Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, The Atlas of Canada Instituto Nacional de Estadística Geografía e Informática U.S. Geological Survey 200406 vector digital data Ottawa, Ontario, Canada Government of Canada Aguascalientes, Aguascalientes, Mexico Instituto Nacional de Estadística, Geografía e Informática (INEGI) Reston, Virginia, USA U.S. Geological Survey http://geogratis.cgdi.gc.ca/download/frameworkdata/North_America_Atlas10M/railroads/ http://antares.inegi.gob.mx/atlas_norteamerica/ http://nationalatlas.gov/atlasftp.html railroads_l_v2 A joint venture involving the National Atlas programs in Canada (Natural Resources Canada), Mexico (Instituto Nacional de Estadística Geografía e Informática), and the United States (U.S. Geological Survey), as well as the North American Commission for Environmental Co-operation, has led to the release (June 2004) of several new products: an updated paper map of North America, and its associated geospatial data sets and their metadata. These data sets are available online from each of the partner countries both for visualization and download. The North American Atlas data are standardized geospatial data sets at 1:10,000,000 scale. A variety of basic data layers (e.g. roads, railroads, populated places, political boundaries, hydrography, bathymetry, sea ice and glaciers) have been integrated so that their relative positions are correct. This collection of data sets forms a base with which other North American thematic data may be integrated. Any data outside of Canada, Mexico, and the United States of America included in the North American Atlas data sets is strictly to complete the context of the data. The North American Atlas - Railroads data set shows the railroads of North America at 1:10,000,000 scale. The railroads selected for this data set are either rail links between major centres of population or major resource railways. There is no classification of rail lines. This data set was produced using digital files supplied by Natural Resources Canada, Instituto Nacional de Estadística Geografía e Informática, and the U.S. Geological Survey. The North American Atlas data are intended for geographic display and analysis at the national and continental level. These data should be displayed and analyzed at scales appropriate for 1:10,000,000-scale data. No responsibility is assumed by Natural Resources Canada, Instituto Nacional de Estadística Geografía e Informática, or the U.S. Geological Survey in the use of these data. The Commission for Environmental Cooperation (CEC) is an international organization created by Canada, Mexico, and the United States of America under the North American Agreement on Environmental Cooperation (NAAEC). The CEC was established to address regional environmental concerns, help prevent potential trade and environmental conflicts, and to promote the effective enforcement of environmental law. The Agreement complements the environmental provisions of the North American Free Trade Agreement (NAFTA). Further information on the CEC is available from http://www.cec.org/ or from >Commission for Environmental Cooperation >393, rue St-Jacques Ouest >Bureau 200 >Montréal (Québec) >H2Y 1N9 Canada > >Telephone: 514 350 4300 >Facsimile: 514 350 4314 >Electronic mail: info@ccemtl.org > All North American Atlas data sets are available in four data formats: Arc/INFO Export format (e00), ArcView Shapefile, Geography Markup Language (GML) and Spatial Data Transfer Standard (SDTS). Although these data are distributed in geographic coordinates, the North American Atlas Map uses a Lambert Azimuthal Equal Area projection. The following parameters were used for the map: >Longitude_of_Projection_Center: -100.00 >Latitude_of_Projection_Center: 45.00 >False_Easting (metres): 0.0 >False_Northing (metres): 0.0 >Semi-major_Axis: 6370997.0 >Denominator_of_Flattening_Ratio: 1.0 >Ellipsoid_Name: Sphere >Units: metres en 200406 Publication date Complete Irregular -180.000000 180.000000 88.228863 0.849582 -2514019.007300 4462879.071800 -3920231.142500 4693624.736500 None Infrastructure Transportation Railway network Railroad network Rail transportation Railway Railroad None Mid-latitude Western Hemisphere Northern Hemisphere North America NAFTA North America Free Trade Agreement Canada Mexico United States None All data for Canada and other areas outside of Mexico and the United States of America are covered by the Geogratis User Agreement for Digital Data; see http://geogratis.cgdi.gc.ca/e_license.html. No use constraints are applied to data for Mexico and the United States of America. Acknowledgment of the North American Atlas, Instituto Nacional de Estadística Geografía e Informática, Natural Resources Canada, and (or) the U.S. Geological Survey would be appreciated in products derived from these data. Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, The Atlas of Canada Atlas of Canada Client Services Mailing address

Room 650-615 Booth Street

Ottawa Ontario K1A 0E9 Canada +01 613 947 2410 info@atlas.gc.ca Jorge Padilla Noriega Dirección General de Geografía, INEGI Mailing address

Av Heroe de Nacozari Sur 2301

Fracc Jardines del Parque

Aguascalientes Aguascalientes 20270 Mexico 52 449 910 5349 jorge.padilla@inegi.gob.mx U.S. Geological Survey John Hutchinson Mailing address

USGS EROS Data Center

Sioux Falls South Dakota 57198-0001 USA 605 594 6049 605 594 6529 atlasmail@usgs.gov ESRI ArcCatalog 9.3.1.3500 Shapefile Where possible, attribute data were quality controlled by creating symbolized digital displays or hard copy plots, which were visually verified with digital or hard copy reference data. Attribute data that could not be verified in such a manner were interactively queried to check for anomalies such as duplication of data in fields requiring unique values and incorrect feature coding. Data for areas outside of Canada, Mexico, and the United States of America are only provided for context; Natural Resources Canada, Instituto Nacional de Estadística Geografía e Informática, and the U.S. Geological Survey assume no responsibility for the completeness, accuracy, or presentation of this data. A quality control of the linear railroad data was done in order to ensure topological consistency within each country and across country boundaries. This data set includes railroads for all of North America as shown at 1:10,000,000 scale. The data completeness reflects the content of the source data, the selection made by the cartographers, and the quality control of the content performed by each country. Positional accuracy varies throughout the data. Positions are dependent on the accuracy of the source data and on the result of the generalization and integration processes. Data for areas outside of Canada, Mexico, and the United States of America are only provided for context; Natural Resources Canada, Instituto Nacional de Estadística Geografía e Informática, and the U.S. Geological Survey assume no responsibility for the completeness, accuracy, or presentation of this data. Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, Atlas of Canada 2002 Vector digital data Ottawa, Ontario, Canada Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, Atlas of Canada 6000000 Online 2002 Ground condition 6M CA Rail Spatial and attribute information Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, Atlas of Canada 2003 Vector digital data Ottawa, Ontario, Canada Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, Atlas of Canada 1000000 Online 2003 Ground condition CA Frameworks Rail Spatial information Instituto Nacional de Estadística Geografía e Informática 200407 Vector digital data Aguascalientes, Aguascalientes, Mexico Instituto Nacional de Estadística, Geografía e Informática (INEGI) 4000000 CD-ROM 19930101 19970401 Ground condition 4M Mexico Spatial and attribute information U.S. Geological Survey Unpublished material Vector digital data Reston, Virginia, USA U.S. Geological Survey 5000000 Internal file 1998 Date of data compilation 5M USA Rails Spatial and attribute information Instituto Nacional de Estadística Geografía e Informática Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, The Atlas of Canada U.S. Geological Survey 200406 Vector digital data Ottawa, Ontario, Canada Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, Atlas of Canada Aguascalientes, Aguascalientes, Mexico Instituto Nacional de Estadística, Geografía e Informática (INEGI) Reston, Virginia, USA U.S. Geological Survey 10000000 Online 2004 Publication date NAA Hydro Spatial information Instituto Nacional de Estadística Geografía e Informática Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, The Atlas of Canada U.S. Geological Survey 200406 Vector digital data Ottawa, Ontario, Canada Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, Atlas of Canada Aguascalientes, Aguascalientes, Mexico Instituto Nacional de Estadística, Geografía e Informática (INEGI) Reston, Virginia, USA U.S. Geological Survey 10000000 Online 2004 Publication date NAA Roads Spatial information Instituto Nacional de Estadística Geografía e Informática Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, The Atlas of Canada U.S. Geological Survey 200406 Vector digital data Ottawa, Ontario, Canada Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, Atlas of Canada Aguascalientes, Aguascalientes, Mexico Instituto Nacional de Estadística, Geografía e Informática (INEGI) Reston, Virginia, USA U.S. Geological Survey 10000000 Online 2004 Publication date NAA Places Spatial information Linework from the 1:6,000,000 Atlas of Canada railroad network was generalized and fitted to the North American Atlas hydrography, roads and populated places layers. The 1:1,000,000 National Scale Frameworks Rail Network, Canada was used as a supplement to check selection and position. The attribute for the country was added to the data. withheld withheld withheld withheld withheld 2003 Linework from the 1:5,000,000 United States General Reference Map was generalized and fitted to the North American Atlas hydrography, roads and populated places layers. The attribute for the country was added to the data. withheld withheld withheld withheld 2003 Linework from the 1:4,000,000 Topographical Dataset for Mexico was generalized and fitted to the North American Atlas hydrography, roads and populated places layers. The attribute for the country was added to the data. withheld withheld withheld withheld 2003 The files from the three countries were combined and the railroad lines were cartographically adjusted along country boundaries to ensure correct connection of the railroad network. 2003 Because the linework came from several different sources and scales, a final cartographic review was done to the full railroad network to ensure cartographic compatibility at 1:10,000,000 scale. Each country then performed a final cartographic check to verify the selection and position of the linework. The attributes were also verified. 2004 Metadata imported. withheld 20060526 15324600 Dataset copied. withheld 20090824 15100900 Dataset copied. withheld 20091202 15250700 Vector Simple FALSE 1111 TRUE FALSE D_Sphere_ARC_INFO Sphere_ARC_INFO 6370997.000000 infinity GCS_Sphere_ARC_INFO Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area coordinate pair meters 0.000000 0.000000 Explicit elevation coordinate included with horizontal coordinates 0.000100 railroads_l_v2 A set of parallel rails on which a train runs. North American Atlas GIS Group Feature Class 1111 UIDENT The unique identifier. A unique number that is assigned to each feature in the North American Atlas data. The last two digits of the identifier are a suffix indicating the map layer. North American Atlas GIS Group 106 111406 Sequential unique whole numbers that are automatically generated. OID 4 FID 0 0 Shape_Leng FID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. COUNTRY EDIT_DATE String 8 Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. 0 0 EDIT_DATE EDIT String 3 Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. 0 0 EDIT COUNTRY String 15 Length of feature in internal units. ESRI Positive real numbers that are automatically generated. 0 0 Country The country in which the railroad exists. North American Atlas GIS Group CAN The railroad is in Canada. North American Atlas GIS Group MEX The railroad is in Mexico. North American Atlas GIS Group USA The railroad is in the United States of America. North American Atlas GIS Group FN The railroad is outside of Canada, Mexico, and the United States of America. North American Atlas GIS Group Sequential unique whole numbers that are automatically generated. Shape Geometry 0 0 0 Shape UIDENT Integer 9 9 0 Feature geometry. ESRI Coordinates defining the features. Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, GeoAccess Division Mailing address

Room 650-615 Booth Street

Ottawa Ontario K1A 0E9 Canada +01 613 947 2410 info@GeoGratis.cgdi.gc.ca Downloadable Data 0.000 0.000 INEGI Jose Luis Ornelas de Anda Mailing address

Avenida Héroe de Nacozari Sur 2301

Fraccionamiento Jardines del Parque

Aguascalientes Aguascalientes 20270 Mexico 52 449 910 5300 ext 5856 jornelas@antares.inegi.gob.mx 8:30 - 16:00 (CST) Downloadable Data 0.000 0.000 EROS Data Center, U.S. Geological Survey Mailing address

47914 252nd Street

Sioux Falls SD 57198-0001 USA 800 252 4547 605 594 6151 605 594 6933 605 594 6589 custserv@usgs.gov Monday through Friday, 8:00 a.m. to 4:00 p.m., Central Time Although these data have been processed successfully on computer systems at Natural Resources Canada, Instituto Nacional de Estadística Geografía e Informática, and the U.S. Geological Survey, no warranty expressed or implied is made by these agencies regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty. No responsibility is assumed by these agencies in the use of these data. ARCE 0.000 0.000 http://geogratis.cgdi.gc.ca/download/frameworkdata/North_America_Atlas10M/railroads/ http://antares.inegi.gob.mx/atlas_norteamerica/ http://nationalatlas.gov/atlasftp.html GML 0.000 0.000 http://geogratis.cgdi.gc.ca/download/frameworkdata/North_America_Atlas10M/railroads/ http://antares.inegi.gob.mx/atlas_norteamerica/ http://nationalatlas.gov/atlasftp.html ESRI Shapefile 0.000 0.000 http://geogratis.cgdi.gc.ca/download/frameworkdata/North_America_Atlas10M/railroads/ http://antares.inegi.gob.mx/atlas_norteamerica/ http://nationalatlas.gov/atlasftp.html SDTS 0.000 0.000 http://geogratis.cgdi.gc.ca/download/frameworkdata/North_America_Atlas10M/railroads/ http://antares.inegi.gob.mx/atlas_norteamerica/ http://nationalatlas.gov/atlasftp.html Gratuit-Free Downloadable Data 20100527 Government of Canada, Natural Resources Canada, Canada Centre for Remote Sensing, The Atlas of Canada REQUIRED: The person responsible for the metadata information. Atlas of Canada Client Services Mailing address

Room 650-615 Booth Street

Ottawa Ontario K1A 0E9 Canada +01 613 947 2410 info@atlas.gc.ca Dirección General de Geografía, INEGI REQUIRED: The person responsible for the metadata information. Administrador de Metadatos Geográficos Mailing address

Av Heroe de Nacozari Sur 2301

Fracc Jardines del Parque

Aguascalientes Aguascalientes 20270 Mexico 52 449 910 53 00 ext 5856 rarrioja@dgg.inegi.gob.mx Peg Rawson U.S. Geological Survey Mailing address

