20260320
08581400
1.0
FALSE
CreateFeatureclass M:\VICTOR\Revisao_PD_2025\Revisao_PD_2025.gdb\Carac_Restricoes AIE_Proposta Polygon # No Yes "PROJCS["SIRGAS_2000_UTM_Zone_22S",GEOGCS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",-51.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]];-5120900 1900 10000;-100000 10000;-100000 10000;0.001;0.001;0.001;IsHighPrecision" # # # # # "Same as template"
UpdateSchema M:\VICTOR\Revisao_PD_2025\Revisao_PD_2025.gdb\Carac_Restricoes\AIE_Proposta <operationSequence><workflow><AlterField><field_name>OBJECTID</field_name><field_alias>OBJECTID</field_alias></AlterField></workflow><workflow><AlterField><field_name>SHAPE</field_name><field_alias>SHAPE</field_alias></AlterField></workflow></operationSequence>
Rename M:\VICTOR\Revisao_PD_2025\Revisao_PD_2025.gdb\Ajuste_Microzoneamento\AIE_Proposta M:\VICTOR\Revisao_PD_2025\Revisao_PD_2025.gdb\Ajuste_Microzoneamento\AIE_RiosUrbanos_Ajuste FeatureClass
UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><FeatureDataset>Ajuste_Microzoneamento</FeatureDataset><WorkspaceConnectionString>DATABASE=M:\VICTOR\Revisao_PD_2025\Revisao_PD_2025.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>AIE_RiosUrbanos_Ajuste</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>Nome</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Area_km2</field_name><field_type>DOUBLE</field_type><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence>
CalculateField AIE_RiosUrbanos_Ajuste Nome "AIE - Rios Urbanos" Python # Text NO_ENFORCE_DOMAINS
CalculateGeometryAttributes AIE_RiosUrbanos_Ajuste "Area_km2 AREA" # "Square Kilometers" PROJCS["SIRGAS_2000_UTM_Zone_22S",GEOGCS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",-51.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] "Same as input"
UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><FeatureDataset>Ajuste_Microzoneamento</FeatureDataset><WorkspaceConnectionString>DATABASE=M:\VICTOR\Revisao_PD_2025\Revisao_PD_2025.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>AIE_RiosUrbanos_Ajuste</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AlterField><field_name>Area_km2</field_name><new_field_name>Area_m2</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow></operationSequence>
CalculateGeometryAttributes AIE_RiosUrbanos_Ajuste "Area_m2 AREA" # "Square Meters" PROJCS["SIRGAS_2000_UTM_Zone_22S",GEOGCS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",-51.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] "Same as input"
UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><FeatureDataset>Ajuste_Microzoneamento</FeatureDataset><WorkspaceConnectionString>DATABASE=M:\VICTOR\Revisao_PD_2025\Revisao_PD_2025.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>AIE_RiosUrbanos_Ajuste</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AlterField><field_name>Area_m2</field_name><new_field_name>Area_ha</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow></operationSequence>
CalculateGeometryAttributes AIE_RiosUrbanos_Ajuste "Area_ha AREA" # Hectares PROJCS["SIRGAS_2000_UTM_Zone_22S",GEOGCS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",10000000.0],PARAMETER["Central_Meridian",-51.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] "Same as input"
CopyFeatures uso_aie_rios_urb_2026 M:\VICTOR\Revisao_PD_2025\PostgreSQL-192-gisdb(pmsbs).sde\gisdb.pmsbs.B060_SoE_Uso_Cobertura\gisdb.pmsbs.uso_aie_rios_urb_2026 # # # #
gisdb.pmsbs.uso_aie_rios_urb_2026
002
Server=192.168.40.6; Service=sde:postgresql:192.168.40.6; Database=gisdb; User=pmsbs; Version=sde.DEFAULT
ArcSDE Connection
Geographic
GCS_SIRGAS_2000
Angular Unit: Degree (0.017453)
<GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><WKT>GEOGCS["GCS_SIRGAS_2000",DATUM["D_SIRGAS_2000",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4674]]</WKT><XOrigin>-399.99999999999994</XOrigin><YOrigin>-399.99999999999994</YOrigin><XYScale>1000000000.0000001</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>8.9831528411952133e-09</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4674</WKID><LatestWKID>4674</LatestWKID></GeographicCoordinateSystem>
20260824
12004100
20260824
12004100
Microsoft Windows 10 Version 10.0 (Build 26200) ; Esri ArcGIS 13.3.0.52636
uso_aie_rios_urb_2026
Áreas de Interesse Estratégico de Qualificação de Rios Urbanos
AIE-RIU
São Bento do Sul
Plano Diretor 2026
Áreas de Interesse Estratégico (AIE) de Rios Urbanos de São Bento do Sul oriundo dos ajustes na lei do plano diretor e de ordenamento territorial realizado no ano de 2026
Enterprise Geodatabase Feature Class
dataset
