20230725 09492000 1.0 FALSE CBW_desigAndProc2018 -11816077.713600 3407884.086400 -262699.130600 7786809.374500 1 002 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) USA_Contiguous_Albers_Equal_Area_Conic_USGS_version <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;USA_Contiguous_Albers_Equal_Area_Conic_USGS_version&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;Albers&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-96.0],PARAMETER[&quot;Standard_Parallel_1&quot;,29.5],PARAMETER[&quot;Standard_Parallel_2&quot;,45.5],PARAMETER[&quot;Latitude_Of_Origin&quot;,23.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;Esri&quot;,102039]]</WKT><XOrigin>-16901100</XOrigin><YOrigin>-6972200</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102039</WKID><LatestWKID>102039</LatestWKID></ProjectedCoordinateSystem> 20230724 15383600 20230724 15383600 ItemDescription /9j/4AAQSkZJRgABAQEAYABgAAD/4RDuRXhpZgAATU0AKgAAAAgABAE7AAIAAAAMAAAISodpAAQA AAABAAAIVpydAAEAAAAYAAAQzuocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAE1hc29uIENyb2Z0AAAF kAMAAgAAABQAABCkkAQAAgAAABQAABC4kpEAAgAAAAM4NgAAkpIAAgAAAAM4NgAA6hwABwAACAwA AAiYAAAAABzqAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAMjAxODowNToyNCAxNzowMzozMQAyMDE4OjA1OjI0IDE3OjAzOjMxAAAA TQBhAHMAbwBuACAAQwByAG8AZgB0AAAA/+ECqmh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8A PD94cGFja2V0IGJlZ2luPSfvu78nIGlkPSdXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQnPz4NCjx4 OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iPjxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0 cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpEZXNjcmlwdGlv biByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4 bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4y MDE4LTA1LTI0VDE3OjAzOjMxLjg1OTwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+ PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEt ZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8i PjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8w Mi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5NYXNvbiBDcm9mdDwvcmRmOmxpPjwvcmRmOlNl cT4NCgkJCTwvZGM6Y3JlYXRvcj48L3JkZjpEZXNjcmlwdGlvbj48L3JkZjpSREY+PC94OnhtcG1l dGE+DQo8P3hwYWNrZXQgZW5kPSd3Jz8+/9sAQwADAgIDAgIDAwMDBAMDBAUIBQUEBAUKBwcGCAwK DAwLCgsLDQ4SEA0OEQ4LCxAWEBETFBUVFQwPFxgWFBgSFBUU/9sAQwEDBAQFBAUJBQUJFA0LDRQU FBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQUFBQU/8AAEQgDZQX1 AwEiAAIRAQMRAf/EAB8AAAEFAQEBAQEBAAAAAAAAAAABAgMEBQYHCAkKC//EALUQAAIBAwMCBAMF BQQEAAABfQECAwAEEQUSITFBBhNRYQcicRQygZGhCCNCscEVUtHwJDNicoIJChYXGBkaJSYnKCkq NDU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6g4SFhoeIiYqSk5SVlpeYmZqi o6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2drh4uPk5ebn6Onq8fLz9PX29/j5+v/E AB8BAAMBAQEBAQEBAQEAAAAAAAABAgMEBQYHCAkKC//EALURAAIBAgQEAwQHBQQEAAECdwABAgMR BAUhMQYSQVEHYXETIjKBCBRCkaGxwQkjM1LwFWJy0QoWJDThJfEXGBkaJicoKSo1Njc4OTpDREVG R0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoKDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKz tLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uLj5OXm5+jp6vLz9PX29/j5+v/aAAwDAQACEQMRAD8A /R/4K/8AJHfAn/YBsP8A0njrtK4v4K/8kd8Cf9gGw/8ASeOu0oAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigDi/gr/yR3wJ/wBgGw/9J467SuL+Cv8AyR3wJ/2AbD/0njrtKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4v4K/8AJHfAn/YBsP8A0njrtK4v 4K/8kd8Cf9gGw/8ASeOu0oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDi/ gr/yR3wJ/wBgGw/9J467SuL+Cv8AyR3wJ/2AbD/0njrtKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAopGYLySAPegMG5ByKBX6C0UlFAxaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigDi/gr/yR3wJ/wBgGw/9J467SuL+C3/JHfAn/YBs P/SeOuzoAWikyD0OaWgAooooAKKKKACiiigAooooAT7tY+reKNL0H7P/AGjeR2n2iTyo/NyNzf4f 7XSl8SeIrHwxpct9fS+XCnAVeWdv7qj1r5i+IXjo65c32u6pttrS1t2baq7vKhXc3+838Tf521y1 8RGjG5rTp859Zfw0hJ9OK+b/AIZ/GmbTdNtZGeTVtEuoopbfczLJDGyrtZd38O3+Ftv/AAH5q9O+ JXxOs/AfgOTXFbz5ruMLp4aMsskjoWj3D5cLxubOOAe9b4WX1pxjT3ZyYypDBUpVqztGJ4t+1V8T ZLzUB4Osn221uyXF7IrMrNJt3LF02kbWD/xfNt+7trm/hn+0pr/g1ktdaabX9LO1R5j4ni53MyyN /rPlY/Kzdl+Za8iurma+uZJ7iaSe5mkaSSSRtzSM33mZv71Q1+tUspw8cLGhUjzM/lrF8T4+pmMs dQqcr/8Abei8z9C/BPjjTPH2gwavpdx5kEh2MpGJIn/ijkX+Fhx/PpzXThduK8H/AGPf+Se6r/2F H/8ARMNe9HmvzTGUlh68qS+yf0lk+LnjsBRxNT4pIdRRRXGeyFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHD/Bn/AJI/4E4znQrDP/gPHXJftF/E yTwH4SS0sXMeqasJIIWDMrRR7f3kysv8S5UL8y8tu5211vwZUt8HfAvb/iQ2HP8A27x14l+1h8P5 RJZ+LrYzyxNttbxWZnWNR/q2Vf8Almv3g3zKu5l+UszV6uW06NXFwjWeh8pxNiMVhcqrVMIve/Jd X/Xqb/7LfxMbXtDfwrezM+oafG0ls7KzF7bcvVs9VZtu35fl2bfutX0F6DtX50eCvFVx4J8VaZrl oMzWc24x4U70+7JH91tu5WZd38O6vvvwr4itPF2gadq9nJvtruISoCRkeqtgn5lPykdiCK9DPMD9 Vre1h8MvzPB4Jzz+0sF9WrS/eU/y6f5HQUUUV80fpIUUUUAFFFFAGfcQvInyTSRHzEO6Pb0V9xX5 lPDfd9efl29asXDyRwyGJQ0u35VY7QW+tT5rkpPHFlqGn+Ik8MXen+KNe0ZJBJpFnqEQkW5VX228 jfN5LM0bL833fm/u1EpRiCR8/wDjO/1fUvEd42tq0eoRt5bQt92Ff4VX/Z/9C+9/FXmeh+I7rxvq zNb2dnfeBbzT28m+bdumm8zy2jaNtu1fvfLtb/e+bbX0bb6LrHxa+F+latqmkx6N4uj81JofJmgW by5JI8qsyrJHHJt8yNZF3Krhf7zV5LpPhptPkXR9N0v7K0bMq6faW+3a3zMy+Wv/AAKvnq1OUpxl 9mR6NOcYrlJvDuhNqF1ZaRpsMce9lhhjiXbHGv8Aur91VX/x1a5P47eMH17xc2jRb10rw/nTrRZk UyErtWSRm/i3NH/3yq/KvzV9HaH4PX4Y+EdU8T6hALzWLWzluBCG4hARm2K2D8zfdZvy/wBr4uub mW+uZJ7iWSa5lkaSSSRtzSM33mZv71fo/CWBSnKvLofiniRmUo4elgacvi1l8v8Ag/kQ0UV2ngX4 P+KfiIyyaZp5jsW/5iF2NkP8X3W+83zLt+VW2t97bX6TVr0sPHmqy5Yn4ThMHicdP2WHjzS8j6F/ Y9/5J7qo7/2o/wD6Jhr3rvjtXn/wi+GUfwt8MtpKXj38sk5uJpmj2bnKqvyr/CMKvc9677tt71+Q 46rGtialSHwtn9ZZHhquDy2jh6y96MSWikpa4T3gooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooA4r4L/8AJHfAv/YBsP8A0njrodY0m31rS7uwuohN a3UTQyxEkBkZdrDj2rn/AILf8kd8C/8AYBsP/SeOuy4pp8uqM5wjOPLI/O74heCbv4e+KrzRbs+a YCpinWNlWeNvusu7/vlvvfMrLu+WvYf2VfiNLY61L4Sv7iSS1uA0unqzbhFIu5njHy/xLls7sfK3 8TGvRv2j/hcPG3hNtV0218/XdOG9RFHukuIf44uvPXcOp+UhfvV8d2tzNY3Mc9vNJBcwyLJHJG21 o2X7rK396v0jD1KedYH2UvjX59z+csdQq8H53GvS/hvVea6x/ryZ95fFrXvFnhrwPdal4Q0qHWdW gljea2aNppFt92ZmhhVo/tEirnbF5ke7+9uwrcZ+zj8YNU+I1lqGka09ndarotvbvcXsKvb3DNJN cx+Xd2jL/ot0v2b95GrOuW3K21lqb4fftG+GvEWk6fHrWow6RrUgYTQSK4hVhnnzGXau5V3YLfxb eTXrdrfQahax3FtNHLbyorxyI25XVujA1+a4jA4jD171LryP6CwWaYTH0eahJSv+Hqc38OPiBovx G0afUtB1O61O0S42NLeWklq6741mjUJJHG23ypomVtvzKy/M3WuiXUraS+uLFbiFruGOOd7dXXzE jZmVGZeoVjHJtbvtb+7XP+B/ht4d+Gen6jZ+GNJj0eyvr2TUJre3dvL86QKrMilsRrtVfkXaq7fl WvGrz9j+PRkhg8DeMtQ8KWkMd9HDb7G3WrXLwszxzWslvM7L5Ma/6TJcLhI/l+WuOU68Yx927PUi oN2ue/a9fXun6LqN1pmnnVdRgt5JLaxEyw/aZVXcse9vlTcwC7m6ZryD4dfGrx74j1D4f2ev/DHU NJj16yuJNR1PdJHHptxC0y7JIWj3RiQRxsvmMv8ArwqtJtY16J8VvBup+O/Aep6Fo/iO88JajeeU ItZsd3nW+2RXbbtZT8yqy/eH3q7EYFOcakp6S5Y/L+v+HBNJbHiN18O/i1rHxal1ef4lWumeDbLU YLuw0PT9LVmuYQu2aGdmO5cqzLu3SKWbzAsbKqr3Xhn4VeF/CPiTVPEGl6Z5Gs6iZPPupppZnCyT PNJHH5jN5MbTSNIyR7VLNuxXa+9I3TriqhQjF8356kubeg3lSMYAqrFY20N1cTxwRRzTbfNlVNrP t+7ubvis7xJ4q0jwnp7Xur6jDY26A4eZtu47Wbav95sK3yrk8V82fET9o7WvF96+heB7a6t4meSM XVvGz3V2u3+CPbuj/ibd975Vbj5lr2MLga2Ml7kfd/m6HzeaZ5g8pj+9leXSK3Z0v7TXxY0VvDd3 4UsZk1DULqRRObaRWW1Mcgb5vV9yY2/99bflz4h4N+DXi3xw6NZabJbWjKsgvb4NFCVZWZWX5dz7 sDorfeWu4+E/7NOsa5q0N54pspNN0aNVm8hnUS3O4f6vCtujH97dtb+H/aX61stPt9OtY7e3hSCC NQiRxjaqKPugCvpJ4+llNL6vg5c0vtSPz2hkWJ4qxX9pZtD2cdox68p5J4B/Zk8M+FWS41T/AIqK +zw14g8gYLdIvu9G/i3cjcNteyoq42gYx7UpGCMAUu3Ge/418rXxFXES56srn6jgcvwmXU/Y4Wny x/r7x9FLRXOekFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQBxfwV/wCSO+BP+wDYf+k8ddnXGfBX/kjvgT/sA2H/AKTx12lAETqrDBHUV8qf GD9mmexkvdb8Kg3Vq2+efTFQCSNt27bAqrhlwT8nX5fl3bttfVrYx6UzB2kHH5V34TGVcHU9pSZ4 Gb5NhM6oexxUfR9j8z61/Dni7WvCd19o0fVLvTmaRZJFt5WVZNv3dy/db/davsv4g/s/+G/iJqse pXbXVhequySSxZU8/wDu7wVbcVHf/wCJXb5prP7HIaS5k0jxEyJt/cQXltu+bb0Z1YfxYO7Zlf8A ar7innmCxEeXEKx+HYjgnOcDV5sI+bl+FqXKzmfCv7WniTR1WPWrG110KrfvA32eYtu+8zKrL042 hR2/4F7N4Y/aT8F+JpEjkvZNHundlEWpp5Y+VNxbzBmMD6t/SvA9a/Zd8d6X5X2e0tdV3lg32K4U bPdvN8v/AMd3fdrgta8A+I/Da3DanoeoWkFu+yWeS3byQN23/Wfd+8fvbvmrOpgMpxqvRlyy8n+h 2UM+4myb3cVSlOPnH9f+Cz9BtPvoNQt454J0nt5VDpJGQVdW5Ugir3GOa/OTw54u1rwndC40bVLv TmMiySLbysqvt+7uX7rf7rV0usfHPx7rVqLe58S3iIH3lrYRwNj/AHo1Vv8AgNeZU4br837upHlP pqPiLhPZ/v6Mubytb9PyPsLxV8WPCfgmR49W1u2huVdUe2jJlmUldy7o0yw4749K8I8ZftbXV5HL D4Y0kWII2pe3pV5FXbz+6HyqwY/LuZh8vT5q+eKK9rC8P4aj79Z8x8ZmXHWZY73MMvZR8vi+/wDy saXiDxJqninVH1DV76e+u35Mkx3YXczbV/ur8zfKvy177+xzaQyXfiq4eKJ54ktY43K/Mqt5m4bv fav/AHyteQfDb4W6z8TNaitbGCSHT/M23GoujeVDt27vm/ik5Hy+/wDCvzV9teDfA2m+A9Bg0jSr fy7aNt7sxzJK5+9JI38TH/63TiubO8ZQp4f6pR3f4Ho8F5PjcXj45rifhjf4urt/Wp1Y+lLSUtfn x/QAlLRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUlAC0UUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSUALRSUtABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQBxfwV/wCSO+BP+wDYf+k8ddpXF/BX/kjvgT/sA2H/AKTx12lABSUtFABR RRQAU1o1PVQfwp1JQKye5zeseCfD3iO4WbVNE07UZ1XYslxapIyrz8oLL05P51yP/DNfw5/6ADf+ Btx/8cr08KfXH4Utb08TXpLlp1HH5nm18twWId61GMvWKZ5ef2afh3/0AWH/AG+z/wDxdbGg/Bvw Z4YWNtO8P2aSxy+dHNOnnSJJx8yu+5h90V2wyvXpS7s8AA/jVyxWJqR5ZVJfeZ08oy+jLmp4eCf+ FDljVcYUD6CnUUVynrJW2CiiigYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUlLSUAMzx0/CmqvQ4/D0r5T/ak17x/wCBviNpninw5qt/aafa6X5ltYGWR7C+nt1vbi7jnhH7 v/j3RfvMsjfL5e5o22/R/hHxNB4y8J6R4gsYZIrPVLKG+gSYASCOSNZFDAE/NtbpWMKkZS5f6/r9 LEXcr6bf1p3/AM0dHRSClrYsKKKKACiiigAooooAKKKKACiiigAooooAKKKKACikpaACkNGayvEO uWXhfQtR1fUJTbadY28l3dT7Gby4413M21fmOFHalsBqilrJ8Pa5ZeJ9D07V9PmNzp19bx3drPsZ fMjkXcrbW+YZVh1rVzRuBz/i7xVp/gfw7qOu6vJNb6XYxiW5migkuDFH/E+yNWbaudzNj5VBZsKu a8Su/wBs/wAHS+K9Fs9Lg1DUPDlzZ3GoXmv/AGS4iFtDGJfmjt2h864XdDIsjKu2PqW+Vtv0U2Px r4a1L4hR/s7/ABEudJn8INd654fLWejas2r3ixyaBJM08Ntsb5ZfLWSSFWbcqtCv3vLryMfi1gl7 apLlh6X1/r/hzsw1F4iXs4xvL+v6/Q+19N1K31axt72yuIbqzuY1mhuIXWSOWNlDK6sOCrL0NXz1 56V8xfBXxlaXlvs+F80KrcTteX/gPWJPLttMZg29rS4Vd0MPmfMyrHcLukjVY7fczL6NNo/xS8U+ KYn1HV7HwV4bhsmHk+G7qO+vLi6Mi7WkkurLasaxhvlVd24/eb+HWhjaeIpRqU/e9P6/4JFSjKnP lloerbs854pRivi7xn8edU+EniyGLSPEuqeI7mLEuq6Bqgja3+0NIzSrJdbpmjmWHbH5Nrtt1mXc qqu6FvafBH7T/g3xxp7T2dv4iW5j/wCPiyj0C8u5IMltu9raOWP5gu5drf8Aj25awpZthKtaVD2i 5omlTB16cFUcfdPas0hauZ8GeO/D/wARNBh1jw1rFrrOnOVBmtJN5jbarbJF+9HJtddyMFZd3zCu lyBXrxlGa5onF8O4+iiirAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOL+Cv/JHfAn/YBsP/AEnjrtK4v4K/8kd8Cf8A YBsP/SeOu0oAKKKKACiiigAooooAKKKKACkpaKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKAPl74xfAn4h+MvixPfadq2naj4Y1BVk+z+IkN9Y6VtiWCT y7GRXjkn+bzoZV8vBWaOTcsnzevfBFfGy/C3Rf8AhYn/ACOGZvt3+p/57yeX/qf3f+r8v7v869A+ tZV9r2mafqWn6Zd6ja22pah5os7SWdVmuvLXdJ5an5m2r8zbfu1wU8PCjVlW5vi+41lNyjyGxRSU td5kFFFFABRRRQAUUUUAFFFFABRRRQAUm6isObxdoln4ig0C41jT4dduk8630yS6jW6lj+b51izu Zf3b/N/st6VDko/EBu0UUVYHknxk0lNY1bQIte0XUNd8BRpPPqdpYQtcr9qSa2azaa3i/fTx5Wf9 2qyLuZWkXau5YPhL4qh8O+GfCHg3VLO8tS1uulaLrCGO4stbjt4XZLiKSFpFj8y3h87y5tn3mVfM 2s1evEfLXyf4M1i203w1ofh3w3e6feeLPBnjjU3i8ONc2MV5qdr9qvYZ/JhWaFY2W3uppF3LGu6H /V+WytXl1f3Nbn7/APA0/wAvM6I+/Gx9ZcZrg/izdeDz4btdC8ceXNpPiS+h0OK1liklW5uJj+7T 92uY23LuWT5drKrblrp/DuuWXijQ9O1fT5jc6ffW8d1az7GXzI5F3K21vmGVI60mtaDpuuGz/tPT rXUTZ3CXlsbq3WX7PMhOySPcPlkXccMvzV3y/eQ90xXuvU+RfGfxe8V/s76fqQ0/WLXWLLwz4gj0 D/hELowui6ZLCtxZSQzLHHNDIturQ7pmnVmSRvm213PwZ/bMtvilBetdeBdf097O5W3uX0lDq8du rRyNG0iwr53zNDIu5YWX7vzfNVT4leEvDfxM+O3ieLWtPt/7LsvD9rpNx4ivZI4xp8/+kXkkFuJ4 2jkma3eGRnXmGMM25S1fF+taL4k+FfxDuLzT7KXw1KUvJrKPVbRYkFpJHIrRtDdNIrK0e5drNJub 5VZmrwqVRqfso1Peu9O9un/Db/efQ4fBUsZTaj/EP1K8H+OtA+IWgwaz4b1i01rTZNv7+0l37GKq 21l+9G+113I2GXdyK+bP2+NW0/wjofh/XLhba6kvJv7LutMLLDc3UIkSZZ45Pm2tDtkjVjHIFF9J 93zDu+IP2dofGT/Gbw7pfgy+vNP1q7uljkNtffZN8Kt50iyMysvl7Y921o5Fbb/q5Put9i/tCfBH 4jfFHS9Jefwr/bXj7wzdeXZ+I7OLTf7O1q13KytcwXEyyRsvz/u9rx7vM2/LL8s4mpLFYeVKpTPC oVJQnde7KJ4BoPxIutJ0fWdR8XQ+ZcHQV1HQ9SN4qXqSPeefG1u/zLMzXUzNJ/y1jVbgblaNo29M 1f8Aa+0z4k/Ce20q6F1P43m8jfPFfXFnHC0MkjR3Hlxsq/aFb95tj3R8Rszfu1iXyr9pOx+LmpfF bW18T3l0l3pdqkcKLetBbS280K280lorMqss2394sf3t0m5V2sq/Pt5Z3WjX3lzq0FzHtb5W+7/w Ja8WtUhNyhhZck+X5+rXf+rndiMdW9tGtKj+7j91/J+Z7pa3D2sjOgRmZGj/AHiLIu1lZW+Vv4vm +VvvK3zL81S6Xq1/od9He6be3Fhew7vLuLWZo5I9y7W2svzfdavO/DnxE+0SRW+pKsW75ftK/Kv/ AAJf/Zq7mvyTFYfEYGpaofeYPF4fMKfNSOv8K/EDxPoXiqw1fQJZD4it2aPT4beJVim82ZWktZYY 1/eW7Nu2wrt8uSTzI9rbt31j8Pv2mvFvxNt9S/sXwPoj31nafa49Pm8Vlbi6XMi7UX7J8p3R7f3m 0fvI2+6ytXxBx6ZrsNH8VahpepweMLTVY5PElvJM0qyfuWVfLjjjuN25fOkZpm3L8zN5bNIsis1e /lGe4nBy5ZyfL8v17f1scWNy6lX96MbSP0b8F+LofGGlzXCWd5pd1b3Mtrd6dqHl/abWVG+7IsbM o3LskXDHdHIjDhhXSbhmvhH4W+MDpnxOV/BWnWugPaSwR3+mWTtcLqumrIzXSt++ka6urfzGkhkh +aSOOT5WVlhr7g0/UrbVrG3vbK4hu7S5jWaG4hdZI5Y2UMrqw4ZWHRq/YMtzCOPpc0f6/I+IxOHl h58rNGiiivaOMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooA4v4K/8AJHfAn/YBsP8A0njrtK8++Da3H/CqfBTl4za/8I9pwSII Q6t5A3MzbuV5TjaNu1vvbvl9BoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBua4zxl8KvDHjzXfD2sa9pf2/UvD9x9 r0ub7RJH9nk3Rtu2qyq3zRx/e3fdrtOorj/iRZeJNQ8JXVt4Sv4dO11pYGhuJ2VAI1njaZdzQzKr NGJVVjG21mX5axqRi4aq5Ub30OvGKDXyd+xf8Vdd1LV/GHw+8WeIptZ1nQr6YWsWpRyPqKwxzeXO 00itJGV8xo1VfOkZf3nzNGI6+sh6Vlh68cTT54l1IOlLlYtFFFdZkFFFFABRRRQAUUUUAFJS0UAQ 7eR9K+Obnwv458J/tsDxbqHhPUr7wprWoRwQT6SZLyCHbZ/ZY7yXyyvlsvmsrecrKqyXG3dtVq+y jgHpRxmuOvh44jl/uu5rCo4c3mOoopK7DITrXJ/EnwHbfEjwldaDc6hqGlxTSQzLe6TMIbmGSGaO aNo5GVtrbo15rrOKXiolFSXLIafK7ow/C/hnT/BujR6VpUU0doks0wWa4kuJWkkkaWRmkkZmZmkk Zss38VbfUUcUH9KIxUVyxFufOPxv+DejS+IJ/FOrpqOr6FqszwapEGgQaG0sdrCNUhkZVZVj+xQr IrMy+XJJJ91ZFk+bfGPh3xz4m83wlY+EJfFukpI90njCPTZ5m1BVVpPNt7qVfkjk5kZY2bzJnk8u Ro5ljb9HeMCvBvix4B0v4W6Tp/i/wmF8J6fpOoWkus2Oiaavl3Gn/aoftTbI13R7UVZJHj/1kcO2 RZFWPy/KxGHpqM58uvf7S9GevgsbKg7PXsfnB4k+G/iH4ZePLS+1Wx1fRbP7bb3CalZ2k0Mtr5m6 RfL8zy28xfLk2/Mqs0Mm1tq7q/Sv4d/tReEPiF4Gh1hNSs9G1donSbTtYuPs8dvcqm4QyXG3bhuW Vh8zKsjBf3cir5v/AMKQ1rWtI1XSNUOpeJrfw495Z6La2t/ax3NvaywyQrayT+arRySRzW837yNl WGNV2/vGjr4m8TeHb7wfq8k6add2Fmt3NbxLfruaNo/vQyNtXdIqsu75V+991a87L6zp0XSn7tV7 c3W3n/wfOy2PVwOV4fG+0j7T3r36f8P/AF6n3B47srXx94ZvLXxFJEE8N2ml3Njqt1LLeJKkiyqW mmQq00Nw0a/vrdQzbomYbo2jj+Vv2tdB0vwDYeEfD2izZ+0QSXGqRr5kbtOs0zQ+dG0Me6RI7plW RvmkXa3lwrt8ztNN/aE8V6b4qttXs5202ztZpPs+kxFWjjtWkZo7WRlVfOjjWRljVv8AV/8ALPbU fxAuLL4lMsuraZb+ZMoe7aENGbicM37/AGr/AKtmXGdv3jk/xba78hwmG4m9tOirVoe7LzS2f4b/ AII/MuKs2xPCqp0K15Up3f46pPyutGv+3mfIFen+EfGFpqNhaWdw0keoRx7XkuJVZZ23NtZflXb8 u1drbmZl3bvm2r6r4P8A2T/CPjHwZrc0PinXI/FGmE3ZsLXS47xp7VtyrHDAsivJJ5m3dJuWNd21 lX/WV803mm3GkzK25l2t8si/KytXz+b5JKpGVCr8UfwPcyPG1Y0Y5hhI81KR73omgap4kumtdI02 71W4jj8xobOBpmVfu7tq/wAPzL/31VW6tZ7KRUuIpIZGjWRVkXazKyqyt/usrKy/7LVl/Cn4neGF 1DTrPxvdataWFxcRtJqWg3LQzaXcRtuhvNu3bIy/vF+6zKskjL821a+lfEfw1vfil8SNcvLDWNH1 Pw3rF3HDp2o+HLSTV7K0dbj94sk0LbrW48ubzmVl+zyNNIzMrbZF+Lo8L1K2G9pGX7zX3f8Ag+Z+ i086w9WfKnoeJafcadpNno2pRKl9q0GoSST6feJ5lu0MfktDuX+JWbzlZd33V/h/i9U8AeItX8D/ ABNsT4CkXQ/t0S3UngbVblY9Nu1khhaGGGaP5ftEkaxss0kcbbtqyNIzNuwvEH7Pev6drWo6do97 pniW8sjcST6bZ3Hl6jFDG23zJLWTbJ825WXZvVty7WZWVm5TR/M+HusalqGtGbQ9X0K3W7tLK8tf 3z3W6NYVWNpI/mXzFm/i+WNm8uRdy1yYKjmOAxVOj7OUXKS/r+nbyNa0aGLh7kuZn6WeEfFFv4y8 J6P4hso5YrTVbOG+hjmAEixyRq6hsZ+ba3QVu7uPavz38G/toH4cLYaV4fhk1LwlDHNIuk+I963O nKsG6Ozt72NpGlj8zcsbTQqyrtVm2/NH9f8AwX+OXhn46aBcan4emnjmtZfLu9OvQqXNsx+6XVWZ drKNysrMp+b+JWUft8Kjso1dJf1/Xc+LxOBxGF96pGyPTKKSiuo84WiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4v4K/wDJHfAn/YBs P/SeOu0ri/gr/wAkd8Cf9gGw/wDSeOu0oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApKWigD5K8B/DfSY/wBtTxZq T2NzZNpzSXekyWi24hnma2ja/wDtDY85m3atGy/w/Nt3bY1Rfefhv8XvCfxbs7y48KaymqJauiXC +VJDLFuXcjNHIqttZfutja21sfdas7S/hPJpPxo1LxwuuTTWd9ZPCdFltkxDcSLaRyTLMPm2tHYW 6+WwOGVmDfNtrW8A/Cfwn8L/AO1R4V0K00RtTn+03f2Zf9Y/ZRn7sa7m2xrhV3NtVdxry8NTqUJc qXu3ZvUkp+95Hb0UmaWvUMAooooAKKKKACiiigAoopKAFooooAaO1I2ADmjPK1yvxLu5rD4f+I7m 3kkguIdNuZI5Yjh0YRNhlPY+9OMbtIyqT9nBz7HTjDDB471l+INcsvDGialq+oTG20+xgkurqfYz eXHGu5m2r8xwoPSvhL4QftL/ABW0S3V9WnTxbarI6DSHQm9L+eVaGFlVmkb+75jfd3feby1r6bn8 eWX7QHwJ8Wp4TYXmp32iXdm2nsTG8N3JbughbzAv/LTcu75V4PpSqQqQpqbj0vr/AF/Wx5+FzLDY qo6cJe9F2+fX+vnsee+KP2jPiJ/YMHibTtE0jw7o146JY2GsxSXd/JG3nNHcTrHNH5IkWMbY9sn3 ZP3n3d3M33xd1bxPDqR8R/EfVEtLq8hSLSfClsmkNBbKm9pPOaOabezqsexZlG7d+88tsr5d4d8b X2sR2uorrGpW7XIWSK5luZYzCWkbc74VmPEs/wB3/nqzLu3bW3vC6eEtWgi0/VorvTtRurqOJNQg dVtbVPK275Fkb5tz/NIvy/L91h91vsqORYP2MatXmn5xb1+Wx+RYjjDM6mLlSoSjD7PLJLRrz318 /v3t6j4D8U3vw5jvfE8Pjv8A4Sbwq+s2thPB4g1i5Z4bOSZYWuc3CHbJC1zas20rGytJu+by2Xu9 a/aW8Fa148tvBNnax+M9J1Czj/tC+025tru0hFxdxWccci+Z8ylpv3gGSqtH8rBm2/M3jrwXd+Av EM2mXZ85csYLjy2j86Pcy7trfMvzK3/oSsy7WZlj8SNR8K+Fb/SorayvNMuorqG7tbpeJ4540jkj Z1ZZFXait+7ZW3Afe+WvLzTJVSw88fhq1qcVe1uy8v8AI9nI+MZ/W4ZVjcO/ac1r83Vvz9e+x9E/ F678C/s16l4P8V6ZoWl+GhNqX9nahJp2mtCtzYTL++VmhhZd8ciw3Cq21mW3kWNvmZW+a/jx8WLL 4n62I9E01tJ8OpL9t+xtFHG11eN/rLqRY/8Alp/DuZm+Vf4dzLWT4F+Kt54a8M6N4T1WGfVfB2kS rdWenWV9Jp8zXCzTTLJLcR/My+ZNu2/L81vbt2bzOc8K+G5/FmpSWdrKkTRIs0shjkk2K0iwr+7h WSRt0kkce1Vb5m/u7mr+e81zarmkY0cv6/Ektf8Ahv6Z/SmX4JYWUqtfRxMeuo8M3YhW3lmuIrlR LsGnusmSq7W+Zl2/K3zL8rbvlb7vy7vTfD/7NsX9lwXHiHVXtJdU09pdMmtgrWPneRJcJJJeLuj8 lVTDbfnZtzKPLVZJI/HnwlPw/t7e8tQsel3Xlyx2VxdebfWyyK3l/aECrtZvJk+6rKrRsu5tu5vr OBchzHDZpDE1peyj8/e8tGvx+5n5x4i5tg6+UVaFOn7WpH09zz1T9NO+6POtN8Rat4L8QDUtL1S5 0fUEO9L6Fm/eruVvLkVflZdyr8rfK235lriv2jvi5P8AFzxdDe6ro1hokvlRxs2nQKrXDKqq00kn 3pPu/KrbvLVVX725m97h8ReDJvBUWk3vhC4m1OORXbUI7/bJIGP7w48tlXhVVY2B+Vm+ZW5bkPif 4Tv/AIr67feI30O2s7LVlTa1ou6N7zaqtFGoVm+0TSLuWP737zc3y7pK+qzzL8XkdWpjacOalOWv dX6vWz9bJu+rb1fmcD8R4THUo5bVlH2vJp281ayt6XaX2bLQ+Sbq3+y3DQs27b/FXQeB/iZ4r+Ge ofbPC3iLUtBkaSOSRbK4ZYrho23L5kf3ZF+ZvlZWX5m/vV03iT4Qz+C/EF1pGvIdLuLXzFla+DA+ Yqsyr+7VvvbdqsvytuVt235q5qXQ7Xayr/Ft+ZW+7Xk4eh9ZXtMPLT5/d6n3T4axVSXtKXLy/Z1/ Wx9FfD671Lxpo2leMfB2p6x4k8ceFZIZltbwQtfzeZIsl4jRw3HnXFqbi4mbz/LjZVufLbzlZnt9 n9rL45+HfjToPhv7PaXen+ItIuriG6tWuI5oU3LDuaN49yyxsy/LIrL/AKtvl/eK1fM3h3x14v8A hfHqKeG/EeoaHHqqLDctptw0DSLGysvzL91lb+Jfm2sy/dZlb0nw58fJfFHh86D4jsJf7QbXG11P FNjqk1i0F7M3zXM0KxyLJt3KyrDHGyrG235mbd3RxU6P7ufxGuX4yWFxUKGLpvnp7d9Tl9U8P382 htr4exutMikttPkms2jj8uZrfcsbQ/LJu2xyK0m3bJJHJ8zfea78LvGT+AfHml60NR1rSreORVuZ dBulhuWh3K2394rRyLuVW8uRdrbdrbfvVpr4F1fULGK0tvJsNLEcc12v2ll89lZtsjRs3zSL5zRq qqvy/wAP+sarmu/DmxbTGXR7K7jvVnkZZrvVVmWSHc3lx+WtvHtk2+X83mbdyt8q7vl+qjga+Npc 7pO3mrHrYjjLh2MvqeKxUPe8/wA3tf8AHyP0M/Z7+KUnjjwlYWWuapa6j4khg82TULGCSGy1SP5f 39sZFXft8yNJlVV8ubcrKoZN3rZY84HFfkJ4J8GyXEzahPcXmlX1jcK1rJbSeTPDIu1t27buVvus rV91fBH9qrT9U09tE+Iuq6foPiGzj3jV7uVLW01WEYUSKW2rHN03Rf8AAl+XheOpgsRg6UZ1V7nf /P8ArU/P8RmeVzzGeX4SvzTj/wAPp3PpelrP07UbbVbC3vLK4hurS5jWaG4hdZI5Y2UMrqw4ZWXo 1aFYnUFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFAHF/BX/kjvgT/sA2H/AKTx12lcX8Ff+SO+BP8AsA2H/pPHXaUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAN618L/tD/s9fFHWYU8Ya1q83juTw7HHDY6Taxx3Et3IzMrXC2qWaxqvmfZ2a3ZZt0ayK03yr X3QeKOorixWFjiocsjalVdKXNE+RPhD+09aaBqlnoWqp/wAIx8NdMij8O6bd65asuo219bx20awX 7RyNHCzf6S3zLGuIW+b5Wr63Vickc18y/tL/ALONhq1u/jXwn4dtG8Q2vnzahYw6bHcLqO6OVll+ zNtWa4WZkbczfMrSKyz/ACwtzPw1+Nmv+B7XVbPRtH1Pxp4H8K6ZBFqAvNSsre70SO3muYZFWH7N DNI3kwKyxzLG/wC7b+8rNy06k8O/Z1Ph/r52+WmxD96Vur/rta/z13PsUUtZGja9puufbP7L1G01 H7JcPZ3ItZ1l+zzJjdHJtPyyLuG5W+atbNerpJEC0UUVQBRRRQA0V5r8cvFnjDwb4JW78FaHFrmr vewW8jXEU00dnC7fPcNDD+9mVflXbF83zbvm2la9KFeW/HiX4iW/h3TJ/hxF5+rx6ikt1A6W7pNb rDMfLbzpI8I03kKzIyyKrMy7tu08tZtUny/gXD40a/wc8fXXxN+HuneIr3RZfDt3cS3MMulXErSS WskVxLCyMzKvzbo/ulRt6dq7vtTFXb3p+a1pxlGKTdxStfQdSMoYEEUtIa1JPiT40fs0+JvCvi7U tY8C6DHr3h3UpDe3FhaTx289lN8xk2xttWRWXbtVfm+Xbt/ibhdD+FWpabdXnjzx34QY6JfanfW9 7oeo2aJlbi0kZZftEsjPC3nQ26+crb2kuNqszfK36GrhTyMk+ntXgf7YGm6jdeEvB8+m6DquvtY+ Jbe7ki0W1lnuYFFvcKLhVjZW3RsysvzBSwVW+VjSr4j2lFU6/vRi/ktU3sr/AJnz8cnp4fETxWD9 2pJPbro0t3b8tep86+IvE0PjC+tVNvb+HdPt4Y4bexsonWzt33KrlI937pW+Zv3at91V2s25qovp +s+D7zSdTe1nsZDsvbK6kj3Rv8qurK33W+8vy/w/dau+8O+BfDWreAtQ8VRXkF54eniK2+qRzC4m 0yV54kjiuLeKRcyCJ1faGVlLSK8bfu1r1D4b+BdD8S+DYLfWvD9raS389xaWskLLugZrdBKwSU8S o9syfdZx5eW3/vJW/RI5rhqVJRoLmp/1sfiX+rWOxmIcsXLlry97rffrpp5bHhWnx33xCa30Hyrj +35vMuNGhjiWCxmVY5HljIO2OPcYPlaNfmffv/vL5n4o0u40/wASHRdTI0u7t5vJvWlu1uoYZNzf N+5Vtu1WVWVdzblb+L5V9m+J/wALrb4Y6oILLxPbzzpF5c8LsscqtJC2V2ozPh9pX5gqqsyDLcsr P+EbvPCWoaLq/hdh44W11FI7ZJbdrYWFxOtu1vLJFMPlWZvl37du1RtkjkaNo/z7jHL6uOwTr08W 40t+TkfLbRdNb/h6LU/Q+C8VDA5ksJXwUZV1pKpzxv32fp0106vQuzfAvwJN4BbxRpk91r09qyQp bWuqwx2d20duszzMrf6RHujVpJLfcsit5ir8u2SvMjfyR30FxZRppn2eTzraOx3RrA22Ndy/xbts MP7xvmby1ZmZvmrqPjr4qXx38SNX0PwnYrfWdwjCOHR7O3kke62s7XMckfnb2b920jR+XI0cextr R7q8h1zW/FHgnTrUeINBmt5r6FZrC8aPbb3MbRrJ5it91v8AWR7lX7u7a21q5eCcblFF1aTpxU4/ atv/AF/w/Q+n4z4f4izlUZZZXbXWHNb59L+ZX8X6beaLHHqlhfzRQWcfkx2MjM0cMbSMzLH/AHV3 Sbtv95mauo0f9qSa1+FF94J1Hw1b6mkis1rf+cFktJm3bpFWRZF/i/5ZrGy/N825vMrzvSfEviW5 027vRZT6po9gy/b5vsu6NFmbaqySbdse5t23/vn5vu1yNxL9ouJZvLji8xmbbGu1V/3a+izFYbFS 56HNFdY6pfn5n6Hwrw7jqVL6vn9OFWcNqnW38ktE+l9b3uj6Qk+F/i57RdbttPv18OT6P/attdzT 27STR+TM0aiNmVfMkW385o42kaOORdzM33uN8K/G7X/hh4guw9veaRr9rI1uzfKvkj5lkjmhZfm/ 5Z/7vl/db5duF8Pry8sreKFryZtIk3SR2jSbljm+VZJNv8P+rVf4d21f7q16dH4wmk8PjSb2b+1/ DsNzDeNp86xzRK0beZ+78xZFjZtzbvl2tuZWVlbbXmZbh6U6VaphsY1LXmhKTdur0d1t5P1PgM/x uW5Tm6weOyxThpy1aWj7Wurap3VuYzLjxpaa18RE8Qa/oumTq0VrJbWOnvGthG0cMfkq0f7zdHtV VaHcrfw7l27a7S68Z6dda3pWrX3hnwreRC2jjuZp9FhmEsflxxAyRF9o8sQDYqtFgZO1VkrC8LeB fAfxK0G9k0HWrfwN4l0+JJ7/AEXWZpP7OCboY/MhuG3NHGzSfdZpG8yTb93azcXY3Gq6Lp+pXEDW raVi402a+mWG4s/MWNpJI45W3R+dtXcvlt5jfw/er8/yfNZ8PyqUqmG9vGprGcd3zbXPr8+yPF5x UpVMvxcqPs+VSj0739f69eW8VanFrPxOg8W+ENJg8Ky6fqH2u1t7ONvIhkWbzI22zMy7l+VWVVWP 5V2xr/F1tj4Z+H1n4A8X6/rnijW9R8VTCPyrG+tVX7ZfTLIzSbvOaSRVb5vMZo9rIrMsm7y2ufBf 4V+IPHeseJ7Cy0iR7/TX8y5tLxfJaNmdR5bbvlWThvlZl/1bVP4k8Go93PZazp0trdx7fMhuI2hk j+Xcvy/7rV9XlmS47M19Y9pHlv8AC/i9b+u1lbz0PNzzjLA8M5msJiKE+X3earGz5nZaa+W9mvQ4 P4aa5NLfNY3N5Iy+X+5jkbd/d+7/ALq/wr/tf3a9Frn/AAL8BP8AhL/GC6bD4it9HLqrWUt1uV5Z vMX9zuVf3bbdzK395VXb81fYHgH9m230HT9Uk8fvp1zpsSi4hNtK0Yj4be0k21H2hQvy7tn3mZd2 Gr9OweP/ALOoexxMfej97PyTjLKMFxNmkcwyKpF05x97SyhJLr6+l0fL9TW1zLY3Mc9vLJBcwyLJ HJG21o2X7rK396tfxzDpFr4w1aDQWZ9KSdo4GZg4+X720jqu7dtb+7trCr62DVempNaM/CK0Z4bE Shz6xOl8DfEa9+E+saRqmhLNZafbyf8AE40ey5tNRt9sasy2vyqt0scfyyLt3Mq7vvSM33b4P8Wa V468PWOuaJqEeo6VeoJbe4gOVZeQf91lYFWVhlWVlIzX52173+xjDr+o+JvGmsjV7h/C0a2+ntZS yLNHNfxxqGkT5t0fl24gj27VVty/88q/Os+y6jg3GtQ05nbl/Vfr/V/3XgbP8XmEpYLFXnyxupfh Z/p/VvruikFLXy5+vhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFAHF/BX/kjvgT/sA2H/pPHXaV5/8ABdbj/hVfgJxJF9l/4R6zDx7DuZvJi2srbvlX h/l287l+7t+b0CgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAb2rzv4pfBPwd8XtLu7bxJolpc3NxbC2 j1SOBVvrZQ25fLm2llwxLbfun5tysrMteifjRx61lOnGpHlmioycXdHwj4b+Gfib9j34sai+k67p UHhnX7SaCw1HxVdSW+myTKu+OG5kjXbHcR7XZWZds0fmKrRs3y/TfwD+Kl78VPCl9eanZaZb6hpt 8NPuJNF1Jb+0nk+zwzNJHIvCr++K7dzbdu3c1d54i0Oz8UaHqOk6hCbnT763ktbqHey745F2suV+ YZUnpXy58PdF8W/sz/FDUtAs/B+ueJPh9fWVxdtrFuWuZ7i4t45J1kESttjmaNo7Tayx+c8Mcit9 5a8yNN4KUYx+D+v6/rXe7rtylv8A8N/W/wDwPrql61wHwl+LmkfGLRdS1PSILm2i0/UJNOlW4kt5 N0iRxszK8EkkbLiRfmVq76vVjJVFzROfYdRRRViCikpaACiiigAooooATikYCg9BXM+PdV1vRfBu taj4f0+PVdYtrSSe0sJGZRPIqkqvyjcSf7v8RwuVzuoJlLlVz5//AGsPDPw5026TxSf+Eftfiks+ mtZR6hdyK1xH9sjjZprWNt00fl+YrN5bN5asv8I2/P8ArC7dQaP+0I9VWNY41uo/M2sqqqqq+Yqt 8q/L93+GvQNL+MmofF268QeLdT+H1j4g8OX1ta2b2kLSXC2MdvDcSNuuBGNlxuvmbcqr5aLt+82+ uX8aaD4jt76XXNc0S60xdUmkuA0kDRxiSSRm2/N91vlb5W+bb83+1X1fC9GdKdSrNRUZ2tqtd+nf +raH4t4gYmGKjSjhuZyhe+ktPntb+r6lHx7421jxPp1xrmuapc3klvujZ5k2wQ5jXy9u35VaTY3y 7V3bP4vm2+efED48XfiHXtTn0Cwt/D9pI9qLWTT4TZyRrbt5m7ajlf3kyrN83mMu2NVkbbuqt8Up bddJto38zz2m3RqrfL/tM397/wCyrzGpzrl+s+zj8MV8PT7j9e8Ksiw2LyuWbY2PPUqPeS/ke6e7 1X33PofwD+0tpXh2f+1n8Ii6+IN/PN9q8QPdqqRiTy90kMLKyrNJ+93fLtVm+XdG3kq/4xxyWt7d yavZXfiKz+yZiuPEFzdzahp4k3eXFcf6tY5l8qaRY9rLtmb5pV2tXzrXcfCubxpruuWXhPwnqmqR Pe3P2pNPsr5rdGmjXzN+1pFj3KsKtub+6v8As1+Z5lk0Z/vaEuTl97liuvc/aauX08PL29L3f6/C x1vwF8ZW3gHxF4skHkXHhzUdJvrVf7QsI/tV2u1vJhjby5lRmbbuWTdDt3bvM2rXofjr9kt/FHiy 01zwhe2NlovifTbjW9N0a9kht7oTeX5wsY4VZVZW8xfmVtsa7t33V8zzj4yt4w8JaoI/E9pFZane SyXkqSWlusk/7643Sbo13bfMaT+Lay+X/CsdYfw7Nx408UR2UGu2Hg2WNJLpNZ1C9W3WCSNd0fzM ytuaTy13LuZd27a22ubB5jmuKfNCinTl52a1699O3/AOaVGpK+Noz5f/ACY9G1r4YDxHrPimbwKt h/ZOiwrM9vc6vD5Lfda4+x3W7y5oIVkjZpJGjZVkjWRVb73mMeozx3OqWs1tdWIsP+P1ZkZfs22T y2WZf+We2RlX5v4mVfvfLTPDfii28A6NpeueHfEtzZeNUuJAbX+zY9ttGrK3mLcMzbvM+Vdqqu5f Mjb5W/eeK3C7ZpV27fm+7u+7XFmGR4KPNOMJQlK/wyf/AA3yPDzPGvLYLnj7SPz3X9XPZ9Z1LWvh /wCJ7250fWNS8O3sk81ubOG5mhvLaFWX93MyrGrfMu35f4ofmVflr0DVvGNp8B/iFpuu+GNKm03x jZy3lpqfh/xBYxtb2MflxrbrA0fzMvlsy/aPM8yby2kb5ZWVvC/D/wAX9f0Gz+yyf2fq0C7FVtW0 y0vZI1WNUVVknhkZY1WONVj+6u35dtbnw6Wz1bxBFJqEk0ekxxf6fcfZvO8iGT9zubcreWu6RV87 azR7lZVZlVW+nyurCOGjRl9lf1oduAzDC5pC8t4x96P8x7VrGt6p4Y+LnhweCNaW21bXYrHRbuMX DNYXXlyQxK1vcRbmay/crGrLJ9o/czLJtbduj+DfxYubLxLpmty20ZurEMFjvZ/LiliWJkYKzfd2 p8qr838Kru+7Wj8O/wBifV/iRHb3NtrthY6XbvNbXupxyfbI7mZWZlmsfL2rJbtG0K7mkVlkWZWX 5dtcZ8ZvAd38C/FFvod5dNf742kXzrZbeRo1kZVmVY5JI/Lba23cyyfK26OP5WbuxGNzDL6X/CfT jP8Ami+3l/w/3ngZrw/lPECjQlL99DWLWjVvwfz/AFPc7/Rrb4q+JodG0TTbPwzf2MbC6uNQRbNZ 2aVEWPyEUqjLI/lqu5mZSoZmavSP2lPE3iDTrddC03WVOnx6bHJqUezbdXCNL5RkZtqptLbV2oyt l2+XbjHyD4X+J83hWPQNSs7pbW6jkaRbiFZPMhbdt8lmb92y7V3fL8rLMys38K+ueF/EHxB+Onhv xZH4b1SfxIFaFrqwupRGrMwkeLZ521UXdH8yxsv8P8P3fostzahmbjVl7nsd4z79bPr1/wAkfhuZ cM5nlEMRhXRlKVb7cPhstI32t573XV6nC0V0kvxl0/4meGITN4QtdO1K2ElvLcpPIwZsuVb5vmH+ vdm3NukfazMyrhsKxsZdUnkgtfJkljMcfk+fGs0kkjbY1jh3bpGb/ZVtv8X3q+8p5pQjRjiMQ+SM vh87n4xjsgxeHx08BRXtJw+LlW1t/wCvuI7XTdU17ULDSNFhjudX1G4jtLWKTdtVmb/WNtVm8uNd 0jbf4Y2av0N8E+EbHwH4V0vQdMR/sWn26wo0m3zZmH35JNqqGkdtzs38TMzV83fsg/Dk6trWo+PL 1TJZWobTtIV1Xa0n/Lxcr8v93bCrK3/PwrLX1kepC8Yr8+zfHfXsU5R+GOi/V/P9D924Nyf+ycv9 pUj+8qav06L+u5JS0lLXjH3wUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQB538DdKis/hL4Lkje5Z5NDsM+dcSSL/qt/wAqs2F+aRvu/wAO1fuqqr6J XF/BX/kjvgT/ALANh/6Tx12lABRRWNqU1/FeWBtLX7RbyyFLmQybWiUqdrKp/wBrbu/9Bb+EA2aK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKSlooAKSlooA5/xdb6xeeFNZt9AuYLLXpLOaOwupuY4bgxt5bN8rfK r7T91v8AdNfNi/E/4xfAnVXb4oi38TeCbaNr2+8S2OnRqyj7PGqwQ+XIvzfbJI4VEkKllZpNyqre X9Y9qa3zLXFVw8pvmhKz/D7jSM7aHivwj/ag8O/GDxY+g6bpGuafdf2e+px3GoxQLBPCsqQsY5I5 pN+JGZfl+XKuudy17Tu68Yr5t+JX7MevXOq6lqXw58WyeCk1S9j1HVdPspJLWbU7nznaZ21BfMkj UxyNti8t41b5tnzNXN33gvxZ4W0S2v8AXbDxlb6VaapFFqN5b/EC6uJ47VNsbzbfMjja2k2+Y0zN byQ+c7eWscPzc9KtXjJ060fn0/BP8bBOKjDmjr6f8F/5n0lovjrRte8Ta/4dsbs3Gs6F9n/tK28l 1+z+cnmRfMV2tuUZ+Ut74rpV+76V8U/DH9ojVrHwZ4Z0+08MWcXxE16CGO78VahAbiyvYbeSW3jk lkt5JLi6uP3Xl+W21vMZm+Vfve42fxxuPBniJvC3xEspbXX55JG0i70HS7q7ttbt13NuhjjWWSOS NR+8ibdt+VlZlat6da0Izqq1/K3y/rqRGUKkpQpyTcezuez/AIUfhWF4b8YaH4xspL3QdasNbso5 PJe5066S4jWTarbdykjdtZTj/aHrUGj+PPDWu65d6NpviHSr/WbLebnTrS+ikuLfa21vMjViy7WO 07v4q6uePcLHTUUmaK0ELSGlpKAPGPir8Dn8a6zf+K9D1/UfD/jM6DLo1jcQtbrEn+tZGLPBJJGd 0vzPCytt+7XydcD4paTptnoXxIttSfQ7LWLnSYdfvryZxdXeJHE/74tNJDtV9rLHtaNdq7WZmr9E cHr2/u14n+0h8KPGHxLh8PXXgzXLLSr3S5LjfFqTSLFuljCrcRsittmi2sEbbuXzmZWUj5s6EpYX