20241212 13330300 1.0 FALSE ExportFeatures CensusTracts_No_Join E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_SNAP_Indicators_Upload_Data.gdb\MI_GeoRef\SNAP_Indicators_ALL_Variables # NOT_USE_ALIAS "County "County (Text)" true true false 255 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.County,0,254;GEOID "GEOID_Text" true true false 11 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.GEOID,0,10;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.GEOID_Num,-1,-1;Label "Label_Full" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Label,0,7999;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Shape_Area,-1,-1;Dataset "Dataset" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.Dataset,0,7999;Label "Label" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.Label,0,7999;County "County" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.County,0,7999;State "State" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.State,-1,-1;Tract "Tract" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.Tract,-1,-1;Tract2 "Tract2" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.Tract2,-1,-1;JoinID_NUM "JoinID_NUM" true true false 8 BigInteger 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.JoinID_NUM,-1,-1;JoinID_TEXT "JoinID_TEXT" true true false 8 BigInteger 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.JoinID_TEXT,-1,-1;Population "Population" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.Population,-1,-1;Med_Age "Med_Age" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.Med_Age,-1,-1;Pop_Under_18 "Pop_Under_18" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.Pop_Under_18,-1,-1;Pop_60_and_Older "Pop_60_and_Older" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,TO_JOIN___ACSDP5Y2022.DP05-2024-12-12T180534.csv.Pop_60_and_Older,-1,-1;Data_JOIN "Data_JOIN" true true false 8 BigInteger 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.Data_JOIN,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C01_001E,-1,-1;S2201_C01_002E "S2201_C01_002E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C01_002E,-1,-1;S2201_C01_009E "S2201_C01_009E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C01_009E,-1,-1;S2201_C01_021E "S2201_C01_021E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C01_021E,-1,-1;S2201_C01_023E "S2201_C01_023E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C01_023E,-1,-1;S2201_C01_034E "S2201_C01_034E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C01_034E,-1,-1;S2201_C01_034E_TEXT "S2201_C01_034E_TEXT" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C01_034E_TEXT,-1,-1;S2201_C02_002E "S2201_C02_002E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C02_002E,-1,-1;S2201_C02_009E "S2201_C02_009E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C02_009E,-1,-1;S2201_C02_021E "S2201_C02_021E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C02_021E,-1,-1;S2201_C02_023E "S2201_C02_023E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C02_023E,-1,-1;S2201_C03_001E "S2201_C03_001E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C03_001E,-1,-1;S2201_C03_002E "S2201_C03_002E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C03_002E,-1,-1;S2201_C03_009E "S2201_C03_009E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C03_009E,-1,-1;S2201_C03_021E "S2201_C03_021E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C03_021E,-1,-1;S2201_C03_023E "S2201_C03_023E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C03_023E,-1,-1;S2201_C03_034E "S2201_C03_034E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C03_034E,-1,-1;S2201_C03_034E_TEXT "S2201_C03_034E_TEXT" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C03_034E_TEXT,-1,-1;S2201_C04_002E "S2201_C04_002E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C04_002E,-1,-1;S2201_C04_009E "S2201_C04_009E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C04_009E,-1,-1;S2201_C04_021E "S2201_C04_021E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C04_021E,-1,-1;S2201_C04_023E "S2201_C04_023E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_SNAP_Join_Data.csv.S2201_C04_023E,-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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_SNAP_Indicators_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>SNAP_Indicators_ALL_Variables</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AlterField><field_name>S2201_C01_034E_TEXT</field_name><new_field_name>S2201_C01_034E_TEXT_temp</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C03_034E_TEXT</field_name><new_field_name>S2201_C03_034E_TEXT_temp</new_field_name><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_SNAP_Indicators_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>SNAP_Indicators_ALL_Variables</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>S2201_C01_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><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_Indicators_ALL_Variables S2201_C01_034E_TEXT "$"&Left([S2201_C01_034E_TEXT_temp],1)&","&Right([S2201_C01_034E_TEXT_temp],3) VB # TEXT NO_ENFORCE_DOMAINS CalculateField SNAP_Indicators_ALL_Variables S2201_C01_034E_TEXT "$"&Left([S2201_C01_034E_TEXT_temp],2)&","&Right([S2201_C01_034E_TEXT_temp],3) VB # TEXT NO_ENFORCE_DOMAINS CalculateField SNAP_Indicators_ALL_Variables S2201_C01_034E_TEXT "$"&Left([S2201_C01_034E_TEXT_temp],3)&","&Right([S2201_C01_034E_TEXT_temp],3) VB # TEXT NO_ENFORCE_DOMAINS CalculateField SNAP_Indicators_ALL_Variables S2201_C03_034E_TEXT "Less than $2,500" VB # TEXT NO_ENFORCE_DOMAINS CalculateField SNAP_Indicators_ALL_Variables S2201_C03_034E_TEXT "$"&Left([S2201_C03_034E],1)&","&Right([S2201_C03_034E],3) VB # TEXT NO_ENFORCE_DOMAINS CalculateField SNAP_Indicators_ALL_Variables S2201_C03_034E_TEXT "$"&Left([S2201_C03_034E],2)&","&Right([S2201_C03_034E],3) VB # TEXT NO_ENFORCE_DOMAINS CalculateField SNAP_Indicators_ALL_Variables S2201_C03_034E_TEXT "$"&Left([S2201_C03_034E],3)&","&Right([S2201_C03_034E],3) VB # TEXT NO_ENFORCE_DOMAINS DeleteField SNAP_Indicators_ALL_Variables S2201_C03_034E_TEXT_temp DELETE_FIELDS DeleteField SNAP_Indicators_ALL_Variables S2201_C01_034E_TEXT_temp 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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_SNAP_Indicators_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>SNAP_Indicators_ALL_Variables</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>Med_Age</field_name><field_alias>Median Age</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>Pop_Under_18</field_name><field_alias>Population Under 18</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>Pop_60_and_Older</field_name><field_alias>Population Over 60</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_SNAP_Indicators_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>SNAP_Indicators_ALL_Variables</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><DeleteField><field_name>Data_JOIN</field_name></DeleteField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_SNAP_Indicators_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>SNAP_Indicators_ALL_Variables</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>S2201_C01_001E</field_name><field_alias>Number of Households</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C01_002E</field_name><field_alias>Number of Households with at Least 1 Person Aged 60 or Older</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C01_009E</field_name><field_alias>Number of Households with at Least 1 Person Aged 18 or Younger</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C01_021E</field_name><field_alias>Number of Households Below the Poverty Level withing the Last 12 Months</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C01_023E</field_name><field_alias>Number of Households with at Least 1 Person Living with a Disability</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C01_034E</field_name><field_alias>Median Household Income in the Last 12 Months</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C02_002E</field_name><field_alias>Percent of Households with at Least 1 Person Aged 60 or Older</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C01_034E_TEXT</field_name><field_alias>Median Household Income in the Last 12 Months_TEXT</field_alias><field_type>TEXT</field_type><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_SNAP_Indicators_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>SNAP_Indicators_ALL_Variables</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>S2201_C02_009E</field_name><field_alias>Percent of Household with at Least 1 Person Aged 18 or Younger</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C02_021E</field_name><field_alias>Percent of Households Below the Poverty Level within the Last 12 Months</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C02_023E</field_name><field_alias>Percent of Households with at Least 1 Person Living with a Disability</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C03_001E</field_name><field_alias>Number of Households who Receive SNAP Benefits</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C03_002E</field_name><field_alias>Number of SNAP Households with at Least 1 Person Aged 60 or Older</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C03_009E</field_name><field_alias>Number of SNAP Households with at Least 1 Person Aged 18 or Younger</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C03_021E</field_name><field_alias>Number of SNAP Households Below the Poverty Level within the Last 12 Months</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C03_023E</field_name><field_alias>Number of SNAP Households with at Least 1 Person Living with a Disability</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C03_034E</field_name><field_alias>Median Household Income in the Last 12 Months for SNAP Households</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C04_002E</field_name><field_alias>Percent of SNAP Households with at Least 1 Person Aged 60 or Older</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C04_009E</field_name><field_alias>Percent of SNAP Households with at Least 1 Person Aged 18 or Younger</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C04_021E</field_name><field_alias>Percent of SNAP Households Below the Poverty Level within the Last 12 Months</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C04_023E</field_name><field_alias>Percent of SNAP Households with at Least 1 Person Living with a Disability</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2201_C03_034E_TEXT</field_name><field_alias>Median Household Income in the Last 12 Months for SNAP Households_TEXT</field_alias><field_type>TEXT</field_type><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow></operationSequence>" CopyFeatures E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_SNAP_Indicators_Upload_Data.gdb\MI_GeoRef\SNAP_Indicators_ALL_Variables E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb\MI_GeoRef\SNAP_Indicators_ALL_Variables # # # # Rename E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb\MI_GeoRef\SNAP_Indicators_ALL_Variables E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb\MI_GeoRef\CensusTracts_No_Join FeatureClass DeleteField E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb\MI_GeoRef\CensusTracts_No_Join Pop_Under_18;Pop_60_and_Older;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_C01_034E_TEXT;S2201_C03_034E_TEXT 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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>CensusTracts_No_Join</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><DeleteField><field_name>JoinID_NUM</field_name><field_name>JoinID_TEXT</field_name></DeleteField></workflow></operationSequence> ExportFeatures CensusTracts_No_Join E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb\MI_GeoRef\Demographic_Variables_ALL # NOT_USE_ALIAS "County "County (Text)" true true false 255 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.County,0,254;GEOID "GEOID_Text" true true false 11 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.GEOID,0,10;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.GEOID_Num,-1,-1;Label "Label_Full" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Dataset,0,7999;Label_1 "Label" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Label_1,0,7999;County_1 "County" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.County_1,0,7999;State "State" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.State,-1,-1;Tract "Tract" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Tract,-1,-1;Tract2 "Tract2" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Tract2,-1,-1;Population "Population" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Population,-1,-1;Med_Age "Median Age" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Med_Age,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,CensusTracts_No_Join,CensusTracts_No_Join.Shape_Area,-1,-1;JOINID "JOINID" true true false 8 BigInteger 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.JOINID,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S2201_C01_001E,-1,-1;S1602_C01_003E "S1602_C01_003E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S1602_C01_003E,-1,-1;S1602_C01_004E "S1602_C01_004E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S1602_C01_004E,-1,-1;S1602_C01_005E "S1602_C01_005E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S1602_C01_005E,-1,-1;S1602_C02_002E "S1602_C02_002E" true true false 8 Double 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S1602_C02_002E,-1,-1;B28002_002E "B28002_002E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.B28002_002E,-1,-1;B28002_006E "B28002_006E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.B28002_006E,-1,-1;B28004_002E "B28004_002E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.B28004_002E,-1,-1;B28004_006E "B28004_006E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.B28004_006E,-1,-1;B28004_010E "B28004_010E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.B28004_010E,-1,-1;B28004_014E "B28004_014E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.B28004_014E,-1,-1;B28004_018E "B28004_018E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.B28004_018E,-1,-1;B28004_022E "B28004_022E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.B28004_022E,-1,-1;S2101_C01_027E "S2101_C01_027E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S2101_C01_027E,-1,-1;S2101_C01_028E "S2101_C01_028E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S2101_C01_028E,-1,-1;S2101_C01_029E "S2101_C01_029E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S2101_C01_029E,-1,-1;S2101_C01_030E "S2101_C01_030E" true true false 4 Long 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.S2101_C01_030E,-1,-1;Field19 "Field19" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.Field19,0,7999;Field20 "Field20" true true false 8000 Text 0 0,First,#,CensusTracts_No_Join,NYAA_Demographic_Join_Data.csv.Field20,0,7999" # 