGW3LL8+SN8 Y3Y+TOe1WvCktlZ/ELRtK8C6tbaZqtqZLq2a7IW11Bp8/aLQMcmT0C4GBjkYrorC6gnF/qAsre1s kymnQXAcABlJ2kISM7l2jI5wK+c7q4vl13TNVs7WaW/huYkt7W2AeVJ5plKcHmPJ+6B8xzjFVg4u bakRfU+v/CsOkLcpNYXFvYRXt3PG9pesEi867z9ot0JH7xOGA/2alujLqOmW+maHbW2kyWkWq6bc 3Mdv5OyO3fAibYCSkmzflQc9q5/w3oerSaBrMF+32GHUkKnU5GHn2ssI5Kow+XMuVfA3dRXcaH4h bULfRdXnvIHm2MvlWmGBD5Rfn+6QF6nvzXk4umozlUp9TshI8K134BeLpdUnc+MtFshhAkFwr+Yi BAEDcc/KBz3HNZP/AAz94t/6Hzw//wB8yV62NRbxLnXb3waL6W9YuJ5EjDOgO2PIdgchAByB0pfs dt/0IUf/AHzD/wDF0/r1fyBtH//SwYJYYxc3onEcxCNyPlHPenLcGO0Ekce9nyS3HzZ61n5+SUT7 ZldQQkZBzz3qxezo8EUTKLdgh2qCMegHFfmJ0BLMI9LsZ1MiDzZgyQ8L8vQmpra+WK7kjj+XePLJ PdXXOAPqanceVHpdtswm1i7LgBc5zu7/AE4qjp9sryXN44JQQjyjwSZT6fQYpWKOv01ba30i4FvE rhX4V+dwUfMBiufuZBPayTQbQgjLInUcnkEe30q+tw1pAsaTYKx7XCjnDdxn+I1mXwP2WRd27bEF XaApbecDdj0FEdyYor3nlrpk8RBIlOFOMZOBwKm0wN/ZE8U0YAE+0Z4O1QDk5qrqU4is0jKncpwq j/pnxk/WrOmM10JGuggit4xkRHjH97n1xWzRZmX8kyAMr7o3kfKgAA4Of5VJayNBpZnAKSXk25Ce 0cIx/wChGk1XAjtLbJXzDI7M3YKQ7AAfU1qTRbNJs4pkxIyAqOy5fd+WMVmxSNAyzSxm7V40GxNz yZ5DDgj/AAqW9LXVnby3TedFGrRl15JUnr6ZGKzY4pry1ME1uywwkM7gcFQOQPx6VWkdI7GCPzi0 MJCIDlSM8/NXPJdjJDbxWl069nPMEgbG3GVxzz71xMRt7a4ga2DSw4YSDI5L/wD167BrdLy1nid/ MgCMc52jLHZ29K5i00y9tYZba5IXypAY2UA5TOB0relsDbOt0eytprxZRwy7mVOuRjH6YriZNRuL gzyFWDwsrAjsVJUg4rq9Hvfsa3juv7xgyqg67gcryOmeaxbnUDqE2ozRnylZ0cxooVdoXa2Ccdua Ik2OxQ2s9vbXd0PLna22ohOIiBz8+OmaZFJFqFvdLcKPOAUgqTjAUYUgfSqdjA89vKVcywQxIsbM Mrt2jn8+KrzS3NlZ3cNszJIWVwCuc4GHUH6VlIpDYAhtl8qTbAQJFZuAWU8qMe9X5Z4T9vlmdhsQ 4ZScRlhhgx9DS2dlaT2tvZZUQuEdS3GwSDOBWfHBbfZ72MobeUkgqzA7lHAIHQ5x61JJ5dqEaPeW kVuoWKEysAv3VA2Ac9Otemx6pcWOk28dkmw3O+QgHgxDCnOR17gVyMsVtNf6ajxxtHKSGToGj35c HHckCu2lN7c3sG22ETIrC3UsPlkjypRcfL93nmuyVTRJmiPN4vF9j4hk1OHRy/kpEi3Al7lSyps7 hhwOO2K35bi60rw8t3YbptOGimKSNuNpXqVHsDt/DmvPl0m4sdV1a1ubmKC6uRJcKqD5l/eDLYXg kOvQV6J4curuwsNO0bVgJkvJZYgrD7ysxAx/slT8w9a7KvLFLlDmY9JLe+0a3v8Ay3jn0w7o/L2y SCKQD5kA++YyTkensKX7LFa2McMUgf7RcxPlGyuCMAD2HSqeklNAgXTk2Ns8wRmUgERk8KvfdzzV zVLlkj06eyhMWnXccgiU7W2S7sGM4/u4yOeRXC73I1OshktrfTGtngEvlyl558fKu0fIuenf1rEn nuIdPggiDSJcSbgf4naHuCcYBzXQaH5jaT9imuRHFefPIuz5Sh77f7wqldzCC6s4tPiJzO6xSTYB 2L8pyvRefWqmDRZmN4yQ2NwY4pHVCTGeCoHKg/z5/pUMdxDb6pcRtjY0DlXzyoQfyq5LaRC4W6up Wd/LKRIQV27fmL/8C6Cs66jEa3d0YgkKWfyM/wB1WVunrtNYqOoRRvaRbxixm1C1lBF0kS8ZAHzZ JH1pIIBG9+fuIjsQFOPvck/nUHhqCY6JBOGjdW2oDFnHDdgfSq2o2E0es3Mk08nl3O1eOgCDOAB6 11Yd8srSNWU1kgl021mnKh5DhumT+9IH8q2luIfs3lQyFnWUyMVOFKqcY4/xrl7DTbcC61JtyLZZ Izz9RtPpnsK2NHihhtpLKWIb5kKgc53ffNbYvllZoESRXxNnqXmOELDzAuC2FU9B65+laVtfwz6X FcMuw+fESAMFuOw/+tVTy1tbPULdUUySxiYFv4UPGc9h2oc26aRDFM28KyMJBgYJHGD6VlSly7Db DSrqa5v3eKJh5WchsAZCtxVa5llNjptxAixeXuVwAfnDN79eRWtoUih5fJt2kZ0ldSDwCE4qnpoM 9hLbuiia0Iwjnpg7Tj+da2u7kmTD/btzeloxbC0/1Z25L7cZ5/GukknuxDbCOR0McXlEAA9M5APu DirGkQRtdi2WNQ6gJIFz1I6mpJYbhVmtThTFIdoGOh74reonZSKTMCw037BbxWIlmmiRQAW6gE/K D7AccVqQacIiMR+ZDNKY3jBLbjjOBu6cVqbYN0yW+ZZooVDbT0z7HFMMtzAYoreVjG8ql5AOUlYb UP06Vmqd3djZY/s5TbreW1syCKRVZy3zfdHykHAGBWvo/hnWtV1f+z4r3UdOlkj81PsxUyPH0+UO MEHHrW15moaBrC6cwivBIys8jghWDZB2jp0FddB4olfxVZ6rb2sTyxaWkSKhIDrFLnA7g8V7eAwc ZSUjlq1GtjzHUdO8YaFeaf8AZdflv4LwzxEalpyo8c0akooaIncM8Hj/ABrEvtP8cadfafJq02i3 JluH8k2Mk0BJn42zLIo2j6V7B8SfE9vrdl4dEFrLaSfbZW3h1I83yj6ZwcisvxbqEU9noiKs73UF 3EfMnQAcHBDAD34r26+HhK8bHOsTPqc1cXXiTw7pd1K2gfb7bSY99x9iu4y0cfTciv8AeH4k/wAq oz6hremX66tq3g/XbOySzwZUhjuFC7t+4mN+mPQVZ8X6n9q1vUbKW7ieSCxkj+RRGolJDLG6Y5x/ WvUfsehy6TL5EiIxsSG2TlWJMRGOG6e1PBYelUbginiXY8f/AOE+8JvFHPNNPaRyqGR7mznjVlx2 Ow1h3vibwbeXWnT2mu6e7yXC8+eFPlkfePmbfpX0Z4NtLm58E6PLDdXD+Tp8BYbw+Nvy4w2f51yH grw5aX/hO1iubazuIY7q5ys9jBKOJmGcld364rqjgEvhZX1u5gJp+nagTJCIJ94LAxNG+e+flJrE 8ReHUs7a61GKBo5F8tgwU5PzgdSPQ1r+Evhd4L1DTtVS58PafdNBq97GrgSROAr5wpjcAL6CsnR/ hjod9ceJLJo9WRLW/wDKhW21SRPLRo1bYFcYxnB61j/Zsl1NFiYW2L8mhpdE3YvLiCW4iCuY5DjG MdPUVnvot/p1zdasNWuZrS3sREbOXb5bLbplSzAbsk9ulT6V4T186trmlxeLNat4tKlgS1jnjt7w hHhV/wB6XwW749sV5h498YfEHwVqU+gw2kXi6GeMq0kVl5E6JIisSyJxxuxxWFTA1OpcK9M9sLPs jA+6Uz6ZJ7VDfLHcWMxkjyEiYncc8jk4x9K+aYP2iBaOtlrXha7tp41ViB5iMF6btrAcVvw/tD+A rm3uba4S9tJJonRSyqwDMpANc31aaex0PERaPo+2jtWsI7grtBRSTsByCPlx16fSsbWrbTLSxkvJ 44XiiTzGJQY45XITBwD14rzbRvjv8LWs7WOTxAltIkaJIJ4iBlVGfug/yrfvPiX8OdX0i9itfFOm zmWF9uHZRnadq/Mo74pKE72BuMtjPlOl6dfWt1bx2DT3cEdzODdS2ymXJHyhlwucetaF0kcEMlr9 mupItQlt9VaS3mgl2t5f3FVmGRxWlpdz4a1aytjDd6fesY06Swt0HQ7jn9KdrfhbTJdPutQWwzcQ wkhwuchR8oBHbFXzVF1Od0EReH4tH8M6wZNXN4QHacqLJymyeMBVymR8vU8D+VaXhZ9F0pU0DU9R sWntftkZaR2Rwbj54zlh7ge1Nn8IWdzIt0ZbmGZlUExTOuMKAABnFYOtpLba5DFJqlw8l7IgO8I0 aIMLg5Gcn61UsTNaXMp4e2phRaa1vp91a3gWWSGwgSS3sXDSF2c5PX5sY7VFfSW4U3OoCW2L20Uc JSQBF3cHzEPdMdAOSaLhJNK1C6twFnvreUy+a48yREPKKAMADBrk5D/ba5uJDcjd/DxtMTZO5QfX vXhVcVJ3MrI0L57ya3hgRhAtud483lijdRjouPrxS3DwR2lu7TeRAOQS212j7EHHTNWEvEXUAoi8 3KFz5mNwb+7/ALu3io9Stm1CFWsbryY4GaSIgAyDAx5aIeOPQ15spNv3il6FM3UF3eq8hdvlV4mQ iQIFHzEY68cmq/8AatldS3DRRIAr5QbCgkUfxZ/hzisbUbmRZEvf3UKxRosbKcbpEOW3jjn26fhW vamxvZ5bxj5UV2h8wAZ+Z8YaMAcZxyMYB6V02jy6FIr20VsbK4WGMzxSuWDKSHjJ4GU68HjFTR6Z fx2lxqCFCsFuZbgE4kHlkIERedxbcGrrtF8O2mrfZLPw5cO2smWRm06fbGJMEsPIlJ+YgYyCc5rl dXn1ix1G+sdQtp9NuvlM1tcY8xGJABOecYT0rGm7S1KcWbk2lW9npGg30KeXcahbzx3Uows7LHhg NpOedx5I9vaufiuLqOSTN0bdTsjjEuWLIeRuHr359x7V0fhzRtOuna/1OMqbK3m1EhJRuuPIGRGM ngHg9Oax45Nc8RXMz2lk10JBv3qAEjizwpcjkqR6cV2KXO9EJozreea4SezjvVg2kFkb/lo+T0/n XU3+q28uoTQt81paQiUoR99wOeMfw5rE8LW9vqGs20OqRZiku2e5Und843EjcPm4x0x0z9a5fXtV S31rVYYgUW4tZIY2b+EqeB+NengqVjSiupvRa9DcsYY8QpGwRxGACT7HpXrPhxYzAskf3TjB69ea +PtP1poLS3G0LM7b5WJ5zknmvrnwSTJptod2d6Kcf/qrautD2MC7yR6LGsYAOMZ9BmtSKFCvQ4NM trKbbkjCmte3g2fKSMkZ5wOPrXmS9D6WEY2M+TTLWYfPGMjpx1qlJoFgwJ8hWPUDH4V1pjAG08gc UwxDBwDk9PShRtsXp2PlL4keCLW3b+17GAQ3NuVkG3A+6Q3H41y3xJuNPnFvrM9ut1HewRm9jwRt fA/fRnseOlfUfirSBe2TAru4ZSPqP8a+NPGn2jTW+yzDMars+vOQPwGK6KFZqVmeTj8LpzRPJ9bs 9JOJ7IBj1WUHG4f3XHrXmN9dyQOWOGA6nuvzdvat3XLyTT53jAIhkORx0PQcVyF5N9oRht+Zsj8h ivXjax81Vdj7M+GesRS/DeOxeYGOKNohG64Ufvtw2t+HrXZ+DnluYo40vGiugZo8NkAIJFwuegYj pk/0rzX4OyFvCc+nKuTbw5lDY2iO5UqXI9Y2AP416p4StLWw1XVrO+k+0WqW6SN+72HdI+CyqOc8 Cvm8xl+85Tla5jptOsUSa5tbKZ4PtVzK73I+YkSLnG48HGQCvTj1p3h7S76KC4ebUy4S7AYuuGjM hO0HPHb64rU8C20767dWN/F5kgm+1RRHIfYQYRGy/wALblGQefWprsazaeINd0qULO2pQmaeALkS /YyGK7R90nPBHUZxWNCm7cyIasRweFbbUbKHUp4LxppJPLuIFkPyK7d1HpgrVyy8KabqcF/JNZW8 cVvmBp52QMrbcqAP7vzYNdosdrNrGkW0U32VryDz72OFMfP5YKnr84znkdO/rXn91d3Vlc6jal1u lvVWQqw+b7KRjfu6eoA6itZScRJo63T9Bki057u10y0JjmRGgO1wizjAn/24sbl4+6VauZ8ILo82 kLpE6s7R3DLCmwK4uUZkLSSEAkCNFZR1rakj12TSbSa4YM9tPsto2kIkmQjPkjbx5RHI/wBvkelR +FY/D0aa+qyTXEl7PC1vbyoY7qB3jZDHIBnBDIASvrxxzWtubUdzLT7TJsmkj8i5LtEFTO5JF+eI BT/CVDfTPNdHLdhNP/s/90Y/ElrCV84BFDW5O/K8YZ14Ve+PU4rz/VZb/To5IreVGutLxNG0f74M 4+Zogf7wAI/ziu8WXTrldCuZLdk1J7tUMwUSwmyv1EkXmZPAD8dOPrmsaalflCLvoc54y+xWPhm+ vJI2/tzTBBfQIUZQbeN/maIfwny8hx/u1q69NBqmheLzIxG6WzvI/Jj3uo8vzFK444Xj8a6Hxhos eseHLXVkSZbfbNalA4iIVw0eFzzsiPGG6j8K8x+GAudW0OXT5rbz4bqzt9LvJUYr5LLI0aMAfmII bAOO1dsqVlYvkZ6xZ2Kr4dsb1LKS0N7E148qsCNrrjCqCdtTfDjWk07whF4ZKx/adPvCLV7htqlJ G86MYXqTk9OafoF1PafD6Cyu4kmNnczaZcoWO9RakhAD+RNcBo4azvrCyjKvNc6ObvzVAlUS28mO ASPn8ts4HNaOThJcoM7fxtZ3Y1CXU7u8SaR5YhNAo4t8tsVmP9z/ABpf7LXVNPvkEiebb6leCLyV x5UZT5gW6bWDACofGNrYaPB9ntb2O61SGKNJWDNH5kE7BpwygEOwDAgdR9KXQbHTZ9O1KW5vpbI3 uqC2jRjgBAvyGXbnqygbuhGK5nH32QlqePabqMd7NGmqeVaxahqVql1GCFMTR7rckj+6dozXrvgq 50uPQYdbl/frZW8t9I4kCpAwkKJ5Y6ngMxGO69ua8zW806O8ubme3jt57WKGaacw+Yo8y7MfmEHv sY/N6nGK7P4X6P4d1Hwfb2OJZr621O5WSIoUVy0+FlfGfkVe+Ovy47VGFhKT2K0LY1ay1aLUNB0E tp9hYD7bH5znfJHM5liSEDGc7nU56Ba7DwV/ZHhmTX7aK3LzWNzHbBDA8m8f2fahk24PHmk/rWd4 nn0Lw/4kt7tDKJrTUkhu1kUCOS2uQrRO7qPl2TYyDjhmrW0XWNRttb8f3KWc39pLqqzNgBYrWO3t oSu/PqPvY9M9K9SLaj7w+h4ffXD+bG2oqrvPrHkzRQRgIFiHklmJxtY+Sfpvrfs7HUdYvPP1i/ih sT589gxnA+zru+TCDJ34bn2znoKr6pdvPpnhUy20M1/rqx3iPEMOZLje4EgGfm8xXbkZwAK9Ki0n TtFW3vdRj09hpunyajHHnzjM3AmhkHTfk7iOwJ7VwJJtt7Gdr7nIyxJqPiXWbO9i+yR6clmqpbOQ dz+SMK+BnzN4fBqzONb8Va9rdtC6QzSXMDwLFtcymO3EaIVOCC68sP6Vkwa+iWmoXcG63k1G+gvh bmINJ58SfuYI1/uthN/oPStv4ZWU39vXExme7v8A+0NSkk8gC3iVYVjjt/KY9SCxTrnHXoaUGm7R H8ja1jJ8W+AvDusNLHa2H9ovPDbRGN7dfJ8rdI67vlJ5P+yaqaxE1raz2UmotJNa3NrbXQtsLujS MlQm0fMBvGGBPaq3ja5stO8Z6TfaKLpFlsL2O9IlJlWUkKQoP8CclsZyDxReWHh7TINMv7uRHXUb eS5EFu+JEneY+Tzkgps2gc9hVVKnKhMl8b+KX1TUtD0yzm/tW6kj+2W5mlx5IeMRsHXHJUnr+lcu z6TfeItXdo4XsoIxAkygIwmUK0mMZLjqTWTb6jDpt7q1/fwu8zpDEJYYtxhXzN7qu3JG8heKzYob DT725R54ZbZ4lmLruH7tjjJ9HZmwT6V506nNqSW9CfT57+BdSja4iCzNBBEhaWckYh3RAH5zwd/S vonwRHp+l+HXTX/D8n2qyieS622+2T5zli0mRk4z0/hrznwhpcGnpBdXdpHBJKzRW8olPlutqNxC L1Zk9jXT3Ot3F94tsr6wjVbvU4ja6jIzCREtT8kRZDwDztOecV3YKml70ilY5TxVaNcavpmk6Xp0 ujWMMVxJGuoKJIWWXGUGQUIkADRk/QcjFJ4YOoWnleehvrXw3ZG5soMrI0M16djRt3kjCr8nVlBG RivTboa7YaK+galo1hqNnqt1NDb2/wBodPKjXhVLLzsYg7cY2mvL/CUur6fqHigW6XEeq6KoANss ZinWH92WiEuWO0A5HUjoOlaV49R6IybLS/Dst14h8NagzWy6nqEC6XdtJvEJkwk8hb+AxtwM4DA4 9K3NQ0p9P0n7RDKlzK1pqGlyxxH9zFatD5U0+wclXxFKmegTFcvoSakl03izVbKWaGFh9uES7kaG 7JWSaBQNnmICjhT6H1rqJnvtP1e41GW5LXM813b24uNqpcWkSEOoX/noxJypAxWaVolRNz4VeJJN U8OWt351zbXl7bw+dcQuP+WcaosXA6BRvHrmqfxK0yO1tbbUbSUXUen+IS9uTPukjXy8s68YzJ5y lx/s1yPwqbSrSzhuJDPHptvNdWF4GI/0eWJ82dzGmRguqPEW6AjbxxVbxP4gXVlubWUxhrXV4bsA MQZI9iJyCAvzJGB1ro9q7Ccuh3PiDxZdjWdBvDa2s8CW+pObaZSJZozHBGhZckf8tGwTiuJjbWLr TrjStKaRbG9Z0msxxGIjl92/sBwPL7816b4z1SJvEvhzW/Dlmy3mp6XdW1vA0IHl+bLDH5cnGNpz wfy7VzumaLfWi6tNqdmtzYQ2M3+iBtxkQDaJUbg53jrjqMVxVqU3UXYRxVzozeCtetH0mW4ubGbS YVjN2ASPKIaTZ2YIGPydQPXrWbqeszf8IHqfh23dmjm0xvtNyqDJlUeYQvPSYKfm9DgV1nxV8Q29 /wCGtFtJrQxala2ixXLEgbY/sq+UuVJ/5acnuAfSsjWItKt/gZLBaXFrBPrMLvepcb3uJXj+aNY3 wApTBwOM9DSdOXtbAeq2et3Om+KbvXtVt7S6tYNKsLmMQTKNyiPyV8tCR8xMp684H4Vr6Of7W02+ n1K9i0HRY9VaSFbrMdxJA8a749g+YI4PHevlqDxBNqN/qV5cxRSHU/K3hoxGFEQX7qA/Ju2g4rtr TXtBneZrx7a4lgUPI1y0jIq/xEcfhX1GDy6cleQXsdVrGq6FoutXep6FeSsrJDDZtaoyCFovvKjM MyAjg/8A6qxNY1jUPFGrReI9QtIlubZo3hflGRof9W+B/EM1x2malrmrakviUWUw00gi0jSEiELn aZencdMV6pr3gm6HhZ/F+n2k0aloo/LSVAWUnlskHZjqa9GOX0Yu8iLnlus+NtQfVfK1C5zIu1Bc L94KhyuW9QTnNUNUs7S9k0/WNYshcvbwPb28gYmBUkO5wF4Ct6HPatltW0UyyabpunQQZyoziV+O OvT8q0LSWKHTrnSLqyWaFw2Yx8uW9cH+ldP1OlKNrEcxWsr+2luobfUJLi00/ZLveEIzM7jajEsO hB5PoMelcpqlzNfXF3pum2ywxrPDFayqNm9Y4xECGH3sn5sYFcb/AGxdaTcYgk8lAzDax3oR02HP YVaOri5KmXt0Cnco2nqoGMfnxXj18kpy+Fjcj6T0a6TQdQ1HwjqGnofD9tdrJdbOGmkaIMDKB823 aRwOOhPeuX03VnsoL7T/AA7FJLE0Be0kY+W9tbbgFkkYYXDYGOf4flyTXI+F/Gk+nm60UQQtdakw htbtly/79vnR2Y8vgfuyeO3pXf2ujS6TqV7pWkXyXMtnaJq+p210d8dwts6lYiU4SXJ3RoDgLt4r wK+FqUp2excR+rLqL+PbO4vJiwTTrrUTHK++V5/9WjOo4QRyg7D3BX2qLXLu8t7KaC6E1zNptncX UxFuxmiYrtVJCwBCJkyjPLDpzioNEuW1T4pX/iSzltbexg81UkmBkjijt28vzpkXPmGSUM4Uf3Qc Yrb8dWiaF4a8Xx3OrNqOsa3c6YJ42jAkkhu5w0atLkcuI3G1Rwny9qSpJ6mljsvA1rc6cNOh1QBf DmhwfaPtlwh22/2TafKjDZChmO4sfw7V4Tf6jrcXh/UfEtjcq2v63A80EVhk3H2jXpxcASjoB5LR JheQDkgV6N8Rda1/U7fXvDs7759RhhR3tFK20LSIFmgRhnc8a53jHcdxWZBaaPpg8A6VFKH1Gz1O 81i8ZU2sxgbFpGwIICSKFzg9V4qZ1YtcqA5zxxb3nhOx8NeH7/dbXPnC7kPliSULZQ4WMy8/8tPL +X3x0FfW/hnwlo9ro1pNdo115sEUB8zKqPk24VDjOBmvm7UfEEd945N6LMXENg8k0Iu7jJBuZI2I lLfeXdGu0/d2r1r03wvreu6xqjaxcQwy/ao/JsLWRtkUybv3mwg/KcISGOOv0q8HUgp2C+x6Imn2 I1ptPsTaxWMkXltBG3AWUcuMHgqRyMfxdK8gbSbHxHrHiS006WG2j1vxHDoyLGm//Q9KtcSSLjuC /LdK7O113/hF/DGrTWduzXlqPtFo9xhoyij5N7+hb5V9eteT+HJh4cvtIgW4nv76KEXtzAqlXSbU w0kyREAk7iy544X6V14jEJrYq5t+NJpnj1nwhpd5b3S+I3tIoTHGQ8EYbBmhCg/vNox6HBNa3iOw uLDTtItFv4Lb/hHRLLDNK8MbmU4EDFIsluAcnPNeX6VcW2ieLfP8UX8ltHbNAfmy8altzTqs4GP3 MRyE6fNW/NolxcR23iTURcadZeKr94obKYIBHpSLvRDISSMINwP8O7FeRWpzqxaS0KtY6m40nUfF Gna/ceHb6LTNV16COe/jn3BVmVdo2qBuC/KG+UezdK5XVPEmlWFlpH2e5kSWS2/sxY1k2xlIOBO5 +8zmQOTgfdxmujuP7XHiPwlHoW+Vr6Rja397c+ZsjbdiMxqo4479etSWOqaZ4st9U1WHTbS3vYka wnZmAS2VhITcQDHPmMcHjipoUqkFyPYUo3R5GZbO+u7mE24v4ReQ28JRiHJVN+4NwQST+6OOg/Gv aNG1/wAQeN9Mt9P06eGwnuJxb+Q0JV49PtCGdnfJwjnAz1b0rgvDXgry9L0Txzo01havobGSSWXc /wBpZWKiJYwV5ySNzdAOO9b+keCdXnvbPxJq8c0Gl+IrlY0uLdgs1mu95PNmU8Kk7D5do+TK545r swlGpBmfIdXqvimz0CJ9Y0e9+0+Ir2cJc+WBDBMyDDMSBykY74571Y8BtrtjoNjJpqwxsJpLE3F7 uEKNchZYnMafey7bVOB94A1j+PRpGh+Hj4asS1/NLdSWpMjtLMkMhyJYn+4GyBuDEBlI+lRaNcJd aVeTRIRcaY9pbfaYnw95GDG3lyRLtjRSoXEh/wCWmMdK66tSXPYHJI6+78Raxq0U9pq0Nulze+Hd WyCD8xgLRptHTDfNu/2V9q/PTwZ4dtodZNzdxfabSKxMe26VDDcpIuGbOQY8SBWV+2Me1foR4v8A Bi3fh/VtVszDpekvaMJ7J5C0VwscOEO/hkkAG0bcc7twr81Ve2sNRntdStFu4tQVcGOSR40jkcPs Yj7qsAM+n41o6knBpGc7npGmHV4dUsPDt3aq2m3E00cVxbbXieCRSVZWBPlHJ4B6beK4HW9Y/srx XdeHrPTVv7azeX91xOtwzpiNd7fwgcqR3468V2HhptKuPEtzq7Xc1nbWMIEkkeApMqZMezONsY6F QcAGlvwvhnyVW1jvIr8FbdlcBGUBh5yPjnYSBt6jnvXiwi4y1Oc6bw94et7vQliuYG02XVI/Muoh JtiyvDvvzwcdeee1czfaJeaJNdalpckN/pltbRw2kaHZNsTPnbGwS0h6E9R0pfh/ZWtp4cCfbzcq 7yA2XHl4B3KI2lIIAzjPfr61s6ja7y1xDqCxAz/bIkdVdTkrujjcEcbvmPuCtcruqjuaJnV+GvGO kXOlqlvo0OjSFWlighIkTYg2CSYEAk8EN6EZrP1bUbPUIILO6Nuvmu9vbeaWgXd5i/ck6mOQfXmv N9BSeDV7bUtqXFxdGeK6nt2kRkdD9zyBwHZuVK5Br0mO0a2+zSa1ory3E0dxPCwU/aLdDgb5Vb+I sAegC9q56tLlkGhZ8WaFct4b1PSdRmj00W+mM82oMS4t7jcWW3UHrG2MZA3ZNcBoDrqWnaNrWo6y LSK6j33dpqEXzQlTgG0kXtgEMH5x0FWbTUL3WLSwjmaW7veHOTug85GIDbG/1hPGR0Fa9vbWAisr jVLRbDW5HLxi0QTwxTJwGmVztG7+76VtF8sbCTJjpnxBfxLHqmiXJ0l7eNZ0NlOA/wBimbEIiYY8 xWH9498VytvNqfj/AMaaj8P9dtzoXijXJFiLTI8UTXQYYuHhONnmRAeYqZQtyKt3niebUZr201FY lns90bW8aN5csJPyxELzt/uqOBXoXw4v4V1mW71KRltdIzPpl9PKskVoFTLJ5rfvuM/dBPehT5IX UTelNr3WeJS6Zb6V4gH2xhHe6NLNbyOMC1YRNsbL/f3Z5HFdtDfQalZazpNzq15p1tpyWz6dqUKg QmSVMy28wUfMrIcZb7h+aszxFZafLHcXWnrFrWnF3uIbgp5fmjbysinHzqxHzfxda7XSB4c1D4U3 F3qFtZXNsNYgS6jVXVomNuFy64A35UY7ba3jLnpqbLUFfQp6rqOs3OoaT4r0XVrf7NHIbe7illzG vlqhjAlYKZBLjJ4+VuRg1Jq2t6vq3iDTtV0G7tvt66o7TRwuzPEk37tLiHzU+aNHcmTGXGenerWv ajFNHfaJaRGGx2qI0lCyozgAu6jA2R7eB/tdO1ef6rpxi0+DUraaA+UfMtoUkKzCNMD7yj925wSO 5X8K4nCM5c9tRQfc9cHhvWbDQVuPif4neC/mmljmN3GGS4jV/wB3NbPbMNyA9G9K89vbObRfGlh4 kXbPLdPHeGW0j2XCLBxlEYhQ39xj/vDtVxPE2hT6Vplv4oF1qmjzXTw2+AwuInXhvuErICCHwPm9 KZqsV3Lot5rH2p9T0G2INw0LqyGMMOCx+ZSM4CE7hyMVXI1GTYcqTPdvEF5bePcajouq/b7bWihl 0q2Ihu/KXHnLIGwFDFcOw554ya801O91mw0CxmsvJisIke22sAQQWkcTxAYI8o/K4+lad3putWni qwltdd07R5bvTo7iC5gVniuI5p9p8txj/VGNcg46461r6tpfgbw8vh2D4ix6jd/ZJLmKG1+yfZ4b mWWVpCksudyIpJBAHOfavGwdOUIPm1NOtjm5rO7nkMwNttkAZN7TbthGUDbeM7cZxUX9nXfrZ/8A fVxV6x8IeNNTtxf2ekQ21tM8hhhty0kUcYchFjddwKKAApznHXByKt/8IF48/wCgcP8AvmT/AArq 17Gfsmf/0+eW3gtrxp7ZQjRoY8dgQM/0rltLkW4uI7Wdjm4Blz13bTnHPTj6V1WoGFAlwD5a3KOJ B124GAcD8RXJeHvOhupJXTJWJgr4BHJwAB24+lfm0FdG6Z0WuTyuLrayRqqRqqg9CQCPz5/zitOK 1e3sppY9hUzooTdjHOCecfpXK+IVaW5ni5xJsBx14GB/Sun1KKZ3thaRBxFEikZ78ZDAdOfWixZr Tx25iNui5V3LrIvOSpxtqLUbD/SprVSFMYiBX1Y84ps9lceTaQqqhWzLlDlRsbkcd6pS3yy3O22H lXAlZmDHcrrAOoP/AAL8xWK30GmYeumVdKvJJQWljm225IxhWI7D3rUs5Yra3mW2k81rgBZA2Nqj Z0GPU1zutahKLRrpj86XAtxnn5GBB4+vNdHDayWNnY24jCiVQ04fG8tjrx2rpq/Age1yteQI9xAZ JFDsFiRSflUO3P8AKnT3MlxrV5Yhg32ZfJCN91dnOTTdK+x3Ov2lq8QC/acyh8nCIMnp61HbXS3u sSzxIq+cXExUfew7HcD+VYWM9zodNmC2c1xaTNJJH8zRHOxQW285Az+tUNSaGCBGnQsXy+QRsxnk HmptJJfSb6aFSykRLJn0Ljis7xMjR6OqgqoWVopBnO3PQce1ZJe8CRBayvHps48naNrOoPVkLjgY +lYhuZ7qY+Y+VUeXErccAdRjr+VaGqSxJp8YX/WxtGqnoCCOhqOHSJG1FZrlkttOtGUzTycqWPKx xjq8hz07Dr61pFWuFi5pK3a6fcuP3rxn55Co9K4kxakr2sf2aZFuZlV5PLfAVh3IGK9IjvFEGnQ6 ZE8Sz3BRnkP7xgo8vJA47fhWNpEU1jE+tT3M09xazSQQGVyQ7YPIyMMEGO30pJ2G4jIL+O0lZbaV ls3gk87IIAaP5VIyB6VFdXJTTri6juJZGSONkPGD5jc/zrS0zUNTuoJHnvJbe5mtmaPcoaOQs2Om 3jjtUOqX9hbSz2mtRGdJbfyYzDhWeTHQnptz+NJoUom3aIJNHxPMm07kCEfeZfmXBFYkKXF1pMiQ QNBcW0hyp+YNGpySjd+PetDTp7O90S3t590TW++LcQTtyvAyBTYYwNOlhW9jkkikO394IyAwwV+Y gVCQuUwNPVZbu1WaNQFIIO3+AuAwH+1Vy8lsppYLd45naR2XC4yoQ56g/wAXrU+k6VfWjWD3EZUt MqMRLHINu/nO1j7VVvdLv7R5LhzarsaTBafacbv7oya0TWwGTBYKb0XMltCjwRMiyMmWVc9QT1z0 +tLpF7Ppl1vK7oPtDtlsblVgoG3GfQ1sSvpy5uNRv44zKkcJ5+RPcZxVKW80ePS7y4EpmkhJaIwu Avynapb/AHw36VvK7HYy7+ysP7ak0+zUeXNmeEzZBVnG5tmfXkCtuZrDTrfVNHuJEhtWk/dzZ3eX tiLRsB2YOQGx1WufGqHV7dL9bFY72zgELxBvMRoXOFkDL/FH3rZs7MHTlv762kxcRw3cg3Bc7Qy4 Yd+NtOelrjsdx4es5rbRM6qfPngT5hGMBsc7U6deM1m2YC6ibrVz505DSFYV3KG27yHzjAXIB962 LS7nmkhSRPIM1uGBlTgknK7ccdOKZN+/uxDOqQgs00Yix5gyBu4XqD1wamD6szYuvKt9pVjdxPCF efyTGvEmSPlbH9xcVzs09xbpcqYPtUTr5KnjnjKj/drPl1OWTUhBYhreGJJNwkI3BiOXHXH0q/A3 nSW3nMSV+Zypyc474pTZaR0+m3rW1hBErrG1vFlh6MegpdUlFvLJeyh5oDAGng4BRs/KwY4wDUeh Rf2jZPZBHQx3SzMzBclAcY3ZrY1SSSx1CD7SnmwXMjIQ+GR4j/yzYD8f0pR3HE4uwS8h0Kd/sjS2 k8nyxtJ8xY+3b8/8KswLLarFdXULKuXG2PjBk4/Meta13YRaTDbWbfu7WNo50Ljkq7cK2M8jo1Qa lfyT3TbIwsaS7VVSCAr8AlRz1HpVzncd0WohBFKkB3rG9vGAsvLEA9/8Kg1O3maILCFFtdIqLKi5 KYOen+cVm63exm6Ny77Y7m3WBR12yRtzjHtXSWUti/8AoqIQrZUBunIByDTeiuPcseG7FbG08uMY 2rNvBbOZMYB5/Dism5tEsZW1CWSJBcO4YEnoeQQPrVhI3smu7c/IAdyjPTe4xjPriodXvp4NMKoo VlJQk4ywwSApbp2rWNX3lFCYtlcCHUobpyf3cm4RgH5gOMnFdAY4odQF2zO41JX27gAVUueP8Pau ImuLpLibzZl2lRJGRjJXH3e3XFb7C4dIFllPy4+Yn7vPQk10Vk+UETDck9w1mjhIx80gZV27W24N SzmKMidgBG8QZ0diQSHwG+XrVK2cE3MO8PHcZRipBU7eeo96z4DLNPHAY3cQuDGrcADdnYf9nms6 fw8xakem64PKnjUXEN0JbeOaNjIVcHYD9Mc1V0u7itm069FpPcPEskWIplUKXb3wST1HFctaci7+ 22vzSLmIvwI8D7o77at2FgI7OK7nAkjJPlxlsAueBgccA8120qri1ZmUoXOsunaPTbK7ksrpIdH1 HzdjxeZ5u8MfnfpV7V9e0K98P38VreCG9+1x3ZEkTKBGrqSgdhjOB61zEVs02msFuHSPzFllMW7D ttKnqePyplva6gdN1jSDfO6PGsKiTlcMNxKdOa9OljqlmzGVBHNXcFnqnjLUb2JEzdxyzwFZCV3N gYJbGW/QV9O+GG0W9mMcj2sjDRGjbcY8mYDbgYP3/TFfKOnTXWm63DaxsbhGVkiMkRKu3TaykkDA /wA9q7WygubXUbL7VpWmSK8pIkiQpgJg4AXjOKMpxc1KUmRKheOh7B4b0S3HhjwvHewPCy20iTgM YyCvAD4/SsDwJp6potlslnhJ1W6tnRXIDR7yRgfhXnCR3EdypXRnth524+Xdyrxv3dC2KnuvEM9p fXk1tcazaDzmaHZKjoN/AZVZTj0zmvdjmTstDNYXQ9c8LfbbS08Stp961s0euSxYYLIu2QgcggVi 6J/adnqvi6aCSBxb6jAHEkZG4yxoM1ylz4vurO/vI7PV57aGcxySA2UcqtIFXc7uuGY/SmJ4mk0y 6k8m+0x5NSignuRcJOjNKigBvl3Y6CtVj49UQ6DR2kIvtP8AGPizbHHcMkNjJtBK7sxbRtzge3Wv KdQlXxX4q1ub7LNp8um281vcQSttkRoFTIDIeQTg/Tpmu3m8VXKarLrAg0+4uNas4jOYbvyl/cHa m3zFzziqFnd2mq+KNQ1SSwOnzeIdNuPNUXEEkPmRAW/BRsl2Ee7gfrV/2hDYlYeV9DylvD1l/wAJ 2Le4BW2uNJLRiRiSuJuPvA/Lxg8/lXA+MfAunT+MLeyg05IGk04SSZAIO2U/MO3THSvpO60pL3Vb HV5m/fWdpJZLGB8pR5A+5vdcYrJv/Dlze+I4NXEihIrF7QQnncTJvDAmo+vQkzZ4eZ8XX/w80mbx Df21wfJ8hIGURjaCX3Dt9BXt/wAH9Etl0qaxgjjZYp51JlRXziVdv3wc8H0q/qnw+8SajrV1rWmi 2a2vIUVVkk2tlQf9n1z3rS8LeEvEGi2V3HqVjtYNO6GJ/M4kKbdu3HzfKccVt7eFtzP2U10Lkfw+ 0DWPFerQSaVZzvHBaNj7MiAghx0jx3AHSuLPgnT4NX1m2e3uLdFlhCR2t3NAsasCCgVWIGa+gbTT 74a9rM8Vvc2UiaJYygruyJNzk5OMH8DXLXELw+KfFXkzFmtntA4fD7wxJJNSnGRi5SPPfD9t4gV9 SitvFWt29taXAhhX7QJ9qgZwRJnP4VFeajquoXwD3Yub1CgMkyAO/wDDGWAwOny8CvUvC4Fpqfia 2EEc0gv0j2SAHh4guVA9BXmOn6dfpr729vC8yW8/leYoH7vecBst7n8K8fN6fue6jaNST0Yt5Nc4 mk1R2je4PlzCM/KzL6kdPp/+quf1OKFvLktSbW2SJ4jIoVWd255APP4V02l2UM7zadeK7B0njiuQ /wAsU0bZ34P3s1jX9wgkis5gjQRAyFo2y8h6OQCOM/SvmJxsXcxW1O4j2MjMrrF5aFcfw9TXerJc G1stVhIZcZ2sQgRwcHA7nv3rldRtJ9JiiuIUhWGdsRoygybSOE3564Na+keZP/aOlRzvdW1rCksE cShmaaFcsp7/AHc9KVSjLk5xxMmW40q+vJlvmuodsrPKItmzptBAPfr+dXb/AETTraSCLzkWaS2S RFlmMT/Md0cnOFx0BGc1XOiC9dLfRJRdrqcTFTOwge4kbgohbHQjHOK6Hx/pMNq/h/UL20ltxf6Z 9nkiOUMc9seOx+UAcnp36c1vRScTWMTBmuNT0/SxdatbzRQxyhRcKVwkjnrG6kMN2efoK6C41zSf EXhzTNMtLKW41a1ea3S8JaRroPh9rk/MztkH228cE1xWn6TbX+5opHhitbqGOUs3mIzTMeeu3AUZ Brtrvwzq2i+GLS7tZCdOgvRc/a7dthSTYy+Xkjhzj6VSgkiuhHaJp954aWKK2LX6XbxztMpWKOCB N5BHG4kkZGcjGMVsTaO9h4Uv7+3U2xtJRNJHFIwIt7oKrQAfw/3vbpUNrq1gNIs49CBnl/tGe5mt NRGNihA5HmL99y+ZE7HJU9q6XXtftNevtL0nw7c2Uc0Vub9nuJSLe4uJsDyJzgfvlX5dh47itqSd tASVtTndOubl59KvNLhuf7MupTPP86yosw/gDYycZwfZuOlfNPiNLu+1zUX83ZHcbI4VH3VYy5Zl HoQMV9SXOuJ4Z8Mpq3hbS00+/adY/KlczfZrjJjlxC3QKAcevBr5X8Ry3aarbXjo0UkqLOc9MO2e OnGT6f416+Dv1O1YSXslUWxy/hiwufGPiuw0K1DJE/7+4287Y1Ocf0r7uhuovDljGLW3aeSP5I4x /sjvXhv7PHhpI7/XdUdMNEsduH7jJ3N/OvoLWNN1K7tjNaRNJ5ePkVgp3dDz/OqxUktDvwENLowz 4h+IF3EWto1iLNlVRh0PTrXPH4gfEbSbvbfQRyRFgGWUA5HTIZazZbvx/pTySx6NPf7W/dxWlykM bDtlx82fyq7L4j8U6pps13f6BeaatptWSG6YXAfccYUD96OOd2SvtXG9rnqpa2PcfDXiuLWbQSyq I5s7WUdOPTtXVm7UJgEFTxn6fWvnfwkl9Fq9vBLE8UFy2VznuBt65P8AnoOlen+NJm02FIIS+4pl ioJxj6Vgztjsb974g8ORf6LqGp28EuM7XcDpXzZ8TbTRNSimksLqOdiMjy8HaR908fWrMfhvw34h ugNQuLgStyDgqTx23Ff5VvyfDvwdcxPp9lfTwzsm1gWw+3HUIeo9xn+lXyrcwm3JWPhvxPphm0yG 4U/vIHaCc8Dkcqx9MjFedWNuv2yP7S4iReCSD1YcdK+9bL4NfY11nTr27W/tb20L285XDJ5WThl7 ntn0r4jPzNIoUGPcGIxzxx+lehhql1Y+dx1Dk94+jfghhdS1PS5is0Fxa7XdiFVV6jnP5/hXrbPJ Baxaol4ZLKRbmx+0NkeckSCRBhQWDAh0JI9MVxPwI8PtF4TGvyxHddyMMOQAIozsQ/nzXs0diL/w zrGnCbN1pBe8WSUA7VX95iNAwUb/ALpPpXiY+SlVtE8qW9jQsPFc7ajYa/qLm8ktGjt3aFVYqY0+ WRiP72R8x6tW14ivbOWGC6tTO2o2kq3Et1I37zBjaJo8jrsyrAdD9axfDkcF1DaWOoqgsr2UyRzJ kF/MaHYrY58rduxnoeKv6noR0vUL61jmEsieasedwEgQo235gBgg/gawpznGmYSVjRhm0k6Zo+oS 3Hla5chdk27i3FywjcOo5wAWyMcY7VXj0h7PW7mRYobxbRzGsu4mLJVo1G4ZHl8k9cZFYGkajA2l 3QWyeKExxvHtA8xoZ9x8wMc52vkHocfhXdax4fVbh9J05lWC+sre5traU+UDtL70ReAzgZ4YjJAx XR8W5CRk2Buja7ZY7h57JCLKM8qxAzKVz/CTwv8Ad9K2NJ1TQtF12DWtMt2K63p/lXP2gAND9nKh Zc/3UDtvIO/Azj5eNbUTHqOm2F7dRtbaorR2d3Fkr5bsFJmXsismD6dfSuYMxsNWtLPVraLULezv PsM4MePtFvdxtb79vG/a8gxt4/vV0UYuL1NLHTXOkX2keTcWlorw6Q8a+TEFK3W/IuPMH8TBfmzn NcJDp122hXtlask1r4bvbjSjc2zkyJbyN5ltIznAYKr7V9ApxXVaC7XFzpnhH7Tus1uBGsjr94wt kRSMxHzj+A/xIAOelb1h5vhr4lRaPNGI4fEVvLGWXBWf7KN9uChwN2zeo+mK3lSUn7gRRl6d4jW4 0GzvNTtlez19DZzx4MkcEyt5Nw8bgeq78V5npOlf2b4lMdnefZTfWNysUar+6kltn2Mjjr+8XDKf 4Wrv9W0260CfU/Cdg8t7Yaw7a3YgkKsTMwWeLjhMPzgkda8+f55E15Lp5f7KllkfavyCEx7X8vOC JQPm+bA4rkrOUZcrBtmt8PPErS2PizTo52ccSkTOEeN7rO8nr+8DKBj8uKt3Mdpb+H/Cmsm2FpYa NryWL3e8rNLbXqtFLlP7qyY3dwKItR0IeP7LVfKU6f4r02K1uUdBEi3VpNv3np88ijkjv+FafiLT dKvPBmsvdFhcW8895prc7WywkW3I+6MdUI+9WzdrNjejOg13wromrM+oaVdwpHf7ZJI0PMdxCpR4 4if+egUd+RyM1yPhbWdbjtfEK6ckNnaRQSRzyyKZFWNFyikMMgcDnGVb2rp/CD3OsQ+H7XUYZJ4f szSxSNIGM+wjGwgDyiOT8w9qr2sl3plp8QzFa+ZDL9tszLIwBMZ5UFRn9582Pp7jFHKpPnQWPOtX j1DwxpEkeSkOr6LDdzlZMyKkd1HcCNGI+UYAOcYP412fhjU7jQItSvFdWvptVuZYyrAlrXcXQ49F 3bsEfezkdKPiDpMn9geEIbiO4tkiW9tbuV1z5ryWqkOrY/2Nqxnpnjoaw/h9I8WkeLPElskN1Mba 2iEM0ZZkt3/1pUdQwfk/7NYVG4VOWDJeux6DFAPHltretXkMT6bCDawwqSHcsdqhH7nnj/bxSWWp XmjfDDxtdald2l7qJ1i/s7iZeZJbp1W3hTI+8zfKdp7EnpXLeDIb3Tr6JPtsFvbo8twGlGI1mVQ2 1f4fnOFh/wBrPfFUNMtb3VodW16zgKWviHxZJpSWkgCSRyy3iyrduoyC8eySDHXBH4dWFkpR1Lgt NTV0/QL3w78V/A/hW/u7R7iznuL+RreLduaS1mO6bJwSgXbgH5BjHen+L5p38Xf2RHLKdO16Qw29 zIinzm2ZiEYGB5RMf59eK5/xjrNlB8SLK70uZtTkijI3YK75JI3jw6cbQu4Jjrhema6bxBFZaR4c uUvRJf6zOlq8N4ZRNbWsdmQCg6eV5eSo9ehpV5x5OUUtCj4c03RdO8RaUuqCF3n1dy0LOTHb2Vpa qNyMuWIWWXAf+JlGa3fhle20zeILSBk+y6jqmo6gJWQ74YhcbUMXfLKD2xzXPeCYbi5nGl+G7oJe i2mUXNxERG6u4/dNgNyyj5WU4yPY1b8LayNN8OXdrpccCajper6nDHczReYvkQYZg3fhgw9Tu6dh nRT3RC7EWq65bHxtJquj2S7rjQoJIE27+Lm6PMvZZML94dc7e1ZPi2yGo3qeHbPctqpEhhiQhcoR hULAMhDLu5wPTtW/p92F+K3i690qx+0x6PYaWII2XzIQZFbYCq/dXzJS6+mOaoSXbeJ9bv8AV3ux bxfvfsSBsZnt8OQxHLAltvTgj2rPF6bimZHhbTtUEunaXd2Ulu81zc6hOLnAKJCCkaA5+X5yXI5z WZotrJeaM/mWkkVnqmqpYWd3Ipw48/8AeKvHPC45HfNdB4a1G+TQ5fFXiXdcRXUd3Fp8txkqZICC N3vubZjuRWdpFjc61YeDdKtRJcXUxu7u681i/llCSUEXG1FyCBjJrmjyiOs8Xx3mgeIINOkJtYbC 5wbgJ50ZLjMW5PULwu37/epNYsvEkPh2LxBJptncWl9dRTRSIWSYJbt8qZH+r5bJDelZOnWurR6n ZzRXgvbiS7ihjF5FwRECWl8sZ/1S52DsaxPEXiW9jtf+EdttbkvdGs3cW93NE0QyoIQtgfMc9eK0 +HVhY9Ts5r/XNE0HW/Ec0lxotpKBJeWpj8yNy+4KmwbyBj5s+vpXmrrfzWWp3VpdO9rqbR+ZsVna 3kckxM/T5QeGKn9K6Gz0K4i+Eesa79pm029gRZLRWlIUiX906+UGA5zwcZpNbtNO0kF/DcLXOjXV kttbwiT94ZbNybkHnJ3MV2e27HpWs4PlTZTRL4BgM1n/AGcl0lnJfIbXUkvpSsM1rMhRvIb+9zlQ BgY61p6dp2jR39rbXECalrem3F1Dq0VzKRFdW1vENlzC/MfneWFMeeDkg9qi8CeHrvUIru8tL86f BpRim0yO5iM2Z3Q+fHyMeUM7B7jtis7Vb7SzrNtNpAf7WN8V1FJM00yTDYPMztG9JE6AdCMe1Vzu EL2BXGG60S38XeLtI8Otb3NjeeVqtuHf/W2l5bo1xGmfuzCZFdVYDY68D7wrB1rWr/XLVzAIHeOG 3t5LYKkS3FvCTJGqscESKfx7Vuaf4bstM8Y2cGv6fLFZeLUcQSSRiO4MnmKpUlsYyCXjx0I54rK+ I+mWmhX+oWNppIsYrfTre5mMc/3cSrGkwQ9BIGAZf7xJ6Vz1ozl70QSO6v8Axklx4r0fV/Inn1aT TzCIhhcpJcKIY9px/EhbA9K9L1GNrHWYbi50+WOzt9Ea0RFUEwkgcIy9CSvzBuB1Jr5x8Q2LWfib T2uIZXN3pVpIAhaKFFJ3bHkbAQ8jJUnbkV7bq/iyTxPoup2+nv5U++DTIvJZ/Mlllk8tGUqcssnz FkxztNdWGq8ytIo8u1Pw3B4g+G2mawkcZVp0EkkfyyQMsChfnPD4wcnpubHavFtU8QNe6fZeGkma 2tLaR7i8jkQNiWM/6OInbGVx8zdiQvvXqr6osGiaNo2mPb6rpY0wtctiSNiloZYdjqrbFDKgy2eT k9eK+ZbGfUdVHnzZt4r5vPVGQ7xEf9WoDchNoAHrXflWF56/M9jOb7HqmiR2U7BFMqR4BSNF3F/q TzW0tjoGoanDYTw3t08ssccdnGoRHckKWlI/gUHp3xVLwX4Zudc1G1sGkh06FyR9okb5uF6cHIwO xArb03xloPgXxA1nK1tqU1tchY5wvmRJsPMikHOWB+nFfVOelkNI+0Nahl8G6QH0m309dJtEjSeC dsALGMN5Oe/XC968w0yPUPFUGoqNNkh8M3Ei3H9iXGBPcK/KOWbGyLdl9g/HHSs7wD4yHxO1nWtW 1BYo7DSZY4baFhvRQRnftY/Mx69OOlej65qE0lxb2UenzSWSbRNMF2YUj7wfOflx06VzN2OmMNDk /H/w/m1D+wrzRrSCznt4zCYYkWMFSuQSVHPt6dK4WPwdrlm7Wsuh3EI3eYZk8tmVhxkoW5X09q3o vjbo+paja6P4amhtJ1ErXl5qDFYLWCBtg4APnStjKp05Faus+MPDvhHVLC51TUtSnvtcto5VuHiO xxJJ5YjfOFQjrs7LThUcSOSLPPPEHwf1CZv7Ys5VtbpgCSq7oye3mR4+XgVQ8R6JbeGdLhtfEVho dxZa0hs3uLcuJvNxv5wRt24ByBjtX0/Y+LfD2o215DZ6pa3K6bd/YrgjaFW4Iz5ZI4JIGeePyryX x94FsdRn07U7KAeWxZ4zlGUytwRyDlGXOR696h1Lsp0VY+NPFXh9/DutXfhqbUI9UEKxmO8tnG10 kAdWVlJwR6E9fSvVdA8dJbfCiOTTIIrjVNQu7uxvw7P5h1JXjFqQeAsbqEwCcEcVzWvfDDXNOtZL yHTrhzblsGMDagU5+VV5xg4xjiuI+G0upv8AEI6DpZQMzHWUgkbbE82mRGZRIuDliOB06VzY9KdO /UxUeU+k/h5oln4Z8N+IvOKOLjULbRojHEXubs2xDXUtqhJy2XO1f9nnvWb4g8IpqviXSRei8jgu Gu7q5kuF26iY4X8hFCjhUbOIBjrkiu9+HN7ofhzwVqfjjUGnmvzPK6IoxtzJ8qIW6NKP3hK/wjOe apFZNU+JfibXLF5Lk6Haadb299Kw2tqJfzXzGOHCFzuUHC7eK+fcYtJl3Kvj3R7zw34s8I6BZzus JjbUdVSKQDdFHLsiKRP0dUaQZz8xHzdqTwLZHxn4k8TGC3kNh5lvFYm5cDz4pGPzAL9wQrbv3/j9 TiuS8ZsdX8U634gXUnudXntbDSraOMmRorjEj3Qi3HdGsa43ZHLnjqKt/AwroukXeu3GoxQSwfbL uaG+kBa2toichBj7xVSWzxlxiueNOLr2WxNjTvW8Lrr/AI/utbt0s7K3ng0DTRZIwhEsEG6ZmJ/g Eh259a6HwlrOlXjadoljpcb2900cMs6MzXSKFwvl7eMZG0YGMEnqMVw0unpY+CdAuNSnV7jxK1z4 huopIg4he6mFyimRfusImUMgB9a9G8Oa1oXgzRNJudV01xf3DyzNdRKzOZAfkLKACu7GyNfZjxmt ayjCpcHE43xVf3q6RBo2tXU2i289wRDCriS48qKTDy3hPGeB5aj8qh8WeJtUe/i1e4uoLddIttkJ jUqx34PmyKBv3yFVCgDhB/tCud17VNR8Qa3PcXbfaLOydW+zhF3SyTYkx9UJ+YZxUev6nHZPFfBo ZorVfts3kqT+94MaBcEn0Ye/y+leZVxD6EOTOu1l7W5sPD/hrWInglgkl1nX5oQFkH29ChTZgrtb 5c4PCgV7P4h0weK21ULpjJb+F7dYLcNchFbzUEsjlQDwUAXH4V5Xouk3es+A4vE8DE3TzLeXNzeX KpDFPC/EKuefLWL5FjwcknNbv/CY2epeC7Ww0WF3g19nu557qTdi3WT94JMYJT5QoYdh9a9SjVsu VnSn7l2ecWbatfDQxpdoZJHvG1B9v32KxCGCEqdoEflc9sd6h1W+sLCy12bWoy1w8LpLcRkRxJGv CII1/gBwPXirfhjVpRpfiDxIl9ZuJdQFtbm7PkfZwVCrcIM/LGx3fL3G2uh+Jvh6Cw0XQdkMerS6 3q2lQTW9qkcO/L+YluzctuYJukfHTrxWXs3N2RCbudjofh6yfQNN07VdQtrmO50yC61DUItqR2ts jAiKJMZEkp7nn73qK9OSxsfEcWp6e99dXNhH5a26wuE8uIIG+6Mfd4wGA6V514hvI/DF1qg1q4v9 NkvmkkuHESTWrbgMIskYDIiD5fmX361a8N6KlisttKWSyvo47tL9bndG8tySxROm5EiwAfX1rup4 iNOXK2E12OD8Wx6XNqMFtp9vcS3gVXurZncbIYXWNOTgfNvHIP3sDpU2iw6HqN7aJqq5jt5pNUjj hBRjbQXP7oSDPA4yQ3aszW7ZR4/1HTNKfyJisEKOrt+6to8zzKc8HG1PN5+Xdu9K6z4UaHa3C3ep 6jbWX2y0tYxHJLJ5k8UC7mmGw55PABxwK53K9XmRlbyOz8ReM9O8ZeHtd0LQry2cnabdpx9ljfbH 5jRxM+PMO4dhxuANfnF4qhi8K6laWvhzVBqM8IQ3IijygBUF4TIp/eBOhOMZ4Hav1J8RJbeKIP7N 0r7Oxgtf7Tsr/wAlZIoSUKeWw4G87s9e/sK/LKxt9L0i2Qvp8WqTJIfOSRzCYrXywQ0OD8z9z2xi uya7CqOyKkWnX2n3Uep2IkIu4p3nYRsyKsrlJQI+cJGrBCex56V0+jrBc28nl3nm6WjgpA7KrQTA bGaNm6Ant071l6d4jtZrfTk0mOaODT0kjju5pAZPLZ8yEqOGZ+mPzoj1yKzk8+ys4LWAfIAwB2Iz bvoTzn2rhrUJS1sclzpY9EsrW8huluYrv7MGjkgu8JhC+5iki8Ftv8+K2dD1jRr29ksr+xSQ3My2 7wxdPKkK5WNv+emAxGK4231PULfUYNGm3rLLHvimZV3FdpZDtPHzL0rrNGNxa3jWUb/Y7lBGyFQN 87urCMncPlKkfdHY15dam09ShzR2tvdX7aFYypFptwViHMKoRMWjGT8wcKNzZFeiQ/EbQm04Tahq bXOqyoIrqy8vdvAYyMyzfxBvQGuETWtV1a4vIdQiWKKQK8sMzGP54AAXJOBlueM8g8Zq450uS9E2 n28ejabFGIiQBJ5UhUsVwCc8YWuecbu8ikkcjJDpNtrE9xpvlHT3nuZXmnfY9r5m07E2nGVPSqs9 3ZxabcNJA8LTYaIhWkaSRfuO392u6t/BGhzXSp4d1SG+tL9WuZBJF9mlhGP3hjjyWbPPpXJa1oOs aTc2WjyQANetHc2cMTZz5hxGSzYBPYrnr1rqoRhUdrlaJkek2MeqZ8QWUkNirQxwursCxuQ3PHU7 V7d+1HiLWpY2GleRHb2phEUxig8hLlAc78Hgkeq9e9STWGq+F9Rh1DULSWy1BpQs9o2FYtjMcqLz tY87uDXY61ot54h8NeF7u0OY4rq/tJDOy7YmVhIgIbH96rouPtOWWxrJXMHw7Y3c/h28Ju447OZ1 iV2KH5M7UlKsPl2NiNvwPSvRk8H2/gvwPr1xZ6xFqtlqN7YyTRwhPOhcxyQyJNGOBJ5ijHr271zX wl0/wz4t1HVtGvoU07V57NsGMt9nuolykqtEfuuud2R7Gsux0lVsfGmj33m2Uy6ev24quZDPptwP LkZM8l0kGW7Hd2rCr7tVxjsbUJJoSx8RatpnkxatL9oa2RZEG4FdqjO3bjsdu7JxmlbW9OvHnsU0 2PTprueK7t5iQIwTnczn+FSrYA/CppE8NNBO0pktYJXaWbayvEi7fm2eg3hTiuc0S68H6c7f2jFc 3GoRCTybqFiIpQfujyiDnJxnjAxxURSfQhor6zYHQtAeTTLm1WDR9ViuW8qVjJGzTeWsYYjA3RSf +O1reGrXVtet/wC29I1qys71bp4tSil2CJbIlfJadGHlzYCMRzkE7uelZOrWd0/h/wAR67pEKPcz xw28kEMnmiJ4m81VKnGTgcHtnFRR3cjC/tr6NDp39owKNOz5E8u+PK72A/1aHaEB9cVUqcuXmjuM 7eDRfCt9obDWXfS30qaHT7eeG4MscQ8qW7EVu5xmGXzA21wvIUdcZ6HxV411bW/h/wCGk1K1hF0k 6yQ3Tbkmhls7ZXVgw68sVy2cqBXN6Je2sGsXDzaXd3ySWcc6SOFc26KzQjcABwcBX4+8BtNdv4I8 JaV4osNOsHuWi0LQdU+0ySuWbzLdosG3UtkkllGB1xiuVSjSg3MtyufOesDXjql29kzQRSyGXy0M wVXk+eTAQhfvk9APzrNx4q/57y/nc/416Z48N/Z+NfEEOj6XJd2R1C4eOTjjzJC7J7bGJTHbGK5P 7X4i/wCgFJ+QpLHRsWf/1OH1W8S20uK5vJXx+8aRuDjjITj6Vx/hzVvtf2vUIbl90Tl5LbHRjyBn p90jvXVa6JnsS0SCzhDrGysdxwv+s9sgf/W9K4nw5pkthpq27qIpb2Sa4+UliEH3TIffrXwNCMVF mp0wQ6pqFjPMzwMk8YVc9ShI5HvXYwywwXrRqHRS586QZG4DuRXE6NfSXWrRQ3JELQhZUMw2gj/Z J/pW9cXWbV5H3ICxJJ4HPHGefeueqaRdzvtLaSCGwDKuxV357nndg/nXJXN1AdWvfkCi2gKq+OCH ctxj1roUDG1hkU7Vtl2KQc9AFzjvWTLaWMdzHbIW+03JmjmYjIVkTr+Ga56cepbVjJ0vR4dQhtLl 5B5Kz/aGQkHOOF499tad1qMV5KuRMDGSBkYX86T7FdWEMcNlGlyojWLzZPl2jj5iO/PSpbi5i3RR 2wEpZdozx1GMsOOPbrRKTehF+hD4PsLqW+u7+NVUMvlxu2O5ycZ68Dtmsi2gs7aGUFi8oGFfZs6D nBJGfyrttGEeJrdn2yQAF2ToMRs3A44riXUPeTXsq/uRNsVRk8YwSAQO/XApx1dhNI63Tb0JpcWm 4k86eJnYMoziMbucVhgQ3GlSWd7IQzyC5VFwclTlt35Vt2fkGJIo5iLgRSkjBbYOoOf6VzNrNJFq MpvIjtaGZCmOrxjPyt05x61MVqOI65sovEE1vpayiOAZZpCMbYgNzSHA/hXO33rF17UItVvLcwBr fTrKNFtYhz5YBxvbHWR+57Zrrru2Nh4YmlK+U95ALaNl/wCfeNd8v5/KK870xft+o2XmkeWGMsgG R+7X5sEd89OnatYdWOTsdh9mv49K0ezMZjnd5WbPVY94YAEe1Jqc0EOsQ6GV8qGxhnLFOQjTDrz3 wc/jW5DdmbX7Cxkcpv8ANlkLHO3A3H8OK89aX7feavciQbxFLNI/Jy2cKBj24rKLvuJs0tOm87xF bW8odreGylwWPTZ0bH5cVdu7SO8stQglk3kx/aBhSdrRnJJ7j8Kdp9uI7C21Fxtmv3gtzuBG1XLd PrirWkQJf3V356Ns8soMnCtkEEFh29qUmS2aWjXUml6PDAxjSJpGMxJ3DLDjFY1zNLaw6n9lhWZP MhZSy+YDlsHjFadx5f8AYyQxKFNpJjIGflgXdkeua5611K50mdPNQSxTs8rqPwx/WpjG4Ij0uNiv 2bekO9xM/wAp3KCwPC9ay9dSGHUPskRCG1DM0hwueNxXvnrW3ZXFrvFw5ZHmba7EZ2x4GAMVXvoI X1ySSWMSxSuPoY3jwc577Vz/AJFb0l72ozTtt8WnaVcWafbNQI3OlzGrqQRypYgYwCO1cjLpdrFY XOwpBchXlKQ/KrbWUnPYgfhXZS3lrBYQ20eQrLjcW3s209VX+HpXOjQNSuLeOXakkEtvMGBOCjS/ LhhXRGSuTcw/DuqQR6hBd3ausLhowoURmRZFCtlR0xkEV6sPJu7SOykw9xbt5e8HnKHIx/DjtXju nWhaK2ubg+RNJGsUSHkRlz8z89gK9o0zTrjT7eMxoJ4EPmzH/loEVf3I2tjHXrWWLcRyehymuj+1 tQtbWzmuUuowvmRM2EROX3IRxncMH2rprK0SK0e9jmkeaCIYMgGcKN2FI7HNQhZL26hiSAS3MU0Y YLhSUYZB+X154qrqJext9WjQt5MYjKj1y+3+fFYRqX0RHMcZ9pUane6rbq6BoljJPzDceGb8BW/b zXFwsWoMnyNtDMu0KRgDIx61y91qEMNpceXCVs76NjJk4dQwwu30+br7V0mnwvaabptgH2vDEkci 8kE46j8a2mtC0ztdIleKFoNrGPLGXHQqGyuPoan1GJp7qGFpVSdts5D5AAhOWZff2qFLRBEYy5CN HiRlJDB89ABVPUNOlutYuLyRpD5ds0AU8AfKAMflRTs0OJr3edXeawYFpGCzwq2dpJGWVfqv61z0 Ail3XE+IrhXCN22oOmQK3LSNILrT72R8G2gjZXxnO09/5c1Fd2N5slkgjEwmlL7DhSNx3cY9KiLs 7Cb1sclqjI89jAsi3A3YJVcfM5wO3611M9oAmTJ5EMW95HYhdgGAvXk5x2q0mgSxlLkyqQwwYzjj HQj8c1vroMsp8wRtNCecMQqlhwCS1dDpzmlGKLSZhNMt7YS3skZ3RlcMeN6gDaRVBiZbFhu80PvO WGc4GB9DxXXXdjcttluFiHlDaArFgAfU1Q+zxbfLVUwRjA4zn610wwE0+Zg0cHcxatbyW8klvbAy JmNiu44P3Wb/AArTsZ/E1xHGuqvblT8uIV2hSDz1+tbUmhi7lj3ys4RfLVc9gaXSbJLS8ktpZFaO IlgoyTuG1genp2rrlCXK0yEZNxbzNbyTRyMinKbl+UqV7hRxWNDppdJAzTShUbLFiWG/vkfSu8dS qzaoB5sDHZ5ZGAc8kgdu1U9PUWkF3d+U8QfbI6EdGY8D8OlclGnb3QcTqPD/AMP3v4ryewimfyLU zjfNygUbj8rcfnWPZadeNd6S32i8hgvIZpRGxDRfLghsEcd67CPXDDG13p8f2nfb+Uybyu1hyScd e/HStzW9ZeG88PskcWy1R4zbhSE2ugcqxOOuc8e/tXt08PTauYTm0cZeeHfEmmWGnXGl6rI9reX0 cMkN1p4k2iVsFlZcMcZ4HPFGty6zocmofZHsruS1iR5BOskPmOg2465UsD09vevUNb8baY/hbSYr Kwmt0i1W0kiPmBkB8z5hyQed3fpXOeIdL8O3mk+Iri+e5hvhOhsIBwpA/gO1juLcnNehVwn7tSgi I131OU0bw3cHUU1qa2s7icyFz5MxJYMv3RkE7vrW/Zw6pqTw3b6NJbG2lYQeVdQSrtPDGQKNyn2y a7a/1bQdR0X7F4ekjjubRVRCF2up8sbye/U9T9K6Tw2vgi70TTGu7W0nlt7YM7Ff3kku7aT82Cem c0sNg71GkayxDUTxOw10anatf2ui6oIN8ieb9mDB2iYo+3ZIW6g44rAbX9B1hb/7KL0NZqPtHmWl xEUeM5Kl3TGcY4BzX0H8PrPwxf6OEULDOk1z5hjmMWFWYiPbtYAHjPSuUttE8rxH4l023u5V09dU N4gEjSeYzIBu3E4rrWCSW4lidTz5fGXgeWTypNb09ZEOGEkqoynHRt4XBx7VLcPoGo2lzJZXdjO/ 2eQqyzwOTtjOAMMe/Fdx4Z0dpvFvjGI7ZjLLYsRLDHIzM0GCTkf4VzaeCtDuPHXiCO50nSZUaxs2 kWayQoMbwTgNxnoa6I5bruQ8auxSj8P6fqFhaXFzpvn4gVQ6qScAZwCOPwpkHhLw++r6bfSWOy6s AyWzZYBFkbcy7Rwc8n1qpb/DXwlL41a3j0e0S2m0eKaKOzllto1YzOu9RGxw3HPPSvO/idoPijw3 q2nW/wAPdbutBuLiIzNFNdz3FsdpcMdj5I49BWMsrlumUsXA9asrma5sba4dcySRq33cfe9q1Lc+ ZPCMfMSMY9K+P9G1j49SapZeGdI1rStSmuLeV4g6qqhLbBIbfGG71reIPiL8e/Ad7YReI9B0iY6j 5n2YxPG6SNEFLrmJuMbh2Fc7y6fQ6Fi4n0vozRTabA6gFv8Aa9AcdK25UZYXe1Aadf8AV55+b+Hv 618WWv7R/ivQEj03UfBkEhUOymG5dTtLZP3d44zXqvgb446r4xkItfBvkvEzIRJqARQVTfxuhH61 EsDNMaxETrwuqS6bpk1hp93GdsjO1nfToQJDu2ncSD3/AIcDNWdS1LVorbTy/wDbMcnkv5xDW1zg h9y+YZIstiqNl49utKeDQtQ8K6l58UImMlpLa3cIjLbcs+5OnTA5/nU978W/B9vLLp2ox6nZXSoc iWxZhhgQDvRmyKnknzcpnywezJLvXpbG5tb6yvdl1c2iTXEs+mJM7yDgFjGy4+XHTt6Vjai8tjqU N9pOp2tvMyQ3fkMsyo8+DuYrh+v90nGa2NJ+KXw7+xQJLrsVs0SJvS4gnjwcY53R/wBar6tf/DnV vP1XQtZsJdU2j5LOUSyTZI4eJsHgD0GBWdZT5LMmVOPc4d7+G41gRyRS/Yb55DKBIFRZJRklWPyq FbPBPtVZ9LtTObQ3DeVbSeU8iRh9y4/1m7rgce1M1G48h3utNDNDczOfLP7xVYHKtg9OO3Suf1OA wXQvLRYx9rSITXzEiNXlyfLfBwvpjtXizi5MwaQ3XoToF2PstzHLOsZkk+UjKt93BP3sgZOPu9Ks 2WoyaQ8Gq2V0ZDBIs+UDpuV+ZFTH3uBgnuDxW14Qtxqki+FtesHgvr2IyaNeTSKRFN92SMHmNo35 +/0rM11brSZNK0jVJ3tWNvNFLCxJ8qaOTaF28jOB24wac4yUbDUTsfENvZSeJ7TR7dIodNuGjuol lkGyMT4yOvBiPbNS+Idb8TaR4ZsvCskTRalpX2v+0ZbuPKTQMyMkayLnd5qcDJ6cdq5nV9N1G80f TLeC0eYWd4vmXES5Qq6Fl3oUDcsOo4zXe2l9rWoW8z6jqq3tlFOgl00gI08ZXagj3AHr8pQ4z1FR S01OmKsjmNc8OBNL01fDmiSC3sZ49e1awjcm4tTcAbVOMDyY1XgjJQnGO9VYNZ3eFtY1aFJV8My6 1FAELk3MUhSRnkiT7rBTjdnnnpXa+D9b1SDXGv7OP7PqVwyR320MIzBvJVZIm6bYxgHP3RnIxiuZ 8dT2Z0a28Myxf2Q17evcGKO3xDAUPlZOz533r85kJ3AYyO9bRsD0HxeG5LHN5qKi6hkuE0xlGAx8 +MvHNG65wVP31Pc+la/iDSdnji4bXLW2ttOexintkhfICxbAjzsQOfXjoQM8VXt9DGu3mt69PqiR 6HZr9ohc5eO4ghwiRxkElpGK4ZhyB7c1sXzab4+s7QoYtAuWC+Yi+aESMZVlyM78kKQPat6U0tzJ xJNaEV/Z3WlRYYWF8kiOcEPDNH5iguPvFQce2PpXmXj/AEL+0fEujaFp1qHkhg8ogdSigOST7AV7 fpUVhc2q2sN9DqsmHaaeGNo3Zx+7CtGwGNgwMjirc1lGkq63bxJIqErNIMeYkZBDAg8jBx2rqjWW 8T6jBuM8NyX2Oc+EumJZeHLpUjCzNJmTjHz9SO3TpXpdi0sAyx2gk8e1YiSx20DS2iqkc83mEJwP mGafDqyzPtJBC8flSxD1OjLKN9DeutEsrz99EzWc2eTDyG+obj8hVf8AsxbOPMt5PJjkZKjA9MKA P0ArWtrmzWHM52lgMDp9KzJLtb2eW1tCF8pQXL9AD2FZRuew4RTKFhYLLqi3AUuytkFj+Wa3tXtE ubp1l/ji2EHoQR04p2kLE6h1YFhxkdsfy7da1dQhJkwo3NjBHcUT0WwRjE8C1v4TeGr6W3+3aPKy wElHtpiG+bnlsqTWnpfwrhW2htdP1DUbKG0bzYlnkDbGHOQSS/6/hXsqxQzpt4coOc9R/Kmm1gTI KgEehNS78uhMqEd0cTrol0fRr6aaRJZLewuZDIgxkmPbz+Nfn94X+Gt74mu2slu7bTILeFXvr27Y LFDGxwR7t9K++fiJL9m8G6s3eZFgXjr5jc4rxrwx4H0//hFLm71FTcJqoZgrAhVFufkyO+a2oT5Y M8athXWqqHQ3YNBs/DEUOgW0r32hmzt20+eIgpKUA8wqV7Z5I4NddpkMdtBrN9bWstwPKaJiceUP nAc+p256e1ULXTm0Czs9Ks3QfYYAvlHDq7up3pyePTj2qE2y3JW50y4F3ZXAdm8tiki+YOQ46DDD nAP868OrJ89z53MaUadZxgZOlWkaw6jb6fdzvqenbLiykgX9zLbkea8UqSYIwA+3HfFeta5qP9oX Om+IdXR/J3vajyAvlvbssjLIO+7dtye4HXpXD+GJfsmu29zCkQEltPD5gUqo3Jvjdf4W2yAq3qCc V0vhlUm+EpvVZLq406cA+egDxtCysqYYjgxDCjuG4rtwzUoOJxVDjNLtJoLaQ3ZAi0xxJbG5XAEM g3vFKVP+rb94q+hKV2mnSRT6lBdyCW3khtMWMZwxBhmG1Ynbp987sZJNcjdTG30+aSa0/wBFckXM SyfIWEhJDD0+YOAOhyPau60qJNG1bSrma4abSb+dvs0kzouTMilo0fv84Vh0I57UsPP3veMNtjel srmGbStX1cqti8K2txJbtlkTG/MoP3jEx56ny2NcV4mt7q2hSRrkXkmlQ+Zapu8u6W3Q8OXHVvMx t7HAx61teI4Y9JtPsV3qDSvaXaBo1LC3K3hwg+QE5Xnk/ToTiFbGWHUzoeqm3uLiGFpPOVg5uIVb b5QjOCpiU5CemD0rsrLm0iXcoaNoljceGG1fV2aG51K6hWSPcIyiylXD7M7t6An94BjNL4z1ZrjS raDVXjfXPDd1FqNjqJbct1AhxlgvCOB8s6Dn+NTg1T8D6zoHhvwzceGtdjhjntbu5tUvpYd4AL/u VkfJwc7i3+8Paut1fwpGug6TeaTGULt59xLKqZImGN3kscH09CnXmtW3GNkO5JqNpcqsHjlbpJ47 K93LBDu2SaVMqpLjj7xHz/Ve/WuE1mzW18TavpOnp/aELzW9xMxUqgiu0MfMYwGwCB9Ntdf4N8Qa 1Y6dfaRrtjFDc+HppLYWwJAZJuYmG7K+UV+7xjHFcxo9hLJfa/Yhnu7qTTStnI0g3FYyGjQyDarb WAXHtyawqNSt3DqY91enUIvBUtx5ay+GpZbOGIHbJJMGxE7kjC/vFw4PavRLTUtNitdTu4o1K6yb S5s45AWhtroN5fzqedoY7cY4715VqtxqOrpPrWnW32e1mjg1TzlUlluIcRT+WMHbufDbWrZvdV0y 11pbqa5e3n1GBdTlgEWQbu2JMpH/AEzcneQOhrn9po0wkzpfAmoajo/id7O1tojfKPLkjGcDMr+c iA/xHHA4/lWfrV2w8MeJr8yP9nvtfkSUYEe0+dv3EdfuFhjrRY6pBb6zfaxrE80Z1HbORarmVyY1 eKYYGVlLN94dqt6VpcXii7t/Ddzgi/1+9nmXdtlMaWgbzDjOSSwx75rTDVPd5ULm6D/i/wCJ7+a2 0a9YPPYaPqVvF9phcLbyNLmNAM/eYAk15boU1tomlXmizvP5V2sju0bfOskd08YXA5G5CNw681vo JNe8Parp/wBrJu/DkLSMvnBWlS15mdM/IylFO0D5u9ed+Dv+Kh8RWOlXl8US4uLq0jmaImVVbypY 28vHO7nLY4zu9q56sZSqXRSPWFS/v7gaNHYwT3ep2MWm2sUkn+ixyZLSTlmxysWdv918d65GXUZ9 E8KS2TTPLqcfitFilgdnKtY3b5Cr/eO3d38zqcV6NILTQPEus6/pskd1dW6rpNnYFXmOUcGYqcfO GIdH9THx1rjLO0ki0PT/ABZqDS28UvjUXzFFA8mKe/ICvHuALNjA7Dha6qcGkO+hqTalYWurza1M htp47yErAYdqyYhDsxb+9ufnvn5mAqPUH0hNP1b7fLLqUz6eY7BvLMcTNcr+9eQDDO8YPy4ABI70 nie8n1nxElxZwnb4g1aeCMfKyOsZhgDl8kKxUYIznGD05Nh3W6gfw3BAFNv5aqk0i7XmWQhBA20s CCpBz93GCK5q1V3aRk3qdx8LtTvLe0m8M6VYvIkFrp6NIzL53lxpNK0gVymCWdtwx8vXqcVgfBZb 7VEk1LVLFb3TdN17UG86dxvmuJZzuDFl+dcAFt3cDsareHo5Gv7u+iaW5vIDLdQWUhERkmAlhZQQ oMjh1Ix7Dik+Hk3jGXwZLpukWdvb2UGo3cl5cALbHzoHMckhGeX6jphmz9K7cPWtHVD0GaX4klsN e+J+uWsUMGl3eom0l2ZZl+w4iijibj5Gf+HH1IqnqN3/AGdoUOiywxR3tpdzSSFiWuGCxLMFjIGN m35G965jQpBdyLZwlI1gu7m7czHzNzQZaT5ApBIz8p5z1NafiC0stQ1ufV5tQube51t03TbN64mC vcZkUbVfAUYXjHTua46s3NiZk2txqt3oVrZTTRDT3vHjjsmxgS3RMiuGPycnjkcV6ZqUM+g654c0 9dU+2R6XpwF3JEFSaNpyMw7yPm+UcM3G3Fc/FoGmX2opaRkjSrO5vrpVXeH8i3KCNGOQQ4Pzcgeg ro0sJfEviDVtelZrqNrl7Wzikm8qIx2ESYXztwGCxw2M5+laxpWQzDuI4L2K4juHzp/z39jkfPN5 Jz5RI2sEJU5aMEE+1O0zRIta021j1J0X+07tmjcNtNrbeSHljKLgNnICZ5rY8NeHLK8uLvUkkV5R alJJLyNxa+WDuaO3k52iE4xu4btzmu08B6tBd2WoaPcaOl2ttps8VzewsDGfMYvvJCn+EjHORVU4 e97w0RfEG38KW3wws9I0d7c3qz2CRRI485tkm6QuqknlV+bjrXG3um6HPr9pCNOktIo4fOhMjtJH cTFWeVscbCqBCB361Ne6bov/AAlPhLTfD532yPFe+ZBGzn5F3OpXaA8wYj5gSNuKh8QeItW1nVpr +3024S8vZM2wvQH2+dIIIumAinop/PArqq1ErBI9it/E9v4R0azuoo7qMXNi0t0UiZhHM6bYzGq5 CD5cjPB+prxzxVqt74mXTVl06SG+jmUQ30QUeU6kSx3ZR8kFymHUjG3PXiuv1LxBr2i6tqdjq88e kywQJE5uZFdWAH7qZPlKjadw25NeV+I7ueDXNUivnIv5THNJcQHYFLDzEkAHy/IM5X8hmufGVbQS QJmj4s+I93rHgS0lnha7vLDUItTs7wOTD/osryXOJZBkN5TeXsz/AA/StL4jnV5vAGtatrEMcxW5 i01rleZJbSWGIoN3HCs0R6ferQ8M+H4F+3WU08U2keIrCb7Np8zuUkuJI9pdRnbG8oAOMDlTnmuL 0PUdX1P4Wav4GgguL3UtWtyrFpUVlkt5drK7OMoFKbeP7oop1Fy6jTR2VnfX10tlaajqFzDLqPhq 6vgzxJJG7GSNIETzNyg7UO5gAQQOOhOHpfm+GtatoNN1GT7PcRi8E4CyyQyvFsYZb5GX52YlcZHH GM1zUwvZY/CV/Lq2JzoGpW0dsYgi2f2aW3kQo/AcSLIcN3471u2nhvxHrfg291aCZRdQyx21rpcC +ZLKilZJ2DIx4JkAWPuqmueonJ+6Q3qeTeJtPm03wE9uERxd3lpYExNIW/fzOyrGeNoADDZnp7Cv QdW+F/iDXPGZuFc6fb37R+XMdrrHBDEkXCAZUxbdo7bvxqhqV1p8PiTwHoAnW3tYdR1HW76a5kCx xJYmZIVdWP7sqDny24JxzzXlPjn9rGHTJNGtvCMr6jPojFpZ4x5VtdSoTtY5ySmTuK9DX0mU0nSp XLUT7Hh+D/hvw5oWo3FjI8N40bPLfXkpfyoh1d8YUYGTkeuK+bL3XPBEN7pcOlaRHZ6LHBKj3V4Q 0sjSP/x87V2nouVG48Ecdq+PfF37QnxU8apcWuoa5PFY3EjStaW37qLcx5GO4xx+HSvKpJNS1GUm +vZZ5G7GQn+fFek6vLuOXZH6Q+Cfi/8ADn4axXggvPt15KylWnnRQCPm3Oi578Aeldd4q/a2+Hev +DpNMNzt1ub5SA4S3Ujo+4Kc/Qg1+XUeixcHHb6/yzUv9mKgIMagepUfyxXNPEK5vGlK1j6/8G+P /htpGo/2jql3Z3s0Dg2iiVzEjk/NM6soDMOSg6fkK+4/D37QHwZv44LWbW4lkxy0/wA3Pc5AP8hi vxbayixt8tfbCimizKYMalSMDIpSxCkOOHlHU/ed7H4PePrV7a1Om3ou3WWRbeQRSO6AgSHaRlgG IzjNZeofCu8hnt7zQPENxbxQ2i2Z0+7xLEyxxeXGwfh9w9ea/Dy11PXdHYNp99NbkEEhZDj24B/r Xung79qH4qeDikT6g+o2owDHK3mIFH+w4P6EVKn2L57bo/Vzw/F4mieODxRpwtJ47JLqW5hlWaNm LGNonG0DIC547Hua+Jf2hEg8N/Gjw9qOl6db6XbmwsxGbf5ElSXzEaQhcfNhmRv92vTvAf7Y3grx fbx6V4uhfRp3wpmiYtER0xg5dPwBH8q8m/anvtM1PxBo+p6BqB1LRn0Zfscm7dt8iaRJVHA5y2f/ ANdXe6dyKri0erw6VqGgeHrXVvEt9JF4flsoJ9KsDw1zPAA7xyoeFiXYAOfnHUj5QfUPhxfaHonh rR18RT/atZ8SSfaLpShLossQmChV+6TvA55x9DXjviPVtQ1fQLPVED3GnaTodnprJKyJun1BUgCL uPO7AYbeR94/LzXsHjh9B8KfB2ysmNrY6nr8ltEruFlliE75bOc8Rw8dufyr5+nHlk2csWfPOkSM /hzW/HV/p7w67rHiOaMLJJ5UaGW48iNIguTu8zdn1Edelahp6XXhlPAWl6SbqbxteRWsF1FKssZs TIGu3Lrn5ovKPA/hcZrU8FRJJB4I0LxLtS903UV1poWVFLpawzRD5UQr8txIZD6E0eIbwSeLdOgW 5XT5fClmbZbG2DQtBd3cgcMW2rnMMZGAOd209KyuoL2peljofifd6He63pumafC9lZ6cZrcqiOjs kGwLKgbCFAflBJ+bkHpXG+N9S0XTbmw159Ve7vcrNFbQoAqRKmUe4JBLM7dAORjgYrl/E3igXcl9 c66GtEijURFWEsjeXIwUqDghGYhAo6tz71v6j4bvNbm8E6JK8ralqTyWVyLgJs8u3JlV2U8iTyiF IGOB68VxPEKrLYy1Z3enWdt8M9Dj8UakY7jWZo28y0AjNykmoYZVCA8tjG7A+leZeAL3VtU8RXts liovrEmF2VgVubo/chQsMKVPzjscAj1rI8VaxqPi3xRFB4bkfTVvJpnslZYM28m3yby4BRc+TGq4 h3HO8seorodR1Cw0ywbQ/DcdzBNcXdu9rPb5F9KoVUVfMkO0ZdSXkUdOBWjjFtLoiuU7fUvBV+sW sWl9cHTk1bybtrSGNpPJbKxTBk5+bkNnFeTWKahpejGKKN7WV5JNN+1y7SsttCWxHlCVUENlxx6d Qa7fX7i/1XxfZyazd6dDqMk0djcJHJLNFHJ5X7n/AFe3JHWY8jkVlaJpF9rur6h4ZtmF59l810CH y4WkttxkZI+FYS9A6N/DyM81z4iMnU93Yt/DY1vh+9jdeXotjbRzxafEzai97tdIrZ8IsanoWx05 zx8tdj4P1K78b/FOB11KOTTfDdlNewRQkAzX0p+yxScA7gkSlV9F5PXNcN4Ngfx1fSeGtNd4mkvJ Zp7mUq0FnCoHmRRlMFzIQF5Py/XNdt8MvD8MV7rvip2lOgrqDWsE8K+VPttkO/5V2lYJH+VV68c8 16uCi4xHR21DTY/GviXWLi61m5ksJIJjZJcyY8vL8oHjIKunTea53xNpuq6EbC70K70+DTb6WG3n jndm+ySk4uBEmf8AUt94DoPXkVr6z4it9U1YaZ58kOl3N8+nadcQySFAcLJveNnJ2JjaePvcCq2o 654f0y3/ALc08Wsv2fyEl+0bZ4FkMwSVcMrGON1O9sDNedjZx59EVFmbabYp9dvNV1F9KvYw1xKg YLHMs80arHGuCX3wJh8H5fcV1vwf09tZPibUNXe3gs7mWBYvPQmKRT5jbYsMAoGT8gJ9+KydctvC d3ps5sYY7cakItR05bvEMkLAqu1Zcn90y8KH2/SvR/BFuus3SXlpYwadd3VhEwjK5ELq2yXZvVhy vDEAH061tGsocsWTJWGtaeI/CK6N4e0e70xHL3kdlbWrSB7ia7jkZZJI2YrsXmQfw4HFfm14htYd RGj399uhs0V7GW4DbI/tGwhQCOo5U49Mg1+m/i/UkgvW16006CC6sLm1tdMvdirLcQO224WMZ64A QZA/dkleDx+ZPxMhvbO+tPDUKu1oji+gUqDM73A2jzUjLfvFVdmAO1e0l79jmqlex0G5sbJormWD bbo0kGx1kM58zDKPLJ+YKST27V3Phq9h8PxrcS6Pp2sQ6lbsbg3MQnaKFciQRYKtHIFPPPoa8q0/ Try3NvZ3FtJFPayO8kDZDKjlmbg4IIXn/Irr7O9k0y7XVLHKeWf+WZAzx0J5/HjmjERk1ozlPQ9V tNE128tb+8SRZ7exit7bZJuDQrD5VvO4H/PJThtuclax4NSe1uNT0vUZDvg8lYTFudpA+cmPaT0x 67uelTy63o9vdadrOraTaSRzwRvDawtJ5LyNKylVUAbSrszOnQ5GOKvWtxJLf6VB86XsnmIFu1EL FVGUDf3OmAxrwKiafvFoo2PiKIw2xvZGur2JfJ8gRltqwH5RIDnLsffp6VNdX04urid9OW3guFMp iGSq5OC5HqW/Ku7uPCN3oMt9P4j0tLeX+ypGsysqrJLcMwWHy3UkTB8kbfX0rz608Marql61lbWF yuo6ajm4gukMe6ZcjZnPDMMAI3pUx9m3YexmjxTcloraG+jSFYY52t2RAJZy/lsc43kBMcA4rp9H 8RvNp19d66sxmllW3s7eQAJHv+Z2RiBtAxlcHrVSdU09INI1HTUurezJ2SIpPlCf938zhsbS/TjA bPpR4cg07VIJLF3M32OWOD5T5hCkfuWZf9pcD3OcVm+TdGi1Or1rV7zWbeSeaJWuXeNoZ7eT/SX4 5G05LDpnAq6l2+s+EfECaXIthNpOoW2q+VKpwsj/ALlx9Wwp2/ia8buoyL97eBcXCKyrHuIkTafv qeMLXT+DL+zu9R1DSJJWjj1LR7qxOB8jOy+cko/vEFDkf/qraGF5XzI1pvUzIdO8Rarb6l4stLh1 163nWdVgCxEgp+8ZRxyOgUcEZz2r6AksdG1OKTxLpOsC/W90KeOW2ZH8/d5EeXhO396TtywOSf4c 180mQ6SLK505tsh8uQzZZg2OP3Z6BMdhn0r3n4T61b6quj2eoa2NP1TTb1baztbuIC3uYANqhbjA 2TbXPBwp5APPGeZNqKmjalDllocPp3hj+0bfQtIfy48Ge2eMv85hRRI8ixlcc4QxyE9DjrxWL4lt tHs/GM+hWtlFZ2tsYzE8bEGNjErGRcgZJYk4r1iHVNT/ALahttR06yN/DNe2NnJaeYbi2K/KwlQH b5fABJA2n72M1454o0pbywuPGMczzwXVyIZ5IQG+yoqqu1zx6H5a87BV26lpEzi+Yy7Owuxd32kQ 6i0sj6fdTQeVkKZ2XeGHH38jnP4Vc8Ja65s9+rzxXB1K2FrOHUu5aZN8LTSEZXGz5SBxn2qneSxa DbaV4i0e7LRwX0yrDKAC8dsqEvnpht+MA8CqBk8J2Lm3n0ifT4hKkLsGdusfmQuysV27lb5eK9TE ycYXt9woHrfhPxxJ4IuIYNO05LnV9Waa1e/nbKOYQ0kVvIqHlcgfz9K9X8EeIIPD/g3VvFuqmJdP JURxJGoEMkpY7Y4lGGcPnI714LJcaKNIjutFtL6S/t3W/hlv/LdVlspVfCIM+aWAx8x2j3rsdLvY /FaaHpVvM0mnajq8t6ybQW8wqpkZgowFQNu6ADd2rx8TH2lpBKOp0Wg+Zp2kW1nZaXb6hHGGL3V3 LtmnlZi00rrn5S8hZsds4rX+23//AEL+n/8Af/8A+vTby2vorqWL+wtAt1RtqIWuWbYOELlY2G9l wzYJ+Ymq3k3v/QI0D87v/wCNVk4+QrM//9XiLm/mjsIblNjK8RnkjkG4Pvk6ViC7kuL+4dyqEJxx hMN0Xj0HFS35cQLMo3hLQAAEcZbGNtc5p1rmO4gkk/e71RGZc8+hr88gjVodazW0mrNFctLvhZCh jIYHH8PzYxXYXErX6SRMxaMRgbQOcj5jx7DHasf7ABEbsCO3A4maT5FbaRhk25Zs+uMV19laRtrA Se8QAuRCqxmPcCAx6geg5pSNIHQC7itVhsJQUmKRoCQAAoHzke+apWFneXWuPf3jeVbxw5Q8fM7f eLAH+lY+pSQ3ty80kkzRxny9kcbhm2nPy7gK7S+iltbmISOqFUVtgwfLBH3W9DXPN2WhbZPdTW0q LZ2uHUfvZGX+I5ztH0rgliS2A8xi0vzFxj72Tnj866PS7eWS1nvmIEau3lqTgsO+PxqhqJghswUh Kyl0Zj1bbj7oPvURRDNrRkiW2uZbbPEe2TB+6TwP51xN/dMLZ51TcyM3U5Iw2Miu58OvDDpN1atx NO+47eW2IQM/pXH+JImSW2t42KtMxD/L/q/mA5x61UNGFtDX0mQHUbNx8qtZSSEY68HGfqTWTeRs /iR40ztEiRGNc8M8ZywrW0z5G3Iu8wWu3LcdRnv9KsaNZG68SwaizBIooluyFOcy+WQAR1oGiXxp Mtr/AKC6xvFb20dspIxhnHz8cV534aSO0mjl8tcSMoJYYCqqktz07f8A1q6L4hCSe7tUjmV0mUys /QExgDA4qC3so57SBbY5hlMoOeVO1No/U1cfhE0jpIbVzB9q2P8AuLLy4ZgR+8NyduB+Ga4Xw6sd vJqQuS4MqMjrHhgVXrz0Br0fUXaLTLCOzmEFugRNryBcMg28A9q4mUzw6ibJSuJY+CjY3sTj8c+1 c8ZdCHI09Rml+waOAAAdQt1yvXagJQEevNallE+lQTyv5cZDSSgSjH3mJxkZ7cDitGaGCa1gM0gd IJNyRAFgHC7Q23AORVHU41t9Ce3jkeFnAUsyZV191bv71DktgGLJbahp/kugt1uTIYFcghRt5wxx 3rgdbaa11Eow2P5cW5iflXcoznP07V1c13IukhHlVjhVHy5bhPl2/wB3HGf61zni2yJGl6rLJk3d vAsiHnEqJtJ/qa3orULk+hWLSo9xFvkhEvzOxIVV6D+VdTeww3MUxaNfMijCeYp4A5GQR1bBrlPD s1vBCYpSzxu/mNHGwxgHALg/0rrdTlsXtZkhmCodrOqrna3UZNKpLlloUYt5bxuIfs4PmoiBTIVy UQ4Iwvrisa5vWW5vHsEVZDnYjn5GYdA2cenFWo5cJM20FokdlbuRg52j2rktNF3rgllX5XWTCEAI oUDO9if9nk8cVrQTerFYt2eHlF1cbEtbZQHA5zn5gD6A+ler2s09y4UjDXiLskTO37pO3HcMfyry a6+y5NnZHes6b0GdyymI5TOP73b2H0r1LSQLa0tLmd/L8qGN1tmO0qyj5genbmprq5nIxrXSrCz1 ia+svNS8mn/exmRdiqAcKvB6/pT9fk0VbCb7Nb3CPqrRriQ+UrCNgR84/hDewq46/ZPFd55ccBhN zyzDqGXcmOR2Nc14786PVdPuoju3o1rBCgJw3DhAvT5sMc1jTjeRKZnQWYaWVISJBGkcbqcFQrHd uHtXZabKst+kjRLKJQoYYxtyMA4+orPsdHudOuLnT7ghLl2AuEGPk3BcKPUAdcV1U+k2yX13aW06 iK5Plqit3cAfeHTGK3qSNEa9oscyBigTfJ5YUfMfY5GR1FNk3vc3iyDck3yknjHHX2/SqGnyGeST ToxtOz92QcbTEcfr61eupbPT3E15J++blVb+77KOv1rqwtBz2NIK70EsLB1CwwqXZIhGNwxGOa1l tmtDsuNSZDkHy7SMbz7b2rJt9flu8pagrEOvuKjur4QKwjw0zjIHOBx1b09q96ngoROpUFe5svqt npIUwWyxyOcq8gM1w+PQdKz31vVdTl8pd289ctuUegb39q5+OH5j502LmUZLk87f7o7j8PxxXM6h 4hhuy2l6RMbbT4W8u4mTl5WPBVW9OOTXoUoKOxbikdpdXljakvcXT3U6fLshIKg+np+deYeJPHNn pOStnskJ6hmYj3JHy/rVmK+WSEwRGOziH7uPbjeyjgls8mvKPE88Oo6iuh2r/uscsWx5shOML6gd wM1rypmTPqfwxfR+INNs9TjZ0Mw2gbTjjqeP6VaeHT475psy7U6mKMlSwHHX1ryP4e6rq/h+SLSb 9zGn3YHZj5L7fl2ZxwfavabuZr63kW3Ox1bcVc+nGBjr0rzcZQktjPlLkEtrq2m5WO43xrLMUhCg Mq43Lz+H5Vnafst3vnKAvdRYKNKo27eM5JqtZ21zI0kMciQQiCTzHbaOH/hJ6/lWFNCxtfJTcI/M RQeFxuYZ7Y5FeX7CV9BNHdyTS2Nj5qIoF1bEk+arHIB6AdfwFWtSu31aDTAls9zf2cfmrEGVxKpj 8ttuDwwrnJra4MsMcbFVtm8na46AqcYPTGMVJq0cFvpMWooxSWOUrtts/McBcnb7e4q4VJwbj0M5 wubkmurHp1vbC08kWtyriRlMgTa33WXHXjHtW3e6xpOoWOqWjzeRdXUYMYdHITCnowXAP415KrX8 H2iSIPJDM2JG5xkY2/MeldrYG8iMdu93cO06qDEclUYjHDcccV1YLM5tOFzCVNXLema1pkl2ksB+ zPDapbytGQPOAUE5Hruzyetdd4f1CAQaZ/aTrFLZmUEtsG9XzsVT/Fz6V53DpM2k+MtLW5hzHcKw kuCoKtGDw2GFdVJYjTJLm6vVtrmO2JurQRhXjnVDnBI+4fyrTC42pCTZp7JNamvpVvZfZJ7O+K28 1zqbBhjCxwKc5OP7w7ZzWhbW+ltNrKPAsLLcxSWzDcm2HOCp/n/hXnlvdRagY5NS0uKeXVpzPGoL BY4hzg7e/Sm6nb3b3l7JBZutjEAVlWdiwGMMhCHGOwFdlPNW7Mn6srXR2txq2l6L4l8VGGOR3vI7 A2ctszhRuGw4ZTyadqCR6P451a3jubl3m0eMRyK+4s6GQgPu4wdv+eK88nuSJ7W0tluIxFbxgeaS 5C57dcegrI1PVNWN3YwzJdZMEi7pRvkKOcfMRjgfoDxT/ty/Nock6HKeh6bMdD8TWdvqs3mySaUY kjt3RnjG8yL5iFWHzMegIxXMeNpNR+36Lf6pKHkns5HU+R5O1WLjDKrEe/auXmvpI7mNfMwIgFhI A8yNUJb5M87c+uc1f1HxZYTz6Jc+J7vTrm2OomyR2tZo3MBRcRx+Xkbg7Z3NgVeBzlNNSRnDD8zO qTVrG78beCLi3AhltNPv7KUqpGS1urKSRjPT8K86+KU17rfinRNOiD77SW5VCVG0gxpuAz9OtexD QtNmeC4aNRLbF/JcnG0ldhxjP8GBVS88NWd9dWd/dIzz2Ds1uc42+aNpzjrwK9KOZQtY6vqkj448 YaRBa+LLKIxxzKbORGygxnOT2rv/AIUW9nFq+oQtEPKSRX2RKTj9y/8APjtXp2qfDPQvEV+NQvpr iK8tQ0CmJlVdp6ZXFQ2Xw50/ww815aX88ry4kVZQh5UeX6Dj5ulDzCmokTw00acemW0niize3Xzb eOwlDeWjZUswQFlVTnr3rnvGvhvTV8RL9ivobq3WyRHyeAEZmx25WuisL9dJu47uzuCsjI1q7MrO sijGCowDtyo6CotVWz1C8ttTivISZbX7NIp+U/aFAwqrjpjPNcEMwg6mjOdwZx/hXwql74ivrKEx CP7PauSx8tHVwcsc5zUfgXSPC8d9L4Y1ZptMutVvbiHzrYeYm6PPlhk2btrdyDjbXp8Ntop1S8u1 vLKG3vtDtoI3WVCTd28snmx4LA5wVHT6VQ0nR9SR9cigtLMyG4W4s3nTdmMnAjikVsx5B+biu6pV i42TGkzhbjwTdaLpeox39tK91p+pNCYbeBnjcIoceWy5+Xuew/KuTu9Gnnt5ZbOz822ubpYI5oWY ASyDzFiaJWwPxH417Drl1faPa6hYz2Aimn1JINqtN5co2Bgg3/8ALNufmB9vaqNkNCjuF1O5sLm3 l1CHy7mCOMXYGM7OnJZf9kHHrXzGJ5YPQ3jHueba74YtLnTbWTUpToVxJ8jqyyC0DD/VBl5aFiwO 51z6muh1fSV1HwTa3fjrUd2oWE8yTXenxC7d42j2w5lHyKM8Fyc4rutEmsLlho8+oRktG5A1IGFH iwdhEqgbAuejZpuoaTfeBfDMumnTZre3eGSC5HnJcI8c7DYSVHzRqecjnFcsJyZ1QhoQ+DLqZvDu h2jgw38cEsMgnOC219ySowzkZxnfgbelRwnUtY0/Rp9Tji1K901WkuBf/wCjFdvzJHEyqQ655Ung DqKijsY7iewuop7cS6DcgTsJC+0ONvkso/gk6jIrN8UaL9qe71u1u5dSi0a7F5PozDasAwFNwC/W FWOcKNvGG9qb2Ndkeg+HTapZG8v0t7W4uvNZI7eRJYAg5wr46Pu27Qvfgc1x114ZsZ9QsbSxMOn3 TwG2iKMJliTeFkkLZXkhgOfm284rK8LXGrrdeINanvElsnSOaFrN/LjDMF8wMoDOpVcfcbb+NdVq F74N1u0nGp2dzvvb+O3gE7Io1GVYtyrOIyrGEKR94nkDjtXQqX2gUk+hia/f63/bHlyx3ljptnus FsZI1aMO52goob5w2Bz2ziqcfim/jeeVoY5jcIiLG3+riC9PLHPb3rs4/Ju76+eWxhkW0aMyytMZ F3Wo+VDvOI9pwc5+bA6dK8I1rxX4f0NJGeR9XmTqlmP3QOehmcEf98K1e7lGFVW6kj3cmeBXM8Wd hJrjfYUsFtFSGGeS4JikePO852MVPTdg4rrLjxnpP9n7/GFrHbRxqR5rSrbpICfvbXZSf1r5T1/4 2eMbbSrnRNH1AaFYXuJZYdPXEkhXj552zIfoCo+lfOOo6o17dSXl87XkrnJa5ZpX57lia9mpllOM bMxxGfYfWGGpn6nW+sWmr+HdP1HS5hJZ3aBoWU5BUHYMEfSqOj2t0816ijLW53ckdx2rzz4Xat9t +FHhOZz80cMsBB/6ZyuB/SvaNBliXUUkGALmNc/gK+WxicZWPQy6r7nMR6NprHZJNctcXDc4bJA4 4AH/ANasLxpo3irypLrQr19LlG0PJGuSyg9v/wBX5VyPxp8J+J/J0/xT4D1CfT7qybyr6OBynmQZ yH4zkp9OlZPhu2+LWoWMN34f8UQ6gh89WhmKSZMKBzg4JzjAbninSh7t7nf7RvRho3xD1TQ4l0rV 547jURKyMVTySwz94oMgn3Xr1r0ey8SfEK61iG50/S7WXTncDzZ5HEgTpnbgD6ViRQfFKK7hXUfC ekavPdW32jzFxGVXGSGf1FVx8WfHGkW5e58BKtrCm7KN8u3kDaSeen1qp72N6attqeqaw9zolv8A 2tCGMsLGWUDoUPJAHtV+28QW+q2UN9aSCWKVdwK+gH6V5Z4S+OVp8QNVm8Pw+F7u1uYOJpCUaBFU /MC2cV01jpB0lLtbdfKt7u4aWKPpsU+nsTXLOLiaxq7o7RorbVkjF4sbi03zxpJ91pMbUyP9nrWF dMmg6ImArfYF3hM7QZXYbEzkdBhuvQVraRcaMWnY6kttfWb7WTG75cZxt/wrm9d1mL+2LTfHG9iq gxiZ1Ecjtwxf8OFB9KVSL5bnl1MbCEJST1OWhmjvReSXNz9ojWYyCZYRJIPMkzujJ+bCjOfmOOOn QQ3mntY3/wBnD4a7Ja0uI2ChhHztZfcHjPXPFb1tp0mnXX2y3XzfJd2Khs+XnLLIEX+HbxxzwOO9 Y2pW4muFnkj+zR3JV1nCkGJgN2zeOWhYgHbgPGDu5U4HkTu2fGSvLVmEkdzNrEtrBNstjF9ptHuD sWKOBgXicDocHv1/Ou30a2kstKW6+1oFvY5IVQk4aWyJTlcjK7Gw3HYdq5Cx+zanIp1CKSAXTSTL IqlomcZLL8mcpsyPlGPSr+nAWWg3FvaXctsunarMirIsiybZChZmlAOxhE45xj5eeK2w7cWYdjXt VC2w8PRSv5yXjIGuMFniz+7dm5wPLCgYJzzXNzXp2WmjXtjcPPFclLeQKipMsvyOqdeVf7nQ46el dNJrNxFPNNbaSZbmOWSxF1Fv8ySRVLK0r5IJBOegHOBWZq2jaz4k0u9utOMT3Hh+Rb9AjbLl/IAl 4Pc4yRzkY+lOSfNeJDNqbWvJgW+wL/UbhoIZJY8SENE43PGpwFODtzj76ljjNdJoke26s9WgtjBA siiG9uChaS9O7y3uXySvGY2GMFSM15za6jBqV8L+4t4NSuZ4Gkg3s58nChwdwxt4zgYPHWul0Kxs dVCTSSRaLEYgJPtZk+zyyljuxKpKjkAHjcDweeK6aU3KQrGfqGtQDWr1biySzj1eImUBt4RbYBJ+ owXUKGG3JKjPeuxBubLTrvT4J1tI47uOSCWRGKv5wxtdG+7wR8ymuI8W23iK1nbU/wCzYNO1PS3W TAjOy5RPvNvbIb5DtU+hw2OK2PLWbwSLw6hNdy2W20voWUblgkK+VIASGLMeAQRjFW61pWYJln4g 6V4g0fVdP8XQxme0vIf7On3EANKPmiX5SRs6gE/xGsHSdZu7nX9Ou9KDXM1hGI47aRQrS24YSPAV HXP8DDnd1xiu0ubtfGOlR+GNc1x7KLH2SOeSEBoSFwC8Y48xSAV3EEdK89giub1dLu7S7jN9o+qt p0gQffWZGKvkcAbhzz8uRU1ErrlLvc0pJ5NVtddvtLQWiWF4bo2bAk/ZL5iGZdhwVRxg+lU3srmf XYLYGC4P2MzW8csn+qkUF5kQY2fvk4G7I9/Tk7fQr+48ZCys99wmpWcsbxjMRSdP3zW0inKZdeUV ch8cZNbmjXVspS7vbWF7bTWn2TPtTyJAuNsgCg7dwHO3K4xjrXPOD1Jl5F3RZI9Y1KK/edGOoRCW 3kJ8pQsLBDD5nI3fLhUKsp7EDiu20HVrGDx5qfiBbXyNJkEttIit+/h8+NVZ4QijGDHnp0OOtch4 c0Yra2HiC7j8/bd7XtZFMCwW+pAlXXy/ld/OTCnHCGt/Fn4W1bX7HUo9Qslh0v7bb55nihcFYoZN vUZO3e2efc4rWgmrMIoytR0PS7bRX1q5tt8HiOxuc4KA2uM/Z5OF3j92QME4YD648u8J6p4g0HV/ CN9pAW01C3M9sGkBwt2UNsvyk7iv3e2zr3zXtuo3Hiaz8GPpus2SgjTbE2jqqtbp5ELsEbO3Blj+ 8R8xIZc9K8i0e60ebVPDuo280kqQ62cQyxlmZ7yMh4VZMbQCQyBiSm33rWs+WSsNvU+kPBnhPSJr yWx1W0bW/ItIpWlcKu2PI/eNIeTJKDk88duDivHNbupNG+ENpZ3trJN9n1IXaTK+Jlij1TzSspXP ybG3LxjknPAr2/w9F4luZLrTTd2iafp090kUj7muZ4dxKhiqjAUKcAA8CvmXxNDNN8PvEV+vmu1r fTo6/eWJLefy5CzdQmQV6bTzXVXbVrDep3mm6ffaVq3hjRbyytTJNfJf2xmYlXMxjKtKuRkeWmeO y/jV2XXLvw1420rUZpYZrrTYp5WLRbljLhz91f76lpE/u5ycVvzyQ6/49t7S41OXT7Xw3px1BX/d oZpYbaGCLK8hgI2cbuPpmuKgs73xT8RNUt7G0ZLfWZbfT5pCAw2ztmTa5ODtgUBgPVqx5Vy6Ilnc eFZ9DHhhtY8Qas0l1rGpXYvIo4o3dZorl7xVAI43b8FucMwHArlvCkF/pvgLxT4rF1ObC0a+ukin VRG6zyTtkIDt3CRM7fpgdamudDXR7vVpdZu4k0XRpLq7ikzn7TE9zs2xD7qkYYbCfm+bt085urub S/hT4e0PU7+W6ub24huZIOSv2WdFljjwThtu7HTrmiU2lqVbuS+DUfQNMsNRvrcQT6Bb+XBBOjkX TXkaM5Ro2BUopCgNktn1OK1LjTdU0/Vo5btGbSU1WeC4jLBJPtQAuLiKGI87FXCDjO0VL9qto9Q0 26ltZNRjYTzxW9plvMvAQkKyKo2gKxTaOM/hXY+ItY0HQ5dD0+Arfw6foioR5ckYt7+5bM877lEn mMA2Sx6VjG1uZmas9jnrrUtFhbxjc6mJf7LudNiFtGmGmSaZNofgj5UkVSR6/lVHwdZ+IktNMuor G6t5kY3KrkuLpIhudVQAjzCx43Y3Z/GszxrpLz+GNE1yzFzHB4n1OWwuFvHwiLMQ9usPfb8p6nIz zivpzwdqpktiJLRYvLeT+z4zJGpSErtJ3FuxX7h5z1xW9GF9WUtjR1d7ey0CWZra4vIfEEA8+DBt FRid7F4sZVsgBh615XZ340DRtDu9KjnitddiWx1Ew7VVpEkYRoQDkMMmNuOfkrqda1DWNa1h7/Tb Iyx2VgiyQs+0Es3ySTspPBAycD0ArgtGlhm0eG70m2C6humjEUsy+XNPuzuRWHDhgCp9BzinUqJO wcx0umT3lnqWsX9rpl3NZeG7RtP0sLKUMNw8m64JIX5QGkUDuSABxWZftKurWsWqX0VyLO7gSaBh tSRbaIPFmXbmPLN/wL0xzU2mHxA/ha20JJLaxuo724uNZNxPvbz1wXeYnaiY3ZRg30yRXn+rRPo1 jeaQ+onUk1GOK7hkOGy5PmKDIx/dvgqTxg0q81yk3O+8RapqfifxFo+jWEcPlzSxK0dzEJZCJQWF t56DcYox86jGUY+mKw4bPWdV8Va3De26zXc00ttCiYHnSoFjaJZGPBULuJ6NnPHICaTq1raCCbSN Tki17zlaN7hIlQiXbD98pgbOTvLLwMV1nhOewW8n16/vLvVNVubN7oXFkwcW86y+Skfz/LscAcuM ehHFRT/eIoj8aR22tH+1LKeO40+FDbQpZqwe1miYMTIEz8gk4z1yT2zXA6PrM663KNLtXttRsIzc q4LI3lsI2/ebQd3OB82OVO7k13cHh640/wAPL4pNxL9rhDSX8EbgSSW7yOUEwQ842lihzjjoRXC+ Ftag0XXGjW0gP/CQ2zwxyTN5pLzyAGfeRgNHtOYz0YnrwTz14++RYk8MXcsetWOmX1okg0+a+kv4 YW3zW8CtGzRbXT7qDDH8hmuv8LXOjeH7DVJZDJdLY2t5q0kAlWObY254oEIG7fjBTaO+3rxXj91Z 3Xh/4pX0OpXgJOkahY3PklUDFI4QoJQDc2SoLY5HJ9a+ivhnpOiX3hfV7bVLT+0Z7+CKd7idBjG3 yJFjkTPyoy5DnAUEGtMOnzlcup+SnxQ+KniL4i6pJJqIFlYWzOLaxVQFhWV95DnALHPXNeaWdnJq ExVB8o5wPQ9BX1T+0T8NvHVlrOiy38MOp2yxXMUeoWsgl3x/aZJIvOKgbXETKO49KyPhp8MZGsLr ULsb2j4K46cdBX00aqULHVGjJS5ZHzrdK0Eht4UwUwDx3xUUMGprkw28zHrkRlv5CvpnU9A03RN8 zRBmBOd2MA1yM3iOeOLz7SzZoOVDxx4HBx1fB7VH1hPob/VOp5fZ6pfWfF3aMw7kowwPXpXqmk6Z bavbfaYwAMZwalt9a1CSJJ5YW8gnyw5XCAnnbn7ua67QZRJN5QQ7j1+XrXNWqaNnVRp62OZm8LxR RNKoGEGentXleqX08EnlwW27sTggEV9a3mltb2TTujEYwAR3NeY6hqktqZNlsu2EZk+UMFB7nANY UcQ+x01sN5nzpNqU+cOqISOh4qW1ud8myb5c+nHB6Zr3nTtT0PVp1tLpbQyOCQkn7rcemA+3H5kV 6NpPwv8ADPiJJkiikhukGDDwO3GPauqeI5VqjijgpP4WfJuq2LWUK3cW4b8DjGB9PyrpfCGqajq1 2nhua83pfPBDDFL8yqzSBSc9hg7mA649hX078WvhJZ6V8P8AS7qyhCXUU6JISQd2UYYrwj4P+APG Oq/EvRLbSNCutRFvcb5TFHmNYgCpZ3PygEZAyRzxVUsVGUGznxGHlB8tj74ksNIk8PaFpep2LW1x LfWlmIraJ9xWzimZLmfcpDCVihKr91Rwegqt8TPF13JF4U0jU9KW40a0k+yXKyjfGwlbJ2P/ABLt jXY+F+9j0zp6rdW39v6rY6jPNbr4V0iKCfcGJWe5k3NGIlPy+XCjDIPzHngCuE165vvt/hrUbNbi OK31C3ie1bLIqzq0yO+/O8nEYUdBj0r56vUkpnn8rW56tpmrTy6n4y8RWsMlzpPh7SbPwvbShTJM xmMlxO4MnIDswz/F0A7VxOj2ceuad4l8TMl1JfjUI1ITHM6YhhjcsckkvwFz69Ok2syCy8D2mpy2 r2s0+opeQxrHiKSJiGF3gnLSPGi/KAMGqGg+N7y/8LwW6W++8uJZNS22wZiJRG0ECLt5O2H55CeV +hwFUr7JlNHQmPSH1WbXYLFUNjbRRWtpFL5gSS3SWP7TuPyysshZhtJUOCareGribUtSuvDun3Mt lq5gWa31FY/NW2Lf6O/lclQXjYiJnIHmY7AkVNX8QjQ/DVn4FjtrWzvbLZbKfKzL5ke9pPMkGTkB mBGNrE8VieG5vFH23w7NBardL4hvR5aFcwTR2vy7pwpRgF+4hyOcnOeKxgvevEcFqdzZ6D4lj1yT wqlu8f2SOG1vo7RJJ4YPLjWSKPeUBbjHmkHbvJ5qtNrGpDWr290pFlvmTy4YkijMywpgfu1kVgMZ B3YyPyr1e/ku9C1/xDr15fvpN/baS0mpFd7ZeUqkU0BLOFV+it1G3Dc4zyPgS+vfCWk/8JhdaZJb 6lqljDPatbw/aFe3hYnZKWLeVM5Jw5YbsdAAK6Pq7kxSjqWLvQp9JntvDfhGOax1XUrWPSFluVje 4aW6/wBInmkUs21I4VO58A/MB7VteItH0TQptQg0/XLmODTbXyp0mRXkghmxHJK0caozBVO5Rnrk 1keEdf8AMt7n4leOrw2smrQ3R0iOCNPNe33hXdQQSZJ32RjH8Ck8A1514kuNYstJ1h9dSa/8V+J5 xEXgCQww+eyCOGYLnc2O2CPwrXHq1L3Rx+I9N8BeB7DQrXXdOmlv9MtbNHsG1GJ32zW7LvSQK3zL uYKdgPsCapaHZ63pnggeEJL4Q2Wsu1xbS3L4eNC2VKCPIlYuM+TneufmIrldT8R67d6fJYtdT6ne W0dtLNHAsggjDHYhVRnP3OSuNvBxitPRdc0eXSbqy1aOG4t44HvlkyoP2wvtWK2BGCqklW3J8xY5 Pp5ODr1ud8xpW916GJY6Tf6zqslof3bCza5t4vMTzGu5B+780/MFAC8oMHPr1Odomn3Gq3V74Th0 6ymsNP3T3rljbsomODbFnOfMfawXGfXpXVeHh4VubjX/ABP4oubTToLOzlNrax5haOa2GQ37vbu7 LtxnnpWZ4Yh0Ce/sLrxBrSWlve6q+r3UJVR9oEMAaMRbeSE8zacjr0ya9GhTvK8ghHmXMj0Txd8R NA8RfDV9I07TLnRLgiK2khmHneW7KTDFvjyG3KBtyRg9ay/BWoeI9Ck07U9B0ea4s4LFL+4t7qSV br7MwKSSw+Y23PXKKCdvOOlWdQn8JQW3ia1sHCWthfQXsQtFd4WeNJWgt2YjJO9tjEdG4OK9J+Hl 5q2oTaottdsusJK1iwkzttrbl4UjRl2/ulIOWB6n1rq9n7WWq2KatuO8cW1j4tutM8PaI21dZgGr CK6kMcCPCg8kxrtPzFSdyAqMDJxX5g/Ee1uoLxtSe6E0t7I8gnC7MMBvGxcnCBSNpB4HSv0YSw1P wlqF2nin+z9Q8PRanLbwTNFut7C6uBHMH8tt3lwyY8s7W/dP22nFfLvj7R9R8GXVzN8Q9BTWobLT 5ktBDA1xDHAZ2AlLq8YQqGXl/wCHAx2qvrns6tpI5q8ex5zpMkuo20F7p08ke1ZLWObVj5zx7woa EOQPMwrZjJDNsxjpVHSdLvri3PnoViU+SZdu0boj5bbifmVuhwwHFbXhfULK7tLq3jYz6TNdPJb2 ca5S1UkqqkS4x6EIDjua0dQiuZJr7ULCVmuL8xQz23lnfHKg4bdtCDKDYcqfrRVxMlOxyWOj0XSZ 9M0n+0vDN9DqfnzGBorok2qMvzxOEK53FhjP5ZzWbrcXia3E8Gq6VEtoFtp2ktnzbqHBJderKmTj b07VX0C0u7DWr2xm4htR58tnE7K03ykCeKMAq/lcHGRnjA6Cny6pq1zZ38t5509rdyRZkK+XYOVb qiFdzckfJ/CBXlYiUr3Gkei2OuXF7YPYvF576OYruae7dW8iORgkcUCk5Dt/DgYGc1VXUbzRfFg1 FBPcTRmS7Zo5TJK0dxuKJIezc5JxngYrndR03Ukk0hL2yhuFeECO5sXDRzxxrlYy2MExH15Xn2qS 7utTsku7ueMRQzP5s8MrosJyPl2MMHpnA6evNcihLmKsRePdZ8zw5F4RiuZdLgRPt1wRtVLu6DeY sjH/AGOgGQN2TiuWsnljt7HWMk740a5aPg7GH+sOAM+WeV+pxxVW+1bSnuLcTRTeRFE527QCWlHD P1JUVY0Sy1zTNGt76PElqBMkBl+YzRA4ykY/h6jtiutUHFWZoiFfDcmsQX32KaP7ZYRJdeWxKyyq X27Yu7n+PAzxUmnPJ4Y8UaHcagkEVxbzRcxYaKSNmwRnpu5O/wBjVnw3d3UNt9o0+E3VhcN+6Qn9 7EWOPJdR/wCQ8H9MVD4hgbVY9SsZo/IjaOUxR/L5sMo+XdHj+6evtW1Ou+bkKi9Sne2EXhzxPrvh K8jbOi3c624GCvlk7tgzxhQwxjtXW+EbqPT7i6vxp/8AaaLA4MYfyhbSMNgnORhmUDAGR61r+I7K 18URaXqd9N9i1LxVotq9veHa5i1GxbyZkXAJHzLyN3zBu+KTSSbOztdPmjFvqsci2+p2MeyaLzSw IYSJ145x/DjDc1ni5e5Y0nKzPb9V8eaTYaNdeIUsblptZ2yTSLMltK0coCB/OCk4BX+Ebu47GsSC 50/T9L0jT/D7DTb7xP5WsFL/AOe31GQD7LcRzzFTudlVHOVLMxyMsaxrnS9M1Hwvpgtta/s2XSLm exIlhnuGbzH86IpFbrIDwxAJxjH3hTdYstJ1Xw/9hW5e9Hh+NbzT7y6X7NLL/wAsrqMxHoofaeed vJ6c+XGC9m7HTFFHXtL8G+HNFfxc+nwzQaG91Jp+nSr5kY1GeSLy0IY/vIIWzIFON4Vc4AxXzFrQ i1ZLbVNTuXVLgpFfSyDkMz5juM9GGSQ567TkDFfTPiLTdJ8WeAre4t7qeO7a4u5DbygbRqSRxk2s 5H3RMqt5UmeoHY1yOj+ArDVtCPh221ezsPEzW6j7LeM8Ud0330KTsDGJU+aNx0cH6Gt8tzKMaNqr 1uZqm4s8rs/EDWFpaaPqESQXWmXsiXE4QAtGwwPLJPrwOOU9816raajf+EheeBjBbxK97JrdlcWz ncI0jiWa2kHAKsiqw2tg55yoxXK2/wAM/HOheXq66dbC6sp2jEU0gTzIDH8hLSBI8rGxQfPz94et d54q0vXPEOnaBNFNc6XceH7hfLjuzlktwOY2IPzqm7gscMgI6HicZjaHtIpPQdjP8U69fWPiPVII BM8JuZJYiA5AjlbzEVdoxtVWAGOwrB/4SnU/+ec3/fMn+Fez2E+lanZQXeqeE7SG8ZAk0Zng+V4/ 3ZA8yNnx8vG45x6dKufZvDf/AELFn/3/ALT/AOM1yvFS7C5T/9bntQ0awtnsvPRb+5SJ1mLsR5Y6 jcqkfLn+HHvXO6BbaeyXlzqMaJb2pZ5DCCpLZyiruJA9/RfzrSht/wDhIbwPZpcfYpGMU05PHynH 7v16d6g8XCG2hs9D02F0tpcsu1du8gbSCT3Y/pX5tTl3NTKjuLXUr2W4h05795XjOJpmKjBwqBF2 qqLjPQZFdegjt910Ftl1HON8G6TYrH/Vxqc9B1OTWPDaxaZHJYzRBo9NSKSQ8hJp3GWVWH8KitHT Qv8AaUTwxootegJzjcuG57+1OUykdzpoEcMmsTRuq2A3kMAfMI+4Fx79awNX+0ul5qDTDzpgGBIH B45PY10V/bSQaJbwW/yRyTBiSc528qn45rCiieRfs93h4klO5Vxu+Xp+Gc8fpWF7gmVBnTovJSYI tvGJHy3yjcd3Gf72ay9W1s3YeMxI4SISBWPIZx0yv9elaGpwpJpeoLA3mSTzq25wByeAn0QgYrNN hLayzB0haRIkQNJwrcbDu9iQT61UbWGdnaxWMWjwRPKsE2z5HOBtfG7n/DpXFXc81/py3Us0ZTdn fHzuzncWx/tLXaapcwafpEcSpFJLHCCArEDOMEjI6CuMFiYPD1kHk3LMJFIi5ONxYg4/3+9QnoO5 Va78l7uYz7SrRxIreo9h19K6rwoRcw3+oPEFAjC85AO1t1ecNLLJBM7wSL86rHlR25yeleg+H90O lQQxlBCw852UlWLNwoOetOorJktmP4uhjazF6WWMbPLVV5EbH7xH/ARUFhZ3E+k26WzIUaHKCLK5 Qnkc96m8VIn9hSZnMsiPsUFPL2s3Xcp+mQa3fCwH9kWkaJmdAWTGP4uQv+f8Kan+7JUjO8S2n2TU NPiW0EcUcBKxkllbn+InvmuVluLR/ElpNNcKCfLjIjTJVwcbR2rpfiDezrDYSbRMbiVcB1wUwpLC M+u7uRXP6ZcWV54vkS4VnmiiBgjGGwxAI3erZpQp+7cVjvlSWN445FAMLuDgckdegrkZr++nZdNt 5/O84yrHI3Kxknpzj0rrZr+aAyIEYXb4xGRw395Sf4T/AJ9q4fSkmeV1aQOkd6cx4GY/MHHI/SsY RvqNGndP9nitX2glWdGz8oLuoycd8YrmPE96l1oiS6ftVp25IYuUO3Py+lPld4JLvTbuKR/s1zvC k7l3YwTkZ6e1R2/2fUdAjs7iOGzkt3Ch5GP7woCCdg54zj8K6qS5XdlWKmjZuJLR4G3xThY2kVtu NgGcitcXU9xDqENvBjYQuxiCrjPDE9eg7Vc8JW2nW5ljsYPNawtx+8AwCzcJtT2JLHNaS2mn2kWo zXBkMl1MvlhO3B4qa0otjOdtJtTj2WpSJJR5ghdUDMpI52jv178VSh0yOy09fD0M0klzeXO6+kGP mTPzxLjoCOuOM+2K0DqE8l1fQW0G2N0dS7EZDxjkZ7cVnRyJGkN/FhrmH90GUkZDnBYg+lOm3YCx 5Fv/AGkiW4WNS+8MF+4G6EAZwRiu3O/WLGMQP5kk+8sXcLtVcJx0J61xkciyXahtqRZbzHyAvfvX X6DYR3cH2e3lVPIZ4Y3R+BwANp98Zok7IiSOinbSp7+6vVR4rqNY0BdlKO20ckHHIHbrWD4a8Ny+ JvE0lhPMY7izAuoy0bHDpIdhUjoNv86i8V6Ld3N815bSTvbXFxHNOkRU+WyHAPJHUcYFdJ8PH1v/ AISy60jT2mVp0jAdmVNqRnfsd3wNvXPPt6VrgoRlJGbOFvZ7x5PNlUpczuAQwIIL9ga39GguY/s/ 7sxFC7DjO7j5CPXNdzrngGXUPEDRbx5whW5dICse3D5YYBIPHIrnGYWNlHboyFrdstGTh9vZm9K2 xGHanylR1djm9T8Sjw7ai+hUHUrzOzd/Bt+Utj8OnSuAtdRvbt3uZt0z3D58yQ7mb16cKvsKw9an mu9Qkmv2M5U4PoqjgAYrHutbFt+9kuVtIIQQY4ly7DH3VIz6elfUYbDKnTUUjtguXY9jtNWjsbfO e3zPnCg9uuPlrEvvHEEMYGnxtdXtw22BT90sTt3t/sjovvXgza3qfiSVYU3WdhH8z+Y2WYDgKTXV xXiacGni/wCPlkx5g52J0CqP7x/T610chpzM7XWdXubaGXTY52mvrhcX068+UuMmOM9mP8R/Cq1h 8tmiooURruAXoOMD6/WsJESwsljOTNMGkcA5OXGACa6LTkxFAjELuj2NgH2o2RcfePLPFf8Ab+qz vp1jJJBAgMXy9PlHJ9fm6VN8OEtbKG4i1iH/AEZGPEoJZB0aNhz17MK+49L8FaPdaZC9zZxyExq2 ejZPfivHPiD4Ks9OmRrOEQiTIJX3xjrWUcStjqqYCUY8xE13Y2lrbWN0B/Zs2I4JSQWib+EMfQet bcGtT2hUMd/kkKSOcr0BryOwv4pxLo2ohXMbFBz8vXirmm381rdSaJdMXK/PbyN/EhH+qP8AStZK 61OBqx7HrGo63eSD+yttlBLHxMFV2d1GOhBrAs08TO/kX18Jo2OSNoUEgZ7VueAPElrpd82navD5 9hfr5YO0Fon6AjOMe/tXpl1okVvMiQwlcnfnICMpHG30rz6to6GTiZPhzTXdJre4lmmdiJVO4ZOY 2Y8sQMCtnUfB1zp2nWlxqv220tLiTzoJJvKIBUZ2oFz973NUdP8AFmjeGPERbW7mG3H2ZI/InmMb ORuyFbB/hPpXV3fjXwFq9jptnPqsFk1jcpNPGjyPDNHH91cOPlkxj5gK6KeEhUjqcdWo4vQ8mnS6 h0OS5ndg32ghba4ibzGVeVK8KDn0OP611uk/8e82oxS5uF+bYyOETcuQHGR8ufQVD8RNY8Ja1q39 reGfFdlaxYLNYXJmUsxAHyPs27iR1JFa3h0LdaFcxWpQzzQOJCzjDRsMjBJ5KmvPWC9nV5TOFVtl e8g1r7BYX0t3auTAz+WshUxsRj+PovfFZcer3NzCmjNHE097aK9p9ndcXEsLkNncQFXB5zitrUBN B4fsbhLaNZVUxyBcnfnhsnn6jArM0n+z549P+03P2e5sjKqzcBApAZA4wGC9uB9aJK8uWJq5OzKE Gq3tmkSRWLO9k5t2KMm3zHPCAsQp9ODXWzvq2qQXCaP4eu3ntUP2m3t44ZHiK/3gJNvzcngniuL0 /wArUbTXNU1GUW0MzJbJHFIvM2fllVGIP5Dd7V0SNbajHrFtHcGyvLeFLaRCSPMkVNuVYAt82Mue 1KnypcrRNOs+hyeoaxLp+pW5uo3hJi8kHBOVYfu2+UEY7fWo7bXLCDTpVuLd0fzGjmcFmHltjBy3 IHsOlU30S8OuS6LzqEriJoZtw6D5vnAJKovJTitWQ2cllqYWLfBZ3BZhM5MkjR/xsoG7D/w8YxjN edOm4yZLmylFI+mWsd1p0sMzzHywj4OIieQA31Hf+tdjby6PpcSW1sltJ5+XZIthCSAjn5sgVzmj zt4iv7J5bSSaN4xPbRsfLijJHDNgjITHSo/E6213rkzbJGhjjeMKiFA4UAKyY5I7ZNbUfdhzkxml 0PRdGlEdqtsyG5aOWRU5G5uSeldBbtIWUyZdGHzAfw8fLmvmTUfA2m6pFYwo11pcM9x5lxHbXEga bcPlV35K4PIx9K6LSfC0+g6hpsDX9wYjNJBJ5t0Zd0bx4Cuv6g9jXsR5fZe0O6lJytY9XiaaePUY YnKtJfPCHBxsTzPT9KLu5iudWsoAjsEmeGcsxjUIy72JXGH7AdPSuc0q1t7C4vtOhmZhDPjfufcY 02tnaW2+3A560upl9VAtJp5LneyiLGwKSp3bGwOox9K8upmNJrRHfLAz5OYz4dHN2TFYMDNBI3lL jeUy5+8SRgKvZc1NNaXcQ8uHVVkUjchk+ZZMkIDGNuQrHjPt6VdhghgklivIxLAA0PmQssfzTjDN GI+rAcE9QB0zT9ON7d62k10YdlllII3j2jygcR7v9oYz+tc0cTHexwPDdTTk8Hz3EUdld6lbsLCR y4bT4ZDJ5gAym7Hy8fe61zkPh+xA0+K4k3tO2GBtwrbQdpwPf26DrXXWT3tnfh7No4Y7jEsnziYq EP3F68N3x0xVb5U1KKPzm+1RmfbJtGQsozujOeR2xXZ9dVrWOmOXq1zKuLfQE1qHTIbaX+zwi2Sr HOygvnIffztKnGcdfWuLmsYdP1G1trpZLc3E0zZQttBhbIUSA5jcjDBwRnoRXXakDZ3ME8JR28wS kgfMCox85OOajmll13FvJcSpdys08ayMNkn+9kYAQcrzya4KtVydzCrQUW4nPXeiab4uQaU/nJby JIIJFQSXSmXlM+YAGwecEf413UWowaMZX1eL7T/ZtusdxHPBG2XUf65toGOAPl/+tXGadb6baXos NVNxeK4cwyIH2iZfm3ny+Qf0rRXGtTT3VvMI7iFBGkyf8sty7ZSw5J3L1H49a1jUskQnZI43xO9p o2paVrM67EubkTXkcUv2cOkp3ws8TfL+7H8W4/Stm6ksdQv7fdq897ZyPLcWb28qwyKkvAET/wAa yZw+7jHYVNFpFxOLW6ulj8i3t/nnuommgEi5TkAKV3Dock+1aGl+F007SbS2ltpLua13y2l1GhH2 dZjg+WCSTgHAz9OK6KlnEtoZq2kOfEc/iS0ttO0czWkNt/o5Vo5kHzyO0B6EseTgcCtWSbTm0S4h 1O5QSQwl7yS0CjlwNhh42neQOhDAdqy7Dw3aWelWVr4nha8+xl4n2rscDfmMt5hXHGNwGR2FTeIb vSdSt5dNt3VTdEoSF2Ijxjaox1C8DtxmvTyej9YrQpSObFS9nT5jwTxDfyapppsrd1sdLtvmisdx PmMMZeckgu/1OB07V4h4r8R21lJ5Uh8vK4EMHGeeOOK3fip4kOlzy6PHbmy1Z023Cfwxg/MHT3bH FYPw++FM3i6JvEniaae30mVswomBcXgB+9k5Cp9R/u+tfoapqPuU1qeKldc8zxnUtYnvZi4G1eR7 gVkkM3TljjHfrXvfxC+FmmaRqgfQ51tbO5jVoIrgu+91++iPjmTuA2KXwV8Om024XX/EIXfaAPDb DDDeOhl7YHXAJ/w4ZYWtKfvG8a9LlUon0l8LrCfSPhbotneIUuIZZDIjdV81t4B9+enavU9KvApt 9vJB25z0rzHwBdy6v4GvbhiXf+0rgAtyWwEbP45rQ0/V/skz205+XGMH19RXzOYUv3jR9XgarVNH 0ZMyNapMy4X/AFbHr8p4P4dK8+HhbQYbmSU6fBC0m5c4YJgnLY2MDHvP3inXFdd4cvLbUbQwswMb xhduc4I71ak0SaNmEUw2ZxhucV5UHZWZ9VhZRa1LFpqOswxFDqPnxiPyvm2E7MYwCQD2HX/69c9d r4lvbuW3TUkNpM8YVDDE0iokewKHU7AM/NypNbq6BMmGIjZjyAM//qrVstPuAxJ2woByEA5q5O2t zdQoRV4xKOg+G9F8NaYthptusUA+aQgYZmB7nv8AjWBrutW1qs+oTNiK1jL47bRyV/Hiuh8TapFZ QLbREb2Hzc4xkV8u/EfxU8GpeG/CyHE2uSPcPnp5EIO1cf7ZBrXB0HWrK54WY45U6bcSbwbqt3Nd X+qSy75ri5MrZPTzOOMZ4ArqY9etdU83RNX0oqlrcEKA4aMt0MkiFct8vTng1gaBDAgfy0Vd68gd j/DXQ38fkajDfI3/AB9R7iQB/B61+jf2VRqU1FxPzKpjZ890yceXo1y/zXn2W72Ilw+za8SZCgbT kFSQpyMY4q7qU2nyMbdG8yYLI0yqCmyVOFfDFSdvQqwwByK2bf8As/VIUtbhQnm5KuuBhiOh4Pyn +L9K5jWXn0G5JkiDRvJG1wrDzlbapG2QqOYzkZbhlODjHNfnuf5RLCTutmephMX7SNnuUFstRjvL K38PSwzXNyAPLin2h7xVkP7syYAaQrgqcfMP7xzXb6frVvLrbnUbUh7uxkS4t71mzDfW2xGSUAZO 6GUbcj5xGuM55qrFc6zqVpZxWccNzIouZjN5YV9gw7rs3bcoxzKQpJUHH3jUfj+2u7rT5vE6R2tv 4u8NxQC/MChVv7Akot1H03KFPzZXKdM/KDXm4aN4Ox02O08PtZ33hLSdKvbd3gS6Z7tVPzxEszJI xHzAjAZP/HsVFeHWfGI1F7mJlurNo7ae7jjKRyQEFJQ4QqwLD51AXvit7Qtbt9S1XR7tHMcPiK1S zuLdnU7L6OPKjgZ3SwoymMjgqvWuuu/D48PRwaxoc8+bBSLu2jdJWurTO8qM9Xh5aJmOW+50Nd8K HNAXLc+bYLyDRtUtbjSwVVZPsXMbE42mDDRkFgx2FcY4PFeuaVBof9orfakw+w+KS1rf2EJAEV8G CwkY3ApO339nTAqhf+FNA1W81m9gmt9QWUi8S4UuC4voQsM5Y9lljbcOoY9PvCuJ0bULeH7fHqlo j3MkLwy5A/cM6/Pujycq/GR/DkEYIzXnezdCV3sRNcp1dzY6leTX+itqcUy6XM/+gSTEp5bfMREW x1ONoY9vXivMZCXFzcQF5b5ciIyTLAxicriUKchvIckEL7Zxya9F8H+IZ5rnVrPyI9Zu2t4Y45kh CtLnaoaZmC/Oo7l/vetY/iFrE61DDbeHrqaDS2dprOTc04tycXEaZUkHDFjk9QuKJwjP3kSldGld JYWF5aXH2RZkuFQG7OId+Vy7sp3HzVPXGevHFcff3ECXP9rRwRJHcp5sMKKsaG5tGyqNg4Jli6kd SopbmdLiJYYJJtXsbdjDukaNGfbkwsFBGP3YHIKuTUlndr/YML27RqJLiSVTKwVVdR/y1Qj5lxlf 0yetcHtuVhqdPqls0kt9YWNyqzW72t/p1wjkKJFbdER2OOV+YMQCcYrkfGviPStR8Uadrtvb/Y7q 5Ag1OwY5KXsK4IcEncJUIMb85GO+RXT6Mi6no1tZ6q8UH2QusbD91skb7m+UchUX7nHzdD61ynxF 8P23hefW/wC0IYfO0+3tJrS5EQMkazPtAkYE4DnKnqFYdQGFddCTk9Ni4s7HULaLxDoP9vfaFtDF NHYzJLc7CxkX93Mw4AEU2wjHRG7Vj2Vyb3UU1VY54L7UtPElwlxK7NN5cX+lxyP0MbSoNm75QQu3 k4rpNIgtfE8uh2T2kduY7V9O1KTy45o/s8yl4LiWEk4mDZ28D5hjnAB4I38GleIJ7SJ57mRnfTkk ebzHnkjRQSqtjIkbDIDjtV1Nkojl5Hsmn6RNr+hyNpl4qaeiqupO0QkaR5mDSBFzwkYxJwu5SSOm a+XdSjm0DTY4DFHI41FLtREcJI9vIfKKgH5tqquCOoYZr6t8P3x8NrqF5qtpcWeneIbQzQzszeSk tuPnR4sApyNznAI5xla+d723fRVubp2N1DBqckAuXCiOORtk2yMgkupYhlJA/pTxz5OVsmW12fUV 5dm+0zRvE+h3zWWrTC2a3MmxrbySMy+eFPAMZ3Hp820HGAa+fbfQp9f+DXjjxDO09jqSalrFwT/z 2XeZ3RgcIpVjhh6HIFdNpmrJq/h3X7e4mWyhWzUQrcALHd/aSsez5eS0RK8gA7R054itYLHQfDHx S05J0OlKjwR2s7ZAnutNiImGQcfvW2EZ4wPrXoR99c6LTvscZpOlWzxaU1zqcbxayLMXAXEeFvwl w0eXPzGENsbntnFeg2moaZpmt6F4kt4beH7TfapfzWzyMY0haWO1tQyr8wl4CqoHc54rx/QtZttO 0bSJ7eGOTUl0SR0ZgG2m8VIolAIx5iMvmhwCFyMg7jXvXiG00q5nm8OeH7eWex0zwpp1mbyNdjrL HebldS2ACCN5746VFOClqugPcTxtHDF8NviRqWn3LWckd/cmC0ZVWRDIQwO4glMCXBA6nIrwzVru yvfEmgR+HftUX2SK2sLWMx7iVsYEZkeL5iz+e8gHsOOBXsPjO+GveEvEeieIEng1JdYtzcXSgLA8 cawllaUfcaSSJiB79hXnumvomneJtF1fRoY11OxhnZEDlJlRxk3UrDJLj5gv1Fc+Kqwb5Ryl0Nnw Vc6sutyX1lK99PYxy3ENrbbSlwEceWxLBf8AVNljnPTA7V0XiF73W9RivdL0+RpfEl5cC1siA/mx 2ZzJuQs2EjIfnHf6VV0HxZ/wjMeoajZ6SI1vnu22Kg/dWsh/dfvcNnZnbgnk9AK5CAX2m3wurO8t rsWVrJCER3ijkMyM8jgffDF878dT1rnbp+z5WY2XQ0td0+38ba9pmgNJ9muLq5cXLELtR0QlGRIw REqbBvyB3q/P4n1u80n+100u3t4tESS0uy1xGYTdYETuoAyEc/OtZ3gHRr668XtfGSK1+w2z3l9J M4mlkldQHWNFOHBDDIBOCa665udKOqX+tzu/9i2RiGq+XFGWh8xgqsEdSrRQnYsvTAJAzitaTvHQ aeh1PhDwB4rstB1EXMSabbG0wSzRyyXsjvyX5+4o/wBX/drlRbzWcUukXEkMk9vqLussceSiv8rs jjBxs+8pAwOhrv8Axr4sv10eS6vrrz9M+wRXdn5QWASPu8treMqDuzgba8TmDWMs1yLkzXFxFb3E 8DQmM2twww6nHEmMjOR19BUYySjaxDRveKm0+yvmvdImhe0gMFrLFE/nLcAHexZ3xhFYZw2QcAYy c1R1Z5PENlp5062824tmkuXjjDqsQb7wjbgkZAI4IAH0rEnSO6e2025uEgvry6+zTapdyBI5VLfe OMqse87fujGMdK19KudPOnRab4kt0hshdyWkdykIzBIMiVozDh3WOQMvAIx83SueMnNWCxq+Bm0O 51hk8QJHLa2/7uUIDh92f3uQRkx9xkg/pXrHhKbRtR8LvoWqWq6ZHLYJcW7WhWGWWGJmZ5A+cMTJ 1i2/Luz3NeCS6loM8Zmsop7q+b5bc5WOExRYj8rYNzP5i53HJ6cd8ezRS6fbWtzrcmkpcarpwtpS 9/D9ntYMg7QFX5Pnxglxtbb8pNdeEcoLlHFs8fufFt/pFhaaPBDHHbW8zywQ/Z1VpobmTzk3qp/e dgBnhdwrR1y1S11S7t5rbMOPMRNixNIZ1Hm4HzKUDYUkcqc+hNJ4Nm8Oanrq6xdm6j1Syt/tNsxd 2jlkOI47ONAQeE5AwcZ9K9R1HUbnxRbroWhWFv5GnWym2N66BtyNuudzArMN+dp3AgHPfilOh7S8 rjsfO1zp+jWGuQavqdoxWCCeK3NvL9qgS4jdTcxkAD51VwzDH3cHoCR694Y1P+3Jp72Nrj7MYDZm UInl2duy5QxRblA2he/XvzxXj0Gp6zonheHW5dPaew8La5by67bPtkaMIphjmwv34poWki8zof3e 48Zr0LwVLDo9lqHh2+08anZ+H9R+2qjb5Y7yyuI2SF0OP+WSypME/wBvn7vDp0+Rq7NIK3KeKQaN rEX9qJq9232G1upLdBNKxBaPoyA8Y+YDH+FeweENDtbbwtG0Y3famDhsfeUcZH86wfH+k+VLcJa5 SCyuZLVMqSPlI2Mu4/Nnrnv2r1Kwt7Wx06w07T122ttbKsQ5PQc9fU5r0YyaVz6vEWmozieGax4R Zbp7qFt7s5YZAO38D/UVw154IjvXeSWIhmO5gW6sOhwP5V9Qy6fvYAj7wznHSmjRlPzYGcenNY+2 ktjWjhk9z5MuPBawRLbxxholbfsZSVDY+8B0JrvfA/gtY76F5IxubBIr1K90hZb2OxtkDTSAuS/A VVrtvCmhRJdRO0iEHuCD/wDqolNuOprTwsVU2Gal4QtbqyFvIFzgcY9D/hXk+rfB+0u53lt40iMw 2ybdw346bsH+lfWt3pnlR4UqeMhs8DuRXOxy2n2r7JMPLmxnqMGuD2sovQ9SeHhLofN2g/BXSrHz 1urTct5GI5gGDiRc525Jzj24+teweHPAWi6IkMelaetvFD8q9Thc57kn9a9QtdNifDcYzW6LJIwA oUjP9KqpWm+ph7OnTXuo8Y8faDbatocdqUCk31udzdFA3cnpwK4jXNL+IHhWO2u/COpQwaXe39pD 9mto0UvE/wAm5pGB+Ufe4xjrXvN/p0WozS6ewBWRSQD935R0NcZ4lZvC3gTU7zdve3WMWiE7QZAC FAJ6Y5YduOazjUktjOCglKpJHkGq2zeK/E2sfYrdbubVdd+wjI8t7iNbdbcsXYgumc89h9aztSk8 Q3/jLVtEnuotLlSFNLuJgxZre6KmO0eaOFsmMkldyg7TjOAKwtK11b/TbHxNrz3MF99mn1HTdsaM txNuEMG99ylVDb3Zcfw8ZqpZNJqfieXVbvULmHUkllvdQzHl3AO53dnXBTcqmHGADkmsqk+58LiK qnNtHfeLVXR/BPiB4JF2aPb2WmCaMZ2MswKFeSAFZAilclgee9UfD0eh+FbSTVvEsr6hqF7ELSwt UCyLcyTBBcSfuiUPyAjA4249zUfxRkS80G48GJcS3un6fBHqt9doBHE629oPsoDndulUsxcdC20j iq3gSyTSvDb+JXL20WhRWgkaQLcPcS30a5tEQbFx/ER/c681tG1rsx6HIavd6v4zmtL+6kaC41i5 OnWxdAVt0iAWaVigG3Yp8tQecbjztzX03oMPmmw0vwxJFcS6hZeVo9mD5TwWtuGRZr4swOHP75Wx 3ChHzXmWk6Tq2u+KtN0DTbSDS5tB017u5EihFklupRnIX5U82EeSh6qC/GRXv/xEbUNBvLP4pWFz EtzpFydJtbW2AUXD3S+XCkvnDYv7xlXI24XJ3dBXTh6Sa52VE4XxNq9h4n8SahpWvi3uE021W1kt rJjFc3bIS8ifIv8AyxC5MYXBLDoOab8RrxIPD9r4S0WbdqF8gnvxYJIY4dKjjWTZKFz5fmfKpA+U MeDUXhm5t5NT1HSZ9UfS7nQZLnWNW1ZwhcySPGjRxnaNyB+W6fKQOmDXlUb6tqLqnhq4uZj4ihUX H7xYRKsYwY5dxAjMZ2tnOPwFayqWVi27npnhjw/qXjXXSLhyht7L7dZsyqLUzIBF5UUSfMIo1wqB WAJ55wa8wWBl12PWNbupnt9IDzNCufPk1IJtC7d+F8piE+fGc8Z5r2m9/sfwFDBpUBgfV5oRFB9g fakcs58v/Wg73jhGd+SoHdc4z83a5Jpul6lMnhSWW8jsjEZ5Fn85t5YhFZzw8hYFv4unsBXPiPhS ZlJW1PcfDNnp2m+IGh8Q6Pa299o2l3TNbxOJYpbm5AZAjKSdyt8jLztrHkmtIdRtbi+so7Oe3CyS 2ZCKqLH+84ZlHmE7gqDsOvrVHwNocF14ig0CGF7W6C+fEbiaH7OojO+SSVtgZ057cn6167rfizwZ e6FeaH4h0y20mLSLj7cCEGJ442Ls9uSucuNuO+DwMVy0KXNNy6GlXVIwX8DOt7Y+F7LVrC3iiVNR 1GMHzVgi34iiQ7cyOxyQp6YBPaobPUtEtPFniC6hFrcrexxW6stvJ817ks3lxPHnEYw0+1CPl7cV keEvHNzomi6jIqiwh10yg3CGMuJkGYgNmXyiAKxxjcc+1ZHw51K81DxLomtXEiSyQ3E9zIZFllSO a74d/MXCBht5UgZzjmuylUjpFLUypto9Huh4Q0KfwxZafdfboLa7iuL17fKpLI/+kKRkbCZZ1GAT 7d8UzwLe+M5/inrD/ZTocmq2ltqMpnVgLp4ZH2QEsBsacS/MFHIi4rfl8K+HL3xlcjXbCTVNN0q3 aWQSRrCkc1/lIh5alQI8Kxjb+AkHiuw8G+IW1jVLnw/qsU39p+H4IFWSTA82RS7W8nyt1ljVvod1 eolGLsdTV1c3vFfhfU38KXVnZ6i80sU0l5cxoqt+7m3GQRhgeVJLKBjPQ8mvkK+0DxL4w8FpZw61 FqB0hJRFCLkF4rOUg+RewAMwgDAfONxiPDbowMfbOueINNhmtTskim1iBVhaKMzPMrHa8bR9mQfN yexUZIxX55af4o1DQ9e1+28MusWswandeQ9zGrKihxG4Vlw4CgcoD8544FceNoXtOJhN2OAs9GEW qXNstiNJuDcpbx6YQFVGxgtNIPubTjcB94kYqxBp+spp17qLR3VktlPBK1wq5jVt5YZb/aKnjnI5 OK9L1S/1vxPK1v8AEBbXw/4mtE22WvWWBa3UKbQn2yMDCRycKs4QGM4DgqOOsnSS2+B2s+H9dDtJ Y6pavEGCsqLM6bTE8fySKrZxgn5frXj4qtKFnIwlCJ5N4U8RaJBrker6gHniuU225t3aGSIs2Nsi DILD7pGR8vvXWzS63YqNMhtIjLqM7yQx38ZCmFh8rIZOEO7Hy/LxXnFpYSXmhobXFubRUd4nzmVS f+PiKRR/EdnDYOc8Yrb1HUtQOlrLFIDeSQ7JrG4dpGdy+PNhk67kyDtHb86U1fYxNG4tdS1S3jGn sYjPE0JlQ7UilX5CoTJ2LtVcEVyZ8MamsaTa3OisjDek7cumMFVIJBwOecYqrdG6tbyPT5Q0k1vJ Iszqz+VOI0+8M465AP8AtKa0iLS20ix1QX6F7wyRG2ePMpkjI35ySBHg/KR16VVPmi7FDND0uxjv Z7rU/wDSbWxVI7gJEsi/NkrJ5hIVfY52+/au+1bS7rR18KWV5ZurXMd3JDBG0ZWWKdP3MmFY/NkD 2yDXmnhnUryHXLa8s/szNODELd2wZrdiNwUHcC4xjGPb3r0m70N7qRriKOSym8uRtOvTIgMao5Jj eNlbydo4Rd/X06VGKqSuhtHIWl1PpwntrtiPssyyJBKFynlnKuGA3ZHQ8GsPWNHjm1VWinEIvJkk mDqA0IkJYuUQ/OvqV/HmtJGa6+1WV9bvqM9/EVMkRbEoXurKOMHn3rpLbRbeG2sdQ1ZVW6hgMMoE pBaLG4BwxXa2cAdj+dZ0qvJLmGkNlQp8P7nTkkY3nhrU2vLZlyCbLUoxHMY+ONsi7uTxuGK5vwvq 9r4fjjvWvYbe8uJ5I288SyF1VBs3smOV7E9c113hS70htb0tEkYaRrUcmnXH2kFhEt0Nnzj+ARy7 SPXAI4rm9E0bVvDV9Np+oxQyanaefHOMFyGWTYSN/wAq9sfKa7ZzjKnqazV0dokF1c6SkfnG40/X XdlELbWhmtz5gAAXuNwwDz2rZ+GMek6omsaNq22a1UstvOjKfNtZR5dypjz1Gd+eOT7Vz1o/9gXt vJbwxQ31ufPTG8lij7iApJ+mfwrNN9P4P8Vm406Evb3N1mKNlwGs7gb407H5UYBv931rlwzW1jej IvadfQ6Xq19oOoz3Ag0+8GlXaOwItfs5YQXGSCMZ7j5ijHH3as6pd6Tq17pljqVn82nObYmN3Vo3 STzQyod0RLu371pAflxs7AX/ABt4MHi97TxtYxzSx69YnTdUtokLv9u03lZMLwHe16/7h7muk8E6 Hp4tLebxStvYPZ2P2uK+dFnmMaDKW54P79EwMNyUP+yccmLoU6Uufc2qO7sU/t1v4l0ew8G6ldta LpPny/ZL2J0jjWU4RXlPG1M7VK8DvjpVPxBrd7c+BvDPihL82PiDSb5tJv4nYTG8s4boQxhig2sx B+THVT7Vn+JPElh/asdrY2NrNf33l3Er30sku2KaIPE8K/JDtA4G/J4/Cqmva7YzaGdG1S0lZX1C 2kt7zS3WN1mWVMMY12o0bZwSmw8fLXmvDKcYWVtTNTt7pfvtO+HL39295pc7zm4l3HzZlz857LKo HHsPfmqv9mfDD/oEz/8Af+f/AOPVxE114ZeVnu9QQTMcsPMKFc9FIZScqODkkkjJ5qLz/CH/AEEE /wC/3/2Fev7OHcftD//Xg0S10+xto7GOeTy0O4EsQCQNzAfTNcY8Lz6rHfeTc3F+mfs8e1fLLAfK ck9hzXQaVpFzFpU2rtLjKTxqhO4b3OVPPoBiodPit9Ra2muZHE8G6ZkUsiKz8dfbHSvzCKtqy+Yy b6+vrO9s9LsZm/diOaVZiDnfhWwPpkV2Nkbe7vJFEmV6BdmDx2z/AI1hXtnOniSS4Ueas4VztK/K F9e4/D/GtuxhKXUksbiOQP5YzliMjJ6f/WoqNFcxr66bxNPURIXuLdMxxj0Jxn6gVhwWzvcRSKwu IlbcwyQVOOpxVy8ur64vba3tiA4g80PtyW+YqMZx1xXPTalcf22yTKjSpx5ioE2HaCVIHBFZxYJm 3arG8ckbDeou/lBIG75c5/CsjVZ4xMs91jyVdFKAj585/lUmkxz3DNMCmPOIVTwSBySvPHNVrpDe SW0UcUcjlxjccDOe1WWJ4nuAbWNGGxXm8tS+VwEHYmmS2USWGkiTybW5lE7lBna2SuK3fFslvdwr ZtbfaVnm2W6Jjckgwd7A9v6Vy/ie8triGG18pUaGFvLnTJ+bJUrz704roS0Yc7zOyW27YOuTn+Li vVHtoV0p9Msoys1tCYjgEglMYPFed6Tp6XF7bNNKoyymTIY/uwWGK9WuLoEzyWKxu0ZwoDYwAcnp SqvoQcTrbONNFrKH86KaJlkYgAhxgr6fn0pLOcaRbwTTWzyeRcKGWPluRtUn1FO1S4ije9lhQPFd 3EcdwZQGSM7QSq56Z9aybjxBMulGyi2xsUG6baQ2VbORxx2qYL3bBFG74rspNX0i1S2Q/aop8kH7 rlDjA9OOKhstKtfCaXWru32i+u2UTFsZi2qP3SY5GD1NaelyW+pQyiK53xQ+W8kwyNsgGWx+XpXI ahq97stFh+Y3N7NM7JgMQABnJ/wpLmtylWZ2mpXO+2t764jAaWRAVj6dM5zXEW4Gnf2q9nGZ3u7l JAhONpxuBHQkCu4v5BaWcNvdlxDIBKRNgtvPUbhXMiGJbC+8sAvMzsgyQ25RhcNTpNLRgkYsGotq c/MaJK7SoZAcfMFz9Ks22nz3dqt1q4Flp0RkBkdsNIDgbokUEuM9xxTtC0r7BpLaxcwLNd3MzCzt JQwDKG+aaUD5tnbbgZ71pySXOpXrNenAjmKzHcFQKoGyJOwH+yK0lLoiwuta0y2gW1tLWaGEssf8 KfOSeSq+vSqep3DGOzbEf2VwoERJ3GQtxleowoI9Kvmwjub+Q3oAW2ugypgL9xBhvzNclqgS5v4L 23OF06VlbGSMMc9PYjHFZqBLZEthDNJdT29wpnmlxOikjDg9QOg/u5qKe0AntLUj5rFiZAcj5QNw zj1HP+RRYullBqd2i7TM3A7hpfp24rTnija6mu3kIS9igYkf7Ufoefuj+ldUHYLmRql1GLS2mhhk czlmYKo2Ju+WPf6Zr0Tw9I+naRc+V5Plh3mxxyWYhf5V5XZYuJGlW4ZRd2ShQVz80jvsxjj8/SvR 7GS1kiuprsyILSPy4QqbY3KHd8y/jTrR92yJkbdzPNevJcJHCG1CJRsDkMsh+6Qq/wCFLo+sQ28e oPJIJ7uySNZQFIZirD5Cfr+lclqmpG8u73UVfZ9l8qKMRAKw3lBn8PpV7wzLem9uCkCLLCsgZSvz OZF3En1bnA69KxpR5PeIUD0BNHn1LxLpt/eRKkd+/lRXLOxRTu2qDsztAPY1zfjaOXw7NdW05j/t BriS0aSN9yuU/iU4xtr1PQNdi0/SpXubRrrToTCVink2p52wvGAFBxtbJ9zxXz38S72+aS1vd3Cs 5xyMmX5s9OvNfRYSEZ2bLprU841x4LC2hluh9pO7eV3ALI7dN544A7V5zft9qkM8kCxgfdC5+UY7 e1dLrXiCC0sUN/EXM+FjXd97AwG5x09q406ql2uy1gUDHzFmLN+nSvfR13JUuEjHzYWMHO3pk+1W RqJQxyMMqpJ2gdWHTiq8GhTuFmuJdgJzt6t/gK100ePkonJOAfqOKTlY1jG5d09rySJ9QujnOJHH 6CuvN6bk6fBZZMrSk49gM4rnLxY7O1itGbL4DS46AAcCus+Genz32vDVJYz5Nuu4bh8ox0z9WP6V zVJHZRo3dkfang66h1Dw7p99Cu5J4E5HsADXH/E7TZJ9IlaIAFRnOOm3rW78KVWPwsbRT+6tb65h jP8AsB2wP/rV1mtWMV5CImGS6MCDzkGuNR1ue3KN6fKz80LWaSTVLxn6o46cHhua9J1DTxqCW9zG xSaMKykf3lGBz0qDVvCU2i+NNWtZF/dyjfHkYBU84H510OmESWxgZQGibC/h2rvi9D52pC0rFaC5 kMQvkyzRYE6fQ43D9K+jfCXiK01WwEV2JJnjVQk68+Txxx9BXztqBFlIt7AAEl+VwOm4cYIroPBO sWUFy8MyhxuAKsxRdrn5ufbrz6Vz4mF43MZqx3Ws2ljrXiqzultftYs0eMtIBJvM24biDwAMcf5w 7W/CulRx27XWiW7T7w7m3hUl/RTtPPp3rZ/seRL2SIh5EVbfyBnAZF3gAY69ax4tPkW8MRZlbGwg japC9xnkFRxXlYmU4tWZyJXeomq6Da3kaXWh6JBay2e2YLHEkZO35XD7Rk9Mj3r0bRNS0650ZLh1 3BIQqgLkksQrb+vaubOnW0NrcSxXUm9vlyXCqhQZBGOxzzXO+AZppLKZwU2wyvFIinKnb0kX3/Ss Y4qUZ8zJcFc35beMWlva7V327ycKpwgydrDI3EgYHTFV/Deq/wBnMmo3iwzDSruMhpEIl/eZAVQM K6t1OR19qh1G5ksYI7jTz5pebE53Ychhnv39ulYcM7XsW1bvy7dJjMbfy+HZR8rE9RjvTniLPmiZ VdrI6nTb6W9l1e9mjS7YTPdRNcTeQ6ZOFU7SQ0gH8A4PrUnh/XptM1G7u55UiuZoJWV5kD/vZFI6 c/NjAGeK5O1lLNKQFZFT5nA6tnIKqf54qJb+ElLh2DNH/dJ8ojft7j+HqT61w1MbNSuc6Z2eq+Mp tbvtNuXhWweG2+zzuo8tmbPyM/lg8gdulYFy8H2iyumVEupLRvPJ+diJOEXHB+X1U4rkZg414Ktw 13LCxMjxkHj+AhemD0rSuTaXEVvE3mxXTyvBOz/dCyc4Hbj6j2pvFSlqx6j7eKe3sTbwThTG4XzY MxqFPRlXj5j3H581vSXFr9gjhhfypLfM09y7HdMPu4A7KOmB6VkWEkaXMEZdM4EbPydoHCnZ1z+F aZ09Q139puJI1JhRIlwcyD5tx9F9cVDr2STKsKkS3DpsAlWDypHkI+VQclTn8O9STXFgY1njklnu o23/ACKFBXnGA2KkSW81G2utI80zRTTmWeGL5WcxnCncB90ZxjNVZIDaRxboRDvQENv3gIp6fie1 er9ZVSlZHXReqsS3V/LHepdlWCyKEKs21lIJIxj1zUi6zHI0UsEQk2Mylidu0euOu6qS+fNcCUyI EfDbQARk5xu7DHTiiaO3vEtBanDy4OzZ/cGS5PH5da8ScT0XXdmgOtLpumrNDC7SLJhRH8qMSNpd 885xxnpWhLevdzmRiIoiY2jHKMPLOF478Dk9MVy00s6zSzTPHGdzK4jGQwH3V284Ga0IVkM0dwZD h41BHGGYjlUJ7d6lTZwe1k2dBDqrLdXErD7NKG7Do2M4XtsI96et7Pe3dqiXghmm+6Zf3arg4+92 xWfHYyNI8shaNkHA39dv3RtPI9elZ8728ciSzbdwV8ycuoYAZB/3q6KbbOj2jsX9Qa/hvlmuzI0c UhiMmQ4LA4O3puFNubhmljWJ8LKcOMdFU/LnuD3x6VWvRHJA8sL4iAUtCx8xV9GVfTtVKK8G2PZi QSSqAjL5YjkBwTjqc9Aasxqyd7mra61caddXa2sqo8ytCWkTapkz0RvpV/Sbr7LqEN5PHGm7epXZ 5gZJBtwBx3yd3b8q55rSazv44fm22shYEjaPm5ds85IHHIH9at6teyPI2oW5iiL42IwYIsY/hbay nPqcYouY+TO2u5b2206w0u4uBqEDOGW2XlxCSTll4O1DnnGKtR6h/akR0vSbgSPAQLaC4ItwzI+4 lQDnnkjOARXD2V5eWYOsX0qoZbdVScAISqg/K4PQYIA7Ee+aguI476ZLy80cy3R2mCaCQxum0cF8 fw4O7Iz7V1RknqdcdTqbPz7jxLPdeJisllJ5pktVkQ7S3AEEz5KvxyoI5zjmuGOo40nT5LqyEdxb l0RcYdVHRXz1b1J5rW0+8tL+PU9L1Ga2aaeSG7tpLxwGDBWE0KZHOcEqaZ4p8OXTWl3/AGUftc1v KZDa9JDu+UmNuA2cZ619DkOIjSxSlPQ8/H0pTp+6eSeNvCWh+Ptc0nxDeoZV0+Joby2jP7ych98e W4OwZIOMt6cV0iSGaE+XtHkqFCJkbQvQKoHH0rmtOnvHmdIUEbwHB8393IpzgBl612MERuVMsjrH efdO3ASVT6+/vX6phJQfvRPmKrlblZiajbprFiIZApdJBLAzKD5cqjII/D5a881O9+z6Xc8bGgUg oevA5BxXpEf7i4ltnXaF7EY98/8A164zxfpZNhdXsIz+7YSADqMYBrTEx924qT1SOr+A9yL/AOHk 0xGFk1O5IP8A2zjrS8RWRilJIA9CP/rVk/AK1aw8AJZN8z/bZnI4x8yr/QV6dq2nLdwnIDNjOOnX 8q/NcY71mfomHivYKxwHh3xnc6FNtmDOgx0Ne56N8SdLvAN7BN+AQxGR2r511HRpIy6kZxXJGzvI pCsAyT3GevauGUUzpoYiVPQ+7bfxNpxbJnjcY4+YcVX1TxpplhatIZN7do1wSx7YA/wr5N0Tw54h vyheTyoh3JIr2HQvCEVsVeYtPJ2LnOP92uZwS3OpY6clyxRetINT8UXv2u+zDAx4X+ID37V8r/GH UI7j492dvERs0Saz04Bf4VaMsxr780vTlt40BBCkr144zzX5+/FGG1n+I2h+LIVCtqGrmKXHRhHJ sVm/CvayWPNU0PFzd8tPU9o8PWyxzTMudzMQQfbP+RXWR2y3enKrHDxFCjezHBH8qxfD8bGed2Az ubt/dyK6WxTNjcDoVjRR+B5r9Jw3wn5/X+Iba281rjnO0k9u9bt5ouoXpN3pkTymRdxyqmMuBt2O M7tpHoOKqQEGBAeuBXVabdN9nZLYkMpALDpn0FeTxBg6dehyTOrLpP2mh5YqXWh6jaQXFubdVuLd 44WBXawLBlQtt+Qr8uNx/Gver6bTvFPh9LqRIxqlgGaCQoZm8scPFKnUxSLhXXuAGGCBTNxuUQXS iXgEbgG5H93PT8Kw/wCw5NPvBfaLdGFstvgkc4cMMHZKpRlbpwcj+VfA/wBmTow93U97mPPvDstn oltrmga2IreTUpEGn3KkySW88L+bDnftYqh2lMDc3zHvivZtN8aXEf2W6Qr5tlAf7WhUM2Z2kDCR SRwkgBYN2TaBmvOfG2gw6nbadfadaS2l3pqNHIZp/wB7cx+VmTyk+YmVcF4yeOoH3s1F/wAJXBqM FjJYWclxqMULabdQRbpftlpsyp6r5qFcNn70fuGJrz+atT3QHaz3UejeJ9N1XwysM2i+IEurURja gS53iZ1AOMgsrPHnGN7YyCBXO+NtAurPWdQm0GNJC1m7alam3i3GKF/N8+FQTtdV6c4ZRjtiskR6 dZaPbahdiS4l+2Lf2oVnlVYpF2qS/SRl+YMCqsv3ccVWn1rV59RvpNQuZfMsbaLTENsAZWjlXdGo jVVzuQkBsk5BFYzxSk+WSIkzE1bSE8M3q67CVvfCeq+TNHLFNhJEkALCPZjKv91umxsV7BY6lq8P hddd0K/g1B5bjd9qVGF9ZqTjyp4sbZDj5c535x8teQeINJ1DQbeW7ijuLHw9rMga4s8bYrO4cbFD 2+4p+9VVLcg7vu88Vf0/U9a+Hcun6pBvk0u9tZf9PO14pWGC80DQ5Ecq52spXeQOQWGS17vuihoc 9HbzWWq/2YtuGS9MdwEuAE82KPc6yo5KgqT8nHPbFdB/wiem6/4Q06/g1KeO6hMsU3kqkb2+WLMC o5dMYHrmuu8RWGl+JfDkOq6bc/ZL3SE/0u2lZVFykgBkeJ41IPGZPNQYZuHHpznhiz8P+IYdV0y/ MEN/cSb7G6LNFKZenlusZwwbrlgw96ydGKlys0OX8KrExhha5W1h8qfcHfcWkt3Ajjlw2Q0g+62W 56DFdz44ktJtG15PI+yaw8EkhtZ84uLW5jCPEZAPLdgyI6856nrXluo6TqHg3xEbDxJp8cgTN1AS wMMsDttZsqQwMbZxt6ema9H1TRGgvr3TNHlk1bSJbJLptPV1nnVJI/3n2SR8byh+aJGIbAxnNZ0U 43RN7HLW/h3xDYRpLp12tvfX9mAApdY3eMZZJdp/iODG3RWB6ZrmNSv7dNP13W7exxqVn9j1XTpD l5re+0+RcwSYbBSaHerFcgFee1Y/hrxFf3NjHBHcvf3bW0cc0jMY57e406QvBKw+7smhGCucZ4ye /ZaPo15rKahLJGUXWrdblTGjebFdufP82KLH3d+A688E/Sqp3i/eKUWtWe83mp2MmheHNb028gvE vJV1A+f+8Vl2gLGMhvvF9sg5UjDEcGvlzxPrqC41PVfs2dNtb1Lt4UiUx2zYKJ8qgjAYgZ6fhXq/ ww03UdNsGN5qt2F0US3Een2lv5/mWbc7Udm4ZOh2pjAxXnHinUdNm1jXtPtIfKg1NFBuGZC6iRPk EW35FHmKMtzv6CniKim7slu57Q3g6xvY/hwZJJ0g1G0K3KwRKSLhyLy2mYkEbDIJI1KAMpYc84rn vEmoXlt8NfjBoWnwO10l/HfTS7S6rCbe3jV1fn95xuYOOFqnoFzrGt+B9L1mxjludRjGmQJBanMc c+lyK6GSMjcJMx8Y7bRWZ4o1bVfEHh/4h/2FaWsNr4ku4Luf5gx8h4IwiIeMfOjc/wAO49iK9D2s ErIv0OH0VLLXofCXh8aTLMZ4reznKSlTOsHkkoqZOxScsW4684xgfQsfiZ/C7+INCazjiti0Vg9q jb408tFRI4mA5wUYKT1PXnNeIeEYrt/F/gS2t8eGBb6PZrNcJuIU3o4uDwQGlETcdF79K7qDw9qO ra14flu7gx3WqINQDXEMjfaZhJ+6LbTwj7uuAAwz0Nc7jK/uETZm+JbW71ayt7DTsWsPiCSKU3F5 8i3D2cvnP8xOCwVTGNueOOvFeX6Zpuq6vr91oyyeVNqOqtYKxX96kEDiSWYnrHGiksX7jjGK9W8c 3NprPxB0Tw9HctHpvhXUAiXIj8u3ie5D3F9ne20sUCewBbHWsv4fzfbtC0e8szHZT2MV6LfyCUk2 3RZ5lc8FgRgqynjoRisKkFF80yo6LUua1Hf22hXVrYXf2uGWYIkyKrCaCBg32lGznaCFyAMAHn23 9A8MHWL6fUNW1JRLHazapcXDqhVkQgNHDIRwNx2njqeK5WC7kt9H1TWpruIlPK0u3tYsoq+aN8ki /wCyBhePvE81PqU95aQWOmWMoiNzbGwnto2kMm2QZVHJOGjkz5hXaMMBnpXL7t7sz6kfw81CzgN/ P9pbTLu/uhptk8DBJds/zyswUNu2DG3AzuyFBbOPW4LfXr/Tp9J0oSx6LYXL2UOIvIVBKRj7QZBv ctkuyDcck7+MV5X8Pm8LaC3266hvbk2l1PZrqHPDIArywfLtjP3R3Pyn1yfpG88XeDrqfT5bCSzS 0so1e9mkG63kjjyfl+YZk3Djvn8q9SnBOFi0fOmrvqBuJvB94PMvtGuxHeKsjCGGGNQ1vhlIQKzE YIxwO1LqUMGpXtm17rG272TGWcxjY1yn39zKTuj547+ntxepyX1t4mPiWSCVIdfaa5uZbgtL5dlI +2K4mQDP7tvmHGMEV191pEN9otzpWnwRXMVtM6xXrbw8mE82WTynQMvyZwCO4ArhxNDsKS0K/hbV tOmuTd2u2f7K8hZ5VRu3SNGB+8oOMjPqBXdaX4ksoNYj1Lw7p7PNeX6XWjytLsWGeRV+1xFQB/ri RIAQUWPIzxXJT6BdWsEVno6w6QdatzIztH8rIR5kUDBf93huNpJ9RUXhyO71xLm1sc2V8Xj8i3uJ N0HmJDsnxNwTFLjaAOR3xzU01yIg76H/AIRyLUNBu7a3ngudJjvUvNQhxiZpWMccgjxsaPcxz0yp xWFq6X8Oj2dl/a013qFo3lmCKQ/Z9Q0oPhLSSMFWiy4AwS2B90121hpugatpfiDVtbeOTTdXRE06 1sn/AHlvdQR4hh2ZIMsp+4mwqc9crXKanpWpWbTaxfNE1088FuJ44zHFDfGLD2qgIf3UEZKMrY+c FuvFdS+G4GroNl4OKreWMIj1BYZZZGlYvHZX6ElYG8zPlZTCoM+nHIqCG5vtSsJPEcukrGtjNK8V xbH5o47gYD+YQGKxuo53gDJwAK537fd3Wn33iHUY5rPVNHlVLzyYEX/RXTfi4TgSmA/vYuMuvyc5 re8O3+vaNoUuoWStqNpdLDbNFaMWhuUCBMFJVPDoM428KR3qobbFI4YazeWNze6hbL5dnLD/AGPe RJkxyAHdEPMI2F0kPX/aUCret+JtS0XV/wDhGtQtLvSNcktt8klzGYWltnXyl2ggZQkeWcDGfTmv VE0L7NaXnhjUS154X8V2iW9lIjbkt2XBGNpcCSI7SGOM4x2xXmVtpet/E/4bX9igS68eeESpsgv+ uuLYRCSa3353NHcA5UAj5wOmGrKpg/aO6KPS9JWxksJJzfxanHd28X2aKUDy4Vwdjbj/AAheh+gr OtJ7Ofelg2+KxIgBU8EqoJxmvH9EurDxH4bTT7G6m08aha/aYZRGHSK4tFJuIQTtZQ6KshTg8PxX XfDeS6vPDUV4unNBaQh0kuARgysxVQe+4jrxXRCXL7p6uHxaUVTZ2l1fxQRNJjKqMEd/wqpJqiyw nyQSxA6fT25qlcw+bK6k89B/UVDeX1n4f0ya8uwFhgTzC7cAY+lZ1N9D6LDs4LxPHrcN0L7TiyO0 flNknoee+K4vQNZ8eaRclN7TtKdw4+Vf/wBVdrb/ABN8C3MC3F1q1mvmfMFDBnH1FdNoHxE+GU85 t3v8lxhSIyAPp8ta2fKdUac5y0R1tkPilq76bKl/p9vYqd0xCO0xGMYXjb+ddlqfhue7g+0W8j/b 4gCkjH7x9DWHafE3wFpUSwfa5AAeD5bYH14p2q/FnwtZ2jXdleNeEEHy7WB5GC9MuFBwPeuaon2P RUJQWqNTSfFNxasLbVlNrKCBh+F9OM9fwr0O31ZJQD1PXA9PwrnrIab4s0mG8aLfFMgcZUDHp6Gm 2dibOSWGOP5VwEIYktng8deK5JTd7HDVkdPpEL3V7O/3dq9BjnP9MV4L8f8AW7DTI7bwnfQu9olo +oSLFlhKZC0MagjJ4+b2yas3fjPUrbxncxaHfxRSJ/oEVt/rPMkgYb3eNeSvJG7gcenNeUfEWW78 Z+IrjUriF7uJtStNGhTcNkk/l5Knj5IuR3/i9RXTGNoHz2Lx8eVxic74jt5rLw5p+spDGE1W2un0 +BnVm+w2my3BlC5CfPhY9vXB7mug8GR2n2bxHc3Gsy3Vrpd6mkFWhE0t7Crxlba3AKlGmkBXpuIw Mdao6v4gtIPDt/pWqxQx3MV7a2IgtELobbT8zShFT+Dcu5wMDJyTyK53wDPDo/hy68a3N9LLc6Gq PpFpCrOLzWtQR0QgNxvtUwPk3YJPpmsqEFOR8/FLsbr6neXaeNtRvLeS48WJcM11bwRq8MUFrtg+ zKFyFgj4jHJxgHNdppfiXQ7PxPpyG8hsvD3gmL7eUcPIl3eSWq/vLhXI3mIDapwTvOOOBWbr/gHV Ph78M/DmoalmLXPEd/ANm8+fGN3nS+Z5QDMdqklQ2MsAc4rObQdJ1Hwl/wAItDOdQ1PUtcsoopbd Csc14xaS8lVsnfFbQjarfdaTnnBNdPs2qtuhNrbHt/w38A6vrXgjUPH3iC6l0m48QXFxrd3ZqIop biKQb4fNlTBhURlsIOm7cetSXdhpeu6cdWa1muvD2lWxWxtop3nee6H7z7Uclt4BPkQjjcdzdRV7 xD9v8Q6zY/CbQtMh0y2nkdYLx3aZxb26MklwwDHoPlhDcMwyflFVbWPw74P8nRLS7ks77QdEur06 jNLnZCuBEyxRNtH77gLkHuAeTXf7qjoU2eC+LPHE+taktxY2x0tb6IQsZZWSEqmN2zeP9WroQOTn gHkYr0z4feErSwFhr2vJb6xoc0cd5NHKfLNvOTwcHh0jGN/TI5PavCtGW28Ra7ZDXorrU5IFaS7g jdU2x/6whSfljUO2W5zXtnizxfDp8HhjR9C06DT49LZ9Y1KWfd5csa7oVVHG3zY5GX5tp2964aS9 67HHzNHXPFPh2803XvG2lWsM1rBdf2bpl/LtXybaNxmK0TO57icbm80Z2pjkcCvIfCOgQ+J/G8sN hDZ2Vpb3UrmCMvJ500ke35DjzJUi+UGQ8IQckc1r6tptnqmrWkfh61vZg8Uf2W2tEyIr68dmfDov l+Wmwnp1AycGvVNJuJ/A7R6rqBikj1KyTS7HTbWPNzLbLk3EgSPDLvmJ3FW781tUXO9TO9nY43RP EelaL4xnuLay+3RQRLos0Vom6SdkG6aRFeQbUmk/utj5fcVan06+v/F8fg65t3msJJ447GOFGErK 8fnFZB94LHjy2IP1PasrRdOi1s+Ldah02DRLnw3fnWnjAZmRGCKttlQFYbRzzx79T6/4Q8U6lpU/ ijxJfabFaXuqRi5e4uJALaG2Rf3UQOQF+bOSx56Vwxpt1LdDWpolY5HxJpVz4Rj1S5vBJY6ZfWck MkMESu0UCuDiV5fmAb/VrtJ3NjCg1r+Ctfu/AvhLGmQNcWWsRZje7iRWF/FxKsgdshUThF68fUV5 /wDEzxRqPim60y9uljtm1S5gSyjuHQeTHH8yNIuMLnqM8Nke1dT4fubTVkt9Ah028bX76eSGC4UI IdOgIxdy2sfZmhLfM+WDPuB7V2U43q2gczlfRGp4F8Y+LvEOq32t2lu17Z3iLBfNJIlvE21PJRI2 Y8koBtIzhjmuriuLKy1kWsM89rqk6W9gi3QRfKWKV5IFkliLB3hZ2jznLI4YivQ7nS5bbSLjS4/C sc2gtBFElmZVwBByGBQA+Y56N1yB9a830G+l1a+tbm30XzLRYpLfWbZphuSJnVN0cR/eCWFxnPXu Oc1lisNUhJa7nVSlZWZ7Sp1tYbLQ2sYEjidTcFptxS4RkkDRuBnc2WcH2I9cfnv8RG8BaV4l1mKO 9lEcU147TSAKXn8xnjMSw4ULEc7S7MZT8xwDgfZ9uW1X+1vDni2/vN06SaVa6oIxEgmGTbyfaE4M rIfkZguenfFfDXjTTNMsJ7hfFWly6lBpEF1p90tpxLazq4BnRpGTfsA+XPbp2qaCa9yb0M8R8Jy4 uNT16x07ULHUf7WkmiubdoMELGNqbwByUVkIYM3B6V6x8N/HS6fo194I8fWMk3hiSCSyaZIWeSxM ysFfAx5sf8W37y7coeoHzFok88FtdrBcSRgyNGzbthZFVSF4yOOc8n09q7TQZ7qMyXbSmbTZDBDe xtl/kLbV+T+JTxkduox1r0MRgoxhys86E2mdZq9nqOiTW9lIUgSBVa1vomG27s/+Wc0Mg+Uq3sCQ eG5BqbSRcaxcSN9ugLWMyRxXUpOWK8bgSpXr/d696Z4dxrEEXhXW8yabqV24tpQczabcnLGRXbAM Tj/WQH7/AN4fNxXNXfhnxXoesm0t9NuJr0IbmGSzR5lkiTJ86Epk+WMHd02EENgg1xKnHl97Q6ZU 