20260731 12594200 1.0 FALSE 002 view_sg_flooddisadvantage 54455.171000 469467.717000 530297.000000 1218557.740000 1 Projected GCS_OSGB_1936 Linear Unit: Meter (1.000000) British_National_Grid <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/3.7.0'><WKT>PROJCS[&quot;British_National_Grid&quot;,GEOGCS[&quot;GCS_OSGB_1936&quot;,DATUM[&quot;D_OSGB_1936&quot;,SPHEROID[&quot;Airy_1830&quot;,6377563.396,299.3249646]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,400000.0],PARAMETER[&quot;False_Northing&quot;,-100000.0],PARAMETER[&quot;Central_Meridian&quot;,-2.0],PARAMETER[&quot;Scale_Factor&quot;,0.9996012717],PARAMETER[&quot;Latitude_Of_Origin&quot;,49.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,27700]]</WKT><XOrigin>-5220400</XOrigin><YOrigin>-15524400</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>27700</WKID><LatestWKID>27700</LatestWKID></ProjectedCoordinateSystem> 150000000 5000 20260731 12594300 20260731 13023800 INSPIRE a23cd103-7381-47e9-841d-083c9aef171d dataset Dr Gina Cavan Manchester Metropolitan University Lecturer g.cavan@mmu.ac.uk School of Science & the Environment, Manchester Metropolitan University, John Dalton Building, Chester Street Manchester M1 5GD +44 (0)161 247 1571 Dr Gina Cavan Dr Aleksandra Kazmierczak Cardiff University Lecturer KazmierczakA@cardiff.ac.uk School of Planning and Geography, Cardiff University, Glamorgan Building, King Edward VII Avenue Cardiff CF10 3WA GB +44(0) 29 208 75281 Dr Aleksandra Kazmierczak GIS Analyst Scottish Government Geospatial Team GIS@gov.scot Victoria Quay City of Edinburgh Scotland EH6 6QQ GB https://www.gov.scot/ WWW:LINK-1.0-http--link 20260731 14.672 https://maps.data.gov.scot/arcgis/services/FloodDisadvantage/MapServer/WMSServer? OGC:WMS FloodDisadvantage OGC View Service https://maps.data.gov.scot/arcgis/services/FloodDisadvantage/MapServer/WFSServer? OGC:WFS FD:FloodDisadvantage OGC Feature Download Service https://maps.data.gov.scot/arcgis/rest/services/FloodDisadvantage/MapServer/0 ESRI:REST FloodDisadvantage ArcGIS REST Service https://maps.data.gov.scot/arcgis/rest/services/FloodDisadvantage/OGCFeatureServer/0 OGC-API Features FloodDisadvantage OGC Features API https://maps.data.gov.scot/shapefiles/SG_FloodDisadvantage_2015-12-23_fgdb.zip WWW:DOWNLOAD-1.0-http--download ArcGIS File Geodatabase Download https://maps.data.gov.scot/shapefiles/SG_FloodDisadvantage_2015-12-23_gpkg.zip WWW:DOWNLOAD-1.0-http--download OGC GeoPackage Download WMS 1.3.0 WFS 2.0.0 ESRI REST 1.0 OGC Features API 1.0 ArcGIS File Geodatabase 1.0 GeoPackage 1.0 <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This dataset presents flood disadvantage for the whole of Scotland using the 2001 Data Zone geography (6,500 Data Zones in total). In addition, the dataset contains:</SPAN></P><UL><LI><P><SPAN>34 vulnerability indicators (as percentage and standardised values)</SPAN></P></LI><LI><P><SPAN>Social vulnerability to flooding (standardised index and dimensions of vulnerability)</SPAN></P></LI><LI><P><SPAN>Percentage of households exposed to flooding - provided for any type of flooding (coastal, river, surface water combined) and for different return periods (1 in 10; 1 in 25/30; 1 in 200; and 1 in 200 years including the impacts of climate change</SPAN></P></LI><LI><P><SPAN>Flood disadvantage (standardised index for different types of flooding and varying return periods)</SPAN></P></LI><LI><P><SPAN>Geographical location (rurality, coastal and inland areas are classified)</SPAN></P></LI></UL><P><SPAN>The dataset was produced as part of the </SPAN><A href="https://www.gov.scot/publications/mapping-flood-disadvantage-scotland-2015-main-report/" target="_blank" STYLE="text-decoration:underline;"><SPAN>Mapping Flood Disadvantage in Scotland</SPAN></A><SPAN> project commissioned by the Scottish Government in 2015. The project aim was to provide a spatial assessment of social vulnerability to flooding and flood disadvantage in Scotland, to enable local authorities, service providers and other agencies to better target work around flood resilience and response, and in particular to assist local authorities in meeting their duties under the Flood Risk Management (Scotland) Act 2009.</SPAN></P><P><SPAN>A detailed methodology document is available. Please contact the data owners if this is not supplied with the dataset.</SPAN></P></DIV></DIV></DIV> Flood Disadvantage - Scotland 2015-12-23T00:00:00 2015-12-23T00:00:00 1 MFDS_2015_all Kazmierczak, A. Cavan, G. Connelly, A. and Lindley, S. (2015). Mapping Flood Disadvantage in Scotland 2015. © Scottish Government, contains Ordnance Survey data © Crown copyright and database right 2015. MFDS_2015_all Dr Aleksandra Kazmierczak Cardiff University Lecturer KazmierczakA@cardiff.ac.uk School of Planning and Geography, Cardiff University, Glamorgan Building, King Edward VII Avenue Cardiff CF10 3WA GB +44(0) 29 208 75281 Dr Gina Cavan Manchester Metropolitan University Lecturer g.cavan@mmu.ac.uk School of Science & the Environment, Manchester Metropolitan University, John Dalton Building, Chester Street Manchester M1 5GD +44 (0)161 247 1571 Scotland -8.336703 -0.721670 60.850454 54.549484 1 GB-SCT ISO 3166 — Codes for the representation of names of countries and their subdivisions 2022-11-29T00:00:00 https://www.iso.org/obp/ui/#iso:code:3166:GB 0 Senior Policy Officer Scottish Government Flood Risk Management Flooding@gov.scot Victoria Quay City of Edinburgh Scotland EH6 6QQ GB Dr Aleksandra Kazmierczak GIS Analyst Scottish Government Geospatial Team GIS@gov.scot Victoria Quay City of Edinburgh Scotland EH6 6QQ GB https://www.gov.scot/ WWW:LINK-1.0-http--link GIS Analyst climate change flooding flood risk management vulnerability assessment human health and safety GEMET - INSPIRE themes, version 1.0 2008-06-01T00:00:00 http://www.eionet.europa.eu/gemet/inspire_themes Human health and safety GEMET - Concepts, version 4.2.3 2008-06-01T00:00:00 https://www.eionet.europa.eu/gemet/en/themes/ climate change flooding flood risk management vulnerability assessment This dataset is the final output of the 'Mapping Flood Disadvantage in Scotland 2015' project commissioned by The Scottish Government, based on 2001 Data Zones. The aim of this research was to provide up-to-date spatial assessment of social vulnerability to flooding and flood disadvantage in Scotland, to enable local authorities, service providers and other agencies to better target work around flood resilience and response, and in particular to assist local authorities in meeting their duties under the Flood Risk Management (Scotland) Act 2009. Kazmierczak, A. Cavan, G. Connelly, A. and Lindley, S. (2015). Mapping Flood Disadvantage in Scotland 2015. © Scottish Government, contains Ordnance Survey data © Crown copyright and database right 2015. The following attribution statement must be used to acknowledge the source of the information: Kazmierczak, A. Cavan, G. Connelly, A. and Lindley, S. (2015). Mapping Flood Disadvantage in Scotland 2015. © Scottish Government, contains Ordnance Survey data © Crown copyright and database right 2015. <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Available under the terms of the </SPAN><A href="https://www.nationalarchives.gov.uk/doc/open-government-licence/" target="_blank" STYLE="text-decoration:underline;"><SPAN>Open Government Licence</SPAN></A><SPAN>. The following attribution statement must be used to acknowledge the source of the information: Kazmierczak, A. Cavan, G. Connelly, A. and Lindley, S. (2015). Mapping Flood Disadvantage in Scotland 2015. © Scottish Government, contains Ordnance Survey data © Crown copyright and database right 2015.</SPAN></P></DIV></DIV></DIV> Esri ArcGIS 13.7.0.1901 Dr Aleksandra Kazmierczak Cardiff University Lecturer KazmierczakA@cardiff.ac.uk School of Planning and Geography, Cardiff University, Glamorgan Building, King Edward VII Avenue Cardiff CF10 3WA GB +44(0) 29 208 75281 Dr Aleksandra Kazmierczak Dr Gina Cavan Manchester Metropolitan University Lecturer g.cavan@mmu.ac.uk School of Science & the Environment, Manchester Metropolitan University, John Dalton Building, Chester Street Manchester M1 5GD +44 (0)161 247 1571 Dr Gina Cavan Dr Gina Cavan This assessment framework recognises that social vulnerability to flooding is influenced by a mix of personal (e.g. disability or age), environmental (e.g. elevation of housing, presence of green space) and social factors (e.g. levels of income, tenure or extent of social networks). When combined, these affect the degree to which flooding may affect the well-being of individuals and communities. The index of social vulnerability to flooding comprised 34 indicators in total, relating to 14 thematic domains (Age, Health, Income, Information use, Insurance, Local knowledge, Social networks, Tenure, Mobility, Physical access, Crime, Access to services, Housing and Green space). The main sources of data were Scotland’s Census, Scottish Neighbourhood Statistics, SEPA’s flood data and Ordnance Survey data, in addition to some other information. The indicators were combined to produce an index of social vulnerability for the whole of Scotland at the neighbourhood level (using 2001 Data Zones). The index of social vulnerability to flooding was combined with flood-hazard exposure data, to produce a final index of flood disadvantage. The flood hazard exposure information takes into account different sources of flooding (coastal, river and surface water) and different return periods. Social vulnerability to flooding and flood disadvantage were then categorised into six classes, based on the deviation from average Scottish values, ranging from extremely low to acute. This digital dataset includes 5 main components: 1. Vulnerability Indicators - the 34 indicators used to calculate the index of social vulnerability to flooding. In order to calculate the index, the indicators were standardised, weighted and combined together. 2. Social Vulnerability to Flooding - the standardised index of social vulnerability is provided, together with the values of the dimensions of vulnerability (sensitivity, ability to prepare for, respond to and recover after flooding). For the index and its dimensions, 0 is the national average; positive values indicate above-average and negative values mean below-average levels of vulnerability, sensitivity, enhanced exposure and inability to prepare for, respond to and recover after flooding. 3. Percentage of Households Exposed to Flooding - the percentage of households in the data zone that are located within flood maps representing the extent of different flood types (coastal, river, surface water) for any type of flooding and for different return periods (1 in 10; 1 in 25/30; 1 in 200; and 1 in 200 years including the impacts of climate change). 4. Flood Disadvantage - the combination of social vulnerability to flooding and the percentage of households exposed to flooding by data zone. This includes the values of the standardised flood disadvantage index for different types of flooding and varying return periods, where 0 is national average, positive values indicate above-average and negative values mean below-average flood disadvantage. The value of -999 code means that the data zone is not exposed to a given type of flooding. 5. Geographical Location - the rurality of each data zone according to the Scottish Urban-Rural Classification (2014). A classification of the coastal and inland areas, is also included based on the location of the data zone population-weighted centroid, and using three distance bands from the coast: 1 km, 2km and 10km. This project applied the same framework as the first assessment of flood disadvantage in Scotland (Lindley and O’Neill, 2013) and an earlier assessment of social vulnerability to climate change impacts (Lindley et al., 2011). However, this research modifies the original methodology to include more recent datasets to represent the indicators of social vulnerability and flood risk. Therefore, the results of this research should not be compared to the earlier work. The research was conducted by Dr Aleksandra Kazmierczak (Cardiff University), Dr Gina Cavan (Manchester Metropolitan University), Dr Angela Connelly (University of Manchester) and Dr Sarah Lindley (University of Manchester). For further details on the methods employed users should consult the methodology report at https://www.gov.scot/publications/mapping-flood-disadvantage-scotland-2015-methodology-report/. COMMISSION REGULATION (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08T00:00:00 Not conformant. 