20231109 15215300 1.0 FALSE Statewide_LandslideHazSusceptibility_20231109.tif 002 355669.000109 785629.000109 4115998.999969 4500000.999969 1 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_UTM_Zone_17N <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/10.8'><WKT>PROJCS[&quot;NAD_1983_UTM_Zone_17N&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,500000.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-81.0],PARAMETER[&quot;Scale_Factor&quot;,0.9996],PARAMETER[&quot;Latitude_Of_Origin&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,26917]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>450445547.3910538</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>26917</WKID><LatestWKID>26917</LatestWKID></ProjectedCoordinateSystem> 32 FALSE LZW 1 TIFF TRUE continuous floating point 3.4e+38 20240322 15535600 20240322 15535600 150000000 5000 ItemDescription Version 6.2 (Build 9200) ; Esri ArcGIS 10.8.1.14362 Statewide_LandslideHazSusceptibility_20231109.tif 1 -82.706815 -77.624313 40.650866 37.146782 Probabilistic slope failure occurrence models were created using LiDAR-derived terrain variables and user-defined landslide points. These models were created as part of a multihazard risk assessment for West Virginia through FEMA's Hazard Mitigation Grant Program (HMGP). Statewide models created for each Major Land Resource Area (MLRA) have been published to both the West Virginia Flood Tool and West Virginia Landslide Tool and are accessible to the public. <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Slope failure probabilistic models were generated using random forest machine learning methods, user-identified landslide incidence points, and LiDAR digital terrain variables. The nature of the West Virginia landscape and the LiDAR imagery limited mapping of user-identified landslide incidence points to landslides at least 33 feet wide. This approach undercounts small, shallow landslides and slope failures that may have been mitigated or removed by human agents. Models were trained and validated in R using both presence data (user-defined points) and absence data (randomly generated pseudo absence non-landslide points). For a detailed description of methods and data layers, the following open-source articles can be accessed:</SPAN></P><P><SPAN>Maxwell, A.E.; Sharma, M.; Kite, J.S.; Donaldson, K.A.; Thompson, J.A.; Bell, M.L.; Maynard, S.M. Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt. Remote Sens. 2020, 12, 486. https://doi.org/10.3390/rs12030486</SPAN></P><P><SPAN>Maxwell, A.E.; Sharma, M.; Kite, J.S.; Donaldson, K.A.; Maynard, S.M.; Malay, C.M. Assessing the Generalization of Machine Learning-Based Slope Failure Prediction to New Geographic Extents. ISPRS Int. J. Geo-Inf. 2021, 10, 293. https://doi.org/10.3390/ijgi10050293</SPAN></P><P><SPAN>These models, which exist as a landslide susceptibility index from 0 to 1 in raster grid form, have been classified using the following classes:</SPAN></P><P><SPAN>0.0-0.3 - low susceptibility</SPAN></P><P><SPAN>0.3-0.7 - moderate susceptibility</SPAN></P><P><SPAN>0.7-1.0 - high susceptibility</SPAN></P></DIV></DIV></DIV> WVGISTC (West Virginia GIS Technical Center), WVU (West Virginia University) Landslide Susceptibility Grid West Virginia Landslide Susceptibility <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Landslide susceptibility classifications used in this risk assessment are based on landslide locations mapped using LiDAR data. The mapping procedure and data used makes detecting landslides difficult in some areas, particularly areas where landslide material is removed shortly after the failure occurs (i.e. roads, populated areas, stream banks). The nature of the West