EPSG
6.11(9.2.0)
0
Simple
FALSE
0
TRUE
FALSE
gisdb.pmsbs.uso_aie_rios_urb_2026
Feature Class
0
objectid
OBJECTID
OID
4
10
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
nome
Nome
String
255
0
0
area_ha
Área (ha)
Double
8
38
8
shape
SHAPE
Geometry
8
0
0
Feature geometry.
Esri
Coordinates defining the features.
st_area(shape)
st_area(shape)
Double
0
0
0
st_length(shape)
st_length(shape)
Double
0
0
0
20260824
iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO
xAAADsQBlSsOGwAAIABJREFUeJztvVeb60qOLYgIknLptjmuTvVM3Tv2Zf7/D5mXeZip29XV1cdu
l06ejPkWECCDFCVRSsplEqeydqZSSdEFCCwsLMRfv351dAIzxlCWZbRcLskYImsjstaeYlfenKVp
StPZhNI0oyiKqJckfP5hxhqyxtZeLxtZ/ne5WG7cvnOOFssFjcfPfE2xvaTXo17S479/ieFmxX0z
m88pjmKK44jsmm3qe8k5/I8I9xmOY4f7LMscZVkqf2MMH5tszBu2Z0x+3vB7fk/d/jhsK+M/j/y5
LH9WRplzFNn1vzPG8O/rjtWt+3sjx4FXXnr+T23xITeuJye/gCZ4Hded5AJ2jmo/08XhHM5h1Phm
xDlfLBbsuLI0IscLUhwWHIuJDeG/umvJK2OL4b1xFNGgP6D5YsbXubq9fQ1bwYLE9m1wH9XtAzwD
jpUXeY2DaP6hcFTsEcRB1TgEHB/2Y52z0n3C37Lz8fd9uEbwtc758hrJ4OxwTI6sLb+PX/d/v7Jv
xlCaLmsd4aXZwRwWnjjhTSoXw/Lr8rTAk49/w4tHL2ZnzQ3ncT6f0XK5oNHoimwUbXUMuA56z/Z6
PUrihP9+PJFoCAubojh/uJSuKRblhgUpv/PRDJxf0uN/J5MJWbugOI5bWzDYV3xemqX5rhqOcLxT
wb1mxLlhge//OYZchi+JblYcgj8v+N+mc5O/1//9Mk359tdoiR1W8PO6v3VOrrs+YPRv9aSHa0j3
M0szOUcX7qwO4rA0NMZJTV3Kr8nNg4u0JJOJI8N/cFp4yvf7/dIF6Gy7cdq1WNBsNuVFmyx71Nvy
BNWUZDqb+igoZqfS6yUUxTHN5/MiDF6z0DjFqtkuUvvZfMr/YnFL3CN5GKeFnFK1d2WxsHnRY6F6
B6Xhnzqy3HkiFXSrjmaXz6INz9JdI8jckcDx5N87eVhs+TvLgRZS7mXunPghhMUclZ0YNg0nrtcO
KaO78ChrZ4clBy8OHQ5HLxSfg+BCyEksp4T5RfUhtGwLT8UWj+iNmDifVDCLKKbJeMwOR3Ad+So/
BBynBdOpYFe9fo+dld70Rh8ySBODdAUPGTghxX7wPZxbHjGzE0sZs1os5nkkomk/O9Mk4f1qc6Fo
2ikLM4+xgqMNvvFp0aEs3/KO9zE71jQVp4KMpME+Gk1J/cOHX2OccPVv/aGXIlJNkS81m9nJYfEJ
lfuQbeWgfVi77sLpBdEQGj/jZq7m851tNzgOOAM4q36vx2kdDM5jNkvZeSFyjeMkTyV0geC1JO4x
4K5pHr7HNZjD6Tgn6WIi253PPA7lwXizXJQchTgpue7snOKEry/A+XQuwD4D9m2GWEHUV/u7lW8O
Y/qgbpIOhn+Ec4KIR49hl3vfaMTkca8mYHr4e3VcuzqtAjOtxwzD49CghVc7F9XscR1W7qz4zi+i
pOJoysWTrcaRvFw4PJ1t5tOGzrafOnY8UqlDOtfr9dlRaCoEZ4VoB04CDq0AduXSSfRVOBC9DnBw
89mcnRS/FseMf3CCJ5mXXCP/YNIKHEc77AAR2cFhyW0F3IofSnFSW3m8eNOsQjLgnf4Maabb0VFV
DdcirUnRmzqtcnRab6GDkvtgzYH6ewKRX/F3/EKrT43tDitI30r4hX9i72OCO3hqg8soAxbhb/LO
Gpw//5+UuJUOIg4hpoQiG9PMTjmaYhCabxhUkTJKl4IrVg0OZdAfcuTFQH66lPcaiZrg0OCQ9L24
z2X7Gp2tYpD9Xp+/Xquxw38BnPHSjMJ4bAaOb9fP1AfOusxGiwBKldC/FZpK/fsZTtAH4IEypo1e
Qg9Gy7BtmpaBZzNUuZZ5RamzBufOP5lxg8zdnM9deHOgsgWnMp6OGTtCBAbHxaVtpGYbbia55pbS
5ZIm2bNgHv79caw3O9IQK6lfZ2drxgcaK6/D8QBWQPXQrEZZjGX69Y77IQnA/LWfxffH4YOO2k8I
qw+biHAvMeVg4Qsl+WMc7CGtSXj90u3nzoqQZvXy8ye4VPn8YVcSvGaMpz4s+Uk59Od6LfbjcUWi
IVf9+Ibt9Vack3Kf2salLsuKVOlcza3ZO+WmcfTkcbCcnEpBhHRmuHK8DsyDHcJR5Rbk/Qz+ntmJ
qZqw8hecKml1LHTmKLHjEOAMGLPZgci5ybB9OBxQGPBJSSypGbaP6Gmezb1jGZUwQKf7NJ95/pBl
cB7OaBuexOV10+PKFT4LPytoH+6XFk/059yq+OarNl9tODMzDe49rhz6qrCWwyImpCqATmdnq2EN
0uIXEO2amubDwGDw9Fcg+ByNmeGols1nnNODoBlieEIuzMgZEgb5UkrVgv1IirWv88qB9MWMI6ml
pytoGoj/lnBm/eKa4fMkFQRtAfiScK7ixuC3x8Z6SPuEwqB4VXXfalnmOBFneLO3bVqIUHjjNGY8
27/+lG8LOrjy6zHlOlLseTos/5Dgk37E884OK454kStedm4nTEiRC1rMZ3yS1FFoKC3s8CgvbfN7
te3FpdJCgijFg+O7Hl9I8CSaM4eK07tM6Al86VyZwChpXY+rfOtwjEaf7f923QOs7n45t+t3FNND
dqf8bLeSvjUhtEqLz+VcM3ZY7CgQJeCJeYToSg2fh8Wo0cqhcaB9TGkCcBJXVzdb20vQ4Jv1M1qm
iMjmNB6P+UbC66ANKM2gyXHq01GrdMukxyx1bcZl4miW5bynqr2USqCpZGbaLbi8JiuqbidMC83q
S7qWThv9tW8xFsIhqoBNTBcu80kChvW5mEZMuOA9JmE2Y2szUG16FEcJ9XsDdl4gUT49PfE2h8OB
REBr2N+h2gFvjyu1TlqbjNAFkl6fzxmqeb2kL7hai09K5WaFDb0hvtlZca3km+OfkcxjT3XEUXVW
r81i7rk6yT1YZN3nFlWFBuIkDCndPmxkrcYgCgK2xc4rTWmxQLOxkDVDSof25TFe5ttuJO0kipOY
I2C8X9tvnG+vabvtBaRTbcWpAuy6n+s30NqudLbBMqWcnMlDXh9yeWO6D0LatPjUzuqcTZyABzX3
jCzEaQmxEnhWnMXMhwLOpWB+lEQ0p2cyWUy0NLSYL7lFBu8HcK4tNNgdbZLNgfyW79WCHFhEvfnv
AqeV94lWbiBtbH87dpp7OcN10YcinY+zCqNwdV51BZt97UTkJ1O6zEXryPmlG4g00syD7C80qd5J
BQ8OTBjlC5q5jB7pE9Eyong5pCgDLQJRmci/MKjvrQGH74X7WOBruNFWfh80sOvP4XU752j5tZjz
2lfbsNBjpPKlYhk/UIUXqMUojrZ8caboL9zfTsrWzMmHFRGzc7rpEWmgktL2HulCX2Qzmrpnmiye
aZktaGTv6GbwkeKoR5aki/8UtsuNdU7X65imi/QUWYpDpB0Dt9zyPlSywaNL3UFTQOYppiot5Emo
HpMGlMGdGUuJ2F9CRj+Zw1KwUHcblTBNlfD9OSwCbTJmnpjvo2tx65QR2l+eaDz7TN9lf2NG+Sx+
oC/2H5RRRoPsht6bv1KfhkdPO84x2j03W8H2jnTK3K5VyQPsV6i0ovDGWpHJ2Txf40kvYacm1Jzs
kiIspf2HjZXId+QE8M6dgEyqRE1p+pSnBuue91tu4kXlLwLBNKMP0c90ndwxwD4wI7p2H5nDNV4+
0rfkFxqZOxq5dxTR4Xv3zjk9P2enxRQS77gKNd3zMNfizqgYo0ZVslaabZ/nN6RCoZJmeiGN73Kv
nbaBz0vMFIzxIh9Wp3VsQ/4987LDqkSgAnRtGo51mIwodjH17IBpEMC4ItZb6LFaK6R5587Swsxo
ap7oyr2nY9g5RLeXZFUCbZgqnoPjd3Cqnme5j+/K6Ude0wzHK8TobCfeZg75OEPOigAk1tcuoPzJ
HNY6QFCrVKdYNEwpSIVSwLm5sUz4BPDd9v7wtqNhbbrH91VGlJgBhKRpRs+cPh7D8mEI/gnaWZOT
Vo5i8jRR23ZO7bScSJevG9ixzqprUZ0VqD4vuTf4HkshiwMcTsQdWXetwXmKzxW4POZFzuWGfYMz
bDQcrSV2tmXboHxuASKp1BmKjlJCF25PcaOefLFdoGkkE1bx2jyPZp8o2O1Jb/HThgCYt0lP4F1S