ERxMJfD+V9f6RwY7B08ywk8HUj8XXz6PTf0Pkbxl8H9C1D4Xaj4uu9YvINT0y+Lz+RYSTRz6e01v bxyQlpFVV3NNIu5Y2kbzo/8Ali0i8J8P/glqPxHF5o1jeaTB4gtY7ya1tjcs0motG0P7lZN3khWX zmjaPdu2ybv3e1l73QviRovg34cyfDfUvDUHijWLfUludB1fQEkksdZf7Uu9Ztyq11D50Pk7VVlm 8lVXa0cc1exaP4FuPGlr4X8P698QNY0jUdRs2sbnwrbx3GnLdW6yXUazRx+WsdvH9nt/9X9nVW2q rbWmVqwzHMq0pxqUo813f/t2/fTX7/Q+/wAslVybL6eEUvdhpF26JdtdPV/M+EK1/DXirVPB2o/b 9Gu2sL3y/LW4h+WRF/i8uT70bNt27l2ttZl+6zV7/wCMv2N/FmtWlrr3glNP1/TbgrbJZ2uoWryJ 5RaHzPOVYYZlZYlbd8sm6TaysyszcR8W/g/D8HfhireMrG80nx5dGNNKh0uRpraWPzm82S6Zo/JV ljXavkzM372HdGvzM3VUr0pUvaS+GUT6qvnGCjQ9pJ8393qeVeIvFDa9rFzfX900l3NJukaaTdIz N8zMzfxbmbdurPiuobiRlSRWZf7tc3eRqq2832mG5kuI/MkjjVlaBtzLtbcqru2qrfLuXay/Nu3K vWeD1s08NagVvfs180yq0LRt++X5vusvy7V/i3bW/efKrbW2+BTx/wBXVOjRp+7t12/E8HLuIauM xccPGmox/T+rGXqWkLdM0iNtl/2vutVe88MXUFmt3HJFLC8jRxqsqtN8qqzbo/vKvzLtb7rfNt+6 232Txx8JNI8N+DbTXdE8cab4mH7tb23jiktfLkkjjk227Sf8fW3zF8xV2vHuj8yNdzbeN0Pw3Lq+ m6vqTrcx6dpcHmT3ENpJMqySNthjZl+WPc38UjL8qtt3Ntjb3amBoVPePRr5VlmYqVa3Kn/6UYui /C7VdY1f7CXtrULfx6fLqDTeZZwSSMyq0lwm6Py/lZty7tyqzLu212958LfFun+HL65triTxB4P0 y3jcar5S+Vb7ZmDRxs3zRr508reT8rSLJHNJGvy7Y/BvxAbwLoevQWdjZ391rdtJp9y2oWzSLbRf KyyQssm3zPM+b5o/laGNlb5mWu9+C+vDxt8ZdIW3vLHwTc3V+LlbaOeSLTppFaGSG3jh+ZtvnR+Z 5LSKsnyxxtC21q8yWDr0avtKNuWPrf8Ar+tDjjksMtqfWKMdKf8A5Mcb8H/HPjjwN4vspvA76hPq dxJ5h0uzjkmjvljVvlkhX/WLt8z/AGl+ZlZW+avaf2gvEvi/yF0zx54K8MyWcJh1R7PTpGW801bm NVmmjmjZVVZLrzvmkWZVmx5m8NA0lPxx8E9U+Ffw10zxp4s1i8sb+SW2i0y1061aa4tmFvut/Mkk eNrdo/L8v5Vby9q7d33VyPil+0x4p8Q6B4cstRXw/qWox2LNPdrax3G5ZPJZVmhbdH5n7nzGj+aP dJH+7WaFfL6MHmFTES5a1Lk/l81+nodc5LGYmNbDwjLu9V+J5t8P/DGtfETXLjS/DVpp8t9cOzxa GbnyfMjbczeS0rfMsaqq7WkZ9rbtrbZGXO1j4l3fw88RzafoLX+m2UNw1vq+kzSXFn/aCxySbo7y FZNqsyyNGyq3y7f73zN02k6HpdnpnhbVLWO70LULjcttrGoNNa2jNEs0lxeeYrfehkkt44VhkZma 1Zmj3SLG2PbfBWLXNSn1/wAV+MJ7DQdUv7hLfxAunzXrX8gk/fXGJGjLRLu3NI3zbm2qsjLJ5fnZ pRw1Cbqx92Ut+zvpr0/U486q4qpDkwkdH9/9fivy7nwHfR/E63urmPxdHDqEVtcalfyamvy2qrMs e6aRm8yRpGk/5ZrJ8zQr95m8ua00C7+L3iCLTfD0Gj6Hrt4y21np4kutk7fM0k26TztvlxqzMrMu 5Y9qqzfK3Y+F/hH4V1y98LaZ8Lta0DT/AB34fmurq31C5ikSPWGaHbHcN9qt5ts8LQrMbdY2jy0j RtG25o/of4O+G9N8cfFrxR8RF8IP4Yg0+4uNIsbW8ghFxLfbtuoXjNGrL95VhVo5mU7bk/ekavmF gauYKivrEp0ub4W29l010tt/wD5XDeywFSpUdBQq2+JJK93107nuvh3Q7PwvoWnaRp8RttOsbeO1 tYN7N5cca7VXc3zHCjvWrQKWv0Re6rHkhRRRVAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcX8Ff+SO+BP+wDYf8ApPHXaVxfwV/5I74E/wCwDYf+ k8ddpQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUlAC0UlG4DqQKAFopu9 f7w/OlzQK6FopM0UDFooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigBKKWigD zvUvgb4K1DXH1qPRv7I1qUymbUdBuptLuJ/MZWk86S1aNptzIrfvN3zc1ix/s+2sditinjnxxFpl tBcQ6fZQa20IsPMbcrJJGqyTeXjbGtw0yqvy7a9doyK5nQpS3iWpW2PiDxP+zv8AGzxpqOsaNq97 4f1nSjq8OrPqetyLGNTnW3kt1khht4f3IFuYVaORW/eQ7lZlZq4/xt8JX+EvirwVB4p0uz0HR57q 2SDX7O+lltbe687dIPOjjt5LX7qyZ3L8qt+8+Vtv6ElTnOc+2K4b4zaDr3iL4W+JtN8Lzww67PZs LVZ7aG4juGHLW7Rzfuysq7o/n+VfMz2qKSngaM1R97m6NJ38u/4nn4zAYfMqsalb3ZRtZptW/T8N j4e17xZ4p+InxC0bTdK8Zaxq3/CFaktxp/h7X7MXF7Lc20irmZYI42kZSrbmbO1TJ+83N83fa1+1 N8a9FuBZp4f8O3WpeYY3sP7Ku47jk/u9sZuNzMw2/L/tLt3VB+z38M/Gnwt+JWv39v8ACpbmx0OG PTrI300drqk9vcXUkxmimVmtLiZIZNsn7xfljjjVl+ZW6T9oSH40+JPhnq1/F4KsyZdT229pYzG7 1K2sllbyXa1XzI/P7NJDM37udlaP5d0eeExeC9lKpXoOMutnLp6v+vIxngszo1oyw+JU4xfwzStL 5qzWvb8TrvBv7V7afo9zd+NrC3m0exaGO88XeFIbq40y3mmWSRYZo5I1midVEKttEm2SZVfy2O2r 1n+3N8I5tT1C0k1u+tYLby/JvptMuPIvNybm8sKjSfL91vMVefu7uteYXuj/AAi8Q+F9cVvFunv/ AGpDaS3OoeH9IuPs6NIZrjy3iSSSSFVh092eGRlWPYGk+7CsXP69+zzpD+FLzxN4YWz13RbGVo3m S1hcT7ditNbyRNIske4yN95fL2MrfMrV62Ew9LFS5aWJXzWv4OxOMzR4Gl7SvhJvvytW/HXo3stO +p9ESftg/Ch9H+32niy3vGa4+zQ2IRobmSbblV2TCPy0b7vnSbYVb70i1543x68MfE6dfPvD46l1 SBf7E+GNpp5KvMski7r+Zl8tpOBIyvthhVPMVZtkczQXHwKuviN8F9Wm8H3emWX/AAk0ERn0mHT/ ALJbpcrJtDGQNvMcPzHy28xflYxqpZNvgPw9/ZS+KXg/VJ9SvNKaaOyuroW8tuf3262kVVuI9yqy qzMzR7dsjeXuVdu1mUcGpYmNCc77+SXr/VjWGcYeOFniZUJ7c0dU7/d/wPkcx4g16+/aA1O817Xb prTWIwQJljkmWbdJ8sMK/LHBbwx/w7mkZ5GZvM8xmh4z42eMtTa68MaIl/50eg6Z9jgn+VrkK00j bZZPvfKrKqr91V+6q7m3e/w+CL/+2G0+6tjompzRSX0elw6RctMY9/zSLa2tvI0cO5lXcyqrMNq7 trbfOPip8HrjxVqFjaaXBqjeMItN+3T6VNoOoxzmDdN8qx/Z/m2yCOPzPlj3TbdzKrMvpZvQwNHA OlTqr2nXX8D5WHEmf5xmVOdPDzo4TXljb8W9/wBF00PMfDPxu8W6DHoENhqSWdxod3JeadqGxVnt 2kX98rMv+uWTbGred5nyxqq7V3K33R4d8PeO/wBqbTb/AFbxxpNvpvgLX9Pa30nT0bzJLOZWaS11 LbIy/wALyR7o2Xzg0e6Py/mr4x0n4AG48ZDRLrxPaXRFtJI0egWV1e6k1wv2lfsa2MkcM3nbrdt3 mLHHGu1mkXdGrfVv7Mfhn4m/BSLWEEWt3fhm0kurW4svGDwaRpOnCF3k+2RzedcSbWQfN5MPks0z N5jeXur4jCYlU4+zfw+R+g4atiIU+WUPe7voaOqfsEDwt8XNB8UeAbvT49DSeRr3S9eeZmtA25fM tJIdshZVbdHukVo5Io2En930q+/Zd+A/gE2WoajoWm6NGlxGlvJqWqzLDJN1WPbJNtkLbfutu3YN T6T+1h4U1/TdUtYb3+zPFMVvcNY2UsX2iHVJI2aJWsJN0a3kbTK8caq0ckmxvlWvlrWfidf2Njq3 hvXfFPibWdUhuvs97a3cpuljjtFuN7Naw/6uRVhjaSTzp4WWSSZt00aqvtYKnh6klKEvd79jKqq2 Cw9bE0pWkumur7fee8+JvjVpGi2sfhaz8JaWPhvfJNbSrpMhQT28o+ZYwix+RKPMLOjfNlvvLlWb zbw58QvhH+z3HqF1b+Fr7xHeXd7eyabqVuscll5f2iOSG13u/mK0P2e1ZvMRpI5GaRflkVm8S8da newAaJDp97BPdXS2D3V3C1pDbzec0e2SSTbGqs1vdLu3bf8AR5P7rbfPLHTXXXLC11G1mjFxJAzQ zTraNJE+1lbzXXaitGyssjfLtbd92lxFmmGyytTw+HhzL7Uk+vbrr/Vj8xyfirOMHOVXNacnQm/h 2d/Xt6627nR+K7vwNqVvrFzpVjqOjahJNbz6dZ27edZRwtGvnW8jSM0nmK3zeduZZP8AnnD91cK+ 8Mva2bXcGpadf2y+Z81vOI2/dyrH8scqxyNu3RyLtX7rN/FHMsfufxO/Z98TeF/COkweFPD+p+Jv Dup29vevfWskd0WunjjIkMFsu5doWZVZpLmJVnbaymRmrzDxT4E8W/A3XITrWjwwtM9wlpPqFlHc wXHlyNC0kcc0bKR/Eu5futG235lauHDZpKpV9hOnK5+8ZTxZDH4v6hGlOL/mlrH/AMC6eux9bfDH xFp/x68B6v4X8c6bcahf+E7C10+S7uriP7bcXlx0RWnhhaGdZIVh2s22Rm2yNJ94/LGi/B7W/FWq WkH9n/YLbUGht7a6knby7KSaRfs7XUccckkKzbtse5Y1bzFZWZVbd9G/A6yS1k1bSdP1HVdD0vxh plvLPr3jBdNWe6vbl7hYmjhaORrpZmik2wySfdaZvmaRWb5r+L/7UXjvxdqV3aXeoatb2zQae9hJ cyTadPF5KxzLerDbyLDuuG23H7xZvL3L5bfKrVNadOjifrHLLt/X9dTKrmrymrONJe7I6C18Ja9p nii18L+KYLWS5e6t7CzafT911elbSbybZJpYcR+Ytzax5uF8yDzLRtqrDtXjvHWj3ukS3VvNZ3Wm 2lpcrbtb6nbra3q7lZo2a3Zty/u13fL8q7l+b5l3eqfs1/tSaZJ48s774tX9zqWp6ejJpOtTtM32 YvGsMm798sar5aj7sLSSMzMzbl+b0L9rDUPCfxo8PjxPp161tf8Ah2yjlmX7DieWGaTyxCzZ+Zln e3/iVUWSZl81vlXp+pxzJ08ZKPwfP+ma4TijCfXqWHnLlc/z8l/Wp5f8Gvjp45tZh4H8ER7tT1e+ W20O91Dbdz6TDI26Y7vJbcrbY5JG27f3MjeXubcv6N+D/CeleBfDtjoWiafHp2lWSeVBbQjCovJJ /wBpmYlmZjuZmZic180fsU/BO20GxuvF+p6bD9uWRrbTPtMC/abSRYxHfNvZEkVjMskPlsv7vyZN rMszPJ9bKp6Zp0qMIVJTp/D0/r+tEjzs3rUauJkqEfdJKKKK7TxAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4v4K/8AJHfAn/YBsP8A0njr tK4v4K/8kd8Cf9gGw/8ASeOu0oAKKKKACiqCtcfa4kEcZtTGxeQuQ6tlNoVdvI5f5t3G1fvbvlv0 AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFJQAxmGDzWTr2vaf4Z0m51HUrmO0s7ZS8s0hxt6fr 7d6PEGu2HhnSLjUtRuo7Szt18ySaQ42jj9fbvmviP4tfFrUvidrU7edPDoUc2+ysJNv7v5duW2/e Zvmb5t23cyq1evl2XVMfUsvhPjuI+I6GQ0P5qkvhj+r8j1fx1+1sY7j7L4SsEmjjcBrzUUO1/vcL GGVv7rbmP/Aa8Z1n4w+NtcuVnufE+oowXy9ttL5C/wDfMe1d3+1XHUV+i4fKcJh46Ruz+esfxNme YS5qtaS/ux0R0Y+JHiwdPFOsj/uITf8AxVdd4f8A2kvHWhlQ+pRapDHEIlj1CFWHG35srtctx/Ez fery6iumpgcNUjyypxOGhnWYYeXNSrSX/bzPsjwD+0z4a8VGO21Ynw/f5xi7ceQ2S3SUfL0H8W3k 7Rur2lem4HdnpX5nV9AfAH4/nw+1t4b8S3G7TP8AV2l/I3/Ht/djkb/nn/db+H/d+78bmWRKjH22 G2/lP17hvjh4mpHC5no38Mv8/wDM+taWmqwdQQcg0tfGH7NuLRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUlLRQAlBHBpaKVugH5e+INO8ZfAO31HwX/AMK9Y3UOoSWv h/xtp9neWuoXl5KzLDItxDP+83W8kyrbq33tu5W2yKf0CvPhra6bcLqHg9bHwnrKxxwPLDYBrW6h RFjSK4gjaPzFjVV8tgytHtwreW0kcnebR6UuPevMwuBWH5lzXXTyN6lbm6Hnyw/E3UmMn2zwp4ZC /L9kFrdaxv8A9vzvMtNvdfL8tvu7t3zbV8m+EPjTV/jB48+I2mweLdZ0WXw9eQQ3NxpWm2NpDdzM skTH7PdW000RX7Pt/eTSM3X92u2NfpqvO/ib4l1P4beHbrW/DXg0+KL2a48y9tbNmimk/c7Vk/dx SySSHy4IR8vAZSzJHGzLrVhyvmcnbrv+hEXd7HSeGvDVr4dsJIYGmu7i4k+0Xl9cspnvJyqq0shA UbtqKoCqqqqqqqqqqj5//ac+KHjPwn470/TPDXiGHRtPj01bm4S3hhkuvMkkkVSyzRsrR7YW2mP7 rK/mfeir13xpb+PPFHwnK+GJLXwl44ure3kUXsizx2Um6Npo2by5Ff5fMXcF7g1886f4P8et40uf DHifw9rPi+31i3t9Okt9duk/s5bS0+1SQSSanbWbNMGk3N+8W3mVpoVZZlZmXWNelSqwjUi3A5MX hquIw040KnLPp5efT+vx4Tx58afFlxHJLp3xF16SB9ONoZi9jarbSTTL5y/u7dd3lRqqLJ+7k8xZ DG0attb0f49eBPhfc+HPCOq+Il8YPaapbzPa3wv7nXYbJWjjmd5GaaZVVY0aRjC3zRwy/MyrWZf/ ALI3i/VPDfhKzudE8IHWtFYLc6oNau5FvY/OWRmMM1rIvnNtC7n8yPa0i+Xt8tY/Tvgl8FPG/h1f Ddx8Rdd0PWn8LRTQaHb6bY/vLVWhjhUm52xsyrGsieX5fzeZuZmZY/L2xVXDVHH6pS189fz7EYPB 16eHk8ZieaV+mlktrNd93ta7scz+yb8LfB/i34eT63rHg3S5Nda4v9H1C4W2jbTtSiW/a4VoEX9z NCreWqybfl8javyqteh6T+y54L8Iy2Nx4RbUfCV7ZySSQXVpdfa2i8xVSXbHeLNGjMqxq0iosm1F Xdtyp9l7UvIrmo4WFOCjY9D203szyu8+D8/iC6eLxD4lu9W0/wDc5MNpDY3135MjTQ/abqFVZvJm bzIfs4t9p3bvM3NXhPir4K+G/wBmvVYfGd0+oTaFHqgvbjVrWNZLjb5bSJZTWixLarA1zHGyXCqr JI8MarHtWZfsjBPWjHzYxiirhadSNv8AgnNVSqw9nPb9e54Hpv7RHhjw3peq6VPf+VrumW1wVt/E mqRwyXeoxtumtHuubNZA0tv8kczeX523y41j218mftB+JI/jTdXfiXStfa9fS5JrL+yb+Oxe9KKz TSfZmst7SW0cas3mSnaqhtryNIy19vN8RfBnw5+Inhb4Vadp39n6jqtvPd2ljplksVraxL5khZsb QN7RzcKrfMPm27gW82/a+/Z10X4i+H08TwanonhHXLGXFzqepmO1truOTy49tzPt3bl2p5bHd/d2 /MGXgqU51Iv3/g/rXubU8PH2tOUZunbbtfu11XdHyV4b+Pnj6H/hFdO8M+GtF1bSdFtH02HT9Q0q 3mjmmmhk8xfMVY2864/fbY42Vptqr++bc0nEfFLx5qP7SnjiI+HvDmneG7Gyso47Hw9aXcce7asc f7tW8vzrhlWGNY418xo4YY1VvLrUh8K2nihGg8U+N900GgQ3WkiHdqcbx+XuWyZlk3W0is23y2Xa vzfw7fM8v8WeAtS8Kqs7eXqGlyLGy6labmiVpFbbDJ/zzk/dyfu2+95bMu6PazfN0ca5SlQqz5pR /FfmfPYd46dOp9YTnGEmufvr9/3nNV6z8AR4h8deJrX4d6WYnh19/shmmjWRrKP/AFk00e5l+7Gs jbVZWbbtX7zK3k1foj/wTh+Do0PwrqXxFv4yLzWC1hp/zfdtY3/eN8rfxTLt2suV8n5flkr18HUr Rq8tGXLzfl1/rubU8JSxMr1Y3Udfn0/rsfYnh7Q7LwxoWnaRp8RttOsbeO1tYN7N5cca7VXc3zHC jvWrRS19Rsj2AooopgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFAHF/BX/kjvgT/sA2H/AKTx12lcX8Ff+SO+BP8AsA2H/pPHXaUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUlLTXbarH0FAbHyn+1l48nm1iz8K2tyVtYYlnvVhl X95Izfu0kHbaFDbf4vMU7flFfO9dP8UNbfxF8Q/EWoPcR3YlvZEilj27TEreXHt2/e/dqvzVzFfr +WYeOGwkIPdn8jcQ5hLMczrV5dJe76LYKKKK9Q+bCiiigAooooA+rf2V/iS+uaPL4VvX3XemR+ba uzMzSwbuVPH/ACzLKq/7LL8vFfQu3cxzX5+/CHxVL4P+IuiX6Tx29u1wsFw00m2PyZG2tu+Zfu/e +b5dyq38NfoCrBsEdx/SvyzPMLHC4puHwyP6b4JzSWYZaoVfip6fLp/kS0UUV8+foQUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUlLRQAlFLRQAlFLRQA UUUUAFFFFADcVh+KPDOn+M9Gk0rVYppLOSWGYrDcSW8qyRyLLGyyRsrKyyRq2Vb+GtzrRwKiUVJc sg2PH/EP7P8Aa3Uz3Gka5q9tdyafdaZJHrWo3mr2lxb3Bj86OSOafzF/1a7WgmhbcF3blXbXyR8c P2VfFPhxJZY7KC+0D7LNbW8On3bNLNdbpGt1+a33LukkXbHJJMzeX5P2hpGhVv0V42+tUdQ0621a xuLO9giu7O5jaGa3njWSOWNlKsjKeGVh1WuKtgaNRfCdlHF1KScd4s/Gbwp8G9b1j4leEvCWrQ/8 I/L4j+yTWtxdNGqta3G1lmj3MqyfL/Du3My+X/rPlr9kvDuh2fhnQ9O0mwh+zadYW8draw72by44 12qu5vmOFA6ntX50ftR/sqfEu8+KC6nZL/wlOlXi2enWWrT3kUcu5YVhjjuWmkX983lr+8+7JJIu 3a0ixrDY/ED40/AbXfBXhO48b20L2ckenyWOpxBtMsY5Ps8vkXN0y/vGjV1Ehj3eTGV8uT94yr49 OvHBylUqx8jzZVKWHShKXLeWnnc/TaikHQUtfTo2CikpaYBRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBxfwV/wCSO+BP+wDYf+k8ddpXF/BX /kjvgT/sA2H/AKTx12lABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFMm/1L/7p/lT6 a43Kw9qa3Jlqmj8y6K6v4sabPpHxL8SwXSeXL/aE0oUMrfu3bzI2/wC+WVq5Sv2qhU9rRhU/mP4x xlGWGxE6M/sysFFFFdBxEqwlreSbcm1HWNl3ru+bd/D95vu/e/h+X+8tRUUUiwooopkBX6L+D9a/ 4STwro+q+UbcX1pFc+Vu3bN6q23d361+dFfop4I0mXw74N0LTJ2R57Oyht3aP7pZEVWx7ZFfD8Tc vJS+Z+0eG3tPaYj+W0f1/wCCdFRRRXwh+6hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACUUtFAGL4i0K08UaDq WkahEbnT763ktbmDcy+ZG67WXcvIypPSvy0/ai+Huv8Aw3+JV/F4kN1LaXy507V7u5kumv7dFWNW aaT5mm2qvmL8u1m+VVjaOv1hJ2rzXhH7YHwv0n4ifBHxFdX+nRXOp6HZT6np920vkyW5jXdJtfa3 ysqfNHja21eVYLInhZtgFjKHnE87G4GGY0o0Z6Wd0+zNP9lH4oWnxV+COg38Cww3OmxjSr2G3jEc cc0KqvyqqKqqyGOTaq7V8zb/AA17JvA5JxX5Rfsy/taD9nPw/rGmnw7e+Im1G6Wfa+r/AGe2t9q7 f3cfkt8zfxNu+bbH8vy/N2N5+0dq/wAbtY1i9s/EvirSfEc1r/xKfC+j3zWNnDsuY1WFWjbddTSQ ySNubyW8yPy1WTzI1WaOP5aMYpc0vu/F6HrUIRqyjTctWfpXkNg9fen/AMVfMP7EHj7xH428E+JY dWu7/W9J03VTBpmuanOrXM8bKJGhkXczK0e5G3NJJ/rtqt+7r6bEnzMMdK9bDVo4ilGrHqY80JJS g7p7EtFFFdIwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigDi/gr/wAkd8Cf9gGw/wDSeOu0ri/gr/yR3wJ/2AbD/wBJ467SgAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACkpaKAOL8efDXQviNpq22s2hkeLcbe4iO2SBiCu5T/7K2V+Vcjiv kH4o/BTXPhndBpFfU9KcBk1KCNgqAbV2yL83ltub5fm+b/vpV+7yOnNVLyxg1C3lguIY57eVSjxy LuV1b7ykV7GX5pWwErJ80ex8dnvDGEzuF5Llqfzf59z816K+lfi5+zDJPcXGseEEjHmOrPo/yooL cM0bM20D7vyN/tbW+6tfOF1bTWNzJBcQyQXMMjRyRyLtaNl+8rL/AHq/SsFmFHHQvTlqfznm2SYz Ja3s8THT7MujIaKKK9E+fCiiigDqfhf4XHjX4gaHpEixm3uLjM6yFl3Qx/vJF3L825lVq/QiMbVA 9Bivn79mH4VXHh3SZfE9+yLeanCv2SJSSY7dju3Nzt3SfK2P4cD+8wr6C3Y61+W55jI4rFWh8MT+ meCcqqZbl3PWjyyqa/Lp/n8ySiiivnj9ECiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKSgBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACkpaSgD8o/wBq L9nlPCP7QGs6Vo93pel6VqVr/bVot5cx20cCzSNH5P3VVf3ysqqvyrHtaRlVZGWr8N/gPq3hf4ne H7XVo7i4s9atbiTQtc0FZS0832Fbi3lt1byZP3ck1ud0nlxqyybmVVZl91/be0C5vPjBp2qRtpNr DZafpkUl7qjFli3S6lLzG25JE2277lZWdmWNUVtzK3n1v8VvFNr8IdS1G+l1GzbS57Cy0U3NhbNH FPJLBf7oZPJRo4lFnuWFS6bJ7VdqrGGb8+r1Iwr1KdT4V733a2PiM0zCMMTUo1Y2jG0vW1m+q32J /wBhX4lReCfjFLot5c/Z9N8TW32QbvLWNryNt1vuZsMvytNGqr95pI12/wB39J/vd+tfkl8Rtd1C zh0m4trCw0XUr6eXXjqOl3b3Nw10s8kG9LtmaQLutmkVVlcMztJu+ZVj/UvwH4j/AOEu8D+HtdDb /wC09Otr3d5Pkk+ZGr/6vfJs+993e23+83WvTyGtywlhua/L+p6HD+I9phfYc13DT5dP8vkdNS0U V9cfThRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF AHF/BX/kjvgT/sA2H/pPHXaVxfwV/wCSO+BP+wDYf+k8ddpQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQBheLLG+1jwvrNjplx9j1K5tJobW4EjR+TK0bKr7l+ZdrY+Zea4n4AR+L4fAS23jSK5i1a1vJ 4Ue8m8yeWPd99m3Nu+bzFVv7qrt+XDN6nXOaD4z0XxReavbadqMd1PpNy1newgMrwyAn7ytzt4b5 vuttb0oA6OiiigAooooAKKKKAIyM4HavNfil8GdI+KUCPciSx1O3Qrb30IG4f7LL/Eu7nH5MNxr0 zNJtrWjWnQmqlN2Zx4rB0MbSlRxEeaLPz0+IHw31r4a6vHY6xDGolXzILm3Zmhl/vbWZV+Zf4l/+ KWuXr9G9d0Gw8SaTcabqVrHd2dwhjkikGdwP9fftivFZv2RPD76xfznVL9LGR1e3s7baph9Q0jBt /P3emO+7rX3OD4ip+ztiVaR+H5rwBiYVubL5Xpy/m3j/AJ/16nypa2s19cxwW8Mk9zNIsccca7mk Zvuqq/3q+hvgz+zVPNdWut+LoVW0wssGksCHZv8Apuvp0bZ7/Ntwyt7l4F+Ffh34d2+3SLCNbkjb JezDfcS/d3bn7btqttXC57V2o28gV5uYZ9VxEfZ0PdifR5DwLRwM44nHy9pPt9n/AIIqqFUADAAp aWivkz9ZCiiigAooooAKKKKACiiigAooooAKSlpKAI92Md6q31/BpttJPczRwW8al3kkO1UVfvEm uC+KXxm0f4VwIl2ZL3VLhGa3sICNx/2nb+Fd3GfyU7TXx340+JXiD4gXfm61qMk0KybktU/d28X3 tu2P+8u5l3Nubb/FXuZflNbHe+vdifB57xbg8lfso+/U7dvU+sfEv7Sngvw1I8SX02sXKuqmLTI/ MA3JuDbzhCPo39awv+GxPCv/AECtY/79Q/8Ax2vkWivrafDuEjH3+Y/Kq3iBmtSpzU1GMfQ+w9N/ a08HX14kNzb6pYRnJNzNbq0aYGcMEZmH5V6f4b8aaJ4vtzPo2q2uoqqqzrbyqWTcMjcvVT7NX521 ZsNSutJvEurC7ns7pNwS4t3aOQfLt+VlrDEcN0ZR/cTsd2B8Q8ZTl/tVNTj9zP0oLZUcjNJuyvTk dq+afhD+06sxh0jxlM3nyS7YtVVFRAp6edjbt+b5dyrt5+bG1mP0srCQbgeDyCK+KxeErYOp7OtE /acrzbCZxQ9thZf5r1JaWkoriPaFooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigApKWkoA+Of20tI03w/rz+LdW0CLXlv/DU2j2Us7eWNOvFm2xybmk2SMyXkzrH5bSZtdylVEjL yHw1+CniP4vfBDwr4Qa/0bR9CZG1m4k1A3UuqWsrT3SwywweYsYgkiUKudqtmWRdzfNXv37X3hBf FXwW1GcmbOiTrqZEcMMuIVVo7lvLmwrMtvNM6jcrb0TaytXif7QHw/m1vWom0zSG8K+HPC+knQbb XNauJLWxaGV47WH94kbXL7Wa5jZZG8rascxby5mEvx+Nw/JialSUeaPLt676/I+SzfDy9q8U480e Xl5dr6662vZKx5z+1hrCaXN4M07TNbhaddFU3trpsM32bzZQzyXNvdyfNNHc+fMrsrN5nl/vWZvu /ZP7K/iQ+Kf2e/A939n+yrb6eunbQ+7cbZ2t93tu8ndt/h3becV+fFzoaN4D1XQza2/9r2e3W3ms dWtLmyt4EWO3O1/PbbLPJJudVZmZorZRHtZfL+w/+Ce+rTX3wPvbWVNiafrVxbxt5sjZVo4Zvus7 KnzTN8se1f4tu5mZuHKqs5ZjKUvd5onBkFadTGV5S93miny+mnU+paKSivvj7wWiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOL+Cv/ACR3wJ/2AbD/ ANJ467SuL+Cv/JHfAn/YBsP/AEnjrtKACiiigAooooAKKKKACiiigAooooAKKKKACvnX9or9nebx 9qmn614XtY11y5ulh1J5JtqNH5aqkzFm+VYxHt2xrubd/s19FUUAcD8JNU1Obwfp+meI5rc+K9Mi WHUrWO6WeaHlvJaXazfNJGqtu/iy1d9XE+FPhdo/g3xf4i1/THvFutbk825hedmgVixZiqerMzN8 27bubbtVttdtQAUUUUAFFFFABRRRQAUUUUAJS0UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUlADA3 PSuQ+Jnjq0+HPhW61u6HnlcR29srhWnkbOFGfb5iecKrNjiutLbeCO1fI37WnjSXVPF1n4eimzZ6 bEss0alh+/k/vfwttj27f+ujV6eW4T65ioUn8PU+X4izVZRl1TEL4to+v9anjXiLX7vxRrl/qt/J uurqVppPmbav+yu7+Ffur/srWbRRX65CEKcOSB/KFWrKtOVWfxSCiiitDEKKKKACvp79lz4rNfRH whqk0ktxHuewmmkXmJQv7od8r8zL1+UN0CivmGtLw74gu/C2u2GrWEmy7tJVmj+Ztrf7Lbf4W+63 +y1eZmODjjcPKEt/sn0vD+bVMnx0a8fhfxeh+kPPpRgFfauc8GeK7Hxz4es9Z02RmtbhcrvXawIb aysP7ysrD8K6Pua/IZRcZcrP6ypVYV6catN3jIdS0lLUmwUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRSUAcd8S/h9pXxS8E6r4Y1zzm0zUUWOU28hjlRlZXRlPqrKrfMCvHzKy5W vi3x/pfi7xxY/EPRb2fUPGckOJbbVNTZLaG1+yXEdvdXTK1wtnaxtLZ3yxqsbTsvmmRlXLSfbnj3 V9S0DwP4h1LRbL+0dYs9OuLixtDC0nnzLGzRx7V+ZtzKq7V+bmvDPAvhfwprvhnwnoviCXTfEt94 w0+a5v7kaxuW+nSS7uL2ONLdfs8nk3F820K27ln+9bKx8PMKEcQ+T+uy/P5HFmFJ18JKlzaS0+/5 PyPlvxB4Sh1z4H+FfEEEWiaTqmt3F1aLZtYR/aL945RgWCW8TSESSDa3nbfLbakflRP5b878G/jN qf7OPj6/vLOwuNStpIPsep6Neu1iTKq/Nx822SObKqzK3ytJ8sbN8v0H8L/AWtfEP4IqbeX/AISE /Ybyx0qVtStLi48P20pxLaY8vbJcSQ/Z/wDWNH8jSR7rTaGk+ZvilpUHhpY9JmvotY1v+0NQvrnU +BcSxOyJEtzuLPHPmCWRopPmj89d2GLKvxWNp1MKqeLp/Ekv6/rofl+JjVy10cwo/E1H9L/f+C7a H6q+EfFmleO/Dtjruh38eo6Tep5sF1Ccq68gj/ZZWBVlYblZWVhmt9vpmvA/2Pfh/feB/hRZST61 eX1jqbi9srGWVZLe3iZf9ZCTGrhZ/wDX7GC7PMVSvmLI0nvbegOK/RsLUlWoxqVI2bP1xapPYfS0 lLXWMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDi/gr /wAkd8Cf9gGw/wDSeOu0ri/gr/yR3wJ/2AbD/wBJ467SgAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApKWkoAZ/C3 0/pX56fFK6mvPiR4oknmed11K4jDucjasjKq/wDAVVVr9C/4W+n9K/PD4kf8lF8Uf9ha6/8ARzV9 hw3/AB6n+E/IfEb/AHOh/i/Q5yiiiv0I/AAoorH8R+HP+Eh/s9WupLaK1uluWVV/1m3/AGvvK3+0 tc9epVp0pSox5pdtjqwlOlWqqNepyR72v+BsUUUV0LY5nvoFFFFMR9C/sk+NjZ6zqXhieRFtbxft dvl1H75QFkVf4mZl2t1+Xy2/2q+ruM+tfmtpuoT6RqVpf2snk3dpMs0Um1WxIrblb5q+9fhX44h+ I3g2w1dWVbpk8u5iTpHMv3ht3Nt/vAHnay1+cZ9gfY1frMfhkf0HwDnSxWF/s6p8VPb/AA/8A7ei iivkz9aCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAEx2ritd+FXhjXtUn1 SbTTZaxcbRPq2kzy6ffTKq7RHJcW7RyPHhV+Vm2/Kv8AdWu2pD+tRKMZfENNrY8P8P8AwR1X4a+G dM0PwxqFtr+i6VcG9srDxCrW93FM0jM3l31sq+XGPMb5Wt5GZWkjZtkny/NnhP8AY3+IPjz4kJ4l 8WwW3hW3uL9tT1ONvs9wWkkmkdo7eMNNHIvyx/NNt/1n3ZNnzffzZIGBn8aRQcEZ3fhivLrZXQru PN8MehwVsFhsRye0gvd2/wCG2+8oeHtDsvDGhadpGnwm206xt47W1g3s3lxxrtVdzfMcKB1rVoFL XraRR3BRRRTAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igDi/gr/AMkd8Cf9gGw/9J467SuL+Cv/ACR3wJ/2AbD/ANJ467SgAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApKW koAYMYyK+If2lPCcvh34m3l2YEhtNTC3ELQx7VZtqrJn5du/cu4/9dFPevt1WHYcV5L+0R8Nx4+8 HyXNvDJLq2lK1xaqjN84+XzEwA24sq/Lx94L717WUYqOExUZS+F6HxXF2VyzTLJRh8UdV/XofFFF FFfrB/K+2gUUUUwCiklljt43klZYoo13MzNtVVqjpOvafr1v51hdR3Kr97b95f8AeX7y/drnlWox qRoyl70vsnTHD1ZUpVoxlyx3l0L9FFFdByhXu/7JXjKTTfGF54elmxaalE0sMbFj+/j/ALv8I3R7 t3/XNa8IrsPg/q8mi/FLwxcRJG7NexwN5i9pf3R/4FtkavLzKjGvhKkO0T6Ph7GTwWaYerH+Zfc9 GfoNRSLyoNLX5Af12FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABS UtFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQBxfwV/5I74E/wCwDYf+k8ddpXF/BX/kjvgT/sA2H/pPHXaUAFFFFABRRRQAUUUUAFFFFABR RRQAUVjSaDZvr0etSJKb6O2a0RjPJsWNmVmAj3bdzMq/Nt3fKtbNABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAlIyhlII4Ip1FAHwj8evh+fAHjy6S3 i8vS7/8A0q02rtVA3+sToF+Vv4V+6rR15vX0v+2NqFht8O2HlxSanulmEnm/PFEdq7dn/TRv4v8A pk3+1XzRX61lNapWwdOVQ/k3inCUcHm9alR+H8r62CiiivZPlCG6tY7y1nt513QTRtHIv95WqDS9 JtdJj8u1jZV2qvzSNI21furub+H/AGau0Vzyo0ZVPayj73c6Y4itGlKjGUuWX2egUUUV0HKFdh8H 9Hk1r4peGLaJ40Zb2OZvMbtF+9P/AALbG1cfXuX7JXhddW8cXutyrG66TDhBllYTS7lVv9r92si/ N/eX/gPmZlW9jhKlTyPoOHsLLG5ph6Uf5l9y1f4H2GvCgUtJS1+Pn9fBRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHF/BX/kjvgT/sA2H/AKTx12lcX8Ff+SO+ BP8AsA2H/pPHXaUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFULS1NvlAZCpkd/wB5K0hyzbvv N/D833f4fur8oAoAv0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUlLRQB8QftOatJqHxe1CB1RVsYYbeLAzuUx+b83+1udv/Ha8or0z9o7j40eI/rB/wCk8deZ 1+w5dFRwdO38qP5B4glKWa4qUv53+YUUVLb28t5cRwwRPLcSOsaJGNzOzfdVV/vV6R4XxEVFVdUt 7q6sZY7O6+w3Lbds/lrJt+b+61S2sckdrBHPN586xqskm3b5jf3ttZe0l7T2fL/2909O/wCFjX2c fY+05ve/l1v67W/G/kS0UUVqc4V6J8IPi/qHwu1jJ33WiXL4urMH7v8Atx/9NP8A0L7rfwsvndFc 1ehTxVP2VSPunoYLG18vxEcTh5cson6N+H9dsPEmkWupabcR3VncKHiljOQc/wBfbtitmvk79lX4 jzWGtSeEr2d5LW4DTWCu24RSLuaSMfL/ABLubO7Hyt/Exr6xr8lx+Elg8RKjI/q3Ic2p5zgYYmPx fa9R1FFFeefQhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcX8Ff +SO+BP8AsA2H/pPHXaVxfwV/5I74E/7ANh/6Tx12lABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUlLRQB8n ftfeGZIdf0XX03vbXEBspPk+VGjLOvzf3m3t8v8A0zNfPVfoB8VPA8PxE8F6hpDKq3TJ5lrK/SKZ c7Tu2tt/ukjnazV8C3VtNY3MkFxDJBcwyNHJHIu1o2X7ysv96v0rh/FRrYZ0X8UT+bePMrlg8x+t x+Gr+fX/ADIaKKK+pPzQKKKKACiqct/NFqsFstpM0EkbSNdqy+XGy/w/5/8AittysqdRVOaMfsm1 SjKnGMpfaCiiitTEvaPrF94d1a31LTbl7W8t33xSoMgD/P8AD/FX3h8K/HEPxG8G2Grgqt0yeXcx R9IplxuG3c23+8AedrLXwDX0B+yP4w/s/wASal4emkxFfRLcwCSYKokT5WCp3ZlOf92If8B+Xz/B xrYb28fiifpfA2bSweYLCTl+7qf+ldP8j61paTNLX5of0kFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRSUtABRWfqGpWuk2Nxe3txDa2ltG001xM6xxxRqpZnZjwqqv Vqv5oAWikozQAtFJmjI9aAFooooAKKKSgBaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKAOL+Cv/JHfAn/YBsP/AEnjrtK4v4K/8kd8Cf8AYBsP/SeOu0oAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigBhrwH4+fAO78cagmvaC0CaksJS4tZAIxcBVbaytj/Wfw/Nxjbyu3n31l3cil25z6V1YbEV MLUVSm9Tysyy2hmuHlhsTG8T80bm2lsbmSC4ikhuYpGjkjkXa0bL95WX+9UVe2ftQ/D3/hGfFia7 aRFdP1bmUIp2xzj73Rdq7l+b725m8w14nX63gsTHF0Y1o9T+Tc2y6pleMqYWf2f6QUUUV3HkhVWx 1K21L7T5D+Z5ErQv8rLtkX7y1aoqJc/NHl+E1j7PllzR94KKKKsyCpba5lsbmOe3lkhuYpFkjkjb a0bL91lb+9UVFJpPRlpum7o+u/gL8dZvH0s2i689vFq0SKbaWPKfa1C/P8v3fMXbuO3jDcD5Wr3f aASQetfmvpuoT6TqVpf2r+TdWkqzRSbVbEituVvmr70+FfjaH4jeDLDVwyi6ZPKuYk6RTL94bdzb f7wB52stfmud5asHP2tH4JH9E8F8RTzSnLB4qXNUj1/mX/AO4opKWvlz9RCiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKSlooAhHYjnHSvky6+NWpt8U/F2u2XjYw6fpPiOPRJPCWpC 0jgWxtYWF1cM7SK0PmTyPHDMzKrTeRHJ8rL5P1rjaK/PD9rz4M3mo3XxF8YaVHo9jpGja3i8gjt2 hvZ5p7LTSreYvyyLvmkba23azSNubzPl8zHSlGCnF7D51TpTnKSVl1vb8NT6L8ZftJeBfFvw/vrb RfEFjJqV7fWujXGkSJHd3m24uIIbjybf94t0yxzSFWjWaFmX/looYGb/AIZy0r4W/D/Up/AVrq0v jKCxENlc2OpCza6uE3LbyXMMbQ2s3lsw3eZG25Vbd5jM275W/YV1L4ha34ws9BXxJqWm+BdEdrq6 hEuIcySR+XbqzKyr5k0cfy/LuX7Rt/1jbvoj9q7w5oel/DO+0fSLSxtNRnWKZTO1mYbS3t4Wj2rD cM0kca24uWjW0j3K6yMNrPJu41iIVKUsVU+z/Wn9dDGjio16cpU1/wAHTv26ep876f8At3fFj4b+ KLjw94pttL8R3FpqJhvPtcCw3C7W8uSFHt9sY+ZW+bbJ97+L7te7+DP+Cg3gjWPsMPiLStW8Mzze Z50yxre2kG3dt+eP9427av8Ayx+83935q+A/EENnqF/INPXyLWNlEEnltG0m1dvmMrSSbWbbuZdz KrM235dter/B/wCBmo/G7Tba30mOFLi1ubk3l15zMRHttlhjkT5VjXdJIytu3MqXG1WaFY5PmcHm OKqYmNDDz5l15tfmfC4LPa+KxkMHQtOK0k3vburaefyP0U8F/HL4f/ERrOPw94t0u/vLwyCCx+0L Hdvt3bv9HfbIOEZuV+7833ea73ywSM/McYNfL/xy/Z01TxVeXl3BpF14p0eC5sZ7fQD4ja2a7aKE QtI7PDt37flaSSSSaRdu2aHy/Lk+fzr3xC/Z10O7i1lda0fX/Pa8s7fTvEEK2NhHMs4Vv7NWGS3a GSbzm+X7rQr/AKndG031M8ZWozlGrDmitbpPp/Xc+yq4ylSnKMk0lre2n39/67H6S59uKRjjvj8K /PT4V/tn/Eq48Uad4cvNS0TxRNqF5Zxw3V1ZNCxeeSFWt2ki8tYVVWk/eeTL86/dkVlavUv+G+rX w3r2r6P458Aax4e1PTpFj+z2F1Delm6vu3eT8u3aysu5WVs/3d1U84wk480pcvqXHFUZR53NJeen 5n13yeooIryHSf2pvhdrGpfYf+EutdOuvs63W3V4ZtOUxsFZWVrhI1bcsisMfeVtw+WvSdD8Qab4 p0mDUtH1G11TTpt3lXdlMs0MgVtrbWX5W+ZWXj0r0qdanU/hyudfoa9FJS10CCiiigAopM0UALRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUlLQAUUmaNw9aV0AtFFFMAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigDi/gr/yR3wJ/wBgGw/9J467SuL+Cv8AyR3wJ/2AbD/0 njrtKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAopKWgAooooA5Xxt4H0zx54fn0jVLfzYJDvVgcSROPuyI 38LDn+XSvgXxF4fu/Cuu3+k6hHsu7SVoZMq21v8AaXd/C33l/wBlq/R6QfMvOB6etfPn7TfwhOuW L+LNKhhjvbOJmv1+61xCq/f3dNyKG/2iv8XyqK+oyLMPqtb2VT4ZfmfmPG2QyzDC/W8PH95T/Ff8 D/M+UqKKK/Sj+cNVowooopgFFFV7G6a8t1mME1s25laOZfmXa23+Go5o83Kaezly8xYoooqzMK+j v2QPFDR6hrfhyRpGEiLfwLtXau3bHIS33tzbov8Avlv+BfONet/su6z/AGZ8WrS38oyf2hbTWu4H GzC+bu/2v9Vt/wCBV4mcU1UwNRPofXcKYiWGzihLm+J8v36H23RSZpa/Jz+rwooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigBmO9eLfDO70rXPiv8QL+OedbtdVuI7QT2QiW5hW1 062uWhkkj3SLHcWbRt5bbd23erfuWr1rUtSttJ0+5vL24itbS2jaaa4mdY44o1Uszsx4CqvU18Z/ Cj/hMdM0+xs77TJvifaeM528SXmh61ZRwhtNkuJG+0W7SXbWcciyNbXElusatuvF+b5d0fn1p2qw jy+f9fedVGnzRbPozwXqmmaChtks9D8LWTX8+nWmnWurKYWt4rpoVljhVVjjla4mRGjX5t00as5Z VQeTftceNNAuNN1Hw3H4l0+01aC3hiv7Zrt3u4UnlVY/JtmkSF28szMzMytGm1uAytXafEz48eHb T4Lt4ku7W+tdO1a3uLJbW+hkt5PtSFlk0+Ty28yGZvLuY/MXcsbRM277iv8AA2sfD3WvjR8SPFWp eB9Ju9e03St32f7NHgx6fCvk2qfvNrM3kxqqr/rG2t95t1eNnOJg6P1WmuaU1tv+HY+TzjHQwsfq lKP7yW0d+j+7bzNfQ/AjeH/FGk2fiyWNPDmuW82maHrlykOo2kKyTNGZlMV35MTRs8kjfvH2Mc7S 21q+4bfwJ4Y+CPg2bwrompalpj30D6gxV/3ktva+W14fOzHHC0isy/aJJIyrTJtdVjiVOL+Cf7NG u6H4V8TeGvEnlp4M8RWHkzaVIf8AS7O6jdo1uETzJoVaSNY5mZZG2yCNVVVjr2D4P32kx6GmgaPq KyW+gwR2E2l3V5FdajZSq8it9pkimkUbti7Yxt27GH+zH6eAyqhl8OeOstPO3fX1+9G+U5LRyyPt I6z/AC0Wl+uvl0NqPVI/h/obDxHrNutlHP5NpOySq0duE+VZpJJZWlZVV2kmYqNqtIyqqs1ctYfC fRf7Yt08Ux+Hdbv185dMuP7JjtL6WN1m+0/aCsm248z7RI8irHHHvkZvLG5dvYR+Jpo9WvY7iJ0g t7Vrh7SONZLmFVlkVZGWOR2ZZlXdGqx7v3Mgb5m8tcvx14gk+FXhHxX4pkkuNU03T9PkvYdLIkkk 89WmkkJm/eMsbb4l+7thWMn7v3fSqWalc92UOZckle58Y6XceHfhbceJ/GFrr2jwx6nqurWegafJ MzwQQy3TW/2y0itG8yNPLtRG1wrI2122r+5RbryT4MaD4X8c+H/Feh32h6hd+L30xrnQG0oyNJNN G00km5Wk2szRtGNqr9yFtv75l8z3D41fs0/8I14P07XvE+tTalqNholnp66bpd7ua0S1hihklt4n h3XUa/NcNHug2/vPm+bcvg/gn4gaxbSWem6BqTWkukzXdhpt8zppDPaXHmS4uLpbiNV8uRfOjhZm VneRdzLtVvga144ylQxcY8n+fX/hu1j4rMnKpXlSrxjyKyX+G9vw0ffRHpXjb9l3xBpvhXx94guf OV9FhjstJk1SGHbqkEEElvdOsM00jR7Y4fOjbd91d0ar+78vnf2EdE8N+KPiR4k8F+MtE0bVrTVd IZ7dtSiVrhZEbbttZM7l3RySM3l/N+5Vty7a+y9Z8WWWm/Be58SeIdSsdX8Wp4LSe70+W/NxZXfm R/fa1ik8lo5Jjs85V5U7d22vzS0HWrnw7rFhq+m3CxX1jcR3drcKqttkjbcrbW+VvmX+KqxlSnlV Wm4LmjLp1/rp8jixeNnkEqUqK9ye/f8Aq35LU/TT4heBrjwj5D+D/EvjKx8S3lpew6fanWzf2t1M sJmRZBqTTRq26EKvl7ZNryN/q1kaPVt9L+Jlh4gvtM074gaDqtiohuc+INI87UraORJV+ZbWS3ja PzIRtO1S26b5v3Q3U/2dfiJ4p+Lvw11S68Tadd+HNbg1K809bpbNrZmAb5ZI4Zlbb5e7yvm8z5oW 3fxLXXag+o6XqEXiHU4dPZ4Jbixht7eaON/s8zRrCizTIPMuJJoYV8vfDH+/2t5jQxu32saUJLnj p9/5H6XLmpv2clqvQxNU8XfE/RbwWEXgrRfEEn2S4uVvbTW5rSOcxNGu0o1qywyyeZuSNpmHyyfv Pl3Vl/8ADSlra3V9Df8AgnxXZx6ddW9rfM0NnM1s09x5EJaGK5aZtzj5VjjZmVlZVZWVm9H1K4st U/sqW2a1upYtRMdvcm1a8SCWMSJcDch/cv5a3EPmMyhWbadxby24vwr4H0PRvGaadofgjTNN0fR5 7q8j1a1uo1khv5kj8yPyF+YLJDcfLub5RCq+WqrCzKcJq3JP77f1/XzFHkknzRFuP2j/AAHppX+0 tQ1LRU+2JYtcaroV/ZwRTOFZY5JJoFWM7WVvmZfl+b7tbun/ABr+H2qXtvY2Xjnw3e3lxKsENvBq 9u8ksjNtVVVW+ZmbjaK838SfCfV/EWg+INQ1HW9WWza41NbzS4GuNRbULP7V5kKw29ztjt51WMxx tHG3VWVm+WvIvEvhVPEHirwxffD250S90vWJrGw8S+HoYLeTT4p1kYedJYec3mqyrMyqjMqrbNMs h/1leZPF4qlU5JKL/B/0uv8AV+mjhqNW9pW/I+3949RSbh6V4L4b/ZS8N6fr9nqWq2fh3U1jt1gu bNPDFjDFdMsMaq23a3l/vPtU37vazefGrMywqp6VvgDoVnoMOmaNqfiPQooY44YBD4o1RlghUgFY 4/tQVcJ8q9VVtvysq7W9fmq/yr7/APgHDOMI7SuerKR2oPAzXzZ8FPD0viKxvdU0j4mePUi1W91F 7dZJFv7JLW31C4ghEc15bzeW3lqvy+Z5jfeZfl+XubPwh4sh0uGXWviH4os9Q8qWaa30230+9iVE fosn9lqzMVZcLtVmy23dt3UoVJ1EpchLUYq99D1zikrzeLSfiH5s8Fr4x0KWC1byRJqHha4aeQbQ ys0iXscbttZdzRxqu4NhV+6rLCHx9qXn/Y/HPg26+zzNbzeT4buH8uRfvRtjUflZf7vWn7Sf/Pt/ h/mT7vc9LFBrzGCf4j2f2d5tX8K6nfzRNOugzabNpszqNqt/pC3V1t8tpF3Msci8qvy7t1N/4TX4 gNpX9pW/hjwrfWPkC5jltPE11P50e3dujWPTmZ8ryoXdupe2Ufi/r7i1ByV0eobqN1eNXfxS8f2v iyfw5/wguizaoultqtuqeIbhY7xFk8uSOORrBY/MVmi3KzL/AK6M9N22/b+OPiQugvqep+CvDGgR QxyS3EepeLZF+zxpnc0jpYtGF2jdnd0pxrQmrx/Jj9mz1TqcZob5ecZr450/xx4duvFkHxL/AOKV l1NPFcN3f3dtf7ZLLTZLO20va01xBDMy+ZdLcf6vydqt+8Dbd31joWvab4q0qDUtH1G11PTp93lX