561 National Center

Reston Virginia 20192 USA 703 648 4183 atlasmail@usgs.gov REQUIRED: The person responsible for the metadata information. FGDC Content Standards for Digital Geospatial Metadata FGDC-STD-001-1998 None Unclassified None en local time 20101012 13494000 FALSE 20230410 16115600 20230410 16115600 railroads_l_v2 002 1.087 -2514019.007300 4462879.071800 -3920231.142500 4693624.736500 1 Projected GCS_Sphere_ARC_INFO Linear Unit: Meter (1.000000) Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area <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/3.1.0'><WKT>PROJCS[&quot;Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area&quot;,GEOGCS[&quot;GCS_Sphere_ARC_INFO&quot;,DATUM[&quot;D_Sphere_ARC_INFO&quot;,SPHEROID[&quot;Sphere_ARC_INFO&quot;,6370997.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-100.0],PARAMETER[&quot;Latitude_Of_Origin&quot;,45.0],UNIT[&quot;Meter&quot;,1.0]]</WKT><XOrigin>-12742100</XOrigin><YOrigin>-12742100</YOrigin><XYScale>353442495.92849648</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></ProjectedCoordinateSystem> 1.0 150000000 5000 ItemDescription Esri ArcGIS 13.1.0.41833 North America Railroads 2009 -2514019.0073 4462879.0718 4693624.7365 -3920231.1425 1 -180 180 88.228863 0.849582 1 This North American Atlas base layer shows the railroads of North America. The railroads included are either rail links between major centers of population or major resource railways. <div style='text-align:Left;'><div><div><p style='margin:0 0 0 0;'><span><span>A joint venture involving the National Atlas programs in Canada (</span></span><a href='https://www.nrcan.gc.ca:443/home' style='text-decoration:underline;' rel='nofollow ugc'><span style='text-decoration:underline;'><span>Natural Resources Canada</span></span></a><span><span>), Mexico (</span></span><a href='https://en.www.inegi.org.mx:443/' style='text-decoration:underline;' rel='nofollow ugc'><span style='text-decoration:underline;'><span>Instituto Nacional de Estadística y Geografía</span></span></a><span><span>), and the United States (</span></span><a href='https://www.usgs.gov:443/' style='text-decoration:underline;' rel='nofollow ugc'><span style='text-decoration:underline;'><span>U.S. Geological Survey</span></span></a><span><span>), as well as the </span></span><a href='http://www.cec.org:80/' style='text-decoration:underline;' rel='nofollow ugc'><span style='text-decoration:underline;'><span>North American Commission for Environmental Cooperation</span></span></a><span>, has led to the release (June 2004) of several new products: an updated paper map of North America, and its associated geospatial data sets and their metadata. These data sets are available online from each of the partner countries for download.</span></p><p style='margin:0 0 0 0;'><span><span>The </span></span><a href='http://www.cec.org:80/north-american-environmental-atlas/' style='text-decoration:underline;' rel='nofollow ugc'><span style='text-decoration:underline;'><span>North American Atlas</span></span></a><span> data are standardized geospatial data sets at 1:10,000,000 scale. A variety of basic data layers (e.g., roads, railroads, populated places, political boundaries, hydrography, bathymetry, sea ice and glaciers) have been integrated so that their relative positions are correct. This collection of data sets forms a base with which other North American thematic data may be integrated. Any data outside of Canada, Mexico, and the United States of America included in the North American Atlas data sets is strictly to complete the context of the data.</span></p><p style='margin:0 0 0 0;'><span><span>The </span></span><a href='http://www.cec.org/north-american-environmental-atlas/railroads-2009/' style='text-decoration:underline;' rel='nofollow ugc'><span style='text-decoration:underline;'><span>North American Atlas - Railroads</span></span></a><span> data set shows the railroads of North America at 1:10,000,000 scale. The railroads selected for this data set are either rail links between major centers of population or major resource railways. There is no classification of rail lines.</span></p><p style='margin:0 0 11 0;'><span>This is a revised version of the 2004 data set.</span></p><p style='text-indent:20;margin:0 0 11 0;'><a href='http://www.cec.org:80/north-american-environmental-atlas/railroads-2009/' target='_blank' style='text-decoration:underline;' rel='nofollow ugc noopener noreferrer'><span style='font-weight:bold;'>Files Download</span></a></p></div></div></div> Commission for Environmental Cooperation (CEC). 2010. “North American Atlas - Railroads”. Natural Resources Canada (NRCan), Instituto Nacional de Estadística y Geografía (INEGI), U.S. Geological Survey (USGS). Ed. 2.0, Vector digital data [1:10,000,000]. North America América del Norte Amérique du Nord Railroads Vías Férreas Voies ferrées Canada Canadá United States Estados Unidos États-Unis Mexico México Mexique CEC CCA CCE <div style='text-align:Left;'><div><div><p><span><span>The user is permitted to use this data for non-commercial, non-sublicensable purposes. The user is allowed non-exclusive rights to represent this data as desired (</span></span><a href='https://creativecommons.org/licenses/by/4.0/' style='text-decoration:underline;' rel='nofollow ugc'><span style='text-decoration:underline;'><span>CC BY 4.0</span></span></a><span><span>).</span></span></p></div></div></div> 1 -180.000000 180.000000 88.228863 0.849582 ISO 19115 Geographic Information - Metadata DIS_ESRI1.0 dataset 002 withheld Local Area Network 0.000 Shapefile Shapefile 1.087 Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area 1111 20230410 iVBORw0KGgoAAAANSUhEUgAAAMgAAACFCAYAAAAenrcsAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJzsvVnMnul5HnY9y7t927/x5zYkhxzOolk0iyRLlpfYjp3adWAX aZoWLgK06EGBpgF6UCRA0SInPehRgQIFGqDpSZHAXYI2SQ+MAo4txJYjW5Kl0TI7Z+dwuPzrt73b sxTX/XwfydFw5NFoRh4hfQcc/iS/5X2f5V6u+7qvx/5n/93/EHcefRTWGUAFAAoRCgo//hX5vw/4 ICX/+G/GpWJAVAFOKxRewSnA6YjcA5UFLpQGGXo4o6FDRBYVDoLC27UHYo7I/1SAjhzMgBMFcCbj zw5Oa8SooBWQhRZO5djzBW7VLZRSuDjUGKJHCBp8+54LuN5oABqZ8rhvVGCEVubDaQvX94iG/17i ZtvBoMP50iLIXRhE1yPmCiYo2KDgV/OrZT4jvI4I0LARMNEhKI1OZ+hCwGHnMO0AD4teGeRwuH+o MIaXNSe/FMdLwUQgKn7nT/7i97qg8M7BIWy2sYsHnvgcdDAIHBcZiI9ng/z/12rAZfINFBxs5AKK CMrAKuBkqaBcgzwCgYs80/JK5QHfA9pbRM0NFmG9gVcBlelx/6BC6xbwymNDFxhZj61CoYgeB73C vz5QWCiLy0PgiWGEjQ610XhrofHq0uIgRDGIJ9HjyY0cm7YD9w3/vesDtsscz887TAqLz40CfNch mAGmfYPNzGKglTwHn4yLmqaVFzexjkEsoOeGUgZ126OPCvvZGN8/aNBGLe/dNR2+fKLE2C9vG1N+ ovwY02YRmyB/t94qn/zPOkY0PfDPvvJHnCPuej6eFqumIm9Ryzb5OK67P+Xf1E3HMdDo5fdWQ6zr yFo0fYe324hBUBjoDDtlhglqKAtcq2l9M/EM9CpecwPxExQab/DdaQOtM+Smwhu9RzA5Jljiyydy bNmAJzYN/uzI4u1Fjw3b41yhUfqIqshQLxoYGGiX411l8e5Rj7HmojeYtRFtbqDnLVQ3hgsRX0cD 3Q9QyMYosCGBhkevAdcHDLWC1hFeRehIK0tTwM3Bp++BXOOoK/D6YY1OnoWrjv4vYuEcCkO3oVZr L8UwNAh83OQ1715Fn/zPNFT09k5p2LRNk8tlKEBXmeZBv3eD3fVZdPfcRLe9jfxd+kC6VHlEFWFC gPYOQYtr4pDJ7ly7Ufm41UCkTax/umOv+AM/rMYtPZ6RxcOxO2kcnj5R4NZRjUXM8cBGwEacI4Yc b7fAu51Gy5DIrDw5xwuADUFCmF5r5N6ih0cIDvcVBsPc4XoNvFsHmAKYRQ2vezS+Q4MKbzcKs3aJ 690CbcihZb4Cpx0aGrOgMPWAURHBNei0xdi02MojrNK45XqU6HD/pESpGgQVsPAabVQYcCNHJ5vG cQqDRx0Nnt/vsTQWymgcNzWWyBD5Wm4SpXHgc/z5ocf9hcH5cYHYNagMYPkZ9DFcmrJO0lpL/mW9 GG8vmtWfP76fVfQwHJrIZ1dObkZ2r0pfzlBLdu/qbdxRaSukWNOuXsAJ6usO16+8iZ1HH8SfLyP+ 1VEpMfa48DgZHB4YF7g0Mnj2+jEubU5wMffYCz3OZQUQIkLuUDRcOD06k8HS9f+0XjJ+NB2MqbkI zGpKoyxuw/GSHMHjW3vHCNHgvgrYEQ9gEUzEiTLDtR7olEIWxJ6tJg0Y5B7nNyq8erOWhcfvgQ9Y