20240416 18183700 1.0 FALSE UpdateSchema "CIMDATA=<CIMStandardDataConnection xsi:type='typens:CIMStandardDataConnection' 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/3.2.0'><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original</WorkspaceConnectionString><WorkspaceFactory>Shapefile</WorkspaceFactory><Dataset>tl_2020_26_tract</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMStandardDataConnection>" <operationSequence><workflow><AddField><field_name>GEOID_Num</field_name><field_type>DOUBLE</field_type><field_is_nullable>False</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> CalculateField tl_2020_26_tract GEOID_Num !GEOID! Python # Text NO_ENFORCE_DOMAINS Clip tl_2020_26_tract co_outline E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original\tl_2020_26_tract_clipped.shp # CopyFeatures E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original\tl_2020_26_tract_clipped.shp E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original\CensusTracts_No_Join.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\tl_2020_26_tract_clipped # # # # ExportFeatures tl_2020_26_tract_clipped E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original\CensusTracts_No_Join.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\tl_2020_26_tract_clipped_with_ACS_data_join # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.NAMELSAD,0,19;MTFCC "MTFCC" true true false 5 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.MTFCC,0,4;FUNCSTAT "FUNCSTAT" true true false 1 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.FUNCSTAT,0,0;ALAND "ALAND" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.ALAND,-1,-1;AWATER "AWATER" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.AWATER,-1,-1;INTPTLAT "INTPTLAT" true true false 11 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.INTPTLAT,0,10;INTPTLON "INTPTLON" true true false 12 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.INTPTLON,0,11;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.GEOID_Num,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.Shape_Area,-1,-1;JOIN_ID "JOIN_ID" true true false 8 BigInteger 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.JOIN_ID,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.JOIN_ID,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Label,0,7999,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Dataset,0,7999,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Population,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Male,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Female,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Female,-1,-1;Under_18 "Under_18" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Under_18,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Under_18,-1,-1;Over_60 "Over_60" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Over_60,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Over_60,-1,-1;MedianAge "MedianAge" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.MedianAge,0,7999,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.MedianAge,0,7999;One_Race "One_Race" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.One_Race,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.One_Race,-1,-1;TwoMoreRaces "TwoMoreRaces" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.TwoMoreRaces,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.White,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.White,-1,-1;Black "Black" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Black,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Black,-1,-1;Amer_Indian "Amer_Indian" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Amer_Indian,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Asian,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Asian,-1,-1;Hawaiian "Hawaiian" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Hawaiian,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Hawaiian,-1,-1;OtherRace "OtherRace" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.OtherRace,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.OtherRace,-1,-1;Hispanic "Hispanic" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Hispanic,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Hispanic,-1,-1;NotHispanic "NotHispanic" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.NotHispanic,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.NotHispanic,-1,-1;HousingUnits "HousingUnits" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.HousingUnits,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.HousingUnits,-1,-1" # ExportFeatures tl_2020_26_tract_clipped_with_ACS_data_join E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Census_Tracts_with_ACS_Data_and_SNAP_Indicators # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.NAMELSAD,0,19;MTFCC "MTFCC" true true false 5 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.MTFCC,0,4;FUNCSTAT "FUNCSTAT" true true false 1 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.FUNCSTAT,0,0;ALAND "ALAND" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.ALAND,-1,-1;AWATER "AWATER" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.AWATER,-1,-1;INTPTLAT "INTPTLAT" true true false 11 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.INTPTLAT,0,10;INTPTLON "INTPTLON" true true false 12 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.INTPTLON,0,11;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.GEOID_Num,-1,-1;JOIN_ID "JOIN_ID" true true false 8 BigInteger 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.JOIN_ID,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Female,-1,-1;Under_18 "Under 18" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Under_18,-1,-1;Over_60 "Over 60" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Over_60,-1,-1;MedianAge "Median Age" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.MedianAge,0,7999;One_Race "One Race" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.One_Race,-1,-1;TwoMoreRaces "Two or More Races" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.White,-1,-1;Black "Black of African American" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Black,-1,-1;Amer_Indian "American Indian and Alaska Native" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Asian,-1,-1;Hawaiian "Native Hawaiian and Other Pacific Islander" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Hawaiian,-1,-1;OtherRace "Other Race" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.OtherRace,-1,-1;Hispanic "Hispanic or Latino" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Hispanic,-1,-1;NotHispanic "Not Hispanic or Latino" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.NotHispanic,-1,-1;HousingUnits "Housing Units" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.HousingUnits,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Shape_Area,-1,-1;CensusTract "CensusTract" true true false 8 BigInteger 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.CensusTract,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.CensusTract,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_001E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_001E,-1,-1;S2201_C01_002E "S2201_C01_002E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_002E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_002E,-1,-1;S2201_C01_009E "S2201_C01_009E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_009E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_009E,-1,-1;S2201_C01_021E "S2201_C01_021E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_021E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_021E,-1,-1;S2201_C01_023E "S2201_C01_023E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_023E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_023E,-1,-1;S2201_C01_034E "S2201_C01_034E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_034E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_034E,-1,-1;S2201_C02_002E "S2201_C02_002E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_002E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_002E,-1,-1;S2201_C02_009E "S2201_C02_009E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_009E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_009E,-1,-1;S2201_C02_021E "S2201_C02_021E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_021E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_021E,-1,-1;S2201_C02_023E "S2201_C02_023E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_023E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_023E,-1,-1;S2201_C03_001E "S2201_C03_001E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_001E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_001E,-1,-1;S2201_C03_002E "S2201_C03_002E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_002E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_002E,-1,-1;S2201_C03_009E "S2201_C03_009E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_009E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_009E,-1,-1;S2201_C03_021E "S2201_C03_021E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_021E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_021E,-1,-1;S2201_C03_023E "S2201_C03_023E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_023E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_023E,-1,-1;S2201_C03_034E "S2201_C03_034E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_034E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_034E,-1,-1;S2201_C04_002E "S2201_C04_002E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_002E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_002E,-1,-1;S2201_C04_009E "S2201_C04_009E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_009E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_009E,-1,-1;S2201_C04_021E "S2201_C04_021E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_021E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_021E,-1,-1;S2201_C04_023E "S2201_C04_023E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_023E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_023E,-1,-1;Field22 "Field22" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.Field22,0,7999,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.Field22,0,7999" # DeleteField "Number of Households Who Receive SNAP Benefits" Field22 "Delete Fields" 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/3.2.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Census_Tracts_with_ACS_Data_and_SNAP_Indicators</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>JoinID_2</field_name><field_type>DOUBLE</field_type><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> 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/3.2.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Census_Tracts_with_ACS_Data_and_SNAP_Indicators</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>CensusTract2</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 "SNAP Households Median Household Income in the Last 12 Months" JoinID_2 !JOIN_ID! Python # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" CensusTract2 !CensusTract! Python # Text NO_ENFORCE_DOMAINS DeleteField "SNAP Households Median Household Income in the Last 12 Months" CensusTract "Delete Fields" DeleteField "SNAP Households Median Household Income in the Last 12 Months" JOIN_ID "Delete Fields" 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/3.2.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Census_Tracts_with_ACS_Data_and_SNAP_Indicators</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>S2201_C03_034E_TEXT</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></operationSequence> CalculateField "SNAP Households Median Household Income in the Last 12 Months" S2201_C03_034E_TEXT !S2201_C03_034E! Python # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" S2201_C03_034E_TEXT "$"+!S2201_C03_034E_TEXT! Python # Text NO_ENFORCE_DOMAINS 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/3.2.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Census_Tracts_with_ACS_Data_and_SNAP_Indicators</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>temp</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>temp2</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></operationSequence> CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp Right([S2201_C03_034E_TEXT],3) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp2 Left([S2201_C03_034E_TEXT],2) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp2 Left([S2201_C03_034E_TEXT],3) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp2 Left([S2201_C03_034E_TEXT],4) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp2 "!temp2! + ","" Python # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" S2201_C03_034E_TEXT "!temp! + !temp2!" Python # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" S2201_C03_034E_TEXT "!temp2! + !temp!" Python # Text NO_ENFORCE_DOMAINS DeleteField "SNAP Households Median Household Income in the Last 12 Months" temp "Delete Fields" DeleteField "SNAP Households Median Household Income in the Last 12 Months" temp2 "Delete Fields" Rename E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\SNAP_Indicator_Layers.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Census_Tracts_with_ACS_Data_and_SNAP_Indicators E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\SNAP_Indicator_Layers.