VlMs0Mm1trbXX5W+ZWXj0ohUlzuFSPK/07kyiuVNao2BS1k6Fr+m+KNJg1LR9RtNU02bd5V3ZTLN DJtba21l+VvmVl/CtXNb6MgWikzS0wCikzRQAtFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFADC vSkb7pp2ea8u/aX1+x8O/AHx/dahOLa2k0a6tBIVZh5k0bQxr8v96SRV/wCBVlUqezhKfY0pxdSS ijxT4X+A9d+KviLxT440zxV4m8FXnissupRz21v+901mhk06az8uT93J9jbyfOZZtsi3H3WX5tzU vg3o/wAFfDcvjjw14WuPC+t+E5VN1Jo2oebHq+krN511uSaTEm6GSRtsm2RZIVVZGWONm5m18R/F PRtJ0fw+bCDwx4N0qxWXXb+OVLjVdOvFutyWkRt7nyFfy5bN1Vo1gaNm/crH/oy+y/CfxD4K8VaX rWkafeDXrrVJ7281ezvUNxIFkmK+Xd5jCr+7KRxxyfehhCxmSOPdXDDCxdL2lvm+/l/wD2MRTlRu k012Wunm1pr53PYAcqKd615N+zrqd0ngGXwvfwzRX/g29k8NPLJbNbi6jt1X7PcIrMx2yW7QN/vM 235dpr1j1ruoy9pBSPFkuV2HUUUVqSFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQBxfwV/5I74E/7ANh/wCk8ddpXF/BX/kjvgT/ALANh/6Tx12l ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFACUmOaAaq3l9Bp1vLcXE0cNvErSPJI21UVfvMTT3E2oq7LB+X2pFbd3z +FeTeJ/2lPBfhqR4kvpdYuUdVMWmR+YBuTdu3nEZH0b+tYK/theFO+l6z/35h/8Ajtd9PLsXUjzR ps+ercRZTRqezqYiPN6nvOPakY7epxXiGn/taeDr68SKe21SxjOSbie3Vo0wM4bYzMPyr07w3420 TxhbGfR9Vtb9VVWdYZlZk3DI3Dqp9mrGphMRh/4kLHVhc4wGOly4atGTOkoZQykEZFAPSiuU9jc+ XP2jvgjYaTp974v0d/sxWbzNQt5HZllaST/WLycNuk6fdx/d28/Ntfor4u8O2ni3w7qGk3qb7a7j MTkAZB7MuQfmU8g9iM1+dVfo3D2LqYihKlU+yfzlx7lVHA4yniKMeWNT81v+aCiiivrD8vCiiigA oqWG3lunZIopJmUNIyxru+VV3M3/AAFVZqiplBXRfD/xpd/D7xVZ63aDzWiLCSBnZVmRvvK23/vp fvcqrbflrnaKzq0o1oSpT+E1w1epha0a9GXLKJ+hfg3xxpnj3w7Bq2lTiWGQ7HjxiSJx96ORf4WH /wBfpXUdulfCnwL+KB+Gni0PcvjRL7bFeqse9gBu2SL/ABfKzfN/ss3ys22vueORZI1KnhhkV+TZ ngJYCtyv4Xsf1Pw1nkM8wftHpUj8S/X5k9FJS15J9eFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABSUtJQB4z+1V4qj8N/B3VLL97Jd+IpIvD9tDBaNdTStct5cixRKy7pBD5zKrMqsyqufmr zb9l34Dap4L1GTxr4lNxoAvJLiTTdDa98tbZbpo12z28cUMayfu412r8rbo/3cbQrV7xV4/0zxJ+ 1VN4a/s86hregaZb22jHUrSKays7q4kjmuLzav76Rlj+xbVj2/dm3NHHumT3zUvG2k6LoLa1qN3D ZaNvhVdRkkUwSCZo1jdWUn5GaVV3Nj1+781eVSqe0qTqKXux0+7udFPETjGVKm9/6/U+G/2itA1v wjb2/he80nV4bi58S3dxH4g07S4rgapDfSTbvLmj8lvtUkc3kyWrfLItpHtZV/1nZ/sw3ngn4KXk Fnr+tr4Z8W3dn596mr3v2SxvrORY3huIJFmmtborIGjjaOSJvLebdHuVt3D32u65+2J+0HN4ej1e 4sfBNv5ySLYGSFHsIptyyNDI3zSSMYV3bflzG3l/u6+lPBnwtX4RfDvSvDusazJq/hXw5JJrN9eT 2ywqdkj3MXlrHvkxDJEkjK3LMy7ZNqtC3iYSTxOLliox92Oik/8ALS3r5Hy+EqwzDMp4iVH4LwUr 9VukvPX8Tu9U8beG7rw3N4iur3SdT8HSQpanUF1WJrSVZp/Jm8zzGWExphfm3MzfvFVd3yycD4Iu PC2seLPE3jvw/wCLGudZvorTTLi1bUNMntIWmaP7K0sdrIvmNG0jQr5knmMI5FjZtyM3eaf8J/Bc 00uq3fgvR01e/WaW6uLzS7T7T/pAbzY5HjXDfLI0bfM25c7mbdubE8Yaf8G7eHTZPE9n4J8qKNtN sG1aGz2pHAxVreLf0WNsrsX7p7Cvpfa1YxvK0f6/A+jlWpUU7ysd1d3cXhmwa4LQw6XHLNc395qN 64EEWHkZwz7vlDbRtZlVUztwFVa8z+MWuaL8VPAelaHoN5b+IoPEHiGw07z9MltbqGLyZlvLhpPM 3RnZb28rbWVt3yrtO6vn+++Emi/HbXGntLjTvhf8Mxr72Vtb6ffv5evXkZEKzW0LbbVTJCrKrRKz blk3eZ/DS1zUvGn7N+seDrG7tfsPgDQdQW7tE1nXLF7mRyJ4bmSEwwedJb7r/wCb/R5JfLUN+5Zt q+PUzCUv4lP929L7/Pbax51DGqrWTh8PTR6v8bLzdj1zxH8A9I8Kw+J7OeaLS/CSw33iex1K3iuE n0W9jX93IkduqwsIRLMy7m82RfJj27bUNXwRceMLrVPGln4g8R3J1eRhDb38l5bLdNLbrGsTblLL uby1+95ivu+bzFb95X2D+2J+0B4M8SeFrDw3pF5qWpaw7rfxyabI1osMc1m2wyNJG3mLJHc/6tdu 5WZWZfut8PyPsjYnqvSvkM+xNB1qdLDv4d/68j85z7H0qWNoYfCrmSvzJdW9Lfd91z6s+HvxouNU /Z3tfAE9muutqk02mLc6rqckUVhDtZhJlYdzw2qxrJN8zLGkkX7zbJ5cXivirwLDperQ634Ia88R eGbnVvsWlz3emsXkuPMby7eSOSPbIzKqsq/N5isu5V3NGvQ6fqlzYeBPh74J8PXy+F9R1Y/25qmt y65NbWTyfaJPIY/vPJXy44YWZlVm8xFj+WSNlb03SviVd6t4J8R694msdN1XwbapJ4afxRDbQxav qhZY7VZbczM0wujazSTSNJ5issCIqx/vGb0YQoYuChX+KK+LtZX/AF1v6WuelWo4PGx+qYifvU/h nskkrtWSbWltXpdWsfRvgv4seHPFHht7nS/Eum+FZpF0zQk0+1mju/sVw0jxiOGNZGj+ZjJFHJ5K hvIMn76FUrqvEY0eHS530PUrFY9D0q4sX0mPW/7NsLe3aRUeSVoVZoWh+xzLHIq/u9sy/LyV+VtP 8SaP8TrjwfoejaNY/Dz4maXGt5oM1rpx1O2NtIrS2tvcEq3z4mjuDMySLH80m5JHdFo+MvgHd6t8 LvCvjLwjrvifV/FuoW41aSNNFmt31OeJt0d1JtbbBPHC74kZma4ZQ0bNJIzSfSSzCo6XPRjz/h/n r5H1E8yn9X9ph4uXLbyb0s7abp9H3Vr6n0hP4C8TfEL4F6bpGr2n9i+IZbZf7W0WaW3ggurlpVae Z5beOTazOJJkaPjc6mSNvmjrjfEnhGXwB8SNb8SeE9I1zUrjTha2d7pcKxQxPbu1u0C20cKrIbeN IWj3Kv3o1X94sdwtd5od58QNN+EsFxffDnS9Q8SR37TN4f8A7TghaRftBkW4aRYvJ+0eZiRtu1Wb dIu1j5Yt+JtLsfjTYaJoniCC3On/AGqR9Y0uyv5LjfLD50ccTNDt/c+dDJJ5kvl/NbpG0e9mWO6t GjWn7XlvLTe6727d9v8AM+hp4p1ouMW4J9ltftf066rqfNltqGu3viaTQraa6A1i5trZNLsdVxfD yZJrmOOSSSGSRZ4ZJg0k3y/6mZpt0zM1W/hX4c134I27and6jqGgXutolpHNJp8n2J7hhN5cckMi rIzboDtb935at97bIu72DSfgzB8EDdy+EoF8WeLr7bFosOuzQKNNhX5Zpt3yv5StcM0nkrubfGu3 +I9LH8OtQ0Hxjpr2ut63q905mukuNYla+tobjy7hWmeNfJWHb9ojjWOJsSKzDyV8vzo8qOFtUhiZ 0Vzrzbt+O29lqvmROnXnRdF4h69Ukr276el2rXd7WR0Xwc8ZXvj74e6Trt/JYzz3UYYyWEckUZYc SL5chZl2yeZH95t3l7t3zbV1NSkv9S8TWlhZXl7pMNhJDfXk32NHhv4ZFuV+ypI33WVo43bau5V2 D/lpuXktU8fy33iTV/DGrLJotqZHlF9HdLbSQ6fHCrPI7KzMu91k/fL5caxyKvnR3C+XWx4muJPD +g3V/Z+IrrRtH0AW8moyahYyXmbO3Xzp/Lkf95I8kbKjTbpvuNtXzNzV6WHqqUOXmvJbs0lDXTS5 x37P+rtffAXQ9dbxLH9luhJrV/q08UcLRTNcG4vo5Ny+X5fnG6XzFC7Y9u3dt8w9tDrjDX4YbSys Z9bnm+zXU90slhJJawyNllDRs0m1pJTGv+rbLssi7q+M/wBnvwb4o8P+NPDnhO8Fvplt4ZVfGmqQ R3lzLfFp7YL5IsoWjk3iMpG0ckbMrb+WjnEUn3BJot5qs2l6hcNFY6jCMzxxSSTxxlonVvJLbV3K 0gxI0Z3Ku3au75TLq7rUbVY2tp81+h40K1aveUVaSdn287fo+/kdPn2qnY3iXtnBMiyqssauqzRv G4BA+8rDcp/2W5rA0nwjLpviSbUjqlxJHLHmWERRx+fKVVWkm8tVWRtsUKq23cojZd21ttJcWNrN q8Q1TMN61xG1vfWkzWX2va1xJHbfLN5k3lR72ZW/dtuZlX7yr3tJPc9CHvrYualPcW/ibSTHpU15 BJDcRzahFHD/AKH/AKtlDM0iybX27dsccmSq7iu35tBpZF1by5H/AHEkO6JVt2yrK2G3Sfd53R7V +VvlY/N/C28jginhvHi3zqfJSVYWkkVZHTK/KMqpKru/h+Xc33amsYZbOzhhmuZb2SNFU3EyoJJS ONzbVVcn/ZUCh6pEx91ux5HdeGbi1/a607XnmhNnfeB7qxjjXPmCSC/t5HLdsEXEe3/dar3ij4Yv 4uktb2wm1KKz8q4vbbzNdvrO4ivLiMqJFVlYw+WqrGse3aouZv3a7dsj/jdbyWN78P8AxNZWcM+o aT4ms7c3EkCybLe8b7FMu7erKv8ApCt8u795HHuXbuZd4aFq/h+YHT76+12KXUTd7NSvP+PVZLj9 8iBVVpI1imm2rI7BPLhVVO0befCylSqzjDTr/X4m1WS5Izf9fd8j5Pm/Yi1Twr4bl1DU9eikutOW OaBLCwnv47qbC7Y5LZYmby1k3KzbZvMjbc0ce1lbzvw38PNU8J2txDqfhubRra/tri3ihmtr/Tr+ 9s7iazsmlm8mSRmbz2t5Psf7xW8y4+VVa33fX3xy+JninRdDk0/wVoWpahrT3KiK4sZrVnmtod0l 5JCjea2Y/LFu3mRr+8uIwu9iqt8/eEvDd18VPiU+ixeILOPxBAJLXxbpivNaBtPk1D7XqNnGFjk3 7bi4aHzluI9wVl8vb+8kvGY6VapGNeHN/VuvodUc0lh37OpG6fXTq+nnv5+RjfEr4U6rZfEa31L4 V3SHxpod8yn+wPDrWMDeZNtjWNoVa18mP/SIZGuGWTdb3CyNIu2OP0LwP+1b45+Gkdpovxm8K6g1 7c3VxaWuswtbWrTvG8fytHI8cOxfN/4+FZY2UL97azV1TfDXw9/wvLWr+w8cauuvaZpyvfWungJd 27RrHNIywQsvnR3DXC3DRpbyRtcK25vmaCTyj4wfCHx38XPjVpV/q2k2uj+F5tXm01JLiFkS52zy RrJ9njuPtEzSW9vGWkVY/wB2itujjh86vncVh61Kp7bDcylJ/D9n120PGx9TFxqL6vG97b7beSuv yvr3Lmlf8FBvFFvqWqWGp/DNLu/huLh0tba+kt5LSGNdzRzKYZC0karI0knyr8p+VcV7LqX7Uuqa XoGlXNz8IPHX9u6nZT3FtpMFnHKzSQlFkDBXM0cYkkjXzHhXKurbf4aX4a6L4G+GN5rNp8P/AApq XjbWrK9ltdY1bT4bKOSGZtshtzNK1vDhQV/c2/yx7fmVWb5u5+Fd1J4ovPE3i+4tprWXUL7+y4Le 4K+fbW9kzwtDL5bNHuW6+3tuVm3LIvzfdVdsNHE/DUrcz9P1tr2OnDKtGDeJS5vK54/8LfBfxp+K d1qXjbxx4l1b4a6i0EUXh/RdJdPskStC+6S7sZPMV/mkX5ZGWTcrD93tj2+0/wDCVeKPCKgeJ9I/ tmxHP9teGbWWQL/11sN0k68sqr5LXG7azN5K12Uk7RtbAQyyrK5V2XaPK+Vm3Nlvu5Xb8u4/Mv8A Dk1e4PevThQVPZ+8bXfUwvDfivSfGFnNd6RqEV7FHJ5EqISstvMFVmhmQ/NFIu5d0bqrLn5lrez7 4rjPFnwx8P8Ai/UrHWLm0+y+I9Pz9h12yPk31sdsigLIPvJ+8f8AdSbo23fMrVkT6v4+8H3NtE2j RePtLaUo15p08VjqUUew7fMt5mWGZtyrukSWH/WfLCoX5r55R+OP3DsnselcCjGawPCHizSvHnh2 x1zRNQj1HSr1PNt7mE5V15BH+yysCrKw3Kysrc1v9K2jJSXNEz2HUUUVYBRRRQAUUUUAFFFFABRS ZooAZ94elfPn7b2t6Zo/7OPiK11FbmSTUWhtrSG23KzTLIJ9xbY3yosMkjbtu5YyoZSwavdNT1S1 0nT7m9vZ4rW0to2mmuJnEccUagszMx4VVXqa+H/Afxw8QfH79on4VTeIEuNA0CVdQutL0nTLzclx PD9p2zTbTHIq+WrR+Z80cjQyKq7ZJlj8vHVY8nsOs9P0/U78HSlKftLaR1+7Us6Tpnjzw78Edb8P 6BZS+LbDTLy1VdPje71K21azlSzWGGzv1uFkjEcMsc7SJDDHvLeUixxyV9YfDHwJafDvwzbaRaQW 6vCsX2m9t92++lWGNGnl3szbmK4+Z5DtVfm9NPwTdXtx4Z099Slll1EownWVoWkWQM25H8n92rqf lZVZlVl27mxubM8b2Ol2+h6qj/2jph1V/Onu9BtrlrmWSOHduZrVfMGY7dY9ysrN8satuaMV6UV7 KPs7+6PE4qWLk1az8uu2/fv/AMA+Vfi4fGN1+1Fo/irw34SuiunzpLo+qaToG+316F4I45o72/3t 5WTHNDFI0O1VkWTcsbeav1J4N+ISeItYu/Dut28OieM7SM3FzoqTPMv2bzmjjuIZWjj86NwqtuRf kZ9j7W4rgvHP7SGl+D9a3zm4e0jvV0u30ez02S6vdXufMnilS2YFYW8tltm3LJJt3SQyRpM0YHU/ DLwXrba7qfjrxilpD4v1e3itItOtVSSLRrGNndLVZtu6SQtIzSSfdZtu1dsa15yiqeIapPff+u/9 eZpiK3tacFOFuVW9f66/0j1MdKWiivTPLCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooA4v4K/wDJHfAn/YBsP/SeOu0ri/gr/wAkd8Cf9gGw/wDS eOu0oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiikoAbxjNB6cUmOg7Vh+KvElp4R8P3+rXj7La1iaVxkZPoq5I+Zj8oGeScVU YuUlGJlUqRpQlUlsjA+KXxS0r4ZaD9tvG+0XMm5LWzRtsk7/APsqr/E3b64B+NviB8UvEHxKvhPq 91i3Xb5djb7lt42VfvKu773zN8zfN8392qPjnxpqHj3xNeaxfyyGSZ28qFnz5EO75Y1+793/AMe+ Zv4q5+v0zK8op4SPPU/iH8zcTcVYjNq0qVCXLRXT+bzf+QUUUV9KfAbhUtvcS2VxHPBLJDcRSLJH JG21o2X7rK396oqKTSe5cZOm7pn0L8Kf2orjSYodL8Wq97aoqxRalEu6ZRn703zfN8v8S/N8v3WZ q+orO8h1G3iubeZJraVA6SRtuV1b7rA1+bFe0fs9fGWbwbrMOg6veKvhu6dtrXD8Wch/iVv7rN95 R8q7t3y/Nu+JzbJVyyr4Za/yn7FwrxlVhUjgsxlzRlpGXb18vPofY0y/un56CvzRr9MGAkjIByGF fnn8SPDZ8I+O9c0oQi2ht7uTyIQ+cQt80fzf9c2X/arm4aqRVSpTe7sel4kUZSoYev8AZjdf+BW/ yObooor9APwgWuf8TWWsXmraH/Z0jR2cdx5l4yybdyrt+X+838Xy1v0Vx4vDRxVL2UpSjt8Pk7/8 P5HbgsXLB1faxipb/FqtVb8N15ktvcS2dwk0ErxXEbrIjxnayMv3WVv71RUkskcEbSSssUUa7mZm 2qq0RSRzxrJEyyxSLuVlbcrLXRzR5uU5bTtzfZFooq9o+j33iHVrfTtOt3ubu4fZFEhwCP8AP8X8 NXOcYR55hCEq0lCHxF3wf4P1TxxrsGk6TB51xJ8zM3yxxR/xSSN/Cq//AGP3q+/fCegjwz4Z0jSf N+0fYLWK284rt37FC7tvbpXGfBf4TQ/CnQpUklN1q96Fa8nUt5YK7tsca/3V3N8x+Zs/7qr6aBjp X5bm+ZfXqtofDHY/prhDh55Lhva1v4tT4vLy/wAySiiivnz9ACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigCPntXJ/ErxlF8PfBGq6/JaTX8trGq29jCrNLd3DsscFuu1WbdJK0cf3W+9XWsp YcGvlmH46eFNQ/ak1G18XmXQJ/DiPouhR6vHHHGskitJd3zSMv7tZFhto4/3nzLJ939423gxVeFG MVOVubQcXGOs2ebfBPwjYXXxHt/FennRfHDyMuu6nrUkbSX63TMq3v2ezWaNvLWZpJI2kt1Zf4Vk ZYVk9Z+OXxc8J/C3S9X0e81u71i48Qad/Zdpp9xdG6tbNUWe3eSQxt56jzCyzOWaViNqbmjIW1J8 VtK8J+H9ah0HVNU8f+K/sMcVrZ6VFHdXAtYZmhSb7TDFMrNtbzm85m3M3+rj8zy6+AfHXxAv/Hmp G7vX3J5FtCkciQ/uFih8vy4fLjXyotxkby41VfmXduZd1fEYjG0suhyUPelK79PI+e4mzNZPhuWh G0pv/gt/p+Wh6X8A9LtPFnxcuDput6rqGteYuo2c2pW8YGpPEGuJFuIjfRtI/nxwOm2Zl3JukXbu ZfrHxR8Wh4T+H1hf/wBoab8QNM1a4judUaG+hs3trKe4SH5La5Em6BpC0TrNKPLaRl3Rqv7n4Lt4 dI0/wjpF3pOqzN44k1JpzHbrJG1jbwr+7KzblVZGk3N8u/5VT5o2UrJ9AeD/ABtc2ng6w0XVLe68 La3oBhurKz1C7uEtL+ZL1FWLyWuIY1/fqzSNJ523M21o1hWNfOwWZLD05U38Uk/6/Pf8zyeFKFXF YadOC5Z/EpLVa9Hurq60316bnq+m/EPwx8e9B0nwzf3p8Qa/ZW90mqy2N7HpN9c+Ww3W0MaTNHPH dLH+8WObydq7maNvLC/L/wC0FrGkWJm8L+F/DureHPDVrqcjxQ6nFPD5kixRhpFWR+Wbz9376NpV Rov3nlssMeh8bPFml+KNFsrH/hGItQ8QyI1+PG9lH/Z/9qFm/fbrX7PG0qrJujVm2szQ7v4mVt2Y +M/Hvii51iDxbfN4GsL2TXNKmmAup41sZJo7fyYrlo90nlRySfM371Y5mZpJYXjXoxGMjiqUoc2u nr6fhq7u7Ms2o4rGVZYGEPe25kt/vtZbeWqO08S6LqXxg/Zf8G+E9OvNV8UeNE1Sef8AfRF1umga Rbj/AEqSTy2hj+0KqzLIyyMqqvJYJ5B4uvvG/wASvGXw68I+NdU0/U7S0lsrS3nadZJZ7e9kzHLN Gskc58yOOPdu8t1Xy87Hk3N9R698Rj4+8P2ureBNWn0jX9LtWjtLbUmkmmufMR4LOSD7RuiZ2uZI 4JZmXcu6VfOC4aTy3+yfC+k/sqrc+B7W61PxS9o92PEMEMNnqP2VdShmkmmjWdptsckXk+d821rd 2VljZWbetFVlzKV48vTqla2m9739D6PFZHHC0vrKcueMbcve12tO+1tXrqfK2qaydR1LVbuOysbE ajIzNa2tuqwwBnV9sKnd5Sqy7flbdt+X7rMtZV0GlhKrXa/GLxZbeOPil4o12wgtYbG8vpGtjawG JZY1O1Zdp+bdIq+Y277zM1YvhvT9Q1C4vP7Od4ZLezlmnaGXEggC4m2r9518tm3Kv8G9m+VWavi6 1o1moyuj8Ng6kszToe/JT089TR+Gvh231j/hJbu+0261FNF0Uatb2NtIyrNi7to2EzKrN5SrJI0m 3a21W/eR/eX2f4Mf8JbqngvxT4dMOka02pND4fTwhqEbW94Wtt0jRxN8sNnJ5bXUyySKzNLbyNtZ lZq4Yapo2g+B9MWPUry3nt9Q1LTNf/saaFZGs54Y1h2tHJtvLfdbtI3zKrMzLuXzI5G9O8IfFTS/ DvhPxB4nvbK0j1qaZrvSoNQsEaW7jjltWtW87/R91vDJZiNY7dsbmZvJ8uGvpctceaMebl0lf/g/ 1oz9tweSYdOnjG1FKL5oq+r2d730TWz827Ho/gX/AIQe40jR/Dfgf4owWV5f2mo6Npr2emia/ntZ Hd1Mm2OO5t5oJruOTiRVaOFptqqzNF3njr4c/EH4tfCO88Ja2LaPxPYiJrHxN9q+yx30n2i5t5me GHf5fmWW1mX5lY3jLtXy+Pjf4hfFaPx54R8A3kltcaZrmixXscuryi5jtxdpJHcRzWvlyN/pbcGS Rl+Zp1Zsfu3j87vfHkq2qaVpGs6lHoVuzC3W4kWORg0bRyf6vpG3nXBWHdIsf2iZdzeZIW9j+1KF KHsnH3bfZ9F+XQxxOe4ephXTp0pSjGOvKurttZaad20l52t+oUdtp3ibTb3UL26vvBGmQQXEhtYb 37DI9lcSJNJeXUW1WtZJGt7ja+4SIskjbo5mZYuo161sPDyy69DpIurtrq3Z8RzSMG3eS0ypHHI3 mLDIy7lX5lVVZljXcvyB+xz8XbDVtN03wDrGsahoVvoaXOqwuL+CG0v1WXzmjkby1lRY8eZtWQiR ftHmfu18uvrDw54t8IzeKLq1t9b0NPEuq7ZTYWeqrNJcxrGWhnEXy5Zrfy2ZlX7qqu5ljVq+qwmM w+Moxme7hMbSxtCGIpv3ZdL7eXr3NLwx4iGteHU1K0S91OKS+miAkMCzIv2to23KCqqsWGyrfvVW PaymbctcXpPgCxvrfUU0a4uoZWvrg6vpuoSKFjkvVaacMEVoZpVjuodvmrPHthSMbdzSV6fFptvD fTXiwRrdzIkL3CoPMkjUuyKzYyVUySYHbc396vFPjt8dtC+EvgrUY/7T0/xR4ly97Y6dqDxt8y3q ovyxqB/o8h+VeJG+zN825HkXfEeyjDmrS0RtWxFPC05VJO0TMi8X+E/G3jHStTPiKHw8lrponv7U Ty/2hew3sMkir9oG2SO3hWVbhWjkZY925lh8lWrlNf0W88NfCPx/fWA0izt5tIv7XxRdand3Ul/P I2n+ZYAeZb26rOzXS+YrR7m3ruZpGauz+Ini2SHQbvx5A8ln4i8O3TWS6dALPVEmlihm82O3bduh RmlVpm/dTeTa/Msf3qy/HfxKn+KfwkfTf7P1TTo9f1TS9OS8/s+6WyMM91axuvnK8ck1vMrXGJF8 ndHhG8tpI/M8duEebnkubXl07q336dP1PTTlGCl9nRf1/W/od/4b8CX9h8Yr/wAYTafZRahqenx2 mosk6ssMaxpt8l/sKvM3nJIrBph+7WFmX/Vxx93q0l/YTy3GnxTalJcx+VFaSTxw2sEiJIwkkfaZ FV22xtt8zHyMsf8ArDXEeHNP+NF1Yu+ua74FsLvzNqx6fo15dRmPavzbmuozu3bvl2+nzVxHiX4v eLPDN5qUYl8MeJbu2nbTbbVdMXV1tILiQxp9nure3t7uPzlmCrtadZNrqoEfmfN7CqqjHWLPMjFQ Vm/6+Z7RqGtXWh6pcy3sRm0Z7dPsv2G0uLm685Vnkn8xY1b5fLji2d2csv3mjVs+1t9dt57BNO03 T9Ht1bz9StHTfHPLNKxlaCZHUqysZJm8yH995i8xs0hXl/BeheMvh74btdA8P+BvCthpVnu8i2Pi 28kC73Z2+ZrBmPzM3X1qGx+IHi7SdVe51LwKNTtJI4rN7zQfE0Nyvm5jMOYrpbSNfM+0N88fzMUV SuPLrqjWjbZ/czSTUJWv+X9bHqcNxDq1sGj89I0mZTvSSFy0cmOh2sV3L1+6y/3latHt1rzNvG2u SahHdf8ACsvGHmxI0aKt9pYi2syMxaP7ftZvlXDMu5Ru243Numj+KGtXMs0cXw18TyvbyCOZVvNI YxttDbW/0/g7WVvoy1n7aPn9zE46mZ8YvF2i3XgGW7h1iwa00vxNo8d/cLcRtFaNHqtqZFlbd+7Z V+9uxtrrvCvhvUdD8N6Lpt7r95qt3Z2VrbXF+8catcyRf6yZgwZt038XzN/s7W+avKvFmsTX3w58 eaOnwz1XwxFqdvfpql9bXOi+XbtNC266n/05P3nltHI272+Yj5q7L4d+Ida8VfD3wLd3GoyRX2qa Fa3N/cppxZnmmtd3mRyAeTGwdWYhlZfurtXcuYoylKvKL7GcpONFX79DV1j4c2XiTw/H4b1yObXt IZJmnvr29kS8aZvlYqY1XaJFknVvLaNVVvLVPLbavI+Kvg9aCxuo/Cmi3WgX1xNFp17q+l3cNlqN xbSS28kt4t55ck0kilW3LI6mT98W3N5bV2l9qFvqsOoeE31a+07UHtktV1QSQw3MkksMp3wccyKs byblj8vKtt3eXIq2vFmpf8U3rZtZ7OU28bR3P2jUnsUgG1WcvcRqzQlY23Bgu77vK/ert9mvaKdt Qi4SlGppf/L+mcroV/a6D4OhfWdRk8V+HLVdQ1e+8RaiqmS1khvBNHFJb7d3mR/vF+VV8trTbtVt qr5w3xc8S/ErQodWspLPw94c1C2kntdLsb9n1qaHEyrNcbI2kjhkZrOPy7RWmVrhWWZdu1rXiTR1 +Hvg7TfAlvpEni7xZ4sm1XS7G41y8hgzDId00k7RyeYsbQoskjW67pZFDSeXNNS+E/hXqPhOz8G2 GrX2j6WPDtpAV1C1lkN0tx5McMqCEjy2jaO3mZbhsN5Z2tH8szSfN5tXrxklQ+HXm6dra6LTe19U ddS1ua+9/wDh7FLwLc3Hwv8A2c18Y6do+k6jr1rpM1tZ2vhZxNZRDzGMMMm2b/SfLm3bm3SSBpLj azeY5ahe/GLSPAHhvRrfw/46th4Ht9C0trDUGsWuriPbJJbKjKI442VvLDSR+Z56i2uljjVtrxY1 lofiG18d6rDeeI/+Eo+IN/oGqXEegxTRxW9nPDdxtZpG3nbmVZJrxo2kkWRYW27Y9rLVWy+BegfE XxJ4eiufAFxaSNfGfxjcatbeRP53k3axxwzWvkw7d0e5mhVfM86zkbduauGjUrxlKlRkuXlsru+u t9fu0vdeh5EYVa1KVCT5JrW71vbzTX53/S38F/2iPFF9b3X9s6zoWqSjVZLUWthcyStbrNvuJLhk 2ySTRxyNDaQLCypJJL5as26F69F8E/Gjw5p+ueI/APh66n8ReJ9Nk1GS3tbsratfXUYSSaNpnbaX kuJbjDJGkarHJtjWONWf55uvDfiz4aeMn8I6PrF/4P0h7yCw0yfUNDgv4byCL7XPbSMzLIIpFu0m k8xpFytwsojhjh216r+z/qlk3/CT2XjG18NPrOn+LINau7nT8alaSXGoO0Nv5DedI0FwtwPL+ZV2 oq/L87SV25bjqdS2Fq351fX/AIP+Z05VKEsHao3Kcbp7730vf1/4JW/aQ+PWpxalpngy0OueGdV8 ibULyXTYiVmEUEzL5dxuWZbdbiH943kxtJCjMrLH/rOP8Cab8RvB11b+K9b0S/8AiB5Isnm0+61C 8ur/AE23kul3NFp9wschbzLORt0m5dwt5I22wSM3vngHwP8ADaz+K2s6joutaVfeM4d8L2umaiEm s7VFjh+zTWsMvl7YtqxrmNdu2LdukXzG9VXRYo7xprYfYnmnFzc/Z4o1+1t5Qj/enaSflWPnhv3a Ddtyte5h5SlCSl3f3dDfC161GEo1ev5X/P8AA+Tfh7c+JPH3w0sfGHhjVtbstWn8S/2qfC0+orpe m6ncr/pF3DZsyzTPHJtnXy2mWPzPOkZUVdte8fDP44WfxE1yfSJPC3irwnqS2zXUUPibSWs/tMSs qyNEdzK21pI93T/WLWJqHxlPhDVNG0DxHr+g2msCa1XWrqGDbY2LSWsknkvJJcKyNM1vcNG2G2r5 atH8ys3TX/hLT/F/iI6rbXmr+FfFi6baeZNZahH5sMLPOY45bffJbyFWa4XdJHIvzMY23LuXmp4W rQUeWXr5lU6U6dJQrO77no9LXmfh3XvFPh/xDovhbxY+n6n9pspvs/ii1drZr25h8tvKktdm2ORo 2lk2xzNuW2lbaq/KvpddcJ85DVhaKKK1JCiiigBlcX8RPihoHww0/wC1azeEXNx8tjpdt+8vtRl3 Kiw20P3pJC0ka/L93eu7avzV02o6jbaTYXF5e3ENraW0bTTXEziOOKNVLM7MeFVV6tXjXgu4vvFF r4o+LjPLJLcWd1beFbXUJ2htbfTI9rLMyOsW37VJCs7M3/LPyV3Kq1hUk+ZQhuzSEerE8WfGzxla 6cX03wPb6HcXloTpY8WaqsNzeXOyRvLjtLVZmbaFj+WSSFmaVY/lY1h6zqXxB1DxprXhbUfF6nX7 bw5Za3BoHhmwWzg1GP7RNHdwfaJmeaNm2xxrMs0Pl+cr7flavMPil4jvfEV1BrWq3n9qaf5osLyy ivLf7LcyRtdKslm0cPmKI1kZv3jN/wAfEO5ZF2s3Rap448R+Ivid4AhtfD0Mtjb6fqFg2nabKsdt f7rGO4jtfOVmhWGS406RVZZG+WFo5F271a8Tg5UKcakpOWuv5dLdzr5VBXVu56F4i+Hfwvj0zWNc h1mxbWrexiePXta137bNYqzTR2skd1efaDar55kZZIx/rI/4tm2vk74W/FjQvh3q3g6WGeXxTB4f 1+5j0yNbjyV03SbhrmGRpJJoYY1kkkuoZvMZtrLGqt9n2/N9BfETx1pPiX9lMeJtL0VrnxFqFvbx R2vh+K4t5LXVrua3uPMURjcjfaGhuVZv9d+72s3nKW5vxR+yxosd34knkvNd+IWr65NfyNo/2R7V XvVW4ZZWnULb2sw82Hy5Jl8th5zRxv50Yj+dxsalScfqunX79fnt2/Q8+pLEVvZKh8Mpat22S1/P Tou59biG7uNSc3VjafZreVntbhZmkl/1aru2GPCNl5l+Vm+VVOf3jKvM/Ej4meFvhatvrHizxTHo ll5ckaWMgVjdNuj/AHixqjTSNH/0z+VVkZmU4Vl860f4xatpHwD0HXU0qIeJbyJ9LFjfCO0nuvEL XH2fy/s/yKVkuPtEkjeZGyqu7b8zbOx+G/wK0bwPdW+sX7jxL4phtoLSPWL21hi+yQxw+WsNnDGi x2sPzSfu4/8Ano25mr13VnUXLS/HodPKoayOd+A1zrPxQuLf4kaxLBp9ilpcaNo2hWV2t8tvGs6J czTXn/LxJJLartZTs8tVPzMzNXugpqjHWndq3owdNWlK7MpS5mOoooroICiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4v4K/wDJHfAn/YBsP/Se Ou0ri/gr/wAkd8Cf9gGw/wDSeOu0oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCN1Bzmvn/wDa38UNpPgzT9DjZ0fVLjzJ flVg0UW0suezb2jb5f7rfj9AMw9OK+RP2vtRnm8daVYswNrb6d5qR7VyHkkZWb/xyP8A75r2MnpK tjYKXQ+L4wxUsLk1Zw3l7v37/geEUUUV+tn8qhRRRQMKKKKACiiigFpsfb37PfxD/wCE68B2yXMv matpp+y3ZLbmcD/VyfeZjuH8TdWV681/a0+H4V7LxhZRHDFbW/8ALXv/AMs5G2r/ANs2Zm/55rXN /sk60bLx5f6c1zHFbXtkZBC2397LGVxt/i3KrSfKv+1/dr6w1bSbfW9LurC7h86yu4mimiyRuRht Yce2a/MMRJ5TmXPT+H/M/pHAU1xTw5GjW+La/wDejs/8z836K9E+MHwf1D4Xawcb7rQ7iT/Rbxl+ 7/0zk/6af+hfeX+JV87r9Gw9eniqftKXwn8+43A4jL8RLD4iPLKIUUUV0nAJLHHPG0cqrLFIu1lZ dystEUccEaxxKsUUa7VVV2qq1MsirbyJ5aMS6ssvzbht3fL97b827+7/AAr/ALW4uLeWzuJIZ4nh uInaN45F2sjL95WX+9Ue7zcxp73Ly/ZIq+wP2e/gpaeEdPtPEeotDf6zdR74XjkV47WNl6oV+VnZ fvOv0XjJb4/r2H4E/Haf4e3cej6vJJceG5pMDPzNZs38S/7P95f+BL825W8LOqOIrYa2H2/M+z4Q xeAweYxnjo/4ZdE+/wDwen5faYH5UtVLO9g1G1iuLeVJraVVkSSNtyurfdYGrWa/LNtz+pU1JXQ6 iiigYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARsBz6Cvyp+Miaj4g+OGs2a6vPqEs2u 6lcGO4la4jsFjupoWZljaSRVW3tYWb5VZY41+Xaqs36qs3c9K/Jn4y6ZZ3+k+C/EulWUlr4U1W1v oNIhm8tfs8cGo3KrDtX7rLG0LMzbt0kkjMzMzNXxvEsebDx/rt/meBntSFLASq1L2i47H1/8M/D+ nzeDdc+HXh2VL/xDY21vfL4luoi1vPItwt1Zyfu5HVIm8xZIVWRlZvtDNCPmWT5g034Ut4V+Jl9q kU9iw+Hl7YzzAz7Y7maS4R0huJtzxQOG/wBHbazKsiru3Kss699+zV+y3rfiTwXb+MdO8TRaONUE 0MSqiTbIo5PLYNHJCytuZWZlUrujjaFvluGaHU0X9qSP4e2tvpkS6H4i0q48rS21ayuJrDUZbC38 mFW2NMywttW9aNfOj3NIsm2Pd++8qlTvGlUrrl09306K3zXmOvPDZpQo4ipJQe95b2tbe+2qe5xH 9pXvh3xz4J8WajaWfhjxBqXiW5uNYt/EGnxRSWgmuIWjuFjd1eS0WNW2yMq+XNFcNv3Mu2HxZrni bUdQup/FzXGjatHBNDBO2nNazOzKzSQLHH5cf7xr1ZJGk+ba27c27y5Nh/jhpWpeN9a0GLxdr2k6 f4nv/K1K+1JrW6trS7/5Z3dq3mMtskM0cartml/cjcskckMbVa+IN54D1bTbu60+0llltNT/ALJ0 /UdNnZbR7OCNY41k8ySVpnWOEfvLf5THcQySbTJ5deZi8KsW+anVUXfW7sr9391+h6vD1dZXKpTc HUja8eVXd0tbau9/z0sP+EvxW0XwvqkcPiCK61+3t5pobFriCMoYp7dbdvtCt5zBVW3t1VYdzKrT L+8XbG3QfGjxPB8RPEd9GYtSOrFY5H0bXJobO10VmtbdXO+aRZo/ml/iWKGaSSH/AFnl7ZORs/DP gDxF4XuJ7bxQ+i39nqCNc3erWqwedaTLt8uG2hkmaWSNl3fLt+WRt395eX8bWcM3xFv9Q8RxMyXt 3cahttPJ8x1kkZl2/vJFh8z5drbpP3bLIvmLt3edUxcaGElhq3vKT+KP67+n+dj9Aw9FVq/t37sr fD2ZJ4N+JnivwvEn9l6zqsOn2asFj+13C2dlJK3lx3EkcatujjmmjkZfLZZPu7W3V9VfFnwPpvhf 9mvUoIJYfFOnW2irZabqd9frb/ZbOONZLdvNj+WRvOjh2+Wq+di3WTdt8yvkDUNY0a1vNNtf7LW3 tWnbVrO4urtLmSDyYZF8m48u3/eLJcLbt5ckaqqr/CszSV6b8N73w3qXjHT9ZM9vptvZm00w6DeJ capfX1qRHC0iv8u523Rx+XFj9z5i+W0atHNtlOMjhqfsanvc/wAPkvmrfLtt2MM0wksVCUorl+Vz 5VLBi3G0/wB1a1NA1SzsblVvLSS5s5G23CxzLCzR/wASq3ltt/3vm/3a+rvDPhhdY0O/0J/hAni1 9AmuW1FrjxH5l3FGzGJYYxHzHIq2MEa+WvzRw/KqiavKGsvBE3gvVLZtEls9i3U2kSPdrdXT3TNY r5cjKse2NVjmbcy7dskm1Wkj2t5tbD06ajJS+LumvzSX4n4zgeCM2wOMlisPVhzL5rXySelvI4LU NPs7dZLOa8M98qwxrNat5kN3ayf8tlkb5lb5o/3bKrfMyttZdte8eAfgbq1v8M9M1fVvG2oWC2dt H4jubFLYX1t9hbzltVZtsjRiRbjUPM2x3EflyM00f8LeLzeMNS17Qjpmm2kehwWFxcWkNx500lzJ Zt5i+S0zfLt/fTKyxxxrJu3SLu219l/s+/BGw0v4XS6b458J34/s67S8aK9tYP380LTZ2R2+6aZV aSVVWVpFkXy2jXa+xfscgy3Cyk6k6nNK+sVfTre/4H6TTwVXCYT2tepeT2017N/8Nvu+p8N+NrzX vDfiXUteia4s0vo5oYpGs1jXybi1XzLf/Uxx7lt7qNWaONV+ZWj+Vo2rymzt/tVwsatt3fxV9vft C/FTUPjPBrvw+L6XI0etrd6ReXPnRTWdrFHeNdy3Hyx+W0PlbfLki3+WzbVmbbM3zhDpb/Ejx9NF o2kWenXer30psdLSeGKODzGZo7eORtsfy7vLX7u75VVfm21z4uSwsqnsry5pe73v+v6n5jmmbfVa HLh/ecpO1klbpskr6rTrY774K+H/AA5Y+EvF/iPxRoWvXlpHara2dxpOmRzW/mMyrIrTSQyLbyfv IWWRdrKvmbfm2q3b23iixGn6NqfgGz1LwbfCwjWXxZq+t6heTkW/kx7pIY9y/Z/Oi+z7mt2hk+6q x7WVav7Keg+K9DbxT4m07QZLyzu9Hv8ARLd1vfs8hvNsdx5aiORbhm8uNtvkru3Y+ZfmkXntct9U t9c1W/8AGGm3UOo3b3W6HUI5rNp7p13bl8uP5Wj86O42ttVlVV+Xcu7jqVI4TAwhGnL2svtdFv5X 27Nbbn2/B9GriMviqycbXuteZ+e+v3Pt0PSvgT8dfjB8RviBHpWq/EK20jT4fLDXWpWFmsUlw0i+ Tb7VhXzmmVZFEayQtt3SK37va3r/AI48M+Nr+SHTtT1f4a6x4vvdPkjv4b6znsJBYzy+Sbbz4rvz pIjJP5cf7nazKrNtk27vnvwl8HdT8S6t4M8Z2t5/Ytp/a9kjLZWkO+2WS+aOFof9czbWjk/eXir9 2ONWm+7X2F4qVtd8S3llrmo674V0+S1urlJ7fU1so4re0VY/OLRyN8rtfNJuZo/+PeENH8rbvosv dWthP9oTctLO71vs9f8AK/oaUKWJpzqYbFJNQdk9Ltb6+dnZ+aOMvtW8X33jDz7Xwx4P1+2Oifa7 HwzpviqILeRXU0n2m6y1mrTRybo9yvthZsN+8k2+XzWvLJ4Z0/TNNtfB1x4Y1G31vT7WRl16xm0g pYzfbv8ASblm+1bfsdv5itJDujWONY1VWZZPoLSo4fCMevWp1i+1PUI5pNbuftCT3kkdvNNIUihT LfKqQtGscX9zdt3N83yz8RPH158J9Q8CeI7vWba6m1vUdR1q4aK3nksk1T+z3sSkiyf6TDDbM0am Pc0jbrhfLh8mOGvYxNOFOPtqkvd+WnTt8jSti/YR5pW5V67JX/4d+Z9AaF4BtfFfjKLxNqvheTTr pLLUNK1C21W/uJ5pY5JvMhiKiRrea3aOedmX94sbNFGv+p+X0m/WyvriHT720+07j9qjWS2aSIND JGysW27VZXKMufm+Xcv3W28D+z946v8A4p/DzTfF+owRQXOoR+URbSSCIiNnUnyWZljPmGRdysxd VjZtv+rj7bT7jVJNYvYp7VI9OjkKwTMQrsvlwldqqz71LNMCzeWy7FXy2H7xvYo1I1KUZ9zGNWGI ipx+Fq5f8mRryOf7RNHEqMjWqqnlyMxQhmO3duXawG1sfO27Py7aEOoW9na6ndfZLyGC1kkkdVtp HaQhdzNHGoZm/wCAr8zZ27t3NnWNHt9YgkSaScFopIf3crKu2RdrZX7rf8CDYql4d1i2uLi5037X pr6xYhTf2djOGMDSZdWZPvJ5gy3zf+PferruuXcxlJ+0imdGOapX00trazTQ20l7JGjMsEJQPIw5 2ruZVyf9pgOlZfg7Rv8AhHfCmmaZHbi0trSAQW9pjBt4V+WGFiZJNzLHtVm3NuZWb+LbWtNaw3Ml vJLDHI9vIZYmZd2xtrJuX0O1mX/gRqDo66HmnxM+G83jrwzpPhpU0/S9Mguo5YbYW011bbreaOS3 imhjaONrdoY5FaOT5VZo9u5lVqu/CXxd4i8WaVDP4hsFS/Rrq0u5tNaM6alxa309u6xbm+0bm8vd 8y7dqr91twrubZblZr3zpYpIjJ+4WOMq0abFyrncdzbtzbgF+VlG35dzeefA3UZLX4SeGL3US9ze 6/HJrsv2OymMUcl9cfaGj+XzNqq10F+ZvuozfdVtuP8Ay+06r+vzGo8yb8zurqz1H7cktpfxxwbV SS3uLbzRxIrMysrKys0Zdfm3c+W2PlZZF061u7ix0ttTMY1GJFkuBZSSLAZfL2tt5+aPLNtV/wDZ b7y1aja5a8lV44vs4jQxyK53s2W3Ky7cAfcw2453Nwu35qzSDR47SIJLLZIqwGTfLcTq+9I48jaz MvzNukZvl27m/iZenXYy5PePKPitb6hrPjP4Zap4d1i1S/HiuTTftMEjeWdPFvNJqNnJtZlkcvYH 7yrtZEX5WUtXqlxpK6tp8tnqyxX8Tzb9iRMiFVk3Rqw3HJGF3dmwflAbbXL/ABQ8Aan4mt7PVPDW q/2T4r0m4jvdOe+Lz2LyJHPGY5oc/Kskd1NG0ke2T5kbc3lqtV/AfxRu9e8VXvhPxD4Yv/C3iSzi kuVWTNzYX0KyKrS2l1tXzFUSwblZY2XzlXb8rY8xxhzyjVj8X3djp1lEu/EzQ/E+vQ6EnhrU4tJM OqRy6hJI5VjZmORZBH8jBpFLK6q3yboxu3LlW39e0WTUbO4EEm24ZPkSR28pmAby845XDMG3JtbK r83y1yHiK6h+Gsv/AAk+o31xe6nfrb6ZLZwLMYbu8Zkjh8mNmk8ld275RuA3M3LFi+nHHovi/XjJ NcXQuY5WitoI9VnhWdLS4jaSRbdXVf3dxtjZtvzLtVmZJNp5kqdaVSm46/8AAdtemnT/AD154xXO 3ff9DifiF8G9b8aatp3ibRPEuo+F9VitpVXTbqK3ljTz48SpNhZBJ8y2+VZpFxb7Y9u5XSb4jfBv WNU+JXg/xn4O1C10HUtPgfRtS3NIvmabJ08pV3RmWFi7RpJG0ZZlZv8AVqK9Zhsxb3V3LG8xaeTz X8yV2UNsVcIGbCrhV+VcLu3NjczGnW1/bXk95HDNHNJayeTOsbqzRSbFba3PyttZWwezD1rojl9G Lco9SqaVGcqkPtb/AJHiHxP+FPhq+tvAvwy0TQ/7GC+ZeWWtacsa3GhWtrNbyTyQSMfME00jQx7l 3NulaRt2za3Z+EfiEsHhvU5NZuNQ1S/0e7mt724stDvDHM32qSNRbxrb7pFXbt2x+b5aqN0kn+ue vI1zcfHTW75vtV9/YPhW3Flplr5K+Y15c3DTHc+3Lt/Z9sq7pFVcN03bq8+1DQPEmuftHX+mav4d 03U/hlqNy0ssOo6RLdJJdxafb4uvMaNkjchlhVtyxssMy7fM5Yhy0pcyju7af16l1Kqi4Qmm/Tz1 v9xwHiL4jWl98RvEOjz+BrrW7rRbpbq11Dxo13ew6bqF39yHybe1mZVZpljjjWTyZPssbRtuZZG7 f4aftCeHIde0/Qr3StT8GprUlvb6ReQaN9l0vWPLeOKKa3DR+YvnxtAu2Qsscaxqsit8zXtf+FMn xW8aXtnLceHrHwroq6XcaboraM/2oLHBceT9pjcxYt98si+X8yssciL5MiyGuz+F9vqMfw/8Aap4 nW10HVLSwW0uIX02Gzc+b5ccVvjc3kqzLF+7j2s0iw/LHtaKuqp7WbXs5fhudeMg6kYypTta11r2 v/Vtrmx8Zi1j4Bl11PveG7i31x5Ux5yw20qyXSxN/wA9JLZbiHqoYSsrMqs1egr0obpXnPwFFzp/ w0sNAvp45b/w3LPoEhjhaFjHayNFbyNGzsytJbrBN975lmVl+VlpfDV/xfocu8T0qiiiuggTmmlq 8duvEV9408ca9p6fEBfBVv4cEkd5o9jHZvfyLtikS8nkuFkWODy2O1Y1/iDNIrboY9xfg3od9ey/ 20upeJLdo1aZNa1m6ura7mJbc0lizfZQudrLtj2qx+VY/LWub2kpfBE05bbmN8RJNA+J3xE8P/Du 7u7PUILXzte1jS1ufM8xbfyVht7mFW/1byXUc37z5W+zbdjbty62vQnxVeavo2taPeLJNbagNL+y yRea9usMEMki7pTGszNcSLGzbdqlt23c27c8L6F4a8LWlonhqHS9H0u/ZpILPSYYYbe7kZFbzF2r 8zeXF/D/AA7vvbV23ZNfFjrFppEpkv8AULmSRybS3by7eH940bTHc3lqfL8sMfvsG2r94LpRg6bc 5bsbl0R8hN4Jm0PxpPZX+pzeHzZCW1024uFaa51G98tbeG4WEbpFVvLjVZI4/lW3jjVvMVZK9G8S fAm7+MngKwu59XvPDvi21g+2/ZbF1sY49bVfMguplj3eXJtkG5tvmMro22Pbtr3GzkvPEGk6TqDW 1vpt29n9pW2vrZppLS6ki+X+JfuhpFZeGbd95f4lvtB0zxZo9mmow6fq89jcLPFdLaxyi1vYty+f CsnmeXJG27b95l6V14mp7enySRo6srpnzRrXxq0/4rfss3/i7xPaW9/aaTrGkDUrGxgbyJ1W4sZL iHbM/wC+VvMkXbIFXcdv7zy1mk9a8ItJ4I8IwX3i42fhq/S3sLeX/hGrO5OnWdpCzbIx50e2OP8A 1zSN5cfkxyqrN+7WZvBvCP7OIuvBvj7S7bWDp/g3VfENxZxab9phsN0lvfXMNtbtczWc0ki+ZDZq rLJ/y2m+VmXbN6Wfg/4Ms31fxH4zu/D+tXOj3EdsZPE0800dpg/LFcSXV5cbfMW53xr8oX7RGzRu yx7fm6H1jkVWcfesv11+70NKacaPJVet23b8PTz0PBfHGoQSfEbw145skl/4Vto+v2LeJPFWkadA 9re6lbTzSLcFY9kki/6U0cl0qrHIzblj+WONv0IjIbOBXiWoatcfFz4PwXXhzxb4d1HU4rq1vbee GOS6gmuLVo7xYHWMrIJGaFWZVj3IrMvlsV3G34u8Z+Ofh3Zp4j1i78OX+h2c8Mus6Tpthd/a9Psp G2tMsyySed5OdzE28fmJHI37v7tXQgsI3Ju/N+D6/wBfmKcvbW7ns9FJS17ZxhRSUtABRRSUALRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBxfwV/5I74 E/7ANh/6Tx12lcX8Ff8AkjvgT/sA2H/pPHXaUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSUAMx82RXy5+2LoojvvDurxQOT Kk1rPPhto27WjXP3Q3zS/wC98392vqHd8+D36V5r8fPBreNPhnqMNvB9ovrXbe2y4YtvQ/MFVerM hkUD1avTy3EfVsXTqM+X4mwLzDKa1GPxWuvlr/wD4Zooor9f31P5K20YUUUUwCiiigAooooA9M/Z x/5LR4e/7eP/AEnkr7mYZOPrXxj+yroP9p/E0ag8c/l6bayTCSMfu98n7tVZu/ytIfw/2a+zlbn8 K/MeIZKWM07H9IeH9OVPKHKX2pv8kv0MnX9CsfEmkXOm6jax3lncKY5IZBncDj9fftivir40fCC5 +GGugwpPcaBckC1u5GVjv28pJtVdrbssP7w/3W2/djKGrI17QbDxLpNzp2pW0d3Z3KmOWKQZDdB+ fv2xXn5dmNTAVLr4T3uIeHqGeYfl+GpH4Zfo/I/OOivWvi5+z9qfw3hm1Ozm/tPQVOWmfCSW26Ta oZd3z/eX5l/2vlWvJa/U8NiKOMp+0oy5kfzJmOXYrK6/1fFR5WFdPY/DTxDqXg268UW+nySaRbPh pP4mXndIq/xKuBub/wCJbb0XwW+Ed58S9fjlkiC6DaSq93NKTtl/i8pQp5Zl/wC+Vfd/dVvt210+ Cxs47aCKOK1iQRpEi7VRRwFAr5/NM4WDqRp0fel9o+74a4Plm9GWIxUuWn9nzff0X4n5s1Q0nWYd Z+2eSsi/ZbprSTzF+8y/+y19CfG/9nvVdJ1y41jwvpZvdGuD5hs7GPMlrIdoZVQfMys3zfL93ngK K8+8P/Bnxn4i1RbOLw/e2Zblp9Qga3jjXKjdubr1Pyrub/Zr0qWYUa8I1lUjFfaPnMRkOOwdepg5 4eUp/ZlG/L/wb/gfUn7NniZvE3wuso5Xdp9NlbT33Iqg7NrIF2/3UZB9Vb616zwn4c1yvw68HxeA fB+n6HDNJci1U7pnUKZGZizNjt8zHj+ddTuDZyPavy7FVIVK9SVP4bn9O5VSrYfAUaVf4oxVyWlp KWuY9UKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKQ0tIelAHi/wAcPEGoeIluPhn4fs5rjUNf sfK1PVJLWSS10fT7h2gM7bB+8kY+YscY/uM8jLHGzV8k/taakviDW9G8MeHvDC6VpGi3rW2l2ljp 5svNF7bWdwsYt22uZvMaTzFWNdnnRK3zP831B8aNYvfhb8S/CnjVrq/PhvVWTQ9c0/T4RNIywrcX FtcBVXescW66kmKt9xV+Vvmr58+KHgXWvAfxwtdf8DavNpemeIdVGkQrpCrqE0FtbLam48uFWkka OOaKaNoVVVjW38v5UdVr4/N/3tGcNfiSfp0/H8TwOJaM6mXRpr4ZNKVv672+Ri/CDUrJPANl4WOr 6hpfjrTdR1C6s9L06WPTLhpJIVsvskk1wFj+0tJMzLJtmkjjjZV2ttWt/VvgDZ+L/GHhyJ472TRo 7K8tru1ttJja9t47IWtoWknjumWeRY3j/wCPfcqyQsv2dmaaOvofUNE+Fnwa8MaNrl9o0N9o82oi 7XXVslvorGaSFW+2DYpW3jka3hX9wqx+Y0e1V3Zr5g+O1jra/HDUY5NR1R/D2lhbmx8SNezXUdtN N5cD7nW4SOzZbm3utg/dJ5sarJti+aPmr4KVDCRVaPN8P4W/Q8qvllDBUIU8TH2kFbyfq9fLXyOS 8K+FdK8F+KP7d0W71Ntd8N28mtLZzaRHqtg8QR7i1kaZWjkSOSKS3/eNCrRuzN+7/gyfFHhnwrZe