BVyss8g0Si9PXiVc178Y6k8fWrYF6pyTyZg1gJil7uf0nSo10uoL0uJ4OaBp/ETOIsq6IySNhza+
aTBxZsdwvbP6aFW7Sdo8j9ZHwE2zkV1wtVxiCCKN3lEdyhiAJxE/SKJEBqG4c6wSBryNTRUIRD4F
xiVAXVtAvFYB0Z+HJwmAdZWGOXWLkAiiAZR01HdXlNKcJubhKDgWh/0YMNJhWa2dzzbNeAE+PGBb
pQF5CWw4D6RoHPEc4WEFpwXHiPQQnR8Q9Vx3yk6eEq7bsZDiH5oqO6xzXFDW1Dl0254+MgF5yqBR
j+WBkQKezzAM7AsfY2oJU+xsND/O5ypbvQusztppmXAQxbYhFMCfoK++Zje08gc4QAbsthsRNooA
AdPGMi+T9d9q0sOTOKwmeX1dKqhgcEhMY8UH35TMNITlkqVg4LSY97EmvcsF+ZZLaSbmyOo8erLU
cqoHz3o4PgB+KLXZt2b7VueaDqHIsC624FqblEfzuZE+2jm2s/I7mDstRFpY0+o4T+qwSiD7HkCg
EExDxixaAQTz4YNOlxxS5hwRl1GSiNKCGl6HB885Vr3+2URVVZMZdHkT01sX0rw44/vqQIvfqCKD
TqzZ5LAwig2VQn9dQ6WUsBXr1JVhXtdLCTZkQlMZNjqNw3phiKykM96Ok0EWMHlSiISwsRnrUs8R
WrpChhkHj/eDusBcKK4GnvnUaYxKZ0rt4cDPqr02HaVTmDqFNvpQ15m1Ij3kGmCSUVCwUawqn3F5
QGB9V4Nz5Y4X0Iliww5MD/BoDisXCcTJbbPEC3G8Xo9POOe9HFElTP5E5IR/BbhHRGZoMh3nU3qQ
Cl7C8Isk6rOaw8yNL0bporPioczDHnDfs2PZbax9G/CKC36n2C/kkrSl5hzTfm3fQXo46A/yfTzK
as0BwpadlVrBGZK2Hh7Z7tt6BLiXpwjjW4sFOyrVO78Esy6mKOvRzD3Tp/g/6J37C8UEDa3VG39B
U5qYR5rRI6W0pB4N6Yo+Ut+N9gJ0O3uBeaJmmnplBW6i9zMikaId4cHjvACj0nXwmYBEsBbOhYm/
ztiZLqSCqBYfjb5w4P4q1a7CjYBoazAYeLAdInRzmkylRIswU9U+L8UiE9HAXEv6mz7Rg/1EV/Se
nZHe9GDCj+melR9w/Og7hFOb2idKaECx6XHbzzY7FNnxzVpFb79ulF4b68IBy83Q01guVvFIuGBS
Eg8ywdRwj2Odu1ULP/HRO9sP+hn8/8ypUtBOpr5IRRFhMLhWZ49ZVQzH0TMDsllENotpQt9obL+x
m+qRRE4TeqQpPfKDoZeNaEA37LQe0NpjJmRdQkO63UrEDYfndg7rgO07pgbT3cNxuWAx8zUD4RcP
aj9MV6bhiCYVc6vysV4SSJyH/F9zO6jDCp/UB3dWfjArvlOBPHw+p39Jr8Q6vkTjCmnUoyh7x31Y
U/ONyGacLoL9/uD+5CjsxtxREg3yqtENfaR79weNsweKXI8SKxXROselVSPYOYGwr9GUY6QZiI5G
2/Uh4ThUEqcEcB/y24pR4Qu/A0RSnZegDvISoqzQDhthacc68vUDnxiRG57zxale8kO39xzTUBUa
LK4oyixldk4zeqKeu6KFmdAtfU8xFc4K/8VuQEN7Q/NoTNPsgWj+jq8JIs/qANuqkGJnx+07ZHXb
AK/Z1SxLZitNp6j+5RN0XgEueViH5c/NoXsBZ7MpDxplFUbM8OvJ7MDXaRIFsfyfW1LMjkk03plc
ig54U1xW/HbobjkKG9t7eur/na9Lf35Do+w9DeOr/MnbYVenM+6dTatTo0qaNVQdwxc+WBT+YC3+
rGhUDn+v/bnFJi9vjRzUYR3Ds2O7HPJy75tUBdtXBz0v40k6WUzLbE6T6J4m5p6WDpFlOfXOJXxc
xFiXdRENzQ3PGRxHD7SgMQ0M5G2kkTa8Xp0d39jBFCi9x5j0l/KPXhvWogJe69vUco6h5xeW8TLh
1IUtPJc6LCS+dNZ0LizG7SvyBMET5lKxqsYgPA0oo9ucDHhD3+VVQ3VU4UxBYF19krI2orPMZpSa
OT2bLwzSgzbBsw19L2XntE4/51CuVf4bguUOJ5hSg1dSJlsuaQE571hezwUx/T2hDuqSr+3BHRY/
9fPWkvaNgfX+gLI05dFYosFuDyK6d04G8DyhPpH7mFMUi46nMgaVs9b96bAmYlrE2HylKT3RkhZM
gcDriekTXFsTCkRTK1KTgvQaPu3D7zODNm+pYsHJanReVyB4jVadSlQ1F4zQAqkSpvxCprNg+G0Q
KUtrUNGOc+l2eB4Wz9OTcPVQJwzOifBFRHOkRgFB9TVcpO2mdMTVY86fqt5p6e8gyXxLPzDR9Ml+
pm/xf3FkOqI7fh0UiLYMD5FCV6u4H1BAYMzZExs5vY+eaRlNOX0dLO8osvHKAgwhhrcYDVpPAkX6
BxZ98XCS85imqiPnHxZZIRpw6Xb4CEtTEpzYHfSfdzWWNl7MeTApxsPjiYMLJoz315UebhI9rA46
Df+G9b9cyuVuvS6I0t7bn+m76K90P/1Gj+ZPGsffaGCuvMrpy0xUNaQY0iRNx8jYRzumMT0iFKQb
913+ANIvffiVoo1KxfM1mvV4FS4tZJF0VD2qg3BcII5qqxqM24ECeOASMauqxa+hmZa12OGgiJjh
DoelSg7nbjgnS5rT0kyp50bNJuMoCVFPZ42YQ7XIEZ773LH5TRgwpF1MV71bSrMZjc03+t3+nd67
v1LPARfb/+ms/J+mypgYwYHobmHm9BD9RqPshiLTlxmNHosJHZ8WC5AWIWI7pVJs26Y4lB5vxmx2
IX7qcUNeRh12P+DRsUyMT615W6/AWR3NYYUiY4eoFvLss8VcwGhuuxFJ2uUSF5XO2hy0RM2YpvRA
luKtDiuXlYZV2NKhI9vl4cD9bllGcZTwLMRs4SizQLYWNIueKGaHEXG1ERFZU5lmXVi7jGuDcwSv
bICeSHtP9/Y3Pi9QWoXyqraY5O/PRez8Ak7huIriwSVaHi36lE6VP52PLkNqguqi+/xP/vGRVWlm
4iuxo9b/tbzaZqAlT1eoE2ZkmQwph3QpJDlm6NOS22dGkI/ZsMtVML2kicQ/rJngEvx9dXv6fnYs
EaD3ARkXE6WOFm5G02hMxo05Aotdj/e2b0d5BQuSN3AWkZEKZHWwxz4YEwD/Id3Qwn3giUEgxfLr
LuG+yJXj5+0Du0H0AQ4Soroyc//cLWyO5n1W/Mk7JCWYuuo19E4Mv2bYBfpRlalUr8mOS1hqmd7A
FAaeFi3qDGFlMJSPPWdDRIG4xaE6tiGFVWdVdzwrN6W2etT8jm9wcNYq46dkEgtueqkP8vuWjkbp
O5rQA6X4G4e+xDFHPAyc8/iAOatCIPrBWDKkbvI50reGRvN9Fk3iBqxKgR7JB/s7FO1pZp4ocRho
W789lU7B5+KLgxSTnPXCDYX04HBg7Hg5O/BOxxcqnF881XsA0THe1dPILCCIvjZ6z1EdVpvOAxd1
Pp/SeDJmEFmE+MqHc6r5hvvGWVvCq7KD8ZHVSjtNgGetpE8+Rag6Pnm6g2xYJihyT2Y6or4BGz4i
F6XcYP2w+EQLO6Vp/MD9jAN3zb2M39PfmAuGDlx+4r9omAdwtYRG7j3jaF/tLzyEY+juNqbNvN/c
8C4R3nQ65Z9VRvucTNnpiKy4tWwuw3vXmQn2P3yfzZVKxEnJ1HKkhGXKyGuw46eEe1QKVX8d0RQu
A4ZFyGtL5mChwZnZ32Fz55lHVmqSEM45ckEU0afrtQsypCaEzip4Q/3rW1JzXeTVkV781AcWpDpO
xjIgHic9ejIp9c2PNMiwvxGNzQN9zv6TsaZBdisaXns+3auRReyAm8Wcfi7NglPDbSZRC6awCIs/
HF5yygW8AqTDufhoat09W+WsOa38sVPW1hs9b3ruir9V2shrsKNHWLtiS3BKANThsIpwfyl5e+bo
ajhksLgu9Tl3p8UVQlTD7J80M8/0aP+kOOvTtfuwmvaoFMia/syw5SLf/gYcI3R+4WCPkKuVf26A
k8GZAtC+wd8B12IhQTDpI3Yqc/sMqJ77F0fmrnSsTStVLCmsi4zDBDitHldSlzSlvpfU2WYhfib3
nUAIpxjjVq2UapWviYxP2LLjcvBdHVUVp5OznPcO+khLcL3L56ydxGHtMr6bS7nsoFKOpERGw/NP
IukbXHcRzj0llEDeMv5zl/1IKS2YxLl0M3YA6AAcOOhaxSVayLqFX8e98t+U04mg0qgOiyPf8H1u
/XaBuvXddek1kBFuTEIPZknT6IlmZOnKvZNNaURgtjstdVb5Ccr3gefC+NR5NyscskAJ4rQQpZmj
4VPVCGlXvbFyVJXx9+J41Qlhu5VBq/5c4rfyeee9Hs4PdN9Djhz5uTwtJN+HdAbfdKhA8U1RN5tQ
Kivn7rAYKHVD+s79N6Y33Js/mAP1QH9KIzdZuqOfPBcKlEpwkYSPtM3qqkklNYbKWqm28uyzlhFh