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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Variables_ALL</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><DeleteField><field_name>JOINID</field_name></DeleteField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Variables_ALL</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><DeleteField><field_name>Field19</field_name><field_name>Field20</field_name></DeleteField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Variables_ALL</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>S2201_C01_001E</field_name><field_alias>Number of Households</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S1602_C01_003E</field_name><field_alias>Number of Households who Speak a Different Indo/European Language</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S1602_C01_004E</field_name><field_alias>Number of Households who Speak an AAPI Language</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S1602_C01_005E</field_name><field_alias>Number of Households who Speak a Different Language than indicated in Other Language options</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Variables_ALL</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>S1602_C02_002E</field_name><field_alias>Percent of Spanish Speaking Households</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>B28002_002E</field_name><field_alias>Number of Households who have Internet Access</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Variables_ALL</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>B28002_006E</field_name><field_alias>Number of Households who Only have Internet Access Through Cellular Data</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Variables_ALL</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>B28004_002E</field_name><field_alias>Number of Households who Earn Less than $10,000 per Year</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Variables_ALL</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>B28004_006E</field_name><field_alias>Number of Households who Earn between $10,000 and $19,999 Annually</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>B28004_010E</field_name><field_alias>Number of Households who Earn between $20,000 and $34,999 Annually </field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>B28004_014E</field_name><field_alias>Number of Households who Earn between $35,000 and $49,999 Annually </field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>B28004_018E</field_name><field_alias>Number of Households who Earn between $50,000 and $74,999 Annually </field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>B28004_022E</field_name><field_alias>Number of Households who Earn $75,000 or More Annually </field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></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/3.4.0'><FeatureDataset>MI_GeoRef</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Variables_ALL</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" "<operationSequence><workflow><AlterField><field_name>S2101_C01_027E</field_name><field_alias>Number of Individuals over the Age of 25 who have Less than a High School Degree</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2101_C01_028E</field_name><field_alias>Number of Individuals over the Age of 25 with a High School Diploma or GED</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2101_C01_029E</field_name><field_alias>Number of Individuals over the Age of 25 who have an Associates Degree or have spend some time Enrolled in College</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow><workflow><AlterField><field_name>S2101_C01_030E</field_name><field_alias>Number of Individuals over the Age of 25 with a Bachelor's Degree or Higher</field_alias><field_is_nullable>True</field_is_nullable><clear_field_alias>False</clear_field_alias></AlterField></workflow></operationSequence>" Demographic_Variables_ALL 002 file://\\DESKTOP-J9TVK45\E$\Nyaa_NSPEARS_Application_Year2_Data\Year_2_Demographic_Variables_Upload_Data.gdb Local Area Network Projected GCS_NAD_1983_2011 Linear Unit: Meter (1.000000) NAD_1983_2011_Michigan_GeoRef_Meters <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/3.4.0'><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> 20241213 13525400 20241213 13525400 Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 13.4.0.55405 Percent of Spanish Speaking Households _ACS22_ Demographic_Indicators This data download includes census tract level geospatial information pertaining to SNAP-Ed relevant contextual variables in the state of Michigan. Data compiled and verified for accuracy by Nyaa Research Services. File Geodatabase Feature Class dataset EPSG 8.2.10(10.3.1) 0 Simple FALSE 0 TRUE FALSE Demographic_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. County County (Text) String 255 0 0 GEOID GEOID_Text String 11 0 0 GEOID_Num GEOID_Num Double 8 0 0 Label Label_Full String 8000 0 0 Dataset Dataset String 8000 0 0 Label_1 Label String 8000 0 0 County_1 County String 8000 0 0 State State Integer 4 0 0 Tract Tract Integer 4 0 0 Tract2 Tract2 Integer 4 0 0 Population Population Integer 4 0 0 Med_Age Median Age Double 8 0 0 JOINID JOINID BigInteger 8 0 0 S2201_C01_001E S2201_C01_001E 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 S1602_C02_002E S1602_C02_002E Double 8 0 0 B28002_002E B28002_002E Integer 4 0 0 B28002_006E B28002_006E Integer 4 0 0 B28004_002E B28004_002E Integer 4 0 0 B28004_006E B28004_006E Integer 4 0 0 B28004_010E B28004_010E Integer 4 0 0 B28004_014E B28004_014E Integer 4 0 0 B28004_018E B28004_018E Integer 4 0 0 B28004_022E B28004_022E Integer 4 0 0 S2101_C01_027E S2101_C01_027E Integer 4 0 0 S2101_C01_028E S2101_C01_028E Integer 4 0 0 S2101_C01_029E S2101_C01_029E Integer 4 0 0 S2101_C01_030E S2101_C01_030E Integer 4 0 0 Field19 Field19 String 8000 0 0 Field20 Field20 String 8000 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. 20241213 iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJzsvWmPJEmSLKZ+R0RmVVf3HLv7QBLvkSD//98gCIIgQBAEyQ/8 QAKPxM5OV1VmRPhNiIiqmUVkZnX1MT09O+ON7MojwsPd3ExNVVRUtP1//vS/7GaVrdti43y2zVbb bbWqqqypDvYwvLe+OfDnHX/ZF9v2zSrD3zv+PM0Xu4xPtlWLme18rVltu238eV02+9Ofvrfputoy r3YZRxuGlq/Df23X2LZtdno42He/f2eV1YZrwn91Vdu2r5aOnZdr+46r2fk6fIa+x1EZPh7v1U84 185z6K21dc3Bjv2jVVVt+17Ztle27401dW9WDWbNwfb2ZFvV2s57eftY18XOT0/2v/6P/7393//n /27nTx95bU3X2cP79/a7f/kPdnr/aG3bWN1U1g9Hq+uWl900rX37uz/aw7tvrR8O9osfGKNlteV6 teXzs9V1bc2ht+owWN11VtW1cbD+Sgfm0baOZuvF6v1itS1W7YvtfN7xbOOobNk7W6vBdut57Xh+ +TWVPys8691s36zdRjtsZ6vryaza8ksxD6qac2vHNWDOYj7XLX/GvNVs0u/jfZijTdVaXXdWW8M5 V/sc5rowzdNYG8s+2cb72fk+nXHjz7r2Yg7jkjEXt8aqurW6wnysrKs1TnGP2z5zfXLsdq3Tcoya quG14fyrbVZhLPxYd7wvjy3un1fPf3E9pvXP9V3zHuqq5ffLduXn7r62OMY+lnw9xsEvJexCXN+2 wS5ozKsaay2uNk4R59Lzw4lwbRgzfFZe22btZf6EZ6uhw8OqdDNV1fIB0lDhYe+Y3Ddj4wbJuACb prd9w4PGoOBB4mHLvuDCp3G0edHf8BDrprV+wPta69qW19APnQaq7rTYTA9FkyhfdMUb00TB78M4 6Xt84aHVt+/BX+LB+/txT5XhocBAvbMd91C3tlVfv5C3ZbHP3//Zzp8+2TReORlhGNq2s+5wssPj N9Z0jT08Ptrx9Mhr6rrBjscH/ly3rdVNY3+RA5Oow/g+WHs42PJ85tf28ZP1335jzfFoVdv+1YwW JmXdHmxvBtu297Zss+34soUGp7gRswoLebC2wfPS4rqdkLfHtk5WzdpYtUDjHxiLWAgyYljgNBBV YyuMzu4zq4o36f3cCEuDsI1u2nZbNxmC+DvOw/nmqxNm8fZq/Zxp7vo9Nr3VVW99jUWL+T+lzXmn sZNxyCfLhl3rxZ87190io+TORrIUbjz4qwrrHte62NA8WGu9rTbZus222cRnsXMtrekcPE0yuHJK NAq45gYLNK0/rd24SzkasW5hC7JjIcMZ90ljl55xfn+7uPVL84JfbbKuvBBeoRsn7ih4YNoV6InV Db0wPKrVsNMs/r6VO1tdV/bweLLnp6tN02LHw4ELmje3m12uVxqprvddiBY+3+Dt4cYmDdLt3zmW MIppCGGoZJzqqrOmGqypH6yq3tleD7ZXMFIyVJgwehBff8DwvvvwwX73z/9Cb2u6Xu3bP/zRvvnd 7+zdh+/s9P69LfOoK9s2G69nM9x701jbDz75/sIGA8+sbax5OFrdtbaPk63XydZxtnrorTsd4e79 lQwXJmrFObJj7JtOc+3+uXMTweJrvmCmcOz0hKr1bPvyvV2XP/OtXdtbiw3JvQdOX85l99Z3zVwY G7wBM4gzjXN0o8dAx63Cwo054gvSv7QmYOg222jAtuz1wEjSo8PSHzk76d/QmJgtPkfbura+hnel dUBjUpqEyiMP00ajTVjLPK4Xn6VFjvduhUOSoxC+L67XrwUGDlEG1si6w2DBQ3u5LdBI8X7dC6ub 9Hqcd+XnxGfhmvM75aK5Abo7MV4HI6bLpNniWeBp4XNwtMlD8YHgB7ghcofRbbh7XnzY4bqZ1fx1 Y12Lv8B/HYuB1ofj9cOht2XGg8S63W1bN5s3DfQ8z/buXcfQ8MtHXEkejJsdhq62fCzugjSsCH3w cN2Vrwer66Nt2NmrjoZqr+Nzf/yCheE5PjzaP/9X/5Eh4DLP9s3vfm/vvvlgw/FkTdPYPI22zJOt y2xth/scrIOxQkj2ax14nl1Hw7R3ne3PWNCL7eNoy7pZNfRW9508rr/CoZBCUEKe7C9e9YPnwdxb lrPV82fb1082b0/W7NogmghLYv4WM4lLY8eGjHmj54IlI0/h5hN8weY5d7tphlehDT77Bu4pRNjh Kyp5fR56KjJZUiSi8+TrrXxV4sC1cZXRU9G9NaYoJT5fRkqGIsJQGBt5S6X3Eh6Rh6v0wMLLzQZI MUx4h/7cylg77qsY3dKLuv9duZIVZsZ9FkaW5knPpJVhyg+JIRit8qIFDQtDfADDk72BMF9y5TbG plXdECqAe1kaLe4yTWPDoeNFjdfZlnXxEFIXdzi2djhoseQBcLObJiMGUxMr/zIPkPaJ1mqDWw1P CnjR0er2aHVzNKNxAi5V25pO+/O8CowfvMU//of/gl+vHcCt8PWbOID99LV13Xsaq/X5YvOnz8S1 mtPRWoSJ2N5/FW8rdlN9n/GYn/7ZmCPLdLZ6eTbbrpzPjBToZsDbh6cTXlXGhvRehUgKOYVvxcH1 4XCJrlaeTfr7zbrIYY9CH0QaikhKvBXXwEVKzEazFzjeAm+F686xITfiO14byIxDJvkaFPk0iBgc 48rBsONUgdlxDUWY6wbcTwMMq8I4AaNzw85PwvUUcyIbnM0WrnPH72h3Ww8zEV6Xm5GH4bwXN5b0 JH2kfHj1sR7B8UPkueFoCXrXA2922S4M4TDcuPBpu9oyzQJr686G/uQWXq45PAbE7gQFdyMGwcEO C+4AHQalh2cBb+Q4KDryC9zhZe279X24+gL286SV6xnuLF6fD5y74e8Q7vXNO2vad1a1J4Z7RhC9 dgPlAO1rvujf44FJNAzWtK1Vx8HGT0+2fvxk2+Vq3ftHeWN/QVCeoRE2RU5q4BU7PWHiS8kL+vGf TYiiGazaerO94dyVV8G/+sJYsvevX+u9EUHwi5B62sBxAsAhOAJI19z288Zq8y+ESVq0WgtKJMQ1 5vlMYwVrxUtEOFXitQrZuEkHhrXC4Nx6J5Fkwr/rPnpCAUkBGDCFelxTHv6VHpEci/h+uYGDGObx HpLlKDwtORr5OuM6EEK7UaahgqNDV8cx9cIDhf0FNu7eIhweGb7yKgPb0vjB5bBq9xixQiw9JbwK 6D4yf/UW8XfsOLXVe2PT8tnmVbuYsjMAyzQZFGLmEA+D3jSYTAI8w2ClyQTwTzOZ3/Ny6T05fkbr VriZBEYba+uT1R08qAeru3cEZq1GGIgdoswi/ePQM91sXhZrYajiSXct8S2b8ax3Wz49WdV3VsOg HWD0f5rxeOsAvgNwvdmerdpHGSzgFxu83862qreqOQqv+hGYorwZwA5Xq/aJy6SxwZZ1tmkfbd8y dgOPv20AE2iBcU457qqYQpmqBDTfJHg0cjlLKaxFWTd9NdZyia0IOSI8StkxN6Kxf6bYMAVyfkP6 LGA5paGwmwxq3sD3OywL1wA8uYg9UyQFxwSeUA7TsiEBmtfs8pJkMGV0lEG8+zzahMIzTvcYGdv1 zqiXYeDtuTAPYgTyPboF8Je2SPF39UCgrbODrfuklOQ2M1vAAeAFCyAnmMhU8GrTcrF5u3g69zbS j0xdmkilZceDUn40xfW3mcAcKpSZibw/IIOJkO9kTfuYvSrQEf6Kafrf+oHRW7fNLuNk3brx+2VZ 6IVgsfUIC3ez+emZwLwAx1XYVtPK4/q51+AAN+gM9XY24waJkAnnFjVgR4asBpXmx50bG+wyn21d P/LcFcLBvbNpPNu8XM32J94zPf3DyR5Oj9Z1AuAF6UTIQn/K/QIc+d8S30oWJ9LxDqLnQKo0LPFt 9sLKcO4+fXSbbIIHqv8EhHMB3d68byphUOO/lBDw9ZZCs10GucTHclZPXl1kURNWdQO/pCvVu+N1 Catr3Mg4xFQYxnsjq/vN5vp2vETriE9uH/tvra070hLEuahoiMb52WYA6CYMoGuQPWhtWq82LaOH f/p7fi7FzcNaEh/TbnLjPvrrUpbEd7B0C8WOI3c00rLaIfrmvbXdd9YO38lI1bD+5cT6x/G2d7Xa dZpptM7X0S7jbD0yiE1t7x5O9ocP76w/9LY9X219Ptv142frP3xDfAuG65cIERkOcjfFpB48NAOe ASCnJw8Omecf81k853K16/VfrVr/5IawsX0d7Px8tY9Pn+xyQULI7NAP9u2HD9Z3PT+nboTvRMZW i2j1bJcOhVyALRSmpblWrGDMedyX5vSU38v78OykZUA7+XWMXmIdZU8kgTiRWatgNAIwz+cOnEt0 C0U1AdhrjYreESGtNg3nM9KYiBsWMA6xtYKLFms8/q5/3DjWte1B6fCEXLAIEnXkxht7+dzK7wML DLsQ742jxUSFtQ5CWo0sQ1XxYfbVId14kMtEHsVr5wSAK2GSsw3pAv22I1NYWucgrQW24OhAYazK u8JZ4b4fre++tbr7YFX7YFvjGNU/DNUPLGazaVlsnCablpnGCZPh4Xiw02FgKh1D2LUNDRoh52PP rG01DraPM7OfVd9b+3AiReKnGi5hTMjWvjOzhxv2QmSrRV/I2bCvO3HgQTWAHFuW2ZZpsm2cOAtP x4Mdh0EeCnAdm+3j5+85X4dhsLZ1XlLhbXDGB7WHIVT2rjzgienJzw18NWM+vqjDxpHbdYtn6VX+ Pv4th3wcCk8EaG1UaQzvn69srTzlgGYE1YjECkyL4aHzy7SOM/FUnlXcTc78JTSJRjaA9WSulDjz aw7ibBtrskgYhDGO8UlG840jNrF72hKzhGEsGPKBrV7luBUPzM1OekgyRM61SrcVN4f3yHXNLmBO DedbDesKHlePC8ludAIq9RoYq4rZvvfWdN+YMePX/SP8++oDC3ix6zjZddbOvyyedUFCZehIM1nW 1eZ5scPQ22HorBt6M5B669HWebZtnm39/HRLgfgJhisyVnnmxNz46YdCH4SxFy4s3O94XWy+1jZv E88NcDs8qXXd7Hod7TiMrEIApqUQKXv18hTaBIHA0MT7i8S7X0CBKd0ldvTKoDGUifwE0XtUGVhV eE0Bm4RXtedI42bc5b0IrwpMyT2cEl5ywyIuWuWeU2Ba+drIHOC6F/xTXm9+ccY1nc6VMqLAwPJd v3A/6OkDWkLCLvHKMA8BO5T3dDMj9H0rklnplkXMSYfyJqegvwvYlAsZJy7Tu3INY1sRGBlxaMS3 2aCJ2Q7Guzy7jFl5aLjDxT1Y1b4z697Z3qI04x+h3487NFEjmwTDhK80T511jEW8Vru1IJfKbRAo DcMECsT5So+lWleW/DSHgX9jKPdTPa5f6g4xL8mUBy6GErDNpmm1Ff8uM19DrA7c1FYhy46yqsvF uq7jlzGzV4DhME6B2/iC1BiKnZXA8BSh3S3qAiqJjFfgYImS4GFoVG/kzy439lylsQfXsAif9MnK MNIPBOcwSNBOOUB4G+s6XzM+v6BuRDgWNIjEsA9zG55ScZe8luI5YHPc58Tl1PkUmYF/iVcgO4xE CIjWSrDVJPWGwQrPK99e0CIw3eqD706Lh3oqIxA1Plt6DkD4tmGsUjjnvqufVPhE7CA5cxAPIdi5 YexgaWsvuQg/Sw8NNVsnW7v3thGrEoP3H8ePO/AMEfodh54bB4BnUnpANAQvi1yYPFH4uzIziDBu GKzue7hmNn38ZPvnJ9svV2veP5j1yOr9tZjyOrS5YWGAO2g0WPgCptN1reg328a6VuQSQCQF8vlv f/qe+OxhONhWI61fJIp2GLpAR7Wxho0BCdmQRXfnilm05AEJtM4QiTw3rCFCG6QceNhGz80dA3dH VOOYrqK4x/3WSAXHKX5OXDYnnbp9YVlNytb5UdTbvhhLx77KjJ9sRL4/YX3ZRt9el5vFZMn0Xo7/ trL2mCF3XduyXhOM1G295w6yeb/fCFrdmKcTOVD6OUew8daIRzPaf4tLhUeV05ExiMkJJpgXDPjs EiMr2TlbV6wGhBqD7fXJ1ubBNpbOuMH8x/ETDsdDuJPDy8hhRcyzm4jmNcMT5L+2te79O9um2fZ5 tvH7T9b2vTXHA8mnf62CaoDne4dazX+2ZV/s+LDboV9Ja/j8/IlE5eAQdM1AA3U4YLM2O52OxPVi jqdgD/O10sZNrwU0gSpzmoJZzrNy/uZMd8AdUdoS5TIwHouHQpEtF8PbDRPjq7iGwIfiT9Ur35ch pLDJkhrApBcNZGBu96jQG0dKLMQKzrzK/LlJiyA/8+LkME7wpBSVYcMAWVzXl4q/EX3h2YAyuy4s oZJhb8hegBOlQmqVELbYRUINITIMmaWbLEiKfW9Iba9GqLrq+2FJvIzCkIUBKssH+LAA9tNQgaqA er9fsYTlt3okr/bt6fblusSMN2Rs5P6vX7EnIKwaBuJX29RaDW9t2229XG2fZmuPB9Yr/iVJp18i jJq9t335Vkain6yz3kbs6CwLg4e1W902dhiO9v7dYwLqqf7g3KtYATiJiKNFij8nylI2rDQSL1dE APih1uAMcGeXlyC/3l+ATnduQ3lkTpifI2005fq8cZLfCF2/dGS8LYHvb3g+L94JTx61tTM8KI0j jQ4MO2grzBCH1+ZMdoomYGMQngjW/o1SCwyWijrl4skO65KUepZV40NMN1wSCPINyW2MnGfUJMXH FAObdiNNDCZjI13M36G+750MFgth/769Kj4bSl64W+0P9qXRES2ACy/VRv7lDoSAzbEhjrWdL1SB AH+rWjez40Di6a+tBMG0fd1Z2//OFngcyzOLzvpBO/U4IVM6G7iwCeQtym2CCJrS8/h5k0SMIgNn E3oBtjwawSfhAZSbcEQgmPdgnAclAuFikK3Twk/GC1t6VPG+dAr2wNgSqdp/78mqRHFIygfZKL7A m4vzlyoKQV/InK743PDmdN8lEbQs1WFSA5sYqE/rleVQkazztCfxNhn7UHxRyN3WfXJtOK5u4OOz 2tCsSXEqX7jTlTtPnzy7AkZ5Txetgm5U1doMvKsIYkUNy8P3MvVacnhRDnRwbSGVEchc9rY339jW PRCv8n3u7/PwmB9F09u80BBwQUKJoKjPApgaeklbDa9isO5w+PUKq+GhHA8ErZvTidyt8fuPNFbD h/dWRYnPr3MxnKt9DzLxYLYgQXCxx8fdhkNr83qx/Vuzh8MHG/qDA84RFgv3CeWWfZd8TKY8C+jj Bp9qBRXuvOQYyRVLmzvDPtCAgPNlj0gmzxd6gN+ETZaCT1Vu/HYrLePzQFk351pxw9KqEe6s68bC VxKtvMxg5uN7hL5tokRkCodnFYsQNJXrFEd5/yVeFVSH5KX6/pASbD6WcFy6GiT2I8cKZYHr/pzq kRPoDksnvglv0YFDWTdYSELwC3b4s3Xt4QZUT/GrP6S8Vzl7ON9CQYHQY6IBbAbxRvhbgOoPtoGx Tu7Lazvza+br35cHxoe9LtTZ2ueVdI8WOw/JjWmjTAW8kuPQpJ5h4NaNEjc4EPrAi/iL6W3FgUXS VSRgYiJV42T7vNj88TMNVn3oCdr/OoZLHhASByg5qprOBoglzo01y59t3SbyzUhMvct44ZDx13rQ Rg6gXYuWlR8pc1bQBO6vIFk9/Rzp/qAI5IWa5XJIUnXcCIB+iAfcbfumf+48H49OkOWVB4Pf4b3Y 5LDGJG6QvcA4ldOHUjIzvKgYyVJk0N/2BUiieBHxKtWKMt3MM9GkUo4ql/op0edOj4+zZGoQNttb PKyXIZvcXFWMg3AIzkpbXcQKpgriUgCVCkfAZUE4QjG0tEji4cq6+iNhZgZibKw33Goaq71B0fLr /CpleXYuXClKZrLcqyDx39jBhYCwD4YK3tSGr90GgNytCsfL1+LZIF2vjdqxK19MqvTfrULtnO+2 f/ED84hhIjCszrYraBAX26aJ91XhmrqepNNfw/uT0QDlQvWIYBUZ8drvuZBs8SJhzFfSHTwz52Uv ybOKTJvDGFI20KKSxfDVc4MflSFSkXy64VnFytFGlMtegiH+GrGyKgydPCeFSznLyzkERxzn2Hbr kNkkPleW4ZSGoISBbkvs7o3FVxmr9NqMScExwalCeRSRGsYdcxh41bKhBFA4l4QDXbk4RVjZRrXY vWn/vFyCLiY0hSj/ogUwjrN9/B7u2TMZwaos2O0AuV1Pf0M9FNkWFJRC/aHvULyabzQWUbjCuAkM +gaeVf1oW/toewNj9crN227TDABvthN0tZiaRlq04kIm6e/X1Jb6xQ9MNHCGZtuvkw1VYwdUGgBs eeXA8xnn2T6NZyIrxK4QdmD3AqcINIMo2fi1x8VVTsF1qk8HW56ebTtfbb5crX44WnM6WNURRPpV LoeeOioi2kerkUJfPtt1Brn0Skhi6I/WdRD2kxGJ+lcpjmiTVdgk6RauD4RPIXAX+lQ0csEfdPKm G7N75EiYkTwaj0Z96ATEK3OfM3o5cDS+D8op4jat5DwFVYOOhJfJYD2QmAlqxu7OxF2MUtIkbmMg JQfKRM2rxqr4VYR4oc0lL1WY1NAeJfuzQjZntbYV54p2Ac9mztdAOoWPSwb5dS4cLUtxmF7McrIU QZsne8Jk2ze7XK40YEOPEgZpWcN4nJ9Ht3qNNa1c0q313f0OzIRSQxIa8x0GnJmtOtoKQij1028n MQcA4N2+2XVS7RsM19P5asu20bVHSPndN4/2eES4+rfpaXFjWFazcbbH/mBdg+Lu+ss1gfPEGsqu B8gMoNJD8jsj9VfzPv06UMqzg/81TracL8wkIssIYwZ56F/DcHGvbg5W99/akQvnI70thCaqkXX1 25QJ17u40GEcPAWPkDCTj26JoXg/jAd+DiVdvcQr6krd84IVrgxizs7nWkYfQ7+W0lxsZKAvtiwT o58Eeq8Ky5Whc6XSdbIdMp2EWVKMmsmhSTKnUIR4Qdx8ZUwLA0ZeWcLyZAT77kCKgiTD8XdEDE32 Yv0aEJoP3ZE/U2YnQlImDTKxItEasgSru7mhtc0PAUcCmIorhE4io3Vdw4YRKCqFWiiMF7glkD9+ /66y/l0pVhdGK7t1oC5sNthWnWytPQx8xdjAAKJhBXCZcYaChDwtZjTTREfZCchoUh3IpMe/jQMP EpjTer1aPc02weiT+d/QUyKIGpMKEsvLZNO62NxAOHCwpkfh+m+Uo0ZpZoRlwLYalV/hOaLM59PC DCPIqPDI/tLZRCzYqjlZPVTWoIJn+URlEsiohEonyZ+UQL4PhQpqQ1pGt7rq4mZlLlWIBka4qGLo qN4oiZfieuXzepr/i5SB1UFtJWK0QQl819VEVCMKhwxn0AMyeJ5JssGi9zsLZdL0eZld//LA/ATO pyRczFVFPIXuVxS332e2Qe0DCTcZSBFsI4TlVRd0KhFH/YUZUNwJ1PZ9Z8tas75M9VcCwmCwEA7C