Virginia landscape and the LiDAR imagery limited mapping of user-identified landslide incidence points to landslides at least 33 feet wide. This approach undercounts small, shallow landslides and slope failures that may have been mitigated or removed by human agents. LiDAR data processing also makes detecting landslides difficult in highly developed areas (i.e. housing developments, industrial parks). LiDAR-mapped landslide locations and landslide susceptibility maps derived from that data are inherently biased against these areas. </SPAN></P><P><SPAN>Landslide risk estimates are approximate. Landslide risk assessments conducted by licensed engineers and qualified geologists may vary from estimated risk and the associated costs. Landslide susceptibility estimates calculated in this dataset are based on the best available asset data, which may be inaccurate. Future studies, landscape alteration, mapping efforts, and asset data may render these results inaccurate. </SPAN></P><P><SPAN>This study is for informational purposes related to general emergency services planning. It has not been prepared for, and may not be suitable for legal, design, engineering, or site-preparation purposes. This susceptibility grid cannot substitute for site-specific investigations by qualified practitioners. Landslide risk is complex and continually changing. Although other existing studies or reports may provide more precise and comprehensive information, detailed original site investigations are normally an essential best practice for public safety, sustainability, and financial viability. These other data sources may give results that differ from those in this dataset.</SPAN></P></DIV></DIV></DIV> Raster Dataset dataset EPSG 6.13(3.0.1) 2 1 0 355669.000109 4115998.999969 355669.000109 4500000.999969 785629.000109 4500000.999969 785629.000109 4115998.999969 570649.000109 4307999.999969 214980 2.000000 192001 2.000000 Band_1 0.999601 0.000000 32 20240322 <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Landslide susceptibility classifications used in this risk assessment are based on landslide locations mapped using LiDAR data. The mapping procedure and data used makes detecting landslides difficult in some areas, particularly areas where landslide material is removed shortly after the failure occurs (i.e. roads, populated areas, stream banks). The nature of the West Virginia landscape and the LiDAR imagery limited mapping of user-identified landslide incidence points to landslides at least 33 feet wide. This approach undercounts small, shallow landslides and slope failures that may have been mitigated or removed by human agents. LiDAR data processing also makes detecting landslides difficult in highly developed areas (i.e. housing developments, industrial parks). LiDAR-mapped landslide locations and landslide susceptibility maps derived from that data are inherently biased against these areas. </SPAN></P><P><SPAN /></P><P><SPAN>Landslide risk estimates are approximate. Landslide risk assessments conducted by licensed engineers and qualified geologists may vary from estimated risk and the associated costs. Landslide susceptibility estimates calculated in this dataset are based on the best available asset data, which may be inaccurate. Future studies, landscape alteration, mapping efforts, and asset data may render these results inaccurate. </SPAN></P><P><SPAN /></P><P><SPAN>This study is for informational purposes related to general emergency services planning. It has not been prepared for, and may not be suitable for legal, design, engineering, or site-preparation purposes. This susceptibility grid cannot substitute for site-specific investigations by qualified practitioners. Landslide risk is complex and continually changing. Although other existing studies or reports may provide more precise and comprehensive information, detailed original site investigations are normally an essential best practice for public safety, sustainability, and financial viability. These other data sources may give results that differ from those in this dataset.