DemOo8YlzUqLLNR82rjf/F6vnFly0nJUAPiRRAu1wvdFevxPygXrP4BxOC+X7T/mIBYy8jWykgLR
nlNpgmJV5h2eygOFhNr8XvfvZ2xLJ+KceBpOW3b8qTk7FgpZLiYWDAvs3uFgJCDqhl3HxWPt9v75
ju8KTRxQRHfZT3TjvudICzSHT9Hf6Yv5F1ffEIUh2upBXcGDrBvN1RB2dbxTzVBNnayS79Pe563Y
bliKX+dkq1ia8IcKORXdJ3Cz5m7MTHykoQDf4bTgHEF9AL4FVvw2Z37wVh6utBZYmRSH5i+CJ6ow
iuGCSJQ7r/B35w6DXJzDyqP9hliWgqfAqeazGU/QwLN829+gPUEJhXVR2NmZx0uZoInoysX0U/p/
Mh6U+kUJrAvNyT9k/93zkdY7gVKEVYls67hwK9FQwxBrVbmyiKJUojePKkw9NpMvOh6m6WGDEH9z
cFgjus1+ZE2vP+0/6EP2b+y0xvSNq4fX7rtG4btG3+jFa9O0cAFnxdHUsoim2nIimW9JyhuctcAS
ps7+fFUjbIFhLr9qeAKHtfsJK6oqhp9WaJUCX2gTAK0AvXwenb+pk/D/Ih3E8AnQBDKXskY7Bkw8
2j9oap6YdY42lrpFWqUv5IoZ4Vt9isbnCqJvrlC63KmNI2DXS/RWYE1NScJMdsTwENbdRwoVREH+
HyB6hgY0hIwKTene/E4P9g+OrCIXo1+A7s1vnBai0gqQHsoPxuERIPcOb65lMmlVj2q5QLq5njO3
lwX3ucHDbNMDeM1HKgh/yFT4GHYapbu85rrL0yuhXj/lDnX8GbffeEwA5MRq+4dWhYB9XYKt4Eb8
IypvEadvKO1Dkz11c3qyX2hqHznqAPgNkiUwI9zMKPkDsFfb2KKTR1IvV7fInRarn3rBOK1seYe5
8tT30QAGEnMKqJhPta1GfZezTBy9yb6jqXnkdBAs+MzIxCCkijNaMs5lTczy0VBg7dMVR2M4h22Z
Rjph1a/aNtPehxUPF7Pl+vAD2tNQymmkx7+4bUde0GriJZgey0VIc+rNgXQQ2BSY7cul4AMw4BzW
jlZOvobAl24a8WBUF1Kfe/qFGeAzQg/imPpmSAvjJVnohgZ0zc5u07ErN4eH2wa8p32dVlgBZudk
PP6EhVZpAwppEiFADfJqdUxV9TzguOCk8YXoamqhuPrArhCRFdRXofHFabR55ugLERnegIIAyKgv
xZNCZ3VQR1X5XKfP+C1ZyjqhAc8QybcRbuucg668X3Kbw1Iimlcxai/33eN+kZskpuFgyOA7DgBR
V5LErOmOtAmJQximnH+VsLlxKuPQGDyk75b/jRfps8dvnu0nskgPjaOlWzDGB/Y5rNpXGP6s0Qwq
rvoZe1mJAiEpIZwVehFtGsvYqSpL21dfQp5UtCtUQBGNsg80og/BBBpHz/YLOylw2u7cT9zSgwiM
nKUr+lCA+zt+nvb7cQuQsfnk5EMbr70IA1BZVncrVhdGy+H3VSJvLostb6BzNW1q3+iwOB9n9c4F
nyTQCeAw2nAApfL6jsapIVeOwHjH09I/3Wq2pV75NTkuGKKMQXZHcXpNtxhrFktkObdjejafaUxf
eNIznAfSJhjjOGhqDvCQfGL2C6YQK+hbiCYKjgX2/hN9opvoB8zuWf0bv+7aBIG53M8qrEAAYyav
9rMRV1m/mV84Ih24u70+Txc8g+ppRvP0ZZW/nT8fV92Utdo3WZP3XUo6CFsbYcEBzGYzFsJDpYMb
Yq2lp6dHurq6aaUnS5tvOR3ZsVUH+wK9KzVoWItQmfiscEAlKoUimbw8y16yXSws8cPwhEevLKd2
nhDYz65oBqa5meR9dw/2V5qbKV1l7+nW/ZA/ncPWkJfvXLCPWUZ9c0s3llge5qv5T/rgfqYeXZWc
pQ5RaDsZSdyQ3md/ZdCf4XakgNCFwgRwM6M5jSnOyooPTSuAMK3+nTvUYHYgZ5+z6T2lVd0Vh4XF
P5lO+IbGgucSKl6fTWg2n5JzALhlqvL+e1EAvruK+qnTQRqz9HiWTHle7RGTfVRtrMtyWqWbjQOS
7U2xWKDgagGEBmM+SQXbSs3CM8Qr6TIhzdh9Dl51IejPWaBiGtE7rtg9mN8ZU0JLD0c/wTHt/LlN
ogYUKpwfzqADSgzRrfmenrIv9BD9zr2ObnHHVVjcN+v6C0Pi574M9TZshaayxbTg9BpMz7sKG9Sm
hDzfL5GBjFjk+hReLGZcpYPhd6VoaR+gdg8wS5qgU05XRULE0MA71jpTeWRlGV9CeljLo1o3gLPy
OjAutPYAcGZZFjORCmOloXrvamAguRvurzgInxIyj0ocFNMbzMsX+bq/L4H2AR4W/h52Re/IRRnz
uAhUl3xVAAAgAElEQVQwwhy9qOiBlHNdbfEKnRXsGMB6W+YU56qQgy/RQhHIWodVJl1qg6WlgWeN
T8bPtASRM07YWYk0LXr65GnV1ErcoCZcHdz6acZyIXzj+FQVlcNt2JredMultOucu+3G4anr87ti
0PnB/sacrWv3cUWfPa/q1RB4qxSHkCkfOqzcQaiT0i8yrJQ1c2PmQiXZoKSwsK0ncu1hMgXCN0Sr
s8K/IRVCWyF9Cb/g4lm6jj7wFOw0SSkyPe4t1IgpPB91LTWnTq92yUbcGmrDpVndrMxahxVjIgdX
3sInioDc80i0hjjams3YgSS9hPWoEHVtJLWFhpvfSGldQPMtb88cR3dQbhgMhuyomjsfaWVRb33u
UZYu5G036LpDAC/r1v3Izutf9v9m7AY6lDo+C5+AChceTHhy4fuSbIkO3dT9CL6v5dAFDkR/xmfH
JpEUjZu395P+yYmvqs8VBaoH7EDlmobTjtcaKwknNIiGFPflIYdIHXw+jQyVCIrt4GF8FlFVOGC1
qV2un8oN5x8HAp6lWm1KiAvGF8sj82pyMS0D3csUQx8M3d69YywJzgvvhzNpHGkxw7kY/1X3pJd+
rKUfRz9nWgMW2q6Rkt6Ml/DECSOfHJgOI4ewd2zNNlAnA541oncMuv8R/Z169orbW66yDwzU4xrL
AAOU6D1Duzqsoma/YPow0wWuonn8Pjwc4Bydowf3C0+lBu0EmFYo81zlZJX2329XpxiHERMcrB78
pv1d2Xd/HlWRlJVAlkuO0CRSLzuqc7lXsH91kfAmyyOTPQpb52Kq0aZaZvxa9U24H9EeEVaRQucQ
xQlvYL5Y8IRZAN54LxwKnlYM2NuIEu9U1ja9eo6XMKNXowlgVfgMeQ+qSrhZfbvHXpWllzG5D2Wb
evfClIvfW8NIl0W8qnWEFAiN0t+bv7HziEzMC3RmHig2EfWzG56WoxWwdcBuleRZ51BgaEnJjylv
yTGU2BE9R58pjaesjyXM/FXGuS6qQgPLN7FDrz9oddm15SVsdwpTXIUzYKALhA6q1baaFkypJ27P
fbrUEV8aZIRRbg2tQUIx6ZaQYYyhISxnzIhvJLmx8Br1Bhx1IcpC6oYP6fX78ruKMWi+FNKdKCoE
rSS+EjOZTCoLVKRfRb9dnGRTk20WC+LcnNYm23SjyQWVym6Y6rIz8XpI19nHXPrF2ZTubcqUh54Z
kXWFmgWuEx44K1FVsODzz/Ta4/IgESHFauok0RrRlfvIon6pTZnoOqYH3sYNfccaWoj0qqKBGk0x
YdCPtdpm2tUAx109R+G/+r1yz6pVwHM0ZdS/ZauNsPQGAZOcmyyZKlBOvTT8j5eKZaEZuc+RFl7n
CTYzwZqUSR1GaEjv0MDMLGFWUiDqD4alGwoLB04P6R//DZfeRQPIsw13OthzvhnZGgaNvMCUPBi0
udjI5A8Z1g/DOUd6A6G6VFpRkFYg8r02H+gP8w+WsMnTTB50CrmSjB844ZnK+wCBN/prqQtcUytY
iF3m0ZozNMruWEYZo8wm5hFty+y4jI3pGm6UJ/EUygNVHGqX6ybwQvFAClPY0ukOpiGfWjJ5o3m1
15emdEaJuhdmik/qQzhwWBoKi+4UnMk1SKJJPRFe574tl3OaYAiEr9LhSxqRExqPn3JZYzg0dRrT
2Yxfxw2KpzqcI0B+3oavamgZE68rrYJlYvInYnQ0Zn0bJrGG/L9OlSnZLsWyQNiuwIz8JgKlToli
03IEksmI+mU2Z6Z24nAdZIEz504jjWq0hBHoPrKC06tiPCtRYEghQ8rJHKw7Gma3NKePlNLfmR8G
NjxkdOrkUHY11apDVMnNQWucUMinwkP2bJ1VCAW8MBW0DQtb52TKncRZCPHRWCpncmPrfLq6smh4
YXGh5aKL8ieipvBJpY5rPH5mxwYHhgUE4il2Ag4McsfswKaOnp+f6Wp0VWgUVSRhxLvKQl/p5G9g
6qE1Gji2QUlhbp4pM5KilTCpOumXBsZkXp+KATQXgLXAHhWfURO9duIBqeLhykJ7iHqRntsaBUts
XwBp4si31Li8bsfdavQozqtH77O/0L35laVzoOsFFYY2DNd46YeKroMM9Fj3Kdwc07D+YC+KrlxR
ZeVj1cPNr/GaPzsDzSz4DiX1Ah7KI6xv998o8oAt0gi90NPphCZuLLiAl3fBhBphDlt2LldX1+yw