gOF3JIjNq123yY6DXMny4m8e8g4PDaHfg63ErN4utcGAIw09oXmF1PWZLu2xCPwtsMrQdzpfzQYA vEin/w0YLBWNIqGx2ny50PtoHMNCWI0wBbcBKWUcwO2mZbILinrhMjP0+5Vlln/qAYyor8nPWqEn f7nYfpVyAjbCau14P39RpVMsONBk4FWsV9uXi63r2dZqsdbxVSYMyAOM93hWMEmuFKfzf4OKEIXG UdmBVH7ILjGx5FDL/ft1jqJer9B8u7l8f8dWsMbhmUNym91+kLNECVIhyEthS8fowhq5Q+h9DnSP uRAZrwmRg1tF1deuJ69qwPvFTb1KcFUGs9yAy9+n+4+sIflYuo5SI74lQBdciArGaaS8DFK6p1Pv FwKA9+pkL4Fj4zRaP8ALkG754TjYuoJdPBQGK8OEmAgyVmCwv7MFce0PlNrAGMFo4oYgh3KdkG6t bKvV2EIpXbPzPDIB8OHdo/XYrf8GjBXVBMaRIdJ2QVYNCq+rMMBWHtY0TdagYUVdczJ+vl6oDtoh pPoVuFa/+AHPGJpbfWf742rbE7hbT1x8YM+3p798Fx8hUyg2DikW1bWuBvVcqOsGJ1BwRIRt93V2 WCto4BJYk8I9vTd3fpI3lLhd4Q4URcaBGb/Eksr8ez73BrmYqOcFw7/p6KGEfDKxZ1nfJCdTpJUT pUBjIYE+fzTJ4LIhhXMxy3tPV5NqfmGooZk2uaJESsG9uA8mPhovbyoOeoiFFE1I24hO1Vm1hWfq xfqJ5V6yqArlUFz+oT/ad++Bk3Q2TcCRRpuvcgFRpwUMpe+9VRcWG7p+RBzvoOG+o7zmaFtztI2q oD9cF4jw7jQMtve7pEHc5S1LSvAPvS/Ko4jX9ZunLLBrzZUZtGN/sBEZKoSzDrrGpABOdb48828L sGPPwP0YFc7f5OG7bP14YqckjAc8rmmcSH8gMP8XMFzVtlpHwH22Hc04qGpa34QimvslryqMkXfa 8ZCO1J7C8GiW+ybCek3I2rhEcmomERfiL3N+VHyf/3zLP8zGYbdxHglcS0hztwpOVYsNXLpekBwO 2XGWLBe0l4xX5Sx70lMoiqFzHx5dzW0omPXqpOogXmaQPDUeIjEkEc7EJfPyrhwlJs8O52mrweb9 wnuramBWB/e0shJpmzMF7so6qz0uu6lgNI72DmTEvWX4hVKHaXrmzSJrCN0kKDfiXKQrAINxDAwE PLTS2mCsahSYio/zNYoLuK5I58Nxup0gGrRX3mW/xWMLygJCvg2UA+Aoq1WtWNAIZ3GvuEd5WHDz RxuxYyKTC7E5x7T+mkXGv2w2Ed1sapX5gAYBb3OS0ilDRPSpZPj/8+8XWdh9na1Zz1ZXk0FadfNy mZJ/lTwgb2OlDjJSIUncJ50xbiP7FY7zqANP8ItelqkVV3UHnehc8f/b98mrAq6bWovx/TIUkcgq Yj6/xlD7LDa5dNrw+m4+Rn0Fvf4wCpdv1UDvyQa3vLN0H2XDmNJpvLvduILArrK3VrwsiKNZsVBp WMYmKiR6AAAgAElEQVSYblVllZFif7DT8MEzKWg2AeD3OblxZNr6jkOpGFIlEMM3tqELLIuYEQL+ PHD45cT9G1i4jjeACArN9GbdrHNu0AQ54mXmJgGAHWxx6DixZyOysVVlC3Tw+86642+k684vfJAn NTTsj1hdrqRAoC7R4G1tB9UtBij/MwwXVTEhk2xXs9B8Y6fl7Ekh8xweurwH7zTjhuHGS5BvH8SJ m02UfhM2pfT6aOobNInywvSOOGd5nlI3LoxfQ0cA61PZQOl4FeYtKAH0ZLyH4htieRlaF3aV61Qh +ufChR4wZ2HB8tLj3dnb1LUGJSKYAanVz837bnUmAicUsYSQR5kxdb4Tu+aooYN3oEk1TkFwq24b RNKyrwnIE5GVLBUHLPFxjc1ra2t1oKEyaFj9HTaECCIfQPUGvDVInfSDPX/+ZE9Pn20cRzscjq4a UNv1eqV3lUB2xPLN4I07vgSB/ns4gG8h5O2pAjF/frL16Wz7ZTR7OEoJ4idK2HAjRRdkm6xGqJE0 oLJXhCNwUTULvpPY4RqJhecNKpDtJiNbmJEwK+9vyM05UljupQTDswTv2bo9vLegFd3iRqE5Vde1 Dd1A6IVZQhqsWw8UP8NPAZ58S9bOxjEOdrlOR5kQqF7psfj27AsFlnStJesMUAbaBZYNavLZOdYF 2Slfize8bav+FsPKMWVkCYAdtdaxv9luQ/1A8pfUCSUGFhrRqVV9cDr4u46UhYVqC0PuqPx3eGwQ yrtebUCDhxZyGaruV72bDBHGHIarrmfrIdPSgM8DVYGVyp6tt+L6922sSrY8jEJv7ftHeqSQsZme ztaMk9VBg/jREs3ybpAkLwvIykUWBcNZMtjf54TQhFHdeFIe8rk8zFaWkrhxuGWU+8+JZpUxJmFC /O2rt6bf1VY3ZLcyO48MK/BbcBzT63Cd9LoQQjYv7ye97qW+1s1VutDB64z7/FkCwm612nPCLZq3 hrTRS8ljb5bG74ELhjfHV+5rSl7E4SshHolSsOikU3XShu7bo6dNYbCiQDqXA6S8wg7NaHz1JIOq JvCXEXV7SxPot3wQswINZF5sOJyYjNCujB6MEtIDhw1GDeA6+Shdb3XTGjjU8K5grJC2/pugLvyi RsulYrxT9bZfOJYIFVEI3iB8ZJ3g182vGqRFFvTmBJMMR6E17t5UFs4rm7yW74nfZCZ6rJ1QN2AQ xK4xGft5S8M8KAoMWLjpl0PxkvtU+RiFy0R6DOkC/lr/vUrl4H29tdW9Ld/HNe6F4be4nb38+Ys7 aWjoRbbRMbNkPGPjkHFkXWHRNgxrpHOOlrhqBN1d54cQ1s5+g4f2ZNY+pB5uYWVZ/7MujPXTuFHl ERMHEh6tsKr25+NVMaGE24ncGsWiv8mjkIzGgXECwN7tKF7W7o2DLODhQKzqcj6Tb4UDhul4PEm+ GO9Hp2xysf42DPQvf8DVb9ithxI242Tzpydbzp/JnEeZDxnz9La+ZNAxp2dr0GAUTPVioYQeE8Fe bw0fwLv6GjD75L/DoplueUVFIxW1tQNmqw4GbdWx64E+Q0TPUh20NLTkNxYAfogHlITN8MJW/2xl CgEZ3Bq5UGaAc4EKFGduFefKnk82t/dWJ5IO2GS9LrEIWxNE5K8rL6Akikr0sZBHL5tSlJ/FcUrE BslNEfS/7QyOo80p8tDqIWE+uaaUt/AKchin50+jXVHvNhysbSub5snWrbHDu0c7n6/s0nR4LGvF fhrmwFQw1CLWyZoKekTo3tzbXp0cY/jtLGRqh00TuVXI/PEBwBMgyVX6PuXBivW2JX71+PiOkw+G Cty2WdwQldv8TKD5381B0cDeum/fWzsvtqP7z/efWNrTPZxo1N4SDIQhYg3hPqrBCjfBaLgqkB2b YhAy9Sb3qALbIks7FrheIOzXW9iRrQ3YJLJ3m83kS6kAOIyF+FvhyWX2NteZOwEiVobsTWmssjfG +YbSGxoVvyS3O5E0oGwzs24t+zMGzhThb9ZsD/zpBlizBhl9F0AInmbg3CTFktmfsaubDL5TP0LU pq2A0bpss4uBpo8q7jGuD4aftCj+Bnr0M1sOJg8rt1SKjiHR0qiggIHrBCB4nG28QGS+t0/j1c5n NKnc7XC62PPzsx1OJ/v2j6sdv/u9c62+fOjeMmdDLiM6z16txle1UCkLZgC1WvPSWNX0d2FS9dcl gYLguW1gjWjHQ3YPMyY0rO6vzzcBAqSgMOwNW4yfr2ezodfvKC7nD9MnKUtwckX535cxg9HyEHFv 0dOYrgnpEAuURMBRoxLqLc4klESqI3EEiJwTSoHH3FMJyt/fAti563MsWM+mxefw8SgESu2xbvJk 2fgJXpFCJw4+f+eqpcdMwvaeiogl3QIVDLW6R1bv5TTDteRkV+Z3he582c399q6DZZ7vtYDFvbHp y2D1Dp/y8I4xgyvA5Azh/ZHfSzO3r6RHqb8EYw7+DWWRmZ3qPdBgeXP+VaJxLMFBGQmAUAxCe7Dx sti0tWTXQnWS4DEY2uPVjoWU7JdCPrnLqlGk+14Bb4C07dVauPAV+uRV1nhac9xQM4g6rZzKzRmd X+8IQ4K6uOVyoSd1oAyMyKsTimFRoPwablGMCvr94ad5XWzcVsrJcNE5aL8uC7+YADkcZahj1/4b q5v82QdTTtiwauu7znbv5INSH84G52+RXEvwnm9yLLV35HlzA4LN5DaUlPcRxc/xS+8L6HhXhtCj 44x+A6JoEe+lwuXEVHLcKRM5Fe4ANA+FB0koqZWYDF6EU3HURZ2fPnuF8XKaTJmhC9A8JxmiobGv u8IpufWvbpnqIa53C8iL4iFzjLrgW7GDcuGLlyaSeank8LaPEdwyaPH1HNekiqyQMGcS5I7lJpZR wS31htG+//NHm+fK+tO39vhP/8EOf3Rdauwuq+oKaXTY1hx+0VtXFe3K0ZZptBbt7mmANmvq1Y4o veEDBRAH1xc3iEWMiSeGvFx63Qh1taKrz68oBzOPvrujIBsP1Y3V154Dk/Pp6ROLWFc8AygveNkN /oai6Pl8psEingPgnuRSLU4C8n9PBisdjHesOh7JjB9OR1Iglo9PXDzDt++tBtEZY1XVtkJrbUe2 KbCs2RpkDKuMMe2QjPF+nCXazkVG6Zk6ZbACJHYVeH8dDIDzxVxOOEB3Lm7vhKOwTjJOWC/AMsMv IK0CeudedhUYG2pI8fld05PWgAQO3gc9NPZkIPAujzHCt7xRYi1Jo05eIgx4SDRnbCqFY3xtyzAs PBs6BOiuzhBRXaBBVWCYzI3bddnuqQ388c7bpWHV/SWZ5aJpajgu8Mpmqo6q50QyWIoV80SoIa7F IAzmZkpuZVV1NvQoWO6tOr63tSitgXGtQXb0h5nSnXeZllBRRMjX7lfrMHl2PAwpMChuD9XBvBsx kt16m02ESvaf8zCy+oVafxHXSCxnz1Yp9+u6YP74Mcnmyep1t76q7XGAAgDUeCdm/ajw6V4QJiR+ B8MSTTzp0i9Lwk8weQG8Nx36/3lnbTQsRcNTsL/RBxBMeAjfXUYmPMhfqRubryOeID0yKYvmBqV/ LwdxIWQNkU08Cpxfny62XkbRQh5OtmGOt8BREA3AoPTkKqmuDW7ObNV2tWo/c6lExCEjpbKae1lg eQ74fM9q+ZTHYkcqXrWGeF4CwIlsoe8jWoPxGWKT92QWDc0ijA2g8z5zE2YrsSCLeq3vHjJQvr6o EqG3iXDsXneZYdT167rpVdIA+1osjEx+rfOlElkUlRnO2fK1Qe28myarLyMJqr64EceGIG8xxASz EGGUMN1eB7Sxwv7k85M4KrBQoVXILnNv8KZrEArclsq6VtZ962p0A5dwXDys/CTde5DCI9xv2U7x YJBiJgHCjRVGe78D9sDwDtVRkij23lYbCLrjYqKgFKGhFCVfwYl+LBMdoRkGNCJh7ii6lzqaa+AA 2HkdqQI69G3K5EEllOD5spC2oN1C9zWOV+2keN222/V6doPlm4U3j4D3BKG7BteAcPA62tB0PN+A c0LiBxMHmcZWBa9Qxd1mNTZdUEsGLwxh5W8oKfEXPUJ1thd+yA3NDf0GxjzKu1BUDfzPVT2F08ZO j922t3pF5bAkwWV4wmMPjOl2Yd9+n3EsrqUqGpdm7hWSMSXILtBci5qyK8nLQHi5uIxwJm7nEp49 t4FP3dJ1faqS0IZZXmfGpaOlWFz1PScquq7HPRUBcOK7Bq8rd3F4cR5nt0PwsEO9Jr1F73uasLMo KcL6dfrHnc17DV8jdilPRr+A5eRFM+SS+4fWSPgWdYOHare5Xmxb0f4bO0kZsPr/eI7VjLvW7JHu zGi3rXbrEfIQ/QdFAjsOerXBuKl2ETsLFm/E8guN1YkTC4JknFAo/2m8tu5nHCTggQ81TQY14brY yaR3D6su4xWGyZbVDoeT5Gz88xWqCThF+KFJIqVHTCRiVT7xL+dn/g00BkwwQsLMKNU0Um0Nkmlt 07bbw8PJTgf0zQMmNqv7Sl3b8XByAHaRJhlrPGdbu5UP9Z4B/fdwwCjhCx4XGr0uEHp8ehZvy+dT jTQ2j6j587b0hBrwhWyUC+ndtLXL6grxfRy5dGbzEO1lBx0A5SH1AoijxoJqtOlGOc0ONRN6X4EV ebySCrTzwTUAA73hnEo0kP2+elOZ+ze4Z5SpDHd/Dm+M1xJgfSa6htGUZxRGqggE7zKsLGaG2mjz yNdDe2zZILyJUjQkmfAWNOa4vaf773XaTKZlm6/oB8ivRjsBAMB5hcIi0rfAT9DSabeuaph6Py+f bd0gwgdwMzwRFuXwqyOeBw8qZJdXuYguk0Eobp3sOp/5kEPsbEEMvc422JECdQIr0VwRMTMaWYZI 388HnDE5psvFbJrsw/HRAfPSWys4JT56IHlCFoaSszfhl0/tYi6Q0d6B/NmmyYQxgMcEA3c6nXju 5+vFzstEiZPH4WADS3Eqqx7QMQv0EcnJxhRJTCB4Dy0aVLRo4WGfn59ILblcRztAroWFw39fISIP SJkcj5yPy+czdoNkmFgK4rSGdR2t2kYtTeI12IRlytgd28PvwHdKL+tVImjqiBPA+j2on2kHDZUi /E1+lOcsjU7JnscRygt47ts2JLqDMonYMGNuZqpGVYgKJjZ5oZyQ6xn5aml4BTvcjxvfLIWbueYx XhS1yeKoSbtZVCkYqrN3D5LxzE0o7hj3KRyN1IXGrWW5AcmfWGEKN0QIRfgiTMnaTbWR+D00qvB2 7CRkn7qHFQxbZvqwebi76ryu4FXoVSK9wdpKhkO15NpVHMAO3SF1LtR5XEw/FWv/zAzfOo5mMD7g S8FIvCIuVh4hy0HNpJsyDu8SvOXxCFb75XK2E8qbXOMqsCsYXam6RnE0PKPGhiPcaNUO4nUi/91e e3Bx0jj4rgZPDAXUCBvny2jrsDNj9luW3PlpR86E3ZrwYsHjnjneKwvPEzM+nUIRQLc/i6vl5SBE QRzwifISdXUIpvatYkMqQ0naM+HM3EqplH7Nq2Gl38aLUpm4nzvDhgOvBTSRwXzvmhOvKZQgGHRx bQX4/ZaCaHg3RUav+Pz7Up1EJPVsPSIkhXzmXZulKS/YCdgYsCxntIsz4VFZNlCRvc0NnvPnt/Sq OFIwLvCuJPEQi4F8PHitnoVQgkYVg8KnMoBFg8afRcZTk6KCrBaOeHTHiDZARfkA3GWIkhGfstZW tALXBfB2WFHvi/k+Lf3lw+ujYud0mRfqdoek6w94bJHVkHeVd9mgHnBAWbysUBBf8zSRDkLOlRus eNTlBGnxxX6NMmY47g2NwgkZxmkc6bmV4QJ1yKDzvm32dIWi5mo7W2/9+zFYpJKgYQeMUDSUDa6U b0TchltwtpTdIu5IVnhpsIBXClyG3lIsOayRBRDIgtfrpQsaoExIqDR2fOip4eV5tVvlqsJo6TOi xCdTh+4B8fTSe/C79OBuFB3KI2cUc11geE6FpQweV/RJ/BF7vU4pMifCxdALSwRV9yT5H7WxsI6U TOJqB3DuaycwrNv+iLfjkH2822LzBLrvK2q34Da42FgRd/OljLOjil1NH2JypHKTKCdIJDNEweBr yaVmzhF4EN5PIyTsRendyLhoZEGA65uDM4c7G7eD7WSL16ROjNMVqU3rQGVAxvIHjFaqkA9PBtSL ZbV6WajzNbVimX/NUwRnJtQhReXAWljIP2PdU9cTl+JnwnBAecClkLmQCPzG7hsToqJ3Z43A3rc8 R7Lj3VBi8WARHVG2UmBVksSVDC04YRe/Xy7ev/EjebHYHObJxqdnblxIPkRaXjJHmxmUHWi8GxZL M3uEZwziqR/kGu61zdWDbdVsyEGzD83W2Hxe7XpZUITIuX5+vtjT5yfrh87+6V++sb6PLHqonhQw QmSWOZ39mjapmwQYfp9tvL/P+39LI4bj3nCV4HwcmaBZeHkuGRW40H2Im79nHsjPE8z46LojEq7s gEcchHrUYj68fZX+ILQGLUFhqMLVW5A+NXJ2b9SHjYd6ljqi578DmTTpOsullLmBFQW+lSrU04CU QvoyMgE48p0A0ZkBWPLiLvR0sr6Q699EZtBdSnyeeFUds4IrCrF5Z1dr1ou9t7PVG27kYPP+YA3+ fudBJDpE0TkZ6TQ0QcAOgJDrcHzghJ7rEa7gVx1MDkD6ZEbbc/B5pB8G2sJhOFF9FZ97vV7oWeF1 8HIeHh6UCboLDeLALgljRKP2pRDWeW4EMQG24zMqcNnyFGDzCooBtlYDF4MefudyQL+ZzGF+PppX b+OROWRGY9mZvRpPzisCefR4fBBGum12XSZ7XibrDwdy1lhpcOhtu05Aa60evPlD4IH43PaobDWw 2g10AmwAem7TBRLVeC4SVUTjlQbPOD1H9/A8hIojxlrZZt0X5oKUOuJeb3lKkem7T+/fwx/VnXxL eWTC581IxzcvQO3SVt5jt0l/PgHueW6yNaCHfsxuu40AbYPZcLIHMEehRKLuPtyTW6joht6b94Is xPpkG+JOXOnBm+TE+oFZFABZ6GApyajUZLowz2SlUkukZoMjUWQgYtBLhUIBffg9djFPF/tDlpsq nAte1aE5UaNaJFY0r4cuJNz3K/siVgY6hFxOxMQL4vIanpYm8YrmoqjnA+ZArmxkFJlAJegKQ4WJ mThXnhIOS/8W5sMwr205+TAuCMnIoXIddvGq1IILA3xw0b0eKgyOV+UdNc8epbh3NZV4ZeHCOEE+ WBlINR5AATUMk8Y3NxIgGXGeeV3Mxu4iuKpXoZeK8ONfZp4y0deLztPff5qho7HxewunnoaHADG8 JSxix/LcUwmHJY2Sw6OYNS1CjhZVD2ajh1lQaWUpVJASkSWk0YHstJH5vj1frELInjKCcK5geNB/ QN6SykbcA+A4qhsTDNbxYbDHD70NB7RUk4qJvTk0WviIJLTg1AE525GbtEmCSxJJ+zWPJy3oylUg Inv5EvR/w5a9uM57cL983mH4ZDTaJIHMayAUo0LvWHNhMdBsGf1LA5PC+F0umLezdT0a6Fb26eMn +/DtB/vm23d2egSZV41tMkyfIabQrU/hYbT50sNyhqy7eUkOIrmjodzgdP4Qwk+goBo7uu1SY0r3 xjaS1MT8JfeCQoExtjAgQwoFoW7aNggHDb11yEhuiK2NtlB0xVtwIwQD2Q6GC/ysGrI2nW0jiHTg eilsBG+MgPcbTRsEtJvNk7q40BggjHjlgGHqOmFWuB9kAMMoFVOBRkPCfMgE5ZkSYPn9BIIhwt/w ua8ZLLZYA5EU4+EddaIFWIS59L4I14DmsNgAJjwyqlCFGKFjTn6KlE+hJoDPYkFvAZw6TwxGXwXa oAlgNxSG+LVH2rQWYYUqBXF8BTpfwJMwL0CKxQJAWBcxSDkwPnexhw8NNrSGm8W/ff9v9GLfv/9g 9vjOx9Y3WTdgfLu3GeM1+TgFvKFMFQr6J2YKbcfcAsYCLW7PSwFPbGo7Hns7PMIQ5kYJcf5X7t6J mU6QxLAD+Memi7EFm4H6VbE96DVl+/nySAU+RUaxSuHTa7jW68druFkOI+MrtyVD0i1/vrelcBXW /H7hVTDKNFDzYpfnS2rvBTlndL1iptDPd73MNp2QCBHXLYFQSckiICe1HUySPRESgk0aDyjvbJkS xrDOd2+oiuq3LsIfVeUF6q5BwA16GFhoZVOgy7ldCB0rp/g/9t/RTQRZDt5VUyFV64Aoa4hnWyJu SzHwZk2123H/zLKedWtt2g9Wjy01dPr+wDDtS9hUyrJsKxfANI12PD28abD4embdHuzTx++pzmD2 Uro42aS3JvT9bxDSMQv1hWuF2uQw+Odn44truF7OLEKH8ZRxm228nmnciPd8/kyDPD59tOnpE5/L w7v3NESC20Q4ZfnFMlMRFeNyeHiww8M76959Q6PyNTr8uv+Nnzufr/bQ4HmqZASzAg02Q6f/Cc+/ aXlfDOF+4MDz+dO//n/2P/9P/wMpIf/xv/5v7bvf/cE/8+VGQBpDo5CdHXCuk8vRePKIBObP1gJm qKKeVdfEzYOJRlcSXb3KkAYvP0e1n4tNOrezx9pQcgmcRRUrw2trttb6ShuZlqJ7oOiAHux6h2YE YkdfQXkxwIGIJ5Fseott6TpezzLej08kAvSrW8Ml5WA4FSJpY+VF0BGZOzonxExh4HGvotg8PoqO JGxVhosJO89p/Zf2L3JSulB2De34HP7eqkOEkqsXP5ObERKo/geFdiVepSfCEwXHImQyCvA4Eehu YnGFlfh5Xq82LqgPUliAh9HX4hnhe3hZ4FtxYLzeUKKBvmMnwLt4CJT2ANGytcNe2bzNNk9Xu16e 6Sn0/ZGgM8MGGpjA4AH8r/Z8uXizVoQoo9Wd+gB+6RABMO/k98dX7nnpOyzWdYI7/ef8V1Ia4E11 KWTMCgF50qlXZGXv3n/DUDCyM3DPt+3Mv71/eLQ/f/rermd5WofhYOP1mryqZRp5DcKC5GEhzT9f kDVbrT1CH+2HgXuCq9OcsKZvhiPxQpKLA0hNwHMWavvR3LnxynGBcX45qnfnhNE6HW15frb16UkG C2VQNlq9PllrVwn7uZcDNZKnp7N9+vRMA4k/9ENvh+OBFJ/H90c7PqDAulz0uh/ZwaxSyrpc5bSt ZclbhInRsTQ/R9YxitdAI+uXfktctciaZdpC+fd7zy8A+5cwxN172HRChFb9VSG7nhfWJTgBisQi 09gW6hC4LkhOPT9dSOP58G1vhwENfrHJ4XyQlUESTrQGC4O4SUsvnMfwMaOoWmVPHvUFrUHdbiPk K1Hhl8Q3/Sm6cKS68JS2DFastHiCM+X4l/OYuNOAm4GFihbhRP3x2co+qOxGxikmYIqzb0DF/BAY vqBoFLc5iIqhsO3JxnG2dj1a3XTOMK/ZSfoZraWgqLDtNm+NX99sT9fZmo+f0mc0ce4i8RC6668Z rDeoLXdr6FYGl70XUZ8V9VpxLlwbtJ9AVkVWEIajOCLExFhDnwxelIB+dcFOKfR1s+szZIBWG44P 9u4Pf+D5YsLBYHGReEjIieGhNEufHO+ji0/SrPA7JgrgLaM2EoYTlBNgcVZb1zY2YKOI8q2Xo/DD A/UK+A7D+vs//BND4n4YMi5VvO5usNl9p14H2y8bme/VsbOmna2F0ULRM8qbkPGdEDLreikiBxVd /GEDv0i1ml0zcIPEApS2qPc29DmdFpEnrUiYBmaK8h84Byw8jpb3d2FdANAFrynTgWLFybOKFVgU 56SMYFohWNgCv15MzFuyJ0L2vK7yp4fseaDa+X2JHEpaFFqLwbhDYx5RS8tOWiHdzKK81KQ5SnBC yz0+VqHnzbWlsDQbZu9uEGzewg54TB2oZ7DToxapsNF+UYWwP8FtTFtntWNG3BVKBqcrGO98J3GM mAS5OBI7E9OmFVKkeGG0HsqTUmfGhPZ6MqgTT6NdLyMbklbVYOfzM13Vcd3t++eL1T2aHgw0ZuG+ ntH2/uNnnz+QaDU7dspIBZYAUDe8zR97vEbWQziMr/IgeO+igDC+SAqkcSvOFeUbMCIwKOR9rUsK tXhPz882wuNCS7bTox2/+0NBWFSvRH4m8BbX8KJB8ppEGnN4NtNknz9+tm5o7Xg80uuAoWId5rzI WNVQSlWWMhWPvxIGS1YlSkB+eMwwBrgGZAX/03/z3/FaTqeH7HGkRXsLa+MArgW9LPxu/PP3LBer D5vVvS9ydDe/IBRGaCjDcjo9Wted6MH2g0IdNA4eDmi5RvojPyfNcXCLynqSSuU4MHjM3IKa4xUf CC/jvkpjVRARfA15Bs5DsQ2Z4gU7hGOX0Dxva2505PK1KN/SZoU7u5xHG2CsXXyw64rWz3eflug/ iUuZN5rAuKvieoTPRUJA1KdhwJw5MZOqYMCJrJ7Qy9bUOZ+UspLBC9OUEgx8eVB2ssFtBZy7MuLt NPEaJ9l4vFFWMqc770E/POjIetSAzDd11bU6KzGm93BxTDZuaK8kkDfoCaG/Ex4NuPXAqyihQfZs yF4ouBY+5o1fc2qLGR2AzuePn+zp80aPAbsytKt+/807w7MfF4DU0FEarOmG5A3yFjHZKhlBhFy4 Bxi97//tX+30+PgDgOcPGbMv/x2TjNcKuZmmsfF6ucleRpgcXpY8II0Bnu3Hj3+20/HE3f06XpnC By6FM0CaOUJANd2EMZrpjU7jCmUge/8eWRyEvrpOGAyESEhTozclngpoHMvlakeE4zD+aPiKsBS6 aaRovN5QQWOrukuKEv7AIarIldf8/psP9s2Hb+3Tp4/8/DiwaJG1RlYwxuOm9AWh4AA53MGm85me VP3haId6otLG54+z/ef//JmG//H0aO/efWvf/O5b/o0dnpvN+iMIqYoGGJkUOJA+KbKcbpAYtAC4 h0emhJQWflaSDW8772NahyxhY3mMz0Z6ypV9+rezrTPGYyJ5+N2HdwzvkRD43R8+2Mc/n32jqn9y LyIAACAASURBVOz/+N/+L/vnf/lnO54O5JR9+4cTo4XQeb9xMVxnSw6Z1vANmXWPqhM3VJsUGxQd CZM6QlrdLU4YZnA8860VmUn3Bimnk0au0JH3CA02JSUmogmFYuMiTUpj5MAhLazcUGA+Cut2m5EB coIc2NUPh4EidJE6P09X4kJ9W9vjER6JrheeAtQLyF+ieqRCzzCcSRKNr59s8toj7jZsYQTJ1ylh Ndy1HDO4TdXKjT8MjW3AHKrKHh6+0UJ1/AmfNc2LnafZPiP9irjaw+DgCQELZSE2jAVI1E1LYB7F xxEa3i2vZDwQnqXJzLIq0SfS+b8Alpa/e+tvaqseoaFoF+KyLMwkoqkF3HOET998+M7T95ttIF1C ZQKWCVpn02TXBaGxZITgNQEDA2n4cBhsHLE4Ji4MbDLTBH7ease2thM0mmAIsaHBqKHcySkgX4yO k7rArWd5j8OwhIu6Tyt10oM8e69IAGMM7wMGdDIYaPesPPMKLA5eIJ4ZjOn4jPBvtaWvoMJty9rZ 8eEb3nt3fGfH9x/s9PDOLvZsG/oZ1ujggnko6ESRaG5qwXkZUYNvziqrAqSAaxVWLGNUyJskAD/W nla8tLQkKRxGcaUaCBqXwGCIJIvnwvvHs657esjLAhJzZ998+95OD4P1B0QQMuBqDe+FcIlK4Qqk fkEsfybROzAlb3wMBySEAZkM0DVGtyE51GICoAUaTRHL+7z1PO1MGMY4c6gdx7yOlJrsDJ7jBEhj duysGLXc1ifsfHJTdSHzutm06AuDok7FMFiVDUttE3AM8jU2u0xQaVDGZWEsrYcAIiNcVX4qpF2R GWwmm1FYTUa7gN948Kg1xG4zdCc7dNBzb61BFtx5TWFog5gqY5e74IK30/cI90D8e3COTh6goVu5 qPGYMCjUL/Jrw9gegMWAQoAF6SAk+gjC81Gbrhfr0F332ebCI6JhcUOWX+ugYuh5v+FtwFDAq7m3 APeLFjQKjMM4KosEDwvcLlw7jBazh6POhSRFlAqBH7cvQkTqDmAoMnLYnC4SihtRegRtLy0YeJ71 2rHBRqisitGdS5fUufptk0Xs7S5xIVVbEY5xTikQSFNMpU1vE0zxmT1UMrApuA4Z9aV8Iqgwv7YW XLfTA8eUtAtkMbuDHeujtf0D7+Hh4dGODAk7OzueiuoK4FpSglGaPzoVS/RSHoIYh5qJAs+Lkq9E ni5hjGKjZRiY9d2CYB2e8AoKD5IAh9b6Q2PHFSGYxhCJgaav7fSIjCvIw4398V8+2OGIn3F9ageW WpkxEgmt98yDugWyPWmwucqph/d4j/oJuvR0MtQ4rRjtNGg8jTbVoJIo11qGo+WRO//g32kzm/fK zovZGV2HQyJZg+NlDSHbyiLrnMKld7TCEK02LbKQIa+Cm1pWeEmKp/WAGztAQRMZME4Y52LRyxDz F/E2fsY5ng8X7rY0jCDs0bwrswjv5jQ82uPhPTGGD9+8twESIsSyYpLkQVDLoCJeZh2kxNMUduYO NpjopwMaVLb2+fnZrshyAYsBaFhB7wps6iOvkcbGMaxYbC8xKVdKXTdrvBtKLNAYX+5LvH94ilKA wG9hXMqHFwdwqeRRvsEjw0KDhDKMHowTPg8YD/ofZoOg8iEA9AgbcA9UkLDazuuFHrHccRBwBfhf oSkluTtb15ob1QGe1TDY43B0rKoIw7tOpNU3qCHleMAIlhQNjAM8NOJGTqKl5w29/IM+662DiQd8 LjytFYTFZ3lw2EOBRQIO4MZTW990Ng6Q9L7yvNh88BhBbSFsQO+0S9cESaMedBu3PS5pyXEhDOJA uIp6RUJN0Wjy/nN7+Vw1km1DRATqwxcKcq7bHhHIqk3q8f3BhgM85HAufMOrFvvw+4zrHR8fMzna CdNUT/WGxxmjEm4scT+nMnljZEogc9riOkDrIADDRJ0/SE+6Be1C6B48URk7MQkiecYO3LFOGMGV hjK3XyNtcNvteTF7XiobV82TNgb3Ztn5IKrpgWcHKridrR0RZoFgiYVBjk2WLG5c9UH8qciwcalQ JRQ3jMLRbz9oICGDAm+L91DDyGgBE3xnnzXIKBtLJNbpyWZkcVZom6OrTNlZt1jg6WZcBpaTAWoT kCP+V+taeB2gUoihHpk03Ov7x0d7F6qpwZMpme+klYm9Tdf8LqvHh+9qDPDCDjdZvVv11fgd7heL C+OBDjqvHZeLFhdDuLtKf2KLKHZ++sQ0fxB8YWT5N+ojaWBwzVjE+NuXMDTeMxVMbz0+jMihO9pD V9kBiYob4NhLbbLz8KMPGs+owXTxQyyyzvXAvoYkifF/fn7iNRxpjA7J6MFAUnrXS6bY5t29zFCH xX0octDcUCt40BKUSdNC97o5Ggp4KDIGiJekUKJFJxyqeoW1XWTgcE6viY2Qva0GN1ZujpwN3mL9 HQ9al6xzjKUeOG5satkA6IF41Yqz+iO7qOtQKy+eI2iTZLF7xAV+Wh2Nk3GE1xfdebwbTvFfeJI3 FKcYE67rAvZxxkvJcE9E2nWz8/OTPU1mE8H5wsMqZqu/qVRRIM+VJxwQjlEZAIAYblqKDRJDy0Yj 