</SPAN></P></DIV></DIV></DIV> Landslide susceptibility classifications used in this risk assessment are based on landslide locations mapped using LiDAR data. The mapping procedure and data used makes detecting landslides difficult in some areas, particularly areas where landslide material is removed shortly after the failure occurs (i.e. roads, populated areas, stream banks). The nature of the West Virginia landscape and the LiDAR imagery limited mapping of user-identified landslide incidence points to landslides at least 33 feet wide. This approach undercounts small, shallow landslides and slope failures that may have been mitigated or removed by human agents. LiDAR data processing also makes detecting landslides difficult in highly developed areas (i.e. housing developments, industrial parks). LiDAR-mapped landslide locations and landslide susceptibility maps derived from that data are inherently biased against these areas. Landslide risk estimates are approximate. Landslide risk assessments conducted by licensed engineers and qualified geologists may vary from estimated risk and the associated costs. Landslide susceptibility estimates calculated in this dataset are based on the best available asset data, which may be inaccurate. Future studies, landscape alteration, mapping efforts, and asset data may render these results inaccurate. This study is for informational purposes related to general emergency services planning. It has not been prepared for, and may not be suitable for legal, design, engineering, or site-preparation purposes. This susceptibility grid cannot substitute for site-specific investigations by qualified practitioners. Landslide risk is complex and continually changing. Although other existing studies or reports may provide more precise and comprehensive information, detailed original site investigations are normally an essential best practice for public safety, sustainability, and financial viability. These other data sources may give results that differ from those in this dataset. /9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEBQoHBwYIDAoMDAsK CwsNDhIQDQ4RDgsLEBYQERMUFRUVDA8XGBYUGBIUFRT/2wBDAQMEBAUEBQkFBQkUDQsNFBQUFBQU FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBT/wAARCACFANIDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDw3SfF n/CNaNqi6TY3SazdL8hyrqvJIODz1bnt09K9P8C6XqWjeEbG31KV59TXeJZJJjMSS7HO/wDi4P07 V5n4Z8KnW9aWCVzJDBua4Yko3XBKkZyc468n1FereIPEMHh/RZrqQnKDbGo5JY9OMjjOM1+RcVyo e1jhqCvUk7y6vsl6WvofsPDFDEYj3n8PwxXq7t27+fqcx8TPE0mi6T9hhcpeXSYyd2VTJBZSD97I 6e9eYeEdPjvdat11HUPsUBYs0xkC4IGfvHjrUerahNqt9NeXCp50zb5NgIVfoPSrXhnw1e+KbiRL VUKpt3ySNgIpJwf0xxXLQw8cHhnGUrPqz9+w2Dp4DCSjKVnbVntI8SWV5qyWNnqMFzNIjSL5cyuM DsSD174rWttsKlt3Q9QMDNcre3ml/Djw9PNbxkpGVHkxkGR2PCliT2z17CuJm+Nd1c2EywWCWk/l swkkZpPmAzwNoBHQc8c14mHyTF5h7+Eg+RO120v6+R+RZhmuX4Kq6fM+/dv7tEbvxG+IVlbrLp0d kl7qm5oN9zCXSLKhuO5yD1XgHrXhYmnjs0UySNMoAZy2VIY4+p5yfwrU1K41DUpYZ72WR72UgySz MSVzgA89Pr2AFaVnoKy3sVstwrum6QnOG4GAPcc8Hvk1+0ZTltLKsOqUPie+r1Z+RZhjqmY1nUlo ltotDL0rVr3Q7hRpt5NHdTR4mliIYtkYB5BIxkfdxXq/wPn1A6Pqttc3M7xQzKsEbt9xTuJK85wT zzXjek6at0t1dTEzzef5MVusW+Q9SAByT8pHToM9a+lfA3hz/hHfD1pbMsAmALSPEm3eSxYZJGTg Njn0r53i6th6WBcGlzza6a6df0+Z7XDVGtUxfOm+SCfpr/V/kbW0B42IwsYz6kf4V4T46+zL48js tNhjeB2jSQKcrECNz4OTySAOOea7Lxp8W7XSZrrTdMZTqELMst1KnyQsvBUA8lgdpzgr1HXiuf8A +EaOj6XpN9qVgsF7czzTNJDhlZSoZGKqSBy3bBrxeGcHWy+oq+JvH2nuxXfre3bT8T1s9xVLGwdG