7rmidTrGtXl/qNtf6neNBd287CS0kZgrbiqybUzsCH73pzW3rniJvL1LSNOhQWd0PK81lUSCPIYh dvyKOPTd9K4/7VLq7WVlMZLf7bILhAm0QG5YCPzGyM7XUjcVJAxk9ap6zZXelXsmPKuryxZo5ADm JzG2COoz7N3rllT2dyEjqvDiJaTzXC37W80UKw2c0aNJLBJK2QFUkYRhncc56ba6C41W4j0ie/1d pWtLYJYtBLazvEQr4i3vuO4bzlnZhhvyrj/CWjHxEuoX2mWEs32QKZyz+VHGzn/VNu2EHdwhUn2r T1nUvGniG0uYdbhubHRra1njwkjiJJCcJGzMcSZZfl77vzrKsuaRdyIaxc3sWpi1sWt1E8k0KB3c bVH3FYsEOOw4PtWbZTy3WmQ+exvftcbA3O4Hyvm4RlYHBX8KqaDqh+xXMzTPIJxFFKWbGCT8456N 16VFeXsFleNb2by3GmMQyqF2sCP4ePvY61i4ahY3zYaxtudPWWS4g2MyyM21WQDcCRjIIx8vp+Vd H4xnfWG8O+J4rtorbxbp8d/9oKlSLu1xazpJgd2G/Hqc15vaylrqeKS9F3aOrkuo+aQHDlFZSeR0 I7Yr0nwzZt4x8A+IvB+9Dqmhzf8ACRaMp2tt24F3DwcFXULJj2rppwujSm7qxf8ADEWmg3V/rtve 3WoCUQQy28gVE2Dd7lg/U/Wq3jWW31vQxf3cMttdaW0UXkElU8qdm2uzdBghgD+Fczovii4gspIX tJZFYrCz2+UeFpOAGAzkfqK29HN/datqOiSNbzabd2DWyzMQF+1AiS3VyxyQWBT23k1y6xmOkrOx 6R8M9SS38O/2NaajDH/wkuxftMMxP9naoj/unfyzGw3EfNzg8oe4rQhNvf8Ah+fw1Z6nHqOu6brD ahPPJF5ciXKx+TPavbk7/KddyvyPlY/WvnS38RLpN9basCp07VoUivrZMr9mnXBSULtGSnHPfbnu a6r4nRR6lqEfji2Hlf2o32XUjEfL23iooS43oQfnAH3Tz1PWt8VSjUSsemqSnDnXQ+kE+HvhjT9b 8Iatj7TAsXnWlvOgdbddrJHCWbBkVHk43L0UZ9/CPF9tpfwy0gaFpFtFNqU5tpb3WL5yIYo3cITB CSEEjKCPujHrnAr0fR/HMUnh/wADwzztIyW32YySnLBw23BPGdpXH+PWuS/aStDbXGk3ViXU3FlO 8bEfI0sMkUkMbKQQ38Rx6elfL4OvUeOlRnt0+RwvX3jrdEh+JUml2512zs7i+AZXlC2L+YisRE+5 o2YlowpOSTnqTWr9m8Z/9A20/wC/en//ABqvOLX4wfGXUbO1vNM0a3vbaW3i2zmCIb2VArkDcMDc Dj2qb/haXx0/6F23/wC/MX/xVe/7GPYq67H/0LF3Jaz6bb2a4jw/mRbshFyfvH2rEtJZEWcSKSHn woAALBB94f7Oasy6ktxFLbZdPtAEcXy5QlOAT6LgVmWUkEE7Bl89k2qChOCxPJBI/TpX5bcDMaZZ tZLWsP8ArY2ikZuuT0PUfoK7bSBYWtlI9qDJNJ96Vsj7vDDaea4a9lga+dIIXgZ5M+afvHHoK7LS rB7K3ju7hDPLMFQKxwCCduPSiS0KiiaYw314L9k8pYLYfNH1IRfl21xqXsctzeOkLAO/O8YOCOD/ AJNehzm3gtZpYI0ijtkfK9cbetcM/lGOKNcMbjexOcsvpxUR7FR3FjumzDKtrujtxgADb2wTU1lc Ry31ncwY2kyMA/Xco4XArKsXl+yyy3DPvTO0dN2B37e1V7O8lmvrMxqIlty8h6AbkQ9QeeK2szQv avcy2moXEsDmK783EbFQVCPyQSfrWD4kih+0WzRAKqxZ29MuTnOK29YZr2+h1OMsIZlTaMA4K5zj t6VzmsH7Tq87LgjzNigdhmtYrqQ2dv4Ss7KWzTVZhI1ypOSvO7HKgA8dhWok8y2c1xHKHaaXIbbt AIwCW/8AQal0yS2FtFBZwlYJCNpXgEAlSee/FQ3U8E2m3NvFgD7R5cQIx8zEkE/U8VySlqJI5TX0 sv7LvFhujJHfToxthkYZj1U8dMVkamoMcRRAPNOJFGcAZwOvHT3rU8V3V0LOOHbHE0D4aONVODjB bP0rNuzcW2kafNDIzwFo0lDgZbnA9+BWsNhHV+GrXZoeqyyq0Uck8jgLwdkcdcXrOpRwaDp2rWsP lefC9uncjIDb/rXscMYmtDHK2LW4RY8KNu0Sgjg15S+n217YaZpCKA1neSJnr5SKNjEg9cirotX1 OhM0fEF07nw/FJqHnuimOeJ1IIRxkNk8Y5wK6R2Gn2ctpbxia83qkI4JUykAO2fasO7torbXvtk1 skxN8lvYx9cqqjErf7KD+H1rq7m51K1utRlWFJI7UpskC/vC8jbNrY9BzWdZq/ukWOWv70ard3Np Yo1tHZxRW4nUtunJdh/EBgFjnA7VZjs47GO20e6mkdbeTzRMjY82QDD7d3HynjrWx/ZcOmxxX6lX 8hVjVGyNzqxzJ+GeK5i6sy8ti1uxRb7hFySURWwxUHvmp8gTNu6l0e01a2i1Jbg2sULzSNCwMzMy hs5bAOARn8q801ySK0nE1szm3mYmItgOwI3DcB9RXpOs/aJbSC4trZMx77fcQSWhcbWNec3mkXWo XBiEgjMMcRV2IxsiGCOfWtqVrEyNFdIvX0O3SGPzJZJEuJMn5WUR/wARPoG/Sty8Rl0GCObLBIYw vyjjDNleOentUEExUHykEzWKQjyy+A0bR7c474Pap7m7mlgtbeXi9cEEDA3nGTxx07Co52Tc559M j+wW8NrMkCQIiKeoVQc8Yyc11H+kW7Sy2uJPtCSugX5wwwRlh9RVLSm33LQu4ljmbfgJjaU6r+tW dMng0/W2sVG1lUgyL2UDJXafp29ap1GJsk06SKzuNbOrWkXneWJZRxuZIyGAx2AxnOKz9D/0uLUd UhlS0tZJo8P5mZePvbfasvxRODput6laTFJyhg3Hk7ZJNvB9GHH+RXGeFNSintTHJDtRY/JQAjC/ NhXIHT61vCnzQ5kVFn0bcazp8uiRx2TSpJlFlSNipmCt1dWHQA9a8h+IMbrpTvETIpljKnp93rjO O1bUviC50O7tLQGC6tNiCSUfNguAox/nFdL4ysYbjw3e3bWCLc2cKxRCPIEzy/KGI6cexr1Muk4y XY2pU3KVkfImqG2klea5t1uljjAhSQfKDnk5qPRIbq8dntIkgg+6ZCAIwOmD/Suz1/wRrek2Kfar Z99qoaUgZUB19efyqjZi5igtre3jyiYwFHHPfFfRymrXR0+xcZWZvW+m2UcP76Q3DdMLwPbGcU4+ XGuWAKgEKqDv9Mfyrd07T55SqxxGQnJ2gDj88VatLXVrK5aKytSL+Y4VWwGPpsJ+X0rkdU7oUGcc /g3xpqaCe10iaaFzvAYBWcenJFdZonirxJoSnQV0KJLibO31UgbQ7+y9vcV0UnhT42Tt+7tdP+zs QCJZZJnAP8Xyuo49BXU23gDxBZXscNpskilQNPMsflRmTHKtGzFs+jKTxSnLQ6KVN8x7F4IktdL0 Gy06NtxjXe7k53yMcuT9TXZPdRg+Yp3cdM8/l1rznw/ps+nXEMF6NqNwcEkZ9q2vHs0+madG2nRS O8vAEZwR9MVzKZ6tvdPEPihrWjf27YxLMovzuXZg8jrXF+cI5xcIAUmUA7ezg9K4nxj4iaG7WTX7 J0Gd0cqQtKPlPaQEBfTium8M6/pep2/2e/f7O1x80EsgK5J5A+bv2rtT01PDras0b6dHt5o2XMFw OeQdjjnPFYng+5t4/EgimZQLm3miPzADcE3Atnr0xn3rL1rUJdOuWtXAZXPUH0459c1keEVOreLN PsRhVmmy5xxtAJc/lRJ+6zjqo+wY7CbTpINP0u6a3urCytmleFhPC88pBxsJxuAI71lya3LFq09x rcaSohL77VCHbj5i3ZcdCK0LSzFnd6o1g21/IJDBsoWjlQg4PHH3c57VFqttBqcTGR4fMeLy5okj 8pslssQR1+vrivKxclaJwrceztNZRG3SOdtQjdtsbiVjn7pY9OnUVzWgx3un3ZWVhGplbC7dqhV/ vYree2WxeNItqouMSW/Bx0AAFX4bK3EpWGY+dO25t3Iyvqeg9O1eXy32G0VNbimkV0v440W4+bdb ZxHuUAN/LpXJ2JEMLW8kRLSMVYydfkONyY9fTpXQy3MUU0wuRGrEsn2eZyofcoOYz/kVzV44mTCs lyWljeNsdPL6omOvHU1K03OaoWZ1jdo9wwmGU7cKdrc8c4qJBHbea8ZVwDgZX76HqMDgYGPrTbq5 a4ZHsrj5FTy8gfIrk4BwKqXZ1CysYryScOYWZGOAUZiOvAzXPNX1Rg0DxrLLbzW8LCYFiZyPLZlA 5CjuB710GnadfCR7ma3dxdBY2G0BUI+4zM3cgVmNGwtFO8yBx5qMVzsLLklT+uK1YnmbTUmjfygL cBlMhwzA5LNWtO1tS0irbCd0maGQJLIVhVUOWP8AESSwHQU9tQ8qJ2kh8/a2CTkZyMA5ODUehtnk qFhU/JMr4bH+71FTzySeXJbyQhsFIsseueV/xrKrylWJtNFpa3kst7C3zwkRRsWEcjsv8e37uAB7 Vo23iAaabe70yeJoIEyokRXVdvB2xntz3+tZ811HKsdtHHJJ5igMdjHOcDKYHv8AhXP3dk3m/ZrO WNooopIpG3qrnnCrgfwepHoKvD1be6VSbUrGxP4g1PU9NurZjai2u7xZptkYicsp3KoHG1eeapBh HerMJllffudQvzRr91toB6Vi3dtMFUzq0YI2gZB3IM4GR1IJqZRKs99OJNqyr5ikjBwSuR+dbShz I9L2TtdmzLHBJIl28gBk2dVC7sEkZWnTFZ4oVSQIWy7jb82QeAvoMVRV9llbQOGRSHjwAH5frk+1 PspZ4TK8Yj+VVjBI59CN3J/SuRwscclY2biUSRxugXbKgOUYblTHJx/Piq91LAI/sqKIhG4X93jh /wC8T05rHz5m8TERrEpBVsgYB+6SBkj6VYuLpdQswJYRB8kYaWNQwJ/u4Pb3rakEJXNm68iyEtkC LgyOiCUurbQvO9WCn6YNJp93Z2n2mK5G2W5YtDNMoZAq/MflOOSc9BWKllLp1pNd4E8Bf906rtHz ccgZxycVbje8tIooXlTySPOAdQ7L2PXpXQ4lyZeMNpNaPdNdvIzMZRsXbEqNjeHz0z2oR2ivdtkj bJWByeZGU9XAPGP9k44rOMsUWNP83/RL1w0kZ6nYdy5/2c+lWorrUv8AhIoEtNk0m8yMsY+faRh0 UHH3cVnKFzPqdD/xI4rq2tEYRRlhDumwWD/eOEPHP901orpMdhbtq1tewT3U8pWOOL/Wpjjhw21j 6Kcbenoawr25kmiupFEZuwxeANGG2yY+42OpPtzVfS73w5c200mtXEejl9hkkuIZWCzKPvBI03Bd 3BOf8K1w8W3Y6E+Xcz9C0e3fxFHe3d5DcNbyNNLC0MiR+ajf6tzJjYRzwD+leg6vqVxctqL3ts39 oLMywPADtmiQfKoQd8Z549uKwydAnmhtbbxPZXLhvN23AnRstyTmRBuOMgEnin6v5z+IdRuJppZ4 rA+bFsYKVgZeqnjjbnn3r1Z0nycsjKU1fQ+Y/jN8XdOttYsrHw/paS3+m7TeXk2QVOOYExg9Mbiw 4PFaHg7x5o/im1X7PJsuI1HmWzDDJxyVx1HuP/rV836p/wATC4vLqdTtuLiVtzfxZckcnrXISR3u k3Md5YStBNGdyOnysuPT/OK+4yyvLDxUTzcVhlUeh+gDR2+oqImbEi/cmX7yj054IqpJDtYWt4oy +Vz/AAuMc4+teBeBvjLFIY9P8UYhm4CXKDCk+jjnFfRUV7aalbjcEeOUbhtOVPuretfWYfEwqxtc 8KpRlTeo3wDYroiXOkR5CiV5oh/st2/4D0/CvV3gWUA4AHrXnVhEYJo5C5ZU6PjDY7A16bp7eYoX HB5x7V8LnOHdCvdn3GUYhVqPKuhz15pENyu3hWHRhik07wbBu8xkByQQa09TtJVJaIbTnIHb8Kqa T4ifTLpINRDQwyHBdjlR+Wa8WTPUUVc9F0nw3FEBhAAO4/wrrYLCCEbnw7du2PyrBfXUjVYNPkW4 yAcpg9RnrWhaz3EnzS/KOtc8onTGXREHiPUl0Xw/qmrOcfZLSVwD/eCEKMfU1+fXi2OCfSvD8sv/ AB8Wt1Fsx3aRwc19X/HDWvs3g86VFJhtSuI4yc4JReWH0r5m0XTP+Ei8T2rzru07RF+0Srj5Xlxi JPw+9X1PD1LRs+X4gq6qJ7xYKIlJXnOf1/8Ar1po4igYAEF16fTrmsxGaKJjkbh6cc98f5+lcJ46 +Idv4Ut4reyCXetXgItYSfkj2j5pZf8AZX+EZ5avulUjSh7x8fyOpLQ9Flv2acaJppVtTkg87YGB aOInG8r6V2ukLcWcCwSDJHJxzk45PHvmvhXR9W1DTb/+2xdzT6vLIZDc8mR2PYj+76KOK+rvC3xT 0nUtPkXxNutLy3RDlVLrNnjcoX7jD0PrXyeNxbqzPfw2E9mj2RtT0/TrFtR1C6S0tY0+aWRgFA9v f0HU1wGo/FbS4U8y0twtqFJ+03r+VGwHdUxuPX0rwjxJ4m1PxR4gm+32/l2lq4Fjbg7o1RuikdN0 neU/cIwK4u+e81LTUOnbZ31WVRGxPmM5U+WGaQj58ldzFF25Q445rlOqMUeqyfFHxZ4jE99okz22 jgGOKWKMRtcSryxUuDsiT/np/e4UZzWHpniWLw9f29kU33MhN5dytkCOB2/cCQZB38FgOCM7g3au Z8XfEfQvC9gukWEcd1fW6CG1tBnOVHzSSnACR91Tq3XHNeDaZ4Y8XePvENppN1dy/aNVuneR/LIi jC4aSQtkDaoHTtxgc1hWhBxbkJpLU/QLwzrEM2s2C2k4Gha5dqUlVxgSRhg7RFgFKMMEkfeYH+I4 pklreWr+Tdn7Xfx3kccN60gikAxiIpIMD5Qobb/F04zWYwaws7fwvDNLqMGl7DpcbKqtiMfcRV42 9yQcHOfSrE3iFT9r0y/eNxcoUt7srnyzEwaOVEA4eKT5XBwcN7V8VUmnVvHY529dD2XXl0690gtq E0EdrqOnyLdRtIXSP7MFaCUIM8DBb15YDoK8PaKbUrCFdNjijv5laO+tYZjDZyyYyXjj3EFmADjG OpBrbs9WabxD9j17T55kkWJVt4YykxhUmWQBuw6np93NPvvsFprGmX8Fy4szNFLHBcKqFYmU7A54 +aIEc/xBvauivJtKaKbMD7Lf+FzDpME6R6P4itZZLeC5RpPImX5Z7N4plDRuc/uiDjGPaqWjsNS0 uz1K+sYft1gp0rUWiJSa38k7Uee3wF+dNiyMD8wA716H4kh1HxBc3GlXk08d9FBJHutW8yZCH3Km M4Kdsj5jWBo9/eanqOrac0kOm6rqG2ZIrgsqyyWoCSq0ZGQzRkNjnIFT7W6sCn0M7xjdXuraRog1 Vm+26TLLbxhUJSSzlyrusnVth2yAEZ65Gal8K66hmhj1MNdXlliPyoZACWTpPD0PAwdvRhwO1Ptd Gv7jxLeeGNU1uCzmxMNPuORa3U8aq8aRSNhRu3hSpwO1cZ4e1vbaanaaxaLZa3p1xJIwt/3LG4jz G8EeM538MnbOecYrG0pSuxpN6mFqdhFPeW/ik3q6dc6hPd2W3Bdk8iQkrwVK5TDIzLjOR7Vv2/ii J9P8P29iGuNY0TVJlWW5OS8c6q6k8kKuVOORVLSNMbXdN1NI7lIPFNncSMthdjyhcQN+/fy3+7ue PcjK5XDVyfh3UNIkg1HTyGF3b3waO4kYRyeTGu61YR/89cErJ6dqKjd9TVq61PX/AA14o13T/iMN C8NZt7vVLe7a1kMikv5CLLHbKrA7flzhu/SuZ1Sz0iz1/wATadqskUF5FZNMi7N9s0zybkQOg4XL ccALmrSQR2F1pvi+ymurGXQxBr9lNKy+ZMiS/Zb1Fb+6ok4z2AJqreXEEvxe1Y6nC6x3lpf74F5E ciQ5hY7QQ21gGx2Aro9nGULISjY6f4K6vqVqnijRrUx2V/f6V9tsbYB/9LgSQg+SVx+9t3B2uCcn huwrkNStw/hv4l6rDfzWtzpKab5cLKil2vb2SMh1GNuBkEY/u+laXw8kntJ/CXiSA+Sun+JrvR5Z SZNrjUVeWFVC5KD7REB8v8TdK808e6hHr/iWP7M0q3eqR2Vo4G1vP2yzN++2n975XyEYzk89c46V TUYJsF2PVNLvdSl8N6lqkNu62dp/Z2ji4jX7zadb4568lrgAr/tV7J4f1rW9H8R6R/wlF0LS3TTJ IYGXCK9vBEPLfuCytERtyK8Q0GY2mlSm5v3udAmvXlVwkkcTR3LgFQSAFZXhz3D7duete2694chm +HFn4eMCNqWp3NlFtlQo5nYPNOVAyUAjdyQp4XaKUE5O66E21PB/Hdy50KytrgHT5nXU/EkiSSeZ LunVZI4yv/LMywmNQp/QE1abSE8Iaf4d+1XsE9vPaQ3TNb7ZDDNzK0JUHO2PcobP8WR0FN+IFl4k Gu3etPFaifTvslq8GSk7KpOSyNwTEo5Pddv0Fq2vY72SxEtm2m6ZrN+dRuFlwjmwjJZmyRmTPPyg AGuSVVSdmOT1sXZIb2x1vw94e1KdZ4UmGoXEC3CJK0kyYkjaYD5M7W2oAR93FYWoa2lvqNxfS6ay ypBO1ywDDYjSfIAT/BtJTnDMcn1x0sniRZIPHHxE8Qael3beJGlsrC6ZfnQrgosUGB8qxRIN/wDA e/Wo9MtotE0DU7XxbaNqNzqUKwwliUG652kiQDlZlB+UjI6r6GlUgrrsOSR6d4UurvS7bwzoCWlq YNJt4ry4smiY+XDcf8tZRJ8pOx+CR/D7Gud1xLLxT4r1TTxHNbaBbXP2q8YyMfs+m2J3SMA4wDK+ 1EXGcnAGKsyXN94oni0vxDcR21rBYy2cz9FENuPOt94GGaRVwm08dPeq/h7xHY3+pRXWvyO6TywX EsbIMMkSAQwFOMqn3lPPzfeHFb8ySRkUrqTU/F9xfWt681k+vxeTK86/8e9mrZWCBIsblVNvmDb1 FXPDw1bS1v8AU5bK1mu2tWs7yeKXzBcqHGJEI+YfaGVQyrn5QemcV2HjGRPEeqPeOXs5NCtgkEUT bY1t2zLPdPKy/IiR8NgZaSuX0zw/qusW2m+OdN01r+ytE+1rZqyrKlkp6oh+Ykgb1jwScE8ZxUzp zctNh6nWWUviG++1eF20+FtYhJETgEywmRMte3CNyEjibbbqMqSeelR+OPDkNjBpem/Zo7B7Un7E mcRrBsP2nziR96WXGzJ4YmrWk6hqo8Qf8J7ba/p2laVryNa2bGRWWa2gn2xvGo5A3tmRDyAw44ry i9u9X8WXtzqGpa7JZ2Urzra3t2oeIoC4t/J7kSSK+CR93mtqtNKFgkrLU2LHWdY0O107WNWCiy1S SSXTJbAiFbeeAmELMFU+XJtJaJuhPUd6s3Hiy+k0Wy8LalPFNpFrJGRceUwcGLLtOen71gcyFTzk 1al06x8e2MWq3kkem30FrBp1vDYqzSBtuFvJ8gKUDjYUA+eP3AzmXHhe38O2Np5kz3ttqdpeR3MO 3zm0u7s3j83aTzJEC3zf3Qd/3CK5oQko6ErY9Mv9NtbnxUnjAaqt2wlt42tItha5tUAIdlOF84DI RmB/drj722tsRWXha4uZ9P1L7R4d1y3k8+3t2Eb2D5Ea3sf92F9wWYArtOHUYyB5xNpst4G/siCO JrkiZ2Vl8vZtGXJyN44PI6dqxFtf+EUK6n4utG1Dw3q6NHBeW9x5ZEkRysRz9yOX7kiuvcEEVtRr S6xKR3ut3GleFPEdq2mvc6bLDcbQyXY8toJMPcrPjtmRSjgMNvJ7muK1FdJ8KJ4a8YfaTL9ht59P 1UW0rRuywy/u5gY9u7ylfdFIhHy9QRzW3p974W0+7uPDGrhYbKxZZDeXEQuBYSeXGzq0rZVoPn8s v0hJQ4weNj4feHVuU1K3nurC2ulvvImjmhXdLE0YlVHQBlEJO4FVVeOc5rb2jvoUmeTyWWgaP4q8 VeD7+7to9B8a28OsaVfJiRbPUgS3nKF3ZSRicD5ch9rVoeDfG15PqOoeEr9YIoNXWO6it4Vx9guY 5Ft7m38wEnaGjDquMFZODXI+INNu9Kgh8Lxot5pelXRfSr0hhvsrmQq8AZwG2xOvIPHy5UjIFX9E tLnQ9R0uaaOC1ubq/S0LwtvF0s7FlbbztG3A65xj3rjeKXtOQ3pyjdHY3F40U2XBDgnK9O/OR2/G tmE6fqdlJbXMYcNwQ3K9c4xzn0qlr1qFv5nAwJW68DpwCaWygBjAiO1s8HNb1l2PqcPUta43+xdI tnz/AGXZ7uoYQRhgPqFrbsBapsSXTI7iMdMrGcfkoNS21hPcMN4PQjNdLa6HvxtkZCeMBAazVZxV j3MPWcfhLFhDZSABLJLXHdVTP44rp4beAIUTpIuxuvzA9jn+XSoLfRzaLnzGLd+BV9EZDnGR6/hX PVrSaNamIcjOhEelQvBbgRpnAGB357cVh654htdG0+TULojERAjU87pG4QBeM8+/atXUWVFaeQ8J /D657V5T8XIdX07wRpVzpURn1O/1yzWONdpbA37VGeB1HesaVNykePjatoSaOKudcubW1OqC8SdY beWzsR5CGSeRgULSMdrZ3SFR+A9K5yKPXdFutHsnKWV/pkhYBm3l71pTK5dW6leEweVAxWlqF5oH hbxloK2c9xPonh6KS8mtpI/3k+qiEOzZcD9350acdBtJ7159PreqXes6hqmoyMblC816clNrTqN0 2CPnX5jyMEceozrUfL7p8Ve+pF4qsL3U9S0yxiURXeuTiG7ltyii2iupdspKk/6wxRs3sOv8OfaZ bH/hKPiF4b0jwtE0fh/wpG97p0AXCW7E4tiw5BJREmckZwQOua+bbXUra51G81i5kl+06en2+3tZ o2Xzy0iRxwp0PzR43l8YVSOpr6I8H+IrjQNOudWe7WfVHi/tGVkidFvmlfYlsjoT5cZJ28DiPP8A drfC1VSjqS3yk/izwrrPjb4rnw1PeyaTZ6XBc6nqsETGSK1lleK3jgikIALTJ8zn+FW9qrfEPTfD fhP4jx6f4UtZ2t/CFhFFOIiQ97f3shxbArnl4j5a7RwOM8VyfwpXxJ9p+JGp69JJDe6hcWdx9rlu FCRwebLJNIsrNjy1GfLxk9OMdO4+G0semaJo/iHVVtbt9Wu57+zhu/3jbsPCMTAM0E5X5ucoO3Nd depGMHOOpcFobWqaPrfha90jTdC067ude16WW51DynMLPFEGEVuiglo40RyozjfjPU4ryPxbLa6J p1jpunah9t8QaqkkOqTuxELeY+yKMBhlI0HO48HpXtZvLC71jSNQvdRGqvFYnUdQMICR2zWUBhyj 9UBfcAvuTgcVxHw20FNebXfjN4ztbKeFzjTLe/XbG3RUmES4VgqgKuflznNc2Gre0XKyWbc9pp/h j4WxX0lnZSeHLJn3M3y3uqXgO03Mr8OtqWbEUSjJ2rk4Iri9Is77WtP0m+t9QM+saxP/AGHGtxC7 Pb2yxFFgDFeI4U3Ty8cZC5JrutTvW1GTTLS6mbVYvDF1HfX4AAhurzAkW3UoCpt4eJCv3VwEzzmu cTxJ400e7vfGOmXMaahqN/PpdrI0O9QbzE8zWgxtLoo3yzegVa2nGKkrMGek6bPpGk31wfDklzpe jeHnawsJmj2/2hcRDy7/AMrbiQkbsqSPXFYP/CUJrF5eeFvDWrTS3NykmljWFjYmeC5kVikWSWjK gbVP3T6ZqPUfH+hXFhP4f0GMt4X0mKFLOeYE3V7cLJlrhNgJDM5YsxC7u3FT6Jr8fg2zvdK0uwt4 NUN7FJayKonBQFWt1eQ/89c42j35wM1zV6vvcsQqRvaxR06/ZvD9t4V8C6eFm1zUkyJkjWRrSIuj w3DEDbu2E8jkAVveF9KlfTFvV0RL/QVgM9ysjov2y9hcssSKAWlhQfNLnALgAcV5JPqdrYnSLYiS AXf2rUtbMbqu15JTFGiAj7yDOwZx1AzXq/hO78TSNrfh3wM8ENtqtzDF9oSRXksbHycu8SglYyuC h45dvxqo0+WN2wtujj9J0+98d+ILbXbhoNLbV7557RIAqRotp8m/ZIfur/Ah9N3tXs2m+IpNO8Wa zqOh2MGp2XhHSTppmed9m9TvuSkoH72WSQqhO3jbivONPsjNfz6veo0Uei6T5gtYWGyBxKq21t5r jmVht37eu4966CyudXku7DRNdsINJuPC1uLlWullf7dPeSl2Pkpy6kc7COorqwd9ZkKNj0zRvHHi TxM2rX+hNYQ2FlEuZF3ToGzucxKypuaI8E9AOlUPAdida1fVLLxNKl7f6xFDeRy2uCqsjCVHVhja Qcc9G2kc10Ol+Db281GW81KS2he3s5Io7Syhe1tQJwWYBerHnB9OlWvh34fk0PxRfQxyRRvd6PDH EIkwkXkEqgQe27Nd3snJq5VzofGGmSaRHc6pb6bb6i2tQ29lqETusUM8hbbHKc/d25xuyOo6bRXy l4s8GWvj/Tdfub7SIdQ8U6PCp0u/tpxJezfZpM/Z7q03bXcx5RWdTv8ArxX2bd2134ugv7C9h+z2 tnI9qQCyecduyRkcHgbGYKex64Ir85PjBY6Xpviq9fxFeardWytNawXmn83Md7YMsQX5eVVgUkIB GRIf7tcWMwd5qSNOZWszxLTJtNuLi+gm0X7DJHctHIYmZmD4GHYNxt9hXZwXelWkc1pFdteiXPnR BflUgcFQo6bq1PAWkaR8SdFbWfGHibSdF1G2kKXJlumtpLlQuESdSmzd0xKGOVGGXvXGyeH5NK1l dM1DULSRTHvt76ymElnKAduVbAxHuBXnGCMelbTtLdnn1KVndA072yS29tK4h3ZBeLadwPyA5PTu O4PNd54J8WeJNA1S30TTc6tDcyh0t9+2SOaU5Z4JcgxM2AWGdrEAkAgVx1zFpCWr7Y5bm6QA3Me4 ojc5VoyPrjFSajo2o6DdWKXNzbmWe2W+VIZTL8j8pluCskfcD/GvPr0lOk4hCpbQ9d8U3ugy2kmt wNJa3Iv44bPTkEcX71m826hmXacldp244fcp46DHHwzt5NH8S+K9O1Gae10Owk1BLS7h2XFxu+fL qPukcjHHzfhXNxeN2ezvrDU44rqyvFQypMgLuYn3LJFIuWjmHPlydOzKRXo/gyTVrvxJDcaTqk2s 6HrdtLbubmNUaKNsPNBdqnSVR0/5Zv1Q9q8FRlQheTNYxOS8N6Vd+G9Rm0y7urS5VbgRtHcyvCrP cQqcMq8+ZsYANjqKxdQg1pP7S0xbT+2NJtp/s8scqLIP3vyJGwJzuB6NGQFI49a2/EGsaD/wnWu3 2qaY9uIZ3YAYlVApCJKyM207VGRHV1tQudUa+i063DWdvLCFubTEcssUgLxmWNcAYZeSOmMZrtpz 2mRblZ4JpImsNak0e8zcP85VZCSXccdB/GP0ro4YrpGvrN7nbDaFZFJOFXIHQ9c/pVPxUiam66ja TbJIGMkUqjDRSE92HqfXtUdnLea01xC1qPt8SKJCDgOAPmJX+7XZOKkro2LU1rczMbjTF2+UOTu2 x5b7rYHr39a6vwj4ln8F+KNO8QKsD3NrIlxKkZO2W3kG2aPnjEibuB06VTt9Aae5t7Z5hBPMP3ZD gYK9Bjpj0zWzpvge1vNE1K5e6hl1bTLiOK2WXKsyuCzY4wGXHAPFc8aig9RXVzodW8O3nhnxFqtv pxW80l431HTLlfmkms7lPMjEsY+8UGAW4O4Y6157dXOq2UEWtmMW5t0ivfKgfrEP9rnac4bBr1i6 inu/hi9nrC79e8Ht5trcxTeZOumzEmUbk6+TLksuDhH9q860nVbPXNGFlZK92suy2uB5Yj3Psz57 5OMfwHHGRTqb36GyWvMW/Eum2+pavb3lwzx2eteVeWs0K5ElrcDIjbsXjYunH92t/wANeHdcuPDG u2F0gubFJJLKAsrMzCONWiZgM4ChgufVeKzlaXR/D134X1m3iWbwZF9p8tHyYrC+ALMPeKQqTjpv /J3gjxc3ww117CRLi6tERWnt5wrTyKEQuULkKWbqo/CuacpfYR0ObivdN+30W6bwjp8Is7iC60u+ 2yuxRopBMDIJI2BwF49c+tfRGs6Rb+PfCesr9ghkltoJBaPdqqpt25KrKfmieQ4CsoyMAZAJrwmX xloHiWe2tPh3dXE0eo3eJ9La2k8y3SI4mlIxxs3D/ZxiuT8UeLNa0fW9StdH1uZUspmt4bpYnH+k RkF1aGZf4CU3DaOelfMSweJlioyjpYxgnvI8nsviLoENssN5p2tWVxEWSSCKePbGysQV+cK2cjnI 65+tWv8AhZPhf/nhrv8A3/hr0G68R/CjxRMdd+I/w4OteKbpV/tG/trpreO5lRRGJfKBAVmVQXAH 3s1X8z9nL/oktx/4MH/+Kr7DkXZmvKj/0cq4u5Rb+W2GgtfMh2KNskgXjiqNsQlsGFr9nMAUxxk4 bDHOf61qXNrPd2IuA8YTzGIkPBXjPt1+lcnLLdS6jCzWu4SnhpGPygdWHrxxX5fFAL+6XU2gd5Hm JZkDAEDIzwa9EvJQtnpiQMHztJBz8uBntx1rzYXcdzrAeyV53UGGMbSSVbsAvcevpXqD2izQ/ZxF 5cyxrER6Jjrx+tKppYpGdenzry6WTEQuEwQvPygZPtziuRSYyXtvDDGNxD5JHICDkHHpXUvb/ZzI EYgpEVj4JzgEdTXJxWt8siyxxM8074GMjhuP6URRrykhlYWVxuUrJuaMDjjb6fWs/wANwSTy3cs0 MsabJWUuBu+ZccAmtDU9I1Q4aWyn2j5sKjMMk8kKKxjPJp07xRJIkjDa3mRGPjOflJ9q6YbAzRi1 e2tENq0bTWileD/rFKtnKkcAcVyxlmlnubk4JbzHPT7py39at6iRCoO/a0p+UDvWfZM0lw6SdSjH Hbj5R0oSSRk2ez6RbOum2aBvK320W1VGTvK7sn865e7uZtJtFsb60+yzSSG4z97esJz3zyc12V/f xWt1Do0UZzJatLHJ1RDGu37vU9O1ec6r9oudU0uG7uROoQqCqlQCzfN/SuSC11NGtDB8SMX1HULW T5WYpcBQ2P8AWqDsz9DV/Xdn2CwsowqTbBsRSThSMBvwxU3iprQa5b3N9CVtpYo9+B80m07Qf5dq S4mt5dX08SQiJ4InZmByXixhVI7flW66WItrY6yS4AtNOmmkZUkaImMH+NR7cdayNSs54r+F+Vhu J/Lfcu3Zu6kHvTNZklVtIsIGCLbWn2kJjIJY7cE+1VQ122qRCOeSVYp1QLJ821nIPT8ayinqbnR6 3BHa6npiGRfJtHZN2QPn8vkgj3rp0ltp7UP5nkiS6ZyQc5CRL1PuMVyGp2c02pXMhBZ4rxXcYByP L5wvaui0+1EqWkLwDbFbG4MnTa+3CqB3zms5bBPYk126W10mW/jjKfZVGzeOGVh0qnFdf2wItixm WAedCAMcBc4PoKl1kQXnh6e2uLiayiKpski2vhlPHX19KoaWkGnh7iZd8s22IOBtO1vvMRxjg1Eb WMEyhqF+P7Mj0y2y8lgUSeQkgtuGcJjtWJFarb2pu9QKyiWUCGEEBiqdQSaha2dNbvI8lvPgkAKk n5o0ytWZbGJrbSZIo2V2J3O2du4kce3ArqhH3RtkptUuNZlmKGCKQJJGo+XtyPfFU54V861kHmZM cbSSf3CMgsvvxWpr6TWyqy4j8uFxEVO75vXjPtVa8klutKs7/wAhpJ7YhnYcKSqgkMPxrHmIuZen Qyfa5pnlBgNtM0ZHDbiucsPrVrw9cfbtV/0xd8hJETgfc+Xv6hulJot4tzBfSbNh+eInjCKx603S YLi1ilgjlCXMrkx5YKfJVvlPoN3XtWihcdhNdeG2stZ06JCzTP8AaTOWyqRxMHx5YH8HTHfNZWk+ ELKBXktXni8g7JvMUZVcZwAO3zd+adoWoyLr/ia5huCtuFy4TDr5K7VZWfBw3J2heoHtWrpxi0u/ e9BQ2uqkeSN5wyg7SwGcnkde9dri4LlBGDr+miOSGG4DWcsAClG+VX4Bzx1xmux+ENxe3t5reh6x etqENvGj24OGVCrZIz3+lJ480/U7ywgvLO4ErW824eYAdsbD5s/0rkPhvrEWl69aag1xHAt5ci0f bjEmcIzY9sn8q7MDO8bnZhaiU0z1zXPDGr+JdXiuZZzFawykAFvlAHUkd+OOa8s1TwFqOm6zdC0j 32cmGt5AcYBOSCOvWvq/VLExpJDJIomY71kGeVXoCAPSuZlhNzEwXgN1B/lx+le2paHt10nJNHI+ G/DYijSQxgHaMkduOa9Y07R7RosSwI5HHKjd+DdRXM2Egt4xCwA2cE5NddpuoRLIxB3cYwa42jsw rXUd/wAIvZbiYxPDnr5czBfyqddA0+1BkkDPs5JZyxrduNQiSIMwA46Vh6nqLRaeZFwnmMIw3HGf rTSPRiomdwZNygBQfkAx0rR12N5dJhuGXaIpF/DPpWZZRW1oQ8twJd3Qtxg/jXWXclvLpskBYcpj 8fWmqfU1aVrHhPizw7dXNuzQpFNHIN3lFQFP88V4JrPhS5EEjpYJbOo3cS7/AJgOMDt2r7Mgjilg Fpc/eUgZ7YxXI694WLKXtsN1x6/Q1pCb2ZxVsJFq6PgbUryU7BcsTtJVix5Vl616B8GdMeXxn5s6 ny47aWRH44O3A5qr8SPDRtrkSozJNJcRqINuVckMCeK9f+FHhIaeXkvwy3M6iPEmQir1xV16iVM+ ZxFk+U9dhG/USJGlRZtOkYuyjIwysdqjg1kajJb74r+wSO3MfySeec5P8M2P93HGKvxWc9hrZilj lWJbWcIXOU6Zwh9KhuLhBaMrWivIzeZHNt+TKjqPXA7dK83Eu9GDOD7TRralZ6Y1ra3Vl5RU4Ewg YGRmI/1gBPTPaoIfJXT5ri2SWWZZPs8ke0B13jIYKP51hWVrPKVuWVXD4lVl+VUz82WI6A+n5VuN qtpp2qxoqyRW0TefMItp3vt4Kt/SuGElYpM4C/uhDeJBFmH93tImKv8Ae6HufwFZE3mCAQ+WwaEZ lB49w6kdF9MfQ10viMYY31wuyNlWSJgo2yRvzyeocZ7VjafrNwokht5YpbWeMQgyJuCqB8wcEZPq tOUeZWRzT3E0eZGjxDLa5jbcobIY45YSr3P0FR2Nx9rluQEAQu7KkQJwx6HDce/9Kq2s4hMLSx7n KlckqCccZBFX7GGRWMEHloZR5kewbnLdMA8dPSsFDldjNItTXRXSxbCHZNLKqswP3do2tx15pk9u bZriEIslo4ZVcjKbhywb2A/wrPv0uIxap5sR82eNctkMrDqVH4c027uMymPe1sJGLvwDvJDBsjOA Pb9K1kii3ot2BaTJG8YkMxSNkGV2hRkcfQ4q/G9yHup33S+WF2scfNjsB7A45rN02VYlkEEECzAk lo127UIHzHr17Vv2c81lFM0Ti4hKvM275mXdhVVTjux/CueVO70AkS3kXUAn2iNDFHlduTtJOOSO PTPtXEWkjWT26312bmYxmRiy+VGHDkbFwMEdgOtdrdQ2sNjGXVt4jDBk5+ZecccHJAFcB9pls1mv rMR/ZpZt0kbsJcysMkgN93/P0qowcAjLlldnR315dXO1JJCr9Yxxyx6ZJ4+nNQMJE0947jEfzrG5 PJGZMt0qss1vexPliHjx5kCqR7B0/wDrU8W/2llu4pnWSL90+U3ehzjpURqO56lOqpD2vbYJ9nuc xPbSANjj2H1/Cku5ZZL6NBEIYoyGjlB4fI/ix6GlW3byprm6hM979qCwOwAUx4+ZiB/CKdbTW9so tJP30Ue9jv6RkHnHse3+RVM5qlr3LZOUhMoysjtEzgcdO31HHtUG6OGz+xud8XyiPcBny1PAOOvp T7fy2urcCY+S8wAC8jeerCr+p6dam3LW9xu8uVoTIR9wocOvpzTpLUzpq7Kt3NDLpksdvE3n28sc mYiRG2VwykHg7eMYPWp9Mt59YTWIbDbJLpjxn9/JFGWVlADAP/F/tZrMuLa4gtvsKwOk7zi3heQ8 xbjkcLwcA56966d9A0ix06W0MEQiSHMjzKGLEOp3MWx+p4/Kvpcvy9V43kZ4muoaI56xi8yRjlbl cLkxbXMuG5TcMYK9TgdOa1vJ0nzruB5vs928ry2rg5bBPzZx69h3FS6tceHbFv7Ut9QsYFVtk0cd xCoeMdNoU4BHTgdOK5seL/CbTSSzatp0uxSUQzojLIB8hznoK5syyt0Ze5qZ4eopP3tDr9MWWHUx fwNAIZ/9XIVIYIBtKBT6kf56VZ1DWILeB44J2FzcSBVRgWXaOquOgXuK8507xd4caaO1+2WeovOz LDa211mYL94mNv4cc89Kz9Z1i/u0MluRMsZKujYWUxY+XJHBcDjrzTwWVVaj7HfKpBLU7m+vNMs/ Kv8AVdVh8xPMxH5icgjChgAT09a4vxr8QINfabT9Aj+z2DxfZ5rosfOljwFZEUcLGceua83uLdLu J0w9zEFw0OALhM8/KDw1clcaVMql9Kuiywj54ZjynH8X/wBevoqWWez0epze0TOf1Tw7ZOXmhuZi y9EY7h0xx0/lXLyWJaMxSDJAPNdJdTXUJDXUTI69D/CQahZkmXMYAJHHvXpJiaPJr6wMEh4yvt6e leifD3x/rOgFrLBvbSH975HVgg+8Uz3A5xU13pKONr4zj+dcPdR3Hh/VLa9tzhoW3Aj0HUH2wcfj WtGThJSRzVoXVmfcOg+ItP1uyj1HSbnzoX65PQ+jDsfXivWdA1WK4ZUc+XOnAH96vhmWDUPDph8Y +D2aTTbtFlmth/CrDoV/2enAr2jwh4/0/wAQW6XEDmK4QbSDjcGz0FexiKUMVT5ah5uHqTw0+eGx 9a3MQmhDgE/hwPrWNPokV6pVsOvXaMHp6VQ8NeKbS8UadqMiJM4Cxy5wG/2SPXNdTNY3ttPvt22k Dp7D0r4bG4Sph5csz7bA4mGIjeJb0rTUs4FiULGMYGAAa2nkEMJPAxxmsm2M+1XuCCfbis7WLt9h ijBLHHC89OTXGnd8qPQ5eVXZ4D8X7q81bWtJ0yxXzZVYlPRGI5ZunygVc0PS7fR7KOytTnne8p/5 aOepb27L7VPeRJPqU1wn+uYeWGPcE5wPY1zF1qz6jdroOlPtB4upB/AufuD0P8u9ff5PhvY0lz7s +BzjEKdayNLU9fjMdyYpjbafYRl7m8b+FV4IiHd+Nv418pXHjHR9a1i41qcSJNKQkKM3yxwp/q0P 82x3Ndp8bfEUFjJY+ANObbFHtuL9l43E/wCrj+oHzN/tGvD7rQklQPAAvHb+n4VGZYjmnyInA0rR 5mesweMrWzdcRgovA2tzj2OKsXHjZ3KLp0W15D8kjnftOfvY4xj2rwQw6lbN5ToW6AY5zXd6Ba3F nbPNefLK/RD1RRzj8a8hx2PSUz0jUvEEUVvNOtzcTSPGPNllbMhVFyQgA79FGOOa1fBt8IdOKtMU /tR4hbQRqDILSGPaGiyfkUnIbHPQ968usDBqmrLZ3eZLdYzNNHkBNp+55h67R7c10Wr6ta2tylzl L02o3JHG6glkAw+BnYMjO1cD8+dLE82pganpt7aeJrvVbyOSZprqUxlkAYbycMOcFFTgA9K+9fg7 8MB4K8PW974h8ltb8V20TxWzyiGeztWy0SLIeBv3bps9yF428eESQz+ItK07UDGkOpiCOQWbbUlC N0YbwVGCMlTuYjp6V6FoPxF1TUfLbxZf/wBqiON41uHhUXBDDy2YjgOq4HC/N6DOa4sYpct4ike9 avpmmw6NJpFx/onkSm50+/twLmaykUj98vlEkoMEMu4Y+bjpXlU8Wm+KLe2sblxptzctNHfH7jLd qRtKdN9tOgLxN1DHacmu0sNQbR7XU7TTL66azEHlz39tEmxCRtVArAPyGA3DJ281w0+n6dq1iHtd c8u8CSWaTSqdrfu/kVw2X2EcBscEAivlatdL3YxJUl2Op0e+u9H1G50GRpbTxZphKR30ybhPaKoK EKeYeMKw75x3NdJeuniqbTdF1K0t7qGf5WhiQllXyiAEkHcHByemK8Ng1YX2iSreX95D4h0x0ksR Id/2mWNSro8uMohUKHH97a3Qmtf7Xe3EGk+Kbef7SjvFPJHA/Aiud24hhgb0bqAfvAis5NpJCcDt tBstTsdYbT9UjlnXw/LCt5KUxJcxRn91Ii5BYqfvZ4rofijp+ieLLNtR0i6k03XdPvEubW6hh2CX zMrNHKG557Fc88dMVwviOe98WyaHqHgm4l1C/ubeZLmVG2Q3G05CvzlTKowyf3vTpXTzeJ7PxVDp Vx5a2a6ZbeUyNEWlAB2FJE/haPBA+g963TUYsnl5TN0J7vxzpWm+G41M+qQP5wl2RrE6uhXDo5Vj juEH3cEciuesNG1bRPE8GrS2bSW19aNFm5O7ypoDs2SA8hR2GMkd66GC0tNMmOpwzx3K2EYK2Fxv /wBIVjg+U642MB9zn68Vs679lvPsOoafqDCFm3yH5W80Dr5gJ/1keMNXMqqaDnvsefSW1v4K8cWl q8cUmkeIbYS30UrCRIpWJUzoSeI2+8FzkCue+IWmeHtDm/4SSytTFNqF81tI3y7YjtAkZR23D5ky O/FXfD3iHQJIbO31aGLWLjQ7t5tNuWQ7dSsIwVurB06+asZJQY/h4rpviJ4dgs/DHiTQr63lvLyy mt9V0y+CkedbMACjJ/dEL43HoyitlTcrM0j0OQsL6ybSLVNQg36dJYatZ3GXLRj7XblMx5+4PM2M VPesfw3Kuuat4S1S81BpL/UJLh7uaAbXa1/swFx3z9142/MVj+Er26udB1nwvJby3M2hStqrSRuE 8q3R0jkkYf8ALSP7hIHI5yMUvhnU9Xi14w2U0NnLHq19I0vlgx24khZlVCBgBxuCj09K0fkayVtj 1C41UyeGfE1l5awi9PnWCQt/x53kBiuLeXecDb5kQTP3ufrXBfEfRLG28ReG5/DytAuj+FLe/u5F XDi5tS+8n1ZyNoPTFa+lXs/9h+JtXls5UmtpIThlLeU3kjapUcfOxA9utc9pmpQ+II4buaBrb7Ws WnzzXByhiIVmTIP3cFtv15qIVWmubYzR7wbX7P8ABrwToFis7yXFi+tXJEfmbogJnwARyq/aFAH8 NXdX8UnT/E0UmmfJaaTZJqEMV43mN5mo2cVqFBPqiTNkf3hWR8OdTudX8K6axKW76XojaSicl0kh uGhf5M8M0NupY/wlgTxXhfjCW7uNU32eom7dzb2TiIH5BbEpEOf7q5HHXOfp11qy3iCL99falf6h HZPOoL3Uk7sy/O7Sqoiyc/Mi/KOSOT6V3fiWSLTdQvZ7aIXMOpwR6f5kknmmOO3QqXQ/dR2YM21e PL2+1eeRXcNxNcat5SC9SGO0Qplkd7k7k+X1WPLfUe1dPxqOrabZrY/8SrTAVaDJQXMobG8zHC7S 21Ac/dGK8yjK61RD0NEI15DpWnyqiEB42I3MzW0UvnNGkPO1rlisWcfdzXa+JI9U1bXbyzhlVtRt Ha5uMriONhGpWLPfrlCO4xXFad4s1nT9SfVLK9dXgzdwEortFFDnKqxHMah/kzgE966Cawg0PSH1 vUdVe/8AF2rXqTaZpdvvS43y7ljSZyMKIl+/n5RnGciuiL5lyiTexheILXWwb3SNTleT7LDDe3gk C7rKGd8L5jgjcWwAe+MV0tnpdlb6TY6/damkUk0nmJYPEVQwLxuMgzmTIHHvW/4F8GXHiTUITq4l uAzl9QlJYteXk0oX7NJnGVhUbowRg1F4y0SJ9e8T6r4GilPh3wuVW/RpMrcX8bYEVkM4MwH30+7n 3GK0lh246C5CKXXW1m8vrfV737PHriQx3iWcS4SGGQFLducYY8yAdTxXpmrR+KtP8OXGtJq0tpA9 ysIdYxDcM8qmMJHCvylMZY88cEcCvKNSi0HTdPj+zXMV9HfQxzG7iDRy2s5k3XFp5TjoqnnJ3Bvr WxFrKeIdU1ESXLv4S0MGV2lcxSPAoAdIz/E5AKnHIxgVNOvJPkYK+xxutWC7dRj8NXCalaajCZdX s/JUYS2O+R7IjPlyOufPcABzhu+K3tKstJ1nw/PcaZYjUIdNnilfUZZ2i860I3xwmPIVfNwVBAHl 4YcZrY8P+HBc+StrJ/p1wv2mO3jAIFk6swZl4+fYcpEDlmO4jjA7HUrHw54Ua1+IHheW0lW2077L rOi7w8Gp2pOZGjj6LdJjJBxuOQOeK6adOUviKtcg07SrW48SXeoWGp/YrES/aBNFKdjCRV8nKvwI w24HA6Y6DmsfW9Pm8L6pb+MdOJt4I7hWitorhftGTiMy+UP3b7+mAen15Z4J8XeENJjn17SZ92n3 00kjxS2xuHtI1JRLaQHqHUhjx39K9A1e+8O6h4O1Pwn4phNtrOhiS9CWyFN2D5kMkbZ6EYfaP7ta ciSauJo4yw13TdGsLXxRo1raxwfbpk1PQwpL2ayA7LmCJsER85lQZAB+Xpx6x4c8Q+G/F+hanouq wwWyxfabctFGJC9s/BcZQ42g7MfqM5HiujRjVbebVNY+e3uRb2Euoxx/PZ3+QQQ/34oZDmOR+gb/ AGeaoteeJ/BnivV4fA8V28hhICXPlyBpEQeZGjHAldF+bd1O7ocVnRq23Gka914RubDxTaxeFdSk S5ljYWCywsLZ5bNQPs8vYloS+7IHmR46nO3kBqV54L1bXrZre5sbl41aawWGRns9h3OIXBbMOD8n on049B034k2Ov26+G/FFpqGkXVu1u8JGGntbyPc3niRsf6sGORc+6t8rV8//ABR+J4l1GTVEcJfW 0MsEt4rs2Fk4ZEbIzH97aMjCnbziu2GC9vpFkzaR0XjXxF4dtdDNhe6heboVDR2qupjiPDeY0jHo f7ma8r8BfE5fFPxT8K6Bp1mGtri/VvMlXO1Y1LfKDg8AdelfNV5eax42uZhYsYrG1VpGeTJ9/wDg T+3Neufs5+GJLT4o6NqOoXDT3NtbXt4kKjCxIsBCO57EkjC+px14rvWVUqNNu2osOuaoj7Z1lPOh YeuOeMn06V57Drsmk3KpP80YOMjsK9FvQG+TPJzj0I9a8x17Ty+8KOCMjNeI0rn1OqPVdP8AEVjJ GrxygggEgEV2mn+ILZeTICB2JAxXw1f3F1YybYndP91vb2qvB4m1qHBW5baOmTSlh77HXSxvLGzP 0bj1m3nT924xwOfpVLUtbtLSHa7AZ54/+tXxXovjbxTcv5EM4+cAcDPT0r3fwv4c1XVBHd6u7PyD +8OCRnviuOrQtuarE82x32n+fr16k8oKWsWAgPc9jXnP7VUNz/wrHTWsQ/nW+t2rAxsQVwjkn5fT A4r6E0zT7e1iVFXaVHyjsMV5n8d41/4Vxd3VxEtxDZXlpLNGylgYmcxnpyMbu3Trz0rTAtKtFHPi m3TZ8v8AhvWfB3iPUtJu73URJNaypdy6c4KK9wI/LxISPu/3lHWqD6Fqnhw65q11Gl7PcQ3ItrKE 7hK1wMB3L9oR91B1IHpXkvjK10/w/f2c9zLIdG1QMtlqCgedaTDrbzj/AJaL3VjhiuO+ay5viT4z 8ETW+m396s+nyfvLaWT97byID1R/vqfb+lfYYrJ6M3zHxkZSR6N8JNLTxX4t0mw8QSfZ9NfU8zPP ldyRLvZXZvmUIuQO3Jz0r0LxL8QvDWm+I9d1bSfNu7XZ5NijMsOyS4lKqWHARfL3Soo/gZa8j/4X 54QuZYn17SWnPyLK8MgUsocHblP4cj5lx8wJr0ybWPBvi3Q7LRtGsLeG5u5ZSJ7lziOS7IV5o3Uf fGeA3+rjjVF715GJyhxjpqaOom7MJxF4h8OwWekGO1s9U1CxhkU4jPm3w3pE5DHZhf3kwAIxheAa 91SC38LPaaX4olsoLXSL6BZjbx7DcC2H/LrKM7YySu4Yzg1xNj4Y0OHxJYWFpqLL4b8GWM11JNLE hU3FyfKkwoPzCTDPlufTHSs7xb4jPxGl0jStLt5IPC+gq0VkZydtwFO5pW43Fm2qoAyK8hRVPRmy lpoaWi6zrHxA8Uy6dc6NpljHdm6n1DWrhTllmBQqRF8u1wFhjAGTjJ5yB7nIugahrVpPe2MCQ6HZ vpr6bHIRAfKTNs+wEblAz5RC5+bkcV5D4KtP7T0w+DvDrx/2n9oiuvEFzLB/oul28MjbVuJSRmVu XihXO44JwATVbxdNp1i6eGtIH9lw2jteLqzxyXGpzSuQyb1HJmuukEAHyoQWwhFZV8PPkc4PUSel zsHvJdT0+fw7b6e+heFtHR1L3cYESooMzeXATvm/6aOxySRx6cV4k13S/Eh0zxNr8C/2JaLENO07 Bj8yFHKyyfuvuedNx5fUKOaTVbvU/EV/YeGNUsri1D3RkktbKXzokQL+/klTaQkm0jftYorAjoK6 XTrPxNoGszR6J4d2CyjaKee0jE8EUd4d6JbIP4Y1AYSsM9eM1yYRRpxXtHqFiv46/te4NprthpcU Wr6s0M9smn4NrFHA6pHGHAC7sDO7sOKp2Op6vd6nFNPDJMml3EslrbW0qxpOI8maGKdwNxhdjvH9 0kDFb3jzVH8QW2meFfDM7uNI2z3Emn48mIEiMW0MygbrmQjcc8KeuOtWdR8Ntquo311BbQ23ia2B t7to2LRxNCN6L5Y+VJnHyzFfv/TmrxtSMNUaQi72OY8I2Ek3hiSXVLBDcyxy6tbLFJG2yKF/Kinu f+WgWBSRbxnj70h5FdK/2GXVINTit4tPsNfjawgurVJPNu7AczTzOcMVmmGYsqDijwndJN4Rh0SG GSHT7nT2m1u4toi8kNnArZtPMGdnmP8ALuzlVHTmm2Op63498OHWvC97FoV3C9va+U37xZLK1UeS CHAx5wx0Axj1rfEVKcKPNNikveO0srVvD1p4f8Py+QGubmfxDfo5OY4rNMRRyAg7jvIbGeienNa3 iO71k/2Z4l06aF2127sbNbxY9srxxkvmJXHGeVY8YXHqK4yw8PXOuy3fizUNamur+eKSK2sbiWNW QxDElmJG5CbuM4+4cV3ejeGb3QdQ0zXvFl1bWuoS6bIqRxo08FnHCN37hXPzSMMIxx9K6ssxEai5 YbIzqbnoNh/a8eqzaXc30SRQW6ymSKEFg0pKhNzEHrntXP3+rHwreaVcKyXet62jaVo0LAAG4cjL yYP+rVRuY8dNo5IFeXv43tb/AMZSz3uvzjTdLBuJ7+CD5pokO1bcRD7rJy8mRnAyO4rSvNJ1qXVE 8Ra9fT2NhJE2pQ3Mmz7VpMAn/dhF6CSbePM7Lk+gr1/aK1wVrHuPgu68WXPguCxuLOG11S0f7KWZ wVkEMmyZ2AJIZiGOD618NfEnVJbzxv488F29rHHdtrB1Swn+9K9xCqiSIKeMSw7sf7SpmvqbSPGW r694Z1+e1s/7G1RfMu44WimJuLWZQiXPkkLJ8zDBduVXcduRXxN45sfElj8Rbi21A7fEF/eRSW1z gSLNcTOqAJKMDCv8vtj5q4sVVioXbFGRYns9I8R+F5/FHgW3t7C8silv4h0qKLzQWZjGt7bJyAsr cSgZA4x1qlNa6FBo0ejX/hy807UrWNZLiOaTMMylMeYiAK1tMeOCSp9BmvavCNnbaPpUGtSzLoR8 MyX2rSWESrjzpJIkkh2nHmRbgwjySP3q7eRXjnjzxNph1O70LQEv/FN9MLhdQk1DT3W5S9M8csU8 fl/NvVMptYBdhHevm6VWcnyXKnJIr+EvCXhrxREdJtddGk6oY1/s57iPEbzAPvjuDnKbiy7SPl6n PSuKiJ0u0vtJ1i0a0RJN0shjDyRTxMUfeo5GApU5YDv0qe31XSo5gFaT7RaDy3+1R+SyOGO9R/GM EYKFf1yK7vTPHnhrU9f0zW/EECxX1nMY7trbE8F+PL2xy3Fs2DuH8TLndjnrXYnUpu7V0c7ipbHB 3Gt6VJaSW/8AZYjkCwxGOSL948afN5kTMfk3dTyf5V0+ieOf7Ejh1HwZpsGl6xaXDXEkkjbhNGQQ sTBhgrz8y9jyD2r3mDwL8OPiZ4ZTw94Tuv7K1TTIfM0q7YZjjk3biik/O0ODsZGG6MY44zXmM/gK TSrvR/Dt/oslnrmqXKxQW0oWZfm3KdzqTwH5B7pisKtahVjyyWoJSg7nI+Mf7P16a/8AHuhwNHpl 1IlzremJ802mzuQJJMd7aU/6qYZCOSH21q51zQ72403RJrQjUreCeCZphb3PyyKw3FVZXby9wKg8 9a63T9B8I2XibWPCOgXEmn+KLVmk0TWJ3ZYprlQBd2jxOAhhm+7EuTvUduBT9F8I2Oq65bQaJNZw zwyJLcaJvLW9tMp+Z7AsA4iBB82BvudBxXPKtCF10Rs4qep474uv7HWde1HWbIiLTtQneRYEZWjC L/CwUD5s9ABXEQ273JaS1meIoWjUD5COR8r4/gr27xx8NprDVb7Q/DEMks1zJJdW0aqAt9HuzujA 4UFiQQD8mOcDFedeD9JXxd4jt9JtpGstaeYWYt5FO6PDYmc4G10QA85PNd2ExEJU7ojZHd/DzT10 /wAQWnnR20u9JDDDcsAIJGH+tldzwmBuTPriruqeKodR16ayhsV1OCdZE89gqzN8n8G0jLckqcfd qz8SPBa+DfG8kNpdedaXMCXFulyu5ZIGX/VnvwQTx09q8q1a6tjLa/Ykkje6RbgSLnzbYlWxsU4/ h+XHv7VmqKnMxVnqdJpHi2LStZtL1IFuI9PnjV0kHlySWzjy5oW5wwkjyjfn1FYHjHwzYaL4imhs da/sqNTHe6U5UyNNZzDzIX7Ljny5geQyniqd5pV3FZ2yCFZILi3a6ieI4YwH5iWUEkEHIbP8Wa9R i0zXtU8GQeDNYgh1SfRCbiy8s75AsxXzYUkAOWh3K+wEhlziuxpKPKjrhP3bGZHfWN34g/4SF5o7 hpoZIrgvhmngdAjh07rt4A9f06Sw+HPwu0i5muPiL4uub2SRBK1nat/pEiv8weaZQSN/BC4HHFec XOrSaPqL2Fxo1v8A2PLizs7sQFJ7Sckh2/2ld+vVV7Gnarod5r/hqDWLRHbU9AVob4xsE3RW+PKZ g+N4aNtg/wBzjmvPgnBm0lzUdDW0G18P+DvjNaXXhqXUtD0W/hkWxa6kIngNwmGDlQQynllU/wAJ /wBkVs67o0914z1Pw1qs8zy2tosdvKzASM7Pve5YgYc3RO9nHRdo7Vg3Wpaxr3heTTby8uJvss0d 1a28zeY9tKqM0W1toeNdvybT1r1bwhcR/ETRdO1d/KXXfD7JBemSM+bNBHmMBCM8ozHr/BiuTF1+ Sp7bsC1R5LNoPhHSZpdM8Vo8Wr2rtHcCPLqcH5GDA8hk2t+NR/Yvhf6zf98H/Guu+JHhTVNW8caz fWmh3V5A8wSOZLTerLGixgg59Fx7dK4j/hAtc/6Fq9/8Av8A69Zxz6FkZc7P/9LNvbsXNm0f+qVZ fu4rltVv3JggSJi84ESFOXJbgIB2B/OtK2Ej/by58wYi25429iRnHpWzb2NhpelyeItUR3dF8uzj TGfmO1nI/QelfmUbItIyrG1MedO090tLCzIbUrwf8tJEPMETjoq/dJXkkHtXcC6ufOaTSFSOB4sk sS2VPb2Jry557nUbtPtEQt7Kx2rbW0QJiUE/dC9zj+I9etegEPHeJLLZzq77VjaM9iMAYPFTXj1G 0U7+W9js2aXiJct8rEnp0O2uJN1NNqllayyskcyCUvvI2LjgH611Or3tzpfE0JEZbfsxnfk7duPw rlNUHmXzfaiqSyIpKIPuKOAM/QVVOJTeh1kt5eW2npeiYxqqrsIkIcuwwuG9K5XT9V1qKSUSag04 kBBjuD5sbc9CMCpvEGpxx6LpC3C7BKzOAeCMj5RgfpWXZOVkmCopRACT17YA4rVQsiWzob3TtD1q W3Rpv7B1BUwpP72yc/3fm5Rj+VL4f0O/gur2CawPmhRCAxD7s/NuUjtjnPSsS4s5Z4yuGeN9rtjB +9wFwenSut8LzXE8i6Zczu+nALFhA25C5+ULLwS5546AVlJu2hUbMuwtJdeLZiVLx6fbsuR91WMf zDNef6El1JqklxcRPLjy3QsCV2hssR/n/CvW4NMjh1EXNuIkE3ygBTvXZ8vmOQSCeK4q71Ca7meS 3VoI7SYRliAvnsOxPb26fzrKLuas808RzNJFYiGU3KxvLCC7DdkSABR9K67RLYs7ahMpkkhtY0wB kkO2MfjSanJA1tborwrNcTzSKsq5+8c4DKO2O+K0Zbv+yYvs8cKwpEiSJNGwYh1XLA9ehraT92xn ya3JfF3GrwSJEYopLAAE8bfKOSKr+GL6bUNTt40jTypXDyNzuDsnyk/Sl8VTte2GlXZJdZdPYOW5 ZpHPLAD/AAql4Ps30/V7fMyvJM0KGIcgIhyc+h471VKyp6mhtRTIup3ivOzCQiOZhnLYBHy131nc QRyQSxv5MSxjAfnKdOnrXAnSzZakq28TF7p5GB+8cDnp2ArYka5gtxd6ef31oPlH3g46Y5rlqLoi Zo2dX+zSm3vfOMaef5YRVyrHrkpXGzTGPV7i3EnlSwu0bo2c7ifl4/z/ACrr9VntYdLjWeNmLRxz BougaRth2+4rzO7lht9QmfJe5ll2zyyfezG3y5/DjNZ04amDN65/eajAIYSJoJEcndkESKBnZ6YN OitbOFrYxhppHZjgbvKVySGGOzCie8sg73ERQyCNkZmJDnGOn0IArNu5rm2v5sMUW8i8yGMcDc2T xjuPWtW+gzRgsI2DSI8sMCFo5PMI53ttUjPOD7V3qw2Cr/Zd3axxIiAExg89P51h6Lp8wtbaG5kL ed5TES/NjYBuTjOCfwro7Z5o9QvLW8O8yhnEgAxyc7RXLMhnC6jbPb6nq1gubcTxt5SlAoKyMpU8 em3FZ8EqLqqMjA7FhUg98KAQBzXomqW8clm13fKHa15U55wfuAfiMVzwgESE7UKR9owC4HQEY5re nV0KTOR0rwnPoXjPVryAqdJuIWuVjbobh0ICqvfB53Y4yw9KxZEvtRsdN02O6hLWGxmjCf6sDnOe o+nfrXoqhzcxveYZYCyAY3E91GQcdcmuU0Gzsp7+51u3lMSSPJEGX5YpHPQY+ldCxDlqxXL+r6rF pOl6lHr1x80yKlsAOZG9gM15rd6NDDb6LqquWMrGaOMABj8/ztgdAORnFeg+LNDtdWjgEsyGO3mj aZHzkKfl4b3HbrXN6zp5udRtHI8mzWARqiHGVA+WL27E+td+EqxUNDaC0Pd7L4jWemLcQeJAyx2w AWcjPBH+zmrugeI9M8Txz6ppHz2JfEbn+Ihea8hmha7uppmV5VnWONo143BQoI56V23w60qTQdN1 TS5IPJjt76QxJndhH5HzcV6+DxEal4s9Gnim9Geki2ScMvAJrJ8mWz1Aop5JXGenI4rVgnTzF5wD 0/OrupRRPcRy8AyxDH1jPFEtD1qTXQIILq9kDzYO3BwOMccVavbR9QtpLK6hLwSfKV9umRU2p3Ny LMyaXbi4nxkQ527vTnivMm+MU9lcfYbzw7eW9whCMLho4+cdAzHafbmtaEebY9WFaxQv/g+PObUN J1a+t7pCGQSSMygAYA29KWXwl44nt3t7vxA1tGwCg2yEnA9d1dY3j3xCI/Pk8K3sUefvbBICDzkb Sc+vFZ8vxb0ODb9rtJkifPziN1wRwQflxxXQoSsUqkW9zpNFQ6dYx2Lu91sXDSzY3F+gPFaUmqRJ aSvIcGLqD+XFczpnjXwh4kk3aJdC/bARxGhIQ57t0raurC3e98pCWtrdfOmI45PAU1zTTRGIrLls eeanBYaiqm6STzZJgzLtQpgYEeG6juadqNr/AGXdmMebNCyqzNnAL7R0+lWNSUm5eWMhAW8tQCGH oOB/Os67lYSLb2zN59tgKl0MqrE5fp15NeZUqPmsj4urO8mzfsNQvbnULG3nk86BLa7ePPDBmixj PpislvOuLeaS3lRkiRNsMrt5kf8AeKKQAQV46UaPePceJ4bbABhFwjKMY5iPTHbIOK47Ur5kui6g FtojR3yBkNkE4/KurFQaw8TlqS1Lc081yI4XQRW80qRtGrlVSNOgce9RTzSabeT21rave2LnK71L 7cADII/hAqpDdXFxDdzWQinug6sYjk89+narem6xdWs9s+57N5I2bJTaBtOSPm9+ntXBGNkQnoWL u4e1tYYViaTT3kLDOTtGMn5TyAew/wDr1m28YgCx2/7ob3aLpwPvYJ7cU+61Zbuee4a5IuQRudvm QDqAccVUeG2ktUvAzbTmSN8k7M8EMB+mO1ZuokQ6hXiAk00LOnmgStJtQbV68gsfXirFvKqMs0L8 sufLQHIPdgf4ar4upLRpoIfOhhDNslO0EAeg5qhpczWtzHcxx+TPkEDBXJx/EW/h7dKy5tTPmLOp zm22XavgO68qgkfcOm9WOOvfitm4tmTZciUpJM5blQysMdE7dse1YF7aT3qReZE7PcXys6TFdnl5 5wcjv0Aq9rJe4upI18xYYsoJWkxtRcEIAOi/hmtZSsaNhp0CSKbcxHbHMGk2E5I3gcsP611to+k2 1rfadOnnNNtk8mUtEdgbcq7lB4J71zF/FdW6bbaPzXeOOTYCQS3HB9RXQy29ze6lfs11b2cMMKSR S3ZPPzr8gCgnHZfTNTqtii9qmrafp+lw3sjYS0jMSwoS+SXGByM5HHXsK4OxSzvo01UZWLlPKmhI VW3cbD0f69Oa6bXLC5uo7MLKJbdonDzQ8MzYwrLuHBzuzgHNczfrapLcW1jdiOaG0kFvAG/di5Rf LVjnHIPPHBOPeieqItqh7WySRPLCn2gbxG+dybQvcY5wfyrV0Wxs7/U2lknkt7N1Cjy8hhIvOGH9 08jPrSafZMk321Y3nVVMd0RhFyVXaAT/ALWT0qdCZry2R5Abe3aWYmTKkqBgKxA5UNyBjrWS7HZG TSsKW3XAS4ke2sYt5sy6/MqEZ2O2cEA8VUTSrrc2qZR4Y42dJpBtUE/d2J/Fj8qnnab7H5c2Wfex jJ5UocKuR7n0qeW6nMUKXRRXiysSR+in7vFJmE2VXuZY5rZI9k8ylZlQrtxuGVVQOuc846VrXaRv axrZIIZVcNPBIeXJHJ29e1UWuktbG4mlZJryVjgShtyqRztYDgpjj1qu1ytlZq0+JftcRlUn724H 5fn69KukTGY+OcvKbyY7SlykyxKCxb5ACDu9AK7HWre31TSdRtUdfJvLKRASAU2uR/8ArrnBFDLc zSl0UG3j4Ycj5GU7enLYxUXgy9ZftdjYTRNEdLeYRsnmrE6nJUIx5IxyQetfT5Rj1BOEjmrwu7nm GsfBXw/oUcXnXVpdzSvGsaxQAEIOXPzen8+lQQ/C3w/NG3kzK7R43RrAAVBGc4x+ldze3ltdam80 pnUugM7H5jtb+6eAo4yB2qLU9fk0m1Fjp16IJnzGkg+aRYc8Fjjhvpmuipjq1esoUnoa0KS3mYkH g+DwU0d7bWMVyl0CHJ/c3nlgYzEhAyo6kkilt5rC5hnntZAxgZgxB+ZdvcY4zXB65dXt9ZtCdQef BYbj8rANwwAPJB6nmvLpfE1zoKW/2O5W08naxP3i7p8jKQe3FfQ0IuMeWYVFd6Ht2o2trcNG7RmG UkeWUYjGe5PXJ6+lYksNu0rWUxK3K/MtwpCyKfY/0PFReFvHmkeJbZrmeFbO5ZvJIb/V7sZBU9s1 Y1jSrqCSS6ikWOQqPkZurHnAxmtrLYlXRg30F5bFzOouY1GWlRchRjrIv9RxXNNowkJuNKuRAzjO xvmhkz6EZx+FdlHqlx5S2s8IjK9dzfLKxPTeOD7g1A2lwMWu4CLSd9vysD5TfWPofbbWfKawmcWt rdwmN72NG5wkYP3j7U7WfDltcWlzeXIZZERV46JkgjP1FdzDDdrbfvrRrOWLG6UgSK3ZSn936HpV GTzrWU6hKmUZvJus/NvUcowH3cp7Giw5SucL8PPFv9jXB8Oa0P8AQZmZIyxA2nOdpJ6DrXUeKPCE 3h+ceK/CgElqw3XNshwGQdWX3rivFPh2GK68+FQYm+fgY3Rk8nf/AHlPPSvT/hnrmopNceGdbbzx Gu62lYZ3x+5+lephffjyyPMrrk95Fvw54m0jWfs8OsTC70u8Cxw3AYrPaXI+4GAxg+h6V9QeCvHd 1ok8XhjxVN9ptc7bS/8AvHavZz1+oNfE/jXwfL4c1OXWdDj36beDbPb9MZ5O3H6Hsa9V+HXimXxL 4evbG98trrRPLYE/6yWA/Kdw/wBkAZP411VKEMQnRqL0MKVaVCaqU3offgsElZQmGEnzKcggr1yM dvSvm/4jeMJb+ZvDXhRlxO4jlulP3sddh6hE7t0J9qqxeL9Z/wCEVu/DFtqK28FwN0UjZ4VRkw+Y MkK/6V5vNeXGjaRb/uiNc13i3i2/u4rfdgZI/wC+jjr714+X8PunU5qnQ9nHZ/7SlyU1qY2oRPD4 itdI8LXExvVjeO5O4uH38NNKG4j/ANgD/wCtXdQ29t4O05Le0QS6lcZZN3OWI5kkP8/wxVG3t38H ae/9jaVc6pqV3hndVy88rHADHsgP4Yqw+n3ljb/aNamFxq85JuWUfInH+pj9ETp74r6m1nc+abPm L4neH5DrUWpPJhryOMl+uWB2nNczpc832O4juPne1bHyDqPUYr2P4jQxX0OkW0bFrlfNlZV5+Ttn HTmvMJNAv7QnVLJwSh27ByJP9gY6PivncXFRqHrYSXuIxLnUFBj8uBymQS0gK49hkZ/SluNQn8mW Argsepzuw3I61rTJbapAFRijOxLDqUYH7rDsazrq1nubiCzZVeVdwJHCgfdOT/sqAa4jvmkTWXhi PWYDqltcsWl4ljVtpUn+E/QDoeK6rwn4f0sXU008DMunTAfvBt81xyin/Z5+YfSuVtdP1Xw7dRy2 LfM/zKnaVCc8Duh4GfWvXNLjeW5itMhXJDSAZILN95vXjFKctDI6O0kvpTPd3G9oYpBITuK4k6qA 3YkcKe3fAwa67wVa2Gt6za694m/dWyzPJGVX5VmxhZGKZOzeecgZrz/WL1rkRafbyEWls4VeQDyc bh03Z+8PTGK6qK4m0++B00+Wk0av9jmO6Eq5VIVA/hbq+6snT5o2ZtE+pdFv/DFraf2dcziyubeB bm2vLRnnZpHk2jykK8tnqCCMVo2On6Tqt3ZeKfENw9tbzXDWJvEVVnJUHduhAwgO44bGSeOmDXjW jazLodyNURkt5rUMqW91G0hwRtK7lwFHoRzXrF8JPFFhpPjbQHuLGzlhkm1GG2drh4ZI28osIzy2 3rwPu9K8LE4Z0veIsch4l8E6H4z0S7m0uSQ6jpESJDfW8bh5I1fY63MJAU5TGSD8hXvxXJ6Z4iu/ D8r6Pq2nMsujwIX3osYbym3Fwv8AFDLx8y9uepNddp2teLZLjUI/Dd/C99dxSKzw5XzYgdqqitwx kC896v3Fhpeu+EWmur6dtT0hWt2ju4Dm2CffWEx5Lh1OPLkwOMivMqLnWhPNzHK+N9GSaaT4keD5 hpUt7dx3LW9hC0ybcDzJDbjH3QTlB25FUtblvrCaDVtQuIba81G1a5s7/TsGzv4wQsUtux+5Lj5X hcBs5wM1T0ifWvCl0Us9Ti0m3s51uLRot8kl1EPmCbCeFOeV6jp0wa7ddQ0caZLcWtpZ32h3aCfV NM2qzQSmT97dWiNlhFnloRgxsu4dxThNS0luNNdStti1zT1vrSWOO9Eaxy2jEqu0YIcjcB869GBq qHn059MtdQiTUDdeZcMhUmESBSxC5xtLKAvuRWZPouneGZ2t9KgurjRnEdzFJdN522JzlPLnjG14 nzlQT8ucdqcza3IdPv4Fje2iukE9tK3llVkJAZvoON38Lf7PNcdaFpaGbsUPEGj2Phi40nVlilm0 a4LRxywoMiU/vQ24fKZNh/dtx8wKtW7pXidtJvdP8N6/e/2tc2FrhZmPGoaPeEmPen8Eqqdk8eQA QDUF0NX0fS7nwfq9r5th4gZbj94hdEeMkGeFF5SeHkyIo+YDcBzXK+Hr6CTxpp/hHXPKsLg+bpyP MBKnnj9/YTArkyI7b1DKcGNsdlrqUH7O0TZbGpb+F9M0vx34Y068ijUasJtJtJ7YgIqTKy568SEY iKtkY2965Pwg1lHqM2gagZLAX+oyKIJcMzSQRSW0izY6MDnByMnmofiJDqOny6Jrlms7XWmym4lC n93bXlrKGMfqHDptP/AT0qLxmTYfFS+8RxSImmXoW+PmN+6ZdUiMqFGXnzFZzkgY4pUqHuXb1Ha6 Pf8AQdBSPw7410myCXM+sSaY9vayZCyKphjdGJ53ssLdP6187/CaxE3iRdPvrP7Sb1mhW2kVmWfz phDkDpgBWww79Oles+DvEV/F9jS+vBttboOt4oEitFBbNMDKMj+IIqHNN+H+iC38VWPiWK5Fraad o1zcwFn8yaOWJGdPLAzkmRzsXHt2qpST91hGX2WVNFlj8Madd65Fdi4t7t5MlU2GVxD5LeW46kjb 5oHPOe1c9q9xJeeENR020S10+7uZLa6dV/1z3WozC0tFRzj7kQlmfHHzrWVbXi674J8PSGVZLfw6 8MGyTG+W7vyJLqVVGFUh2VfoV9RWl44tJdN1680pryC3u9Lmks9O8xiIWa2XLTiXGR5bs233GBxi ohDlbJjvYw/DOmalPLq6IizWWk35s5ZFyN7I7b/s5A+ZFVWkdv4Y/rW1Jq19cBLLSR5our6e1tQZ MeXawjO4KR91ApkYnuRUmk6mvhnwPautpDNqmoWtz9lKfvbmNdR3IrFAdqySqSOeypx1FZXhea38 IWd5rV3dWo1KR20+O1LbmiUrmZz/AA8YCgZ5wc8c1qoxLSudfY6hoXhi2tijyXerawyl7RD+7EEM hSBd5HymZ8kr2iTIHNJFFc32lal498R3rajfSagkVqXPlq29yJLkvxsTKBY04yA2M0weFtJ0HT7G 21u5Nh4h8T+Zd3km3e+n6aRhRHD1N3cZEcYIGxAcDNdNc3+qeKtdHhl4NO0mz0xI7cN5Z2232cbE ubpBkPKkWFQfd83d6V0WVtERy2J9Hj1jxLe6lpujXkksNlazXWr63boYI2WPqERiP3gHyxt2WvRr TW9FutM0+zsPD/2SCy05fsdmWLrbxeYFuZ5skM9xINpTcCdnz+tc/ZS2XhCTV5Vle+8HRpFLPJeY ibVJ43+WNcYOzd/dG3tnArO8QatpXiB7vxTpVvcPb64TbXN+y7bm6lLYW3gjGPIhQfIVIDOOelaR k4x94VzG+ImvWPiDWTc6ZZi0F5KYodQjgKxTzwjaqwrjbJt6O46gfSt3wp4o0vUpfCulado/23+y 5MJ9sPnfapDnb+8wMwwSZO3GW+7ziuP8X3OrX/iWB7m2cWtssdhp6264ht0QjakarlQ3cuPvV6z4 H05dB0nxDcOllrOlRRwLqttFgTWbg7jcRqSCoHV0B9xzxXHTXNPmBIhtNOS2F+bTTtQ1nUYHkume OForUzxnmVUGHBTkJ/dHGK3tG8c6Br9hLpl9pEemzRMY1EaqZ457c+YWVGwR8pLcjnn1Faw1LXfC niLUPEVhbXXiXQNSaOFp1k+1zwrGNrtGq480NkFpcZHcVh6le6Tq9/eeILzSdPt4ZJE0mXUEZXuG iaPdHPtOMMWG326muzlcNgszm/GPgi30zWJL3TZ7i70LxH9nMckwCxI2d4gfZ2kI+Q+4WtPQrSNr WG1t9An02+SxnW6g1SYCbUFkj/e+S4J3bOiBei8V0t6t14U07+xNfsf7X8NalJBGjFt0+2SMgRMI +JZBgPH5ZU/KM9M1lqkWveD/APhGfGG37ZokiJp2pFjFeW24rtMjf3whDF/uyDgnctKWG1vcGZ2n n+yvO8TSRC+0fW0az1SyT9z5YmcQgCMA/JNGvVgMsmMjfWF4P12y06IaDr8RgsLS6ke5F0jRSSPI Fghg8wchdqjyXXqAFznOdTVrHxZ4OtLi+vJpbqGZDFdaxFAJpI0XYPst/GOuzCvbXaL/AAjfgZNb +narN8SPCl+JNX0izutKDCNPkZbmzZVbdK7fdj3bTGw+aNk/N06LXS4NaHmHxdeHSL7yLf8A49QP tsNtNueS3lnVQ5E2A2FRBHtwQCD9K+GfG99Jck2zAyxkqXHAVnc7VGB1x14r63+JWuDWb77O91b3 r6fbrZvd22T56wvlZnJ43Mp5xkHrXxtqU4l8V20VypiEW+U7xhWeQdR/ujivscvoqFJabnFJ6nU2 dlHpOlDTlAyo+Yrxlz94j86+jv2erC3i0rxB4mcb767uY9ND45S3t1V2C+5dgT/u184vIfT2APrX unwW1kWNleaRITslvGlXPGN4X/CnmytRsjqy6S9pqfRtwvyBeMr8oI9BXN3EEcoIkwf4cHjH41te d5nQ4HpWbdwlomJHGM8fpXw8UfVyZ5prfhmG6YrA20E8juKNF+GC3EwNwC6nnnI4reuPND5Lbume 36133hnWLfKQXJKyJ6EEEVU5NLQcYJnSeGPAmk6UAy2yZAGDivWLGC3RQoGR0wMcYrnrW4jl2mIc Y4OetdHa7gwJOT+FcDbe50JJGzGuO2K8/wDiyI3+Gni5ZR8n9lzHnoGym0/gRXfg5+vt6fh/n8q+ d/2lfFUOjfD8+HY5D9t8SyxwhV+8LZHVnf6ZGPetsJBurHl3FVkuRtnyQPsGoaG+l6pAl7ZXKKJ4 37YHDoezqT8p/A15FoGkpc2134Y1Q/aodL1aLyHk+b5XTpjsCo6V6cZBDbISoACbiRjoByP65rmf Ddp9p1Fp3UoJZWu5FPH+s/dRA/8AAFyK/UORWsz4OUtWzxnxtodn4Y1z/Q4Q9pJhzGw4RgB90966 rw940/s2eF7OGaXTpWAnhCg7DjaWjIz0610vieyh1PUAkoBUbV556jmvNPEdjJ4U1K0vrAlYTtZ4 gSFLYwwx7iuSVPk1Ra1PtXTNcn8Q6AmhJmSC4uYLw+Wg8y6jjDKIyQe2evbnivY/DvhOS/sG1iWf 7Pplqdt3eQRAbp2KrHpmlRf8tGzgST8L5hwmcMa+OfhDrrXdjNEY2t4Ek82AY5UMSpCk46YOD0HP 4/WLa94k13TdJ06xeVbyGZo49Sjb5NPghCybo4FUZnY4CyH7uK8LM8ui0q6RpTqW0Y/xhdSeB9St PCegaeseqaw0MsGgyuLgWTL/AMvd1dKf302d22KQkK+HbOcB+oSWHhPS4bv7JPN4p1iV2El1L9ou ojJgSeVCCSpnPWV9rOThflArb0nT7nwxocviS3C22jTyXkn9rSnN7qEsgVvKhE4LRszps81yCMNg CuA0m41OeWz1/Q7NY7jVLpYre6YAWi3Eo8n7RMxGRhNyw57Ay8M4FeDUnfRI3T7Hr1t4c8NfDi0s m8aXUEus38MifYBc5Do37tIVPG0N91vU5xWl4s8S2vhrwsq2EkFpq95cw2+n29jM4bCHC4PJeOMZ 3Z7fKK5PWNPFnqp0LSv7J8W6teWwhvLu8jklttLigAVpzuJKxKNoQluo+XNOisfD/gSwtdVnnutY NxdxKt1ti3SW0bjEyROf3aO49jsrxs2wrlGKRcdCWa717TdE1e8ltNM0e+kELtOswt0SJTmSdQFJ Dyg43EfKo7GuK07WNRt7LV9KhW/W/u4opo5LKOO5hme4yjHzcZ3gDO7k45r2T+xjpQh1nVNRXV5N bupGut+yRILeRSmCx6Rqu0YxisKTwVHZ3c95ba/BLcRzbFms2FvNMI08z9yikqB/yzz3HSuWNG8v YXNG+pz+j6f4i8N2x8H3didKXxDaiWySS6+0xPKpxKHztG6THCNxkmu50/SbvU2lu9YZoZ9LMQNq tqI4kYLwqKuBtGeOTivOvD+t6x4tfVJ5dLuvE3kWZgv7yHy7f7AynfAkKyHy5ZozncoIJ6nqK9N8 NpoHiPTLK60eG5ksbAlZ7mWR48XhU+ZbiJzlNuOjHjPaufNcPWqJUoLQ2ik1c4rSrLSNH+I89la3 xw1j9snVsDEynZIspY4Xfw2R68V3mu20mowTa42t3NpPaWM7Qwwuk0kbzeWjCI84O3Kg4PY15hrV tcz3FrfPp8j32muv2xlIe3MC/u2mZn25PlNgrngjNa97ZafFr+m+H/DdzLokesR7TfXSia4jVFkC SwJnapliA8kN6hmwKMnoYiLvTe2hFTl5Sfw/pGga38SUh1XU7rUvIsYtQvJzF5FrLKZVFtC3yjPk gB3PWRx8wwMV65BeNdeItRhExkspoJraS/1NQI55llTeIh/CgHyhsEDivnfw7o91eaWLi2MVpZyW 5iF5I8uEv8FPnZiobABY7MgNXrPg/wAN67rl5oy6fq1zf+H/ALA6ySXUzFZpY2QTQxgqSoYZy/fp 2r6eLrSqcrOWKR6PrMWs6j4w0/wread9u0a60+9sXv4ZTFOqgR7zLIMbWG3gDrvzgV4P4vsPDtr4 tfwi9lBZR2Goeb4fmuZVEUVxEI5NkWW8wfMMlmGFfivbPFPh4+HNQdtF8SarYT6v+6Rd8LR+ZEo4 3zYKBV5J6YwM9K+Ivit4nufCnjS7srextNaNqYBZ3F5GGuzJNCs5QbcE4aTGerGuLOoTqQ9hFajt GOp6j4nv7e58Rap4evJ0sM7NVf7LGWhQWqhZYZw/+sAcCZecbh6YrivEviv4i+DLPVJrPxFENJ1q JJtO1XTbdElebzI1+zmXaTkxckluVGVrb0rVE1nTtViso4V1S1C2omlV1e6kjb99b75sHyw2+PLf ywa7T4laXfeJfB1t4c0fS47aGJILiGzD5WSeP93gqu3aI1c8A4J74xXzOArulU5ar2HV1Wh8OzOs jNNJK7zMx3SSOzF9xLEszfxHrknn9K9D8MaZda1Y3ljo++5ktFZrg28IEkUcwwGEjYG0Fcda9M0P 4e65beCNU8L6rpf2e/1XVFmtLuSIKvmRxbQhOXaPA+7zg5wM4rj7r4ZeKdD0eQalIYYpp/L1CAXs SQsI+YmIdl8xgcfIPT5gK+xWY0K37uk9jgcZI9B+Ddk3hy81HT/GOnRx6ZfMt3DN9ohjNtcKNm93 jlBXfGcE8jgcV6XqGpxaN47j1mZk1AQWMEGha+m6a7WHUiUWGSKPC3PlsGTePmVfevkyOzv7a3kR dJVQ8jAXQ8hm3MQASEYgDH90HAr1z4V3XjG3t7nTrbRriR/DKyajZyx2wKsz4SSETN+7Dc7kOcjn AzXn46HuudjWnOex7aus6LbWkmjajcRavFG262s5Y2FxA6Es0Mb3GCwTrGuQy421mf8AFK6l4zt/ FOkILPV/sV350CROJpJ2hxHLFE3Kt1BXPzduayJdC8N+K9Q1maSO60KfUIlgJYPcafNfwsG+1Yfa V5+VmPB+vFamhX1/o3jWy8Darf3UeqIoeQ6bbxfYbgbPMWbL/vbd15XhyHHp0r5CWChBupGevY6l poWbPXb2bT7K+8RaPImm2Ra4jvAQJtPDgp5koKh1Xb/rV+7+NeYfCrwzqGjfEm00q7jBt9G0m9ub OeJVZbxbo5SVJl+8GU5X1+tdB8VrsRa+lnqk9zD5ltFcabe28cjw3Fpuy8cqKdw2NkSKTx1waj8H 2Ov+DZ31D7MupeHtOiM9vPYyrcNZG5YrLDCyH97F/wAtXhI3L99Mk4r2ctcYw9onoxSoTeh0Hx1a 0vruztkZMrYxs7SqTFuQnyhuXlAxyT+ANfOesaIklrpd1dQTJbTrHm7O3zFZugMRIzGc9uc16z8W Li01HVhrOj6wfso0mza1kt13R3hBAeNXPTB+9kZXocHivDZdS0dIoG1Fw+oz3DqYWDvGQg482Too f+DHSvQp1JTlzU0c0abW50KaVMtv+6u7e5TS4ntmuLd/MZFUlgjK2PmyemMV0nh8a7oOv+GtREq3 8djKdQe4WBo/NkZD0T+8oBD9se9chpservHbX2l6fbaFbK4HmzyhVl25V8x/xkLkZ9hWpqPi3UPD ukaLK16unz6m1w8Nx5JEEjOdzAYyRHjjIFU+fm0Ki7Mm+IFpBbX2n6xZPJc+HdbgSfS2bptL7pYX z0lhkGGHXv3rN8P3qaRqN3o91Arrq1xLCZ1ydzM+6FXbONiPjk9ORXQ+G20rxBO3w/1i+EWia24k tbhTutrbUgFMUyP/AARy8pKOOME1zo0JrTUbl9bkk055XeGa0JAlD2pCSKQfunepJb7pGME1WJko Q1PRj8Jcv9Evdf1XT9aj1Sx0ks9tOLO4ufKmeaJgXDKvU7fl211GqaiPAfxAtPGNrJEuk69KLe/s oSSbaQoFDsQQD5yrk44BBzVSQ+CpNFPia41T+wdQlnns5b4WyzFJQiyAyBuf3ke0/KM/K2OlcF4g 0Cyl0qy2a9ZeJRdCSNrnTlaLyMpvTz0bBBDj5Tj+8Opry3eppbQwXun0VrvgvxlqerXOoWLzXNrc kSQyW7yrGYyo2YAOOBgH3BrI/wCFe+PP+ed5/wB/Jv8AGvKtE+O/jDwnpVr4cud3macnlHduzwSR 29DxWr/w0z4p9/8Ax7/Cuf8As6BWh//Ti8N6PFPp0t/KCgDgopyegA/HH0ql4zaRtQNvtC21lbBY wON3zAk4/wDrV20F4TOIrFR5VuY4zgZC7uw/rXJX+lNq/iC4ZyyIbfeWH94j5Qv1INflil7xorHm 810YWLRtJGzIgJ4wOeMV7NbPLbQW20tcPGm9GkOeo/xJrwjSLnzvtl1eqswtZVQKeBuJ29enFe0X G5ZrC7tpNweDIEf3RsyP1NdNfZDZg3K35u1tJtksfmGRmkO4FQSdobsfauREi63qM72kbRiSbaSx A2hOo57Yrr7OaeeSSC5JcWiH5FXC7mJHJOPmrGW3TTNKu5onYXSBQEC7gFP3s+5qISswOG8RsNX1 lJDOHt4n2gqPm8uNcLgcdcV1XltJcQ20QENpEY5brGN7vjhB/gKy4tPhnv7aZBIkkoMhVl27VQ5z z/e4FdNpzwT6rLMW/wBAsMKSoyZbgjDbPUnof7q10TnpoSzehtrvUoxFKdlscrFHGq7pH7KucH5e 5HFa6SWml3lrpdmQ6QnEj7wMZHJJHX0B/KjT5HFrea5cIkcltCIrMgnEe4Y2p/u8845PWuJslu7X VLGZUR4r6VIJXf8A1jIx6AgYUjqRiuNaocTu7h7h7gRQLIltFKyYjIQx7RwB65NcXdG4vrqVZNsc mweXI+Au4nOHHA/Guknkt7FNOazHnk3EkpM3OWJD9vp3rltevd99cx7cso+YKuEOehqKehq5FS2j EVs+1Bci0lk2uMbfmAB28etXrrTBBbySspknuOI1blEIjK72/oKPD0CXOlfYpV3QmcOTv2eXj7zL 9Pyrb8QiNtNjTOQ0ijDHDsCuVJ+oq5S94aZyfiSHUF0fTb+OONb2CEiSIHGVHy7kHY8Zp3g2Cw3X mo6g/mSw+WtuQwADN3OOpq3exs8Jtgg32SxncDk8E/Jz169q5i2u0i0ua2QiGSS9VXCryAi5OatX lGyJZ6zaCK1uorq7iaR0DqDu/vDquP5VmzpFNY3KPci0lClzu+UKpHGfr6Vh63ctbXGlWRk2Stav Nw33jnA/pW1pTS6vp5sLwrBHcQAmWTlhlSvy/wD16wcGgJ7q3W80C1sUK82e8SRk4Jik3Nt/E15Z qckM7g2offNM28kjqWyetewW9vFY6fbaH5iCSMuY5M7tykjOfT1xXk2q2wtLknbvmByT09sgf1q6 RDJ5vIiubeaFSuyYqQeQeAR7Y4Nams777QjKXdpjLH5QVQDhhz9AO2P8a5W5nc3EcZJYfLwvQdcH NdHa3DRRWltKo2paxzAnPUZBB/CtfZk2NXwtqEkvnwSyFTPdCSNeckKMYGf5V3rahAt/LGf3saAO Qv8AAc9P0ryzwztOq2z8gpKZR7knt7YrrtQtfIn1GS3RhL50kmV6YyeP51hUggkdBqU9xKs0c0yO rpkRjouxwVX8a5fTp7KOQxZe5uwrBDyoG3jABrX1C5kN3qVra/KiwZDDhS5VSuS3AODWPDc/YpH1 BLRRfQy/vEPzpsfpgj+91NZxjZEDBZJFZEK0pUMDO5P3+PzHB7Vl+FbS6s9QjtEEk1jO5uIYZfus FYMhAHfjrWxphlvYrma1iPmrJgozZiw4O7GP7oFM8G3UkVidUnlcwi82wRddxYYYEnkKKblaLA1b 6F7mGf7FHGs1+7O4kzuZ4xtyM9MV5yhkXy5HISOyXa7Hk7lO0t+dezy20MWoSQwsokbJ8t/9WccH nqteDa5a2cs+ow+YPP8APYNaxhtoAbK7M9Rjk+9a4Kd1ZmsGdhZ3F0dQeeWRjAindxgHAGWGPf0r 0jwrcSzWuoeZ80iPG2Qcn5xxXjtvLClpdRXc7pY3MDkyN8vlLHzwBzy3FbHwS8VXHiLX/FFnNCsV tBbQNEqnrtbBPNe/luFm37SPQ3ptXsepfbSeA2SOTj/AVsi/N5ZoDxJEdw/oK4nxJYXen3X2m2Hy +gp3h7U47omKQhJD2NehVjc9ehUS0PXrG8kazSR/mjxhhnp9KxNcisNQt5LbU7O31G2wBtnQOpHc YxkH8al8Pzx/vbO4BwvTAznvnj61rTeHLp1L2xV42zwe2ayptwdkezh52Vzy6w8O+EXd49H1e/0B gfmgguWjQehUNkCm6z4P8LXxjj1vVL3VVYjCNdthscDIQD8a6LUfh3d3Uhc2sbZ5DLLtP+RT9L8E 3FhLxBhyRjB34/OutV+h1e0otc3LqbWl2em6JpbQaVZR2VvAmVSNQqkn+9wST+NVL2++zWZtkyZH /eSlR3PAHHpxWrrLJp8Men43smJHA5LufuLXmaao2q6Q+o6Kq6lJcyhQ0Dl3DklWjaL+HbjHSuWt dp2Pn8fitLRFdrZP38cMcdwpUq7glpD2wP8A61RztFe6g0kd35KtAu4hejE5YH/61WJBb2139lus vIHEAXbgK20HBx0PWudlEFrc3EVvLvjVigJ7juBiuXAQ9o/ePn436nR+GLOW58XaVbQmNpbh5Y1k J2Kf3ZPVsevesfU0MeqXFoAJLmxkEckceDgfN824ZXtUmhzBvEGk7nxtlKDkDAKMKsNap/bF/NDK POuFRDEv8ITJycfXrXqY2knQszKaVzCu4YdsF9DN5q7Qwcvho8f7uP1ro9Svp9c0yCW9mbWyqsIU G1fKIXuwAziuU1+wv0bFpEDHKuwhItwJ9QRVTTLyeJXSGIR7UChoXMbBhw21TxXzsk4mb0M6fU0i RZpozbFCN8ZQ8jdgNyOeKbdXt1ZGeCQwvau3mJnOAhPDDb/Kna7Hf3bW6qoRAxQyZKsxI3gMQDuH PpTXltJbVo7qNJJYYwyrvYZdT05H3R6Vm4q1zItW91FHp7kyJI9x8q5+YhQSD0+lVLa3S5AfBE8p ORJwAqjI2CmRaPcQWdpc2xiuFuGmklKn5Bn7ihjjj261btILNY1iVWZlLbTu+SNz3fuAO1ZLcBjQ vcpbSXjnyUuI5ASPulSp289a2JXgvb6a2zARvdnT72B3yucfhmsVXntlaO7ImKNuVuAD06Y7+lVd QvGuZ3QwKrQyEkH5yGPAIAwa2krmlzrrxbQXlttmZG8pXT5AAPl27Tz7VZiuCzSEshkljKlH5UCP uo+uK53UbnUluYStst0qxwhiGAIBXk7Tjp7U6GWSdbVpYtysrIpt22kY5BdeoYj2x681PK1qVfQ2 9Ws49T0y1htJ44I7iVl3jLKrRqTlWONuOaq3dpAXe6+zfvYWQBmYEkIOoXv2PHXP1rUsreK4023h YGRLVWOOnmY5OQO+Kp3mpqySrGnkiNRcJvAYRhegBP3TzUzkpbBcuS3kcUIihkBLzDzP9gFOMqOu M/pVAeX9pF5b28TFd0ZVm3Bg338gdOemagZ7K4jjlZdkyDcpycMSOd3Tr15rF+zapcSOsLea7jbI igJmNeRms4lc0jdeV2kXcNpWJDh+gBOePp0rKkt5ZIYrhwiykfO27pwOcDJ/Kq900rSou3EszKrD noq7uParr/bJWtAqRqttGQAcecSRwuzgEHr1rZoiTGfapHvCyY2yqcgnYWCrgkM3G7HQEjiqct0Y 54ZISrRQr9zjaQeR16E5596z7pZ5IHQWhglyCUCEbZO2F6A4/CoHS+SwM92wmsTlZJYyA6MxwqlR zw34UKJkbs1wJSbiwYfvkVHtjyXVf4k9DWz4KbzLq4t4XRg1lcBAuAwUIcb8HIriI2e6O+KIx3Vq AxLuQT8mD0HrnFdL8N1iudRuHXLXdzbTRtvUZUiIkAEcV34KPvopxZn2unTTStcSSfLIvBznbsjw RIB69BXKX0lxNIzxwFlcYKggbABxz9KXUtbvEgZHUrbtKv7tUA5WMZDEHkZzXJS6pFffvLiLYH4D PFlRgZ5ZCenavqsowyinV6nQtiWQISYpY5IxkEgjd3wMkDP6Vyus6LFL5k0ynaG4X5QAScBsV11q /wBp2oLn91tyF8zzEbPQqpAK7QM/jVaPzJIpLkQb2uSVhjAMi7VGBwBnPPpXtMzZ5p4X0C+vtc1X R4iTJLaidCQdpaJwMjj8K7HUNa8XeDb0Pd2n9oaNdAyJA/34iPvBJByOcmvR/hSk1z8TtEiuYRHH dRXELKeMFos4/wC+kNfTHjjwHpksTwNGvmLhlTHJEi9B+tc9SvZ2JsfGGleKvCuvhRbzNp1w+cQ3 DBec4IUkbWzxyccV00dnc28264Iy3yqYVK7j3YDBz7Y7VgeOPBUEN7ItnYlI7eHanG3ORlsj615C uteJ/Bq2728jPaz7gbe4xJEWGCRtzkdeoNbxkmhWPe5mnRnm3BFjGSXJCxoTjdkfdlc9ARz1qGVE liYSReXNAN5WRcCNQOFcdMt69K5/wx490PxT5NpN/wASvU1z5ccrgxu2Mfu5Gxk/9M2/A11bR3EE kUJh8qSNt0RU5MRb78uW6seyHjHNCY0cFqNmDZi2VWVfvWzMBmKTo42g/dI5p2jarZQ21tdtGyTW 5KGUZIQKeVYjp688V2OraOY4/tNuvm7cMY1P3scsQD3x1/8A1V5vbatbaHrswuYWl0+fbPDJBI0U kRbqqsuQR7Fa78FOzOXFwvE9fk13w/qNmbe/vbZUcdRKh6/Q1wejTaH4X8a2Gpadc+fHK3kT+UGZ GilO1lbAxXoGk3GmX9sLyOTUYweplliOf/HK828VazolrqUMl7e6hbojZTOJg+PQLtC/rmvZm7Ln keTBXdonV3J1ux8XDwssqz6Y2pLFvaAq0Vs5zjzPutgcV6BdGxuPEt9cyxxLcWcpt7ZhuumMQHHy fdTivCdR8deHL3WPtbfbTqKyRBQLaMM0keAp2tk56cGvQPCHxCvPF3iOXTi0igRSyy+bbxQNuwMA +X3pUcUublZVShOKuemw3utTFo0iuBDtwTcPHFFt/wBpUzIR/sgrXP63qKx5cvv2LsG0BVPY7VHQ frW5fzLa2kkxbAVe5J69OteMalrf+lxxycsxD7OnH3R/KtsTUUI6mNCnzOxmukt9qE17MCJCBHCM biqj/Z44Pr0rUv1gsdOa7mKmRiFT5h8vPIUnC8DrnH1pYddsUYSTR7yOmByPb1xVO6ZFLXvn/aLW 4+QhQA0jdk28jC9OBn1r5ipPmnzH0EIWjynL6jpFrGxu7keVMgZIinyPLv8Amw4OBhP7w6etcloj pqXiWDYSbe0V2eZOWBIwZEUffII+Vcc12GnIPEeqT2cUg+y6fHi5dGJX5TxbRk/8sx1durGi1ubf SdUvfsShLhpkWM7eNy42gY6NjnJ+X3rBs0R1MmlNFb2+o6gUSabFxb26EHbvBAuJG6K0g6xj5VpT K2kRQ28JIvpQLhyQQy26DGUJH3yccf3awbGXW9Af7LFZS6lauxeKN7ea68piMkoY85H+yao3d00t zfW182Lu4CS3QkjeEO0qhgBE/KgJjHOOaz5X0Goky3txdSAxFo52KrhCFZWl6fI+B8qDmu5hvUke F7rEdtbkSCORTGPLQFYl2nGTsBfjNecwNPbxG4lViZUKoAPMTfMdpOOG+VAa6nT90EYlkVoRD+8C NKI9nlttKbPmP3TnmtFsWtDqdd1mwvb5beKZp0A82Z1JjDcddrYxwc16t8N/Ei2ZjtftkttamUyJ EGIRvmCzIzDvgKVHTrXhtvNLI13dF3XzHXkyQphZ02YGQTxsrtdIu/K1iz33BSGSRYp5RsuGSNv3 blui4GA2B1rkxdNSptDlsfSVvJdX+p3d/wCH13TWsfmRQoFh2M/ULjvwWIJz6VoWl/qBgbxrFqMF jBdxra38lvEY5N25kIkXBXA7yV5e+sXen6jPc2SJNdzRgC80mR7aQx7iGZ4gMLJn7u7oODxWxD4o bS2udL8T2/mQX9qLq5nhnWETIo5cMpERmP8AcfaOO9fJQah7r3OdaDfEml6xHoen3txeQ3NnaL50 e1A06bTtQE4G1SP++hz6487n0vWoC3iXTkmSKJlaOWRMPFNjewm2fwsOVbH0zzXp2i63Z2Opatp0 17/oWrtARqGpR+SgjaIZhldXMfyjA+Q4YfSu+1fTNFm1u3t7q3k8PB2jttO1iCd5pJk8s/8AH45z DcKc7duciqeF5neJUYHjvhDxnLf3k2jasXaDU2kupraPy4ZBKgH76xyfLleRc74SQkv3lweKveIt L0fRtasrSz1Aapp2sBH0+S4LLJLbOMPDcxgdA3y7uCDwec1neLfA2o+Gt8OsWMTaVM5W1u7GFmgH ltkFwnzw4z8mQQOuccV50usXfhDWLfRfEllIdGup/tUMofc80WT5ptrjpkDJxx82NwXvDjL4ZIvQ 9T8M31n4kj8QaHPustYs/LnhL3pWWK4hfyhclcH93GpVZSOi4fnFeYfEXwy+k63Z6298bOHViltB dE7JLXVbM7zBOBna46Nt+Uj5l6165bW+jarpCj+2PJ1K0u5LnSPEJQKPsz/KIbiMDmN8hZoGBKsd 8fDEDhzqc99qmp+FPH9h9l07VplN2YYRvtbtEMUFxDnP3PlyV++g6cYHRCUbWTHF9DA8W66Nbjfx g+neXc6re7b0But0RtuYtoOMLgMhxuZWyM4qnb6pBqltZWeHkuhBOpIAKstoxeNSP9qPaBjjmsjx IZIPDFzYXl2s17pV1Ak/lHCXLRsRGjngpNCGBjY43o2w8gCneAr61bx/4ctnlZbTUL7aGJ2lDcQy Qukg/h8uTHGPTtUyilqjTl6mz4CuFjjubS7uBFDpavKku0gSbkjEMRHIODuzn0+ldD/wn9lqCahc xqTMmnwR/aICIXFwX/eeWBxg7dpzgBeleV61fX+k/adK1IgXFkXjupoG/dcukcilBw7AgHrwM10P hex0fUNc0fTb4mz01NPh/tGcEcMiSy75CeAMlc+oHGeK5Y0U5akOF9ToW8R2radYQ2WnRm4dbi/K MmUEoTzjGIxwqiQ/M+fuqvoK821C71jxEdY12fzZZHG2ZlOY1luXJ+QkfLvfJAHZTWnHHrC+GrvV p0ePT7KygsLMxlQ900sgEKMfvZbO5z32Be+KitNWgsdGtdDtw8Mr6ot79p8zKqltHsiAhx95GYtl voB1rpklFlxijuta1m7ub+y0YTLHb6NEkYa3iEUjXTQoFjGOQB972JLcZFdJa2PhwwXfiO4gtIdI 8IgQ21qx/dz6gE3Q2xz855JnaU/8tAB0BFeUxaqtpcHWJ/Lae6b7MCxPlRfaWCSM7jOC3dhXX+CN KvdS8Sr4Z8P2kniG5tBITIV/0VWMnmNcSZAIizh4vMPzYGRg1tFc2w5I3tEi8TeMdZvBpECTa/rT Nd3mqXf7m10+F2ygjZuWdh8vAz2QV7Zpmi6Vpnhq80tUvBokm2TxD4gXEclxcK2Es7RGG5rjovTM foSTixd6Pp+kCLUNK1JY7PTWSXU9YjJJup93P2cNwx3ceZjLthYhgECnDa+IbjWND1rX7dJ45bSY 23h+GRVXSbaKP5Lid2YBZ3Mm6aeTlOQCWyK6YR5NzG5q+IvD2q3uoW/ifxZpdvpGg6NaRW+l6Esn nXaEkKGmxlTOf7gHyjI6isG9vPE19qqeEtKNlHe2kM9oY4mjjjskK/6QksjDBmZCAZc8fdFamgC9 8a6hPA+tRabZTqyQ64qsEW3t18uSHSRL1c/de4OWPb1rtrf4caXrOjQ6PDBDonhywu0urecIPtEp Xn75YsS7AmVnOWHGKU6LmTys8e8LWV5JbagY7m3s5/D80JNjIJUJ3Hy1uIuxSPHTsfzr0fXtM1iw u/sMsf8AaUl01s8E1ihKzRvk+VcEcnOSeRhu3FeoeJdR0S1trH7RfLLpWpuhuLi3hT9ynCB/MUHa vbrXmWla3qnhXxLNZeHdUXxLDbzytJ5cii4NrGd0e4/dkZCTsReq81nLDqGlwcrGVpF9qXge8e1u I4xpeZrr7Bb3GwC2ml/eXFpz8skTDE0YznrjnFYsZsdRkSPzLf8AsZ5ZLiLVZgV3IXbMJ2Y2/Nx+ 8GcjHFdbrukeEPGkWoW+lWg/tJ7xZrY3L+T5fmqGla3zkYBH7xE7VxkXhaTw3Fq39oPFY6/p1z9p lklZmju4JSBHEIlzv8wgOhxge1JxlNqETOpLQ7Ian4g8SeHNN06WHKQ3McdvNYusVxFdWr4VzG+Y 5Au4YOVbGQai8T+IbO+ljPivU40ntpYIZ08pYpZII3DuMKWDODuPXHQDivGvEHxBubeCXQrGaW3t nuJLn7FaEFxJLzIXfhYyx5xxXl8t/qzqRb2tvYL03ysZpB6Y24GfXJr6bBZC5LmqsxdbQ+jbz41a jpUl3aeGrye5sVfFk1/CnmxxkYMbEZ8xeWwGHQj0rwDWvEzqJDdSQ6dDM7M0a7YFO87m+Qc9Txjj 2rjb9b+f5bvUbiQDjEZEKn8EGK4+8s4bV/MhiVSepOSSfqc5r3qeCpU17qMpTkxLzxhb2uoxzgyP Cw2SHBWNSecgHBb8qm1XQrDXrOXUdKkMl/lZoznPyKoQoB2zjNcdqVoJif4g3JB+nYVh6X4hv/Dd 4ACzQZyAeGX/AHccY+tXJEo7ezklaBTKhJThinOMcc9x+Ndn4R1AabrMWXxFcYB5754NR2dxofii EahDILS+4zJHxuxxh0HFMm0owMJV2sV/jXpWdaCqU3E0pyUZJo+vtMuHuYoznczDt/T6VsFHZSMg jt3/AJV5z8Nbz+3rT7NFMEu7VfnhH+sKj/loo/u9MkV61b2c0O0Z35HQck18Hi6TpT5WfWYefPHm Ry13p+8/MgAPcfSrmlaSqzCQNg+mK7r+y4rmIN5f1xkn9KItFiib5FIOeeTXJPax0wOn0tIoo13H JA6YxXVQkkDOADwBkZP0/wDrVzFhaRwgccdOSfrVjxD4p0XwVo0us65J5MKD93Gn35nxkIg9W6e1 ZRpOT5UbSdlzF/xJ4m0fwXok+ua1KVgj4jjQjzJpO0ceM5Y9+wFfmh4z8a6p8RviA95qTjNvG0hj jyYrdMbYoEHovWtT4o/EvWvGeoNqN4diopWztFP7u3jzx0/j/vN+VeXeDYP3lxM43yXEmepHIOPr jPrX1+VZX7Nc8tz5zH47n92B0HiDVbSC4sbKfc0NxKBKI8NtQclQPf7p9q2LVF0y2uWeXzZbmVpX fZsGCMRhVHRVGAKzdV0F21G2+0JgnMr8ceu0VW1C6V28ndlVxnbk/hX0S2PGZQZzc6rEMZ2HzWA7 AnFc14p0q+8R6idM01A7wrJLIW+6kYX5j+HGP0rttE8PaxqFxPdTpHZQy8K8zHKoP9j3rYS68PeD Y5/s6PdT3p2u0n3pCP4MLnC55qJQurAnYp+EdPi8HeGvtN2JZAkS+dHuDP5jZ+WNemCSK7rwz49g syrpqxsJ3IJEqvBg9e42/e4615dNfXOqXKNcgrFGcrGvABBB3f4V0dmp8ss4VgeucH88g5ojTjy8 j2E9T6AuPEs3i+a1ufFssuv2tqjLHDFOFh+b+IpFkH+uMfX0e/8AG2oanpOn2WnXYuf7OaJ7W1eG OEwSRvzLgYWTCt0/vZ4xXyfbQwRMbi1ijgk/vRAxnp32101vqV3Lcw2k833wzv8AxHYoGMc55rjx OVUqu2g41Gj6HbU5I9Xj8E6FbQalPNcR3Wt6pK+GubyRXlt7W4mTChEXbujjJBHyfLnNXfDd6z2d xpWmxWuoa5q3/Hy7R+dZWEDt94xx53kZxHCntn5a81tLya/0ldAjcWQiYOnkDy9wDbuvUMx5LdfS vpXw5qGkeFtB0LQNE07ULmaFFkl8qCOGKDcfMfdcMcAjt3I5718nicvdKvyzWh1U58zPJLbwnrml eMb3wlp3m3dglp9qtkmVUCfL8xMbfdGcsUVvkByBXDaXo2geL9W/siL7VpsenMrajf283nptMmHT anzOVbG1lG5RyR1rv/EeqePPG/iiw+HHhq9tzq9xLJqupypIdun2rEZiu5CvzzSQ7UEShcDO71rt 5/h14e0HT4PCl/dzaaEUG2uo22rJBgtMYWUDE6nBKnOR0rxcRgVGp7VaHSzvrjW9B8Iv/wAIraeU uj/YZriZbZxIx3/K7zFOWJPLN1J/KvI7dodGvDfWMz2VhrCRm5jVSFcqcRTBCMb2Xh16sBWTqA8P +G7y1s7e5laJvLYTwYN3LKo2ozRH/XLIpyygZA5rVg0ObWU0yzmiNzBPDdxJZWj7ZbhkOyG4Q/wx gDc3cEV5uKrzlUUou6K5raI7HxG/9paqvhnQ7mfxBLqdutvaibYLWzZAGmmXy/uKUOGJyWYbQCK0 /B+neG4Jp/CeuyTX/iGzuJSLj5kW7jYDyJePuExlflXp/DxmvPdJ0HW/DUJv9V057vWt/wBklmt5 /LiuVjcBrlQ+3DMMNj7uVYe1E0HhyTxno9/f6o8OkyzLp0oi8yJla0g8+K63EZHmMzLjuMAV6+X1 lJWaE1oamseCvhoPGC6dNeXKaaLe5Mkcd3KUimleJA0US5BkkZioxnkE9K6zwNdTeAPFk/hKeR9R trKwlfSVUEyzIZIwkbdSrguFYEYGNxNcpqGueGCraRDo9tcavqFqLgGJkkkkkeTy1t4WVuJEjQEk 4Cnmu68J+HNYbxpY+LdbubKS9msptgtd/mWzIm2OFgG+eIKeRjlxuzzx6qs5XiZWPTBpCeKJ5p/E FtDqUBIktk6pZyxh43iWQfeORkvjn0wBXw58Qv7G8K3HiLWNUjxr2jWFrZWiynzSXXCeZFIwAM0Z 2qW/uoD619l6Z4sfTorrTLlfL1CC3nudRi2kzW8g4a5Y4IaN3xs25+XnHyEV8N+P/GWmahqFnpni i3HiXRrhI76a2lkeOIIrOvmZi2sXY/LyccV5uaRjeKtqV0MDwprHinXLE+MfFa/abu9GI4pRw8CA Dd0XZ5jd/vdz1r3n4fa632zVrfXsCzYI9lNI6yNuMXJdMFvLBUKp744r5g1P4mSDxFs8P6ZDaaEs Ec0ljMC/nRuQhhLH7qqvKf3Tg+tb1vrc6avqXiTSJXk0fxHHHayQ4Bls5UC7LZ/7uADskXiRf9oN j5DEZXWk5VZrRmUaivY+lIr/AF3w5fLd+J7myvLG7WKCe1sgZMt/FM+/G1wSBHt6d68j8X/DbTtJ vJbv4hz63cbGa5iuES2MJQHO5YmfzA2CM/LzXcaOF1Twzb3PhqKR72CUmZSvmytbwP5Mvlr1LwSh dwU/6uTPOBXkHxQuJ9W1Man4gjMkUVj9isZpH+aSWMiSWSMdXVfkGSOffmsMpi3iLKVma11aJgFN At4xpelX22whee5juri3SKdfMwzxonTnGBVbwvq+kJCNO1XUfEqW9wzNJDokm1Tkc74TnfjHJXGK 429a7TTrOW7gSFU3BcgFgD/GxGBz14rc8Labqo8S6Xf6JKsV4knn2zrIIwDGDu3lwfkwCWP93tX2 7wtqLjPc41UaR9G+BvGvgC0sX0ybxJeBRuuNLN/au9/GBtV4GeMvFLCy4OCee+AM1n2sUWoTxaRB a6hfact+HsJ4Zvst9aK370RwORmQKfuxsx9BxVew8HWGk+ITrOpWEIgl+azTaqlHlJd4tyZBj3/O rDllNHiPXNC1TzEudTt7OG0kJ2NcBZBPHj964T5s4xs29K+NxVSF/wB2bxqNOx6Jd+J/DeueGvs2 ttZaymh3LETaqrWV5FvbBDofuOcbWbpmut8P6fpbaHLZeBmNgial9oaW1kQsEl6SiRhg+VjYM4yv WvKbDUNN8XaxajVbRtXM1kJk1F1WKK8iRtqn5fmkdCMMxGVxyPXntO8TGx8Z3FpYaLLbo8j2d4bG 5COdnUGOQtFJnHH3DXiuhWa5aLs9zvpV0tz0nW9Hubjwvc6PdaJpmuStrz2s8RdrCMhlaSG6t3h/ 1U0mPnGNu7BIrwzW/hveRzfZ9G0/UtKW8hcG21gxCNpI+WWG8jzE7YPSZF45z3r6Oi8cHWTa+HII lt2JLyhoAkkhjOUwGACt/CTnGa4jSPEcslxdJr+k/wBnaxazjNpNc/8ALM/IGjUMyO7qQDtYHtg1 rlePxntWprRbmUnGRyuo/B6bRfAtjPLbH+1dCcBhaymdZ7WV+z9HaIsD8oxt3j+GvO/FmhWNl8Pt AsL+N7O61eyMr2soL/Y7aKQrGIy33fMfJ9do5r6J0S10nQ9DvdB0JLvw/r6GYrEZ2lS8Zv3nlxmY f63acGB1D4GEBrzXUV1v4iQTT+KPCdwjlBZw6vaym2laBCSPtNnMxi9N21c5zX1qxsF70mZOh1Pn s6JLpJ/trTQF0e4PkZaQMoLLxuhDfdyPlP3u3pXp9xrM3jew0uSaWGHxb4fj+z3jygtHqelHA+1L 3MtoVXze5j555q7F8A/GEWlzJ4QEV1BIVaWC9/clo1GR5eySRVbH3ePrjpVvRfBPi/wf/Yfii9sJ F1LR75G/sW123aOWJBimnT7ivGWJGB2B711VMRSqQ0dzeEZRK0vhG/v/AB7qfw5trV9Q0fUrVd91 tEagBVns9QEnCgDOTznaSuOtc7Y6LpPhjxxo2iTXDWupatIoM00BkhmjSR4Q4Ynd5LSI3l8YYc5w a968P+fqml694M1CO8g0qNZ9Ot5ogY5re1uAwWElscwZIUDI2lccDNeSfGfwTf2Ot2K+Hmm1nUtM 0awlUCQSTy2cYfHlrwdsMqOQAM7Tjsa8/L8Wqt6U9AaclZI4fxpoOt3HinU57nRLi8kklz5+nkJb uu0bSiPgqduNwI4bIrl/+Eb1T/oW9U/7+J/jW4nxU8ZX4N3enT3nkJ3NcW5WTgkDcNvoBj2xT/8A hZHij+7pP/fg/wDxFaXtpoc/JI//1NrTwNK0MpCMyLul8zsx/vNXDQzutxJrVhI37m1k3FiShkTo 3oPpxXbeKFW3sZZnfy7ZbUrszkhj0JxXhWm2t/L4fuf9MESANHHFksJXZsjgc9K/LqEb6sC14Nu5 rm4j09Yo5W1C9S4csNv3OW6Z/lXueqRItld6l5oxapIxHdMYAOB2/CvHfBka6deyXs8SiRSsCxrh mGOSy4Jx9MV6puN4uvxIih7hYoQpAUYLYPIzWld6otM43RJJ5LG5uC2ZJPM3ODn5gAVNWryZGs5M SlWbaueBuP8APNOjSLTYodJSMJMvmRlUYsGQfPu3Eenf8KpRyTMbmEMsqyFZo1Kj5FkGFK5/nWDe o7luO0bzHu1jVLkw7FZ+rMflXnpj29qmWwj06bT9EZY5xBFvnDnapd+XkDev8I9hUmhNNb28kzq7 CNtm5huzhc78c/jirczW01953ltPKyRyHCkkKDnHtmh1LaCNjV/3li9orIolQtiHnGeMY6/5+tcT oUFs13bX0l1ENlwkZhkDfJIT/eAPzEdP/wBVdfqsuUNwExNcEeWhGzavBI49up//AFV55o+pp4j1 zTtDhtZYYNHuxdSbELpNh8lpSBj5SMDJ7VVLVMaOh1lYjcWtq7PJBCZjCrN02r06DNcTqN7aXAe9 jfY1w+xn6btg28L7YxXaai1nHdGWFJGWJ3AYL9zj5gO3b/PSuCnWK/uYLeRERIfn2sNvU9SaUNQk zqdNj+waK5knnT7QpAIGB+8+o4AxUZuLzW9PjuLUiQWt0FjGQd6qu08nHSrOoakU0Z3gCObeRVMh yVVcbcY74HT61HA32LwyPsrP5tvMJQ0gVCzScOdg5xjpxTYXZk3xt7vWY7S6RkkeQoGDFeAAO1UL jTYNc8IlNKf7PLYakxbzf9dNs+UkEdV6UqxPLqWlyvGzyXFwhkkz8qIQQCT06KKr6dYXltoVpeiN jK00zOW4+8w4/HFdlJR5TSJ13i2a2P8AZNzEqmTyjBJgDdjpwOv5U7R3hs/DdtdASxpaHynRuTLt JHG7H6Zrlr29a3W3nv4UgXaSGznaHPG7uK6B2jexjF1NJEk1zuU7cHhcceorCpGxbG6le2culxah PbuI5rgxwxo+GBC7gcj3AGKyvEepRanaQTI5inkysBx82F4ZWPT3re1pYLbw1bGKPdapOTGCoXdK WPzH0rgcm9juLZnJllAdR/cxxuGPY/pRSinqZcpVmhW5mVlnELlVjJbvtG3jjiulkSWWG3fT2Vza Rm2k6HqB2z6GuftLK4t5lW8KTLanpxiQDlc+2eea19GuZftUySJ8srEgRgDLMATg/XNdD2Ebfg2K 8trsag0MksNsux2kA+YoeiV6HqdxLGI7tQBchPNAxlWUqcZA9PXFebzh4GBSR0R7iKNU3H5mJ5XA 46V32vCxsbae5R2k8tAHt2bA8tyFQ7v7p3dq4pvUhvUzoNZutQt2na2W3t55JQMkDC7EaPAPBJJw PQelNit4LCSWO5BuF1EGSNpG6SKOUGOpAxUqa3Yw2wsNMi85baVV3NFvjZAgjBVjjjt+FVdMZhdy xXVvJJCR5kvAHkuSm3AbGDlsHHaoYmczp8tslk8cqta3Dzbo5ApAmZflHPsOOlbGkSQol4RMlsqJ 5ZKkOAwOd2z+uK53V7hrK4n015IzHZXOUlj5Z965BGeAB9e1c3p8y3TzW4uVeCaJwkp+ULJycHHr xVKnzIm57HFO8txY3jzF2ubYSZUd1/iAPXNcFqmkI3ia6hwYftrNMZDj5EHLEkdBXpFhBFPomlzi byZLO1MJZAPvZ2/L+VeHfEPxFdal4kHhmyLjT7SGOXVLjhXuccxQkjkJxuYL16V6uUZdOrPbQbly nPfEnxihvNN8LaDIYoZsefc4BkaNRkhe2CRXQ/BbUo9M8d20EuEXV7aS3J7b/voDj/8AV714rd+Z feMpJpFBFlAAvbljnB7fWum0m8exuo9Ri/1tlMk8ZHGNrZIH4Zr9BWGjTpeziRSnaakff+pWC3EB UjdgcfSvHda0aazm+0Wp2OvRumMe1e8ade2+qWNvfQkGG7iSVT6hxmsvVtFjuE6dv6V827p2Z9HK Omh5TovjLypEN8Nk8XHmDuPevX/D/jbTbyUxpOqs3VSRg9uleI674UnXzJLdSec4rzmWC4tZf9VJ G69CmRQ4o0o15QPvMXdqwVug9uaydS8TaPpqF3mTcvVQefbNfHFpN4mvJBFb3F3uboMkD0r1rwp4 GnDi41ZzNKxztckr+Iq3Y2ljG9EjutCSfWNWOr3S7Y92Yx3wvAPP1r8oLnXdT8PeKdXbTrma0kS+ nI8tygY+YeoGPzzX7I2trHaoqLhFUEE49MGvyS17SrfXGmvI1UTXDygEdmViefqK7svipXujxsxe zPSvC3xl8YyxotnfR6ncqP8Ajw1T5jJgfet7kEPu9nz6dKxm+LXieeeWHT7u1sLoMd9tfwhWDHqB OMD/AL72/jXgkYexlSWVT5fOQOMMB/P3zXpEtr/wkejJdH57+CMGK4x8xXp5UvZhxweorvjhKa2R 5fOz3b4Q+L/GmtfEjT9E8URpboYZbiICIIGZQAGVwSGXn+EmvWfEOt6bJ4i1Oysri4sHtEjt7gXD Iw+0kmTdGyYbZs6jPH5V8XeDPiDrXhjU4Xsrg6bd27fIzDeobp8wfII9q7a68V6N4k8Q6lqPiu/T SW19Eiv7m0gF5EAgA8yNARJCxxhyhxt/KuTG4fnhYL3PofVddu9O08T/AGm5ure7fZ5ybnjRhjax PJCntxjFcNqSuuo232O/kljvpGl3Lxgg9s44r0ay8H6Z4p06STw14rsNUtDEqQDT2dFjwRw8ZIcd P4wP61HrXg7Vbb/S728s/Itizrh23ZxwoXGSPpXzcqDjoxyMywuJLra10rvcxttjDcK4Ppn+LFZd xBFZayuoK2yIqwCyAkRsDtbI78DpT59J1Gy/smQgTPqH76PyjkBAc7sHpjp61du9Mngu51hmjMVx avM8TOBmXGeDnmuOVMysJdandyKE0xo5JmT5UEUceF9DvwOn41m2mseVbG2vbYxzsmHjCrIoXd1U jGc/pWPfWF0NJtGiZ7eSdY5PPUEiP646cetehvp8WkLbrDtkdoyXaaIbBjnzTIP5VlyqI1E5mKPC F9rBZSFxEQ+MsMKGzwfwwK1L9YbW/uUhY215I0hOTuVgeUVmOOe3FRzaTp0sExeNrZjE0kkif6s7 eTsYYJLfXirviW3tryxi1aN5AZbeNdki+XvyMCRSxzz3960irotRuTXMMmmXE8N1I127xIS0zCIo doO1COuOlc3pniB7q4u3trMJPYoksxaTzpFDDdlY+O3HSun1uBZTba23mCylMVsCMFvOQDs2No44 JGDVeeBfLX7XAoNxCDv2KGPUYZl5xUtaD6Fi5mvpdMtLizLia7Lvg4CAFiMjHt2NUNTeztopYJrp md41QIBlmdfmwo57dzx/KtW21HQ7eCO11EPJFLIkkUTEEHaDyrDBA9jW9avp0lwRO0KQIQEZSFlY 4A+fbntnHtXP1sRF2Mm509Wjt0xGF+zqwjbqxRc8kd65USTE300imPEe0RucBSeyuDzXsf8AxIrm ynvUsI7udV8oSOrYVEHJO30JrmE0fTZrHW7mPTmgNu6gJFPl48J5gCKeMH1rWLib88DzlYraK7ke W6mtxbqqBG5Ukowyp6mrzra2ilHvXadiqASN8rZHHYY9KzprS6Goqs6/N5EZ2lmPnR4PB6c49K6S VdG+wvfWkVuXa3X5BGPMLsMRYyx4TPP8q0mrEzV0ctl7kPZ3by2syygxupOzK8E578cVHO32K2u9 PhjlMk6Kk0BGcqekhDYHHt0rvL3TbBtLisHt98sa7+IhLPMrn5kWRiBGDge4rz3xxBpVxb6jqllF bxRvAimF5MmCVAN6KjHBYnn0oprU57FTw7ezWNpe2ksZaSWPEBXBZsfxcE9OnFdn8LhNb6zHZNFJ HPcCQhdrfI4jxwT3P/1qYLeNY9S00RjVl1LSpQJBEAy7djq4CBcbNuOg9Oea2fh3rdrpGnNqOqRy S2aN50kgzv3wwZUZP8JY9ME16eGdpo6HU0seG+IbiRZfJkVcxbl5U7txbBySR3rnZIF8gALEjMrB WAMZ3cAAYyKo3N5LcJPdTMoknfzGYmQZLHd1YH9BUpiluNPka3YmQMgj8qcPzknG2TBr7bDQUIcq BMda3dwkV1ZyMDcA7EDNy2flXEgHXFbUmot+5gbDG0TEcjBiwYDYBuTuOTzXI2d4zanpN0wwJz+9 DADBUZ5HtVq5kk0u+m86Uol39w4ZcOeBnPH5VqKx6Z8NJrS2+Jnhm6RVRY9QMfmJlvlkRwAzk4z8 2OK/R2fRk1zXZLIXCwSLbRMYwMyHAb5gW6Yr8r/Dl/Lp2u6HdSgL9k1O2I2qQxDyLuOBxycflX6q aNpg1DxTe6w1z5CRQRQD+/uZdxP/AI9yK460VzDPIPEPgCG+s7uaSUhbWXypHVcn26V8n/G/wXb2 dn4WgRFEU09xGjKACd0e8buncV+jmr+RaWN9a6a2YpJC5yM7mGBu/M18gftFypG/hmDy9pjnMuUU 8MsRAPAPXPpSg7Mg/OTWNIk024ZcoVBXGCOMjI6cg8dq9W8A/E0DydC8VT7Qg22l+3WIn+CX+8h6 Z7Vt6xpUN59olibzYg8CsyurDAjYcg4I6dDXh2saLPp2HdGClU5K8fMmeD9317102EfYV0Li0jUM vmNcDEPlYMZzzlj2ULjB79K841iwhedXlQNG6kg4AUsD1Uf59u1cL4C+JB0mJfDniVnn0iQ7Ukzl 4Mngepi9R27elezazYkWym3dZxKmYpY/naVDwka+iDH3uD64ORXRhp2miayvCxf8NR28ERtbr5o+ oIya5b4h2umvbRywxkskiMH9MHggetafhmJ4Z2S4la4AGQf4Tn09hWR4+lCWoi4BJJx7Dmvp1yum eA/dlY6m+8NLd/GEa+yLsg06O/8AQGVY9ofAHWrvg/TIo5rrWQmLjUJWlkbg9+ldMo36vqt2D88P hy2UZ7GQU7RIPLsLeAELgLk9uRWkKEU+Yh1ZNWZkeM75bSwRHON53n6A+1eAG1n/AOEkFzeSNLHd AlQvYY+QLjt6jrXe/EPUpb/WU062O7ydu/HQBTnntTL2eC4sENmE2A5YtyowOew8og5x/erxsxrX lY9HBU7K7MGCa0DzK0e9YCMSL0Pog9D6Vx+qaxeazqB0TROZJMRySRDd5aE7Sse3Pzf3mFWdSn1T XtTg8L+GY5bi8mXJIxuVSPmZzwOB1JwFFeueFPDWneC7TydJdLzV5Noub1OR/tR27HOFHr17140p WPSSLegeEdI8F6cbTURHeXuSTbQt+7BUcGd+/wD1zHXv3FX7lf8AhJo5YLlore6iBltLgRBVjbb9 1gBwpHyjJqePTTPH9tvrlbWCRwvzAtI8h42QqPvMeuQD61qaJ4V1zx/5mleGg+j6Vby+XfXsx3Fc HnMi/wCsb+5GhHJ+biudzZfKejfs7a5FNDdeGykj3VtbySxSxSGQRAIBIJG6bWZv3J/vKVr55/aA do/jb4jD7ZBcw2YBP3ubdEH5Aivvfwn4P0vwhAbbSbcwmXa9xcMVEtwQNo8zaAABnKoPlWvhP9oB 3t/i54inIA8qxtbmIMwG7bbRI4A/2Qc+9TCb5rFo8leeKUbEDJaQsRIWcBTu+U528np2rqdJv/tW nwl4TA00mSiRIscmFeNyCSWYFhmuE8yWOG1t7WMNcTgopHJJkPPXj35rtbDbbQQWix7Ut4GVDncH lBzgt0GSCcCui4lqdHpNvP8AZsJtVHk8oBEiXhJNwPzZPT/PaurvLffGFdzK74YFxlQRwCgUKA3y jn9K4/SnS3C+bIVaGUEBrlYwd0ecBVG4fd9a3reLzp1a2PnSKxT5TNJ/sAb3yPTtUy1KUT6g0nUt O8T6JHomjaF5+tCzSe7ntkQXDzH+ORQ3zI3TPGO9c0/hfxP4LkPieWyV9KZTHc27fv1tRIQo+0/L 8kTdmG4DpnjFZXhqaXW9J0Vrq0keDThLard2Mzpd2cinf5xmgXCE5AUMCCBVzVrq60nxFG095J4s UFpJTvmglmiwcxzlfkdhn5uOfY5r5XFwjFsxnoT6lb6VdQXtk8f2Ka9TzEtVCm3R1wFOQR8mAOy/ qTQi3XhO0udG1SK80eSS1lk8mCUxwDzuY5lIBieMnncV+XoTTrq40zSdOS4mIeONFZ7ra/l28ZYf u9hJyuMbuM4/KvXdf8L20WlP4g8FeIf7JaNVmhW3eCS0vPlAMSGYiPbn+EFeMDHauWhGc1cmHMzy +3+I+q6Np2kwC6N1Hp0LKsMqYJLrjc2MxMD1+6M9feuc8UIJHNvNpVxBpOrMbk2l7blYCR9+SwYZ /wBZt/egHr2xWx4l8OeKdHjh1BdKs7o3luIZrrQLn97G83JF5akPbMe2N6/7NYSeJNI1vQ3h1PS0 8MyRSJbXE+nSyJD5icKZNOm/ciXu7QOHq5Up8urL5WZU+rTeFtJib+zZLbSUAuLW9i/fweRn5jvA IY+Z8gRsYPB7VYl8T6b4tt4VMarJFKpVlLLIphPCvkZDHkAenFZUfi6+0wtf+FmnTyYHhuopbWST TL5QArLsf54mkAyRlgTgjHWvLdcj0u7uYfEvgxrmKC6aQ3EcikwW8g+ZjHOfvRt3if50IxyMGuT6 p1HyHc+JpbK31XUriXU43/4SGxezvPNhDfLn9xKzr/GHQYbHQYzXJ6YLXw7d2Gr3d1idZd5e3UPF llHlzcjO0fxY7/SutjsrrxHFa6ykYsWsrWSJ1uGU+dEeI5bbbnPmAkPGc4YFgea8/wDELSz6lFcz RsiXLRwW0UajzVZcKoMY+Vc44+neqg38JtHVWO5vPDWt21vcX3lm6tdSeeVxNH+8l/56SgH1L5XH UdKk0G2ntbRI5pLa+tLYFEtpQT54jjHlR7QMs27DMG4+TbWXa/EXUtHuryxk8jWY0AVbmVyJIQ48 oom3vnnnvVzWvEWuXMNppvhm0a3u9jbboMqyyLAAQoR8Df3J6mlPmTRm5T2MvRw2mQL9h1CK+1aS V/MjbgIsa/uoo4zwHU7nzjr+VZWv6nFfatZ3VxMxkWyWSUBQQrMm5UQYHIB596qWWia7ZOb+/tXE 8l3Jtnfbvdo/vkY5xzjOPpWNKILp7uR0aMeXMYdsoiVZIMY+Y/e/h4rT7ZpFHpHhnwrq/iTxfZ+E 7ZhazRQi4vWkkQrbxyoJHd9+EAt4uc9A5H90V9G6Be2drYN4J065h0LwQskhvNQtw6XOobE3+ZNP gSyK4BO/aNyY4HFeOaZodvpWnW9zr9z/AGlruuODFoMXyT3TgblkvbjjyLYtz5ZBZgAcdq1PEd2m sXbRzaoutXtgSLtbAN9iRS3mJBaqSvmYcfPLjYcDHHFd9raoynqdpf8Aj4avqtvf6ajQ6XoTpcaZ btEhaR4htW8Ntyryjjyd6mOFeiu1VY/CPjfxZPbf29YXWm6DqMhmNirGfVLqFvmMtwjciJj1MhX2 XGK3/B/w9uEz4v8AHN8tnczus7eY6ysyEDJAUhzI3HMicdQq9a+gPt/hzT9UjsPDai9unUmVxLJK jMB1llGdxX+FAfxrohFv3pMLnKTeFta8RT3MNzfGKXQraCGwCQqsFpYOn72NiABvlACjC7h9Oa62 Lw34VnRbnRrm6sr/AEa0JeATOm0P0WVZB8y8HI981wVrfLfX11YG1vNJ1yW4WMzpNHISMnE0qgFN h9JGOB0HeuevfC3iWGOG/wBR1GDUXllnju0hkVUW1U7RK5Y/vC3RecADAA4FXGcUSpHYRa5a6da6 v4XvRBbx+HFEkBjnWSO4tpG/dxrHywaMk54P5VzcHw6tPF0lnf6PfS2seou0s07bVFrHH0aKGMhs sTySV7kio5dU8FabPos+kC4vUhti15LcQAsZo/usA8YznbggcY/OtS38YXKa+82m6adTuJ4ZY5ZL KPy7W7zH/wAsFA27l+6QOM8msZWnKxMjmbeRdC0pPDOrafFcx6ncCS0v1ZTtto3InkiHO3d95TlS p45xXjvjPx1e+JrqXTNH1C4bTbIeTJqMriSeRVP3Yye/qx6dvStf4peJ9S0vwjo2goRbzQoouwoK jKjhdrDPHR+cFs14na3sdxamSH5VYZAHRfYfT/PrX0+T4KMI88jlqM6AXFraRfZbNAkanqerdwzO fmas+a8d33Zzt4xXPSzSAsjZJU449e/1qW2n80+WcE+lfQ8yM0TT3Bfp2rDvyJEO4Z9PatWdQFwO AvaqDqkme3/6qmTA42aFuM8Z6+1c9qdgZwdvVVxkivQJbYHOayZrYHIqGyTyVJtS0m4W4tZWidOd y9Dj/Zr1Hw98Q4Lh1tdV/wBEnchdyDMbf7w7Vj3+mK6Elf0rhL/SpIiSF3r7cYrCV47FxPq3Tbtr a8iu9MkksrlCGiniLce6SL0/HivoLw58Wrri18WWTahGODeWeFlHu8eCr+5GK+GfA1sdY0iSKW5u op7WcR7oJWixG65Xdt+9g5PIqjD488eaFf3FoxW++zzNEWliUk7OOq7cZ+tcmKw9OrH3kdVCtKm9 D9ddC8U+DNQszcWuph4AMFvLYFD6OmCV/lXQM2iSgS21/bOrjK4cDOOOhr8mLH4z6gk8El54bV5l beHt3YOG9RvDZ+nSvTbX48/anw3hEox+/JKTHH/vNtOT+Ar5+tlEr2gevRzO3xH3l4m8beGPBtg1 7quowmTH7q2hYSTTEdEUDOM+pwK+E/HnjrWfGurNqWpgrj5ba1Uny7dCeP8Agf8AeP5dq4vUPETa tfS3FxPaW6ycDDfw+ioMk/jinxWbTY8lvPPYLEea9bL8rVL3pbnJjMwdT3YnO60wt4GAJM03BPp2 +XNS6d/oVrEyOVfqCOPetafwxfXE0ctyPscSg4Mm319M1eRNG0ZGmuT9qki/jlP7te3HG0enevXs zyzLjm1/X2eOyQyrGPvykiNffPU/hmtWzt7LQHjnnb7dfZDHI+Vf93t+dc1feNoZ5zFC7TlP+Wdu PlPsW4rG/wCJnqsmbgmGFufLj449CarnVhWOx1bxe9zK3lR/OQRsTJUHtk1xSQXd7frc3TeZIDwM njj0rZg02KBNqoFz1Iz/AFq/DBsIKDb6+1SrskfFEQw2p9TWyr+WuM4BqvEmWyx/GnTYVBg5JrZa AalhdNvXAzV+1vAb+eZmD7dqKBxjbz/PisW0YwQNJ/cUsPfjiq0TPDao8gIx8xGO7ckZ+tVcVj1C 01NEkRSTy3qQQDXuc3j3xlqfgeDwR4bCy315P9mNzNI3lQWsi5kaRBjO3sfwr5asLtpJF2jJ69ia 9P8AD+tPp9zFco2dmcjp07EDt7GubG4dVKTvuXTfLK57DoXh/wAO+CxY+FtH1Yz6ne393Lda9HOY o7oYwxeSQkeZE2U/d89Oe1eweJfCXhjV9Dh1HVNYlkuPJiRZrkufKjA8wmBH6zHqOT7+lJ4N+H2k K9zqM7aVqVlcQlwslvGzbZE3MI+6qW27s4JI4ryrxfZ+HLnTvDel6Jp02tzX919njWec7jGCfMt4 BkGOIfeaUn5OQp5xXw8qd/dmehzXNhxYadLpeh+HLeS+nvrm4WBhcBbiVkKsrTO6kjzIz85A2xqO OTx6FD4C8PaO9q7aummeKJizreWzBY5BIgVreOGQlHttvY5Y/ezk5qnovhm28OaJ4jtWsLK/8R6r K9pFJZN5CxwFdsRyzMYEhHQA/N97qeNey8HhYVl8R3kWqIbqKCG3wG8mBwBIQzDMru2fmbt+FZ08 FTirpAnY5LXJNd0O6klu7SyXRdHeENbxu6xX0NwArRp5pzDKchlJOzjGa5Lx54e0ditp4OZrjxLI 63h0mWVVW3ZT5qcglYWdMjGTHzhQc0kGsaRrfi/UPDOgp5+mXT3GnWujm4dEkMQ/fTzFs7LZeS3Q 9FWrEGieI/h14yt9L0m90/X9GlSG7jjuWVbt2tAUmto7sDCqrEPFFICOnIrlw1NqTNI2aN3wrYiy 1HSfEsN1YakPFSvMywxJbmyXylCrIq52BMY2rwT3xX0Documf8JHYxWbwvJiQEIAG2hOOB06ivFf A3iiyki15dPsbi1tP7VU2ltNHuuLczkPc25jQM6bGDshPVPu5rM03xxqdnrdrcPaqI3vri/uJLOA tewwgYlWaB87lK4IKqMjkfNmvThUhFGUk1ufWWuapbaRam5SIXN3K8VpFEuN0k0hxGjHsOdzE9Fy ecV+aH7S2k+HfDurTaRHvOsQrZzxyIPkeOSN3uHdz90eYuVQevHevpnX/iykvi6Oaxj2xtZrc2gK by8SOypKUUxuJJF8xYo3wRv7HivlH9o+Dxdq3xAsLjxNY29jf6no9syQ24dlt0ZptscpPHmLhif0 4FaU3Cck+xNV6HzVpExa6bAUAWgGPUiQE7vevQdA1Y6dqdrLcxlLBnSO8C5O+EtlsKuCWH8Pp+lc R4VsY7l9UvGZPs9taoQTnDlpQg24HPGTxXTSRRkt+7gwG7SdD9a0rUVNcpxPQ9+8PeMPCtrLN4f8 B2erwzXkr39q7vIsj37LtZR5blokeMENtfHT0rlNT/tz4l3ei6Fc6LPpWuSSSR213dLcpa3Qlwwj nyrFHQLkOp5GK4TRtV1rRpDeaLdLZyRkMSu5hnoCcA5G3IPI4r6Sudd8eRXNpregS2c2lXbWkYeR vLW2ubgbEBIZWEb53KxIC52kjacfG47DvCVeeEU/mdUG5o8r+HWk+FrHWYpvGGz+1LK/mguLK/iE 1i6RnypAJlJ8tweYy67WHQ1Y1v4O+Izq2t/2bc/aNDst2LwSr5iQXAwqQxoxyhUpHnK5G7Ar6D0u z8QSavrml3E1hcanNDFeWc8cXyX3lNm5tpvMYusgBG3JbI6eg52/1pb5by2t9BuLd726thftYjZE Esx5ixswU7QDuHQcYrzqmeVebnjrcr2cbFq58PXEthonhey1GG0m0OzjWee73OiN5YRcbcfMG5C5 zj2r5j8X6Lp3hjWk0271JfEOrspmnuLe3a1AkkOF2O+7zAV6nOBj6V32ra2Zbm++ztO9nqaA200b Bmt3Vsq6fXkEt2rz65sbzxFNNqX27+0JU2xIbtxJ5UYB+Ur8pxnptyK7MqpTjNzexm0es+HtTsrT TodDuby20u2VI7uDdKkgilUfvWaZScM/TavoOKSPxh4I1RZbXUNPfUm5ZGuEjVZOc73ZcSbu+7Fe OvpNzZq9rBLFYPIrSeWVA85UHPlZzn0rFs57e9kFtqFxJMr/ACiNWHBI6MeMEV6Ty+E7yRmkz6+0 i8T4jW0Z1i7Pg610pttpPJMHNxsXHfYxRhx908DHvWBf6N4IdXEs0jJcutnHqUSgBrhP3i3FgnCs dyhSP4SDt3EmvE7m71DxAj273f8AoOiQpGb6Yx20Nrv+UiaTJB4AVECs7dhV7T7yyi0OPQ4riTWr UXguJILf/RYW2YLQh2DSDLE4O1COBjrXz1TK693U5jrp1EtD6X1iLTfF+lwQ+IAuo+JbSDyIdQuY /s0N9tGEFwseTFKOsMm35X79DXfWPgHTX0O4u/FOrQ372s7RtfwSlXSA8LHcONxdlJ27iAW614rr 01v4i8AX9j4cku7vULWS0urV3geWW6iB8r7PvjKeXNHv/eEsR8vmD5a5fwZp9lJdRwal4htvthhl S6thcySxwRsdoMNy3yShsYO/gNwCeK8dYWrGlJ1GdMuW1z1KDQ5NO8Qjwb4kjvLq2uriPzFlid47 i2R1dJ7ee3BwYmxlWxnkdK8A1fR/Fmt/EnTG8Q6ppmizXmpx2cSJPLDavbQzqBDE6K8TtMmRhmD5 bnB4r6TTxXc+GbWCCx1WK8vdNVrdZ334MOd8KXO0blXa4Cvg8DPPFcVd21t8SNbfVvDesDTPEEdz byatYybGeLa6utzFGEKTpjDJMFzwN2K6smx6heU42RMk7e4eh/E2Ww+HN3o9x4d0m81Oxv5JLbUr SymaaeCIbfKuIVc/eHzjGCrYC8GvINevtb0S20rW9Y1u08RyRaVK9jeS6fb3Et3GGCwtFvXzIo33 sJ4t26N1Ze4z2PxH8N/adF07xX4ivr/Xby5afSbtLeNI4/LJeS3ZkUqVkDgHerHkHgjAHnMGn+Cp /hnLpmnazb6kqv8A8TTTbeaTzNKvGcp9ssxNtmaIptS7hBbf95SrDNe7Gph8WuaiaRU4rVj7L4ty SWkDqfD1p+7UNDNp7B0YDDAiOTb1z93jFWv+Frzf8/Phn/wAl/8AjlePXHwq8YGTdY6bfatasqND d2k935MyFQVZNyE9OuSee56mH/hVPjr/AKFzVv8Av/c//G6v+z4nRZn/1V8TXM15bPZW0bzGaPaZ MfIXcYxk4ArzDS8adY3umiBJp2j/ANaVyY9nUg16HqouEWa3t7xrXzgfIJHyybedv/168gnnvH1O dFkkQTr5bmMAAKvb6GvzHD6omLOl8Kbr27C28ixRWxa5kcBldsjnoenSvQtMVZzKtrMtxNMPNmOM qhUfKMk9TXF+DIjE1xFBCwE2wNKy48oAnueO2AO9Wf7Vt7BbVVKQXtxeSRzLHkblHGWHaior6Io3 9pLXEKH9/dqyASAKpQjDDJ/pWD50VvanyZAjQ4gQvjhU+VQT/dB55rXvribd5TAxymPMQIGWI/Qc 1x13bNeaFd9InmniZlHzHCtlxxmphDoyz0eycWfh+0sbb/SDLkmbkLJvOS2SM98CksJJru6mDYES bYgu0Dkd934VkyrfWb6BpkY3sy28lzu6oijzfujj0Fbtky/Z7u6topEaG4c/vAcSKrFVYVhUjYDJ vLy0i0gXNwzWcUfmKgtiTMzE4AXdkDP97tWH4R12S5u28+28mwtfmRYm+WOVv+WsvQyyEdc8DsKj 8aXATQ4DtKOZApIAGMt8uKp/D61tZnv72Zv3trGvkjJK78kNwOrEDvXRTVqfMJuxsX14trdPcQR+ csswAUEhNuwMeuPpXA3kj390yWRKCRwgQYPynkjP412+vXkUFosErG3MpG3IztVvXGea8++zyyXT W1nGzyxfIrJwCW+UGigla4XPQbyS0h02GzurYOVkVfLCHJI6ZI4PHpVW2CzwyadMZPMu7lCJmwFj Rc4VfpStc/2XYafprRFJdsjeaTuIKDLls+3FZeirDq2tGP5lhgi80hcMqBTnrn39KTW7GdJGkNnq Lz31sZE0+3doYIj8oEacO+OrDsP5VyGtXEzaVaWzSSTndETIcb13HJWTacZ5roHJjfWNQmO+O3th EigAZN1IEz17CufkvIbe8zETb/aJRBKoUcbTgtg9xW1PujREcxWbyo7gH7Rbt5canhPLU927V0Vz BALd7uWMywgqwycpH8qjCquTyT6VHPp0ouXuDAiRRSNGwDAlj2bB7dz71U0LUrXStHVZZY7gz6i6 RDJCB4jlgS+Onbt6U6krrQq51PiC2tpPDlzaSRyI9ugkAGSFkAyvvXh2mzyJf2s8YZ7l1kV4xnJR +V4/lXufmS3Xha5vtQbyASshkb/looBTK4z+ANeQW8tja6bZaZIP3almkMo8ybYnJfcMYz0XBq8G /daJizZNwLJpGvbhYdyYRcAqDjOTjPy1Wh+z2rfbN4cMMCUEEhjwxTnGCOlVbm4t73R31R7doo7L dlUIAKH7vC5PAGBnisyaOR9Tis5FLW0WycrIAqjhTjjjOK0cNLEtnbafcQTW0DxStBas25S4y2CQ QTnoSvSur1oRaz4av/s4eS5t4VdNqtnyYJPkB45yM8VxqvNFpYaFitrJJ5BkuE4HHQKBngcDFd5p 17oNhpMlxNdTxmaMRAxrhT5X8IH+0e3WuWS10M5MyvD2q6JAqWDKbf7fALfPzfKuSwwCBh+c/St7 WJPsEcSxBLhrv545yp3k9MZHX7o61zZjgOpRaTpZub+9kkVJFOCsZQENjBG1gTg57CvTL/QLa4Mf 9sXfywkNHFBgeWwGCC3U81tDL51Ze6Rc8j1A3c8cAsrSC5v45Ua5t7RGmchV4cKASSv5VuQeD45o oE1OGO3srdvNWFVHnybgCFl/hUduua6W80bTrGyLaM7w3a7vKmDM20tydytgEZriZNVhvroWevSR Qzwxqot1kwsrj+PHynn0xX0ODyZRV6gJHX6ncX0zokEQRIsIkEYzgAdOOa+a7i5upfE+ux3ahMyR EDpwqcdfrX0bpNpHYQt9nQw+a25tpb8lzyK8u+IOlWolk8TWLGOUFIJom+64PGTjPTFe9hOWm7IU 0eUz27CWe+hj8wuRvVep29SKjsnjkeOWFhLayna3+w2NpB6ela8F5DO3lxlgATuyMbf931Fc3rGn SpL9usiIbuMdBwsqgfxAcbsdK9e6ZjY+2fgfrLan4OXTZW3XGjzPbkdflxlM/hXuksQdO3tj0r4y +A3iGOLxIbXd8utWyjHQ+dCNyg+5HFfbltiSIcdRnn+XNfK4+nyTPpMJU5qepxN3bxKcOMj3qgmh 2F42fJTIrstQsAy7sAZrmzb3Fu+VLKD3ANcra6HUl5Fm30mzsQCkaqegrorG3XG7oMdKybWKSRgC S5PXvXYW1sqx8naO5OAB+J6UtWLnS30MHV5DbaddzL1jt5Tgey5z+lflB4T/ANJimtZflkSZm59R 8pHpX6B/Ff4iafaWU+gaG/2ieRCs0wJAUd1Wvzm0G7e38QXcEnHmTswGOgzXt5YrLU8THYmE3yxJ dZ0LzZL21IwXQyR9sMg4xUngWC+i0+STC7Zz8uTzx14Gcc16BrFuJvs10gABIXgevGa898HyNaa/ e2PI2SSBSenXsOletY89M9CsdPslgW31WwtdQSQHzEkiR2Q9NyMcEfgaVvh/4CuHEsBuLMqOttMe B7CYSV0N3bCTy72A7WjOGXgAg89qpyWLSxi8t12SDhlHQgVTgmNpmYvw0tYpF1Dwpr8lvdRLkLcf uWJzxtnhAx/3xWzonxP8TRQy6F4lnXVBbuY2W4kWO5ibON0coGGPpuByKj0qC6N4Z7WRFtV/14O5 eg+ldbqPwl8E660Or6rrl2tyymYGwt0DMgHEfz5z/vEVxYqhScfeQU73NO38SaXqlnDZaTfGYLdp NJZXke24gkH3vunEkbd9h/KugvbNX1C7kIKwtaGaFF5hiLZyUzxnPQeleO+Jfh6fDmnnxNaasbfT 1eOMDVdiXCseECS2u8EZ7MiEetegeGYbm5063muL5L6e7tJfOeA7428rIIGQuWGOTjk189iMJGKu jo5TWklubCw0+zaVJGECP90LkDnkD5ec1fhu7a4Mey6UyIfmXHymP+IEnjFJMv8AaNnZNFIUhmgi ULKgDEA/eXH8q0tb0C2W0ka4kihnVh5cQjDI+3r6EH1968KtGzM5ROVd55LfUXur6NJQjAWkalws RI+Y5FaF5pQudJtN0M0k3nbQ7vhI41O/5V/MeldHb6BE8KSWp2yLE4KZMkR3D7u1+gBHYdaxtKD6 laztfW0sM0VwIXSNw0Y+TaCuOxrShZoqkje8Vx6dMYrO2cIlxcKzSSE4YonAxjjH0rMitBqumh47 s+ZHF5e3rnBIyPqWq14jhf8Asae4wY2t7yBEzg7v3iqSOvaks7SXNrHHN5ImnYIUUASIVQsPwOaV SFkOUS8dF0u2tp4jFuuHGC5wxjy3bPT0pbK2trcrDFHbMU+UlwdzY7mt+3hhjtNR3yecQiLkYOVD gdfwrMicK0yB3WNOQhxzj/aGa5cQtjlcbuxoQSQFy0cEQ4KEA4TB4PHfpWZO2o3F1fQwytE2xWMc YASZWXbzjP3cAcVZzbxgfaZJNxXIVfTPsKzrlraTUxNnyi1m6AvkZIyVyR2rnhHXQcKdmY0UhfxG q3p8xrYbG+XcACAoVQemM43dPeulX7Tpca21mkIjt8eW5jRSF6Y+XPNZNvZqurb74i3k+zxkxoD5 L8fNubrjOOPWtaCC3E8JEi7Qfu+Z83PGcc9K0xM1oiqrIYtQ1hLpwGBSZvvOVkXPTOOCK4/xFp08 iX4jlkgjul8t/LRfJk2kHkR8jd0ya7Mxaba3bhZW3RN82ec+/SoruHTri1uorbY00iHjPUk9R059 +lZwq8sjJvUwpUaxuFubRYbaL7JPGws5DGgDJ0Ygdc/h61yWq3K6X8K5ohL8+qXEKFSSrA45I2gn 7mO1elmSxs7+z/0crDPbyROCFCswUj7p4/GvFfEF1fNZaZo88aw20JeeGPgksDsLE9enAFe7l16t RHSjyO5fFq5UqFLDnzWGQBgcsK1LJp5LEykPOoc7SywzhSsfUEYPcU3VUAwNuR1CYIx+AOK09Ks7 WeyIMKgkls4A7YPr2r72OiKPPLtf7O1GK0YbbdC0icgDOMtwcfXiupeKHVtAgSaSMO+3hTyW5ZV2 yYxz6GqHijTjGFu0dtqAptIXHzHqKqaVuntDp7bishVFZeQABnnPTt05qhXK2jvcjz9MuAxvLYFv Lxj5k/ebsfeHSv1s8K65FctfRQIDJcrbyeYfuxq0S8jPftX5GTSG68rUQhjmhZra4i6quOW4+8Ov XkGvurwl491jT9GtMsI4ryNHjZUQiVY1VWDEgkMvAA6bcH6c1WOornu3i/xdY6Vc2Wjw2v2qa/Kg Fm2xRIZhCzuepO9u1fMnx50qw0v4j+XA7y6lNbG4uJWc7NhCpCsY+6qjy+f4uag8V+J7FvMl1O4M bSosKPGHZlUHjaqKehyc464rz3WfG8nxC8Rf285V5bewtrGQhiAzQbzuK443Zz0qVG0tBJnKXjqZ p3kECG3eYIWRlZfLUMPm2dM+vauG1exgvUaKVbVFkfCvG3XyY9hyOx+b0FdjcSZhlKTbQXuc7LnB 5dV/jApphN5JJazCV0ZbjCsYn6yAHGOTkAc4roQWPnC906a32iRSGd9pDHoeuM+lem/Dnxz/AGcP +EW192fSp2KwTZJNs8hwffy27jt19aNQ014pzDJBvIRnbfhSHVtuMZ44rkZdGjeWWJCfMZHO0889 M/0p7EvQ+mrfZp19cWzrsCKGUDGAvrxkYz/nFeda3OdZunmTm3X5UPbJOM1P4O1BfEWhDStTeSS8 0tVYYYj7RaOdpzgH/V/d9vzrtdWtba3VLKFAFfaQAOgJG38693B1eeHKeRioWlzHocEZN14obvFa WVjx/sxZNE1xFpWkveSnCW1uWP8AvAcDj8KltiEsvE87nb9q1MRckD/Uxov+NedePr6W5t7Lw7aB vMudk04ztIgjOQOcck/pXpVp8lLU4qUHKpZHBWLTT3zX0wV3uCXlLnbhT0APA+X3+lc/r+sO0raZ o8e6d+JpAMcdAZF6e6jrTNe14pINH0x8STnEkgG3O3j5sA5PpjiqmngWcgtLeEzyP/yzHDyMRnr0 L4OetfKTld3PoYw5dD1LwRFbadoMOnxWn2ea+3PcXSHzGvDuOFcrkj/rnketdtK32WMF0S6uH2pH CTtiiI5xKy88/wAKjr3rCS6Npo9vPIYUghi3kn5XKYwGbb/qdh6N95jwAa4bVNau9TaO0t2jWIRM 6yqm13UnaX2ZBQsep6t3x0rnlE0TPUhoGp+ItDuPE0t0Taypc2ttMNruJI4/NWPap2pGCCvy/N61 9m/Di+0/VfDWh3mk2gtLS7t4pEgjTAXdGCThcj72fU18zfs7m2vvh/r+gupmOiamlx5bklmjJ3Nj IH3kducDPpXuPwOaXT9Dv/DkILT+H9RuLFc/3IpS0YH/AABx0rDrY0PY7yH7PA7ngopJ+u4V+c/x yntH+K3iB55FiePTLaVGLDcC9ssZCg8bcDJ+lfohrUjCzvByDFGwz64GR/Kvy9/aGuN/xU8QTRHb 9msbO3YH+8Yhx0/2qmluKT0POfDdudQ1B9VkjJVHjlTMQlCksFcgkgc4/CujieGVomjwzPdfKF3F tjKyqMLlB065qnoWmS22jpIY0MsixNlwSxLOegyF4BXrj9K6UWMY1G0iaWSY26J/rHBUbGLqdoAx jp1712NBA1dP1qHTPEulW1xJvsopIFuYU4Z0YkMdyjdtEbnJ612nxO0qHSSP+EX1F57Rrqba6XUj BlEHKxuRgplBtO7r7V6l8Pfs974Zu9OuZIoIZXMSSEosiblzwxxleBx2JJrmfiV4ntfFGm/2bZQS J/wj2nJEEDD97MrLG77Yz0x3OMjn2qNg1exnfDA+JtUmn0u3vRaXNwgukU3UdnFNJDndtYMpB8vh C35V3+maK2o+IxZavdLaXKmb96tzGGkmVAVDyP5iysfbr7dK8o8IxWMdpaHVLH7fawTn7TAr+UZ0 RgShZgSMjjdmvVNY0nSF1uTQdM0mezaUvLa2F3M9w8ZCjCpJEEyh64Ik47gV4OZULu4OPcvv4MMs VxcalqguLlLOSclNQFsscbHaUfcPLOOu0nB6ba42y06Xw5d3FtNrtxa6SrR/vrXyJhLGB1EUUrhf M6MUUnIH7siu48Oy6XJdQX17dPFqqpIYV8qzTTVhXj5pTbzfvUPDCXYyj04qu0f/AAkUlzLceKNF s9QScxxQ3d7BCIYFHyMHhVQ6BjlMLkd8157g4q0RJOK0Oau38VTaPCPC+q6hqkUUpZbZJfsyiKIl vmVlRS654Plpk9K4uw1DXjfXuhavbajd20kSTyxS3ltcIklwfllQqzrvfH3lyARhlxXpCWXhiyW7 1HU/EEKyQMgleKW1bE0XCo5yRJJ/FHJ8wZeuOg5PWYdKme0v1ge80zziJ9RhjiQv5nzLGIii7Tx9 9WKH26VEVK3vApMbfw2drpt7pcEN3b3JuA0F5cXDZTaNgWWOJEhkU+hjGPYYo0iwubbw3qFulxND d3waaWMMhhzGcgbYwq7N+D/s9DWvbafFeadKNubWTzjB5CuLryk7uS/lxxrjBOOvrzWRrKeHdOst Nln1aR0mCSp9lVUdZASZCzP8uI+Bvxj6muWq3oS2eU3viDUY0ilaBovOUo8bAb1cfelwMBQcdhz1 qHSLq28SalBbR2ommnUzfvY/MAUDKuw9UPCn0rd8UxzWohgsr+0NtO26BtqySMrfxggquOdx29MV h6nImi2zNAbiWS5b7PBtCoI2Xli0yk/LwSFIZfaqhG6ujaMtDk9fvBpt3Mlxb209zbz+WUWMLgnO XXb5YBPYdh710eha1favpltdGN47xD5NnBNMcSqpVTKHJLbovujnnHtxylyljLftLMgN2SMPdmWV Gy3/ACzBITnj3HoBWlql5pWgy2V0832dNPBX7IkPzPMVyNueNoySenUV6KpRcOWxXLcuWl7fS67e XE26K28syB2nZg+WztVWwVbPJAFZttqMtjLp2oW9vE62/wC+dpYxIv2iNyF3qcZD4T3GOnan+I9c stSvUms7eR2nhBYRMpBBGVZQOi7evvWhp+h2+q+FbXWbt7mzW3vAJblE3wmKR1iQ/KVxhyOX/lxU KmkuaRonZHZr4nuPFd9NqA0xojcMtvbx2rBrmedcLiXzFkZt0hLsNvJIHCqAPorw/wDCseF9KvtW 1fUUOuRhUULj5pwCZA0sjYBiU7NoXBZvavMPBOsabpPjrS7rw1pynVjcEiOLdD8pjMTOMfKquCQc D5myOM5H0F4gkn/4lPhzTN1vreoPBqxjRoyiS/LkSP8AO2043DB4PrkV0JJowmjU05bDR/DemWOr GWK71R5GC4jEMSBQR57GMO7hR/e74rpYtCfUrmE6NqcugaRNCJLMxwy+dc7eu3ccKnrxk/Tmo7Pw rpFlJPceLLVtRmd/P+zSTQyfMMvuVF6YXjb6D6mtXxLc3sOj22qx2g8OWMCq0csMied5UqhFAVc+ WDu5cHpWqp2j7xlytnI3MditjLFp6lpJEea9vIwwuYolchpPKBG8sQcBOx/CqH2m5yE8K6dfypbI 1x5U9kyx+TG4+R5DwTtOQBwPbgVn2+k61D4mvNP1+S0tJtHcERRTMbi8aSHdbxxPktJ83J7V6c+h eKNRtLa2v1+yyuVnhilnEjIesilec/7v3c4PzdKwhh+YjlPOp4YZfDc91qGo39rJptyfItoYPLVF l5PmSFT5jngKAfl56da09J8SaDrOtaXNqmrNbW2j27W5gAeNp9pz9qS4b94Dn5WDHpgDpXfQaRrl 5HATp0N0bbdGXlvcLIYG+T7RF5bDzR3KhRgdug8v8U6Rq+iLq3imexnhSxhNvKJbnzraKWVyySwh lH7mQHaynoRxWtOhJSVhSbPnX4qa3/wkeuajume6jty0MUkzAyFRx8x7n3rxDw5dXKRSRxDzJYWY hSRjC9R+IrqJrkXOrXq5yJPmz06naOPwrzvT7ttP8RSW7OIw53qffGMV9zQhywSOZ7nod35E8MV5 bHdHIO3UY6A/SuYW9a01JPmGyQcfyrR3NY3oiYH7Ne5kjA4Cv1Yfj1rD163Pk+dH8pHK9a2ZJ3Gz z48AgBwD9OKyWYrIY8cg/wAqqeGtRF5bBH+8nUA9cVrX8SxyefnANNMCq65yMYrMdMMcVuIFaLeT kHqO5rPljzzjAyKLCsZMsRYEHBFYF3ZopZcdR04rsDHxjHSsm9jG3IGSKTjcYngmEJ/a0EfR0jfj jlD/APXqhf6Ux1S8mSSdDNOWKLIoXJHTlTXR+CVA1K9H8PkA4/4EP61auF36ldDAAEzfy4rPlKRx V4uswxp5Fm06ADciyhQ+PUgVZj8UanbxrEfDrIo4wkg2/kfeuzjj2jCgfWk8hST6ehPFPlHc5IeN 9eh/489DEOe5KjGPcA1m3PxB8bPlEtxHjpkt/wCy4ruWtYXPzgH6E1m3Gnpg7OgHSs5Rfcafkec2 mv8Ai7U9WtbWa+eBbuVIWKDG1SRnnk/rXZappl5e6hd2l1czT2sEzRpHI5IIQ4ye5yecVz9/bfZJ hPH8rKVYc91ORXrepwqNXvGTG13Dg/76hv60RgK5ytnpFtEFRF2nHIUAZ/Hit2K2QYA4I9KtRxED AGSTwR/9apWRk+8MEVsoIlkMkRHIH0zVdQSQp5PtV9hI4ySOlQRRnIJ7Z5FVYRIihVznms6eXcww CQKvzY27MkA9wP8ACswDMu3tkD9KYGpHj7JLIFyFAXBz3NUbqQp+73YTrj3x09a0LhxFaW8QO0NK WbHoBxXPXEpluVi6knnjuOopWA6DT3aGJrjgN2xXZ+GLhrgu8w3EHBHGcY71wM8hhjit1GS3JHTi un8KyL54HQMSMfQ0CufTkPi/T7T4czWV1qMFjNps8dzFYtCrHUtsnywFjhnJJwq8Y6812enaYPC+ s2Gs6uba+8YeIo1DWEcf2ew0u3Jy0fmq6DCpwxJy7DFeLeD5LC814aZqSxyJske3E4DpHcbCEYK2 V3emRx1r6L8NaP4U0fS7WS/N1rniWa3t5jeXMc14kCB8CKJ2jCqVHtzXyuY4fkq6bHdRleJ2c+t6 DZzfYdKnsXlt2+0Na2dvLK0gbozFTtIOOmcZ71xfirx7rVjZ3FxoSP8A2jOsJ+zmBYo44WG9dwUs 3mzMNkKL87DPRBmug8U+PrfT4NVh8PgWN3ESby/WMSLZqceWApA8y5fhY4AfQn0rh44da0W/0/Xp 7TU/7QS4kKWcjJKrTzRhjJO7Ab52A2u2B5Q+SNQAa8+XYsy/CvhXx74dF54wm1K3k1zVkNw9rEEd jIozHErSIyIFBOEwcNwS3WuySXS9c0Nja6hcXPiawuvt8dzPBJGk179wJJFGi+TI0fyqB93jqOBr t431y/jGmXXha6mhYJDfxIzIbiWcBla1kO0qkWMluKYdWvJtaksHd7a28NX0dze2UcTHes+DueaT j93g9epPFTGKXujTseevc6XpWveEvGcWkXDyW0so1K78uRt1pK+wR3Stn99buww2cgErwBgepalY +FodUttUt7FoQbj7PcT2lv5YuEZGUWzOduS7lVAB4zkVx9t4qtdZ1LWbPWdFmstJ8QSzpHbByDPK ibGCgABTMjfL0HmD6Zn8DeKpr+/0rQLhG1G+8P30iSW0m5J3ntwY7YHjYgZWMpY9xWL5b8hbfMbV r8NPFHhrxfP4pu4dN1OGKGcWdi7urP5se1IYN27BhjBUb1PmE/w182fF+efSvEmlpDdaxNps/h+D 7VBd4hmtpFeRlRQVIjKfeA+bBO0kc1+kl7oraldaZqVzPNBNppaTyIWBidpEMbrJuX51weOnrXz9 4g0fw/40uNO8ReKPD2n6zfpbr5EtzCCY1Y7igwOnJ+9muuNKEFYzlTufmlqGrR2s4i0vU0a2VFMZ 3/KFKB8HBHTO36ioLW+srgyfa7+2gbB2Heu5ccZ5PzV+id14Y+G1mES48EeG4ztO55LC3IGThVwy dSPerY8E+BJlZf8AhD/DQjmXO6PSbVdoJ4wypkEfWtVXgkZuh0Pzd0+8uRd/6NdpMXbyl2j5ZCBz nHX6V7l8N/FdzfWeo+DNWtba4sLmJYpJJgHSFIZNwYKDuG1j8vHHNe+a5oV/oKMuk+HdF1Lw75n7 9beziW6RJTtMilRhwvHHFc54WW3uL+5ENlbAzxz262d5Z28VwsbRBTJEyruRhkcMSAPcgV42Y1IV 4NTQKDgy1b2k97oun6trOnTifSLi7e1TI+1SC2ynIG3KnqFydwwR1rj9N+Mnga/uY9RXX4tF8QJ8 tztkRI5kjPENzDI2x1xxwdy9s12ng7XZVtmF5fJN/wAI9b7HWVtpkVmWOCcnqSEBhOP4tpPXNdfa f8JNq2ofabO0TTb6JhLGheK5tEB4Bli2AKyjtnd7V8pl+WwlJw+43kup86T+F9Oh1G+1XXPEENpo sjpNbXEEbXyRvdkssYeAMipn7rNg/wAOMg15veNYQvM1rfhLozhGGPKBVTwTuAPTHB59q+qtc0Px BpeoRLY6wYdQ1iVlvbmIQx24tu6/Z2Qkhh8ufX07ei+Hr+HVbOx8I3SWtk0MjQw7I1eG4iQfutrS ZPmKc7wRn0yMGvZp0pU1y1DKVO58CrHrN7eWkdlAb+WR2Kb96Lti5IRHAPGc471raxod14X1F7LX 7S50+7nMaQErvSbcPMyhQHrnp1r6y1DxPeypqFroGtwa/Msk1rFpN07FYvJbY8m/loJI8Zb+Ejot S6fdR3FjLfm6sbPUdPtmtmlvHaa2W2kwJEJjPOzOYmyvJ5AHFVUxjpVYxa91iUT4u1a41ODRZLdo Lv7Clz54ke1maOBpPl8zcY9qFvug5Ga6PTJddsZYbWfTLhbJUwjXFq6NxhgYg4UnI9M19Z2drZaf JBqsPjSHxFZWlpKkFruFzawyn5B/o8DY+Y/LluR/erzyfx+0XiiGLXLjTPDF5ptpG5WO3bUZZNpO 2ALG7bSBgrGzDHQ8V6kqEXGxTgecx+KvHNzJY315puoW1tp2xrVpbK4WGAZJJ2kCPDfx5XnPWvQP DHiOw8PzXNr4g8FjT5nnFxa3AtxCwMnzrCJZVQ+WXDMqZYKSBwABXRm9+J/ieR9dtLC/OkRwkO96 og80sw8tzAq/ux1z5IYgEfNU0/g6w0XS72LxprFlqratKJjZWkTqqTniQrdCUzh5Puyc7CAPlz08 fNcojKi7FwOU+KOr3Fn4nsdQGmyCS8giEkUEe6RSshiffGo+ZQAGdCP++epZNPe+Ib+LxProuLCT T9kdxq2nRxC6iWH/AI9Zlk+RcYwCsvOMqGb7ot6rPo2ta3p39oLd6zb2MckdkEuWhkAYK32aa42t uUAfuTkFgcEnGag17RPFPhSbTL/w1o9oINWR44bOK5lmuFhC7ijq0ZDRBcHMjbR1xXzWXUJQh7GC vYfPZncePLe41r4ezSx6i9xHZ3dleG/hgywAR18yaKJnMaguA+zcFyDtxnGF4nj0bxXpemXOl6ja 63cu0NtN5tpaz/aohj94GC8TRL/rF+RmXrjbXNaRda5pZfLW+kQT7LGSyuIZDFKbgktEV8z0Xc0k e0r1HPFdXbaha6Vc7/C1jpuia5abZYoflMtxGMGbF22XlDlSDn5wvDYrKcPZfDobSlzQsjg7Hx/o OkWy6dofj7xloGnwFhDp9jb21xbW4LElIpZz5hTJJG7pnHQCrn/C0bb/AKKr49/8AbH/AOKro7qD w5rdxLqtusOlJdMX+yMzQmFs/MmwRMAA2cYJBHI4NQf2Hof/AD/W/wD3/b/4zUf21bS5zc9Q/9aj rE8LJczTp5q253CHDYVY8gBe/wBccV5HbzBbSSSXbufIiOAflIxzjnjtXs8k1rKJ7gzG3mu2fYXI bI+7hP8AOK8W1aRYJZoeDHbYCgEHcejHC8da/McPIzgbOmW89rcWrxXG2whg3TFs5+U7unfPb0rR sZrTXZk1S0tpPtMs7Ab4wVWLOCxI/i968/1S8nitmghn8mKWMnZ8o6D0zx+NegeC7/z9FtL8A2sN ij+cVyPMQchQPc+ldM46XNrHRagdOu0/0aTzZoIuTISoRcjkevHpVPSdOu7q0eOAh2dlViBwhBwG 9O9OQM2kXcsMPmPIcP5i42K+DsBOPWtPw5LJbt5lq37q4uFi8mPlSvT+tc7Gi/qQW28RXk87SQBS oSVeRgqoX8OMYq5tnurCbZJ50EMnEgJBZmPXA/hGRWfr2x9ZtbJB5fnTebOx5JCE7VH0GK3NJk87 T7xopfM8y4VQXwu0JksPzrCr0HY8u8bSZsdNLwny7cDc65wzb+PfPFb3hyea28PWjTWkdvLcfuo5 8FCVGQHbDfMRg9qzfEeo2k9mxu4vtEccjlYxkcFcKeOM1qaCYtK0ua8CmS8iWONg6/u40k4XaTnc cDnjrWrf7qxnJFfWlR7gz2/l3VtJAEAYbWLwu3IU885/z0rkI4ZGvbltwCgxlcjuvJ4X8OOK9J1q zQWqX1pOgS8jMxwP9U20A4xmuJ0aFjqqx5jkJBceYwUHnJYZIH4daik7RY0bHiuO1t7FSq77krjC 8BoWxvAwSMkjHXNVvDlsul6dd3gADPJ9nYpnuC7DPtxW/rcN9cRSR3aRCEO08TvKkIO1eMbiOM+l LY6HdyaRoEFldrKkSzyzskkbHe7bQAM/Nj1ohP3dSjIeGe5sNfiihLzTDT9qL1KicH2xXHy2k0mr EMggBvcxqXyNrHJxj9a9UudOOk6MUlhM2paldWxuZyxHlbSSipsznaPmPbsK4zRNNvNT87XwRL9h E+OPnLAbF2rWtKpFItM1tL1KPWb66il2wxvuijyxJZgTngA/yrh9QtNN1C3i0e7T7M1teOxilGUz /eyOma6bw9Zto8NxqKIZruDKRIV5aeRQpTn065FU9ea7vdWVEiMU2IY541ACrtHzMXP5UU2lOy2B M0LMm78K30Hm+fC9z5KqrFh5YTIUZ6cDNc3b6dbakp2wIItOUAq54BYYU56ke3SvQIJ9Im0r7Jp8 b2q2V3HLMWKkSGPqM9TmuHts2/iQ25gYxyRfaBGv+rccDk+ntVxqWJbIdV02104H7QGjgvoxHMi8 q0TfxLj+6wp2p6VO9zb3Fm48mIeaHdsMMLxx6fWqepKLnxOtoG3Q+WXxk7FRQDhfTmuughlubWNZ 5FNltClwocxBjnj37Y5q1zOyjuZmFeSXkWljZEtzOE+1eXsLiZj92NcfMWOBwBn8KuaL8PfGmvPa 33iCZfDVokkE6Wk/72ZigJIMKkBRnH3zuwOlex6ZHaW0S3uhW483ZiSeZgZfQqD/AAAei4+tMvb5 HHmRkOUzyPUce9fQ4PL1BXmNIp6Vp+keFkvYtLs3iuL2WSWeeVlYs0nPGOgzwMdvWoZ5A7byNxPU 1W+3faCUkfaw5x/+uqcl3jIHQcZr1IJRXujsSyZx12+g9hXF3VvpMmtw3eqbHWJcxxyYG5geDk46 fWta+1KO3iLyOABk89hXz94h1OXVL2S68/y1QFYgSCoHvXRSp8xLdj6Lkv8Ay7lJRMHjfORgJjPP yg14r8SPEy2ekyWtpF9rLT75xG2w7F9P71cfa+K9Ys4zZvIHgYD93IBIpx6bs7fwxVmfxLZ30f2f UdOjY9PNtwYnX6ZJ/wA/lW3spRdxOV0cNp3ijR5tv78wSYGI5AVx7Z6frXWK898Y0coERsoVzyCP m5707xFew+JvDMHhiTUGhtrF2ksxc2tu7IzcsPPjj87afTdXlkFxJ4dm8zTbwvBCqq8LEu0rZ+fa mMIB74z15rrpyb3Mmj1nw7c3Wi3yfZZDBdWsi3FqxzgMpzyOOOnFfT3hn9oq8SJF1zRUlPIke3cx EkHBwOR+tfKNnqUGtwx3VoQkiHg/dKt/dIr0Pw/YQXtq7v8Au5FfDBs8evTNY4ujFq5LxU6a91n1 