GoUqBuS2wHenHt9WWuaU4ZzTFb57BJwwHR7bGeIB38Ioi6UCXm0i3ph6eG1gAWxbh0PHfCMXY3m+ Mnhq0iGLAYdVhT+9ucA8WAy5iGOBxWKJkxsD2YQDWtvATcl1FDCEx+akwhtHLUKXi4FwJhlNPgxf w5k+ChazpcfLTYPdXOFipXBGK7R9D1VYzL3Bfu1xamCwjVZyrgCbwrhPyIPIjCnOGj3Iem7vnmd6 EUAeNln4KEkT3R0H8zBGvH7scHJs8a3f/b9xuruG+fPncN9TT+GvP/gMvnpQ42xWQocOLQy+u1fj Zy+cwP/x5hSPTXLcrCMe34rwRx22xhXOF0sgWth1IPtTeyUr52BXFt/Dxh4qapyoLO4fA68cNbju LW45I+Pq6xansgwjbdH2wK1e46Dr+e73fXrdOZzwAf3I4aWWlhhwFjjoCtzqvSTwSjZHit1p8Jiz tFpj2gfcmHc40AoqtMjziL05jd8Qne1xQvX45c0S+xH46sESnS/xRl3j7MBi1wKFaXBhHHB9OsWe tdjIAk5PhrLpZ15hBoVJliGPXco7VIZp28qQMDQUey8hlEKMRnIQA4ftXKNzClUxQBM7vL5w2KtK zNoOWauxv2xg8wwj5NiOHhmfX1KCn8z1/lm460oxL3c9R9tgkVm8Me/x5sJho8zx4nSJz//cl6C+ 8g9w8uAr+N3/8R/jr//X/wRvVSPcP9R4e7/Hz54N+P4bNbKwjVGp8NzcIc8qvDiPuH9Y4R/dCnhy mOE3tyoMsUT8KXYgMlpKi5Xc1S0eOznAiNbQAdF3GGlgWhU4mnq0OkBpg2lf4V/td9C5R9sx96vE MJl7fH6nCrxW13h6u8R8z+OmM4jGIeqlhCFmFcMzmPWS5Ca0Ke+NhD6v9B5RFWh7BdX0UCoiCz3y 2EtIRdTM5/RLGbwq0PmA5271eHo7w451eGRgsRzktJwoTIsi1mh1gWVIyNSyN1DeoolcJx2OuBEU wyreV4AhmscnSymHDFjnPQqToa4X2MwtQp7j1nyBSyVzMo1iPEHf9yhUi874ZLx/govkAzeIIBQr r8HYudYZXjic4fnW4kwR8c3DBX773AjmlsE3n30R/+1/dBG//ESN//lrv4tf+u2/g2oY8WQ2RFF7 fO70AH1c4rRRuNb3WDYZPrMdca7s8Ftji1s2w+/PFni8KvCAdeI+k+daw374qbhSgBWwpRye3B3g hJ8hY9jBYNVGvNVneHnhUdsMmW+RE/q1QBNyGMd4KlvhXR2A/D2Rtny+Vri+sPiXjUNeWGjbQftM NgS/haHHKihOMEv0kCzfBGzrZK2n7QJ7TKLpfVSGXlvYnhs14GudQ1QOpQdO5zM8uVPKRh2bnqk2 8hBQ6QZWG3SMLoJBEQLOZ4SaHZTy8CbgajCoGcdnFrFjSLWCcCUL5fPIiEhuOgsafWBONsK5uIRy NfxuJc+Thx4dU3nrkTGEcwzh8L41cXdW8hPZIALbKYXcE3Fw6Oi2dYnNqsBGVqI0Dji22G967G5X +Nrhefzn/3APv/FMAC4OMP+9/wVHz/2v2FJL4PK/hT/8Xo37Hv08HnroSfzCz34RLx3XeGxzjDfn CxSZxy+OC1xtA64uW+yMC2xxOqJD7omsreLaVVL/6blWGRqtGSFP4v6IGCLgmZ0Mu0y6Gdq4AaBm KKxGZQMmuof1wMXNATK0+N6sl8WsgoUzfOaAHha7JqK0Ga62TvIXJvfMXzobxTtMHWsFxKICQlCy OJkg2sBFTo9VAaYV/HXDOjw1yXBCNYglsDAWS6fx3GGLd1QmiBNzv1wZnCozDMMcj4xLGN3IUzJh JfKUIFDOB72Vh6c54NcyJeWLQoSFwYks4q9sZGizEn+8fwQdh9BBobdBPkv8nIwXw62IJmq8sN/B DStsW40JIW3HvCtDrrk5ErLFNZnenTxRUAxlQ3rmVd6Q8LFPeIPQhWXRodM5Wmvw/K2Iqurx+28v 8RuXS7x9fQ9/48IpXAwz/L2/9Xfwf/7d07i8DfyDf+bw8td+F48/WuCXLmS47+xl/LM//g4iNvH6 jQM8+GCPvp3i8uYQf7o3x2JzgMP9RpCVjdLj0bKA7ztordEUvDWdUC/G1p8ykJgTE1aTwnuktdvN gS9tWRSqgfKZQLrfn/Z4fLtg8Ii9tsLSKzTK4/XDmSCFMbLOQYPEiU6ASKkcntrMEIPHtS6gM6mI awhqSIEt4KmxxtGiw9u9lQ1aKuBsYXBKeyyVxvWuxs4ww9VpQNsHvFp3yCuNkQkYxhZZZvDwxGCn azGxBYzNcX0xw+5AwXcZ6r5HyYTYWJQmoPOcFwXXpaKeynN4X6ewMuQYRQutCWUz2Iry7AeLBgic x7RwuTluj98KEJKLoaaK+M5ige3Copz32BpabOsOp7SB8clIeuOlOGlWhpORBtcp8yzmPYx2Pu7S 4geEWNzZGWByvLbo8Cd1i8t5iWJngG8e9virZ3fx3EGNfaPxO3/vv8Df/4f/Db7wuadxfPxt/Iu/ P8Y/fnaE/335Cxhf38HZ334cv/blAfr5V6HMW2jLL+KrNxT2nAf2O3xppHGhMnh52eA1Aus2udFd x2psQKdZIVaycD5d1xq8DZigx+XtArvaoYqd1JNoXedR42bscBk5rjYBrxy3uH9c4PKwgcIAX9lf IISAzBt0hosxYOQcvrC5iW2zwEIH7NoOt7oBgAJBtxJO9RwX1+LyVgk9bXBz2eDy9gYO6hqLHLiQ dbgvzzDWDqe2NP70VsCVupANfWngcILVicgU2eOxYQmrWsxCj1iV2Fv0mEaN1xWwaQ10E5CxWtZp 7A4HqC3wbtOhbxyKmMOoEteXc2R6ji+e2pIayZt1wMvzgKUHOmLO9G6CTt6ZRHqO9SUhtSBgA7je 4eLWEGdUK/Uiry2W0SIwhAseTll47zFzTgCjLZNDGYdMjFS4Xdn/eKaYHvEDLsaJfezxzVs1Ht0a 44TyqF2HGhmuOcAPFP50L8eXf+7X8Z9+4eeQ5xn+yX/1t/B3/68FvvZ6xH/yXz6Fk0/uIaqXEY++ AY0TGI1+Hd9aBvx5Azw0zPD0VomTGviz64dod8a4cn2JXfR47OIZ7E1v4OSAUKaF07QOny6Ia+3u 8+Bx3zjHuSJIFftGDeyUOa50Lfa7AVqX488OI+o+4JFhjrOmgQ0dJkrj5ycKpS7x5iLg5TqghsVC R3z/8AhqqMWaG/kex2ohimAkF2C41SJHpXo8tJmh7wNGrK9s5HjnsEc/qTDIezD+GekBtOlkP79W Rxy4Am1XSwWeoZvaUjiXkaqi4GInNQ+LDO+2HrcaIm0BQcKrDLpeSM2EyyZo+rEOXrFgWWEXBV6Y Rhx2EZ1U/AkMaOTRI/AXq5/x3iFQVB6a+VKw2APwjb0FntyscKoocKNpcavr0NcNTm9tYb9pEaLF fsdQNMOIxc/K4IQBLgwy5KoT1oB8Lmt7q++LH3cOwpjzVshwZpKhWSzwHafxy7tDfH/W4MX9IK7+ 3KjGE+UxnPs9RL+J//C//0dQ7S7+WraE7r+KefMujFeww7+KXp+Cx7a471/Z8vjSRMM7h+cWHi90 BuOjgL99YYTjPuDAzfBCY/GbZSnZWMZ491PnQVKiyCrS9U5hv3FgSkv+UV1Y3OhYJW7wpa0R3nE9 rrkOm3YBm3HTM2TqcEpGf4GHJ6XE2c81HXy0ghjaiq+b46lNi8ksYGgMzgwKvLRo8XKtcHXqJB47 X0acHRss2iW2rcHDWwZT56WQqFAjsPgbIoxwvzLUocGJQYGbC4elNnhptsBkq8QgKgwNK1sa78wW UDFnlSUVvqRGxsp4jiA/M/hlUVkjJwagWkydxs3WYsga13iMd2dTWaCkmzCXSNVxJiDvn0gBfFmw jCwkAzVKfP0wolAdBkYh0osE4OZxDxf4zWKa5L2t0tC1wg0oXFk4XNrIcH8GyWPCGmz6wWL8h55g 9UNgXq+wg4C3jhp84dwWUCscRIfNDLhcWXz9qMevVq+jvfm/wehDLH1A1nwDxl5AGzdQxsso7BcR Bpdh4gix+T3M3B/jy5PfhDGP4Y0mx9T1GMDi588OEfsOr04Vfv9Wh42RwbQp8CsnFKrQCOz4yWAU H/3iZNKL9MriVs/iYAFjFGzUuHrTIcuAn90YIzCPmNd4aHtbiqeuaZEZkhBTrcNEvrPHpTFrEA7X G4+jYPDs3hyf38qxoyM+OzboXYdBbHC6YNir0Zocb7Q9GudwqdI4MaK3coJfKaI/xiMjkdEpZDHD KLOo0WCoIorQSMxuMMCt4HCz9Xio9LgvGOzkQLE9xCuzFkcdYVWLjrBvADLVYacMOF0onKlKxGWL waCQTfDW3OGqizhogTenC6mY90IfIYDww+duXXe7/WdBvSKWLC2kVQ5tMuHSJtZGfE8pgi9hZkRj 9dxRwLUyx8NDh/O2hZENJZjZx5mkM9INgPH45bOb+M5Bg0Vv8IWTBi93Cocm4me2HUy8CVWcA/AE BlmNTOdYhHcRF9+GLnIsuhdQZVPAn4cffxlj9ziao1dgts7ist1FtAqveoPrs1Ym7DgqbExyTFve mBP3vK4IfNocCBdFcuIrbJ6Wqrfo6H2zHt5bfOOwIUcXlS4w9nO0yxalrVAYDYcMRy5iSxOcpQlY 4pmRgRkazMnc9bkgYsLiRYfcWriYY69Nm/NM4XGrD7je5Tgka3bmEIPC2SLi4ZGB1R4mKlzzS2Tl ABPbY6cCLmUFuqyHOo64Pu2RZwYnKxYdl2gsC41RCnJPjkizYGgLzNDjcFHj/HiEsVToSXGpESrO zxJZKPBY5fBo8Hg5t/j+jOHiCtLl4Nx2HB9uFpmf8L2pOJ1CStknqQiyAm/WpMbVe8ifErYS0dWA G6HHqa0MleLffvTw/ANDrF5HTIPFv746w69eLLC37FFE4LPbChOV4cayg67+CvrBr2B/Ocdp+xKO 9v8fVNUZTCb/PhZhjMpsoKu/ic59F8PiP4Y3TwOTCqq5glAAXTaG7RQIWOUq4sHM45Vpjc/dv4Fr Bx6ZIz2BS5ETkfhcn5pLil+rwZJJpUlZMdRCLvHxPHTorcKmZjLbCU/NGqB3PaZeYxEtBoXG661H oUoY77BTBewQBROuVCHfQUiVdnIPzFcWOJHleOpEhhePO1xZeDjHWDv9B9ULhZ6hhY8FTDDY6x3O K4+zI40MC+gQ8PRoiP+3bmCYiLtWipZRR2gPlEZhtNr43gBDbsiNAiqSQh8EgNCB1BJSSBhuO6lu R11giEK+o1nZ7cSzu4uU9kHDedc/r5m9zOvv3ljrTZEKsj/geeRnwbdlI910Gu/2GS5YCNLFsV8j XCkT+nARyQeHWEFjwpjUZnjLGbywv8TVnQxu7vDMiQL/9N2A/+DBEn96s0bXZfid+55AWb2CpfsT dLMXkMcT8MMvwuafRVHW6JcvIg5KZOFFtPVXgP4cYvVzuJR/Hre6Ef7ouMG/d6bEmXIAOI8HhrQY fFiHwhVSI/i0BFkc3kwQE6FfrmaHMTbRP1bSE1eVfph9NtdVwPVDoNCkaHBoC3RtCrNuFBG7gxI2 9JiwL0QpLH2Fd4PBjWmLcVXAeHKqMlxvApaxkp6NP7lRo3W08vRmvHg/Fjcc8K15QPARHXoseyfF RKUtes/+EU45IWWPsVG41QF7vcawYj0lIuNGCSwMGwlPiCSxxpIsNf8tLc5OsYpBhJkEVRqGDktf C+0E/A7WZtbW/UP4jvexvv+CzbT+9/e+b52Q0/jkeH3mkI00TuW5UH5oACTkEy5h6nD5aBtEiIoa w9jgszs5vjsNOAwF9vYCfvFkJQthmQH/9I0ZntoY4pGLCm9PZ7g4/mWU/hG0069BFQ6qf0l2/aw+ QrRDDIsdRD+RBD8LN4FuilnsMLAt/vb9Q1hl8Wq9wGs3O/y1XcKaU+Q+/1RtDl70FLSssloE1WHh zEETKpVk9jYlWuwoN5JWFm1o8a5jo5HGTtXimY0C48iQhfgV03WLbxw7vNVFWayke6gFLWfyTtJT ojwaVaAR+k+qTzPK5h95NwtSPRYeMeYIhrmbhzI9rtZeEKaTGXtQclnULblg2uD1ukWjcyElXqgI AQOv7je4vDVEHluBhEnS7hSr2x5G5Zghx6yZ40xlYT0XW4bOlHjt1hIzlf+lxMSSf60KifRyh8Hi e1OHRelxacg1x2r/mkKFH8+DyNeEHE8N2DizxI28koTtD/eXyGFQmi380kWL/YMWv/9mjV/brtAt fx/Kvwsz2IKX0HMEU3wew8kMUW/B9B1gR9DGCLoSi5Po1BC7aojv7M+wpy12EfDrJwfQsYF2E6nH JK7RpycLoYseKoNd22LCHEJrDDMFmxW4Mo+4teSCK4SXpQNzKXY+iSleeR2D6TLHFXhs5T2iy3Ac DK61Dk0g4kOrnLYEywX0DIIkkXbBKjOT1ZW95OskKg/cPNwoCTwgZSQPTmDizgQ4oaYXeKsjc7Zf TTK9QIl3oXBtxmp+juen5GhpLMMAx/McVYAk221Y0X48ae0Kc+fRs0DYADbj8yzhhO6+Toh/8iHx unVk1XAB7x1MYdCQaxZZYE2AAQuLMh0fAvq5d5K+4gM5sG+gx9/YHiDcDHjT1/iZDYOvzxqozuM7 1zO83jjcX+X47sECu6fuAw5+D6FvYcMGzPgC3PHzQHgI453Hcbj8JrYmzwDxdxAHj+KrVzsUuzle 3JvhxOYAx3s1HiSMAvKxVr0iNKGfos3Bi4n5tmnx1HaGUawlHE2k9IDTY4vpqMCtxmO2mOHUzkiq 5c/PHQ47wsC9UDiYxF9pc+jGCnNBGK9EaiT2T80F/FSZaAllUosraSHScyE1hfStAqkoL/R49lmw 0JqTpOh7FEWOjjQWeLShA0LAVkbai0ZugIN2KUU3LfCyxkI4cAHBQLwOWdZee2EOE6HzOpcaCu+N fU77JFh2LJgO5N6YL6b7uhNaSZ4um3bV+/MJXSRDythxDBgOaoM2RAkXaWRYaFzvCvXjVtJZjPGG 1spjW3n81rkx/vCdBS6WEzwz0HCuw6tNh6602MiBWFa46R7HfcN/G+3iD5BVD8DFXfghMGg9buz/ T8hCg5m/hcHwb+JKP8E3+x71tRaPj4FJW+MXLhiBlqU1PhHuP33JudhHg4PW47iNqGxKzLlIpekM EWPVYjTQCEWGzKSC3e6mxXNTj5dri85qRNtLrsLnE8u8arNNDKX0LdK37jIJgzqyfzntXOS00pEV alpz5g4M4Vhd6TCxQKUDBmzn3RjBdTVePu5xoCogdHh0o8KjgxaFb2XRz8ZWuggzo7DfB7y+MDhg f4rcl4KOHcahRUEUzbNGwU3I2kYpgQw3NqHqTnNje5Cm1+REmfRdhJwUiH5AGeRjuxIfK9H96U2Z sPcuoityzDqPjYws517C/rRpfwwUi1OXMeFi0z16nIhz/M0zQ1zva5he4WRpsbQBT+YlgmmhXIE9 