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Orig_Make_Copy FeatureClass ExportFeatures Orig_Make_Copy E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Demographic_Contextual_Variables # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.NAMELSAD,0,19;MTFCC "MTFCC" true true false 5 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.MTFCC,0,4;FUNCSTAT "FUNCSTAT" true true false 1 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.FUNCSTAT,0,0;ALAND "ALAND" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.ALAND,-1,-1;AWATER "AWATER" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.AWATER,-1,-1;INTPTLAT "INTPTLAT" true true false 11 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.INTPTLAT,0,10;INTPTLON "INTPTLON" true true false 12 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.INTPTLON,0,11;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.GEOID_Num,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Female,-1,-1;Under_18 "Under 18" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Under_18,-1,-1;Over_60 "Over 60" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Over_60,-1,-1;MedianAge "Median Age" true true false 8000 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.MedianAge,0,7999;One_Race "One Race" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.One_Race,-1,-1;TwoMoreRaces "Two or More Races" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.White,-1,-1;Black "Black of African American" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Black,-1,-1;Amer_Indian "American Indian and Alaska Native" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Asian,-1,-1;Hawaiian "Native Hawaiian and Other Pacific Islander" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Hawaiian,-1,-1;OtherRace "Other Race" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.OtherRace,-1,-1;Hispanic "Hispanic or Latino" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Hispanic,-1,-1;NotHispanic "Not Hispanic or Latino" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.NotHispanic,-1,-1;HousingUnits "Housing Units" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.HousingUnits,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_001E,-1,-1;S2201_C01_002E "S2201_C01_002E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_002E,-1,-1;S2201_C01_009E "S2201_C01_009E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_009E,-1,-1;S2201_C01_021E "S2201_C01_021E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_021E,-1,-1;S2201_C01_023E "S2201_C01_023E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_023E,-1,-1;S2201_C01_034E "S2201_C01_034E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_034E,-1,-1;S2201_C02_002E "S2201_C02_002E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C02_002E,-1,-1;S2201_C02_009E "S2201_C02_009E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C02_009E,-1,-1;S2201_C02_021E "S2201_C02_021E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C02_021E,-1,-1;S2201_C02_023E "S2201_C02_023E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C02_023E,-1,-1;S2201_C03_001E "S2201_C03_001E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_001E,-1,-1;S2201_C03_002E "S2201_C03_002E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_002E,-1,-1;S2201_C03_009E "S2201_C03_009E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_009E,-1,-1;S2201_C03_021E "S2201_C03_021E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_021E,-1,-1;S2201_C03_023E "S2201_C03_023E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_023E,-1,-1;S2201_C03_034E "S2201_C03_034E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_034E,-1,-1;S2201_C04_002E "S2201_C04_002E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C04_002E,-1,-1;S2201_C04_009E "S2201_C04_009E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C04_009E,-1,-1;S2201_C04_021E "S2201_C04_021E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C04_021E,-1,-1;S2201_C04_023E "S2201_C04_023E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C04_023E,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Shape_Area,-1,-1;JoinID_2 "JoinID_2" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.JoinID_2,-1,-1;CensusTract2 "CensusTract2" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.CensusTract2,-1,-1;S2201_C03_034E_TEXT "S2201_C03_034E_TEXT" true true false 255 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_034E_TEXT,0,254;Tract "Tract" true true false 8 BigInteger 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.Tract,-1,-1;S1602_C02_002E "S1602_C02_002E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S1602_C02_002E,-1,-1;S1602_C02_003E "S1602_C02_003E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S1602_C02_003E,-1,-1;S1602_C02_004E "S1602_C02_004E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S1602_C02_004E,-1,-1;S1602_C02_005E "S1602_C02_005E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S1602_C02_005E,-1,-1;B28002_002E "B28002_002E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28002_002E,-1,-1;B28002_006E "B28002_006E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28002_006E,-1,-1;B28004_002E "B28004_002E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_002E,-1,-1;B28004_006E "B28004_006E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_006E,-1,-1;B28004_010E "B28004_010E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_010E,-1,-1;B28004_014E "B28004_014E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_014E,-1,-1;B28004_018E "B28004_018E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_018E,-1,-1;B28004_022E "B28004_022E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_022E,-1,-1;S2101_C01_027E "S2101_C01_027E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S2101_C01_027E,-1,-1;S2101_C01_028E "S2101_C01_028E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S2101_C01_028E,-1,-1;S2101_C01_029E "S2101_C01_029E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S2101_C01_029E,-1,-1;S2101_C01_030E "S2101_C01_030E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S2101_C01_030E,-1,-1" # DeleteField Demographic_Contextual_Variables S2201_C01_001E;S2201_C01_002E;S2201_C01_009E;S2201_C01_021E;S2201_C01_023E;S2201_C01_034E;S2201_C02_002E;S2201_C02_009E;S2201_C02_021E;S2201_C02_023E;S2201_C03_001E;S2201_C03_002E;S2201_C03_009E;S2201_C03_021E;S2201_C03_023E;S2201_C03_034E;S2201_C04_002E;S2201_C04_009E;S2201_C04_021E;S2201_C04_023E;S2201_C03_034E_TEXT "Delete Fields" DeleteField Demographic_Contextual_Variables S2201_C03_034E_TEXT;S2201_C04_023E;S2201_C04_021E;S2201_C04_009E;S2201_C04_002E;S2201_C03_034E;S2201_C03_023E;S2201_C03_021E;S2201_C03_009E;S2201_C03_002E;S2201_C03_001E;S2201_C02_023E;S2201_C02_021E;S2201_C02_009E;S2201_C02_002E;S2201_C01_034E;S2201_C01_023E;S2201_C01_021E;S2201_C01_009E;S2201_C01_002E;S2201_C01_001E;MTFCC;FUNCSTAT;ALAND;AWATER;INTPTLAT;INTPTLON "Delete Fields" 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>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Contextual_Variables</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>S1602_C02_002E_TEXT</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>S1602_C02_003E_TEXT</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>S1602_C02_004E_TEXT</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>S1602_C02_005E_TEXT</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></operationSequence> CalculateField Demographic_Contextual_Variables S1602_C02_002E_TEXT !S1602_C02_002E! Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_002E_TEXT "!S1602_C02_002E_TEXT! + "%"" Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_003E_TEXT !S1602_C02_003E! Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_003E_TEXT "!S1602_C02_003E_TEXT! + "%"" Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_004E_TEXT !S1602_C02_004E! Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_004E_TEXT "!S1602_C02_004E_TEXT! + "%"" Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_005E_TEXT !S1602_C02_005E! Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_005E_TEXT "!S1602_C02_005E_TEXT! + "%"" Python # Text NO_ENFORCE_DOMAINS ExportFeatures Demographic_Contextual_Variables E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Demographic_Contextual_Variables_All # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.NAMELSAD,0,19;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.GEOID_Num,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Female,-1,-1;Under_18 "Under 18" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Under_18,-1,-1;Over_60 "Over 60" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Over_60,-1,-1;MedianAge "Median Age" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.MedianAge,0,7999;One_Race "One Race" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.One_Race,-1,-1;TwoMoreRaces "Two or More Races" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.White,-1,-1;Black "Black of African American" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Black,-1,-1;Amer_Indian "American Indian and Alaska Native" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Asian,-1,-1;Hawaiian "Native Hawaiian and Other Pacific Islander" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Hawaiian,-1,-1;OtherRace "Other Race" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.OtherRace,-1,-1;Hispanic "Hispanic or Latino" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Hispanic,-1,-1;NotHispanic "Not Hispanic or Latino" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.NotHispanic,-1,-1;HousingUnits "Housing Units" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.HousingUnits,-1,-1;JoinID_2 "JoinID_2" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.JoinID_2,-1,-1;CensusTract2 "CensusTract2" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.CensusTract2,-1,-1;Tract "Tract" true true false 8 BigInteger 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Tract,-1,-1;S1602_C02_002E "Percent of Spanish Speaking Households" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_002E,-1,-1;S1602_C02_002E_TEXT "Percent of Spanish Speaking Households_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_002E_TEXT,0,254;S1602_C02_003E "Percent of Households who Speak a Different Indo/European Language" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_003E,-1,-1;S1602_C02_003E_TEXT "Percent of Households who Speak a Different Indo/European Language_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_003E_TEXT,0,254;S1602_C02_004E "Percent of Households who Speak an AAPI Language" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_004E,-1,-1;S1602_C02_004E_TEXT "Percent of Households who Speak an AAPI Language_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_004E_TEXT,0,254;S1602_C02_005E "Percent of Households who Speak a Different Language than Indicated in Other Language Options" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_005E,-1,-1;S1602_C02_005E_TEXT "Percent of Households who Speak a Different Language than Indicated in Other Language Options_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_005E_TEXT,0,254;B28002_002E "Number of Households who have Internet Access" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28002_002E,-1,-1;B28002_006E "Number of Households who Only have Internet Access Through Cellular Data" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28002_006E,-1,-1;B28004_002E "Number of Households who Earn Less than $10,000 per Year" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_002E,-1,-1;B28004_006E "Number of Households who Earn between $10,000 and $19,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_006E,-1,-1;B28004_010E "Number of Households who Earn between $20,000 and $34,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_010E,-1,-1;B28004_014E "Number of Households who Earn between $35,000 and $49,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_014E,-1,-1;B28004_018E "Number of Households who Earn between $50,000 and $74,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_018E,-1,-1;B28004_022E "Number of Households who Earn $75,000 or More Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_022E,-1,-1;S2101_C01_027E "Number of Individuals over the Age of 25 who have Less than a High School Degree" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S2101_C01_027E,-1,-1;S2101_C01_028E "Number of Individuals over the Age of 25 with a High School Diploma or GED" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S2101_C01_028E,-1,-1;S2101_C01_029E "Number of Individuals over the Age of 25 who have an Associates Degree or have spend some time Enrolled in College" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S2101_C01_029E,-1,-1;S2101_C01_030E "Number of Individuals over the Age of 25 with a Bachelor's Degree of Higher" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S2101_C01_030E,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Shape_Area,-1,-1;Tract "Tract" true true false 8 BigInteger 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.Tract,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C01_001E,-1,-1;S2201_C01_021E "S2201_C01_021E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C01_021E,-1,-1;S2201_C02_021E "S2201_C02_021E" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C02_021E,-1,-1;S2201_C03_001E "S2201_C03_001E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C03_001E,-1,-1;S2201_C03_021E "S2201_C03_021E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C03_021E,-1,-1;S2201_C03_034E "S2201_C03_034E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C03_034E,-1,-1;S2201_C03_034E_TEXT "S2201_C03_034E_TEXT" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C03_034E_TEXT,0,7999;S2201_C04_021E "S2201_C04_021E" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C04_021E,-1,-1;S1602_C01_002E "S1602_C01_002E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S1602_C01_002E,-1,-1;S1602_C01_003E "S1602_C01_003E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S1602_C01_003E,-1,-1;S1602_C01_004E "S1602_C01_004E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S1602_C01_004E,-1,-1;S1602_C01_005E "S1602_C01_005E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S1602_C01_005E,-1,-1" # 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>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Contextual_Variables_All</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><DeleteField><field_name>Tract_1</field_name></DeleteField></workflow><workflow><AddField><field_name>S2201_C02_021E_TEXT</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>S2201_C04_021E_TEXT</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></operationSequence> Demographic_Contextual_Variables_All 002 0.000 file://\\DESKTOP-J9TVK45\E$\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb Local Area Network Projected GCS_NAD_1983_2011 Linear Unit: Meter (1.000000) <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' 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>PROJCS[&quot;NAD_1983_2011_Michigan_GeoRef_Meters&quot;,GEOGCS[&quot;GCS_NAD_1983_2011&quot;,DATUM[&quot;D_NAD_1983_2011&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Hotine_Oblique_Mercator_Azimuth_Natural_Origin&quot;],PARAMETER[&quot;False_Easting&quot;,2546731.496],PARAMETER[&quot;False_Northing&quot;,-4354009.816],PARAMETER[&quot;Scale_Factor&quot;,0.9996],PARAMETER[&quot;Azimuth&quot;,337.25556],PARAMETER[&quot;Longitude_Of_Center&quot;,-86.0],PARAMETER[&quot;Latitude_Of_Center&quot;,45.30916666666666],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,6497]]</WKT><XOrigin>-28810000</XOrigin><YOrigin>-30359300</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102968</WKID><LatestWKID>6497</LatestWKID></ProjectedCoordinateSystem> NAD_1983_2011_Michigan_GeoRef_Meters 20240701 16422500 20240701 16422500 Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 13.3.0.52636 Number of Households who have Internet Access Demographic_Indicators Data Source: American Community Survey (ACS) 5-Year Estimates (2017-2021). Highlighted Variable: Number of Households who have Internet Access (B28002_002E) Fields/Data: OBJECTID (OBJECTID), Shape (Shape), State (STATEFP), County (COUNTYFP), Tract, (TRACTCE), GEOID_Text (GEOID), GEOID_Num (GEOID_Num), Label_Full (Label), Dataset (Dataset), Number of Households who Only have Internet Access Through Cellular Data (B28002_006E), Population (Population), Number of Households (S2201_C01_001E), Median Age (MedianAge), One Race (One_Race), Two or More Races (TwoMoreRaces), White (White), Black or African American (Black), American Indian and Alaska Native (Amer_Indian), Asian (Asian), Native Hawaiian and Other Pacific Islander (Hawaiian), Other Race (OtherRace), Hispanic or Latino (Hispanic), Not Hispanic or Latino (NotHispanic), Housing Units (HousingUnits) File Geodatabase Feature Class 0.000 dataset EPSG 8.2.10(10.3.1) 0 Simple FALSE 0 TRUE FALSE Demographic_Contextual_Variables_All Feature Class 0 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. STATEFP STATEFP String 2 0 0 COUNTYFP COUNTYFP String 3 0 0 TRACTCE TRACTCE String 6 0 0 GEOID GEOID String 11 0 0 NAME NAME String 7 0 0 NAMELSAD NAMELSAD String 20 0 0 GEOID_Num GEOID_Num Double 8 0 0 Label Label String 8000 0 0 Dataset Dataset String 8000 0 0 Population Population Integer 4 0 0 Male Male Integer 4 0 0 Female Female Integer 4 0 0 Under_18 Under 18 Integer 4 0 0 Over_60 Over 60 Integer 4 0 0 MedianAge Median Age String 8000 0 0 One_Race One Race Integer 4 0 0 TwoMoreRaces Two or More Races Integer 4 0 0 White White Integer 4 0 0 Black Black of African American Integer 4 0 0 Amer_Indian American Indian and Alaska Native Integer 4 0 0 Asian Asian Integer 4 0 0 Hawaiian Native Hawaiian and Other Pacific Islander Integer 4 0 0 OtherRace Other Race Integer 4 0 0 Hispanic Hispanic or Latino Integer 4 0 0 NotHispanic Not Hispanic or Latino Integer 4 0 0 HousingUnits Housing Units Integer 4 0 0 S2201_C01_001E S2201_C01_001E Integer 4 0 0 S2201_C01_021E S2201_C01_021E Integer 4 0 0 S2201_C02_021E S2201_C02_021E Double 8 0 0 S2201_C03_001E S2201_C03_001E Integer 4 0 0 S1602_C02_002E_TEXT Percent of Spanish Speaking Households_TEXT String 255 0 0 S2201_C03_021E S2201_C03_021E Integer 4 0 0 S1602_C02_003E_TEXT Percent of Households who Speak a Different Indo/European Language_TEXT String 255 0 0 S2201_C03_034E S2201_C03_034E Integer 4 0 0 S1602_C02_004E_TEXT Percent of Households who Speak an AAPI Language_TEXT String 255 0 0 S2201_C04_021E S2201_C04_021E Double 8 0 0 S1602_C02_005E_TEXT Percent of Households who Speak a Different Language than Indicated in Other Language Options_TEXT String 255 0 0 JoinID_2 JoinID_2 Double 8 0 0 CensusTract2 CensusTract2 Double 8 0 0 S2201_C03_034E_TEXT S2201_C03_034E_TEXT String 8000 0 0 Tract Tract BigInteger 8 0 0 S1602_C02_002E Percent of Spanish Speaking Households Double 8 0 0 S1602_C02_003E Percent of Households who Speak a Different Indo/European Language Double 8 0 0 S1602_C02_004E Percent of Households who Speak an AAPI Language Double 8 0 0 S1602_C02_005E Percent of Households who Speak a Different Language than Indicated in Other Language Options Double 8 0 0 B28002_002E Number of Households who have Internet Access Integer 4 0 0 B28002_006E Number of Households who Only have Internet Access Through Cellular Data Integer 4 0 0 B28004_002E Number of Households who Earn Less than $10,000 per Year Integer 4 0 0 B28004_006E Number of Households who Earn between $10,000 and $19,999 Annually Integer 4 0 0 Tract_1 Tract BigInteger 8 0 0 B28004_010E Number of Households who Earn between $20,000 and $34,999 Annually Integer 4 0 0 B28004_014E Number of Households who Earn between $35,000 and $49,999 Annually Integer 4 0 0 B28004_018E Number of Households who Earn between $50,000 and $74,999 Annually Integer 4 0 0 B28004_022E Number of Households who Earn $75,000 or More Annually Integer 4 0 0 S2101_C01_027E Number of Individuals over the Age of 25 who have Less than a High School Degree Integer 4 0 0 S2101_C01_028E Number of Individuals over the Age of 25 with a High School Diploma or GED Integer 4 0 0 S2101_C01_029E Number of Individuals over the Age of 25 who have an Associates Degree or have spend some time Enrolled in College Integer 4 0 0 S2101_C01_030E Number of Individuals over the Age of 25 with a Bachelor's Degree of Higher Integer 4 0 0 S1602_C01_002E S1602_C01_002E Integer 4 0 0 S1602_C01_003E S1602_C01_003E Integer 4 0 0 S1602_C01_004E S1602_C01_004E Integer 4 0 0 S1602_C01_005E S1602_C01_005E Integer 4 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. Shape_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. 20240701 iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJzsvemSHOmxJXZij8g9sxagsHSz+/KSl3NnNDYaM0k/ZTbPoAfV O8jsmn5IY7KhxCHZTRKNBlBrbrHvsuNfRFZWVtYGVBVWb8sGUJVLZMQX/rkfP37cnM1mNa6wNMuw 8H3ESXrh53V95UvuxZI0xWzhw09LDJ/9ANNyoOn6rV9f1xWSMIBp2zAtG5p28bVlniNNItiuB8M0 5fdFUWC5XOK/Tmsscg0AHzX0skCtG6irElqZA7qF2jABjb8HtKpEZ/ka/+nAAz/lL7McR/ZT1JoB zT8B8hj16Bmgr72G71uV+N/+sIff7o/guu6V3+Xnn/6E6dkJ6rpEt9eF7dgw5ZjVe32z9zeeQp5H U6tR1hqqevva5pk2DEPOu8vzbxhyb3Q8D7Ztw7jD2vxmH2bmdb/UdR22aSHRMlRVhYc2LhY+zs7m CPIa1nAXluO9xxsBdVlC438bzoqmGTpMy0IWR7C9jjg1Gr+jDk0cD5dtDQ2VYTWvKnmAqLk413wF n5dVNd4cnSG3+zg09pRz4u8G+9ceJP9T73C1/fjj7/HixQ+YTk/w6tXPcIsc3W4XltUe1ze7q3GN 0VGJb6prlCih6dwE5J+XjJul6zjoc8OwLHltt9OR++OhN+9vdgeHxatX1XRUD39RyrJEkqQ4mc6h d8foDDyulPd4J+UIaolAtkchdVmhyHMYlg1dN+Rn3EH7/T7+RYtxuExwkgABnIsvlPdcOxd1DaMu 8EQLEReAT+fmbLzmCuM7zf0Iy64jUZ5lbndAvFm4i+/uPUGn08Nf//JH5Fb+zWG9h3FDyvNcomlH om9LLoTBc89NjiHWFUbnpGv6KrLln+3fvzmtT8BhMQ2MkljSs3YTaS+Srmkoq+reLlSWZQjjFH6S wxzsweoOUHHnK4r3i9LKUhbYpbSprpFnqbwvoyou1DbV5HMZtUz6GqKsxGwjDVYrmt937T01DZVm YOnsoihKpIa7SvuuPUZGbpqBP85yaFaIP5gmJgN1KdaPuT2/PEZLt9Htaej1hoBWiIOnk/1m5+eK zigMQ7lMpmHCMEyBB3iu1LlV13q5nGM0HqHXRKl8fr1az5evHy+JYegoykIejNxd1zm/PhKtfYu0 PprD4g4UJwmiOJGLLXm+RAEmTNOQnD1JM1kgdFzvs7DktWUFvjova6SVhtLuojPag24YyNMY5XtE dtwlyyJvnJEmC5E7KzEovltZFrImiW+10ZUsNqYJgIT8yhFsOrtmZW9EbsS3fHsCaAnQvN9trNJ0 vIkqjOYRJjYxlEqdZ12XBz8hKQr5WNey4Mnx6hiNdzCbHSNLU3idDr5247UjnpSmuaTmSVaj57rw vA4sy5bfVyWvfS3r2veXTbTUXENxVuov58jlRWNkxbWf5wWyPFdQiW2p9/mWFn58h5VmuVwY3ujc WQzdkB2l47oSSvPGCuMYYaQisOvwrRaXonPin0VVSeoXxbF8TgEdut2BOxhjOJx88BeigyryQgB1 Wah1gaoqJQWkAzNNBcS3zorG6CjJclTcO3UNSVHiUmyntuEtaaqG2rAEkK8F/breCLhrq924RpLm OJ6n8NNUbiLbNOBxU9A0zOJUbp5xt4MnwwF63S6GozHm81MkSQbbcT77KKtdH5tp1g0vkgic140b ph9G8OMMldWF193B3pM9PBmNLqTN/IzZ7FTO3e7eHiyrwRm5edQ1LNNQl7g8P571Y+KGzQ08TlN5 Ltf8enr4LcL6iA6LzooLgRecTqrruXJjrC8o/pyRFh1aEEZbL5g4KEZrcSwVuCgtUFsd2N0B3J1d jBxP7Wbyvhdv9vP97o7WvCwOl1INZKTldLqw3U5TEbx8U5z6EX56N4VfmdBRo9RM5LrCNzbf97LV 0IpEIq0WbL/OjKqAXSVoYjvYhgvD7cJqoiUmL2HzXN0zJTJIyxJBHKHf68F1PTx5+gInR28xm06x s7vz2VYM2/WRJIlEKtwM22t0nXFtxlGE45MzZJoJd7iL/sEL6KaFMk+3FlqyLEWSxCACoOsX1wAd Vp4TeG/W4QWYkpttiSCKkDabeFnpssF13G/Vwce2rXdYt+PBcWy5PwkG0yltLiL+u62YMAJLsxR5 Ua6AzSgMMQ8TlIygLBfW6Bn6pAPopiwsSdnoBK88NJWmSUVHfeCtvpBumHC8rkRVaRhIesn0jwvx qhuhrAG6kFRvAHPumLcF/CXPLJhj3pwS1jVGjo7vel306KA0wDV1uPY55aF9y9aIlxA3yZoIld+h 3x8gDJZYLKb4lI3OKIoiBGGMfr8nkQmj8kUQI8kKBKWGsDKRwcDLvgnP5N8YgTKNL9X6aEp3jEpV VRVCGeH5dve/h8u1ZDswSH3hZ16BrTLa5nteRQkhTkhHVteXk0I6NGYEVVULpcFzbDi2WvvtNfkW ZX1Eh8XIijk77brdTnJ5S+XytmWiKCsB0Bd5gdNlhNJwoNsuTK8Ls9MTEPS2jkd2Ok1DniYr7Eic T0NBuPp1muzSBkwUaaIWYvNe20wwjloTx1rfGCFte48WBBFSzw2vr+EYGsYdC0NWQbcfENK8kHTZ sRVuuIwy+GEK1+1gt9+DaVoSMdaGjbdHp9idjOQG+pSMGOiZH+PNMkdQ6OglCQxdQ5QDYeEgKywk lYakZmSq44lhw3VM1DodVMWSXkMhabGmtVRNN6BbDuxuX2FI/FlVCmeuowOWvt15Eirg2t66psUx Kg7f9kiwFGcl0Ijnyd/Xq4TfHNbj2NY79C4RjVTXGkBeOEmpKSBokpfo9zrQbWcVUd3F2l2L4LuU mwmUW86NDmsz2mpDejqxq0w5rTsd3sYbQEUAK8cl32D7c+XXV59bph1TP8EsSjHyLHiOhaN5iCjJ JP35H17o6NgWokLDWWHj8N0hLMfBDrHGj4xnyXmsKgRxhuNlhNfLHK8TG4Xdg7GMxKmUVhc1uXVG 64gq2CjhdB10+wTK7+54WRU2qhJdvYZnO3DlPNSXHVaRw2ywq8vHzkhKOStmDFKoWfu9pKwOcVxP OFnr3/nW2Ns3+2C7GXS5g/GS8aIO+z3sdi2YVYjldIrccDFyfqPSMu6gqxuWW9oV7yX0CV3C8fPS zd28Cp1kkWfi9Ayzv/1JvMkk7L/NO151sPxVAa1UUZZEBgTgV2t4jeEuKfTVnzBbBng1z3CcWxhF MTzNR5jmWBYajt74sFBhr2Pj1TLF/zVjCjTE3iKCY0BA+Y/ltGRjqCpJnf70ywl+yVwsWT3tqkiy sNwH/OwKtmHgYDKR49hWuWM6yMdWuov8voah6TANFl50ZPl5RNdWBUkcJc62/Ri+0Ro+O4fVGkPm f/3Dv8htejad4d3xKc7+9v9C9wbo7+zD9rpSzRPqQsORab3SimIgrRAWPNdVITn5YHfcxBhVcZFe x+dq2Qrvvd40HbXThUYcq1KfoxXV+RsyHeUNRIzrFp+hnLouKeq8srEUF2Wi0gpkWY5/exPAqnNk RYmMH2d18NfTM0zPzvB85OHHH3949N2e14eg9NvTBX6aV1iYu0hd71ZFiPswQgVxluLP//gHJr0e hoO+YFUXrKGjXHeduXEVVQ29LgTP4g94PWQTHvQli9g8s1fhV99SxIexB1lR3JHotGi7O4pktxtG mPkR8iJFkZnQdGPFNheOFNtiDFMW3zkIaqh/02FpmVT9kkjV0LhwFL+Kr9PXUjH1h+m4qrDHSO2G Xi+CuR/UeKQzHW77OprHOgmRDjjPoPFGKG3o2hVs+LpGXNRI2AXEm4dRWhbDLGK4eg3LchGWrFIZ TUhYAoYlOFCY5UjSqwsL72MsnvhBKPiN59ro9Xqr92eKFYaRVMvmaYXTVMNZ7mGqWyjNzi3wwPsx bnizMMHMD5GHAdx5hn//0sJvOr0Lz7NtRx7zxULSzm2RqKS0zd8ZadG4dlrIY8XdWv988udqRabm 6/n+BPiFr2iY8Dzvo6fqX5I9+KpiYy8f4/EI3bMpfj6aC4ve9XrIkgieaSn8QByP2gnFUTF0bx1N A6S30dd5ZEQWM1nftfCr+HypJVUV0ihUrRfCrdJvxK9uF2Bd4wyuqhC2/C02TjfZ7VVOheeAHLCU EVr7tkWKrl5gTDyrY2Ia5ViUrKzx0tWw6xyjrotO6qrv32AqV70/HU2aZhJJEAQnWM8IYttz4zhC kJIsWUhER4fVOrJ5kODVLEZcapilGpa1hcTsAu7tMcYPMeFPVRVOgxTvlilOA0bSJtgj9fLJRfyJ Rvwq56bROJarzpOsRUnem+ZoiforidK1psLYYnU8DxlxMdOUVDEKI/yZ9Jg4h2XqcAwdv3+xj2Gv I8/5ljZ+uD3ONtgAmbu7O/j59VuEQSYLo0gzmDZbYBpyaVGgKguJtIg/CSencTam7chj3eiYyGpP myZmcWpCHM3gz86EPEpAmu93lXExygJ+H87XbUxWvaF4WrUFmFdXK5Xzrc5xO3GRFQauhWejDnqs UOUBqjzDtFZUDa/wMe53YFgDlMiwDCOJBuiMJArleS4VyZGRLJ0Vn5PkxGwqjAdd7Da4zHrVSygJ YQTdHQFGiShaqoi2BuZBhH9ME/zRt1C5A9S2ij5s7eZm7i3fHE18eqcrwOscJak4zeO4RgYXMF0Y ZYZiy7ImDWTpzzAY9FaRWctU33YtkpTxba34h3qGKDbQ6XjCPWRhhGTVZRBJ+xqdFfGt07MZ/u+3 KQ7DElpVwKlTDD1yGS2J6r45rM/IYdEIoj8ZD1BpITK2mTx52qR2tTimtgLI1E/A8iwRTpUsqm3c mS2E09Zsx21adEw47tUtLMpZNVjTjfZhwCoridSC2KogIb2MtnCybKNC1ARZ1I9I0wSRX8CuO+j1 +zDTCHUSy+8WlYnIp3PqwUyW+MfbP2F30EHfs2AZOpK8wvEikhSl3/Ew2tnB8MXv0dd0BPNTZGWs iJv8/Kb9RxrR41hu1jAKhFVv1pl0KPhhiJ+mGf6ROCg7bElSqXAXGfZMRham8Nrqm05ZeznJGK8r WC0H7paWlxXeni0xz0zkDQ33OuPasi1n5agkom8ay9etjZ5Ojo/lnA2ERmIgkIjdlE311eEJ/u1P r7EsDSFZ21oFzzaRag58bw9VzwWKDEU6F6fKlp76PURHvtlHdli00XCIgCX5pFIOqpZ62oVUSSIr Q5dydZYmAr6vR1sqpK/EsZV5Jr9fRQdNtMZozDYYdZGxfg09tVIppCInKlrChXvsOorCne28BWWr UbbE0tExNSzSSpFXTRuFoUHcU5xhXmvwNRe166lsUwOyxgH6GvC3MEQeqy4Efg6dR5rbsLQK//n7 EZ4OdoRmwt+53QHSRYrDo2PpZhiNx9IjysghqzRJR9k65PsB8jhAmcY4xAAn6CN1uudOvq7Qcw28 GKn0f62x5QaPdZ5uOSY3n9ueRuVU4px4Xksnvd5Go4k45bOzIxQlme3kuqUoqhKOpZQ6qrKSrg2f DrozRA4N8TLFyeyNbDbT7pmkxydhDq03RpcxWBgjzktEmo3S6aOi49V0GFqNTp3Cc3cUWfVRNE++ fHt0h0UcpBPEOPX9hhx62R3QMRlC5FSYjJSkpSS2/swGh5BqosKvmh/Lgm5Jpi2If7XV6FvAdz0N mqmqSMrpnfeZybMKqkDoqKxi9bu2R/KcHd+SD9fUrppVKlSyKoclGl1X35nsJbRZRGjSQlYXY93E aVlgkSYIDB0xbNRMLRugpb0RcoNVxQ7OEmtF01DJlgVXK5HqTJG91eebjoOqO0RaFvCTAFgsBT9L Kh12b4hseYYkWaLMYnmfX/wSU89D4nSFtLq6XuzFM3R0PQeedzf6gqTwBRvhL1dymXaREW+YBsa9 jhw3HVWYZDghV63QkW8tHV92DY7jYjiayJoj34oFn3ng42S5lA6NPCDJ2EBhuNA6HVj9kVIMyRIU