0 6505 EPSG 6.3(3.0.1) Flood Disadvantage - Scotland Feature Class Esri Feature Class 6505 OBJECTID objectid OID 4 10 0 Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Shape shape Geometry 8 0 0 Feature geometry. Esri Coordinates defining the features. dzcode dzcode String 10 0 0 Nine character alpha-numeric code of the Data Zone as listed in the Scottish Register of Geographic Codes. Scottish Government dzname dzname String 255 0 0 Official name of the Data Zone as listed in the Scottish Register of Geographic Codes. Scottish Government Names recorded in the Scottish Register of Geographic Codes are normally those defined in legislation, if applicable to that geography. izcode izcode String 10 0 0 Nine character alpha-numeric code of the Intermediate Zone as listed in the Scottish Register of Geographic Codes. Scottish Government izname izname String 255 0 0 Official name of the Intermediate Zone as listed in the Scottish Register of Geographic Codes. Scottish Government Names recorded in the Scottish Register of Geographic Codes are normally those defined in legislation, if applicable to that geography. lacode lacode String 10 0 0 Nine character alpha-numeric code of the Local Authority as listed in the Scottish Register of Geographic Codes. Scottish Government laname laname String 255 0 0 Official name of the Local Authority as listed in the Scottish Register of Geographic Codes. Scottish Government Names recorded in the Scottish Register of Geographic Codes are normally those defined in legislation, if applicable to that geography. pctund5 pctund5 Double 8 38 8 % people under 5 years old Kazmierczak et al. (2015) Percent people under 5 pctovr75 pctovr75 Double 8 38 8 % people over 75 years old Kazmierczak et al. (2015) Percent people over 75 pctltli pctltli Double 8 38 8 % people whose day-to-day activities are limited by a long-term illness Kazmierczak et al. (2015) Percent people with long-term limiting illness pcthhltli pcthhltli Double 8 38 8 % households with at least one person with a long term limiting illness Kazmierczak et al. (2015) Percent households with long-term limiting illness pctroutocc pctroutocc Double 8 38 8 % people in routine or semi-routine occupations Kazmierczak et al. (2015) Percent people in routine or semi-routine occupations pctltunemp pctltunemp Double 8 38 8 % of people who are long term unemployed or who have never worked Kazmierczak et al. (2015) Percent of people who are long term unemployed pcthhwcunemp pcthhwcunemp Double 8 38 8 % households with dependent children and no adults in employment Kazmierczak et al. (2015) Percent households with dependent children and no adults in employment numincsup numincsup Double 8 38 8 Number of Income Support claimants Kazmierczak et al. (2015) Number of Income Support claimants numjsa numjsa Double 8 38 8 Number of Job seeker allowance claimants Kazmierczak et al. (2015) Number of Job seeker allowance claimants numpcc numpcc Double 8 38 8 Number of pension credit claimants Kazmierczak et al. (2015) Number of pension credit claimants numfamtaxcred numfamtaxcred Double 8 38 8 Number of families receiving tax credits Kazmierczak et al. (2015) Total number of families receiving tax credits (working tax creditx and child tax credits) pctlt1yrres pctlt1yrres Double 8 38 8 Percent people with less than 1 year residency in the UK Kazmierczak et al. (2015) % people with <1 year residency in the UK pctnoeng pctnoeng Double 8 38 8 Percent people who do not speak English Kazmierczak et al. (2015) % people who do not speak English/no not speak English well pctnewaddflood pctnewaddflood Double 8 38 8 Percent new addresses in flood risk areas Kazmierczak et al. (2015) % new addresses (01.01.2009) in flood risk areas (proxy for insurance availability) numhistflood numhistflood Double 8 38 8 Number of historic flood events Kazmierczak et al. (2015) Number of historic flood events (proxy for insurance cost) pctaddfwta pctaddfwta Double 8 38 8 Percent addresses in Flood Warning Target Areas Kazmierczak et al. (2015) % addresses in Flood Warning Target Areas pctnewres pctnewres Double 8 38 8 Percent new residents (less than 1 year) arriving from outside the local area Kazmierczak et al. (2015) % new residents (< 1 year) arriving from outside the local area pctsocrent pctsocrent Double 8 38 8 Percent social rented households Kazmierczak et al. (2015) % social rented households pctprivrent pctprivrent Double 8 38 8 Percent private rented households Kazmierczak et al. (2015) % private rented households pctibsda pctibsda Double 8 38 8 Percent of Incapacity Benefit/Severe Disablement allowance claimants Kazmierczak et al. (2015) % of Incapacity Benefit/Severe Disablement allowance claimants pctmedcare pctmedcare Double 8 38 8 Percent people living in medical and care establishments Kazmierczak et al. (2015) % people living in medical and care establishments pcthhnocar pcthhnocar Double 8 38 8 Percent households with no vehicle Kazmierczak et al. (2015) % households with no car or van numvolorg numvolorg Double 8 38 8 Number of voluntary organisations focused on local community Kazmierczak et al. (2015) Number of voluntary organisations focused on local community pctsingpenhh pctsingpenhh Double 8 38 8 Percent single pensioner households Kazmierczak et al. (2015) % single pensioner households pctchiprisch pctchiprisch Double 8 38 8 Percent children of primary school age Kazmierczak et al. (2015) % children of primary school age pctwork30km pctwork30km Double 8 38 8 Percent people working further than 30km from home Kazmierczak et al. (2015) % people working further than 30km from home roaddensity roaddensity Double 8 38 8 Road density (km/km2) Kazmierczak et al. (2015) Road density (km/km2) numbreakins numbreakins Double 8 38 8 Number of domestic break-ins per 10,000 households Kazmierczak et al. (2015) Number of domestic break-ins per 10,000 households drivegp drivegp Double 8 38 8 Travel time to GP surgery by car (minutes) Kazmierczak et al. (2015) Travel time to GP surgery (private transport; minutes) pubtransgp pubtransgp Double 8 38 8 Travel time to GP surgery by public transport (minutes) Kazmierczak et al. (2015) Travel time to GP surgery (public transport; minutes) pcthhgroundflr pcthhgroundflr Double 8 38 8 Percent households with lowest floor level ground floor Kazmierczak et al. (2015) % households with the lowest floor level: ground floor pcthhbasement pcthhbasement Double 8 38 8 Percent households with lowest floor level basement or semi-basement Kazmierczak et al. (2015) % households with the lowest floor level: basement or semi-basement pctmobstruct pctmobstruct Double 8 38 8 Percent caravan or other mobile or temporary structures in all households Kazmierczak et al. (2015) % caravan or other mobile or temporary structures in all households pcturblandcov pcturblandcov Double 8 38 8 Percent urban land cover Kazmierczak et al. (2015) % urban land cover sisocvuln sisocvuln Double 8 38 8 Standardised index of social vulnerability Kazmierczak et al. (2015) Standardised index of social vulnerability to flooding sisens sisens Double 8 38 8 Standardised index of sensitivity Kazmierczak et al. (2015) Standardised index of sensitivity siprep siprep Double 8 38 8 Standardised index of ability to prepare Kazmierczak et al. (2015) Standardised index of ability to prepare siresp siresp Double 8 38 8 Standardised index of ability to respond Kazmierczak et al. (2015) Standardised index of ability to respond sirecov sirecov Double 8 38 8 Standardised index of ability to recover Kazmierczak et al. (2015) Standardised index of ability to recover siexp siexp Double 8 38 8 Standardised index of enhanced exposure Kazmierczak et al. (2015) Standardised index of enhanced exposure socvulnclass socvulnclass String 254 0 0 Social vulnerability class Kazmierczak et al. (2015) Social vulnerability to flooding (classified) sensclass sensclass String 254 0 0 Sensitivity class Kazmierczak et al. (2015) Sensitivity (classified) prepclass prepclass String 254 0 0 Inability to prepare class Kazmierczak et al. (2015) Inability to prepare (classified) respclass respclass String 254 0 0 Inability to respond class Kazmierczak et al. (2015) Inability to respond (classified) recovclass recovclass String 254 0 0 Inability to recover class Kazmierczak et al. (2015) Inability to recover (classified) expclass expclass String 254 0 0 Enhanced exposure class Kazmierczak et al. (2015) Enhanced exposure (classified) pcthhanyflood pcthhanyflood Double 8 38 8 Percent of households exposed to any type of 200 year flooding (including the impacts of climate change) Kazmierczak et al. (2015) Percentage of households exposed to any type of flooding (return period: 1 in 200 years including the impacts of climate change) pcthhc25flood pcthhc25flood Double 8 38 8 Percent of households exposed to 25 year coastal flooding Kazmierczak et al. (2015) Percentage of households exposed to coastal flooding (return period: 1 in 25 years) pcthhc200flood pcthhc200flood Double 8 38 8 Percent of households exposed to 200 year coastal flooding Kazmierczak et al. (2015) Percentage of households exposed to coastal flooding (return period: 1 in 200 years) pcthhc200ccflood pcthhc200ccflood Double 8 38 8 Percent of households exposed to 200 year coastal flooding (including the impacts of climate change) Kazmierczak et al. (2015) Percentage of households exposed to coastal flooding (return period: 1 in 200 years including the impacts of climate change) pcthhr30flood pcthhr30flood Double 8 38 8 Percent of households exposed to 30 year river flooding Kazmierczak et al. (2015) Percentage of households exposed to river flooding (return period: 1 in 30 years) pcthhr200flood pcthhr200flood Double 8 38 8 Percent of households exposed to 200 year river flooding Kazmierczak et al. (2015) Percentage of households exposed to river flooding (return period: 1 in 200 years) pcthhr200ccflood pcthhr200ccflood Double 8 38 8 Percent of households exposed to 200 year river flooding (including the impacts of climate change) Kazmierczak et al. (2015) Percentage of households exposed to river flooding (return period: 1 in 200 years including the impacts of climate change) pcthhs30flood pcthhs30flood Double 8 38 8 Percent of households exposed to 30 year surface water flooding Kazmierczak et al. (2015) Percentage of households exposed to surface water flooding (return period: 1 in 30 years) pcthhs200flood pcthhs200flood Double 8 38 8 Percent of households exposed to 200 year surface water flooding Kazmierczak et al. (2015) Percentage of households exposed to surface water flooding (return period: 1 in 200 years) pcthhs200ccflood pcthhs200ccflood Double 8 38 8 Percent of households exposed to 200 year surface water flooding (including the impacts of climate change) Kazmierczak et al. (2015) Percentage of households exposed to surface water flooding (return period: 1 in 200 years including the impacts of climate change) indanyflood indanyflood Double 8 38 8 Flood Disadvantage Index - any type of 200 year flooding (including the impacts of climate change) Kazmierczak et al. (2015) Flood disadvantage index - any type of flooding (return period: 1 in 200 years including the impacts of climate change). Not exposed is recorded as -999.0000. indc25flood indc25flood Double 8 38 8 Flood Disadvantage Index - 25 year coastal flooding Kazmierczak et al. (2015) Flood disadvantage index - coastal flooding (return period: 1 in 25 years). Not exposed is recorded as -999.0000. indc200flood indc200flood Double 8 38 8 Flood Disadvantage Index - 200 year coastal flooding Kazmierczak et al. (2015) Flood disadvantage index - coastal flooding (return period: 1 in 200 years). Not exposed is recorded as -999.0000. indc200ccflood indc200ccflood Double 8 38 8 Flood Disadvantage Index - 200 year coastal flooding (including the impacts of climate change) Kazmierczak et al. (2015) Flood disadvantage index - coastal flooding (return period: 1 in 200 years including the impacts of climate change). Not exposed recorded as -999.0000. indr30flood indr30flood Double 8 38 8 Flood Disadvantage Index - 30 year river flooding Kazmierczak et al. (2015) Flood disadvantage index - river flooding (return period: 1 in 30 years). Not exposed recorded as -999.0000. indr200flood indr200flood Double 8 38 8 Flood Disadvantage Index - 200 year river flooding Kazmierczak et al. (2015) Flood disadvantage index - river flooding (return period: 1 in 200 years). Not exposed recorded as -999.0000. indr200ccflood indr200ccflood Double 8 38 8 Flood Disadvantage Index - 200 year river flooding (including the impacts of climate change) Kazmierczak et al. (2015) Flood disadvantage index - any type of flooding (return period: 1 in 200 years including the impacts of climate change). Not exposed recorded as -999.0000. inds30flood inds30flood Double 8 38 8 Flood Disadvantage Index - 30 year surface water flooding Kazmierczak et al. (2015) Flood disadvantage index - surface water flooding (return period: 1 in 30 years). Not exposed recorded as -999.0000. inds200flood inds200flood Double 8 38 8 