hq6fvSfbpa/z/Ar6k/kaxcReUjfZ9luFnBOcAEkD6njJ7Z4rl/E2qr5skNpd/aniYYK8iP6YJAHH OCelNvtUnlutVnjmkuLa9mForxxEqh2gHcxPUkdeKTT/AAfLqsflaWWuXs7dpZoVOW2rwMKOpy3T rX6hKcaUXKo7RXU+DtKo+WCuz6Xib7dpe0qzLNb4xnkbl6dffrXhsvhnWtG1C2hMU1vcO6mN0cME JbAYspOMEZ9a9l8G2uo6f4Rs11V1e/EY3soAAUD5R7YXFaMN3HdWcclrIskcnKuhGGHPIP4V/PNP FSwFWpThaUb2v067H9GZXmVXC0bqGkkr36aGJ4kW7/4Qe6tyZbq5EMat5eXZnBUE+p5BNcv8LfEx kaXSZZppWd91uScrGoHzLyeOfQV6An2yK8lSWKEWPkqY2HEm8/ezXAeItPPg3xnp19beV5d/IVKM hCxZ2qx4Pvmrw0o1KU8NKzclzL17eR24CtHE0KuDlHV3a6ao9E86dllAY5x2Jz+FeDaheXfhu1uN Bklm8mdg3kQyLIoIbOFIYgD17knkGveRE0MLEMzF23c9R6V5D8R/DotdYmuRpch0oRxyyzbmWJJM 5LZ9S2OnGcV9BwlXpQxE6Va1mk1futvmfmXEVGpOjGpSvdaadmtdjjrrVZZF82F5IBJE0UUy5jZX BBDjv0zyOeOlbXgLxFcaLryLNfmO1kO69lkYSPORyPU9Sc9+eprntdnOnb9OaBY3QqriUnKt15Ve vHPX+dW/h3odx4i1eOG08sQQoJGmnQmPbkA4QH5jluhYHqa/V8f9Wlg6k69uTl1fl0/4B+d4N1li 4RpX5k9v8/1PoeZjdqG+ZYxz97rxxXL+LtDi8VeHL21tzHe39sS0Cx3O3yrgA7ckMACMng8etdRu 2hVO1gq8Me1cRoerrpvjjW9PlkitrK5nLoWB3PKQoABz0OT/AI1/P+E54VHWov3oarzsz90jgljq NWEtfd2/M8IvIb3w/fPY6iVsXRzI0EoEe4f3sMemOjDgjpmur0Fns9OOqWd3dR3DtiEebhpD1GAe uTg4Pt617y3/AB8MHfBXv2q5cXjmMRIw2twx6H/9VfeS4254clTD3fX3tH8rH59DhVU5uUa2npr+ ZR0W+nbR7Fr0TC8MEZmHzff2jd/D65orRjnYIo2y9OxOKK+Cli1Jt+yj9x9WsHZJe0l95nTyaZ4T 0tmuHS1iQ4aYoFMjAY3bVA3EgDoM15Hrmq6l40vhJBavLBCSI0tYWdVDHqSATzjPNemaBayxtJrO tTk3M0R8yKVRHFapkNgDkkg9ye9YOsfFi1htZxpNm0lwSU82UBUAzjdhSd35jtXo4WVRVpVIR9pN vWV7Jfh/Xax91lVL6rPlw1LnktL/AGV+H431POdY0O80drd7yEwfaIfNjV+CVBI5HVTkHrXrGh2+ meB/CUV66K0kyK2ZAokkZudobgkDPH41g+B/Bt3rlwutatNM4LLNCWIJm5zknJwMjpjnNdt4k8M2 /iWOwjuJnSK2lMrKq8tlSMZ7Y69668RiqFSrChiZe4vit+RHEWPrTofV8PZzW/a//APCvE0t5qXi KTUZWhV2cCWJMjkDouTk4AXIPXnkcCoGvn+wzGOLE2drkkHJJO4kdhxjHtXsv/CrdDm85pIpGdju E0j7nTnopxwD0PqOK59/g3FZXSSvqm2wgj82eTYwYkffG3ccApn5s8H+E1+g4XifK3H2cW4qOiTW /krXPwevkOYKXtGk+Z6tP/M4CxkvJJoxeNHIm4GR0UAZHA5OOcGs+8s7i3kurmCSdpnbyhHHzukY 4AA9cYHvXd+dpnjDVotL0xIbHToVISeZVjaUjhiWGcrxnJAPPQVv+B7Own1L+zrXSUvrS3Yzy31y UDQuQdmIzyN2GxtJGByRXq4rNPquGliJU3dK/K2lZdL379tzz6GXvEVlRU003a+ru/LT8djhvCum 3Fnrmn2TadNY6tIUCS3O4CMkf65YgFw+QfvMRg425ru/HXjWXS5jo9lI0d9EgFzI8RBIZQQUOcHg nr+da/jyzuL6Gwisb22h1KK486GGa4EUshCkAR56t1x06VkaT8MYbe4bUdSummu5Zd7xo2I+AAuT gEn/AAr4r+0cDi3TzHH2Vr2hu2++vTstrn1CwOLwqngcHre3v7WX+f42OB+FHgW51LWEvLl/Ngt5 jMZmXersCMRkEA+pySTXo3jnxAbeRrBYDGLWBphIcbHLKVUexBB6GtnXtU/4RrTYbawhRZefKtYY iMR/xFONuQegbAJNebalPca7rGoiS5ae9i2O9n5ISTy1bCI3YHG8kgEHtxjPp5e8TnGOjmVWHLSi moL9fz/Q4cXGjleElgacr1G05P8AQw7Dw1eXkcUSObI4kuHk8sHBY7QCOhyBu6d8561ufBvRdRs/ EGp3rx50+SNoPtKyDAcMDtHOc4x2p9vr0sFiVjSFru4ykIkbyxz93ap+8cn6nHavQ/BejzaPoscV wRGpZZNnlGNwxUbgwwOcivQ4oxSw+XSpvedlr662OXIcL7fGRmvs3f4aHUSMv2WQE8+W3Xj+E141 8J4ZT4oZkMiots/zYynVSAffrXscsglgCAZDgq3YgYrxnQ/GFv4f8VXMVnbvHpc5VEtSwzvyFzuO SccnGe9fk+X80qFaEFdtf18z95y+M54bEU6au2l/XqevrmaaQOx2LjBU4B71V163SbSJ/nhjVRvD XSLKuRkjhzjOcc+9T4UKv8bO2ehx1wOK8g8YfEG51hprFU+z2Ckxsg5LkHuceo7YrlwWFniKnu7L dnBluDq4yt7miW77fI7rwJ4iu/E2jz3N2VMqybBsXaBwDjH511Ug8spyykLw4rg/g+6yaTex7QFj uQ/HPBX/AOtW9418VReFdFN61u11NnEcKEDcSRnJxxgEnOO1a1sLOpjXQoRu29EjmzZ0sHXquXux icD8b7WBLzQGtLC1+13TSGWfy/mbBjGWIIzwTyc9MVpfCbMMl1bCWcQsGulg2J5YXKqDuHzZx/D0 x71xeoeJr/xFrgu76WOSyty0KLDlURiQDtOMnhc9T17Yq3caw+j3LXlhcQmWMq0SSowTOTgEjAI6 557V+wLJ688lWX1X76Xqr7o/GlmVKOZvG017t/R2PbmxMMlcgjn349qz7T7DrVvbX3kw3C/8sZJY gWRQexIyvIqp4R8T23jDw7Bqlo2EcNG6vxmReHwMnjdnHeuB8EeNLzQ9cGj6osgtxIIkjZfnjY4C ryQAuTk8V+PrA1l7SD0nDofsuAo/XqEq+HldpJ77o9Tnsxhix5PJ55qJXQn5gS2OaueaJjwOG6+w xVWOFlYtkbewNeYcJpRWcpiQgKRgd6KWPZJGjeenzAHhxiir5WPlfb8D5z1C6utYmYSvNeTZ8td5 aRwM9BnJrr9E+Fdz5Mc2q3EFhaqczIzguFxkHH3RnIGCa9YGl2/nGUW8EZVs/LGOPxrkPiPb6nfQ x2Vppsl5aumxXiUho2fgtkHB29cEY9QTg19dhsY8fWjh4SVKPVu34bHu5pxPVoYdywtKz7LVv7lo dNY6xpEm6zsJox5SkLHAv7sADJwQNuOakjmM+3e3y9sck1i+CfCsfh/QfKnBnu5n/ezEDKAdAMAY