6mbPae8VIiNsC1XEOIl4lLwy5bFtbBM7B9CeGxx92wZA3KyH9KCmcXYP8FydllbGjnmzysSXjHvr
MCAUKlf5QIkgumh6TJq64+2DgaTj5TK/I5sUfKXSIogSemd+oJm9p9hZuqHvRVrYeHzSRvmk4TCS
A7Cvgz90Me13LiwrSnzLfuUJOFI9bOda8D2nwxf8CPfafQgwq1MvzHVpYMh5a8MyVs4oHiSKT69g
lhhqkT9Qy9y7Yxp8isIDyguFxdhpOJAQkyierCKSx/PdZlPfAiMLAd4vSTDNRHWoKg4rwDmsdZxe
IqpiHCoo0cJJ9gcDkZINut/xei3+UJH7bWrqQJHuVpnRhzZJAw2P8sLodUghQ9tqV0elpjeSjQVP
xANF5XLzVK2OHZ/KWOAr+4E1o9AMHGXoObwVAD3NaidMh9tt7FTX5B/SxAt+ls312Nsc9VnQQQqu
XxgF6n0Q0mnOxWnlxNcWUuQVq2wuv/fw8NHf5Z8d/NyQ2N22qa+RBwsePp7pjqpKFItchuAe0nOn
IDeeuADQhWJQNgE46z+Qm3N9GhF5p6QVQ14AfvqJpo/WpsyJSY2kbXhtHYN9ExXgHA37if466FQ9
mk8sh4Jx8CFDfBfjqDdP01zJ0Yelef7sGv6OpT4Nk3f05D7TY/qJoqzHUeDW49hhXzcxs6U44CdE
7zEFZ5OV195qOT/k450Dibj02QGd4ySFHlcTcfE9unOdqxUrKA3FXMUYhMHILpjwCWwJb4CDmkye
uWKkOBY7lR0qKqyq0B9S0hOOUJXeEKYb4rQSDv1gjbCmoErWxPQG1Rv2uDepkVFe6V/oOfp/mEjJ
2A3oHHvshkbFzNSGk99wvtbd/NDLEj30J5ag6QH8bnQk23c4P7e1H+24tzB/4GwZUrGrycOsPBG7
+nDTKCtksh8KIuAHRljVlVdXugPyqPaYjsFtcY7s1HafJfpSU1ydoQ3GZYt7M+b8MLixtT8Pzgqk
PVT18BpA8cZtN7px37Coti6lEP6MZM5N+Sa79qOdmukuHyvDR9HfB4eFRmU+jobRIvdsJr6lBN0G
nsu2i+nDAdtCj9+MJpwe1j1y91HTCD8n/FcNW+NRXSalKIuZ1hB+dE5BCbCcl1jeVlTF8zxhVhz+
klt22n6I5fecd0YyUp5/U+zfCwihx7Bj75kU4qZcAOr1VsGCWCgM1yvOBWA482JsShEoBv6JvvMO
BE+OdUMJTAlo3O3gkFru4kh1XuGxTWTtAG/3/GBUX9nbck41GlBnizYoKE/uyy0L8TvsB/YJ8jM5
GFtJU3Nd9nCgRYP7YP37MFtw7pUmZNZieQf9u/Y9Pk9mhRtXegNLaPvzXT0XYftHmzQHxaN0mnZA
/i+7gfP1VUc3bTTfVMHlCEuwovIbQv4UUxz2vJD5BJcg7A+jCSGPFmxnmViL6kDDD9ihU6eoXBb4
xdEMBToX08jdsWQvMK2Eq4XrIWft3dTuA16IL+xx0145VNMoX9BpcRqrkUglymqSvm1t2oYrCYKN
UrpWjYZ2BKD5HmJAv9zGtC4iD0mJbWmm5RW4upT80hyU89dctf8PvP/4jE3XIEb6pzlqtX1hl/aX
TZbfhEFJlXfOP3by6CpIj0IMolqxKlEbmCzYLMqSJ2rky6VY+MerFApLPaYres+ql2B+IyXERJd1
+6oLqYkSQcjt2kSU1cqtDrUAqRMzD69RCFDgfYPT0nRt/weJRJoaaeELWl3VSGvfdFSjdVWgbbJP
imkqHvhinDMY9PAqzAUPFh8hbjs0ucfkD8P2OnHm9ee2CdUkZl0rL7uiU0sOYbU4gv7nqxA5Sa4k
xoZQUUZtM8alB6UHKUfKs6Ka7LucLOEqocbfllNuYnjyo/kXPCRQHNDTBykYWcB1+yqKoE2iqurD
Ji9qBIBvaMz0joaUmCE9ma/MOu8ZacWqbcSuuX5rrZTxrEZkLF2cJSznbE3EtAr0FbZpGuWUX9z8
ftwL4YMhfGg0/1z/zY7wxrmbC/l3/KDXB1jlfT57UTI6rOjjlb8HzSlUCK5qkm2yOLxQnCYdcQGX
Fldw5CGQLA7FsZa2/ByqQELUD+mMIxML07vJUxEnK8sEbD2GwypFhE7HUT3wFyRgQHdY+Rtlwa8Z
wVQXRYWOKnxPdT/0e6SoPTtgLAsOFPiapFO6tstOUF5Zf36r/K11+4fCw2D5nr4m/8nzFbEAoMWe
vw/H4Jn4bVSoctxui8fVPsmwRxJTmXaaHB5mC6/MnHc8fC6xBisUGo3eUQxSgYLqPShRLByUrG2F
KPh+9LLsm+zkcwnlgDh2qv29Oi+kciKyVvwuivQJKOSyzDUbFVWwfA/fhqBAdQgI92jELTpj+5Xu
7a/Uz/7XVfDZa4WJwuYq7pJX0JStXKLzrGJCoRNQLSu+SbKEVTgndM8TZoQXJbI/cGDhfjWtyFa5
YHmaGkACQ3NFz6bvK5SrFsICdUNY23ZWoYUzB+dzkVGqmxlQa6/UWa3c0yAhB3QQjZCk9U5B89Xz
pb3BiMDmGIrMmFW9c9vosJiZDvkW3/UfckV02OLBohHObcXxyI8FpraNjSxhpg81vZpAyLlaZ9pA
jRMX8jwOYgF2ImoLGLp+S6lb5JGWsM1XcZx166yECzakRYROq4iyEhqaO3qk/0Gfon9A5JzRpdgN
6EP2V05hV5zppuOsrQKvOgyJtDAwAmPbrta2XoUOf9e2rH2clZpiWBLhZbu3dL1un5Wb6oqhS0Kr
rrKWN629QgaJ0Zwd8MLCYekGWC+9QtcPctO2K2sszAfxN70xeQHqzVrWeApL8tWbsOTg0DqUecDa
p7qyWS8QnG9Hwn9gWseqGGqkAf5RzI2/jzzkE+A7eup03yQ9gYRvGWxX3C94odGCXAfAy7ToK7qy
d/RoP8uYdzgSQruWFkZeGIX6Fhndf3lJPC3mJSI1ze+zCvVFnRVP5NlhP17irMJt6KIC/KD41sYF
FhSX3oItvby2CEgG8tWNrs/uAVDhsHQx+Sgqv7/9g5zDONwwwU2zA6OgZof9A9nfCLJJXawaWYnE
q8ZeocPKd6DCFcoVHXwZNse7Qs1y/y9L5OKzmdwnWlKyb4dJEUMAWiqiYH3Lv8COmCsUpKyisrpZ
wK4RxWBDmiJOK6Zb8yNlPDhiwhOTR9l7IXVWq7kbPmedammoSJFrX/GxGspMRqlZMs2jul8hfiUl
dbnjmlyeFce+p+nCAsicP/wCpxWek0tV9nyJoSiE9r22hixvs7W5UPWzGYxEzxrzXAqOifJ5djV/
7/HnCLBZmbyiFRoeIFo03+pTro7BrE9UBUw1jVU9He2504ZKAPasepABW/JKnXxztnfiw0qd7lPh
rFCZddSzfYpB4nSeI+a1xmsdlZJsm+/ARqfF53TpKDEjepf8TI/0ifscofuOK60olEYOteVo1VwP
H2ZV8D04B/g+tj2mVcBBAj9T/hSkZvhz2bml7Mzk74Rk2kQoQqPsNk3THSU3hilik4LEazXDnLrj
HXdZcZTWOII8KvApln/ysYRHf1Wfu4lJxJNRn4Xj4hLfCPlwOOCzLqpS56RP4XWLkZU4vRg/pFiY
b7PAEI0lpV55Yk4TQnTaMwMi7zT2cVor+MuG3ADl/JvoIyVRTL+n/07J4oZ6DhOJt0RMONaKAsEm
yzluW2bUcdSw7PM0+6Ud0zR7oISumNzKEeuazwudVTjHbxtvDKkgSLQLN6cv5l/0OfonJa5HN/QD
jbJ33MHwGP3B+B6llu7cT3RlPmy9Luz4dmwh28XUQeHeBzseVesct3l7/oqwjhKTFFpbx3RYelOq
dk415E3Qe6XEuh0WdHhDq8m0FulpQ7N1iWtUw+2A6Uy6OrbyxnK7l8RdzEVaBkoHvQhz6STCeV5M
aBx94d+hggduFCRQsHBYK2pHyx1eDQitDwRthbJzqDjc0J/R/6Cb7Hv+TFTn2jJ15k2oAcD9rrMP
NIseadz/QrP0F7pOP/JcQ3bm67YfOPhcd2mDyXss9bNbeucS7qv85n7loa9j8006H5yjWTamW/cT
je0XSqM5pRlIpgX9IYcCgLxhDL2Z0JDA6eo3LxTsYRr9oxqmERe4RWhhO3oD8wlNMe3qAJRD2vbV
GAJVDHEJCEkN00EQPlUiAheTG2/9EAP8jKdUExb3i8NtX87XiEwVVVFShXRvL0pobL/RIptItdRA
195w9a46NXlb6lfFNUrHEgxx4Ag17dGN+YE/e4powhKnY6GjLKRY6ttL2nBWvD/gxiwHNHART7sZ
mBk92S+UZH0GxjF5p3S8la6DEHNqwp8CgRQPCKSBcdqnqX3gxvAH+4d8HrcyvWNt5wUckn2mXlZE
oc/2M03MPS2hT89RW8ysfXQT4LqtI+S2YSF0oaKJS4hUYnbiGSuZtmUMJXj8aieeWusOa1OVg6EM
YDBSdVvnsMo3snCoVDtLwXDFo6o4TS3ZcVfcZoPl+6YRJOtE92norhhkxpPaQJbV3NPYfKUMgxx4
nl7Go9cTA7ypL9ygmpahMB0MnVWOy6kuOlJSlu+R+YE2i1jcDykQKBfX7oMwipVksgchcRdnle9n