ialafmoblWHD7Gp4PiZNRpMo7yjMEGsuYClnRbO0GhOrRMfZDvB/s7vZas3ikY0huMMKSpmiyFMY bBy+AhTn4rIct5GEyQWDaZv6yOdiFYaR1wWMitEXw/21qOsmGzkG+mztEKJpUzVs1CeZrsq/K94s JippHlGOjL9jBa1gmtFI1axAfCpYrhwWOUpC1RLneN1x8TfETqSnEWRT60hqHXEJaJmOmqB2G9ls vI1qk1LY1SYmp2vkJ138bBY33P4IputhcfQGIc+xbkuvZ2+0gyUdfxjDZEoDA6f1GLk9uOCsmpOu jvue6RTsljgLEqW6AA2moUvP31mY4o2vig+bLVSrOu0WmgGd1t7+0wajSlDaHnzNQZYliNIUhmnD 6fbhrYlG1mzz6g6RxCEMKkDohqiktRuhnxY4K/j3i47a1IGhrcFpOkG+2e1ta+vUx3JYNIbgHUtH tJzB642E2iAN0OrILj3fsh153MaUcylhtpjIJvFmI8Wjc2M1kQzpm6OxK1KTyBec7CYWfpbEKvq7 ZgEz0qiLDGYJlLoBC6wuMrVrwPg1QP7SsZSlRAuEse9i7CDYefHjpZ8Pdw9guz0cvn0jiqrZ5FlD in0cW2QVTnKDcBBOgzN4loa0rBHVJlKd2mOXX0PHRgIrU8WrjDjW0g9EcYTOmi0+F95jbc2oZmdK LBmyUazDEs1ftl4OceA6cUkWQL45rNtay2vbdFqq4HPXlX1PNhmPBeuYLQJMl8ci3FdoJiyvD68/ UNIvH7hbp7Hia3E5KXxMAetyMlgdahqvq4KyM+8Zsrcn97bFsbaB95rvNhr2kdc6ktLHaZbhdzs2 XFPDPMzweka8SvXNETdiMsgURbC3NEQRhVgUTAfvJ9Kh4xfqRLePwY6FrEqQl4Zcq+qReFa8foVu IoAhhYiKfM4P+HpMB09nU8zTAqluiST26neV6oUNoliazen0JMIWeaIMhhWtNjVFq6gxXUbIUkIT usgMtde2qIGA0mvU9v/WtnOjbSq4bkbI7b8/isMiCMk2EjaQdjsO0ryUR5RmiJcniKhJZFgwnY7Q Fe5ywensLAGkmYOt/WJFXWgwKWplaYqfIz3Z77sLrg7tZo/VwsPXfR3eBKyAZXL4mqg57HRt9F0b rqXjKC4RZCW6to4dz8QsyeFnJTwbsLomfjq9mtVP3IYUEH7nNpplSr0tMhShRaZJhonxeAynV2G/ UMc2jwucJTnCQkPVOHvhdzGCFCqAIgB/aCrUJP8qapLHPVgNqdwtogTzrEKY10L74JXJK4omKuJu uc7Pq1lVtKHl3PDO30fEA1P1eqrAciBJbTrS2cBN5zS3cDpfoOda6KxpwItaLyvjomRKHa6vC47X rui9bM/DdefjozisFRu+05EHjTubH/hY+qFUXXIhhcaoohw5F4fecLNkCs41GJAodm6/AduOf+Fe Scqg4q/ryJz3aqvrcPVnBVGKWVwgIF6mG9JEzlhq0HHgWgbcIBUVBDqsp0MHg7jA23mKnqljZDn4 fpDhbUTGPG86bXuE11A5pMNAHHvTtL1+mA0WSGDeMkxYdgktTMCBQhl5WXUm6VleWRLxhWmNXxc1 rBhybEJmde9GVdg0Fh9MvW1zuqeosQamcY43ywwnCZ1/JSRa4QXWQAYdlcZIaYuz3WyHqDVoVdMz yvVDYirPMCu7MOBrnswoGHVsKci0pFM6d5GqMU2Rcnof06654b9UZ/VRHdY2MH5nsiMPAUWTBLP5 HCdsns6J55ionQ40dFVjsTDhb7+Ly0lqcbI13o4p7OcPiATu1IfY3HjXOMcFHVbCip4jqd5ZlMMx E9mJO56L7zwPA/ERJVzbkkgVRQqDEL1m4X966eHffonwNoSw0NeNPCu302s6AEgVYuEjQ5Yk54fX mgaRsGHESqfG7oSfDqdKhqfBicySirFsq6qxqAzMGdzpBQ6cQnCbD3VYJF0uycnL6i2zhrYBWIxa 2GB+tXvjcb/2c/zkA8tyTRnilsxV9e2V0KJcR4kwDSlEsP2KrVTKabmoLA9hMhO8TOFfirPGQg3T T655yjJdOARN6XKp79pUvDfWuLZ202/rWfwUHdfm8b7vMX4yDmvdhCPkunj65An29/bkwgVBiOls hpOTVzKvUHN7UuHyuv33T+fu0W4JYa1m4W0zLuK01pA1kRErYEeFjcVpgkWc4V9f7omYIpVH2Tie J7Fw0J7uTcT58CBY3fvL2c84TUqpLLbGe4BVvPXPlkEc1xQK2rSxKiggWOOssJBrlqrM8d5zNjoE moKGrhOkxgfb/rALz4oRxaobgpaxJaag1Pa2M87CQ4GBwc+vtyuVpilOYp6bu98wWl2ioxXSjr6s LCmKrIyO3RtAy2MVnQkhtobnOuh2O6JgIg3ZNZCzDzNW8uLvjk/k5XTwhEjIiJeGf1aJWX3WdInC WqelNSe2BaFXx7bWM9uSXz81EL11VB/iUD9Jh3VhQk9zEZS0hyWAPRcrcQhO2glmS4Rso7ApvdyX dpxHBTnbY2wwjes/u5aIpg7WFSpWW6ua+hLFyDNFQWjfXzTpCxJlM+i6K/gQ6R5sGYlD8tkUxYPK quQleSaJi2rBym6/lvZeOL5VxHmbymulho0K76s97u2vpnNba4d8b5PRax3vQqTGSqnSrrriSMsS HZcN9ZefQGHEo7mPsNRQ1BfnS97G+PSBrWFoAoWfIERng2SsKQyrzEXjv7IsHM7P8N0+oylLCUmK b1NRFLsCsjwTx0VqDB0qnRn15FpqBq9XTCFKRl4arzMdmxoGQwe3zUm1982HOofb3kc3PW/9OD40 +vtkHdamcXfiox0Uk6YpwihCGFtCLiRInZcRMipCcLEatpAkjasUJt/DVL8jdb0bOYWmkkYwu02z bjJZeFt6G+kU2G+51/fgubVM1Wk/06g1dE0lx8PPkV5L47wRl+8pfDT2EJYVvt/ro9QT+JlyLX5S oMxy2Pr7L5a7vDIuOMhDTSu6TcouuA4jROkNvVw0kFJJy69r9ayuuKT0Q3QAp8slAt74bBtyHXiO I05hHiUSwb5PIZVnk2uLDPtm0M5lY9Qlje7EBy3MogRJXlzs5OBGQcUIKkFYivtHR8WIihLPPM51 I+Ylb60pRySzFBqHRafedjUoHp6qgLdUis21v1U+unF27UO9J53hefeBHF/TEdK2YymAQzlRUSje sgF+qNP8bB3WplFhgI/JWP2b8+jmiyUWQYiIGAEF9bQKVUmypy5pjJqWs9mXePuVqzS8OMlG5us0 KQgbi9VFvNkxsipnSeVz28BaZmcvOucN2e10GCGe5sW5+qreSkabgkmtG8mV/3Qwwk4/UU3PJGAG GWZLDZPOZSXOh7C41hHllUQKTG9utIZDJj2IWyKjVoFDDRU5Jwpv/yY1wop8rRJ6nMgN+KTfhalx riCwTPL3VpflGlrk1KHnDAB7tQoumUTcBmp2EJSG0HboRDtr171VI6Fz4RqKEnWsWx1KEzlV/F/r zFLlwMS5yMwFNRCmTS3boTGMxpST54s0parakJlbAnTZkJ/b5nsqqjI4EKenNUOOmyjw/Cyrz+dn USa9/U73TR7+YhzWpnF8Ox/PmwscBAGOTk5wNgsQZpXoy/cmuwIkt7yru0RedCKMplhZc7u9axUg 7myirrrOBbvMCyt1xfS/6ZjbMep8tPad8NKiVaTy0MYCyTIpMA/iWzksYaGTeMthElukrCXdKeiw E1HfuA53u/TerDY38sekLcxiOqz3v6kyzUZ2iem/YQaZ+Tq0PMHCnuDtIsXBdAb3mXfhhubf6BR4 jiQNvOO1qdrzshGUrmNGVJIQLa8mMivbSKqplnNTU1XLai2K0ySiUvJNauPZNjhZ3rujtMLYBC6p qqS7D4cpfzEOa914wui8vnMcPDtQIXYYJ5jOFvBPT5Exb7C76MocxOtL5gRx2RpEEyXQnI6DKecj fqG1pu4Gtb/DC+sHauS+zjSEeYVFWuLgHt/1fcRr2t5B0gh6jo0/HIwx+2WG46RGynWw9VM2/7nR 5nPh91cQgQnC29w0avxlsUBZnOI/Zjl+8/LFShurjahUukWY4X42k3otDSNdiM6kTUk325Y2UzY6 MuKo62mfivivGAfol9IwztfJRCHXkQhNFFg4h6GJ1DZJoa3TlAiPw5qhugLo/FoHuRpys14owhdq KlRWfV88QRzNJBIgWSGYQpBkWM6PZVqw5ZIqsc0apnOTslSNeoRT5Iopf+ed5AN1t+5ARyIWJHgb R6tTtaHBQaTyxJ7EdpQWq3rsobzifbjESTSt0hR7dolSZ7UNSEsNQX25l681TsQJciX2d9VorbuY ev373dBhmqGqFmpqU5nhZQfoGBWStnFdfYKiQ2z2Z0rVc/3fa5F5DcwCDYtcMrTNA169WaB3REWj +nWB4XCA8WAg78EbU6arV6WoRwis0EQ+dDSMdATGaGcbvIet2rnuYLed5i7YI9PEJlXk2pJKd9pE i6xM6yplbZ1Oi5cpx3XuPOmYGJ3xfLAQIeksI7ealKdzp/XFOqxLMxTJeWm0j3gTLX0fxmyJRZih yCJkMUdneZfwrZY70zosrtKMWBKDft4A7Q68No5KfabCJy73J37ILnr719JZ8dHuqufH0SpmlhIt Kg35q8NFRTCtpJT/3cgVB5UVBWYxp+OI6PllL7rC35hSqFmAH2Liqpod/31OX0pCaMrvWsgNs9Oz 0XGZIq4L4SiA+QIq1fic9mZZVVrXj63MEZXlZYe1ZpXpSl/kPwIfrw9PJJViEYAOi1SdmKA8oUlR /FBzMpmqCUaUkl9HegSrw60Q06dpXFeMuvhYNwXet0UkRVLmz1SKqgazFBwBl9BZqeKD/I4YbDMv UvT0eT7wFRq5LjuTCcajEX598xbHcx9xyOqILooNvIFXFR2DAnHKWHHkyVNpodKg55/iEGQbpjPg 7lGplheqpIKN3erOv23V7Cq7y72qPsuAI1jW5UVOZ1YYWdPKdD3OlKfEQAzs9odyB7NCi0WMVxFv 08vwjNDTAAAgAElEQVTOjmWOnlFjYN0vnqGqtFkDPzcOrN0oWmntLTc1Z2Ou/t487muKo71MoZON v7o6VzgU00ZSevjzGzVHkjckydHz5RLzRAHhVHigOi17J+1ODxXB8CSCWabYn6hp63du0P8EjFkK qbaKfqPLBsaIyjZNmQnAMyYV3KV/Pt5POMhqjiSli8R5857CV2y8mZ4/O8BkMsbZfIG/vf4Z5mAP Tm94QY5ZqZ4mooDKKh+F7/SVHLMibCpndg7mCzaRJYiDxWoRs3RP/tSj2Opmvqr+36Ytt3mzu+Fm IyPHD5MODia9C/jFe5ssYuVkF8dvUfA6cLMgfYDnnJUx2xVMsjMYP+pNLaPb6gp6rdIiRqBb02Sd 6Y2Hv8Yd/NMshFZk8ONMmtWHBwerQoLa29YagYn1lDlOjv6BnarGsN99ODnvh85yLEsUXBlhthtZ WQWSDrebuSjWEtNqns/n0uG1KfFX7bBoquXFW/15PF1gvjiGz7YWgn6OB8frQKeGV0XNK0P+LmJy xIGKQmgKjLba0rH6fw3LdtcGyCrn8GE30wMs1Fu95d1SMdvQYJuqvH6flodLjDoWvDGrbdTGIjZF ki3xRA1pPMN8OWUPEjTTlnPf6Q/kz7u2Xwm/LlNYynWbzMjRYI4M1E3ae7hMMc11JFtiOPbD5s4I f3wzw7SrYzAYor97IM52HQ+9kHI29AdncoClf4aiWGBnPPrs1EtbvIvpoqLsNNQI+X7nLHhVnSQn UclY5xQp4O8SQjnb1Pq/QuNJI0bA6ganO7l+hGWcSMju1BqKKBUGeU58UaakGDDlhJYS0p6nVBfx r00Mi+PN2qZaYY43YGU7DOFTlCFp01+y6G8bZZmCT9zfdyHDP/EXsLUSg467ahguS+c8zRZRvhSm nqOsOZY+Q5FUSIuY0qrQLRc6hQBXSqwN4NtMLl8vDEgvKx1fnsrfI6a9IindbzoMlDQRzbMNOIat 8E8C/EkGn4oP68XZlfx2JY3R7xLipSZ6lgd3k5O3xeiw7E4fSZEjTnzhGw4G/XtZL+vf+X1eK+u2 SemuspYvxmiJw2qJIaeZclz8OYsOfLROS1j6zXsKnaLBnb9aDOsq4wlh68+g3xci6tJfCrdkGYSY hynijFLNicgAc1gEgVFqoJeRj6qRx5WTuiZDvPEJ6sbKchzNQkSpqtzZVGGwTXQdNX3GkAjFUOzh C68+V5xo3u3CW98pApMd/eYKEm98OcY7pLKMsASPuQdrB+4mixPsDTgm/jyd2MTH2Nzd73NxZ0ji BHEco0yXyFJDGpFr3UKUl0L6LDVDiJ88313HQt+zBSuRm6MosDg9FsVQy9CQxGyV0UQu2bA9cVjk xDGyY5StyJ+FRH1MD4VH3JJ/ZZpQBc+o4BiagPOl3ofRdWSdMIq7TTeGUoedIC5LTOeHGI0G4mQl XWIU8h7ntqUN8Puuk1al57Q5t1f1LApB1VStQbL5JttJr21/JD+nK7NKFW2Ckda2fsdV+xgjsi3c r28Oa4txIQyHQ3nQntYVjo5P8ObtO3RHI3z38qWkj34Q4PXrX7FYHsGnrlKtJs8Mn36vKoirDnX5 fyP6xgUX4H//P/+Gs4waCxo6JrDb0fHP+x1JpQaejafjLsbDfrODNZVKEv2KUoK0c6x5jYi4VtFq OTRXGRdcfosmWSFirC3gG62uRcPLeh+wfTUvEBdwvyqN0DMr9Lpqvt9NRkllPvqDgXoPjiyLIkwX Zzg99fGrNkHsDEVRgfFvHyFe9mP0LEbRlWBL2czH5MUunu6OVrSWP/3lZ8yWvrxfFvrQbQ+D3X3B OFloETnvuIZeOuDsbvlKmo6RXeG7oYP9UR+zIAJ7ufuuJthU6C/QH++uOa3z6ynnQk6F+pnOiJ0Q g6GwHZKDeeNHUXyJinAdCUScDjdWy8J4OBTgn9Fp+ztxMLaaF8pKJqObzWyEQ0tYdeeGQo4jJyq1 x7naUDVN7pNepyPv1xorga2j22bn0+Av2xftsDZ3hvc14h+sKg5EDVVRJGjU8vrxh99I6EppYpZh 2YX/06u/oCQbWucgDAdOdwCn05ULL4BirWPQdXBWajJunhSpMACOkoZ/oqcYuyH+/ZM59gce9sc9 mUR8FmR4dzoVx9ji5a1bcLRC8JSnI9Wqo9K4a+RdWprAfZsGLKNYohMu0nYenwgmNjfd+rUQtrVI yLCR/VR+7/WH0gfKWy71p9CSJXZ2dt57mANfRyKx63oSQY+OZvg5jTDlUBHDgQ8bP4UN34rDMJIS u3mKYLnAKQV1BGNhpBfJBG6z6yI3+T1TnP36D+HmESrgZjXZ28eB4+EJeO0tNScgqVGc2ZhGnnxX vcgQTUsEMlMzxKnTwejJc1HaIN5Gx8chw5TUlgFu4x35HkkYyCSjp+M+Rv2+qqBRI043kBeq/Yek Zv7ccWzhQzF6UimvIb/nWmakQ4KnKg4p5nubijESGg36itxaMRvIMFvMm9YfpnUWBr2mmEKnww2C EAkJooaiItCJ0iEyU6Ek0mamsCJBby4dEk35HqL0Wn25Dms1W3DrSbhNj9/NJotjo8FZmlAb/Kq9 6N2O2wyQ0NU0lqJAUcWiKvH2JMSboMJpZsGnUqUMqiB4z3ydZMxW6akWCR0KGfacAru9BBMPsLUC U3TVCPuWF9QcS1cDPEsXoL/9ZVu13DxXohvV0DCuriIqZQlFFbiFo+D7UqUgTzAvl7DjKSrfRb/X URQQLm25Dow9NJhuVyYe1XmKKlrKOetbTIlrlOEZ0kAdq4USna53aX7gXUyV0xWRmGzq7/crZCcR ijjAQmd6qIOUMh6iXRaYlEv88GwPA5etJqr/Mo4TPNnfUyPnDEOoHb+8eQe/LNBx+zK+jlSWyWQC 2+Gxqs9kONyql5JkVRGk52SeJpIo8j6m8zmy+TGCJBCnx42O5ysKA+RU6WgmClVJgJFniuQSz0eL e3IdFqUljoJN0aQ/0GHRkTFCkmqbQ+0tpRghLTWW2bDaowsqD6s+REamDbbkOSweKYiC77HugGSA LrOKPrMBKkuk8r147RQZdGODaqI0Or6CU48akJ3G4+11e4iTeIVxfVEO60KbwZYUqBXtewxr8RTP MPDy+TNV8chzAe5FjHBZ4k2d4nie4v+bq0V/lQQLXVZaAr/6NTQ/w+t5gj23xsHAgDYccHDjpdeU BE5EduY8qlJjyBT7WI1WJ8M/xzQqxLl5dYKe50jRYNO5CZaTZUoz6zZ9k3UJPY9hRWcYeBp6eg2n SmAXahdnMUMyW2q0VxpqihSS3V3m6NvqRul01a5P7CnLmIbUoovWqtLeh/H9qU31Is2RlRnKbIHQ 6EgFj85FZ69o7mN//BvpjFhhOHWNTrcrNyHPDZnoTB/HwwE8z8PuzkRV81i42eLgpbm7uZFFcWMN I+LNyzUCjXOSMpHsZ9TCQ0qIkaVLNejWqDDq9jAYDDZwPCWoSKMzOXdO6rwyumodTauJpmYRqKiq dV7teiHbvAXSxWG5zqowsfndVk6O492kck46Ty5ps2rKvnz/8b0Y5RED5mBeeW4z1JYRGc8tmVjb 4v/P22G1mMcWAPZTYtdzge1RiLCu8MY/xR9nVAO9XXrDiIOR2KIAjKjGzmD7ReMOzlCaNzoXID+f WIFMHmbKmheyOH45CfHfDmPsuzV+P6rwm6cTSVflTdfWFtMBAsmw1cxH1YDMRaSGMqhYiZFkewAl zDLGLpb4D08PsDceSkR6VXTr+z5moS9/3919eeF5HxJN3dZ2R33U9QLV0Sle5yMk1gClYcmNH6b5 qpetVT2g41xZU9367uWLW0fw27se1Lrd29+/8nV0IFmaCrbpuK5EIK0zafFK4pqEIqj6QJPxc82E a9d1Vk5qk2YiPbeeasjmqF5GR0wB2cnQBgK8hlLFvkGjvz0HdEb8zDwjMH95AAf/JaTRBiqIk6Tp hlB9iGpM3jnv6otxWG0Y+7mMUOKFI3/m2dDHRDvFISa3IgD2jRx7QxfDvicsbu5a24yzC4/DHOa7 E/z4bF926FM/wp/fzZHorky5KVIN00jDLNWhp3N8b+SoJ444piQKLoC+/L84rGbhcJDo9Nd/oK5L OL0RLL2Dbp0iqC0R9dOqAh2jxn/+55cYDXqrVoprB+o2kdPHoHMwWtjfGaPX8TB6c4Q/FQYW6Ekk 2DVJzVBMdPZg2o2U0brM7yY88GDHSfmWXlci2KquEVCplOxwTVPtOtyYihwRHRYpGEkiaWDroEzL FLoO08C2/Wjd2vdRsIrq76MzId7FFJOY23SxlPmKBPkZAV1nvB8djuUThYirr6tw35vIX31PlW4y umv1v7aeD3ymdn9jCR7PmJdzwQy7NuZ+glRzVkJ9VxlBSGIiFvk61+zmlWaASl0RG5XrGsfEzOYx ApbwQXUADX7IHkrqZBb4DxNgf+AIuM3KUx3HsDvEm0ok4VKqYL2dp1LtzKIAyeIMQ1cX7aRaq0QM cLdrIc4UBkemt4sCvc7wEm5xHa70MU1SItToWsDLagGrSBGkFcJSl15TpsR+lIhz+92P34nw3mOb NEAbVJ0lX0lNGq8qVbXjjb0iMDepa5v+r35PWCLNZANjxMMNr00P24k9Iu9DjlRz3RT4bjb4ElQU JOoKNzdFq9cShWWL29Vru52a3mJdwoGU3sJSNgP+mxZE0QUs67N1WJ+Ts1LjoFK8OlnicMn2Hqoc aDBrpliK+0OTqdLrEsRt9wxD6xtwJIHqpatfvV6NCqtR6tRg15BGMfwolUX81IqxY2UYDJ7A7Y8b prhSLc3iFHWWSJRRRQvEWYSa4HseYzTqYTonJ6lWob+pQ1dQkzRHd3X2fn24MsOmKfyNqa61Knnf S4TTkEbZU9opK/SjEqfLFFlGThUkDYzTDFHoI44iwaoek+C73p4jTH1NpertDbytkrZ+cxPsFxVX gQSUIxIszGrBc6bA5Upqua388VPpwGzLXglIrooItzjm25wf1UjeRKsNPiaUCXFU6j2oErJpn6XD +tzaEmi84WT0uh+hyHV4mgkDObKaMCsXAvXSa7D5YNNh3cU7M6XhazymApbBqQ3yBgEnHUcpPEPD b/sqQnJ7A9jdodpVm1RTpIrLHOOBi4Xvo0zViHiPFTOSDEtZuud8qAaQdvQKfb1+kBtaFC/jWG4Y EnqJ1XS6HUnTxJk14neuEBNvn2JKNOA48qAN4hhjPcNJUUuqyJSV5/D4OMf0bIreYChVPDoznmdG HcTbHjI9bFMnkWiBFBrVz+9wE6xrUNFIgWDayDRMOSylQyUYV6NYIROr6ciI3zXp8Ieqbqxbm7K2 fYVtZEfeVtuik2ZfCHFU8JXPKMoSFdBuFz/sdPDu6BRn0yX+nV0ih4l3RRdHZRclJ19XrI5wJ13r K7vloAh57pocjGNqcDmIgkMvDBdxQhmdDOO+hv/4ciSL1+10laMSQrZqb2kJrnQSB0/2BWxVKgkl lsslKlOlkWq8FdOqAhpldVFiaK/PUb4fkxRHKp1K4G5BEUbfl0LGwfMDiXxOT07EiX3/ww8qChBi 7fYjuW5QCJ1Pt99HkqWKFsAhGNSu0g3897/8BNNPxLlZOnlvFaIoxNMDNs9PHiTqOidgGq2gMVS1 1VDDW9/7fRVQzweNBZl4rWmf5jaOWPTaRTHh/idY00GxGXrVX9gYozpyyIhnbdpn6bBoLX3/QyVb Hst4jP/84/fY3d3F/xol6Dg2fv71Hf7t5xnyowBppfhJdFzvO+GY49VPqCn0C+mOmkwTknFTlo79 J7uYuKc4MEL0Oj3FXq5LAZVb2gJllOs0gKeX6PbGKwyCDuLdu3dYLJboPf0ejkEBGaYbOXbCY3SN CvsDF/uj8b0uaoLedBp0JJIy8O+OhWpRYjqdoj/o4ez0VJ7X6/UFYBb3vuUQWCpnWk7n5zVTmNsH jTfMydkM745OhPc0WAYYDpUUSq/Xxb/7l9/h3dkchjdEd7SDqsxQhSGmwQJFeSocrYcwNSFI8bh0 kjfbwRDNJHOZTn1PKce600jzXKKcFjeVOZMeGe7evUZabeVy/ZLJcBlOj9oCvn/eDqvV++ZN2eTe n7KxGkPsYG+sJpN0XAuj8QT/aZHAj1P8t78f4c9BiakQtFSkc5fvxMXLMessKRPP4liumgu8zIR1 bpN9XmTCc2JKlYqIXybYFc9ntJiioxfCU1q9J2f5xYnwt4bPfoTpdpAECxm+akcBfjMwMOx20e92 Vuzp+zJGT3LePE8im5OTE/jLpTgwRj782Wg8Vtrla1W8bUbHzPeLwhDHx0fQJFJ0Vdm+Aa7jtITV G4lDDkod+dxHhz2e3Y44rQlbfIoKSRzCoga920GRhnhoazUiK/7ZSA+rsV8Ag2huHoJh3mPngkTV 63jYah5irdpsSPtr+glbsP6u709idZKyKqhoDUKxEOG+c87al+Ow5H/qJEmOf5W29idmLeOatjse YTIc4F+bwZojs4DxKsTf/QKJ7sgi7LpkGF8Bdq7GzNOaNguRSiMJkqlRKRGWMND5jCqXCdHi3FvV CC6OpjWGTGp7oLCbFu9gnxpHdjmDHQyePEc4n6FKfNhVjq5nYDTsy/M/dNfdTNUU8bZY9bERO0qT HJpuot/1MNkZw7YdAcZvA/TKLEfpkbPlPZlu6JUBz+xI5EK6hkFSpu02k3uonpmgogJmEEszN6Pi GjniLIBOvK4o0PVsaVp/DKvFiTQV1gbblP7RJqqsSMi9oYf0Qz6b543rok3h2vFeBOeJed2mOnz+ XmrNy8xFUSBVHCyhUsjUqy+Mh7Vu2kbD5edkAvw2eMH//O9+gKP9hN2jCG+qDirDFqFAnUoQ6yZA t3JWmrRtcBXrQkk4d2DyxFWJQssiOFWKgWsJ5sJm4AolKmJcZYY8CuDZOlzqZzepEiuKlDLJYWH8 9CmyOEK8nGHkGdjf2xUi4/2YuhnWnblil7NBlprySjZ3Z0LyrQw9W0EBt73e7Vi48WQi73d0tkBu ehg8eXnheXTeSehLPyN1mvj307NjaHkkZFMqO3hWhSz1hTLQ7w/QaeRuHlVbqiZ+qHTo5cFImjMI 2P7T6KXft51P6TlP1ZToXioKJqzwCf/uhmyHpNYoTYTILKlmx5PoiptoEAZSEPhiewlVhYtyH5/G aO4P+R5sFv3X336PzDjE9Jclsv7O1ik5WpGoJl1GSS3dQWRNzge8irG3w9JFmdOMpnjWAf5pbyhR CSOrk3eHUjGajIZ4MhzCddgrp96PC+b09EyiK5Nz/SwL83evsNd3RPWyrazdh9EpnZ4co9cfCKG0 dURpFkv6werffDbH99//Tm5W358jjpfv+VkVTk9OkesuTO/iTMdN4w1EdVmOhiPL/+TNz+habIQf SZoq2uONg/0YWGrdRFwk7qo6cy2EV9X+pAaoPLS1ERedGKEDpc6gBDGvOmY6JHK7GKGxB1E4WNTF agT+rhuC8dk7LCXZogT7P7foatN4/G9P53izzFB0dhrNqi0m6a+OutHgan8mgDOjrNXzKlhlgp30 CL858PCkP8Kg4yi+i+dhb6KkU/r93oVmWvV2teBq1mAHhu0inJ3A0Upp7r4JL3qf7z2e7FwQ0eOf u3u7zch1INVzdLt9wTzuJHezZoysCOjGMGF1h3A2htBedWxUUBDAf7CLOPbx6/FMNLqIp+1xNsBw KMeZFamU6Xl+pC3pkRxYTcfV3OOGrkgxHFxhWnQC7QzCh/38Np2T6I/dAbbql20boKUNJ00lFeRU HKbhnqcY+NK83Vwb/nkdFvdZO6wL+fpn7qxak1HjFIi7lgHf4HWMoFqToikJhucRGYcXjEofv9ux 8WzSkypPGwUwYuLIKT5TFAI2TdjPihNGgL2MA0mJpB3knm9EaQ9pOFTr1v6MJW4OmmVxwNM7CAMb vn+ZVHidLYMIJ4sQZ0mJyvQwyitoaQJjoxJFcJ4qFZs3DaMty+vJ5OjTMEOUmuhrLjS7g05/LCkN hQOX/gLzZQjT1DEanjcpP7TVbQuVTKFppYcUQM8Ejfyth464zmcsNhIzTWVReFzQVk3VdFamRX6X u2LbM+qirtZN+myfpcNqpWRah9XKtH4JdrA7wvOwwC9vIqRUyVwxR5UItqR9jTDctVaV6FYxnlkJ fnh6IFWYzajouiZjPpNqBX5McbccplZh0N+7spfxoUyqRVUNx1UVpFbehTSF25pgVosIfz6JMKs7 