ePjz4OniHmafqMee22Nv61Qf4y+GH3PBY3r+gcIuf1r5/i0e2X5vOiA6f6xR049a2odOtVxmaFgf SRDj9a8hUIj/ALXqHq0vxqliX/iVaGmTwGuZC2PfC1wWv+OvF/ikGG9vDFAeBBb/ACRD8F/xqkum rkbPmz0KBjn8gR+ta9tpojOZYwo/28foFqlFI5q2PqSVmzjRooeJiU5KjJHtXzl4z09tC8SW10Bt SV+v419lyRoiEICBjvj+lfPPxZ0+G8tftS4V7bkEV20JanJRn7xWsCt1pVswO4+aR+Qya8z01/K1 AzsMPJI3UYxuOea67wnes/hWaXIMyswUehIwDVoW8bjbMF2oc5AGTjpXqI9JM7G2YrbhM8HBJ9eK u2u75o1G1T/L2rmI765cCG3VIwOAzdTjjpV1ryW0QmSSSYr94hSQpx0wBn9K0sVZly/tRFA4Ev7j lzEx2gnAwWPXHtXu2gaRFe6Fp0sge3fyo2dGG1vkOQPZT6eleU+ELC2u2j1/W43ulXH2C0VTtlY9 ZGUjJ29hjrXqt14pg04CXV9Qs9Mtx0jmzJKVx3CsNv6V5mMq8z5UbUYdWbGs6DZ6/YNo+oxw6hZS MA8Odvy9cDbg/jnNNsfCXh200+z060t2toLJZIYFjlf935nDn5i2c89a8u1j46+CrF/J0+U6lIDw VJKj8Ark/p/Wuu8H+MNV1y0ubvVrKO2TeBbCFdvykZywyTXFOlpqbnQ2/gTT4BbxW2oT+TCu3ypM EkA8DKgY6dq6s+H9HtAt1dmWJlbcCnz7c8Z3ZwO1ZyXUTIXBxgdakm1e4hg2wzB0OFZWAdcfQmuC phYT2RLiSabHos9q80t/Kl6j/NGybgBuwCT7/wCe1cVp8H2aabTLXZLZ/bpBJMTt81QP3Z9s1JqX h+2klj1GyuV08xsJJI3y0D47j+5z2GRXH2qXFiXjWRRDJL5gdlwrMwztXg9/esPq0YbhBW1Z23ib RZdO0SRrdvtUMxi3yRS7WtyTlt6NxInbI6UjaLdR61BY6TeQTRzXm2JzuZUtSiZ+hyDjFcrZTvqK T296nlNtZQVG0Z7ZPGSOOMdq1bSF5LRWVHQQKsOVY4GweYGxx1zzUOVJaWG6sD1y78O6DY2rRNqb RxyJsKOyAkIdxIrMXR/CKTRJFczGVnVdplXHOOwry8WDTaXO7XkVvf3EUk9vExBfcYxxyentWV4b k1O+vpZ3Ut5ZEE25sbGIwSO/vTc6DWxyyrR5tEfRTaDpN7b/AGrfJhcRqUcDkHpXL+L9K0jT7GC8 dGKxTiO5jZ/mWORSqvgc/KcHpXMXXOo/aJG3wApLhj9x1G3cMEKRwKpS29pcySCWXzBewr5iktjG SWP51jOvQivhNnXgkdt4P8P6TrE1y95B9qtbWGNIpQ7qXkX5WPUenpV64t/CWma5Jp+oeH2FuiGW K6jmaRmK8fdIG0E/L+vFeb2sstlZLFZyvaNEdoEMhHv1PuawtURtQVb67uZ7ie3wQZJGG1gT1I6j 2PFc/wBew3L8Opz1a8Hsbd34hkub0S2VtEkCOXRJEWUrkYEb5HQe2a7Tw3e2mstcaRq2nQR3M8TJ BNbkgJMFyo4H3T/k159Y6jY3DSGEgBQC3C8knNWXlVbpVh2xMBvUgY3A+hFeP9cSqfCcntOpj69r LRm2tby2SKSyfDgfNucfIS7HHHHYdK8p8Yag95rkQgUiK0tycxeUyjJZjgNz6V6bqFlYS3UayuIJ YAxhQgszmU9d3t16V4bqcCjVtSjtQ11DE/lJIbaNi65GThiCO9fW5FKFSbaR10anNuYetySpBDuD FwBuLRDsOciPNXdB1JktnTfbk7DtGXjOS23GGFVfEwURwIyhRhuGSRdvA/55kYqp4duuiLIT8yoB 9p3/AMfTbIAa+vRubmuK1xDdAptjhZG3YOCCCeGH0rhrC7NjeCOQ/u2JwSSenuf8K73VVAnvAw2e d5WSwK5wMcOf8K4fWbKZ4xJFmTCPISjiQjoF9+voKZBm+K1awu21aFFZbmMwXEZyOCMBvyxX1v8A Diwk8TfDPSIo5vKuLdTPaSEZEc23bhsfwMvyt+fpXyezrd2rxyybX3PvRpWwAgUfdI/rX2R8ArrT rL4f6cl9KsQaaWKPOTnax9O34VjVAr/8K/8AEFykl54hhW2jfE+I5BJ5IQYCNJ3ckn7o2+9eBeFI jayX0cXKi6dAT8xIViFIA6fKK+4PiJqttpvgrVtRtpUWSaJ0R4vnQgD5d2PusWIx06V8TeE4hBAy AYzICd0ZXPTP+9U03dAkXzDMyYNxIflY/Okbg7pB97K1AYnjlfcUcNHIpBjVesmc8DPtTYkg8uIv FHnamf8ARJEBDzHujf0qKLyg4dAiBrfBCiccGVmzg9a1QFe+0+N53bZsE3mqP3aJg7gw4GfTH+Fc BPpE1/F9otUaK+tdkTMBGB99upyOo56V6qVVtMVmTbJHcqQVR4/vO2Rj5j+dcZqQm0fVkvoV/czO yTgRHaQoyc547mmiZI5PSrvUtKkbULZMX+jyNOIs8S20p2yx8dRn/PevdLbTda1q007xDYadLcaO 1xExuEeIKqbhkbHcS8Dj7v0rye9hisbqx1WKRntph5UgJTcElYxkYUngE+le8fs/WvhvX/BPxI+H ep6fbza9Yqt7pd48SNOiW0nKRylS6/3sAjiuqjXdNqxz1aakrHW2+l6ndaFdvYWU928l1d3txHFE 2+OBXXLMhGVxivma91e71zVJ3tFb7bqDbjJk4htuijBr6W+JvjDRLfwfrcZtbe/13xbMkOmREAyW 6yMJbi4jGAUbACBzj614bpthBoWnCST95eXODIwGWlkPGwcDhePwrrx2L5moo58HRsuZnJvoK2eM /vpycL9SfWultdFg8Nlrq4aM3JRTMzDe0OfueUpx+8yceg61b0+7W2kn1i/Kp9nzGiyYBt2GTnaf vb1+579Kg0EnxZ4qNuFIsrRGuFV8Fmkbkbj7/pXmXPQkef8AiTVtSu9QeK4LLY2TMVt0l5MrDBkk P8UhA5PTsB3rsrYW8clmpZxmAYZVBXLEMBk88L26ViT2TXEhu/LDkTzuu4KwUibYvXHrjrXY/wBl wSyNHcRskqxRSszKRlQMEF/7vHTp71MkET1b9mzUbW08c6/oVxNFFaaxpnmqwYBSYQyOxJ6YDKTX ceE/ihoWjfEvVbM3DPDr9xZW8bwrgrOqfZJ5GzjjcE5744r5lt9Vn8N+OdE1G2mEULxNaiXI2rvX KbccYVlWvprVPhd4o8f/AAXtNd0+xhbxfbxv4ukaOBY7i8SdmjVHwRmd44xPkYDkYwD153pI1Ppv xGm21uQv/PJuFJz908cewr8n/iTqX9ufE/X5JHzC2oMG56iBRGoz+BFfq9qmqDVvCNn4ieF7aS9s Y5po24ZWkgDMGBAIwcjpX5Do5l1rVL6QbmuNSYg8c4lbpu/pSprUk9Kgmto4445m+Y87ACeApxjH 0qrDdzzX01wIRtRZNjSuIgSsYcbQ2OvNTTkWoTdNtXMUZ+fZgFmTkAZNZ2jxm2hgulj5ZoJcrCBk EGOTLyn+QrqsUtDs5tSjghtJPtC4uXjMilGkwshUHaEX3AHNC6nfWaxXK4mkMWws9rJsCkneChbn kDqa54NnSrfzG3SKi7vnbDeW3GWj/wB3sKuXawxsYQ0aiNnAwlyMZcnr0rN6lI6a61HU5NNVoflu LoouVj3LwMkszttx7fhX0PpV3ba/4P03VNVvJ5tS0yHbc21zbNNbIoAicr5WNnyncCrD5uor5k0q 2e9lFw6ttUBYWeNJFGTgsHkIAw2Oma958H+NNXsfCE3ha2uoYITM9yiMi+bslK+ehZVlj2tyNjbP Ud64sZFOBMlc9Z1jXPFWi2g0y/ilthbMstjqEUSwzqiYCs4iKxzRyZGRgMoxktVTSPG1nJrcmk+J rK2N15fyas4aZVcrn5kclS+BjJKj/ZI4rFu7+Tw9p0eg6tqUa6d+6+zTlHnkELj/AI9+DlsdVIX5 FI6iqNzbadZSy2WtxRectv8A6FLcRTGZQ43QeQkTbmLA/wAX3e9fM1K8oz8jNOx2dwvhWXTLfxB4 F0iwmujDNHLd6h5AByMvbyb9u4YBKxptHQqV6V43rfijwlq/h+a7sdCtxdJLG099FaiPfNlQqLIg QHeowQ3yYAKjmq2o6DAtlbtdTw2Fpbylxc3LRmWPIyMxA7pnLY2Jt+XOSa5vxDeiW58vS7aK2ikx FeTAFIpyfn2yIi/NypUDjP3MitpVXNbF3I7drGeG1a0eWP7PFJGL0ruKCbszZCyf7YBASnXNvc2d tpTapB/aVizG3zxclUlPHlwrsMwfsAflHWvQG0rStP0z7TcXiarNp9zHFDJJIptbi2mIeO3+y5yi InPyFiCCGOK276wOhPBr/heCC307TrgM+oSxlIyZBiWSMBjlQeBwNoxj1rD2TvczZwN14d8NHT2G j2tpqSMWBEMPlvEmQiRYdFL44D7OEAwTxmp/+EJ8Lx+GY9Q162je2vW8mVrRRI24NtjnjRuHAYEl VAbb2rqoPBOv+IZofEtlbJcW88w+zNMnl3CPdIWcxchv4SzJ0HdayLrwjJpel3bNMPtMXlxi1uMk 2zyYjb5MKp3nHPG0fjWNSDjZsm70Plm2sZNW8Rm3kc3NnptxsBGAs7bjs2An5c7ScdkB6Vh+K4Fu YRfXVxH5OnyGIAqZhIH+YtsyPvOpwf7oX2r2zQPBME+rIdK1aC4tre3mE8ygNJ/aVwDFJJsB/wCW CEbFXI49zXhN5otzY3+seH5Zo7kW6T2yvFkiSSI+YHUe4X9a9ShOLlZHXB6E3iPxebyLSJ9I0qPS biCMeTLHkyCNt3mIo/hjXr34I5rZj1rXl0i50GeD+z7O/n/e+crwEup2xxEtjerBA3TAcfjWO2h3 cWnwanY3ZkuNOgjBZMMGPJaNh8vTfjGTu49hWPqP9v3UizeIFlS7sNqT20wCTw/xBvKdR2710NU5 KyJ59T0Pw/qdxpt5a60t01tdwzo6mIZ+WJg6kr3AOCFz36V90/DzXLZ9DufHkarqd3qttgxySxtN AN53qIoyZDGZCVXG3aFx2r8745Cbd2uYikUEmBuGQI7gNG5BGACvX8OK+6vht4N8W+DPCuhaZ453 aLbpHJfWsJmjlLEuhtwYl+bzRI+8Anv061LptCqHtvirUdQm8JXPijSbC2iSwDW0+M28jxtgS7Cx AdG7D7x6DOa8duPFXiDWNUsddsokvbPTdPS2mlu0fC+YwVX8l8BHIACpyAyH6V1OpX0Udm/iPUYY LzV7K7e0ttNtyYrWN42VhvJyJZlf5vmGAPujvW54O8G+IYvEcuoeKIXzOrXQ+zDyra3mmX51Kc7y 6lvm5O7r1pSvKVjNsydBg1jwy0MXhW3tNW1maLzZ5ogJZI5pj832qaTMavs+ULuAU/w969a0uDXl sluf9HW8gT7LGkyvJgAZXdJuH3icMQPTHFa1hYJows9F02WS1sEXCRoRtXb93kDr19avXNnFFCxj vboGSRQckAFia6KcHEDC0bTta0PTGLXwJZ8mFogURmb5+ckn65rxb4+6lrOm6LDpOoXyzx6lvk2I vlyYgI+Vh0KBjkV7Tc3qxi3E+oTywS3bW00cTq3zL9wH0+Y18kftBaytx46vtIWVnXRNNt03Md+G kJkkA9ySM124SHNUsTUtynyq948Wtj5iTKGGSerZyO1c543ge2ls9dt8bMgHAH61V1K7khliuc5M TbuevB7/AIV2Qit9a0+60mTlZkDR/wCyWGVYV9OtjhuIkp1vSFZJAZCA8b/3ZE+7n+X0qrHfDULL dKfnOVK/3WHBBP6VynhC7udJ1C58O34MciP8meMMOn51sago02/fGFhvBv4HAkU4YcevWmncDC06 7l0rVNp+VZCOK9hlP22wJXqoyMV47rESvmVeCpyCPrxXoHg3Vlu4UhlPzR4XnuKpAXLJt25CeQR1 46cVZmVtmPf+fSqdxELa/dM55JH0JrTdQ6bvUD9OKoDOZSMdx0rKvUwDW2y7D65rJvBvG0A5APSg CfwPIia5dB2CqbRmOewVga05VH26cEg4fJxisLwdKtv4psllAME5aFwe+8bR+uK6C5j8m6ukHBWR lxxxzSsMVMSEKowe1B4bB6dM1Cm44OSCOnSplVsDIyPUkUxCgBQeM1SlIIK4+laPbBFU5FGaTQHn 3iBTtdxwFH5Z6V6tfrm+yehiiJ/FBXmfiGL/AEd2Ho2R+Ga9OuiJZLa6TpNa2z59RsHNREEyuoEY +Tj8f8Kk3HnIzmq6OikDIy3IHH5/SpwV5+YDHqQPz9KfMOw9sYAHWmj5cADrTmB5GMbTgj8OmKRl ZQcjhMgj0q0mwsVLhsEY4AHOKoQ/NLuHSprhuMjjPaks1JkGBk56ZFAg1Zwht1zwiMSPrWdp58+6 eVx8q4IP1NO8RTA6g8Q+UptQAeuBn+tMt5YYUFurqX2ksAR2GRSTEmVTM1xezM7YxIVUD0U44rs9 EYxXMJHAzz+fFeeWJMkoU8s5JyO3Nd3YM8a7jgkEDPTimFjv4NUl0/U7fUY3Ie1kV0IOCvb5T24/ CvrPQ5PF3i7Tb+Iaitlod1ZxRT6lcxPGBENzMtuAFw5BKtOCF/GviKW6IjwOd3Hze4/ljivrHwTq mveL/h3ouiDT2uvD+nMY9Tk3xB7ght0NmAxDeUvU7cljgCvEzqmuRSOjDs17N73Utc0fSfD2nSx+ FtJuLdoF8oxx3VwoP+kSqGWSTghk8wZb73pXoi6/e+D7FNLudCldZHaz026lYyPNcOWaOMZAZW64 zkkda57TdeeTxD4bmlW71TXb1WkuGdIrZoIASmHiGPLHH3SPpxiuz8UajBLo0OzTftE6X0BtYdyO 7zwSbkVByfX5v4OpOCK+bg76nXY8z8Uf8JLPa+H/AA94mSPQbC0jlE93E8bG5aVsRiGFTuMrkgED oTnpXVW83iyHVI9P8TWdzDpbsl7HAgjLs+RHEk7eYFbnPyAHntjk9jbW5hEVz4rgGp6tdt5ZdQqp EobzEgjzj5VI5fgs9XfEl3fanYH7FbRwPHLHctLNMsccYgbzMngnZ9M1cIpO7BxPGfFuly3vjTy/ EWk3uqX+qKPsYknjjiMEXySALb8IYiVbdnjiu18CwReEfG8Om6zZiw1a4l86e6+RReIInxL8v8X9 8djkelcXr/ia917VtH1OOf8As2C0tXll8uRJJYHkdAhhZUzIH2/IGO18Y7V3HiXwvd6z4e0+LTIZ G1TSpjeacbk7J5NwbfburHO6RSdwz1xXLiIpvnRMD3fwB4lvvFOly63dp5MN5dTCyjGeLeJvLUk+ rFSa83sn+z6XYL1At0GB0+7XoXwuv9OuvAWg/wBmkFYbeOCVDgMky/LKGHGDuB4xXlukyNc6RYSM 20APEc8Y2OyZP4V0QleFy2c1qXiLQtQtNStb5rWWxQeUd8mQzHllwgJB/ukde3Q457T9duo9OEMR kt7k+W8Vs9sypHAPl+WQ/e44Ofr0qv4e8Dx6fretWn2n/iQK5mtrCW3DF5JIsSN5xz9zBVRsPUkd jVrXZvtekaPLHCmn5UtHE4Ms8ce3ywyqw2jK9Sdpx2zmuKs+eLUSFzNmXq+smK3FxqEElqbBoZrW NnIhuGLZHmpERuRNgxnqcVD4fuNKsrzUvEGtGV9ajfc0kaZMZ4lVfkzEvmfxgnsBXXP4a0hLTQLX xJPDf3U2+KORd8cKFkLiP73UBcZP3var1jpUv9pXep2mpy2lv9oPlW8ARYjsRVBO8Pnvx096wo4S XLaT6Gji3ueTad4a03XLPXNfC+VD9oljNsyhfMtplDum44xgfNH6MA38Iz3GkXuq6XqSPcX1ve/Z NO8q8kRTCs1vjzLefn5dz45OelR6xf8Aiax1Cee1ihn0mFYzeyysij5twLBd3ZRxtOKwbzUbHSL7 UJ7zS45LnTYzLYpaSAm5sp13Slk4K+WGLbefkJxXHQkoSskHkdTH420PVdFk1xoY01PTB5a5AUG5 Yf6pH53KfpWCb+zht2uobu6OvXCLHc6O0SXG4yfdSNkAPP8AyzcnK4rz+JtUa+ktNG0LyYJI/tMU lxIrTW8J+XaoLdWx1I3d+BXV3nh7zotM8QeD4Y7K7gt0ea0llImDucbhIuRIHHUMwdf4RW2KTqO7 EZo086fZ6hNrNitpYQ3SmeaOVPnnm523ZVA5x3kTI/vDvXZaZpcQ1RbKXUo5rTWrea3eKJ1nWLEZ kU5VduCRXOxeHNQtpLuK9t7e5triVWSQobhIyo/gV2ABzyQR3roG8KXUNtbahpt/E2sQyLcLCY/J t3izgqY4T+7JHG5D17VEqHNCxTWhxth4f0RdPufDsCKkF5utrwDy/MInA5DlRghtrKSOMYzVbTdR 0vQtRj0yyNlZWsUbW0t5KQrLsbzDI79pT91iOe2K6edNJEMdmmjzabqCwym4g3GR8zSAiVX5MiLg 7W7dDyMUaZ4cmg1ObUJLZru6sVSVjDjdc75CPMK98JgP6Zz615iq1cNVUZvR/gYzvsjrB4h8N6qE u7vxZDJBxshtX8qNc8MC2DI3T+8PXFee6zf+HZPFemw+GLOW9tI4wZ0hgK28jSsPmM55dem8D+71 5r0LS9Sv7W+1q+1K6tUmgS3mYwkP9qiOYeHQkIIcLkp1z9a8x8e+Mr/V5jpFtGogj2Tw3iFRknIw BjOFU8g/xY7V7tfMKUqdg1sdSZdSvL2W8trCKAokQk1K/SHY6rnZHBBEN0axg5BLBv0qe3v0/ty8 07UooU0i7jVb+CGSXy5I2y6SxSqAUUsfuMf7wGVIxzkeuajFHZw6jOsbNZAzSPAij5wVcKuMbiuF XvXH/wDCYanLPp2kGyEVvpkKrBaohDyS8eU0z4H+qG792M8mvk40YSq+1hv5CUtD1jVbeLxHIX8Q SG+sbViNL+y2I8u2RNwV3kGDu29VTp057yeFvB/hbUxHoklxFfyahBKpvYZfMjguMPslSJuYXOSv zKHGMcHrx9pqz6UjPpkOLvyIYbp/JZmkm3EMXiEgQn+44AI5GeBW9bz2njK/htFuo/DGpRwOZUCk GWWHDLLZyuMv5in5oiPMVhg+tceKquM25IcVf4Tr7HSdP020hsPFXhq+l1e3UR3EtnM8kEhHCyIw AHzrhjwMEkdqtfZ/B3/Qr6x/33JWxpfiT4hPp8BM1lfHaR5/lyRmTBI3MhkUq395cDDZGBWh/wAJ F8QP+edl+T//AB2vJ5sJ2LtI/9fG8USE6HemWwFubfBLKpVQw44/u7q87g0+5/cBWWJUXaeQNq/e Y89a7nxlGzTxgySHEb71D5R9pwjFQ2Og715608ZkM5cDaCGYgnBOPfn6V+aUtIisYN2I31uOOU+c vmBGBAO5c4x9K9c0if7Zay5KRWdrIFhjI2jGOipiucuNNhsbhru2lW4uIlKiTYAiuRnauOD155qz pYuF8PXtxBhmF0jsxUEFQPmZGPp7V0SfNFFJnVSg23nxWECiRoi0kXIxvI+duvI7VseCNNZJI/m+ SAoCWG7BwCT07/SuM0m81KfSodSuPKdb4CMPEdrbc8Fs47V3WnGS10iSKOURzNLlSM/cx8uTXHO8 dATOUkvLOXX5b28haJovNS3JG5mkB2ltvsK6rTzM9mFtZftFqDkiWLb8w74O3vmvM/Fskl14l0KD yfskxHzgHAaQSD5j9RXqbvN5P2e7uB5DDOw4AG5iF37eDntz0qKsWkmU2ecQiS7g1K6mj+ZbmWGN GBPygZDZwOB0rstN06JdEw7I1xNJFN8+V/1fA56Vy9mYfsl3HGpDmd8/MTu2tt/AYA4rZmvI5VFp ayrc3JmELAHgAcbQPaiTbVhNEHiCCJLa3/0ZY5xhlCuSCoJDE496q6LYyS6XFqElmtzLDMRBAPl/ eMMEktj7o5rV8UabLPZ22wvHJb4WPkfMM80tvNDZX1okyyzW8MJAQY2CSY7tzZx0xSXwgkQXNut5 4itLHWt0TQxIqh1BIO4FePQ4rq9QSKJYPsqfv44WMdw6D5I93zFQMLjtmqo03TdPnu9TFlJdPeZ2 A/dTaOOTnbzyK5s2+sarHHpou45b92IG9gkaxKOYRyOMZ/zisZWfUGzr7u2D+G0+2BwGiNzaux3l dgyM9Oo5rKttsmgm4dSN/ledGp2iWOMZYA9dx+la+rTCK9tUdf8ARIl8pzuBxG6bQFXuK5a0ubx9 LTTtHsri5lktyyNEp3q6naN3HoKuEG9EByc91eQ20uI3gDy4gRiSdhbcFP8AnNaVrpqa1Z3LiHzb 5LndGTkZDjHzY42pjuea6vS/A/i69toJtQspXKyM7pOqxAt/CwyRgetdPpvgbXre2KPewWlzLJvk S1TdwpzsyBn68/pXdDA1paKI0zjdHghs7RNMV4mTIDtsxI7ZyXw2OM9MdqyprZLK9m1fy/LW2t5F IkYH5ZG+XA+voK94T4XtfTSieDUHSVsjAWLGRyNxBJXj1q0vg34d+HWkfXToenIPmkN3Mk0in2Un j8q76ORVpO89BWZ8rWsB1GWO4trRprk/u5nhGcc5AIH8OOuK7zUIYbDTBDCqrnDyFehb27YHSu6+ IHjzw3Y+H44PhrNDqEL/ADXl5HAI0EanBSEgAnPcjivM5tQj1TRzdQnKvHlQO2BnH4V7GGy5UpXF YpaNcyx608RZgsy7ioY88V2E5CONq4VxlsD8K8yed01CxeFzELiLZ5kfDc9CD60aH4nv5ri70HWJ mmubRmkilY5Z4wcYJHcV6Fi0jrb0FH3x9Qf5VBJMSpboD0/KlDFgCevTA7dqRwCBngUwsefeLJrk WE6pwHQqSGZSPfg/0r5Xn8SXttc+TbvMREcNHO/nRtj0zhl/A19lazapLC6EZBXpXzfqPheCfUJH RAC2fbkV0UZGbiYEHibS5WSO9D2bv3PKfQY5H41vxtBcKPJljmB6GNgf5GuK8ReGZo7VLiEFipwV HeuattOvQG8lXSRBuYjK4rqUzOSPXBFtU7DtIOeev5VCYIXGJEHPqAOvt1rzK28UazYMI5JPPUcb ZVwfzFdVYeMrCbEd1GbNj3xuU/jzWykiS0+kXNjP9v0HEcoIL2/RXA9M9G969h8A6lHrjzLaM0F1 DGvmRFiHD5weAfT2rz1JPMjWaEpInVChJH50W09/p2owa7ppFvqNocgjG2VByYpfZume1TJc0TOp DmifSX26+sH2zyNIucYeuv027gusOrANjgZxWNpN5Y+MNCj1W0Tymk4mgP8Ayxkxkqc9v7p6EVgA zabdFCSuOo6fTH6V5TTW55clbQ9QWJjkkkZ7j/69RyQ469faszTdSMwB37uBwe1bPnBh0BqGZGbP AvknDZbBwDXhXj6yM2nXCjjaGyPwr6BuYvMiLKMNXkXjmEJptxJ/dTk4+gFaUH7xpT3PlzQZJ7ab 7Hgg+aseM+2a9IkYLiFRz3I/nXndrcfZ/E2du4vIQQOcYX/CuyuLp94t7YZlc49c4Ne2tj04MvXV 0bQCOzc/aGOAyjJzj7qjpTJLBdOtTqFzdSQTScEJg72J6Bs/rjA6Vc0Szj8yS7Yh2TKhuwbr8v8A Wu/0/wALQau8N/qluXGzbBGchGGeuO5qK1RQV2bQi2cDP4n8VanGNK8JWq6VZKnlFwWDNzgkv94k 9flKjtz1qbSfhRPqL+f4iuZL0j+F2Kj64Gf1r2jTfD2mWzeXasIccGGXjGP7tdTDpcluAUBOT161 5E63VHXGFlucLpHw70DT4glvYJGy4O/A/nivSbW0trO18iFPLHUlyece+K0II8KoYZz1HsDXH3uq W1tLJHqU1xbXaTZh+VjBNCe3ygr9ckEGsnJy0LSRtC6OxUSQAsxxt6fQ1afU7dw1lPGI5ym4MmAD g1y9zcRtIk9mPtETnjacke3bpVGSZxcvMwKtGoYg9ucVHKB1OvSk6ERnqUDA9GAbB/Sn+aLuOO0t 3RJoJA0QlXIIAxgCsbXbpTos3bftYe2MZpl7APs/2lW2AISSM56ZGMdKzlBPciWqsOvbZZ7h3M6Y GTIYwQvm56J+HXjrWVqWoDSFhe9V1tIj5c86o3zCT5goccbvrW1bCK/0201GOMzeWdrxKMncOMjF R2/huCa7Fyun6hJEW80RiPK7xxkEgHNeDiOaM+XlPNqRktEjhNb1fUPsxlRI/srJJKDGcyRRqQAk mfY1rafrdxJP9tSSNJdQ+5GjbniKDJZgPvKV4/QV6Fa+Eyjyy6f4bYG5yshlJG8t137iTj8K2LXw j4k83YugWMKMMGQyqG4/AkAdqKcJvRQMlCXY8u02w8W6nG8krWfkxDzJLEsyzurdHTPHH9z8xWxG t7JGqWVjIm1B84B4GM7SCK9Xh8Ga+0iyST28ZXgjY8mffcuPanyeAdUuVT7dryq6ZP7i1IHX/po3 9KueW4mptAr2MjzeeK8EMcq2xZsAFMc5/wDrVk3+jatdCJWiZ0IIeJvk4PY17hb+AbWNNsup311n s7KuPptH9K0ofCGixwrE7XcgUnG+dj1/KnDIcR1LWHZ8y2nhjX9InkeK3EdoxDLvkAIx271sNbSe Sk13ewxSRchWcYHtkV9Hw+F9FwqfYw+MD51dyfzPatK10PRrfCLYW6g9xEisf0/rXT/q/UnvJIfs T5jfT7B2W7e+aWTy8sbPDsu3JH3ucfSvnuA6dd3EkpETNNKTlmXdyc9a/RjxVcLovhLxDfAGL7Pp 1yQVwMZTGBX526HHcD7Iu59gG5ifIOPlBOTya9zKss+q31ub0qaRR123Lh0kjcFEO0L8vDdD8h/p WJojkSkySkLG6nDBezH1XP610niKBJp5t8IwPIAJhZxjnJ3Lj+VcNpM/2fUFj8zaoccCU9QeuGFe 6kancXjo14LmHpOEGV+TkDoSMjoPQVnTRR3Eskc6FlPlId6LIMAlmyV2uMcVp3zGSZrhshkaFMk4 4YY44C/1rNkvXhuZI4xlW8wKVkBwQQq5VsHrTIOS8t7Wdk3bBKVO0EpjzHyRh8/zr3T4OXVwmj7T MQyTyhc/Pj5yCBmvJbi7keZre+hV5I97I7JtxtAA9q9K+DlyBHIGZSsErs4yONhJ5x0zmonqSerf FHUVi0TSfC1u4cyPJqV8I+iw2gOwED1c/wCRXhfh4GOFjtQtKQzlS+0HA6kA+natjXb3+0v+Er15 5AFjRdNhwT9xiqNjHqSawdKurNLViCEkzwuFOfkyeecfLwKmEbDRqW1rKoiwkCbfJBK3Esf3ZCTj GRUkscyKHk43Qqig3DOpbcxB+Yen8XXtisxbiwbYvkQEfJ/EBtDA4HUDp9KuQGyO1o5EhGIl3Fx8 wJJVR1xWli7FKG/8iIxqUBa6GVLyxhgZHPBON3XuBWjrGmW+pK8qATQzlmBKSHadpGNqMfbrWfeR RzwW6+fy0qM2LgDIMj7c8buvPSl0q/EUn2O8l8+R+FkibaD+82dvQ9c1IWOUXS7qXRr/AE1ll8v7 O7IdqogYAOPvHcMMOlJ8PL/W4PF08+giY6pquk3KIY5IoAMQnzQzSgqVMfUZBruo7Bra8uP9Inkj kYllYZDDld3PbNeW6boE3nC7t9V+x3NlM9sUt1dptrsyBlIIHb6L19qpeZnKPY998fWUH/Cex3Nv FE40nQNJs7ZGG3JeHzFwPVgw/Dk1w0b29j5msahIpYE4E689MNCY/wCJePkA5/CpYNWuEjOp6vdz XJ+VNzTGadJkQJl5GA3cAD0HQcVy8Y1DXNQlur6RVEe4RKmGj543ZPU5/LpVzabuTFaWK4uJvEEx uHZ4bNAwt4gRwCNuXz/F2x/AOFNdh4Qlj0LUr+TBMjFhuJBOSmARjpxiqeiaa0cDtKN0gH33XCEE 8Yrr30WQzl7iMIrKZPkwCwC1kW0cPBBuitUIyUX5iIwcl5WflyRt6dMV0p+zxXOoSBUTy44hkb1P 3OuH+T/gQH4VmJFKLuBoVUGPYoYIzNgLk8g4Xr6VfkS2mkvZVlwJLkhnG6QHGANokxx69qrcpI4L x7DINM+0+WyTRsCsjKoycbeJE+VsfQf1r9YfAvjrQ/DN1ZeHNWdbN57a2igckpvihiEaIGPfrj8a /LLxNaCbR7vy2gkOxziLMTfMWPK5I9On4V+lHh6xtPin8DvB+uosbrNpcabxuM0dzbJ5bgdP4l69 65q8epR3vjywS2t5tPscXH2+Fnt0UgLllb5ix4EakZJOAK/GuSFLXXtX0y2lS7W0vjGs1uu4S7Z2 G5N2MDFfpH8Qo73wR4EWyn1qa20640pJryQsA9wdzmOOOQFn5J2MBxge1fmLpTRvqGqXccLeRO0k 8Q8syfKHyMAYBxyM+oqKLJeh6vqIKyBbRmkEzDzDGyrtEcoYbsAt0z3qvLbAfL+6TyQy/KrTfdl3 DHmNgcZ7U6aQT4ELF45EkUnaAceXuQ/NtxzxVR3jlVmkdSZd+S82eHhymFjB75rsRRegj3TtCWYp GZA5BC4OSQCVUAdfWte8lt5ZdsVwZHkwMee3JYZAxurN8Lxi5jvZowQw5BA2kfIv973P8VW7OTz9 WtVd2BG0sJGjPRf9jnt6D+VRYpHVafp9pboYjaiaRVDthC23zPkJ3Nx/dr0TwnITcaiMTRCKHz2t A8cC3AQ7ZWyAwymMj7pwTz0zw88e8QtNH5qo3RlabajfKQFJCDsefSprCS5tL0vbBUuGjkiB8qJM vxk/uWLcbc4PWuXHK9J2HNXR6tdwaZo2pyyeFZrrR3Sb7WtxJIJei5jC+YZQQhJ+6FyDtbOKT7W2 ovDqV7dyvqsbIGmk6Ssp+5D5ChIww6EL8p5YHoOKuLp5ILdopVmlgJLO+F3luS+xicH/AGaxr3Vo ZLomOYmVG3bk42k92XtjFfByqz2OTU9M1jxHpv8AadvFpOlSaXLeGSGZR+8dnUc/apPvys3Trz1P pUoTR/EOmyaDqvladoltHvummHmzNM0exY4LcbSVbA4DKEPzdRivKZNdktrX7TJFNDskEqXcbBv3 gbAfIyQCOq9amGoPNcXW9syRkP5lvEEhkdjk71Y7lIycsBjPcVoq9S402dtpJMfiA3fiOWLVbzWb IRWIeUxubmL90I2kWNYlkVNhJb73TeMV2Gta9pXjy5ni166i0vS0aTzRcnEl3Mh3HcFYJ5Zlj2EA nK/M23OK8b0nV7q0v2ezhjeCO4ZH80goY5sIw+fAGSB0+tafhrxPPApt9QFmywyFggVJo3XnZ8jK ygkE5DdgK66eMcX7yK5rbn3LpyT2NpBqttc/2ypQSWts22ORFkGF8lY/ld9mFXJHyjHPNfOTXkPi XxndRqxtyrNLciZmQmPZkxuH+Xbt6MP4T34FcNa6xBY2i2VlqN5awyW7JOljPND9oR84iZQxWNOc kRgZ9qzNV1qNxBI1w8+pXl2f9IYgsfMAC+YexVflx0q8ZiVUSUSZVE3ocq1k3gXw5qM915Msqaky 2k1q6LGWuH2jnhSVj5wF684714rYGxh1KC5vRJttpAIwgDZYnaV7fjxXoWv+MPLa70m70wX9hEpj nRSZJln+6k68ALmMsOcEH86wNOh0S7tdQtLyRYDNGHguWjDNFtdXba2f9YdpUVthoOMHKR0UloWr S91/SdSudNVIr3T5iGto8ruRoxv3Kf73XIPA79q4PxNq97ceJbmfWnNyNW8uRi+0PDKECoN6k5Xo A5PTmvoKW20l9T027WyLeVEhk+0lXxGoLHzBxsZgw/D9KOseD7DUdN1nWP7CMk8rSJD5IIcbflVo k/u5w2O2PQ0UcVTi7NEcyueV+CfD+oeJdVtA8SzaPPqVvDqAMm1gRIJJYsDJbeq4QgYJIHev0H1/ yvEmuWMXiS7tU1QtNEux90Gmo64+zwIM+bMgH76Q8Z+QEKvzfnv4dnisdXE0L3ME25bYgBFD4I+S dTtyOQBuPHpkV9iy6q1wfDwMseoSwxRBo4oURYYlieN1LRgFiZApzjr145rrq1beg6krHtGheHvC K2UWn6hbm5ae5e4toZSTOVQ5a4buWY9+m0AdM56Ea1YXZNlIWjuIZ1R8SSCN1B3ZGWzjbjrXmFrr fhe88Uy6w1pc3GneVttkjjAkUHAEjDIbBYH5a1J9e8OQsseoPLB9klZ4402rJ85JBLc4+nIxSjiI 2IieqMdL4ZFjO04yj7ugz6/1rAutUsELfaiyL5kezdwCGOOfbjr/APWrzi38ZxvqyWlix1Jpv9Ik XaI98cY+Uem/dwT0wK3rnX2u9CuBBayeTLakOzof9Zv2vGjEY4x8vNWsRF9SrHYzReFNSWSZVgEo +5IPkbI+ZSQRzwOp5r88vEOuPr3ifxXqrKAt5JNKFAIwqnao5A9K+uta8RXVt4Y1YxGZ57e1Zi4C kRRqmN7cDHXr3NfCdpcbLm4D4/fwPH9TjP8APNe9k6Um5GOJlpY8/wBXRZImDHAHyEdOAMAitXw/ qwSK2eYkNAfJftlf4elQ3sbSxldo5GCT9K5a0lEU8kB/5aDAz2Ir3UrHIj0vxRpVtqgtriIi31Tg 20wz8wHOx/Ynv/SsueZtd0dt48vULNsyR8DY6DGPoeQKtHztc8OskD7L+wHmQkdTjtXL6dq5vZP7 RijCalaJ5d1CePtEXTdt/vLgUxkMFwt3a4bllAVvwGMmk0y7fSdQEik7WIGPQZ61m6sY7O+TULQ/ 6JejcAp+62fmB989qc7Cb5vvKwJyKlSJuewatOHSDUUIIlXYSOxHOPyq3BMJI1U5yRkDvjrXE+HL 5tQ0240WX5pk/eQZwMkc7fx6fjXtnhvwHHfaTqMl9fyRapZWf2tI4gDEdpG+NwevH8qcqiQ0cN3X 8aqXMbup2nGeBXQ6zpdxoup3Gl3bK8luEbfF91lddyEcDtj/ADisl1CgnHOeQe39K1uraAcjb+Za albTITuhlV8gdwd3869A1YKuq3rKcrLL5o+kg3f1rh5UbzSMABucfSu11MZv23HkxQH8olFAEC4x ntUyrmool+XjkntVoDb7UAG0A89MVWKkHJ6VZz+dQuQelAzk/Edvvs5XJ+UL0HGc+/5V1mnQ3F5p WgLbo8808IgAUEnKPtA+QE4AILcfdFYeshTZyAjjb6elfUf7NhtdF0XX/F7wrPq+j26afpaFwrRy X8gUSEE4Me/G8YPA6da5a1RxtFbilJRV30O98KfBbwz4ejubHxPYP448SyrFILazkdLK0Xc21pZx KkTLKm10D/vB2QgZq5Yad4W1DxBrHhmH4WeHbiew02zjmRbtRLK53MJLVvLjXcMnd8yOT1kxgVqQ +FvC3grUdQ14mWCzIttOuoop2jh1DUmbMk0yKMDaW6KABz2Fd7qMOmW+knUrmHSXtESRyGthCpCK eI5h8yn5flY/n2rphgtL3PznHcZVI1LUV7r2PnLxv8IPD1wst58MzfwaxE+bnwffKDexQbXZpbZn YtLEMfLjzA3QNnivm8yedArRMCjAEDBXgnHQ19zWekwaRfXXxD0O/vr6+W0h1iwnmunkLaYigXWn beBjZuYd93ltnKV84/Hfw/Y+HviHcy6bIPseuwpqiKHR9jXBbzFJVn6upZfmPB6nrWXL7Kduh9jk +arGUW+sdzxKeTLcY44Aq9p6qJI1OCznHH1BqgAZJm3HAHOCOmRWvYAC6izwMhug7cfzxWqPVPN9 d1EJeXN11czyJGPVg2AazrOJrVWuXO+4mzuPXk8YArNeSbVNZnuy221t5pRGp/iy5ycCtWKZXlOO QnTHr34qRbHRWKiKNdow/Qn+mK6iIEIN4z7ZP/6q5ywRt4lzyCCPyrpBx1I9apDL7SDA7Dbkfyr6 L+CVzLeWQ0e5lK6cNQSYxYBzPsxGy7umCOMDrXzIz7sKp57CvbPh9iw0jVdUuwv9m26CG6KMRdFn GUS0Ea5MrEY68AmvNzSSWHfMaUtz1rRIJdK8V6t4o1DUJ5obKWe1Nw5LSXbW/wC88gJHgE4IHyj+ pr1Dwlb39qH1/ULUnXNXcqiTZjEETjekMOTjac/OQfm9a+S/DfiHUfDmtLdrbSpqUskkdtp0rSMI 3uFEajEmWZyDjkcjpXpy/F2607RdM0y1sV02/wBIvES8e6kkYRBDh08pwCCOm0MCPSvh6eMhZnW5 n0XfHWgBdXUPlJatvAwXI24GdqZwK5Bfidoeo3V3pejhNXvZJDbtHKdlu4BwVBYdvTH4ViD4qat4 gsGe1gtNKF35kUVyJJS0ce3mVlMRVdw5ABz+Oa8Uvdbu9OlV7PW1u9QuEjjivXtm84bifumU7OmR u20VcbD7Jbmj0628SrbarN4vTShDbapcWunae1jtuMT2oZFR4oQGG52JX5Mdui5r27T4NUsrzTJt RtRLLcXETXih92xd4yZAMjHqOfY189WnibQrY6TLZ+HVnvkl+xi6ixExiZMbJpovKAYrz5mMx4x1 q5r3ivw94butPPhixtICHj+0MDKy+asgO13Y72T/AGu9TGtCXUmKPZLO/t/hj8UdVBUSaNcyxyTs JShtob04ilMecNEJMx7+Nm3nAGa4+X4o+B9Jsr7TbW6u7/UtMubuP7LBaSxFm89gyebOqQrtDcku PQV5vqvjbXPH/iG4urmVmkv7WXSN0AWOGMPmSOLO1zsk2yJuYNjcNvPFV4tNlvPO0i8tIP8AhJ3j iIup5ztu7K34JbejI9xAMCQlAZE+bqDiVi4wfKb6M7y0+Kdhd3UCW9vc2NvDE+p3t7M8DW8VqVYN GwD7wzEKAVBGa57RvjDp/iC+uLi18O3d20BZI/MvrKFY4SxGdkko69fnXI6cVimG31LxBb+Hri2A tMJcW4j2QNelVIikuHiQthD9zOAvYZzUWq67o2iwrZNpSTQTDCBShCSI23luGznp2/lXLDFUoTd0 ROaWh1mv/EK8MthPeeFNQZYdzJb2upWB3sBw7bWdht553Bax/DfxK1HSoWgHhm4upP3i4k1O0jkC hi+GReOF+XP/ANesS58SXPiGW0jW0bzHaAsVKgtEpx5e4AY4HSuQ1X+1po9QuJ5IbKSSeTAZhGxD tkOuflbgYHP61vHNYNP3TLnPTLr4mSWmgw2B0KO7jvUmmjN1eLHFFG7+YsEhCNvIJZQVwK4G08eW qJMtppNs16skctvcxXsbfZfKJyoQRYYHOCpxkcGr1paapN5mmReRJNbQxsSHLCF2/hLhWG4/ktV9 RixZtbutsrRE7445l3Ag9sqpbPWscPXpyi+cbmjpn1yPW30PVPCVtYaKxyjWl9dSxLcX8b7ngS4Z Zo42K42ZIyOld1r3jLUPDPh2zuLnwzaC61Fik2nm9aJ4JB80mX8pv9XxyF5JOOuK8o0Gx1XUre+t rpY7PTNaiRrW5zGXtbtDi3maBt2I3/1b5UdckV0HihNc1ix0jwxqGmqdW+yveQyzslvPJcr+7niA O1NpI7cBq82pj3Ga5bWGpHRfDXxz4o1fxDa6dH4dF1bPDNc6iqzYisUVsApvz5zSHCrkpt64qprf j/xP4RaO+1LwhotvqN9lBZPrrNcBQTiRgtqVDepB/CvPYrrV9JtPsFrcfZNTnvSl9JbS4QiRAyRl xxkHG4flXK67pdnpt3cw37ytqIcF5JJGeRsjOcsPmT6kV6mDxalJ9gdQ71vi5c6pPHBceHbK0vDK XBj1F7kFyMIRKYo2iIb+6Np7+ldBZfETVLmLTdK1gW2h+I0b/QdSE4e3lugwHlXMYC7NynHmKcPw uBnFfPVx9iLiI+Y7Fc7Qq8jtwOfpUWpyS3sljCyeaFjZTHHFgAngkg9flIBGce3epxsqVb3YrQhV rM+rX17wpq2s3NqI7S31a2svI1S2mjntB58J6wwuMoMD50POe1ebeO/iZ4k8Na/eaR4dvTas+Lq3 c2luwjtbqBJI42d4nK+XyOvNcp4W1u41TUdJ0DxToUms/ZJkTTdThQzXUe9fLa3mRNslxC0Y2pks 6H7vpXs3g23uZNf1dNc8OWP2Ga6lhQmFr1rjTraH5FSWTdkHaBsCK3ZstxXzFakqLvLVHRK0o6Hl 0njXT9UWCe8t45LW4iiaeaOWP91NIoLYWQggpn73Q+1ZNxp97p2nyzWTG60lN16tza4mOyThZRC2 cElMEdjXmWq3VlqV697b6LFZfb2LmxsZH8mAjh2/fEtuOclMnaeOmK6PRL7V4WurbTLqSyluj5Nk eJC5T98wiTpzzwe/SvQeBVJc8DliujPT9IuNQmku4tSuUtn00RSC4ljiY+YduxHYbVT5W++M4NYn 2q+isZbu+tk1O0Nw6m8kkacQ3J+4yeXszgry6tit3wh4fGoXrXV5cxSXU5lubeO3fe1zMy/N/rMK PQhd34DmultfEljq+mT6RaQX+lW6W6xrPbx58gMNjKFTIXysfN7+9YxjGqmmQpuLLc3i3xBaOLaz 0v8AtGGJEVblnLGX5RliWfPJzwenSof+E28Vf9C8Pz/+zrc0/T4NPsoLLVvtOpXkCBZLpFZRLjo2 F45GOR161c8vR/8Anyu/yevBeCpdzb2zP//Q5fxmJdOvobi+hZbe8tBtZOPmAAYH6dvWvPtGS31O dIozI8EEnCRrkbl6dv51teMZpn1LULATPN9hiVMyNn5iAMgfTFXdBe7sLJdLs1SCF4PMmnRfnWRh kfN0Ffm9kluSYWtmWM6ZosksqrNMZGkIU7mXnGxeVxVnxRrdxdeC/IERjcXX2cSKu0suOox+R9uK gsIZLW/uL77YPMihOS5HmFu5j68epxWd4zEdt4es7aSeK5jllaeJg7HacZI3YAJ5/pW0feskM7r4 fxQ2fg+yRsPNcvI2x+QrA9ErqNQvRaaLdXUmVSPMiIwxkMcZ46daz/DqCx8N6Ot3cRXojhEsaJhT EzA5VjVjUdP1rVvDF3b6Zpct5cTbVgWJCWbGSV3HtkfSsHTnOpoh2Z5hEk+o6zps0kzP5RcmR+eA wGB/ntXpc6yi0vrw5ED3cKIxB2tgOQB+VUNF+FfxCu/Ia8sYtMbyWBN1Om5WbgcIT93Jr2GP4ayS aeltqOpfKmzK28ZzlBtHzNj+Vd88trTskjRHmVosNraMZQZXhaSU5XgZbcAMVwun6rarFbSzSx5l bYSxxhlYjIVeua+rbX4Y6RcRsrWFzqKsFyZXbHAwAAtdlp3gLTdBtPMaz0rRLaI4AYxxn1Jy2T39 K66HD9RL32No+bX0nW766E1nYyXRdQ2ApKgEfeGOBXXWngjV5L62uriO3hVFQOHkyeARgKM/rXpm reOPhdpH7mbxAuoTqMGCyRrhhg842DFcjcfF7TLV/wDiQeHbq6V8lJdQlS1jx67Blv0rZZNh6f8A EmTYguPAF7qQkg1DVZJI5t6iKCIsApPHpjitPRPhPoMTJbxW9xfyxMx3Tvg5PY9Mf4V5z4j+MPxC toN9sbDS0csFaCEybSBnG6TGOORzXz1deKviv4puZV1LxPeRxOxGyLEan67eeld2DweDlf2auRc+ 8bjSfCXh5YxrOpaTp3kj5VaRWdcc/dyayJ/iF8L9MtpXstRv9fNqf3sVhbEwrjtuwFFeJ6FoGl6b ok0otkuLyOBZQ0uJGdiNr/M/P61QTUPLudS0aMG1SUhwByF4w2VHr9a83E5zCi5Qpw1Rbeh6V4o+ OcHhvSpNXsPBPyCWONFu7pDIxlHynZGDgfjXm9h8d/id4nkeK3trTw5ZBXzJbxCWYDI+6ZMgVn65 DcXccelXn7mOQodrLnf5YyPn56dOK6Dw5oMUTiEKAJlMWT23Db/XNelgMdPEUZPqZKbMrxLfa9PG l3qur6jdxSj935t0wV+cHaIhGMZ6iuZ13wbZ6jptpYIse69iaRiUXK4YEgNndyvvWheXdis/9ksL i4e0HIHKKgYblUduetWQZzpSjSryJGdMxkx72j+YjBJ9q+dp168q/LKRaRbGn21rYJptrGEtYUES IAcBQMd6840u8XRNQutEuX2W1xukhLfwnHIrofDniSW8kl0zUgEu4CQTjG7nqBWR420ozQ/a4uXg +ZSMc+1fWJF7EM86SWdvNbsG+xzgkj0B/wAKwfFwGj+IdP1pCUtrkhmI6YcbWBx/hWUlxP5CXdux Eb/JKg9RXcalpSeKvCUtkrfvok3xN9OcD/CmgRfsrv5HUyZYH5TnpW2j+dGWXqvevFvCmuS3EQhu QVni/cSg5HzLwa9M0W+Vp2t2GMjgH09abQzXYC4U7uo4NeZXFrHDdTlhjAcgmvSmQrcAoPkbrj0r ldUso5rqSBh8swK/TI68UkBgR6Gt3pyecowDuPsOufSqHh/wvDMl3dGPIkcqmccgcVa0vW/JtZNA vo2S5hYqJOfnj7c9K6/w5c5untyoEO3CqB0bHShyaHZdjwL4geC0jtmvrVNjRnnivFfsUyj5kIDe 2a+7vFejC506ZSoOUJP19K8GbwzHJakome3StqVbuZOmeIwzanpjefYySQ9jsyAfqOldbpnjmXcq atAsq9POjADL2yV6Gu307QftCSQlBkHBGOuOKwNZ8Cp5cl1p6lZU6oK3Va5PKe3/AA21mWG7/tPT WN5p1wPLuhFjcv8AccpwflPtXteradDeRrKF5I3Anj3FfB3hTVtU8G6nBqlqTsR9txBnAkj7qcV9 3eH7oeItPtr3SIpr+G5jEirbo0rIG/hIQHGOnNY1l2ODE0Nbo5iEy2MoPauws76J9rHqOMCr8/gj xVenEGgaq7Hpi2f/AAFXtN+FfxJkkUQ+FdQ2txmZVjH/AI8RWHIzicJdiFpAI2IOQf0yK868YQCf R7yMLuby2wB6gZr6Cj+DnxSSDf8A8I+QF/hW5h3H6gPXmHijwZ410yKf7d4a1SEbWDMIGlXkY6oD WlKDUhqnJPY+Cv8AV667nd843Db13NHgc1v3d3HpFnJczSZuJDwy4yCRgAfhWfNDjV97Bg0XyshG GVkYqQR1yDXU6V4Ni8UWdxcXmSiNsiPOcjv+fFetOaij0qcbmv4c1fT9StbfT7WKSGZhiSOTgHPV gw4r6S0ee1ltY9NmA8qMKqY/hxwMHrk4r4xmstU8MXfkNIRswI5DjGD2zXoN74p8YeFbSRJozb33 2cSQ7gG3LKPleMj5WGMcg15lZuozqp2W59PXGmJLOdPuXX7Uqb0kQguYx/fHb61nRrqmnyGOZWCA ZUnhcdjk8dK+DdLk8VDVV1u2v7mG/Y72uA5LE+/TI9jkV3eoXvjPxJGLfxBrl5eQnrEWCJxxyqBQ fpx/Ws/qzRrznufiX4paDpiywtdG/lj+/Dp4DspPTfL9xPzry/U/Guu3tmbzTIksd5Xkn7RKVz3Z 8L0/2TVLSPC8X9m3VoECoyZ6DHBz0/8Armo7BHs1e0u1GACUk7FfwpqEUK53XgnxFeXspmubp5Zt 20PISS3bHQV6ZOBtupCTl0Oec4wK8F0kvpeJoY98btuXGO5r2lbh5rZ5cYLRiLA9SM9qhlovaxIT 4etn7uAD+ddGyebZY7bMY/CuZ1fyh4aiSORZDGdp2sDgg8jjNdLCd1mB7DP41k9xmD8O9audJ1i7 gRsrC/mBTyMjgjn2r7FsZbPUrWO+07DwuASFf7h9CB0r4S8NzrD40uU+6HJFeyWfiK+8K6vDd205 isppIxcx4+Qx5VS2PUDP5VvCVtzKaPpFICPm2vgHOCR/+uopLZQ2/wCZs1bs9Q0rVo/P0u7W9ifk BfvDPYjirptmjyfLZsevb8671boc12jnOckGNlx7Z/kamRSxBG5FYYwVP8q2QhUB1UFj6/8A1qZv Yvg8Mew46cVVu47mTJEBjOR77P8A9VQPER90MT1GFIz+VbjRzzDlDgdM5BpkkDAZHykDGAeadhI5 8TSkYBlR87cY5X0NcBdeJPEUF7JZzyRRlDlWKclexGeK9Pnsbh0+1QMVaP7ydytcf4q0m0v9OE7M r3EXMZLbTtHVe1eZmbquk5U3sOW2h418RfEOsS+CdYtjeSTC7CQkFETq/I5HpmvnDTLW+gIuPsay BEY4VYQwyezAA/zr1/4qts8N28Shkee9XAPGAi7sZrx+wT/RpJJZJRt+XEJDEkn+LcKzyGrUlRcq m5VFu2pU1KO6Rp55bCVRJK2CRIhG2PaOUYivPnlEN+gd5FQPj5n3cAf9NAK9OuBqEfm/vXOVOCNp 6t36HpXF373hnKuVfaWILLjjscHP8/8ACvoI9izctpI7mw1FxKhG6Hy8yAZ28kKPpVOaD7VEF2uW Xywp+SQfPLu7HPan6XM8iyRMidlLMyBUGznA9SOhqvNaWYKotw0Ji2kxsFPCJgDcDzVCsZV4u5JJ ScOELuCpQcs3ygEY5+td18LvB+uw3OneLLbF7pGtRzmZ7clhDKjEvBcKOh28qcYIx3rzbWjdafps 0dlcJI21BlSeAoOfXmvTvh1b3+haRGlnfKguEDXkccpye2EZf3b9tyms5uxmy5r8E+k+DLK2EZ+0 aheCSRcE44aXaQp7cCseGW7aBpxbxeXCXOwrKGIVSOAM9xmtDx5qMN9qmn6YWBktIzLNuy+GkKqM lB6fl61XtBG+mXMoMaR7ZmYhpNoyW68ZH4CimOIQ39y08Ya0QFypJxKAN0e85XFTW2oXUsYZLGNR tgbBLcdSwG4Y9O/FVYjGssSo8aBWiGBPcDpAc9qfp5iMTkbWAht8jzHfjBHRgP5/pWjNCBPPjtIb hrRTE0kas4aMl5PMbIJI/IjtVa+vbNyIZ9PRJ0AYeWVPBfaOi/j1rZbbb6bbyyr5SrepklQgHzN1 YZ2fgKkstVlvdU/0fASNBk7zyDkj5OP/AK9QDKOjXMWpSTabHYJ5kMEsymXaoHlnO0sOeeuMVQsr bPmWUO61815RI0mAfLkO6RSvGQTwjVevri4uZ4hYnbMCoyMA/Jyep5z0xn+lUvEt4JxBZXWXuZAq HBxggfxg4O3+LjvQQc9qJe4uU0tYXSOHaojJMahWIGfX867bToNICqnlMoXJDKwOAT8vyjnnGelG iWtppRgnYkAsu4+Z5ZkI+bqQa0I3hubRfNDy4SNwilJMbmOcYweBU3CLG2NrBIYxDqKrEUG5bhdu ct2LY+leqNDBHod1kKXNu2H7nsNv5ivPfLWCyhs2kXyGeJYyzeWxXOdpyP54rttZc22loFJZZ2ji QEgYDsB2600EjyeWO8jn3LCsjncUYEiNELAKSw4B2jpVq1a6ktop5bWVpjHxIH8wEPJ1wBnoPSrd pY28M9wA3ypGWZVnIOQWxyeFGKzPtt3HZWZtrt2d1CspKBQMEKAGAz9715rQpGZr0sc9ncrPEiTM hwD8jceobGeD29K+9P2HNbj1L4P6p4cmGJNB1qbCsf8AllcKk0eM9skivge/v7h7BxfQJIsyMSdr I6jHY8r+lfSH7B2t29h4w8Y+FWJiXUNOS9EZxjfZOVOMADkPz9K563wjOe+Ofju5guvB0F1bQ6t4 e0u7vIZbOXMcc8U8isIpZQd2CDjgZUD3xXgfiiS1vvHF9BHqMWpPDAIpTaq0NlE6gfubRI8fu4xt TkAt97vWF4yvdf19ZLLU7lPs9s0k1ptBBL7uQ5H+zwOnSuU8GROl6VmAkbluGOTkqCP881lRRnJn o9pGJbLT02k5MakiIk85U8MRjtS3iz2+nrjzIshCxeREHyHbjC5/h7VagtLyK1UEFYkWQNIucr5b HIweTtCjsay/EPlJEI1RnZ3wq8kcjPIrruWjpfC6RwaPMSyNvyWYplU+ReoJyelWNJEGZphLI4hY bPKgVQfLw2OM/SkgVrTTAjW85KRYiAVlT5kBy3I71u6JBqTWyGFXAfli2EAUnB5LH+VTcpHSPHHM HWWJZ0cglG498Hr/AC/rVSaGQxhilrbmQ8mH92y56kMFUfrWnbRz2iCG6x5qjDhSp5PUHp6ntVGS GxGUnu/KklPI2BsMehO1Mj8/yqKqvBo1fwnN3MET28mmSXrRrAS0cysQ+1vv7m5Yrx0I/pWxdCWb T1eASStIjSCfcgAkBwH7ceg6YrE1S5vXvVe18pobdTGcL8jOOd3mfw/Q8GtjSrDXoXWTVbYramNh MwXiZH5wEXpjoD3r4erT5WefNlm7tCYri/v4TGwWINIJGQsijcWEKghgfXGawJ9IluZp9UguZUaY LPbsgeNnBAyqodvT+835V1Oo3dtBGk08zhDJ5cYBwYyfuRSdeo7elcdbag9hqKDK29s7AFd275Xf 5wuenPpWMWZXJEe+tJnur+GKK4jz5qsMbwEIQuF4zj0H1rpTAp0yQXFrKSoXKJ8rZb+7gZbrxgdK m129vLq38mHbdKh3iSXC7t6/cLDtwCK5SfXJbSRLm+uTGkEpzli65b70bFM4H8INUuaZvGVzXgub mNY7Zr028tv5W1GIB2nKsHz/ABDtUsenC3V5bZhczBrf5ZSeAzhWYDgll5bp0rmtUv7DWLJfElpY yR3crnEzZ8plQYktwxwvy8YJ53VuQWyoNNuNkrwsON7/ADqoUb0K8ZyGxn1pSbSsS9zndTT7F4mv Fby3tr6JWnExOAZPv52MPmGz5R71z6Jp9zdiLSypt9R3SkODbplf4ADk4U528Zr0nUdJS40y/u49 OikMol8tv4kcoTwOeQOfxryK40fVfDa297IBEL+BhMke4+S3SLzGI2h5FG5QD09+K9PC1VNcrOqn JM9Et/FOlXfhj7MS1xqVpbeXcR3gb7QysMs3o4HQcfd96wdK8aTaMUigYTRXLRI2UMzhHP3VQY/4 FgZ/KrNtqFg9vdXtygvILiBCiQbU+6u3JfqDnjHenafpGnatBZalYW0MCxmKKOKbiSaRv3bOzqc/ KFXbgYxnvT5IJu6NFFE+raHHb3U+v6dayxsbuWOVJAdhWIKYyobne2SCcY2jrXfWN9qVvYWt3f2f 2MwxuEG/ajJJgtEMfw+/XNZN5p8eppcyT3G94fJiZJZSI42bsp4ziM4FNuVs73TJn1Ao0LTpEEM3 +sI/ibn5Fz6dcfhXFXm5RVjnrzR6BoXi9raAyW0pguo0ILQYEZVTnAPOcDGM1YbxLMRJdaj9nniD o0+792ypL83yg14hdhUlju2S6s4rdUt7WWKH5TIowCAPv7B7ex6V0SQX81tHZ29w0TR+XA80i72I zkiNzwOfX8O1ctWlLl1ZyKbPYbjx9YyOfsljtilGVw2xtqnA2sB93pmsfUviC1/cNpU2bh0GxYI3 xtZwNvTjb/tV4c2gawuu2+iG485bvLKJgUKryNxbhRz1AOcdu1dHc+FdTVbtVv1sNSlCBNvyIWQb WQO3AU9uaqNC2zBzZ6VqmuXaeGNYsZJ1Md2Io2ReFPkt93PdeM+hNfOt/H9mnjl3AlnyzZHIPU8c V3JOu29vqtvrN9Fdv+4ZRCF2wqcjZuHfArmNRiS6iZHxkcZznH8q/Qsgo8mGuzJttmDIkFy8kcTb 15IJxXDaxZGGUSrjehPIxjpiussWFtceS5OBnkY5qbWLJbiDIAzgnj/CvcsMxPDeofZ7hHDkBioJ 6gFau+K/DxkmXxBop8m6HMgXjJ+nFcXaObW5MWPvnv2I4r0aw1NljETjKnAOaiUbjTPMDfLdwNbT KIVkkyyAcRTHjd9DUOl/aZ5fsECl2Y7QeABjjr/9eug8S2WlmUzR7obqRlQBcbSO5IrQ0O0jtmgS MDh1YnHXBxlsf5xXLKXKHKj1nwNpOg+GrU6nq+b3UruMiCKMgsBztJzwEB5Jr2bwRJJ9uuLYtue4 0y6h46FjET/MZ5ru/Ev7PeneGtH2eE9XTVdc0HTpNS8Q3E7YM6sR5axfwx7EVtkZOWUjIzxXm/gR 1bxFpeekhaI8k8PGR/X0rllK+pokcr44H/E2s7vO5b3TLVgSD8xWIL6fSuFkyVPIGea9A+IO+NPD jEbR9iaLHp5blf5CvPWAwDjBGQPavRw8rwuZyOcunCTqQMu5Ax+ld1rS+Xrl4uOIyiAf7qKMfpXA or3Ou6bbAf626jUj23Dd+ldvfyefql/P1DTtg+2eK6BCQgAbqsMc44x6CoY+cj9KtAD2x/KgCAkc rUAyBgDIqUgggqQfWmOTjoAfagDG1XcbOfnkI3HtivpD4J6ZD4g8LeM9ANml/LPa20scB2+a5ibz nWNgwfLRxsBsBbPYDBr5v1PA066YjkRt+or0v4e+KL3wLqFl4nsEVp7S6gV43ziSGRCJY2xjhgOm euM5HFc1VJO5FSmpJxW59az+FdNl8L2fhvwjKUs4r228Q6OXk81pot2+WEM2d0i5O0E+g6Zrrp/E RuPDywxW2sm4vLi5sEWW3BuPNEbLmSIrtEbfhgd65rwpdQ+M9Fuda8KvZX8Fxcy3dz4ZklW3u9Kd jk/ZbgMFJx823Coc8fLgV0kep6hJJ9hitvFnyRRlrdrFAwSXIj/f7Qo3YIznHpjrXdQxEGtWfkWZ ZLjKVTlcL63R5/rvgfTovA1t4ev57mV/C+ltEklnMYPN1G8AWOEbQWfLFR5fcHn0ryP9pI3dn4r0 bRr++kur3R9Ds7WcOyMI3XJIVkVd+4MGyQDzwK968Xa1beDdOsdY8SSJo0tun2zRNEtP387XbeYI rq86hhuG1lydu7JORx8QeKvEOqeLvEOoeJNWKfa9SlM0ip9xM8KkeeQiDAAJ6CuGu1VmlHofd8K4 Kth8PKddWcvwOejG3IHBzj9asy3H2KCW8JytsplP/ARkfrUAU7/y/HisTxTd+RozxL969kS3A9VU 72/LFdLZ9Jc89hDWtiiA5kbCfj1roNNjjhZVAy3YHv61hLtmuyE5SL5QffHNdbZxEDJHsCKgLGvE zJhscEdMVcE2R049aiNnOyrJ91eM5PJqK6ZoIwsfynPJqkM1LQFplYjKr19817/8NvBT/EC31rwt bXr6ddfZ/ttqYgMySR8LGSCrBTz93mvA9O++m7qeTXp2m/am0rUjp00lreJbvLBPCdskckQ3Aqcf hjOK4sxwzq0XGI4bktpaamPFmi6f4t1G3i1uCS3aazkaeWZI4AWRZJiD0QHbzgcAmvRPFdl/wkPx PvpIoUt11kwfZvty5keV49rtEhwOTj943y9814/ceLvjHqmmQzTWljqUHkpJ9pt0txO4xt+dlIlB 9VDV6Ro3izQrnUtGj8U6e39p3VqdLe5tQUaONXWSOQsxO99ybF5HHHSvyfFYeVKp7TsdcXFnP6rq Fyt3e2SwXMElvI8EloxyY2U7ShK5U/hxj2qDQrX+111GGKB47+zsWvHjPT7LAf3oRm43KADx9Bmv WPEGneGpdXu/FM2jai3igt5txps0yWL3AZQvnLFJhcuuNyq2T1XNc74Okax+Ilhq1pbXhk2Sw3el xWExhitpEC7FndjnsMtwTXLTxPNDmcbWJa1scNY+I7nTyFtVeWG7lDvErZ3Lghtp9W4NTajrkOp3 cJT7Qo/1Zicod6jjO70q5rfwu1rQotUutO1KDUbmxP2iSyhiljuF05vmSdPNClyn3biMfNHjIFeX 304uL65mtyY4hDGrK2FLfxYTHHPr3611UJRmuaIcrieqWep2dvCn9mz+VKlwJRCZFG+S3lEioefb j616l471W2kTTLzQ7W5ibWpYtT0y4wvm22Jdnbk4+dXXGCBg182aedCSOW5k00mUKSsZIYiTOAQQ ecZya9DTU5o/AekajZTM9xo2o3enzkNtxHdL9ot8+2TMBjjitKiVr2N6cj0mwj1vVfEcZ02e0sZr NTJfJPcLEkYhbcskMkmN8EuSAvVX+UgVxvjCXRYtQ1OOzlZpri6VUtpLVvKfO1g6zDAby2LAqx55 HoK8wuptRs9Vtb6LVPsk25LOBZ/lKF5MNtRuo+YFvXFekfFm2uYtXHiKwkeGwt7tLLUYsEmyvQmx WCdorsfvYD93duXORiuSUbyUhTd9T0KxNloelLdj5XKeZm6VTJLtjyvyDAQemO1cP4g1FIbWS01Q aVPa2UWXaXzjLACmBsO0Elm54OAK85udbnt7cRxXIuLd4/8Aj5OXwuMfxdMDtWFf65ql9aS29rMb pJF4KsQVzyxYH9OKqNF3MnWR6jLfG901ZUng/s2NN9tZxyG2hlIGMSSEB35/z3rzTUJnEVu1to0V jdo213hYMgZvl4JdgRzySBWjB4pgvWstMuNPNwVRYvMfLBSowBEnf5QM++ag8SXFrPFcaCcWpkjh 8xlhMgJlJ2RS7P8AVIe59a1pUPe94wlIqA2KSNp+owrb3t5C7QGD5oyPmXzDszjp2HuK+i18QmTw Pp3jn4geH5317RrKD7VNExe9htAxS3vEAYDyzjEnGd20t6n5z0K30TSJBbagl7fKqIkiCUQr5S5z uf7w5447V9P6br3h298K6fqUXnXQtUa0+zhvMlNuwxPbS4zx5a5Q9mCnHJrmzCtSjyx5TfDa6HN6 d4w8F2OiX15Lc3GsL4qhazV5YSHtLYyA+eyEqxZXIyFI6bq8d8QW8eka5Lpt7f8A26GRpIPtEDBj I4AwVYnGGHJ5rT+JUK+EvEcOiRpcSi9jWbTZZFM7X9rcEPHMqqB87fcYDuPWmeK/hnqXg7wFpfiL UL6aHV9QuT9ptVwi21uVDjjnDnv6Dirp4ZQd+4VY2djhNSlh0IySW5luY0k25Vg7JxnGPT9KqXGp x2zQX/2hZGmRpIoJUIM20HIB6BR0yDXOXS6heIHtrsr5szJFCVCtuUZDJv2+ta2oT/ZdKtfD09qd Qu0VkhKjezbvmY542g55Fd9OhoTFHv3iDwrpniJbHxh4d15tB8GX9ukkg3AzadfLsJtHK8/MBmF/ r1Kiu/8AEOq674Cg0e1+w3t9H4ieCRtcku2Iur0Ovlxq0eWideC0jrukbO8Mo4+fvBfiFPBdje2d tottdTXhQ3ktyZ5I3AbMSJAuIlw/CsdzbuR6V7h4Y8fr/Yd5aQ2H9j6bZ3EranZS7oHs7xBufy/M 3beT8h9+K8XGQlTfNJXR0QlyvQzvG6eGfH0dt468L6empazqd7/ZupyFmtZLHKGOO/uokDq+0hlk lQmN9qngnaOEtvhjFYSweEU8TWty+lXsF1JJbtIC8aIzlN5UACVZMs2RtAX3rrPB3iLwV4VvNUks 9V1iOy8QRxmSC5jDNAU+VZ7O4C/IwHHQo4HzV29/rB1KDS9Zgv8ATpG2CK5kijK2UhiIVDM6ZNm7 qRtl8vyyw2tsI51ni5cvufiRJpyueWz6j4x0aS41u0tYdNumRLC1ncLNtgSeORo40XeYg+OflB98 V0ni7xFq9p4mSee91C4sNdtGv9Og+1zwoDJldn7s+WDbzfKyY+YAHnNWPHniHWtHuNJ/s/RLu/j0 +Sa6lsIoElYqoG5MoGLRMfnL5PGOetef/CbxdY+Mpbzwb42k+2Q6hezXmn3dzEIzpl6kQHzOMbIp sBGH3d4B606Mf3Tq22IqHSw/Fq9s4Y7XU/FdzFdwoqSpnbtIGMYLgjHuBUn/AAuMf9Dfc/8AfY/+ OV53rXwE8L6nq15e6hf6hb3UkrCaIlH2Op2suWIbgjGCMjpWZ/wzt4L/AOgpf/lF/wDFVw/WsF3f 3GN2f//R8ktNH8Wa8kzaP4f1PUJJcAymEop+rPivU7T4feN9U0i2tr+G30J0J8xJp1ZSBwu5Yyx7 f/qr6c1ObRNLQt4g8TQWioQWWSYMR7bARXnGofFj4R2U3kWct/rkx6C2gIBI9WxXgvJ6MPjYWRxu j/CHTLe3UanrImuyS0klnHtPPGzcc/LXU6Z8D/A37j/iQy6xIjPIhvJpJVDEdlUxgfrXIa1+0WdP BTSfCphYKXT7ZIDlQO6rXil9+0V8Vtalmj0x10+GbG1UXbt4zwR0rvoYfDpWgh2R9u2+kwaSDEmi RWUMQA4VI0A6Dlv0qK88XfDzSbKSXXPEVtbSg7Rb+aJG/wDIf04FfJGjQ32uadban42vLrV7m6lk LxyTOIUWMfKqqCOTnrWf4g0GwkvJdLsdNSKIJCUWBURlYADcx5P5muGvmVGlPlUSuY+irv4y/DtX t7bQtN1PWZro7YQkPkozAZzufAAx3z/hVVPid4yvoGn0rQNO0mCOUxrLfytO2R12rHkHH5V4pKJr YxWVnKGmiRVYJnlVUAjPJBz6V23hiSdtKtpZVL+eGlCPwiqXK4GfXFeXiM+nb3ECZwvxH+Lfxlju bWwi8QFILxJObSIRKGjbBUY+orgvD1n4h8QXE974kv59SeOIyKty7uFPXgEgV6P4js4b7UI4ztYQ FtgB6+Y2ePyFbHh3TRBcxhuFYbTwSAG+UnivZwVZ4jDcz3IkzP0S18nZdQRAIkTiR0UDacY5x+P5 V0NxFZzYktovtMjOp2yNtVFxtC991czpcF1b3E1tdv8AYIgjwysc4dgccgfSuguJ7LdBcWmqQpbQ HY8O0lmZf4hgfSvisVzc+pBoTeHNMvL2O+1G3aSwtmaRopRt/e4CrsXPI471jvo1pHqdxJaqnkvK WVVAAUEccVc8S6vHp11bFf3uoXJRBuGUAwMnbXSWFtEZpcMXYfMwKhR6ce1enkVVxr8r2AQ6fbwx 2l1IvzP/AKN1IwM7s4rz250//ibXkt1nF18jeWwPzFsjleny44r1LXBJZ6Jc3tuMSWOJ1yDjjgrX ndzHby3dxeQj7NG0ccrR4DI0zDrnP8qnO6DhiW+5tB6HFS6zNp9xIGt7m2uY42YfaPmBUHB259QM 179pUcUltb3MA+WaOORT6hh1/SvmbxhdG61wMMKVhVQMsfvcHCjn0r3/AMH6iLHwhpiagC13Evk+ Qn+tb5sLgfSvVyFW0ZjI8o1iN9J+JWu20TiBpVjuoM8rsnTcRj/eresIoRbFIGR1WPJZOhPfFbfx bto9Mv8ASdbjLj7bC1m6QxrvdkIZQXPcBsdP5Vi6S6NcT24J2j5BnuBwOlbvB8uJczalseZeL4Jd KvYNdtuChw/TkV11jqEOtaevIbI+o59qvavp0eo2c9nJySrLz29CK8P07Vrnwvqb2N0D5YPB7H0r viW0b0cJ0zVp9OnGYLn5l9u1dn4cmNjMbXPyq+UrG1WXT9Utob+KQCRMY5GeadY3KOwOfmBGKGJI 4j4laJP4Y1NfFGnIfsF4Qt0g6K/ZwBUWk+IE+0wXcZ6EMxBySBxj/CvdZ7W01rSZdPvl86KdCrA+ h4z+HWvkDUbG68E63No92GeBDvtnyOYyf8e36VpDVAfWdrqUN2oeHI78471n6zHsnjcYXcAQfpXI +EdVF7EsUap5jcKC3p/nvXcags9xZqxjVXiYgg+w7VDBanB+J9JnkljvrNAWkQBieMVoeHw8cqjk spHNaeoyySaYCkOSnXLVQ8Nqbklt23a3IXtUvYpI9NuYPtNoVYfeGMV5Lb2SiS4t2GNknSvXYERh s5xx3rhry0+x63cJghZVDDPvUpg0eeW1t9k1OTGcMeRWzfadyLmDuOg44xV2+0km5NxGSS3YcYrS toS8AjcdBjHHFVELHlOs6BDcwSPHGIycE8DHB/Ove/2I/Hknhnx5ceEL2bFtqIZEDYGOeAD7NXGy aYNzRsuVxntxXjWgalc+FfH1lrdqSr2F15mBj7ocHArsoa6GVQ/oOOdp+ZiV4OM4yOOMYrCup8n/ AGgcH/69cXafGD4dTWEN1JrsCPcRI7JtYspK/SuV1H4zfDq2YbL+4uySTiCA4P4kVskjFtdz3cKv 2IjORt/z0xXMSXU6EIkjqPY9B2Hc8HnrXmMXx/8ABnleV9ivtnTOxf8AGqs3xp+GhXdLf3Vo3QLJ buefT5aXNElzXc/NT9pLw2NC+OOrSrhYdaji1Bf96QbZB3/iBrR8EWcMnh238lccvzx83znmtT9r PxZ4T8UeKPDmreFrz7VJbWk0Nx+7aPBEoKZ3/wC8elcx8PtQCaVaQ7sBl3AHj7xJqMVK8dDShuJ4 t8ODUbdlWJGdQcbhwPUDHr0NeNBtRlu4dK1FDGbRGit0LFgsed2wFv4Mk8V9eSWyTIHXo3p715L8 RbLS9IgsdQnX/TZbgRW+OGbHLZ/2QOtcdOp0OiUepytnoaJGGUDn27dq110m3b5ZBtPGCPXFdNZQ RMgwQ+75gF6YPIxVySFx8ioqAdCw5/CqcmIxdLsTtaIrk4ZDnjI7da881JYYBPavu3RbgMds816z bQlZN5JYd88YPoa4zxNp9qhmuDHuklXapHQH1pRQHOQyN9ihhGdu1OBgHiup1DxVbzxR6boUcjTK oWaeUYSL6dyfeuJctHGr/L8iAHGR7Vbt08i1CAYaUbiT6VTQ7nf6HFu8Oaki88l8+uO4r0XTJvNs IWH8aL+Q6VxXhmEPpVzAeA6HkfSul0GYvpQDfeiBT8iaxaKPN7GcxeM5HBx85Fex+JTnT4LlPUdO /bFeECQnXZZl+8srHj0/yK9vt501TR7LoxS5jBx2xxjFVYgh0fV59I1SOKJ3hScB0xlO1e92Pi/V UiUTSfa4cdH4x9CK8j8R6ctxqNiFwsiIAgH8q1NT1WLRdPzMVBRM+mMDmtItoJJPoesp4/8ADYvI tNvJzaXk65jjcnaR0+8OB+Jrq87CdjeU5wePmGPWvhrS72XV55tWvF3m8lSOEMP+WanHHpmvoj4c eKnmupvB+oT/ALy3J+yOT8zgDPl7m9Dz9OK6qVXozGVOx7Xb3M0bBkCliPvM3Axx0qy0xdtkk8Cl vTPWsWXzONt4nA54H61E1uww/wBuXfjIBA6V0NmZqNKDJsaQEjkFCAD9aybu1traY3EuHtJxskVi CEY9Dg/0qtJbXTYk+0xse2UA6ceoqjLbzTpJDe3saQlSDgqo6ds+lY1Y+7YcT5t/aQh0zT38N2du sJLi6ndSQo6hVPT8K8N0FrcRzt5cSRgNvMTZBCqDj0/Gu1+ND3Nv4xh0t9Q+3x2VkvlyRvGAEkLN jPrxXA6XKUtrgs7HO7rIpxkBcZGBW2HpqMOVI0Rd8q3dYzFBPGG2Y8phKoDKWPIJrjryIbhieVhg ApIpUHIJxuIFdpdAbjvUsUBG4xB/uxADmM+/pXN3cZ887Npw4GA0mflTHRlFdCAxbAGP7SsOHIZR GE2vtJXowFbus/LKZ3LkSkoAoHfocCsWNMm6EcZ/4+AB8gOAOP4TWzeiZbdDBG6zJE7JsiA5HA4z gcmq6CbPPvEdxGNPkuEALM+Iz06Drj8O9ddo/iWx0HRNNsZ0BSwhMgc4DSTXI8w4HXAziuM1qa1S 5sV1dGktVkUzxxjazqB8yrjgE4xXMahcyalfXGqzRrFJK5by1+7Go4CqD0AHFRONzNs9N8Ove6ob /WbmSRJ72UNgPtwqkBR0xwK9CtIdllPaK7l2R8ESAOMk4w2P6VwPhqOCPToVfyx5uwklpepk9l9q 6+ERvocrZQAxHnewU5PXON3b0p2toVE0VicSk+fMDncp80cnytn932oZRFFteVplZYlw7b8Kg/ug D0rH2wozSSPEWEkihPPcYbyen3ccVEZYzHNFEVdF8ofLKzrkRDPbNVuWQPNPexWaRIEhjmaRSqlC 2OSATwOvcflXV6Xbi2+0Sk7GdAQN3ChR9KxIIY1fSwgVGZZG3AMeNoHpt/M1Zgm2aldRAB3O1cKp LdADxj+VSOWxnzxXNjZS38ACzzjCSNjai5zzz39ua4yC1vJ9RGo3rMHZPKCcHaF/Wun1D7Fc3UaT 7RJavtjAlaPOMHlDx6U3SbZbm9kdcsqDbktuyzMeePagzOm0FhcQrFOzRtbnIG1Tn5T/AHqvSaeX sRgRyFkiGXiB2gDJ5Q5H5VlTPGrbYZNqosh2iRU4VeuD9avlpkSC2TzQftUaZLJKMBASBg8fhmlY Im/azNLaW5t55NsEuCVZVPyr6HrXX+JMvpmgRKfmnljJ4znau/8ApXJQH7FqQslKFZjI+2RjngYy ABXXeL4gkHhi3UAv+9fg7QdsXqemM/SmkEtzzS5djb6l5bZ2RqgwysRuPHynAA/Gs+5tZYtsMeXS GFCAoVwcA/eBwe3ar11ZtLDI67nWaZYwFVX+n3TgVRZgzPDeyrNImBhwC304xjg+lUiznrqOPa0K hYw7KrKm6LocdGyK2v2efE7eEPjbo+oSfLBcyT6bOSvRbyF4xjGRxIE71m3KxoC5d8B2Plk7/ccH msjwGk1rqtveRqpn/tK3YMrGPafOQoduDnBqJLQCvfW7/wDCK3N1OxM7Ss3+7tONv6VyemLsdVSU xsyqCFOCqyKDuxjrk/8A6q+gPiF4ajtfFmtadhl06+lS8tgyhR5d6vmALt7RuWX/AID0rwnT2nsL m9TPzWczQFh3MW05OR0+UHFRAiSOktCZJGR0muGc7wGJ+667epx1PX2ojlXUtWto7WOOIQr5pIZP vbc4+b6f/qpJNUdLpWiOBLAAuDkt5ZGPp7VqaDYTQhZ2V2M0mCxWAYDxMwHzc1oNM6GW9v54vLlv QgYw8HygOVH8S5rpYhdOhi+0ySIFYFVOFBVdwUnCLiRB8vJ5FZulWU8kP28ghLeKCQjdGmQF6cfK OhrH1XVjcP8A2XpojvWhHLhmlBER3IzM2F4Q4xipNEb7a5a6fqJgs4zIGSRXLSLlioDo+F43YOK1 PPh1FkDL8zlgF2ySdTnnbjvXH6foOo2k0Be38tUWWPl1IyVG3hR7VpPBdxqn7jeodXP3sY3c42e1 CZSZY1O9MSLp88y6fDLtRPPUxQs5xJgDk84/ire0bxn4wXxEz6xaxJY2cDQQXdvlnVOM7VPDbuAN vSm6R4D8XeNo5brw/Zx31tpSq09qpVZhNkiJ1SXllC9cU3+1j4L03/idWU+i3yZzDdxnzPMzgeWM bcH2r5fMacotpROOa1K/iKK1E03iSx3SremSOfZtMUcy9SI+vI7kcVljR7fW9JkvbBZpLmxQbYI8 Ol3GW8vZtPIdM5OOorTtLBNUu7jGjy26PIguekkcrXEYkDFQQVyD1AxXoukJpOi2bW87hB9pjnQs rKPLVdnoDyD+leJJ8nQycTyT/hE2ttbv/DuuXE+nraor/YRhlJMWY2+UkgHn5QcjuBWpqWgaBod7 No1mN9pFbedLHKjBd20MxGc53dQM9a9Nuj4duGe4hmjEsknnoVi2IWUbQrNzhfXnrXnHiYz22rSy 3l4Z5ry38xIxFgcYIETjqNv5VVLESk7IcUbWkPpOoNb6LE+2EFpcxqq/vCASWHQ/KOeKjvrP7NDa O2qLbT+czRWwjDpctG2EO/8A5ZhRk7s8PgEDFeQRXul2E/8AaFhJJaXollZjE5cHcuWUbuOATkCu ne10hNKh1OTVjcXLW5lUZyqLKMsEXrnpwB97mqqUJJltHtfhxYYLGPV750Fy0bZVnaX5iAu4qpUZ J5Oa8s+JFtc3AhRYhPtYTRRQlhtDDa0ZRT8vYjqecr3ridQ1dNC8Oh9EMsWy4855GJ/exvHk71kw c7vSuMsy2qXd9cSKTuijbBZ8bvuKCwIPr/8AqruwmEd+ZGtCOpswwR2d42mTXTf6UxklghO90AHS Q8gHoTg1raSfsQYffGnXW9Pk+faRvC5B4696yrXSLe3MCxwqrxvG8SgFtzDczIT1PRgfw+ldBAby 21b7baM7vLbrFHvb/WSF9iZB46Zzn2r058q1O/Q29RmluHkt7N4yl7KLi1EnQ4G1s+u09vaufm1r TbKyubdBsKBYb07DIVKnHKj7i7uhPGa27OxjikaS5jYw6YXhRVO5iZE4f8PlzjvmodG17T/Dt9qQ fT4/9LCm4kLktcBvmEezHIz1964asI2vY4cXC8bmMfHEsk8Vtp0pjW3jdIbaT5Gmwc79394kc9OO Kv2PiD+15GtpVaSdYyd6KQg7oq+4PrS+IdV1OW7/AH2mhNPx9okCGJmKKcFJRwTtzllX1zWfYWmk mJptCmktopWSRYfNLAKpPCjHHtzjH51hUjHlTZ570RyGoa3c6pqsdpqemTT+RCsJhWYwo02f9Y74 3flWlJ418SaRcR2c8G8ISgiu2+0IyAcgEDp/tYqt4mvpYZBb6nZkybNltcQsBu5/5a44zXN3Wr+I ILEF7oxSxjbCURXeQc9CM8etd9OjzJWRN2+h7B4f1FLrwkl8YIrY3t7Iyxwg7Sq4A69gSarSxsVP Ukdc+vfp+lX4IpbTSdGspCXkgtEkkz/flG856dMjikPQg9K+7w1JRoqKKRwVwqrOWAPHANbaqJbY MM4244qDVISgLJyfaq+n3UjAwseB0FdIHG6/ZmFkuY+ADgnvT7a5cwbt2Qoz+VdLq9qJYWOMrjaf Y49K4nTX8qZrVyCCrAZ+nFZNgVdSlE+rQFWwCgbGO44rttCXdOreh69ME+hGfT0rzaA+ZfrkZ8sb T7fNmvStD/1g49x+fFcFTcbP0PfXdN8TfA7UE8PajbW2l6b4fRdZ08qI76TWVnhYXLk/62KYo4HP fGOgHz/4WnNv4i0uaU/6q5jDY7HODXAWLnaAQMkjIOdrbeRuHdRnp6811mnt5d3aSEkLFLGeueAw 79enfvWNtDU0PifA8dlpRbgw3t9Bz2CysBmvKmOYsjqcHH4V7X8YIR/ZjP8AeaPWJGIHYSBGFeHR h3QZ+UEZAP0r0MN8FjOS1M7Q4vtHjC1QfKYo5JR7si5/wrqJlAvLhCcjzD+grB8Kf8jvbBhwbe5/ SPNdNegLfXQ28iUgfliulMkhXqCvepmGQMZqIDbx0GKn9Pf0pgRge2MVWkBLYHTtVv1qq+d3t2oA xtZ+TS7pvRD+ldEjltMuDgriW3/PYRXN6y2NMuQP7p/WuuuU26XK6npNbjA9kP8AjSt5AV7W6uLO eK8tZXhuIfuSRsY3XP3trrhueld4/wAWviXM7SS+LdR8xkSJ2V1BKRMWVfucjk4yfr7eeHpTF6mo VCHYrmL11eXWoTNd6jczXU8hy80ztI5z/ve/OM1lOQZCc57VaOQCP8iqH8X41cUlsJ66Esanceeg yK8/8R3qTa+bYDMWkxHzPQyP8zD/ANB/KvSrdAHBkGVXlh/sgc/nwK+eodQnktb25nO+4vHcyN/t Mcmok7aCUepe0iR13FhnJyce5rv7OTKIvI715/pHzEenrXo2lqiyEvwOlNIDdh82RQ79hgAn04qh enbhe2eTV8yqOV6HtWTcSfaL2OBOhwcdKoDp9Lg+VWzyBxXpnhdsXbRdNyf/AKwe3SvPNPTAGOR3 9scV12h3TR6pC38LHaV55weKUknFknAaxa6tDPJfeHJo4bwXstutsq7U3SfKrbs456YPeu48K6x/ wiOsJe+I9Wg0zUHspreOfyfMFvMuNzrLho9w6EfeHYimXXgHWr/WteXTtREllcSpLJZFEIV3+YMw JB7ZUg8VwfiDwXZWU9s+rX0yabLve0MUZ8yRzw7S5yE9MdT96vzfFxhGq6bNG9DZ+IHjLwv4i8TX l7qF5dXjyiJJ47FSlnMYh+7lz+8y3ds8k1wb+KbweXp+kahJb2blSUtvMj3PFkQl2H9xiWx69elc lMIr2+ltBHBYQ2alo2iJwRnjIHLMfYVT1KG40ki0uGDErvKpEwIVgGUlhx3/AP1Vaw8WiOZ3PvXS fiDpWmWNqtp8RY5vLto3a01gebqVhfI6rIyXCrh7Q53spO4j5cNyBD4m8GeAm8qfw+nhW41zVkkl RrC6m843JRpBJZ2rlU2sSdqH5R90elfAZntSm4Xquw3OFjJ64yM+2eM5/Cut03RvFN1DaPb6XLLa RZWCS5jXy0SQ8kMfnwOvH14ziuN5bCEuaOh1Kt0kewWsF1cXUNrpWoW+p30IKT2a5sL5ZMlZC1tc 7N7ccgHg11ugWlzJpvjPwrq1jNZXUemLq1nFco8bOdJkaUkZAzmF5B8uRWRNLAul2Nr4vtrXxVJK rKZUjP2iHjauySTO4D0fOexHSu88HWV/pE2h61ceIf7Z8J293HaX8cvnRzWkV/G1m6SxvuaMAS5D AmM9vQTiKSsbU4xexjx+KvE/imxt7PWvsHiOy2mJYNShhf5Y1G1knVUljJ7EN1FekaPqs2q3Onwa nFHHJb2i6TdNIN8OoaYTmOOXcMia3P8AqpT8wGO64PiWvRaPour6n4ZS6+z3eiXDadJayDa7G0Pl s249eV3E+9YVzqtpuYtfWsMm3ADPk47HHOAfrXm/VJPY5vacsmmem/FHRdG8KyaHbxWF2i3cLv8A a55/Mgn8tiCoCgDzFbPmZ7YxxXlML6LJMruoijcjKrIEcgHcRiuu8PeJNM+w3GgeIpYNR8L6hKou be3cyzWVyFG2+td2MSL/AMtFziROMcVm+ItC0DwXqMdprTx25vIRLY3sIZ7a5hk+WOeE4OEJGGXq jZU9K3VGUdCaiv70UWJrS21bUYdS0+8WzltjxGkZ4UjAweg7cnvV2PTGi0yZtMa71KZAJpXAAkTY MEA91Hpg/wCzS6JqaTiGIvA8LqYcwuih0A5Y/wC6eTWrpF/a6VcSXEUEt2FYwEIy4cgkeYTkfKMd KclLoZ3R5LZalqGo6+YrWLzHjLSiCRElgPkLvIZWZd35/rxX1j8LviB4U1zSZol0q28Oak1wsks4 UJBeTzJlWG/mIjb8i42Z44PNcHb2v/CZ28j22mxTtZRLe7LREUrADt80RqMyIp4kweD1r1D4cS6B qJi8JahqNnqmiahbOLO3mtkWW0uCMlY5QBx/sc8+9edmkoypXa2NKWjujUOnLqtjpV/4hhEV1oup t9jEQzJM0knnGKDYd6DcN2G4V9w6Yrkvi1NYav4l0x9cGt6QulQyTSeQltJbTy3XzuGuGO3zAuF2 jdgeh4ra1TRNMTSZddmt57XVvClncK7o5L28gk2xzRHOHB6SA9Bjms278cx6z4abX5ZJr60vdPYP pRdpLa2v4JRFM4jbAWM5VwR9DXnYPEvk53r0Oyeup83+NrvSLye5Tw5pOowWsUaW8lmzQXBEw5Ex lUh8MuPlUY9643QI1g1KSwuittdvEwWFGZiiuO7cgEjjg8V6Do8/9uXd7BphKSW8e+WaS2EYiKZ2 smDhhu9aLy9mSwha/khXVooxErrGqfaMnbvCDkDuT0r6WlK0bGCepTm8IRakI49Mu7l0cJ/ozDzJ BsH+rj2nqThge3WvpPx/pT638N471rZTf3Kac+stkLNceXHseIydNySbST1OK+S59Y1nTDJBrVmi Pb3CtZsQyrJEw27gq9c9iCa9l8OePvFNh4MvrO3jitNRliF1piTHfDeRJcqkkc4cY2OmVAyNyjrx XPjMNOXLJbI1iZXhnRNKl8Hz+IfEKNDo/g/XLq5+xsCsl27W8CR2Sn+ESTq2/GRgN0BrtvG3xH8J weDdL1DwTczWOtah5k1/f7vs0lvs4ltwijbhs/IoG1UHzZJqLXvB97e+GLB5IZtK8HaazX88Vrav Kz3lwd7q0p4KRDCIxOeSa8nl8NaX5kIt9VsbWA8CPWkkRDkgn50JjG4ZUhsZzn0rOFWlWk5TOecu xs+C/iNr32S7ig1KTV0sJ2kNrNE0bWyufmMOwrLEg/iQHHTjtXst3qmmy+DbaY6WbrVvIXyZ7yLe 91CuSsizJxNtODtPIXj0rzbUPBEdrNoWvXqKNb0+2SGO+tT+6uo41zDtePKu8PKMD9+MIeTmvTPD /iHTtK0hLdpv+EgsEs2vDY3MRito7lpwVhDqf3ZZdzowwRs+grkxs4O0aWxpTlde8a2k6vp8mmWj avHb6hfGJfPuROP3j45Y5IOT3yM5681of2p4e/6B8H/f9f8A4qvlS+8ZfEW2vbm30y5t2s45XWEv awXZ27jgee67pAOgZuSOvNVf+E5+Kf8Az3tP/BZb/wCFZf2PLshaH//S+ftTubaKe3jtdMERWLE0 rFpS5Y8HL966gM0F7b2ryPHH5KtHswqux6g4xXLu0sM73wnLEIpEeAVcA4+YdqmEbzul3FO0XzM6 gfNgt8uPbmvhKlSUt2DMu7XyNev54f8AUkBQHJP3hg/e6V2miaDGUjmSMbXG4Z44riZ7aWC3mwxJ bd5hHzMWB7+le2+DIludCtHPWLcvHOcE46e1ezlE9bMixsrpwj0XzCP+PeXcMHGA67R+uK4Vlk0v U7oSqmoXuoDndIVEMY6DIPevaUsfNsL23IzugfaB/fUbh/KvGcRRa1LNftP5S7I42jVdrnbwWzzx XJm9PlqczLjqQ6fIYtditFjERuE82NUO/tl8HntXeeHbq3th9lXO+RCqea2BGgP3QDXPXCrBfLe2 uJWlt/JXgDDZwWHp6cVuaTCqyvcPFudhs3yjONgy2P1r5+rqhpnO3d9FP4lsI0XCQeaQ46Mjnbgj 2xmvUbS0WIrgfdHB+leK6rckanFPZqQN7SBjhf8Aa9uK9/00rd2dvc8ZliVjyOSRyRntX12QT/d8 hMzyfxh/bNl4luirLFp1xCJBIFBJMi+WV5/2lPatTykkv7KwwkjIczIgXdED1Y9OK3fH2jS3X9kX sceTEXglLttjVGw6s2cc8cVy11ceHINRuvEdxIt0bVP3aW4LADhQ+eMnOOP8jxc2wrjWaQJD/FNg tz4pS4F2uy0gjdI1xnljuOPcAH/IrtdB1SC9vhOn+j2/2cR4lZQFKnoT0zivJ7nxNZanrEN9cWhP mmOFi2RwGwCAAOcV11mfsrQNeOpQ3XlMoQBBg/KpxkZIIrnwMHGpFyYWPXLf7Hrlvf2MTl4nVoHb qDuHY+1fOmoXFteSHSliuLgwYWQSSbFJhXbwqZz0r6bsrdLQLHCABGSQFAC5H0/GvDvEOnovi7UI xiG5guEktii4EyOu4h8dPY9Pxr6XO4JwVQlaHmtpf3txbRjT447J9zQusChX+UbuHbNeq/B12uod RhuWad4JFmjeQ5fkYI3deD7V5h4is/J+16jaSZS1ulY9vLLKOw7djXoPwl1eO+8TSMtvtN7a+Vuj zt3J82eOM/jXk5diLVEKTPSviXYWU3hiKa7H72wu0mtiP+ejZRgfqP5V4hYTm0vFccAtyM1698Vr 9hDp2mgjktO49xwufxrwdncMGweAK+nr2voaUmd/rKtF5d9CPkYcgdK838X+G4NYtxdwACYDqOxx 0r03RLuLUbM2c393b7g1gyxNpdy9pcD9xIep5+nSue5vY+Zop7iwnNndggKSBXoGmXIk2FOSO+a0 vGfhdJ0NzbryMnIrzjSr+WwnEEhwBxg1dyT6H0W48xNpPPA9ua4H4maRo3iezewtruEavZ5aEfxK 4Gduff0rc0S5WQAoeDj6VyWvRfYdUF7CNrFixPv69OuKcXYDxbRdVu44tsbtb3Vs2HA6hl6g19F+ CfEc3iPSJoL4hrqDuONw7GvEvFVpFY6xbazCoEGrARy+glA4b8QK0vAmtjS/EsMLn9xefuzjOBur SSurgme2fesLmAnJAIxWZ4IheOW4bBKluM/StDUZYrEyl227iygDnPp0q14YjMFlkjBY5x7ZrCRR 2CtiUdhkVg6zcQ3WrrbPHjygvzAnk9cVolz5i46HH6VgayfK1N5gCTwQB34qBlmSxQl9i9/Xpmqa 2JiDCRSrBuTn8quxTPHPG6gxqyhtrflW3L5E6AN94du2aEByM1ra7wsqGUnBwATxXjs2k79Tv2AK IskiYx26/wCHSvpBLHOXkbkgAAY6e9eVGJP7U1O3KAhZ5W6+2OldWHZlUR3XhHWp77w1ZO7EtGoj Y4x9z5ccV08c8sj7XHB4zyfbrXS/swfDHwv480/xNYeJpb1bjSLiKSFbafyl8qaPJzgddwr6p034 H/CyzmVW0SS/IA5u7mRz0wOmKv2bZ5tbDybPkbho8qAzDrjmuZ1f7UYzv+VAM4bgDtxX6RJ8Ivhk kIZfDFmBjoFb/wCKrn9R+DnwsuomEvheA5OfkklTp9Gx9KTosyWCkfi/8QkdYxOMhg+0hsc5Ix1x 3rq/DcxtbCyYEqBGvqK9k/ay+Eej+BNLt/EPhZpI9Pu7yOC4tJWMnlOwJVo3PO045B/CvHbS326b EoG0rGmAOgG3PXrV1VaB34WPLoe16DqwlHkscnrgcnp6V4V8art5dZtcwXG7TZIGJwht1guAVzvD Z8xjxjHA610+j6nJHGVYncnAw3PPoP8A69ee63Z3tzYeKI3YTQ24gkRRnhVcSsTznPaooUbux0Sk 0d4msajZeHob20a2i2RkvJONwGP9k4x9KxbT4j6lBOsWrWsNxEVUqYwEyH6H05+tcLF4m1SXwh9j Nul20lxNpMsAO1fMkAMcnPvkZqeys5rzQYLe/jMV3ZF7ViR18jgc45olStuNM7m/8RXGoqDbKbS2 HzbAclvqafPdtf6K7E5ki7/jXG2SX0UT28kaugBG7I4/WtvQ3YpcWrfMHHQelQojOdmuCVIPzHbg AVoDVdkKGXBbG0AY7egFc5eTLDI8YjZpUcqN3CDnv3q/aW6kiZ2NxKwGSRgKB/CoqmhXPa/BV4Lq 2KFdjMcEcdOg/StzSGEFtqoHSGQ/yrz7wpefZL2KNuFdgPp7V6IqfZ7HXcjB++PoR14rnsWnoeaa Yge/uJyM4LV3fhvULjTr5GVRNCXVyjdCR9K5LRYgFeTH3jV/Vdei8PWn2oRrLO3ywRE4Dt2DY/hq 7EntN74j09YV1C9NvYKhAE8zgBexHvXjHjHWpNYvI9MtJPOFyQN4zgqe4/CuBtIL7xFqLat4knN5 JGAVU4EUYH8MajgAfrXX+GoFkubrW5gvlxZSLgAbVOc0JgdXpSKfEOmaRAD5NhH5j/72MYNZPiHV ZtP1Zri3dknSTdEyfwlejVc+HrPea/f6k/PmAgE+hPFWNK0b+2tautSuObeCVlUEYDc44ovrccke 4+HfGura7pEEuox+RdKgDhF+V/8Aa4/X3rZOq6gc4Zs9sdhiuX0uWGKZY1YBVAG0Dp2robuZ7LG9 mjV8lcqecelenhqkZaM5ZxaF+3X+eLiUZ7E8fyqDddSIUl/eK5wM5I5ODUB1IcjzXOOMbD16elLH es8qKokc5GAU967vZxsc6lqfLHxBl87xxqwS3YCJ0iAjC7cogxjP1rATzPK3rG5j3FWG3JOT2UfL +tVNauft2u6nqJj8wz3UrAmBjwHKjuOwqeSNVsIflCoZUJzG+OSc/KM1ktDrWxtSRWpjDSxvbl5C NxjZON2B8y/lVN3iaYHzjIR5jbQ4YdfSlszCLbkxqxC/d86H70nHYileFbkTEFWAUEEAOSFJz0p2 Gc1cWKxJxhi90TlUJJ+bPappof4tsLGKNgQFf+8PSqQEsVhZQPGqjcWBIeMEFiQeOadLLCGJXC7g gG2YpncT2x+VOwmjy/xTazm6LIv7uJA+QCAPmxWXFIPLU9ODyH9R7iuj1m5jjE6qyObqBoiN5fBb kdcdxWRHHGYYIipU7lbGP9rpzTMn2PWtKmkitrNPNZCVhIxdKgA3H+EA4rpbeYx6EzrIxZocAmVT n6OcVHaQxKIdqAlQuBtHAX8PetTyo/swt3jGzHRlyMewxSsaRRz1zfSRtcP58qndPz9pjPzbQvpU sUm8XEzyb1RmUkzKOBGBn5RWzHHavI29UUDORtXv971qZ4I/LkQKMSZJ244BGM8D+VMozbJQ19aK WBaK0YcsxPJxxwFrM0maO61vVZXcYtySFyx+UttB4Hy8+5rptLQNH5rsS6q3OR2PTgVl+FYxu1q7 ByHkjjAB9CzsDSsBQZrdf9JglLsOXbz0k5IYgbXwegFWPD9v5OkvOzYeaRsnj+Ee3vmtDVbS3aBV eJBvON21RgYx1Ht6ip9pERRF/djJwMDB2g9qLCsc9dCQyyIDIV8oAcIVG5tpwDz2zV+Qxu9h5qCN mu5CkjQFcY+X7yk56elS3FrA21poI3aUYJbPQH2PFYmtwW1n9inhBVYpfm2sTt3HORUgkemzOV1M yo7qEtwNokGC0jYGUxlc10/i65i/tHSbbdveKxuZFVcOeiLgg4556ZFcJLqBtIbfVLuRp4LiWC3d iAdsTHYGDD721z93FS6vPa3urLcWE5miSGXT5ZJfmxNHOA24DgKcfdz+NBMtyhdjyoY4zGMteggN GRgDnhUOF4rnrmW81AFdjNHklSUjlHX2ORXWw6dcNcRMV2qk7vhd0YUFflYJzx7VZOiXTyOdsXBw HbAyAMfwmm32LPPTpF21pctBtLBQuEyDzyflfHQUujzrpclvBE24LcRyKWVRzuB/Ljg/1r0SLSbi Nw7XKE/d8sDhvasLxR4fgi066eMLFuicBJAyndIMZPov8j0qX8I7Hs3xk0yO2tdH8ZRQu4sPMs5w BlWV900A5I6HzMHFfEeiefHJqouZC0yTlpJCD8xkPLbT69ea/QDUreLx58FmmKFpb3RYLyMMMHzr NfNO4D+L5XX/AIFX56+Jo20jXJo7aYrFdxxy7VPy4Ixzn3rnpsiWhsNqW2a1SRAyhxHyQBg4Ocet enp/ZiRJ80BIMRIKKSvyADjnsTXhmiLLeajE5feITtIO38xuwK9j8wyqbdZWQSxQNud0UACJ+fkz +VdIobhE2o3CPBpytFZyeXG6xqFL4O2uo0yGLS7cfuol+X5vMYNgo5jkBCegNa2maSs2nXMJVWl8 wgvlmUbhuRucKw9+On40gvYbdi9w6rGcPKiueQT5Uq7bcDrw2CaVjY0PN1CFX3fMYgxAESxjMLFh gk/xJzXRr9hljEyvgMoIOeuRnPFcOb62h3OM7rcct5cce97ZtrZMrE/PGf0re0q6ja3ltrdgyWsr BdrBgIyNy84x0OKQHrH7POuzalrfim3tRst/slu+1iCrFZtm75ef5V9QSrqVzafZ7kQ39uOPKmiE uM9QvmAgZHvXzL8A7zwz4d8aa63iG6jsoNS05IoGI4aVZwwU9vujPb+lfasGkabqdt9o0q8LRON3 zEICOx9qwnG+4pWPANX+FPhTXLm2updHn0m5gOEn0qc2sgXn5cD5eM46d65vWPg5BFNfT+Fb+Jft ARY7XVQVddpzjzxkMWBx0H8q+om8J30+ds0A8vJ++c4A71zN/pk0XB2SbT1UblP1J5rjrYGnNEOK PizXfBXifwrLBdXejtcWCzv5iRp9ptliAIVd8eXw+c5xwRXlrXeuajqsNn9jtbS3sVDiPzxcSqXX G+MnGE6fJ94Y6V+iJsdVhYPbwlGOckfIvXvk4P5VzuteEdA8QQ+T4l0/T7sAghnISRT14aPGP1rg /siMXeJHs7bH5r6BaR6jrZ0fzrVZY45j5ZImAx8pAbgK5Ofw4qfVLd4IwLpotPm0uVTEAATLbZC7 UY8r93sCa+q9U/Zj8NJcXF74O106TNcM8jw3aLeQFm7q2FkT9a8z8SfCD4pWlssdzbxa9DAHlgm0 2QGSKVF/dsY5jnPPHUVlVwc4smUWeTWup+ENS03+z9QuZri2vJmYTOvmS2yW+UYLjlnkHGCPuEH1 rR/sPTbW6xoyumkFhcscfuikaAR5bqM56VhXGmafpAP26G703VtymcXsbQRu0afOxJULubk/Iec1 1vhaC7JgtZrSK50fy4ZTGGGHYrueQSkgfwfMvboOa52nB6BTdh114eFz5MtnIkNvbgOXxkmRB8iq c/xvgfSuchmll1Q6UjDzLedjsGAokYbJMkfwxk4HuPxq/r+n3HhMxrZagyT6hHJEYmKvGC2OEyei 8qCegxXO6LHdG+8qzgjhmWZfLiZhtbCgqC390c7j3Jrend6yOund7nU6bqL29k1qluZ762MhQZyu yJ9zeYR/dx+PalsdPtLx7qW5CXUixtKzsgDBlGSqdsZww9hVaPzY21K/uBLJJfblzHtGXIbzXAHR B2HXp9KyNbnjAstG8kXVu8PmygkrslCBhsYYPOefY0VPeVkOolKFjlvGc+m6j9m1PTmLamzFS9sS wkwoBlOwnYVwOe44xVJdfh0V2jk1CKWUIPNdFBViBnG0dSTxzj6Vs6Br9xMTb6ZpC2M0y+XH5g/d bkH3CcfKD6nAp3iKw8OWzLf+KjHeXjLkw6Wnloe22WdQQB9Oa6KNKPLyyPHa1syncaZqfiiyaKyC yNOgnUZOCcZ2k9iPT/8AXXM2fhmwgvbTTL7UZDdySxoba3/ecyEZG4cDArUi8Sap5sA0RYtN0dD8 llauuCxHWRsEuc/3sV23hprK41i2uNqW1xBIS8bbWLvtJ4UVvQTjNRQN2NrWdRitr+RGGAG8sDp8 qDaOnsKkURyIGByCM59qwdXhLkOMHcc5xjGevFT6ZdxtmHIPYD1r7WOisJCX8S4257cA1yTSNFc4 HOOwrsNVAaPKYBXtXEXdtdOm5F5/zirQGw8/mQMccjtXn1/Gba+E6/dcjOO+Tg10dlfMknkXY2sf lx29KxNbZrW48qZN0T/dfjjvWM2Bh6cga+uW7CQgGvRdFDbvU54NcFo+SXlHId8g/wAq9D0jAbPb NcclqM9Fs8AD1roImONy9VGfy6VzVmMgc1vREhGXvjgfpWUtDRHpPxUjSbwvezgk7bi1uc/9dIB+ P8NeAK3mAjoO3sOlfRXjeIXXgy72jKyadYTA+4JT+VfPEarsDdcgH0rswr0JkiDwyvmeMBjho7G5 K/XAWul1BSuoXSkf8tM/pWB4VJ/4TV8c/wDEvuSP0NdTrP8AyFrrHQ7CO38ArqRBnKd2amBBI9hU UY74qwOM81QDOg9O1VX4z61YcnnofwqEH1FAGHr6hdMmK8jv7crXWTAvplyW4CzxAY5zlD6flXNa 1Gp0y5A5yuf1GK6SP5tDu2B5FxAOf91qAM5WIGAc46Cn5J6jBpmM4KjGfSjJz9KAEf5VO7lT2qrG PmzjAFTScjnipbWASceox9aAJZD5VqXPBk4+gFfMjs0KT27DaYpmBHpX01qQ2AqOBGvT14FfL+rs U1W+U87pCw7ZzXJWly2ZrBXR02iHeFVuDjnFd7D8gG1u3H1rznw9JvI6A+uR0rvredvunGBxW0WZ PQ045ZIweeAOpp2ljzZmu2Odh2/jWXe3JAVE+Z3+UAY69q6WyiWwt44OGY/Mx46mtQOlsZR5R3fK c9B6Vr6XdJ9thwDuEo59q5aOZW+VDtx6f/Wq/p1z5VzGWHHmLg/zqCTtdY0u1k8VyXpfybm7t4ir AZBaE8K316VyPi24vW8PF/EV3BDqNpcGSMRp+6dHOFVD0ZgPvAdMV6ReaNqPiDxZ4Z0qyEhi1JpI 5jbIJJkRNrvJtP8AAiDk+9eE+Mvh/rvh3x5rHgLU9WW7s9GnNzCJsmKSGdfNWWOIHhmQjP8A+qvg Mzw6eMaZSWhxcgtdO1yz8QWzs0M74nMa/wCryMZReQyj0o1bU/FWuz3KaLDNNpdopV2aMRqy9dzF sY+ldo3h/U7Tzbm31W4+xW67rmJkWXKp8v7rd9OF61tLo7Wls/22C9tLOO88hxPE0OycAFlI7kKQ emOaznX5EhJ9TnfB1hNPYTnWpwLl48W0EPllnUHBIjUEsBWp4h1q68PaaNNFze6cS4kiWezaNWwN uEJ6q2OgqxY3FlY38t9pk4E0T4gnj3SO6YxlwwG3ngAelO1fXtR1CzZPIu7tICYssPNSZlAwyDBA z6g4rn9pzT94OY5Xw34jkvZJDd2uo321eGtj8qDrjbjj0ruF+KX/AAiyj7Ja3yXUodMy7djQONrw TQniRCfX7vJBzxXkNzqHixZwbVJ9MST93jcwXg9wB1q/pmsvaXKSalaLehcq7S8SHLABgT7HgYGa 63Ri9hwk07o+q/HOmX3jvQLHxn4WtLGXxH4rtReC2hXAdLPbDdtaq/W4jUIWjzllbeMkkDyHT5It PtPLeziaeYiYzbhcEMRyPmA5HcdK7rTE1TU/hFfaHZ2pg1jwdq/9v6XIzkHaV2SKGXBVgvzgdPkG eKzw0Pi63kstWudO8O+O1ffIkjLb6drYPA8yQcWl4eDnIjkHXGa50raHZVipRTRxE+sPbiWBreFo LraoPPmSY/iyMIP9nn2r0LQPGMcWlXHgHxPOdN0XV8C1uWhWQ6ZclgUnCPuJt5CAJowcY5FeH+Kb FfINlrnhu40nWYGaNjGrxlFjIUny2xnt9zI/nRpHhy+vV0661TUoplkYLZw3PLyxn+7g/d+vFaSp R5eZnNfldj6S8G6TY32n+IPhL4z060sNdupW1HTbqxAVmljhLRSRyH78UnbHynJB54rz9PBXxdsd Mh1S40bUtI0i4jR0lmiTbjHG/eQ+GHHC1s+G7159T0TRLu7Nvf6feh9H1GRMmxcHc0Ej97Z1zuH8 J6UnxI04Wfiu+k1nXdQudQnvPNtpUm3SPBON8JQltixgfKvHAXHavJpylCrpsy5pCfD+z+J9jqNj qfgjR5bjUtNlZoI9yDdBLjfE0ZYbY3Hr9cZr6B1v4S+EtQ8Sx+Mo9VuNCtmv0utR060UFGlOG8tH 48t/M5bHGK+arGz0fULmLTL29b+0dPuIiJJZVh1NbdZdz+W+RHKue2cjGVzXvmt6tBpt/rlympTX GnWAku2llyfMP95gBje3r0J6V5WfVKkXH2e46excs/EOteI7S51Wazh0u/1fz7iwWzZrh57ax+X9 9FIAJAyn506bcdxVGx1P4d2lk/g6d/s8fiZV0yRbR1U6ZbjFx5mW5/eXGBhh8qd+BXPeCR4Y8YfE zQ7jQdQ1HSLnQdHuoZdM1BPLijit0M0pWYfMhJILqwGPpVm3l0TxPNafEa5tFtdFt9Kv7vVdKmUJ E19Gfs9urtgF0Mkuc98Cpp4O0otaI1vZXPH/ABxpGo2Otaj4d1DUYdJbSpfs8kMSloIXU/d844aV v9vHJ6cc1x+nXOmWgkuLo3zN56xma5hG58nahQH+At9PfFd74i1ZvFfhG2+IelwS3Wq6E0Ok6/BI F8+SJPksr8qePmUeVK3OPlJ615hfa5aT6vp8NlBf+THPvX7eGLK0f7wAOB80e5QQOor6CjFuPvox qaPQ+lfBc3hv426TJ4X8R2sP9qaECdM1qGLyxafaH8vyruAfKFkkAXr16dKXWfBmq+A5F8RXVlay HQ/B1hbWltclvJhv4p4LRnZT98iRnmQ5+Y9e9eft8VLfQtPE+jW9h4futVgGbF4ZZPNSQ5d5J42G PnwwyDsbp3r6k8M+LdL+IXh2TVtVSG90vXZIvIsbkK+0RRIsqI64EhWdNzf3d2fevKq1KlJub+Eq nLQ+MNd1nxte6VPLqviy9uomGFjvL94bfCjB+TKpjPy4C1x1uPGur+JdP0zUJktNO1CRQ0ltskhM bA/NjkfMAOeOa9jkg8NWHjq28Vx+GUg0+CJo10q4mN4sQaMwy/MeNpZiwAyR+ANcVqGn6JLb21lo NpdPIJZE8y0lWHygknyyksDnc2GA6V6lGtBQ90ya1PRdC059AtZ9MtZJ4bO0O4l5T0HQxKc/NnI4 Ht05q6dX02F9R0uO+j8K3Op2uDLcmN7W5kjuFkhLqCXiBaLY5AK4NR6p5UFraMb03MnkpG7XMZCy EZDOrADOSewxXzx8RbaXUPEV4NREzy2SpB5ojVk8qIbgVAPvz+tcVDCqrUdyr2Pri28Dx3ttBeL8 NdMuRPGsglgu5kjbcAcqsRZAP904+nSp/wDhXq/9Eusf/A65/wAK+KtNfXRYwCx1zVY7YIBGsbSB Av8AsgcY9McelXfM8T/9DBrH/fctdzy99yuc/9P5+uIPLkVoV8xpvmIUcDHUdvSnQObmC7iRY4nk ZUTJIxg9K03ugitEoAbJwVplpOkC/cAGSzFlGDxt4zXwvs9B2RXuLizgPlW6YAVkkGDiTHJKk17X 4Ct4X0WNbeAwHLZRgwYdhuB9eo9q8Onkih+4Q6lDjPPPfgc/pX0F4Oh+xwaYru32i7gDTb2zuP8A D9MDArrwcnTqJiaPQbOPyJEOAdrKSPUBun614ffSSab4p1HS8RstrPI0fmrn5QN27HunSvcor21/ tWPSc7p2RpCVIKjb/CwHPP8AntXnnjixtNP8Tm+WCSe41DTPMc+YIYlW2Hln5+SN3HGK9rN6CnSu QnbQ4aJ2ubtrwTLiVd0aKMbMHHT8K69xdRW0OIwiEMxkkIXrwTg4OO1cRo1950HlWtvFYpExj/cr uPX+/wA+v411WlRrE89/PC8kbROvnSZfPGMHPQY7Yr42rBR3LOQntLEKS1ybrkx4gXb9V3t/MV6F 4ell1zRre0t5fs9hDJ5TIuHchDnDPwfyFeca0RDZ3SljGyyBUI7Z/iwMcYre+CErCbxBo8rZ2vFc IevykFCRj617+S1LS5UKWx6l4y0+XVfBWsWqjEtrALmMZwD9n+fB/AYr5tstV0xfBWsPeERSRFpC keATkbl/DOBxX2NDHFMTDKoMcwMUgPOQw+avhLVLLUdMstf8NyiPZd3P2BtigzYhfGVPYYxXXm9O 9mRFnaeEL25uLmxv4o5FjwQZJEDAEjGAPqetdHfXhsWTToY55B9p8xwxwPMBBLZ6Y/Gua0hY7bQr iz85HciLylUnhRxzz1JrcZzbGx0tjtPnrk8uQGG3cevy8V85KK9pc0Pqqxk+020c6dJUV89eo9a8 f+KNktr4j0vVgpRb21a3eQZG1o3yp4+teh+A7oXPhq1BfzJLQmB2PHMbEGsz4r6W994SNxGwjezu UdmPAEbcMP619dWp+2wtkYyPDPFlz/amlTL58cb26k3ECxg+fEP+WqAfxL1xVDSvFUtpr/h+600P aWm6BZLWKT5JY8hTK6qDuLZz2x0q2mmXc+2O9uJItP3NMZljwqHaAoOf3h+mMYqCx0+20m5XUNM0 s3IkLxPccQ2QVuT5UaksTn7ucV4OFwTg05E3vodh8StUt7jxXfLEd0VqVgjP+yByRjNeZm8jOFxy OOPaoNTlu7ydriQMFc5AP075rLWQA/McY719Be+p1QjY7DS9QNpdB14B/nXpF1Bb63ZjIxJtzn0r xCN2+8G7ivQPDusmNljkb2qJI0uMUNCzadfjIPCN2Arynxn4XNsTd2q5GN3HoK+htW02K/gM0Q+Y DcSO2B3ri2G+M2GoDercK5HT2oUkFjyfwXqR85bd+Dn1/pW94vj2SRt1DZP6YrmtW0uTwvrsFwBi 2nfhvTNdh4rCy6ZDcod3TkehFWhHmmq2Y1nwtPb5xLbZdCOqsnINch4LRrvWLeRgMxgE+3OM11un 3qxSzQS/cPTHfPUVU8G2AsdRuC5BEl3kY7L6VrF2ViXuel6uhuL+NWOcnCgdzXeWK/Z4VjIwQOfr 3FcXa20k+sm7ucqkXCIa7KKTf35rnkzUubx5y+grmvGs0lpDNcxOUYJGVYf7wB4rdY857gVz3xBj MujsU6tC3I7MBkGpWoHVPAks4kJ5jijBH4CtFbZZeUGT6CsLwdqtl4h8OWuoRuq3Txok6t1DIu3v 64rba++z4tLCL7TckfOTwI/XpQBZaE28RGclvx9sV5bp6ed4o1ZcA8ytn616DHd3TTLDPKd7EADa Tnvjj+dcJ4ZUya7q07cA+aAeOcN6V0UvhuZy3PoX9lfUG0/4nazp2cJqek78Dj5oHH9Gr7ts1LXb N3b9K/On4KTjTfjL4ZdeDdw3Vvn/AK6J0P4iv0Y09iCHx04z6iuimyKiOuUZgx7YrNliGNoHJ/wr XX/UE9Kxp5HUA55rUz+R8QftoaZ5vwl1GXad0N3ZyA8fLiUDP5GvjCOLYo/ulR/Kv0I/a1tDd/BH xK6ruMKQyH/gEyH+VfA6oTKIhxlVP1+XNZYh+6i6aMO2gaO7J6BwTg+orPv7xdNudT83b5F9YOpy DywTaOK6a3Q/aTHKMlelcl4yiTYsfR0ikck9NqjmsqErTuXPY5nwxoEN/pmps87JbyxW15tXO8TQ nkjjocc11uoSl5IJ34Eh5A4A8wc9K5z4cXgaMWEgO50uoMHuUxIOnpzXR3UW7S43/iiwD9V4p1ZP mCGxhy3H2S8MXOx+n0p1nKttdrLETjIz+NRaliTyZgOWWqUb4xz04qS2jI15Wh8UvCpwrgTqev8A CO1b0Abhoxndz2HP/wCus7xE1tBq9hf3UqwK9sYyW4BO71+laFtdWkqD7PMkijgbGBzVrUzuSwXs ltMHLHKENXvNpMmr+H7q8h/5bW+G+q188XiFCHQ5HQj0FaGg+LdU8MyTCzEVzbXC4kgnGVPbPHSp lApM9CtJY7DTvtNx8iD5ueMrXnRmufEOqteSDECEiNewWmalqt3rIiti6oJD/qwMBQOwFdXollHC YolGR0OPSoWm40zQntvsmnJaQAGa8wg7cdf0rQ1gjTNMttIt+HlXDEfzq3YRx3OqT3sn/Hvp6BF9 Cx5P86qWkD6vqb38x+RGxGD6DgVAHReE4/7JMUh+UOpDn/eHt6V6Dp9hb6VYW0CzRjCbpHLAkZ5z gZxXGjauEQj0Fchr+pajc3n/AAj3heL7RqLAGWY8xWitxlsfx/7FNIbZ2fiX4hWehbbHRwb7Vbk+ XEF7E/dbGOlcmfFGs+Fp4IL+/kvb+53S3ccr71jyMhR/d+grm7qTQ/hrG0FvIdW8U3GRNcnDvHx/ COij8c/pXkt1F4k1OaS7mDI8p3MTyx9vX861grMlrQ++PD3iG38SWC39i0amPAlQgkox9cdvQ1tN LLDHLJ5nMSSMBtOOFzwcV8ZfDW68V+FdZh1drsw2GRHdRONyyRMcdPUHmvrDxNqk1j4b1u5NxKyw WkpBK4AyMCvXo1XKNjjqQSkfHVvatPD5kgiy+5vmLhiWO7+8K1k5iggSJWMZ6FmVePQhif0qPTp7 M2ce64i3ABQpkGc1oXKxwOjOwU46lgP54qrG6HQ292jKfLYAeX0nOcLz3B71agSbyp3lSVT5XPmN v/kOfyqe2kikB2EHGc4YH7vXGKsvvi5csgXkhj1GM4pDOVv7SFLSzCbQEhJwGaIg9uxPXPXFYcrK kjxws+P3Q4mUYKjnhhXdPcw+dK8o3RtGApIBH4ZrA1SPTIl89SrNySFGcnGO3pRcVzxPxFJK2VZ3 wo4y6tk/8BFO0S0bUdUsLKNkRpmXIkOB8ozTPEpUpuRdpLYNJpaLJdQCXPlIpYg8Z7f5waozPaw1 5AxglIimjKbo2STfuZScgY6Y7jitQTznS1uQ4acrGAcMRlsAjaOffpXMabry2sK2lxqSvbBeIp38 zb8hGF6lRk9q6u6mgsdNt5nciAvF5bo7OpBI6A5bA/H8KC4sqNcXUSSSCWPIDkARy87ZNo6Y659K laefyGlGScsgKxtjarbe/NRC4ju4iqTK6OM/LcTvjM+fuBQRwOeKiaNJNIR43jlzyTiUja0gGAev r1oHc1bXzo7uWMmRYkijCIUCrliS209z0rP8NxXP/COxy5kLaheTkYjBJAk8tcjqfypd6QahfSQh VZI4eVjKlhgYBY8N+FT6DAieH9BIRcH94N0Ug+Z7mQjLoSc/L6UDG311cuYlE0a9gPLlBH7zZyOl a+jsLuFo5ZA7NI6lhx0+UcHmoo7VLiK1BLqxWActOnLSkntS6Tp0kUYmSTpM28FmY/ezyW5oAr6u XdxDE8QWJsfM5U7QpY7uMVyuqi8urKSNobeaaGNJCsUgfqN38Jr0W62i48jzlTeEypnUZDkrwpGP 1rBMCRTARLKxHlsFBRkPygfwZpWA5Ox8Q2kOkSRyMzWkv3kaMt5bow+eM4+Uj0r1fWPGWgeImtLW x0e309ogxIMi3Edz5gBMsiY+RzjP4187eITHaX18LZ1QTuytDEQNpYDnC5Xr712Ok2Uei3lvPJax W+yIo7W+C7EjqSTipSIluegHVNQswbpoxBb28WXAHJzzhB3H04rQg1qaWWPcpfcqnDfKoDHaM9uv Fc+13bSW17DaKyFUU+bL5kjMcjjt8v8AujFS3WlNcTJKjwsuADvU53Bs4HJG3NBZ1f8AatvGRtju Jmbr9nQIoyCV+ZsZzjtXP+KpbzVdJt/sESqUxNJb+SMyqMSMkj7s8gELiuZjgltsJiIogjIUBuSF c+vH3q1opI7m1trcxw5tgFAZdwOFI4wc9etJIdz6h+BE6XfgdLdsMNLv7i3IxnMTssi5z7SNxXwT 8U9Em0a88l4iXttW1TTQ5AyyWkiCMf8AfLCvub9nH5E8VaHISypNb3kYwQAskboR/wB9JXjv7TXh VrSx1fVoEw9prn28kZ+VL6CNG/8AH41rlb5ZEyPmHwvbLHMGfqhR2wu/vz8temaTqOuxQxpZ6dI4 XYQy7IAdm5Tu2gn7hwOf8K8/8KXU9k1veRBM7MMGxjpkivR/+FkaBZLtlzMwGTFbx+Y39AK60RFm lZ6f4muAHlYxL5Yj/wBYxb5Wbbnd1GMCrcPgy8kyZbl3WQyFgrcBXGOg965qT4sThibDw9IQm757 mQqcIMngKRx6Zq8fiP4mkKL/AGaqA8fKVJ55XGdooNUzpR4UjQlggYklmzk5JAB9aGaXS5odykJs CMQMbtnQn8FxVTTfiGt0EF35gYmEsJJIwCjjZj5VPRua27XVdM1aBTIfOaPMrRoHZSAfLbLbQOOt KxRV16B9R0qOJQRJ9oVg0fYAdCa7TwVr3jfw5Cv2DW7iGGNd/lyfvEKKCSDmqdhY291eS2tuNsFo nTOcnO2uvl06KHSr0O/lL5EihwN23cu3IXufQd6561RR3MqhP4c/a7vl06zvtdRFSaX7O0Y5dmxg cLztYc9K9v8ADPx38OeIb5fsOowWiXcYWITHCoy/eyDjHPqBXw3pvw50fTvPeIz3qTW8DxmVNlwP KGSfKUEoP6V1UT2kTxJY2FvGL6LLSQoHkHP3iD0z6mvElmkE9DLnZ+l8FhZa5BDcNqyXrSjgIpVW Hs/SodR0mPS4Vl/slYoxx5sj7g3uOtfAulaxr2m2H2jwRdyQ65BIHjtWn/dzR/xbVyc8cnsDxXsn hX9ofxJd2wTUrCHVoI90c0cEoV4yuV+dG4HP0rqoZjSqeRcZHr91f2wmMKweXLwPlUnP5VFFbXlz IHW3kRAflIXv09RR4U+LPgfVMQpJDZXZO14LlVikRh1ODw2O20nIr1+333wJsmjeJ8KN3U55yuOl dyalsacyZ5OVkMMltd2K3sLHa0dwFmXn2dfT0Necax8HPhzrqMRoc/h26lJ3TaRM1uMnuUJZD9MC vo680PVYwy/6PIF/hh+ZuO/FYMlkiErM8hbGSMZxU+yi9Gi7LsfD/iP9lnW7iWaXw74wtdVD9IdV hNtMCBhdssR2Nx6ivOtQ+H/jv4fRrJ4h8H3Sx20flte2kS3NsI3GXPmW+T+ajHT3r9Jo7KyKsZI5 BngbTtz+hpqaf5c2bQ/Z2I6F2UNz3P8A9asp4WDVgufldHeWKRIEkWVobllYI+VjEq7139wDx/Kp LzTpbQKkqql/buFeNuRs5JI+i4r9I/E3wx8H+LED+KfD+n6lKF4uI8JMpxgfvIyGG3tj8q8R8Q/A ax8QW8q6TNeaBdW7yLDHqiR3MUpH3ZftMbB/mHZgcd645YNr4Sr+7Y+AtR1S5fDtdRGIorLHGN26 M9yOq1K+t6tY71tLWGNVwBvjMqMMf3gCtereLPgR8XdBQjUPDsHiPTI9ypd6My3FxEuflKqu2TIG ONpFeRLdSafeTaffWouLxGUKl4JLaV8DYMwvgggDmn7Nrc8qdN3ucjILXUrm4ltwLe5kH7+H7tuc HH3l+77jge9dl4Is9QsPFtlb3p3qYZZE4PH7s7drH26Ve1fTb/xHZx2w0i109rUjeUQIzD0LkV0/ hPQZ7S7glivZHthBN/ooVSqBVOcnr1PGK3w1aLqRRLl0LWoWryR8fKAOa4eBjaXI28Hd0zXpLHdb qxHyFeAc8dq87121ktrkXCfcIBBxX2UvIR14kE8YZwPm4OPpXNvPH5jQyDHUZ/HFWtJvvtEflZBI x7VnaxbMjGVenfFCYGRqemIyebCTvXmuX1G4mn02WykHzRDzAx6+hGa6WG6CL8xzXMeI7wHyreJe ZMsSPQdqyqy0HAdpKhYV29Dg13mmbc9f0ridLAESj8vzrtNNyGAzgcVyBuzv7Rvl+XmtyB8kZHI/ /XXPWnr2rdjbpxjkVEkWmez6pm68CeYfutoa5A7GGXbx+tfOFoM20YYfdAB9uK+mNMdZfBOnsW+Y WV/DgjOdrbsfhXzPaA+UAeeOT9DzW+HdhsZ4axF45DdF/s+5B/HHpXV62S2pS4IA+Tp6bBXN+GUV vG8gIyBp8/4ZIrrNchVNTl2jACoR34KDFdkWZGTGQVCgVNjAx3pqJn1BqVk4x7VYFfdjPf8ApVfI yac64yQeKjxQBW1HabC4B7x7f/Hq6EoI9EniGM/aYM/98NXOanj7BKTxnaP1FdDJGyadeZPypNB/ IigDNUYwOwGKawzjj6Hjn8qUEHjGTSHggY49KAIm5O09fWt3T08mJpWwQvAGKx0Xe+Oo9BW7IQkH lqMDH60gMHUMmGVj945/lXz+dKGveLoNIEq24um8syNnCnBOT+Ve/XrYglfqQvT8K8J0iX7P4p+0 sM7lk2k9mdGVenvXFjU3HQ0g7DNLU2k7W05XzIXaNsYIypwcYrrHuRGm8jcFGSeP6V5raPLIQw55 JPueucda7vTIxCsc92d46xxdAM/xNnr9K0oP3EZzWpuWMTRj7dcJ8zjESEdFPf6+lbVk7SNhyPx/ rWHDK9xIH3b+ep4roESaMAKEOR/9et7sVzobSzXB5AbsBnoa04rZssD3AxjjpXNwXMqEHDADg4rp NPeWRstnA6FqdhH0N4MtNIv7uyvdYgut2lRpLHc2hdWtjnBZ2jZcKw65zwMYpnjrwvpfxL8ZS69Y 6rYnUEWF7iWQCTzLaAbWnsSDtMgTgxOAQ3OK8x8Wz3EPhlvspJDCIyAFhuTOCPlxxz0rzvwdr3iC 18UaVAxEFjaXwDpblQ0aAcFepAzjce/SvgOIMFP6z7aDLWxpXWv+VPfn7TNf2a3Egs1v4Ak0MIyF MzKqh5ip5IG3nIr1/wCK2of8JR8Gfhxqt5qXkreG4W6uIYxLuYoi8gd8r97rXlPjnxjs8SrZ+OfD 9lA0lwJJ77TBLZySZPDuFYxvuHXK9Pyr27xrqfgj4t+BtJ0vwprVtY6hp93JcW1lHmDzvNiCtFGJ tg8xdo2qJBnOa8DFyacG1p3LjFWsjxTSbRLLw/NrFho0t9Y2JTdPBbPNGmNrBJ5oweHxnnGK7/Wt H0jxpruhR/D+KC0g8aYurOUAi2tY0G2/SZUJ+a1fpGOu4Hoc1wsHhP4jeFLHT9V8Ftqem6po88ya iY2m05is8mYWKzDypAoyvBxXoPw8+I3ibWPF9np3jHw1YwfacyT6nEYLIrchdonkRWMZduAzKEZ1 x/drWrFv95T1JVNnk91EV1LVG0vU3OhwSy29lcyRIs04U7POEZJWMHsCemK4KPwHFBFe3k+rDUZd gkUwSK80bhs4IyVx6e9aHxU8H+LfC3i3U9DltW1CzgZHjvbKGaS1mMi+Y/lum4HBbB561bsPCepa N8PtD+INnZJZrq99NpxuRcGJ/Ot1LmN4m4AbaQO5Kn1FehRlOMLsHFo6L4X+KF8NePdMZ9MuLmxd nW/SVneS4sirfaH2/d2+X95h0/EV0SfDGbS/E194Zj1G31GGyuFbMxPmrZtIRwWHzYt2hdT3+Ydh UviDQPi5ouhQ6/pPhmy1OJYZN1zo159vaEShRJ5lsv71EkHJG3Fd14Nht/iVc+BvFNpdi11CPzvC uqmRRui8uEy2s8ycchEePLcfu1FZYhPlumdFODcTkB8SNDuPN8B+I7k6hpCXP2O2VWH23TSAywSx XDD55EwcxOSjrlMg81w/iDTfEPgyWwvfsljq+mamI203U7aMtHe2tuS22Jm5hkUHDxNh0+nJ6P8A 4VT8IdR1K58PeCfHQ1XV1naWKXXfLstO1SRMjybef7yyoWJ3t+7fsM8nN/tDxh8O9T1fwN480K41 GwuXJ1Hw89rJJbTj+C4guY/mSX+JLmIg5PIwcVrTUVZXBrm0aPMYtY8UEyaoljLY2chd/POXxhiC QDyTu4wBXrmgNJ430G38JeIzHY6zauR4f1ifb5Jd+WsbvgnyZWHyn/lm/oMUkHwxOp2q6z8KYdR+ yPEW/s3UBNDqEToNyxgMVS4VjnmPbx2rmrXwZ49u4Dqeuy6bZyiRVe1uLyyjZ4wCWZcSnayns5BN VUUWtOgvYzW60OnfxB4fttTXwOPCzaTqti8Vlq2t6nbi61KOY/fW1UAw2ydoyp3uuGB5rorT4ma5 pD3ejHTBPohKxQea0ckbxr8gNy5/es5JzgZxgela8Wk/8LB8JPpNzqMVx4r0+0W2je3vommuFAzF buyff8rGI5PvDoa8NfQtPtLaGW7murK7S48t1vpyBAV4lXb0Eg6EnoK4a9OFbSS1QnBw1PtW4bQN e1zw98SNbtpNP8Q20TWzy26qI9UVoWheC5ydu5R80c2fmXjmvGNXgh8EeEYPDiyWN3f6neS3ceiv d/Z2NghHkrvz88krZYLnpjitvw/4luPEGq6dfL4lsbiayhbTodKWxms7YvDF5jKrlmRh5a58/arZ 7c1X1bR/AFxND8QPGWoy3um3CC2FhZbYbs3oOUEly5YJGEOcp+FeXGE+dU6rujTl5qfMjzzwb4o0 zTPE/wBkk8HT2YvrZrHV7YXNxKl1p7ttmiCSBg0kZIdO+5eKXxB4Rm8KeJ7zRdI1y91KSximltbe a2yLmARF4pI5I+uU7EAjGOorlbj4r+F7XVzqttb31vZ2cyzWdu8v2m4XZxzdDaHBI44x6177q5s9 GXxcljYz3+v+CYRq+moLhllm0e/CTS887xAXB46JvUdK9RQnC1jNWaPAfDXwqTUdVjv9RuPtCvmV 4RatKqxuAUzKSfuenuK+nvAfhbVPDGlaOs8ELNp02pOY43iDC4n2+VGsIII3nY3yk984rxTw74tj l1Dwzc+RbzTmXyrOKCdhpn2m6BiO5FzIV+ZW2uPvjFaHxUuIILvVbfRpriWXSrsaNFdOiATXVgoS 4a33Z3Et/F2z7ccWYU6mIpul30BLlidB4h8N6R4GdW+Idh4hukuI0imNpYbbBXyML9pLkjGccgcf Q1hz6J4Z1qD+1tEkuNLRUaNLMyKkieW+3eNvBUIMjP48mpNP+NPxRtNLbSLSS+uI0s91wLyOO9hE f3X852XcN38CLk/QdM/w1DNYRX15rcO1JEaBLmL55ZQ2A0YtG4BCnnJHtUqj7OGu5m5a2NSSxkQy 6fO0g/5akg5O2PlmRj90cdOleB69oyWt1qOp6HrGYZsbbO7Es25mXEpdwvB3YKj8K9a1u/0Cz0oa NdSXD/aYHns5mLQurDjEm3LdD8yd+1cf4ctdTsL+5vfEV289lMubQFgRKZBs2tg9uvTINduCcop1 EOS6HjOmfELxHodjDpVgw+z2oKpvX5hkkkH05J47dKv/APC2PF/95P8AvmvqWw/Z0XxjZw+JYIbF U1Bd+1w24FflYHnqCCDVz/hk6T/nlYf+Pf416X1+iP5H/9TwGK4imla3tibm6PSKD94//fKZqxNo V9CVuNWuLbSFbHNzLmXHtFFl/wA6kl1y/MLW9tKLGCT5TFYotspXp8wjwT/31WCVGSfmBH8X8X4c H+lfKe6hHT6JHoZ1a0t7N7zUrkS7vNk22kCBep2As7jHqVr1xdXfU2imjvC8IlaJIYgIjIi9WGzB wP8AaNeJ2ynSrGe6MkUV1dRlQrkZWEcnH+0xp+m6kbWzjjtdy3EdndvvyBgzAbVB+nespO8otGlk fWWj6XbWSs1rGkZlO47FA/z71lfEiGFfD8GpXFqt0LSXynDHAEdwNhJ/4EBWz4RvE1LwzpWoLn99 bKGz/eUbTn8av+KdO/tbwrrOmY+eS0kKbeu+MeYv/jyCvp60PaUOUw+0fMXh4W+mQrF57srb5dzD EagNgZP0FdjbJcNosOpec32bDsqE/LKULZ479cdO1eTwanLcrZm3Yw25jIYcDv3/AP1V0OoajcWP hqGTUZJPNh8xIDIT91zkYx2wa+FxGFldGziR3Wrzaibw6lLGkMKhbYxceb/sDjrW/wDCNZZPEMus eSLexVTY72YBmllK7VYE9QR6V5Hqur284sIhAsZXypi4JXeD249fXFezf8JN4R0jSNOvLLTrCyeS cSyJHLIzMyfMJSvr2zXr4Ck4TTIZ9KIu4ZX5SQOcYxkV82fE/TX03xhqFyq7I9QijuoiDgM+NkgJ xxyK+l/Nt9qz+YixSBWDMwA+fleePavFvjxoU13pek6slzbWMlpI9q815JsjjSUblYbVLE7lwAtf QY2i6lKyM0eKaNfRWLzSuiXDMhyzjiP/AGe2a2vDN1qOvaxvFi10lsVaOaFQPs5HDO8h+XafRsY7 VzFlrWhabEbSNZfETS43Ncr9ntk9THH80r+xZlqlrN5qPiFFih1GS1s7fHl6fF5cEZIPZOFf8SK+ bjgH1Zpc+oPAerzWZ1TSLUxX8kcqTRpFJlIScgiRv4vUbc16elndal4dm0i6uT9ruLaSM3C43KW5 BXPpjHIr5W+EGt6nb/ESXRtQZTb6tZ7NhyrLJEoKlEcDHHXBIz0r7FgXYI5AB8mDx37e1fT4SK9l ymcz4csdNTV9bhntdS1dxa3AeTfH5MbRx/JLnrvBrpLbS7ay1DUntLW6t47eSNImDCSKVFP/AC1A Hy+nBzXq3iTw9o1hrK2kLZuGLTpajAMiueWXjJx6dK5zVhcyadPcSvJEjusJhaJYtxX+Lg8j8K8i dTlqcooQa1ZkXNpFOm9lGG56cDjoK4/U9DjwxRQpHNehWwWWIDqPY5xVSS1Y7lI4PSuxM60eMyRy Wsm1q1rXMY81Tya6HWtHJQsg+bt0rl7RmwY36qcfrTA2tW8W+JbDQLj/AIR5YHvYBuQ3EfmZXuFH FYvhP4maJ4xi+zaov9n6mnySxtwpbuVrTVhFjnGf04ryPx34FeeX/hIvDn+j30XzMkfBfH8QrSCi F2e46xoi6nYSabcv5kUmfInHOxuoNcjKJ/8AhHTY3nyXNm/lPnuAPlP4+1eMeH/jB4k0wGx1ALIy /KRIMZxxhhXdHx7f+JGhiuLa3tIhyfIBJfA7kihxaYro4G+uPs8VxMD8wGAPfoa6zS7K4aOK2iz5 6oPnHZh9cV55e3MUXiK0huV3wm7iZ4/VPMB4z6817pJqum5+x6VD5YP32b7xqnoJPU2beR0jSKV/ NlRcNJ61qWczBiD0rnYSRGWUdPX2q7ZXnmkg8YrGSLudYTnFN8T2/n6VFxwAQfqeMVXR9+09OlbV 2vnaQy5yVPFSho8R+HV6NO1C605jthMpGOuMHFfQK3FlpkTGIh2cbsjqT6E18zwiTSvFRxgLLJkE +/WvctuLX7UBkBeBV8ugI7WC8iuYBLbxr5jglieAMDHXt+FeY+EoxJc3zqOCZm/M9a6nRbmCWzcu dpT5iOmcc1zngNFuftkicYJBzjvzitYfCRLc7rwTJ9k+JXg66X/lnepk5HAkJUCv0ssl2Rt35Of8 K/NTRIgvifw9dxYHlXsHTt+9AH86/SyxJaSRe249K2oy0JqI66M7bX6is9YTM2zt2rTRMxgZwO2P pUqRpHwCN1bXMvkeDftCaLHdfBXxzC7DjSpZQMf88+f6V+W00xSWKVOf3URA9QYwf8iv06/aU16P RPhD4rjnJFzq1m+nWsfd3nGDt/3V59q/KxLsXNrZvGd26CMH6qoFYV3fQumzcmnjW5V4zncm78uK w/E2hXOtQCWylxIYWAQY+bcORk/Srkgy0TqMDG05rX0e43KU2/NDJgfzx+VYre5rJHz14W1E2V9D d42/ZtYiLA+kiGIj8TXtM0CRw3Np1COcfQ8ivGmssax4t0lPlKus8eOMeXIHB/I17LZzfbLK3vjg +dbKX/3kG0j9K0qvZmcDm5LTdp8brwVOM+2a5VJTFMVI+XdxXqGnxWxsgJl3552ivM9Yi+yaiEXh WbIHpSTNGz0jwzNph13TtM1u3iu9N1pHsZo5QDgSj5SpP3Tu7iuC+JfwY134fTT61ojtqXh5XJM8 WTJa/wB1ZgOcf7WMYp+tyyQWFncw8PbusoI7bSD29ga+87G60y6sbbUI7WPyr6COUh548MJVyQy8 8exFdVCK5TCR+XsOs3ohAeXcDyPp6/8A16ng1QyEZPJOM19h+OvgF4U8QtLqnhC4t/D2pS8m23mW zmY8fdAzGT6qCv8As18b69o2q+F9Sm0jXLZrG7tyVKydGxwGRjwy46Efp0q1TC5QtdZuItWa6SU5 U7QD0KjjbX0B4W1mLUv30YAeBNzL6cYGa+ZLKyvtRu1t9Ot5Lu5djtjgUyMfoEyf0r2K20bxz8OR p/inWdH8i2d/LKSurqR2SZVJ8vf/AAZHUfhWM6V0NSPd5bOS3srbS4+JZ8yzt6bucZ9ulPa6tNMt hEoAKAcevFeXXPxMvtRZpEtYrJpB8xVi5bHHVsfyrnZ9duZ8tM+AnJz6dK5lTZrdHpN34pd7u20/ TgBd3shRGPPlgDLP+A6e9duLf+x9MXSvDg/0+/3brhsF1BPzzSHufTPSvCPAglvtSvPEMwO23/0W 3HrnmU8+oG2vS4fHmo6W/wBntLaK8Q8KCpLKPqOT9KUlZjN+y8BWNivmJIJbliDJNL8zOcdQau/8 IxAhy4+6Tz60WHi3xLehGbT7e2B4Acc/gP8A61aOr37Qx4kwkuMnHT3xSuB4j4/8QNBGdK0wgFPm 47Mpzz7cV7z4g1u31T4RXGuos4nvNPijfIUjzQfLYAsRjkV5C2i6Xq1zJMrbZ24KnpnruFb+pXH2 TwFqXh5JDg3dvLGgAY4L/OOcd+a78LU5VY56kdTz5bhogAS7LuA/1kDdTgcEZHSoNZ1ywilxMxBC HB8pGxg98kfyrPls9QvSFCEplRjbEPunr3/z+VJN4Ze5YbV81djbySQvJ+n8q7lIdx0Wq2Fy/l28 0WZFcKVgG4FiOuDWjcaTr1rDcS2d9hPMO7cpDLzjoW2jjnrXMv4DaZ824SAZB3KWbtn2qZdK8W6J bEWd3HdRydYJehLDJwSSP0pBc6GZPGls5/4mFhcFIVKiaEbnUHAX5Sfzrz+98T6gkjR6hpwtZMkM VXPQ47n+lehWHiqC+NuLxJILpYgjqxWPJUdMheV/zz1qh4h0OPUEN3bRxMw5L8twRk0CPH9RvYrz o3G4HAXb29K1/DqebcTOoLBUCjHr+PFc1qlvFa3jwR9FHT3xnpXV+E08u3ml6biozj0+tUmSdMQj bUllwCDgGWNMjuMLmvVILtrXTLaJCwVGhxi4CcEjADMOa800yOKW48trjbtVj8sYPzBfoa9KS4iN rEblnhHmRsJFRX3Yxj5SvFHUqJzcsEP2uCZXCP1U/aSSp80n5fKAP61tlZpbaANP9oWVIsZWUncH zzIccY/vZqsuxnDGe6dVJJ2RqnQsTtKoOh961JDGi24j/wBYvl5MjSMSANuOvXtk8UwM+7HlyXpl SXztsW8y7ipG3jy2+UDt2FbNhDBDoXh0SSqJXs4iEWX3lYEgE+orndelRE1GRSjSSrGoYBgDhFO1 S5+9j8P0rrbnV9TtNO0dII4ltU023EbKAG2tDyTkdcnHJoLHxQ+Udqxzgw+TjMhH3TuPAO7p/s/n VS3t7+SHMRIhlYuMk8g/7Tn/ANl/xrKW9jSSOdbSVGR4yzRv8p2pt/vdjVm01aSK3SOJGVcYCyHc w56dx1oAsTRPC3+k3ce6MjEYYjopYc7AKksbCxnmlRNqs5TlJA56DnkZqeG6gn3MZLpW+XcACBja eny0+1lLXg8qaS4b5cpIAMAAewqbgeJeLIJbeZkkDTbGlZWaFR1cKOU/Stu3uEhvoo2fgRHcCQhH StDxVoX225le3e1aUzDIfIK7G3fwk1kWdrqhQXF5Gq3DAtvEmFGW4X8qFsZu50ssnk2GqT+aoRgF DNK/GSARnAxXQf2l5BiR5AqrHkE3BjHBRcgEDNYlvct/Yd8skr+c8igCOZFJy2Orcdu1Xbv96G+z iXBhaL5BAc/OCf8AWfzFIq5qzx27syvKpdHZQGu8d3jAxj2qpfWr2sNo6yKRtOXM5H/LMkdh6VYt ZLpLkxA3BXzCVCvABt84MOvTgGteQSy2NtE6ytOhKlTJHuC/MA2fxoC57R+zHcRy614mh3B2axtW +VjJjEjd8D+9XafH7QILnRPEIkHmC80qRTn+9Dl0OB3UivPf2dTNH491WykjkBu9GLqWKt/qJYQf mT/e9K+ifiRHEdEkuLsb0KNE4x/eHT9K4avxFWPyP8P6fFrEETXc5RHik2RK5XLI3zcLz712MPhW wjjkW23Di5K4Z8YTaw9AKwtCKLb2zRnCRTuANwX7/O3PBxx0FdWLmFZBHKxZp+ChbP8ArlK8L/wH FdsdiLF//hH4o3m83yd2+VArZOMw5PV/WtaTQwtlay+YjuWgIPlK3J28Dd9fU/0rJh1eNctHCUzy CYxHjzE2k5Y+1b6anObaO6gZHeTy0b5ui5/2c56UyrFZvDqRx8+aCEcqFVV+5MCPur6e9aTafbWC zEh2f/SUHnLKx28cjBHArQhubv7KxdQgCT/L5M2MK4HBFRXN9mS4cyRZWWY7d8iE7QOFDjjg/Shl XPSfh/befc6p5hDCNYhkZIIPpXRePJotL8KTz3E62olngjDtwA2c9v096zPhnIg1LVNNIxJPbx3C Hk/KjYOfzH9Kq/Gy6S10XR7eS2Ny0t60+1UEhXyhjOPT8K8zG/w3cxqyseWw3l1qMr3RlLoo/dyS 5jMi46Bzg7sduBTtHheMw/ZlRQU3RySK4ckZ+UFAc/7tR6T9q1O6Bf7OUsRvaI5EgJH8Cfdz7ZzW p4QGuah4mg07wferb3MQzdi4l8mJhnLR/dOW9l9Mba+PlG2iOZSOqvrCxsfDAuY4DcXMyqVmRsSH dxIsT8HGPSuAu9V0GxmuHuNMMc7fuGmTAMirxzH2OAMkmrfjHVINcvp7y5vGGkqRCAiOnzjKsRCu Bu3D2+lc/qCNZ2EM01tPcIzOIyqHcQFzk7u2OtKgrfEWmdhfW+g6fpUVzNqEUKzv+4W4mGCCoO1c fd+ucUui/F3xd4WuDZ6HeXUtqSABKN0Azz99ScD3zXCeJrKxs7KwvrEi61W8ZVSC4xsCGMYwh4Az WzdWfiWPSpLjUzbz3X2eNY4TF5UiDbsYBk4OMZXGeMV30qzhrcqDPojQf2lvFHh4favEUUVxaTyJ EsqNyWZN2xeg4H+e1fUPgr42eCPiBbp5eqWqyOMGL5IZh7ENjP4V+YPijT5ItD0LS8vmaS51CTzA AVMrCJVwP7vlHFc1ZaJL5iyRhkIwAVJQj3ytfSUJtxuzbmP2S1DSG+RtNuDe+ZyEk+UoPxwK5ma1 vLOVhcWcqEcrnJUnHSvgXwl418feC7aS7sfEM8dlaxmSSG7BuIti9cqTu5/2W/Cvorw1+0/qiWUN 7rulutg+Q89i32iIBTjc8XyyIfwNaKqluXGR7fZLqHntKLBpF6kZIA+oq608cbF5ogg6bS2dvsB1 /SofDfxb+H/ja2EsGpxXAX5iiYjdSOPmX5T+YrtY7DQ9UfZYTAlvvhsfJmri7gcHcGHKzRlYyORv xu/DIODUd54N0TxnYmx8S6ZZaxA+cRXVukrdMgq2QVPuDXUahFYaMxMIxPHwQ+Wz7jnFc1eeJZLn KtenzOuEjPygewH9abt2EjwHxV+yP4S1VzdaFe6n4WlAH7tJUvLYN0yIpm3jGOgcV5Fr3wd8W/C2 G41rXH0+60i6Jt4bm0Z1lMzgBd8cgOM9wCRX2S+oRyIS9xv2/wB6Ed/96vBfj1rL3Vl4f0wPGYxL NclNgXJQBVLGlRoQdROxjWpq1z5UjbMMkJH+qbaR0wfpWHqkDyQlVTcQOnGB+FadxOttqRaMForn LZPqODT2WOXlRwehr6hu5xXPLBePpdzlxtU89MV1Au4NUgzEwzjBHp+Fal/pEN3C6uuQeMdO3viv OrvRdS0iXzrVmaPrgY7ewPpWTQx1/b3dsSpgLAd+MVyGrEvJbApsZd4I+vAr0CHULjU7Z4o5Nt1w BuBx+P8AjXnN7539qSpO4domwSP6YrnrlQOmsEYKvHGOPpXX6fy/5Vy1mMIg9q6fT/lcNWL0Emdz athRW5EfTvwK561bcoNbkLcj04/lUlo9x8Ij7T4Z09WG4C7urXHp5kWTXzjHlGmjPVJHX/x4ivoT wHJ/xIVQniPWoMewlXb2+leCXK+Rql9ERjbcyKR6Yaqo72Blfwq+PG82Wwp0yc/ltNdVqhb7dIe2 yLAPutcj4T2P472MNytp9yMAc4wBXaeIQkWsNEnTyo/0FehTWhmZ0R+Xc3+fSn8mNm4yOnSooVYr waldSBjjB61SAqOOoPfH8qYVGOKV8g89O2KAwzgHpTAxtZbbptx7bMf99Cu5uYx/ZN4ynhjbOf1F cTriA6ZOR2Ck/nXWLOJNKv8ACEFRbqT29v0oAwVyO2KkVSwyBk0uO44FSIMY9DQBbggZBu4Gfx/l U9yOAvcVZt1xGznpiqdzz81K4HO6xIILZlB+8pH5ivCL1/7N1Szm43LKkh9wte3a1uaMjqMeleB+ I5N+rsqkYjUKCOegzj865MVZxsi4HS+JNFXQNcmkWRGtroNd24U/MFdsYI7c8fStDSdQ0ORgupEi XOAM/Kcenb8650XdtrmozXOs3JSZyNj4/hUbQoHsB2resvC+g37FIdRWY4yckDFRh7qA5nodr/YU gUQSR8/7Qrfhso3z5RUntXlreBFgw9rK24crg9cVt6Rd39hKIppGbGBgiu1Mzsdy0b27bCo2nuBW jas25R95fSm21/FcAK/THcVeEcasrxt35GK0JO0vrZ7/AEkWqplpIPLycheuf8mvENVmh8Irdb9K jkup9vkSPNGdjMTlkXJc/QjFe9WBgu4BDInmeajoRzzgZ7V5rrPhXQZWN9caZJMlvGBi1yzSgnG5 xuQnZjHWvkc+aVSN+oWPJJLPw/4jtzcXF9q0mqyx7gZEQQmQH7nQ4/pXqnwu8fy/Cmx1VNK8Dxar qd4MrfXrtOI0QA7VjTaEGfmyOayNEMkkN3J4P8QQJYK+17fVLcsbeR+ojJznj8q01tGSyuZbm8fU oYVA+zMWhDbj0VYl3fpXzlarHl9m9hKRbi+M/jq68QXOq2w83WNct2heG8Hm27RZysawsP3ag/xV 574gbUNf1a6vtbYatrd06efZ6WqBVVRsVGkIIiC4x8qE/SurvZor3QJ9Ku7e4j0/O5/L+SVPLwV3 swy208AEdMVyjazZ390bizvlsprEr55MQt7iSPcu7of6j6UqMaa+CJoqjNzT01iHT47Tw2z+Hrlt u6KOWRZoTjgtJks5GOMduw7WLjxn4w8N3Fl/bvjDUde0iOULcWTSZ8yUqQ3lJIWG8Z4Yj1rmpPiR 9s1iKC30+D7LI2xCrkzEt0zyVzn+f1q7baTpi2ouZdFf+0Iz8qq+LhmXlfmX5V59ulbWs/eD2kke 7vr+p/2yGspoLdtL+z3CzpDFHLJABv2uYyD0OMN82eowK7Lwp8TluPGo8Px6m1zp2ttJbK8qxsS0 pKxRzTRD5yM4x/DzzXyJ4VuvF2oateaW2pQ6c8EB+S+iAlnB+TYucMc5JznP6Cul0rQNcUQz3Qtd IWyljY3Ngr+dcKhIKOWySMgdBWNTDpbs2p17NHa63Nq0Ws3JGg6Vb21jM1tDYC0hS4Tyf3boJ3/1 e11+7jgDrUtn49nmeaxHim+84REJZNftJGqjqiyRHgD+7kitf4j6NF4mks/iXbXkenx+KrcRaxGx xbrqdgFjlJG0hPOj8uUbuvNeVXOr6bopktpNXs7i52FT5JDfIf4N0Y2L+BpSpKWwqtSSeh0cmr2A lMurXixNbv59hNHdM9zFLn+GcneqeowR6c5NbWrT+EPEcSS+MfL0/U5wynXrSKJZ2DDC+fAf3VyB 3OVk9Cetecyw+Cb2D+0fsjXNqp2SHz2kCsPm5yAcAGsy38UWGmiew8P3RvbMncYbrB2c/NsBB4A/ z2qPZNaoKeJnFnbeJPBfif4e6fo2q6rNJf6Rcyl7HWdL+e1lPHlq5VQYJGPPlvjn1HNdlcWo+LMC XlgfsfxCs4B+7kjULrkEY/unhbxEHp+87fNzUGn+PptFtI7PwsZpLu+jGQtsXt7iPG5op4G3J5XY kp/9ba0jQtE8X7rwXMngvW4JUdYrV/7R08vgugi5S4tX4zhXbGPlQ9KwnU+09DtjOFT3Udr8Hrr4 dXZ1nwiNOv8AQG1JGupr+8uY7pbMRwm2leKeRY5IXBfO1ye6ntXkWqeD9e8AeDtWsPG9jFqdna36 okcUqyC704Ewm7hKn5GicCRCeTu+bivoKHQJdSbWrbxTpCW3jHxRo01ppt/FtOn68FKOWDICPtBR dsi8O3GVFeb6RdTa7YHw3qk80enW0ElmlpciJI4E2dF2ASbVO0EMx7dqxpVVfmRFaLpLlPNfCXga 58S+NdN0KW7ZvDc1u2qHUYtjQy6XCwWTahHyzlisLp/CxJ7ivcPit42utN+L2m+INPubf7F4Ys4N PXT3UFZoWQi6tWwMlCHZee/0rz/4faj/AMIEslm5tg19cSQPIkfSZG3AbSAMq20tzsJxmvMNc07w Rrmq3b3es+KtO1tZWe9GoQQygTM24lEjaIhCc468V1e2VWV76I59nzI9d07wHZ+Hfi9pMOm3Uknh a3j/AOEs0+NgBbNptnE10it3DRSIsb/7Q968Oa5Ov6y8ctyLOTUjLdzzXjZhi887nFvFFlmkcsp6 jnrxX038Ihca34I8T+BtLeDxNq2maNcW2lXX/HrJBFfSJ51o5lI2pLsJGCVBzkjNec3nwG+IcOoy /afCeqSSCRdttHbpcWzIDwhkgZwV465H4UqeJhze+y60bxuirofiuK82+HfszX9zEhgt7go9uZhA pVpG6rxtwBk8n2rprrTNL0+JklYWULb5pp2lKgHPLFuSCT39fyr0P/hW+ptp2haKfJ8M6vbCaC2R 7a4aFrBmeaNZWdEQSRSOy7N5LRt7AVyXibSNUtLhrTX/AA1oGp6XdRx29zPHqbQb/JAG9JGEUsJT AJXay9smuCpJTk+XYj2TPR/iDpK/En4ffDfxX4es4NRuUJ0md1IV8lMQjfxtLNCVyR1YetfJF1pw 0bUm1a4vptKh1H/TEsprTLSQngGMSbWkwf4lr6o+H2u+FvC2heI/Cvhya5m0m4HmywrK17bwTIwd popceYA2PulQD1yKz9QufiN59xrnh6W30fw6bZL2e41jyGtCHDPIFhk3+dIUA2woUx0ry8qx044h 4dr3elzavBWVj5rutW8QanO+oad48ns7W4w6QQ2k7pHkcqGCkHBznBxnpVf7R4t/6KLef+AM/wD8 TXvKRfB7UlF9fXfh6a5nG6R08N3lurN0yIoL9I1+iqPUjOad/ZvwT/57aD/4ItR/+WNfQ3/ufgce p//V+ZGumBBxx29aVZ2dTnkA5HbpUEuFOCvPT8qWJevUD0r4647kFyxkcFzuIHBPOPbmq28qBgkN 0GM9+1W5Ux+NU5B8o6Dnr06VpFbCPr34Kak2o+DJbWaTfLpl00XTHyMu8fzr2i3YKwZxuXOGH+yO o/XFfLH7P+oNHrGsaQzgrdW63CqectC3P6EV9UxqNvy/NxyB36cYr6bDT/d3M5I+BvFltNoPiHWf DxIig0y9niVSMblLb0P/AHyRist9f1ExG2uWMiFVjw2eFHGPfivbfjV4P1GXxqNZtFgt7W/sYjPd XMqwxLLDmIh2JBPy7eFBY15I1v4S0zCs8viWb+7EWsrL15OPOcfgteHiaNptF82hx6JfazdwWNhb S314iJGkFtEWkKr/AHlQHA9zxWxLoNrpcAXxXq6WjRLj7BYhby7I/unafKiP++zf7tWdU8R63d23 9m27xaVpZ+U2Wmxi1hIP9/y8NJ9WY/0rlZbYRgbFwoHQYGfbinTnFBY+2PCdxP4v8JaJdefJHYLG sSwfKHzbHaDK46nj0HtxWz8UdJbxB8OvENi675Ybf7bEoAX95bnzB69gRXnH7PepLc+FtT0oY/4l t6rKBx8lwmeB/vLX0NHGkp8qbHlzAxtnn5ZBsIr3YS5qZDR+YttcNcYcHGRuQjtnn/8AVWJqTXuQ qrNcxM+SgAZTgc5Xg11V7pT6Hqmo6I/EumXU9qc4yRG5C/muDXOXs0Uf7r7Owlc84IUn33AFq8lf E7ibPW/g3Y+LrrxDpuqxJJbaLpVws8sl4GK5z/qoC6luc8gcDvX6GQ4MZxznkf55/wA/hXwz8MYf GniEC4s9Sk0/TdIXyhKn7wo3BZI1ICnd/Ex6V9q6dfW82mRagJV+zmMEuORwcdRXRgqzc3FkpnO+ I9OhGpJqkxQq0BRWkBPlkHqB6fjXlXim6ikhihSc3bxyfNMzYHThVUdFr1vxvaxz6TBcGNrhYJsl ImxuVxt5JwP1rw3Ubyec28clultDbEgIBg8DHzE9ec+tcGIof7SmXF62JLGQBAAeT1B/xrQLA+1Z UZjVQy8Z65wP/rVTu9Vht1OXUEe4rtd2dSRuS20U6FTycYrzXU9Il029E5GLZhyxxgGmal41itwV hl+cdxXnOq+LrjUiEvLrEK9EJAX8atRYjqJryORsK3Q+9W7e6LLtJxxgev0rzdPEmixYE9/Cn1Yc Yratdb0uY5tr2OT/AHSKqwzF8a+BbLWYhf2arBdrwWXofdq8W0/ULrw9qj6bfNnY20kEHHHtmvqK O7gmQ/OCBxx1Oef0r5l8e+HrnStVe7OZbW8Y+XJ3Df3T/St6WujM5Ikubq31LXbKWNj5izLkey17 GmBsmU4f+KvBPDUDnVLeRx8wO7Ht0r3VGBReQW+tFZW0CJ2UcgMOSRnFR2M6mUp0IqGMK1iZlG4x DoO9OtXs7tRJCdsg5x3rnaLOygfIA6Yrprb97YSr/dGTXGQSEEE8f/qxXXaSxaN0bgMMfhWZSPHP F+mSQtHqUQxsOc8V32kX32vRFlyPlTBH0rTgtbbU1udOu0BDkgH29q5nT9MudHlm0J45JZDl7UoM 71PVGHqOwrS+iGi9o9yqW98yupAjc4b/AHe1Z/wnu/PstTiQYZiFGe3HNU59OFstxJEslyI43UyO GXZx0Kjnj3rA+FmojTXvN+cSThTxuHI/2elb017jMpP3j38K9ktneQuizWYWRcjI3R4YcfUV6Np/ x6+I84XGpwxb8ArHbRpj+decPe28hjsFjM9zP+7jRByzNwAtUJ9C1jw9cGx1uwutPvF/5d7mMxu3 PBU/xD3Gaypt9Dlxs2n7p9Aw/Fr4gzr+81t0B9EQf+y026+JPjkoSfEFyN2ANpxz/wABx/KuOvvh /wCOfCmixa9rdnDaafdyRxptuEkfdJypKqDjp/e/Kt/Tvh74k1rwfdeM7aayj061SZmQmTzysIyx Xgrk/wCfSrXOee51NkeVeN9b17xdHNPr+p3F9DZxP5CysWCEjDsv+0eBz2r5f0R/MhWInBywAHTg 8gYr7e1/4ba1pXgy08Yyz2j6dfhIxDEZDInmg4L5XBK45xXxNpcPlPImMNb3DoR6YbpTV7anVg78 /vHVsBgM+VKMFx19jWfp87W/iCOHOYpfmPbBwf8AGtGdwrOe3mdO2K5y6uGs/EenFl/c3LeX9H7G oPTZyF/EsHxYvIMfLexEAdPvRcfyrs9HRrSC908/8uk7FB6pIM4rkvFp+zfE7SbrHEiwDd7ZKf1r 0i+sDaapqEgGUuIlbH+0vy/0zWlTZERWpDbfubBZPReK898QqXurdyPvf4101lfTPp05lORF90e1 c3rZLi1c9SRg1mmVIl1aPdpyJ6jaf+BDHP4V9Z/B+9tNX+HOjm4uokubHfaTKYmbb5ZOzJyO1fK1 +oNuiegr339mvUbNbbxJoN5GkjRTQXyBtoO1h5bY3EelddKdmZTR7Y1ppCEb7tcc8/Z12kY6fOwq td6b4e1GJYdRkhvI4xhFlt7d9v0JYsPp0rtFuNCgxttU34A4aE9B6+aP5VWlvdMOdunws3qZ4gP/ AB0mu2xkcvaw6FpUBhsM2sPI2W6xRDn2U/nzWPe/8I3qEE9jqMEt3a3amOaOZgySLjkEc/z/ACxX XS3FqRzpFoy+pmBz/wCQz/OsyR7WbiPR7ND6/M3/ALTFCQM+BfiD4OHgTxCLe1kaXR9QBlsZWO/a Af3kMjHqU9e45rzPUL2Z447G2GZZiqjHr0GK/RHx14QtvG/hO/8ADr2UVtcMpls5kjf91cL9xs7Q MN91/wDZr8+fDtnNb+JhJqVv/wAgZ2eaFuvmRnaq/wDfX8qxqRsrlRZ7xoejf2XpNppUIyLePBK/ 3zy5/PNdTp8MVnITbxJuAC5xnNUtMu7PUAVsTsV+qDk/Suv0+zjhdWYDIHArzJa7m8ehs2NoGkil lHzcY9jiuY8UI11eizifB6uR2xXbxZGBjgcgVRuoYgTMEw5OS2PQdKRTR4nPpN1bzCa3n8vZyT7f Ssm91lYZbZ71v3fmqGO7aNpGM/gfSux8RM0bPt7ivFNba4/tW3MIZvkYkDjJHI+9xXZh9WZSPW7Z 0nija0Ysu1QDGHx8zkZLHAqxq0LQSp5MLEtDIWKKpLben3mFea219LCqPGyyBexZp2GOV+VcIPzr uX8RwXYijlWOR1ilUqpBPI+X5QD3969JImwEagJWjSOVFDHJEcI42Aepp0VruSR2d2IjUBmCLg7M dUzitFba4uGMkViIyTnDbF4aMdmxVm20q8iVl2WcayhclicDauDwgzTsFjk7jQ7G6tNNmlT5ymDK zng9O4FUbq003RmO+6YHMf7sbMscdPmwTWrdxatNBbraTyHY3lqIsgkjjGGzx7muI1HTPEaMzS2j zKc8hfMx36daklo8+16RJW3TIY2foQgVjn1xxW3o6eRZeWw+7zkjFYet6bfLfwTXkDQK7qkUbrtB A9M+vWuiSSbJVhsjGMDgH275/SnHzIOv8OF3mkMSs+7aAUHTqSeWFdvsYJEI7aXzfl83cR0wd2Bv rz/SNQv7U+ZBME28j54v7rDHIrunv7qKCI/2j+8lXHzMiABY8/3aotaCJFqTyb1hcMAQTiMchcYP 3u5qe6eaNVM1q6EyoSFchnO3gOMYIyO3aspbuYyBRqeEZ8viVud0OcfKvpU6JO0dvKbyaQy+Xkq5 KBymwnLAdscUAUdeElxpt6zW28o0ZYiZpPL+QA7flAHTgZrr4rC91XSNKSJU+XTbdMYYNgAbScH2 9K4bxHGYbfUIzJN5duIGALKTu8vvs46etdK8Kx29rI0MqMNOjO5YN4PyRgc5Hcnt/SgdytPpWs24 ZJIzwDlgznPOB1FSWqQxBAxyAATnnLZOa0LxbcpMiFkx52w+TKm0Idw6Z681TnH2e6iYnasihtwI GCwoC5deS5SYLsk2EKMjYe2G75447VLHNc2ku+6guCDCcNEMc5yV4bA+tQ/bbgMJf7QR1VXcf6RG eY1CgYZe9TRyag8jIk48vG3eUjk4AHPHrUBcllK3SN5sXyMc8xuSBjPOB/WsD+zLYymCAR2xPYuF zk52gE9c+1dANR1l5nTepCSSKIzFEQAIw3ADD69K0hPdzSA3GnruQA5GVzgA543LTEeblZVEcHKr JIgI3IX2lztO7/61WJopJg32osz5IDROSoAbavOR1PHT9MVuPY28cavc2JKxToXk2RM2MkBRjGB7 /wD6qkZNOtG2zaYqOqxsVLxEgGXb2UikBiLZbgZVM6BwSCzEqNzkHPzeo/Ktzf5VswmkbyxLyFYm UMrMCDjPy5Bpsuo6TapiTTowcMu3fGM4fbnlPxrDlurGYfbW0xEjl3lHZoQH2KT6fWgD3z9nW5EH xh0yNSxjvtL1GL593JCwyAYI/wBk19Y/GXTXX4a+JbmElXtrX7Qo94zk4x7V8EfC3xDpfh/4xeB9 RkiS1hlvZLJnJjKqt3G8IyVGepWv0K1nxbY6cWeadPJjwZlBGGVT82QT07Vw4hWlctWPx30lGbTG 2bgyHzD8oXD/AN358e/PtXVfZfMKYkYB0wDu2rw4KjIB7ZroPjhqFzb/ABQ8T30LBrLxFKby0kjj KKYgVREUtwfljH3fWuR0jVXv76NoIY5Is5YuSyquO4H+FdkNiWbtrp9mk0cYhggZ3UksQc73aXvn stdCbiwttKtGJX955UxQo7EbzuwEjwcZPYCoDdXKxH95HBDhRlATwByMHGcjvkYq8Ybq0to2ET3V w6+Y7QFmRdoyAW+boMcAY7VRRbWe3a1kkSRYQVus7kuYtoZx69qbNdWkb3WZt4HmgLFd/LncBzuB JzWQINb1Bit9ezY5URRoFUZO7HAxjnkCom8L6wZBH5ieU7oHknBRY++d2B1pvYdj3H4J6DrH9veL vEetgrv+z2doqf6sRsd5I9PuDNZvxrMd54r0LSnFyILCxaSZrdWfHnvwHC5xnb3q78FNQ0iy8S33 hzS7qa7a4sWuLiZtzK0kBGdpbHAUnoKzviTqEc/xF1a0j33VzbLbQxx28jL5aBfnMh2sp6/KuCe/ SvGzWXLA5q5ydjcWGiaUtqEilaCGSFmjUlzltw3JuGW6c54qoyyeCr250ySK1hg1W0iuXiVWZw5j bbLHI24hxkA7e9aOq+E57nUopp79rXSIFad57l/LjgWPoJRjezntgn8OANy91VPEGgaXrcqi/t7I tpj3KwlJWslfzDtVucSH5Q4z+HSvlpT5Wc55tpd7JJ9jWJW8+3VrqOduV4Pzbxu2nb3rq73Ur7XL YRTH7PI8sazyJI5Ro5R5Zbyifl2D5jjmsy0jW8vHsLD/AEKKSJrN4pAAkXmDhB33YX9571j+HWgm 86K2ldCYGKGXlyY37g4ySB0PA/Sk0nqgOjGgfYNRa+t7iPUYUkjETRRkMyt1ky/3CDk4GQV6elcX c6tPqFxqpONXMJLKo2x7lJ2/x4YAcN0rtfDafa5pVkhia/tE/wBGUySK8au7LskB+STPJKgfl0rN 0fSLb/hJU0k2Spc3avbiR1BjdZWCbdoxsIxxxW1JptIadjor3QJI4tIsZzvew0uzt3bg5cx+bI2f 99zVux0SMHLKAeD/AJxXo2oWqXGo3c6qQrSuFB/hUHCgfQCiHTsYwAT9K+thsjbmZyeq6ZGuhagJ B5cRgIZj/CmeTWBpkH/CH6KTLdTKbtgHe5yBnJAICA4THy9Oetej63FCdMlsGKiS9VkXKnB2/Nhs c7eleealC+sLKt1DJNNGW8kzsTEsgIRSCjfdVgCOOg6V5eKqXnY3pvQxdet/D0el3N5p9xaW15bT 7XkjOPKaUgsYvu9Sw6+uR6V3Wj/Ezxj8OBN9q1NdbtrGygvTDKN8pSQBcLJ/Lcp4x2waxLnw54fu fC1xFcWr3KRSDz4wC6mdtoadv4nCnlfYV0J8K2emW+q65PJ9p+2aYbdFVS6t9njVIlRMcE/e2nuC BzXL9e5FuJzSPf8Awz+0Fo+tQWx8R28uhpc/6l7iIqhB/wBs8fr+AFem2E3g3WpUFrrbyGYllaIq UI6/KO4+lfnfeaJfanlrW2ktvtG0wJeExswBHT5vlIXkA9eh9K0Lux8QaB4kceGIbxAsaH7RZ4gj ORtBYN+7YnHIwa2pZqtpGKxCW5+jcmg6FChUt54IJUjIJ/Cvi341Sza78QpdMsYtsGk28Nqq8cHG 9yTx1yK0fC/xW8ceH7o6ZrVr/aBfhUkxb3DqPvbCTsbGOx/CuA8deKzfX2taxp6m3m1ucyoJBh04 xt/DpX0GV1qdWejHXrJw0PO/EmlfYmhJlQpbfekzjc2OgrAttWjkIjiwSO4FcldJreqTtFEZb/y+ ZFhBbB98DArd0rSLq2H+lhYzwQuQWxjp8vSvoed3OOxp3RvJQGtpvKYn0yMY7elcpc2niRS72+qM w5Ox0BH4YruRChUkDJHcd6hdBuBHAx3py1C549eXXiOCYG5AKj7xiUD5c+tc4beSK8kVwQXO8Z/u nmvUddur+zhaVrdPLAJD9Tx2IHSvL/tk2o3Rup/v8AY6AYxXJVjqjSD0Otsui9+K6azz27VzFl/C cfhXS2pIcNjHt61lIk6+ybCDPWt6FhkDFc5Z8Dn61uxHpUFo9l+Hr7tJ1xcf8e89lOPY5ZePzryT xVAIPFOtxrwFu5CB7E5FemfDeViviCBfvPZxyAevlyf4muG8cjyvF+q4Gd7q5PT7wBFVR+IJbHLe AdPur/4k6ZYWjQpc38U9rAJiQskpAYRj/bfG1e3NdTr6zprt/aXsJgubCVrWaNiCUkhyrrlSQdp4 4rjLO9v9E8R6P4m02FJbvRr6C+jjc7UkNu28IWHI3dMiuv1nWG8R6/qniJ7NdPbV7yW9a2RzKIml OSoY4zzntXoLQzGxJ6dB2qOZ8EqMHFTKwwzHn9Kz5Hyx9PSrQEbNuJHf0pNuBkc1Kqr/AHQBSsVK jAwRTAxNXQy6bON2CQq49i1dYpxoN7Ef4ZbYfXhq5PVy32U8/fdB+ors7qEw6JdvxtkuLcKfopP8 qAMIc5B7cVZiQEg9hVcAueuDnoK1rGL5yW52jtQBeKhYwq/j2rLuThggHQc1rkFRvI3AdOlYE5Zy U3YJNSgOb1ycWthPcsMts8uNfVnOBXzxqMbm/dFG90Cj6naM17ZrtyL24SJfkhtxwSeDzz+XrXkL 6ff6leTXcKCFJJWbzZcRoAT/AHmIz+ArlxOppAbptnY+fHHfebcyyNn7NCQvH+0/YfSvS7Tw74ZB AmsDGx/u3L5Hpgjr+VcxoGi/Zrn99q1tAHOGkt8yyFT/AArkBR+derf2PHZKtzaaSt0vZ3lZj06l Tirox92zFUfY2NNtYDGkVlM+1OAsu5sY9zk/nVm70xj++FtHKB12dv8AP0rAj8RfZ28iSA2bDsqY A/z9a6PT9XW5x8wdexHGPw/+tXUmjAyvJ+QhV49Aclfbitiz89lYR8lRn1xgY7VPcR6ZeNtuQyMO RLEdrD6AcH8aSytLywRriCUXcWcHbw+31K02yjp9AvJFljhn2hi2QQce2KZdWA1HWJ73SL1xZaaW jntriBhcpKRncpVgkid+vAqPTms72VVi/czg4AI/lWpdw2Uep363k5sRc7Q5hbLzbAP4O+F7/hXx /FMHyxmgKGq6tqvh6SGx0vUWkSZUZJbePFuuW+YtGQcN+Jqj4n1e6R7m/Sby5rgBhNJj5SABn5Qf 5VFp2t6w9/GdStrv+yNzRqZLdl+Qfcd8L938P8azdSshe63LH4Y1e0H2LEqrFdCGd3Gd8fnEMgH9 0GvjIRd9SWaml3VldGPUZdRhnR1VZGMjybWz82YtvHPrWinw5Pi/V4bryLe7jg3Ov2iLBZjhtpdc cAdjniuVvfDUn2GDVvtP2W/mw0kktyjFGI6Syx4BNdF4O8LX89/b6jLrdpbmbfHBPNFL5TEghlZx KRyAef5U51HDVMI6mff+FbXSbtYNZ8OwRRHa0U0Rh2O6dAjIo246/MF/Gs17nww97bxtewR3xVi8 doPPedSDlTjA4x1HNaGqW2r6frF1pjXTahptlAsssWkwFtm9wuSjk71A6sDmr0Hh/QobT+29KWW4 +xOqfaIUBliBUkttcKeM8gZqo1rrmkypHHp4ri1G0u7ey0S0vbOyRfKjuswfKPTIZi2fQis7S9a1 BNO822voJZdnmtZywsxUdCiJJgtxxlSa9M1bw3eX0MN1pWvi+05OJlKR/vFOOC4Xdx3TGaoT3trb 3Vtb3c1na3BDeQLjywIEXuDxtHtWvtoszRofDvULT4neGfEvgKa2t9P/ALTEcljbyyCRTqUB/dEE /wCrEiExNuBGcc8V5s+m+GJ7QtZ6Hb2RR9l1DOzLLBziRWyPvRkYrqdNv1h1VNQ8M2a31/buk322 3YJGmGwD28w9eMccV13xK0fw/wCIYbTxPPG8dj4keWSYKWXy7+D/AF8UwQH5/wCP0ZTurdT7HbKS