Flood Disadvantage Index - 200 year surface water flooding Kazmierczak et al. (2015) Flood disadvantage index - surface water flooding (return period: 1 in 200 years). Not exposed recorded as -999.0000. inds200ccflood inds200ccflood Double 8 38 8 Flood Disadvantage Index - 200 year surface water flooding (including the impacts of climate change) Kazmierczak et al. (2015) Flood disadvantage index - surface water flooding (return period: 1 in 200 years including the impacts of climate change). Not exposed recorded as -999.0000. classanyflood classanyflood String 254 0 0 Flood Disadvantage Class - any type of 200 year flooding (including the impacts of climate change) Kazmierczak et al. (2015) Classified flood disadvantage index - any type of flooding (return period: 1 in 200 years including the impacts of climate change) classc25flood classc25flood String 254 0 0 Flood Disadvantage Class - 25 year coastal flooding Kazmierczak et al. (2015) Classified flood disadvantage index - coastal flooding (return period: 1 in 25 years) classc200flood classc200flood String 254 0 0 Flood Disadvantage Class - 200 year coastal flooding Kazmierczak et al. (2015) Classified flood disadvantage index - coastal flooding (return period: 1 in 200 years) classc200ccflood classc200ccflood String 254 0 0 Flood Disadvantage Class - 200 year coastal flooding (including the impacts of climate change) Kazmierczak et al. (2015) Classified flood disadvantage index coastal flooding (return period: 1 in 200 years including the impacts of climate change classr30flood classr30flood String 254 0 0 Flood Disadvantage Class - 30 year river flooding Kazmierczak et al. (2015) Classified flood disadvantage index - river flooding (return period: 1 in 30 years) classr200flood classr200flood String 254 0 0 Flood Disadvantage Class - 200 year river flooding Kazmierczak et al. (2015) Classified flood disadvantage index - river flooding (return period: 1 in 200 years) classr200ccflood classr200ccflood String 254 0 0 Flood Disadvantage Class - 200 year river flooding (including the impacts of climate change) Kazmierczak et al. (2015) Classified flood disadvantage index - any type of flooding (return period: 1 in 200 years including the impacts of climate change) classs30flood classs30flood String 254 0 0 Flood Disadvantage Class - 30 year surface water flooding Kazmierczak et al. (2015) Classified flood disadvantage index - surface water flooding (return period: 1 in 30 years) classs200flood classs200flood String 254 0 0 Flood Disadvantage Class - 200 year surface water flooding Kazmierczak et al. (2015) Classified flood disadvantage index - surface water flooding (return period: 1 in 200 years) classs200ccflood classs200ccflood String 254 0 0 Flood Disadvantage Class - 200 year surface water flooding (including the impacts of climate change) Kazmierczak et al. (2015) Classified flood disadvantage index - surface water flooding (return period: 1 in 200 years including the impacts of climate change) ur6class ur6class Double 8 38 8 6-fold Urban Rural Classification Kazmierczak et al. (2015) Data zone class (number) in the 6-fold Urban-Rural Classification (Scottish Government, 2014) ur6desc ur6desc String 254 0 0 6-fold Urban Rural Classification description Kazmierczak et al. (2015) Data zone class (description) in the 6-fold Urban-Rural Classification (Scottish Government, 2014) dzcoast2km dzcoast2km String 254 0 0 Data zone weighted centroid is within 2km of the coast Kazmierczak et al. (2015) Location of data zone population-weighted centroid within coastal areas, defined as 2km distance from the coast dzpva dzpva String 254 0 0 Data zone weighted centroid is within Potentially Vulnerable Areas Kazmierczak et al. (2015) Data zone centroid location in relation to Potentially Vulnerable Areas, as identified in NFRA (SEPA, 2011): coded dzpvadesc dzpvadesc String 254 0 0 Data zone weighted centroid is within Potentially Vulnerable Areas description Kazmierczak et al. (2015) Data zone centroid location in relation to Potentially Vulnerable Areas, as identified in NFRA (SEPA, 2011): description st_area(shape) st_area(shape) Double 0 0 0 Area measurement ESRI System calculated area of the feature. st_perimeter(shape) st_perimeter(shape) Double 0 0 0 Perimeter measurement ESRI System calculated perimeter of the feature. Simple FALSE 6505 FALSE FALSE /9j/4AAQSkZJRgABAQEAYABgAAD/4QCyRXhpZgAATU0AKgAAAAgABgESAAMAAAABAAEAAAEaAAUA AAABAAAAVgEbAAUAAAABAAAAXgEoAAMAAAABAAIAAAITAAMAAAABAAEAAIdpAAQAAAABAAAAZgAA AAAAAABgAAAAAQAAAGAAAAABAAWQAAAHAAAABDAyMTCRAQAHAAAABAECAwCgAAAHAAAABDAxMDCg AgADAAAAAQJYAACgAwADAAAAAQGQAAAAAAAAAAD/4QchaHR0cDovL25zLmFkb2JlLmNvbS94YXAv MS4wLwA8P3hwYWNrZXQgYmVnaW49J++7vycgaWQ9J1c1TTBNcENlaGlIenJlU3pOVGN6a2M5ZCc/ Pg0KPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyI+PHJkZjpSREYgeG1sbnM6cmRm PSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj48cmRmOkRlc2Ny aXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJm MWIiIHhtbG5zOnByZWZpeDA9Imh0dHA6Ly9ucy5hdHRyaWJ1dGlvbi5jb20vYWRzLzEuMC8iPjxw cmVmaXgwOkFkcz48cmRmOlNlcSB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIv MjItcmRmLXN5bnRheC1ucyMiPjxyZGY6bGk+PHJkZjpEZXNjcmlwdGlvbiB4bWxuczpyZGY9Imh0 dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxwcmVmaXgwOkNyZWF0 ZWQgeG1sbnM6cHJlZml4MD0iaHR0cDovL25zLmF0dHJpYnV0aW9uLmNvbS9hZHMvMS4wLyI+MjAy Ni0wNS0yNjwvcHJlZml4MDpDcmVhdGVkPjxwcmVmaXgxOkRhdGEgeG1sbnM6cHJlZml4MT0iaHR0 cDovL25zLmF0dHJpYnV0aW9uLmNvbS9hZHMvMS4wLyI+eyJkb2MiOiJEQUhLeHFubUMtYyIsInVz ZXIiOiJVQUhLeHFLSnZWbyIsImJyYW5kIjoiQkFIS3hvQV83WmcifTwvcHJlZml4MTpEYXRhPjxw cmVmaXgyOkV4dElkIHhtbG5zOnByZWZpeDI9Imh0dHA6Ly9ucy5hdHRyaWJ1dGlvbi5jb20vYWRz LzEuMC8iPjM1ZGE3N2RjLWJkOWUtNDkxOS1iMjgxLTU5OTIxYjJmZTlhZTwvcHJlZml4MjpFeHRJ ZD48cHJlZml4MzpGYklkIHhtbG5zOnByZWZpeDM9Imh0dHA6Ly9ucy5hdHRyaWJ1dGlvbi5jb20v YWRzLzEuMC8iPjUyNTI2NTkxNDE3OTU4MDwvcHJlZml4MzpGYklkPjxwcmVmaXg0OlRvdWNoVHlw ZSB4bWxuczpwcmVmaXg0PSJodHRwOi8vbnMuYXR0cmlidXRpb24uY29tL2Fkcy8xLjAvIj4yPC9w cmVmaXg0OlRvdWNoVHlwZT48L3JkZjpEZXNjcmlwdGlvbj4NCgkJCQkJPC9yZGY6bGk+PC9yZGY6 U2VxPg0KCQkJPC9wcmVmaXgwOkFkcz48L3JkZjpEZXNjcmlwdGlvbj48cmRmOkRlc2NyaXB0aW9u IHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJmMWIiIHht bG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyI+PGRjOnRpdGxlPjxyZGY6 QWx0IHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5z IyI+PHJkZjpsaSB4bWw6bGFuZz0ieC1kZWZhdWx0Ij5VbnRpdGxlZCBkZXNpZ24gLSAxPC9yZGY6 bGk+PC9yZGY6QWx0Pg0KCQkJPC9kYzp0aXRsZT48L3JkZjpEZXNjcmlwdGlvbj48cmRmOkRlc2Ny aXB0aW9uIHJkZjphYm91dD0idXVpZDpmYWY1YmRkNS1iYTNkLTExZGEtYWQzMS1kMzNkNzUxODJm MWIiIHhtbG5zOnBkZj0iaHR0cDovL25zLmFkb2JlLmNvbS9wZGYvMS4zLyI+PHBkZjpBdXRob3I+ S2F0ZSBOZXd0b248L3BkZjpBdXRob3I+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlv biByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4 bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRvclRvb2w+ Q2FudmEgKFJlbmRlcmVyKSBkb2M9REFIS3hxbm1DLWMgdXNlcj1VQUhLeHFLSnZWbyBicmFuZD1C QUhLeG9BXzdaZzwveG1wOkNyZWF0b3JUb29sPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48 L3g6eG1wbWV0YT4NCjw/eHBhY2tldCBlbmQ9J3cnPz7/4gxYSUNDX1BST0ZJTEUAAQEAAAxITGlu bwIQAABtbnRyUkdCIFhZWiAHzgACAAkABgAxAABhY3NwTVNGVAAAAABJRUMgc1JHQgAAAAAAAAAA AAAAAAAA9tYAAQAAAADTLUhQICAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAABFjcHJ0AAABUAAAADNkZXNjAAABhAAAAGx3dHB0AAAB8AAAABRia3B0AAACBAAA ABRyWFlaAAACGAAAABRnWFlaAAACLAAAABRiWFlaAAACQAAAABRkbW5kAAACVAAAAHBkbWRkAAAC xAAAAIh2dWVkAAADTAAAAIZ2aWV3AAAD1AAAACRsdW1pAAAD+AAAABRtZWFzAAAEDAAAACR0ZWNo AAAEMAAAAAxyVFJDAAAEPAAACAxnVFJDAAAEPAAACAxiVFJDAAAEPAAACAx0ZXh0AAAAAENvcHly aWdodCAoYykgMTk5OCBIZXdsZXR0LVBhY2thcmQgQ29tcGFueQAAZGVzYwAAAAAAAAASc1JHQiBJ RUM2MTk2Ni0yLjEAAAAAAAAAAAAAABJzUkdCIElFQzYxOTY2LTIuMQAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAWFlaIAAAAAAAAPNRAAEAAAABFsxYWVog AAAAAAAAAAAAAAAAAAAAAFhZWiAAAAAAAABvogAAOPUAAAOQWFlaIAAAAAAAAGKZAAC3hQAAGNpY WVogAAAAAAAAJKAAAA+EAAC2z2Rlc2MAAAAAAAAAFklFQyBodHRwOi8vd3d3LmllYy5jaAAAAAAA AAAAAAAAFklFQyBodHRwOi8vd3d3LmllYy5jaAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAABkZXNjAAAAAAAAAC5JRUMgNjE5NjYtMi4xIERlZmF1bHQgUkdCIGNv bG91ciBzcGFjZSAtIHNSR0IAAAAAAAAAAAAAAC5JRUMgNjE5NjYtMi4xIERlZmF1bHQgUkdCIGNv bG91ciBzcGFjZSAtIHNSR0IAAAAAAAAAAAAAAAAAAAAAAAAAAAAAZGVzYwAAAAAAAAAsUmVmZXJl bmNlIFZpZXdpbmcgQ29uZGl0aW9uIGluIElFQzYxOTY2LTIuMQAAAAAAAAAAAAAALFJlZmVyZW5j ZSBWaWV3aW5nIENvbmRpdGlvbiBpbiBJRUM2MTk2Ni0yLjEAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAHZpZXcAAAAAABOk/gAUXy4AEM8UAAPtzAAEEwsAA1yeAAAAAVhZWiAAAAAAAEwJVgBQAAAA Vx/nbWVhcwAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAo8AAAACc2lnIAAAAABDUlQgY3VydgAA AAAAAAQAAAAABQAKAA8AFAAZAB4AIwAoAC0AMgA3ADsAQABFAEoATwBUAFkAXgBjAGgAbQByAHcA fACBAIYAiwCQAJUAmgCfAKQAqQCuALIAtwC8AMEAxgDLANAA1QDbAOAA5QDrAPAA9gD7AQEBBwEN ARMBGQEfASUBKwEyATgBPgFFAUwBUgFZAWABZwFuAXUBfAGDAYsBkgGaAaEBqQGxAbkBwQHJAdEB 2QHhAekB8gH6AgMCDAIUAh0CJgIvAjgCQQJLAlQCXQJnAnECegKEAo4CmAKiAqwCtgLBAssC1QLg AusC9QMAAwsDFgMhAy0DOANDA08DWgNmA3IDfgOKA5YDogOuA7oDxwPTA+AD7AP5BAYEEwQgBC0E OwRIBFUEYwRxBH4EjASaBKgEtgTEBNME4QTwBP4FDQUcBSsFOgVJBVgFZwV3BYYFlgWmBbUFxQXV BeUF9gYGBhYGJwY3BkgGWQZqBnsGjAadBq8GwAbRBuMG9QcHBxkHKwc9B08HYQd0B4YHmQesB78H 0gflB/gICwgfCDIIRghaCG4IggiWCKoIvgjSCOcI+wkQCSUJOglPCWQJeQmPCaQJugnPCeUJ+woR CicKPQpUCmoKgQqYCq4KxQrcCvMLCwsiCzkLUQtpC4ALmAuwC8gL4Qv5DBIMKgxDDFwMdQyODKcM wAzZDPMNDQ0mDUANWg10DY4NqQ3DDd4N+A4TDi4OSQ5kDn8Omw62DtIO7g8JDyUPQQ9eD3oPlg+z D88P7BAJECYQQxBhEH4QmxC5ENcQ9RETETERTxFtEYwRqhHJEegSBxImEkUSZBKEEqMSwxLjEwMT IxNDE2MTgxOkE8UT5RQGFCcUSRRqFIsUrRTOFPAVEhU0FVYVeBWbFb0V4BYDFiYWSRZsFo8WshbW FvoXHRdBF2UXiReuF9IX9xgbGEAYZRiKGK8Y1Rj6GSAZRRlrGZEZtxndGgQaKhpRGncanhrFGuwb FBs7G2MbihuyG9ocAhwqHFIcexyjHMwc9R0eHUcdcB2ZHcMd7B4WHkAeah6UHr4e6R8THz4faR+U H78f6iAVIEEgbCCYIMQg8CEcIUghdSGhIc4h+yInIlUigiKvIt0jCiM4I2YjlCPCI/AkHyRNJHwk qyTaJQklOCVoJZclxyX3JicmVyaHJrcm6CcYJ0kneierJ9woDSg/KHEooijUKQYpOClrKZ0p0CoC KjUqaCqbKs8rAis2K2krnSvRLAUsOSxuLKIs1y0MLUEtdi2rLeEuFi5MLoIuty7uLyQvWi+RL8cv /jA1MGwwpDDbMRIxSjGCMbox8jIqMmMymzLUMw0zRjN/M7gz8TQrNGU0njTYNRM1TTWHNcI1/TY3 NnI2rjbpNyQ3YDecN9c4FDhQOIw4yDkFOUI5fzm8Ofk6Njp0OrI67zstO2s7qjvoPCc8ZTykPOM9 Ij1hPaE94D4gPmA+oD7gPyE/YT+iP+JAI0BkQKZA50EpQWpBrEHuQjBCckK1QvdDOkN9Q8BEA0RH RIpEzkUSRVVFmkXeRiJGZ0arRvBHNUd7R8BIBUhLSJFI10kdSWNJqUnwSjdKfUrESwxLU0uaS+JM KkxyTLpNAk1KTZNN3E4lTm5Ot08AT0lPk0/dUCdQcVC7UQZRUFGbUeZSMVJ8UsdTE1NfU6pT9lRC VI9U21UoVXVVwlYPVlxWqVb3V0RXklfgWC9YfVjLWRpZaVm4WgdaVlqmWvVbRVuVW+VcNVyGXNZd J114XcleGl5sXr1fD19hX7NgBWBXYKpg/GFPYaJh9WJJYpxi8GNDY5dj62RAZJRk6WU9ZZJl52Y9 ZpJm6Gc9Z5Nn6Wg/aJZo7GlDaZpp8WpIap9q92tPa6dr/2xXbK9tCG1gbbluEm5rbsRvHm94b9Fw K3CGcOBxOnGVcfByS3KmcwFzXXO4dBR0cHTMdSh1hXXhdj52m3b4d1Z3s3gReG54zHkqeYl553pG eqV7BHtje8J8IXyBfOF9QX2hfgF+Yn7CfyN/hH/lgEeAqIEKgWuBzYIwgpKC9INXg7qEHYSAhOOF R4Wrhg6GcobXhzuHn4gEiGmIzokziZmJ/opkisqLMIuWi/yMY4zKjTGNmI3/jmaOzo82j56QBpBu kNaRP5GokhGSepLjk02TtpQglIqU9JVflcmWNJaflwqXdZfgmEyYuJkkmZCZ/JpomtWbQpuvnByc iZz3nWSd0p5Anq6fHZ+Ln/qgaaDYoUehtqImopajBqN2o+akVqTHpTilqaYapoum/adup+CoUqjE qTepqaocqo+rAqt1q+msXKzQrUStuK4trqGvFq+LsACwdbDqsWCx1rJLssKzOLOutCW0nLUTtYq2 AbZ5tvC3aLfguFm40blKucK6O7q1uy67p7whvJu9Fb2Pvgq+hL7/v3q/9cBwwOzBZ8Hjwl/C28NY w9TEUcTOxUvFyMZGxsPHQce/yD3IvMk6ybnKOMq3yzbLtsw1zLXNNc21zjbOts83z7jQOdC60TzR vtI/0sHTRNPG1EnUy9VO1dHWVdbY11zX4Nhk2OjZbNnx2nba+9uA3AXcit0Q3ZbeHN6i3ynfr+A2 4L3hROHM4lPi2+Nj4+vkc+T85YTmDeaW5x/nqegy6LzpRunQ6lvq5etw6/vshu0R7ZzuKO6070Dv zPBY8OXxcvH/8ozzGfOn9DT0wvVQ9d72bfb794r4Gfio+Tj5x/pX+uf7d/wH/Jj9Kf26/kv+3P9t ////2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsKCwsNDhIQDQ4RDgsLEBYQERMU FRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQU FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCAGQAlgDASIAAhEBAxEB/8QAHwAAAQUBAQEB AQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1Fh ByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZ WmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXG x8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAEC AwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHB CSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0 dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX 2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD6wsf+Wn+7VpW+Vqk03S7rdJtjWX/d kVqvLpd1/wA+/wD48ten7OUveifm8qco+7KJl/xLR/y0/wCBVrLo11/zxX/vqmyaXdRbdtvu/wB1 qPZVP5Q5ZGfH96iNf31aUek3jN/qVi/2mapG0O6RdytEzf3a19hU/lDlMWT71ObbubdV6Szurf79 u3+8vzVV3Rs3zLtasJU5R+Igjj2/N96m/L5i7W/iqSZVWGRlb+GsWPVJvM8tbef7rNub/wDZpcoG 1JVeRl+bctR2Msl00m5ZF2/wyfeqSa3k2qu1qgAmtVuLdY23Kv3qo3FjHZx+Z5kjbfmbc1ajNt+7 TZIlul2t8tWBj29vDcLuSRtrbtu1quWtr9n3KrM27+9Uy2uz7m1v92nKsit92iRcRrRMv8NRzWq3 Ue1921amaVv92j7Q38W1qgsx47eFrpo933VVv9Z81bVrbrbx7R92mrbx/wCsVfm/u1L/AMs6uRiL t+X5aayfw07Z8rU3dsVqgDW8I2v2jXIfu7Y/m+avSbHVLW61f7D5n75fmZf9mvIlb5V/h/3a6rwb eR2Ed5dNulupP3ce7+7/ABVz14x+KR7mXYr2Pucp6LFqTPI0kbKy7vu/eqh4hup9N8N6lqen2bXN 5aws0dtu+WST+H71cvHqUzalHNKzKu75vK+Wuu1ue1utJFtDIrWyrukZW/irz6dT3uY+lVeNaEuQ /PPXrrULrWry41Tzft00jSTeYvzbmqhX174o8B6L4yt9uo2qtN/yznj+WRf+BV4X4w+B+saDNJNp cb6rY/e3Kv7xf95a/QsDnOHxP7uXuyPzjEYOpTfN8R5z/dWjd/douFa3kZZVaKRflZWXbUP2j5f7 