G3GeeuTxWBqN9r95rUsUkf8AwjWiWzkvqXnDzJIweWXcNoG3GAQTx3ryp4CjiMVOjhKi5Y/ak935 evS1z45YyrToxq4mD5pdEtuuv6npdratJv8AKXdsILnIHY9cnpx1rzDx94uGqSXGgaXPN9vWQxTx QgPhuvOORkHpkemDXF+KPGGu+K1e0huX0nS7dwGjjYPNPt6FnyBk43YAGNx5qD4ayR+H/E1xqF9c TKPsru0MoDlCSCEXaBycZPY55r7fK8hll1N46tHnqJXUd/T5nymYZzDGyWEpPlg3rJnawfCuXT7C 3ubXdNOyozQ3DbCCAMjqP4s+4robrXNJ8E/at048zC5tUCsyHHcgbuS38RNcp4h+IWpa9YfZ/Dlj dmaZeGeREnY4J2qCdoHQZ39+lQ+B/h7q95psVvq1qLCAKC/my+ZJnGeACQT6knjjrRVhWxNFSz2s oRV2oK3M9f06WVx05UqNbkymk5N/ad+Vf1vq7djPufETa5460i+uC/lrdotvEwVjAu7GOB154P8A tV7KsZ2gSDoAevTtWXoXhPT/AA5LcvZRBJZwFeR2ydvGFH+zwDjuead4o8QS+GdCutSiEc00S5jg lkKCQ5AwCAcd+x6V8lmmKpZpiKVHAwaUUoq/U+gwNGpgKNSri53bfMzE8bahFb6naCTYp8lmibdu LgHPAAyOTXimtTwXuo3VuyLe6fNKS0cjYJZQoBDYBGMEZB7kdDWZ4s8ba5deKv7Qlup1jVVcwybv IjByTtH8AyF4AANcxY3wl1aPU7qKORVZphD5LxxoOMhQGfGSy9uTySOtfs+V4OeCwdOjO10ul7H5 dmOMji8TOpC9m+p7Z8MNLTXNYs7FraCXSdPiLrIoCyJJnI3DG372QCB046jJ9suJo5JirspYfNtU gH8utcP8OfDp8O6G1xNFGuo3WJHaN2ZSn3kByBgjcc/zNcx8PdVfUvG91dXUm+5uIJCWC4JOV9Bg cCvyfPcQ82xVWcH7lJWXn3/r0P1/hzJ3Ty+daTs/ifz2X3Hr0eWAHORkdTxXH+GfAtr4X1Ce+e5k uZyCgOwKoVjzkZOe3p9K6sTx2kbSXMgiVRyWYKB6k56VzFj48sNb8Sf2bZRSSBg7LcDhW2rk8EZ9 RXylH2/JNUvh6nv0PrTpVFSvyte98jqYl3Mrjbgc4x6V5J8RvDNh4fitLi2ExkuppPN8x9y8ANxw O5r13y1ghYnjHGf514d478RXWtX0lncMix2UroscaDG7oWz1OQO57V6GURqSr3g7R6/oevkUK0sR eDtFb/oUfD2vapprRWmnXa2aXFwrFpTgBhkDcf7vPI6V6J8S7q3h8AsNQntm1CQRiGWNMiSbKk+W e2RkA8da8ktbQ3OpQxsSqSuIyV9CQK7T4kKYXt9EkVGs7K0R4ZHjzJ0YNkng4CjoBya+vw+EVfM6 Cho0+Z200Xn9ysefx1VpYfAtpLmlptvfrc8xvpppvs9v5yQ28+XiaRTskJPA/AY/OpbK4XUI7m3V zblBlbXkEpn5ipzz9B6/jVTa0cpENp5saxgRs5BK4XJ68joP881QuvDuof2hp0sEfmBGclpSOuMY GM9QfX+dfrq3ufzLKTtoaOn3F1p6xyWQn07lf9IibZIqdAQFOQSWHTHU819MN4d0yRopnsrZrvcJ GnaFS+eobcRknjrXnPw61u1sbPWPtunxs2nqsklxGd52lgAgUjsTnOf5VL4i+LDzQyxaPDJbsxXZ dMRkqOo24P061+ScTTrY7FqhSpuPJvLve3bofsnB2U4qpQdWjK6n62Vtz0a31S2aO6fz1VLZ2hmk f5AjDBOSeO4rzjxt8RLi8uGtNKvfLs8486HKvIe43dcA+nBrirnVLvUJJZbiSR/NbzH9GY9zVaH/ AFu4fdAxjHSvKw2V06E+eTu/yP2TB5LSw1X2lR8z6eR0dtrepQ28SJOgRUCjIGcAfSisyOZPLXIO cCivX5Y9j3fZU/5Udvb/ABG1K/8AE6t562qvKIo9PR1YsMgsvzYG73PQHtXo2t6fca19m2X13pbR qXZLdgGORjDYPb2JHPWvPvD3w11BPEEc+pwfuI3aVJo7hfMTBG1T6g46V6bcMbjayEEE4VemAPev E4irYCFalHLlG0Vq1/Wp/PvD/wBeoxnVxTd76X/yf/DHMQ3ml+DpAb/xNc3UzEokc0jylOOdyJn1 zlvwrjfiXb3klvHfW19dXumXxWeBZN7QoxJCgKRxhcnp+FegTeB9Ju9SudUvImupZGV8NIVWMKBy MY/u9z2rPufiZpSa1BYrClzahwpuWkVY4ye68HIA9MVzZXjZYXERr0YOo7e9sl8tNLH02aZX/blD 2dFyc7au2i8rKx5xp0msLpdjpfh/QzDMZCRcxochj1PnN9w4B7gYOO9dND8LY5HWLVNbuJdZuoWl ldQZMheCwLD0Pfp2rd8UePrHSbZG0xrW8uJCSdkgKRnIOWC9cjI61w2uePNS16xEM5gi/eqyGFSr Lz2JPTFe480x+Kl7TCwVJO/W8n6tq/y0OTLeCnWgpYr3l0v7trdoo63RfC5sLqXQ7XVPskK2qEG1 jAncFseY8jISCcdFIGRx3rr9Fs20u3W0F1NecE+bdPukbPqcVyvwv0lr6GXWpnle+lkeN5Hb5WUh SDjHrmur13ULfRdKudRmJEdqm4sBnGeOn1Ir5DGSrYjE+xvzybXzex6WMhTy+Lw8LKEFrZJLRaia vdrZ2pKsizynClWAZePv7T1ArwS48Tavren3kF3cG7s4p5TD+7Cq6l2AO5snB5OAeM1d8beJrrVL No7i4UXLRZjCrgKxPAGP4c9VNcvHNc6haHT1ubiKBEWNsZ2rgYGBnnkfp2r9hyPI4ZbQSqJOo9W+ 3p6H4tm2bSx1Z8jaitEu/qMsrG48Q3F/dXBV5cBPI8tlAGc52n0xjjPT6Z7f4R+Eotejnvbli9jG xACuoDyFg20jGdu3jsfQ1zeh6bNcR2mlaek41HLAuGDFjnPLEjIxk8mvfvC+iw+G9Et7RFhklChp riGIR+ewGC7AE8njuelc/E+bfUMMqNGVqk/vS6/f/ma5Blv1yu6lVXhH8X/wDSRVRRGFzFHhVRR0 wOnvXNatJpvgLT1ew04PLcTxxi1ic7nJIBIJyeMjpW/b6vawzXFs8wSWJfNbzMgBGOAdxwOvFeUX DR+JvEcGqWdtKofUop38wElsEDouem1a/Ocjy2WOlUqVm+SKu+0n0TfmffZpmE8vpwoUtJTsreXe 3+Z6hJaweINLmtbpZIBdIN65wy55wMjqKh8P+FNP8KNN9laaQz4DecwbGM9MKPWpfEmsW3hu1n1S 6P8Ao8QLBVYKzn+6oJGSR2ps3ijSY9Pt7+S7jSGWMMqhgzMTjKhVJLMCcEDOK8WMMTKn+6i+STas tde33HsLG+yg8O6lla7W3zZr3EYljQDgHkjHp3rwPxs0V/4i1GS1tltFjkaNo92d7AkFug6mvdNW ka30W9njOx4rSR0ZsY3BCQcfUV86TXs95NNO48yaRixZVwCxOScV6mS0/enU7H2nDtO8p1fkNt2W 1vrR2+5FKjMoGSQDk17lP4f0nxjHBezROvmKsiMrbX2dQGxnjknHrXlHgrwydf1qSaU7dPtQZJ5A