{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# What NYC boroughs should we avoid?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## (Or, what borough has the fastest taxi drivers?)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "from __future__ import division" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import sqlparse\n", "\n", "\n", "def psql(query):\n", " print(\n", " sqlparse.format(\n", " str(query.compile(\n", " compile_kwargs=dict(literal_binds=True)\n", " )),\n", " reindent=True\n", " )\n", " )" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ " \n", "\n", "\n", " \n", "\n", "
\n", " \n", " BokehJS successfully loaded.\n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%reload_ext autotime\n", "%matplotlib nbagg\n", "\n", "from blaze import by, compute, transform, Data\n", "from odo import odo, drop, resource, Temp, CSV\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Rectangle\n", "from IPython.display import Image\n", "from sqlalchemy import func as f\n", "from bokeh.charts import Histogram\n", "from bokeh.charts import defaults, vplot, hplot, show, output_notebook\n", "output_notebook()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Load up NYC Taxi from Postgres" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 41.5 ms\n" ] } ], "source": [ "d = Data('postgresql://localhost/nyc::bigsample')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
medallionhack_licensevendor_idrate_codestore_and_fwd_flagpickup_datetimedropoff_datetimepassenger_counttrip_time_in_secstrip_distancepickup_longitudepickup_latitudedropoff_longitudedropoff_latitudetolls_amounttip_amounttotal_amountmta_taxfare_amountpayment_typesurcharge
0039A16D739D799891C9211A55F731263F6D14E7742FE7EFFCE16E55CB62274FCCMT1N2013-05-29 08:50:232013-05-29 09:12:27113226.0-74.00914040.704449-73.97466340.7565420.002.0023.500.5021.00CRD0.00
1207291076F51439066F4430B049D8E4A7808B339F91B9527FEC548FC5B92E6A6CMT1N2013-05-29 08:42:322013-05-29 08:51:4315501.4-73.99875640.734665-73.98210940.7462160.001.009.500.508.00CRD0.00
2DF0002B656F1AA6D651D3EB6C4AD21570DD4A4383927098D2CB5A90C049005DECMT1N2013-05-29 08:40:502013-05-29 08:58:50110792.6-74.00811040.722893-73.98328440.7543220.002.7016.200.5013.00CRD0.00
3EBAA2041372A9CEA19CF32572A0D1752FB7456FD36320B37B976E15D9B41C760CMT1N2013-05-29 08:43:522013-05-29 08:57:2018071.8-73.99777240.746155-73.97982040.7614170.002.1012.600.5010.00CRD0.00
4022562E7A0FEB7A9D4A31B06BF0B82E8B706829B6520B5C6A15EF434DDE3A8D4CMT1N2013-05-29 08:42:102013-05-29 08:55:0217721.7-73.95081340.786579-73.96683540.7639580.002.1012.600.5010.00CRD0.00
" ], "text/plain": [ " medallion hack_license \\\n", "0 039A16D739D799891C9211A55F731263 F6D14E7742FE7EFFCE16E55CB62274FC \n", "1 207291076F51439066F4430B049D8E4A 7808B339F91B9527FEC548FC5B92E6A6 \n", "2 DF0002B656F1AA6D651D3EB6C4AD2157 0DD4A4383927098D2CB5A90C049005DE \n", "3 EBAA2041372A9CEA19CF32572A0D1752 FB7456FD36320B37B976E15D9B41C760 \n", "4 022562E7A0FEB7A9D4A31B06BF0B82E8 B706829B6520B5C6A15EF434DDE3A8D4 \n", "\n", " vendor_id rate_code store_and_fwd_flag pickup_datetime \\\n", "0 CMT 1 N 2013-05-29 08:50:23 \n", "1 CMT 1 N 2013-05-29 08:42:32 \n", "2 CMT 1 N 2013-05-29 08:40:50 \n", "3 CMT 1 N 2013-05-29 08:43:52 \n", "4 CMT 1 N 2013-05-29 08:42:10 \n", "\n", " dropoff_datetime passenger_count trip_time_in_secs trip_distance \\\n", "0 2013-05-29 09:12:27 1 1322 6.0 \n", "1 2013-05-29 08:51:43 1 550 1.4 \n", "2 2013-05-29 08:58:50 1 1079 2.6 \n", "3 2013-05-29 08:57:20 1 807 1.8 \n", "4 2013-05-29 08:55:02 1 772 1.7 \n", "\n", " ... pickup_latitude dropoff_longitude dropoff_latitude \\\n", "0 ... 40.704449 -73.974663 40.756542 \n", "1 ... 40.734665 -73.982109 40.746216 \n", "2 ... 40.722893 -73.983284 40.754322 \n", "3 ... 40.746155 -73.979820 40.761417 \n", "4 ... 40.786579 -73.966835 40.763958 \n", "\n", " tolls_amount tip_amount total_amount mta_tax fare_amount payment_type \\\n", "0 0.00 2.00 23.50 0.50 21.00 CRD \n", "1 0.00 1.00 9.50 0.50 8.00 CRD \n", "2 0.00 2.70 16.20 0.50 13.00 CRD \n", "3 0.00 2.10 12.60 0.50 10.00 CRD \n", "4 0.00 2.10 12.60 0.50 10.00 CRD \n", "\n", " surcharge \n", "0 0.00 \n", "1 0.00 \n", "2 0.00 \n", "3 0.00 \n", "4 0.00 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 45.6 ms\n" ] } ], "source": [ "d.head(5)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## What is the bounding box of NYC?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 718 µs\n" ] } ], "source": [ "from IPython.display import HTML, IFrame" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 1.45 ms\n" ] } ], "source": [ "IFrame(\n", " 'https://www.maptechnica.com/us-city-boundary-map/city/New%20York/state/NY/cityid/3651000',\n", " width=800,\n", " height=600\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Postgres has some funky operators for doing cool things" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " box_dist \n", "------------------\n", " 39.5120219053786\n", "(1 row)\n", "\n", "time: 18.7 ms\n" ] } ], "source": [ "!psql nyc -c 'select point(40.477399, -74.259090) <@> point(40.917577, -73.700272) as box_dist'" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "39.5120219053786" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 5.81 ms\n" ] } ], "source": [ "max_distance = d.data.bind.execute(\n", " sa.select([\n", " f.point(40.477399, -74.259090).op('<@>')(f.point(40.917577, -73.700272))\n", " ])\n", ").scalar()\n", "max_distance" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Reduce the data to the area defined as NYC" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 196 ms\n" ] } ], "source": [ "# http://hafen.github.io/taxi/#reading-in-to-r\n", "\n", "min_lat, max_lat = 40.477399, 40.917577\n", "min_lon, max_lon = -74.259090, -73.700272\n", "\n", "valid = d[\n", " (d.trip_distance > 0) &\n", " (d.trip_distance <= max_distance) &\n", " (d.trip_time_in_secs > 0) &\n", " (d.passenger_count > 0) & (d.passenger_count <= 5) &\n", " (d.pickup_latitude >= min_lat) & (d.pickup_latitude <= max_lat) &\n", " (d.dropoff_latitude >= min_lat) & (d.dropoff_latitude <= max_lat) &\n", " (d.pickup_longitude >= min_lon) & (d.pickup_longitude <= max_lon) &\n", " (d.dropoff_longitude >= min_lon) & (d.dropoff_longitude <= max_lon)\n", "]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SELECT bigsample.medallion,\n", " bigsample.hack_license,\n", " bigsample.vendor_id,\n", " bigsample.rate_code,\n", " bigsample.store_and_fwd_flag,\n", " bigsample.pickup_datetime,\n", " bigsample.dropoff_datetime,\n", " bigsample.passenger_count,\n", " bigsample.trip_time_in_secs,\n", " bigsample.trip_distance,\n", " bigsample.pickup_longitude,\n", " bigsample.pickup_latitude,\n", " bigsample.dropoff_longitude,\n", " bigsample.dropoff_latitude,\n", " bigsample.tolls_amount,\n", " bigsample.tip_amount,\n", " bigsample.total_amount,\n", " bigsample.mta_tax,\n", " bigsample.fare_amount,\n", " bigsample.payment_type,\n", " bigsample.surcharge\n", "FROM bigsample\n", "WHERE bigsample.trip_distance > 0\n", " AND bigsample.trip_distance <= 39.5120219053786\n", " AND bigsample.trip_time_in_secs > 0\n", " AND bigsample.passenger_count > 0\n", " AND bigsample.passenger_count <= 5\n", " AND bigsample.pickup_latitude >= 40.477399\n", " AND bigsample.pickup_latitude <= 40.917577\n", " AND bigsample.dropoff_latitude >= 40.477399\n", " AND bigsample.dropoff_latitude <= 40.917577\n", " AND bigsample.pickup_longitude >= -74.25909\n", " AND bigsample.pickup_longitude <= -73.700272\n", " AND bigsample.dropoff_longitude >= -74.25909\n", " AND bigsample.dropoff_longitude <= -73.700272\n", "time: 66.8 ms\n" ] } ], "source": [ "psql(compute(valid))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Add a column containing the average speed in MPH" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 8.07 ms\n" ] } ], "source": [ "trip_time_in_hours = valid.trip_time_in_secs.coerce('float64') / 3600.0\n", "calcd = transform(valid, avg_speed_in_mph=valid.trip_distance / trip_time_in_hours)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
avg_speed_in_mph
016.338880
19.163636
28.674699
38.029740
47.927461
" ], "text/plain": [ " avg_speed_in_mph\n", "0 16.338880\n", "1 9.163636\n", "2 8.674699\n", "3 8.029740\n", "4 7.927461" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 191 ms\n" ] } ], "source": [ "calcd.avg_speed_in_mph.head(5)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 40.3 s\n" ] } ], "source": [ "s = odo(calcd.avg_speed_in_mph, pd.Series)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 2.37 s\n" ] } ], "source": [ "show(Histogram(\n", " s.loc[s > 120],\n", " title='Average Speed in MPH',\n", " bins=100,\n", " xlabel='Average Speed in MPH',\n", " ylabel='Count'\n", "))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = Scatter(df, x='mpg', y='hp', color='cyl', title=\"HP vs MPG (shaded by CYL)\",\n", " xlabel=\"Miles Per Gallon\", ylabel=\"Horsepower\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Assume that > 120 MPH is invalid" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 45.6 ms\n" ] } ], "source": [ "data = calcd[(calcd.avg_speed_in_mph > 5) & (calcd.avg_speed_in_mph <= 120)][\n", " [\n", " 'avg_speed_in_mph', \n", " 'pickup_latitude', \n", " 'pickup_longitude', \n", " 'dropoff_latitude', \n", " 'dropoff_longitude'\n", " ]\n", "]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
avg_speed_in_mphpickup_latitudepickup_longitudedropoff_latitudedropoff_longitude
016.33888040.704449-74.00914040.756542-73.974663
19.16363640.734665-73.99875640.746216-73.982109
28.67469940.722893-74.00811040.754322-73.983284
38.02974040.746155-73.99777240.761417-73.979820
47.92746140.786579-73.95081340.763958-73.966835
" ], "text/plain": [ " avg_speed_in_mph pickup_latitude pickup_longitude dropoff_latitude \\\n", "0 16.338880 40.704449 -74.009140 40.756542 \n", "1 9.163636 40.734665 -73.998756 40.746216 \n", "2 8.674699 40.722893 -74.008110 40.754322 \n", "3 8.029740 40.746155 -73.997772 40.761417 \n", "4 7.927461 40.786579 -73.950813 40.763958 \n", "\n", " dropoff_longitude \n", "0 -73.974663 \n", "1 -73.982109 \n", "2 -73.983284 \n", "3 -73.979820 \n", "4 -73.966835 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 378 ms\n" ] } ], "source": [ "data.head(5)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SELECT bigsample.trip_distance / (CAST(bigsample.trip_time_in_secs AS FLOAT) / 3600.0) AS avg_speed_in_mph,\n", " bigsample.pickup_latitude,\n", " bigsample.pickup_longitude,\n", " bigsample.dropoff_latitude,\n", " bigsample.dropoff_longitude\n", "FROM bigsample\n", "WHERE bigsample.trip_distance > 0\n", " AND bigsample.trip_distance <= 39.5120219053786\n", " AND bigsample.trip_time_in_secs > 0\n", " AND bigsample.passenger_count > 0\n", " AND bigsample.passenger_count <= 5\n", " AND bigsample.pickup_latitude >= 40.477399\n", " AND bigsample.pickup_latitude <= 40.917577\n", " AND bigsample.dropoff_latitude >= 40.477399\n", " AND bigsample.dropoff_latitude <= 40.917577\n", " AND bigsample.pickup_longitude >= -74.25909\n", " AND bigsample.pickup_longitude <= -73.700272\n", " AND bigsample.dropoff_longitude >= -74.25909\n", " AND bigsample.dropoff_longitude <= -73.700272\n", " AND bigsample.trip_distance / (CAST(bigsample.trip_time_in_secs AS FLOAT) / 3600.0) > 5\n", " AND bigsample.trip_distance / (CAST(bigsample.trip_time_in_secs AS FLOAT) / 3600.0) <= 120\n", "time: 177 ms\n" ] } ], "source": [ "psql(compute(data))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## A small aside, with `odo`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Let's look at the distribution of rides by borough\n", "\n", "#### Q: Where do we get borough geolocation data?\n", "#### A: From NYC's own API!" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 565 µs\n" ] } ], "source": [ "import requests" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 2.76 ms\n" ] } ], "source": [ "def get_kml(wifi):\n", " if wifi:\n", " result = requests.get(\n", " 'https://data.cityofnewyork.us/api/geospatial/tqmj-j8zm',\n", " params=dict(method='export', format='KML')\n", " ).text\n", " with open('cityofnewyork.kml', 'wt') as f:\n", " f.write(result)\n", " return result\n", " else:\n", " with open('cityofnewyork.kml', 'rt') as f:\n", " return f.read()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 2.83 ms\n" ] } ], "source": [ "raw = get_kml(wifi=False)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 8.91 ms\n" ] } ], "source": [ "import lxml\n", "import lxml.etree" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Parse the KML with `lxml`" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 4.92 ms\n" ] } ], "source": [ "kml = lxml.etree.fromstring(raw.encode('utf8'))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " \n", " geo_tqmj-j8zm:geo_tqmj-j8zm-1\n", " \n", " -73.97867494308187\n", " 40.70615701593643\n", " 81791.8316066984\n", " 66092.09005639808\n", " 0.0\n", " 0.0\n", " clampToGround\n", " \n", " \n", " \n", " geo_tqmj-j8zm-1\n", "\n", "
    \n", " \n", "
  • BoroCode: 5
    • \n", "
  • BoroName\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    lonlat
    count36999.00000036999.000000
    mean-73.90521040.674753
    std0.1231010.102569
    min-74.25560040.496100
    25%-73.94330040.598200
    50%-73.87890040.632800
    75%-73.81560040.777900
    max-73.70000040.915500
    \n", "" ], "text/plain": [ " lon lat\n", "count 36999.000000 36999.000000\n", "mean -73.905210 40.674753\n", "std 0.123101 0.102569\n", "min -74.255600 40.496100\n", "25% -73.943300 40.598200\n", "50% -73.878900 40.632800\n", "75% -73.815600 40.777900\n", "max -73.700000 40.915500" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 14.9 ms\n" ] } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 127 ms\n" ] } ], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = Scatter(\n", " df, x='lon', y='lat',\n", " color='name',\n", " title=\"NYC Borough Polygons\"\n", " xlabel=\"Longitude\", ylabel=\"Horsepower\"\n", ")\n", "output_notebook()\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### After we're satisfied, let's throw everything into postgres for later analysis" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 170 ms\n" ] } ], "source": [ "boros = odo(\n", " odo(df, Temp(CSV)),\n", " 'postgresql://localhost/nyc::boros'\n", ")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    lonlatname
    0-74.15340540.580831Staten Island
    1-74.05050040.566400Staten Island
    2-74.05030040.566400Staten Island
    3-74.05010040.566400Staten Island
    4-74.05000040.566400Staten Island