V6LW/y42TnwZs/kNbBYXELvXsHDfRTH8GcCfhbUGvS5w/WCKsbH4jd0JHigaDLhA+sAa2U/F5Y3B Ud/jjM0kzOFQlS61xnIxcHJYnOPvHE/qIHx2ojDOajw7Jx9rLCFQand+75WiaCbIHqXVUqHfsXM8 uFkg1zn2Q8RzRw0W0YhVJ8uXST77TR4YAJvaoO+4cDvEQmM2KHCw0NgqDM5mNWJkT4WWUIpJ/gk6 D+VwbDWOvEOdVci8w/22x8PjTHrQ6dU7ZfG9I40364gNU+PiWGOcBxQu4PlZjYsbG7C+wS0WTcjI 6g2uNg2OjEngheyOT26HrNetoF1CZSFoArRdwDGA3dLImK9f+2HQrA/MQdZxnIB1ukTwLDh5nM4s cpOh00vcWuT451dnOLdVCRN3FoDf2tR4anAC2XiBunkT2gT0u7+Kr1zbwa5t0PcaWxu72D3l8e84 C93PMRRUiGoa2Qoc/HRfpL1swuG+UQXja8m3aPe9kt7M1KG5CjBSS7JC5j2mymIBcrY6mNLh+rIT at/7Pp//494ivYMWWrcwsYT1GU6qGYa2wKsqog4sCpLE52C0x6Co0LULdDmQW6Jp7MmocLNr2eyK AZGrnPBsL5EBPQEhZN79zVjhe/sLxFhgEBzODS2emjgMw1Ro7U0s8a1Ddh8qfGHspWA4VqzmKHgb cK4AhuRvZRpnctJYesQy4oHtEb518wjXQym8qk+WMnSnRUNmgD3zrLY7jWWlcew6nOIcrfK0pMTy EVEshhH0iXwgu2JJJto5yMCRRPGLQ+DBwQjfWQahAjQR+OeHwB/c9PjSmXP4UvwzhOM/gKrP4xc2 HoBt38Qw/yLxMQkLTKhFQgY+Wylg3IHqPl1XYuwyhmaEzW18fsLiH2n5jMczsVQMSWSryGuVwLFs GFqoHFcXCzjT4Hyl8HSpsDAaf+4cbvSpIa1VmdDYB4qhV4faFDK5Dw6AR0pyrzT2+ho3UODm0mBK 6FcBVnmc5ud1wLP7DS5VGZ7KuElbHGl2fi4xycfwcQlnybfSKAPzmMSzEsRHF3j+oMZUcxuBhCA8 OY5CnGTHIb3N8w1w7Ds8vFEIX27oIlTOkCWJMJyelHjh4ACfGW5gZHq0xkAFhw03w1PbJbq9IEoq XLSSXwmthAnzx9fzk1oi0jytPRXni+t1zpBYA6PcgsFhykfUR9wgcsPhPW5rfQN3mlNSQraravza UCGMLHoZlIBO5Xh9egRb/AJGJz8P7xJVOmRflAqyj4xRfRJzkGdJSP6nKxW/g3JwDGhtElEwCoK1 wYYhWcKEcVMthPzYoHrJDZwqMc0nuCosgQaPTFhC4+s7eWVNK9c3+NXTJ1D1Da66Hq8sPEqlcWmy KVTxq0uPBb0E6ezB4WQF7HUBL089evbv06xEhSfGhYREz+0vMWCMSrGHAJAD/Mh2iZemS8klciH5 KRz3DPmiUFo2sgH2e4PrRHyIakWPU6WRew2aGURAw5rM8QLnJgXOxZnwsqhUIpaam5S0cwecqDbw rbrG/VWJw0WHeaewXZItHJFnCrFna2XKT2hsBZjlzx/rzN+phty96Yi81r3Htb7GxVEFrTlXUfpU PnLL7Ye/mGq2qDqFaKwUlx4fjkTqpomkIzOLTzQGCdpE/+zTth3ef6XNkQwFPZzoVsHhVKaEPUrA VuR4Vh1yosARSonxG2Px7XePcf/2Jh4u5vBqIaGKyCMEoHJLfGFrE0XfwuiACzZDnwEL5gV9gycm DqU22GscuhEr8Q0KpzE0WkIutuYyTHIq4pXDOR7fyPGzpxmGsTBIBm7Ke6zv8OCQCNkQV6YOmxV7 2izRMPGEAAAgAElEQVQ6X+CVPaqqUOKGYVeGYDwmcLiwMRC0TYmtZy7Fij9bcCGUeLIedGSSzzAm gQzkQS3agIXL8A1qDaiIL+0WKOIStcpB+butPMeryxY1w6Cg4QypLAm2/iTJqCRYski6UWXYIK+P bF8RtvuL45WPaYMwGjOCerFgRoNAdUC6Tw4wrQaBQMYhHBCJ0n9a+mjvIrpRfGFDezywPYT2i5Qv RTI+Uy0igfC9yPa8OOtxbNnH0ImqIWFahq1ErnrjkVuCICxorLZX6PFQqWH1qmYSqaCo8Na0x1yX 2GLTUDQY6IiTVuHYsWU2hXLvdBrTI4fTucJloorSuBSFqsGRz8lsqxnuKVFGLPMCb3ZLLDlRhECp U7VKWksTUcXUdxJXXpRGgSTHwxbYUx4nBmT+iqbKqlajMdNkZS3x+dEGlI1YdK200uZkZFDlpNLY iRFt6/FmR44ZVW1SWPpJ20rqQy7g4fseo4FF62uUmtQZKSC9pzflE9sgEp+LZUkcpWQRmERmcIQ6 QXiNeUYqghHTX+P4a6mBD0dK+Alfgpkn4IDolIPGW9MaelSKtA2tURbvLNZG5Xhh6bDfWDy1UeKB cil0GdYT6D0lj5M2Vn5qqmtQbCFmhEFTwsyQg95h01aCEl2bTjEaVfLtrI2fGZZ4Y0pska2ywAgF Yhvwmgs4rJd4enuITXo+x3qGQs+EWT43k+7DN9sWV+bU1SJLgShUWiDsXLTGoKB8KEM7JB4YN1Fp PM7kCqeHnNzU9ptHhpJUo2FHY8SlwmAzb+BCJ5QVTjv70llA7HgvaPHo1gjVEvj+soUPTNpX8qVr Gr20F6eqeBr1j8OQJkGLobQrexhTYb9zInhXEc2TEU+1px/UqfvYNgg/lZO1poOtJb6Y1hIuJiKT 1PPS4mDiJwQyCbsSopDoZJ8uz3J766483ixkeG4Z8Frd4jOFxkNj1iiWsKz4hySM1ouFBI58xDsL g+0ix8jQogcpXFmThr0Xqkif6s4rcIfjJTCxaAJ0eHCiYW0pVpxQhosKpwqNc5nHG70Im6F+43Vc OnsG3UGNPNN47q03cPnEBt58+RVcfOQxHBwcYbZ0uHT+JB7enqDRAQ9v5Hj1qMEB73nVBiVVi5Bi cyuhpZbnZsHxkZHFS9MaLxwpXB5blKJkQgwr1WsKqvnmK9E1ZZHnCpEaWHyeSNkgghFa2noH2Qj3 WxJiPSoAe02HRcgl1HOGemB8D8NXihn++HPIfvsqozRrJvWR1+c9DmMu9b2J67FR5CgISK3qUnez fz++EGu1tFOnHUv6ySqQsiwdgfCY2JSsHde1YEEMTlKfMvuKITEpWyU+7Rdt/xwFnm17jKoKJ6zC ldrhnSZiHqggyOqzxevLHgcZMArs5QjYNBq7VQaj6Dck5RfP8r5Lkl8FJT3ZCZJMrPF1UTFC8zOC EtLgxrlzeGe5QJZnMBsD+K0hrgagu/QZvFgMYXZzbAWHE5vMGRai7fsQW4VPDPC1/QZLxbicCT81 rjK8sbCYaBYPKZHKxc/79Ti3NcH14y4RFMnXVfQAqzF5DzExVeNT3/naZCbG9qgyQr2ZlA7KKOzY DHtbE3zr+hyHsRDZIBrP9HxroYYf72ptwLsu4Ou3eqjSousTEplPxmj9Aj1JmoZdlSm0vfsrP0YP sg6VEoSXyv4pWVTaCzT4zBbVLQKus5Clc7w179mHJYiKopdx9DIrntCn+OKUk9Zho8Gzx6nz4cjT I+ayYJiHMR9jznEqM3hsaFGgS14yOkSvEFaQ6r16FVLmxsXMSrq070k+J+opiSop8pxsma2txWFm YCaT9PkSr2iBNN3GRDo6tdU4PalwrugkX+oiEbCATeVF+X1JcqFswIh9ZXClZvsq0BzNsFVlODsw 6Bw3UIdzZokKI2HsstZB4umam5kQT0Zg3MDvX9jcZFvyGB4FWc02Q9s7uL7FpVEOPaOwHfvTU89H 6jL88ecr53irEm9xfTYOZ6zBzNd4/uAAJwYD6XfPrVSyZHOkza4+3g3CaaMLpYQ/uTsT5fGZyQDD 2KEymYgzU+bGKbaJstjY4LwF3ukVyqxA9BHPTReiyCfJvKiP87NWfRaCqkjwkTrO1onVXdyaZL3u wHy3lcJXdyhLgBDlutnmNnWa+Ly/a4NzYlLQwc1LzSdPdiwnT+gemdQgArVv+Y2hEIPQWzKeGC71 K4RE453FAqO8wC6LoMFJjM+jAOYdyXTAxBiMS4uC/eCB1I1EWUmyOneoGStqYuoIDw6PjUto1eGG 91jWRM0SH2xUZZg5olxWNLk4Dr3OcBA6OKnuMxei/6KcDjsZc8ynNWpdIo9kvHKndDhTFNguSFBN ms1FxkXWQLO5iuNFkQXFbZbcR1J3XlHhg5a8jMk6/2ItGCd5BWnnnFEiWMx7oseGIVAAPDDJ8GDs 0SqqzEfsuUR3EU9E0YYVXvoj56kstRF1DB3OW4+HN4Zo+oAjD9yse+mnyVWPk4VGLg/0CYRYMq1Z hw3X4UJZCaW6xAKT7D3ItBDcBpK0aoytxo5WGCmPgfEYnbA4bhwyrZBzIo3FcdvjoO5wg5wkgT1W dZrVGKVkcvUNks9QaZAJb0qt13Xt5KqZEK8Jfmnk0vu4yNkDTjYsG5WMqJUzaS6Vx2NbFV47WmKf TFtB5qgSyLln3ScJXBAipVdRkSPBkIh/sCBPgBVoETGinm5oUTI5tKUcM8Dkvjxa4vJmjlMFUMpi SuEqN+oa4RELzQUmxEiFYejwzFBhGYFbmcWVpcMBGcCOdAqDdtWFx1Cl8Ey8GdoRsUmoW1L9dbhU VOhHBi/MWjSiJs82V4uvHzSikzvMjLT1ni4zbK60dwX0lg3M4xxoebUIOwiqxQ5EF4WOf0qMIomQ MrKSq6w5Wl3vUfctNgYDtH2DDb4x45klCouosMgVuhgwI2Qt9PlUheeGTbJaH961iOnzQfpxPHWG lzMMtMYDVYmLhcO8D8hXMrsU1hE2nP8YN4gQ9HSPz28NscvlY5h4KoH4Ehf1Xm9K5aEztC0iyhBx SmucLpMYIK02F+upSiMOrcSJTSBdgBOgcdw6FLnBlm4xh8W12mMe6H3Yu0zrlghropZxOwRIUKu0 gdIDcRxWEp3Mkge+x9kM2MgtZjHDtTmwQ+SGNYhNja8dOihfIbcNTg/Y3denGoKnagh1i1PYxE7A JGqQiIWUP3hykuFkrjAIOawhGZRdfJ7YHppeo+S5IERwblNt7oiJr70l75usXRYmRfs3OkHS8kGG k4McB97ia3tTdHogiSlVGzn8OTcdRUNEgTE1ovG+og9ofcSMYY7E/dTpiui0wpHP8O3aoZx3OF0M MKs98pJj1WJHWWSqEnSKlemmI5MiwIlmgcWwzPDycYcXZsDP7OaY6Dkyn/rvE3JJj2SRZRlU7JEZ HiBEuMJh6RT6mGMn63D/xhgv7s1xreuFvydjochtS/SdD3vRk7JD1ocZHtqZ4FRw2FMaz+1PsT0g oGDQ9Sz+BhGhuHvFfiwbhMviC1sFTuhaJG2o9MdJXPX0fMAVYeRwmFb6uCMl/1hukhOKkotIRXb2 bCtBSQYM3ajOlwO6SoFCipyBpijwThPw6myGpcmliYeymhTP9reFyxIywk3D5qCMveG9R5XlOJ0p nGXYAo/9RYcTGXB6o8N2OZC+5jJ6XDAB11wjIeLZAbA3Z2Vc4TPbFmWM4B/fXixwEHOR9UzAhZPC IW+SAtDUoBKJUOrmet4Xn47haerBFsvOxSt1jITRU0hPLlbs5WSnJO7H1Jk0ljentVjzwharegs/ NWLbzfHUyS28cFDjdYpUBYszlcGIfSk+KZXUwaPpapwabmE6o6wDv8dI8p95qph4vNW2YsB2mogv DIYobI/rvkXbKtzsNd6uWxgzYPcPDmPA3vE8cboyjWf3ezyyuY1ds4RVFPJeBcMyJqTMR4kqeHQG +42Yo40LBdsAi/mS7HwJT+g96P0JS4ucz4+wQTp5v8YocuEoacA7ERTGJ7ZxZUZ5o4gdBp3aoFE0 WdwlHzEHkcSJCADzDZX6EMY6YlO0l9gws5L8l2Q1HUZy7/1hbi9uWSBMXm/3NyTRbF5CbgtR6BaC lYs1JU9MCWWFnH++j1vhYgXsdj0mVPgQUQODFw473AqsvyRdpgoBl4dBOut8luF43mN7BHT1AkcL 4v0G54cZSgEOWAhc3D4L5emdHDvzFlsZqQsRl7IGk0Eh5LyC56BYjYuDEq/NPW45j2nnRUlxqQt8 e+4x7Q3Ol0MMpTYR5TSpAk0CKVbC16klNIVAPB+ETT4Ubn+rZp7E5NphbNg3HqRR6Wi+wKCc4OVZ i5o5gRBpqXrS4cGqwI5f4Bd3qEiT4c2ZwzeOgSp63D/OZMw3C4Ond0aSX+1nJV6a04qT2NijIZiy Iv3lgRvZSWuuRYNL1sr3X+16HEhfSStCDTS/yhc4NTSi0LKdD3B1doRbxRAPlxzXRkJXSeI5J5LT +XREDSyqzEjIpU2OPMtxsjA4JnPZrxrB7oX6/UWXhJsahyjxjZs1HhoajDKD467GG00KO++bcHP2 ogt2tzj2j/RtnMSqJzJFlTtgYDVOZh7PTNiumeqqKWFe4TM/dJPTUiYLvJbIv2fyJeflpbSJIQfD kzsH1CS+zZ2MQjgsSUvLa1wgkW/H4u02SFvptKNgdo4HpAd8LiHLoMpwOJ8LWnPKGnkOEYmghZVn uNPsXwSHy8MV+hQdHtlMFjsdCJMqz0b1eHQQ8TjRIpHmibhaB8wb4PqSZwQqTNsaPQqctBG/uGtF VIBGgBUQOgnJc+ghYsRhk5i2318cYGDYawI8sTOWg4nEI5Q5ZtFLor+ktnGRIfoW50cVLhQeuWpR +g4hG+BNNlwRXSjIwaKxKKWPfMCzR6zHQceNDjy5yTNeFF44BA6RCdOaPukgaLwz73FxUIn3Jr3o VJWhKgs8u9fieswEpj85DND8wffy3AQJTqqIfaeFKzYpUnGUa0giDR7JsOr/If3eWYs+arxxcCzj RZSNY8PuSOZ5SfL1w69bhq6iYSY5YY4Xjxm+e/EsBDO2NO9R46ZrsWUzDFfcOl4/4naMGGcRnz+1 gU1PogLdJHsSCPd9ugp8jFVb5jkq4nyhcYG4YjTg0uw6j2FOH9DBZBplX2C4kt9JOFkKZ9LpRmuv ljYyqRrsaaFyOvspEi19TfAETO/R2xxtz0NlOjxigYdHCu0mwxbIMWgujvG9WY1j1wgdfWJLRPaS sD2WPK6ul37qt+seY2WwmXX4JVagYfBGD/zRTRInWJ9g+dWiNRETS7gyos4omB1wMJvi7drhfG5x sWTIqeTIilusfusMbx4u8fhuiTMpOZFFOh4MYJsFTpAcqZfY2BrjX5ICLwLXARcz6maRjLlqg4oF 3pk2yKsctqgQOsLSAUe1w8wMJcIgNRPsnWl7HDsKVFj4wmDgFhiwHMDEWYY3tcEK6tY7zNmMp3LU AvemPn9RaFxFDz/KEQgJOEnfwfinsQaVjjgtAoksZPc4oStss4BLlE1ebz7aBsmtQxVIGyGun04j 