7V9Atdjzh0i7m+9Uf2df4flo81m+6u2tK68K65awxzXGl3kULL5iyNbttZf71KUox+IcYyl8Jk/d b73zU5ZWVvmanfZ9rbW3bq774J+A7Xx546t7G9jaXT442muFVtu5f4V/76Zayq1I0qcqkvhiaUqM 61SNOP2jg1ulSvWv2c/BEPjLxhJfXsay6fparMyt91pN37tf/Zv+A17lJ+yf4HlVmWO6i/65zNXU +DfhTovw70lrHTWlaO4k+0SNI3zM33Vr5rGZzRqYeUaXxH0+DyWtTxEZVbcp1kdxby7W8z/gNSL5 bStGy7ZN235aq2emw/aIflb7y/xVYtot+p+dub7zNXxB9wOvH3TbVb5Y/lWo6reTIrfK3/fS0/zp Eb5o/l/vLQMkVd7Kv96prpt9w237q/KtVVuI3bbu+aqPiC/urDS5riwt/tl0u3bH/e+agDas2l8z 5ZGWNfmb5qkW+uZ7hV8xk3NXn1x4/wBa01dtx4dlijm+X73zM3/xNasPijWrf98vh2do/urOsy7f mbburTkkHMdU1/M0jbJJNu75fmp11eTKyxrNJuVdrfN/FRa3DXEi+aqtt+bdtqqu6Wb5juZmqALN 1dTLIq+ZJuVV3fN/FS3U8nmL+8b/AFa/xU25TdJNJu/5aba5fWtSvIvE1xCkjeT8vyf8BrGrV9lG 8iJS5TqLqSTzFXzG+WNf4qLqWRfJVWb/AFa/xVHdP++Zf4lVVp118ska/wB2Na1LD+0luNv2a6WX bGu7ypN22pEupvJmZm3bV/irmPCe5Y9QbayxyXDbfu/Mv/Aa2dSvfseizXCr5m1mbbu+9tWq+0Bo Wcn2qTy5VjaNVZvu1xC+FZl8TQ6l9slW3jj8tbb7QzRqu3b/AKv+9/tVV0/4saXH5n7yB5Gka18z zJNvmblVl/1f+0tdJ4R1qHxG0jIse2H5dytu/vL/ABKv8StWkeaPwkcsZGxN9n2xs0cn7z5vlalV oW2r5kn/AAJVapL21ZpI1iXdGsf96oLezm+0R7o227lqNSyRoofMZTJFuX5fustPhtV86P5Y3Vv4 vM3VUkimWaRmjk+9/dqe3maW6Vm/hVqWoivJ4j0vTbprWe+iiuFZtys23b8u7/0H5qoXHijR7C1V Z9StYmkmXavnL/Evy/8AfVYeteAdF166uLy6hZbqbcrTRttk2+X5e3d/u1kyfBPR4dWmkaaWSxa3 jhhtm/h2qvzbv4vurRqM6238TabdaktjFdRvdNu2xf7u7d/6C1aGpLcTaBeLAqy3H/LFZPu7tvy1 g6P4F03RdWh1C3WRriNpGXzG3L827d/6E1dh96z3Lbq25tzKtSB5xpt14ys5lWLS4JflZv3jL97a 393/AIDTbfxX4q8668jS7VoY5GX9+21W/wCBbq67XLq6t9Pmk06zinmWNlaBpmVm+X+Fv71eF/8A CW6beQw2t+t9ZrCvl7bbbt+Vv4l/vV20MPUqR5qcThq4ylQly1Zcp6reXHi7+z7G6gsbVJmjka4t vM+63/LP5v7tVrW/8YLdKq2Nq8m5mX5v975a5fS9NsfFHlrZeLrmK3j3LJHc3DRzMzfd27m+7Xqe mRLbtJN5iyNDC3zeYrfw1jUozhvE3hWpVI80JHHSNJ9nbz1XzmXayr93dWTeMq7Y1b7v3v8Aep3i Lxbp+kqyveQNN93y4G8xv/Ha4O+8fSN/x5Q+V/00k+Zq8/D5VicRLm5eU8DH46jT9zmOsvtWt9Jh 8y8k27vur/E1cXqnjq+vN0dr/ocP+z97/vqufvrq41Kbzp5Gnm/i3NTd3y/N/wB819vg8ppYX3qn vSPk6uKqVPh0iDfM25t0rN95moZPm+Zv++ap332xvL+zsqr/ABbqryS6gsfzeUte6cJqbt3zLTd2 +s1Zb5flXy2b+9Ukn259vlNHtVV8xarUC9tb1orPsft0sjNOu2OijUD3Twzb6xFJ/wATe8ttQuF8 zbPbW/krt/d7Vb5V/utXR1hR+LY5Y2/0eT7396hvFH923/8AHq+dwtWnToxjKR9LjKtOtVlKJu1j N4qsUZl/ef8AfNVbrxA1xayRrD5TMu3durn/ALL71vPFR+yeZUlL7J2Wn65b6lM0cW7cq7vmWtGu G09m028WYfMy/wANan/CRXDSLtWNVqY4uP2iqcpcvvHS1FNaw3H3442/3lrFh8TSLJ+9hjlX/Z+W tCPXrGX73mwf7y7v/QaqOKpy+I392Q1tBsW/5Y/+PVb8P+BdM1LUBE8cix7Wb5WpI723l+5NG3/A q6fwT/yE5m/uxH+dTX9n7GUonVhaUalWKkcNeeEo7W6uFs5miVWZfmWjT/CeqXzSLHHHJ5a7m2tt roLhvmm/2mre0X/QfDd9c/xP8i1y1IRjTUomsMNTqVOWXqeE3l1cLeNH9nZv3m1m8z7vzfNUljcT NdKrx7V2/wDPTdur0S40u1ulZZbeNt38W35qxbjw5Jat5lrtZf7rfeq54WS+E8qUTJ/3akjbb95q GX5tsqtFJTZFb+7uWuL4SB0lusq/+zVX+yt/CytUkbMv+7R8rf7NZl8wN8v+6tOb5vvUKrbvvUlW QLt/dtULNtVV2q26p+aT+61QA1tv+7UkcklvIskTfdqFpFWuw8K/D6TW7dby4m+zWu3d93c1KUo/ aOqhQqVpctMteG9NuNcsZrhdsTL8se7/AJaNVeTdDI0Mu6KT7rLXcrpsNnbw2dvC3lxr8rbvvf7V cVH4K1qW4mZ9Ujn3NJJ8ytu/2V3V4dWMeb93E+nlg5U6ceX4irNE277tNWJtv3qy2luImZUvF2r8 tTWsN1LC0i3X7uubmPN5jzf9oDQ9PvPCrMmntPqjSLtuYIWZlX+Lcy/+zV852+mzNdQxq0ksm75Y 9vzf7tfb1natHH+/mVmZt1cvb/CPQbDxFHrlvbyRXUcjSKqyblZv92vr8szpYSlKlUieTiMHKtU5 onnP7N/g2G4t9e1K9jiljj228cd3b+Yu75m+63/Aa+kNB1S+S6t7M/ZXs9sas0cf3V8v5vl/3q5+ O3VW2oqru/urXTeG9PmWa4keNlb7q7lrysVjqmMqymkfR5dB0oxpI0tc8D+H/Edu0Wo6XY3kcy7v 3kKq3/fX3v8Ax6srwn8J/DvgPULi80az+yTXC7ZN025VX+H71dQzK96y/ejVdtV5GZmb+61Ze3qx j7Pm909z6vTcufl940NyxbmVl+Vf71QXV0yzeX+7bb8vzLUdnbtLNHhW27qdHpN41xJI0X3m3fer PU1EjvNkit5Me7/vmpLdof3zeWybY/4Wqpq1rdabY3F15Kt5f95q4tfihpqyNbrqmmySSfLuVZmX 5ZPL+9t/vLSjGQzuZLdWj3RMzf7O2oW+X7y1Hps0k+mw3Tx7d33lq7DN9omVn/1aru20gKixNcbl Vd9FrZL5iruZVX5mq1br8u5m2fNu/wCArTpJW+ysztuab5f+A0gOC8dWt14mjuLeyuorWSTasck8 fmLGq7vl2/7W5fm/2a6XRZmsrOGPc0DbV+Vvl/h/y3/Aq0rjTo9yxpb7o2Vdu1ay5tLmib5JGX/Z alKpLl5SOX7RrXGpLa2LTSw+fuby/wB38rVSt9e09ZlZ1ng2/wB5d1XWt4VtbeGfd5iruZl/vNVN rKHc21d61Mub7Miy0t5a3EMawXkTNuZm3NtrL1LTbWbXGvH1CJVZV/dqu5vlqyumwp0Wq62rIzMs Lbm/vSVnKManxkOPN8Rdm1SznkkkijuZ9zfwrtqK6uprhvMSNYtq7VWVqqrZXHzL+7iX/vqpmtWt 45FZt7bfu7a2jIsz9JtWsoZFvGXczbtqzbl+7/erZ1S3tZ9PWze4aDzIW+7Hu+9WN4Jt410tf9D+ VpGZV3bv4v71dDfRQyssjbfMVdu371XIDlG8A6bb6bdSJHYyxtJubbZ/eZm3f3qseFY7XRprjZ8q sv3Y4dq/xf8AxVdHcQx/2bbwtuWOSRpG2/3VrMuFht2Xbbw/MvzfMzUpSkVGPMXf7ct1Zf3e7bHt 8za33ttV4dSjX5tzbf8AaWqm6P8A59ov/HqctxH/AM+sH/fNT7xXJIvrrG9t3nsi/wANSLrEn/PR m/3l3VmLcR/8+sX/AHzR9oX/AJ94qPeD2ci9dXELKrKu1tvzfLR9v32qqzL8v+zWbeRK1r9oX903 /jtSaXat9n8x9yyNU83vcpPw+6X45lbbt+bdVyO8VY/Lk3J/01X7y1lfYm/56fN/u1T/ALUkRefm ajm5STavI5LiOSRWWVVX7y/er5Lum+0XUzN97c1fUUerx+WyyQxy+Z8u7dXgPib4fapo1xJIkLXN qzblaBfu/wDAa+gymvTpylGUviPlc8o1KkYSpx0icrt/4DR9ok8tlikZVb73zfeqSa3k+7LHJEy/ 3l21C0X91ttfWaTPi/egNZlX5V+9Ui1XkXZTo22/7tK5JJNL9nXcv3qIZVuP9mqsjNLJ81XIYfKX 73zVZHMSbfm/2WqvcW63Cqrfwtuqx8yrTd396q1LM/8AsaPduWSRWqxa2a2O5tzMzf3qsbd38VR3 ETS/7NRIBzNuXctFNjXyl3bqKYHpVr8sf/AqsN/47RZ2+61Ws2+lmt93lRyT/M3/AC027a+P5T0j SX5l202T5aLNl8ttzfxf71Tbo/8AK0AYtv4mt5YVk2t5bL8rbdzf3am/4SCx3MvmN/D83lt/FVxb CxVvlt49y1VXQ7FbiSZod3mKq7W+6u2gCNdes/vMzKvzfejb/a/+JarVrdLdR+YjNt/2vlp39k2L bm+zxr97+H+9Ui2q26ts27fvVADv7vy/NVyxv7izk8y3uJYG2/eVqz7eX7RGsix/L/vbqm+7DI3/ AAGrLjLl+E0odZ1Bm/1i3P8AvL81dHdeNlt9FtdOurOa0bduaT+Fqu+G7PytP0dY12yTNuZl+9S/ F9mzYqBhV3fw/erKOKcpcv8AKe88LUw9CVfn+JGdDcR3Ee6KRZV/2alrhIWa3k8yCRoJP9n7tbVr 4maLat5H8v8Az0jr2qeKjL4jw+Y2LyxhvY9sq/8AAv7tYdxoN1a/NEyzx/3f4q3oby3uI/MSaNl/ 3qmVlZdytuWuidKnWCUeY4ttvzKy+VJ/tU1lZf4flrsprWG6/wBbGrf71c3rGkTWcckkDbody/7y /NXnVcPKn70SeUz4/wCJqdGv+9WWtxdLdSK8LRRr/F5m7d81anmtu/2a4yQ8tv7tEkTbflWq6tI0 m3zGqwsrfNU8wFrQ9J/tLVLe1+95jfN/u17Br11HoOix2sXysq8f71c18KtL2tc6nKVIj3Rr/s1q +KJ7G8hbz5GiuG+ZVrz8RI+rwFL2OGlU+1Ii0nxWtx5drJHJuk+VmX+H/aq1rVxNDZyQ2a+arL+8 bd8zf7tYHh28t7WZoZFVZJP+Wn/stdL5dccZc0T08PKVan70jzz+zbeXbuj+7UiwLaxrDF91fmru 20CxS48yWPdI33l/hWuW1S/jvLxtsaosfyqy/wAVYShynlVcP7H3pGXN95v9mnRsy/NTmt2/h+Za PuqqstQcpoaPdWq3UbXcLSHcu3a22svxt/bDXV82g2+mLJ5km5tQVWk3Nt2su7+Fdrf99Ves4rdp GW5kaBV+6y11Hh2b7RZ3C/bPP/eLt835flrqpS5T1sHLl90wfh3b3UVxeSav9mZvm2tYxqq7d3y7 tv8AwKu/W9t1X91bxp/10qrIslrbx5X7zbv9mmNcRyt88K/9s/lrTmPYL8d5cTMzbo1h2t8yrVaO RmWSQ3Eku1v4W+9TFlVYZFSSTay7drfw0R3Ei2cm1tqqyqu2nzAZ2oI2qWc1rLNJ5ci7W2tWP/wr XTYrppII7Tzvl3bbWNW+Xay/w/7K11MMqyrJJLGreX8277tQQxb47iZ2bdu3f8CojIA0u6m0mzjt f9bHH8v7xfmq8uoWcitvhaBm+Vmjqot0zfwq033Vb+KnXSL5e5mj87dt/d0cwD7iG1t7WWb7eIre Nd0nmfwrXnPjv4rR+H9UsV06O31SzaPdJtk/h/h+Za3vEGqaC9vNpOrXkUUl1HtWNpNvy/71fPPi K1tdN1y8t7CZpbWOTbGzfNur2sBhYVpfvInzmaY6ph4/upf5npUfx+2XkitpbRWbfdVZvmWvUfDP iiHxVptndWTSLbt8rLIvzKy/er5SaXavzKrV33hv4tN4Z8Mx6bZWMcV1Gzfv5G3Kyt975a9DFZdH l/cR948nA5xKM5fWZe6fQMix38zMkm2Rv4W+7Uf2OT/pm3/Alr52m+KXiK40m6sZbqKeGaNo/M8v bIq/7LLXsngO2uLfwbpLXG95JIfM3N/tfNXi4jAyw0eaZ9Dhcyp4upyU4nRyW8kS7pF2infavWOJ 2/vbaz9aWTUFbyvl+bdtp2kr9js281Vebd91vm2rXkc/v8p7Be+1Lu/eQx/8B+Ws7xJdR/2XdSRL N9oZdq/db71XrGdbqzjke3jVpJGVdv8AdrA8ZW811dafb2drNJD5375vmZVX/a/+yraPve8Bp6Lb tHotnD5ka7Y13L8sfzf7q1LJ/rFjX52/2auqytujZVaOOP8Au03TY/3zTMv7uFd1OXvAV9RuvK1B bdOfs9vt4/vfxVkVPJfzSyMzSb938LfNTo7pV/5Yqrf3l/ho1OuMeUbDYTTfNt8pf7zVaj0+H7vz St/e+7TrW4WVf3m5lX5f9r/P/wATVmTzNrbl8qNfvbf4v8/+zVUYkykVpLe1ik2sv3W2/e/3v/ia j+zwy7lWGb/gP/Av/iac2oQqvyxru/2fmqGTUmf5du7/AHmo90PeIbhI2uFhZvlhVfl/vVfjddvy tVDzZrhv7zf7tTLayT/f3RMv935d1Z8sTKUZR94txsu6sqx0e41aSRYdqKv3mb7ta1n4fuGkWZ5m jRfvNItbMMlhs+wQTBWb+7UOlzfEYfEcVqekz6TIqzKu1vusv3WqraxTXUm2CNpW/wBld1d9NJp8 0IgnX7T5LfxL/FVX+0Ps7tDawRW8f+z95qn6v73uyDlucy3hK+uF3S2Kt/s/LXnPxM+HMiwyatZ2 bQTRruuLZV27l/56L/7NXtH9pXTf8tG/vf5/76WrFrqMl7E32iFWh/iVq9HD1pYapzQkcuKwEMTT 5ZnxfJ8zbl/8eqRV+bdXoXxU+Hc3h/WJr6yjaXR7hvMVlX/Us38LV5+0W2vuaNSNaPPE/NK+Hlh6 kqcxvlL5m7+Kpl2/8CqHa38PzUbm/iWtjnJl3d/u03cv8NNWWjcrUAEzfKtG5lVahaRZZFVamk+9 tpcwDmb5Vopzfe27aKYHpSr5Sqv+zQyr8v7tWb/dqTfHLJt+ZWoaLc3ytXxh6QRsqr/q1/4DTlWN m+XctNaJlWhV/dyfw/w1YEfyyttSRWapJFbb92s+zs5lupGf7rNu27V+WtBWb+8y0AH/ACx/3qr3 jeVC3zfNVppf4SqtVW68lpI42jZt391vu0ASW7eVbxqrVY3bvLXbuZqj8tW+61WNPlt/7Yt/PmjW GORdzM33aguMeaXKez6Ja2DNbRpG6z2se7/Zry7xlrVxrOrSb18pY2ZVjr0PQdUs2s7++juonj27 VbzP4a808Xalb3uuTSQSRsqqq7lb71cdH4vePq8z93Dx5Ze6ZK/e+ZdtR7vmZt1SQy7o2bduWm/L t/u12HyJG1usv+y3+zRZ3l5prfupPl/u/wALVJ92o2WtI1JU/hJ5TqtP163vbfc7LBJ91lZq7Dw3 pkMglvLsK1tEv3WX5S3rXkflebIu35WrsYvHMo0+DTbiMLEnDTL/ABf71dkq8qy5Nj0MLVjCXNU+ yM8RaTDql5JeW8aQTfwqq/LXNyLtm8uVWgmX+Fq7KGaO4j3RMrL/AHlourOG6t9s8ay7v/Ha6KmH hy+6Zyi60pVDh2iaL/gVNWt660GSBWaBvPj/AOebfeqjb6bJcMreTIse7a25fu15lWlKjqzCMZfC bGi63qGk6b5MVwypI27y9tWLjXPt9x5kqqsm3b8v3az5mVm+X7q/KtQ/7VfLVa8qkpM9ynKUafJz GgvzfdbfXa+HdWjazWFple6X5VZvl2151HuVt26rC3TfxfNUU6vKb0K/sZXPUbhJFsWVfkk/eN83 +7XnTbV3ba1I/Elu2i/Zd0yXCrtXd8y/99Vl7d23+Ja3lKMjpxVWNbllEbHu3fLUi3HzbX+7Tfur /tVGzbvvVkcBIytE1dF4W1Czt18m4/dbm3bv4WrnYW/5Zn7v/oNOZdrKv8NXGXKbUqkqcuaJ6PDL 5n2iR22Rsu1dtR/ZWb/VMsv+796qPh9vs+iwrKrS+YzSfM33f7taVvbwzSbo5PlX5mVq6fiPpacu aPMDQqsKw+YqyfeZWqH99at/Ev8A6C1F0snnM0qsu6izZvN27v3f8W77u2mWWFaP7L+9Xymkb70d Nmib7PGsX71V+ZmX+9XL+LtXZplhg+Xb83y/w/3at6Dq0dututxcM11MreX/ALTf3aXNzS5TnjWj KpKBs2a/6Qrfwr8zbv4ajkWN5FWDduZvutRdastlazSXTL5Ma7pGb+FVrgPE3xc0nTdHW80KaO+v JJPLWOT/AJZ/L96uijQqVpctOJNfE08PHmqSOT+PEWmxeIrX7Ov+meT/AKQyt8rf3a8x+81Xtc1m 48QalNqF5IrTSfe/hWqP3fmavu8NTlRoxhI/NcXVjXryqR6jZPvbf7tNoorY80P935a2dH8aa5oO 0WWpXEKr/Csny/8AfNY1Vf8ASPti/d8lm/8AZaXLGXxG1OUoS5onrekfHrVIcLqVtb6lH/fZfLb/ AL6Wuj/4XtoNwu2XTbqDd/zzmVq+e421Bd3meW1Hm33nbfLj2r/FXJPL8LU+KJ68M1xdP3YyPpBv jZ4ZWG3jjjvtsa7f9Wvy/wDj1V/EHxx0jRrW0ksP+JgszbplbdG0f91a8Fs1k/eLKvy7vlanTWq3 Sr/rFaNtysv8Nc/9l4aMtjeWdYvl5T3vT/jp4fuFZbqSWxaT5W8xdy/99LXb6T4w0XVNBZrPUoJf MZlb95t+79771fILaHbqzMys25t3zN/FXuXgP4b+GrzwzpM1xJPPcNbzK0cd1t2+Z/s/3q87HYGl Qhz0z2cszKriZ8lQ7pdZ0O68zyNSi/dyeS22Tdtbazf+grVqOy+0RrJbTRzxt91lb73+7WbN8J/C 2orNJdLdN9o3LM3mfNJu3fe2r/tV1Gi+G9M0TS7bTbGfZBCu2NZPvV4HKfW+0K9nbyRKq/LFuVvM 3feq3dTtNNbb2Xb5f3VqdtJmj+WIK0e3buVvm/4DTrfSpZfkuPlWP5vM/wBqmHN9oxG0plZvMkVI /wDvpqsR6fCrL+7aXc235q32s7OHa0hVdy/xVYkuIbW1WQfd/hp8oe0MeHTJAu6cJDD/AJ/hqyWj tQDbwqz/APPST5arzCaeTzFulbd91d22ppFWORtzf7P+f/HqgylLmKV9DNeKwuLr/a2r92m20drZ qs5hbcrL827cy/7VT3Ui/LDE224b5f8AdrPm861t/MRo5dvzbVXa1LmLjI1pI7e4ZpIpl2ybpNrf xVzF14htbfUI4/tEUU23/Vs3zNu/+yqx/aHm28jJ+9+Xa275WX/erhfG1xp8U0cd7Zy3PmR7t0bf 3WX5dv8AwLdVc3MaR92J3kmpWsUcatNF+8by13NtZm/u02TUobdl/eRszf7X3l/+yrzFr/wjZ262 bx3TRxtJMu5W/iXazVRuovDN55K/6dFDC26T5fmmXavy7v4aQ+Y9mt3t7+No32yW7L+8Vl/h/wB2 vD/ip4It/Dl419YKy6XJJ5e3/ni393/dr1DR/Ji02z+x7vs6xr5e7723b8tc38YNShbwbJG6yfaJ riPbtX5dy16mX15U60Yx+0ePnGFp1sNKo/iieJ7d3zLtao2l/hZaNrL833W/3qFuFb5X2tX22p+Y Dflb+Gm+V/daptsbfdbbQ0bKtGoFWS1ZqjaKTzN27c1WNrbvlajc38S7qy5YgC3DfxKrNRTlb/ZX /gVFakHqkcXzeY33qj+ZfvLTo7jzdy/981H5qrJtZdtfGnqjmlZfutTt7Mq/Luo3L/DJUc0UjL8r M1QBJ/C275aaq7m+9TV3LCu771C/3moAG8zzPu/LVX/W3zfN/q1/vVe3L6VDHG0TN827/gNADvu/ drDkbzZJJHs5HaT5m21vbl2s1Nhs2vJFhg3SySfLt2/NV/CNR5ti94FtbWJriSXR7me4khk8uRpN se3b8y0axokN1bxx2/he+trj7zSeZuX/AHa6LXPCutR2em29nM9tJCqyN/o7Sf3fl+VvlZfm/wBn 5q79Va30u3hb5G/iXdu27a5Kk+X3on29DDxlh40qkTyTRfCGrXlrJHb6e6LH83zMu5qp31jcWNw0 NxC8Ekf3lZa9ps0j3M0jKm1fl31YuorW6t9r+XKrN/y3j3LURxH8xyVcnp/8u5Hg0bLL838X92iv TPEXg3S1026vEtfKaGNm3W0ny/8AfNecqqy/dauqEo1D5/EYaphpcsxsf7qPd/F/DUP+01TTbt3+ ytZ7albyw7X3L5i/d/2aZxFqO6a3ZZLeba3+y1bVj4q+ZVvI9rf89Fridti6svmSqv8ADTmit2b5 Gkbd/wDZV0U6sqZfNL7J6tbst6sbQMsqyfd2/wAVdNrPl6LosOnDa0svzS1494T8SL4XknubZGum RWkW3mbarN/DR4Y+O2l/EDW7m0vY20jVlbb5E7Kytt/hVq7oxli5fD7sTupYinRpy/mkdPfWPlr5 kf3apq26t9tpX/ZrOuNNZW3J92vAzHLeX97QjoVSq9JFFqFpzLt+9SV8wdYxu9H+8tPopi1Fjl8l fl+7TmuN33vu1DsprfdquYNTQjl3L5bfe/h/2qtQsqsqy/6v+Lb95ay49zLt/iX7tWoZfN/66f8A oVaRkVE9Ls2tYdPjVF8+32/uWX/0GqOravDps32eP5o1/wBY1cxouuSaXJtf97ayfeiqHWpVa8ZY maW3+8rf3q7JT933T2Y4yPs/7x2drqH7vdFNujb/AL5rN1LxJ9jmmt1hXcy/My/Ltaua028k0+6W RdzKvzMv96tjUtX0/VLOSSWPbdfeo9p7ofWPaU9JcsjBmaSWRpGbczfeaneH2X+2o7h5FWFf9pl3 VTuJVVdrbvm/u/eqSFVij2/erDqeXGXLLmJPjd4qW38Px2cDfvLxvL+9u+VfvV4O3ysq/wANdN4o 0u1tZpriDUvtLecytA33lb+KuZ27mr9Gy+nGnQ0Pm8xxEsRXuyTfu61DM3y/LU27c3+zVeRWZt1e hI8qQR/MvzU6inKvy1JA2o7qzW4VWZmXb8y7asbVX71Hy7W+9VlxiZ7aHHKvzTSfNUcmkrF92STd t+Vt1a3+ztWmsy/3VqtSyvu+yxxqvzfLUkcvmxs3+1TmWOX70dOWFVjXb8q1EfiAjqZpZIpNyMys v91qasfzL81ez6L8EdP8uO41O8nnaRVbyIP3ar/wKsMTiKNCN6h3YbC1sXK1El+COqXF5p+pNf3k tzDG0aqskjNt+Vq9F+a4uPM2+VGq7VWsTwz4X03w5HNHYQtFDJJu+aRmZttdLDbtcfd27v8Aaavh 8VVjWqylTP0TA0pUaEadT4h8P+jw+dJ5nzN8u1tu2rnnXEfyxXTO33tsi1WjhWVl8yeNtq/dX+7S NdRyszSwq+7+Jflrm1O0nW6+1TLDeR7m/hb7rLV/UrVXtkjEixKv96uaj8QQ7l3W9zL5M21pFj+X /wBCqWfxFFqOpJH5NxErLtjaSP5d21mqvsgX47XfNb7GjdV+9tams00TNvhb5v4qq/avs68Ku7+9 /FTVuGiVf9Zub5vlbbXNKvGPul8pMu1m3NIqtu3NVhVZmuGXanlx7V/2aq/2g3lszSNtX/nrHuoX VI2VlPk/vPvbWaOojXgHKEKr9okkdVZlVY2/4FWRrFm39n3zWsMf2ry28tm+6rf5/vVs/uf9IkVm t5Jtv3vmX5aj8poo2Z/mh/iZfmX/AD/vVvGUZFRPOr7S/ETSM32PT2tWXbDLNtaRlZV3K235d27/ ANBqbTbXWLqT7Pq1rYtp8kLeYqr827d8q/8AfNdhZ3EcsbWsvyxs26Nv+ebVXmt5LWTbKuxqs1jE hjVYlWNF2qvyqq1xPx8s4bfw7Y7G/efaFZlZfu/L/s13SttZWX7y1h+KtD03xN5MOr3TRRtJ+7VV +aST/e/hrpw84060akjlx1KVbDypx+0fM+6+XareW3+1/dpsMt9Msm6NYvlbb/vV75ffAnS5d32O +ubZv7sm2Ra5+++BOqRfNa31tP8A7Mm6OvrKeY4aX2j85qZXi6f2TyVf7Q+6yxr/ALVSW8uoLJHu WNV+Xdt+au6k+E/iqJmVdNWfb/zzkX5qw7rw/qlhI0dxptzBIvytuhau2OIp1PhkefPDVqfxxZT8 1W++v/Aqb9n3fck3f71ElvJF96ORf95dtR1rzHNKLBlkX7yrRUyytRWgHeblX7rMtSfbG+7LtlX/ AGquNKvy7o4/mWoZIoWVdy7d392vjT0iNdsq/upNrf3Wp0MrKzK/3qha1/uNu/8AQqdHcfdWX5lX +L+JaOX+UC1Izbtqrv20blWP5l27qay7m3LIvzUSfwru3VBY5Wj3fe/76p21m/8AsartTd+2gCxJ 