y5GBlRg8kEjHAro9C+JVzq/j6302wlit9Ha58qBI4tpniLcMdwyvQjjHavbrYfFYmq6mDlyyppty vay7adX2PF4vx2BhOjh665pN7adTsv8AhDNJ0nS721hsYXhkjd33RqWcjLKCQMnB6Z5rwXTtSnmt dPkkYpuJUqM/NxwOOSQOBn3r2T4lfESfwSyfZ7aCcuCX3Hew4G35AwK8k8k4OOK8Bh1AzeIobWeR lXc0jg4OXYccgcd+PTOa+r4To4v2VTEYp3VSzTbu+qZ+KcR1MN7SnRoK3Jo1ay6WOn0HW7TSr67j vp/s+kXrKt6jguwQv2K/MACPr29qZr2i3Hh68ltnAkh4MUgVgjKQGGCwGeozWZq1wk0zJC7TbY9v lKcDONpHB9c+xq5H4/ey8P2C65YwaxYWu5dshkgnVnY/xBiNoGOSvbHvXp5pltWtNYjDK76rRX7N ef6HucI8VrI5yw+LbdKWvez9CrFMflRm2oOSDwKerBmCxryffvXW6P8AFjwJZ6f5R0q4t47rmWJU 85cjjhy2cds4FQaba+GfiXql0nh17nT5rSNWmWWBfLZWOBtw+c5B6mvjp/WKPNKvQlCC6tafgfse E4zyrFVVSjNK+3/DGXHZr5a5aPOBn56K6Nfg7rW0YngIxwdzf/E0VzfXsL/z8R9J/amE/wCfi/E9 tmbyo3ZjjkszlsAe9ctrXxB0fScq1w13OjbXitgWKnGcktgY+hq54s1Wz03RLz7XeR25miaJI2bc 7kjHCj5iORzjArwYRFY1G3Kdc5ABr5jLsvjiU6lW9v66nymVZbTxkXUrXSW3n8zrvE3ja98SL5Az a2W7iFG+96bv73Tgdu1c1Hbt5ysWyACD6A10Ol/DXXdStIrmO3EccgyBM3lk++Dzg9j3qy3w912G QWy6ZNw20SLlo/ru9OetfS0quFor2UJJW8z7ShWwdCPsaUoq3mjsvDfh/wAPaxo+n6s9pA/kxIs0 kmUj3Ko3FlOFPJ5JHPrVy58C+HtSuodQit4mQgnbaviF+wOFOAAR0HXnNN8J+AE03RntdYgWSUyH 7s8m1lIHBAYDqPSujeBYlSKJFWNeBHHhRjPPSvkcRiJQqS9lVk/npb7z4TF4qVOrL2FaT107W+8l t4kghCxIkcWAiqgACj0AH8q8j+LHioz3UNpaTbIoJSjhpGjVnPBDA43BdueAeWFdt8QtSjs/Dtxa /IZruMxIhcqQDnngEkDHpXkOuSRtq1hDaxxtdWtvukXBY7j8o5PY4OP5V9zwllPtJPH1lt8P+Z+U 8R5lyr6pB6vf/IqzQw/bC9xdIZc4YSY3P34DYPtjrx2oh0aO4vhNZ3UkMGQx2AAFQBlTkd6zZtIF 5fS3t+9w9wsSoV4UR/MMAAcnj1POa674dWd7q+uWsscgh0i0ZZt0US/vCMYjc5Byy85x271+lYvF wwWHnXquySPgaGHliq0aVNXbPT/CHhiHQ7W2luI4ZNQkBke6CHcdx6LuJK4BHTr3ro9oY7MFD0Pt UeQ1uoxnHv0o8sw87jk889K/nLFYqri60q9V3b/qx+34ehDDUo0qa0R518QNXuLXR55/sC2Vzdyt p7SXDMzyImGRk6KATk8g/Wub+GMk8Pji3UiOSHyJNu3G5TjngfQd63vjNdM9tpkQgkYJP5gkZAY2 OMBeuc9DyPx9NvwzDF4M8KC+uLR3mdt0otlR3kBc7TwccA+vAGK/Q6eIjheHklD3qraWvnv8tD5S rRqZln1ovSFrb9tl8zkfjlqqxXywMNqPAjsGdsHDMeRkDpketcn4ds0uNamaKZZWhjCrHGqgKzAZ 2gMcEjP3cdeea9v1aHTfFNr/AGVrFk6NcBpI7e4UEsq4BcMpIUjf0JDD0rJ8O/D3TPDOqpc6fI9t DE24W7fNyQQSXPJ6k9etb4DiGhl+B+p4mm41IJ27Pt6fd8zDMuHcVLHOtf3ZNX7r9H951t4qTWoh m+eORPLZSfvBs5Xr6H9a818OeD9Dmi1qe/DWyw3MsIO8BYFBBDDI684yc/SqXxI8XXEmrSaYJY4Y 7eRZlkBw+4BWU57Y9q42O8lmupn+0vLJOhMzBj+8yf4jn5vxr5fBYGr7Jz5+Xmsz9wyzLa6ocyqc vNZ6eX+Z3fh23PiTwXdaRZbTeW10N7T5EZUuWypAJzhTjgVzGi/CzxDK1zcIkFihf9wl8zq8YGBl QA2Ace3Wuu+Dakxay+OA0eD0PRsV6PNH5cQbbxjDfSun+3MTlM6mGw6TTd9dXqtT4PiLJMLisxlU qt6effX8z541jwvrHhFJG1eSCa1af5JkkJWXcMhAeGGMc8D2NcfDHptlqMt8kTfaFj2worF244bg 9T9M96+k/iB4ds/EXht4ria2sjGVliurx9sUTA/eJz0xx+NfP2rQp/a9jHp89te3O2Tc0MTqLfLD ++F3ZBP5V+j8P5z/AGlh/wB78a0dk7eXl+R+RZzlf1Gv+7+F6q71/Rkdv9vl0/UJIbcWnmACNZUZ uMkcc4HPPHqetUVtLi6mWIvJhpAMcYbAU5yTgjqBketbepw3dnpCpbTB5i6qPnwcbuSMnP5HPtji q+m26fbkOx5hEHUKw+VRnOAQOvP0r6y9j5978rM7xAttGWee2j2KVUKiY2d+q8knHIBxz9BVbTfA +satqg1Wzt7tRaxJeLJprASLGRhdhDZ3g9UXkdwKv3cLah5CJ5k0LO0r8eWw9M7ckAY4P61Xs7fV o47qK1lktoVlWMok3Q5G4nDbep69TWNdVZQtStfzV1+aHTVOM7zvZbWdvzR3UXib4hRxoottbkCg AM+kQlj7k+VyaKWHxN4vWGMJq8pQKAp8uLpjj/llRXzv9nVP+gaj+P8A8iez9ah/z/q/h/8AJHT+ IG1f4haozwafi3s5WhXYQduWxkk4z93sK7Lwf8ObPw+ft12UvL0DAG0GOMjn5cjOQf4uOvSuiku7 bStDe/ZlSyX96DGgwQx4IA45J7etc1afErTNQkhthFLayySLGkZyQ5Jxyw6fMa/G54jE16Thh4OM F2P6PlisViMNyYWny049jtkusOePm6kDmtCS7SC38x/lCrlsAnGPwzWKzJp8LT3BOxeWKruPJxmu Q8ReKrq/t9RmgtzDpVmE33MjEeeM8hWU8DGQcjJrLLcpxGZzSpx9xPV9v+CfE47MqGAj7797ojpY fEVlq00v2OZpvKJLZidRuyO7Aeo/Oq3iDxZpHhaKN9TvFtWmG1SFZyucDnAOOT3ri7TxJYQ2OhW/ h+CHSn1GP7U0MiMzKjBvn3nOX3Dqytn071z/AIuki8QPcWqRSSKgKPM+MM3cKDkdP84r6vL+GI4u q6lS8KXROzk/0SPnMZnzoU+SFpVOrV+VP8zJ1nxDqXiQ2l0LtpJ+WeJlKfuiCMKMnrhvoRxjmqdx fywXsepEFLeOPbdysy7pDnCoAeeCe3PSsnXLGyk0211O3lKwqmIFuZMqvJCnOM92PXAyBj01fEkM cG2SXM8oAjitMoz8Y5wvB4BO71P4V+t06caMVCCslpY/OKk5VJSnN3e+pj+MdRnjMNpC+w6kxiDY AYbmx97jucZJ9D2Fe1/DvwivhLw8YJEnju7hllngllEnluqhMKRxjAHc9a8l+EukXOreOPtUllCd M0mN1CzJl1llXPGQe46j8zXuV1cLBG0vmxxoqlmYnjAr8y4wzKcprL4baOXr0X6n3HDWBi19ce70 