    " ], "text/plain": [ " lon lat name\n", "0 -74.153405 40.580831 Staten Island\n", "1 -74.050500 40.566400 Staten Island\n", "2 -74.050300 40.566400 Staten Island\n", "3 -74.050100 40.566400 Staten Island\n", "4 -74.050000 40.566400 Staten Island" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 15.6 ms\n" ] } ], "source": [ "Data(boros).head(5)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Make a table of `name`, `polygon` for each borough" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 2.45 ms\n" ] } ], "source": [ "bounds = sa.select([\n", " boros.c.name,\n", " f.box(\n", " f.point(f.min(boros.c.lon), f.min(boros.c.lat)),\n", " f.point(f.max(boros.c.lon), f.max(boros.c.lat))\n", " ).label('boro_box')\n", "]).group_by(boros.c.name).alias()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Get out the SQLAlchemy table" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    avg_speed_in_mphpickup_latitudepickup_longitudedropoff_latitudedropoff_longitude
    016.33888040.704449-74.00914040.756542-73.974663
    19.16363640.734665-73.99875640.746216-73.982109
    28.67469940.722893-74.00811040.754322-73.983284
    38.02974040.746155-73.99777240.761417-73.979820
    47.92746140.786579-73.95081340.763958-73.966835
    " ], "text/plain": [ " avg_speed_in_mph pickup_latitude pickup_longitude dropoff_latitude \\\n", "0 16.338880 40.704449 -74.009140 40.756542 \n", "1 9.163636 40.734665 -73.998756 40.746216 \n", "2 8.674699 40.722893 -74.008110 40.754322 \n", "3 8.029740 40.746155 -73.997772 40.761417 \n", "4 7.927461 40.786579 -73.950813 40.763958 \n", "\n", " dropoff_longitude \n", "0 -73.974663 \n", "1 -73.982109 \n", "2 -73.983284 \n", "3 -73.979820 \n", "4 -73.966835 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 427 ms\n" ] } ], "source": [ "data.head(5)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 187 ms\n" ] } ], "source": [ "nyc = compute(data).alias()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Join on both the pickup and dropoff latitude and longtiude being in the same borough" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 2.7 ms\n" ] } ], "source": [ "joined = nyc.join(\n", " bounds,\n", " onclause=f.lseg(\n", " f.point(nyc.c.pickup_longitude, nyc.c.pickup_latitude),\n", " f.point(nyc.c.dropoff_longitude, nyc.c.dropoff_latitude)\n", " ).op('<@')(bounds.c.boro_box)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### Group By `name` and compute the average speed and the number of samples that went into the average" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 1.87 ms\n" ] } ], "source": [ "sel = sa.select([\n", " bounds.c.name,\n", " nyc.c.avg_speed_in_mph, \n", " nyc.c.pickup_latitude,\n", " nyc.c.pickup_longitude,\n", " nyc.c.dropoff_latitude,\n", " nyc.c.dropoff_longitude\n", "]).select_from(joined).alias()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time: 1.91 ms\n" ] } ], "source": [ "per_boro_speeds = sa.select([\n", " sel.c.name,\n", " f.avg(sel.c.avg_speed_in_mph).label('avg_speed'),\n", " f.count(sel.c.avg_speed_in_mph).label('nsamples')\n", "]).group_by(sel.c.name).order_by(sa.desc('avg_speed'))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SELECT anon_1.name,\n", " avg(anon_1.avg_speed_in_mph) AS avg_speed,\n", " count(anon_1.avg_speed_in_mph) AS nsamples\n", "FROM\n", " (SELECT anon_2.name AS name,\n", " anon_3.avg_speed_in_mph AS avg_speed_in_mph,\n", " anon_3.pickup_latitude AS pickup_latitude,\n", " anon_3.pickup_longitude AS pickup_longitude,\n", " anon_3.dropoff_latitude AS dropoff_latitude,\n", " anon_3.dropoff_longitude AS dropoff_longitude\n", " FROM\n", " (SELECT bigsample.trip_distance / (CAST(bigsample.trip_time_in_secs AS FLOAT) / 3600.0) AS avg_speed_in_mph,\n", " bigsample.pickup_latitude AS pickup_latitude,\n", " bigsample.pickup_longitude AS pickup_longitude,\n", " bigsample.dropoff_latitude AS dropoff_latitude,\n", " bigsample.dropoff_longitude AS dropoff_longitude\n", " FROM bigsample\n", " WHERE bigsample.trip_distance > 0\n", " AND bigsample.trip_distance <= 39.5120219053786\n", " AND bigsample.trip_time_in_secs > 0\n", " AND bigsample.passenger_count > 0\n", " AND bigsample.passenger_count <= 5\n", " AND bigsample.pickup_latitude >= 40.477399\n", " AND bigsample.pickup_latitude <= 40.917577\n", " AND bigsample.dropoff_latitude >= 40.477399\n", " AND bigsample.dropoff_latitude <= 40.917577\n", " AND bigsample.pickup_longitude >= -74.25909\n", " AND bigsample.pickup_longitude <= -73.700272\n", " AND bigsample.dropoff_longitude >= -74.25909\n", " AND bigsample.dropoff_longitude <= -73.700272\n", " AND bigsample.trip_distance / (CAST(bigsample.trip_time_in_secs AS FLOAT) / 3600.0) > 5\n", " AND bigsample.trip_distance / (CAST(bigsample.trip_time_in_secs AS FLOAT) / 3600.0) <= 120) AS anon_3\n", " JOIN\n", " (SELECT boros.name AS name,\n", " box(point(min(boros.lon), min(boros.lat)), point(max(boros.lon), max(boros.lat))) AS boro_box\n", " FROM boros\n", " GROUP BY boros.name) AS anon_2 ON lseg(point(anon_3.pickup_longitude, anon_3.pickup_latitude), point(anon_3.dropoff_longitude, anon_3.dropoff_latitude)) <@ anon_2.boro_box) AS anon_1\n", "GROUP BY anon_1.name\n", "ORDER BY avg_speed DESC\n", "time: 35 ms\n" ] } ], "source": [ "psql(per_boro_speeds)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    nameavg_speednsamples
    0Staten Island17.61064483
    1Queens16.693146603622
    2Bronx16.1745953475
    3Brooklyn13.1086031157388
    4Manhattan12.7876977726282
    \n", "
    " ], "text/plain": [ " name avg_speed nsamples\n", "0 Staten Island 17.610644 83\n", "1 Queens 16.693146 603622\n", "2 Bronx 16.174595 3475\n", "3 Brooklyn 13.108603 1157388\n", "4 Manhattan 12.787697 7726282" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "time: 20.2 s\n" ] } ], "source": [ "speeds = odo(per_boro_speeds, pd.DataFrame)\n", "speeds" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/jpeg": 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3n9hXOsQ3HhvQrmK+ezM63Ok2M+/zbeNhthqFGNKnKNOzcVe9730T0vbddOnk2VVq1oVJ\nxc2rS2Vmu619H+J5J8QP2YP+C0/ws+Of7O37OPjD9mf9lJfih+1MfjH/AMKkj0z413VzoM6/BHwb\nZeOvGg8Raj/aP/Epa30HULZ9PZlcapdb7aIWzLvrwfxZ4m/4KOfDr9sKw/4Jx6x+yV4D8c/tweKN\nI8N+IfCvg34Z/EA6t8Pbbw1r2k32vT+KPFfi3Uby307RdD0PRrCfUNavL3WNOs9OhXbLem5eC2n/\nAL7Pjp+yVqnxg/bK/YR/antfGthoem/sd/8ADT/9q+DrjRbi+vvHX/DQfwr0j4dWP9n6xHqFtb6H\n/wAIxcaY2qXf2mw1H+04ZltYfsboZm/Gf4N+F9N1X/g7B/a78TXTXH9oeFP+CW/w5XS0Q2/2Ytr3\nj/4R2N5JOslvJOZUt4dlu9vcWxUTTrL50cmwbOjSas4K3q/nre+vUz9tV/nb3Wy6/I/HL44fsP8A\n/Bc79l74X+Ifjr8SP2av2UfjF8OfAmk3Xijx54R/Zy+JHjbV/ivoPhHS7R7/AF3WLXRPF0Gn23iV\n9FsluJ7yx8JweIdXkS3nmsdLvLeNpF+X/grqn/BTD9qf9k74kft6fs5fs7fs/ax+yv8ADTS/i5re\nual42+Kl3ovxC/s34MaJeeIfGBg8Oi6tknubfTbSZ7CGNSmozFIopYWJI/s2/wCCeepajrf7VX/B\nZTS9av73V9M0z9u/wXpOm6dql1Pf2Gn6Vc/sUfsvXVxpljaXcktvaafPc3l3czWUEcdtLPdXMzxN\nJPKzfiL/AMEYLS2sP+DZ39vWxsoIrWzstD/4Kb2lpbQII4be2tvh74qhgghjUBUihiRI40UAKihQ\nMCkqFKLuoJO1t35efl8+oe3q2tzv1sr/AH2PyS+McX/BUr9mr9jvw3/wUE+On7On7OWmfsqeJPCX\nwd8bWl/4T+K97q3xDOg/G6Twwngtv+Ee+1XKx3twfFulNqFtLGF0xftPmzTtCFk+nvgb+xX/AMF0\nv2n/AIYeHPjr8Nv2aP2T/g98OfHWk2vijwJ4T/aP+JHjjSfix4g8JapaLf6FrF3onhGG+t/DT63Z\nSQXFnYeLYPD2rwpcQTXulWlvIsjfob/wV10q313/AINaP2e9Eu3mjtNZ+B//AAS90q6ktmRLiO31\nDWPgFaTPA8sc0azLHMzRNJDKiuFLxuoKn9O/+Cq+rfHT4Y+Nv+CYXi39nT4M/EP9o3xB8Kv2rfFX\niDWfgv8AD/xr4Q8E+JvHvgzS/wBk747+GbmKbV/G2ueGfCVzBpN/rmj67eWuoXDhvsDXNlYSXEEQ\njpUqa+yu3X/MaxFaO1Rr0Sv99r28tj+QDwt4z/4KW+Of2wbn/gnJp37IPgXwF+29oPh7xF4t17wn\n8T/iA+l/DjVPCOiWFvq8Hi3wb4w068utM1/QNY0+dp9IvtO1rUrO8lhurMX0Wo2V9Z23tvg79nb/\nAILWePf2hfjZ+y34b/Zw/ZHf4xfs9+Efg746+JWnXnxl1a30K38PfG2LxhN4Ln0XWG1H7LqVzOng\nbXP7QtklEuls1l5i3PnuIv0t+GPxs+Mfx8/4Ocv2T/G/xw/Yz8efsXeMLb/gnF8W/D1t4P8AiR4y\n+GPjnxL410Ow8Y/Ea9s/Fi6z8L9W1vTI9Kgu9Tv9DsrHVL8alBcadfSraw2c9tJP/Rf8J/2QtQ+G\n37d/7X37ZVx46s9W0/8Aae+Fv7MXw40/wBDoE9peeEJP2fbX4nQXur3niJ9Wng1dPE7/ABBgNrYw\naPYNpi6ZKZrq8N0ghPZQ/l7vd9bX69bIcsTXnZym9FbaK/JH8Tn7OnwC/wCC1v7V/hLxv4z+Bv7O\nP7It7oPw7+M/xX+A3iifxL8YNW0mZfiB8G/FV14P8aW9haz6nbS3GlQ6zZzDT9T5i1G1ZJ0iiJKB\nv7Qvw4/4Kp/sJ6R8MPiX+25+z/8As5eHvhB8R/jb4C+BkWufCf4rXfifxHYeKPH8Wt3WlSvost9f\nNLZraeHtUea5A2xyrbRMmJi6+j/8G+3x/wDjtL/wXt/bx/Zvn+MfxKuP2fIPGv8AwUK+I1n8EpvG\nOtv8L7Lx2n7UPhzTz4ttPBhvDocOvPbX99G+orZi4Jup33eZK7nd/wCCpS/E39p3/gtl+0/+z/8A\nEr47/Ge8/Z0/Zp0/9kn43/Dn4AW/jfUm+Eln8RJ/hL4au28QN4LumutGt737bq+t3j3lla2129xr\nWpytOZLp2Mzp0YpylFaJJvW+rS7+n6l0KteVWEIy1b2aja27T02sv8jyf9pT4peLPhNoXw0tPh34\nZ0TxZ8Q/jJ8cPht8BvAGj+JdSuNH0C48YfE+/vdL0BtW1K23S2Vgt9bx/bLhVbyLd5JirCPFeQ/t\noeOf+Cg//BMiL4c3v7eP7PvwH8NaZ8X7L4ot8M774UfE+78XSXmr/DHRNE1nUrDXrZrq6eyjmTxF\npEVvcIVFy0lzsETQqkncfthQmPx9/wAE5iRgD/gqH+xqo4ABJ8Wa2c+vTp7GvrH/AIPcP+RK/wCC\ndP8A2Hf2uf8A1Cvg5UUadOULrW73u1bRaLppt+B04uvUo17UpcsLRly2i9Xe+6e/rovVnK+D/wBh\nr/gvZ8QvDnhP4j+Df2YP2L38H+OfDfh/xj4cOpfHPU4dTl8P+JdLs9a0qS7Q6vCLa7k069t2lhe2\nBglZo3UlSD5p8Bf2cf8Agt7+054K1j4jfBz9mz9jm/8ADehfE/4vfCHVLjX/AIya5pd0njL4J/Ej\nxL8KvG9vb2lxq9vK+nweK/CerLpt8N8epaebW8QQ+aYU/pg/a/8A2fv24P2mP+CSH7Ofwv8A+Ce/\nxu/4Z9/aKufAH7Lut2PxHb4k+MfhVHaeD9H+H2ly+KtKk8V+BNE8Q+IFXVreW1t10+LSp7a8lEZu\nnhjhEyfzQ/8ABr58bf2lLX/gp38QP2Y/H37RXxl8e/DnSf2bf2pPiJ4w+Gnib4k+IvFHw1b4/wBv\n+1f4Y0Hxx8RtB0jUY9Nt7nUfEmq3viPXpfEGpaPb69fX3ijW7y/2XF/NGuvs6enu+XX1118vP8Tl\n+tYjV+1l9y79NP8AI7DxV+yd/wAF3vh34v8Ag54F8V/sx/sZf2/8c/HmueAPAdvp3xs1WS3udc8O\n/DDx98XtXGpXTavNDplrF4Q+G3iVorybzfN1J9NsY7Z/tEk0PC/s6fDP/gtD+1742+Pfw9+Cf7K3\n7MPgy4/Ze+MHjH4BfFz4mfF34oeJk+F9z8XvA01vH4i8I+BJ/C88/ibxLLp0F1Y393qNjpV3pFra\narpYv9Ssry8jtW7X9rf9qL/gof4R/wCDgT9pvx98HPFPjT9oH4S/sIfFj4Qa74W/ZH8cfHC98DfC\neB/jh+xdqHhDUdR8OrqzanpXhm8gn8V+K9WupNJ0eaS+k1S/tbiIwancsf0p/wCCMv7SHgb/AIKD\n/sb/APBRb9gL4meNr39mT9tD4n/Gj9ujW/iV4Q0bxRDf/FHw34c/an8UeI/EVr8T/h7qt1e2d34+\n034bT+PR4CfVrT+z5rVPB2jR6hDo+n63oV9eJQpOTilFyi9Um7q6TV1fazT7a+Y/rGKjFS5pKE7p\nS5VZtPVJ233uj8ufixJ/wUI/YW+Mfwd+Gf8AwUP/AGffhVoHgf4/+KLDwB8L/wBoH9njxpqfiX4X\nXPxE1CF7mPwVrdv4omXxPo+tNAkk0VvrmnaDHqNrBf3vh641+30rVTZ2vCf/AA31+1L+0p+0J8AP\n2Ffgx8EPiHP+zJpnwf1X4m6j8XPiJfeBrgQ/GXw9qev+Hk0MRTRWt2Y4tD1OG7JZntitnIY5luG8\nqH/gr58R/wDgp34V8Z/sF/sFft8fCb9mDSP2cvCP7RXwV8XfAj9s34S6N8Ur5PiR4j+DOh6t4R03\nw5r+peLvG3iZPAPjzxRoWty6n4n8L6tpltc3jtLcaBretaDZanqEf6Uf8G/KeX/wVC/4LRJtK48I\n/wDBPng/9kp+IBz+J5/GodOm6qi4705StrZe9GOnrd3Rt9ZrvCufP7yrRhze7zWcJSs1bulZ211V\n9D8W/hz8Qv8AgqJ8W/2Z/wBo39sPw9+yt8BdC+B37IfiT42+Ef2gLbxh8UNS0z4h+H/EX7PmgWvi\nP4n6RpHhmO5kmvrzSLO7jsLVTHIt9qsdxbpJHDH9oPrf7Hfg/wD4K0/8FKPh2vx2/Y4/Zq/Z18C/\nAeWQ6d4c+If7UPj3xdo8XxE1rSriXS/FK+CNK8Dpca3caXomuWd9p8upajpNrpbTwPa2mq395bXk\nMH9J37bX7K+h/ss/8EtP+C6sXhWHTLHwt+0H4b/bN/ab0jSdLtUs4tK1D4n/ALL3g+y8dRXEEbGI\nXWp/Ezwv418Qu8axrJFrML+UjEiviP4j6prHgH/g2C/YIu/Bet6x4WvbnwL/AMEnVnvvD2pXWiXs\nsHiv9oP9m2fxDaSXWkyWcr2mr/2pfQalbuzJqFvczR332nzpjI/q1C9/ZrTpd26b6+X4s5vrNfX9\n49fT8NND8XPHni//AIKpfB39rj4T/wDBPf4m/sefCPRP2qPj02uXvwS8Ux/EPVP+GePihoHh3w/r\nWvaxqPhzxotxczW9zpMXh++t9W0bV7qx8RaRPc6KNa0PSLTXtHvr77AP7An/AAcOs/z/ALLf7DXl\nlh5ip8ddXV2TIDBXPiFgrFeA21gDg7SMg+8f8HgHjXx18KPA/wDwTt+MHwk8beKPhb8XPAHxi/aB\nufBHxP8AAWr3fhvx14T+3fBVZdXi0DxJpskGp6XFrUGm2tlqq2lxF9stUWKUkKm32X9uv9qT9pLw\nV/wat/Bn9p3wb8d/iv4W/aN1r9m39gTxFqnxy0Lxxr+m/FG/8QfEDxp8E7HxrrV54ztr6PXLnUfF\nFprusW+t3M140moxajdx3BdZmBaw9FO6prdNavS3zD6xX39o+3Tpby/q78z8K7v9tfxJ4H+DnxV1\nv4ofCwXvx1+GX7UXjX9j6P4SfC261HxMPG/xx8IataaAdB8ITC3u9V1mDUtYlubfTfsGn3l9qhS0\nhsrJrm9jRPuCz/4J9f8ABwTqHgAfE1P2Yv2J9JDaE/iGL4H6v8YfFTfGpgIftUXhmS/0/Up/hbF4\npmiIiSCbxfFp0V2fs9zfW8imNfkjwb+zRoPwa/4KAf8ABIr4VX3ivxl8Rl+KH/BRnxL+0J8R/Ffj\n6+tNY8T+OfijY2vhnxPc+IvE929n5OpXeq63D9s1OW4gkubt7u/ka5S4nWdP6cv23/iN+1d8Gv8A\ngrP8D/jB+z9+yp8Xv2yPCXhD9gb4meDfGnwt+GPxK+HPgOPw34j+KHx58I33hXxtqMPxQ8V+HdDu\n3fTPhf4o0YXWl22oatEskVtM1tZz5chCnJOSV020r30S/rrqb18RiKU40+dxlCEOa1neTipNvTs1\nZbdep/Kx8Jv2rf2sf2n/AIx2n7G37Mf7G2uXH7bGhah4jsPjV8Nvi3qj+EfCnwOTwdq1noPinXPH\nOqXMulXVro2m6nqFrG7GeK/ka4trPSrTWr28s47j6v8A2mPg1/wWT/YG+GmpftCftR/szfs0fFT9\nnrwlNb3XxU179lHx54417xf8NPC8lzDazeMNd8PeOIrXUr3w7pjzxzaxquh6Pq1noVik2reIZNF0\nO0vdUtv03/4IleLvFvxW/wCC0H/Bdj4qfEv4EeJP2bfiNrWm/sLLrnwZ8Zaz4L8S+KfAsuo/CrxI\nLu21XxD4Dv8AWPDOoTeI10DS/FQl0jVZVkg1W2GrRpq8FxDa/T/7D2qap8QP+CbP/BXeDx3qepeM\n4rn9r3/gsH4WnTxVf3eviXw1beNPiPpNt4fY6rNdMdFttL/4l1tpmRZW9ji1hhSABBXsoWty3+bv\n95m8ZiG0/aWtbRJWdu6ae/U/nxf4yfH/AOPnxr+BP7PH7CHw9+GnxZ+I/wAd/gvr3xz8Lz/FPxZd\neDPDUPgvQws108+oWs0Ua6msJJaxe4DieOSASb1BP0zZ/sHf8HDcVxC1z+yx+w3Nbqw82OH476rD\nM0Z4YRyvrs6I+OjPDIB/d5r8E7PTPHvw2/4JWfAn9uz4OfGj4u/BP9oj4CfCa48G+DvG/wAKfGN9\n4N1S48J+Mfjr/wAIRq+galqmkmDV306TStWlY29vfwQyywwCeORUAr+vX/g5i/aX/aG/Zu/4JIfA\n34pfs/8Axt+KHwZ+JWt/H34AeHdX8f8Aw28aa54T8YaloevfCf4nanrWl3uv6TeW2o3VnqupaZp1\n/fwT3Ekd3eWVtcTh5YkYRCnTktY3cZNXd91Z3Xla3T5bmuIxGIhOLVRxjOnCpFRtZKUden8yl8j8\nWv2LrL/gsF/wUm8B6j8U/wBlX9lT9nD4d/C/QdT1TwtJ8Sf2j/iD4v0/wx458Y+G9ZvdC8VaT8O7\nTwa9x4h1a18PajZT2WoatPpP/CPxX1vc6dF4gu9Ttbqwg6vWPib+1/8Asv8A7TXg39kb/gof8AfC\nfwd8ffFWz1nUfgX8V/hT4pn8VfBf4v2/h9LZdZtfDGo3893q9tqVlc3tlbXmk66dJ8RaZPqOkNqv\nh6zsdb0e/vf0z8Uzav8ADb/g0m+EereDvEniTRden/Zg/ZJ8Ut4jsNYutO8Qrq3xG/aC+EPiTxVK\nmsaWbC8A1G88U6zbTuZDc3dhdyw6lcX0s91Pcd9/wc82ttHpP/BJjWkt4F1i2/4KefCXQrXUvLX7\nZbaR4j8L+KV1vT4Z/vx2upnTNNe8hVgk72Fo0gYwJjRRjBNxVtO71t637EU8ZiHUgpVHJcyTi1Gz\nTeuyVvVanykUlGWLN8rDII5YnvkkjH55/iq3D+8xu79e3Ug5yMdj1q/cW5Q4A3dQSAMAf3gfXGOM\nc+gwMvtrLIALDBweeD8wDY6df/1H1OX1iGnd20v3Sa6Xe/bSx7l7/wBL16ev9Kx/K9+x54Z+N/7P\nHwp1H4W+KvDU/wAOP+F3GOO51LVdNjbUNU0+c20/2O1Gp2r7Xkk0vcTA6OFD4bGRX7N+Cv2C9E8X\nfDjw/wCF7/wlpng3SojHcDWYldtfmkcSE3qi5QNHuaQ5VLgJ3xnmvrj9qfwEl/cfD74uW+j6PeH4\nVapHfPockFtDBf6YYLmwRIYlgYGUTaiHEcURIEYIPAIyPjD+3B8OfgL4f07xf4/0G80XwxdWsbAT\nC4ivYQFbcLaykaEPH+7bDB1Gf4cCvMqVMNSrVY8qquaSXLZ2krb9tVb1/HajgoUW6dWoo8vK720b\nlbZXSas306n1h/wbPX9n4b/Z6/4Kd/sLT+IvD938WvhB+2R8cNbsPDjTGw8Q6r4A+KfgXwr4e8He\nObm3urjbPomt+IPCGt2UWoWVrFa2DQwRXzB7yze45r/grZDF+wr/AMGsnhL9k39onVfD/hf48Xnw\nA/Za/Zy0jwTY+INJ1a48Q/E7wZ4z+GWveMNO8N3FrdeVr1h4a8O+EPEWvanqemfaLWLT9PV9+67t\nFl/k0/a8+MsX7ZP7Vdh8aP2NG8T/AAb8faLoX9l3nxS8BeLvEPw28X6ukYiiimk1nw9eW94kos2S\nykuILlZbiGCNbl5RGoHb/BX/AIJn/tP/ALcnitvGv7TnxF+LHjvTNJs5/D0Hjf4z/FrxZ8S/FMNr\nczPczJoOo+MJJri2s/tIupxbxXBhe4mMzK0hLntp42LUIunUjNpe60rbLW91p12+V9DyK+U1o1Ze\nznSqUpSbhKMnfleq5o20avbRu+6P7ovH/wC1iP2V/F3/AAQIs9V1Kz0zwT+0n4bu/wBmfxncahfG\nxtI/+E8/Zv8Ah9r3gF1Zz9me7m+K/g/wFpkP2jaPs+pXkcckckoJ/JX/AIOEtI8f6z/wWI/4JwWv\nw003SdR8WD9lj9oBrI62qnTLGGTUtbt7rUJjJaXsSy2sEzyWnmW7qbryV3ISHHzHa/8ABF/9maTR\nvDVl49+Jv7UPj/U/C1xYah4Wv/EHx616e18Ia1p8MS2OpeCbKSxmXwxd6e8MX2G8sZzc232a2EE0\nSRKK94+BP/BPr4Lfs9fEW3+K2k+Kfjb8RfiBpmj6poOj+IPjJ8UtX+JF1oui61zqGn6Q+p29v9ht\n7nGJFhC+YQHcO2WPZOMZw5JK8akWmu6aV0+2jKw2V4mFajUk6SjGpGT95tvladrKPXTqtzxP/gkh\n/wAE4/g7+0h/wSC8cftJfsoeFNNsP+C1vwl8XfHHw3pfxp0b40ePfAXxA8BfGjTfi1rWteB7HxFZ\nR+OLDwHYWmtfCe+0bT4rfxT4Vk8K+KNHu59P8QQ6hbNqxX9Tf+DgDxbaWs//AAQd+H/xI8S+C2/a\nD1L/AIKufsY+LdY8M6bf2C6vqNp4eMuifE3xL4c0pvK1NvBdh428SeGdPur+K2jsIrrVvD9tcbbi\nW2jH51+P/wDgmb8EfE3xR1745fDL4g/tE/sufFDxYXk8U+L/ANlr4xav8HtS8QyyTG6ll1JtMstQ\niZpbgm4kjtkt4HnJnMPmlpD5ddf8Eh/2bfEOv33jP4m+P/2lvjF8Vb260