91V/vV13w3uLO31JZri4jijjZm+b/ZXbXGxyt5m1m3L96tbT4pLeGPfrywWqqv2jT42m/ds377/l mvyttX/vndWdWPNE9TK4/v8AmPX7HWYdUvJPImjnj8z5trfdqOO6i1KZVT7qttrE8F2syQ33myN5 lvCtus8jM3mNt3bv73/LSr1jpF1p8d1Mq+e3l7Y/Lbd8zV5VTmjI+2Ft7hby4kmiZlt1baq1pfu5 YoVaTymX+8tYelyrp9usMu6Jv4lkXbWgsqy/MjK61nTlzRHEvyWC3Uzbo45Ldl+797ctcz4x0vTd D0GaVbGBbhtsMbeX/E3zNXQ3LeVcKq/ejXbXCfEzUmlvLXT1bctvHub/AHmruoe9I8nMZRp0JSkc b5u1fn+Zas2fh271iOf+z7XzmhXLbV+aq0e5mVdu6vXPCeir4a0tZizPLcqv+yytXXOp7M+awOD+ tS974TwnbeWszK9qvmbtsiyfLRHLdNI0a2cf8O6vWNQ+HtjrMkjXF9LA0k0km6ONd3zf3m/iriPG /wAONS8H6Lc6paSS6pbRL5mLdW3/APfNXSqRqe6i8Rl1bD+8veicR408Rt4V8N3Fw8McF9N+5hVf m3NXz2zf8tG+8zbmroPGXiqbxVqEbN5kVvbx7Vjkb5l/vVz/AN5q+5y/D+wp67yPl607yPTvh/8A HDVPDlxDb6rNLqel7fL2t/rI/wDaVv4v92vo3RdcsfEGnx32n3EdzayfdZa+Ia6Xwb451bwTffaN OuGVW/1kDfNHJ/vLW9bDRmuaJrRryj7sj6+urFZl3J8rVlsrKzK3ytWR8P8A4paZ48t1jVls9SVf 3lpI3/jy/wB5a628s1ul/utXwmY5Tf36Ufe7HvUq/MYckqxMtO8yP+8tNvLVlk+dahWCNl2su1f9 mvi5e5LlkdhY82NvlVl3U1biP+8tC28atuWNdy0eRH/zzWgNR0dwu75WVqk3fNu/iqGOKNfuqq1Y t2WKT513K3/jtXENS1DKtwu7+L+Jakjb/lm33WqjtaKb5fvfw1cZtu35fmb71bRkMkZdvy7qjm3b vmqZdrL5bN838NV5IpJVZVb7v3lb+KqKKca/bJvM/hj+7/C1cz428VTaXeR2tlNtmj+aT5d3/Aa6 DVvEFn4dt4VuN26Td5e1d1eS311Jf3011L8zSNur6LK8D7WXtqkfdPLxlf2cfZx+IhZvmbczbmbc zU77u7dtqnJeQrJ5byKrVJ9qt2Zm86P/AL6r7WPkeCTLt2/dpvyt/C1QrdQ/LtkXatOW4h+95y7f 96r1Ak+X+FaG3Mq7abHcRtu2Mrf8CqRW3blqQG7f71OXbuplLt30AOb+FqP4vlWhv4d1N3f3arUB 21mXdTW+6tDN/E1NZmb7v/fVAGhoNu1/rVja/wDPaaOP/wAer6kVWurjb80EO7au371fNvw7t/N8 baP/ABbbhW/75+avp3T2j3W6tCu7+9ur5XOJe/CJ9nkEPcnImVI7eGSO3jWLy227vvNtqP7Qszfv fkb/AJ6rUa3DRbmX7zVMqw3G6T/VbfmZa+cPrB3/AB5x/u2XdJ/Ev92o1aO6bay+VI38S/dpiytL ceZ93b/6DVmRVjVZoo28yT7v+zVAcI/wxkvPEF9cM32lpm3MrXH7varblXbt/hb/ANCaovCPw5/4 RfVpryObzWZVWbdIv8KyL8qqq/8APRq9J0RNqzyVjW8jeXHJ/E3zVXN7pP2hsnzLTFuG27WVWX/a WrLKrLuj+7/Ev92ovs6v977q/M1eZXpSl70TWISbWjjj2qu5tzVVmtbfcy/vItv/AAKpo91xcL/t NQyNNM38O75m/wBmuJ+8WQLa3Fv80Um9W/ut/wCy1ah1BrWNWlXbJJ/d+VttUl/0++2q21f4f91a ddK11qHl7t3zeWv+7RGXLrAgvzabDfQrNHtgZvu/3W/+Jpq+Zax/Z54/NjX+Fv8A2Wrm9VtdzbZV 3fKv92o2uvNZvPXzV/8AQa9uPw+8HMUJNNWX5rVt3/TNvvf/AGVYOsQyLcafHukika4XbHuWPd/3 1XYw2e2RZlbdGv7z/aqhcWEOrSSXFxHukjXcvzN8vzLVxKlU93lL7WUiW3+r+b+9UP2dljZmVi1O t5WS3mZWZfu1NZ3k3mNubeqqzfNUmZXs03XUf+9Vaa4VZGZ5FX/earf2xvm/dx7trfMq7a8Smaa4 bdK0krf3mbdW9Km6h5eOxqwfL7vNzHYfEiW3nh09UminZfM3LGytt+7XByWcMv34Y2/3lqw21Y/m ZV2/NUMN5ZttZrhVj/ir04OVOPLE+OrVPb1JTf2irNodjL961j/75orvLPRvDLSbW1bz5Fby9vmL H823dRUfXJfzM7FlVdq/Kjzm6v77aypa/MrKqt/eokv754/+PVVba3+1/FWlRTPNKcNxeNIubP5f M+Zt38NXrpY1+9Iqt/DTl/3ttU761kuPJbzNm1t33d27/Zq4kEkcvlfK33akqPb8v+196iH5vl/7 5oAko2/xUUbveoLLFvb/AGiby1+9J8v/AH1Xq1j4IjtdPjuHaBbhof3jNYx7mXbt+b/gNec+HYVu NYsY/u7plr3HUv8AVNCm5tqqvy1z15H0+Tw92UjLsf8AQ7OTzJo55ppGkk3R7d3+7/3zUkzeTart +XzGZtq02aGNY1kXzPm/hai9+WRY/wC6qrXnn0g6O4kaOTe3mrt+7J81Qw2tq0is1osTL826NttS LtSz/wBpmqpqkv2XQb64DMsixttZf4aXLzAaVq63l2qtCu5m+ZlrhPHPg64+3Xmp20y3MDNukTd8 0dZR8T63b2sl5Ho2oq0e5fLe4k8yRfMXy9u1dvzK27/Z21Np15q/iixNobS6spbySMyrcMzNCu5v m+b5vm2rXZS5oyPMx1GNaj7xY8B+F5L+8jup4W8lfmj3L96vS7hbppGVIVaNfu7lq1ZxQaPp8Fsv KxKqij+1PmdWi2bW2/M3WpqS5pG2FoRw9OMIlGz0iRpFaZdir/DWhfXS2EKhfmb+7Xl/xq+M158N 9Bhm0+wF3eXEm1d6s0aqv3mZqwPDP7R/hXxbIq3l42kXjf8ALO+Xau7/AGW+7W8cLWlS9rTXMiZY yhGr7KUtTe+InwL8J/ES3mufs8ej6i3zfbrRVU7v9tf4q+IfEGk/2HrV9YxXUd9HbzNGtzB92Tb/ ABLX0b8fvjZcaTM3h3QpImW4td1xfRtuZVb+FWX/AGf/AEKvmhZdv+0tfZ5PTxEaXNWl7vQ+Jzqp h5VeWjH3upCrVIzfLuqxa6bJql1DDZxtLcTNtWJfvM1R3VvJbzSQyxtBNG21o5PlZa+g5vsnzsYy G2d1NZ3Ec0E0kEkbblkjbay17N8Ofj1cWckdj4jka5tfure7f3kf/XT+8teI/Nu+7Un3ayqU41Pi NadSVP4T7kjmt9Xs45oJI57eRd0ckbblasm4s2t5Pmr5o+HfxU1LwHMsa7rvS2b95aM33f8Aaj/u tX0/4f17TfGWiw6hYSLPbyf99K391v8Aar43NMnVX3lue3h8Vze6Vqikbbtq5dWbW7f3lqnMtfAV qMqMvZ1PiPVjLmCOX5ttS0yOLbT6yKLNrLuXb/y0X7v+7Uiruas/c0TKy/w1oRyq6rIv3WrenLmH Ec27d/tVY2+b8rfe/vVD8v3qm3LEu5q0GcD8RNBvpZv7QX9/axrt2/8APOuD8qS43bI5G2rubau7 bXukku5WVo1aNvlZWrF+1QxSTNBH5Uy/LujhX7v/ALNX0WGzaVGl7KUTy62B9pU5oyPE7yzjljmk aNmbbVOT7Kse1bWRtu3cy/xV0GqWs1nqnlyzea0n7zdt21XZtzV9lRqRqRjKJ4coyjLlkZbeTayf 8erMrKv/AMVUbNat/wAuMq/NW1/y0X/Zpyt833a2JKtvYQ2+5oo9u6rC7V/ip0m5qaq/N81GoA3y t8q0N8yrQ33V21Jt+Xb/ABUagR/wtTfmb7v/AH1RGrbvmqX+GjUCCSaG1VWlkVd3y/NRvT+8tR6h psepRrG7Mqq275aq/wBg26+Z80ku7d97/gP/AMTRqBqW901uzSRSNFIv3WVtrLWtp/xE1q1k3W+t T/L8v+s3Vyc3hy3lXczSKzNu+Vqc2g27bfvLtbd8v+83/wAVWMqcZfFE1jUqU/hkfRfwj8dTeK7e 6j1O4inmj2+SsXyyMv8AE3/oNelRtbsqqGZV+8yt/E1eOfA/wHb6Xp8OvLNI9xcLJCsbKu3buX+L 73/LOvXZ7dYY1YSbty7tu2vhcZyxxEowj7p+lZfzSw1OVSXvDvs7ec0sseyNf7v8VQ32qNpvlzSL I32htqxRru3fLT7y4t7OxtVuZGiaTcyt96uf8YaTD4u0u3sYryNo9zLMqzeSzK27cqttrh5o83Ke jzG9beKrW2tZV+y3fmMW+XyaZ9ohvIYbiH/VyL/d27W/irz7Wvg/JqlvGsvmRLJ95o5o90jfL8zN 5f8A0zWu30u1ks9Ht45dvmKzbtrbvvNurWoESwjbW+WpriKSGPzFVXVl+aP+7Qq/ZV8xv9Y33V/u /wC1Ua3EiSeYrfNWEo80eUCFtrQyLE3lSN/eamrcTWVq3n7mZm2qrN/DVi4tVZfOgX/eX+7VDUNz XSwqv3flWvKqRlTNSOH/AEe3kk/ib92v/s1T6bF/rJP+ArVDW7+3023kmnmWO1tV+aX/ANmrL/ct rljfQeIrWC1ZoWa2a427tu5mX/Z+8tFCl7Sf+Eg7ma68hVt1WN1X727+9UMaw7tzybVZvurXnEei 6hDeTSL4sjnkZm8uBpPmVVZmb/0L71V2W+i02GRPGEUFvtbb56/vG+Zf738Pyt/31Xtcv2iT1u42 vefJui8v/lorfLtqNbiOVpFWFkaT+7XDWNxDpM2nyHxFFPHJG32iPzN3mM3zRt/s/LXXQ3Eb2fmQ SLL53yqytu+WgDMuvGel6X5kM8M6yf3ZVqxY+KLO6t1mgaCLzPl2tt3UXmkW+sxrb3MKyr/C38S/ 8CrgvEHgW401pJrRvtlqv/fxf+A1vGNOoePXq4nDy5uXmidtp/iD+3L7UNPZmjhjjZWkVvvfLXnv iLw8ukxxzQXHn28jbV/vLWXC0m5tjNu2/wAP3qjX7zNukdv9pq2jCUZe78J8/XxixFP95H3ivdMy qu2Pduba1Ud0e5maxkb5dtNVrqKTy2vNrN/eqxtuPm/0xfL2/wAP3q6onlLc6y3fTbiztWn8KztN 5arH95t3y7fvfw0VHa2+pXWm2twmuWyzeWvy/aPLVV/vf8B3fdb71FeVPc/SaP8ADRzrS/3aN3zf LTmj2tUm2P8AvMteofnBX2yf7tWGTYsa/wB1ad5W5l2su2msys27bQBHt2tULfK1WGbd96o5vmX/ AHacQHec3+1QsrU232su1m2stTNb/wC0rUgNDQLManq1hbHpNMte03FvI1wzQSq393a3zV5t8NbP f4gkuGX5bWFm/wCBV3m64tY2VWbb97bIu5a4sRL7J9Zk8OWlKfcuxyyS3EcM8fmt/tfeWoJLpmmb 5vvfw15/eeCdauNY1i8tdaWxW6/fblVt33lby2bd93+FdtV/+EI1RtWs7qDxJK11+8WSSRW+ZW+6 u3dtrm1PePR/tCt8rKrbf7vy064ihlt1hk3eWy/MrLuX5q8+8P8Aw71jTdUs7y48RSXP2eSOO6Zl b/SNqyf/ABS16NHpslxeeW00bKrbWVf7tHKMr3Wg6TYWMKtp8DSN833a0vD+kQaVayTpDFHLN8x2 rtpNQsZrzUoVMf8Aoq/eb2Wl1e83RKqdG/8AQarm5RD/AJfMZpG+7/eqjcXEd00m/au35lb/AOKq qrMuNrVkeIr1rW1WNfvTf+g1lKRnVqezjzGDrV5/al1JvVWh+6sbL8u2vPfEXwg8N+INzfY/7PuG /wCWln8v/jv3a7i4lVYWk/u1RXVIVXdtZv8AZrOlialCV6cuU+VqxjW+M+cfH3wd1LwbC11A39oa f/FJGvzR/wC8tcDt3fdr7GuNShuGZW+ZfutuWvI/E3wgt7/xVbzacv2bSZJP9KjX/ln/AHtv+9X2 WXcQRmvZ4r7zwcRgbe9SLXwV8A/2TD/wk2qKsTeXutVk/wCWa/xSNXnPxI8Wx+N/FV1eJCsVuv7u NlX5mVf4mr0T42ePobPSYfDumzfvpFX7Qq/8s4f4V/4FXhrfL8q16uVxqYmpLG1evw+hz4iUacfY QBrfb/u03Zu+61SR3H96nNErfMtfSHnEP3V2/wAVdl8M/iHceAda875pNPm+W6tl/i/2l/2q49lb d81C/e3fw1Eo80eWQ4y5Zc0T7W8P+JtN8W6XHeafcR3NvJ97+8v+yy/w026s/Kbd/DXyT4F8ZXng nxFDqVruaH7s0G75ZI6+svDPijTfGmjreWEyyRt96P8Aijb+6y18lmeWRqo9zDYnmId38NLU99YN F8y/MtVY3/vV+bVaNTD1PZzPXjLmH0+xuF8zy2+61V5Jf4VqS1ij8tpP9bIvzeXWUZe97o9TUj2s 23bt20Myytu/u02zuPtFuzN977rU1fl3LtrrLDe1QyRQ28fmMzRf8C+7U3y/7VZevSttjXb8rNWt KPtJcpnOXs48w3UPC+n6yvmTx7m2/LOrfNXn/iLwlJpKtNayLeLu+6v3q7CGWRY/kZl3feXdUcnz fLX0OFqVsN7vN7p5FX2db7PvHk9xqX2OTy57eWJv9paauqRt/wAs5f8AvmvSNcs/tVuqtHHL83y+ ZXK6tYWdjIscW7d97b/dWvpaGKjW93l948ypT5Siq/L/AL1V5JVi+98zf7NWtsa1XvrdbqHy1Vfm b5trba79TMjjvI9rbt3+6tWtyr0rJXSVVlYRt5kfyq3mf7O2thZf87aNQI/4lanN8rfNTt3+ytDb W+8rUagNVty0fK1O2x7du7/vqhYv7rUANk2/d3fdpu3/AGlqxdWFxbyfvYZYv95dtV2Slcdmevf8 LEj8G+E/D9vpy22oSND++XzP9X/ndW94X+Llj4m1COxuLf8As+4ZVWPdJuVm/u14v4d8OXniPUls 7Vdskn/LRvur/vV00nwf8TWv7xIYp1Vl+aKavAr4XCe8py95n0uGxmN92dKN4I9p8cXDQ3VrCkfm rHH81YUcrbo2X733q7aGzaW1h8+OC8ZY1jZm+992mQ6Xp/2pVezWJv8AeavhJ4eTrc/MfapSlqaF 9mKO3jZvmWH/AMeqnaqrzLG/3WqfWNr3zKWZdqr/AA1W/wCPeSORW3fxV3v4jQbNI0kjM1R8KKvX lvt+4q+Wv3v71UW+6aQySGVodrD71Ok+750C/Kv3ov7tQx9qkty6tuVtu3+JqxlHmjylmJrGn2s+ l33m27X0fks32Nv+Wn+zXHyLptqyyT+D7nztu3asnmbW+Vvlr1HylXfMiqysv7xV/hqMSxyxrHKr fL91l/hp0IezjykSPLodU0HTZriaLwzfRSQq000jL93cvzfe+9Ul9FpdvqDfaNDuZ2jt41hbzJPl 3bm2s38NeoTWKzQrHtWfzG+995agvtSjs9sfl+fH93av3v8AereU+WPvAeXR3GhtIsk/hW+8z5fM 2q33q9I0G1jbR7PYv2ZmjVltm/h/2dzVpttWGPZulh3bpP73+7Try6jZmkfb9nhXd/8AY0AQyH7L Htb5ZpP/AB1a82j03xxpcitb3kV59ovmkkWdv+Pe3+baq/8Ajv8A3zXf2+uabeTbpd8Ujfwt92gv u6VlGpGXwiPKl8M+NotYkvFmgW4/dyLPGse1m8v94rL/AHd1O1Dwf4wvLzzp7XT1WS6k/eQNtVYd vy/L/er1NnpYbiS3Ztv3W+8v8LVvGrI5qmEo1I8sonh83h+6s5P+JvD/AKYv3fm/h/4DVPyrW1um VoWT5fvbv71eral4FbWdYa4W82xyfMyt95f9lao6T4cXTfFH2O5jWePy2b95H96u72seU+TeAqRq cvL7tzHt7/wzLYwrdaLdedtXcqw/L91V/wDZVor1WGWFVWMW8fy/LRXA53PtIQtGx4Yyq38W3/gN V5GWKTy2b5tu6ppF2x7abJtbb/FXqn5sNt5VZmZWpy1CtvHu+7R9n2srJI21W+ZaAJKdsH97/wAd qrNdfZfmfasbN/E1C3kf8f7v/eoAk/1U3+zVr+JpG+7/AA1Xupo2m8vcvmLHu/4DUdvdRs3l+Yvy /wC1Vknqnwstdum3kzfekkVf+Ar81dDca19lb97cbd3zfNWV4FvdPs/DtvvvIkaTzJG/eL8v8Pzf 981NqmhtqW2azkinVl/hk+9XjYqcvigfeYOPs8NE0odShuI9yxwT/wC0tWZGt7W6XbaR7l2/99Vl 6DpMljtjlh8hpJP7taFxEzySTbo2Vm/hasqcpSjzSPRiTQ+XcTQxxq23zNzbqvaXNCk00atum+9J WTcXH9nx7V/4+GX/AL9rTPDdxsa6uHbbDt2qzfxNWsZe8I1NNmm8i5kZiy/dX/erA17VNQtbxVt4 1ltY127du6ty61C10PT4hcSrEzfvP71cy3irSd21ppn/ANryaipLl93mOarUp/DKVhkPiuF223Nm yt/0ybb/AOOtWDq101/eSTf8s/ur/srVy802S/Zry1kW5jkbbuX/AJZ/71GoaK2m2/nLNuX+JWrC XNI8qp7WpH+7Ex1+Wo9QikaPbbqu7+JatfL977tRstZHAYs32hVZfLjVv4Vq5YxXETfvY49rVemV WVfNXdVG41SNfli+bbTp0JSl7pnKUafxHhfx8/sVdchW1hVdY27rqSNv4f4Vb/aryVq+mvH3w70f x1bzXSsun6lGu5rlV+9t/vf3q+Z2+VmXduVfutX6lk9aFTDxox+KO9z5rGQlGpzfzEaruapN3zfL R935f4qaqszbU+Zv9mvdOAmWVZV2v8rVHIjL/u02Rv4adCzKvP3asA27V+781anhvxVqngvUFvtL uPIk/iX7yyL/AHWWs3bu+ZPvf3ahmbatRKPNuOMuU+z/AAT4oh8b+FrHVUj8pbhf3kf91l+8tPul 8q6rE+DegzeHfh7pNrcKyzSK1xIrfw+Y26t2+kWWb5f4Wr81z+EI2kj6bCylKPvFdot1RrF/eqei vjOU7tS3prfM0f8Aeqx/E1ZsMvlSKy1pXDKq7mbav3q66Yxu5lqGZVljZZfmX/aqncasu7bErStV qx8M6lrjLJO32aH/AGv/AImu6nQl8UvdIu6kuWnHmMtls7dfL+aVqos37z5flr0mPwBpNx5Me2dG +6zLJ96oLj4Z2sO7/SJ9v8LLtavRp1YR+0KeW4n+U89b7orD1zw/9v8A9Igk2zKv3f71dZ4i01dJ 1SSzikaVY1X5m/i+WqcNrJcSeTbwyTyN/Cq7q7KdWVOXNE8idH3vZs8vZvm+6v8AtUNu3fLXsVz8 CY76OzktbptNmkXdcQXK7l/4D/8AE1seD/B/hvTWmks421G6t22tLOvzf8BWvXnmdKNPmj7xvSym vKpaXunmPh34X614jVZPJWxt2/5aXPy7v+A/eravvgXrFgzN9stZFX5vvN83/jte5x3UaqqlVWHb 81ZNx40037VDp8UjTszbfP2/Kv8As15H9p16kuaJ70spwVKPLOR83XHhnULdm3Q7v92stt3mN8rV 698TL9ftV01uyq0MaqzL/eryH5vvV9Bg61TEU+aR8jiqUaFSUIyDc3pW14N0v+2fFGl2bfdmuFVv 93+KsX7zVpeHdcuPDOoR6larG1xDu2+Yu5fu7a66nNyy5TCjyxqR5vhPpaa6a43RybZI/wC7Iqsv /j1Uf+ES0PVrhvtWj2bfKzM0cflt/wCO15ba/Ha6X/j60e0l/wBqORo//iq7j4f/ABGtfGV9dWcW nz2ci27M0m7zFX5lr42WFxeH977Pqff08bgcXL2f2vQ7HS59N0u1FvDpUNtCv8EK7a1be8069H2d Y3jZvmrJawX+C6ib/e+WsXxdq154N8P3WrQrE0kO1Y93zLuZttedD2lapy/zHrTdOjTlL7MTS8Va lDo2l/are+ZgrMzKsfmN8qs3yr/e+WuJj+KX2W4s5J2ngW4ZfJkubVVj3eX5jK3zfKy/drl1+Okl 0sa3uiwM0bblktJmhZWrH8ceN7XxvZ2sMEN9BHGzSSR3Nx5is1evSy6r7SMakTxqucUI05Spy94+ jbiWPUrhprWSOeNtvzRSK1O+zt5yqfl27f4a+QlvJreTdBNJE3+y22tzTfiN4k0vb5GsXG1f4ZG8 xf8Ax6umpk/8sjz6fEEftwPp+6uF/eKn8Tbmb+9TYbXcqySfdb+5WF4B8Ryap4TsbrVoVnuplZmk VVX5d3y11f2jT1t1kbdBH/Dur5+pT5ZSjzfCfUUqsa1OMolWSzjiXc8zfe/hWofmuG8uJfl/hWr6 vp919y6il/uqzU2TT7plZYni8v8AuxtUcpuMj226/K37tW+Zv+ejf3arzQrMrSRfd/iX+7T5rG5V VUo+1fu0iq2n/vG/1zfdX+7/AL1PURG11DpsLefM0Uki/dX5tq1RbWdPXos8v/fK1MtvbytNcXSr LJ/DuarVvFaywtIbeFVVv4VrL3pDKUPiGFZF8i1kZm+X5pKs6xbx3sa26yeRtbc21dy7qmt7eGFm m2rF/wA8/l/ipv2eN/u3Mf8A49Ry80eWQHOTaDIrfJMjL/3zWrDH9njiX+7VPWNct9F1D7LLtZvL aRmVv975f/HaRfEGn3DLGl1F5jM0f3v4t22op0Yw96Io+6X5Pm68VJ+FQQsrKrRtuX+9VG18R6fe 3EkIuFWZWZdrfLWvLImVSMfika8MXmyKq/xVYkmjuJmkkj3KvyrKq/Mq02x2+Yu5tu5du6po7OZW X5tkO75mpxKI/srRsrKqyq33aKdJcTRXTN91v7v+zRVe6M8EWXdtX/0KnSL/AHataHo0mqSMrMyx r/Ev96tSTwbdKzeVJu/3lr0JV6cZcsj4Kng69an7SnE52nL/AHf71XrjQby3+9Du/wB2qMkUkTfO rL/vLWkakZfCc06NSn8cSvcWsdxtWVd21t1Qroduyybt3zK3/j1XKkb/AFf+9VGRm3WlrceY25lk ZVX/AL5psOgw7o1+ZmZlX/gVaDfdrpPh/pcepa8qyruhjVpGp83Ka0Ye0qRgXNL+FWi38K/LPL5e 7dH8vy7mZv7v3fmau00nS4dJ0230+33LDCvlx7v7tZsi3Gh3SyRSNtb7rf3v9lq6ex1aGe3jkb96 27+L70deVKP2j9Fh7qC6upLe4ZYpG2r8tctqmrXWqalDDYSeQ0LeY1yv3dy/LtZam1i8tb++uNH+ a63L++kgk2tDu+61WtL0uO3h/uwx/wCskb7zf/ZVPwjHXFvNcQq275ppNu5v/Hmq1qyxxaDJ5S/u /lVaZHd/ao5ptu2Py2W3X+6v8Tf8Cpl823wrcN/0021mc9f+HI4+6b7Qvluzbf4f9ms2a1ZZNzVc umZVk2/e2/LWaupahFHta1+Zvu7fur8tcUo8x8udF4Tv49NmuGnZkhkj2/8AAt1WNe1JbySOOBt0 a/N/wKuVXVL793/o67f4l20R6lffe+w7W2t8v935quPux5TqjXlGn7M0pP7v92iFZGkVU3Mzfw1k teX25d1rt+aprPXPEml6hbta+GZLyOaP5rndtWP/AIDWkY8wUqXtpcsS5qEUlxC0cXytWDJE1r8r rtavQNJtbjxBpa3GqWa6ZqG5lZY/u1T1DwvceWy7Vnj/ANn71dVKrKj7plicvqfFE4NvmVlb7rfe WvP9Y+Del3mqQ3lrI1nb+Zumtv4WX/Z/u16dq2g6ha2q3EELSw7tu7bWG0t4yxttX7vzL/tV7uHx FSn71KR8/UpP4akTx/4seD10a8j1KyhWKzm+VlX/AJZtWl8IfBHmyLrl7H8q/wDHqrfxf7VegapY ya5pslneWsbwzL8y1ct7OT+z2s1/0NfL8mPyP4V/vLXqSzCUqHsvtfocvsff5jg/iN4B0e40+61q 2mj0+aP5pNv+rkb+7/vV479/7vy12nijwR4i0GG4s18/UNJVvO3RtuX/AHtv96uJZf7tfQYFctPW pc5KvxfCO/5aKqq3mfdXb/FX0X8GfgZpF+lvqHitXeX70cG793/s7q5r4L/C9ry4h1jU4/lX5oY2 X7q/3q+gflgj67VWuXF4rmUo05WXc7sJR5Ze0lHmNTWtDk0OPzG2yW3aReorjmZd3zVpTa9cSrHC 8jPbr92Nqr/6HK3+rbdX5njsVLEy5eb4T6Plpx/hlGSXarbfmaoZLyOJZP3kjLHu3bY1rUa4jija OKH7396qPzL/AMtJF/4FXnx5Y/ERzFq1uoYoVZV82SquoM11Gs3/AAFloVdtSQqrM0bfdk+WtaFb 2dS/2TOa9pHlOu8CeGptN/0i9sfmZlaNtu5ttdPdK32iTcv8X8C1wHhvXtQtdUt7W6upFs2/ds0n 8P8Adr0W3W4ul8y3uPMj/wCeit8terVd2fRZfOm6XLAqR6pYWt9Z29xJHBdTN+7WWZVaT/dWo21K GJZFaaPa0ix/M38X8K/71ZHi74Yr4y1jTb69vJ4JLOOSNfIb+8yt/wCy1zWrfBS2mjhMep3ECx7f Li/usscfzK38LfL97/arM9Qz/GUq/wDCTXnzf3V/8dWm+FdWj0bVlmlZlj8tlbbVjS/gd5EMjX+q NLeKvzLGv7lf3m5dqt/3zV61+E+n2twsz3lxP5cnmKrbdv3l+X/x3/x6ur28eXlkfMyy6vGt7WEl