PvE1gtjJYpo95JpFrJbkpHcx23lwq0ipt/eN5c0kknzLJJt+VfmavU/Hl78PfC+h6p4e1/4cReFt Yt7WSVbPT7n7VJa3Hks0CzSfdmjZrjzPMVlb93HG0bLGrN4tptu95pDahqGqrLqc7SXE8V15jXEs zMrMzNt27maSRvmb/lm27+Hd8Xi8Z9SqL2UYu0r26adGnb57eh+i5Dw7Tw9CUI81OM18Sund9V20 t80a/h3w5YvJdmPxWun/AGe22/8AE3so7qORX/c/L91vMVpFb5VZlXzJF+aNVahcWcrHxBqGyzfT NNZpn/st5G/ds3lxyRx3G1vJ85oY/mk8xVmVtsm1q2vDGg+I2jubjT/temWE1s0dzeq0kcb2rMyy btvzSR/uZGZVVvlhkbb+7bb3Wrfs233hyyRtZNm2s3scCQWl1PtbSt0m6a7mZGaPaqxSKvzMzRiS bavkyKvnS9pmMpVlh4xj5aLytr/w59Ryyy+h7ClW5qnwqU9fm7W/rTQ8HsdSFj4iXVQ1zpc0Cx+V MytHskWTdu8xfusu2Pa26u/1pb7wV4mtNXi8VaP4g1UXbXi32j3v9oNHIs3yySNt8vczfNt3M23/ AFirurV+HvgvVJ/Fgjhv49D8QadH9usoJ7driZriG4WPy2hjWSRZF2ySbfLZtse5l2/NVv8AaE+G ui614gt9XtNXvNd8ZeIp7qZoYZY722m/0v7Pb+S0fzJHJHHJtj3TNHtjjb+9So0ZVMNKrU05bW1+ Wnn09AzDMKuFqRjSpe135rf1Y634H/EXWfDfifxVqsel33iPzpmvNY1jR5fIhaJZPOkmkSSPy/mW GXy1bym2yyLu3Mvl998L/h58MPiBq3iLUo9et/E/h2yi33qtDNp8GlbmaVoYA64Fu0jTSfu5FaHy lj3Oskmfmfw+dT8L6jJrTeItT0vXLOwWG2Ek832ueOa38uNo2kjkVVWNlb70f7v/AFbfdZdP4ZfD e91bw1qEemz6Hqc9pqqzXVteNb+YlounzTSSLdeZItv8syws0kO2ObyW3K0bKvt5fjvaRjRrQ9ry 30l/X4O5wY6niE41aHuxlvrr08n96sfR/wAEPhZ4e+EPiXxXpOqW2ja3b/2ktrGrRrNeaVb3LNDa Rzf8s9tzubcsaqy+bH5itHJ+567VviDrfjTSZtI1nR5fAfiCOFbjT59VhZ0muJJprPyYZ4WZogsk 1rG1wrLI0dyzRrDuWRfFdDm+Jvi7UvE83hbSPDfiKBrq5s7/AFjRbS1meaQbpI/Ma5W3gmbbcTW7 TRx7mjkkZo23Qsx8QPFnh7RfBuj6Z4/8N3Og6pd6pDoI0+bTruH+y/D8F5bs0K3Sqy3y7bWJ/MWR m3XTbdv3T9lhcT9XjL2ceWK7rRu/f+te/T5jG1K9P940+bXTTfp3t8vuPnj432emXPj7Vdd8N6Vb 6Z4Pv7yVNM+yTrJCfLjhkZlHWPcs8beWyr5fmeX/AMs2VdXVvgvCPh3Hrfh3W7fxVf2aC71ay0t4 ZPsFq1tayfvE8zzN0ck8is0ayR7V3M0LKytofGbT7bVvGWoaZ4V+yp4asjGtxc6drf2vSxMzXl0s cLMscK7Y7h1jhVd25ZFXczMzaOq+KNVu77TNI0uWw1RLSW4S2XR9N8uO/muW/fK0Uka+Z5issPlt H5bRxqqrt+98Vi5U6Vao6kea7/Hy6f8ADW63Pmsv4TjmNausUuXT3ZdE3duy0va6X/BPvT4L3Elj 8A/A721rJetD4csjHa25RXk22ybY13Mq7jgD5mC+pFeHfE74qeN7zwp4Q1HWPhQsfiKN7e/U3Wn3 FzHBumj8vbNGyyW9w0sa/wCi/vNyvGrNu3R18teGby38P/EKLWtHvdQ0K3KM3/EsjWOS086Fo28t ZGkbavmSKu5lkZV+9Czbl2bHRY/FupaprV9qGkeHbGWeadoY5VVl+XzGht7bdu+7uWPdtjZtsfmb tte3iOJYVMKsJGj7+nXVW/z8z77KspxEFGpi/dUfnzed99fv8tme2eF/EXiH4I3Oty6e2tx3FjaW 97qHhbVmkktJ5fMWPba3EkbN5MdrFc/xbl+xqytdQxsw9m0HX/EXiKa+vrvwWdWsdUt4RKoeO4+3 6f5cvmWsbM0dufJuLoqskm0XFvuZfM27j8/fD3xLJ4Z0HSkt9P0+00uQqdWttAjjhint2t1tGkvJ p91vJMrXlrMsKt5e6SZplVvmX3L4M/F7V/HWg+Lra/8ADmoReM9FENnczae1m13cwyNJ9nbdL5cP nRqZDJGyqqvu/dqzNCv0GXYiOKceWXL15fv76fd9+jNcZTkuaSj8/u/rruXfFfhXV/iH428T6ZF4 p1S18OarYLp5s4b+wWGdYpPJvfs6+TPJ8vnOshYQssqxpuZWUw+W/tBeA/EXxk+JXgHw8dP+12tj aapNbX2tiS1N68MkEMiziCNtqtJHHN5irCskdwqRtG2Gb6D17Qf7Eu/Fms+H20XSfF2oND5dxqV1 M8N0zrDDD9ojVl27mh8mPbu2/My7mkkjbzlV8dD4wzWya1b3d14a0O6u7/Wr7QJJYSt9cRvFaWtn DJHI6pHYn94skxZtyn5vlX1cThY1afJLXmf63/zsfNPB06tKcZPR/wDD/m7fgYfg+z8e+A/gL4K0 7wPomj+IfE9jY/b30uDWVZUa5mZoZpG8+OOSFoTecbnXzhH5e5Y2kXufBvgH4m6TeaneD4n22pW1 7qV5cG11fw1cbYpDIF8uEPeK6wx+S3lqvyssjNlwysH6brHxHt9cmuLLT/CniXUltreDVrP7PNo1 1ZszK8Uclwsl5HJ5ayzyNErblWRWXd5i7umudY+KL7fJ8KeE423ruZ/Ety427huA/wBAX5iu4Z/h 9G6VdKEFG0ub8f0OenhIRhFR+z2dttPI9GJHTgmsGx0u21LSIEvNMdVlLXTWmpuLiSGSQs7IzbpF yrMy4Viqj5V+XbXn9l8Ytft/F2o+Gr3wda6nq9rbpcm18LeI7K8ljVid3nQ3TWskf3o23bWVvNX5 quaT8VtZvLy6jTwB4uup5EhvRYz3Gjxy2kMi7I8x/bFkVGaGVv3nzbvMX+HavXHEQ3X5M7OWM01o zt/Cz6hJodq2qxrDe7T5kMZZvK+Y/u9zfNJtGF8z/lpt3YXdtq99mkW6kmNxNJCVVRbMECIyl8sp 27stuUH5sfIu0D5t3n99468ULMJ7L4ZeKGl27Gt577S1jkUc9ftrbW5Pzbfm+638LKvhX4+eCtY8 Jw6xf+KdM0maIrb6jbatNHp81jdfOrQTQyPuhk3QzfKzN/q22syjdVzrU5O7lb8BQUo6WPQVs4Fv JbryoxdSIkTzBR5hVCzKpb0HmNj/AHm9a4L4B2qj4N+Apw0geTwzpcbAzOY8LAv3VztVvmPzLgt8 u7O1cSeIr7wn8S/hj4zi0nxLo/8AZOp2N1aXut2VxDPDbM1vsaWRlbblY9jfMw+UL2riPgT8GfA+ ufBnwNfaz8P/AArJqdxo1rLJOdMhuGlzCu2RpGiVvMZcM3XazMu5vvHmlUbrr2eun+Ruo+67nt6f aftkrPJEbfy0EcaxnerZbcWbdgjBTC7eMN8zbvlVbhDeSQYl3Roshby225bd/F91j8v3f4fl/vLX k2m/s9eBdW16PVj4D8KLoskEc9rA+hG3uNzDIWWF9qrt3yMwaPczSR7lRrdWfqP+FA/DTv8AD3wp /wCCS1/+N10c1V/Z/H/gEuMV1OxsL6G/RpIJknRZHjLRsGUMjsrLwfvKysp9xXj/AMTtS+C/jmW7 tfEnijwpY6/Zk2seqLqtrb6rpk0cm5fKm3eZDJHIu7b/AHh8y9RXVQ/Av4bNJKG+HHhZNj7VZtEt fn+Ucj5Pw/4DXXaHoOm+FtJt9M0jTrTS9Nh3eVaWMCwwx7m3NtVflX5mZvxrP95ONpW/r7h3indH nnwA+IMvxA8P6+kusQ+JU0XXbrTINegZGW/hUrLE/wC7ijXcscyRttXazRsysysK7KLRtE8P6tYy xzDTJ7hWsLWzW8aG2mb552WO23eW0mFkbcF37Vb5torz7T9HW3+PHjLSry6mjt9estJ1+yiFtI0L zWcxjuG3S+ZD5ny2Cs0axsFaHaNyeZXpPiHw6mvraQztHLYrLIbu1nR3juoWhkjaJlDqrL+8B/eL Ivy/d3bWUoX5LS+JDcY83NsQa3Dq1n5VxoQjmmn1K3kvILxpJA1udsUvklpFWFlQeZ8oKtsb5N0m 6rPh2HUFW9u7+adVvZUuILCeONZNPjMMatAzRsyyN5iyPu3N/rNqnaoo1O+XSbyG5ubmf7HcyQ2R WV4EhtpGL7X3NtZmkkaKHarN8xj2qvzNW4DXVfQi7seb+CYF/wCEw+KNjcCSCS41eG5WNZGjla3k 02ziWZdp3BWkgnVZF/ihkGdytVbUPCumX3iK7vbWxk0O+0C8ju3msbK42X8DZuJA2yNPNkMklz/q WkZWkbduW4mgk0v+QT8dP+ev/CQeHP8Ad8j+z7r/AMe8z+1Pbb5H8W/5ejk1qOPxJFowtL4yNatd NeLbN9ljAdVEZmPy+Y25m2rztUltvy7uenTjNNNa/wBMmpFTauZ66taa7qGp6PqsFq+nzD+zjZ3v kv8AaJiszSwsolbdut1jl8to1/dybvm3MsdnTm1jVo9Sa787S4pRGlnGsEa3NruhXezN5kscjLIz fwqvy/dkXaza9i13JZQm8iihuSi+dHDKZUVsDcqsyruXrztX6VWunWLUI7hobpkjhmJkiZjGOU+X yw3zM2Plwrfdb7u75t5y07DTHaLqg1azlnFtdWey5nt/KvIvKdvLmaPeF/utt3K38Ssrd64n4F3U eteB7jX1WZ01zVdR1GO6nieKW6t3vJvskjB1VsfZVt1XcP8AVrGv3VWqvxG8Ran4ZktNA8OJf6p4 x8S29xDZ3MhZ4LMw2+37dMrDyY41ke33KqqWMnyrI3ynsPAPhg+CPAvh3w6bn7adI063sPtPl+X5 3kxrHu27m27to+Xca5+bmqen/ALlpH1OlrP1HUrbSbG4vL24htLS2jaaa4mcRxxRqCzOzHhVVerV wl98QtY8VWNz/wAK3sLDXh5beT4g1C98vSPM242o0fmSXDKxXd5arHxIvnLIjLXn+s+B/EHxg+Hf iLV77xBqt74is/7XsdK0nSrk6Zp0d9a3ckNtcoiN5jSeZaxyL580iozsV29oqV/5I3CMe503he51 XxH8TrnxhbQXXhzRdQ0x9DitfEEfk3N7dW9xLJbzx2rKsixiOS7b55Fd1Yfu1Cq7en2NxeXUjyzW 8lpEWaNIZgvmfLI6+ZuV2XbIoRlXhlU/NhvlXxP4o/HnxRoUZsNK8GyaTqnlRyS/27qdq1xCk11D axyQ2tm11LN88p/hVRt/5aN+7ORrnxu8RTeDNd8Qy+MfCvhKzX7Vb29vbaVdX13BNbxr5kLyXUlq q3m4uyW8kKsyqp/eKrGoozgnKNO8pdf6egR/fT9nDc9vaHU9B0y1jNzq/iG4sYZbh5cWaTagyqQs L/LHGrMWDKV8td0fzMq/eXxRJoOk2qa34gvYNJttOkU/2lcXn2VYtzL8jybl/ds3ljy2+VmC/KcL XhnhO8sfiRc6pqi+MvEHjT+x1a1vF0nW/skUSgwtJzp9rbXFww3XX2faskcnltho2k+W94B0f4Ua Z4m8OW8ngm2tfHOryNdo95Z3V1cwssl1KLmS4voY5hIzRSfMy+Y3yfejVWHXy17vlh+P9fmaSp+z +P8Ar/L7j0GD4peG/iNZ65puhjTfHNslx/Z15Z6dcR3UTQyW/mN524eXtdRJGvLRs2FZl/eeXr+B dQvNQhvpdVEiX5lytvI8ivHauzSW4eNlVVdVk8tmTduaHl2Zdq+JftOapoWn6hF4p8PQ2c3xA0Vr OePXLi5mgtbG3iupo1hluFKxBZpmmtWhaRd3ms0ny2+5L2kfF/xX4u8K+Htb1bwL4p8P22qTxvFq mk3i6pAunTGO6O6G1XztzRx+SrfZ90Yk/wBcrM1Y+2dKXs639f5fMt0XyKa2Zk+HfhTrHjCH4r6d e+IfEvhqC216+3WOiyWcJu/MkkvI83EdvJcMrQ3UP7tt23c33t3lx7XiL4G+B/Emg+O55vDQ0m/X TLqNPEXjCSS6cySQLsumuLjzGWOFYYtreZuj/ebo0b71L4Z/GGbVfiR42k8PWd94ps/EM+n3elpa WzR2m77AY5L6e4aPbb27NarDs8ySZZLeRfL3blqpqdne+KLnQdJ8T6hZePvEd5brPF8PdJuWsNEs PJuLWOb7T5KzeZHDJDcfNcyMrNG0ccLM21fGhKFSnzKN/X7l+HbUr34y7f1/W5zfgHxJd+LvEnhf 4geG4f8AhIvH169vf+LtB8I6nJFYNp8lrLbwfaWuJPJ+0wsqssG5Wz5nytt86sX4v+Bfip8fri71 nTNN8Z6bo1zDI2n6Xf29rp72hWOSHyWhbUl+WZZpvMmkj8zbtVVaNmWvpX4K/AnRvg1Z6rJYw2n9 s6xO9xqF1YwyWtvJ++mkijjt2kkWGONZvLVVP3V/L1DFbxwEqtPlry+7+v6ew5YjlnzQMzw//aX9 h6d/bX2X+2vs0f237Du+z+ftHmeVv+by927bu+bFarHapJ6Ciql5Zw6hazW9xEk1vMjRvHIu5XVv vKVr3I9jz5N20OX8N/FTwt4y1y40jRtVjvr63RndY45Nu1WVWKybdrfMy/dbvXYFu1eA2PwZvPgv outa/wCEXOva+pIht7uFm/0XcC0aqsi7pPlVt38WzaqruryrxF+0z8Qprg25lt9CuLeRo5Y7ez+Z m/uss27ay7f9mvepZb9dqcuEl7v94+CxHEryejF5vT5Zy/k1X33tf5n2ksm7BHzLTsda8g/Zlk1e 8+G4v9X1K41J7y6llt3uJnmeONcR7Szf7UbN/wACr1vcVx7CvIrUvY1ZU/5T6/A4r69hoYnltzK5 PRSUtYneFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcX8F f+SO+BP+wDYf+k8ddpXF/BX/AJI74E/7ANh/6Tx12lABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRSUUALRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAlDKGUgjg8UtFAHxJ+ 0F8I2+HevrqViN2i6pLIYkEWFtZPveT8q7dv93/ZVl/h3N5JX6OeINBsPEukXGm6lbR3dncKUkik GQ3T9fftivjH4t/AvV/hreXF3EjX/h4yDyr8Fd0WfurIv97+Hd91ty/dZttfoeTZtGpH2FeWv5n8 8cX8KVMLWljsFH91L4vL/gfkeY0UUV9eflIUUUUwCiivdfgR8ALzxFfad4k162hj0HJmjsrhWaS5 4Hlll/557vm+b7237u1s1wYzGU8FT9pUPZyrKsTm+IjQoR0/9J82eqfsy+AW8K+Bf7VuEIvtZC3J BP3YefJXrjoxbsfn2n7te0cjHHagINoC8CnL9a/I8RWliKsq0+p/WWXYKnl2Fp4WltH+vxHUUtFc 56R5z8dtWGhfCfxLOYjMJLX7KFVtuPNZYt3/AAHfu/Cvjf4b/D3UPiZ4oi0iweOEFfOnuJP+WUKs qs23+JvmX5f9r+H71fZnxx8O6j4u+GOr6VpMH2u/uPJMcO9U3bZo2Ybm+X7qtUXwd+GcHwt8Lw2Z WGbVpv3l9dQg/O/8KDP8Kqdo+767RuNfSYLMFgcFNQ/iSZ+ZZ3kFXO87o+0/gQh73rd6f1sjofBv hOw8C+H7PRtMjZba3XapdtzHLbmZj/eZix/Guj7mjn1pDjb7V87KUpy5mfo9KlToU40qatGIdO1K PoBQPrXlHjTwf8TbrVdf1Hw548gis7qO3j07Q5rCGFbMrNAZ3+1tHMzM0cdwq7oWVWmX5flrCpOV ON0rm0Y8z3PWBj60cV8uN8TPi/8ADfxu/h3V9LtfE8P2RtZS4Z5riSa3Mtva/Z45LWwj+aGSRriT FvI3lyIv+1Xt/wAK/H8fxQ8B6Z4kjtvsZuvMikt1uI7hY5I5WhkVJoyVkTfG22RfvLtbvWNLEQqS 5eptOk4Lm6HbUUUV1mIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeOfE+91uX4yfCfSd Lsre6tEuNQ1e9kmk8toY4oVtd27/AHb9vlCszN5a/u13SL8//th2vi/xN8bPCHhNdYk07w1fxxRW 0kEflKr3E32WbrIPtMirKNyr92OY/Ko3s3s37R3hu2h8U/DTx5fXUtjpXhrVxDd3VvOLdrRLmWFV naRpFXy/MjSGVWVt0NzN91lU034veEYNc0/WbrTbGwuPG9kn9t6RJNaSJd/brdY4ri4tlZ2MirGt oFj8sxeZ5W5pN3y/O42k66nQh8TafroZZlgo5hhoUVPlvv8AJ3/I7Pxmui6h4itbMz6Xa60sZntt QuGTzbW8hkgS3DbbiOZlzfbWjX5ZFuGjZkEwWX4v+IFmvgv4tX8oI0z4b6xMJE01kexhkjnjVEiL WiqYoZgok3fddIP3izT27QV99WfhewtPEF/rCoZL672q0jY/dqFVdqjH8WxdzfebagLbY41X5X/a x0mDwHHoni0aFanV7OG3kjlt50luEvI0aNLuS4aL7RNDbyC1VvM+WVpLfdt2ssnRmtGn7D2j+zr/ AJng53TdTCRnN/A7/L/humx4j401HxVN4B13S/ilZ31h4mv7m0vtFvbuzkW5umkYfaIvljaHatu+ 5vmSSPyoY23LGkcfoX7SPwl0/wCH/iCKz0CztdH8O3McNzLeX0+5hKWaExxbt00kca/vGWFWb99u bcvlqvJfB+zHiL4reBJNVv8AVdZju7GHUEmLWcQ0yRXkt7eS3ZlX/R47prhfs6xxRq/kqv2hfL87 6L0/xp4Rtde/t3/hPZ9fbR7aZRY3C2lxfjbezyXF5BIfl8v7PHNujt13/Z1j2ru8la+LWApZlF05 bytZy308+uj6W0Vj63JMTVwmBptylKMb/Fd3V9Ht92nU878K2MPwVk8N+INTvfEXhrQ4J5pbeyv/ ACY73Um+z7bpGtVVmVZJI7VY1kZlVYWZpI2aFm4z4d/ECfxN8QNM+1f2X4fsrRbV7dZrk/YLOa38 uKO6aC5uNs0nlL5Pyssir86/vI65r4jeLrLX9P0mz0rSbnw5o9vawrbWsjed9uWPzo/tDzsqs3l7 RDGu1lVVkVfL+6eSFmy6BJctAipJcrHDPIs25tqs0ixt/q9q7o9275vmj2/Lur5PGY+VCvHDUvgh /W/btb7+p9rhcLCtR9tV+Kf9bfnc+t/hF4B8IeF9Lh1XR9E1LXfGaRSqLV5JIPs8kc15sJkZU8tZ Gs2tzIyruCruhj86RW5Kbwz4GvbHxtqVnb2t/H9gutV0nSRFB5csafapU3GKFbhIVZV/eLJGv7xb VmLRMrfNVxeT3McEc00kkdvH5cSyNuWNdzNtX+6u5mb/AHmaqv72aQSXE0kvlx+TFHI277PHuZtq /wB1WZmk/wB5mrojnVCVO3sYxjFbd9l2/Nu5w4jL8SpxhCo/fer/AJV+V9kvU+rPEnxS8OW/iW6v IPEelzw6bJtmupkm1C71CZZ/Ot9r7Y22eZbbvJhaG3jaSNvMkVvLrhPgvokXxH8IXj2HhW0g8PwT 31xLPqtwsVppn2lmdmW6kVpmkjtVhj3bfL3NDI0amFZJvIdX1TV/Adwn2e+jS90mdbyy+y3Ed5Cl 03lsrLtZo2bcse7733VVvu19a3v7MfhP4hfCHS9a0DRdU0DUf7FeXTdJbVPtEbMyeZDG3nOy+Xv+ YbWj/wBYzNtZm2+nltSrmbqT5fej0/q/3f0rr0qOCUY83xdf6seI+A/iJB8CfEnjjwne+JtEjsNb jhlXV/Comm+xTSXEa4jnMbOyx28k0m3c33dqyLIzNX014z+DviD4nf2BHqcVtoesaHdXNzbeILS+ kvlVbiGbc0KTLuWQXC27eW2PKjC+TMrL8vhXw5/Zcs/HWr6aPFOpXltcaTcWtrdaDe+dGt0skP2z yYLhlXy2WOaTzLdVZt0cjbo/MWRfYte8VeHfh9p/j6GSC6VPBMItdN06HVJrR5bWTT7FNq3Lybvl aRVKwt8rNDJ5bTNEzfo1PEU8Rgoe1jy/zL5Xf5fqfNZ5PAqpDEKTv9rtprt8r6ffoeMfDmztLLxx 4t1nVWutU1fUdSvtCvtA03TbuSaK3WNlfzGik2yTTLZyrt86T5pFnaR2jar2q+Ffh7rXi7TNQhl1 DRnsNJnmtHjv8rHDb3E0kElrJDu2+XJG6zN5c3/PRVm/eXdeIQ/tPePLWzn0xNUF1oly88lxpt9H HtlmuGaS6JaFYW2yNJcfu23eWs3y/vI45F5jQNb13XtYvbPSbKGNbqC4kksrW6khiW3jX7RIreYz blVYd3zMzfu1+81fnn9oSwt4YNc19O2/n5/L9Dy6HEWT5nWjSdV811b3X3t2+5fqfVvjj4L+EtBv daC6DLdW+6aOybR7uRW2q0axlPMkm8yZrpvsci+W+3dHJth/5bcp4E+AcHinQbnV3e+/suS0UJcx WU1xIrNcbVkVYVZfMWPbuhj+0Ku2ZZJLdtrL4vp/xT8RXHh67tbmGDUYJ7i4uZbma1tbxpJJNqzT RyTL5is3lr8ytu+VWr0rwxqPj268Wa7qEmoa8PEf9oyWuo6ZZJqDNczReWu6WSxhkjaGFpo9yrIr LH8se3dHXPh6mFxGJT+r6bS20v8A167n3tSOIw9HWr+Z6FJ8H7K3s0n0nTr3T7PWtSktdR0Fr+4a 32wqJFR7NpIbuSRYfOuI1RmkZYY28uNZvLh7Xxl4F0qx+CGk2miW2iR6FvBur6xH9qSXHliO4kP2 xofJiVrizWOSS4j8nYqM23asVfLL/GmTTfh3c6Cb/U/BPi7Qbq8W1tLVr6P93NPatLb+Y115kckb RzfLIrRsq/8APb95X1v8D9as9Y8C6XYeFrWxvzpOjw6Tq39h3kJu5JirSTMfLuYljUyR3Ecc25ma a4aZdsIaSb6zL44SpUlGPxW/pLqvNfpv8Y8zjj5Og7yha7vdb3Vmn1t6nfXGl6Fft4F8O6Xf61oi 6TPI9naaPqO1HtbB1geO4YMyyQ7jCjRsfM/eLnbtk22NBtZL742a7LfCS2vIPD+iTNFaXUhiEnna srJ0USrhm+8v91tqsq7a3gHwsniKz0bxFLqei6yLvQ7fSNUNhNcX1lerGs4mVJJLgpIvnSDbJJG0 m1ZlZm8393U+GPhBZPG3xPuNRupbzUU8Wxz/AG61LWcjQrp9rJb28nlMvmRxrOy7X3K3LMu5jX1+ q9nF/wBaM1lFKLint/mey4HpRVO8t1vLWWBmkVZFZC0TtE3P91lO5T/tLSNeQLeR2vmxLcyI8iQl h5hVCqsyr6L5i/8AfS+tdJymJrng+x1i7a+iT+zdbaJLcaxZxxreJCsqy+UJGVv3ZZeY2+VueK6R Pu8c1m+INDtPE2h6jpOoRfabC+gktbmDey745F2su5fmGVJ6UmgaHb+HdLgsbSW6kghzta9u5rqX k5+aSVmduv8AE1Trt0MVDlldI165tvDegan4qXXZNFs5/EGnf6JFqlxYL9ohVlLbY5mXdt2zN91t vzsvXcK6Wm7aHFS3Njx74xfs2+E/jBb313Jb/wBg+KZbeWGDxFpoMdwN8axsJdrL58bRqI2WT/lm zKrLu3VpfA3Xr3VPAtvp19qceu6rod5eaJfajG3zPJaXDwK0ytIzLNJGscjfe+8W6Mtembs4z1Ne K6PM/hH9qDXtPl1D7Ppni3R7fUobS9aT/SNQty0Mn2X/AJZ/LbxwtIv+s/1bfdVtvn1KcKNZVY9d H/X4fM3i3ODg3sewrZwR3kt2Io0uJEWOSYLhmVSzKrH0G5v++jV00CszWftP2Nfs32rzPtEOfsnl b9vmpu/1ny7du7d/Ft3bfm216RjuzTqurS/aHVgvlbF2tnkt827j/vn86o2erCa8aymXydQighuL iJVdo18wuqhJGVVb5o2/2h8u5V3LVi0tVguLyRTIWmlEjb5XZQQir8qsflHy/dXjO5vvMxrPXQRz HxG+Ffhr4paHLpniHSrS+L289vb3UltHJcWfmLtaSB5Eby5Pund/eVa8h+HdrqvjnXrvwp42+IHi a+17RXmvWt9PvbHTbXU7YXF1axzRtY7bhVXy/wB5G0i7JNobPFeleOPitpXw1ja0fTfEWu6pI800 GlaPptxeXU6q0TTNGzfL5afao/m3hR/q1+ZdlVPhr4W1a58XeJfHnifSv7N1fUTHpum6fNJbzTaf pkPKo0ka8PNM0kzqski8wru/d1w1IwqVo8u/X/gnRG8Y+98hp+D1ncahPFHceJ7O0jWWMSXPi7Vn aaQrE0cke29/1Y3Sqyttbcvy/L8zZLfBfwNql4uq6vZDxLPb3EcVtb+K9fn1KK0ErRwyosczSxrI ZI2UL826Rdu5c/L6rY6xb6jeahBA7NJY3K21x+7ZdjmKOTac/e+WRDuHHzY7Gs2bR21jVrXVYri6 htZLZDsZzC0ZWRJFHlmPcu75lk+ZeFVWVv4cq2HXxQXN5af1oxc00zhtO8B+GvBHxy8Njw94f0nQ ftPhzV/tA0yxit/O23WmbS2xRu27m/76rb1z4xfDOWbU9E1jxj4XDxPJYXun6lqNuBnbtkjeN2+b 721h/vL6ivPvAPw88Ea42s3eteCLHV9Xt/FWr2Oo/a9AhlkRpr6S4tppWkj8x18mS32vGWXbOGb5 V3R+0+H/AA1pXhOwNho2nQ6VYIxZLKzTyoI88t5ca/KoJy2FAyzM33matqcaluaFkn/VugSavqcJ /wANCeCLRNb1CLXdQ1vTrFy91faXpN1f2Foqwxsy/areBotqqRI252Zd7Zx91YW+MvhrVrV7zSfD HiHxQmrW9tDC1r4ZvEN/FIzbQ01xDHB5KrNv3STbdrSf8C73xH4cXxRZ3elah5U+gahZXFlfWbxu JJlkCr8siuNq7fNU/LuO5SrLt+bF+Cd5dap8HfAt/e3E17eXGhWM09xPIZJJZGt42ZmZvvMWJO73 q5e2lLklJart/wAEPdtc4i+s/Euo+O5fHdtoqeBYdG0maG5HiieCSDUlkeOSX5ra6ZbTy1s4VNwy yFlk+aNhCtbs1t4r+I3h8WnibR4fCHh+/hW01LSElOoX9ysn7uWHzIv3dvHu3L5i+YzRSbv9Gdfl 9B03VLmbS2ur7TptPmjMge33LM3ysyhl8vduVtu5f4trLuVW3KrdSkNiz6nJc3YtLS2mMtna2/ne b9xt21UaRpFCMqrGfm8xvlY7drjST+1uLm1sV7zzfDek6daaLpNsLWCS3tBaruhit7fckf7tY42+ 4p+Vdqr8vzMi/MPO/gzcPD4u+I9hPoq6Bez65DrL6dt27be4sYFSbcq+XJI01vN5m1mCyLJ8zbVZ vUmNzHdqI4A0Rb97JJLt2rtblV+bc25V4O35W+9ldteT6L42jf8Aaj1jQo7SYC+8I2919plR4Wja 11C7hZdjLllY3G5X+7tXcu5W3VNTRxfmOGsXoTfEL4Gx/EjXjrUlzN4f8QHTLjTI9T069Z/KVbhZ LV/KaMKzDDSfeVo2b5WZljlj8p+NH7PviSbwfIdS+JF3PpGnW9rbWtnqenyav9rvGha3877rTRtL NdSLtXzmXcrK3yxxw/Ud81x9ldYTHBfTF4oJGhe4ijba2xpFXb8vy/MNy/3d3Q1zN/ear4p+z3Ok LeaPJBbyXEcepW8iq7tJthZ4VaPzN0azN5bTRtH5kZkj3bfL9CjCMZc8Va+5lhbYWq69Janyb8M/ C8ngO+8Naboeradp/ivXZv7I8U6JrSLeacLWBWtWkxbiOFt0zRx/MWZriTyfO3LcLJ9pa4sEcRut SmsY9FtY2ubn7bGNsbRskkc3mM21Fj2M3K9Qrbl2/NwHhvTZfAuvW9rcf2cgNxcPqGoNfRQyXJuZ Yktri4ihigja6mkTy1XyW+6+2bd+7m6jxJ4ItdQ1RvEFlb28PiRLRrWLVGtVnuYY9k21bfzHCxt5 k24t91l+VuNrLtUlbVnbiqzrzUmeQePPAdxrXwh8ZDR4tW0fSG0CTTLDRdN/fXF6turR2is3l/aI +dyyW7bl2su7azXCtH4H8aadrXwn1KH4eC11S/g0m68ouIY7l7mS3hmt7dvs90rRyeX+52w7VVrV hH5Kwxxt6N8QvF2ieC7+TW/FuoXllpUIWCxS1s7lnWQw3MlxJvt9zSRmFdxVl2xm18zr5bL45rX/ AAmE17rXxKsdC1fwvq3iiCLS9O0+Cxnk+xR28u+O81WG33TNcSKrLHtjkjjXbHJuVmrwJTdfEcql 8WnW6t1+XQmnCUtH1/Mv/CHwP491jwLfeA7q40Xwbomjx2Oj6hZR6fDqdzKzWdvNeKhZVt0WRZhu 8yO5/eTXHzMvl19C+GfDNh4SspLTTopVEknnTTXM8lzPM+FXdLNIzSSNtVV3MzYVFX7qqK4n4S6p fXvi74iC50uewD6hYXNw0s0beVeSabaedartZtyxosDeZ8oYzMqr+73N6rXVh6UYR9NPuZjUk3oO oooruMAooooAj9gOK+ffjn+z1ceNNaXXtAaNNSuHjiu4Z3wjrlVEob+EquNy/wASr8vzfe+hKTj1 NdOHxNXC1PaUnqeVmWW4bNKHsMTG8TK8O6LB4e0PT9MtyzW9lbx28ZkOWKooUZPc8VrUbaOOlc8n zPmZ6UIRpxUY9B1FFFIsKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooA4v4K/wDJHfAn/YBsP/SeOu0ri/gr/wAkd8Cf9gGw/wDSeOu0oAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigCM+hNN8wKuSQB61Xvb6HTbWW4uJUhtolLvJI21UUfeJNfI/xl/aM n8ZW13oXh5ZLLR2kaOa934ku4/7uP+Wat83+0ylfu/MtejgsDWx1Tkpo+bznPMJklD2mIl73SPf+ u59N+FfHugeM1vjo+qRaj9jl8mbym+6e3blW7MPlbHBrpVxtBxj2r86fCHi7VPA+uQ6tpM/k3Efy srHdHLH/ABRyL/Erf/Zfer7s+H3jiy8f+F7TV7KRAZQvmwrJvMEu0bo26cgn054bvXbmmVzy+V17 0TxuGeKaWeqVOceWrHp3XkdhRSUteCfehRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAlI8ayK QwBHuKdRQJq+jPC/GX7LPhbxBcT3GkTXGgXUnzbIP3lvu37i3lt/3ztVgo4+WvF9a/Zd8daZ5X2a 0tNV3lg32K4UbPdvN8v/AMd3fdr7X4bpz+NDLxwN31Ne5h84xmGVlLm9T4jMODcpzCXO6fJL+7p/ wPwPg/8A4UH4+/tL7H/wjVx53lebu82Py9u7b/rN3l7v9ndurW0v9mPx9qFwyXGn2+lqoyJrq7jZ W/2f3e5t3vtr7c3Zo212S4jxktlE8an4e5VGXNKc5fNf5Hhnw9/Zd0LwvqAvNYuv+EinUMqQz26r AFZcHcnzbjy33jt+b7u4bq9yVQqgAYFLt9KUe5zXgYjE1cVLnrS5j77AZbhMsp+xwkOWI6kpaK5j 0wooooATHtRS0UAFJS0UAFFFFAHEfED4V+GfidDaxeI9La/Fr5vkyRTyW8yLJG0csfmRsrbHjYqy 7trfxCum0/T7XSbC3s7K3itbS2jWGG3hRY44kVQqoqjgKo6LV8ZrwT9pDxnrXhfxJ8P9OsNQ1TRr TU7q8SO80uJS1zqHk+XZWMkkiyQxxzNNId0kcnzRI21drSLzVHCjGVWxrFSqNQPeqdXmfwR8XP4s 8M6vDc6tPrOqaJr2paLd3NxAkUm6G6k8rcI40jZvJaBtyrj5v726vS/WrpzVSCkiJLldh1FJS1sS FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUlLRQB47+1B4usvC3wV8RW13d2dvda9bzaHY/bpWhhkuL iGRVVpNrLH8u5t0m2P5fmaNfmXgPgb4p074j+HbBvEESXmv6fezeDILfX5ri0utRt47eFrxb2BjI s02xZ5fLbzF/h3Rs0jD6J17QdN8U6TPpmsafa6pp020y2l7As0Mm1ty7lb5W+ZVb8K8M+E+m601v 8RdZ1ARAt4s1m7024uCztKlvNHChkmaRljjX7P5XllV+WFWXbtDV8/jqlbDVY16X9Lr/AMDzsdMZ JU3Gx65ZzXesNFrdrDexzsEtf7K1SRrWO3UTbbhtqq3mSYU7W+aNvLXy2VZGkbyH4zxya18PbLV3 uNX0HxDc2gFnpP2KC9Yu+77bZbLhUFx5iyD/AEdpP3n2WNoY/wB2d3sPhnxRoer2enf2dqmn3T31 v9uhSzZV89WEcjTKmd20+fEx/wCuy5+8K8Q/bA0XW9K8Ly+KNFle5uYJbH7PHcEtBpTW0k9w13Gr N5fnSMYYcsu5l/dru8zbXZjai+rSqy2t/lt/T7nl42Llhqi5Oj9dO3np958R+CfiOmgzaIutwvqu h2RW1vbKV/laza4aT9zCu1ftC/aLzbJIx2rcfL5bKrV7/wCCPGtnq3wj1jxXrvgnStb0MadaaLZz RWkc01leRW5hHnSeXHJ5bFIX8zzP3fmwrGzNu8v5Q1LUZdN+IF7p1tbrpmkSGOIRb7jy5rT928M0 m5VkdZMQy/6tVZtrLGvyqvbeF/F83hmTV9Ds9XvLjw7fTqYNFkMjWF3ErLI2795ujkVoYF+X95tZ v3i/xflVSdTB/HL7Pl2dvkv66HDwzWxMYUsFXqRly6Wv71m7p/LZF/x58QtX8UzW11q0yXM9laR2 dv5dtHbwwwq3yx7Y1Vfl3NWLqk0/2zT5WiBtmhkbT55bVYGltlkk/fNtZvmZty7t0m3b5e5ttSXN rHeW7RS/db+7WV5M8lxf2FrdzwRywxxz+WzeWY1ZWWFl/i+ZVb/eWvkadRVOaVR+93PruIKWY0a8 Hh/ep6ci29/V/kt+m/Q6Pw1fapJavZWIeefVoo7V4YYdzT7pI5FjVfvbvMjj+7/do0aaSaG6vbS5 ktYYLWaSe6jWTase3y2VvLVmVZN3k/N8u6RVb5WqrautjGnnBJ/3TQ75mZfmZdqyfK33lZty/wAO 5fm3L8tGi6Pa+X9nuoxcrHBOVYTxwf8ALNm+83/oP3pPur8zLWC5JWZ9RF4+EYUYRXwe83/Nol1f S9/TcsaT4juo9BuNHSWxii1uKEXM11FukHkyLdL5bKrMrM0Hl7futu+b7u5fSdN/aa8faL4Z0jQd O1SG2j0kxi1uI4UWRYVh8lYWXb5bRqvzfMrNu2/N8teQ6m0DeIkhgjXyrVZGhuY4WhkuV3fu2mVp GVZNvmLtVtu1f725q6XwXcTWuvWk9pb2mrXsTL5Wn31l9qjv5PMWPyfL2srM25v7v3W2tu2169DE 18M4xp1OWL7et/62Omhh4yw98RHmlH+tfVbnsnwU8UPZ/HDQ9WvQ90+s6lMqXb2tnGh861j8x2m3 N/pEkkkMjQpIvlbpI41bzvLXjviBq+qfFKTwfcXmlyW+lWOgnxDqOn6lqUqfarSO6kjSM3E264kk mdpBCxZtovY1jVfnkk9f0PxRY+H/AImfbfFmrva6vfeHZLjVdDspWtLTRhcs8jLGzSJ9kmmZbQyN JIredIu3zPO8y38f0H4oTfFS/lj8TXt34d+EWh250+DQdLnbK77WQWcKsvzTzB7ZZFab92rRt8w3 KrfoNGVKhRlTqVNJP/J/K1v6R+SZtKlmOLo5ffSTekXduzv8ra2Wvb0+oNL/AGkNG8AeCraTxzpf iPSvEDKbuSzvtOkH2h5Nk0n2eVv3fkx/aNqxySLJGsW1l3LXzt8dfE2k/EZ/E/iDQLCDxHb36LdF rcTLfaLDBawrdC4VVX5W/d/vGaSNdsm35mZqreB/idoGuePra28dQWUuix3U00OoQ2crGG3jkVob WNZI2ZbTciqsPlrIqzN8yq0iti/Hi60rUPiN4wh8KWGrpqd5qkljHF5beYbiTzre5WNluG3RzSSL 8rR/Msm1dqrtk5cdjpYynzVJR5Yy263189e116rVH6Dg8u+qTSmry77IzPDEltovhnw3p9vds4gM d9qVjrC2JtrXzfse7yZFhkkZmaPdIvlu0a/u2jbbM0nvl54o8MeIvhWNF+I3jXRVm8RB7ptUV7u7 uXt43/0gW8bxxNZ/vrSNow3mRvKrKsLLtjrmvAXwo+Hvjz4aa1qAvZ9C1yMSXFtHd61DdGCNVCrN MscKtHb+ZIvmMyfKse7cFY7uM/aj8L+N/FHiqw13Vbu28QaTeWs+qae2m2amSx0nzFkxNPFH5cgj WRfmWSRV3bv+Wis3Ng6lfB0ZYrljLmt5q22u1vu1b+R4HEWOnhqHssJDnl52/HXVf0zo/FNj4a8A +P8AxVd32pah4fknsNM0e2k8OQzahZaTs8yPc1zIsbStA2nQyblKt5iPHtm8mRa8l+GOoaJ8Svir pGiahFY2fhyF7W1XS5rRJGaGado5LW3kkbzv9dfzSbhJuVd0m5mt41rrbD4y6S3iW90TSfDEXiuT XFttLjVdRurC03NczGSK3hZh5MbR3LW8f3dq7mZf300dan7Oen2fxU+IthqIn8O+GdRkQ2epWVtp dr9p1MrbvtmtVZWa3Yru8xo1iTdEjDfuZV64YmNSUXRiryb08+1367L1PzyOMoSxccNha2zb02u3 0fXS2n+R9T+PvgD4JvvDVjfWHh3QvBE9je2eqXN9bW1vYSw28MySzo1zCu6L92r/ADRuvzKvz7d2 X2fwa8LahpVprVt4k17RYL+/hvWvtH8a30kOqRsVithJJJJ8/nRfZ1+X5vuqsjbVY+gTf8I00ltq WotpEl1oNs11Df3EyTS2VrIGXzvOf5o1kjjbc2fm2t8zbc1r6VollpP2o2cPlPdzvd3EhZneWRsA lmb5jgKqr/dVFVcKqrX6DSw1GSjU5dT7qMpRd7v9DxfTfBPi7UvE08PhPxn4w8G6DBeym+tPEVvH qb3Cq6wq1lNcrKyxyeTcN+8kZl/dt5O2Vd3ffC+61Yah4103VNau9e/snWUtLa8vYrdJjE1hZz7W EMca/wCsmk/hp+k+JJrXR9J0fU9f0+LxLqNpt0ua/j8m51GSO3jaSdrFvLZWV2ZmhVvlXb8y7sLw Hwu8XeKtPuvHV7P4N1bxMb/xFceVqGkLYWkE32aGGxfbDcX/AJi/vLOT7zfMu1vl3bVdTkpyjb9T aSclr/XzPf8AmivNNP8Ajhol3YwXE2ieMLGWRFdrafwhqjSxMyg7W22zLuXlTtZl46mrv/C6vD// AED/ABZ/4SGrf/Ita+2p9zn5ZdjvM+lHWvNbP40aNJ5jTad4rQMVeOM+DdWVo1Kr8rN9nYM27dyv GCP940NC+MvwktbnUr+18UeH9Gv7q4Zb+PUbhLC8EsbMjLNDNtkQq275WVfvM38WTLrU4u3MvvBR k1sepMJPtCkFfL2ncuPmzxjn/vqvLvjFcWWkeMvhPqtzawyXKeJmsIbgtFHLGtxp93GVV5GX5S3l syq25vLXarNtWtofH74Z/wDRQvCo/wC43a//AByuP+LHjn4T/Fb4d674W1D4jeFY4NUt/KWb+2bd vJlB3RSbVmXdtkVW27vm27azr1YOHutXNIRaeqPbcE9G/SqNvbTQ3F5I93LcLNIHjikVNsC7FXYu 1R8u5Wb5tzbmb5tu1V+efgbH8Ivi74Qa9Hw/8ILrumzRWGsWlrotnMsd021WkiEXmbrdpN22TdtK qx3fKxr068+B3w3t7aWWL4Z+GbuSNWZYYdGs9zt/dXcqrn/eYU6dapUgpxt9/wDwCWlF2f5G5Yza 3fWdsbbXNIumhu1W6nhsJGjeONfLuIlxcfLJ5yyYZmby/usrMu40fEnjzwn4R1lIfFninw5pl2JB dafb6hdQ208MbJ5e795J8xLeevmKq/K+3+FmbKtfgL4Dh1C8+0fDzwbJY/K1s0OiW6yrhfmWRfL+ b5l3Bl/vY2/LubrPDPgHwz4JN1/wjnh7S9BNyF+0f2XZR24l27tu7y1Xdt3N1/vGr/ey3iim430P FfiFB4f+K3xi+F3/AAjN/bnxXp8663d6lpt3DLLaaSsau0M3lzZZbhriFFxuXbJIw+Xdu90168gt dJnMmpHSBcBbWK+Dxq0c0jCOPZ5isvmeYyqqsrbmZV2t0ryn9mfwlF4FtfiRpkdr/Z5j8ZX0wsVi jjht45I4JLdIvLZh5f2d4G6LtLMu35dzeox6pdTXwVbKSXT5JTCLgt5ckTL53mNJHIq/u8xxqrLu 3ebnaFXe2WFi+X2kviZdVuTUd0i1qDNptne3VrZyXkxRpvs9r5ay3Mir8qqXZV3NtVfnZV+78y1p 1BdXCwLubd95V+VSepx2qvYtdNCwuoY7eXzJAqwSNInl728tixRfmZQrMv8ACx27mxubsjy7Iw6H n3gC3Ola58W49HsrQ3P/AAkPnw2zN9nhedtKsJPndFbZukYszbWb5mba1enL9+vLvC9jcab8cvH6 QOXtNQsNI1GaHzpFWORvttu0iqzMvmMtrArbfL+VV6svzeo/xVhQ+H5v82XU3/rsZdrotvYBjb+c rtcyXbsZpHaSR924MzN8y4baqt8qqqqoUKu3i/gHbvp3w0stJN3LdRaPe6jottPcKoka3s76e1gD bVVdyxwxjdt+bGe9elV5L8E9R1OPUvGWiS2qtpmn65c3Nvf7lUt9uWPUPJ2rJJuaNryRWk+Vdqx7 Q25tqkkqsZev9fgL4kz1sfWsyPUEe2EtoPtqibyW+zSK21lk8uTJLY+Vt27v8rfxcVi6L4d0zwnr V/eyXEMmr6/eYa7uIbeG5uNis0dvujjjMqxxrJt37mCq3zNUXxC1y9s/CPiGPRZjb+IBYN/Z0lwq xxG7m3x2qLJJ+6Z2m2rt3fxLu+8u7o1sS0k9NR3h3U9e1vw3bPIunW1/JbTLNNvMgt7pWCqrRIzK 3zeZ5irN+7ZfLV5P9YPE/GniTSPCv7WFh4jufElnpUFnp1joGptdXMKW8cVxDqt15czN/q5PMtbN l+Zflf8Ai312ni7xBfeAW0Saz8Jf8JR8UNdt43m03Q7prO0uGto9s80ksh2rDH9o2r5m5mZ4V/hD Lf8AhR4R1HwPoOqeI/HeuTaj4svYw2s6teSRw2sMNuZBGsKqfLjt1VpJN3ys3mM0iqzeWvm1OatK MOzv/l21fY6E1HVbMx/EX7QngrWrqfStK8TSXkFrcWqXep6DY31+sE0k0Rt41ktY2jYyfMvzSbd2 1WjkDMleefGL9tqP4d3GhGHwrqkN/MWe60PWbqzhk8hoVkVpFhkmmt5lZodqyRruV5Rt3bWX0v8A ab1J7L4fzW93FfR6NcRzST6lpNtNPd6fNDE1xazIsbx7As0Kt5jPtVljVhtkaSPy2+/s74A/DXTP +Eb1u9/4WdeL5lxpuoLaQ3muXHnyySzagZI5GW3j/wBKZp1kVfLVv9Ib93JWGIxFeMpQjJRt1t+n Xy2FRq0JT5Xuun66Fv8A4WpqOrWuiQatJ4b+H3g69Ns+j3Eypq2jLcWhjnWFb61urdYZFkj2+TIv ls1rIEkkVttd+Phb/bHirSrHxd8RNf1nUNPgguPKt7mHSftU0kd1H/y6wRyBQI5nVVuGb/Wbvl+/ 5V4J+AvgXx3omiXcFvPYaXol82gX4vfD01naa7Ha2ckM1xcQ+YPLmWRrvy7xvLaORjGysyru6Hwn pPjH4S/FDQvBvhbWNX8Q+DdF0ia6v/DjaVYwy7WmaG2K3Xlwxs0jMZfmk3N9muGb5m2Vz0+aMo08 THm5ttb/APA728kby5JL92/69TF/aH/Zj8Jrodhqcek2Gi2lrq7WlxqUuoSST3Ed/th+3XU0i+ZJ NBeTJIqySsrRxt848zam/bfEjUNA8N32majOYPHmh6NDfaQ2m6j/AGlZ+I1kWT7LiQp9okj85ZLX bN8264j/AHkk0kMld/8AHzwZp/xA8Mah4Zu7DVp7rW7JorO4ha8awgu0+W2+0LCW2jzLjzNzRlP3 G6T/AFcdeMaZpmuWFx4I1a2+Gdp4MOma7ZyX2s2eq2c8Ivrm/wDsN/bwwR+cxWRbhlbdJH5fkxr/ AMu8at1VYLC1PaUo7+Xquw4zVSly32/rufTPw78Iv4M8OJa3mpza7q88rXeoavcwrFLe3DfekZV+ 6qqFjVf4Y440zha6/HFAx0p1etGKjHlRwSfM7hRRRVkhRRRQAlLRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHF/BX/kjvgT/sA2H/pPHXaV xfwV/wCSO+BP+wDYf+k8ddpQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFJS0UAfM/7VieL 1sY/KYN4RMimZbeNg6yfLtWY7vmXdyrLtXJ+b5lVm+Ya/SLWNJtta0u60+8i860u4mhljJIDIw2s OPavhz4w/Cm6+FPiBLdnNzpd5uayuWK7yq7dyOP7y5X5vutu+qj7/h/MKfJ9Vl7svzPwTjzI8RGr /aUZSlTfxf3f+B+pwFdZ8NfiVq3wz19NQ05/Ngfat3Zs22O4j9P9kr/C38P+7uVuTr6V+C37NcU1 vaa54uhk84SLPBpLcLt6/v19funy/wDZ+bduZR7uZYjD4ejJYjaX2T4Xh3L8wx2NjLL/AHXH7Xb+ u3U+gfC/iSDxZ4f07VLaO4ihu4lnQXELRyBT2K/5VvvDIrexuFMjRUXA6U5W+XNfksrX0P6xp80Y RjLcdS0lLUmgUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAJRS0UAJS0UUAFJS0UAFFFFABRRR QAUUUUAFFFFABRRRQA3pWB4v8J6V488O32ha3p8eo6Vex+VcW0wyrrwQf9llYBlZTuVlVl5rf60d DUSipLlkPY+Vv+F6L8F/jYPhlHpZ1Hw99r3xWmnx3WpasBcR/apLyRvNmaQLMbjzI3WORVaGRPMV ttfTOnalbarY295ZXEV3aXMazQ3MLrJHLGyhlZWHBVl6Gvgv9tzwymjfFhXufDUup6J4il026bWJ PKsxFeLut3tft7R7Vhkt4dzRyN8sm2b7qsre+fsyfEqG+uNS+Hn2241Gx0GOSHRb67sJrO5e1gk8 mS2uFaNVE1vut/mXDNHcQsyq26vBwmJnHEVKFT4b6f5Ho1cPejGtD5n0VRRRX0J5oUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRSUANbvivDfD/hfQtD/AGk/FK6b5enXmo6Rp2syafB5IE1x515HNeeX INytsWOFmj25+0Nu+Zty+4rx+FfLX7SfgXVZvjB4F8Q6J4o1Xwhea5byeFH1O0+zuiO7NJAhSSaL 7ytdNlS7boYdq+Ztz5mOk4wjUjHms/8AgFczinZXPVtL8D6Rbz+VJYS+GtI1S1tRFoq6j5LC8WSW 4ZRHD8scsaqvzwzNuVNuNsMbV1lpo82mahC1t5TxXKsdSm+yxrcXVwI40juJZFZV/wBXD5bARsTm LbsWPFcZ8QfA+iyaTb3GjTWHhYv4ht7m8vtPsrXzbqeS6hVl3ybVEjXEduzN8zM0KrtkbC16Tp9r FYxlLeFIEZ3lZY1CqWd2Zm6feZmZj7muqMp1OaEl7qtYqSj/ABF10/r+tD8qv2kvh7B8NPipqGjW enw6Zpkab7K3jtmjZbdpJNrNIzM0rM25vM8x/vKv7vb5MfN2dxBrHgaGKzG69Op28klx5cKrBtjk Vdq+XubcvzfeXcytuVm2tX11/wAFHPBqzeB9E8W2ekzz3tjcfZLrUYVXy4rWT7qzN/rMecU2/wAK 7pPusy18SeGdJjvPhrrkjrcfLN533W8tvLjk+bd935Wkj/y1fk+c4H6rXlNy6q3zPg8DleJwWbyr 0tpPT03/AAt56dD0f4Y29r468RaNpdw7xfb7yPTpJYWX9zNI3lq38W5VZlbb/Evy7l+8v2J8Q/2L /DUuk6vfeGJNRs9USHzLLTzOslu0ir/q/wB583zY+80nys277q7a/NfwT4su/CusQ3FtLJC4ZWSR ZmjaNlbcsisvzKyt/FX1j4o/am8V3PiH4c6r4e1PUdW8UrZSWuoaGpk/sy7kaSSO3b7PGy+ZMytu bb8u7ydv3WWvTyXJcLVxFXAYiN+b3ovyP2f61XzCNOvhpWtujzPXPDEnhvxJd6drkMn2mx3RyW1r dxssc235dzR7lba23cv+yy/K3zLWs7GS8kMUIj3rHJM26RY/ljVpG+9/sq3y/wAX3V+avYfjN4l0 n4uatJr8enSaDrS28cLxRbbiG72t96STbG0bKvy7tsm7aq/Lt+bx6zaBbmF7pJJrdZF86OGTy2Zf 4lVtrbW/2trf7tfKZ9kGNyGvGlio/u5P3fP7vUrh/PMtzunUrYGpGU18W/y39NBNP8La1d6hqlyt hMsNv9lhkmmj8mGJZlVoWmmb5Y1k875Wbau2u11DQtP0nUNR0pNQtdL1/RUvJr37RZrNaTz2UbLb w27SbmZpmWZpFk2Rt5ir5bbY1bG+G/xN8a+H9HtvCen2Vg2h+LLyJrf7fpQuhzdN+9WNVZZm3fKz NHI37lVX/Vqq+k67rug678RtDj+FsmqXngS5j/tAW2k6GJRfX1rC0c1swma3kkj+zbmeMyN5jXD/ ACs0ldGHwdGVPmj8Wi1/Jf5nl4zPuaNWj7OScXy/fbX01ueQ+E9ca28O3ml6ho1vN/a0f2KxmuhD bWMF0sSo003yqskkdvJHtaSTbC0zSsrNIzViWPifU/EWlRJqV7fXaWtzIypfXLP5cksiySSL5n/P RmVm+7ub5vmaqzaK01jp1gbuVrSGP7Q6rKzRrcSbWbbG0a+W3lrHG33vmhZtzLtrM03VtP0uPVVt bhmW3VZpFm+Vt33WXayr/dX/AIE1c9WtKrH2dP7NvTf+u+3Y8fhmhUw1elXr0PZRjC3NJ3cuu1l9 6+e50cuqpbRyWv8AajRW9xJ81p53yyTLu2tt/iZfMZf4tu75fvfNS0fXlt9QsbXU4kkLzW7X9qIP tH7lZFmbb91t37vbtjkVtrbfMVWrC1a1knvNP1iePyLxfLaO0/1n7tfmbduX5W+b/gO3/vn3/wAR fD3WvC/hn4ZXKawINL8R20upXn9n20dt9jtyscKtcTbo/O+XUNv76RVj3NGrbWoo0W4e0j73KvLR 3/Fa/wBan2FbNFXqyw1Oai1+Vr39dt+nyNTQfFnhv4leIZbpIdM8O6Xa3lxeah4hvtOgmni0wSM0 aSRz+ZHNeSTTQx+d5fm7lX5rje1ee+KtD1XSviBp+jLr+ra5reoW9neXesaoZBps1yjt/pf76M/a bKGBZF85l2sqySf6v5G7T4bjwP4J+IU3jLU9U8L+HtIuNKMUWiF7jWWWSe127xDG5bYzedvjkZmX /V7R5kclZXi74j+BvFmn75fCth4bs9GuNQXSNNs43t5LuzmuI28uTbGwhkXz5po2WRo1YSK0LKy7 vcXJLD3rzXNf7/672v2vsvyXGqjj48uIqclaD1d3ZJLpb4b9L9dDhPA+tWMlt441qfw9Y3+p29r9 vij865toEie4W2uLeNLaSParLdr/AHgqxsu3958vqfxQ8B+NPh34o1K18Az3PhrSHsLO81Wa1mTR LeFopJraH55Ljcqsc/6yZvPk8yRdy7WXxzwzJfeMPEGrz6bFdX/iKeO73xhbqae+8+F1ZY44V3NK qtcSMzSMpUbWX+F/sf4e64nxS8OTwRa1d6doEhjTQ/FNqI0kttcuIpvtrXUazFoZppL2RVjbbH80 fksrNCzdWV044ik6Uvdl9nv16/O33X6Hi5Xk0auFdOrLk1bjO1pSW1+9nzL5dz3vwP42j8feA9B1 W0vLG11LW9LGo21v5nnqgZV3fKGVpFjaSNWZdvJ/h3VN4b0jUZodPj1nX7vVNY0XdDc3FvZNp9tf TNGu2Zo/m8zbG38LNF5jN8u6NRH8++LrjVvhP8QPhxrKf2vP8J/D+k21mzXkaxyfbvst1DAZFk8v yWZbiONpG8uJWcLIy+X8vvXgfwnpWhajrLWtnqBmgu2iS81ZTNO6yW9sz+Xcybppo28uNS0jM26L Z92KNV/TqKkqcYy+JH6S6MqVKLcr3X49UbU/h+8vPE0l/NrN0NJFvDHFpEKrGqzrM0jTtKv7xtw8 tfL3bNqtuVt/y4nwV/5E3UP+xj13/wBO13VvwPc6bp8Vx4Wh8Q3PiDUvD6xW15LqUivervQSRNKV Vd25G/1m35trZZmVq5v9meG/k+DOh3uqSzXF1qct5qouJ444pJ47q7muI5WSNmWNnjmVtit8u7b/ AA0ql/axT7P9DN7P5fqesVUt7O3tZruSKGOOS4l82Vo1CtKwRV3Nxy21FX6KtR6faz2kLpNdz3zm WWTzJljVgrSMyxjYqjaittX+Laq7mZtzMqfavtkrPJEbcxoI41Q+YrZbczNuwRymF28bW+Zt3y6x MQvPtSWUxs4oZ7kI3kxzSmJGbB2hmCttXpztb6VdYe1Z99dQwtawy3K28lzN5cS7grTOqtIY1z97 5Y2P+6rU+e+Sygubm8YWtvbqzPNMVWMIF3M27+Ff97+7T5VYRW1C6vFmt49OitLubzo/tSXF00LR QHdmRQsb7m+X5Vbarc/MtWbS6jnuLyNFkDQSiNt8TopYorfKzDDD5vvL8udy/eVhU3kRtMsxVTKq lVbHzANjI/8AHV/KmXtylrGT96VgRHGpAaRsFtq578UuVXHzaangvxm0m0ufjX8KtcvIWs9La6+z Wev6RcWsNzNfMd8FrNJIqt9laH7X8scjeY0m3bu27vfIPMaMGRVSTbyobIB+tZGt6FYeINMbS9Y0 +HxBpl1Jma2v4I5oSu7zF3I3ysFZV2/Kx+77tWD8A2/4sb8Ox/1Lmnf+ksdckafs6sl31/Qvm54R 8jVvNI1L/hKra4tNTv4LGUGS6gKwy2/yhRtXd+8jZvlHy7o9qyfKsjLJXTdDgVmaVHdw6XYreOZb 5Yo1uJNytukwAzblSMN83oq/7q9Kyrz/AISCHxRa3UUiXOhiKaGaxhjVZvNeS1WKQszfMsYF0zbW X5XUBWZRXTfljch9Ev61/wCD9xyHxC+H+rWHiB/H3w/EKeKljWLVNHkfyrbxDbp92GVukdwi/wCp uP4c7W3Rt8vWfD/xtB498Nxa5a209pZ3EjxpBdo0dzE0beXNHNGyjy5I5lljZRuX93u3fNx0NnaL ZWcECGRkjRUDTO0jYH95mO5j7mvm3xX4W1rwB4w+HzaF4p1s69rvi9hcaJc38MdobDfe3WoSQ2qt taNvM8zdI0kyqYV3KwC1x1OahLnj8JtH31Zn09tqlbahb3k12kM8U0lrJ5M6xurNFJsVtrc/K211 bB7MtJp+oW+rWMF7aXEd1Z3EazQ3EMivHJGw3KysPvKw71Ammr9naK7lfUx55nH2tIztbzPMjUBV Vf3fyhW+98i5Zm+au/zMvU80svFaab+0R4osLizuvOn0TRoohG0cjPH9o1DdcCNW8zyVkmjiZtuV ZtzKsf7yuz8USa9b6poTaJmWKS6EV9FcIn2ZbckM0jNlZFkVVKx7dykvtdcN5kflvhvxDN4q+MHj zxfpy6heaF4Nhm0RbW3X7Q19deWsl/FbxsVeORWt7CNefLbbLtXdI0g6/wAU+MNO+Esmo+L/ABjq em6Los5W2Epjae5kxIVt4YzHGrsqr5s3llZW33E21ljj+bz6NRRhKctrvU1rxaso76Hon2qH7S0A dDMiq7R5+ZVbcFbHvtb/AL5NfP8AZ+JPFt58QPjLbeA9S0C8ubC4t9RSxubQXn2qY2EdusBmjvY/ JfzrGSNllj3L8rfMrfLNrWnW/wAQPH2rfC7RJIW8M6PJp2q+IrXULJZ7a2CtaNZ6baR+ZGI45I7W R2/dyRrub+Jtq+waHpNv4TmtdG0fSLfTtAW3ZoY9Pto4YbWTzNzKVV/+Wnmbl2x7V8uQs3zKKJfv 5+7sv+GLt7Nef9M8k8V/tNeHtE8G6XDrVp4gt9a1OGNZ7K40PUtJnWH939umjYRtt+zwtJM3lyNt EfEnRq0/D/7VHhXxBrWn6bb2GpwNfzx21jd3Yt4bS9Yt5c32e4aYR3HkyssbLCzszN+7WRfmrqfG 2ueIfCPg271ez037d4hvYoYI9LW+iaxtr51Kx/vZ2gZo2maOP5drN8u2NWZqo/tG/D2++KXwT8Ve G9O+TU7q3WS2Xap82SKRZlj+ZlVdzR7dxb5d27tSqOvBS5ZXsuxCnTnL2VrPv6/h0OL+Buh3njrU JPirq2v+JLK816a6g0bTNQuYjDDprXUckQjtmh2wtJDax7trPujzJu3NuX2TXt/9mX817dW2k2Vn cRXX2xrmRV+zRNHLK0m1o/L+7Iv3mTbtZtys0dcH8Ifix4N8baBbWmn3y2N34dFpZ3+nNZTaUtjc yK1utq0DNtX590aw7pFVlXlmCtW7N4Z1LS/ihba/ZaTpN/b6iJrfUNUlaRdQsoBDCYY4mZmDQmSG TdEnlrumSTbuWRpNqEeSn7pdRPmfQ+R/jt8btK8WWml6lo3inzEsrm8sNJ1jWNNhnjupmh0uG4Ms X2PKw+TdXzSR7fmZcrn93Gvr3wh8VeLHtPi58SdR02TX9dDWulw+GtLkZl32dqrN5LSfNGskl1Iz QtH5kbblZWk3LXl3x80HWvGH7Uenano2uPpDaRb2yXzXN5HbnTbffdeaxmhkT921vG9x5bTLcNHM zLtVW8n6J8MeC9Lt7rSfEM+lXlhpmhJeXei20kFxc3587c1zczM26bzJfMbbb53HdudWk2x2/g4X nniqlSp9l+i7X6/1Y8ChKrCtKLjd316X2Xnsl+Nt7m3pvjDwjHpF5Yz3GnXx0m9Yam2maZJ9kGpr JDNIqqokX7Q09xEyx7mkaRm27pEbHPfFn4SvrXiK11/wbv0Px+z+cusLNJHbbY4JbcSTo1vNDNIq 3W1VdVkZdwWRVVq9Ms42vNYa6u9Litnt4ngtLid1a6KtK3mL8u7bG4hgkX5tzcblVo6ual/aCtaN p7W5VZ1NxHOrZkh+62xh91lyG5Vt23b8u7cv0kqUamjPa5lDY8btda+LjeFIn8MSeHPGtnqci3Ok +JtZmls5EspI/Oja6tIoFDN1j3RMud0bNGv7yvHvh7op8SftFad4butZtNS1HRf+Jvrlv4dNrY6O sdqI47SzWO33TXElrdbNv2po/LWH/V/vPm+uLe61qTWL0TWdtBpsc6w2zecfMkj8nc82Mf8APRvL Efy8Rs+5tyqOA+GOk3Nv8XPilqcsduR9osdMNxEipPdSRxyXQaRVRV+SC+trdW3MzLb5bFcNfD60 /e6/8H+rnRTrqMJq39f12PX6WiivTOMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDi/gr/yR3wJ/wBgGw/9J467SuL+Cv8A yR3wJ/2AbD/0njrtKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApKWigBnPHpXLeNPA2m+P tBn0nVbfzIXO9XBxJE/8Lo38LD/63Tiuo4A9RS/jxVRlKnLmiY1qNOvTlSqRvFnjvwl/Z8074a6l LqVxcf2xqf3IrmSHy1hTADbV3N8zc/N/dwvHzbvYsYXijbilrSviKmIn7SrK7OXBYDDZfS9hhY8s RaKWisT0AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKSlpKAOB+K3xB8IeAfCmoy+L73T1sprK4b+y7qaHzNRRI8ywxRyMvnMysq 7e+9R/FXmP7NM3gTUPEOtarpXhPw94P8V6hbRzta6NfreJPYKfL8yFlVY1VbiOWKRYV2tJCshLLJ GzeU/Fb9orR9P+JnjaVvC3iC51Y20Ok6f/ZMzWd3Ja6deXE17O10ozDbysssf7tmZls5PM8n5ds3 gPx98SfGuv8Ahz4j67BeaTpEXiKGy0eKe1dVurHU5oI3gby1XzIY4zHJHcSbV8yNV/fNIvk+Y2qj 9v0j/V/+GPShRfsW/wCvuPt2ikpa9M80KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooATHFch8SP CE3jrwrd6RbapNot0ZILqz1GGFZXt7mGZJoZCjfK6rJGm5T95dy5XrXYU1sDNZyipR5ZFRfK7nz7 4A+PHgvx/o3w28R+JbK2s/E9/asbC7vLFV8q6eZbOaO1dizfvJM8RsxWPa0m0Mu72Zrh9at7CbS7 pXs5njm+120ykGHG/KHayur4Vf4fldmVtwFfPWmaQnjz9mn4h6/4YmkvLzxFq2qa/YyWtsYmd4Lt ltmgjiEbiRks4WVm3N5jbm8zlW8V8A/tM22h3kd9qUmveJNAkhmiuwfJttSsbjCTruvw0ZuPMW1E YHnRmXydvkrtjjbwFmEcI4xr689rP+vl6X8jnqYqnTxMqFaah/LfTq7rXTT5bnvP7dOsS6Z8A7qy R7dTqN9b28iTK7M6q3nER7Rjd+53fNhdqt/Ftr52/ZR/Z9T4l6h4ot9fmvNJgsY44bm3gPlXUc3m NtRldWXb+7k3Ky7lZV/2q+iPGGk+Ef2ob3V7W91PVYdE0TTrTUoIrTQkW6ure7i+0R3UM0sEkjI3 liNY4Vjk8y3mWTzMqq8DrXwj8b/CTVribTdcu7LRfEepRjWWtb6GG8k2rNdTSR3Um35Y41kXzpGi aRkbdHtZa8TN8L7bExxNSHPT8vn+ticPlP8AwqPHVZJOMFyb6X17dn5rXyPiL4neHfDPhP4hGHSb ibVvC7StIkcUjQ3P2dZpFVWkkj+WRo41bdtZV8xfl+8tdJ4Cu4fsF1f2t+8WpaPBDf6XPIVjmnmj mt1aGP5lZWj8yST5d3yxt/dr71+IX7L/AIA/aS8J+HtZXUNWtryCwMdtrq26x3d/8iJHNdebCJJs eWGX7u4M23hq+I/HXhu68G3q+E9e1e1m1DRpvsVpJaS740ZZML5a7m/ikZmX5drM25VbdXm5qquB pU5P4vsuP9X/AB+8+iwFL2k68FPkjKP3fp96PXNPh8WfEDwTY69H4cuI9LaWVxdJbQ29r5nAkm/d qqwx7UiVmb93ujkbd97bwnjTVPD+ka2L211G48Ys1pJf3RvhJDLLNt3L5391WkbayrIzN5MjeZH5 kbVL4f8Ai1GvwL8U+EtRvLrT7S6LXgGm2ytLcXXkr5StM037uHzI7dv3aszLuXctYfgP9mTxH4w0 m21OTWbG20zVvLZIrG5t7q9upWX5V8vzljVlaaFW86aH5po1+aRlVtMwzKrn2FoUa3vSpr8brX7t NV+evFlGS0+H69atS/5eW5vknt89d9LiQ3Gr+H/BsOkadq+o6XDfTNdLE0Ma+bGsbW6yLPt8ySP5 riNo/wDV8t/eba/xTaeH9A1OJdNvofIkguLprb7H9hWLdD5lvDG0c15u+Ztu1trL8ysytukj9O/Z y/Z/8YfErXvGGleL7/WvAdloU62d7a6fbmOS6mkhmVjHcMzR7ljk+8qybo7r+7IrV5T+0Z8L9H+E uo2dv4Y8R6x4oubJRYa+uq6dNCtndbd0bKzKq+XMqyNGu5vljZt0it8vn08kxscN7Wpb0Z15rnVS NCVXLafvR6vb5mlo/gPxt4u0rWX8M+Ev7butGDS3s1lqcM0e1lby1jjX5pm/dyf6tpPM3Lt/2ut0 j9lnTLWRPE3jjxB4k8OXVn+91fT18C380Vn5PyzGO8jaSFl2xsyzfMu1lbb/AA19e/serpL/ALPH hK60q2nthcRSG6+1xosstwsjRysxX7y7k2qzfN5ax7q9u+o619RluQ4fD0o1PtSWvb+vzOWpmdXG wp1Knk/n+R8Aa3+z94N1bxlBbeEvit4Qu7W6CxWsF3q8Et7JcbWWOHbGP3itIw+ZdrL5jfK2Pm9D 8L/Au98OfErU9D1HVLnVvAun+E1GpaRJdXUlzDHdySNIlr9mVGk3TadltscfmLIy7W6V9T674f0z xPpNxpmsada6nps+3zbS+gWaGTa25dyN8rfMob8BXiPg34C+C9U+I3j3Uf7Gi0yTS9WtbHThoP8A xKms4106GRmjktvKk3SNfTeZuY7gsa/8s1rWOSYfC1VKnD4n/np10Np4yVem4VNvQ+UdX+FPxd8f eJ4NYTw14itX1B0vbaW6sba1u7OcLdSiMsvlLEvnrMzSDy9zSRzNH5ksaN5PrGh+INF09b3U4Lqa 3t3k01Hnha4t4g6ecvlzFWgbzFneZDGzN/y1XG5Hb6z+Lej+K/hJ8TNAj8O/ELxLeXDWzXs2mXWt NMsrNJ5Qht4ZmuGPmMxWNZIZNrbW8xvLkaPr9L+EviT4qeDdF8SeIde1bR7868uqLoet6NGzwyxh rWGSZrKOzuGk+VJBJuVVj2qysq768KeWwxNWVKHNzx9PI+CzXhKhW/f0qzjz67t9fv8AvfzMb9iT R9O1rwn4qjs9Kv8ARX+y21q1/Bqi3MfnS2y/aHtWEf8Ao8jbYZJE3MwbywwXy1WqnwL+Evje18N6 LoWq3dnJd6H4nh1eNdTmikiKKka362sUfmLvhk8yNW3bN11IzLHMq+X5Jo0Hir4C+KPG2heC10vx HHpSSfbfE3kXEMr26/ZWvIVaO42x+Wzxq7Lukj3M26P7y+peOv2gtU8VfETw2+k+G7XXLjQw12sW l6rNc6dfRtcrC27zNPRpdk0K+XJG3lrN5bfvGXbXr4LGYanS9li7xcP19F0t06HqZfhY18PRoRlG U/h7bXul+llq7HsPjT4e6D48+N1raT3F9Y65Y6JqV2l5otpJbPY/bY7e0S4a83bftO23nWP5T8q/ dHkqzWPh/wCCNO8XeLLrxDr+haFq+qaEY9Mg8SNaKLy/voMJc3Um1fJX5oYfL8tnaHbLGWjbzIkq 6x8fprXwBp+sa58MvGVtZa3DDAW0eW1uGWSdcKsbw3SzLuZtqybY23FfusyrWHoXjLwz8I/Bcdnq vhvx18P/AIfSWM8kC3sCywWizNDlWms5Jrq3k8ySRlMjK26aX5vkjVPpfrELxnH9fxPpKntmoQu9 NOnnf8eh7neXUNx4ht9Oi1/7LqSQfaW0yGSHzZIfNj/eMjKzbfkaPcuF/ev/ABbWXA+Af/JDfh3/ ANi5p2P/AAFjrzH4c/tW+CfGniXWrIjUdR1HT5Zm0+/sdIur6W4sZJFY7VhtFkiVW8uNlZP4I/3k jfNVj4G/Gzwvovw90nw94n1qw8Iar4esrDTJLHXrxbG5dlsbZmfyZtjKoeRoxjcreXuVuap4ilKp CSl3OXlny+8mvk/M+heaOa86j+P3w28yYHx74XiCNtVm1u1/efKPmH7z1O3n+7Vj/hf3wz/6KF4V /wDB3a//AByuj21P+ZE8sux1jMLszQQXvlvDOnmGFlLr91zG27djcpHvtb5dvDUz+3LJdcGkNMU1 Awfa1gZWXzI921mVvuttbbu2/d8yPdt8xd02n6la6rYW97ZXEN3aXMazQ3ELrJHLGwDK6sOGVl6N T7Gxt9Ms4bWzhjtraBFiighXZHGoGFVVHRa2Uo2M7SG3lms9xaSMZA0EpkXy5XVSSjL8yqcMPm+6 3GdrfeVTVPS7Xy7e0/dXkBtVktUW6umldkVgvmMdz72YRqys3zYY7trMy1eW8ga8ktfNia5jRJHh DDzArllVmX0by2/75b0q5x60rJO5V7qzKN/ZW+pWM1rdwR3NtMjRSwTLujkUjDKynqtebfBNtWl+ BvgqGO0tWh/4RmyWB/trxvI32ODaG2x/u9zGQblZtoVWG4ttXvprwW/iCC1aWRmuLaSZI/MjCr5T qrHbnzG3ecv95RtGdrMu7jfgFptqvwF8BW0drCsEnh+yaSERLtZpLdGkyvT5mZmb1LVi/wCLHXv+ g/8Al29DrPCWiSeHtLSze6FzFHxCBCsSxJhf3aqPurnO1f4V2r/DWP4j8Y2vgPT766uzNrOotdW2 7T7GVfOP2mcWtqqxzS7Y1ZgF3blVmSRuPmrf0e8aZbu3khkhks5jbnf5jKy4VlZZJFXzP3bJuZdy ht67mZWqh4j0jTrnUtF1bU73yE0y5LWsbyLHF9pmH2eNj3Z9s0karu2kzH5WbYV1qybu0cnK4U+W H9fecb8bPiXd/DrwfrmotpBuLeziE4Au3tlvLc7Y5FW5G37POrSKyr8zSKuI8sW8uv460/8A4Tex 0rxX4W1ldC13TOdF1e8lI03UvtEqxtZzJu3SQzNHDtdV/ihkhZuh8z/aA8T6O3iLRovGem21ta3r fZNGW/nksdkUjRx6hFd3Cs3lr8vMke35RFta4jmuFXtdPl8MXfxyuNP+wDQtRS0XVSukQ3lrNqMn 2+W3864aLy1mRdkbbZo3X/SGZWaPczedOTqTlByXLoref9Wsc31icK795W0Vr/ff/I2/B/xm1PUP EA0Dxn4I1TwZrMtg1/b26O2pxXCo8nneXLboy/uwtv8Ae2sWuFVV+60mxpfxu8E69rr6Rpes/wBp aksywtb2VrNM43Nt8z5UP7kMPmm/1a7l3MNy1zHx6g1/w34p8B/EHRrDV9etfD091aanomiRmS5u bS6jVfMVQw8zy5I4W8va2773y+XurzXxLpfxG+M3hv4U614g0Dw/4n8Mz6xpmr3VnYmSJfstxAm5 prWQMzeS0snzLcbWX/WQsu7bz1Ktem/Zx1l6brTtt9x7DjFwcv6Xf10818yv+zX4c+MniL4V+DNR tfH+jaNoxmvL9x9h/tS5v/MuvM23UjSfeZmuN3ltHJH8qtudm8vqX/Zn8SfEC80XVviT40l1N49V /tfVfDNui3GkvJE0i2sdqJFVoY/LfbIrK3nbm3fN81eg/DuTSfHHgmIaFYSeF/CJWS2Gi29lNpN5 a3Uc7ecvmQyLt+dWVljX7275m5Wu6sdDstNuri5htgt3cLtmunYvNIgkkkVC7fNsVppdq9F3ttC1 tTwVL2UFJ326uwLE8/7yHXbQx/APw78M/D3Tbu38Jadb6Zp+o3balJHau7RNLIFDMq7tqrtVdqrh VwNq10FvDDcXLX0bySC4ijTctwzRFF3srKm7aD8/3l5bK5+6uOGuviZ4L+H9xd6HdeKPD+kXlncr /oOqa/HFIscpWVm2yMWjVRI3lx/d2qqrtXbt5zx9+1J8PvCHg/VvENj4u0fX5LJcQ6PpuoQvc3Uv mNHt+8zeXuZW3KvyqjN8y/LXbz0aK5U0rGN6klzSR6TqniCTw9YatdahAz21jbSXn2lGhgikUeY3 l5kl+V1VV3NJtj+ZW3D5gtdfHekw+EX8VSXRPhv+zzqy34hfCWojEm5l/wBYzbdzcLnt9773zz4D 8SaB4P8Ah3/wiGtaVf8AinXJXfV9S8J/8Ipqc0M8W7aTaQy2sawq0yrNhl8pZXkUeWrK0eVer4y1 Dwba6Pe/DjxPF8PNH06O70zw29zp93f66YJVuI/t6zM00Mf7uFWtYYWZfNZPuqsdc1TEezheOvlv 95NCNao1Keif3nXz+HfGniLx63xZ0Wwtvh3ZWttC1xpPiK5ktn16Hy/3kuoiGRo7fyY5HWPckkis n7zaqqq1vEHgz4o/F2bXNLm1qPwtpE/iWzvLm1tzdmdLKGOFWaxvpI1VG+0WryLGsK/N+88zZKrS dBqviLxL8F/BNlpdnoPgbwZodhH5jXUmuXN4llaRurTzfZfs0Mlw3zBflk3NLPHu+81Y3w28DfFr UPDsGonxpCLm91u68/Utd0Ca31i2sPtW1ja/aGkSJZltoWWBrdVXzN38K54/Z/DFJ+9q+n+X+R08 1bR6L8f6+77j0/wf8M/CHgdtKGg+F18LypdfZ1S3sY5GuPJjuY45JpVWTarRvIwmZlkbzFVm3O0b djcY0wX2oXoWeO3V3h+y2ckk6Q7FZk2ruaRmZS3yKM/Ku1mXc3HXvwn1XVIltr/4l+MLizMsbzQw PY2bSqrK2zzre0jmVW27T5cittLDdUE/7Pfg/UtWtb7VdPuNe+yNIsEfiDUrjWYmSRV3L5d40qx/ Mqtuj2t+6X5trMp9FXiuRQ93+n0uJ8rldsrzfHjwr4VjaLxF408Mw3FnFcSarD/bcM1zaXCtuNrH CsatMq/vY/urJ+7jGxmZiss3xM07UreLQdG8AeKfEFhLaPHLaf2CdOtYoF2x+W39ofZ42DK+PLXc dqt8uK7Xw7pdl4c0q107StDTRtPjnkijs7SGGGKFdzt5myNtu12G75ef3g3KPm23ta0XT/EWny6f qllBqVnJt8y2uYlkjfa25dyt8p+Zf5VUfay3dvkEnG+x5rfTfFLxxFZC10zTPh/bJfmd5r3UGv7t oY3YxpJbwosf7wrHuVLr5VYrubla4DQdH8bfB/433HiXxJdprejeLrq30LUL6x09baKOaO3gWwuj GrTSLvma4t23Mq7pFbhfLWvdbjT9XF5PeRam8kSNuttOWOOKKQLE6+XNIVkk+aR9xZNuPLjXbxJ5 vFaDa674m1Y6b4igtzDbyzDVdP8A7RaZLyGSN4YWkt2t9rxsi/wNHGzeYWXcrRrs8M5x5ub4f620 9PnocUq1OlU+G/N6+ny7nrq0dK81+Cuoal/wjuqeH9XuNRvdT8N6tc6PJfalJHJPcwrtmtZWkj+8 zWs1tuZtrFt275q9J54pU588eY6GrOw6iiitRBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBxfwV/5I74E/7ANh/6Tx12lcX8Ff8A kjvgT/sA2H/pPHXaUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUlLRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAeLeJv2WfBXjTWtVn1sajeaRf351OXw6tz9nsBeNHFG11thWORpGWL+KRl3SS Nt3MxrmfAP7D3g74b6tBe6T4i8TERzwzyW1zNayRS+VcQ3CI2bfcq+Zbwt8rK3y/er6N5oLVzfV6 V+blNfbTirXFpaKK6TIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKQ9KWkoA/PPxX8QtK8J6t 448O/GHwjqXhzWpb2HWGm8EXrLp2pL5sa/aPsV3N9nuGZo1Lfu5GZfM8yNWhdq5fwTpvwR8cavqN j4i1qTTW+x22ow69o0ElgrN924t5rOTzljm8ybzG+z7ofLj3L5Sxybv0O8deOdE+Gvhe98Q+Ibv7 Bo1p5fn3PkyS7Nzqi/Kisx+ZlHA71494s8YfCbxsrvqHgi48SeGvEGltrN74xtND82wSO3huF/f3 C/vY7iOOOVFXb5kbSKo2s1fKV8vj7S8pJ/4l/X32NMRhsJjof7RR5vP+v8zAtdS1n9nm6bxJdXXx A8ZeBJ9JhjjsZ7CCRrDyl+SR1aSOSHbFGxbdCv8ArF8xvMXFdHqWtaD+1F4VuovC3iOz1i3095Eu tOa2S1ZpJCvkyF7i3mki2xecqyRx/Mxbay7TtzfhT8VvDr6hJ9iu9U1HwrrviBdO0DxHeakt0kLG yhkeyka7na6jaSa3m+Roxu8yPb95WqQ/CT4feN/Atzp0nw8t59Z0+7jsdVh0KzsrS6S4j8uaTEod VCSKV3eXJu2zbflYMF0TdaMqEXdO/wDSfnvtr955fs8RgKqdBpx/lff11ONj8L+IfE13rlt4d8BQ 6Lez6bMl99mvpo7Ky+02kjC1+yt5K/apIzZrIf3kasscjKrV8UfGjwdqdj8TNY+36eLS4mlkkuLf SljnhtZvmbyY1jbaqr8q7d3y/N95l219M+H/ABJq/wCyXrlx4ymg1bxP4Bvbk6BOk928d5a7Wja1 eSCaONo9toCqxyKvzKzLIsc0dY/xO/aU0L4sePLiawtNHjsLrS5rW1k1Kziku2VYJGVWaSTbDIs0 knl+Wys29flkZVjr5eXLhaca878937tt/uS+b/4B9RT5cdFU52jzRT32v0137aHzLB4J17StB1d7 23/s9oriOym025/d3Kybv+eLfN96Pb935d38O5d31ZJZ6Hpum6VajXbfVL3U9FhsrKO40/7RbzO3 261ZoStn59vtmWGRVjj3SSsyt5m/zV8gj8OC+vbnQr+yvlvp3Wz8mGLzJoJvMVW/0dv9Yy/Mqx7o /m2/N/e1NF8WR+LtLtjeaPFNrybrltShia5kn8qORpvtELs0cyyTfvJJG3bV3fejVY1+ao5jerKv OK+Lb8/w/HXQ+nWDVOMacZH0l4a+M/ijxND4Om1exl1jTw8MgGp2UtlJc3FxdTR2M0yQxtHHH/x4 yLIzeX/rvLW4mjXyfPfib8QvB2h+GfEPh/xF8NtBtPEminT9IgsbiFWurmMWrNFcfaobjzo1UR2+ 6NWk2qqxtI3nboeE1j4oeLNbh0yxuPG0mq6Ho9t/bkljrFxZ29xM/mNDcWsckjTNMzRzTbY5v9ZG zfuWXy1bA+LHh210PXrvxboV/puk2NxqEn9jNoEjPC81rN5Nw0e1ma1bd5d1HH8yLDN8sm5VVv0P H5lVjho+xjyxst9dGvPft697n5bxVjMVgI1KdGCS+1/hfVJ2Wj0t306o+rf2RPjNBr2pD4caf4ZT wpp3hvTZna1vr2a71Ce4+0KrM37mNY9rNJ5it826RNqqqtX1RuMZ5+4P4q/NL9nn47eJtJ+J3iG4 vtXuL268TQMbkrFG1xc3Eas0P2ct8qzKpkWGPa0cjeXDtVWWSP7duPEfiiz0DWr6PWtJFtcxm80P VLm2a40+KH9/Nm4uIZF/d+TGv7zy41j8yNfMuG+ZvWynFRxGFjNy/rpovIxyPERx2AhVUl1Xprpo ttLM9UyAOSMV558Hf9K0vxDqcuZL7UPEWqm5lP8AH5F3JZw/L91QtvawR/L97buPzMzHlfgR8cNS 8deFNQufG8Gk6Jf2Ia4F/Z3Pl2V9Z+TDcLdRLM3nRosVzblvMHy+Yu7azNHHw01xP8Mv2W/BXjvT Lm+uL3T49P126tLmVLo39xeNCt05kuI5ZImkWe4+aFl+aeT726uypVjGSqP4bP8AQ9uT9nTcmmYH xe+CuoeOfiBYT6tNLHC+meXfeKjYNY2ztbq0c8k/yt5cisvmqx8uOaJljWRPJWSTy/8AaU/aG/4T C88D6rqPg+90SXw/q1wZt95NE7GNrfzII5V2tHJ/e+Tcn7tlb5mWvadB8UeN/jtqmrNaaI+k6Nea bcfZb2/u2t7rw7rEEfk+TE0LMyt5ksjFpI4pJI5pA26ONY26zw78IPh14L+HWjXGvaM1tP4ZuDrF 1dTKzXEN9FCrSXEgt5JNnmLDFJ5YZlP7v5d22vl6uDeInKphZcsJ/Ffra1t/wOfMKsczy/2EfJxe y3Te/l5W0PlL4Tt4k+EfhvQviTpd2ul6DfLq1rHb6tKlzbyyR2iyBSqtGytcT2kirtVWXyUVjJuX d9a/BvwjrPjD4N+HR4l1e3aDUdFt2WPSoHtZIHWZprWSExssMXlxtGu1YeWjXczKqrXIePvgLpsf wX8X2+ieGby5t21K/wBVtdAe12Xa3oFzbrJB5c0cfkgeRJHHsbMUbfK7yLt9n+GKnxJ8LtJg1nSb vyJ9OitZ7HXlkkunHkqky3SzRR7m3eYrHbiQfN0bbXqZfgfYOVOXw2/P/hjxcuwtXA4hR2SXTv37 dOn66+deNfEHhzVPHfjLQfE2q3aaFcQLo8r3/lwQaJIbF2a6tpHXy9kkd8IZJGPmRyyQRsrRzx7c j4mW/jjXPBPiPw94i1yT7Xfa7a6Kr6Lp0lrpsdhfNb28ckjTbJJWVmkYx29xJ+8mWOTdHuVfYLr4 epqXg+XQJo7ZdNkv45ms72KPUYXt47hJGV/OVWd5ljZmkk3sskzNufaprzrxho+u6fprWVvoun6Z Fe/EXTrhVkiiW71K1Fxb3Elwq2/ytIskcjbpPm+z27NJ8ys1e5UTlQlGR9bBpqzf9f1uXtB8D/EH TfDN1Yat4e8H65danKlxezQ69qNptmjSOGFleSG4kaRY7eFvO3RtvH3dy+Y2h4ftvjDoGj2tr9h8 NalcxpDE0l94iuZI9qylpNv+geZlo28tWkkkZfLjZvMbzGk9Tmez0W3vL6doLGFQ1xdXEm2NMKvz SOxx91VHzN2WnWN1Nc27SS2k1m4lkQQ3BQuVV2VX+RmG1woZf4trLuVW3KIjTduXnf8AXyMPaO1n qeT6ZF4y8O+J7jWdY8HTa2JI5k05NC1uO9ksRNIkl1HI14bT92zRwNGoWQx7ZF3LH5ca6HgrVLDx hoehQ6BpMtrounazdWep6LdvbvJpk1u021HG6VVWOaOJo44W+XdbsrLGu1vVmrwbwSsuj6Jca5qM 1vNbWnivWfsB+xybNOjbVLv7ZPPI1wsP+pWbbOyx+WrMq+YZPLkqKanyyf8AWhSlzI6Pw/8As8+B bdL2bUPh94PFzdyJMbaHSo5Y4D5MatGryJ8y71ZtypGvzf6vduZrOlfA34Y3ljF5PgvwnqCQs1vJ c/2PZuWkjYxyZ2x7Q25WVlAG1twxXa6vYzaoDYSWtlc6VdRPHdrdr5m9SyKYvJ24dXRpVZmb5cL8 rbjtk+ytpGii00ayto/ssHlWdjn7PAu1cRx5VW8tOFX5Vbav8Paq9jT/AJUQ5N63OKuv2ffhhcTW jt8PvDKtDKZFMelQopO1l+ZVXDL82drfLu2t95VNcpY/su/Dzw5p+h+H7HS9FEtvYzxM2o6VZ3l/ eKFVWuvMkjLeakjRNuKtH+8ZWj+aMp33w38Hat4X+HehaJq+v32paxaxxte6lNcfaJZZvM8yRRJI nzR5LRruXd5e3kN81dUt0GvprcLLvjjSRi0LiPBZ/ut91m+U/KuWHy7vvLmvY0+xXtJwbinoeIeD /gX4ItvC4k8ReCPD/h6/v9Ymlt7O703T5ZIY2umaG2VisinzIl+ZVZtvmyLG0arGsbPg/wDE7UtB +DfgGKT4feI5oP7H020t7qO60tY7lmhjVDGHvFb5ieNyq3qq16HrmqarpOtaXpLR6re2WoTSXLav YwRt9hVZoPLt5l2kNHJ5kke9V3LGu7+GSZfNPh/49tfAei6j4asPBPiDUptG8T3lmtpo+kqkNt9q vLyaHa0jRxrGtu0LM2dqrcw8/Nxx1YKjVjJ6J32FVq8tNzfc1PiE/iH4i6NHYzfD7xhpkttcC4tN S07UdPguLdj5kbtH5eortbyZGVWZmXc25o2VdrTa1qniPxXFo6658OPFkkWm6n/aYj0+/wBNhjma ORjaxyL9vPmBcpI3zBTJCrY2/LXmvxI/bg1P4d6/Lp938Nb62QSmOP8AtXUktriQLjc/lLHJ+73b lWRWaN9rbW+Vgvsi+H/DWseJrb4iaL4xu7J76FI7qaw1SO4sNQgikVVjZJfMjVVbeu6Hy2zNJ825 qwozpYipOFKTb0v/AFY8OnjcHiJTVPWUbX3X5nIfE7wjdfFy408v8ItQtdbik/ceItR1mz0+TTgq SNHIlxayXMxZZGG2Py2Tc25vu1Wt18UeBfBP9gweF/DN1aeD7LfKdQ8XXSXoh+zSKskVxJZRLCzK zqsyssa7ZIx5e1vL921KK8uBapayeQPtCNNKrqrrGpLbVVkdW3FVjYfL8rsysrKtcr8XfA+l/FTw jP4K1Z7uK11jY0jafNCk0ccMiTFwJN3y+YsaMVVj+9X/AHl3lhnHmqU5e98v8j1YUaHtPaciuzyX 4rfGLVPiFceAfAvw71lvDmseOtOXVJ7u6s2afT9Nkj8zzlkWTas22OWNVXd8x/1kO1Wbs/CHhzxr 4N8PaL4atfG3htIdMih0i1bUPC10ss/l26su1vt6rI3lruZo/l+WTptbbxfwJ0W38cfGDX/G+ka7 dXvhPw99o8N2EH9v32qrd3DNFNNdeZcN8q7DDH5a+ZGzR7lb5UZvoDSb4FoYVN1dCaN7ozXCRxtC rNmONk+Vl4ZlX5f+WLbm3fewwtOdaTq1flZ/15nZUcYJQj/X9aHjHhb9mGWO31NfFvii/wBaXVb6 6mv9PsL6+sbCeOZcN+4judqySSZlk+9H++kjWPbtZesh+Cfgh47gv8KfCcc8d2sKr/ZtoUmg3J++ VvK3fLGx+VlU7lZeV2yN0OmxaRpmn2U39ptqKxx/2nazWcrvc6hDHapC00qwt/ph2sv8LD5ocLuV GrtcV6Kw9KCsoI5lKdtzgbjwjB4L8P29p4S0+PQYPtDyXdr4Z021gNw7QPHuVZP3at5nktubd/q1 Vvl3VL8SoIfEnhW/sl0nTPE0FrcRvfade3Uiqpi23ChlhhmZm3LC3l7fmVu/3W0LrxbFZ6xqtvf2 k1jpGm2YvLjXLp1htIvvFlZmZeFUbty7lXncysMVfjsLuRVmeb7IZwst3DG7TbZMR/LE0nCx7UZW URru37vlbO7eMoxtdbEe98RwHwt8E/Zo9cufEGkTXes6td/2hdalqEULRStJG0aw26lmmWGKMtGq zKrYkbj5mVeY+Ln7S+heE76/0HwdcWnif4jiMouj2NnPfTN5fmZhb7Ou3erK26NpFaNWkk2ttEcn l/x9+KGu/GDxpB8O9DiutL8CxatcWviDxDY3/wBnZ7W2jh/tASltqwwxrcsreZuWRvL25+63hng3 4kfDz4a/DfxFaadpq6D48ae5tLbWdPm/tG/27bhVkjuiqwWyr5kcLTW7M0i+YyovyyL87is4g6vu r5vy7f1bsckcZCUk7NvXZXvb+tO9nbue9+J/2dPEk02lnx9b6j8bdX1m9jCJ/ac2mab4dba0lw25 ZGby24VdsK/LHt+VmjVvoL4N+DbDwP4Vt9Jtbi+1K702GHSrjUL+zkt2mWCNY41jVlVfJVfu+Xlc szbmZpGbzL4J6L8XNL8Lw2SQabZwTXrXT+IvFkMsur3sLW6MGms452/ebtsStJdKyxwqpj+UV3d9 4F+ImratBJqPxG+y2Hyq9r4b0yGw8srubzN1wLtpGZvKXy90a7d2d33W66L5X7bkfNL0/p/1Y7ff qWqVZcum3b7tL/0vP0iS6lXULa3WxmeKWKSVrpWj8uJlZNsbAtu3MGZl2qV/dtuZfl3VJrpNZscx w30iLdBdqK9rJmOfazfMULIGXd/dkj+7vVsN5jZ33xF+FOqXlvq8WpfFbw7ezNLZ32mwWsGpafJJ OqrbzRtJHHJDtkVvOXbt2yblWPbtLbxr4r1mC6udJ8E+H9Wn06S4u7fT73xTJ/aUMnmXEO1la0aO KQstxD8szR/K6rI0Y3V1qunH34v7rl8krpo9gNvG86TmNTKqsivjkKxUkf8Ajq/981Z7V5z4F8X+ HfiRp11FoT3+g6lYSeZe6XNbNY3unXFxG0m6e3kXazN5rNuZZI2b5l3bd1dN4sstUvNHeTQ5YYtZ t8y2ZvTN9maTay7ZljZSylWbru2ttbazKtb0+SfvRejJkrHFeNfjZ4Z8P/EOw+H2sDUIL7WLSN0u opEhjRZpGgjVXWRZvMaTCjylZl3BvlVWZc3x14i17x340m+G/h25uPD+YftWt69FDcJNa2LLGI0t pGjWP7RMzTKsiu3lrbyt/rF2rX0aTVvj9qsGuPc6t4b+G9vl9Kt7K5msb3XWK7ftU0kbLJDbfN+6 jDK0mPMb5dq12/gD4V+GvhnBdReHdLawF15XnSyXElxMyxxrHFGZJGZtkaKFVd21R90CuJSq1nb7 D/L+uv8AwBQi6T99/h+Zp+D/AALo3gDR5NP0OzNnbzXEl5O7ySTzTzyNukllkdmkkdv7zMzdPSul 60lLxXdGKiuWIr33HUUUVYBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQBxfwV/wCSO+BP+wDYf+k8ddpXF/BX/kjvgT/sA2H/AKTx 12lABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUlFAC0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGdqOn2urWFxZ3tvDdWlzG0 M1vMiyRyxspVkZTwysvVa4T4kXcvwh+Eeq3vgzRrCyi0aNboWUNmoght/OV7qRYVkhVmWMzybfMX cw6/NXpfWkIGKwqQ5o6blRlY+bv2e/hH4C8TfB2+vE8Pg23iyGO11jT5r1ru2Elpm38u2k82T9ys kckkciyM3zKytwoWt4e1DxB4C8F+NdB010i8UeHtVtdNh1/VLlfsd9aqsdxFJdMUVVkisGWO4fb5 jLCu2Rm8vb9LAbVFfOXib4N/EeC88X2Xh2+0y70PxBqtzq5uNY1aRbuOWW1hjhC4spPL+yzQrJCy v8qxxr/Duryq2H+rwpujHVaaL+rfoOrJ1YTS+X/DnF+KYxqHjLxPZafBpNz4fNjJp91olnpiwW0E lqsfkzTyMoVo4Zo4490yyRrHHJ+7VZNrcPpf7NNnaeH7LxH4Ba41XS9RsbWHUvCdw1xY6h+8aOFm ZWkkUN9ohkm3Sfu18n/lpGtbY+B/xC03SNG0+bwjrHiKbTGkFxHfahptxb3E3nD57eaSRZFhZd0y xzQttb5WjkWSTd1Xxs+IXjNLrw0PB3g3xppOtaIbi2nmuNMmv3MLRQ4/ewtNb3Cv/FuZpFeH+Fvv eVm06OJoylKhKPKl3u/RbfL/AIc7Mvp1acoS5o9dNNOu/wCGny7HzV4f8C33iaz/ALQ0e3t/EE2m QyajqViIvMhsIY1mVZLppNsbSbljkjt/3nmKreYu1drU/wDhEYbiyt44LjN8tjdTvpyIzLH5W6Rm l3LGscfkLIyqsku5o/4fMVV9s+BOg634k8C+K/BelvpuoeGdekh1TTvEpS6tYZL5ZI1ktVFxCrTt 5do0m6NT5fltub5htx/iJ8L28KLJq+natA9l4cuZrXUte0/zmkg1BVkmhiIXhFXbaqzf8s5rtvmb btj8BZJQWURxcf4n8v5u7v8Afe3kenHNsZ/bH1RQ/dWvz+fSJzvgz4Va7qdnreueGvDevLr/AIfn sZ/MMNtcFL238xptvmLHINp8tlt4/N+Zdsituj2+f/Ej4deJvCuh6dHcwSah4YtY2n0/VfKX95by TY3L8zN5Kybfus0KyTKysy3Eck3v/hH4lXHhfVPCl34y0e0vL5rqa7sr4W8bmJrlZLjzvM+2LDDI 0l5G0kbRx/u3hZm+WPb9B/Hfw6Pil8DdasURbz7Uq3drJpQa+Z4o5POSSHCfNI0MZ2p8qsziPzPm 8yuulhqeYYSVOMpSqRivLporfd62R4fFWFqZnhpUmvet7r/S/ZtLsfmz4U0GL4heK/D+jfaYdKnv ZrbT2maJXEkjSrErJHHGBuVGVm8xvmZHLPudVr7Z8E/BPxn+z34g8P3WkC28X+EIJ5JdRtYUaKS0 eSGOKW+ht28xmmVYW3bZHZvtEkccKb2Zvkz4C+AfGfi34jaLB4Zlu9CuLlbhE1xYGMVtD5O24ZWX +JY5lVfmXa00fzKzK1fqjpliml6fBZo0zR28YjVp5XmkYKu3LSMzMzf7THce9b8P4P3JVakXGXf+ vxPzThvK6tD2jxFOUKkZeny8/M+CPjH4607xd4X8azeFdFbS9N8IrNpj6dNbTafbWcM00lr5lusK q32iRpmaSOZoVZdqtDM0fmL7v4S+I1j48+H+k+BtQ0bWBG2kzadrsmkQLFNpU1vCn+stbcM0KzMs yxxsq7vL2+UytXx/+0t8RbrUf2jtb13Sru6im0XU4YLBrgB/IktGVW2K25QvnRySbe+7cy/M1afw Ft9KfVPFh0l9aR5oLTTbS4gmsvtCedMqrcNFMytu+1rZsvkyL5Xmf67cqszebKWL9ly+78L8/P8A r5s+po8TQxNeng3TS96Sve2iW6+fn0tqey6N4wHw0/ZN0FdL1O5srX/hIVs9B1rT0ZN6CZpriSSG SZfMj3reRrHLt8zEW5F3ZX2HxZ8QvD3j74RWOlw+INP1HV9Yks9GuI/OX7UPtM0drdSRq0cTeYsU 0zK3lKP4tm35a+R9d8Q6Y3ieHStf8L6PotlDaQ6hptxbaLbPcXsx3RrJ50cFvDcQxm4kk+75E32G NVZlkaST0HTPhb4t+LFjc65feELK78N63DdQQQ+GVs4PsMqxxxx3CxzSKzL50W7y1mX5lk/hkYTd 2ExEFN4ZavRf5yeu3b5anuUY1MwhKLcFCLabWmtldL810aat1PqO7+Hem+MNY8DeKkj0vUb6yuPt 02s6dNPai4WS0CtND5MhWTzGitV2ytIvkqy5birGjaWPhJ4R0TRtNtIU0LTpUsYfLeES3MbQnYzF mhjjnmuiqfL5m5pF+75jNH454W1Y+G9Q1zQ/DejSWeq6pJeXumalp+n3S3KstxJC0t9NcSN9ukX7 ZE3lt8scjMs32fy/MXu/Dvxa0y+8TWN1a6svh3wtqIuWttO1XQ3t/t3lRvdTahBc+YqrDIs0bs0q /N5bfdZmr6COOovTm/roddpSvFO6j/w/yep1vg3Q49O8QRS6Tc3dlpcOniyuvDr30csel3G5biOP ylWTy3Edwy4WYRrGsKqjL5bLkeMNP1a1+JXgma71OK80258Ts1jaLaCOSzA0W/WRWl3fvFZl3D5V ZdzDcy7dtTQfiTpHxT1G40sahN4a1FbySTQpLiGzh1IrC0sE8kNvO00m7dDdKzSQxsI5GXbuVmrT 8Yz6nqHxm+HujxWlqunWpvNfkvHum80+VbvZtCsPl7fvahC27zP4ZPl+7u6MR8PzX5kyjKMtd7Hq dU7R2kjJkhkhO5htk2/wt975f733quUVucwx/umvn39kzUntfhX4b0yWxGmy2+l2lxc2sMLMsn2j atrNEokbYrRx+ZIyx+W0kkjblkjuFX3D+1ozq39mbbj7T5H2nzPsknkbN23HnbfL3f7O7d/Ft215 HpFv8RPgt4LvNLsdK0rx3ofh+3b+ypG1Ca01OazjjHl2zQx2kiSTLt8tWVl8zC5VWzWE5ckk5I2h 8LR6LpfhXUbCfRnk8SagYrK0jt5rCNY2trqQKV8x2mWS43Z+b/Xfwru3fNuZ4g1jUfDc32uYpe6b JK0UdvDHHDMsjKghTzZrhUYvMrRqNvzNcQj5QrM3nml/HLwH/wAJdqV9qGv2ej3jSjTmtfEUUGla jp0ao0m5vtDRytay4hkjXazBpm3fLlYfTNF1bS/iB4ZkudPuludJvfOhS6sL1SJEDNEWjmt5Dt+6 SrK25ePusMLpGpCWzuHvxd5oZ4V1a81S3dNVWOy1QLHO+lusa3NtE3yq0ixzTL88kUzKytjbhfvI zVfXVJpN8C2VxBeMtw0MdwCY28uTapaZN6oHyrKrfNtY/LlWVYWjv9O1C+uPPl1OzuZIRBZLFGsl o33ZG8zcu6PG2Ta3zL+82s26ONef0uaLx9qT6vYeI7W+0y1nT7LHb2UfmWsiqp3LLJuP76OSVWbb 80M8LR7f9ZLr8XvCtfU6DV7K0+y6nNrVxE2iSWfl3NnfCP7IkY3+azbl5DK2G3MV2qPlX5t3i9n8 YtN+HOj/ABX8T6g0muw2mq6fcPPpUG23u/P0zTo43gZnZCrN823zGZVZeu5d3S+PZNM8WaH4gj8U 6AvjXwrDp1tqOnnSdMkuZLqSeS6jC2+1mzIsPkDzI2Xb5jSEorfL83WvwV1jxJ/wseC2i1DXNOvb aIm78V6lqcesecun2Vwn2qO1jaGVoTcfLbtH5jMGVfljrycfVrU3GNKPM/8AgPpY8zFVsRGlOFCP vW0/4ax7p4f+OlvovhuDXfHN1dWOmSRyym4j8PzCKFmuPLEUzRzXXkyxujKyOy/65F2q0bqvrHhf WNNv9E0OXQbQ/wDCP3FjHNZTwRLDAkO1PJQRkqy5VvlXbhdjA7flDeR/B34B6f4VuNO8Vahd6Lq8 m4XWly6Do9vAIhOnl4a4to1+0xqsh2v5ca/vHkZf9X5XuF5ayXCiOO4ktfnRw0KpuKq6sy/MrfK4 +Vu+G+Xa2Grow/tbXqx/z+8ywMK/Lesrfn/XkcR46146Z448OaTJq2oW1vrkFxAlraQbgksbwss6 yLGzbl3crJ+6aPezfc2yJ8YviBZ/DXw5/aGpJHqFtfJNp9roX2KSebVL6Rc21ujRhtocRyqQ0bZ3 r8y7cP1OubJIZkunudKs7Pyb1tQSdIo22SeY0e7du24j/eblCskmNx+bb4/a+INR+P3j7RILfSrb S/D/AIOmsdbu59Utmuftd9cWfmR29uQVUeTFceY0jfMsjQ4j2/M22Ic1D3P6f/DHpUuRVeWf9L/h 3/lsVf2dfDnxS+GHwe0Dw5c+ENBSWxFwXW+8RyRTfvLiST5lhtJowfmH3ZG6/wAP3a6XQPiD8TfG 2g315pPgrwzphaSa2s7jUfEc8iu6ZXzGhjs9yqsm5WjkaOQMjKyxmvTmWz8QaGRHcGaxvYMrcWc7 LujcH545I23Lw3ysre61At5o3hdNP0oT2WlRiJY7Oy3xwjy1aOJVjj/uq0sMfy/xSRr/ABLXJToS pwioz0S8v8jSc5SqXa/4c8mgv/idq3xSv/C97r3hq3Ok2drrFleWugag0dwk32iFlmVb5Y1ZWjfb GzSbvlkX5o/k328LeMpdQvn1r4g61Fu2yQf8I5olnFZqojbMaxzR3U24GNmZmba3nRqvPy07wJqt 1rXxg8d3k1i9hFFaabpEcbu0shkhia8laRlUxx/8hSONV8xt3lSMvyrWv8Mvhc3w3/tAt4s8UeJx eeXz4k1H7Z5G3d/q/lXbu3fN67VpRpuXLv16vuFSpKMkkjktY+EF349m0Se7+I3i2/toGW7l0u4m j0xmikVvLZhawwSJJHIqSL5m75oWXau5mXjPh74K0bxlqnjbw7q9xqHjmfRfE8gkgvvGN1e6fFar NDLbxy27SHcyq0i+XJHIrTWUytIvy7foy8uzHcQQRGB718SLDJNsYxK6CRl+Vs7Vb/vplUsu7cPL LHxNcWf7QjGfT7/TLDW7KTQh9ssm2zXVizXUMkM0bNH5c0F5dN8+1t1oV+8rLSrUoc0eb07/AJj5 5fDe3Y89+LP7HvhXULG1m8K+HNG0m5utat21JZo7qaP7O8/zNEkI3p94r5atHDtkk3FfLjkj6Cf9 lbRrnTtJvfDetTWeoQ6nY66mpahA1wbqa2b/AERXhV4Y44I4pJo/JhWM7fK+ZfL2t7P4ja503Q9c uUt49SZYJJ47OQSlZNsQ/dttWRsMy/8ALONvvcRs33l8QapqOnRn+zNGm1i4df3ey4jghVvMjUCR mbcq4kZyyK52RSfKzeXG9fUMLrLkOSFCMZqpb3u/9bfrc8y8LfF+LwP/AG/oHxDh03wrrOi2a63c XVneedZX1vPI3mXEOVWRT9oaSPyXUtuMe1pN26up1D4d6D4m+IkHi6HUbqLXNLg/su6isLvasse+ O6SGYD5l2sY5NqldyvtbcjbaX4j/AA0Txpp+nTRXNxbeItJvhd6ZrlvMkNzYbpB5oVvLZXj8slGh dWWRVVW5/eL5hofxW1P9n3Hh74rapLd+de3l7D4su54/IvbXdGcW9vCrTLIslxGpt2XaqiZkk8uN Y6q8lONOpqu/n/X9amsqHtmlFXXb0+f9eZ75NcHUFmg0u/tPtlpcxxXXmL5/l/6uSSNlV12yNCw2 lvu+YjbWX5WimtJZdWhNyZHhhmNzbNbO0Kxjy/K8uZfM/fbjJKy/Lt+Vfl3RqzR+IvBHh7xebQ67 oOl64bNvMtW1G0juPJb+8u5TtPC/dpniO41DRfCs/wDZ8hvdYWFbaznvLeSRJblsRxPOtum4R+Yy mRlVVVdzfKq8du2rNU+iOA+IXg/SvE2t61qNxq9x4J8V6TaebpnimD/RtlipimbzW8zy7q3WaNvM jk27Vb7qeYs0nHW/iD4x/Fz4aXdx4Y0XRfCp16HfDrGsazcedNbyWqqt1bW8KyfY2bcrqvnN5bK2 5WZt7ZmqfFbW/jZNpfg+98B/Ybe/l0i8vNJ1Jme8WNb9ZLh5reaONVtVSzuF8z5vMaa3XarM0a/U A2/MBwO9cHKq8nKDtFr7yo1k6amtb7FHw7odl4Y0LTtI0+I22nWNvHa2sG9m8uONdqrub5jhR3rV xQKWu9e6rGQUUUVQBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAcX8Ff+SO+BP8AsA2H/pPHXaVxfwV/5I74E/7ANh/6Tx12 lABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABSUUE4oAYOeh4puevOfwrwT4s/tMJ4M8RRaToUEGqTW83/ABMHm3eW q/xRKy/x/wC18wXG3ax3bfR/hz8UNH+Juky3elzOssMmya1mCiaL0LKGPDYyD/ga7amDr06Ua0o+ 6zw6GdYDFYqWDpVP3kf6+Z3NLSUtcR7gUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACUUtFACYrK8Q6HZeJ9C1HSNQhN zp19byWt1BvZfMjkXay7l+YZVj0rWpDS0kB4dF4I8YfA3w2dM+Glha+KPDcIuHt9D1O68m9sZJJt yrBMdqTQrvkby5mWT/ps3yqvnGj/ALb0psJF1Xw3CuoWbMuoRNdtavasrNuWSFlZo2Xbtbc3VWr6 xVSuflx9DXm/xq+BPhb49eHItH8TwTBreTzbXULNlS4tmP39jMrDayjayspU/L/EqsqouOGvenzx /l2+5/18jzcdQxGMs6Fd0pd7J/en+ZxOhaXon7XHgNde13wzFotrNJ5WmatY3yTXskccjK3zeVhY 929drburNtX5Wqn40+3/AAxGraDpj+J9Uur63sotK165ke4mgnuGaGeEXsytHEsjWlr+7+X99dL8 0asrw2fhxa3v7KPgG18MeJ4Z9b8HWEs72viXRrKWVrWFlkuZGvrddzRqJDIqyR+Yu3b5nl9Wr/GL 