5Zf8ZfQgf9DFwSMUKslsTNpOvXZwtkXlgFGWoSOu4iOG7Jq063b/Vf6z9ka31WpSgY6XZvVMEvzV VlqjaWsNKMPXshjVS7hApIiGpHIeA4o6C8occXY4xsFRjT++lRqjeJHGwi9Kffgau7kV1cV5fYBp R4Fnh7MlhUF5/obCzBciVUo5ntqReq9RLztkgbKgBVQWJRkm7+p6W+N6b4XeQfHq8ydHGPmlhMwk UNLgV6bFxSyNB41Dw1OxjAazwxgaXMorOf9QJI5oHExAyDJ8d6/BVOVSfedGy0yGPHTpPEJD7WEl BsllDreaHtdbh7MVD06KGBL6XqGHNE8c1UmuMSgMdoPGrU7hrYXHviO9Pwlk/KimWCiX7HhcUXwU NY8zjYu5w5An+NYQdnbH/DSQqLlSq0sbRATkhR+V2idZ3U46Qu//It6wxR/dbHFulOGBYcDQE7HK V/qqK3WmVe/EXxptRHIsTmKiT/A2qCRiQjoxltUKwsBCyIsRI4H4eJpZqv7epb6U2kZXZzLSysuJ sxRU93fXZMSu3W4xy8MCO4PE3BLNEQ6+HNOh4bzFdw+OcU2xtjKQr0ga6QyHemxmCqUtcFjXOA4Z vndU42JpJJQa0MsFh/HQYqRq2WyUOL01p7ektA9jey9Ubj7fdkaa4hI7VcROMUJ3sMR9my3OWlbK ebwUYdfVYiftwgPDCVHAOaiM9fqc1JYMFyYVCt9hTKE4HrlACJ2STMbhdDXAi8dM4ldcNeaKhHA5 /sZiHOb4/KkKuxSmMxVePPaYUCS7CXixj/jcJNV3qNjOMexosAiihCCHD5WlwVAb3Fh0cHmJm4tO zhRhCMgrUSaT6v0aGn//ckhdhpxyJuJlCDiRG+nLn7U8Fk8hep4WzBw+HbZ6u1BIhQd6AWmHXGXu zE0/KGKi2zyCxWzqcHPW4bHtHKcsjxpLCbdoQ63hybsoCD/pS+rJd6nurX2DEGUFitZwXScunvGn WJlAhZIkkLAuNPJHxsuN56lQWhZv7NjjoZBRdI1sAXEfRJtoHOTUi1V9hGOR4t+E/wdY0+PpkxM8 SgZs0656IaJ8VmUsBvLGgKPS4MqygYkFzuYmHZy5OhaPfSfnSyOshPtHBosRdY4pEBpQZmmh9I5e 0WGuNFyo4Z2F8j1OliUmnsXP9Tyt1FJWg0ZOGDV4vXK4sGmwv7fA20czfHGH5MwkM9STJaCYj3IF dfiZU0M8u8c6UWISEGyQQ31CxE6mMdIMn9L3HNAjdI2gYmxyPzfIsU3+lYzTqlQrE8YaEykhwK4N 2BkTBl7gUlXgleMe7zQKrWa7c2r8Ws/rvciNa6qOzDIpPxY4mRPW52FAPMCH2vnpoO4ffDu9qbjc GYtYUi9g3YC39UGtkYRLO0l4rmOEeDRHdbLAUBZEGgTpFlv1R9yto/qpuOS8m2S1yiKDc8wlWJdQ IkfE9lkqH2bWruoQafBNlmHeORxPlzg1HEqLK+FihgdU+RAhhVUVg0ZEzvMQKniqqMtGErSKHiag imxPTSEIF4SwZoVikVjRp63CYFLiRm1xo29xTlpo11XrtKG5+dhjzeIaETRZhJ7SQFq4RU3fYpJZ 0Rvj8QrnxiVC5xGEVvH+S+5PBA2og9XiAg9p2KzEG4yNkxyAhUEaU9ZgIgGU6LBheqHD76/UHaVp TOAsNnhFvLg0MC7HgQu45anZawTaJRt4v25xcpiQT+Zg0gIgniiZjqSmyAIzRe2YIjS4b2gld9zr vORbSUclScHe67lu54irM67Y5y9NXIa/jJQl6LU4n8Qw7j6Lxm4XCj9/aoiDtkEBixtthzeWTIzu DRNIJTPJoUnosR8z/NG7vZwvt6F6XJyUGHGAOOGfPub6OtdOZDpStnk4jVyp5hBZEfYQBImQH8MJ CT1DSG2mwxH2l1OcHLIYmILIqHM54++oC7ixjDhqWmxXGpfGbC5mcMa8hKofK/Rl5aw5ltSzojXu V6dxr30X7d0gehTC60rEeKbl0sAk3prC2evOzoTUiHicwOHJaw3EwzF6SkcbnLABpXzWvYu3a6kh eh6ewBthsGuoSMgwOhk6llO4humpNI+CViW+e9DjXda0VmEpC4JrAJ8UlpemiZdGRfZMzs9KPTAP jHs8PMhECO8l8qK2NnGSi1JRsDzJ+HCckm1PrGD22u9qFlw7nDFUiCnw1qzG9S6pT94reV+LyYn5 VhpHfcDNNqUUhGrYt8J7GpjUJXn3srU+OIzQYYs1lNhLuyWlY16eOSxES4g4ODcDi1nk9kc5zehU 7rFdAktV4NWjHld74IiWs7cwyzl2qgyTPJMe64+D03/ndOgU80uuIPnCnbrJna6SD/6+O3z/dQZ+ p/YipBUiK4QZGQ/71XNLg3/AZkn4sUcxGaKLwLLTIvVJy0iKzFSgRIOHhmOcHSbS3vNLSvgH3F9w 8FO4lkLRBA1TaHlQGtHCXd9LUo5PCminRjmatknJO2Hx1QJYZ0g9D+bkxLLpiX/ngxQW1702kgWR ukOYWjZHYhE45k3iAdIwSHFstSBFdZ7MBZtOteLOe3XupE/+8tDKyVevc4HpAq/PPI78qsd9TRYX Hlta4HdakhlisH8EGPqAyxOFzwwB6zrMYHAjaLx5s8WpwuLBkZVTd6dkTniNQaawyTZiVt1X/LIi Z4eRx7ELGGdDXO8agcjvtfdpyNfH3PH5KP1T5QVyERKIqHKCFOnoBXrl1Zm8aYMQ7iLLKR2cSRiy w+WqxKgo8O1Zi+OOoRdtVI9M93h8YLFhFE6RcBhaIcGZSglpjy5qyzTYoaQmq6ZgjM09+nFc6ZCX lBgnq++cg2Uo9L6X/pBvvOc/pf6LdU0lVa9X0jEkAd62SlKpkN6Kd1ojmlc68zhTFdjNM1zrIt5s FF5aBLy66KTLcmwintkkvSUlf0ncIW1HHqTZc9Gs5iNVjZkk0wAQSGAk78QTpMe6szkSgg/YfMVt Wh3PtuTZILTuxKaY7ErvfModbr9XzvS4szmEHsM4PjHG0DF0owdyiUrDYuxguIGXby6xz3NOqLTo c9zwGjyjk5udG//OTKX6VHpOyn2mxcfQKvcOn9nM5cgGsh04zGNFA2IwrnIUscHIMjzMMLcl3lks cTCtcXk0kqJmGR22pMlKow4Wb9Ut3lg24sUV4+N7r5ykybUykORj8QfxFjLOAtvA83g36py9t2Eq qXAQYmQzkM4GmHcBzx/XWPAYNMV+6B73jTQeq4yIosnJtyuS4Jb1GIzJYUkN+PwkIfxFJmwJ3RKb Lh1jHz3mSnnDel+nFWW5eJlYiyX8MbdhUulfHfSSuD60iaS6CO1lRf/gC0eZxqMluUtejinYdwN8 63iJI0VGAdB6hYeHpK0QUw8YsPp1GzBYd3ozhIs4nVsRcKAwT80aRdtI73ZawOtzYZNXvPsJU6SW xnW9gfl3BfMo9qXLpuKkE9VK3C1BHNaJ8N1Z5u3bc5h7gyuLDhtZgXPlhmjzHiPinaMjfOHUFhZO 4Y1ZjbwIchTaWqbp7mMIEiuAoelKK1c2L8OfpNzyxlGNsaI8EPlb7NEPeIhHP0ir74p3bQO65Qzz aDDDEC/OAh7UCg8UDC0DjlpIeP+Oi1hQ9tWnHOxexem1KjxzrPSUBm/POuTDVCQkO0HCU/b7E9m7 67028VpY6uchKD3evLFAawboFQXheMIqxYuVVIFN4wWH36rYYsnGGQnAUfIhBdpNE5msSZd6QRKH 4MOt0b9wE929RO6cHMSJEubCnTjso10rAeiEpJBcSjw3woUgCn1c7Jbn9a2q7dw7e73Bt2cU4eZ5 f0Oxcq9OZzjPeoftUPMAG/p1IVrdYQ/zVts+ijC0phKj8dCGwm0Ksy5iyJiICbjkSGvq/h2UJvHV 0vYRqk1alXKeoIyHWG5i/ismwiqceA+3a/Wc/LX2w6wHPLIxxLXlUpjURVHg2SOHSllsmhZhUePC aIArU4/grUiUrnVu11PkBIlTKJzH5XGGnQyYdw06CntYg618gE3VSR5IQGLP84TdTOSGGEYJ3V95 XKYRKjS+edRj6TO8MmNum+G+UuHdvsFLi0byF55PmHSJV5P4vlWTbjBJN1EIMcPbzqObN3h8s8S2 tFcQdHi/IbKJP2VxTBgzDLCw5PsnDhRZsfxSfugyaLzlWViBwHISDgtNke66k9f0vsSBcygyNuOk 3Sjvv81GvdPHQcu/ziPe8zB3eQJO9PrPZH7e6zIrMGE9NkLwk4jzI7Qmrs+YSHe4ogCntlabDF3K E1YbnyHfOPd4ZpPK7aR4OJHy+eJWJWqIM50Brku1k9VRQPwIcehKYZAzWU8LjUcht95jq8zlaDoa HquTbu9tZtpd5M8UCjJUWB27QHWulRekJSRcK9/FYl+I6KzFlKgWhbYFJEiwNoOx1GKV5opWtkKP B9n5CI8DMJTRmBQRZfA4PWLvi8MDu0bOPHx1HnG49nOSYyg5SzHEHo9u5nio5PtaAUbloNK+RcbC LBUOxVBY3GyAW8sGT+yU2CXHTETteDZlxBkV8czI4u2mx14f8L1ZwCsLLeRIqs2n9uQE4n8AWemu 604FnobphnM41/VyPg18gYyfZ3jk310oFiGuI1/gIATcqjtEk8uxYWtZ/RS6ELPi6Ump74Bn4PHo YDbGtI6s2BKZ1XjnuEbMDJ7aqrAVa2FNKm9uW633nGx6lxW7ffs/sFm4iWjB+PsPXdV3vV8gW26+ j6MV7fZnp43CvUh6CZNvBgMZAQueIJUpOUiTYY0wU3UjwzaJBmU2wMJ1yCnfKTFvwnPEmknSTglQ Hi1A8MPi1qLB2GZSL/HkNuksiViTvSD5xB0oQnzKCr6kx2C4Uzuqi6TfOQ67RNuo9E7emXxu2unS BiCts+ugdQVTi5hcL4n2Uud47qDD1Je4VPDv63R2oVnKZr9cVbhUWTy/jHhp1smRCyJVK70bVH9v 5QxBmBJzZ3Fz2cHkA7THLXaGQ4GHC+Xl5N2Lo1yKoOlssxT2s3hZhIgHrcV9kxyvtcDzsyBaYXL/ t+dm5fXfX8b4wEs8J/vg7RDv9kfITBTwYJOG765Psdeiwh/fmsPpEp009CR1cfDQeuGu8CxqVjfZ 8AKJCesmwYErlavVztUIdozCLxE7Js8aveGx8u916T/KxQm+1+a617W2HtwgfD09ycfQzvz+71lt FEF3YmLasv00GoO27TCSpDmNGyexFF/msSRRL0vU9iTHthIUEqkkAhAat5YLjAcb6LpOjM4ezxnP DXTvcaIg+5a9EO8fB6kiUPuJa5FSQ3OyjAeYNR2Oexozi9Z5HDcezvMEKoXNkmCAB/dtCs+Id1FO SGOv5dEUFa4fU/nRYky9LLbXyvHWAbkrcOgz3OgD7ssDzlaZRCGHC4cj9nOsCPdXWoM3G9HMRNxL HOoAACAASURBVO1ZHNQINSFVg1HT4UKl8NCIx1Vzs5Ljtc4lyd6ms2ZrMmuGrXRPbpPg2QDvduyj SaDNe3LPv9iFvOdil+Hr0wUeLBnedcipcfwDm8zSYrSqBGKOPCxxcVhJsxIZpm8taix0JXEfh0bo JCtNVyb1t8v8oHxKj5yJEpmjxki1VbMI9xE3h9wc4da+/8Dw6u7rzrj8xSMU7xGL/0gXLT89iSfZ LtHtZUMUKUxJ6i4kEWosOx7oWYJHXRJVku5DoeEQ2RJcVqg6iy6gykpkVECU5F5ho7BU3EmmSKci 1r0cI40U76Dn/XhgqxriuGkxyNj7wY3cSTFxzBOs2Asu35nqAJ7HAzjmQgq1yfHqIuJWbxGW3NaV sIFpvcn0pejv+Ry46XM8u1S42Slc9Rb3ZTUuFhRc0LhVr8JDggOuk/YBAj1Tsdrs2hQRWxxHg5cb hQPvcV+mpPhXSqcpdQpyvD1tsVFVoqlFH0qB7G0Eoem3C0gPhxjNNY/0I8iXEvRiIfNCbjBguqlZ ymCtisWe9BrLcyFoGdiB5lWOa5Ss6YMgDI+eHOPmtMGNJkhZnxytJB2v5RTVs4paSRXeaOe4MIh4 YDBC5QdyeIskuUJ+WJeYPvxFL8CNwevDbI73epBVsv9D1v69cp8f8Q5TgKP1ip6S/EKqASR8SORw vMa1OeHzFg9vl+g9RahTnzWTBFJFOooqaIMq5xz0UtRTeSFYPesbFGDjJhxIgxXV4Nfp9Do4ohqj wbeOeFCPxmc3nZAGB+zXYOmdYhisgygrzVEslDGUcOSkSUWbaota5EXfmdM6r07IkqtDB4XnjylL Ctzvgbmv8MqswVR68QOOeWAQq/XH/GyevJUEsRnWPbg5wvWjmQhBkIcmjFwxsgQQclF/4XqrMotT NBiMGChUgYgz4yGeP27QOeDyRo6tIp1hcuPQ4ZhFyR+Yvo9C2GD+5QxwZdlhwxbYzXopfq5DNjFz dwptaciXBOdh8GJrUOy1EhJQcxcr7+GEp+Ox6Wo8vjvERlzgwUlqp7SuEYpFR0WNQGTi3pSGDxNa cYNUFVWTPvy19gxcf7SMUsv4RK4U/0tdanXYjYTCK0smFelADV+Hx3Z4Om1EYG9ECNKMdNqOKNmG hqfNCmVlLizS1/oKb7cd8r4X+PfMCNiS12jUnUdZ5KvkdqUessL2CXhdyDP0LnUmciOQctKZDG8t e3xmTFJkj0VosOUY9DEv5LnuAbWp8HIdcdBH3KhZq5FzmN5j1AxVM5XGlbaHbrm4tRjHR3ZKKTaS ur/fKCkWZlKLUFiaiJePFqImQmXDjGxqSvoGg8Ky7SDRzGks3qDoRBtwdjyCb45wYQhshAW+uKFx tSGKRpEI4MJYYZ/tAOKJf3z2OBE0GvspLL59vMDnt0rsZgSBVxejgQ+a/OL2GXsrhFK2bOohHqDH k1sTbMgRuxQWS8cVJ6mbFAaUwi9K2rU/Mj2ZOc9tLtS9UbAf5hkSG/2T5bnIElr1cPM8wVTE5IAz TEghKP+etQ56Zxk91pQynjF/KN1yD1QDbBPvDxpXmh6z1uGJcYmNLEj9gv5XckEXULI2QijyrjtI CTqLZvz+WjopZ96h98xZCuEvPVSRjDlH7og+lrjpe0xMJiLZzCdfPujwemflFKa1huT7xlh1q/MQ WRxm0a3BeDMT9ZG3D+fY86STk0bS4MHKYtop7HvggTFzDEOZE9xogAM5PpqokcXAlnhrvsC+z9CF DO864PoBC8sW77YK56oRNsuAMwUwKjJ883ghp3RtVWNkXS3h5o89hyJiSFDF4v9r7zu44zrPM5/b 2xRUFomULFm2krjEiZM92c05+9f3nN1sy2bttRNbllUsk5RIAiAG0277btnzvO/9ZgYUqUJBEqXg 06FIAIPBxb1fectTLpwU90qmCh5Gbi1f521gaeXT3znA1tnoEQVsqWwMYAw3wOuxIxpLUVuKXGXF