udvY6oupabbzJHNFubcvmfe21y3iRpvDerf2tZ26+XcL5ci/w+ZXa311DpsbM0MSwxxq33fu/LXn 3iLxwupRyW9rax/Z2Xa3mL97/aWs6UeaR3Y6pGnRjzy945/Ute1DWflubhmX/nkvyrVFV2/xfN/s 0N/s/dqO48zyWVP9Zt+Vq9CMeU+MlUlU96UjjfHGqXFreMrtJ9lb5vl/vVyv9rRqu5lk2/7tekXl hdXVvJC/lsrLt3Mtef6lYalptxNDJHG3l/d2/wAVfQ4GrGUeQ4KseWXMRrq0P3tsv/fNOj1SO4Zo VVtzLu3NUMy3XmNt27aJPtyxqu2KvTMSzXr/AMAbVYv7Yvnkkg/1cKsv/fVePwq3lru+9t+atCHU rqztfJivJ7aNm3MsbMqtXHiKUq1GVOJ24OvHC1o1pR5rH014q8Yab4d01W1G+83zpFWPbH8y15n8 SPG+k654Rkt7C8WeSSaPdFtZW2/NXk8zSS/NJI0v+0zbqdtbydq/71edQyynSlGpKXvRPVxGdVMR GVOMeWMiCrtqvlQs1U1Xc392rkzKsaqv3a9qJ86V6PpRWh4ds/7S17TbX/ntcKv/AI9UTlyw5iYR 5pqJ9K+G7f8AsvR9PhCq3k26rtb/AHag1a8uLi42y/drTVdq1BdWq3Ee1vvfw1+Y1OaTP1Kl+7XK Y61JDLMsirE0m7+Ha1EdvI1x5O1mkb7q11Oj6Wtm23790y/My/8ALOsIQlKR2kljdXWm2rLPcNLc N/C3zeXV6O+nWPdcrG0f+0vzVg694kt/DMMKyt++kXdtX5mauCvvH+palHcRttXzG+Vl/wCWa/3a 9SnTlI4a+OpYf3WejyapoeoNJDLNFDMvzMu75aSzVdsMlnNHLbyfdkX7teG3Fx95V/4E1R2uo3mm 3W6Lynj2/Ms67vl/2d1bexjI8innHvfvI+6e/wAlxDdyeX8sW37rLUK27LNtk+Xb8zbv7teefD2a +1TUJGuPsMrR/wCr+yR+XJ5flru8z5V/iru9Qv7e30mZb6Ro7f8AvL97d/d/2q5JRlGXKfRKtTlT 9pGXunNXEv8AaGoXlx5nlWqszNO33dtMh8Q+H7ixkje38hYW+WTb+8b/AGq5TWtcbVNsMS+RYx/6 uBf/AEJv9qs37v3vvVth8HyR974j5itmko1P3XwnSa9dW9no/wBhs7rzWmkW4Xy2+Vf95v8A2WuT huvNkZZ22ybvvN/FVr79Qtbq0isy7mWvQhTjTjynlYjFVMTLmkbmj+KtQ0mRds3nwr/yzk+avSNF 1y38Rw7lkbdu+Zm/h/2Wrx9vu/u69F8I3Gn2ugyeVIs80cbTSRxfeZv7tYVqcT1crxU+bknL3Trl mWWRYVXfD91f71Fc3oPjWz1Jtu77Ddf3ZG+Vv91qK4+Sa3PpI4mjUV1I5D4W38Os+EfM2r9st5PL uF/56fxK3+f7tdlp8v7z97Hv2/eWsHwf8MZPC+qSTWGqebbyL5cltPH95f4fmrorNpLXUl8v5vM+ XbtrXGezlW56XwmOXxqU6EadWPvRLl9YWP2hVaSSJlX7rfMtZ914XjlVmiaOVf8Apk3/ALLWvfS2 dxeTb45Ffd9+NqqTbbBm8qTzZpI/3fy/3q45KJ6NkcXqHhe3lZmij2/7S1y99ps2mt83zL/er1S3 0+SKP70bN/F81Z+raNa3Hnfxr/6FWtOrKn/hPKxWXUa0fd92RyPhHSY9UuLjz41lj27fm/vV6R4f 8Gw+HrO4uIFK3E37vbu+XbWL4Z0GSw02TYvm7pNzNXWao/2eOxs/4tu6tJ1OaUgwuDjRhHmj7xXu NPj+yt9oj3eZ91f7v+1XH3l1caXeeTa+XLJI23+8u3/gP3Wro9e1r+w49tvIss0jbVgZfvVjabo0 cSzTXXzXlx/rJ/4lqYy5fiPTNDw/ptnFYxx+d++ZvmvG+9J/stVnUnaa4WxVWgjX/wBB/iasi1uG 0u6aOVd0bf6xf7y/3lrcuGa8XzIl+WRfl2t92P8AhqakQiMjWPy5tkny+XtVdv8ADS3lvJceGZI4 o2kbzvurU2lwNDdL80f/AH1WpY+dHJfeXH8qr8u3+9UxiRUj7SPKcJH4Z1Gb5jaSD/gNW18F3zf6 yNI/96Ra6e4eRI1V5JPMb5m3N92o47dmXcrbm/iX+Kp9nE4o4Gn9qRhR+C2Xl7y3i/4FuqaPwjYq 259Q3N/sx1t2cKSxNu2tt/vfL/49U32WH5tysrKv3fu1XJE0jg6MSk3hrQ72ZWAMTbl6HburWOjR xN5luclV+VaqLFGrbtu3bTftUlkyxxN8y/e/2mqvdN404x+EqyK0TMrqyNRJb7413/LHJ8vy/erX jvLbVF8udVWT1rN8QRnS4JruY/uI1+8v/oNHL/KaSlyr3jh2+G1vFbwwwaheQRqqr8sn+9u/763V yvi7wXHockPkXkrrIv8Aq2at638eXi3H72NJYd27b91ttYOtapJrV9JdSf7qr/dWuujTqRl7x8tj sZh69Llj8Rg/YN3l/vm3Ku1m/vUf2Wyr8txKq/71alrayXl1HDEu+SRtq1Y1LTZtNuvs867ZF/8A Hq7OY+e5ZcvN9ko29v5Vv5bNuX+9WD/wrvQW1Rr5tNilmZvu/wALN/u1vfxba3PD9h5sn2hvux/L H/vf3q6qHtOblhIjl5jT0uyXT7FY227vvM1Vr6/81tq/do1a/WJvLZtq1iyNHcSK32ja33flryMy xnNL6vT2R6tCnyx5i9975qdG21las9VXbuW6Ztv92nL5cUi7rhvl/havneU6NTU89f8AbqOSXdu/ u1XjuI5W2qyu1SVMpSJGVL92m0UDiany3Vqu6hbiRVZVkkVf9lqhhuo7e3jV/vSN8tSKv+0tdnvc sS4S1PR5riSHy1WSRV8tf4v9mkmlklt41k3OdzMrM1EyLcKrLNHtWNdq7qcvlssMbs0Uit93bXYf Xx2J5NqyXjOrbflX5aqf6L/dm/76WpJrjybi4+VWVm+61PmjhljhYNHBuX5vvVQjP8R+ZdQ3lqsz RRsvl/8AjteNK37xlr2LxJdWdg1x595HFJIreWrq3zfLXjO75vl+9XoUvhPls4lTlKPL8RN/6C1N ZliVt7Kqr/E1Ob5l/urUdxbx3lu0bruVl2tW582c3q3ir5mhs/8AgU//AMTXNszSM25mZvvbqsal YSaddNC//AW/vLVPczfKvzNX1WHhTpx/dnnylKXxDl3M22o7ht0m3+7UjTbflHzN/FVdtzbmroJE qWRfus33dtNWRlX5dtTfeX5tvmfw0ARr+7Xn+L+Gm/M0ny/xUbW3fN96rS7bf733qAG3FurWskbt 95drMtZv9lxs3+sk+b/aq9IzSN81H+q/3qCLlGbRl3f66avTPhv8IrrVI49Yupp7a1j3NDt/1jbv 4v8AdrY+GPwn/tDy9U1ldsf3obGX/lp/vV7bGqqq7F2qvy7f7v8As183j8w+KlSPqsuyyXu163yO AbwDb7ZtmoX0W5mZf33+r3f3a6TS9P8Astrb2qtJP5caxqzfMzf71aFxa7pF8j5t38P92rdqsdu3 kp+9ZvlZv7v+7XyvvfaPr4x5viCGGG1Zm27227WkX+GsfxJ4kj8M28ar810zblX+Fv8Aaraj2ozR wbdv/LSVqzda0uz16z+yyrtVf9XP/ErVvT5eb3h14z9nL2XxHkV9eTXkzTTyNLI38VV1bb92rmta bNo181vcKu5fusrblZf71UV+Zq9X/CfAVObm9/4hvlbm3bfmqZd21dtH3V+Wj73+9SMS1p+o3Gl3 S3FtM0Ei/wAS1a1zWbrxFMtxcSb9v3Y1+VVrL++3+ytOVv4loNfaS5eXm90b93/eptWPllXctUWv 4VX5ZFpyMix935qj81W+61UWuvNb5pF/3d1Hmqvzbl+WsvaE8xeXdurttH8M2epaXHcWt00WoR/e kVvlVq5Hw+1neXUcN5dR20bNtWT/AGv4a9b8OeGV0bT2j8z5vmbdt+9WdWpyx9097LcP7aXNJe6e fWvg++bWIbe8jZYZJPmnX5laivT7Td5y7W2qvzN/u0Vz+2kz2Y5XRirN3JLdV+9FNDK25f4ttZ99 ZTWusLMsbeX5m5W20xmjupI1S3WJmb+Go21Kf+1LpoppFXzGVV3fwrXOeyX7z7H9qmV45E+b7ytS XUtv5Ml4itub9zGrfw1HcXisyrPbxyttVmb7rUSNZ3VvHHuktfLbd83zUFmev3ab5LXUywxrv+b5 qvtprN80EkU/+63zUs2nyWcKqscjySfNI23/AMdqOUQ7T4lbUI1iaSJt3/fVaGpLDqF8+1vLkh+X d/DUfh1XlvZJpNzNGv8AFVPVm/0eRYP3q7v9IZfvf/s1rH4STJms7VdSuriBWZZmVvm+b5v7y/3a 1YdImaPd8q/xbWb5lX+9VCzfymjb7yr8y7qvzahJeyMu3yofvMq/xVH+IorapZR3Vuvlfdh+WOT/ AJ6N/wDE1l6feNFIsbf98/8AoS1sQy+bJ5bf6tv4V/hrF1KLZMsi/wAX/oVaxlze6SdDp8Wy48xW Xyf4W/4FWnbSri+kaT9zuX7v8TVzen3kkMasu3a33lb7tdHH5LabCskaxJI3mNtqYlSKLSNNI0h/ ip9vKsTNu3fMu3cv8NLcW/2dtv8AC33ahVWYbV+9SJLFxthjWFG3L95m/vU2G9aL5W+Zf7r0Qxc7 n+7H8zf/ABNOhupLi48t1jaP7zbl+6tICbzFWHzlZtzfKqy/w/7VU6dcXHmyfKu1V+VVptKQDag1 hU1TSZLa6aRoV/eLtb5l21NSSKs0bK33WXbRGXKTKMakeWR4tfSratMqXEVz5bLHJ5as3ls0fmbW /wCA1a8L2snijUGt4JPlWPc0qr92u+XwVZqrLuX5o/Lb/R4dzL/tNtrX8K+GdK0G4unRWV7pVVxs VVP/AAFVru9v7p8/LKY+05oy905bR9Gk8P3izXSrLMzbYVVvvf7VZPjLUl1LVtsSr5cK7dy/xVY8 cf2hHrjSSxywQx/Lb7v7v96ubXdtZt1XThLm55SPLxNaMYfVoR0PJfil4t1qw8TLpenXjQQyQx/6 tfm3bv71fQWmwSaXodrDLI0s0MKqzf3m218++JEjuvjVosMvzL5luu3/AIFX0ZeRNLbyKtfUVo+z wkZU4+9ynj0P4kuY5+b963zfNUcdvG3/ACzX/vmpJPvURt81fmDlK57uoLbxru+VfmXbTvs8e3/V r81OopBqNWJV+6qrTqKKkyCiiobhmXb5cfmt/Cu7bVR94qIak3y26/7NdX8NbVri31BvLVvmXbu/ 9lrzTWLy4+3furdWj/h/eN8v/j1dp8P/ABN/ZDWtvP5a28jbmnZvu/LX0saf7iMTLC1Y08XzVD0t bPc21Zo/M/u1JMrNNbs67JG+9/31UOmst0sN1DNC0O7/AFiyVX1DUrfSZoZJZlZpJlWNV+bd81cn L9k+2dSMY8zl7pbZY91xJJu2+Zt+WmSMsvlxxqyKv96pJLxfmX7Om3du/irnfFXij+w/sq21vH50 jbvm3fdojHmlyxIq1o0aftJmN8Tm/wCJlb/8CrhZNyqzVteIPEkniK4jklhWJo93+rb71Y8237O1 elTjyxPg8ZVjWrSqRIY7xdqsvzbvvLVhW3fvFb5ax/N2xtWbq3iNdGhZlt7mWT+GNfvf7Lfd+7V0 oyrS5YnDzfzGl4i0NtWhWSD/AFy/+PVxN0rWckluy7ZF+81a2i+MLq8vmkut0Vv93yG/hWtDxVo3 2i3+2QfMyr823+Jf71e9SlUw8owqdTmly1Peicir7W3U5vlZWX7tEa/xH7tSLtiba3/7NesYDW2w /N/F/d/u06P978zfe/vU1bdmk5q0qM0iwwR+bM33VX+Gp+H4gGsyxN/00qjI0krbq6i18KrFtkvJ Glmb/lnHWlZ+ErNJN0q7v7qt/DXF9fp0ynQlI4u18y4bbFG0rL8vyrXqHwj8B/arxtU1S3jlhX/U xs33W/2lqNbeOJvljVW/3a2NF1mbQ7xbiL5l/ijb7rLXlYjGVK0ZRh7p6eBjSo1oyq+9E9baBlhW T+Fv/HamhZrrdjb5235t33WWqeh3kPiS3+2CT93/AMtP9n/Zq9JL9oZoolWKFfvNXzXLyn6RGUak eaISKyN5Kf3fmk/2f/iaj+Xy2WNlih/5aSN/FRcXsNvYyNLuW1j/AIv4mauK1jXJL9d3lyfZ1bbH BB/FVU4SqS5KfxGVWrHDx5pGxrmvLE0dvZ/w/M3/ANlWLHqV550iyyNtk/u1i6hrNrps0cKzWa3D LukjnuljZasahcLcW8cNvcbftC/LOq7l/wB3dRVynEy97m/M8z+0qcvhMfxRrMMv7lV82aP7rf8A POsOxb93Izt975d1WL7Q5NNkX7Rt8tv+WlUZrj5lVPljX+GuilSeGjynymJqSq1OeRpLu3UN/s1n w37RN83zR/3avRtubcldcZcxzjlb1+9/epy/N96mt833aF+Zefu0wBm+b5ahXS7W4X/V7ZKk2/3m qGS6Vfufe/vUpSj9oCrJpq27bfL+VabDawr8qx7t3y1qW9wtwvlv96m/ZfIk3f8AfNR7P7USeUh+ wW/nbvJVvm+bdXcQ/Ea8W4tY3WNbNV2zLt3bq4/73+9Qq7mqpU4yOyjXqUf4cj2nTdYs9Whb7MY1 8z+KP/2ZaK8ctbyazmWaCRoJP7y0VyvDnv084jy/vI6nrNn/AKOs1033YV+X/e/hqlp9v5sir/n/ AGqs+Jbpf9DtYFXb/rm/3f4aihuPssK/KvmbfvVwf3T6cdNL50jN/eqHduZlWpreya6bc0kcUa/e ZqtLY29xuW1Zmm/ut/F/u0coENvps0vlsy/K3zbv4VqzJcXErRx2fmLCvyrt/iala8Wy/wBDC+bG 3+u/2m/2a1oY49Jga6fduZdqL/dWqjEAvJ303SWRpDLcN8pasWzfbGrOq+Wrbvm+X5qdq1x9n+zx zs0vzeZJt/2qo3FxcXsixx/LD/CtEpAR3yxw3W6Jv3f+zUkMvzbk+8tQzabMrbm+b/gO5ahX7RAr Ksfmqq7ty/w0gNCa4/dssa+Vu+9Vdo1uIdrfxf8AjtNjuFljX5vvVMq0AZ+mystx9nf+Jtv/AAKu 0m3RTLGiq6xqsf8Au/3q4vULfdukVfu/e/3a0NP1lpVZZW/ebdrNWzl7vMSdLO26Tb/D95t1RMsa x/dVV3fdqtDdSKvzfMv+1Uy3Uf8AeZf96p5gG3U3lbYz86/e2t/DUM22HT5GTdukZVpsjtNJu/ia kkVbq3WGKRWmVmbZ/e/3akspx3DMyrt3Vckt5Lf76slRW6NZxyXEi7ZF+WNW/vUtxut7eO3Xc0kn 7yT/ANlWpAer0lRtayWqs3nRNt+9Hu+ZafC/mx+Zt+XdtpAOoofvXN/ETxpa/D7wjfaxcsvmRrtt 42/5aTN91a1p05VpRpx+0YVKkaUZVJfCeOftBfGvUNG1638P6NdL5dn+8vPlVlZm/wCWf/Aay/CP xQ0/xQsdvcMtjqDf8s2b5W/3WrwW+v5tUvJrq6ZpbiaRpJJP7zNUcasjbl/4DX6VHK6P1eNL7Xc/ L6+MnXrSqs9A+JV9ceFfiZa6siqzR+XcRq33W2/w19HeD/GGm+NNGjvtOmWVWX95H/FG391q+P8A XtUutZs7WO6m89rf5Y2b723+7UfhPxhqngnVFvNNuGjk+7JG33ZF/ustaPCyjTVN/ZMKdfll/dPs 6+sNzbol/wB5azWiaJv7rf7VcX4R/aH0PWFWPVlbSLj/AJ6N80Lf8C/hr0qx1TTdbhWa1ure8jb+ KORWr4/F5JGpLmh7p7dPE8xk7qkX5mrYaxt/7u2o20uPd8rMteRLJsRH4bG/t4mb9lm/55NTvss3 /PNqvf2X/wBNmpv9l/8ATZqw/sjE/wAv4j9pTKf2WT+5To7P5vn2t/s7qsNpLf8APSm/2TJ/Cy1l LLcTH7A+emc+0e1po3j/AHkbN/DUK/N92tTVIpLdo2eFvl/5aVntErfMvy17UObljzR5ZHm1FyyN jT/Fd9pNnHaweV5a7tu6Pc1V5NevLq+tZLqZp1hkWRVrPaKT5flrNm1L7PcNGzL5i/Nt2/w0ckS/ b1uXl5vdOo1DxRqF5dNMl08Ef8Mat92s+6v7i/kja4uGnZfu7qxbPVo7yZY0bd8392tb7O25Wqo0 4xCdepU+KRH/AHm3LWbNeebHt27a1Ps7fN92qMlnHYW7XFw27b/CtTKMpe7E5ZGffXUel2fnSqrS Sf6uJv8A0KuFum+2TNM67matTWJrrVLxpnX5f4V/u1myW8i/eWvpMJh40I2+0cdSXMRxxKrfIu3d XaeFdWWdW0+VvmX/AFbN/wCg1yMK7dzf3ada+ZGzSJu3LXRVpRrRtImMuWRc1aKGLULhbVtyq3y/ 7P8As1Thia6ZV27m/wDQqma13Sbq67w/pK2sa3Eq/vm+6v8AdWirOOHp80gjHmkGl+HI7e3ZrhVl uGX7rfw1a0fQ4dLVtvzM33pG+9Wgv3qjuNysrfw/3a+cq4ipU3O6NOMQhtVt5GbczN/tVM396mxy rLTl+X73/fNc5Qfw/N/wGrGn2dxqV5HbwLukb/x2oY4muJFVF3MzbVr0fw7oMejafuf/AI+pG+Zv /ZaUpcp34PCyxNT+6beh+GY9Jsfs9u33tsk0n/PT/P8Adq78sq/L+7tV/wDHqj/tq1t7dfNVoIY/ m3L/AA/7Vcl4i+I9nAzR2e6dv4WVdq151SMviPsnWo4WPLKVi34t1D7VGtmnyru+7/drzL4peOpP h94Vvrj5VuJNtvYwN/E23/Wf8B/9lrc0vULzXGumTZEyrtVm/vN/FXzD8ePGTeKPGUlnFIzWOl/6 LHub7zL95v8AP92veyfC/FUn1PlM1x0akbwPO7q6mvLiSa5kaeaRtzSM25mruPhr8XtW+H15HH5k l5o7N+8tJG+7/tL/AHWrh/KamtGy19jKPMrSPj4ylGXNE+6rG6t/Fuj/AGyKaO50+6VZIZF+8u6u P1CwksLxreX/AIC396vHfgT8To/D943h3V5P+JPeN+7kZv8Aj3k/+JavpTWtNh1S1W3Vt15HH5kb N/FXy2Ow38p79OX1mnf7RysNnGvzN81WP9lar2s3y+W/3l+Vlqx/u/eryo8v2TIPu/71RzXCr/tN Uc1wq/db/eaq+5W/iWolIB0jNK3zNSUzzh/eWrEKq3zMys1T8RI63X+Krkcv8L1DRW8fdKJGi2/7 tNZ91Ojl2/K33aJI/wCJfu0gGq1FRyK0sLKrKv8AtUUAew3tnDqVxNcW1x5UjbV2yf3V/u0yTTZr fa0sbfd+996oY4me4+X7rfKq1emumtZtsEjIqrt+996vHP1EoVpL/wASyP8A6epP/Ia//FUkN/D5 yyTW6sy/xRfL/wCO1atNLjvJmunm3wL8zbl2tURj/KIsWKx+St9eqqsv3W/vVm301xJqCnzNzSfd 8pt1OvJptevPLgXbDH93+6Ks6fax27K27cy/M1bakFS4i+0alM3mKzL+7jVvm27f4qazQxTMu3ZJ t27l+7u/3auwvHdSbp41fb83mr8rVCum+bM0kEnmt97a3ytUGg3S4ZIprlk+Zo1b7v8AE1Rf2l/r I7i3Vt3ysy/K1WfNWKx23MLRSSNt/dfK3y1Bptv9omZm8uXd95Zfvbf71AFTU9Ijt4Y5oJGVpP8A lmy/NTfsl3DHulh/dr95l+arF5qs7XEiq2yH+GNqsTXsq2dvJH+6kkZmb/0Gl7oDNPZbfT9Qum2t tj8v5q4e+1aHTbhmkuILOPcyx+ezbm2qrN91W+X5lrQ8beIbrTVs/s+2LzN0kiqvyt/DWPp91Z68 212tYpmbd5FzbrIu5lVW2s3+792t405L3vsnm/XKbqypfaOl8O+I49SkWNZoJ4fmXz4Gb5WXb8rb lX+9XQbfl3KysrfxLXGf2XJZ3Xnfuotv8MFusf8Ad+Zv733VrY0/VGST5WVWb7yt92SpjTjL4Tpl KUTWmb7PCzfxN8q1nq1Na8murja8aqqr/D/DTtu6s5xlE2pyjL4TTj1DyY4Vu18/5ty7vvKtSQxK 0017FJ57L8yr/FurO3xy/wCt+Vvu+atSQwyW8bXC/P8Awqy0uYshW3klkXarMzNt/wCBVNfSqu23 i/1cP8X95v4q1/tC2+nxyXTfvpPusq/NWNcWbQr5isssP/PRaJR5QGreLFGzTsqxxruaRvuqv+1X xz8cvidJ8RPEnl2zMuj2O6O1i/vf3pP+Bf8AoNe8ftCXGuWHw9ZtLhke3uGaO8kj+9HD/u/3Wr4/ 2szV9nkGFjy/WJfEfE5/ipc31aPwlG6v1tbjy921mX5flpsOuf6R5P3ZP7rL/wDZVpSWcf3m3bm/ utVVrOPdu+b/AL6r7Q+MLEnzfNTWiWX733qdu2ruqu0rLJ/s1Mv7wFj7q/LUkNxJbtugkkgb+9G2 2o423f71DfdpDOi0H4h+JvDit9g1q5WNm3NHJ+8Vm/4FXXWv7Rniy3h2yx6fcsv8TQsrf+OtXmH8 NMZtsbNWbpU5fFEqM5R+GR69b/tN6827fpdi3/AmWrX/AA05qn/QFtf+/jV4nbr+7/3qk/iqJUKX 8pca9T+Y9oX9pvUv4tFtdv8AszNXUeH/ANpHQ7/bHqlrPpsn95f3kf8A8VXzhzS+Vu/irOWFpyNY 4ipE+2tD8Q6X4osftGm3kV9b/dZo2/8AHWrJ1jRms2aaBd1u33l/u18r+D/GWqeBtWW80+Tbu/10 Df6uZf7rV9XeB/HWm+PtH+2WTfMvyzWzfejb+61eNjMFeJ10qsanuyMnd8vytVWS1/fbtzfM27+G t7WNDa1Zri2XfD/FH/drLjXzWr5yUZRlyyNJR5SP7LHa7ZNzeZUnmyLNt3fLTbho2k+bd8tO+VpN 1Ikj+0Sf3qdJcSKzLuqm0sis37vcqt8u2iS4bzG/dtu+apLG6hq39m2/nPGsq/d21x+sa9/aVwsi Q+Uqrt210HiCKS6s1jiXczfN96ubm0lotPj/AHbfavM2sq/3f4a9jB8kY80viOOrzSKsl5ujZVXa 22s3dfL/AMvSqq/7NXI1+Zty/dWm+X9oby/71exzHMa3h3Q77UmWaW8jVd25Y9v3q7SG1vI7hfNk Vo6p6XYfY9u3+6vy7t1bSt9oj/2lr5uvXlWkehThyxI/4ty07buVlb+KloriKIlg2/dqb+Hb/FUf 3qdt/vUAaWh6zHoM0lx9nWeZl2x7vurU19411K8b5ZI7Zfuqsa/drmdQuLiK6XZD5sbL96oWvLra 221Zf95q05YnVHE1Yx5Iy5Tem16+vFWOW6kaP7rKv8VZt0ystea/FD4iax4S+wrp1rGrTKzN567v u7a41fi54murdWa6jtWb7yrCtelSy2tiKfPH4Tjq4n3v3kuY+kvC9v8AaoZNsixeTcLI3+0tfJvx Es5NJ+IWuW8/3lvJG/4Czbv/AGavSvg38Qby+8WTaPq19JLb6lHthZv+Wcy/MtQ/tMeEmt9et/EF vtls7hfs80qr92Zf73+f4a9LC0pYSfs5E15e2oRcfsnju35ttDfL96o4Zf4W/wCAtTrqJvvf99V7 Z5GpDJF/EvzLX01+z/8AFBfEGmroeossurWcbfZ2kb5po/8A4pa+Z1baqt/daprG/uNJvIb6ymaK 4hbdHIvysrVx16XtonRQryoy5j7M17TWlaPUIFaJpP8AWK38LVmzMyqq7v4fmapPhf8AEFfid4fh k3RpeQ/ub60b+H/potXNY01rNtv3o/8Alm1fK4jDypykexPll+8j8MjDutq28m5dy/3apqtr/DHJ WnzVZlm8xtu3bXlHKVd1uv3Y5KsWbW6zLiNt396hftH8Sx1Jb+Z5f73bu3fw0AaVOX71V4d22pl7 V0xNQp0cvlf7tNplICy0ayr8lFRqzK3y0VYHrkf+hR+d96Rt23/ZqjHdLcbm3bmb5mq/cSxyx/e2 Mq7du2qlroM0UbXEkbbVXd8teDLmlI/UC1HcafcXENqJGgupPux7lbdWzcSWJhjs1vrdEh2+avmc k15dca94fv4ftl1b6vYtdQxx/u1+bb8zRt/6FWP9j8Kyq0cDah5jL521Y/3m3d/lq3jEg9rW9jsl 8toVWNvlXbJ96qscy+TM25fm/drXkUml+GYo7dW+3eTcR/aIZ2bczbpGX5d3+9/6DVqx1Lwrpv2O 6ZtS+0Ltm8uf5tu37v8A6DRKIz1eFlt7WRtvzblX/eqKOXb5zbW+ZdqtUei69a6to9veRQ7YbhmZ f+Att/i/3auzW6tHGscir8vmbW+X71LUCNZt1rJ5qrOq7VVZKitYrd2m8rdBM0bfNu3KtSzWsy2s a7d33mbb81Q2/wC6t5pG+837tagCpcWFw0e7ct0v95fmpdS/cyRw/wDPONVqzp/y3G7+GNd1WLeQ XRVZY1kZpFX5qOUCDWtNt7yzhs7iNZY1j/4ErV53rHw7urdpJtMb7TGvzeX/AMtP/sq9MukN1dTM rKiq33mqFbVmuNqsqN/D81aRqSicGIwdLEb/ABHmGj+LprNltdRVpY1+X5vvLXReVDdR+dZyLLC1 TeOPCn26zm1Bo9lxCu5mT/lov/xVee6fqV5oknmQNuhb7y/wtXTye096J5McVUwVT2Vf3onfQ38k