416Fd+JfBtjpmp2uo6GY7jWLzXNNs11QadHEsix3Ee2ORWw0V0u5f9XJGjPmMNHJ40MPFwlbSrLf +l+D/M+hpzn7sZO6X3f0+3meMavFafADS28QWdjHDrek2/2axC6ZJa3G91khWaRmt5P3O65uGXzm jab7P/F5e1aOm/tG+OYrjwfqvjiWbSrhbC+vNK1ddKlh/tEsrMtnNu2xNDJstW3QxyfKYvmjZmkW n8Zte+HHxc+Inhfwjp7rpGh+GL210h/FNrJH9jn03btkVmWH935cirHC3zQ/vJGbarbqvfDT4M2X xM8AaZHrA0nwvbaXfTazPJe6lI0oj/s3S5JLjYrBWhm3CSRd8bQ+dHtkXayv4+FwFSnhpU4VLyi+ 9+z79LPfyN6NR1I89Vpe9vv7j3+eklt5+Rw+j6b4c+I3xf0HV9b8OINP1q71Rr5RqM3k314tu0rR 7Y900bedPCqqu7cskMaq0is0nGeJPhufEnii7n+HGn6h4g8NXsrvYmGwmQ2zCFp2smL/ACySxxq3 yqz7127dzNtrQ0PQ9N1nxX4Re50280y31YQfbG02SNsfabxgv2WP99LPGsELRsvzNHNuWRty+W3t Hw98WeK73TdP+GfhTUPBsHiQz3DTatJp8m2Vob64uo47dlt2g2x4aZWZVXbcqsaq0bNXi0cBDFS9 lWlGP977Tf4b2ujx864VwOaOEsNFQh8XNH+tt3/w555q+q61a+FfDkmtW9+YtP0mzg02W11JpPsM dxCzbWVZG2rcRLJujbbJt8tV2xr5bfbH7KcjXHwD8MtJt3E3RO3/AK+pq+HPiJ4a8QaP4gtNKj1y 38WxXsVnDBHpzb2WSG3W3jVoPlkb5W/dybf3sciyRs3mNt+nta8H6FrnwN07xbc6zM+q+DdH1O30 bWdJ1OSyszdSKsfmW8kbeYyLJH9nj3fKy7v3bK0db5TCeGxlWdSXwx6vu7/Ky0/4Fj6vG0oUcDRw 1GKd7arTZcpLpHwPk+KPhHR7fUtfsLS70xLrS9T0mxsrVTpzSKq3ENu1rJtgkk+SR2fzNxb/AFcc ckkDdhrngvwx44+KRk0kWsOvWsD3l9qEunteRXSvuszbtJ5nlqP9GaOSF1LFWk8vy5N0i/EsugeD 4dS1DxnrV7d+JY7m5kuY9EhuY7OR5muHZo7hpJppvL8lVZpFWTc023zlZdzdh8KfjVYeE/Es3inU NEmVb7WLy6bWNRu5tqzNt/dvFDthZlW4mVm8tmX7UrLH8vlyaUcyw0vZxlTXvNb67bPb+tduvg4H B4jnqTqw5Kk72Sd3Jf07W6dDt9e8f6r4N8RfD7wnZXGm+M/GPhy+T7fe2unXBuEmlaZrxYZo1kMy +TujmZoWk3MsnzTbvJ9o0G3uIf2lNItrmO20mO08H3Nra6HY3azQQwrNp7faI1+Xy1aSSSBcxruF lu/uqvxq1jDqni82TS6XYXaRx/Z7Txlfbhp9xE3kKtxLJbruWOPdJ5PyxtthVvMaNYW+3PhPqkGv /FDxVrFrrM2s2eoaBpFzEzzWsotla61NWtle2/dssbKy53SNnd+8avqMFivrlBS/vfdb/hvxPZzC isPCDj/KevzrM0f7oqrZT5pF3fLu+b+Ifw/5NXKSlr2j5sSilqhcmddogVHbzEysj7Rt3fOR8rc7 c8d/VetAFbWPD+m64bP+09OtNR+x3CXlsbq3WXyJkzskj3D5ZF3cMvzc143r/wCzulrHe6V4f+I+ v+AtHvrlrnSNE0ieOCG1v2jmaTy/l3tC3+s+yqyqu2Vu6+X72abxWE6MKmsi1OUdj5mk+Efxy1GG 6mPxW8Pw6vptxM9i8Phu3k+1SeWyx3FxIy/uJmjk8lvLVgsar/rNzbui0vwt8ZfEV5Il14m034ea BbS28FrZafGNa1CWGFpI5Wa7uV27pl2uHaORseXuVWWTzPVoYrlde1dVmkHmwQtb+ZCzQRt+8H9/ 5myu5gu35dvfmti7sbe8t2huYY54W+9HIu5T+BrCOGh9mcrerD29SS1SPF5PhN8QNL+23Vp47XxZ 9outk3h3xBp9tbaXd6ezSeZBJ5MLMtwfOkZrhV2yMqK8ZX7vG/Cr4j+KvCPiLVIfiB4f1/Rta8Te I4fPkXSvtek2u61tbeL/AEqJVVfOk2wr803l7FWT5t0jfUPqc815Z8aoXTwWmoWFpDLqh17REjW5 ZoFeRdWtAFZtrFVZlUFgrfKq/e2rU1KCor2sZPT5/mHN7RqMl+n5Ho9mt0quLiaKaTe5V4kMa7d7 bF2lm+ZV2qzfxNltq/dEj3kMd3FamaNZ3RpEi3fMyrtDNj0G9f8AvoVxf9s/Ez/oUfCf/hU3X/yu rn9U8OfFnxRo8lrdeI/C+gfapJFkj0/T72aazj3OYmjuluofMkGI937uNfmb5cfK3Qqyb+F/cTKL t7p1nxL1q+0nwXrUmhTMfEttp82oWFlCgllu3gCt5XlYLOjsY422/Nib5WVirDx/9kG3s7L4H+GL tLqG+1PVry51hrbS447NAzT+RN+5VIRJHDvXduVlUhfLyqwrXo1vofxUeNJ7jxd4UWSSKNZbaPw1 cPFFIC24qxv1Zs52/N/dU7V5z49+xHqFpJ8L9G8NrZ63BrWiX13LqhuftsNvbXHmTRtb5YeW0iq0 LNbttXdL5u0yK1cVR/7XT5tNH+h1U5R9i2z6Q8ULrj+H74eHJbCHXPL3WjarHJJamQEfLII2Vtrf d3L93O7a2NpybzxRodrrmieEtc1CxvfE17ELqOyWFv3hh+fzxHlvLXfHuXc33l+VmZa35Nc0+Nrk vqNqot7iO0l3TpiOaTy/Lib0dvMj2r1bzE/vCn3d19inaa5uYIbJjFFGHG1vMZiv3i3zbmaNVXbn d/e3Db639045Oe0NDk/h0MeMPikB/wBDHD/6aNOrcuGm0fUJbuW61PULa/nhgW0SGJ4tP+XbuXy0 WTazbdzM0m0tu+SNWK8T8L9Xu7jVPijqMuiahZ3f9vK/9lTSW7XG5dJsNq7llaPc235f3mPm+bFb 2s+DdLjvLq70+6TQNa13U7G5ub6MkT3ptWjf7P8AeVirQ28i7V+Xa0jbTlt3NTbUL+b/ADLqc0Xo r/8ADFnxtqHiaHw/Zx6Bp8Mut3V1bQyNJia2so2kXz5mDSQtIqR79oT5mbb8v3seY+LfDOh/CH4M +F5/Cs92NJ8B6xDebrILeMYVuGt9R8wbW+7DcXjNt2+Wy/w7dtex6bqFtrU0d5aXk2xE2zWbR+Wy tIsciedGy+ZHIqMvytt4kO5fu7cjXL7QvEGkW3hnxH5dhceJ7KeBdBv7yOO8uI/JzcQr5cnzMiN8 zRs23ru6Goq04zTfX+v1JhSaqc/9WLkllZ+fpOlxRWmpvprQzSrqF201zax+XKsNwu5XaSRnj27m Zc/vG3My7Wq+KdA1vxDq2kmy1g6FYaddQ3ha2zI998zLNbzIw2+S0TNjq3mOr/L5I8zgv2c/G+pe MNC1XQNSvLqTU/A+szaBc6l5T7dYWFWjWaRpVbDt95lWRm3KrM22Ta3pfjrw9eeKfCeraXp+tXXh 29vLdoYdUsgrS27N/Euf/Zdrf3WVsMutGSqw511NZxV+SZpSWaS6hFdlpvMiRo0QSuI9rMjMWjDb Wb5RhmXcvzbfvNuwviH8N/DnxS0A6N4n0iPVtOMqziKRmR45FB2srqysrclcqw+VmX7rNW9qFnHq NjNaSGZYp0ZHaCV4ZFVhtO2RGVlbr8yncO1Zni3xdpHgfRrjVtdv4rCygimneSTOSscLSybVX5mZ Y45G2rubajGtHyyWpMea6UNzzP4ceNH06PVvAnibxNc2niHw9qEcVpqGpSKtzqunNcotrJumhVZf MZhZyMqszSKWVt0kbVo+JfHK/DfS9HuL3Q/tPj3VPNstO8M6LqUtz/aE37v5mkkWPcqxwxs9zNH+ 5XcN3zYk5r9oS9tfCujaf4i8Mwafp/ifwvqs17B9t0S6kgMdxC32+Rjbr5nk7brzpJo9y+ZGqt+8 6bfwH8IajcafYeNvE1pqOk6/dWUtlZeHZpUW10LT2uGkit4YY441VvLW23NIvmfulU7du2vOjKpz exXxd/L/AD/Pc6ZRUYc7X6f0jf8AhR8N77wfHqer+IL6PUvF2uOs+pTWatHZQbWkZLa1j42wxtNK 25vnkaSSRvmbC+k9qMGkzXfTgqa5YnI9R9FFFaCCiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOL+Cv/JHfAn/YBsP/ AEnjrtK4v4K/8kd8Cf8AYBsP/SeOu0oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAiwOvVq8b+Pnxkf4b6TDYaXsk 12/VhGzMrfZ07yMvr125+X5W/u7W9X1BLhrOZbaSOG5ZGEcksZkUN2JUFc/TcK+FvjF4R8QeE/Gl 43iGaTUJLyRpodS2YW5X/wBlZflXy/4fl/h2172TYSlisR++lounc+D4vzTFZZgHLCwd5ac38pxN zcy31zJPcSyTXMsjSSSSNuaRm+8zN/erV8I+LtT8E65Bqukz+TcR8FWO6OWP+JJB/Erf/ZferNj+ zfYJd3nfbPMTytu3Zs2vv3fxbt2zb+P+zVev06UIVY+ylH3T+aadarh6scRTn7/xH3D8GfjNZ/E/ TTHKY7PXrZf9Ks1bg/8ATSP/AGf1XOD2J9R9eK+K/wBm34fah4o8bW+tLJdWWnaTJ5jXUXy+bN/z yz/tK3zfe+X5fl3LX2pg4xmvyrNsNSwuJdOjK6/I/qHhXMMXmWXQrYyNpd/5vMfS0lLXkH2IUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABR RRQAUUUUAFFFFABRRRQAlFLRQBG3rivAfjR+yH4Y+Kkl3qGkXcvgvX7tPLvLrS4F8i/Vplmb7VCN vnNuXdu3K27azbtqrX0B2pKwq0adaPLUVwdmrM+QPFnwB+LVl4wvPEuiJ4U1CBLm8vG8PT3dx9g1 OSa3aOSR7Vo1XzpTJcN+8mfasscPmeXDubnPid8M/Edn8Jb5LL4aa/J4gzbwyTXdvp+qxmFII7H/ AEeGO4kkt5GjSGZmhjZlaPa0jRqrL9wbgyj9KTPPSuWOEjTcpxk/e+e5VHloqcYx0nvq9fx/I/Mv 4ZaZrvw/+OnhBvEWgXvhvwXHPDYS6t4jnvdPsv8AR4ZJj5ckyxqsc11GbxYWj/1gVf3as1dv8Bf2 Z3+KOl61rF54g09jpurNYLp4iXUbC4j+zrIzNJbXClm/0iPb5ci7fJZWVtzKv36vYYwKP4jXm/2N RlKMqvvKN+nf/LoevLNJ+yjSoxULR5dPV/529Ej44+I37FfjHxFqU+rWHinQdQnuJFjOmS6S2mWk Fusflxxx+W03yxqkaqu37ufmp3h34X+IdS1aX4VfE/XdT07TbvddeFv7OvdOuLaa3to4F+zxtJar MssMbL8yrH5i+c23hmk+xWAwo3YrlPiJ8P8ASviR4ffSdYEyKsq3FreWcnlXVlcpkx3EEn/LORT9 1vwO5WZTpLJ8NCbqU4/Fuuj9Tn+v1pRjGcvh28j8/fhv8N7LR/FGteCvE/jXTtA8Xzag2gQWl/4Y m1BmWSSFluIJmZVTzG+VZGXdtbd8u5WXsNH/AGJ/ikum3Iu7/wAPhry4kjm0uW/uo4F8tlWC6Xy1 ZZGZfOXbIvyxy/8APRtsf1Z8KfHWq3l5eeDPGD2dv480S2t2uPs8zNHqkLQx5vod0MX7tpmkjZVV hGybc/Mteo8L/hU/2Pl1f34x79X13NsTjqtSrGp1Wvz77Hwz4l/ZX+J11qN14k13VPCHnw6aYJ9Q vNXvYx5K2n2dpJpPIVm/dhmfc21mZty7Ttr3f4AfD/VfC+v+KdXu/Dc3g3Tb6002zstDn1GK8aBo I5GmaMx/KsbSXDfeZmkfzJG2+Ztr2XUNPttWsZ7O7t47uzuI2hmt5kV45I2G1lZT95WHask+GRpX gkaD4fuDoP2fT/sOnXHl/aPse2Py4m2yf6zb8vysfm213YfAUsPLmhJmNXGVcRBQmzpOKdXFv420 /Rdd0TwvcXd3qOtXHkwtK0KhjugupFmlZVVPm+w3HEa/e2/KqsK7JWB75Neoee01uOoqCHzFU+aV Lbm6DHG75f0qekIKKKryTJC8ayOqtI22NSfvNt3f+ytQBHaeQy+db+Wyz7ZPMT/lp8ow2f4vlxVW xuzPPPayyWy30WXa3hufMdYWkdYpG+VWXcsbdsblZdzbd1XoVkWFRIytJt+ZgNoJ+lQJaqt9NcBp Q8kaRlWmcx4DP91fuq3zH5lwx+Xd91cUC2Cxs4LBWjt4Ut0aR5Ssa7VLO7MzdPvMzMx9zXB/Ha/t 9L8A295e3ENpaW+u6JNNc3EixxxRrqtqzMzNwFVe9dFqXihYdcOiWluZ9XbT5dQgjuJPIgk2sqBT Jhm+8y7jGknljbu2+ZGsmJ4u8Z+EdRu7nwvrM8VxBslfUI2mCxQRwpFMy3I3BlhaORdxYeUyt5cj fvFSTlqvmhJXM/bQhK8mdPZT38mtahFcwRx6fGsQtJEO5pDhjJuO75Wzt+XbjbtbcxZljzfAviq3 8ceH4tdsGvltb12Bs9RgEE1nJG3kyW7JtDArJHIG3bvmLYbbtx5H8C/Fesj4YL4O0/UdF1Lxr4YF rarZ3l/GYLqwVbdkuI5IAWMMlvIPLk8vK7lWRZGVmk6q60HTNP8AjlpviE+I9LfxhdaO2lLoE062 7zWZvPOE0ceWkZo41mzxtkZf+WI3VhGs6nJOK/r+r6BUc6c+Va/8HqvQu6npPiTWPDr6R4f1h9M1 Cx1dFnv7y9We5aNSsjySIsbLufcGW3Vo18to+Y1zDXnP7M+ntqN18U9dttXfStN8TeIZrTSzZSbo J7iKN/tGo2XnGSNvOk8yTbtkVfI2lpFXjP8Ajpr2qzw2ngqITaV428eXCaLcSQwx6o0Wl+ZJ5jMq tF5carPdGOTy2Zoo28zy33ND6l4X0fxNbeKtF0iHSo/DfgjwtHNaW8a3rXa6pF5McdoytuWRWjXz lkWZWXcV2+Y22WMqXqVouf2b/e9Onp6djrqctPb7du/TX9d/ky94k+C/hzV/DfjXS9P0uz0q88VI 7319HbK7G427Y7jb3aNv3ijj59zfeZmrqfCel3ui6DZ2Go3seoXdvH5T3EKSqHUfdP7yWWQtt25Z pGLMN3et5jkda8U+KmpT6f8AE3wzb3PjvXvDenX9u6Wllo2mwyIbxZo4PMmmeOT5X+2Rxqskfl7l Vtytt3dE3CH7xnFU5Yt1ZvU6LwRNJZ+JvivNDbSX0kfiCJhbwsgklP8AZGnnau5lXJ/2mUdK9IT7 teJ/CDVCvjzxCll4hXxtYapp+j6lcapYm2+zG6MV1Zz3C+Wf9XJ/ZsI8tWkKux+6u7b1WofE7RvA a6TpnibV9/ijUMzLo9gkmo3fzeZIVjighWWSGPy5FWRolysfzfN1zpVIxpc0tN/zOqScn935HbXt nDqCrHPFHOiyJIqyLuUMjqytyPvKyqw9xTLi0S4uLOSRpVaB/NTy5XVWbYy4dVbDLtZvlbcu7a33 lU15E37R1rqXiq+0XTNC8QwrYIv2q/u/DupMUaSLfC0dtHbs7DcMMsjQf7O+uisfilruo2dvdxfC 7xiIrhFkRZ5dLgkVWUNh43vVZW6fKw3A5pqvSk9BR5pL+l+ZX+D2r/ZU8SeHbm0urfULDxFq7Sbo d0LJNdfbI2Ei7lG6G+gwrFW/1ny/u2x6Fp99FqkTSwLMkSzSwkXEEkLbo5GRsK6qxXKna33WXayl lYE/N+i+Kp/h78TvE998XhZeFdF8Yaamp6fpsupJdWUMkcEFvfW8zbU864ZUtdq7ZF2iRY2+aTd6 NdftDfDvwxo9oJviBo2uCGPbc30eqWck22OFmaZ442XezMm3bBGzbpF2xhc7caFenGFm7eptKClL 3NX/AF/X+Z6xnb8vc9Ky9Q1L7PNDYwm1k1Gb94lrPceWzQLJGs0ijazNsWRTwu3cVVmXdurhbT4o eGtOawhsbPxVZabZ25t49Mt/BepJAqgptO37HuXYqlVVSq7Wb5W+Xb84ftBfH3UfDPxX0zUfD+pX 1pfNHHHp+n6tZ3FhH5bMu77Qt5DDGkMki7WbczfJu8yPavl9Ua1BRcqk0v62PNxFWeH5HGDleSX3 vc9K+Lnh+DwXocPhG68b6vGnjPU7XRNPhkea7untZrtlvIZJrgzR/LFeSeXJtjk2xwqzSeWtfSir t7V8HeIbPxDb6X8Kfit4g8RaTqngbRdRiv4tRt/DZivXa9uo5LuS8t03Qw7dsm2aPc3mbdu6SRZF +8Qe3UiuPD1fbTlPyX3fI9Krfvclooor0jmCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDi/gr/yR3wJ/2AbD /wBJ467SuL+Cv/JHfAn/AGAbD/0njrtKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKSigBrAN16VkeINBsPE2k3Gnajb R3dncIY5IpRncOP19+2K2OCtI3K1UZSjK8TOcI1Y8k9j4d+LnwI1b4ZtLfwv/aGgvKVjuEB3wKfu +cP947dy8dfu5Va47wN4L1Dx74ms9H0+KRpJXUyzKmfIh3fNI33fu/8Aj3yr/FX6DXVnBqFpJBdR RzW8qsjxyDcrq33gRUOl6NY6Lax2un2VvZ2sedkNvGI0XnPCr05r6qnxBXjQ9nKN5fzH5VX8P8LV x3t6c+Wl/L/kzP8ABnhOx8DeH7PRtNjZba3XC723MSW3MzH+8zFj+NdH3NHPrRkBa+VlKU5c0j9T pU4UKcaVNWjEdS0lLUmwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACUUtFACUUtFACUUtJQBw/x E+F+gfE/Tha6zZ5urf5rHVLb93e6dLuV1mt5vvRuGjjb5fvbF3bl+WuG0nWNb+LX7P0ttHq+paZ4 wt0/svW30l7X+0I723dY72GFt/kxSybJFjfcqr50cny17duHTHavm/wdqmjfAX4teN9J1aS+sx4v 1tdY0ya/kb7PdRyov2gwsG8lXimZtysscnk+WzNJtXbyTjy1YtddP8jppzsubqtUfQemx3a2zfap 4biUyyMGhiaNRHuby12szfMq7VZv4mVm2ru2rk+KPCFn4pk0v7XLdRf2fPJcx/Y52hdme3mt2+Zf mHy3DncjKysFbdVfxZ8SvDXgnwXceLtX1eGDw5a7fMv4Va4QbpFjXAjDM37xlX5fWuK8UftP+DNG vIrHQV1H4g6rLF9p+weC7b+05I7fO3zmZG8sLu2j72751+WuiclTTlLoYRu37puLe6t4dg1Xw9pe k3lvNFui0LU9Xu5tRt72VoGmzPJukmjjWQNG3mYH3FjYllUVvhz8GdA8E61ceLP7JtrXxxqtu39s XVjcXD28s8jLLceWsjNtVpF3DjdjFYi/He+8ubTdS8CeMPDGvJGkZuH0CbV7CGZ4XfeJLNm86NXC qwVlZvMX7v7wxzeF/i9a+JPGHhmbw5faj4q0PxhFdXUMkiQ21rpFvY4hnkRWjjnkaS4lhTa277zM NqrhojiKc/dizrUakk1DTTXztr+Wvy9EezUmRQfu1j2E2ptqV+lzZ2kOnxlPsU8Fy8ksy7cv5kZi VY9rfKu15Nw5+X7tbHGlc181irqUd6yPFafbEg8x454mjZVmVmjaNSWysn3l9PvLurQtrqG+t4ri B0mhkUOkkbblZW7q1UtN0WHSlbyYYYz5txIEtI/JjJmkMjsyBtrOzfMzdWZmb5dxo0iZyi2a/tWV rmv6f4Y0qbUtWv7bS9Ph2+Zd3sywxR7mCruZvlX5mVfxqZYJXt4RcMHnBQu8O6NWZcZO3dwuf4dz fjTktViknPz5dvMO52b+EL8v937v3V/rSe2g5X6HP33hfwvc+PtL126sbCTxXb2k0djcybftIt9y iXaOpVTIq7v4fOZePMbdkt4P8F6t4j0XxVd6bGddup0n0671PzIrsSfZXxCkc2Gj2xiSQ2+1QreZ IU37mro7izsY9YudUS3a91mCy8sRxzAyrCWZtsas22PzGXlvl3eWu5v3a7ef1jT28MQXvim71fT/ AO3Y7eGH7brB8qxt4Q0fmwxfN/o6zMuS7NI25o93nLDHGuKim9F1/H/My5I2fMl/XX8PyOF0jwvp nxG+L/xL1HTLrUtO0+3h0zQ72+0W+W3+3X1vI1xOrPC3mbo43t7dmO1grTRj7vy+m+G/Csml6lPe XwhvLm3T7Dp91vmeRLIbW2yebI/7wt9+Rf8AWeXGzfdVV8y/Z+8TRLN4ma00TXE8Pa9rNzr2k6rd aYsa3UNw0K7mMaiT5pmmaMyru+ziNmO0LXqV94te01zRtMbRNWY6k9yv2uK3V7a28nGGmcN8nmD/ AFfr321lh6HuqdtXd/mzeU41KjW7Wn3GT44+Gfhfx4ujReKoW1C7tNQa70y4a6a0uYpvmdVikhaN vlVfu/8ATJWbcy7qD8F/D/8A0EPFn/hYat/8lUvxW02yk8I6lqFzL5LQWF1AZhbRzZiljKNG5aCf bDu8t2/dsP3KsysqsrdTqFhLex232fULmw8mdZn+zrG3nqp/1T+YjfK3fbtbjhlrqdGEveaK9p9l S17djwL41fA3wtL8I/Fd94X8Jp4s8RTSq015JMNS1SZY5I4bqOG6uvOkWRYYpI1VcsrL8q7q7b4i eGI/i34As9e8HR6Pc61cPpmraTqWq27ojLDMs8W5lXzFXy5Jl28H99Ivy7mruP7OhuvtOnjTH021 t7uO5SbZB5dzJ5i3DOqjd/y03bmZVbduZezVwTfAfwPoGnSXV0b3RbHTLi51KI2Wv6hp9lYbjKzS Rxrc+XDtjkkVmXavzSfKqttrz6lCzk4R0fy/rdiqRjiKbpVP6uQ6bpl1H8VtKS4fUbGLXfCl5aXl nJOolRLO8hW1bzY5JJPN8vUJt0izfMdrYVvu8v8AHn4maN8JvjF4A1W0vruTxFq80Wk6jodo8kn2 zS2aQLM0KxyEyQzybo9oWSTdNGu75ttL9nv9nP4Z6h4NsPEk3hO01i5u/tMVtfazY/8AH1Yrcstp M1syrGsj28cLeYI1ZtzMeZG3e7+F/APhnwSbkeHfD2l6CbkL9o/syyjt/N27tu7Yq7sbm6/3jWcY 160Lq0b697G/uUnyvW2hyun/ABMlvbrUJtE+HHiy9tWnUyXrWVvp32mTy48v5d5PDM21dq7mj/5Z 7Vztrm9F+KUdx8RH0vxRazeEtX1C+26fZ+IVhgbyY441jWzuI0khumka4bcnneZH50iqvy7m9yrK 17w/pvinSbjTNX0+01TTptvm2l9As0Mm1ty7lb5W+ZVb8K6pUpyWruZ80Zbo8M/aK+M+tfD3xVoW haNZW11Lq9ldF7i+lurS3tI1Uhn81PlkbcY22p+9URsq/NcR7fLtB8ca38P/AAno9h41m1SHxKob xR4ggP2ttQutPVtgYtZw8bRb2/7y5mVl8swybYW210l9+zj8U9QbT9DudV8MXnhzRLWbSNAu7p5D dWlnI3l+dNCtusc80duse1d0a+ZFG27727334d/C7QPhjp5ttHtCLm4+a+1S6/eXuoy7mdprib70 kjNJI3zfd3ttCrxWKjOpJT/mTvF7Lp03fnfrociwbhX9u6z/AMK0XT/ItaL4ysda8K6Zq2nX0eo2 95Cvk3CTRzRySblj2tNb7o93mNtbZuXIbb92sTXPh/4Z12LxBe3/AIVso7q5sGspbrUord4mjW4m mHyt5iKPMP2hmaP5t0bMGZNq8P8AFPwTcfB/WL34p+B7JY1jE154s0WC5MKarCFVvtKht0azReWz HbGGkWWb5tzfN2vg/wAZeHvFXgEt5i+INF02Bory/wDtVvqKs9vHEx8zyZpmaRg27b8zfKd33l3T aUuelNdNP6bv+HzOqUNVJDLPwxb614Eu/BGtS2VhZXUV1pZs9MM1qzQM0ix+Ukjbol8uOTbGu6Pa nyN5a7aPgZ431Pxr4FjXXojD4p0WeTR9bTbj/TIcbpBhVXbIjRzfJ8o83b/DXmvxP8N/EL4dfEiP xz4QsoNX0t9Nms9Ss45RDDaQxsskc/lySfM21VjZY1+ZYWxsaRfL1PiDeeJPhvrGm/EfS7K48W2t rFJpniWztmeO6bTmb7TbzfZyyqs1t5kg27dzLO3+rVv3efPKnV53B9u99tvT079TlpYj2kpwnHl5 e+33/wBW6n0MKWsnw7rll4n0LTdX0+Y3OnX1vHdWs+xl8yORdyttb5hlSOtaua9f4jcWiiimAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQBxfwV/5I74E/7ANh/wCk8ddpXF/BX/kjvgT/ALANh/6Tx12lABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSUAM 3cY6Ghido55+lcx4z8d6X4C0ObVdWufKgjO1Y1GZJXP3Y0X+Jjz/AD6V8rePv2mvE/iS+uYtGnOh aV+8jRYlH2h42XG5pPmw38X7vbt3febbur08FluIx/8ACWnc+WzniPAZJG2IleX8q3Ps2SeONfmd V+tUtN1ix1QTfYr23uxBK0EvkyK2yRfvI23oy/3a/OfUNSu9WvJLq/u57y6faHuLiRpJD8u35map 9B8Ran4Z1Jb7Sr2ayul/5aRHbuXcrbW/vL8q/K3y19H/AKsz5f4nveh+f/8AESKftLfV/d9dfyP0 iyBjNHTtXjHwd+Ptl8SZP7L1KGLTtbRQyRrJuS4wvzMnvwx2Zb5f4mw232f8a+Qr0KmGqezqxsz9 YwGYYbMqEcRhZc0WO7UUtFYHohSUtFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFADe1cd8R/htonxW8M3Oh67C8tozLLDcQOY7i0nX7s0Mn8Mi5PPuytuVmWuzpvFRKKk rMadj4F/aa+BXxp8P+HbGw8K6zqXi3wnZpfRbdLj+zaglvdSRbrWeOFl+1R/KrblX+Kbcqqq17N+ xT8D9a+E3w7lufEl1I+p6xDbmLTy77bC1HmTJE0bxq0cvnXVwZF+Ybj/AL1fSYUdaK444blq+1cm yubsLgYrwL4heA/+FO+Mrv4veGLM3NlDb3L+JPD6S+WskEnlyXN9aKzLGl1/o8TSK3yzLF/DJ8ze +9qoalp1tq1hcWd7bw3dpcxtDNbzIskcsbKVZGU8FWXqtb1aXOtNwjKzIrjS91i9taTtp29mk861 RN4Zm3MfmVl+Zi2fl/i/Gqq+Ho2bSmlmuLiTT7hrmOWdtzvI0csbM3HTEzfKu0L/AA4X5a4X4C31 3pug6j4D1O40+fV/BEkOjGSxdh9otfs8b2tw8bfNGzRttYbmXzIpNrHt6v2qadqv7x7/ANfqJqzs ZUukWTtavJCLma1nkureS5/fNFKwkVmjZtzL8ski/L91XKj5flq1p9lDYRskESQI0jyFY1CqWd2Z m6feZmZj7mrooNdQru1hN1ZmpagbGFXW2uLuQ7hHb2ybnkZUZtu5sKu4Ljc7Ku7aN3zVHrXiHTfD rWYv7qOzF1I8UbSnC7lhkmYs3RQI4ZG3Nx8tcc3xMn1fXtX0zwbZR+K59OmWyu5PMNrZWN2vzSRz XR3eY21lXy4IZGjZcSbdy7ZlKMV7wJO5seOPiJo/w/09LjVL6CKaS5t7aO1xJJPM00hVFjijV5JH bbJtVV+by2+6qsy8RZ+C774zpYeJfFSat4f05j5lj4Zjle0uIVWRjFNcyLIzJP8AKki+R5MkfmND I0g3VteD/g7baH4itPEmtXX9t69ZwSW9jsh8mysPMdmmkt4WeRlmm3fvZpJJJJG3fMqttr0vvjpW CjOU+Zu0e3+f+QSUHHlav/X9anmGh/s1/C3QNLhsbTwFoM0MIYI19YR3U3zNu+aSYNI3LfxNWTp/ 7LvhHwr4lk8QeDbnUvAuq3CTQ3D6K0LxyxySJIU8m4jmjjVWjXb5artyRXs/NLTVCnHZF+0n3Pny z/Zr8QeGdS8UX3hv4jX9vL4htJLe5m1i2kuprZ5JJpJJbb7PNbRxtvmZl/dttbcVx5jCtPwh8Vk+ HNrpXhb4kNdeHLxYLo2+u+JNZsZre/S3mRPluFaNmbZNE37yGNyu7duZWY+3Bveg0vZ8vwS/UqVV zVp6njml/FS1lmttA8F6breuXsM8lpLDr1hqttHbS+X5+65vriFmjXy/uqyyMzTQbdq/NXkX7T3x S1660qT4e6roaafq9xNpeomPQo218T2bXUifNFNbRqzLcQ2y+W3yyNOi7vmavr/aMUBfpUuFV35p Xv5afnf8Rc0exi+ELjWbrwro9zr9vDZa7JZQyX9tDzHDcGNfNRfmb5Vbd/E3+8a3aKWuiK5YmQUU UVYBSUtJQA3qK8K8QaXBY/tGQfZrm0MGqaLDq+taVaWqteytp9zstJmZZA21nu933WZvsCou75lr 3X0rxTxY2r+Ff2iNH1uUb/CuuaP/AGSfJgWeWLULVri6Rin+sw1q14q+Xu+Y/Mv3a4cVKUIxce5r C7ukdLod/qfh3xVeabNplxc6HAlvbQanDZbpCzDH7xlb5l8xm4ij2x7ju8tVrWutW8O3Nxb607Jr kmn3M9pDLY2ZvpLGdRIs6qYUZo2wrRt/uhfvN82jpbamuva3FdsGsd8MliViOFRo9rKzfxN5is23 +FWX5vm2rzll8PbSfXbPUZbSK+azub5Fm1AbmaO4Ueeyx+WqRu0kYX5V2tG0jbmaZqWHhKnT5V5/ rZaW0X4rzJVrFD4X+RofjL4leH4ReRG31lNVgt7nzmjEN5bRSNJC0ny+W90t78qNtVhJ92vUfavC fiZ4VuPALeCtb8I2t1qOreGZJTNpVmI/tF/pDJHHdosKvH5zRrHaeXjd80cK7WzXsPh3XLPxRoWn avp8xudPvreO6tZ9jL5kci7lba3zDKkdaeHqbwlG39fpsVO7SkbFFFFdxkFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAcX8Ff+SO+BP+wDYf8ApPHXaVxfwV/5I74E/wCwDYf+k8ddpQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAZHiLQ7PxRoWo6RqERudOvreS0uoN7L5kci7WXcvzDKk9KPDuh2fhfQtO0jT4jbad Y28dpawb2by4412qu5vmOFA61q9KPvVPKr8wX6DqKKKoAooooAKKKKACiiigAooooAKKKKACiiko AZ93NNOFXdngDNG7dj3riPjB4gPhf4Y+Ib9JJoZRatDHLbnEiSSfu0ZT2wzA1pTg6k4wj1OXE144 ejOtLaKb+4+RfjN8SX+JHjCW5iJTSrPdb2ahm2mPd/rtrfdZuf4V+VVX+GuAoor9mw9CNClGnT+G J/HWOxlXMMTPE1pe9IKKKK6ThLWm6hPpGpWl/aSeTd2kyzRSbVbbIrblb5q+1/gz8ZbP4naZ5cpj tdetl/0uzVuG7eZH/s/+gng9ifh6r2i61f8Ah3VrfUtNuXtby2fzIpoxkD/P93+KvFzPLYY+ndfF 9k+w4b4irZDXv8VOXxR/VeZ+ktH4cV5Z8GvjLZ/FDTfKl2Wmu2y/6VZq3DdvMT/Z/Vc4PYn1LnPt X5XWozw8/Z1F7yP6fweMoY+hHEYeXNGRJRRRWR3BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUlLRQAUUUUAeSfGXwi1lJp/j/QdP1CfxV4fkh3rowU3Ooab56td WbRt8s6+X5kkcf3lkVfL2s3zdp4P8c6N4/0eTUdDvDeW8NxJZzo8UkE0E0bbZIpY3VZI3X+6yq3T 1rpOMV4/8KzaeE/ij8TPCb/uL++1GPxTaqwt4/tNrcQxQuyrGFJ23FvMrMy7vmjZmZpNzcbvTq8y 2Zp8UfQ9i9KTrmlryb9p7xXdeC/gX4p1i0l0+OW2jh3Ratatc2t1G08ayW0kYVtyzKzQ9P8Alp95 fvLtUmqcXJmZmy+EoPjB4w16ZppLTwIbdtK1C1tUe1n125IUy+dMrBvJgXbDtXbJ5i3UcnyqySeq aD4f03wrpMGmaPp1rpenQ7jFaWMCwwx7m3NtVflX5mZvxqh4D8Fab8O/CGk+GtGj8nTdNt1t4t6K rybfvSPtVVLs252bHzMxaukxUwj9uW/9aFaR0jsOopaK6CQooooASloooAKKKKACiiigAooooAKK KKAG/wANcj8RPh7pfxJ8PvpWriZAsi3FreWcnlXVlcpkx3EEn/LORT91vwO5WZT19HFRKKlHlkVG Ti7o+evhr4m1/RV1TRLjQ7x/H1jJbQX+mzXkNrpN/JM0jNqVvIsf3ZPJuZpFjj3eYZFaNmXcNr4f 6dD4X8Qm6ltl8OT6nLdDUVn277qaO8aOAGaTy2ljb7WvlskO5sRq833Vk2/ir8L9a8Y3EGp+E/GN x4D11YGsbm9t7GO6W7tm+ZY5EYr80bbmjk3Zj82bb/rGr5x+IX7Xmp+HvBt/LHYaHD4qs5I31XSd ame7VtShmht5obeH7QvkrHJH5y+S03y7ZNoZpGXjpxjB67pPV66ad36aaG8qqhBy6Pf+vx67H054 08Pt4j1C3l0fV5tM1izkjF9LpaWzXMkKq0sMMjSfMsbSKrbd3zBmVvlZmXP+CuixeFZfHXhuybZo 2j+IpE061WJI1tYri1trxoVCKo8tZrubb/dXauflr4V8H/FSy+OHxk8H6pqH2fRNZ1TULf8AtS0t riSztprxZmWFoWjaSRd0fkru+95jSfMq/NX3j4B1K1g+KHxO0hLiGe8+3WOqyLBID9njmsYrdI5P 7sn+gySbf+ecsLfxcb1KEaPsqsZc3tNfTfT101+T02XPCpzJ2PS6WiiugQUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QBxfwV/5I74E/wCwDYf+k8ddpXF/BX/kjvgT/sA2H/pPHXaUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAYPijxNp3gzRZNV1WWVLNJYYSYbeS4laSSRYo1WONWZmaR1XCr/FU2g6/pvirSYNT0fUbXU9 Nm3eVd2UyzQybW2ttZflb5lZePSjXtA03xVpM+mavp1rqmmzbfNtL2BZoZNrbl3K3yt8yq34Cua+ Fvwg8PfBvS9Q0rwvbzWOlXt79v8AsckzTLDIYYo2VGb5tp8kN8zN8zt0Xaow/e+0/ul6W8zvaKTN LW5AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUlADW6V5r+0Fps+pfB3xFDbqJHWNJmUsF+RJVd zn/dVjXo5PzD8azPEWjw+IPD+o6ZcFlgvLeS3kMZwwV1KnB9ea1oVPZVYVOzODMKH1rCVqH80Wvv R+cVFXda0mXRNYvtOmaN57OeSB2jOFZo22tt/wC+apV+2Qmpw54n8aVISo1HCfQKKKKogKKKKAPV v2ZNXfTfi7p8KKjJfQzW8u4Y2qI/N+X/AGtyL/49X24rZ6V+b3h3WP8AhH/EGl6n5Xn/AGG6hufK 3bd/lsrbd38P3a/R+L7iY7ivzniSjyYiNT+ZH9B+HeK9pgauH/llf7/+GZNRRRXyR+tBRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACCvPPiN4U1nUN f8I+JPDcNnPrWi3FwssF9qU9nDcWc1uyyQs0ccmf3y2knzRt/qe1ehdBR1Wsp01UjYadmeW+Cvi5 b+JYtN8Pa/HqHg3x3fW0yvp17YvAGmh+WZrSaRWhuFU/vF2tJ+7Ksy/ere+IGh2vxA8Da1oostM1 +3vka1a11CRvsxkV9rbmj3MrRsuflwysnDK3zLreJfB2ieMbKOy17RbDW7KOTzkttRtUuIxJtZd2 1gw3bWYZ/wBo+tcB4k/Zr8G6pGH0S0bwJeeRcWc114TihspLm3mj8uSGVfLZZF+6y7l3KyKysvfL 96viin/X9dhSipLR2/r+u50HwZ1C8vPhvocN/NNeanp8b6Rf3c0jSNcXdnI1rcS7mJZlaSGRlZvm ZSpYKxxXe+tc/wCDPBukfD3w5a6FoNq1pplsZWiheeSZgzyNIx3yMzNuZmb5m710Na01KMEpDbu7 hS0UVqIKKKKACiiigAooooAKKKKACiiigAooooAKKK5/xt4qt/BPhXVNfvIJriz0y3ku7hbcKZPK Rdzsu5lB2qGP/AaANz+LpXkHxq/Zq8F/GzQdRgv9MtdN8QXW2WPxDa2qfa0lVdqMzfekXb8vls33 fulWVWW98Ofj1oPxF+Gup+PYLPVtE8OaebgyzatbbWeOFd0s0YjaTfGvzL8ufmjZf4a8Rm/bI/4W zq1tongSa/8AD8l5Gq26rpkN/rF7cecuVhh837PDHHGrtJJcSD5W+Vfl3V51eth5wUZ6qRryVFt/ XkfEfwW1jxP8L/jVpt3o/hzUNX8Taa11BHokVvJ9p8zyZI5P3e1m3RqzNt2/w/NX6Bfsb/Dez8N3 PxJ8VaTLP/YHiPXJI9LjnuI7p2treWZfN89ZJPMVpJJVXd822PdubdurmvBH7G+o+JPFQ8RePdTv 7aObS47V7I6tHqGpT7pC0sdzfLawsF8v/R28tmkaNmTzvL+VvrDTtOttKsbeysoIrW0to1hht4UE ccUagKqKo4VVXotefgcPUUueov6/Tz76diXShRb5JXNKiiivoSAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDi/gr/ AMkd8Cf9gGw/9J467SuL+Cv/ACR3wJ/2AbD/ANJ467SgAooooAKKKKACiiigAooooAKKKKACiiig ApKWigDw/Vvhf4n8M/tEWfjrwkbS40fxDbrp/iiwu7qWLb5a/ur2P7ytIqqsYTb/AOjZJI/bQOtL xmlrCFKNPma6luTluLRRRW5AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAlFLSUAfKH7Vnw5lstai8 XWFvJJa3AWHUGVdwikXasch+b+Jdq/d2/Kv8TCvnmv0d17Q7DxLo9zpupW0d3Z3KGOWKQZ3f/X9+ 2K+Fviz8NLn4Y+KptPYTS6dKPMs7uVVAmj/iA2913bW+7/e2ruWv0PIcxjVh9UqfEvyP58444flh a8syoR/dz+Lyf/B/P5HF0UUV9gfkoUUUUAFff/wl1weIPhv4cv8A7U9472aRSzSFtzSooSTdnq29 W5r4Ar7M/ZT1j+0vhbHa+UY/7OvJrfcW3b9x83d7f63b/wABr5DiSnz4aFT+Vn6v4d4j2eYVKP8A NH8n/wAOe00tFFfnZ/QwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZ+p6Xb 6xAsN1F50azRzKmSP3kciyRt/wABZVP4VoUUAJj2rJstA0zT9T1DUrXTrW31LUPKN5dw26pNdeWu 2PzG+821flXd92teilZAFJS0UwCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAopKWgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOL+Cv/JHfAn/AGAbD/0njrtK 4v4K/wDJHfAn/YBsP/SeOu0oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAErK8QaT/bmh6lp gu7uwN5bSW4u7GXyriDepXzI3x8si53K3Y1rUlLcDkfhp4d1rwl4J0rRde8QzeKdUskaKTWJoPJk uF3Ns3Lub5lTapZmZm27mO5jXXY4pOKdUxiorlQ27u4lLRRViCiiigAooooAKKKKACiiigAooooA KSlpKAG+npXN+LvB2leONJk03WbJL22Zg+1iwKsvRlZeVb/dx3rpOCtH41UZShLmiY1KdOvB06ke aLPhn4s/AzWPhvdXF3FG1/4e8weXfjbuiyflWRf738O77rbl+6zba8yr9LmhEkW123DPPFeD/EP9 ljRNcM134cl/sO9YM32fO62dvmYfL96P5mX7vygL92vucv4gSj7LFf8AgR+JZ9wHNSlXyvVfy/5f 8E+SaK9F8UfAHxv4XkcPosuo2+9UFxpg8/d8u77q/vP9ncyr/wCg10/w+/Zc17xJ5N3r7f2BpzbW 8k/NdOPlYfL92Phm+98ysv3a+lqZpg6dP2jqKx+dUeHM1rV/q6w8r+lvxPGbW1mvrmOC3hknuZpF jjjjXc0jN91VX+9X2P8As4+AvEfgLw1fWuuxW9tHcTieGBJN8yNja3mEfKRtSNht/vNmuw8C/Cvw 98O7fGkWCLdFdsl7MN9xL93cWftu2htq4XPau0OVXrXwmaZy8cvZQjaJ+1cMcH/2NV+uYipzVOy+ H/gklLSUtfNH6cFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAB RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFF FFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAJSbqXoK8W+MHirxl4r8AGP4S2dh4kX W7K9i/4SO21mOOOxZF2r5RVv3kjsJI1ZWVY5FVm+XdWFSoqceYuMeZ2PUtc8Qab4X0mfU9X1G10z TYdvm3d7MsMMe5tq7mb5V+ZlXn1rz6+/aP8ABdro9xqdj/bmu2ccrQQz6ToF/cwXkyyeSscFwsPk yM0g8tW8zazEfNXEs3wp8P6bofju+tfJutQ06x1bTtT8WSTa1ei2jb7Q628Uk8txG8MbtJI8fyx+ Ysjb1jYLq6t408U/Gexls/h3a6jpGgXtlJbt4u1hGsrcrMtuy3FnDtW6mkWOSTy3zDDu3fNJ8u3i qVqlvdkubtv/AJGvs0cb4q/bKe88eWXgPwl4b1VPFv8AaP2S8hurK11FY18uQsq+RfKvmK4j3bpF WNRJu+Zdte2/Ca/8b6j4furnx5Y6fp+qNfSra29guxharhY2mUTTKsjYZtqSMqqy/Nu3VR8Dfs/+ AfhzfQXugeHYre7tY/Jtbq6uJrqS0j3SMywNMzeSrNNJuWPbu3ndmuS8Ifsv6f4e+Nuq/E3VNZm1 zWppLh7Jdkkf2fzGk5dmlfzGWGRbddvlxqkY/d7vmXGnTxUJxlP3vnol+rKm6UlaOh71RSUte0co UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcX8Ff8AkjvgT/sA2H/pPHXaVxfwV/5I74E/7ANh /wCk8ddpQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAJS0UUAJRS0UAJj2opaKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAEpOtDVkah4h03Sbu0t73U rW0uLqTy7eK4mWNp24+VAT8x5H3fWmouWxEpRguaRs0UlLSLCiiigAooooAKKKKAGMu6uP8Aht8K /DHwg0OfSPCmmf2Vps1w128P2iSbdKyqrNukZm+6i967IUGo5VJ3Hd2scj4b+GPhDwbfPfaB4V0X RLto/INxpunQ28jR7lbYWRV+XKr8v+yK63tSjFLRGMYq0RuTe4UUUVZIUUUUAFFFFABRRRQAUUUU AFFFFABRRRQAUUUUAFFFFAHF/BX/AJI74E/7ANh/6Tx12lcX8Ff+SO+BP+wDYf8ApPHXaUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUU UUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRR QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFA BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAF FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFADee9ea/ET4I+H/iNrlnqmpm6FxaIIytvNtWaPcW8 th+Lcrtb5uvAx6V/DSbc1pTqzpS5qbszlxGFo4qHs60boco2qB6CloorM6gooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOL+Cv8AyR3wJ/2AbD/0 njrtK4v4K/8AJHfAn/YBsP8A0njrtKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooA4v4K/8kd8Cf9gGw/8ASeOu0ri/gr/yR3wJ/wBgGw/9J467SgAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKAOL+Cv8AyR3wJ/2AbD/0njrtK4v4K/8AJHfAn/YBsP8A0njr tKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4v4K/8kd8Cf9gGw/8ASeOu 0ri/gr/yR3wJ/wBgGw/9J467SgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAo oooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK AOL+Cv8AyR3wJ/2AbD/0njrtK4v4K/8AJHfAn/YBsP8A0njrtKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKK KKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooo oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiig AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAC iiigAooooAKKKKACiiigAooooA4v4K/8kd8Cf9gGw/8ASeOu0oooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigA ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACi iigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK KACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACiiigD/9k= Mason Croft USGS Gap Analysis Project - Boise State University Cooperator PAD-US Technical Specialist 208-301-8288 Boise State University 1910 University Drive - MS 1935 Boise Idaho 83725 US masoncroft@boisestate.edu 9:00 am - 5:00 pm MST, M-F 20230724 ArcGIS Metadata 1.0 U.S. Geological Survey - ScienceBase 1-888- 275-8747 Denver Federal Center, Building 810, Mail Stop 302 Denver Colorado 80225 US sciencebase@usgs.gov None. No fees are applicable for obtaining the data set. The File downloads in a .zip format https://doi.org/10.5066/P955KPLE https://gapanalysis.usgs.gov/PADUS withheld Download or view data from https://gapanalysis.usgs.gov/padus/. The PAD-US version 2.0 data was developed using an Esri ArcGIS version 10 File Geodatabase format to utilize the expanded capabilities of the File Geodatabase for overall size, attribute name length, attribute aliases, coded domain values and topology analysis tools (See PAD-US 2_0_GDB.zip at https://www.sciencebase.gov/catalog/file/get/5b030c7ae4b0da30c1c1d6de). Users may download a shapefile (.shp), Keyhole Markup Language (.kmz), and ArcGIS version 10.x and 9.x file geodatabase (.gdb) formats at https://gapanalysis.usgs.gov/padus/data/download/. Various Web Mapping Services (https://gapanalysis.usgs.gov/data/web-services/) and maps (https://gapanalysis.usgs.gov/padus/resources/#maps) are also available. Data scales not immediately available for download may be obtained by contacting the PAD-US development team at https://gapanalysis.usgs.gov/padus/contacts/. https://gapanalysis.usgs.gov/PADUS https://gapanalysis.usgs.gov/padus/data/ https://doi.org/10.5066/P955KPLE File Geodatabase Feature Class CBW_desigAndProc2018 2018-09-30 2.0 U.S. Geological Survey (USGS) https://gapanalysis.usgs.gov/padus/ U.S. Geological Survey (USGS) Gap Analysis Project (GAP) vector and tabular digital data PAD-US Version 2.0 Protected Areas Database of the United States (PAD-US) The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee; however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas with over twenty-five attributes in nine feature classes to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.0 dataset includes a variety of updates and changes from the previous Version 1.4 dataset. The following list summarizes major updates and changes: 1) Expanded database structure with new layers: the geodatabase feature class structure now includes nine feature classes separating fee owned lands, conservation (and other) easements, management designations overlapping fee lands, marine areas, proclamation boundaries and various 'Combined' feature classes (e.g. 