hsEQA+pFI6I4pQfzB0v9oXoo+JJpRBJtuIN8HhHRxq5PPTeiyH6mMZyQmdGQ7rh1CE7rwfzO7UxL
uuHrgUjs2Xzhxm3FvfD3eA1UDXzhnoMTk9afw0QA/PDheyFlKmGjSuIrMccRVlTqNd75flund7fG
dsp31HnIBlfDwFL53fOhNMdn/pHHlMJxUiGfKGRolxjbh7jZPGkUESAA7jiKaBS9F64Zohy6oz/t
P1gzivcMxQGHhmXw0j8UI6rUUYXkxsDp69O2PH6raGLW48eiRVSABYfIAXgOZgeG/ikkXx7COM0P
sEKOfGyf9btm6bN/YFl2JnAIMtG53EYV3mvroqwcFA/0mIzXDMPDIzZ9jjjVcG7AhAcbHxFf7BKO
wpA632U/Us9dcwED74NTm7mxEFJtxjwyOP8mUfKullcFA0UN7UesYnqv00wAKtf8etO9aira9eF0
8Q29irsBNN5ZeY20koUSusjtkfohZMQFVFni6mAE3SacGU8KDv6TXx0+N8bm8ZQQro2mrziRCX0f
/S/0YH8nRLx9O6JJ9kTf0t94+swNfaSBHTEOp60FZZE4pM+Y4ShqrnyOWKu+kIeR9xXHeJ19xw3S
AJs/2f+gH93/ztjRvg77JXpWfJ2XRDf9D/Ql+oUm9MD7B6fyJfonXQNvc+8EL/ILtuygZBZkKC4Y
Vn95nmHNXYk08MpJhRJRrjgFS0P3jhY048/Gz+gUeJf95JvH/QPOOH6c3Ee/0b154IfMIL2igYOW
mzjXNo23p5CJx1Zx3+vEl0uYzLOv4ZhQ3cdkJpbW1oyFoy1+R63WV1jB5eKFH46c9776IGddlLU3
xRsXY5Yih/W9ZziAXsKRCgS4AKSHtmmKiTqtUxg+Hj2F0voTvA5Ax1m6ph/yp2U/xuTiK/rq4LSw
oL9nXCeJRS21il1p+Zvfm+uCFd5+pYWGlUBvyKaWnqJP9Ef0/9GH7N88HlOMOjp0O1FwEDRafKBh
ckv38e90n/7C1xoRFo+7R6nfYzZ4KIUwgOIa3C8WKG2G1JR1hjQO2x/RXY5DIV2/c3+hUfqBU9Sh
veb0vNo6dOt+ZEc6NY/0bL4xLnbIJuQctwweDiyOiAc25hTMIYAp1JrX5Licx5/xUA7TX7nX+buC
mpQrC+sYuaJlC1Z+0Psof01Ve6PDyvGjIOKBoSogN56MjMKr8La4gXEQsKV3Zk1y21MS7iTCWtNK
gLYBEpoDAaLIHF0l72lh5jSxT/TN/cKA/XB5Vy79Bymu4mVFuujPoQ5ErZwbBqLdFV2nhh7tH4wh
Ib2BI+P5Oy4pnlBbzllePX1Bfxv4Ui6N6cZ8z43xD/QnpTQjF4lED496C7hx8rlFUSE4lcX/b/Ug
KkFTPESgIQZAHQ+RYXZHiYXKxepDTkQJreBYlNCCpvRsvjLHDWPFDmYV7LcadcriLqZSXbo5hnfS
jcKeId0IviG8RzaZZll19+xGh1WtzoUKAhoH57k6blKOVnwK4KcNI+JquqOnsWZtBMw7w6grY+k6
+sCvP9FXmtsJD3EAvlVblQrShZIF/XzVC46oDakNnkTP9gsvOEQLiDKQBuVjwpocXRj17XkN4D6S
bERXINdGEU9uXkRTmqQPnB7j+AF+Qy4GB9Y3I67+7fN5df2TvDgMxJzvCYS5oXu/8fjF3SWMX11l
H2liHzh1PBTdYRNWp4oEsJAl/xqwLdOwGVz/acMHrDqscJvql/RXFZ6UAumMAfkhrPmfBrIoTUrc
F2GGRErX9HhUFjCVb+ZXWmRTXiLcS9VwQVSrbCXcjv+LJCVylp4I5fsHWpgJJbbHjdOItJAmbivd
Vx3ArtdBJaWxCREcHNDAXtMTfaFp9gguOo3MHUeBT/ZT3mKUZM2jmVBRwr9Q2lcA6nCIqZmzyGAj
3TM+gwnduO/omT7T3E1pQEtm1x8Ex6LmWlvV9p5LNJO30DV7f9P7blsmEDftOatuVEE3XATgPyFg
DVM2sEYQp8Ko2rASRQHM+MWcj30U3dI4Aw9oRplZlp7i2iu3aZtN0jngV8DNwMYHgfLe/sHscIDx
iCD6bnRQkqReQyw44DLAYnrZkEZ0S3+gimq/MoELU3hYksYmPFxi6yKuOKmVirM8Avm8ghMHDha+
H5gBD75vEmGy4zR9BtxTt6TlARxWsMM7tfYIbppctNNyZUj25dtr4HtqW3Pqd271dQUXpdevEjH4
4Z48ZH5L+Luy7aPPHGz+WTguiAlifmLPjugu+oE+uX9yBADnMXR3zB/iEj2X0l92HOzsvaICqA6I
XIDnIE1MaeExr+HBK4aa6ms6Y5YxXS+/pzRzNKUnbp35yd1wZVF1xjbtixJ31xkasRFVfo3+xWk3
KoCoIBpMAt/xepk1/Y6tWFGJb2R6/jDRfD6f52OsLjVFdC2e0yb3aKDW4Fm7+l/Aj9FyZWyFTxWC
ymm2pBlaXmrwEhDLDIax6uCHZb2CZgjO6Wfyv0d68uw6KosF3ZD+QeXAfc94zXQ5oRk9cnsIYzmG
6Nb9wHwt4FtVHtCui06kWHp05YTFjTQJjuwQ/KJ10YFOmPFtA9wDip7IzC3JmZRchkpms+1tO37Q
XOZmSjM7poxSUUfNRjTIbnaOKJFEE2Sx3eow4BfbnkgH4APM+pSICxQIpI2XVUm0ftBptYXvpbYT
cTT84LCqkasl+KZkxaxUTjasCJY+PB+MKJrsqre16QDzHT5SpLX1ZLv6pmswm3mwRPSOiY1JOqB5
NqUsW1BqFwyUO/OFBuaaS+1gYtc645pBn5twGXZSO5PKK43ZO2BZirsI3wzOXXpKXWaoZ0a0tDP6
av+L9xBpauR6lJkFO+9Bds3HLVhbs0nXWp1GkQH0BjSFo7tA2PC7VfrwSQN3S2N6oIWd7fS3O33I
Ho6rUOsUjBfnN49gz9xxmWBEWqvbDfhYdVZbJUTriLbDaMhXIlVq1UybmtfchOys4ND8z8oE1m1s
slBD6dDW6N7QaT4BmVUrh1EcUS9ClTCmfjoS1Qm34ArfMprwlBzgL2DJj9z7vFWkeg4OTpItS3Dk
x9DEcp1z0EAiYJbgeRg0ztAwc9wwjWME/wmGqt6MnmhOgsH13Q31CY6rfMvxUFWIBnILjXDN5NpH
gglmPXZSEBTc7+FluIqJiBSRWpvWRipUzCeQh384ifrcnZb1+vetTqAymx8CJcVR/ZejKkOC1WQV
xctQthhgrAcS1322hozAunatUB20NScwneqxzrQlZl3fE+Rq8LSBmgVYuuCnmKWlu/QncrTkQQtw
XqxE4KxXgTh8Klc6Bs8Erz2XTYstwbQi3Sb4ToPsjlNVOKlx9JVm9okVFPA7vIbG5aVbICtjZQr9
Wxgqn8DApCJaVDxRHY3MkIwbvfDIHbcWcV8hNIRaLFbn90ML29TIShnyYRXxnB2XtSiu4QSgmtyO
ltymvtncYQn4Jx+IKhg/Tf0iVZJjOFFEI6yBl9Soo+BrOqkXgx3bhgMKG2uPaU0Az20XQsN5OK6o
B2BYohJWb3dDGqa39GQ+0zf7CyVm4BdnO6qYTa2OWa+tEnmVOBgcEZpE2eDWlRdo2H+IlO12+ZNs
F6C6xUMPWNQzPdo/6cl+pl46ylUf8BkzGnOT+ZCJsQG+1VrlifiBERvUb4etnm/ug+OIsD1TsUlM
nsGDT9u7ztUinoegSqxgDLx8PoJi6XUWa6VPc+iQihCmhLUb8G0t+UAHr4VUYm77p4QAi+srClUg
Nq8+tHjzHtoU5+FxRf0e62/puQAGM6L33JP3yf47pzjAZwDMn4rlH1p4nqsRtS4iflAxpUX+YrMi
IE4IphhF7MgAlGcm5SEXIeeMmelGZGGAA26ahL2ryTiNBS3thBJ6x9haW5YPO4FyCd+nm2ksu5ic
86SUJu40AOPIFuLcL7ZwPmmNxdrzVBLQ8/ciLkZuPNBU3iMDAQptLImc/Nt8/5BO0FXnxDME8T5t
FtX9YweneTsuSpmId4y0sJUTrdvCsfC6tBQnsbDj/XGjWvg++5k5VVAVYME6+sbpVKNtY06yGYuO
F0QGjzClTZ0VTCNwmSu3Oa7Q3/PCNqmMDHPcNFN6H9plgFE9ms/UM1ceoG9rYcKhpFxVRSqKFLUN
k3ta3CGuH9J9YHFo2sZXG9dF15EqYISOy5xhithmN0vVD4UWCzXBd1yvScfU8QjwLs4qVBnUfkau
rZIAABt4SURBVDlNL4qmx/LB5ER5L+uiz1l5TTESW9rZs6gS7iEUyGRLEwWLPBU+lbtlGsSchjQx
j4xrYREDmF7HWseyg0gdnNWz+cyLAhHboUmyeZTtm7fzYkuDSdS8rbwoA9a+NHDj+/AWQ/QFlVD0
/CFlxrukEtjGNZEI7sp99ENi24reJLK6N3/Qo/mT9bvgvFgfCpqoDUUft5mc/6K1zfn+XYFYzi9N
bNNhMaQQSDbpGo0BGGuEUaeOWfczcIuwebe6w5r2yUkubm4tV6tiqd6TiNQYHwk/x0dix3iaHOIj