2UtmCL1ym64ldl/F0pgU04iFhNZhLnsMjwup0R6EtJ1ALzyC0+lghwOKicHAdpygUHnIJLjbQ655 ZaBX1S3SyTA2CHVwEoQZcJmBjWSv6a0DLj7cbXhaUcqTRzjCQWVzyKf5CpWEulZohPt5LcTk9HFs 6rVLE+k0t4Wi+GGNMVUYF1kf/A0eBK4UpMvyXFwoyEaihZYLwBF5v3kRGDObPZwOxK7auiJhFTMA oRN5bs7YDyP/Q+P5yt0khj2yzFFt8Zqxqr5AfYBnGCqeLGxPr5dhDcUMnJsCjq4Uy6YhTmzGs4Tn hbrWeZ9shPwPPBzOccERwhAxxlrQKidTC3uRqvWZqU+CZ6IDt9LGmMFlRQvKBiv9H6x6F4PszIZT Y1UXya/bMO7W8Xip16XNxYuYE/s8wkOv+Q15Gg5flOtFuBpzwsmcHhIK18r3FMZKnLTQmFdzVnV/ 1oXSv6J8NI4YQwfa6e3NNtSrzaCQ+DODqsmrE6G8AIDuCPeY7kbDnEJVIEinkd9TfB6m2ouqAeyy NgvuJfgzYLQ3dtgGm48NM1RcdPQz3YLzdMKRTg9He3gEbiSqBJQNJoSK9OCcjOcMjHwTVbGDwSOs rWEYgoLgicDltkJQrLcGX4s0tHO4p0VykwUFn6bN7OY8UoWRf6PE4gePH+BvvbVAg4CJMBAeFoyV mlgguSFNbDLeUY+Igu26Zpijgms/NyWXNRGBE7GEip51zs7AO+6b2j6cQA9QmQuoBaFGAA8jCK4K cY0bzT3O9qVDonHeLxC4S7DGnUld3Lj/Thhq3mPl/cY94dJDgVMGVM8Gmy1vPREvC6DfvVKMFTc7 Gu7Orij32jxL5uahQwKcRsp10LjKRXPQFp9xoKBDBmYFwyToJwPg0ddQBMvCH4v5XOPab/mP93Mj 87Y8jLuhjQRXS6B9OXeFX8sT0thlTy0yeqIWgUuGaAhYo9OMijgnPZ90Tmi/F9O7oE3ENckwKhOZ Q2nM5c0eemRkZ7t6AquNi/P35uxVZDJoCTX8JxAGi9iX/hW1mrHLCCcScCZ3PtxILIDzuNh1xjmR phf3CbWPKNUYxydUi8moetwuIqOqyOFdKYUtwA8qpUwXw+j1UGxAeJJZ9+meo6gzbq5C4XBjbedk 1g3hwbMt8xO7Aa0EMQ/W9w/Ur2cGi8bLJ0wofL5RRkL2PEHj9RdrL58wiMRhyYeaGYgpD6D46fNn hVXA5R7e2bp+z9o1eArn5zm95/PnT6lZRpBuv3snxVXgPGD+TwybMpMcz6HvWgL5IOBSjwqhUgvZ H4TuumeMTYSeDOfeqI987YCnC8Y/PMEI15UdDqA4vzbu8348gKECnyMl5I1i8nJsIwnSNkpq4D3U GwN+h3Z1xAU7W+bVnhEm8hqUsUZbtnarrANlJK00eTrwnAOJ0rOrrXbtOamYSBFUnXtAyYBuVEmC DRUEYbwJb7LMQtesKCsGKsfA5B2p/ZYbAd9Mk2fF9LfDBbgGMtDdcBCoUwIgQj9RiVxGBh7Ahua0 Cn8lDFgctB0SNsB11By38DLDqEao7PzCNHqgxECFFHgrEgXAw4Gbplb1+cbzOCjOXFc1BI0hiR0w DJwKIcveZxkA1PcCzsZ5s8/nhQ8ZYC0KklHQTAJ+ja4x0D2fCLYHEI9TE8+gCkJ0sImz6iGCKR9S N8HvKsl4L4o9U1NJ31GJl4CZW1t97Mm3gWwuvparMosozG47AL5gR0tW48sgzQ+4Si9e7l1TfGd/ zSMJljlVItyDIHCPsA+GwvsbIjQbJ9VFYuGBfgEQm6HbvvNnGJFpHoXFFRyuRBVw/I1NL4rbkfOh 1+BXMEzwoKQCmwaY/4N3gmsrvZevObApMRsLnftCgULSLLfDC6MY+JOuTxIzbAIC4qrzzL504PVP T0+p7ZYyaB4KIkP4LO19dIzp19kewVZvVJ6C9r5T1dtSd7bVB7ZpS4kRKpiCIR9GALgU8Djhuihr kSeZeqw6N0orLTo2c8NmiJlD++Yui6geDE5ZAHDOmkT8yg0ejVQ8HqcikAyeyds4PO+p7Nw82efP T8wOMtqod7uMT1532pLtDwN2GHo6Ewy/nUx2HxrqEahfgW3Y/GXEIqBVs4+ISYCxiWMJZwK2AxtH ZI5xFK6CWzxVWPCGZVLSsiF/RZhVdEkNVzoaMnpnEP5aA4Wu0Jcr/QB1mulQ96dXC+Cs7XQCh0sL dl29gQMHRx6N0rJ54LGLQNmhb10LqwgJ/JsURZdY3r177E/KjfHONL2SiCAKygOjPAgZy43ZjEUA 3EuAfTQVuD9nThG/fsjQIIGgEICG6AsembAZgcJJ6ZFtozRZWQ+YDIRwLRgNGK3UfNUZ9bGGZ2BW HsIB0GdWzzsAYVF9WaVUTVyjQ/G9AY9O1XH+t85B5VD2sxQzn1GB0yOAh5XRdh7e/Yb9DuOMl8DI ULkD4aQX3v7QkWygY44klxb4G8aE1wIm/4JSHsgKiQqgab7YYgdb94Pt8GzwuX6/JLGmCg/0nVyJ AapmEPaivdnsVUYTnZZFl5AZ6bw3n89nL8uJYusZYXH6fSwExUSiYYTRcLpAKrNxzCiNwlZshLN9 +vSJrABBDGbPlyc6EaAeQU2FOO4J0t+1K5bkZMP9+JIcyuyk947gGASrPl6ZuwMRkuCch/OCeY31 2RUhIYsJhc1AHhYnDoYzi9UdIJswsQASw02lbMzt4sy9BFXmAWN1HbEgKzv22LWRWcSCcJ9tR1i3 2/EwgMue0r95cuqIa5GXVXlDySPDNoWcMJRqm1RMafdeIu3s2Uf3sDT3g+EvRUQarqaijlSEl2wQ i/AIGoDsaaewsQVY75gXia1JMvY2QXOzRB1AZc0f+EypW1Eohb7FDC892P1FEXb5e2arWlESgl5Q UgzCq2IJj/fIA+akMPdHeodfSk6sDUUOMadepZdFAToNJwxWzrjqmoS3hbT0zUHVDIV7MCZ4fxjl 8LS/JgzFa4Z+sIXS0y6fFJslukv3g6oEeK0VkxIVMmY1qCAQ7xb2OiPywCYWLbtiHRQ3C5rN4t2k oIIA5RNgNKD7xDzEv1CQQA1jqsWFGhzELPl+E75Hpr2eFnqkA2ukCSDZGVUI0J3D4gbuCK/O6TB1 APkZsknwjk9cYM3XeaEoADiQkkY3u1xRcK45omcmAi28+uPhoUgy+Nimh6nNTYRVXH/gVJEBTb6W 2AR+baxcYDQhzTOGuqQ1gL1Kfg12ft0MKhhQVxqsVBkFtFNXeY4kUqOez/9zSQycD58DEunnMxaF 2aEb7OF0sr4FODvTPIC1rJhY6d/w0BTxh/Lp3RJynSFYeRITXZ41GMSRZVIiRrtkuJyplMeHE++L lhGRyUHKm8+VOj2YlBgos3pYrenlPYKTA1WHCZjXAmuu+ilI2TRt/4WiaIVgAr8nXv/Du8cXC+jN Mp2+s+MJpTZf9hzCEwMD+rWz4f3iYR389eJm/fgswduHAH11Aw8d/Bf35pQBGdRbeRl4jucL6j5B gOxfsOHLg5yrM4QdRe/Iyh4/rBXF8PcIsu3AMUP5Tw6BlchgWEZFi9auz8+U/GsHzVYFUYs11fxi k7r9bGzS+LdoxMLiaPhohYS2Gy90pkolbpjP4ADCM18R8o6pn4EUP6OWEesTXK1zwtlmFF5THkqh V6iUCM4V1KOf6Y/xPM/jan9+Xm1ig185LKpIkIoE1oCSNrU9Pz/bNu/2uw9/YGIr04GymCfloXGP HgmRWBwUDpZTAVv1TvMufoDNnBUh5EOCoK1eBTjaEb0mXZVgODY0MDU7ecoDiYtgmNCg7g/uLrwh AI0q5pzm1c5XgO/hMaAJheQ2Dn1rD0fgVpSG8J1Dk1O9CaM2MHPsbybVzSwouUxBVJOJSzyOUO4A IFwdksFSc0tlzQTGCwOLNC/+lrueFLFIuPAB4DeRendJ2BXM8Wd7uiKDiQwaDEHz6sJC6IUmquPl SpIoFsR940tJltyTmbLL/MOew9d5FlBdwMTDF+kPkEjkxFCT19gV73cN8ucu5+TdBJYFzKcsJYqQ MbCy12sGX7+vUHgIqsAXqSYewkWzWoYqvC6V8XzNIb6asqjkYBV/w8ZyONS2dp1dz7tN40X6/cfO xrWzacXmVluHnghJTRV9UxoW4d80vkj3HBlJFf8ylELIUsjAhAIr/U6//8abapSZwwDp8dYOmd4a 3D/P0KNgfZ9sJiUF89fbynvvBqiVMCsKrwky0DYZlPKvqO54Dw/tyZbxMw350Htia0dFRWfHAUma Xpw5dqyOteoRTNH5fKlWYsChJLyvUv9ADXEovXqcnNRCWPfqGW/SQXwKtCyGVM966zs1OHQ1qiID IWtP5U+fwHJDvT4QjNSr3HJMElII0EiRrjvKagCyS4xPjSxahiOpdX0URTvLWhIzt7FwmLJMYLtJ unqrC7He01KI7r0eSuIIjA0/0i0n/6UES/L9ZgOZdw3uuI0vpnqzDR7jikWOYeutgoigncjzUmuv 9oVcDEOYRTymYFQrsyR+jyAk9YIMzyzxcb72eCMsjSNCMe7W6JocY0xYYGYdIrNwhcHSDq1sILN3 dUOsqcF5vEsQZ49THaLe7ccnIvRPgOh5kbu+WlnyVGOOybOlAivLc2Agb1/3xaGiVx4NSV/RZ/dk RDc82HhZbJk2a/rGprG2lXI00JyHZ1O0+poqWzs1wWAjjNc+N2GssTm+Ogw319m8gc3Rn8AJXEuL cUqNueMdwj2GQblSaowSCh2InvCsQOpE8nAAHNdbvR3owNgOnBCF7cLgToeTPTy+twEF7/S6lEDg yiQFSqFdcNxCxRhUGTkQ0rmLewxHRRud1wF7BUciqPpdtYdDwegOb8TdM73MSYv8AwyRLnolKM9H 6osZeACq9iVLHB+kjho4N2JrZSqCe+JTwlUHlQnhNaRM5G1LpLTjBtcpyV0U/F1yXHJoIJnVnCDQ /QXwGPyXfDW5niszeMUkVsrVM885FMaCRHoY+ACM8AJ8ajTUpGIIhv4h0wfcgO3oXFZBkB/yMSCh inKA0ChCGYZDPtElU/IaU/61IxIhX34VjAG8EGQEw6hGofH1UhGsj7DxnrYRLczwmufnz0k1IgwW d1ZK40Ca5e1Q980jEgPwltgVXOA95hI3RC+21nlRRycaw885mHUklhXcrdzlJoXShyO1w0BxWK67 rRdgUaj22G1lN6eoDoDHNrOonETcKJ7/qSUAP+motO6SAfPf7kgkzLavoxn+ZScob4FXgcrT2BHS OFtn9dZat252fEAHd8gIjYJkugca74YP1yXQV9WYsvELlUpFnwiZdRowtE5zyRl4W8g2MmtIwySs qywZSovN8WgcJJZIf12GA6AteUT4UFbzy4JSnKvG7iU1SoSIF3A1KHtb2bffAIAWsbRjZgLROZGF 5L6S5rDPJllmcUaA/iO0Gtfn3HgxC7y4/pZPmmiy6LtgksVIzTA8k0jiI/gc0Jd26y0fxg27Nz0F VyRlZKKeSoAnZJnD66MXBgDSQ00xcQWURktusYSpSmhbN9q4/L82PX+yYfy9HYZHzyw29u79Bypm Er8ZBoZXMEwAL6PuL9xplf94a7KCLvLF40c6Yi/enjzKt/5+WwuXw4B8kO2/byw2Z/3eG3r6bx2B H52fP9sVLd7XzstrNO8O/WCHFg1nleyIRhQ/FA6XFI74nPxHGWxkHXHAa0MtZmjn4yDNBkKJ82gf //TJDg/fkO+G8p8bIUF3tICtQdMcooEtOifdNcX9axwbDcli3T5a1+yGPFKzVvaEdY7mLryDoGG0 dnj8zt4d4GnBECkEP/atTXVv10UcxYcWTHTIQSuRozZUuSdEFISLFiNKDM7NCgB6aPYGbcPpVu5C 4WgFWusPC3rd85VRBySvIspgiEsVwNoAF5yvr+wERmpYSnoe0JKKsgMVEkfDCwZw1G5HzZjZ0kAl Ab39YCSyF6arKEsryqykMgxK/EWpAAC70ZbNSXgOp0fJRPL6YCyaQ2qskHqm8dZVwJkLVRUGCSSM ZA4AybwLhD5X2AlmTOCO22IjyKlnyOuqMSx2OPaLqGqb5ifWCR4Pj16MnPvWRR878YxULB1hVhiN UlX0R3sxXzpuOZrpiKwer+0L+FB4xFFS9GMPbFLISFcDOv8cXHs+S6AgbICO2oT6VIzlDLzFd+Y1 9KY0xgirw4OESsVlkvRM5OC65BGpoQk2kfDkEe62CDGLhrm8LyR9OggYqrj8LawMhdU2VsywIRNZ HwZp54dVy8N9N94BzN/K7VSZjFgcRVYyFVh7MfXdRsG5hCwiPdaKGcbNW5INXtQ/+2cC38X6JA5s jy45o/NgzeF7fWH1zAybl/0gvKrCWg6ZY68M4TlhP0YaPzoCfn3l3C1FHUv1mLhPYP+J0yHj5CiR L4pg6oYKQt5ZzXo2OtSFBzeEA+OFvvKIIOrRFYJgrHTOnChYeLjSa8cOOTIuWQkB3ljWc3dcwq9X dUze1NHDQaiXQuEhuCY6VzaceF1nvfqiUaMI5/Iein6n0RklNLyCQc2kAo0WXO1eu0QlqQ10pHam jO6Txakq+l5WkO6QiAC562A7CXRw0qB8MbOpa0PMH6oQAOHF8cJOpMxiyxALQDmBcD3lVGpS1u6J wwLu0GJzg44lLx0Poi4gqb7ilbyuQJEnk4iZytK+eYQn81Wpx0wQDfLnjNIYGG0QOCEffbPw9Foa cO/ojAdFygB6MEI6BxukG3w8txq8OqqALPaMLBvAcHZ5Xq2aBUVwnOfZ703zDyEpxlNS0LmbEDa6 dgADHg3oMEEwlneJE6q2dtati10vm63oXERKBIta75RWY+bknwWa+wbl2cCapVKSbxLHMM97MlCh pMoopWZPTxy5yw269szW7uinCZAbfahQZtNa1dbWy8rJo29qGyg/5IkXF9rjWTyCUfbRJYUYcUgk kB2RqsmVRH01kG+4emciQUPBMLjpnO1M/sJ8FUT0ZLByzZ0mquNLroUVYZfAcGX+gkNDFJ8eBQbN JZSjJsnpAXLxAIZKy13qEBgyrHzVnOEkbLNegVslQ8Bdb1tsQu1fcjVVzxS3Ihs8W7UrgxE3mOqi UsV4odvjJDtxiGPROdPbDenmhhI8smnebJyhsSSDRG0plL7sWBTQ5sKkXW2bnhhGBq0i1WWxgSSM NCYfpIRRx4hteqfULjB3YFmfnp6sqxGCPFrfnazvpFAQBdXCup64o6sLDLhKzlXx7jPQRGLGi4TK M70G8qsi1Vww2uEp3RcVJyb7nbhgtkPeoMJrBF83bNBtkjICOUOvnSttkHotSaL+2TASI2o9O+nG vwTOZaAD34uDTWWxe6ONFoF4ZG3RVELZQ44hUvIQJeyP/Lzx+dk+ff8n9dfENULDDdk2YGIH8Owa qy/P8tTcACavsantOo/WbAsbbSCE1AYQxjcMmLCkBZgRDB6SHQ2Kz4Oq4Z6jvzaXyc3EmIgzNaBe OJdwG8WiR28CnAMREgzmPlpvZ+sqjEJnV9TuQnAyPLptsX47W1+N1tagDzW27APpBqihxSHPxxkD 3kXp/kjd2ChZGLXCvTP4M4+tVM9BATrVcFHOw+gCPLHgXuWkTKpfdA+UzlRQpwLDSp5P0BEYIgqD KCkClKYoY3SGf8roKYRSSCXAW96QMCvsblPKHPCyir5n8dEwVJwYTvTEecg27gBEKyQQ41XkvLhF KSOoru2GsxmCYqFexJ3GO/MgGwfW1X5NBpqfzVIGUDtQSoSdXsWjAno9o4OaOrLcYSSBdwG307UT aULGFBK8mERoWuDdoMNLpRFHLaP3x6sbgfZtvdgB1bTbs23bZNcrvDLseADnN1JHmBpGl2nvdxjn DeoAjBDKKmCwAjQXh0mvAV4G44C6Q5wnKBVlZi/4L/dHSLDEQnzdEwNrfuTnoO3Y0/VixxO6FqnU Bge0qFqXtT4vI+cC/HR0qUbfPta/Qw2iP0lO500v7fb3uB+W6gyDLdeRpEkYkofuHe/nMl7tPKOh rLxWhj1ta9/88Y/yHMfJpk+fxV06HKx9fLThCMPi+WHGP5gzIb+D5i21XVEAjpZq2LzcowP/bGhB bu7Z7myaznZ4DyOLeTrbsl1trt4LswEMAwURLX8W+rLLjgEzxe+RuTx5+DXbYBcVsKBxCprq1qAj gLs1WVsB0gk5FxRvY83B2EEv4Gp9dSHbkE4p171nc8lZlPfWotu7Fv0b414+AdEqREwt6Rq3Twlk cW2k2PiPEo+sIUcVdqSQpimgHJYlhZCCE1PVNTKxcm8bRWYiQf4vnS50frxXWi5XKFhMnrEjPhRt nYpmjMJicrv6IMOpa46sJegRlGhOl+NnT/SKkOLImb9kofkyuf9pYgOsdYJnVMen4WcXBLHAhAAA IABJREFUnYrKo5dx5WJHXRo6wEhiVhlS7OAtWMrOzSplXjGJxisKZ0cvGelpAKmkAFWEvrMBzP5e oCN2SDZHRfdo1K1yNiGUUzskEnvBkVlrgthgZt97RmI4uwcILhLQMm8RFqRLejuTvKIo3g7jih0Q IRaZ1pChQVKghRCfl0l5OVFkCIMRru7buShbInho5Tbb2tXWHQbbUPzsYDPr1ODIsFffSnYyjAwl qP0EnFeOkf0YXE4yNE61YP0hShNmO6KUBPc7K5Ci4mgKq32BITN4mG3q0IxXxq8tuWVR11goHKSq CXCj4Al6QgclN/Xa8hrWyxkDZ1VXW9OpbjawOOKcxIWRgYO5kvYciKg4yO7yFnkTIhNfJStwJxYc R2d0hMDAi9XFh5AHHEBmyEe7oGEFw3MQTuHxKiLCaBxspKonEVqIEuxQAqb7n+7zB8e9+P/bz0YK GShdmjaU0+GXMN6e8PO1hbrIG4y6SL7FJJPBKs1Vaq8lTZ+SApAvkntDCreyy1aIfEU0WnR2iUxA kuhIYWjgZQ56R01igOZhBn1ckrFMUgxumxPwGBuE8DC9QuEsRAdR2pPbjWcOGAYPWvWXEdgFlCRQ djBoh+DgRfMLTCZNEK/NTy4dvBssFsgKC/NB9iQ3SkUzTfHVlN1C8gFlPiDhcTfHZ4EL44Y8TfAd 61uM8PuFrPc15HZhocMcBXEyPCgpTXglP9UccZ+jna9edI60PCShh54Gpwf5chLQj0+jnj9UTV3N FNgb9a/gTTvWgoMNGyDROxzs8Piu0PKSUgA7AKGcBhUV9L4y4fSXOHifkIOGbjuM1jTaoZfBlTS0 vFtWQRRgOd7TguIRVIavvCbx6CJB5QcIm5ezzc8X4jz9w9H2BuKB8mpgdPCQVQgcNYNK+RMVxsJ1 LxYdAZftwvmwodjaVUCZVPAMPFuVeUNW9RHMrccQXlNiCLiSC/OJRQmDNTmNA2sdNAZgT5Qa9sa3 mQf1cw9tROAWouZT84Z0CJdfVvJfBktX6HbnrutS0nTX+g7BUpkV3BYGa7GZ5kkAujRx5I1gEMAo x7CKZyKpDIGc1HZOwl85DqWqIigSzm0KgqoylcF9cktbNsfgj26EeMHhDZYpdr/dIPDRlYxwUsJw 4M4Y25K7XnaSxIF0zWrnabXj0NkB7b1QmkfPSmU4EtgPUbJsvHFu1FfBw0C8fjiiJ5+6yAAPiTpB 4XPybqoaEswPDPXyovVHlYpcscAUbCPNDxFTqiS8sqCgU46xBaFvOABnusWnqL2OBhOXM6WgP42L nZfdGoRTpGeY1fAWQM1A7ajjPyQH+mLAAr3OaFoxM5N3gAfsYoakAPS9HaG9z3u+Uyx1IBqk1Bsh q1/4IFfocLS1nuzp+ZnjOaIpCrhh00hwHQZqANWgKg16+1M/MTn9POB1z+gV0FnVt9SQR96anjNB HGwmzrEDjuRUA+BT+GpY7qMvbIydjUoMbb11XI0D/Cqbd2TmOjvVow3YQEEVohoqzg18cLKhAtCt wusWVRiV621xfUjPnatvRd0nIqWGoe4+j7DiPm9/medEJd56sBn9AradHl7S93LOVrAI7nUfCtBd xiQ6LZfWTFZaVlyLSDeIvYLlGx4aSutGgL0yjHo38SLfDV5eiPAhGD7G53fWNP6Vm1iYJO/6kbIM gakA9GcICU5Uzi6EMFoypttoSyqmjm7VckcxiOM627xiSqCwWg82MocKMWUw4VbrftFEQ7IblAUh vlbbeM1hMAQD+eD5uRMnMrXQg+jqWaYbrW9PdiDXME2bXS6412cVp6KtE5UsVNicF5oyWgG+37vq MDzwKoh1ARRnkW+LHl7Wo9htX+zz8xOlmnFeBGqeOyZgjQ1oOT8xFQ5WNL1DbGyLCs+VofSQ41WW eQlu/PAiID7pgDTOl6gNX8lgp7796cisI/hGwd3E+ylA6K3DGJaGJ/ELLE5maVmXCAC7kCS6SePr 84K5voM+UcN4LTZvC5M+KGKG9xpzv8HPMFfVYr21xLeYsQYOxs1Q7eW5Mj0zjmwfqkCIWkGy1PXa iWWFh+YORscwbSZNAZlGJxZ+VWj4w0fekInj0hOElhgXf4BZWb8+ximKtP25UNMjFm2EdrHYA7GP UE1eiqf6UXFOWkBMnog9Y3gF1FNDy0O8QMTEm/eLiA43TlMojxwW3i6+AOAzx8ozN9AyauHWq0yH EsvrRVkgP6MwtUhDB63BjWK1UwL64QiwXD9z8IpMg94Xet4aKyUlZAyQ2YLkxjyB/AmvBC43JgQy HsKjFNpJIQCM6MjO5d5wjr8R74MxFpDLUgf8i+Llw8m29coQJgxWJCXeohwIQHZDEAadLY9BZEKz Di0qGFRgasjQKAiGc9mS5MeA2MO7elWvRxblIrsHwD9aSf0CkxyeEL03gMbAAjmOZv3hdQ/z5l7x PzwPZOiw0CHcCFa6S8+wG9DlQhoDuUj+jFvwsH6ESuqrn43nhtAZktPYGNC0d+isgsf1Zm1hlLoB o8IGgI13ccqEgy07jAyynwr/CFM47zEWNZ6n0kzaQOXlenuuInqBzhsykVgBkM0B/wr0D4Wk2KRY ovAX8YKxue8bcD5ITUl5VfzGqKH0ZFKKYPJ8oqZ7CT7f+lfRkyz7XS8yQzd6KlGY6a29vR5PWcSS ABetE92qMvxxg/4yA17IfpAQ4lIwAiTjnNFqaeiRUfGwDQZhxCIvWPHppDI8IsOGlnRlj0fgVgh3 FelrWmSzLLjNZWYd44vPhvFAFmo4dK6jJCxCZStSzkQ9FlovQSaaofOKMhwVYTPBcN9BG5nGHmoN AJKd0EpZngeqPqCfXr9/XVlKEBFBOQCWg35GCO0wTuO0WH/s7fjuHbE10haYjl5tgyQQOiB58wrI 46xXpOlXqzsZL2Ba61QRv/pZaqupAF0Gi57Kho7Soi7QE4BReUXkkGE9wPw0x3RgwZOi0Li+U6vn M6EI3TunY1PjyGNcfoRK6msHDHv//p2t54ttl9HWT8+2nwarD7vVKCL2xMWL98Vc5oajrGGCNhIW vBOKEZ4KVGwWFuwCnEH2wd86GKWE0nqJkwUpVGop8Npa19UK8jPDwgYt9dRo5Jc9tClCmhwlZyga R0aXPScWCSyUEdKNrVBpjpPRQo42GiWmIXRPJ+ySW5UIZ5Del+qBXHYWT6OrTtUzVqaKZ9FdJjp7 ZEmZ3BH6Jb1ahjAD5OlqXNpD7l+AkNxXoOZIL0DvGdoTjRtbfDH8DYOKnUoeQ/bWHH+Aq1rjIUrL ntI/IP2lZKVC0fsicRm92h4fH+wwDMS0guITBpH1dZCuoOoj/R0vahUOFp6sbk0F46dTzwp5hFyI /6FZtiwf2TsQtV8zOUBixTfIFX/hiBAUjWBBEZ9RkG29PT/9iSzl44f3ymqCqY+Oy9No9eNDAvtJ b7hcrQKA3XQ0VM/T1ZDHQkcdGJMvkkq/dG2FAmpcK7ykmoqPqsV8q7kHPM/r+Wznpyduaszuyk2m isLGPoTIoD4R34JRekAW9HK1A/oCmPFe+Voa7Jch9VcfWGQAW949EstbYbRAdRknFUM/nKwGtvUD JUtlKORpVBMp+Rh3Tdxp2s4MITusR/pWTsL1Co7AXalCys1/ZeQxbxNBfDx/eG7zDqxstwWbZ4M5 laVrfslDIX5ra320CyStQSbFpgl0GSEw11pEfvGfSyRLlUAELbJTmXnzleuyqz406cM0foqRG7aR F2isgRHHKTAvYVel7rTXM6XWRyW1ID2Z9F1wM5I2Q5G1SBPbBfhkbDVhIreI7Ju6SnvjUa91Wyo0 aNU1izGvSngabtAJKoCUumt8l73HsPdRPqR7YGMDYDrsUAyDpy7ZJHXi92yWUdk4X22nG6yiavai YzJDSolyWCPrhFovxxCZad6t6TcuhhYYIkqalif7/HlhkbVS+4WulHOrqGwaksrbRnwKYSlS7+DQ jPBcvNsNMQQncwbrGWB6SMXM15Ea5vACcM4rMlAP6oKNcIq97wqw/4eO0vNm9tCvpQalAK24KIeO Ehtx0N4yIuTsHQAqQ7tqohEiW5tqCyo5WWow2dH2C/j3IgHKDguV1tI2hPNMOvwCmbHAIzv0M6zZ oHZDsxUUTn//yZrjYPWAJAW7An+lcawKYqV+Ztsse7BtHwjs46cJcA28F+h0kdcV0s0kuXF5Y02M NFYH28ApJJUGns7/z9y7LcmRJMliFpmR96wCunt6hkuhkE+U80IR/v838BvO0zmz5O5MN4CqvMU1 Kapq5u6RVQUUgAJmYxcDdF0yIyPCzc3U1FQXlKiRs/PbB6s4aJ1WL6wbtjaMZwoPdmNlW/DneD/F xk8xwE9Eozle9kXWIAA+pCCiZ5jlWlLU98whFY/4gM6QDxG1UuM9XXKf/RObvsTLXOZkUnbm7+v9 /SgUHMLinnNLxff4gUkh0IOeVUHx85hbjMFpP5dgHHs3EosZx8CSVe7Q4Vw7fbzc2opgpkoiTKJH yYqaHS5Bep+R+kTiHGlinTpBPm0QLBPZLennkjyh5p2o4Ck9/MpOh9b67mrL5dYB+Cx2F2YQwbOj jDICDGWna1IUNquldcPZZruNrXYyYu3aLmamWAYiG2FOi/Kx7Ti3h7S+wZmB7wXX4W+gJ5TZFH8X GZZLJBs06h3AJ5XyC/OLNMBgt7Ni1oiABQ5WzFty9tAq2yygRz5aRy1wwMvC6bRYObvupSWena+1 KXv+vFgiI0OtW5bS13PDbAslNwLnfL1SmfjVpXSltcThe9miUXmBoD3KLjn6AANlz58BQIJ+qB5a zLUSusGWjHE2PINLq6ii8GM6uOnMXR0GgbbjepxzY8J5YToCf7LOfUFriA6beCFZlZCa1My4gimO tjfkRvJsUs5OhpQ+zsH6Rrt1bIr3iSDl0inuxiFgPA9Tx9YR4z3lPJrKOMd4vHOnml+tUACKONQi jTklfQUduQVoFK6SmgG93DLlEHiBE6T3pDC/MiqZyip7HAw4grOgJxdVzYY1JDjm6DSivSwKhQi0 2OkU/EFngNQzmfeBu/AcHfMLKen4YBNJakkPA99ChruGC/PNOAUMKJApYZwn5Fg0iKrvwz37XbUH W8Nmq/dWY+G4kiSMIPB9fA6O8kDHC7LLbC2O7LSONcTtFOS+/cjng8FgsuCRFQJfqiEOh8U11Ud7 /mWgca8ZyhmIttBkK84r3R1uJgtbQpmhudj5+KCGAlq9yCwxr4lARcxRXLY3cUDCAo2Mare17vFg /els1+PJlnd3ooFAgrhoALzyhS261/pPtn5JgWjHlc1gJHEFO77lrG40rNoroPY1O4zz69kW1cWa 65YBK3hcP/pgGsB7hAApWAkSNKJDkdHKKRCtuyCOOuhcUg5i9ozk0BhFYJABz9TNJtL8U8izqHMI shpUH6S+4EQw7wJotnCeMgo++z5VX1IaFPzEtYr8gl04amVlHSTn2fN3JFIGkwDhEXxff8+4kdEf EJiZB6dxTgx8MZLE7yNz864Yz0H1fdTTyXjDJWaRRa0Ajl/VwcJnxDEMrZ3aR6or6nXDqEPzdgBQ EXAQ3BC8IuNLBLo0HqX/DnY8y2CypdXyT84lfkSZjWBN1QBfrF2Xh93Bo7qMg63f3UtFwLORu/e/ SKQvsgOdkNu6gZA5UEkBZeX3LGZtVNlEhOeAYNXJ8RkPM69W9yVpHQxDt9aeztYfTzZDhlZf7OLU ERx0dYb43HbnFvO4R3Or2l5lKAeqe3v//j0NS/tLa5/Of9jm/t6Wm20SWvy+w+8RZJju7my+XjPT Ajjfn8/MtJIMzVeU1dPDnxPCfqBK3HEcph0aq8eTP71oKgG66Ww9u1hdtdaNtV3rnVVzjX39rINB C11DN6ah4jFji5uuEgsBpugBK8mqcDH5gLF3vSIrkvY6OBwZv4oRmej0OZtJmlYjZgenDPiYExQv RS636rZJh6tUDsg9ySC5BWZW4mf6+ZRJ+RR9IpUyzcxjPyUPRC8S6hHOrPLPRycUZpgRIITzxDC4 go6oDHlX8xnHcNK9GdImx8tloHNwdkUIlAUkplZWX0X4K29mZFUphBWSuPohBN6WZFAO4LqyAJ1e fAwns91ldIHfhW52h88EFyN0xRKOAu9GgdWTB4ulR0Wtb6hWQB77a8dnnn9iC8ySmKLLmyS6TZiH vHDEdEM3QIPDNpsd5/c0qJw9JJVdRvPHtavQMUT8YNwHf23O7Ay4GbIslJUz+BwkIP5tWN98b3ZT NaGA6HKlM89g/cMjBmsZvGYg2H5H4Kp4bTVjiSl8OD9pTaZ2lrIaEhtA5p1xnhOlWFIF+QlH7ug6 5y7OnRwt17kDX4YBK9n9TMmcJcDH9iazq+JiOKHMR7Eyjd6BdALaDJu6NGDDhziec7j9ZINHlXfD PA6E+rtQXkiYWCwun2BPKJtzmYhHhGNwHunxN8wk1ILKgWxKE1wyByDT3AczZY9d6F0xYcsjHCpt S035fB7BOsv8KuFmOaO8TjO74udiju32iN+L+TYpNqhEhW+h5hlh3SXeUfHRFcwwLgSGMaRbMFs3 6VZNh1fTV7G4kfF4wAtpkx91YGIDOBM4X8+NI8VBSgLoFeC31QvbLFfWXM78Oo1hXYkUzyN038Xl 0jUli995SjK80OwklzmuG4T56Ggt6ZnqO+kOTw5ksEupUlzbzgZMSZzVzcRCva5GNgXYBPjGYFml DjTIvi7BjU2Ys6pObMas4gD/RTSDcH1kufavOTjKTVqrKBtqiPGBCOIogwWxLI3cpP4cSyaUb87K 