'Fee' + 'Easement' + 'Designation' feature classes); 2) Major update of the Federal estate including data from 8 agencies, developed in collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 3) Major updates to 30 States and limited additions to 16 other States; 4) Integration of The Nature Conservancy's (TNC) Secured Lands geodatabase; 5) Integration of Ducks Unlimited's (DU) Conservation and Recreation Lands (CARL) database; 6) Integration of The Trust for Public Land's (TPL) Conservation Almanac database; 7) The Nature Conservancy (TNC) Lands database update: the national source of lands owned in fee or managed by TNC; 8) National Conservation Easement Database (NCED) update: complete update of non-sensitive (suitable for publication in the public domain) easements; 9) Complete National Marine Protected Areas (MPA) update: from the NOAA MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; 10) First integration of Bureau of Energy Ocean Management (BOEM) managed marine lands: BOEM submitted Outer Continental Shelf Area lands managed for natural resources (minerals, oil and gas), a significant and new addition to PAD-US; 11) Fee boundary overlap assessment: topology overlaps in the PAD-US 2.0 'Fee' feature class have been identified and are available for user and data-steward reference (See Logical_Consistency_Report Section). For more information regarding the PAD-US dataset please visit, https://gapanalysis.usgs.gov/padus/. For more information about data aggregation please review the “Data Manual for PAD-US” available at https://gapanalysis.usgs.gov/padus/data/manual/ . The PAD-US geodatabase was originally developed to organize and assess the management status (i.e. apply 'GAP Status Code') of elements of biodiversity protection by identifying species and plant communities not adequately represented in existing conservation lands. In cooperation with the United Nations Environment World Conservation Monitoring Centre (WCMC), PAD-US also supports global conservation analyses to inform policy decisions. The dataset is robust and has been expanded in recent years to support the recreation, natural resource management, wildfire, emergency management, transportation, research, and public health communities. New applications are frequently discovered. Multiple attributes and a flexible database structure provide context for data to be used at local (depending on inventory completeness in the local area), regional, State, national, and international scales. See https://gapanalysis.usgs.gov/padus/resources/ for more information. U.S. Geological Survey, Gap Analysis Project (GAP), May 2018, Protected Areas Database of the United States (PADUS), Version 2.0 Combined Feature Class Lisa Johnson USGS Gap Analysis Project - Boise State University PAD-US Coordinator / FGDC Federal Lands Working Group co-chair 208-874-3102 Boise State University 1910 University Drive - MS 1935 Boise Idaho 83725 US lisajjohnson@boisestate.edu 9:00 am - 5:00 pm MST, M-F Julie Prior-Magee USGS Core Science Analytics, Synthesis, and Library PAD-US Dataset Manager 575-557-1566 PO Box 25046, MS 302 Denver Colorado 80225 US jpmagee@usgs.gov 9:00 am - 5:00 pm MST, M-F United States Common geographic areas New Jersey (NJ) South Carolina (SC) Kentucky (KY) Iowa (IA) Minnesota (MN) Tennessee (TN) Alaska (AK) West Virginia (WV) Mariana Islands (MP) Puerto Rico (PR) New Mexico (NM) Indiana (IN) Delaware (DE) Oregon (OR) U.S. Minor Outlying Islands (UM) Hawaii (HI) Utah (UT) North Dakota (ND) Arizona (AZ) Colorado (CO) Kansas (KS) Oklahoma (OK) Georgia (GA) Idaho (ID) Washington (WA) Connecticut (CT) Arkansas (AR) Texas (TX) Louisiana (LA) Wyoming (WY) Maryland (MD) Florida (FL) Nevada (NV) Missouri (MO) Alabama (AL) South Dakota (SD) United States Ohio (OH) Montana (MT) Wisconsin (WI) New Hampshire (NH) Virginia (VA) American Samoa (AS) Illinois (IL) Guam (GU) Maine (ME) Nebraska (NE) Marshall Islands (MH) Federated States of Micronesia (FM) Massachusetts (MA) California (CA) New York (NY) Mississippi (MS) North Carolina (NC) Michigan (MI) Palau (PW) Vermont (VT) Rhode Island (RI) Pennsylvania (PA) United States Virgin Islands (VI) 2015 2005 2010 2007 2017 2006 2012 2011 2009 2014 2008 2018 2013 2016 Land-Use Change Geography USGS Thesaurus Cadastre Theme NGDA National Geospatial Data Asset NGDA Portfolio Themes Land Stewardship Gap Analysis Land Ownership Protected Area State Lands Public Open Space Protection Status Local Government Lands NGDA Portfolio Themes Land Manager Private Lands Public Health Parks Governmental Units Conservation Outdoor Recreation Federal Lands Biodiversity IUCN Category Public Lands GAP Status Code Forest Service Bureau of Reclamation Department of Energy Tennessee Valley Authority Bureau of Land Management Army Corps of Engineers National Park Service Department of Defense Agricultural Research Service National Oceanic and Atmospheric Administration U.S. Fish and Wildlife Service U.S. Fish & Wildlife Service Natural Resources Conservation Service Federal Agencies and Programs Land Stewardship New Jersey (NJ) 2015 South Carolina (SC) Forest Service Kentucky (KY) 2005 Cadastre Theme Iowa (IA) Minnesota (MN) Gap Analysis 2010 Bureau of Reclamation Department of Energy 2007 Tennessee (TN) Alaska (AK) Land Ownership Protected Area West Virginia (WV) Mariana Islands (MP) Puerto Rico (PR) New Mexico (NM) Tennessee Valley Authority Indiana (IN) 2017 State Lands Public Open Space Delaware (DE) Oregon (OR) U.S. Minor Outlying Islands (UM) Protection Status Local Government Lands NGDA NGDA Portfolio Themes Hawaii (HI) Land Manager 2006 Bureau of Land Management Army Corps of Engineers Utah (UT) National Park Service North Dakota (ND) Arizona (AZ) Colorado (CO) 2012 Private Lands Kansas (KS) Land-Use Change Oklahoma (OK) 2011 Georgia (GA) Idaho (ID) Washington (WA) 2009 United States Connecticut (CT) Arkansas (AR) Texas (TX) Louisiana (LA) Wyoming (WY) Maryland (MD) Florida (FL) Public Health Nevada (NV) 2014 Parks Governmental Units Missouri (MO) Alabama (AL) Conservation South Dakota (SD) Outdoor Recreation United States Federal Lands Ohio (OH) Montana (MT) Wisconsin (WI) Biodiversity New Hampshire (NH) Virginia (VA) 2008 Department of Defense Agricultural Research Service American Samoa (AS) Illinois (IL) Guam (GU) Maine (ME) Nebraska (NE) Marshall Islands (MH) Federated States of Micronesia (FM) IUCN Category Massachusetts (MA) California (CA) Public Lands New York (NY) National Oceanic and Atmospheric Administration Mississippi (MS) 2018 North Carolina (NC) U.S. Fish and Wildlife Service Michigan (MI) Palau (PW) U.S. Fish & Wildlife Service Vermont (VT) Rhode Island (RI) Natural Resources Conservation Service 2013 GAP Status Code National Geospatial Data Asset Pennsylvania (PA) 2016 United States Virgin Islands (VI) Geography Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This database, identified as PAD-US version 2.0, has been approved for release by the U.S. Geological Survey (USGS). Although this database has been subjected to rigorous review and is substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. These data are in the public domain. The Digital Object Identifier https://doi.org/10.5066/P955KPLE for PAD-US 2.0 provides the persistent reference that should be used to obtain the data for use. The U.S. Geological Survey and all contributing data partners shall not be held liable for improper or incorrect use of the data described and (or) contained herein. All information is created with a specific end use or uses in mind. This is especially true for GIS data, which is expensive to produce and must be directed to meet the immediate program needs. These data were created with the expectation that they would be used for other applications; however, inappropriate uses are listed below. This list is in no way exhaustive but should serve as a guide to assess whether a proposed use can or cannot be supported by these data. For many uses, it is unlikely that PAD-US will provide the only data needed, and for uses with a regulatory outcome, authoritative agency data and field surveys should verify the result. PAD-US is recommended for users seeking basic information about more than one agency or organizations lands. Users should seek authoritative source data directly to answer questions regarding one agency or those requiring more frequent updates. Ultimately, it will be the responsibility of each data user to determine if these data can answer the question being asked. Inappropriate uses include: Using PAD-US for applications or analyses associated with one agency or a particular unit (agencies are always the best and authoritative source of their lands data and many publish updates more frequently than PAD-US). Using some data to map small areas (less than thousands of hectares), typically requiring mapping resolution at 1:24,000 scale (as boundary quality varies by data source) and using aerial photographs or ground surveys in areas where data are incomplete. Combining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queries. Generating specific areal measurements from the data finer than the nearest thousand hectares. Representing boundaries as a legal representation for regulation or acquisition. Establishing definite occurrence or non-occurrence of any feature for an exact geographic area. Determining abundance, health, or condition of any feature. Using the data without acquiring and reviewing the metadata. Protected Areas Database of the United States (PAD-US) 2018-09-30 USGS Gap Analysis Project https://gapanalysis.usgs.gov/PADUS Protected Areas Database of the United States (PAD-US) 2018-09-30 2.0 U.S. Geological Survey (USGS) https://gapanalysis.usgs.gov/padus/ USGS Gap Analysis Project geodatabase https://gapanalysis.usgs.gov/PADUS/ Esri ArcGIS 13.1.2.41833 publication date 2005-01-01 2018-01-01 -180 180 -15.386142 71.311956 The PAD-US database strives to be a complete inventory of areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational or cultural uses, managed for these purposes through legal or other effective means. PAD-US is an aggregation of “best available” spatial data provided by agencies and organizations at a point in time. This includes both fee ownership of lands as well as management through leases, easements, or other binding agreements. The data also tracks Congressional designations, Executive designations, and administrative designations identified in management plans (e.g. Bureau of Land Management 'Area of Environmental Concern'). These factors provide for a robust dataset offering a spatial representation of the complex U.S. protected areas network. It is important to have in mind a specific analysis question when approaching how to work with the data. As a full inventory of areas aggregated from authoritative source data, PAD-US includes overlapping designation types and small boundary discrepancies between agency datasets. Overlapping designations largely occur in the Federal estate of the 'Designation' or 'Combined' feature classes (e.g. 'Wilderness Area' over a 'Wild and Scenic River' and 'National Forest'). See the Entity_and_Attribute_Information Section for more information about PAD-US feature classes. It is important to note the presence of overlaps especially when trying to calculate area statistics; overlapping boundaries count the same area of ground multiple times. While minor boundary discrepancies remain, most major overlaps have been removed from the 'Fee' feature class and this is the best source for overall land area calculations by land manager ('Manager Name') within the PAD-US database (data gaps limit calculations by fee ownership or 'Owner Name'). Statistics summarizing 'Public Access' or Protection Status ('GAP Status Code') by managing agency or organization from an analysis of the PAD-US 1.4 'Combined' feature class are available at https://gapanalysis.usgs.gov/padus/data/statistics/ and will be updated with PAD-US 2.0 in 2018. As the PAD-US database is a direct aggregation of source data, the PAD-US development team does not alter spatial linework. The exception is to “clip” lands data along State boundary lines (using the authoritative State boundary file provided by the U.S. Census Bureau) and remove the small segments of boundaries created by this process associated with State or local lands (not Federal or nonprofit lands). Some boundary discrepancies (or silvers) remain in the dataset. Data overlaps have been identified and are shared, along with the U.S. Census Bureau State jurisdictional boundary file, with agency data-stewards to facilitate edits in source files that will then be incorporated in subsequent PAD-US versions over time. This PAD-US Version 2.0 dataset includes a variety of updates and changes from the previous version 1.4 dataset (see “Supplemental_Info_Readme.txt” for more information, including story maps and other information that describe PAD-US and various applications). The PAD-US database is built in collaboration with many partners and data-stewards. Information regarding data-stewards is available at http://gapanalysis.usgs.gov/padus/stewards/. 1 -180.000000 180.000000 90.000000 -43.515819 Boundary discrepancies (mostly small slivers) between agency datasets and major overlaps associated with fee ownership (mostly between Federal and State lands) have been identified and are shared with agency data-stewards, who will edit source files as resources allow. Users are encouraged to review topology overlap errors associated with the PAD-US Topology Overlap Assessment (https://www.sciencebase.gov/catalog/item/5b043619e4b0da30c1c367e3). The assessment identifies all overlaps (minimum distance between feature coordinates to evaluate overlap relationship = 0.05 meter), large (greater than 5 acres) and small (less than 5 acres) overlaps, between Federal lands and between Federal and State agency lands in the Fee feature class. Boundary discrepancies (mostly small slivers) between agency datasets and major overlaps associated with fee ownership (mostly between Federal and State lands) have been identified and are shared with agency data-stewards, who will edit source files as resources allow. Users are encouraged to review topology overlap errors associated with the PAD-US Topology Overlap Assessment (https://www.sciencebase.gov/catalog/item/5b043619e4b0da30c1c367e3). The assessment identifies all overlaps (minimum distance between feature coordinates to evaluate overlap relationship = 0.05 meter), large (greater than 5 acres) and small (less than 5 acres) overlaps, between Federal lands and between Federal and State agency lands in the Fee feature class. Data set is considered complete for the information presented, as described in the abstract and supplemental information section. Estimated completeness of the PAD-US 2.0 inventory, by Federal agency or State, is available at http://www.protectedlands.net/data-stewards/. Qualitative completeness estimates balance acres inventoried and the quality of data/attributes in each inventory, including fee owned parcels, easement interests and major designations. For example, Federal agencies estimate the percent completeness of lands data submitted for PAD-US 2.0 as: NPS (95%), USFS (99%), FWS (89%), BLM (85%), USACE (80%), DOD (79%), USBR (50%), NRCS (85%), BOEM (95%), NOAA (95%). USGS seeks to increase the completeness, efficiency, and accuracy of PAD-US updates in collaboration with agency data-stewards as described in "Completing America's Inventory of Public Parks and Protected Areas: An Action Plan for 2016 - 2020" (November, 2016) and "A Map of the Future", published following the PAD-US Design Project (July, 2009) available at https://gapanalysis.usgs.gov/padus/vision/. As a compilation of many data sets, attribute completeness and accuracy may vary. Federal data providers reviewed the translation of their spatial data files into the PAD-US schema and the aggregated database. In addition, the PAD-US team summarized and reviewed the final database and each feature class for overall attribute accuracy and consistency as described in PAD-US 2.0 Summary Review Tables here: https://www.sciencebase.gov/catalog/item/5b043677e4b0da30c1c367e5. As a compilation of many data sets, attribute completeness and accuracy may vary. Federal data providers reviewed the translation of their spatial data files into the PAD-US schema and the aggregated database. In addition, the PAD-US team summarized and reviewed the final database and each feature class for overall attribute accuracy and consistency as described in PAD-US 2.0 Summary Review Tables here: https://www.sciencebase.gov/catalog/item/5b043677e4b0da30c1c367e5. A formal accuracy assessment of the vertical positional information in the data set is not applicable. Categorical Assignments by Designation Type: The areas in PAD-US are assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant 'GAP Status Code' (that also identifies multiple use areas) and the global 'IUCN Category' standard. In addition, a general measure of recreation access ('Public Access') are also assigned. After locally reviewed measures were transferred from PAD-US 1.4 (described in the processing step above) input files are checked for completeness of required attributes and that all domains and standardized field attributes are completely assigned. In the absence of other information (e.g. local assignment or review), 'GAP Status Code', 'IUCN Category', and 'Public Access' are assigned categorically based on 'Designation Type' using a Python script (See PAD-US 2.0 Default Categorical Assignment Process at https://www.sciencebase.gov/catalog/item/5b0435cde4b0da30c1c367e1). 2018-05-04 PAD-US 2.0 Review: Once all attributes are assigned and feature classes are aggregated, the PAD-US development team conducts a final review by summarizing the database (See PAD-US 2.0 Review Summary Tables at https://www.sciencebase.gov/catalog/item/5b043677e4b0da30c1c367e5) and reviewing tables associated with each feature class. In addition, 'GIS Acres' are compared to known sources and spatial data are compared to previous PAD-US versions. The Federal Lands Working Group also reviewed the Federal estate prior to PAD-US integration and a draft of the full database with the State and other inputs listed above. An assessment report documenting the changes between PAD-US 1.4 and PAD-US 2.0 is available (See PAD-US 2.0 Magnitude of Change Assessment at https://www.sciencebase.gov/catalog/item/5b3aba56e4b040769c18b497). 2018-05-04 Multiple Feature Class Implementation: The major feature of PAD-US 2.0 involves expansion of the multiple feature class database structure; primarily, the removal of overlapping management designations from fee owned lands. This was fairly straightforward for Federal lands, building on FLWG efforts in PAD-US 1.4, as Federal data-stewards defined the 'Category' (field in PAD-US) associated with source data submitted for PAD-US 2.0 as fee parcels, easement interest, leases, agreements, overlapping management designations, proclamation, or marine boundaries. An automated script process compared State and other overlaps to Federal fee data to determine feature class placement (see PAD-US 2.0 Multiple Feature Class Script Used in Data Processing at https://www.sciencebase.gov/catalog/item/5b030ea2e4b0da30c1c1d72a). 2018-05-04 Source Data Translation and Quality Control: Individual source files were translated into the PAD-US 2.0 format (unless provided in the PAD-US format). Field maps document the translation of agency source data into the PAD-US format and the PAD-US development team reviewed these crosswalks with agency data-stewards (See Protected Areas Database of the United States Source Data at https://www.sciencebase.gov/catalog/item/59cd1685e4b00fa06fefea46). The PAD-US development team uses a Python script to generate a Quality Assessment and Quality Control (QA/QC) report each time a source file is submitted or translated to ensure the major components needed for database aggregation are included (See PAD-US 2.0 QA/QC Script Used in Data Processing at https://www.sciencebase.gov/catalog/item/5b030e3ce4b0da30c1c1d71a). Subsequent process steps will outline the general workflow and processes used to compile each major component of the final database. 2018-05-04 Source Data Collection: The PAD-US Data-Steward Network includes Federal, State and nonprofit partners that provided source data files for PAD-US 2.0 (See Protected Areas Database of the United States Source Data: https://www.sciencebase.gov/catalog/item/59cd1685e4b00fa06fefea46). More information about the PAD-US Data-Steward Network is available on the PAD-US website (https://gapanalysis.usgs.gov/padus/partners/). 2018-05-04 PAD-US 2.0 Blank Schema and Manual: The first step in the PAD-US 2.0 development process was to update the Blank PAD-US 2.0 schema (https://www.sciencebase.gov/catalog/item/5b048e4ae4b0d8682b96361d) in response to feedback and the PAD-US Action Plan (https://gapanalysis.usgs.gov/padus/vision/). The blank schema provides a blueprint of the database for the PAD-US Team to load input files for PAD-US development and it is shared with data-stewards seeking to submit source data in the PAD-US format. PAD-US database aggregation involves the compilation of thousands of agency lands data files from nearly 100 data-stewards. Data aggregation and development are an ongoing and evolving process as user and stakeholder feedback improve the process. The Data Manual and schema has changed with each PAD-US version to better reflect user needs. The schema includes all the components to build PAD-US 2.0, including multiple feature classes ('Fee', 'Designation', 'Easement', 'Proclamation', 'Marine', 'Combined'), core attributes, and all standardized domain tables ('State Name', 'IUCN Category', 'GAP Status Code', 'Designation Type', 'Category', 'Agency Type', 'Agency Name', 'Public Access'). The 'Marine' and 'Easement' feature classes also include fields (in addition to PAD-US core attributes) unique to the NOAA Marine Protected Areas Inventory and National Conservation Easement Database (sole sources for data in the Marine and Easement feature classes, respectively). In addition, the Blank PAD-US 2.0 Schema includes the U.S. Census Bureau State file (from the national sub-state geography geodatabase: https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2017/2017_TIGER_GDB_Record_Layouts.pdf) that served as the common standard for State jurisdictional boundaries and the 'State Name' field in PAD-US 2.0. The PAD-US development team works closely with a large variety of stakeholders including development partners, data sources, and data users to develop and maintain the "Data Manual for PAD-US" (document available at https://gapanalysis.usgs.gov/padus/data/manual/) with updates regarding PAD-US 2.0 expected late in 2018. 2018-05-04 Transfer Locally Reviewed Measures from PAD-US 1.4: Input files were reconciled with the previous version of PAD-US (1.4) to transfer locally assigned conservation ('GAP Status Code', 'IUCN Category') or recreation access measures ('Public Access'), and 'Date of Establishment' years, that weren't provided in the data submissions. See PAD-US 2.0 Reconciliation Script Used in Data Processing at https://www.sciencebase.gov/catalog/item/5b030ed5e4b0da30c1c1d734. 2018-05-04 Local Agency to Agency Name Crosswalk: A python script was used to crosswalk data provided by PAD-US contributors, referencing the land owner or managing entity for a unit, into standardized PAD-US geodatabase domain tables (See PAD-US 2.0 Local Owner to Owner Name Crosswalk at https://www.sciencebase.gov/catalog/item/5b3e9228e4b060350a0efded). PAD-US retains source data as provided in the 'Local Owner' and 'Local Manager' fields. These fields often provide additional detail to compliment the 'Owner Name' and 'Manager Name' fields, respectively. 2018-05-04 Database Aggregation and Load Order: Input files referenced in the “Categorical Assignments by Designation Type” processing step above are aggregated into the PAD-US geodatabase in a purposeful load order to achieve the necessary visual and analytical results associated with current database technology. Load order accounts for boundary size, type, and quality, to ensure overlapping features are visible on maps and to support assessments of biodiversity or recreation. Files in the PAD-US geodatabase feature class loaded first remain on the bottom in cases of overlap. Feature classes were manually loaded separately (agencies referenced below are listed in geodatabase load order within their respective feature classes - bottom to top) before the PAD-US 2.0 'Combined' feature classes are created. For example, the full 'PADUS2_0Combined_Proclamation_Marine_Fee_Designation_Easement' feature class represents this load order (PAD-US feature class and a general description of agency source data listed from the bottom to the top): 1) 'Proclamation' feature class (FWS Approved Acquisition, DOD Military Lands, USFS Proclamation Boundaries, NPS Proclamation Boundaries); 2) 'Marine' feature class (BOEM Blocks, NOAA MPAI); 3) Federal fee lands (NPS Proclamation boundaries required to fill fee data gaps, DOE, TVA, ARS legacy data from PAD-US 1.4, BLM public lands from Surface Management Area database, USBR legacy data from PAD-US 1.4, USACE lands, USFS fee parcel data, NPS fee parcel data, FWS fee parcel data); 4) State and local government fee lands, in alphabetical order by State; 5) Fee lands from regional datasets (DU CARL, TNC Lands, TNC Secured Lands, TPL Almanac); 6) Federal designations (USBR designations, BLM Herd Management Areas, BLM Grazing Allotments, BLM ACEC, BLM Monuments/National Conservation Areas/Similar, USACE reservoirs, USFS Inventory Roadless Areas, BLM Wilderness Study Areas, NPS Other Categories of Wilderness, NPS designations, USFS Withdrawals, USFS designations, USFS Special Interest Management areas, BLM Wilderness, USFS Wilderness, USFS Wild and Scenic Rivers, NPS Legislated Wilderness, FWS Special Designations, BLM Wild and Scenic Rivers); 7) State and Local designations, in alphabetical order by State; 8) Designations from regional datasets (DU CARL, TNC Lands, TNC Secured Lands, TPL Almanac); 9) Easements (often small) are loaded last to remain visible on top of overlapping records. 