+OYC+B4Xwn0a6iL64oET6Ec0n/mmRySA6AJk05BnhPYfOCosaAavzZxn+bWZOuX7XH1YhU84/0BZ
JwuzzjgqwEgzZpz7iCGMwNhZX9MNOfpq/0VTNyyoGy+0Eg2kRUPde0qPTOXIXOqrmBiNZhmPA1rW
VjRXpRBknCbK5ORzqyQeYl/CYiAsDmkG1Rw5fJLm/CP5YS2EEbKiVz9cUkNwtsLP0hC7bkePYbgB
4rh9oL/apW9jSZX5AZERA804o5/tP2lhfuHICU9nHa+Fpzj68xCF4fwgOvuQ/c8Ha8dZCeuD9D1s
sVLcZpftcuRpZZJ1tbcUix1qFPiCc0b0KINlT7cYtTVI9q9Q8wA9Aq1C38x/8UPmffZXunHf876i
IvrJ/pMroj3Wd2v3GkUex9LWHocHnz09tiX3hg41bmmbWVElDe/LGBwiWMizCm+majon/KH9wr91
XerHrZWtmkg4H24PqvwaYFsMYC+lB++n9P/gNhREWg/2T54og+WCxTvIbuku+8kvYGlLaetshVOS
eAZilq5w6uoqNhpdry3GrPksrH84O8a1KpJCcAq32Y/0xf4nnwtw1oD5ncK4emmeuUBCqaEb+o4r
mzkeC6qKeeKHB9RMdWgIomYooEL59FBm8iJI5CuJC3Zap8S2RGZo86T1trDlWErvm9ilPhSDY/ON
jvtohW+zPPM4ljjdDjysNiyUGEGqiBSRCx5IGyEUaD7QwIz46S065eghxCAGYXgfBGD3h81EVz8B
JU/7PUVD1VRf2kMnby2oE3naH2wQrUdc3HELru5Vh6sey/DZTEExv3I6OcueWTjQ2Yzm8aM0YqOR
2gEXK66LKKNCdFCa3w9pJm+mLroQlJp0bJOJz8eRkALCIh9aoQ/o66nHLLgv0Pe06Xvb2EHdVl4m
9pUQ/R4ech2T/hItTJmVqwZMi8m1rsdVMx4Xz0THhDXF8krjgfYn7wfzr5XIikFLUvg3ew24qJmU
HBJysdCBZwEfQvUNPYDqEATmlulLgknxcC9x8LTkaEwE+l5+n4Bgi62D6QpFVR4zZsDGl8G+cTqg
IXpra/S1jlG5rTqKoiBWKGscc73o1KBjWLxyYJUbNKwE4ld8eVpyViVw1BrROvdPjII+wegiRQe8
AMf2heq0Qtwwr6hRoUiASolMv263bFzeF/l3JRXMQ/L1arD79CVWVVmrm4aa6NLOmWzKTstdCW7E
RYcpvwdpNKIgOJElQSNL/hMe18tNWPY9jmxRLMDwi2n8jYeCjNL3NKQbEEzYSZ6aQ2eCcWN1rT2H
dlyCpQmGdQwnuTpI1dQ5E+TIHjCutGe82IKNMRseUKYXple9+MOHmscXGAx7NNdaKjwoWBWobnMf
apvRg35RjqsruFZdV8LW/QvaLVT1o/o3oNdm7j09mU/0zf4qU6ANRJmf2WHBoYPHxuPYMCKMRBdM
qqsvN6R60nPoKTappYG55c8D9eSa3lPPDFtt8Wk3RczybpR9G6lXhDjX/r02bB9Ps35r/CrIf8G/
kbE8h9kxbBVOKpx3KOzqwxo38h4xlD8XE2chzet1v9N2qhycr5GFyR9yDfoRdTtIrTZFjCxYaMbc
xgRahxI/UXwAoD22mODzkSOttikL4FQBv5rQI48c+878jeIs8cHg+UMT1jspdVr42mVyDw+XmWGI
cDGerhiwotdLvj9Fo3ajVcptO5CQgRcNMIw27NQhNeycdYjCJti2jVNOtwoO89xEl3JNEgJ91YW6
ws8L1EObRoGb3sfjT12fK6Q3GCdGdzIY1be/xBk07dsDtrWFZ+YmzK2CDPV39D95km+/LnM9e7N5
Sxwm98xlNmjNfaTwRBhV9fsY3VZgyrPZrDT9hssLgHCYDnRcWsVWhxWmLdqQEDqrUPp4r+rhtjtB
yFvsMDU1aTs3D5nE5+i42m2vdTL2nSbeMQDYLipLSMmn5oEHujK3JkqYH4ZUjSPtPTGtaq+i0mPq
boD8PsoE2K5W40DRbNMWiKfMJ5rYR47YRtkHGtI7z4fb0jN5pmaCNSIFrYym07kv9EhLXXivh5hX
OPy3imVXPuXoAH8zh+Wf8vJC8bvwgLTi08R2aWhGupIx8Fyw5eVkFw3ar9nabgBnMqqRoQyibRUz
fwhDLVAdA5gMrAhAN1qIlnZBY/eVO/FB7AQFY61Mjbe6361UB+XF8jF644ZlmguOyYzxw15jdA+A
+8X0EvqORu4u51YdWzmkbbO53laBgyIygqmz2QTQKza2btvHXn9b5xKqwoKOWK+TMt7ZgorCtm3w
EyGsVFYqZm2csLfg+MKuRDgs8LtwxMCKVG4Z4DYcBRYrps2AMAmtKsxGhJMCprNxtl8Acaxc14bq
sbi/MNRjaac8MAIqru01Q1fMCFkCDgsRyF36M13ZYnjrazJrI+r3i06LqqPa5/4/xZrZ6LC44sCA
tMcmwuiqDnzd8kQKw8t9nJ2cWPk0/qwWq2Znmw5Woo8XbcurF2CR3mGysnvHzgqsbaSAELobQFXB
GNaQQqTDQ0vNgNVE4dwkNay3pte1LtpiHMlJhW5mJryPQ7qmJILKQ/Gwas0gUMn0CWjxP3FFdmCu
dsDF5FwWaiLSS3jOZgKpIKzt+XyW622dW1/iXg5LWbN5hUjbNGrIhPqzAq/h9/nv/N+3qRvUhom8
zXnebDk/y+uWrWWeV/9GxfXC1+EUmEUvwDbO39yO6Sn6g2f+2SyhfjRkwDtLHc9b/NP+OzsrvB//
3biPLaVoXkvK3w9wVp/sP1itVCdd36Y/5IipPqyqbWP73APY4sJO6Hf6fzk1RhXyHf3MEV2zKJCF
frhvEM3OYOijgvnOQbzw/Be98YTlwWCY0yBCAcpzdlxbMawq/2cbnpKrOGgKkPMEm6WApzB5upyn
w4Kpwiuz4XsJSzJvogXkuGOlTUNlaqD2AJkXGRF2Re+Wf2V/nUQJudSQS33qzUNS39HE3NPYfuNI
DF9C0FQwWip3jRZ68PB64mnWX2hpptIjCba/I+4nBPANp4mJQ6HxQoLT8jBFlSnf1PBu7i5A+d/N
6cl84UJEmi3omqVoVnEzOHoQWfE+EEYxZg32zv0Majw79ZQrq6IkewlmgrRQo65zH4TRyGEp9b9p
+bLMmD6/g65arsV1hhcIFnLguE9tA4lUnZW0Syg24YR8acYCLvN/wqBHvyKcFraLMW7SV1iobcA5
Mfs+jTka+2Z+85OgFQKQaA0SK4iK1ulOwVny59MDZXbJPCcA6n26zvcbNAIMnkB7i+xhVO/wuCsC
DK2xjKQP0tRNGUBhoHPIVGht+YFyBgZoQGOeo09Pm8D24aDgtJdmIdORaMA9gzhWneKjjPtLcVZq
eeZTkoQSQqg6tFOrQexWJfRfh5g/di52qLaXNk3K/F4nPrjJqgZpk5kTNVMmhUqFg/vi0MoCxzWg
a7JZIQy37RxgeOsoiylCm0ry4Ben5+UgLOLK3pxfh6oBR0gkrS14H9JN0CjAVp8R9gsgv2WlA8wZ
RGgl/YBDkZ+pozpURr/BMU3sA1c3MbNQ9afqFGrrYAhhyQ+5Egqn/S77C0d8cFo4Du2ZxXmeUcSO
DJN/rvy4Nh2mkZ+jAyo0HMNMSR1Y0sSwR/Bc1n6DKmExU/DSbV0UpVK0527ShI5JReJs6tI9LLin
6CtN7D07L35CYminAQXymq7dexqkN2Qw9HRNW02dM0SqA5D+Jv1YOldKukTKiDQJKR6L8tEVL2oR
tXugCZRVXUw32Q/Uz0aeW4XUVCI9VmpodBJ0f2J2xigEYLtD966I+gywMXxhkIq8ptrx6uwgHwOd
MXWU4HshvWPg3yz8g1qE+jDT8Cr7yMeP6Oq1mw2GxoT41jlM8al1WGHzMYDbXnLckVRtmzredW0K
El1cgMfyeBakaTQ9hJKDLkDIoSASGNAt/Zv7v0SehcPjghi7KWWR/rPV6G1TWsDMc+rRbfYD3dD3
AmibCU3sN04fZ/aRIyvQJkDGXHLKJdvq+dRPrbG2FtNsYrqjH+kr/cIih2jngbIpHBWwpif7hT+b
tfXdgEX2wJjHvsK1/wmAn564xQeOSPcDjg0Rmxq2e0s/leS834pZf92Fu7XMq4qnBObjTXIj66Y5
X5qF6ghQANU2hfD3l2J6DKjyAAtClPA1+5UB3yt6R7fZ9zKgwhURlFAjRK8p7Purss8Fp8S5Kas8
Nt43/x9wHbTPXNFHTiswVBZ1NSgugC7xFH8Wkmb2ga7dd4x96d83NYn6I4psQhm08aOvPIEInLFp
9kxRmtD77N84ZUXUeR/9Ss/0hadII10GOfZj9je6yt5v0ce/BBT2GFVF3BcYoJvx/Qe3gMk+JyWO
IqJScH0zJf+yTBceFnk4V5GD/qCr/VIMcPODu2dhOeA/V9E7HhkP6WSbYox9oBLqq2glWZcamRd9
v15/nCuVz15n1Vac/F6BPAu+oKCQeednENdAYcHSFEqeZknP0VfeX+A/+zQxwwEDqEdax+x9MyEw
VHopJk1fUc8MuPo5oCu6p9/ZceELx/4u+4kdpmhaXf49fmgLCaYSgYPHNc85XMdaP7nDUpE+2CUt