5q8UIKn+5SMyjjMk8DkD8Ijc4IREEApPtMDDpHygUi79jGtgMcdBhgKlNldn0GavaXVwmRK3y3Gp lAtxXin7JBaWn5PWOTKgAQqp1LdW4FrNRdjzn1YgKTLMKJojQEcJKlffsnGQA6ear9O5zAi8QRRV GauAF6RbdTldsic2ErF3bejAl1rYartNGBx1otyivnSjwYHFB1njcb205WpBGZHXHBrLWbITBzJn mrl744NZTF3TdQmfA5+h9Fa8PXj/us42cBYKt2ioNtx4Iso6DPOPbkTrgb5Uq8D3U3D0e8EAeB3t cLk8m3m+yYHnd7Wk9td8u7HhcFSZeDixGbLYb4VvVd9XfgubisOH4q9rGwdgkw92pYAnnrupNPbP OwT6X0YMyJvta3SqXQXFG2A1radD0cBb7an/5tymdJMQNIIk5AsxVUXe+YuiKEPXUXAGMO04jopW vR8XY7DhxRshHcKzoyiBKEXCTMy7jB7eojhL8slJViYuQ4zzeAaZvo7XnHmHBWAjbMjC/MsDcQKF M1efZV16EY0mxQtGx5XqqRPjCM+m/LVSIpMSwGxyEZS3oGMEoz99Rj8zzPzNa92zsGUnZYPu2YPG ToqFjvKI+ufDxkboPVG14ssLkDb0/m8ODS8xxPqtc3efPxhwcJ6UO3mlx2AIzxVO0aEO2oJHBu2q 85lfx9gRM1Iw+pdLkm8XhUggfoaD6rXrhzmBZQ5KR3MpSmeN/uC132pAWKVibfP9ntnciEZH01r/ ARpkS5tt1m+e5cXaGCt0KoFZLt/u83zDIUNdZHsgyF7sNJh1ZP+7L6FE7IJFHqVglB3TbAt4kh4i 2jTmFRdLnOWeJtAU0OR4okXmgHV06VIZJeAcOVkOhPFznmG5igSJqmjUhguZv33wc3FICWKaw0Tp GGB3+kz+Pbb8r1dbonbPBW7h+JJxI+FIoTkUWsIZH4tAGeebh7MzaD61vXjuyOYYiYVfYFgJzyLh N5uNJmAeRp8+Hzd9uH0mD4sCBMZXBh2I9vE+Ql3CJVfeCs+JjBBBhLSEMOyQkd8XAypn4eq5Da20 wMauo5Z6aVWGQfCwHQPtI648pg8k7qd2P7Or0P7ifKR+EuUhKBKY4xSLWL9P/BFYFC2p3jBo+QC6 cK6ZjW1PDtc4wK0H0tEY83leYO/b3tKtchC0v1J//62PhLMaVEM2nGHFzCTKxKQ4mjtxBQCaAogv XM4xqTQhtB0OOxE1hAT4b2kmKOb5UsAq3HBSAGMwgZ6PZp+ICdEhRugaKBKEB1G2uX15EpDzLAjH 4Ckv6AvRBMjkB8/v3I+t1NQmzMuhaNjTi6YRfodxvtGZcwEcx8VCTlqf29JvRhaasThlq66x6Jle jFGkUrCgL/D3vJSlq3QE/SL44WtIIliygzfzkjxxoYpAQihAcwxOvxpEdpFEL9fepltWLHgErD7M ZUsu0uuyCAa3urah7akACiwItAwcpLPAZs6c8kHC61xaUXQWVpe47RpiZVKSEJ8JKrBhprGYzW2z AF9Nz18QfJtBrke2GBJXTSzussnzbQc5YGgCLJfMsjCPODYNP+esX5IGAVIqsqLvKVNV/kK6MiG7 9q8+iH9XtXXV3qo5dPUhX6SMuZa+UygqKIMircCDB6kKdJDBbu7qlSmIZd0pEU+dCuGqpSj7QjZ5 ktPEomWwCFPHIugB4AdO5U42qKMwStC0R1ImcudMB3+qqm1Vb5LoWhoGLTK7KGE5u+gDy8rNxGdP vKyiBJRrbjQKnuZuFEcjUFkEyMQrU4EbLdo0iV5gK5nBX7C+i7pQ5Ln4kwtuKvo2CDxbW63u0kOr 9YT2MILwYPPRFQJmM47RoMsndvXnHsywFNMNXjot4i0fZZzj+XRSN89L1W/LGKRtNSyge98QcH8E gM15ycp2UK6gA3j4FGqbQ9nctI27FS353Gy3ez4zkJc+HY8sJ9F5pfghMDInx2q8Sc9uA2cf6Ifd YXFJfqf+Bqeglz9excFvYFjXbkPaRnc4WnU62wIjPts1M7Lvwrf8+fivdEhteGb9MLNqOFg9O/Dr Nbt/cdDVwjtAMViX+EBqdaafn1iB+8CcXiTFaisWuCRyfUEG1cEDJfkWHthCV50Bk8EMc08N/0gr 61YRVe+lucJpIMA8ozJECOLkecYoDYmcOdcKmldRdkk5MjvtZC5XmE9qCiAhXWw4RJAMLC/4W8pd gdGF8mouojMfjB2rqtWQrsvUpOtGSzHvTPKC+cOFIOiGFSipULqBKIrFCgwHnULSG3AVB7gAq4RZ brAwXwCx+bMYcxJuBfZxNGXe+uBsJOSM68G2WPDf+B6kQSxqSskA59zCIuzhkYYIUGkF9wrXAQHo 8PiozMlHkeQwrMwLX5dSKxoSAy2/NN4kgB5BLNyMtBkas68rZt8uoPJcrUfwWPVWYcjZmfISUX1+ 4PlVRwg0VnhO4Qe4IEMe/DFI5NQYjdpunlGC+LoDRio4ghv5rz5ivhlWfF3QGpKzahAWU6kX3ToP OYGd3IzPhFlpJAGppR/UB8d7Qg9LXBtv93uWE5IvOqYXHb/b9S0JlmLW3wLZ8ekiuIaBE/h51Gv0 TElaXuWRCROY98MD7Hien/2soGKkoOSlcdRzLtnjmdANBde1lyjuh/FgZ7Cr9FX3NHAvsOHbHqoE K+4s5L0VATmJtrpSBInAwFPaljpZZIfDoadHwBHelG3D3JQU+0NIEr+0eNz0Yu7a3v0IWZm8cb3l QTyJlktfj4mlbijNDWQ/Ft1RjKBAJRaTBGhGQPBQg+ci72pECeNM4q0F54hgPTqTAOQxA4jysQaB FU7lAzGyBRsZ4q1FpxTPBygT0HdCzKipci1OIjecGBuiqcZ3BJXZjOA7eFoVzvV0IVN+vDTkiaHD +D2HRDe9m+3CfS+RSfLT/mMPZn7jwvpqe6uH5WWTA+kaXBY7uBiE8TEePGMKMvwD2YoB5SMWIwZO kTrjZ6SpBHwoO0nrgPIm/k9AvxbbFbgQdiUPPFHOxftMR2CKciryG9eUUifRDVCDaxWzginzww4v HGwWQvr+M9FNrGcYaVEQU6kVmZReIdAkeTvma5hlpsWmju5lTAcEGJ4/n7zdoI2FN6rrlS2L4Bmf 1nkbHlQADHfWdw/W9ycuUig1QKKjH5Z2/+5XjeS4tyFwK2hC4YF/uQuUNwMSKili8mMeSnz+9WZN rtjXHqIytByjQSetGq72eHhgVsng0rW2BPETuJJXBnS53mje8vHhE/HQGIbna9LKDaM9HQUQmWl5 sMN1RCBDp3Gz3SYTEBwMgpgaaBv+LL7H7mPX2gEyyWDYI3tbr21h6PK9AR1khsC1IkcLGmZXZHeX 5jsDVqb5QF6aKzCELifwC46oEuLZ/3EH55OJ0TroHg9pBIncy9ICDVmHAJpZWoXUMQxLyaq+2vkB JgXyHJSVufSfaFOKWUMsphhs5RwbiGwoOwQwc2eEAwjgK2YA0VKGPD8Y8gBD8Vq5U4mvQw9dNkyY Q0MJ6LNI3rFUVqNzfrojRNkrAD2zgBXskrxG4kHJe1Ffj0zOC0Mfmr3VoI7yMP1f3GAH3aPcDDVX nYPzqOj35qWfvz7PmnLEc3v/y9oTPZ1FcwFuJZNYKpomnz+x2pV1Pb/D5+6tsmwMoUKe6PPONd93 BGfqaw4EpRY41Yc/bTNf2Lvdnc0QBFAGc25SNnEE8EGg9XlKStf4swc+GY0/FjAEwSxnNkWBLDSC zLmVA3Q+ciFfHnJtMtvt7iaa+/g3znXmGzwVM+ACjTISgfQNSuwKgRjyNR3klbOX6De+muY4KRYJ PtaZA+Du9aTngM+3/A+pTAUz4vmXxRS/71D2DQ/UgjiqskG+98Fxitk06eco65KbRkjCRMah181A s+bkcJM00oKfgfomKhhQYEaULQ3m/vDhhV8RIK5nFLfb7jV8mkeA8qKPD8HMZgbi41YPKDI7GDnw +2OmYSSEK7+WTjfE/6N7pxJQJWzM6/lrpcyOeZewCWe6CyBXyUj+mA+Ex7wijvyu+WuJiFVie47r RRMkOGBpn4txKadu1NCJSg0FfsUWCzQedikQoCt4BkubiqHPl4JizPtGkPhs8x/jKn1zfM1rIwAA t5n3g9VYTENn7fySPAEhecMMy3ljIoO2pDlEEwFih2tXLKVhw801QVYVv0f8zy0SJX7fAAAgAElE QVTUgAv6Caefx/tAVBAlJobPJ6YfcFN2/0Z2HLGhICMce28M6ae+8+oZqCk9zxENFlE5vjVoZekk +B8iaGHjx8Yp+ELYsyy32qtKtJ8xwaON/jkTCs+wUrxyjEXOcQVDnezwLnf14BS7mdu4zJ1DumFw jUfZg/LSszi3e+9d3U6SGNgBK9rWi3rgsjfFwHFmTkmaAhkVJJHxNzW5MEQZc3tJi8u5Hcy2IkvS XOMV2NI1D1RrBMkzSy9Bs1h/dDghuwuSZjHbCKzMS+iYpQxKRJG3ZJNav743t2Q6kuNkjCjBM37o 4K1bpClQAZfBZ1tZXa+5UHCA1Y0BZ4jrzTln+Fwp6EGUZpwaVr9CATVJNv/XOFhqQy65GzTjtxGQ jj+hyhCTAggiUJKIoEXqgWtH4YhS+bmDHpTIwNC0gASMNyEQdMIBKPh16EoiG4sZRfodFq+F98Pv DD1UCHCuoErMbdzDbiscbL7jqFQeMozQHg6Tw993zwiPMKtG8aXxmAWVSYp2WoWvorP/8ykQPqGb ipHJolLElZQq00Ciy9mXTmWLNMXhl1cyu2ecR3ITA/jeXWEuKpFAKBvAmIGmk25moa6gzy4Sx8IR Sgxy4lEgUhmFIAWFgzn1pFZ0wqGhBekXCE4APF3XiXwpBTkN1kJuZqPAOMIJDu4jym7oOc2RGGBy rc8zugdfsSsmxr1/BRjYiNEmjhIoYPLcU1Ito1ZSPCaETudXhTFBDJI7343teM5auiJpwJ3Aprih CHSGhjwyTGh/BVYGjXV0BjH2QSWEZwIQ7yNNN8CFQ8mtUYj/ageDKqgKVtl+u7fdZmvn85lgN7Ib PFvhk8junYPmKBM3W4gbhpZ92pKfPaKcQ7BBMMS9Q6Dh+2y2DHTS1h9oFov7hPfBe8MlXOeaLdri D5n2hEZWmhyIzfsNhsdnuLe8jxB1/D43ImreQ57mipIWAfbRangaMrPy7J/vi5US5fyPaco8d8Qo +4SMkDZ1B77V3g9rcLmnIEOhJnmMoYwC3MMQolwcYC1fRwQh53PBShwYApxCKCnReBs1ykvhXtF1 hG07rhPJnQg4s5qZ1bLe2gKCY9RXD2dl2XHpkIGExmUw5Nmo/A3MrFpwBARiycyjsCYoZuaqCszK 9DrqngaDP8rEOF/V/qFAQZlmsnWV0WFGrOvB3D1ZO5w8e3NsIJV5Afb7S0ammh0e0/XX/0ewRBcF vCKRHNsOFvVSVLiimwQAOQxByyMx/zGrP3LAG7IjFzcO+VkP4NPDXazh7dhCdWFu6+1aX4MdPQTw WKoN/D4yH0gjc1MkGRTZUEcKwmazJq1BoDm8IltrAE6/pHGFrAkORyyHVcJdrx8ZYKBuwaxqISUI +flBFTP4fvr9cOBGkNPmMTLjA7Vig65rZcTgVhvITr9RBjuTygXY/uBkfe+dE30JMs9Luw5ba+Bk XZ0lvezQxPJ6oc3YOKLs/TFjWs8dwrBumFP8X5Zq0ZnzhZHoD1guQMdzKVO+jnZ4/Ra/41wmJlHJ st114NXyykRPLqQAyRX4lvWOnccScAdWAR2dkHEpF1iuxPUakp1xbhU1uQRml8Jn+MnWAxEyjRIs D2A7mOvAxhDm0giSY1chSBgEz9CJ5+ecQStb3UsWDgnfyhyuaBVn/pfvJ8X/JewrBUtYJqFhgQUJ 1Uh2LaxBIKpXzDSeaodr4BtlIHK+tfPLqEtJ7tDbDzZHSZeoHi+A/2jcQHjwdDwz+MAlG91EdYEF 0oM7hZdCNsDu3eWsjWKxZGBQwOv4HisXNqQhrLPiKblTZpv+UfFsg0yKcTYEObx+lJMbAPTApArM CwGLDRDqx7ubuLtnR6kKzBbnyGZAXVOBoodMNigYbyHPY5B5nttsgLyF1sdbHHwOGZhhnLvjEw7D ikB2F/T1bK2Fjlbq7P/4gz5Xt+xttfBDIVPzaplfpIKBQHeSoQh8JneywHbP4z2x+KQDTo80F+nD f8vBw9+Z8IweEi0ct3GfAydCWeopfbyv85Iy7pM+gWc86uiBI4MBSppP8FzKxR8Zh6ga6ogG4K1a XaTNeF2pICoCS0csOomiefReSiqghJmrglGez4xTVXsd3VBkjxrExa5Fj8VCPicyMv2a69RzDgwL s2eJNF4XNl9tSVysKV38dPfjpxg7m42wOb/aam4cMkX4IrbzI4KVZ0jc7HBOy5gxnf4csqPD4WTH xxPNOtYYQXE+DjJFQBCbzZZZC5VaUW4hYBlcq9f2/tff+B7H44EgPfWe3LWamcFCGSe62GEMkqYE KL0jhVYJ/Zmdjgd+HZ09BKV0/WNOFJpjbngR30NARPYH9j5e73xRx5FYG86hrTSWVKhFfM8xw7gU Bt4vrs3/RiUaX4HZ01ZZu52IaWGzpVAAuvFMIlZv8XavOmpc0BzlfbA5AeXhdY8UGgJ36uqpwyLb 8whjuWsoID5lA9G+DrhMlO003Cx6guNkSRxGBL/AbYICELIfQULFIXlmZDs6b7Hm9daUa2ZWI6Jm GEeUu5qaCCgXcyCKzCyySwL2FUDpYF4p8LLxcM0kWwSqfmhY4k66gf6AI2iu5qIixHvTgJWZogIF lEo5zIzgOlu56qICJf3cqPEeBFR9NvCv1pva1k1lnz4d7eF8srv/5d+UWd0EnyAHLg3g9WCrmfAW YVcqNN/62RNO1lsFEwYA0wuYW6BrOe1A0noLZdu5tXoxt/39znY7STRHLspGQtOQ54QxHJjKKpNR pgWOFX6WUjIEzhtmN8FWRxbKbqIz2pG9pUmGZ4/PY178fE7XyfQIp4l4pYFXwHwjskeOIdULOzFF nHEe8HuPisB7Zc35bLPhjmYsb3MT/f7MIA6wtRkbM+juuwNVjKYRb73tJuen/C2PmiMgReAJXyrg IZpiz0wHLnjnJsXoicZngqmukkvgtp903G+3F4qSUkdIiOQAV46tJGxNob4oOyUlksquCI7RgKNd mctU8JwDKNdgdE5f9VAF/yqXccWYTfwkP2dIu+DaROckFb5P79VkAFvnhYCUbiWZ0QhMMXuWdbEC X0JWqtTc3yk5GeWJAmass7ntd8D6lrboZhzfeF6pU68L24IlFTcrawa3NENX6wd0fjQQjBELX80c br8NAuJAtQ1KDSmBKtMsAhqaL31nZ4DLfs2R2Wy375OCRCiM4tklvnU5qytIXpaXbBhlIkjeuEv2 yyUw3j/KOmKw4X/oQDpduz3740djt1tO2Aiukd19+PiBA8urzcZW89rOfWs1XhPUCudufWt5WHG0 CBZ3Tj34Lj7WM6/P53RpPYB4iPyNc9vMRbTG09z2sLVfJYetIFAHHPSW8EKtxRDt+wCSS8wkdgvn dnv05q7iZqXKhtTpUwo+2uFwoDccH6DE5/GSiTcqm67Ciiv9DK/Q04l9BaYIUshIIpsrpIrTEXZd Od2fvu7tvunuuSwRpK+VuqUeL9kBpEJDAO7lJpJ/PitSPH0XBd/4jDp34GkodzWKE3lEXO88XP4k K0wD2XnxQ5lgPVvYOCxs5KDu05KL6fy1tyXZ/eqGXrBB8d69PXgamQ31qWqVcDH2UZ4XHHlOh6Md DxdrWjhKK0hMOGwQLISMMMRGOGAsze81nH2e6YIiSAEAR1ADvqVu3clhB2VhcAiqUJ46NSLK1yA4 R0ccWV1zvUxmCaMrGcESQRK4Fv5sNzs3xtBzio7tYo3ScU6naXGZRWbN/gjfeFQx5eHOQx4sXnuU ZOeUICTEwqERXOvZigbDwHAXdqIcE/BPwyQLurT0BA2YRNXZvC6URN7gqEl9d5ym7IhJySBE7zKb iBj5RJgu1AyRvei/sbP9x//3H3Z//44kPc5nwd8OHQz44DWNA8FiCS/rDR8q0hnoLhUcledyW1dI dY5WWJE9PSJDyuibWOsZ5Yr4K6UJgaVJ4K9QVKWlFiWicVMCO/LSOQX7cLjW7po0D2MOcHpq+otV YARgHwFyhYtJhpfOuWxmxGYCOFQYGoet3YcPM3BPrgvuJUwnoOLtWSToHA3+haD5BrZWtwfLsnPD wLHbbynjkmYHA+xGF+7S2OXxaI8PJzv1o+02ANbljYgJCN5FsMWpge+mtwXPSiTQKRE1d2B133EO h8OjSyELNMdIDbvWntlGgMXPJgepcSCtAc/1eq3RHD3Pok4sl2viVZpDFFEUGBheG0GRDYHlwja7 ne3e3fN81q51hcxIz923HxU7376GkQzQs7JwDHKqBzfecRqcGOMQvFGyO2mYwYncyJBSAvYHuoN0 uJhyWseGTT0D8nm1y4iAPrfewAHUcyaMUc5Hb/VU1cJtMseaM30uTpewHx/yLUubeAj487jJSLUJ ZGqBSor2ygfkf/7Pf+fM2K+//kJMAoAk3uNyaVjbn04XupWgDY0H+a9//Z3yvbQVT/hCXOhCSfQz i2u6a4VDdBk5IuhGyMLrRmruXHXH5KRhlTumINYl60PfgcszzEchKzr5hrK5TGsIcYyQQInyr8wI izK2AN3ROWX3lMKG4JThMQLja/p+6iOgFBxsN1c7BAaiDek12THnrQ9kMMClJKgHs1OX/ynGYTC2 Yi1kl+ckrYKvtNxubLnJfCiK7XmAAQblODafh0+fPth+d0eeVOiy69lU4EFZKMY5tLIOLNnAaAfl Ac8sNlBkUPhd/J15Ujq3CDwIVghE4GnhdlC5NEAq4lQHfk6O+6CbuUTDQN8/d9wWePsI+u+D7Pr9 1/2K/8H9W8wVTFAyB8SMe9APzPAw13puWusct8b31z7L+Z8fHziaA500iidCvsh/brta2fu9zHIB u1EHjo80nj1t9ExvmL17F5yGrBiTemMMazLYHDiSd7yws8SC0scXKTMFLtIaas86lEbj4Tqfj7Zc 4obBiWRhf/3bb1RQ3O8wSAo1RdloCSzEirnaao2vYweATdjFZlALuILiiXGbrFiay6MpGWNyAwur rmdr+ZIWEGM8LtsM9rsY/3m2L8ZurFBwiOCp4B5dzQApQ10hNx10PmWpGGVdLkVy+ZdEn29Kyvhf 3TU8LJdxZi2vEWRkcYeQfUYXdHpNIO2/QneHAViDzT131B8TrHBgowoGeozBICD0l5AbJj+AbtJ4 2NG5rKi8sLSu7e00nu1yvtj+fi9HaHedzheFKYIGyMOt2gnJyHQQaEhF8FKO9vUmqAIZwGIhIqdK OcdYidlg4SrrZRY0Q3dSg82EUZ7ZMPnezM5AMZEtHIml6OI67YE6XShG3ljZcwb1iPXKrl0vykTx PazjSwtKQs+ABRmZhZfnS6hR1HPbrlEyB9NdShQsfzGkTgnpvN7wvWHAxoLSEE+RxANWs8EWXEeV tSBv/4BHioqjBVqdSIxKHYfi4Y+yqlhEXttCFZSkTqqdNBRAQ0aKaAzS32b3u2admP7C+lzYw3Jd 22ozs939ip0cxuwetXJLnhDIpujayROxDKpxs18AlVPGpKxFu1hgkV5KpUxFAamnogQyk2w/H4YZ qUWQAkwMIxcZXHRDYbNVgMqZy6XxnrACk1FHZrZHhkd7wRsQXzhCZIy5wQCgvOHghEQPQSUpA3p+ AdTxCAoj2AQO08vKDNyrXDq//UH8CDb3HpSJZ2H+7nR2+yk5W9IZmyXraHOMuqBMxOByW9nlBKKl UzwQLNAowO5PVVEtLI3IOHGXuuB+z7j4wN2q7QoXncJUFgcxVkgsDxihyQoOwa/KEj2iLyTah5dK ZfMAPz/49yRlk2WYsMiRrVGnndy4N94o5mi0LK05X6yCSUjxLUI2TnZFcMKTt8T1AF0D+miL2n7Z 72zw5xbBCfdDhirQDAu6S25z9dgkYcKKnL5iIJHnFJ3NRcWOex6/9xZHLbdnX0ypDHGbbjw8TiSc vmVwlmK2UAFNGykwBew0MkIA/gAbcxkOKytBysmHDRdug+9FdwF/dD5BqwRVgG3/K1r9PuPkIP9z R9Amwq9OyU2I3k0B8Bji7q8DR1hAGMUS5mLGPKAz1UV0ddwslYollSOaAXgwdH6hyM5zZryQxn00 J6KbqtgjQkF0/6LDGkz36K6GbE50U9srdjEsIgT05x+KAE0Br+PBwoOLQ8GKznQ35eMbH36fRFlA AGrtigFm6JRzcYBRXdtsJTstjtXUCzZskI1we2Iw820TSqVtb5+OR1vOZ3a/WRFQPx8emYGhXPvt L78rc8ImCbcfSsUo4KEjFzQEZiLu3Qj5mXEx2ul0ZFBFmSggXyWmAHW5+DznvIQ/gDtoCeYYGAOd u/GQH4XGA7A6vOd6TWXS0p36+y5zRSIqJHfAdl+kucKrrRagUswnyh63w9erooR8mcaVIaGhArgg vuGM6wZdYBhHjHa5InBHt9ZNh9+oW1grbRVoG+MnkX0IP1JGkYeACwUCt4xXqaQIDByKGtsdOj0A qjHn5AS9uB7uKEPuUhoSzhSDGI1Iabez22lnXeLhkyPTHHK5FR27/Jki4gskd3E/BkmI52kmMEiz oeGlcSNefi1+Z+jrRmCAunV6hw4GWAZ/7GViWWsqwHGbIguUdLJAd13vosFRgur82QxUS9TZ3Xue XBBhVtRLx3lhobnCauwvDbIA/OMHysfEEcHq04cDswzkW4BmIWHMADqOdjw8Wt9eSLXY7Lb2/v29 nU8Xa7uO2REyk/kwkkSM/OFhnNGB+j87WMv3NmuOtpuZ/QXW9q1mSDN4Lu5ZzAn2RTaGAWaO2Piz AeyVmCyxr4vVtco7cL7UUVeQjeFngfnIzrQ22C1zOkO609j453Nru94ukPk+nGjdtb2/sw01rKo3 M2c1Xzi5U5hNSXPe/dzGVr7Wl9+OlQTK9hG4KEbuRuvGys7jkoICtV2sHjGBIMjiShma5U1A/HrC GPWwogTCwlHr29lOyS6rXGjFSYdeU/rEcB6Z25qzDdksFTcRuEMELfUdI6vLjtMRcsrrRnMK76Kl wcuiSza93LcUgnKwOP9spnCE2gF8EwUQOgNKO1Fwn7wkDmG+DGkHh+sWancRQQRelKTkN0mnvlSe 0LjJLBFc82vmI9y0Q1QQ1wJ5RztWNhBLK61DHL9zPBFhFcEBmWPnGvwIUlD9aQHSB9P7Bx8BrGPe URwrkRHBTMe55vJJwoHLObpSCzvbhV8k9gUgOQw2roPdYUbS3a1RslRjbav5zJardZKcRiAKdVEc HMthhqTMJzZFBRdd+3g/ZlcebMI+LGVmSQ8uuycFvkqsJ55z4lfghmmm8Hg6sxvaQ+yyGWzRdho7 eot7UJG4ZnWNZ0mbAHGZ+PYbb0qSLsbmAUxQrubynKptVY22qFAugrsBQrJEJMM9PZ5RHVnG6OXj OgXdVUYV1vRRoyMDYboXvn7FPFsKWIFvOS/L1SorDnYGNwVLLPSngv8joJqnw9+LzqQWfGA2WKBY 0IlvNSF+xoPi/6Wa4YZTkhUVJpwePmiSZ8bXQeZzy0YvAx3LcDIs/hX6WkquSxqDGPv5PDRSFPLP zJ5QtiUV1TworR1QDPx8b0pl1JL7pd9G0Dkj8JDJXpJboWOEkSA4IeOhMVuCTD272slLcnQFCb8y gKqU/BlHPGPCFLWWwJ/CkLBm7UTFqDoRL1kOctAdeMvcVigdKSoIZdbeft+urKVsS2+7/d76Bgoe M3bxCGPEXa+mOE5wliLoBEYV1w8AO0pSmrf674PgOaBDhj/eOGBwpUpDdo3ONApRJjTPOONr4QCH 6YxHDKXbFZ9bPCw637zBUeEzOmEYtvdQJP1xB8k+pJhkjBd5CsB3zNrGXC2eyQzRSLKmTwR1BLzU GY/Y4zw5bSrepOlE1K61Y+Dng/RYhIMwLo1FnwwZStcbnXwud3xsJH6fbU0B0Spl1f2iEJ6Xb0My aA0JYZHPVHKVwPVT1dDJf4dFllMQSMb08yuzlgh86aKzDNUMY/wuVVJ9AFnKFMpwFJoVqIaELcXc ZARKJ3V6ua1ZyegsxXiQfjLOKH0AN7fQJ3HynQ+K4/OAAYDUu726ImoxVgVgHdS9TYVOFNBAbw5Y ZbvFlYvlCGUHDExTkfQn6V2xPvX7Q0wSXbMFSz1mg26vheyoAUMd0i0HzPCNtgQZtp7bPYiYA6gw Z7seD7ZZLa2fz2xc1Pbbr+/tfFoyeCFwUMIYnSy25YWBAUdVZ1BehCwVEdSpPpo7tTDuAN6F15h+ hCuHn4Vr5WdOOC0C1tTgFOA2AhY6lXGdQ+wvyvBp8f9Gxxzg/sAxKFqB/eAdySnXmqlE+MFoKwO6 N6+qyoZOw/YzZF1ja5sZeIDKXD9BKRabGLLXvtNcJBsUPScJMMZ0Phzs4x//4PvVMD+A9HEM12bh Ocd/Et6T9Z1ibq7sLsKhRosW+YdE9ESI05ycpGHwQTJwh4MqUdSxckwH/1e44uB12MmKGUDiOJla kavgVLF71hFjPaEAMT7hcyXZY5tZM+AzeIFFn7YIPMj2Mnk0Mi6ID+pZyBrycSSAnt0TOQDlEaPc nVTSUWZIt61yH5b2nZvtYvxBKILIXhGsCB6DtmCjrchfm3K3MHLD+8Hu1I/HreJ99bk0DrRCCxwk TcC17WgfPvwpYTtXcegvZxuai11cBnm13RCcvjYtyaTY9MAnejy3du6v1GzfrFbSTfcS7AjbLRhC IGABAD6fvcNXc2h6t9snEmhovQd73S/5TYnt5WB8FpY2iTinX0nekuUBGeulDZfBDocHlaqula7X zX/e8phhbhKUCsflftzhHEZu3AAY4rOp7J9MDZyPdmkOvB+zsbMzpKJ6aIy1Vq32ttrueSHOnz5q MJ7SPhf7x3/8p/3+17+xvO0xoYCA1bQ9xyBIjPYFrFInFAhc0TCs48k6jgWYsx5PsP2DjBOgPmgI ociYiJeeZQR+k7O2UG1wY1IPbPIsnGI2zwF35RiPfyHEFyYgPF47NFXh/IzgInDSH89CGSGXiRqs LEvgKC9DDkdM/GgWlOdX4l03eNuzASSEDfHjcrxGsKJMTUFFUPrs84Ewo5hkb8WK8BGip47Qb38I 7xkoKQPNccj3vttuCAdwqTtnKoaEyVZHYMUz0GK4uVErHhkQ6tpEzq2kNw+Sj4hn1hzPHA+ZsYTM pUV4EeLhx292dccMKhRCRbNQt5L3xnmEwLmCU0iNMZwPupt2SlMheH3QLhbzmW3W688Sl2mbBqoA SkoEXmBmjutF5/GtcKzZcsEuLErC7x75+fybOecQG2nIKJXcSD33bEKgPO0Hm4HrhibJYi47uq63 7RpabCMnIOg5API5+HLXlW22e1tCehq0CpJwGbAEsmOGDqYQAsVwEzWaoCRLCgkc6qQEilQGcmnl J+ldwxiSDjCf33X3FqR6MRLgbCCfqYMlFx5guOdkTXTqNjkHRNlPLO7qM4v9mZ+JHTEaCe7xpxuK YCCihTqhuSGQ8K6bbDP+BH4nXhUkYrJ4oaBAjf1E2swGQ3RyPPNJYnw32VVWjNCVbMFkp1xNtqaK pBy70HKuTufTA+ccHow/KVgxUHXWI1j0ck9+d/+OTGqqc4JRTgMTlU0SywOlobU1We16yOGIA0xp EdgRlCyawWy5tZY7cc9nCk32u/XS7rZ3ifsVCgpcVyCCgul9PtO6nlpaKA0x6kPpYjybCE4tsy5g JsfTUeNLfcfzOzYwohUdBPcHXcJ3W3AIp1hRZBYB5tPsYrW2Cppl49VOh4u6vJyNHM1CG/MNjoqZ B+K3a2O98SD09M3UuTfwrsiXjG9Exx/kczXZ1qslLezOF7MVpx3k+UD9shqD1TOW4ZiznLF6mNuv //a/S03VOWQ46sVmbtceOxwAR8nHpJ3dh5XjHMKxGWWkNJ5U/iRiJ0Hy+CCKvIlAqW/4RDtKg9yy Z5+uki47FmM3niedNJffcwwE4xDidfC0Uhbjp1m4Ud9c3SJAYdG72YQf85mL2hH7mmJl4YIduJTw MT0YVF11yoCuQV2kc+FBqGCFnwotfJ0RAhJIdi9nWIxF/D0ErKUS7wlFRMa0C8jfTM67yOYqSECT MfNdDsGvPai1DiyoH20P1Uo8R3hyo7vmgSFwnyi7PkI5tB/SMDM+f83gdrEB4LWbOvAjcZ9xLGgY bL/d2rvdlmz0mM4g4xzuzdSyko57ezgkYimNZvuOpSLGdFCu/vnHP5mR4esIOPu7O4Ltl+Fqpxad twU73vgkfC7R3r/5/MTCcM6wEqvQCNgzQK5dvws2ZJ8abNzggL0tC66aC4oInlmIbL79EZ1TrEeZ 9dpVFVACXfqRwRkWgMvt1u72K9uhQxpTI5Aqp/ABriGyMXALK2uQveK5AHk18GGPhnUNJUymreCt 4IH2TpmPo6R2reMyYbYQcjJY6MLsYyHl6fNJN89t4oNKwLdg5hEZBG6uyr4IdozNhdeg6A0BWHr6 l+gGUVOHq4wXexPW91PaQ1AcpKFeBr1Q+1SJl0gXTpLLmkOiaCgY6vwje+J3o3RzFdNyFMojsKgJ LxFhXRIFmUA3ukTLbMSd8xY/Rm5G22DteHkabX2NV2hBNdCaT47ePz7DQrpvTWuLIoCQkNm2dro0 djidbV63NkLbH8D0ONgWP+v8LADyxJ9oCS/AGyz1EVQG4DPoGgU7fujs2gtsjkH0Dlb0UGB1RyEE ICwmWXFJP4scNWfPh8QLAgsJ0QiM7E55Fsa5QWw+6swKvgyO4tObRnE+jBu57la07vG5lswW4fKd x5Xe8piBloGgjHPmyIn9gAPPXmuz61nZFeGaUPxAbNBYHycY5qB3IElB/cEz9DpMlJ9ovlWzEc6N pOIAue4ZmUTdCVysBqUeZoxozzKLDG6ik81UcuBQWRMEz5CDiZOPsRNaXVGJMAxDs/tNkNc0DqPf y7FEfC8FOuFmERMm0+XxC3jYPODlwWTH1+BI4lZZiPplxpXB98yv8m94FM/nEAFPyqg508xno9Is