lBdzxW5TwxaK7T3Z0+2vVceXEThgRjB4x+NV7K58PTyW2mWEW5UCxTebGEjQ+hkOOf51vSfDvwfe vCkMF3GCfmuLR0KxA/8APRZRn8AM59q7G5srSLTre3E6xRoY0MlzAWErAdcxpywA/A+1OpOK2OO6 K1rqEmnvImh3lzI0f7maKKCaBtgHCiRuNv4CsxYry4VrzRIRaX7yYeSKZ45CcYC/u+PlHc8/ga6J riO3tQsWqwmBmLRzpJHNt9nVTnNYl9ca41za3emxCWz+7IhUObkseg5Gzp9fwrm+LcadtUe/6ZrF 98QtIg0rSrRY9eRI5dQ8NlzBBdSQ4VdR027VT9ivk4Jz+7lGc8itPxF4Zl8cx6pBpOqQXfi7S7d7 XVbIPEJZWUBhMwj+WK4TADOmYpOvyVBq2qt8NfB9lotjd29nresqJp5NypOsRGZJVPJWQn5UO04x XG2/ijUdVs5XkmksrzSJobi01y0jC3aSY2Ks3l7ZbqPsykZGdw/u1z1Yxij0aOMi17OocB8ULbxB /ZXh210W/uLO+ttLmutQi2Z+Z5TGyRAd41GW5LfN7V5rPp3iXx/4S0GWOYy6xoTyaVM0zlZHt5WE to2ed53F4/mr6t1/Qbn4jWcd1Y6zbaT400NPtM6/etbsH/loqJtPlscs/l4KNndF6afhj4br4fns /iDf2kL6SssU7aXK6XEct2zcC3kiY5ETfOi/gOhreWYKFNX0NpYZ622PmzwJb2/w08TW97rEd9Bq VvLGt/BeDaBbSfJNtXaPvRuxBwexFd94s8WeIND8Qat4NtNMspU0Ob7NHIHnikZBjy3BV8sGTawb AyDmuK+IHjDxP4r8T6z461HTpbe1v5GYu8JfZHH8kKtuwnyqAMZra+IF4LXwd4T8fyQm7uri2GjS 5wrrNa/6iWReqNNbOrAHsuazrQVWqpNaHBFPlscK3xOvtTkOlLeXNreuxiktoGnZiVODGJJZX/kB 71i6x4avbyS2OqQS6amCRJcSI8q7uCyhGy+ABx/OtDS5dC1Hz7lJFt7ne0p+0EJIzPkkqxwWXOel bCafHcaebrSbeCS5dXiV1ZQpYfeO8l8V1whGnL3ImTqMxNE0a4toWRNffS5ZiYoDaXD7rtE53LCG Vt3qu4c8VzQ8R+OtLulsvD3iXVtWtmYGIQi4UGT+NTBICNw5B5OK7lp7Gzt7LR7vQLaIOuZZiI3m kbPIDgBl575HFYt5fPpVmq+HgbVpbhppbFiX3qBhs9wHGOQa1ioX1iP2jIJfiRrRkb7dqUn2gHEn nWkXmBhxhsw5zTP+Fj3/AP0Eh/4CQ/8AxmsaWCS7kNybKNPN+ba2/Iz26flUf2B/+fWL8pP8K39l HuHMf//W+W5cvtbkd+PcVNAuzOSfaiZ1MfysNorc0fQNW1eE3VjbYtI/v3c7CC2XHrK+F/Wvj1Bg c/OSSR3A6DBwPXiq0cUtzNHaWsT3FzL/AKuGFDJI5/2VAJNdu9l4S03JurqbxDc94bPdBaj2a4fD v/2zT23VFN4h1UwNYaWItCsZeHg0xPK3j/prOd00n4tj2rZR5dwOj8BWN54P8WaZf65Nb2c7OYks GkEl03mrsAZIyVh65y5De1fUNqNTuNag1C92wx2iSBII2d87/wC+SwB/75r4XCSae0d3BhTA6yrt wvzI27jjua/QTT3hvI4L2PlLmFZAf94Z7fWvay+acOUiR5H8f9PF54Q03WUHz6TfgMQo4juV2k+2 CO3rXykEPT7u3j8f8+1fe/jHRl13wfr+jFd/2iylZB/00iHmoR+KivhyC3LwxyMPlZQe/HFceaR5 ZJkwRjTKQVz0xUVxblo/nOxe5FbFxAoI2DPt9KjKqVCnnd1HbjivNjI1PT/gFepZ+Lr7RVb93qtk 2zOf9bAdw5+hNfYKjcu7t1Bx0718JeBbyPR/GuiaieES6jhJH92Y+W3H0PpX3k0kMGRcOsal9ily FBPQAZ719Fl1TmjYiR8T/G3Rv7N+Id5dLHiLWraC8BwP9Yo8t8fiB+deGXtpJc3WUjL5dRGgBYu3 Taq9SfYV9rfHPw/Y3MGi69qd1JZRWMslo4hiEksgm+dVAJVV5T7zHivma61xrOJrTwvZrocMoCPO jedfSq3BV7ltpjHT5IgB/tHpXFilyTJtc9At/BF/b6NYwXPiC5062aFJfs0SmJ0lbLSRsm4IrH1J 3e1fQHgsW+seG4rSNplsrB/JigduCvUF2/iJPP8ASvnrxr4pvLvWV0nTxIuiW0EUUEMu0qSIh+9H TBzmvcvhPrWnXdo+l2m77RHaxTShsgbkOw4yBRgqyc7hax6zq1sbzQr+z25Z7dvLUHb8wGVOa+X5 YLvRUkutebfBOvmQOGVw49FAyc9sV9dBASu4cbQCP0I/DNfGnj+8g8F679ins2ubOO4kbyC5BZWP Y8gEduldmMp+9GSHHc4LWPHHiG5YwaHpjqg4Dyg/ToAcVwF5F8Q74nzVjiVv9o/y/wDrV6k/xH8I sg22l1EFGAnyr9BkZzXP3vxX8P2P/HnYqGA4MnzH9ahJo6Tzpfh/411HBluWKdwgPH4tita2+Buu 3GPtEkw3d3k7e4XNXX+JPi3W2KaSi2MB4M23BAPoOldR4d0fVXmXV9Q1C4Zo2DmdmYA44xt6Gr5m xWMyH9nWwUKdT1F4zkErD14zwGbjpjtXmmn+D/AV9dS2djrl/peo20jI8VyIs5U4+UjbX0pq3iWa QYgAQDA3CvL9S1nw1dSi11i1sdRmY4VPKV5j/ueWA+fxpwm+oWONm8N+PfDsnnWV2mq2o5DEkNjt kc/zqS71Jta0+bStatHsrl1DRiT7rSfwlGxgn8a7w6Bq+nWf2/StM1fTIZP9WhaOZCPaGfcwH0rm Jtf8WmK6jGjW10yRMQ1xGYHDBeoQ8Fh2INHMKSPJvDoHnvKc7k4/Gu0F3LBIsmSR6cVw2gMsURVj sdm+YHgjnHSuxv1/dBlH8I7Yq56iWh6TpF48kB8ogkjIB/lWZJKLW9Mqnyz3Gfz4rnvDd+yv5W7t 1rf1RorsA7MTIOCMYrBIaZ6BYTLPDHIh3AjqPWuw0aT97tPQdv0ryrw9M8UJhPY5AOPr2r0TRpQb pBnAJANS4lo0JtMu5PMurP5XVzjHfFTTX1xfQrDe6cszpjaxLKRx6jGKr6Hrnk+JdU0S6Pyu6SQZ zxkDiut1KzmiIkhAdO4FZvQo4PVba4h0q7mmlSNFicCGMFUAI6nqTya574YW0otr4QKFklb5C33e OPSum8W31xD4cvUnjRI9iqBg7vmbn+VZPgRZLKztHlVgLhNwxnueOldcXy0jPl949J0MReGL+21m 5T7ZdWtxFcuBwWEbqwQfKTz0r7Y+OFrY+I/hZH4imtvLurZre5gP8cfmMEdd3phunevk3w1rVjpP ifRtW1QltP066W4nRV3syqCTtGeT7V9sfE+9tfEPwf1TVbUFbe4tI7mLeMNs3qwyB3wOmaqhHQzq xvuYHxXUXPwf0+dhyosJeQOD8o/qax/ACNJ8DPEMCNyiX4+nyZxXc3Ph658dfCPTNIspo4Z7qztC skuSi+VtPOPpU/gn4far4Y8G6r4bvLy3uJ9Q8/Y8KsEUzR7fmyeefb/Gug5FD95fyPJ79hqH7OAL ZDWbRj6bLkf0avzJkjmt/EOsNHE7QW947SFQSFVsHJwOnNfq94h8NjwH8Etb0LU71LmSQP5bqhRS 8jrtVVOTnivyj/4SCfR/F+rjT4Wj1GeWSJ5v3ex4JflKuGye3GAaymnYqnpURsXrO7GO3QyEydVG R1zmuU8UlobvTC5JKXUODjpu4xXoOjWNx5IuIZgFIKsDk9BjI4H8q4bx1Cf7Kt7/AHBTBcRnBIyx Vq50dxzvxHjKa/YXS5Vo7cMnvskxxXrmvSoLVG/ilwoPTqN39ax9T8MaZ4iura4vjKph4XY6orK3 zbXJUnFN1u4jlu47aA7khj254A3EdMe3TpVzd7ISRyGnziTSrof3TtP0FUtSUSR2qnqASP5f0pbK 7im0u7eIbWSfy3X6HFTTpva2BGO1CGxL0k+WBzn/AAr0X4I6gdO+JFpbZVRq1tPZ4bpnG5eMHvXn M+GcgdQCOP0qSxupNH13R72EMbmC6ilAVSWKqcEBepzmqhLUiR+iwjvAD5sSjpx5Tj8uV/lVRpbu P7u1D7qB/OSvMb/xt4EsLgwtIjzZGIZEi8wk84SH95Kf++agbxRLeJu0fw3cFB/E9lDEv53Jtv5G vTitDA9DudVnAKSXEWfbyieP+2lc9casshx9qxjg4CZ/RjXKt4j8U24yfD5RB0ETaep/AJJ/Wsy9 8fi2x/a2m3umIcDfcxyCLJ/6ax7ox+LVVgOmlurR8FnmIHp8uf0NfNPxU8A6udWm8V+EIftaX6KN Rgi+aUSx5zOi8ZDA9FFfQVvr8F8iNazxOJvuZZm3duGzhvwNWjJdhmCpBCw6nB/U5o5RI/PTRtVv bfXNPMc0kTpcorryp9CHHH5Yr6JbX3yHU/Mea7b4naLpt7oD63qEcB1HSirW00eI5gXO3bvAGV5z tI/GvKrO2IUSznKnkCvOxFO2xtGR6j4b8RPdDyruMpjox6HmuxudskISM5L8AcV5nottdXLExLsQ cbsHNekabDHb7Y3Jds9X7fSuQ1Rwut+H5WVnnk8ke3J/DFeJ67Z6dC5QM4cf8tZSGJHdVXPFfWOu Wf2m0Y5528V8v+IV+wXQkk+dUkwV9vxrpwz94mUTlI5kX91DbyTqOFMowoHbg7R+prXtl1chmgmh tAMDCjBOe3BH8zXTxzaa6qzCNC44TG9j/ke1aCpZpaC7eMSxiRU2v93Jz/czivWIMLT7TVPMjkuN SMaqclWk27gefuoU7e5rp91hIvy+VcMQDhW9D/vGsJtSe9EYs4baMbV2nY7Y+YjuPapLbTL5ypF3 9nBDZMUTKBtbHtTA0k0u1miNx9miXfKFMmxztGc4zkZ64qvqsNpbIqW0AiyOMpwMejN/jWf/AGRr C2rOb2SXFwy4ZJR91xyBkD8qttH4gtYht+VNhYsY0wdpA+9I1KwmjyfxOqtJYyRkf68cJ1+7noP8 BUFhG28FEZQOhEQGPxY5rb8exsksMUiqhkkZi6BGI27sYKH6Vm6JGrIJSvUZyAWx9aLGZ0Np55jb 5HPUZCxZHBHc11/nSrFCB5sfDZCvGv8AyzI43f0rlIY7XeBuiG4bT93p1rpYhEIU8/y1jB+RiFyf lP3cjH60yi5FO4KZlkB3fdN3FxiD2FW0UyC1b5nb91uIImZRtzzIvCD6ilhn0sk/vIdoOWYKnQJ1 4X+7/ntS3s4WK2iR0dgYiODgfKcEBQF/M0AY3i1cW+qBxtIEAA2+WSNvZf4uo571JdyxIwjdLdWW CCHnzww+YDkgY/h9KyvEMjSrdqxLl57eORQGIkDbByGxn/gJwK7HUZ7aC/uPN+0K0cqoFZXwBHll Ax/s0mwMpJ/3UpEkYLRzHCzyL1bHcc1r6hDmGIxna6AbduB29TXONqdrkJ5hbcoYB0I++eByK6dp POX5vmAycAg8f4dqSYGJaXU95GIpfP3pGyglYZBlpdoAxit2G5lE8sW8gK7KN0Cxv17FCePwrJay 0oldixsxVTnAOdz7hyKv2rRW7r5VqrBw3IJ5BbpSAz5rLfczyiIYM0xz9iYKcR44IPXNbtnHAssa ERxsWVSRHLHkmLocjHb1qktlbJM7SR7d8kpUCRhgsoCnP/1qtRIEkE6vINrq4CzOMMFxg54OfcCg C9fWinTruRFGElinUCE8KWzuwD8v+9+lQ3MsiXUg810LwsQRceXuKyqQeV561PDKBFqFk0eUnhBz sONy4wepLnjGegqa4tP7Q2uWkgZIJEyBGQ3mqMEEjcOU9KAOd+xzTXH+kynG7BH2qLqZHHH44raZ EWysJU3bc7WPmxITuHl/eZSp9KttBO+/98z7SSCywn+4eoX1X9frWbqU7RWz2ZcsyzeYpKRnjcT0 A47UAcbq1y8viXw5abN/m3SlWkaJtu5U+b93g/Jgnrx16V6Fpesv4m0G+1NZ5479IJZoBIoUmSBi rB92QNnqTz0ArxBrjU7PWYdUhVGmsz8u+FNvzJsI4xnIJB5rsPBfi6Sy1KHQtSgijsLmWSxeePIn R70eYkquc4DTLkjnDewAo5Va7IZjfEHx1q+p3GpeCpxHNY6Zc/ZkkKZmPkspXGMKem3OPu1k6Pc6 ZptvGl04818SBANzHZkbRyT+FcBdrONd1OG9mZ/KuJGkkH3mJY8/j6V1Wg2cDgsfukrnCGTdliMk fKDjgcmhAkei2us6HZW4uLmEqADGDKYwTiLP3Bub9KtQeNtZURvofh9mgl27TLI3zfL6KOh+lcys Ykzc7Ud1VWACLIV8s+XIoC/ImV55NdjZRX50xwqiY25MWW2yFjG3y5zhefrxQbJBD41+JIjV49K0 9kTawQSJuOxtp4Yj1qreeP7sBtM8V6cbISr5bCeFTDIQ5I2yK2OnfNd5ZxySnyijx7nkQYljAImi DY+WJuAR0zWfrOlQ3UEsN1F8r4ZwFUjIC/8APPB7elSyz1T4Q6ZoaeK/7R05VRv7PlXO7kK7rlQO Ox/KvNPF3jXxD4K+Imp3TaJcJqeozSeSJoiqfZ3bZHIhxh84BDAkAVh2l43w+vbTVbK+CrBcBrdF HmMVYgSDYP8AlnsyT6GvozTLy4m082jPa6l4fgUzx21/uk2JJ86iGUfvYjzgbDx34rwc5rwglGZx YmSR4lpza9FqzW2sNZ3WpY+1G3lEjQ+XjhJiD+8y3OEGOzHHFbOkar4rNxqunarPbzaw0SS2jIGE DqTnZErDamBnCAY/SvSIPCGjeIVjv/Dl59iluSyCz1DaZSyn7kFwjBZB/snDe1c+3hW0tvEtra61 qM0DB1W2iCGExujbzvjbOdx4Bz0r5WvUsr9Dl52cTr+haLpQudbtStpYGwW4Uo4jYXHnFjEMJuc9 Q7dxXHLeeH7fxEVszNNcXzI8drMNo8mWLdK27t94BOe31r3H4laRodvdCGWBWstLdfs7z5L75h5n koR16k4x/SvH4NZ0AXs8k2nRW6yGFTLHAd8Xl5HcBtrbuf8ACrwteM4alcxtnWZE0y0vjJHo0d1a yXdtayMP30KDazyuAdrN2zzXU/DzRJNVePV724NtNo0ovPIjkW4hu7crujKn73DEc9OMe1cXp+g6 D4gtrg+T5piHlxyEEhlU8BVkOenA7cV674E0xNKv9Q0rTkmWwitIlBOVifzJN4WPj5dh38dP5V04 SzqKKBS1N2G0IiVWAyFAzgDPHXvVpbdV6dcf0zWuLcCMEjGRkD2x6Uww+nU5/lX2L0OxI838TBZ7 2DSJH8vzrSSUOodmVhllXAGMMFbvmuOuxa207tdbrC1Ay5VN4jj27QhHRV/2s+9b+vSXtn48h1Cx wGhjityHTdH88eAW9D85/CvL9V1G08U2FxpVzOiQ2TCG5uERiEGcLx0fn5SMnmvn6/vVGVBHfnXb i1tdPtIbIus5YyFSoZ7dOGBPQx4bjHOTWze3sTaCdJR3ltbN45BcQng5y8bHv+73/McVzPhvw4kG n6XolndQIkaTm3nmdljCKd7AH5hh2OcY4rpLLS4tt9aRGNZFtJQyrKPKkMcZHysBj5k+UMBx+teV W5U7ESV0zhzq0U2s3Wt6trMVvpliSEhgHnR4Cg7gTljJ06DbxWhr2tf8TMDTf9NtZ4YJWtJCEmeI 42sg+pz7jpWLe+G9K0+3tLTUNKsYrC8+eB0y8ojTtMxIPcDO0ZpviC2vzHp0mhXdhFbC1jt7kffm QwMcbj1wF5C9KztFnBKJta1eNe6XYuLiFZkVx5hGTBubLdcc4GB0x065rz/xfr1p4eEOp/Y4tRW8 BMUcwbCY4+tehaVoeoa7o8WsI8KKhZoijb2dFP3ZEwVAbp147V5r8SfDMniNdPh0YJCtu4W6ypMN uJBy/mdCoxwByfSvVyCt7OvaI7dDgD471nWWUSSJb22cCGBQiqPT5efzrYsb8S/LEhmf/ZBasW2h 0DQ2W3tIP7Vuk/5eLnmJW7+XEOMf7wP9K7LT9R1G9hafiKCPq4wiD2AH8v8A9Vfo9J3WpTFEFxJ8 3+qHcHiopbbk7TlgM5zU5ut3y7yw70cEdeTxzW/KKxgzQakQuyUOCNu08Ad+nTn3ryO6Eqarcefb fZpC3+qXkDjqMV7bIpwcnK9wD1FeZ+J0aOSGS4Afc22GYfLhepU5rmrRKjoOsT26Y46V0VqSCO5r mNOPAH0I/pXT23B5rmkI6ezPQ54roI2BANc1ZtnpXQx/dxUFo9S+GRzq+owdp9Mn/wDIZVq5/wAe KR4hJ7XFpA+fooBrV+GU2zxfawk/LcW11ER/vRMQPzFVPiLCF1PS5RyJLNRjp0bFVR+Ictjz0A4H HFXrUlQMnrVaPnHHB6VoxR9K9MzLJZljIA4PQ9KpsCe1XJlyo9BVYkZpgCAgHPSm464qRcEYzioy mOVNAGRrJxYEZyWkXGOvWuxuJjL4YQYORdpjPfbHz/OuQ1D9+9pbDq0oI9vyrtNSRbbSrSJcBZZ5 GA65+UHpQBgRLsJYjit2yVgD71nQKz4BGPat+0gYHJ+U9j2+nFCAq3bbPkzg+vrXFazqBjXyI2Ak cEZ9FrV1u/SB3KHdjgAevevN7yU/NLKc5OTjniiwGTqmrR6fGW8vecZAPPIGOe2KbaeIvFl1YJ9q 0mO9sGGAskX7sgcHHBP5Yqwvho6hPDd6rILa3iO5Y9okd/TJJAAr0nT444FBhvpGAPAONgA6ADp+ FYum2x3PM7a18G6nL5LQS6JePxtDEw+mdrcjmurs7TxF4ZZZYVN/Yr0MWTheucc1t6to1lrEbLIq eZziRAAePpWBoWs3vh+8GmXzloGPyPnG33PXNWkI7WzutJ8SW58llkmH3o3xvU+46/pXJXlrJol4 Zo8rGTyAMdPauq1KWzjkS7vbTaWAKXMHG4f7W2tKGbTNehEDsrvjAOAOOnOKbQGfqEHnWFpqViQ6 Sja4AxhulZ1pqFzZXokjfymRcEHofYj0rudC0VrJZNKMvnQTncgPZx0+nFcj4j07ZA8oXy7i3cgk fWl0FY7a0vtHuQLi/j+zyoN5ZSAOOaz7PxjAmrjyhDd20x8tQzg7GY43MB1UjjGQRWRpMkM+jNJI C8xYIu3AB74yfWrcty5YW8VvFEsisrxSyINpA++u0dsV8XxJilNqkJ6HQzeI59JeSwsbQ3tmR80c hLqnmc7EIB+X2J46Vw+vX+haLqUaS+Dba5ivcOZTH8sRzt5x8pNdFa69cQx/bNQvwkEoCq8iKB8v 3lO3P4Gtv7NHqFt9tk3TwyHfFJKmU9Pl28Y+tfK0tNTOxjrNHbC1isbezstKVstEkRfaWGd3yggf lTtNuRtuFn1OS/sy6sF+zIixc43b1PQD2qJZ9SlgvRY2lq9jZMA88SlGLgZyPU+w4/lWfpGsSwSn TbySYRyNG8+0KpjjJ+ZhuGxmA5AyKUkuo1oamoK148TvLe6d5ACi6ikQSOoP3Wyp+XPbiuy8KaPr sdrqDeHTLqv9nxfaryOMxmeNFOGkKEgvjqVUE4HStbxJ8OtPXw5L4n8LatPro08i9mhjgUtNYhSD NGN5YmJuJhjIQ5xwcedeB/FtzZ6xaeIPD1+P7Q0KT7UbW5DQGW1XaZHhJCknGNykEMvI6EVy+05o N0+hvGnd6mhNb3d/eT21xJ5tndKJIUgiEClmIVXfpyAO1clqFlqOkwtpeox22pQyBhC3CsOcjeXx wfrXvvxJ0HTLbxZqd54a1IppVzbW+opDCPOWNr1d/kAdBnl1APCkV5lBLdT+SurR3FoZSRBFJb7d 2O2VY4rXC1uempGdSnZ2RyGmeH/CLCODzhb3U0fmOkc5hyV6op4yB/nNeueG7PQr2UeAxdmCx8SJ i3kmfzDZavB/x6y7ujJLkxyd9jc9K4nVre702W1urvRw9k7FUvWjWQYHEgUkHOOO/wD9bmVl0ee5 mXS2e0mspB5kbAxFsMP4WxtznOeMYrqVTU1w7szpodHjF9qGi6xpL6N4k0zKzKN6hVyVJhJ+VkPU HmudjgNlbpp+pHVNfjicLGYWW2t0bd8u7acyHn73Tb+Ve66Ro83xF8Mz3cphfxRowP2S7YFRskyT FMUbLoeg67T6c188WWpw/wBuP4O8ZWq2N1IdomDRtCM5bnfhYyRx2rScHvFDrUOR36HReIrayDtY Xfhm3lZmxtWOLzAEOAQy7TiubtNG8O215AbPS9RW9JBAabdAjMcqeGJUr0rQurDQ2gW0e7vbrTg4 McFpIl0pkxnG1PMk6f3WVa7LSPD2k32i3cvh+fVX/s5GaaCEyRPCWBJPlSL19wDj9al1VCJyroZ9 0uqSa7ZX97fozT5hnlDmaYlUEcPcEqmOAABXtV34b8FXHgDQEvtak0qTUVaVNQTTVN1Jtba4Zmd2 Qdu/t6VzUHw38EaboWh6jHa77h9L+33I80+dLb+ZtMwkVV3mI8tk9/QVxGqatDCbLTr21iubGGU2 WmiCV/mhkbcFlh6E7jncprzZVPayvDoa8vKd1pfw+vdIvLbxb8J/FsPiG6sZkn+xSmO1uotvHyI5 +bP8aui7h0Neva3cXV9o2oax4W0z+x/EGnxx3GvaAUGw7iN15HFE2JIHydxQ74/XvXzPrWn2+naz f6Ze3FlZNps72siTSbfLeM7WVX5A574IFexafr/hdPC1n4r0G61HR9c8NDyrW58yO9t7l1OP3jwo r+VKMxsWXaOh9TryOouSR14XENe6cbqcegeJbATzvFpt9IPOshcsbnS1mTjfIihpBt/hik8yPIBZ gtcoPh34gm8CeIfBeoML4XpXWNN1rJnilu7V90geWIyKxaFped249/SvRfF/h/wz400GX4keG9Fj uJVYHU7IsVbTGcjzJ4UBBe3Ltnbu/d59K8z0TQvGnhi5n1/wm4gtUYSXS2t2pV4sZKmFXeM5Xp5n XPStqa9mrKWx2OpDms1ufO7+CrzTr23a91a0ewvZEV5rJ0lKbmxwCfc8Ve1fQ7Kzn+y2ep3OoXVn KB/DHb7e+cdTjHI46819F6z4d8HW2pzPp9r/AGXDrcf2i21GzgARArEILizf7rA5DNAcEDOys7Vt JtLXSIH1FJ2tJ0+zRaxpO2W1mIUqoaQA7HGfuOFcY6V2/X0zgrYdx1jqjwbT/wDhItLRrlbCOa3K hg9w8QZT/stn1rT/ALUFxu/tKF4LoNx5R+ZQO+4AflXbXPhXTrnT2TTLC7vrpWVob+5uCf3kfUno Av8Asgdq881bwf4qiuC09ul08iFy1n85wOckZ3D8QP6VvCvCRyM9QtdPgnt4pmc5dQTtPFT/ANlW /wDff8xXg0Wq6nDGsUN/MqKMACEkD8ak/trWP+ghP/34NHs49kLlP//X8WuJNK8P3M+n6bpInuLG R4TdamftBUxtjKW4KxD23Fv6VkX+pahqs6zatcy3csfEYkOVjxwAkYARR/ugfj1rv/izpcWnePNW dQVTUBHfqAOB5gCsB9CK85ZXQsuNh6c89PpXzGJ92biC2RX273+blu2TWnbQR5BC7j3I4H5VmlFB BGc9yOlb+nYCHgsa4pN23HYo3sGY8FSQM8YHpxX1t8Mb43fgvSCW3vax/ZmPcmM7f5YNfL8ynHAx 9e1e6/BK93Wer6a53NazR3Cj/ZkTb0+o7V6OVVLVOUUke7xMqSqWXK5BIPdSduK+Htf0htH1rVtJ UEfZLuSJQf8AnmW3KR+BFfcgj+QA9F4P8uK+Y/jBprW3i2O8X7uqWUUgAHWWNvKYD1OFX869HNKf NC5EGeJXEYRcsM4wAegNVHhDgR7TubAUAEkn+6vqfauzk8OfZEW58R3v9kxP80cIXzLyUdvLhBBX t8zlRUceuQ6ejR+GLT+yN+Va7YiW/cevmkYiHtFz/t14FOPc0sZz+F3sIUuPEU40oY82O1CGe9dU +YMsA+4P9qQrX1rayyeJbXS9WnmJtpo47qGJMBVOOrHu3H0+tfHjqWDFm5dssRkszYILEnn9fz61 9T/Ca8OoeB9PRiGeylltz9N2R/OvYy2oublREjY+JunSa74D1y2C7riGIXMQAA+eFt/H4DFfDjx/ aGLxgkEZGPc5r9HAiyExt9yUMre6sMf4V+fupWE+male6PISxsriWDHTG18Dp7Yq8yjazCBr3Dpc W0MhAZoogA/uvsa9M+FuponinSzI8aGWB7aQcDduGU6e4xXltrboYwjsVGMkHirmjN/ZetWF/Cvm GC4RhnOcBhnivKoSUaisEtT78h+YYbjt6cjtXxr+0rEtt4j0y4U/8fVj5xXp8wO0+3avqybXAL6G 3sE+0CSUNMcfKkZHY9M/jXyr+1HBJd+IPD8NqMh9PcA9gBIRX1FRJxIS1Pj+5nnu5hBbDJ6Cui03 wrb25FzqJ+0zH7kfUZP8AFdTpejJZKUihWS5PVvSu40K1060vUN7erbXTr8lzIodYW/2Q3APvzXM 5HQS6d4f0jQLOPW/F9xFaIMGC2JCj2G0dTVK68SeIvFs5tfCWks8SfKs91+5hUeu0ct+VbcmneDr e6OoanqX9s3JH+s37mbnpv8A4R/srge9LceNLiVRYaHbpawrwFjGOMewqLlGZB8PLSNln8d649/N ji2twUhTn+6teqwab4P8DaW+q29pa6fAEB8yOMCR+4+Yndn8687tYbi4dI5MySyuM9zivQrvRbPV Ly1m1IG4WyRRBbtzGrAD94R3YdhU31KscQLzxj44l82yiXQtIzg3FxzLIOxSIf1A/rXSQ2WnaR+7 iJvbkcedPhyCO4A6Z9K75LOIKNyhyMH6fQdBRNJb2qAiMB+yrgZz+FL2iBo8D8R+B7HxNcG8TTza 378i5tgIuenzjo1cLrfw88UWFtkWLaioXiS2X5hj1QZP6V9eQWzlPNn5YjgDt/Kq9xcWtqu5nw46 Bcg/jihVGRY/P+Ce40y9Rb2CW0kH3lmRozx/vAV39xcxTWhmi67c5XBr6fvtRe7UwrbxyqRjNxGs gH0Vs1xd54S0XUBi6sEVjwTAohx9NvH6Vpz3BI8p0KVZYEkX8fzru9IuB9pjJ6A9PTtULeAGsIW/ se5aRc8Q3HJxnOAwx+tYlrNcWWoJDeK0DhgMP3+lJlEniuR7DxdHdxkgvFG3HHTrXtWg6215apu+ YhefwryPxtGh1C0ldd2+2I+mDWh4Sv2hmitt3Ibbgg46YqWrjuaXxbuVbQIljAR7udYfQnpj2rc0 0fZtMt7crsMaKoJIz8oxXF/FK4lebQrOUhSbtT8oyOPpXbR6dpc+1vt21x13sBitJfDYhb3NO3tr q5HkxW8kpkDBfLBZ2JHO0DnIFfS3/CUfFu+8Ex+CU8G3SWptEszcG1mD+Wq43fNtXJ9elfOmjTaV pOoR3GLjUlCsJLeC4eJnUqVBLRBmAVsNkAdMV7IviuH7EJr/AMH30iSIibtS1G+aIEj+EYVefTNV SlZHHip2djstL1b45aLotrZxxwWGnWYjt0Mz2SMq8KoO52bjPWr/APbHxhuBKsviCxgEbBTm9s16 jPUDHSvF9W1Oy1RraS30XTtJFsCu2yjcb88jeXLZxWOTEqhYoo1IGCQg5x1xj/Ch1kcDxDtY6Dxj 4k8RajNLba7q51I2RKqySCSFWK9UKgKx7dODXxF4qT7F40aVScPFGSevT1r6j1STcn2WMABiC2O2 Ppivnj4mWv2XXLG6UYE8O3PuDj+VKErjws71LtnY6FcMNPXoFbd6Vy/iexttQ0S6t3b50UyKx6Ji ptEvgNNjD9V3ZNYGr6pbw2crTnKuNoQdScegpKNme22T+H9bl1Xw/aXDn96i+U2eDuT5f6cVBJMo uo2xtGcY965fwgzwadfRSAr+/BUHsGGe1ak8+J1z/D/PFNx94TZymhyjyNbhzyl6SPzrpyzGSPHO F6ngDiuQ0LLvqoVS3nX3AH0z7VevBPr2pNpFrOLKzhA+03DtgkDjamOta8mpN3Ykk1pDdG00qP7f dKcOQQIo/wDeJ6/hVyN9U2TKb8wySqQ32PMXG08GThyOOgIplwdE0GEWWmSR+V3eQjOe7HbzWIPE Okgdbi/YKeFUrFnpknrW0af8pD8z7+8A32lWPg/RRoVjLavPZRNcC0s7dVkfGGZ3PzMSck7smumk 1K5cFkhviwHJ/cDv7q3evzvXxvrc9tDBLe3721hCEtljn8qKBQc/LtYc/Wt3Qvib4gi1UX5v7nWZ kTb5F7cN5RBGB8qEdhXZFWXvGLPuOS51E5Btb3HTDzRDn0yIxWfnU5H3LbSoMYJku88fgB/KvHtP 8Za/F4as/F3iXwla2Xh/VZpLOy1KK+Bha4Qf6uSJyZFJCsVztzjivY/CGtfAXX5Y11HX9QuJ2ZY/ JithBEknTYZZPMPXoTiqFqcTqXgezupXubGCPSL1uWuLOdV3f9dYSPKl+jLn371iSx+MfDiF9Qhg 1vT4hnzbBt0kY9Ws9xbHr5LN/uV9J+OPB/hfTdOtdS8NaWdP2XHlv57LcNKCvB2N0/OvMAsyYK7k B5ASCNefyzn6Yq1EXMfPvxI1vS9c8CvPpTpc/wCmQif7Ohfy1GS2/bymDjhwp9q828HW1ysEdxeS MwmAFtE3PynuQM19GeOvCcmv2M2oaIph8R265guUaO3kfHHlu+4h1P8Adcba+RNf1LV7CS4sdTtZ NK1e35Plr5att77f4eO6/L6Y6Vy4incuLPsLSLFIrAKyjf71oLbOpyDkj0rjPhx4nTxJots8jZnj RVbPqvy816aIwPfvxXkzumdcGVhvkt2RxyOnFfN3xH0sokrKBuYE9Pyr6f5wVxgeleUfEPRWutLn kRSWjQuMew5FVTlaQ5HzZZXS21tHFJKYzj5mOFB74x1f9K7bQdeSGB4QxwjpJzuAwDjIVOf1rj7L QzPGs/mB965HUsR06H0PFb1l4VtJmYmRYyoOWJ9OcDB559K9uL0M7Ho7wtcrmPzCr7lVtzAbVO4f 8tM0kdjDBOrlSAXlyxdmOGwwwGesTSJ49Oh+ypNLKitGVPkHjzFzwXxW7b3rTCOQyttVY94fylHP ynjdTuFhq2tt5OoR5BVpPOjBJbdkDGMucc+2KjuGikjRlXeMsBjyBwRgjofStJrKGS5uYiN+63Vw q7ONpwRwd36VnMhSBZEDD54xhVjO0Y29A9FwseQfEZP3NtuTLSyAAlUOMKM8pgfpWXpUOyAjbtK/ LkrjOPTnHatf4gwzTxWUtwxKwupbfEVJyMds+1UdKgVYS+0A5GSAVwfoetNMxaNq1inaPcvnAHPA ijAHbjmtyaR1it2jV/OO0cIgI+QgbtxxWFEqniNFdi45MDHg5HY4rXMOLGFnQMNyEboS2MA/wZ4o uCJ7Wa6nj3kzEjeRtaBekAz16itmSf5reHcX5icLJKvXaeNqAbq5qyhecLEiBWZgmVtuOYceuK62 2kklgtBerlIxCAypFHjsc9T37UrjOc1rbc3UyEBib+zyDL5m0Y746V0N49kbu5SW2j3+aSHaSYY/ dHPA4qtr0HmGaSIiYPPZSphkYDLY6JyK6qeK8S9uUkgn2i4PzfaVx/q8YwBxzSewHluoxRRNE8JR o2khJRZnP3Yw33GGP/Hq6z+0IJLRpY3ydmcbQxGPbgdxx0rUk0+X9y1zHKYd43lplZRmLaOgz1rA 1Hwk4jkfSpNxIJEOcg5HbOP50ICGKSG6KmCN5QphUlrMcNt3ZIQn9K6HS7aBoUlQqjlAWBhMQ59s /wCFcNcaB4itZlurXTd4U/c+6u4RDrtanWt7dWTLLc2t3AqoocRpIVDbe+D0H0oA2ZYo1jkCvbNu W458mdMZIAPHp7CnJPZxXkbloC3mnH7yYZ/dgDaCMGuVh1uxlyyXkauQ4I+0Ohy0now9K6O1Swub n5r1JNxmkB+1bsgABfQH9KALH22GNbqWOWJR5BXOX7IOpxu6k9BUseurbWdlvzDhOf3xUHEZ6Mwq tc2VlFZam32pd7YQZnZVZeBtyw9qz72KFFGyVdojfaVlVeu1VHOc96AOki1FH2n7Q6sUIJ+2Rlf9 RkZ+X2q59mW60+eRWIZHBMnnxvwBk/NgD8K4BjKuWhk3hvMGSYTk8RKOa2bXU/L0+785MqzuykCP GR+7GONv5mgB9zonmyFWlYMrY5mt8enr7Vx2srb6dqtrBI6JbX0Qs2nDKRDcRyedDKSny/Kx/wC+ c16dbyPIcETffZcAWwBxIo4/Nq4nxhZzX2mX9lNb/wCrCzqxCZDj7uPL4wR97vUsLHn3ipANcmuZ UMP2shpFJA/eqf3int6Hr0rvNFsLYaTbs6oRNEwILMRuYZT5F4OCB3rySe/m1bSy85/0qyKJIWHz SAjakmeeRja/qMelemaJqD3GnrFvCSQqiBlwWZDygOccn/PFVBii9TqHktrc+fqLEs2QIlAVVyBx 5f3e3H3v6V22lyxzeZcwjyhjO1ht3blBOR2HPX8q8j80vMn97OCc5kCn5yC54XB445rrbGd7RBBB gb02tIiNsV4TjBd8Ekg/TimzZHewXeo/Lc2/mPGiLJmKM4+WTDDLlc9BzWLqmia3dzTPHK7Dc43T A5QKcfw8cfU1gWVg9oz6jOrliJ/JMkTzrIPl4OSoHPStiPUAj+YEiJkctl4p1xubHY47VA0U7bwN aPMr6nLLLIfly+R19H9Pwr3Hw9bXFna2Xlndp7MEXDk5SPg544HHevP7XXrW+06NZ5UScxBhlsZO Cf4j/OvTtLeW08KWby2zyJFCXby+ZNjElsAdQM5PoK+b4lpc1BJbnHjV7t0O8a6V4ctE+36RZxyN Jtkd1Us0hI+6invj2/HtWNp3xJe6t20/V9NOr6ZbbR9lvs/aIwOnkTAB42U84Py1ta3rumaPo82p JZreRbY4o/s6HarE4+aXOBx1x34rxvxHcaDr7NLY3t9ptxEAZII40cNg/eaUbX2em4n2FfMYOLiu WRwwdtD23xBpF94i0mS5+H+qC41e4hSW10zUJUhmTI2lonPySOVyOTnHy4rwy/TXtAaxg1OSSw1I QPFNC6x286EHIA3EfJjpjPFdffR3sujaFqNzm0uQD51rEwnDRnmNn7p9TkitaPx/cs1vp/iu3sNY 0Ajy4F1JmW6RxyBBc7Sy+gEmF969GhCG1jWdtDy7T7OK/sbLUtV0Vpb+VmjEltLMpDKTtMjGQg5A 7LivpD4bKLrS73UFnknW4ljhHmPu8t4Uww4x90t/+uuNj8F2HiGxttV+HmqTXKXD7/7J1F0juWUt l0gdsRTDPAGVPYV6/wCCNLn0zw5BaXtotndebK00KIY8bpOMo3KttA3D1rvwFD99zBCOpoyRkEgg cY6VEIlLLgc5GCPrzWu8PTPB6DPFUp9lukk8hCrApY544Ayf5dq95vRnafOfirV7abUZTbTxrG9w TPFMWRnFu2dq8c8AEfiKwfDdvotxrscslh9mt72ZYponPlKs8jCTcFKglThWXJPU+1az3s93CjmQ GO4uEZGgUgDB/eRl29fX39quSzQ3OpQywqskluFt1847kkXIXKZ/5aKea+Vq3d2Jp8pW0qwW0s76 LTpXhhhvZmjtpGByDcl/NgK4wq9GUgcEV6HFb6RLdWF7bQi2t4x517JMzMwaMv8AuwuM7XBGBjt9 K8zd9Xm0qS5t45LfUrFwiTy4J8vz1Y/98Nypxjk54xW9eeJbK/g0S/uIZIdVWIwSRrDiNyspwcg/ cIH3D0+nXzakJNmKuY+s+HEfTIY4vMa/bbJMu0M07DJAznOANqhR071zht/FOi32nT6VAGZ2YSxJ 5cjiMDk4k+Vvcqa6qe2ub+C6Z5DZXULOqeW2UMc827K5wUK/MMf3foK5/V9VOhyWgfWZoYL8J5Vu 8SMuSMbVQfMM4z96t6UDOSOs8O6xbX1pqiNPbWUcsLtJFbP8ibflkcZJ2Mq9s438iuC17w/qUumz 2NhfW9tpF2FhhAd5Zl2DdCz7Rg7/AO//AA9OmK0PDfia31a+v7C/0t765tNxlMsawRmGPGyMhSd/ ryfbpXVXf2PXJG03R9Qn8m7OY3YKxE33vLGAPkXGNv4VdHmoVfaRJjG7R8teGdKit21G88ThoY9P fyntz8rSy7c4z/cI5yOtdy1xFqUUclzOLeBFBjgiGAi+gHGT7msn4t+ZFcabEZhcGOyZJZQnliV4 jt3Mp5+UDb+FLaaRA0FuZizSPChx26dK/Tstq+0pqZUkbq/2fEoFsm7Hc047XHzJge3FWRZQQqdg yenHSqxR24A616JBmzpDHztOD3rj/EOnLc6dcLbkM21XAbttJ6V3skQUcjNZU8MG0hzhTnJ7YIx2 rOavGwI8w0xgQr54I/SupgI/CuT05DFI9u3WJvLx/unbXVw9h+lcTQG/ZevauiiOAOa5i1J4UcYr o4myuO9Z2LR3/wAPpvK8ZaGTwDchf++xitT4lQEf2QSOY2uYifTa+QP51y/hKRYfFGiO7hUS+tyz ZxtHmDP6V3/xQgAiiP3Wh1GePn/dzx2p0viG2eRQqDgenAA9q1ohuUcciqCxsMtjYuceg44qRJVX jfwO4r00ZlqRsL9OtU85GKfLcwfd3ZJ7EgflmqnmkYAhbPqMH+VMCYDkAHn8qkxgDn+mKRFuGbKw 8dMtxj88UjRXbNsLIAOh6/yzS1A5/wAQJdC1W6tZdlxbHzIzj+52Fek69ZGPQ/C2sWmoXF/Y67Zm Y/aWDvDdxN5c0eRj5Rjp6Yrk7vTppojGdrZ9hxjnPJH8qt6WdTj0aLw/ceUdOtrh7qBAm2VJZfv4 kydqHjgCnYB9u/kR71QkHuAcZ9QOaZqOqzWcZR0ZUcdf4ckZABH0q3Z20KXL7lWQf7WSOOPWszxm luYtPMcKQxh5PMCg/NhRtPOOetTYDirmfz2MkrHHtnj2rAt5JdR1BI7SJZ/JbgMcLuHTPrWpb+Mb +zvVtidkWcBVAAP1GDXe6jE2oWf23TLaKabYPMtmARj7oV70JgYb6j4o09cXVjHLF3AQGtbS9Z0W /wAJLpyJOepDlDnp0FYujeK7eGRrUpcRSD5TBO25RjrhTzWxK3hjUTmRRaz5yHj+UHFVcDVvLnR7 MD7TbS2ufuurbh+Z/wAKzrrTtA16IxtdZk/hJwCv1rpLCOCa3+zySLcxYwM4PH41z2qeDIGLTadm ByM4B4qXqBu+H9OmsYG066mS+sm6Z5I9qz9U8KSWs32vSiY88jbwPpXI20eq6XLt87gYGDnNekaV qtxKgim/eKccnjA704gc1DqWr2LI84+ZW696764tYNWtY7grgyL+8OOpHXiqV1Z292OgQE9DUI1K 20dxGLmMyQkSGNm64OQPxrlxeIjRg5SJuc8tnpbmDSbm3uFlLyeXL92EMD0wPmLEe3FQXehWl1K0 gv0t4Y2RY7eEB2TjHXOS36V18+v2WqHUG0/TY01Mt5i3yq7Lb+YMbQuRyw4BStfR/B+rPY2WzTYt OnlYTxyC8kndvmxgpMo8st6YNfmWKxbq1HUIs2eCahHqlhN/ZV/bywyQ3O6QyREyG1YcPgZXn2rv NKt4rdf+JfqUc8PIMZLbsEYx82On0r3PxBZ3mimBJwIpJCzTwRuJZowBnPy8LnrtIFYVzZ2Go6ct 9o9lZ6jLLJhhLuSZWx/GeAv48VwvFdLC+Z4Re6Jc20P2ay1S9WOA7/s0km2HDHc54/mfWtTwfoXj DxHp8kugIkkdrMI2kaWWFpP4gORtbuOPxrq7LWdC1WSaK8tEjvrJl3B5oyuxjtAED7G+U+5BrW1r TvHGj2z3Hg+7uYBaS7ZB5JC5bjeqBmTBGP4T7Vq6/QZP8PNZ8SaNr0M0dw9lFb3BfMEiNwvHl+Wx +91HPbO4benqWhyaJdRWvhyLw5pFnq+u+Zd28+oxF7e4lkfa8VtMgMyeUT9xdu3pyDXgd7rY06L+ 3Nb8KFdSijYyXsRKubjbjzW+7tTs209O1ctc+HPiPbS2d3Mkup2dwy6mLeG4LRvv2/Mqs4OSoABw GHHNYrC815RdjSnUs7M+nNUvNJsLyz0/QZHvLvQrOCy1BbyB4bnzIkEWJLOVdyoUj+VgWUoQKn8V +B5fHGnMfDEUGm61HGlzqOnWrqigDIa5strEZ+U+bbZB/uA1zGjeNZ9Y8NnxH49ew1t7S5lstD82 NotYTy1G8POrhvKXzNvz7hkVJ4K8f+LLrxq9hdJplhovhy1Elpd3T3SrbGWQEMnlKWnlY8EEY256 CvOdGq3an0OvmgQfDy9vLPRrrwNqk13rOhzyR3jbo1a7snYgLeWiJu/1LAGSI/fXdtBpmtC2tPE1 74W1NYry+tY/38sB8oupTerJIwG5JFwylW5B9c1oa1fz2lpaHweYrDQVvGv57dYJjJJJuJzIIyDI UXiJd2Bnp2r0GfQLDxN4UsPHZlmtr3SUm0lrCGz+0tdQ+YJIC6s8ZREDsucZAPtitFUbd2csl/Ke EaJe674a186lZackFnaBpZfLYrG0CkDYzuo3yNnj/Jrtfij4V8H6pDL44+z29zpVzBBLsEbrcGY4 WQQ+UNrAcEkn+tY/gPV9Kjv9Zm1Dw7PDa6YI7i50y7i3RzxNIsUm0Y3DywQ4ZeAOc969hv4/B99p J0rw7dC10V7l7dIXLD+z7piRsJbcRGXyFOcHt049D63yrlZ0xfPDlZ8oXugafbXcOn+EJ77S7/UI QsdjZZVdg+YvPIxY4/2sj0FWLPw18bIZzB4Zup5F01kupLiO4VmTcdqgbsMenKL94dc16JpNjqHg 67liutFtr6QblFvc3Ztm37sYlnCs+zH3VTn3rq57mbxLrOnQWehado94myMR6csg3sWyGkndjIxU cdelYVsYoQTepyqnrqXIrzUvE3heDxN4fhe01rwTcvcPbwoEf7MWHnsiAsGXOSy/d2swx0qhbeBf CGvXNl428BGfTIbqcS3OnQMHtbe6Ugstu4/1SS/88m3DB+Q44r3XXNW8F/D/AOy63qmhwW+uIdkt t4filWN/tHykyxbgpjbPIZfm615YdZ8M+FfEyad4T8Nrp2m6pkgWxMcd5sb5kRJJpBuifhl2qfwr KVPki3FnTCPNozlfiV4SvP8AhM/ES2ihtP1iyg8QyPOUW3gDt5UkW4jcW85SFAHTGazY/DV3oFml 54kmS3tLm2ltZbeEgq1vKm1wZB8oOOVx/EtetfEjVLTWfDmnGFY7nw7NBIhMfykyJIXEZPZoioGz NeJrqNvrMjLp2oXk17F85+1IoCRr/CI264PtmsvbzsrHO48sjvPDnhC++Cunab4i8OanNrd9rWoL EMR5hGlrAGYyKMhzukBLfwjt1rJ8R/C628S29zrvw4urjQtNnmeXXNBs9pMF1tCtJGv/ADzKZ+UH HOVGK7S58vS/AXgF9XhVmuoLllt7mRliWaZkEcc7LyIyGA6YBI3YWsnRddh02y1DULeCXTPE2mz/ ANnyx78bVQSP5ssfR02AL156g+vRTxFRJ3O+lNT0kedyhovB39jR2lrrv9i+THLvhSWRYrjeUkUH ptPy5HqM1ylr4lvPCAk/s63m0v7U5GJ03RTYGPLmjK7ZI9vG0g/h1r6CsrGy8TalP4i0KIadrlzb va6zpyfcuPMHyXdvwNxUhWdcDv8AKDxXOf8ACJS/2/oWneMIVuIXge8MX3opGQFV+djlQH9vrxWU LU25SM6vNFpxORttW8O6zp4W08vT5Nm+SKz8w26jqW8nJljXPGV3oPUdBhajpzW2oRSQTLYzRIrI 6YzOD2VlO04zwfz5zWH4nn8Q2Opx3NkJClv5sdvcgCMeUsvCupwCCq8cdKIvFMQgtYrtby2GpN5q AxboDKgC/LENmM/34yrfXpXVD4eaBnOcZ77k8ng3T7mRri6jMs0hLO20HJ74PHH4cUz/AIQbSP8A ngf+/Y/xrsbTxPo0dvGl/NLFcgfvFIhkwf8AeeRW/AgY6ds1Y/4Srw1/z8y/9+7f/wCPUc8zL2Ej /9A+OVkVXQNcQc5mspDjrgeagNfPrO5PzjJNfYHxU0s6h4E1N4wGl04xXyA9f3TYcD/gLV8fhg/z 5zu5/Ovn80hyz5kKDIGTA6j8eMVqaY20EY3j1HaqZWNmA6sOcCrltKVcEsFXoCe+egA7n6Zry3qt DQ1pEJ4x7/h7V33wlv1sfGaWbEbb+B4hkjDOo3qB61zy6BNFAt1rcq6PbSYKCVWa5k/64wLlj9WA Wl0/W7DT9Vs/7Hs/Ij85Umu7lw9zJGxwwG3KxDGflX+fFbYSLhUUmKSPrA67ax6raaOiLO0oImdW B8naP4h7muO+LDXNvodnq2luIJba5EMk6DMix3C4+Rz935l6rg+9dNZWFvaystrEkUfIULxkHoeM /rSeKtOfWPCur6ahzLJayNGT/wA9IwHTH4qK+nxMeam0YrRnxddESEyN8xkOWZidxyOpJyW/P86z oQPMKA5A7nv+FaVxh0DryrYIxxxjNZyKAwcHnPP/AOqvkNUdFy8yL5fXHpxXs3wMvNh13RHbOTFd p7c+W2K8ZAJHJJxnr6V3vwwvfsPja1QlfKv4pLY4yPvLkZ/EV2YCTVVGckfWS7tqsODwSfQ+lfI3 xb0kaX48u5UBVdUjjuwQOpA2uAPqBX1uZoUkijldUadiqA92Azj8K8l+Mun2cVtpPiGfTjqU8DvZ rGciACQb1aYDlgGXgD8a97HQ5qZCPnrStLu7yBr9po7TT4yA95OdkI/2AeS79gqg1tJqFjZJJDoV uUYrhr2YDzmyMHyx/wAs1478/rWHfale6lIt1qM/mMnEQ4SKMY+7Gi4Cj2H51f0jTJ78F4mghiHD NPIsan6ZIrw6NJyfulNH1P4a1K1k8NaXf3OIx5O1yoxuaPg/7x4ryD4p3f8AwkkEGqW+k3Bi0iOU rJJtDSIeTgeg9KrW0E0FrDatd6VJHb52xy3O5RuPOFU9a17bUFtyFjl8P5HHzCSTI/Ant7V9VTot w1M7nzNH4gsLpA+nHh+VJ/kc4xUCSGRzv5JPr/8Ar/lV7x74KXRdTbWNPeA2OoStIRah1SKQ87cM BwevTFctDevCoNwMFemeMiuOrTcWbRZ0TRTTuIY/lHVj0/XH9K6nTrWG2jwg4IyTnn8c1hWmpaQt uJFu0Ej/AHkJAYdqng1OK6YxQMBGg+Yjvn0rJo0ud3os8cN4kzEDjC56AV3FpPA97GPMA3HGT0ry I3WAQPkIwO3GK1YLp5VByenP1HFS0M9ourkJlI2UjpkYrDe9ggZZZW3kVwwurgKAXPHSpt7GMljw aXKguelQvFqUYkgnZVPUdPbp+FKdItuGOWJ7muS0W58gBCcD/E13kD+YmSQeKloSKqadAvQc+tSr Zwg/cyam3flUE9yYR8oy3p9azHoZ2oNAjxwRKAWPzY/oRWRqPh+31OLbJEJVXkFsZH+6av8AlMX8 xvvVqWzD7mcHHArROwjyLxJ4V1u9m05rCz+0x24dJQHUEKR8vLEVkRabd2cylrZ0eNgWA529+2e1 e1XVpeXOUZljiPGB1/SmxWUFogCfIOhOc5/OruB87eNrvzfEHhpc8Cdcls4Gex4r2GbT7iWQr9gi X0K4Ofep9b8L6Jripe3+lib7NKix3SqUZJOoUSjgH2x0q1BFqF7cQ6bp4llnuGCQxLjc3HX6Dua0 lJNaDjvYZpOq654Yuy2k3culSSoYy8JWNmQ4yhOO9dpeeIvEWsWot9a1u+1GEOreVPK7ICO+3J/D ivQvD3wMhuI/P8QazKl0eRFZqCi/VpPvH6cenrRqPwe1q2Zv7Ku7e+h5IWQ+U+B7dM/jWXOloc2N y+vL3oxPLVKJGEPp2wKY0kOPunAGSeAOK6248D+K7WNm/suS4C5JELJITgdgDk15/Jf2U1lLeW9w phUvHI7YXynTh1kVsbWTHIPIqoq+x4tShVh8SsRQKLiaVyp2q20Hr2HGa8v+LtkB4eGoKPmsJ424 7Ix2N+QOa7PSZPEniezbUtH26NogJ8i5lg865uUz/rVRiFSM9s4JHSsLxdpmoJpU0Or6jFqtlKNs izBbVyB2R0zznOBj+tawpPmKpXUkzxK1vZBoQmQHJbBA+ma4XVbxvtUcN05QFeAf4T26e1ej6fp2 h21kbdtQn0aJ2dli1eHbn/dkXPHoT2rjPF+l/wBkzxXs+mtqSXQHkXJk861Y4wMLFnJHocV1Og7n squixpkrpA3ljMjncVHsKo3WrWvnrE84knzxDEDNJ9Nqjg16f8MfhPffE6++yeK9bPhHRkiEn2m+ hZRcYPMNnAdiuwHOSa95vfg9HoOunwj8I7VLW3+z28tz4h8R7d6+YxH7qFFViG6gbMj6c1cKK3kZ 1KyitWfJuieEfEj2Ml2tkuj6dudmvNZcWiZb+6mct+VRW+jeEoy1k2u3esyHCtDo0HlRZJ6GVyM5 9ga+jfiN8INB0/RraC51fUPFXi7XNRtdKt7y6TyLK1eZvmaKFT/Cudu4+9e86X4HPg/T18NeANFt dE+y5gk1e4UNO3G15Ud8tvf2wB2roUl8MI3OCdVyXNKVj5b8H/B2z8SAHQ/A+qXs8UgWT+0b1LaF F7b28kdT/AOT1r0m/wDhX4Q0uSWw1/WBbalEAZtO0QvqT2/HSQLAEQf7xFe+W/heC102HTf7UvLa KFGDC0k2bnf70uT/ABn+9yfStnQNN0bwxYDTfDlmlhDu3kht8jv3kkdhudyf4iQfp0r0cLleJqaq NkefWxtKG0rnxva/s/X3ia6ljtNN1FINuVvNQ08WIKHkFWjnXPH/AEzNS6h+yYmi6Tf6mnitrPUL NA9oj2262LjBxJcBh5e4/L0+tfaF1qiQxy3mp3aQwoMPLKQAvPbPc15VcalcfFHVbbSkVrbwXaXC teyMcNqDK2BEqjAKjHzHgY+XrnHp1crhShepLUwoY+pOXubHCeEvH/hDwd8LfEXwr8c+CbrxPIW3 28ChVjuY2jVVkSVssmGBIkTPGCPSvibQ4dRt7iSGG1mshkLHHGsjB13ZAkkjAChf73NftB4l0HTN dgWzlt40a3jWK3eOFZowgGEQoQQQOgrwbUNM+IHwr8OS6hHrl5No1uZXuGm0qOIW8snywhD5g3Jv wrcnAIwuBXguCTPfp1HJangXg34b/CzxD8Ok1bVfEV8vjNcGSyn1YRsuZAPkhKk7dpyD1xVweCNX 8Oqbjwv4g+0xpybLUpEkjkHoswHyH0LLj3r6I+HXwS8bR/Dmw0vxJpOjapazxSXVzYX+m5u2M2ZN i3YkDo5J+9j5fTAriLv9nj4neGtahu/hpBBbaRKjSGx1G6jIgcD/AI9XRiwIY9JkbO3giq50tEaR szj9N1621G3mW/aGxkt38q4EsYCxyIOYpASMED5lP8S8j0rk/Hnhqz+ImmJaw2kj3dv/AMeOpsqW 6hj/AAhXILxv+I9K9O8ffCf4leHdFuvH99bRT32n23magEnhnUww/OBHDGq/6pvmUkHgHJOa9T8J /DrxN4w8M6T4r0/W7T7NrVslyC0jfLvGSp4PKnK9un4VMpXHKPKfnv8AC+HUdE1S50S6T7Le2F0P PiII4I6j/ZPb2r6qWWPrjHtVz4rfBvVvCDP8Wr68t9S/s0QwXUNmrB0s8FTI3A3lXIPTOOK86k8T 2n2SO8hlWSKVdykHOQee1eNjI2loddKV0egqeMgcVh67bmeyljYcMhHPfPH4V52PiHcRShZYV8no DnkgV2Nl4o0/WrdokYJIQPkPfB/CuZLW5oz5K0mT+y9a1DQZScLK0sJOTkE+nfH4CvXNEitLqCWG YbiBj5Xzn5ecADA/A1518QLJtJ8V22oFMK77MngEPxk45GPpXpmjW/lwg+YZNwAOeBj7vAA9K9yj JcpkZ0GlaevlvHZPJtWE42Me5A+8RUqQXUChLe0a3DIrH91H03kddxP6V0Eek2EYDG3DHgDcM/d5 FSCxsF4+zKucLkALgZz9asZEIHXU18wYaSCXPyJyFOe/zH8BWHKsfkKPs20l1zugHZv9kj+VdLdW 0bCSRdvmrG2xyAzIp9DgVh3GjwxofkBX5WOGPUck9qAPMPE8ZaxmUJtMW1uA8X8Xoc55NYNsJvOj V2AVznC4AFei6tpxOkXwBJKxSEZ5xsG/iuH00CSBZFO58Zzxkj+VUjKSOlit3jYOshUZ6DJ4H0+t W2jIzGZcA7SMdeOP8K51rmSJSQ8o25zu2dPl9/rRBcubaYIxaX58HK7uOecEgdB3oEkbIkljwEUu VAIHT5gO3y1uabeSFPKmslaNAOX2g4H4VwQuSit83zBWwfOTPQEZ4PvWhHdRiBlnuQ5j3YUzDgAh h93r1qQO8ure1m0a2ntwsZiMTMFwMhZP9mu4u0CXlwjyFFMzn5hx83oa8r1e400h7mNQhW0JwrHc Dvx6BenFdrdW1tJqEm63WNTLA3AnPDAryw4zQBcvIwbEohBQ8gjGM/w1iQQXX2mNoXwF5BAJ4Ap6 onkRyIPui3bI84j5TsPB49K3rlI2EgyNrjAIJAxzz60AVXhZw4lvATjG0heDnPpmqVxaQmdVaSHd jIEig5HTis8R2K+SZCgz5W4Fp1BYAr6VPpksSxRODGdqLH8jluB3O8ZFOwHP6j4ej3O3lYYnqO2W x2ArFtLS2ivRDFGDLnjOM/P9fpXUC7tJY2Q3Ea4E2SLnOMSDHylf61Um8PW11IWhvH85FdR5Xlv8 ytn+E/0pADabczTzQTw/LIBIQAGzj26CiTSAgyIj06HBwM1EkOv6fZ3Uiu0xhZY95UF1G/Hrgcet bz3V9CwZon2xRO2I9gLFHDdz3/z2oAwGtVjU/uFYrxjbnkHdj8xUZNqIPLnswVYZwE6c5OBnFdNP HKZTE7SAD5AxRWwfun7p7fLTGsQtrbSo21nYLJiIsVJ+XBx74FAHHg2pfctoobdnlAeSQ3etO2mu lj8mGyAjc7fNGEUKx5yO9a4juIo18wybvl2D7MwyTExPOR3FRPvknhR2LxF3GPJZP+WSuuWNDQHz x4u0r+xNVN1brstdQ3naRgDPDDv06j6/hXTaNIYtDtdQRwm1TG2ThcE8Er1YjtXXePNLW80qYJ0j TzACrH5kPHOOMg15noUsg0d7ZuCJTER1PzDI6/WpiSzt9ORWmkkmdlTOCEThC2VBC8gct6+9dvo2 lx3V4NS1mHiNhII5X3yPKPlJyMKq4A4Gc1y+mt9mg3HZnDSFJGYcrgjnGOpHUiu70+5MgmkeWNjC 7RDyiGB2gc89PvVcjWDNGfSoJ/LAQx8seGcH533HowA9OlYVxpzM0gtHkBjjLSSGRsLnPHP1rVj1 S+u4oljtGkknEf8AqmVgrOTgHBNazQRW2lS3F0UUrHvMakDMgfaN2cCoNbGbp2k2NjbwXF7Gxe4V FQNyZC3C8Y7YzXIeJdc1bSNWtrH+0Lu8h0xFW+dVdXZc8K4AGIgrfd/i79q7i2vF8Qai16xxHb4E BOflPGDgfStW50+x1o63YeHJYLnXtRXz7iW7kaBYmUAfuzjlQnXGRXj5q9jkxSbVkYovNbttTtG8 MXrNb3FpbTvCuFgWPkGVwcjb7dT2zXXarYeHtLmiTxLmMzRbibefEbSkbzygDqGJ+6fwrnJPD8Ed zI1zb6nZTWtlAkd7pX76IRqD3PysvPzbsY6itKz0CTTkuLXQJr3Wbi8t/tlvdTpGyxLAc743BIkz 0K9QBwK+VnSfxI4JRZy15out33hmS1kdrBoL+KeBxMV2W5jwTuJYsgGNoJ5647VtSLq16zaDfwtZ xqvlRzzRhzdRRfL5inG3zDnhQd3tTr3xHYT+E9U8P6nby3UMM8UsEVsdsqAjqWxnah6HsOMcVnan qF3efZLm+vvM02LymiQEyyLcx8CQMPUcHpV05Nx1HYvavMPATR2+o3EbWt1G0dvFdyAPEir1C8jl xkcZzX2BYCS20iy+2yM06W8KySTMSzMEAJJYn8K+NL+5sNe8VaPpd9bQtLrEtnFGjOC6xlhu6g9c MxPSvra9hl1CeSMu2z7oC/lXvZTTahzs2p7nKzeKrm68a2dhptyz6Qq7bxWgBjkYhsKso53A4P8A njqPELwx6HqZll2J9leJn4JAf5BjPfJq3LpUFrYxwwIEMbh8AYXI7+uawfFOsaVpOhltctDdWl1c JEyxMI5QdrOXV+hwFyFPevRrL3HY6UeEandWFhftpekXNu8yRiNRJnMapECCQPl553LnPzZqjqTa jd2ek3yCGC1spg0qQzbJUkXgiJD/ALX3h+FWfEfwul1m0udU+HV8fENvJtmnsiVj1GONjlm8g48z P8TKT0/CuN068i8R6db6brKPpdzDcMpnVcTwyZCgOh5O8D5sj5T8wzzXzlSgzOV72O6h1i4lm1Sx vjN+/UCN4cSq7Svyikdtp/wrodV0S8sNI07Vfs0Tz28TQyMyedG1wkkYO0KeFwTu756DFczpumXX h3Vb7XZdUEdleoFsbgoCstxnABBUkDYrru7PgnHFdH4U1a4g8IX2tWOrxGa3vGitofLErrHtBl3Y 4BZeOBmsXTLSMbUBe2AnxO1hezTZijuIzte2mBx5gIBGMHAxnGDXJW39vW1nOk0No8UDMsV3GDIU 56FWG5PY4xV3xdJrWn22neKdPu3TR5fOhv7c/vgZ423JKEbnEkbk4HTbWHpfia28R3bzTKifZYlk 2JEUabB5DydMHnA61Thy7GFVdjR0vW00q7to77QLPTZpZRF5s1xIZp1k53bADl2zkD0ro4Zmt7+a 403To0trObz1BbIRh0fHBXP61v2V9bf2eWv1XTpLtvtFjIwHn29vja+ZHB5c/wCrx7nIyKzJfDlt dXV7qMGp2kt1HDse1to5Yo2yNxLPIABjsBnmnbmVjWlTdjz/AONoi8S+GrHxRbp+80+5kgnOAN0V yuUbaOmxxz9a4PSLgXWjaZdq2GEIjbcf4ozgivUfFccK+AtMtZAI0ktrjzoYx88qi525Iz1XrmvI fB1m8sN1pdy2BY3LlmPGUdd+fxr63hyfuOi2Y1Y8rOwg1RdxQwl+2RnFXXO4AqNo+lLPPBZg29lE snYEYwM1jG01S5G57lYR6DOfp0r6j1MixNDI445xWbPDKIzvjyFGR0pzadc4+a+l+gx+lZ01lfId 8Vy7KP71LZAeazJ5OsXa5wBKXx6B+eldNAc4GMEHGag8R2yxG2vXAEzOEcr1dQOM/linW5+TI4xw M1wzYI6C0zkV0UP3QOw71zFs2MY6V0Nux2isSzXtX8m4ilABMciOPcqe/wBK9x+K1uJLSfy324v1 fcQcDzUxxXgqMdrcfMBle3619D+P1E+hPcINxmt7G5/76GDTpy94DwpdPB5eVnJPbj/P5U42UcZy Nwx/eOauoNn0PpTXI9SQexr1EQURDGXUsqEjoSOn0q2qpGc4HPT/ACKRUXcDjkdh0qyMY+Zc+x7V SQEmCYhjk555zxTkjwc7cjt/kVYijU26yKOrYP8AKpSQDjt7UybkATJG8cD8KY7KoLLwKuecqBsr u9/wrOkYSuEAwDQKwlixbzZDwNw/lXMePdZj0O00q5njWW3nunjlDdl2j5h/9austkVPMDNgRnOP Y1znjSwn1PR4YrQwNLBcM4W5AMbb024z/D+NKTfLoVFnBeM9Hjsp4L2yyUkXzAB2Hb6V3GmefN4f h1KxlYS7cKMDDbeGB7iudttX/tAp4Z8V2v8AZWoIuLaSQbUkUDG0E8EdMGtTwt9p0x7zQrnI8tjL ApGfkYZOMdeTWUBksN5ofiVfJ1q2Ed0nHmx4V1+h71rf8IFp06hrW+84AcZ+9XJahZeVe/aYRgsR j2yK1bea9SEm0IDL/DnFaAa8fg/VdPO61mLDrye1dDZ3FxbgxXx3EccVw0PijUkJjl+UjjANTt4n i3FHyG9DzUuQzubmOzu48hAGA696yZbi10yPzJm8sfdUfxMT0AA60nhLT9X8datHouiEKz/NNNJx HDH/ABOx46fwju1fYMPwZ+G0MCR2TTwXgiSKW5mZZ/MxyziNseWXPPykYrhxeOVOOi1LhSlI+R9G jvrrUDLrsU2nWKpmOMDM0p7Db1/SuvjjttVdI4YI7Jrlvs6XTxqWiZeAGRucn3Fey6p8FZ1JvdCe 1uZEDqkaylWKkZH7tsc/7uTXhfiDwnf6FJA97pM2nXrkO73Hm26xheN7Pzkd1wC1fA5jXxFad5ES ptbnVWnhvWhfWDIYrxB5kd/LhY9ggBKkKMAj6chqdZ6j4bt7q7tPEepaxa6pcQ+Tp/2NR5Vq24nf JuJ8wlcZwvy96ktNRu3vdJ1Kxk+1WE+Y7u/sSpkWNRtYIv8AeOPmLcv0xmqh8X6dfXsGpWJurWaW 3a3u9O1SFXiaaMlGuIjyYvMX5gmevtivJnD3blr3eh6J4WvPCx8S6da6tf3Go2NzGI2EUW0KzLtS RhgErnPOOc1oeGfhR4m1ay1k/ZrCz1Kz1B5DHO7266hCRmMhkyq/J8uCRhuuK474fSnQ9Cu/EWq2 9vNqb3f9nacBgJIiruWSU5OE/i7YPFXvHeua38RG0CytdYt/D8+jvKZpNClctciQKAxQ7QpTB6kg 5zXjRnKVe3Q1Xs2veRlaxpmq2+qawk/h77JqWiwiTUIXtRePHbI339oVZDHznepYHqOK4u6dZbJr nwPN9skSaNnaB2jMyjBMblmJx+oHsK9Ug+IGv+EtX03WtUCX9ho+2O31TWGY3EcTDbKjzRDLI2T+ 7ZTjjFcbrVjpOtX3/CR+E72+0K1N0dQnivbVbuxMe4Oscc9viQQjOPnXp0z0rqp3b94wlTi/hZzO nave61fDTdS8DSXUcxlmEdy0U0f7pdxKrIfm9l4J9M12Fv4Y0zxB4b8R+LZ5CZPD0tpbmzhm8lbZ JhtD5T5k8tjtMfP8XoBXph8DaYsmkePNBin1TR9YyR9gl8u6tZgjLIzROPKuI85xymT71Ts/Bege Hvhl4qtfCdxe61rfiCX7Pq4vIHtpVIuVkj3WwXIQR78srEEn5TXPicfG1odDSlhm37x4LpUOu6bp jw6r4ctb19NMjR3MRjeSZJMO0hVSB82BnuT2qDQvEmn+INNns/l07zXZpIziIWoA273LdD1wvX2r cOm6DHraz6HNL4e0m3Vzb263TXMbTv8AK/mQyMDtUf6sdjWZqOkaFrF1qMCSW+pau8TGC61KNF8s KMgNHHkM4988Yr0lUjJaHJJNM9O+HvhPW5bG5Onyz3NpcvI39oXEkZtJjnaNkmdx2KCpKrgVg61o nivwpLNcLFcQWsStGRDd/ahLv/iTY+VyO2M59K5uG513w7YWyaU0si2QhMmmxJviIcYkI2PgA4JO BXVQa9d6da3F14Ukslv7sjy5tXXH2e3B3OsSyn/WduvvXNKm+bQadjX0e/8AEF3qWmzaZpI1QDPk XJRVubNTmMo0hZPMjPO+Ni2R6HAr0G6+F0uo6je6iNVjtNOurPyoLKFyGF2fmV5HAP8Aq+QuNx9f WvnrW/EtzqWhXGsefBfXTXhtntIJ5Y5I5Y+VJySA2znPTtWj4V1DUJE1jWtek/sTS7Eiwury5YRO bhl/dIIUAaRlzkSKPrWNXDzavE3i0el+JNCu7u0tE8VQeXqtiI4Hn27rW8RBlWjkH8XHQ9OlYyeI ovA/2S4tdi6/qyySWZkXclsrHYJsd2x9xTxnmrMd3dwNDYxPqGt6UxUSLcxNKsyEYVoTHv2c89eo wa0L3wgviC4s7HTZjaOhW5vGbk3FmgANxbrJwHiIMcsW3K/exjmuOFKUqi9psacvNscPa6iljb6x bXF0+o3lqTKbq7lCy3d1IN3lGQ4Xcq988dAM8V1mmaenimO2S5eDStIvDFcG1uosPHJtI3xvjzFk X/nrxleuc4DPD0Xh3xLa3l5ZWSNY6TeSW2mNMokJkiGHmDDG7J9q7rRWttWsLeO+gEst1HJa3W0g bLu3zkd9ocAEf06V6WIlypMJOy5Uc99m0bRru6+HN3fWt6dRiF1ax2nmW6F15VAxQJDdMo+Qrw3Q 9a5S38FS6hql5qsupXR06GDzljl2wSRkfe82DZuSQf3RlT2yOabqmg6zosGrGXU7X7BPdtPPc6iT OXjcDbDBHGeNuO/oOVxXYadqsHh60ttW1C5uZobjaq3PM7bGjJAuGIJIOMHjG3AyK5MVWVKzj1En z7nMalrFp4h8F6FpCw3dzfaJczRS+S0cf+i3MSIGZmwPvqF2fxfhVjw74V+JVto083izQ1h0ffDp ltfX0sZ1FIZH22wnWPcTFC33JG+dVbbhhzXQ/DfT7LVddHiqDR/sy6as6XOnmaMp5hXdFLbYbKDd yqMCOflJwa7DWvF8usSTeFdSklRL9FkmsY1EEp6MFZ5MjrgqwwO9deFk/ZyUhRUos4KTUNC0Uvpm oagNLvrGcyRTkYMcj5C8gDf937mfYZrqrq+Hja+0W2uIIbXxJYqt2itlkvrM/Nvs1HVpDgBWAAb5 etS+GLLQ7zwtdeJYrhNcspG3TRXaK3kKku2VnVs72XhCc4HB+XrXmnj/AMMjwTJp+qeHzeXtg750 p3uyX0y6VhOVjmxkq+1XTk96zpQjO92bRlbRmT4j02+0qVtOF9FcNcFTHLGoMibmOY5ImxtfPBB7 1Db+E7kssWpNJPcbN2GVEKbeQm4BuPu/d/pXUKvh748eZpOvQ2/hv4k2/mG0uU+SDUHAGI5sgZcH HzdT1rzCRY9IvGkjs5bC5eXN3pbtLJHDcRko8f3htzyecZ9639nKnFI5K0HF3RDJ4U8azSPL/Z+k SBiSGAbp2BxInIHB+Ucio/8AhD/Gv/QL0n8n/wDj1WU0iW8BurPS2MMzM6kxSdyc/wAR4z0p3/CP 3/8A0Cj/AN+pP8aq5lzS7n//0ffZbWPULa406YDy7+KS2YEdpFx/hXwHJD9jeS1mIWS3kaEg8Hch x07199aXfW2pwfbdPmEkcTsgfBGJFPPXHTGOlfLvxBnh8JeNNUi0PTo7a8vCt59vnxNKvnjcfIjP 7uPv8xDN7V5mZw5opkQZxVv4XvBarfa3cx6FYSYKPdAmaX/rhbD53+pG2rlpr2maSxj8K2rQTHht RvNkt2faJOYoR+DH6dK5SZ5bqd7u8eS4mk+9LLl2I92Of0C/SrMAYEbWCgf7P4V4cmo7GxuOzzSN PKzTzSDMkkjFnbPYucnH0x9Kz5lI+XOD26cY+uO1XUJAGTn6/wD1qjkAKdMkcjP0xis4y1RXLc+v /Dd4NS0XSr8NkTW0e4j+8Bg/yrpYnSOZGcbl4JHtkf0ryf4U3q3HhhbTIL2NxLGB0+V/mT+deqYG 0gjI5H9K+voz5qSZySXvHxR4g0z+xdc1PSGbK2VzIgz127srx9MVy0haPOw+WF65x1r2r4w6elv4 tivTGNuqWiyEjA+aI+W36AV5Xp+i32rPK1qFhtrcbri7mbyoIVx95pTx/wABHJr5fE0WqrSNoPQq pJ8m5zwBkntj+VdLptlJo15Za5qbjTkgkWa2ibP2iYqcgpHyQn+2cfjTY7/SNFIGgp9uvFGf7SuI wUTPe2gbA+jt9QKxLmaWQyy3TFpbgYeaVizMT+HJ/wBkYFTScYSXcGz7CWC5utQi1C9mLujebDGq gIm7njvnB64qLxzdxJ4W1AzSJG/yNEHIyzK2cLmuLsPHdiug6fEpLX626I4eKRkVlG3kAVgz6hc6 i3nXGqXcr54Een/KPZdw/WvrVFTjZmZgaVaeCLJ4rmdY9QuT87JLBIQuecHkA49q6uDXPCcUhMek LEDydloGB/B8/wCfypsVjcz4dZdadT3W2RVOOOMitA6Jey8Rprb+z+Wo/StaOHhBaCbEk8ReHmAE emXGOgCWsY/DpTk1vROqaVqGT2CQp7VpaRo+uadJcy/2DcXwuYWtz9qmU7VbqU5GD71SXwhrgA26 JcTBeMSXvpwOldKYGNrT6Fren3Gl3mkXpgu49mZJY8K38LYHofSvjLxFZXei3s2k3gxLbtj5sjII yp/EV91jwn4gOceGoYiuCGkvCScemcfrXl3xR+E+v69YjV7e0ghvbAFpQtwrvLCP4ev3l7e1YVoc yCLPjlp3T5kOSOQFx16Yru9IkNnaxrN80rfvGP16D8qy20qCwiF1cHO3pn1HGD0qql+S4BIG7kZ4 rz5I0uekRXUc8f3sEcAfpWpp9yRJsPQ8V5gt3In3TsPfqM1qWutGMhy2SnY1m0WpHtXliPBY8Hp+ VSu6rj0rkoPFFjd2aq7Msy+3/wBaoDqjSMdsnHvUlXO3ivEQ9Oc13+iajb3EEnzruQDgmvCV1Kce 4+gqWDVWSUOuVJ9OM49qlxGj6AluUPQgdOKovIv3ic5/pXmEeq3TqCJDz3ra0TUJriS4t5X3Dt+A 96z5QOqe7xkLyBTre7LHOMEdDVDaAvvSwKWbjg0yrHSxXqEqJTj3NN/dXT5LZVWwAP8APSsgsVOy YBw3cdR2pVdIgzZIUDOOnTsTQJ7GxbWmq6kw8Paa8s/2qdZY7dpX8lZF480pkKAB1PGe1fRPhHwR ovhOEmCQXepyLsmuWGN3qsY/gQdu/rXJeBdFbRtKk1i4kQahqCAIFI/dQ/wr9T978a9F0y9jwVkZ TIfpgn1zUTnpY+jy3Lny88kWdR1X+xoTM0IESjLSbxuUVl/8JIJYDKrGWNxkMv0z+f4V1txY6drV obPU4w6v8uR8rLn+JSOmOx9a+YLjwV4b+GfiW8uPiEut3nhu/ZWtNYsJn8u2U53RXUKZ+bPPmYpU aDn1PQrYlUo++j3fTtbsTEybumTlgQc56dR+Hoa818X/AAT8H+PJdQ1CLW7jQ77WUWLUGg2tDdhc fNJEcfOQMbh26969b8I+Bvg34ls/tfhLWZNXiIBPlX/mMuezL1HpzXoVh4B8KadNuWwmlZen2iQs uR3Irtp4SpF6Hl4nNcHVpcs4an46/FbS/EkXxmufBcmupZ2emxQFJbKZxb21ikPms2xf49n8GOuA O1chrnjjxBrEy2Pgy0u7DSbVfLtzb27y3kygY3zTbC25up2kda/cuPw34btpZri18N2Iml/1kiWy F34wdzbScHgdegrVt5ZrRdtvpyRJ2Ece0D8sV6a0R8hPlb91aH4UaD4cv7rzj4tsNS0mFgJEvJ7e VY3boUZpgRkmuw0zRfDuhXSXmnate288TCRRHIAoK9MxnK8f/rr9o7tNN162udE8Q2Uc9rdIUlgu FDRyIeCCGB5/H3FfCHjv9kbxNZ63FafDlrV/D15KZJXu5ds9mD/Amf8AWoP4fQYHvWkZdyb+RT8H /GrVdWFhbeM0geG4LW+nazCsflNOPuwTfLiCVgMpwA/r2rrtHjvtS1+FobeS7v7xzdEOQGEcZ2rk t09+OCa5nWv2LvFclxZ2mg+Irb+yWFvPejUN6M08T5xGsIZSoA+XODnNfT9j8GYbLULfVZryGW5g t8DduC+ax+6QMfulxkDuetbzlDk5epy1qHPK58j33jPWJfivZ6cUgs9P8OasIieJ5bzUZogCkb/d RIUOWY+vBr3W8iuYiwkSR1Q7V3A8nsMEdawta/Y/8P8AiHxNeeJtd8cakLi9n89o7GOC2VJP7y8P g4AyR6V6f4R+A3g7wtqtvrc2t654lvrX/Vf2retcRK39/wAoALu/SuzLcZTw0dVdnNjMD7SyizU8 P/Dm1uLGO816eaKWX5hBH8u1feulTwN4Ktyd8U9x7ySOR+XFWPFniVtDSOCzsf7S1G45jgGQiL/e kI6fTNcDJ4o+JlxkwaRYWgHdoWPb/axWVbMa85X5jSngKcVsdu3g3wHPKJJPD9tcsg2j7QvmAA88 IxI+p610cEGn2yqtnpFuixKFQRxgAL0CrxXhGo+J/H8ERm1HxNp+kwjqVWOPGPxNeZXnxNvtQuBp WheINV8X6m52i30oF1HHVnVQAPxrjnUlL4mdkKEVsj7F1HWZ9Nspby8WLTbS3G6SaXAREH3uuK+d 7O/b41eILPXNZuEsvh74euPMsYrl0Q6tewn5ZWjJ5gibnDcMcelcVB8MvE/ilVn+IN9KluxydJiu Wcye1xMpIwP7idejHqK9P0L4daEtxHDJZxPEFEaowyqKBj5RjCjAHAFSa2R7Tf8AjrwdbKFk8QWc LLzhZAxHt8pryzUPiZ4Ct7mV01yWdWbdiGCVuf8APtXEz+DdLhu5VjtkQK5Axjj9KSTw5ZiNgIVz 7gH9BUJWBl/Ufid8P762utPu5dRubW9hkt5sWzbtkqlWxux29PWvm34F/GbQfAXgDWfCHiCw1PUj 4Z1W5jgaEIFS3c70Rskd1Y19BaToloCFWFPlfd0HGOvFeFeCvDtvd/Eb476BJCvkNcWBAA4QyxMM gVV9CobEOt/tOeEPH2laz4R0Pw9d2cl9F5X2q8nijjRt3XaNxb/drwOy8ERwytu1ON7BD+6gX5fL U84OMCvE9XsLjwl4p1fw7KjvdfaN0CKuWkRvulR69q6TTPB/iPUCtz4gvZNNtz8yW8ZLzsOwx0X8 T+HavPr3k9Tejoe62PhvRrkmPy4rhE4YowbbkZ529KyNW8IrZyG70cshT+A+3arXg+C50GM2ulWG y3lYPI07ZeTHGWNelTraypkx7d3J29sj1rkvY6Gj5k8cQPruiTRXC7L+1XcmeCSPTNa/hC8ludOh jkADiFMk+u3nFaHjnU9NsFkZITK6cDI5Ge1c14IugpTdtRZ0B4XAyenzt/QV6GFbsZtWZ2n9qyoj BvIA2j/lo3TzNp4A4/GnT6mE8xDJa/L5oxvcn5cdOKzZoL/95GLqVo1VsiOROz59D/OmNbaipkRJ pcuZVx56gEt+HtXctgZpXk0D3lvG8wVZFcMvmgEgKMY9axzrxggcPsmVN3WQZAzgdM1rraj7XYzT A/vCVyu3aCy4UEnpn2qQQw2kMxmLxqN43KFxzyM4BP6UAc5qGp291p100UaOHSSNjE6sV+Q54z9O 1eZ6SpbT4U27iY9uCM9efu8V2OsahHdxyQxbCmGbK4kBOMHoARXBaXdypGpiUbemB6A1XQykbSaU 04ZmtV2k/wANupGNjKeWPoM10OnabFHDc5twnl5BUxRp0RSemR+dc4l4hyrqoJ4wdxPJ2+vv6Vow XS+VIqbfLk5KgHoVzwKEJGsLRUQ71YIgxwLdP+WQ/rU0kNpBZzzSqud0gGHjHTAxkcdqwIb9nAIg 3BlPHkt/Fg9/pXVxXUC2VwEtnDESqXMQG3bnPDcD69aLAcx4qvbh7YW8SykGzkIRpBt4PHygZ7V6 NqV1H9tSUSKpe3s5CWuGXOSvVFBrmPE0aDTzhGYm0kZc7AAVbpkHd/DXZmbzLKxuIopXR9NilBCg AhVjAxUgZUnkeRJtmj+SKUf6+Q/clBHQV0AJMSSZA+UcA9gOuRVBWuYmlDWzmP8AeKQ2xfufN1PP NaduRNaRODuzHuIPJ+YdKAOcW4hGcyKhQZ4un6+dgdUrd06OO4BCnz8cLtkVyMH0wKo3DytG223d QPNxgR8BCrmrNqVgAhnDplzjcVAyD320+gHFXlpNA8iI88YCXA4mhPzCQEjmtGFdRjulDM7qJJzg mEggoMbiBmtHV47Gd7iP7EVH7zk2x9AzYIrJtJYGZrRICjb35MbDcdo6np+tIDU+1u9tqMEwkbfC NuUjOQFU8AEKOff/AApl7ch7aCTyCxMTAHaHwPK6HkelZMpkQai8ibYfJxgxHbtwB93+LirreQbe NpiohKEKCCMDZjt/s0AQNJIGlfywGKucfZ8DIjj9GrXldRYXUQwDFLIQDDJtG1g3KIc/kKy5rbSR HLKpQDDnJc85C/0qV9otLhN2LZjIZGDEKDznLUAAeCKZcJEDFIowUmXlJsY5b0Y1ntcRj7P5TIxQ xLnMm77rA/e47VazCWOyd0AYnAnH/PQdMj2rPmkUrEySF2JBHzZ6KSOgoA5PxrfKumyLldzZYctk FgTjjIPPrXFeH7JrnxFJaxhWjkCMVLFRvHuOnStjxtdRhGjfgA9PYMP6VzmlTJdsbi3LE+bkkHHA 6Bj+NUtESesyDUUQjESArIAEuQxUeYJBkEZ+6Bxiuhh0n7Nsa9me+E0nmSorBFRXUdTxmuR0iQwQ J5KtIWIbjG3aSIz8zdx7V11rb3UllEVPzTukeSAwC7j/ABEjB6VDZrTN/RxBa2cbxRw2y25tidks hB2MynO3rzXPajrFvcRrb3du8ttKAABLkZaYvuwfQHpVyz1CytUMd7KJBsjkKG4BIMU3zcKPQ/pX SxjTXinaOdLjyAxwc5DRNluo/jQj8qRqZukwW9mkc1nOksAXp93IPUNXZ6NrH9mazp/2WIvbXHmR ykqMJHt52y/wknt0Irh9X1rRLYhNKCF2PDAj5lIztUnAJqTw/q0bS4S1iuWdtscc+4hXz1I4A9K4 8dSVSm0waOuvLuWwvLmdLmS4eYuiwxqwHkqBuiOOAMdD3qlFLpkdlHpmlypZz3Kn7KWLJ5akkqDC Pu4P3sfeHIqpqOtX1s63WoxC0vGSTyWfDKJgfusF4xjtnpWHqWp+L7PULGeN7TUb3U5khMYiFuFG 3I+YDG1s7Rg5r5yEbKxz1rJWseeNq+tWfiBr3xbbTaR5cSxJdadF5a7Y3KGSMNw4Jrt9Mu7aWOKH RprjXIVLTTTvkKyZCEyjaACD/dqDW/sc76HFqVy1rrL3Yt7eOLDR5jl2sjliRuU8Ajt26102p6Wx ttRKzzWt1LP9k/c4K7lfd7A9O3JPam436HJGD3NL4cWEfiTx5Y3eo2n2e78PzT/ZTGnyqsSlVLZ+ 9GQ4wf71fWkMIBzgjp1x+uK+ffg/p+rXniu+17VtSk1T7JpqWkbSwiGRWaUs6svy8gp1x04r6UVA EC4GcdvWvawi/dlwRQuVzCVIyTXi3xOuBHb6FpuIN13eSN+/JBASPGYR0L/P0Ne4SABef/1V8zfG RdTutWt7bT5USLTtPaeQAK8gd5C6kKcc4jG3HPXPanjH7htE8rk1S6i1XTNXczxw2McWLm33xtAV co5EsfOHXBYcrkZHWvb9a1nRbxIR4/t/7bjnYJa63ZxJBqCk8QmYJ8szFOpx2rxoXCpDZ3Ol332V b8Qi7tblS8kD3PIGxRgLLzjPTpXQW/iSzmt/D+uz2MlrJp8MhFofmha4jPlLJs/jJG32C9K+edWc ZcpLfvHV+KPBGtahoqR+GZbXWNFsJGuI7q3RvtFtPn547u17bto2yAYGK4jw3HrN14cm0Wx/01zq zwT3MI2tbm4gcPwo6xnDHPJwcVQW88QWGs22u+CZJjrMbZWQXIikWPPdc7ZEz8pyDgelez6R4/0j VLmW38aaKvhrXZnhee9sY12ySKpZGeMELINrfeHODXRWso8yCfkfPmj6ssdp4mC3U6z20kdzdWyn zBDJDujkktyMh4ZInZvlzgY7Yru/A/hq+1rVJbhpYbTSLfa+7n/SZCd2yXK/KOPmPQJ9a7DUvC/i rwytpqvg8afqOjwmD/iZJGs8SWhByk8K5aPywTgEfMp29sVettb8y7a20iGF9FubC7ERjkJk3qhk 2umPvfxH/YAA705w0THGBw9zq9vdy6qc27SPefY55ZGaONlDbiyuw7cAKB04rsdJm8MarBc29jeJ KyKrTBkA27ef9Zxn2rwm81S2e/MGsxXh023byopIoh5ltGp2sY2XIYexGfpXaTaHpGh6hY32i6lP cEIXVDtxcxyDCiTH3cH2rGcV0F7T3tDrfGeqeH7/AE2z0wW+oxyxW1xOmxYpGVUYO7xocbht7DPS vCbu103RL+C/0K8m1Cx1SGNJXmt/s7ZDfu3Pb5gSMr6dulepRajLpBtJLi384GQmaCViA2Dz5bj5 kO3p2PcV5X4y0eHSbr7ToN3eX+nXxe5thKysrQp8+w4+7JC3y4x2rsyys6dRSFiNWPiuv9JdRwMk 5PP863DD5qjEgAI47V5lcazMl6tvZRG5nuCNiKCZGY8/KB+tdpFZXVvGn9sXKl3HMMZ4U+jMOPyr 9BpzujkLR8gSeVHJ58g6hfmC/UjIrMvtYsbFSs0ybh1AI/pUl3bXF/CYrCVYIhwwjGMfU1l/8IvC gPnjzSRzx1py8gPOPFhlvbmDVLaUXFkPlwp+43uB/hV7T5S6KD1A5q/rGh6bbxtHbK9tdSD5QT8j 44wR0FYmkuSo4we4/wAM1wVI+8NnYQHBHpXSW+NoPb2rlYScgd8/1ro7djjbUtWGjYjXejgHkqcZ 6dK+iNSb7d4Ws3Iz52hIePWLgV87QMOAenf8a+h9KcT+C9G4yTYXcB/4BI2KyiveGeN8FVx0Cj27 VUYEd6k3kInG5cfz9qquzOT7dK9eOxBdt2AOMjNTyPz2+orEW5KSBcDPTGM/TpWvGN6hj36j0q0K 5oQApAc9M5xTxgj3p77fIABxxwKjVNw9CB0piInZVGSM1DEMsX28dc/pSzvv2wKfvH0q+6CGMDIx 0NAGTbjL3SDncVYfhVXVo7mfS7uGzcJcFQ8e4b04OcFff2NT7hHfbV+VXjP4mp4pcsEcgqwxgdu1 JvoCPJk161nDab4hsDPag7ntyxDxHvJAx5Azzt6V3lvFYarp8J0++aW5tiXsp5/vDj/VsR1B6Vz+ v6Ct5Gzx/JNCSVYdV5689fpXnsF3f6ZOZId0c0PzOg4VgP4gP6Cs9ij055RqUb7EMN1bHE9uR8y/ /W7j2q3Y7UYHOd/Ue3SnaLrWkayI9R1BRDJbJn7Uvy4H91/UfWrl4umvi80y4WSGYE7hjj2pqSAg urC3EqylevOADUaaC+q3cdjYW/m3EpwEAzjtuf0UVBHNcvKkMhAjDAlj2BO3HFeyaVqNpoUf2TTw kSvjzJD/AKx2HHzN6ew4xXHiaqib0abkz0zwT4c0/wAC6QbC2/eXN3iS7uARh2H3U/3ErpVv1LnZ LtPUAnoO2a8tXV3uAWDoSBwBIuT+GeKtW+v3CYUKFzxyAf5Zrxpzvqz0IpLQ9Yt9QuUkRUlwcnDI 2VHFdJb+J7lIRD5skkZ4ZGDNGfXKMWGK8Vt9blWQhkBPXEfBP17V0kGszTkK0YVvU/K35jiotcbi ux0OpaD4T1dSTpn9mTmRZTc6WVtZGKcruCqyN+K1w3iP4capqdybvwv4gt0naQTi31K2KxtIPWWP d3z/AAjrXZQ6nBC6yuc9jucEVdXxXpEQKPNHjPKlT/hXJUwNOe6IlSTPFfiV4L8aWr2+sQaBPPYj 7KkosX82Bf8AnuzeSC20dsL0/GvPfC+ueCNS1K7t7Szu9HubeB/s85Z2QyQth1Cna3zg4Hy9q+v7 LxloyS+ZGfKccB4iyHjjJGBn9asajfeB/FHlR69ZQ6hIhzHJNbjejdmWWMqf1rzMRksHH3Wc1TCX 2PBrvSmitReeFZWu9TtZkjvBfu8ifZ5Rhz5TA5AIXGAa422huba6neS1/saN3VGghUmCSVRxJH3z zj0yOnSvoS+8CeHdSt9Qg0vWNQtZbyNYVM0huVjVeyA/vB+tY918P9btJYPsEVnfRwQhQ1yxlDMo wH2sQQe/SvFll1amrWOR4eUTnbHxl4intf7Lt7+50W7t3+0bFkUmcIu3LRSArtxjIUhj6d6y9J+N nxCsgNW8RwztfaFLHHY/ZoituvmA+akw3HKuqrwGx0x6l2qXDyXsVvqWlLf6hGfLmmltzBhXG1li U4O0jvVOztNUsX8iy0e0u7OzaKaWC3mxCkeQcAyk5lx6dGFeRPDKLacdxKrKPUZ8WNY03XPF174s 0/RLKTT7W2QzSwyxxskmzEoDBfmBwCFePJrKfwL8PPEwuNFtvFEOkXVy8YEt3GVjkeVQ7xibZ+5O flViCrL909q2r7R4dSuLjXbOcTz7v3lzcRC8fAO0xIvCqB05GeK5y006/itLkXlshg82a9t2HBt7 cDcIixHJDpng/wAZFdNGy2ZpLEKW4t94WfwZcnTtXlXTWsx5sHnz5+0KPumGQH97EezDj+HrxWYm sax9oh0ay0eE6bchWlvZ4xLGyY5CKefqeP0qC/1yx32uleJIxPZXsBm0xmQstl5xxIPm+byn2glB 6A8c11Ft8QdU0++GkXlhaaxAgb93IqW1yY14LpKoKykduxp1ItapE1KNlzI5a/1DRIJ2sLn7Ut5E yS20VnbL+9dhsALKCXfbx82APWvU/AVnomqS3k2qzPe36MLhLKaLy2jLrzK4x8x7deKr6TeeDdcu 2uPCmo2kWpB2jks5wsVzn+IK44PTqv4VTvZtR8OXl1dazLKsE0wVriNWD2keCqq6rgkcjDDPNSqr 2SMVudnr/jDU4rSbRdHMmiRqo8yNo3SWWNjh2EgJxtHQryPSuF0TxSPCXhqabSQ+q3N7ctPZSXcx eQFBhwsh9R19W4YcV0F4i6zYW6PepJFLccBo3MrR7PvENyW3EfN+lcVrWk2SXFv5d6LeTSCq+QkY KLnn96vTLZ56HvWkZfaaOynpqd5pfjnS9f8ACNxqOjaSLG9sSZdSsoPma1Qn/XpGP9ZCx+VnXlT1 xVLwvr91J4gGg+TsfWpbS4s48N/Ap3ksBjJHTPauB0fwtLomoWusaLfyaXPG0k0c1mftVu0jfMjp nlI/4biFxg/eTJr2Pw54hbxbe2Oqx7bLVtMngmv7AbgyYcAzWwUfvbdxkkHBjrOvBVIe6HLzFnxR oen6gkiTSG2mi80W8qyKEyTzmPnd3OccjpUdlp9trngWS00qzfUdU0m0jkFnaNiZZrfCNLHn+F0O duDk/Jx0rmtUhlupdevrOWS8hgu3xNtDnez5XZkHI/hOP5dL+nvqfhvSW+0aiUvPKZ55osQJGGO7 cWGCrLjBPqBjvXjV6bsk+guXlMuwZbSPTHstMnh1Lyit1Z3CmB5fLBJvLJ5O7dJLfG3OSuetbGqa npl7qcD38hvfDF1MqWGoFmhm0y7mVlNrO4y0Ak3fKrZjJ+ZRg1d0TV9O8T22p+I9euLfVzqwWS0j jWUSCW2Rg80ZLARkDlto5I6VzfhiSDxLNPqXg6+hnuZrdhFDMoX7dFu+ZGjYCNxnnnv8wxXoUpN6 lwqHrGi38dlHrWi6OIJF0K83RwNy482MLdQXkOM7ZHO5XQFPnyOCRXLX+nvb2pm8B3aDSdVuE+1a Ze7Zjp9wSF8tCefs0iEjG75MKVODXP2fjXT1u9N8T+I7OWw1axlXTriJvlSWIsIJI1lH3XgOw7Wb PWuV8S+NH0Tx/q/h7xPpgudP80iG/wBPXy5YVL/JG6HCttBByeoodNvm9muhpKHVHRfGLwvZ2fif TpvCUUbCeNWlt5LkxTpBbv5bzWsjY+fGzPO726U/7Za+Kr240jxtHZWPjs2xsLDxDOpjg1KFRlIb sgriXHCTEegNYvxfnbVfDll4x8P20evx+H/Mju4412XFp9sKbJTCSGGNvLcjle1UJdV0Q3UPgz4h zDULh7UxRatbhfMMapvEl3Cu7aISM/aEzhfvLjmt8rlU+qxdRa9TOTto0Ok0D4haE39ktp8sf2UB QhlWQqCMhS3GSAQOntTPs/xA/wCfKT/vof416xpni3W9GsIdJuL/AMySxBgLSbWY+WSoJJz1A4A4 HQcYq/8A8J/qv/P6n/fKf4VzfXUR7BH/0vadEt49NtEs4BiNSWOcZZieSa8S+POnbLzQddQZNxHJ YucdWT94n6E/lXvcCbVGeo61wHxd03+0PAV7Mn+s0ueK9Q+gVgrj/vlqwxUOam4ma3PkoMTkMcDP HNWYFXcMZbP5VCPLxtMjcdCoBzT4iPMG3JJ7txXydjc3UyAFUDikZS2cnHoKbHkKUyDnHPvQ7bQx b5VXqf6Vm3qWmet/B29CX2raY/8Ay2ijnB/65HH67ulfQy7QQhIBJ4GeTzjA7mvlTwJ9r0nXLPXZ h5FnLvtgJMK1z5gxsiU4J+bHzYwPUV7zHHf3mpWl7elI1siWghjAJRiNuGbvx+tfUZdO9GxzyRh/ F3T9ObQ7LWNRtGvDpdwU8sSGIMLgbdsjDogZQcCvmnUtUu9TEcM7CO2tmzBbQr5dvDjgbYx1Yf3z knrkdK+vvFumjW/COtacwG6S1d0GMANGA4/HIxXyrpnhi/1CKO/uICLdsYRXVGcHr8zH5R+dcmYY ec6nuBFnORo8jEheSc5duBnqc11WnjTNNmju31OL7TCMxuoJ2N6gEYJr0BLC3iCmLwh4diCgDMjs 5OO5Jbqep4qwxkCr5Gg+Gbc9isav+hzXbgsrhBc00DZhp44lH3PEMm7uVRAT+a1F/wAJtO0gz4hu oz6rsz+WP6V0Zn1VRwmgw47R2kZI/Q1JHf6yv3rq1Vf+mViBj6YSvajykGF/wl0ky7X1vUpvcMBn 8BxULa5dv/q7/UnB7F2z+ldE95qUoG7U3AHQR223H6D+VM+1X44Oq3ZHQhYtv5UwMSO9vZ+XGp3G Oux5P5CplWeT5W0/U3+rSEf+g/1rSMmH51DUFz124XP50rSBxtE2rSfSXAP/AHzmgBsVg82N3h+8 mHuzCraaSFILeGJPTLuwIxz2OP0qolorn57fVn92lbn8eK0LfR45emm6nL7faSB+pxR5Aed+L/hR YeLissVhdaJcA5BtZY9gOMcxs2Oa8U1X4H/EnSN0thFDrFsoLnyp0imHoDGT/Kvrl9Isov8AXaFc gf8ATW9x/wCgk1FJa6NHjGhwox6b7yRs/lWU6Sa2A/PmaS+06drXU7aWymT70VwhjYZPbPX8DVuO 7V1z93b2PHWvcPjHZ291r0DCzhgRLWNdqEuo+dsHLeuK+etdtW06QSwjCOMhRzg4zXBKCvYo347g od0b81vW+oFiCSc9xXlltrKOAOVbuK24dVXjbls9+lS6aQ7nq0N+pxknHQ4rQDLIQ6EYHNeY22p7 u+0HqK111KNSCGIPpms+U0Uj1fT7hJID8wBU1LDq8FlNvEoDZxjPWvNIL6WZT5ZIDen5VYh8vzMt kn/a5AqeUpM9rg1lZfnQ7gfetG3v/NBK/e9M9cV5TaTPHtZCTjp/kV1FpqCknDGOTH3ccke1Z8pa Z3VrqEcs3lkgbhgA/rW5pNpHrGt6fpjybIriT95jukXJ6V52sFvvWWFySe/v3GK63wQkmoa/cXUU rRHTYwkbDOCW5OalqyOjDQ5ppH2Fb6XazyBYsIoG3A7KOAPwFZGoeH7vTmaax/eL1IY/5Fcja+Jz p4j/ALQWWKcfxKCEb0NdVP8AECy+zqZhntlCOfwrklqfaYeMkvdZm2fi/wCxXS2k91GsrcCNztPH pnrXpln4hW7tTDcRq0b8OjAEEHj5g2f5V5Vq58K+KbNorlU3N91sBJAcfwnrmvOtPufGnha/FlZS HVLJzhA5wyr7/wCNTH3Sa9VN2mj1XXfhf8PL+KbUk0ZNPuwrMl7prvZ3CP2bdFw3Pqtcr4d8OfGv SrcJo3xNuxEyALDqMCXiqOwBODXZ6DPf6yAb+BYSmGwr5znoDXptvAY1XGO3TjGfrXt4KpNwuz5H OHBT5YHh1zZ/tOAfufFmlasp7MJLNsegKrXKXkvx+tMvq3hK41Xbn95p2tK5P/AHxX1ZGjHg9PfF RzQDG3HPsB/n9a70eJzI8Q8PfH7xRomnrp3jv4b+I/OteIbiGOKbcn912U9q25f2nLcqEsvh/wCJ 7nHADCKJfzLV3V3pMN0hSVcAjGR/WsYeFLED/VIf+A81UrdiHI4SX9oT4hXJ/wCJT8NI4lOf+Qlq YRvbKqDis6T41/Ha6Oy38H+HbQdQz3ck2O2OAP5V6vH4dsEA/dLx2x0q9FpNqP8Alnj/AHcCov5D 5jxb/hYH7R14wMNz4a0sesdpNLjP1xWjbv8AtC6pzqHxGstPB4C2elqSB9Xr2NLG3j+UJjPc1cS3 jXlV/lVcz7BzeR4U3wi8a6tcNd658YPEMjS43LZ21tACMYxnk9qvr8AtBlwNX8YeLdWUcbJNRMS8 evlivcERhgAcVZVCRyKZLZ5DpnwK+E+myJP/AMI8dSmHPmancy3Z6+jECvUbSws9OtxaadbQ2NsB gQ2kawpjtlVAz+JP4Voqu3p3pNhoFcqhCM9/T6Vq6NHi+Tp/kVTCHPStPSQFvkzx2/pSsBxeoQj7 dOcfxk/rVIxKV9q2dTTbqNyuOA/9KohAOD+FO4Gfptuv2poxwM9B1INeBfD2e3i+IX7QHiKXItrf ULGAnt+5gdiD+NfRVmoTVIkAyXHHua+LtO1UxfDv4t6gko8zxF47msQQQD5cQCtyfaqfwlwRwtw6 6xrM/iK5gRL28+UMq4dYx91N369q27TT4ldAw3NwCePzGeax7IT3QmuYIJJobdfNnkRCY41Y/KWb ooPbJrWs23NI+TlRwDxyOK8SpJs74RsjWn0qU/vIJjgdB24qndzyW1ttlIWTPAH0q4t1L5Cgdh0H 61h3c5vJQu3JXqD2xWOpZ4l46IeN2P8AtH8uaxfDQdbGyuAqqPL5JGM4PGDW78REENq7EYJOB6c8 Vxmn3T28MKwsGaIdFTJO3sWPFephNjKb1PT7KV3Q+ZNJIshfCliMbxz0x6elbC3Omq8TttZiIy3m MWJJGM1wX9oxmSORghBVW+fLHnrhV4rbhu38nywWjV0GCdkIG18jHeu0DeubvNqtxE4McTDYFGNp VsfxYFc9rdhPJfTq0zZfBTHGBj26/lXQSOk9ldpndh2Od/mY9BggU+9C3TBJOFkgHIUR4O0clhn9 KAPKXtbmDz8ukirG/JGDkj1FedadfqACG5YA9v6V7BqLrBY3/mvkQW8pzkH7i8c/hXhdtCsix7Ub aeOwApowmtTpv7TdMhZR1JwZMdCCOAK29OvIXyMgcDjzCScEjPI9K5gaAxw8bsSeiq3T860LXT9T hcKhuPmGBuxg8+tOxNjeiNsp8suhYZJHmOc7QeOnpXT27Qy21ykSxSv+9KjEkn31z0bGK4c2d3BO B/pAcEgFQCBnHfHvW7YXs6Q+Q8crCQEnzmKj7uMjaM/lTLOl8VLmOIYIZ7KcZMZB4J/irYspVk8P 6LiKBnbSth8xJHbKAADK8fw1y3iK4j8m2DlciGZQFZuAR78Cum0e5s5PD2joyXEhWxZAY2ZR8xYA cYqbAX1SI3JbyY2LySZxbPxuiB65rX02YLYwl0BXylVQRjseorKhMLz7ooLzaXU5Ej/3No/i7Vei Gy0CxqYtg8sJISdo96dirFC6dCkwEMZBkkAxaseXj9c+1WNN3pcNMIlRpJD0i8vPAyeSah2WyOW+ w3EpBzvDt8xCY6BgOPpVmKIzO0ttBJGT8/71z2A+6CT/ADosFilqiQG6u5AIEIMylt0ynJiU85GK ylk8jUUdJkDGcgYuGPBjBwARWul9ayXUsE/22FzK+5VyQf3eOAOKneFZZI7iOe6EnmAgSIBn90B6 HHA70WCxWYRzWup52E/Z85EjkDgdWIz27f4U26hdLeBxISPKc/JKo/5ZccvjPSrVk84lvIBvI+zY 3NMisQUPOeicVaEdvNZWrSShVWMqHaNJN+Y9v8Q/ujrRYLGIZJk8xZJJ0Lhg2ZYz0gUj72PQ9qtX KtLp11y5mZ3OAI3J/XbRcw2MRZY72DOGBEkC9PKwMAY7A9qglNqljdRG4hdCSxcx/ulbHRlB5/Oi wWKMnnRyFyJMliMeTHxiUDsfX+Vc7LMyyxLllICklolXjYOpB9Kr6pqFvE7Or28g3NtKxkcEsfX3 ri7jULjYTBHtXbj5c9vbmpEzC8VXpuJim8sAORwf/r1Q8MPsgk3fLhzg43HIAY4HTp71j3zTzyEe U25icjjjv2rS0D9150TD7uGP0ZSD/Sh9CD0OLxELZFskiZ5VGcriRhuC8YOFXpnrXc2d1qT6K1xL 8zvKvkx7PMK7WBB7AV5Zp8oS4O1RI7DK5yFwe5r12xnP2MWkQJvXjIEifdUHngfTFJo0gyC10W6v hM13I20rOvDRpjzGHpmtW4bT9FeSG2uUuJj5gkC3PRSirjOOckVGlra3FrFpiSbbZcCXCHfI7fxF vrxxWfL4NsJQiRWTqznOQjZZgORxSNivqekkxx3loPMtJAGOPmAOPutjkfUCp/DsurW2s2Npodid V1LLzQ2QPzS7VyyrjJk2jkDrUkfh7xfpUbvbWd0kRx9xRgqegOTn9K3NJttWe9t7ue0l0y9tmDwX 7P5UsLjBVkZejL6/gamps0K50seoWXiOxZbtrjSQVkaSVYwSrMoXLq/KuDweOlX7Saaay0ixle21 lrWYwxpeRmMkJzuWRem7A69B0roH17wb421qSw8W6nHo/iSyZEg8QWqbbe/Y/Li/tj8hcHH7xfyr l/EeheKPAeri28RpFfR36u0GduyZk5SWFlO1xzkcg9sV8xUpNMhWZaTQzarFpWu6THNY3EjXNjA8 oby2XlwJeCyp/eJHpXSeKLg6qsg0NbKaSVE8ixv4dsUrxD51jnbCuXOAuCDnv3rhNNv9WktLhlub q7mhUR3CxxgytGTx5Qb7rHJyvHAxVTw9FqL32q+Gbu9liy3n2ltd2mWu0UCR1j7Apxyp6VF9bFSl Be7E+ivg2t5L4aea+uDM6ymEKzCRU8vgp7bDlep6V65j9fxrz/4XWK2ngnTpBaR2cl4HuZYY84Dy MScHnr3r0Ruhx68f0r36MbQSOST1KUgx718n/GV7STWZriZo7drJ4xJKWPyhIvlOB3y5wOnFfWRU vIg6fMBx3r4Z8b6qupaxqt81jFqVrLOGEUqliVSR43OF5PyYxxXBmcvdQ0O8P/2zdeJNMvrcQ6pb 3jC3eV9rFImGf3jJ82FA+7t47dau+PvD91JBpV9p0q3tpG++C5MO6B0lXCbtpyu11KtxmvLNetrP w5dReMNN1GTToHk862tLZtkmWQBfl77O/GMYr1PSL/WPFPh2xl0S7Fk812bqWJlXbGAHNwh4w0RY eav905HTBry509mhbnnOh+EdEvPFL6Xq90VvtRukjT7OcJEZHG2ME/djk+4j16Ybua4S5i1qGSw1 bRLuS1/s5WXzorSd2ECz9THjmM/xFWDYHFYg8W6SPEtpe6P4fS8ube5tLkSN8nnlJAfMaOPASPPO GOB+ldVPoY8XeNNditE+26lfabHOJHYJJJCreXuaZON8MkOST/WrceaNjWEehZs76TwnegeC9ZXT PEI3TXbSRsbOYsFSUSZ+XbFGRt3ADOO9dnpfxE+Hmqa80eq2LaTqsyskt5ZW7NZ3MbZjIuIFB8vd n/WJXM69p+m+LtEttNsrW4gk1OWENcvH5b3M0C4Kyj+GMMmH6Z+UtjivKry61DwV/pOnTLnRo4NK e3bJF2V/esyZHPzMVB9BWtN2WoqlPl1O98eeD/EWgfadT02yGs6Zdsps75ZPMtdh6b9hyNvoRXla 6U8Eya5qOp3VvexDEEUUYNsioRgZPVa9MsfH1z4WnXVdKTUdFt70+XIZ08y2dscq9scgxjpuwK3r +++F3ixopNRvoPB2sXBVI7q3Jn0q6LjB82A5aM/yqbKWxzQd3c4DU9V0i6vZBfBUi8wNIYTw+9Rz kE7dv8qoS6PY/ZW0W/VXtr+6EmmNaXAwXlX54HLDIEuM/XgVteL/AIdeL/Dcy392In0m6iREvrUe dbMoHLeaudo2iuK8OWNzM2o6ho066jb6PcrK8Cki5UKMm5tgoKvGBw/8S4Bx1peycbDrdyrYDTtE i1TWUjSKe6lNpbnOWgigG11RjjHzAgt+FVdPiN6n9t6vIYLTGY4+hfPfH9z/AMerS8UeZrWqJc33 lraj95KVURqxXkgKv949fU81wl/rLajqfk4+VPlhj6Kg/u/hX3mHf7tGUVodwPECFdlvGtvbLwHY Esf92MDn8artqOozKQkM8ino3Cf+OjpRpdlGuyWUeY57nt7LXWIu8YUY46V2RYWPMtY1N7e1ZdUs ne2PQHEihh0B6FSfauK066hkmYxIVQuSFP8ADz0r2+902K8je3uofMgmUq2PQjGfqK8Wk0ebQNUf T5/nXAaJxxujPT/A1hWi7hY6iA5YfpW5bNgkmsK3wQD0rYgcA49etc0hm/ESDuxwa+gPCTmbwVoo bO0Xt5CxHZWUNj9a+dIpT6de/avR/D3xC0DQ9BtNGvpGkuk1P7QFVgPkdAjAD8Ky66AYDOImYN/A SuOD0NQgq54NQSyxtNKy5wzswJ/ulsgflTFJDDacmvWjsQaSIgIbABxyf5VZt2AOCPl9aqplz/Sr 0KbVPfNWhMtZJHA4HQUqScHI5qMbhxjNMYgcAc0xD7ZFnuvNbgJ0HvVqZjyo6n8aoQkoxxxntVa6 ndX4JxQwG6gjRQi5iAV4WDHj+HgHpUYaTzMEopf5sjp69uKtJJHJatG2WOeoPX61zMVy9v8A6Ocb rVipJ/iXGQfyNJ2HY3rmPLLJnO5cHHSvPvE+iu8a3dsdk0PzIfX2Ndza6jBKn2WaRMycLjjBqpK6 OGilIQJ1LEYUjuTWc2rDseUeGpIZLqS0u0X7HqgNtODnasmeDj2NdT4IBt7i/wDDN5tE9k8mzd1b b3Hsa8+WeH+1dQWxBe1kbIYdPMU/eXpXboputebVosrujVC3RjtTB/M1zxeo7nca3M+m6Tq19AqN LaQF16FfvAEfhXE6N4+hvEVbnIY8knvzz7V0V15Zsbmxc7oriJomB7huCf614FaWMlteTRkFhAdu 4emeDg+2O1cmPWzOnDysfTtnqsd0qtblXAGcHg10VtqE0pULbuWHXBwAK+fdOvzb5YOW7YPGMcV6 74f1W0kSNwV3p8218kNz0JryZnoU5x6nplreXkMiB8qff0Pqa2ftN4d0i3BCp1UMTuHtgGsmGWLV mBt41t1I2/Ic4J9q6WLR71PLRtskcf3hHgMR9O9c7udseUwp53uJ1WxRix5YM+5mPsKnR9atvme3 eFRyxIVsD8zXa6fo2mSMVl+02pH3ARjrz8ucV0ltpVlGJJoZmwuF3NGjj8uaabDlicHYTo4VfsP2 gsTjyz0ya2rZhHMU8jbEOoLHKn04rZk0G6lVpY5/LGcZWDa2Dzn5BVM+Gr123i9VwflIKOm7jvxx RdCdNdhxvTF2MWDwytj/AL5rWtvEz7cJdMBjaTICOn0rndQ8J6lYx+eZYyvUIsmOB/dH+FYX9n3R O8CRM4xn5sU7kezR7Vb+Mr2aL7PqNrbarbLhdksayfJ35blfwxWAPDPw6voljg0ubSYlO5IYCZrZ JC24sIZCcfMSSAf8K8zM97GuIIvtEnZWzF+TcVbsvGD26mzvtLkgmjbA/e7uvP8A+qs6tCnPdGFX DwfQsah8IT/asuq6THa6k3DrFbyvYPncD/qWbbu49cGuL1FvFUN2IdU0m+09NPyzRTbgs0a/w78b Dkehr1e28QiSItHIQV52Hhhntitu38UX20JJcuVGAEkO7jr91+1edXyeEtYnJLAp7Hit/b65rlrO tjoNvetLbb3+3P5X2ZH+VFjPA37TnHpinWmnyatPNomnxbkeGKezngZXmguYI8Sw5bB2SgPtH97b 7V6Zq+j+EfELs19by6ddN+8+06dM1s7MemVBKPj3wO1cPZeBNc0i41GLwtr7ajeWPk3dgL8C2aTM jMP3sfQh1/GvPnllSKsOGGmk0zzV/Dmg+XY2+txmysA5ljvrXcbggjIR9g5znIbPWvW/D13pa6LP PpL3niW2sEECm5lBZSPlxcbsHaAOgyc1iX/hy9stW1u9hguW07VHM32aNPOhsb1RvubZZVz8m4kx HptZcUX2l6Ro9hDpGvTLCmpwQXoWByvls+X3Z4Hm9ck8ZBFeXWvTfLI8+pScXqJP41S9OnaVPpP9 m21xP9nuJ7Nyoljd8KxyMgRnrjGa8T8ZTa/rOs6lpy3UVxo638s0EAl8sxu+M704yRtIDE8elei3 nhO2vItBh0lhc41AT3N555Zp7ckeWF/hyBnpw2apePdF0++uJb25u47aCzzZ+WtvsaRo3+80iZJ4 9aVGsloax+BlT4eaHrp0OTULKU38dsmzUNPsybmQQZLAjaeSv3jjpXcaXp1hrcn/AAk/hrWmtNSt 1X7PcG6SJxIv3coeT0w6457815Pps+sw+INH03wxqS6LeWs811YT2WUACIMg7tuWfBHORiugtfDX iPxLrOo6hdfY7HVtR2S2EoQQxvMZSXDgfLuK8dvzrR+77wo+6fRngfVP7T1+SOeMab4gmt3i1HT0 YNDdLKP+P2z7HnHmRjkc15L8RPF9rbzXNh4jilksdNBBsIQpUzgDDyDIJx/AhrH1SFbW5ig0m7Ol 3+mTsLeOQHcbmNsA2x+8ryYP7psA9a7Eal/b2m6hrVj4dit/GoU3ep2LRB21KJcK91Zqx2hgRmZR yvPFR7OMnzlVG5LQ5/RtQ8PX8dkt7qhsGkKTxRNAVkWRhwrAAdAfmAGCPan6jMP+EusdB0VY7uaB D5ElohgS3QEkswXAVM91ODxio9Q8SLqV7Zf2tpZbUQm+1+x7miQMNhlD8bcJkZboRjFdNpz6e891 aaLb+WZp5VlllPmTtbo2UE0x6hei9BxXDiJqhHYwj5lnxfOJ20yXULmyvv7QmZ7uOC1cfaHWFVkd 0I4JwMtwSefWultfEB1Hw1JNrqrrujwt9kuYI4A1+mV2RoMD97HIpDHPzxsp+lU7bVLPQtRUzWt1 ewz+WZYm2kxKw2HbjLDevzDtmtLVLe88PxL4n0SGW/0K5CQXMsLBHMbgtE5QfMJYZcgkLzz2rCg5 35r2Omm+iLt5LoWk2E1poskk8kOmxRFDCj3r6epXJTIHm7Mry3OB9RXn/wAPIdFOua+8ng14LBre 7leRWVpFt54PssjtFk/NJHLkp0yN2Plrqry4sNRtdG1iwskh8W6ZDPNHcusgDEbgiMw4ML9x6V3H g++0bxM6+KzZW1hPAn2LU45mYSQPsMUkTgcYkU/I4zuBwK9GlzpcyHJHzVFZanaxJa/atFYW4EIN 5JOJysfyjfhgM4HpUnk6l/z8+HP+/lx/8XXc6D49tdF0m30mawguXsw0RkkxuO1iMH5T06de1a// AAtGx/6BNr+n/wARXDKqr/CZ8zP/0/doWOBkdef0ovLJNVsrzSpBlL63kgII/vqR/OhCrMxTlckj 6HpUwyjhxgFDnr/n0qGrxM+p8DW8boot5Iz5kJ8ts/3lOD0q35bxgAYAPRBznHp3+tdL8QdM/sXx trVts2RTTi4hA7rP84CgZ7kjHWkGgWujqk3i24ks5JlzHpVoR9tlHUebIcrbJ3+YF8dFHWvl61Nx kzZFbSbDUNYlNtp8PnyoMuT8qRp6yOcKq+5IraMmh6OQ9qya3qCf8tnTFjAR2SPrOw9W+T2PWqN/ rU19arpsMMVnpacrZW2fJyP4pWPMr99z5+grNLBsbmzgY2n8sACsk0tiyxPqF7NexareTNcXUTq4 kcjI2YICj7qDt8owK+sZ9WsrS3j1S7by4Z4xMjZyxDjdgdz+Ar5Qi0q5lGWjKIRgZIBIPXrWzcWe p3Ww3Nzv8pAkZeRPkVRgAL24r6DK6EuX3jOR6nf/ABBa6WW2i0nzYG6eZLJGxHphT3+lZdvrN1KA YfClqynBDEs3Hb7x7dK89i0mZlOLlF+jk9Py/lVtNJH3ZLpmYf3d5/lXsqmtzLY9FOu67EMp4fsY F7ZCDH/j1Q/2/wCInYn7Pp8A9C8Q/LJH8q4VPD4c/LKzqf8AYkP86ur4ZhCncZMjpiL+Wea0C9zp 21zXRyz2EQ9QY/6GmjX9cPyrq1nGPTev/wASazrfw9YgfvhfPgdFiCfl8p/nV1NBsduItNv3H94l if8Ax1KdyNRzahqbn95rVmB/11A/QRmkE082BLrtqR7s7fyQVcg8NAc/2RqEg9xKB+iiryeHORjw 3dMP9szY/XFINTGMMJP/ACG7XHtFMcfTgfyp4W3UYOvHHosUvH4bhXSL4ZBx/wAUu6gd2Z9p/wC+ mAqZNACnC+H7dCO7sn9ZP60FHKgaUP8AWa1M30t3/wDZpP60FtBQ/Nqd049reMf+hN/WutbRZR97 S9PjA7l4cfq39aT+zXjGEh0pAP8AahY/yP8AOgDjmvPDoyEurpz3HlwJj8806O98NDlmv5GHTa8K /wAl/pXYpEyYHn6bEfT93/JUNSELGedQsw3cJG5H6KP0FO4WZ84/E5LC8urSXT0nVWiw32hg7Eo2 cgrjsfSvEdd00XVmoI5XJ9Oor6b+KUQlutLfzFnGyRcqpXHK8c4NeOXViBEVYccV5leVpFxR4Guh SAbFGN2PSsq4sri2mwgIVcDiveYtJjJZyvA6fWsy80JZDuROR1qXUK5Tx2C9lt2KspOegP8A9eta 31MOfmwoHUHtXWXWhh85QZHTiuWu/D1wCfIjPrmkpImx0NlfxxMDv4Pat77dbnlG+leWPZ6jac8n 2pq6tND8soINVKFxq563BrU8BGwgrXX22v2d7EILgLHIBxIBhs+leD2+sgj7wyecHjFaseoNw45O O1ZukaKR77BceUVKSbwOevNdb8NdR1Bba81fTmMlwkxaW2IBEkYP8P8AtYr5rstcnjkX5ijHjn0r sfh78S7LTNQhQP8AYru3kIeNztWReh2k8Z+uKzdN2O7BVVGd2foRonibw9rFmiJcLN5gyYWALqT1 Ug9MVO9n4Fd/Lhto5JD98IC23646V5rpfib4eanbLdM0VvdjDFyvkk57HgA/hXRNrPw3mT/T9ojU dRMYvxzuUCuBxdz6b2sHHcv3XhLQgTN4fv3sJeQUY+bF69G5FeR/Ej4hax8LLC3lvLSx1Ce9l8q3 gWRlZtoyXZOoUVF4y+I3wl0Wwurm11N7m6jjcQWcF5JKzuB8qnZkAZx1PavgLVNS1DWLo6hqlzNd XEh3Eyuzbc9huzgfSurD4dt3keLj8wVOPKnqfqZ+yz4x1T4jeHvEniDWyovl1FV8uJQIo4/LBSNR 9K+qViA4HTt9K+C/2C9QDx+OdFJJCva3YB7ZVl/oK/QYRAfWvYpQSVj52tNy95ldEx9alEZI6c1M E6cVYAHTFdBzGW8Q9cVB5Q9M1ruo9Kg2HqOBQxsoiIdxil8pR2q6YiR6mmmMgDjpRYlFQxjPQfjU gXHFTiNvSgxZ9j6UDbIwD61Mq8DNCxkdulSLGevaiwNDAMmnFfapRGR9Kk8uiwFUL7VesFIukPfI /SmBKs2oAuYvrRYGzltZTGqXPpnP4EZrN2qRtxkn/PStnXxt1SXsCFz+I4qnYhRLvYZbGV9qLaDW 5moTBd2lyVOIpcMPQNgV+dOlW2v634Y8P+DvD2mvqV7r3i3xFcsFlEccbRukZlncg7URMntntk1+ mN1AJZQyDlgAR/n3r5J/Z60/VtNTxJd6paPa2dvq2o21mZF2tKr3Ba4aM91O0Lkde1ZVKnLDU9DB 4dzlaJnxfDD4qfD6xuLnRZ7TUoLlCl1DYMwJBGAWgl4l29gT74ryjTZYjLNaRMfOgIEkDArKn+8h 5H5Y9OK+9dS1y2sbYuXzJISMjjYAMkjHSvlzx58O7L4h3U3iC3Y2dzFiKGePKs23+JtuM9K8T20W 9T26mW2j7pxWIkXdu2FeG749j6VRl8uXKL8qtwxXrzzXA6hd+NfBNwLbW4Bq9qhPltJ8sgAOOHFa uj+M/DXiFvslndfZr/obS6IjY5/uHo34GrTPNnTcXZnmXxILRtZwKSQ8jYZRhsIK4GC6juA0bk7l GcM249eeF4r0nxxGZNW0uKRtih5UZjjA4xj0rn5tH0V33KJbkjkEZC/0r1cKtDlluZQWza04kIkU 7Qo/dkj/AGduTVvTItTjmXyIcIwb5ioX6YZznqPSrltaz25LWNvGoUZZsAFR+uafbvfSlmDkBf7u E/8AQef1roGei2MF9PFKHliTzcB/N3P/AA4z90VCVuWMC7bYFD5URZSSMHB4HTp3rlrO1uraX7S7 rImAxjkRjkexJp1/PqtzHv0+0t4YGztZ1ZevU9fWgDlfiFHfxzrpbMkMl9GHdg7HcityBx6jFcnY eH7kqpZlb2Uk4H6dK6l4ZLuYLrVna3kSAICsrh0552YbPvXGXkXiLT9QubXTkM9srbo2LDcVbkA5 PbpTMpanaxaRjEUc6RE9fMkC5/LpXRW2lSq8bPNBMv8AcDhug9c15VFJ4hUgXGlzsTwNjqfy5960 7HWkCyLdaZeJcuMg+Qz5BGR930FO5Fj1J9Llhjd9qlhv2gEge3H1qleR30MMqfZGWLZJiRDyvTHX gfnXDx+I9LhZw+oSQsd+BIksf3mVl/Suos9Zh1COT7HrAkH7yMbZSfvY6B/p3pXKMvW2WSEMLc7R HKAHf7y4HJxz+ldPokd4fC+gGKIDFkxwjKT8sjY6jPSua8TsJYYbgzNid2QngjbtyR8tdPoUU03h Dw7i7ig2W8kIVpChG5pOOB7dM0XA6G1j1EMzJAQu7jGOgX6etT/vvLEcqN5nVun1rnIL27QiWTUg WdoWA+0EA+aMHAx7V1mmT+dYq7v5mC37zO7O3jqf8KLllFdO1hm82JG8l9u35wM9m4we2Ktxpf6e vmS26yoULBZJATuUDphenSqF3dSiZYlvvLwY1Vdzjbltx4AxTrS2kvjEpvZSxhJzFIcAMctncBTQ FTUrbUi0t2sEoQvI20FcDKr6qOM1PZ3V+D5bQu8WQpVVQnaEIPQ+tRvqD/bZbMaoUK+coV5jyojX /Zx2qqJ9WtbwBLsFGkIJWVWx+6B5Xb2pgbnmx/aWX7NMrNHtWIRKxUGMguTnnbjvxVdp1RBLDBMz Msa/Og2/dJyAOByMVhX/AIi8nfLcyAl0UySLHhT8u0ISMe3SoW1vVwiIk0SoIlO1VUBONgHJ9TU3 Fc0bzUtqlnjAVVC8ofTOen4Vx93qU18xt9PgMzucMCPkXtwOMn61qzvql7K/mXEVvaxk5JaMM+Oc r1/u1rPbLp6pFDfZhKE7mcszevCqP8ii4XOUtfC+oyAT3Fsw9NxxnHHTpT7nR42j2Im1s4JbAFaV xqmnRxF59QywzggHGQGyvzkdSPasO68XaRbQS20TvMRuCqEOfmxnoMcH3pCkzktY0i1tElma8RWx uCpz0rkNMjmLPME/eTtgJjrn0x6VY1nV5dWk2xWU6AOWw3Gec8Lj+tUdOS+iaS9kYRwxAnCnGPYV SZB28tra2nk26Sosj4HLfMT3Uev4V6RpV1LpsYusQWqRhZWCgyTSYG3G36V5Dod/Y2kn264iFxfT P5aHgiIOOOD69eOa9W0m18SXA/tHRgtnKinEzfMynHzbVPHI9f0qWVDc24/iF4ainGn3EEiXSRgj 7REI9+wZU8kDp711l1rc8c1tNCNsSSM4UEHCBQONua4q78Pa1rtso12SG+gZXAiklQFFMZcsCvOR 71m+DoNW8OXI0G9Mlzay3SxhRGsipmMMcy5ztwwU+4qTe502qazrMGo+S15KIrjbjLEqFyDkDv6c Vxuuavq2q6lIZrtnQyFvJXKIAB0C12WvaBNJFDcW+ZRE4jjaNg4YE/LF653HtVCx0mbe1lKtr9tv 5AsIZnDyXEf344yBtU8fxEDNc2KrckLkVJaHPaR4ettY1GPU7+ePS4JYMzDd5hSaMcIU52bgQ2e/ 1r2uy8Za94d8MwaT4o0yHxT4dg2x6rpsjnasc/8Ax73NtJ9+CQgYOD1XpzXBab4Zjntzq0MgWQ3g VhJIplt2Q/NHMASfLfoOCR9K7TU0vfCf9qx3emPc6bfQqXa3ljD2rRZAJdldWXnheM+1fLfWvfOa FRI2n8FeGtW0a58W+FdY1DxF4ctYmuGt0zHqelz8BRcwJgzAd5Rk4HA61zd3cJqljpq/bE1bVdIl ZbfV7WRSVN0SsSEEgKCB+9Ofb2rK07XfEXg7xdaalpNtbvdC3EQWxnMfmW0nQsBxHIpB3xnI3cCu +sr34d/EbXobq+0z/hDvFs1xhYoY82upgHzGSaJfljuPlzkfLXRC0qisKTPp/RbMWOkWNkBxbwpH zzkKuPatB+mKlHYenpz096hZlbO0hhjPBzx+FfRJWVhdihcTLbRS3DOUEKO5b0CoTn8OtfEji7s7 K6luLMT6nZ8RrIgTZIxws4dT8yrxu7YNfX/jCaCPwxrHnRvLE9o8TpF99lk/dsBjvtbtXzHb2djJ HPAsguRp+n5jj2u0skiKI2HmfxpLgEY7/Q142aSV0jSNzzTxRqsegpHdaxp1ndX80Hz/AGTny0nB 2vkggZYbR044Fb+pT2HhPZpen3bWH9ofZ7f5VCiOG4UPcllPKqzYT86yLLXIP7VsrK1a40ea6uYF vrTUYA0CIMeXcQO4GVG0hVbjmue8QTw6p4hlvYLgLHNfKxcNlivCgyI3TA5I6fmK82cmtLFKlpcW z1W20w3lmJYZQp8lvOIaPEb84aP5hnH0969Em137JYWb6bcizhe7uNPnuGUhpS+Lq2AfjEWd4x78 9Koavottp/8AZniOzhjSS/mubK9jiUFkmiwc7T0zH82MfSuB8QXMVwslo89z/pu1UEyiSOGVVwzI BjG4YA9Oe1TTnrYyleLPrXwPeWEmhWZubcySy3pZYzlN0su1yqOeoDDcenGAa8i1zwAl7e/2hNqQ 1HTDf3eAkeCrMQ4hnHSKUcgc8jkcVe8LeKIvEGh6VHpjPHP4YEsc8c3JnhMYMNwSOjmRQjHpjFZO rI0V3ceKvCl6Zmub+dNUtEjkmE7qw5KKMMh29R07HORXVJ2R0yd4WZBczXdtaWms/Y7d7S6jjtYY A7iaGcE4YclGDdPTivKrey03RrTUYr1ZbnWb+6Xzp/J3RxRsf4Qcjnjce2celegyX9p4miW7uXms raaVpoNMgYqbe4xh2XH3S/HDYA6iuU0y1a6sdYtLu3vxYzokt3e3HLxiP+BBnJlY4wo+vSlTmtke ctHoaHg/xn4w+HeuLo+myXFtpEw23FreEzWkm/gbImyFB6NivoXRtD8L+J7ey8S6faxeC/EF3uvJ Le3zJZmQBo8sn8IcdMcY6jivmbxPqlh4h8P6NfSRz6Vd2UEsDW0mQ0rBgqSJnGeB+db8njLXn8L+ HmsJQt9pFuZGcAAvbNl0jI/iK/8A1hWk5yuuU3drHO/ESx1vwn4pbRtZMZtrqMXVq8RLW8yt1aJu 4B4xjI9BXiukXznUBJKfnJzz7+n9K/SnXNE0bxBptrpWu2Eeo2cdvbnyrlcFGKBiVI5jbJ5wenGM 14Z8RfCfw90fwzPYaNpFvYatdyRLbyW6FpUZTk85PykcdK+soYr3VFkxhoeXaXdlgDnIBrro7qMO DnB6A15tp0roQrnDL8uB2I7V2lu4nXkDjtXr05Jq5lY7W5n07yUeEHzAPmPY5GK8b8TWIvvFjQ3T XdvCIYRbvFGCjJgFzuOOjEivRooC6EdF68c8DjtXfeAvAXirxrciy01TBYFh5l5JxFHzgFQQfMfj oOPWprVYwheRpRpSnLlij5ynhXT7qfT/ADhM1sVVmA29QCOPxpkmoW9ivmXUioOw7tj0A7e9fob4 s/ZV+FcekXOsWt/feHr6GLzbm9a4EkEhH3meGYFOT2GMV+dmpeBrH+2Z2a+nvLZJCIzLjLqpx8wH 3QfQdPSvKo4lVnaB14jAzo6zK1vda34gLQabH9ith96ds5I9q6zSvD2l6Vh9v2m5PLTS8kH/AGc9 KvwW62sAhgXyo0AAVegqwuR96vShS7nA2WUXeeABj0q2o24GOKghIADDmpJZlAzjmukk0bYHd8wx k4rV2ADjvXOWt6g/i2kVuQzMww+DnuKuImT8qpINV92W56nsKdK+3CngGqMl1GiHLEEcDGKGxFkk +YCjfyqjesemOe569qqm63kbTketQyXnlAs3IPrUOQ7EcN4sUhGQqnj6VT1mZLZo76D94ANki+qn vXM6xe+dnyhtI9KzLe9nuYWtps7QQcjv7HNZSm+gxLq8/efu2IHVCMdOv6Vp6ldLqunRR/aHhmDb ZV28SpjHNY0doBuJ4x0FadratOQCpA/Cs7AQWOnpAoCAYHAGMcflXUwRLFHg8VYFjDFBuZirDtWJ d6vDAPvbiOMDBpAS6hMIU3Fto69ewFcWlvvu5rhBhXUDjv71BqGpX08heKMKBg5bnAyM11iWfzHH Iz7CuLGy0NIIxkt4933eD7VqWdtcxv5ltIUPYH7prXgsCcZx+VdBa6eqkN1ryLm6ZTtdf1bTT5st s7bf44TnHplR0r1Pwh8WrcyiO6YSELjaVz36GsTSNNF1qNnalii3E0Ufy4yAxwx9sD/Pau517wD4 Uvbi8gtJ5rpbafyo9RWNGkG0ZKHAX36nPYVy4jGRptKSL9vy9T2TRPEng3UgpRWtpO+yT5C2eflb kfhXpFnoOlXMRfT7pWY8kEA5PYMOv6V8RweF5bW0vNU8OeIIb21sdjNFcxvbM4dtoETEsGO7g8da 9A03xHq/hK9itPElheaewwcSjCNnkbZE+U1dPEUp/CzeOIutD62j0ppWjtkt4XZhgsrFAD24rLuv DkEbbZHlt3LbWZfmXH+fSuC8O/E+1eORpg3lH7h4JJPTkV61pni7S7m2DXCGVWU5V8DGeM+vHWun 2RpGsznV0SS3kaNit2BwCMt8o9ug/OuYNjYifyyptxIDgdOR0AFeyx2GlPBE0UjLKTj92pK4YfLz Wovh/RrWMpqJjkd03JuHyn/db+maXsmae3PB10SfO+EssCNjCMM8fXj8Kw9X0i1kjCSnzI3b5hNE FIxxx/8AWNe23ugtbWc+qwzW2m6cF/eSXbDySMdDkjB9Mc1883Xxj0S4uZ9L8F6Jf+LNTtnRPIih PlEdC5Rcnyx/ecrSsXGaZi3Vj9iykNoIk3Y8z7oP+RVSQX2R/o7uxIG8AkbcdK3v+EH+Mfjd47rV 7zTvBFk4BmjsYvtVwMdB1CDOPU1NafA/wDGS+tapreuXCnJF3dvChJPTyogBt+lHKi7roYE+s2dv Gsl1qNvp23jFxJGq/Lx/Eazk8e+GbfUoJYfEVoZkRonW3Zptyn5hwobowBr1iw+H3gnQTKdP8Iaa 7SrwzRLLISOjF5MmrdqkMMphsDBBMvWHyY1b6J8oz+FS0hKRzXg74iWs95qGnWGoAzahELpEa3lj UXFqM7WaRFU+ZHlD7hcZ61JJr/gLxHogg8a6Z9qs9YZWZRDMsrwwS+bCuUQsArFsDA6811cN7aPM sNwDN8wPzdMr2IXnrVy9szEfMsibZicny8tH7DGSVrixOBp1JRb6Gc6SlueZWHhP4URXs1x4U8Qx 6WGk88Wl8k4iifOSI/MRQo71lXngrWUuJ9TsI4NaL3EksU+nXEM8ZUnPIDF1/wC+a9cg1LWo23TR LMo+X5AvP/AWqyt/Yzl5bixtldAOWgWNxjnqCM/rWNfK6M5XRzzwyatE+TptHNpq98ZtCn1C91Fd sYchVtueXG7ay465AxW1pPhKR5Ql34lt/s5z+4to2mc46kynGPbHSvpu50fwv4jjxqWmpdK/BkWR 4pVA5wCuBjNef618Go7uc3/g3XXsZ1G2OK+jWUDcu1lEi9OOmVNeTiskqy/hs45YaUTGto9NuLmy S41CPVdUZ1NnMtv/AKY0i8ArgbXYDA3HkVQ1rwTcWN5bsl3/AGfdW87XKTwLtkil+8xBz37gcH0r k5tI+K3wz8RWPk2M39ky2E1p/aMO6dIWALnLLny/m6Nx6e1VrTVbrUfD6atLr0Nzel2iJRmkZSve RRkgkV5zy+phvjM3dHuuieG7Xxnq+oy2mpvLdNBEJLRCixllPMjIAGAfdkgDbmuMuNBufAmnzw3U 9tPqU90EES4fCSOfnIz8u1cDaa8/8J6Xq0uv2viKW9tLmS1ZYLS8tZHjNwWj5RxwuFO0c969S0+x 1C/utLewhtby1g077f8A2JNKsVzPNd5Lzq03yySo+UCFlAGOKynGM/iJijxO3t9esPGGoanJqM+p RXcUjJDK+1DcMAq7u4jQcKPWvVNG0zxI+lyC70tbeDy0lnZpCyTyHLAqAeCVwpGP1JrX1GbSdWvd NsYbH+z9Q+0COTz4zFNb7V/1cinPbIHqenFXtZiig8eeJfD6aiRHHH5WjQSyFoDclo3hy4wF+U46 7eeuKqcoy0sdMWtkjK0G8uUtf9J+VknktL+RIS+wGMMkvlddjx/l254rI1jTB8OvEsmpaXJBqVjq NvGNU0hZvME8O3cDEenmIMOnPqPas/TfEmqt4hOh3bT6Rc3c0drM5hMFxbXVu7StCeD8mC8RPPD7 u1XdR8Q6shu7a4l8+2guxc226JRJDGcEoHx8wbAx2XqDzQ6qoaTLlHl1Zl2/gTTtXjOqaPDaanYX jvLBcSp87I7EhW+ccp93GB0qb/hWZ/6BVh/3z/8AZ1ja74WsPFWrXXiOUz28upsJ5UglEUfmuo8x lRWAAZstwB1zWT/wrfTv+fi9/wDAn/7Os/a0hXif/9T13w9qA1PRNNvU5MsCbseqjYf1FbVvcW12 88VtKHe1bZKBkbW9PT8q8o8I32oW/h6LRbNoJ7q0dg86tvhh3c7Fx/rW987feu20O1ayE7vI0012 4eWR+C2OM8cdKzw79xXM2ea/GC6udA1HTNY0mGO1vdTt5LeTUQoa5j8oj5InP+ryp+8nzHpkda+f F+SV5SMyOWLNkknJ5LHkk+vX+tfU3xfsP7R8HC42F5NNvYZkA5Ox8xuBXg1h4XdisuqsI4wBiEMo 3kf3iDxXlYzCznU9w0hIzdPgnvmKQEEDGScYX6iu003SltJN4tmvJj0+8QPoFBrqbDU7uwgFvpd+ ljEoxsjlVMfTrWgmueJJV2nxNdA+kd2Tx77RXdhctjTfNIqUjOjs9SlANvpjBvVlmx/6BitBdO8U 4BGnKFx94Wzn9SoprXN62ftXiKXnqZJpCfzxmqXk2kjEvrc0gPUhpHH58fyr1UrbGdzVTTfFDn9z AVHciBFH/j2KeNP8WocPMYR6boIv1JrHWy8PA/vdVmk9vIc/qW/pTvJ8Jxn93JeSe3lov4/M9WhX Ng6TrZBabWLaM9w97CP6Vci0vUbdFe58UQWiOD5eJy6sc9tqGuf3+E4cMYL4nvgQj9WNWYtS8KRn D2F6+efmuYlyP7vQ/pUgbIe/2lD4ohYjtHPcdu42x1AZyWAl8SgDv810/wCm0VeufFPha8thax+F ILRRtKyW8iwy8DHzSqhLD1FUV1DQpPmGgPOw7NeTkfoooCxNmFxx4hldR0Kw3ZB+mWUU3y9Ocfvt YuGx/wBOjHP5zVL9ugCjyPCEXPQs102f5VB/aM+4+V4Xtk9zHM3/AKEwoCw4QaAeXu72QHoVtYgP ++mlNLt8PIeJLxgP+mdsP6mhdR1cH934dtuexst3/oRP860Y9S8ZqP8ARtKFuPSLT4f0+VqQFBZ/ D2cbLx/bzIU/khqdbjRcfu7K8Ppm5XH5LFWit58SnXdFFcoo/wCecMSfyUfypzP8Tpk3Mb9B3zIF P/jq/wBKAM9ZLZ/9XpNxMOwM8zfoqAVZW1upF3ReG3YehNy3+FH9n/EOZSxku09fMuyufw4praJ4 ulTbcXCjPXzb7j8fm/rTA8d+KjXEF5psEmnjTXWF2KDflgz4U4kzjp/nivK4ZzKvlSgI68HOe3Sv Rviha3mla3ZxXTJMY7YOTDL5qjLHgnLVwq+XPtlUjAUY9/rXlYj4jSKFWDJxwcjkike0iA3Oce2K 0YlVowwwSh3YHtxUchjkJMYPPXI6ViaGBPbwNn5Nw7dqzpNOD5wufauzSxH8QBH0qGWCPHyD8BTT FY8+udFjkGNuBj61yWoeGEc5ABI7Yr2RrZWJJB5qrc2sVtBJdMufKGRn1pqTCx8va9pA0yVUTlmH T0zzWKk99FwjkegPQV7NJoj6hM17cjl8keg9KyT4YDyHjgfhXTCojJo8/j1S6QkTDeMYBHGKsPbW WtN5xuUsrhRhjJna+O+exrrZvC20jK4BOKz9e8OmysImVfnlfAGBz61opKQNPQxmt9RkjFmurPdR p91I2Zl9e9WIdPZoZDNkmMdGZjnH4/0rtfD2gGKEO642jGQuOtXLvTBHbXDov3ioGeOpxUNxK55c u5xVvY74VlhUKSOoUDr+FVHt2HAH6V6NHYeVb7cBVUY4rAvbYRvleaIs8udRt6n1D+w3ei0+J+ua ZnYNR0gnB9YZM/8As1fqUI8k54Izke9fj9+y1qX9l/Hfw23RbxLm0YdAd8e4D8xX7FYxx6cflXVT N7+4RCPHbinbcdqmApCuK3MxhjUgZGaaY1A46VN2FICM4NBSehBsA4oK+lTPt3YBoO0Dk/kM0Elb Yx+lIE59amJA+n0pobHagdxRGMCnBVHHpRmmEsSQBwKCgbAOKXIpoTPJ5qXb6UE8wzIp8DYuYj2z TeB/+qnx7fNjbPAPP/6qdh3RieIF/wCJq+eAUQ1nWpAkHbjitrxEn/EyUjnMSk49MmsRBtZW6bev tj1pN6DW5tTMI7KVhhXf92p46v8ALn8BXn9yIolihS3CW0XyxkE4AXP3h/CW/WrnjDVpbPRTdWRW Wa2mjYwqcs6n5SoA9jmuSn8Qtd25vLm0fTlgjDb5TjGeny9z26fhXi4tSb5Uz67J1CMOZvU8t1vU h4i8aL4Zt5QYdMCG7VDjaXGRuI7Adu+a9HW0RlGFEUCL8iDjHGccelec/DzwtHo7X15Juln1e8k1 C8lm/wBY8sxyqNjjZGoVQK9cmTKDHAYgY9c1501yuyPahrqeQ+NtGsbrSrieaIMRlYxj+LoCK/K7 x+8dt4imsLWRibSQl3U4O8/Tuv1r9PPiHqk2oyHRdGZDLzDG7Z2LLglnJHZOK+H7r9m3xrcXEty2 s6TM8zFncySqxLcnjYa9HAUZS1PnM4qRjLlPMrDx/q91Lpltq2y7azf91M332UjbtYn72O1er2Wo RyMrNgc4xH/D26/5xXZ+Dv2ctL0q6jvvFGpxatIhG21gjYQA4xukd8M4UchQBzXjHihtZ+HXiG40 KTbcG1kVo3YZ862c/uyPw4x2PFexCLgeJGR7LJBstlcq+HBcsQpxj03YxxisGHWdNjvdgvDG5ORI XhbYSOARnH4VyWqCfxFJbiW5ZIcb3K8JGCPVcH9arQ+FPDMxEgM9wUGNyv5MQ7Z3Sbj19BTuaXPQ rvQ5tRhln1DUZXCLvBXbgjHoD/KsWPTZlVItPu2vI2iwI2Ei7e/QcfrXW+EtDsbW2vLLSbd7SSPZ JJIVc4WThdzyP0P0/Suml0mZFQPqAjmUfvBC4yQOMt83+femgueTroc/mx/2hJlQOAp+fgen/wBf /CqOoxWltfvBaOu5IwzBRnD9sjPpiu11fwwLhftMOsMJUPXCsdoP3Rg5NeEeKzr/AIX1h7lpIZot QYyxMBgHAClSmcjHHaghnpkKu7RybFZiN3zRd8fXNblrGkdvcqoVJGLY+QhSAcL8vQ8e4r56i8c6 3EVKiEheg2Y49ua6ay+Kl9bw7JdNikkA4dXK9/TtSuTc9ijs4LgN+7hSVGcqQXG7OEUcjAwKe2iW CI8d1aW8j5lCyOPNztAVOmDyK8mg+KmZCbnTSAeMq3Tkc/zraPxJ0O4tsYlgmDNnO5QQenKg5qkh nQ+IvDWmR/YEitIgwEjMV3cDH908DjvXQeEL1LLStJTdtjgkki2iZYF/1nBIIJP3q881Lx/pF5sg DtsCMhLApyxzkd/8/hXc+DfIvPDyajbSoPs11MqxyorLLsaPAJPI4NIVzfu+EtppYJmDpbKrQSwy ZxIykk8U2xbdbtAocBZZAC5B4zjnFWWvNMKReYsUfDZTanylHB9z9Ks2tzYwQSW9vt2h2dSmMAnn HbHNBdymRL5zlZ5VjBiyEljVQu7BODzV3T4Lov5Qu2iYARhGZJDhC3IA5rLlS3YPL+7VinzblQnA YHPPWte0v9NiCQm4iS6Z87AE5AGeQv8A9aqQ7mBe22ptql35eqweY3nhdytJhtv3dqjjjFF1Fr4m 5vbafcciQ2DjJ8sdGyMcY6110+taa10WjuGlMZilZYwcg/dOVXj8Kq3et6ZbYi+zy3AABEijaMht hHzH0z2phoeYXfhu51C3dr+6cvFMA6QhWwV4XGQc8Y70svhnWPMkgg1OQFOokhgOfTA2k/Ka7uXV NEW5kUyKiSRGPLP2H3TtHToPeuej8b6Ha28Et9eW1tdK2JQgYgqMr0B3dMduanQh2MMeCdd3Z/tO ZVQEARpF0APGAg7Z71ck8DiXcZ7i7d9hBMk0gKtjGNoYD8qqXfxT0JAPLv5Jm27SI7c/3McFvest /i5pqJIkdreSsxYrIQinB9eaNBe6dKvg/ToZkd7G2fBySYw5B6g5csT0Hc9/pTrnS4LaNoXAV4s7 CFVTjPHAGOK8+ufiuCMQ2Nyw5yJJsZOSf4c+ornb34k6pcFvKthGGyPmkZ8ZPuKQnY6zUIJYf30k wZgpOZXwuBz90D0rz+a/Z2Z3dXi6g7dnbsD2rJuPEur3ashKosg2t8vTHp/9au4+Emk6TrXjfTbX xJZi/wBPeRTJA7FVbkltxTBwB160pSSRNzS8LeHLXyY9TaJfOdQ/nsCwRZA23CnAB2jj617VpNtb 5Zk3TvJlmaUmQZPXCjAFJ4Z8F6z4w1q80LwbYCXF5M6mPEcEFrC7JGzycDbjheMnHGa+ldO/Z48b +SkdxrOjpOOdx86QD3HyD+RrN1UlqzejRnLWKPGYbWylCwIimMsQqKoXJCbXXPuD61ylzYappUj6 hE3nR3gYxtG+3BDncSn4V614o+EvxL8KBbiHT/7WtobgzSXGm4nboBnyW2yYGD0zXkEOoR6peC1m V7aWwiMPlzBo23SNwvlv82dgPWlCom9DadKcdWjP1XUD9oe/s4jHGu4hlztVsbVlUHHzD6Viz+H7 u7Gk6wYXWNJ8mRCQd23IDr90Ek5LEiul1SSBbppWBFuwJO7hdwGTj29+n8q7fw39l025t4bsboJm inkhYZWWF4+cg/3RxXLmDXJZmDXNseWwarBZyR2l/o6w3N0jMJI5d++RTuxvB+Ukc16VonjXS9ct WjtpittJbeb9mkZhGxk+WRJHz83zAHts/EVyninRG0rVLYWF9EEDpOksp2qInT5SoUHlfuj9axfE ttqEkljNr9umkeIIARDq1gD5V6jDJM0B2mNiBy4Cj1yAK+ZnRic8qVtGdDca2t7Yahaarb28OqaS 72EdvFHJC0SsPNUkt8rnIPzMCcnPQiuq+EsNrrvjexuZ7W5SXTYTdR+emwRM42BzlR8xzj9eleV3 cs0mlW2naveXE/2eXFvdRxjeLeUc77jA8xQeoxn6dK+hfgok0EupR3PmyT6fbwRi5YhllE2WOwc8 cAjPTOK6MLS/eKwctj6KuLy2slM104jiGCc88e/fOK8/8E+JNQ1u71v+0bwzWYuV+wCePypkRSVY YHDLxke1bc9gb6Y/ahvXP3edo/8A1U5tPgivfkGwKBg45GBivomaWRzvxLimvfDMujw6qmiyalLH F9skHyRjJO0EZwzngZ4AzXzjHJfwTxt4tf8A4R/UYdNt7KxnsyrQhrbJeQ4JGWzkZPSvdfiN4Zbx fb6Rpdle2qa3a3L6hZWd3hYr/wAgKrQlscbQenfccV81+PLjW72/uEXRJ9G/sxphPoZZAsUwGDLA SpbawxjGQSCRXiZgnzlPQ2fEVhD4p0zT9R8XWv2XXlijUOCRFc2LZIkj9M/NlGxz93tXOeLPDCz6 Z9ttVDxJN5ZYKoCoyeWI/l5JLjHPYD61O3iRdS0u3i1OaJJ5bBlE+H8zzAn7tQTtVfLPUYFaMl5F u1K/nMUMcrW87EPynlgqI+pB8yUpz6ivO9rJOw4SZhrb+I7/AFG2a+YXV3ceTC6qVQSCKIqoYZA3 hc5Yck/hWJrNpLcRkvbyfxTBFG1gy5HQ9uBz0rtormLRtQ0+/hia8azIeRWctsu4nUBcqDwwI/I1 n39pdzxT6pqMkm+4jlDR7sP8qhmCH+71rp9nqpHT7G7Mf4d3k9heatfAiLydHuBAWA+fy0MrKQcZ VRyT2Iq//bMa6jDY29tNoVzqSRbFhmVY7kEBy0TDI/1vYHjnNe6fAbwpa+I9U1orbRuF082KQnAW OKdG3nJ5O8/K3HTFe4ax8IdA0Gwj8mw/tKKR9sMBVGW3bGB5Y4x6HmrqLmCvhz8/ZNaaXVpW19Zb NTN9glSCVYY4jk7XBXBkRh1JzzwOMVd1fxHBfyiPRJJ4NHsY8weXt+a7fOSuOSoGSeCa9g8X+AbL UdZiMFuivcwBPLdPuCFnRsr3xztxzXkF7YT6a1/p2k5TT5GYhQil/wB2uzjH3STnNYeytscMsM46 s5qZb37NHa6jbxyIFPli5kG7y2IbORkj2xWnpWmTal4g0nQrR/8Aj8vILSMFyqMq43DKgltqDgD8 a7DQtO0fWtVu/wC25po7OztFGYlDZcAAIv5V714G8OaVahPE+hwQTW0IlJncZkhBTA256buh/n2o w1R81mYok1u/kuNRvfs/yRtM+3/dBxn9KztK0SL7U2oyjdMBhGODtJ7gH+laEFuT5YI/gAA9PQDP pxW/FH5aBenSvprmy2Pkb4k+Hl0DxIb6FdtnrJ+0JjJ8uZDiRPx+8OO9Y2llnCEnJdcAD+L2r6p8 TeGbDxRp02kagCqv88Mq/ehmAwjqf/Qvavki31iGx8SwaLdOsM9sNwPCpI4O0qCcdhXoQxXLTCFJ Smkz7F+Gfw00KeCHV/FWL3OHhswSsQ93x94+3SvqVJ7WztVFp5drbwKW+UCNEVepIHACjvXyT4L8 ZSQoU8xZUjwChIG0Z9z+vavPvix8cn8QW7eDvC0jw6eCRfXCnBnP/PJSP+Wa4ycfe7ZHNfOtVsVV s9j7GlVw+Eo3S1NX4xfGO48Z3P8AYWjTMuh2jnZ63LL0lkBxwP4F/wC+sV4ZbWpLfaJT9B1/H8ax rSZCfM6nGMHH6/4VvRXKn5f0H/1q+uweGhSjZHxuLxc68uZsmEeWG7BB9Kjk+Qn09/8A61WHbGOx rKuX689K7UjkLRmCLkVRknaQ9cCq7M2w87geo6VUaTbnB6UN2A14ZUj5c8Vtw6nCEHz7dvauGknL 4FVxL15IxUc4rHcXesSSArHg+h79KzFkJw8j5xxisWO+iAw559eaimvRsARsnNDkFjea5ijA2t36 VkXN08hKhgeuQMde1ZD3MhAKnBDdSP8ACoWW4Ofl9+BUt3GTta7m+Y59qUQxx+3tinWun3FxIDIx UD1zWv8AZYIMGU5Yds1KVgM6O1klbhDj16Y/lWqJYdPjZmfLAdsVnXeqRxRsEIJXgLXOyLcamx3f Ih/hHU8etMC5feIJ7k+UpIFU4LaWTLygexxV2DTvJH+rBxjBNX2BWPpgUrAZDxgId3p6V3elwm5s YJ26sgPHtwa49guOeQeDXofhFFn0nBGTDM8ePYgN/WuDHxtG5rTNCG0wfu+35cdq1YrcKCQPwq5F AOGBwDzx71oR24PI5zXjs6LGt4TsLa41u0W9D/ZYyZJCgDMVUE4AbAye3OfSodPTUdR1KcWgl8Mw 3eXs7eS5CtfR7sM+Hx0BGUFWLNLmES3VnaT3LQKpcWsnlTxqTjzImGfnXsCMVNf297efa7XU47y8 sdEkN2/iRk2GNEAIiTIJaYPhAItuTw3SvDzB/vLGFRanP+I9QuNOtpNO1eNlFtIkMt0y7AqZ3LjY MDH+1ivUtdu7y8+HUmZ28UW091avb/ZGUuE3ZPmHkx8dSV6dO1czctpGtNo93p+uXVmDE0d/bzQi V7g9jIZVK/L/AMtVZdwXpxVnS7bTbS8s9J0LUINRubifKNpcaQ28cKfMxjMP+sLc7QRvx6dBwVXH RxNPZ2EvfDdr50M/heWW8uriNW+y6ag3RNINwUrKQGAHXHz452is3SfFniC2uHsJbSU3MbGJoPuS 5Ubv9W2G6c9Ole4+HUtm8VW3hu1v7Gx0e8L3pgt2jguCBHvMseAVdgcKy/xDJzzUHxEt9cubqw8T z6zDPoK3UZspRbebdWRZSolu2chljY/IpxtA4zxXoUcXOBuomV4f8fiScQpdPZ3Wza0Mv7qX8mx+ HH8q9y0bxuY9q3Z860jj82Uzt91YxkgEA8/Wvl2bw/d67oVzpl1P/aGu+Ho4VvJLseXcvaOwk+02 rpuVzEOAA7KVIyuAc6dt4Y122ht28BeIpdTWW2czWepKbe5R0GSqRnhw4zkJuVfoK9Oljoy3Kib8 keqfHD4kXdpdasbLwvpsazTrbtnyYugjjT7vmTHI3kH+VfW/hnRPB3hPQrXw34I06LTdMjUK0cY+ d29ZJvvu3cszV+bI8R/ED4Q6/wD2lqlldWNtq0KyMCySKyRnKH5M8KTwCAcdOOa+hNC+PWmatGl0 7CGUMNxhOY92P4sdPxFdUKikVN9D6VvfD8Uiym1PlzKesmQuR7j+tc+ujucR3sX2h3yCIwI2HoQw 4P6Umh/EK31G23h47iHqQ5G35ue1dJBqul6kFdXKtkYVjj+Vacg1JxPMbjTrmymkSPFxEDhlPyyL 6A89PpVCO1hlEtubP7fFINrwSgNz7BhlfquK9ou9Ls9Rl/cxKpjA+cZ69eveuXufD6mYzGNpHQ/f UlWH4jiodM1jUT3PN0sY7KZYbNegyscjfvI8fwxy/MDz/BIPxFMm114JFgmj+055OP3RTHqgz+Q/ CtXU7e6s32xRM4OSQ/b3B9awnmuY1891wSdpD5Df8B4qTdamlDdapcxm4tPKaLrnoaqG7vrl/mMW U4O5AQPrTXeSNPMS53lxtCuqiM/RxgBvrilS/WQfZ7y3Fv5Y5kKg5444/wAM0hmhEmrIBNE8bx8h tqccj26/hUH9r6pbxqoiVip6eXnjpnjn9KdFqkhnjjtALsqN2AQuB6A5/pVjEOpu01k7tIP9ZC4D be3Dg4/WpJaJI/E1+MKIkUt/d+UjH14+vFVL7QfCWuqkmp6PbxTq5f7TZMbOcO3VlliK8/UVfXR7 iVS3kedFnCupAZfpzWVqEF5ptq0pVriJXwQAMr9c8UuVS0kjOcExmm+FLbQ/CsXhbw7qKGOJwY5N Uj84AE5cnyioZy3O4j9a8x8VeE/GwtVu763D/ZMhrmx/exbAdyTIkY3boX+ZlK/dPtXXDxNPZMCl lewnqSkKyqB7srf0rdsPEf2jy2j1Hynck4JKY6dBgcnpyfbpXnYjJoSfOYRopHkt7aeM/HtkStlL Y+ItEMQkMeMxNAuUlWZjzE4bcGOevOOlZPjvxTp2h6fpOq+JdFuH1O+ie31CWPZIJLm3SNB5JcFX 3QhMbSBnvxX0RePa3/2Ntbjt742biSKWUEMrBcbgyc/8BLbfbFcdqngrS7yd5dCvU0ywuRul0m8g W6sHkPBdBuWWCQqTloyVJ52mvM/sWUb66GdSg90cImj+JvGelaP8QrexdLyz2NFHezql1dwW6+Xt JGQXVeMsQeOazpIpNd0Mi3d5EJby05R1kXh4nBGd46FcfpVOLwB46+H/AIjkuvDNo+p+FtSV2vrf T5jctbTZ+SWKFsP7kBecnPas5bvWoRqGvaOt1NdWkjT3Om+VId0TSI0wuISMjd/DNgFPcVhi8J9m SOWpCbPStI/alTwbpdn4X1Xw7Fe3emQpC825Y8gDKjbt/hUhc98Z71o/8Nl6X/0KcX/f5P8A4muG e18BeMW/4SVPDkU41DD+ZN5byHaNnzME5PGDTP8AhD/An/Qr23/fMf8A8RXF9Uh/KTc//9WL4W3b LdajYSfKHjWVPT5eD0r1a71vSdJ2rqN7FalhwGyen+7mvEPDEE2jXv8AahikklWNl8uNBzu465H8 q62WbSrucTTeHtSkk7yyPDj9c1hl0JclpmctDo7/AFbwjrcDRXfir7OvaGGNjG/++RGxNYEeieCW +UeJnnI6LFpkrfkTt/lU32zToB+40KcN233ixj8lAApg1TaedCtm/wCu2oO4/R1FegqaQkyzD4V8 JbvMGo6hIo5ATSwP/QnFaLaV4WiTCXGsD0KWMEWPbLyH+VYMviwQN5MehaIjDrukMn55lP8AOr6e NNYhAFrp2gQLjnZHCx/WQ1XKDY8WfhZnO99Vf13NYIT9Rya0l0vwkVH7nVGHvc2oH/oo/wA6zZPG PjZ8GJ9PjU9Fht4OPpgH+dXYda+JdxbNcLqkNuUO1Y1hjEjZ/iXZCQPxb/CnYDSh0LwnOCsej6nc H2vI9v8A45DVuHQdFh3Cx0DUIy4wR/aE6gj0/dxJ/OscSfFS4TnWLhVPXy9w/PbGartpXxGmx5uv 3Sg9me4X9AgpcwHRwaBYxENb+GGDDoTPdyY/CpJ7DUUwqaPMM8j5Z+nsWxXKf8IZ4wuSc65LI3fZ Jcsf1xVj/hWevsm6+1WQH0kVzx7+Y4pXAvXWjeIZ2x9luEXjCtME28f9dKyX8J+ImPE3lr3DXyoB /wCP5pjfDiVPll1dVA6Hy4jn85jUDeBNMj/1+uRrt6hRbK3/AHz5jGi47l+PRLy2s5PM1VY74thZ F1SIQhfR0ALt+JqEWmvREN/wldouPW9LfopNUv8AhHfDEHTU7udRxmIRr+uw1ah0PwpJ8smrXiHs rMT9OPI/rRoFy2JdWxiXxuiAf3ZZH/QCm/aJUGX8bTH/AK5pNS/8I54WHC3d7OPYMR+AwtPTQPCs b4iS/l9QVYfoHpWC5RlmsTzJ4mvZs9dsTj9WNUM6OWy2o6jIp6kbR/6Ea6gaH4ZPzf2der6EhlX8 y9VLjSfD6EP/AGfKw7DzEP8ANWP6/lRZBcwGXw4SC0mpTHtmWFP8TSNN4ajOFsrt17iS8QZ/75TP 61v+R4dQfLoUrnscqf8A2nVWSTT4AdmhYB6fvMH/AMc2n9KaaC55T4mFjJq0QhjEEVxCPkaTzOFb nLHk/lXn1zAbGaS3jztYEgY9ea77xwxfUNLcWj2StFK6gmVxJtcAhQ7Y/KvMdR1gpdCVE3SEFVjC 4xjtivOr/GaQZ1emKPKabbjOAc9a02giU/KB747Vz+mXN4XRZoiqzLux74rfWbK5xtPTH04rmNUQ 7ASB2HemNaKefWns+OSaDMignI4oAY1uoAAHSsLXAoiW2xkn5iKnu9b8g/LGSqnk8Vtx2dtchLxQ H8wAZP8AKlcDkYtLZLXcRjcMjgcZqlb6WrsTgZr0KeA+WRgYHHH5Vk20K5ZjyB7dKSY7HHXWlqJo E243NjNct44so4p9NtgOTvY9R3xXqkiq91GGGdnOa868UsLvxXY2+cDKJx2ydx61vTd2QzWgtBaW qowGR2HP8qytcjjaC2Qc75ucccY4rr2t4ml2qzSM3r/hWdqFor38MC/8s03lRUJmdd2pmA0CCLb1 OOfauL1OEbgq9Qa9DvQI129C3tXE6g0YkxkHaOQO3+fpWsWeQanwsvW0b4peDtSzs8jVrfJP91js wfrur9wjHhiMg88GvwWs7s2N/aajCrM1lcR3IAByfJIcL/nFftf4S8f6D4y8P2XiPRJPt0FygyUO CjgfMjDjBHpiu2jNdTqpu8bHdbMc54ppZexrnm14uMx2+B2JP+RVQ65cn7kKc10cyFY6skEYzikC xqck5Nccda1E8KI19OKhbU9TbnzMDHVV/wA/rRzIaR2529dv86FYZxjp2FcIbvUm4aZxzisLxRce MrDSE1Pw5DBfkNtkjuC7Yx04Q4FS5DUD1aSZRnoMdvSqrzxoctIq47ZHHHfFfB2rfHT4kQ38unrH YWkkDmMqluX5xk/ec9q5XUPjf8TLW4jZdSjkVMM0SW0SrgHLKdq55Hqaj2yNPZH6KtqNkvJnUmq7 a3p4HMpPsAa8S0f4neB9X0az1VtUt4JLiMF7c5Lo/Rlx35HarI8deGpGIs0vLxvS3s5m/XYKaqCU LHrb+IrIcKsjZ9qiPiOLkJA59iRXj2o+P7bS7KbULvQtVjs4BueaaERKP++yP0rOsPiNcazaJe6P oWYZPuNdXkMJxnAynUU9QUT2pvEE54jtgO/Pb8qZFr18ZkHlJtzn5ea8Zl8Y+IM7ZLXRrPB5Ml48 hH4RJ/Wsafxv4sZyiSaeqkYSSGN5Onp5nSmky7I9b1nxHquoXsht5fLjj/djaAc9zXNXMtwwP2y5 klAP8chQc9sf/Xrx26udRu1YXmqXzGTj924RRk84H/168B1Dw9r+k6//AGhrdzqWoWVvN5lvdwyF 8L2Dxg5XHTgGoqtxV7F0aKnK17H2bJd+H9M2ya74gi8yY/6LYwyoGLeyIWZvyq08Mms+WBGYLSNt 3luMF26ZK+g7e/PFeJ+EPEfgnVdYF3EEl1mOLylkcBJVQ8lRuHI9RXo2o+MILQC0tSZpehji+Y8f 3scKK8TEV5TlaKPrMDgKdKHPOR2AaPTYykZDgDksOgHc/wBT0rz/AFT4iaRcA6TZ38KSMWV51Pyq uOVRhxuHtXmPib+3PEyNb3uvQadZBv8Aj2t13bj6yPvXP0pul2vh/R7QWtvLC7ry7GRdztjq2e3o B2rWjls5O7DEZ1ThpDU1ZYbe8ZJxIYoY1McEbfKwRsEtz3kPzU77NZLnfeyqvbhen4MKrG90JCfP ltjjgDzQcfgab/aei9RNbEduN1fQ0YKnHlR8niKzqzc2XltdMb/l7ncHsGIH5hq8h+MnwztvF/h9 tR0Rsa3oyGSNJHJa6t85eJcZ+ZSAy8+teof21o8YH7yI+yRkY/Knr4h0uMhopG3EYGyM8c5444qm YpWPj7w//Z+t2M93GNk6IIysiDesyLho+oA5HGe1bOl6XeSxi4vX+xKeRHbvmVv9rzOij/dGfeuy +LOgmUJ4r8IWyQzKu3VYo4vJ3pnMc+QuN6dD3IrjLWYLbQRJL5quuV8zA4HJ27etc9SNtjqWyuaP h+9XR/EsVlZxrBBqIKuqgN5rR/vP3u/JavQGjaG5Tch8rO1/3UMUeD2Gea8/0nwxrviy/hPgrTJN VurKRJRMoAhRlO755nARRkYwGz7V9K6X8JvGviMpcavb2HhnPEhjka+YjP8AAPlXp7msnVjHdm1P D1J/CjyW+snhTaRGFOSgJUcey7a+fvixpnlWemX0RLqk0sZ6fKWUNjjH93P+FfpbZfALw4sKJqmu areheoieO2X8NqMfwr5r/aS+DN34V8Iya7oc82raLDcQPOJwPtNodxQOXQYkhO7aSQNp/CphiYN2 RrVwFVR5raH5/LCx3ELkD06fhjr7UpjI/hOBXQwRMmQsZULxjr+v9anFu7dFOewArsSR5XMcqVOO 4x2ppVsHK9OldI9pMxwibiOwHNQGxuDkeWcjqODj64p8oKoY8ksskCQsqkJk5I7muk0Hxlr3h2wu 9J01ojaXsglkSVA+GXjKnqv/ANas1rCfqVwP8Kh+zfMVBBIwcZ5rOwKR1LfEDXNzs9tZOD2aI+uf 7wrZtfidfxxb5dHs5HuHLsUeSNSc8/Ln+tcCbN+BImBzzg/4UxYFWKLfKqqu75TgAelOw3UPQp/i pdu23+x7fO0AfPIcA9eSf6VzV94t1fUQrRbbDqWFsWDNn1LHI/CueSJp5NsC+YwAG2MFz/47n+Vd lpfgbxXqW1tN8PaneF8YMNjcSA/98xmnykyrHKC/1gNuF9cqxGCVlYZ+pBq19r1mWMrJfzSKQBky N2PPJr2HT/gX8YdRISz8Aa4R2ZrVoV595jHXYWX7KXx0v8bfCTWqt1+03lpDj85GNPl8iPaLufMj W3myMZJC/wBWyf51MtnAF3MefQDpX2Vpv7FnxeuFBu00a2U95r8vj/gMUL/zrtbD9hvxkxU33iXR Ldf9iC5uWH04hH5VVnbYn2sT4IW1tmB25cD0x+VSLpssjYitic9B1r9J7H9htwo+2eOHBzyLbTQu PoZZj/KvYfA/7Ivwz8NtJLrlrN4wuG+62rhVgj/3LWIiMn/bcsfanZkusfjs2lFR/pDRwjvlgMe3 OKgNjY5Ci4jc9gGBP4YPNfv3p/wa+GOlov8AZ3gvQbcnrtsIDjnj5ima6618NaXZLtsrS1tFXjEN vFHj8lP8qn2fmHtT+ey08JazqJC6dpV/eMeghtJ5M/QIhr1jwd8KfifbXdpqlh4K12WW3nR2QafP GGUHhSZFXAPfPav3RjgVV8sXDqPQHb+WKeLe3/jd2Hu5P+FTZbSKhKTeiPln4I/Cy7+Hnga2sL6M waxqRN3qTdCZGHyRd8LEnygdN2TXtcdnIvGxFA4XHp2ru/s1iOkYOPTOOfpSloIlIEQXHoOn51x1 MJTk7ntUcyq0o2ikcKdLnfmNsN2znj2GK8Q+K3wm0L4gwRvPjSfEFsCtrqcSLuG4Y8uZRxLC3Qjq Oua+npZI0GcqoB5PpXAawizyzBSDgbgRjjjtXLiKKpLnps9DBYudefs6mx+VGpX1ppWl6rp+u2/2 e50SUwX0atvIlifG0FmGV6EYHIrmrv4m6LeajbxJN9ku45o0XKNlWXC8NyNpHbOK9b/aC8Iaro3i 3XPFiWcz+HtYa2mluFQNDFcNGUdZj/CGYYDEbfevmi707T1SJlgjKyrGV4Hc4xuP0rnq1FVSlI4K 1P2dRxZ6jd+N9C1XXLaLXNWFktlfsI5s5VIgAknCgEq4/LtWn4m8VeGbm5aDwreJOiIYZbyZzIzk nOIvMJbaOnNeJX/h+ye5QPaqY7mYKrMThSRnnvjHWm33hODSmR5LQcgkSQliP8/hXLPDQbucrld3 PUk8S+Z4eXw/IsdxfQfO07KNksSDCox7HnjA7V9QfAu0Mfg2WeUkl72WPPJ+SH5VGfQdq+ALdWQy PFcOItoZwGwdqncf0r9JfhHp8mm/DTw1bSEmSWzWaTfyS02XJP4Gu3B0ooyq6no0UeG+n8xxVS5T M5YcnAFaKEYyKo3B2ncT1BB9sV2tGaPFfiMyJfQXIZYZ7e12Qyl9jEktKyR8dwMuw5ArmbDx1o/j +WHwb8VNNEBu7iaw0/WrHEc1rdIpHkTDrIhXoTz0xXi3xd8VTf8AC3NYSQySWmjqLOCAEbUY26+b x/tPw3fb0zXmi+KprjUl1a6DPewSwyBkBUu0bjbu/wBpBxu6noa8urQbdzZI9L8b/Da6+HmoaJJq lsb/AMPTCSJtbtG3QsVk+R3Y/wCqmCZ8xHC+grL8X6pe6Nr8sUcEb3MLyxzWufk8yJuH2sP+WkZy B0VvpXaeAvi4dI8IeI7i+KaistxLNfaXeozw6itzsba5wVQ5LAEYIrrfE/hnRvi8bn4hfCeT+1br 7PG2q+HZdo1G38lABJCnHnAJgEgnOPWs1ST3HB6mTpOn2WraTfatpFybiG4tlZhHMuFu4/3gVsfO hzleV5H1ryyLX4Iltro+c/2hBlQm5EBUhhI+cg888VwMclnb3s09u+yT95HJCw8so4kzh1OMOuD9 7kdPSun0++txGTM6bQNwYOCAW+YhlwMj8Kl4SUTtwrSlds+sv2dvij4Q8JahrVlqqXcd9qy28Vq0 UDSKY4ldpDvyAOcHnFe8an8dPhi+hz6dpWvrHdRATSJKkiyFD8+1CQQGwR3r8+PD2ojS9S0++JVV jk3LsYFQGO3t65/DpXoGr2fhaz1ee11MR3iuiO6Nx80hwANh7KBSjCxdWabudNcfESK68Q3OuXBh tPLRtokkCko5ztXdgdOpHevO2t9G10zaxpFy08jy/vog6jailjx6+n+RVDW7Dw7r15ZiKKAx2UJi hdhs2qTkgEn5ufWq9pYabpzo8Mm2OL+7IuJOfmU9OMYH4VMqN9Tmqe8M0mybUPIQRmFmnCsjttYI VJ3Nt+npXs/w3ilMWo3tsHisX09bJocsFeQXJKy7cd468n8P6ToUn9syGB7e6S0keFlchA0jhGkZ uein5R616/8ACzTxZeFbuVfOYXF95KGWTfiO1i25U9Orc+/St8PQXNc56kUj0iCIFieuP6VbZeQc cUsSbECnjHAPrQfQ16rMyhOnORwOR+B4P6CvjHxtd2k94LgW0am2Kop2DcAh9a+1pMcEdM4Pv718 XfETRp7LUrmaFhJaTSSEFQdwXPy8fT2rtwcbtpmFSpys851fxFq7edHZXUkltcZO5WCsitwUGDnn vXOWmoCHan3QowB6AcY/Kqd0ZIm3RHaO2OM9uRVUXkMxxKmG6ZAxiuuEYQ2QSlKSu2dvb6gxwwro rbUsYBPUZ7dM15grvGQ0blgen06VpRX5GN/A/PNbKRieuw3auu3III6jP9adKAyHaa8/0/WFDbTI COgrpRfMwA7YreMwLj5Gcc1SYd/X2qbzOcqR2pkj7uMjPfFJsCoQepFVmjbHXFXwp/D17UqmMHnB A4JA6VFgMoxyPwqliemKmi066lIXYUA9f8a3o3toSJVYEVJJq9sictkDoB0osBRg0Yqcs27PYD09 qumCKFSznA6DPH6Vj3ev4yI2Efpjkn8s1hPNqF++1M49W7fhTA3L3WFgzFCMt7EVhtLfXhyAQO/8 q07TRkTDyDdJ3471sRWUaAALtP4U0hXOdt9LZzuk5PY+lbawpAoWMdB3rRCKnQU8gEcDtzihISkZ LSBAWYcVXaQP0H4VonGfub16mt/TvCeu6xYtqWl6Rd3trG21zaRGcr/wCLc//jtDiNtLc4p4HkyV G0DPFdv4AkeO81KykH/LKOaPP95TtOB9DT9E8F+ItX8Q6ZDaeFNQ1iKC5BuLa5sp7e3MZHzedNMs SRbeoZmGD68V6VZeBtH8K6lc/wBj6qNaUMw8yJt0KBmP7lX/AOWxToz8fMDXDjpLkszSmxVtgp24 5Hy4I9O9WY4+cAY9K0lgIGduPagQgkYHPTivEOlHa+ArW1vDq2mLeww6texBLO3dissqqCX8lsFQ w/4DXF/EXX9U8N6b4c8OfZ9TBM32u9t7zOXmZtheUjIIRRmPGUyORmu28PWeqvo+qXOiah9j1BEK R+S4SeNGGHZXPMePVSM9D61x8XihnTRtN8V2kvivShqE2mxLfM5ORFmSeKRPnSROTlWKnO3mvEr0 17ZykaKkn7xxUWmeJZNXuNI0G5j1e3EAle+upliYEt0eNcmaRR0wox2IFd7osGq3lsPDWm2kml6j fXDTpZ3UJhmlO4LmG5QNFny8/wCs5+pzWy3gr+yLiPS/Dtz5+m2TGGK4vY1mmQkfdMikIUAO0cZ7 Vztpca7c+Kr7w1p99caSdLtmBuBg75ZAuxBEGXaB9cmuJ1OZ2SM5VNT13SPDfw6j+ICarqsFzL4t P2iFLUMy6bDJtH2aGSFRvd8DjYVTPqeK9ZtLPX7bXYNYtG8jUY4Y7fVbIQq9pdW2Cdoj253ZGN/z HHAFfLfijxpqreJ9H1a/TZcQSrZ3TW2Cs52BQYt3+rcuBu2nt82OtcVpHjuXwZ/aPg24Esn9pXa/ aYbtlvkcufkZZ94CyjJwIwMt6nNdTlobQqx2PqmTwdrk/wC+0yKLStSe5hvBDM6iGzMLkiODaFcQ sO0hC/MVyCOepv8AVNO11dZt9K0+MXUVslxGwWQNYzxfvZIyrKflPOyRCBtOMcGuP03xjqvhRJrq e5ln0qSV4baOWQGEW8QVUDMRvRw3+tBBxnOMVRT4j6j4f1WPWdOkt9a0LV5PszWYMdve6dKVOY/N h2pMu7G0lTnIb64uSex0RnBHjc2oaB4nkvNL1i4XWNIvHQy2us+butnUMXMEyvHLDMjKqjYdrKR9 7pXiuuad8K9EujPokmspfM8gjgmkdIwqtzEhU5ZsdDvH4dK+sNZFhq1vceG28QIkpaCS3m1IRIry MMiJZ4gvlOu07o2UZIyTXiPivw1qV3eT6RJoVxqXidfLeye2s0fTUL7VeSSV923u25TtHcjms1jJ J2OarVVy3fRxaJph8Q+DdZuLzT2WMvabHaaLzQOm5tzkdwCCPTHNdHoPxa1OzuFt7yI3LYzhcuwy