yqcIwZIzze6odotUOfBLXrRMck68Rsq8u3mCJO0imvbs3cN2qmVh7YiWfW2jJz9GOe9iSsOF4HiI mHQT46PtIdG+VZAnj3AXB9MbeOfJCh9VBlOPmCkH+2jxk/0AcV/DNB4WDTANWDQZNJskiR6egYQA tkXHJN/DKxHFrEuBmJswRDiUyLUvpdJJXLR7XBy0Y6Mt3NLFRxW5Morlsvfw07+no81SUXxp0XgB 3l2yOWeEHMawjz8vcSmLmuLcLOSejMkD6Sq4folJQp6+igMRteU4Oe4ehHh/BfyxHEQjGBH6oS6W RYtm0WDqOtgPHXhegH1yGEvaaV/N5ie1P28gavUePiy5uTn4i5R+hpqE+LLnC0puMFzvA9V/kk6y Ch3YCxJ4EONZIXURY5Ogc3L4Lg/5AU48FBZEauU5k9QC8nhTSKFkiil8AlFBAdZdgqL1xaAlFZ0C llZZulVVDhFfEI0rPd1E5GCQkxEsFVjlIHzCgSH0IowAInRFpFnLn564Hu3uxztFXWfbpxl60M+4 uQNBSn6exlusRlH7tXTZA/JgTI+qayWk4NusuwDv5B0eFoXwLBKvE9ejI5fC08Q/GSGy8sUTIk0t TGTo7QiDRRJsWk1Qw4l8blZjWGVUbBevKeiNwFe4uQSBIw3JkFgpjxCSHl4T4IOywfsVp4iiHbgj s8giMPSde4KdRcmiDCMF5hPy1AYtXYIb+eK8D/FPT8huYV7r4n0idUe+KKpL03iQz9AaHpCgxk9G lHjy8K8r5ldU1iScfkBqOAFW5MsUPcqgE0o0+zHrShuGV6cGqjAeQqo+LoDId/GK4NZ4Zn5qegwc geFTyu/QN5KSrfHxLrVNmwo/yxKczz1R+GAs55FMwVyFN3HoXD6rg6HixI7wCdgM4h/W0Al2fG9V Y9RXCL0aTlXCc/bxoOlYhAMM1TciTMjZEGE5avza0EYVM2QBiJY8F5SAjrSPwAUxYK6kBHypKXgZ 4KgbwXBKfRasa9ihNRjl7srTLxTueusRicsGYyyhU0jbuWIl7rC3whSHvotJ2OJOVCkPQq5dN4Ce oDxmh5Ls8icpxdVSdshRXNRG+N0+icLEEKHF2tQYxfSi1w1FzCwNdcZ4lLEBx885uPAC/LkgZYHY J4UPiDvW8LO29wS6CAaXrJ2WrN6xnc71Fq02bFKOg70sQUw3LT4/CjQ8wyCJW+JbJHTFDME6zNcO PqkIglWslMo3qa4vvVEEPOuFX4py+7xhERMbMA/NoRwH7y8M1hGNd9QnXjWWfOJkaEm8nSWW3qiL g8Qqqlo06JhzENZAaEIUo3FrROQ7NDR7VM4wvy6IfuFuX96FrYB16fNhd4j7ABdtiF/NZrg7yvBG xL5Cg7YK0Pj0AuzxsOANTnG2anE7djGlJm3gIWwqnfS0XGYM3QJmXakCIFVBvN2uusrdeAxDjNnk OVwwXA9ylbZT/QXvv6Xm8n3KokQYUmhaO+68Ir63QSWNv58nPn5MaHzfIvM5zZV7szHMHEx4CC3b UH8HgCYfnuzbUpVSQhKTYpaRL9oax1mCo0AT4hMKXbMrz8qjR6UTTmbtqFcMjxY5LqiDu3GeJSrA CnQzdySCWU8s5kLci6gIyVPV+qt+dm1SpYcuVgWqlPkOTza1YNae6/YU53ApTdv3uNG7+DD08VFV YMLSeB/gpFEdLIGfUPE+DDGvyWD86opVm8rnDo6MKYXxItwvclTc4OzUJYdaYoXhYVsRBpXvoRZT h1tZirezHssqwK+XOf6pbvDLQzaGQhEYmHohbvnKR5aknQ9AJHu2YEKby1CiNO8qpK6PRe/ht4sc lTfGcUQYQAWni3Fae1i3DoqaogQNTqpGGlQPKogSB8uT3JkZ+/PEomf5q/v7iBzg/GItJ0eWBiIM Zmc+TwZTa2WIpiokRlGFXIoLwyq5BOf4ojeb1N2YFaIehiY4rQvD0ovfYZ88bXJM/B45pWXYDxAe yBbWojRl/lAuqqGnoQfg8PA8KfkyWadOLhVDRuzcBx4CPxarA4W0ODBUJwnpjssdt5eNQrlW7HIH cvpLkVMEHHjSk/2nJwgBpy5xZ40CJ0fjiVTgYGoJGbMkwZPzGfYOJhIKMa3+FJKBkBsa6iDAWQmk CU2VeMLzPmv/xy4OHXxS5KAw3uZWHGHN8LTNBUlAqZ/WbXDH6/FXEw+/ZQOzYSbz1ShHw+GpIfKO fK3qQJLAJvhmbYx4fSBxq+JMGcJcnh7M9D8uDD6hPdAAa7jogf9yOmg4Oaxl1zhKNM6lxzpPnJqa VO5QlBNEZ4l31jQx6XEQRXjcOyJqxg7uUQjkDfDESXBvUYldGJNC6icpJondZL0qclXW5aB8Puhw 8c87a4oGkGwU4W/2EnTFHEXTIkkTlOsCveEEqpHuT7Am1KFtMGInfHvWbm/gF7zR29PGguJUtLtr BoVyPviA7S5gyp1Zqn16VKvMv65K9Uv3ZRN4f1FhP3bFfXzCk9KDLmbGypRbklPGoPFCTKzow3AK 3YpYdlce9hIM9dgErgePST19NN+k1Tf54ExU1SNSpRhdeLEPN85gmGMMyGBGamVvkB1NBDnQDI6x l+7ZAAhkPrXnElsWwe9zuTfsobMsziobqa7h4E4lhQZGAB4dfdk2NSgoJDhsVo3XI+4N3phE2HcL /GgvRH5WYdkn8gIRCxedYA2nv+jY0Bvkg91nyeegIfaXOqeUVfAUXmpTUaJvnoeVF2JRMdnsMI5d cOsUV1VivEyL2PPxw5QwZ67ODh+WwIqIJtfBsivxLwtyG4wgLrnVKA1YY+dnjd3OMh8MH1nb9CgQ 4r/MctwOIvygcTApS/hxD3eSiop8npdIPBdJFsH4HfzncAm+yGC4sDl97LWwUTrskoI30PLacB/1 Z4liuyiZ2AcS4F2KE9QG+xS0JhbJobg3MaWN9HZKhj9Un+ROK3xslW6V+zPwsjV3oEAENWeVJ641 Ye7wRjrWtI9m5YrmoFw0PG1Ei4AnyeYE87FerZCGnMyUf+0QjRKUrUElVbXLs2Fb9HAx9Rr87ADY c6iyyISem2Mn/BSGfXqkaaOVYbjoYVHGlTlJGOE+zYCE105/xho3fYP9KBQ0wi1W0yYB/m1eYu6w yMD+j2qiXK1b1SZJv4q3crHoXfzLSYm69WWCJhc8USIs60houD/bc9DIA6gx8ZgERVhRer4pcJSQ 9tui7iI5ncTPkOyugeG10Tb6AkNpp6wohfi4bjFzOrwxGYk27nJVwk9jYZER/lFczJDuU47gqzWe BD9FrVmrtjH4pEjj8jnfQ8Ydw9dzmmsWJW6m7Oa2OBKuid2dXWHwCTTAUy44fz2WbM+Nj7yscWvM BqKa3ii7W4BBAoBkcVfRx5r3cXH83SgQoYrTHvi/FzkqdyRCe/Q3ZKPX5jwccRwiCUOYoqS8lfye 9lY97/cSmVBpopJpMeQ3LY11BpzVJvbXYo7YwA2Le9Tn+NkkxF3Hxe+Xa+TGxY9GKe7QtLSvRGOX faUbkY9XsgD5ig1RLpKhmnjFpf2r8wfptR+inkKEKlMcucf9UkF7R5nK5Z8tStzaS/CgBh4sHTyh q+vUg29anLqEWGxr/CrctqUMPWvsJlgcuoNs5fjZblv0Dt65aHHmNvj54QjzOsdZS28ND4fJAZaz Bcb7VK26/DO+aA7C0yNkblDX8vezYPvPg8wQlTB2gckogNsVGDGM2r2SQWDCZnAOw03BXXXSQIyi WNDC7LTfSCPBXYnSft8LeUrKsFJNVN0pUqYZwiQMgsMEf6akDiWMhkrlZpoNsk1O6CHnIkt10xGU guX1P6eYQbjIWe/hf5x1eC3q8Fd7PlLxTdQTU/4vGwerpUqoYo7F5032ZO0YjFof/zGN0fhEGmgR qHZZaPDktA/dCnfjEKZ18XFpUA02GVetEfLcBfJ5Xeunh8TwDqNiPhRXGkcq2+mI7AslPz1TgISS //kEmJkaP9ob4T84DZKow4eMXYtt6XV3UXwmhcp5Xsilk8IqWTCcOek8fLAu8VpCiX8Hbhjik7zF q7zRZYcoJHqUr1X+uCX6P+PuDPfHClPY1zz7FNJq0VA+HiApltyjnxvi5kuo5SEkE0q4h7NC0a5H MWVVSQRtcTeqUfQR/tssx5uTiTb7GCoNv/XjvESWZpjK7k0xb0mR9VSiAELX4O4oxsVMkbY80ajf xTyIuZ4zCHPw3jF5t1U+2XiGBNfdSAXp78RCtUCSCDdyAnxU1dhfO3gjo6cKvTioZkLVFVW/FAUV qdRpK1NwwCI8pox6Isk5BKgpN40+LMqxP3ZKmU+ryuBMwrirR09cXYjFsu+G3LOVoT4OSvzDfiqh wYM+wweLThhkvJHl+gJPshT3Zh6eGMKim6s1vXpq8EE/qDwkjod9VsWWFcbkcrgxmvVCGp5eyJ1M F7zs288QgeXnWaQNBIQ39ELEc3HnflheyoAq2HyvTQafdX0DcoDfQlURafWwXNw1OE5DOV2JktUA SqVu6Ol3zMZWQPQsa0oeakLllyVADS/yQoTtRdjh4EMo4auWTW86PaJph1FKwhBwlhs8Mg1O6ga5 rFueWKq8snVf2i70Tc419DjIO2mGvoWQslwX95sSP6TJkNyPEJ5ItyrwdUMGs/dt+Jj3gmS4Z895 FS5nYp+FHm5NUyxnJUoS1K5YSue5s9HCwT+brLQddFsShcaeYswGrycGb49dTPoYf16V+EPtYEV0 nUBSDDovxANkuLes0ZKLzLja7lJf0xBHoxZ4ryzxVuYjTEYI+rVYGLOcuajWCCuDdMSc5PmFAV4s rcNo8MPkVVqVkiR2mzDr0uk7VNjYuNzwx59xOHNSyNcow0+JoTAWBl7CA4MI2OFnE+mgEqUUkLjA G+MMTqNFEvaB2DQ8GmtCy3CKP4vvXJPiy5K4S5lRFTKY+h0mAWHyS5n009DDDyKKsnl4kDv43RzI XTI4uLtrKXpTBt+Et3plzDFCt8NBHKCoDFZNj9qjPoGLuXGQxTGKqpRFQv2rlEolA5Nx905fZpt+ +kYp9InVQRctc6Ka1GLmIax2frqy9lXGZ27Xz14cm+hz+zq5aQ5ueQY3MwdNXeFglIly4Htth4dl IAYljE07gTuEUr2gVpPedGurxcvRsvHXMezOWbcB/rCgaU2BN2IXB47yEg7jEbzWYDlfI4x9xDH1 XnauZXhe4ifuh1iXpeCeuDN6FLXmQw21FMuhXHnyxFkNWiMdj6QSJFQAj/Bu243e+RFDcs9ZPFsu RaFElVs6LRcPDT29Yx0OadVA9RGH6o8GYz8RhRPCQrTcDumbdERHx5G44A57tGCotMzPMEpzJOue lXYdfpCoBBRj/EclNbEo06nuxPZ22CnMhc/QitB89mEogcR5QcTuj9MEmVPK9bhBhFlZIokTURNh SCtIJ/3VtMUwoBh2mfj68fbEIZmKsCeSuV7L2DTt8FHeoNhASHav7mtZIHa/2mYDeoyyi643lrsK VT9oKfZK1ON4b4zFxTnaIMbvziusOupLDReo7jDCc5fP7TqgboQdvl6LW1W3YN9BpUFL42DmtziI CMnocdI4OKYXRhJjyapURLEGtXoIIm1I1lUlnoqB14laOhusfNckiaU7zwkuah+7kH1OuHQEphHr WuEj04hEIkdKsWya6fd6AmEnZCehLm3qoRLqLMNAJu8N6rYXf8O9NETCki1lREnaogCCoEZ58g+h rkB0GnhhJL0exvFc8KxmMZ8RHvpQEGA1TfowYhSqTz/oOtz0gJuZQT3ycNoFuL/kJNTvbNpWfDtY FhCNXzfARaP9F86bQ7/GTye0lSglDyQ2js3JG1Eg4deSz93UiKWRu51j0k6UPo5qajEP4vRhYi+c k5Zej+q1yNfRD/PtjF3vDg/WpBx7dF1RlLMIQSiB3PY9NoiJL0DRfX6SPiSoIrpg8VkSerE/0cHv KQEZ4CDOUJRGypQPT5YoOpraOwhY0yYLbgAgvgxj28dRYCZ1mmjywsQ1CDKcLdc49x3coPr5QQZj CmWZuS4qhgZBINUoJudlRUN6drF9lEWBglWjIJBdnjv+bh6iMXcvpvWZp932uqk0Nxj0vcT7ZPi4 rCuRQaWKIE+aZUHKqYYohM9TcZGvXDM/8T347PvUFYIgBP2ranI2GL9TY6vXJisNhGs2BEHxBoO6 44mZSIXR+tPb6tLmfjnbahr597e9Crenwl0cqlEu7jkR/vnxCl2fgTrP5Ji8mdC3hJq6iQAZdd40 KmUkGZSDZWPEbm0c0p23RtvV4gNyXlaIPAc3Ql+sFEiLvaCHYZQgahRdzvtmnyOrgFw8Sd/h7dTF G6mLFTe7shYDpTOjhqu7DcTdxuCL5yBDvMkmj9REJO5rpBYeeTFgIixLB4+WK9Hp9bwR9ze4tMji btZzN1TJzZdrDAmBBS6Kg1OPlSmwckPkbY+9hJ5+a7iEm4szlXaxefI1JA0NQtcMBahAYhuF7BAH FI9md7xtZeJTdEIouoMnoO3UEgpSMn5mRWenf8JBvgR1wJhXUEBjElFjiyGvEfjHlJrCaJHyvtNj UBZ7iDKvpSwbk6EYqRZu23k4kvCMzVpCfxz4hMNz8g/NS1taETsjVh5FtcduiCzFqgKBnC3yC7AK pvCdtqKzYDR4plPUoZXT6jhgR3wloh1USKwcTzj5LOSQVUg4EH/nWTFDTxMeLwDRToQVRVksFGDH o4hIh6TyxLXqxHTi6us0Ne5mITLmTjwjBiAlQaKKVwD81Jdy8HKei0YbT+cvU5W1Q8CKw7R5aqhX BXcXSo2ywjFOM9EvvXexQsnmjEBnGLv2aHu625GGKRZJQ5OPNeyXxzdkE8VaQWlC0WHwymgktmr/ 7zTHoo/Qe3RY8oShF7vs8nJBDBMoiKSyxEajuEv1A6RkQAuXJQGLoXzMCVBV9O9jXG4QRHxQwyRs OphSGXUc2i8bwHvsnnGCCu6zR0OmJiEYQYgszkQilsSltSw8X5RVAsrDpolIoVYIcVF7eG+5xNgH jkeB+AOyQCIKim2LkChnlmIFkEhdLcU2FYZeh1ywDNGIVSMp3spyar9EIf6Kt9uLUqRthcLTYgXh JCyl3x6lVKTA/aoS8tRRHGO+WAqWjO7G4uGCHnfSTOHyvcGB72I8JuSpHpAwfEasFrqo3RbLrpCI hYtOLoiYNJlramtNnvvZ2uAgjXEgVuQ9kizE4z7CJ3mN0pLpbLrQf4kTZNP0Gd5AfL598gwa8csu yxpn6wK0ogelaC6txp1UagjwFB79ddakXnQodNvyrkvE+MNFhZ/sx3DdSOr0764NLsoWr6WRbA4u QX9D1uVtEkqdKCrXwKqLsgrdKNqcHlwYWrZ1BAtmTwnbYwpCRRULGFCGnkRNrX/zNfIehM51HYqi