m8tvjZ8WPjhr/i74v6L\nc6BcPfaRc6H4zks7O7sbiwvmN5Z3JglubO7RJ7OWCUZbKVaFP3bSurKyVtNNrtX0d7bmbyjEucox\nlScU9JylJXva11ytpu6/zP2G/wCDkb/gl3+1p/wUB8DfDX4jfs6/HfwV8JvA/wCzn8HP2mdQ+NHh\nHxb4z+KHhgfFHRfEXh/wnq9jpMOn/D/w9rOl+IIrHTPB/ia1a08WTQaf5uupb+TPY3mprXk/gb4T\nax/wVW/4Ne/2af2df2VNW+Hvj74j2vwq/Y3+HviTw3rPinSNNsNE1z9nf4tfCz/hZnhvxcZpNRTR\ndStdA8B65qtvpetQ20+uaa9jcWVq8WsadDP+Xl3/AMEpfhGJZ0uf2kP239asJhPBd6NrH7TPiW60\nq/tLhWjmsry2TS45Li0lid4ZoHnYSwsySlwW3XPCv/BKT4M/DvVrjWf2ffjP+11+ynfapp+n2Gv3\nH7NH7QXiD4XSeJItNSSO1l197PT799RmUSzNy6Qo88xhhjMzgy8XTTtafry77ba66PWwv7Jxdm70\ntHoud3d+q9383+av9J/8F1NG/wCCaH/BNj9qL/gkNN8B/gL+yv8As9fFjw5+3h8J/wBoH4vat8L/\nAIXeEPB3iDw7+zl8PvEGm6T4i1nx6/gfRtM1W38K32oeI31XRYdU1K1h1XUfBeo3On2Oq/2Ffvp/\n9Mvjb4FePfHn/BSn9kr9rXwpHoOs/A74f/sf/tSfDXXvFNl4h0ueSbxL8ZfHX7N3ir4fto1hDPJP\nrWj6xofw98S3p1rT/O0+3SCzEk3+n2pk/lE8C/8ABLT9lbw43xGv/F+h+NPjt4v+LFrNY+PviZ8e\nfGuqfEb4keIIZ0gDyXXii4jsJ474/Y7YjU4YY9UAt4ohe+QphPMWX/BKXwD4e8Ny/Dzwl+1h+354\nG+EF3b3VpL8EvB/7U/iPRvhIdLvFuIrzRv8AhD00WWzOk3EFzc209nJK6zQXM8crOJZNzWJg9bSs\n0mm0tenfRpj/ALIxVotOk291zNcruktXGz3V7beZ+qP/AATe+NPw6+O//ByP/wAFl/GHws8R6d4v\n8JaJ8BP2cvhu3iXRry11HRtR8TfDXSvA3gzxrBpl/ZTXFteQaJ4u0jWvD088chR77Sbsx7ovLdvr\nT9g39rEftYf8Edf2jbzVNSs73xt8BfDf7a37M/jO3gvjeXVl/wAKd/4WHoPgF9RWQ/aYLvUvhQ3g\nPUpvP3Ca4ubiSGR48Bf5+0/4JDfsnaLZ6Wvw3u/jP8GbjTNJs9DvNR+EPxb8QeB9V8SWdrO90v8A\nwk19ZxzTaxLJcyPPJ5vlxxyEfZ4YAqCuGX/gkP8As5+FLe+sfCnxD/ac8IaNrd/f6r4j0Lwt8dvE\nWi6P4r1HU4o7XUr7xNp9paLHq97qVvHFb395cZur23jjhuZpY1VRTr01vzL5afem1+PT0uTyfFxu\n70mu6k9bWvvHpfr+p+yv/BMP4BeOf2qP+DVbwD+zZ8M5tBtfiD8c/wBlL9pn4W+DrrxTf3OleGrT\nxB40+L/xm0PTbzX9RsrDVb6y0a0uLxLnUbix0vU72O0ilaz0+9ufKtpcH/g5zv8AStO/ZH/4J0fs\nxvqWlX/xA8c/t4/s4R6dpNtqltFq8+hfDXwb41sfFHii00KbGo3fh6xu9Z0mzvtTREg0681fSLa5\nPmX8YX8ZrH/gk98GvD2l6foPgf43ftf/AA98MaVHNHpvhnwR+0Hr2g+HbEXNzNe3JstJh0uS0szc\n3lxc3k4tUhSa6uLi4kVppZHbv/hb/wAE5fgH8JviHafFtLf4gfE/4naZFbQ6d8QfjD4/1/4heJ9O\nS0R0tvsN1qckdrE0CO62hNo5tNxa1MLfNUyrx5WkpNtNLa19t79G9TGGArc0XLlUeZXerdlZtJcu\n/rZeZ6n+yBYPa/8ABf8A/wCCbcojYQSfBn9r6JZdp2NLF8GvGbOgbJGVWWM4z0YYHWvmj/g4Hglk\n/wCDlH9hGZI3aKAf8E7vNkVSUj8z9q/xkqb2Awu88DJGTX0B+0H+yX8O/wBorUfB+s+Kb3x94U8T\n+AxrS+FfGfw18Y6j4H8WaJF4gtYrLWIbPW9NRrmKK/tIja3Ea4WaCWeCQPDNJG3zpH/wTL+AMsF+\nfEniX43+NfEt0uhra/EHxr8V9a8QfEHRD4Z1KbWdBbw/4pubdb3Rm0rVbifUbE2QjW1vZpLmBUmk\nZzFGrGFKEXduOjsurd29X5/ga1sFVq1pTThaVtW3e/Ko7WfXrfzt0P63v2zdf13T/wDgrL/wRc0O\nw1rVrHRPEP8Aw8Y/t/R7PUby20vXP7J/Zr8LXmlf2xp8MyWmp/2Zdu91p/22Gf7Fcu09t5crFj+Y\nPhb43+FPhX/wdzfGXwN4t8QaB4eX49/8E7PAvw48HnW5mtbjX/H2laj4F8f6R4X0S5e5htBqmpeH\n/CXiq7htJ45ptQbTUsrAC9mhin/EGT/gmB8JdR13RNb1j41/tcax4h8NSa1/wjHiTUv2gvEt34g8\nLjX7NLHWR4c1aS08/SF1KxjSz1D7C0bahaRpb3xuoVCVsah/wSr/AGXdcsdSHi28+MHjzxRePpU8\nHxJ8afFDW/EHxI0e40RmfSptG8UXESz2ZsnYtbRvFNbwsFeGFHVWGqrwe1+nTTXzv0M/7MxPen/4\nE3+Uf+GP7Hfgb4Ovf2J/G3/BWH9qP9pHV/C/w7+CXxK/aL0/9o3w74z1DxJpLWcPwq8G/sq/Az4f\na3rOuL9oQ6Jep4p+H3iTT7PSL9or+/MVi9rFKNSsxL+CX/Bur4l8NftWf8ELP23f2V/hX4h0Wb48\na9qP7aHheb4fa1fw6TrGh3P7QXgK/T4Z6tq0VwR5fhrWp9eis/8AhIrb7RpUV9p+r2TXP2rTriJf\nzc13/gnL4V8eWWlaN8af2lv22/j/AOBNB1Kx1DSvhb8b/wBpPxT47+HsM+mvI9g0Xhu8sraOMWnn\nTrCYJ4pYVnmWKRPNfO14q/4J8fBTUPHelfFL4Ua78Yf2ZPiTpGir4eg8efsx/EnVfg/4qOjJBb2p\nsJNT0qC7YQvDa28dx5KwG88iOS8a4kUPTlWgmuqdtU097W0ve2uvbzK/svEJNt07q1o8zd++trLp\na++uumv6ef8ABwX4x0n9kD/g33/Zz/Ze+J+r6BB8fRoH7EHww0bwPp+qJqUuteK/2foPh/4m+Id3\np7WqC4uvC2kR/DvVLaXxAIYbNpr7SYRItzqNrBJ+yv7R1p8Q/wBrA/8ABK79qr9lr4keGb34KeAf\n2hNE/aI+NnxJ8O/Eaz8L+E9Q/Zs1r4CfEvS/E8ovZdRhPiLw/f6pr2jWOq+GpnuGhCvJrsFvDpOo\nT2n8k2g/8E9vg6PHdz8TPi34p+N/7Uvj+XRptAh8YftQ/FHWPi/r1npU6zpJZW17qttZssSi6ujC\nkgmW2kuJZ7YRTN5o44/8EyfhlF4N8R/Czwx8e/2wvAPwS8W3eo3niH4B+CP2gtc0X4KalJqsvnah\nHffDx9Lu9Ivo7tsC5W/W6ecAedJIwBC9vC/W3R23e1v68+xEsuxEUneDbbvHm2Wlne1nu722t1uf\n0SfHCaG5/wCDpr9hm5tpYri3n/4JafF2eCeCRZYZoZfiR8VHimiljLJJFIjK8ciMyOrBlJBBr7B/\nZk17XL3/AILXf8FT/D15rWrXegaF+zT/AME7rzRNDudRvJ9I0a71vT/2hf7autK02WZ7LTrnV/7M\n03+1J7SCGXUP7PsftbTfZIPL/kOu/wDgmD8FpNatPFTfFP8AagPjXT9FPhzTfHZ+PPiaXxnpmgtL\nJM+j6f4ilge+tNKlnnuZ5NNhddPaa5uJDa7pmy+2/wCCZfwps9R1TXrL46/tf2HiHXLfRrXXfEVn\n+0P4kt9d1608Pi8GjWmtamliLnUrbTV1HUV0+C5eSOwXUb4WC2xupS0SxVKNr82qutFt94o5fiJX\ntyadOZ9N38Oy6nWf8G+0M0f/AAcoft3yvG6xTH/goiYZGUhZNn7WHhBX2EjB2tgHBOD15rU/4KH3\nHx/8Yf8ABzX+0B+zV+zVqHww0H4qftPeG/2bPA+ieJvjBbeIrnwNocXhD9lnTPiHqE+pr4Wtr/Wl\nNzZeH5bOB7TTr4i5uIBJFHEzzxchN/wTY+AFrbWMnh/xN8b/AAT4ktF11br4geCvitrXh/4ga1/w\nk+pQ6zrzeIPFNtAb3WW1XVbeDUb77aJVuryFLmcSTIsg5mH9gK8+EXxc+D/7RP7NXjkH46fCnxZq\nnjBPH37R97rnxmutV1eTwhJ4Q8NXmqW929uusy+E4ZjqGj2WrvPo6XunaVBqGm6po8NzpNxDxVCa\n5J3tJ63TtZNNXtd+TS031sVLBYmlJTXLdJcri7tuyTsmtteq/M8g8feO/jFrPxp+A3wa+O58BX3x\nU/ZX/wCC5n7NvwE8Xa78MINbh8Ea/e+CvGmrWE2t6IniGK31YWN3dmVrdr6ysZ/KkhEtrFJkV/TN\n/wAHRX/BMT41ft/fs3/Dj4t/Cbx58L/B+k/sReA/2qfjJ8RNN+Idz4rt9R8WeHLr4c+F9ak0rwYP\nDfhnxBaya19h+HmsQKNcuNIslur7TT9qaI3LQfhf4G/YN0PUPhN4r8H/AB18d+JviV8Q/iT8a9V/\naL+IfxT0e+l8J+Jr/wCMOrXU18/ivRdQsIxJpd5Z3c893Z3NpBZG3u52ktLWzSOGKPIvP+CZHw21\nm11HS9b/AGif20Nd0XWbO90zVdC1j9o7xHf6NqWmalBJbXunXtjLpxS8sbq0lmtrm1vDPb3NvLJD\ncRyo5UxSxNGmpxXNy87cEo6crSstWrWd7J7KyFVw9eq4Tk05ckYy5nqmr7tJp+b+Suf1K/tw/sRf\ntC/8FA/+CPv7Ov7PP7MXxc0H4JfFK88Dfsr+Mbbx34j8TeOfCWn2+h+FvAGlzazpf9sfDzSdZ8SJ\nPqkV3Fbpbw2qWs6eaLmeMKgf+Yr/AINqNC1XwH/wXJ+Mvwa8QWGgQeJv2ev2Sf2rvgH4v1fwvc69\nfaH4t8YfDX9r/wADab4l8WWN94mkfXbyLXNTuJbuK41RYL6W3aB7q2gnaSNa6f8ABMX4eWi2kFh+\n0h+21p2n2kNrbWumaf8AtLeJINPtbW2jjhhtbe3GmM0FtHDGsaQwyIkUShIVjVVA05v+CX/7PUMU\nFx4Z8V/HnwR4q2a6upfEPwZ8Xdb0H4heIz4m1WDXdebxL4qgtpL7XW1bWbW21S//ALQEq3eoW8N5\ncLJcRpINY4mlUlFRU29baaK9vPW90lv18rpYCv7y9xXWnvPVprS9tNL+p9E/EyKU/wDBbT/gtI4V\nwjeL/wBihFcAhSyfszW5YA8ZKh0JA6bh0zXF/wDBI/8A4J2/s1ftj/sJ/t2/EHwR4IsD/wAFkfgX\n8Wf2pvDPw0+KMnxi+JXw7+Ivw5+JV54ZmvP2e/FNpBoXjzTdD0rw1Pqd/Jo+m+ItU8MjQtQ1LSfE\n+mawL2107Ui3ZfAH9lf4f/s7/wDCWXHhDVPHniXXPH13od94z8XfEfxffeNvFniCbwzp82l6EdQ1\n3Uo47u5XTtOmextUkLLBaRw28QSGCKNfOfiL+wR8I/HvxVk+PHgzxd8av2d/jRcQ3lpqPxT/AGbf\nijq/wm8a6rDemP7YL7VtMt7sk3nkwm8a2itjetEkl59okXecfbxpYiq5XanyvRXcWoxik9eyu+qd\ntHfTrngassNRhFx56bk2m/dfPJt2aTTcbr8d9L/pB/wcMaz8QNI/4I5/sS/Cn9p7xj8PtS/bm1/4\n4/sajVdN8O3tpc3Xib43+F9BvYviz4u8C6bDb2l4+jWdxN4j/tLX7HTNP0S1i1qLS0ez/tzS9Pus\nX/ghskif8FFv+C5pdSA/wp/YVaNiMB0HwW+Jabl9RuRlOO6mvz28B/8ABP34U+F/inp/xx8d+OPj\nt+0p8XtGgt4ND+IX7TPxW1b4veJtBW1ne5tTpl/qtpaeX9luJZZrUTJOttLLJNbpFOxkrofij+wj\n8KPi58SNb+KN741+OXw58c+Krbw5b+K7r4QfFXW/h5beKLXwrp50rw9Fr9vpEJXUTpli88Fo00m+\n2W5uvIeL7TLvbxdP2sZWlyqnKN7a3lKL2v05e/oQsvrLDyheHPKrCdr6KMIzj8Vt3z32tp5n6z+B\nf2qm/bJ/4NSvix8Z9Q1e51rxdF/wTR/aH+GPxEvL/wAr+05fiD8Hfhl41+GPia91BYSQJdduvC6e\nJrV3JnudM1uwvLj99cSCud/Z38J33/BSv/g3D/Yz+Ef7KuueEfG3xB8AaP8AsI+GPF2iX2uQ+HT4\ne139lb46/BvVfipoOsPqYzp2sad4f+Hmu6rpUV6LeDxHbCwutGlmt9W04zfjxoX/AASB/Zk0PRG8\nM+G/Gv7R/hrwRqMd/FrHw90D43a/pXgTxBFq8H2LV4df8M21mun6hHqtiPsOpxuqx6hZAW12s8Qx\nXp2i/wDBKL4LeFPEd74k+AHxm/a1/ZN1bW7DStO8Qv8AsvftAa98KI/EcWkWotbWbXFs9O1GS+n2\ntLI/7xIRNcXMkMEPnODp9do3Wk13bS0+5sx/s3E6/wAO66cz19Hy2PaP+Dv/AMeeDfiLP+x3+zD4\nU12z134xeEfD/wC1R+0X4x8IaTcQXureDvhl4e+B2r2ula94ns4ZGuNItfFupafrlt4akuokXU18\nM+IHiIWyBk7/AP4KHRvL/wAGdnwCijRnkk/ZX/4JpIiKCWZ2+IH7PgVQBySScAetcN8Iv+Ca/wCz\nf8Fl+KWrmHx/8UfiB8a9A1/wz8Svi58afGl38Q/in4m0fxTpkmka7bXfiu/tbVhNqNlK0N1epaC+\nnQLFNcy24MJ8m0n/AII9/szJJYWWpfET9p7X/AmmjRUsfhV4h+Omvar8L7W18N3VneeH9Ni8HTWQ\nsv7K0e407TpNL01na0086fZGyit2toWWvrMNXrZ25NNW92n56qy/XQ6Fk2JkqcYypuUpPn952jfl\nt9m7sr83RPRX3Kv7aHiHQfgB+1v/AMEov2rvG9zY6H8NPgr+214a034m+JNUu4rDSPCfhX4kQWGi\n3/ivWdQupbez07StAtNMu9Svb+/uLaytYrcyXk8NuJHX+qnxn8L/AI1j/gp/8Ev2odF8Vadp37I7\nfsbeOPg74/lXx/YaZpXiP4y+J/jL4F1P4HR3Phh76CPxQ95ZeIPFdh4N1WCO/jt9V1240y1MV9rt\nhHf/AJJ/E74B/DT9oH4c+Ivhd8WvC1h4w8CeJ7UW2p6XqCtuV0Ja1vbG6j23FhqVjJtls762kSeF\ngV3NFJLFJ+d99/wRS+Bmp+HPC/gG7/af/b2u/hZ4D1jSfEHw/wDhFdftPavP8MvAGs+HZDL4d1Dw\nT4Vfw4bTwze+H5Np0S/0t4L/AEsqrWV1A4LFUqipx5ZXeraaV93fv+Lf479+Y5NXqV3VoOEoSjHm\nUm4uLilHs78ys973vofoH/wT6+N/w48E/wDByj/wXA+CfijxVpWh+PPjT4d/Y51/4caHqTvbT+LR\n8KvgR4dTxXY6PdSFLOfVNPtfiDpV+NI3nUb6xW+vbKGS20nUWi+ntO8K3P8AwTN/4Jef8FT/ABT+\n1prXhPwNo/jH49/8FHPjR4OurbxNpuoReJ/Dn7RvjHxhf/BTQtLcvbl/G3j+61/RNC0nwnKkeqjX\ntTttNnhjkLlPxF8Uf8EXP2SZLO7vbm6+NN349/4SHSfE1j8aLj4ta5dfGnSdZ0G1Sw0a70zx3c28\n15E2m2cFlBYyXFtdy2UenaelpJCtqgGZdf8ABK34X+Mte8I638d/j7+2X+1fYeBdS/tXwn4Q/ai/\naL8Q/FfwfoF+YxEJdP0S80zThaMYlWKRYbmNZoUEFwJYSyG/bw10kn2a66WV7tXd+550smxkeVv2\nbTcU7SbcW7brlTdr300PzB+Kfw/1zwN/wb52+h6xZT2mqL8LPhrr9xbTxMk6WviT9oPwp4ptsxuA\n6BNP1aCVsgfuxuI4yf6Mv+DqfQ73xP8A8Ed/2W/DmnQvPf69+1f+yxo9nDGrPJLc6l8JvitZwIqK\nCzM0kygAAkk4AJrn/il8Bvh58Zfhh4k+DHxC0oz/AA/8VaPZ6Hf6PpciacItOsLyyvbC3sHgi8uy\nWznsLRrZI4fLhWBERFQAD4v0X/glR8CfDHi/w34u1f4iftKfEtvBnijSfGfhbw18Vfjf4h8beENG\n8RaGzNpGpWnh++to4Bc2Alnjt55JJJ4obi4gSVYppEaKdWKUtHrJtbWey77eZpicBWcqT05IU405\nPX7Du7JJ62l96eyP0O/ZS8N6/wD8FLf+DYP4afs4/s46p4O8TfGnQfhb8HPgXr3hi91geGYvC3jT\n9nr44+Ab/XfD3iqTVmml0XV5vBHghNdtJLtIrPXF1TTdQ0wQ6Zqtp5UX/Byr8SfBPjD4xf8ABJr9\nlbQtctdW+Mw/bZ8LftIat4Q02VbzU/Dnwn+GWiatpmqeJvEVvbl5NFstWv8AxAYPD82oC3GsjQvE\nb6f9oXRNQNv+dOuf8EyvhFbfEbxP8V/gv8Vf2nf2SvG/jdVXxjqn7Knxv134OjxOVnluj/a8dhY6\ngZojczz3AtInhsYpppZIbaJpGz6X+z7+wD8D/wBnrxv4k+Lel3HxH+KPxo8Wbz4j+NHxq8caj8SP\nibqqy2sdjMtx4kvoLRpXnsoYrSe5a1N7JaRRWhufsymElTEwUbK/NO8YrTdrvfz0tq9O5z0svre1\njeUFGMk3K7baTvpGy1eitfS+7Ppb+y1I2b8E4C9xuweT169+f5tVWSxa1kVGBLE8bOTyvTpg/eOM\nDk/hXpUWmK6GIR5lwXLDoPxxkHB/DryQKybiwaJ/L2gyc7e5z2IyCcenA6egrFx5UnpaVtNFul0t\nfVtLX/gHs+ydlead3ukrpK3Ta7t+t7n4L/G79un9o39r3w1B8PP2aPgb4m0/w14heOzvPFeq6Rql\nk9pukEyzx3Fzo6lYR5UJ3LconzMMkg11XgD/AIJEfGL4q+KvCviL9rj4r6h4n8OafaQ3c/hGz1OY\nWqYlYmxeG31QxthJCSzW2ecFa/oC8P8AhbQfDWjW2keFND0Xw3YwIIo7bSNKtbE+WoBUE2qRZyQO\ncdq1WsZs+c0sxJ9S5zjg4OcEnHPFKhl9Kmk5PmqdZ9726+Xz163PWlh44h+0nJ6x5bJJdVZ67rut\n9fNHzb4D/ZE/Zz+F+n21l4K+GOiWZhgW2+0vZ2ct1IEwNzyNaeaykrklpDzjJxxXv2n2Gm6TZQ6b\npumwadbREkWlpbxWsY5yW8uGOJSfmJB2A8tz1rftkJBYoSVJUcHkEHkgdMduPxqSC2lnZ5nRg4Ow\nFlIIznkggYHTH155rf6vFauXvXspNX0stPzV272ua08NTprXXVWvbpby0vbpbT8GTrFJbptUELtJ\nJUfLgc/MR8p7Y6Yx6YObLB8u4KSCARxuLe2TxjGep471rSxG3XbkOjkblGeDz1HP+1+PNNit5JGw\nOE5Iz247enPUZ7kVtCm4Jpybu9+3w6Lp026oc6Cck4vlVnu73k7WSvqtumq82ZMFuZmwU8s5OIyv\nUccjgAY47EY6bh1gv9PICusYZlB3YUdMA5Py89B8p7ZrpDZzOFnydysUVRkEgfKD3yvI/Ie9W2sJ\nZIi7ME4GVfIbHPAyP4fQDr1rmq6VJW79XbovJijh91KSt5Jdk+lv0ex5LcfZfNCLakyEjezLgA9u\nSpB+p6+tbFnAmwb1RSwG0YUgDHGePXj+vAzf1Sy8tgEj+QYLyAEncOwOMqOg59cdqghgdDHgOygD\nGcnAx/n/AAFKMoxS5o3cbq9rp35el97r9LWD6stbSWj0t0tbR732/Flq3jQJIvlrld3IUbsknI3Y\n68nOOnTjtAbdI8S4LRHO8OSzB+20Y6YznA6+ordt7b9yJCpzjvnOctxjH+Rn3pwtXdJCIyST0A7j\nHYc9u4P4Vn8N3069/wDg791bzdxPDP8AnXZO2t9LPp29TkbiNIcGPZKGJO35VK+5AzwB7Y4HcGue\nvdNuLrDFflD9cbjt/i57/TGPfrXoB0gly4HAXBxkgcdz0/M5PrVtNMypzGcNkA4b0GMZPH4H6d8l\n3/L+K8v+CvkY+yafLZ6S3adtLJ7XVnbr/wAE8wbQzNG0YjHygHcEAHy89QMegxnHrnrXxD/wUK+L\nniP9mr9jv46/GTwmFh8TeE/DOmwaBdyRrItlq3ivxToHg2yvwrKyNLYT+IUvIRICvnwR7gwHP6dj\nTUtYUVoThsnkE55GQeBx+H59K/Nj/gq94X8K+IP2Av2m9O8Y6u/h/wAOnwXpN3Lqcdt9rNvq+m+N\nPDWp+G4vs/mQZju/ElnpNlIRJmGO5eZVcxhGuOsoq2jkr37XXRXv1+70FicOoYavNSSlGhNwe3LJ\nQfLK70SvrfyurdPb/DX/AAb6/sEeAPC37JOnft9/tFfth/F79rH9rvxhYeCtI8Q+Fvix4h8OeFk+\nLl58JPE/xc8TeHvDen6FplzZeHvB3hnRPA3ix9O1zxHqd5f6rLDZRgeZfRada/njqX/BMT9kr9nv\n/gqf47/4J6ftyf8ABSn9qnw/8D/EfwR+Cnjz9jJrT4y/8IP8QfFfjH4wfEjXvh6Phh4jvrbwX4q0\nrxJrFlr3hjWItJubDSfDdvdafqOkzXiJdGVK/Yz9ib9s34U/8FWP2avg3+xn/wAFX/2Svj7+zD+0\nc1tp3h34aeNPil8Pvi18BvDHxh+JmgfD7VdMg+Iv7Lfxyl0zwVf+EPjRrfgx/Fuu3fw203VrfV5d\nAm8TWOiy+OvBza9Z2/8ALL+2b+wv4k/4Jt/8Fk/2S/2bfGPxK8ZftAab4v8A2q/2I/jF8BPjT8T9\nZ1fXviRafBe5+P2t+Hbr4a+JtR1JzYzajoHi6O6kv7nw5DZ6HqciQazb6fpF3ql/ound1l2X3L+v\n+GPhFKTb9+Sum2+Z621S+fmft9/wUi/4I4f8Eov+CbfwC8a/Ebxz/wAFAf25/hX8Y9d+FPxr1f8A\nZn0Xxz+03Fe2vxH+K/w88DvrOieGrbT9J+EPmamsniXVPCVnfac9/pkl5DqyQwXcTM88Gr/wTM/4\nIDfs9/tffsEfsf8A7Tf7Un7U/wC3ppvxu/aH8Br4wvLHwT+0hbaB4Vn/ALau/FXiXwja+HdH1T4d\na1fWjN8NNI03VbtLjVb3zbi1v7qO5eJolrh/+D17VY9G8J/8E7L2SCG5V7/9s3TVjnijlRZNa+HH\nwo0aGdUkV1E1tLfpc28gAeGeKOWJkkRHX9+PCXhn4rfs0fBP/gg58DPAPhnxbeeFvB3iv4T/AAy+\nOuq+G9B1nxJpfhrwl4f/AOCfHx58Jx3XjLVtOudW0vw/4d1b4r3vhC0j1XW7q40n+25tJttM1FNR\nOlmY5Y9l9yFzzt8ct9uZ+Vnv/Vj+OL9nr9mD9mjUP+Cqv7aX/BOf/goZ+3f+1J8KPhn8F/iRZ/DD\n9khtK+NWo+DvGvxS17x747sIvhz4f1zU7zwP4ni8Qa3qvgbXvDs1u8OnaNY3d3fG9t7qeymtyP1E\n/wCClX/BG3/glB/wTj/Z78deP/iB/wAFA/26fhZ8XvEfwl+Nupfs16L45/aeS7tfiR8VPh/4GfV9\nF8M2tjpPwjEuorL4k1XwlZXuntf6ZLeRatHDb3kLM88H52f8FpPBUfwr/wCDkX4e2I0+3WL9or9p\n7/glb+0HYXksKSXkC+EtdPwR1SOzvJri7uorTUtW8OXlzeW8J0+2kntLVGsHWytbqT7l/wCD3BFf\nwV/wTpV1VgNd/a5cBgGAaPwV8HHRgCDhkZQynqrAMCCAaSS1XKkk9NN7pNvbv+Q+abcffk20lu9N\nbJb9kme7a7/wQy/4JXfBD9kn4O/tY/tf/t6/t7fCDwz4z8BfCHUfEfjfXv2orWz8NW3jT4heEdN1\niDTbWO0+EOq3VpFeahLex6bDKZljREge5eQqz/Ff/BNL/gjx8LP+CiX7Hf7a3x48Eftg/tmah8Rv\nhl+1J+1v8F/2S/Emh/Hdrr4fa94S+HWheHr/AOBXiDxTocnhRNT8TSeIJNesL3XrrT9a0CPVbW5j\nNlZaYFEJ/oh/buH/AATtP/BHX9ngf8FQx4oP7Kx8EfsqDUh4QHxIOsHx2fAWlDwWFHwpz4vNubv7\nUJhb/wChE+X9u/dYr4p/4NcviB8K/hL/AMEsv2ofiBp893p/wY8Jf8FAP2jb7RLydb1bnTfh8dF+\nDVnoN9fjxJPa6pHHp+h3Fjd6m+tSrqsNvBcyXiS3yPE7suy+4XPP+aXf4nv33Pwz+L3/AAT80X4C\nf8EH/hL/AMFYbT9oL9s1v2s9Tt/gte+K/CXjH4yvN8MLXU/F3x0034d+KNFuPAM3hGw1yzFlpEtx\nbQ2F/wCJJLiz1KHdfm52S27fTP7Jv7FHgn/gqJ/wU1/a0/Z1+OHxj/aU+HXw0/Z9/Z/+EPjXwdpP\nwD+KNv8ADh7vxJ4wu9M/tS51w3nhrxPBewiC+IgjhtrKVZw0ks0yiNF/ZT/g5n+Hvhb4Xf8ABDL4\n9+DfBen/ANleHbX4xfBXW7PTg5kjtLrxj+1P4a8ZavDbkgFLQazr+oGzgO77PamGDfJ5e9vj7/gh\nnGIv+C2n/BS9QCB/wyp+zmcH3k8N1lKMfbU7xXwVenVSp2/BvXz+7eE5/Vq3vS/i0er6xrN/jFP8\nz5J/Yi/4Ie+Fv2pv2w/+Cj3w3+O/7Uv7Wt9+xR+wt8fZ/gv8MvBlt8Wp4vG/j3XJfBtp4v165+IH\njW18OB7jTPCmh6roMsdr4a0fSNU1a91O1El3bWljcW2r+V/8FD/+CXf7NfwH/wCCUsf/AAVi/wCC\nSv7TP7V3hvwFbeHfBfiLUfBXxF8c6vrvh34jfCz4g/EDTfhpdavBpPiiy8O+K/BXizw/rfiKx1V5\nZ7jV7G80fSLmzg8OWmo3UevRfTX7L/8AwUk+Ov7EP/BbH/grT8P5/wBkn9qD9pH9kb4vftb2c/jn\nxx+zn8GvHvxZvPgH8S7Dwxo9gPFWp6V4L8J62+u6drnhifT4vEnhyHVbHxFBp2h6ZrHh7TdauY5N\nI1Ly7/gvd/wSf/Z9+In7AfiX/gp5/wAE0Pjb4hsvgCvh7QfHfj34C+DviB4r1H9mr4ifDvU/FsGn\n6j49+Gvg7UNaXTPAeveFvEtxY6lrPgey0m18MM2k6rcWmg+G/FGm3C6tpyxtbljZ+SMOed0+aV1s\n222rdL9vI5j/AIKLf8EwPhj/AME9v+CcPwB/4KDfBn9pD9tPxB8aPFni39lFvE+i/En462fij4d6\npbfFrT7HUPF9o3hiz8FaFdCFpp5F0uNtaZLWPbHOLtNwbuP+CXX/AATA+DH/AAVl8Lftp/tKftPf\ntCftb/D/AMOfDn9tX4qfBr4VR/CL46WXw18AR/Dvwdo3hC+trq4tdZ8H+I7Q3iah4kmtnuYNRtIl\nhFtE9jExjuLv7t/4ODp47b/g3v8A2aLmUgRW/iH9gmeQnoI4vDumSOT14CqSeK5v/gkzJ8Sfgh/w\nbFR/Fj4a/D7xr45+KnxP8Z/FH4j2Phb4e6DeeN/GviS78Z/tYj4af2zo+g6FcXV7eXGneDtHg1e7\nVoHGmafpF1qGq6Re2lpdWl2vZw5lLljdRa2XdNdOlvkVzy5OXmesr7vovXzufkB+19+wIf2YP+C2\nn7H/APwSx8IftKftlz/so/tWaF8JfFusa/4i+MD3XxQsV1vXfin4Z8U6V4U8eJ4SsdHjgju/A1lc\nFT4d1B7JdTkguHdzbmH9CfFn/BAb4e+Ff+Crnwd/ZDm/ag/bsn/ZA+L/AOxX8WPjLpmpH9oWD/hZ\ndp8cfhD8VPBHh3xJo7+Jl8GNpQ8JP4S+I/g7Urawl8LxX82pT6gYtWlgtJIU+0f+Cynwwubf/guT\n/wAG+fxnttHmNnq3xY+K/wAMNa19Sht47nw+/hTxX4Y0icecZEmli8S+Lr21Jt0SVIbvE0jRFIv6\nRLKf4ZfEj4ualdwKbj4k/s36he+E7p3ito7nSofiv4I8IeK7iOKYLNcy6NrujyaE7IJbNZtX0BzP\nbTLptlcO1GK2ilrfRLf+kTzz096WistXou25/Er+wF/wRg+Cf7XP7Z3/AAVg/Zt+LX7Un7eZ+HX7\nDnx4+Fvw1+DDeG/2irbS9eu/D3jHQPHWqapL43v7rwJqdrrmpwzeHNMisrrT9P0SGKBJRLazzymc\nd5+0b/wSD+GPwn/Yg/aO/bw/4Jqf8FIf209a/wCGYIvjVqeo/Dr47eMJfH/w48Yaj+yz4o8U+Evj\nT8PrjRL3wL4J8RWeutrngbxRYeHdaktNV8Palc22mxxafLo2rJ4itv1I/wCCMX/KU3/g4n/7O4+A\nP/qHfFirv7Nn/KDz/gqH/wBjn/wXC/8AV4ftVUOMXvGL9Un+g1UqJ3U5prqpO/5n5wfs/wDj6f4w\n/BH4Q/FRkgjk+Inw18EeMbtIE8qCG/8AEHh3T9S1GCJM5Edvf3FxAi5OEjA5xmvboNPy6OQrPhRu\nXHByeQTnv29sV8af8E2Ypp/2Bf2UpHWWVz8HvDkbMQxbykSeKJRgfdSJURewVRjoK+8dGs2kEsbR\nuFTLKzKQccAYPGD1+hrxJpqc1vZtPa9otrd2PrqVNyp06kn704U3JvtKK+7W2v3XsZsEFwH8vDAA\n5ySQOvXOenQ4B6V2mntsu4pNxAWMAtkhQ2R1Jxzx6g8e1TW2nxzKzk42rjPJPT27jueAPTkUNb+U\nRbpko/zF+F+blME9cFcluvOfSop81T3drJ+8t33X3f8AANVSvbVXe68lbrvff/gK5vtfTXGEJBRD\n1zuB9vXOBxnJyK2LadfMjQqGHG5QoPpt4+noB61y1ukqDywpx64bpzjp17nj3rstMtMot06nLAqQ\nAeMcAYPQYA68mrhRalFqbkoyUmnsmrWXZN26LrodCfI09HyO9krX23fpt89dz0zTbi3+xRxqEDNt\nGCoXrj2PXP8Anit02hjVXWIyeZxwNu33JI/XtzXB6Kmy4VXLyMH3DhiqjOPunjIA4A/AjFeu2zkL\nCQUZSQSrbcj2APU/59cdagpSSvbmdttW7pJbdW/n9x6EG3BSb82krc19bdb7bq33nH3mifaYXMsA\nbA4BbPzADHGM9fy7157f6G8BDNGqjPzqMc8jngDPYhcHsO1e/JGJ2mDFY1UkqgIGOSfr7Y4rmNZ0\nyOXbg7jkfKPmOOOf19uR24ro+rX3k7XTVr3SVrr8Hr67XuhqLtaPLqpPztbR+u3pc8Vn0ZZpI7hV\nwqgDlMbcc/T2/E81T1DT5TPGUiVotuQThSevY+npz3z6161JpmyIKEJxnjB4yGzzzn3zXP6jp5IQ\nhW3jAVNp6fgN3fjjg8cULDd5X1b0uvTbS91q7GU6CnpZK71S2eq0WnZdt7WPLZLMy27RyRASKSFY\nICoHXG9RgnoOlLHprKm2b5SQMADHHvj+fXnn1HqQ0q3S12uBu27wcnO7H3R3H+e2KpHToyhbIEzA\nhQeFwfXJ29AMZ4rOWETlH3tFqr230Xay16+fc5a2GSSaaTva6XS0dH+X3dXp5uUWIERIp4CghSWA\n479ex65GciswQ5QkxMzCQkyOgBwW7ZBOM+p/CvSTpEecx7RweTkDP8Wefb09KhlsIBGySAF9vAVs\nEjB5GMc45OePwHLrx5eWPktb7vtay0v6dzndJJpX03v1fl2VvPR90Sw24WQIsWOcKB2HPuO3T1qw\n0bSyGBFJOApXGPT14H8/yroltGRieMq37s8tn8B9OfX3q5JpoYGeNQH2/wAOPm+p7+3tiu6UWrXW\nj79bb7ef4Hfh5uXMmkmkmrNt207q+vRKy7dTl4bALkEbXJwM8gHGPTnH17VcFjcEr5jKq5+Y8DzB\n74zk49R3NbkNmQgaXIBJAwen+cfzx76b2MOIwzthscjOO2e36frxSOlNNJrVO1n6/jp10OObSRNK\nYwu1c5zyeeOCRx3/APr5xV1tAEUR2tlj90Hr979cc+n5CvQINOSMRRgcZ3q5BJYHJxjqRk46fz5v\nz6WkhDOQqqBkjjH5nPbj09+KBpe825W00W2vS79V8729PKItOuYmjEkRK7zyDnpg8jt9Sfy5q7NZ\nCZhhcEfw5+8emOgA9x6Cu1kt1iJjUGQSjCvgkIfujHHoev0Pc1SbSZ4n3RozqDuZsbeTwOuMYP8A\nnANYVKEZtycpJvR2fS6638vku5o6acE1J899l6rTzVtt73ulY82v7CNkkgaMqykhunUcdh26cE/l\nXKvayQyhACVU43g9Acg/y547E817XPpMTDfJtMrjLoMEgnPUn16YPv0Ncje6R5sxMYIUbgVwOoJ6\n+o9+vFcc/cnKKbkopXbe9+W9u1tddd9Ohio1raxStola17WWtlp+Ot/VczZWckoCjIXnsOTk9PT6\n4H6c9NBo0ybQoHlsMs2eh/3evHcD35qxpdn5MyxSHKBh7DGe3Q9/f6V2zWCyJEUkyrfKOm7IGOP4\nu/GR/M46oUYyhB3esU76b6aW8tfnZ3fSlFac2nXRdV09fXyucOmiLbzSxyMXjlXcp9sZ5xnAyTyP\nbjirY03ytp8rzFx8qsVIAUYB9sDgcg+1da9kSvysJJUBGBxjPr83BOOvTPbuaZQ7WiyuSQWJ/gI+\n8AeuBn6jByfl5Pq0bL3ne6d9+3fTv69tEh30SsrL167t69X17nJXdkxhMhAJDbQhI+XLAcD06DIG\nR9Oa+CP+CjnwD1/9o39ij9oT4PeD1efxd4v8GW83h6zRtsl/rPhfxDo3jHT9NjJIUSandeH00+IF\ngGkuVU5BNfote2yKkciybpGYxtH22ggbu3G3tk9Oc1zF9p5Vi5TemwvgfxYDE5Gf5fpxjmrydCUL\nWbutWrW1jva/59NDOtShVp1KU78s4TptxdnaSs/w9emmh8B6B/wWq/4JRftK/DP9hd/+Cjnj346/\nsiftTf8ABP74h+B/jvqnwv1D4WfE86X4q+Nnwm+H2s/DyYadrPgD4feNbbxH4D8R6p4jn8U6R4Zj\nu/CniO4XT4dE1u3XRl1WDVPxr/4KjftkaT/wUP8A+ClH7Nf/AAUj+F3wz+KPh79in9jXx9+yf4A/\n4WT4u8A+INN8SePtF8K/HrW/jD8RPiBo/gy3TUNbfQNLsru5tNI0yOw/4SCe1063ur/T7TUNct9G\nsf6T9b0PRdYWN7/SdOu51DRxyX1jbXTomdxRWnikaNc5YgMATk4znNE2P2XYj7Y4lCqiIoVAqj5V\njAICquACBkAcDjaK6Fib7RtZpXve60fbS9+uq+R8vPIIU5tPEuad0kqSi4u11zPnfNbTRKN/Q/n8\n/wCDjP8A4KPfsX/8FZZf2EtF/Za8W/EXx54T+CXxF+LGofHvU734LfFnwsfCPgnxufhHYtqVtb6/\n4b0O9164TT/DPiWd9P0CSbUUNgkf7ia6tWf9gv2rP+Dob9lW18efsT6f+xT8UvHepfCrS/2j/Ddl\n+2dLqv7O/wASIrjSf2ZrbRLvT9YttJg8a/DS51m71aG+ntNWim8GX8Hixf7C+wJHdw6vNNae7KjS\nMwZAgRiEOOqjOOTk59e3tkYWq8KyzBFjDEEjeeik/jnjOecdemaHiLNe6tWvta7pPprv5fMz/sJa\nf7S/P9yvw/eafifzv/8ABZH9vP8AYz/bk/4Kef8ABO79uH9lTxL458b/AA//AGfdW+ENj+0lr03w\nT+Jvha+8NeF/hN+0VpvxP0/U1sde8HaZrHiqVNB8QeMZIrXRhrF1CNHWzitLee8tkve4/wCDjb/g\noT+zB/wV70b9kzQP2Ftf8f8AxR1D4DxftP8AiP4krqnwi+JXgWLRrLxR4K+H8Ph5YZ/GfhrRItSn\nv7nwvq8DQ6e9zLavDH9pSP7RB5n7wPB5YWMt5jEgrg5wOBjr3AYnv3qH7GhKyPz2VTnAYYxyecgj\ngc8UvrWj9zpde9ft/d9bvS3nZjWRxTT+st23/dJX9P3jt+J8+/FL/gsp/wAG+/7WH7E3wb/Yw/bW\n8efFrxb4Y8H+DPgtF4u8E6Z8HP2pvCUyePfhl4R0/SYhH4k8CeGdD1qW307V0vGjFjqkdpf7InkE\n9udj/lJ+zl/wUu/4J/fAH/gkV/wVm/YM8E698SPC3i74/wDxz/buv/2PPAkfwp+N+tx6n8I/iR4a\n8O+GvgKt5411fQdVudLvLjSdBtrS6l8da8niWwa3W88U3CXc0tzJ+7f2eRt2WVSpzgA9OmO5z/Q/\nQihPCGbGGzxknODwfpgmqWIuk+TdJ/F3/wC3TKWSSTtGs5O12vZWa1S6VH3f+W5+YX/BSb/gtT+z\nH/wUZ/4I1+E/2PfDuteO5f27/HOh/skSfEP4Xj4NfEzStB0/4g+C/G3w61v4prYeL7nwpaeDv7Jt\nJ9G17UdKkh1UW1zZrBa2ck1w0cb9B+yF+3b+zX/wTD/4Kw/tpfG39r7xN4s8C/Dr44/s6fA7wp8P\ndc8P/DXx78QY9U8QeG5NMbWLGZPBGga61iLSHTnkM18LaOR5Y4YTI4fb+iDW2SUQbwOHzj5W7nnH\nT3//AFVotNdQSFUE9sDpk+59uuKxq4lwqQlyJ2jNNXt8TjfWztbl/E0hlDVKpTlVacpU5p8mq5VJ\nJW59U+d63Wy06H51/sG/8FyP2Zf2Zf8AgoH/AMFSfid8aNE+LQ/YQ/bq+NGk/E74R/H/AEj4PeP7\nm20PXNA8OT+HdZsfGnhiLSrbx/ZaT4q0HUrSO0m07QL/AFLRdV8OgHTmsdek1TT+e/bK/wCCmf8A\nwTB+Ev8AwRu8ef8ABI//AIJS6l8bv2ttQ8S+ENV8HWXiObwH8SodF8GaJ4x8eJ8VPH/jXxj4u8c+\nEfBc2pTodS1jS/DXh3wpoN3b6demysdVksLLSrie9/TOfQIbxDHfW8NzDINrwzRpLC6nqrxS7kdS\nByGUg9MVLY+G7PS7hhp1hZ6etxhnWztILZZWAwrOsKRh2XopwcDIGAan687fw1f/ABaW/wDATP8A\nsaN1/tDtbVezV2/J89kvk7eZ5B8U/wDgsp/wb7ftY/sSfB39jD9tfx78WfF3hbwd4K+DEfjDwTpv\nwc/al8JSx+O/hj4QsNKTZ4k8C+GdC1qW203Vo71kFlqkdnfiON5RPbnY/gniL/g4U/ZB/Ze/4I++\nHP2b/wDgml8RfGFt+2H8K/hj8NfCPwn8OX/wG+J+paD4XuD8QvD9x48uJ9Y+KvgTXvCmvLpXg678\nWtEfEd7L/a1yFeyu01F7IV97PAyqsUuDv4Y7T8inq3Oc8p931IyfWIWCO5WIlVQYIGQrjG7LDpkD\nOMD2IxWscXzJPktd2+LzS/l87/IayVaXxL1bX8FeX/T3zf8AkfJ/7dH/AAXk/wCCYn7RXxK/4Je/\nETw18WfHF54j/ZO/bT8G/GL4xNffs8fGex1DQvAcvwe+I/hnxq+kzT/Du1bUZB4r1Pwr5uj6Oyz6\nj5VrdRaXPb2X2jTm/CH/AIOHv2Jvh/8A8Fav2w/jxrHjv4pN+xH+0J+zR+z5Y+GfiEnwW+MUpsfj\nn8FrrUdBn0aXwKvhMeIbaPXPDXjLxBJP4kfR308nwxpVkLwNdhB9ZTWMUSNCAWdxtLckAEfQdcAd\nfxqO20d1yQARnk4OQOMdT9c+/v1pYlO9o7db7L5ry/FeZjLKWpWjWcl39nbqlb43tfX5dNT8/P8A\ngnJ/wW//AOCeX7L/AO3T/wAFh/j58ZviN8RfDPwu/bE/aD+EHxB+AfiCD4E/GLVpPFfhjwx4Y8e2\nWtXd/pem+DLjU/Dk9rdeINMiSz161sLi682SS0SeGF5B7N+0D/wW7/4JkRf8E9/2nv2LP+CZ+n/t\nFfGr4vftM6B+03onhbQdM+DfxIsE0v4jftc+JPG3iD4h+PPFnij4l+H9JtbfRNP1z4meJtbsNK0W\nw1WeO1sNP8OWVppNr5OrWf2BYaVG6mUf60ZCIAMggcZ6YBwMVsW+lyrnJZQxBbcORnH8Q65x+vFZ\nSx1nZU9X3ltt05dVq+qt1sdVDIVV1liXFaNxVJOVtN26ml9bXi+9uh8o/sk/CrWPgt+zb8Bfhdqd\nqYdX8B/CTwD4b1y3l2N9n13T/DmnRa5EQpZcx6sbxcozAgbtxFfUgsN8YdV8t3XLEDC5IwcAfXJ/\nH8eih0p2JAwxx1C479Rg5H59vWuis9MjKBJBjaBjj39zwR/XPeuRUqkryS1crNdWmr6PXv59e59I\nsJSjFQXM1FKOr1Sjy6P81t1ucVZ6aLWLayiVn42/l7dPqen63jpMdxESAI3T5vLGAQfYenPH0Fds\nmlEXCOI8puyCR95cc+h6+vXGDW2dCW4AZItkhG3oCT7Zx/8AX7VdKnGDstZNXb0dtVonr6b6r8Ke\nD0vaWycXHe118tevrd+fmVvpToMHOAc8HOQvTGB19/lJPfoK3rAPGfKUHDt1IAyT+WTn1I/lXcxe\nF5II8Sq4XO7Pyt1ydvHb8PwNW7PSbUyLI6ZQEjkBfmX3IJ5x+PTvWkEpOKa0clFva1+VK3dvv01M\nvYzbSasub3uu9nq3a1tf10M/SrRDOrSblY4DYBP3+Bxgjk8f5xXVizZ7uOFZHRUwwPP3iAW56cse\nmM/kadp9gY7tXKjyd2dpB6A7s/gD2rrEgUvJKIxx93r05I56YwO//wCvrhhowcWnLR31e9rWXorP\n77HXCKhHlW3TX+vPRGXIjQuQBuYAbmxy3fJJHP8AnFQhGuZtyw7dqjOfUZ5z0Pf/APVXTLFHKq5G\nCBgjHf8AyQBSosI/dqPmZskjgjHRSOePce1dBRy13GIIVJjzI78jHVcH5hx6+nH0qhNp8EsiyuAi\nBPl9c/TBPHoPXtxXWyO0ly0UkSiONcITj7oPDde/IydvQ1j3MJnuSBtWNGwmGOMZ9j1bnr+Pasqs\n5QScd27flr8gOFubFsnamVXOcsM7c8++cdjjNZZtSt0MqvklSCf7h46DH+990HvjJ6+i3NpDlVMf\nzSkBQu3q3T7vQ8Hrz39aybvSAV3SbowOmP6kc4A+nvU0qkpuSklo1a3y16+vkY1oOUWlFpt2bSXN\n016326203OHntSCBGpaEsCx9exHPOcD/ACKyb2OIqXSN1lACqSeAANv93uB+H613htniIVnzEOQM\nHj6Z4x1HU9/rVOe1iEnTCtjg52nsMjjjAz1549azxDScbu7SbV+6s7O1t7/1ocsoOm1zfaV13slu\n3/wLu55d4m+PnwT8C+L9L+HXjDxdaaN4u1hBJY2l2l0fMXMmGLxWk0YXMEo+aRfu9BXV6d8V/hnq\n+r6foGi+LNP1TVNQdUtLXT/tDnbkjfIWgCqNysMPJG2ccYOa8E/aF0T9nDxB4Tm+J9/H4N8T+K7S\n0NvpOqTXFw09sSwIRDFNCQ2Jpf4T98mvj39n3xp8P/g58SfBunpptjFefEm7R7l55JH+wiSSaIfZ\nC0zbYyLNCC27lj2pYjE14zi+anKD2UXeUdYuzts2k15sFXp05c0UlzpJJy16av8Aq3mkfsfHYpvU\n4Up94HGQW78EYz/h14q4mnRseVBQHKDuDjJ7BefbFXmhtUtsq3mgxrNARzuV0DgKwxwd2ASPx4qW\n1Jlj3lSNvUHg8jPv02+/PpjNdSk2ou26Teq0va63e2v9PTvjJTcbO6ktdlbVL8Omy8iRLRlUbotu\nANjMD93AyB3HQHr9e2HT2bzoQWK7CGIGcnByAOP4vzPX66STmZIg4BwwRRjbjoB16+3ftzVyJGaY\noIfM2plgRzgjOeoHy898cZ4qjedLkcU2nzJtW0fTR3/Dz9TnorFMxsAU4O0BeCT1Jz39xj3pRZSR\nNIQ+4NuOHBOM9QMD0PB/+vXXLAEQZjyQSSn90dcY684z19Oe9VjFHOzbVw2MAEdecfeyB160G9Gl\nGVpu/NGSb7bppbvRbdPmcU2kwCQ3LJ1+UoDwSec8g/rj+dVJ9DhYmZVUKTnG3nk9zjBP+e4rvorI\no+ZCrJj7o7Hjj/HjrketU74KrGFEwTk7lGce3X27EcH0rB0Iyu38Vne1/Jav00v5/f0OK3SV1snt\n/VvkeYz6b5TEhVAzlCFG7Hvkfljv71PDHIqbQW2oOD0wSODxxjb24HFdNdWZcpiPOeOhPPboQBu6\nfhxSxW4YNEyncCBkgY6A9ecD+vrxWCqzh7qStFqKV02tEuvz6rfqtTL2EG37z327bf5Lzv8ANGXF\nDDbKsjh28wjJYjHI6/h6c+nNVL3T0WfcjEhgshA5+8A20Lzng9+59+N1EFvEY2TzGLEhmyQoDDAX\n1ODjj3OcVmXCSrL5qnYoOWGeQOOecAgY/H1GcV2XlyXtrbbrzfda1+t/kcU42k4ptpO22t7K+ibM\nQ2SzSsnIQDgv97kc9vXjp1qnd20AGCpGQ6HpxxjOcf06cVttcqknmvGJ1YAJJ74wR1UHHrj8qzbk\nrOCQFHzsfTbk4I9en8+teXUjVqT5p6KDW+2jTu979r7W8tSfO337X/D7v+GOB1OwiityyiOVkdiG\nUHIyRkdOue5HSuQu4i8IjEZaRhkHAJTtznBHp6Y78Zr0i8igVmMeWJYmQHJJ459uc9f/AK1c1NHt\n3MgWMEkAtn7uDx3z0FaQW9tbu+1t0vN6bdrEShGfxJM4+K0jCZnA3rzhR971ByOx4PPUHkiq4CiS\nR0QBQSDjqRj8j+Xr0reMKs0hDZbnCkHDc8j/AGjnqPXn6V5oVACKvzkZcbcbV5zn14A79/xo5Yvo\nvlp+Rn7Cnpptv57fdt07+SOamtTJIskbNhSNy/xYyxBOBjHp2OfanQwhdwcbsn5S3OOPpj06Adcc\nk1qbDE25FOTnPygjvjHt19MfhV6O2WTfthxgA7sfeJGRjkn/ADzSSjZKTvorPv6JNdLbbuxEsOrL\nlSvdO+i2tp89X+ByZAZ2ChsqTvAwcjPPIHHbrzziqothNOyouNi5Oc8gKSOmevQ854/LsYrLDPKs\nWCxIOR3GVz+PUUosMSNJ5fzFCv8A46AO47dP07VSaeq1XcVCE4u7Vla2qe1ouyv/AJ/LoclFaohJ\nKBtxb+Hn5v8AP0FWVs0yPlXIPIK88H0/A10iaawUfuwO5J9Pfk/mfxFXo7ES7tgBCgcEY45yAMn3\n9c/pWVSnKbTSvbTRN2Te+2nTrruiq9GVXlcEtL8z6tO1tr32djkzbxHCtHwMZIAByBx1/wA/riSS\nzDPEzhVKAbBjaQPfqOevfHvXWLpx+8I1BHOWHB9euPr3NJdWDyxrtT94p5O7pznru4/zgZwK53CS\ndmutvv2fo76MyhhmtZrpdLv+PX/PQ524sYnjyFBdSNrYyOnHqcE5PQY9Kgj0wMyEFEGcscEknr3X\n+LOPwHtXYx2QEDBo/mBDAHkkBei4Oemev8hTltQyDEYG3jGDwBxg8469/wAAO9ClKOz0WqWj16+m\n39bmjoQvo5NX21euj3urb76Pz3OZGkxM+5Arkn5vvH+nv0/Hmr8GlwxKU2bixB5HAIz75Bx7cdx0\nNbf2dYSdkRBIGeeOc5459Mdvy62bWB5Wx5YCjqOOuPpn6ep/GumlK8W3um033sk7vvuNUILWS2d7\np9rdrab36/OxnWmjKCMKUUj/AFg+6COg6ZyP5gV1FpoPmqCHM+TgHPJwPpk9Py9DWjZWDcRGIvH1\nxxjJ2n8OR6H8a7fRNFPnCVyYS3CpjK8gY4wW3H8jxxWUqMruV209V5bbWvqrPrbre7LpYdwanBN8\nzuk73s3H3et16dTlrfwy8b4jg+ZgGK9cA47e31OOemSK6bSfCwmZvNj2seqlfc9OMf8A6vrXrVh4\neQxJIsT+cwDbiBghs8exPvjB9OK37bSkRsPFsk5yGPXHOeODkZ9+g+nrU4xUIWs3a97/AGnFXv5/\nfp+PesLNpPmWqW6e7Sdnpvv69jw9tA2SGLyySDjCA4A/Ef5/nv2ulKIhiBkZSN7EdeMY4HT3yP8A\nD1ldJgmnOxMkHDEDrgHIO5gT+I7GrMmkMilViBVvlyPvAe4z/L8BxULDwTW7Svp56at+X3q+5s6V\nZWi3GMbLpJXSt5a9bbbdjyqaxVY1zuYHAwc46d+Ont7CskaZuQxeUwKMXDADnBzjnnufy9Oa9dvN\nICxmNY8nAYbe5x06jn1zjPasW40YiNFxICGDswAOACPlPPRsY4+anGjTi07XafMrvZq21rdvL0Jn\nh4RjJ67LSy8tfTrb+nxcVrICheNQoGA3QDAwM5/oOa0IQhjaMAK3K59T0HboSeOa6abTVaFZgjYQ\nbcEfKcHGev55Prx0FUZLAMiyJE2UG5lXgnbn5gc8jjJbHqM8c7HHZd07K/S19Glrq9u2/wAzISNo\n928YIzgevTn6HH/1qht4Wedm5G1QSDnB78YHBPTPPatZPm3syh+Cuf7uB0P5VUBkwrw4RQ3z+rdM\nAcH9M0E/h/XzImtvOdmlG35cDAw30PGM8nmqE9nDFuwW3DqDwTxxjaO/5g/rtBpXYsQABz0OR/T/\nAD35rKnEksjq6jyiTgqTu7fQdR0/DpWc4qorNtW23sr2v5X9HoBkPC0XlyA73DqwHJwAen1B7/dx\nz7hsjPIhWVfMzg4YcD65GT/F8vPPpVy7iNvEHiDHcQOQpIP6egHqTnjOKZFKJhsC4OACxAOT+ZPY\nj25+tc806XLyOMXKz07K1vJu/bf1sxp9beq+d7deyOduYkEe/ao+YADjPfsMcE98f1NZd3aGRPlB\nKOqgsozjIGP+BAemK6tbcCRlO2RSpGBnB+vpj9P50zBcRbiQFXPC5GcZ+TAK8Z59j24qacXXleo3\nem9En6Xv+Nt/zOTFWlKDdt2vR2XyvZJLvfVNWR/Ip/wT++BPiH9s/wAXa/ZDxD4l0/wh4b1tr7Wr\niC7nn02CzD28X2CBp3lDOsksT4kVW2vy/Sv2B/aR/Zl+FH7PepfD74yGXXdX8K+DXt7bXpHjTZp8\ncT3E73e5LhNuGeNfmcKd/Tdiv0j/AGb/ANl74b/sy+BrbwZ8NtGh0y0aFJdQvShF3qVztRXe4d3l\nduERtvmFcqDjivaPFPgfw18QfCmseEfFelW2paDrVs9tqNpMgcSIxBLLnBVvkByrKfccVvRyj2EV\nUd5ynbre2iXVvW9lft+PDgcOqlPmqptpWvf/AAvbbvrre9jnPC2t6F4q8H+HfFPhq+t7/RNV0m2m\n066SQOJo9iqBldykoUZCAxGVbJ71urvjVVEZR3HPDYbPAx7c88e/asT4dfC7w58M/Cdh4L8K/aF0\nTSvMFjDcySSNCss00xQGaWVgoaVsKGK4A7Lg+imy3ogwGZQOdvTpj+91x/wHr713Kh7OPvLTaN3t\na3TfXXv92p6tKMIK0ej3+7pbbTpf5u5jWu1nRRwVIY5A+8MZA5HOR39Oa6KJSkhmRcsygbT0x044\nPHPPv9azJNLu45IpIwNpY5APZiGOce3PIx6Zrbs1OSs2QwGAQARn1O7r29zxz0rOouWUUrK6ei1T\nSSfTa29ttjdNyvfWyS+9r8kvlZaEQMjFyFwwwSODu6//AFunb35qUWRRFmXG6Q/MDxgnnnGSOff8\nKtPA6uoV+OWYNgE59eB/9bgcU1hKziEMFI5BPIAC7ye/YHtkY4qDsw38P59d9l+HYrtbSqGwM4/+\nvjjOf0rOlgJcmRo0PPDEgMOecbTx+XvWu0sm1sdyP4jz1+hHI/8ArjNZMkyy3BheM78EKQ3Hf+XX\nocD6ZpNJ7o3d7rRNdb/Ky/P7tBY4IVV8gMT3UDAxn1GcegHcdO9VJrNLgFUPI+U4ABx9Bg+/X6j1\nuuPKBUH655HX29cn/wCtTUAYZBCsSeg/X+fesnRhe6VndNNvrfbb5L9BmHNYogXJJwO4Hb168dvX\n+uReQHyWaFRKzHa6kYAGTnkZP+fxrpbgNuVc5zwPfn6Z9OuPxqjPAQsyDAfZnts5GT1HXof61sYy\nowak0tbO19bdf69djz6W2uirOE2opxsBypJOC2R7jPbrk1TKFflPB+nHP17euffpXZrE7OoOMFcE\nEAgnGc8Hgjr27ZJrCvrcJKQx+ZVwu3HON3+R39BSaTVmrp9DhlK6tZfffb5Lfp5HL3MEQ55B5yfX\njPbp19APyrAlhib5SCwHIwMYOOp46c9+B+NdhMiIjO4LHoAB+Bzzxx9M1iOsc3yxgoe5IHOOMZ/T\n29awnShGMpJNdl0V7JfcZnLyWqtv2sFIyVZQAwIIPPTHcdD/AErNmiVSI2LMT1LYy2enOMDjjqen\nXtXST2k0Tk4yrHqD2PcjPfPHPB4rNnt1YnpvA4PXB9MZ/r/jXNqn0s3132SX4gYws2nfaqhVAbHB\nIx/49yeTnPr26vYCNvLQ5Kgg456DP8II5UY59MZznOxZW1wOWAKnuTgg4+UAY5BH+RUTWkqsN6Hc\nzdQOcEgHsOB+OfoKGr27Wb7NPS3z3AigXeAqx/PjBCqMD6dfU5z3zk9qvmyfcAyckAnYMgZ69u3f\n3q7bWW3bkEHABbbycjoee3/1u4rooIEZUAGW6Luxg9uTz/Lj0rNwnK6hokru2vZNXduq267dbDUZ\nVOZRbXKru/2V5O1r2a+75nGPYTowCozDgn5cDrj6Y7enUVbXTZ7hwAjR7ckYXuMdDtx0GfpXoFjp\nMjyyowUgKJOBkZP8PIB6/jx7V0FrpCElnAG4YwAOmBx2/X/CnSVWimpTd24vZdk9mvv9L7LUcJ05\nXcnqotRe69fJ21/TQ8zh0xmjeO4jLOeVlAwSSR6KB36Y6ClttFjjmQsWPOCWAUbfrg/jjJ75x09V\n/sNLjcAGV0B2lQfmO7HPt+ZP505vDEjxFsMTj5TgAkA89MdNuM88gda3pQjWV6i57Xs9r7eSTte1\n+24RfO9LtJ21v/wbXPM7zRLe3kgkXaZiwbaG3bk56jd3PoODxWbLZOrunl4QkNnbjbwR259ew5r1\nEaAWILDMkbADO4kDg4weD69ao3WhSiZeM8jI2jB+bPoOefwP5VnOjGLcElZW6au6T31+ad76eo3S\nnNJwbSVrtp2bdvLVdlu/I4eLTmnYOEO0rjOB2G3GcfTucnNaVrpMwc5hcKG+U7dwI7jPPYg+w4+n\nXppJiCh/kVv7o+nqR9PxzXVRaajWmYkzIAMEjkEAbup+g6fqAa2oUqbhs99r7ppbpfd331E48q9/\nfRSsraWu/wA9vM5rSLEsCSvI4UbepJ7Z/pjHHPFeg6Xp0SgPNFvdSMBgQASQD02k46j34561mWFv\nNbBSY1JVixDDpuOR/LP8Ndnp6q8ZkfjefujoMYO0EZP/AOr8K6YxUYpJWVrJrXRdm1qelRSVKNnp\nyq22isrd9Ukv1Oq0+1cmJUQlGCjIAO38T6Zx2z7YrZnsIy5iUfvlQENgcrkj0+vQZx0q9oXkSW2V\n4yAMNjO4epyT35/nWteWjbl8vaHkjALpyFGTwP6/ifQ0+1rWt/w1vI3UpqyUtFrqluvw7/hp0ORW\ny8icNGCQRljgYzj1HX3/AMeKs2sUrMyEFmDfIuPvDuQOcYBC/wCc1vLYvDF5b4dh/GMk9d3PfHP8\n6ILSVZlcIflAXoc9c7hwOePbHrTDmlr7zd99v8l/w+pzlzbHlAmJA25z12gAnnjp/wDW44rKby7r\nMUa4KcNwOSO5PTsRgZ5/A139/bEICLcq0g5dgfTvkf8A1/wrDWzjjnjjjjxI4Ix2Zi2McZySee3J\n/MMql3CSSu7d7fPUw4LA+WYShZSGwWHUnkDr+XaqdrpLSySLtfCEjAQEEEkY+hX+ua9CTTWR98qh\ndo+YLjChhgA8DuB/XmviL9v/APaN139kD4C33xU8PaVFq962pWNkBIWEcUVzeQ27v8iOu8JMzAFc\n5XkgU4wdSSjB2m7ettPnt009Tyqk40lKU7cqvq7d1fXvrZXd/uaPpS70JYNxWJUhcAsWGCW+8wOe\nME/TBryb4qeNvCfwh8I6v4/8VS/ZfDmj26y3so2hYg0iRBz5jINu+VQMsPx6V+cf7H3/AAVfs/2u\nvi94J+Efh3Q0S9l8OW954vu3DL5d+mn3MsoiUb1K/abZxuIjJB+6OBX3f+3P4Ct/F/7Mfxf0WUyw\nfZ/DctwjKvDSJPE+FbIyB5YI7n+c4aca9Z0UtYT5ZXtrHS76efdfijGpPmw8q1KVko3TV7t6a6+u\n23k7a/lNc/8ABbX4Mw/Fux+HFt4fe78M6xqaafYeLEwY/wB4zqkjut8IArCPsh5YcV+xujT2evaR\np+t6fIZ7LU7aO7tJVClZYZeQy44OMEcE8g89a/zrvD3h6U/EjwTplzdzJu8V2UEs8g2MYhNvBBJy\nvzADKkfpgf3Bah+3n+zT+zn8NbPwx4v8VRr4n8AeFLZZ9GY25ub7YYyRErXiSyEm5HIXJ2n0JryJ\nVcTHNqtHnf1eF7w00SS69O359DzMDjGqNSrXnJyUuWMZaWemu6t+WnQ+1rqzPyJnI3BiAOQRnj0H\nfJ9Oaz5bRJMhmKbMgYAJ5PfFcF8Dvjp4H/aL+G+k/FDwNdpNouqg7UyoeBzJMgWQJJLtJ8knBbgN\nj3r0wws3mOGBUSKBg8MCM54PtXfTq0cUpOMbcsravWytr2TdvVdb3Pa0cYTje04xlr5pbeT1t95h\npD5bsSS5UMcEDnA6HH51mTvK0w3A7RjjnAHvzg88fhjvXQ/Z5N7EkEHK4/PGBnucdsd+elUWiAuU\nLDeMZI4A6ZPc9Mdh+GK2hBQTS6u/oui+REoQkveV7O6u9vtP0vda9up6DqscNjpxu3ZvItoTNKse\nXlC7sFFTOWYZyRx9K8iuvjn8NdHfTLe61Xbdanc/YzYSqkVxAzByZZImm8wAlNu4gDLDvivQfEXx\nC8CeEdL1HWPFfiHSdK02xQtqUmoXtpFGmCoaMJcTouQXViuBwfSvgj42/CbSv229N03WPgb4q0nw\n7beFtQjuf+Ep0sWkEOpSIJD9lMtpcW8coZbgPn7RJ/qwNvGB6+MnOnWlTpLWOjsr6e7Zq/npskuu\n91EJwb5IKKioqyT1ei1S7fj3P0IuINzQGzPmJJGk2VOVMcihhggYJAYd/XHvNY2NykpklDNnO0DO\nP930Pbj061n+EdJ1HQvDHh/Q9RvF1C/0ixtdPvdQCAGeaNBmUsGcsT/vt7nFdzcQ5hVY5VUjksBn\n0AwT1zxgf5ERhWre9ONleNpN20sr3j3tff06Gut+lvx6f8H8DHtwSjwjIl3lzkZKoCe5HAwcE9Mj\n0FaMdg5h+0RgbgBuzwjY6HceB6njr+uDe3A0qx1XVLidYYrCwur2eeQqsax20TOxYsQoGAc5KjHJ\n74/Ej4r/APBSXwV8UNJ8Y6Vp+qa/4RvPhzqeradeanpL38NhqH2K7uraBTPaNa25aT7C3AeRnaTH\nzHrzY6tQwNKLqLmlJtR0vvy3u7abWXWz6ke3hRacveTTjZp7uyT0tte/bY/bu5Evm+YzNubao2kl\nVwMB+CFPTrjHHuaattMdwDl5DgoV56fMRxjJ9ew/M1+fX7Onjj49eLvhZ4E8f+E7jTvGXg7V7hY7\nqPUpLWHU1tMWnmy+fOtxeM8cUzOO5K8FWOa/RK3YPbRXCxGCd0VpYixcRORygJ25wcjJUZ647nlj\nNTjGS0UkpadNE7N79dV6myqStFp6P3lZaNOzvtb9U7lOWJRDtYqkpPI3fTqeDzkjufbvVKSIW20l\nQzyDJIwWIbuTg449QfX2rWaykuZBJ8uzaC3zBcn+n484/GqUyuZ/JKhy3ybwd3lj+8cDAx79h061\nR6NPEQUVzxV7Wbbd7+7a/RX37J9UUpYixUNjb94YzubOOG9/y9qngtlf5chCO5Ixjvz6+o9j9KnS\n2eMSxkhm4KsSpwO/U8f570MoVA2Tnuc4zggc/hS7a7af8P5/5lpqSunZPX069b/j0KMtpIzYJTK5\nA/A9QPw/zjNULqJ9rgAZxyeOcZHB69P061qzMyAEE5Iyc885H+FViXcEseo46dc5H+T60w0fzX4P\n/M5praVclgAoHX/P+epz6w3NqpRJWUMQpbJxgnLMDzjrjp+HpXRSQyMrA7ccHHQls+oFZ93EQoWQ\nhUwB36cj2PtntUTfLFtaWt080v8AgeR51a0ZcsbWS0tro9d/w0scpe2CTxo6gCQ552gDDDrhRj9f\nUVz0+nm3cKSuXUjA6dCefxBxn19jXbyx5EaIhKg/Lz/D/ngdqy763kNwhmGGUEjHGcgrt/Xnj26i\nuKEpOpBPWL3SbafTtprvd2V+nTJpW3u/uttdX3ettvPQ88nhdGxkkOuSCcgHJycMT07dP61lPbBp\n0QISzMCMEkDJP1GOemPWu8l04zzHCjI+UJu6DnjI4J5/mOtVl0t4pnkZQ2z5QAe/HAGP/rfpXX7K\nGitdLbdaab2+f3v1J128t/P0/E5eaIgrAmBJtyQO2B146HOM/wCNTtbsBGWGSoA4xnjHt9fT/He/\ns4RSGXyizvyDuHy44I6nOe/T8RV6OwM0cj7NrKQF7Dkccc5A+X19cVjXjGnBOKs3JX1vpdLrfbf5\nLshmZFbecoGAoVQW4ALfLnA4Gfp1PfFalhbpEr4iflvlyCSGOeRuwQPxH866C10+JVjDREgKpzvP\nJKjJHJLc88jAHoK3LbSgVJ24Vy2RnnJIPT9O/rU4Zrmkr3suW2rVtHu/Tr8tC4zcb2bStbum+z/P\nXst7GTa2cnlmeIEu4AYD7uB9GI4yRk4z6VrW9hPI6ZypyOvTnke3Xgd/U11ek6OrSeWo+UhvlZu5\nPGcn6dwc9PU9VaaFHG2LhQSSMBent0Pcn8vTNdThGVrq9lpq9vv1KVOVWzvqpWSena13vp2uzhFt\nbiJsBeWOCw7Zxjp19u1btrbsUClGLY++VO3vnJ+77fXpzXZjSYMFFjG/O7JP8J4Xrx16Yzz29dKH\nS4xEUWMZOOpyRg884OOBVKKikkrJLRK9rfqdFKgopua62W6srq2z67eR51LoqhTcKDvZ8cMcfMM4\n2cj8cGs240kuwYqAx6csPu49vr2Pv0r1+60XbGiIVYMAxweA2MDofQntgcHOOuVJ4fl+V2AKnOBu\nx0GSM7v8aXJHt+f+Zt9XcrtXjHRWWzta++t992+7PKX0K5uQItuBnOQfT0wM/h/UV0tlphgVIxgl\nUwQcE5244455GPY9M16DHoXybiuCCuMd+D78D8B6c9tKDw35sW6NTHKcZJJ5AxgjJH3j0A5z6YFN\nJRVkrI6KeC54tyu7u1mlfp5p/L/hjzKTTGaXZgbWxx0xu6/h1/yMVYh0qWNwsZJXrgsT9e5/L/J9\nAbRGWRYyA0oxljgAdccZxjj8ce9XI9DliIeQDHPIbnrwepABxx6DrTMZYecHJRlNRVk0lpZNWWvl\nur6dtDnrC3uVCohKDIUkcA4yeBkewz1rt7K1uEgCyyAqRkEsCwPPGT8w7nqePXHEY05xs2gBWyCR\njngevH8X559q1IUcDy2UkIuF5P3h6ED9MfpSvrbyudsKUFFXj71tbv7tn69NLMhWLbklicdj9Sv+\nP6VIsEzksh4XJPUYA79uh9fTnrxbS0QoZsncCMoCM46ZAI4PTvnpWlBE4g4X5JMhTgbj/DjbgkDp\nySMjgc0yvYwSsl26+n5dl5/Ll5hPc7o2ckx8KAPrxkY6/wCfWvmf9rz4meJPgh+zx8QviZ4Xs47z\nXfD2lTmwSVdyxyNZXkyyHdHL/q3t0OShFfWUttMzMIo/LfBwzYIbP1XGO2Bnt3zXyz+3TYRXH7I3\nxhjlRZJB4Z1JyGVXQldKv8HnIDA9MfxdPbSkoyrQi7tXtZX236aro/mzx8dRrUoylC6SjraTtstv\nO3daba2R+G37Cn/BX74xftC/HjwD8EfE/h2yEepHVjreoxY3ssU1uLcADT41+RHcEB1zjnNfqN/w\nVjtvC2q/scfEPRrvUNMnv4XtpbbTWuYWvI3juCwlEBYyrgqG4XBOc8A1/Ip/wTe+K3w9+Bv7b+he\nP/iRqb6T4U0f+3FuZwDIPMaWPYqrvj5JjcBcj2Fdt/wUL/a8v/2gv2pfHGofDHxp4gl+FWoRQ29v\npz3t/FZTvEbtZJFtDc/ZyshZGGIyMY68Z8mGNlhsdXnKXuUnJKNtX8NtX1SWmulnpufM1sVzYCVB\nxcqspSXO7O1radevpprqZv8AwSm/aN+Dv7I37R2vfEH4sX62mkDw5FbxTpHEZRcpFqimJFaeJixa\naNTh1Ygj1r9Jv2v/APg4Q+F/izwp4s+GHwe8G3erReLbKXSJdavreeKKKOZ9onQlLqPdwDgOo9D1\nr+XXxno1u2vS2s5MyynKgtjDZY89QQxAODx19awrTS7WG5js/s0SSLLGw2KsZwW4BZByOOAen0rz\np4+phas8VSvGU25JLlvZ2avfay62v5dDkpYqcMM6EppOyTXV7dL9f+G7r2SfWZdE8Q+HfFV5J5/2\nK/t9WMIUAkwymRogVBIwqnBI7YwK9J/az/aT8O/tMeOrbxr4d0+40Z/7Ei0rUbRpZlE9xHMHMskb\nlQwMaqPuYGMfTwzxo0nl21sTsjSHIJ55w4KjOOAOvOD2rzDRrELcTPGoUcgAYOQ2OcDknPU+lc+D\nzOWJlUnUh+8kpNz+072WuytftZ6PZnNUvGKjFpc8k16pxvffe71a76n9zv8AwRzs9B/4Yq8LT2sv\nmNHMn2xAxYiXzL4gAbvlwoAxgdc46V+mdzbvDGNj5jlcOBnpwdo4HYNX5mf8EVvCVzov7GGjXE8q\nXEGsajFLEjMCY1/02MgAliMlCeg65P3sV+qWpaaySqFBwR8vzAjtjjtjpzxXdktZVPbJJK03bqvs\nvVvT5PzPsFf6vhb2T9jG6W3wx/r0tfU5cxknYGYMOcnABGOnXPHXn8z2rzQErlQA/ADZGOmOc5+n\n+NaDWrB2jDZVQWDdmYDBXOfXI5OPalf/AFISWMhiRgg9QCO3T8q9zRLskSfF/wAdPgh4R/a9/Z88\nQ6boGv8A9nR+K9NOpW+uWV6JPs8skkBaOSS3ljKEJCxIeVCCwJHWpf2CvhjZfs/fCHQ/gxa3p1lV\ngOoPq7yGeW5mSeS25ldpHxtA4Mz9Pz/n2/ZL/be+K/7JGjaj8FPjDFqeqaB42sHXw3fTXJS70Vrg\nKsU5M7XEscQ+yONqpEQZDzyc/Zf7Dv7bFt4I+L8Pw1+LHjK01ZNedo/D2q/aQLfTRcXCvHbSvJLc\nCRwPMfKyRD5wMEg162FzvB4yvF1aDp16jUZqSaTTcUtX1TvrtvfufEUMx1g3K0n9pS/wpvfX59t2\nf0gI1uNwUpuJBK5A3MBtG7GBuAGPXp+NtfLJzjJfAYE4AHT8fpwa4mwuILqK3uLWZriG5AubW5R8\npLE+GUjrkEnIIO3HfpXYQqJkjWQsjq3BHBYHP3zjkHjuPyNfa1cnpwo03RknzQUrq13dp289N30v\nfXp9DhcarpSacXJJtu9npq1pfS1/PQTUtEsdV03UdG1JQdP1eznsbpAwUvBcxtFIgbHykoTzhq+I\nJ/8Agmp+y0fCni/wmdCu7az8Yam+t6vei5/0kT/abq/k8u4W3DqnmXMn7tTyCB2r7/g04TQzln3m\nNN0QILAEAsMf7vXqB+HNc5411yz8K+GJ57q2e41K+hl03SbWNGd5r6/Q21mzIFcmNJ5IzIGCgKTl\nk6j5bH5dFXVSi6iSbi5JtX02u73ve3Vbeb9dyhW5fdg0rJWSdlprfXV2699rniP7O/gbw/8ABb4a\nXngvQ5HXwb4a1G5stDaRiZZIlmW2AZmG6RvLijDHbn5ua9d1LWNH07Q7nX9Wnj07SbeBru4upAEW\nKBEMjSOW24JUYJODnOeK4X4Y/C3xhbaJEvj7Wobn7TqN3q8Wj2MUlslut5Is0EE/7+4jkaIKFIAT\nlTwgO0fMf7Zz/Ffx14q8OfAj4c6VLpvhDxFo2oJ4x8UvA32OwhfSrhrePzBJAQwuoAhw7n94MgZz\nXg1aDo0/aqm420UN7LTWy3u97p+i2N5QtRj7srvy0S0s9vW3fqfUvgn4qfDb4haGniHwd4w0jUdF\nguGtbm4N7ahxMqoSux5yw3GWPBxyW6j7w7WaWNlM1kokgmO9JlG4SqSMEFeGAOBkMfx7fyL/AAv+\nEv7RHwB+P3iL9n6z8SeIrzwxqss2o6NqNpb6rJpcogae4RopElkhDSx6cqH95khh2r+hz9jT9o4/\nFPw9N8MPEnh/W9I8ffD2yW01S51K1uIbfVo4pY4vOiaW1jXLSXA+/PKcKT715lDMvaVZUZUZLlbb\nfK0ndK1m1u9dOnl0ww+I9rN0GnFRlZyXnyta+nXuvJn2WHY7QoG5hhzgjAP44wPvHPTGSM1RuYGj\nAERMhxkg89Tz/nj6VvSW/wAr4wpKEHHXPXGe/wDj2qG3tswMcZYHBOPy4zngdeOg9K7k20m1vr2t\nfZPu+h6aa5UknJJJN2TTttba6t16bnMyDCgv8sndQAR9eec80oilJClQI+CXA5zgd8YwPTp756bE\ntuWj8w27koflJAOckY5A6D/9VSFAYhGRnKjrnhsdicH071RwSbTkuaSSa66p9LbaWXk7pdjFe3wu\nQ5Y54B6dOOufw5qhdWzvsQ/xY4wTjB7ZyP8A9f4V2L2wVYiqjIXJAGMg+3IOMHOVyTj8Y7my86NZ\neEYLwqjHA6/Lnr/X6Gk1dW0vpvto76k2k7Ozd22tNHdR0d/0t0SOFEflOEKjPQcdCf73U/1456U2\nezEp86U8LwueOcZzjHGfy7dea6V7HbhihJzwQMkH6k9z659qsDSXkjyVYgZwNvQ8/wAOGJ/Hn7pP\nslBK2iuuqSW7vt/XkJRu+61T3XTXppa+l7XscEtovmviI5JID4xn0xkcn/dP4U9tHLEt8+WOcbSe\nP978fx9O49Eg0SMRrI4BZMHG3nrj6/l+PHFXrfQ2dvOiwV5O1hwO2ME8Yx6e/wBaL9jy+9du6SSe\n10tbp+f4PV7Hln9lqoKMOTyCRyOh4zn09P8A69610ZsECIMme6E+3T9fr+delDQoLhy06lCDtBCe\n5PGB055+6AOcGt7SNAALiaMsR90/w7cHHHODjk88kE+9JpPdJrez7q1v+CaUleotFHsmnqtH0Vm/\nmreZ5na6JcFX2x5CjKgjjHH4Zx2610Gn6MdyRurFm+boSOmcYx1/l6165b6DbxRNIoAb5gUYZAC9\n8cev+e9yx0ERyecibi394ZAGT91W6Z4HriiyT2SfXSzOv2EanuR5VLR3uvLVtNfLW12chpujwrHv\n5WZWKhcAcAjd1Az9T/hnWm01flRQQynczY2kr0HUDA/ka7abSlTayokb7c/KoGGwercd+47A81UN\nuOWlAeVeMLyCOfTj8/xpjVF0L36tXbvZ9N7v/gde5zMtoscKvH80owCOpOPbGeODg4q3a2jzyMj/\nALp/KyONozwf5Z7dPxxssqYCi22HPDYxj8sfmfWrJtQHRwQDtByCQ3QfKD+XP9RQddBRak5JN6db\n9U1bTy1200ZmW1qURvOB3hsKCeq45bnaTjnrxUV3kCNFTcCwyME4HrWrHBIQzTlt4OFbjPPUnnp2\n6Z/WlWJPMX+I5xyOB/LP/wCugyrO03a+lnZd2lrZWv8A8OWbC2tnUeaBnAGzIHYZwSPw9/5dELO3\nYKV+VQCByMYxnHTGcdwKzbGASAv8gEZ4GBk59cnnt1ycYHpXQJEjoIz8m4ZG0AcAA9eg6cd+tBtS\nnaKvPWLb1d9NFre79Fr67WyW0yCKQThQ4yB0DA8BT9evQ8cVblsEc8JhCBjjvweeM4xlgf6YNaUM\nSR4iJLKOSX5ySQf59/p6c3AVOQQAAeOevv2/r60m7WtbdL8enn+lypz5UnZPW/8A269La3362S3O\nbGihsNuxjpkAY78DPPI788U4aQyhiGJ98cH24XnOOT2zzgV0cgGEwD0Oep6YH6Y/lSxg7FU5xjBH\nPGfp69veuuNuVaRu46uyT0s7XturdtNGvPL2rVnZPmukrW6LTZX2t/lsYA0xVi37zvwc8jPDbRx6\n7STn2+lOFtsAAbA9iffHO0k+/wDk1uYXJXHTnt/Q5/p+lBRDgEcfdGPxHHbPXB9a5km7W6/l3+/+\ntr6OraN2raLrrd2V+mzf/DGA8DMPvAsvQg9M9Mj8PY9K+Uv23rZ/+GS/jOFUqR4V1Y9xn/iV32e3\nr1zj619jC3iUhgSQ2BhgSOMcc/1z618r/t2sIv2R/jS6oEP/AAiGsDC/Lg/2Tf4PB6/y/n30Y2cH\nKCW9naz0Udr7K3prrc8jG1H9XqylOVnBLf7SaX3NPXvfuf5onicOfFmqCQEot/qDMeCyqLmUk5HQ\nY5yc8Y6973hzUbd71XsXVihdNowfmUhSePvMDz05qhq0D3HjHVVLsUnu9TRhuyQDPKpx7YJ5xzyP\nWn6HpVroeoKLVmbfNJK+QcqXbf6YxzxgH69a+HziSjiaiWnNOWr0bsk23e3d/PVHxd9Wm3ZWaSej\n1XTffbR69NTmfEsckviyOaUsshJ/dkkDA39sEDk9h2Bqm8bf2rLLMoQBlEb/AHed5wN3PPNbfiR/\nN8RxXO3J3jnDc8sQOnb0/L1qzPZfbLks2REzKwIGcNuBPOf8mvLxcrwj714qMVy6OyfKt9Hqt9V0\ntdIlpO14q711TutuqT677F3xHY+fotvNL88phLKG4Zsq3zEkEn8jyPTivIPDN1LHNeQSKGdXBBJB\nwBs47N6/dweo6V9CeJrcQaFaO2dwttoIB5ADcHvjnHf9QK+dPDEZTWrtCSfmZsNnnBC5BI3Z/THr\n2rKnejJ3uvejt2tb/h9tPmKom7JK/W/VWlHb836H98X/AARviW+/Yf8ACUxdWVZ0VViYExHzNQOc\nKCVPXsDz1r9MNQgKBYRltwJ83kuOehOMj+Qxz7/iP/wb+/Eex8SfAHxh8NJtWV9Y8M6tE8WnyOWk\n8hrUTExREgt810BkZwWIr96NQ0uM7QAwYZzlWHQ88EDjvjkc9M17+SqKp1bK0uZ82mv2fivZp69r\nbWex9bG3sMNrr7KKa9IrR/n6o82NrbrkBfm9yME+pH51QuYgRl0xLGV2oAdroe4xjGAVP48kmuxu\nNMUsVChfQqDjH1+9/n8RlizcjYRvk3lEYqSQGIAUHnC+n0FfQUY3unZ6Rv16q/3/APD2JbSTb2R/\nJZ+1F4T+Bnxu8DTfFDUvF2n+EPE2n6VbxQN9nukjcx3EUjWUHlWrkzlZZDxIRtPIxk18UTfEP9mj\nw18R/hJqcnw51P8AsLw1pEUut6tI1kk/iDXotRu9tyWa1Vnja1eNf3kYbMY/ecAV9KyePPhD8SfG\nth8Hvib8O/8AhGPAXh3xW+qaPqVnClr/AG7YtbtbI10bmabfnLScQRfNGpPofpjxl+zZ+y18dfGN\n5ofw9hZrbw14eP8AY+mYgEUd2k0bJ9sKwKpJEr8xyA4ZemDXsVMDCU/ZwlSjKjyvmlHllNpwvb56\nK2m71PyOlV9k5OTtLlSS620bW7fo+9ujP03+HXxh+LHj7wz8IvEvw9tbe20a60+O9bw+iFnudFtr\nye1MUjR3CQB2WOMZbaCCTtxX3R4G+IfxC1rxINP1vwGml6HPbYXU2+ylorhFiRw3l3TvhpGkYYjP\n3ep5z+Uf/BNb4jXugw638P8Axpe6HZS6LfNofhaxlMyapFBlWW2tg7mIRySFn28LuI5Gef2Cgv5b\nKS5u72f7Lp0ETTPK5CptCBh8x4yQTjAAr9ByTDRxeG9tXxUafsYqLTknGySu2n0W2j20WzPUwmNb\nny3k23FRVne9lt16vtfc5n9oz4x+Mfgl4Dm+IHhfQYfEVhpkqR6xYrGGnMPzllt980YEjxxSCPhz\nkjjtXnX7O/xp8VftB30HxG8W+DdQ0HR7S3gi0Hw/cRorpI0exr+dVZ4xmSOK5jOY2+8cFuK+YP2l\n/wBvz4H+H/D134KufFGm3Nyus2s0lnczmRbiWwmlUwuqRgkOzlCGYfKfvDrXYfs/f8FQP2c4NNuL\nfU9Q02xv7mys7ebTklRLe3t9OgaKN0QgsDNH82N56cgHivjc/wAdgqOIp04Y11Gpr3aesbXW9m9N\n9n6H1uAnXnUg1NwSWsZ9GnG7/r112f60R2heNbjAHngSbwreZGz8lScHG0kYA6HvXm2qaVrfjLW7\nrRLW6Fn4eskj/tYgOlzfSF3V4kmBKgF0UkOh4dgNuci78Of2gPCXxh8Jw+KPhZpNz4qsWlmtcWj2\n8lvb3EO2MlmeSHgOw3KpJ4JGBg12/hbw140uIpjrGnjSJ77VLi9uWjBVktZGjkhg4eT5kKuD1X5s\nDFePCpDEpumnOD101XTs7b99NE/T62M7xjGUozbevLrvbS999/y0ucz/AMKu8G2EmmX1p4X0d9R0\nqIW9neSWiPewxrG6BmmUjLYdwSvXJwAOkNn4D0DT9RudWs9A06y1a+TZfapa26xXVwu4P88mSWXc\nqtt9R7DP0EdCjR38vfIuMhmxkdeDwM+v/wBfNZw0UgyKgXqQFYZPcjHofpj8Oa5pwpKV1TjF31aV\nmtFe7tfTpb53H9Vg05Jx1V278sm/ds2vPVbaem/kT6NEpIbDcY3BWGfZuDzyf1qa20JQjhT8khye\nCT124wRgduPbmvR10WSRzBJF8yk7iAMZ9ecdOn4k+4sQaM8LFTEcA9cArjGRjBOc89/bFcjik3Zb\neSv9+mj6eu56FONoRikvhSlLu7RtbS7fe9tfM8nuNCmDlY+EHG0ZPXrknPUegIPvVI6HKQeQcjkL\nnOPoP8K9xGkRsBwDuHJxjHHbg+/Zu2PfJudG8p2CR7ySSMAZPPUdDxx17d+lF7/LT9f1OOWGXO5t\np63fvRadrapX06N37a+fksejOqk9CoJGRz2H65P86jl0qd1K5T73v2P0/wAjOK9Zi0cSiTzUK5GF\nBGDkdeufT9fpmWXQEVIyyA4AACgZPpuGSc4/r70DceX7DV1dWitlZX9E7b/lY8ej0idCGZd4HTAL\nZPPXt/TIq9a2U0rsI0DFFyykEYGQO5z9OT+deqrpQhAbywQTjaQOv0wAOoq0mkKV3RwKHGA+wDc2\ne+Cenvjg4oEqcX7qSXNvfv0d+j8/Q82hsI1b5rUHIwxKjH1/H0I/XmtqLRVKB0iEakchcDJ6Z4yf\n/wBddiumgMC8JbH3lwOf6f735DvWtFZK0ZCptRsHAA+THB/L6+5pXf8AK36Lzt99rerfzBYbkjz2\nclda3utLapbW9Nk7bnBx6JkYYKAeQT+XpjH69a1bHSpUZflJA+7jvgD0xk8fpXZLpUdyqowICdDw\nM9ex56/WtyG3ijjCZG0cDg/3T9PbjpmtaWsk2mvdT1Wt3ay17a62tZNb6CkkouyW1l08l9xx8Wjo\nx3zAlSMkc4PUjtxnqex6+9aK2YQgRodoGBkYwBjPQ8AA4HHtXRBEHTBx6rwPqfT9Pwpm0dQq554w\nvU45Pb/6xrpUHJuKUe97bO+112tva+y00Kp1HTd9HZWto+q97vZ7W7fIyZraGZRleigHn0Azz79f\n8KxJNPjO4267XPJz9725HGc5+909K69RuU5XnkdF9Pb098VVa2KqWVckA9OOx9+enTOD0Jz0xqQn\np7j03dnu7WXr6IqUlJRT6Lq7321s/l+BxoheIkzAO6nhOgY+hzjPHb+fGJVsmkHnYZSo3FCBj5ff\nn2PVRk/ntPZrIwkaMhiS3PX6H6d8YPp6GIw3CuBEw2d14we3v9eD71hytbqXzW3zt+bubUZRjfma\nV1ouvz6WWlu93qZccce8vOfvqflzkqecdh2PHUH1xSLFbM7MpIKHIwCBnJxwc8n+VaxtgRv8oSTZ\nwQRnIwPcbuR/h61aW0hjQO0CKzegIyfQ4z6kD86LNu3LL1s0vv07/wBWM6soubfMrNLVNfyrZve3\ny7djMSJVKuI/LDk8464OMkY9eP5etaoYoMgKMjOM88nGRx6/h+lMKvMwHlMoQdCBjqOQTjP074zV\nw2xKhtp2hQO4wfTrzjd/9f16IRfKvdu7tu8W76q34rXTXYylZXu0k1bXTW/5a6lU5YZyB2OWB/Xs\nOn54qeFSfRh1JHsfw9ev078VPFbB1XjKFjnv+fPT8P6VqLbIqhUXIUenOfqPXn8ueta/VuZKXMk2\nlJxen8t0kter7EOcYys5W0snvbSNr3016WWvfYy5BK7F0wNpyAQRkevA68Y79OABVgEsUL9wM49V\nz/j+fUVc2IuQ6hTjjAHU478f59xUWzJ4Hy5H685Gff8AXp0pSUYqyaly3Wm0dF8t22vTfSwKakr9\nUnZ6We2q1/Lz9CDaxJIwTk8Hp+menpjivmX9pr9qDwT+yt4c0zxf8QVZdF1K9SzWRSAIZGSZxI2U\nk4AgfgDv+f1TLGylQiEgkZwAccHIzxj/APXX4Tf8HAsCJ+yFp9wcBhriJk9V3WeptjAwBz6e/TNG\nGhUlGbhFyaTlFuKavdaXs9raXt63szzMbi5U6CnBtyS1im7tpqze6Xfr5H69fBv4reGvjP8ADrR/\niVod3axaLrMTzWwNzAdkSyyx7pdrDbnyiV3BSRj+GvDP+CgOteH9E/ZA+MjaxrWmWBvfCeqxWS3N\n3Cj3Ukul34iWCPeJJC5IC7VIJIzjIr+Ej4Kf8FIv2m/gl4PfwJoHjLU7jwjjyBpz3MgWG3ffkQAO\niLhXbG85JOOwrnf2kP27/j1+0tqWkaf8QviJqlz4atbWC2s/DAu5DaOIo4I83EZDBzlT92QDDNnr\nWccc6FOp9ZsqkU7LZtppKOttdbq61sn5Hg1c3lUw0aTvepNJtxTcVomm99LbtdNL9fj2MRDxRqtz\nIpKNf6g0UhxtZGuZSu0ddrAjBb19K2LQ20utiSRdqbDkHtuXj1AyfT/AVyiyyXOtywoQrr5mzB/c\nqRkRcHnPQHJySM4I4O74es7+3upo9VdJrueV/s+0k7Yw5KZyf7pHA4x7Dj4XOcQsSpVY8qbu48mr\n+yum+m//AAUcLnC9+ZJX0abd9uibtpo7+T1MbxM1tbaskhGIo5HcgjooBb1ORjg5xnp9Lv2yCW2h\nu4CDDcyIY0GM7cq2SPoc8/lWL42KWN8ovDuEm9NpPGCpGecg5ye/5ZrHtNTS2trW2tFjkSMqq9Tt\nxtGAcnH4jt9a8anCbpt2nJtK97tuySuv1tpp52BTjLRSWqdvTTXZd7b9tz0vxHqEz6fBEIQ8KIw5\nGc4Vjznr25Pb8z4XppgfXR5Eflv5v70DABz1GBkjJwM9OpxxmvZr3UhcaZAjxfvFhYbF542N1/H1\nPbj1PgOn3jxeJJlIMX78gK557dh+J616mUuUaMtFzXknF201Wqvs/l5X7qanZODirWdnbley1fkt\nF8rPe36SfsPftm69+xV8ddP8faYbubw/cIi69ocUu2LU2E9uW3oWRGBhgVMvn0+n9kH/AATs/bx1\nD9u7wV4t8V3/AIYj8M3HhzURZwwqIx58EyXEyuTFLMMlYlzkjkn5fT/Pt1WaQ6nZ5BJeRCFycnnB\nJH4+v/1/7Ef+Ddp0u/ht8VNPC79urWDHaPTTrg8Dv97kevPPSvTqKeDjTlSqK1ecXOMfis2nZ9vJ\neiPVybFVatd06tnGCjbqtHHVeWtlbyXkf0Gyws0Jj8gLKSQXIH3GxnkH0AK9fp3rGefRNPkaG71b\nTLa9aCZ4YLm6hjfKx5WURvIrHZkNgDHTjpXS654g0fw/bym+uYFuPMWKC2OTcTSPkJHGoBGWIC84\nG4jJIr5P+PPww+EGuW7eLfFfiW60DxOdJuYtMtrS4iF81zfWgFlGI1hnYNM/lqmQoBPzEV9jRVD2\naqqoqNTkppqo+VSejdlpdW12/E7sfXdKpJxpRmno0mlyNW3Xe2ltvO234uft0fsD/ET4t+HoPjF4\nasNN8A3vh+NVuNF8PRTCKawUnbfkzxXTkmW5WPImC4jA2kc18U6Z8Av2pP2fvAGn+NfCtlqfi6L4\ng6lHGNdt4jIzQmB4zJcNHFAFiD2WxgiDlR71/XR8Efg9rXhX4H6B8KvHUsvinVdO8MjQtV1m5USN\nqBSORvtDkJGrMWKnIjUfKO9eHfszeFdGmsPGvwF0O+07UdU8C+JptO1iO6ENzLpFnJZx3R8uOVJR\nE4e/iwCqgBxk8199muCwOK/e1ZRoVVHm5aUlZ6RfffRu3noz4ilw7Xq0YVqk+Wo0pTbvzJaWXR69\nrfqfzlfs+fsu/tJ/GD4/QeK7fw0PDmueEbm11O5uLNrtYdcljjtphEyyPKDIyyohxFFgxnJ65/Z3\n9qPW/jB8Sf2I/HWr/Czwzq/g34l/CmEx+KdIu4fKutTtdPtNSe4vLPyZLlnR47aFgZHjyZUbbjo/\n9mf9nn9obwh+218TNH1fT9c0r4VW0FzdaB4s+y7LS5uiNKkQRO0XkOAZblQMkYjPHHP7A/FHxF4B\n+Fvw68R+IPiDLpC6XBpN1DrFrdpBbtraCFl+zNHGsKzTXMcbIoLKThgCpLV8hRrYijOvTp4ipDDc\nri1Jv3vdirPV6f8ABse3lGU4fB1r1YqtUlZwT3+yrJa9W9d18z/KU8deNPGeoeJ9V1TxXNqF1rJ1\nK8WQXjtm3u1uXFwgVCu1kmBXnkFfrnD07xH4hXUI706peRm4zDKUmkVUQnYoPbABOPXGTX2j/wAF\nJPCC+D/it458SeF9Ei8O6P4t16fWNG8O3cbw3tpY31zNdrd28J80GC9juY7mNnmYFHUqADivzk8F\nX0884W7lkkkkaXchA2qVJ4OACD7+3bBr5ith1Vm53bldvXe90ouzv2vfb1PdxdOUZqUabp01G777\nR0vprfa6as1sf3M/8G7n7RWqt8O/FPwU1i2udYt7XUZdYstVTEsloNUu5pAspd1wgFsqrhGPByfu\n1/U6LeNSQAJSPlL8ZI9+g+tfyP8A/BsPoT3OsfGXVZUH2ddM02GJJUVuVuNWUshYMRwQcgge9f1+\nmBAdoAHGD2/EA+o/Divfyj2dGDV27JR1ty29270vre+u2hyQxThUnGLScWnJ3e7as/JrS7V/vObn\ntAeUTk8EDp+mT06Djk46HNVYtJVmJKH5uT8pOMc569O2fTiutSJWJXB4HUEY/Tj+X45qzHZjjCgh\njk5PuSfTPU/yraVGEpOe6ld76J6fls318zoWZygrK9/5n522Teqsn6fezkZNLQKGEe1gCchepzx1\n9sev5cVROnjJLR5yeTjJA7c57dc4P15Nd+0CldoHQkY4Oe/Gew/p3ziq720a43KM85xgcceg+nPa\npeHhZpK90rXeulnbbTZW2sRDM6i0bm1J3bTaS1ja2j02fVfJ68J9kjwwMQJznkEZ6fj+HXHpVFtM\nImWQKCoR8rgnGecdfz5454612jWnV/4cn5cjPp9ev+FV3thtbblWxxkDt0zgdM1g8Hdt23k7avy8\nu+ul+nkd8MdfkfO7uyV21fWPR7ef336HDmxRHLY+UknHPGccdfl/kffmiO0/ecksrNwCMgZPt0Bx\ngfljvW7NZSHcRge2SOfw7d/zxUUcDqMEZZe4OfbGfX29vbg+pq8XdLa6T0aTje/466+vQ9V4tyiv\nevKSSaVkklbX8mn/AMMsq4swse9U5B7ZOOR35H54psMCsACPLON+dvPGcjBx/h8p9K2ijEcKuOpB\n3Z4/P/DpmoXQrjKryNvH8hg9PT/9VEsG4K6V7NOz6r5rbfTyuZqq7X0bT11to7LRX1t3t92xSMKk\nkEAHOA+OvBweev4D6dfmnhgAADdyMZHX6c+3NTxLlhnOBz6dR+P5/Sr8USs24ArtPIJUqc9ccHPB\n+lSoydkku9rau8kml2S3ve11oYzxNRwUE9bdG3p2Ss10s3+BWNuY1ARSCSM4APGBgn8Md/wqf7Mm\nAduSAM5yMHvlQf8APf20QVUAAA+v1/Ef5/m4KrckdQc49/y9OfWm6NSD0i7XWrS2dnqmk106nP7W\nSsm276Wve+y7btbu/lZmX9mjP8JPbjPp9T2B+v8AMEaLkCLnHU5yP8+/XHrWp5a5z39eP8KNiegw\ncn27f/rq6VKak7p26trzV7fe9evbvPtG7au1tfed+TTsrN79ddDHMII+4fUkA/8A1zyf/wBfrG0Q\nORkjjHCnHt7fqPfrW6Yl7BsN9Mn8h/n8aha2APTJPXhR/n/9dauMk76u32d1d2S8uze/foVHFR2u\n1Zqyu7393ZNP8umllc5x7c8gjpnAOOuPqPf8PTmovJUdtuNueM9OOeSemfTofpW9NAOWA+6eenPT\n35H4fzqr5Yyxx17fw98A9/r/AF5rGrB3V3yt7JNXuuW3Lo+m2i10vudUK97PflWmvdr8le1ut+m2\naYgMFVB4AGAecfn37j8KFieQjMZABB5yRzjPYenp/StLYOOMk8fX9G/wpxURY4Hzdx7fgPX/ADzU\nqnOKTab0t6LTa293ZXX5aB7VWaStrFpd3o2nfy167dSpMqqg2gjO0c557/T06cZ6UwcQkYydoBHr\nhfQe/HuTz1xV5owccdc44HP4fSmpGeV2kBgeTyo/Xuc49PfNaUtVZL3tLqzVlp+HZ7ai5laLk22p\nN2Te3u6a7dbWstNyvaYEQUghgxPI5IBB7cnP+elXIO/4/wBKfHaSDGACGPUdsdxkntjj19a0orYq\noODjPJ4ye56ZPTH+Txryzf2fVx21tbpbXy89e3PXq03zRjKzb01vZ3i7O35Xvr5FJ4gxLEZyOgHO\n78+O/sKptE+SELAAnA44xggdAOe3Ppmt9mi3dAQOnAzk8dMf5FMWMNuwo9Tjj8vf/JrSOEUnGcot\nPqls1ZNdLNrXtfY5oYyKSTbT191SveyX3Prb/gGUtwVXM22FEH7x5mVFA56kk+n+Oa/BD/g4Z8S6\nFZfshaZYJqlhc6he64hhsYpg8zL9j1RTIqLngOCrbjwT06ZX/gvj8dfiZ8GfhT8NrT4Z+KdQ8J3H\niXV2t7y8094o7iSP7JqjbMvHKG5gQ/wnIr+OT4lfGz4mfEK1t7T4keONe8WW9v8AMiavciSKGQs5\nzGihI+d7EErkkkfXz8xzVZcowoU2lLRNWu7WvffbS+t9DyMZipyimk4wadrK97W1vbpf5Ptrb5hj\n1id2MUQ5LAFeOD/tDB4NYfiKR4PEWgzrMDICgmAYYVTJF94dxgenXFU/EviCOLU2g0W3EYOVaU/f\nZyRjAywz35x9MkVwstvrNzcfa51eSSMjY5LfLnBXbxx064z8v5/MYjFfXVKU09bO6j8K89v00Wmu\n3hK7V9ru/wDVrdv6Wh6pql6sV401tJ8+CWZG5y3fBB9T+f0q54c1OZNWhuJ75iFIDeYw2KOgwcdy\nOmM5P5eYQ6B4jvHMuZkLAEseQcjgcqcn/I9Kut4a162kgSSdi8pGFU/MgyG+bKDsecE/j38icKEY\n8spR1srPVrazXTVPp306gldKy3S0td9NPuX5djpPiprv26+htrciRvMy0iHIVCRt6cZO3PQE/WsH\nTrtofIh3nzAVZ8jJ5wDng+nuPoMYxPElhqNjd28U7guRGWJOX2sflbkfeGPy4HStHSliW8VHn812\nKYwUyuGzg4/Hpnt1FdFOFNUrqafZWSSVla2vW2q7vua0IvnVkm7O9lr07f032Pa4pbtbOGQgKjxD\nax4Jyp65Hf1Pr06Gvn2/mlj8YoCCGkuNz4PCggjLZ45wMf4177qN2kFlaRoruUhGVXHUhhxyM7vc\nH6jFfOuqSPJ4qjlQPEwkywk6v1Pv14ztx0z2rHBR5ak3LROTaV1v7ujV++727M0xEXGEbtpXSf2d\nb6ad9e++6PRbqR7jXrRY5dwBTqeAfl6D69vwJ7V/X/8A8G3WsyPpnxg0FvmkW5tplyRuyunNjaMk\nj72Dx6euK/jztEnbUYZjGwIAbAXjaCOn1I/lxxX9Xn/BtTqyzfEX4taXPOkBmt1aMSttYsmnwKoA\n4Qn5gBxkdq2rzhz4eMpRS9tTb1SdueDu3forv03OnKZ+zxDc2owtq79Fay21tfXd6XP0s/4KkftP\na5+yX4w8EaxpekWsp1Kyu7uwu7x5o0N7DNZBMCKZELLLJtXKnbjnPNflJ8CfjB8WP2gfHL/GLXPj\nDotp4m8T62ukeG/Buq30+20mtb1rG2uooI7N/lidreVSZXzgfKelffn/AAWC/Zm/aH/bB+L3gz4R\n+G/At9caBpduLnR/FEcN0LSB2fT55vOnjjdQnmuScq/CE4xla/BL4wf8Ex/2sv2WPGfhXX7HR9b1\nCbSb+z1CO60ObUJrSSW1ntriS0kVookUzOoicpGHByQc8118SQxFWpSqYWalShThfkqJdI30XV+a\ns0eNm+Mx/wDaNqUZPCqavKL0avHr13aXnfXof3v6h8YNB0NI11+JbW0vdMklsdYiUGF7qSORBBK5\naONGBBbAZmwRkdTX5/8A7L/w4n+IPxv8S+MNGN74ZTwt4xOr+LtcgmnMfiaRbOBBpt0skiwbHt57\neUlMuPIGFxlqXTPCHi/4kWf7IfjCXWRY+IcY+I/gbVHW0+0wnQtfZh/Z87bllW7e2b/j0Vv3Q6Yw\nNnUv2aviZ8Qvhd4y8MfCD4nP8MZrr4ji88ZayFgSeTThokcU1rbzT3ELphvs7BopY2+Q4J5FfSxx\n+IruNRwqPlVk5SevwpJpu1u/T1PsMXisPd+w5oc3Kua3NG3uaJJ7Pp2Wmp9reIv2rPgt4M1bxhp+\ntapMieCIGl1/WLazt5LKMRW8ErRG8+0x75THMrhW2/dPOBXz34X+Clh+0T4nv/2iPiwNZuPDYRx4\nH8AvLdQ6XcaWgTbqF3bx3EAnlle0jniSWG4R47lvnC4U+OfDX9mPSZ7u28FeDNavfEXw48Kara3H\nxW8SeJIf7QuvHmqxw75rW0vdRnvJTZy2tw0Dta3bor24Xy2YcfoL8c9W1Pwn8MYNL8C2lvDeat/Z\n/hrSoo9kMFlHc2z2ySIqRlQ0ZiQghRuOTnHXroSq1ZN4jl5Omt3qo2Ttvtq+ye+py03GpVpuk5Nv\nlU56xtpHRJ7Prp663PwK/wCCuf7L3w4/aT/Z98Za9ZeAvA/h3xH4As7h/DmrQ61d2ettHplvcEW0\n9jHDHG4ZrSCNd7SkKoAGCQf4GNKsZNH1++0maQLc6dc3VrOc/u1mgkkiYqx+8N6HaXAyMHHYf3x/\n8FBPhV4Z8D/Defw5dXvjTxL4s1XT7nVNTs9C1LWrmKKe+jN3Ib5bK9KQwxPcEKkkXlmLPAUAH+JP\nxvotn4V8f61Df6TdafC2oXBgW/tXSR2luZScvOoeRgWGSSTuJPevn8RWqYXFz54pJq0bap3so9NO\nl7etz3MYr4elCEnO0Lu+sr2TfM1o/v1P6RP+CQv7Zdl8GfCmj/CH4ZWenWfjnxneSp4s8aa3ObKy\nsrOd0a2jgmZbq3kmUy3KpG0UWXKAnLEj+t34LeKvG8zS2HivxVomtQwbLq21K2voXuL1J2KgGCK3\njjVAiblCM2SxBPGa/wA3n9nF77U/H3hrQPDV1f2M+o6tp0ZOmxyPcuJruIKsXkusmev3SMA+1f6B\nv7IX7Ja+DvBnhTxRqev+JptTv9JsZdRstX1PU5G3eQsit5V1eSGEF3b5EQKu0gcYFZZfXxjxrpKD\ndNyTXvOzT5X3X67dxUJYJ0Ze0hCM5RUfatK7ty3VnbVfL16P9QbK4SdFcAFNuQeDnJwuMj2xnjtW\nnF0H4f8AoYrmNHtZNPh8gvujHyx5YsVHPc9ScH/6/U9GrHgErnHXPp689eQf09SPs3FxSTurXuvV\nR679dNdjwK8Yxc1CXNC/uy77P9f6dxuRuIzg5P4enuOTz7VFKpZsjqBjnP8A8V/XtnqRUmAWDHAP\n6fjz35zx6etOK5U/Nx7cenX39jn36mghrVO+19O97flYzFQyPlHAAOCpGMZ5/M+/9M1O9qdpIONv\nU4Gcj+EdcVaisgoLFskkE9uQcg/5ByD2wKteXwc49vT8cip543tfbR2W3+e/3fIzVeopL4XbXbWK\n0tq+636+pzstsVBbHQdwCWI49v8APXFZ7wBwQB74A5/M9uf6V1ssG5T83p9B7Djt2/znOaMK+1VB\nOF544z39Oe2P8aqM4tK7u/PR/Z02VtFbq73OuOMaSVrJKPfpbTv/AMMc/wDZ9vUE/gM/1449KRLZ\nWPrwcZA/LPT17V0TwkKC2zr65x6c8jjH+HfLRbY5zn2I/px/np2pq17Pr17fprt+J1fW9N7ddFft\np39b7+tzC+zpGM7RyfQEd+/9Mce3dDGMMQcAY7Z9R7+vY9gRW00BO3hevp24+hP4fnQLU9WCgd+B\nnHb/APV71bjBLSS5ku2rWjdvTpZpLe4Rxdm03pp6bLS9tlt3vd+ZkRxjkYJPfnGc9AR247+lTbcK\nRs4xwPy5/p1+natMIqbl2g5PUA5/3e3UentnPNSrEjjIBHH/AI8TjIODz/8AW74Jlyb0dt003ZtW\ns1t29O68jOeLb6P4km1oraaR9G9/8zNihD4yOQAT93nJ/rU4tMndjjrxgHoRyoyQTz6/rV9I1TcM\nAlhnpg/eHQ+gx688n62FCKO/Q8HPf/PHOKHKyu200307238rp9H2Of61U20ella1rabO618ra9dD\nGFnIcgcAAnBBHH0/+t61G0WSYwDuGcHg84/XHuO1b0pULv28DAwuCSfoB7dR39ea4vxT438KeB7F\ntZ8Yaxp/h7Tm88RXOp3UFosjQR+bKkZneMMwQgqFOSSFqHUilzSklbZtpbNLVNXe9r97Iz+s1JTi\n4rXSPKrX2X320+/R3LbWcksYcZzu2uvp7eg9fmPr+NHNp9oeyF3atdJjfbiRDMvT7ycEeg+nHSv5\nrf24P+C1vj3w78VpfAP7KnhSXxPbeGNQuLPVtZmtXey1F0W4h/0WUWV/DMm9opQ0bkEAkcivyF/4\nak/by+KnxrufGviHxbrnw0s9cuHubhzLc2+kW0TKf3UEcyWdphXQH5FQgHp1z4GacQ5fl8ZTq8tR\nU0m5Jq0Wkra63Tv307d++jWxVSpToQUpTkn7qV1FWWjequ0/zXk/7zLiO2hZY5bu2idl+VXkVXzy\nVwDx6enocYqlfiz0yzfUdRv7Wzsozma5upRFAkeCc+Y3HQZyTjgiv86n4rftw/td+HPHOsadqHxb\n8WTppt0Us9Tge6SwuY1jjbzFkjuBbsN7EHBI4PNc9r//AAUB/as8b+EJfDGr/F7XZtLlA814bye1\nuCoWRdvmwzLINwcj/WdAp64A4cJxjTxVH21DDKVLTl5nrpy/8N5O79Mq2Y1sJUlQmvejy3utn7qa\nb2vZvXX0P9ATx5+1d+zn8K7WG78a/Fbwpp8E08drGF1WyZxLIxRcrJcQ4AKkE5OMYr5c8cf8Fbv2\nFvBV4+m3vxSt9RuPJEyNph0+6icMgcKHXU4zvwQCqg8gjr81f582v+P/ABBqltcHxV4z8Q69IJBc\nQpea9qUyJJGW27VkvJBjJ+8Bx+deWr46tvne5dJQzFFe6ummmVssihTLuYDPYvjAHaofEtXFO1Kj\nyJvlva1mlG69Pv217HHUzGbmnzPo3Z6dNe6ba6d/v/v4tP8Agup+xM15PaNr2ppFFI8a3L2duI2C\nMQGz/amPmxnvweK91/Z+/wCCsP7JHx/8T6v4Y0Hx3a6PJpcXmrca29np8FwAZwfLke/mDfLDkYXn\ncK/zhH8VB7gJBFHcFkLMqooG0rlW+VTnjJIx2G41JpHj3XtMu2uNHurnSZo2ZTPY3E1hIy8jEjW5\njZxtJ4YnqeelQs6zJWSceWNraJtq0Wk/lo/PVefLLM6q92NPmvZtrS6VtUtV6ea3P9U+9+PnwT0/\nSrbXb74leFLfSr1EktLt9WtBHcKyggxlpArfKQflLYznvXK3n7Xv7MunafqN9N8YvBQXT7Oa4kDa\n3YAgIpCADzxkl9qgHHXAPUV/mRyftE/EHWLaz0HWfiB4pNrp6hbGJdZ1JbaMgbdhC3ixkbVABx2w\nRjmqN14z8Za05hh8Q67Okh8udf8AhINTRZIhyXZRcEFSB0Py8HnnjafE2LpQUq1NcrtHmTu27RSs\nrrRu/TZN9bB9fpzSbpTUm0pOLdto3a0VvL5LRb/q3/wVG/bI8e/td/HbXtFsdYtdR+G/g7Xp7bwQ\nlo6Pb3FoImKXfmRRDJJubhNweUEDG70/PfRtC0C3voo9aMd1PFgzwPhoh0J+bIJ6jOR2INcDaaut\nhbt5V1/x6jZNLNKXlcnBJWR2LlstjIwfwNZE2sTT3KtaXYG8GR3dwxZQ21hkkk/hxj1xXLDFV8bX\nVTEwtScbxVtvhtp5q9/u1sc2IryqKCpq6/8AJeXR21vZ9deqd9j32fT/AAmku6Pw7pcts5ws0YLy\np3zsCdTg9W/GqXinwH4E8W6YqaXpo0fUghVZrdWdJZFGASJHCIS3opxkivHtN8RXFrqMMclx50LE\nKWyCqksOozg9zzx+PTurfW20vUo7pr6N4Y3juIYkZHbPDsrxZ6FuOQc9gTiuh+wlGUY07c2l+mnL\nrpprbr+pj7SK0bs/w6bPZ7627M+e/F/gvxP4YVFjZTYI7ql+rHBkUqojf92qKd3yjBOfm7/LWf4d\n1D7LbyT6wY5bpS3lsTkAZOORgn67f6mvufx7baX4r8NDV5NKuLPQrmySG/mWCRFhvBCES5TAQIDJ\nvlYqyq3Un5cj4B1fwNFod+TeX91dWLP52nzQvI8UkMp3Qo7JJtDhCmQWYgnJrwMxwbUJVKcXKyW2\n211bz6bpdXZ6IpVYRkry0e118LaSbV0k9b79m9S5runWfiO6j1CWQxxwpGmI1U5ZcjI5HHI43dvb\nnoLXw9oun21tcSpFu2q/mO+1mAAI3Ddx6nv+tcb/AGpelhbWlmVt48orEMxLKMMzEoeehyTjOKwZ\nW1Ka/Fte/a0E/wAsSgy+WMZbjkAYB44PfgCvkqmMxUJOK92mnbmu001y3TWrf530O2cvZw9oldXW\n2r3Tfwpffd6J6bnrGp+KPCdskfm3lqZQpUxpJGxGFJAIDk5ycYI7Yz1rzXUda8L3eowX0EKyzL0C\nqCSck44bng/3R681VvPAGgC0uZrnUrn+2i2YbQAsrEleMGXcv8fKoeBTvD/w91u6vY4IrQWoIJWe\nYfu1GeDudQuPf+fflnm7oqSqYlRk00m1b+W9/nqvO+ljiqYqEovnmm1raTSs7RaX3PVX2u79T0vQ\nXm16G5ks9MjtDbRFUvZAfLJwpGQylM85x6+3B6nQfHvx8+EGpRa78I/Ft/4b1OQNJc3mjzNCbgIQ\nBHIkcW1yQqqMg4C/N7bGjXdp4V03+xL250+Wd0yWDwrucZGGK/f645+mOay9X8ZaL9i+yWlysmpm\n4RYbSEK2cq2cFW3YJK5G3vjB7eRRzjFSrz5ZuqtOVyu017tnB3a0vfe/e/X5ivnGKo1ZKn70U/da\n1f2XZq700auvO3c9mvv+Cin/AAUCvYbJNR+K+sGdYmsWuW8hbmK1lZUciQWYkDBI1OQwzjOR1rW0\nL9vD9qDw9pWp6W/xR1LVPt9rem6m137PfsJ76KR5PsrX0VyUdZJGEflsmCF27SBt+bpPEk+lMp1j\nRZGEih43dZEBUgHn5ABwR/h64HiPxX4YkjXy7KU3EqBSixExo7KuMsD8wBP3iAMdz1rtqZ9mE3Gn\nNuCXutJt8y0Su/vTXd2Mv7axU4+ya+LRadVy8z6tdXq1a6v1P9NL9tL4q/BT4C+Ff+Fj+Ib6w0zx\nX4NMuo6LY6aYIHmcI9s1rcQ2jIyr5VxcHEkbDcFG0nGP5dvjH/wWpk1uLx9omjPqWheD/E2uNqFl\naaG9zZamVWzgtDv1Kxihu4VZoHJByvII6CvyB/bG/wCCiHxW/aQ1bUv7f8T3D6bdXEs1zCjzL5yu\nWDRx5mPlx5IITaVBXrX512Xim01GC7W6uXhSPO3dK29ouCTuyGJ3E9Dx9c1+0TqvEy/dv2dJK8Wt\nH0fztdeXy3/UVGlQXLF+0q3tNtK0bJaRbTaV9NN2ldrp/Vp8Nf8Ag4m1j4c/D238BW3wgsbuwsYD\nbnUjdQLc3is0j/aLyf8Ashpri5/eYEk0rzbVxuwuK6f4V/8ABwlrHjH4p+E4/jF4PtNP+GlrfoHN\nlBFLcWshu4TBcsINOSRzBGZRyQeR8wFfyX6f4p0ue6Wyt5WktZEKTGR+N+QMgkbQduDnPeo9Y1S6\njuYks7lPsiAgofmGMg5xvwxXoDjg96SniqdvZScldXbf8vLv1d7N2f8AwRQruEpKFGCUnzSkmua7\nUbW6p6b3Ssnsf6avw8+Nn7Fnx11Swn8MeKNE8Wa18RLSKT7FfCyvry3iEUcEtmYJ5p5rYAXAj2+W\nn3QSnGF/nO/4L5/sv+ArPUfDHiD4M/DWRrTS3P8AwlOraBo7iz0+VDZDde3GnWXkIWaO6MhndPmR\niT1x+FP7Jv7V+r/szfE3wZ8SdFvzd3nh68hll0y6ffa3Fq1zbzXESwyt5QkdYAqHazKScA81/db+\nzH+0p+y//wAFEf2Yvidc22n6fDf3fhPWG8ceH7sW5vrO9TStRNxqFvHLaxS+Wk63VxG8ULCNVVg/\n3Wpxn9YnKjiYK6jzKe70ts2tHfXe9/JHo4fEUoRjOp7SsrKDjfaUuX3rtL4U1+HqfyZ/8EWP2cLz\n9oX9rHQ7CKMW+leCf+Jxq95t3xCK1E9xaoHzGoZpLJ0+VsjI4zjd/ofaXpFto9tDp1tvMFqiwRNI\n5kBjTKrjOSAB0HbpnHX+TT/ghppXg74RftlftE/DvRgLvTLxrkeG9WigMkKWkN54gZbWW9UPGsyw\nCONo2kRwxAKKeD/WvASQwww2kj5iWIKnrnP+cHnivTyzDUlFSUnOSk0pbNL3bLtbZb2VtLnm1vaO\nvNRdqS1ik2tJWa9bJPV3LvllCAx2nPTd1H5H8vyz0qcuzEYOP73A5x+efbg/iKgEgc4flhkghQFH\nTr7Yxk+uPlpwb5iuTvOSD2Hrzjb0Ge/Xv29eV07Pp22110+8wd7tS17d09Nb3100LKnIXJ5Gf4en\nr39f5Z4pwcrjGOPr/jVUFsnLKAMhcHBOM5+vRf1qWPLDGe+PyHt+NSZSgkm7vfz62XTb5L8jQXoP\nf+nH9KdVMTkBh6EAHHqT755+nHtU28kc9wM/z/8A11k4O7tZXvbd6efy036b9CORuz2WjvdJfPyJ\nqryQqcsOG9QT6Y/z/kVYXGOvOBxjrn9Bimv90/h/MUop3V9LNXs763tbp/Wva8vb5x/NFOVQoBO7\nrnP/ANfj+fr3qHIc45AHJ9f5H19TxwB6WJVDAZzwfb0+nqM1Cq4Bzz29vbPHXrx/hXQ7WT5m3fVd\nVtrv+PpextFpK+2q6WfSz3frfTqxrDOFX+Hr+Prn+lKFfoOc/p/+v/OOtGMch/wY5B//AFVOnmjJ\nAwoH32GFBPOcntgH9am6t/m7L8u/UUpbNab76PpZrye3nqhkcLMHYkHkcdM8+uMevbn3wadsK+g/\nMY757f55964XxZ8WPh34EjWXxX418M6MG/gvda0yCZgOD+5lukcEEEH5c5xjtj8t/jd/wWt/ZH+D\n3iafwzHqt14vubYSC4utHFxJaQSJsGzzoLW7ilGGPKyAfKc8isJ4ujBN1KkIpb3eivZdLa67d1qZ\n+0u73vG6T1tZ6X7f8DV7n7AYdiRGPmXkn5TuGOSM9ew9u+a4jx/8RvBnws8Lar4w8d6/p+haLpED\n3F7PeXdvC4RY3kASOWWMs7hCqKDlicAEk1/Kx+19/wAHBWs+IbGy0L9l/QG0a7gdJtS1rWoPtUbR\nK0cnlQxTW1kwZk8xflkbnHBIwfwj+N/7cf7UP7Qa6vJ8Uvifq50HUMmfQrO7vrPT3WMuVUWq3zwg\nAEcGMgjgnHA8avxDhYN0qL9pUeui5usXutVuu68rClPSPPOKT15Vdy6W1T0vd9kk9L20/sT+PP8A\nwXI/ZL+Gfhee48Bate+PvFl0WtNK0ezs5yjXs2IoDLLCl4oUSzIRuiwxUjjrX8837UX7a3x9/aQN\n9qPxL1/UPDPhxJZ9Z0nwvo7XFhLZWtyD5cEslk1q8r+WixsjwBgRkrzgfjN4C8QeHr3X/C9jo+my\n3+sweItN2SpC1w0u2/tgoYLEzFRtbJLEEMSBjdX6O/GHWdJ0TxeZfG9zaaebyytv+Je8sMRjAklk\ncNAW4O3IYFBjbz7fEZ9xDipU6lKnKcam0OW+qdm1pZflvtoerllKnOqpKLk7qzfTSPV627rp00Y3\n9n2+vPFKrrllZT2tkJD9na+hkOoXxVQwmnM6C4dnDDLyF2JHJOMV9i3Os3niLS00fxR4dl+yWDu0\nRtY3jnkjVSMmaGESNkuRtLHp3r5++FPx6/Z78PSJBc+JLOyFrbqsccUIMSsDjG2MBQQCf/19PqHw\n7+0P8BfECSnSPGui3F3GdvkXskEDMSMYUXEqEj5hxg5549PybPs3zOvg/qtDBYqVSdvaVU5KKbcU\n+ivv8ul9D7bJsLhI1alSvWhScZPlg7KUvh6273v9/mfIGpeJPh74gvNY8Nah4JhmtLR2h8y801Eu\ntoVMj7XNaGUk7y2/fkfpXxZ8Y/2efCb2t9r/AIA1mTQLsq8/9j3l60sEhCklIzPPEi5K/Ltj4ycA\nYav0X8c2Wi3N/falpo06eLUCZjLZy2z71ICghoix52jnnuDjt+Xf7Qw8U2msNLoV266escgmtnLy\nFeQCVJdQucHPy45PSvpuFMzo4bLaVHFVZxxPI4unVvfn0dlzavbRfLsfOZ1ldSWKqOmoSozaaqLt\nJxaeivou17vqfG/jTwNq2ieGk8R3uvWjzm8+zS6ak8TzKPMlTzNizM+0iPdu8sLtYHdnFed+F/Cc\nOrag2oajrMMenqoVt86osbMMZAaVQWQ8npzgdTx6XFd6Zq63NhrxuHumk3BnlbZuXdwQVI5J9ccV\nz134c8P/AGWW2tpZo9jmR4opWCEgljnaAoJweMAnPHQ5+iw2IrKTkoNQcna2qa0s0lu3pbVPpsfE\n4mH1Sc4PmaT0um+kdb69++tuhDrFrpng/WbWS01iLVLKe2lBkiKSGPzYhgNskl+4HPXGcDnmuc8W\na7p0GkxXemXTSXcz7ZIoVYsQdmciMkjOXzlRnAGaW1g0uWSW3gtJpBDuDyTZdDtYrtXemF6EAjr+\nVV/tFrHK8Nh4bDzFgqvMiNCSrEkoWhCZPsSem49TXqQzGVrRp2UrbpLW8W9HboraPW7ujKFpRTjZ\nLRbJNrs+vzVvRaGd4Tg1bWopXngljjB3q7xyK+CRjG4KcDqOeOeDzX0B4KtYNOeVZ55t89vs3yh2\nWIg9RuJXPGOCOpPU1yVhql9HBDE9lDbyCNVlWGFQFbByD5YA5I/PGMfNXYaDc+dqEUEyptmUFgww\nnOecH6dz3NeZPFTxGKjR+zzxk7aJaxSt0v1vb79S7JNKytZtb+Sfrv17bsoyx7rh7b95cJvO5oyy\nq4x/s7gFznHXg571t2mnSQ28t9NbeTGi7FJl52nLE4KjoR3H8sUmqXcWlXvlWdqVG4lpnO6HH/fI\nj7Z5b2HUVdupJPElvbafpplMs8qRu0cu2NFKkMzAfKQD75HAr7KlyqnBdFCNlo1tHS22jaWq0v2J\nn7sHy6WWn3mr4J0W68XXxtNOtVSzhmH2rVJeIokBy+C4CM4BOBvB+U4BxX0n/ZXgLw1BGlnbRX96\ngQ3t9fTrJGHQfMIbecSKB1HyPg7QeOMfO974yh8Hacnhjw/uiLbftt2n3ZJMHeGKgEnLsMlj07Gu\nCXxLPq15FA95IxclXxIwXlgCWXdhj6Ht+FdUKUnKPMtLq715baN28nts7a6XOI/QXQvjXo2n2T6R\nq+iWGq6FMnkSWkdpb+W8RDISFWCRVcISVYKWB5U1wWt6p8HNTurjw5L4ami8P+IonSK98yQXeh3j\nhvKeJ/IWZYI55UOxXijCIQSFJI+ebTUodKCRqzO4KMRMd4ZTjfszjkKeMZxkcd65jxV4zvZJXi0y\nwVI1MPmzOUeVcnD4iKiQAgEDHA549euUKDSTg3FX5oys09LPra2naw1CU01GyaXe3XRfPY82+LPw\nh8a/DrWmGkal/a3hW8cnStajlDq6EsVinKyShZQhjz5km4s4BG7OeBtLLxGJ4pryd4Z4CqwtMhZJ\nGBIyN52sCD15B9Rmvr7w5r3/AAlOkS+FdY81rC5tt1qZWLJb3iIX81AQRECyRZAxlVC5615fNHaW\n96dB1No55NLlaBL4hUEuz5QwDbt2NufvEDI6nivzbifB0cPVjXo8ypTd2lom3Z6ryfXy7mjx7wtO\nUKiTaSTT1T1VvJt9eljz2fUdZsbeW51Wxgnm3bYZoIEaTt8xKRlsYJ79eldvaeMJ7fQNtu++/EJk\n3Sx4CDOPLBYY9+3stP1Xw5LdI159saDTN25GO6RWXGeNrBcDr/d59qxdI0eK4upYvtCXVgp2EKPL\nlw3PJJLAZ9vSvzvFwpVpL2jbSXNG27acG72tft8utzxMU8Pim5+2dN2u47ark2t039XqlvbxK98Q\n+IvF/i3TzcWlza6dayHzLmKORIpCsucFkRAQQTyzFePzk8Q2Ot3OsWzaE4tLm1kSU3Mj7VJTHDeY\nygk8EZ6jPoK9y8USWHhmC3s7KO3eBBvlCCOSWNdzEs+0Fm4PQjnP1ryGZptQurvUrJp5rMxMPJVZ\nA7y4XCouMryrDG057V0UJ0r0qdOmqcafxSt8b92332aW+2rdzwJuKcrSTUGmpO3M7tJXfZb66W76\nI60eKtc8RJZaVfK17c2yrDPJAhxgbVLBo1bdwM9eo474ypPDGrG+lFhKrSRYneO6wNka5kYYlYYI\nUHA4z0xXL2PiG80dgLFWtLy4lWLfcRMAjMxUMzSbM4+oPQj1r0e3+2pZPPLctdajchTLPGTgow3M\nFVWfjDEYJ6dTnNdahCEnKsm4NtRcXZ83u3ut7X6s6sNTjKHM5xUmm43a96/LrfddmvLu2fM+va9d\nJNdQB3KkYWTJO3nAAJPAODgj19q5VtauVtjCrtvlPku5OCUYZ9fUA98egqnquo5kuYs/NjBPTndk\nYz3Pf8gKwo/PkIiMhR9wnWQkZwBtAB6kd+meOnYfuGFpuME3azjay2d7O/byP1Vz5VGU3zOWjcXa\nKbt83f8AH7jtrPWJdMVIVkJY4ZmUkEBvm69T+vHv06Ky8XSzO0ZdmVvlG/JPORjt3Pbp+ePKzcsr\nSI5JYps3HHtgj/vk8/QEVZ04y3FzE1vJsCuivuzhmB6nA75PUV1GaqRcrWlfb02666W6dvQ+i9FN\nrqqI00oSSD5sjJZSvKkDBIIAyOuD7194/st/tLfFr9n2bxFL8JvFU+lN4l0PVNE1eGOSZUvrO+sb\nmymWVI7iAO3k3MoBbcFDHI5Ir8wNOn1CwvXa1d33qA6Z+Viy845GeS2Pr+NfQXw31ZY4ru6u5Ws3\ntUkZ8NtYqyyHjG48gdehPWuHFTUJU2nyzk1unqrpNXV76WTXyOqlJ0pz5nem6bbit76Wt+v36W0/\ncv8A4Jb/ALffi39n34x+I5bzw5o/idNevJrvUpZ7GP8AtSO5nuLua5eC8uJSFJaZvuxvkKAD0Nf2\ni/A3/gob8DfjLBY2VzqMfg/xHJFF59hqkwEZnYAOolW3ggUbwcAucY5PFf5qfw1+JOleCPEq+KrK\n6upZDIxdVcqXJJJ52KOdxyC3B+lfa9r+3nptosTR6BefazIGF9HLAsyNkEOGLbuvTjPTpXq4aMqM\nYNSTi0nay6tOy3el/wCtjzVi4XvUc7qT5Wr/AA6W9el29Nl5n+mLp17Zapai60u+tNStpVEiXNnc\nQ3MRQ9ADC7gdc5bGKuhXCYIOSOc9untgccYyOnrmv4OP2dP+C0fxg+DltG2lTazreiWxTdomp3iX\nFs8QZE2oguIVXcBxvP8AD7Gv6gv+Ca//AAUcP7ell4lFz4Mk8KX3ha0juZ3Mtq8N2GltYDsjt55n\nX57kMN+Pu4z3PoRxMW1GSaa31Xlbvq1ordiliG0pODcLtX+1o4rX5PZX8rK5+o6qYgGPP09/8SSf\nrn609T15xnnH3eufrjH6n6UkgxI7ZJ5OB37fp7c/yqu80NvE8txNDCBxvnlSJQCePmkZB+RPoc11\nJxcb3afS+l07WVrb7+t79zZzVlu72suqu7q9lfr29dC3v29s/wDAs5x/LP4VMDgDkcgcjOAPp2P9\nc9M15bqnxO8J6fcPatqMd1coDmG13ELjqXuNjWygZXLGQdvfH5wftF/8FSPDPwd1ddF8OeA9a8QW\nltqVhp2reLPtFl/YlhLdyKjQMsjwzSSriRMwmVQ8TE5yMzKUYtXe/K13adn599exusNXqQ5lSk1b\nR6dt99tU3+C2P12EiYAxtIHXPOOnAz3xz+Z7UryoVIHJ6jkZ4PoemD059a898EeKIPG/gjwp4xs2\n/ceJdC0nWY1UHCjULG3vHTp0Xzwvfp34rrtzknCsvGM44Azz82Mgd+vHP4PlTtLmV1olfe7SXl13\n/E41TlZ6xumk9lK6aVnr5pa2t89bxYsvK4655AIHYhjx14P8qgkntIYzJNfWcESAmZprmBAg77na\nQKuccscZ9K/K7/goH/wUg+F37L/w58WaBoPie11H4vS2s9hpej2U0n2nTLt9sXn3B2Ko8vdIQFnD\nBovpn+P7xf8A8FN/24fEunajolx8Z/EEMOqKzXDQahfp5Mbsu5UzcNgAISPlIya83G5lQwVo1HeU\ntEnu7tLpps/+AauE3HnXLBR01d76x2Sata6v8j+6z9oD9sj9n39nXw/ea5478d6L9ptrSa4s9JsL\n+G8vLyZAwSBRaG6KlmUklk5xz15/lQ/aS/4Lz/tG/Ea/1jSPg7p9t4B8OWt1Nb22otAJNQu7MErH\nMstpd2kiPlv44Q2F5HevwG8efF7xjqt/FqvjvxlqnjGVVJDardS3bKSWJWIsMqcsW9wWAzu483f4\nl6tdx3D2Vo0EKqXRmAyccZIDg59c4OPWvnMdndecf3SajfRKyb1W7XTy023OabTi7XclZRlLRbrV\natLW/T0ejPqD4q/Hv48fEq/uNb8d/FPxXr8jmSRLebVb54I2lYOQkc00ojUEkY3d8e9fPklzOkjX\nN0xkknyZifmll3c5kfksTnqevXGc1zFl4y1C/QLfzZV925TktkHJUncRwefUn/gWbcOpQXQljS4U\ntgiN2J3AgY68E4I9MfpXy2Kx+Km3Kd4xlNXTenTT0+VtNGZxg5e7UezW1k3or2t5eWnmad94lhtY\nMW8bRhdvmfxHcAOeAO3PGPXpgHzzxD4wfUWjtg7FSPnXJIIwMjHfIB4O7r1q9qVtN9mkkluo7eEZ\nZ5mJYsMnoAS3TjhefxFed3d3o1o8P2XzdRu5H3eYOFUZBIzIq9A2Op+7WGEnapKUYa2TdkrpaNp6\nel/UmdOCjzU+ZWs2m7u1lrrZ3t/wH29N+E3jmP4ZeNYfF8sIMNjE72kBXgXrxyC2k24OSlw0T5AB\nyOGB5r1TVPEF58SvFt54/wDi54gutUl1qZpdOsWM4gs4JWZ44hHL9oUELKV+UqM444r5qguk13Ub\nPSp7RomlmhELnBHmRyBkHGf4gMHgEgHPNfaXg34DeJdZ1/w7J4mLHw2I47h4mkjiTy0RpYwTI5U7\nmRAAA1cmMnGU5zcJKTV46J7JXv8ALY+m4dw9bF4mn7JSaXKpxdle/K7PVX1v3+9s5vWbb4K+H7aJ\nrjTpbe5vEAi3qB5hIJDjNpyrYCjkfWvMtX8I6K0SaxoTyaYLg7opbaURE9SGBVVIbjgkAjB9BX1D\n8UPg/pfxC8TGx062isl0hDb6bFG0bLNHEX2MoTI3ncSCSPlHavNLn9nrx2tk1s2q/ZLG1B8tJTIV\nRVXPG1sDrjd+HvXDg62HXMsTKTk2uWMo2X2Vpo0lbV31/JfouMyauqV6OHh8GtSNm4/A3L8rr/I+\neIvjd8XvhNqEVzYeIbzxHpMJLNp1/LPdHy1bmGMyTBY844OxlXPSvbrz9oLw38VfDz3dtB/ZniyC\n1ZNT0m7KqZGZSS8AKRo4wEzsaQZbHXgfP/irSm03XH0S7PmmDMM11y0cmGBLEfex07e/rWLrXw0a\nW0tPEmhubbWrJGeOaI7RcIrFjG2AGIJVV+8OBiuzE0MBiKcV9XjzwlzKcFq37uzVtPyt9/wGL+sx\njKNStNezlZKOttYpXbt22899dOe8VawttqUTT2slqskhZ5FUrj5xzwoZhznjtgV5zrV5r16fM0K8\neMO5DZ3kyjOAThlxxjOT949ua7HVtT1LxNLGl7ADLaKkV0ApwjQgIMnceWCljyOemK6Cy0e1Fuk0\nEsVlGibpVlBxlACSNuAM4J5PHcV3YbGKmlShTTULJJqzdlFabNNWtv21ufLYuUZylGSc5aXlK13o\nvPT8PLRo830SbxRowMF/NEWuvnZZFJZ93JA/enBIbPTv1xXomm3surNbRKTaPYO3mxBWUvu2oHyo\nAJJBbuQRVMX3g3VdSRb3UgLyyIVHUuIgV4wP3ZJ+6c/Nz68V3lnqvhGytLm5W4tWkTKs4HzSDPQH\nB5PzfNweTinmMpxoqVOlUd4xl7qej912vr87bLzOOMVFWSsuy8vW+/8AWty3HbXaC4SBFuGkJlaV\nuTGhO7gsSeMHgflnqyynCGIghpjJtMqfLtUDOCRznnPHHPcVzdx4+8L3Jijs9Ta0kkPkSqWfCjB6\n7U54cknoe/WnvNa6TatPZagt9HLOQZUY4RSA24kquOeORzn8vOwSqutTrSpyj8N1JWe8HdO72W/y\nvsijr/G2rOukWdjbRmSS5cL5w/1igh/vZAYA45Jx16it/wAJWOoeH9H1HU8l3jtyYBvGUYorZ69m\nB9OvvXIaPZ3+uPLcRyq9rbAEM27CjI56g/xY4+o65rqvEGqw6T4cXTxc7buZfnbcxVx8wwpPOPu8\nEnp0zmv0KglUjFRajZRve9ul9t3stNdehE1eDV+iV/Rr/I8s1fUtQluNyMgjuBKGI5KSM4+bIOc9\nedvQ1n2Ekmnb7i5ZlliLEuCTv3/MOmT04ye/fNRfaraC6zK7FXUkBT8uTg+h75OTj8KS8ImtZNrp\nsYgYfOT6N1HQEZ4HrXopWSXZJfccbbe7b9Xc6248VrqbWAeY27IUXzoy2QilMsSvO4jn5cEHOFyT\nWBDrqvrWox/aLiSBPLUTF28x3LOuCCA+0HHJGMEZIrziZdQsZo2RxJbrNGyhTjq+cct0z/s4Ofxr\nfs2ia/uJmlxcXFuiKVPyo5RhjvhgSM5yMcjjFY4hy9lPleu3a12np5K99Px1LpyUOZtXTsrLTXV3\n/A+hfCuvafG/2J2dbxgmxi4cMGO0AEDjdg5BPHTsa5v4kWUFheyG7kZZdSh32f2RX82KSQsd7lVc\n9FP905HbkV5naC60GKO8uJXac3DOkoJ3nLKVXPHH0wOfc16uZ7jWv7Ov5lDXU9nFs84EgMVYhmP4\nnvnFfI8S0Wss55WdSnHmcl1j7tu12vPv3OLGUXVjK0oxu729Lebv5vbzPNvD2tanpdjPpfiPWmbS\nIFxGZVmeeU5VdqqXeQ4Xn5UOOfSoz4ps4fttv4feWS6eIsZnV1VBuUAkMsRBPGOe/vXS6z8Prq9n\njvdUdEljlM1q3W3b5SMOoLN0LH7wwQCP71Y40CxtblrWGWF7m6OLqRdyqFwFwuQCo+VePmOc5r8q\npyjUcKlnKKum9NW7b273Xn+R5eEw0K85xqXtFWun1utX5eunlqjkNMknuXv7u+mknnCMFSVndGBU\nErgkggnjHH1xU2m6xrVh9oNjBZQxK+8rLANiEAkOvzxjkZPAyM+o467UPCqW6htNuYZZEUqyZ+Uk\njcdwyM44PymvOPEP9q6dFHZ3ITzHYykR4+aMEjbyxGCGAXHv/drshRU5fuvctZyUn5x6q3ZWta9w\nr5PFxnGMl79ry18tvud/u3NC6vxfK1vqNpDcxvIs091boolhZSSGT/WScBidqgnhc8iuv8L3+ian\nqJ0uz1oWLWturrJfLIonKx5aFCwiBYbdh67jjivN7bzWt4pbaKe2vSCHUgtG8eTjeIweGXHG7oTX\nOa3ZNdhLiWSASabKbgJbK0dyGVhKwJfPyOU5G3kHqOtexQwvtqUrrmtezd9/da6Pu7fPpqTQyj92\nvf8Agatro7tNvWz9Lb/cfN1xqkjXLJIo/escSHJLDGc8fQ/UnPTimTapcLMIM5+XapycjjjOcH8Q\nKpxhFuojMQYeACeh69Dye4OevqOlT6vYGHbd8/ZichgOenUDj0wfXr0NfsFBNUoX6pP7/kj7qDm4\nybUpSSu01orWVtl37FuS43Q5V/3pGAVI3ZHI5xgf598dR4dh8qExzEpMTvViCSd2SMH8cZyP1rh9\nIaF7lFY5UsOGzznHB59/f8K9djsDIUXIX7TCGjfoVCAL0985H496VasqXLdNuT0+VunXf5XT3NME\n516iTXJFSVu7Ste9radWlr0XYtabqF6t2IrmJtkRysv8TqMEDccjkDj2z07+o36X+kaNbatJDLbw\naqXjiDYHnBCq7+M9pAR7HjIqDw1oC63bWtqsQedZ0jaYDJKh9pzjsQOMjOBnHFdz+0BKui2/hPw1\nECEtdPjmOQBlpre1c5BH94HnHbFeQ8X9ZrQg4tOEo3WyavG9m77vdLtsew8IpUq9ZzsoQlf+9Jar\n81paz30Oe8BeOdE0a6uX1/SDq9msDxJbgoTHMySKJcO6jKsQxweqiny+K7OW7up7eF4LSaZntYXY\nHyYyxKIMFhlVwODt5xkV5r4LfQYtVil8SXDRaUXkM5TYXIwTgb/l+btn161c8SXmi3mu3KeFGmfQ\n43zbmTaHOWcHcIyVx93GDjrxX0UaqUYxUXZRjq720te+972XfbRdT5KdZOT92156pXTsrWa2100X\nofSvhbxdctZPFDK4VhtK7gAVBB9Cx9Sc1/Vh/wAG6H7Qnwz8A+K/ilofj/xlp3hr7d4Zint5dWuG\niikb+1NOUrGUic7gsbtk4OAcV/ID8NLS91TWl0lJVh+1ptV5iAit8zljjJBwuD97ivtv4d6A/g+D\nXXj1aOa8Nv8AZrqa1uJ4vJVZIpCEeLy2zkADdkYb8a5KmIeHm5SkkrprdLS13269vPTr6dClLEYW\nTXNdaxSSva0XZNXu1ruvv0P9OW6/aA8Ea3pMOq/De+s/HlveIzW2oaZdQpYR4d0LzvePZsFBQ528\ngZr89/2nP2y/gH8K4Jb34yfGeDVtWhDS2/wy8H3uZZCqswtbwXlm9uzEjY3l3wX5/vbhmv4qfCn7\nW/7Qmj+EU+Gngz4la/4c8ERkotnY3MPzNyCrSXFvPMAQzE7ZQepzg14B4x8V38dzNf61qt7resXT\nM8t1dXMk8z7j8zsHcICxAPyrjHQntNTPKcYrlbnJ8raj52+Ly1fzN4VKdOMOSMlOKTc3aUnJNJJp\n7q6a6Pz6n9AHx0/4K7+J/Hs+o+Cvg/baf8HfBc8ckM+tQvMut3cGwIrtLBc31sGkjdmb9yOQpwOQ\nfkH4kftdaD43+G2nfDo+Ntb1vV4td07VLu1WaE6XqDW901xcXN0stvHcvMzSM+VdVzI/HIFfjnp2\nsX99Fe3wLQxRsVR3Y/OxDEA9Q3Kn5SPqPlrd/t3T/DL2cmpHz9Rv42ZXGN0SnaEJ5Qjarqeh6D8X\nVnXxEIzhVcHJc1pNrVpbX2t87+WiXRDMMVCHLGSUZNc1rP3dFp0TsunW3mf1S+Bf+C6XjPwH4Q8E\n+A9C+F1rLp3hTw7p+hG7mK7p2sNOt7GKZGXVU+VjBv5RThugFZvjP/gv18abSwvTZ+CNMsA9vdQR\nMDLuWSSJo4pfl1NhlXYSZB/h6c4r+avQvHy61bvAuYxa5IuAcB9ucKTkj+EgfXjBzWRqutX+sJcQ\ntmOGMMRIc4mxnBzkg5xngA9eor5nEZnmWGqypyxCkoK9+bTTl00T2V9Htu9UcNatSjdNJttSvtdu\n3y/4fzO/+OXx/wDFHxe8feJPif4unuNQ8QeKtTnvrpzIXjgEs7ziCJWcttRpXA5bCk855Hiup65q\neqNA9pKLTzkCOrH5x1yBtJznPqenBqEQySwxhmQeXkLuONrbcZOe/THPPtVVILbzRmXz7wDLEk7B\n/ukY5B9R0/OvNrZxLFO9ZSk07KWqS+HRbPfZb97PUweIlJPTS+ik9GrprRbW7W9VprGunaU84stV\nkEryJkyOd20knP8ACOeB1z+mDFf6HpnlyR2NyiRZIYLwzoO5yo78e47YNSmzkmuy+zftJ3sc5ReQ\nDxnGDntmntp8TSsyHzU/5aHnKMByBjGBt69RXI8Zyr43fRpS16rotemu7aI9sr2a279F7rb2Xprb\nTRHNXWk2sVtizJe4U8Z25Yc57Afe6+561i2+l3IlbeCjjIXp1K5xnJ+mMZ9B3rtEhcb9+23Qn5c8\nMRjryG5z+OOhyaxLtVjZpLa4Eso6OxPXtnA/P5edvNRKrLEXpp89nF2S9HbTW3fZXKVbVNxSSvs0\numt/T9Gc1e2V9KWjuAxiiG0hjhdvUZXPp/dOcfWs+HRpr0+VCgihUE+aqjggElgDkAAgN0/U1cu9\nes2d7e7vALoceUpGyQn7owVBwO2D35xXONreoO8kNlJCrqWG3LZEYzyOD820EkfNznnvV03Woe9F\nSXMmk3rdO13Z2e2/T8ERLEfu5XSinFXdu9reX562R2nh/T7Ky8SWL6lqoaS0aB0jGSSAwx0jxzg8\n7vTv0+oPjb8ZtdHh7w/o2g6v/ZEEdjComjkKySARjAJXdzx6AZY+tfHXh1r+a71DUCyb7WzB3kg7\nmCyBQvB5yMr06ip4LbS/FdvJc+KL+QtZoht7bcByGHQcHGPfsOg6c06dapNOesYp7fLW/W99E766\nW0ufYcK1vZt1HNwty2k92nZee637XXqfR3wW+I95prTan4i8T3N3qG8G2WeYEAZQc/u9xBDNyD3G\nDzz9F/EP4uRW+hxXR1OJo7u224R+ASGIJyPQY96/PZ7HwfBYvewX1xBPCh2REooLL83PJb8j/D7V\n4r4n+Id5dwnTReu1sjiOPLk7Bnr046le+Qe/e44GOInByi4csW7pb6KyX+at13Z97POa2Goziq6n\nCpdOMmk38OiTXl0urdtn9C6pqv8Aa+rLeIwl+3S+Wtwv+qXcSdrbsOThc8fLjPPUV6lFaG20/TY3\nVWQrsMcR2rlnJLkHnPUnnvmvGvA9rBJBplzflz9ngV7cwbTDP8xYGUyfNuyTwuDgcd69RbWo7gPZ\n/ao1EUqgQKcvGpBO8jaeFzzzjnsCaiso0F7qTULLa2raV737/LVrtf4LFVvbRr1LXcpNpK/SUXv5\nJr5d2fO+ranB4W1bXbU2TTSXFwsqBQuRlGPHIA+969B9c8Xquq6tfWFyGZ44JUZxH0ZRtLY5PXBx\nnv8AhXpfiqyMer3Vy8JcybcDGSVCAbuemRg9ep+tcTczIS2LSRotpWQlemQRg5PQ5/PnFGFx1CM7\nVI2s7JvzsrLXprY+UrU1KpJ3Sbs2m1dapLuuvz2e9jxDTY2nupSYP3EJZnkJ5dhksDzxyP8AOa24\nFvJzO8dvGLJNxY54GCegZuoAHPH58V0c2jWcpmMCm1iky0jklQOpf+8AOSOmec1wusXkMcsWlaRL\nJMu/ZdSJyG6L/DgnDKfzOe5r6jD4iFZJPl5GkldrmlG0dLdHtfv06HE5xi2pTjKa0007JXs7W2fS\n3QlMllLcRQWyCaSQ/OyZ/dk9M7h13egOfwBrv9JjvbWO4jZ2GnwqDN15IYZ7NxyOcfXirHg3wQ0D\nR3oiJWQL5m8E/J1yM5555x0zXrT+GrS8tr/TrK42PJDtIPy5O5fbrx2FeVj8UqFdUqKXM5pKKton\nyvtqtXZb309biuazXvOy9Omr3189Omxu/DzXLOy8MajcSnNpcT+UrNgkQ7IyCOg/1gPY9a5fx9fJ\nevai2f8AcRLvRs5H3j146YPp/OuPvIL/AEHSrTw7azu8qyGGYpgh+rdNoJOcHp2713U+hzX0Vvay\nDM4tRuU/eJyD3OSSTnkDBwfp9Xg3ONCFaUbp293R2bUbvT8FdfqOpCXJLbbv6Xfyv/S1OJtrvT/K\nVLgbXHyhz1OeBs4IHbAI745p1zLBJA0SuGkVSVIH8HPB4GSBirupeENQ3rHENvl5IDBuvHp6eoOT\nXA2ovNJup7W8kUPJKAGJzlMkMBnHXIz9Og79qxD54wcHeTsrJ6bd7d/8+z4Xfrf53/UY+orAwiZj\nJAkmWUn+IMegIHPGPQcGtTStW01LmO7WF5FSdfMt3wMfvBkrjA65PLHpXKa/9mtlnm84SIMMAuMh\n8k5PT+IE9/esDTNdWa3OXADShD0PBbbn04zznGB6girrpSpyVrt6Jed0/wBBH0rqXkatPYRIhWK4\nkhmjg4JUMVYAbeCQAv8AF27mvadTsItPttJWXybVI7C2AY5ViwVuDjPPbBHpkEV8/wDgq4kvbixj\niHmmAxLG5wSNmM4Ppgc9MnjtivU/GOu3Eryw3dnLiBEiEigbCiPyx5BA25yQPy6V8rxVVjHBSotq\n84xp2W60gr29d/zRM3p6v/g/odBcxalqmmzyK0Q0+0QOXYn5xwvykY3Z3AcjqDxXnDWFjfQm7hma\nC7tQVaMZzL/F8uFJxyOSR0J6Guo0bVLS+06GCG4V9OC7JdrHkddrbjyQSOM4HY9ir29pY3MklhBA\n8ko2xF2Y4zg5KhgM9eme34flOGl7H921zWbTVlrqrSV+u66aW7HHQpOjKpL+dvtrov8AgatfeecS\n2k8dtJMZ5LOZd0ghDAG4CZzwAevQHjp071yl8tzrURvYbeSe8WVIfJkGSqAFSRu2qAdgON3fv1r0\niSJLm+jl1KWOxnkk2OzHEewtklQd3AwAc+mfWreu2NhcRWsGn6gl9fyFVWK1CkDkj5mCIemM89Tz\nXsUqSlFzikoxSctdtt99d0/NK7OmU3JRT+ymt+/9bdjndP8At8Vgp2afZPt2vdThsxsny7RsZj7E\n4IJHHavPJtJmvNTki0qP7frN05W61SNWFrHBnCxHOxyHjYqcLzg49/bLT4Zxag1u+rTNFGAGe1DE\nB3BG0N0PPIKhh1+X1r03TvDujaFZslnZIhiXJWNc79o43EsWwR1we559MZ5nHC+5Cdn1WluZcurf\nl8+vV6OFRQUotX5lZa6brpZ7b/0z8q10bTXuLqKaUrFACYdm0lXBxznr6dT+NVry4jsojFKvn2+G\nUB1+mSMd8Hpk8du1fqPN+yT4bhge633svnElmSAOmBznd5vtjGPxPfzTWv2atAMjLZX3lkAgx3SR\nIuf97e5Hpnb6e1fs1CrW5eV0kkno9bO/Lovvtbppc+neKoRhy0ryUrczaXVJt3S2T2V91sun51ab\nHbi6WZRlWcYQn7pODnjJx6+nFeiHVRDeWybcpEqDjg9jxz0z/jXtOufs/eJNOmYQ6WlxaDLwy27O\n4fBwCDHGASeeMkcZzXlOo+AtbXVYkvLC406GMhXmkDhcLgH74AxgcHIpV1VnBycOVxV9E+tt7v0V\n+m19zGjiIRqJxcVzO8r7brRO9/m9F0Z7x8P7+0t5raeNdjSTW52tn5jkZIGSO/fgc9M1jfHHXRrv\ni1YZQVNlp1siAjBKtboB6ddoxz78VzDeJrLw7HbWenRCa5t9m+RyQo2kEsNu8defy6iuX1LWLjW9\nSm1a8KNKyRRgA8hI12AcKOgx1HPfvXDhKCnVU3pyq7S3V3HXbd30XpfuexWxVJYOcFy80003fVpq\nN2l5aPXfa9x3hu10S5vre2125ez0x5c3cyhGdYwQeA7BeRkfeHofWt7XpvDWk+IHt/CNz9r0URqI\nZpkQO7/vN+RGXHQJ/ERk9q5+w8m8v7WC9ljtdOllVJ51CsyKWAZgrEDoT1YY5HHNS+MNI0DSdZjh\n0LUZNRs2QMspCjDEPkYSRwvAXAyeozXvNW0TvZRs2vJdNP67nyFV2m7q+lna6urJr599738zqPCl\n/cXPiqzW3ums2mcRpInG1iGy3PYA8DGK/QX4f2dholrdaPqOoyXMuquWnuZCC0bsq9OQDzGOuRgn\nOMGvy30HUxY65Y43q6XkfOegB9TjpjjPrn1r7HvfiFZ2ms2Okm5KW99FG9xfQ4lkiJZwAoJVR8yD\now6/n4ecqtKnCNNWi780luleP9Wfy3PpcgxWFip06zaSte213a6u/wAe2x7JrFvrljfXOnaawCtM\nTbyk4LoQPn+6RjJIwO4OcGuQ8Yq+hC0v9VuvMkkhIcZDKSW5xkAjpzn6/X0i11m1m06JgzTtHCEh\nu3AVnBYsGYKzDPO3qenJrxr4iafdXNgkrztci13SNGPmwmSxyAc8EqM/lXyOCzCrTxbw9WUVC8VF\n8qvZOPyd7aWV9W0jjxNWl9Yqcilyc7cbNN2069NNNH5MenjLRtO0mzt4Ck32hjOxyOHWVgFODjnc\nRg5x+FZD67p3iS9E7yOHgAiPQKqHAYDLHoExx0/LPg0t+rGOKOTiIuQu7IGGweBz16+v44rofDE8\ncd6wdm2PG0hHQFlG4ZII9e/t7V+gwq054eNSnOMlyqN3ta0dGlqnb8tl05vbSV9Fu7b3V389ut/L\noj6f07XtJtjDpMCiC3WMPPMPvSEqGbcScYDZbGe47Cn6h41sbK9S1sEF/EEQnAJB4+cEqwDEDqP+\n+hjFcTpdjFq9okzblHmeW7RDJKB9rYGR/Cenf16CuvutF0+wit2tIG8zYMvKCWIKgcg5AXt16V8P\nmLwzxclaUb+7e7s5Xi7u+ltHa930SOKpTlN87fvK1kurumr3aXSyVt2WV1jTtTlWdHa2kXG+DOFJ\nPGOpOSeeTxgcZqf+zTc3SXNvPIkY++MADbg88gHGcY+ZfbtWZbHTIVZjCJJyBkr8qZB6DB4IIHX8\nqrXfim8tJPL+yiO1VeThgWXngfL/AMC69MdRivHnQlUfs6bum+7+G6s1bdLyaV9lsWvaWTcUtbSv\ne6tbp567991a509vaQRSSSyXLbQSSGwvmAdh06Hnb9Peo7vWba0RRFErRyuFLruyFAIyRkj3xkj3\nFeN6h44c3Upson2OWEgYMVGSfmXgjHT+7x2HSqEdxcSNaTrcySGY+ZJbZB2/MwwBkkcgDGBww610\nxy2pRg61VuSTVr76qK0T83r6Xs7lxTckv5rK/XfZrRdfw31Ox1rxRD9tMdqru2NuAOCTg4GGx34/\nWuS1m8vmt5V5giG1mZc+YwI3YII4zlumO9Mv9RuDK32KwxcR4LGRDtPAywJVjkbhgAEentbhS61C\nSFygJUA3KEZUsCOcEYACjGMDp+Xo4WGGot1HZz5Ypp2aV7ead09/W9tDNz5ZuCV0ra6Lt6K1v07H\nBvoN7qcyXkMcqwxqrPICckjlixJPBwfwB+ldgul2bJCiRtBJHGHknyT5mFBIyTjnBxnuQv12phqA\nZRboqQplXjRQQ4BAIGFP8OenXPfGa0rO3vZJWkktx5UsTRqhX7pKMu7IHXJDHPcfk6uYYWTcJKMY\npWi49buOlrXs290vm7GdR2ahLZpLR3aV00rbOyvp1d+xkaBLaTx63Z6ep8yC3JmbBJY5kxnk5G5T\n0HBxXjWqa5eaLI8kyN5KsQWOQnHUEjC87ge+c96+k/CXh9dIXXr6aEk3cJXcyEDrIRzn/aA7YzzX\nm2t6LYXlldx3US7ZA5DEfdYjIAPGDnHeuOGLpzk1GN4cyitWtbK3lpu2+v3nuYLEyw0YRXvaRtZt\nW2S5rNf8DqrnzxrvjuK6jyt2YRjmOMgIeAMc5OM9On+HlTask2tWMRllW3u7pFaQYIUHJzzhRyo4\n/WtrxR4D1yLUZXRCbEsWt1jGdwJJGfl/kTk9O1Zvh/wrq1zrmnRtazjZdxAkQ79hHzZKt2B6luOt\nexSq0qdByslJp+bd+V6O71Xldeh6VWpisROnKN3FxadnJpW2bv52t/w6Pva2tr3SNK0yeNwB5EYh\nKEnz0ILDzQQAjdR8o6AdDWPDqM1jPPeOWiMcgZ3c9iNxHIPyf3m+nevTrexuGstPgv7ZP3VrHHFJ\nCzyKy4zukBVVjYEkHGcd/Woh4O+26hDFJBIbebhlVD8wJHJJPzDHTJ6cHivkcXX9+VNK93dybvpa\nLXS3T17Wtc6HTccPKLupNJvo9WnZvbrtpf5tFZDba0YWSBVEkalT/ffaMsS2TyxJGBjb16U+38PF\nZXXUYIvs+7CKiLlhzhgSAeDj7vX5vw7Z/BF7pNuJYo3aBCvlsFLFBnJ3EAAYxgDP0OOjrOCPypPO\nuVdwVyCygrjqCM/XAwOB3PXwcZXUd5OM1q2tvs2+fXfstT4/MeanWk3zWVulk9EtLJ36d9jgLjwP\npd0XdUJhycxKuc9c55BOfY8fiQcqx8HaNptzI0GkQcj70qEnOTyMk8k8/TvXqU0drGgIuMMrZIBH\nzDOQMZ53LVW41GFXSQQgxjapBHLN0yc993PHXnPSlgsxxUHFe1mlok23az5dbeWmq+drnlOcY2lU\nbhF9bO+tla7vsu9+pxcul2cytFHK9s+cMqoiqp44HQ47cjvjoayP7M+xXXl+bMEf/lugUN0J4HIP\nUenX/vr1+NLSZXlmSKGJFDySuAAi84ySMDoCO2T71Lo2r6Z/aX2GxS3WC5Uouo3Ucb4kJJJiWQPG\nVwMghl+gFe1go4nG42EopyjGS55O62cb7X6d9/M9TDN1EmpWSTSaveWidtXrfo0eGfZrG51KKXVb\noW9rBP8AuXdUEjkLwMHAbIJxgnpjHSsfxLrf2LVHuNJuGYRYADHAZQF643ent6c45p/GvQdW8L69\np+pw6kNX0a7vgzsiRxNAxVzt8uAbQNq85bHOPSqXiqPTtUj0iOFHjnljSQRw58xzkj5lVwTlvTPP\n5V+l0oVqVGNO8WlFNvmd/s6bW6fLV9bEVJ1VzQUej1V9tLPe6vZXXTyW9y08ZwShZtSJMhcqAmdp\nJOcckdR1/njpy2ueDtM8TXc0lhq0ltcSRmaJPkwGChj1LHAJGCv4V3Fn8H7nWbS3mnnbTbXhgZCV\nkdvYOQDk56EdOlacXwXngmJTUrmMjagmjBbajgcff2YB+9jOP51CvL2kHZOzSSd77JbpeV722ujC\nTbe/Z+jaTPli/wDhX44nS7GnyvexxsQ/JO9QWBIxGcEAY7epPFcVeaJreiBYF0m8hniUmfcjfOVG\nS65Y5BKk9B0xx1r74l+H7eH4Dcf8JFJO8ab3jHlgYC7juw5OevsefxdpOt+HdQj+xeIrO3mty4hM\nogiEgj3bDKXXy2Y7eSGYj5ew69cq03tFb26u92ltptq9/QdOPPK17WjJ7b7K19bb9rnxv8MPiZJp\nPiTTbS8jkjlFzGrRXIKKyO4RtuCSeCTnAr7v1+0F3c/btPBaDVIxcbQA8ZWTcxRS2SecdcCvJ/iX\n8BNE8aHT734danaLqltPHJb27mO2klRWjLIxjMjkoitjIbJPJ719f6D8E9