fQc9P896zPF/gbwv4WfS9bH269iUJBcXNhfxSRW74+ZPKVsyF+u3H3cdKxV8Oaf4Zv42skvkMMYz cW0UckEW/oJkUmSLHQgnrmrhmcoPUj6yj6X8NfFrT72Yvb3otPMCqI5XAXPQgseAc9s/1r2WDxjB LGlveAQsf4l5De5xXxrB4f0fX0s5Z2is7jVFkjtbiMkoZW+6zMhLJh15Dj16VjRXnjPwdqp0TUrt bdxJJGI7xsxjyiP4jgclhgrnOc+9ezQzKE9zWNRNXP0EWPRNXtikxDI3AIzx+I6fjWBqPhO6trMP YBHiZtpLYdiM9Fr5SsPinf6Dfvbar5+lXsLBJhnzkz7Y+Uj09q918O/GFp12uILncNwa1OXUA4yy NxXbGpGXwmin2JbrQ5ogUTNpOeoYBVbsA6n29q5yXRbyOPZDbBwesa/Oh9xnla9n07xD4e1h2WaR Y5+DkjZ15xjn9KunQtPvWa5ikYt0wrY29h0/wp8pqqvc+azZ6gqD7ETZsjHLnIGO4LAZH5UvmXuf MvvNkVW5beNpA6fNH/7NivadU8J3bSG4eF+MYkH3SBx8yj6elYFxoMUyFLWZjOilipCIzD2Zc/Tp UuJfOjnLfxNcRQ+XDGgAHyu5Bxj/AHTWV/akV7I819JKArYfaoU56D5TwRiteSytFtWF7aPGVPFz EcMnbD7cgj8qwroIEeA7Z1LAIx4U9PurjipegbnRf2dEYTcW92pVcExsSjc98dOKUJZuieYkEgDY JaNN3X1ANcNBcTWrstoTFJHklJcDB6YVW5Prx/8AWrZi1hNgaSG1mkQDLKAHJ/3TinzMdjtH0dXj xYTLGzfwEAqfw4/lXPXdprVjKpRFmC8fKEZfyIzWbF4hN2zJFtJXqo+V/wDgNaZvHQLOWdBtxlck H+lK5KZDBqJ87Zf25jZPu4+TnrwR0/IVuW+uxrKJkufKdPlzLk44/v8AOB/nFZstzYaogiuCpYja Hz6cds/rWPPo8kSZWRtv3cYB46ZGOD+dS4p7ofKjlNV+Evwy1jUbnVJYry3ku3Mjx2d7cwwBm+8U RWIGTycdSSeM4rP/AOFJ/C711X/wZXf+NdodOuF+VopGI77T+HTik+wTf88ZP++WrP6tEz9ij//W 56O9WXCro2iRAcfMJm/9DmIrVgj3kbLfQEA6bLPzP5Fv513jaf8AFBPm/tyytlPpEEA+m5RTDaeP 3BW48a26gdo5lBH1HFdlrGTMmz0i4mGY7jTY8d4NFLfkdhrXj0CZmAn1aRSOgh8PM35fu6yLjSfE 5bFz42VUPXbcEf8AoMwH8qrN4cjmyuoePIkI6CScsPy88/ypiOyh0WRPlOr60hHQRaLHAn471FX4 9NmRh/xNPEUxHaOG1Qn6Ywf1rgYvh/pl5b3N0njfTJbWxUPO7ecwjB4H3c/lmq0PhPwbG+8ePrWI gZBis7hmOR24oGekx3tvA5Dar4oGzgo9zBCB/wCP1P8A2vooYE6lrBbv52rIP/QWrzT/AIRnwFyb jx0ZOmfK0mds/wDfQ/OrK6J8M4V2DxXqcmO0elFQfzFAHoNxJ4enI8/xBcwIeSDqTyH/ANBJqnHB 8NoSXu79ruQniR555Dx+ArkEsvhchUSarr04A58u0jT8gamEfwujBOzxHcKOzG3j/TNS0gO5/tP4 aWmDaRpux1WGQn+tRXHiTwTImx453PfbBMv/AMTXFeZ8NCQsWha5LyB+9vYo15/3TUl6vgTT51hf wleOdu4btVDJgj1jDUWQGw+ufDo436ffSHuuw4/J5j/Ko21z4dLgRaDdkjpzCv6bq58at4OTiLwP Bt7GbUZ2H5BakGt+G14i8F6OQP789y2P/QRRyoDoU8WeE7b5ofDrgnvJNAv8gxp7fEPS14i0W1QD pvuv/iYq53/hI9MXiDwx4fjPoI5Xx+LOKQeJk6R6JokbD+5Ylv5yUuVAbTfEuOMYg03Tf+BXE5P5 BVFQt8Tr4jbFHpcA7AJNL/OTH6Vlt4l1I4WGx01AOnl6XHn9S9WU8QeLGAW2hEeP+eWmQL/7SNVY BX+IupnlXsEI7rYKf1Z6Z/wnmuXLBormIv6pp0A6e+GNXotV+IUmPIN6PZLaGL9fJFSyT/EN8mWf VFz6zKnb/Zx/KmBUj8Q+MpwfLkvWz/zwso1B/HyqQ6h43k+VV1Y57Kip/wCgxCmSHxcwJup7uQdh JfuMfk4/lWVLFqz5+1XG7PXzbosPwyzdqAOF+IVvr8mp6HLq0F6kxS6MZuslsIEJxuxxXkpQrcG4 BS9duWJGGXnBA4x0xXp/jeZtOsxJA6+dLE8AkV84V2BbB+g7CuL0axaK3JmXBdTggY4P0ry8RpIu DNVMvFAwOVB2gA/4VRaSWGdhnJGf881Lp/KyW4GDE+VHTr9al1KFmfcnHy9MY6VzGtyAzAqA5yO2 Kms1jkm2OPlNUY4WaPaT1GavWke8bu6fhSbKIdS0RGibyxgDtWf4W1UWk0mjXx2gndGW479q7xcT Rhcc45rz3xRpDBVvoOJIOSfb2qVqOx6LLHuRh19PyrKSMQxZI5Y8fnWLoHiCSe1WK4+YgAAnrW+M zYB9e1JxGjIZAJnk69AAP5V5bJFNfeMhNGobyZMjIPO1cGvZEjWJpc4JzuBPbAryrw5Lv16W5UFw GmkYZ65OBW9F6NkPc7K3klacO0YQxYGFB4Ncjqt5u1y4DRebsVVw05jxwOy130MjQyyNFFkXDKTk 8KM9vWvLbuy+265fTuchpcfgBilBHPin7liaeWFiSILdWHdmeY/riua1CaViUSVVUdooQo9a6f8A swKjKi7c9+vSs240z1Tn8e1aRZ5ZxczSklWklZMDnO3B9sV3nws+LviL4Ta819pim90q6bF7psjk JKOzr/dcdj+dYT6ewyQBtrGuNPVi7KPmCkjHsP8A61bxmXCXK9D74k/bO+HZgEsWjan5zqGMbImQ xGcbgcGt7wz+0fpPjOznvrWXRfD0cMvlBNb1BIZmIG4ssSgtgV+Z0lrt5UZH5dqpPb7jkoGx0zg/ h9PpWlzb25+o9z8dfDUZAuPiL4WtxnrbJc3Lc45GEAP5V43H+1dp0niDWdP1bW7i20a0O3T7/TrA StdjdyWikI8vHOM4r4Tlgwu0orDPAwBj2HSqpgnBzg4P+fWmrjVc/QaL9pX4bXzmJtd8V3U+dqII oLffn0CbjXknj79p5ZJYNM8Af2vHaR7mvJb29k8yWQfdRQIxs2d+Oa+SwZ7PUIrmEmOWHEqFeoZT kHnuMV7x8WNDg8QeGdD+J+i2e0XCCHVJUiRVMgGPMIU8YYYPHvVG0Ghml/E3zQ0t/pzI9w42NCRJ uOcMZGc5JbPb0r3BvBCtBDIJpzckZlk8jcGyMjABXH518b6SFMSqMgQlGI3dAjA4/Sv0TiSa+t4b 63tS8F1BFKhaXGVZAQRgGnTV9DV+Rwtt4UNpLb3EM17BNAQ6SQhUIYcZO9n/AErrZL/xZI6PNrWp ShP4ZLuX9cMB+laP9lz9RZxp9Zif6CkTTrzJz9njHYFicf8Ajw/lW3sCOaJjsdVvZfNuJ98mOC+H P0BOaDZaoP8AlsBnnO304xWu+lXLIQ17AgPdVU/qc/zqL+xmJz/ahBH9wRf/ABBrSMbCc0ZyabqT gZvdpHOBx/Wpf7OumXbPesB6K+D+oP8AOrv9kK3Eup3BPtIB/wCgxUn9kWi8HUJz6gyyE/oF/lWl hcyKo0cOQPtbNjt5n+GKn/sqFetzKM9cMwH6H+lT/wBkaZjLTTuD1BeX/EU3+x9GC/Kkre53n/2f +tHKyXJGdN4W0e6YSTRK7r0cBg3/AH3wacdB0ND8xRSMdePz+atVNM0pQp+z5HuAR+pJpzadpIUF bEAdMqF5/IGk6Svc09tK1rmGdM8NxfN/o/4tub8s1MIPDCAb/s5I7ZUfpnNakaacGAjtVCjgltvX 8QBU4n0iM8xw56Y3pxVcpmnbYwd/hlN214c+isp/QGmf2hoAwEbce2EJ/kDW++o6QmMXEMJXqBIq j/P4UN4k0eMAm7tCe+JAT+hpcoucw1vdLY/LDN9Y4nOfySrIvbf/AJZafeEeq20nP4lav/8ACT6T yyX0ag/3Q59uCBSDxHprYMczyE9SkMj5/wDHKXKVzGFrZ/tjQtR0QaXeH7fA8SF4gAHI+XqVx0r5 k+H+ny6/4+tfAnirT5bO1tS089u4KSOIxgKp4O1up29RX12/iO1Tcqx3rlh1W0lwfzjqjdajb6vL AH0+6WeI4huZrUqUB6qHZVwv41hiIvldjowtT97G577odvbWEi2OnwR2lrBCAIYQI0jUdl2/1z/h 3tmkUqqxIAYdDn6DpXnXhdzcLHuYE3aBsjsB8tehwMLSSOGTBUn5PWvmYvmep92rQSsazW12yf6P CcdBjp+tRTeHv7Z0+70vWrWO4sb+F7e5hc5WWGQbXRh7jj269encaMyvazEgHYwC5+natRbuCMMG 2r9cCvdpYKmkpXPma+eVZJwikfnFcfsPaiur3X9j+Mre30Uvm2W5tpprpY/7rlZFQ7egIHTGea6K y/Ye0/cDqXje6kHTbbafHHj6GWWT+VfepvLcjhkIx7cd+39ahN/CBgNx22gmvQTifPzhKTufHFp+ xB8MU2/btW8QXuOSBPbQKefRIDj8DXZWf7HvwQtwPO0G7vyP+fvUrkjj2RlFfQtxr+m2wJnukjA5 +dkXj/gRFcxd/E3wXYj/AErXbGIf7dzCP5Mar2kDP2Uu5y+l/s2/BXTWTyvAOjttP3pkacgY7mZn zWprnwH+EOvaW2j3ngTRPszYwba1S1lUg/wzQbJPyYfjWfcfHb4bWp/5GC0kxxiIvLn/AL4U1g3X 7RfgJCfKup5j28u0nOf++goqPbREqMi1pv7MfwQ035IPAmlybTjNz5k7fiZHb+Vd3p/wk+HGlBf7 P8IaDaFP+een2+fzKE/rXj1x+0p4eiA+zWOpXC4J4tkiH5u5/lXK3n7U1hEcLYTqD08y8tYf5Ue3 j0D6uz69s9D020ULaRQW6jtBFHHj/vha0vJBGzzpWHf5ifzAr4Luf2prhxi3060OeAZNQkk/JYkq r/wvrxvqpUaXpCylhx5Wn3t0f5KKh1/IPqzPvopp0Qy7qOxLPjP5kD9KjN5o0XDPGCe+7k/lXwSf HHx41Mg6d4e1MA9TDpPlgfjOwqf7P+0vqS/u9P1ZQ3Yz2FoMf99kip9uxrDH3UdW0vcQjBz6KpP9 KrvrtjB1RlPqyhR/49iviWP4WftBawAt9ctAH/gk1qRsf7wt4zmr0X7MfxNvip1DWdNiU9ma/nPv ncyLS9vLuaqhE+t7rx5olkMTXlvF/wBdJ4Ux+G6uYvPjN4Fsvludd06Mjt9p3f8AoGa+fR+ylc24 zqfjGxsW7mKxh/LNxM1WU+A/ww0s/wDE0+IuwqPnEMthb/mI4pCPwxSdTzKVFHqVx+0P8PI8BNXj nbPSGKeX/wBlArGuP2jvDC7jZWmpXar1MVi6r+bv/QVxkfgL9mfTM/bPF1/qJ6kHULmQH/gMCL/P 8qrzQfsj2jAzaQ2qPGeDNFdzZ9M+fKP1qObzK9mjWu/2obCAZj0K/wBvZp5rW2U/mc1xur/tYX1t 5a2GiW6vI2MzXzSYwPSEV0cnxN/Z60OIvongWB3gAZSba0j5HuxY1bm/a68P2EZOkeFIoCqgqv2p F69RtSPFS59ikrHmq/tC/FvX3e20Tw/53mDhLTTNSumK5zkHitPRfip47hlvo/iN4OvNMiih822l mjeykm5IKmCV3JQdztWtef8AbQv41Mi6Vp9pEhy3mPNKfbGCuK4jSfH9z+0bc6zqmqadHpVlpzxW UMtizN9olwz9JAQgXjcxzxx14rOU/dub0oc0uVHv/gL4raR4ntbmz06N7lrLyY52iiPkxu33lWZ8 B9o/u5r0tZ4XXKjIxtyfTGK8c+HHghfBPh200fMRKDdLsXaZJWH7xmJwST0HHGK9H+32tuqws+WA 6Z54714NfFc8j7PBZdGlBPqfOP7QniHWvh+NE8X6TBFqFkZJdM1XTrkFrW+tLpdwjlTnncnyNj5T XzdrfgjQLnw+/wAQ/he0l14VicHUtLnPmXmhu38Mgx+9tef3cnbgGvsn4paZH8SPAHivSdPi81ra wM8Ei8gXNsfMTGO/FfnL4R8S+JPh9qmneLPDd6tteSRhpIsbopYXG57e4To6HOCCOP0ralUXKeLn dFwqJmdrF9b3Mkgtoh9kACqu4fMp4B49Rzx2qtFFJFaR3NxH9k085ESK7M7444B5xx9K9u8S+CND +JmkXfj34SWkljqNoDca54UiO57Zzy15p46yRH+KLnHtXi6WN9qsMf2mUPGCUDlsKo+6qf7PT7vW iR4dzNiWWeKWythuFwEhQDGd0jbPT/ar9RdOsxZaZY2MYwtrbxRAf7qgd/pX56+CNHF/430LRobb 9wb6CRpsEE7G3lfp8lfo3xnjpnivQwmxnIOgFVJFEsyRHpIVUn696tt0rG1S7XTrK81KThLK1muD 7iONn/pXTfuTE/PnxJa22ueK9d8Swzu8093dOygAod5bH4gEflXA3cBuZxJaoTM8yLEIwenfOBjr Vm0u5bbLb+h3Ak/eGO/rV2W7vJT56hreV0AjdVCoAOo+teVOo7nRczL59R0Q3WlZSS2nlkPygHJA GPyGK1bnUpvC3iO11nwvdNpt1YMgtrmNisnmqgEhyONvH3eM+9ZJgnXy7lpCSzFgGAJz+PrWxJZG /kiLOryTfIomGFLVmp2KSR7H/b/gL47rbWHjprbwV8QDtitfEUEYWy1HI+SO/RSu1+nz4yOoP8I8 a8XeBPEXw71lvD3jWxNldE77eUNvt7uPtNBMOJARg/3h39a04m+xxfZzFEUdXjC4BHA+bPXPsP1r 1fwn8Wls/DreBPiVpp8YeC5sKsMxzeaeez2spG7KfwoCMdmraNYUT51W1tiRFIzbepzxlQSxArqL uLz5UkuLl7V3hTJZyowBlAMn3r6Hsfg94atC/iDRLiPxd4C1GHaLwKVvtOkRwVS5RfmXBIUyr17i rGs/CzwVdaZPqkVnJ58SYB84tGynuuOM9qb11NGfLM9rOrxn7ezRsPlIbI47d6gcaguTHOku3nmP kj6cfpXVax4SjhvBp1yXKQD9yVONoxnn161zj2UltO1rO5PkkcIcM3sKq5m9jf8ACl5e3V1qWlS7 S+oabNHB5fygTwkSRgnPseMV9b/DW1li+H3hs3JzJdW8l8wz/wA/Mu4Dn0Wvi21n1Szu1vtLufJu baXdB8gO1mOB1xnvX6ER2/2RYbPAjFpFBDtHAGxBkfnXTQMavQcVPb1qMgVP/PAzUTdMVqQlYydU k+z2FzOX2KkLnPPUjb9e9fO/iUf6fs4mEcaITwcnyxn9a+g9bjM2myQKeXZDg9G8smQL/wB9AV8+ 6o0y3ckc8WyQsTx3zzxX0mTYdcjmzyMdU9+yPHPEPg2GdWutOIhd+WR/unjHbkdK8mv9JvLFil3C 0Z7P/Cfx6V9Nzwyq5JUkDB596oS6atwrQTRrIrfwkAj8c111cHf4SKOK5dz5iS4mtcAjKdweMitG O+tZflfMbHHAH8zXqOo+AbeVm+z+ZAxHVBuX6GuH1LwLrdkhnSL7TGvXyvvf989f0rz54ecDthiI TMoCNuUkTPqpxV6O/urddqyAgepFc1JZSRMI3VoX9HUr/PFJ9nuo+hz6AisfaNGqguh20GuOMbuA e+a3rTWFkAXjP+fWvMEF2fk8rcRxx/gKuwi9BHybeo49qqNS5LVj024viFBXgAc56Z/CsKbVcElT s/2VrGW1v5htlkJ9i3+FXI9MZSN/Tv6Vpdkj31WeXCxqEI4yKnSK5uF/eOSD3/TtVmGyigbcoD1q qoABxsB6dqYXKdvpqAhjy3Y+ldBbwADC4B74xVeNgcBAXPQDHX9K6LStE1jVJhBZwxqXwQZZMBf+ +a1hBy0RMppbkMZ4+9n345xUygAZYgZ5+teo6V8JzcTbNZ15Ej25eG0jA3H03y5H47K9PsPh14At YBaS6Xb3BbDGa5dnnJ95P5cYxXdSwU2clTFwjsfK6yxXEgjtx9obOAqc/wBKvR6TqVxMI/szQDuS OR7ACvqJvCek2R3WDIig8KIgCB+FVZLRLY5ZA6k/xdv+A8V1Ry1/aOaWYX2R82TaLbRHbe2Grzgj rAyrjHpGVI/WqFwLJJVlgv8AVdOkXC/vbFg4A54eFoj/ADr6h+z6fcxuzBUbtujB9uo/xrBn0zTm +WUPJjpjAHPTpTq4NJaIlYlnh0MGv6/KlrN4g1C/smGJYpGuVTaOcYklYV7ToVhHaaVZ2sSBFgj2 oFJAwDWfBdwWJkjgtcKxOeRmussyZ7WKQDblRjgD27cV89mtLlhc9HBT5nqRGH2qMw4yFOGxkAds VreV+P060hhHTGc9jx05zke3pXz1tD04lfRdc02y1iOWxn+zTQr5d+JSwjZN2CBnCbeeeetcRbXN xb2Qt7eZFs7S/vbi2ltS4Vo5HxkAgt90DP8A+qo/FM+j6fZa5Z+KJ5I720mVWcRhSYZiJICpXO5M nB9x7Vw416/v9KXQ4rSNLuTSr1kFs+754TncjDj5lPrXh1ItyZo07WR63eyeNpNO8qwjgv8ATbwp PBCyiG4VkHylJ0YHnoQQfWuCfxZd+ENSvLDxBpeoabfRKLktI8N7uhkGwmGchJF6kfeyPTINHhbx 9r83g60t7extTY20i2ttfzFy+IlDFTGBlue/FbPxN1JfG3wxj1JbUQajotwl4WJJQxoxVkB+8vJz hvlrCnGz5WYWaPPbD+wre8iOnnUbizuZBfOl7g+VHGcqUdCGfaTgljkiuiuj4tgNxrGm2s9zeGdL ki3toJDK6nKuNys4TA64GB0Oa4u78PeNGudJ8TX8enQ2Fzam6jgSf/WxMuGBaTZkj0BwOnbFVbb4 jf2NZvZ218/m6dtSFgkjxQq4O/eW3jn+EDjNaSpu/u6mdtdD6c8UwX8GnaTPdzh/Dtze3IvNImjz 5kdyTILiKaQgxyovy4fjBzjkis3Rf7E8L+LLi8t9atBoFzaQ6jZeen7qZ7YKWt8jO1xF2HzK3TIq XxPq2jyw3Ojas9pc3Mml6Xb3iXqvut53tV8ubdH80bbTh/l2njvxVnw1rWk6TpEep2cFnq2rW7yW kdiI5I1v7Mp891beWhKFNoBLDO7IwOledObfQ0qdzLN14f1Xw/qWrsUvtBhvcQ3NpERdWkczmT9+ koPnQxkgAbcbT3Oay9M1WaTUtUk06+k/sZ9jXsaSELc6fJsjePYVXf5oycRqWXd0Aqro3jXxdoXh nRJdFGmRvpV1JZ3SXggkuPMLnyGZl2TKmzBYjcn8RGOaxPGN1qFrrOmfZr+2VfEBMQvJmjlgHnOP l2ux2RKX5f0x06VPs23oYymeifYl8LX9z4l8H2cmn31lvgi8PXELFrVJYdsRlycSQjI6gfN3rX11 7zTpbLQ9Ya90DW9Ut5zfuZV+yyEqWijlX5gGldWULHKuBzzya8mGravp91q6alrNv4kW8ji3yWcc kkblR5DxrJDszIFjUEN8vH4101xqx05F8PaPAl7508fE84l+0mAlAGhOXQpFu2hX68fMcCuatBp6 k28jVtYYEsohcW8xv7dFjtZrPMA2yKyN88bfvosjy/mGRjOe57jUvFlzren6Ba3VqoSOHa0d1Gvn JFGBG8YY85OAUcjdtx81eZ6vew30TWV5o11rF3FBJOskUnkNbIhP7uFl/eLggfK3PHPNeZ6F4psd YstP/tYabdahPK62tpeNdyttmYKimXdtD9/bvWuHozceZAro9cudYVYC1zd2c8N3H5CNdurTB0bL ozHk/wB0EA4GPrVfSYPASW2palq1/d6QfkOnGxYPKZP44jC2Vbb/AMBOO9c9ZXXhTWvDGr2GoOPD 2qaJ54lcgSLNbK/zmGVk+cxk4dRyQOM4rotEh0vy4NYg8QJfnQspo9l9kja3uo5l8t3aXbgStk7Y 81qq1SHUpVXEmh8X+KdAtdL1K7m+36dqWUsnDjzm2nGGh5ZTnj5u9er6B8YWs7gQXU7W9wpw1veR mJlI4I5xXhvxDutLst89vaN9ujeO7tJ0RI/KYPiXJ+VuQCBG67c9D0rtpJLaeS+tNdm07VNOfbdW FxcSxtc4vR/q5CmM7P8AllgZzxXZSziUUnM6PbaXPrvRPiZYaiii4nWJmwFBIUE+zdDXYf2poWr2 wW5jjm54KkK/4EYr847v/immkj0rUpormIxj7Kx3RuQeXO4fKPpXVaR8RNejkl+/cLZJG04tP3hj WQ7ULL2DHpXt0cwp1NjeMrn25L4bM587TJ42cYASZNjHnOC4z/KuV1bw7eXdxI01kfkQ79jbeAee eM/zryXw38VnYvDDfLI8fLJJ8jBvQq33T25r2rR/iJY30K2+ozczDHUYz7AjnHsa7bJmsZNHGT+D LWfbeWN/Eh2gNDLE3bjCyDOKx77wReGFpARIevlSrlTjuH4Ir2x9I0zUIlksgCW5Dx/Kfrs6Uk2m X+nkyw27XMYT5gOXPH90/wBBUeyZp7Q+e20+5s4CeUMJGVYLMn05wy/hmoJOontHFuo+8YSWQHuC G6V7Pc6Xp+oOI9n2KdwB86kde+ec1yeq+GbjSJC97CJoD9yWNCpz7t0/OlylXRylvM4CfaLeG4gf ODH1bn1HFankQcS28uABuKFuwP3cdq565hEckciOgBO0qBuXPq3b9DVZ0uLiZXitgwXBbBKlsenO KmxdzsjeKTlSMduOnt1o+1+4/L/69cx5bd4rhT6bo+P1pPLP/PO4/wC+o/8AGgD/19NvhbqMeDNd aaCeu+QNj/0KnD4bqgLT6zpcC98SDH44WvRF+EckXW4nkHH3tyAf0q3/AMKp0kAtcag6eu18f0Ir quyOU8qfwNpUIDSeI9KIPA2sT+WFH8qdL4M0qzKC88Q2tqZBuQEPyPVen8q9Wh+HngeHd9r1K4Zv 9m4UY+vyinnwX8N03E3sisRjJmQfmCRn8xRzE2PK4/D3hhMFvFaMeAAsLsF+mOaf/YngxWZpvEM7 NnLeXbN9eK9QXw/8NoOGvVfaAMtdqv6byaR9O+FESbpjDN7tcSsP/HTS5/IDzNNK8Ct/zGdTkHot uR+RbFIdN8CoSz3OpOOmSIx/PkV3/mfCWBjttdPkx0H75v8A0MEfpUo1X4axfNFp1oSOgSMD/wBk H86XN5AeffZfAC/dN6/+88Y/UHFPWTwVDjy9LurgerSkD9GxXcP4l8BRYYaPDIR0CoQB+ZFI3jzw ui/6NpXlgdAI/wCWGNHMyrHFLe+F8jPh5pF9BOcU5tS0BRtt/CkaHrl5JG/lmulbx9YMcJo3mH02 n/ChvHMKKGXQACeMHr/6CTRdCscu2t2oHy+GrJD/ALSMf5rVc+IJMAQ6JaIO2y3Bx+a1vv4/lQ5G gKATwTGx/mBTD4/1yQ7bTSAD6eRn+RFO8u4WMJfEOu/dtoAg/ux2icfoP5VcTUvF0uPLhvRnpst1 A/RTWifHfjtf9XZG3x2WEj/2cVWfxx46l4aN+ep2j/2cmlZiIyPHM3CrqQx/dTb/AC/woGl+O5+J E1Hnu8hQfn/9eqreI/HU52rJdKvYIsOP/HUqNp/Hlw3+svxx0V1U/wDjqf1o1KRbbwh4uuB+9t5p R/tXYP8A7MKjHgjxF0ezRR/00uQP/alVxofju9Qu0Gozr/t3Mn/1qaPB3jGXK/2VOSOgkuHx+XmE fpVcvmA9vBmpp88sdrGB6S7gP/HqpS6HHbkedPp6e5+Zv61MfAnigctoluD3Mkq/+zEUf8IbrMYz La6RC3bMsLH9A386fL5iuebeONPa8vNO0y3lSSCJXlkeIKAM+w+npVL5BIijHTgelb/jC1m8PmOO aa2eS4j3f6JjCgHGG28Vw8MzOizZxuOM+xrzcT8RcUWynkaqhyCJsdPUCtTUVWNwOqgY9v8AOayr hUiKSHqOh649K3isd7aYJ5Yc8+lcxocysi7xxtA4AFWEwkodeB1I9aWS0jhALtg9Ki3D14xSaKOh t2JbPXdU11bJcRNGw+9x+lUoSWjXacZx+Falu4IIY5I6VFxo8mjspdMv5rQnAPzIT3Fdbpt8ExHI Rlu9bGtaLHfRi4hO2eM5U/QdK4+NZE8yGYYdeeO1Xcdjo9QuTHDK6gcRsR+AryPwQt1cW2o6rFJj yyIymBzk5rvtUkKaHd3PmhtkRAHpxzXCeBjc22gzumNlxPgkY7DHrWkNIXM5bnrAjCWS3JbBCk4P 0ridPthLukbGWJYke9dBL9ojspZZJvlWPbs4OMjrVfSoAYFORgqoA49KmLsjhxr6E7W8SIBkEn6V mXFsMY7D2rqvs4IC8ADpiqs9sMcg/iKmMjzzz+4syRgDAPoKzE0z9+v+0GXJ7ZU8V3c0AVuuBVCe FVMblgvzDmtYT8xpnmU2k8YH4VROlMMjH8q9G+xkqrgZHTP04NQNYknlcH34rTnY+byPOG0wnsCa hk0wgfMv0rsbx9OtMi6uI4iOxcA/981gvq+mb8WyTXJ7FF2/+PNiri5vZFJPscXqdkYmtpsceZt4 9cV7f8J9Y3SXngLUI2u9G1+Mq0Ocqjgct6AHufpXlWpXV9dBYltFihWRGYs298A4yo4A/Ovo/wCF Hiyxk04eGkghsdRs0Zd8cYjNxGpxvLrzuAxxXXTpvdnRC584ax4bv/B+uahoN38zWXAcjBeJhmN/ xFfZ/g65u7zwjoMscaOv2JUBLMOjFe3HavKvjXYWjQaL4hbes7SnT5pABjYV3IHx/tV13ww1ae68 H29i2FfSnltuOC38QbH41pTXLUsdF9D0byb0sQwjB9PmP88VJHY6g3Qoo9uf/ZhVdp9UBw2AoOB1 zj8qajXz5IZQM4P3s/ma7OZGdi+tpqCMHZ0KqeU2qufxyala2u5d2yTyB6Aoc/mDWabe+IJ81uem B/jSfY784zNKF7YXpQ7dALKafcMvN3KhGerx/wBFNONlIBt+1OSe3mH+i0xdMuGJZrls/gP6UNpx HWbcR2LAf+y0gB7BsArIzeuZX/8Arfypn2C3A3TPtbv87n/2anf2eGYDOU/3x/TFP+wRg8sNvrv5 pisRiz03gtIG+oOD+ZqYW2i/xordhnAGfr/9enPp9ooHlyBz1w5zxSiys8fvFBJ6Ben40gsXdQ0v TbJ443htkBj35thHIDnnlwG5qmp0lV3q8O0AcHyx1/4CP0FElnYoykxwxL12oo3HHfJFI6WICNE6 R465VWzz7f4UDHfb9HQH99EuP4RIo/8AHRj+VR/27pi/6uZF+j/0Wka5s4/mN9sVewVRj8P/AK1O a9037/2zA45JC9qCrCDxDZrnEm4HsvmE1KutQyLgCTB/2JD/AOy/1qqda0GM5OpqSe3mrUZ1zw4z Dde8ng4mOf0FBJdF+oyI4JDnuUI/9CFWYr2SQjzbRtvAJwo9s8DPf1rKbWdBjUFPNuV6Eo0rYP0G P5URa7ZSsFjsbv5iBu2S7R2BJINRUa5Wjeh8cT2fwXepNuKjYbFPKPfHNd/LdecqyxP88Z+U44HY 9a8C1bXo/A+r/bzKqWt5GpbHMf44713OkeKtO1aBbuyuBJE46jsT7V8lN2P0OnTTgrnoN14qMIgS PdKYV3ShXKZwM9Oh796+afFPxvul057f4eWX23U7m4ZM6kqwwxKx5bLkbmyeMsBXsV1Gk/71CHb7 oyQOv0r55+I9lp/hzw/ci10hppPORluY2JEajOTLyMJ3yoNd+CxqkuWR8vjsrlH34bG5o3gf9q7x VGkjXAt7Y/cZdVt4UGf9i0irprf9l745apdRXOu+KbBEBy6S3eoXDEYx/dQfhXx3YftPfEy3K2Wo a/dWdrC2xIdP228QC/KD5gBdhj1NV9T/AGgvEV5IXufEOq3MeMbHupCDxz/Hj9K9H3+iPD90+7V/ ZGvQwl1nxnZWwUdVsA5/Oe4H/oNX/wDhnH4X6an/ABOfiH5ZUfN5TWNuf1DYr81br4wXFxnbDLcM epmlZ8/rWTP8UdQbHl2cKDn36U7y7Duj9Px8PP2Z9JDC+8YXd+V6/wCntL07BbWJazLnSP2TpLiC G3kupkw3m7pdTIY9O7rmvzBf4ka7M3yeVD6EAcfTArLufGPie8cB7ppAv91duB9BT5WHMkfqsNQ/ ZX0NPLh8NJc7B8ubYy5x/wBfMxpV+MXwL0cE6V4DgDAZDLbafD+O4BiPwr8nf7Z1i6cCS8ZBnHJJ xUVw9xjLah5jE4xz6e9DpvoLnR+sEn7V3hHTht0zwvaw7f794E/9FxA1zt9+2aUybbS9MjI6GSSe bH/j4Br8txH5i5aSTHXAB9vrVaZI4zhixI45xSVFvdk+0P0dvf20vEOGWOTTYB2MdpuP4B5CB+tc he/tneMZciLWnhJ7wwwJj6fuv618DGaM9sjtntTDKvt+VV7KJV7n2bqP7WXjS6yp8RapIp6hZ2jz +CbRXEXv7Q2vX2RcXd7MO/m3Erg/99P/AEr5mMoHAA4pnmHml7NAe4XHxi1aVgyW6EepwT+uaon4 meKbhc2sSImDzwO/tgfpXjnmEU7zpc43Mo9Af6UKnEm56TN8Q/ExYq06g+2CBx2//XWVP4u124bc 18Vz747f56Vxe7b26+ld34ITw0JJ5dbmjSZP9WsvKlcdR24pScYrYunBydthtk/inWlP2Frq6XHJ jRmX8xxW3F4I8c3BhVLK8cSuI1O0gBj/ACr2Xw78Q9B0HTk0/TY452GSJFZY15bIGOv6V3+m/F3T ZQftVpGjjpufjGP9mvNq4uf2Yns0cBQfxTOC8H/sy+MfEF0B4guBptp/FJ56yNtzzhY88+mTX6Be EPDfhzwRolr4b0C3WG2s16Af61xyzvnJO44Jr50t/ifqh0S51yw0a6uNKsnCS3MICwRu3RXY/WvQ LBPH2qwAXmnJpEcmGL3dwJMKwBG2GLd9eSK8yrLE1Xa2h6+FjhMN7yZ6xqPiezg/cRuZJOgRMFiR xknt715PrPjXQRci01zWYLaP+K2t2Utj/po+entXzV8YfFfiTSvEeo+DtN1OaO2slhE0kCiJpWlQ uQWGTtGQK8Ht/wBw0nmuWEysPXLHjvUQocvxEYjPop+4j7L8WfHS2h0S88P/AA8hKQTRvDLqDcRh T8riFP4mIypY9B0zXyQ13LNDHaSYMFum2LAAwg6DPH6/zqO0uPKhEcsp8uH5VQfdHfOPfvxUUkkb sdqDD9QO+eapQseFjMZKvLmka2g6/qHhrWLbXNDuJLK/s2DW9yjfOh9FAxlT/EpyCK96vrTSvjUD qfhNYtB+IcSbr7R0wlrqyoP9fZE4CXOOWh/jr5mkZUZNpKnoCcbR9M1bhuZ7K4jvbG6uIbm0Ilhn ify3jcHIZCBkMD3HXpW0ZdDk5Ue5/BPTpp/iNBFKr7tGS7edZV8t1c4jUNGfuHIOFr7V9eOh6ent XiPwk8VD4hPd+JtZ0yFPFFkkdhf6pBhP7QgYbozMg481T95sV7nt6Bct2Hc5AzivVw8eVGMyBj+l effE26W18BeInaTyfOsXtg/XBuGWP+tegvgDA614Z8e7po/ABtgdv2vULWNj6xx7pGH/AI7V1XaN yYHyFbaXp9s8hvPLulcI0blSWxj5htFTTyabeYsbZjJI77oSUMSrxwDuxVeedlji8v5VD7uBjg9u 3atBILaKciTdsZxtJfpkZyO9eC5XNzGuTbQW1uLtX89CxCY3BwD1B4/lVqOe2kt1liCAou4D0bOP 5VX1X7bKosnKzG3/AHqycFtvQDNZ0lssMIMSgJPHvz68VVgLZmLq3yMrMu4ZwM8noPwq1fuz3LXL IFLMQMcYXHGB09vWozGLm0huCyIY4ghJJyxB4AHbinXBV4vm+dlAOB16UoyGmbngPxtr/gXxPY6j 4cu3tXnlWKVcYimQ4BSaIEBx7nn0xX0ZZa9oXxatLi28IXEfhTxYjSmXQblgthfsrYLW06j5JD1C YAr5Gt2Rrm3RSzSeagUDHGcH+lXb22jGqzGzeQeXM5iYEgqvmMQR0xzz7fpXTGqh851viW7vdI11 tK16yudL1ew3rPBdDDbm4UqT95fRhwa5K9urGeRCrNNKOFGPmz6e9e72PxL0LxfpVp4P+Ots2o2N ugjsvEdvgahY5GPnJB8xPXdkEdV9OF8e/CvWfh0F1iwkTxL4QvFAt9bsRvjXfwnnKpPktjjn5c8Z qubsS5HOeC9L/tXxhodgUbZcX8LSAj+CM7j/ACr7fkdpZJpWJJkkZsn0J/wr5S+C1sJfGMFwzEjS 7S6nYgYzhQi569TX1WqkKoznAAz9OK7sPsZS1HZApjkUpFMcH9K6LAcN4zvp7SHT0gIUmd5W7cJH wPxJ7VwxnttZgInjCzw5GQOSOnB/pXWeM2+0ajplkFLLEklw5HYYwAfyrhby18phLFgGTk4PfsMV 9pk1O1A+dx7/AHpVl0uQPh1DRso2nOP0pgtIYGaSTAG0jgE8elalvJJjypM8jp6fjUd5bt5QOcL0 wa9FwRyJmPD9mklERYoG7gfzrYm0RGw4kAx/EMrjjj/JrMtbPzJACOfQHHSukECxx7VfAA5Df4Un BNWHzHLyeHdOu0aPUFWdB2dFJ/pXKXXw38Iq+EikjDdBC7rt/DkfpXtOn2Mcn7uVuO4bHfnrU0+i W2/YQVBHGOlZPCRfQ0jWkup82a38NIbcJLo95KykZ8u42yDI7A4X+VcJPoGs27fvLdHA9zX21N4f geGPcuQFwNnOKw5/DNqyOpTIx3HT9Kwnlq6GkcVJHyElnqAyEto1I5xnpVKZdYjYLHEoLd8GvpvU fC0MP72OIKD06cjFcDcWEYuTbnndnKnHJA7YrnngmjaOKbZ4dO+rbmHmkbevAHb0NNsIdQvrkIJx I+3ozj17Yr0XUtJRY5ree3CshwAxViO/pkfr/SuUh8NyGUyQK42dG8wEDHbjHftXJTmnozrnsdLp unyIY7e7jEM25QGfoR35/wAK7gRtpd23lTFVzwBx06MP0riIp78IkOElkQ445P5Guv03w3qmoLuu 3KRjtyTz9K7qFN2ujgqSRLd+Jrx5Th9/GCCSckegFSWviO/wpMzxAEAAArx6en6121n4Vs7aEKhU sepZSc10dt4bt7lAjyQISRkHHbvzjFd0YTOdyj1R1fg7xjZXcP2O/aOZ3AVZGAyO2CQa37/7HZxG V7keWTgAE8Z/CuR03wr/AGPOt5Fqdq0IbJjwPyHNd/ba1YTxvHcIkg7EhccemTXVCTtqZSS+ycLd 69pUcRTLSADaAqsf1xWNbXyXFyDboVwcYb6Y9K7q/m0ktmOGIqR1UDg/hWTBNp1uZCBtDnnp6e+K zldgmZI0aOVpJGBjHOSnIGRW5FapawxRJL5yqnBPFPfWoMbYwjK/BJ+XGPUd/wAKssEYRlH8wFfv HHP5V4edwXsT0cul74wDgdCMDnpVe9lFtZXVydoEMTSHeQF+XBAZuy8VdEQ2898Cub8bXMdl4Q1V 5IvOSaE2wjbgEzYQE+wr46/unuRWtjxnx/r1h4l8JXAaWGfU9FbzXIOXbT3G636dfs8pZM5wU2mv M9I125069shZgST6VpH2iNSAR5oVnfOcYUg4Iplis+hrJdYjmi02H7OySoGEscox5GDz+89+lYdt vtbyLVreTyn+zvCoBDbVcFNnP3to454rkaidbemh6b4F8Y+HLhL7w7eMNNsb2R7y0eZiVhlbkxgj +EnP0qxruv2EN/Pa+H7iS40aQQmeJy5hlZPmkQMOApfNeLWOhG4vYrODLzv8wLfIo4xnkgdfQVuL Y6hoV6kjxL58XOJ1Mi5A+91weKynRg9UYWPpaz8f6PP4Z8Q6/p2rf2TJLssJfDsuZS8EilFngdjw E56DaO9ZN74K+HGvppHhbwdfXw1bW7u1tobS4jURAXJ+d2nwoBC7mXbu/DrXmHhPwRqfji6TVbm7 j0TSTLieeNgk0i4y7W9ueX5UKcbsZ6Y5rvfCtpbeBdXvPFsbXGzRNH1J4ra98t2ivbiL7PbMskYV Xz5m/AAZT0z1rl+rqLbizWES74w8SxeJvtt3a6TbCSGRra4uICBcXlpC2yJpmbI2hVAXaMj6c1z3 hfWr6K70/ToNODb7wS2JmugrxJMoijzM4+RRluc7fxFea2l9qOmz6ZKpintn8uRYdgQyx7slMKTg kZHPr0r174yfEOLxj4psr/S9JfT0t7MWxs53MjxYY7AsqgB128qNuV+72zSdBW5SnCL0PcPiT8Jv Fl7rsHiL4c67DffZbeEMZHt5SLhQY2VHjT5mC9yfUV5nD4P+IN/4fs9PXwvafabVriO41aSLzGeW WRXMhRyEiZcYPB39sV4nbeJry1INvDdWmSC3kSbdwx7KvPvXW6L8TdU0h7lrO71G2a4ClijbmJT7 uSevPrUrDOKsiFQgUtV8G67pWlSXOp2kkUMck1xZkbo45Z94WRIipB2rsOFxkD2q5YeKYGRdMu3u Umu7KGW8EzRxSmND5zi3nUBwUC/Lz87cU7WPH8viFIbXXNXu7uO1eSSP7SrPtkfALrjvxjp0rpPC HiTSNR1S08K3cWl6hpuszrCyaigt3geT5VeG72CSIrx8u/DdO9KtC1O8okyox6Fzxd4b8U3mg6P4 h1Eajf6Ndw6dEbqR/LnnkEMszGLPzcxFfMf7itgH38q8PW1r/blj4cvNObT8tJHZz3Lu0dsZozsZ /lZTtxjcpH0A5r6t+OmveMtP1fTLS+sZYPDGn28dno87W/lQzMyAzncPlEu5ChjH8K14Tda7/aG6 O5ijnTBHySDGFGT+fGKjBRm4E/V0cPHeR22iDQpL17qeWaMaaXfMMds+5Zm5y6uxOEzxjFe4zPqN 14U1jRzqNo8ehQWlpazXXlwStEuZIPmQoxbcMbwMjGDxXil1psZv4p0i+WFlAH31CI2QoA9+a7OD WtLuNVbVNe8Pi8YrtkRFESsqfN82Oc/Wt6mG5uhEqKOovNa0vXdQ8Nalrp8y3itF1C+SD95cXMCI VaPkndulxgZ39eNuK5vwdrfiDUrNv7K0w3l1aWy3itKg+y2flNt+aIpuLhsbRzxzWdr9j4N1XVtB n0C0urHTooGnIeXdDDlsSDhiU24PU89hXQ+FYH027SbQ7u50i4J3OIpP9cinJeLd1VwB5kZ/3uK5 MVTjSpbBCkkrHp2iR20+lJ4q8RaZ5c6DzR/ZyxzK1yW2op3sAqq53qv54q34GvftWmeL7m5W7nub OVYriSQxq8p09/tKSNDGGT5ZGyzKQVUY6CuH1218a6JaQanoTWEVj4zhWV1VUuvswgY4l2Ah4jkj YvIPOR0rptam1G38H6NdQ6xFa620+25vViMSSSRnbJtthIdyKuFZ8KCOCKyw8NOdGsabuLrwk1vU bWG9itBcamL++tryzZVm+0Mdyu78s0J/HGazLS4voEint7sWkSws7x6gH/cyAfKI5IVYukmDsYqB gc8qabJeXMmvWK201m2lrqcF9EtsRtDELHKmx143DJOMAH16mbxLb6foOlXF7aXTw3Wj6jLbFbj5 kFncnJiZwd7Rc7hzujk+6NrEV6PtGlzI6409Du9J+I2p6LhtRhmt7ZHGLhf3luC33QJ1ymPx/pXs nh74sWsoC3U+6QtgHv17f/XrxHSPEfj0aHaeDvBOrRabaXI8rUbW9tre+imSZdxaOfu7Kfuuny8Y PFcD8QtPsvCFzp+ieFtMum1G4ihMZEkjq++Tyf3xOQiqeScYHTpitKGb05Pkb1MJSV9D9C7Lxbot 4qS3TK+ThHIXgj1xmup/tGzvpk+zzYLYAOdobjGA4BH4Yr85LfV/Gnh6/j0XULMT3MsTyxi2cPvS FgrOp4BAJX8xXc6X8Vr6wcWmpQT2jvziaNoWIXg4J6kV6KxVO24JJn1dr+jWf2tpmszCsoCiaJFK bs8+d/CntnGRXLSeFoGcRmQkod8ZQFcgH9fwrL8LfFW1kk8t3UL0TOQOnQ57/Q16dBrXhfUnRpoW s7hBkNFgKfqRW/KXex583hG+diws3nB5DoMg/Q57dKb/AMIff/8AQNl/75/+vXtC2tpKokWWKQMM hpANx+vzD+VO+w2v963/ACH/AMXR7NB7Rn//0PQD4bvXQKmma+SevmT+X19ctmlj8DX4IlGh3kg7 iS/AP4hqw4vAniN2K3d9MRnkyXigHHGRjH8qvv8AD5Agae9Tj+GTVMY/4CGGK67oxt5nWQfD+EL5 h0II3YzXqgc/hVeXwNZo2bhtMiJ/vThv1OP5VzEfgnRV/wBZqGnn/fvmcfgA+KZ/wjHhhGIlvtLU KehkLfl81TbsM6tPDGjx4ze6NCB7of5mp30fw7Eu4a5pCMO48ofpg/yrnF0XwIigte6WmP4m5z+A NIbL4fRnK6vpkR9UiJP/AKDRYDb2+G4yVuPFGnKB3iXOPyAFTrdeEYkx/wAJMJI/RINxP0wprmyn ghUzH4miDZwBFaN+u0H+VV2k8EoN0ev36yj+OO0kP5ZH9KV2B0jat4AB/wCPy7uyO8du4B/Hyx/O o5Nb8BAFmt71iO5gJ+nJYVyxuPAbjEupa7cEfxLbbBn2ziqzN4BA3H+3Jx33bV/9nH8qqzHc6oeJ PA0A3fZdSI7YjCD9XFV28c+Dw2YtN1BtvdpEA/mf51y4fwHyRpmqzn/auET/ANmNRSTeDNxEfhqb I6GW/wDbuAp/nS5Qub8/j/w9JwmgPLt7zTr/ACANVl8f6MGxH4dtQe3mTbv6CsVrrw8i/ufDNuw7 eddllH/jn9aeNYsli2RaHo0I9tzkfyo9mFzVl+JcEY2W+i6cmOxOf5Gqv/CyrocwaXp0bH/nnBuI /Ems3+23TmC30u3B/uxdPwJFO/t25I4v4Ux0CQr+mSf5U1ARfHxI8XPkWirEPRLZP/iTTD448e3J 8pLm8JPRYowv5ALVI6teT/I2pMp7BI40/Pan9KiD38o+a8ux6BCwB/JRVcsewFibUPiDcbWkbUyP U7h/QVRli8VuB5635H+1M4U/X5hUv2C7n5RL+UjviVv16U3+xLtZA7wTtkYxNHkfhvz/AEqrRAoz aVqAOJVic9Tmfnkd9zH+dV10p1OFS0T6uhrSbRI4yWmgCjr+82oPy3UzydOhAV57SL2EyH9MmiyJ scN4msUSe0gfyv3qsf3LAg4PRsVzW6OCyZmIKIeO2a6rxIltdXcccMqvHDHkPHjqT6iuLn8th5V1 CY7dGxHkHacd8mvKxHxG0CW2uvtw8xjjb8oX0rSt5/K+U8KDg/hxWNAFFyHhUhCu0ce2c0XnnLgr l04PHauY0OiktI5vn3HHuaatmQCQ2VA4FY1pqnlqFmyB3PBAro1MM0ask2Qw44/woKHwPtAwfYit RCGXcePcVmxJDHkq+4+g5/lVg3K7xGcD9PyqLFJGpHMEXBH0+lQy2mmzkSyhQ6/hn19KzbklU/eN 5S44GPmP4D/CoItLjleMyl2L8hM9aVhXOb8bQWlpoVy1odouGSMjGemc1zXhPTJhpenRqpUSF5nB 4PJ4Bro/HZjRLHTFYbnJlK+mOFFdRYWEdvbpAzYfainH8PGMfzrdfDYzerOM8SXMWi2T3UglkW8n jtkjjxuZnJwOaxdK8aaYto0zWt0FhG0goowPx/pWL8VtT/sm68N6WXLP9ra+bPdV+RBx75rymDxB PFbzQ3Ei+XIu3CncV+uK7KGHUlqcmKgpM94l+JegxBdttdOTkAKiduvU8Vjv8UdPmGbXTLpz2LmN OvT+LP6V4AbuWQfuZn8sHqRj2/zxXRabGzAfMSmOCc1ssHA5/YI9Vfx7qEufJ0pU9DJPn89oNZdz 4t1a7ULNHaxBTnCB2P5nFc2hVTyu49+1NmkXoE2gcVosNBdC1TRoXOu6/KpCagY16YjjAOPzNY8k eoXiZubmacDoJHZh+WQP0rSK9Bjae4HNAEpBUByB/smtOSK6D5EZSWLRc+UgHsBx9Ktwof4Vzj61 oLFcSLhF496lSzvkVlij+Y+nFUl2HyozLpZY0dnGBgcD0yK9H+GdtC/jq3ndzFAwfL7DJtG3Odo/ wrgNQsbldPup5SMRx/d5zxivVfhfFBa6zBqE4Y+XEzDGeMggfrSZZ67458D6b430CDRxrFxZmO5S 4Mn9nlgdg4AG9e5zXIfDa0vvC97q3hvU1uWk81GjmhVFDsq5X5XOBvXnrXqL+KLI53bzt9UY1yWq +LbG0vTqUkbLENTsIuFPLMrL/UCq5FfmBPod2JYlABN6wHq8Cf8AoJY1IWU4C2dxNuI+9egfokZr GfxCsTeVHazvs+UgRDqOO9OHiG74MWmXDN9EX9M1SiK5rtBcyIwj08RH1e5mcf8AosCs2Sy1sk+X dQWw/wBmF3/UkfyqEazqzkn+z5VPXDSIOPzqQX+qt832EY9TKv8ALB/nVKNhCJo2rzZY6o5H+zbq P5sanGhakuN2p3A9ljiX9eai/tDVfui2VQ3bd0/8dpTear1MEY9yzH9QP6UwHt4elclpdTum9i6A 8f8AAKF8P2hXEt3cnPZpSP5LQsmtTKGjjiCnuFf+ZIpdmroCXkRfXCk/+zUASHw5pyodu+QdBmeT +Yx/KnReHNN5EiBV7bpZXJ/DNQKmpOokF4oH+4AR+BNSouovkf2moPQjYgz+BFAE3/CP6KGGLeNm PBJD5A/4ETUi6HooJzZwHHdogf51n/Y9QRizXyqD/dxu/wDQTSiCVsb7ueQHgkHr+QH8qANKPRtI BB+y2iFuQfJU4x9asrZafEcrBAp9RDGD/Ksc2ER3b5Jyq9MyMAfpg8U1rW2jxmSUjocTPx+tA7nS BY1HyDAHQBYwPwwlSxzqqbgCAvUnp+mP5VxzWtieWDls9Gkb/wCK+lKtjpJBaS2Tk8lmDEj8SaBH ZtqEW0YlEQyCdrMM8f7wqrPe2To4ubpVWQbSGlAyp44yT/OuZFrpHAjtYeeoZcj2wMVbW10vaQtt DGR6IDn/AD9KTH5nO+K/E9p4YsPNuHttW0NAEkbzAJ42PC7s8MPpXkWl+JtGnuzdeEtSNlPncYgw aNvYr2/Ku1+MlpBP8NNaW2hWJoPJnIjH3gjjJ6DtivgX59+5flOcgjgg+23FeVWwMXse1SzmaXK0 fpToPxOUbbTWf9FuennZ/dsP1xXbaheRahbKsvzxS/8AfLqe3v8Ayr8z9P8AHniKxiFtLOLyAfLt uBuO30Leleh6V8cNY0+zNlEJ4os/6lHR0H03jI/OvLq5fNO8T06Gb05RtM9r139n/RdU1GS/0a9a yWYljAwVkXPJxWbB+z5DDlbnUt4TqRGvSvOY/j7rkeSsMr7egPlLx9AKhvv2gfEl3bPAlkgB/vyn 09FAFaxjibWM1UwWrkj0zW/g/wCH49Pi+yt50wcKBkH8lWvm/wAb2C+EfEc2k2xtrgJHHIW2DKE9 VIBPPNVr/wCIPi28nNwuovbcDCQ4QDn1wSa4iR3lkkkkZnaVtzscEk5zkn612YeFWPxM87G4nDzV qcTS/ty9/hWJPZYk/HsaR9b1KRDH9pZFPVVVVH6D+lZm3v8AjRiu48u3kSifPUE/jn+eK9m+FXw5 sfHttql7qN/c2EFg8cSC3Cks0gJY5b0ArxMA7v8AP0r6/wD2fYpV8GXzqoCXGpMc7iM+XGoH/oWK qDJl6DtQ+B+hHSL06Pd30+qRQs9sJ2QRvJjO3Cg9RxjNfIUsly0rxTL5cqHaU27cMOox2x34r9Kz OYyCWAJIYYYZHYY+lfGvxt8NxaR4tXVrSPZa67H9p4+6J1OJQCPfn8aqZMTxnOefbtxTSV/Kpceo /wAio2AORjrWVjUTAPtTgh7GmgY4qZQTVARNGcCnpGxA4qTkGrawyEZHFCRLZAlsWxk/hW1Yaduc M4DAEH5vQdgKighZQN/Fb+noMjJ4qrIybJsBWCqOnoMf0rX09oxJ84yew/8A1/4Uw26AZ5zUUYCT jsKfKrbFJn2F8JdN1DxF8PtR0i3uTbW6akZZPl3bsRptjPPKE/eGPxr6m0+V5bSJbhArxKqkAYBx gD/P5V86/s3SpL4b8QWoAPk3lu5GcEl42A/kK+hmuUs45Lh8+XArSMOpCqCTj39PeuOTtdI0Wu5+ dXxFvzfePfEmpKwIm1K4QZAPyw/ul7/7PpXCNDF8m842YAboAfp+te6/EHwdBZ2lv4i0Zze6H4ll eaz1GTBaORj5klncLwFkQkkc/MMEV5VcabHaW+xZRIJGGSTt68bgD0r5+q/e1NIvoZxhg+z/ALks 8h5PHXHHFU02btrjY4PfgHvTzIsByQUj+7yTvb0IxU0dnHcssnPljpwc0yi1HbGcYbDY6DHpXUaD Fo0Jb7Ysjyt1O3Cqo6YPXOayWw0IiB5/vjg8cU4sGjGThYhyT2AGSeKEx9D69+AOl/YvB9xqGCDq V/LMA3JKqNqg16j4l1iDRtFvLp7n7PMkTLC6RidhLj92fKJGeepz0rkvAEU+k+AdCtAQJhaLKSPW Ubv61sHTFvJDNc/vSuWG7np2x7ivdh8KOdsv+FdQ1PVPDun3mstA+oSR/wCkNbjEZbOMhf4cjnHa vDv2hbxTb+GtJZvkuZLuaRR1wFRVP4fNXtWkxiyUrGuyNmZiqjp/tV85ftCrfDxTo6yxslqNPCwT hf3crNK7SBG6bhxkdaxxHwFQR4BdWhlhRY33ENkZOMA81oLbBEBuUPK5AP8AFjjP6VDFC24NOQgU DP8ATmnrCWZ5xM0iRg4B+6PpXibGpEYjbyNdL8xZDnPAwO1UpP3loJY2DRRsSpA4255H51IZvMUO M4bv6AcdKbB+9PkpGMO2ePbj6U0wM9XQlinG7BAPb6VI0mzO7OGGCRyR2pJ44kmMaE8HHToazZCd 5QnC9SB3ppFWNJbRoJ7e4aQFlkRsAcf5xV2VZGkeGABTkyuSfvKfStLSx517CJV3xSR4BAztYDAa o7uFhcAnflFKMFbHC/Nnp3phYx7a4ubS4jvoCsUsMm5N6LKM9vlfKkY7EV6T4B+KHibwNfXU9m8F 7Y3u97vTbpQbS5Dj5lKqAI8/7K47EVwthGyTm4n+ZGUlVZjwSOnAxVCc+VOElSNg6kYBz6HJpwdm S0fYHgey+HF4upeLPAEN1pU1+sVjqGkTAeTYy5Mm+BxwVcdFHAHYDAr0AEYH+0M/h+FeS/CG3eLw ZJfOu06lqEknH923AiX8K9VEixnD4C55PQYPXk+le3T+EyuSkfgKhkOOP84xmuQ0HxPNrGrXmmtB bvbQlxBd227DBDtwyuASfoK6sgvhcZY4AA9T2/Sri+gmzhfEN7bi9VM8xM8cmByPk2/purhbpxJt K8kDkDg5xWq0g1AXd5kMrXl3tb1USFV/QVlQI8jN5QDNGcY9c81+gZZC2HR81in+9ZFHMu0b8rnr nOf0qSa4gkXBOAD0OfT6VBc+fHISVIVhtYf1oVIZU8txtZe1djRzIWP7MZRIGMbDpjoas3U4jCvv D9vmUjj0p9haZkChQR/tc9K0pIkBO7nB746YpJCZNpWpQL5YmJI9QOmOlbU+t6dAFaRmfJwAB2x2 zXNM0HDqAQeoXjpVjT0066vYVux+6TJ+bhRUSdth2K918SNFhLCPzMJx8ygfzqrB8Q9FuMvOWTHA JXr+XpWJ4q8K217O6wQ7ITyrL34/xrxu/wDBeqRyk2c0idhzjpXLKrURpGEX1PctR8W6VeIFtpGY qGGApHX0rlVh0y/vkzIUlJPytgcEdvevIY9R8T+H3IlkimK8BZQW/XArA1Hx7r7TZlhtnyccIePo awli/wCY6adBv4T6D1rTYrvX5LwzWolLR+TbO5VPkjA3OOvQA15WunEma51fUfstsXZmMWOcHlV9 vSvPF8YeIWujLAVRmwAACduB0HXg12/hiPR/FMv2bWJZo78EuVd8Rnj+Ae3pXNScXsbVaUo7s9D8 N+Lvhrbr5cdtK7DjzJcknHfGK9i03X/CV1GPs8uwYBGAFx+FeL3fwqtxAsluQhYDbIOcen09Kx4v BvijTCW0y7BwOjf4V6NGpKPQ4Zwiz6kiTSr1B9nlguAe68H9aqT6Zak+WJXtx6ruGP8AP0r52i1v xFojr/aliWHTzYyfz4r1nw9r0eqwb7eZpHVeUfDfh/8AWrrhiVLSxlKi0dbB4RE8xlmvRcoFz7+3 FaA8P6fDCE/jP8WePpis+wubkCR0jIYdDnH9anOtSZMdxavKo4JTgD0xVPluSV7vSHCDEeQx++o2 5x+VTrZSRgeTHuCryGIxWvHeySRgNDKB1HmEHj8DSzSOFydqnGMdjTdhmJHFIZowyAE85yDjn0xW wgdl+c7iv4cVQWaK0mwzBiw6Dk1sWaCSJX7HJHbvXgZ6/wBwehl38QFTjjAPuK85+Kt29r4V2IAz XFzGowMkCMGTpXqG0Y6YI614l8YpJWk0Kxj6L51w6k43YAQc/jXxNV2ifQRWp4tqsN1d2rW6RGQp zPJjGSx+9/vRjAWsuXTcvEpA2bSQU6frXYrKtxPJ9mie2SR/njPK57EGsyG3iuUubWQeW0UoBLcA 7umPSvN9qbJmPpENtCbi7ly0ykRoD/CuOoqO/wBOguY59Rvrph5a5jWQY3Bf4Qw5OfpXTWPh7zLa ae4kW3+YpEY/mGF/vdsUzUIPD8OmSW1i8lzdTBWMh+ZU2ntnjn0pe0IZBp+qW8tpEl6JLSzjjzBb Rzltpc/N0GY/U88isjTRaXeoR6CkrRrqQePzpsmPzCC0AUOehfA9eemKpmOS+kCrEQyNnc3BBI4A A6/jWjHZC2hM8k2ZIGWQqVA2mMhgcnocinz6AmcjBIyzeRHZHfbYMgC/NGU4ON3INdNfQX8EGmxv YtbXEClY54N/myEHcrzAnG7kY46CukvLC3jZb6WfyX1ZJbySSCPzNxPP3h05rZ057nWYvL1K5M91 IiR26zOCEHUM3yg9MUpV9Bs57Udf+Ielw239oSO6XaA2/nwIrlOVG7uOnfFZdl4n1qKMXt7ZL9km fazR26MW29cMw7Vu38VzcebNDp8l21vKbcXMLMzqEOcjPyhcnvWXrMT2MASK4n3SPvYySAKhY47c enSlGd9wjMzr3X7bVbeSRLaOJLdnlDqoBK4746cCqFpqOpWOo202nrLaXdkyzROoQSI4XKvl+O4K 8Y5zXdeDra00jxbGfEU0mz7JdiK4091k8q6EJFtLIgx5iLJtDRnKuOCMV7P8UvC3gnTpLLWr2TZF cW4bTtIswlvhp1SR0diCqW9vL5nkgr0YIOE4zr41U5Km+o7nCX3jTxD4v+FepRa5q93f3Oi6zbpd 214RM8lvfR4t5kZ1BBiliKFgMnzOeK8WnbSXUmLdkEZBU/Lk4zjj2r0LwxpV/ev4p8NRwGH+2dFu porcMJJvN08C9gVXUDJzEewBrodR+GNjr3g/QvHHgxHigmtoo9TtI83BRkYRyTxjrkkfvI+fvbhW dTF06CTm7J6FOSSOR+KXw9l+GXipfDcuqpqMU8AuIbiJGgwMhdsitjBGeo7VxFvYTPMIrfUBG275 W84qBgZGc5zX1X8XNJ8ErLa638StTvb/AFWHTEg0/wAPadJFDP5agATXtw4kWIYVcKFyRivmq40Q Ssu3y0AgjkO58/K/K9AM9cZwPXiunB4hyp80tyOZMhaPUtOj+zvqm/7Qd2UdZFD8Y4AB/HP4Vtye MvFTalbi0vIIJ2ZLZUij2M5bEfDNuOW6Hp+HSuObS5nUTRpG6rk4RvmUg+/9K674bWr3fi+DU5Y4 7i10CKfV50kxjNsh8oZ/66MK0qpSjqXHc3tfg1lvG8szWcdvFLJFDmFchJbVNpgDDIyGz9RirXxA 1/VrXUbjw/4d3PZ6UfIt5bmZZT5sn7y4IB27sk7Rjpiq/g6bVdH1BrrUJnaK9LNJMxBSSZ2J3CNv lfa5BzjK449KpanDqRs5dMXMmpxyMzhm80O7NnJY4Izyf8K4qP8AFt0sO+uggv5NUkii02+bQNQz H5a3hzaXMgweZgN0O5umQEHc1h+OvFnxK06/vPDfi22OmSSbi0bxlgySYcSRurGKRMH5XHBpJrqJ b0xQjzYvlBCAKvOFYYPUexB/lXXRajdWVpPot5bR3uk20qI9heL5scbMcgxAYMb/AO4VB7g16HNy 6WNVHm0Kfhr4sWnhjwvqWgalpN4tzqhJ+0Ru0EhSRFzG2ccDAdCgz26V73Y/EDwFo1joV9qMuo+K bF4P9Du5o4xPCzHdJaTEttLRkHGTyvPrXO6fqevak7al4XOn6xBLD5dzo2safDNJCqdfJ4Cygf8A TP5lHbvXmt5dWFmNR07VdJhsrTW0zKumZWBJFOYZvLJb54m9x8uQeMV89Xw9KcuZqxlOlY3Pit45 8L+JtQ0zVPBmmS+H20sSGSWW8Km4EgXysQJIyRlGB3EffDKMfKDXuMb6n4n+GI0jxtp6aNqVjdpq Nq0v7wvayR7JiE4kiLMwZVbA9K+L10mfStXtIrmza9FtNFMWh+5cRoyuSnGMOPwrudP0Dxl52t/E VYxMmmXEE+rXKyDfEt85EfmxNl/KP3M/dTHbpXficJH2aUXsRFNbHSPqd/own8gzPPaL5pizhnyQ AwB789q9I0X4l32mrGjyM8fHzE5+Xtx1rzn4ladd3Hhrwz4gnsfskd9fajZwSxyn96luYfmRDhgu SeT+ledf2TqFi0i/apozGSrjzicEHB7GuzBYpzpqUtzXc+14vjTtjVd8QwP4mwfyqT/hdf8Atwf9 918UC81BBt/tCTj1cZ/9Bpft2of9BB/++x/8TXoe1I5T/9F0XgnxBPwNEdv9qQoAf++qup8OPEh5 TRokP/XWEfyrIm1bU7wZa5vpAeyxAD8MR4qKP+2ZDiC21KUj+6jD+SV2crMjon+GPiYgf6PbQn0a 6iB/Hipk+F+vR43vYr9blSP0H9Kx4rHxXKAEsdVHoGEg/oKnPhrxpJ97RNRz6uXUEfiwpcr7gaq/ DzVFJMl9YRD1Du369P0p48BzowWTW7JS3f5//wBVYo8I+LOc6Kee8jg/zersXgTxRKQp022jJHB3 oCP1NHK+4rFuXwjp1qf9I8UWh9olZj+pFVjo3haL5pPETnHXZCmf1ersfgTxHCMOtlCR3eWM/wAl NB8I62gKi800D/e/wWpv5jKy2fgMD95rF9O3oscKg/Tk1OI/AUfQalOB1xIi/qFNO/4Q7Vgu/wDt fT4x3xv/AKYqAeFpwf3+vwRg9dilsfjuH8qr5gSM3ggDI0bUpfdroc/klSrfeE4x+68LPIB3lunY /kFAqlJ4e06Ikz+JRkekQJ/nVU6f4ZT/AFmuXDMO6pGv6HNO3mBsDVvD6f6rwjZBvWR5D+fzD+VT Lr9qq4j8M6RFj1jyf/HmzXPfZfCAH/H9qE3spjA/RaZ5XhAcgai2PVjgfkB/KloB0UniR+BHpOlQ n0WFDj881E3inVPLKIlhCOxW2iyPplaxo/8AhE4xldJu7gHuXcY/IVaF94bQfL4flz/tGRs/qKaQ E/8AwkOunrrBT0CRRpj6ALimN4g10ghvENx6YDBcfkBVdtS0lP8AVeHFXPQkqv8AN6zpNSk3jyNO WIDnDTKv48Zot5AaTX+rEBbvUr6YNzh5nTj2AxWNcW8ly+J/NuEHQSuZP0JNLJfXM0hmnZ3c4AZ7 osVx2BC4ptxds5ZpZ4cH1mc/oFpgVv7OgQ4+xbD6eWOP0xVqKBV+UWu4HuAP8Kz2lhK4F3GMf88z If61C09uuP8ASVf38knP4k1VkBQ8TwGBoZXjEEUwCc4GSD04x/KsqOSPesD7SAejcqR/d56V0Mr6 TfR/Yr12mhfO4JEAenZs8GvnUeKl0fULq3uGkS23sIZUy5CgnAbP+FcFeg27opSPWJNItxqBt/Me MON8RTHy56itWLSR5bRGVZiMgluCR27Vx2l+MtJ15Ydk6Le2/wApOdoYHpjNdrbT72zIdv8AWuFw ktzW5z0uisjvG0RSI9CvPHtioLaFbcyWkAkVf9rIrrZ5j50bJwuQOD/Stm4iieE3MRDYGCMe3uKi 5SZxNlDNbuzHb5O3g5B5o3T+YWiXc4GF4PX19q6O1060uIzIbcFieoJHHTpVu300RSsySYLnkH2H vRYoztI06zU/aLl/PvScBpCThuvyr2qezmZrxhMBlWAB4HQ4qvfJ9kw6HIjk3Ahc/wCfpWcblY5L iaeL5ljaTJOOMcHFCjd2JvocdG7eIPHV2334rNsqPaMYH5nNenvaSLamUP8ANgtgDrxgV434Ft7m abUr6KTa88xGPTPNevxXn2QRxTzIccnPsM1vUjsiYbHyZ8VrwXvjINK37myjFqpbt5Y3E8e5rzNW zvTCksmeMDmus8RLd31w+oy/M11cyvxyFBbaK5VxK9yxUhj90YGOOhr2KKtGxyT3LljD8saPllbs OcHFdRat5UYVV5xwDmsyxgkQLEFwDgn/AD2rpINO3thmAJ6VRI1ZjxmMfL35FF7dExKvllRvXke1 dBBocWQZJQF7/wCf8KZcafavf2djFJ13SScdgMCgqxWi1KWRwY4OV9cZwDWlb3bPxKpBJ7cVu2+m WkcRGckDJx+dX4tMszsIGFz1NBJBp6QyD96PLUDOTzkVpXa2KxGa3+aRFyQPp1qaG0hx5YBDbsY4 xzWlawQ291veMFVVgeOuaBnCXME2p2iQohUXDqoJ7jPPWvS/h7bxQSSySqZgkWFxz1bjpXKS30UV 5bQom7yi0+BjB2pgD8zXpvw+0xTZXkk/7sBo0TrnCjk9KVtSjuY721d0/wBECgFSRtbnn2HsK4iP w82o+G9RsdWhNtc6pqLX0jLgtEyShotpzjG1B09frXf/ANmWBADyyOOgBzj+lMOkaIAQyoPXIOfy zWvKSQNfyNud4owW5ydgOTyeuaY2qxYCiaJT0xlP8Ktmw0aI5ESEnttTP9alEGnp0hUr2HH9BTQi iuoqgx50Q4wP3g/kKI9VjwV81QB0x835cf0rTVbTIKWzHtwc/oBTyIo+sLe5GR/hQBm/2m7odkjD 0wj8/TFQi7Yn5jM/t5bHH51qGS3AysG4+rdfzJqudQtgdvlxIV6ksBgfnQBW+1Hp+8z6eWB+mRUT XMw6GQe3yj9Aau/2pppbPnQKR/trR/bOnjhJo2Yddg3D9M0AVBcXTquFcY771p++8zkjjtkjj61P /blkQMIWHsh/wqM60pYhIZHX1Cfy4oKsQO9yGXKhOM5DNg/jjFSt9ux/Cvpjcf5CrMeoRNHJ51rd uUGUWNflJ/2s1Cb26JBS0ZweSORjjp2oJESDUdu5mjOPVW/rTltLvPMiAn0QcfnSNeagwGywx9W4 /KmNNrZ5jggXtgsOPwpcyAmaxuQeZCfoqini0ndeZXwnbjmoFn1xhsdIQ3PCn37U5U1Yqd0kaH8T TuMTyH4P+kPjgfMMCphYhfuNKzdT8x/wqAW+unMn2kbeAAI8077BqjYWS+Yc9QpHWpEOu9Jt9Us7 nSboM9vfQtbyl8kBZBt9P4Tg1+bGqaZcaHqV7pF4uy506Z7dx7ocZ49cV+kX9k3JIY3jsw4yV9D7 V8e/Hrw8+jeOhfbdsetWsd1kjAZ1PlyfqvNTIpM8QxRjGCOtKM4BPJPJxVmBkUHdznjp0FREGrEZ RiMHgAD9eaXyhgDpWtDGrhhwQKl+zADsKpIi5iCPAqIxkHjpWy0OGJ7e3TpSGEc7RjPb0pNXLuZB iOOlR7AOtbQth3Jz6CqU0W1sYxn1osCkZ5XALIPmHTt+Fff/AMP9IsvDPhLRtH+zB5xEJ52ZT88s 58z/AMdGF/CvhzTbdbi8trRF3zTTJGqZHzMxGO9fpPLZmKTyRdKPKVU+T/YG3Bz06URiJszBMpYg WaYXIHy9e3evK/i74ek8Q+B7t4bPZc6O4vYMcnb92ZQP9pcH8K9maO03DzJAPo3t7VUeK1eZVJLQ 4IdezK/yMD+BrSWo0fmepD4IAII4PFNdEK5PFdP4s0B/DHinWNAK4WyuXWMn/nmx3J/46RWEUAJU 4xjqKysaGaY9pPcetSRjOMUr4BwDkCnpGM5HftUJE3JFjyeR9K2bOMN82enGKoW2GcAn2xWmibWC LkH8q0SIepfS2Vjk/jWnbwrHKoUHnvSLCxUc4B7/AIYrRtYgwG7gp3pMROYWB3nnHGPXis9VLylP u89cfpXSRxny/mH3v8KyXi2SNjqGzjpgUX0sB9R/syXJbUfEVoz4aW1ifHp5Un3v/Hq+h/HWoHSf Buu3+cGGxmK46hiNo5r5W+AGq2WieIb2bUH8mG7gW28wdEZmDKW9icV758bL42ngO4jYgtf3Nrbj sDucSEj22iuDFPlg2aRZ4P4L8ZReG5brRPEFjHqXgvWQItT08nDHYMJcwMc7ZYvvAjGcba5z4geC pvAlzbLbTRaro2uRG40bV9uFuoj95XHRZ4xw68c1x964+z+dKBlmDLzjnPUA16H8PfGek2unXXw/ 8exve+C9ZZXdoxuk0y56R3ltnoUP316MtfOwqc2jLseTLbB2eWRQQPmGe3/16swlpSqKCSfTjtXa +OPB2o+DdbXQdSEdxHcRfabC+tzmC/tSflmiP6MvVWyMVy0S+VIFdgrKeFXnt7Vo3y6FpkbKYYwg B3dSMdBUFuhuZorPcweaVYwPXzDt/wDZq2mXgv1Crnn1ArX8B6ZDrHjLRYXGFe6WQj2jBb+gqqT5 pJDb0Pua3s47e3htVGFgRYx9FGKvRRhVc9Rzj8qdgk59eanC4jYHr1r349DnMQfu15O0eg9+/wCl eDa94/0X/hLfEXgH4hQNe+DriaOJHj/4+NLuFjUfarYjPy787x9a+gljEkiRtjDsFx+tfn74ju/7 W8Q6zqeN7Xuo3EoOcja0hwD7e1ceMnaJrTRu/EDwLq3gS9g+1TJqWjaoPM03VoBm2ukYYHI+646F Tg7geKxIrmBLMQKu1U5YNwxwMdOv04rtvAXj+Dw5YXXg3xjZDXfBWrMRd2Bz5ls5GPtNoedsi9dv Sq/jr4dS+C4LXWtG1CPXPBmqjdpurRA/Kw4+z3QHMcwHrgH615koX1iWedzxxo+xWAV8Pjt06cVL ZxSqxuWKsUBwq4GARjNVULOTH1bO055x/nr0q7YQbZpg7h9i/wAP6fWstirGPcWsMe1vvuSOc/rx VKGx+1zMh+hAPr/StF/MaRvLCu3cKD07e1dJpyeVakNGC7EEx4HIPGSwquYYqWq2sSJZyAqFCOy9 enbpwKoarqMK+TFYEgp8ruedy45/Wpr4T29vMYGERDdh1rCz5sZl+9gckjbzQgLltcyeQFlYkZxt UDOO1QzrFtk80fMq5DAYI7/yFVomHmKQeg4POacq3N1dJYKCTcSJEPfzCAOTj6VcVqRLY+v/AA8i aF4Q0DTzxJBYB3HA+eYlyfryKy9SlvNZDWYkaKFvlYDIzzXT6paq941uoAjt9kA9liQJ/MU62tYz KoUYC9cV7sY2Vjmjuc1pHhux0SWN7BGh2cyIrH94Op+h/SvQoo55YZbuyU3f2WMzPGB+9VV5wVHJ 6dR/jWbcJiUYx+A56Vka5q914f0PUNcsZPKu9OhMsDnnEmQEB9VyelElbUs898LkSaKioVYGSQyA dizE7T9M4q/FavbLvRc4Pb3Fa+ja7afEDTLjxEtnHpl9A6Q3og4hmmKCTzUXtkcketMkgmRwYmDb yexr9AyqaeHgfNYtfvWYM6tktKSyvzz1FUZNPdRvDbge+RmunHmPvW4XAPTj2rJvS8TCPyyeBggd c+wrs5jnRQtZpbPJhI64IbsfX/61RX/26eNX80LuPWPjJrSQBY9ky4B6AjBqCYyRw5C5H07fSi4G RCt1wm/5jwWGeatW1m0d0sk7FivIB6fjioIppVfcyHFMvL1UjfdMEbGAvU5rJjR6Ibuyu7TyyV3Y wOeh9q8y1ixmS6LI2wY7HrVLTtWa2mjG3cA3Oa9Bv47PUrEzW2GkA6Dj+eKG1LQSTi7nj2oeG7aW EyeYGkOCS3OBXifirShp8drcbWUyzSAIcfMMcEYr3m5FzHceS/C5A56Hvj6V5P4rVbjUfD1nIJfL e43MJCCAobHyj0+przMXTR34Sepytxo8umwwytsYptb5T0JGSGrorfTI9T2XNqfs9wnIZf4fcVvX bfboXinQCQk8FPL/AN3cPpjpWPpqy2lwI/ujOMc4608PTsRVm2z2Dwf4teMJpGvv9kuV4inb/VTL no3YV6PdQ3Fk6Tyw5il5Up8wx615daW9rcwLDcRqxkHy5AKn/ez2+ldd4V8V3ehtLpbE3NtHwbS5 AIC9cxOe31r0o+pyu99DdW+028LRTrlO5xnHasi201NL1Y3WlsoSYAEDG3p2HrXdyaN4X8V2q3sC vp13/wA9LdsbccD5TgVw+oeFfEOhzCSExapATkMMq4H8qJ6aoSlc7fT4pb10tyhVs5J29B3zV2+s niZRtO0egI6Vz/h7Vbq0uBJeaTdGJ1wwC5Hpw/T9a7i6nivQu21e36YyR0xWsBSRkW8AKldzsfTP QdavwwK/7rG7PTJzjFRJbXETlnC7egZO/wCFXYY2aSJkB2hud3FWSc9qFtOrF3QKV4U7fXitjR72 1u7eWO3kDyWUhguFXP7uQDdg9OxGD0rW1jT99v5y4K7gGUHPfr+VeBaV40u/BXiDWH8T6JNFYarc Epdx42/I2IwGGVzjnBOa+a4ga9mkerlcfeZ766jHykHIPTkcda+dvigz3fjWO3jIAs7KFSGP/PQl sD0zivoSwvbHVoVn024E8LKDjGHVTwNy9e1fO/iyWC98aaqVtjdXCyrDGq55WNVXr0GMmvi8V8J7 sEcY98HllEaJbEtjc5wBjjjtVC+Rg7xO/nQExM8oHyk9etak/hy4W9+zXJEcD5ZkX944A9e1Qahp 1rPCwtfMeKNcbCdvT07HmvKTLMrZeJdtBbXJCnny0+cbMYO4D+dMljls5orVSALlgiblwoFdxE1l Z26SWcCQzPEvnE8scDofasu5uzelZQoiaLCgkZXP+yKVxWOfvdLn0uYGSUPlsxlD1/rUFzdtHNkI GLA5RsFcqOjY7GtO9mlubdrhiJXG0eftIOAcYRfwrHnilUNumVd64IIBO08ZOKaFY6qNpJtA+zW8 axBIw7L6b28zavoO1Yc01lPJEt1b4tot7NknexH3QrDGO1aeh2ZvoPsf2o7gVSNk/jyvCt+XU8VU +wol2umQs2oXVxiNBEoCDccbQW5LY71NimixpVjeQrOtpKogmh80Ozl1RjhgOoJbtjpUE9gls4nu JROo/fEnBDE/3x22n2rZuNPSxuoLO/g8m5hADoTuyrfdKgcBsVm3L28TtcQB02j5dzgsT23Dpik5 EtE9p4dsdNurS/1O28m3IWd4JHYCdSd22PZk7emfevbvBR8Na98UtH1zWnS805rKa5nium3QwxRR mDyMHgKvUe/PWvCHnsXjgmu3nnvHGQhc7Vwf4W7DFb8GoWCRmz0zNpPKwwoPnNkkb1Xu27OcYrjx NNyVxNNnR2mr6f4U1vQtS0fSJYbbStTmvNPlMUhnu4pHaPyVEgGY/IO1QM+ten+H9D1/4c+E9Y0L wPYy6nqkPiNpY7a4jEkdtp/TcZQwUBohHlv4SDxxXmreNPHzaW3hbxLd3F/p/mxutrqUY32kqkMs kGVR4XHbttNeiaZp9tq3whtbm8v49LNvr0wm1EM5lht4wZCsSK252mkfaoI57EdK4MXScqKjPVXH H0PH7zwZ4l8Z+KtVu9D0y3stPvp2vri/muj9itEPykT3E4HRgSowcjhRjFT3ei6P4GktX8CeLZPE uuvFMl/cWdvJFbRZXKNFI+5WCtz90ZznHp2X9l3XjG00TTp727sPA8VxcFmmZTLHHDhpGlDYQyuO Y8/c6e9eY+IrXSH1Kc+EY7m30yPYLYXEzGaXa3zPJwB8/OB2GK6aWJcmohJHNXNgs8cUdyu9ZHy0 qjDszOWkOUHqT2xVtNLbRo9ZitpGghuoFt2MeWbypJAcnHbHWrlzd3lsV8mMw7yVUSP5mQO2K1I7 m3i05/7RnL3JdXGE2gJj7uBmvQlUY07HORW+jhhZ6pqf2wKMRKRsWMsezcHOcdq7waVc3Wny3pmt mv8AyAtz5TZOYxtjlbH+yO3WufljVJPtVnCrtImOQNpB45z6U/TmFtctPby5uYRmOUE53ZwQxPG3 HG38aynLsVBmdbaRHDAkLKv2VGDMxYcKAOFz6ketasH2LTdcsR460mS40q6uFN8ELQy+XMPklt3X O2RR86/38Eelb66g0dzBqNlZWN7BvCmK5gEyQEDDRNHkAgqMhjXTy3vhnx1ctLc6SmkW1mkUUGlW dwyreOFkCxJO+TBv5b26DrSqY1wjcqTPP/EVjqnw58U6r4X07VJLoafIn2O4tl/eSRuPNhuQP4W2 MCfQ98cVq6tdN4ts4pfEsa6Zq5J8vVjH5cVyQMf6ZCOVLdPPUEZ+8vevW9f1GDRr3Q9Kt9May1TX dKg1KO4mjF2Fyhj8m5RlVv3flqhdW464ql8JXM3xJs/FV3o8BsEjljmNuwkhgmaLyvtQS4zvVCTu XqD22iuSjjeeF5q1jSFXSzPEbbStDHl6F47k1fR7m13S20+neXOskMn8KDO11z8wKnnsB0Hqnw2s 9D8Ea/Jr2h+JIfF/hvWoWsNa0+5i+zXbWcwKt8pO2TYTkelc74q0nVbV7uDVL3S7UQPLcLp00nkg ywrzPaLGCIWkIzlTsfJwBmqPh7So7rw+/iDTIf7UtbeLzdTtoQDLYS4C5eIYbyW5xL0XpVuLknyy 0YNK90N8e6d4n1fStEi0yBtX8M/D1J7DzoCDIsa3DNJO8ed3zoY+cdua8ome9u9RnnZSFuZvMCg/ MAx+VcV738Nz4t1GDxBpXgS7tZLu5tmvDYSrvkuoYIykkMDjhJShPX744ODiuP1Dw3HJZ/brDbA6 KpFsAQQp+6Qew+prowNblXs30FI84lWxhkaKf5ZEOGDjkGo92mf3l/Ku6A1RxulggZ+hO9Oo47qf 50uzUP8An3g/77j/APiK6vbisf/S0pPiJ4vbJGrQxA/3c44+gH8qpyePPGD/AHtclwe6bsfh0rf+ 2/DuBso12xHf7PyfoWqb/hJ/A8QXGn6lcZ/iaKPHHpXTzLsZHIN421+EhX1G8ujJzlLSWX9SwFRP 4y1udtnmaih6cwKv82Y/rXbyePvDip5SeHb2VPeWGLNVP+Fh6cihLPwvawn1musn9KfN5AcONc1L f80+oh+xG0f0NTHVNTk5aTUT7mUc/kldgPiTfKxEGj6XGMcbnLgfhiq7/EfxN/yxXSLf0xb5/nT5 vIDmxdXsw2RQ37nuPOkP6KB/KlGm6k/zLo91IfVmlP8AI1uH4j+NwNserWsR9IoEX+QrNk8ceMpD um8Q3AHYIoH5YFHyAjHh7xDIMx6DcH/vs/8AoRq5F4Q8YTAFfD7IvrIgH8+tZjeKtfDGSXXr47uc 7wPb6UkXjTVuWk1S8mI/vSAj24xVAbQ8C+L2cEaXGpPTITA/OtFfh342bAZbaI9gmCw/4Corl28d 6s3yrfzBegJlT+WKafEmvXC4XULlkI6Cb/AUtQOzX4b+MScy38cLL3Z9oHtgCmf8K61gZM2tQIVO M+b/AI15+dRvmPzTTuf7peQ1Isl9IcC2Zj6fvD+fFHvdxHoH/CAWUeFv/FUEbHkgOT+oqOfwd4Wi GT4mhZfUs3P4VwItdUbcPszsRjGImJHtyKu22jeKbqSOO10yYhzgO0Kog+rGiz7h8jp4fCHhGeQo PERmAXcwiTOAPdiKqy+H/ArLth1G7GPvNt2g4+tZlz4b8UwSsktqjhOjxn5P1ANZrWOtoSrrIAPR s/0px5u4HQLpXgJM/wCk3kzDurEZ/SlSz8HxAuI7t1+jH/Cuf/s6/YjzM5PqVGP5US6dJCAbq6ij A6AOCf0NVYZ0LTeFUO1dMu3YdCcLn9ab9s0Mcx6EQR/EXX+Wa54WdtwBqIUeuSQfypf7M0pic6mC /wDs5P8AhRYDoJ9esI7C4SLSYw0cTspZwMMQRxtz0r5KlsYbwFJhl36lhwf5V9CajZ6fBYXVwt+z eTGxKGPrxXlEVjBfGGSIeX8wUr05osZTZ5NqPgzYPMtwY3+8GXI/LFYSTeLdFuUls9Rmdo8HDncp Hvmvf9UDQREMoIDbAemO3SuRawEhJON39KlwiwTsc1F8WNWWHbq2jpKUPD27tGf1rQ074xWtpOrL DdW8b/fjlAkQ59GH9ajv9CgcrlhnByBxmuWuvDltwdo59/6VhLDRZaqM9ysvil4buLZmguokc9mP l/o1dTpvjfQ7oIj3sW/P8Lg/yr5Fl8Nxg5zj2H/1gazG8P7CTjBz9OPrWX1Jdy/bn3NrN9pUEllP JeKjzMDE64YenY4/OuG8QKotL2SGYyS3LBN78KQfT0r5PWynhAH2h/lOVIc8fQZru7TxN4gSyNg1 55kJwczLuK49DinDC2Ye2PcPAEcQ0t2B5MhII4/GtfWhFp+kajdCLcyQnEj9mbgAV4bo/ivX9Oh+ zWV4UUYx+7BxV/UfEGvXdlImpXrzJKeYwoCkgZXOPTrSeFk53KVVWscZf3gsjHZzKCrYY+vUN3qt NZwjbIqBS3KjgYHX8a89vri7vJpJZ5Wlfcdxbjv2xW/obaqYt1rKSF42yAMOvbdiu2Omhg0dLDZX jErHbFcDOWPX8anaw1k/dC57c0kV7rK8zRcAY+U8flWnFq1yuFkQjtwDxVEkEGn+IWX/AFipjHXn rxSwafrNxNI8N0A8Z2s7DJJA6cVrLqjhH27iVQt0H+etR6XqX2eJWMbOJDuJ9SeaB3LVlpuvoDuv UYk9MY/nXTWNnqKkrPNvx0wQKgh1CCfEnlkH1rShucMRjgdO/wDKgEdFZWMkpB8zJQc/Wrp09o1Z pX4zwDVW1u9kDsnKjqOmMjoaxdf194bSO0txuu5xtAXnaD3/AK0mzSxmyNFNrT7cLFEqxKR6/eJ/ pXuPhOxu7jRYp4mWKOV2Yl1J6HB/+tXiGl2MNsA1xIslxjJDEY6dOa960jXtPs9Ls7XexKplgsLn G45x0x0xTiSbq6TqQztuIlA4P7rd+XIpo0zUHjGNQcIMghIkBHPoc1THiO24KRXLnGTiDn9ahbxJ M3CWN7jsDEqg/rWwrGidJnT5ZNRvCfTKr+mKVdED/wCturt/QGXH9Ky/7cvW/wCYdOcf3nRf0OTQ NX1h+EsFRR0zOnH6VPMgsan9gQBRv+0OM9PtB/oKUeHNKPMlupP+07H+tZw1DWGIBggH+9KT/wCg ipPtusHGBaqM4x+8P+FMk1B4d0ZACsEG70KsxH60o0myT7kNuo6Y8ocf99A/zrIe61SQbGltYye4 RyRj61BnUh8v2+NP92LqR9aAOmjstOjG2YR7vRERR+FWBDZoPkLAYxhCo/pXNiz1WVcm6mdR1KxK Kd/Zmo8ZuLvb6DatAHSbY8YOcAf3uf5CmiO3Jzh8D0YgfoK559JuXyr3N4COcGXH6LUEekNIdrvc bRyczkDHtQO528NsZQyWyDBGWLyEcY9TisxjbK+Sm3ZxjOQfxrlv7DsJWZZbaVivOWZiPwOR/KpI vDmnnLfZUT2fcc/rQI3JJtOBLvIox1BlA/rUP9qaPHwJ4k78uM1mroVkDtNtbqB0wpP9atNpNouF VIsjt5YH86XyAtL4h0RJDm4gLAYDFl7+1SSeJdG3AJcLjvsJP8gaiTSoVxhVX2WEY/lT2sXDBFxt 7koq0co7kB8V6VGTiUFh7Pz+GKcPFWnyjahP1COefxFXo7UKMSEgDoVYD9KmWGPk+YzLxkl//r0W EZkniODgJBNICOSsWf8ACvm/9od49RsPDmoCGaOW1kurffLHt+VwrAD6c8V9TyG2RgyIQo4+Zv8A GvHvjtYWmofDe7uUVPO0y6guYyTu3bsxMAf+BCpkNM+FmByevpzUkA3ZXGSelNlVAdqHpyDjH04q 5aIrkspw2OvqfSoihy2LdpuiLDp0z9OlaZiTaM5weoqCOFgCy84UA/XOa10hII6Enn86ZFjIaMdC uPzpfKGAAK1JbYsQ+fu9h3qIRdicH0NAyiIcAcdeD9KpXds+7IJPv6cV0AQMM4/yOKgukxbkY+8e RQFjZ+FujNqnjjSrYFvLSUSyP/dCZJK578CvtdtGuXcvJJclWOeJQp5Oea+dPgZpzHXdQvfLDm2s 9uem0zNtH6Zr6ca5vztzACPqO/NKIGc2goFLMz8djLnFRPolsUUGMOrHBDSE4zj6elbBkuVxtTbu /EVWMt8uC6R4BxzwfbNaID5S+PnhldO1fTPEVqAsOoIbebaCFEsIyhwfUV4OyMADneMfe9fevvH4 h6DceKvBuqaYIVNxGpu7fGCRNANygfUZ/wA4r4XiMZjRirBcc8dCKhoq9kY7AfnVqIHGU5PTFRTq EYgfdPI+h5p9qOflGSKnYVzVg8pv3ci4bsRW5YQidym4bwOMjrWbbncu1k4HBIxx6VqpbgldhKEd xRcSNaCCYgo6bgDgAcVo2tu/mbSuM4HPFVLYShhls+vXtxW2sMrEFcHvSKsWo7dtyrkjB6DFZ95D ++kPf3+lbdl95YT1Jqhqy7ZGyD6cUBY7X4dO2dQVeP3UeflzwG56+1el+M7vW9V8HafpUiedBpNz 9qEh5fyWjKBMH72wnNeV/D52S4uYmJXfDuyP9n6V6DmPcXM8rHjOFHrnGSf6VjWoe0g4lpWPM5LZ J/kkhwrHdzx15z7U2C0SEyCEKq9myDt9eB1B9K6rX41+WeIGO2H3zKQSCTxnb/DXLSyWdsTLCGun bGQW2jp2A/SvlatB03Y1jY9V8KeJdC1jRl+G/j2TOhTy79M1NPmm0e6Y/K6dcwOxxIvQc1wXijwl rXgvXJ/D2vxpFe2p3rJF80VxC4+SaJuQyMPTp07Vjxww3sRmWFTjgq5wvH8POM5r2Xwrq+meO9Et vhf46u1trqFmXw1rcjZezlkGPslwxzugm+6v901cWpKw2rHikcqtEY3TcMcFsg/jxXq3wUsFn8Zp OVwLK2kkAx0Y4A/PmuK1bQNV8O6tdaJrcTWeoWDmO4t5Bjb6FOu5D/Cw6ivbPgLpuy417UtmIz5V qufxkb9PatsJD95YmUj6Kx0/lT3OFI9sCkXOSW49qJJBtxjFe4Z2MS9u/sFpdXzZH2SCWb6eUhav z5jhjFsHbduPzNjA5J9O9fb3xHumsfA2v3CkqxtfKB6f651T+tfFqrA6LsbDKoAP4CvIzOeyNKa0 IJfLdA+NpwOB047/AFru/AfxBvfBputKubNdZ8L6qu3VNGuADFNH/fhH8Ew/vD0rkoYIhw0gOOcn p+dOfZ5DbCCd/wCWBjIOP5V5yk0Udv47+G1lounx+PfAE7a74Hu2x52S1xprk5+z3S/eCr/C+ORi vPNOLR3DyLiQHLkAcNn09sV2vgXx3rfw91GXU9IWO6tL1BDqOm3SZtr2L+46dM+jYrrPGPw/0e/0 WT4kfCYyXfhv72paSObvRpjywC9Tb+4zge2K1tzbFHkU6wylmjXygecIPUc0+3LRIAo3s/BB49gf zqr50ckKyp91huwvTHY59KlhLLJHuQnJxz2Dcd6zt0Yya9GYijSIrbeckfe9BXNtFOiqzjKEgAD0 xiumNtIoddpaOM8naDu9/m/pUreS6qJEDKo4C8f4UJgcuYHKgINrZ4ArtvAenDUvGWhWcwLgXQlb PG0RLu5zWZcQxMS6RrGD90A+gr0H4UWi/wDCTXV9tObDTp3Hs02Ilrpw/vTSIn8J7s2JS0p6uzN/ 30c1ZtYwpLetVlT+HpjjH0rRiGF47V75zIrXC/OOOetef/EeVE8KXFuzbRezwQ4PfJLn9Fr0Kfl/ qCK8b+K84W20azxuLTvcY9di7Bx+JrCs7Uyoj/hp/ouga1bYGftkBwPTysfyAr0IPCFiZmWVW4IH UYNeVaIgHg4TRMQ11fu4YEj5YRtH9eDXYaJcpfRGF2BlXBxjFfaZM7YeJ4GMX71mzePvLYQKjHIz /DWbIXJ/efK8bIAR6DrWy8aLtSVcHcr8cjjtUt0tvLOWAUDPCj0NeukcRzM8kiqJOGI/vDP+eKsT uZbb9zFuYgdMHJx0rSurO3AwgPPI46cdKyYbiaw3qmSuTgEdCeaq4GGdD1K6zKdkI6bG4bp37Vym q+H57PHmHLNyDwcGvRY9XuJJCrrwDzjvxVW6V7gL5o3qvTPYUnDmByseC3N1eW9wdy4A6D6Ct/SP EVzGVMecdCPX8K63WNItpFdlTJIyMD0rhZdOnUFrVMHgYx6CslHlKTudfdXVteyqygK2OQecce1e E+KLe4tfEOnyyRuIXjCRO2Su4N82foOtemabLci5RLiN0CAndj8K8/8AiTcSvNpjhsqjSOGAyNyg Dkfz9q8/HVbSgn1OzDw3ZBGXg1M28sgkB5DBgynj+EjOf6VuzwRK6ueVY9T2OK5G83SW9tdxqkUg UHEWBHz/AHRziuxsHS/09XPLbdpz2NddN6IwqLU9M8LLFLZyLIqyBXAz/s+1dBqnhaaeFrrTJAZ0 Hyow5x6V5noV49jlBgOBgZ4/GvbNG162kFvHNweFJ4xXRHXcyd7nK6HqOs6YDb3CtBgg4KfKce4z +tdynieRkaOceSz/AMS4KkdwR/8AWrpdSsrO6SOeBF+YckZ7cf0rOk0xJUVfKO4cbk4x+FWotC0M /wDtDcQY5Nu4jJA4xjHBP+Fd5bagxjAVFbK9GHbpwcVyS6TeWbbSyGPGdxXcT/hW5pz28qBFlj3x 9iTk5OauLsJm7Ghdm3LlSv3TjvWjBYRQ4BTO7oD2pkCvkbWVhgcCrG7UN20FAp6E84/CplJE2IXa FobmJvk2dscZ6cYrgZIFWeSCRFkgnBDo/wAyMO6kHt/nNek3ll5GmSzTOHllYAEdq4m8jG4EDBzx Xxufz5qiie/lkbR5jkX8HeRILzwxKbCdOUgJzGCM58s/wdTweOa8nVNO1O+F3FIbDVJ5pfMEzbra WUsOQ3BXOCfT9K9+ubv7Dp15dA829vLIOe4Q4/z/APWr5Zgt7YQs8zsQAq4VeWI7lj3/AFr5jF1U laR6aOwNtcwTbtRREmiXeWkb92w9EYfernJUtt08q3KoTziJCzJ+B4xWlHca/a2cd3pU6XVp5bh7 a85YhBtOCfUdqw4brQNWmZNPCaHfPhktrjKwtntuNcip3V4FXMeVWazlfzWbBHKqR8vTPNSadbWs Km5lvS2zGFk424rXntr6S6+wahGbQFCSMfK4UcFD0I+lWNNsNOUxw3KK7Nx8+dpzzzWT0GRS6ZpW rQQ22m3AimZd0kbKSHOeoNIvhO8VkjhhLxBiZJNoHGenNaVjYJZyyXCRZMLZC+oznAxmvQP7S2af G2mxyQSrJ5js2DEARjGOtTctWOMm0uWyRn0mJbZk2Elhl2yccVbtvB+rzXEOr3UsGmT2L+TBtj/1 kkg4fg/wplfqauT69f7GkVUkRSFLEfNtHO5azotbt9T1hH1CSQnCBAXIQBDnjt689c0XLSRB4lez jaa6khNxKNqBl4dvL4HXgV51e3zCMLDCnmzECGPqFxwC2e9dTcvNKxWU+ROzD5uNqqvOCeeMcVdu fAmuQaHP4nuIdtjLbf2hZTqRLbXKJMI50ikXOJIQ3mMrYYBTxgVEmhSaOSuhbJY2yyWzx3bctcKR hiedu09OK734e6x4Q8Ky23ipmvZ/E9oZViVo1NpGG4UrgjnHDE9O1cRqNvf2SJFdWkkLNGkwjkB3 COVd0bHPI3Lg89qytI8ydGLHEEjbVHToDnH51hNKUSVY7jxVLeR+I2j1a6e7ub1ILmWeSQ/M95As pUM5yVjJKLz91RXq3wo17w34jA8JXOmxalaNZT7IwDxNEfNE4dfmV+cDI7Vwx8VW1hLoPiLTrZY9 YstM/s26+0RRXkMnknakvlyfdIibGfbNdDb/ABH8a+da+KD4U0O5eykWK2vP7ONrvdiFVVMRAfP0 rnxMb4flT1JNTxnBo2neCbzw1oRlgudNlhfUsybrcyXi/vY4s8kJsUkn+8fpXhOj6lpltrkMeo3J aybKtGPmC8cEt6V9C6no/h5tU8O+K/CX2u0tvEEepzatYXeZ/sWq2sfnGAo4GCRuKof4MEcV4p4i 8BQafaPq/nzJGYfO8ud4XYocNu27g69eVxnPas8HKMVysTZFfaZaWcsM2hTx+XKzMyzPuyM5Yxs3 Sude5lv9QjhjYqksqxDIz8uetbOtZbStLeOKSOW8jIhD8bYRwxwQMbyPTio9Aj8ia81I7ZpYFCB3 5AJ54/8A1V3ktF3xLeR2N0LaBMCFQrdlPGO1cjb2+t61fiw0Cynubkpn7PaIZGC/xOccD0BrSl8m /uWuXieTeQC3JXk/xH2rX8OXy6NJdXUUshtbd8lIpNjmVuFG4Y6YztJAxzRKVldDPWvBkcOm+DLB 9Vtn0HxVjUPsmprayeYr2xzHbXyFSrxyj5BkD61nSa3ounTTaxrWhyaPd4geeyijHmW9xJu2TR9f 3Mv8B6Bvl7Vy+neLNa8RWosriSe/j+1rcQTrLIzKygBlDqAGyOgc4Fd94i0zWNc8deGdA8mTTdb1 Qpp039puJNOeCdN8ODGCcZB91OABxXhy5vaOnPqVdPQ6nxRrPjjWl0fVxo/iC20eW0ZpLa8tfuLC V8yUSKM7ZBhg2eMVxASfwvo+o6hBL5lneQSaPbuV3F/OcMxgx95sdWxxmkFp4nu21NYdT1iJY5Vj urM+fdTwtDuilICNteNtuN3AAHIrm/GV5qNvL4Tt7JnWHR7e3kto3UJGxdhJ5gyc7n4D5A9BXTS5 b8qGZPibSNfje2k1exvNMmSFzBHewyLJNaj5wynBykZyPauS0W+1DSLqPU9DvJdOurUloLqByJUL c/MRkMrL94NlW9CK9U8LfEnxtpXiyPW7W5fWL3UN8cttqUrSxvG3zPEN+PKAx2wR0I71peIpvAek +KbfxTL4clWfVLOK/g0C7/49Y5pGIeeQqcSRtj92oOD1+ndKvy+64lKZe8GfEXR9I8UeH/G39nJp 2v2t5FDdvpzKdO1GGYmNm8rH+jzc/ME+STkrtIxWD49Y2XjLXrdRJaWuo3VxeWhlRojJbTsWRl3D kDOMiuV1/XdQ1aS51LVpB59yCP3cCoqbeVSJEx5Sx9E4JAroh4v3W58P+LLeXXfDUscd1bIJgb3S y64aSxuGAOOCGgf5XA4wamK5JKRrF8xyVrE0VvHHseTaMbgOtT4b/ni/5V6e3wru22vpHiLQtQsZ FV4biTUBau6MARuh/gYZwV7EU3/hVWuf9BXQP/BuK2+sRC5//9OtJ8OfEgXzriS0tx2+0XceeOP7 9VJfA95Gd1xfaeR3H2pT0+ma54XMJJH2TBPTEbsamWTnCWJz6CMn/wBCFdtpdzE1j4UsDjzdXsYs dR5m4f8AoNV59C0KIhP7Zt5G9I+FH0ylMC3x2hNPlI7YiH9AavRad4jcYi0mcqeh2KP6U7PuBRXS NDjI8zUQw7hNxx/3yFq+uneGkAMepPM3cC3kb+benvU/9j+LUXeNNmQe2Bn9KkTSPFUkZ3Wqqo/v zoP0OKXzD5DI7fwtFxLDf3PfiLaPyZq04JPC4yyaJfMv/bNAcfQf0rO/sfXlIBFsuegM6n9ATUsf h/X5WwHtEB6szMfw+7SuLU1xe+F44zjwq0j/AN+a559sgYoGsWIG6LwragjoWmcD9CM1lyaFqcB/ fX9um3j5Vc/+yirdr4aNz80mtRpIBnmIYH/fRHanp3GayeK72MYj0LSLcDoXV2P5kinf8JhrSN5i Wujw7v4hBnp+dYT6NYQN+/1mYg8bolgGf++n6fhTH07w+jAnWZ5PUBoRj8cGptEZvv4y8SEjybmy jH/TK2Xj86ryeLPFcuC+uSRj+7DGEx+Arn5I/DUZBGpy89mkBJ/75jqMS+F0J3NcTP1BQSH/AOJ/ l/jVe6I0LjW9blIe4166U5wMS+X+fNV5L28aDy5budgeTIZmDH6sSOPwqe01Sx0eXzbG3njllQrk IXyp9iT/AEqlLfaRI25dMmUnqWiALH1+bP8An8Kqy7Bcrb4XP7yV5mPX9+T/AFFMMen8hjz6bycV f/tCB0Crpdw23ptWMfrtprXEzgGLTZkA6DzQv6YpoLlOOPT1yY4jKR6ByfyqTNmw3fYd2O5Q/wBa kD3j4LWBwOnm3LAD/vkinytduRvtbFNvQGRj/Wk2MiE23/VWKqPXys/qacsszMAtpz2IRR/Wl33C kHzNOtx3+Uv/ADBpy3c4O0X1mPdYBx+lMDm/F97cxeH7tJEEZdlQ425wevT/AArz/wAPxyedHkDa o3mu98ay3V14dnLXYuzbsH8uOIIFUdThQK4ewvoXgDxEIzcEnHTpTRm1ct6xcwFo96B8vnArGils GWU3MGRvwMf/AFqr3U9ut15Yl3KPlx1BJ54rR8yAKkMaqxB7Y54oZJjzT6G55icnsecY/Gse4k00 tugtnAx164NdTMsKRHci5dcADtVAZ3bVAAx0xSA4y4t4pQSLaRjgcgY/lWTLpsjH5baUqBk7nCqB Xors3AXCDofwqnMCcjnA465/lRp2HocD/Yzbc7EiB7cnimtpUYxvfj0A/lXZNA8mExuHbtioJLWN Ww65YfkKEGhj28EKfu4VwfX2rRvBaW9lcXEmMxwOUB7tipIbZYxI7OIxjknp16Vsad4bv/GlzNoW mW7PCLaSWQgDIWNeSCenNO4j5rit3eEnZlvvH8T0ra0yQKI2ljZMcHBx39KsaPB5rrHygJKkn/Z4 /pWpBaXURLNIki7yuSB6++KEhtl+2vI9wA6DrmtpLkOoIjDAfTmsUowyxOMjGAB2q0rMI1CHgdaQ jTbyZ432p5YKFSBxniqmi+ULdFm5EeQdp9zxUSTurBkGdnUHjNUd08UrvaBVEnzNG+f0oA9Bjn0x YyI3MWezLUEl+FGbRvMCdCw2qPXBbFczC95c7d0nkxj+BT/iK1fsf2nBmzMFwAH5xx6cAfhQND11 m+mAijmEz5PEY+QduW71r2FncMFkjCiSQjfIecYGMc/SpoLFI1GRnHQdPyx/hXQmBYoN53YA6HjO OelSzUf4f0Y6rq8cE0m6ztiJJ8nGQP4c/wC1XuCtZJkPEyjJGPNIHHHGDXL+FtC8nSUkmiYveHzW OCML/Dz9K6qPS0j2jaCR3br+VWkZ3G/adOUn5EPbly5/xpvn6eTmOGNvcBj/ACNSfY5XIEe1D224 GAKcNMXYzFto/i3MPmPtVCuKLmEY22q4HGQhwf6frUUmpW0YIYhAD3XFOGnRZwhycZGGP8qsLpcA bJRHPHDcn8qdwuU/7TtQojEinPOAoqz/AGyqJvId8jA2gDAHtVsafbH7qFSOmVFPXT4Q/wB8Kvfb Hk/nTEZH9qpPncJ8p0yuBj24p5vyVA2sR6Hj6HpWqbAD7kMjrggluPyApPs+3ARCnY7nAoFczmvZ VUOp8wY6Df8A4Covt8jf8u5JPUEk4+la32eUZZFUD1PNP+zxnPmFAAOAp5z6HHagZjefdkjbGVG3 u1SefebBtRQ/fPT9K2x9m27SkSMVxlj3+lRRrb+X5QuBIzd+tBVjNWXUZmSGGJJXJ4QE8n0pZYtb hlaC5VIpBglH6jIyAa0xFHlUL8k4XqP8KmuIILSUW/mt5gGeFLdeeoz/ADpXCxjFNTYbHlhC+gj6 fjxUw+2hf9Yg7AY6Y9K0nMgX5yXUdMLj9aj+128fzFSDxy20dPriixJniC+yd1y+PTaM/wA6RbSb d+8nk+bp0FW7jWdLhYPLPGm7GSZY+vbPNUJPEmgL8z3VqQOha6h/+LpgT/2aHy/mNx3z/SlTT4jg q0jevOKzz408ORof+JlZLnstwG/9BzTG8feGMrtv4G9Snmnp7qlAGsbRW3ffbZwcjpxxXLfEHRbO 78E+IY2XfKbBmX2ZCHXCj6VaX4g6KGYwylz0ykFw/wD7TqLUvGNrqWkahp9raXcrXdpLCCLSYYd1 OMMwUVLWgH50sNuBktxjmtPSVUvLu5wufyqtdRiG4de2T26/qavaOABcyYyEjP6moiVI2Lfgv/ck A49K2I0Bde2B0+lZFjGgWMscgjmt6MEAJH95eh9MnNBI9lVQXA5H86yiqsytICSOlaeSFZW5JqAR 8Egcj/CgASMZEY5HXPp3xUU0aybyeFUj6cDFaMeFRPVhmr1taQzQusgyq5duPQUrge9fBPThb6Bq +puyI17dRwJnA+W3Qk/+PPXsciKDs84ADOOleW+CvCt5F4T0p01q5s47yNrowxQIf9Zkj5mGemK6 tPD1w2EfxDqjE8KiYXH5L/SmkBvukMq+VJNgdtuc/wCFRRQ27o8X7xghGc8k1hDw1bkmOTVNVkZe NpuNnU47YqGXwxokbFZzqDHODuupG5/Aiq1KsdKsPlyRuiSjceeDkj07fjXwj488NyeGPGOraRKF iheb7RCc9UnO5QMcD0r7Fbwx4aBIltJXBGdzSSH/ANmrxP40+F9Ps9K0vxLpFuLUQTfYboK2dyy/ PGx3Z7jFSS0fMl8myQcEYPQ8YxUun7PtAz0YYp+oYfy5lXbv4xjGKZaridD6HJpWFc6a2gI3BR78 +1btsu6L5RtK9RWIshdgAdnoa2badPs5jDfODyR6H/61SETXiABV4cbV9a2YE2985GQPrWVp+MBe ACeh9K3irCH5RlhwMUG5BaYFxGD1DcD0q3qMQCl+pbjmoYF2yrkYAwc56VPqBa4gLkbQWwD64Hag DS8ETvBqUpSFZ3EEnylvLGOB1rvnu9RbpY28aj1nLYx7j/CuG8BwNHqFyVG4tbNjPOCzrgelelkz 7twXIYYwFH4UrAZbX2qbDF9lsSGXB3eY3B9eg6e1cvqmhQvrE97p+mPHZXRDLbQMT5TlRuwW5wWy R9a7d1u5lMOSoI6BQPyNVlsNUnjbAdoyNjHdsIH+faubEYdVIiizipdJntI83DpbRP1Vm3sT2+Ra pSWcLIyrMXBGNvlY/I9j79u1Gq6TqdneR2jrJKz5MLhlcnvgkE4x71o2drcBA99bXZcdPLUAf8Cb /wCtXzlSm4SsbJ6HrWialbfFXTLPwb4huI7bxdpKFNB1eTG25jQc2N0565/5Zs3sOtem/CjSb/SP DM8OqWr2V79rlSaCRSrIyN5Yzn6HHtXyTdajpgjZU05IdmW83JklDA5+XONvOMHsa+5/Dl3qV54e 0q91idrnUJ7aIzySY3FggGWx3xXoYJXfMZTR0R7+3FQS4weaeeBkDCjv2H9KryHgkfn9K9MSPIPj NcRxeCJLV5TH9svYI8gdQgeTAH1UV8qRbVJG5mY8jC5xntxX0h8aLe61C30Kyt/LCrLcXUjyNgDC pGnX6tXg1xa6BY2YEd4098MBihIXd+IFeFmGtSxpFWKi5UFU5wMlSBz3qJbh5t0OBj0AwBTd7Rqr N8uf7wK5/OhZCo3txnkbSG6fSuBsohMOQ0c38PTBIP4V0ngvxXrvgbW4te8NXX2W7iGHjb5o54/4 opV6MrDjkVzrSlucZ3cU8nYoDLz97pjGP504SKPcNe8FaD470u58f/C2za3mgPna74aXBktSRk3N mOjRk/fX8q8ijgglthMMFmG7JUjGTjHOPpj8KseH/E+teFNXtvEHh+8On6lbkGKReQeclZFPBQ9x XtuoaLo/xpt7rxN4FtYdJ8axJJPq3h8NtivVAz59k3/PQ45Ud8jHet9JIGeJSCXYPKCuGGCRxj14 rNkBxtA4TGQO561fImjLpsaIqzRtE4w6FeqMD90jv/k1Ve0u7eLzyEQOOF3qW/FQcisttxjSVnHz jhOmR049K9i+Ftp5OnazqHU3M1vbgkfwRAyEfyrxS3ieSQCMsz5+4gLn16LX0h4FtHtPCOn+aDuu Zbi4YEYP/PNB+QP/ANau3L4XnczqvSx18aYwTz61aIHY4zUMfAz1GKfn06V7hgRTLkqB1JwK8F+J 6teeItPsom2fZ7PO4nHzSPu747CveZiVBOOg4Pp7184+OLv7R4u1NTjbb+XB68Rpj+dcuKfupAjb 0xhDp+n6a5C+VCw7YJY7if1q1e2Fxay/2npvydyo6Ef57VjpOsuuQwFhtEawqAMdEAr1HQ7aCe1m t58gZUIT2+XrX3eCj+5jFdjwK799szNK8QJf26hx+9Tqv04roJDF8rxDO7Bxjke1ef6/oE2mXxud PfA6lV70yw8RujCG6ypHQntXX7Zp2ZzOJ213dfZG3MdygcAVW/tiKUpE0OGJ+9jOPrWnGsWqWwdC rsODt9653UtHv7dFeJCRn5SOpOe/41upLoTYWYLHITs+Vz1Haq82pQ+WYlOZIznJ4/Colt9WmjaD ZgZBbAPGKxbixmVnYtzGDnAHI960jIlxubVxbm4XzYQCHTP581xlyTazbXVdyjJAz/Su207VLeO2 jRnw6LjIGevWotc0kDbdJHgOucnHP5UTjzLQm9tDy/W9VnjgV4YxEQQFPUndxivP/HRgkstNMTbj HOVfggnMY7cd66/xRKZZYLVEDSNNHkBSW/djd8v1Nct4mtLiVdOimhlt2efKh0K7gozxuAr4/MJS eMpx7HtYWP7ps5jTImTNhNwGPmJ6YI/+vW3bR3Nk6rtBjLckdvyrOmieKSCSGQshYsO5XJ6Guztb +0CBZSOeQSOnr2r6Cn5nBI1o7QOFPAJwdw9K7DS4HkcBCNvAIPbFchBqdvHMAWVo8YBFaUF8rfvb eTYQcEV0OSISPdtPvbpcLvQ4ABHXIHAIFa8urCDDJaRXKKdxf5lII7Y4rxCz1W6hmSRSWC9wen5V 21nqTbGxMBu5IaqVUzcDqTrmoXUxSOPCtzt4/KqywxpN9pKGBl6/iap2+pyx58oo2SOcY7e+KJrm 6ncPEQR3HX8gKl1ClE3DeWz4HmvC4wcY6j2Oa7bSp7hyBAA6BVJyRuANeSAFn2zMxY9mGMf0ruvC SPHc5ickgqpBPqeP0rKU+wrHbaxIx/0fsuCfrj0rmLmHMZbqR3rRa4N1c3Mud2ZCOPQH/wCtQwym MZHP6V8jmUr1j38FG1I828Zziz8J6mc4aZBCp/67FVx+leCWw8yF1kUkbtwA4/H6iva/iVKI9Cgt hj/SbxQc9ljQtx+Irw6KAbURzuYDjI/wr5XMX76R3olle5eMQKAoQ71zkHPTP5VLdxR6rawWt3bh 0gHDHuenpx61GGaKRsADjGOTj6dasW9weIicsenv+VcUZtaopHOR3ureH2NrCx1LTX+WS2uBu46f KT/jXT6S2j6jIn2SaW1nwB9lmIxkd1Y/l/nNVbtWCfdZxkH5e1U5LaK5UExfMv3XOQw/EV1e3Ula Qi1cR3UWoO12ZbYjkfwofTGeDU8l3KNqSMcY+4pJyPfFU4LjV7KAqSNTs85MUzHfE399fw4q5ENO 1CMf2bKUl2/PbTth15x8rcVnKi3rALjrm6XyRAqbB0GDwM81jNDDv8/budTjC/54rRFlcNOkFxC0 I5Q7iPrx6/hT2jtNu6BiAOOOprm5hqQh0xGjC54Klj/eGOeD/wDWr0/wJe+KdL0e600abe6j4L11 jBNEE2xrIyeX5kBbBztJSQxjBHB5Brzy2keL93IQ6ucMR6Y/kKrW9xqNldW9xb3UiS2Tq1swZsRl TuG1TkAZ7Y5xzXPVTkrIHqd18Q7S1vPH3ii31K2ntXt7kJCs26OTyo41WBih7OoyuBgjpwK8+kjg 06HzJPmLJ+4CrhfmOM5r6I068s/jfpUui+I5IoPGlgRc2mrbQtxJCJP9IjcDAlTyMlUx8jr/AHTm vGfEfhO/0PVJNMJL+TcyrCzOgMkcLbY5GRSTGsg5XIHHTpmpoz5VyDQzwBJpaz3Vt4q0Z9W0mCMz NJApBiuiwMLSyL/A4Xy8dNuak8T/ABAOtXm++tDcJzFHbg+ULVQvBtmT7svTBI/2e1dJ4L1nTvBO r3ba1qUN7ZanbPY31pYB528pgULedlEyoOQBu9MiuC1drDRbqNvD9891bCCN/PaJY5FZSyGLIzhW G07Rzggds1hOKlV5mB79ptzBrPh6QW9758GvWCx2+o3EJhlsNYtYWtt03AJZ1YcYyQxPOK+ernw3 qWjXuk6r4j0N7KKdRHvmxIkslv8AL5iyc7sjGM/ewT2OO/u9Y8Y+EvGElj41nnlWxcR3Wn5V1h8y 3BjMYHAMYcHI6dOTV99Q0NNHsNIvbAeJvCkcza0qWkzWstpKT5U8Ny4BUwknK7hwGxRhU4Nt7Mlb nmurSXmoLZ3mr20kZvRJNZylRtaHzMHYgADDJxn8qyRbSQE25VZi7gCOJh1U8bj0/CvQfiZrVv4x 1ew1jTNQe8SKyEC2M1uluLCOJhtiiKSPFKh65AU8dK8+kjuEgWURkBjlRn9BXpQlctsyJ7278mTT XjWBfukJwTjnZzjH1Ga988P22lzWmjy+KdT0fXNUtyGRJ49rBo+YYboRjE0anoxAK9O2K8JZllt5 454VLydDzvyDgD8ema9E1vStG8L6WLTTXluNR1Zttw023KKMMUKccKTtzntXNi43SSZLR32sfFXX dHN3D4g0W3GvRy7ljtI0t7eNCuE4QYeNhznrXj3hi61/WfGuk2moaxdxnUrjy42WUqI5iC0DAsDh UlxVLU21O+3XskklxcPiIuWyqxpgRj6Lj/PFNu86XPDdWsh8+3CTI5zhpIz5m48ccrx7VFPCxV5W 1YQhY9k8deM9b0fU4tI0Od9M1Vk8/V57N8fapCwUbnPPBBJX7pzXG/2nd+KNTtJbT7FDr9vsgazv EUW2oQL8oxuGA6g/Oh4cYKkEYrlfG2swXPi7Vr68ZmLvGxAIC5KD5Bj0rn9Pt7jU4i6LJK6kKIuA QydMEkdsc8VeHwsYwTtZmnMexz/D7Rv7Q0KbxTZX2l6Ul9cadq8VjN5klslyN1ncwSR7meOJ2Ocj OI9rgcU/4ojVpo7Hw14miguNY8Izzaf/AGrbkr59pIvmQ7U+75T/AOtiOeMkdM1oeHvFureBtasX 8XwSCOeBIY9XtFMVw8Xl5MZPAkMeRn/lpgD+DFd7qfg/xF4z0+O/TV7PWrO5tYo5btLplmDxEsJo mK7RjjEbAYAK1yvES5veY9D5RmtHtcxyxgr2Xlevp3+lbsl3Nf6VFc3C7pNPkW0JwAwhYb4umOjb h9K0dH8H+MtTsJp00lrh7RikpjeKJSyD5s5YZJ64A561s6Zp9iPCd3/a1i9vqdxqi6TcY+Vljkik ktpAuT8yygjPdTXRPEwtdsIzSPNDHZOdy2iMD329+/Q460eTaf8APmn/AHyf8a92tvBmgxW8cc0M c8ijDScjce5xU/8AwiHh3/nzj/M1i8zoi9sj/9Q/tvU42/caWkTHoGkXj/Cpj4i8REBdsEXp/pBA /wC+VIFa0fwn8QPjfq2lQp3b7RHx+G6ph8LFRv8ASfF+kQ49HDEflXXeHYysc6dd11sk3tnCe/zO W/r/ADpP7Z1fPz6rCo7jBb8gRXTt4A8KW+PP8f2smOojHf8AAVF/winw7jP+keL5JSOmzO38Mf4V ScRHLteXIbeur8ntHbDH58/ypq3l6GOdbu/fy0C4/p+ldOdF+FEf/Hx4gvZMdBFDISfxzR9h+EML A+bql7gfd8tl/LrR7vYDlXuPOO+TUb6dh0LMv9BUElxbAbJbm8dAc7GmH3vXI/lXXH/hU65ZPD+r TsO25VFTf2p8P1UeV4Ludqf3nALfUjB/WlzrsBwJuNKGS+9yeoafrTTPpHDLaIw/66Oc/lXo48U+ Ho0xZ+B4PlHBlcn/ANmpIvGvlnba+FdKg/vb1J+nJNPm8hnnrXWkkj/iX231IZj7VJHPbOQbewj9 vLt2f+Vehr4411STa6TpFsP9iJCfzyaqy+OfGG4lbyC0z/zyjQdvcUwOVt31WU+Va6c5J7JaZY/m P61aGleLXTzYtPnXDYACBW/757elb1v408S+cHvdYuJEOQYoXEGfqyJu/Ws+XVtfu5WaTxFegE8J 5km0D09f1qtRXK3/AAj3jxuRYXYB5JJAz9Kim8OeLlAku4TCB3mkwR+FVpFldXMl/d3LknnMmPzL Vnm0gdv3rSuR1y27P/fVHvdiTWPhjXtolmlht1PeSbH9f6VF/YF8o/eavYgjp++zj8qrpp+nDO6F mbsGYY/Dr/KlFrp4z/o2B0wSP6CizAsRaHA2Rd+JNPh9sM/5EU2ew8ORYB8RGYjqI4OB9DTFgswN v2cKR23kZ70/NrECxSNB67jx+dSUiBbLwyTl9XunXvthAH8qspH4JT791qUp/wBlFH+FMDQNyMMf Uc5qzFHdOc21u8memyJmz9MCnYY5Y/BDo6vDqkwdWUD5Ah3LjDc/dr5hFuIs2v3GJZcZ6MD0r6wi sPEUrBYdHviOeEtZQOB/uivnnULRftV1E8ex45HkOeoYHkUJEPQ4JriGwuSssbMyj5VXn261vQXb h45CmM5JAHTIrJu4SblXThn4IxWqu5cqCflpkGm0oYrvA4HamP5ZJHJ39MduKq/NxyVXPpRGSZWV DjHCk49PagCPyyrkMcge9TDbtl6BR/8AWqwYgq529s1CwByvTuMd6AKbD5Tt+XjJPTj2qnMeRir8 rvjO3jbgAVjuG3jPJ/KgC6kAvCsKKCOSVPQkc4r0v4aWAe+vr5iTEEjtVA7NJlm6Y4HArzFZXSHb b/K7thnxnagBYlcd6+mfAlj4a0/wpY/2nPpx1C53Xcwka98xXkIKoVhIjyq+lK5SR8OXVr/ZOtXF u6lRbXLgr/wI8fSpT9nNoxtCQDKSQ/bJrqPilBb2/wAQdaFjs+yvOJI/LDhSrgMMCQlvzriLWYFr uE52o4b9KtCZZludmAuD2xkVAbkMy7B9SKrXNukjDyH3k846VVjRo+OVxxRYR0UUo27WHB6EVZEO d2DngAH69qxbdjng4B/Guos1TgHpxUgSW8PlqvPKjmuhtpjtT5cEdMY5571nlC6nAA5wK0rcKJ0U cjH/AOv9aC1E6uJ7ZtjyIHYenH6VrrZi9mgSUhQ7qoDEKBz3NYdjCgYvIMjORivQvD5D3gvYWkRr dcKY+oJ46cfrUsps9LW38tVT7RZRqgChfN3YC8fwk0SNHEgL3dtt7cS4H/jv9ayZL/U5RiW9vJCO CrOcYHHY/wBahCyspaUFk6Ybk/rmtEmRoav2+wjw39qW+4cYWOVv/ZKa2r2Az5V75x/2LaU/4fyF UY4oEAJV93YKVUD0qcyt5JHlIWJwCzHAX0KgU7MLFW51+VABZWl5M/TBijUfm7kfpVQ69rLcLo9z gd2uIUH5KKuKtxIojMmG3cFeFC+2KvRCJBh2iB9Xdsj3wDinYkwhq+vzYVNKjQ/7V3n/ANBQ05p/ FsgG23s0X0M8xx/3yAK1Q2AU+2q2TnfwMD36/wA6f5y8ESxhsdcA9OM9KYGRGfE5IVv7Ni92Esn/ ALMBU/l+IZB/yENOT/dtCfyzJV37VBG2+a5G5iABtAyfypXurUOEaYq3XAUigdynZwait0G1HU4r i3Ay0UMCW7n/ALa5cjHpUc1rqxkJGuy26PkrHFDCQF7YJT+tajX0LMFLtk9o8qDj16Co5NYtE+Vm kc4xtBy2B/wKgRmDSrt13N4g1Hnj5PKUD/vgf0po0dJH2HX9TlPoJ2H6qo/lV+HULJuEikUHqH4z j1zTnv7eA5EG/nOIsfmTQO5UXw1ZSHa+oaky9G3Xkp/JcjNNl8G+GnfEIu7pdvW6kkiGR1wPM/r/ AIVYW/geQ4tPLBP3mZSDnrjGaux3+mKwE3mEqMjYEILdhnIP6UrBcxB4P8LFC39nbwGwdzf/AGR/ nVhfCHhRWCx6Nblj0LgH8zzU7amdpdlj3DJUZPHPQmo11fk5jRFI5wM80xE6eHvDUIydLslP3cCN Tz9cf0q1FpOiBsRaVZoV7iBTj8cf0rOOqs2DHgKMZ+X29BUo1WbH7oOGPXIFAG2kFrEy7La3QD+I Qp/9apzKyzKIli8sjmRI1Dr7KOlcy9/qRBEYKDGRlcmkFxqkiqd8u5e6qRn9RQB1fmT7Tg7SDwQo H54FTQSXaOHkckrtYBeFwrcg8dwcVxZOqlcvcXAHpux+h/xpM3+3EjSYPHzMO/HaiwHwr470b+xf FOs6Si7Esb6ZYgO8bPuX/wAdYVmaTE3kX5wMBAvHrnjFeyfG3w60Wv2mtkqF1aLZJ8w/1sK45x3Z P5V5lotr5Om324f8tIwvsMEgflWaVh3KloHVVB/KumtlDqGPGV/risRE8oqR9PpXQaeGdlUfwjOP akIl+yhlPtUHlARhgMHdg/litoQHDjOFH9RWbIAoCHkg/lSbKsVNyDbnPyDB4Nb9jGx0+5mAy8i7 UUfl+uayBGskiox+XqxHau68KQx3mpadCEDJNf28eGyPkjfe3FLqFj6Zgls9Ms7WwBmZLKGKBSB0 EaBf5ilOs2iqXWOVOcAvwfqDRFb2bMQ4Zi+SBtJPWuu8N+FvDGrxSyan4ptdAlQ48me2aVmTuQwc L+QrWT5STiV1SBd5VBkLkF3I3E9hTDrK4GLYgr3J3Z/z9K6PxFpOg6Hexw6VrcWv20iEmW3heFkI /hZWz+n/ANasEixEW/y3IxycAEfhtNNO6AqSas+3AiBY+gI4965fxRaf8JZ4Z1Tw7LEqPex/usDA WWM74zz9MV1B+xlSYw4IOMEjn9BUDT2ZVQisjqevmDG4d+PyoaA/PciV7doZlKuOHB42uvBFJAm/ GOg6g8ZxxXqPxR8PxaT40vGtIzFZ6wq3sAJBCv8A8tVyOPvV5rgrhsYGAT+XtWZJqQPhSjHp0q7b gkEY281mhiQCAMYB4rUtf3hO3rjOPelYaOu08KO2QR19CK6slEVfl+V16joT0rltJUAAMecdPc12 Ese+CFDwU4+nNI2Wxj7GV1YDBJrbv13xWsSAHJ3Ede3YCqM4EVyoJ3KqdfWtiUs1rbPHgP3x6HpS GX/DtlNFb3k0eUcmNMggccn+ldCYplOz7QUKn/ntjp04DVR0SC4fSEj43zXTMN5A6DaM/rzVv7FI Mb/LOwYOA/b6A0AVJ4Ud8S3OWHcynn9aoSWNsY/OZ4pCDx99zx64BrVFs7EsrcL2Ebj+ajNVmsv3 oLAncP7vQ+lSQUoZNPgkDGQIM5+WNj7HGKyJ7nU9SvmSxRzDGxwjNjcoGOxHWtj7LEpxIkhJ7YQf 1qzaQ20X2g+XOsmwCBtybdxPO5Qa4cZQUtjSErGF9jF1dW+nGxe2nmeKMhFyPnYc8Zr7whjW3hht k5WGNIxkf3VC/hXyr4MjubnxVpFvdKFiFzvZlI6IM+v6V9JXl5NK7rC20E8496xwdPlQVGZfi/xS 2gx6dBpxgm1O6ukjW3nUFZIicOVkz8jCuuuGURsynK9Bnnr0rz+40G1uw817Glw+RtLZ+UryCD2/ CutthKdOZgxdoV+cYztHbA788cV2MlHgHxail1PxJaWgl8mKysBxuA3GRy3APHpXlUsFnBEfJtzl WC5xgHjru5rvvH00l5421RESKQRtHbhgckeWgGBXLPFcSQJCxEm0/dALdO3y14tdXk2dMVoZbWt/ 9lUbIXjDZB3s/fodwrEuopbW4zNHv3jIIBCjj14Fd7b2BuWQmFIol++CrfNjiuf1iK1EhVN0AQkO EXerZHHrg+lc0qaDlMCPcGG4Kqkdv/r4pZoUUrIx+YcdfWqzSIm079xxgL344H+c1Ax3gGQjAOcG seQaiWxPDHON7K4HGcc8+wrXsry9s7221LSbqW3u7OQSwSxAxskg6MOmeO3SuWdi0g8kALjr7/jW vb3F0yrChZ2yOFxz+FUlZ3Gz6Plm0f41Wz48nQPiDHDscghLXV0TkI3TZL05HOfUV4HcaLr1pf3e lT6XcJqNlKYpoTFzE6nBUg9vTGRjHau18PeH1dBf6lbzW80T5hAYLjI+/uHIxj617jZ+ItO8XvH4 d16/tLHxV5fl6fqbNviuWxgQXXII46P1+ldUaaktSWfMreJte0e2msrdotPkVGjk8uERSAkYOepz 9a+ktKgktNH0uzk5eCxhDHj77KJCTjPJJ5rwbXdJ8UXPi+Dw54u07+zr6GWKAqqBUMe8MJI3HyyI y9DngcHHSvoyQ+ZPIy8KTtAxj5V+Vf0FdmBp8jZz1JCr1J7nnFPzx0pQoUYzzTMivRFYaRmWNOvz KSPxAI/KvknVZpLrWdUuhkCW6kbp0XcRmvqu+nFvaz3PQQQvLnpgqh5r5VEU1wrzQwyB5FJkY5wd x64rhxctYoaWjN9plh8S2vzf6ueMH8a9i04SpJMinAhkIHQ8fhXzveXOzxKHBG2O4UHGTzxX0Po7 suqzlSGSZhID6ZHTmv0LAy/dx9D5yurMs6tDvjzt2tjg+veuGl02G4C+YgLA4zXquoQmSN9wB29A Onr2rg70eWQ2MeuOtdNRJmEWYSRanpoElrIfKU9Dx7gVtW3i65SMQzxgFOQevJOa0I5cxgYBB6q3 rUSabHL5hkjQsOe1TFWCWppJ4wtpQA4U54IIOT/3zkVk61J9qjM9smwuMMVHUVhaxojACTTcoW/g 4A49Kq6Pf3unzLBfqVifg55xk10U2yGuxnTWNzbqtxGpC9/Suig8URXtrHaXfHlfKfdfSujuhbPZ bI8OrMcsM8DGOnavM7jSALw4PJ4ABAyfSio3HYiKuxIdJvl8TG+tFhm/s+2lu4hKVK9gAwGD+tcD 478UaxrN5pr38ybrQy/ZVjTCI3A75PIr0SfQbvxPevHpMsEf2GONJjI0i7s9QdnX8q8v+JHh+78N X+l2d/Ok7zW0s48oEbTvAA5FfG1ZN49eR9BTjy0DnBqEAlkjEY2nsv07VXVJc5UZTPvVGGxjmiWY bgzqDlfXFbeniXKwzDcTwCOMY9a+litEeVLctwRecGUoUOMYHT8K6jT9AumgE0MhZW4I9D0pukxx vciMhcdDnp6cV6Pa2xso/LI+V8EbeAPTFCgTzHN6Zb31hMqzoTGzYb39M13shhIAjTap6qRx07VS N8roYFUMPU9QajiAnnW1aTyJHXKSN91iONmen51o4oRtWcBuVZISMjgL0/nVyFZbdmXbk46HjpWT HZXdu/zYDjByODkVuWxa5fD8EDqfpUKIFi2Mcv8AriI2HQMf5E12nhxVt/tF2zFQiM2CP+ea5H86 5W3ty0bAAMvYnnHauqLLa+HblNoVpCsSle+4/N+laSjyxbJL+jqBa7gd+8k5x17VpOMcevSm21u1 rbw2/A8pADgd8c/rmpHPAyMEdP618NiZ3m2fR0Y2gonhnxVuUS50ex/iIml+mMKD/OvKFxv3t8uV x39M5xXXfFi7SXxsttux9isohjjAZ2Z/1wK8za+ZbhDJuRDk+xz6V83ileodcUdIWj2K0zhAOFf1 qGaIRlXXguMhl+tVLeccGGYqmOnXn6Gln1EtGPNiLBOcr19OlcaQ7EsslxuEkIZBt6Hv6/jUIknW OSYMwYrhR6VJHKLiNju5PaRe3tg9e1MVDcSAFdkanb+lVYC7a3bbF80YMa89h+P/AOqs26SG7Pm+ XlifvR/Ky4HrV2UxqjQxkZzjJI//AF1CI0RguSN+FBHeiNRxfuisWoLu+tVjt7/F7ZkAhj88kf8A EdtX1W1vIRPYNubo0bffXtjOMVWLLG6+Y6qFGCVP6VU8hZJxNCBE4IIeI46e3Q10e2hP4hWLkijA x1HUdMfnUJYMyoOD04PX6VJFfBVFpqnMK8JMuA6Z9G6/p1xTnsrtEjltRHexM3GOJR9MHB/CsZ0L axGmR2xkiu4pbaX7PcxljHMzFdpx0yOQPX1r2fXPAGq+L9M0Dxj4Wthf6rrzSQ6pYxyoUtbi3GwT JK3CRyoAzhz8hZccZrw8zQSo4lBidDtwf4TnofStLw3eXsWpppsNpdapFeHY1jbM6vL7Iv3XPH3W GCO/SuKvBpc9thl/xF4Y8ReD79tK8Q6W2mXxiSZY5GjkjaE8CRHi4KjH4d6qeHToia1aR68Cti8q N50XAilDArI4HWM4xImOmDXsfxV1XRp/BvhqzsbYyNYNcr9pvoiLqyjdlMdunQpFu3KVYentXimk 3dlb3iXF9ZRapaSJskheQqSD95opFH7uQf8APQAr2YEVz4er7alcD1T4o+LPBHiXVtWvrHwlLa61 d3Xz6sdTlljmaP5crBwhjIAC89OK47wLrkuh3lzb28z28l7E/wBmlRkxHcMMKrJKGRopPusrcDr1 FS+I9IsrbTNEufD0MrW13bzTmSVAtw0RkGzz1jyjeSVZN42gjBwvQUpNFgPhG38TW0rmb+2H065j ZvlQ+R5sBX0yw4FXT5YwsNFbVpbm7urm6vEhW8DbJvKiWBeOAREgAGRycDrzXPOss2yPcOG2qM9K 6/XbyLVbNNVWILPC226IODHu4DehBPrXFNJPGPMRchRwSMfNiupMckSwafbnUYhfzyW9qsgM0kSC Uqcjb8n8Xzf/AFq7T4q6TNoni57GWcGcWVtNIQpUlrhAxCqfwyPWuV0q7ka7S8uYUaOw/wBJcNnB 2HcFcAc84rTbUrnxpqUZ8R31wZ9Q1FJpLvaGkhSThxEhxkDHyIeKzd3IDl3u7u0UQqT5b8EeqjjI qCXz51EW4M0xSMK5wGLPhMntnIrq9afw+dVuJPDFnNaaXEVggS7k82eQpx5srYwrMQTsHA6U7w3q E+h61b+JZbEXkdkXcwyMEDhkIPQE7ueOOelat72Fex0XxLtdEluNJ0ue1UeIbBP+JvqVuqxmfdEo RNqjnZ65zzXD+dDGDGCHWRRGNwOeO+evT3roPFsyTeINRuoovs4un8wxnjYCAehPFclNC8gDq33e oHPQe1Z02+WwjudH8RajoVnPYmKHUNK1LC3Njep50Euw/K2Mko69AyEH3xgVveHtUg06edPC9x/Z 6XmJJtJvd1xauAfn8q4jxJGuOPmH19a87tbqVbGVbpizLgRYHqcnNTaXqEdjqsV/JFmFCySRr/Ej cMR+mBWFbDx5XYTZ9P8AhzVrO/1W0tfDkSabPcTSWeoaMWjYss0eYri3kJAkaMjBH3tjdMAmvMdP mgnhvRfjfc3sqQrKo2QnULIkCJkPzxyspO3IAcj5c9Kh1GDOky+INJgHiCKeFFuLZRjyfKb720fN kqw+4QVwanW51Ozm1LUZ5Ens71UMn2ra8ixr86okmN0hUtw2N33fpXh1MPo2KclYy7zUPKupYvMx 5bbcZ9OKrf2n/wBNf1ror7w94d1a6k1OXU7i0ku8SvDb26SxxswBZVY7TgHtjjp2qp/whvhr/oN3 3/gHH/jWccOrIj2Z/9XG8uDgFV/4ESc0NHYghjHbqB3ABP45r0iTVPDdrCfsOHkB5B0m0iA4/vOZ v5VnHxFerk2RWEdiLaxGfxiswf1rs5/7picSktoDiIW5x2ESmr0YunZfIh3f7kBP9K6f/hLfEigH +1ZYY/8ApmSuPxSNapyeLdYl+aTXNQlHoLqZR6dyP50XYytDpPiK7AMOl31wD08q2kI9Oipj9a0I /B/jFwDFoWoqPeHYB+eKxJtbvbnKS3byY6mS5lyfxMnP5VRM6H7/ANnlb0lRZsfmM/r/AIU7yFY6 l/DHihATPZG3x18+4gjx/wB9OKqS6BfKA9zd6XEB/wA9NStf/jlc+bvgjzbZdvRFgj/+I/rSxanc wH91OIT6xoBj6ACj3wsbUWjgf8xbSY1bpsvBL+kKvVpNC3jcmo2sgHUQwXsv5YhrCk169Yfv9VvS o6jLrn8iKrvfTS/Obm7kHTlmOf8AvpjS5pAdafDsgUOJLp19U067A49PNEYrOljs7NwZBezbfvIL eOLPt88/Fc1JN05uSOozwPp1H8hVYz2C8patu9SRu/EgE/rTUhm7cavpN3N/oWk6lbLjYQpgK+hO 95Wq+qsmxo9FnkwB8zX1sn/oMbf5/KuTW/gVs4x7Fzk09dXjBx5cefTe3H4AincRr3V55bH/AIld uW9JdTJC/wDfu3B/Wqg1W8+6lrpY9A09y4H/AKD/ACrOfUFdjjaCw6f4damgkuYxiKFMHqSmW/HI qgNq11K4ljdbhtCtB2JjupSPw80fyqdpIMBpNX0wE9Db6Uztx7zM1ZCLqU7blhYlf7sQA/lSSadq MZDmFgTz0xSsM6CDU7WJSp1m4dh0EWlWSD9Ym/nVpdesrd1l/tDV5GUcCOOygT/x2BW/WuTFjqDf eBCnsXAx+H+FKNOccKSzY5AAGPxzRYDsh4vMbCRZtemTsDflM9v+WYFJJ4rvmyLWwvD/ANfer3bf oJgP0rmINAvrx1XEaA4HmTSqoB6g5bt2q7/wiV6JHX7Tp3BxuN3Fg++NwpXQFq58QzPbyi7022ck E7pL2d9mF67DKc/jXhussjSNcRniQcbcY6e1eyT+FDb20011q2kxiNGbYtwJHO3ngJmvDNXdpbqS VSQAmcfw9OwpxMmzDjG98SYCk9euMcdqkfdHvx09vpVFHyRgHHWrQPGOqnnj24q7CJCJMAA7h6VL bq/zNggqcZpWkCLH5f1wKZ57Ybrhj6UWAtO5yuT0HP8AOmSFVQMCQenGKrsGZY2zxzkn8qVmGFBI wDnNSBDcSlRsjOC3eqcce98sei96tOhI3n7o4z/Ks1zIWCrwqnd6cDtnpQBe0i3l1XWoreFmCJ8h 2j5VGN0jH6f/AKq91S+kUKqQqygcYj7CuK+GNhOst3q1vYx3uweTGJkMkalvmZguQP0r11r3xCw2 R2cELkcGO0jjI9gdoNKxaPlL4qgx+Imu3+QXNumBtxnaCCR+leSiQSTMYiwLJltvcAdxXvPxtsb9 J9LutRyWmRlwxHY/7JOK+fkQi7idcquwjg4496uJJcjzCuRwDjBHUVejVZkI/i9azzwfTnoe9aFp gyZ28d6YiW3gIkVWHy+orprWLBGDkA5NVYoY2I+XJPY1rxxtD8pHHbFQNFuIsoC7cnJxmr9sFRlf ALEH9KzxuySo5xx7GtGzVXKlT868dKDRHU2kbLFE7kkhtwx6V6Ppsy2FqsUUU0kj/vHKgbST0AJ9 sVwltExEadT6D36V6fbC2WCKGWMkooBDHA6Z7NQkTJlX7VeHl7Z9vvJjpx0FS+bIMMbZsYzzKTn9 KttcWEIJFnB06/vG/wDZqie9tkwwFvARgkgBSeOh3bs/nWiIKZM4PybYyfVicfnilKzbgWmDMO3S rjazHgCO5t0X+6EjGPXtU8erySJiKdSSOixg5x/un+lOwGcYnwSdp5zn5Sf61Msc3HlBzjptX/AY qyuo3rDgzMe22GRenuKeNS1GQ7GE6/Usf0Yf1oAqtFfOC7B8+p4z+lTLaarGA7LLER0J3DA6/wCe KeBeHPn+Zs65PlkfgCwqoZQZ1A+cdsmMdPX95QBIIbiYk+ZuwcZK5Of+BMKsnSJ2AeaWMjtudB/7 Mar/AGxNwaGVU28cuQc+mQDSPOk7DMiJt6tvL5/8h0ATnSCq8yx7fVZBx+hpRp1ugwblU29i+f0C is6S63ZiklR1HHCE8fkv8qkjdIgVT5wemIiP5NQBbbTbLcFluI2HUkKxx6dqlFnoyf8ALQM/c7W7 fRgP0qiHkJ4M6gdgCM/+PUhecsGSOUY9GAH5YP8AOgDQ8nTQOfMcN0IQDH6mrztYpCiSW6Ii8giA KxH+9g5rCK3UrHdbb+nDOB/Ja3I9N16XS5r608POLSHPm3wWR0UDrufhOPagCItpgyVjGOozgf8A stLFPp7ptNuCozkiTj8MY/lWbGl/wAcr/dLyYP8A49TGt7tiXIiHoPmP6nJ/WgDXivbUyeWtlhcd dzE/zptxdWqOMQqhPqrn+tZS6fcDiQxjdzlsY/AEU99OYjZJNFGvqiL/AIGgDQm1AKqtHHEAO5wv /oWTR/aUcyFgYRKBjarR5P51SGnEAD7WpPQEBRx+FWf7KcKA00gHXjcMfiAP5UAPF6HjAOC4427Q R/47/hUS3flSKwBVu+yOYfToKjFpGXETTy5xjDM/P8qkNhFEm4thN2zIyeevUtQBznjvSv8AhL/D d7pU4cXGUntJXD/JPGPl6r0YZXGa+X5rM2OmXKypsdmjUAfwsqbT09+9fYQs4o3DFS3PGMEcdP4j Xzr8QraOPVbm2i4G9iD7g4rOYHk+0noMjgdq3NJUu7juOBWQ3yD+nvWxpdwqSxjGGHAP1pAdDJF5 YGOSQMisBwXleVuEHf3AxXXGF2i80DCnvkc1h3EcaELnAAzg1LLM8jacdQRjI/2q9M+H1pKb+1Ee N9uZpefVVwMH6kCvOI+F85xnBwgH97+E17N8OEFteyK7gFLN3YkkfNI68cUnuB6kiXUeWWR8feAO P0xWjeJBvha0upp/Mj3Teaka7W/ugGM7vxIqRbhGX90Argg7jIwJXH3ccUizrISg8zbzwWLN+ea6 bprYgzjGXZ2ZcqRgAAJz/wABAH6VXEEwzG5zxnncQB74Iq00jRS4kLMp6h4yQKhu5kYDZhFA52rt z9cikVYrLbfvVVCImPygqzKBnnrmo57OUHbJOMr3HzZx7tSyS2rhc7CVXaAHjx9SKak0JQo7MB6b hj8MEVLkKx5j8WtB+2+F4tXTLz6LOJCRty0E2I5B8vpx3r5euwxuflbcByRyOvPc/wCNfds0EV1a XGnzkm0vYXt3JyRiQYbGAfUc+1fEup6dc2N/PYTjE1m7wkEE58tto9O1ZtktFWMGOJk6K3OavWQ2 yfKMgjBFRRx5i2Y5yD0x0FTWbATYA79OuKYkd/pSghhjkBcH1rr9gdQpOTnp0/nXH6S/IkQfNGwU +2RXaM7uCVXBHWpZutjMuoSPN4+6mQa1NEDSKIpX3BFzjp2JGKqMkhgllf8AhGB781e0CMs5TODk 5PTuaQzr7KN4NK08yMw82Nui5IwxqQzjJ3LIxz/cAxSzappKaVpseoXMdkYo5o134HmfP257ZArM l17wtnCazC3sd3b02g1NwNLzcrhVZju6HHT86i2wb283kjnGVFZ48R+GCdq3sjgf3VZvyHln+f8A hTX17w/giIXUrDsLaQfySnYmxbcxKMQqTu7Mw4/If0qLccbXSMD03Mf5is46tbN9zT79yemLVx+R Yr/Knf2rIBiPQ7+Q/wC0oX/0KShxuOx2fgt4IPE2niUwwJlsFOOSOhz/AI19E7NxJYYPfjHtXx8l /qZYqNCuCrYBDSQJz13btzMMdq+ivh94kn1zSjbamPK1Kw4MburySQ/wvleGI6HH1rncLAzvZI9s A2jknrRbSS2txHNE2HXnPqMjg+1STN+6Rfz9qyryY21pcXGf9RDLID06Rmpk9A6nk/i7RLDWdNvP H/g0k2HnsusWbZaSynJx5wA/5YyeozjNcBa3MFqm6LAkMW5iAv4bcGrPg/xfqnhXV4dQtNjmSPbc 20g/c3MLH543XowI6ehruvE/hTSUsYvGfhCRpPC99Nh0GDJpty3BhkHZD0B6cj2ry5q7OiLPL7hp 5/373LRxD7yghR07AVzDasJUki06Ca4WQbVLZC5IzkYFdVJELsyXdwBDJC3lgryAg/vDHXOegrn9 Sj8lxJaqkzE5RGQpnHA2hayaLOcl0zUBgzRkEjnbjI+tQypbRYRYwmBgLycHHvV641y7njMS20cZ Pyuw+8F6YGT/ADrGBB+bjPYD0rCxSRLvX+LCj2rX0vXLvStz2GwknBLKDkfjWLgrgMQ2e9J5hxgD I6gDtSsDR23/AAl2q3yFZJzGAMFUyB16nGOO3J/wrotHXVtUmW1GlWESJHvM9xAcsAcjawxz7iua 8K6BfX063TsltaowbzG2nBU5HGeR7GvcrW6e8D77yKcI2MQjtnoR2/CtaUXciSO007Vf7X0yHS/F ELX1zpS/adLv1yk1u6DHkt13w4PAPOaIhhRgDnnjjHesPTLZUlursZDCIRYDHHzHHGfaugBWPIxn HGenTpXsUVoclTcd6VH+GPrxVfUb6DSYIrm+SSK3n5WZVUrgHaepz19BVplKsyEcqccdK0uUcz4o maHQr8xgFmj2Y5OdxA5/CvIYfDt/eiEbbp41GCN4Tjj5V9q9e8RXSw2YV5lgMh2KW5zxkAL0/WvJ /st/qNwbWaOYLjIGDGCfXI/xrzMVL30UloeeeKYvsXiG8TdnZtCY54wMGvofw+RJJYspXEsEDZPp tFfO3j2wn0bXzag7kkgiljJ46rtYfga938EXAudO0m4AGVtIxzj/AJZt5eR+Vff5dPmpRsfO4qNp NHrc9k2SoYbUHIHc4rzXWbP+IEKM9BXr7DcSEOHHOD37159rFr5iO7KOCcgV60oo4YyOdsl3JycO owvTn8as+Xdv8xYYXj04/Cq8MhjKKy85yO2R04/wroohE33RyRnApFmahWGGNBhXjY/N1yDzg5rI v7OJ+SmMruHtg9K17pxHl1UZXPB9qyZzLMC5UnCnp0rWBBzem3TxTtGwZoWcg47elauoWiRuk8Sk shLErg5GM8CsO7nFuHSEZZsEbfXHrV2SeQ6OzYZ5FX/ll97n/PWnUfuhFe8dD8K4vD2o2mt2/iCK a3N3exvb6jGcNblEYbXUdUYtmvKvjlY/Y/Hun6bLeRXBttLR/OtSJF/eSZXcBkjIr0v4eKr+HpZQ eGupOnU7cAZ7ZrxDx/ZSQfEq/DxnyZbe3lQqpcFAu3oORz16gGvi6FpYxs+hqK1A86tWl0y4lgcZ gLHjrjntXT2thHcHz8YJ5GDjp61VSATyyE/MEXrnPaum0hI47Yf3wTweOK+pS2PCk9TrtO022MaY KiQgEn1+n4Vo/a45m8l9yBPlBPOccVnaXJINpI3KM4xjjHr0rpotGgu9kqoweUBgR3psdiolhMVM 0eHA54rd05EkWRbiIMvHBx/kGp7eC5sMLsB2cnjp7+tbUFrHeJ5kURSQjJPY+/pTURXKC2giG5Hc A/dB5x+NSspOB948bQBgVcZDAghcZBB6g8c0+C082RFjXaQAcVokTzGxpaSKAskY+bA+XFdBqton n6XaAczu0jAD7qrtHSsu1hcyoseNwOAAa6JLeR9YeafgWUCxIB3aT5jyfwrlx9VQps3w8OaSRoMA cnFVmUM4VfXA7e5q+y9arrxMp7L83P8As818He6uz6SOmh8WePLsXvjPX7g/OqXKQ/hEqrgfrXKs WGwNgrj7vXFT3M0upahd364/0u5nnI6fekYjj6Gq0iSRyDzV2n3rwa3xtnVFCkKpPlny935VZimk KlZFLDGAVxVTeSQcBh7jP8qAGLB0DYHA68/jWIWLpfIXcSpB7LnFa0d35luYVkQLnIYAAmsOAsVJ c7R0yD/kVMscIO4gOeoK57VJNizeY2KAcyr3A+X17U63u/MG2VTGUHGMY/WoPMViSY1b0yPTjtSn a8J3AJk4AHHFKxJdnkeeNVgdHCjlQBms0F1ceZC8JHcE4/KmmKFPmhYo3r34qZLudePNMgPByKdi rDzdwtGYjl37Mf7vpU9veNafPaSGE91PK/Qqf55zWZIW4KkZPGBUjhgFEe1gByW5xVQk47A0dPHq Frq7fYr9EhYriJ04w2eznkfiKVdI1C1lWWCR5FViySQttkV0Gcgr+PKk8D61yALZ+c8YIxxXQ6Pq d9a8wyJLCBgwy4cHPGNvBH1/xroi41NGZ2sei6R8UvEOlD7Tf3Ul9cWkf+gXBIWe2nPBO8bWIwNr K2cHnrgV6D8TNEePSYPiPon/ABItYu5xaa7Y28ykb5U3RzxDPO/+MRnBUbvU14vBc6PqtwsErfZp JP3Z3nmLbgHa2OV+7x14rQ0i51vwZrun+INPjD3OkyxSGCbJR9rZ8t1O4YP95c+v92uSpl/J/D2H czjr2spawW8lyXgt+YkyUyj8OodefnUdfm7e9exXtp4V8O+AYfG1joUt9ofiG8tpINI1O9ZTbXMa yRt50ibJJlP/ACzUbfU+teEaxaJaIdQ0kBtDv3kMA4/0ds7vs0n90xjhMfeUZHevU7fxjd+GNC0i 81K3t9Vtb6IxyJOsVwjLt4XyZM+WyDjdw/HFedi4OGwpHJQ6rD/aT6lJpNrp9sR5NzZWkUkcf2dx yoSRyQV4YZOc1i+IdGj0y4e0UgRqEmgYMdskcgyknPqOvvmvU/Fmr6Hr9xHcWvhm4vLjVXhmvNb0 2ZzIIVGyKK0WVfKjAwPNBwzAbcir3jbwXYW/w/8ADev+Gb+41C2lmkihaaMwzpOOJYEDEMEYjeqt uCsCM7azWLjopbmsFzI8MeH7HpoB/wBfePvZTkDyk6Kfqfm+lLayxW0tpPOhnhhlikkjVhvdVceY FI6ZUcVNeSKs3kSkp9nXywhONuAAR0FRfZB9leZZMSqScL8xGOMcV1Ba2h2Pjj/hHz4kkHhqyTTt IMEL29sr+YVUoCS7nrJ3Ncr5rR5g5MTBSWBzgKcqR6HI7YpJ0k3oGGA8SsM4BO3rjOKoPGQrOz/u s5YEA/IGBbp6rntVR2Je53/i/WJb200jUJ9OSOW8tUkkvFjctNOqhWRZmLdFwWHP5VxDFRkI20+h GP8AOa7T4jwwQeK7+z0s+Tp9gLeK2tA58tP3KBtq9Fz/ABEcmuBw7sDuKe3XFKCstBMnDhi42FF2 8nqD64p8cS5jETEr1bp06DFIAdm0Lu7FjgH8qltVOecgZwT0zWiQFsvc2UaLaySK6/NmIldhxx09 s/8A6q921fQW1PQNOa5gt4bfU4Yn0zVEn863kcRghJRn93NnMci7R/s5IrwK7l6HJkIPA/u49M49 KjiYSSLsSMMDuxt9emPpz/Q1jVwylqKx67FZaLbRpBqmq6bFeRqBMnns218cjICg4+n+NP8AJ8Lf 9BnTf+/r15tHqWpQIIYWwicKHRmYD0Jx26U7+2NW/vr/AN+2/wAK5fqoz//W1IPh34nmAMU97OD2 gs52/mV/lT0+FfimeTDadrDE9zaxx/mXkzWTNqfiy4+a41WdiTyftLkn6hpDVDdqcrf6RqW3/elr stMwujrW+EXidPmk0+9K+slxawAf99ZqB/hlqEJHnJAAeol1a0UjHqQK5GSwif5pL2KQHvyc/oaj TT9PjXf9rCSDoIo2OfxXFPkmHMjubf4fQLmS4v8ARrdF6CbWQ/1/1SD+dXx4W8KxfJNqeiOezLd3 cgH5KRx9a86MFnLjzJpSP9tW5/8AH6eLXSEBwkhP+6Dn8yaPZyHc7ObR/DUWV/t3TlA6FRcn8siq ZtPBsQzN4iiYnosNlLJj/voiucWPSF+drSRwBxkIv64NIZNOxmOyIHctLj+Qo5GGpsk+EI251e7k U9oLJEbH4scVG03gYsqxw6zOxOBuaJc+20Amsc3ljECPs8KD1abfn88VAuqLExkS4tj0wvlqR06F gc/pTUQNaPV9BsxNDZaDI4c4LXUsfmZ+vlMR+YrPn1ZnGLPR7aD3dnm/9CxSjxC3USW6k91j6duh zTP7eJHyTsrD+7Ev8h/hTsBVS81jPy2toR2xABj8zUom12TLJ5aD/pnCnGOP7pNQTao9wf38lxLj sE2r+GBUsN9dMjC3W5AHGAWH6CiwyZf+EgJx50mfXyVA/D93UqHXmyv9oXIHoMj+QH8hVcz6rgZt rgj/AG5Gx+RpiDVJ9sUdmXJ7Djv7GmBde11mVdkt/dMp7CcgfzFVjoe84nucn0ebJ/8AQjVqPw/r 7n5dMQY7lv6FqnGg+JMcW8MK+rSIuPwzQBk/2FYAFvNibHqxJ4/CrEOmaP5Z+0Tujk4UBRtH1wKv jw/rTndLd2dtjrvlTkfhU0Phm4mcINc01c+s4OPyoAyDYaUpwbsyDHACMR+oAp6WWlY+WdwO4VMY /AkfyrcfwdaJ/wAfHirTU90Lt/I/0qRPCfhtAPP8WW7kd0jbj/vqj3RamB5GkpkrNLgBjyqgdMDo 3FeMamSJJx1LEgc9vqa97u9G8HQ200lvr0lzOittQQkB2A+UBhXht9FGzq0hAOzcQevNBDVjkvLC uqq/AH3ankzkFewxx2qbygJySBk9APbikeORjkMEGemPSrJGLuCqQMEcDtU7O7rtB2474zQUwB8w z159uKfBEfMYMRlhwBwKAElUrCqnks3bmlaPKAvgruwegyK0N0KFGduEwCAM9qrXLblAgXYM5y/6 cCoAo3zgAJkKg5A9R9Kxbhi0D/P5anAU9OT2rQmQ43OfMkJPJ7e1W9N0k6jfWNoxwss2SScYA5J7 9PpS1A9e8NWd5Y6NaW0FrOnmJ5jkjbnIyDgc1upZ63ONi6dLn2QyH88GtJtR1NwE/ti48teAonfA AGAAEwKaFM4/0jUZZMdmecj8cZ/WqtIDxX4uaXdjS9MF5avBsuG4ZSvbtxXzbtQanFGhypBP4ZxX 1h8T7KE6ZYi3c3ErTNtRdw2jHLDI68+lfN1zp6LexQEfvkXcSDn8D0oiwKMkShzngcDJq/bRkJwB x3rSFms1upI+bNRrG0X3l6NwKLgWbUur/N0HrXSRSI8OCMbQT+tYUSELzyXPT0rRA8uFgP4hg+1I aZNETnA6Ow/CtSF9kqLGvzE8/hxWVbq5ZEHQDP5VrRnEikEZoNEejaOsFxfWULpuLNyCQOnJ+9kV 6S0Oh9G08tjOCJoxn6KsdcD4VVobj7XNwkC7Rnjlq7v+1ymDbXCBh05TgD64px8xNDHi01tvlaeu BwQZOv5JVuGJWwkVnGCOn3z/AEFVG1W4ndSbqVnX/nnjaf8AvnipHvr2RQkrTkrwCN+eeff+VaXI sWpLa6YgCIKB0CKw/KmfYZvLYsrI3qF5/HNVA162A0Mkg9NpP6FBUeL2KTZDEF3dQSob8sii4FgW RGS5kZvdl/w/rUi6dbkYkY+wLrj+dRPb3rfvY4dhHGCyc9uqyf0pjW12FAd40J9ZuB/P+dMRINM0 4MMxp83AJYH2561M+mQwtgGLYSOASf5LVB5Iwu2+vrJAOP8AX4YY9Qyf1qh/a2hWxxJr2nJj+EyI T+Yx/KpuUkb4tLTnHlnB4wpPPbsKnRLfeI1C5Uc7VwQfoWH8q43/AISjwzCcf2/aKwz8qsDz9Fbv 9KU+LfDEoG3VZWf/AKYwSuPwKqf50yTrw8SSeWzyMD0G0Af+hU43NouYZFdCRnPBOPz/AK1xg8Qa dLgwLqNzjsljcEn8dlObWQ3TR9VkzwP9GKf+hYpcyHY7PzbSFS/lvKzD5SWA/TB/z+VNNxaRx7gu DgZAf/AVyX9r3jxFV8N6ioXADO8MYHp1lH8qP7Q1dgGi8NOwA/5a30C/mFkb+dHMgsdf9qtmiMvl gsMY+Zv8RUcV5HP8zW7O6Z+VUlfgjqcE4rj/ADPETktDoFkjf9NbwH/0FG/n/hW3pusfEy1tLjTd HNtZ2s3+uhguLtkbcOfMEMI7cdaV+w7F5dRhY/OYEHXnC9f+A1E+o2hny5jGOgUr6fSud8rxk2If tejwjhVXyp2K8cD+D+dSNYeNDtRtctYwv9ywlOPoTNRdhY6A3duzFkAIP8IAOMD1Uf0qd9QTa20M 5PZlcHpjH3a5mTQPFEn+u8TqB32WC/zkkP8AKj/hENcYB28SXmz1itrOPP47TRdhY6F9RjljCxLt YfwsGP6YFMa7mKBJLZinc4Ufzz/OsBPCE8u7d4j1QlTzh7VCMf7sQP61LD4Ksrl9kuuanOSOhvNv /otP60XYWOghnunVh9nBCdCCuV/AOD+lRg3gkzFGzADqSy/pzWL/AMIHogkKXEmovt4+e9uiPyWn /wDCvPCLLuNjPKB3ea8cH/vp/wClF2FjVmklR9yjY3GSzk4yMdWQV83eNpjN4ku1bgq7Z6fxHjoB 1r6Ig+G3hHC3H9g27LGQP3sb9+OrsK+XfEo8vWLtMCNYZnjAAwAinC/oKme5JyfkvuYDk7iKS3la G4QtnG4A8Vajx5pYk7TyfrUklqsjbk5PYYI5pAehW7Ce0xuwQOBxn1rHltop13v0XOR0zg4qOyd4 I139eOPpWsWjYMzcjoR60rFXOTj5MZwSEbdjjBwen8q9e8JahLZQXFza6beamZNseyzA3KTzzuKj riuAjtVz5eBweOnQmvbvhfa+VFqcoH7sSRxkgKTlRuPUe9Fh2F/tvW+fK8I6wR6SywqOno0n9KhF 74pnfcnhR09pby2Uf+O5r1ISxl9qq27PCsqBfzGaVjNG376HCn/nm+APyWtCDzHd4xZePD+mwE8D zb1Dj6bY8/r/AIVJEvj/AGhYrLR41zjLzyv3/wB2vTLja8CzW5BZTnLO3Hbtj+VQxXMLZBeJmz93 ec/z/pSsO550bTx+5x5mkQDsY0uZMfyFJJonjaZhJPq1gjMOSunuenHVm5ru5kExZABDk/eGT/Wl cJZ4jKrISM7mQOcflmlYLnAf2B4oQlm8TRwEjkRafGD+G968M+JXh660rWmae4+2tdrFd/aGUIWJ /dsCq5HG31r6ukuJIiXh2A7eVaNASD6gjivFvizp0dxpVnqELBpYi9uwXGFDjzV9O6ntSaBu585w ZPOCpUAEHnpUoiVZ98eEyRnH0q8kO4uUGQ2MY45IoWICQh+MdPy9qBWOl0j5Vzjnoff3rvIQdu0n K8A/lXFWCgxBMYLEfpXY7tskKg4D47egqWarYkuVWOJsEMBjAHuabp/yXDkttwpwB/8AWqxJCTBJ K3RCBzjms6zO25cnqeB+dIZ6LZ2yNpVnK8duW+ZlEoQuPMfHyhvoKsbYYcLuhTA42AHp9BjNXPs0 D6Fp9y8sUKhpISZXCchgV2k1RJ0+DKf2nZqeWx569uxHWlYCUvCR/wAfTDA9G9PQAVSkuB0NxK2O RsUnP4ZH86aL7TR11G3J9N248/Sq0mpaaxIF2mfVELcfgKYEbPA+fMknP1Qf/FGoMWzsQWnAHban P55/nViS60wgZupT6AQuP6VXa501V3D7RJ2ysR4qbsBWWz/ijumxjGGjX9ADVzTb99Ou4b7T4ZIr mA7kZpSVx3Uqq4wazTdaSo+aK7wAOMqvb3xTG1HTlIaGwmYDtJKoz/3yaTQH09oWvWniLTlu7dRD MnyzxZP7tvQ5xx6Hp/KszxnOYPC2rPHkM1vsHUYLsF/ka8H0jxjLot7HqFhp4gZRiRDKzCSIn5gU 6H2r1Px5qtveeEba70yQS2uoXMSqV64UZZT7jjNclWLjFti6njIge3twFlDSqqjaxwQBx8ua6zwP 48uvB99OzW632lXwFvqdjIMx3MZ4b5T/AMtFXlD+HSuJmJeOUOcD7xZjuOeihVHvU1xcNLHBG0ap 5YUsygKVIPJHqa8rmOix33jrwhBo09h4g8PT/wBoeE9dP/EvuS2TbyEfNbTnjDoOAWA3Y9a5C7is 9MiJuJY5ccYlGOPZuMGux8AeL4fC8F3o+twnU/C+sbl1CzJ2lAeBPF285OCMfz6cj8Sfhzruj+JY V0x/7V8L6jbpd6VqEcirFJB0KOWIAmQ5ypx06U5K6ugizym5mSadpYVEascgAY4qtnjgYI6n1p91 BbQXJt4JPNC8F92ckdefr2qDe29UUZB75rmRsPbpgD5h05qWCGS4cW8S72kwMf4YpqqXxgjA9R/n NT2rvbTJOh+aNsjHBx35oQHqXh3w1YwyK2oT214GxiMNwh6bW56+1eq29lZ2Ufk2lqtvEWxhFAya 83k1bT9e0hX02FYL+0cFSwCY4wTlf5c1u+Eb28ns5oLuYymGUgO3GSQTznp+NdcX0REj0ix+W1Mj 8CWbGOeiDmoLvVUtoicAnHGfWo5pWjtLWEZy0TSZ6ff6fpWX9lkkkDs3yg4wec16UdrHHJXZx0lp r+rarLrE8yG0hQRR2pGcjrnOcdfavVbK4kuLJJ2XAH7vcM7dwHTnGKqJAqQKgH3jnirFpK1qz5Xz oXGJImPDY6YA7+9FijN1QsZoNh4Xk4I9OPWqLNL0twHZu7fdHHPSum1PTQtpBq9tIbqxb5XGMPAx /hf+h6VzyrHCPkXYrDPtXHVWppA8b+K2nagbCw1e4CSeTO1u5jBGxWGU5PYnNdj8PJf+JD4fkXBF wJEweOPOyM5rqdZ0u31/SLzRpwAl3FtHPKvjMbfg1cP4RL6X4c0Pzxh7Ka4icHqDFOQx/SvqMlr+ 7ynjZjSs7n1CsEmAWGAcEYA4Fcrq1kJPOIBy56D2GBxXb6HdQ6jaIOjx4+vTpS6nYY3SEAKepHHU dK+rl5Hi7M8EuYw2Gb5Snygj7wI6fhWpZsxOwHLbQATx3zV7W9PkhkIUAIx4Ixj6Vg+f5XPOR3Ha oNLXNy5to0tzPNtABP3u9cpdagbmSS3twsUe7IZcnOK6IWgvrbzJptwY/LznGB3rDubGJJ22two5 xx29K1gQYc9mIrZ7kKHcHAB45rm9QE1rYS3UzhS5HRtpXb1xiusa5VJGDjfCpBwe/FVrizttXntr S4Yqs25mjGNpRTnnuPwrnxlVU6TkzXDwcppI1fAtt9m8NWYnDKZpJJ8DjIJ4z9cV558U44l13R7s jY72kisQSvCyDAB9favX7Uy20SQLt2INqjk4UcAfhXh/xK1ZpPE/9nmUBbG2VHiwCGaQbsg9iOK+ Ly6bninI+hxEeWjynn+lzBXkXbjexAB4xWnbC6tpAzrnB3Ef3l9RXPiCa1kWXG5SScjuvXviu20m RLyKFTw2MJnuDztr7K58+2eo6dp9rqNit5pxAeMDcO/I54NdPplkEtS0YJZcYwcY/A153aGTSVW4 tmdAzBXA6pjuB3WvXtKuLHUABMotrr72VHyuMZyPStI6ibLZsY5oxO5IdcAlfvEY75qY2rQwsbaR X2r8qyJkHPPOKfDFPG77Msg47YI9qlZkEgRX4xnArVIzuQTi3kWNmjCOgGQOm7HSoLFEWfckh3Pw Bj1q7MbaRSS4UqOD/SmWVm29Wmj+6d6nI59siiwjWhgw6qpCy+nQ5rp9oAGeX4yfXAxWNYWourlb hV2NGdx6n/OK6A8vuHA9K+dz6p7qgetllPdkDYPtmsDX7j7Do+pXudn2ezmlz6bVPpXRtjJrz34m TPa+CdWdFLGYJbkcA4ldVIFfLS2Z7CWp8ow+H7g2Ec5AYlAQp456HnjvmseTEGftQkhxwAzNxn6i utigkuYjDZXJdcb/ACZVUnOPXGaksnOi3UVzqem7oOctG3mxDJ5zHgivEnG+p1KJx32dnx5YYg+v zfTpVT95AWjVuB1PIx7YNei65Z+GdQi/tHw68VlMBkxR5jDn/ZTPBrzueSVXVZUaN16iRSpOeOpr JxsBXdiSMykHv0/kK0bK4mO5YyCF9hxRb2BuWWGK4jhlPP71tisc8AEjAqVbabTzKs8Q3L1KMJF/ BhUtEku35tzyZ9cA/wAqY6jG5SWHsKt6ZC+oTeXFJBHno0soRR36n/GrM9r/AGHfQnUbaG/gI3kR yny2U8Y3rk0crFYyY5ZIyTGVIPHzAdvSpIYLi5LmFXfAyQoBxj1rqp4vBN3E1xbNf2E204QkSIGH bJxxXJrPcwvtidFPcZwSv4UmrDICNrAScMOhOMj2pxIVdx+QHjJ6Z/z2rZ0zStQ1lzFF5UQVl3SS yKiqp45zz+XNdx4t+G9z4HjSPWdQ0qeWVd26xvs3SbhwjWx2nHvg01B2uB5nbpFMJD9pQPGMoMZy fTimHU9WtreWxjOy3nwZYgqlHI9f/rGuhvfEMd7ZQ2Wq6dZzi0Ty4Zol+zzADj5jEPn/AOBAVzPm 4JWN1dMcB+cc9OKFoJoB5pBfK/NtzGw/hByRnr0roLDxBeWJVGJnhX5WjkO4lD1CsenTj8KwduMH YR/n3p0bAP8ANyD1xWkakkLlR6jYa3aSxXUWnqvkahze2V0RLHL/AHPTO3naww654OBisTVtB1ey tZLOxD3GnvH5zxNta4t1Ubicnlkx/EMnrnvXEs8cZDjIwc8cY9hXdeGtf1t7yy0y3MdxIZc2zXDh BC2MszSnomPU47d61tTlvuJoj8C6/f2I1LSLRLiaw1RUW4hhWR4Y1J5cBQdv+weM/XNfaemXdj8W tEaxlt7e7to3ZLW0hPlqk3kGPYXXBSRpFyS2MGvkmTStCuvth0O5LSzoyXNpDOYFl2ndGC8TgSop ORzzXoPwz0jxLqOsaXo+hS2elw6d8q6jBsXVDITumdm+XzowOkUquMdOa8HM8ncmq0XsOLuzyfxl o0ltqTXt75lpd3IeO5jd45BFe2+1LiFmQlcjhuDgg8d64+4SWAqtzHtJAAlhJBz/ALVez+IvCusN canD4umisrzxXd3Vzo7ykBbi+sJFjLxYz5cdyjbRuwucAHivC4Lm5tnkhkjBAO2SOXKsGU4YHPQg 9c10YeSkjSUWjr/7R1Ofw/Y6bDMtzaRXEk210Xd5g+Xh/vYxjjpXNzalCkE0Un7r904AOOCUPrWj 5tpP4eaKxs3SewuzczSKxO6KdAgyOoCMBXKXm57eYysW3xtknGDx09q2UNyWtT1LxjMi+JL9JVyz iB3Yj+9CjdRmuRe5iwFTYp9jkVpfEiS9sfGWppIWt98VtKoXvC1uhiPOOCg/z0rl76w1PR7lLTVr V7W4mghuow4+/DOoeORcZBDKex/WqjBcsWJrUvnU2iJRgCCO2M1bhvrSVQjOo4zskXofYisEykIF fZJnvjGKjyCcgAHGAATxTUQsdE1oWw0REa9eCNv4c1PG6QKGc/dPVQay4maAFPNDrwcE9eM96ZPf MAADx3HX+VTysDbOttGSi6g0QHRfN24/DHFJ/b8n/QUb/v8Af/WrlzeISTsHPvSfa0/uD86n6t5k 8p//19Bfhz444/0UQk/7Eg/9lrQtvhd4vmz9okhtyO7KD+RLD+QrmZNa1ebP2nXr4g9gv+H+FRfb 5Fzu1nUWPorFD/6F/Suy0zE7RPhTrO799qEae/7pf5OaX/hWLKzfaPEccOBj5WU4/wAa4d7hJATJ eajJ/vz/AONVmi01j/q7iVh/z0mY+/RaLSGd8/w80eJc3Hi9EIHUhgD9MKapP4S8FQf8fnix2Hfa HP8AhXGbdK/i00MfWSR2x+eKRpNPT/V2kAH1b/Efyp8j7iujqJtH+GMRAXWdQuT6pD8v0G56qyxf DJFLLa6ncsPXy0zjj1b8q50ahbRk4S3yOeVJ4HvVm6uLuC3hurm1NrA5zFIYGiVj/suwwxo5Rmzq Vv4N0+GORdC81ptuxV1G2mkUEfxpGHK/j/8AWrJGpae5Ai0URheAu4Nj/vmOs8a0ygiGXBJz8iqD +Py1PHrN6c+XJMx7bVA/9BFUhM021AbR5GiAMf4ikh/lt/lUyajrLj5NMjTH3SYmI/IkVnyahqk4 +WO6kI4wFkBH5YqWKz8R3OfL028kz1LLIP8A0Ki67i1LJ1XXFJQWNsp9RAM/zoN9rpX52ghC9vLi Xr9eaZHoXiTkDSpM99wAP/j1WYPCfiW4yE0pl93+UfgRS5kMaLzX1GE1JV9lMS4/Q1E9zrRyJdak APUCUkf+OgD8q1f+EA8YOgeWG1tR/wBNroL/ADqo/hLWLf8A4+dU0e1x3a+jwPzNHMgMuWDKjdrM r564804/WqrWOnSZEl/NOw7+W5/nWuNDi3YuPGOgxAdT9rjfH5Nmra6P4Qjybv4gaYZBgKLcF/z2 g/yo5kBzP2HSVOA85I6koo/maR7fTBgJ5mT0Jdf6V1C6L4Ef7njdrjHXybK6bH0KpipP7L+HYOLj xHrM7Dp5OnuufoXwKOYZzKRaUoGLdz/214/lTzJZJwtruz3MjH8sYrq4bX4XDq3iS8I7CCGPP/Av NqRz8MYWyug67kf37m2X88ymj2vkKxx8txbOvFukZ2nBIYsfxI5ryCWISli+fNAYZweB27V9A6jq XgVdK1BdO8K3SXjQuIbibUFYRtjrsUNmvm6adoLRXQB5HwowQMnv7VS1IkUYVlSQHG33NWJB6HcK rWoffLHMBuHOck9cVbf5eMDA+lO5JW3YxhR6YqRAw3bhgevpTCyts4wAeTVtfnhcbfu/pVANMgCh eMZ4zST5QAH1/SpI4d4Hm4wDnJ7VDdTwpKFjVppOx5wv8qgCDZk72wFzgA9WzXbfDvQ7/VNYvruz hEzWUSqSCQFMh4HGa40oUUzO25wCwHpxXpfgLw4brw/9veya4NzKzkYJ+6cKMZFVcEegvp+o26Yv UigUcBjICOPpzx9Kzrm6tIVH2nV7KNc9DLgAD1BxVj/hFLZMvJpEabR3jj7eucfyq7Z6TYQ3H+l6 ZuhiGWW3MMEhzwMSbDj8AanUqx4v8SNUtY7Kzi0q7GoSSLJkxsh8vcRgjYTjgd68FiLreRGZtzCA Fs8NhhX0J8Wr3StOubeG1t1gUQ5FuZRORyeXfamSTnt+FfPiu0uozMVCvJBHlRzj5c4H6UIkuwzb cFGAU9M/p0q4QWO5uT3x0rIhhzDubCheCo9RWjbzuFEedw6c0Aaca7tnGDnPbtVicFm2/wAvyp0U YVgSO39Kcy/vQ2OSMAetAEywtHwrYIXpV+1gMbxSEZAO4Ad6qxIQ77zu9RXT2FokyquMA42/z6UG q2PR9Ej1NLNbizW1jDknfceYWHb5VjH8zWnLD4nuCM6tZwDbgCOCV/8A0KRa1dOkitbGCDZaoYlw WfBJz+NXF1CNUMZFvIH4DqkZC/TDZqvZsVzmP7M8US8SeJXiUf3LFQOn+1MaefDt4zY/4Sy9mwOT HDBF+jI1dCmoRwYSGSNxzuURjfn8j/OnjV5cFo7jyyP4Su0j8QtPlRLZyzeFG3LnWdWkZv41ngX/ ANAhWnReDLFnEUtxqc5GTu+2yHHPcR4ro21d2kDfanjlAznchH54Bpi3kUjFg08Uz8NIrkhj2+6x oshXMv8A4QLSWjLi0uZx3MlxdsP1eo4/A2hFPMj0aCcDg/LI59P+WjGuiF1OzLGySzDoeJBjH1pq G6jZkigLqezAqR9Plp6Br2KMXgjRkAMehWasT8u+GDv65rQi8OW9upX7HYwP0AiFuP5f4VD5dzgy xxyMc4KsUPH0yP5UyScDAEaxSDqWaMA/kaQ0aMGnSRvhDBCB2SXb/Jf5VLLavHIfMu4lB7NMxPT2 FYz3loqgz3lqnofNUVVfxFpluAkmq2bjpuE6/LVEm+be3dWdriEBec4kl6fl/WkNpaTqGDhwMbmE bHHp1YVgR+JtGQqZNYSSM8narOOO3Ciq0fiTSnMm26d9zYUW9pM6/U7f8KfyA6yGDTyGi3cdx5YU /wDjzUsQ08boz5jL3BWH8OcGuVTV7UyjdBqUhHRks5lUD/gWKdc63Aq/8e2o7vaOJc/XewpWQHUK tnb4AikYt91gYtg/ACp1upbBC1s9xDG5XzDDcyW4b/e8sDP41w58RQFQrWFxMD/C8tqn54bj86sW XiG4tJJLiHR1KFAubp7a5VMHPyr82P0oHc6nzoDGXiCOduAPNbIPfnApUmjkh2rGjSIuMyN5mf8A voiuYk8QXk8nm22kQktliVmcL/3yq4FQDVr8rn7Fp8RJ4MzzHP5xY/WgLnWx3j4MLwwwkcZEY/kC aat0baTbKVw3QrHgj8wa5N9a1ULtaDTlA/6Zlh/T+VNTUde2CSGSzUL0EFuG/wDZ6BHWyXrh0Iuj LCvRdyJj17DP5UyW9geXzIZnkYfwhlJ+mARXHi411mLpeAluSI7QKT/49UhutaPEupywsOqtEsZ+ mXT+tAXOuN6k4J/fQzdtwkK/khP86UPczAxvDLuUZJXzx+m3+tcYW1GQ/vdRnQnvvg5+uVqzHFfL 968uXB43QzRtn/v3g8UDudBczS2dtJc3MLSQQhpCzluNgz/EtfIXiwh9VuygAWY+Zx0GTu7f4V9E 