X7t9zKv/AH0taEcqyr8rVi6bq1nr0a7W8qZfvL/FUzLNZybv/wBlq5pR5v8AEe/TqRqR5o7GrWjp N5cRzrDEd8bfeVvu1R024tb9fLeTyLj+Hc3ytWjHb/2fBIrNGtxJ8qru/hrLllE6C5Jfw38jRsqy x/dVW+9UcOntFcbrabZHu+ZW/wA/NWMysrbWXa1SLdTLt/eN8tHN/MHKO1KX7VcSK8e1fu+Xt/hr 5v8Ai98B/sv2rXvDka+T/rLjT1/h/vNH/wDE19Nx3sN/tjuo/m+6sq189ftFfFr+wVm8K6JcK19I u28uY2/1K/3V/wBpq9jK5V44mPsDxs0jQlh5e3Pm+4rJkv7W4hZWkZd3+zVxrprePa3zL/vVTkvL f5dyxbdq7f8AO2v08/MCnst/+f6Rl/hWiSBW27ryT+JlZa0LeK3lXzFhj/u7lqb7HCsar5asqrtX /ZoAp2d1bwKzfambd/eq9DcLcRtInzKtRrZw/e8lf++akhVYvljXatQBXW6k/vKy024umZfmqaa3 /iSq7Lu+9WL5oklqNt0a7akVdtU7dmiba33W/iq4zbV3NW3N9oIjZGWJdzVX+0Nu3LVeS6W4kbc2 1V/hoW4jbbiRf++qIR9pIznU5TUjlW4X/arc8F+Mr7wHr0OpWnzR/dmg3fLNH/drkVuo1+bzFqwu pQsrK7fN91qmXL8MjWnU+0fdHg3WbXxzpdrfaXIssMy7m/6Z/wB5W/3al8ZeFbbSVj/s5/NmZd0y qc/98145+zuuoeCNNutQWZpbPUF+W0b7v+81esWeuWrRqsUc/wB7c38Xl/8A2NfG4jlqV5RUvdif T0pU6mH5ftyORqSH+L/drptc8P8A2rdcWq7Zv+WkX97/AGlrm4dqK26vLlGUZcrMJ0+UI1/eL8y0 RxNuZqjZdrU2oMyRVZd25f4aj8qNlkZlWnLKy/xNUnzTQ/7W6mBm32jWt/Gvmx7WX5d0fytVO18J WtvcLMskjLH821q2mVlVcrTmXbCv+1W8a9SMeWMiJU4yIv4qsrKyxqy/w/eqv/EtO3f3a5yxzX8O 5flbc38Py06G8W4ZlRay102Pa2+Hd5n3v3zfNVrT9LW13NEqqu37u7dVkF5vur83/fNV7i1a6jVV ZlXdu+WrC7V+9UM0qs0kKzL5237qt8y/7VStQKM1q0TLuvtrN93c33qotcQ3SxwprUXmTM0atHMu 7/gP+1XhPiq61BdcuIb/AFCS+kt5GVW8zctY67tysG2/7v8ADX0tPKeaPNznJKuWvGUWuXGrSWeq ao1zJZyMscn/AALdWG1hcbtxumZd3zVofdbb97d95qP9la+jpx9nHlOOXvDobia1uIZrVmimjZZI 5F+8rLX0d8Pfihovj7R5NP1e3g/tplXzrafb5dwy/wAS7vl3V82/MreXu/3ar3Ct8si/Ky1jiKEa 0dTelXlRloeyePv2cbzzJNS8MsstrN+8XT5P3ckf+yu6vH9W0PVvDNx9n1SxnsZP7s8e3dXbeEfi 5rnhdoYb2RtX0lW+aCdt0kf/AFzb7y19FRy6D8WPDdv/AKKuoaXcLt+b70Mi/wB7+61eZOVbDR97 3onSqVLFfB7sj4z3L5beV/vNTVZWj+6q7vlat74heFJPBHjDUNL+by4W3Qs38Ubfdrn9vysy/dZa 7Y1PaR5onmyjyy5Tc8C+NL7wD4ih1Kzbdt+WaD+GaP8Au19eeGfEGi+MvDMd9ZXDNa3Em3y2/wCX eT+7/s/NXxSq74/mX5q7L4W/ESb4ea0zOrXOj3Xy3Vt/7Mv+1WFeh7SP9468PiPZS5ZfCfSWqWEl nceW/wDwFv71Uq6uG8s/GWnrcWjLLayR+Zb3MfzL/wDYtXL3lq0UjRv8si18biaEqcj0ZwtqMqWO 3/if/vmnW9v5Srv+ZqmrnjEyDZtop3/oNNrQoP4aKKG70AFFGyigD3O3sre1kh/d+bI3y+Yy/db/ AHamhgkuLpY/OZmX5pGoWJZJN0VvLL/vfLWBqnjmz0OS6sZWhgu23M3n3Ecbbf7yr/dry4x5j9NO hvljlk2/ZY3hX7rKqtVeTTbO4jbMMG1lZWXy9rba5az8URy3kMMUkW6T7vkXSyf8C+X/AGq6OHUJ P49sv+8tVKP2iuUWS1jiVY2tYGjWNY13Rq3y/wB2i6tbFpvLNrGm1fL+VVq3DcR3Ei7lZP4vlqKG 33TLN5sbKzbv7tQSLJZxtcLDFJGqr8u3btqjcaxZtdXUf2qLzI5Fjb5vu1cjSRZpJpFZdqs1Yv8A whul3l80k8O5m/eSbpG2/wCfmrOXN9kmXN9kzvEmufZ9QkaC42rCqxr5clQ2vjWZo4Vkuop/MXcq y7W3VRvPhssXzRWKsv3dyyNJ8vzf/FVTj8OWthcKyWrRNG3y/erxJzrRqc2xzSlUidzpPibT9Q0u a4eSCLc2393J/d/3q1NHe3umW6t7lZ4VXctc2ngvQ1jsjPZZkhh/hZl+8277v/fNdBoekWuj6K1r ZNtt1/dqsv8ADXr0vafaN4832ibyVit5PPb/AFjfLt+bdUdxtijhWPcvy+Z81SXG37pVomVdu37y 1Ctw3k+X8rL/ALS/drY1IZvlsVU/8tG3f8BqvHptnFpXktaxNHMzfLt/hq7eLbvIsbNIrRrt3L8y 0+8tWeRY4mjby127d3zU/ej8JEoxl8R5xr3w9ks1+3aS0jRt83kbv3i/7v8AerN0nxhcWcnk36tP D935vvLXrNy62/lwvG22Nflb7rbq8++JGmq0NnqCRqrMzQzMq/eb7ytXTTl7T3ZHg4nDywn7+hK3 901Le3h1mPzrKRWVv4ahk863kVZ1b5fu7v8A2Vq8/sdSuNLm8y3kZW/u/wB6u+0XxhZ64q294vlX H+191qKlPl/vHThcxp4j3Ze7I0LfVm+66q8f91m+ZavKq3C7oG/4C33qo3WkqnzI23d93+JWqnM0 2kwyTO3lW8a7mbduVVrm9nGTtE9fm5Vc3IV+z7pn+Xb93d/er4f+MHia18UfEDVLywhiit1k8nzF X/Xbfl3V1Xjz9pTWtW1TVrXTZFi0eSNrWH5fm/2pF/3q8bX5vuyLu/2q+6yjLamGlKtVPg85zGni VGjSC4g+0Ltb5f8AZaoW02Pasf7vav8ACq1a2sv31bbUjbZf/iq+nPlghtVt7dVRty0L97/Zpys1 v81SMqtHuRv+A1YEf+zQv92j5lVaSoAOaRlVl+Zd1O+Zf71DbmX7tADfKVl+WqcyyL838K1e+ZVp v3v96jlFKJl+VHu3bVo+zx/881qxcQeV8w+7U0dqrbW3fK1XTly+7KJhKnzFP7LH/cr1D4W/DeHV mj1bVLf/AEWFv3MbL/rm/wDiao/D3wC3ijUo5pY2XTbdt0kn97/ZWvfIbOO3hjhiVYo412qq/dVa +czLGez5qdP4j0KFD7Uh1nEq27Ki7V3VIsrW8yyI21l+61EK7fMWnMu6vlY+6egdFpesfarpfPm8 qRl2srfd/wB5aj1DTY9WZprf5bhW/eKy7a5/bVy1164s42jZmaNvl3L95a7I1Yy92odXtOaPLIq3 lhNZttlj21Xrso7i11KxjWeSKXzG2/L8vzf+y1zepaXJps21vmjb7rVFWly+9H4SJQ5SvI23au1f u02ZvlX/AHaFZvlX73+9TZm3SNXKZgrN91Wqw25WZv4f9moYfvbv7tCyrtXd95aAI5PmbdRQ3en0 AFLHK0S/LTdv977tQzXHzbU+Zv8AZ/hoAsahdfZ9PmukhknaNd3lx/eb/dr5r1jxHNqWuaheWfn2 bXTbWVZG3f7tfSluskEfztuasmHwbodhqVxrC2sUV037yRpPur/tL/dr1sDiqdDm5o80jlqwlUPm f76/7VOX/wAdroPHH9kr4kupNIk821ZtzKq/Krfxbf8AZrnW+9X2dOXNHmPPl7obW3U75tu3+KhW /wDsqZVCFb7v3vmoVvNX7v8AvU5fl+b+KodzLNwrLVx94UpcpM21V+b7td98CfHk3hXxculvJJ/Z uqN5Py/8s5P4WWuBb+LdV7w3uXxloflKzN9uh+Vf95a560eaPvGtOUoyjKJ7x8fvAMes+D/+Egn/ AHWrabD8zL92aPd/lq+Y42ZYW2sy19mfFCL7L8PfFTXUn7ua3Zo/m3bflVVX/vpa+M4VZoZFH3q8 zCylKPKdWOjGNTmiTLcebG2771Ryf6unRrtXb/31RJ9016B52p698Bfirb+GY7jw7q1x9ksbht1v c/8APGRv73+zX0RqWmx6pawt50X2ry9ysrfKy18IV7j8B/ilJ9qt/Ces7bmzm3R2c8n3oW/55/7t eTisN7SMpHpYXE/8uqnwnq0LfN5bVJUl1YNb/uWhaJv+WbL91l/u1DDLuXb/ABV8nKPLLlO6pCVO RJ92j71No+lIyCinfe/3qFoAF+Vd1FRs25qKnmLPb7jWn0vS5dRvZG2wrxH6yf3a8UvPL1HWpNWu 7WG7vm3KskjSLtVm3bflb/aatjXvEt9rkNvbzsvkw/wqu3c396sVvkX/AGmrOnT5Y+8evi8wlUqc 1GXLEd4fRfD+qQ3kEcUvk7tsbbtvzNub+L+9XqHh3xVZ+Ibhrd7OSzkVdzNE25a8tVdtSLK0cm5J JIm/vK1XOnGUTChmFajL3pc0T2q3uLHzpoYb+3lm27fL8za1Ra15lhY+Y8Z8uGNmZq8Y2tt+Vv4q vLreoRwyWq3k/wBnZdrRs25dtYPDxPQ/tbmjKLiWtD1zVFvoY7e8lX7RMq7d25fvV621wzWszFV3 M21W2145oerR6XqlvdTW/nxxtu2q22vVrHV9P1TRxdeY1jAu5m+0/d/3t1KtE6Mpqc0ZKUveJrH5 ZGk/55rup8MrXUyxy7ZV/wCmq7qXT3t7qx86CaK8t7j7ssEm7ctN+0Wtq0myT5vu7Wrj5T6IfJcQ 3EjM8bLu/iVqseSv9m/JJu3SfxfLWS2uaK9vDM99BBHJ91vM+Vq0fOjXT7WSCRZY23SKy/xLVajJ WlZFbd/q1Vdqt/eqK3uPNm+dY2Vfmb5alhuNy7m/u1DG6xwszLu8z5aNQI7VFmutzqzbvmquq/at Vj/2m3NUGta9a+H7Frh45f3jeSqrT9DvbfVLeS6s5o5ZGj2+Xu+Zf+A1Ps5cvMZe1p+09nze8WJL q6WZt25N3zbW+7WZ4kt/7a0G8t/Lj8xV85dq/wAS1o/aLi1+VmZV/uy1JDdR+YrPbru/vL8tOMuW XMKtT9pTlCR4RRWp4m0v+xtcvLX+FZPl/wB3+GsuvWPz2UZQnys6TQfGtxpf7m6/0q3b+9/DXn/7 SHxQh/s2Hw7ok0nmXUfmXjfd2x/881atysvXvDOn+I7P7Pe26yr/AAt/Ev8AutW2G9jTrxqTidcs dX9hKlzHzD8v8S7qb9njb7rbf96u+8WfCrVND8y4tY5NQsVXc0ka/NGv+0tcL5W1ufmX/Zr9Ao16 daN6cj5qVOUdZFeZpLBfM3bY9rNuX5qjXVFlbbu+b/rnt/8AZq0Gt1uFVvu/7tNazjZdu5lroMzP 1R2uIdttIu5f7y/dqo1vcQt/rI9rf3l+61WrrRdtw00kbSr935WqOztY7eSP/WKv95m3VQG3Zqst rH/e201l2tUbbovmX/vpaFupF/2qyAdtb1p235aPtn/TNakjulb+H5qsCNt3y/eo+apFvPvfL8y0 1bxm3fL92gCOa3aVVXa1dB4N8H3HijVI7WL5Y1+aST+GNa5/7fJ83yrW54Z8YX3hm6+1WTf3Vkhb 7si1lW5vZy9n8Q48vN7x9GaTpNvoenw2Nqu2GNf++qtLWf4b1lfEei2+pRQyQRzL92T+Go5NJka4 aRZvK/eeZ8tfnVTm5pe0+I9eJsLTd6/3qyV0m6ZlzfSfe3Nt/ipq6HMsaxpdMqqvzL/e+WswNimt VHT9NktbiaR5vN3KqrV5qAGwytazbk+633l/hauujuI9cjkjeFVhZVZW/wA/7VcizV0XhdlazVfO ZWVm/cfwsu771duFl73Kbw/lMG4iazuJI2X5lqvWpri7tUm2rWe0X+y1c1SPLKUTMcq/uW+ZV3NV eSVYpNrf+O1My/uV+9UeyswK7XXzbVVmanNLJ/DG1TVMvzUAV4YpvMbz9vl/MrLUy262/wB35mqZ k3bf/QqN3937y0EBt+X5v4ahuIo7q3khljWWGRdrRt/EtT0nlUwPnn4heCJPCWqbotzabcNujk/u /wCy1cnt/hb/AIDX1Fr2h2viDS5tPul3RyL8rf3W/havmPxBo1x4e1a4s7jcs0Mm3d/e/wBqvtcv xnt6fLL4onn1YcpX3fN935aN0f3d3zU2OXdVWbKzN/s16pzSND7yrR/D95aprebV27aI7ratP4fi DmiWvur/ALNelfs8+GV1zxlJrE6r9j0tdys3/PRvu/8As1cj4F8Eal8RNYWzs1aK3X/j4uW+7Gv/ AMVX1FpOjeH/AAH4Tkt42+zafY/vLiRv+WjL97c38VeRisRf9zT+I9LC0L/vJfCcL+0Zqn9jeC49 L85WuNSvGkbb/wA81+b/AOJr5r27V2/xfxV1nxI+INx8RPE0l4ytFp9v+7tYP7q//FNXIyNtVmra hD2dPlOPE1fa1JMGZV+81QyfN92q7XUcvzfMlRtLDubLN/vVqcfMWalhuJLOaOaJmimjZWVl/haq LLCzL80i/K3y1J9ojVdvzbtu7bRygfWXwj+LFx8RNLms7pvs2sWMfmSSf8s5o/u7v96tiRdsjSI2 6Fmby2+X/wBlrwv9m+4ZfiVCqM3lyWsm7/aWvoa+0n7PDdMqyNHHJ8snmblb/Z2/w7a+YzClGPwn 0VKUq2H5pfFEpq3mrTl/8eqONdq/71OrxiAp0n92m0SfepS+ECneW80nl+VJ5W1v++qKsL975aKz LJl+b5lb5aNzbm3VlyRXS/eum/4CtOjhmuo1/wBIZWXdtaugg0mbaq0blaqscE1uzb5Gl3VYjX7t AEm35v8Adpu5vLprfNTmb5trVAEljateXkNvH96RttepX3h6HV9HbSTuihkjWPcv3lrg/C91Dpd5 9suIZmhX5VkVflVq9J0fV7O+WSS3uFlZV+7/ABVxVuY+ryn2cYylJ+9I4ib4T2Msm2TVtSkkjhkh WbzFVvmZm3L/AHdu77v+ytSyfB/TYrqZnvLyXzNsnlsy7Y9se1dv/fVdvZ2+6ZVb7v3m/wB2nzO0 szN/EzVzSl/KfRnDQ/BjRYbWNReXkCM33V27d26Nmb/x2vQodIW10qwtbeZZI7ePy1ZvlZttNurW Xcqp8yxrt+Wlm+fTrf8A2WZaYguIJLJWXaxVvvNtrivE2m3lrrHmJ4g+w/aGVYbaT7u7y13f+gtX Ttql5ZTbYlaSP/vqoNQ0/TfFEkP9q6fHcsv3W3bWXdSj73wk83NqeO+JoryzaG1fXl1GNv8ASNyy bl3Nt2/+g1c0G1vHsWktdetoJGb/AFe5dzVDrWm6bcatdNbqyw+Ztj3MzfKvyrVGTRodu1Y/l+78 telKlzU+Q+KliuXFyr/EeleDdS1DQ7Gb+2NQa8s4VZlkkj+VtzfLXR2es6bqS/I0Tf8AXCTb/wCO 15N/a159hWxNxI1su3bCzblWqv8ADuX5WrkhheWPvSO/+2Jc3ux906b4kXVreapbzWsiy7YfLk/3 lrkasfehb/Z+aq6/NXdGPLHlPCq1fbVJTCinbf7rU2mZHbeCdN22s1w6/wCs/dr/ALtcP8TP2eNN 8TedqGg+XpWqN8zR7f3E3/Af4a6Twr4m/stvs90u61b+L+KOvQNq+WsiMssLfdZa5Y18Rg6vtKcj 67C08LjMN7KUdj4D8SeHNW8Jak1jqlnJY3C/3l+Vv9pW/iqjG3y7n/hr781rwHo/jjS5IdetY5dP X+JvvK3+y1fKvxS+AuqeEpJtQ0lZNV0X/ZX9/Cv+0v8AF/vV9rl+cU8V7tT3ZHzGOyephv3lH3on k7XDNJu/8dpskS3HzJ8sn92mstJX0B88Mjma3ZlZfl/iWpmVdu5G3LQ22Vdr/wDAWqHc1vJ/n5qv 4hD+aKT/AGl+7Un3f96oAk+VlXf/AN9UTbYodqr95qbu3NRIu6P5fvL92rAjVflr0D4b/D5vEjfa r1WXT42+Vfu+Z/8AY1V+HfgOTxNcLdXEbLp8bfe/56N/dr3RbVbW1WG3jWKFfl2rXz2Y4/2X7un8 R1UqXN70ixY3EdqsdukarCq7VVV+VauSRfLuX7tUYYvKZWb71TLK0TfK25a+Rj/eO4kX7y0VJtVl 3J97+7UMzbZGpgOZttQs+6hlbbuptBYV1Xhtbf7KrJu+0eWvmf8AfTba5OT7u3+98tdhp8tvZ6LH dJG0W6NWbd/FXZhfi5jSn9ow/EHy6tcLu+7Wfvf+9Ukn+kSNI0nzN8zU3yfl3eYtckpc0uYmXxDm lZY4/mam7pG+9u20TfL5f+7UbN81IkcybaI2+Whm+Vab/FQBYX5qFVlbcfu1GrNtqOFmaRl+Zvm+ WmQWtyr92o9395q0LPQbq63M/wC4jVdzbvvbf92tTT9LtWt5GS1klkX7v2ldvmV0xoSkaxhKRza7 m+VV3f7tec/Fz4W+IvEF7a6ppej3FzE0flzMq/dZWr3KS4WwsZPtElnYzfw7m+VV/wCBbaZF8TvD Nn4ZvbG/8V2cNwrbv3DKzH/Z2/NXZSjUwsuenHmNo4enU92pLlPla1+CPjaX5l0dov8ArpNGv/s1 aEP7OvjK4+/Daxf71wtewX3xw8FrarbtqV1ebW3boI5FZv8AgS7axdU/aK8LrZrGuj319DD/AKtZ FVf/AEJq9/2uJl8MTg9hho/FUPO7r9nPxJZqrT3mlR7vu7rhvm/8drpPDv7M8lvdW8muXTXKt8zQ WP3dv+1I3/sq1m+Iv2ntQvVWHSLOLSrdV2q0i+ZIv/sq1xsnxL8TXkkkja9eN5nyttm2r/47W3Li akfelymH+zU5e7HmPpLWte8M/CWxjjimjs4Y1/c6baKvmSN/tf8A2VeB/ET4sat4+ZreVVsdLVty 2kf8X+0zfxVxdxcTXDNI8jSyN95mbc1N+9WtLCRh7z+IiriZVPd+yUW3LMy7dsdE0W2P/wBmq58t RtF/d+X/AHa39mcfKZrRNTOau3Hyq396qXNYSjyk6j2T+Km/d+am7q7z4c/B3WviDMsyxtY6X/y0 vpF+9/ur/FWUpRj70ioxlUlyxOq/Zj0aabxZfaw67bOztWjadvuqzf8A2NfQGtbbCzhs1k3fNuZv 71U/CvhfRfBfhdbG0hlW1jk8zdJ964b+81V9QvG1K88xvlVfl2185jK8ah78Yewo8n2iFu9FG7c1 FeOZAv3qJPvNRRJ96lL4QGrt+9RQ3yrRWZZc0/Q/+EhvFs1kWBpFb5pF3Kvy1of8K31TTY/Lt44r mNd237NJ/wCyt81dla+KNBuJN32hYJvu7pYdv/j1bdqsd+vmWk0Nyq/xQSbqxlWqR+yfS0Muw1SP L7TmkeNMskcjRurKy/Kysv3aF6tXrmraNb6pHt1Gz3N/DPt2yL/wKvOfFmkW+jX32e1aVo2Xd+8r WnVjUPMxWX1ML732TJVfmpq/M3y/eamrL838S1veD7Nb/XId6+bHH+8atZS5YnBRpyqVIwj9o7rQ 9IjsNHht3VWZl3SK396r0Ol2ulzR3FtCsFxJH8zRfL8tXVt4ZWXy5tn+zLRdK3nMxVtv8NeVzSP0 KNGnGMY8vwkkdwv2eSSWNdzfu90fytRa28bTeYkm/b821vlaobr5fLh/ur83+9R/q7P/AK6N/wCO rUmwyRJIZPnVlaqXiS6ul0eOaK4ZWhuF8z/rm3y1qWbSfZ5mXdKq/Lt+9VS/ih1LSb+18vZJNC33 fu7l+atIfEYVlenLlLcMtv8ANt8yJmXbu+9XLa94hs9Ns9QhiullvFXy1Vf7zVy+m+NNQslVXZbm Nf733qwbyVriZpH/ANZI25q6qdKUfiPAxOaRdPlpdSOP+9/dpq7v4adu2qtNkaRo/k+9/FtrqPmB 3mt/F81O3Rt97ctV47iTzFV1WpmWnGXMIkji+9833lqHyvKjbdW14Z0WPWtQmjdmRYYWk+X+9WL9 okiajmNZU5RjGX8xCy05lbaq1MtxGytujprLHL91tv8AvUEmbHq9rLG0m5l+995a7TwH41s42msb +8SCzjjabc33l2ru2/8AoVY+i+EG8RahDawwx+Wv3pP4Y1/iau+vPB/h9bdbOLTYJYVXa0ki/NJ9 7/4pq5a8oxjyyPeyqhUlU9r9ko6x8VfD9xeQ2sV8v2VY1kVl/wBWqtt+Zm/4Etaml6la6zp8N5Zz Lc2sy/LIv8VVV8A+F7ptyaTbW1xtWPa27y2Vfu/L/D91a0LPS49Gt47OC3+zQwrtWP8Au15rPrzy f4nfs96b4q8zUNE8vStWb5mVV/czf7y/w18w+JvCupeE9SksdWs5LO4X+991v9pW/ir7+VGZtq/e rF8WeDdL8YafJY6vYx3Mf8O770f+0rfw19Hl+cVMP+7re9E+bx2T08V79H3ZHwJtal+8u1vu16x8 TP2f9W8FedfaXv1XSV+bcq/voV/2l/8AZq8nb5q+6oYmniI89KR8JXw1TCy9nViQqzW8ny1Nu81d y/8AAqay7utR/NE25a7PiOYlrrfh74Nm8Yal8+5dPhbdNP8A+yrWb4R8K3XjLVFt7Vdsa/NNJ/zz WvozR9FtfD+mw2NlH5UMf/jzf3q8XMMd7CPs6fxHRSpc3vSLFjpsOm2K29vGsUMPyxqv92pF7VGy /vtwb5lX5lqZW3LtWvjZPmPTE/hpV+X5qcy/d3NTd237tSA6NmVt33akmj83bIn8VV6sWrfejP3W oAbuXb83zf7tRs27/ZWiRWibbTWbbQBJZ7f7Qt90ayr5i/K38Xzba6LxNcLFax26fLu/hX+7Wb4T uG/tCRVjVlZW3M38O2jxFcNcalIv8Mfy11U5ctGUv5jeXu04mXUy7fLVdy/8CqGiuUwJJvvL/u1H Tm+9TaAHN/6DTW705vvNV7SdLa83TPHI1vH/AHfvN/s1cISlL3Q+L3SOzsJrz7u2KP8Aikb7taGr app/w+s5L7UWVbWH70m3zJJG/wBlVq9rF/b2EduvkxtdKu5Y1bcsdeT/ABguJrzwXqEkrMzM0f8A 6FXfR9nGUY/zGlXlw/Ny+9Izdc/aRjiaaPw9o+2ST713fNuZv+A//ZV53rHxO8Va5MzXGuXUat/y ztm8lf8Ax2uThXav+1VW+v2s7i3jWPcsm5mb+6q19hTw9On8MTyJ16lT4pGpcXE10264mlnZv4pJ GanQqqs3y1hQ+Jo5YdyW8zfxfw/3ttTrryy2f2hI9jeYq7Z227d38Tf99V0xjymFzQZm/vU1vu/N WbNrkcUm2SNvvbdyt8v/AAH/AL6otdchvLiOFY2VpN33tv8ADQI0NqtUMlqyfvIGZW/u/wB6rH8V LzQBBb3G7bG/yzbatbKhmtY5VXevzVHHdMszRz/Lu+7VgWm70fxU1vu01m2K3zVAFG+m3Nt/4FUN vBNf3UdvbQyT3EjbY4413MzUSMssjNX0p+zr8NI9GhXWtRhZtSvo91rHt/1MP97/AHmrzcRVjTjz F0KEsRU5YkPwv/Z9h0uzj1LxBax6nqTMvl6fu/dw/wDXT+81e0Xl1DoNvDGyrFMse3yIPlX/AL5/ ho1DW7fwzbta2O2W8b/WT/e8v/7KuPaVrqRpHZm3f3v4q+Tq4yVSR9HKNPDR9nD4ia+1KbUpNzfd /h2/dWq/3V2rTv8AZWhV21x/4jiBV2rTqKKQgqGa42yMtTVVm+aZqAGySM1FaVroN00kfm28qq3/ AAH/AMe/hooVObWhr7KRXk3WsjLP8q/wt92vWLe/0PRobezaRbOSaNZmZV+Vv7teW6xK0u2FV+X7 zfLWbH5i/wAUjr/d3V59OXtqfvno0cR9UqS9nE9/hkklj8yzuFnj/wCmbf8AstYPjrRG1SzjZFb7 ZCvy7V+9/s15dpfiO80aaOSBmVv+Wi7flrom+I+rXlrNG8i7mb/WKu1lrSNJxleJ6ssyoYijKnWi c/NbtbybZoWib/aXbXcfD3Q2ls7i6VfmZtq7fvVy+oazeazHDHcMsu1vlZV2tXqPh+1j03S47fd5 Unl7d3+1/FWlWXLHlkcuWUoyr+0j8MQaGaFtu7/gLLTo7ya3bb8yr/s/dq40skI2uyyj/a+ao2W3 lbaytE3/AH0teefWgt7b3TfOv7z+JlqSaFbhV8hlZVXaqt8rVXbTW2/LtlX+8tUG8y3Vtrfd/vUA asjtarbxtuRvvNTbebzb7cflVm/9CrAuvGX9l3C288Mk67d21V3f98rWfN8TtHVty2t8kit822P7 taRjKXwmcpae8cbrGnSaZql1bOvMcjLVHd/era8SeLbXxfdfaraxa1ZW+ZpPvNWLu/vfNXpx/vH5 7XjGNSUacrxD5ZW/utQytTl2/e+7TdrUzIN3zU6Nv4qbu/vfNTtq7fl/ioIOy8A2t0sk032NWhmj ZVn3bW/4CtYsfhLVJbfztqru/wCWUvytW98N9ZW3vYdPm+60m6Nv/Za7bUNQkgupoV+ZVb+L5lri qVJU5H1WFwdLFUY+98J462jXkVxHHLayRMzbdy/dq1pPhO81u8aG3VWVZNrSfdWvTPtVrL/rbfY3 96JqueXDountJBu8y4+WPcu3atH1iRr/AGPT5r8xl29va6HZ/wBn2C7YV/1kn8UzU9rdZv8AVNvb +633qr0+uKUub4j3qdONOPLEjZtrbWq5b3n7vy7hfNh/h/vL/u1CzLKy+au7/aX71FxatC33t6/3 loLL62H+jySQNujb/lp/d/2WqpuZdsMsbMv8P95f92pJNQkWbbF+6WH5VWrMNxHdMv3YJP7v8Lf7 v92l7shlCa1ZVbcu6P8AvV438Tv2e9L8W+ZqGjeXpWqN8zKq/uJm/wBpf4a9mmaaK43Puiko/dzf 3YpP/HWrqoYmphanPSlY5K+Gp4qPJVifAPijwbrHg28a11exls5P4WZflb/db+Kqeh6HdeINShsb WPdJI3/AVX+81ffGtaDZ65ZyWOp2cV5byfejkXctedw/BvTfB7XVxocLbpm3NHI25lX+6rf3a+up Z/GVPllH3vwPjMVkU6MualLmj+JyPhXwvb+D9Njt7Nfm+9JJ/FI3+1W40q+W0n3dv3lp0isrMrLt b+JWqv8A8tG2r8q/xV5EqkqkuaR5vLykyrtZm/iapFVWXbt+amqyy/7LVJt+X5fvViURsrK3ytup u5v4lp1FABQvytTV+9tqSOJm+7QBYmXzY1kX738VU5PmWrlvLtk8uo7qLypP9lqANLwTJN5l5Ht/ 0f5W+7/F81V/EH/IWuP+A/8AoNTeEbiRb64tdu6Nl8zd/dqx4os9si3S/db5Wrt5eahHl+yby+Ew 1+7uoZf7tC/3aPvVxGASNtZvlWjd/s02T7zVpaXpLXStcSq32df7q/M1XGMpSCMeb3YjdL0v7fMr Tt5Vuzbd395v7q1sahef2THJbwSbmb5VVfuxr/u0alqUdhDDHFDGt4se35fm8uubjuvtEkinc0it 81dkpRpR5Ymkpez92I5vmmbd8zN/erk/i0v/ABQd8y/w7f8A0Ja6xv8AWLXL/FRfM8A6p/sqrf8A jy1nhv40fU5amx87x075W/hpsfVqN7f3q/QDyg8qPbtWNVX/AHab9nXbt+Xb/d21IvairAJLddy/ Ku3/AHarrZwpIvy/98rVxvlVf++ai3f7VQAm3/gNO27fmaj73+7TWbdQAlafh3R49c1SOGWPdDH+ 8asyu98A2CxWMl033pm2r/urWdWXLEcTD8ReC/7Jt2uLeRmt2bc27/ln/wDY1zbRbFVW+avaJoll VldVZWrzfxR4bbRptyf8esn3W/u/7NZUqnN7sglEq+FdEh1nxJptq6x7ZJlaTd/dX5mr7IsbiHw/ 4XWaJViuJl2wqrbvL/8A2a+Xfg7prXnjaG6VVaOxhkk+ZflZtu1Vr6M8Ublkhh/uwr8v+9Xz+bS9 7lPZwb9jSlOJiyTbmp0P+rFQVZh/1dfORMySiiimUGyjZRRQA1m2/Kv3mrY0vRYVjmmuJts0f8X8 Mf8AwL+9UPh2zkluI7xo1aNW+833a4Px14wbWbqSztf3Gnws3yr/AMtG/vV6OFw3tPekXKUaMeaR ueLPiQsUbafozfKvytc//E//ABVFebqvzUV7Xs4/ynnSrVJO/Me4fw0fw01X3U6vyU+rDatV5tNh uJFk/wBVJ/eX+KrFPrSnUlTlzRFKMZfES+GdL87xBa7lZo428xtv+zXqsnluvmeWrR/3o/lZf96v NvDurNo+pRzFd0LfLIv95a9JaJlX7RBuX5d23+Flr041/bR949jLIRpwlGJGtvHN/qpP+At8tRyQ SRtytWPs8dxD5kW1W/ijb/2WoVkmt/l3Mv8AstQe0R7tv+9Un2hmXbKqzr/tUebHcffg2N/eioa3 3/6iRX/2W+VqAKOoafb3FvI0DNBcbW8vd8y7q82uNB1i/wBY1Bra322LL+58qRmab5l3bl2/L/F9 6vTrhWi+V1ZKrxs1vJ5iMytWlOr7Mwr0o1o8sjy3VNLutLaFbqFoGkXcu6qatu+98y13nxIQXWn6 ZePuaTdJCzf+PVwfy7dqtXp05c0eY+ExVD6vWlANy7f7tG1lqNlZW+ahW2/drTlOYkV9zfMtOb5m 4qP7Qq/e205WVm+VqCDuPhvpsdw0009v5qrJGqyNXVap82pXH+9XE/DtW/tSSRriSCOHazbZNqt/ vV2mpSLNfyyRMrxyNuVl/irysR8R9xlX8CPujtLs/tVx8/ywx/NI3+zU2oXS6hIqsuVbc0f+yq1b W3W3s/sfmeVcTL5kjf8AstUWtZLfarfvVX7rRN8y1iewVGt2Vd38P95abVpriPzGZY2iVv8Ax7/e WnfZVuGXyvvN93b93/7Go5QK8K7FaZv4fu/71Num+yw7W+9/rJKl1K/j021aRWXy422ruX7zf7NZ Fje/bNP8y6b/AFjNI3/xNEalONTlkZVJS5fd+Ik+2fL5zR7FZtrVJ9uh/vVlzXH2hlVV2xr91aXa 391q8+tilKpL2MfdMKUqkYxjL4i/Nq0P8UjM1Qzaovk/ut25qr3WntYeSzNuaaPzNv8AdqT+zbra rfZ2+7uruoxp1I/vKvLL0MKlfERlywhzBZ381v8AK372P+61aUd1b3H3ZPKb+7J8v/j1VYdDvJY2 kWH7vy1T1C3m01d08bLWGIth6n7j3o+ZrQnV5OarGweIPCMeqbplbyLjbtVv4Wrz+8sLjS2WO8j8 qT7v+y1dVb6pcWszSRSNtb+H+GtZbzT9et/st/Gq7v733a1w+Yxl7sjgxOBp4n3o+7I8zkXb8y06 G43bWZZP++WrpvEXgi6sF86y/wBJtdv/AAJa41tP82Rvmk3fdZVavoI1I1Iny1WhUw8uWcTUVlut 235WqOoY5WVm3L/FVhm3R7v+Wn8P+1SMCGFmuJPu7VVvvVca4VV2rVO1WSKFlZWVmbc26pKciIlh l81VkX71Tf8AHxb/AO1VOOJm+7UN1fyW900aLI3y/eVfl3VUQCbzomWSJmWRfustdhDdQ69pLRq0 bTNHuZV/hauHW/mluPLKyLt/i/h/9BrS0+4/snUo7ry/NjX7y1rTqez92XwyNacv5iORWikZXXay 0KzNXSSW669C037pVjXcs6t/D/dZak0e1s4reSa1X7TNGv3pF27v93dR7B83umsab/7dM/TdBaVv Ouv3UP8AtfLurS1S8bSVkjSRWkk+WOJfuqv96o7zXo4oVjeGOe6Vt21W3Kv/AAKuXutSkvJJJNrS zMv/AHzXTKUaUeWPxESlGPuxC6kmaTaqszSf8tKkt4Ft41VaLO38pWVm+ZqkrzzAa3Ra5v4kLu8B 6x/1zVv/AB6ukk+6a5/4hLu8D61/17/+zLW+G/ix9SJfDI+c7O1kvLqOGCNpZG/hWuit/AN9LHul migb+796tzwf4f8A7LtftE6/6VIv/fK/3a6Ovtp1/wCU83lOAm8A3kXzRXEUrf3fu1k3Wg6hZ/62 1k2/3lXctesf7tJzURryDlPHpI5Ft2Zo2Vf9parqu5q9C8XXUNxoN1u8pfL+b7zfL/47/drzO1vV ut0aSR/7y1305+0jzEyLm5W+VWpOarW8X2eRVf5Vq5t+b5aUfeIBflXdVzTdWutLk3WsjL/ejb7r VTb/AGfu0blVdzfJtq+XmKPQNJ8YLqS/Z9rRXn+7uWuia3+0Q7Z41b+8rfMtcz4J8Ptptr9quNrT TfMrL/Cv92uqb7teVU5eb3TY6TwLbxq155XlxSR+W21flb+L7u2vQPFlmyyR3C/NHMu3/dZa8n0O /h0vVIZriPz7VvlmX/Z/vV7FtXUtHbyrqOeGRt1u2771eHiqUpSPZoSjPD+z+0chzUtu3ystSXVq 0UjKy7WX7y1XVtrbq8Pl5TmLVFN/2lpy/NTKCmyf6tv92nU1vutQM1LP7RYeEb6bd8zRySR/N91d teK17dp6tceE76H7zeXMq/8AfNeI19Jhv4SOfFS5pRJlXbRQz7V3UV3nIe0Sfut237vmUL/eWmx7 l3ea25WoW3ZWVV+Zf7y1+OH1+pIy1GrMrfNTmZvObau9fu03azN81Aalhfu13mk+IJLXSdNmdfMh +aGRf93/AOxrz/7v8VdRYrt8MWrf3riRv/Qa1jKUYylE9DAy/eHYw+TcQyXEEnmw/wAKr95f96hr pokjjf8Ae7v4Wriob+SwZpopGjZf7tXLPxrHcNt1GHa3/PeD/wCJraliI1PiPXlXp05csjqf3Lfd 3RN/tfMtc02vNFcTQz27LtbbuVq1Jr+3is2u0mjnh/vL/e/u1xt1K1xM0jfeZtzVvORhiK/s+X2c joZPFbWrRrA3nxt95Za0rHVLPV45N0LQMv3ttcRuZfvf+PVpaXqy6arboVlVv9qpjM5aWMl7T958 Jc+IFqq+F7fZIJdtxuX+FtrLXmrV02uXk2pW91NO29mZf+A1ze5q9jDy5qZ8/mFT2lbmGqzLR8rf eXb/ALtO+V/9mq95FcN5f2fa3zfN/u10HnlXUGt/OVZ93y/dZaq7bNW+Zpdv93+9Vhvtn3nhipyt cNC25Y1b+Hb/AOPVZBXWWzZmVZJ1Zvl2r/FXuHgu6sbrQ4b75msbW3VVZvl+Zfl/9lrxNvtnmfLH Gq/+hV3V1qXia10fT7M2tpBZ+SrLGrfeX+9XDi5cseY+jyeUuaUTuG1y1uriaSK4XzJP+e/y7as/ aGs22qv7v+9/e/4FXmC6oyzN8u6PdW5p+qXFuqyW8zIv92vmqWP5viPqIzOsm8u4bcG8pv7rfdq3 D/xLbdppW+993a1YMPihZV/0y1jZv70a7aqeINc+3xx29vuWNvvNXZLEU4x5i+Yq6tdNrTbt3lWs P+rX/wBmo3tcLHbxLthVdu2q8asyxxp93/0Kut0vQfJaNvvSbfu/3a4acalaRzx96XMM0vw+25ZG VpdrfdWt1orX7Q25m+83+7TpLpbdVWJt0irt/wBlahbbcQtIF8pl+8tevCnGn7sTcbqyzW9urRLF L/Cvmxq1STXHyw5hj3eWu75aZay7bebzPmX5flap2WO6VpmWVf8AZ27qr+8Mbbf8eLbZPIbzPvbv vVXul3L88iz/APj1WvKhS1/1jMvmf3aqzNH/AAKy/wC81EgOA8QRR2+qTLEqqvy/KtZ3NbevfZ11 SZpdzM235azZopF+ZP8AV/w18rVj+8kccviLGl61eWXypulj/utVfxJBp96y3Edv9lvmjZt38Lbd vy1XWVmb94zbabq1/bxab5ax7mk3KrN/D8u7/wBlrvweJlTqRjzHPXjGpRlGRz7Ksv3vvVVkVlVl /hb/AL5ohuNzVYWVWX5q+1jUPhivY3StDtf5atfu1/26q/Y1i8xk3fN81N+7VCiaCt5sbf3qq/Z1 Zvu/NTYZfKk3U5rpm/u7f++agC1b28Msf+rXctNb/aqO3uvLkVm3batXC/xL/FT+ICva3E1hMzW8 jRM33qkbc0m57iV2ao2ibdup3+8y/wDfVVzSiMkj2/MqrTVi2/w7VoWaNW/1kdN3R/8APRagRJ8q t96m0Lt/hZf++qdsagCOT7pqjrirLot4rLuVo/8A2arzfdbdVHWm/wCJLdf9c62o/wASIS+E4Pay 0lKv3qd97czLX1p5oyl3N6UeV/dajbubbVagUZNDhurWaOXb5cisrfu1+bdXC6h8ObjS901ltuYd v3VXa1ehatqkOk2bTS7vLj/u1wOreMry/wBywN9mh/2fvNXRS5/skSOdZf8Alm601vlXav3VrqtH 0Gz8QabI0Vw0WoR/e3fdrB1DS7jS7ho7iNlZf/Hq7IyjKRBX+v3q1vDuhyatqUe+Nlt4fmZmXbu/ u7a3vCOlrZ6e19LGssk33f8AZWuotYtq7m+83zVzVK/2YlxiWI1VV2r8tDUlKrf3vu1xFg33a3vD fiiTQd1vKvn6fI37yD+7/tLWC1NXdUSjGXuyKhUlTlzRPbPtVn4mhhazVpY2Xas6/Myt/dasG+sJ LWRo3XbJ/wChV5/putXmh3UdxZSNFIv3l/hb/er1DRfEGn+KtLk8uFvtm7dJB5nzL/e214+Iwp6s aka/+IxVlZfu1Itx/s1c1DRpLVVk2t5bfMrbdtZ7Iy/w140oyiZSjKPxE3mrTfN3NtqLmrem6Tca lJtiX5V+8zfdWp5hxjKXuxNzwrF/od1uZWWST7v935VrxG4i/wBMmjT7qyMqt/wKvcFW18M6PeXn 2jz4WXzNy/xfLt+WvE2+Vt27+LczV9PhY/u48xljI8soxK+1v4/u0U2ZvtEnyrRXdynnHuHmt/vU 6GX983y/dX+GmfxU+OJljZv7zV+On2ZIrfxbm+X+9Ue3/aV6Gb5dq02jmJ5gkXa33a6i6v7W303T 7NJlbyY9zf7zVh2du33j92oZLhmkZqr7J00qsqPvRJtQuvtDbUb5ap037YqyMrfL/wABo+3xt/Ev /fNTGPKZzqe0lzSJo5Wib5Wq1HKtx937392s9rxfl+783+zQt+u75du77y/LVxlyk8w7UtXWwXy1 +ab/ANBrLt9SmZvM8z5v4l/vVcvLeO8VmZds396smOzkWRll+XbXt4X2dSPunmVpVIy5jY1S/wD+ JSvlN80jbqw47xmX51WtKzs5NWvFt4v4v/HVrFVdvytXRGPsfdiYVXKX7yRMzM0nyttoZpG+98tR 0+q5jAWaWSXdHu+Vvl2tUf8AZKttZppFZflqSFf3y1c+8q7a3plRKcOl+VMsizNLt/hr0TxAzNHC rfw2sa/+O1w8f+sSvSl0n+0tNsb4yfK1uq/e+bcvy15+Y05VKXLE+hyn+JKJwf2Wb/nm1NWWS3b5 WZWrvF0OH+Jm/wC+qo6xpNusMf7v+KvkJ5fKnHm5j6b2RzMOqSK3z/MtaFvcR3H3G+ao5NJjZfkV lanafataqyuvzbqyhGpGXLIiPMWo5ZF2srfMv3a7Xwnq7al5lu7bZl2tu/vLXGKu5toXczV0C2cl nD/Ztv8A8fUnzXUi/wDLNf7tenh5SjLmiaROjmtZG3SeXtX+5ViaVWtZGXb5bbVVdvzLVGHUlVo4 5WZ2Vdq3K/eq1eK00iqi742/1e3+L/ar2jYhtV3w3C/xfK1CxXSrtCy7aPs6q277VGrL/d3U5orj d8rSSq3zbl3UASLD5VrN5scm3crVh+INWXS44WgVvm/vfNWtcfaIdNvGZZPlj3Lu/wB6uK1R5tUj j3Mv7v7vy1xYqr7OPLH4iJSKfnfb9QaZ13bl3VJdWv2jayttkX7tQ/YJl2skiq23bUPlXlv8qtur xP8AEc45bNpfM83crL/F/erN1yVbCzm2Ks6sqr83+18tay3U0sfl+Xtb+Jmpt1utdHumRVaRYW27 v92roxXtImdSPNGRw6rtpaRvlptfZH589yRZdtSLtaq9C/K22tYzDmJmj/u/NTfs7f3Wqxs+Wo5G 8qt+blKIfKZajkupIt25mVV/6aVaW4/2qryWay3DSOqs38LbmWqjICFbpZWVflbd/tK1TfdpzaSt vItwsce7du+8zVcj/ex7nXdQBRoq95UbfLt21G1rH/dqAKuyippLXcy7Vp0ax2/+03/jtPmAFuvl VWX7tQ30Ud5YzQ7vvL8tOb5m+6tV5mZdu2NmX+KrjL3vdA4VpVVtr/ep33l+Vt1U9Y8611K4X7LJ t8z5aoreSbvlhb722vrY+9HmPNNqT5VVakj+WPd/3zVG3laSRVNWppf3m1furVAVdSsF1Kzmt5fu yLWDJ4FtfsLQrI32hv8Alu1dJ5q/xU1m3fdVqcako/CBR0fw/a6NG3kLukb70jfear15YW9/b+Td RrLH/wCg0bpNv3akVmZdrUc0ubmAp6TocekrMtvNI0MjfLGzfdq7SKzK3+1U25Zfvfe/vUSlzARU U9l2/eqncSzW8m5I/Nj20gLTNtb71C7fvVjtqkm5V+zt8zfLUi3l1tkbyfu/dVqCDSk3fwrRHcTW ckdxbyNFNG25WWqK39x8rNb/ADN/daj7fM0kcb27L5ny7qXxFxPTPD/xLjvGhh1lmgkX5fPVd0bL /dZa6Kb+x7y4aO1vI4JPvLuZWWRf7y14rspvlf3a4Z4enU+I644yXLyyjzHtS2FjFIyz6lbLtXdt WRVaqeqePNJ03T2tVjjuZG+7BBJ8q/7zV4+tv/dWpo18r94/y/3VqqeEox+En61KPwxNrWvFF9rP /H1Jtt1+7BH8sa1hszXEm1aP3l1JtH/7NWo4liXan3v4mru5eUwlLm+IbHEtv937396ipeaKepJ7 Ky/vNq0MrO3yr8q1N5TMzbNu3+KVqcqRr/C0rf3mr8f5T7Hl5iFbdm+783+7ViO3WL7/AN7+7Ttz Mv3vlps0StbyLu2rt3bqqNMOUm3bV/u7qPvddrf71ZcLbW3fbP3e5vlqFYmX5V1CthFy6tY4m8xV Xa1Qrt/urV7b/oKq7ebJ/e/vNVFopLf7y1yT92QDtvy/dpvy/wB1d1EatK1Omuo7Lcq/NJ/46ta0 qUq0tDOUoxjzSBljtV8yX/vms+4umum5/wC+akt7W61a68uBWnmauytfhj51mrS3Eizf3vuqte5S pU8N/iMY0a2L+CPumT8P226xMv8Aej/9mrj9ciaz1a8j2/KsjL/49Xs+i+HbHQbfy1k82RvvMq/M 1eceNLCNfEl5tjlVWZW/8dojL2lTQ68ZhJUcJCL7nK+aqr81Hmqq1aksI/7zL/vLVeOJYrhVaRWV fmrTlkfPcpNaxSf6x/lWrW35d26o/NVm3bqG+VV/76rojHlKJP3n8PzV6N4PuvtnhVVb/WWczR/8 Bb5q852rt+Wu5+FswnuNRsZGz50W5f8AeWpqx5qZ6uWVPZ4mJvr833atWNu0rMx+Vf7zVfjt1t41 aLbPt+9/s/8AAaa26T+Fmb+7Xk8p94EixqsbJCsqtXLeKrVbXUvl+7Iu5a61fLt42aRvvfMqrXPe KIvNt47hv9YrbV+b7y1hiI81MiRQ0Hbbw3l9t3SWqr5at/eb+Kr9nE0Ubbm3SSfNI396s/Tfl0XV pG+Vdsaq3+1uptv4o09oV2Teft+Vmj+Zd1clKUYx94iJvW8saqyuq/N/FVe48Sf2TdfZ7dfPh/5a K3/stZ669Zy/8tGVv7u2stmkv5PORWlVvusvzVrVre7+7ZUpHd6Tf2uoNugkjb+9BIvzLVXVrK6u vLZLhrGOP/WNu2rXKR6XfM25LWf/AIDG1dTZ6Nfapoqw3SyxTQyNtaf+JaI1JVo8soklC61SOK1k tbZpJ/M/1k8jfe/3azY7eS4+WKNmb/ZrqI/Da6fCu63aWb7zN95Vq/N5ax7ZvJgb5flVaX1eVT4y OU5qHwzdSr8zJFJ/DGzfNRH4emgmWS4WNofuttbdXSyalArR/LI23b833ahjkhTdsmaLd/DKu6tf qtOJfKU49Dhij2jy0aRd22SsrWtB8m1kWKPZMytt/u1vyW8lw25ZI52/2WouIpIrGNXVlbzGatXS j/KU46HhkPzQr5q/N/d/u01rVW/1bf8AAasNFMslxHP/AK5ZG3f99VFzXt/Efm048s+UqRxeazf7 NSR2/wC8bdTmtd0m5dysq/wtUKzyW+7fHu2/eZVp+zM+UtNuVfvf99VVaXczNUkbfapG3N+7X71W PlVlZY1/75oceYCutqzLubb/AMCqORGibaystXqP9mp9mA6xbzYfLao/KZWZakjVVk+X5akm+ZVk FaRAqruWhtzVNvZtzKzU2SL5vlWkA2H733WquytVqOJvMX5ab5Sf89Gp8oFFpVVtrMqNt3U1bqH+ GRf++qsXln5sbN8ssm35az1tbjd/x5ruX+KjlA5XxBqULaxMrSR/wrWfJa7m+Ta3+y1Sapprf2tc b7dV2t8tN+zyL/Cv/fVfWUY/u4nny+Ij+xt5e75V/wB6ofmX+KtDay2smfvVRZo93zqy1rKXKSQ/ b/Kba7Rru/3v722pre83f3dv+zTfKVvuyL/wJVahbdvvfK3+78tHNEC5HKrU75ao+T/lqd++i/vU wLjfdpv3arrdSL97a3+8tSfalb78f/fLU+UC1HLtX+8tO2qy/u/+BLVdbiH+8y/7y0LF+83JNHSA r31qzRtGreVu/iWqbWt1tbbcfN/CtdAyrKvzbd1Rtar/AHlpcoGXZxTRK3myeazf+O1aj+8ama3h /ikpu63i/wCWm7/do5SCvIv7ynR27N975VpzX6r/AKqP/gTVDumum2/M3/oNHKWTNcRxfc+Zv/Ha jjt5Lptzfd/vNU0dnHFtadtzf3amZvl/ur/dqwGrtt12pS802nc0agNoooqQPdWm3fw/LTVbd/8A ZUMrL95aI2b+Kvyc+yJGXdt21HdS+VC21fN2/wAP96rCrt3M33qtaboc2qM2zbFGv3pW+7VxHGMp e7E5XzYVX5rORf8Adpu6F12tZyeW1ejWvg23aRY3uJGZv7q1dtfDOlrb+Y8MjfNt+Zq15JHZHB1Z HCQ7pVt/l2/Lu21JJuVf9qu9uNJ023WHFrGysv8AFuqouk6fcM3+i7VX/po1R7E0+o1DldLs/tF1 DCi/MzfNtrbsfAen2t1JNcefeL/DGy7a39NsbWxkklt7dYpo1+VvvVo/2vcf3l/75rop/u4nVSwM fiqx5jPs4IdNXbZWK2yt97y4/mq2tjNdfvJ2aOP+JpKe2qXX9/8A8dqpcXU04+eRmquY9KMeX3Yl r7dDZ/Laxqzf89JKqSX80sm59rf70a1GsTTNtjVmb/YqRrJl++0cX+81RzSAztaltZLGRrq3tmjV W+Zo1rwdre43TMkiqu793+7/AIf7v3a+hL21tE3Q3Enm/wB5Vj3V5z40t9Lt7ry7BZFm+9J93bXZ Qqe9ynz2bUvc548v6nL6Xod467pWjZmXcu1dtXv7Guv7qt/wKtTTZfNs4f8AvmriqsTbi3zf3awq YqpGXKeNHD05R5jDh0a6ZW+VV/4FW54NjuNH8S2czL+7ZvLb5v4Wpq1JC22ZWWp+uSNqdCNOUZRP Vnsbe1m8x7tI/m3bWxVWSPTZGY/a5mX+7Hu21n6x80NvdP8AKzQrJu/2ttcvJcSS/wCskkb/AHmr jrYn2Urcp9epHbq2j2vzFm/4E1QXF/4f8tlkjWVf7v3q87updQt7qRooVnt9se1fu/3t3/stZ91r OqW8e1dNVpNyx/KzN8zLurmljJfymcqp6RHeaW0PkwacJIWkXzFbd/31Xn+tfDu3WONbe4fT4/Mm 3LafL80jM3/s22r2k6pqSzNHdWawW8jbZGim+bb/AAt/n+9V64uGuJN33V/hVf4ay+sS5S4y5jn7 z4U6lfrM2m6l5CtcRtbwNM22OHy9rf8AAt3zVatfhF9ljkjbXtQ8tljXy1ZlVfL3fd+b/gVdxpMU lvaqr/8AAf8AZrXV1ePzpV+Zfu/7VepTfNE3Dz5LW1jzcSyN5a7Vb73/AAKm7rq4s5NskjPuX+Ko VVruZtzf7TN/dqvdTebJtX5Y1+6tdFP3pFRiWoWvNPbzHWXy/wCJd1XFuGumj82GOWFtv3mVmWqN vdSPbzb1jlVVX7y1ZtdyyQ+XbKsci7mZVb5a0kEiFvsM38M0H/j1I2leb/qLqKX/AHvlaqTfe/2a lX7orn5iRZNNuI/meNtv+z81LDcTQt8sjf7tLDdTW/CSMv8AwKrtvftcM3nxpKqqzfdpiPGPElvJ ZeItSjlX/l4Zl/3d3y1msyt95a3PGULf8JRqDSbv30nmR/Nu+XbWL5W77rLXrU/hPzzEx5a0o/3g Vdq/L/FTdm77vzVV1hbprORbXaslcbZ6peabIyo3+9HJXdSw/to80ZHDKfLI7aa3WWNo2+VfvVDu mt9vmr5sbfdZf71Y8fjJtq+ba/8AkSo5vGX3dlr8v8O5qr6nW/lD2sTqPrQvasm+1SRtB+3QbYpG VWrn4/FF8rbmkVv9nb96phhalTYJTjE7hfmanQ/6ySM/d+9XG3HjC6lj2xQxxt/e+9Wa2rX10ys1 xI3+61bQwNSXxEOrE9C+7uWiRWXazK1cnJ4qvtq7fL/75r27T7Cz8X+DbG8a6Vls7fbI0DfdZV+a uTEUKmHjzSPQwtL63KUafxHncf8ArEobvVjQ9Z8L65pK6pBrUsFvu+7PDuZvu/d2/e+9W5b+P/Ac q3kjyR2cdrtbdcwtHuVljZWX+9/rl+X71cMq8T0YZVXl8XunN1HfNJFYyNE22RVZlauk1TXvBt5I rRassW5vs8fkRttaT5f4tv8AeZV/4EtTaP8AETwbBpMMdxfWv2pbeOSSNod0n7xdy/Ltb5vmWj28 Y+8VHKaspcspHh81xJcSNI7bmb5mqFd3+1X0je/C3RfFnlalFblY5Y1aOS0by1kVvutT/wDhRuif L/oD/L/08tXvRzajy/CzklkeI5viR85r/qWqHb/s167q/wAIJdZ8SzWej272Ol2qqslzJubdJ/Ft /vVZh/Z3uGk2vqzov977P/8AZV0vMKC+KRxrKsTL4Yni8lvGyruVartZ7F3Rs1dx8RvAbeBb61tf tTXizQ+ZuaPy2X5ttcn8yqu2uyMo1o80TzatKVGXs6nxGb5sy/7VTLfsv3lq4rf3qj8qPa27bRyy /mMhq3kbfeVaNtu38O2mtbxv92m/YG/hbbVc0gHNZr/DI3/Aqjazk/h2t/wKmstxb/7tC3jL95aP aAH2WZf4Wp32eZv4ZKkW/X+8y1Mt038Mla83MBVWzmb/AJZ1Mumyfxsq1aW6/vfLRN821l+7T1Ah W1t4vvbpWqbzvl+XatR/c60bv71GoDfK+bczbmpy/NRlqXmpARvlpKX7lRySrQA+ioGl+X+7RS5g P//Z