U/TybGDgcPHOmWB7C3h/7mHVAC9mwUiCbBvrR+Ax2HFirMU5LWDrz4BFin6P7k0AACAASURBVHz6 8HiwFQ1BEc7mdsFOtsAD8uP1t5VpYugW5R5oKbr/CCAnllY9QfMKfoHo5PUdVU8xwIzricWOz8GS iuNfPteG8ZNhRp7Wgp/Fu7H1iiM6wEWQNWERxDxhSDEjgOLxh3pIdAljyLmkNnDgHo2gGjplDsaD AxY+BaUaRNyl24gVTRJ/fqILiebApW2tAZ2BQfHrHa9fc9DODaV329n8B1Eb2B2/tj4Y5hhziGX6 geRxs8Z9gSCjJj3UWIvpGd/AYxli2gTPMJVjRoOLo5KjDJvUVLZcYge62qUD9oCgJfBrQYty9yQk xd5njShbDMIbXiJjQQxbI1jEGkvAkGmIKyejBT6BKBFcjZEpNXYjl1jhOENkHkzDnCoBThjSXWVe ytSCVFl0LJntIHZH0MrcHwXKbPWVInsw9Xm35RQc+FFJqYimQFZWVYALkwhlY9r1M6bnXo/h94f9 42bO8OkOm7trfY8g1ZPD057O1jetsBd48y3QYcUC72m6AKxkv4fpwtL+/vd/Z0cMAnX9cLXl/b0t 70Qs/NEHcAeMnVzswKBFu1xmOo0dLi1lSOotZFbMxhbZs9mqxggJ8BA4D+v6opSKkhF0Bimyzuz9 bmO7/X2iGOB+UYG0uVAxAWUWshqSKCHWR4MKGVcAL0OZKBa8jFenUs3KBpEJ1V6aopvVX82Ol+lQ sVfsTw78PrqS5zOcokZmhjjP4/liHx4e7bHpOMYSKOnb5leVaAF40UtjM3gwvvkeFU0eqdYGvSbz NTU1w2xygUkQ4JVYp+5f4NQdra/IogB1tsS5uO27sGg/XFQv+YeooSSoBYyRA9nOYxYQrOS21xzS bBZqBtk4AbN2oULg26BjbVjwarmHskHM7el6gle0sor+gyKMBv4Vs33h+hySxHrP3NvLJVd4EIYj clwsJwimjqK4ZplHVsDxjrstZ4NdmPZLYiMFtNumHmOpnHuYefHBi5Q4sDxkefmsnzCEU77lzYn0 sxl4lzY87kVjTdcZcGbuZZx7m9vOd2f8NHAfTtnPoe2+pu7Y/T0WtNRNkXnUnRt7vLDI3vKIEmu1 2drdZueDti6a9+GTdc205Y4SbbffkPkezRktimwSqzIvshOw0UN5QcfCpKKwWo5pSFng/mgViKAr AO15JhZBCwGF6gtJFkmDv8iuoG8V+mBZDidLc/vNcr2sLHmkL+PcllJsKDIwZsu4B8DTrt0LirBv cP3ruc0WtfWPR2FMb3qom4pgtXQ6kxpODuekrn8hA0XOpVdhXNshTKBKLWILCkFlU0paOKsYno+O U9dR+uBe4saAfcs3GTTGMQ4z2RHSdCF2FQ0Hp0EWd4YRMI9d3wPVxIY9omsQyrzrGCYMPj+Y+Q46 cU4EJgkXRfbp7GH0bAL/CtQ91CXiMntnMFEVfPTIhe/QsQI5VBSHijdDF8JHlYJeELOO1M5ynC1Z m5WdyxxkySubdDdLzv2EfVHc5LjY6CRBvxzqmCiFZMxwt4HjdZ7z7Ht45skVRrwiAJYa4cFYC0Fn qG7+DP6Vg+iY/gf+IypKNFA0WoP2Nhc8rbI0gByNDZVxalLgJiUdQ8ZcL48nXgSZL4WPJ7qE3JoB 7ksGXCYo0TEuR2yAU0na2AeVWb7h/mqshxkW5gZpZ56nC4C7odC8XFs7Lp6mMcj6cC1ORwW8thWY D7E8SCwF+f+HlYQ9pGZ8nOkt3ihmU6n8gUaPk5oLmpPADw86QRcpkp2Ae/LmyS3VMfEwvgneZODG mRzHq5zUATwdrqCwQJy40tzTqPp9vlDQEoPbyaY+c6eX8MVLLW6X/OWJiBkuqoP7GLreFIKIAmKU lcHhiquknS2rampgOS5GuDrneSSdS3CsQjVBh4KWojomy/0GOG01423pDukie+kb/z17Rlzvll5R km/FyQqeQ8bh9L1ydy6loVUqhdPwMLR0RqkXa5rWbjFkPiGPxivq2Kw3dsHAPoN0LNSfw2wn/gC3 GOizD/CgbPk8QVcJf6/mUEf3sYuCNybeXb4eST44Zi6dEjE24ki9+P6e8ah0dj/BCoEr4AtXzXVy LfGsVryxIKF2rlISXwPeBbpF0ofj2qxsaCGD09n5pOc9Z2R5CBmlKjM0DthXdoW3Yhjo/oANpMLn RAbi0MpbURu4htHhhw4WNpmwz/PEJBKJPPMbAUvrvXih/E9vPGXT5GI9OmewFEl0tQZXGfVsho8N Mpo5tIWwM2C3MNsukOVM3WDCNktZVJDHAs4Oq66FzcI09YodBp0F8GKQToIPM9o4w3Cr0wSYBoas i9ySy8pMA8tRikUpqfZ/ycFS5iQcSmWqAup1FPlQej8KQLgwOBuN4+TzT+3bwuMwSgMx+uNcYx5Q HdHwdIzxouCmKNB5KE1Cg3liIFHvolGAhVMvbbE2W9cXgs0Y8pXsz3PPQOR1crOmatRPY7Zbpi10 vR2a0Y6gFVRX29Xi4tzt97aPMq8yYlNo/yMYleA3DgQlEStdz8qVEvizroP1uePqP4NsDOVkOQgN zs/+7p7dNAn/KXPjVuHGELjG2LARigATwEE6unoBYzx++khJGtqGeWbL0tyJjmiYoGOJF97u9rZt 8fz/w9qUQfyYo4I7OnT9Y8F850GW/thYfb3YBsPNHNifXv9p7RAzgU/qiQKjxf+GjWCWTFcmDohl Mcm0cACQd9qQC8dTMBMYTLTrr7bAFogsizIxwm3YCSwcnvXf05GFBJZzTAdBTYsTs3nw38NJUily Bs7KzKpB7X92VqAcEUvP9dSn5ZP4VDHHF2NFicbAi5VVKIRRuOU9Ju6uILKREsvX0+uEFLTOvgxa qWHgQTl0shPlI4kMhtvPc+YYwYrXOZbBK3+m6e1nSJ7NbYP5tu3OtZvWz4juxcMQ40hqP7gBk/3M Q+qfYX2FjNtVLOH2TV3/6BrTzYTyLBTWuwlYhAucywQ/wYAB2pnLDZcH+X0DS1BkTMDD2CEFplUv rG01iE9lCCd2ykEHAU2ZLEiMIIiGByHOOTqOkBmOzmLJkgnhQAQ7kGPDkuzx8cFObW/iZavExTKC eOEJlI71Dxw8r8DGNxuwltiFHSYa7197iAjb2tp60meWsKJL3e5YF2k1xG95ma7OYK6IcnWhn3cL QGTbMAQudOuC1F0+55SXCT5RlA36T5ykshfeOAiA4SZHtpAWWNSbWrhKEh1T8LakAol3dFxbigqZ fAAH4lkKxaH26NwUdiU9lWTpGR9T7xflYCaFup5UTHtPAsHMMyInaJA7EoyuLF743EJI4zzk7egV 9b34eZFD46bkHcEDWGJOlNct/3luCDmd/XxhEGMF/WTpXa8gspYPVBwx7Oyh+plx8Z9weCnEtgi8 AMfRzteZNW3nATcvHmQxsOgk1wzNA5djEQ4H8FXNBeovBebUdyKe0qsxOk46SDOhWq43WZBdzsxW PvoB2oOE/FBeggStHZxNgtWaDYokEQx2PMUVaqpEAKhHRhYOO3pW8wam66+uJTJAYFinbpDlu5dP pJXBEuvGOeitjwpNGTw4QSD9xkPOPpA+7m05gzWcnNNLnPaZ30p/h4v7Z94gxZ8QtAxOJ3mfrpAS FmIsCYWXBMaROybIpOhDxiHRmV0a/AwequgWxlB0qGiiPMxhQCTTqE2RKTkDPj1i0sghzkL5EA1P E+ch9oJz8DnGCKyhiZQ8BzPBk4anQEe8HIsZv9ywU8YhjrJnY/5N/A5kx1Is8pol6AxB6UD5OoBD xM8SN8KdXVzvylmxuRfpfLYgKVLnM7qYUfqFKWWYWVO7CotlwUClbPDpQ34brOJQYHaxVfu5RwDg bOJgrIVyMZVtTidmWCif4rSlWYVgME+lIb4X5qf4U6py0uHFXZ/xwYDVh2wMAwzEI51fFd4EuCUM lKRNqMnDTc1tuUTBE64p55v8ftSBrxc21H0ys0AY071URjVxy3bZcJScqxPKXZA3Z4QgmGnNFrbc YWMH9efHYYoVFCQ2GxubDm3ib3wV51yOAxVA1sCxyxnW/G5PQxeXa6zx/O08SB79e0BBquzinmEh Uc3FFVuFYTqtQZyIgsCZzqQ0PXW28Dh3r78hAWVYGAwdXr+HXZUyppBYQWZ2KTppeFoyi17lYZR1 4caj9/AMuwDGNbMXw8UE190PkWVnqEhEK5kpqWc12BkoxwxD0zzknADpdA28AUDANwOWOKXm3NnD h0MmHFIx82pzyNJiMUJWBOxtsqOx4LRYoimBn6GgIdv+kPdVhwUdNbiUEJj2Bc0xM0j8JgOAzz2h +SGQm06Q7X56fsVyELQYZhrQibrW/AwIPhxZcYUGXFaakQLQ3mzoSoP7jp9BkFIJJg9CfkTHkjBG s1qBQa+nE+40WdNKuCXLUXa9B2sAcjuWCUxps0VHVfQJBdXezqcjSZ2XprH3794Tr+J7OqUhGWj4 Z2jbjqx5lJA4TwRYPE1d31rbiekOXtluu7X5Ymn//PDJTuCkMXBVtljXJHX+qCyrYoZVcxSJMuXX 3TcA79H1hqjiXPhRgjXy96O7p6+4i7lv9EpYYqPNTZR4YUWXoCYFRyte1+0D+YNOHNXXb9vx2pmy HDIypcZqx7aw6EHsGl0aWOMK4lNI1UF0BZxIaJ4L7vYsCuVmUoWQpDBfmxmBKBCc60NXGww48MCK Iie6gg6Xp7aqWqJFNhbdRqcuyO4Au6leJ8qGKMvKz5/lYJy8QWYy8GtQB0pjCwVtWBxxAbCr6gqb wD2ospkzQ1wutNGreWc9IiDb4Vmvi2MjHtSTJMyrHq2c0bEtj+tRCMq9/ih3yq99wIUNoRe8Bxes FhcJGv4AoWfsLOtVwThnZkJ/QAj4rbwLKHwLkiwAynmcZRcmF2bcX90zBPjT5WJ/Ph61K3umg9nD +Vw6VAiO26UwULweixSA76B9gCKD7IlSLJVtt1u+HYKiSlTJIKMUjO6htLVq8rQiAOK8AahjoYYV Ge2+wIlDMMXnrdG8aliCLkh83XAS4YcdVUVtdx4uoPetoFkJLKTNPVGV9FURvV0iqgp7s0zOztXf DWetBAT5F35fw88EWwi86+s4aoKGob5Yco9A2wLgyaE5vDeyAwnxgaWOGn2C57hhRGKCMxMLQLsY Hi4ib2qOBZoUtRC5VAHaIcj5+0Sa76Cyyip3n2EJ5O+fQP+CcoCd9IqUFjtv1noXcO8zgOWFmyS8 CigsD5YL291hdxfIr7IFpVu2RZrNlzbCPgkA8AJjJupwifAIEBqXx8dXwJPxHREd2eDWoysO3Opl buFtS7ykVOgZ/doHND6PWvB6pXQNn9V8enpKPcZBrsbFCBD6hBZ4XTMrQvDAgqWKaMiFeJaRWOtu 4onMS1mzOy+3QQ0QuH5qW2uHkQPJsK5CKg6siYPJFIRzwHfoKfYH9VxSHDBX6cC65JI1ewfFWWR5 ONiRhJBg3ybRPc6aoiTyDiCef6wd4rvIK6DwEKB8KtMF2PP3CW+MNqsr2+1W9D5gifqjjkrKpsL4 ZIWW8PCvfZk0zxJf8w48E5JyNhaHrytizlibi2LCBd/HPcQadhB+QuXJG27EimxsrJ+qHx8P7sem yXFkB7QCWqAkaShdu6BO9IIzh7qBsGHSbBcCWaR/SZ6Y+IU4V+r4YSK7NHfwD0qhvAXZsmKMizHP U0Sl5eqlArdlIw+MiqWgSzdTw4rZnFjvIYwXISkY6DB2THyvABO9aNLwccZVtC6fZhrsVi3xRxSN 0pY+Ka7yBuaB1sxyjq5UVreEvhVm6hC8ZEeFPxg/wee6qdILUDdRIV6IH8oibz/Dl4+edAQA4zCI DBMLkIrRqscEwOelaXAtmvPJViDe1hB0VLZCNVGUTIuFshzHpriYvcuWzh2TBvWCwSPRB0C+PB19 PvBKMBtjMgO4fvWauljEBjH/V+cgQKnlzqkobhyKwNM0Zwaezf6O78cOJX0IEYxgagHxP5SSsqZn cEXmRpdohwGQXUEtE6U/3ac7e3z4xHNU00AZXHdp7dK1dgYeTK/Ope3v73+Iy/b00AOEbJA3wrG0 r93EwhdgIusWcLrLUAH75fQK13xxBlzLClixNkVVoNyCVzK5KRZAe2Id6EXSRAiO+s8/PyQDSlxA 7hp0ra1tsbra+1/vmVlh0WPWB+sN5RtdhjuYTqBE0oKs50p/RSrNLXXp4sjtOZdfRJKS/O9wvXgd nAX9AvPCw6OnTb8FvZzJYHXS3VJEjpEflXHwoQO+gAulxa6BZezEysjoejOpn7O5QLlzRDBkcExa 6wpWCMolEJ/mKx28D6eVkmmPH126U4/Kv+iSPh+LJg+4NyBK7lb+OSP3qaWkzOtYzghMTdfbxwM4 cujEiSKg576y++3G7vdbGj48d3a4Fnh2Pv35webdYBeYb87ntl5tpCYJ+ywEHDf5DP8AfO2Wz/Pc uYEmoAypsg5742rvJgt+XV+IzZG9bTYr6nHhkl0afEbdG8xalnwuBFMoSiS3n9apPi6yh3EelLma MYShxMCMjNZkKP9guEqJZt0XBKv+Utkd3HEKrtaP1iSLY2T1NloF1yGUiF/xvsyGr1ICuc30gwYV Rht6XRcDKCSeWDZzgFlrTeaq4RMBcw65lmdIJiqxKAM1exzPXP3+/bu0S/AkvZ4H7oA5WaTNajPm IWc1WZACIWvprevz5Dm6CDSZYIT0bsw0dcmKCN7GjMWf+VuZt6HfS7wA+ROC4unT8HEh4uIlHXiO 06jbAHVzgJ3EruhS7dV2UnlgrnbTAXGqQ2RjDtqq71FSJvJ1yQ9hZr5PvnazqrDcoFIg5vznEaOn 4n4vQ0y+XyUBws8dWHBgpNMpmcTKsTDUFNs+rgctsHyO71a1lGJ0bWebxYrOxmt06xCg3EGc7ste OoX6Ag8a4zw9x3zVpKCA3wM3Ctloe2qROrATRoiCrx9g7/SIV8bvrwiMu966612V9y24YVS+9MAS bfcIsMj0kBVWlVygqy5080t6pJ6dyFBRmdABm048P5bOMD3U9ADWDFWMclTtSwfv83WgdBFEIW9P uUS9b/mD059VhiTifwSg8DXQ2kzv6WN2sfJKflcKWL/99ovbFQX7tFDYnGkgMU9iyxdZ9af0I3pv 82P36BlkxcJWoRV5iRQUkiNNpH5uIpqkTYtTT50+4hhZeRMdJ2FOeHWkpBm3ytIxCo4angY5FRkc pFgBRWfx/5yZiaN1hYdilKZ+GuVFS5Adn+Gkw+OhofCBe7E759lbfBc41Td38fxEnvl9qYvGjX/5 0AziYA9HqQpEcBhTUNLior7WMNjxDB5SRUwIGUW50Kkw2jS2W63UQKH5QMvBbQ78eikM5VBiTT64 3aL8ouqoHGY4NuNEzKCB4JwWXlZi4OrTxVVrnSMUTkFs4xYdb2ppebnW9+F44+q3HJlpbOafQ2NQ KvMS1oKgTNNfv9be8aZ7NYmieK4A5g9WnS/sMuKXO3SB+R6AVTBtiGFhtzf7yV3bGTqskEtu+6/A sVzBFh4BEyXR6ZGoRUXXb/rEOdxB+8BQIO6LIIZKStBRkkYvG0iTy6XvsVMpIt/0jfzt0pAzZwnn cm1BViVxPXBk1MoEU13mPsKVKBNB3KqjHk7K0Py1EkcmMVtFg8idBe12qI1xakwrXV45eFHTWSwv +KLs9HEbStK48zHsGzDXn0s/BLOy5JPj9JQQG6figcmxutA7kmpqZF+Z+f+5I1/p7314nwlWCOMA rSH2x4HQ2ecDFkidkKzhCAw4R3OWAgF24t/oxpGKATY45W1BRQlWt645glN/OvEeniCeh4WP0Zau JY51aelOZ6ulu+X4dRbNQaB2stAC4N21DGLU9w7siwGjsrG9UL8dDx8eAzgFVe2ZYoBI/1l+K7Xn 7NsFGVnf2uUijSu8zvlysePpYp9OjdyGYAK8XtE8Vbd6ZhcsdKf9IBfA/GaUtJDNeTg82uEC2WSz xbFJGT46lAEnIE4v13MTnP9zgxUOZHV0roH08yu7hcyuR2iUjSSLPt/YKTIf53A+uz0m7iEOSXSz DPTdPznNc4MIcrCqEY3VZQcqHDV2WCox3JYZIeLvrFaBn/7f3IVxk+F/AtcdqAki9e8NJjxtN7N+ sbIVSX3KqGSkmKNyComkHMhtV7iOuoD8eG5Gmmcdby4cOxGhuIXgFAEsX04sNGYyzBbA9yqnyKUp n0FsEf/je8wKE+NepWmcf9LkikwwMZerV2ZG33IUZQcD7XTUAcYMF0jMXuc2cCf5PEiO760XC/vb r8B2/LPxXvkkPbMHaHFphAWBZ1HDJxDduDyCgQwJncFf7t/z8fp0PNv5BMt0KERs1ZnDC3YX22yX 7JAJ01PnFcEI+I/Aat0f6EnVJIjObE23m52wTho6zKi3hZ8XFUJGEfg9ZH4Cml2PzCWWievBAgzm rexIz+3YjdbXayoxQExwaDq6Se+3G3YCD49Hq+o1N14stXt8PsemuLc+Plo362zsfVG6ow50ySWN A1zXpWm+IVZxIxhw7YNkXH31a4TnIdcXNqLnk/Lp77glahbjeXJmheacB+ZnRsAybUjvGRxLQUIZ xyv7j/pnVDnKckN1FwcAqqKwCNG5cESOzpTY2glrcBZ30Br4xlR4kGkiduUOwmx4PdqMLX2sRlhV 6KAHppPB68iNggjqKTwliSVKFoL02Gljct4RA1cejXZpXHoZwiIPEvEtFrh3+CJ4pYchFBxu6v1y MHmCc+nia3Tg1tHn5Ufi247PBzvMrcFwSUoDXwZ2pYY5s916NUEKInFlJ3OhESpmDk7bmJaCHWfV FtXctmvQB67MOmzWcawozoCNEx8sBhYVM3egOuBv6VWFSamyOeIoM8wZLhPOKjVSTqHaspb1PF4L LlD4fswBSippYFd7hHwyF6yb+vpri6Ashh4+czMg6INqMtoMjjbgtJNhrSF5UiFQBmLcyG3ClrX0 zRAMwNFT51w8Lswf4mugwnwNH454IpRJKWsT8jtzNggWz8xcfuEmJ5lxjumUxM2Xf6mYRpkeAfEE cI6sKMZmYs1EBz29f/JOyFSGp+efItAED8w4sGdYMjr1UOEyMSJKxs31wBDlVgCMSM1dm0pAd4gA etCCflMPDGBha7S4afIA5UaQ6nx4NeZGmLlFAMK/w+lZO0N2Uta/qUjKajJGiULYItQZHAxnmooA 6XIzV7eMnzQC4qIELpZLQWRsEZqSljwzar2bK4n5+YUBar74b3tMt0adQ/56sr/CuQT35hXHtFnw 9AjJ5dvPxMUOntn5YqsKZqeygUfT5nNrKSYESqb5zINSGQgD9I/FBiFDYkfOkqctGMtFGKGGfLF3 cb00w98ka3qwYwAjbaOWxjvKUA9GyIromVjPSFJtYcc1E8jvzWH+D0tkMOIvJ77dBuqmFQIYAiiC MZQkampetchu6JgDeCSbbbykMiFisc7zfDzZ+XCyrsW5g1W/sOt+J5s0Nxt9bdCS3AzMZgarlsJs v/AbJC8HbfwWLipFM0NqKTfJwoRinMg5xWRKCa+8hMFmEDI9NFlxNEcwX+bOIOdiTJ9LnRjRAJxf Qc4FSjllXPxvP2mUB8s52uKV9e3MeqTw6M55Wir4AUHJo6ovsJwiimgZVvBpd/KxGjLXSXLE3Jds gKQXrwCV62p8zQH/ER2ihjwudhCLXpre0zW4n+1QREqqzDPTKfB7GgEp8tE3DleR8YZTj79X2sF0 HhTLI2b3lObw/Uf1gtFEZ1cMNK/c9OCFg1kMMikQKwvhvPCp/NIgcBBJMQWgbqFLVKMbDQVSf2sJ 8LXEl6i26lSL2HxF37na3f07GrBir8YIDmYEEQAR/Oy6to+PB3tsGlvd7zzTyybCAOUJhywWttns 2FEEBtdczj7CA8zI7HT4ZPPtxhYbKMAqGCd5GxdZTFfXu4k4v65pabRR94Pdw65svWFJixJ5BW7X RVQKgOmvJZ5yHhc8MJh9DOpWfvF3Yg1NvhZCAf7ceWWk6xN96aA+C5LQcyqBzNDEUwB7OVzx5fn+ ce2RDKVZwhASy0VZHHFN+WZVWHJlGRaxfDT8rAzLyZezQdwsEGawC1O/HcqLeJga2+62NnZh+SXp Ctxo8TkQzKQfFF083NDUpfQFqkUstx0nd7giRFHTkB+FkkbAY8aeFLHF+4pB5vmN5nQe4M7yLyEH w6vic4bTjuAPya0ca5MvdezUkUVoL4M9koZiXluWft8RM3Yo9djqh0POYkln5uMJXKXWZpSSVksb aCdKIxiP5s91S3d5+RDpEKRSzGzGOI2+o9dSN5B8wMUygfYh9EfSKSqG0NPyMR+q7OO54/C98E7I gkPckTbzSw3gNn1jh8cHNn44juM4FTE0gPgklVbKZPA9EGZXK1EfnLiqwe6MO+J8MPrTnc5yl5G9 j+3WG6t3a9dpq+zopfvKJaRPXUMKyej0i1dp7K9WIo/K/eUL11qYsGaAC4glnsNEmPbsnmXf5OHQ X0XQy8yAp9DK7eHorFdJ0+eDU7nFCPBNVhEdhamRaHTU4tXVkkSNhoTNzVHpcIKdH7bjDdf56XgR aW+2tB4Suc4eV+ASaErtJLTU2eHibSXDGothuQxMS5b3UnIIbM2la1xWRg+GpGqAfzEUhfxM2ivC sVnloNMsFbY9uqegWdzK8pKW2enbZzZxiSMbvL2tOvAMUg3gB0uWTN/eybDzuZ0ORzv1R5vVS57H Bfrx+JG+tQ0AbIjhYRPgWE4Mtzsf7jPSI6UUcmoKeHdSjuTghvU2R/kFcwlQI0AUhYwMeVYqG0Ox AYsdKgrLxSKrSTiIT2NVSheD7ArPgdoOragVWfssB1nyypA14RwRLAGuozRFQCZOJyOOmGzg9Ujd Mg+wCKqnkzUPB6uATbmhSMWMDOeUteFpClODmlHzPamAAfnnlQfEz90qOicvrPt0sdkrpWauxWQA mg4ZD55yo6bv7UB5EU1iUsEvwKveO/fpn46D1VlvyuUdkpmXp3D+srEqZwAAIABJREFUYOhFHOPB bkWNd1+sXqKJauAZDu7b4mo9Oi/Hi42LOVvGlwu4PCc7X068IXiA4G7yeHjkrsxy0+fyAu9YrWCq AIOFwJj8hB14p+ifXy/wtJJ7My+UfP/SRUgyvAqsGB3IFXW5+3tL1ukPuQzzqXSmqJnVO52n+syD 8NX62hlfC8yqFDbTJxR+9fbl6GfOKuRcVis7fvxkx4cDXanr9Z7cpnq5sOXY2K/7re02G37u4+GR WQnxI1AeQrWhkDXWJ9S1BvDMLMl/Jh5+Sc5IRiYco1Ey4r8x0oPB5IAu0DFktlWJI4VOX9Ak8E74 G88fiNLDoIAHcBvTOOP1hLlrDpFzpObu3roWYpMDsx2UnRdYrw29LdZL6ekjaH2GHBoBkI7XMHI9 X6zqeio6MGCNGG86W2NnvufdXSl5ZDzWi6UN3cWOGPKGICI1pj/TDfYMkrI88Kt8DbUhhBjDXDiN lElRVnIvzkivxNNjykBeG78Y2cw3HkFADQgk8bBCrUAfIIODsEa6JLCLci/xYciR0HhKkCjDuj5c ZGIlA1s5XlpbtFe2tO/fQ1JlYXdXDb8GmW61es8bT0XIxNnSuaEMYDqdQH4x5HU1Qq5YGRlnlWAn TpxKACHxLrLd3eCRuIF3It1MIpjsKVvi6+dAwWzL81587tqFLvyCFA/V8wEpsgo87Pg8r0nlI3PM 97DAydwPkfgMdMldg/znsag9y5rP7WyVPUJor0YgUNdwic0I2BYF+xRcgn6Ae4xMGsHmcL4If/Py Sp9Leuz4OjIMjLqExAxeH9kUZxOpTzbYZTwR40Egwu9jY9RsrExMQWU4E6fq+DMxJhUgPKRi0IWL kgsngA7fBcA7GRK5gxzBBn/oJI1nDFJBeO8vAeE8397O57OdHh84wkRhQQyHb3fcvCNxuJwxqhZd vfj1a34tDM0D79oMX35fZKYoUZFp4dWQkktI/7MHVEUuPZybIZ+jsTtc248fP9n9/Z1rk3Us9R8e HvhcYnIG3+NEQ0lX+KqjIGrfkMrr6QfNdAbtUNEZUyclic0zSxHTWMA2iIR4gLzdSYcZLWLggpv1 gjjBcl3RUJGdSV9cwf8kTZ+M6m4qQO8t0yglRIVQih80As2Oh2yzpVYrO4zQiiePBgEYXUJhKlI8 9fmm1DAMQb1sLZVKADYZMoh4mw7Hz790gM+EB/V4PNl2K7+74NlExwv8JPwM598AuNaY13RNc+Aq qXwJkFv4FUN2sjT7uQcC1lgvadQ6oCkC/aprR1zx6jpV8SeduwcW3vd2sHYYxF6fhfCe8B04OkO/ nkP5zjBnsCCRVURTZFO4NjScCHa8BzYc4ncpaJL5rgc53Sl08CLbUAdxkRyqgVWBnxVCk9HhCtln 8RSvdq3x31/eLNTl1DMKdYhVvbINsrTzSRy0uSgTSYMeTQIG1dY/55Lfx88DQF9UM+vOMNbFGN3n pWqIrxGvdj5WgSW+dEAh9nw8Wns506kJAex0OtmnT5+4meB6YANarVfk3eH+Xs6N3d/dfXeunwUv pmsqUOr0E1HRMQhhaNgzBhlKRFbh1AGWi3oIcPOQc91mKRo81d+zOVJL/XzgRHQXoZWTsCQ8nNLU Cdo/mNfZKDVpVRV8LWVHAAg1qkOJF7LvFRgpJ1ON1rnqAyF1/l7I2Uw7blkxNGNWiXGbZJszbvWa rDfKFwDUOF8J1EE7Ccaeyhiq6r398ceffM/Vem3bLZjXYIpDvndFLXAENTQtoiuXIVHXJHLCIjcE x0zwNXngTTs/063q2w5iOdBOr5d25n4lWzMMc4PVLtqM/yzleYBFikuEM1iAJj62ZOaTrhJwLrIr /Jlp06BtfZQF1GhvCHwjK8K1QacPOCiyaHW0fTYW/25dAyvkcwo7Mehy4UIgUzg3sh5jdjf0dJjG hgeJYTwZCiB6flGS6jPB+WtqCPLSwTKwufCzAVjfLVYE2zmbCDL2ACWJhgEFz0TTNnap8DkxmoS1 iPOXaSye281yZY+YAFj6eNtLJ+FBGmNzfD6wGTxjS1YeJA1jY4AFGjhp8CqlOoU08uVmIzUI3Ift bsOOvTqipVTTSxdmyrWavnf+NzJNUEmiIqlT16mkbjpGoiXrvA2vJxPfotD80Ztoql1sVgfhQ+4Y rFjuQmGc6qYSLn6HUR8SFmZy4MGDlMBOL0+11sK8oBTeU1NAozfobHgLmhgbFrCXijOxlaOInHE4 WzulPk78ngZ2E8O/0Ou5BTen8sSfxxFmPgB7f7+Vy/alYXqN6f8g4GIu83yGmmVjC8rr7qy5CNfD n3/+8w/729/+yuBmnFi6mdyiXVWjXZw40org6QiSI3fwoIAUZ1o8HJk8+7ojsiZ0/sjo5gZW2Xo+ t1/2S/v9118d4C7e6vY59XupweB0YckDWi4wCrS0/X5rHz/8yUC3gR9jPbc///jDO34FPcADEbKA zWbDxYNrDJkYgNnRXQyhP6mSrNipYzZFDXmdIBoF+yhDiaW1zGw4muMcNEINywUGJF91vRCYzoej jU1r737/q60WywnUQoedFo2KLV2iz1BtnV3tl7s7mcZedcnA5qeG13ptzSEgkS8QQnFtIIuEgAUX n7UgmZcOzmjS6Wdn+7/8os5q0HeSzHQ05FRVSXE4SKTRWc//W7x6OudbsvVtE+Yf//gn1wSfeQQs ZC8aSETZg+hYvIFzjuLFQWCLljoDFOt9n62jWkN4EWIngKREGJQajUkFmnrQK/ScIp3WRkB1O7/4 jpOFPkJobpU8qlJVkzPYGfuqauwoMp5APEL6Tp9YyLlg53aJG+FYwrJUFoZHYQZJYy0XaMZnwdXb Aw8cFtcwrNUGP6H0gx07pIHXXGAgBy6X/5sIuz7wizQ8uiW//PKe6TdAYV3nrOCJDzhcjnb88Kcd P31kpwoBiyXE6UBrdzzgxHAwL8iWekf5YOzk+N7+/a+2/+1357O8PnihuF5BIA/qquNox3Nv//1g 9v8+toQC+KBjE+jO9td3e7vfbbhDgy8FOgQ6V3NQYPJTS7gB8MCpxzCGVDxpQtoDJF9Q0wpuO7g4 KKXY0HHCJxY+Onyh744gwF360trj+WJ3+x2zQMwxSrVBnD6WZPibOu4wrNUYTzSAGChcGwsHsqAx fPO+cL3I2ke1AW4W1FVJhtXQd9NebH7WQmfmCCHHATpTIzPVrkNWJpcqfs/VTPGe+D6J0SyRXz4P rLF6tbYRwfsLwDuhClyz89nqOXDErci5RWMpzdaqVxu/+YWsKp2N/69Py6Rw4+KdKWZlsm38UD2l NExfrhzaKZUWCEYH7wrdOPzbrdtvXyXGaCT1G5T/Qn44fAA5SoI0NbqS6uYFtUAjQ/k8o5tZlmzp cySLr16ZVlBhafNFC4j0Dnr9CHpBG023bvJ3GfyDU3Jzq1+8WcHujlJCA75iXUOaF2UNs3aUxE5S 5RzlCqWMB/JdxoLiQDaFDOHcjFT8UtawzBIuGOyl7I9knDEMjMl9PLToUrXni7pcKBeA13yGZvDc oTGayjZQU5gBYxntj8eT/cexYbdL91Yz+b/Me7u/q+w3d8XBuc7rJX0KNbWQLyPHUxosGLHg+QQ6 hgfLr1AspVIr5wd7u3ajNVQvAe8ONAoRaUeMz/hG2w7KWJERDgNwQuBpeAbUIIgRnrjfgCioJuoE 1HiyCTgj21pgfEdd5KfEoOnBz9QBh0MwFtEUGV/T9rZcgSmv0h+ZCjYwfG5eI8fMKt+cQSSNJ42u Q+5S9NkDz81qSRoFTEY/RzllTYRSb72yBQUOfBN3SKEE05VzhTV9EdASMeHld9Ez/vnT3u/3kpp2 kmwtYPuWS1F2pvxrieEtpiq4TRz0dKoDA46XgymDKkqB3JYOYbvAsJyjEwC/64KHAB8yLKkhZOpB ZkJF+IuoH+oBwjDkMO3GFQDNoSDg56RZRL0GXh9jOwL0b4mN5UW/5Z9EUJ7+zJcGjnnhie1t3NZK XcMgR8ZrirKhklUB/SkXi6Ds5WSXx8YW73+xzbtfbLW/S7N4JDpiR3WsgQ0KjGjU2Gik0rkBFgSX l832qxo6lGiBAi3a3NCOZaNgtP7Y2se+sw+0idOxRAa+Bh9oQ/3z0gnnpY7q8aB7DcMJ0hi8OQHq