67aXMeiapKZbqaL7My5kaXjLBecnGMZr5b1fUonusE52sVPX+Hjvz2rOe4hkC+ahUjBIyMcdKsWr O8zbuAE+XqOnFV413L5qnKnP45qWSQxKpBAwuCPQUNAbkUqbQH69zzzWjGVIJBwB0H61i2yB0J/h YqPpW1Fbh1UkjYhwR6npSHEmtZQLkEkBShxnv6V7b4Vv7HTNIFvcyXCzPJvYQwyPjKjH3FNeEzAJ IW6EAAIOc+lfQOh2oGmQvezbXnRWEUeT5Q24H8Q6jmmi5M1pdbspznytQul74tCP1KCoDrCINi6d qbgfw7EQDPY7iP5UJDbk4MlxKg4OIWxx9JKdutLcnyJJ4iO0sRCY/wCBg/zq7GZX/tSU8x6RdRkd MSxKce+Jf6Usuragw2x6SwOODJdrn8Vy38qklu7QygxRMYwuC++EZJ9BtOPxNQPPDKp8mSNyo6Bo uPptwf0pAQteawVxLplsyk8eZc4x+URpjXermMxwQ6bCeGx5k8mD/wB+1q75lsqqEjZ2xkje/wDJ aVri32c2rx+4abH6CgDMFzr5ODPpysfSCV+v1daoa7bavqOi3thfXURg8ssEitBGNygkfMZW9TW6 l1AIyogilH9+TeSP+/gFVpLmJmy62xIBGAqqfbGW5pNAfLSoVMMiYRQuCMdD09vrVSFVkuG809Dn P/6q6TXLWOw1HVNPi2/uJVkQrzuST5gR+eMVgomzc5G0DkmpA21JjaIxNx6V1sBabyT12sGzXn9v dI7QnKkISPTr9a7bTZg8IUNypwPw5pWNVsdLOA1nOzcEspHasa33L5rN8zueg7Y/+tWm4L2knflc 0WcKHftJBYH5j9faiwzprGeG+0iWz2BmheOUCTBA+8pxuwPypHtrhBho4AzfMAuwHPp14qHR9Nur j7VbJF5skkW+NQCT+75KqB/FjkCmR2zXJYxRs2AG+UDgHoeR+PWkK5IZJAnmNNGB90AFgQf+A00r GE3G9hbBwQWYMPruUVWltZoVLSLsX1JUc9O1PWO7QI2cZ6YIP/oJpDCWK0yGa+hGem0E/wAqqLHZ 79ouGkbHVIjxj0zj+VWhaXU3WRY/rj+XWqklqYtymdMr329/TJWgmwxktS3S4Kd+Av6GmyLpqY/d St2++m7gemKb9ljzH5l2gDDkhS238qkhsomYKkzkE8lImwB09aAsCS6WqgpBcnPaSVFU+/yDd+Rq QX0txbPphjaKzEvnoFLttkxtzk+oFT/ZY7eYqDeSAfceOLqB7HmrARCAdl0pUZIMYXOfXctRUjdW YrmZCkcJO7epwNofB69qREaSUsi4IU5T+JsHGecVo3FpLMB5BA2c/vGTBHf7tYgsVgd3MjTKf4Cc 4PqGHQeleFWpOL0OmLLKTSElQuWU52EKx545wCMV33gbxjBJban8P/iTFHL4I1VOHhISXS7gcJd2 /dcdXGMd+ejcDp8to4JAe3CHAMfJ3A9x1x+FLKsMIe6mdZGnk2MsnyKU6/dYc7un09Kzi7FFX4i+ A5fh1q8ehXsf2g3yfabDVFfdDe22AQ8R6FgCN3936EE8CoY8Z55wAOnsa908E+L/AA74j0dvgz8U Jdvh+W436Hq4bMuj3LEquC3WBieecIDg8EY8x8YeDvEHw88QXPhPxPAIru22tFcR58m6gJws0RPV T6fwng1FSPYuLMCFoo5QJwfK77SN34U6cwsR5Bb6MR0/Cowyg42gkkjOR1BqzaQw3F/bw3F2lkhb a0jDdgZB4x9PSsyju/CWhCaG4k1GSS3V1GwJtzjqSysOK6qXWNP8PiPT7WFbzD4UxFQ3OB8237vX 0rz7xN4iurnUZ4LW+8zTkwIxGmOT1OfTNei+D/D9jMllcL++muDGZBIuNuRnNdNFakS2PUbiFXmI QALEqoAO2ByOf50qRc49TU7cySP/AH2J/Wnxr8wPYH9K9RHKPkUKqjsOKrSYXmrcv6VWf7nTkihs Blnqlzpt00tsqusi7ZYZOY5V67WH6ZFaGoaPbXdpLq/htXktEXfPa8NNbP3AH8Ufp14rmSSST+VX dOv73SLyLULCTyp4uQT0YdNjKOCp9DXHLc2ijPhkJUMSrcZyvtzmsrX4fLgtngjAB3NJgDksa9Ju dIsfEqy6p4djWK/j/eXendGChcmWEd1z2FcJ4jieLT7WWQOkpOx0YbWGfbjtXr5JdVtDhzL4Dp/A +ukYhkfAPIxxg9+te7+db39qrAKzqBkdOcV8m6GTDl05Izx344r2bw5rk8axlmUtwAD9K+2py6Hz VSJt6xowvFdZIxsI6Lng468V4xqWjvpUhkmBCPkBx27c17xJr9uLeaW4KRkITycc/SstpNI1qxEM 8kbqSOYypI71q4mUW0eO6eYziJmAWTcM9MEDjgevtWxqVlBAouJZAilcjIHGeau3Wk2elXRcXKXU QdtoOFdc9OK5TVDc3ExknUlF4VT6Y9q0jsWcTdx3F7dutuP3e3IBOMc/eJ4FbuijzL+QZAeCBUGG 65PJrMv7nbJHb2IwoXDN6knOKn8Mq7zXUxbzMqMbRjryOv8AhXlZs/3DO3AfxEdmojKiRW3hvk9T wc180eNo3l8Y65ZvjM7xMhPGDsGBmvpeGIGYLwgfGeor5j127/tnXNe1KB0WSOZxHCc5kjhAXcD+ Br5rI4t1T2ce/dMTRL9ZfP0nUtqTKmFL5weccfhW9Z20tlMbc5UqcxEe1ZF5p8eo6dHq1sAJYSuB 6kD29xXqXhvT4fF+mEIfLvokBUjH3142npwcV9ekfPs1LWYXMcbkDzAArr/ePau20dIYIf8ASBi1 dtoZfvIc9vauBso5ba4IdCJoxsdTwSOh/wDrV3+mmJYsqN2R0IyDntWi0JO4t5PK2LI2/aQY5V/i HYHFVZ5gL8T27DOfTrnrxUlvIIbQwygeQ3KhSP3ZI7GsoqByVOBnDD+daog2Ti7mLxgICMsCMgY+ lbCwiWIW+VBXlTyOgzgY9qxbAwx/uycOxyOuDW6qxg5cg857/l+VTKVgJNFt2F202502Dp1U/Wug Vfqe/PvUdnGgSRou5wMY6Yq2F/lnFfH51PmrJHu5erQuVHXANeSfFu5lh0Cytosj7RfIWK84WKMt /MCvXZB14wBXhHxdv9mo6Jp6ByTHPKQO7MQicfTP+cV4lb4WelBank6MYTFcCIzFful0O0Z552ZN PljvtTSRLeL7IrjadrfuiCeWwe/4VfjgupWVxGIV6kKcFh9Rmtg/ZYYmVo/3xG5SRuIx6kf4V5UV 0Oo4mw8D3c1wqRajHFcoAybV6ketduz+IoXGneIbfTr6xjj3NJkxvgd/nUj8sVfV9ZvYE+xeRDGP lIkB3Y9sD+tNuPC0M8W86jd2+5cOIpCYm+sb5XH0q1BIR5nrFp4RiE/2O5lEknzoqbto5ycYDD8z UXh3Srm+mW606zS7gVxvjuG8tWHruTJP5V3kGhQpEZbfTNK1aJRsaS3UQzcdmUELVv8A4S+1srQ6 eLYWssQxGk2EVcHoCoP86zlAVivqGnX16v8AplqbNBxlUinj2r06bZB0xnFYsuh6XIRDFHPPO/3x Y3I3KDyN0MqBvwDGi91nxNq8JtorSVUyA7RCRlIIz83UY/Cul0Pwvq9hGY7hIxFJj97az8r35ilj KH3+7TjTuFjgLnwi1pJu+2QKy8+XdI9qx77SeQT+Nb8WoeHLy1exn0G4t3RQrTWsfnoPfcuTXVar 4f1C6jIujbarCH/1cm63cAd90eU/MVkw+GxDKkmnajLpJXrGroPwG3G78V/xqlTsFizb/Djw5q3h WfWYfGOnW2pRSgDTL5fJuhF0yAvzkt/DtU8e9eb6pcahLLM2tvO7ggFpAwyw9M8H9K9gvU07TpbC 91TSf7euLcEub4C5syP4CYofLkyvX7xH8qxf7WjvbyWVIINOtZMME0qULEoUYO23mLNu/wBkE1Mo iPN9NU6csOvxxxsWdtsc4LIUXje3Hrxj/wCvVLVtSu9a1Fry/Ie7uCgxBGqL8qhVVUX2A6da9H1b w5/wkek6prdhq9rMvhuBHuYXhFlI6+aECrIAVkm3uAqfxcAV519pWw1Gz1XQpZoZrGSOaN7lV3CW NtyEbGxjfjPArL2ewGY0FxGxVwyMvVZFKle+CG6flTlXjJwfpXSeMPFWq+NtfuvE+txRRX96qCb7 OHEbMihd2JCTu+X1rmctkkdCehGCPypShYBrBG+UDPrmnqpU7TwQRwOhx/npT8ZPzdKQ5GD0FHLs AqKyESxHynT7rRjay49hxXeaH4uurSBH1hftNqs2I2i/d3UUqj/Wo4wRxwRkAiuBbzFxxx+tCy/3 cq3bOe/Bqr6WYmfUGm+OL3X7byjcx+I9Mt9PNlNptyIkm2gYhaJ2XKsrfMCCPmHuM+J+M9PluYl8 SXMaWupvMbTVoYx+7N0qApcouAQk64J44fOa734R3vgY/wBo+HPEGnzQya8sNmt/FOrPDIXytxFG UHllOA3zncMdKt6jout+E9bv9I1qCDxCvh6fbMbSQC4e0HzJM1u53NE4P3lDBcnngVhQp0VJqKsw uzw/SJpobuYrISlzbTwygYAkRhux9OBVLRbF9R1nT9NMTyG7uoY2RRnKeYpJx/dAzmvXJPB2jamq y+Ebi2cSrJOvmxsLhsc+Syj7rkZHQcj3GeQ0eLWfC3iSG9RZbW6shIyuV3bkkiI98KQc1pOk1qik ijr2onxPrM9x4nv7nEcbWkdxHGjlIIWKRqFBXKqOn3j/ACr1n4o6PHrPw88JeLNMnt9RXw9Z2+my 3dlnaY1wgUoQGRlYbmVgCN3pXgyGPcBICoLBmC4PGeevtjHvXsvgvxb4JskvtHuvDdydHvkRb0i9 luS6I4O9oQEw2VXOzJxxXiZhGcFGrDp0Ikzw3O0EOWxgkbl7DjPbP4U9YkG1hIF4yN3cHp+FfRfi jwJ4q8R6jb32p+KLOfwxej7ZprQF1VYmO0LBAfuOvRieKzJ7H4WeF4GZjb6hcoPus32iSTjsudq+ vTil/a0Gkoq7DnR4IpiJI3h2XJwpBx74FRurY4OO3IrsvEXiOTVg1nFY29jaDBWGKKMNweGZwufw 6VyWOSV6jkDjn8B/hXq0pNxuykyuE9hS7D6CtRYLMqC9xtY9Rsfj/wAdpfs9h/z8/wDjj/8AxNO4 7n//0NVPAejxA/bPiBo5x1FvukP/AHyoP6VZg8I+EvNCnxnJOg4P2ewuXP4KqYrIeHUtmbjWigHU C7c/otUpYvMwJ9YJX/rvMf8Ax0E118sjI7YeEPAoIEF/rdyfU2MifkGFSSeFPAtrH5l3beIpQO5F vCP/AB91rg2stLxh9S80e6sTUAs9FRjmSU+8UOe3v/j/AIUKjLuTodg0fwptlcy6HqNzIPu/aL+C Pb/3zIazxfeAI2Vrbwp5qj/n41VSv5JG5rHH9hxY2yTfigX9d1Ry3OlBT5aSSkYyWdBt7duafsX3 Hc6GPX9LCvJp3gHT5Ik+YyCe5mRcd2dbcYx6bhSXfxA1u5ZVOi6TKseQguPt06qo4G1ZJAB9f0rP XxCILX7JC92lrggwxXkixHPP3EZQefUVRGoWoZmitIXGScMzHA/AUcgalm48V+LZ1C28WhWJUZzb 6czPj0O+c/ypIvE/xAZdqa3HAuOPK062X8iS38qiXWIFOVsrZGHB27+QfxFV/wC1QsgdYoYj6hN3 /oRq1ELkp1/xi5KS+Lb+Ju4gjt4/yxDVaW/8U3XyS+LNen/2ftflj8o40/nT5NbuXUbmicdtsKDH PsaT+2LxkES3TKB/Cny/0P8AOjlj2Fcz3sLyf/j8v9Xuj0+e/usf+hikk8JwS/8AHxZXNyDyDJNd yZ495SKtNqN0pPmzSkcfekOR+BAqVdQ1OZCENxKo7Kp6fhT0C5kx+DNFDnZokMzf7cRbn6vmrieD 7CLDJolnG3r5MOf1UVYjj1FwDb2k3Jyflc/Wniz1eQH/AIl87c4+45/9lNVZBccugtbrujtYIiOA B5S/ov8AjVoaeVXP2y2ib+6ZD/IGp4fC/imZhJFpcyr2AT/9QqeLwf4ulJ8rS7hW9SP54NTeIame tlAytv1C329MAv8A0Bquy2UThPtAmAGN5Rgo/wB2tuTwP41xte02c5POCeP96p0+HniN1D3EsMRP UOyEr6Zznt/ntSbiFzIV9KwD57knjPkDsMdc0EacnzxtKwyCSI1H9TWyPh7qDMETVbPGcf65P5Kf 6U7/AIQGCN9k+v2iP0wm48j6A0vaoLHI6pc2n9n3rW6zF/JYAl14/Af4V4hOoL2W1CMR5fPdq+nb r4e281rLHHrPnM6MCIoJ23YGVUMY8DPvivnPWZPslnmUlGQ5wABjBxg1UZJkTZRiidnaTYA79z6D imz2zfKzrkYz+VJ/aisqSTfLgAkgYrYhu7KeEZcDK8DI9abEc1IQq5I4HqRU9vv2cY2uMDFaM1rp 0mN0+1V+nJqO0WzjdIg2/DHqO2aLgIYncL3GR9KdJGsSlWAAPp2rTvXSBUhijwGO7OPSsK6kJjZs bmLYwO9IBqFZFbczKMY6Z9q9i0u1ewsIhHA8W+NSXWM5YgY5OK8p062czwG4XJZ1yDjaEXnHOf0F e3zePfFEjbLCSGwiRQojhhtyeBjG4xA05FxZV2SS4ZYJznjK72J9uUxj3roLjwtcWlhDdQ6jZ394 5Tfptss8kqeYueX2CL5e+GrPg8ZeNLr93JqEiZ4Eiq7bePvbYgp/KqU+p+Obl2hbVNSnU4VSrXUO cHOVUsOOfSs7SKueK/E2xVNceC6QQmCFEKlQuD97oc15ElsianNICxkRUGfbYMV6X8QTLc6pP9qe R5EwrmRizEgAHcW5rgVTN3KY5AX8pCRj/ZA4PSrWhFixEkcyHYNrj7w6dOKZFCoJVeuasiPZbh8F T6GpIA0rBWGHbuOPagk0LUszHeRhRjP4Vp2+x5wq4Y44+tUzNHaRqk2Mnjj1NXrQmOF7gptPY9M9 uKCrGnbwJscv94nAFdF4bRnu/myIYhktgsev8IyB2rH0u2uLpDO4IVhgA8d/evQ9Es7YK7TK4jAw CoXkjucg0Iq50Yv/AA7HGFhj1Sd8fMSkKA/T99/SqA1bSw+2LTbtsdpZEX+Rf+VX2i0tyqiGdto5 2vGoz9MZ/SnhdIXANmJinaWfB/8AHSP5Vpyk3M59YjiI8jRIVz3e5z19vJFVZ73VJj+4gtLNMcMM yfy2VuldP4B0/I7ZklK/+O5qUPZxYZNLtgPUtKy/qKXKK5yqXGvupYajHgcfu7c8f99SMP0pHbVH XLa3MX9ECRnPt8ldat5EoJSysVLHGVUZ/wDH8U0XyxuGQWcbD0t48/mDT5UFzjTaXEgPn6vfM3o0 4Tr/AMApYtLhZT5t5duew+0yMv8A5DK13g1a5ONlxI2ehW3wv/fS5/lUL6rfuf8Aj5lQZwNhAOfo Yz/OjlQXOLTw3aSt89pLOD0Ie6kz9f3jVcTwfCMY0RXUjtDLn8fMUiujlv7vkTXMvH9+UIfx3ItV /tSSEBrsHngB8n81ZaLBczI/B6wHdDokMR7b7aBPyYov860YPDt/B8tvbLAH5J/0f+aSA1C19Zhi Dd8j5SPMc/hjzGNLG8ZO5UZ1P8Ufmf8Asyf1piNAaJrX3ftRQdgLw4/Le1H9jyBgt3dwKwHLeeT+ eYyKqfZoifmj356B0Ab8/L/rSS2/kjAtpQxxw4hHH5qaAJW0WxLkS3lgAO7EfzWNSf0qRNH0lQV+ 3Wu3uUjaT+Uin+VQq0qj5I2j/wB114+oMv8AKg9pTNDu95iD6cqNwoAuLY6YABFdMR6xRyY/75Mr VbgXTbaXekk28ZGUhQN/3y6sK51pbJgWnubNm7b2VW/SPnFRSanpsOxpL2Bo0IyE5IHfDcAUAdFJ JocjeZKJXdudsgt1J+mI48fnS+fpGP8Ajzm44O6dGz/3wQRWTd654fBj/s6W6tYguSJ2Ny7k9Cu0 gBenFZSatZPn57mQ558qFj19RlhQgOpNxpag+VYSoeuRNM4P/AQxqH7ZZkjdpcTsP7xmU/iGA/nW Et5FNylnqT4O0/6IFyMdiY/1qx586KCmk6i8YO0eZEU7dNwVf50XA121Cy6HTrEe0kaA/wDj+P51 Kuq3Sx7bW0t0Reflt4mXHsVl/kK58XV47EwaJdY9DdAfmGkpfOvWbA0jypGGAJLlDj65fNArHRpr eoY2xnZgZ/dRZ/8AQS1QPruskndePGPRTtP0w0X9awJP7WUgvpVki8YMmyQf+gtVnZrmAu3T7eM9 fLRXP/pPxQMj8QXl7f6DqFrc3TuJI+VYgE46Z6V8P66twsskg+UpKQwx0r7Q1291Ow002rXcc5v9 0ASKM8LgMzYMaYwPavme7sYZb64ur75LZ3Plp08wj+VZy7gc1oV48gEVwpKPwM8fTitpoC482QBQ OAOn+elWxFaSoBC0YI5AXGR6CknC+WsUxOWOQDkf0pAWEO0JED+7ABrT005lkRsgAggfhWPaTJI6 Daw52kkEVuwJtZiMDGeTxx70DTLFrbf2jrNraBFZ5ZFUbmwFU85H5V73vhJ3q0DM3QCXGAOB90P/ ACrxbwo5n1vSpLKP7TJfNLCq4O0pjJYkYb5cdq+hG0LRgGV7JEcMVyqTv7dGzTihs5yZ4TkSNA7e jbXH5m2P6VnLqVirbTKIz0wVYD8MIortF8Oadg406F5D/GkEKt/3y+KtxaIsA4gVlY4GIoFI/EEV oScK2qWWCVvEG3081T+BE4H6VC+orIA0M7yAjPKlsn0/1jntXpR0qaNxhXQn+F2AHTsVLU02BXEi sVxkczMy59MBKkDzhH+0R7vs12ZP7ixg/rsOalhilbP/ABL7mNvQbYznsceStd69jFKxfNurD+MS ZPA54xUK2du77fPgDAcBlJ/9BcfyoA4l475R8tpJnuJJAv8ALaabHBqhO42kSsOhedmwPwm/pXcf ZkRdskqJ6Dy3OPwDGontioIAaVMZJSPGPwYNQB8wfFGzu9G1Gy8QeXu87ckgjyy4VRxnLemetZem nT9RsgqtvDA4IxyPevWvi9NpEHhNItSykslyGtyyxryqkEZVAT16V8vaXeHw/qf2SaTNvJ/q5UOU BPIJPPymoKR6M2h2cSeaMeuCfTjtWvYMtpC3ygZDbQOmcCqN1H50cPmTKIsZyAdvPOePzFXYrWR1 QRSo4PH5cUFE0msGMeWZAocZP1qzZa3GJFXJKgZ6VzN3YzlpJJflKnHTt0qO1t7oESRqWH8LY6UB c9r8N+I40vIbzebdon3RtnBDp9zj2b26celbeoSQXV5Jf2fmIl2WmeOKQqiyH74ADLhc5IHpXjFn ESd0g+dD1PAz7V6houoS2mlJ5ckozIzAL83A49DUsSJxb7x/q/PA4IdpW6cd5mphs4tpK2SE98Rl 8/8AoQ/SrEuqlnaQyTgsT0GM/oKrf2mS5KGTJPZgv4HOKlsZC9hKQNtomD6QRj+cf9af9nv4kI27 AB2EaAfgqr/Oo2vBKdzZZj6tngdvlNME0pGIoWdSMHgkD0FFwuTMl6gGLrYWHaXBx9Q9VDuZikl6 xY84edj+vP8AKm+RckDbatzyPkPTpS+Xd/w2bEYxgDGfzpXGiubaMk+ZdQ7VBJBkLcfTy/61WC2P lyss0asuNoCN8y9eOF/nWqlnqU7LALPYCOS7KAPbPSnRaVqTyeWltllOMGYAY/76FDYzPhWwaRXa WQFv+ecJJ/PfRqZso0FxF50wT7/nRleOx9xVx9LvdxRvLjPoDu6e53UHRrqMlJbu1AcY6xj5T2+4 pFc9WnzRsNM5GXVrdpFaN/KYHPyK6g8e3FYOsXE1zcq1xKXVx8gOePfnNdPf6XpujPHKt1FchwcJ E24IR6jk1h32oJfxi3itsso/1nG7/gOK8KrGUdGbROdeEbdjKPmGCXwc/n/n8q+j/BWv6J8V/DkH wm+IV4LPVLTI8La9If3ltKwwLWRz95G4Xn7y+jKpr5xUuG8p+QOPmB/TikZymVRSccqc7cN6gjPT tx1qac7bl8pu+IdA13wdrl14a8S2J07U7JvnjJ+QoThZImI5jOOCPoeQaxZJP90bc9QOPbnFfSmh 6jY/H7w5beBfFFzFZePtEhZfD+rzEhb6MD/jzuPXIA/RhzkV85XNjeaTqs+k+ILGWzvNPlMV3buN siMOvtjpgg4PanOPUIs2vDGmjWL8Rz24ltoAXkGdv+7nHavfPC1tHDqywRqkcUMckhUMSAFAVP14 rx3w1dwJfPaafHIkDjc7ykZGBwOOa9r8I2spW7vbk5ciOFen8TbzwPYV0YVXZFV6HYqOMEYI6+xq VBg5FMUcAE4xxxx2qUV6tjmTGyNnrxmq8xAiZvTpUzdRnoKzrwkRNjjOAKia0GivjGM4H5CopnVR nv61FglQ0hzjp/KgAEc9O3SuO50IsWl5dWtzDeWczQzwHdG6npj+H6e1UPHurT6pPbXMsKwGZcyI nTcPl3D69avRiRRJJHHvCgAIOSSeK57xncotxDYtgTW8Q8zH94/Njj0BxX0uSYaSvV6HkZjWTfIi lpF3FDmNzg5zk/liu4sdX8qaNs7AM9APpXlcMqbiWTfkZ4xzW+kpSJOfz96+ghM8mUT3Sfw2msaS Znncl1ymxgMZ9cV5jefD2+iH+i37wnPOK7bwP4hVohYTsMqMLg4X8a9Cu4fNXcuBxwR2/KuqMeZH K5OJ8v3vgDxDEJbm31Z2lhQuA/Of51x+leJtZgZrPUPnV22lv4hjrivqu8jQRsmB91skd8djXgWs +HA155sZwCc4AwBkZo9jJbFc91qRLDJO32q3jKqPvMp2g9gc9j9av6K4hvNSQttIZMFzkkDgdhmt OIQaVprPIoYRoSMrnGT6d647RVk1S8unR0RS7SKxB5z2UfSvNziajRcWduXxvPQ7S7vGtbS5m3lm it5HHHcLXzppMDyaPdSO4xKvmYKDczMOSrDnj06V9BataJZ+HdTy+W+xyY3cHpmvGfC0MNzoywjf JE0SmS3kxtBP8UbDn6+9eRw8tz0MzZzGiTvZXpsJG8y2nJIz2yfSu80Sd/C2thyD9mm5yARhsda4 PXrG50u7jbB/d8rn0znFegx3kWsadC55MQ5Br6aKPG3PZdQ0aPWLKPWrIhpMAyZ9PbFUYoJUQKRg EfeHGP8Aex0qj4G1s2siaZPkwzDah6bTjJzmvVPs3LRKVYE8Mu3O305/rWsUTc5uIEWqK4G7AGB9 0+5pSQ2Ix+PfpW9c2ywOgKAA/KOnT1qukEZcsh24OOfQccVpsSOtrcjaCM4Gc9RWxJEuwbFIIHOf un6Uy0tpMGTdui5Hy9j71enxDbby2QBjB781lJjS1M+48Q+H/CtnBceI9Tg06OeQhWfcQew+4Dge 5rq45YJ7aG6tpY54LiISxSREMskZHyupGcjpXzZ8UtU8SRLFpmilruxlVWkskRW8uQNy5U/fyMYK 4Zea7D4UeL9Ej8P2/he6V9MnsXcKJm+VWlO4xFn5TDEhAeCMV8PmE+arc+kwsLU7Hr8kbc54PI/7 5r5++IAtb7xTIZTsks7eK2Qr23fvefzFfQro2VjI54zgHucV8oeLdVSTxZq0qpHMsV00bB8R8xqI wA3U9PSvMxD0OqBSWdYJm8grOoHKLnc30FW4XlnZkEXlMBlSeTjHSswTtNIjfYjaxxfKQZAUYD34 NdDZLG8exZEikbjCDJwfzrzkjpNGzikt1RLmRiknSOQKFyfQEZFNv9LvLVPtOgzeRIv34pBmJ/6L +FX47f7PbBbhXnjyMYy361oeZbyQ7olJI4I6EfhWlgOThuLK2lSW+iSxupOC9t918j0q5qmlWl+0 E8hRpFORMoIfb/dKnKn8hVz+z4ZC5Yq47Bv4eKxZZL20lWFCjrkMWct9MYxxS+QHSx215aLjTmtp E25zsKMeP7yfLxXMpbeJrm5N5b3v2V0yqpcr5kLc46LyPy9617S4nt5SzRlo5jkhOVX6flXTxtFc Q7+RyBjGK0SAwIri7n2RarbpDIg+9ayEox9SGGf0qzJBZmVLqaBXdBgFl3hR9OKddQvF+8jJUjpg dRT7SWTJckuPTGP0qgOaGtPFcyxadPaXeMbrbd9mnQH0V+Oa24NEbUbe01bWdPj0WylnEUUuqXMN rDcGNwZFR3YAnHHQVsaXp2m67qS2VxZxyPcMkXmtEGMZJ2A8g8DduzxjAryPSpb3xD4407wR421P UfFGl2moT2nkRTOwkdA0aOnB8uHeBvIH3KzaJOi+NU+oaPqzeG7O+gu/BELR3dimlIht0kKBXWdo h80qNuI83PXI7Y4PwR4Ou/H1xcWukXtnb3EGxktpI5pHnYnkKsWcBf4mIXCnPpX0RoCa34G1afwv oSJ4O0mVpJXs7fEclxdhRhpLucPkSAYzwK8F+I/irVPFHjCbVNR0aLwtfRxRxNZ2++Agrn945jK7 3Ix+8TIYY6jmpcUBL4/0rwdpGoS6DpXhzUPDviHTpfLvYJL0X1g67M5tzIBNt54OTivPcZ565981 tX3ifW9Ws4dP1a5a/itT+6e4UNMv/bX734ZxWSepPJz689fyrCTAj7dKeuMZOaac59qUAelTcByl cEDkinBRn58flSKufu/lS5OMdKQHoHw91P4faBqUmreMrLUNWPkTW8dhDFEbZllAy7uZVkEgx8pR c8da6bxTB4Nt/EWi3ml3WpP4PvRBd6XqgdpZdMEsmy6gklfL7FYfcIzyfmIrxnLKPl59PTPqfftX 0B8IPEUdzpGo+BtSW1h0spJJJMIpZJ5YrzMLxPCp2MqH95v4Ydga4MXTcX7VBDcsNo+q6zZ61rf9 mwR6fp11d/brvT5GW9R4yptr2GP5l37SGkSMhZl/h3KGrB13WZTNJpniaOG4tzBGYNQiTG+OX7si eX95CynIA45GBxXvnw/8MXfgbwxq1l4hvo3ghSCZNiiXyon/AHXltJwHTAHuv3a8X1Twdquj6JrW iatdsdJlulFrexx4t4N+JIbmEnpFJJmGdPuxs6sMZrmwGbWnKnP5F2TOIm8BJe6a2q+H7qGW3SVB JECJPLjY/JJgdFV/kfGeSh6VZtPAPiPQxDrGl3mmahFco0MluJnUywsMNE6yxrtJZcf7LLx3FR3n hbV/BthoOt6TqT/2vqMv2drWFQ37xk4VVGfNDZKYI557ZrX8N+NbK8uV0bxBasJiZlilgbfgyLiR ScZG7G5W7OMnG416dRU69N8j1Oeb3I4bXxN4Zdo7FBbxyZe3guo1niSQ8sI2Pypu45X2JHJrmPHN 1Brlzo91Doyadqt2skF5cW+IYb1wwWNngORDcrkiXDbHPzDvj1G61S70+MfZJhfWpX94RuZWlT+9 ESANy8gjqQR1GKZcT6OkU9xpsS2+qKI5pkDBlhMyfLPCkuQww/3lOenHevm1hauGq87jciDPn7WP D/iTw+sc+uaPeadBKcRzTwssT5PUPyp9+avyeH4bXwJZ+LZWWSXV9VlsYI1H+qjtot7yBu5dyqj6 elfTel+P9Wfw1oukavDa38MNmIdRtjsYSS7ypb5eFd0wxwByea88+J2j6Ppfw/0DSvDTs9vY65f3 BgOWmt47uEHDqOdquMA130czhUlyPcuNS7seBBYiMsSp7j/P/wCr0pdsH94/pV1WtpFDvMVY9QME fnS4tP8Anufyr0te5pY//9HRg+Hnit3AFhMAeuQAB+BrRPwt8UvkO0Nrs6CSdBxjvt6VVbxt4iuv 3kOmeW/bEBb9JM/yrPm8V+NmUxspRf7kVvbx/wDsv9K6vfMjUl+GuowZNxqWmpJ6NcIc/wAzTI/A AYhLjXdORf8AZMjflhQKxW8R+K5EaNpVhkHR3eBGGfdMfyrOaXxJMpEmtsmerm73Y/4Dk/zotKwH c/8ACBaVAMNrkcgPXFhOw+gxxVw6B4Xt7RrF5HklkAImFo46dgGYAV5oEEdtJDMbK9uZOl1cXFy8 ie6L9zNYf9jWwObjVUAbnALM35lQKShID1WTT/Da/JNNqDqOFISBMeudzZ+lZs1v4Ph5c3EgHUPd 20X/AI7kmuBXTtLRvl1GRvdY2P8AJsfpStZ6TuEklxczZHXCgfQjdTUXsB1txrnhK2wsekicdMtd sV/8hrUa+LPC0JymjWqHpnN1Nj80ArkktNNkIaOC54PPzIFP0yM1MItOiz/xL5UwcZaRefoNtaco HTp4y0bvYWSoeh+xuxP03MtPk8d2WDHZ2lsoPQR2cK4/Dzq5kSaeQSunZx3aSQflgBf0o+2QoPl0 21GOmS75/WnYDqIviPdQDZFG8J/2ILRM/i+8/r/SnyfErUiCrfavxuIVH/jkBrj3vt5/49LZF9PL 4/DJpRqsyYIFoo6f6hAfzbn9KhxA6ZviBqu3dB5o3dS12e3HRYlqFvHfiF+YVkOeuJrlx/3yHArn Tqt0xylwqn/pmVHA9sYp66tdggf2jOdw6bwo/QUcsewG6PE/jCQ5jidz2Hlzt/6FIR+lPk8ReLZA RcWSv2zJCV/xrlJ76eVh/pkhx1KvJn8cY/lVaSSNiWed3PbLyHt0weadl2A6oaj4scZQW0APTbFC P1K/zpwufFsTA/2jCjEd5Ilx/wB8YrlRCZBu+yysB2WNmz9cmp47OZMYsLgA9AsGP15qrAb0mveJ duLvxGIlJ2/u5MYH/AQaqNqmpE/8jDI4XoVmk/kV/rWf5F/GzK2lzFj3kC+lIRexKd1t5A9JHjGP 1zT0JuW3vZ5GR7jX53xyFPmdc9eCK4jUFtL++ubeUGQQybTuPXA6812UFrqE5Bjt45A2PmLjjntX kHiCe5XxFf3Vq2LcyfMoOfmUYI/SmZvU09U0CZoN8MZKrwABxj8KwbVXtYGhkQK4buO1eg+E/FNj qKfYLr91McbCa3dY0i0uxiZPKZm4ZQAD6U7AcBYWFtdR7tqtITwCQR+GKsSWUlozkxgGPqR/nFV3 0TXtMuZbi2j8yBOEKgfX/PFb+n6lbahDKl5bmOWL7wbjdUgUGV51j3nBxUM9rBbJyS8jDKj36Va1 HUbWKSKC3QGQHJI6ADjFVpp1gHmE7rib7hPRR3oAv6Cxi1CIKN08bFy+f9nG2vQ21G5HDzMG9Dg/ qAOv0rg/BtkJb6+mndHMYC7W5GWOeB06V6CtpbRFgtvmI9ljzkdurED8qCkiFb3Vpkk8pr10TlxE suxR77UwPzqo2pI7hXvHPQ8uST2HzcflW3DcagkJtNNlu7K2Gd6RBo42yM/PsAyp6Y5qjOLm2tGk Yh0RCY1QqGBUZ6YzxQVY8E8Z4/te4dm3RO5zjnPGP/11w6SJHqYUH5ja5X6jtXVa8GvD9ol3rI58 xnbnO457Vwl+yrf2rsQVwVbGAcZ6470CZL/aU9zNtkXygOef84rbhk3KpLcn0GKpNpNw4Dwvvjx3 4xmrkemzKiZJAA6HB/lQQXdLvbeTVD9sVXjVdu1/5ivQFutGkhijgKF0bO1u59q5HStHtJiWlX5z 0rRfR4ra4SZSGVDuOO3agdzvYpR8qgjOcBV9+ccV0qGW1iEUeMtySEcnnn+HFZWgWNsIbeeTHmTn PUEjLcCu6XTbsttt2YjkAAhcfXJppCOV+1TbiskjEZ4xHKf0Mgpxnuc+TCrue+YwMcZ7yn+VdkNI unxvKqV7iVVI+op76VM2FxCwHcykMPyFXcDiiLxJdrfaCcDlUhVenqQaQi7jzutLksTnf8ig/iEF do2ngL5Ul1BsPVZN4x+lRrZ2aAb72GPOQAAZAe3U/wCFFyrHG/8AE03eY9nIyKc7ZbogEfhS7b+U ZFknl+ovXkX8lau3js7GFhKtzICDjMKHb+Wf6VJ5WnFyVlndv9hQrCi4WODW2vpRzHZsByMFnYfX OSf8/SrYtb0jCpZDI4Hl7Wz7bkrtAum527biSTHG7Yp/ElaqmayKNHIklvjGC8g/HkD+lFgscj9g u1OLi58gjkIYxx+P/wBarsUepAAC9lC9vLVSP/HTXTR3tgi7Yo8r3P2gv+iv/SnRahZRMTHHHMMd Czkj/gJ/xNMVjmH07UJuWuLiTHPyYGPw2/1pP7IkdlE0lzKT/wBN/LYfVSv9a6J9S0/J2QQg9w8L Aj9P50NqtsVC28Rhc9SYsqR7YoA599CiYEzP6f6yQnj/AHhilTQrLAVEBJ4B8x5Afb5ZP6Vvpq04 4WGdMcBggwfwzVg6xdK37xCV+iRkcUAc0ugWIJQwqpTqE8w4/B2b+VaI8MQqgeS1Ty3GQ7W4XH4m PBqx/aPl7sxlg5+YSSIG/PB/nUb6yCAjvHs6eXJOrjj6H+lADP7JhtnSMQFtylhm2iHHs6gfzrR0 +zt0uomktZriFDueBJvKZhjoJA2V/KqMeqkEpD5KH+5Gztn065ps95Ih3E29uxwPuzDr0y3I/Si4 7GvewRXVyG0XSbjRoW/dmI3xmJPUuzNzjtxUlpolq95GuuTyGy3fvTBIks6rnhUDfLVO/s9RsIY7 q4vNPuCxWNUsh9qnUlAw3ABNowfWqCzX88YYyyMuOB9nRSPpl80mwsegtZfDeCZ4NMs/EEyjBWS4 vLeD/gOwxYrlZbS3Wdmh22tnG7GIlvNIBPQ7cKT+FY+NSK7VN2wHPHlAj9TTEt9TdlA+0xlh1kmV en0Q1mtBWNE7XkCC1WdB0mhTAPHcbx/Krcagx487eV48sRsNvPbdmuckglST97LznlvtLjH1OxR+ VVzDZoxaa/t4+eTLcylfw+Zf5CtLgdQI4jkI0gYDiORYx/NRTophaJKjxbfPAVX3ABSDnPFcnJc+ HolO/WdPQr2aRnz9P35H6CsqfVvDcY3W+s6WJM/NmNXyuOQOWouOxj/FjW205dMubaRS8aTFtrbw V6Hp0/OvnK41e01u9AuUaJI12RIQFUHox4Ney/EK60K/srUf2japCqkGZV8sjJ+6q8Z7Vxeh2nhS NBJp8MeoXPJEkzCUgn0QdKhsk4uXwxbXaZ01ZCR02FvpUMXgrVXGbrUXtfRFO9j/ADrtNS1y9hP2 e2tGBUc+a2EHHoBXMHUvEMgwtxHAGz/qkz+HPT8KQFN/B17Z7JV1abIOQG9aluLvUILA2TTeY8so Ekg4Pl45FJaxTySBr6eSQ78Hcf6V150mIPFA68Nlx7rjqPXFA0dL8Np7XT9e0zeRizgkKLu2sXkU hQCcAV9Ctr235lRUBOSZLyIL/wChV84+DNPEHiGGZI1e5i3yxiXIQlBsG7GeOK9ca68RNKJY49Oj IOcb5ip98KopplNHYPriXJDbYGZemy6c8e4VTUDaz9oxGRbls+k7Me3/ADy5rn5b/wAXXABe7sWK 8fLFcuw46ffHaqMj+I5x8+pW6lfWzJP/AJEc1dxJHUzXNyUKzwJ5fTm3nfJ/4Eq1Ct69muAFTjOE t0A/8elFYEUGuSYVtadT3xZQLn881Um8O3kspln1G5dyeqR2yn8wuaQWOshvbqQeZbzSp3ISKCM+ /G80LPf3m7H2n5O7SRKPwxGxrnG0O4jT5tV1R1H8P2kR/wAl/rUcmg2vl7p7y8IPRZb6Y5/742ig LHU+RfPhGaTB7SXL4/IQiqk0awqIpCpYn+GSVh+JAT+dcofDWinPmxM6gZJe6mf8wXpE0Dwwu5Vt bQ54woZs/mxoCxB4z8Pab4q8P3ulXF3aWkqKJrV2cjbMnTHmyn7w4PHSvjKeMwFtPvkwqZVSOdmO mPVcV9xQ+GNIYK1tplsx9RbBsbeTyfpXyX4q1jQtT1ia6sbZ1Du7EnaFXPGNox09MVArGfpGo6/p qIlpOl1bEcQyt8uM9FODXpekapaX0Zklsns51O35W3Lz6V4vpeqy6YzpKhe3V8kDG5SDjiuhj1CO 7yY9caAuckBQmPQc0Bc9lnWxceWt38x4IbtS29qIwVEyBU6c8EGvIYzpiN/xMPEFw4H9xwPzwK7C x8LadqkUd1b3008cjbVdZ2PbHP5elAXPULPSgFJg2zB85KsG25/2etdObmTTdNtbey0mDUXBZWM7 CPaf6/5+lfM9vPrHhbXNyXkojRuNzFgwB6Ma+ofDnibw94itPJ1GaC2lljIK5Ay2MB81LGmUP7e8 QMRs8PaLC6cB2lyfxGMVFJrHiibYZbTSUETZQAP8rY6/LgfpVmSK1T91vmlKHblFO35eOSDzUBWz 8zAW4WMjjGCeBz1pWGRHWfFwG6O6sICcAlbZm9vWo31PxYVIOtQwgjotqR+XzVPKdPXjy55A3Utt U9O/Bqp5lmfuWbAL3aTA49gKVh2M6SfXHYNJrspPTMdvH3/3s1W8vUDkPrd7/wBs1hT+QNbn2i1y TJZxum3kGRxg/gRUC3UMZyLWHJ/vZP4daQ0Y7WdxJxNrOpyKeoNwAD+CqKgbSbB+J7i+lHfzLtwD +S/1roDe5I22sKj128fzokvZUBEQjQnvHF7e9AznToehMP8AVl/X/SZGz9RUaaB4dTJ/s+Fie7eY Sf1rdbVdQOCCyjHBZFXp74pn9qaoMsspXPXkf4UMCguk2DxyW1vp6RrMuC8EXzj0+bJrHk0V9Lh3 30jwvjET7Su4Djv9Oa2m1DUJDhrubHpG2P1AFMnt2vIvKuHcyj5oy7F8MPZq4sXQUo3KgzhUi8+X G/kZwc1CRtJXk44q5I3lu6NGI5EPzKTy3bNVWT+OQEMc5A7Y+leE1bRnUmRJJJBLFPBO9vNC6yRy R8MjqcqwI6EH8+lfUcItP2kPD6pIYrP4seHrfdGxYRprtpGMujN2lQc8D5T7NXy0xUkhVz9eK0tF u77S9Us9Q0y6exvrOZZreePho3U8EEf/AFhj14rWnLoxOJ22jX+n6FdSWT2RhumcW9wLkFXilQ/M rqPuMP4h2+mK978LvLLo63MqCMz3MjbR/dXCrj8zUXijRrL406RceNfDkFvH8RtEtA2q6bBgR6tB GP8Aj4hU4/eL6dSPlPSrnhq3eHwxocco2zfZBLIpBXa0hLFSGGRt4GDXoYaFncwqs3Fzx6+9P6U1 O1Oau0wImIJrK1BtqqOOorTauQ8SSESW65wqjcQM9c4B/Cs6ztEuKLshSBS74Hfj0+lU47wTsdkZ KjjceOoplmQVD3Djb6Eg/XoaSfUlRvKtEV8emK4EzoNY6nJpCm4Ee5DtDkcmPByrfTtXj+pSajqW pXl7IHPnSMTgE557V61bmS4tyJxjfwwI4I9D7VyVleaZpetSQ3C7rK44jJGVWTHPJ7V9Rk9V1Iez 6I8XMKSi+Y4FLPVCx8tWUL2J/StmKXVREI5Ecn1AyP0r6A0/RdGu41lBQhhkYGOtdAPDdkBvjIAP HH5V9FHDt7Hk+2SPmm2/t2F/tFqHVlPUg4r2DwR4z1uW6/svWoGJcZSUYwMdq39Q061s5YQnAbhv T6gVlFEVt6RlXQnBHHA5yK2p0XF3InJSO0YIXJcbVYkEemRzXD+IJbWwhYXjeWsXzgY5x7ev0qjr 3xA0vSoZIWYSXcx4hRQxHpu9K4K21OLxPPOL5h5zRARRn7i7T1HvXR7axkqZmtrl3rFy9uAYINpK 55+Uf3sVu6ddR6ZbAkhymQibQFAPcHisNLOawdXiKx7wzE/eBGcYrUiS3CyGcq5OeOoAPPbpXwOe VpOtyn0mAppQuat9qUeo6LqFukX72azlRAvzZJHAz06184eD/EIsbuzt5vkZM28ynKspTjDDtXuN rCd0kyFUA6IF4YjoT+HTivAviCqSeJLvWbaJYo5fK3LGNpMkYxuPuR1rfIKrU2kLM4LlPoHWPD9p 4i0sXFod8qpkPxzXl1iLnSZhC8fAbBHv0xWx8LfG0bo2gX8mwvt8snv2716d4l8JC4jF9Cod4sGV OhCno3HWvtbKpHmifPpuLszjEu/KmjeJsDqjf882Bzz/APqr0zR/Ff27Z5oCyqwDgdu3SvOfsawM VnQbOhPuDgYq3ZSW9nKzxj5+x6Z9qwT5Smj3CaeSRMucqBkM3b0q7ZvblE811Vs5DZ4wf8+lefWO ow3qFEkYEABkz7du1dHob/aLmGBznkDLAdM9hWrmKx2bzmJ1gtwpLnnGap3cLbtsuQNwwBz78V3E GhRcuq7QehPPesbWbcLJDkbSoIwPcVxYmpywcmXQi5SSRyNlocMrXNywBHzHd0b1XntWZceFdN1o NJMDbXyEhLqJVLgE/ddTgOv15Hau/tIRFasG53DgfpWZaxneewz09MV8RKfM+Y+nhHlVjmNM1fVv C8yaV4h2zQJGximUkJ8m94wjH8BtJz+FeSa+LS1ElxrFmx86dgJI1yW3uSCc8e3WvobWfs50TUTc xrMkdtMwEgBAYJwfbHrXzzBq2p6dZQ2uuLDq2m4VZI8EyIgIPyH2OTXLXN4dzDFvahx5fmOu7eu7 kcdMj9K27OLyhJcPHht208c/SroWK8sI7/TSLyzbcJTGB5kO1VB3L161nQSp9qGJmdFO4lgRn25x 9MVx8pqjZh1AODG6yxBDwDjpVpo5oozc2rCTJwFbqfxOBVeSW3Vd8ilxu48vrn0NEc6xMVJbkZw3 HPoMVUWBIlwAfOQHzo/kMbcJzyT7/hUMyiSfy5j83ykCPlce9XAltIvmKsiPjB6ECqqaa7TvLbwM jtgeZye3HSmBuQeTHGUQkMvQ46c9KtxwqjeaDneMYyD/ACqhEr267G3MVIaQFcjIHqKtGdmRWyqs xyMDqPwqwFkG8EbsgcADtWtofhLX/EBnfSIEuDaAFw8ip94ZGMkelZR2tyOSPTimhthy0mw9gGIP 44IoFca0yCKS1vbSO9tpQySwSZAcMNpBZCCPYg5rV+HXh2y0++vtM8HJe6Tc6iYmS8eQXLWAhG3A lKiVI8HG0Hnv2NU9NurK3vY7i/06LVIAeYJZGjXpjhl5zj7vvWjY6bqX2K58ZaRZyf2VaXLQyRea ZJY4SM4lC43R543f4UrCZ7T4P8IeIfC1xNb67faPrOl35EjRzGWaYbBktF524EnJ6Y9818k/FH4t v4+iuND/ALD0j7BZXTLp+pQ27Ld+RG5UKrHbtBAyRj8AK0Na1bxF8PZNP8UeAtTkTQNXuZV+xXAM 8Ftf2+2WVFV/nCsGDghhjlcdK0Gn+FHxb069urmJfBHjxYZZTHaBmsNRkX5iyxKDwwwHwcg1LQHz nuRmYAf59KkI7AVArtMiTO28lQMnH8Ixjj0xipAxOB3rlktQA8fhQvpRg/WlA/OpAMAdePpSkgdO nvSE/nS4zj0pAJnuDUiMV+bcQw4DIxUgHqMjGPbFMwDkY445o25woND1VmFz2z4Y+L9FsJ5rXxPr E5kvIxZIt+WexSOR12MzKeGjIBw4Ge2a9H0jxjfRW134F8SWUGsSaM91LcwBx9m1LTT8ivasCVEi oTld3zcZxXyaGYKUzlR24+nII5r0/QvidqGgaFb+HLPQdKktIvly8TEyF8AkjryTk8ivKxmXRleS RLPUPEcsOg2tzqPhmJr6CKGS5sd/F1b2jqBKyq2NssSsFc9VU7h3NeGr4kjhgVdM0+G0ZQGEiYba D02k/wBa9T1TWBo2racl39l/sH7Qk0F7bwiNrO8VdksVwQSCjqQD2KkZrgPHnhe00fUX1jQCzaBd zFE3fetLgDe9tNx3U+ZE3R48Fc81hgMM6fuyHGnpqetfDxdM8T/D3WtS1O6S11PwzebnvYgEK20g EkZbPB4yB33DgevnMfjWw12CP7cPsOpwfu4LrA8meNT+7SVcZXbyMjjy2KdhXmhkmtYJrXzGWK5C LNGrEI/lnKb1HXbk1T3Bxk4ZWyOccDtn3r2aScU1LYxUD6Q1HTJL3T/7Q0eJbm2V4281ceYsbDB8 1l++YnKqzddpVuma56K4lurtfOGLmFmhYv1JU4CPj+JcFSK8z0TxPrGjXKyWN3schV8lhmNsfKN3 XHynHAPU9elezvFaXGk6R4g17Zb2+sCKRH3hlZl4ZG7gkLsLY44P3hz52LyanVfPT0ZEqR4J4s02 +0bxFf6dZyI8EbhoztHCyKJAOPTdiud3at/eT/vivue0j1eG2hj0nRdL1WxCAwXRkijaRCMjcjnI YfdYdNwOOMVYz4o/6FXSv+/9v/jUrC1UrCuz/9LkFWaQZSOds9SFkb+WauR6LqM6BodOv5c/884Z D/6FivQW8X6pFt+x6PYW6kdWjklznp2Aqk/ijxCVZ552uGHO0n5VwM/IoIxXXzS7GNjiJ9KurfZ9 qsJo93qoLcdtoz+VbkHhDxReac2o29h5EA4RG+W4f02x4r0O5vfGtjo/9sf2hoMCiJZVjNxE16VY 4GIlDN09T/8AW5K58TeLboBG1mYA9BbyeVj/AIDg/wA/yqryCxz83hTxZEitLozQAjgzFVH5sKq/ 8I7rW1vNNhaKBks0ycD27VbeyvL2Qm7mkuHByTLI56/7wI/Wmf2LZrIuIFLNwf3Lsx/EcfpTtIVi gdNYIFbULbI9ZlA/8doGnwKQf7Yskbvyzn9Aa2v7EbOIrGQBe4gO78cH+gqyuhXvyxx2syl+gYKR /wCPc00hnONbWb5V9S3+uIGP6YFH2bS0UKL24bHI22ozn8zXW/2BqG3BsmRF4Ks0SqT9G5pG0W/t 9oSOKEFh0mjQAfTI/SmM5hLXSh8yjUiD1JEac/g1H2XSpOUsry4PcySxjp9Oa6l9MYHE19arnqWl Y49MBRTW02DnztQieMDtHI3GPULQByS2+nD92NFKk9PNlk/oKfHBboCx0y3Xtl5JMD88V0rW2mRA EaiAvVcwHBHT+Kp2tNJxh7qRs9BCkZU/8BY5/SlYDkmW1LASQxKw6LCuc59Sxq6vkQKGhsQ7Hqxi QkflmtwJpkRG2S6Kf3flX+QocacHDLHcOTjBeYJ/NafKTcrLeK2D/Z63GB/E2zH4Kv8ASq88rOd4 tUt3PIcMwPp/HtxVyWW0cFYrcsw4O6U5/DaQP0po24UJbKsmPlM3mSDj1IajlFzFIC9dfkaWNm/i L4U49PmquLJslpriQepM3y/nz/Ott2Wb5mgticDP7rPIGOM5Pb1pJLl9gTybSNVOSpjQKPQkGnYd zBFinWSZgO2Z2ZSPoOKlEFlEwMYidvUfNj6A1pPqaJnN9Zxk94zEn+fyqM6/tUG41tAg7JJj/wBB FFh6lz+1dTtLVryW8ZbW1UnhfLxxgBQMCvnw20d7dTTQqzG4lMhy2clz8wz+te0atd2N/ZrFcXLX EN3IsaFixDP1HLAZrO8jR7VPKtAkJAJBxgD3GR+OKdjPQ4qHwnY3+JLQhGtztQrw2RW1Jfz6KDaX jC8AXOD1Ws6a8OjXPl2LeZvOd4/2hn/PFRaPq2nXFzez6t80xACAjPSi4i7H43htI9qadJkHcM8/ nXPTvrGvXTztbi1jbHOAM55HSvWLeLT5YPM8xEwvACgnrXP39/Z2j4d9xTnpj+VSBxZtLbTZWkuW DtjrjOOOKwY2m1C8Zj8wdxGuONqk1evJn1WaQD92hblj7VLCHsRFNDtEaSDedpOecdv8KAPTvA1l bW2kyzO08ZnuGx5WOQnyjr9K64nSlbzIvMlkU4wZAAPqMVjw6dqFpb4W70+3twTIFL25cBzu+bc2 R16fhTXuoFjPmeII1nB4kE9uEVe4EUYb8c96DRI3TNZs2ySGWUDB2mcoPXgcZ/AU7z7fKqmlqecA O53E++D0x7VyT61YqUabxSzRdxFGGJz0xiM4qCbXtDWHH9rapcjkqIllYdfQIo/SgR5p4umt9H8R 3dqbForJ9siLyUXKhmAzzjOa4TWtGgv5bE2ZVZXjeQDA4BYt29q77x95FylrdWJlJZdx+05DnHGC D2rxqLX7y01OGW5i2Rsnk5HGGUdM/wAqB2H2l9Ppkptpycc8ZzxW3Z6n9pY7Tn0H+FRv9k1VclAH c5I6VgzaRdWk6vYyc7vungH6GgzO7ii1T76wPj0XirER1JUKSWc23OQSM1S0bxLPH5cOo2zKqfKC VJz/AMCr1DSmj1AhLOQFepGf0FAHLab4iudOuLeN7aQhZEOCp6k5x+Ve76prNrdi2e2EyJPEJCIo 2PX6GuJZo1u44WC+YSDt2jjHfmrN9LrVrMLfS7+OKAIpCG2STZjqCTjqfencqxsPLPJtK2d05/vC Ej/0M/h1oaa5PBtp2PoRAD+PNctcHxZcDa2tPCg5PkW8cJ5PqVb+dZj6fqbD/SNd1Rl/2ZkA/MKP 5U9R2R3u7UsBY7dgrdVkeJP1ANAXUdm2Mqobqq3LL04/hTmuGj8NrOP32oahcRnub1uPwVh/Km/8 IjpAbEkUk2evm3Uj5/M/1o1HodybWferTy2sWBx+/fr7kgVWeTT4G2XWoWUee4mzj65df5Vy8XhL wwCR/ZFvKeMs28kcdwwP86vxeHNIiO2LSLVHUZ/1ABx7FhTDQ0W1jw1HlLjXtPAHYBW/L5yapt4k 8Jx/c12KY+kcBb+Wa0IdCWMoIrFV3DIxbqMD6j/CpTp17PJ5FtFNleSFUHA/SlqIyT4s0GLlL26c f9MrOQ5/HZT28VaVJjyrfWLgdgLZlH4blFdFp/hzW9Tu4tP0zT7i5uXP3FwuF7ltw4Hvn/Cuh1f4 YeNdDgN7eaTmAfM7wXCkRgf38/0p8y7j0PNZNfSQhrfSdbkA6IGSID6guKauu3oyqeGbiQnr9pvI 1x+AfNbzaf8AJnEKj0NyBQtlG5jXdCgJ6l96/wDfQqhGKdW1d1/d+GLNB/01vv54Vqa+oa24C/2J o0RBGN8s0uB7gRgfyroTYQoQPOgK7uTGjy8ewAqJrKEk+UxZf9q3ZD/LH60E3OfGqeISp8r+xrfH HyQSt/h/KnRan4w6JqVhAP8Ap3s8n8d0groRbBcbbib6CJVH5nip2togP3E9yzDqN8KL/OgLnOi7 8Uy/KdcKDuY7NR/6EzClNv4jkwDr18Rzjy4IFwOv/PM10EfnKjr8vm8bd0qkdO4XNSNGuwgxtOzL j5pGI3E4yAqenvWbRRmT+FfF1tYLqF5fautm+NrCeDnPcJGAwz9Kx30S5YDzb3Vj/v3bL36cYFbg 02RZWmFvCAoCkiORs898qP59K0Ps0fnLK8UcSL/DHHnPv87UXFc45vDdu3MktzIp/wCet/L/AENR /wDCNaAvMsMRJ/vzytk/UtzXbiQIx/eRHtkxwhvYHDmpPtEbhQ0yKUPVfKDfyJp2Fc5Oz8MeG7qY qRp1tIqZDOjy7iONuOTSjRdJtz+8s7XGcZW3Az9ODXUG6m3/ACyO3YHAOfptizStCZgGZ5pGH8Lb 2/QIMUwuYQsNODBRbJHjoFt1HH4LVi7tTY2VxfJbOwt4ZJcLEOdi7gOgrX2TKo3GSIA9CXGB9N6i nLLE8scbOrrkZRnjGdwxzmQ9MUBqfBOqz6h4sv2vb8tLc3LgIpGQmegx7Ctuy8Mx2lyn2R3iuIiP 3sfHzY744613F1p9np2q3hhRI2u7mWKzEYwqJuOSce3FS6hBHaaaLeJiqOMZHU47HFQIwbi/vLV2 N9qaXAxg7gCc+hC1zr+ILFc7vmPYIrDn2yBWkNLsE3SJCMjuSWP68VajtrWRtsiDp6CgCtZaxpd3 GYxKykdVcY5r0XQ7u1njS3uyCkeVWTOGXdzx6iuCbSbRwUjUbhyOMY/GmW1ubWXcykRIdwGc9vyo GmewaZpVzp2v200k24M58qRfulGIOR9MYx+eK9bkjn8qNRJIgHVw8YDKOMjmvDPCvijDrHMS4tpF khYjJjy2JFUdPmGK95ku7iNiNqowGSd8rnae52xDt6GmixYliSMgC4eRzwwl2fyFSiKNW33EM4Tu hlJ3fpSOl2iCWfyyrgMChndse+4r/Ks+S58jk3MRQ93TBGf9+WqJ1LklpHjNvbeWrd2aR8fiSP5V TjsYLcEuschbqWU8fmazZNY0uIZn1Szhx/e+zA/jmQmsuXxZoEcm1ddtM+sc9tx+ChqegtTsopoY gQkVuV/2lQ4/OopJNziVJPL29AqIV/ACuSHjHw6JPl1d7jI5MDsWz7COEfzqObxTpEuPsp1a4z1/ c3smfptT8KhspI677RcIzLFL5JcYIAwGGeRjFIXvCxOduDwANpx/3zXHSa40hVrLS9Vwo+YPZztn /vtlqX7XdXGPJ8KaizkfeaBRnv0kmP8AKi47HVNPOhaQyuZADgBm6dfUZ6egr5H+IPhFNI8S3t0p C2F0wuo84yDJy68E9DX0Vv1vaAnh94VHzHdJZJn24Y4rwr4ix6lc+IXN/B9lEcSFYtyvGqkDkMmF PvjvSJkeQRpFMx+XBKEkn1XrV+DQWlwZ1AzjA29vxrorDSraJWvLsMqw5fYRgEP8oIPfPNRajeXC rI0WQW6Y6Lg9KokqDTdItflmjWQg4IAB/Oun0XVo9NkJhjEUJYARqMDOOGrn9N0dNUgDC6C5J4GN wOa2F8GnG1dTnQrxgAbaLAenz2GmeJbUXljtadfvw8Hdjglawl0AadcQyywgBpFUAjB5x0rg7aHx R4ZuPtFvOJ4UOC4GGHOelewaB4ltvE0EFtqeLe8hfMUoIXdxnY1TJDR6C2kzXA3p8iHphtv6UwaL PFzJHgdnyxU/Q1Wn0VkZ0uluWYEEobqbHPIxtIGPSqo0nSiC09ioI4zJLIx9vvS1Bqa66OhXzcoc nblmwB+f5VnyWdjE7CW7iQKcYDKVPvyartpOkjhLC1DHByyKcj0yXJpP7Ptl4htrMHr+7jj4+vym gViYQaIEDPq1qkn91jHgD3AOaa7eHkxnWLXb3xyT9NuaatnJkBAvsEjH8glWFtrlT8pkQ+yEf+yg VIyi9x4aSUrDqIdCPlwrE5x6AUwXOkgsBLO444S2kPb12itcWmoPna1yx7YBP8iBTX0u9c4cyt7b lX+bZ/WgDNiudOGP9AvJ9pzte3cKfrkiqUksIZimm3J/i2hVTAJ6ZMma2n0e4XG/bg/3pQx/HGaa dK2bVdrVCxC/PJ+PoaB2MIX3loyroxcHgebPEp6e5NU/tFzg7dJgGf4pblT/AOgqa65NIjR9hurR D7SEgAdgOKq3FnYohke8hdj/AMslyXHzc9W9OagdjiNTt7jUY4i7W8LRjA2GSTKnnHCAdfpXJAyR yPFJCVcHDA9R3r1V7TTncFDuXPIaIZxn61manpWnTxyvC88d0iExFI8RsAc7XHJrzcXhub3kaU2e byQv1ix6/nSRwyscEYJ6EnFTHOCHBH96PgYP0p4jDDdyMdFPbivK5Wtzc7bwrrdx4V1Sx1jw9cyW ut28ymCWI53cqNjf7BzjFfWGpSyT6jd3EzAyTTMz7RtGScnAHbNfIvgfTv7Q8Z6JbsAYxcrI4HdI vnYfkK+qVcvlucnnmvTwN+XU5qpYXvSE89ePaheh5wcZ59P8KYvzKHXDKwyCuCCPbFdxhZiPjBx2 rz/XWMupOqcuiiMA/TJNd8zchenr/OvP73zJLm6mk2oJWKoc9unasMRLSxpBFW0iiO5Zf3J9T0/w q3O1vDav9mxKo4JAKnP41jSyzxq0Mg3KmOByfr+dbVlqFsYCk+ZGTGFC5x9cVxXNynaarc7w8zuk UW0Ff4SO/JrRi8LzeILC5vLgrBBjbGWYIhGO2arfaF1No7G3AP22QW4BGDzwSOnStrX9PstdmHhW OZ47PSIUUKrFfMI4YnpX1mQJeybPEzR++kedwa74m+H9ybbVUk1DRs5S4i+YooOOvfHtXvNh4itt T0+PUNNuVvLeWMMSv8J6YI9fw/CvBJvCPi7R90eiXf26zd8taXB3Lz3GelUtGl1nw7fTmOxksUuA fOtwPkLf3lx0r3Y1JRZ5kqalsfQz6rFPNGsxId/lRVGcnbnB7jpWRqFneX9jMZr1dHtSMCQ/LtyO zNXmdpqGuPfx3kCmKW1G+Nm9jjp+ddO2iXWsxfatevnuJNnyx9ETI67e9dPtWzL2aR5zDp2n2d5J DcLvlVlO5Du8xWXcHBPXI5/SuksUs/tySW6BNuQQvO5c4z65/CqXi3So7J7BkJRktwpZeOVwBt/M 1z1pcSafDIzyE+WpXcv3uPrRe2rHvojvb8S7It+5wJWTgDhcZ/nU1vZ7bbbKFdWwRwBn/ZrF0a71 a4E19dwZVislsG4LKRgnB5rcWUt987UPJGCMfSvgM0nGWIdtj6bBxap2ZBN8hZ9giiiUvtjBPbGD Xi2p6ZcajNNO6bYwc4xzhunH/wBavX764tE0+7KyeWvCvkbsE8AcVyE89syL5m3eqbSU44z0Ir2+ G6N4OTPPzSTukeKNpFxDKGh+9ESVZeD1xX1R8K/GUutQR+HtbGy/twVjmbGJowPuH3rziGTRw3mY VivC89K34k0uMxX9lOsV5bkSRMCOG+lfUQShseNJ8x7X4h8B/brTz7Q5JTGBxjn8K8jvPDmoaefK njZlz1x144r6J8G+JrPxNp/lhtt9Adskefve4H5V1N74dt7iFJnG8DIyex9KqcFLYUZNbnyDGZ7S YZO1l9P06V6B4f1GSS6hkb+Hv06V1mr+AxOsvk/uZOSA2MHv2rykC70G++zzjYchBnpyeTXI4uLN U7n1dZ3gRIZZJCQwzxk1kXEn2u7Mx6E8c9hxVPSH+0aK9wXX/RVQ5B+UqTjNXYI90pYD5c5X3HrX iZxVdlFbHo5dTV3IumNQpA6gViopVjn17V0W3qehIrFdWWRvQmvnZHsHNeLiw8NXyKcNcCOEdvvN zjOO1eO7TEVhubdkbkZUDgHnFd78Ubk2+i6ZCc7Z9QBYKcErHGWOPxxXn+nTxXy5SRguMbnPOa5q zTlY1prQrR6Ld2ly2qaBdiwmxgphvJmHdXX1NSW2oQavLNbXduulaxwfJkGIptxz8h9fpWj9nuLj Fs1wGRRyNwUisnUtKhuIRDf5faDsctlx9CORzWOhpHoLdWpTzLW6hMMyMVAAy3HXp169qqqLiExJ E+5EO1gy4289s96jTXrnTbUWXiUyX9js/d3iA+ZDgAKHA7e9a32aP7CdSs7mK5sJxnzkB45xg+lQ 1YZKmovbgKxQxk4J4zipLqS5dRd2EpKxYJXJw2DWZbJGZNq+UQ65Bl9h19Kr/ZLqB/lkzG3Xyz8h 74FFwNx9XubhA1oizNu+ePO0AH3PWt+3EzRr8gzjgAZxj6Vz1jdfaJMJbKhjARkjfBxjqa7LT9Ku 9V03Vb62mt7YaVCJXiuJQjSc7QI84zjr0q0wKbtEoAJBI447UhbjJA4GenOBWPb3sbEiUbJvvE4I DDHUcCtazivdQMqWlrPcmNd5ECFiE6FiB2pkjE3vnBwCMDA9eldD4c1iDwtfJ4m1jWv7B0a2aNLp 3y32gsCy2yxKDuMgBzkcDPSsK10t9S1O20q9nTS/tDbVlvFZUXaMj0HPI613/i74TxeI/Dnhvw1o niO00/UnvJ5VSRC32u5MIVSzwljEqLlVLDHzUDscb8aPB/iTxbpVv8Q/DGtN4p8MR75UsLaFAtjF ImGkhSP72Av7zI3e1eH/AAn8Uat4Z+IWhaloktusl/cLYzG5VWgNvclVl+Zx8uMAgjHoa7n4W+Nv +FZ+KNQ8FeKbO5vLVr7yVtbKTz0t7yA48yJU5ZOMHbwR94d6g+M/gLxPp+uar4yuPDcWm6DfyxMX swI4o3bg+Yv8DO/XA61L0ESftEeDtO8I/EiU6bFHb2euwLqCwR8LHIz7JMDpz8r14bH1U9R78Zr1 m58Y+JPH3gePw3rE1jqMvhxhNaSXS7dRaMgjYj9GUfxAnPTjivKeDzye+TwT9a5qu4C9elJn8qac /wAPSncY5P0FZWAQjkYHzUoJ6HOaWMluVGCOn0qYqSRu5/SmkBGATxjkVIqnk8KR0qQDyyrqOn41 JLOJCflGT3q+VAVjtyQ6hsYwRSdwueD1GeeueD+VB6n0+lSQRrLdQwSSxwpK6qZZclUDcFiBk7Rx Ut2WoHQaX4huNGmuTFGt1Z3yg3FpcfPHL3BKnuM9R1H0Fdz4T1rRJdNg0W50afUYp5TFqNk85dJ9 P+V4ltc4ImtJNzxg8sjFf9mvPr7SbyxtYTdQiJUlaATDlJG+/wDIw4IweMVklnjk81AyOnR0+VgR 6Ef+O+h5rBwjOLsxqVjrfH2g6f4W8Zajo+jvJdaNIsd3pk8nLPa3CeYuSepTmNjgfMpz6VxQVT/q yfm6AYI98Yr2zwr/AGN4l0bSPCPiW4N9DO8jadeIwiu9Nvy+XtGf+KC5wCm7+P03GvPvFfh+HQdR b7K08ukTtss7i4xvEowslvKV+5cRt8jRsA2OgxUUa13yMGjmrSFby6gthLHAJ3EfmzPsiXeQMu3R Uz1bPHeu91bwD4i0S+s9P1mN9HjMElxKLp98NssBUSsHXcrRyKymJ1zuzj3ruvB/wutvEPgq38RJ qUV3perbra9ieJFu9Mv4GIQRZ+WSKZCEKkjKPnnaK7m0mvfDvgDTvD+suNWhguWWGzvV82eCwI5i lZM/u1ZSyntwAK4sXjlCXLHcwnPoeKWHw28Yanapf6RLDFYTlmtxcTrbyGPcQrNFv+TcPmA9CKt/ 8Ko+IH/P1Z/+Bq//ABdYmpeN3lv7l7fQtGli8xgrzwtK5CnHL71yOPl4GBgdqo/8Jlc/9C9oP/gI 3/x2tfbVO5N2f//TtC50NIxImk+W2PmWVpTj6/8A1qb9q05CskWm2hKHg4ZlH1If0rpotAt/D1zY 3uuwNeQzr5kEZ066uoJCejERbOcdFbrVHVZNLvrx7qHTdUQsoQpp3h5rWHA7hXcYJrt9tEjlMyHU olB+z2NlGx6usIOccAcksKe+s3hTZGluJW4DLGowPTDL/WnJHa7/ADk0HxLOOm2SC0tl+hMk39Kq XBuuWtPC86xdf9L1CwhA+hBJpqZJI+uX7jY1zkpwQoVSv5gfyqMaxf8A3BeTndyFBxk/UVXjvtXj ffLo+iQqRtIfWGJC+n7iP+RqY+ILwoFj07w3GBwWkn1K64H/AAFRRcBj6lJI4R55/ObqHMgbjpjA yaiYzncFSV5R3lUjA/3m5qKXxPcxYEMfhu3/ANmPT7uTd+MsoAqp/wAJn4kjOI77TYFHQQ6RDhfp 5kh/UUXA0FUy4M1vsXowdlIz6jDVaMV3sKxRsFTG0R4YdOOOa5yTxZ4rlbzG19/lGALextICP++Q aibxL4vwB/wkmsFTzgSIgH4JHSuwOsWw1iRVYWT5I4Voncf+i6I9D8QuONLuVyeGWGQDj224rjZN d8Sv811rury8dGu3AP0wo/lUB1HUCQZLmZ897i4lb88yL/Ki8gPRpNC8V3fFxFK/lLtjGxYwF64w zLUP/CMau4HmyW9uRnmaW3ifr/eaUV599skdl8xLdyvf735qzPWjHqE0HzQ2dojDoRbRfzEdHvdg OyXwjeTfNPr2jWig8GbULfP/AI4HP86kHhSFjtl8ZaS+0cRw3E07H/v3F/SsC28UayrqLdwr9wkQ GPoVArRuPF3itl8uG4ut/bBY/wAn4/KotIV4mz/wg2kSAGXxFLM47wWeoS49uI1H8qsJ8PNJwXW7 1a5x3j0ub/2pIv8ASuQOs+N5xskkujGepZjtP61nStr6v5ku0gdSJdo/XFHLILxPSF+Hnh10Pm2u su6d5LezhPPPIkuCf0qaH4c+Ftpb+y7xWHe4urCHP/AVEn8q84j1DWYlG7UIYVH8ImPH5MBUcuoT Tgfa/EAIXoilmx/48R+lHLIZ6ongbQV5+xQgD/ntqUKgfTyrYVMmjeENOjeS6XSgV/hfUrh3OOw8 pE/WvFzPaPv26m8h7EwnB/QVATbhJGNxI0mMKFgKr909yKfJITZw3xJ1db6ObWrNfs9np00UsEMT M4AjkUE5bnnk8/4VGLTVfFMdxe6YcBN0hHHYkbcZrmtBmju1vdD1M4t7xG8onpuJ5BJ967Hw82o+ Dp0+1Kz6dIFSTCkAZPXIrfZGB55Lqup6TdLb6hbmNlPIYevTmuh0rWvD8xkNzFiYhsDnH6V7lrfh bSfEMAkIVy6AoV5OOvavIrn4T3Akke0nVgzHAPHFQ0UdPazWs4xaSCViB8q/XNOn01po3neIH5tv NY3hvQZ/DmoSR3jgKVyWHA6ccmtXVvFdjaRG3gPnSnoqdB9T0/WmBzl9Ha2sTbysZT8M1mR6lYXd o+n2xM17c/uokjG5jznhe314/lWDeTXGqTos67135CjgHPau98J+HrXTtTlmWF1uEhEiSqeFE/GA NrYPtTKsd7YEXNxawXcctnpyRhZTb2MMsy4UcgP95ie5PFP1i3ka7C+H/wC2JrTy0Y/b47cTeYM8 IIcDbj1AqBbiK2k8g3jQSDHytIc8n0WNafL5iLt82QE9cmbnn+8ZB+gqbFooxaL4ilXKxzqvpjH5 4pRot3HCWu1K/wAIG4HJ/L+tDMAcyXTuD1BbOfbLTY/SnxfZ0BZVRADkbWg4+uN/86YuY5Dxjo4n tLa0tI/3kbbnGckDHTj3ryefww63iWE9r9paPFxc4yVRiuUXP5GvRvHur3mimObS7rfPN/rEZwcD H8OwenqK8aTxvqlzEIIb0xyeYW2S8F29274HSqsTzHQQeHb+KVmhi2q5yc/rWpHpu1R9qKxspwck dx1HPFcQ/iXxU7rCzickEAggZ/I01LnWnO7UNOuJIjz8jbl/AVJJ6vHNoEaRx3M0YCrg7mGeOKtW WoeH7Kcz2d+u/oqIC38hXnFqbF9p/sz5j08xF3fiCa9G0WWyVFE1u0S4+UJDgA9P4QaAOn0m/i1C 5G9XKcmSYbVyeiqGcjFdgwsCiItp5hIHHnEqSOoIVXqrpdtZJG11bcIBhiMrtbtjpk1ZfbGR9ovI V9RI5Y/+PyDt14plWJ1htwreTp0cRP8Ae8x+/wD1yA/SrEca28YkazgT/bMe38OSlc3PfaRbNl9T t4wFOF2xYz+Lfzqn/wAJL4bhO+fWLeAAckC3yePYM360+YEdgL7yzuiS2iB6lVQ5/OU0r6pNJEqG SIhen7uH19i/8q4E+PPCgYj+1vNwcDy3kJP/AHymPyFTf8Jl4bePzIzf3Jzwkcd3Jn/vlcUcw7M7 BNR1FkKedtj9I1xn6bYcVJ9tvXUgzzfMMZ+c5x/wBP51xy+I4JebTw7qt2TyALO4/mxH61WbVtQd /Mh8Gahj1kEUI4/66vmi4WZ2GZlO5pZyD3aUjH4Gams0QPMiZYYzvUn8S0p/lXJm/wDELbvK8KIg ZcD7Re2a49+ppwuPGRAEGi2MHAGft6NnHr5UbU7i5Ts7DV5NIuVvtJvltJ04EkLR7se+I2z9K2NT 8W+IddtRa6trN1qFrnf5L/c3euFhH5dK87z49kGPK0yBWGT+/u3zj02wjp9KtR2PjwfO+padb7h/ zwum46D/AFmz+VQ0uw1E6BpXaMyL5iIB/Csgx/3wqVELpiAwWSQEdX8xf/Qnya5uXT/Ebti68S2k RzyFsYx+fmTiq/8AZd0eJvFzgekNvZIfTvJJVcwuQ6lp2GWaEkf7QUf+hSH+VPjJkGbeKEj0Lwg/ yNcyuhyO6q3iPVJhjOI3gQD6BLdv5VMPCEcp5n16cf7N1MP/AEXbLinzD5To9l0SBsQM391lPTj+ GMVII71+6pt4ILSL+lc4fBdkAVk0/Vpyf+e95ef1ZKQ+CtDxiTQM5xxLcuwP/fy6H8qOYOU6GUzg YkeJR33PJz/48tUpLmwiDG41KyUdMZQt9Pmf+tZ8fg7w9EcxeHtLUn/np5DfnmSSr8Ph/TIcGPSt IjGf4IIP6QH9KOYfKaNp4m+HNnYNDq9vZ3N6dyi5k1OO2Ee77hEKFgcH35P6c0vinwmi7X8SWxmB 2FY/KXPqeEbvXpWiaF4Vktmk1PVX0y6DsEt7DS45wVC5VmfykAyeMCqCw6goH7+dR/CEjkH8ytQR ocGvizw0p2Lq87qD92BJmz/3xBinDxJp0qlYv7VuPQpa3zfkFiUV3Lm8RCxuZgo6krJ/NpwP0qOO KS93eXJO2zqpKpnPpl2p3HaJxEequ+Fj0fW7gdybO55+hZl/pTPteoEsY/CuqY/24I0B/wC/s9d4 2miAf6WJo93Qs0JB9srG1XLbRPMVpEjOxOSQTkemfLjFFykjzuC51tG/0fwrIG/25NPT/wBqE1ah ufFIut58OxRqcBle/tVIHTcAiZLAdBnFd2unwFh5zLt7FppP5My086dYb1/eW8gzjYZTk/RTN/Si 4WPizxrdEeLGht8pDC5VR6c4z+narN7N59uFLnbgbR68VB49gFl4nuNgwxkOD/d5PHesT7Q8qRxn hkXn+VJGYjSSxoVzweMelathEZVDg5wpFYsi5jAY4erdnNJZruySp6j0FVYDolt3cM+ecYAFSxhP LKiPMmwjuckHpT9O1OFyCMDbyR68VpPENy3EIyknpxipAzdMtXe4hZh5W50UueAQWAHT0J7Yr6Ff wR4fkJY2d3cLnI8zUZiPy+0Yx+FeQW0DR3FpNBGXVZomC8ZbDE8bvpX0vJc27tI8tjeOJW3A/ZXU Y99zoP0oL7HDweBfB6y5/wCEftiTwTPcGVs/Qyt/KrC+EvCcb5i0HSEIO35oUY/+i2rq5LwI++C0 lhVByNsEanj0kuP6VUfULNSrCG2BPOHms+D/AMBZ/wCVAFKDQ9HtTiPTdOhUHjy7McZ+kIq6yW6L +4EcYXqUtsKPocqP0qxLqLTKgV4Ap52JOhGP+AW5FVUmlPyQqEUE4EX2lsn2226j8qCkhkN2JsPD dSY5HyoFHB93/pTbn7SsgjZJ5t38aKrZyO5/+tWjjVApG2QbuSTFdHgfUoKpyfaIlKs37o9d0f8A 8cuAKBW8yjLBcxp5jWtztXgFsADP+6lS2cczN/oAMMqrwxcr1H97C1FmGMHF1DEOhRvsg/QyvUDT 2MfC3cQI5yGtVH/jsZ/lQGotyRDMlriF7naN4ad+p9SxxXivxP1bTrK6ijuI45723iX5VO9FDZKn POT7Zr2I6nayMIY9QLFh/wAsy/H/AH6gFeI/EzRBd6tHq8cpe2mh2yPIso2yRgKDulVc5XFASR5K 95LqlqpnkZtojbbgBRkYwMe5zXKTxPBIViZht6nPtXTWDQvIkcaY8/fCF548vgcjj07/ANaoSxCK eRdmVQY5PFUZmRC93E3mRDBzuJj46dM11Gm+KLm2kjS7yFPrVKGFN4AQAD0z356VLqdvE0YkCdeD jtgUrgeo6XLDqwBDZWf5SOuM9DxT18PxqG2jYynAP44yDXnnhXUf7Mu0XPybskenIr2Ka7WSIuCH 3AdM9/pSGmelxXsMUNutxp8dzcLbxpJIyOxJKgDO1T6VDPqkpaNrewiiEWRhYC2f++gK2YLDSxFD NqwuJBJFEYxBcx24jAHO7dyxODWNJBosO4XOq2tvHnKLJLCSo/hUtnrjFZstMhbWdQHA06JVPBJj C/8AswxVNtTuTG8JXyFftHtGf/H6kkufC8B/5DlkR3xJGGBx6bTVA6t4a+Yv4hiYg8BNz8duEj/p RqO4guLpcmKQgnudp7fjSKbyQ48yV/Urnn/xyhNc8Nj5BqdzPIeoSG5Kn0wNq0LqegFj+4vZT22W so/9GSf0FKw7oXF2vG2cqf7zuAfwwBTAh2khAqj0J/8AihTJL/SwSV0u9YH+9DCnT3LVXk1fT3cM uizho12qPtFsn5gA0WFzotKsedq+WfYEH+bUjx26HO1Gx1QlBn8ecVCms4Ty00JTtOcm8Hp32R/0 pkeqPEWKaLa5fk77i4kH04QD9KTK50S3DWUz5gg8pQP9Wsm5Rx13bKrErj5QCo5+/J9OygVbn1fU p41jOn6bDCgyBsuD/wCzCs9tSvo/mR7FT02LbZ/IPMR+lJIOdExguJRuW0MieoErAD/vsVGbHdgG IKW6bVH8nlIqr/buqKcfbYEHQhLe3TGOgxzTTreqy/KuoSY/2I4V/wDQYjU2voFzD1jQ7qWKXU9P tmeO1QtPHH5fyqvBcxoDwPb61zu5Y0Q7flPf/wDUK7/+0talUxvqt4UZSpQPIqFWGGBVI0BB9Olc VqVk1s4nto9iE5CE42+3zHJry8Zhbao1hU6M9E+EUQn8VXN78u2xspmB9Hl/dD/0Kvd2nSBME5C8 ZPtXkvwtgmtrTWLyRgDdPbwkHBOEDMccA9+fpXfS75go4Ax0rowqtAyqbmN4l1nU7m2Om6DLJby3 G6OR4yFyjDlfpVvwZJ9m0r+yHjkie2diQx3YMoBPzc98mrltY7ZQ5UBR1+tatpKlneLMyb43GyZM fej9v9r3rdEotTNy3H8JPHYV5wkHmzSGViFBJXPTNekeIrQaVpL6ur79MlRljk7oc/cb3HFeViS5 u8xK2ABghe2O2a5MUaQQjtEZcNIF7EryTjpmpw1uyqd6lgMYzjHr061Wt4LeAkEEsOMtg85p6+RC xleNWAPX0H0/+tXMkaM6DRdQ0zS0v/EdwPM/siHZbhhjdPLwOPpnnFeTy6vqz3h1CGcxzOS5P+0T nn+dani/VALa20+0AC4a5ZW/v52qT+FYeh6Tc6hIr8tG/Qe3T9K+zyuly0U4ngYud5nW2fiXV/NC STlGOSD2zj/GvX9G1FNW02O4vI1ZiACDjnA5xXKaJ4bt7Nx9rBCkZyD0/Cp59VsdLZYWRoonB/e5 zhgcKTxx6V7lONviPPcr7BrV79jYiC2b90+fm4OD2Nc3qWuXyxPIvylMqR6Y9x0rrvt+l3Mw+3OD H1LZPfnk1z2r2VhJGTA+9J1LYU55Awac12EkSaPe2/jHw9/YUv8Ao+sWW9oRIQUmRzu2BuzelZtx YS6LtuJ7dmW1aJyhUFW2ODsf64FeWu9zpOpLLbTeXNb4O1iBjjORXvWh65YeN9Ke2ucDVLdchQ2P N7ZP0rOS54OIR92Skdn4m8PWfiq3u/F/w1iYkHffaMxBktywyzRf7I7DpXi9lHLfyD7ZKzBDgQgl eR/ez39RXUQ6zqPhXWo7nSJ2t7y3cHIB2leuHHHB/GvStRs9I+KVtc3/AITSGx8UwqJLjTdy7brP BeI8fp071+eYmm4zcT62lK8Ez558Wypp8Fra2yeQz75CARjjj5s814zP51xGZmuJEdgDlSOeK3vF Op3sfje60zU4/srWMf2OSKX/AJZSgbmDen51RktxJIUjAAU8AEdM4FfYZVT5KCPBxs7zOVt7jWI3 Kx3GR6EAcDityGbX5Iy6SZUHGMAHP1rp00hP7NlndPm8xVU111toaSwJOvBACkDp0r0ZwZyRkjza y8TeLtFnF7ZXjW80bDDoDwR0zivqT4e/tELqIj0TxdAtpenAS6QnyZe3zcfK34V41J4bxzwVJ5DG su48Am8Y+V8gHpnHPOfrWMfaR2KfLI/Qyw1bTtThJSVXD9CMHb9f/r1yHjrQ9A1a2xdskFygzHKu FDY/hzxXzHoNl4psLL7O0kd4sXyqztIr4HrsIB9OldadK8XzMrHSdMlPUGR5Gzj3ya6PauSsZqly 63PbvDGo2VtZNp8Ma38e3ypFwChAG7BI9K6S1SNQCg2qeQo4255x/SvOPB+q69bG30bWfD0FtDfS +XHcWc3CvjI3IefavTUXaBj6/nXzmayd+Vnq4GKtdEjDA9c96x5QPMYDtzWwTlSKzJhtJ45xXjSP RWp4P8Ybi2k1DQNPlk8treOS5fgk7CwUYx64NeZ29wolVoY3txgLjaQntgYrpfivIt38RLiI422d hbW+Dkfe3SHBHfkVzcZurRXG7zBtJ2nkDntXm1X750xWh0Ek0kcKs4WQHggD29RVKK8cyqA2xM4J fnb7+pqK1f7UY0kLIz4xt5HA6nFbAupRG2FEohHy4wOPoef0qIsonZ1iXajLKsvDKQCrD1Hr9K5p YJdM1UXHhQIMD97Zs37uTkEqoP3CevTGa2IhcuXllcPDt3eWwx79eCKoW0aP+8jHltHljnrnPA4q 72FYngn03WBJ/ZzfZNQiDeZp8uFYMeoUHr+FTWqX0GLfzMSKu0xkYxn2/HFZWo28OsBVuNsF1H/q 5UO11PsR1q3b6tcQFNP8SJ5EmAkN/EAAwAwC35DrT0ewXNycJbgTDCPIoB2jncOgp1nqlrJLsvY9 kxAUHqrAf1qC+hu7MCS4ne5iT/VyKF2tkZ7Gs82tu6C4cGGST5QzHg/SpV0M6xoYZGDgb++euM84 4ziprC/ayvFmtnkY24YPCHZQd3DAleQCOD7c1N4PstKvdYsLLWArxSeZEhkLJE0gTMSzbBnyy/XH Ndb4zstS0WHQ9M1Wy0/TNUhsfNv7HTCXgSdpDhvMPzNuj2nB9fStESc5p3h/wvDqN3qlv4guhpeo WkvnaDfxSXJjuVX90be53Y8veARkDCcGpPDMt7Bqtm9pb3F1HLIIXW1WQtsk+RssFI6Hgg4GMnFV fDekwa1rkGjXWoDSo71zGJmQybH2kIm1cdenJ/Svpm18TWmk+K7f4f3ekz6SgiaGznt2WSKWLyj8 4Cqu3K8H5uGFMo858I/BafwTpmvW2iaqItbvr1HstTkh/frZowAtmLAnDHJdkI3N3rwL43eOdY1v xbqHhQalNNoWgeVaJGrDy7mSIKZLhgvysWkH4AV7ZbatPGNb0P8A4SU+KtHjgktNVNozjUNNUhoh dIjfMNvcrkcDOK+RfFfg/WPBF1HYXYjuLS6XNjqEI/0e6hT+JWHIYDG+MncpzxjFKexJiw2d/cQ3 V9Z2dxNDYKGuJY4yUh3thWeQdFJzg1XOVZgwKkHkHqD+PP510mrSeGrC3sF8IajfKL602arbzM4R bhQBjB4dTlsA/drmSdzE4AJOcDtXLMBM0noccCpAnQkHHrVlbbepYEADqORUAV4kJbqAAMc1onCA bgCOOlNKW6IAW2v2YdD7VWLYGFHFVYB8suciMfKO1QLuxkgHHcnFNxkDDdT0qyyjjcu4gcAUwI1O Tg/KPartrcTadcw32nTfZrq0cSwzcHy5FwQcMCPzqi25MgcfWrVhJpySFdYW4MDKQJLZlDpnOMK3 yuOPun/61JvSzA7e78R3njKHUrfWZY0vhm/gEcflxzyAfvfkXhZCq7/l4Pb0rzzzGKrjIUf0r02/ 8J3/AIc0Lw/r9vJFqWn3dy+oWt3B8gmtxCu5ZF52PHh1Ze1cLqulXOm3JTDvbSNmCUj5XQcZB9Oo z3xxXLS5IuyBO5klMk7uBwTg45BGOmMEdj24r6S8OandeOPCJubNIJPFOjbLXWopI8xa3pp+VJbi Ncbpkj3L5y/OHC5++K+dESNyWMgAHX0HpXU+DPEmn+D/ABBa+IdS0+4vYrBvOCW8xt3LLggE8B1b GNpwDx6UsVSunyboTdj6B07wvcfDrTdWhS4ln8N+JCIpozhmsrgLus7hJF+SaGToJOOgz1GeF8a6 xE+j6sYXnttUtLfTlknV9sTwSTMCo6/N5j5IP8Ir2Z/Feh+EUjn8FxS3WjKoMumXBaXdFOnmvGiE H7gZQqj7u3I4rDHh1NR8KT3Iay0zVNZhW0tryVWm3rbt5uzygDk4OOm4DnGMmvmaNW9ZTqGcfiuf JWq3VtqV/NfC18gz7WKRuAobaAxA7AnJ/Gs7Zb/883/7+D/GuyutA0VrmVo/F+guu44YLdAfl5VV /wDhHtI/6G3Qfyuv/jVfWctLua2R/9Sxr2oLPGqaJY61byhiXnv9RF0GTH8MIAVCfY8Vy66d4guV B85hv7M/Ppg5U/zqN7yUkiS4yB8pzuX9GdB+lUN5RvnKnPQNsHH0Z2rvsZGvFomrwbg8gXvzJ3I9 gKcthIm0XFzCCRlvOmzjt2I/DmsseYy/u1VR2wsf6FUNOhF8MoQ/z8AqDz+UQ/nRZAbiWGhp813q MDDHWNWb/wBmNVWXSz/x7XGfdYyx9uNpqAWuphMi2mABAJkQjj6tIpP5U6LTdUkLHaIV64dgv85T /KpA0I00gRYlnulbvtgwPb7wGKgZtJVw+ycgDGCQv82AqH+xNSbhfKwwyAWTt9VNalp4L8VzIJbb SZZIz0McTtntn5Yx/OgCi0+gbAIYJZHPXMo9f9kmqJktCfltUGf+mhfH1AUmuuPw48aPhjYzEDnD IyY/BnWrsPwy8RlA93JBah+qzT20LDHHRpzQ5JBY42G6S3JZLGGbAxmRXKjP/AV/nSrf7A6R2duo flsRZXp6vJgV2jfDARKZLrXtGt17b76yX8/vH8qjHgzw/ECt34y0aL+6Irzcp+vlQ/yqPbIXKcM2 q3SDy4HihA4wFjIH5u1IL/UTmRW3diVjT6fwRNXolt4M8Isdlx4lF4cgqlpa6nMMfVY1BrXXwH4I QiaSTW7lR0SLR7xsn2aYgUvbeQ+Q8clu7wHc1xKCynCxhufxCL/Ko1v7oIW8+QL02mRh/N0H6V7b H4T+H9tMGGh+JLsMORJYW8a/i0rrt/CozY+D7Nm8vwvfQxD+O41LTrdsj335HpR7XyHyM8SM0UuH eSI59WB6evzv/KgtCcmGNZOOTCgf/wBBi/rXtc2reE4FD2nhnTWdev2rxLEw/wC+YN5qv/wk2mu+ YvCvhUNjq9/qM/6RQc0/a+QcjPHUt7zr9nnYHoDE65/75ANStBKq/PZyhh1GWH/oUma9rPi4xFXt rDwtbFBx5Wk6lcsPXmRUB/Koj8QtYtvmS6sLReoFvoUMZ5/67zCl7R9g5Tx1NN1KfJh0+dl44yGz /wB9Of5Vch8M65O+YdLeMY6DA56cbIie/rXo0nxU8TqT5evSRg9DBBp8X6b2/nULfErxJcqN/iHV JVX5lAlgiXIHO7yYj/6FQqkg5dD5413Q5YJbuzkDefbTbF3ZLAjtkgdKms/Hn9l2EmkeLLV7yEAi MrwRxxn6dKWfXbn+0pbi/lMxuHZ3aQ7mLsc8sauX8emybI9XtGiMy5BIBBBHFdLehg0VtA+JX2G2 eCzRnUjaqNywJOdq4rb/AOEq8ZXG1LLS0tnblS2MgGvP73w74ThQ3FncrFKpyvlu2f1qkpsFO661 C+uM4/diVj9PpU69gO1utM8R6hJv1e5w7dVGFUDHbmrcfg2xt7c3NxcAL6KRz6VxwvPCiRAta37t 3L5P0+8cVSi8eWSSfYrLTL2dVbodmCRwMjJxVWA6a5iW1ljg020DyyH5ix8vCZx/SvSFXw/HErZm huJgskkNrZXU+09ApKkLkduelec2154m8QXEVrp1mtg6r5uAy+YAD1MjHCj27167oumwxwMNaF1q jFWYj7Rb2rGRwMfvNzkIoH3VTn61LRa1IR4ouLGxg0zT01NLaMSBl/sqNPMaThi7yShmPbDH5e1Y c+pTZItfD92V7Mxs4yB6AmU9OldQ2nW91NM2nadcafb5yBJcxv8ANjkpI8LMRn8ulNk0i9jjB2Oz MduRdSDHv+7hjqCzjLa912Mtt0Q4k6GS8teP++Vaka58bOd8VhYwlTwzXcrN6YxHDgfgK7ldFZ4n mN3Ex/6aXN03Tt88q1QSz0zeC9zZl8dCxkfI/wB+c4/KncdvI8P8W+Gxd3j3up3Nvbz42yxxOxGQ M4+bB59cV5xP4U0ZLlftU0SpF+9feMHJHCg10Wt2etyX+pfaMBYJmxlxjaG4wR/KuVXTftWq3UM7 u8jRLKygZ6AABW+ntWiMWXY38P2g8uzjViMjKjOCRXa+H7hJkWGI7jjhTwPpiuQXwq5cF5TaqeTu xmti38N/ZmFza6uiyIePM29voaLCO+ns76JE8vR4J2bJJk44A9RV2w1DW7dB9l0u0tyqhtwLOePY 1X0rxF9niB1a6hdV43xsPTA+Wuni8W6WVQQ28twQMAxoRn8xRYCzZ6FLr2n3NxrEf2yKNvNa2DeS vP8AEeYx+ZrMHhXwrGwI8N6WR1zNPbnrz03SH9K6/SNU1K63TWtj9nUAhfNSaRm47Lbo44/2q01v NWz8sMilQAWjsrofj87QD8qk0izjbbRtGhBNvoWkRj18lJPy8u1J/U1sQ2siMBa2FrED3itrk4/K FK2JNR1b5QbiSIR95I4U/MzXhP8An6VVbW5TlZdSUD0E+mr/AOzSmgqw4DXduULxqo6i1uOn4zp/ IUvk6s4DPM7L3byYVH5vcsfzqquqGVtltfpjHLx3kR/SC0b9KmRrllBhkmkQdXDX75PsUtI8/nQK zIZ7e4M0cTLPI7jIby7MjjjgkP8A1qePTLyQhA7g+rzRJj2xHbD9KeBLPnzYbggcb5YdRb8jJNGK hZbZR+8twFT/AJ6wxD/0det/KgWpdGk36kLlwM4Ja8mwP++AlQJa2rNJHPdQqYjgmWa5f9XnAP5V RWfT1BZBZxgHJEjaWpP5ebUy6pbgARzQRxjnEU8WP/JeyIH4UrBYsfYNFDhftlgR3+WNzz67pWNJ /Z2jbnEM1s7AcGG0hkyf+/bGnLq0x+aO4byx2WS/fP4JDEPyApf7VlPB84jtiO+Y/wDkS6QfpTCw +0iHlL+5uRIGwRDbbRj22Qf1q+IrsH5YtRjx0I8z8m4T+dZRnadgXtyV97VNx+vm3bfyqzE8Sx+U dNwT0LRWcIH/AI7J/KgLMtiRFbbdLO79Dm4IAHvvuB/KqkrWoLCPazD7qyXULR/8CUStUSPcAlYb R0Udllt0B+hjtR+lXFbWlB/cSJHx924nOc9PuRpTsKzKiTwrlVislbsIiG/9BjepQj/aYriLY20H 5FjudvIxxtt1H61K0eqOTvMaL0z59wev+9MP5VQeEx8XE1kiEHd5m0454wZJTSHqbUFzcQqIhbsG 6FjDc8493aL+dNub++OS0siEDHCwKnT+Lzrs/wCfyrmG1PQ4B/pGtaXAF4wTZDj/AIEDVGTxl4Qt iQ3i7TYoV7RzW4OfYRx07BqbiTrbMZGuUIPUyz2SKPyaT+tXf7RuJ8CF4m2njZJET+cVqf5/4Vha X8WPBmmCdoPENhqNxcKEjNxCbxoR1JhXy/lzjDHnI44qpqHxG8IatdS3okvgLh/MMOn2WoLBz/zz ijARR7CiwrHUs2qXCshjaVSeQxvWU/8AfFulNH9owrhUeIJxzHfFRn0Ek0Qri38SaBMdsOga9ddj /wASi8bP/fZx+lQR6vCoxD4J1mQZzk6bDEP/ACI4osOx2UrykAymNsdS0EYAPsJbw/yqu08RIEl3 AzL2K6bGf/Rkh/nXMf8ACQ3MeTB4KvIx/wBNG0yDH5yGo/8AhKtc8tkj8LxBT3k1bT4yPxjziiwH X/aVwD/aKs/bbcWyEf8Afm0b+dSR6g29U/tC5mJI6Xt5g4/652ydOnGK4xPFfi1iBa6TpaMOMHWP M/SCI1aTVviFNH5a2mjLF8xAebUpMZ65CQAYosVdHgPxU/5GIudxKsMly7sc88tJ830z/wDWripX KeW0fAfgk967b4hvcJrV0L4o1yDG0jRhxHlgPuiT5sexrnn8uQBXiwccY/ziiKMmRRRLcRjaPmwR n1rVW2LW6xgZUjpWOpEDCOMELncw98YrdspTJEpzyeDj19KoRivZT2sgljG0LznPauy0jVrWUQwu pSQcOOzcY4qu1lHty6k5ByCcDrWXcadsmVrcbcLkYPT2qCrHrNnZK0dpewlTbRXCecM4CdgDjrn8 P5V6dDb2yEbzBAH+bc1vbDjGf+Wjk14z4L1iWO4e0uWxvi2OePlByMj3Xg11Vt4I8CXEKXR0ayle cea5uJUPzNy5Ie6HU/7IppDR3n2zT7Vi02rW8RXjj7DEQPwQn9ayZPFfhy0jdn8TQq2ceWdQiTj1 JjjAH51mR+EPByYeHQNIbb2K2zdPciYfoK1YNC0ZI/8ARNO0wEnAEduPX1Wz/rT5QvEypPH/AIOj TJ8TQu68EC/nlx/3yKot488IHOdR+1KecCO+lz7A5A/Su8i0y6jTbDbPB/1zguAOPQiOIU3yLvG5 zOVzt3ESYz06tcqKOUOdHEP4x8HTBPsWnXdzJ3A0q4nBzzwW/wAasDxPZooMHg/VXx3GmJEPzkrq GtGdjGju7H1MHH/fc8n8qGsTARG8aNv45ltlP/fKwtijlF7RHKv4rviV8jwjqUew8FzZQ9fc4pp8 R+JZCFh8MzRlTnMmp2sZyeQTszXYto10hAWMYYZ2mYkf+Q4FAqJbG8RsfZyAOgEtwc/ltH60+UfM cv8A8JJ4zkyH0bT8MpV/O1B3JHt5aV5n8S9b1y9tNP03VUtrdgkjoloZCuzK8HzAuTx/nivbGWNb g28rwxy4wY5klIP1aWbFeR/EawRNR065Z4JI44XTFvs8sfNnszeo70mhOVzxWytmQORJ5XkSxuNv +2WGPx203V4WjublV52yE5Xp1xj9KsXNxI95dwpEUXqABgkoeM7sDvVjULjzLyZ2GQ6o+SMdR6UE nOwXON3OOnPsR6VvwC1uNsTudj9cdu1ZX2SN8FW8t8AYIzn/ACKjZJbdn5DbSChXHrk0WA01t7aO ZkAOBkZP1r0Xw8dsTqwJDAbc89DXBIgkc5AJcbhn1ruNBn2zRRsMqq7j/wABqQPRte0jTLjURNdW VvNK9vDl5VBJxHjkmRc9vSsuPS9MiDTQLpMOzgxsY45CD3TCyD8z/Su31S+WKWzPlQvut1PzIC3X 12mqa6teABUgjBI+6IHbA9fuipY9Tlg1tD/q2jBHQKIx9Puwf1qdZb24IS285yeix+fz9AqCtue/ 1eYLiJ49p4MUT/zLCoZdV1p4zBLPN5YIbA2KOO2S2R+f+FIoyHS/UsrW1yJF/hcTAj/vplo+w6tI pdbKTYvJbacf+RJD/KpZ9TvWwGvZPkB482MYyc47n/P4VVFzLKjA3bsrjBBnyh/BENBSLSaLqjAP HbRpnnc5iXj8jVi50TVoMLPcwxbeQolHIIzwEX+lZwYuAPMeXHYfaHXHthVH6USW8ynfsJK8AGJw PxLMKBWJW0uTA3ajA5b+ETSEj6jAqObSbWMjz7+2Geyh2P5FhVRkznzBsPfPkL/6E5pEmjUCFTbv 7jyQR+MYoHYke20VVyl7uYccW8Y/UsT+tILfRW63k5wM5iiQEe3CmnzwiZwEfAPQRtI3Qc4VYh/O o/7MnOMJIR/1zuM/qy0rASpHpIbCxanIncAuN3v8qjH5Usp0aOFl/svUyxHBklxH+O5lP6Ukej6l JjyYHI7fujz/AN9zf0p50e6O0XFgAexkSBMfkzH8xUoVjHmn8NgYOl7uP+Wk6f8Aszmlg1TSreEx W2j2BYtnzCyltnePKL6963Y9LundkhkRpAMmNZYkYAem2LJ/DNPh0jU7xHeGd9kPBLXUwX8PlX9K dk9xpHSeA7i3a1vre3VQY5BKIVLNgScYDNgtt213oULx6ev6V5VZaVrWlTLqNjNDJJF0PmTyZHRh tZx2/wBmvWS9rIqy2kwmh2c7V27TjJUj1rBxtsMsWw4c5/DFRuDnd7Yx61ZRdke0/e71Gy9Pbmou JF2w1+z0e3ubPXovtnh6/wAQ3lufvBZPlMkX+0uK4nxVo1/8O9QgCsNU8Nasm/SdRjJKlXP+pkAG BKo4564rK8b3DxW2nwxDd5k7s3B/hQEVr+A/HGnJZTeB/GqLeeFdVfDqSc2czcLPEeq+/wClcdaS crM1gY0vkMqyLfrGrYICBc+xamy2Wm4BMockZzz8xrR8U+C5vh9qUdtqDfbbG4Bksb4ECK4g+9/D kBwD8w/HpisHUNShmhj+yKVCL9wHkdfasUiuhSOhR6lfC4dgITGq4AzwK7/T7C00y03qB84CA4A+ Wua03dmxiJIE4XIPJ4HOa2NfuGiaK3hc7YguBxz61+j4Gmo4eNj5bES/eM07SW81a4aytGDPGdw7 blUY+legw6DpktpPYapBERKoIJ7HPPNeGWWoXOm3g1OB8Y3Kcj1Ne52F9BfacLyIq6ENlW45xniu 6nZ7nPNWOOvPBFnp3meSz+WCR5Mj5XFYTeFNFEqqhnjkwGCB/u7uP84r1vU2F3pwkX5JQvmEHG1h 6A9K8xvLiKLVYpgGBdVwD78Y/Cq9mSeXeKPDt0ltG4QO4bdwQCSDt2k+n41ieDmaHWY4kR4pd28L kHDezDnHBr1bXYwdJmVGLyrIpA9tw/nXEJo+2+sL+1LBXbEiD1XluetKVPlTkVB9Cr4k1y9/tOSB JhGMYfadu9mOR19Kr+HnuLHUP7ShmuLO7t2V45oXO5D0Y8dunHSm6hcW0Gp3jpHFKVxGVlyxyB/D itJtYuYbRfNGEk+7HHhcAcc1+YYubddy8z6+jG1NHnXxA1G9174nan4nvcfadRmgnlaEBNx8kRlw OgyVyavIBeXySgYUQBz0GSRjtUGtbr/Vobho/LKKGfL7cgfKMdt3tnFX7ELDMxJyihY1LdcepxX3 uXq9GLPm8U/fZ0qWSSab5OSG2g89+2a2vD8bNpuxgcxOyn8KpXjqIAySDCFSAM1saPI3l3zp825l RQP7zcV6zpo4rmpp0cc8hVwNq4JyM44rohZQiIhSRuPOccelZumo1s4CFSW4Oeox0rpt0gUOOcHH 3TzUOA0zP0mBY5AhySx25GOcnmvX4XhisI9ygO64OOMdvavLtNhkluHfaOASOcY59K7y3t7gxx+a oycc4PFZyiuxTsza0aKJ9QtLgNuSzJkOf72Mdq6XaFyFPGT/ADqnb28UVsCi7GkIBOMe3StFlAJU ZwMjmvms7auj1MsWjIeAPr7Vmyod/wAvG7+XStYqcDnpVL7kodsbUO4njsCa8CR60UfHnie6udT8 beJbraBGL90RuOViAiHB+hrLlEaIxHLJjO09sVYkVpxJevubz3kncgH5fMct/WqkLW7qZ0ZWCcDJ 4ry6r9651paEmlxXsZkliA25JCeoI4xW4n2GRBFLLLZXZ4BXBTPrWNA6ygzREx9OQcdD2FdJpckt 2szQW6bi2wMx+Y+4FRBiJIIdetI5BdN51u33JQoP8qpg+Q/nXAcFhy+BtPpjFWLlrmG/Fql3JE6L l0mGdwHphT2qFppbrIOdyngAcYx7AAVTYETGNjlWwvXO0f1x+lW/LgurVo7yHfGOCDypX6dfyFRL FJMH3uPMQdTwuPbHFL9meMb0m2jq5DA8Y/h7UJ2FYxv9M0RmudEke5sUyXtnO4x5xu2E8YI7ZzXV QXFrq8DXOliOWFcn7PLnIHBwOnIyOKVDDJsdGyOgyMY4xuwOpqpqWjhJ4rzSs2l8FwZ4sbWHQhwM /mBmtE7isa1nJbFgfL8mQHYQSQox2x/U1aiZfOfy2VTn7mSXOfds8VzS6358pg1iL7Ld/Nhs5jbI BByOvf3roDaXNp5m0pdbGK/MMEAcfIy/4irQHc/D/TrvUfGujx25jVrWb7TK8p4McY52jueenT3r X8b+LtQn1yBLS2l0y68OzTLbSOS0shPQtkYwwHC9MGsL4beKNL0XXri81ZRaQyWjwxyzA4Vi6Y+6 CfmGQfb0rf8AGek3fiLxjcT+H4HvYb/yXkuSpjt9zRgSskr7f3agAjuMnFA0cj41l0DwD4y8MfGP RLGWe28RxXX221SUJCb14wJUbgkZO4mLGCRxzXlPi74mW3iHQ5PDGi+HbLQ9LubmO8lxJLcztJGf kCu+RGB/Ht+8OK9g+O2oW3gbRU+Hul6BA2h67AbyDUJZml/0lH+d4uMeYEAI6feNfJUsqAsm8OVO 0jIPHQc8VE2wsTRoZDtiUnHA46AU+SNojiQYyORxz+VTW8F+8HnW9pcvAmQ0iQSNGuOoLqpUY788 VEkMl4wVNvAPBJB/WueSEMDMflzkDtT938PQ96rOGiYo5wQOg5Faen20MoJmHQccgUooCERs/O3I /wAKY+EJ3cdvpVy4lMYxHwo6VSLM2SxycZP0+lU0ARqGJY8j0q1sVRnO09v8inQNIMEAEEcAjmnS JwDgZ9BgYosBHxzvOcVCTGy8NgfTt7D2NSM+SApwfT/61G079xwDxx0zxTXoB7B8JJfEMsGs6RZN a3WlWMH9pyWU0hEiH7kklqgBzuX5Zlxjbg1qWui6X4uvvDfhfVNQuNKtLS91DSrS4tIxcAtKRJG0 qtgRoi7V4yXHYYNeK2V1c6deW17ps5tru3mjMcq8bWJ4z049fywa9J1fxP8AZdRu00s2928lzbvL J5LRRfaQP9dGIyHQE5BAOOvtXkYvDVFU5oCUDkfF+h2uheJrnRtOae4gtY7YK0r7iXaIF+eNo3Zw vYV2nhTwIt94YufFmnaja6jeWYjlt7WFMPBdQNuaK6V+DFMhwjrkE969D8UeHfCuuabpPiZ7T7Y1 lYvHq1povlpfxvF/FJK23MO3YTgbuwbrXI+AvCsFxBq3iqz1KZYdK+xyQ3kCOrbDNuuPNjG7hVGz jeAW3Z61m8cnQ8xM6Wz1HwxewW2pxM9n9oke3iv5JVlhguVXcqTwrysYHyiTHHvVrTr3x5FB4ogC ma50a0/tOws12yF2hcGRon4Mn7rey8DcPXpV3wp8N9O8R3fiDVrTVNmlraXNzb6RFKYgs7jdDI8u dpi8wkfd+tUNEv8AxHoGo7NStH06e1jkSHZOJGhFwhDKrdMfxKPbHevEnUT/AIZKRTk0m31NzqCD w5Mtx84eeG+glOezxwMsYYdCVAyeTyTTP+Eah/54+F//ACp//HK8Hm1DxNpU0mm3Gv3tnLbO0bRe ecAg9R04b7w9jUX/AAkHiH/oabz/AL/n/GvU+qy7l3Z//9XYKafGVSz0fS0XudR8QWCMPoIEcCkk EkZQLP4TjU8BI9UvLxh7bYLT+tZ/9iahCBuJUNjIM9wf/RZjH6/4U2XTSAPtEsDLj+Iynn38yfH6 V2WFoOlnuXlMIn0mFMY3x6fq05B9t/lD9BWc7XlqgQeKXGeqwaFtA9s3N4v8qeLPSVxG1/YBieQs ELsB3OSz/wAqvraaME3w38jlSFCWtrFkj1wsGfypcpJkDUGQr5mvXzOOnl2elQ/+jJpT/Orkeqah ARs1jX2XuI7qytwB148izkP61eCAnZanVAg6ny54wB/wGFR+tWFksIwPtQ1CXb1EksiKfTiSVf50 7AYtxcXN2wbHiGcLxmXVL05/782ka1GdPtTgz6Fcvj/n7vdVn/SSaGrktzoxdi9nGIx/z3uYf5Pc Pn8qRz4fKJI1nYqVO4APGc+wESNRYDEvtPhdABpFnaAnkjJY/wC8Z74np7UyG0hgA+z21ghHUkWe /wDDJmI/z9K6aPaJEnTTrZQPuqLe4k6+hW2H86sS3eoKoykkTA/KIbWYYz2IZoaLAc/Bc3NqDPay W0EidXiWN8duSlmcfnWrb+I/Eqoy2Ws3sfqYPPUD/eKQRfoRTrq+v3jX7TLL8n3VZII0Xn+7JeE/ nWVPdRCYGe8zIeVH2uzQ/wDfIMmaFEDVfUvFt4hjfV9Ymx1QzXe0/wDfy7QVmTadf3IPnpcyt3DM h/8ARlzKT+VSm/ZSBDcCVj/08nj6CCzP86ma5vZFEfmTTHtn7ecf98RRCqsuwXZSh8KXcgDpYtnt k20f84SavR+GNQ2/JbomOp8wDH/fu3QfrVK4S5ICSh29TJBdEfnPdIP0qlIbVSqFoUz1PkWGfzku nP8AOiy7Bdm62ltHhLmaCA/3/tMzKPwEiCqrWOmKT5+safsXqT8xbv8AdkuG/lWQb62ScJHeRIVG CBLpycnt+7hlNXf7TLMES/bavGEvJO3vb2afoaAuyYW+hEMI720cY4MdrC35BY2/nWraJpUUIQJe XWRw0Nm6In+7sgrJluZ5Mec90Ux0aTVXz/300Q/Kq7xRTKGawZovWSCVv1uL3+lKz7j5jdeJQpa3 j1VweBvjli/U+XXP6/4j8P8AhzT1udYF4ZJ38nypJ5Cu0jksvmN/KmGC3f51sYUVe32bTxn/AL6k l/z+Vc74v0+LVtMgguYPLWGcSKEW3jVuNv8Ay7og/OmmROWhz9xrvw/1tDaadizmYYAJ3KWH15p8 VnqEarb3Za9sUwVccsnsM84rj5/Dul73iMHzR8Dnpx7YqAaVf2Pz6Tfz25/2WwB9QeK2sYXPWZPD elzCK8XT45XZSRsIB4PccVUngv1+Sy0YK44BO0Y49BXnCz+KNwJ1e4JHU5U/l8tW3sNTvVxfa1du h5C+ay/+g4/lSGdNJoGoXsoOrS+TG3WOPpge9YFz5OmTyW9rNb2KD5TIGUzMMev8NZ7eGbD/AJaT SNjrukc8f99VO+laVZxgKu7d0Hf9aAO88F6fbQQXczSi4kdgyytKyKUx9ziORs55wBXbyajZZVpm jmn+4AzanIRn/gEI9MDivNPCWqaLYmQX801tDFu2xwQXM7u7DBA8lWwAOeq/j1r2ez+LF/YaIdD0 NNc+wpbyW6wwaTIquspw7M0n3i3rIMjtWU5dDSKMyczhMTRMC2MeZZ3LdP8Ar4u1FUX+y24IZYhJ wf8Aj3sE7d/NupP1Fc9/aUNuR9j8F6gWAC75IbaLheBkyTDt7f4U/wD4Sm/t8/ZfCP2fdxm4v7GL /wBmb9KdytDYjurCH5vNgaQ5wIzpUfUf9M4ZDV+1u4S8MTXTBmONv2vHUZ+7HZov6iuYPi3xXKCl to1lDnjD6oh/MQxGn21/4jYXEmo2NjZ2ixM7fZrm5lkZsYHysix/+PD6VVgcjy/XXkurmRFxmSTk 9z81ca2oraNdXGlCRXuWWJ3lAO0Rj+A+9dbqD774Mxxs3YAzzhSR19x/niuGsIonsAZGwXkJwT61 RiVwZbpi08zPJ/eBPT6VpWml2ZJaUH33GpLW2gjLbiMYwKvoltj5XycfyoA6rSLCwjEbJbxOOwK5 P613NvLbxYcLhs7QBxjHsK4vTriFPswXkocnGK6uGaOSTKqCUJPNAHYRwLqNtEGVAgJB8xGcc+gV o/5mq9xp9nA3lySWMKqMb2tLYdun712/lXPX2k6Jq620up2trctFGVX7RIiquTz8jTwg/iDVCPwx 4QgYH+ytE5+6Stk2Pz8z+tPlKUkdD9t8P2qFG1yxs9vXH9nxfltSqj+NfB1syxXHjGGOMEZEd5Gr f+Q0FEOm6PDtMNppqqvQwW0JA+nlWDfzrUgZ4/8Aj2yMdlgnQf8AfMdrF/OjlQ+dGGfHvgrcy/8A CQXM69AIpb2TPp9zH6VMPEnhS4UGGx1fUM90sNQlDfi+a6D/AImzDIN6E94Lof8AfIe6Vf0oex1F zulSY7hkG4SFfbjfdSn9KfIh+0OeXU9PyXj8GazMpGBnT1jX85SKG16eDmLwTdx7RhRNNYWwwP8A elroG0GHeGNurSn+FXsC2ffZFIR+NW49Iu4QTDZsMf3bsJ+kFnRyIXOcwPFevPFsi8M2UZByPP1a 1XH1EO6lPiLxtcjbDp+iRbuuby6myBx/yzgIrsH0fVsKZB5QPbz9QY/+O+UP5VA2k7z+/miQA8lh OSPxmvKXKg52cibvx1KwLDRkYdCLTUZwPxMaA02WTxqBh9Xs7Yf9M9I24/Ga4T+VdO9jocZKSXlq WXs0dod3vmSeT+VWbeHw5EMyapbwH+7bxWPI9Dst2P60cqFzHCmfxMF2yeMZYvUQWunw/wDod02K aI9QnwJfF+sTE8YiurKLP4xRS16XG2j4Xy7++deyW4kAP/fm3FWYw7/PKusSwA8KP7R/qIxS5CeZ nmK+HpLn7upa5clupW/uAOOOfJsv60v/AAg9jJgyadqlyR1Ms+qkf+0h/KvRbiK3kGX0i8k4yxn8 9RwfWScfrVbOn7cQ6NZR4xzcPabjgd/MuD/KtLDucCfh/om0s/h9fXNwZz/6PvcUkXg/wvCfk0PR 1YDOXisjx/wO4lNdsNQ08NtSy0qNwMZW4sEx+Qf+Rp6azNFLGsVzYAg5ws+84Hbbb2Z/Q1PKLmOW g0LRoGBtrPRoDjOVhsOPp5cEhzWvBZOnzW8kKg94EYY+nlWCfzrqjq+p3A2wIrMTuBii1GQfRdtq g/Wq1y2tMV3LdZ64+w3hX8fNmi/lVWDmI9N0+aeR11K+1i3tgjMrWNvdyu7/AMKjz1jiUf7W0mq2 o6XP9qMenXmqz22xRi+hm84nAyDsu4k9cHHTFTSXN+mBIHR24+e1tozx6efentVCXUHix5l8sLN9 0tPpMHX23yfp/wDWpBzELaRDw0qSs3YyR236eZdP/KnJoMMil0tlcD+J5dPjH6Quagiv4I9rpfxu x5Ui+tR1/wCve0fv71eN350JX7TcTlugW61OcZ9MQWsY/SkK4qaQZABbwhnzjMU6n/0RZf1q/JoG owKJJBIewjW4vT+WyKP+dUUtZzHlLW5Y5/htdYk/PzJYc1XOmHBeXSyJB036Zj8zeX5/UUCuX5NN uEY/aVwQM7JWvZMj/gdyF/lVZLDRpObi7sICjDiSCMYBGf8AlreZ/Ss+OG2ts77SC2YckvFoMOPb 55pT+lPh1S0jc4vLdG6YW90ePHvi3tXIoHqfPHxVitY/EV6lrNFPCyQgSQ7dmQoOBsLL+Rrg0uA6 IhJDIowenavVfi4Ptep2WpG4E0dwgjaRZ/tLDyzjmQRQqB04CfjXjMJV7gQhdiOMg59D2qVuM3Io 2faeuRweO1adqxiTC8E8jH1qlBIsABxuABA9sDFaCsuwMD8xUED8KANSK5R9oyTjgg1qQFPMCsO2 MgevSuSXI+YH3rc0+73zxqcYY8j1xSsO5062qRXMNyuFK8Fs5HJGOMDFe5+H9RuW0ix2Io/0fGSt 4zZHqIbYj8mNeRqu6GIqpKqdrYAOVJ6V6Ro9mJNIskja1WIxfLG/9oSFeT1SJ0T8v/r0RHJHSrNq ayeYqS5kGGEdrfspwO4k8sdu1V7g6q8S2wS5t4QckCzK98/fmuh6+lUpNNWOAyyrZybSB5Y0yZm/ Dz7ls/lVVLGCJRI4hjU9dul2cZH/AH2TVkWHyRmBgWnmQrlsSS2CZ9vnuH/lUMdzDaR7Fu44EY7y r3tkArE5yBFFIauefaWke6HUWhVfmARNOhLe3+qOPz/wqnLrmmxlS+uyKzcuG1KOLb/36QZouP5D luI5mLx3wcnkmO8mbke0VmMU9vMny7wyTk/xganKeP8AdWP+dYl14y8Ng+Tcan5iD7zvqlxgfgjC sy58b/D9QAb+2nI7PJeS9Pcuc0udBynVixJ+b7HKTjgm0vGH5z3AFMFssCFpLYrg9WtbOMf+RZ2r h3+IHgDeClvbSKFwUjsi5Y/7zGnQ+PtCJX7Ho1zMvYQ6ev8AMDP60cyHY6yOa0iRgJ4ImDZ/1umR bR77RIRXnHxAeG7TTHjuUuB5kisEmjmIGFI5iihx+tdXF43uRE62XhrWiW+64tWVVwc8DZjrXOeN da1PXrC0OoaNe6WLdyYhdcCRmHO1QoIx7mpkwseA6jEkV/Lgqinc2W+4vK5ztqK7Ubo9jYBhXBB4 PHbvWrfQSXmoOtuBmJGwSwiUYKDG9sBe3aquqW7Q/Y9+AWgDHDbuc9iOtAjJgnZJdkpyfunp6Vda IjaU5DjnOO9ZTFo5N4G75iSOPStdWMqR4BQ+n0GKYrmnbKgZUxnC4BrrNFH+nRIWyCQvp1/yK5Kx 4nx/CentXX6Ww/tG042fMM/gMVAz2qfTLu+gsJ4mjCeTt2ukrcg/7DAfyqJdEZLfzZLyFY9+0qLd xID65eUfLWJrelW2oRafNcXlzbgQPEFiuUgQkN1O4HPp0rn/APhHNFVkZ/OuCcD575mx/wB+oR+G KllxO+Sw0GMyLf3LOGBCvbtBbiNvVvM87d+AFUAuiQcNq8Ue3gsotlJ/75TNcl/wj3h4n5dPimYn B3SX0vPbH3RVpfDkYQzW+gWyW6cNIE81vT/VtPu/SkWa8mreHEO1tWL7ejfaNnH0jT+tEniDwSnB vblsdSZZ2B/lWX9ggh/1dpCig4+Wztl/MySt/n8qYzrGwi85I89VQ2MWP++Qf5UASt4o8FeXIsME s8hYfMYnkwo6rycmlPijwy5/0Tw7czE9lsj+HrUD3kqNiK6k+UdPtwA/8hRrUZvHlPN25Hp515J/ KgDRj8QXGA1l4Qv/AGzbIn5bhVyTX/EbL8vhaWAYGRJPDF+gAx+VYrW3nfPHC8jAZz5Ny44/66Nj 9KiGn3WFMNnIc/3bKMf+hPQBoXGv+J5FCf2fZwhe0t9HwPoOazv7W8QI2VbSomH96eSX8wKtppmr RkFreaBXwMsLOFevJzhjV+9029hlCWGpQarH5e4yW8zRqrE8owkhjOQO4XFAGMl94hSQPFdaXC3O CtpNJ19O3WmtJ4lfhtcMWO0Vh68/xVda0vnjdZbqzVUG7abqVjxx2A/kKqPp5C7xe2hBAz8s7+/c ipAhMOtIwb/hIdSVmGN0NvBC3/fRPFU57KRj/pviDWJieoNzDHj67KujTrMLl7+zBPOBb9B/20J/ nUJ/sa3cIuohlb/njHEhB+mDipsBnf2TpUjBTdXtwcf8ttScn8PLU10/hy/g8KzyQWdvttbhw88e +SZyyLgMpYY71kNfaKhOb66bGMAPGn8lqaXUvD0kQAl1C5k3HgSvgLjtsFJxA+hFmheJJoXDxONy MOc5HT6+tIzhOv8Ad/nXmPg/xTZwzR6NJBcW1pOf3U0hkKJJjHO9eM/WvSmTafLcbduBj27f571j KNgPMvHeoCLU7O0UA+TF5hHTJbpn8K5mKye/gzF9nQj1Yc5/hx/Wp/GMnneJbzcC/lLHEgXHBVfm zWDbdWxtTJzvJAxzXj1pfvLnRFaHvfgnxVo15pY+GXxAm87R7pz9gvCSz2M5/iDc/Ix9Pu9+K5bx d4dl+H+syaHq9oqvKoltbtW/dXMbciVCPl6feUH5fpiuFgtIZH8uY5RhyV53Z7c9K988N6povjbR ofhn44vNjx5Oh6qxVntpeqxPn+E5wPUcela0582hMtDg9Ikie2j1SXjrtB/hAG3pWRdyTSzGSQbQ c4HsBW1rWga14VkXwzrVutvqFrLNHJHH92VP4ZU/2X4IzVSO0kuWtC6FNhKsD3/ziv0jA64aNz5f EL94zLiKtY3UbZ+VhkfpxXfeBNUeD7TpkjKysm8BhkDA5xXIXtgbS4uWDgoeoHTBPTFS2EzadeR3 cK5ZFOQO4PY11R3uc8ket6ncRy6RNaI4YTKFQKeVKn2/KvJWv/3pNw2ZhgIOe3WvXZJra402S6tN gVwGICgY/vV5BfWET2EksJw3mbV4wfeuszuS2OrQskqXcv70uAMjOP8APFWrmB4PtDW8gIRx5ZHG A4y3J4rkrmwmjgBaRgTtUYGCehrr7u+87TEEC+Wxhk34x+82rtBwccjOOBWeIdqbKgveRh/2ppOn lmTM1xM2WWPk/rgCuL1PVbzVJtiIW5wqKOntk1U8m8ieeaGEFA7A9jwe5pouZ9xc5TbhhsGO3avy bE1L1WfaU/gKcwmR5vMhlTy1UL6KwGcMp5/HpV+2tPtEcTs5Bbkqfr/ntSCS2uZB5+15pBhmySwB 6A5NS+dEkY2kYjAVQMjt6V+j5V/u8T5TFy/eNHUaXb20xlEi7vKX5h2+bpWlokMhtpI14ZZXXjsU HymjwhAokxOATdI5PPtgGtTQJI7K6eG69TG/TKkcgr25FeucdzqNLsohbi4Mm+ZuCp9AOtakYjT5 mbnB4B4P+RVVb2zEWEj8mUc47Ybjr0/WrlvAZod64RclQcZ5Axik1YLmxoMaCRXRARnJPX8BitQX csU8jxMX+buemP8A61T29stjZiTOAMKSOMetUpIkhCEMGEz9RnvxWEkOLPUIm86yhlIyHQMAPXOe 9P3KzZGR7Vn6ZKj2QRWy0LeWRxxj/PatEHBNfH5xO9ax9Bl8LQuBGBmuX8TX6aV4e1nVGk8sWVlN JwM4wuAa6ZiSOmK8w+Ld1Fa+ANWSQsq3jW9sSuM4eUMcfgteTPa56EVqfKo1Ga5/c2zS3CMNpwpX t7cVoWVnFaRlrxfIAP3SOWyO/HFYFveTWLP5JhEfUGVQSR+C59utdZaWdxfxLO7COFgceX/GOpGG PH514qfM7nUiklpLBmRBm3PIPPGaltrtrZhNEwZkPI29/wC9WlafZ53S1e6ES5+7ycgdvSp9Tt7C Jn+yMsxUYCr94e5q7Elgu2pPH5tz5Tj5gSvzZx/L2pkCXY3xPcriPP3ONw69TUFpPHYwiaZjIxHC uMAfjUQlF2jPJG0RzlAnO76e9AF+NoSwUArGgyT6nrg1AxheZfLOUJ54OR9RVUQxxtxh+M7CePX5 8cZ/GtFFUIRGV3beMeh5Kn2oASCNYrl5ZGIZsD5eHGPY+1a09xC8ZVnL56ZXb34zWVGJmlSV5zJG g2kOMheOwHOKkW3s/MOPlLnPBxv+maEBWlszeAQzRo0TfKo6dSMkZ6EAVrwQa7p8ElyYJtV0mEf6 RPEhZrbdhFeYKDhdwXnpjNQ3TR+T84LKhK4BHHHXg0zw34j17w5fR3ehrLkAqybVeNwCGG5GHzc9 zn0xVqbFY1kEF6gurC4W5tpz+7YDIOV3EE59OOBXWaZrPiUWT6bda5LZaLMiwstw/mRRRsxBVFbJ Xg/MV2/nXmlzMHuJdVsZEt7m4kLzQZxEx5JYKANrNuIxgVoXM2leLNKOlXBe1v2iUwn+LzoozsVh 90pI6jJ9x71qNI6PxG8Xjbw/a/Dj4ZQSapDZ339otNdyC3TCoyhLfzc7AxPK7V47V5PbaHc/DzxZ pr/FbwdJfaWyMxtWkzFPGfl82CSJtsmxj0OK6LwNqMkM0Xg+5tn0fV1V2tpgg/fSBTKCwI4bZzG6 kj5ewra13wl4h+Ifii914a5bXNqV86ZZ2kWe1toxiUQKEKHYFLYG33pWFc5Txt4w8PyTHTvhc2o6 B4buIS13pTSSRQyXOfnbyRIwwwCk9MkV5qtzjAhxgcAjr+OK9Q8Yf8KiudPFr8P7WeS6mnjeK5Nv PbrFbg/vI5UlmcSErgIVUcgk159MsNnHwPmyeBjp0GfaokkIILd59pLAgdRwD69asXLR20YS3Oex DckZ+lZpuWdQOg9Rxn8KSNJJW3DcQR95RgZ6f0qFYlsBGJG3OePfHH5Vdjt948yTaypzjpkD6Vct rCNELSurkdBjrntxSSQF3ykaRrnGfwq40G1zC5iurAZ8pQo7d8D60eWx+Y4b3HWlcOmeA/b6Uz5g CVPPHFPlHcGmt9gG35z64/nUexypAKgdqmbygFOB0/EUxBNz5ZyOoBoUQuSpbrwCQMjk8dB2xVu3 zNcLBLgLdr5PODgk5UgDHceveolVyp83gj047VGw2D9zlXAOG7ggZBrXk92xPMzdTxRrVqY/sdy2 mvbDaBbYjycbSSRnOe+c12PhDxAl5Y3OhX2ryaLqk06XFlqKxKIGdV2mKcKflEhOS23HHNcbqlrb HSLLWEumeW/Zt6EKoRtv3ff5gaxLdbVpo1uZmt4HPlyyBPN2I3G7yiV34/ukivPq4WlUi+WOw0z6 As9WuYfAfiZ3uIdQv7qyi0W8ljaOOFDI5ltnyvJI2yRk8BiVrzmx1nxDp8+m28922nxalcLbSQyY O6Fn273hPQHOUcHnkivQ/A/hFdNnu7mx1i38QeFfFFqbO5kt4ZIJYJoz5kJaCTI3RuBhQTx04wa5 v4mT6fb6wNGvNMOkmCBXtbuEFhJFINrIyE7tqMhxsYFcnPGBXh4Z0ViJUUtyzUbxd4TieS28SQaY 2qWrtbXH2pG80NATEA2FxgBQB7Ypv/CYfDj/AJ9tF/74k/8Aiawj4F8NeLtviK88R2dvc3yq0qpc xRqZEARnCMykbypbBA60n/CofCX/AENVr/4GQ/8Axddf1NfzDuf/1s/zLQliGhyO6rZIo+pLSkfr T4pYIuYXTLd42i/TyrQfoa9MaPU7EfuvAdpaxjgC/wDFNsjfjHbIanW08UzgunhrwdbrwQZ9Y1G9 bp/djQV1c5nY86jnlLFd120R++Imuy447ARpj86qSC45G26ER/56eeW/8euQK9lstG8byKXWDwRZ qB1XS9UvD+G/aP1qwuheNY0eY6nosIHe08KBvye6njFLnGeG/YuCZLNSmOpitzn6s80h/Onw2zyY 8m0hG3j5UtFx/wB8xE/zr12XStZeRZrrx5PYsv8Az76Xo1qfwMsj/qarzQ2lvEG1D4l64TztEGqa TbZ/C3t3x/31/hT5wOGttE8Uzlfs9leBc/L5XmYb0A8m3z+tbMfgbx3KQf7KvpGPI8xb0r+G51/n +VXIX8JgD7V42125ZiTtbxFfy5HTGLe2QflVS5X4TMD9utb+/AyS9zfa5cEf8BLIP1ovICN/hh4w mG6bSlQ5wfNKlQT/ANd7n9KiHw9vbePffXei2EiHBW5ksYGwf4gX8wcVWh1T4QWNwk+leFrD7RBj bN9kk81T677m8DA1WfxN4LX9+nhrSVnznfNb6b36k7zM35k0vfHoTyeG9HtSfO8beGLcE/wX9qCP oYrc1YisPAwjdLr4j2SSDACQXFzck/hDCuPwpLT4l6Zac2mm6XET/wA8Es4//RFm5rQj+MmvRsF0 +MY7LE9wMfQRwRUmmK8ShFpvw/nYKvi++vT0C22karck/jxmteLwL4UuDviXxVehhz5Phm7z+czm qkvxY+IM4LR2s7j3F+w/J541rIufGnxCuExLFs8wfL5kGMewE1039aFTYrnWw+CvCdoC58I+MrzO AC2m2NmPT700oqymiaPCv+i+AtcjI4xc6xo9sn44ZiK8uN944kbzN4hx14s1I/IOaGTxtcIAbyVV PJCToCf+/MC/zpqlIfMeuLaxrbxWa+D7ARxu0iR3PiiPhjwSfs0BP60g0y03ANofhOAjjEusavcu oHb93CBXkZ0bxFNta41KbpjEtzenH5Mg/Sqk3hxyAt7cwNGepfzpl/Ke5x+lWqL7i5z1+aDTIDib /hA4HXlBHpuqXR/8flQH9KzJfFmnWDCNNZ8NwIo+ZrXw1FkfjcXTfyry/wDsHwrbKVm1jT43HVFg s42PfktvI/Kn48JQqFg1lXPpD9nH/oqChUx+0PSG+KEFsDFYeKbmQgcJp2jaNbY+hcyY/KvOvHHi 2812Wxmvbq+vIvLISS++z+YvzHKoLdI41X14qzG/huRMLqGpSsOogW6YD/v1EB+lcd4ouA14IC5e G2jAiD7i5VhuIbd83U96unSszGcjl3to5pC9q435OEXC1RlgvoyQ1vIxB5Ix/niq09ttIe3cpIwy CO1SRalrURVGYT54AIFbGZI0UphJ2+WxPAOP6VpwWkoiXeclu+BVManqXG61DAdhxUjajfkAyWWA OOv/AOqpKJrmxZI1jDBudxPA6dqf5ELlNyAscKoHXJ6VUe7u3A22eCOMmltRqIlErxKiqdx5HG05 /lTsB6J4Q0gzaNBJdWuqXFuRMR/ZvlqSfMP3nmfYOMfwGtGfw6pkcWunXRiLfILo2Ur++5i5X8lr L0S6ns9G02FL+SzDQ5ws9hATvdiRmdXl6ew9qvJrCSyFZ9WeZx0B1Zc4+kFqf50cuoFxPCuogACx wx6Kktmm36+XbGrVv4f1RpESONSWOzK3Fx8p9SYYE/p/WsuG4a4EgjkknH+zcatcH/yFFEKE06ea MmazecdR5lheycDsPPulH51dgszWm8O6kjMHuohgkZaW8I49nmX+lYesQDT9OuPMnimdk2kRb/r/ ABTSH/P4UkVnHDE8n9nRRle8ljp8f/o64f8AWsnV7mP+x3ClOZMYjFmAOP8Apz6fialpodzyjUAp nlmyAUjmIyR18s7c9K4uLzY9IiTaHJbOQOnpzXV3MbO1+Y0y62r7AcBckhRn9a56NnhsreFtuNue Mdjg4qRFa3Z5JAJD36dK1jGhlBAOBiqSPHwT1zkdOlXVCPgqTt9KANm2lFs/7o7jjiuk0y8kQ8sD hWJ/E8Z9K5CEBdwTj3PpXR2wDyRiPlhhQRzjPtQB6zpt6La3iEYtZikeCknns43fNnEMDkfma1Yt R1mVSLZZFB7xWmpTdOO8cf8ASqR0+8itltpr2AADGw3eplR7BImjT8qU+HIJ4j5jWMidctaXVx+s 13/WquFi39s8QeSzSi6VV7/YJUH4G4ukH6VRbVr1Y91xcGMR9N66ZER9fMuXNQNoemRcf6JGTwTH pFmMY6ZM8knaniLSLSPadciUdxBBpduF/FYM0FW8ir/a/nMXfVomx2F7piY/CJJP8/lT7bUgG8y3 1F+OMx3spP4fZ7Ln86hl1nw7bApP4lYD0fUYYyPwgiQ/rWbP4z8GxxlG8TtcnHzbtSuiPYYVs1Vw sjo4ftVyxlR7mUkEHb/bMxPv8iR/zpVsr5zuksLiYDpvsL49P+vi7XNcf/wnHw6KYkKXT4ALebfz foXOaSPxh8O2J+w+HjcSf7OmyS7j/wADyaVxWOrFqsbtJJYIgHrZ2MX5/aLqT+VPhu7WKQnzLeHZ 283RYcfgkb1zVt4r0+OTNl4Nvrj/AGYdFRMfTCZ/z+FXh4q1lpCbbwNrCFugNtHCBjj+JaQWN59f tt6ouroAvRV1GEDPXGLazBH4Gp31iWQpHFdu27oFu9WlB+gjhQfrXLP4i8etkReHL+1Rf4Xv7e2x 9TkfyFJH4q8ewkqNLsIye93rcJA/J/8AP6UBY6sreS8NBczqegW11aYfnLMoNI2nTuoVNImJPY6W P/a93XGy+KfGsjjzZvC8BPZr95D+GzJNNOq+NJ3/AOQtoER7BLa8uD+GI8VVx8h27WFxa/K+lGEj BO600qDk+7vIf1/wqxDa3W1gIVhkPQPc6dCcH/rnbt/OuDaDx5Ku6XxFGi+kGhTsv/j6j86il0/x hgNc+KdTX0+z6XFAeOP+Wkq/ypXEekqNTt0wZ9oHb+0ZTj8IbZf0pkaX7k7r75vT7Vqcg/QxivKp 9J1KTD3Xi3Xn9B51nD+BzOf5VTbQ9PYD7RrerTn+6dXtUz9fLDGncLI9cOnq7NJMIZWPUtDdy5xx /wAtbmhdP0+MElbZc9c6dbDH/f6V68qHhHQJhmQXlwfR9Wnk/MRW2f1qzF4E0Lnd4ca4z0Jl1af8 wsaCldisj1DTrrwppeoRXusR2Wq2kOS9hKtjZRSsfu+ZJbrv+U4bHKnuKTXvGPhG7vPtunTab4ct kRIls9MvY0SQg5Mkn7vls9NuBXEaX8NdOvbyCzs/DNjHPdN5cb3NrfJEuf78086qnT0rodY+Gtl4 ea2hlj8M38twGJTToYbjy9hI/fG4mj9O2aloVjNuvHXhaNczeJpFJ651SYDP/bNVNY0/xD8Bvxda oJwDwZb++lzj/ePP51pjTbC0PyQ6PbnrhE0WA5+p3kU9bqKNwo1WygYdAt7Yj8P3VoT+VMdkcxJ4 8+HDkjEE5P3Q1tPL+hcinr4z8HgBbPw5LMfSLSdxP0yDXVHVSzbRq/A4/dXd+36W9oo/Klk+1TYE d3dTjrhYdcl/TKfypajsjn08YShSNK8IayinoYdHiT6/8s/61Z/4SrxjKmIvCWvqvqYkgPHrlRWx /Y11cKQLe8uSew02+f8AS4vBSf8ACJXcaM76TcKw6sdMsofpnz7t/wBadpDPIvidea9q+gwXGr6R PphsXKxNczxTNIJcFgNnKY2eh614bsDIrdGGABjkV9M+PdIeDwfe3BhMXlvHggaehzuxyLTL9+5N fNAXySWJxgDjHepEy9G7+XyQQpwB7VrRXGbeMsu1sY/LisyyAkQ57VeZFG3JxgjI44oEa6xxyqi5 CsRwPpU9rH5Vyg42nufUcVkxysWXZ1U4BrXALSxtjJTHSgD0PQrgvcojHH3jj3AwOtb7aFpl3EZ7 7XNRhlO4NBDqUNtEu1yPljMbkcD8etclpLN9sicJzGcEV6x4Z1X/AIlwzeXcUnn3B8uC6lij8stk fLEhPelEpnJr4J8PXDH95qd7t+YbdRnk4x28i0BqeP4faA2N2hXcuepdtVlz/wCQ0zXpMt5HdoQj 6ncjHBaXVnCtjrtUAHniohaeaUSTR7mZWGGMtndvn3/fOKsi5wI+HuiLL8nhYMo4zNbX5/8AR1xG Ks/8Idodvgf8I3ZJ/vW8P/tW9cfpXbvYWkWEg8Mxxt03S2VvGMDsfNm4qKS8tLVgP7L022PTj+zI lPH8Q8x6Vgucl/ZGgwbfL07SbRf9o6Oh/wDZz/n8KuRm0QL5NxZIRwBHLbrj/vzbH+daba8y7vst 3ZQseABe2oQD0xFE38qeniS7IZReWzAjBImuZMe6iG2A/I07LsFysZ7lhiO7DAdklvX+uFitR+hp 7R6i8SlzcujcAi31J0+mXdB+gok1C/nUmGWTBGN0VvqLk9u+0VdudJ1dLVJpnaeK4wWijtHLgqOG cPPuB98dKLIZjvp8/H+iO5Ydf7P3E/8Af24z+lcD46tZbXTrJ5Lcxo9ww5t4YMnbjBWNnPHuRXfu kwPmt9oRz3aO0iI+gkuDXAePGjbQjIZBK1vMjYMtqSu7gnZBkgVMkB4hcxxNqGJU3xJExkwN2VTG c5P0pNWi/wBGsGVAsbW2VAGMKp46e1Tys7XaGLDNJFLgMMqfkBwR6cUzUzjTdNACqRHMpAAAA8wg YGf8+1IDAO0kHbgjjHrVxBtjC9d3+RVUJiQFj82cA9uafGAEC9SMHP8A9amKxoQsyFCo5zjj612G mHzLyIDqOpx3ri4wUKdwDxxXa6Kxfy8HBd8Z+hqWM9tRbFY7A3tzPagRSYaFgpyWHUAZqKZ/DwTC 3V7cuOB+9kCn64B/nUGqXUUA0+IyKh8hmOZCv8Q54BrIa/hk4W4R+3Etw3/oKAUrItaF8Gw3nzLO Vozx8sk3I9ulQTro7DeNE5Vdok2knr/tn/P6VnvvY/6sufaC6c/ripFguCFxaSBT/ELIpyPd3qWW TPPp+3EOmRxtjaWcQn8yT/hU76/DDGqxWlohVcH54huP0GcVZt/Dl5dWTXovrGIohZbOT/j7cA/w oI2jH4vWLDZ6hNGrwrMyt2ElrHjt2BpAF14hvbvbGJIbdVXgROBn67UqD+2NUmURNc+d/CoRXfnr jhKtLp2pyh1QXJ8v5nxd5CjOM/u1/kP8ans7aWKSV0ubCSXa0Rhv5LqbaCPvKNoCn3BzQBlfadSb fiSeMNjmO3lzuX1JI+lVJBdF2eVrg7/myYgOv+89XG8PtvAaey+qpdS9P958VHFokIMhaeIFNpUr Z8NkkNncSeBigDOkkwAHnfanGHeBAPw3NVOS9jjBTzkTPQC6jGfqFU1v/wBnWvkNJJPPHIOEAtoF Q44O7NZ8jWMK/NdTk+iTRRjP/fOBSuBj70fKh0b+I4knc/kiCpBbFkMvlAoOuI52P5HFXFudCQg3 F8Y/aS+DYH/ASP5U6TVPA8R5e2mHYvcSnJ+lIDOMLIdy2ZUY+XNsv6b3/nRvK43bkx14tYvw/iIo bXPB6SDZbWhj9Vjlkb8MmmR+IvDqvkWwkP8ACkdljH5n+lADmu4kX5nMfu92n/sqVEt/EB/rkcHp m5uG/SMD+lajeKYTj7PpV8do+Xy4AoH5rSSeI9ZumITSdROUCgMqoODnqFFK4GSFjuW2JapKz9xF eSn6jd1r2nwTr9zexLpeqRyrPbD91O8TRLIM4WM7/wCIdueleWvq3iZ1Yf2Lcxo3JEl4EH4dMVCN W8QKwZINPtyMf667MjAryOAT/jUzV0B1WveXLqV7c5IlM7ZOMZwce1ZyDz4io8vK/MQw647CqUt1 Jdv5r3UM9xJ80qRAlQx544qrJJLuLRMUwOeMdvwr56unGWp0x2J5ZLi1kLPtQnpsOSB2qIXrSOWZ QzcEMeOR06fTg+tVR18wuCf9vrUqvHuUAAEnk9KxU3zaDkj1a78S6nr8WkahrEpu7m3hWxEshJeS OBiE3H16flU9teSE2ysd7xsWcYIzj8K5jSw82mW0mSUhmkyB/vE1tWj+VewSO26PcAQcdD1r9Uyz XDRPk8V/EZJrkzLJ9oJzBcoGUjtk+lR2cyuqKPmLDH+FS6orwILeXDtbuVQHHzRONwI/E4rHsJ/L Qv0KkMpIxg+mK9GKOZs7/QdRmtrt7SZcwSDBGOjY4P0o1qweE7UU7d+5QPdAc8fjXD3d08N3FdIx QmI/NtLZOe/tXqVlPHq1tukGHFuAxA5G0DlfTNdMNrGLPILq8kiubWG4LeQ7nLEnvxxXT6o8FpaQ LCzKrqd+0AoQWHyg9c49K0dU8JW0/wC+gjzLhtqk/dYD5hjpyBXPkq0en2O/EcqooUSZZchnUMnY Y7iuPGu1Nm1B3kjgmivHVxuAG5mIYlcZPAwahjYCPZNMIzjaAMn8qsX1w04XCqrHnIIwM9ayxMLW UvIizQsuHAfDY/2eOK/Jq0ffPsoP3R1zH5dvFIpYo0qr2OOasW9nG1wIpTgDn5eM/XrUkzxtE8Kx vAuxSkbYJO7oQaSzEyxTXBGfJUZx/vf4V+nZWrUIryPlcVb2jO503UPskqeUANm1VJXOMDp9M+1b l1M11qMd2w8tXQJIFbbgjpgVzt3ZH7PBqFqTgSIWHQ4Kjp7Vqeekt1Guec5JAwD+deqchqRTMWdX YtG2QQ3OK9G0O1kkitbMjcH+YqcgmvNYIJ52uJIUd/LKgDGFGTz+let6NC0djcXjHD2q5A/vY7ev 6U2iTcnuW+1C1VVA3YzycYqvfCP7VaxPn5t74GAP3YBqhPP5s9vcIG/ehAQOo3Vni7N3rc64CiCU RxknJwnLfmaxdrO4anqWjoxhkduDLKzEKOBn6Vq7McdajtIfIgjj5DY6dOevapmzXwOMqc9Vs+pw 8OWmkROP/rV4N8fbtYPDWk2R5NzqAlKjusEZI/Uivd3baM/54r5e/aFneXWfDNgvP2a0uLggf9N5 EVc/ghrz675YXR1Q1Z4VbTQMx3KxI4UcHI+hxXW2eqQiIQSuYlZSpAwACeBXJ20AdQEGPQkVfML2 rHzdswxnljxx2FeNGR1GrPZbZbd4V8wkDLBsN/hWnG5hhd5lfgEeWMHNczHqEoci1Xk4OHOcDHbF XBcmbA1FZImToVOAAe9aKRB0sH2eOSNpoyowDs8sg49a3LkC5mVVnRNuApIO7HtgelUbRLzy0e1k DxSDafN5JHb/ADirUMyGSS0a2DNxiUg/Lj8KsCDULOS2hB3RzRLwwTrg92qKFYowF2ySKccD39xW lcXywxLbReW7A9QM5H+1VGOdpHYALDuBAyM7sf3VHNADCDGrKzbYyehIGB6A9ao3UcY2vuIj/wBk D9SSMfgK0mRZLbzIx5gXjao71UunR4Rb3A2yegGCe4FAEEI+zswkKyhumD0/LPaul8GadZal4lgt NSvI7azZhK6ytsEhUcLu42+vpXNARMq4zuA+6McdqV5zHIu3qvIXaCpzwd2e2KEBu+LYbI+ILu30 q2WKyt3aFfJ+6/l/L5gY9QcVveDvBU/iaO6hWTh1SK1n25kW9T51RcsoyFbO045xVfR4fDWuWc2m eInk0u/cItne2weRDIekckIBzuP3eRiq3hXW7zQNThuNO1CDVNMjlR2LRH905+UsY3+ZJBj5iBxg VomBaj1G90LUYr6R4NQl07i2u/LVmtnDlXjdWD+WdpfHOPu+mKr+LdUTS/A11c6dEYb/AFGQQ3F8 u7d9jORMFZR+7DuAvptOPatfSYde1PxKbLUfDEM0V87f6TbNuiuXY7k2KSCS+ePT26Vhkzw+amlQ zQyCSV5bGZfMe38oGSR1Yg8AIAUI4Iq0yTwWGVZsGNg2DgFQMLzjHH5e9TtEjQmWR/nH3VHT617H 4otLfx39j8R6MYDcQQvugSFYxKjASIvy4G5DuUHvxXj8jvBOYWtlEkDIxjmQFTtGfmGR8p9qiUQI o7V3BlEi4UcqQQ547VrWp8lfn+Yeg/wpywpNdvdx20dlvbPlR5CKT1wOaay43KxCk9faqhHyJZcz DMoLR+WM5OW+b8MVNPtEYaM7O3HPGetZaRZHnTfME+7jipVJlyqfKcZ9M10NmYPI8h5bPTrx2x9K jJwMAAk08Yb5Wx8tRjbu+VcH165rMsO+So/Cnq3JPocH2FRj5gOce1SH5PmDcj9fzppAStIJEKnH HvioUkOdsZXd2J7Y574pY0SXneMrzgjGKnjiDMN2B6Y61qQakM9zHoV1pE6P5LTRzqCpw0chOdjY 7SLzt9aw1BAXdnOOc889/wDOK9K8PR6XqlrHpt1fM6SCS1eORBvt/tBGHXPyuAyK3XivMZd5Gw43 Dg443dq46Ek5uJZ0vhDxDr3hu6u9Q0WTZZL5f2qOX/UtlvlGOznsV5/Dmu01LWrTxvc29pNoo1K+ uN62lxZzvHcWm87nUgqUdQy7yHHf5WFeQ+bcJBJaK+IpHEzRn7pdFKof+A5Nbugy3dnrNnd2N0un 3EcgaOVgShYfNtfb0Vx8hNcWJwMOZ4hLVAenaf4G0aG0jj1RruS8BYyGBFePJYkANg5IGAeTznk1 d/4Qvwt6aj/36X/4mvbdC8E+H9a0ey1a38T3thHdxLILYWEE4iJ+8nmvy2Dnk1r/APCtdG/6HK+/ 8FVtXzLxFbuLU//X1YfE/jOMM1sRDjqWughP4Q2608+LPHUzZe/b6m4vG/8AZ1rjNwdgNkecclRf OeOO7KD+VQ+TDv3Sxtx0H2Zj/wCjJ/513+yMuY6Oe9193Lzauoc+qzyAf73mTGsqd/tRK3euxKB1 VoYBn82J/Os3YYkL7tibsAAWUY57c7z+tTfbI4UIRlDj0ngX/wBFxf1o9mTcVodMhw0Oqbx6xxxq B9NqZNTQRabKuZL29uGfOPKEy47dY4xn86hGoO6ALmQj1mupv/RKipZBdXMBzYSMDjDeRqDZxx/y 1lUU7IenYl+x2QxI0WqXSqR8skc4U4Pcs61JPFokshkk8OiJGO4rLsKjPp502ap/2LeyRhF02Up/ tWK9+f8AlrIwoXS7yNQsdtKgHH+rsIwPyV6fKu4uYer6PbAtaabYRMD0eWzHUegJpbfUCAPJist/ QCJ8kfQQxGriQ6xsWKIsmOh8+Nf0hgFXY9P8RTrtTddEEf8ALW7kA/BAv6UXXcLFX+1dXTCoEDg5 CiK9kI9zmNafJqGuAGWZSB6iwl/9qSgfoK018A+ML3b5els6nnKW90f/AEbJWpB8HPGNwyhNNMTH +IWkQI+vmSH/ANBpe0itx2OLm1C/TBadow443R2UI/DzJm/lVZ9UumIQ6jIWHpeWS/l5Ub4/CvSJ Pg54stW8x7uK3K/eIbT4Dxxj/Vk/rVM+Dtbgkc3F/hYxhSdRtIULevy7TT9tAfIcC88hYL/aNw4J A/5CFw4GR0IgtxirPkK8gV4ZbgY5P/E1nz+BMYrrbrRIotn2zWdKiAHzm41yQjP+ysb1nSaR4PkJ M+s6NMzcfJcX1x2xwFDZ/OodaHQXKc7NpsAcf8S1EC9WbTpGP5z3XNNe3s4ZVAhhQY5Jt9KgH5yS yH9K6T/hFfC+6Mw3cM+35j5Gh3N0QPQeZiugtPDGi3CAQW2suwJIktNAhib6FpG/pUe0iHKeef2j BbTKUvI4VXjC3mnQgf8AfiE/pTo9ZzNhNSZ887RqN2+P+/Nuv6V6hJp2i6aCtzba8resp0627d2Z uKrN4g8K2w2TWlxIR2uddtUA9M+SCatVF2DlPP2mW5mUiSSYnADKdUm/IuQP0rgfEIb+0bl52Rgq gIEyOijOd3P617ZJ408GliTpFi7gjJOszy+wB8tK8Y18RyXt1JII0EjkqsZJVVPQAn29a0jUuZyO OnZ5lUI4XaOSKajXCCPYQCD3NVgCkhXOeexq9EwaRUI4PFWQbdi8rS/OQ2Tgbc8/SrxYvtyerHg1 Hp4A2xqQpQsQfTNTSKEG0cnGT7GlYojYtnaBkZ5xnFRCd3WVVK7USQYPAyQeuap3E0w4R8f1qBmU abcb5fIV1KGTgYyR69aYH0FY6bC/hjSLcabLZ37W8EkuqRyhJJEKAiLbJ8irjAyRmscxW0TulxrF yjL0V9WIVf8Av2wH5Vzd1pFyZX222doQKw0+LO1UA+/LKN35f0rPNtLBGS961iuT0OmW49+G3moa kPQ61YvDUjn7fq9m5HafUbi5zn2VjVuHTfBwDmJ9In3DOPsl5d5/NM/SuGTV1SJkGtq5HQf2jbDH 1EEJx9KcmorOg/0v7Q46A3OoSg/9+IoxT5JFXR6XDYeDo8GCO7lO0bvs2gIij6GUj+VcR8QGsls7 eDTY7lIyDk3VvBbk/RYSen51kTWhvUXNu0hQ8f6FqcuPp50oFZPiOE2lhbK1tLBucnEluYCcD+4W bP6VKj3E0jyq/uDBZX/Jy8aAg+m/gfkKzZoUlgtmWM4CcAdAa09Rto5LLURJ/q9sXCtj+M+n+NUb iK7jWNrMuiLGMKcNj/GrsSUvLKMPMX6cHHFW7dvnRegbp1qAapfQkrdRLKMYzgCrVpeRXE6KSEY9 BSA0Yi3zDseAa7HSIfJjSRm2ndneCvy7R2Y4C9e9cfNBJEMg7hn+tegeE7eG4uwt5cfYrdIXYTts wrdAB5mU/OgCOSy0Yvtu9e1CZjx8+twx5P0jVzTE0Xwu8hQia7buG1O+n6eojgU13632lwYjfxRd DkfMt1axDg5x+6iBpza5ol1PJCt5fXEaH5Xe9uX3HrnEB6VXIizho/CmhSP8ugx3A7brbVZ8/iSu a0V8KadCyrH4TtwPU6O5P/kecfyremfSbpxHDo95d8jLBtRmYjv8pJH0q7Fp+m4Lt4UunA4UtYyY H184gH8qBXMD+x4rPaBotraqfWy0qDH/AH9mb+VWGdLcb4ZLS2EfBxPpEWM/7iNWyY4oWPk+Go7M Du0NhF7/APLVx/Kpn8QzwzKWgs7SNV2oHu9Oh298jy8mpsK5kjVJDH+61iM54Bj1JOv0t7Q/zqRr m/ZArahNKWHafU5c/QxxRVrnxhcELGL7T7dcbcLfvL+OYojSpq+pSY2XcE4PG5Yr+4yPwiAqkgMq PTrmWI+Yt1Nn/p31OQc/9dbhRUSaJbybv3Cu4/h+wouMevnXjD9K6Fjq77d4lkUcq0el3L4/77lT +VQXFhdThTJBfIgfcfLs4IMtt4O6Wc/ljFA7FK28OkIJUssEjIdYNLjGPbO+rMWhyx5mlj8sLxhr mzj/APRNsaiMC2q4ea7weokutPiAz9DSJdWa4H2tOOok1SAD8o4Sf1phYti0eJsvd7A3Qf2jc/yi gX+n9amj0+Kbc0tzbyxIpbDy6lNwPbzVX9KhXULCc4iNrJIONxvbuQnH/XKMD8jSPHDdExfZYG7k +XqsnPb+IZp2CxELLTDJvjjs3U8/LZyyf+jJyfzFWf7J05Z0McVqJXOdq6faxk+5kcsaqjS3D7Ir BQD94jTLps/UzTgVG+iTD50tfLXvjTbNR+HmzGpJsWLia20+Tc9yYSpxiMaejt/u4i/CpX8SaVtX ztXvIyP4Be28Z+h2xr+lZr2pgXMV0Y2TPCro8C/qGI/CrEeqGCLamquSw6f2tZW5HsPJgNAF3/hI dBcL5l/cyFuMNfXcmf8Av2MflTXutIn24juJx/dEeoyH8C3Wqq62XJX7fEzrwFOr3bn8fs8Ioac3 QELrbTqw7/21cD8MsFp6CsazRaG6KP8AhGLqcDG4tYStu/7+lQO1VIIbmC0HmeHRbk8Am1tYsAHg He47cVi/2ZCG2PZQEeh0q9kPTPSacZ49q3x4Ju1szqOdPtU8rzlgSysY5pFzjaI5JmYMfQ8+1JsQ 5dWvLeJgNP0+1J5yZNPiIU9v9YcVEviS6EoZL6wTA/1aapAo44/5ZRPVGKwurdlki+2QNj5sx6RB t9vuM361qCTU0TzUu53VepfUraLH/fm2/kTRcdvIiPiKedyP7QtRJjOI7i9m/wDRVvio/tWp3BVE uPMAbdlLLU5tx9CcJxVoS390fKEqSPt4E2sXhJ/4DFFHVGeynuUMcxsi2P4ptUl6cf3x+pouNItz R6rcMHaK6YnvFo0qgY7fvrgD8xUH2fVbcnfJdxoxwQtvp8BP4yXBNZNvoy3PSHTSS20K9hcSkEf9 drj8jUi+Gpt8iebbWuwZKR6VaLnHo8jvj8qVx8pkeMYll8NanDJcSSkQEhJ7vT25Ug/LFBuc/TJr 5H5Cwh8A7RnAxz3FfZLWLPay21xqcyRXKSR+T5Onw78rj5/LjzjPAwQa+PJ0+QADBzgYHTHb9KHY l32LNk371EwRkcEdsGtkwBgcDv3rCiOx0lHVRjAx1rpbe5F1CEUbZYufqKQxbeFVK+x5x2q8gkWU mPgsQvb86zhKcnb8vPJpX88yKYXwuQfxxQB3ukS4h1CYttaOH5T6kkAGvTtF1GLT9LW1nultwssg CS6jcWueeSIYIzn/AHt2favK9OHlacQ+c3MkUZxjorZPWu00/wAX6p4dRre20jVbwzSyzs1hlkG7 5QCyoxB/2c1N7FWO0k1CyfJ+02s/PCifWLo/yXNCJDcrmCwSYL126RqMuP8Av9KBXNxeMvGc0pkg 8FaxdYG0fapJsfUAgY/Oi61z4hXUi+Z4NaLauP8ASLwpx6HdItVzInlOmSxXLbdHk3DndHoMEePT LTTf1q9aW9xMjGFbm3CdVjtNOgyfw3la87lv/HrndLo+iWnGP397GpHYf8tTStdeN2GXvvD1pvxk CfcOOBjyw1S5hynd/bNSTMMM14TnnOoWkOPr5MXFOEmoTqDLduNvBEmuTscf7qIK4NV8aFfLj8T6 ZArnn7Pa3Eob2IWH+VW00fxq65fxPOVA6W2kzH8tyLT5g5TqprBXkUM8Mpk5y11fTLgeuSgrPbR7 SSYQ29vYGXjLNYTsABzjMs3esV/DutmFEuNf17cGLu0VrHBvB4ClZHGMHms6bw4/3bzX9cYDBImu 7SFDx1wWJo5h2O3XSELI8cFtHLITgLp9quMcY3PmuT8d2SJ4TvGjl3yb0xGEto8DOS37pB/PFY0v hzQpQfMvb27PVQ2rRAH1+6p7Vmavoek2ekXtxp9pscRqPM+1y3JCMcEbWVEXPFJsLHjwYRSW88oV owJB82cbfJc/w59K0JVhbTrTySjwq84BjOV++G4LfWqKyyGex2KG/wBKaMMBnKvGydPx6VpwR+bp cLZLgtKPmUJkiMdh07UxGENrFWwQPl6/SliIUEEcjuakuUaFghOB1FLHGjqRuH09aYFiFi5yBwME kDjFdTpUZjQuD8u/I9uK4/aArmQH5Rxt4/Ouw0ORJRCv3c8EfWpkB7JdeMJdPitNJttDu9Q/dxy/ aLeMk85BjBHbuazz4r8SOQ8fhy/OB8qSyIqj0CjH8zU97CkUlvbyDBWGP5d03OSf+efH61mC0tnG 37I0n/bGV/8A0LNZmq2J38ReM2XyxoSxL12zXiqBnk8Zqi+s+MlRkEOm2q8Ha91u5Ptk1OumqrKY 7CVBjkNar82f7oOKmj0q/IAjt51znBWG2QcccZ/nVWGUP+Eh8XgGM6vo8AwANm5jgHgfL/IVmm/1 wuzDX9OjfdkiG0dyCeSOa6BtG1hSAftQGD8nnRJn8EXt+FdTFPrd1pI0JNF0UKV8v7T5Z+1cNu3e apUhvcEVIHmu3XZzmTW58MMHydPK8ZyKb9i1Ar++1LWHz/zxt0gH5lq6eXwteSzmKb7KJB2a4uHx /wB9MP51G3g8qokeSyXPAfy5H6cchnzQByjaWF/182sOD18y7iQfzqs+l6T/AMtVlOcf67VUGePR WrtP+EUgTc8l/aIVUEBbePnceOrGoZNLsrWRopNXKBOMxwwKCfTO00AcTJpvh4Y3W1q/bEl9NJyP p/hT007SOsOn6e+MDKR3cp9uwFda8ekwKCurTyOy/MRJGFHsMIKgml8NMVD3M0q/KrCS5PC9yNmO fwoAwDZxoSyabbjHG5LCQ4/76NSKboY8qPaM/wDLK0t1xx/ttWjJ/wAIiXdorQ3QBIVmec9+OATQ p8PLj/iSNKDn/lhIw6cdR/WpAzjc3akiS4lQjn5pLWIgdugJ/Co21BFH73UDt9GvR+WFjFbFlLaw ypI/h+OWNRnYLUxgHtgkj9atf2pdRRNHBo8URZmJkZYkbaw+7luAB60AcsL2zkYxxXyzSdSqz3Ev T/dA/lUnlxXJA3+YehCwzvjA6fvG2iurPijWCljHJb6U0OnwvDHBI8TRtuYtucLtLNzwc1X1Dxbq dyYWlm0qyMSGMC14G0j+JcHP9KmwHNvpF5G2Dp9whADYNkiZB6EbmFTDTdUXYYrSeMHuTbwd/wAa mbW7yVSPtizMYxGrRRueO+AeKqyXGruwZZr+UdAFgjXtjqxoTAtro+rzFcQySknhXvQqn6eWuK1b jQdTj06S/mltkMAG+GKR3kXnaMZAzWMw1GK1WfdfGcnHlCSFGA9d3OPpWaLvVEkPnWwVRxi5vwBg /lXNiKUZxLUh4UlyjH5l4weR9MmrAgmAL+WMAE/LyOPpmq8pt3eMRGMr1YxsGzx69KkF0+108zKZ xzkYGPavDjS9+xq3odxpMskOj2kAwqSvK0hP6VqeZFkqhAZR0x0xXMRX6LcTaXhlkto4JHLDjY+F OKt3rC3vTFI23DfK3dSP4TX6xgKXLQjHyPk8RK82za8SmWWGzmgIdpF2hwMcx896oWkhIkaQgqwJ FQXt9Hc2aW7HY8O/OBnG4cY5qDSyDPHBIwCEkZ4+6B+Nd0YnOzQ1KcrHbuPlzEyt6EFgeK6TRL82 t5aFpCsDq0cgB6BlwP5Vxl1lnFpIXJUMVzjAwf8A61bej77yUW7ofKbmNxj5WPfrWkWZyOn1Oe+t 1trlJ2f7PIGG1ckkja276r7Vhaq1vDcWUlvMk8DtlAvJiAjOEZeMfe9TXZRG2W2liuW+SIfM7Ajj 1zxgV5xe6i+qz2l6sscqSFpITC/mIF37cAkDHT0Nc+LjeNjXDr3kcO1y8oEWcnPyjHYcVHNbOQCo IVDnOMfgQe1agEcQac5SPsXx3PtVY3MciFFfYp5y3Ir8lkv3nzPsFrEwXiJg+3RzSn7O4P7w5O0n G0Y/hzmu02qbS4aMhdwAz2wOnFNttNin0ks5P71nUEDCvgdPb8ap6da3htJbN1OFBx+B49K/Usvp 2pRt2PksRL32dXp1/iJ7eTpjYykjqPSprFXluSyrnapwrEdvesWO3KzeYzFdvIAxXWQ2wVYpDhg+ W3jg8Doa9Cxhc6jS1UW8oYuNibzJHjIz2PY16DYzC28P2ckro4uJCgzw3HG4iuN8Oq1xbXSLuCTL jDLxlU6Z/wAafqMk9t4d01ZY90gjfIUd92APyp2EbkN8Y3NvdIzRxz/Ng4ZCvII9q39NitL/AFq2 eJm2lmc5AGcctk1wupXs9rcwXeFEcyIrAfxdiPrXpPg1IhqNzqEjYgiRFjbgld5wfl9gBXJimoU5 M1w8eaaR6BJe6dHfR6ZJf2qX8wBjtmmRZXycDahOTnsAKtHGM8DvgV8U/FHXru98Uw6drVtHbPa+ atrewKVx5ZzDIsw5+ZsBhnII4r7K0/VbHXrC11jTZxcQ3SLll6iQD5lI7EHrX57N9T6tLQWbI2rj g8/nXx58atRe4+I19bBCU02ys7f0G4gy4H4uBX2XsDMoUcvj8eOK+CvHOo/2p428S6gRhW1CWNSO crD+6X/0GuLGTtTNaa1OfRFtSJFfcOwGTU0V5GhJlj3qc+xrHnaTOI87BgDj2p8e7A8wEDoPevHu dBt+VaXHz28ckbDHK9qrNJcM+yZi6J0Y8dP51ClxPBgxtsXIyvr9asIWndrhyCTwUwdo9KqLFY1o 9V1KJY4zMrqcBRjHy+uRWrbavHJcJFdfKGO3KN/TrWCYoQqLPiFG/uf0zWzDplrIVNncl5B83zKB jFbKQWN2RImuTEpIOPkAO3t149qtRaXDbsJELMM/Pubd94ZJDdvpXP2V9Iko+0HcC20qQNw9Petm eaNfm8kr7xjcfyHFUtSQuAIwyxPHtQdGHP1GP8KyWjwpneRnbjkgA9P4fStqKNZ0LxxnceMFMZAG c1Xlj3Mkc6ttHTb0OeaYFQ25kUsHO0jjGPTuaY22KJfLRHbPBJ6469cVoSzpBEFiDlAfmUDnGP8A GqEz2b25fJUKpOSQCigHP58CgC/YXTR29zfW0CzXGnxfa8EkY8lgUXj0Zg57kLgVxPh7Tf7Tnkv3 nnSG0X7RNLbt5btI7bVjVh2JzuyK9dk07xdYR6Fpvg1be21Ka1e41C4miAdFbB3yNKGXyxz90dvW srXtZtLg2+naNBBa2tqA00tuoQXl0ow1wQQOGOdoAppgWbrWZrm5GrXGqNpkdrFHEJIA5MaIMDYV 2nOfmrsbTxT4audMj1mS5a4nCSwGWBJQZAfl3yrIMg49TzXBeFzDfXj6VcW/nwzwXAIkHyqWhdBI cf3JNp/SuS8J+JtU+Hl1No2u6THqmnTAG7sZ2aEOcfeWRQSMnFCeominfWWpeC9athplyJ4biGK+ t5o8BZI3Y/eXopVlIx7Dtg10af8ACO+LYGtrm3NnqoVHt5ABvO08qM9QVPQ91Hauf1LUG1m/udQd PJjmkxFGj+YsESgKkan0AFYdwsCGGWbzJWU78MwTb/ukd69BQXLcycjQ1fRLzSJtt8g8uXLR3EWf LODzgdj/ALJ5rOQCOUq67xjnHOO3Tmuz0fxzCbL+xvEAF1YOxILISY9w2MCP4sjHOeoFQ6z4Idmf UPDlz9rspVUrGDlwSCSFP+8On0FZNdhXOPMgkYYOF9BT2klXKKowRjkc1mqjSMwVsEY4b5cfX3qY MUPynPY49vrWd31HYnJyVU5zjgLRududhUDjAxTAcjEfyk+vNThmC7CAc9OtUhktvCrxkuwUjnBI qJZvLidiQFHUYzwPWojCzEHG3HU1IphiDSORtTDFj0Xjt2x9cU0wOo1nwV4m0HSLPxDf6eBo+oNE lvfQsskLNLH5iLlSSCRkcgfMCvXiuVe4lwfLjCY4Jr6C8M6f5Xw81PwL4p16G1TxDDaajpNoQZDa zb/MjYP0AnUEBQQAx5wSRXPWfgvQPF8Ot2nh601C01nRtO3x2l5JGz3js8f2e4TA2iNv3gkQE44O ehrzaOZqz5+4mjxm11HULO6ivbNf39tIkqK3AZomzsIPZsYauo8U21jD4iurjT1KaZqflanYqeCt tejzY0H/AFzyY/8AgNR+JvDw8JajJodxqlprF3Ag8/7IXaOFxyIhIVUOw6HZkA96u6rrFnrPhi1s vN82fREgWN3UJMlr+8jMWRw2133KVzgda65VYpqcVuGpyryKrkY3qDg1nyuXYEggbumSM4x/Lt71 K8TN8xYDoMD29BUQ3Rvllz1xntn0pzqXZaR7J4a+L+o6PoVlpc93pzNaoYwbrTjLNtBO3e6yKCdu Ogrd/wCF43f/AD9aP/4Km/8Aj1eDfY7CQB3kuVYgZAIwDij7Bp3/AD2uv0rz3g4F8h//0JmuPhi3 zf8ACTWjn+7FFfXJ/QEc/SkW78ARtutzcXp7CLRbiXP+75gUCuHXWfFTKIhqtxEo7fa0hA/79R05 z4gn4bWpJgOqLeXDDp6JiuvlZnY7+O80mT95beG/EkiesOmwQb+3WSrMcrRg3EPg3WUUdWu7+xt/ 0615c+j3szBnV5C38TLcy/nuP9f8KenhiYjmyEpHULZKSPf52P60+XzC56O2vaoxDQ6VZ2SrwPtn iC1TH1UDmo7rxRPCg86Lwy49F1Sa4b/yEoriI/Dl7FtSKCWIZyB5NpFj165xWpH4c1plBSR4+eCt zFGQPbykH86PZMXN5HTaf4our6Eiw0rS2mQ4Vl07UL08ejdPzrZt/GHje1JS6vn09V6fZ9CZVH/f yuKGg6sd0N3NPsPXN7dsB/3zxVaPwrZOcYgd8/Nv8+UkfVmApeyQc3kd3N478SSMFuPFWstGeQYb Sztvy3uKhfxw4Qm88WeIZ1HAjN/aWvTt8pb+VcS2g6VE+0LaIw+8VtY+f+BSOf0FaEOkaDFF5t3r FvaOo+WJEtg3tknP8qr2KHzly88XWlwnN1q1wen7zXcjH/bOMfpWYuv6PtIbQpr1uxm1a9f/AMcR QKaX0OI7jq7MCcZEsf6FIxUq3OgZG24uLte582d2z/s+XS9hEXMV/wC07WYYh8J6acdmhvbjGf8A edf5VXe9uom3W2h6fasf+eeloP8A0fMf5VptFpMxyum3k5bv5Fwc+md5xSpaRW4OPDkgVujPbJH+ rsKtQj2JuzJOsa5ECVltrUDk7bbT4cfjzj8qj/4SfWsEp4geP12XduB9P3cfH5V0X2hYgC2jxxAd Mz2kYH15NJP4iut0aq9nbBPup9sQj6kRJVqKXQm7OebU9TnRTJrl7MW6Bbq9fj/tlGoP5VDJZz3O A8V7cBuoaC/k/PeQPzrXk1i9ncf6VbA9A0TXcx6+qR/0pryXk52uZpk65jtL1s/ntquVDuYi6Iod fL0t1LfLxYRLz9ZZauHQby2IH2CdB/dH9nw4/VjWvNJrEca25huhB94BbGKPH/ApZQ2frVd73UY2 IlnuI1PBLTWEHbvuLGiwuYjGk6vHLHGxlAfpH/aUQ2+/7mIgV574hUiS9jP8MxB+bfkj/a713r3i Rkm41FeRgH+1YQduR18mLn8TXD+JZo5Ly7ZJlniBypQkg9uvf8qfyFI4xcOS3ygLwKvQx75EU/Ln nP0qlZ2zhSzgkE5x6VpwxTPIrhgv6/yqrEG5p+PMfPAAwD61JPPAmQ2dxGOO/wDSnwREWzPjJQZw PrWS9tIQWL96kopzSpKwA4IGQFrRsg8iW8QjObi5ijyV3ceZkgcdxVUIkJOAOF+taumzSx6rpMJX 90JJLiQ7c/LHHkZKDd+FAHp90+lXMrS2/hiVvNYs5kskYnnqpkb5c1Ra7SzZnsdAisFHBMrafG34 s7ZrMW1hlzLb2COTkgxaPcSBvq0suKtwW12wBtdLvICjYb7LpdjFyecYlc0uYqxqjxWywxps0pNh zmbUoQT9REDRbeKdR2yyW+oWTlzkhZru44/2RFDxgVTU+IG3+RFqkRTglbjTbQ8evlxs34VBLHrq o73BuF2ck3GuMvB/2YYgf1/wD5ybGimq6xe4WO5SVv7selahOQPqyoP1FcJ4zjuI4bf7QHVpC+A9 mbTJx/dZ2LVvrFdXmdy2z47yX+pzk9vurjP51zXiqCHTbO2kZLMGZ2X/AEaOZRkL/GZ2JNKVmDR5 ldqh0/UGY5+SPIHO3D89Kpy3QgaKPIMckakdeKgub11tdUkt3KL9nzvHOD5g/wA81C0TXNhbsSrM YlJcfnTQGvMY5EKiMNu7iqtvAv2kII1XZzuqtbzxwcSEsB6dBXU2NkssLXK4ct8ozUgTImQoIB3d K7nRIXS2LpG7E/d8pbVivv8A6UQlcekbxuqen+GK66TxD4J0K2i03xHGlxdyBZFRoPP2jGMYPAoY WN5r+W2yk1xPbEAHd/aGk2/X18uMkfhmopNdRwEn1dDj+94ilYfgLWEfzrmm+IPgeBALGwKBW3AR 2MUb8jHXApIfi1bQZWHTtTvVHRVCp9PudKftEUb/ANs0+5TypJ4LkOeCbjWrn+Srn86ki0q0n/1e l/aFboyaLqE/Tj/lvOAfyrBX4q+Ip5ZJLDwvqU3moqYYzNhVPbC1LbeLfiLI/wBotfBd07sP+W/n 4X2wRU86FynSR6fLbgrDotwVU4BHh+1gAz6NNM38quIutx8LbX0YPQqmjWuPyQkfhXFnU/iuWDw+ G7S0IJYGeSNdpPfDuKpvffE9j+/k0CzOSSXntjjPPZjT9oh8rPSxF4kEe4tfmL1bWoYx+Bgh/lUb 2WsTf6x45OOlzq9/Px/2zQV5c918QbgbJvF2jRITyLYmTb+Ean+VSrpvim4wj+NywP8ADb2d23/t MCj2nkFj0eLw9fS5RV0xMtsYyLfT8kZ/jcdv8ilXw7Jb+YSNLg2HbmPSw4J/7aTE154vhLXJ1Im8 Ta5OOmYdPmUY9PnZKiPw/wB5AuLzXpMD7zrDbj8fMnIo5ilE9WbSjZmNW1mKFXHzG0sLBNmfZy1Q 3D2dvOY08T3ZUDmYPZW659gIgfyNeVDwH4ZTcJ1v+Ovm6nYxZP4bj+tLH4T8FAjOnW8hHAM+rmT/ AMdgiqbhY9Ln1Dw6qg3XjC5fAGc6gwz/AN8KP5VkzeI/AcZb7RrzSRouApvLqTefc5H/AOqucj8M +FR/q9E0ls9y+oXH5YUCtq10KAQR21lo1oYVdpFC6PdTAMeOGdhmi47GdL4u+GwIWd0kiXgqguJs +n35DVYeO/hfEW2adC5H3SLAbh+ZrqYfD+qRsq2+kxwjbx5egxKQB7zyH+VXV0nxJbnLNdW4PQxW 2k2nHsef5U+YVonExfEnwnES9ppr3BwceVp0EZ6f3gPwpsPxEGAINB1W4IIb5bdV7dBtFd26avEo 87WbuNCcbZNWtIs/hGnH4Cs6UQxn/SPEKYxkiTXJ274+7Cg/Q0+YVkY0HjXxe650/wAJ666MSQSX VeT7AUx9c+I025v+EQulQjrc3JTn/gRBrUMGiSyAf2ha3BP3v3mp3X5ksKemnaIG2xwxSA9fK0i7 nJ+m9zU3AwW1b4jF/NOg6RbSKAga4v49yjqfvSVB/a/xAQFftHhu05yo+3Q8HORjDH8q7WLRNMyJ LXS7ssOAyaBHx+Ejf1rsNDTStPJOseC9U15/MDKskdppsQXGNpSMq/XnO8Ubj+R4kL3xxz/xUfhi 0ZzuO0tIQTyTwj/5/KnGPxpMweTxvp7lv+eFjcSe38MP9K9Vlstdlknmh0/VoYC7MsT6rZxJEhJ2 pwpOAOOv+NQyQaqzeXNZLEpGf9K8QPg44/5YJRYLnmf9h+MJxufxnfOvrBpF03T32r+gpf8AhE/E EnzTeKPEchU4ymmGFf8AyJKtd82jSykC4h0YE9M6lqd3n/gKqKlPhIbtn2PR36ZKWN7P1/32Ap8o jzmXwTcSjF9rniWRAOkjWcIx6fNcH8KyW8E+FlPl3d9qDKvOLnWbCEfTALn9K9lt/CSIvmSQadbl T/yx0SIt/wB9SSHNW4tHlRlYXrwA5+aPTtNg24PbduquUdzxK28L/Du3uY5n+zlo2Viza8H+ZSCv y28B56cd68X8RQm11a+tuqxXLgY6Yz7gfqK+3Xks7Uj7T4l1VF6N/pthbAD2EcXHT0r5X+KNjYQ+ Lr64sLkzW04ifzmm+0MXkjHWQAAnIPapsTNnmikcBfXPHNa+nMVuN5PbH4E4qotoY1Eitu38YPGB /wDrq5CuwbiOPujj0FMk0vJ2sOMgk8+4qYRjOB1BHSoIA2VDk5HPtWlZDzZiGAAUEk/jxQB0dyyQ 29pGfkCsGyeK9V0dXMBjZjEWfMYaO+k35ReAtoVGM+pzXjl1P56xR43FEJwecYNe26F4gk0rSJIh dWlnDcSRmV7y4lgDlIlGE8pX3YGM9Kgdy2NPMmd9hcTnHPl6Pfyj855h+tSrpc6qdujXSL6jSrKH H0M0z/ypD4iaRyyajpbMRtO221G6bA427Sgpw1W+uWCJPK4OOLbw5P26YLuB2rRW7C1LcWmXm3dH pl2hJwHebSbfp/uq2PwFPNvqqt5Ln7OT1D61En/oi3qkW1oMUh/tsDGRt0i1hP5yyHH+fpUbnWVU eYmtsAcnzJdMtx+bZI/OkGpca1uWGA1vnofM1PUZunHKxotJ/YMhl2ynTGPU/uNRuMcd98oH6Vkz 38pU+c8ue6za9bJnHTiGPj86pvqEOBuuLADoVm126lP/AHxDxT1KS8zoP7EhQFR/Z6Y5/daMTn8Z ZSKsR6WEx/pDQq/Ty9OsoNuPQtnvXJxS6dK3E2kMT1x/aNwf5jNWhZxuweOO1lRzgFNInl6cdZCT +ZqLi1OhkFpHn7Xrl7bgcZWeyiz2x8kZxx7Vx/iWTT59Cv44L2S5kEUhXzL/AM/hCCu1ECDtWuLS RSGgt5VI4/daNDF+W8mq2sWt7/Y+oRSRXA823k4litYR93H8PP5Uhany9CUeW3+8zJdRSd+xweea 37aJjo6oYfKAuZMAENjMS9wT1rAtEBlgTGSZUDbuR/rBjjgV11ibWPRWVERUNy2QgIUMYxggc4qg