j+po28m23Kjg8kA0651OK0jae6dYLdeDLMwVeenJrkNcZ/FXhdp7TUBYwRSOkv2iPCSqCB8x5KjP QjOc1N8P5rW/8Kw2Ek26VVaOZVb5lDFvX2571+e/VuWl7ST2dmrbH6gsJaj7WT2dmrbGN8Xrq21T Q9MlsbyJ0+3BPMhPmL9wkqdvfpV/wZqVhcaDa6EmovDelGcgY3MCzFtuQQeOoGcCs74vafHY+FtG ghDMLW6+Tcm44WJgM49u+O1cDpfhe88dObawj+zXHkbpLhypjhBJ/Ek9PlHHev0nLsFh8bkKjWqc kYybTdtNXv8A5H5nj8XUwWdueFhztpadWv69T1HT9O0L4fapFH5V6ZpFYC5mO4Y7KgAAIPr2q/df ELQbOEySzT7SeQkLMuD6kV43qVnqfhvUjp/9oRyywJ5PkQ/MoYgEnPBAIx1H9c40ZmktEW5uGLR/ LGrseozwDjnp0xXpx4VwWIhCrVqSk2k7p6PzWjOPE8VZhOvJzSvfqrteXQ9n8RfC+O+V9Q0iQR7l 8wwyudhXbnKnkjvwc59q8vWFo7jeFyuB09a+g9HuLf8A4R+yFtGsVs0AAhWLyx6EbCBjnPbvXiPj ELb69qCwDyUSZwkcagBfQAAYAr4nLsROU50J3fLtfe3mf0Hw/mFTE03Tqu9kmjqvhr4gsdCbUxqN 2lrHIibN/R2BPA461m658TtXvdRElrOLS23blhX5gVz0c9/TjHWugh+H2iTabb2s93GdSukWWImb bLjAOFj3YI684PFcPJ4D119Sls00yUyoudzY2YwD9/O0nn1/CrpvCVq06st/Pa3dXN6VXLsViKtV tOS0d7aeh2uvaxZ+MfhzPcTIPOhwmD8gEw27sc8jB715PcK8LSlZiqB3kIUYO04A6DkDn/69dtrP h+fTdJsrIMIrS2LX9zMHVm8zbtkiGMgABQfWuKVptVllSB47OzkDIpgUeZJhsbgAMAcHPQ1+i8OY X6phHyvSTbXkv6Vz+buK6tCtmc1h/hjovP8ArYrwaVNJeW1tKuUdTInmOFB4JB2k/wC119q0PDOn Nb3DyRvDKEBUx4D/AI5CnJ9vakv4T9paIopRWIVx8zHC5xxyOo6VnWs1hpV4kLWmbnyfN8sjOeTx v6Dv7cGvqr3R8jblldm++nBtUaS4EUcMcakSRls4JJPJH0/+vXPzX0fheFZZVF5cXU7BxCSqk5wM DqAMmunmuH1LSbMBbdGbEkihEO7gDAOOCR6cnFcrPo8zEupWZ4st5QGX5JwPqM9eeBTi9NSJq70O uWFpFDC9uIwwyEGML7dKK5u1sdZe1hYNeYKKRtc46f7lFUFn2Pof7Pa+PvB0cdhFskjCwQvONuzY VVmBUkYIU49ataH8LtM0jyppt1/cR9JZQAu4HIIXnBGB3rjvhTpbeFNCW/ufKmub7F1LNBEXcxtg qrHG47c9+nNeto2Qm9uDyN3Sv5vx85YapUo4aT9lzNJ9++p/QWXZlipYGNGcuVtJtLz/AEf3FfV9 Ne+0m4t0GZJFwvue1ebfFyGTw94ZsNNgjklvruVUQWrfOxXkhQSoPbrwMZNZnjT9oy58P63caZB4 O1S4hhmMH9owhXw2SNwRScjOOGKmtrWPFaarZpq1/FDbmNPI8zPzEE5KgFjjvk+wFfo3DWX4/B07 YhKMJapbt/dpsfm+eZhgcXJqjJuUdH2/r0PNvEmqNpdw0d7eS3eu/KLa+kUbCu8Boy3HZiw7ADnv XQmG2jt4PIvYZkdQzFJd/wAxHKjOAWz+HpxXNa9EnizUI5XdGiidZI41YHzQufvZVskdeD+VZHjn xY+n/Yf7HSGZlYPNuyGK4wNjHqc56nAHavvIUlT5Y09F26Hx8qj96U9fzGeONXiis2iihd7eJhMY 4QXRQDgFzkdgDk9fesPX9cvrrUIo4LMzSJGDIrplh3LDsB0/T3q+0UN9b28Er+TJMyo0a4LbQcgE 9DkA11nhf4fza14wlfU45JNLaAyRXVoCu2QFQEZiCuSCxwKWKxVLA0XWrO0VuYU8NWxtRUqKu2dV 8JfA974Z0e7u9RiNnd37hnttyMFUZ2EMhOdwOeeR0rtfJhaVPOgDbGDI0i7gCDw3sRWt9jCLFGsT FFjUBup4GBk1VmsAxztCntuyK/nbHYyeNxM8TN6yf/Dfgft+DwqwVCNCGnL/AEzzPxb4g1qOTU9N 1G3keC5UpZtBErdWyCWXr8o6HmuM8P8Aih/D+r292sAuPJ3KQOCdwIJ+te/fY0baWRWK+wwP8DXN aH8KdH02GT7RCt68j53SA7VGTgAZ9D1zXp4fMMPGjKE4Wv0XX/I+3wmaYeFCVOrTtfouvn5FDxas 3xH8ES3NnHPpy2xMw+0qU85dhyVI69+/arPwnmsjoQtohCl/GCZWiQfMhPyndjkdcDtU3j5Z9J8L wWulxeVAX8hooyAPLZWBHIPHPNeZeFPFU/hzxDM0MPlQJGpnjeTG5AWAG4gk4znGPpX1eX4CpmeS VKNJ2XNeK/R+vQ/IM0xtDB5yq0Y2VrPrp0t+pP8AELw/dWvxElupEH2a7+deVYkBQucc4x83XB/n WSt/aXF/fG5h86C3VIwqgEr3zjPHA7+tXvFXi281rW4jIht43d9iH94I12fd3YwOc9xzXO29useq XUvkhUndVkM54kTHJ9D0/Sv0TLqdWlg6dPEW5opLT+u258NjKlOpiZzofC23qfQnhLxBH4g0OCQy 2zXYwksEEm4rj7pK9Rn3qKz8F2tv4m1HU5m817nd5cLBSqg4zkHqcjse9ea+FNW/4R3xZPPIZ4rR mZJLe2bh8qoXK8A8nvjrXuSbSU2Or8HDKcjFfjefYCeU4qTo6U57f5X8j9XyHNZ4nDOKlaS0fn5n z74313UJvHEN3p8kmmC0Y2kZwDkozKzDjbyDgKa9q0fXodS0VNShJEZhaRWmG0ZUEEkDnGVryr4p eGP7L8W6fcW0TR2c0rSzSuTw7BiccdyR3xgHjmui+H2uJZ6bJY3UuLTDx27MvRjvLZ46EkAf5NfW ZjlsMwyvDVsJC7il68v5s+TwWNqYPH16WJlbmb9L/oZFxeSeI9P1ASSRW8kdpJqFwyAsD32gZG0H 8enSuZsRBb29mkhWERxFkbbuCLkYHHXr2x1ruPCvh+2bU7q01KWMtqFvJCVjxF5ifKAoweuB2/Ws 7xd4IuPDepRyW8UbaPDHhPMVQu4gjaQDxjP3iOc9a+np46lQxssvk+XblWytba/c8Kpg6lXDLGRV 9+b1ucjMs0zJ9okxN5e4LBhQzbgeP0HcfjVaezivb6QbysgsWR1UhG4bnAOeMkcjNdJeaalywN0D F5gxvU4Dqo3MoPHXbgUl19ia8+0CAKscW6abncoGflx36frX0F7I8flfQxpNPW1WNJJHlTdtDAYE XUHbjHGT7H8Kr2umx/aLiZma4by9sO8fKuDjHp+NbLT3M2oTosS744EkVmGVTcSAD0ycZJwajELQ C0hLG6dbUyHcSRlsEsB6dfwIougcOZlKLxFFFGiKo2qAowF7fjRVCPR7eSNXea83MATtAAz7c9KK 0uZe8fQlnHL4Z8J2azQqt0tskHlyY2qwUZDDPPPpT/EXiiWHwdLfLbPumHkmESkEBiUzuA/Hp+Ne