QsgWo+zDIkeMaeCdOXYsfzN5UTp/hBwwjDxUZKgjQDMwbFGiA7NSMAxgP8BuqUfomeY8pF8fAuPI gkYExoFD/8wMaRNfdpDFYscG0ZwvdjhCnQIbmHf3SBa92json6IBYCsNcYN175LRiYBZuXx000gZ 1tV/BwSTAcqan7lZBJQ1KC5LJdiCP69ASoUM0Dbmd7amu49KP66llJkVayHOLymfxJUv/372pJ75 SjZgxfHu3fvJmqqDthC16eQlIlgFF4jdv/j8ChVRswYnI1qRgT3pNcUijw/LAFSMmeheiAzqo9n+ 3tI7yoqDAvf1FqEuoBIyPx9OuQBQ78YYel/Ui6PNB2m7owKBMJmY2DJ11WdwkqyD7ulzTIaJb9jt N750rzmwuGPXSMOdhRtJwusc3/JbNHl9ZQoLW67WtuznNltvWQbeMCGKAtb/DTb49WooKvRxvMPF 8ub1Q7pY3J8+Ptgff360T49na4kXVvbnubemG+06/z6TBZRH+EOMCVSJ1YpZBzAp6WWN1o4N1Uyp twXsrjvbtRcpGR8dGWuETT4HqctbZnSyDkOJGBLH2ERA2ERJzrIR5qybrcp0lHLwRUR5hnPsQIYG GN7bYndn23e/pOWsTLGx08ODnQ5wCKpssbnP9xHPFsiinmFKblkjPFKD8YkOBswrS9uHh4/kay3W RwLjq7utbecIeF/qFEqrjs9zaPe/9LM0Yw3NhmAIxKRFGBSHY7NTe56+yqvu83NrJ/4Jw5cyZ6gp WOavzUe6YEs/kYvIqU1a3Gkf86FTqXgiyDjYzqCAN9UIkH7VqQlJl6oMAE72DLXGzD0PhqZnTREo CjOLpK0TpqoSZit3A432QHjNHZsLA4tylCBHZi//Ikim3O7pDf6a4/bnFcvLkkJBNndES8ps/l2A r9gN17ONZstu8KfSlzCddVi7pQ/4deeOAwvlcDrbPw6Nfeor+zjO7Z+HjgoHTX+l8sEEIfiGA2d4 Op/tjw8fiSn9+suvDFgIYiyX3LiCpNFxsPWyttXynkFXrtPg96kLSESUeN5RYzXlHSSNBL6Fd8nZ mU8vpWtUZofmfjlWg2frcj7a+QJfhM4Wm63tiS3ZRM1ihHXYZmfL68xO59bGoZGMDzwRx95WgAEG 0F6kqY9uKyuXwmE8FyyVNSCHIWOFnj1EBTAx+1qzVDwfmL+kc9PUkLc8PJVwJYupT0FJXP7iPZwI g6r5FOcqak+oZoiyJL6eBsQFBUyDWS3/v/JMdXfLRZGHgb3bkB7+LJbHk0pSxSHz4uhSbCbOPJcr TvEODITxwdzSK4JV1MzB24ppcLcXy+VYnJW/tmeDYGTFm/D/GPAAriL7ClZyFtPIKW++afkk46tv e0xLyvw5pmTW8hzyeYCFfZ2D5ybJ4OfOr+hlFn993+cAr+x4ONmlgRPOlSqbn84dx8sprTtDCzoD 6Qtqkgl3DEOOcGiOa4DLnqSJWcpJ+WCwCyV+ESSR3SAToi9hjLp42YwRLgUoGEa03PAgSJiyfnS+ HN6IspMdwJhB9KFgZB/QAAPGCLoE/hu4GV47MiAppkqhtD2DTgG2+04SNiDG+lD0AEeftrPt/c6W MxBMoR4hakULukU30rsQHWSWo3AC6jQk5qedxqAqf08IZeIbM2wKeEIH2cq95kDDhbQPBK2XfUz0 3o45x8jaFCv+8vODe/Dw8JjkgeQXAf2zMGdWwMoBTNMwuIZoLv32228O0+T34h2aUgj8ISpKvxJM zBpUfrHSw+/McrppuNUXSzKRP3GRQAyMLgAftZDuwIMdJVh4IjqPht27dB6SVaWhRZhMFN5ngZ8l e6hgxafzFx6GU0Mg61AeuktNjC5qXkozjjk0x+vmBV+G8y8Cnp/hnEyPiOx5yBRuP5HhlXidXvVq /bXi4DNK1p9JHsVixIwoDQrmZkdMRrQXa8a53S3M3q+A0QXGJ8uy+1rBTE45g2zi/ZkjhuSzc5KM kW7Wfn/HDPJwPGiGrpFHIBcbwGfKtThc4CUOg0Hf26VpSQ3g4DMwLwSS60BqBBa/ApUWEpU/PKPC gfd6fHy0j58+8D3u9nfspIojBuKlHKrZsa7kLLVcbPh4N6eT1euVjRhQxrwfMEOM1HhJG2vscq6Z La63O01ygF8FfX2fzkjvFWNqlf4mibZHZ+7Mf6+vtV0HKE18eR/KzZhbfPbpIbVerWoEf+peFc42 XzoQfP7+93+3d+/e8XOCboL3lmhoBCwRgjXcvbLjATxFKGrs7d27O407FUTfOklUeN6ZqXv+AeO/ igHQJ6mekxXjAgdISc4TdyvsnD0DQzxUctbBw5aDklQ0HUcqs7wYYiZ/S9kTWfEj7UI5TA0cStgP l1MiCN7cAWFgcyW7WOxYM8BeKHlD9rLSVrCIXaEmk0wnnz2L7ZcCY0rdJYkT2VG+bllY8Pkj0MYs /5zzq7z49Z7SbUew6vAovWLU5y0POAH/Zbex7fuVdszrR/t//sejfWwG+8tvG/u//u29/Z//6++p wUIy5OnRNpWck/HcAQ8iQZHjMS0fWO22UBpVacXOGWgA7gaO7GS73evBx9Bz01jTCBgGZg2cC6q+ KEsfWnRFkUU1dj482Nie7bf9jnOryNzwjGzutjwXjvLga379STSF1RdK36a1E6xPEOggUHg628Ph REDk8eHBLodPDET0n4T43+lMCZn9dkdhw/back62G852gDRz21MLHg0LDmzXn5j1499kjtdqJEiB FWawYryPI3A8qbVCEwuBvG1QDqPr6EHlC+V937Z27Tpb7bdfFnh0aAVTLHp2X5BBfuFAo2S/33F0 h+KGRadT0wei+gjegdfp1e7ulrxfiB4fP/2n7XbvmE0Dq52O5kSm9ESRMONUZSKhIOaGqJSAEdYD aQ0bEVAcwB1QPgSYHCWiOE+ZQxWSMWHCmjWtgjQZxhOBaaX3R5nBEtS7nV7eBWg9FQtz7hVn4GTQ qjEjAZxBnWAQsys7W+C53HZNJxlncaUSPq7InaRncgkdelrpguslyweHP5ydqBOG5b+TNgo8fKPK A+mO/9xj7iDvGvpUpBos2MxoifnUdrde2+/Uk9KZIRAdqi7ZYQEzwmcMLhQLdWAqME3lQPOVYDX+ Xq7mdnd3x6BGWRlnWMs5OjI0lYso4fBwc7SFQ9OgATT8M7SNfYKF2Xxp9fJKusd7x6fCWCL8ElHq YVHd7zHM3VCF9PHTJ9ILzm1nBxB6wVI3jJzt7K+//UYZZ5wXS82htxWY31iUxJh6ezyd7Y/Txf7Z SO9e/Kk5bLoZHMlj68Gxqkn3YDLhawCLuIGsauBnLJdDAx/Xt2Dxv8Svwu/RxUdE3dcMQOuBxLN4 a5j65QNmun/5/S+kpiBBAc6nuWMnmXL+NU5DCjCwl3gcOzufTvbQY+MKly4dX1byKrKHFLjif4lk YmGqJZwwn5SaI4DBfAffKU1H3W3Z6f1Rg2arrwz6S2g51EAjqD3F0yJjy4AgDq/TU5s0RoKyNyJE CUOiRvZWUrTGCMS1IAV69Zquw9NkuhzSyThUvr1R5rpKanFF81G2igPUfPkBcTWvbwLNv/dASQTJ EiyUMIJN2WRIo3g6j0Pjo5g1kAQxTEWQFRJDwq7vw8oxlmKu1BD2ccCPIjAlbSuWFZpLlIO5SiUq mNZz26FhitJyXNjSdta3mMfsrbliKN8Z/zxXbQzd2PqAujZYdAXX662t1o00vmB+QaVUXfKwH2Ng gyjgxukX0Ag7HugQxObAbEZyLg5kQ3DXgRQPghvmIJXJX+0K1v4wJ16GYDUxhUVg7zqrF9ltOZE3 KZCoUvtF+WTozrUIGOKpyT3m9c+NKElf94zwumCuMPEi5VcaNl8ldqk9Ht3LwRaLizUzjORgyBxd YsxL+mjOsycWF6k8w9QZLL6USsGgKcRITlZ9wNKk9Tcm3/GwgX/CoWOkr2CZh3KhY2L8iiQ1wixV H8x5WRTd07loIDnMTcXZwqOZOVIeaBzbikWlixa/J6YucLRErUCWQOvlILqmC5D/la5HybuKLoqf V8rqQj0Vc45PW895cDobRmaQc/ra5eFirz89u8JBE4pFbZf2bPOCbxVH+khVydlCZqJWPHLl++1a aps+K8aWO1yma5V4uG5oJoB3hSCEQMAOoa1sHGAsoZk+8KqWK8km4z7H5EAA1na95znAcuw/YRxc Y9oAp6bmEORlRD6dJzMLZGngaOHYNhfbrlZk2bN7RWMIZQoPFApEQDvya7udjC6Q6eHgOYI1P5vZ Ow+4zeGRmN66rmyzRMkVWlcIhlf7eJb0TbqW0YCaSb4lgnWyxcL30ZRoGlFlnoEHCKGcTgxu8xWU Jb781ESG7wnQZI2+9sidRTECQsL8dlJEBQQC/J7kYgTytj3ymmCTuTJoPQlYRYi63nwltd2DpySZ FWydsJsnhuTBgQqhPpTMuTwIE3OcBsEIqatsvyi57M44/HcA7ZNdwjEQ9x+cUg+ijIsGQJSMfrFD vM2bA4jwiNoC6503xq6m2OTDFWYZGo6GYQXzIBcE1EXP4zq3Qeq5m12iT1SPiNGfm8wpUXL4fjYp AXM5nl1tcKiMFZP7XxGxRPqd2zirrEfJdTnRGoojHwN2Rs3fUTIG59trbnSc1dZAoqU52W/v39Oh BgELNu/QSMPnx6KGdLGoA5JBBlAbaqK73T7non5NEnZ623VFZuHPFUoibJpwMEemAyrL+bjkgC+e Y8rfoHSEYgi6no8PzAmALUl3y2cI8X2yP2tregQviPWt7ALLMqqRzu3w+Ei6BbEtd/JBloXf262W 9sv7d3a33zO4RdaPJw144AGd1+JaIyhL8vnKgIUJAHXDpZZ6v1nRfo2k0JcaQLgGbWdzZHxfmCNM v5L+RPUgbFWb5NeVhzp8TCvhsVOSdUxxLFeYF0X2vne6g6Yins+wnvS0pqDx5GfTApTEsL6oejc+ Cy2SKg1oMg0f4FmoIKI5QYWx5K4cs4sOfoYzc+4KqhWaOEvhcMMTFw9MwdUxr8DZqN8FsFMjFpG5 gfEOk0eNG6mEYR6WCKzTG5OL46+5WUGzuH7md0ssscQRY4eagv7AP7wI/iog9C0PnhMkRfqBjkn/ 7S8bO3Sj/R/vl7ZfogvX2vnsRrwo2ygBg9ILGI86bViIMXKDQ1r9zsNjUHENLR86xv3Bwo+MDO9P cimGk9uO9IpTj8yskiaX87bCpBWlIv7wno8yaeXQcQc6gbI0UjCcCNra3NoOYzhCSQGeNxdJrjhJ ncHV7FdJ5ziYHNpcyrA0BSCipho/S2Ro+DzBLWC1gm5mp24hZJGCb8hcEKW3s46Ca+dJBLS4IOrX gG6fZl9vNnVgXMhs12tXanjdM5MrjOyoFaDQNz410+enfK+QciK2lcnT+szVcwGrTM90uho0jvP1 E/U1l62tipNIfcXQjJJSQj2DSBvATTxoKAVh0JovS3QVEwCXjFaFeWUemNjKAPVpQuAqDfrA2GW9 vENmN1skXhiyqLAXi7AMkBiyzupSgstEvQd5Ixb8rsl1ndyrrw1aN42L2x3myfzm0+0DB8I8ZuYG N4r9VwUsYoj13Lrr1baLmf3f/3Zvbd/Z++3G3m2WXPzAcqhbBUoAA1E8PAhocJOGKoW6Zr5ENacH KRa07plVgGwZztQC0XEwe1lIagbvBU/Bx3awqjNbz652t17QbAJs9ehKwrWaQZLy6Vkbqx1Ge7io pNwvawa764DWC7JI+RTg+ehmaBIMJLTifUl/crljDEajpIWhARRE0eFMZF6AzBSqxMjLnPOA+Bzx mRgKqMaKwWQETahg6PnAZgC58bOvRfLtCpnp6PCnTfZmsJnXnYPp8s98rmR89vam4JTvd7AFvueJ e0nkMn2/iAdsLhRVxJMM66lkyvS/y27VS/N0cm0Ncie6RmY1HmAYpwxoGaNL4ThSzAy6kl9gTZnl 7t1E7+ZFxhXvq46jLmQoiiZVK9IVYngamd0mUw0qYCLUaSDJkaCwB+hcmgWbvtgNvjM2fBHn9B13 +lDka4Fvd1ezE52dX2/n9aMOliuzyja12fvl0g6nQYGCShEq0aBOsNvd2alBBgOiq4ilHx4P9h8d gtFAb8QV+HGY8cO9QwbjmRe+D06WdK6MPKpHGEIA+3KslDpb8MYEHokSrZY4H4BzZGTsTq5W6jT2 HeVbDicoN2xJ3GTHzQmrKBfpCQg6AcIo3n/sbIcB71Vti/3WFvPK/vnhAx1ucF7oan04HG3AbCys vexqv727pwvOn58eGMTweWBw8bffflFX1CVuQtAQf9ZedgI/Q+YWd/fheLQPj0cj/EwuljcgriPV HmbXVjYlu+0ThAn4Mc6TEj748wr8iiawIACH/LjXQuWa/9rjFta4PaIsRjZIuZ8gzIZSy+e6hCUN /6VZnzKQ55/LP0u7rtKCGpIumL0EaFiHUSqARvmclZpc+HfbD3ZusPviwQTGi11HM41UXqIbMXSC HN+idLIPYqdRAp0HCXNuQiFbQ1iPQSlWD050swgMhlNQ0mz/MUHh+deNYKk2b57ty3LICMmDp83/ ig5hOlNfcFCjnEGnvRvclr6lYgMyDiRJVT/aJkZH3Djk3I/2J0qtHgsP0i9Hq+vWlVaxjVxtO3Z2 t5jZOy+BgnIAKgTKBmRdx+ORNAlgRC03LPGn5jO8jgJQxj0V3h9O8BSAjMvc3t3fS/EAhqX6UHxW losVO1zIYOmD2LUsvS4XdNgQDbdmqzuSZRdUK5nZETODWJRDZb/Uld0Hox08RBttgTKVdAxNYSCD RHBclhAHB/+1aeN9Pzw+WjtcKfzYouGw2toaQYR9Kbj59Abi+QDiAAAgAElEQVTf2jOuZXux3/d7 4lSTogAEWQhBrteUY/5cwFGyNpr1Df0Wl26f9/UwyPPPy8vv67ANS+M88ZCCZfWKLmHs82XQKn7y 2eZ+fI/fjVEaAtod630xZhGAIoB4zyBMS/WLBL/xjB8uGHuAflVlix74A4JLAO0Yx8DNQffNO02s UpFGBzs8+F4eHIllCQMCxyqZ8xQfy1sDr2IO/7ijQApiTnPsrbfa/Qf/haVgeZagFsDks1vadWyo NXVqOjteLqpdh47SMwguygYGgspHbEY2c/JuxbY8BiHmaHqg3Bdhzu5hxLvZJh12cK1QSiF7Cinh yFBoPuKbJrOXm9InaBFgpc930oDfbbYiq3rACqIM/kaAwrPTYTN0vEsA+YzmD7v5yvp2iY6CjSQ2 V7Ym1WHOsSQEGOJM8DEEL2kOXhY2aFdFdTgjQoGyJpFjoTPfNZ39/Y8PdsFrD71tZ5W9266SdRo+ z8Phas11zg7o+dLaLxQWLOxUXCmCXod3W80PvnAkscwR8jMXW4Lg+RXD8N91BGbprt8MWMmgtZgl /Pxr5NGblwCyxKNIsSvNuKQOG7AjaAdxmgAqCcBK06CvSr0cHRzHcQ11ALQIPijVOKdN5BEAPgBc ALodAw+ExqBxtFpWtqoh85EV3///5s5rR3JsS8+LZDAYPjPLdffp4+ZCA0EQpEvpBfQgeltBEKCb EYTBYGY00kzPaVOVlWEZQRPC96+9SUZkVlZVm3OaQJk0EUG79jK/Me0byHYCFRkb38pc8iTsRddQ DDiueEN8rN7uz8HTk4/wG4P/XzbPr5vp3W/p24EdL+Q+Ym8nqzW3YJX/C+EZrrY4tm8AjpYnW84X 1trOtuXGqjpQcSZeztFL2gIIPNZ2ZBEBg+SSBArAKj4ksewlOu8rqej5ovs8p/UAJvWsyldiNztx 7mWQQgrXNAJCndqSCPN1s1i4ZpjeMcgZ6SEPgx7xBZ1LyOuk2IBGvAKm68kTtJBQTnOwY7Q4xrYq IGBjsDoVyJPmOdd0mgkBYkXuDf+oWuqa9JNO7yvuKxr3Nfr/VWN/Wu+smsxtlY/sxdRVXN2N2+lD qgROZzsKpxF1v4ZTUlClYWAlh51HBWOQjfFeXNIeLWuPNlPZHh1whvjCn2+7eG4G9/912ThUefoI cDQ2ia/7Wpeb869i06fPCISN4kblYWvQyyL4NEyDnV8YOYcBZqD1S0HFszFWg7sbUnaoN66qELMk Apb0iVihIX/qc1rLEW2TnA2pOHpYEYvlTjrEO+FeNLl0J9peeHA4KHD0c2T99wcXz8eHpx2fcqEu X9dTonrCc+wXREMMDwync2ZNMJT9NWRYbAI4jka2ozy5f297ZIlPJzukueWohh72th0lttm8t8OR c0rfEIS3++TBt9tBIA6a/vMit1fzQkasm3UtA9AO3EuGdTral+iJYeeV56LHkGUxcZumlE8HOyW1 bc8oIOzs4f29eljFdKogtNttbHT03qcTkf1BnwSjYMFbWxgaJ1dpJaDI4NcngDPBJ3z6J/drM0Et MFqlnAUHtpUHIsEICEFih/1WQRZnnI7ILJMWzw693HVe3Q57+iyVaoOzED2UtlfnnZ9IQSL16gLI R4R3xE29K7Bm8lp8nF1FB3VUXcfnysZJa8WIzIqJud9/7pPQewj82Pvu+r4fVm4MBKJ9m8eBoMcf Mq+4fTBgXYvSXVaAlzs8bMR3r7sAdCXOsaJsY/LhHe6Bc3RsrnPjpHYKDy2rUq7eVNQI9ZGwL8r8 y5uksoiqoQOpRHQPNlQMPL3sJ488JLyXBiWdv2LQp77odQQc1yBr8ua97wNTzutzdSm9cb09Hl4M z62/v5dM6ud1n00pHAiioH55WFnxDZT0ryNYsQnQCDKbB/V+bdvdXpy5fd3assitCRghSqxVVtg0 cX15Nkq0h6y0P+0aO5xHNktTezHK7Y+v7qzCUZo+Ve2KtsixkKWjtEFGJWE8uS5XNmoBgOa2wBo+ MZtj4Ao3sa4VRAgWsynmHU7FiT2RbIRYHpy9zJZBQYJAxX6JEhb8BfweiW0IwK1eUpb4JwJDCCht 9PqPR7KyyurMH3pej3FEp1gA4HSz1n17Fpo9sRnSONhwUfoGLSrSHt3vwZmrCU41w/vNlXyjwUeh Jj+f7WKAZtW+lJRMhlZWx9wYbNJGq2yaNFLTYDpJRXN5j/Isxfuyd7L6cTdLBI0Od8EDtRgE/DTl +KIzlh/bsyXh0+oDIfD4L/hndw90vzO9bVdPh9FJ5mSP4IvFg44EabfocmEwz460yl3Ik0QJHO9f 0Sjvd/EMl9nyMwrYQnR55NaEwftU3hZzJ5cYCLxnMMhqrqcfCSv13mWMSaUJnEH9URlCaA7H3ko8 H25hdgn0iP/2Jfb1me1hHB3+TOVHaTmaR4GLdjodrALgmIwts6n9WrZIkSnmU1vfP6gpfthu5EIz vbl1o4Xz2Yrx2NpzZjWrt8jHZ2vQY0fsDzloGZAkNs0yW00KezgerDy5VjxCbi5g5036A7ItoUQr a66tWZGerUjOmsTBbRQZmab2eKxMCcS8PBJDNsM9Oc7djzCKKVKuHUqHTkRreQ80tbJ+d41JbRFc mDW1RUseviHu06DtVdp6diAPxujEY72TNaDSkt526ra+ybkQoZwsySlrPKRur8d7ieheYb111OBJ H8FxyH6+djVVFmlkdUL5ytbgus5i/QS6Xah3azXRnMmkJXhbDoUsQx/YLfSCsOUza+XjtoZvEfhL Vtr9Xgi8USFDw4grIOl1efhRLuGjHXgilsXUrs9QfEVSkIrYp2h+Gg46osejyw5231FueYLNfdRx F4q5R7Nzs3n52OMwBfvURUbBwYX4CF0Et8tARP8hGEwE8b6eZhDeLPTIOJ7vv//eHh7W6klwk2IS wfbwsJdZJIRcbLdi74ETXkzA+fQqrNdI3kfnt/uhd9wYLihAtZQ+hw4EC/q6Oh2s4cnM4NWFcXP3 118Y3jBK3VCjyCw9M5XaMQaTCacQ5hqcZJYBa2mQIKav6Y7BVp1slZ1tkra2HCVWnE92v96ILAxQ MyOtYhos0btGSO+HdWa7LGTXGQEc3fXGjvudTccjS0uAqnxO658btMIIlFV9EHKda4aB6cU2yJTl SiO38tL25dE2ApDWNhvnctWhvNO9G8wkCEbb7db2x6MyPR+UIClTeKnTCcO5qoQzFca6A3mQCVbS 9o+g0JDLSDnCGpW5u91WMJIo7keQTAmiZOYER49k4XkMhGdZ1V9TwpwuhmDONIPZEQdgYQh2tbhG lsezE8aBWXBUZojZIO5QQEXQaO8gGU0rAr0bJccS9PkWy0cDVo/FGozbnxj1Xzed4wmLQSDqBvk1 a9VMF1s9WIHx/XHIiCgd/YpFL0BWjHgvuQVX1OYRPiOUfFxUN7R18KcytwhUjHeKh9Nu2hMJ0j42 D1Mm7YP3UyDGkjGcTthHARakB7JX9sWNDEOfC8GNhAjb69dv7OZ2dWE/9ewWVC5YvThjaH359zlO uG347dWdOmVyPtqZ72VTaYkLWEdZEeQ34imPNmr+9dW07BcIbtEWfrJYKJAjHVxWjU3HDi/gAYXz dyPEeyOtqvt9qX27W+T2ejEVP3FXHm19ONi/PGxVpmDzRWYmQKh4hp6FbcpjUC0Y2WzlXEOOkuBw e7vyMi+UYzwsrhoKDMMlTcgIZd3+AUwSxwP2S+7R47Glo4MdtqWdebDoXUlWybM07veYcYvwLK12 t3SXt18oKWOvCoAr9ymwnWO9t9lkIujCpj7aUdPPkyZTZFxv5hO7vVnZhKwxS20Gcv/oPEXkZZym hEUc/bq91Uwk4e6ySNRo3o9k7+X2bvEZdelxenYTlYF8N9LaYrUQk7RAbbvoqz61eR+ZhYBFnuMl iL99+1YL/mazlbXd7373O4doIHEzHttvf/u1pXk0Tb7u6z5WiEBg6ome1PVE8HLHOu7bsxO0uFK5 jb0TgBNPq/UchUyMtmIQ0It/vJ/Vf7Zcmn2MEaY5bl4R5WG8BPNpY4Ad9hlI2Odu+uihSauLwqFT 5fUrdQ1ymcCAaaZycZtNmUYBl2CFculdt+L25irlht+MbuPEw6AGYqdiGnmITwPmLhqPA/FCH3uf Q7BC1dPbr8f9wY6byizb226zVpmzWK4sleidS5swPRWFpURrPLXpYtlPPKGNTOcOkvyZMVxc6+li bqvbW0vZj8o5gTyQ6ulEnBXnSgsPASRVxjBLapX6jVV6cMk0SVRp1elREkiSa4dmVZiQUWKkDrSE REProJGvZE/rYSMD8oXNcWOdHA1EZBD1uFXLHNR7l5RrgkuwA/QMpUcFT7JWpjZmjSXwiDLk6Hiy OUlM0kQHwV+eLKtqSzMnTC8Bp4KUb1orG7NpzrAHbNbZ7hYzZaIQoN2pei9hSZ4LAtXttBDFKBlM PnUtgwKpcoKopsq5wMKe7AyhPAJ1gGewiduLrta5tmnaKlgNn5P+eYpmEPGnz4NGPdNEF39r2+1a ZTaLiIK4hBgXClxg9viMomiUTXr/0AcQjztRwdt0sH1cXubqoRrsYtjRy+zq0WvVGORAfaURB452 VqTBJEwOg2lpOHnuKtv7IYrgG3o7qvcFHI3yIIPAGCmawXjV41SUdAm9tGGA9jSri+LCOVXcrKhV +pRzOhvbbEZgWgQpkyjL3GtteVN85fseJjG9/O8g4/xgeRjE6Zh8UkIFOV8ff/cuRJwHmqrY2Tfn vVYvlDDB6EhGJYcO44oG3AQHygea4cL9OMYlo1+DjLAyix+PWv7QfcIkrlktLQFsWZYS26MHyNmQ yUPI+lwDK9BSEpQUCFgeCAprNa1iSkjHOTmP1KMpKb1ZDFCoDDAFssymPnXu3w0a57IrCz1SGt7y +vN+Y8O0WpAA5w3uEP87gc3KrADYmY/FgVQ/KrjmKECAPqd0yp0XiAZYJNETOGRYoeDl9/caSpJ0 18jSM5sVBBxXNAVLx8TawpRvPi1MptOBwuMyyUd5Ho4ULN19ug3nTtP1sCBIMjla20NP4hyfjpYc cisoPRdzZYTR8YaFBNFpstdCiiiD6x8fXz3UsQSM+vfRQ/Apw1bv1R2PO9vtHqyqDmqfwEiYTL5Q EI73O1UIVYlnpUxdA1PlYgcGX159lOu4fOJ22YjrgY0fKhE7eo5a6dxkrZNSkYTsuINOYnXNq8F7 deJ3IVMJYNEEW6XgcOPBILiADAjNITK5WkPQ4JHHn2RQAuE6yYWG7uSVo9oDpWqNrrQ76nh97aUI PQZXH3iM/o/8p0fnfeB6okZvADXGYBenTwqWJ3hqwfHlTKP3EOhC/eh3NB7ZhB4WJWExsylZnign R3H6IidP7i8T3I8R1uuuStj3UIr+Am0vjf7JBvKRVZvKNruN3d7e2d3tC+1jLIviNQrQK5GmJ9O5 jWYjg5CyORxo3liaTG21yIWh2x3Pdi/0sdkNJWCNKejJChQtpUs1FsZvUnhvSVLKATNF4GbV172m spSsFZli11EfS40aeRg3E25UWkKhSTqwKhu4qaj9ruMNU0SODWdoBg14Pe7W7zVNnBRjG0+nAbrj QRPkf1nSkyx1f6wfJrYvD6o+cBr6f9+9te+POAIltszOdjsr7Dx13JhnJd6PUrY3ds5m7KPhIFRy 3EliN6geLGbKVpWB0bNKWpWBYMjULhl4KXRE/6Gxe2jpeIb6tHmwSwdtbL1+a/f3D1aMyarIoOhr 3l30t/iXoBV9KePP9ElXk0n3KbXPz7ACrOtR0+0yaD3mIPrx8wBVrtrgmgiuCRVQwx48+jo1RvUh hGIYImMtHn/bkyTPchzhjIyLl3MCS3eGp4Hd3yFJhjIvwewzG1s+Ru97ZOMJky9HFccLpYlMoA08 CtCREXC9SoQSV0qPlIudYkM4syGoo3DJTRd/LmPP0FyXxrdWsMqOVWLn8dQWr76yRXB2kVcfD2Z8 vwEuzjW8s4sgTlnoDeBfopdFxkHBlwjtnqWVVlia2+dONz3IxQQXcEDFD9utbUrke0tryq32Hj4i ZfdyubIq2VlSn3QeRIhOE3v14oUwW0wMSyRtyFBzeifwBclW6+7hJhuhgR4zYHpL+PshEFefU0vb VBJI0eqLQEeP6xT+EAw4gaOyDBZtPnl0pQdKITeX4F/055nsHUMpryv69geJAVIiv1rdKpgRXAh0 y9US9z9BIpokt2OT2NvN0Q5tYundwsq6tRnwjDQLzXsPxuyrppQhu9JCkGR2PDFUKM2K0oqbsRWZ +1ICWiVTrfgT74fo0RA8HqNm3OXWw5pErZMseSjjyGrlfUmWS/+MxZ2FnveiRRGFci4195+qNj4F pP1JActzmz6L6VPCLlxdHWQ/DiWACH8VSJSqiQO+xDMlL+S6Uq0ffV2lKjGwXFTcwZew7xJ2mlW8 7wVrPaqGXie0/c8JWD6id28/0X6CZVSfPPlKEYmZ11us/R/tv/Yrwhb6YNGD4oK8COBa1Cx46MWx pKoNQZcUnhSarEq65kOvxMujefo6/nk29lF8Nhqv4Kjg7W123ugtfCom3SgCBVPk1hUKmlNlJVCS w1agUMNxuSGzwjGnsdkosTfpWOXRi8nIXiwWIk1jFAGPkWsF0jxy/1SiFoUdBTcgmNBTCv1KOS5X djrshF8qclQUnBLii4QrRvDAURoyZOFppclN0OV7TLi09JHpAGKlP3RwsraLWfo5J8Ol0W6j1nK1 Q0z7aQ2v5+GPJrmOSYS+syhGsgVbFjjtuNRygqoEU0Gpnrp7D6cpqrLy51jTBUzsUFU2SWtrYBxk 9Pgy3Tc/PDwoENNPkjKESlZ3oH79+rUmn49jxqXiLrewPwN+V6l3l41tOuWeZCLKRH2uoNX3snuI wrPT8o9snxCw4mrtypyGJ1pnnhp/ft3DYpUNActwHOZrbkw0ec6W5Ey7om1XKK+6tlKslfvcqv+E 5ANHGe2e+m92gSPG0xjgBu9zPbpl9YpN2Y7npcAU/NMCbcMJms8zz3uJHfd0i9+JkxiffoYeDlmb 1AJwLm59DJ3y4FEyOG6NaZgm00lhVrjzyoeu1l9607RwNpXjzeFYqbR5tz0Ipb6Y0Fx2ekpqtaF4 Lt5dZsHlBihDKWgLDxFc0upho2CHUN2X8BaBI9DADYYMTJtQ4VdfBE310JAnEyKQjJji1ifbV62l TdDdasi6ztacDgoKBAj9/hgTBIcSyM4r6KaTxfC+HBPTTAdo+iBA10YGq27COs5H1pxCpqFJYy7H 63M+sSoZaULG7wudHhytKdegMfG9l/OxZHB4iOZ5YrfzqaatdbQ4G7voHxsTTPXYwrQSlyAHIEJK xxGqteZU6pjY2W//9CdNt1+9eq3szqebiOVV9uLFy2AKcTV0u3huelS+Q4T8+RgXOP8wCXa2SJ67 iYZDiJ7Pmvqq5OMZ/6gfHX54izrMyoUC6vtikndZ5Hh5Flyco0nFOHcfNtLeHJxMB/33TxhG70Bs GoYe/98nNV0uy62LY4uZ2LPv5/meQxz8BlKvK2K/tBrGSU3vIPyoDo+g2eEuBUyV97t8GinKg5qz uMNgFR9vCj4bnL90JSxBfSA7G+tslcLLO1iWzq8Wj1/HxopPv+jNF1/qIdmfasn+0meiHGG8jTpn Vp/sxc1Krjg8tNvdztabrR2OoXQrZpaevbtJMxyi8qLIFdBQ2nRIgi8e79+/t91hr8/43W++7h7E fqfCgCQuVs6/UkZBaUdwibAWb14fO/VQFiksumJ5yM/ZB/pgB7z39geJ/x12O8sge08K9Wppmseq IM2LgCXzaZv6aVqkwHtNbdE2KvH5/4sZel3hpmkbNexj7y3LRgrUfLZmptGMQ9l6Y2lb2zw923xe 2B++fmGL2xshyA8HaEZj++Nf/UH3IWU2W2xvcC+TdXkLy/u8XVVzJVjTQzR8YeCZdlUWB1hrch5a FdfP46N7ZVCN9P//8HP+iQSPYTkWxvTd9GC4M8Od670FvYfiCg06NAWAPlj1Lxo2scNBDEq2pyaV vR/F4yN5rDzx1PYErCNMKl3hMVIy+l5XNzm6okk8/dmX5e3QQ2eYVUq5IgNMSE+Ah0JLYigtAj5G tAnPzOoAe/gF+uY/edM5Qx6FcupY2fv90e5xPKaMCPQj4ASoD/yw2VlqeyMxeLev7Yd1KWAoGurg jQjkHOeBEX8KQHKic0P2JdBkcM4hO7gzMoSRJGMqufGcxO+DiIzZ6JTFAlVOVQCJpZPcsuJWkAH6 qGQ+Xga6qSp9t1h+kcnQeCfADe9vhP+29dm+P4JCT+zVZGavl2MJF1LGum0YsYrBgGPBhhQZB1pS