OV1GIPIVB2FMDB7is8ROgKkAMvP0HrXYanp5YR5wkmM+gxXKNMEco+Dk4FMC0IGlh5IwRyeK1tOV 7doiDzCd4wBzjtWcsknlYjQOGwOxq7bPMlwMoQADkfypSQ0fSFz4luLJoY7dbPZNaRkSTZDDeOeg 6r2xWJ/wkt+VKG7gQOFyBvPb/dFM0lbrUNEspIYpXlTMZWMoDgcr96rX2K/O5DDg/wB6W6j4x/u1 maIrjxDqEbK8d2NwBAC2rv8AeGCeR6VU/ta+8tohPOu9BH+7tT90HJ+9W2NF1NrV7t1gWFQMu1yz DGcDhRVNtJvN4jBtEVuFLNKQfXHAFIZlG8uwCA96qt1G1F/maqPcXRYczgZOd0kUYPFbH9jyxyNF LLaxyA4KiB268jrSvpTQ5864DgdVhtFzn8z/ACoA52bcmOPMG37y3Prx/DVYyWDqhUxbxlWMs8rJ n0BT29q9I0S10PTfMk1rRE1wyOjBrqZ7XylXqipBt+9/fLEj0xWDe21qNRmZdRmtbWd3kij+2Kwg Q/djbK7jtGAD6UXA40/ZmxmKCXjGFguJc+3UUCN1wyW5A45TTG+nV2ropE8OxQsLjVomlbu+otwA M8hcdT2rFuL/AMHrETHLZmYqpy93I4B6sCc/lQBXdr0f6q0uB2yILaM/huNV2u72PBIuUz0zc2sO PyrT/t74fLCCqWzzDqgjaZTx/ePP0p0XiXwgkRMdiHk27cRWZCj/AHfkNTcDFa/uXO17lQO/mamD 7DhF4qq8u5iDLaE9MtdTyfntUV1UvivT3CDTdGvIwFAJ+w+Zux3GdoH5VD/wk+q7h5Wh6hOMg7Wi RBwc9M5qdQOYaF3J2taEgZO23up+vTGQKlj0+8kI8iGQ5yB5WmHt1++1blxr3ieUEf2JdwIZDJiW dYwc/wAPJxgfSoh4h8TxxyCPSLWMSYDPJeLuG3oRjpRcCiNMvlIDC9RjjA+x28OT7Z5q8mk66HMU cWos46jz7eHaPU7RxWTca/4lM/2sw6VDKMEFpmfBH5dvSqI1rXzNJcDVdMt5ZM73TcxYHsaNQOg/ snVJI/PkieSPd5YM+olhu6YKoBSTaBcxXBiuLayt5gu4qZ7huMccg4965g6lqq5VPENnGzEMRDbs 2eO4xStc6tcDD+Irl267YrU9fY8UWA6NtCYZaZLB2I3ACGWbj/to2P0rUsfCqTW7TpJa27KQNgs4 IyVI6ruauG+xXkmBNqmrOTySsQXPt3obR1z+8m1Zx2EkqKv8xS5GB6aPDPmoEfUJJQoO1JmhiQMO nyriuIuN0U0lrKVKxsFJUAflisI6NpWcyW00pA/5a3gA+o61oCSyWBYIBb2oX0uPMY1y1aPvpj5v dOlvMtcXV6cyZZRHMMYMYPC8Vc1eV7e7gmmAKyp0P8TelUbO8E2mNZOhaVDyOMkP0f8ACtXVbTUo bWM3VrI0MWNsiFZMAnAZgpJFfoWEq81GLR8vWjy1GihdCF5BJH8jBRjP05X86bprT22q2+7ayDex Xg84xioGinuoUV2SF4GLbgCflbgZx/hWjBFdWOu29pqgRPM3+XPwI2yg24/z1rrRkzV1bH9txzFT scAEem5yO1aGnQrZXojUY28fMOnJPFLrtpD5NvNIQAoCuwzwcYB6Vk2urX0E9nE3lvHsClm5bIOA fyqloZHWXszJDOqPIsiRE/KPmzjgKuDuf2rnJOLhU/eM0ccfMqqj8ZyCqgAHBzx/Ouk8QIbS3FzG 5kW7jLEgn5SeMjaCe2eK50xy+aBLKGZVRgxySymPPfB6eoz/ACrGttfyZvQWqOFW8E8jR7fPBJwO w5/Cr8McZDr8oRF6kZAJPb86ghsLZxudkUdcEkf+gipJLkEMsQ8wxg9BgA4G3rX5JOX7w+wgvdN7 QNWtoRc6ZMEY2z5ccdWUY/T0rTutSiDN5IUA8gL029jXi15qb6br4uMqsVwqmTvzXdRzm7VJI8KU RVI9QT1r9VynEc1CK8j5DF07VGbX7xpVjGPm7DnrXeLZ408+WVAwMqSOx7AetcPaRv8AbDMT8mAo PQA+9egqIre03zyhleMthQQVwvHWvQRzMm8MXzkzwqdo5B5yNwOcccDjitjWgZ5rSPgSThsYGcAH HfiuI8HPILW4uQRtllLDPeur1G58+8spN4zDBuIXgZJwMCkI6OCKy1CKSxnUlS/yE4ARkXj8zXRW VtcQaatqrENMTI5XjoMYNcLY3LPEXQYBk2YHUHvXr8UHlRRQjkqgXn1A5/WvEzyry0eU9DLafNUu cdP4P0ue0Et/apdKxIZXHynI75/yK4Z7fxB8G7o67ohl1bwhMR9ribLPYkkf61F+/D/cl+8nRhgZ r32/hxZImMYINEG6SElME7WUKRx8wwQc54YZBBByPSviWz6VSJ/D3iTSfEGmw67pUoltVUSyK2C0 YVdxBx1GOQRwR0r8+PtRvvtF9JtzcySTHb0Ilcvnj617xrWiXXw6utT13wicaFJE8epaavytax3B EbyW2f8AlmTj93yUzlM8geQ6voq6YsV/YSC70e54huAPubT/AKqUD+Lj0Ga4MZ7ysjSCMNI13bc/ KPXOP5UrZBZQAcDP9OKukNJHvQfu84z2z6cVCVV32MOSAFK/XmvLaNyMIgQsQdx457e1NXKnbng8 nBxUqgANgk84wactwVRhE3fBBPOPyoSJuX9Pb5ioLP6KHGD+dWzutJGuYEyWGGwxO33rIsJxaz73 HzcjHU4P1xVg3L5l8rcd5GcjGBirTC5qMt7cSRS7AoK4V14zjufyrrdKks3DSvL5bqNpjJ+8QOvt XCw3V5CoEOQB3K7v0rVt5b29HlAKXuR8rKu3Cr2rWMgsddJrMEVszlhuVcBR157flWTHqcc5JCN0 yM54I4H+RTbfRInUfbGcqxydjEbcetaT29pYw7YnjbGThzk49Ku47GczmPa4UDucnuec1HpYsJ9W tBq0ssVjNLsuCrBTsIOdpUHb0A3YOKy5buWeTyS+1Sc8A8fiavxWdqG80s2cY34A/Dr0/ChMLHce LtZ0WeGPRfD0VyltDkS3c1w05lTACqmdp2rjqevXHauPsdOWe9P2yZLTT1ZGmuG+5GHPzEj6dAOR 1wBUFxeecqQxyYCHO5sYwOxNbUOqaHqGk2XhrV9JWeK3uWk8xCw8xpDne+P4sfIp7KKuxJ6R4b1u Lw/qMV7Hp+kx+HL6Nvs27LzTpFx5hY/dOMbgcc15/wDGCXQX1WzNjarb3Eq/aZwjM+xHUYB/HnHa n+MNX0jR7/ZBA2oXNmsSw26xkQQqy+ainA+Ynd8+Blj2xXk0cmpeLNQu7lvt97fOpa4aOIO25eik L09AKFDUB9vJbrnZO8axgfKgHWrdzd2koMcjOgbvtBwQOmK2dM8MaPp2mXl14ukvdM1NXU2dmYiG dNoLs20EYY4WsGHT4/KSaRdkvBJHOM9sV3Rk2rIwktTFtbZrl32ttAbGR3A9c12WlapqXh6SN9Mb 5Y2DeXJnY2O+O1Ylw8cJC2y+c7HG0Aj9Rx+taUVy8oMdzbYPYA9OKErCO8OnaF46eSXTfL0zUGTz PKbgbhyV91JLYI6V5tquj6hodxJaalAIpIjtR15SQdipGf8AGgyrYXKXEReCReVYdRj/AD9K9A0/ xJp+sJ/Z/iiNXScIUnzjO3OA393OeorOSHc80UgKFChW78g/hxU8cgQnK7lx6dK6nXfBtzpKR3ll /pumsrMWiwWXZ645I/A1xzMDwuc8DHYc4qNShGkLMMNge5rW0XU4dK1KLUJtOs9VEI/1F4paEk/x FVPNYRXP8XBpu3y/m5wB9M1nN3VmVY9n1LxzoWqaBBDqOnRLcPuC22lp5BsU3BtqTP1DkbtgyFPQ jNWrbUZtY0XVfFXhY3b6xZXMaHTowxeGBl3GdfKJbykwuQOB3614aLjbwrYJrrPBeoeJtE1uDxT4 RSY6logNxiIMfMiyFlQhQcgr1GDxzjivK/s+EVaOwpIjs9Dk1rw+2uaQ019dRTsL2GJdx2OGaOdM ZOCVZW+n0rGsZ4bTUbe6n4hkJjlwuVMcoKu30GQ34fSvo238ZaDqdx4g8R6To1t4VTVbTyrq/s8/ ZRCxxumjXIFyhJVdnEnHbJrwTxa3g15YbPwjZ3y2trD5M89+8bNcNxho0Vf3YxwVLemOlb0q7/ht BExryFrS6msp8edbuYjtOclTt/8Ar/T2qq0xPBHA/wA9q9O0uxXxD8OfEl5a2817rtv/AGbHcqi7 mZbWYqLhccjdA5WXH9yvLA+Qdrk/h27URrcxryk3ytzjr7mjC/5JqAnPIFJTuyj/0YJNUVQAWtbc /wB37RCqj6+Xn+VPj1+5Q+Wl9aRqf+ebzy/+i46sx2Wr7GaCK/RYxyBHaW68+wDGqbrqeAsh1AZ/ hbUQq/lEg/kK9Gxz8xOdTvAd6zLcFzjK2V1L/ML/APWqf7TqgXzkSZABjcmnbf8A0Y9VV026do0u bcQqxwHmv7iRSRzjgDNSnwxMyCdLfTQrcg7Z5C3PH33pAV2n1NW+ea6i8w9V+xwD9XYiqjXE3zed d7x1wdVhXp6qik1ag0Ul9yzWluUPa0T6cbya1BY2tsRJPq6I6KOIYrWPJxwMYNAHMNd2jELLe2pj bjDajdSkH02xRClEdlIdgign9GW21CY/kSP1xWrLd2ER/ea/cAMRvWK5jiAUj0RMZ+mKin1XwTAp 3axJK3pJdTc+xUYp86HYrDThtz9kJU8AR6QFJ/7+zH+VOMV3bqCEuYwBji30+DGPruIrMl1/4fI2 +ae1YdsrK+cDnlpCP0qFfHPwvs5C8cKNnskCHp+PNP2iDlZqm/dUxNdzkBhw2o2kRH4QxEiozqUA Plm+WVX/AIZNVuZ8Y/2Y4wKxX+J/g9WxZQOvOcRW6D9dv9KmX4kXJ0+4mtNH1G4s7pvJ85Y8AFed qYUH8emafOhWZfY6Y4w0UF0jdQYdQuOn1IFCQWix5gs4vL5AKaV0/GWSse08f6sv/Hh4T1Kcsd2H 3Y5HcMP61KPEfju6Mk9r4LMe/nMzIij2wxpc6KSZtwC42bbWC4ZAcborKyt8ewZy1WCmrtiJk1Rx 2Vry1iH/AJCTj865R774j3CeXPpOk2EYOQbm5ijAPsAaqi7+IKZjGuaHZKD0Wbfjv8oQGp5g5Ts0 0/U7gMjxzELyFn1S4wPwjAqN9CuXUgWltIOM+ZLezdumGYD9K42Q+P7khW8Z2gH92GOaTH0CoKcN F8YS4WbxVqMh9IrGYL+BOKrmFY7Gbw3HbCMNb6fG74+Uae8ozj+878VYg0N7dg7y2tue/lWkH85D XBf8IdqkuPtGvaywxlgI0X9XYVAPAuj7x9rvtUJ7ia6t4QfzY0+cOU9PVIYM79elgU8FYjbLnJ6E Knp715V4nMcV5qJtWEiBztbg5HXrmro8EeEASk247xjM+pL7HjYpqhrSQi6mhhQCGMbUAbcMKMfe wM04bkSOdgnMyBmUqw/Ae3FWYPlcMWOB6cVEF5UYxkc/lUoUs646CtCDqoZjJbSLtwNvH51kSpcY f5cL+HSr1ozJbyORnBUY9qrT3zGRkSPjdj2qCiGDT3mXOMKcc1qwS65aXM9xomjzaq8EEVukezbG DM5V23f7KrVayuJpiMnZHnaQO/PatrS7e2uVu7u7Ec5muHQBoLicIkKqoz5Py8tu60MBE1b4qJua Dw5aWwfnMk6DOP8Aeaqd3q/xQuCn2mPRrYxHcvmXUPDeu1W611P9kQso8vTVdcDBh0Qn2+9I+asD RNVWMOtlfeWThdun6fb4+m8k1m4mqZ5/Ld/ECZgk+vaLHht2BJuIY/8AXMGmyweMbk7JPGVt/uQw TPznPZK9IfS9XiCG4W/RT0zd2UHP/AFNTPoWshQZHdo3baPtGtnGSOBiCIH9aOUWh50PDniu4Uef 4t1Bw3A8mxnx19So/SsjxB4XvtP0lnutRv7stMnz3VsIkTt1Jr1iLw/NcSPC4sXKLuJmu9QuAMep BUfrWFr+i2tnpLIo0xhfboUe3trgOJMdVmnY9MU0hNngL2YiS+Vt4xC7r5ZBY5ZQAAeMVo2UlqEt 4ZSwURqvIAPSsm6uboWV0JiJCISoHTncvU1YS8s/LgWYhW8oYz1zj6VVyDSvNKgk+eBttRWmqtY3 KwMMp3zWbFdylgyklM/lTr+Pz4kkjHPtSA71JDfLI8IwpA55zyeor0ZbBlMTwaJIhEcavK9vas0h HG7fPJ/SvMvAMJvIr6K7K+XblGbdkqRnoQK9kt28OySI0fhETOD82zSp2LDP8BkI9qJFxMkm8hG0 g2gXjD3GlwD/AMd3fyqI6vMMBtWt4yvG1tYQZ/CCMfpXVo0sVx5tl4NnjHPD6XBGACSRgyGtVvEu pwHjw+1qQu0CaTT7bHH3hz1qbFHnwuobl8S6jbliPurcajdEj/dRRT/7MsJyI9qTufuhdM1CU/8A j8gB/KupfxfqscZjnls0BbJaXWbZSMDp+6B/nVG48YXc8Mlqus6XaLICpZNQmuGC5zxsj6/jRfyG Z8Xht5HH2fSJw69NmhxIW9/3slXo9E1OFgPsV/HIPukRaZaD8cBiPz/wqaLxFcLZCwh1bTJoyoGU s724kOPVioNNilunwbcSufW20SQ9PQyEUXJ1Jl03V5SQ0d0AeCDq8UYOB/0zT+QqYaNPKB5q27kj gzazcy/miBaaJdXZgDFrLYxt22Ftbgdv43FP8zVoskrqca+pu7G3H65p2GQ2/hq2uc3EcGkuN2z/ AJfZizdxhjk1aj8Kq7uv2XTLYx9SumyO3Pp5hrOlu2OftEnHdrjWowcd8CMYqmbjS3w0zWC7eAG1 O6mz26IOakVzpv7F8gfLeRx7Tj5NOto8fQtmkQ7Zxbvrd9Gw6+X9khG313Ktc2raS+REulOVPaC9 umOPY4z+VTeXbh91tZwZPUpocp/9GP8A0oGjZln05JGW81q+fbxubUoYwfT7uD+lUnu/CEuRcahv k/6aavI5P4R+1OW0vdodLK5AHTy9JtIfyMtTNJq0Sgbr+2YdN02n2uB/wEcVViTMc+CpYmIjtri4 zhUZr24Xb6kqOTx06VoRHwbvUReGjIgbkx6XdTMxx1+cgAVXl1Bk/wCPzVJT/wBddajH5+Xmsu61 LT3BRr60kPYSatduB/3wP607IDoFltYnkNp4VmjRjwYtKjjbHTGZc4pk2pa+pUWejz2cIYEiRbKM sP8AeNc75mhTDkafKRjJWO/uCT+PWnLHpkv/AB7abBcHt5OizO3HvI4osgOjl8Qa0GLmNIFB5V9U s4z/AOOGs2TxRe7hi90tAO0uqyOy/wDfpTTIluSmLbTLqMJ18vSrWH8vNYmrPn6yEAih1RUA6B7C 3/kp/nUMditdeIpNQj8qa90lwFK4jF/O2M9OEFQQSzEbbRlDYHMOjXkv5byKv+frrnaReMvpPrMc Y6f9MlFRx29vdLJ/al7Bp+3G0HULi9LnocAFQvHvWlxDJI9SnBZzqbNx/q9GjiBx7yTCmTx6sjKz jWQxOQzvp1vkDgcuWIps2n26MHifRpYCdkcssd3IX+qFv61BLDHCQkkulR78kGHSi+cf7UjUuYCR rmVObmS5x28zX7OL8/LU/pVKW908tia6swT2m8SXL/8AjkEQqxFfop2x6hLEBwDb6NapnHpuzirI nvH2I2q6oAe6raQbee21DRzgYLTaMzhANDndu4Gr3jn65xmp47e1aVRDptkzH/nl4avZvyM0mP0q 1NcNkrNq+r7N3Jm1QIMY7LGFxWHcS+HgzLd3kkgzybjWZTn8N4x+dHMB062t+g2W1jqEbHtbeGbK 349mmkrx/wCLVvPIbVrmO6+1IuH+1x28bqufl2ralk79+a3Zb/4dxv8Avo9HbH8VxfTS8Y6/6w1z HiHUdNhhfVLOKGPTbeJVtooQVilZvmUoCT0Y5yaCWjygRSxPFaz/ACvsy4/u445qwWL7nGRGMYB9 sCofMkuHa5mO6acl3I/vE8gVa8oldrce34ZoEWLRy27d2HA+tbNmqiNmbgtwo/Ssizh8x8YJT1Hb iurgiDDc6Y44H04oAjs7cz3MK93JU47j3r2Xw3BrH2G4bTo9SMZuGANhNBGPlVV+cTj/ANB6968r 0pNt0ihSApyf6Hiunm+36dYW6o2khZ3md31G6aBtwYbQgUHPA7ZqBo9Bkj1k7hO+pgA4KT67DEB2 6Imf89qrS6fdNgy2VsRxk3OtXMoP/fKqK86/ta/Y4OqeG4RnIEQuZ2Gef4VxVlr/AFq6JY+IrCQs FB8jSbiXOOByQOnSg0sddLpkcJO/TtEHb5pL24H86VLC28syJD4eg24yU0+WVh/32ea5mN/EMjER 67qTE9rfRNn6yMKV7LxC+PM1LxK5/wBm1gthj8Wp3Cx2cVnPGgI1CziLDKm30eHBHtmoVn1ASeVF rN/kdDBYW0K+vGFriJdNviT9pl1w5GMy39tCP61XfTbQAfaHl245M+upjj/c/wAKRN0eiqL6Vl83 WtdzySfNjiAA/wBxf6VmPsIL3UmqSnJyZdSaMnnAycjtXAzWHhscSxaSc8H7RqtxP/46AKkWz8Kj CQ2uh8cfJBeTn8iKTZSR113N4egTc0aM+DlbjVmcnjvlh/OqA1LwcIyJBpkLlQn+vMrfMvLAhj0P as6G0soj+5tLRQOhh0STP4b61baC8/5Y292wHzfutIgj49Mkg1VhaHz9P/okzE8+UQ3HAOw7q0bS /Ith5u3CXCPtTO3y2VlyfxFN15NuvXUMn8LkEMBuyTgggVz0MKRubSOMmSXzINyoekeAhZs47frV GZ6Ik8VyFYEOSAoDc4z2FYN7o3n5ktwNytyO9cvBLOqiRPMBwMEZOOP89q2LfXZlOJ4mdFxyflIw OvagCxb2ssJC+WQM5OM4P9K6a1gxIGmPzMQFHsap22owXYKxSkEjAB4rqNOsy8gYjKgd+5HpQNM6 S3ur/R4Y1tp7GEx/Pm5eQMN3GNqA9qYNcv5CxTVtNRs43Q2txL046kCteS3vxjyJbnJQfLGkfH4t zUR03WJSA09+AexljjH/AI6KzZdyjJq2sSxmKXXZpI2xmO301uccj7xHeq7zaxMBv1DXH5BG2zhj A+hbOKvtod5133EpP8LXbn26BRTBoM2H820ViMZLyysRn1xijQaMyWDUXGZH1uX0Ml1BGB+lUjZ8 Ynsrp/Qz6woX8duK3x4T8uaS3+yWRmXlldmZh9fmqBNBtcNIBpq7OCFgyR26tmpGc3Jp+kg7pdN0 /nr52pyyZ+oFWrHw0NYZxo3hfTtV8oDcbSCe82bjhcngfrXSjSmUn/T7VAACNtuuCPqARxWjPpup 2cJeLxG8CkBtto3l544yF2//AFvrSuBwUtpZ2Fw9i1tpVldQsY3gOnyeajKOVZWHBHpUX24x4WK4 zxk/ZtI4Fde2mqGfz/EE0hcBsrLglj6k8/nUMumaKEfzNYupJlGUjWU5Y5xjPsKXMBzovdYf7k+q uD02WMUf5HNIbjWSoBl1oqexeCGtBrfwPFbR/aLqd7kyMrpJdMqhB90jB796pyS+BFfd9iVTwTH5 zy447cmgCnI+oEHzY9QZf+mt/Ev8qyZvLxmVEA9Z9VJ/RcV0f27wUpZotHDY6DZI/bv1zTk1bSEP 7rw8rA8/JaEfTr/hRcDjGfTgCSmlH3kuZ5f0/wDrVXDWBOUl0zP/AEygmkNd5B4hnifKaCZk/uNA F2/TpTpPE2roGaLTlQNgYbyYyMHI7/1oA4gSfKDA+7/rjpjP+W6rcEWrS/8AHvBqk2MZ8rTYo8Z/ Hit8eLPEQleX7QkbsSdrzwBRkYA+XNQzeLtflRYHvrNQdqlRKWZsf7nX8hRcCmtr4il+QWOruoHT zYYfz2g07/hHvEvJOlzjAzifVAOPXaMGs64v70uYr6+ihZBhkCzE8j0xVOLzdxeC5lkJ4BhtJmX9 SP0p3A3U8M65IRusbEZGctfSSe3SrR8HampUSDS4WYbl3LJLx+eKxvP1dsDfqcgA2hUtFjx9NxqI 2lyxPnnUTnk+a0MX4dTWbkx8p0EOgiN2jur+yQlcr5ECKwI7nfmmnSYYsNL4hkUHtHBFz/3yP6Vz TwKDt8gzD1k1JVGfQ7f8Kh8u0Vvmj02Fu/nX0sp/TH8qFruTy2N+98vSjBq9tdveSQSCK4WUYMkE vHb+4fmr1zw611DK8fm+faPGZIGbqu37yc9Rg968p8O6MdauzbwjTmgjw85gWTco6DDvx1962rXx NDouuQ2gd5NNDMkhYnPz/K232FfVZHK0LM8LMopz0Ou8VaVLpMkupWqbrW4wcA42sR9z8Pasy41K fUbOKG3to2YxbXExB27O6+ma9KmEYt5dOut1zp93taJxjcF2g5X3rB/4RySyRp7RhdWzrjft5Azk Zx37V9HUpdUeTGppZnNpa6nqNn9muNO3pCUKvA/zIWGckHGV9qyra1IvvsU4CSxSKjDB4b2rbm1S bTbs+bG0CtgN0HYY71Re8hkvUvgVjMhQH3YnvjNYtWLR18rrc/YNNkKgJvikDZ2/vfkGduCBzng1 jX9lLZizu5IzGPKkhAKPGpEfIYCT5scld2edtaRs5EhmnZxHIFLA4z9zjtnjgVNrVlFNZXk0n2eC 5urb7RBE0zyTy4UtLhDkCJF8wfL3NcuN92m5HRQXvJHjKsfs+QNzbRg/h71DEs7jg9RyOfWpFKsf 07Y+laMKty1vEzEDaTz198V+Qz1m7H2UVpY888UWYW7S1hABeEDBPRjyOa3/AATqJJNvc/NIF8sn 1Xsar6yR/aenStht+6Mgg479aw7a5XS9atnBwocJJ2G31x+NfomUyfso2PmMYvfaPobS4YEgP2kE 56EA7RkY5+lGvXUiadJsbmRQGB4xnj+Var6LqkFuLmW2ZLZkXZ6NuHBGKydatydOjc8lcqc99w7/ AEFfTpaXPKLPhsGPSYtp++5yRwCBwDWzGN8kIAYsJGQ45wANw/WsrwhC1zaqsZ4VCuPVlbHFeiad Zf6QUUbSTkjHJOKIibKuh2N417Z4BVHkQuGAx6n9K9zEPnTKR0zmvKL+UQadcQW77ZlYBWAOQ2ev 5DpXsmmSi7tra8UZE8SvkjHUf54r5jiSDsmexlDV2TX4DRLGBhV7VQiXaobOxRkjPHStS7/1Q45a swEMGUHB2nB9Mj3/AAr5Ro9y55F8YWXT/B2q3MYz/aRgtCFwMkyKTg9vlU18zaRqdzosjrbr9ssb gfv7VxlJFOQSB2PvXvvxznlXwz4d0qVw8t5fvLIw+XKwR8e3V8fhXz0qiyI+zXHmAH7nHIx3rzMS 7TsbwR0d7p6w6edY8OyG70h3xJGQTJbZ/hcddnbP51g4glZngARNuQBg7fQcdqj0rXL3Qr/7XYkM Hws8DD93KhIyjD2xXRXGkQanay674eH7m3+e5siQHg4OSnqnBP8AkVzShfY0TMOFNqeZhVU8YJz/ ACqiHhM+JflU8EYwAfarKjzPnXBycgj36Yp7QNOu9lRgBnH+FYtWEVVRXc5+7jOPX8abmSNWRThe o74pSo34HycdDTVkQfIFBPvkVNgNKOeSWNYnYFjwcDHf/Clxa2z+dA3kMM5GSxUnstQROSP3A2t0 J6g/Smyc7cModfariykaNtrl2i4mO4rjYVQ4PGMNUVzc+c6ygshc9QMD8BVdWV1I3qp7nP8AStux uWRd0yF0ThXC5qrjKcaXMwCxMHY8g8YwOORWiLW7hMUl0CEPylSDk89eKS5jtmPnWMjbQPn3DGDn oMVRkub5iW898p0ycjg0JgbTmyS2LTQojLJ95j8vTofeum8G2lnql/b6fZWFle3N0xRkvrp7eMc8 eXjHzc8AVL4Fv/D8sstjrqpE9wP3d2EDbBgAgqxA69+tczqC6JY3rT+H9RmnET5WJ1CvlT1QD9Dn OK2RB6Z4l8C/DGK41GOK/wBQ0fWdMWQSxXNziI+ScOY3cAuFzwvfHFfPul+Lda8NXd1d6FcbReqF y4LgqrZDMhwATx26Cur1i+8UeL5rOK81h5biCMxxi+UeWMAkbnC9+OTXHaxZaq+oTabe+U1zbErx xuXttPGQR0o5uiHYdqXirWNe1Aanqdybi4iVYE2oEVEQllAUcYycn1ot9eheVEuYvLUJgyqeAQO4 FP0DwxeazLOrXH9npZeUjsYi4DSHCg456Cuh1rwmfDslrc3TWWp2sszrGV3RkvF/rEdDjpWtKbRL VyEWsflCawvAhkUP8gU54/SsK4vUEpjlGJO747/pS3mpGKeTMohE/IjiUbUUDAAxnHAqeGO1BM5A ZsAkkZrqU09jJqwwLK0AkOZcE87ccYqSCbzI/KeMgtxjPHHAyPTituGWO5iChsH+E8cfgKz53itn 3kq69OOMnvintuJI2tE8TXOgn7PKGurByC8BJ3LgY3Rk+3Y8V0+o+HNC8V2g1bw1cLb32xRPDJwp ZeDuXqDg5yOK8kmvdw+RcHqCewNFhqd5Z3QuLSQxSKBnaOoz09P/AK1YzrRNOUu3NnNYyy217GYL mIkFG9uu31HuKpzbiAQfx9PwFevafrvhzxUkOleJYvs1z8gW45G/C7WO7sdoBx0JX1NcD4i8Lavo M0zEC6tYc7pUBLIM8F8duRz05HqKzlHTQZzLcr91Q3GMYrtPh94guvDvia2mXWZdDsbg4vJoo/M+ SNHYHauDvB4UjP3sHArikQyAM5znHI6c9KlXaSuTtUjHr7j3rGa0sD2se/Pr2h69I2nS2o8JW+v3 CreXm2KOOVlP+jXjwof3To4Vm2hUZdwI6V5RrfgHxB4X8QyeErma1vLyKFZ43tXLxzRFd++I8bh8 uMDOPWuZlkklkD3Db3YYJxuONuBnPp6V7v4K13T/ABB4WHhTxjZXV2LItcaTqNmgkurPaV3sjD5t qn59p+XjaOtefiHKlBuLJ5uRHD6DcXXhjxFq3hdL82y6xZ/2dJOp2qk0qLsJI/hydpHcGvNPJMbG ORfLkiJRkHRGQ7GXPop4r3q78DarqHiZpdINtrupQh21O2f5IXjCGNZ45CR+7kXDHHzIenSvPfHu ki21RNUtP9VeoNxY5zIF3Bsjj94mOP7yNWWErLlV9y1UT2ODMTUeU1SA4GNxHt6fSjd/tmvQsVc/ /9Lj5NZ8ayNtk1fToRjvLv6Dvt/wqo0/iZv9b4msxn/njC7n9MV1C6RdoxCR3MhHGAIowPxGaswa RfXDhTC7hSMqboBgMf7Cmu3Uz07HGGz1uf5pvEt24HOIrN+pGONxHak/sSQ4LaxrLnjASJYye3GW Nd5JoMj3LQpDDED0+0XMzMvHqn+FPPh5om2Si2YnqdkswH1NAadjz0+FrOYj7VPrM/pvuYogfbPJ qb/hGvCqfKdNuWYcEzalyP8AvkV6Rb+G0bLpPBbp6pZoPb+ImrreH7CEYfXBGh6+UIFwO/UGgR5V F4f8HR5/4kNrKxOB5l1cTdf9zinjRNB3f6PoVoo7KILiT8g1ekG10KKP5tdnYqwG1rqNBt9f3f8A hUOfBuM3GqSSkceXHPPJnAxyQB+lMDk7LQbYzov9hwrGeSYtOG7n0Lt9KuR6fdxE4s47ML/z0gtE P8zXSbvBvlsbK0luLkkBVaK5kTbjnOeafFb6cFP2fw7cHPQixZV/DeaOVAc0EkQ/PdxIB1Pn20f/ AKCpprSQsBFPqybBztN/KwB9lijFdi9xAsYRPDNwuByTHBGPr8zVEmtX9rCY0sYoo2YH99fW0YA+ i5xTsI49V0xj5X2kSK3cpez/AP66cLK1AINozofuGPTZDk/9tZBXQv4lurcMVutODNzk6hvx6cIh qGLxdfqZHS9052mXYzrHdXB2jsuE4+vWquFilDpd9/yx0y/ZgMqyWVrDj/vt2P8AntVsWGvKiM1r ejJ4Zrq1iX6fKhxUc+t6lelfMuVYIuB9n0u4YqvTCk4z+NXrfU9aEYigl1cxj7og02GMY/2vMaps LUibTtVkAS4iVSTwJdXYtzwPkjQUq+GtSlkaN7CzzF/FJcXUuPxBANQPd6s8hWf+1nA5/eS2Vswx z94kkfnVG4uZetzJKink/aNchVfXkRqaaGbI8J3bSlG/s1CVzvS1kkIx673qZvD88TLHHqMe7gZh 023UJx/ekY1xou9JYfvZtIOTjE2r3MwAHtGAD+lH2rQWyA+iKw7R21/cE/yFAHaPpn2fabjX53U5 BT/RIl/HFeK63cPPqlyUw4SVlxxyueDkV3VrNp7yKsUNuGDDabfQpmz68yH9a4S9tpo726uI+0rZ zgcZ9O307VpAzkY/mDzNjrs9PxFWYnURhcgMBgH8ajvJYZDGjYDDqelW7a2tiBvkUMTgEmtCDfiG bNRGoVS/P/AVB/nWdJ8gAKrvKk4HJzWq2+O1gtYmDlPMZiOnPA5rnpvtRZ1iIL468cZ96gouIube c7lRYImkOOu4DgcV6RZ6uNP0rT9LjutLSG0gVMyamIjl/wB5IXRAedxx1zXl1vpykQ2U2+Vr2REf bGZWWJTvlbaOWwvGzvmvR2u9SaQyWp1oFzuAt9GsoV9eshzRJgkWV8RQhhKb7RWcdFZr28wcdQFT H61XXV/Mm3w3VtNITkmDRr2Yk+27Aqdp/EpjH/IyBSON93ZWY/8AHAcVnTW+vXCkzW2oyHr/AKT4 j2qQP9xBx9KnmLNndrsuJYf7UZm/jh0OOHH4zPTpD4kC5ml1wgH/AJanT7UD3DFjXProUkqCSbTN LKkf8vGr3lz7dBjP4VVXwxCWZl07wxEF43G3urg/hufBpXA27iaccXF7dR7sEm58QW0eDnrsiUdv f/CvOfiFeI2r6X/Y11Je6RZsrSSvcGfdcTfKfm7hcV3iaKbVCRdaNaAdoNDDt+G9q4bxpcDTxb3s 9wb2MbhKTZpbRIo4BEafXrTVgaPOrkW6f2wZ5NscihUIGf8AWS8nH4VvjQ9Kl8lXt9m2JevXpk1h iz0+98+HTbwQPejy1jfBUnORg81gRWPiGe6lgmvwvk5jZoDuQleMBh6UEHbvo1gq/uTIijsAaks7 Gxjzu3NuPG4gD0rk7GO9huAt5evPHjG4HGPbnFbH9iQXxHlFsjvvPU9OKAO/0Gwhjt7u4ilS284q m8H+6c/Sugku9MkQM01vcLyHe/1m4tlZuuEjj7AcccV42sGh6beRWlxqk0khOPLTLrk/wnbkc16N 4d8WeF9GtnjcSXEryAgRWDzkqRwMlcD8BRLYqLNR5fD7gAf8ImPZ7jUbz9M/1oSTRk2i1l8OoSf+ XfQbyfP0L5rRHj17lVNjpGs4TvDYGMEduw/lUn/CU+KpmaSHQNekVl2jzBFGo/HeDU3NB0F5d4xY zTN2zZeGIk/JpiKuxzeJGz5TeI3x1C2tjZqPyyRWK+ueMI0Jm0Mx9i1zqUUf8mP86yJdd1pCJJ49 EgKj/l41jcB/wBc0gO7I19wonh1yQnoJdYgjB/79r6VWeyurhyjaeGIXLCbXJZMAeuwCvO5PENxK qxSat4cjVCcCNp5iufQJx+lMh1OeMlIvEVlb5AJ+w6XcyE8f7VAHoKaVB5iqNO0VC3AMk15Nj/vo irK6ZDbvsL6DCeuY9PeXb+Lk5rgln1a55XXtYnBGP9G0cJx7FueKsDT9akXlvFlwMYOIoLdCB7kj FO4WO9kgvYG+TVLYxrzvs9HgXrz1b64qa0h1C5DNJr2oQIo4xFbQf+y15o+jXDZ86w1x0PX7Rq0E Q446CqEuj6GnN1pttn0v9dDdPZeKQHqlzLFAMXOs6ncDPT+0UhP5KBis6a88MRIftmoeZITjy7jV JeF65JBFecC38KxnB07wuBxw91cTt+O2plXRUH+iJoEfYeTplzPj6ZoFc6j+1vAzXEcJOmuHOMCW W6lc9ggY81o3eqeDdHUXF1YLHak7R/xLy7k/+PCuatri8Vh/Z8sikHg2Ph9gVI7qzcg1on/hK5gd s3iWYE5JGn28POeuZGOPyo5gsIfiJ4cMBi0+0n+0F8rs0rKhRxt27amsvHV+m/ZpetXasfuR2CQ4 9MDFMNp4nT5pjrmOp86+s7ccD2yao3GlajIx+0rO2DyJdeQf+gj0oGdJJ4z8TXNusFv4V1pQN+X8 1YN28AfNg8bcCsmfW/GU4ZZPD87A9ftOpxLjHTGD/M1jnRtODAXX9irn/nvqtzMfyTg0f2f4eBH7 7QCAcDba3dzg+3rQBZn13xKyxJNp+iQeSCFFxqYyM/3tuc1kXPiHV5H2zal4Wtz0wJ5JGx/wGtyL TtPXaYZ4cdvs2gnj6F60IbOYsBDdapID0EWkW8OPpkUE3OKGr3jKGPiXRUIPP2axuZ29PT+lMaW6 d958WS4PXyNFPHtl/wCtekfYtXl+4viV8cABrW3H44HFdN4YOo+HbuS5l8ISa95yeWq65qAkWLBy WjWMDaaBHjCWmp3aB4Nb8S3UO7bm2sIolznoG5H60r6FqH3pG8Xzn/ppPbwD9W4/KvU/FFkfE2tv rOoeG9Ks5ZEjhFvBezRwqqccRxnGT3b1rDj8I2TYxpegRkD+Pz7o498mnyiTRwf9hRHJntNTU/3r vXoEX8QOaz5NE8Ng5lsdMZvS78QGX9EBr1KTwxYWUqRv/YFrI4yoh0kNj/gTE1oQ6NOVBg1a2jCc ZttKhGMnsxGKNCrni32TwZFlfsXhiJv9u4u7nH4KtWkTw0gX7Onh8EcAwaTe3B/UV7XLpOqwELJ4 m1KEjjEFnbxn8wP6VUn0aYKktx4k8RzFx937TDBj6lQMVVkFzy+2lXbmyUsRwDa+FSV646yjk+1c F46e7vdWGm/eSyU5BhEHzY/iiXhCB2r3S503w6FZr7UtXnbA+V9c2k5OCBhgOnOa8V+JWgxW+p2u p+Dry0mtbiLZcQSXvmtG8QBJZwSSTnvQSzkYhBaoqOy+YMHqOwx+tRHULISBpXYfNkjsBUEA1WB0 8+0huCeAYpUkJ/4BnNWG17T4MRXdt9mHpLCUH8sUEmzBqunPGIY5FUEgtjrxWlaahbeakXmDa5xk +1c5DdaBeqWg8pm/2cVt2+mWcxhKIFJJwR0HpQB2emZlW9SzCszwsue+COv4YrovDf22+02eVftk ckFw2wWkEM5aN1Dbj53Kc8DjmuI0vzfDs0pced5ylSBjgDqM16h4Y+zWuhX2rWskMSz7EeS7Lxwl Ek+QMRgjAPGKlopIsR6Tr8mPKj8Ryr0BDWdqv6A06Xw9rGzfcWuqlM4zca3HGo9j5aiqK6nCPJEO u6SjQdgt1LhhxnpzTrzU7jUYUS41yC4RH8wLFpc7KG9e2ai47safDcuY3fT4ZRKWEf2nWLmQNtGW GF29Pw/Go30C3gR5ZrLQ4VQncZJrqYrzjBDN+VSxS3UqEDUNUmjJLAWmkhRuIx1bkdO1Mls7yfcH HiC5D/eBt4I8+5yf6VXMTctS+GbOz/4+W8P2b/8AXmztzwPvvzTotBm3lYdTsoQDgmDSYTgeuHz+ lVZNOu7n5ruy1mYEdbm9tY/59PzqlNZpHzLZhB3FzrigfktBVyS5nnsmEL6terubajW9jbx7scZ+ 78tbkIWVYY59a1ZTN1JuY4wuOOQFAH4GuPnGioQssWhoynP73VpJMHHdRULajoELBTL4aQtjiNbm c57c45qWUjubq00S1fY+sz3I4yDqRI/mKzQfBofbeTRhT3lupJCfwDZ9KxLfVNLdgsEulsX6GDSJ nPHpuWtNL53OyCS5J6Yg0eNPyLVNgueF+OI7NfG14dP+eyG2WIrnlSoOecHr61hW6TpeK6xzSJ9t jYiPoMhCd3twa7rxvpl8/iGHUJY7kxXlui77yMRsWQ7MYT6CqGj2Dz3S7WG15YFkUtjAVl3Dj69K 1uZnMwxzwSyRCPIjdlz9D7VvRRbmxOnykc9KTznldtjIqSvIRgdieKsGxjL73naUnoE6VQihd2Gn 5V7aTypBzx7V1/h3WIooPs96VJjbIc9QDxjiuc+yW33mts4yOTjFaOnWcLPHuCpubaAOvPtSY0ey 3d0UkLxXSQp5Y+bBJClc44FZ/lQ3By18zl8MCFY8AfQVXe/RMRkXksSKqApE20gDbx0pr3AYALp9 +T90HAXH1JrI0Q8zAMWjkuA68bxFjpx3pst1dyL8kl3jac7lUAkc1DtvJV/daVeH0DuAD+vSmCy1 cuqnRiu7u9wB+hFFxjJGmeTzZUklkk5LGQIf89qrvhVwkLIM8/vwOvsKvvaamoJksrZQmfvTEcD2 GKP7N1FgPLgsYgDtwWJOfx60tCdTIcRoQWRJM9CZWI+mAKqyQwLiUw26Ke53vz9f/rVvyaLqSSmJ ru2tyq52iLOM89aij0vUZJFjTWVyxA/dwquPxPFIo58rZOAFSM5+7tiZwfXAP8qjO2E5t5BCF+U/ 6Fjn0FdFFpclsxuL/UpdQhV9j25UKsmGyUJTnDDjIrodabQtXRU03QY/DzJM5SXTjMkhi2jbHJuJ Vhnv1/CgDzIPJjNv5rEnP7uyTnPoTRNcaqxCRJqgwMFUhiXJPua6KfRNPV3Mq6gyZwDJd4yvUnHb 6VFPofgtIGkuAl0+8IsM12y5Qp8zbg3Y9qAOdkmvAkYAvpS45DTRxY479qz3bBPnLsPUibVBwMeg rpTbeBYbgmG208IJgShlMjeWR8y8568d6jS88Hp5ZeDTolVsvtjDkj0HymgDkZWscjzTpyAc5kvJ JMf989arSS6TyVl0oEfxLDNKwrvJfEHhGCJltJoYCGyHW1U5XOdrAqB+VRv4/wDDNtDJF5guZHOU fyFj2c59qAOHjnsV+WO5g47w6az/AM6tqJCpeCa6cnBBisUjzj0B6Vp3HxR0MZT97k9x5afl/wDr rGm+KECsfswvHDekiL04H3VbNAWL32bWLz5VtNZlb1zFFx2yx6UNpesEmJ7XUA44xPfhPzKjHNZz fEi+uN3k6TLIXXYyky4ZffC1WPizxNcxG3h0K4aAdIykrcjvkkVKEzZXQNXuGSM6dHnnmXUJJM/X GKVfCshYi4h0ZGXqJZpGb+dYS6n4zuBi20DyfXMeO2O7cfnVVp/GsYGbKyt+Oszwr/Mmr5RXOn/s 2K23KlzpEJHUC180g/nWpHZ3MWwpqMbAj5HtdPiClhjjLc968/8A7T8WJw+saVZHuPOg3D8FBqza apq6XdvLfeLbN4ElRnjjkzkIdxwAoFVCCuhOWjPqXQ/A2qyaVPpTX58+4ffczFQnRRiLCjp681zn iH4afZYmRF3nbtBYAcDsBnpXld746g1S/m0y91qaytJ5GdZ4ZNhDfw5HXmi28VeKPDBN1ZeKLLV7 ULjyr2QMQOgA719rh6lKNNQSPmakKkpuTO78J69d6NPHoGtozWbv5cMzA5jJ6qPavULzS72ymLaa 4yM/I33COTz26V88XXxb03UICmo6XFHOOTJEhZQw7qa7XTfidLFZrOwnubLccTKpK45H8IOMV2UM QtrnPUotnfTfYL7EGtW3Eg5K4+U9M1z1n4Z0pbyZY9QWaJf+WZU7hg8flVKy+Jfhi7ghSa4jMwVV bcVU8D/axmukivvCl/KLq1voreZjhgSBvUkdPwrpcoMxtJGxdQxyLbyRHbgMgwOD8oz7VMt1HY2e mXjL9pK3EkMsbNGiSQE5KuHwxXG4EJ7VVu30rToMrcrOoHyhWyS2OlecXHjO1gC2DRRTT/aGlhEk HneX8mDjptyMYP04rgzKcPYvU6sLGTmjX+LPwsf4daol7oE7X/g7Vj5unXJAPlbzkW8hGeB/A3oM deK81iuLmJNkchWOQ7cDjoPU17V8MPiZaaZBN4B+IEY1DwdrMrpiT5jZ+cxIK552ZP8AwA/MOuK5 X4l/DPV/hrrMFoj/ANoaLqhEml6gnzB1Y/LFLjjeP4W43D3r8prUbztA+yhOy1PJtWQyJbXzJhbe dXDdiAOn41xeuwZlEvdl2MP5YI9c16lr9hINNttItiGeOVDLI5+UMec8VxusWM8UarJCEJY4xyD6 FTz16193ltB0aKjI+axVXmnzH0b8I/G0fijw+nh/UWEmp6XGIMSYPmRRjMbA59OK6TWvDv2yzuoo 12uuZUA+nX/62a+NtE1bUfCut22v6eSZLVvmjX+NO619veHvEFp4r0yLWNOw4kXBQH7rE8qRx3r6 LDTUlys8yrFx948d8H38mm3rQyxgMhwwbPfqR7+lextJLJJaT28e4k9Vzx9a4LxNoskc8upaZ8jA 7ZUUA7cD86v+E9Rv9YiaAycwHaVHGB/eYVqo20Ick0dlcKUtFaUY82RmBGMkjo30r1zwowbQrY7t xQbMj2rxzUo9Ot7a0EU++SMlQN2SzHnGPTNer+C5kl0YIpwyPhvx6Yx29K8TiGn+4b7Ho5XK1Sx0 91koT3HGKym5TA9K1pgSp44rObhTk8KM/lXwp9KfLnx8vh/wkOh6Wg3G102SQjI4aefGPyjrwp5X LjYTGMnhRjj3r1H4yXUdx8Q9TIX/AI9IILbA/vLGHI/AtXnEau+QF+UcAnrXj4mXvnXFaCwIrYLD 5ie1ado11p9yLqxYQ3G1lPZZY2GCkg6c1lyb0KhOvftV8KZ1yXAI65/pWEXYdjqXsrbxLBLqeiIt rqsOTdafwA/IHmQ57/N0rlgwbdGI9rA4IOQ3PHzD19aYJpLO5jvLOZkuoiDHIOGGO30rrwtr4sVp bYRWGvQth4y2IrlMMd2T/F0rS3MtCLnETAEAAncvHaqQkcHYw3g8Z9Pyrblie3kkguY/3seVZWHz DA6H/PSsRlO4hTkBuorBxaKRfTDMNuFbHrgVcjtJJMsxCgc9qz4jsfkkg9TxxWr9rVwBvCL0INCZ RGkTR5JIYDsR29akeTbDstsybuuR3psdwjiSOL5+OM0xp/LiVVAwOSelUBbtpHWN4UcBXAzxjDZz TP8ASYSwYYU5GRyT+FRI21C8Z+Z/XBwPah8HAlYlwMg/TjmgCSN3ZmiiB+RQcMnPIx2961NGFvaa kdQmtVaReAWz8oI29BntVWylaLNwxO7bgHIPGf8AParMst62PLIQEEqXGO9XcViXVta0zT4mSOLz 5XBGxGKqoIGWY9SfQVl+FtE0/WZRqfiO6uoNNLkoluD5si567j2/lVuzDvK8VzKkoADByqjuMj9K 1bKT7Hau4Xb5SSIig5xkjCr25600I7rxpeeC/h/p+k2vgy0S8i163+2tJdEyqFifaC6nBZ88c4wM 4rx6PWdL1pXi1SXynLMyIPlVGY4YrSapaXN3bi4keQ3FvEqRRFcBFUnhR+JJrlfssEBV7qMjJJUH B+Ue4zVCE1G1WK52RXAdGBfcDk4HGDitvS7U28XnzTlonGQvYE9659zpxYF0ZVBP3c4xjvV8NoiL /o7TMwTgsxx+VEJ8rE1c3Z9RhRFFuCGJxng/yrIMu5+uACT8/wBO1UDLAw3LGwHHTvVsNaNtCK2e CS/8hSqVXIFEUgk+3Hcc0vyFCqptPQ4zz78Ux/LA/ddfU04SMqjcc1ikWkKry4KklgD07DkEdfSv QvDPj6fRnt4tRQ31lCNmyTHmCNxtKBj1G0859vSvOi0hBAyoqJomyON5+nAq41uV6i5T2XXPB1nq NrJrnhaZTHgb414VipwWwfukjD4x2OOleYzW8ttOba5jaGaMlTGwwV/CrGh6/qfh+5E1iRg43xvn y39io+vX8K9Yt7nwt4+tF0y/jay1RfngZWHmBgAMIx+9uUbACeoX+9XRpLYzZ4o864CR5cueNvJP 5f0r2Lwf4p8VeFYZ7jSNI+y6k8LRW8lzbSBXifYZFXcAN3yAjtya5SWw8V/D26m1DTbkxwTM0Saj FGrEKo8z59wPlZU56Y/I4ktPHmu3N0v/AAkOs3txbO65dpNyxq2dzlQvYfTj6VwYmi5e7YXLck1D xbq+t6bJq7t9lvrDyoZHt8xqPm3h/K42/L8pycVopqWn61o9ro2qXqzvrtqZ4rvAFvb3asWWNmHR lPP+67+1ZqeO7AaoupR+HlvJEcENK5DybQ2NyqMNw2MEelIlhHqFk1/JBGNL8R3FwY9ucQ31q6SP B0ADmGQFQOqk/wB01xShbUV1E7m8/Z61e9mW70248i3miicR/e2uY18wZ7jfuxiqv/DOPib/AJ/R /wB816TP4n8V+G5W0LRjHNp1jiK2M6hpFiAyiMxOTsGEyeoFQ/8ACwvHv/PO2/79r/jXP7eZfOf/ 01/tS+2+UtxNsBxiKxzn/vo0vmagBuja+TPdEgg/9C/xqsNH1eXDeUjr3DXjsP8Ax0U6PwvqFw5S 3srKWT0VZ5mHr/nFdd13J0Ay3zqTJNeMF6+dqEMYx+AqlLJpzYM0tvnv5upSv+QVefpXSWXw98UT kFLMRljt2jTjuPpy4NdJF8J/H5O1LS7YZ42W0EY/M4x+VVzR7knmBn0b7qyWBY+qXMxH4cZqRHiy FhRdx4zBpLdP+BE17CvwY8eKgDm4wfmI+2RQgexGP61b/wCFEalsLXur24YgEo99jHHfBFZ+1h3C x48r3kRGI79iOhSytosfi3P51ca81YIQ0upBAP4ru2gH5KvFekf8KS0xHP23xNoVmiDgPcNKx/8A H6WT4WfD2zbdL440NTgZCweYRx2wxo9tELHkb3u4Fbi5Zh6XGr/Ln6Jis8vpZbbKdLLH+/c3cv5/ /rr3ODw/8E7BNl94zW4lB4FnYlB+O5Dmo43+D1u5SPW9UufURafFGf8AvpgP5U/bxCx4Yr6Sv+rk 02MjtFaXM2f++qtR3ESMPI4LcAwaMq5+jS17M2o/CmN8LD4jvRjARSkY/TFK2ufDxo1ih8Ma6QnT zb1QDR7Zdg5WeTm41ZBmJtYx2221pb/l0/lTHfXnwXj1pg3QSXsEIP8A3wK9K/tbwSkpuF8C7pR0 a81MkHj2qRvHHh+1A3eD/DqLn/l5vHfHp0xS9r5D5Tyj7Jqc5Ilsjk9fN1h+397aMU46Kz48zTdJ YE4zNfXE38iK9BufijYWcgay07wZZbu6RtMR+LGs24+MCypj+0fDykfdEOlK236fKc/nTjVv0JZw osrAS/Z9/h62A4YJayTbfblz/OtCOwSLm3vrFPQ2uiqxI/2SwatL/haHiJmP2PxAAp/gs9GRP/aZ rNuPF3jq9Df8T7xLcI4wFgt0iX6AbVxWqkxXLJj1GOPdHqGpuSeEi0yKEn/yHgVfbT9UW1FxLqet Ip6BpFhH0yEArjJG8VXCgXMviWRPWS6SEfq4rLvNDEqrJe2Etyi85vtWAXP0yf51XvC0O8ktbTyz Jf3moSIPvebqbjdjseQPyFeQ61dm2v5Rp8ai2D/L824AHnr3+uK0k03wymPOstDhLcZe+8049fTA qPWjpOl+VHEyLEwD/JjaB/s8dD24q4kSKB1W1aNXNrGJhxtxkZrOuZrlmB8lAX5AA+7jj+lXZdUs Iolkt9Ma6YtwOn8qdHruolgIdESEf7TYx7YrQg34czW4gYbFEeG28Zzz7VX8gIFjjBUE4PHaqM+r 37lQ0SRgD5lHzYPoKI767LROB5QduHftgY71A7m3plzc22sxGKJ2mQeVbhSAxYjcTn8q2Dr/AI8V fKHhlU97i/Ck4HfNcT4eg81rnVrm5g+0GaSFJbuX7OvlqhYpuwSAcgZ/CtyS10Ln954WhkyfkP2y 6Izz0A/WpcWOLLNzrXjdAS9todmDyftN5vI+vOKyLnxD4lnObnXvCyFBjC/vSAe3y5rUh+wIwW2v tFUr1FpoN1M3/j1aKnUCu61vtQO3PNn4eiix+Mo/U1PKVzHLDWtbZVX/AITTSYgvQWli8rKPbCkV JDfa6/3PGd/Nz/y6aMx/VkArp1j8QzbWQ+K2Ujho4bKzDfSnf2J4guMbrLxHKp/5+NahhB+oiBos Vc56PTtWugWfWfFV5zx5VlFD/Mj+VUdf/tPR9LTZBqcpmfY39tSRv5sb8MiqnGB9a69/CVywHnaF EB3F7rt3K31wiiuX8Y+HV03SI5IbbTLKSW4RSLCWeaQr6uZuw9qqMbCPI21+20Ym7Tw6LS5jRgk0 eWCDDAkL6jrU9h4o1G9hmshPbW7IxkkSVBGWboMnjrkVY8+6tkeK6gW8tQrjcn3xlSMKM4/WtvU/ D3g2PVb2a4gR557eOYLySwZY2H6VViDnY9VuxIEudIs5SxxmKUcn6A8V0lheNDdxtd6e0EQPKRsO eOMGual0nQFb/RtAZnU5BjZl5z3NdHFbaxNGZTp8MEYUfekZ3HGP88UrAbejWrX9yyR2N3GwVXij 02KN5mYnlsyEAY+tdo1n4mVf3uneJvL6gT39parj2rC8O22hxGRfFQE9qUzhpxbLv7KX+U/hmuk0 9/g1Fc/8TGHQ7eNQxbbM9zJuA+UZ+b6Gkxow7iyuFObuy2qf+f8A8Tov5iPNZ32TQHY+cPDHv5us XF0V+oT/AArp4PEfw2twFt7TRzJn/llp80h5PHIjHNdLH4k0wLH/AGdo94kx/wCfbRn2Mn1IqUyj zaOLw9EwEV34YjJ7RWN7dHj07fpWnb/Zc/6NqcSn0s/DeT+HnD+tehJ4i1yQn7NoWtsB90/YooFA 9i7DpTX1nxq2A2j36KennXlrCCPzJ/Wi4kzkoY9WchIbzxDIOy2+k2dsPwJFWv7P8RyHDWni2UdM PeWtsp/75FbjaxrhLfaobS0VV4a51mNuf91M1QbxBcB1ZtY0GMAYKteTSAfRVFIsrL4d1RwRNoV+ xPX7ZrhHX1C0h8Gs/wA0+h6OqoMk3Oo3U5GPXaatPrssi7V8QaPFz1S1uJv++c4qgWM+4pq08gfg m20VmVvxap1AsweFbcqWSy8M2yev2eeT/wBDYVqxeH7lA0dpqOjq0YD7bXSkYr6DLkiqUMOrtEEW XxFdRAdIbKC2X9TxS/Zr5xmSw8QSKcDD3cNuOD1+Wi7JuzQbTru2QSza5LHHyc29jbxYx97GFNXI 7SSWB5RretyQrwSrxxZx2ACg/pXLT6TbncZtIkU5P/H1rSA89yFNIbLRFUvcW2hRHqftGqSyt+S1 Q7GzLDpjOBNcarPFjkTaiylv+AjbVJ4vBcjnzbcyDgEz3x3D/wAeNZX/ABJRho5fDUIP/PKOe5HF W47y3XCwanYKp6mz0RmIx2+fFKwxkz+EIp2WKwsRCe8m6X5emcYzU8eueEtkYstEsBc7fmX7NKwB 7EALjkc4qz9runGIdR1QgcYg0qKLP0zmpU/tyYAkeI5EHQfuIAfpgcUxjotaG4JbaMGBXkwWDZ3e i5GKu/2v4mkRo7XSbyLcuFY2qRbT68kVmy2V0+TPaago9brVo48/XA/rWY0Fgp/0qCwT0+1aw7jj /ZVsfpQTc6L7f4iVFEllcxyjIaSSeBM++Cf61Qmv9dKKt3cQRheQZdRhX9EzWOZfDUQLPceGIGPq 085449aauqeHxnyNS0VXAyDbaVLL+pBoEWn1J2z9o1rS48nG1rySYceuwcfpVS51K2uWjMviCwJi HlolvDdyqf0ANW4NSgC4tNTuMEZP2TRNoJ92YCtW0udZvZ4bS11LXN80iRq0ttHbRKZDtyzD7qD1 oJuzBsp0i8z7HfSEyFS/2XSZHyB1z5nP5VpwQap5eLZtebPUw6fHbj/x6uz8SeCdR8PWD3E+vS6/ cR3ItDY2OoFLgNtDeYC2FMQHU9K4FrC/lIA8NTyg/wAVzrKEcdzsB/lRzBZFiSx1iVCJrTxDJ6br qCDA/OqEum3iH97YXqoeCbzXYVx/wECrT6JKmPO0LRIieMT6jPMef9lVqqmlhnZEi8LWrIcY8q4m wR/vEA/lSC6Kvk2cC/6VZaTgj71zrMkn1+5WRcr4awTM3g9A3/PSW6uen0610ptrqN1i/tPQomfh fs+kq2PoXJqWOLVPOkjt/FWJYk3lLTSbWM4HoxzTKODTUPDkTlLXVfCkOcDFtodzcse3fNO1HT7j XtMlSxjk1hoXXAs9HOmqinOQExmXNdb9o1a4XYviXxJvkIXbGltCDz/eWPC/nXTeDfBFx478S2Xh +TV/E6wzjzrqSTUSojgHUlUQY3HhPxp3FbueWfD39nzXPiArT3l7PoenWsyhH+yfvpD947FY7Vx0 969e1/8AY+8S2OlmXwJ4rbW7tOf7L1m3jg88KOkUy/KG9Nwx719j+FLKHT9ItbZbcWlt5jRW6sSv yY2jOe5x1z716hFYTT2aRyAvFyRyMr3Uq/1qpNWIR+Fa2sFveXNn4h0EwX1rLJFLEuYHieI4YFf6 9D1HFbWnz+G1RX+yagWd+EaVNp2rngkHtXvX7Zem3nhj4jaV4r0ZAD4j09lvx5BkD3NnJ5JkOBx5 kTIOn8NfJdh4s8SzOq2ttBGWG1ZRbsy7+mOmATUcxdj2e016O0itZNJ0y3YzOHjluf37feC7doHH 0rtrG7vLQyS6xqV5DcyxyJbxG1a6jjxICwEA+TBGQp681p/sqa7qOs/FGWw1lobmHTLMSrGIF2xy srK3yY7Y9+a63446rP8ADf4g6hpd5cXNvZ6rnVLGOIBsRSH5kbH3fLk7ehqHMZxp1bU8eXFd69OB wDb6bHDx/s7sfzqIy6/cNtW38VTZ6ktb22fTvn9K4t/irZ2y+XHNeS4bIlcgOvsMtj86ypPjDYQs GJdypyDNOgPPbkmi6HY9HFjqt0QsmiapO2eDcayq9PUIKonSLgMySaBaKOv+kazKQB7hAa8uk+Lu mSRvBFZIokYswjmJbn2VSRUi/E/WJrcQWOmtJCoChFimkXHviKq5wsemDRGLDGl+H4ScY82a5mz+ HAP4VNL4furaRfPi8K2e7o/2CSQ/+PGvM/8AhJ/H94EuYfDtw4UYjaOzn+XHHQgfypX1T4pXq/8A IBu2X/prbIPyMjip5mFj0aOGYEpHr2lwsn/Pvo8K5/3d2atWU9/eP5Vn4rnSXcY2ENjbQn2OWFeU n/hbLcmwa1A4/evZwgf+PH+dVpIPiM523GpWkAI6vqduv/oANMZ7JcWsr/aoz4n16eW3xgKY44nA PzHcqZ6elZq2Qd8XF7r0sXUbrxsEdRjbivI/7N8ST8XXifTYgOw1GSQ8cciKMfzqu3hu6mIL+J7a Rj18uO/m9gOlIk9L8UaJZHRZH0aC7N5EfMBmled2UDBG1unXtXG+F1tL5bK8vZZ9OCai0TQsmS7K sTEHHOPQ/wD6qm0XwxqNhO11ZaxcTXPlOsZisriJcsO7yHH8Irn9J1XxJo1/E9/qkkdvBdOrTEKz xoDgkBs8kMPpVJktFrT/AAxq9yIp7c272dwcJL56xqA3J++RgjoeKnuPCYtZdlxq1pACeomDdh/d Jrc8H/Cvxb4xvrpvCujTazbwShjcHaluSjHP7xvlJdcDjoRXuenfsn/FvUd5vbbTdNjRhcIZboOQ FGCm1I8jIx3p89hHztDommW0o+1a5EwDshXa7DP1xz9K7TRtD0dLy2jl1p5yzjbFaW77mJwQMnA6 D2r3kfsk+PLnMk2q6SY5zFMxVpOJIhsYp8vRl/Wqfij4SeIPhJokfi7VJrTVY0uobSQwOyJAWDJD NK2BwDhTUtjR4jffEvQ/7R8+JxGba6aQRnbjzEY435bp2I5qq/xcsY9Uk1oZM87F3RWi8oFl24Cc /wA/8KtxaRbTr59rbaWTIxcvHbq/LHcc81fh0m6jb/XWkan/AJ520Sf41OpqcT/wsewCAJbTN8mO JGOSecjah9ab/wALAuLiJYLfS7m5jT7qfvXHXPZBXctaXKsUGoOoH9zyxx+C/wAqiazY/LLrF0VH JCzlMfguKnlA4xfEviXAS08MyhSCOYJCNpPIyxFWP7Y8aXUU7SaFLBMEBgLLEEznB3B3NdHLptpj aZnlzzlpnP8AWqjaRof/AC1gWVvUlm/nRYDmXuviGxGI7eBe+6a3jH6KarPP40x++1nT7bOCf9KG fblEWusOnaRCN0dvEgGAf3S5/X/CpI47ccxxsQM42QqAMerYwKYHDumvybXm8V2oB7RNcS59+1U5 NOnmO2XxLcSZ/wCednK2fxYmvSY/tDnbFaXTEjjgDj224/lSvZ3csXlJZTTLuBYeY2OByrADoRxT sB5mnh22bITUNTuSnJ8u0j6f4fhUreFbeQgtDq8u0c/6iIYr1jVWfU7FIYfCOmaVJEFEdxZK8M4R cAq7biG98gfyrNbw1qBQGfSYmB5UmZsYPPYn8qQHnh8Maeo4069lxyBNqEcY/wDHBTW0bTI8efp1 sg64m1In9ABXeDwvcPuleDToAilg0uTnbxgc1C3hS43h5G0uPaMk+SrEfgxoA88ktfD8WSsWhxe0 k0k5/SpFfR40MudGRPuiSCwklXP16V6Gnhucfd1a0jB6GK0hX/Go30Z4xh/E0+CekUKD24wvX6gU AcB/aFvCQbS+2Ht9l0kE/huBqX+09UkGI59auM9oLGKI/mR/SuwfSbEIzT+INSkbOAisy5HtgCoW 0TwtIwWe+1J++ZbqTH48igRyDjXZcf6P4kmJ7STRwj9AKpSwajgG50q9cNwDcasF7dxx+ldXNZ/D +2yX04zsDtJkvSA3fPzN+FVxc/DOIiRtMskcZJDTLIwJHA6/0oTE0cVJax9ZtO0iLv8A6VqbP+eD VVks1BOfC8OOpzJMR+Ga7WbxF8O4sCx0ywBVf+Wqbzu/7Zr0/GpoPGvh2AK9rpFsZUIP7q0ncE+o xxgVXMFjhY7q1QLt1vSogeF8jTZX5HYHGK6/w+ja2ZNHs7+7udTuGX7NcWtmLeCGMD96ZlkTay4H 96tNvH+pPtNnp94kYYsFhsJdoPqobGPeu98B621xFruveILOe3XT4YkVHi8tpnZt22MZ/DHpXTha XtaiizDEVOSDkUpPh7pFzaD7VH/aM7cy3UygPL2JAXoP7vtiuVvfhBp7JG2nXDWLROXUlFkTp0PG f1rvda+K+u2Fg95b+G7awtIVCxrJL5kk247VHHT+Vc14l+Ieo+H9RtNPvLS3ubm5t1meG2fcsRbk x5OMtj0Jr6xqhD3WzwoOtLVHOt8P/F+n/wDHu2majGflAO+Bs598j9a1tM0jxtowkS10Nwr/ADOt tPHKC2OuMg1n2/xMivfmWxvbdGO7LIWQHOMDFdTY+KtF8S2t5ot5qX2Ca4izHID5WMNn7zbSrcU4 uj0Yp+0XxIt6Vqfh7Wp10XxLpUEGpxvjy7yARyHA5xuxn8DVfxLpPh61ivYtJ0+CzSFVPnKCu915 wPQc449Kspol2sUdu3iq01W1ibdAbyRJHRu2JDhhkds1YvtAk1+2azk1bTbaMYZvKmViVT1yfetJ P3NGZL4rnzZcXHjCycWsV7dbO21t6YztHbgfjXp9pFFaWdjlNk81uJJWPVmYn19q9u8FfD3SZpGs UuY74xAv5gbcF29CB1P8q4/4gaE2i6raW00ccEtzZxyvHH8yJg8EH/aHzY96+VzqE40bnrYGcZVL HCtcBywj3f3dpUHIxgjn2r2rQPG+v3nw5uPBuphbnSYrqOSzlly00HkEOY0Y9vQ9uRXjDRCMYycj GSO/+favbvDdjZrDa2GpxRwybQghlBjbbONySKejZ/SvI4bwvtK15bI7MzrckNDjLDT7gONVngdL Zy0QeHDYk6DzI2wdpHTisG7todUt5rVmEKqx8sofuY+7t/CvcNYtA8EcEls0cEed3TLHjY24dePa vFItA1nc0SKAu8sTjtnNfpHsrnzHtEcjP4fgtoSsk2GXpjGeRjcK1fhj4om8Fa8dPvjnStSOGIz8 kq/dYVuN4Tv5C9w0oYDpnHGOtZcuhR3Hhg3jKySLPIA2M9CduKz9nKMroamnGx9SyfZZ5FkJUG6X cQoP7zI5PpXOaJpZ0nXr6SOLYphQljwrFzwPyrnPA/iWSTRrS1vgftEK7fM9AvHOa9LaRmhERG7u R1yPb8a9GKUonE/d0OH1J4rzWEghsxFZjcxnXO6OT17ZUj0r2f4fywmzufK+ZMRqp45x715wulOh GI5Ng+U8cnjtXpvhIG33xRQolp5ayIw4O4cMpH615mc008NJHbl9S1VHYTt8oAwcjt7VSjiaRwg5 LHbj6mrU3C7dvI4HTtxVI3a2SS3L8RW0TTsfQQr5h4H0r82b0ufXrofB/i+8bU/GHiK/3ZW51K4C EcZVG8tMfgtUIysWR91Ryc4zWWjPcRh3bLSkyN25Y5pJnZmCryBwSfT2rwqzvJnbHYmnkXzQdvJG cE9vp2qJJTkMBhj2FJ5JkKspAB7n2p6wFXw5O3tgZ/lWdxXNCOJUwzOUJ65xUMqmOQSJIVI+64+8 D2xVmKOJ18tsgDnA5zTpIkUBiN4K468j34px7k2O0N3Z+NLQW98BZ+ILdcRXIwFmj2hAkgHr61wF 9ZXOnXk2m3qm2ubc4aNhjgHAI9V96bLII5VeH5So+UZ5GOnT1ru7TU9P8W2tjpOuLtu4mNvDfH7w 3H5d3crnqD+FbaSQupwnQjjH14/Mf0okJ27QM45OBWzq2lXWiXklhqcOy6ySGX/VOMkbo2PUZH+N ZcUojcPhTjs2R/hWLi0aN9hbN1jzGEw7Dgmr8qKBtKhgR0yP5VCzMG3xqNx+baMcVKZA+CyZfA5o QII2aJMRgZb8QO1CQy3BJYBQRjjuPbtU8coQgKCq9wSRmrkkylNg+U9c56fypjIooEgUlx5oA/iy MelEt2XXBGVxjGc5qjJNKuSxOw+n/wBakgVoo1ZHyBzg+5p3AvHY67HHkjbycHgH/Oa07vTbldGn 1SwlguIbSLzCmSHCr8vmEVUzBdRBC0iSIQe2D7U/yp7e1nt2lk8qdCmyPgFW55wOntTUgOc0y+v7 m8AuQt7AEO6OViFGfQjFaR0WxfLFN5Y7lQZCr7D/APXSWdsqbfKjZPmGc88EZOTxWyb3T7VmXDAH rnnPsKq4Hnd/pE0M6xsDbwPnDyA4HfGarnSlR1RJFKsOuD6V6xc65aXFuYDGwVk2YdQcAd1zXFyo quDDGAW7dcVMgMaPTvIxK8gwo+7046ZravbC508Rrc27ReZGsqFl+9G3AZfVe2elP069t7W9hl1S xN9apJiWGN/JZ1P8IcZwffFe86R4O+Hvi3w5d3uhXGp39jZqwezuJd95orycmUIMmeAnDNjoOeKw qVOUicuU+bpo4gpMjhCCAOMDPpVfPmMkY6dj0z6V6p4a0i103V5v7TWG7udIsrpZdpE1tMJEzb3M R7gqR9DxXBaJ4a1nX2u4dKtjcSWFm9/OOjLHGBlgDjnnjtis44mMm0ug1K5lq+FKtzjkAU6NwTvx wOOeOn1pFt51YFE24GTn0NX44gMNM25TxkYxmtWURGeMnlMY6HPU1A86LKJBmN/lIK8EFehB7HnH H17VLOLXB3HkdMe1ZzN3UZPYGjmE0eweGviMUX+y9eUXFnMFVZnGSjod0bMmMMF5B9Vdh6VD4m8A xeQuseFjvtXLMsIbIAc5jETeqkGMr/ex2cZ8sRiyfvFCj164rpfDXjLVPDUhjt3860kILwScoR6r /db0x3APaumNdWsyLGDbmeymNwJXhuIiWyo2SKSO2fzr2jwJ8U0061l0jXraylFxdW91593CGhaa AbUkJGPLm2fKWxgrwauTWXhj4lWyyWsw0/WLTcUkTAkWN/mdWU8OEc7177GkA+6K8n1DwR4t0i7j 07+z5Lp5VJgktl81ZNnyOOfukH5WBHTDDqKxr0LxFJLqenS3PxZSWT7Naf27C7tIl8DERMHJbI57 Zx7Ypn2z4wf9C9/6K/xrHtvB2rC2gC6prui4jUGyitI5UiYABgr7+QTkjPIBwelT/wDCH6x/0NHi H/wBj/8AjleZ7Azsj//U3E+Nfja3j22mo6NZLnrbWcfP4tVeT40/Ee4I/wCKqEYPH7i2RevptSuN WO6cjy5r0g97XS1GfxerCabqMhIWHWrhT1Ajhgx+DLxXaqcTO50cvxL+IUzf6R4l1e4HQLGroMH0 wBVU+K/E8+TIutXRPUtOyg/U7qzP7L1Aja9jqzj/AKa30afogH86jHhy8lIK6Or+9xqUh6f7pal7 OPYfMaba/rZQeZorOD0M95kfjyf51mzazqpyDpulWyjqJ7rgVKfCtztLvpGkhRwxneaX8gRU/wDw jskCjd/YlupGQIrHecD3YgU/ZR7E8xhzeILh8RyX2gw47LIWx+AFUzrkpBA13TFX+7DbPJ/QV19r ot5cS7LXUYyB1FvYQp0HqemKsvpV6p2DWdQlYDOxFijC4452pVckewcxw4v7ybGzUbpwR/y76W5z +LHH6UCC/dxti16Unp5drDCPwZq7ltBVlBvNW1CRmwSJLsIAP+Aqv5ZpLvR/A9oo868lup+M+Zfk oBjnGX60cq7A5WOU/srVCn7y11kf9drqGP8AD5TVV9GjQk3GnKvq95q+3P4KT/Kt1U+H0TAvaWBU E8yzvK3XuAzCpPt/gdHUada2eB2itHlP4fu8fpRYVzk5NP0OJs3S6BCR0Et5NPj/AMdp8R0mEiSK 70CNR08mynm/wH5V28OsW0bK1lY3OzskOnHn8TGuPzq4+t6ow8u20rUwDz8sMaD/AMfkFMEziYpw TmHUdx/6ddEb9CasB71zmO6145OMW9hbw/q/NdJJq+tl9r6bcbEH+ruLu2j2/X5i35mqza3fW0xu oxp8EhUp5dxqGdufZVI/Wi4NGRJYarMpDWviCQer3tvBnjuFwagHhzU3K50e7cHvc604B+qpVtvE d3GCP7R0MMeu+e4mx/wFRVd/EVzJ/wAxuwZRxi30+5mA+mcU7+QrCv4QdiWm0LR1I7zXl1Pjjv0q 3B4VO4FLfw7E54Gyzllx+LN/Sqkeoam//HvqmpMvcWui4/LcTVqP+25OceKJ1/vLb29tn8WouI17 VNO0CSS+1e7s7i2txtaGx0+O3+duhLsDkD0FeL67rGh3WpTzjTLu7O7kyygA4OAAFHA4HavTrnS1 8kS6ta6juB/cjU545Rk9SqR8fnXFXdojXbojhuwfHWtIomRy769c79ljorRhM4DTEgZOfQVfW68R XACrZpCuM4PZj3z3rbsdFm+0t5zlojknrzg+1bV3eWsI8mPmXbjB42446frVkHO6fZToxa/KsxOS B+tas97a6aovGIRo5P3ZYHJlwfLVRg559qoS6oqtym0LwcetaGneTqd3aXeLl57NyYI7EJ9o3Y5K tJ8qAe9QBt+HL+x06yVb+G61G9ZCTtsJplMshyzNuQLwOmO9dVD4i1BFf7J4e1ecuNqbNOWLA9cl h1rBltdZCh59O8SPnj/S9Xt4VY465Uj+VY9zZW6ruvNMtkJ6HUvErYH12n+tKz7lqJ1Mur+JZG8y Tw7qqJjCh7i3txx67pDVS71vX5jD9r0i0t4oG3YudbgjVv8AfCA8e1cPLD4btyDInguLPebUZ7s/ pxTEvPDwk2w3fhaAjp9m0a4uj+Bxz+VJjsdfc+NNQPy3Oo+FrJR2fVJJdvHRRGg+nWsq58XvMgU+ J/D6L0Atre+uD+YPNVYtTlTAsNTdQO+n+GCp/AutXIr/AMR3B/c3/jC5Hf7Pp1lbZ+hcA0WGQw6p dSgfZ/EEkzHr9i8PzSt+Bl/xrM8Uxare6OZJxrdwInUh73TIrG2QDjdlDnPtW95XiGUlZYPF8/8A 111W2twMDuFxiua8U/2rp+hzsmn3lkbkopa81I37Nz8pxnEePpTTE2eZSap/Zp+yKytgLuJ5LE1g 3d0zXltrBgDlW8gOc4OBuQfka7nR9Djt5hdXMJuJhnaoXcRnlQQa3WvNPt57JsWyOk0k7pMvQx4V cAfL/DTJMDw74gupv9Fnt2gA+YHBG4Djv+ddYbyORJgrjzMDjp1NcssVzdFrm6vRFG3zBYBn3xzW /bajocEcnmTIIQiiSTk4weSTii5Vi5Z3kVtulluobdgvDyaedTf12pAucf7xroofEGokbLLVNek5 wPsPheCEDn/bSsuDxxoEmnyafaNqNrbsVIu7IiCQleeHYghePStaO00XUhG39r6zerJyFk1ifBwO SQCmB71DY15lgS+Krg5ZvHkw9TDZWaj6LkYqBtL1qXd9q07xI67ck3muwRKPcheQPxpZ/Dng2Er5 1ssm7ljcahO//ocgqP8As34bQE7tP0cYHDykSE98Hcx4/GouOxROm2KH9/pemj1a+8QSS8f7QXil 2eHYiQ3/AAh0DDnPnXNycfgf6Vpf2t4Bs8CJNGtz6xwI/wDJTUj+OPCqEfZ7u2hVRgCK2UY+m1f6 UudC5SrDqehxY+zav4fhP/THR5Jj9FLZzWvDrDBc2+t3kg9LDw8qfkzx4/Wsq4+ImhJHtXULhS3J eKLb06c8D8qz5fipoIB3y3dwF4JaYDpx1z/Oq5kacrOpOrXj/euPFMo/6Z2cEP68Y/KlY6jL1sPE kwbA/f38UPb0Brz5vi5o5fCxzT+g80MfyGacvxN+0AfYNGnuMcjiZsf8BVTU8yE0zuTp91Lln8Oy uBxm51dh04/hqE6Nccv/AGBpCAdTc3k8x/EAflXHr4x8TSH/AEPwrc/NzzbTHn/gSirH9v8AxIlX MXh6WMjp5sCpj67iKOZEcp18emTxJ5gs/DVmo6n7NNJ/6Ewz+VWxFqKKBHfaTbj1t9MGfw3NXA/b viRKd0llYWIPVpZrUHjjpvOKpvd+NRk3HiDSrcjgBr2Pp/wBDRzDsz0+3h1qeUxL4kdMruJhsYIs D2PNNlt7/cyz65rUxTsjRRAjpxtWvIZ5tRddl54307cx+5C883GO2xR/L/GovsMlxgHxNcTt1/cW N1J25HzMoNO5R7Cmk2rqWurzVJABuBm1Bh+GBiqM2l+E0dftdqtxv5YSajJuH1+evNY/DCyn57zX rkHqItM2f+jC1P8A+ELtQAXsfEj57t9ltxj8cfyqbsDu8eArbIj0zSjg/wDLS4Mv/odB1jwtBG5i fR4COQEtkfOPfaa4o+E9IjjG7RrzI6tc6pbxfmVP9Ki/sHw1FjfpOmjPGbjV5Jfz2f0p3J0O2k8c aIjbra7tbZAvSK0GAw4yvyj+VRz/ABQ0MIqzarONo4McMceW9eK5E2fhmJtqW/hqNh2Burg/ltq5 BDbAj7ImjqR2g0iWQj/vvFHvBoWLz4r6HwRd3sikY2iRVDfXmsK7+JeiyjiO/mjxjBlJU+xwDxXW xRa0Bm3eVf8Ar00KOM/+PN/Srgi8UpGSsniAcHLR21tF0GeAUbt7mlYNDzX/AITiGUxmDw/fXJH3 SUnb/vnYgxn0zV5PGXi2TP2DwhetuIP/AB73HPGAOdv8q9pi+Gfjm60wawdUnhtJLYzkXOtRRTlF PTy44s7z2TI46kdK4a40O6wDLbXBVsEG81t0HI7hH/SmkLnXY4s6r8S5My2/hGdcnILwgEewLyDp +FMe6+Ls27Gh28C9cym0j/VpWrqW0izVT5r6Ih9ZtQnuP0381Xk0nQY8Obnw2g77bSack/Qg/wA6 rkRNzlpH+KjACW50uyYcjN9ZJjHH8ANZko8eSZS78ZaJbHsP7UUkD0xHFzmvUNM8Pf2xfQaL4dkt NR1Gckx22n6CpP8AvO0oVFTj7zEDtXuWjeAfBnhO8Ft8TfFMuq6pEwj/ALA0i1CoHf7sUrW8ZMj5 P+rDBR0zxUyaQ02fHVjonivW7oaVYeMLXVL+QhUtbBry7lP/AACGPaMYHJxX6D/Bv4TXXgTwPHpm tTyHX9bn+039zC5SUADEduMlsJGnOM/fJr2TTLew0CxhSx0yHS1mAEdhaxrEzADOZPL/AF7Z4zWd ph1CXUp9e1kTKUja3tbWTAjTJA38dSexpLXYpy7lPx294tnGmkRI1xaYMJf5Y1K4LZHrtwP84rqd MuprOxt1sz5qqTFKpJYKd2e59O1UNQtrGVk+1zuLxHJCr9w7uX3UadNG15qsTpsjkn3hOnC8HH5U rkm8iXUOoNaKriR0LhZI1MTBWyRufOCfw6VwHjb4LeAPifYT27W8mlajLGU+0WRFtdLnnAIGyRf9 lgRXoVrKqwxWNuZJo5N5kkkfLRhucc1DN9nYR7WZfJwBIudw28cUpIk+f/gN8AIPhPresvPqMms6 jqbwxidoPJMNpD/CRyN3uOD+lch+1jp2lan4x8NX2oWMF6BY3kMZuLSW7XKyRttAQrg4JPWvqNPE V9a3O2fdeQHOW+7KigevQivnX41XkmtaX4evZJr6ygXULpFjspPLkYtFgF+D/d4HpRYdz5Rj8O6c v/Hr4eiQrwCmggZ+hlbP51ox6PqkePs+lTxcceVpljF+sgNdF/ZGitk3VxqZJPJudQEfTjJ+YYzW Pq9v4Ksog0X2e8bukmpb2U9j98VpYoeLPxLEn71b61jXnJuLK2HJ/wBgLiqEiX5c/aLkID/FLraj p7Ieapxah4V+VE0vTFZFOZPNdyzH/Z+boKsRanpcShbfT7a4Xs62rtgjjgeVRYl3IJbezB3XV/pI Po+pXEjfjjrWe6eHvM8ttW0ZgegEV3cGujtdVu+Bb6fKSM8w6cxJz2wVArTTV/FCnbb6VqTDrlbK FCP1osLU4Ty9NQmO3vrch+9toE8vT3c1MluxYPDJrL4OB9m8PW8f/j0ma6m6v/GdzKJns9QjCjAB e3iyPc7mFRTah4jdVkaxiDR8f6Rq1vF2xztH9KLlamctnrMiYS28WMp7+VYWi/8A1qRbHWiAv9m6 5cZOAZdYt4uffywf0rLlv9diikE19pkEbkljca0Hb8PLTp+NY0/iCDykjuPEvhi3VGyAbu6uMH1I BGfyqRI7F9C1CNC93oZwBk/addmfC+6qvatH4g/AHxR4t0H4f22gW9jYTPDNeahPPcMIYvtbh0Te w3PhFHyqvevPrDV7XWL6y0a08X6DNc6hcQ2iR21ncEsZ5FThpHI/EjAr9EYotP1mb+0rK2aCC2SK 1heY8hYVxuihHEWcfWnfoO9ib4K+Go/h94C0jwVZ6xBqUmjRslxJDG0O93d5C2w8nGSMn07dK9vs 5raVWSWaYCYFWO0jg15cj2kD2GrFjDLD/ozqib1ZZDks3Tn3966q+EVpcxLBJc37TDzNzkmKJfZV HNVJCTOkntNOREt7e4uQ2PkUY24X69q5NVMlrcC7u2aWWcwCLyY5gVTDfKDlW6DHXnNLM+lPp0sb R3qTSt95Mopz8o3EZ+WqGoCGwl0/T7CyiuJtNhSWHzP4WSQFyuCOShY1mOx5b4l8MfDbxHqcEHie wbT7u4GItW09fsU3XbidUIiJzxyPQVT1L9lW2ePPhXxMJZtqsY9RgUsFIyPmh2Efitdf8UdMh020 k1a9ixpsc+zUTETvWyuR5ck6cceU2yXp1FS6v8J/E+t+EbGGLxFG+vaYF/sjxDab4w8bclbpI8ho 5BjLKT8w3cZNZyXmWj5G8WeC7rwDqSaT4xsJbaaXmGSIiWGZB3ikAHQcspwR9MGuVjudMRGI095W JIUbDgDOBk1718c72+sfC/hzwh4s8RJrmvWF79qe6SFVlWExFXEqpkKGyNpJy568c181JFC6YF9d ORwSEwM+w4oixm7DcwRJIG0pXYqSrnICnscVmR6ncwFibC3ZWBBBUE/MMZBzVQ2iKQu69lLdAMAf zpy2QLACGXOejv8A0FaWAtrqc0cHk/ZIAQAFc4B49cZpt1rmp3AkUTwQo23Khj8xU8MePwqvLal3 5tIyw4JaT06fw+lVnsyOWtrRR2LSkfmAKLASvr2psNn2qJGLbvMQksMdhVd9bvY5CzXcMb5Bzg59 ecUxh5fzf6EmOCysWxjimNcwxBnbUrRB0LGPP9RTAq3usyXczTz6iNz43GJMDjtiq41O4mBP9o3G F4/dwkZH4/Spn1O1lwq6zGfQRQL/APXrptE8I+L/ABTbS3fhwz3sEMiwNIZbW23SMARGiy/MxwR0 AFTLQDhDd+bjZLe3GMqAsIPXk00vMSZPs9+zbc4EYBOfzra1C0vtOvp9K1Ea2bu0keKaJcOqOvDK XjBU4PoapYmePZHpGoyBecO4XJ9c5pIDMlh1GcbRY6jsHP8ArFXt71QbT71seZpd8uTjL3iLgfgf 6VoNo5JMg8O3EjZyfMuhjJ9ec0Jok+cL4ftYye8lxuBz2piuYbaXOVZpdKgQrnH2jUD+HA6ZHvVL 7HCWX7Ra+HIYx3a6kkZfwzXSHw9eFwE0nSITk8s0j9D3+TH609dF1D5lQ6LDt6hbXdj8WKj9KBnJ 3A0WJR9n1LQVYc4NqXxj6E1X+2xoB5fiHTosdrfTGkPPpla7pdL1Rh+713Too1YJmG0jXBI92qtc 2l1bEQz+KZlZuMW8EQBxwf738qAOUXU7rOE8Ramy+lnpG0fh8oFOEl5cnm88V3Y9Et0h/keK6b+y mkjVzr2qTIeB5MmwH8ohUMmgaa6/6RNrd16p58oB/wCBDYKdgMM6NNIu9rfxG+f+WdxdCMn8ia6X w7cWWg208V3oszLdXMQVLu7a4VcDG9gvUdiMiqLeFfCi4JsL5o2HzM1w2QfdXlANSWmn6DpEwns7 ayhLZRzNKPudQcsxP+Fb0KnJNMwrR542NrxXqP26Sw1PUwsg026hdZIAAjrG4/cBOiiMKMY/hxXP +PptNuPFUPiieN3tLmNSJB+8EUmPv5AHynjoDiqOueHrrxlFLqt/rtno9rZhY9Ls8qVuTjEpjPyh m9fyrw3yNecwacouJnuCI4bSMvIWLdFWMZ5/2etbYmsqj0MsNR5UfSnhXxHpmrTNFZAuIgudoJwB jjgc4+ldnLYadegCaBJFznDDODgeoFfIun6vfaBeSW95HJbtADHJEmYZI2BwVdThh78V6h4f+LYt ofIv7EhM7Y2ilbaD/thsj9KyUmjZxPV28O6MR/x6K2WWMhAI2I7gEZxj+Hjr14rorL4W6AxfxB4b J1GOORY54JkMV7Fk8s0AIWRfV4mOO4rziy+Jmg+bFLqulXEtn5gLeTMgyByQCUADfUGvpXwvfaF4 xtYZPh1qia82nCdn0a5jFtfxyTnHmeTkpOiIPvxHdxmvWwFXX3medjl2ifPmsfafD2oXt7ptwlks ibnjmSQxyjOGj+UZST0Vse9ZNrNJc6ZBdTLwrEbGJOFPO3nP869S+LNhe6wttqenQRXIhvUsp72Y mB5SEd0gmP3c+jnDdjXBeW9rDNZy288M0LjcJV+Vsp1Vhxj056VnnbjKi0yMC7TTsLpenTXeo20E GxireYRM/lRHYudjO39/p9a66SW4ujHFbpcW7giQQvN5xg3fN5QcgYUHPHauZ04qZpg8kUO6I4Ey 7lfp8jem7+E9quaZdC+vxpOfIE7MDIGABGemax4YhGNJsebO8uU9Eg8R29pb7dRmjck58uV8bf8A 9VYt9438M+YVF3DGdvIjbJH1HP8AKsvVPCFhDuWMK8gGBuyx4Fco1n4e0UiS6UhWXjnbk/nX1jrt Hjxpx6nVj4g6Mm5Lgl4MY3KpA6delcze/EPwjBYtYwGSSM8oFU8NnrzweaoSa/4OnAj+y6hMiKQU hJI/z9KhstV02FydC8GyPIR9+RWZh6ZJzWbrtlqlG5Z0DUdb1mUR6Jps9vC/37i5kEMYHdtvU/lX aXOvazoej3+vTa/Bu02B3CQqGV5FwsafN6scVR0/xf4us5A83hYiB8LIqupdl6YBP+FdN4f0jwrq csmo6JoE97LKx82PUfmWJgeeCQuB/u1Lc7e6O8ebVHmEHxA+IviDw1/bNrrfk3cbEG3iQHdGOpUD uK+h/gPq3iLVw7ahJcT20ETGWSZdqlz90D1963PCvgXwvc3dxqMegWtu0UUU0s+nMyRpMzFHilCH yyxxnHHFewWEUdov2eFVihQjEaYCZxjLfSvnszx7hF0merhcOpvnS0LzFQNxGc9R6H/9dcX46vRp 3gfxNe45GnSxL/vSjyx/6HXaTOixkbuOmfXt+VePfGe5MHw8uYRnOoX1rASP7ikyt+iV8rLY9iKu z4/VWWMRAZKcen4VKICOmCR1BOKvrGbjPlhWwecnbjH1qF0CPhjuUnBC/T3rwpO7udaGhmh2gIB7 j0qwzhwVZxyPp2qB5VIC4YKvC5GT+lQrl3bzwyZ5C4x04pCJUQqxMB2hRtYHnNbFugcLHckx4O75 RnOO3FZdv8mBt+VDu29yR2rQG7yzMrhwT0zgjPbFUgG3NorqX3dW4IwMeg9qrLnZsV9vltkkjGeO 1TRbfNTepwT83oQewxTrjZuaMJhc9BzgVSYWO40vWLLW7FPDviFjJsKmGcfeQ8k7Sf1HtXGa5od7 4euo4r0edBOokhuUHySqeQfY+3B/Ss9gFKsqhSuNprvfD3iSwu44vD3iiMXNjIyxeY68qgB4z2wd vNacykhKNjz3d5bEqdrKdpGentV3zEONoxjng/4VueJfCV34fH9oWsi3mkPIsUcoO14iy7lRh7D+ Lp+tc1EWQkk8EYyec96ylFx3KTLgDyMAACeuCeRV9IgRmQ5ycEc/hjNUEkaBgy9COnFTSXwK/MDu PChcfzpJlFlDHj92AwT1zzjt09KkEKbxOuApwWUd6EjS3iV8lnzuIbHp0pjTIrIhIDMN2MHimBNH mRiRlF/wrRhLSMU8xgFBJzgdvfFZkCSl/OaTLDoB3FaMN3cncJURlfgEjGKAM26uLq1U29usrbxn djPaqcFrn/SLh97dgfl6cdDiujjmFvlieF+Yj8OlYwmW4mcRRFtxJ9hmrAZLcCdgrcleAV4FUysy S74+FIyS2OMVavYZ7YK3lhiemOCKzUuZWTDptDnDA/lUSYBNOtxG2MGQ/KSucY/KpdCk1jTNWs7j Q5ZrfUjIsdtPbMY5VY8KoYdRnsTjrkYoidBu2x7R0BFNw6gOT91gVK8EfiPT1rOaurCsep3l5Fpv iGWLxzd6dJe3cQivjpyFUK53mOZosYJOeY+R0yOlbnhmO00H4im3DRJomrR7rGawnkeGCO4RtsLy SfvNkq5Dxv3HBzXhk77nJOGYDJIwCT9O/wCddN4R1zT9Gu47fU4W+xyIYXaJzjk5RiD2U9SOR2rz cThWoScNzNqxc13wJrXhrU5dPji8/SgGnt9SdRHbzR/xHfnqh+XaPSuEmkVSYwwcJkAjODg9RnB/ SvbfF0tzr3whgmBMz+Dddw7DJBstTT75GeVEq49ia8J8x5F2h9zKBuHBI+vpzTy2vKpS97dGlrWK 0jI3LBQeOQDTlUZDsMrnHQ1NHB3YbyOnbPpxV+MxOgJwpTqOn5V6AyrJ5BU+XzgdDVF25GEx/Tip 3RkYmIEEZIB47+9R7J2YZVQT3c4z+dAC2dxc2lxHdWjtHNCQwde2DnnHb6/SvePCXxPhuoV03xHH l8q0VxH2+Xy2VxwP3sfybs/eVG6VwV/4UtYvB2k69ZPPLqW+4GpWyLvjhhik4nJHMahXjz1U5zXI M/kgkdWwdv8Ae9zj608PiVK6XQzkkz6Dm8HTvIXt/E+uLEwBQQIs0e0jgq7EEg9cEcdO1Rf8IZef 9DT4i/8AAeP/ABrxKDX9ctYVt7e/uUij4VVY4A9ql/4SfxH/ANBG7/76Nb81Mj2Z/9WxJrWsGWNj phRY23ASXsKgnHU5yePr/Ss691jUbmfdO9hbnJID6hhR+CL/AErCjbS1yIo9BGe8dpNMR9Nxq5HM dw+zzIF/6YaTgD/voGvQOezJ/wC2GGP+JzpELf7LTzk/98qv6GnDVpJGBGuRk+kGmXEh/DJP86UT auFOybU/LzyYbSCJR+LDih01STBkj1iWMjjzLuCJeOOiqcfnTdikiYXV9J/y/avcDH/LPTEjx/38 yacsV+QCya63b5zbQDn27VQOnbxifT5WX/pvqDkf+OYqs2k2S9dO01SveWeaX6cHJNK6KsX5rORl Pn212Rk5E+rRxcdv9XWeYdKxieDSwVHIudXmkxgf3VIq1BpMoYG2sdLXI48qzkkP/fLEVsQ6Jr+5 VNusEbdDHpar+XJ/lU8y7hp2OXRtGX5ov+EdUDriOa4I9jlqtrdWDqBDNZc9Ra6NIcfQkV18fh/x MzEJcagARkCK1VRj3wKf/wAIR4qugP3Ou3CnnO9oh+QwPwFLnj3Ecr59z1t59UX2t9NiiX8Cy8VJ 5utSAKsXiCbd0ImtoM/lg/pXWH4Ya7MoB0y/k3dnu3GPr84oufhHcwWyXD6Yrkkhg0u5R2ALyzD8 gKPaR7ktHDSQajuy1lqHzDk3GrIo6f7Jqg6WqcT2+nJnvPqcz9fp1r0mL4e6fZeW95aaQFQEsJbi 2jPT7o+Y4qpb6JpdkD5l7oEBY5RYZIXKj0JC0e0j3Gu1jzgzaTHw58NqF7Mk91j86sxalYupaC90 uJl422mmSSk/TtXoyz+H4XKP4n0u3b0iSVyP+/aVXl1DwlH18XPOB1+z2Vyw/BnOD+QqXViOzOJS 9vAC0Wo6gw9LTSUix+eamS41mYbkfxI6t3UQQZ+gbFbs2v8AgvBD6vezgdvsa5P/AH1IAPyrHPi3 wajbTJqUhB43fZ4wv5ZxS9sgsyvLa6sSFltNdmBGcXOqxwj/AL4jOai/smMgtcaMhJ6m61edvpny yRVG48YeFHlJisdQvMH5ibhQxHbG2I1PN4wFxBtsfCt05wPLeSadgOeyrFGPzNP2qDlJZdHlVWSy 0zTrWUAZNrNPPLj1LzYXFc3c6ZqdrK5ZSjqmSDg++a6jQ9R8Q6lePBqOmtZ2MaGWaUJICVXkKdzn r06Vyup3/inU76SayjEcZyNpA5HYdq6qTujKRtaZPPdtFbMu0AZJJ5IHc4qW6ih3lo4AW5yx64rl baTxXE/EcIc/L1GeB7GnSa9c2zBNTtTGTx5i52/jirINR720hUfaEWQHoFGSe2PWm6U8lzq8dutu 0EEu+KSPnJT+M4BB+b7o57VLbpp7xC7gUFuSpQbvmUbu34VnWrf2jC9o73PnNbKwMCiWct1bC/7x IrOTGkdtcaX4DtEjMmj2sMy/fe7+dcYxgK5JqkLzwNZkNBbaUHXowgg+X9M1yA8HAt5s+nazI7c5 kht4eeuMs5qc+H7SLppV0AMAtc39pD2rBuZ0Kx2UGu6XKHaz1CxtRjn5UjB/75Vj+lWJPFOjQWmP +EiDTKOTEs7fhhYwK8+az0iEnzYNNix3udaP8oh/IVGbrwtGpjlufDyE9mu7u4/QKKj3xc0Tq5/i H4dhAMt/czELwBA/XHq2K5Gf4rWoYvHb3joh4LzqnHtk0kLeGWbNqulyMOM22l3dzk/8CIrTg+0Z Y2Wn3pX0s/DYQf8AkQ0+WRNzBHxVi2M8Wku5T5syTBgCBnHANcxpGsX3iKaa71O98y98QW0ggCkC GGJDuiiQcAZwSWNd1r/h2bxHp0dtrum+J3tIJA52RWlkoOMc98EHGOa8EvTHZzA2KutrbAwwIzcp EBhQSMc+vFXDQW56jZazdR258x2jkGcnnI7Nj6e9cXqEGs3l9bTRh1USyQkKRsBTGXkJ6DDA5961 NT1DR5LGJ2aeC8/dq5gUOkg4BkGSuOnQ5rotLWfWbSbSvDtw2lwRv57m4UT3dwsxxJKpX5UVSgG2 t7kpHNaHpy6hdW9pdajbXE8z7I4jL5UIHXdJI20bf19K1dRi0u7uF0rSrmO8tYjunlhjKQlx/DGS AWT3I/oa4vW9M07Sb/y9Ima9uAW85jtyjn+8R8oH1rRt7yeyg2WsBmLcbwRjjjjpWU2VBHSXu22g jjJDnpjJPGMccDp9K4O9uYUmhutzO9ksmET5fM8zjy+O3ANSXGq3FwJRcR+W3TH8WAPuiuXuJpdy xDbCxIXJZcqvXkVFhSkep+HtG0jxDFBM9/qE2ozhpJreytVlwRy2Gdvf0rq5Ph5Yptd7DXgv/TZr G1B79Wya8N0XX4NOvIri6g+0WcAERg3sqyR9ZAzoRhnPT6V9S6LpEOo2gvtJ0zwultJGjD7QJ7id SecSRSHjjvSsNM44eFNDgQedp8qe13r8EXH/AGyWk/sTwfECWttFX2uNYu7kn67F/KvTl0i/gOYb vw9ZHOAbXRYz14HMmavNZ6vCZI38VMmwgP8AYtOtVVSfop/Sq5Q50eYW+meGXP7ux0F27eRY3t0R /wB9gZ4rbtdOljx/Z9qAp6iz8Ngfkzn+Yr0EWOshFz4h8QXgPH7pkhGP+AqP0pjeHre4HmXEmtTk Zys1+8fbHOGFOwe0OaW08VOFaGDWlTHHk2NlZ8f77mpPsnit0zN/bKL0AudXsoUP/fAzWsfDvg2J 839sFPRjPqBbH4M9Zc1t4EhLiCDR1y4X9/OH+QDOTgn+VPQXOZEumsT/AKTJZljyftPiFmx9Viqt 9g0IH95e+GYz3E1zfXTcemCM/lXSJqvgmDeqS6Rkn5RHGzAew2pV6HxF4aVAi2y4A4+z6bPJnn3j Ao0FdnG+T4bQjZqWh56ERaPcTflvq5AdNGEtb6fnp9k0CKPp12mQ12i+KZmCraadqrJxtEOmmPp9 WFWxq2vypcJFpWu7LpBGwaKGMFQcgbi5ZeeePpTuF2cik0+wNDN4mkH/AEyt7S3U/kpxV1LG8m/e Jp/iSZTzltQ2D8cRjFa51DxGqCI6TdIVbJa5vIYyfrwTVSS+1By32u2sLbcOTcasOfYhUX+dFyjC ms4xJ5cmiXMjZwRPrEzc+4AHtUo0qAPsPhvTIpcA4uLiZx685fn8qsvqcSDEl9oESnGP9IabGP8A daov+Ejg80LN4k0KNlGFENrJKw9uc/zqbANXS4lyXsfDdvjknyS/6vn+dbC2epRRebDeaRCjDKiH TowePQstURrZ3hY/ECup6/Z9JO4/7uVq01w6ETjVNUnPHFvZxknjpyAR/SmBoxprcg8sa7IpC7iL azhUgf8Ajv6VLDp11LG0s+u6o+OiAFc/gvSsRoryRsw2PiSUsc7TJHErfgHOKjm0+7kIM3h29Ygf 8vGpiP8APGf50rgbw0XzY2kll1RkXj95OyKfpkn+VZ0+j6SnEiueOS97gg/99L/Ksg6bbL80mg6V GVOd1zqpY5x/F8oP6/0pGNrAN03/AAjNuBnOZmkxz/vjP5UwLken+DYmb7TZ6fIfvBpbktjj039a j83wahLSxaOh9drt06dmqkNS07fhNZ8P2vZRBaPOePQAN9ev5VG/iO2T7vixWJ6C00jH6lP6UIix q/2v4eEZWzjsI5c/K8Vk0nGOf4MU6XxBbmNkRJgQDtFppzfNgZxyB6elZI8QyugA1zxHcE9Db2Ii /LAH8qguLm5uohGkPjO53g5PnbMZ4OASAOPancpI+hb/AMT3Hwo8K23h/Qba9uPEuu2sd9q2r28K 77SOfmC3QyHCvtbgAcE7upruvhj4MXwrp0HinXoJ2168XdaWlyVDWiSclpCo/wBc4xvPJH3R615n 4Q8a2eo6ppAm+EFxq3jaCOK3tNRu5YVXZAu2Oe4ZidojXHz+Xnj5TzgfT4kuLYLJcR/aL5mXzJR8 kO8jlYs8lR0Bx9eazUbsUpaFWSKG78yEzzT30+PNaBCzLHw3lp2UdsZrZt5WvJUhlszbR26bmjkB BQAbVz1+tKqazp4Z7e12+eeXWbJ3Hv8AhVIXUlzFLZ+Ywnc7JHIKsAe+GwePpW1rKxmtSG5t55Xh uZj+6vrkYBHKqVIxkVYuV2zRiMBZeh2jPvmrmsWl01itrAA3yqY37iUYK/nVtLvVNMhluptPhnkZ FSRx/ByRz7c9q52jS5cs9Ltb3R/tdk+26DNuOThtpI2n61zlwXhyjjYy9Vrq9KENtocSxYUozLIR 0Lbjxk1g61eW8kaRXCb7nf8AudvDe4P+zQhHNzyKkR3EZm4z0wo5/WvB/ij4a1DUfDOu668FteWm j3ljcJb3ztHEVlR7eT5lIwUB3da9s062vddum8hQioxV5G/1aEHH/wCoVZ+KmiRR/CPxTpFnGk5O nyzMk2dszIN+XI5Gdu1R6U7jSPz08hw+JYPB1kyk5O5pmBH/AAM0w3y27Ef8JB4cswnA8uz3E/Tg 1c+walG7MNE8NWjjlu7L+BqwI9aIU21/oVuW4ASzDEcdtwFXcDMj12WNSV8axRkdDZ6Xzj2+QH9a rvrzytj/AIS7xDOx7waeV/U1uqniN45WfxRBarCu4iGyxkZx8vQdahdtTaEST+LtRVWBJEaKnC8H gHgUXGjKWJ7wDfceMdRJ+7wIl49DnFJ/YxkX/kW/Elxn+K6vljA+u3FSXFrprpMZ9d1W7kQJhUlA LEnB5qtdaVoUasEj1W4fsJbo7foQoP6VVitCrN4bidvn8EIw/vX2qhAPrVR/D1vGOPCvhmP08/Ui w/Rh/KryaD4cdRI2iylxglZp5DuP1qFtH0NXLx+Hbbyx1MrFnz9WxU2HYyGS3tASLPwLp4Hylm8y 4/Mc1THiCC2JA8WeDLLb3g0rf+C5FdZDb+G7XyhLoukK25izMqDC4yOCaoS6pZwblE+iw5IbzAkK lP8AZ6elSTYz9H8dWkeu6Sz+PLS5RdQtS0Nno0cSyASodvmdVz69q/QjTpWS4uoN27y5ZBx/s9CB k9vevg/TvFmjtrelWE2qWM8dxe2kLwRxoXkWSdFZQVTjIY4Oa+6ba3ht/Fmp2MhKJYyBZ1ydzr/y xZO/7xcA/TFNPUmR09mt0sT3SIyQSKck5O8jsorubK+uBZRxJbspVAwDdXQ87cDPrSSxfb9KEVuU E1tiRFXhePlJHtVbT5ru5/eJL5aBCmD94NyPy4rV6kI6O9vrsRrpzpBHG67nEfJVeuDnvVW/hYWm mySQ790y7G27T8wK7MgVT0ti8LSj95MxGMgc+ob8a0Jr67vdStbZoSqWZWcqy5yw4HQ1ky7mPrHh 651nwtfaLI277bYz2aK7BwGaIgfNztDMRkHj37V+cun654x06wGkprl1pgtw1tLbwtOixyRHy5F2 qR911Pc8fUV+mdra/wClX6mVFMknmIikhoyQA2U64Pb3r4G/aP07xD4V+Jb6jpOmLLpvi2GO9jZE 3L9qjTy7pXx0Y4RunvzUSRUWVvEHie08WeGLB9fDP4v0yRLaDV7RCsd1ZDIZLkZG54/+WZH5g5z5 rJaSJEJpNRkCp8pOFBPphc+np/8Aq5X+1vHb+WiaM0a44BjjHbHRpV/lSG78fHAEMEB7iQwDH4b3 xSUSzsIrKVVkAnubgIFdi3lrtU8Z5/pVb7HaNcNH/pUgUbi4LKM/UDp61yJHjiVWEmo2kKt1xOOc HgfLHUL23iaQlJNft4R1wHkb8OAn86rUDtV0nTpV3XdhPKegPnHb+Rx/OpX0vQ4sBdLjcHGR16e7 NivPV0m73F7nxKD32okjfgMy1AdDsn+eXXLuU+iWxJP5l6YHo1zHoMDTGyt7K2DbdolRMjjnd175 qnc6npaf8er6aNo6uyKwb1G0dK4dPDmjls79TuSPSFY/1EYqYeG9Kb5v7L1CcDu85RT/AOPigDsJ fEmmbBHDd2qbcEsAXYnuARnjPT2qnL4p08SeY2ou/AUpHG3AHPHy9Pb/APVWEdAsUUkaGNvpLecf lvqu2jWgB8vRNNY/7cpfH/odKwGtceLNHiQbbq5jfO8lU2bsdjuwKxr34iaDKoictlGzuEkKMfY8 k4q6miy7o0t9I055pXCLFb2zTOcjsFjWr0djrURmSaAac9s/lvDNp8tvKuRlSI5NpZT/AHlBHvRY Dk28daZJGYlgeRW5x9obn2Hlg1F/wmLSFDaaRJIUIKY+0Pgj1wldPJb6tvIa/KHphbZF6fVqrm21 Bh82pXeO+0Qr+XWkTYxW8QeI7l3eDQJC0hyS0Mh/RtuKhF945b5U0ZLdT/0whT/0KStv7BK4INzq MoHYzqP5A1Vk0yNUJNtMcdfPvmI/754oGZbReOXIKpbW+R13wIP0zzVK7t/GYwbnW7GAKMAfa4wQ PcCM1fk02LBUaTpxzjmedj16cdaznWC3wuPDtlnoGVpCPrn/AAoFcypo78J/pfjG0iHXi5kfH/fA X+VYstnokrZn8UiQ92hhuJS34hsfyrpDqQiY7fEOkW/X/j3s84/UD9BUc2ryPsA8Wyzs67lWz08N ke+1TincLnPR6N4al6Xmq3YPaKwc5/76z/OrY8NaCVZl0fXp8qRxbxRfKB24z29avC5ubn7t94lv CBnEMAix9eBSCza5YRf2T4luGkKrmWcqvzHGTg8fSnfQLHceJX8KW/g7Q9Pvze2t/ZGVYhEib0jk AOCz8ducHNeR2F1dRmO1F23lGbzlI2/LMBw4Iwc/Rq+grjQfEF7e6HqHhfw9/bOpT3p0zTw0fmD9 zGJJWO8hcLwCzcV892UIur6f7VMNNdppPMZ4WZVk3HcuyMnGGz2op2Jkgzq9jq8l9G7SXVwjM86w 785PzZByOfXNZmo20u9tQ2mSOYH7SgXHToRjuK7GewW2ObPUrTUW7pHFcRsM/wC+oH61VuuYCHTy 5CNqMpAwTjoOhrW5JF8O7bUZfEWnpo2kw+Jbx5WKWMqCSOdUGSJF/hVum445r7J+I3gvwzovgjTN Y0fQY/CGu6k9pe+TZnyprO5CEvsZG/gYkDHUd8c14D4VhvfDWpaZ4w8FX7mNS0NyygRuJNuTwBgg EA4YcjpXrfjnxdrPioWWq6+YVKM8MSQKVBZMF5CORznHX9K5ak5c94s0S0sc5c/EzXvEvg670fxg Z9R1i0vt4ltbZXkvJUiCxPK6japCnnP/ANeubV5ZLaKd4Z4UdciO4yGjHTBHNUr3S7a7trePU7i6 tvMZrvbZu6ttmXagfywT0WoYNP0bSGmFj/aU08qDJZ5JUwRwMtgf4UYyc6sLGcKcYu4++Nw1rKLW Xy7jGY2H54Arzu58Q6j54SRvIulPEvy849MivR4MWskFxK3KkMRJ/CvTB5ra1Pwdps9l/wAJAbpo YJXAxafMULcZceW20Z967cjlyx5TlzD4kzy211vxPqpRH1aZCMgMCFwB9B3rp9M8J3lxJ5+oTNcE jcVyWJ/Bv6V6N4f8DXsNnJf21isVoF3G5uZEQEf3iW2n3+7W/rIXw/Ha299qmnxQ3UgUyW0rXew/ 9NNoO0e1fVUKd9Zs8apPW0UcHZ+GI1njgswYXmbbt4GD1zj/AOtXoOl6Z5FrNHazOpQAGUghT3OS cbcdOcUabp3hj+3ZobrxFNllUQ+RYfu3kP3VYyH7rZ64GK6STWvD58M6lNa2cFrqPhxlef8AtW5e beN+07LUKIiqNjggn0zXTz0oIy5JSMvTfCN9rTLcaZDPLC7Y+1XM5trTI6/vjwceiZrTi8L6Jo95 anUP+J292XjgWINa2BmHWJ2OJJeoyDtBNdbpMl38WdAkubC4fVbySOKSORXSOCxlhO2WN4xsEcTL hkIXtW3qb+FBbSWPiq8OrX9wIJp7HSFAEV7EPL84XRwg8xPvgBuQO1ZPEwS5mxqi72SNbwheajqU 9nDaSb7DyJA9pEgSK0ni/haJQBj+6zH8c13QRo5CsqMnTAbg4AHavn/w/wDGHwT4r1LVfh1exWnh rTbvzHdhdNas0tp/yzurnKsSeHBVwOK6PwFqNhD4u1bw54d8RT+I/Df2RLqD7RKbn7HeByJI7e5b LSxupGckgHIB4r5DNKsKs/dPocBTcYanrsyLkDGAeMn1rwH48XTrpvh3TlPD3FzcsPXYixrx/wAC Ne+3AZcYH3RgjnBP4/1r5V+OeoE+K9H09TxZ6ap6d7iZm5/ACvBrvljc76Z45t25ZELn/Z5pIZME B+5z0Jx+IFSRnbhvlJHYnH8qiFxKGdMnbjGMcnvXjG4+VDG4lUFh1XnGB/n2pDLLM6mVdoC5XHbn moQ5LAvu8sjGDjdn0FKJJIGD52sPug8gL/KgDThVZSjQ4IL4BPfNQNmGcw7jv6A9l9qc9xPNGqrs RM5LjC5+lHkBcAnk5OSD3NUgJvLPSLLFsHj1zTwWG8t95uhpg+VcP9724yKQMm7eePoCf0pgRvCj INmW2HLY4qsQTyoBPIw2fu/hWiZHEeEzt754NVAsqAkDJPAFSB23hfxV9khOk6rGtzZyBFxJg/ws AnPb3qXxL4Ui0dl1PRJTe6RdLHMOn7ppFDFexwhyv0Feex+buB4znHPbFd34Z8WzaFdxC8BuLEPl 4SAQqsCCMegDGt4yUtGQ9DkVcvtCkEMM5x7Z59KcI5FZGkXYp4AHvzzXo3iHwfbPYnxB4SAms9sb XFspz5bOCd0fQlcq2R2+mK86lvC6KVGOc5PT0OPpUShymkZGvPcLdIIgFTYNuayfs8pbecyNGRhh xjj071JaBDJ5pw+OAB61at5WRXaUhUXIxj171JRFA4iJcnDjPPpn0q0lxg7xICvTB/wrLmeSXGeQ Pukcf4Uw3CphdpX8KANvzd+4NjyyMEgVq2TWlvEZFJVsdxx+tc+14qQiNyCG74NVpWuLhtqt8vTH Y/XpTuBa1LUTdEBQAiH75yPz/pWUw5yBuz0FT/ZVjO12MueoH5Vpw2yqpdwJGBGAARjAxSYFO0ka EKWhwM45FNvpI3ICJsGc/LVq4kmK7eMN6c4NZd0DEqlzyT29KAKrMWOCBjODxwBjuaegiyqvJ5aq cZ3jbg9jntWlpkOhSOsutXdzAqnHlWsWZGX/AK6FgE/DNd3/AMJl4W0m1Nv4b8Kws5+X7RfESSH3 JAbj2yKzloRKXkWPCvioCDVPBviELd6b4i09tM82I/6nawniLKMdSh21yniTXdI1GCDT9F0Gw0qx ssGN4oR9rlIG3Ms2N/4cj+VNt/EV3JfiS50myvYiGD2aRJEsisMEBwAVK9Ub+A88810OseHLf7Ld 3+kO97EoW4gRDHLuUk+ZHNsOEuIh1ZeJFAPUmvOhTVOq/MSu9zzgnAKY5XOcjGD71ReEpzjBPf61 bVw0sCu+1HkijaRRllDuqbtg9j8o9iOeK3PE+g6l4S8T6t4W1bEl5pdyYC6jCSL1SRfZ1IYfWu11 EnymhgL90ea2ccZPPb2qNnAPyltpOBuPJ5/lROuCu0kjvx+NQH0IBA45Hb0FOXYD6v8ADR8Nabo8 I8M3V9erdW8mnTRtKSYZLiHycyI+1kilG/y3wyK4UkV816sPD73UMXh+CW2s4EwTcM5uHIGHEwcl VZCCDsAFa+l+NNf0XRxYaYwjvvtfnx3z/PIkQjCrCAflADjd0Oc/jWv4su9E8RaLp3jSygGma7Pd S2Gu2kBxbSTiPfBfRxYGzz1DCRRxuHr15cLh3CbZlaz1OGDog2bD8vHftR5qf3D+tJ56jgMuPz/I +lJ9oX+8v5V6Humlz//W7EN8P7RisniqWfPH7q1kbp6YQCpHv/hmE2yarrcqkgttgEWce7Y/nXjk V+1uNkM+lQgDgb3kIA7HO0VDNqsTjy31GyB/6ZRSOP0Jrb2c+5HNE9kfW/hcB81nq98vqboIMD/Z Xirf/CafDO2Qi18IkzAfL510efxIrxCCWKQbUvnkx2hsyfyzzVwQXEoAthqjg9WS0AXj/ewKfsmF z2SH4l6AikQ+DrEEDBEkhk/9kpw+Lflf8evhXS4dvQtCWwPr8v8AKvGGsL7geTfOp4+eWOIE/nVd rFj/AK2zVMd5tSUfyNH1Z9y+ZHuM/wAa/FcyqtpBYWSqMYjtYyeOP4if5Vzt58YPG0+d18lkU4yq QJn9P6V5W1rECDKdIRV7yXjyH8QuajaTSUIH27QlPokU0zfqKf1YnmO6l+J/jWQceJpM5ycTRrj2 +UD+VYk3jTxZcuxuvEtySw6pcOf/AEE/0rEF3bL8yXwUD/n301m/LOKuJNPIu+3bV5S2ABBp4jz+ JBo9hEjmK8mp6tej/SNZmmYH+ITyjGO3T+tVntr9mWRpbt9hyrLbMf8AgQ3nB/GtJ7fUnXabDXXU jkSyxw/TgVGdL1aQ4GjTuRjP2nVAuPqAf6VoqUewrmZ/Y3mne8N5P3JKInJ5/iIo/sQL8xt3A9Wu oo8fgM1s/wDCPagcbtG0xdvQXF9LL+QVSaevhjUWPyW3h+2PtbSSsP8AvoUezj2Hzvoc7La2MZCT jTYgO818x/SOoBFoxYsZ9EBHQHzpT/6EM12i6RqsURcavpkCoduyHTYyef7vzf0qzb6VfuHVvFk0 RHQQ2kSfkRmn7OIuZnGRCDI8qeCX0FvpshH4fMavxi8PENtqeDx/o+miPH4vXUvoFw4CXuv67cE9 AsqLkf8AAYzj86pN4d0tlcXEupyhRyJ7xhz+G2nyIVzHeDXdpX+zteK7c/PJb2/5Ac1A9rqBQG5t LpfX7Tq0SjH0Rs/pW0dJ8Fw4E9vEAMZ+0XrHnHUKzimY+GVowYpop2+mGJ/Ek1SSEYfm6hbQ3EOj wwPK6KZI7S6lvpiucZYIDge/SsO41TxbbSeTHYuoXqhhOR9c+tegXWt2ttDt8GSnTXnwGOnK0DMg /vOoQ49skVhGXxgs8dy+uaisS8t5k2/JPPCtmtKaIkzK0S/1W5ujG+kyyyYLZVcbeO//AOui4VpV 8ua0myc5V4yQfXitOHxR4si1B2XUHu0GIAl1bQOjB+W+VVH0/Cob/W9cZbmG60/TpC92qq76f5W2 NVAYr5TggHrWhHyOQWNbCSRtOaYRkEmOQYAY8fLj6Vo6TYXF9OsSaXdzSIu7yPtTWe5Dn94Xyu1c 5/zitCGcT+KUtZ9N0+KwgSRp8tcRxnaoUfMZN/U5wpHSkVpNL1CfUjf6PaJcs0lrb38ksUUUbN8r LtDSs2Ap+Y8A1E2NMut4O1N22y+ENMLsRg3+s3c3XnopOfzoHhKeI4Gg+EI2z98xTXeMdfvL2/3q ZceMzboN/ifw1aJJ8ybLW8mzjqQXI3Y9R/8AqpL48t2IA8dRfTTtDO7n03Oak0Ojg8O6whAS88P2 iqN2bLRUOOfWRwKsRab4h+Vk8Tyop4H2PTLWInv1ySK5T/hJGvMpFr/i/UM8lbPTI7dW+uVY1N5O pXw3DTPHN7jGFlu0tQfY/IvagVztR4d1K4VPtPiTxFIzchUdY1x77I/6/lUVz4I03yGmup9Yun7C 61GWNfzG2uP/ALAvnBV/BWoPH3Opa85GPoHUU19AjHLeFPC9v73WpNL/AN9ZkJqB2F8R6T4Z0jTL i8tbK3W6hi3RPNftcSK28cqhk5/KvA5jaySSwOPmG7IbjnPPFeva1aLa6TcgQeEoIwVDJpQ8y7HP RDg44OTz+VeQeMpWg16/uGVzbSMssckQBO1lH3utTJjgi1Hb3y2EcOmi1do2J3Tt2PO0Cs64u9ej H2d7qGwjZwWFmv7xgO2/OR9f0rGttWsn5jnhuD1PmNscD3BxTLrVbZQQ9xAg7BWMh/QUk2gkS3Fs EtvLRmg3csyHLk+rk/ez1rOg1nVoWFv5yxQIOZCoLHHP4flTE1rTjGUNwXkztVDEQM9etY877rhI sqd+cgYHQdKZNzc/4SO0Q71Ds78eYV7+uP5Vf0660MgyS2Mly7NyscgiV+/zNtLGueguoEWMXNvE ygYyOCfT2rXjtGuIfO0SQGRefIPX3IHHSkI6pdS0HxLctbahpUGkLAAIzp75nO3/AJ6CQbWHHQAc /nWp4d8SS6Dqy/Zb9hE+63ElxGu9ov4RIp3Dg9682jt57m6Tba7LuEGQuSqDC9SSTXZadDcy2Vw9 vFHO4GXdx2PYZ/pSuVY98svEkN3AAnijW7uYYWWDTbK2YK+OAp2gnP0raSO8uI972njm8OS2SY7V TxzwuB6V8++AL/UY/EWnadprQRTXV1GfMYsvT/lnvXO3Ir0nVPEehReI9RWWHV5tOKxNHCbsxvbu VG6PKltyccZ+bGKrmRSR3A0SfJVvCmssR0N5q+0fUjNKdECANP4a0qHHe91Vnx+R5/Oua022sdai F1pXhCS9QjPmXF+8ijPPzLuFasHhu9GHh8F6Hbn+9LIWP6v/AEp3EXDFaw42w+DrI+ruZsfrSrrC 2wwPEPhqAeltYlj+B5FOGneJUGYLHw1ZEdP3O7GBnqAaniTxME3T6xpFqpHH2e03H8D5Wf1oDQRP EkmQB4xK+i2elbv5rSnVpZ8j/hIfFUx/6Y2YT8sYqeKHXmQH/hK2RS2391C4HrxnFOewuJg0V14j 1S4AxkxgKvI7ZY/yplWKzRXM6q4Xxde9v3riL+tVTo0bsWuPDuqv2JutTEY/U/1o/sTRju+0X2qS YbZ80qrnI9AtRweHvChja4e1vJQj7cPdP83+1tVRQFhF0eyAyfDWmEA4zeaor9/bJpTaWUGCmneE rdlPBe5klP4bcZq4tj4fXPlaXCgAIVp5ZT24PL0yeTw7+7MWhaVbBYtspdTIZZP73zucUmxWGvqq 2uS2reHbEDpss2f8ixpq+K7fI8nxXbnb/DZ6ah9+KBrOk28Srbpp0MqrhysEOM9sYUnpTk8XosHl SajBFIDw0ap0xxwFpc6Ju+xaHik3OZI9S1ibywpYQWoUMCcDPf8AKornU5705lHiZ1b5FWI/Z16f T+tVj43s7eRGl1SRgrIWRRwyqOnQd607PxBLq0MV5ZzNLHPPtEQYbxnjKjPbFTKoo7j9TPNh5kYI 0DXbskcedftt/wCBdKrDRW3Y/wCENgUjjNzqA/q1M1vVV0dZry/tZhGSBvkdTvYcAIpzzzzgVx58 dWJwq200pQjAaTjgY6Ln9P8A61SqsZbAelromljR0u107Qk1ZpvKWxdZJNqZxuM4fA45+7VdLPWI Wx9j8O2YJ2gxQ7iccdx+tcFH42aSQPaaLcTSICEWOOWTGe4VRzV6717WZZYm0HwrqhuNn71ru0kj U5HITqfzrTmQtT0qwis2gY63rLwETxxhNHtrcfuifnczTRu2U/uKuG7mqV8L1LqQ2Xiy6isGfEG6 1gW5I/6abNqD/gIxXmcR+JUgxb+HntF5IDQKg69csw9albTvirJmSS3tYXHAMktrHwPfeadw5Tsm eaaQRHxZrE7ltoWPy0wQOfXj36fpWn4b8Dy+OdY/sXRtV12edUD3dzPcvFBaxg/emKg/8BX19q88 0Xwf8UvE+tWfh+31iyS4v3CD7PdJK0SdXkMcQORGOeSOcA1+nngX4deHvAfh+Dw3pgkvgn7y7vLv Dz3k5+9JL05z/B90DHemtRN2Od8E+CfB3gDSJLTwqVmvGUG8v7yV2nusHnfMQeP9kDGOK6KTU31J g39nyXaQnCPEfk+oxj6V3U2mJLAYUiiEZ4ICAcD8KZBZXFkgghRNnsNoH5cVS0MmcS+p3SyRR3Fp eIm7gYXap/A1v2pjuppDc5850Krnj5fpxW7JpNxdQSRyOI94wOSce/Ga56eW6sZ/seoxIboRhYZF HDr6UpyLijX0O1869T5xJFZc+mc+3tVvxDGsMDxseJ3jBKnHHP8AhWFaTf2VOlwpLbP9ao7g9vwr Z8RPHJZedG4I2b4/cjlV59cVi9y7HHf2pc2EUiQbT5h+4Rn5iOqj19zUFro/nymW/ldpHBJWNtmA wxgt3+gp2k6fd6jJ9t2hIeAJG4wD6V0WoaxpXheyNxPLFDGGWKSeeRYYUZjkFnbrRdDUTQ07T47K 2jiEawwQAhQRtRcc7vdvc4rwH9oz4jaP4O+H19bNMsl/4kH2KzjLYeRWOJZQB/BGv3m6c1yfjr9o u0td1n4M0qXxDeKzr58i+Rp6kjhjkiSf6DC18hX8mv8AiLX18SeK5by91c7QJ90QaJE+5Fbx79iB P4R+JLGobvsaJWRkR+MNTdt1vodwzgYwqTnHtwuKtHXPGupMdnhueQ7er28owo4/iUD9a69I9Vu0 +7r+CSf+PhVB57FcmmSaLcyH95pupTEj/l51NgvH94YrVLQi6OJZ/iJcEMvhiVBjAZ4gowO3zMKD bfEsnc1nFa7uDue2jIz1zmSuom8P8gz6HZp2zc6iT/Nh+FM/s+xh+RrLw9bhe73Ak/rVWFzHKPae OWx5+o6fbbhyXvLcZwP9jd+hrNl0vXWAWfxfpaN3H2t25/3UiNehKtsinZP4ahA5G218w/mFP6U8 3cUKYOt2MQ/6YadnH0xHS5Rcx5iPD9w/yy+O7HPUrGt1Jj6fcH6fnVf/AIRKxmfMniu7nI/599Mu H/ImSvTv7RMxCweILph6W9iEx+e3+VTGa92Bhc+Ip93/ADyRI1/9C/rTsHMeYj4eaFNt2XfiK+PO DDpaLn6eYzUjfDzRbfroXiydhx92ygz/AN9LivRW0ua9O6ex8QXA7ZuBHn8RVKXwzHJu/wCKYvZX XtcamRj64wf1osK5zvhvwXotl4k0G5HhPVreKLVLKSSa71S32xhZ0Jcxx9cegFfoh4ttpbbVz4gj U+ZGGguAuAZICd3/AH0ucg1+dNz4aePc8Pg7SkmTMkcl3rErFGUDadquc7Tzivtj4c/EiH4g+GI1 1gfZNYtwttdwScPFM+RtPH3JMZjboRgZ3ZFFgke6eHdQUahH5TCWMWwKMOhVmDL+ntXR6pYyW17F Fay+R9qCSMcZ+795a8t8CZj1D+zCNrLKIlzxgZzg56Y/CvW/EFzEZ4A7/MrFQB2UcVUXuYsxI0v7 K/8AtGBLbE5U5AZGDBgV7dOMGus0qfWWuJbyOF53mwCTtHA6Z6fpWAq6ejxWsswQovnAtg7l9+ea 2otTsR8kN66svOFUjrz0AxUyiarUfrCPNPDdXzrDLARjzkwMH+EMvFc/418D6J8TPC914VvJdk7r 9osblCfMtbhPuOCO2TgjoV49K37++i1GB7O4vVaGRNpDYUg+vIqe1ns1ihEE0cl1Cu1JCR86gbcH H0pcrsN6H5X3uh/2PfXuj67H9lv7CQwXUb7zskTrls9CMMCOCpzVBrXTHAjlCyCPIA8rkfrn86+5 Pj38O5fEqp460GzZtT0+ApqVquA11bRjcJIs8GSL3+8ny9sV8YtfWbKhh3SiXiNVYjCnpnHWkVFm SLKwkPyQZUdCIV+nv/OrIsYwu37Kdp6DagH5ba0TfwOFC2OdowcMx5HfvVWK4CZM9gJGJ4Lg8fTi gsr+SYxiK0CgdzMq/opH8qHaTA/491wOFEpbrxjAIq6bi6lKxiKFMcjCKgHsTxS+bdiTyjLb2/lA nkoRz6H1oAzScKRiB+cY+bj8cGljtVlJJ8tFPfySeg91q8bi6VgftcLHaVyMbeRjsO1QTT3ChWa/ jYooACBv4e/IFADUs78xtJbFmiQZJjhCAfTJA/IVWms9Sy5mmulZDhlZQvTj1p7ahcySNM05jZ+M r0HHUKcCqkkwkBdrkv8AMSc8/e5PegCeKw1W3ZZYZJkYDKOZwjYHUcjp9DU89pq+oSoL+drhwAoN zdMxC5yedn3QDwM9az41SWZHJd27NsDKAR9DTHtWKqDDIR3IAGf/AB2lcm5Jc6XHDHE9xPAPMyqA MzHjjnkfy/xqKLRbR13/AG6BOcYw4bjju1RfZpI8BLYnuA7Y/LioigDYaIM/X72enpg0guPaw0n/ AJb3RZuQMRg9OOvP86qG08OISvlTOT32cH86fIi7dqrgtyef/wBZqsLdNmzyd49snFAXGPF4TDAS 2Cu2CMy5G3B4NRtc+DbYk/ZLRyBtBYKDgfX/AAprWttKNgt42xxzg7T78GqTwzRhVSCyQLwpcFsc +m3H60CJ7rxXodu0aW8NhbxAYbdLzx7KO/0rNk8f6arDZLA3cLBHLLhu33VH0qOSa9UnZeadbKOh EIz+XSsa51eaIE3HiiG3C9TFbqD+G00rAaU3j2WZf3NlczHbyYtPf19yK0PC2sa9rWppaG1vYYth I861jgRnJ2oo5J5JrgjrljP/AKzxXrFyCcHyIAuc+h2ke3WvffgfFoen67pmt65capcadeOZmkvv vIluD5PAGArSFjn0ol8JSPf/ABxomq+HvB3hzwja6/beCvC6JI2ua4zKLhWOB5FuvDFpOrFee1fn 7p954asp501yCbU4VlYW7pc/ZklQN8sjLjd8w565r9BPiLZ/DT4gapomveKdbE+h+HEu5H06OTAu ppFzDyhJyCNo+X+tfDTavbeA9dvtJ1PQbe5t3kMtu0g2yPbyHcpDPuDYB29O1Thn3FMq3Vx4TvIw NMsbjTZD0ZdQaZD7bJFP6GtrwN4b8Y674mtIfCmmR69qOkL/AGv9mkRJIzHayIRvR/v/ADEfJn5v yFLPq/w31VnYaDe2kpPJtZlh5A74AX/x0V6x8Ffhj4u1yx1Hx/4C8RSaDrWkzLJo1rMqs1zGB8zS vx+7Zx5eChX+97bVZ2iRBXPcfCekfD74meFtf8WeGtLbQ9T8VTx22s2bnjT9WsS2WjXA2h/MyRgZ HbPFeS2ukTa34egt2dYJ7K/nL717lQsij6kV9iaY17qfhuz1y40SPRNb1VUutStIlGFvlTZIzEZD ZK/KxPK4r5Z1N/7J1LxfaqhT7LdPcop4wLhd38zXlQqNvQ6lTPn34ieRNr6zmPWHSe2UJDpQxGEi dwu/HQ4wK4pNOjmz5fh3xDcKe01yYwfyrrfHN1Db39hbzahqVk6WhBTTgSW+ckFtoNcRjTJ23FPF WpbvTcMmvSobamUkdTpiajZotnD4al061dsyyy3cbMF65O7LGvQNJfWL2z1axtftj/ZpFb7PblVV UMe3OwKCc8Hr715bo3h7SNV1eytH8PavD5rgma/lcKiryWIyM49Ku+PRqUesnVIGktY72JF8yGTy +UBG1thPbFVGSpzuY1Ic6sd7pOg6zPaPDq0LGS+s/Jc3TE7JSpBbkn0HtXW6tp2kX3h6PR9f1Ox0 pLbykEwkgDKUGDIEDhj27Zr55sdAt9bt2+0yyvcdEdnZ+Tz82f61WtrAWDSWktsiTR5UsqgYx0O7 Bzn6168Mzja1jg+oPqz6GU+G9f1SwZ9fe41RLcQY0+As0yQLwwL4XcVCjr2rkfivFZ2ejx3badPZ XmsKZAt8x81xGMlzEvEYLDoTyf04i1kkRYZEZoZImzHKv3ldeQVOe1dZb+LbXxZ4xudf+Immr4hM dimnwwALGimLGXI/2vr1rCrjpS0NKeESdzxeDxLNbkyLDPbhx+8a2ZghHowXt/vVb0fWb611i1n0 XU50aZRGpiYgh1OenT88V6p4jvvAz3403SfCtrpzWq4eKORgz7uV3+vGOled6LY20HjPRrmzszaY 1GATQqxIVRIEfgjPRzmuNzujr5EjCuLrU77VG1BVSa6ErF5nVSZctz5vHI9wMgfSvvP9n/TNY1TQ YvGMkVnYaUzzwxWVupaYTxnymMr4A8sD7oFfEjW76fdaj4bn2RXen3j4muFDExBiu0E4HzdQf619 G/Bf4efFGayufGvgXVTpTCQ/ZbC5xJZ6sIzskhlCn5GBXALKexGMZrnmtLlQkfasjAYU/wAR24/x +lfGnxWvPtvxI11o3BFmYbQAYOBDCBx/wI19deGtYtfGGk6R4isEeCDWIUmML4LQuTtkjJ77GyM9 8d+tfCXiC/bVfEGt6lt2/bdRuZgfQNK20ce1eNi3aB1QM8RjaWlJcDqAMY/KpGRkjkKgHC8Y/SmR kKVbJC98/rTI5JXucouQTgZzz6V5hoSDy/JG8HzCMqfSkSF5FCk5HofX1q/JZO0XnF8SjgAenc1T W5wNoURlCAc9xTsBIIkj+V8MT6qCB9KfJPhFUcjPsP0qiHaVi3LHsARgCrXl/KCSNy9B+tUBKrO/ YbR1B/z6VN+7BWMnOeQRVQLGQN3Q46cfpWjHDAcBnCunHPpQBXYhT1VlVep4J+tQseNxbb09xUky gSbMjBHGMYppjcIp2naOCaVgGll2kq2GHQ4qGQb+5BJzwKEjcuSRgDoRjpUxG0YwcKOtIDovDPiO +8PzKUy9uXDGEdBg8sPqO1dvrXh7TvEFpb674TRWuriCNbu0bG2SdSysY1H3ZGwpC9DmvKElCglM ntiuh0nV7vRbqG+093jdHjcIchS0Z3A/XPet4VOjJsYW5Ip9kGQxbLKeGU9MMOxFTXLCbcFyV+9j 3HB6V65qOj6Z8RbBtZ0Qx2msRIokj4VZmaUr5b9MOqsoB6EDPrjx0JcQSNb3Mb29wjYeFxhl/Cic LbFJj0yF+cEkdvQVYkmjBXaC+Om7H61VeXccAjA496VGPXbk9vwrMsfIpuAqt3OOeB9KtfZ0tdpL jnjCnPIqo0q7TvA3DjH9eKaB5xVwACBgAcdKQE7XccTkxkliQCrDJ/So59QlVyU+Rc8gcA1FLbTB lIGCRknPeolx5gH3yOoNAFiSaQ/dGQR1UgZ+tU3ZmXHlksehokVVJ7FuuDjFR7BJhQ56ZyPQVNyb iMm0DczZb06fTFTRgbS7kgLxQoBOXPyj0pjbWBAJwTn3pDRHMBMGVjlewbJX8QMHkcda2JfEeqy3 MV4k7WbW6JHELdtoQRgAYUDHYf8A6qyHXg7jwKjDYGFXOO3X3pNJqzGd94Vs9C8UeLNHbUp10+T7 fby3UflFobhY5VlOxVYFHfb8ygHnJHetL4v+KdM8Z/EfXPEembks7hoY4zIvltmGNEYsMnGWH+el eY2yRLKZW3EoN2U+8AOWKf7WOnv2r1fxJ4Mv57b/AISXS0XWLOZVlTU7OIiG8jB5JwD5NzGTieFg N+N65HNcdaEFVU5O2lhxeh5a8iYfK7Cv3hkcHoQcVWAAwCcqec9a7HwP4OufHuq3+jadd/Y57LSb zVIg0e/zJLXZ+4K7htJ343dV7jvXF2/mXtv59tBJIoiMzBFZtkYAJZsZ+VQRyccVu6kG3qSh5ZlY bc59Ota+g3Vjb6gE1mOV9Ju1aK6EancEI++h/wCekTAOvb8zWPG+08ZBHU16D4bg16TTJ7zwxq9v cXFo++40mbCSumPlki835ZF6gheVPY8UVJcsLikzmX8Na1Axj/s6/u1HKT2iloJUPKyRkAjDKQcZ 4zg88Uz+wNZ/6A2r/wDfDf8AxNdi3i3UwzCPTvsq7jiHOzZz0IyvPvgZ60n/AAl2q/8APt/4/wD/ AGVef9bkZ86P/9eGDw5q0XW/tY844hsQMfmf6VbOgampwuqXLD1jhjQfnn+lcqLmSRhtu/EUp/6Z wCKkFu7sB9h164x2muhGPx6V6BhsdKdAuC4FzqWqMpOSwmCgAeny/wBapPouhvIVkN/MB/y0ku3U D644NY76WGHz6Iwx2uNRB/Pa1Qf2eFJP9maMq+ktxI38jQNG1JZeE4CCY4CMfeluXY+nQOKqPP4I gw0selk+hAf9TuqhvihOS2gwN2McBf8Amv8A7NQmrNGSq+ILOH0W3tFI/ENT0FY34fEHhKBAtpb2 gf1hsxx/5DNWz4psnTbaWl0G7GKzI6enyD+Vct/bDsSja/fyt6W9qAD9MZoaSa6+9N4guc9Aq7Af x2/1osI6Zdf1iQlxp2qXDYwCYhGBgY4JK/yqr/aviBS23RroEn71xcRKfxDORXOfYt+RJpOrzenm 3YX8+9L/AGYQRnw2MHAzcaiefqAf6UWA3J9S1uVkM1rZIVBA86/hAH4IpqKfWtSigEMl1odtH3Au JJD/AOOgfyrJFnFFknQtGhYcZkuC/wDQ03zIYMnz/D1pjk4QScfilFgFfX3UDPiLSoveK1llIHoO lM/t+Mj5fElw5xwbbTO//AmP8qBrbxArF4h0uHufstqpb1/hAqM+IEcEnxTeFiQMQ2w5+mCf50WA b9sSdi4ufEN0xGMQWKRqw/3gnFTCCeZAo07xPOp6LJKsY/IKKZ/aEt5lYtQ8RXuBkiBNvHTnCHH5 1V+ywTgl9H126bu0twy9OnoBxRoOxafRZH4k8L3T54zeaoUH5LIMVC+lsg2toGhQoO93qDuOP+BP UH9mb8rD4UllIGf9Ivzj8QJKsDS53jjt18K6JHHGxkAmn8wliO+dxOPxpBYh/wBHth/rPCNmB2UN cEfmmKswX0sjeVb6/o7nrts9N3ED/gLJ/KmfYtQgXMNt4dsVXsluz/XpFn9a5LxD4mudMlOl3U9s k7x+Yr2VuYyFPG0kjPPvQFjoL3WL5IpYX1iW5icGM4gS2BUjp8uTXJeRM8KfZ4nSBSBksT/M1ww1 u9Ls0oztPA6qB/tfzrpLLxgqj9/hVXt0zgds0C0LjxhAd0jArjuRjnPY0x9RvreJ9t1huSpcBsA9 +fYf5xVLWPEuiTWkhRWSUruD5B+boOn8utc7bzTrbNe6n+4gRcsHwvH+1nGAey9aiSfcd4k9jqnj TWJ7/wDs6dkSwtpLy7uFVEVYkBJywHOcYrq/Cl9K+kPcXd7oEZeVmM+txG4uH3IoPlkhvlXj+leZ S61Nrd+1s+6GzaML5ceV3xgdNq4zkevavavDjaVa6aZriGxSaeUvF9oRJSkYXZhfMyvJz044qYyf UqXL0L8niq6VRE/jvQraKPISOz0zcEGei9AB9B1qKPxiJMr/AMLE1CUDgiw0sKP+A84/StiHxJpF mSqvpigcfuo7eI8emxR1qU+OtLjRlbV0R8YVI5GKqD6hcZrXnRBg/wBrw3Z/ea74y1EH+GKJYvbs p/lTfs9pcg/8SbxZdnsbm5KKfyUCuiuPiPprwiP7SsoQcE7x0HbOCfzrB/4WFpasZJd83BUHbkDJ 9OaXOTYWPRbZseR4IuJSeD9r1BsfkzCpH0SSHlPAehwv2N3dbsfh5hH6Vjz/ABD0oqqQWcqhB1jR dzEnvlvy4rHuPH8wAEen3M3OVUtnaex+XNFx6l7xTpt9b6ZbTXul6LpEbzMbZtJWMOzBcMJDtB2/ jXnLrNb2m2/GyBD8sqndt3c7eTyvNLrXiLUdcnR5dPeyHKea+752PQcgKOKRGZImhWYeVMh3pKMq vPb/AAqXqXbQ4bWNI0ObJsyL+4b5mNurxQoP+BqDu+ny1zMunR2xQGEL3BZg35iu1n0ln+ezu4lk BxtRtuR9DXO31tqcDmS6iOzGMhc+3UU0ZyRmQqDdwKFQjOcBcCluRtvfkkIZWGBgYwxpLcpHcLKS Sp4wDWktiHufOypUHdgnk8cCncnU2LXy3jkF3bq6KdofbjGPcVopoEczCXT5dh4OHBJzjsw5/Sse 3jksjk3vlSNztHz9ecFelaKap0ZrRWKnOQ7L047VIz0GPSbO30sXtxNHJqBLKRtwduNu0L3ziuan mubgz6ZFDLDJcbOIx0RTnPHWsie9urpBx5XmZCqD2Hqe34VFF58A2RSuFYAsQxznHTPWocR3OrSC HQpI5LZmRtuQsb4lZiOVBAJUepqRGa6ZpLnZFGmPLt4ht6/xbudxHck1zlrfTpIkUYQu5GXIywHo K1Xk2SA5IPYjsDgfpTaHzHR2NxcWrrc20gtZsfwdvrng/nXVQ+LJwF+1RfaJkBAaNvLz9QvFeczC BropbStJAzYjduCVHGT6VphxaRHaAoHDE80CuemaFqkmtTXsZUWktnbtcQRuJLlrl16xrgjHHtTE vPGlxslh8KXO6QDbut5hnjOPmIHFcTo11Otyl9p/7iSE7jIGwBgd+3NdxbCW6gQ6jFeXFxdybLaO O7kt0kVRyxUAn/61Sr9x3HrbfER/nGjfZienmeXHgY4J3SU2Sx8fSDNw1lbAD7z3cI6DHYn0rS/4 R6devh+GMngG51I4J/Mfz/LpSJpkcRG+w8O27Z5Mty8xH1yx/nWqQXMf+zPEjqGuPEem2+OCv2lz jIx0RSKonTURsX/jey3AcCJbiTGPqF/lXXeUFIH2rw9BkcCOIscD/gJqeGcxHC6/b2xH8NtYf/YL RYfMcKdL8PykfavFU9wB2gsnOf8Ax6p4fD3hib57eTW789jFaKmP++s13omuWYH+39RI7GG22Lj2 LMKa8Dz5Bm1+57HlUXI9MM38qrkJ52cmnhnSFwF8P6/cEngvJHAP0Wry+FbF+F8MSIB1FzqJ/pt/ lW+ugiYAS6VqtwQes06r+irTl8JwjP8AxTGwZ4866k/+sKnlHzMwW0LT7fiPw9o8betxeNIfxBlI /SrVvJNYKGtZdC09R0VGcrkf7IGP1rpF8NTx4EehaZCB1MjvJj8NwzVtNC1BCZVstDthjhxbrn0/ izQ6ae6JOSm8Q3+9Wk8T6YmzIUxWxmZe3y7geo4Ix+NXl1LUJtLmv4fF0zLBgNHa2cfc7QEGV+nT iui+z3sQ/d6tZ2+CQfs9vFkY5PQH+VR+bcFcP4olQPgDZGqg45xwB3x2pxpxWwzjItT1C5Yj+1vE 96RjhIdijjoNpP6VL9gurs7msfE123QF5zEP1AxXWO9lI5gbXtSnlPBSJ2X39qzSPD3mGKWe/uZE 6pJKe31Jq7IRNo3gBPEd/Nbww/8ACO29rCs0lxrl/JI8jjgRQQxFS7se29RUevfD7RvDt6LS0fQP EWIRLJdLK8PlPnmF1eVvn45wzAd8VHJbaNcR+Y2kOyAcecdyt7BQv+FaPh3w9YeJdesdA07w+sUl w4eS4uAwjSGMZlkxgA7VHA//AF1DQH0t8DPhzaeHtGPiO4sLK31fxHtijS0Xakdk7ZVQ55Yy9WPH y4H1+pI0VDmTn6Db+lcXa3FslvbrbfuBAI/K2qML5a4TjtxjpXTwaxZTptuo9uBjdCwOccZx1q1K xDRro7M4COCvcD/61RXk+C6AlBjt2FV7a40qOQsszpns0ZzTrjUdN3BlhkuGHGMbF/Wpcuw0i/ZE qBjcVK5yeRTdRtotQgNu/E4+aJsfdYdBWQ+sXoXbDFBDH0A27mxWZJrWoxB5XuINvYlMFazbvuUj JmuMIsk2EKZMh6cr8pGPrXlXxK+Jnh/4Y6Tb6j4l868vNR3tpukwAGaRQQpOeAgPXc2Mdu9evW+l yXTwXt8hS2QloYWG1pn+9uI/u9eK+BP2ovEljf8AxaisjriWL6HpMNvJGtu1wxa5dpcfL935Sv4V FR6aGkUWte/aH8e+K1lt9IWXwnZiQMgt1gmuAq8cSzbkB9cIcHpXkt/GviCV9Q1XU9U1e4kcMz3t 0sh3KcZA2kL/AMBC1xUdzoj4MWuX8+f+eVqFx/4/WrFHpkxwqa1dKB8wVVTJ/wDHv5VmkarQ3YtM s4V4WaRuWA851wgOCzBRyKvJHplpIfPjUZVWUxSSDgtjK84rlH0gOfMt9D1cqBwXfBznpkoox7Vr 22lvuEjaRNYesnmZb2yMCrpkyZ0y3Ph+Rm2x303UDfIQDg4xnmlA0eaPc2jyyR5xh3zu+h2n+dOi gsZAq7bxyB8yrMFBPfBL1IbK0dv+QTNK3YSTI2T+AY1vcysSouh2zKkOgj6yuRjPb/Ip9zqukQkx LpFocc/PKD2+oqNdNaNcxaLD1yN8hOPX/lnikMc0Oc2VjBz3J4/NRTJsEGtWaTxm5jsbeJVYspRJ QePl245qKXxBdOAIZY4wuCNiHBHpwhoW5mV2MV5p1sw4O3Yx/Ebv6UqahMcxvrCRv/0xiQ8f98n+ dAEn9v6zICnmXCIe8UL4/D5RVa5utSmZT5mpBgMYVQM/m4p0lzI4y+s3bt0+WE8fh5YqGTyidxub /b3LExn8AdoH6UAIq6rsYCK6kdm3eY7ouMDGMZP+fyqs9hqUyr5kMoAyQzzADk5/hBqZjYN/yznk A7mUnP8A4/UDW1i4MiadK5PTLbv8f50AZN1pl829ZLe1eKQZJmuHUccHCjFePtrHiPwn4jj13Sb+ NLpGCzCWQyRSx9DG6NgOm3jGfcEGvbJdOhwJU0uHJIGJAxwOnTbXjHjG3sjdOZZo5blX2fZ44XCK o/2vlWpqFxPt79nT446D408UQaLfxvp+oWumyzeU0nniXYACEcjJ2r0zzj6V9X2NwL2aS5vwN06M fZARx/Svxb+GviQ+C/iN4Y8Tz28UFpp99Gs+0kExTfuG6E8AHdiv2d0y0a5ZIf8AlgibmYchhnC4 x6+1Km+hNRG/otrdX6RwoiiNMqJHHIwei16bY2KwRhQO2Dnvxj1/pWRY+XZwopXe6jGFwFGDjr9K 2YHuArPKQi44HGefpSm2QmNurGymVhcIpUjkYGfrzXGxaLcWM7R2F0sls5/1cqAle+FYCt+4uuTj 5z057VUiMzzDeuR7dKuEHa42yP7LqFqQ7hZMc5Q5YAc45/X8a/Pr4z+D4vBXjqeOwBttJ1yIX9qk QwISzhJ4o+RgK3I46H61+j83kwRPcuoKRLkjnOR2FfKv7SVnNqPhPSfEgk+zzaVqSxExBuILuPaQ ce4B6daRUWfGUmxnPlmdx/P/AD9ajSG4k/1drcMcdWBYY6cYq8J2fBOpTMR3G89PcMB+lDeY+czy XAIOPnJPH4/1pGpWGmXu1dumtjHJcld2T6GnjTrwAb7aGJB2kmAH6sBQ1tp5iWSaORmK7gflGR68 gmmx2VlxI0KOGUuB5g3EA452gUXAQI0e7ElngdW8xWx7HBPSqzsHkVJZoyo/iVcgflWg409JIklt FgSUH94d8ijK45wcnkUy3nXGZUhjVxknI49uTQBXkFrEAyaiZHPVFiOB2GMimb4hjbPPjsFjwD9M kVM4ilja4kkCRxdCuFYseigcfyqNXgjgedp9rAfcGN2T77TQTcbKYIyWk+2OMdRtUc/U1TAsywC2 87M2SN0wGQPbBqzcTWjYQec6tGvyBiwz9cD+VQqy+XtSKYsrcccAGloIvaV4c1TxHPJb+HdFkvZL eLzZ5PORY0UttBd5dqj86pazpuqeHrxNN1vTH0u9MayrDLtyY2zh1KfKynHVTWjaXj2aia2gubKX 7pe3baZF4JDHOMZHTFVZry9upJJbv94xVVEk8nmNhc7clznHsOKQWZhG78oo8YDEkEEIT0H61DI0 kkhmaGZsnOFjwo7cVrNqjwB4jPZ25c537wWHpjOMD8KpyeJIYTsbVbCIKuAA24k/hQFmULmO4niY m3l2k54UR5wKpNa3jIG+wc7cL5hTgHvg/wA6nuvEOn3DL52qpKF6iKNiCMdOBUEniuzckzXFy42C MKsBBVQOikinoFmUzp18/wAqWdptH8BdQT/wHbUDaPr9wrGxWwtfLbYZHQnDYz/CoqC48Z6TbAp5 t7wVYZZI2BA4xuYdq5fU/iBpNyrp9macht4M12g+bPfYxNTcdjfbw/4nuXSKTxXDbNJ8oEUBA5OM Etz0z0r2O2g1aKBLe1jgjjwqhpXYybV+UAhR0AH96vnLw54lXWvFuj6fBY2iefeRjepMjKBk9cn+ 76V9Kx6suoGe4s7BrhlkPmqzgbGY8Dj17YzVaMaRT1Pwtc3MRnadLW6aNlSWNDtWT+Bj14WvH734 Z+PdSEM15eWupNCvloTM0hQdcZZBxnqM177HrOrW2VfS2jUcHA3gfiMn9K0INUtJQFuLbbu/u7h1 98VpCPkKR8wN8M/GVuwke2gHTkEAdecfLXsHg27+JmkXlqXnt0hhQRmSJ4/MSILg7BF17ZByPxr1 CKLTpULRrImRwFkP4Zz74p5tdNDhxE7SKVUE8H5uOo60pK+jEtDek+IHiS0iElxNFdMrbBldrLzg DKYBx05FedX2qNruu6peX6rCL+JPMCEkfuUXHX1x6Ve1KOa4Ky7AqsmMDjgjn+ded69dzW3hjXNR siZJ2tjDCo6xySPGiE9+Mk9Kw9jGOxpzM8uvtYutR1K/u1mis42meMM0jZ2Lx/Dx27Vl3WuQQqPt Os2qDAXAEkjg468n+lclb+CtUZM3OpW1uBxmRHbPr14/Gpm8Facg33vieJMHkRw5/r/SmpWViXG5 6z4GaK+tzqMV4bqHe8MZEYQAqMk4Pua9I1Lw7a6rpy2c6AmWP5s8Z7jHTBH0rxjwRf8Ah61jXw5Z 339orDLJOXkjMfEm1Tg99pXP+cV7hrvia28N22m3V+h+y37eUZYwDtIHVl67PUgZriqN81xOOx4w dLuPDeofYbpcxz8xy9BJjv8AX2rcvdO/ti1Nxa/ur63ATacYkH/6q9I1LSNO8T2KLvUpKoaGaI7l 6fK6H8OfavOV+36RfCxv18u4t+Bj7sidnQ/Tt2q41bjcTl7W2uXuBayRtEyMCwf5cAck89ap6cRF Kb9trx3QadR2KlyOB68V6jqU/naZctGzIRE7h1wWGF7VyWraVZ6ReaTpdpI0kEVraEu/38zyB3HA 9a6VVvoxOJ7MPhLqOleCbr4iXVi97rFykCWenWiEyytOdkRkIyQFHLcYwBXl3i/4b/ELRoLTxZ4k tUtTd3KQzGKQNJb3EY3xmQL6gE7lz0/Gv0Jtb2aLVniidRbtFEioATIjqBuJx/Ds44FeDan8IvHn xR8W6prfxA1uLRdAtJXttMhjZZVitk4jnZdwRVJwzZO4jjisadXWw3E+UG8JT68W1BfEFlcam2X2 zpLbtITyR5mdnPbjGa7L4MfbIvGVt4V1TWtT0K01BnhuLe0umtmMrAGM5GRkkclcblOfU1x0Gl6J Z391petT3RkspZLZrjTnilhcRtt3pu+8pxnNakmkaHolzb64l5Dr+jyMLWVYf3F7as/+plC9mSTB VunY8V0yV4kxjY/QK2tNP8E+HJLPT4TbWOiWMzRqSW2CJGYEk5JJbNfnpaBzbwtISWkQMcjuwyc/ nX0+vxIj8T/DLxJaLMbnU4bDy1m6mRX2I7lR/ssT0r5ohbIjwMbwDn/D2rw8a9OU6YEgjLnnOB0H HFW1ZYl7Yx1HGMVWOUc7Tz789PpQztxnB9cCvNNC405KbQwA45J5qsUUHJIBOMk9/pUkQEjhCozj J7YA461HMiGTZEpdSM888e1UgGkGBvkwF7n+XSnNOZXWNTtUZ547j3qsYpCcKxRM85/T3qz9lJHy BX2dfXj8qYBHE3IbOdoHPt9KujHBxuxx9ay0lf7uckDHU4PtW1DuXDKoAxnDUARhCLgeYyHC57fp VidJIwu0ZV+4O7PFSyTWy5drddwHUdCaY0lultlY9wbn5T90/SgCk8LhS44XoM8HioRE7MoJOT2p rkAguWw/TJ6VPCkpBZTkH+LPpxxUAP8AJEIIJZgD0Azx+FKzSJh4TtA7Nk/pUSGQuRtO3B+YHp26 VKZDvHART6d8CmkBe0/VbzRblL61ba7Aq4/hdSejAfjggV6deDR/H+mQ39u66drNokqu2ck7281Q 3cqBvUHr92vIZZztZY+g74z29qtWN1cWpE3CHuR3X047VtCdtGBNfaXfaVdy6dqcJtruNiGU42gL /ErDqvoaoFihKqMEHA+te02Gp6T8RdNj0nWR5OqW+3ZN0YkR+WrKe43LHlPTJryfWNI1HQrr7LqK bPtBDRyfwsMZxn1HTB5pzp9UO5kGTdlTlnzyBjFThYtmDlVHYdajaIqrMrAL1Ydf5VCDJknIGV2g Y6e9ZBcveXO0G5TgFuN2M46VmFcMZDzt4CgYz+VSsGKRknJUgcZGMe360gJUkjqOMCgLkjmLy12x lXPUDmoipA3EEtnGBx+dOEqD5uSACOnf6U6JJJn2RpvJGTg+3fJqBERALKr4/wCA9B/hTwjRtg/K T0JNWnRkx5RUMo5GOB9c1HK0rxlnbIIwQP6UDuQsi/dHII68cn8aqFWQkDj1+lWWy4TacFBwD7Us gM8g34HHJOQfSgLldPlPyMVbjkHBznPUdOMV1HhPxjrngy/nu9IuJVt7kMs9tHPJEkmRgN8hA3r1 B2/4Vy7LtxGnHqajZgi5VgeCSBzjHJ4qZQUlaSC59AfDTxxDd/GvRPG2q+TZ3d3J9hvxwIpjdQeQ J9+Fw5cJ5nygN19a3Ph18O7rwV8UbazvZoW0bUvtml+WCUlK3e5fJZDwSPu++OOhx5R4Y+HfifW4 tPvrG1Yi5YyxFgHR04G+JoycOvIKuQfQZr1gr4q8MrBf3102p2O4Szi8Oy4tZYSsgYMe+cEZPOex HPiY3CzfN7PZqwvaW0PlvE1pLNYTqw+xSvbsWB3EwtsOTx6VrWMul20pnurU3e3jZuMf5lefpzXc XHh/whrGoXWrXHjSKym1C6mupIzaMRG87l3PXoO5xWVrvgfWvD+sppjtDqMF5A95Y31qS0F3AE80 ujN/EF+8hw3tjFenTmuRKQnJFNvFV0WJ8hB7FmbH44pv/CVXP/PGP82/wrmwqADJY55yOnPPFG2P /bqPYRHyRP/Q4U3UczdNZuj0BwB/JR/KnC3DjMei6o/X/WT7R+PIr1228e/CKxDmG71K8VwVy9pk H6df51Qf4sfDFW2weGdUnVRjK+XEG98HOK6va+Ri0eZLpd2/70eGY3T+9JcA/nnNSrpOrBY2i0Gw hjlyV3ZkLAHGfl/GvR7j4reGHCi1+H13cjGQZrlFX/x1aqP8VdalKx6R8PbWKMDAWaWVs/8AfOAP 0pub7AjjotN8QH5Il0uI/wBwQOSP0OK2YPDfi6QrGLy1gB6eXBhQPqcV0o+IvxGXHk+DPD1seMGQ bivHoW/nSyeNfjFeKNj+HtNBONqwQ5H6n+dLmkVoc/ceF/FEbRRNrn+s6CJFwe/UZpkHgfWrqVVu Ly9uIn6+Tnd+grQn1b4xSEmbxjplmuAR5YhX+QOOKzJJvHc4aS/+LcFoPuBY3dvlB64jWp5pCNCL 4V6lKwEVhq8ijoZDIo59gP6VOnwg1eeXyxoZ29zPIx/TNclcRyyKf7U+Kt9d7u0InI+gyRWI2jeD JCDd+MNanyeQkUjE9uSZCKPeA9Wg+EE1uJPtWh2AcDCGS5Rdp/vENJk/lTY/h7b2XF/deH7XjOTN AScegJP+fyryU+HfhxuybzXbkZ6+WqnH61YGg/DMY8rQNauW9S0S5/FkqtQPSbrT/COnhd3ifSLI xqc75IsFs/wpEmePrWT/AG34ChDGXx5bvIRjEFvcNj3AArkP7M8Dxr/o/gK8Yj+Ka/2ce4jUVIi6 cGH2XwNpyBeB9ouZHwPfc5z+VNRYrm3d+PvAwigs7jWr+5gicsTbWrR+bxjDszjI9sVhXfxK+Hwl H2S11e4CYUE+UmOOgCk/rVhdSkgbFtoHh6xYdzGHx/30SP0qrL4j1zbl9U0i0RD9y3t0X8wEH8qd hXMWX4heGS+bbw9qpycHfOoVh9AtVJPH0QGNL8ITIW4zJcM4A9FULWjceJbqchJPFYHtFb4x+lUB fzykj+2dTufQw2p/ng1oqbFzFFfGHjGXKW3hyHaV4/dSHGT68fyrhPEc2rNdrLqUYiu7tdjpt2hF UcDnPtXpJtmnGWi8QTMMkM22JTxxngVxXj3S0sr63FszyqlrGSrsSfOb5m5x/DkCpasHMeVOZ7aZ 44bgwsTjDcj8M1dVtTRNxC3QXB5+UYPrUbLc2zp59nHdSFQTnJ7e1a8U4NqLu9hW3jc+XDag/PcM D3A6RJ3Jxz0qiDON7LPLF9ptEgCOPunIHYYHTNWmKai0ltK5HkOfLJIGMfX/AOvVDULB5omM8+8y EMgjyqnB6c9vTjpWbpsNnerLPNG4eM+Ww3EZyOuBQBty2lvpGiy6nIksjXNwILZUBy7D5nkYjpHG OBjqx9Acdt4X0qbXbK5bU7O9uTE8KxpZKDKuckBgeAOua4S11HVI5ZVjmkgtfKSGOFfuBV5ZmGDk tk88V33hmGK6ke0Sy1G9Hlbnj0+YRszEjDM59B71BdzrbbwTZoAI/CmquMcCaaOL8MGtL/hD7ZOD 4aSM+lxqSKPyDCo4vDQuCXHgzUJcnJNxqnIz9DV6DwzlgqeENMjZcYFzetIc+hw1WLmI10fTLfaJ NK0a2Pq97vx+pqyqafGp8t9ARx1KjziP0NaqeGtRXkaH4ftSvTcWc/lWpbaTrUbCMXWi2RIziGzB OPqUpWQWOYN9awDd/aWmxN03W9nux9Nqg/rUTajE8ZaPXr1mHG20sSP1LV3P2e4B2S63GhHQ29qE H5hRUM+nSGFp38QXkqqOUhwv5fNmiwWPFPHLvdabaxNf3zDzw+dQj8iP5R/Dtyc/jXkD69BbgwiR J2XHA+bPpX0rrml213aiVLmbC4CyamRJHvPHFcFeWU9ouF8V6XbqvGyCKMtj24J/SpY1c8aXW7st uktI3jzkgRkbR7GrseuQIwCRHH93lgfXhv6V3c7WrKQ/iuScEY2xQDJ/Ja5+XStMlZVN/qdyT1EF kxP4NtxScymjnng0a6k81VNo7c4i5XPuD0oOnaai5/tWIk9A6jH0yK3j4f06RCI9I1e6nBwHuLZQ FT0AyMVC3heUkmDRL0L0G9oUJ7c5ziouKxif2fbHBiEcp7GBzn67TUBSecPDCVdlwCCcN+OcfyrT l8L30ZE0ejNBIoJDyalED+C4rAlu9RsZwNYs4HA43o4yM9PmXrTRLiaqyyiRVkjICrgnBwMc9aWS 4jPKsAzc49M1c0/VLRSogvnBkJzFMuR06ZxXTRXNvcwoVgs5A3GMhen1xTbJZxdqzC4EjEYA6jqK naeRiAZNqtnJNdh/ZltKwYaQsmTwY3wua07bTbCFlZ9JhjYHJJfIwKAschp9wUEkccfnCTAJHOOe grp7Twx4j8QNuWFbe0UgF5jtwB6LwT+Vb63VhEhEfkW8ado1B2/jWtZ6tpTAHzmuWHHqoFBVjY0j w/ZaWVMmdSvEX5AflhjYdCw7/jxVa51VxdyyRXC3F/IAvmr/AKuHedrKn+1t4+Wql3NJfIIUdreE 5JA+Uso+may9N1q0S8bS7LSL3UzGf3jWiqpQEcYY9/rRFhynrMdh4ZkBmh0YMIsRgXBmMjMO+Tx+ laYh0yCLjRrSKTHGHyo+vIrl7bRyka58MXkjEdbm+O/nnJ2n86vx6Xd5zF4VsYh6y3W/8TnNbok1 W1SzikREt7G2TGGMnluSfYZ4obxLDCd1rPZrgYKBVA/DGcVTNtqFvndZaHbjursGx9KQXV8nH9qa Nar/ANM0Xj25FMVh/wDwlV3nIvI1POQsZfr0xgVZfX766+7JdMGzkLCQvtxgVnnV5Y8o3iO364/d W4J/QU3+1YZZBGdfvZmbHyww7R/KgLFr7bqRARILx/mySIivTtyaVINbdmP2G6nGSwErKo5GOBzU Bksy+Xn1e62/wcr/AFFOC20yjZpOpXHOMPKR/NqBjjpusMNrWXlgLgF7pV7egH5UySzmRQs72QYA cSXGSMeuMfyqZLEtjyvDTsG7zXHH9atJp88YIXQ7G3X1dy3P1xS5gKQWOIMU1KwtwdwYRfMxUioW /s1goGrK0aE4C25Y9cdQK00fUYW/dnS4BjoOf6/0oe7lGfP1ewtT28uNSf1pXApwSaYVfy72+JPO 6K2I9u4qL/RHOPL1Wf8A4AqHr706a8ZMeZ4mAU/88ohn8gpqJbmB/wDmN6hcg9QkJXA/75H8qoCR rRZUyNG1CUY4LzKMY/EV7L8B7HT4PGl7f3mk3FmINKlRTJIZDMJWCMqjOOnp6V4bcw2DMjPJrDry 21d4347YyPwwK6PToPG/w4e+8exeGZ7O30GLdqcMuowNMLWUAoxtyxZmIOQoOQOwqJaAj7zj0e6i BbTnGoW68BogN644AaPr+lSxo/PmRbD6EFcY/AV598OfjH4H+IVrFe6JdRTz4XcsT+RcIcYw8Zxm va4NYtz8sl4Rk423cIJP4isJSuXyMxoy5CsWUqOgzVp7qMRlZGAI6AEfpXRI1vMhISxZR3bKn9Kr tPp0HI+wRsOpVS5HpjPT8qSnYOVnOpbahfkrbw7Iu8k3yxgDvzz+QrSg0+1sissEf9oXPaeUfu1O P4Rxmlm1B5XMsazXKKvDTDy4Vx3xwMV4r4y+PejeGEuLDS5B4j1ZUz5NsAtpH2/fTjjA9EyaHVQ+ Q7T4jeP9D+HGhza/4nuhPc3B8m3tkYLJM55EMQ7Z7nsK/L3VvEWt6vrOoaxeeLdLhu9VnkncQWrO yFz8qB/JJxGPlGW7fhXXeKNa1/xxrLa/4saDULnBitoVykFuh/hjXcOBjr95jnPpXOCLy8yGy023 CnOWRSf/AB5/6VLZskc8+vTCQiTxtettJBNvZvEBz6jZSDVoZTifxDrV2p4+TI7epmP8q3Lq7kAa 1N9ZRhxyYIYB+GQM05teubS3jtZtbWW3h4UQqqkZHQkJu7+tK7JZzDG0lYuf7aulP3d7fzwp/n+V b+jWdt9ojlhsLy1POJGJfkf7Hk/1/wDrQf8ACQWUxO7V7kMOMKZjk9PSrsWrh2BiuZ5VVSoEqs2d 3GR0PFOLsKx2DRzFQZL/AFOMHqsNsw3Dty23tUatC/yvNqc2Rt2hCp/V8fpVGDVtQiiS2W7KLHhU Hlr0A9DmkfUdSwzNqEpJ9CqqB7Ba2izNxNOO103OY9Nu5ieP3nl/Trj+dWI9HiQk22mxKRySzqP5 Rk/rXPnU5Swa51AvGMfuzKyD8CKqyajpAfzHuEY9MmVice5zVc8SLM6lLC9RmEdnbgnnmZj/AOyr /OpX+0jK3MlijKONzdPxZwa4ObV/D6qEDwrjJPO5m5qFfEGhxrsWVFHvFlqPaRK5WdqzzCQQ/b9P 3PwgQKw+hG5jSyx2/lgSanEC/QoiHGPZUrh4PF2jWL5tpJGdm+8UX0x3A/lVd/GWn26fI0yHAyMq Mnr60e0iPlOyLW8Z2yazKFPJKwsAMf8AAcD86hkubdQP9OuiGGRhWO4D6YFcCPG9moaSOLvgmS4U n/vnNZcnjWz3nyLRJGGRjzScd+AFNT7RBynojyaQEkeZrlWRchWAXdnHTLVx2rXVtK8ohkgtI8sj qI49+FOM7znrzWG/itpmG3SfOBAUlklOB17L9O1ZU+r3MuS/h5I1bnmCRuT75FS6iYKByutaX4ct lnW3kadFBETNLn5tudzYGPpX6/eGdQ1DSNI8O2k03kSTabayxs4D718hNybvVT/nGK/HXVpdSvrW aCOySISKygIgQdMevv8A56V+w3gK+0v4ifDLwxNcMLQX2m201hcg/wDHtcxJ5EsbH0LIciiApI9f 03V58q12ouI1+88WFcY9B93Fd7aXFjcxboJQ6gfcbII+oNeGaBcXenXM2jaqphvrb5XQ/wAQHdMd Vx0rsZpUmwE4GOccHpx0raRFjsWFxcuRbw/LnBI44rRtYPIQl9qzDszDNcLbvdRKfKYqD23E1bEz kbjxIep5J/Ws3LSwWNrVLqOQLZ25BTO+Rh0z6V5X8UtAXxX8PfEOiRjbOlqb+A7sfvbQ+ai/Tj9a 7ZUIJkO5vc4qxb2UlzBf3TgpB9lmiHbeXjOcdemPSob0Lij8lU8U6I4jnju40RhuxFCxJ3L03Adu lVP+El0iMKIZLhtoPIj6g9eoFZNt4Q8KC1jgubPUrlkLKf3xEZIY8jAH86up4X8GxoFi8LSXIzgl 5D/MsKaehrYc3izSbJSGNyu7gPI0SYX/AGdxAqjL8QtAg5yOOm6eJcgf7pNbkWkaMp/deFbZccEO scmew+8x9q1IbLySGsvDunwse7QISfyU1IaHn03xR0VQoit4JD2HmtJj8kNLY/EfSbm6hF3YF7Xd +9+zwSySBccbQUVeDXrCR6qI/wDR7GKHPUrAQv4YQfyqQ/8ACSOvzSeSF4ASNv8AFapFWR423jbW ZyY7PQLmVeCClg/PGOuQM+vvTx4g+IUqlodB1ML2BtVVQfxZv5V6v9m1lv8AWX4Qnr8gU/q9MfT5 W/1uqMhHXEkQz/6EaZB5QLv4rXGfK0S6Uf8ATWSKIfXG0VYi0f4r3gLGxih9Q9/jP/fFejtplkql 5tWkds4BNxkD/vmMfzqFrLRXH728mcjqUmmI4+lRqVc4JvBnxEnwJrrTrfd6zXEx+nGB+lV3+HHi Nvnu/EWn2oHJ2W7kkdON7CvQ107w3Jhk8+U9BuaZs8e5FSvpWheWTb2RaXHDSRYXJ7ksSf1obA81 PgJAP9I8ZBSOgitoV/L5qryeDPD4OLjxjeNjgiIxryPQCvo2TTvhSdIMNrZ6zFqKW4xclEIN1t+5 JHs8vyd3AIbOK4u3t3McS/YUMmwFxGu0Zx26cfhSuB5EngrwYwLSatq16B6ykA/98qf509vA3gIY ZtO1G59CzysD+S/h1r1029+EGISu7uG6c47EVTa1n6vJGh77nyTj8aVwPN18J+CIgCnhWV8dC4lO fr8wFRy6Pp0A2WPg61btmYoAPwLZrvJYIRKS75ZOGAZue/HNZM1paXCfvEncnOI0eReDnHSqA4V5 tTs3We10rw7prwHKOSGkRkOeAgPJ6fSvY7e6njkg1WGP7Fcl2SSFgHWK5j5kiI43RHO5Bjo3WvFZ 9ItraU3D+Co2ijdWkluLk7tiSAsVy33sA4r37xDbS2lpNq1jGZ1kMU7qBljhdodV9QmA3sKnm5WS 0djBquh65IsimLw5qH8UL7jpsrYwWjkGWgz6N8tXLnTNRsj/AKVArLgN5kbrNG4z1V0yDXg48RzS KdrZXO4lDhM98cEfQelbmn62UcXFhObOWTAZoG8oke+3/P8AKui/YnU9FkMCYLo0DHb7f3fxq3bN c3Ei29nBJczuNqRxoWZmxwAB61zNh428YWDeTZas7A8ATRwzLz/10Q/zrRm8f+Nr9RENbubcQozj 7Esduowef9SiUpTfQOU1vHOm6h4bS307WLf7JfXibxAXVpEjUbgWRSSuePvYrw3xre2Vno6aK00k T6k0d1NsJGEib9yoIP8AE3zH/ZX6V11zFHqUFzfXt20SW2y4ubyVtzcH7u71fpjvWBrdvJB4Rt/F LRLbT63qaNEjgEpp6I0SL8/AOBmspVLLUuMTzrT9Nt5FJRYZGA4aUs/Q475qzNa3kPNu2lQ4Ay7R Dgn/AICTWpLe3EMUaqYbiRzgBdgCqemcA9vTNaNtAJ7CNpntYJPNYPEY2kA5G1gVX61lcux5F4gu daWeO6tdVtne3AdUgXapx2wFFem+EPGGieMLA6Jqyxmc5zbyttYn1i7e+evbGKwNS0tBODeQxSwB ciSGAKxI9ciuM1LT7KNkl0qOeC5hG+GWONRtkJ7+1PlJaPVGttZ+HM4uLMvqvhu4b5ouVeI4zuH9 0j/vn+Vdpc/Y/GGkQ3ti5k3bnt5WHzoy8Mh6cGvN/CHxMNzcPofiOOOzm8vCTSH93Mfu4kDdMivW NFhstKi+yafCIIQzOYweAzjO4f7J7VhONhNnmqXsYtLm38wKRHIuCcFSVwFqnrF1Hc3hvLcmWNEt cYBB/dRqGUZH97Paui8WeH5mefUNMG24tzuZBwJY+vP+0DXJRTi+s/tcZ5B8qUfxJJ1X/gLfzyK6 KTTFJH3fpHjLwvqixO2pm1d/IlbZhW3R/wAHzY+9/F7Vr/ELwlp3xZ8PWHh638Vf2DbxXX2i58jZ ILiErtMTKWXpjjmvjHS9RjvIj8m2ZOJEAGFI9PUV3unxQzBQ0Y8wkYA7/pSeG6hzl74sfAjw94N8 O6TqHgq9n1W+hMi31o0gkaWFVBSRNqkrsJ6DPtmvGbLRvC+vwiHSNVuNJ1ZPmhi1J0lgcr0CTKAY jn1HBr0rxB4NvtR1B9U0vVn06cxKgict5YZRgFCnIznnjrXkOr+BvFmluZZNMN7EclntWM+3HQle v6VtDsxHos3i+91LTo/Hp0xtK8W/b/sU01qix6fqqxRCO7hkjXMa3ICJJ8vytuZx1rP1K0sdStD4 l8OIfsRb/SrXlntJGJAz328enFT/AAa8ReGhFq/wo8bWlxNZeKbpbuEEFJLK6hhOy4hU8rIpHOPv pwa52zutS8KatJciPz5Ajw3sDDatxGylMlR/EM5rzMXDWzNab0sRrk8ltxJxx7ent700t5Y55Pt7 V1Gu6TZrbp4j8PuZtEv3kwF6277v9W/+zXK5BC5PBz06DtXkzhY2J/N24w+Btyf8KfCoKsygo3YE np+FUwCGSMFX6H+mOK3hDFHaMJmXzG7DPHPSpQFIeWjMCOQo5IPPNQNgAiE4d25b0FMuZCJ0SBsK /ULzgfjirEKyO5whYL7Y/lTAtRwxLJHgYJHX1xV+YeSQ2OMYFVFR3+cqRjpSy3CRoHdiWU4280Ds RqeCp+fIzjt1qVWMalQqqR1FVoW8+UgDaCQMn6064kYbxkZz/wDWoEU2+V2xhiemfX8aEbY4Vz+A OOf5UigEPvQsv9/nAqAwuN2MHjj1qANEnCMRnPTinKI1h8zJZjxjPTFVRG0aKxycD6/ypiyDdjcR zySP8KpAaMaqBjIBbkj2p8gj3FyxVVHI/wDrVWV5WkIRi/y9QMAf5+lMlaRgNoAIIyDTAsxySI6z wlo5I+UK8MuBwR+deu6F4i0jxLY3HhjxTGM3PlmGfvujJ7noxQkcd8V5KYjgPASSMcdcflUXz7d8 gAK8jHDD/P51dOVtx2N/xJ4W1DwzJE12pubC5ZjBcpwrMH2GOTHQ9xntisBmH8CrjsRyfavU/C/j OCawl8K+KAs9leRyRwTvz5TyKB8/sCF+brxWB408Cy+HLl73TS0+jPtaNhyYldNxU4ySqncA3TAq 3T7COBaRyQEG4njA9D+lJtCYB+8TzUmwIBsAVcZJHIAqN5IY8bdxJ46D9M1g00AMwVmXOFPfrmiK 4kjYi3O0tw3HX/IqusTyNz+RxVpUWPKn8/SpAllkkdSxxggD+lVS3RRxjrRJNkj07AVEFeQ+WOpo HYbJM5ODjHbHbFPTeU+YliKiaPy+B249xSxyHd5a8hupJ6UrjsW1ChQjD3xVZ0jWTIGMjooHBqUr uHztjH3R61F3J9OgpklzSFvvt0NvpNw9nNKcowlMIHOCcgjGPwr0vV/Gd/JpL+FvEeoNr9uwVGnP yzRFGDALNyWwR3B4GPavKH2n5vug9R+HaoNzADdhV56c1lOm5Eyjc6qS2t9andNL8xL1gGihn2nz 8jBjUoceYuC2ON6njJyK6nw94hu9Q8HXfheZm87SVknsiclkilQq8Y7jDen8DewrzDcZNqrzgkcd iPmBBH4dx7eldV4fabWNYV727ghu5V8mSeZtvnLKNv7wjAyCPvD7x645rlxtL916DklYwNRtJLa9 mhVeA2R/wIZ/r249OKpeXL/dr1DxB4Xvv7XuNjNIPkwwXg/IOnFY3/CL6h6P/wB8n/CuCOKnZaGd z//R4VzPA7xNfaZH5bMp8sl+VOOCByPSoW1FU+/rVspH8KR5P8q6KPwxroVJ47TSLZMZVsk4HpgC rX9jeIdo/wCJjZQL2MMJYfhkCu/nMbHOQ3NpOJXm1+UGNA0ccdsS0jHjbntgYqqzK4IW51CZeg2w sM49TmuobRNZyPM8QHf2EdsowKkTwxLIS0+sXzqRyVCryOOgo5/MWhx4C7dottQYdDv4H6nNOFi2 f3elvIvT97MqjJGf71dpB4W06Qbpr68c9PmmAxg+gom8N+FYlBeOec5wd8zHPvxQpoLHHNp8yDf/ AGTZxkf89Jw359aRUeMfc0uEjp82R+grqBpGhRpvh0qKNegMjs2ccd6cI9DVBmysYOOTIF/QE1Vx 2OYM8wOH1HTYTjjZGX7fh/IVB9rkLbDqyj/rja/15/nXZHUfDkAH720QJwQoQU9fE2gof3d9Ft9A F/oKLiscatw24Kuo37jpiO3Az+lStG0vzLHrLg8ZKFF/WuvXxfYqSFmknXsI0Yj8MYFQr4qEUhkt bW73uhjL+Q79T1UHgHsKQjmP7Mun4bT76QdzJMFz7dRSHQ7oAFdDgRfWe8B/PDV0g1LUNn+jaHeM xA/eMm0/Xk9fwpu/WGxnQJWzyTLKiH88/wBKm47GKPDuoqAU03SojjP7x2cAfkf51N/YuppH5iz6 bbkjOIYCxH5r/StOe61t4/Kawt4lyCTJdr/Ss0S6vAGkSbS7cMCC0k/mfoaYWRWMOox5V9e2gDkJ ZqD+B4FPhsJrlGeTxFqrAfwQokYP/fOarS6gwObjxFpUTdP3cTNj8jiqza9Zom2Xxd8oJGy2tuSo /hGcnPvVmbRW1Wz0y2s5TFqWpXVzglIpZztyOrNhT09K8a1Oa+vZ1t5pvNc8Fj/Dgd8YH6V3Op37 31rKwZ5Gm2ss5AUiFTwOO7d68zCym4uN9ykEcWVUdWbuFqGKxWW2vk3LbqZFHBZT055qa1tLQAGf cGY4cn5uM8DHue2azft2owkI7FIW6bSBxW6sMBTMUjEqPMZz9xR0zn1zU8wzR1JLO0tZni2NcRQs AOoBYBQAfUbug6V5hpHnQrcI2RuZSr9M7flOPXkV7Vqms29/pCWWlW8UixxxwqjrkswO+Rz9SBXk