EJgGS6cSskhOpwxCP2T83+JhFSF0G2SewbwdYVY3+IsxofEEnLKrTXVzPTlp7+y7ahFtNWro3de2 mks5rEA1Zlj8l+MTZd27kjbupw5ei0K8vzZovYhpheRWASrcpRpn4yKjvZ34AKrkkUqT8jTRvo0g ihmmSn3C1SqeaQWvxd+XRYZX0gRdx9yQfBEVp1PjP54cwIEDHI6V9WpLEa8nDe4Qxdd2QiNXy28G 1frkPg9BQQbHgHcawgDGpMIM5JHei0jzo0Ixm43HY+uqK83f3rATlbjf316UOIxCMYpZ9SE+YuhU 9mgDR5L4qKtEO0kSfemRbAdPDLsgLF3YwrktpP5ZtGHeZ8ljAsLSPXmP3QqYLBauWEBOIhK/7IKz J5SEc7waz8UBfKwiIqBL3B1FqEwrxKiTqpdqHRGxXCRk9rFQEFAkkG9uNX9FNIFC2NuGpfuU8suu +iV71/sOF0GM31+spTxNf8TKdXCaGzkNmJRTbKJsHPiJennslok/NZcGuVXpqNfULe4Fal+XtAi1 faXnNGRl2pLRyohGfC7khBYYiyit2EqmYi2+SHYsaF6fYLh6IBINinvWwlkMhbl4eEPoG/l9WR07 cBCGhaXj4nFpJIYltJ+nxb26FSTwj6MANwJWTHTXerr8YSc3JzUXhJ3ou83CXX69zVGkGSZaukP/ Y0jUdych+yr2JLEhGxcU/7YLhTs+SVEMF+8kHj46b9DUPl4bMyRrcL6u8UqUIaQvu2CZBgHnAaMl fwSCvhUQeJbipfuUPhm74Jx4d8Ie4YGP313UeOx6GDPHCuzu3iFLfDDdoiYXTyzRKbANx+1PkFI1 Zzl1Q2iPV5sWoySWxUzApqjHMAx9Ti7B9lAg+tCqNikgUXLvG+aY2qTkkafOYxoBfN527/PFNXc/ lQjYKb3u1LgHzVI9Gl+mnOJqBnkU/EWbjiA+Lg7lPbtdiHnX40nR4yhl/lBrAiswbX5Do0zFwQl3 4zTSdXKq8G9uNixLyuKhMN/g2ssJzp8ZEQ7u6ufJnaF5z66qCDcv5iwBTykpG8eoa7N5fykQDPrE JfFOpsYkiTCnnBBK/NBvEIymeFCVeCvU04fVJnmuzDEkhxz42Ty11FJrCKEduQ/KqhuAlzsLR0Wm qYdW4qgP8LNpiKOqwO10gnGbI5Q1qOZBrMhp/0QxYeuWJ0uLUaieHtY3ha/JGyPl7HGWoKobRIxc bP5IxZah7ybhkdAh2DRkTkgdhBZrL8bDVYMzwyJHjyNGANTMXSrL0paNv0iXhFVDGRZSZGXrFTqt g/Pn6ebg92rYbp1gg/gPPjQ+rFZi8qnn42G+ROI5mPgqYsbyqhOQwchkuyHlDnmZi84Us9+6LhCG AVxWpOiLF1F5kcxCTdSZXHOiSygrNm5UqfcR+kMXfggjOuYzwrtgudkXToucOGEnTTmZcnSKJcGJ PRzfxZHvIO8dnK4qzCbs3xToW4XCcALS6lpUeb0A5ZB3KktEE21HKkVkLnrSkxHBiGdD0qQIU4kn SYsbfi0nLalS9miQ2icXYqAoCd7XsnUwN8CNRAsOlAMil1/CLwRI/QSpx2uoJAdg2F8RrExDUQAA IABJREFUiRxwwXJDUkwWE3jaPNDjhsqMbHae9CH+9cTgvEnQ+jV+OepwOwhxb1kK5J4FB9IF3MEr 5GkK8NPj6wM+fQ+GeIFUHe77Ff4qDgS/xQd0nnPHCsWnguXgJCa4L0DueMioTEnYDCm9oaW7aqWI pwAbiyGrWexzRC7y5VJOCS6OqtGKl4L3NExTpLDu8lxE6TjVvERs4cSgQcO2tpEQZr1aChOO4nNE 8+bzBQ7JcGS52eRojC8hDPkfbPCxYUdZ2RHDbBGf5sxO8YBQmrbB7aBDZVaYZpNnKFFqbEZabhRG yh7dSC9tJ54NG3lHCdFhEeF2TGesWhN0hkakEjsR8sDFRbnCQRAhdWNcNB3eLyH+h86SkY4rJ0rT 1qgoUEFN3TBFWTuouh4XtN1iCOWWeDN0cCf1cO88R+cmm9+1F5NapVd93rheIJ8xOLEZcj0sXZyV Ht6MM/w4rTEJVri3rnHfA44bD8chc4q51PItu9H3AvTsA9kQa+DAc5KXeb6BxY/HY9SmlmZiIGC7 T4fGdq6JL/ugH8CF19SkqapWsP7xkaaBxPmEvFH2kydcV1bo3QCtlwoYsew93Jz6GPPMIPK2pQK9 +ikmToBaGI5UX+wRRCnGFKWjVNBQsra506CxJA3Adb5GmmUDwHLbQ7G5WRzHEgoGoWoYJ3SdJaRX +jEOapd9EJK36UAbSsh02rn4Y97h45K7PnMH9n8aZG6DN0Ypxn2H+0WNDwrdTJjEu04oPZnQrfHD LEXUFvjBvovROsfHnYsoybCi9laupeTBt/Z6gbzIkMjS9eE3vPUt3i1Xwoe/G2V4c99BVnV4UFU4 PVnijYRQiwrlur50aFsXLC6UtVnDDwKh/PJvKzbHnZklXHa/RfPLkqyI4wqomL4Nb2XyCQNJ8U82 4RckW+Cjlu8dEBAsbjHHpIIhjWKoqSwgywA186nIEx91n9z2jtUeBxfrFYIkw77r42FT4zfLAuOW vRQfU58/i6BUJrwu7UsEr6bESBdVbaTqydKsGIuK+DiEsyJSUI6DXPKnGC37QARYGoPM8xEzHI1V +unMuPjDbC6h561xhL/cS8QkltWth32MX53m+ENNfxEuThKpFD7ERUUP9LCv8Yuxj0m/klZJ2Hu4 E3m4nVEPYIkHpYs5o4PrE+SrDRUUYzgyoAH6QJpz7+WcpB5uB4XoWpXeBEFM4hOhtFratcod/Lsg G8+nJ14sbzOJproARV1dU/ukjjcoYDmFhi6/th1c1KZBY2okoSd87vW6RBxH6uTErImGluVKm5rs 2PuxnDJUbyc0hadUkCRwajY+XWQBCc/a7ypWnEgdnDjCyh3hLI/xwdlKwIlcTH6aYK8t8dY4EEuH vb6GLxbQoVKBSdwKQ6HNdrWelEM1Q34/lUdSsWjqEhBwJgLp7IR6ATo2Xymq1zmipcWl8A83DqTq RqE4IgS4EZ10Dk77UGDtPHXUwUp7UDzuO7dB0JX4i2ksJjtRQ00DrdjxP9qu5ry3foT9mCo0jajE f9a4DrFeYLBx9XFR4odRghEWOHd4ImRSGakMldn1DOECyfMcSZIiouLJAElZLBYCQbEsQ7uYeIpo T6WV19tKCRcSlVnIVyEQMS9KaTxK04unT9MgJ4gyiqUUyh3VMPyiVCmR000nYMnlOld7BFq+EQ6U r0UNpUvGmHlsmOa4aGkb3SKcxDjyXCxZnmWHeuXi8dzg9UmGn48cEedgUaAmvMWljRzDIAovEA7J Kp8YzquO18CoZFUr8V00fSO4tnJNJxNPPjdvWvxp1WDPDREW58jzMyDKkExSkCiZhj4yJ8P8SYtl z1NrEKyTboZ25qkPjS7Cn5fAHy9ydD7JehGatsGxG4ASCVXQYOqU+GFIUgCf42c/6+sF8gKDE3rR 9sJ3f93vcTOhXSvFEYx4bnAHtX2OLMuGk0H9Svj3ZDKW0KuuB08+VrB8f4vJ4o4recmg5E6ELf3P Wc2pFUJPlLF8SdTUuVt3VALSU8Fz5f1E60yqYD1KUSMEIla+CH1pG4RpinVeYG0M1vCRxSEywkKi FlOvxbwoca/rsXYSZBMPmZ9g/ugRLjp6hiTwCSAkGoAaUosFwjiWJJzwDgrfCXSGjb4glGoc568I qA4oZoQhVusCy7ZA74/wg2iCrp5jeuu23Jv5+QyBn1CCEaEXIGhasaYjsrku1uom5sVSdbszCjBr S5zy1KANB1U/W5qNUvYoEE8Swv5XDXDT8XEwTnGazwEnul4gVz0EdeBEWDK2jyIxuOmbGlWv+cds NpNFlKapTPoNs3AH4csJzER308sgvGLAcYmwUucgX1Mogd12BTRKrqDmIxtBCOG7syMvYYQqkrCU LASugZHHBpkm1gx5XAFJUhWF/a9xmmDSdrjRFfBGrGSxqkTxOGDcGPGDb+NUpHcezmY4euVQwp5f n/uIRj3GoYMo4Gnmi0Msy7JFYxAFytFnhYvXzZBUysK8oAZoilL2f/aBhD6AHovVE4zHU0EYt8Rj 8QSKVCapLQrEdYs3aNlGDn4covMTPJnnGJNVGFRSdCBsLXdJ0rIMC9oFdjh1Ce9kPhLiMYBH8+Jz FwfH9QnyQiuEca2DzFF9J0LXuZPSEpmTcG9vbwMT0RBra+Ngm2270BPbEWelRzBage78EUWhw1BC soBl2EsdZJWH5eTjv1k1Y29jRUtkwuVJiGqJJtZQjaVivufFfC7hXUt1QumnhJozOECV54h8UmIj dIGPcDLCae3incUSF06E1Bnjp0mIacjSb4jfna4Q+RmicYi6IkOwwS8zapJpQ5lFKipbGlNjnS8l FOPJxx/L35knqXTQVwXKoiaMGSWdrMQ0tUGajVCvCgR7EzyGhwUrXm6No6BD5rCnVKO96WOBEI/W Lc6KDpWyt6SQQT4IF4mYJQy9ni2m7Iv19K4XyAsuEDbPiBfq6gJxqnAIknc4YS1V2U76XeDipbcZ For9uv14V5aTeQnDNO7G1l7Ofm2z0IaEXjSmhCqscTk/3l2QXBDT6XQDGVmv17I47PVy12d3omXp x3VBU9mLngLSLl51A/w0JUx9LTTe/czHSRHhdF7BiUq8Ns1QFiE+zpfihnUUN5IvGULdCUnxYlSr FWLmY2mEi8UCs/kSKXOTyEd2c4qeFbKG4EsS0PbEGOijwuDRiUFKPbVpgKM4xKooBC83q1w8rjrM m1pUdASevCMjcBVt7esF8iJDEkKl2Ta0HCBWiBDxoVG2C8ng5OMfniQMuS6/zRYQuBGf28FB7X68 KydksV4WA2b1d1n2HSW00NMyMU83ta72ZaFxsdrr4x++pz2t+DFJVKJp5Xko8wKul6EolEG6FxLC YhD0sdhavL9aIMnGiFbAxA/xtxHh/C3Oc4PzClitHSFoMQ03VYexqfD2K8eigyWhYJuIv71QB2L1 iVgZD2s/w4Pa4PGFwZrOy0gRocFP93sc+2t8uA7x+3kAOngQlKj6c0rLYGXLCklY3o2VFn/R5XK9 QF5gcP5mboe9OELQ1IiCGIv1EtMku4Ty3V0oXBycpHbHtj4oJPKQC6Honq2C9m4IZqm89r1sw9FO es1TNN/ggmDuwQXDiU5ErD2RdvMgDi4MLpINJD8gWthHnq8F+r4q15hVMXo/Ec/KmjlRFMAh67Ls 8WbY41/dOTxnhLIqBEGbmxD3zsnELBFEFJ9YY0IUwjiBqUthCPKXjAPFlzEZZ7H2ovXwTgHcpyqn w54Sw1JKHalT7q/OO/FSX7akH7OfrhuCdsO3C0AxZE+LRr34WXK9QF5g8BkkfMDMM3h6UKSdUEXh il3OL3YHcxGbpHOwskN+A3sGnOxS+vWZrm6prvbkePr04CLZ9E12mnLbE0y+Y1Mk2EUMy88eTiwL Z5HKmzFivionkmDwfLQEGfYNFnWFfhRisVqocZDjYnaxxH7QwQ96vJ+nqLsQy+VCGopv3eTCJUgx gCdWfFqKZShaCxixhkP8GCtoDsSz/IFxsfZTRVP3hIOoHhcHT6J8OAy2XKRLigmbf23HVw+yrhfI Cw0HK9Oi7lkajbBa5cJZWBW5YpKGRPtZSfkubJydb9JBRWNXKj4GTVlIDsHww75efuLwHjb5t2Vh Tnx7wtjX7/58O2y33paSLQ/e8k1ksKpGyzgmtARRrktkFVG5S9zeDzmzEadjNJ1BUdf403mJIpxg dn+FLPbx6rjF68eh2LuhLaR5KUhjQ8sMJtjs2ygagJRggjWjOEFjHByEYxwFNbpGZb0VPY5vfVwv kBcYPNjLlk3BRmRDiaOiPhZhHjZcsV10m3TbnZwfE9OkkPgI68bBJ7MV9rMEBwmhJb50eskUfNYp xElu5YSsgspu+GX/Xq1Wm3COw7Ic7aLi66yG1+YkET56I+rxDPL3Ix+/GHlysjnlSv1FalaPSNkF fvzqFKuyBg5STHxHLPlYUKA7PPd2LlyGeCJ8EfAkIo3Yw3KVS9k4R4Rl4+NMZIJWME6gXHQJurZy q9/muF4gLzCYCCpERAUqXIIMpT3BUEb7GhwK7KN+biSlTlvOZT5iFw/52bf2UlCuKgpY6bKSppdD NZUSaiVuN7lotEqHmHmMK85U6sOiapDcoRXWQuEJ6yfOngR5J9JAbBqM0pGS4wb7BJ6C5KbwWplr dG4Dp8ypk4LRKBVwJM1wSH8d+aH0O0YjZ1Au8dGHGdYFAY1qkc2EnwQ0fi6eZvCKGjV1dIMxPqhp muQInIXKAJop6KmhsHz9mHirb3NcL5AXGKyQVL0rpkHqiupiVefiYCvVJLGgdmVx8JSwOcOl9xh2 fQGXu632JuibbppNP4TDhkKCdGU+0qgpJ2dQKM1DnkwKuJAQZlD4kIklGqpa5eEp57sBirXCWUyr Tq7klOyeItT9smLbTddtKl+0i7D/7lyK+xB3RXZjtml4clBogdAWpW4T60l+R4hZvsaIXPC9A5wu CfJUfopqBVtdla2mgWqYffv8o+sF8gJDJHgcFx+3IW73HkYO+dgGbe0gzIil0sacjs8OpG2YY/sk /GPptLvcc+l9SKNMfcspZEcZICa8nORVo8IP7Maz3MlQR7gihJmsOZG18kU+SlnVmO7tCcivKnPk OfFjjixSlqO5ENicHI/Hm4lvS8X25NNTUXFnuw1MLiyaYAqauG+xLunXGCKnsJyX4MPTAo+Nj06s 8cgdfzmlPey4XiAvMNQMVN2w/m1RoR5HOGDXOFC8lS6Oz88w7emw67jLBcLJ9ywNJ3pDsix878OH ONw7UhagABZ9nM2fIPR8HB0d4MHHD9AeAEVeIE1GImy3qVxJZ7tB5DIPYZJMw2M1POXCZCORE//w 8HDTTJxMJnKt/DcXj13U/MPP8XptfiPXTTYyxe1YUGg7rHsHJ62L/SjG6arG2qdlNYsRKsC91XJ8 +cb1AnmBYSnJjPlP+wDFhcHfTkLc9lmJUgYhq1AsR24bVTvfLxD44pmNw8+ynaAqIl3Ok4B2chUy QjGqGnHsYhSPURA0WNJgJ0AajzAe7WP26AnSZCwkrdUyx3QywXy5xNFNF/fuf4zX37iDMFZVFVsm tkUGV2ykNanfXTyWD8+v86Sy/R0uHC6gqigVEEnISFXCzzLM8hanTYmSQnw0BNo4536+cMK3Oa4X yIsMeabaqGL/o3CAe6sc070AY0+NbYRZN5Rqnx6cZJxIX3Y4hG70Oe6+dVOIT9po1OpY7CQ49qgn AmQHt1S/NnCw/5qGSdNXDjBaMyzrcPfWLaSZj7/4yY+GwkIjemC7Ciy8Rm/gtlg8GT/P04Knhl1E