6HRtFEEqBYn+Bv7UVqi20ZPpMKnVflp7SJwRXkx0DCcDDHcm2G2irL/8/BOmwTuUSSJ4DvGZx2xB qoUG0WC5jVyRapKmF2sHoz7k5yvFh7YjoZ0e3UryqPRPYaSylprIjO1D0kN4g07bZ+mw2r4p7Y7E ys/BqKv0dBhi5+0ZjuqRtOuI5m4ju6vlsfQN1tfMEqTpRYKJXeHHne5WZ3WT8fyGfiDRVcXKjWHI BGy9qco9llGpoK6paPBMIs+IfY1JfKeqZBCGOEo1HBs7qNyByBTbRYqhbgu4vm4yq/GK4g2ldkiY Lcsamm2gNixElQY/ydBzU8FvnG4Po4PvEEYhfn3zM75/8UzRPR4R36pbuo9QD9T9QeU1ponSvkUq xEN6rY3+WT5aY1WwFQpkdVEpNqhGawUJKFLrFwG6t9ITrcQr7XPHrdaNu9LfX73C8fEMB6MunvWo U6QWl3zLWkec6HiTmlhWV106aTjDsFxi3wKGg533OkdcVE8ODjCfzYSYSQoBgfbHBpa5Mydxitev fsbx4VvkRQLd0DAcjW79vf52EuAwcVDZ5xO+g7yWxyXme9Pk1mpg8T/FtK+kU2Bk1fhxbGOaljhb ZuhYBsYdR9QN6LDUy9XQiP6zH3E6P8Zk0BMe18ewiuB7I50t1V+micS5ZF3dL3drm22+P6MtPuiU JNVn1GwYIqNMagRlx9lVcZV98g5r09O2EdWX5KhaazWsON1ld/AEhk1uipKUFQXQosJPUaYUR68a ckFnE07xtFfjyeD9WcmtBhSdFMN1amo9ZpTQGo+fnUCGSX2nGpqhRprf5XvFRY2s0lftROu2eRaV n6qQxbF837LMJeriKDfqrlPNk050OHJk0ClboHQSVxvNclUQ4IZqwu4OsJgeScXuY1nd/E/IpZWS 1FYbvqJDqA3xYdPEC8fTfFBL+Bb1UZG/UcKI13GwPnmHtUmCW4+svkRT0rE2vI4JXfr8zm8wKfkW CWaFgZRb5lZ/pYmqg5ecYq/bx7h3d35QK0+yHknJxF3O+sPjG8+Bw8cHsCg4Defi2LEanJnDCTft 902bXb0ucgRhjF5PFzkZRlfUhHK7StHAJE/NNIRgKlhNloiDYwOxHK9sqE07C39mOqKcQfxGpGCa DbgVB3wsfKsWp8SUULpVz2cCNIoJymk9bDVx+3GdzyOk+ORnWyVcqYl+4U5q06TpNKfqY35xpiGb j8MYCbnqV50PFh9MGzuOJpN3tlb/bjDeWAS0WZaXQQSkPfS60gLzWNe8nVd3X9a3ALcAfKGDULur Ejkco6bsSiaqrCchG8816FUBfxni95MdIUPmGQeymrBsB1nKFmEl90MeETlqdVHAMUmEZPsTZzeu Bb+UTun0kOahVCkVGZelf7UJcGQZm6xvyxC/b7a8jJRrHJamMeIh0nU+5PQxTGZA8lymSvrophT1 k3VY7QUkK5bDCr7EFHCbselU5G03blj2dXEoBccyXNVhSNa7mQf4fuKh776f5AnlW+bzudKJ4qSg gZp/9xjGSIaLlwuXDvK+rvnYM9HLa5wUmaR5Vpni+a6HJz1Legtfny1xWHWRwoReAx4lDgXfacd0 KC/EaiUdDhU/SZH4NfekEHLQZbps4g+Oai9pjcffn+wLWz5IQnm96ajRbUmeYbEMYBVTPH36REDo x7S66RVkvMdgmg8W7Tj+QqbWPGK0xeiqVRq5yT5Zh9Xa1+KoyF05PDrCLM6Fhc0G3E27aZiyXWXY r2bYG+7K7v0+1u/3BVOgEcNaJ3M+uBHDIx8nDO8Eqt9k5EPtJtSQP0HpDrHv1TLVeR4q3XPK/BQ1 /9Rg1JpMPGqQlnMaQFN1Y3Qi7HLy02CIftmviYbsJIFjnorsTNunKV+JFTGvC5ODaJtpRaI3VZco 3A7ycIGT06nISrMn87HXe9203JAto6Iu0iCoa0aHzeEW20dyPcRx4EtwWF+LESs5Opsh0akC0N26 cK/FbutKmOi7dilNuu+bUtFZyYDMZqLKQ5swnCnKV3FCzu2bme/6nZ4OMpnmchzPUeYFphr7NNk3 aeAkqrAgVgUdjgF03DWHI+e9kmGjfF5ccsCDjqh0xLFxRFhYAYdRib+d+njRM2HaJkphnJ9Xsskf Wy9aGJQQ5vTpqoQfBQjOArh+Ao996xx5xo2DOuyP4MDqJgXk5/LvLSCPxnmJCu5HwLc+S4f1NURY 0s5QVlhGGezRuBki2oTtzWxAxR4uZCckveHiGwBGmaGjZ9gbdG5N7lyNe2oeraTHfTuMmxxWEATw gwh7e7sSDXV7N7fM3MWkzWfQhaVrMA7PMA9j1EYlQn3TMMO71EFQqluha+uwh7Zwg/h8poCU1jkL UrzJbISV6tqDxrao83OUV8CvQSGy18OOJRVeJYjXtlI1BOdWf0ucQyVOU/MGeHu6QDL1ZQAtpaaf Qke/S71zJR/8GFY3jkukahoZG+W4mAFcfN7Hsk/WYbVz0j6XqTgf3ECakSDZhsbNjLg8l2nILKkX sBDECepMFw7NhVVTA918gf1uiYO955fGOV1laiadGrnE80zB//Wx5I9hjOZGoxGOpguJtNiraBj3 p37QpjP8ruNBTx5KUhn4++EZ/o+TU7yLdxHVFFDU4cYF0niBlwNbpnNnRYlFnGNa2shFzXT7uaV6 wtvMQBAv8cPYxYuJhUrQbUVGJemW0RT5bOz/lE2hqRIOxzt4k1o4qnOcWRZ+6yQ49kNE8QzDri14 8scAACAASURBVCNk4k1KifaAG0oLzAsoT1Se0jsNP43crXWdrPu2ddXTz8ZhraYTfwXRFY036t/+ /nfklAaJI6UuScmV5TH2h30kVYKzIEKq26io17QBuosSw9DGjzuuGo9+y/MmAy1dV/oDGcFx4m7x QBPsr7KWhyPSLR2239xPdZDRIit586WvNj6piGlS0JgHMc6iAq8WBV5FLqqKLSGqwkew3S9NvEpM eLBQFzWqpJDzAp3g8OVzK0NY20Z03YRh2bAdb4XL1Hal9PDzDF63f3GcmpIPxcuBJVOHolJFXf3d FzLvyI+WmP56KFOJam7idHqGjhfffffgKbvCtlRqS+6bKh8+rLNiNNkO//28HNZXkg7SGOXsjCeo zQiWQ/F/Qro1Ogd7ouT5+niKKFgiq/qoZZGuLdSqwkhPsd+xMRxsx76uMmFvc6JKkTfjoz4OTsEo Y3cyEfrAfQgBLvwAR8sEhzEQ1660M1FlNUxKJMSfcoqa51jkGuZ1RyIIFbaqdIhKrUlJViUfFD7k WCCW0DiBaIuToEJGGgE2te4V031dE0o6FXQO7rBlbNrla1TLBKL9noPa9pRYoOfKGDKNKaPtil6Z bnC2ZAI/mItsNecEPHS62Eb9Cj9tdNjW19+9fpbqKliHJTYd16fpsNoOCXwdxgjjyZN9uN5yNYBU yb70sQxCzJMS0wzIqOKwPlGHE0+yAM+7GnZ6d5/eQmclErVlsZoW/DGM35dj4D/EuCuTQxbFKX6d R3gdAidVF5XbhytEWx2hVuJdXOAk1kH9AOpIJXDgyAiG9eNZV/8Q8Ekip9pQvYSXjHSJMhEhRNEs 3jBiVTRSVbataQG6ZcQVexsvngfD7EmVMbaUSobB1FLTsUwK1IjFsYlk8QMbnW5LMn1IkpaaEER1 WFPmKDKN5tpUl0P7NB3W12ZtWrS/d3GQAZ3WP94e46+zHIf1ALVOqkK75GsYVY5heoaXz3cw7qsp yjdZ29rU6ohxlNJ6c+rnaPweJH+yynp4tsRR6SK0J9B7I/mijuHih6dDEd/7f15NMfs1RVRYEsl4 Zg2nMmE2ARXTxo601jRvTgdl2Cqyuip65SBaRlVpoPhaG3d0C3FcJ4EkUZl29TVjilnkGUzbxWD/ GaLFDGeLmUznHo/vjwJylanDV+PgiGE9JNWBhQZOmWaVVOlrlasZht8c1idqypmk+PNZgsPElFTh woouS9hVipfdSjTFN4mm24wgKlMIRnHcyfIb1B0/F+OU5n+cLPGnOZCb+xh4wIEDVFaKs6RG2gzn 2O2a+MPzEf54dIgXnRrPBpaMYNeJFpWFioB0A0WRITTVFCMqutZ6rx0icMURtPT2Wlp7yH5fb6qm rDAjBTqbi15J9dNRg4vYFD/7KiMGZliWaO9naYLB3lMc/jyHGacYDMpHSA1VJVuvSDDl+nk4+IA4 pgy4aNY012trn6TD4o3VKjN8TW05m+zfd4fHmJcWMuNiCZ0rpVOGOMASPz7fh+feTBJtJT0odcJo joxyDtH83B0Wne67oMDfIgtRZySpm48Crqnhed/G84mNxTJE36xhmwb2xn38l98lcC0dg64L27RQ 5GkTvTiCE5Fi8d8DQ8lJXTE27JJRAdbpoawSkf9RVos8T55RYcKEsTFMhKoE1Mlnb6JJxdfrpns3 kbHO9zEMxP4Cel3AMpS88GNYOzn6fLrvw3gstukwxY+oKWY0U6qbwa7mJz/Cq/3ZVwTC09K8wN8P pwirASr9IkZh5iF2jBQ/DGwMe11hJd9k3KVaZ3WeGn7+55Ng+iwFFloXtaXSYk7TTilcpwN7fQ89 rnJqqDGKQY3vRzYsR1XyeO7SqITmWrDYWtMM8zCikuOHbm9cmwTlQWypXb90Spz0Y0jlsB1Wuxo1 X3IqUApbdxVOxkZsTt5urJCiSCWUCc4sqBq5GDrpOArBTN4tqCeVCAz+8PI/ynmrYScP9yltlbAd TMLCEzXdaJ+kw6KtwunmAn9JqqI3mcwCTAv8fZogsQciYWLpavoc8aZR5eN5X8eLvdGtnFWLC4h+ tqYJ34oA+01ytJ+SiWZ4ozJKFYR2AXPwQVFzwCpF8qpzEBtUSCil6uRYpmgv+VEs/YqsdLlNlCAS K6QKUNFUSuqV3PyuroidVwFL7Y83K9quBlFnpaOi8U8qXci8Pf6sYYwrna1CKoGeZarJyqtik1IH DZIC84hpLR1XoUaykQelGchzUjQ6qOMcznwp8xOl6PKADms1rOkBK4WbvY4CXRQFrEbe6JN1WDQ1 f+28tHndmPEvybjDhEmKdzFQkvXspNhzVYr8ZnaMH8cmvhsPBNe4jQlY2ozu4g7NkFtY859Cr8Ud yt2HR8eSkrAhm3r0YhowJA3EzFcS98SjLBDj0xDMz5DEkVAY/EJXigtFLuPO+o4pN/pgZ1fOJbET nh/LAA7sHIWst8s3pgx5aH5M/IlG/IubB69IR8sRB0uJmPjZTDUtOtu2q6Ad9lFVsDTg5WggAnaU qBEtreb3h7Ml5tkpfj6NkVcW5TvV+XCJZ7qA3UORncKczfHbJ8MHn2sgxQQWAEwLaa5Ufx/D/JDc xFrklT9ph9UaLzBDYqE7fAUOS6ZCOxaeeyUyM8PTQR87HQvRcoqdgw52+h1hhF9nLdZA4HLQ7wm4 TJ4To5SPhVu10jFyfLeMBIi1Lf0AMz9GpZuwGb2s0S9c28FY19FzekLKpOVpIqEANbDSwFdTrWsL x1WjOmrVWGjAuK7wwoSM8WoJqzKUwzTwfHco2mI3HSc/i99JHB6f3zSpMwrJ0xR9Vwn/nVMaztev pFaZLnr1ZMCvTwyi7fR7+GGvxF+PlziBjcKwzofnNo7U13t4U5bonM7Q6fUeFM8SvfiKUyGbEfaP 1KbD88sBFtxoPw+H9WDw3ic8o7Dr4X/5l+/x87tT2HWCuqhQBlM8e7Ij3JubFqbVpIDUzZZJN5qm RoPfYXjDfdvMj/DmjINZDTwbOiIwyGO8dqGmGRZRioxYTrzEYDK6MLiU39NuiHscAksjGM1eOJbg K8eRsVIE0HP2D2jSXSxIU1gXiGQjXBOxEphGa8ZUqaGg15lRFdIcbRIUNs8jXhV51bAcVzmrLRut pIVNNLWtf1MIElWFsFCUCwgoxlRTA2xq/WsoDRt+5eF1sMBLTui2HzYLUSPFVOTZUm0fw5TQ4ieM Ya3bKvT8CqKr1dgrx8bvvjtAHgc4jUIE0RIlR8nbT250Vnw9HQFHT7Xz8dR6r9YY7Y+/Bfhxhp9P AkTmAFoVCrv7OofFaDAj9q1zdl0JxyYA64miQ2vSisSoMUskFRR1hWY6MfGevFSsdQLYWplfGLib aSVmUYmzhU/9Ubn1GL0xMjKsTByWGrsGuJaBruussLObd9PmHF+eg7v2jDUl0C1wh5BhixIhhfUo 96IpOWa9TFGVpFuo2zevdUxzA0GUnmNZD2iCKTfF08deRZ+Fw9ocPPE1WNtX9fzpPtJf3iKaz+FR nvcW54DPYVMxH+s3AefB8Qb8WGA7K3CFbiPu7mOZvRUhvOvM9wOUhgev7yI+WeL5s2eiArpucnPW MbI4k/SM353DIgiwz5MCqcYePg0RMZcivVAZZax5klUo0hMsYzoH1YJCx1eDuvHkA2lS9HjSt/Hb F3sS+d4uglHrVaCMtQG1arBF+1cF7Is8cFFcGqySl4VUCVvXoJNDp1kiQJgkS2ic4sPPKDKJHheL BQauaqi+b9XW9e+l0tGPk/N8Fg7ra7bJeCwtOlEUY7aY30gQleq63GhrN0kzrYQYAEvgH8vIKt+1 K/hFDNfWRGv9OiN7Hd0uLLeD0HC2MqyljUnIl5kA8yD4bZriGM9yYAZXhOgqs0a9ZbUXdYWTNMbP 0xCLmNQBAlDqhtQ0pnUaTK3GvhNidzwUh3UbU+C6oRyWTD1Sx86KZMt8l6GtaYqj0xNxAgT5qXjq WIZsLkyH38xjFVlxzqReYKejYaCbeP36FXqWOoecZH2kd7EM5oh6bOaukCYJdvf27p/mIAoOPD7V GP3YzaefjcNqOVlfU5S1OayAKcp1y0MNFWBP2kUyIc9dEIWi8cTd/LGtBcmH3Q5+ewBEb45Rlxxb bm/X+E4zvD48xTzJMbBrDLoG7P4Ip2dn0CbKga+/t2obaXg6loNSK1BphQzroP79BYzq0ucp9dCc +Hcpvc5ieplDKzKZ/1hTLZSx2cYmcPOIexNpHELPDAlIqK1F+sRKcrmuxBkx5SN5VDT56UShIU1S vFkkeLUsUXECT5Xg2dDFjqsjXs7hoMAPT59g1OsgSgsMpgHCeYR5EGDM9LH9cvdujXyyOF75pnhM ++Qd1vlQSCU9K/ru+LqsVVW4afkx+uo4joDsbVuDmsTL6CoT8Pmhx5WvWzvK6Wy2FIIkR2TtDbv4 fcZqjyvFg00jX+p4HuCvPkFyF8uoxqAKoccxxtiC8zSj0eloFEmTk24K6GWGrlEhJHdJnNnVfCo6 IoXJNOg9z1kcoeCoL5vn08ZuR5PezSyJlC4822zWv6c4ZQLn55LKymkxZaVjzQVbYzVSRT2q+Rpa jko3pCLZAtnE207DFO/CEtPCUMNP+buS7O8CJ2Euqg3Eq6jvNagqwQPnniabAtPmlj5x/9dUrUcG xy31+DGDrE/fYQmBjCxlSnQwvL79LvelWDuK6jqTBlnLEskRznhbEW/JBcrURJLHdFbnZM8cp/Ol DGLYGQ8xGg3x47P9K69hkKR4PQ1xpO0qqZiwxsxfYLeY4buXu8LUbx0ho8gWmE6LCp705NXSZuNo BfY7BvKYoDXZ70qg77LxxiY2VMGE2hQ4WKJKYxk3T1fnaTleDjtCqWAvHx2L8NpIMxB6QoWy5jSc dVFF9XflsBR/iRsKq4btdxcH26SOLd+L38sPYxz6KRYZnZraeArdwmmYSOFgFpbo5JQFUlw6Fi6e 7E2wv3t/gzuuM1VLUFGiGkr+eOvK/Byiq3bje0zp3k/JZHrvDQ5LWm8c58LwCb6OBFGK2K1zlx5b SZX4DOVjROPphuu3TCu89kvUfVMoAygzWFqJ5yMXo0FfQHapHmaZOGfeNGGSYxGX2Huq2o7oyHrd Loa2gx1OxZknOMws+JQ13mI8JAs57DoDakPY545bY8C7wygEO3qxP8JgNBTMiFXE2F+iMxg17O9q VRhacQXZLMwpQFEgTsrt9GDZW/TKJMpSwsRc5GzJ+unXQxxXHhKRYVZPqzUDs8oBgTjNAqrOGLVu XZrS81jGqJbww0PLzXxWDkswmYaOz0ZdGbjIcF8Y8F9epMXvl2aZPFgdEoyj0alihHRtdGXbMsRh 3VjtouJDq9X+2CaOo9fDD4IBKULmukVRJI7H80ictOU7JkWNwnCktea3ExsIfIRnhzjyE7x4sodl GOGnX09wvIjxX/7H30o0xXTKJjfNtBsBOA5JrdE3dHx3sI934REKSja0y4VcJmJUdIi0RnOpkpmP TM0AZzjCZNSTPr4sijENUkzGlUSxbLXhuU2iUKSPuUbdbl8wqyb2kHvYtKuVYB9TwStTtE31GU2H WRWiB1VQCbXtPQSdG9NHG5nMEPx4JuNYHzsf/NQd1nrTcytq106Drm/ZRP+5OSv2+TGNYnTSRpjr DPGrHMP2YQUf9wyp6ccGvC2VzSxNEcQZgjiFEWXwbAOLKMO7yEDhDiV5E/mb2kBmduEJ1QA4WsQ4 XMQNRKAJNYItzZJq8Y3piDiUg+oUho6dwRBddwEzjGS0lyZTPMhp0lGbdPACyginqdJKcVqy5owa NjWZpEJXYBblMsFZnI9pCHgunC7BCEtkSSxOySZJt1UV5dCL5prwWLecIXXM9cVI+eWTHUyyQpRR U86olPWvNLPirMQ0KJF5Q7yeJfIdn04GD869unzoLUHkm8O66gwpLsoXmhbyspN2QND5rpU8kd/g yKq189LiPMLj+UR6Bouc6gQ5LNuSsVlRaaCwWOVi32SJWVhgBhs121lKcqY46Z08rBq9QjVrH/k5 /JT9d4703nGiTVLoMB1Tnu8ZjbwxAfqGizR0LXQ14lypRHkxP4P4U6s/Jd3SBLeZ2qlKHSOYOMlh mZpwsXgeWwiQjoNR1sqoAsoomINsm57E1RoVdQNW1a64Bk0a2Rq10/dGfUwYbWZcC63DYpSmyfd1 kCOyxzgL5tBmKfLax9izpF2rVax9aGuT2G/E0c0TIxGVOjVtKvgY8/Ie3YSXUwoWcltrFyYX6eZo LwG8iaGkFFr5RFRB41j6AklLmC4CmP0JuqM9FFGKv5+kiLRcHIdGfKQuMen30CFFwc7gFJ40Lp8F GeISIlrIbz9LSkSFhmFD1Bx3PPhaLN+dNTemnBNHx8QqkMcJOu4Ah6UtkdTKmghm3eJCw3QRwtZq dCxg4Hoy9mubEVinNA0JqzTFu1q9OSpGffL31sm145Ga5haRvqkapQX1Ozpadipstr84loWerUsU /mddxxvfx/zNUvpO/+nZHvo9SrE8fOLUMvQVX60ZZ/a1p4Sqb6kSxrEiCKrJuV+itd/1tliTYHtN qkXshzKy6+PO6fiI+12HfT2mMbIiLyo2e4ijEs7kGXTLFk35OCualAeNYifVJBJkqQmrTuDUOZ4+ eYLj42MYeYCeocrqQeDjJLPgeF2RiCblhTjeUOYaMtVTjuP7vQnCOJZqKdM0jY1515xm/iqt2SgO GU7bc3T87uU+ut5l3phwswRr3KblzppBJj2NJLOSNNo6Kq5lVgkrpr1UJClVQ7Fsz8JuV88hHsfX tRLKdYN3Ud55pGfQXXLzhjjLIjxNMnRc+5EclurVlGNaSc58xQ6rxaokcmjKqMQBvrxksBkGQWXK pkx9nUQMjWkNy/uOgL8l0pw7e30hsmJ3+8dktV8yuZ6aCNl5vb6aAENtqrLA3ngA283xyzTEmWBF 6sZ9O/OxZ6Q46KiUKAxC+EkJi32SjolXR1OEGVNGQ27gPIkw9wHHNFescpJteVdxMpBJQqUO7Fu5 ArDXzBkCzzscNGGJpFFrZLmPPR1GlSGLVZvM+ispp3zOwaLjKaH//+y9Z48kWZYldkwLVyEzs3R3 T/f0cqZnBuQX7vLTAiTA/8BfuH+AHwhiAYIAAQ6W2OGonR52d3WJrBShXJmWxLnvPXcLz4jIiKwU ntV7C1GZ4enCzNzefVece46toA88B0aFB3GEicdRH13P0h3vvvVQuBbmlarPbYagJVU1ZHlkfSg0 G6pzLd170gMnPWtqnlzHcvmDdBl1j+OdG+89pqnsFr6vBsD+Oiz5H704OYrU5fgpuivlrEokJJe7 pZu3oTcOAgHQMgVkV40LQBaLtb2BuOMbZ/UhUO23mVqM6u9UgWF0wR06cAPV9vdcIatblB04YSgY rNpCLPUf5SjSosIstHEwVQPQv1+myJ0QZWcLf1hk90iDEEXfyvvzJ4jIQmrBcl0cTsZwIgWF2LUT ZyKFePKuD2uBzAI9hyIQW2zb8D60O87t6dlPowmp2VyFdsZxMQ5DHI1GWt3GFNDp3Dpkro1klYrk vTgqA98xaHg5d43pcpxrNMoCE+HokAyQW5gvXwiTwvu291lS3luHReOlp/dm70agKj89f6WioSKX WtNdgFASvFFolE5NMQioaIupRjhgZuTz81KN4OwTQd92VEQdE+EI/G4ZOZjFzsiRRWeCg2W2zg5Q 9CUKIvT7TtgePpvFODqMkfYurpwZKn+KVQucpxUeEyjquKDsIIvVRPhbtnIi7N4dMmUcjzdI86Hl HmtRrnBjXTtufW25aTIF362fkuvKkPj5wasiEyLfFceCC+MmMzSm60zbXa+S6PMmEVlh2fR8lOR+ t687rE2fTmTCGD26qvv5noguJR1W5F94X7bXDssYC+3vE037vkxwZazt3MFawN2eXOwGdDlUEDHG lrbNgdeyQpImkmrtk7OiUR6+aXs4WrrJgCylTqNFTAObNE9k4nQAP5au3KpscdZWOE0SLNMMTx4d o7Fd/MOzNcrZV+LYliwN1TamTY0ZwbJ5KqMrLIRfczxNjbLIEQhh3/UFraAjZu1tFzwdf5pxQ6lw OJsIqnzoDKS2RPHVTqd1uyf+YzlYzBD1NXhLr45ZF+gNZbHUv97j926k4oSIcNjE+FN0WPLFS+2B ADoFEt23Rfg2oiv+3IZi5zWgPhtTwdd1RoUHqqc0GKOR/btOPMcFcVbzHu6KKZuKBrnIauLO4GBN 0di8xrpVggyBQ1mpAouqwLffP4NPZ913SMoWi8pGcn4pxWrWoxJyDUecwatxFDk4HEWIxtNtJ5VD x7YjNaXaKmSRSRFczMJyMUfR2ehc0vB2+PTRqZADLvMafzhLsG4s/MarcOqwFvUQzJN2zDd8J6Yu SfjFXemDihAjwXoVaaLwbZ6qAQpn12YTe78piInsWJqo2vejGr6/Dku3RKRLqPl9fipm0gx2rtie vu1mNrJc9+n6KH27/ZPtkuNqWyyzAudZj5fUXM65+ahz4vnPrxKsaxddXUgURHQ5FzFrXGGdY9Wm mEcOyNSePE3wMsvwd897lN0aY1Kbj2KMJyECh5ztDlLwp8MRsk3kFBAIyuKCOEqyOmy7aapeZGNR dVLU5yjOIx53WWGVV1iUPQqmmTJ9oL8r/adMI+yUnHsSBbLrx+iHG0gTiHNkCliSvtlcG1HeLiUt 3Cq34lbohOsxlFQRFlkpeMwqgFMw+XBygLpJZIJAxpbesW2i0g3E5t07rb11WGIG4Y6flplOHlO4 2+ThZdwjDFS6dw8oB2ENfM99MCPRxFS37khzXONp0uGMisxupMYUaH2HtKrxw7rDomrhtB08QaKb CLtHXnXI2x4rFrcdDz+sKzy/ZNTTo+4sPD70MLYCWJGDCg4yK0DV2sizDqtK8a17fYPDwMJRrJWD qLrj+dcojVkQb/IWq8ZGaztY56yddTjPSALIrqNySgLGrSsFsbFUJ5LFcxPd8N9ZqaJSDzfaxrXh 9B2KIt84b6O+LemUHr+6NTjSTCXq8yx1zHS4umvIY1H4LwvBaIxmnkjH+X04LH14oMsWVSD56vo/ XYel+J10KvgTi7DoXO7CXfG8ubjuw/8l70fWyj1xWHTEqyTBOiulg3eR1XjRT5G4VFAeoPGpy1fV SGpL6lXDxMZyPYRkf2d3rp8APovpDsrGRu3aCIOa/DNCVrhbjyLdcdLZSDR9/YnV4MT1EI3GMrd4 M4SGmwLR7OSncvDscoV542DZcmTH41iywk4xhdeQCVVwlneQuT9Wkfj4JPTxeDpGHI+EA2s+v0KW pfJZ0yml2bbMDC41BdPyVlgAP4sOSVLYVjlz0TjUdEJ91cswNtPDMKJGpaqpvS8ztWV19d498n2v HRbtpziGw5vQzAveKinvOIJqvs/5871K3rR7AmN4eX4BhFNMv1BFcX+dIpmzG2pd0yZlHYvHXndc 6uY8SVho4Xjk4CjysUg9idAeHQSw3QBpTkxSDYwsdMRWuRb618zRHY98HE2uc8Hv6h0+XZa4KGw0 Lh1kj+/WFTrCRgjO1ZsKubAaL0AYxQh9JV8vkU9dwm1KjPpG2IPH1BnUx8SifBjysyOMRqZDqc9U 02DfNZNHJ1UWmTQKfOmosjPHLqqCPhA0y6iL9D2UFgst0s3czErxLkwdj5Liex8hxd47LBovhgmj P3ZThHqtLJJba1cERsZUJX49IwWdH2ERJTuD2A+TubumgV+3cCkcMRnjizZleR0vNGsCqVwmToPx 2EIIBw1VbhoLdUmywQZ9UuH7C6raWPBCH0Xv4bEPnGghiKumQ8OOIyNwS9U4JTVjPWh4zZT8sjxO TBPR8Nf1H5UwxnneoeJyYKjXlNeiXxYlHP5YWyzUZuLCVnp5B6MQs9kMRZ4zdESarDcYQkkTmcLt pva66K7qYHcQDEoNy9VD1eqItv+ugJv88stkgfHY36h7vxfTyY+kzJLqv9tV+lE4LMO98bF2Ck2X SMCC2mFdWxCa1YBRFXfcUFPFvNZZkZyvLKXl/iH4rm4zYqkY6yme9UYwUKHdyZiL1XHxt/hk7ODA H8HBCD9vlHRUU+aYL9b45sUa//xdgmVG1LaHg2MPZ7mFTyYWHh3EmExtOEElAqOrmlzoqkEjhW7+ 7LjuednBWhQ4alnzmmg4SSNg00sye2Z0dD0il8XvCnVfoabs/ECrkOR9xFlJGjYYQ9kwwvR62Frq 944GkRIvx88iz9bNm5NyfHePEav7wABvrztjSRWpKE1H3RRwrPczmrN7b3MiheXHP+2iuzbFAjkk DPq4TPAqpHgmXke4rbbfrDASkIIlDLWOoLeZnXydERhJOpp9A4nGYYCcbsmoIruOdMICu8dRwEiq xlczNf+3bckDyWqFZzZwtqjw/XyBuu1x7JEDrceiYOTkYjoZ4YtxiJNpKTAIkfASdoYtOknBBPQj lo2Lysa6qpA3LWZxgKxqkJS1dACvqJZjhTj0ChwETNEsLDogJ7eWsIAqAbAR0y/TABlea41YL9ta