wrrPgRxmmPoF1QZlX8oce9S9Z3/uEES2x355RulDoSnaYJCx714qGLMBIyB7IiiJ9hbt/QYCld2i /NG7yisincPwXMQBmhZYTWQf90ifEgXouMpdyRnNmR8rbXxiSdgng3Gn+qctGqA+ziR6baxQ0sTy hzSyy0rCTsdiP7646/Bd97SuTtrVAX04A/tA+RRKUVfAGrw2mLbGhv5TW6TkqOck6Zqns7XLfYg4 l7CvYRX1rDV8bvier6aUDY7J0pRGDIG+RP+1brFUdiDsWdZfW0TkVAZ6Q5ux/ghj1PKgB5RGPbIz 62NjVdnYy6qxOQ8hwMb0bFuymxoSSeH2WjR6RZb1lfx2uXQFh9DL2dKEr52Yz9WcAk6k/OrcdrwE jBNbn7S5ozNBIDu7e40NFidOudyf6VWVXuZH12sa9jVVSE5ZNNMQJR0FITomhAxcyNxRK90fbD6d KHuijNzs9tIQE/aMiSSO0/TckGg+ll7WaliQBNE+13fvuLsxeMjevbCmOAkrRrbmsAEnWQ+u0DNX b9iXfu43veWRiPfncA094x1M4ePd1AhU8k7OQOXl0Uv7+/2zYA1dthOzVTWMo9370M4+lmOxT8Xm QDmXSgnQ/RC0enBlyKmGfadnx+8B49SfgwvPv2FGduluc/kefb8pUHWCKsRzmzA9ISWOtuXPbdFB tyunw7nx4B51zP19R6nTe1y/nhUYueAwCWoABfpDN8xFf4mJ30/Z6MeRleB6s4PneTxbnTSWtbVL AXHGN+/MwBsFAKfMHegXJZltT419mY8ViCbzpR0eNpaPXWqGI8V2y6dirnxAkHGpYVcM5T6i6Tub YWhAHzWg2wPAMyLYYz+GGyevHfogcGYIYMBEvIkPxMLLLmVFIlXnqjMo3FdMJ5vUCmw5dX/TW/Ps BlAoO32kHEaZ4mFvE2SYcemResRRU3MGCM5V5VyNDCAF5XSWFzruNDHb7RKbzxaBnJ0I5yYmhqab eBoWVmNScQ40nSerlWevXDdt/vBzx30blXwd3CkJ6wDMfa51M0TTO+QHzKRDhWLr6anXDALW5279 znQ2W0KOX/6WU2d6pQM1noPaJNOZYhxhBtfyy/EzIjgzfQYZHhrfg0OLr43lWp/1PVbnvCzb+szP B44fH7HGC/v0qtBv3rAHVOhyNn0G6edD1kcRha2gxs/JHHx1FJ1khMQ071FbxbkMpRANCs++Rc8e 7FuER1xesz/XRlbyfrO20+Fk329Ke1jvLWlr+2I5szfLhTKMb6udpRPvd0C1OY2wuncNc5DgWhQo uyRr7FlwVKE8NImVZFDnxsrD2kbrvd6nkzivjjYdpfaFMFJkKZPOqBV3HgW189nevX+wvXwIE5vn yGY7ql5mFPnYfthsZZrKub2b5Hpvmvx3L1+pbYBsy5zeD4F5t1GDe7/dqgf24tVcwF5lcG1jm7Ky h53zGAlcbe2GGuWhVG8UBkBbuHaYYC7AKY6l3hd8WUbmuN1oUsh7oPjwflfaGfsxCMzTWeeODY1H DuhxOBS08rsFXcOc57OgSFl7/GvenqC/qDsbE5KQ2V5Q+Z5SM+n+7nu+Pbzo8evCXP75gNX7j304 tXORvX7K5/sZy7vOdWwA/fQH0m9mlAd8xOzKgtbhpuIrfHuuBo6qDxe/MQCxRmHB4Xs9FVmGPbLh Zz0ftIZp79PAt3CegoGk74/LO8fXdOPhcCwdqDJkeN0UUgRvtND5Ohdcg6zrcNrZfrOz5nSyuaZX vUCbGPu8nn6CsDuOvwJJ/ctvXgpS6vzp+/f2zduNpoQvF2N731Q2AvOTJtI6QzJYS977tattEFAm ub2+XcoGi/NAlnF/bO2lozyUnSMfRIFJE/99FWR5sv5ajOUtSYnERNUJ0PF8KjNS5srw4mi7slKD PJmMbXE6qqEvClGe266q7S34MDzzoBShIsLYHlONIPoH6gzDikmWWJmYgJsP+71NDwcFN+2vJuRE WM+OmYi2GUqrtdVkSDX66WbNfC65HHfPzi0/z5SJicCdeRBXcFDDHiJ3I84hU0R04NWDE+DWy1in 88Q2g1+bD3ZZ/M70KkiIgKDU+yhM+Hs4GDpAlLy5O8j4nwg+w88Pv9MhKjVoe+L56VTAgkTytb11 33v6GBT6/MTDGyVj2HgQA18tROJuZEqaLaF5lEEzt4MPRqZcDCcw91lWPEnXJ8EzokHjbnByHnOT PpIGfegon1AF7elAV+TwJ89RzBQ9a+qGFQpWwZIsrE4XGK9uHNx76XRYLEqOGgqR2fb9vVX7g41k 4R5vZLPjbu9k4dwxUaNiaiOwWzDMf+mMK2bSVW0/vF/bt9+/td3+aDf5nT143SsT1S9ulsIjUQ4W +1IPJJpQTTO3Vy9figyPainlIZpYZQUei8BEdukaaQKeCuHvlu1xOCT1DpGlUYjwxr3YAGi0hDYC fERlMngons1259Z2OdNa3HjGltfwMgMAmSGCXKddnoasT2YQYbzPhfSgAu81UZAFOCtnn8wJ32DB yN7SM9guAksiAcC3W+SkPaND/HBfnmw+S22CtHTAs0lzLPXnye3S6ItBbaH0pJQdq0+l/pqSgJhQ XPpgPk5CLpODqAAsOZnutZfGL/o93b/+7Plz7a8Wmyt7LZoAACAASURBVCpQe4a/233U1Uf7wGm4 B/FH/bPDtXUlF4Oq5AJnMWhFQNhzrOz45l7K9CcE0TGXFg4AyW4PHCagh5DVa5xZUjOVae1YVgLW kRmwIuYj5GUGmtRdcyqI0z+qcZ8OGI/T2U9vUvd9sKfGsJeN8vjn6dgeMqfuaMIvhcyKG98F5eJn cd7oTThn8XFgcYIplJ2qPFh1IsAz7Zqqkb15uPcMIh/JyDP2dTjnq5evbcYUCSsq++U3Rw2gXAln rbZKllWZZWN0qWa2nE6FRYrnU+4xxdSK6Uw9oTcv7gT43BDIKrNF1lpSH8WPrHFIhuaDV2DTWtGc HLkPURxKEjQdMq5jIzXSqBzLVBG3HeAFPPCoka7Xa9sfT+pVlTzwB8wwRmqeF4vSkuZkv8EFaDqx utxLhJDjYgoIJsvR4WCeJspu0t1OWKgk37vdF56Eo5Hty20gb5NkMYnMpON1v93b//yXrf3mdmb5 qbX03TtNHsd5Ksu0f7lf229lJIoSRKrrer9eCyBKmfv1tFDWhmsP00IwVi3lcEHfLkCHPml96ifh /lVYZAPk4dohi7jh183hJS5ucBa9COXfwDvx/KhTbokKqrpDXBGDHiD3rJRaYhsjtuOdK3k6M0Tx 143+9m//TtyeyJpGKmU+n4mgyI4tFvNOczp+0MVhduzu+Hzzv4EBRB8ntfJoLE+mgWtOdlbq7uNa f4DbY2v7DHVKLvRlbXsdRD8Enrx6dAavvyz7rvtinTTOdbT/wOd007APaL8/VSrG0bRG2APiZ+Qi ghh2N6Gnyksn9lano5DXx1Nm4+mNTRY3DlC8e+1nH/UDiLFBcJC3wpnYg9Uv38/yeJ8yprKbl69t d8qsvd/by+WN/f43r+3NyxtboRfVQjqmP5dLISEvis48YjafW3Z0SeLl4mzz3OxmMbXFfC7c1mwR 0Oh1bbc7t/Pi/DDqX8yX7hN4PttcZaVP+pA1oQeE9PB6X9q7d/f6ml4R+8Lt1qxubbW6tcVkLgWM Ik/tdjG3m9XSthugDrPOzVpie6mf50g5itSkm5tbZWrRIVtTREUsPAdSOx22dpDG/clGLX2qyt4m pVk11XGd9htdR5RU33//rdXIFxWFbbAnIyhp4tragf4cAVo9sMSmwC7gm+Z9O+ETrtbgK55a6G1k pU4Lyywi1nu9q8j00Lk9t7ZvMMNAHZV7eiBlo7/Yl771329nE8hEChI+vKDspjR25Qw3KWEP2tDK GDmDnZXdlRG54PFZcei/S0A8foD6Zllfh4YekmMFukCmBnxAvndZT+jLZJlbf5EpsB9USVhAAbxj ZepS2M4e+0c/Qs8GK89PI0Ho0z+n6zt9ZIsBKRJ/3TWoJ2trUki5QL/pUeDr/+9jdzSZuEmnNl6s rFis3EW5K40Ty68D6EDZ4s+xEXiKLLVXlH1Jbofbyl4sJ/ZyORX4k+tdHqMrNIhvt10TDq12QUTX 9EZJ02wxztT3iosDbQNBEjJoLGPJt/C7eyZ285mVCPgFTFUHuhRZeGLbEpXSxsqGMhO35UYDAReb 9DaFroNwUamyKoLkIctcGeF0EtGZ/VYfLCDQOfXiL2apvb67la8f13R/gtpD5hasx1APPR1lNbbf kxXtLcV5+jy2aUJpn1jF9eQP9mHlQfCPUV7YMfAvyfgWtysbrxZ2Kk8K3CQWfB6eiRKb/OzLPcAb qjHvIFdneDiotsM9gk+D/yifzMRKHCHF430MGH12IOUFtyttIHcuP0SHSKBArIAlR5kQsL766kvJ qcjqKMg/RORs3896entKH6efBgwngPSpoizL8OSEDISyRv0XtN/RfCK1BzbgKeen9p+GE8QPnqAr Z47hxLGL/0/IwF4TND8tu+s/M2K2KFlI1TlmvqYXEtciAKNInTObkJVZCEBxVYvihRC0JyTTo6Vl Y0xAHS3/iffIL77FxF4yMXlqi5cra16NFEAIEukeeklux/JoIxRBodMkZCFuBU8G2WZQWPy+JGDR A0rIGg+l+j+SSgm9vyi1G1HkkSAvYjLuzINBAw7U8Ovoo81ncwUfDB+ydiQVEbIrCMrsk/fJksFi 456I1fHo/wIMRQFVxF80nrwRT3nDFJTpJBNAyrVyv3Xl2gT5Gp9Ccj6Ense2bNRYOz5bUyEM4JZh VCQaUEHUJqtK0Mka2WIxU7B69duvlVDsv/1Ox7kYjxVIsxkDg8lnX7UhfEC6c2pLxN5qwKKRfjCN RW8NtQvMOsjIMm9jeD/qMxb8mNxIhvls5TkyrPspt3K+6Et4e3tz0bQePog01RwP9KEd+PQde/SA D2KQshSN/dH2PmuESx1Mj4sJyMd0pJ+b5F1/7nV51h/JQOBvsF+Pfu9K2yqqRDx1vBHjE1/vkhq9 KwgP7D/8wz+qzKAMV8nX7rWaSxYmCLUxwmZFpkyfTMaylMLwIR8t3NTzz5g5fdLGMddoSLX27eZg 3+zO9t3JzUP+uMzt69XEbmwkK0DKGLBa59xLtypLbN/W9t27tVQ4ydJuxyMxiKuqtcP5JM4delmU h/OC1kVQnR1mokEFNArJ6d4CEY4IYusA0DfLmc3y1JrjQZnMZL6y5csvtWxpYSlm1shR2d88YvRi vyai4INTWXCppldW23frtSWI8pEdLpYKOMgHbcuDqEeSEbbEJqPM3qCYmrQ2njro1FmsTsvRkEE6 aa2ywe3xZHn+xvma06nt1murtlurD3tbA6blPpmsLJMy30AL7tkteAp0EzlvvGsyrazRJZrU9Jel Wmu7ZmSnZGRt8A8cloE/bovDEl29wZ5dVjwqCT94GJ+ARXpuYwWRq6/MJfvRaAT4+Wfwt4/yGfEC gNtuD3ZOuNlmNhm5XbeXkcOGXfjfxSj18pz1K8YA2PqhxEgo8x6Bf33Yj8/FZfYpzmQQnhoGuOH5 7aeLfdBzsrVZWSLMtw/4H+8peqke1DHrWtKzs1mhXk9L72eVWj6ZO/3/V7RJBriu7V1ZGS5d2wak Og5FmX23O7lZBFkVjsjSWgoLKkardWsH5IKlUnEWKfnFamHnuvY+CZrw51QZi9zA8zxw/FyviqEE si2eAYGG94UDnaot2c6xkh7UZJrbzXQib/K6XmhxZv/op+jaMCRCHqY52fGQ2C6FC4vKnveu8vZs JXLZ6E8ho8OkLIz6URvdt6WCEVCUtiJz9ukhB120ULAIqBjFIuA3tQOqp1kAXspcAskWKg1Xj6X3 O85SmXPMujaNI9sFdAXYOs1sfjO32WJsWU4gPVqaAEbNPp5ZBXaHK1V4KY6gpOMBnVp3xjPhnNq+ zaxOc5V/Hqh4n59z0RwiAS63Z4GjP7XngfY5F1kM9nEupUxAZmh+u7W8f4ZPKl1OhR4a9X6aQSDF Mbi52pc+mjzuNV1nYhHUGdFd58fZnb/5BTwhNggfTwp79qRnT04PUdKqfx6DXDtOVXJdkvpkjAzX bwgX7WuaIBioPzEAOqVEZYHKElc0mE1vbDT+9bjnxE3WW1Vj3+4qu99X9t27g33zDv32zGavJlZP V95YRoYYFU9ZtrkWeVYgfV1YnmSSVeZ0/WnrDjmIzvHQ8gDvDygTtDY6VtZWtRWUizTN40QQjh86 WYfKRs3JDmUp6s8pze3ldGwvFjMRmrf0sEYOfjyfmLCV4hZSOvLz4wERQMq3IOAnuo5nEw/bg2g5 TGPpN0UjCu+dpTJZFcoeZxrKRfiNA1NX+rQ0ywEIY87B9ZcSa8hqFLRAtGsgRk8wt8Vkai9fv7bF 0gGpCmSj3M7F1LIpgn05+stOE4rmsFcYJ5m14iwto1g3wCDzP+BZUB01qZ5M4XLCrCBAo8YwsgRF 2zSzoxrrju37c2+fKC/D9jnBy/svOIjsdrtOY+dPf/pWD93vf/87J5jmzjTn95ZLdIbQIYfI2WM7 hmqbXrm6amecSF5I2jwKWMNYcd2UCv8ECIcmaiGARvBpp2sdVU/1sUm3krsGPD2pQqBMb530aPmL c/gouXOJ2NevXw7gDjedqoC/R8z4XKfdVSX3oqVU+8oD3V/gpvnYRrZCMPlm19i7+7394z/+YH// 998r0f/6P/2VLX77QsEJ96EJsIfCl5PlZGLprLBm6db077Y7+7sfNvbf/vkH9bH+/ZuF/fWrhfBJ 52Zp1Tk15O0nHYshgEaLwkoC1q60f37Y2E17lKMMgnj78di+nM1tjnRwktr76mxrRuZtYtO6sXlz tGUxtZublSaYGzktuXoB/SKRiYOabmssGlOb5ksNEXbbjR0JOE1ls/HUm/UEPkjPh4MWbEQEtbaR wckQmKzRVQiAOxyQcw6GGrrzJCbomcyoGNvN69f2+uuvbXV3p2yt2Zc2K6Y2Zjo4X9h+7FO6jGdI OnSP8ytK1oeHB0E6pPnPMUyn9s0339gmwGG++OILe3iPZdtJkI3bl7eWr27NpoCTgxXZT9wetVo+ YeT1cZuvwf8+zpcLwSRoQHGgGqmPXLOHCx4hDKTSaAHx83/6p/9rL1+9lLgbK8pXv/lK0xxWHgTc xuNgftl9RnRo9nJKBxwj0zAqxG8NvvdUWRhVOlUWdBrqg6N94rArlDHhiPHjnDLFJ5xJ0iNNYjs/ lqTd24UGue/bpSehswFCwIoBMJSOCtZtZsV0YqtiLvxVzM5+TRtKA2RYh3Nup7SwQzOyh52X/Wiq uyRK8MAj05BDTaZeHrimWC5j1pDmlZXJUXidSsIVyJfkEt1jiqRMVLK7EJ99YusOzc4fRDuLxUXN cSZZMo842XoN2+IkGk0FdxO3m3Fuq+VKU8N//n//xx4e7u1mdaNGvOvl14JfCKxaVbacjDsAK+Ub +0CFQM9xnE/t/WZjD5uN9LG4pkem4Emq42BooAl6yJKk3y+fwB7DlqQuKOkqCK6ZtnpxJzoQQee0 29vh/caSaWHJbKZMTIRzy+xgqSzvcSmayMij3zhHojvp3Hh/jK9pOXB+J9OJguvhUOr3R8jZGERx SNi+T5+y9eICnnUHzLC+5rnW0AKCe1CxEHVIvbIoHfT4c4B5qAR6Mgt5tpp8ahtGDDBcM5tOi6Ce yY02cqjEZCJ8F1mNMqdzq6+py3k1rjQur8qNABoZmUYvk5SGRqpB18vqe0MXexPJxcNScLCbw+8r cGAmGfpYH74KgbygStUxNp4pfkj98zId7zBb3fRpGDBDezdowEvBEghDtCrnXFFq2EgP+fVk8Ney adBAlpHU1o5aW01Tu7kZi9u3mlGqNHYq9y6RE8CJXOcNmCg5FrW2mk3Fy7sbtfbvXhQqb17PcjcR HRiAKKpHkcMBQ4Am+mqc229vlzZFJZ5JIyBEy0TAfr/D+LS2m8nY3syXClj1YWdNVrifYYpZApZl t7In84WhF/TzTgP3L1ZeaNfvnFeHOSvONwReJpTYbwUzV43m5Wa0E2/QBw7eNnCqJDAND6psmhSG u0c+jqeTkPEqPY+17e/XCoJnRDFPtR5mqZ/iyJO60YXM7ga3oEpfghKZnxKKvh0iy62gZca/i8UK cXNLp3OZl2QFldKnLZDqH4oQbeJNauIr41rUU13dAfArwZr+olgH7VnZM6YcJC9dZaJjdKDqyMFs jnvgBD69fd6YMmZZRWCfxw2jTbYuwQkP780NPLgIRAvI2ZCJucInFrn+ksgG71VBB7XdB3a9Z60P GueD2BoNLi+Bno8DzfD/53NujaSL00HAehrrFQ/6wqhC3wvMeE1JexvyKEnteuC45bjpa9QcoyVB CBMF4tMQH3/WTcz9trXbSWp5m9ndMrc3r+f2x5cz++r1Qg+UG3KA4zkLE4XwHBQbzCd2h9Kau1tl ADe52X98s/D7KUsVsHr98sCbjBzWwYlg2kQGxM1Pg1+LJllaVdl392tb4zY9yux2kturu5Xuve/Y B6RS4AtO5nZ7dyvoA+8LRMEzP7K6MB2UvZi/5/5w6Gy35BLDTtBm4HfpJ+FqdB65czd9IoYtqKq2 qMxyc0dbeu9liYRNIiHPDTecPSM9g9rDZqcSFvyVzSZC/ndmterHjdTD09Sygyv4JjegU6WeMXpf 0fSX47m9ve2fP1yO2sQO59SOCe2Oz7OFY3/5HN5nuy8leMBe0B+cBENigidyNGTkglbB/6THRkCT hwJKrG4GS5+SbXQM9k9qAHYF4jAV+TwQpW/BAqkbqV7eUJEoLaXNIA0yUOILPTDnA9Lsa2tWsVar ymQacVleQjkqvB/dfi5GSkf75O9fBqtrEGbGChrwN7Fp73+ef6+nP5sHAIT0MHNwWWZlXcHY1W9i 18fP8Of7M9ACf8zm+5na8uZGBqSLVWWvXjb2n//D7+yPX72yxWyiifDmdLItjd12J5nil4ulZXUr QvfD3h12lvOZffXqhZdhYRoY1Rt44GlqdIGbbUBap8Sjf+qMAg8yvH56bOycg1/LLG/BhDm9hgyf gCW8G9Zc0m8f+SAIoKgy+pPVGhQ5KJWeFNNdrhMORmw1foXl1vcBqWBcfFRJ1NZCVMbclPcAqiAx Qm+XCN4jGkyQ6db97QBYdzxPRfh+ePsgdkCxmNv8ZumiTR1p3mTKAZl6yXkmAA6SIpXBQZqHz4xG stHFJmaPIEU8WEGef4om9vzG6wmawpyBYkd+WjSlTBg8yfmMXOVhnrqAokpGentVY/V+bYf12sYk AgTe8PEjvNZOAX5B5bXbl9o13hjQX0/Cvdid/u54tCVP/0onJxyD0wCi8Aiyf1nepaPEGJhRhrF6 G3QIZU4x6vdo+48rLHz46+7oLnBWjwOQ9le6SI4ds3MxwLt86oUNeVRwFfFxsgPmouIF/TyySE2R 8NuTV6GTxiW/25lcRtPWJ4/6yc/+pSKdIAS7ra3v39mxOltbtbbMG/tildqhOtm79/dmVeHZD3rj QVaIAAbokUUAPXcmc2yALr/5ThfeXZdEg0ltd9jZvtkp0KzS2r6FT0jZV9V2e8KlyY9P/L6AvBYH jgleeZDUdJ6OxV87Up5C3UElNHVlVIIhvahDe1A/ble7BVyCuQUk53Rks9HZbu9mtri5s/b7t5ZN 5tZUR+GrAHGSabXZWCjwBkmY01FwDwKPq6VmbpKBHLP0493hSOa5UVE2NKNzpJHzwkY8zCzy47EV s6n4t/TDdGxyog53oeYxHcu3uz401wnCAuQGC7t+sfV7g6KLliO9sKc5rR/fQNpDzO5EBgPJ2UHi 3qoBw4XKYrPbu/krWRj9SOzrcQp/eafATJnsTlh0hkJW4HpWZ0VmmmGOcfEUXNMONQ77vs9zh+AN 8AHtJIb+AUD18qGJj1u/UrpmvCe0OumcfJQdg2qlSJcDYvF1kPmxkIxuKnk1wRj8RvC8K0WkxYll uUzUD3CsWb/6xmkjK7333iI1x28mblBvtocRtjA3rjHGxEgyyYH6g6MxAQpIQ3nC57ix0XliSeoL DJ+N5Eo0oGWVR/COlLuTch6Wu2HC+HNMe4Ybagiy3Toi8ex4svl8qkXxvslscmrsxTyxV6u5TTP3 iVHvKWQowAbIeEA2qymMJnzVqu80HY/0gGvqvEW07mDJ+WTJBBAqJQfwCDcvjSTlHrLiiwL/m01Q ynQ9dx5mrgX7zBCoFQsBqIIbpCoYsC9kuOpZuQAdUz14sCLzM9XTQ4WsNhVDLm4jP6M3RSbFHyAQ 7nruWSilnj9/ToMjjSBg7fYHtUOoeqYFckKtsFq3N0tlnGeGE7O5jD5cQ23AxQ1yRXo+VGb6cxWN UyQsGExMolzNEDIE26BsEynEcpAXRqufsXWN86EkHX2q49HL18AS0ESU4MPwZML5SyzDWYk/Ua9+ MAmX33sv/8AFINPCVZbUzNUA5hPnVHUcoU6J4Wokd7ENsU3xWz0cIQYoF6sblI1R7VPf4iGPlADX 1aqw2daP+/fvkPrBiccP5XmE+3MZ1FOvCa/sNJQOh61Gw9stZaE7pgBTcBDowev3s6fCrGrqJ2iS SInHCjQWUplPkK8bSqLoNgEIDGaoLu3vbHlvyAd6yxGKi9k02QZEeS3cEIvNWYHRbL9d2/LmTuNu nwRFwqpnGvl0plIFQvTPtfG+9J82+6PtTrgjETxyk2va8Wxv68SWUsHJ7AYSc/C2E3euOoUpstks YJEwqsCZGaE+kXtHeAFORYxWUNvtPbA3iSUNnKZU54/MBgyVlzI9pERDDPVi3CFJpgthI9DRGGcy R3ABiwt+kEUA52yp1ei+9MEIXUeuM70ySTZrnx0nx89VDlGaURol7owTgwqZGlsXYMCPYeagnx/t 7f1OP1/OC1stJurdvbyb26s3NzZ9Obd8casmOJ/bPWGRRqfeXhzkeA+IbCQa+cZsyhksYZgTzg97 BfQDZYQ9bIALzPnnb93knh4UfUechvYHB8aGPmQ6nys4oT2G+a67X3/4U0deJ/t0o2yYECBBApxA S4NtdqVtD1h9p7Zk4hcmU7E53D/TQ+XA4cPu323OwWMvwhGGKHQhbMPXw1ZRrC5DQHJOE9/3JrQj cr0RCsH6mjZz/TANkebX3/uUTT2L497e33/rsIYaoTVHB4NfEWEXnfLjSdgyNoCh9/f3HaBRpqeT ka1usHBHttd1w50A7L0WyQtLy8knJTo3SoEzsxlGqtz0hW4mFCkrEPLHgyycKInYJ8qbV2/eKPND Qxzkd8SWMdX68vd/tJsvfiOX5p9LwYHz8Pbd2v71+419+3Zno/FBcjLoWO33Jyutst05sYfp2X64 n2jfWQhZjQFmagom/p+bS4A32+mhd8OIozo4btqQV0cr6qOtdzv7p3elLWdzNZHhAW7WqVWFZ1hR JDFCbvga3h2BC/Bt3IA61OXOLD3amZ4RpdLtXLhBeQqeve8EJSiHrNOafff+3vanxvJiKg4kmFIt WKdKLj5csmK+tNWL11qoDltwTaUyjGjppnsiQBvorz2sN/b+oZQzzqvbhf3ht69ttSxsCY1oPrN0 jEEG5SKaWExZ/ZHBTJ3JY5Q78+eTYUYU23W2ST8V1BWTtBOZH+cYMZeSLJ6ERwYTP/mG8MHa/iCr tqosbYTaBYgApqZgLTtbsceKwE9tI2nSBC2aKL4lnjrea2lqzGdk5ROmEF1r6uKh13f6Qf6TgSA2 3t04NXzL/+Hqh/fopn4hqyKYaWXL/KZTtlAHVXSUHoK8sGeKvcro5cd/Wk/r+S2WgmhwMzGqbFzk tlzxoDgMI04JCU7igAkf5Bi0nleIuwk8MAwnwbYQqMaaJMWCO7rlcoO5qy9g2sZGWSFUcsHoPZlY nuVWTOfKsDiS8epFt4qCEaPkIRjwYB62W2UfGhaczzZbrSxHNdN+no3P3R9O9t//5hv7H//rG8ki f/H1jeULz40Zxx+zka1GmW1rX8VrskRUaCh7k8y+37pr8mG7ll8g425UKW5XNza9WUg6hckzfLs8 f3AIgErwRj5+UHgIWIzEyXg475FPGD0lwVMdoTXR/J7Ouv3P4Pgxodzv3IMwzezt2x90DXge8pYS 12w2HgmoyfdB0rejic4pWR+lGwvVw/t7UYfIgNDZQrfqsN9ac9qrN1yd864nF0tW6dpL9WBkoyK3 r7+4tb/+w5f25Zevwt2X2fHoKhyn44OaVFQn79drm89mshar4PhJYWUcWhSObxou1LGp3oTSD3Fl 7YlMMjzwxZz7KYfzT9rI5lh8DqVV273vRzG2YjFzg10MMWKg+sz3HykkCFjmo3W68YJoRmcXIXCR +Hii5Au/5yXicGT/1O/69KU/ptgLu0KqP9qCrxsFEdQMrHODUL8SMteh6I0sAq438gEjePPHS9Nc 7gulBGVJQzY65kadBY85p0/4EcVj88wpgkIjIVr9Kxf86YKRgmzXHHZCddvw+5QSPHw0kilnKEvc qp3sJZ0EE1kC1vJ2cPrDMZ/PNmlqKxYHS4Psr/aM7ANS8M+UXSF5/f3btf3vv//O/vYffxA3LluM bXweAaETbKEeoTxgKl1RI5WUOk1kOKR1Y9+uD8oM2/3Gjru1HkB0ytMzOClXuHBQbzg6DEzBDDW1 5FZUJgZeJ1kOKgfcLjRuGZKQWQpjBewidXfveF20yFSVbMH4HcpB+lGrhbvxuNggC7tfOxaOGcoP TNF43tGDZ2ql61bbSeoKTLdcHZWAZfXJe1/FVAEr9pBKyO2oUXA8EzdN/frLV/b112/s7u7GNmsU KsAjer+TLI3ykm2/2UisMGvRc3fiNbcWAYzF6jKj8kAlVQwyKYYBdLqiHLcjMYKO74+g5gXSe8sQ gT+ou/JOUJRoi+CUzX33Eyh/HZBB6FrhV0KWxANAP4veASPeQLCNN4y/JnD0dOFJm2Nm5an4Jbrb 5YH7TCv0sII/9OMJ23UPiowqtUmWC4nMzcHUUJgY2Q359MW1SmNPLLz2Z3ooKSNYlV+9emXlYeOK iTnBatQ1NvsdDpQfoZYvQaVRX4hgdShRxKwG7+V1BeVu3eAo483J2KNoWhRavVfVFhip9gFL0sFP bRl9nekFXfvnyqziRpP9hx/uxUeb5WebT0dWpAizVe5YA9Yvba1gYNJW1gZcEzb0DTSnppW6KBSa CdLIiNGNxzafTNRAJ1iJDEyjNnMtc8rp1TI3K3B4nkmpszztbd4WCgDoqpVI9pDB0Hckewoab/O5 c/Eingoc1aE8CADqOnATS8cTGwdb95WoY15qU7opS8WFWhPj2kqN5xIF0Ol8aSUEbFRNUYYt4dCe qEWVCd7e3umcMWQAYPr2/l5l83jc2E2W2Xw00j12c/dC99t+hyS2L9QeWJ2vyD6QSbmmnfcoeR7I 4KMgZ3/PeVdBkjBBv0pVQWCQXG+f9cSoX9aqlG4PR2u2O+dMTsY2eflSC2XUQf6pC+QT1JzQxguO u4QDKeIETJAm74EB7ziOYIsliEE34rt4IrrMK6amKqp7cXwv655A0Mb365Gm/m05iDA1TETQZMog Q4vMbbqFzdKUMXzeJ5ykj/WzYsbmo2hQ+ppXabOL6wAABExJREFUaPV2h+HLpr73Syil3W6J17m2 l6d9fP943GkMznsI2a/SLoJm0YXad8chUCHniQEf/9YHcdYcPf3xbsPPHaCGGw8LSpG/v5vZf/0v /9bu13sRlv/m/mTrys0gFlMkceb2xSqzNzdju5tk6j/JvBRKSpLYv3lzp2Bwfy/mvOzOocV8+eVX tlqt1DPk3IBAn+wO1oxaa3FT5jGgD0Y5nFY2XSzk75cjfdy0tpjNrK1xT3ZddjXcZzNbzBeOE0Ic P2Sj4KpQK+WaIN3LVHoP1aY8KNtTQ38y1cAkmqTy+vU6Uy/onDKx5J586RkWRhmg3XcbW04nUi9l IBCz7ZPMUNHeH9uYLCutbDbxzyAIj8cze/W66Br1JsGHvpqJg5ThvRszt+76kFHB70QQj1JSels/ sty73tgXruF6o4Y656RAtJFsSuaxP6/80UXAGjatu3JKDPjKyuqof09HejE+ZaCxGEF5gNEmWHTL Ihxe1tje37/Xg07jOb5/hzxXCekdQTd5FBGv8zmMD/bwUetR7Z658H40OrG/ahoeDLoN4TUhaH4K 1qrbt49wovz3nNOVn+kFxTr/aSsyUl83nYj9N19fyJLIntQzUHYWJzdOYyKYya6NYBTOgYJV9OFj 2oUueF1CtlNA+Itt4H+ORwBPtqJ0erW0xWxsyUNp/1qvLUNogTKaxY0gMAICUtjNcqWsihUfWk6R oxo6tSbP7FwfgcUqQxH0IlCyIpK8t2f33qC3CHxailYWmfhsNJEjjy+sE6PlryA0hofHYgD0wjNY giRYKMrUtEACGRhPpt6Z9Mnrk23WD50LszLqUGpxn/O1ynz049KzTUaJjeb0x1wumWvWrBYy2JAS BMolTMxQS0AOGono49GqkmFKbUW+dH5d1D2nyfwhlF3yzH1Lqd56+UevyhVBnZ3xk4MV0/LyqCBl KGWgWMExg+2aOZwm+M/9tM+5OJqPkp+9wCqhBBw2ekDqmgyGh3Vsm822mzzA/CbT4MaczZwrqEmJ uFVjScb4A+6IVoKaxvYJEisrG+FDlzyFzwrfi419/ddhD5xzZJSp7aWtlPhq+7GT9CmqpB++oKHW lyjch80mO7WHzEfmarh3+vUATskS3XmY8s/7YzSDHeFeB1G0CGnQuUpdSE0B61xZXe3tnBUKCD/L avmZm4YQTEQf1rpxJ+lIZRjNY3ABd7ORFdPMGo5TgZmmqQNfNSbAvAFXmZE7zgjKoSnqTP2hh91e tKdoXBqnrDK3QLqY8mdgYtvCFJBKAWYmmTCEBHru0aYGFuHqCe3OFVwhMruPn/fRKGMIWAYCe5S4 mJ4s6RzYye9EGpdgCsFYJSLBZVlGZtHOQGgqK9K+5oXlY6AvtVoBQtS37qRTARINKpv0krNxLvkl AnjnKtkNtezTr01srKtX5TiySDb/SUFEfaraGoLVoRTgk2c6n+HKNPGG+s98L8aKhS/+P0fh/gEm cgHSAAAAAElFTkSuQmCC