fJ0tKXOxMMQjLiyIY3FNDkcOir7Gq9MR/rAwot9LoLnVo3p5l4aO6wXyAsOqYYjOhnjreXhsAkyb GG+gQBp4qOoarp+KhgflONkAsHgoS1vdaPdecny93BS0fQuBzw+vLcu1TEaBzA95AE8j7vIWQsLq Fa+Q3WtO8nWxRBDxpFANr9qspYDAPZysxZzQEiks6GnBheAOJWKRVTVGPscFYO3m+D42ubdlY1lc bYO6yOkFipDVviBGVrtIA2D+sinRfs64XiAvOFQWiTBzfkT/8RDvzg3yoMPPjgMxvqSIAiHlbCvk pcbqNjThZLI79HK5lH/bP3bhWEE6CyuZTPYkOgn8SEIrWQwB4/8Ws/M5RqPRQNvVcEn+RytjlqHp CVkaZFko10N504a0V8NJzuvQErPNKXb7JN3Qv+FJws8xJ7GCd+y3cPD7mNTLwm5ZiUtFwpReiI8M 8MG8xoo4k5f9yHhqXC+QFx67iveKqK28AB+1LsoLYNI7uJ36CKsCidsjjRLpl1CtxKNr7FDl4oTb ZRfqO5NXQR47VTtoFaCqIUS6isCDnDja72iGhTYZa7mVUqFkE0ptiGVfH1jlKwRhgGysOQ872InP UrKiiNn3IH2XC5eTn78PTwx3R/7U4r+kFGzF7AIXEfOXNkDoR/LzCA0ZRbRacPCo9fHBRYUTQxmi QBACnyez87KN6wXyFcaOxuCgPKL+FR8bFw/7CH86b3AQTsRX8M2ohVcarOjL7kWgXjv1oGy8L3Dz Yee2HG4VcNR/S4OQuUuyReTKS4bTRtxnWRF7ut/CXILSQZQZHbrs4bDz2/Ky78cbVUj7vrYLX1XV oAmsJ4bNUXitzDVauhaHPNVI3KJ5EnAR7+G3Z0ssukYsDcTjUAH4X9lN+Jse1wvkCodK/nDC0nTS R+t5eNj0mM0rjI2HaV4h79doRaggQBvrxGXXuxmAe1RwZ4Nxt4HMk+QSIO8Z+CxOXmHwDTmOhbxY KLsFLFrs1S7shHkFiwZcgPZ9LbjR930JnbhIbL7EPxcXF1JFE7YgKb81MA8S3DM97q3XWInPvchr K4VY/nq5ex7PGtcL5AqH9VThkEkzfK7wQvyx6fCfbh/i1S5BaWjy4orXB+muxtlpxlEEgTt0rPCL 1XotX1suFhiNR4gpNz8MO6FtGGQrYxbxy0XC6hMXgHW9smGTzS04mPDzZ/N1dnHYJN0Z9ID5estL sVx7qkt2phGF95k/wm/mDWYUVBDHqa2VtApkb43UvkvjeoFc9djM3wFWPpwq522PP6zX+EXmYx1E +N/nBUrHw9t9j79MPLRBh9VFKY62NCJq+0y+n1wPTrKDo33BQp2fnCOk1yBttSkLykk8FA3shJSE u3fFMq4kMJIatTAiqkBVRd8PNbdpakUFWMvutkeURChRCaiwZoXMUK/XB9EtZVugLmr4DnMpB01p JO+o/BDv5jUu+kQg6rQaIOzlMivjaRf678a4XiBXPZ6aBQMIHW3v44NVgwPfw6tRiSQq8aQ7xPtd gVfiCPvtCnHqIImng/q4jmqQ87HhzSgb42J+IU5XTscwpt+KzfmD+rls2NTyJdScHoAJSrL7DEvO 7KJTApSTOxB2IMvQVbFEQSZf1aIKM8xWrficR2EmEHR2u19NUtwKAswNtXdb7KHDuqulU98Zusyy qkefQ4Znl0vX38XFwXG9QL7msZHxcRrUjo/fzQMsRy5+fhjjeNHgT7WLP65q/K3E8wvhhLh+qMJr O07DNvQp2xJRFKIsKwEsMv2lvx+RgVQ9pGIj4SPsRVCgLU0ytJ5B0rQoCgeLeA+/Lxus2xCm6ZHE KVAVGAcZxqmgC/Fw3eCspca9GuQQAEZrgj8ZI65NMxrt9MAvsgzHoY89t8HN3sUT8WmnMgrzEnwv xvUC+YaG4I4cYN0Z/GHt4Eme4xeHKaKixQeFg191Hf5yPEbQLpGwnDocIcwr2GNgQm271gyxokTz ja5p4fpUWOzR1j6WizXSJKGmCcJUMV2FifFJ1eHEi3C66lF1tuQKzKkmAheP+VdNsDytufk1hYGI 1OmQXNPudE5hbIf6th1+tcjxDyMPB0GH1ynisGzwiArxYiLz/Vgh1wvkax6bipPoNGkZmG63o5hS naV4fK8aqncQ5Rrh7TDDm3EuyvBuF4hAWpQRju4j9iOYvhG5f9uXcIMBpi6GoMBkOsZs9kTRukT3 Nj1+X4e4X/AEUz8NKhBuvWDUhUkvUxeCdlkGdchNgjN8dQjJRNibeYooofQInBp3I0+cpUDXRIev GYS1WQrevPt3a+FcL5BvYFhTexKLuDuTjfg4p5W2i5teg5+MyCIM8d6yxW8qYIYIb9YtRj4XB2mr dLOlzlMriF4Oniq7JjosBYt/Ie2dDw+lDEt9LsqMnpUVapfKIWquSorvpS7OZQPCS1PYeWo+2/4M f6PaCfF+3uN4KrRK7DUxJljiHK40IsUQjdcnpQplFH7X6rzXC+QbGuqPpJ1k7sozOtEWPX7fubjp F/jlARNxD39cufiorGAiB7+AD59y6bTWFtNhhZ9YzrmFn1vIfFkWCjMZWIyz2QXgJQidVBbY0Mu8 pB31VYbfOjhrW/z3pYe/H0VIUYgQtddStZFK6jzhxA9XdYmfog9/F8b1AvkGhsTysp9Slo1sOcqg 6EwlVulJG4FmUz8edXhSGzwxET4pDYrQx5s9Q5cWEW2b6eZUq0eHBRJaGVMr5hBHieQlonqSJFi1 LvJZg85lPnLVPmG9nEyP6w7/56IWGu6c1mo9k/QGkyDEilRjWrqxHC2h1r/TEEuO4svels/8nHz+ OxeJXsUQzfPh997qX7lOixIefjXv8Hri4U5MBGyDkxKY1S5+bWLMTYW/HAFRSEV24PysQF7OcfPm bazPSzR+gSwaY100cKY9Tk5PsH84xXicYUVzGhr5eFyknJ70PHze3We1zfLAd1FTaputZp87X2eO IWIPDk47H6c0ggXLvS3CXpuNZQexTxMhOzFs3uZl3zLd/Js/QWRP3Ljj6ngWV4yS+C+DpMu3Nbaq Th580nOdBqceZTR73O46vH0wQdus8HGrJpLvNj5OZi1+OU0w9WqEfQFvf4pioXlIOV9hcvsAxarE rdu3EIcZ0NC3kPea9peEm2yhMM9+Khye7PLScLSTv2dxQfd9ynvSMsChJjEMgpZ+HAydqGbfw4tC BllopQjAcnFpoZdoxCGqg8fFulO4eNYG+r1cIHY32Ag9it1WgMJUcosEeiDx9yBR8zLflW9ocKcl ArztYvgtORU1zuDg7MladnsuDqdljmEwd1v8r3mBvx4Dr/54MgjjsGsNeAVvdoXDOyOU3QL7N1Ox BqB5Z1kS2asgSGvM+ryNW7grQzdegkKH3HJPdv1uOAHpI38cubgVB6icAAvT4Gbo4kbooy1rWZCP 8xxn9EbxIqyqUNUPOyNy64SViUfJDh/mZZ4OV3eCiObqdgjmp+2EM01eAJtWYoksjq5q6PjvfVAB nYw+KpbTb5xmMrp7U1mQSoRA45Wy63p9LLL+HxYVxn2BvShGIJLlqrnFxqJIEVGL1+Nj9cHbfsye yYlRX3SZ4gOA8BmDcBOeGhM02PMpD0oVeyPGmOdGjN2EJTnrXZwua0zCGH0T4Dc561QNYrfHcdJi nEa429SI2wrLuMInfYTzhiJ5+szJF7F8QiKN1X1qd/Z8HxfIIC9vR0PDGEsmqhsxj898YEydpLpG 7QRo6RnBwictgAdA9Mu7l1z92OQjQxKvPG1dOJe1Zrm392JT5vgtkiyhVwHK1he8FU+aluom/H6P 7ugq+clTJHU93Igd3C/YgxEElYQ/qkGli5FwdIZX087g7w4cjMwKnheLCQ6f0W+WDp60dKZyxUaN +MQeIc5ZYRsMcXiNee/iXq4syxgebkc9DpHjp14p/Zqll0iu8nBZAXECQ3Rxy1NF5Y9cGOnCi5CD o0LazJm0ccmfIPY76mHyDSUw/tc/ATz0rpHKRt4GaPsCP7s1wWzd4uNFIUy87y6U7WqGfdjbe7B9 +NuJwBCM7rWONPySkFZpnCiUZnPEQJMl1Y7RTAfMzxdI0wSOV+EXYYCw7vBHLigvFGdbwYcNIRTv Pj+X1UscensIvbFUoRqPLrMu7hWak1y65uGanh4SnPEaewefFA2KJEVCZynax7dAzQgiToAmx93M R8SDoyqRJhHOWw+nJVC0Go0Y0fmiDhgXhoNm+PibzF+/5jKvxsmakzCYoKmii8ezBZ40DUo/3Jic eMMJK53b6/HMwXt10vb4r48MbiQJ7oxc3PYMmuUSkzSTk6RzeSLQaZbyPFQ4YQPR4JXIQ0CjmabG oqODkw+fwMXAQYhaDEjfPMjg0omp5+caXNQeTk0g3f8vc7JLUcB1UPY+zqoOroTW1P2lKy3dp/Rk KFcdfjR24DcrTKMUldugHjk4NcBHeYSzkqeTgcvmpuQxzGf0/b+pSONrXiAMmniDWMVgjlbDaX3c E6lJenyzjqNeG7x5qub99V7Rd3mo2qCHpRMgL+kfXmI/oD3zBAdth1tRhLg3ErIGYQQ6SLPD6PQe Dp0Se84adxsXVeQDrsEo8OFJT8ZFV3dw8kr6Mn3TYG4MHsRHOKlF3u1T6NzPe+o8kW6EDo4iYFm3 eNy6WHVUhPThg1ZwLD/3+HXeYhrsob9ggSJCYRqpkMmO6XQ4iFwcstjTOHhYGFn8wrgUNMC2ZPwd bhQqSE8nPmPaoVGmwZe6uA7Zy1bW/no8a2wDMC271ohEEIGsRa8E9l2DWxErSh6mIUBJOEb0vWOE P86JSXu2aUL+O8OVRtTWeXivlyXTekSTGE25xDjeB1OFmkXa3hcIvqgP8xv47KT6xKjg08UWATY6 Ph6ZXq4PiOR69VoczM2WHcnM44zsw40gHzvv1qTew1kFnFa2LG3LXQrv56RiEZtJD3OjTkKw70Un /XohXOUdZIeddNYzAKdFDz/vxXJ5Gno4ToBbfoCJ52LRNPioNsirGjc8XxqTgVuLPUHkxTBFCZLK jRvhybJG3jjisx51vVil0QC0h0He9lg4TNiVOfi8K/yiT3nLfnne73n567aTo5urgzSMkNflsDiu Nka/hpp8b6i+yjsRhiArhy5wahyc1iHecRspJTOk7fuJhEAfty3+XJf4ie8jWS35/v/RAAAGcUlE QVRxfOcQ/Zj+6T3q3ID2mv84ChEnhLUwr6F8vYO2MuiDBO8+KXC/DbCQkvI3/xvrYaLdfvFLcVwY qXpdL5Dr8enpMsDQbdVL9acktCUCmMHsoEmlQPcGjHKfuDH+ue5wM56iLlqMKdvTeljPFxjTmLQj zJBaXg6qVYneCRB0BCEavHk8wZMnTPiv9nEoC+Xzzh6LOlYYfdO2iCk1VF0Wu7iKcX2CfA/GAOuS 8fQEYQhid1x92XYBMTwia/B+F+PBUslRqdPheHSA1OvgNBX25gZRmePoxr5UyeDFcpos6x5PqFv0 Jfq9tlTMXFM59FqY0eyiQ+N48FtCWSrUVE9p2TxVK4TOwl86lqaZgxDioj1/5h6F4SlHZy5Vrbyq cb1Arsdm4XDCLiWJDtC3qqE76Uu8OeZp0mAUxKBmSd/XKFjbok/bl6hu+cNpYyiK5/ZyGu33HW6M UnxSNqhaequ3qJwQY3HtrVG5DmqPRQFWONkL0bLAUDOAR2YkPRUpdzQ4aV3luF4g12NnaJndcyqh 3va9jwVC/HbV4g7jtZMFbvoObh+EmFfkzVdfOCdWDa8OgdNjRIdgz8Gh72IK4L2TR4hGe7gbNxhH BnkT4FEZ4bSLiUFBII1NLguq21NU25EQ0fQtao/4sFLqYEZOlquFMF0vkOuxszw0Umtd0n3J6VBo NmEun1Qe/ubmAV4LWtSLcxwUOWLsIx++V+Ef3c6/FfCoFDE12XkrBX4y9dGsF5hEIWazBbL9Y5Q3 JriZRgjbHKYl03IlhqDvzUtErocs8AXqckKrBzi4nQWYmha54+LDusBhMkHZ9fhkoB1vEeUD6PJ6 gVyPqxgbtgrxKgM3hINUXur4vjMvcc/pcTvZw6tHPf5zbfCnIsCHdJHyHdnFeTJM41CweI8LI6J5 fdvi0Ovxg8RD3JXoowBtQfhRgMLz8WC1EFuEH8QuImoVOQn2Afz9IZeDjrcEsm8VJQ3c0EPjRHg1 DZC3Nd5d003Lercwd2mk39bRHmLAPr/IuD5Brsfnj14714suwIXj4Wxp8DBvsFetcedggjvHCbp1 jbEf4X5t8OFihldHI/zjvo975xfwwxRvTYDYNdq74CQOFNBYzM7w9zcZaLVwOwo9eHQfUui9FKx3 cGnW453dfuYkTYGxB0y8DsHYxSTvcJoDc4rmkaMiRQC+z4tzjK8XyPX43NENquwBqHtFNISPky7E uXuEP81bJMUKd6IedZzgw5nBzJ2gyFtMwg4/CFqkkx4RcVgdd32it4GqbRBRdGK9RFITk8c5zG6/ A8PkXwhbn9aCF5Hv1sPcdXBmWtyhx0lfY5QDb5c1fn68h/st8NuLEgskckoRMfCiGL/NAhHb3QGe /nRHcheq9m38+2XFL/Yvwb35Zu5fq+EXkcM9d2Z+zqARvFSI2iTIqWm6yoVvQnpv0/X43RMHf310 hH2HKlpAQX46pU8dD0lImVJgvL+HgtpcYSTW1F5dwE9DNHTAJQw+8MTzZBP+uR5B8aIUv08eDPP3 1hczUj/ypMBwh7YM+wneW3V4aGopHyuFYAOdlTBMQ8htr/4SI3Yg+PlMxPoBWhzS43sDZnFe2ol4 Pb7p++fIf7oLb8lOUuIll4cuW8JA1JKvO6jbrzxgblrcDXxhTPZeiM5jRkAHLoOA2l8BbagTbRAS 0RpH6LsWseuhroxaPmSRuubuTGZxOhHj3w5lWSOkeHeWCL+Fjcw7Xotb0wCPFgYtra6ItafDV9fj SVlj4ZNDT608Zk6E6rCp6sAVMp+CIMlhYl9Gjjy7LuQ43cjUbzuW386/Lz/kl3FcvsZv6z69XPfP HfIMpTL4OLt3gmyUoqGRUJLhwf0H2J/uoQt71KYSqaLVci06XpUp4Ec05nGwIATm+Jgs983SUB77 IH9BtABntu+gWBUw80aUXLyIZKwG5XqBwzSCG1FnkmLbreDOXqeRkN9j1gX4aGlwv6yx9KhTjA15 T7W/HPjVYo4//+bXlA+QpMgQIUlivb2xw5Fjo64rOf6Hxq59b/w7+Pe/p/vnyEwi7KMX0lU0n2H/ 8ACziwuEjo+TR6e4des2Hj1+jFuv3kRbd6h4CoQhVvlauPqjSSKuV37w543NCH/n+WwhVtYxQ7JW 7enEikE8IBssaXWXjdELQUuV7nl6KP6ZlhNGHIj7UDlKhQHWbiIVN556+qy0GZpVJf4/xPUpj7Yg bHcAAAAASUVORK5CYII=