KHOkW8cUUBD2ytbr1a3uaEOAd9vXLVGYu2EntYdc/QSd9h1814Yb+jJgSGaJ92lS/9NRHp2zmivs /8Qdlhag6D/C1NAgvFnrIPM4Z9qG6Zt0A5lSTCcPYqGQThwpSErytO8XlGE2naBJSqRljsnRqTw2 8lzEBw5+IahtC6OIasjXz3edFVjnOTjB8vjAERgBBa59q0bQFYjcUHi/hKwwYDTDUrnaCGQzI8DS DdHvRBiKJ71EXtV4fjHHN6sGSe+jEVFVVWs6GEX46oiYNx/nl3PhoQIjQgPe9QK4Lav4rowSDY0R zcgPMDs8VOnZA03oY265q4VjK4qRrhYI4x6+s3WEfV0Jp/snh4cS9f1LsZTo9n2bqPRwrlDzgL3L vfOjcFgEjraDljA7Q7d/xfuIudK0MQRxa54qYzwXAhQfSpljhp73KbIyxuFk122kJuXpUoUa9wAA IABJREFUAjBHWGCctxaL2LWnywr/8LLGi9SFd/IIrgqQUNo2SNDyn34o8F8unktESmd2nrBW1iLo KwSdDQeaxO8GaywPc1Bs1BatQhGAuOHZjHDJj77hvtIbJQerFSeffUsEvWE5vsXe7HtSUZVSTrJ2 PluNrqlyAqXMGIl9iCaVzDOyWUEl6nc8y/hROKzh4CzLk66JsvZvrd7KmEpMkRFHpaoJ0z6mdHRW JLRTXAb3v9maOwr3H9q2tTnd0ZLBDfUnFz/TQOFBFyGJ7SLMmx6LEljWDizPdM9Uu5w/Z3kLq2DU oySmWgGfqo1gI+d12zFZyumRCeIuI0TCt71XOnVtqbBPG8f76ifcdUU2/6ywV9ualdQyuaHxDthI 7uy8TsBOLbqa8AflFfnUripwtaI8mXreMssxG73Hgvv1m1yfG/60HNadC1Dfa4JNes9qHW9s3CEl cdlO3AdSK1EUKM6A1+q+ZqS7KDCxj0bQqOKk59iNGupmRMROIDfgruZYTCVR2JC5kzUvl8V5Vq5v /G63j0vkYVw8u46ehcABYpvD4fdz/JsYi/9ra4FMUGQiIlSCDq7t5IcOIU8r+K0Dp+bTK3lNaPfw 2AlEjybvUVhqztD4GKZpI4/o+1yOtCKZ3w7ltfy9axEx0ibn/YD6e3gWY2esOpTEe+kNvHYgo0PP X86xJiZtXSAK3zNVspzE0M++W5aCvXJYpuNw2+yVor7/CJzUwDY3nwF3698ZWWwxOA+jzlHUx83e 1a6MFXmBpqHSso8qzySaukgrrKtOCtlccF1dKXHYQb2JCjsUTrXFYd1iZv7P3r6O/mnsA4+jHkch ObDud5x8negRWkCelTibUxm7xM+nPqrewqoopSi/qFnYJ/dTtUllGRd+EnaYxaE44qt1geplNtDR JMyiwVdTByOfjLAO7OVKy7xf52Qh28ZxHAojhyms8xopWIuWFDPU4JamuGH61bb44/kc3zy9xL/O K3xWtfjkA9DLqJS4F74xQhs6Rnw/dYdlvmRhMqTa8U5NR8j8Np01NTT7MXotcyMOOb4euh+x2L5v ncGhkd7GDmeYHTySlJAqxlerHs+KDp1xNL0PokgtjUantd4I0+MYY+86xcvQrFI5BQRErG+v3NTt 8NW4xZOT41fUeW5/s+0fv/3hAi/XFRwrwldPTvH86kqmBzqrx1UNNA5HXWzFVqqd3ZFdw43HKKsW L+say2YIgGVDwMW0ahCOIoSxqa/tfts9vNoWTv5Gz5eOwlAcHB/zPAV1oXVMo/VGxfEi3kdfnRyI EIVTfo2+rmVw+0OYUh1SJhvSO7o33X3ANhk6X34RQuB/B3uD+uPDH/N9bUP5ob9EweuQeVMob5ks PtzorAQasafG8RZ27KRW1UOUbYrO4qCNFIc3tvMVGhDkENy5a5Z2RoKRGjKDOhYCl2ysXPw3v17o hln7KwvFxcV5Ql2TontpehtJ2eCb8wVOJ5HUl0pSrTvAU3LJIxCiPbvv4PY1pnEkorVpTSFVSswP T8kSsKmoSutISR/E5tTVyFmHJYfg9UZNto1IsSUL86nLdJczi7BkKLzVGQjPPOa8om2hzDKEbYHq gwp/bbGGeg763Tms4Qd+ENMj3/1A6VnqEze0ez8mZ0VTLXfZgpSzkkVpWtnb0aOHnBPrV/vGyjA0 2XwodloUgj9jqud1rfTwqt7V0vE/0na6YYbvinTCffPq+6u6kYLFyDG2HKOxtg5L8EQ2Vq2Ff7oq 8RfUBKwaqac9noY4S5Yo2PDpKaza4iS0MIkDqWHJXXpDd45f6aqoEa5TmfULXEN/PHgOeoG5dI4H y6b4KsGvnSa4J9C2hVVXylE51DZ0RayCOUbZduLUqqaFG/go0w9cMNkQ87+Lt1bX1x06gQ/pDASu IGyJKuKiSaz1MWAX7jI5BZ1GmFrEzg3+UKd1H+n6D2ki3U6aGDKFklfdAo69Dn3b46Lu73RYXIhM a1hAZ9RS94wsrrN43nRT0IGTGjoLPOHNEq7xQVnBiETQQRluqd1ryC5d3jv4Nge6ixyjrsThOMAo 5JjUSj7aR4NDr8NXhxNEJPOTuT7NDrPz9fHXs4zMFSnquMDpONBsC4YCWS9yOiDD124olg2ujA6p Jti4hGNxGoL/pp5fky+O5+74CMYT5KvLDzp7q8gM1cbwtheuKRO5mzm9N9jp36aZdMDsghsw4Eds pmPE4qg3oLa9yRQ9x+vTPKEoeYC6zocwpcQ8EgI91lyCsMXBzIK/yrF+mZD34GYIQk/J+AYzu8QX Y1s6X2eVg4VETDtdix1jN+9C6kUdpq6NmDzxNww7q/fQn75zDRmfkAqG//ptbuPQduH3LiaMxHRE zE01DlwczKbb8zUB367DsixkTgynK3DoBhgfHm0+X3FImVFHVY+kc+ff6ejlfbnBOS68KNJiFzVK Csp23UbIw5AABkGIaegqxtYPZKphpihz3jYDKadA+COraDjTpoYx3x/VqgJUivC1WuCacvZjt81O 9xp+7eG1f12Ey38jIwNv7H11WLLwpDanBBRIxyKpftugqcrNHF7U5RJJOdoZxJ4N320xiQIcHx6I KMUJ5w6vUlQLahYafNHNc3d1b2PeeEhXDvykxuMRo6B+Qy09NEkHBxgx2mlo4ZPIl7rWs2WO8yxH yFoVtjAB3pesJZVNi4TjNg1nBIE8r6+JgeyaRxER3V3kNfnhYoGsKBEHnsw2ynURfJonoFuJBtkF JDC4LK7xd9mOLWBZMjM4ri/AXF7TfL2QDuqHXDm8JYUd2CUk5O0GP+zSWpWMp9+8cIaPvZ9Cnemh 3H7JdyXrP0j7dhORXheU3Eg0qQPV/6aO8z5h+vB5d50b1YBZw9pHk2Fs1q2IOyMSn9zqVQVPy7dz 7OUwrGE3FU7GLiahJ6M2TJMZGXQlKXVcqQ3Rofh9j8eTQBzMs3WBlPN/rAe1lJHvBIBLEQ5CBWT8 BlwoFkSNi2RYVSFDyLPAEVkwVYwnhq3Rclz2JlU8jT2R44LtCbShrjr4no3IU0tEpL/IiNA7WNXA fE35LvJSaU1COuLN18xB7FYcDm+QzKrwsi9Ric4gcJE1KBoLftlhXqcImgqnB4GMaG1SRTrGLBEM FylkOA7kiKqzEtBgc4rNlxUdZ1XgKLQRzKYIbosq34OpKNFARVTg87ZMoljSMe9+oPzjHqSIe2ma 2pj1EfPfRplaE7lJsmGcz2DA+e633V538+dN110WZcsxn/10WHSky1UC2x/B9pWjIXA0iBVn0yS2 8VlvYVa3OBoFmEShIP2Nlalh5bA312Ea+ZKq2Ujxr6sVFusKad0jLHocRJlI2UN37kK7QW9X4rgu mgLzKkfeOvj1sY8vj0eYkk9ZvfOGusdEfOPQxyQMkLVAVjdgrKK+Q8WGELlA3LSgnjQfJvaKPPE8 Nq9ipNhs6IpbdkWLHElGfn0gs2sUlYt5S9FToLJ8tJYLq+6xqGo8cnscWSqKMs0BIy6hRFXVl9+J MjhrgzXyLMVlUqJK15i5DSYHp5ICf0hVdBX7KqcsM+JvUVCHa44kia8UVoZRwbt2WkbS+2NKAHlJ lNNiAV27KO1giEXRfmsjxXXfc9t1WrcKqu7pFTOsp4u8wsHh5/DjMeqqQF0U4qwYHcT8ibTT2GXf kNrRq5UtYpAOJup1v/1+gXXjCpDTKWskLy/g+pGkab7n4JPTEVB3qBrSMpORocVV2eMo9vC56yMc TW49fqdvpBOXFxXOVikKmyloK/xWpJY5jFx4DbGCLSLPxiyOhKRPJhfosFyCtdR5lXQmV2u8yChR Z+Mk6tHTMds890FXnuM1FhlNqbijU2jWqAb6lHRi7Cw2NdNpTg5UyJIVksUVvj7PcBy7+LPPD94b JfLrjHVopvlvG3RjALTuQ2orb9txvcn7CafUnnTIzED2K91OMz7xwPd7XfoovEyeJxxY+zaUQ8bT NM0QHjyCw4Fn1e6VBb1bcxK1GqbW0mFTaVlPQCgpnl/DHmqMlDPPLyusSMPcWDg6CDAZB6hBRR0H WUPcG5FQ/J5UqnKX0VlykyH9zF+exhLduI4PizxPtoVPHx3rWT/TIFKNEhq/6SGVNaMvjvMoYho6 IgVPsAn3GCD0BSLRZeIQ6dpIp1xlqUK6u5oVItDdTiM7VqRAOse/+fIRYnwvRI+nJ7qYvwcmNWkZ waK/eHsbK+XtRJDlrie9yxTROMD7sGrujZlOpnRCXuWZV7uL/e7OibqEHsc8PlzYf5PxWqyTDN+9 OEcR9XhiU6Wac3U2InKYDzukfQenLnAwGstNTXoceoGTk2NcrFZIWK29h/EKUAnnb770scx7pHWL IstRO76IUDSCSNczETp6e50xYjqIY8S6DsTjS/IcV+u12kx2pOKvQzFaeF0pUfbUB8bHPn7j+yga Ajw9HI0DxGMb36xqBaCFJTOLvyAK3u4FBMoolGlmXeZwHVWzUu+v6lp1WYJJ5cEkEs6xr774TOZQ b9Kq/GCmKVLZrFTcWD/+LXnPs9bJaPu1Z/pO61qD2aiPwQzQk4me7CAajmDaBZJnC4L9x5/TTXUs aatrmpF9AtBSSGKeVXia9KhJV7wqMfFrqQudkjm1Z2JkGDZ6Yao4jKkTaKGkYwNwOJ0iKQqkzVYG a9eGXGi81tOxh8C3YeVUDOqwzGp0YYhm08Dp4dsdDkaBQBHuMslQqdIchPJD43dM50WncLZOmecT s/HKa9kwOAodRJHuZHYePKsXmEHZqJeFntpkiqJE0dYSRc0iH49moeKmd7jRkanUF/CrlBx2QKZN LePTmI5Hch9MJrenuB/KjMSiKVz82DuUkSu/A1GPIvL/fgdx/9b7fd9vUyd74Gs/ZDRmkPhy3AaO MQCCvg1ywWH98MbrrKlJ9sVZ0dZphou8w5V7iN6ZoM1bjMoWp02Fg6AQUdPA20YnvhchjiJJb6/Z a75boR8ezGUSBPrNZY2LZSMA04hUyHQMGovEtC20W5xOI4FL3GUqYr7+GL/bUUTQqIPz5erWa07G z5NxgGg0UfLtZq0MCuDEo1F1+vOIg+stvMBBFHvCv140SptSIkEzurMBB2+Pqy5z+G2DcBztb1Yi 7BOK614BZN88yqLT5j3Ce8VEkfeOJXeLwm/D7iNhtU9mbkSTXghx2c4u+GPtrussOCwKPHyAifzb jMf0cpXhrLTRTR9LTSqBh1QK2C28RYE/++xTzEZKVOPuN7v7n6mmY64LoQ3Pr0qcL8im0OM4ouAD +eJ3j09Hwffont2BgLtz4kRqWERiC+zgtntadT9918Lh0alwa5kDVCK4tQIPsxPJCItMFrr4bpxZ mSaw7Qa2vR8F9luhPzpl37KPvlmtmk5qNpnIEPi10Zz3aT/W8X3oyMLgrB7SAXxT2y3C8/dUc7jv g/F4Vus1zvJesEmunWDqtJjGim1ARmws4OXVBZZrTyPMe7WwN9MN2/NbLhfIywqOpD66/KD/T0d9 5Ld4FDYquvU64EmMWUwRjh6eZ+N47MCJGp210UFQ7bnHfLXGC6fBlIIW5FzenoB6fy6srkG2XuFS HIdSgjZW1RXqLJXUlwo9gkivK3E6TGsJMxAxU+oWMt1j00XznS3THIsKSBsNSWka2Mu5Gg5Hj7Fd 45ODkcBTBfEuRBTUF1TD3YIVM5uj64LsWx8SuvCgrp7mrOvfsG7FhoJocw74zT6Mw9q5UT8mUzeQ acC/O+cpeB4NUmUnkqR4FErNy2JvFHJ4fM+u1lh0IeogVsop6ITpYByp2hSZBoreRtUo1DvrNcRl qdrslnBF8EmMVFhY9YaKQbpW2PU4msZ41IVwyAnI26ib4qhqRHGYTw+JHGDByHGFzYHvwCOadz6C ApiEDj47OrxWXTHfoLgFoqk1xbOwhuhj4N+ZluQWB7dtcVZV0cDpfXSNonwRNtkgQFeQ29wRBDoj vqrLcUUW1cZWLBZkSa3VyBavDewOrh/Co2CsXIxNznttDnWzSfYfw7pR7K9qIkP9/lBj5sLIalck 940c1o+pY/0UFHB0Zv5O8VCqO8xUoRMlZUIZ6LD2BdJBmaxFkuFpAiROhN4dyfHWXSuYqUOCNPV3 bCiOazQy8ye8Ujdcu54jKJxDZNF75/7geXPHnbZAHW4Tv03ZmdFbxzCmEuJAONv6WAYgbXsRYj05 OLjxfHh9kzQR8Os0pPPdOixirOIo5iARGjosx9UTD0aiy5PfvSCQwriM2AShFO1t8lyRl55h37VT 6mET+8XkSY8JycbEKE6nmOpZW/bShvUr+/7TE3cZozlS2fB8hRba9zfir2/LBnvOg21Dv7Njb+yw PkR69qELjYI0Fxrf9wHFUJEFI6o8LyXC2hdjakNn9YfnV3iJIxSbOb/XV1hVSePVDWt7L93eOTbs oMN328YjhI8SS0WamJ6s7/LI9pm3NyroDK6WC1wtl7KzT6dKu9B8mJrfpOYRZ11VNzGMx0jTRAE7 LQdtkUm6ZyAU5rOuNQq0KVFWpssdXIuUMiVqq5UO4mK1RtGrbqHCHDLSbkS4Y9KkmI1/vNCECMjm ORbLFcqqEaDndDKW8zaiFj/uMxQGS8QxWtJFPdxP1NqhboDGb+Kw3gbE4UddhrewszzUhFjf2hYQ GUVwUORdHoWhiSamS9DzezSKw2N7frHA7y5yPO2PUXsjxXCn/nXnz+tmOmeSUF9zWhzqrmSeY7eV P3j1znffCxDT62sZy2FtJ+wrnE5ClF2DKwpadFtHKlQ1NzQrFD9+ixWR8y3gdi2+f/5cjlNFswrC slgu0Dg+ev04a23LmgDWQjYTu6slQgmiaONsNutl5zOPnQKPQlvgFlEwkQ4qmRh4eJUT4evMRVvp mYamgpsvEdYJgriHPZn9qPXAc315fol1UcMdHSA6HCNbLfDiaoGz80tMRiFOT0+le/nmdWYF1hW1 bRllenhGplD/r5Y+XnFYdxH6bVr3A/6sh6LgDeWKYhd9ePHwffN2yS5n2s3v0VluOpLdfnUFszTD y7zHizZG5cfaWW1rUSoKui1K0rRBTHEGVGdyg7PYLiKyt6clw3ek24tR4ucnY4wDFvSpfFwicGys ygZ5bkmxe2u6ZjVQrDHvKNGTdGBbsOxte57M8KnOHZlTPdS2q4e4PUnNS4o/lI4IuTodMLPJ4U4Z ugG76C1ZwTRwcDwJBE+1mWkkaLSzYBedQDTa3oJVZZh2Cf582uFgcoyp7wrDgzEOmjO1YxMjikgZ fbOpsbFOhF6XqxXK3oU3PoIbxrCptxjG0kAgPo4R1rPzC0xjX6IupopvZFynrgNHnNfDs8LbfMo1 xlHaMHIaDuPe9GbDf7+v49o874EnYD539xjftakB2G3F6m1gre7zmTI3VjeyiD50Z3TXWIcqyddL aXdhr9vSFRt8+SuvKQthHqBKMoVBd5+hiP40Id49TOrVPWf8iCJXaUPb+DJsXTQdRZBfMaZ2y2SN gqyelKYPfIS+j7P5FSpeZ95T3NXbDqMoRNpx2Jne0dGUz77QH2VVgWeLHC8yB73jYeT2GHtqZIwF eQVx8LYOXDOHGgfJrmJVO6J6xPoRgal0FBzhaamw0/jw6wxWtsTIrfDlZ0eYziZ6zOn1DSwTNXJc ihueOOK2F4R9CR/uaAY3GqvGgq20I+n0SGzI59MhJxU/P8eRt4UUPMTkWnKjfUPKZAlqbnJYhv/q 2kXQtssesAG1Df4cRl03vcfbNjPO8z4XMUduDB+70Iy8h0BL6ld5LricfXJYQRhg4iYYtTnWTYje JpTbl/qO1NnaCmfLTOYyYp8MmWqR0VmR5fP2lO9hN7Wik3GEHpgRKKMDuK5wS91kfE5eURlnIekf hwupeDOOInz/8gxWSNYHBQi2mxJTb4ymdgVOsSuw8fxqjX95lmBZE20fIhq76D2q3bjiMAWuIedN TnYLE5d1KHXUfL91rmhnSFBIhDspdUI06IoEbpVg1oeI+yVsO8Es8GSw+aZsxCfjqa45DY0bHe8d qtfwdiVjadH0aMIYwcExfDYVBuM8jVvKgDV/eA6zx59hdf4CaZkgLkqEnFZ4A6dlSALeZMHcGmFt hR1vftFD3liNE2wJAPdpof0YEzqZXqfBr+HseiumB7w5Z7dv3O3EAp2OPHwyabHqQlhlIpp+eV7h cp6grHt882KNPz/x8RdPRjg6mChBBdeVQvVtV8903O5blyFJINk813mJWcjoJ94Q39306rqukGQd FtUITsjntrgqKrxcrlDUjD10sdl2UJBnirgrvt01B9tjmeT4+jzBP83V41+5DQ4jC7YXY3RwiGI1 F6clDBWej9Cz8KtjzgWyk8ha2Aq/XYb4pnC0YlAHp0twXL7EF2GNnx1P8EmVYjo5hO8/kuiJMIub rol7wwwhr1+SrHFxMcdf/ea/w3R2IPcSRVZfluSxv8mBvKKcgHh2hGrV4eLiAp9++qmAdh9qgkez f0z5RqWyQ2ftvk2nYsjtaHc5rneLYHr7pkZvNMDwHTthU7cSqlymJ3vo9GfjEU6yBv/8+z9g3XlI Ww9pYyNrPDS9WoiPWx+dFyJmkVjjiRTP+83dThGJ2onUh6ZwVha+iBqc+qwVqceJ+ZI6i67TMLIZ RQFOuhJFlWPe0BX18LtKKu+ceTz0fOkGNkIV1GN8cCyvIxVOWWSiIp1ZHuA5eohXBg3lnr1Iazxd VBI5HQcdxmEklNCmNBaOxmjKSoCkdFgyEO96cH1fRYJMsVzWGOyN0jOZKjja89nRBAeTsdxjplO3 m728zlarNRJixKYnuFivkTatnGNWVsiliK1wfTymMB7JeTMtVClct1EsEnEOLwTHrensLct/s5qz UC69IQRKk2MON7G3Dhw1C3qIF9otku/fEny9vc9CvxRIK4W52sdr5fsexpGHsVVi0dhIKgdpq7if AouSXnRc9FLXhVKF6bNrJDUcRkK8oUVhh+lv10kt5SajKvPxOFRRj349ZbYk7dRpGB19QHaEEcmm OnhpI3OETh9KqvQtIflOLumgpEBkPHUUUwJ0B13EMwbTDL21dZBCVdM28HrgcBIj4hC360iHTxYX pxCYphmmBc3wwR9CvwmutfoKVstpBeWwRm2CoyiWjeChWKiOHcssk0ioolIRyQG9GE7kIWPxXnyU jdb14bICLp1HNSnB8zASZHLsXLPaYfExN4jhjWbiBMlh7wfBw46NvHCWzkreiMzPEGYOIqyHvsW9 P+qO0N5EWB8CprDvJsh26uEJ//k+uisVPZNz/TD2cOWNMCrJnqnwTGPHwnlxx3FrEKRCQ5vH1GKh Q2PRur6B8YKvYYTWWp7QIPO1pwdjqTkpRygcoarGiV6ojU+nMQKvwnQUIa9qPFsWeJGzJlmi6YCJ r7jLzEcJHEEiEO1MBsfM7yTJFSf9JPLROhA0v9SRbC5OFQ0zghS0vp4V3FIeKyPmyeL5a8iE3bc4 8RpJa9+kI1eR0C9LRUrt4vIKafQI7ShA6DiYwoVj0yFzVIiKP8ro1OVackxIqH9UFCOND81hL5MV bYfKCrCcv5BrM+No0H1ltU25SEva0eU8FI9lIqyhvdPRnGFR3vxuTE1xbye6/6ttjSE7I6x9NhaL J3GIA+8EYdNLy5wo7cANkFzUsrPeOCTsuAjYJRyAbzeLRTprFqo8vRFYmmc5XiRrvFiT3cDCv/+b n5E9HlWeaYQ5RRlUp5BONY7HGGsKlrpN5D1Jn/xt0qGsM3wSUyLeQzjoWqr05fqx87G6afEiKUQ0 9fh4igkc2E2BvicQggKx26xic2/vMDCoriGxaJwh0gNebYtHY0/k7B9qHaXNsky6jZ9/+TMU8PGi meGy4SZi44uW2DR9LjplNqGOdA+bWiHstWrPZpPgjGiSYZ7kKKoGi1WLIEikOTAav9ngtXrvh2/A Coe4vR3e+SzhEIqwC0vgriXy8x9BlPW+8F/SkubNtGfF9hsJ9GSqxIHtewi8ED5b90WCk7DAoe/C e1ChlkVvFZnclBLyxl3mLf7xfIlvrmo8GVl4cXaJsdeL+rHUYuhYZNaykTEZ1alTRtaZsd1i0tfC JnFeMhqq0FHkIR6DTcZtR+vVojSZQi8aC3N+CqUFeR9UOawqF8KrznKQrhaCYG8aR+o+XJ+khIlG U8CwM0jE5SgVLEZaAlJ61Unex9IkwWq5FB79Z1cJvmmmSIJYxpJ6irIy8mQ9TKe52Xq56V4aR8qI iY8RQ8Z0XR2ihbOM6XOH2o7Qjb/Ai+QFplH2YIclGEKmhW84sL3Fzb2jGtZdNizEKyrhLTzhvqnh h0ghh85KpWyqeCnYGf7XXY8kf4y9SaH1Q0VY08iDw0jQ0i1xi4PDLv7tr57g0TQQuuEHmTnvG1k9 OXFgiXT8msPDKfC3f1zgf/jVKR5P4o1zKvNMFuMufILjVBS+GE8DzFcpXiQt1q2NbyoPV8+WAleZ +cDEocNp4EUj+IMoUL53PQBER2PVpQxZK4yWAobZBEraCre02dwsW9KrnjADRol5jqaiMrYUkdBV GbwJX2e/kcNazK9EjGIVniKNp2idYDvDuXGG5traAmmgKtBFWmFV9fhyVmFCAKs8f4ssFzAvHS7/ xQmwrFyk7Zt0CpX2wdtKo96Lw9od6RlGKgYctt/L06Sv6u+CydKzauo83l5aq3gg9p/Ngg5rFHqI ylIvYldqM77V4vFE1bdI78L0jMazYdTB31mcJ6vCEITLWgxBmSxq3xTEctqAQEgQfd7VILPy83mD tDqShSXvo3djPpe1JEZbxtq6FADnyOUgci2iE1Vjo+wspDl5InosnRYjwtZtB1W/EKfXdYp1IE1T VLXLCW05T6aDRJ3bLjUYmSm0+CFhl3I48dujzlusizXKNpEiNKEqnBQoyDDRNQjbFP0J2Vcf7rBs zupZjqSCK+8QrUeZMmfz6XIt9XMNySS7lKg4hN6jrBWGTX7Iw8WokM2PTulfbqq6mH1SAAAgAElE QVSMlo115yJtdIfxIXWszV80p9gDsXa79t4iLBNZbYj7TcSCj8j0LOGWbF+pBLN79bYiInGA3NR4 U+zRwPOu8Wx53icBMCHxETtQvMNqzvgp9WLBVg1qznyMwMt1TgEK07ZXAUpV1HA4TugwjXn181h/ uUoKVasSymVIUZggzklgKbS7HkwWXiqRw9rO5tAhJmS9KCys8xa5EfqkwKs+o3kFzBktOxZWyRzL ZYlEnki5rQrxoyO4USTpIBWqn5BJ1Xel25aXwNcLNgV0ncgwbhQ1vjvPcZ400nkcCjN4fYUjhop4 OINo13WI4xj+aIqmblCHmoVCon9VH8srHlclDQbWq/KqRZNXyCpVsyJ9dNYAdVqpzYR4tCoTEY2k VLONxgp4yBtOX9QPLrwrfg61ET+w7P4KJOK982FtCpO6bWwekzhlz6OK6/OFWn/tLR6yGVkSatiA 6jgs6mIvjXU23ry//PRTeBzz0HWQPF0LPYwU0DcLV33nTKnStME/L5lebes2Ug/a9LBU6veKsWSU 2Fi0HiryxlsW8rbH//VNInWvx0cz6c6pSELdVwqoqqxsgUUG/C6lV5uoO/+Wu5/f7dPzEv/ptwtB tI8t4HgE/PcHj1VnkqITvYcvHx+LbiKZBS5WKZ6e09lux5SkuO24QknDYn/XX4+igr7FrFsi9B4/ KCXse8JeiE63kdsBFozWmtK01RQcwOpw0dTIqlYGthlNsYDeXhbiwDgITq2eHwpCOEhpIwqL6MXh 2rDqXYoJD3VfieOn/uH9j1ULoDIabAyTxX1frBpQw6d/MLmNITp+Fxx2n9d9KDNjOlJ/E4zJ26s3 KeqVbTdpn43pBnfsUEjWdhabdMIGgqg6qhD81GbucDDK9cqp3nTuOnIhZsgyBIoK4ySFY9fdFM15 kzPqIVjT81RNR0mKXWcSvdN2j/F1dXE+gXUtAwjlotdOmzQykVWLgzDP5TuTQfXXIw+zMRke7o+/ sliL8gM8O7vEovXRTA7Ru7peaChx+g7ndQqr9FQRnpGOUO9YPET5kUPZMLvqc5U/hmmt/psXouo4 0VBgOp3iIUbOLZE643cvX/b91rCZiRx6uA+qD3SN8QEfj0l0KEO/GgX9Ft/X7ED7xNCwa9xlKTja 2r6Spq8VsyS/z6YoNAZp4HReGaJ/kyqsojz2rAYBNPFdD1Fk9kX2zJIiM/FFonlIx6WpizncK/Ji HEKuCOw03Vj1s10P+tqTiLDMhTZZPaqirs1m2VN7sMPlKkFWuAILWKaFHp6uRfGZzjwmTgs1ZjE1 9Tg4rVOptpJrMrY9eDbhGPfnuCoKxbiQpCl+WNe4sg7R+0r9enNptRZixXGyzt75t+Gl1xGZzMky /mWcxUPr4fSVoPwdqxTsGLPAkLVBXt8Hm0r9Vc3y/rh3XnPCSYZBygcXNNtMnH9EQFKJgEw6yxD6 Lb7vhvJEp5z7Zor8rUTedAgmhyjKGvOmQd6aweEKwZhc6m/fOJc2CQC77dGww9Z1OAg8LaGlOLVI osexEsO91VaV1LLIwqAK86rD25FuuqixKtjeV+9vwMzizPIKTdMh1i11tYFobBU/q+vxbFXCZc2t bdRkghXKJkZc1GqdI81rxE6L2cEUEyoz64K4QCEEAd8hpUJ1USHyle7esM5707VfpDm+PlthntVY dS6KULOe6trV5kxYemFU0ypFHungWr1gqeiADBkiX61+2M1Tw/1S5qhcuHaPIPIENsLH/YKqiDcP l99lKigx7Kz3v6llAxSMGPbLYRmifc7Omen+P1Xbd3dNKmHWRVpvhNnJE5xfXeFFYuG8ouhAh7Cx 8LgFttWjt2eEJpxMQhyHFnryqtc9pmNHWCEY6TKaIiJbyburRoi08ZO1Si2YGhFNTohCk2CRl/h+ wRrJ7lXvpQFAEdSbTDpwPXDGrmHrAp0Nq+3Ruw4s30MU0wk1eJ5YCNDBHfUIBnANM4DcWg4SZ4Tn l0vhdx/FodQBWUy/aePO8wJn6xJ/LCMUo8+kYSDvx1TUHBkdl8lf5d9Z66rFWR26HR5FI4S+A4+y 7w5/yKihFH84NsQUm59d5UylORu51T7Mr0r06e26kXeZij7N5nC/u1xFwfx+9ijC+q+2NYNBkix/ h9ZkX+xqvkQfzTA5enTzE94i5uYm670AoWtj5PPm93F6OBHmzm0jZzs0bB4zvmLrAzTnxtuM5hl5 SeRkw0cjlDKLnpAPG01ZoiF4fyNT30o0Q3gE6Y9/e8k08hwHTo3AAX7xy1/KTOEuZdOzizm+X/Yo xp+p9NIlzspoF+p7p5MZKYnm+nC6OWnb7jDyKzw5OZRozqRp5hptroxOKc1A9NBkXlIUtR96aXSD iu/5QDVogVzQ0bkq+9obh2WApDLs+BEAJ9+ZSRjcCp/1PpkZn6l4w9Ut3EIBIcs0RZlyNIepBCEN JdIkRMCeoK6dCD2P5pKqibcyU8Y6dVGDwCwIq7RBZu00HqKXMRZ9m0oO40qEEvgdDnxiwVz0TYX5 qsbVOkNjKafBpxJ28dnhaMMiMuzoqpu/Q9Cx9rT9B56Dy7lp0Hl0gpAnV6GCrRdAlaquZlPIWA2L O0w30yTDP/3uCq7V4ctjHycTF42fY1l0ePrtEr+ryCJqw3c6/JtfPcbh4VjOr8lz/P6PK/xjVWIS WjidufhD8g2+PIrwCdkbpmNpKlxeXaEkBiyKdC1MI+aler5FgisHpgGgAyoZo/TEKGoIS+CIzu4I nSmU8/taFY2kv0wLkReI3pTMb/MZqiv8kGqHCM9yKkak4/bAhh1Dyb1NEV6f2KZW+JaUp/fZyChA ZyXUMntm3O0sh/QoHixizwTvZGGeVHiekDG0xwglvpuUWNSqK6c6nupLZMpGkKYCPg2Hi3UKw5qL qcVQ+UbToWzl4VUU0VguUjuCQy3BsoddsVWvtBHZHVN3TYeRYwl3lz2A0mygFJqIUX3ytivm2z2O wh4XHYeRHQSehbbSzzBdRs5JigrO9ve66fH98zWcvsYX02N8fjCCjwqzwMLfXWb4/mWFurMQOR0e nygnJOeSZ1ivM7ho0fUeruoO3y7W+MNZjq8OM/z8dI3PTkZIeAzBCL4TMcvbHK8KWQZhCxsF/NlA HPR5Wi0q/lcUct2G1huHThoc3a0k7GFetLgkJ2MHjOwGk76VdPKh9Oam4E4gxRCHdu/7bjCPuTcO yzgjUdrQjkoO0VCsDCh4BbT5E3Va3NVYJ9pHp2ywc44fiNin6CZ6KeZlhR+WVIDpMLVbBAkQD7qc m+SCd37VAna93c2ZulCSS0CcTGeUc+k54sLIiw5MaFG2uSbHRZaNg1XdoHctKSzLfRJyzk15IQWl 6JAUFKlQiO6i4mPa+TGKshzUlqI95vIb+xYeRS0e+wWq3kcRe2jrBlFjYeL3wukOL9KbqK2Om+M5 DtUrSl3bsjCOQ3x2egBmXqxTfXvW4tszAmnV584vV6jlO1ZdRVIsP5nyWBz8fuHgonDw/bLB1y8u cXa5xL//68/gzR4hsF0EOeCX6nw4KCR89IrbRp2XYYLg+BCFPQwWzmpRdwTTlmgGjSJ+n6ybSS25 azcOi+exahz8UCgXMUMN33eFSoddYjJL3NdpqckN7UPfYGpySKm+Fw6LNiT6U+G7xs5oB2bApbJ3 6h3hQyeNbzttZVRl9Af3zSSFIsdTXaLjQrjBNvVU4X4apB3mL50Di8ozHNC9CQ9FzvSmUiBIRiDk cLnpeRQu5XP4YXQYdGx8Xa4H6fWGVjnA7845hNwJTxV5ReGxrjNwgOLfLIR2g795EuEvjzykF1c4 9Tz8f02H1TTGweEUvmvDm9wOmOTHxi4XfS/PDaMAj+2ZiGqcTpeYRb3Q4jCCm88TXC0ygWYQkfGb T0I0TodV7aDzA5CzoslqrCsb836K+NM/QxBGAhadNSkeWRmSuhR8V2aHWDPqkkhvwCa6y3bBCM7K EYxmiMNXMV9llij1auOO2RmUS69+Z5PB9kMJfFfLSxwdH9/bYRmoDofbpR71oM34evq4Nw5raLtA UvN3c4HY8dmE6B/wGNUhvL0UlY5KkL17GF0ZY0rQoBPudKaGb8dYH6pgVamKqLytY7CIWWIExcXI ecCO4FNXASUZ4ZiFStbPcLyNxGUkpBXJr7HTIwxsFC2pgteiCt3y+OscQcfIiIXyFl1NFZwQjT+F VdU4/uQQkTuWDprdVhs2TilZsLjfFBDa0q7ByOvw7/7tL2WTdSYh/t8r3hcBnLZCHYT45a+e4Ohw quAE/B/PgfU49Jggw/kiwyrt0XF4mR8RTqR4HxzGmwK87wd4dORiNhkrvqqyxPfrGknKqNUAVodr wuCsCNys0fSEONzczDGiFVaaSuScphkqFix1RMrgmCmj5VlYXeSYHXSk0L+XqUkGdvx+/FrZS4dl 7JWTeyCzw7u0XbGOH/NFbOh2RFBhP7uDxij9tMxr5OsF4sPTB76ai6fZ4JA2tRdhwDO1l+33zJRL kd1xvESLexo6IjorQU73MtA88W24EUemtp/mURQ1ChE5TBsbPFtk+OHpWtI07vQcoG4tYossFLDx xxVwVTfoGqajPUYHAXzKZzGarwYjQ/rzFcWxZuf0LRzGWye65LgMmeKp5uwE8GMP02koEdeB1yHy leAD3yBZq9R3EgGV5eF3Vy2ajmBSG5Y4K9O8sIVBwvfID++jDn20dg50Kc7XK3ktr9N10K659HTf rwYDxrK6w4t1hbIolT5i2SBlqUsHUa2hPWqBnEPT97zfhbxPcF+ku3kDbKEh8dunGtZ9bHOhNb7m w7ssvN0IiwXYPY+uaNS/S/IFmnyNfnb88AkFcUiacZSpJQvtMj5jq8hKHLYudnDxmwKy/F0V4VXn cCveOgkcfB7biGJ/A7jlehQKnFEkrKKMHl6ua5ytOexsRnuYcqr34dr8IbXwQ8ro3UYAG1+NLKgz VKnjoGS/kf6SI2CqS+eyPcFNK19+BPfVoswLuCMHh36Pg5DjXUostLQtPJr4GBNPZrk4X69F75DY KRbjs7yQJkdvPoEIdkYsvF/aViIxq84AJ1aX5FoarSJQohGanmNlt3wtlo28s/E0UaPKSt+MI57q YnJ2gIo/hDXY4ViyAc91bhTC2DUNInnze3sfa1ivs01a+JGN8dzb9KDnfXeuD2X8DkjM59UNKtK3 sN2soQAyyqGL7PItab+zqWIpMA76YKTqkhpr5gSTbSpTl+hrSlSpLilrQjJXKTOIwrKngZfb92TE MR17mGp20V27PlqjxVyvnZSe4dQsqdLZ1B04wcOZESyhQVYK4KrJqB2Z0DIrah1B3JMdgUUfWlsh sCgE0aDIMox8wikCsO5NmXiO2mSth1kU42hKamMbPztLUXUKc3TodQIsFWA1lOOgw+JoEBH866qT n7530Qdj8jzfDIQjg8YOanxok9DDySTC07klzktFs9ton0V4niXHrqbHT5CmZwJ1eJ3DEtfK67HZ JH6cfRQOq78B3rAPkcgu+aD5XRaj5s/ivvu6FFYNeSo4w76nhLQoDFE2GS5ePEXXuzh0a3wWMxLq 4PcNRn0Bv1G0IuxgVS2vB/mfVMfO8seo0wTr5QqtZePR+GDbAeSYzypFUqXwrBbjUYh4rOTBBIHe sXRew+pUZ09qI2WH1dqBSzDmteFdZaIaU5SiiDNyKgFzGnozhRFTHUUxPSbGqAVlgox69HSQjaJb EawYSemoPB1yzIVMpx0iu8dvPp1JVPd0VeO7hEVu1qiA40MHh4f6s7oa354vsJ6v8fIixXxV4De/ OML401O0Yx/TaYD/+b/9UhSmOQ94mZb4l8tK5hp7+WzWvnR02QfonA6dT6rjUqfZtwF36YjYwGIU z6bVoCmyIdRUqbN0P817SZeWURqjP4WID6IIq/lzjJgf3pPAj7CTt9HZ31uHtYE2GMgDHxywfprn 7IO9ogpEByT1GRWeS8fzjtczFUzSbO8L7sZYBA6DAFaS4GA6wX/jNPjZow4BR1PaCpPZTOb5yqrG xSLB336fC46IHFKMME5HOlJzbbg7XcCqoRRXh3llg2pYndXA8yirRYcEfHkQ4ngUqChLXyuiwwNy cslCtDVzg655MdW7XOEsqbHoPHzx5BCdrVJNRkZkEOhJr8xOo44A6RTsjmDUAudzzgg6gl1idNMJ Q0cHjwDRx6FIvfOLtp0O4yiUcwoKHa3pGlVLpDshGDznrsRyvkSZJBjZNX72mYvHY8C3W0UMSC56 plqeL1FmQg6rjhuZwVNJ/qavlo6C2EFndF6kiir6ps5qT/hDgx/O57hc+ugIZ2DkI5urJceX8RhJ j8NoSIN1STJIihxS1AgklUpE+u+vu1OVJmEnHUZ+xhsZr5nnbcCue+ewhpgLQ8MhP3tUt7oryuqG jnZHMeU2Xi3WAzJNofsxGG8e7sasQTF66l1PWBSmHnAyO5YWPB1KmhfIVmus8haLoodvKRYFKUn5 AcKYi5TS8KpgQlBjss6xKhUjp9NbuMo61H0J328xcVvEJyGOYh9p1UgqxIUYuD6CwJWIRJDbXImU lvd8WTBJ3WPO94SLySRQxXzWz7jY6bBcf/s7Iwn27hsX56saaVHBtlocRJZgptLGQda6Ul8iLEE6 fdywHNKnKBwhB4XpfMQh9h2KokKalRKhjO0Kdt8LI+th5OOTqS9OUkriTSX4rLKlA6wEbb5OK9XZ IxtrUyvnB0fFg5yKqGoZvGYUib7EbBQgiujQd2ZyBb3v4GVSw5LxR1ciRpEtk+FuXjczHK7wcYpz XjdChA1Wba7y0Trxf53D4oe86SC/2tRccVgGIbB3DmtI8Lc7LrDLobUvNhSQxcDBmnTwpq/VwDQ4 qlKW1V5ir+40qUFVOF+muOoCoSROnRrj8RjLRSaLQYZ1rxJVrJWUZHuT2wSfMgqho9CMAk26RltU cB0fU0ZMhB/0wDxr4WQVWq9CUU6wyh08vcpwRqaFtsXnMx/O4Qhh4GORUt+wQhT4iEaqo5VSpp0j LBuOKzPGYu6hV3hXNr+5aBDbLU5CTxgeuhLIWmezKcnbaCwXoQYc5SHWaubTiRGXBfQ+U0oFxxg7 1DM8xGHQY+xxoBtY0pnxvm9rqWn94TwTp8TrRufVO4E40bpKkaa5iNbK8zuS8rVIa3YXfbg9mSp8 hHxjj3wZOxCHrsW6LtB3TPk0n4YJxni9iW+rMpUGSue2k2vJiJSnmqQNzmEjr4lCobTa3WvQZEWK JePhtxhH9aIwkKF3cyZ757CkOTQg/3/l3/fIURm7Rv2s0wq3v91ZyfO0HH1eFBJdfWzGmbTAD3BW eLKoPQu4rF38/b98jf/yzRppyUJwizTLUR4+BtybQJeakE93/eIDB9G0wRMuRkIfgli4x6uyRtOw c9ejLHI8ryt8tyjR2Code7lIgSIV2bH//M0CgUPkeIBJvMb3aYvcGYGKe5s6Fc2MtNxRX2Tqx3nC yG6wWlVYdiHS3tNEfMP6jaKfyddrOG2I08jD6WgM16VUu43mCYGySveQdTRGDMKhXhFC0OPy5UKl ZnRKToj/44/nWFTs6FmYhhZ+/akL27NR9Q7mpY2zFPClHqj0Exvbk/NTidrg2u5ea5NG37aEJKLi fJNC4VNRqClS1A4jvxrrIsfXTS1ah785ynE6vZt5VHQPDXHiG3gsRqtxFL0fIdU3sY+FD+sm26Dv NVborvMwUl58BsN+oT75yIyduUdHU/ziPIEdjLBqXazmBc4aH/96UeN8rkZVGH48clJRhkHgvjo/ KOgCRfkCu4HFQjXDDi4+P4RvuXAjpis9+iLBf35ODqtS6JFVWR9SbGe66VsZlgRrWzaiRYmDoMLR oyPFkqDHVQYHoDqWrGeJDQrv8qvaNEWx2fGQ1T3ynqX4m+mPZCA7HiEaj+B4AzYEaS6q2T0BoDK1 qhUbKuXF2Gx5VLLhUiOwydNVYmLXPCnpzEWsg5GOOl0jWaeo8gKh7q5SVJZRKz8m6Ct4FNeoek4J CV+74rdiFKeI8+hA1iQY4IERTiKwEk1S2LXI8xIvrjLUlhIKoTPtOo6Id2jTDMurFMt1LVHh6V9Z +DXboTddC7ke5NBSMIqHklHyurNGOo63oNm9c1ims3YfpoZhCrYvEddtHcObBjlFHFL/Tr6fj6V2 9eruFyLsrrCsPCQYofNCkbDyAhdhpHiZRFHG0CRoDJZVF1v6EgGAmuhUP81RoElpr7MmRCchqsou FlWFy4xL6LqDYV2IKRGBoBR7KLiY+g4zFswlOVdNkFdqv0MwqKHv5b1IdR8yFvQuqsZCJZJXCmS6 Yc28NmCsuodyvKyLaRFV3qtMl2XxUySWnWCyDwj5oyOblTiUwBeHVtYNZiQptC00vQWfnQdeLwJk nRYILGQCl+e8pQ3P9+B7iiLa70r86jTGp4cjcYgC2tTKy/zMNEvx9RoohDOLkVQl15rRrcxaNj2u Slv49nmFVfquuOqspkNSUhmowdgGVrmNSlR3bhiE1p+54Yl4wO3N9RMFAeI4khR/lyr8Vof1IEfw IwrimzrA4IDv85phfWtfnBbtri6mmolUHUTRuWtaWRQfo8lsoeMgdC38kFdYkKpOqrktZlNf5K9Y cCYMofeJwOatTzYGph2K+5yRj8h8yi7PXV/X8Th+o5kbDJpcFn+jEOEs1BtQgzE6RnYRTdylisK6 RmZYDQSEqqNZRj18b13wl9/576bO1bA4rp7LVIxCqyy0q6iDuLNecU/J69V3erXuUfbk6nIRUitV iCl7rItKun30UhW7CW0Nm3Q8vkolGyrzuAH1l5Dm7BhXmOecE7cwCm1xYG4YYjJ2EYRAWPOzK1hu AD9gw4HX24Pb5Pji0RRfHo/Fiaimj4aL1DWWVoPvUtYMVf1LrgmjW3YB+bXAQUUhihtWs8jda8fE u3qetkJSSId780yhaT7d736iY+ImyIhqRGfl+zeq87h3RTs3OYJrpzJgUHhTpzXsCj4kHbzGB78n DmtXf3H4mPxd/ylZEJVM2lI4pj5WYxp0ejjF9+TGyhL0luJ4F6xOrHQKvd5BzR0cLVzuxqYfbgQq uOAFwK6dh3TWNE0KI4CmFmxWWTao8xx9WcFj52mAI6JrIrThILClG5gxGuttkaVqszWcgCGLFkCV DqJOhTbodBEuVHQ2/O48au9ZmHgder9FTw1GEjPYlpBNlI3KBBjRcZ6QmLCq7/CHrII1L/B45ODz CUdxZnKNLkvg+1WHgoVtgkbLGj0vBN+fx1/0oibNXCtNLfz2uzWeXtUiO/bVcYRHJxO48SEsXle+ A512uUbvj1SEamYGpRmlQK7qMqs41BAGqBuQabAhilSNDSWyqrt6d3zXjIpjPR31w4L8Y6VqNNwk oEHQ7WYY+/b1aZpPHje/MMQ4jsRR3eYL3BtrK0O2hFtsCD0wbAq3jc1sgJQ70dQGWPYG84E3KUYP AaYfshZ2G1/QpntIAJ60qck4uR/O9k2M98jBbIaTeYnLvMaq8fH90oLVkD5G45lYRxGmDVtSHMpt CRW2H8ISnJK5IXpYFALVDryPDsShZKsVnn5/gX/6eimL4OQ4Qjxl+rldJCw5f34Y4H/6y8f43/7x Gb5bAZk4EQuroscsjkS6Xr1xC4tjRfwsGQkyPOupOg6mc0SMc27ScjDhbevH6h4tVtIg6Hw1AiOp rp68YA2oLFPAjdF6NtzAhR+p57luBouKNQZqIFgvOg6V6srvhm1Wz95p3tnNLN2tlHedikQFkNtU yNIEl5Ttspiy1QoLxVS0Zo0qR91Typ6RMCNaw6XFdavpkW9DpG82YW4IFr5fAudrJftFSufB0zTT g61mB+9wVoI5i0IEQYCAEaJuUtxlG4dlHIrRR3vdgt/864CKVn1xzH5ffe6Q/veVC4H7mYT7ussh yNxBFGPazBvU7gdwWMMIio7fEI/xDFV6YEZEOpldexjv4n4ab7DjkYvjvMPFukLcZwh9Fny5+fdS WD6bZyJawSIyB4+FMXPIJCpm5l0UdkeNmABZXgsVyypt4DEqaRUKSUZFdJTGdI3Mo7NxKAo6luC9 OBLCObwG3dVKeJyIQp/E7GpqnBFT2GF9bfO4ciwmJen1+ImwU9AJ3zSOQukxDW9Qnz6sxRquder/ bWt1G6PKsrTlWrhtiSdTF5GO5g4mLo68FnGfw7U4nCMFJdguIQy10EXHnoUwGqFKGnx9keH/+S5B 05PnvsEnx7E4Babebe2pEUFVFZcamKTdhlaanO66dvWK06Iz0+IVPAlCKxZJKV3ggwMt4jrAS3IM yEwU3GQGXMKpiYDp3x3IgKG5m5RMD0i9SWSyeQ3fS0BzO0c5QKq/DTcixfkdXBZ3baPKrDYmDdx8 Tya1DQ10VTixbuBICSIkSJSP0ZFpaSUdin/sdjiO8KRoschyOBM1UyfgSQ+oQwvzpdJaYXdKkhQB KZJC+frJy2NGREH/WeYlsrSUmtHYJ9cUd1lOBFDnjx0stUlSJHS5SmD1jTzqEZxKifmyR8LUkp1D Lmx20jxbyOoIBJVIQ334AKc1MIGDl4Pn3HEh9G2f1h2er0us25X8/uIqwcWiQNKR6QqYBR38aFCf Gbxn6Lv4y58fI6azFA4p4PAggOdbCEJ2zDieSFdAJolQpMsYYUaRjQo+/vFZhj9clEKnTFEQO2hx ID7YgdUp5L1iTNWNAjNcTqCtYyO0WhRyr+rRJT3u5Dk9JqGNbuLgyHLgMfqtGlwlJT57JTjRLK53 7MfSVQ0C6TY/RHjGNYtsF6T5JjYcoXkXJsyFOjIx6aswYBogqY683nc6KJPzOqIaCqsaiINxWkKo v8OuKm3zj9xYJH00KrH257BiC15LriQXtevgijxSLF7LHBuLzBD0uCtFd11Dko1O7fZkLSUtsRLd VFES/zoduziMyC3ei2LOyAaODmc4W2QCaPy+qfD3doU8LeCQ6YC+pe+QkwpGBewAACAASURBVKi4 Z0ymYgciw4VLS+pZA6pgI/I5uHXkmOhiTWFeUqjXR8WrCsjIdCoYC+DlRYIXixLrxpVBce/IlSFi +eaFVYLRnwPb8xB4Ab48cfF4FIiTriRSUWsqEqkwG2WmOOupaFOXBcqMPGKN6C+Sx17wWKKW2itI hW4wSLSv0f3yfRDDVVZoGxWt1nWLwGpQy7Wz4DtA7FtwGJ1aHqZujJOJp5olTYnCKvAybfEXLWt+ w5oiD1NRKd/ktITby/MwHsXSUX2IuR+L4EM/+JNfbt2yw8bcnY5C1QDouN6nYrJJQyVyYpWGtakB Cl89rmo9nK7fbQ6Iw3oAN/a+GusfgbTXPXx9scJJCExituV7rEqdCjKRs1ywIfaHi3rDPcDai0Ph UpvI6x4nYwdfHEdS4+OdH01jnH52ghHHYdoC68sFqqTAk8+P8b/8u5/hP/zv/4i/fXaF32U9/u5f LPziEw/hOFJpG6MRpj76OJXqsQfEB5s0b3ASArcwNTQx0iFLTVvXS/vktsli/R7qj5ZAzg2+Cyjt Ehy5LsFGRC8ajnFniXYjX0J+rCO3xihs4XgBnLrADxeJFM8D18Io8OC4RJurDVHK6bq7TMdAQKoX RAq/5FoIQU2ARqYAWDTsi1bNPBruMc3ZRY6rq4tLZI0tfPOsOXGeWcaKOEYUefjZoxhOPB2IsSrH ly/nuDi/wvNcUSazYC73Mtcjh93BDermy8T7hJtc/ADJe2PuR+Gs9DCxEV3lpLk4Jj2Lx5TA17Lg rzubIaPCmxyHmhXcvtbUq8zNQCclv8sLdDQ4UAQydTYjw71JYz9yI27m09NDfPViiYs2xIt5j3We YF0Ay2UOn+ovowlajpno19i9iZgtoS8WcCOxTqwhSdeuFmpjLtw2T/HiPEVXVpgSMyBy7b6iN7Fs rLpOtBCZELJgoBFgAq7czp/o7uSGTvgGu+m2GGK13mC5xL6NcWAjqZW6srDU0DmWanNz3EbYREWh Wkfs/JiCgP+6h92XCAPSGFMrzJKoiiM4RZrIKNLFOoPlpPjZ6RR/8ckI2SrFf/yHl7hMgZPRCSYH EWzy3TPC4gSBPkki6RPhxndR97rDqOFofAY3GaFG3glqpMvt2GidEOu6wXy+xJMngapbsmlBh6qk hnYuoyXIdcHvUR/yDWxvgKO3mQAtDae7JvAzKPFXcFw62qHdlGrt6tUNXz+02x4zaZ9+ty2VyQaa YaTPX8Vf6WdeWw8mjf0pGKPeySjCV8cx6jTAWVrg+Zyt7xZ12WDUpBh1QDCxZPCWplyKasAr3Lsa LpHI1FL1HgI4q2SNPKlR58RGNWjqHsukxIr0NDWZHHocOEwXuWBYXtZFd5vIJoYM6jvjEuFgjddV Ut/aNdIICyMqBwYHtqls9RyQJkZMpWSKHUJDaygM1lZoa8p+7SiwNqWwWHBci7YqLJSUgk9ULXNp F/jWp4xVJao6Xx24CDjG01GUpIJbZYL87mUNqHVAXBQhMWXD+UCyXABP2h6TyMfhyBeloDRnbU9F uJ5cc12YGpyZ6t7a4rCGj5omANNvp6lUR1SvLzox1hGPYxux3eMqyXHadXJ+dIJmGxoaoy8W10fs CvqBOLw3us+wx2a6gqb2ox5ktMWdamf5a6dhanKyS+0WdXdwUjeNAt02HiSvNB9palOa+XGTcjww auOXZqAg+4Ile1PjOVAG/dPjCc67HqOgkZSCKPGMQiKsPbVqLu/JYQDfpwQ6laRr/LDIBUnOK8A5 4UPfQjwOYLUWkitbqF1IgvfJkY+mUAO/z68y/N//9BRF3eD0JMb02BPapuMJkdKOsGHy2lJAy+CB xl4v1DaH45uZMruK0RzghNt/I02MclgW2oLv6cKhegwpf6tGKya70vR5WQK5dDCvR82UvOdMpDmO VUXlaj6HGo09rrpKePJ5BQ7cGrE7w6OZK1MQbV3DctSsZtvUguAXhgyQQUIBRnsXyPsAKcdyKIQh kQ7rPUwdayUaojFuMuBspghaKkIzpiVBny2D2zMCVeNAhDTGbofjGBjxejk2irLCOq+QNdwUepzG Fo4dbjIWsiyDy1Tc4pbQvwII5fcdh9GPclZ777BUFKO8hBmI2EZUW+qZoSPiGIax25zALh/7bRHY NREMcWSq00Uz6d3ue9z5uTu/k9/bdaufhMOi8cY8nE1hX85xOCaEYCzsm39cdEhb5TjGsYP/8VcT HM0mgrv59iLF//r3z5E3Spll6tv480MPJ4esufio0wznywwHkwqPjqd48ewC3zxP8HJR4j/8n9/i r399jF//+gni2YGKeoo1YrvDodtiZLd4igNUFuMBLsAefzZq8dmjQ6Et4ebGSQNVZ2Qg5CunOmAu 3X63FvKEtSQXfqhwR9l6CT9QGK9MhoNrFB1T3uvLqvM4qN2JjNcuzmnzO51M18JpCiEjjF1GLDUO +gIz1ge7CldZi8uiQ9I7WLYt/ubLE0X0h0bqYuerDK1bC0sGB8/HIcGtDL8KWJUGmDIlNNitpkFA +a6ejtrGzLfxV8ce/urnB0Itzee1TYXR9EAiuheLBH88a5G1nUAgIhIs+j78wMbZ2QVG4SfS+ROS VoFAKEZSRoesWfGa/1jba4clubLgRVSqNVzxTPkMwI47Gm8+oed94OLfJd679VikPmbBlRxdFZqv P/86ol3pxV3fabcBmnKGQth3QzH+YzUBAo5GGPXP0RQVFq0vN3pr+0zEOJiD1nYQcL7EsnCZlHi+ zFQard+DURG18YIo1PTHjkRine2LLmBtR6h6pmz6+jJCFWFXlfKwu+i7FqajQHjSj+Hg2TLHRa0o VSRiYg3UdrBOUvxwsUBatTiKPRGriAIPo1s6V8p53T5dMRDZeuVxoY+WzEDVNAWjZKh2BIbRYmKX OLUzzFcWXM/F46jHzFHMFZeXuTirZeugsEPh33pxfiGO8um8xLPUwrO+wWnYihjv7HQGxxohOHCB aLwBwwrSn5EeJdWcCJ2fo6mpc22jYYeRadtkjDgiZRCFV5XiMr8Xjym2S74winTUOEt7fHuWAUWG X4QrHBzMMBqNBVvle+4GsiBjQjfgO3c3+tdiP/dJqv4uU7V2jb5nb4n4Eo0YN1+5wYCZAuFQ5/Dt HMNOTq67TsZU2tq9Mn4zBNZKV9BgxxwHGaWUasUF9VMx3pxc+ILFQovQdRT3E3qRuQ8dlXIzugrc VphCWXPiYma1N8lL/P5ljxeFMPrh5cUl+ipH0bn4+ptzXFxlKBg0iPCD6pKJo9B4othucBQHOJqN MRoFaFcr5GUjtRzfa5BZDVZrH57jYp4VuMpqrBBhmdmY2TVOmgKut5ZzEaaBwXfT5KksRCEK7Hsk KWltSIjnSS2pbogw91GXObIkk1k7mtuVOAxtHE5jAV+u0xzLvEdKiTBYiEMfs0mEiQ94dYKyUE4n 7yukbgU/cJHVrTBFcNzoIkvg2T3OmjHyzhFyxHnpwOrJKsFN0kXpx7CEzUHVBo3CkMK4kfaZgDYy edpCoUOKoLHjSESsd1xJQcnXluWZ3N8ZJcDKXLi9KlJcrzOczzNkqwynn9TC38UxnfF4JKM2dFb3 oQe/iWL8NvsoHJaCKmz5pRTubTsGZEp8Jj28jfjvbZt1y/zlBtU//CKMEx28RsAQPyFnZWwaU0yh QJVyRMfBr04n0kXkYiDFCRVsErIRELWuGyoyaEySuKzD71/kcC9SoMzhNDliq8blqsBV0glxHMGe 3GsZrSzmiWqfuyup2/z1Jx4OTqeYTSh0AXx7meO7yxzLWm0YS7fDN8uFqu90rYzvtCNPVGrIqlDV DVZQ+ClFQ75t6kRNCR85orD6/8v7ruY4sjS7k96VhQddm+me2VXHjiIU+yCFnvWiH6wXPehFEQop RqEN7bS008NpQw8QQJn0edMoznczCwWQ7AbZJEfE3A42yAJQlVWZ98vPHIP5dIy4bBC3CorlZNOK BE3jUP69RpLmeJloXNOepzAZ+XBGtkjmsAGfV9pGS0CZtoXRKETg22hzE2aTCTA2qxoYSgdyVhE5 A1IDUVWwfRs5HLEnI4SWWDN+HkSgywiAQFBeYYT/qApNyTKxhSl0HR3gSeWRn5Fr0ZTe2CKt8Ogi lUyT3pP0PjQKS5rpeV4jISKFVQYVMWj51ZioTVt4sYTucPFmJFr3NwhW21+vP/5pB6wtorW1RXyV