F7c6vdX13d6nD5KPCtvAmwRqiA/dRR/6F1NNSBG/bzQXNukkb8Yxu/3flb9RXc+AtXFkL1rbT7++ mkZYx9ktzMqrz988AZ+teW7ILWy8i3bf5aAFV7HrXUeBNQNtJNb3up6lp1vLtcrppxK7AnHA46Ux o9205NUaNorfwtrEu4Ly6rEo298kk1vQw+KktRZ/2HLbwFt5MtxCrlh33ZGK4e3sdHvCSy+K9R3Y x5juuOO+BW5D4e0z5Wj8LavL2/fzlc0x2NZLXVoGLtDYqWPPnX6nHr0NV5YtjCS6vNIQr3a5kfAP sBU6+HoVx5fgxMdP39wG7dyCKtJpM8WGGgaRa4GQS4PH5mlYZT+3aYF2v4h0yIekUTGoft2krjbr xfH/ADwtOv51u7LyMBjLo9kD0ZB5mcD/AHaqtqd7CSqeJNOhx/zzjx+WQKrlYHL6jeabLESks9zj gebbbU+tcJeaheIwSK8sLNF9IgR+J9a9J1DUJZxsm1v7S2MrtUgH261yc8FyMywWdixPV5No5x33 A1nNMpHIPrVwg/f+LoIsf8+8ClvasqbW7GU/vfF+qzHoRDbkfTnkD8q61/7eDYS60mzXPAMZkx+A AFV3vdejx5niyytg3T7PYMV9+QazsOxyPn6TN8ovvE15n+FIyoP4hKfHpen3HMXhbxBen1klkQH+ Qrp45728mFunjXULiVz8qW1ovPGTjqcf59qzSIrubypNe8R3hPDKqBM/pRsPQpp4aV8+V8PLl2A4 +13gGPp81Jc+FNVmQxjwPpFnnALS3Q3D8cmtVtA00OYp9P8AENw44AkuWQDPP8FRP4T0jd/yLF5d N6PeyH88mlzINDkpPA2oQ5ZzpVqMjCpeD5QOcDgCsG68mydre+ntomJyMyqy4x/CQTXqsPg+Cbcl t4BtyyAMfPlcnB79a1IPDWrWvNh4W0qAl1j2Min73/XQH+dPmRPKeLQ2Ud2C8N4oydoKEHHvXX2+ jQiERKWd3IAKj0/HFd3eeF9auGMTWmhQFR3jiTbkZ4xXO3HgiJDum1SxiYckwmMKuBjip9og5SS0 0GCHP2qSOBBglppURfwGfaoLnxR4U0r9zp8UuszDBP2NcqPQFsYqq3hPRbeULJ4kgycElVB4/wC+ D/Ogw6NaBUi168lA4226lc/98gA0rmiic9PqXxB8RGRINDv47VjhVjUxkL2GSB+ddl4V8I65pOJL 3R7vTFDKzPHfBpCCc8oCQfxqO2Tw1kGVdYvCTwGEhA7ccj+ddJpcOlwXEctlo98uxsh7jKDnvjca aY2j06CG0kTKxanOh6Aufr1pGsLViBHol5NnniRgPx6Vn22qXoRvOuwuXyAW5VR0HFaJ19fJeL7Z FGXOfmcnHPT/ACa6FNGDiWE0Z2wsfhkAess38+akGlX8XEOi6dGvo7gn9awBrFlHve9uIrjK7VC7 gB7jBqk/iTSAQ7XJj2gfdICjt0NHtEHIdsllqqAGVtMtz1wFyR+QNMaPUg23+2raLPQJHuwfyrhp PHGkwnJu2kPqSACB6Vnz+O9NutwVWlReflOCOOOgo9og5GelGGYr+912UBfveTFtwfy/rQLa3O3d r95LuyBjCjGOvb6V5fH40jUFLXTJnR/vcSMT9eKnj8Qa5dALZeGriUfwt9nkxjuASKOcORneyN4e UgTPe3JX5TmTqR3qa5tvDNuIh9neRpI1lCmUnBZuA3OORz/hXAef49nGLXw1cIMYBMYUDIwRlqSO 2+JIwP7OMAzgh5IhkYwP4qXMHKehXH9iR70Omw704wFZuvTpmo7fUNOti0R0eIyFflZYi20j1yw/ QVwn9j+P5cCa5tLfaM/vLoDp0wBkfrW9c+F4V0CKVfGci+IJCPOgdE+xoGbnZIDvLBeemKl1PIfK b0mo3EckbR20CKRlyFCge/OSKjudevssjyJsbA/dOi8Kf8iudt9F8IW19BLeaq91b2wbzopbsEXU gH7sfJ80ce7l+SWHHFczPoOlXNzNc3Xi6JZJnLNHZ2pEa552RqSTtHQZ7U/a+Qcp6C/iMKkim6WM PkjMvC7u/rmuR8Qaimp2MluJ4L+STZiO4uGEYKnOCWznOaxj4Q8KMQTr+p3BPaO1X9Mg06fw5oEU O2xl1eVx3mgVYhjruCjionJsqMUea3mh6t9ta8tzpVrdg4WWG4kDjHT/AFf4Yr0PQ/it8dPCwiht PGMVxbIMCC8U3Ee3rgblz+tYD6MtsRt1424PO3Y7YyfoBWZcaNaEAz+Ip5ieQsVs2R+JIrDmN09D 3iy/ak+KVrH5dynh2Rz1b7NKCfwDCnXn7UXxVu0CJqOk6WuMbraxDt9f3jHHtxXz+mgaWWDLd6lc H18pEH6k1s2/h3SwRutLx0J5eRowPyz/AEqbD0NXxP4/1vxhx4o8WatqyH/lg1zHDBj3hjCj881j WuvaXaII4Ip/7vErEADpwg5/Gulg06wtm2pbKMcKGUE/oP5VsLb3UcAxA6EsNuyEjI/FapLyJZ5+ +s2UjM32CWZiSeknU/h/SkOrZXamhn2by3J/WvRjaa5IG8iyvf8AgWIx+HIqs2na5kh7VUPT99Oq 9vdqepNziI7vUPLzFo+09v3A/rTor3W45VE+nSGHnIQQxNnHHLV2C6TqL5M01jF/29A4xx0AP86m a1MCjzdQ0tGGcFi0pH6VQaHBC48VSAN5aRbf70sY4P4D+VXoNP8AF90uTf2sKf8ATW4OPyAreR7Y tvl1yzBxwI7Zj+YOK19Nh0iQbjcxTOG43W/6/MaSiScV/wAIxqDHNx4m0yBj1ANxJ+gGKnTwbaDB n8XwYPXyrKVvyLf4163axWMBDC/toQecRwRjH5ZrUN7FGMpr75PACQg9P91Cf0rVQFc8aj8F+HWP z+I9Qn9oNNwPwNaEfgPQHb91Pr1x7LDHHn8+lemJrNnLKYR4h1KRh96OMMQPbhRUkh02Vd7z6tcZ 6DzSucDnAz+lPkJ5jztPAeiKDnRPEN4MYHmXCxZP4CrUfgPSsAnwhd4H/P1qWPzFdUU0p3Hk6XeX J6Zklzj26k0qWFtMSsHh5iQfmDSlj+I5pcoHMf8ACI6REf8AkUdKGOnnXu9h9fmFWRoGmJhl0Tw7 an/aIkx+prefStmfL8OKmO0xZR9M8ULpcu3I0HTo2HI3SZ/9nFPlGYwjggYBG8P24Tr5dsjf+hCp k1CJCAutW0RHa3skUD6fLWvHYX8xaJLXRLcqMlGwWx7DJJ6042Oox+ZHJe6VbtExRtsIbaR/CTtN HIgMGXW8gqNcuXJ7RwhQfyFc1d3Jnk8wsLwqP+XuUR89sgGu+JlS3lmbxBbERP5YWO34MmM7QNo7 f/rrnH0G61nz5UFpfNFC1y5uv3GEUAnanGTz90Ek+lQ4oSZwd2T87O2iwkgDmXJHBxnr0zXtnwD+ Kdn4Umf4b6nqNqbK9uhcaVPn/RfOkGJrJmb7hlI8yM8fNx1xXmbaS0KgJa6VHkZw0P8A9kP5VTvr W2udPubO7l05J5Y9qGCOFNuSPvM2SOM47jt6UkrF2P1Ms7601aBLbVbM3othtj3ny7y3/wBnPG4C tu2stGzgajPA3ZZ4ensSK/MTRPjD4u+GAttLvdWs/FenRRIYBcT4uYscbFuVHYdMivffDf7ZngCc iLV7y/0llxjz0+0R+nyyR5zTcyHBn25bWOmnP/ExDY4wIz/UVpx6bpoUP9oll9lXGfoK+U1/a++D 8and4yVJB3FtIcfmKzdS/bT+GFuNlpqOpazhc77G0bHPYllwPzqHMrlPshbe1iG5LQ5HO64YDH4V 5r8UPHdl4J8Ialr11cJJcNG9tZQlgiy3EibVjQHGSM7jXxjrX7Yur3m5PCngLULvulxfuwxx12Lk YrwzVfFHjb4j6pDrPj3Tbq9Ns2bZS4SC3Gf+WUI4z6t1NJFcqOjtPPtYY4na1LRgcht2T1JHPrV4 Xt0flWeAk8jy0U4AHPGDUUUkwspbNbZiZJfNDfd2juBg4FQFGg8rbaFMl1lBlyH38Zx1GBWyJuWP tly5ONR4/wCmaY4/75oM7MRGdSuGY8KACP14/lVdGMcSKkEQFsoTJOM9+3WmiSZ5JNwjCyEEMeD6 gfh1phcWd1VXMlxcsI+WZmx0+p/CmvYAqZDG8oCLJneOFY4B/wDrUpjQStJLLHktn587SfXH9MVH LOkZLi6RmY/NhDnIHAOR0pCLUej+aD5NoZSgJcoSwjA9dvFVIYIpJJIlRAIxn7p/qR/KnJqLRxsk dzLtbhhEhXPrnFQLJECeGwcAcYyKWo7FySKW0EDtbBo7hiiFQeq88jdUsjxx7mEq7yitGUVeWPY/ hVB7m3JPlLL1xyRn6/0qXekhDR2ZXB5YvnPFFwsE17PFHkSZklIVFjCcNnr909qsT3V0m+NZhK2V VNzHGO5ITj8KoMyxRvGYfLJOSR8xH40nnjaP3e0g9QCM8UmMuS3x8hoJryYyGRP3kRc4QHldpI7V AswkaZ5reMwnASJ9xwAMA7s55xmq/wBodCxDKue4A54qL7dn70pYrxgYpWANS8mW5V7LyzcBVLwE AKmBgFcZPT1FPiDLModd0bZ+Tg5JAzzx3qkXRZZJ+ssgGWAAYgcDkVGrx7lXEoO7C47UWA0L37NN KTaw+QisQFDd8f41l3EayTebNGG2xhQOh3euakE5DskVuSoPzBupqGbdk+TC0aYyAxBI+tK4GDqt zZ2NtIDZyvI4+U4835sHqPfNXPAHiy+tvDTTeJdRs0trW7lhgWWQpequc/NERjZ6f4VI/wBokBXg nr1HH5V5Z4m8K3V55kqw2pdnLeYZF3EY6cg/Ss6kbjR69rOk6LquNT8NXaWzzZaRbdldGDd9uflb 6VXhUwRwxNcWylQoO9CpOPfZj9a+YZPD+t2sm+3MVo3UiObaf/HSP5Vai13xXpbI76zHKoP3ZH8z p+dQrpWHZH1WtslzCxsjH9rT51SKRWjkToQcYO7jjio7uG6v+NOtLvzpUIkRI2OwnjHy183WvxK8 RWlylz5tnJJEcqQpXGPTAr1bRP2iZdOZvtNuzs21g9udrBh94MDjvSlKfL7o4wR7l4e+GmqeJ3hs dfifSvCdm4nvnlASa8ZekSr1CL/eI6k+1Yvxn12PU7+003SbW3e2VvMZZCqpHHCpVNo44J4H+TXI ah+0jqGu2klpa2c29znMoZlXaeDtA+b6V5veSrrN7Nql/Y31/d3IVpHb5E4UAKqhcBR0xmuanGpK XNMvSKsjft3uk2pKlrbIP4UK9u4qffGgLyOxVjtyvv8ASs2C3mWNWh0qRGHB8wkgHPQcYrTV5BHh 1VG6gAjjPb/69d+hBl3JjxhWO0nhmBPt3rj9RtbNNzNdFy33gIzhT+ld5JI2HjeaEMSMgsu0ZOPW sa6Yj5GvrKINnq6nkewzRcg8ov7fTpVaPypZCB8rKvHp3rvfA/xDltjHoniZmRVxFaXbqR0OFjl7 7fQ9vpUM8UbqfN1e354/dZ6gegWuYu7TTZwIbu7acEnGFLc+oIHXFDjcLH1tbH7TcBGZdxQEjg8K f1B7V5b4u0uTwVry6lbxg6dqgz5Z+6XJ+ZD9TyD2rnvhh4wmbUI9Av3Z2g3JZzyfKWRf+WTZxk4+ 7+VfXEvhjSvHHhY6VcjL3AYxTYBMbY4/KudPklqNs+cQyXEMeo6WwaJG2tNjBjY8+VOB0J7H7pHQ mvQ9A1VLrLIvkTwAEoSNwHTI9s14iBq3gXW7vSblns9RtR5Em5crLDn5WKN8siOMfTtg12enajBf 3MZsoxY3ind5KNlGPQlCecD+4c/1r0XO+xk0e/2eoZQrMjSLyRtLfj06VrxXGmuwaOa5gYccHePT /PWuQ8N3uk6nFNp+qamujakGjW0uJlxYyZOGE8n3oW6EHG33rcu9P1bQphFq0LRgcLMGEkL46FJV +Vh6c0ouLGi3dafYXN1DqBaGS8t2VkmkjCyjgr94D+6cV5d450kBjfKqs8Z3sUySN3J/An1r0kuZ cM3VgCB+Has68iS4DxsCwZdpUD+Ejv7ZqatHnjYIyR4v4e1w6BdS5jW40y+yl5bfwtHxllHZql13 Qo9PS21TT3+0aVfDMMo58shQTDJ7jPBrP1CzNjfGCL7p+YccAEcjB+hrZ0DV/wCwxNYahB9s0O9P 7+34Gx8bRIucYNeBNa8rOnmMWytLeRX+U7gARx2Jqa5DrMYkbco9AK1td0KfwxcQz2spu9KvATZ3 a/xDJARvQjHQisAm6aQuh6+uPxrnlCxQ6NY3JLrk9c9OKuZHymE7OOCTjNZ333wWGVOCoxz+VJM7 opiKBy5wD02/SoAsxlhJIjFhx1Pqad5K4Uvgkn6nim2yTEbLj+EcDOTVjzPKZSyjC8Dv1oKQqo7q Fj+XnJyMZ5ps4WJSnQ/TPXmky7ne7EHrx0NPP75icZKeg60ElQH5DGxJ3c4Hf0xiok5QnuOD+HSr RRi33SD0GMc1ABnPGCvY96VgJeoUMxUY/h/rUKxjduI5HapQyAgEEsentUDTj7pXdj72OD/kUwJ0 yiHAwWyM5AOfSkGFX94cnoKrIGaQD7wHO6p2IBAKk46mgCaFnLkKeRzgHHFW1t3mZX+UccnORj61 RhDu2ACCeOn5Vfv1NkI1+Yqy8igpFa4AjQq+2Tfxgdx/hXoHgv4jyaJNb2GtxfbNNRxlZPmKR9Cv uhBPv1riUmRYHRbfeWXbuPbIrKntySsgOCMADpnA6GqhKwpI9P8AGnw7a0iOueEy97oqr5rwJ8xg iZ8B1x1QZXcO34V5bBGCg85vuj8scfzr0LwR4+vPCM629wftOnSBlkiIz5avncU9c/3TxXW+MPAt nq6y+K/BhjmhmcPPaJhfMZkyZIgcfOWU5Udz6Vs4KWxCZ4orpGpORk9KpSSE9AW9uwqyucf6v/Zw Qd2R1BHZvUUzzBC4CBTnOfzrmlHlKEijLgM+FC8cd6c08duCFXlumevFVpr2QARLz3yOPpTFtpZh vbk1FykHnNIxI+Xvj26VLAnz5YYHqaNkcfbnv9Kb5gfheT/h9aQy0WQNgNkD0HSmZJJfGQO//wBa kZSiZOAPwqAuoAUZI9qdxWJjhmx37GoAMHgcenpipd+MEDoKiDghlJwW7egpXGSxRpI8ImlFvDJK EMrAkIp4Lba9j0/RPhBplvL/AGrr7eJJPvCGJGhXOOgUY5PTqMda8YBOB2zwCP60uS37sHG3jsf6 ClYynDmPU/8AhaOt2BNno8awafAdlvHPF58iRj7qmQnLYHA9sCk/4W54t9bf/wABR/jXmBRBwwXP +fejbH6L/n8ay+roXsj/0qEtx4ljjZJDpcK9CHu88enFZf22+jyZNZ0W1ZeF/eNIRUC6XbIxK+EE I7efPn+tWFt76I5g8O6PbehlYH8hXo8phqVG1GFNxl8WWiM3JKW5b+lRrq1gSQPFVzLnqLe0xn8e f5Vp+brI6nSLceoUHH04NNN9qUed2vWMJx0SPJ/AfLRoIy8WM7b1u9buh6CPbn8gKUW9qx2RaVrd y3o7bR/SnyaozkrP4n5/uxRqGH6mqJ1HTi219fvZif4V4/lRoO9jQTSd5OfCl1Iewmusf1o/su4V sx+FLKM+s84bH55rNkOjsR50mqXJAyQN54/CnRWOnXIHk6XqMo7Au4/LOKQamp5Gp2+GWx0W3A/v sOKmFxqKpuOpaRaL6xxA/riqDaPFGu4eGZztOC074X8cmp10e+GJYvDmnxE87nZTx2z1/lSuURtq coO1vF9rDjr5UBz+n+FQNf25b974uuZs9oYc5/KtNYdbUfurPTYh6xgHH/jtSRxa6WBF3aRE8DbD n8iMUXJsYbXely4U6lrVwO+1GApiWulykbLDWL0nszMM/niuiFlrkys0utrGq8kpEo46fxVANKur gjytcvZx0IiRQP0BP60c6HZmONItyfl8I3UnoJpiB+IzS/2PcR52eErKHv8AvpA38/8AGtaTwzMz YH9r3Lc8jft/QCqA8K27sftGl6jLL2DecR+PAquZdxWIlXVAQtvpuhWoXqW2/T6Vz/iTVtZ0vSp9 +paYrXIa3EdpCvmAMPmIJHQjjNdM3g+4MgW08Hz3S5GX8t9vzDHViK8b8TeVPqsy29qlrHaKLeGJ FxhlPznnP8We9Jy7GbOZuNU1u+t49OnuytrHEEVcAbUVcLlqzIrOecQbITsi6FFJyc56n3Nac0Gx S7MASei9/wA+lVYryQFVxI4ToAdvf2rPmAV9EtgzSXcE5ZuQD2z/ALNdBo32C2VYtTt/MtVbONwX HGRkVE1xGFVpISzHpuZjSwa5ZQnyn0K2uMEEmXcd2DnBxSAi8QJZXdyqaPNIttHGd7PlGaRjn5V4 wqjj3xmuKvbCGOQzrI7yxJhurKwHoO1dhcpDqLSXUEQsyz7hEudqD0Ge1Ns7SC6dLS/Jij3ZaSMb mWPOW2qP4vSnsrjijjP7Pv5x9ljtpmEmN2Ds2gDpnivRfCWiatp0kElvImns5wXI81gBxnJrtNP0 T4fRyH7RqOuXLZwoXyogV9GwTiu6iHwvtsFtIvn2jo9/jPpnArmeINVExBcXyAibxJfOAcAxKq/4 Gpgu918/UNSmjbj5plXJ+nWupj8TfD+FX+yeELd324D3F1M/T8hVKTxfoS4ay8MaYkyEclJJO3+0 a1VfyD2bKa2umRACQXLZBYIbonAzj5gucZ9KbLa6HDFuaxgeZeCrzMzf06elV9T8beIr37Ott4fs UijkLnyrYKzcYw2T05496s6Vr2vW1rDClhDB5ShVzbpu6ckt6k80vbsOUA+igj7PplshIVSMM2Oe fWtVYiNy2GmrIh+55NoW59fmFOfxR4kKffFv/umNfy4qrL4l8QkL/wATRiT1DT7V/DZgUe1mUoIk msNcngNumlzkvhQ624TJPb5sU8fDnxI7RtJ4YkaPjie4gRW468msaXW7xn3z3cczA8FpnbGB6Zqn PrszkbpomPXgZ/mTUuU2HKjuYfhpr8a4utN0W3jJyPtOoRAKP+Abqde/Dq9lijjHiTwrpSR9VSWS UnJ/2Bj9BXl1xrdwmSt1GiAZB4qgfFM65VdSU8Z4TOB+FZ2mWmj04+CJLGEJD450EMMkSW9pMZGz /CXx05NY0/hq7QEr42tHCcKIbGXp7fhXm0viq5AONalRcZ+WI1mtrU87ALf6lcEnLFIT0/KotMtW PRJdJvbdN0PiiSVhkYSzKZP1JqgtrdgFp9XvSR18tQn5ZxXEH7TIpb7DrFxnJBLbAfpUZsL5m40a 5AK8ma6H64P9KrkkFkdhMtm25pbzUZj90kzKmcdAcHpWcqWXLurbsAgSXQPI/OsQadckbBplrHgd ZJzz+Rqz9guEQfLp1pjjIHmt+FWoE6BLJpa5SK2iJUjBMhfGBzmqz3C8+VaWoXgj93k5xjvWklpA BkaiCw/55wfy4q3FYyHkXc7jGc7QP6VfIZtnPi41HcWhs4wMcbYlx+FSeb4hkBKxFQOcrGq4roG0 nzPvG5YDuSFB4qJtHswNskUhz1zLx/OnylXOfaPxI5yZBGOuWmVePwq0lrdyupvtWMYPOIn3kAcf 54rUXRdJAMhthheCSxI68d617W10+2AYTRWhPHEeT/wGmkJsq2vh3w9OglnfW7s9jEoVT/46T/Ot NfC2gMFMfhzWLjHdpdoP5Dj8q2oriwDAf23qj/7EEWB+WKsKNNlJwutXX94OCP6VrYm5iR+FdJjd Sngp2P8A08XR/Xn+laX9h28WGj8LaJAR3uJ1cj9TWgmnWrgGPw1qU2ehaUhT+lTroVzIfk8HID2M 0h/pQIoYMCqrWnh61H8OERwPXAANINSnjbA1rTbYDgLDaj9MACtqDR9Si+74d0y2ZeBuYnH0DVcg tNckcosGlxlfULxQBzJ1m5yAPEspHcW9uV/Kmm7a4JMus6xMW6bI8frXWG11oKc6pp9qP9iIGlit tVdDt8RsAneKMgfgP/rU/kTzI48WkEgYmLW7onGS0mM/gM1ZXw7HKFI8P6lID0Mk+B+WK6qO085j FN4puHPfMZ/LnH86o3FnocEzxTalezuMKSp2ggjPAzQMx4/CuzlPDiMR0+0T8D9RVmPw/KpIGj6R ERyd8inHoN27+lTxab4cvX8tYry7aMdJJhGMevP+NWpbDQY4VeHTFkY9riZlQIDjACd6LiuV/wCz pYh5j/2NZjphRG/45wackixn95r9kg9IIV4+ny1NZrYYXZodrKJzhQpfaMHozYzmrcl9YW0bGPw0 FmClY2G11Eg4DE/3fai7GUpbq1LgP4iuGjz80Swldx+oAx+FU5rfR7lVje91jURNuFwFJjVV/wCW Yh469NxatiPW72RHiWyg0+bbIdyorK7Z+XGTxn6VQl8SazIkkckVshXIAOxO3HSk0LU446fPEDEm jC4AG0yz4z6c/hT47S+QfurOwgUepBbj1rbvb6Q28Lr5NzNhBIjN8o5yxGO4qlezR3BeVhGmAygQ 5UdcKcfSsnBdi7j0ttRwfNnsRnjiLcRjjtVuK0nWNLlZ4Ejlk8kS/ZmC7xzkMfQday7S7nSYzypg Bdu1VJ49ScfzrQk1G5ltrmySCaSCaPy1jdDtjL/fkT/aIAH0pWKuE8Fyru8mrSttON0UAxgcdcj+ VRx6fc3qSTJNezQQEb5E2BUz0zuasxrDVLhWdYPL3AKfmKjH+7TjY6hHG6f2itgjKI22scbT1yvf pTsSPbT4nkQeXPITwGe4UD16DNVjbaaYyHsfPw20o8shfcDxjgfnWbcPGmVl8RTRg8BYIwc/nVJl 0piDLrN3OV6Zjzj6Yx+NGg0jppdPgtifN0u3hClVdMPJgMAc4yPantpkot/t0VpZRxhvLCGBC3P3 WGWNceYdGUgZv7jdzgA8n25p62OmOwKaFeyP/ekLcn6UrBY3FS53xqZYIIwcSHy4UxjjOOT/AD/p Vue/tLbA+2NeEjrCURQueMjA6dawI7FAP+RaYH1kc9a1beBo/wDWaNa26gbvvgnHTpmhIDTn1AWw h+zJE8rrlnG87mJ9se3arFvqHiFV84y220dQY1OPxzmlhntmiOdMLtjgJx/OphbQS9dD3K3UFxnG PpWkWJosw6rq1rKJZbuB4s7tiRQoSvXOa0bnxG1zDK1vqZhnaSPy1CIAI+jnKjgjtVOOBkUNDoEM UYGAX54+lSW66nKpe2tLFIzxldpPHrVEjm8USHan9pyny12hVB+bjHJXH8qx4TZLHKiyX8jTY3SE ueQc8VsN/bKD/j4sEP8AsqhxVWSfVRxceILeJe4VACP0oGQJHgBTZXc5Rt6iT7uevQ5/nTnsmwZV 8Posj8+YeMN65I/TFRtdjcvmeJXkUDGI+p/Ks+Z9M8wG41m8mD/wgH+lAFtdNvfLdYtPjj83q8kn zfgRyKnQatbStNG8ERZdzglW5HG7B6mswNoIBIa6mC9jxSLc2gYrbWsg4wC3PH40ASToJbQRT3sZ +csu3adrE5LcVzVzYWJbEourplJYbMhTkdBt9a3skxv5ESwnOASo5/Kse/juACJNTNsCMny4wDge 9TJEoyBp1kxz/wAI/cy4AyZZeuB3ANTxWCJ/x7+F4Xyekkp6VVmuLZP3cut3UpZekeApquv9jNw8 13cH0V2Hb2qC7m2bfWsBbbw/pFoo5xKA+D6/NWPf6N4jmO+Sfw3bZxg+VGeh9M1ZhXQNu2fRr27J +65kkGB+mfpTo7DT9x8vw2znOQZRn6ZyaBnOyWl1FuabxnodqVONtvaKrDP4YqtH5XO74h7j0xFB GP8A0Fea7j7OEKiPQrSEE4+ZEyOOi5q6sd5Aof7LZwKTx/qxj6KAaVgOHFvo8oHm+L9avHBA2x/I D+grpdPsLG3kD2897OpAwZpH5rdF1M2F+02yqOuCM/otEtzaDaJJw5H9wM3/ALL/AEoURMnCLnoA PckmhkzgbyuOTUTzRHhVnlBxykTcenORUiyXTkhdPunGMZKquf8Avo5rWxNhQkOQfM3r6Bgf5VMG iUszKMdCGPT6f/qquIp15NiYzjgmSJR+tMZb4EboYlx2eYD6fdFFhWLETJAryxqCq8fNhutMaSOP PCgk9Bzg96SeG8glNvPLZRSIVLfO7gBl3L8y8Ur2ONhkvbZCw3kAM3X0BpXCxPHJN5bKkhjx6AZ5 qCXlzG8hfp83C9qijEYLD7YwC5+Zbf5OvUHNTyGxSGOU6zcsGPEcMar+h/pT0KKuOe6lSPlIzn8q kijYfMFIPXHOD+FL5dqST5l0zdiJgAfy4FLENKlWRJIpW2qQfMuG4b1xipsUI1mo/eZx7FSc59zV NwwUo8irz149eO9KINJjYvDaR3KKMtguWzj/AGsVbK+HWj82GyZmaJtsUkBHly5wCGzyCKLAZrTW sX3rpPxKgeg4zSm7tjlY50eQDAVGGSelTKsSSKsEKxKVT5WgXtjKsT7jjHar0JKtCXjMyFm85SEG V3fLtyD29qkDS1bwn4m0O3jmu9KuZokw90YZUlNqrLuR5EQnCkc/5xXJmdSQ8cdy/GAUibaT3IIO PxroJJYhetOJJ4YJjmZSwVtufuAqQMY7YqiLmzSXfOwWBM7UEmCMjjJOKAMj99I/nrp1yWAxv8sH Pp1alxMmSNNlA28l/LUfkc0fbtPjikUXcUcsgAR9+WQA55GcGlute0z7NHAs1uk2/dLKmWMi44Xb 2weamwEEiSH90mnKrdSN6K344H9KoX2nSTQxyXOlxiCQlP8Aj4Dc9furjHap49bs08xmlSTzF2M4 ibPTr0rMkvdM3bWuJgjMCVEZA4GM8/Sgehn3HhPT53Y2uiWckcK5JnlfduHYBa5u48LyRo7w+H7C Mhd+fmbA9cV1Vxq1mIggNwue+UUH264rKbWrOMMDdzqMAECZMH24zU2C5jweFdTmLFLG12wx+bK1 vAZPLQ/xMQCFHua0/wDhCNXjYOXESEKwP2dI22kfKRuxwR09qiHiDToxNHHdyLFOhjljFwVDqexV Rz9CaqN4i0pn+YlyiKiZeSThBtA6HsKXIFzobXR9UErww3UjgMFBEkUe046lR/T/AOtVl9OQSv5t 3cqQMYe4A3OOp47YrkG16xVvMisGJJ6+UxP1zQPEhztTT3HozRgYxzkZI61Vgudva6Vod9NAH1OC zWRCSbhpH2bc7ScdegofT9BhjurddQt3mhIaKWGItHODhSpzyOOa4r/hJdRlceRaEk8YPlr1/wB3 OKlbV/EOOLaNfo+f0AqeQLnUX0enym3VFMsFuuPkiA345GR+lU5F8+GDbZsrRRtwkAC5PQfMBXMN c+J5nJW3Uk4+7uPb2H9KXb4s/hi2n3DD+eKrkJudELOz3Kz2twzSLiUBY1wcYGOlZt1aTKpNvZ7F GMBnQdBjtWYbbxdIclkUd/mRcf8AfTCqkula++TcXkSfWWL+WT/OnysZl6mJVj+1Q7LeayYSxuCN 6uvII6Z//XX0V8MPitZazbWkN3cRW2rvN9ne1ReJZSMiWP0VgOR2NfOc3hzqZtYhTjGBL6/7KjFc zPpI0eePUdO1NPtNuweN03EqwOcg4GelRVp3QH6cePPAPh74vaHBf2xWPVbNVFvdxY86N+Q0bDun sfwr4a1PR9Z8Han/AGVrkJhmi/1bjIWRR/HE36f4dK7v4VftBXFldrYeKbtre6lk2RX6KFi2MMbZ 1H6EDNfVmrr4P+I9gbDWorYyDawaJw0f7zhHDKSUPHDVyqpKk/e2KdO+x8waJ4hg1S3kgu1Et9HF +6fbj7QoGfKlGD86jpxyB65ro/DfizVtIYxaddy20ZfcYS3mQse+UfIx9Kb4l/Z88a+EmOs+EWbV 7BD5wRTieEqdwKv0c/Xtx3ri5fMtrwSzQtZGdQ7QSoY/Lcj5lG7Axnpz0rvpVac9mZcske+2niTQ 9SY/2hYtZTP9+408ARsemXgY4/75NbdpZaJqoNsuv21lnAD3cLp987SykDqB26V47onmuyx2h3s/ OByAP6fpXYiC/sZJQqiSZoxuQAZHGQSef5VcrdykjN+KumaVBq8ur+H5kk0s3T2iFRjoilZNp5w7 bsV5T5m7dkZB6k85J617F4p1SyvvB0iSxolw0Vu0e0AbpBIuP++cNmvHnIY/KAAM4x3rwsfbmXKb QOv8Oa5bx2x8Pa8pn0i5bI7tCRzuT0PNYGv6LLoGqi3Ym6gZVa1uEyPMV0V8H/aG7BH41kgMzBCu Qx69OvFeh6Fqmn31k/hXxCD5RLmCdMb0faVVlPbHyg+341lH31Zlo89gbc/mbQoz1qR13zpv/wCW n8vX/Irb13QrrwvqLafqHlzHBaCSPOx484Vh/WuXje5uJ/MPy84BH8I9q55QcXYDRLHcScsOBgY7 CkZWCElefQn8qvC124fC4OM5zzVw/Yp0CxEK69R/hSsWUPMEkIMa7VVcde9OtmjbKP8AKTjBBIJo e2+zANyzP92NRn8hVQh1zldrDoMZoFYluJTI4ij+Vs8N04xVZWZXfDB25APp61JtLMH8v26d/oag 8g+YdpAB5x0AxQSLvUooc72B5wO1MESlv3C8YJwf8ae2RlQuDjg9fyxTvKdYwjH52GSBxhai7ALf YoyM96m8tnbGOW5XP/1qLe0YDcg3AnAz2NdFY6OWmW4uTk4wq8j86tIDMtbaSR/KV1TPqQAMe9T3 No4LNMyvHjAbP9KbdWMkGoZWRXUMNiDgIcdeetQXtyZASTvbkccL1qrFIjETQ8BgUK5HPWqkhZQp cDB5A+oxUBmkQlCuxsZBPI6dBSqo3FlbcGHRucGpZJOsQZcyDaSuMV0/hfxfeeFbxPs48y0cgyWz MdpI/jU/wtzXPMoJ45OOaz5ZoUkw2CBwKcHyise/eJfDejePNFh8TeFHjt9SiRobqL7qy7WBjVwP usQWAcd8Z4r5yu7e5tLue0vongu7SQxTxsMMjDjGPfse9b/h/wAS6j4cvjdaawYONssLE7JEPBDD 2HQ9Qa+gI9N8GfGO0tLq8nl03UoWhhmlg2+dAGXy8vnmRM7S2ewPPNbOPOtBK+x8uqRG/Kbm7H0q f7S5GASFGBx71pax4fvtB1e80LUzELyylaLzIspDMqtgSRb8fIeCPQVVutJ1Sya2W5tJI0umRYZP vRO0h2gh1yOnvXJOPK9S7mdNvwWzwOxpVPI+XHFW9TsbnTdQudJvVEc1m/lyDoBjufSqbxPGUMiF Q67lJ4BHrn0pKwXJXZsAryKTzPmG4bX7H0qc2N75iwmAo7RrNhgR+6YDDfSmyQeW7REqXQkcHI49 CP8AGlcSYgYliB0PQ4x2pOhPc00/Ln8wPalHONoplkgJAyR8vYU1HQ84w2OcUjklSoOB9Kr+WRxn g+nFAGgoyATHuPrxS7R/zy/lTEWMIBuNO2x+pqPmK5//0y08BR3cpt/7Tv5W/uKsuTj2VK1LH4Qy XTM76PriRD+IhiX9xuZa9wbxt8bbvZHZeDLmKQ9M2+3AI9WfFRLcftIN850uG0x18+W0jA+gO7+d N1ZE8p5lb/Bfzcf8U1fSf76pj06l61bf4LX6o5TwqkBYfuzNLCAGHTjJ/wA/lXS3UXxuuSDqHiDS NNA679St0/Pag/nXNX1t40jBW++Ieh5J+cLqcnHHqvX8qPayHY3R8F777NGU0vTLOcAZkeTjPf7o pbf4M6+gJudT0qCA8nG49OozwK85vUW1ilkuviNpEjp8yRpJPKzt6da47UdUTdFFbeJTfSMMyeVB KsaHrjn73FV7w+VH0Bf/AA2s7Oywuq6ZbMMKZC7EDPP3d3f61Qj8EeElMZ1PxwqunIWERKBj0ByT +deKzQ+G3sJCur6lqWpnHkRRWpigTIyd27rzU+i6FG6mbVo9UmBHyRweXGCR3Znxj8KLPuPlR7hb 6V8ONNuFuD4uuLryzvA2RsM+v3Tn8qy7w/ByWWSe51i/ncncwU4H5DaP0ri9J0/wrZuzar4VutYl LZX7VqSRxqvQjC1uS6j8P4JYJrbwFolqYHLYub4vliMDIAOQO9JwkKyLTaz8DLYCBbW5ui3UiTlv w3iqU/j/AOCmmyKkGhyyt1ySCEx681R1jx1p1xZzadHbeEtKgmXYfJglkdR6q2F5rAi8e2OnWi2t nr+lIEXaPJ0lGOB3LNnJpcg9DvLb4n+C7Y+Zo/gy3kd+RJIjMWPb2/z60f8AC8L+Pfb6d4dgt5Ac lY4+R9f/ANVeZ3XiyXxHLHb/APCQ6ldzqG2pbQQ20aqo5Y8YUfjXFvqOjZKkavcbj94zkA++Aoql DzJuewXXxz8bvuW0sYo93UmEH8K568+MvxKmUnfb24HGQiRkfj/9avP/ADtNkGY/D95eDp+9mI/M cVmTwxTN+58HxIRn/WSZH8z+pqvZoLnUXPxA8f6jKqaj4haKGX5Whjk3MdwwOF+tfNN5e3sNzd2l 1GElikeIgf31PJ/Hr1r1di0FzGlxptvpiOwwYmUucHPy/jUXi/wr/a8KeIdJljkvLhVS9gBAk3KO HUH2AzWsVYwmeOx4ZSznc/fmrNnbSGUuAMDoPXNSW+mXSv8AvoJEYkghxjHPGelaKWurx/6v5QOg 3AGmQXJtH1KO3+1GFWUc/LyQPwrIBRf3p/eH+6OMVpW+pa9p84chpD1IILgjpjipZ4pruUTpZNE0 nLfLgDPfmgDnzI0Uikc7j93+Qq3KJLGJ5nbyppYS5z/yyjP3B/vOelb+n6dZ77nDGW6SLMJ4CI5P BNcrrflLbi2hlaVRJtdzgmWQdxnsn8Poc0myooi0v7Q37zczc4JJ9OO1dPBHyGaRQpOCG55rM0vT ZCqKrySMTztA2kiuvsNJkkkCLahmAyTP0/Ss+VdjpQiRhSB9sjAH/TPdWjGygHZdzSY7RRAfyzW5 a6ZqKgbfstugO0FV3E/TPWr5g1GAqp1TyhJ90RQgZxx6U0iZSsYkUc0qjFvqNxj0+T/CrP2C6JGd JviMf8tJev5GtwWM0k4tpr+8ckfMU+UDjOPyqRdMsWjGbu8mLP5YDTkBccFq0sieYxzodyUDHSkA 6jzLnp9etRnRrlCubOzjz/fcmt06NpJnMIj8wAEs7ys+MdeQfyp0en6TJgWmmxHs7TBu3GfmPNKw XOYltfKJBewU+gz/AFrOuAIwF8y0BJxwQM13c1ppaAxW9hAh/icx5/Ks6SPR1jKvZp5rdGCDj8Kp iSOAuEfaGhjsjyBkkHj6VnyzXicNNp0ZXIGF6HpzjqK9Bkm0ARCKTSVcxnmRSAzZ44HbFQR6nods GWHw/FK2PvyzKM+h2jpWZocRdXkLRwnTdYNuyxbZyVzufOcxgfcTtjvWes0twwE+v3hUnB8m2C// AF/8/hXZvrFtGWaDSLJJOf8AWS+pBHQ9sZ6Ut74rvbi4luZ/7LilfGXSPPbANRYZxLWln5mPtuq3 LDjgOM/gKfb6PaTsxj07UpyvPzM2f1IrpX8X3Spt/tO0GOP3cIz+NRx+JJ1kVk1Dew5IjQ8fWlYl sr22gjfkaTKWI6TEgD8BW1BpLIVi+wLG7fdHXB/Go11vVZmzE0zliCegzWhHeazKBIELSA5Jk4/K tYolsnbTniBE7tAq8HagH4VcttHWeJv3k3yDfhiq7hntzUfmasSDLcW0Q64kYkjPsalaWZPnn1q3 G3oUC5A992KpInUln0a0jgLhpJ3GAI8nkkZFXH0i0i8gRhcMv7w8kq3oayTfWvDP4gdx1+RVHTjt moJLzRz/AMv15Lnn5N3OfYCqsM6yLSNNdSk3mIGGFIUDbjkcd8mnwQC1Rc3MEcxyeYkODn1NctEm mSlXjsNTuge7B+fxIFXxZhz+58MXD47ysoH6tQkB00muG0tcf2jBNP8AdP3QFHXIKiq48TRx4e/1 UuijgJIUzx36VipbXaYS38P28P8AeEkqj+tXo08QuoVLLTYAp4LSqcD6AGgA/wCEpt7kYfUZCM8B d5BHbB60jazpkjK6x3c5UYyiyHP8qmc69gb7+yibp+7wfpj5c1TLasCfO8QpCP8Apmv+GKALEeqE 5MWmXsvPG4HioN96Sxi0S7DOcnL7c+mcnpVZ5rZVL3HiiQheuACfyJ/rVM3PhxuW8QXdxnsqqB+Q Y0E6nQxnX8KRpCJsO7bJOo6VOZPErSbtlhAxG0gzAfKa5B5vCmPmuNRkA9GIz+VSq3h5l/0fTNRu M9CxcZ/Sgo6Zn1kcTX+mw44z9/p7ihbq+hBY+I9PjwOnlufyNYarZoQI/Cs7jjl2I/nV1couLXwr ErHn95jp7HNAD3uLUuXl8TwbmHWOME/rUL3um5BbXriTIwNkC8+uMZqdG1fIC6RZQZ/vGM7fxJqx v1/O5E02EHg5UFuOOMUAUBcaIfka+1abPBxlM/oKGXQsjGn6pcMfSTr9eKs+d4kTj+1LVAf7q4/X GP1qA3GpDJn1+JSP7oXP9KAEihs3YNa+HbuYjgea5H8sfyq2trcDJXwrH9ZJCT+IrNkuoQC1x4mY cZxuRen4is37fo0gWN9VvLtpHCrHC5zyOvHb36UAdB9n1RSTHpFjFjk5bpUhTWFwSLKEEfwjP61z Ui6fBMq3FrqMUcg3I8xcLIv+wSAG/CpUbSGY+Xo15KD3YOBjH+1igDeEmoRZD6raxjHIAH65qOa/ gOd+t5YDonb2HtVGIRSgPa+GjheMyJ6f7xFWmj1Z+LfQIYm/gyqjp+NSBnTX2n9DdSzN0yyng1Re S0fPlWz3BAyM9D+FbLr4iRTG8NpbgdeVGD+JrOmXUs7WmhDMBuUSgE8dsdKGgMnzbxlLRWVtEoOM txj9Kek2pKA8V9YwEZyBjcPrkVHcaTfyyYdIgz42DzCcnHr0qvFp926iA21vvB3ZKPuI9MY5rOw0 xXvbl2zdeIkjA4UABvy2iqUk+lshDeIJyw7Ku3+VbA0HVDKLd7KNJJeV3wEYHp0pFsb9XCR+XbFc hx5ZyMH04pjOWjm0kt82oXr89RuOfw/D2rbg/smQZiN3KSMBjyOenrWo1jeT74hqJ/c7cpIBFnPP Bzz+dObTfLWJZb4PPsXEYkTaFzyWbNACWjCNT/pM8TDgFuwrUF5a4CXepSAD1fb+QOKxBbWCJunj jduhDTZ9v4a0rZ9JgLGaK0MZTCBQz4b6kU07CZN5mibi8l/PMOwJ2gce55qEHQGVigmkCdQH2gZ9 AKnW6gKnyGtAR0IjLD6dKF1O4DIzMko4DIsKj5QegPH8qu4iqZtIGQlpdsy9gSCf0pyzRsBs0C6c HoWyc/iDWgNWuI5zI8kzx8qseFQAnkE/SoVv7tsyS3Ehf/YkwCKLgKhuVGIvDpTjgsQv5Z/xppj1 UtgaIit6tJH6e9Zt1cwSuolmXCDrLKc/Q81VN/p0FzHcpPbRmPOFd/MG7GM/e/pRcDYKakcr5MSZ 5x5yD+WKgltdZ+XzZraIdh5nas6bX9PZPLMtkhfk7Ez9cAVRbXtLIMSTohAzkRMWKjqeh4ouBuG1 1FgpFxbvuJUAHeM4z7CqkdpqM7lLWNJXCjcyRYHzHA9R3FZMviPS9gjN7cCMlWPlqwzgdunaqk3j aNYFtBqF6sKY2xhgBkY5OG9qmTFY3V03XdzFmw0eQR5YAABxxkUR2Os3DpGJmQS/IpwAOOW4yMcV xcvivTz8plnlx3edVz+A3H9aj/4SrTBgFCpHACSluvPZPwrPUux3TafdCNlXUHMT7vKKYXft4IOT x09agjtN7LFJcb227uG4A+prh28XwjattZ5HbKynr16AD9Klj8RXbKTFpmR2YQyDPbHzMKoD0GLT rRZBJLIjouSCckgkVLbwaYbiISMY4wDuOzcfQfSvOP7b8Sytm3s5CD2CRIB7ZYn+tTxXvjec4Ebx 9v8Aj5iAx9AtCFc9DvNkQI06RXIwAEUBQMUmdQ3JhnVNvIQgfN+VcJ9i8bzkkzLg8YMrEH/vlBTW 8L+IpQPOuo4we+6U/pkU7D3O+LXRATe0YPMnz8sf/wBVJLeRSyh76SJ1C7QJWBK47/eHNednwhqI x5msQoB1Gzdj67nP8qYvhzT0+W58RJu77UtlA+nBNVdkndPeaEEKJdW6NtwXD7z9SM0Pr2kRBcXs UuFC4RScY74xXGNpHh0RpnW5AVzloioY/Xy1/pQuk+Fj/rLvULgeuZ8H/vlBRdgdQ3iHR3lL7ZnL lcqIiFO0d8iopvE2lIUl8mZpEG0ZwPbHUVhjSPC6phNLv7xSM5CTMP8Ax4rUiaVoCfNH4Ulcf7UK f+zuaVmK4lx4w0YBhJaAN0+edF6D/e/Gqh8f6SgGI7aMp03TFyB9QK10tbaPaLTwskXpvMCfoM1a jOof8s9EtU9SZlGfwSMfzo5RnM/8LHh2kW0cLk85jSZ8/ktRN491Sc/ubaf6RWcpxx74/pXaebru VRYLOEnod8rDj6YqN5fEMilBe2aJ6COR8f8AfT1Vh3OMbxL4vmA2aXfEHv5EcePzamDU/HT8fYrs FuhJgT8ySa6xotWJBbWYYADjCwRDr77qjVpBxLrzyqy42osffkEbQf5UMLnLMnjiYEvbuCeoa7AH 5KP6Uz+xfGMp+b7Mv/XSaWT+QrphawS4d9avmVeMCVwP0UD9KDp+lhiftt3c46/vpH/DjA+lRYLn Mnwx4nlTMtxZov8AuSN/6Finf8I5qiqI5NXtogO0cMa4/N63pNC007ZTp80jP3bcf5tSro2n/LHH pjWylgGk8tXZRxnCtnt6miwXOdfRIWH+keJsY6geSuc9OMH+dVW0TQ8kS63M5HXEwA/8cU17Zr/h DwVp9jLfeF/Eb315CyIuk3unRwPP5nJeGaPhQo/56A1yP2DUApAsoPLQfxMFxgdwMfyp2C555/ZP hn+O9uJR0OHnb8uBSLpXhNTuEFzMOhIjl/QlhXd+RevzGbQDbuwrMcfrTpdH1ZoknzbkOCy7U3Ng HB7E07IR55cWHhSIeZHp0xI/56Rp+hZjVYDw+yjZYSKc4x+6Tn9a9FTw7rlzFJNEsSJGAzyPACq5 OOcjA/OnW/hbW50LDUXiIzysCIBg46ErU3QHnP8AoMfEWkzuB0/eH+Sp/SpUed8LBoDt6AtIf5AV 3aeF9QdJpJNalKQ43GJl4/nWd/YmkI0r6lrssiRxlvLEu1mfA2gEe1Fx3OZZNUX5v7ChhYgcvk4/ 76amj+2zyYrG329flj/mSa6D+xPCDs81xqZjXduEYeSQkEYxgUyOy8ERNIHX7Ui/6vfHIS7YwCfp U8wXMLztXT/XajbRD0UxjHPbCmrO6fbtm1hRu4wCw/klb8C+FIWT7PYbcYDmOAtnHoD0q2ZtIWR3 tNHnycmNWVUHX+tF2PU4qX7MhKSajKzADIQzN/6DgVUOnW17cLFEb68lbOBDC7lQBksec4FehQag 8cjNFo8kiFANk7oAH9Q3FJHqF5BKZUs0STnBFxswx46pn5cdqLE2PNf7K0vPzRXcmD94Jtz26EH8 q1X8MWEXEumagGChsOMcYznaK60X1wq7mFqZlOQd7SY9igHeo11fUY7wXyXUInQEY8tnBGMbefrR Yepyh0OziGV0mTP+06/yqxFojO8aHSYYxIQQ0pxt5xz2roTq13/q3vkUEAZESjGBjI5qD+050cON TmIX/ZjK8exOKkNTidZ8Lm9EkMUFnbyo5TcELdOCB+tWvD2i+PPB0Uup+F9cRWuf9HeFshWwucEH PTPHHWtSWXSFYvPczM0jFyBIByxyTx9arSzeFePtCPMT2edjg+uB7USjfc0R6V4f/aK+IvhCCCG7 0+MyW3lrLIr/AOsiHBJRjjdivWbf9oTw347tdVttS0WO78pAyRzIglKtxvXHXB44r5SmuPCiH9zY QOSOC6yMAPxqaxn0hJBLb2cFowH+sjhZSRnA+b+nSuSpQjbmRUX3PoGy1vw1aSSnTY/syShSQxK4 Zeylc8e2K3H8Qw3E4mF8wL4AYzY2/p0HQV4UJV81RkPngkEEnjNaG4Mvyr19QOfyrz3XktGacqOy 8b2j2V3Zw+f9oiuIDcMwcOA7tjClfT8K45mYsOw747VXw7seoKDIBP4VLHyN3UZIB9cHFYzlzDsO fYE25JbOePYZ/wAio281goH7tg2Sx6g9DTpFODnjpj6+tQW7ebHvZj8nU+p71nre5J65oeo2fi+z tfDfiGTyZIyqQzkcxkureYCOoG37tcXLo1zod1LZ30RN2nftJ23j2J/WueNxLbyRuhKPn5SuDgjp Xsul3mneP9MXQtTIttUhw0FwDgoVVvzQk8iuuElNaieh5fdyeXETKDnPGB0NLp8fIlHAAJAx71S1 Kz1DT9Rn03U18ie3Ybh/CQQCGU9Sp9aniE0sbJC2xUXfnscVzyi7lKRIZZWl8w8MucuWAIHbH6VA 7puYLKSDjkZz61Zu4txRZHQOUyOPas2JSQWIXK+nGagovsWX7zHJPylu9RmKQBnJHld//rU6MhyF 8wfLzhu3FCrK42qd6gYxQKxCrwIGYoC23APoParFpZTT7dinL8Bm5x61ZtdP3PudcRjtxXR+dbRG NcYCLwo4+nWqUAsNg05LdY/MPTnHG3NVNQv9jlUBAHUKfbrzWdqeoMu5fMDSDkBf8KyVk+RpJpMu 3YelOTJLU9wztsT5i+Dn2xVU7ZcxM2xepx3qm1x97b8h/hBqxFZyTOqht2SDxwcYrK4xjBGyQWfH y57e1Wre0AUHoeODntTmVrPDFWABxtP8Q9qgLyXILZIkZhiMcfL7e+KBC3gmhB3R7QehORmsZwXY BRkD8a9U0r4YeOvGUYHhzRpdUl27txZIUUDgEPJgN+Ga6CL9mv43SqGbw9BFnu19DjHuBWipyYI8 IGRLhSMAdu1b2kapqWh30WpaTM0FxCVww6MM5KuOcrjivdLX9l74vSE+da6XbDHRr0YOf91DW4v7 KXxPdFSXU9CgP91ppXwPwQfrWsKMkUynby+FvjJZHT9SK6brkSlYyMHYxHyshPUZHK+hrxu3l8Wf D3VjYSQm6i+VvszI09pMqsTkJztYEY4xj0r6N0/9knxpaXMV6vjjT7K4iwYzDaytt/vYIYdfpXe+ LPBmp6Jbx2Pi6aC+t9ShW3TVbRPJj+1Kcqs24nyZGx8j5w3StalBThtqYybPlfxNLp/xKsG1jR4z YeJ9NV5JbCUEfbIf4zbSEY8yMY+Q9V+7k8Vga2LTVfBHgu4sAqXOn2s+nTo2FaVlfeyrnHzKeeee a6HX7fX/AAPraDWhFeaakhaK627G3gZQORyrqMHj7/vXT6ObbX9ObV9N0iDVY7l5BqEFhLuvbWXH l/aJLFgA+AM5Q185U9pSlyyREYyM7wlb6R4n8BfZPEN3NYXOjn7J9ojQNIwiYusb7sYU5wa8x8QR aJahLbS28yVXxO5OT04C8D0rp9aksP8AhGV07Q5ZZrizvJZ9Sgbcs/llY1Q4YKdmAfXB4Nc9a+Ed S1vWdD0fw4f7QPiUhbBzwP8App5uBwU9elRQ0m3Jlwpu5yQwWwf4ePypnI4WnXMc9tPPaXKCKe2l khlUHcBJGxVgCOoyKrBmJHOB/n0r1Iu6NyyrBsIDhu/pSuuG2r0H+FVQDnryO4qVGODycDv60wJt snYfrRtl9P1pglUDBU/lR5y/3aBWP//U5t/F2p35YTa14j1A56I0u0/yqtJIk/zNpWqXee8kjD27 sa2l0q8uCA3iIgN3jhU7fzbj86qzaXAG2za3fzP0xEyLnHrwa7OVEcxQSyBA8nw6AG7zzgkdu+f5 Vb+xamq/utGtIgON0sikfhwP5VoWPhHRr5pd93qM5RRw90yjd1x8uP5U6Twf4fALNZu7DjEkzyZ/ En+lL3SrmS66lEv73+y7cA8E5YD8uKpm/lRi7a9YRk8ZSNSePrW+PDuiW7HytFtWXAO51zk/jVsW FhCUKW9hCAPm/cqPyP8A9ahSRJxMupK+Vm8Tuw6kRRKPy21HHcWj4KajqF1njCrJz+Qrv21HT7QZ +1W6KOOAAP0rOn8T6Q8y/wDExgRVHO1lDMfagdzmBDZSKWGnatcH3BA9O9WI7LjdF4YmkUjgzSbf 5mtqLxfoMeS1y879gMnH5UT+LNIuVCi1u5QOgVHIP0ouIxvs17GMpoFjC3/TScNj+YqeO11uRggg 0u3DAsAAScdOwqR9cgf/AFGi3nHU7cfoaUa/qaEPHo0xkJHzPhcY4+7QVYsDS9fGDLf2kCN2iRsE fpn6YxSvpl+eBqgHoI4AMfTk/wA6pyat4jnbzF02Nc85aQD27U5b/wAUOAFitYs9CTmkFhz6FcSD M2pXjgjHyYQVC3hiCaFpWu711Tt5pUGqlxJ4mbPm6nZWyeqrz+bcVl3AusZm8URqR12Kn+NMhowv E+mWumQR3NjG4l3YEjuWP/Ac4qlpMiarNHBdO1tJnBkH3cY71DrZhdFP9r/bZYuQrsNv6Vy1nrel i4Ec0whkH91h3NPQhxPXpoIoJESO7tp1VQADgdOOauwWtrMNwSE+42n/ACK8qaA3Molt5SydQRg0 5GnVyqTOpz24qboXIesyhbWIkCKIDnJC+navNdR1yOGWVbq7MySA/LEAMj0FVrlrhotpumKDu39M 1zN3c6BZRNNqd6rSA4CIN8n4KufwouilArJe6vrF2mmaXGLWBuXWJeAvcyMccY6/Wr0V9EJlWwkg +zWrFVnkXcWbGGdR6E5A9qxItSl1JZrOysLi20+XaJByss+Ogcr0T2H41v6XaiJFWLSAioAAX7Y9 v8BUuRpGNjooddnRFIuo8JwBFHg9OBwPwrUh16/YBA0rkjqEHp3qjH9tTB+yW8QHQjHNWlvLpeHn iQnsqr/U1NyjWi1TXZypSA4iyFDMFxnuKkW48QcEx2yNgBWkJbt7VkGVGA86/fP91MAfpT1OnNjz JLiZu2M/piqQmjcZ9d2AS3tsgznAHT9RUUhusDfr3lt0xEiLj8cn+VZyLp4YFdNlnI7sCf8AP5VM NowYdGQ845GMfhmgixcml05lAfVpWCgDardTj7x2is95dCJ/eXt8+OP9Y+D9BtxVpG1CNgYdNig9 Tx/Kpd+vsCVS3UA9Tg/0qkFjPJ0YgFYLy4z0GGxxx2qq40ckqdLuFJ7ndx+daznWsfvrq3hb/dqt L9pHzXGqx7QOyikxpGNcR6ao3w2E3uOefzIrNlh04Af6BMc/wlscfhW2wUnm/UrjOdo7/jUckQ2D y70qB3BAH6CouWZHlWar+60V/bLk0ipcKCYdFhQN/FIu79TV6SOMAb7wknjAZjn8qcI7PBDGSZu4 AZqQFVLfU5ANllaRL6hVx/Op0g1NTtN3bW5JwFTYuPrimi3t937q0nYnsIyf61ahikI/0excYIOW Cp/U0wI/MwwWfUdw6ERc4x9BU8VvpztuX7TOfUBvyq0BqEPK20cJbu0gz/KraQa+yDE0MaA5P3j+ gFMgZbQWiEumizyEd5MH+ZrXSeaNGFvoUQ+pjU/1rOjtNRlZVfVDGrkDEMeeavPpMgZ4ZtRupDGc NtCr2+vFaJiLMI1eQbl061hAB5Z1J/HAH8qslvEAiG25tLcDjq3H6AVmpo+ny7kEt9KB0JcqvT6U q6Lp/wDq1he8P91pDgfjxTuItPLrJysusxgDqYkzjj61TaOA4a98RyY6YAQfzq4mi6fHEZjYWfyH G1m3t+uKZPDBCVe1hsoAmCylPMY/TjFVdAVm/wCEdUEza3cMOnytgZ9Pl4qtu8PoT/pF9MP7o3nP bstaX9oybAkKRiQgjzTEoxk9AuP161It3ewwxFJkkuFbcxdQAoA4x7UgKatoIVdmi3sw55bzcHn3 20JDYFv9F8KSuSeB5ZJ/VjWnPqVzJCcTEzuQXLEYHHRcGo1v4IHjLSsHRsgmXr9VzRcBBHdJ8sXh 1LYj+95aEfXn9KtJPryKCtjZQqeAGdATj/gBrJkvbCR5vPuY2jY/ImSCrHnJ5ptxr2jCH7OtxHGF AUFeWGOfl+ppXKsa8V5r8/Iu9NtUUjJd2446/KB/KoTe6oX512yIJOPKiklz69SKxx4j02NXVJQq vgkFPb2Bpk/jWx8vytpKf7C49uBtFHOh8h1CWV5cQGV/EIjA6iOAg/gpJNRR6Z5wdptavAiDkmJE BI6Abv6GuK/4Tqyt4wsRdNvYkCqU/jy2uAPMUSbenmONv6VDmLlO5GkaVJLHD52p3UkrY+WRYUAP OWx2p50XREEm6G4kEfyjfdyNnHTpj+Vedf8ACfBZD9nhjd8bTtYkgYyQdueKzx4/mLbYbVFB/iVZ CP5VSkHKemjSNJ4Y6baEkhQJJnPBGc4zVgw6dZgL/Zdg2OAUyw/WvIv+Ew1ORv3FtI3Bxsgck88j pTxrvi2XAisbwfWHH86fMg5T2IXsCb2itrG12KAALcMWJPrz/KrP9sS+YUa5jt4sY3wRReZ7A5xx Xigm8bzjm0u8H1aNFH60qWXjCU8W5j9fMuE+n8OcUuYVj2Q6vOsUUK6pKywrtVQiDb7L1xn6VDLr E6qUBlO9eCzkY/SvHJfD/ji7yp+yx98tM5GPwFJ/wh3igqvmajYR56ARySfk3FMR6n/asIK+Zcq2 B84knIz9KrXGtWuxwt3CgfoTKWZR6A5rzQeC9WQfvdXtkX/ZhPH4MwpT4Sx/r/EDHPXYqJ+XJqR2 O1bXLQKBHLakdCz5fOO/emR+KpbdZPs97FFv+UlYv4fTkVw8nhnSekusXbsO4lQfy/pUSeHtA+Yf 2hJMcdJJW/oKAsdfdeLLyaNYpdWKqvKgKe3THy1mHXQGMn9tTg8AMoI4B9MjH5Vz0mkeG4sMIopj 0yWkY8fgP5VZjsPDQXK2ik9MeUTj/voipCxo3HiSJ2LXWp3c+Tjcz9vrn9KypNd0piu2SWRv7xkH Y9+pqYReGIcb4Cjc4CxRr0/3s0gn0MAiC0lY/wB4PGucewT+tAys3iLRvlJgZ8Y583IyP+A0o8Tw MmLe3zt+XAEjHp/s4/lVuOay3Bo9IZ1x94ytj8QqrWraiaRgIbFEBGQSZD09if6/4UAY0WvahNgw 6ZIuCPmEbfT+JhWkLvxW4Jg0qWMHo2IlJHv85P6VvW6SsC0rxwhRztjZsfQMx/lU8bW2Pm1duOu1 YB+HAB/WhRBs5xU8czHBt/KJ7mSIfToCaU6F40nGJJ0QN/ekbA/75H8q6URaYWG/VL1x6KcfoM/z qcxaI+VP21/cySrn+Q/Kq9mK5yCeE/ERO2TUYR/u+YcfTJFLJ4N1AYWfWIkHXmMn/wBDfH6V1wg0 UrmPSLq4YdTIZGx7/MRThFaxY26AvI43RbR7cs1P2fmFzh38I20Q3z6ySD/dihX8qYPDuhoAZNZm IHXEiAf+OAivSH+1wnbb2FrvwMLgAgEVZS31t1LJbQIQNx2gDA/756VPKFzzlNE8K42/abm6J6ES TEfgFXFXIdG0azLPY2k+6SN4ySkrZUgccnoa7iaPXRtDMigjIAfJx7AYqi8OpSbt1wqKo4Ixux3H J/lVKIXOJbR9Fj+VtMlGzj/Vlv1ZqozW2hxNt+wyx9+UiQdPxIruktbpcol2ZcDcQwXH8iaYdP1V 3/5Zgoudu3KY9cKKGI4ZZNKKgJpLyH3cc/klWo7qEDEGhDHvJIf0VQK7W58L67bqpeby3cI+wgpt WT7vcdfpVWTR7+3A83VQFODsDgHkf7RP8qhMepzqSagTmPSoQvTBEp/QkVL5Wryrjy7e39dka8fU sTWtLaWQUNLqgjQ9VZ0Jz7BRUsFjoqiMHUIFR9zHDnKkcA4RcmnYLmXG12iLv1Lyx/sCMMfyBNXC qyR/vtTuXUdkbDH/AL4ANac7eG7WUiG4mki6CQh2OSPvKG/qauR3uhmOP+z/AD227QXeFefXgE04 sRzhtNJnUCUXk+eQGedic/jTl0HRAokOiyMewfOT9dxrpJ71/sscNhFOrn/WFlA78bSKhe5uAgm8 sxyY24duCPXmruBlwaVp8eTbaGqEdSQmB7E81OtrPF9zSLONeSWyuRj1Ajz+tT/bHAZZLi1iwAQS ytz6gFhST6vpwAH2u1iXaFfL7w3OSdvX8qVxcpNLFqlvM1pPb29pKgBMbE5UEZGRgdRzQkOsH5fP t0LDIwjHge+arzeKLOXzPLuod8pyWETscD0ODWe3iSwWQyNNK8h4ysDAcYHGQPSi4rGsbO/ZJpZr lJBEQCFTGMj35pr2NzGwUXcoZwCAI1HUcc49qxz4ks8yPm6YS7VfCBAQP+BAelQ/8JZaxP50aTCU KoJeVU+4MLn5j0pXKOs03w/d6wJGtLqeZI4zLIwYJHtQEgFgRySmOBVGPT4JSB5k8wCAhmnPy5Gc Hnn2rjX8aWdojQR7I4idxU3HPTjOF9+PxrOfx3ZRjaHs1BGGTMh5AxkAbRjFSmKx6c2iWcRBLxl+ 4aYntjvT7TTdKdvJk8iMxqZMszYGCuQMZz3ryZviDACPKWJic/6u3kY/zpv/AAmmpSH9zb3Dg84W z7dwCwP86vQVj1OZNPh8pU8tpJPvFYyVTBxkhgPw4xT5Dp0BeKM71jLBHVP9aDjBIyNvpivMo/Ef iq5A+z6ZqPzDOVhWOnG68b3DD/iX3anph51X88MKloo9YnmsvlFpGHG/5m8ra20jHy9elNub61j+ zw21pI8dqMCeTCPJnqGAAGOwPXFeTyaR45uT8tou3HWa8P48Cof+EY8Zv/Bp8YPcu8mP0NCHY9Pu NQ06WVTFD5MYwxR5x94HPXP3abJrtgRILlbaSP5iEMv3WbnPy+leYjwf4pLKkmrWMGeojtyx/DOK vx+A75wFuvEE4YfwR28aimFjpbjXpHE228ghjmOMICcK3UbgCfyquviUoSYbqOMcEJFExVSo7DB6 1jL4BtjlbjVpgfby0P44BqYfD3Qtubq6urpe4+0//EgVNwsW7vxPcXqmO4uZAsmSQkEaE5/Ks46y sKDbdXSADGN6qPwrXj8CeBELRHTg7DgmSabAPTk7gKryeFPB6o32bTYoyP4iWdfw3E0iuUx5PE1u kTxtfTbH4dXuPlOPVRWHJ4h0YEyMwmJ6sZpG/LbXTPpXh234XT0bH92OMfqcmn/Z9OyPslhswOm5 B+gSlYOU5AeItK4aO2DgkfdRvb/CmDxFGwxBZYxwB5BOK6icabAc3GnyMx/iydv4YUVB9ssOPI0v K9xmRv04phyo5wa3qOf3NlPgdB5KqDn3OKsG/wDEIjEv2WZI36fOgzzjoM10aTtKB5WhGY+yOMD8 T/Wr9va6zdYFnpf2YryQ6ouP++s1AcqODkvPEbfNHE6BexlBH4gURWviK8Uy5to2U4IZXY+2B/8A Wr0ybRvG0isn2Z2hAB4MYBz6dKzv7J8TwR75XMAAyH81Bj06ZNHMWkcWdE8TuuWcOvokLHH4ZqxH 4X1J0U3GpvExB/drGq4577jXRnTtRlO+51GM7Rn5p2/HhetYt3p9hEvmyTCVc9VVyadx2Mt/DepY 2famIz13xxjH/ATUB8Nj/lpqQVR18y5/9lH+NbDS6Pb25tkEpEnLBVA9+pPWqSzeHxt/0a4MY4O4 qOPXv/OlzBYrHwzaKFaa9jZT0wzHNWk8M6OxLNdxxjHQDdn6Emnvf6CkoUQT3EI4GXAYfTFW7WfR pnIi0a4m24ztfPX1AU4pXCxAmj+HY8JLMNvvHyMfjU9tB4bgkGUaQKc427fp0BreMujI3lx+HWcA ZPmSSHH1BUCpkm1F1L2fheHYpIBMcvGDjkg4/GkPlOeM2jvKfK03cTwAzkDnp2rWhtlm2CLSrSM5 xiUyHtx3Aragt/Gd3E32XRre2TgbljBxnv8AOc1Mun+LVQrdSW9vtIGGkhXp3AOf51LAoz6TdG2B MUMVyH5WMHgY/GsaF5Y3KJklMg55xg12iNNaRO11cLPIuCDHICx/BayNQ0+FFW8sp0kaZt3kjO4A jJPSuHE0E1dFJmOryySfMcEfNkDGfzqZXbkLnC9vSq2JjGZihCA49P54NWPMWNSobbjrkH/CvN1W 5SJPTcef05FVWWRVZVUAIwII/wBr/JpRMW3HYdo6/wD1q2dDMVzeSWkvC3ltLBE56JMRmI4/38U7 jMSbjleccgnnHrU9rPcQXCTWcux4huB6fN128dqevkbCWB3jov8Adx2qu5j+WWPgg8444oTsJo9m iGn/ABI097SY/Zdct4XeB2x/rEQHb7qwUDFebS21xpUktlcQ+TPbHY0bdAF4AX2qppeovp1wt3bn EyOGDZ5BB/wr2yey0/4oaI9za7bTxLYRPMGPHmgspMTDjI4611RSmjJbni1zKrKikDzM88jjnpVY AknjaG7UTW09tLJaXUXlz27eW6nqrKcH8OOPapUdLZ+fnb0xXK+xomWorPIJfhfXjitCKaKEDaAW 7E9vwrIa9kY7tpUDjNVp5GKfJJx6j/CobKNqW+ZChC7ucAD6Vmz3svmFHA3so/IVUZ2O3LZfqDnG B9KI0RnOH2Z4GW496dybipFmQyblYD5t3p7HNOkVm5XOPQgCowckjGQOAe3pyOK0LK3a4YvLgRgj GfXHWiMebYRWs7Jpn37d23nn19PyrsbHTVRlBkCyMM7vTPNSpBbxxZQhQQDwOKpveJA3mcEH5e+M euMV0RgkBtX9taxRgPIruecpg9BjvWXYaXYDUYk80otzIitj5ihZgC3HtmstbiW4bYkeeoBHA4+t atkXW909kG8C6gBJGMZkAPSr5U2B+i1mlpZQ26aBIosreNFtjH8q7VG1enf1HrXoFhrC3QEUwVZg OSf4vWvBdKvpNOVUhG+DgvH0VsjtjoRXoEE32mBLiBw0YGFJ+8p6YPvXe4KxknqeksiksR0HRSMc n0pLiIl0aJGjVV5BPfArG07UDKiw3Rw/RWbjcB2rcmindUmZjtj46cVjaxpcbvlC/vTkeowR0qve RWl5azWd9AlxbXSeVLFKoeORD94OvTH657ip8kAgDKt1WmnO07l6ClcVz5q8f/D+DSbC5kMLan4a cKreYvmyWWGzsn7vDjIWT7yKeelfFfjn4fX3gqaLX9CunudJBIhuoZWMtrj5kDyJjKBCCj/N78c1 +rhLKPM/4DtIzweoYHrkcEEYxXhvi74d/wBmx3l54btjd6Pcky3ekDDGEj5mltQfvJgsHi/h3ZHt U4xqR5ZIErHw9F8UNWuG02/1a3sdSv7QGKO7mhBk2EjPmbMb887s8VteEfHtt4eF/Y6eIba68q6/ s66UY8qScfcjZsbdx4TOKreOfhhJptkvinwrm70x94nto8t5RU/6yA8fu8YYoeU6fw141GY5ozj5 kdck4yCCOv8A9bp75NeHiMBFaWGmWfsl49w9vJFMLkH5o5EKvknqQeefXoa0bbQZJojNdXMOn5JC rMGDMVOMbR0/EVr2+rXOp2lrZ3UpF5pw8mC4bg/ZpGBMcjDLMsZGVPUA4q94s07WSseoXk9rqC20 C24ltpQ7qqcgyrwefXFYKraXL0CUrM4QxFGMWVcqcZU5HXtQ24L8vGeuR0rS1PTpNGumsp2Uyx7f M2/dGQGAyfYj8ay3+76D2roTuuZFph5Zf5i5yaTyD/fNICuByaXK+pqtBn//1a7IkMkqRKEXZ0UY H6VZs44wQQgzx2FQTf66X/cq1Z9vwrtMjE1G4niEvlSunzfwsR/KuK1G8uweJ5P++j/jXX6r0k/3 q4jUetSUcrcX999odPtMu303tjt2zXQaRHHOqmdRKT13jd/OuVuP+Pp/8+ldboX3E/z61KA7i10v TNwP2KDP/XNf8K24bGyUfLbRDHTCL6fSqVr1Fa8fQ/57VQDJgEACDaPQcdqzJpJA7AOccd/atS47 f57VkTf6xvw/lQBnXksq7drsOOxNYkl1ciHImcHnoxrXvf4fpWDL/qfzoLOSvb69MrZuZe38be3v UUUkjsN7s31JNRXv+tb8P6U6D7woAuxRRmXlFPPcCuwtLS0ZF3QRn6qK5OH/AFtdpZ/cWgDF161t kgGyFF4PRQK+ZPElvB/aMh8pM8fwj0r6i8Qf6gfQ18x+JP8AkIyf57UAc1ot1cpO6JNIq+gYgV3s N5eeSP8ASJO/8Z/xrzvR/wDj5eu7h/1I/GudgVbuaZyN8jN9STWrpUUbCNmRS3PJAz1rFueore0n 7kf4/wA6AOss5ZVVMOw5PQn1qfzZSTl2PJ7n3qrafdT8f51MOp+p/rQBoRojKCyg/UfWugsoIMj9 0n/fIrBh+6v+fWujsuoqgOis4IMN+6T7v90VdkRAiYUDjsKr2fRv901al+4n0qwKxYgpgkden1rA u7i4VGxK457Ma3n6p+P865u8+431oA5qa9vOf9Il/wC+z/jUAuJ2DbpXPHdjTJu9NTo30qiC/p4E m7zAH/3ua6GO0tGK5gjP1Uf4Vz+mfxV1EXVakCT7Habv9RH95f4R/hWraWlp/aAHkR4wf4RVL+P/ AIEtatp/yEfwNQWYS9H+pq5KzCxssEjlun++apr0f6mrU3/HjZfVv/QzQBfid/LPzH/Vt3rat7eD yyvlJjyhxtGOlYcX+rP/AFzauhtvun/riP5VIFW3ggWTiNBhRjAHpWVBPOjlEldVw/AYgdT2rag/ 1n4D+VYEX+tP0f8Amaogvu7/AGyD5jxApHPf1rCvbm4VbjbM4y3OGNbT/wDH5D/17rXPX33Z/rVg PgmmEJxIwz15NaEgAgyOp6/nWVD/AKmtWX/j3H+e9UgKcnBXHH0rKuXfyx8x79/etWXqtZFz/qx+ P86kDNnmm2f6xu3c1zM1zcZP75+38RroZ/uflXMTdT+FUA0yysxVnYjI4JPpUj/fRe3p2qAffP4f yqdv9YtMChefJFIE+Ube3FUWllSBdrsvy9iRV6+/1Un+7WbJ/qF/3agDmXurkk5mfqf4j61fs5ZT Ly7H5fU+tZLdT9T/ADrSsv8AW/h/Wgs3bK2t5b1vNhR+f4lB7+9en6fpOllVY2NuTgc+Umf5V5vp /wDx+N9f616vp3+rX6VSINMW1vbMn2aFIeT9xQvb2q1F2PtUc3VPqf5VJF2+lJACs2cbjip2+4T3 5quvWrDf6s/jVAQPxFkcH1Fc1d3FwuCsrgjOMMfSulk/1NcpedB+P8qCThH1LUXQb7ydvmA5kY8e nWrtnNNcFPtEjS/75LevrWL/AAD/AHxWrpvVP8+tUSzsNNsbGTb5ltE/P8SKf6V1UWnaemNlpCvH aNR/Suf0r+H611idB9KCjHMMIHEa/kKzLxFSJ9iheOwxWwelZN9/qn+lAHIyfNMA3I44NdBHZ2jR KWt4yeOqD/Cuff8A14/Cupi/1S/hWRRe0+xsvLP+jRfe/uL/AIVTuf3YPl/Jyfu8dq1tP/1R/wB6 sm87/U/yoAy3nmMxQyMVO3jJx0HaqFxJIlwiK7Ku5uASB930q0//AB8/98/+giqNz/x9p/vN/wCg 0Aa9ooKx5AP3f5V6DFa2wacCFMKibflHHI6V5/Z/dj/4D/IV6NH9+4/3E/mKqImbmpwQwXEiwRrG PLU4QADlc9qoQM20cnoK1NY/4+ZP+uS/+g1k2/3R9BViG3s00baf5cjJvmw20kZGeh9arTXE41Uq JXA+XjccfeqTUPvab/13/rVaf/kLH/gP/oVADPEMssms3vmOz/vIBySeNq8U+UAWaY76gEP+76fT 2qHXv+Q1ff8AXWD/ANBWppf+POP/ALCQqQJ9R/dajZCL5Btf7vH8R9Ky7uWVb7SQrsB5j9Cf7xrU 1X/kI2X+6/8A6Eax7z/j/wBJ/wCuj/8AoRqkBzM93dJd3uyeReJOjEfxGsa6vLwfZCLiTOP759Pr Wjc/8fd79JP/AEI1i3fS0+n9KmQGKbq5ut0lzM8znALOxY8Ngcn0rT05Ea+kRlBUE4BHFYsP3D/n +KtzTf8AkIS/U1iUdRBbW+3/AFKdT/CK2ILa3KrmFPvf3RWbB938TWzb9F/3q1WwGfrH+iq5tv3J HTy/l/lXm13quqeQx+23Gf8Aro3+Nek6/wDdkrye7/492qIgNiubmdczzPL/AL7Fv51aihhZ03Rq fqB61nWn3BWtD99P8960AZdxxxhRGgQewxUJY705PAqzffw1VP31+lAHGape3iSuEuJVA9HYf1rk Lu/vmJzcynju7f4102rf62SuOuup+goJNa0kkkQeY5fp9457V6B4bsbK4ulWe2ilHHDorfzFeeWP 3B+H8q9N8K/8fa/hQUet2mh6Iqrt021HHaFP8K2ItL0xZFVbOAAdAI1/wqO16L9K0o/9aKlAVrqO OCIeQgi/3Bt/lUSs3lIcnPP8zVi//wBUKrL/AKlPx/maoAlkkx99vzNOiJx1NRS9Kki6UAWrYBi+ 4A/L3qqxKF9h29OnFW7Xq/8Au1Tk6v8AhQBnIxZ8Ek/WrRVQRgCqkf36uN1FAFUdD9atui/ZJH2j cImwcc/nVTsfrV1/+PKX/rk1QBh/wRN3OMn8DWRbszKMkngda1/+WcP4fyNY9t0H0FBZ2+i28EkL +ZEj9fvKDXQabp2n+YP9Eh/79r/hWJoX+pf8a6fTf9YKANSO1tlbiFBjphRXP6lNLFKwikZB/skj t7V06ferlNW/1rf57UAea6pqmph5FF7OB6CRvb3ribnUdQZ3DXcxHvI3p9a6jVf9ZJ/n0ri7j/WP /ntUAX7a6uftER8588fxH3quskheQlyeT1PuaW2/4+Ivw/rUafef6n+dSUWYWbziMnGP6VpW6Kyt uUHnuKy4f9cfoP5VrW33W+tUM0dPtbZmk3Qo3zd1HpXb/Y7QWSYgj+7/AHR/hXHab96T/e/pXc/8 uSf7tSBl6ZbW/wBpl/cpxC2PlHpV2yvr2ziuRaXMtuNsf+rdl7D0IqDTP+Pqb/ri38qZH/q7n/dj /kKAOZ8a6vq3l3H+n3H+pP8Ay1f1HvVfUNQv5obZZrqaQbI+Gdj2X1NVvGv+ruP+uJ/mKZef6q1/ 3Y/5LUFI5q0kk8u5G9sZfuez4H5V1VrbwfZpZPKTeIo8NtGeo71yVp9y4+sn/oddlaf8ek3/AFxj /mKCTV08DL8D71X7j5PLK/KcRdOPSqOn9X/3v8avXXSP6Rf0pT+EpGPq4H22Ze23OPxqoeY+auav /wAf03+7/WqX/LOvFxHxFIiH3mHbitS1+W5tscfOOlZY+834VqW3/Hzb/wC+P6Vmxle+AGoX4HAF xNj/AL+GqDdAO1X77/kI6h/18Tf+jDVBu1IBp++v0rs/BE0sevJ5cjJk4O0kcZHFcYf9Yv0rrvBf /Iej/wB7+oregZSO5+LUccfi+1MaKhe3YtgAZPzdfWvKo+qf7o/lXrHxd/5G6y/69m/9mryePqn+ 6P5VNXdlRFTv9atlF3j5R+VVE7/Wrh++KxZZWKj5uB0NVx9yrJ/i+hqsv3KEQXMDcvHp/KtuHhAB 71i/xL+H8q2ofuD8a3pAWh91v9wfyrL6vHmtQfdb/cH8qy/4461A1sBIPl+X6cUmn8avYY/57Q/+ jBSt/qKbp/8AyGLD/rtD/wCjBTjuDPtK5+SFAnyjHbjtXU+CWLXMiMSVa3JIPQ/hXLXf+qT6f0rq PBH/AB9v/wBe5r1PsnP1O5l+7H7bcV2lvJIdNiy5PPr71xcn3Y/+A12Nt/yDYvr/AFrnqFlrA3n8 P5VNH9wf571Efvn8P5VLH9wf571kUVdS4ljxxx2rOX5JmKfKQUxjjHzVo6n/AK2Ks7/lq/1T/wBC o6os+avEkcdv4+8c2luixW6raTLEgCoJHjbe4Ucbmycnqc818P8Aj6CC28f61DbRpDH9oVtqAKuS FJOBxknmvuPxV/yUbx3/ANcbL/0W1fEPxE/5KJrX/XdP/QVrPE/CJGFoH/IwWa9mlVSPVTnIPsfS reuAJcaJtAXfDMGxxkCU4B9cVU0D/kYbH/rsn9aua7/x8aD/ANcp/wD0aa+fr/EKodHrKJIniWSR Q7ifhmGTxMo6/TivOOw/D+Vek6t/qfE3/Xx/7XWvNuw/D+QqsJ/DCBC/3jTOKe/3jTa6TU//2Q== "
       id="image575"
       x="361.02182"
       y="203.33975" />
    <g
       inkscape:label="Layer 1"
       id="layer1-2"
       transform="matrix(0.13362628,0,0,0.13362628,5.2827804,173.73163)">
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:40px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="242.22574"
         y="45.235691"
         id="text930"><tspan
           sodipodi:role="line"
           id="tspan928"
           x="242.22574"
           y="45.235691">Teensy code</tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:16px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="214.76024"
         y="103.43179"
         id="text934"><tspan
           sodipodi:role="line"
           id="tspan932"
           x="214.76024"
           y="103.43179">SETUP:</tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:16px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:147.702;fill:#000000;fill-opacity:1;stroke:none"
         x="223.76254"
         y="82.929291"
         id="text956"
         transform="translate(-197.79926,5.662286)"><tspan
           x="223.76254"
           y="82.929291"><tspan>Define all ports </tspan></tspan><tspan
           x="223.76254"
           y="102.92929"><tspan>and create </tspan></tspan><tspan
           x="223.76254"
           y="122.92929"><tspan>variables</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964);fill-opacity:1;stroke:#000000;stroke-width:1.03774px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 135.89995,121.05732 5.30501,-10.20866 49.53384,26.21436 4.47727,-9.32251 7.44271,23.68023 -22.873,5.60028 4.66338,-9.58953 z"
         id="path962"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:195.632;fill:#000000;fill-opacity:1;stroke:none"
         x="191.36867"
         y="185.56943"
         id="text4508"
         transform="translate(25.225656,-71.420877)"><tspan
           x="191.36867"
           y="185.56943"><tspan>-Start serial connection with </tspan></tspan><tspan
           x="191.36867"
           y="200.56943"><tspan>OpenMV canmera and with a </tspan></tspan><tspan
           x="191.36867"
           y="215.56943"><tspan>posibly connected computer</tspan></tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:194.868;fill:#000000;fill-opacity:1;stroke:none"
         x="317.71005"
         y="227.06509"
         id="text4512"
         transform="translate(-102.19194,-69.813821)"><tspan
           x="317.71005"
           y="227.06509"><tspan>-Start I2C connection with the </tspan></tspan><tspan
           x="317.71005"
           y="242.06509"><tspan>port multiplexers</tspan></tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:196.078;fill:#000000;fill-opacity:1;stroke:none"
         x="191.36867"
         y="185.56943"
         id="text4508-3"
         transform="translate(211.63662,-40.692483)"><tspan
           x="191.36867"
           y="185.56943"><tspan>-Start serial connection </tspan><tspan>with </tspan></tspan><tspan
           x="191.36867"
           y="200.56943"><tspan>OpenMV canmera </tspan><tspan>and with a </tspan></tspan><tspan
           x="191.36867"
           y="215.56943"><tspan>posibly </tspan><tspan>connected computer</tspan></tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:199.86;fill:#000000;fill-opacity:1;stroke:none"
         x="191.36867"
         y="185.56943"
         id="text4508-3-6"
         transform="translate(211.67585,-72.480656)"><tspan
           x="191.36867"
           y="185.56943"><tspan>-Connect unconected pins to </tspan></tspan><tspan
           x="191.36867"
           y="200.56943"><tspan>ground
</tspan></tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:143.292;fill:#000000;fill-opacity:1;stroke:none"
         x="191.36867"
         y="185.56943"
         id="text4508-3-6-7"
         transform="translate(25.073645,1.7975766)"><tspan
           x="191.36867"
           y="185.56943"><tspan>-Configure IR sensors
</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5);fill-opacity:1;stroke:#000000;stroke-width:1.03774px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 374.0662,202.38131 11.49809,-0.39196 1.4892,56.023 10.31967,-0.67785 -16.97328,18.11228 -16.1109,-17.17486 10.64367,-0.64686 z"
         id="path962-2"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="M 15.696084,144.93231 118.12971,146.77982 157.59656,73.404007 14.486848,72.537784 Z"
         id="path4711" />
      <path
         style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 213.11714,85.098354 1.65738,107.552606 378.8577,2.02656 -2.14221,-101.182476 z"
         id="path4713" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:16px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="359.72479"
         y="293.56763"
         id="text934-7"><tspan
           sodipodi:role="line"
           id="tspan932-0"
           x="359.72479"
           y="293.56763">LOOP</tspan><tspan
           sodipodi:role="line"
           x="359.72479"
           y="313.56763"
           id="tspan5594" /></text>
      <path
         style="fill:none;fill-opacity:1;stroke:#1d0000;stroke-width:1.26264;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1"
         d="M 362.19331,795.64875 C 264.25556,787.28094 181.2982,725.16535 147.18423,634.65746 90.626355,484.60355 189.02767,320.85913 348.43024,299.77461 c 128.94313,-17.05558 250.60008,70.13629 276.50567,198.17242 20.15788,99.62869 -23.2344,202.09332 -109.34472,258.20185 -44.12969,28.75444 -102.68618,43.83269 -153.39788,39.49987 z"
         id="path4745" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:16px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="306.4743"
         y="319.84521"
         id="text5598"><tspan
           sodipodi:role="line"
           id="tspan5596"
           x="306.4743"
           y="319.84521">collect sensor data</tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:8px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="341.14001"
         y="332.21109"
         id="text5602"><tspan
           sodipodi:role="line"
           id="tspan5600"
           x="341.14001"
           y="332.21109">-gather IR sensor data</tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:8px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:101.323;fill:#000000;fill-opacity:1;stroke:none"
         x="349.1778"
         y="338.96719"
         id="text5606"
         transform="translate(-5.5403341,1.5596866)"><tspan
           x="349.1778"
           y="338.96719"><tspan>-select color sensor N </tspan></tspan><tspan
           x="349.1778"
           y="348.96719"><tspan>and gather its extract </tspan></tspan><tspan
           x="349.1778"
           y="358.96719"><tspan>color from data</tspan></tspan></text>
      <path
         style="fill:#000000;fill-opacity:1;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 436.6423,346.76487 c -1.00253,11.34871 -1.11244,23.59463 -14.14641,31.63929 -6.6483,4.23117 -48.25327,8.4906 -75.54617,6.13516 -3.45546,-0.42283 -23.14014,-24.35114 -20.82336,-28.75226 l 11.89849,-7.41811 1.11891,1.79766 1.91891,-5.70473 -5.76198,-0.71384 0.91427,1.66101 -4.23628,2.68765 -10.18068,7.11304 c -5.54855,5.91637 19.68562,33.94817 23.03058,32.65766 30.29955,2.75684 56.2728,1.25897 79.4664,-6.97654 10.62047,-6.02561 14.38613,-18.02335 15.13461,-30.89892 l 0.59151,-5.45439 -8.04061,-1.38313 -0.31977,2.77515 z"
         id="path5610"
         sodipodi:nodetypes="cccccccccccccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="313.88461"
         y="434.13651"
         id="text5666"
         transform="rotate(-8.2465204)"><tspan
           sodipodi:role="line"
           id="tspan5664"
           x="313.88461"
           y="434.13651">6x</tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:8px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:95.7652;fill:#000000;fill-opacity:1;stroke:none"
         x="345.74963"
         y="397.77167"
         id="text5602-9"
         transform="translate(-8.2312746,-1.2253776)"><tspan
           x="345.74963"
           y="397.77167"><tspan>-collect location of the </tspan></tspan><tspan
           x="345.74963"
           y="407.77167"><tspan>goal from openMV</tspan></tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:8px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="340.58344"
         y="414.91052"
         id="text5694"><tspan
           sodipodi:role="line"
           id="tspan5692"
           x="340.58344"
           y="414.91052">-Read compas data</tspan></text>
      <path
         style="fill:none;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 304.84362,309.1661 -1.34763,19.74881 4.5901,51.21316 15.88107,35.76963 c 0,0 24.7881,7.17501 30.00938,8.14532 5.22127,0.9703 36.61382,2.81443 47.36371,1.99807 10.74988,-0.81636 46.60379,-15.3715 47.25116,-17.60341 0.64737,-2.23191 14.76097,-76.40027 14.76097,-76.40027 l -2.68902,-21.12672"
         id="path5696" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:13.3333px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:133.266;fill:#000000;fill-opacity:1;stroke:none"
         x="490.44293"
         y="412.42523"
         id="text5700"
         transform="translate(-1.0919691,38.24416)"><tspan
           x="490.44293"
           y="412.42523"><tspan
             style="font-size:13.3333px">Print all data to </tspan></tspan><tspan
           x="490.44293"
           y="429.09186"><tspan
             style="font-size:13.3333px">serial port for </tspan></tspan><tspan
           x="490.44293"
           y="445.75848"><tspan
             style="font-size:13.3333px">debuging.</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 452.63573,402.4063 5.28963,-5.27368 25.4955,25.95918 4.60324,-4.88811 -0.0505,16.11565 -15.25393,-1.03183 4.7709,-5.01674 z"
         id="path962-2-7"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-9"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 537.17444,496.31666 7.4693,-0.0375 -0.0904,36.38532 6.70994,-0.24523 -11.35682,11.43413 -10.13147,-11.4499 6.91962,-0.21899 z"
         id="path962-2-7-2"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-2"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:13.3333px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:119.339;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792"
         transform="translate(-0.64226502,-9.486363)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Do the color </tspan></tspan><tspan
           x="500.0155"
           y="579.79233"><tspan>sensors see a </tspan></tspan><tspan
           x="500.0155"
           y="596.45895"><tspan>line?</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 496.15066,550.04059 -0.72886,7.43375 -36.18412,-3.82214 -0.44434,6.6997 -10.20889,-12.46977 12.42874,-8.90356 -0.49195,6.90559 z"
         id="path962-2-7-2-9"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-3"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="519.26428"
         y="494.92142"
         id="text5839"
         transform="rotate(5.8898211)"><tspan
           sodipodi:role="line"
           id="tspan5837"
           x="519.26428"
           y="494.92142">YES</tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 527.75341,594.2137 7.29887,1.5869 -7.99682,35.49578 6.60282,1.21909 -13.57059,8.69227 -7.40054,-13.3783 6.80179,1.29028 z"
         id="path962-2-7-2-9-0"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-36"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="-458.63602"
         y="668.10687"
         id="text5886"
         transform="rotate(-74.175348)"><tspan
           sodipodi:role="line"
           id="tspan5884"
           x="-458.63602"
           y="668.10687">NO</tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="385.60623"
         y="545.41431"
         id="text5890"><tspan
           sodipodi:role="line"
           id="tspan5888"
           x="385.60623"
           y="545.41431">Move back.</tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-10);fill-opacity:1;stroke:#000000;stroke-width:1.34273px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 411.18431,529.52966 -13.24816,3.65109 -12.4011,-78.13565 -13.75219,4.34065 15.61048,-29.77882 22.55219,19.53835 -12.19981,3.77476 z"
         id="path962-2-7-2-9-5"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-5"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:13.3333px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:119.339;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4"
         transform="translate(-25.103357,95.314456)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Is the robot in </tspan></tspan><tspan
           x="500.0155"
           y="579.79233"><tspan>posesion of the </tspan></tspan><tspan
           x="500.0155"
           y="596.45895"><tspan>ball?</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 466.42363,645.30113 -3.27234,6.71443 -32.58726,-16.18546 -2.74998,6.12545 -5.22654,-15.24467 14.75156,-4.01724 -2.8663,6.30186 z"
         id="path962-2-7-2-9-52"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="695.32977"
         y="323.95016"
         id="text5839-5"
         transform="rotate(30.124431)"><tspan
           sodipodi:role="line"
           id="tspan5837-4"
           x="695.32977"
           y="323.95016">YES</tspan></text>
      <script
         id="mesh_polyfill-7"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 478.62393,706.82933 5.83974,4.65717 -22.89915,28.27593 5.37878,4.01898 -16.01733,1.77809 -0.70535,-15.2725 5.52559,4.17097 z"
         id="path962-2-7-2-9-0-8"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="-233.98264"
         y="827.42963"
         id="text5886-8"
         transform="rotate(-47.869365)"><tspan
           sodipodi:role="line"
           id="tspan5884-4"
           x="-233.98264"
           y="827.42963">NO</tspan></text>
      <script
         id="mesh_polyfill-31"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:13.3333px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:119.339;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4-4"
         transform="translate(-156.67005,177.86142)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Do we see the </tspan></tspan><tspan
           x="500.0155"
           y="579.79233"><tspan>ball?</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4-9);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 337.90583,745.34621 -0.1976,7.46678 -36.36444,-1.23545 0.0339,6.71433 -11.07102,-11.71105 11.76308,-9.7661 10e-4,6.92308 z"
         id="path962-2-7-2-9-0-8-6"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="371.64801"
         y="710.85425"
         id="text5886-8-6"
         transform="rotate(5.0743235)"><tspan
           sodipodi:role="line"
           id="tspan5884-4-4"
           x="371.64801"
           y="710.85425">NO</tspan></text>
      <script
         id="mesh_polyfill-95"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-0);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 357.00421,723.20907 -6.05938,4.36758 -21.05357,-29.67568 -5.32052,4.09578 2.60695,-15.90347 14.89696,3.43911 -5.50648,4.19618 z"
         id="path962-2-7-2-9-52-7"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="765.39264"
         y="127.9407"
         id="text5839-5-2"
         transform="rotate(54.50602)"><tspan
           sodipodi:role="line"
           id="tspan5837-4-2"
           x="765.39264"
           y="127.9407">YES</tspan></text>
      <script
         id="mesh_polyfill-6"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:40px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="304.20572"
         y="667.4967"
         id="text6230"><tspan
           sodipodi:role="line"
           id="tspan6228"
           x="304.20572"
           y="667.4967" /></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:13.3333px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:119.339;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4-4-1"
         transform="translate(-206.89856,99.912641)"><tspan
           x="500.0155"
           y="563.12573"><tspan>move towards </tspan></tspan><tspan
           x="500.0155"
           y="579.79233"><tspan>the </tspan><tspan>ball</tspan></tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:119.339;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4-4-1-0"
         transform="rotate(46.550189,221.44663,259.81089)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Have we seen the ball </tspan></tspan><tspan
           x="500.0155"
           y="576.45914"><tspan>in the last 10 loops?</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4-9-6);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 185.91288,664.45664 -6.99229,2.6267 -12.53945,-34.15641 -6.20805,2.55803 6.68417,-14.66417 13.47476,7.22343 -6.41383,2.6061 z"
         id="path962-2-7-2-9-0-8-6-9"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="658.91205"
         y="15.163838"
         id="text5886-8-6-1"
         transform="rotate(72.969382)"><tspan
           sodipodi:role="line"
           id="tspan5884-4-4-7"
           x="658.91205"
           y="15.163838">NO</tspan></text>
      <script
         id="mesh_polyfill-71"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:68.1166;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4-4-1-0-1"
         transform="rotate(4.7038079,375.56485,-3791.2835)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Start </tspan></tspan><tspan
           x="500.0155"
           y="576.45914"><tspan>spinning </tspan></tspan><tspan
           x="500.0155"
           y="589.79251"><tspan>whilst </tspan></tspan><tspan
           x="500.0155"
           y="603.12588"><tspan>moving on </tspan></tspan><tspan
           x="500.0155"
           y="616.45926"><tspan>the field</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4-9-6-5);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 151.58376,549.33852 -7.25681,-1.76928 8.88362,-35.28427 -6.5702,-1.38417 13.7841,-8.34952 7.06303,13.55951 -6.76728,-1.46038 z"
         id="path962-2-7-2-9-0-8-6-9-6"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-56"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4-9-6-5-3);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 163.05386,496.28457 -7.25681,-1.76928 8.88362,-35.28427 -6.5702,-1.38417 13.7841,-8.34952 7.06303,13.55951 -6.76728,-1.46038 z"
         id="path962-2-7-2-9-0-8-6-9-6-3"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-90"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4-9-6-5-3-8);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 177.87669,442.41075 -6.08098,-4.33743 21.35044,-29.46281 -5.58647,-3.72489 15.89897,-2.63415 1.52304,15.21272 -5.74116,-3.86883 z"
         id="path962-2-7-2-9-0-8-6-9-6-3-8"
         sodipodi:nodetypes="cccccccc"
         inkscape:transform-center-x="27.666848"
         inkscape:transform-center-y="19.799176" />
      <script
         id="mesh_polyfill-561"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4-9-6-5-3-8-1);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 210.05054,395.90213 -5.60056,-4.94221 24.27883,-27.1005 -5.17195,-4.28187 16.08599,-0.97823 -0.0561,15.28867 -5.31095,-4.44102 z"
         id="path962-2-7-2-9-0-8-6-9-6-3-8-0"
         sodipodi:nodetypes="cccccccc"
         inkscape:transform-center-x="30.097023"
         inkscape:transform-center-y="17.071601" />
      <script
         id="mesh_polyfill-30"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4-9-6-5-3-8-1-4);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 252.21842,358.61516 -4.25592,-6.1383 30.05604,-20.50689 -3.9977,-5.39463 15.85307,2.89762 -3.71121,14.8315 -4.0946,-5.58239 z"
         id="path962-2-7-2-9-0-8-6-9-6-3-8-0-1"
         sodipodi:nodetypes="cccccccc"
         inkscape:transform-center-x="32.715613"
         inkscape:transform-center-y="8.598446" />
      <script
         id="mesh_polyfill-75"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-0-9);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 313.29385,642.25356 -6.26394,4.06878 -19.59061,-30.66116 -5.51277,3.83311 3.37465,-15.75843 14.71277,4.15704 -5.70337,3.92438 z"
         id="path962-2-7-2-9-52-7-7"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-4"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-0-9-1);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 281.81658,591.81425 -5.95228,4.51245 -21.76317,-29.15927 -5.22019,4.2229 2.22264,-15.96172 14.97557,3.07883 -5.40368,4.32776 z"
         id="path962-2-7-2-9-52-7-7-8"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-8"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-0-9-1-3);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 237.17143,544.98566 -6.51529,3.65275 -17.55493,-31.87041 -5.7504,3.46644 4.39239,-15.5056 14.41124,5.10516 -5.94654,3.54513 z"
         id="path962-2-7-2-9-52-7-7-8-3"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-38"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-0-9-1-3-6);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 213.86566,495.58007 -6.85416,2.96852 -14.20952,-33.49609 -6.07425,2.86125 5.95243,-14.97615 13.81475,6.54975 -6.2774,2.91951 z"
         id="path962-2-7-2-9-52-7-7-8-3-7"
         sodipodi:nodetypes="cccccccc"
         inkscape:transform-center-x="-15.661285"
         inkscape:transform-center-y="31.897104" />
      <script
         id="mesh_polyfill-76"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:13.3333px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:101.395;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4-5"
         transform="translate(-166.49997,23.56864)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Is our front </tspan></tspan><tspan
           x="500.0155"
           y="579.79233"><tspan>aimed towards </tspan></tspan><tspan
           x="500.0155"
           y="596.45895"><tspan>the goal?</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-9);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 330.32013,564.36187 -3.27234,6.71443 -32.58726,-16.18546 -2.74998,6.12545 -5.22654,-15.24467 14.75156,-4.01724 -2.8663,6.30186 z"
         id="path962-2-7-2-9-52-75"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="536.987"
         y="322.25034"
         id="text5839-5-4"
         transform="rotate(30.124431)"><tspan
           sodipodi:role="line"
           id="tspan5837-4-8"
           x="536.987"
           y="322.25034">YES</tspan></text>
      <script
         id="mesh_polyfill-1"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-1-4-2);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 354.81555,569.68819 -6.94074,2.76005 -13.19087,-33.91018 -6.15794,2.67641 6.40228,-14.78942 13.61054,6.9642 -6.36275,2.72848 z"
         id="path962-2-7-2-9-0-8-2"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:12px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="620.94873"
         y="-161.64262"
         id="text5886-8-1"
         transform="rotate(71.872734)"><tspan
           sodipodi:role="line"
           id="tspan5884-4-0"
           x="620.94873"
           y="-161.64262">NO</tspan></text>
      <script
         id="mesh_polyfill-51"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:13.3333px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:101.395;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4-5-1"
         transform="rotate(65.987611,497.52556,363.28629)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Turn towards </tspan></tspan><tspan
           x="500.0155"
           y="579.79233"><tspan>the goal.</tspan></tspan></text>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:13.3333px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:101.395;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4-5-1-0"
         transform="rotate(50.559937,475.20209,245.97224)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Move towards </tspan></tspan><tspan
           x="500.0155"
           y="579.79233"><tspan>the goal.</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-9-8);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 243.3836,456.90739 -7.25651,1.77056 -8.35937,-35.41213 -6.46969,1.79631 8.39196,-13.75833 12.51288,8.7849 -6.67971,1.81949 z"
         id="path962-2-7-2-9-52-75-5"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-86"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-9-8-2);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 307.51857,442.34461 -6.33832,3.9519 -19.01852,-31.01925 -5.58294,3.73019 3.66637,-15.69313 14.63313,4.42923 -5.77516,3.81792 z"
         id="path962-2-7-2-9-52-75-5-6"
         sodipodi:nodetypes="cccccccc"
         inkscape:transform-center-x="48.32508"
         inkscape:transform-center-y="25.465562" />
      <script
         id="mesh_polyfill-29"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-9-8-2-9);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 278.49628,398.26031 -7.30866,-1.54119 7.77449,-35.54512 -6.61033,-1.17775 13.51593,-8.77704 7.48412,13.33171 -6.80973,-1.24765 z"
         id="path962-2-7-2-9-52-75-5-6-0"
         sodipodi:nodetypes="cccccccc"
         inkscape:transform-center-x="49.712915"
         inkscape:transform-center-y="-15.269373" />
      <script
         id="mesh_polyfill-34"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-9-0);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 244.54903,698.61932 -7.46914,-0.0614 0.57216,-36.38092 -6.7126,0.15635 11.50723,-11.28274 9.97897,11.58304 -6.92191,0.12735 z"
         id="path962-2-7-2-9-52-75-3"
         sodipodi:nodetypes="cccccccc" />
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;fill:#000000;fill-opacity:1;stroke:none"
         x="644.72876"
         y="-300.04681"
         id="text5839-5-4-8"
         transform="rotate(94.612744)"><tspan
           sodipodi:role="line"
           id="tspan5837-4-8-0"
           x="644.72876"
           y="-300.04681">YES</tspan></text>
      <script
         id="mesh_polyfill-566"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
      <text
         xml:space="preserve"
         style="font-style:normal;font-weight:normal;font-size:10.6667px;line-height:1.25;font-family:sans-serif;white-space:pre;inline-size:119.339;fill:#000000;fill-opacity:1;stroke:none"
         x="500.0155"
         y="563.12573"
         id="text5792-4-4-1-0-4"
         transform="rotate(11.335767,35.742683,-853.13042)"><tspan
           x="500.0155"
           y="563.12573"><tspan>Continue last </tspan></tspan><tspan
           x="500.0155"
           y="576.45914"><tspan>direction.</tspan></tspan></text>
      <path
         style="fill:url(#meshgradient964-5-6-2-8-7-9-0-0);fill-opacity:1;stroke:#000000;stroke-width:0.673746px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"
         d="m 254.74519,626.82836 -7.46914,-0.0614 0.57216,-36.38092 -6.7126,0.15635 11.50723,-11.28274 9.97897,11.58304 -6.92191,0.12735 z"
         id="path962-2-7-2-9-52-75-3-5"
         sodipodi:nodetypes="cccccccc" />
      <script
         id="mesh_polyfill-69"
         type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
    </g>
  </g>
  <script
     id="script936"
     type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
  <script
     id="mesh_polyfill"
     type="text/javascript">
!function(){const t=&quot;http://www.w3.org/2000/svg&quot;,e=&quot;http://www.w3.org/1999/xlink&quot;,s=&quot;http://www.w3.org/1999/xhtml&quot;,r=2;if(document.createElementNS(t,&quot;meshgradient&quot;).x)return;const n=(t,e,s,r)=&gt;{let n=new x(.5*(e.x+s.x),.5*(e.y+s.y)),o=new x(.5*(t.x+e.x),.5*(t.y+e.y)),i=new x(.5*(s.x+r.x),.5*(s.y+r.y)),a=new x(.5*(n.x+o.x),.5*(n.y+o.y)),h=new x(.5*(n.x+i.x),.5*(n.y+i.y)),l=new x(.5*(a.x+h.x),.5*(a.y+h.y));return[[t,o,a,l],[l,h,i,r]]},o=t=&gt;{let e=t[0].distSquared(t[1]),s=t[2].distSquared(t[3]),r=.25*t[0].distSquared(t[2]),n=.25*t[1].distSquared(t[3]),o=e&gt;s?e:s,i=r&gt;n?r:n;return 18*(o&gt;i?o:i)},i=(t,e)=&gt;Math.sqrt(t.distSquared(e)),a=(t,e)=&gt;t.scale(2/3).add(e.scale(1/3)),h=t=&gt;{let e,s,r,n,o,i,a,h=new g;return t.match(/(\w+\(\s*[^)]+\))+/g).forEach(t=&gt;{let l=t.match(/[\w.-]+/g),d=l.shift();switch(d){case&quot;translate&quot;:2===l.length?e=new g(1,0,0,1,l[0],l[1]):(console.error(&quot;mesh.js: translate does not have 2 arguments!&quot;),e=new g(1,0,0,1,0,0)),h=h.append(e);break;case&quot;scale&quot;:1===l.length?s=new g(l[0],0,0,l[0],0,0):2===l.length?s=new g(l[0],0,0,l[1],0,0):(console.error(&quot;mesh.js: scale does not have 1 or 2 arguments!&quot;),s=new g(1,0,0,1,0,0)),h=h.append(s);break;case&quot;rotate&quot;:if(3===l.length&amp;&amp;(e=new g(1,0,0,1,l[1],l[2]),h=h.append(e)),l[0]){r=l[0]*Math.PI/180;let t=Math.cos(r),e=Math.sin(r);Math.abs(t)&lt;1e-16&amp;&amp;(t=0),Math.abs(e)&lt;1e-16&amp;&amp;(e=0),a=new g(t,e,-e,t,0,0),h=h.append(a)}else console.error(&quot;math.js: No argument to rotate transform!&quot;);3===l.length&amp;&amp;(e=new g(1,0,0,1,-l[1],-l[2]),h=h.append(e));break;case&quot;skewX&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),o=new g(1,0,n,1,0,0),h=h.append(o)):console.error(&quot;math.js: No argument to skewX transform!&quot;);break;case&quot;skewY&quot;:l[0]?(r=l[0]*Math.PI/180,n=Math.tan(r),i=new g(1,n,0,1,0,0),h=h.append(i)):console.error(&quot;math.js: No argument to skewY transform!&quot;);break;case&quot;matrix&quot;:6===l.length?h=h.append(new g(...l)):console.error(&quot;math.js: Incorrect number of arguments for matrix!&quot;);break;default:console.error(&quot;mesh.js: Unhandled transform type: &quot;+d)}}),h},l=t=&gt;{let e=[],s=t.split(/[ ,]+/);for(let t=0,r=s.length-1;t&lt;r;t+=2)e.push(new x(parseFloat(s[t]),parseFloat(s[t+1])));return e},d=(t,e)=&gt;{for(let s in e)t.setAttribute(s,e[s])},c=(t,e,s,r,n)=&gt;{let o,i,a=[0,0,0,0];for(let h=0;h&lt;3;++h)e[h]&lt;t[h]&amp;&amp;e[h]&lt;s[h]||t[h]&lt;e[h]&amp;&amp;s[h]&lt;e[h]?a[h]=0:(a[h]=.5*((e[h]-t[h])/r+(s[h]-e[h])/n),o=Math.abs(3*(e[h]-t[h])/r),i=Math.abs(3*(s[h]-e[h])/n),a[h]&gt;o?a[h]=o:a[h]&gt;i&amp;&amp;(a[h]=i));return a},u=[[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],[-3,3,0,0,-2,-1,0,0,0,0,0,0,0,0,0,0],[2,-2,0,0,1,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0],[0,0,0,0,0,0,0,0,-3,3,0,0,-2,-1,0,0],[0,0,0,0,0,0,0,0,2,-2,0,0,1,1,0,0],[-3,0,3,0,0,0,0,0,-2,0,-1,0,0,0,0,0],[0,0,0,0,-3,0,3,0,0,0,0,0,-2,0,-1,0],[9,-9,-9,9,6,3,-6,-3,6,-6,3,-3,4,2,2,1],[-6,6,6,-6,-3,-3,3,3,-4,4,-2,2,-2,-2,-1,-1],[2,0,-2,0,0,0,0,0,1,0,1,0,0,0,0,0],[0,0,0,0,2,0,-2,0,0,0,0,0,1,0,1,0],[-6,6,6,-6,-4,-2,4,2,-3,3,-3,3,-2,-1,-2,-1],[4,-4,-4,4,2,2,-2,-2,2,-2,2,-2,1,1,1,1]],f=t=&gt;{let e=[];for(let s=0;s&lt;16;++s){e[s]=0;for(let r=0;r&lt;16;++r)e[s]+=u[s][r]*t[r]}return e},p=(t,e,s)=&gt;{const r=e*e,n=s*s,o=e*e*e,i=s*s*s;return t[0]+t[1]*e+t[2]*r+t[3]*o+t[4]*s+t[5]*s*e+t[6]*s*r+t[7]*s*o+t[8]*n+t[9]*n*e+t[10]*n*r+t[11]*n*o+t[12]*i+t[13]*i*e+t[14]*i*r+t[15]*i*o},y=t=&gt;{let e=[],s=[],r=[];for(let s=0;s&lt;4;++s)e[s]=[],e[s][0]=n(t[0][s],t[1][s],t[2][s],t[3][s]),e[s][1]=[],e[s][1].push(...n(...e[s][0][0])),e[s][1].push(...n(...e[s][0][1])),e[s][2]=[],e[s][2].push(...n(...e[s][1][0])),e[s][2].push(...n(...e[s][1][1])),e[s][2].push(...n(...e[s][1][2])),e[s][2].push(...n(...e[s][1][3]));for(let t=0;t&lt;8;++t){s[t]=[];for(let r=0;r&lt;4;++r)s[t][r]=[],s[t][r][0]=n(e[0][2][t][r],e[1][2][t][r],e[2][2][t][r],e[3][2][t][r]),s[t][r][1]=[],s[t][r][1].push(...n(...s[t][r][0][0])),s[t][r][1].push(...n(...s[t][r][0][1])),s[t][r][2]=[],s[t][r][2].push(...n(...s[t][r][1][0])),s[t][r][2].push(...n(...s[t][r][1][1])),s[t][r][2].push(...n(...s[t][r][1][2])),s[t][r][2].push(...n(...s[t][r][1][3]))}for(let t=0;t&lt;8;++t){r[t]=[];for(let e=0;e&lt;8;++e)r[t][e]=[],r[t][e][0]=s[t][0][2][e],r[t][e][1]=s[t][1][2][e],r[t][e][2]=s[t][2][2][e],r[t][e][3]=s[t][3][2][e]}return r};class x{constructor(t,e){this.x=t||0,this.y=e||0}toString(){return`(x=${this.x}, y=${this.y})`}clone(){return new x(this.x,this.y)}add(t){return new x(this.x+t.x,this.y+t.y)}scale(t){return void 0===t.x?new x(this.x*t,this.y*t):new x(this.x*t.x,this.y*t.y)}distSquared(t){let e=this.x-t.x,s=this.y-t.y;return e*e+s*s}transform(t){let e=this.x*t.a+this.y*t.c+t.e,s=this.x*t.b+this.y*t.d+t.f;return new x(e,s)}}class g{constructor(t,e,s,r,n,o){void 0===t?(this.a=1,this.b=0,this.c=0,this.d=1,this.e=0,this.f=0):(this.a=t,this.b=e,this.c=s,this.d=r,this.e=n,this.f=o)}toString(){return`affine: ${this.a} ${this.c} ${this.e} \n       ${this.b} ${this.d} ${this.f}`}append(t){t instanceof g||console.error(&quot;mesh.js: argument to Affine.append is not affine!&quot;);let e=this.a*t.a+this.c*t.b,s=this.b*t.a+this.d*t.b,r=this.a*t.c+this.c*t.d,n=this.b*t.c+this.d*t.d,o=this.a*t.e+this.c*t.f+this.e,i=this.b*t.e+this.d*t.f+this.f;return new g(e,s,r,n,o,i)}}class w{constructor(t,e){this.nodes=t,this.colors=e}paintCurve(t,e){if(o(this.nodes)&gt;r){const s=n(...this.nodes);let r=[[],[]],o=[[],[]];for(let t=0;t&lt;4;++t)r[0][t]=this.colors[0][t],r[1][t]=(this.colors[0][t]+this.colors[1][t])/2,o[0][t]=r[1][t],o[1][t]=this.colors[1][t];let i=new w(s[0],r),a=new w(s[1],o);i.paintCurve(t,e),a.paintCurve(t,e)}else{let s=Math.round(this.nodes[0].x);if(s&gt;=0&amp;&amp;s&lt;e){let r=4*(~~this.nodes[0].y*e+s);t[r]=Math.round(this.colors[0][0]),t[r+1]=Math.round(this.colors[0][1]),t[r+2]=Math.round(this.colors[0][2]),t[r+3]=Math.round(this.colors[0][3])}}}}class m{constructor(t,e){this.nodes=t,this.colors=e}split(){let t=[[],[],[],[]],e=[[],[],[],[]],s=[[[],[]],[[],[]]],r=[[[],[]],[[],[]]];for(let s=0;s&lt;4;++s){const r=n(this.nodes[0][s],this.nodes[1][s],this.nodes[2][s],this.nodes[3][s]);t[0][s]=r[0][0],t[1][s]=r[0][1],t[2][s]=r[0][2],t[3][s]=r[0][3],e[0][s]=r[1][0],e[1][s]=r[1][1],e[2][s]=r[1][2],e[3][s]=r[1][3]}for(let t=0;t&lt;4;++t)s[0][0][t]=this.colors[0][0][t],s[0][1][t]=this.colors[0][1][t],s[1][0][t]=(this.colors[0][0][t]+this.colors[1][0][t])/2,s[1][1][t]=(this.colors[0][1][t]+this.colors[1][1][t])/2,r[0][0][t]=s[1][0][t],r[0][1][t]=s[1][1][t],r[1][0][t]=this.colors[1][0][t],r[1][1][t]=this.colors[1][1][t];return[new m(t,s),new m(e,r)]}paint(t,e){let s,n=!1;for(let t=0;t&lt;4;++t)if((s=o([this.nodes[0][t],this.nodes[1][t],this.nodes[2][t],this.nodes[3][t]]))&gt;r){n=!0;break}if(n){let s=this.split();s[0].paint(t,e),s[1].paint(t,e)}else{new w([...this.nodes[0]],[...this.colors[0]]).paintCurve(t,e)}}}class b{constructor(t){this.readMesh(t),this.type=t.getAttribute(&quot;type&quot;)||&quot;bilinear&quot;}readMesh(t){let e=[[]],s=[[]],r=Number(t.getAttribute(&quot;x&quot;)),n=Number(t.getAttribute(&quot;y&quot;));e[0][0]=new x(r,n);let o=t.children;for(let t=0,r=o.length;t&lt;r;++t){e[3*t+1]=[],e[3*t+2]=[],e[3*t+3]=[],s[t+1]=[];let r=o[t].children;for(let n=0,o=r.length;n&lt;o;++n){let o=r[n].children;for(let r=0,i=o.length;r&lt;i;++r){let i=r;0!==t&amp;&amp;++i;let h,d=o[r].getAttribute(&quot;path&quot;),c=&quot;l&quot;;null!=d&amp;&amp;(c=(h=d.match(/\s*([lLcC])\s*(.*)/))[1]);let u=l(h[2]);switch(c){case&quot;l&quot;:0===i?(e[3*t][3*n+3]=u[0].add(e[3*t][3*n]),e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0].add(e[3*t+3][3*n+3])),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;L&quot;:0===i?(e[3*t][3*n+3]=u[0],e[3*t][3*n+1]=a(e[3*t][3*n],e[3*t][3*n+3]),e[3*t][3*n+2]=a(e[3*t][3*n+3],e[3*t][3*n])):1===i?(e[3*t+3][3*n+3]=u[0],e[3*t+1][3*n+3]=a(e[3*t][3*n+3],e[3*t+3][3*n+3]),e[3*t+2][3*n+3]=a(e[3*t+3][3*n+3],e[3*t][3*n+3])):2===i?(0===n&amp;&amp;(e[3*t+3][3*n+0]=u[0]),e[3*t+3][3*n+1]=a(e[3*t+3][3*n],e[3*t+3][3*n+3]),e[3*t+3][3*n+2]=a(e[3*t+3][3*n+3],e[3*t+3][3*n])):(e[3*t+1][3*n]=a(e[3*t][3*n],e[3*t+3][3*n]),e[3*t+2][3*n]=a(e[3*t+3][3*n],e[3*t][3*n]));break;case&quot;c&quot;:0===i?(e[3*t][3*n+1]=u[0].add(e[3*t][3*n]),e[3*t][3*n+2]=u[1].add(e[3*t][3*n]),e[3*t][3*n+3]=u[2].add(e[3*t][3*n])):1===i?(e[3*t+1][3*n+3]=u[0].add(e[3*t][3*n+3]),e[3*t+2][3*n+3]=u[1].add(e[3*t][3*n+3]),e[3*t+3][3*n+3]=u[2].add(e[3*t][3*n+3])):2===i?(e[3*t+3][3*n+2]=u[0].add(e[3*t+3][3*n+3]),e[3*t+3][3*n+1]=u[1].add(e[3*t+3][3*n+3]),0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2].add(e[3*t+3][3*n+3]))):(e[3*t+2][3*n]=u[0].add(e[3*t+3][3*n]),e[3*t+1][3*n]=u[1].add(e[3*t+3][3*n]));break;case&quot;C&quot;:0===i?(e[3*t][3*n+1]=u[0],e[3*t][3*n+2]=u[1],e[3*t][3*n+3]=u[2]):1===i?(e[3*t+1][3*n+3]=u[0],e[3*t+2][3*n+3]=u[1],e[3*t+3][3*n+3]=u[2]):2===i?(e[3*t+3][3*n+2]=u[0],e[3*t+3][3*n+1]=u[1],0===n&amp;&amp;(e[3*t+3][3*n+0]=u[2])):(e[3*t+2][3*n]=u[0],e[3*t+1][3*n]=u[1]);break;default:console.error(&quot;mesh.js: &quot;+c+&quot; invalid path type.&quot;)}if(0===t&amp;&amp;0===n||r&gt;0){let e=window.getComputedStyle(o[r]).stopColor.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$/i),a=window.getComputedStyle(o[r]).stopOpacity,h=255;a&amp;&amp;(h=Math.floor(255*a)),e&amp;&amp;(0===i?(s[t][n]=[],s[t][n][0]=Math.floor(e[1]),s[t][n][1]=Math.floor(e[2]),s[t][n][2]=Math.floor(e[3]),s[t][n][3]=h):1===i?(s[t][n+1]=[],s[t][n+1][0]=Math.floor(e[1]),s[t][n+1][1]=Math.floor(e[2]),s[t][n+1][2]=Math.floor(e[3]),s[t][n+1][3]=h):2===i?(s[t+1][n+1]=[],s[t+1][n+1][0]=Math.floor(e[1]),s[t+1][n+1][1]=Math.floor(e[2]),s[t+1][n+1][2]=Math.floor(e[3]),s[t+1][n+1][3]=h):3===i&amp;&amp;(s[t+1][n]=[],s[t+1][n][0]=Math.floor(e[1]),s[t+1][n][1]=Math.floor(e[2]),s[t+1][n][2]=Math.floor(e[3]),s[t+1][n][3]=h))}}e[3*t+1][3*n+1]=new x,e[3*t+1][3*n+2]=new x,e[3*t+2][3*n+1]=new x,e[3*t+2][3*n+2]=new x,e[3*t+1][3*n+1].x=(-4*e[3*t][3*n].x+6*(e[3*t][3*n+1].x+e[3*t+1][3*n].x)+-2*(e[3*t][3*n+3].x+e[3*t+3][3*n].x)+3*(e[3*t+3][3*n+1].x+e[3*t+1][3*n+3].x)+-1*e[3*t+3][3*n+3].x)/9,e[3*t+1][3*n+2].x=(-4*e[3*t][3*n+3].x+6*(e[3*t][3*n+2].x+e[3*t+1][3*n+3].x)+-2*(e[3*t][3*n].x+e[3*t+3][3*n+3].x)+3*(e[3*t+3][3*n+2].x+e[3*t+1][3*n].x)+-1*e[3*t+3][3*n].x)/9,e[3*t+2][3*n+1].x=(-4*e[3*t+3][3*n].x+6*(e[3*t+3][3*n+1].x+e[3*t+2][3*n].x)+-2*(e[3*t+3][3*n+3].x+e[3*t][3*n].x)+3*(e[3*t][3*n+1].x+e[3*t+2][3*n+3].x)+-1*e[3*t][3*n+3].x)/9,e[3*t+2][3*n+2].x=(-4*e[3*t+3][3*n+3].x+6*(e[3*t+3][3*n+2].x+e[3*t+2][3*n+3].x)+-2*(e[3*t+3][3*n].x+e[3*t][3*n+3].x)+3*(e[3*t][3*n+2].x+e[3*t+2][3*n].x)+-1*e[3*t][3*n].x)/9,e[3*t+1][3*n+1].y=(-4*e[3*t][3*n].y+6*(e[3*t][3*n+1].y+e[3*t+1][3*n].y)+-2*(e[3*t][3*n+3].y+e[3*t+3][3*n].y)+3*(e[3*t+3][3*n+1].y+e[3*t+1][3*n+3].y)+-1*e[3*t+3][3*n+3].y)/9,e[3*t+1][3*n+2].y=(-4*e[3*t][3*n+3].y+6*(e[3*t][3*n+2].y+e[3*t+1][3*n+3].y)+-2*(e[3*t][3*n].y+e[3*t+3][3*n+3].y)+3*(e[3*t+3][3*n+2].y+e[3*t+1][3*n].y)+-1*e[3*t+3][3*n].y)/9,e[3*t+2][3*n+1].y=(-4*e[3*t+3][3*n].y+6*(e[3*t+3][3*n+1].y+e[3*t+2][3*n].y)+-2*(e[3*t+3][3*n+3].y+e[3*t][3*n].y)+3*(e[3*t][3*n+1].y+e[3*t+2][3*n+3].y)+-1*e[3*t][3*n+3].y)/9,e[3*t+2][3*n+2].y=(-4*e[3*t+3][3*n+3].y+6*(e[3*t+3][3*n+2].y+e[3*t+2][3*n+3].y)+-2*(e[3*t+3][3*n].y+e[3*t][3*n+3].y)+3*(e[3*t][3*n+2].y+e[3*t+2][3*n].y)+-1*e[3*t][3*n].y)/9}}this.nodes=e,this.colors=s}paintMesh(t,e){let s=(this.nodes.length-1)/3,r=(this.nodes[0].length-1)/3;if(&quot;bilinear&quot;===this.type||s&lt;2||r&lt;2){let n;for(let o=0;o&lt;s;++o)for(let s=0;s&lt;r;++s){let r=[];for(let t=3*o,e=3*o+4;t&lt;e;++t)r.push(this.nodes[t].slice(3*s,3*s+4));let i=[];i.push(this.colors[o].slice(s,s+2)),i.push(this.colors[o+1].slice(s,s+2)),(n=new m(r,i)).paint(t,e)}}else{let n,o,a,h,l,d,u;const x=s,g=r;s++,r++;let w=new Array(s);for(let t=0;t&lt;s;++t){w[t]=new Array(r);for(let e=0;e&lt;r;++e)w[t][e]=[],w[t][e][0]=this.nodes[3*t][3*e],w[t][e][1]=this.colors[t][e]}for(let t=0;t&lt;s;++t)for(let e=0;e&lt;r;++e)0!==t&amp;&amp;t!==x&amp;&amp;(n=i(w[t-1][e][0],w[t][e][0]),o=i(w[t+1][e][0],w[t][e][0]),w[t][e][2]=c(w[t-1][e][1],w[t][e][1],w[t+1][e][1],n,o)),0!==e&amp;&amp;e!==g&amp;&amp;(n=i(w[t][e-1][0],w[t][e][0]),o=i(w[t][e+1][0],w[t][e][0]),w[t][e][3]=c(w[t][e-1][1],w[t][e][1],w[t][e+1][1],n,o));for(let t=0;t&lt;r;++t){w[0][t][2]=[],w[x][t][2]=[];for(let e=0;e&lt;4;++e)n=i(w[1][t][0],w[0][t][0]),o=i(w[x][t][0],w[x-1][t][0]),w[0][t][2][e]=n&gt;0?2*(w[1][t][1][e]-w[0][t][1][e])/n-w[1][t][2][e]:0,w[x][t][2][e]=o&gt;0?2*(w[x][t][1][e]-w[x-1][t][1][e])/o-w[x-1][t][2][e]:0}for(let t=0;t&lt;s;++t){w[t][0][3]=[],w[t][g][3]=[];for(let e=0;e&lt;4;++e)n=i(w[t][1][0],w[t][0][0]),o=i(w[t][g][0],w[t][g-1][0]),w[t][0][3][e]=n&gt;0?2*(w[t][1][1][e]-w[t][0][1][e])/n-w[t][1][3][e]:0,w[t][g][3][e]=o&gt;0?2*(w[t][g][1][e]-w[t][g-1][1][e])/o-w[t][g-1][3][e]:0}for(let s=0;s&lt;x;++s)for(let r=0;r&lt;g;++r){let n=i(w[s][r][0],w[s+1][r][0]),o=i(w[s][r+1][0],w[s+1][r+1][0]),c=i(w[s][r][0],w[s][r+1][0]),x=i(w[s+1][r][0],w[s+1][r+1][0]),g=[[],[],[],[]];for(let t=0;t&lt;4;++t){(d=[])[0]=w[s][r][1][t],d[1]=w[s+1][r][1][t],d[2]=w[s][r+1][1][t],d[3]=w[s+1][r+1][1][t],d[4]=w[s][r][2][t]*n,d[5]=w[s+1][r][2][t]*n,d[6]=w[s][r+1][2][t]*o,d[7]=w[s+1][r+1][2][t]*o,d[8]=w[s][r][3][t]*c,d[9]=w[s+1][r][3][t]*x,d[10]=w[s][r+1][3][t]*c,d[11]=w[s+1][r+1][3][t]*x,d[12]=0,d[13]=0,d[14]=0,d[15]=0,u=f(d);for(let e=0;e&lt;9;++e){g[t][e]=[];for(let s=0;s&lt;9;++s)g[t][e][s]=p(u,e/8,s/8),g[t][e][s]&gt;255?g[t][e][s]=255:g[t][e][s]&lt;0&amp;&amp;(g[t][e][s]=0)}}h=[];for(let t=3*s,e=3*s+4;t&lt;e;++t)h.push(this.nodes[t].slice(3*r,3*r+4));l=y(h);for(let s=0;s&lt;8;++s)for(let r=0;r&lt;8;++r)(a=new m(l[s][r],[[[g[0][s][r],g[1][s][r],g[2][s][r],g[3][s][r]],[g[0][s][r+1],g[1][s][r+1],g[2][s][r+1],g[3][s][r+1]]],[[g[0][s+1][r],g[1][s+1][r],g[2][s+1][r],g[3][s+1][r]],[g[0][s+1][r+1],g[1][s+1][r+1],g[2][s+1][r+1],g[3][s+1][r+1]]]])).paint(t,e)}}}transform(t){if(t instanceof x)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].add(t);else if(t instanceof g)for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].transform(t)}scale(t){for(let e=0,s=this.nodes.length;e&lt;s;++e)for(let s=0,r=this.nodes[0].length;s&lt;r;++s)this.nodes[e][s]=this.nodes[e][s].scale(t)}}document.querySelectorAll(&quot;rect,circle,ellipse,path,text&quot;).forEach((r,n)=&gt;{let o=r.getAttribute(&quot;id&quot;);o||(o=&quot;patchjs_shape&quot;+n,r.setAttribute(&quot;id&quot;,o));const i=r.style.fill.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/),a=r.style.stroke.match(/^url\(\s*&quot;?\s*#([^\s&quot;]+)&quot;?\s*\)/);if(i&amp;&amp;i[1]){const a=document.getElementById(i[1]);if(a&amp;&amp;&quot;meshgradient&quot;===a.nodeName){const i=r.getBBox();let l=document.createElementNS(s,&quot;canvas&quot;);d(l,{width:i.width,height:i.height});const c=l.getContext(&quot;2d&quot;);let u=c.createImageData(i.width,i.height);const f=new b(a);&quot;objectBoundingBox&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.scale(new x(i.width,i.height));const p=a.getAttribute(&quot;gradientTransform&quot;);null!=p&amp;&amp;f.transform(h(p)),&quot;userSpaceOnUse&quot;===a.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;f.transform(new x(-i.x,-i.y)),f.paintMesh(u.data,l.width),c.putImageData(u,0,0);const y=document.createElementNS(t,&quot;image&quot;);d(y,{width:i.width,height:i.height,x:i.x,y:i.y});let g=l.toDataURL();y.setAttributeNS(e,&quot;xlink:href&quot;,g),r.parentNode.insertBefore(y,r),r.style.fill=&quot;none&quot;;const w=document.createElementNS(t,&quot;use&quot;);w.setAttributeNS(e,&quot;xlink:href&quot;,&quot;#&quot;+o);const m=&quot;patchjs_clip&quot;+n,M=document.createElementNS(t,&quot;clipPath&quot;);M.setAttribute(&quot;id&quot;,m),M.appendChild(w),r.parentElement.insertBefore(M,r),y.setAttribute(&quot;clip-path&quot;,&quot;url(#&quot;+m+&quot;)&quot;),u=null,l=null,g=null}}if(a&amp;&amp;a[1]){const o=document.getElementById(a[1]);if(o&amp;&amp;&quot;meshgradient&quot;===o.nodeName){const i=parseFloat(r.style.strokeWidth.slice(0,-2))*(parseFloat(r.style.strokeMiterlimit)||parseFloat(r.getAttribute(&quot;stroke-miterlimit&quot;))||1),a=r.getBBox(),l=Math.trunc(a.width+i),c=Math.trunc(a.height+i),u=Math.trunc(a.x-i/2),f=Math.trunc(a.y-i/2);let p=document.createElementNS(s,&quot;canvas&quot;);d(p,{width:l,height:c});const y=p.getContext(&quot;2d&quot;);let g=y.createImageData(l,c);const w=new b(o);&quot;objectBoundingBox&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.scale(new x(l,c));const m=o.getAttribute(&quot;gradientTransform&quot;);null!=m&amp;&amp;w.transform(h(m)),&quot;userSpaceOnUse&quot;===o.getAttribute(&quot;gradientUnits&quot;)&amp;&amp;w.transform(new x(-u,-f)),w.paintMesh(g.data,p.width),y.putImageData(g,0,0);const M=document.createElementNS(t,&quot;image&quot;);d(M,{width:l,height:c,x:0,y:0});let S=p.toDataURL();M.setAttributeNS(e,&quot;xlink:href&quot;,S);const k=&quot;pattern_clip&quot;+n,A=document.createElementNS(t,&quot;pattern&quot;);d(A,{id:k,patternUnits:&quot;userSpaceOnUse&quot;,width:l,height:c,x:u,y:f}),A.appendChild(M),o.parentNode.appendChild(A),r.style.stroke=&quot;url(#&quot;+k+&quot;)&quot;,g=null,p=null,S=null}}})}();
</script>
</svg>