2018-05-04 0 Shape Feature geometry. ESRI Coordinates defining the features. Shape Geometry 0 0 0 OBJECTID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. OBJECTID OID 4 0 0 Mang_Name Land manager or administrative agency (e.g. 'Forest Service', 'State Fish and Wildlife', 'City Land', Non-Governmental Organization') standardized for the U.S. See PAD-US Data Manual or geodatabase look up table for 'Agency Name' for full domain descriptions. A python script assigns 'Manager Name' from 'Local Manager' (PAD-US 2.0 Local Owner to Owner Name Crosswalk: https://www.sciencebase.gov/catalog/item/5b3e9228e4b060350a0efded). Use 'Manager Name' for the best depiction of Federal lands by agency as the 'Owner Name' field includes data gaps (i.e. 'Owner Name' = 'Unknown'), where parcel level ownership data are not yet available from authoritative data sources. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Mang_Name U.S. Geological Survey Manager Name String 70 0 0 Own_Name Land owner or holding agency (e.g. 'Forest Service', 'State Fish and Wildlife', 'City Land', Non-Governmental Organization') standardized for the U.S. See PAD-US Data Manual or geodatabase 'Agency Name' look up table for full domain descriptions. Please note there are instances where 'Owner Name' = 'Designation' rather than an 'Agency Name' as expected. 'Designation' is applied to designations overlapping fee as ownership (i.e. the 'Owner Name') is not applicable, while it remains a core attribute. A python script assigns 'Owner Name' from 'Local Owner' (PAD-US 2.0 Local Owner to Owner Name Crosswalk at https://www.sciencebase.gov/catalog/item/5b3e9228e4b060350a0efded). 'Owner Name' also contains unknown values where parcel level ownership data are not yet available from authoritative data sources. Use the 'Manager Name' field for the best depiction of Federal lands by agency as several ownership related data gaps (i.e. 'Owner Name' = 'Unknown') occur in the Federal theme. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Own_Name U.S. Geological Survey Owner Name String 70 0 0 IUCNCtDt The most current Year (yyyy) the 'IUCN Category' was assigned to the polygon. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ IUCNCtDt U.S. Geological Survey IUCN Category Date String 4 0 0 GAP_Sts The 'GAP Status Code' is a measure of management intent to conserve biodiversity defined as: 'GAP Status Code 1': An area having permanent protection from conversion of natural land cover and a mandated management plan in operation to maintain a natural state within which disturbance events (of natural type, frequency, intensity, and legacy) are permitted to proceed without interference or are mimicked through management. 'GAP Status Code 2': An area having permanent protection from conversion of natural land cover and a mandated management plan in operation to maintain a primarily natural state, but which may receive uses or management practices that degrade the quality of existing natural communities, including suppression of natural disturbance. 'GAP Status Code 3': An area having permanent protection from conversion of natural land cover for most of the area, but subject to extractive uses of either a broad, low-intensity type (e.g., logging, Off Highway Vehicle recreation) or localized intense type (e.g., mining). It also confers protection to Federally listed endangered and threatened species throughout the area. 'GAP Status Code 4': There are no known public or private institutional mandates or legally recognized easements or deed restrictions held by the managing entity to prevent conversion of natural habitat types to anthropogenic habitat types. The area generally allows conversion to unnatural land cover throughout or management intent is unknown. See the PAD-US Standards Manual for a summary of methods or the geodatabase look up table for short descriptions. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ GAP_Sts U.S. Geological Survey GAP Status Code String 95 0 0 IUCN_Cat International Union for the Conservation of Nature (IUCN) management categories assigned to protected areas for inclusion in the United Nations Environment World Conservation Monitoring Center (WCMC) World Database for Protected Areas (WDPA) and the Commission for Environmental Cooperation (CEC) North American Terrestrial Protected Areas Database. IUCN defines a protected area as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" (includes GAP Status Code 1 and 2 only). Categorization follows as: 'IUCN Category Ia': Strict Nature Reserves are strictly protected areas set aside to protect biodiversity and possibly geological or geomorphological features, where human visitation, use and impacts are strictly controlled and limited to ensure preservation of the conservation values. Such protected areas can serve as indispensable reference areas for scientific research and monitoring. 'IUCN Category Ib': Wilderness Areas are protected areas are usually large unmodified or slightly modified areas, retaining their natural character and influence, without permanent or significant human habitation, which are protected and managed to preserve their natural condition. 'IUCN Category II': National Park protected areas are large natural or near natural areas set aside to protect large-scale ecological processes, along with the complement of species and ecosystems characteristic of the area, which also provide a foundation for environmentally and culturally compatible spiritual, scientific, educational, recreational and visitor opportunities. 'IUCN Category III': Natural Monument or Feature protected areas are set aside to protect a specific natural monument, which can be a landform, sea mount, submarine caverns, geological feature such as caves, or even a living feature such as an ancient grove. They are generally quite small protected areas and often have high visitor value. 'IUCN Category IV': Habitat and (or) species management protected areas aim to protect particular species or habitats and management reflects this priority. Many category IV protected areas will need regular, active interventions to address the requirements of particular species or to maintain habitats, but this is not a requirement of this category. 'IUCN Category V': Protected landscape and (or) seascape protected areas occur where the interaction of people and nature over time has produced an area of distinct character with significant ecological, biological, cultural, and scenic value. 'IUCN Category VI': Protected area with sustainable use (community based, non-industrial) of natural resources are generally large, with much of the area in a more-or-less natural condition and whereas a proportion is under sustainable natural resource management and where such exploitation is seen as one of the main aims of the area. 'Other Conservation Areas' are not recognized by IUCN at this time; however, they are included in the CEC's database referenced above. These areas (includes GAP Status Code 3 areas only) are attributed in the 'IUCN Category' Domain along with 'Unassigned' areas (GAP Status Code 4). In addition, a few areas are included as 'Not Reported', these areas meet the definition of IUCN protection (i.e. GAP Status Code 1 or 2) but 'IUCN Category' has not yet been assigned and categorical assignment is not appropriate. See the PAD-US Standards Manual for a summary of methods. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ IUCN_Cat U.S. Geological Survey IUCN Category String 70 0 0 GIS_Acres Acres calculated for each polygon converted from the Shape_Area field using field calculator "!shape.area@acres!". See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ GIS_Acres U.S. Geological Survey GIS Acres Integer 4 0 0 WDPA_Cd The 'World Database for Protected Areas (WDPA) Site Code' is assigned by the UNEP World Conservation Monitoring Centre (WCMC) to all areas submitted to the WDPA. USGS maintains these codes, assigned to overall protected areas by 'Unit Name' (includes 'Designation Type'), between PAD-US updates. Areas identified as 'GAP Status Code' 1 or 2 meet the definition of protection by the International Union for the Conservation of Nature (IUCN) and are submitted to WCMC for the WDPA. Other areas will not have a 'WDPA Code'. A derivative PAD-US product, with all 'WDPA Codes', is sent to WCMC for the WDPA following each PAD-US update. This field is incomplete at this time, pending additional code assignments by WCMC following PAD-US 2.0 publication and review. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ WDPA_Cd U.S. Geological Survey WDPA Site Code Integer 4 0 0 GIS_Src The source of spatial data the aggregator obtained (e.g. WYGF_whmas08.shp) for each record. Files names match original source data provided by managing agencies to increase update efficiency and data transparency. This field is in a state of transition to fully meet standards as the original 'GIS Source' is not always provided in aggregated datasets. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ GIS_Src U.S. Geological Survey GIS Source String 200 0 0 Loc_Mang Local Manager String 250 0 0 Comments Comments from either the original data source or aggregator. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Comments U.S. Geological Survey Comments String 255 0 0 Unit_Nm The name of overall protected area following the PAD-US Standard (i.e. full name including the designation type in Proper Case without acronyms, unit identifiers, special characters, space or return errors), complimenting 'Local Name'. As null values are not permitted in this standardized field, categorical assignments are sometimes made from the 'Manager Name' field and an auto-incremented number for each protected area when data gaps occur in source files. This field is in a state of transition as data-stewards move toward common standards. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Des_Nm U.S. Geological Survey Unit Name String 250 0 0 Date_Est The Year (yyyy) the protected area was designated, decreed or otherwise established. The date is assigned to each unit by name and designation type, without event status (e.g. Yellowstone National Park: 1872, Frank Church-River of No Return Wilderness Area: 1980). This field is not fully attributed and data gaps are difficult to address. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Date_Est U.S. Geological Survey Date of Establishment String 4 0 0 Shape_Length Length of feature in internal units. ESRI Positive real numbers that are automatically generated. Shape_Length Double 8 0 0 Shape_Area Area of feature in internal units squared. ESRI Positive real numbers that are automatically generated. Shape_Area Double 8 0 0 EsmtHldr 'Easement Holder' is the name of the organization managing or holding the easement standardized by NCED to Proper Case with all acronyms spelled out (e.g., The Nature Conservancy [TNC]). This is a core attribute in the National Conservation Easement Database (NCED) provided by the easement holder or data provider that was transferred to the PAD-US 'Easement' and various 'Combined' feature classes. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ EsmtHldr U.S. Geological Survey Easement Holder String 100 0 0 Loc_Ds The unit's land management description or designation as provided by the data source. 'Local Designation Type' is not standardized and complements the standardized PAD-US 'Designation Type' field as more detail may be available. Null values indicate designation related information was not available in source files and categorical assignments to 'Designation Type' apply. See the PAD-US Data Manual for a crosswalk of 'Designation Type' (e.g. 'State Conservation Area') from source data where 'Local Designation Type' may include various, related designations referenced in source data (e.g. State Natural Area, State Ecological Reserve, State Nature Preserve, State Critical Habitat Area, State Wildlife Management Area, etc.). See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Loc_Ds U.S. Geological Survey Local Designation String 250 0 0 Source_PAID The 'Source Protected Area Identifier' (Source PAID) includes the persistent identifier (e.g. numbers, letters, or combinations of each) associated with each protected area, when available in source data. The intention is to maintain a reliable connection to join authoritative source data to previous PAD-US versions (to transfer attributes not available in source data) and support links to other data. This field is not fully attributed and significant work remains to ensure persistent identifiers are provided by data-stewards. Several Federal data-stewards provided 'Source PAID' for PAD-US 2.0 and the FLWG plans to continue work in 2018-19 to ensure this field is operational for the Federal estate in the PAD-US format. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Source_PAID U.S. Geological Survey Source Protected Area ID String 100 0 0 Des_Tp The unit's land management description or 'Designation Type', standardized for the U.S. (e.g. 'Area of Critical Environmental Concern', 'Wilderness Area', 'State Park', 'Local Recreation Area', 'Conservation Easement'). See the PAD-US Data Manual for a crosswalk of 'Designation Type' from source data where 'Local Designation Type' may include related designations in various formats (e.g. NWSR, National Recreation River, National Scenic River, Eligible - Recreational, Eligible - Wild, etc.) or the geodatabase look up table for 'Designation Type' domain descriptions. 'Designation Type' supports PAD-US queries and the categorical assignment of conservation measures (i.e. 'GAP Status Code', 'IUCN Category') and 'Public Access' in the absence of other information. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Des_Tp U.S. Geological Survey Designation Type String 75 0 0 Loc_Nm The name of the protected area as provided by the data source; the 'Local Name' field is not standardized. This field may or may not include designations, different formats, spelling errors, unit or area identifiers unique to parcels; however, it links directly to source data files. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Loc_Nm U.S. Geological Survey Local Name String 250 0 0 State_Nm Name of State or territory spelled out in Proper Case. U.S. Census Bureau States and Equivalent data serve as the common standard to apply 'State Name' and State jurisdictional boundaries. Protected Areas Database of the United States (PAD-US) Source Data are available at https://www.sciencebase.gov/catalog/item/59cd1685e4b00fa06fefea46 and http://www.protectedlands.net/data-stewards/. See domain descriptions in PAD-US Data Manual or geodatabase look up table for details. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ State_Nm U.S. Geological Survey State Name String 50 0 0 Loc_Own The name of the land owner as provided by the data source, to complement the standardized 'Owner Name' field (e.g. 'State Fish and Wildlife' is a standard 'Owner Name', while 'Washington Department of Fish and Wildlife' reflect source data in the 'Local Owner' field) as more detail may be provided. A python script assigns 'Owner Name' from 'Local Owner' (PAD-US 2.0 Local Owner to Owner Name Crosswalk at https://www.sciencebase.gov/catalog/item/5b3e9228e4b060350a0efded). Efforts to complete and standardize 'Local Owner' in cooperation with data-stewards are in progress. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Loc_Own U.S. Geological Survey Local Owner String 250 0 0 Own_Type The general land owner description (e.g. 'Federal', 'Territorial', 'American Indian Lands', 'State', 'Regional Agency Special District', 'Local Government', 'Non-Governmental Organization', 'Private', 'Joint') standardized for the U.S. See PAD-US Data Manual for the “Agency Name to Agency Type Crosswalk” or geodatabase look up table for full domain descriptions. 'Regional Agency Special Districts' include limited purpose governmental units that exist separately from local governments such as county or municipal. 'Designation' is applied to designations overlapping fee lands as ownership is not applicable. Use the 'Manager Type' field for the best general depiction of Federal lands as several ownership related data gaps (i.e. 'Owner Type' = 'Unknown') occur in the Federal theme. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Own_Type U.S. Geological Survey Owner Type String 50 0 0 Access_Dt Public Access Date String 4 0 0 Mang_Type General land manager description (e.g. 'Federal', 'Territorial', 'American Indian Lands', 'State', 'Regional Agency Special District', 'Local Government', 'Non-Governmental Organization', 'Private', 'Joint') standardized for the U.S. See PAD-US Data Manual for “Agency Name to Agency Type Crosswalk” or geodatabase look up table for full domain descriptions. Use the 'Manager Type' field for the most complete general depiction of Federal lands as ownership related data gaps (i.e. 'Owner Type' = 'Unknown') occur in the Federal theme. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Mang_Type U.S. Geological Survey Manager Type String 50 0 0 Category The general 'Category' for the protection mechanism associated with the protected area. 'Fee' simple is the most common way real estate is owned. A conservation 'easement' creates a legally enforceable land preservation agreement between a landowner and government agency or qualified land protection organization (i.e. land trust). 'Other' types of protection include leases, agreements or deed restrictions. 'Designation' is applied to management boundaries not tied to title documents (e.g. 'National Monument', 'Wild and Scenic River', and some 'State Wildlife Management Area') overlapping fee ownership parcels. 'Marine' includes outer continental shelf lands managed by the Bureau of Ocean Energy Management and Marine Protected Areas identified by the National Oceanic and Atmospheric Administration. 'Proclamation' defines the outer boundaries of areas without internal ownership defined: Military Lands (Department of Defense), Proclamation (National Park Service and Forest Service) and Approved Acquisition Boundaries (U.S. Fish and Wildlife Service). See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Category U.S. Geological Survey Category String 12 0 0 FeatClass This is a new field added to the PAD-US 2.0 'Combined' feature classes to identify the original feature class ('Fee', 'Easement', 'Designation', etc.) a unit occurred in before combination. This is included to assist users seeking data in the 'Fee' feature class as data gaps exist and 'Category' (which would otherwise define feature class placement) may be 'Other' or 'Unknown'. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ FeatClass U.S. Geological Survey FeatClass String 12 0 0 EHoldTyp 'Easement Holder Type' is the general description (e.g., 'Federal', 'Tribal', 'State', 'Private') of the easement holder, following the PAD-US 'Agency Type' domain codes and descriptions. This is a core attribute in the National Conservation Easement Database (NCED) that was transferred to the PAD-US Easement and various Combined feature classes. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ EHoldTyp U.S. Geological Survey Easement Holder Type String 50 0 0 Src_Date This represents the date (yyyy/mm/dd) GIS data was published or obtained (in the case of infrequently updated files) by the data aggregator. If month or day is unknown date is yyyy/00/00. The date an aggregated dataset was delivered to USGS may also be assigned to address data gaps when the original 'GIS Source Date' is not available. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Src_Date U.S. Geological Survey GIS Source Date String 15 0 0 Access Level of 'Public Access' permitted, described as: 'Open' requires no special requirements for public access to the property (may include regular hours of availability); 'Restricted' requires a special permit from the owner for access, a registration permit on public land (e.g. self-permitting Wild and Scenic River, backcountry Wilderness registration) or has highly variable times when open to use; 'Closed' occurs where no public access is allowed (e.g. land bank property, special ecological study areas, military bases, many easements, etc.). 'Unknown' is assigned where information is not currently available. 'Public Access' was provided by State data-stewards, Federal, nonprofit partners or assigned categorically by 'Designation Type' when no other information was available. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Access U.S. Geological Survey Public Access String 20 0 0 Access_Src An acronym to describe the organization(s) that applied 'Public Access' to the polygon. This field also describes the methods used for assigning 'Public Access' as follows: 'GAP - Default' is assigned when GAP's categorical assignment of status has been applied, without additional review. 'GAP - Other Organization' (e.g. 'GAP - NPS') applies when the measure is assigned in partnership with GAP, including review. 'Other Organization' applies when Public Access is assigned by another organization according to their methods (e.g. NOAA) is assigned. See the PAD-US Standards Manual for more information. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Access_Src U.S. Geological Survey Public Access Source String 150 0 0 IUCNCtSrc An acronym to describe the organization(s) that applied 'IUCN Category' to the polygon. This field also describes the methods used for assigning 'IUCN Category' as follows: 'GAP - Default' is assigned when GAP's categorical assignment of status has been applied, without additional review. 'GAP - Other Organization' (e.g. 'GAP - NPS') applies when the measure is assigned in partnership with GAP, including review. 'Other Organization' applies when IUCN Category is assigned by another organization according to their methods (e.g. NOAA) is assigned. See the PAD-US Standards Manual for more information. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ IUCNCtSrc U.S. Geological Survey IUCN Category Source String 150 0 0 Agg_Src 'Aggregator Source' describes the Aggregator (Organization) credited with data aggregation, version of PAD-US when the update occurred, feature class the data reside in (unless mixed), reference to the original source data file, and a reference to describe the State location to manage boundary inconsistencies between agency datasets (from State data-steward submissions only). 'Aggregator Source' is attributed in the format 'organization name_PADUSversion+featureclass_filename.filetype' (e.g. TNC_PADUS1_4_SecuredAreas2008.shp). State aggregations also include a reference to the State in the format 'organization name_PADUSversion_filename.filetype_StateUSPS'. Aggregators may not always be able to define the geodatabase feature class as data may be mixed (e.g. MNDNR_PADUS2_0_MN2015_PADUS_MN_1.gdb_MN, NJOGIS_PADUS2_0Fee_OSPRI_August2017_NJ). Organization acronyms are used and underscore replaces spaces. A data aggregator submits data in the PAD-US format or includes nonprofit aggregators managing regional or national datasets with required fields for PAD-US translation. USGS is identified as aggregator when data translation is required (e.g. USGS_PADUS2_0Fee_BLM_SMA_ADMU_Union). Protected Areas Database of the United States (PAD-US) Source Data are available at https://www.sciencebase.gov/catalog/item/59cd1685e4b00fa06fefea46 and http://www.protectedlands.net/data-stewards/. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ Agg_Src U.S. Geological Survey Aggregator Source String 150 0 0 GAPCdSrc An acronym to describe the organization(s) that applied 'GAP Status Code' to a unit boundary. This field also describes the methods used for assigning GAP Status as follows: 'GAP - Default' is assigned when GAP's categorical assignment of status has been applied, without more detailed review or inquiry. 'GAP' is assigned when standard methods (includes management plan review or land manager interview to assign GAP Status to a protected area) apply. 'GAP - Other Organization' (e.g. 'GAP - NPS') applies when the measure is assigned in partnership with GAP, including review. 'Other Organization' is assigned when another organization applied GAP Status according to their methods then (e.g. The Nature Conservancy). See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ GAPCdSrc U.S. Geological Survey GAP Status Code Source String 150 0 0 Source_PAI Source_PAI String 100 0 0 UID2 UID2 Integer 4 0 0 circle circle Double 8 0 0 Notes Notes String 254 0 0 SensitiveD SensitiveD Integer 4 0 0 Shape_Leng Shape_Leng Double 8 0 0 Rast_Own Rast_Own Integer 4 0 0 Restrict Restrict SmallInteger 2 0 0 GAPCdDt The most current Year (yyyy) the 'GAP Status Code' was assigned to the polygon. See PAD-US Data Manual Document available at https://gapanalysis.usgs.gov/padus/data/manual/ GAPCdDt U.S. Geological Survey GAP Status Code Date String 4 0 0 CBW_desigAndProc2018 Feature Class 0 dataset Esri 8.1.2 Simple FALSE 0 TRUE FALSE