3m0WRgrhWHAdLaaclGMcc04r4KUmp15HqWnlLiTg4j9gKJA2wftMsI1ltuD39c2IbuxH6tmRn26D
KppOJpLR8isqA5X1ycC5929VFvSmKKtKFg6dYAjm8/YYc0K3BKYHxeSssOqn9oEdrSh17s4wRzS3
oBE9mN8Zf7pafs+8KmBNwMfwH1LjucMgjwnTMvAaPo8dauesdjJdL6hAaxeM4tzHcFxlhxUArop2
XH6EJZFU2AiqwzBy6eej7IVUwgA+wySiMPyaNgLz1BxU1pyRap95pmf6ypU1/LXwkBwv+JEDU/1O
cKoUI99laCu4V1DJxG2j9IZd16Q2u2tEuraZWb7JU8EiwsrfVGmQl7mEAnzL/yMqerSf/IgsTLiJ
GItq6kyYlmBwDi2rOwyzW5msrcKI3sUjJZV5jvgMVXu49Hv7dIbzC9kZpUBU2+UO5TdKo+qNnxe4
wA74IZqHVPo8holjkg51NZBEjy19zcvGTJkHhXmEWETVhY9J0DzwIOvTU/qVvib/4iGrN4sfKcp6
XABJYhlbpQJ4eWeBdxhY+Fz5yvEFJjY0Mn6/ZpJeanftBCSA8wpFrxkHt66ZNviJK3JP0Se6j35n
R80gd/YDg+EAyIvobdXBuFBZgohu0x9ZIRTXV88H0xLMlB7sHxytQnnix+x/8wMuLvvePgdjMnNS
TKlW/tahspay4qj/IGl0rhd2uzTTBbeu4/xYpvP3PmYDdijK9eGJyIj4aEn39jf6ZP+DU7Qsyui7
7G8sUgeJFw5Sct31oGtAL5F2FngSLFdEUc0Bm3u5XOt4QuO/t/Xk311VC2qdVeWrcEVC1OT+PjOn
e/MbEzVBN8C/+B0wJzibUGYGjv8x+sxpHkB2fhBh7FWlawFOcJjdMP/s1v3IUWznrNq1EPfGvSbA
vPQutum4Vlax9Bb7wQ90+SbSztJycGqnpcxpaLZDM5zPL+RaEE6nlobmI0VQT6BMJiLbW+5v47et
cRiheB7/HJD/YNwoElAg1u5bGMZ7NY2StE0DRdEmZ4DPgetR5iZFhVKEsXMMCykeegpBNoVCBAwN
0QlNfEXRibNC1OSZ+31zxUz9Wmkbsuzs8C+c1+u4s8/P9P5R2Rp9cOKrLSrE2hX8Wi6pjPSW1iI4
rVPObysvW08x8K1BZGMmWmJSDC40Ii+MuufeTAWw6yYzbyi85xK24G1tOOYQfwrpDuGsuV30zjYZ
WmKus+/oyfzJDgnRJBzYyF0xKJ5akDalqpjPMHSOm4xBj5g7KDksmR6Bih8cHSJXpJDa0lN3fOLo
Vtt9OmvfNFMLAfm2Wn1evZC5niRYcdLg/elkFdGSFpiPZqTRBYtT5z9uBi53bjhu+gjy1Ra8H/pn
YdN6G2J2cBjo4QOZ9dl+ZaeVZAN6l/3MWNSE0LM3Z4eFT2QxQf8fXp+7Z24xYnUJJ2k26AkoQGyS
hmlb+72z7RZiWUgRQ1J6uC53sVfvsGB6cqQci6pXEbUcu9WgUaRT95ZQAG9nFWojKqFNJu94B7qy
nztqtm/aFziPu+wvTHB9Np99BHQtNcQs4sIEqoeplepfsSsi76yN0XBWANpRmDj04NLO9jfcS2FF
UbmP+6y/N+Gw1FDilzC1aCDGqO8kadjHdkpTGRVNAxteZC0385MuKaaprHtvngL64RNqpQitoXro
JmMnRVeCjRmkrlIhhHJE33zjqt4TfVnheoHBP8iu+P1X7iOn0Z2zuryKojLmcWWx/ppGW2/KYWmE
oZULya0Xucc/Nba1zlZkqXfczVwW1xpunGZ8Cj2I1WhpizpKeH74b1/ouBi7ImAdoV6aVPVA8WBq
qREJGdAQEFHBQQklQfCwDpO6PNO1VjRYg8fVTPX0zTgsOUEA3MWTb4o0zs00pdvXN+QTgXyDNwPZ
nqVcfqMH3aVUXL8vIQj/QhMUbvUWBIEUbPUoSxir4qoqxVKIYJWHDji/dAvbfMCa557TtBhpt85x
vRmHBQvB94LNXT+v7awsYJO/xLjMvEzJ9kTtAQ6sHpPaHsUdkhogbgzjvbzaRGev1kweaQlko6C8
qIasVhTflMNS03L9LhNxT2ltOwdEV0yXMJUoK5hm1Flnx6dBAGOVYAK3pUz3K1cU35zDCsuqb3Vx
4hwAy1ppqVlpfWlvYEhnnW0zYcYnJPxuIZ1qnyKmUb9JhwVJDKU4vFVjh+Wn5qxQHUIO2CXOaO/s
lZhgzr1eocH1Jh0W8mRjTs92P7WVRret41a97VPU2YmtrLBi3p7DUolgWaj1pf+3En0xz8oWpWW1
0Il16WBn52AhfPNmHJb2Nen3aIhWC5VIL2kK7kuMKR0QZ2CpnUJGJteR8nphnXV2TvZmHFaYEmpP
ofxcAHoh7QEVxNduII+auBhfHg4nvSSd+87ejsVvrXSK8dyhBIvolqv6qJyOSyGUvtSUl+UiYf/n
Gu5lwarOOjsbezMOSy0koqnUhRJJQx3zc5GiOUqqjPTYURBpoY3Hsvps57Q6Oyd7cw6rauKw0EtY
SLq+piEcu+J7AnCKQgKPNNwg+tdZZ8e2N+2wCkGxKI+mctG7NwC8h6bqkFol5X5DaIF2UVZnZ2Rv
2mHBtEUHprIXLKX3xqIstXDMF1cPgXF1eWFnZ2Jv3mGFFjZEv2VTxw3RtQUki9MOfe/sPOxtr8zA
oIslDZeXNQX6EBZqcXfBVWfnZJ3Dyg34VfoivelX57Sg0Np5rM7OyDqH5S0cYx9qTnfWWWfnY53D
quFkiWRrO3PULtoAXXXwVWdnZJ3DCkzSwbdHaVixLdrunXV2KuscVoWHBJb3W08HC63tjjTa2XlZ
57Bq5GdkWvTbNdWP7xqgOzs36xyWN+BWsLfQP9hZZ5dqncOqRFedtTcstbPO2rbOYXkDux08LFFt
6FDnLiXs7Bytc1jeuNnXS6uI03qbOJaSZvGvTizprLNzsc5hBYaFCulkTOjo99+mw2LrAszOztQ6
hxWYtua8hSEU2/oIkRJCxUKHc3SRVmfnYJ3DWhmw+jam5mwyDKDIKBNdrEh0sXgoRZcddnZi6xyW
N0QSIX7zlk1JtHDicRIXfZad+mhnJ7bOYXnTqTFv3VmFxtr2C5kghEhr6UT7vrPOTmWdw/KGRSnD
KESpoXNcYuFAjiiOyC3EsXfW2Smsc1glk4XYOauycVTlm8LhtBB1ddbZKaxzWIF1jmq9hcM54LSA
Z3WRVmfHts5hedPxVkIclbFfnQXmfPXQSfWQbFFZ7ayzY1nnsGqIo5hTmCQdjrWuMAFT6kfntDo7
pnUOKzClNWSZqI6iv7BLEzdNFxJli3zEfWedHdg6h1UxJY52qU7DaAuqDqbTzursONY5rLVpT8fH
2mTq0DsJms6OaZ3Dqu2lcxTHnZBfU+ui0c6OZZ3DqomuugW43RjbMygedlXCzo5nncOqMTgsSMxg
VHsHum+ggXS6750d2TqHVVmErFDA9AZVH+3oDXWGc6NVwg507+xY1jmsimlEpWOudEF2tmrq0OG4
OmpDZ8ewzmFVTDGsLrrarcewi7I6O4Z1DmuNOgH+7dLBBs4dwn6WWH4GaXSHwXd2SOscVsW0lzBJ
OsC9qcFpWWNZpbSrGnZ2SOscVsXQ9BzHRLPZjPr9/puXS25qHRWks2NY57BqpIHxBUqDSgN3tt26
wkRnx7DOYdXomLO6ZjeyfqciBTCsOI75HHbk284OZZ3D8qZDVJWL1UUMO44Fy0x+7lRyptN/76xt
6xxWRalB/jWvt1n5AMemwyrYYcUSnXZtTp0dwjqH5U2Joq/RNE2DIW07lKnj0pRaOwY666wt6xxW
YK81sgoxpdlsysKEh0x7lVAK54jPCB1mZ529xDqH5a1owXGvWtbYWhHb01aaQ2B2jF9BapqWFNmI
deC7ydGdtWGdwwrsNUYBIMHCNEUDLR0RkALjWh1dN/V6X+xLnRYIpfhyppPt6ezl1jmsmoX2Gkzx
OIgRFs5KDN/jSyt5cGrOSeQVNntXz4U6taamzpA/31hKqcOzOnuZdQ4rsFAq5RLwrNChhM3HIQF2
U7qn6aBW9ebzeenYwwZwUD4ATPHY+h2cFrYBLEuGe3RjwTp7mXUOq2KXotIAZwQcSiMpOIWcE2UM
/6wVwSbHgveA3b8uBdRt43N37QDglNMa/nLp64hgOzuNdQ4rMADSiCLOPS1U7AmRUUFTkMEZoaje
rk5309/o69oNsMv2OWrDf2f+EOjs/K1zWCW7DIelUZWmdDBN2Q7pFBTDQmSnlIUmxucTKjSIXOOO
5tDZ/tY5rJJJ5HLOgYDiUwqcV1n6x8L49KuJ09J0MjIF+B9WKTvrrKl1Dqsy3utYi38fC/lTp8LZ
wn5B3Y/wd+tMHRTez9GZNZQumxFK11UtO3t71jksvxDm8xk7Kq1onaNppNLrJSdzqmFaiKoiTCgS
2K/+xnNXJ9+zLcpSSob+bee03ra9eYcFB4CRXtKugqjlvJ2VOITT5qzYB9Abyn2JMhot/4mjKcEE
i59lYEWIg+n31T5OdYwazWlEqfLVndGbtP8ftWQwE2pUL8MAAAAASUVORK5CYII=