Da7rfjDxxrWt/wDCNXGu6hp9hF+/nsbUzRRxg48528tgik8BmK16V8MdQ1PzbXTtT1OS+TyG87zl Ul2CknGAAOeoAr8wxXD8MDho4qolKUJc8t1db26/I+3w+dTxlaWGp3jGUeWOzs+72OT8L6TNrOsW 1vGr8MC8wAcR4/iYZ5A/Pmui8U/DUahFby6r4hMGnWqsHhhgEQ3HhCGLNgAkdck+or0NbWHSY2js 4Le0TfnbFEqnOOBwPSvNPjFMi2mkia8ngikaYOsblUc4jwzDocHBAI6nisI8Q4rOsxp0MPL2VN6d G9Nd2t+1io5Hh8ny+pOuvaTWvVLt32W5yXhSWFPBU091HvmANvCW+7tAOGH1Iz7joOeeV1S3vmt4 ojCwAmR/LyC3lLgnb6ggD0PzHitXVri91m2SLTYXMexUEkrbeBgsQCecdaoym3jutjK6SJEAy7Tu JAHzYI9TzznA4r9SjHl3Z8LJ82i6Gvoul3muTRxWkKXssZ81ljKoArY3bCzBui9Bn8K9t8G6bcaf oqrIyJE7rND97eFI6OCOG+leI+DvGw0rxNFHZ2Mc7zCOPfeRbnjYsfmQggrkE5zx933r3vVdYg0v VorC6fyFmgacXMzhF4bG3nHJHPXtX5txVWxlRrBxppwlqrb+7q/0PvOHaeFgniXO0lo+2uxYudat LO4gjnuViaXcE8wgBioJwD0H4msPTviRpOq332doryNh/wAtHj3Dj/cLfn0qDxFrFpZyb4bGPWJL OIzxFh+5yflIMgyqntlxjkd6d4H8SW9x9otL3SbTR2DFYvLkJdFI5DseCSxxwSMY6HgfGUsmxLw0 q7oSendL8N393c++p5tlUH7GpLmm9FZ6J+ux1CzxTSyKO3JCjgjPes7UvEmnaEqRXFyv2uTPl28Z BY9Pvc4UDIPzEe1c3411bXdKvprCyVbCJYlaO5iAkmJJI7jaBx0wfrXj2vWclpe6WJ79Z5lH79+R K0m7JOT0HJA9sgV7mT8L/W4xxGKmlB6pJ6v1ey/E+RzTiD6q5UaEfeWl3sv8/wADodc8Q6x4g8RT f2jO1rZW7/uY43dYSBjL7MjcNxIDHPHfoKyNPuoPtt2k0Pmi5cos+QP3YA+Un8fao9XuXsJoJJoW aNnGWAJCp0DMduMcgc+gq1p+m2+n6hfvLbebsgVpgit3JIK4zk4CnA9RX61Ro0sNBUqMVGK6I/O6 lSpXm6lV80urKpX93AhjjJ3TSLGxODztBQDr1HtzUkOipayMymaSeZNh3PgAL/dA6deTz0ra0LTY G+wSTsn7tSEEjfMuCC/HPfGe3So/Ft9PBYNqVrbR3mmrMIWaEfvEcjO1sj5RgZz/AI1c6sYWUna4 RptpySMHSJH1S2glaSWIrPkMr4bchx1A5HA79sV0mgfFLVPD0/2RreARSFSpuEd9nONqhXGO/HrV TQbFpLTz5wsUbxiKaNkaL5yOee/U81Vu7Kz+2aWs8ha2gl3yBzxGoXhic5PJUfgawxGGo4uDpV4c 0fM0o1quGkqlGVmamua5N4i1+W6vJUzNEkMNvbOwhGCzZOc8n8+KlhhvNJsUlns5LKF5AV85fKJf dngNjPTrz16GoxMtzqxtvPMvkwNMsmB83PDE9B1Nb+h6vB8Qlj0XVLiO2v7IDyfKQt9oB65XPHCn nPOSa4K9T+zaUHSglSjva94q3RL8fvO2jT+uVJKpN+0e19m/VnI6XqkupX0t3cNO8fmlI2h6RNnl sfMWGPRhnjBGK9k03+0bWzZpzHrGmSIJIzIAk6L1C7eQ4HHU/U1ia1DofgextrSDw3Hd4UEO0S7F BbGHkwTn6+1UJPiovki0n0i3TTvuNCnz/J0wAcA9PYV8DmeZvN4ReHovl/vW27rW6fzR+o5Hwnjq UPbt3T/lf36PRr1QvjDRNcmdZDYWt9Ey7/tVnGySqoXBHLMqnBIzg1zcjWF9atBbiTS55mMTLeET JtI+8z4XAB7bffNeg+H9a0zXLtk0t7vSZIQGjtndQhBBJCQhip6HOBxnNb+peFtG1oFLmxVS7F3l hURuSeuWGCa8mhnuJwLVOben9bO6fyPo8XlmVVf3WNw1pfzRXLL13szyzTfB82oardSwOb2MQxok tmAYAVB43EbsjjjpzVK38ParHOjalbNZxrE/kwsB8xJx3yMYHTivRT8L7a2vZ5NO1G80xGQKy28h GcHuc5qGbwhrNiotrXWpbuBsuy30P2gblBxglWA4z6dRXv0OK5ymk7NddGn+qPk8Rwnl0k3h8S43 25l+dkcdCZLeFIlCEIoUHevYYorurZdSjt4kawsXZUALNpNwCeOpAjwKK+g/1in/AM+fx/8AtTwv 9VaX/QZD/wAm/wAjmb/4SusmsxaR4v8AEfh+DW4Vhv7XTbpIre4VedroEG4ZYnGe59aybHQ77wDJ BaR6vNqF3aI4F9cpudwSxwQxPbj6UUV9DKKqxcKiumfDySotTp6M9bt83WjWN3MEM0yh22LgZI6j JPt+VeU/Ha4+1afDZhdrwtu8xjlWEgwRtGOmB3oor8nyGjCOdNJWS5rfI/RM2qSllau9+W5yGhMb jQRayhXFuEjaQj5pAWAyTnrz/wDWqWTTUt9SkgG398wjDrGFIyQTnHX8MUUV+yrZ+p+ZR3RiQ+FV /wCEgsbcXLKZzNJKyggMFOFAAPHbv2q74hvLiGzNlBPJDF5atlXbOCwGODz06n1NFFKW6FH4Wi0u pXPh/wAC32r28zs1raectszFYi2QvIUj1ryuz1DxHqnma43iS7gulIkhjhG2OIjkALnG3tgYz3zR RXRRim3c4a8mkrM+uPgh4zv/AIgeC49TvztnDLHwc545J6Zyea1fE/wt0bxTAC8Edpdq2UuoIwHH zAkE9wefzoor+f8AMas8DnE1hXyK6207H7TgIxxmUwliFzOz31PHvEmFezWJUjK3gtQ20MfvD5uf 5frXSzactvMSuzeofLeWAWxtABPXHHSiiv3Km3KnFvyPzBxXtJI3vAfgeyn8GadNds13K8RKyNlW XJOeQefvd/SsjxFYT+FbiLTLTUJ3gu4pWn80KQykbSu0AL+OMj1oor8xy/HYnEZxKhWnzQbej202 0Pt8ZhaNHLY1qcbSSWq3131Mrw7Yf2jpJ3XE0ZMhHlpt8s8D7y4yep7jtTfGHgWLS7NLyKdcSSqk kflkBuQP73v6UUV9bLFVoY10lL3eY+clh6UsKqjjqkivpdnBqiSyyR7JndbWTy2YI4Vc7iuepz0B x7Vs+E/CK6fqB1S3lhSaOcLtMHGACgHDDAA5+vrRRRmFWcIzpp6Ntd9NdC8FTjN05ta6PtrfyPU4 dMt9St5obiJJY2HzKy5z/hXld9pei3/i9dDj06SzX7QITNDckls56hg3+e9FFfj2BvGU+VtW8z9z y2pUjCTjJqyvv1saPwrtVju9WHXayBTjkDD1d1z4kNo+ty2Y09ZYo/kZvOIZm3DkHHAx2wfrRRXd KlCvi6iqK+i/I9ipShiMdU9qr6L8kdZpunQ6rGl+waK4njVt6Mdy8dPTv6VSv7C8sYS1nqcv2rfl Xu0EqAHjGxdueOnPX1oory6Vapzxpt3W2uunzPhsRRhackrPXbQ1bPRtbms4HbWLTc0ascafxkj/ AK6UUUV7fu/yR/8AAY/5Hm+9/M//AAJ/5n//2Q==