x7bgDNv0nL+WmsPw+pssr8/+tgMaNynvRrZNcbpPQ8v9pmtnEsI0ElEbNTwDv3/g4Mu7MzFhXccJ vj9ZQ61KKeGXKZ2kaZRqi1Inm8PLdSnqnpaqcDQCpk6Lx2UO247kTm8RqCj0G+DkxRJPXqyRN8zY LHw9uYeA8su9+ujD0wyPL3LZXPQBfEbNiFa7/RBqcDgPMKbGPLj5gLwycLI5FVeNSffZkK9yjFSD vb09CRinlYWUXW/pC2n5Z25bTt3yjohwFsMtPJKxSN+xbK18IDQbTfTWDkM9C0ICjELjkL5lIm5M JOta6DcrehgqgjzpkDNBaXgom1qa88IlFAJOC4dYLFebP7iGEulp3ix4zfldBZ/9JAaUhmASSlBb SBtCS1pYSYnvXqZwXFdnkoSVeK3mPdYMikwDA9hhiybuYHoGdkM21SsZPsh1L9xR6t+/f8jUJxGw 3gUrtp1q/jWa2gONtD+ADb9xW5mCNb5yXQlYZL3flkXGPTf+X57mUEaDMX6CZ5v46v6e9KL+8DjF y4pTMMBvMoR1gtQNMQltzOwWRgmkXQPfZGeFG9nAvYMA0Z0HePjTEg8fr3E+oBIMOhqbWDYmpkRu c2Ne6SdqXtyuR5njSoCk7J1ddL4WwHuFM9dTVt60eA4Fx7Ul4f2GS4sPMydLjQ6+YSMQCEwvj9M1 5MXpioGlZJWjbk3k6zWWz05hTT3M5oHgrf7vSSZTu9bpBOpQFQoXtJZv1qKrz+vM91h0GwgJQp2G 2NmnTY+NeLnEyarC2ZLDHWDPM3AUBbDpkk0Oo0ut+UYbTNCMghAHy4dBXXuyFBh8+verFUs1OFRc oNmzdDrciwz83XyEB3cOMJ1Ob6Rpd6sD1qegYCr9KhH11/019ioG80jBb8ld5+rPC2iQTWYi929R wOIkLXBMBHaLjDQVvjWLaGwfdsnNW8OlC03TYO7U+GwnwrdrJRmSH3Az2QijAF4ZwyhTQczPZiMk WYZOZYjMCoc2+1imjNeZpuz7BvbmLgKPZWeFIs9Q5BTLq7HjtghdS+AIHP0vOgc5ZQFZjqpK3Gbk OhmQ7sN9ZoMV7TN1s5Hp2TTyJVMsswxtRe6sLVpdebzCWqVSyhGu4bO/3VKLiv0tEwGDVVWIlru7 6+NItMBa1FUhBq/MxI4dA+O7+/gff4nx7dMER3MHk/kIP/10DseI5UiWOXBe+Zj4ZFZQpsYV6y0u uu+0lqvNaUm6t20YJl0Bew52T9ehOoVRlTIMGCbxgnrowc6Xn0CnGQBCSq8FYuJ3mtuJqENjGTgO gC/uHWEckmrzptHD+1m3PmB9rAxLhgLMq2iLvlX+DX/a6+Xi5hc/6Pn9qyyOsg93A/yrL2a4aChn 0uLbFwlOi06AkFnJMbrmsrFpfFryk2O/p9FcuI7N3044bXbvE1hQQtjKMbYaxA5t5vk5krqi1VLc wBRPv2frAtn3C7heLjy8s0Sh7Gys2eNRlvR/spZUIUO+v0gU1k0mJ8EUYcEGTb8rGFio7qCDgYFU bKssJKUNpjpx1aEizssiRII9HQOLnL0sBgbCOPgsBIkaUKanJW04cHBrTT2iuUNdIm1tlFkr8jtW 5OHB1EOsbBRliWlA0rOL7xgEa4XANeHaNvK1wkHE6SsEF+UKBUkPpLQHpO6ec/DhmQ3GAqztMDJr CcxlXsNsFOy2gSt/tCghoQ5Ewxs0hd006Ss9VTRt+I6Fz8eGwBeqeYhibcApV9idTaRi+NCCA7c6 YH3MNUwxB6zY9p//H5VRP/SkcGca4DcPZti3Z3h6usKfX+b405kOCJbqBJMkPD9KRDMTYt8mZxNc 9yfzskGbFqALludYqMRuixkp4AcuPFq85x0830IQenDCEKbv41lc4ac0RW2SdN3CbWrkZiDSyXpd biiCOVc1gx9TQAM2jR3I+eOmBnAQdPBcW2zEBIfF4+K35NsdDLrn0J2GZGrUAt8oug7FdoLC7xDC Qq6k03PnGq0XJhZhVA7tf1RuaqaF/amPfxtqJyJmi6uMZg6WWMmHto155GOmOowjW64pGyamfGpy CZnd044sjhGGnkxh7089PJjxRlDD7iyclVRHreCbLQJH8wsk3MkApFciFfBr77jNHmsvB8S3u09w rphDGFjbCuuTldCkPgYv9m8mYH3oLGto/LMpTGK27llpAq/2ldNyuNePQReOtyvFEtiC6I17cKMZ /LhCnLZCsrVhYULunO3JJghQYRcZTuIU66pFKeY2HVRNSWQFZ+bDDTzYdo3HLwuxszLDCJELnD8v YIU+rOkEjRdK70r8VHsXHlYtrJh0QHgNt60XyOO6JE/10igDub33Udz8xJVT1TfLey9Eq5dp1qYP WuCPX12L18LwK4PB7uVRWAIp0MMlOv54DrAzjcRvcBVTewow/UCmhaZvIxr7+MJ3hPjPDPNuYGMy a4XQ3SoTSVbi9LTAvbt7mPgOvtqf4LPdCEVGbmeN//wwwZPzDBOnwYP9CMpuUCqtZ8E+a+uE6EgF EkKhgilqD5p/yQyOtnR8b/SfJNSCcjofSyfp1gesD10WXp9GDoJ9Gj6tm+8iCCcE6D7ruqIXR/vv T1+x4frizVY2rGlgZ+wjJZZKNYg8E//+7lgcd1hC1KpEvGjxP6sa66JBXNaoslya0tNZhMayBEcl U7yyRcWI4Hmw/ACf30tkc7OEkxzJoEwym/5s61AtwESyMoVyU17Tzhf35k5pJHjfwGf5pC289GL/ qy5yqIQg3xa2RR17DVjmanI6zLCRn4u8MiEY+wHNW0kDMnDMYMuUsLVQmjYS9o7ydZ9OEZbD6aiB eeTh9/fHOJgSvwRdmpoMBAVexCWeJC3cvV3UyoCyWhSuic+/2IfdFPI5eWaLSZfjUVKK801aKhSN iXS1gj8JtGqOUMBMcaaW4Y/0W3U2zAJ5854HKAfl8CldE69FV57lI6EQjSpxXqVQ8x3UcY7nJy+x WpYIwvC1dKf3vW59wBrWh9B/H/SoB0T7Nm1I/k68kEjeDJtgK7jxIhImPKc/RBMXn6Td15uWRZ5a laBpFnjx6AUulrT9MhDNPVjdCGanMA1DGJ2J1VmLZdLi0UUjyPa7O3NMppFsjq4pkNDkIqklG/BH EUrLxVla4elphi6gKzCV7viqhmR14WgkfSeqXhKcK0CpawkACyFCW5ll1abbSwVpqMGwVnGJi5cr 5NW5oPGPJgaOd33M5yOYwUiAnxd5g6wWMRWMSQCfTeA5JmZWjQdTGwmTEzo5ExTbV1ksDSUL7Bv6 vJ+ROM44+PTlCqrSmCki8Knien6eou0mmhreaACp49qwa1umdswsV1WLP/7lTIxWA9/EbOahWJdQ lkKV+cgzE3mWShls1gXcthCMV3KeYwwffsjP0YBnWLjnVZiGDLJ07K4xdizMZzSIsFCVDdapj/PM kp5b3gXwfA8l3YBILXoLMb53WX8zAetDBsDXlXnDxfiKm/AGaqHvekS+UxqHfCuaql6n83yqS4Cg qhC34qZI4beV4I+gGvz4VAlOiZpR/DxowJpUBlQNOFQDdWzx9qsUdZ2Y6VDKJMBsb95vVCoRKGTs blsm3OASnMDXIBTAcj2dLRlau+FS30Ov4TeExExcE88WXX62BL5FxXSR4+VSgW35keHA27EwdzvU VodUnI/o1tyrQTBQ0G+QjhgwkXQm4s6UxrYaykpOh6lHJeYRPcG/1ZpdxKR9+2QpE+OWLs4ymKtl iuk6AYyaUz1iukwB1TZNKRLJLCWfJ0oGGiRhE983G3nwuwKoMlysE5kcMthwMkvCuB4GWDhPK8zn NmZhIKqkJFAdk9I0dtGMXKSBBbOtRECRaguJZ2FZAqvaEjqPYfsIvRAp+4aTCV7nR/E+199EwLrS BH9PQ7k3NdIv9eS3cDqv+d1tBDXLQt/3xB2GF+ttaM7zTi6UmLbELDQw9am5ZAlJ98lFDnNUIjcS 2bwrc4xwUuKOz03aoKobnC2YVbChrHmFe/tTdLMp1mmCrNQSwBSd08OwLRVynSptGsfyHfZlrmGr 9M/TLks3nfmvfs62cVAWUUZNbMHYAKaBg+OJjzsjG2tO96xG1BUMU8n7QmtLo7w0qKigsCypUuHA UQ1MTt1Yv/JYqQ5K+hKDjVB3II3/ZVrhJGtQqRal6rDjdjjwWoQTYp4MFFTXpT+hSxJzClUpRIEt UILlKsXuhHlegJ2pj+N5gKogYXuFx1Q8bUItRcwpoO0g8jqcJTWyxsbuYYhoMoZhOVIWs5nv0hDC soWTWWWJ2LaR3+gZVE1NtPpq37vjJ/axwM9/EwFrWIPshzi2/Yom4caf7Q18xZtMSwYqz2CkSjoD JTiYVvPkf+orzhucXOSIqwq7AQUWifwGTNfDwRfHkgWVbLzbnkz9fjedS6xJ4hgnJwuclxbsrsI8 tDCdjzDe2ZHnVWWDZd3iNOfnNyg69cqlGggn9mAGUdlNLZMxq+UEsLe12mCrtBqCw5FfrwVlU0aQ oEgh4NGYTJdUY4elqvYHJNQhNBrkZYmI5SSDR2vAKojML5AsDKSilU6TUWpyEQVfI/IszKa6OW0W axy6Hu6PAziOLRpaP6xKvCg63D3eFajD9y8y7ExM/P6uL4qjf4xdJJQ/JZizSKQBnpSdwCjYXxIk PxtgpBBZLSbI8aekxrOnBSgucdy52J2P5Lr/x/tjnHsX+E/f/4RFbqLcD9DNIvHk4KdJetJPL54J j3BvZyoMhPFsV3pgjSqkpDRIG6LSRbaE0cTY2/1aJoUfetl5kWuZ1YFV/QkokL7NGgKDnty9qp74 tksbTLy7aunmuAbXZ1Eq0JmfOI18wrZf22s69vDgzgzjLkCTLHEwn4hMb8zMITIxm/hSuizzCn96 /hIXhUap0+tv7tXYnfpiLJrHMZ798ATL//NUntd3WikZQ8vBgzuBZCqdTR9CBh4DZxmwVClMkzIq RM0XaFrCDrSKw25gYDSbaNt4NpKbCq0oGQitfkP6Xb08Q8lS1HdEEv3ffBFgL6Ipxgo/PF7gZMlm e4ustaXBTbpYGHl4UVooWppc8Bkb1FSG4DM7DqZuIBAIml94gYvJOJRNrgjgpOcig4DoeHE/5gJ2 HQe+9LaMOodZE5VO8iGld4BRRBKjglEpfBHV+O7xCoXpITUdJFYF1ZAQZGs6k8oRVC2KzkJZ+GJm O/c7KRXL9QqJpYQwfu9ghonZoTAtPEtaPLw4l0Dtr00YXiglZ1YaqKUPR7XUFEEvkf1RYA36Rajl zLsRpwgfuAj9yEuL+mmkuW4I6vvsu4SsXyJUa4Ao71E3C4pX/Oa2VFVvw2Lm4EcRbGsu5gruJBD9 r0a1mIQWDmZj4ftZyxRGs8AyE/lxjO0W44g65ro5XaQ5FusKj15qQvLuzMZ4ZsMJXRxGY9E2j1sH pTjCAJmUZvwbM6UWPrXKBfNFqEMH3zUQhb5I2HC8aNQmOkIGhitCHKYVasoR19rXjyX7aBygQSX8 RA4UZr6NvfkMT1YVTok7YMnk+WiUIQh7YqFc10Qp0AYLlUgQcwjDflmnLe1tWx8HGQ9WKfZkgnsi 37DHnPGaFW43p5oG31UvS1RXokVFfbasJCG6we7EwSJVKFOFjGKKgQ3qRpO3SJ2wp88TGTSo2Iai Ia1r43fHYykDnTqH37nY9zrkWY5lTJmbBKu8Alk8Y89ASyWJiuqmvM49Oc7I93DXi+A67kdJdmy+ UMPxLUmXdS0X1W3KsXTZdqlD9a5v7jp04cprbDI5Bpyfd9DdOjDd4+ozQK2fdYvUGySdtUT32xy7 yNlMZ5Pa7QiaFgpO6HsISjrsOChpbEAJZXEm7oSrJjuWeu8kJSsK15mIahM+ScWWg8nYQlm1SAsT rXr9dEo6Usbwh9Ufp3Iap7WxuLoy6NCuzv4oRJkXMI1cOluZIOUNGFWLsQkcRA6+unuAzl5BIUFt uWhsRwJNzeFJSR37EHC10ih16TtV9iamhAd0UAWzL6qC0l22gtsquA4hGQa6qSP9rR8uCj0VNIC5 R6/CFuuyQJpkODreRU39iJxKCR2mIxemQXVRTQ4nfzLYc+XaUm2Hk7VC01V4ntZirErvwlloidNQ UzWiGvr0xQLrVSy9wqYqseeZ+GJnhMMZpZRrPKsVHiYVEuWgszxMww6H0+BGUjLvY4kJBS8YbhQ2 fQfX4tsStIxrEAQhJb9lULhJZiUKpP3FfqMMrj8errbWfavbMiHkEhUCTt0ov2L5OCO0gDbrRoe0 rJCSngNTJnGbBnivMUa5EuVEwiOkWQP5cTM68Ri2KCxE5Nt5BkaeBoeGrSmZhtbc7/XTtCl7L+r4 hoPsdcs5fbvqywQRsCt9SjmzlKyRMrC2HVzYqAxgRVpOZ2ES+bhDX8TO1hNBQ0vgVJlCRBH7XlKb iH6UqYApGLAq20bq0GLLFwmYtkgwbmvccVvMJiM00RT/+/EK/+W7C8Stj//4GweHEftWDdaqQpIr zAIPlWHhIs6wHxhYZDVm0xBjt0PQFiI7fW9/LKKT/D0/LBDnJc4vFIq8Bm1dn50uUWaFYMwWqwz/ 619eCObsztTCVwcefntnim++/lzUQhnQ2vYMf/xxjWeiVZpjttvBvdPLXXyEtWm6y4idhqREF5MT dAv88rQOu3as4RI81FsEqyHIbU8ZX7ckSPWTwZsG+0FNldOfhr0OGTXfkuyq60QffGJzgpTh4ck5 znNd8tGX75lRwf3hXMoiSqmkSQqzceAT6lAAJ5WBMzJ8+ZlXNXZ37l/T+AAAEtVJREFUfHx+d4Z/ uL+DyOmk3PQ4hm9KLJJCFEyZkVE9dFHTvoq1FIsoE8tFi6qg0e4bjlVsr8j7GyAol2fPiSKEe0Dm 1VgWlLrx4XQm6lYhhoOHp0t8du8Y97/8jWQ+j14s8H2cIZhNpYSkUzU1+jbwK5MZJMGixGVp81LK D1dFDttv4VoNolGIyXSGIosR+DkCu0Sc1/j+osbLzJZsrC5rfD61sVitsC4U1osEZUm5YjbFS+RN Bz+wQXMhXldpXWJBoUJmo2khdl5RYAmq3jIUbKdXZDA77E0cfLY/xr2Jic8Pxrh/51BEC+M4xvcP H+LRk6f4ajRHkD7FuvWw5+5gOhl/cHu9YeJub6mby/8ZsLR1Ej75pbmnl8SXQaL4jT//mob8dVDo z/Wi3pZkI2Uk2foUkqPu0C2JV0VRSAZFUm9s+sibUgCUWU3oQIuK75lid5Qr5oStJkjRk7KNnx8l VLSnOhAYDo7nHv7d39/D4WyELI2xyEosihp1S0ljH3vzsZSXVG734kIIz1R6coIAbprBLIf+lKZO bT7nXjJFgzi1sYg06sWwgdPFSpRTSRou4eAsq6FIUi6paqoQZwb2DqlWEMBSNQLP1u0AYqyEKE3Z VP3agWvjzo6Hu3tTSQashs/dosgy/PnH53i2LtFJAK/wIuug8gQX7JcJ16jFy2WFc9uW98zpHKIG 66YQ7FVRkI9IPB97YobwB0OH2avCeV6JyB7dnPke+fu0k+bkcRaYAnx1LCUIftd3MPZtHE2o0wYB 7K5XS+zu7uLkxXOcvTwVfawd38Y3f/dbxEWBaeTi4GD/gzbct/elrT30Br+9Sy3yDcBxWzC/Z59/ ikHrlcdvoCd9Y5llURR4O9kF3e/qbZPou8dG6i2JWMSSMUCt4CMzfdRGChaABBoyYNGNuaub3uTU lDKLTfFtWsywdtjU3Qvw5Z1duZFmVYWkbXCmNFrqiBmX64oG18HOFFlZ4ZzQEFpBELck54bP3Pcg RfeYbshEoFO9VJdocu6owinBij/ZSknL36WKARU8iSuL0xplVqNVvNGUWKelqIuWzGTy3s6LXSej hU+XZ5fZC6EGetxjkfDMoMZhgLRgcjy/iHFK0ULTlIxpXHSIDCXqFQcjgmZtcbLuLAcNsW1JiadV K5xruX6qTmRfJr6N+dgTCAWHETzOyvBguQZGRieqDr7vwFLUzTIw8m1RMHVsV6SSTdKeaB1W5Ohs UyAfaWphZ2enN2ylRnuBti4xDx3c2xthPA4wijRc4kOsbQgRl83R+vYDg2LgYI99Bbl9RRPlLdem 331dMG348mHe8HbQ2YZtvE4M/12ndFo36dXNJreAXo9+e2n8ldbHuoSP3p4lgZhTOdtD6NqYeizT mBSQPGuiLKgjrK29WtOR0iT09TSO5QptvMredCT0NML6IiWgtsJZUooPXykGqUDF7K1QcNwGnaXd o1nmyV25bUTxc+SyVKc3n0ayM1AR4S54Bf6diqcU1Wvoe9iIsgIVNgmcdD0XI7HFYtnPhrbbK8Uy YFjiyffjiwvBLj1dZJoYTJUIs9W6VyNLSl/fanGRENeVyLGNDCWOz1QGrd0ABsuymviuGn5ZIIpM ofiQe+lNIt3rMi0sL2I8O8lw2pqI2NN3GYpNVBUBp40Edct1kZEJ0JgwohEmXougyaAKA/ORi6Ig ILeTvpXvcIapB22E/pylGrt2tBOJ0w1pNzxeUVjNEuFXkqKTxAsc7H2G3d2dD5ZdDZnV9r4Uq/pt rfFhU2+kfQde3IbVfi3ruCalceUFtx/s5Vd0idabkA6qjYPZ0XtWKrwEZ/YN8Y5a05fv70M1uYcS cTAw0DAHfY/XQUof07A+rZz1lxff48wFvnZauEGDhLpPpiOWVCTm/vOPMX5KqE/uwDOBQ7/DP9y1 MBuFOE9L/MvTBI9jNtANXKyB/57l+KdHS8lE6Ws4ilxBZnOd1zlWcQrD1drudVGLzExIETtSSkIb o1ko6g8kMhNYaoqFDU0Z9J/O8WEUMQKzg2uaAnZtrBCu72M3irDrEiLKWnesp4vUteI5zpY4pa/i owsR7Psx9dB0DCxUYungRS3mB/saulCXyIiOr7Vj0CEDudvBCTwE+y7+fr+E1xZYJLngshzR7LIk 8O9RmUI01w2cJy2+O2nw2U4rHL9paIqhhhpFUrous1KgCBwSrBWl7lewLIXOVAiIFQtDnNem2MnT rDYpCqhSIXKpgEvdCEPAut98foy7R/uXpZjozo9FB+uLL7/UZhPjkWi3f6xgpY+jt5bmBqKyo1xw ggthPa4xWlvqGu+wLnsGly/cP0bMy1V9j01Wcnnkr3kzm7Tsmo/gFQ7fYMSqLcC4xLW5lxv5oOUX /Qt7dYBBrG+7jzZMCLX29e1bNNz0gwjRmL55FgJLY5Rs10OcVfjxPEdXcCraC1l4vti4H4c+dmwP 9lmJMqVhLq1GNbaKQEyesjkzDjtE5+uA1RDxzk1jMhCY6OxG0PMC0KTCJ9sbjg/HNeX1jSoT+V/R ouKTmqwN9SXHBj5NR33fRxfSyFX1qrGtgDYJtoTBgKSHN5QUpjNOWtRQDSlZnZhOkBTMBI7nl5NO g3ISIjF0+RkxqDRZLtljmbcwKNTnMghYOIldUUTY9YHdwMK5Iu6K+LAKvlHhcGzhcOYgtBqUSYUV 7extC3t7vrhXq84QbmpL/EirhJhc5PQOKGCsCzlu9tQI+5r5BhyfJWSDpq1wHAK/vX8P88llmccv ge/h+PhIzoFlU/vKQkat+KaF57lSggpbQ7JP+72qn7wCa9hsdN5w+pMhQl6suSksL8/0zsdwJWi9 /u9bhdFbBpJNMOhBm1s2zPJl8Czke1SKzcnL9/tKtNh+zHj9Q69CFrZ+cOtnBuWFQdiPExh55X6K qEU9Lp9x8Nm4DW0sVTcwXAuerwXrXNeG47nSa6Izi5CgacjQ55ss1J6sFA532PzVrsytodsSEva3 9HhklioTrX5TEIjJYMDyjs1u+Z7V46x6nRtmt3Ids8nfO0EPk11Jy5TgoKSfNVQRfEz6WbqXJYhy akOJeQOFCHntKjnPZa2v48io8Ls9H7PQE6rMSVoIb08ORPpklKCnDCDbLqVMhzunw9Q1xOaMOoJ1 S5Crti7LCoXzrMPTnCoJBbK8wsix8R/+8QEip8V6HePJWYK8bjAbiWK8TBT5tuluTewahwfyHkxT ppB1w9LagmpM+b7V1Qhpt22bMlEkLGM6DiUIXXFvdl3JFKUw6BV1aUXGG3NFTXpyMk2WqQGiHnHw tuvn7L6G4LUJhVIKdpcGnwST8itLmV9fo/Yl4Ct/f9d1+YaGgKexN1v/HoJBf7GzDJBhAiv+4QBe FxyuPbY9Rb0Uj7k0HpBeyEaXrS8B5SbQA0J5CH0JKo9vHHV0yjCU3ixVdVb26UcsKcF5/fQXMgnB plXprKQqMfGAuxMLU8VRf4fIaVCWPTXHIiRCbyQi1XtTyk1Dnv0h8SBkMOEa1DEZbLZcml+/rk+A O8FMZXmLrmRA4l3DQVYYaOlP2Faga73EXUqnGCYmvoVxRK2tDllno1A+GkufM6cthXR8NPGQqg4x 5WtcX8pBfbw9KVsyBWDk2tgZB3I95GkiU7mMNzqWvraBqgJOYoUn6wqWypDGGeZHU3zz2VxkZBbr HFXD/dmKjhbL0pi9PgOImNTV1HjXTX8mIqFtIis0uJmNf+4GmqMOWY1YyLPS2sIEav/P3jhYyJtb e08+FgbBS8wIbzaB5711zPg59RMeC/07OaSwXxfhBk0bQcALzOHj8IRuvrbKwJ8Jfu0ADCQuiHft 3iDiUp/q163tO4EGLfYnWbIoTYDVBhQisi2jbq1y2Q82euIz78BMpwncvQ0ZliQtlUKZpfLZEGc0 ZLk0XPgNx/szEkIMlKoRQ1PHcHA0D3XjuKvhdRXMppDshhmRMvQd3xGbL1NKO1lNJQYXgiD/xTLg 6ofLG8Q6U/hpwfPARy63Q4sSY0vhYOLAD/Xj7Nvc3Rnjy4OZ/Pt8YcOIGgSFbp3Q2OEELs7iXmmU SPchO5TMz0TnEmRpwDYKTPwWd2eRtGJeVAUWcSref4qTvYD9JgfjroOfJpIR2TyqPMPj5xc4XWT4 ln29usTMb1EscpGCqetOAv8yBaJxhBHVRMtSALoynGx0oz70CW2gd2GAkgoT7PG1RNSbWC7X4uvI slISVGkZ6QD/y+f+snf7PoKVXD9KYZ2konH/xmJT7Kn7bKtSRT/6/LTEHXRfX188w9xgc/28j+ff thHjf/Slk2uTwnCXErvDz7BBOVAYeLcAN94ggaKFwj/5JRcdtb/9EOF4qjNJbnaP/RXdZqCulZbc BeKsRHu6kM1EOITABBSQmwRpdpJp9c+sKS7c9OxP9T0sQzBWPwcp2aonN//ul7gbCfZ8S3PzcjUE NXBiSDKdvLdSn1uhXumMhFSc7cyOMA0i9TfXhip06Wk5Yu91eRTUxaKelIXHz85wukyEB7jMaqyK CqOjCHtjFwe2IRiqFycXqLoWf36yxj8/zuF07JUBRxMgtA3EijpgnhCVNXC3hdvmMFQLH6T8UApG A0Pp+uNbJlRdoYIpuu6V0WHdaTdqlu+6D6V9E+UzF5L+ja6AG/eHf6lfNWRWy3UsX/nvn41Am6zK GKZa9ScXtCSh6fFOOrt6vwCKwdV56FXx7iIpsrRH3nDieKcTc9X+H/1xfurVIC8ogkapymB5vgQD 0SEng6LiZjfhR2PphQwXqmPXEgBIdVnFLVZpi7jWJaAW1dMleG06MjGrKTMtQM/h2uw987jqQvey xBRiew3tAaYJ/NqIn5XAG2hCyq7PVl9Vy+nR7IffZ2OJJBaanBYocgPxmmj3Tlt95S2asi/xq0Ik aTqZJDCYtXCZR/b9M2aOI05Lae3WlFgtKvzhZYyzVSIZBI0hTlJqYTVwqgLxzMc48nBv5OLQneG/ rUo8TxKUTYeQ2u+uhZaB0LPEUCL0HaEr8f3INdjWoNk0JZp5r6DWPHWuaMHGT4Ty0/PpHHWeAXkO h87WrcIoiqTJTjApK4Gmebsg9HPV2BCkrmdUr3t+xpwky7Wg4eAq/UsHIC/OC6XWv0Q52k9Ngmb4 YKRh+p4625rwfNnQl7U5Cdsb4PIYBOTHPiFH1j1w9PL7n/7EkNn4YrHEsnbh2RUsJ9NZel2jVpWo E/CGV2QpTuIKadVKY/nlKhW7dj8whCd4EFnI6LLcunQIk/NVUehvXSBf11jKgMUScGZgKqjWRMn+ ExU5uxpBZwnOiP2usaGQNgVUzc+d/ls97koaw5x+uTicbg1/egkiLgYVbtiTi0TiHANOWnR4vFAC saiLDBeVgaTpJYf6Jj2PjQHLsxrszkKBHTCTJo+SFl4s/6u8w6PnKf7rH89QUvxOkR3Q4DzXNz2X G7QjPs3B0ZTPYeIPf46R1rn0nghKZbl6NPMwCk3EaaVdpmlRb9kSbNKikQDFzK4x+dmy99YL74m+ u43Ic5GqSoIsp5T7Yx/TUSDng0vUWm+4XxgrWEW8KWC9zo39TYFQCNuKaq7Flde/UbrEno92h9Ob 7rpV/PteA83lfTzPpXzL5YX4PgKDxnFxtN3+bA3+ak9NZ3rXJyC3gfjMu/rFcoVnzRgz5PBtLaGy wfQJJaeSXsQ//WWBp+sKlbhKtfjXxz7uhAF828W+6ws8oTV8mcwRniBI+PUCFxcJsiRDpugMXWN/ 4oq88jkjHPWyDIUD28HU6hAYDe6GLR5RzYATM6YZkvWxNKMUs4vQdnF/1L5SJsr7yTO8XOR4/DLv FUuFOAQSdbioi9720jUDMcth31KPgcVDcbYXwvYISadTUIO7u5Oem2fjh2cx/vD9GmlnYMfSRhvn lMnpSO7u8Jnl4HA+xs5sKuW04+jtSvgEm+xEwe+HBhyrQ2Z1AlplJud7RKSTDK7zRelBMfOXm6Ue 7BBISxZAk6dyQ+H1OxmF+ObBPkahzlB1X/bm+5DtDgbBgbt7+ZFeBWv/0j7Rbaha4DHbDX15jRsf zVvUpr9mDZQV41c2x7dlZTbP/UZowjuu3jx10Nu62TGxFLwa4AQDdwuoOcxC1mWLxPYw8UYIWP65 rrivSIbLTMMiGDLFabnGo4SNd47bDZhegOl0grHvoHM8eByZ88Y4uLWYBIAWOLlQyCrqjLvyOVLr vTCAdUMyDv9toLZDuJ6DXbvC4eEMp2cV0rTSPTTiDlkyEvbQX19v7H6ZlWC8yHl8xdK+v4oGnYkt vYm+L/mGm/oWo0SLSfYcR7mhAiE6ZCyBmw539ub4/df35VnTJBaqDtVPuahg8cPLDOuyxNHcxk5o 42zdYdZWotTKODNzTJnU5g0duEmernEUmhi7NGClukOBZbyG4/iYhi6Odie4c/dYG3RspI5ufk0S UzYE1eE9bv+5Cf2MN/U404yB15my/D/YfKUE9W2MjgAAAABJRU5ErkJggg==