bsvhnp19M8g1/S9Kgb\nUNPlz5chRQHeGRx5jcliMRoMc+w+U4my7EYi06Tk2knyW2St030v/kVVpRjHd67ttaba7Xtvffpu\nfL9z4cAtZra2WW0nlkyDECFDcDBBPGNvQDuPSp4PBfiG4gj+2SeVbxKB5yOxnZQc7tpUDcenBPPp\nXUXLXUEUdzPCyLIci3cHepOcjJ+YHjvWzbamklnECkmHG0PlgqHJ++M8Y2j2r8wqxlQryhUT546t\nW0STTd769e33nJSoVq02oL3Ff35KytpZvzTu/lpozirnRLKSzt7Pa7szrD9qlGHUnOSPmYZzk88N\n6YFdNpOgeHtOkixeBZdoy3ysxckHnceOfTB7jFT/ANnQQxm/mkkWFJg67xhJGILBULEggcjgetbG\nmaroaxyiaKK5vB93YQQowcBlXjkYyduT19at45uLpwlZq3Ola70Wj+X5ejImnTm41J04pNJtN7Oy\nvq1rd/J6CI9oZ2dfOYQ/IrHG07u5IJx0H8uelX2u7e35EqtvAJQNltpxwR0yM/5xVqG1TWFMtlPb\nW7Rttks2ZACD/FuKly2F4AG35qpyxado6tHdMLqaaRcNGA4iBPI69BuA+YDOOvesPZ0al24p6O7d\n+tunX5W3ei0Jq1sJSipe1U59HFpqKfLv1SWr9SWH4ua21tLpsJdI7pxFG8kQLRIxU8bgeeDzkdfw\nr1HQ9X0AiG11q1S4j8gS3kj4SVhnkqwG5Oo+646epNc3/wAIJpN5aPJZvGfKQMkoIJzlcd+evXPT\nvxWK/h3UNOmFzdTQ3UcTCOdPOQCWzIBIB3NtffgAgFsD0r+i4+7tZaW9P6+Z3czaSV1yxs1Hrqnb\nbTy8j6Q8GeHPDXj+6+xeF4hJNAjB7W9lNuiAMMFJJGffkMucDue/T0G7/Zi0jW2ki1rSrBLOBcT3\nUEkbCN8BsmRVXnG7qwzj8a+aLDX4/DCW93pUo0SK9iKxzJegTbtwBLSZjcDKnqem30qtdftPanoN\nvcafp13qmr3N2QDAGu7iJ2QNGWL5kTqeVIwQc9qbk5Jp7elr6L71+txwfJJSTe9km7Wemuln377L\nY8n/AGlf2EvE+iCPxV8No49Xs5BI91ZLKBLFGpHziNBM2Ngds7QCMHNfmzf6Lq+kTXWl6hayWuo2\nsrxzQSB0YFGIPDBSRlTjI2ntz1/brwXd/GX4qj7XNqU2h6K6qjRIzCSWIrteMx+ZCwJXKk4zk/nz\n/wAWP+Cf9z4ulk8VeDfFlpb6u9nH9sstXkjiWedI13lJLy8jXLSbjhUJyw6muaFGEZc8dL67dNNP\n63u91t2U6imnKpUacd1ta1tV0633W9z8ZbSRbhbawnjEYNxHHLMCVMaPKiyNnjOBknJXODzXX+Jt\nE0DRZbeDRtSXVfMgjklZyhMUjAlkwJJTwRgDg89BXTfFf4EfET4R6nHF4psYltbqdYhqVrcx3FqT\nLIqs2Yd0S43gj950Pbg1yGs+FYNEhs5bDU7fUmuI1edkkjZrd3QEoSskhOGOOduO61u3d3+X4LTv\nsu5nOnCcnJSutEn/AFbft9xwN1KbadrnycEy4U8g545UgZHTt2617N8PLFPEmr2NqzlmLo0rSsXd\nQMnCliSACR0IySa8Zu0Zpdkr79rbmxwR0B5z+P8Anj0f4aTXlnq8WoWcuGhlA2Z+8gHA6qT1647V\nGIpxqYKryxbqcsviWzutb200u0l6di6T+rxly6uejdle70ul/wAO1vtc/Q2MWCWEVjbxgCCNUBKj\nnAOWyee59cep7V7qw0y4jeOYqfNXZIrYxg4BByV65x+I/DK0TUhqlibuCPbcqmLiIYIjbBzgAD2P\nQdauW+nvKXuLh5DGxJK7SCMccH07Hjpz65/H8ZF0swnGbajB80Wr8yfura60/Jd9EcLddTkve0fN\no91p+Hy9ND5l1L4X6p/wks9tZCMwXLtLAxZSojbDN0GFwX6E/Nj1HPp2kfBSyjEN3fX00V+YmTy4\nSwhGAFB2pKqA4xk7c9S1dJavdXWu3KRSBWhwsJY/dTau4ZJPQgfTnnrXtfhvw3HqMkEt5qGyRFJK\ngkqcEEgDdjJx6fj6/oeRu+DupLl9x3ab0XLd2u9dHbTtt01VV21V9HezfZLXyWmv67eLaXYt4d0i\n9trSNprmG6wzyKWAQytghW3jkZPPA6Z9Y7CPVZHkvbwtJDMu1UK7VXAOSFAxgBhjIHT3r0bW9Gv7\nTW7p4FSSwaSCJg4VNwLFN2DyeOenHHOK6b+yorSOJJ0haCaJdqZQEFlGSOpGPl4Hp06V5HEEIUXL\nERppxmlaW17uKVr+f599urDL2sVJ7pJ972ta7fW9/wA+54nc20kEUkkIyJMlGbjDBskbckHoQeef\nrXJXdtrs5aW4glkiXIBVGC7WBABwoHGf4vwHFfSc3hzRZ7QPJcbI4TuIQktk4HRWB7jjr3FT29x4\natI49HlKTC4XcsjxjcAecbmyRwvJ/D6fIYfM5UbP3XrZtXckk4bWVm2uju7a92uyUVNNO8Xf17eW\nvw9LnzHYeFC8BYW5eS5feCBkruGduMN0x7Y55rVtfA12Zo5PLeIJhgSDlQD3Bxxnnrk4/P6IGreF\nvD3nRJAtwwTKOYtyoen3ijAEY9j82OeK0/Dt3pmrQTXDQkCZW2kx7Qo3AHGQOMjtx26cV21M+qSo\nunBXb5VdrfWKs30tv31JpYeLkrytpdPXe6tq/l1ffRI8YsvDEDSRfawkqn93hEUMSehYryBnPXPB\n7132meAdNVpZZEaJGC8AE5yPlxkjPHX9Ohrbe70bSXmZ7eSVUJZsRtIf+AjBJ9eOwx0qnrHj6Oew\neKzs5IJIkKRytCyDDDqxKDoR1JHrXkyzCvK721VlF6OzXa7aunrdK9jnpRpyrTnOL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o0c9vdKqtENoIyMbm4OTycZIP+eF8deKoUsbPTLdwJ5yIpUVwdowMsT1+bGDtrP6lh6817Snz\nPfRJK610222Xa77HT7KhS9+ULJpJNb30XTz2/Jn5k+Pf+CT/AOytb6Pq/jHwP4avZdQsUa6l0+fU\no3gmt1ZMhY2tEjLZbP8AEcL09fJ1/ZL/AGQvjB8NZfh9daFefDzxVpm+PS/FcFwAYrhUZQtyLWyt\n5nQMzOUa4jGdvPSv2GsrcRaVHYyTsqXUTQuikjejIRh+xGezcfrn8mfjVpV18P8Axl4gsLbBt5r1\n7qBYxsHllUUp8xwwyCflwuT6181xBRjg8LOrh6V+V+8rKXurk1atpe+9ra7Hzef4zE4HCqtSs4OV\npWjdqN4tvqtdLu27R/Pr+1B8F/GH7LnjB9EnvrPxb4f1F5YdH8Q6bErQ3UJfbGlwEnujBMdrA+c6\nswQsV5r58l1Lxqlml0mhwx2khDbt8MsnOSDtXDgAZ4weD6V+4HxS8JaJ8UPCmtaFqFkLm5MMtzpa\nlVMlpeKhRJELK20Au54xgtw2ev4u6nqV7p+p6lp1ubpItDvXs5xcuxhMqM3loY8J8pjRuBx/tDNf\nlmZToVYOtCg1JxTk+jbtrvol52v+fymFzyGKp1G4Pm91J73b5U010Sve3d+ZyCa94m1C2uLq7hFr\na2vRp4jFuKhuBvK5HykZAPbkA4qlaaxb2ED6w0qJrtwVt7SZkAiVXzGpC8AkArna4zjvnmXxfL4h\n1m0S7kvI4IOEawt2KwsoyofYshXJC5OeSWPSuL1/TNYms9Dkhihlso54F3xhQwmEkKgOS/TeD1A4\nJ4rgwaVXljKm48y6+kXdO2vya0fYMNVlicQ/bQtHXlja11eLWui2+Xc/UTwx4X+L/wC0Jd29t4J8\nAapp9payiH+076GOGKGEjh8PcRSFQXzwpJzx3r6+8MfsGHTntR8TPEMV1LJsln02w8xW34wY5DJF\nNGenTcR05r9XfEk2j+A9Pez0iztNP85MiK2TyyOgUDGQenr1+oNeTaXHd61fJfSbnkuWJ8tvmCrn\nG7A7/L696/oU/S1gKdCnFtqopfE7q9tHpe+t9Nrvoch8LP2fPhh8PYxd6T4asYrqPDRXk0EbTIwA\nIcOiqd2RnOOT264+jbHVxBH5RJkjhwIwCpSIDJAAPbnP/wBauO1DVVtoRbb4RsyGA3ZwMgjhskd+\nhweo655F9bkh3Ay7Udc4JIJA/A5+9g//AK62oyspaK/NZPXRWT9dmSqdOLdoaW0Wj7Wbv2tfT8T0\njxZrsE82nSzlduUUgZUYyi+h6Y6/iT0r8yP+Cl/iqDUvCXgLwel6qL9tnvhbKSz4iuLK4RsDIGeP\nmIwB97bX2b4l1djaWl6GBjhKo2ScAkjD+20qTxkdOnGfwy/a9+LsWs/F6LTIbpb5NGtJI5BJJkRP\nPCikJggqUaNgQQRwPvdlUbTg03onbSyW33vqn/w5liKkqcW4u2lr6rpbXulpp1tqtj5l1rSz9m8+\n3DpcwT+YCHXzJFDhhjjGCAxOccHJ9+b+JPizWdO0XQr+ymkiWVEtbtW3CNXSNPmJBUFt788noOhN\ndK7NqMq3kN6TEyoXQHADDl1bIzgBse3oeBVP4kaNc638NLuOBxNJp1x9rQgcRpJJbqenPKow4/3R\nmsnre+t9zyaXNLmlK75n9620XbotDzDQfiI6X9ut/cxkKdkzvli+QVyvJ4yeB82Oo4rzf9o34R3X\nxl8D6xB4R0uC+8TQPa3WlSM8Nq88cd5avNC9zcvFEm6Hz8F3RckDOTg8t4etNOv7xHuyZJbSYkbT\nh34ARBu+XaHIPbv719//AAt0Sxn0qO+8UMtnarAEjtYgBNcKAGG7duQoSeoI5X8y3X5fd/w5lUlN\n6PRNSi1p1001d7aafM/ej4Ef8HX3xp8U/Hv9mf8AZ18Z/wDBNjRfh/o/xj+OHwY/Z/j8YN+1dD4m\nvdAX4i+MdD8Epry6NY/BKyXV7jSrS9n1T7GbrS7a/ls/shvbHz1kT+2+v8pzTtN8NSftU/8ABOfW\ntNtLa0v4P+Cmv7G2lW4jRluJdMl+JEF1LJIcldv2mGFflP3hnb3H+rHUQbfNe2kmlZNaWTW7euu/\n4HLUhyS5ddk3fu1dn8rH/BUr/g40+KX/AAT2/bn8U/sY/D79iHQf2hJvDvw48AfEQ+Mr79oRPhbc\nPbeNbAXElhJod78MvE9of7OugIkuotdaS6jmUmxh8l3k/Kz/AINpPiT4s+N3/BdP/gpR8fPGvgWD\n4ZeIf2gPgL8TPjZeeALXxNb+Mbfwl/wsb9pH4W+IF0KHxNbWGlR60mnm5a3W/Ol6c9wsayPZwMxQ\nfJP/AAcIeEtT8Yf8F2vjFY6XctaT2v7K/wAC7151ONiJZwx5J2sRhpA2cHp0r6Q/4NX7HUdN/wCC\ntn7VNjqdybu5t/2EFj88nO9B8b/hNt6hTxyORSUm6ko9Ek9u9t3fzdtPyHyL2XPrfmturfda/Xvp\n8z/Qgr/O5/4M4PAVnr/7d/7e/wAUrqytLi++Gvwftvh5pN9M2680+D4pfHHWvE+oR2MLRsqpet8N\noxd3KSxSxCGKALLHeTFP9Eav8vj/AINgv2s/B37J/wDwVR1vwj8TdZ0zwx4F/bQ0D4jfA3Q/Emrv\nBa6ba/GPQfigfE/w70e71OeSNLGTxW1tf+DtJiZymo+JfEvh/TvIaa5guLa9NPXT1s/0uZq+vpr6\nXX62P9HT9sv4q+L/AIFfshftTfGz4faamseO/hD+zr8avid4L0yWGyuILzxV4E+HHiTxPoEV1b6j\ne6dZ3Fn/AGrpdq15bzXsAntVmiRmkdEb8QP+DXb9v/8Aap/4KFfsJ/Fn4iftc/EaH4rfEP4c/tOe\nLPhvovjZ/DHhrwtq9/4Tk+Hnw08d2ljrVp4Q0vQvD91Nper+M9ZtdPurXRbGaPSRY2E5uBZRSV/S\nRfWNlqlleabqVna6jp2o2txY6hp99bw3dlfWV3C9vdWd5a3CSQXNrcwSSQ3FvPG8M0LvHIjIzKf4\nmP8Ago7/AMG1/wC0f8FdN8cfFr/gih+0n8fPhB4e1zxLq3j/AMafsL+Fvjz40+F3hXUNUutMt47+\n++Cvi3TfE+jaZBrE8Gk2Ol2PhT4jLe77YWVlp/jvT9L0fS/DtD77+Str97X5iP0K/wCDtDwTp+u/\n8EkdZ8fy2djLrnwR/aP/AGffiP4ZvbiKRr7TtTv/ABbN8Nrh9KmiKmK4uNN8fXkFws2+3lszPmM3\nKWskX8dj+II7HTRcrA02PmJbBKyDIAG0Dtn7uQOuMjn5Et/Ffxk+Ll94q+B37Rn7Rn7b154n8I6x\na2/xI/Z2/aJ+OXxF1qCw1/w1qNtqEMOveB/F5SG7l0nVbW01G1W9sEubGeK1vY1jZYbg/RFn4iS2\ngKXUcUtoCQCBwXJOd2TjHUdjxX5hxhjYYrFYSlTp1qM8LGvGv7aCpt+1lQdNR958yXJJqSfL710z\n0sGnC+sXztOKWrTSlfR7Pvp00OS8T6fp/wAbPHn7J3wdu5E2/Gr9sP8AZ5+F2rW027yZ9I8WePbT\nS7pZVWS3cxh5rXzNt1CxBAE0Zw6/66sMMNtDFb28MVvb28UcMEEMaRQwwxII4oYoowqRxRoqpHGi\nqiIoVQAAK/yzP+CbnhrSPiv/AMFjP+CXfgoIsFppnxy8ZfFW8jVJnVbv4Q+ANW+Ifh6aX7NLGUWX\nWPDaWkEsj+QLiZFmjnjL28v+pfe3ltp9nd6heSeTaWNtPeXUuySTyra2ieaeTy4leV9kSM2yNHkb\nG1FZiAfq+EqXs8loy61q1eq/VVHSv53VJNeVjDGy5sRLdcsYxs/8Kf5tmD4Q8X6H450aTXvDtybv\nS49e8WeHRc/uyk2oeDPFWteDdZaB4pJY5bX+2dBvxaTo5FxaiGfahkKL/nTf8F0vh3D8LP8Aguf+\n0C2kgxw/tF/sr/BP44Xtsl5vaTWfD4m+DsrkSpGYXeDwHd3H2d5bnYku+CSG2eOztf65f+CA/wC0\nHqH7UX/BMr4YfGvVGR7vxr8Zv2u9bLBr2SQwa5+1h8ZvEVmZpb9IZXc2mtQGNEt7WK2t/Is1toXt\n3Qfzw/8AB174Gfwz+3l/wTe+MljZXhn+JPwb+P3wW1S9jt7gWc0fgHVdF8X+HdLlu222X2iTUPif\nqtzFah1uWSOSVleNY9np5x/yK8fL/n3hqtX/AMEx9r9/uaeZWXTcMdhXH4nWhBetR+z/APbj8TfC\nMMfn2s4l8kpcQxzxSZJby5VLHcoCZO4nhvx4r+in/g1/8P2/in9tj/grF8W5LHTHPhzQP2PvhN4e\nv3ZpNY0+wuPC3xA1jxFY2pERih0rWL7w3oWoXirOGnvLCyPkkQbx/N7oEfivU3ttG0vRDFGszNdX\nKo4YNIVDHPmEZVlLE4+vt+zf/BvV8aPDP7IX/BVj46fAH4p3cOgxft+fBn4Yaj8I/FGp3Vpaabqv\nxP8AgfFr6/8ACAtcXcluo1jxNoviLxZdaPBE7TX2qaRYaPb2097rNgtfnnCWf4DFZ/8AUoYik6tX\nLq/soKavKpTq4apOCV/emqaqTsteWEnsmfXZ/gMZTyiNedObpRxdJznyv3YzpVVHmfbnlFXdleSt\nuf1+f8FP/jp8Rv2Zv+Cd/wC2f8fvhCY4vid8J/2dPif4y8D380FjdxaJ4i0zw1etp/iWSz1O5tbG\n9XwvK/8AwkRsbhp1vRpf2VLDU5JU026+D/8Ag3G/bJ/aE/bn/wCCYXgP41/tP+OP+FlfFu1+KXxa\n8C6p46m0bQtB1DxFpPhzxEk+hz6rp/hnTdG0IX1hYatHo63FhpVl9pstOs5rxZ9Qa7vLn9vvFnhX\nw5468LeJfBHjHRdP8SeEfGOgaz4V8U+HdXt0vNK1/wAOeIdOudI1zRdTtJAY7rT9U0y8urG9t5AU\nntp5YmBVjX8N/wDwUa/4Ny/2yf2XtE1v4j/8EdP2oP2pLf4H2Gp3XiXWf2E/DP7RXxH8CeIdBfVr\nye58Q3XwM8RaZrlpoPiWPc1tMnhfxpZDxa8EN1LF4t8YambLSZP1Ruybs3ZPRWu/JXaX3v5nwqV2\nldK7Su72Xm7Juy62TfkfoN/wdk+Gw37Jv7D/AMS4lsUuvhz/AMFFvgqhuJIpH1JtO8VeC/iZZ3+n\n2TJGVW1u5dMsbrUEeVBKdOs1EchAaL0T/g0ywP8AgkrZAHI/4af/AGiMH1H/AAkWl81/IT+yP4Ff\n9su91jRfjX+0/wDtneL/ABV8FPGlprWr/s9/tK/Hnxl4ruPCXxB8J3Vzp0dzq/gPxbZIYtW0O+l1\nTSZrl7Kx1TSpJr3T7tLKS4lhk/r8/wCDTuIQ/wDBJ6KEdIv2pf2jYh9I/EumIP5V5+FxMMVisQ40\nq1KdKlSpVI16fs53560kkru8VdtSTalzXV1q+3E4aeHw9ByqUqkas6s4OjP2kbclJNt2VpPZxeqa\ns0ndHov/AAdPf8oSP2qP+xu/Zp/9aY+E1eG/8GwriTwt/wAFYHU5V/8Agql8dWU+qnw14KKn8sV7\nl/wdPf8AKEj9qj/sbv2af/WmPhNXz/8A8GuUnm+BP+CqMoJIk/4KjfG1wTnJ3eFfBByc88578+td\nz+OL/uT/ABdP/I5E/wB3Nd50390aq/U/V/8A4LUf8okf+Cjf/Znnx1/9QPV6/wA7D4M+CdJ8N/8A\nBNz4q+Pbu3gOu+Kv2cvEGjWMzgLMlpF8NJxmMsAW3wXr9GJI7EYz/on/APBaj/lEj/wUb/7M8+Ov\n/qB6vX+fXo03hy9/4JqNpGrG8t7nS/2TtZ1bTvJCC3vdRu/g45t0l3Kxbyp7eFnxsOGOGNeXmcFO\nrgE9UsQ5W0tdcltXtr+d+h3YKbjRxtm03Sgrr/G/wZ/oK/8ABFj/AJRJ/wDBOX/sz34F/wDqCaRX\n5Z/8HOJK+H/+CSzKwRh/wVZ+AxVj0Vh4b8a4Y+wOCa/Uz/gix/yiT/4Jy/8AZnvwL/8AUE0ivye/\n4OjrsWHgX/glZes4jW0/4KkfBG5Z2OFQQ+FPHEhdj/dULub2Br1ZpShOL2cZJ+jTTOOg1GvRk9o1\nabfoppn5W/tra5/wyR+05/wTS/4KJOdNtNL/AGe/2p/DnhH4r6zOBGLL4J/GyGbwT4/vrm5lbyLa\n20fwxP4kFncz7YrXUdbildlXdX98n+h6lZ/8u1/p9/bf9MrqzvbO6i/7aQXNtcwSf7cU0T/xI3P8\nC37d99pvx7/Y9+NPwckjGpXniDwDqM+hPEIp5F8S+GTF4o8N+Sjksjy61pFnbF4wJBDPKinDkV/V\nj/wRe/aob9sr/gl/+xv8cr/U5tW8W3nwg0TwB8R7+5hube6u/id8IpLj4W+P7+5juo4m83V/E/hL\nUdaEkQe2li1KKS2lkgdGOOFhTpUYU6c+eMVa7d7Xbdvx/E680nCpjas4bS5b6W1SS/JJdNT8hv8A\ng1i/Y3j/AGW/CP8AwU8urzw9faVqNv8A8FDPiv8As5aDf6pYwWdxqHgL9meWTRdGeziDC4t7T+3f\nGniWCaMQW9pLPZobc3CQKYeu/wCDoL4h3PjX4O/sXf8ABPTw/f2y67+3R+1V4Tt/F2nGG4nvB8GP\ngbfaN438a6qpt38u2g03xVqXw+u3a5jP2mG2uzbEfY7po/6Lfg38GfDHwU03x7pnheO2SH4hfF/4\nofGbW3t9NttNefxP8VvFd94t16S6FszJe3K3t80B1KRY7m8ghge5VpxJLJ/IR+1j8Q5P2rP+Dgv4\npamZL66+HH/BNz9nLw38GPD6OdLutHT41/Ga3n8W+MtUsZ7MPJHcy+FvEsvhrUobq4nu7O+8GpBL\nDYTLLbJ0K11fZtL72l+pxQjzTiu7X3bv8D9OfDXh/wAL2+kWljpdjHp9np1hbadp1lCsccFpZWlt\nHbW9rbxhQEit4Y44oowPkRFGcDjtND0HTLWUTyqAGPViuSM8dPzHf1zjn5nvfGU0BjW1uCPL4UIT\ntPY56enPToBn10LDx/qtxIkErttbhXLcDpjnOeMeuPU1316VOnClKCs2027N9F+LS06310b192hW\nkprnlJRaTd9bbdd1ZLfb5s+ttd1XQ9I0aeU3QCojyEBsbMAkY4PHFfN2gXk/jHW2v7WeOa2W4IV2\nuIg4CknB3OuMq3p1z+HIa34oEqTaddXHmrPE6mMsTu3qwz2Hynkc5yPUV+a3iH4ieJPBnxC8Q+Hd\nP8UalpVkZpbqBIpVRYkaST5VBiPAWLA/lXnZvm9LLcHOr7JyqRjblWspXS1dtr9nvfvczx2ZwoQu\n0mk0uVXstrPs+97b9rH7t3ECWtrHPLeQAQxqvkyXMBcFcneMPtPBxX54ftq2mmwSeGvEEFzBFPcn\n7Pd7JUcvk3L7iIyWz8qjLZ6fSvzdvP2g/HWpeLRp8fjjWprCJjGZROuGILekQGBkdh2rvPh2urfF\n34inQfFnii91bR7FRJGkzh/Lfcq9DEoUBZX/AIfw618F/rhhMfQeCqYOp7WtGUE2m3q4pXXTVvXf\nTseFi8zjmVB4KMEpVHaF1v8ACna+6aXndp20H6bEge51DT5Yp7y1Id1I+aW32gsuDtJyzYxzyOhx\nX5qftNfA8eGdR1Dx9YXBh8K+KHd9St8ER2GpAeTEWwgA3iOeQ7Q/X7wr9oNf/Zc1zT9dg1bwRrXm\n2MybJbSTdgZI4YRwqMYX+99CR0+e/wBon4R6nF8JvGXgLx1p/l3msaVd6ro16V+QTWsEsMflklm3\nb7gYwuSFPauPCcPYutP2dSj+7rxvFOyai+Xfe2jvE8Kllk8DGdRJKNNrmTSaT9xtvre+nqfgMh0G\n8RtNtr9XuHJSNmfKjoqsPkA+Ule3Q96Ze+HX0myGlS6kGNywliZXJAlch0wSDj5nTsOnbpXh08Ul\nhqeoWWnrNPdWV9cWSlOoNvNJCxydvQpz/wDWr0S01m1i02C38QXwi1lMNCJSw2RqAyAYUncFC9CO\nSM14WPy7+zsROjdSlTvo7XgnZdtWui9T0oVVOlGpSpxk1F6qKTTstPvt+m9j+wbUbqfxVqDPeN0k\n2xrg5K4znH589uM9jXRy2K6Bp5aFoxNIn7sgkMBzheSPTg5/Cr+kaVa6TC9/doBtBO5iOFwP6nvw\nN3XmuE8Q6umrXDRWxIiWXajhjjBXAIwxGckg84JHpg1+0vftZ9G918z727ta7t2vp9xymrXZlP2h\nCWcswZRznJbPpt98np71y19fyyxMdzRsq/KrYBB7beuAP1GcVp37XdjL5EsWAsZYSNu+Y7hjH8PK\nn0P3e/Bri5LnzYy0jYYyEBRwwG4rjr/F19u1JNrRN66aX19bA23qyDXPE1lY+GL1dWuFhLyJDbF2\nC/vXDhMfViBx/wAB9v5ifiRqEusfHTxvJ9okmjg1eRY9pVlZRdXIAQ45GFAIGRgk9q/fL9o68lsv\nhl4mv/NKGxtZL2J1ODGLaCeRj0wDkL04yOAMCv5vvB2ry3Wta5q1yzXM99q2oTRytguFa7ndD2A+\nVxnJPeg48Y7U3daKz89X6P1PpvR9TsNMj23ULLJNvAVeSzEc5BYABiw4AGOccV1lsz6t4b8R2s8H\n2eym06XyAN24vCJJVBBJGCQo465PtXlGj6jDEz39+ouQGMaK3JR2GFOFwMZYDnnK88V3nhvxJc3d\n1dWE81ubR43URDHmLHMHQLt2gZxwMnk8A5xQeJGTirNtpNNLa3VWS+Tvb8z4u8NTWNrfvd3ji2vL\nKYobUnHK4Cs+S3IY+vYcV9M6H8UIoo7KWeF5RbhYVEZBWSMc5ILD+8eg4I+tfJ/xSTT/AA/491jR\n2L2plne4hWX5XaJpGxkbjgfIfUcfWqmma0qrFMlw7W1s28EHId8bcEZ7g8/MAeaTvdW21v8AodFP\nDOaUnJr4m9btbSWmjv57dPI+/vC/inSfEf7VH/BOE6WhiEX/AAUs/Y2LRnAIX/hZdrngEgDe/rmv\n9YCv8ej9nfW7jU/21P8AgnYjYjgf/gob+yFP5GTuEifFvRI1bacjAV2GeMbgABkgf7C9O1jirRca\njTbbVtX6adWf5on/AAcO6pr+jf8ABdP4x6joAnaS3/Za+BBvkgAO+yNpDvWQ4JCGQJkgg5AGea+m\nv+DVzW/+Eg/4K0ftTaiU8t3/AGDY45U7rJH8b/hMr55PJbJ/Wvmz/g4W8aL4I/4Lq/Ga/khgnivv\n2VfgXp8iXH+r2SWkMpJ4PP7r24zzXu//AAafXUV7/wAFYP2sLqFo2jn/AGHZpR5R3IvmfHD4SSbA\neM7A4U554pWV2+r3/r0t+Bo/92X+P/P7/wCl0P8AQ9r/ABI9A1H4batovjTwR44ttfvr1/HnxA13\nSV8K6Jq2s6/pEsfizUbeLWrT+yraeS3SG6EUTvKyQvI0cblXeFh/tuV/m2f8Gf2tadp//BVT9qrR\nby7MWpeIf2UPizPo1r5Fw4uo9I/af+HEmpMJ44Xt4Ps6TRPi5mhMu/bAJWVwuVen7WMYc86fvpqU\nGlNOKk1yuSkr6dnpcxhLl5nZP3bNPVNXjvqjsf8Agkz/AMHPfxz/AGV5tD/Z5/4KCaR8Vv2i/wBn\nDRrXTdF8G/tLReB/EzfHD4W6HZultu+J1rqFktx8W/C2k6YFnudZe6k+JGnQWVzK+oePHuLLTNP/\nANA34JfHH4Q/tI/C/wAJfGn4EfEPwx8U/hX45086n4U8b+ENRTUtF1e1SaS2nWOVQk1reWV3DPZa\njpt9Da6jpt9BPZaha213BLCnI/tbI8n7Kn7TUcaNJJJ+z58Z0REUs7u3w48SKqIqgszMxAVQCSSA\nASa/kJ/4MjvE/jG//Zh/bc8I341CXwB4b+Nnwv1fwldTyySaenifxR4C1eLx1pmno4KwSWlr4e8G\n319FEwRn1aGcxLJM8k2kU4pRcpTaXxS5bu1t+WMY3flFdSPw/rzPpD/g6/8A2Dvh/wCI/wBliw/4\nKV+A9A0/w5+0T+yp4l8Aaf4y8VaRpcial8S/gp4w8X6V4In8LeLLizaKG+Hg3XvE2l694f1XWI7i\nXTNKbxHodnMq6zBAP5DdOuLbV9D0xrPy3t72KG7PlZLstxGJYm5B5aORGABXg/XH+jZ/wXmXRm/4\nI6/8FDRrr6hHZD9nHxY0DaYIDcnWVudNbw4kn2hHj/s+TxCNLj1YqBONKa9Nq8dyIXX/ADe/g34b\n1Zfhz4Puxbyf2i/g/wALXB8wsWZ20KwYgqQcvkkt759OPgeOqEeTA4iPuzTr05tOznD91KKffkfN\nbtzM9HL0pTlF23hKN9r81nfytv1tsfr7/wAG6XgGLxl/wXD0jWHsbqa3+CP7F3xZ8cwXAtGnttK1\nvxR4q8N/DaM3Fx5M8enz3+ieOL5IGMtnNcoJYY5ZYTNBN/fl+1v4xuvh3+yl+038QLK3uru98C/s\n9/GjxjaWljaR6he3N14Y+G/iTW7e3s7CWWCK9uppbFI7e0kmhjuZWSF5Y1cuP44P+DUTwdH4i/bi\n/wCCmvxcn0xzL4M+Gn7OHwl0zVvJRobT/hKJPE3iPxRo0dybd5YXl1HwFo13Lbrd2yTG3SSS2vHg\nSWy/uV1TVNM0PTNR1rWtRsNH0bR7C71TVtW1S7t9P0zS9M0+3ku7/UdRv7uSG1srCytYZbm7u7mW\nK3treKSaaRI0Zh9XkdP2WT5bDq8JRqPSzTqxVVp+ac3fe+9znxjTxVfltyqrKKttaD5brydro/lo\n/wCDPrxfq2rf8EndV8B69Z61pmp/CX9qH4teFodN1u01izlt9I8RaF4B+ItpNZQarbwRRWVzqPjL\nWT5Wml7U3sd7cS7by5uS3J/8HcPguQfsyfsLfG+PTLi4g+EH7dngXS/EOsQNGBofhD4jeDPF1tq0\nlyHnQi21DV/C/hywMgglRbmS2jkkgEoEv9SvgT4p/DH4o22oXnwz+I/gP4i2mkzwW2q3XgTxf4e8\nXW2mXNzG8ttb6hP4f1HUIrKe4ijkkgiuWjkljjd41ZVYj8S/+Dm34XXHxP8A+CLn7W8mnwxS6v8A\nDQfCf4vaYz2tpcS26eAfi94I1DxBc2011LCbGeLwhN4jLXVpJ9se2NxZRRXK3klrN6GIpKvQrUJf\nDWpVKUvSpCUH+DMqU3Sq06q3p1ITV9rwkpK9td0fy0aV4UvtJi0aHS9BE1vMqSX8zRjepdEZuQ4x\ntbJwe45r4h/bx8W/s/azc6f8NPF+reLrH4meB59J8U6FrXw+8MeKNY8YfD3ULxbXUtG1ey1Pw9Zv\n9hlcwW95AhvLeQyQxTW80NxCk0f2XH8Z9T17wL4K1zwpYCVfF3hTw34hju9pEMcOtaVaakq7kGNy\npc7WAjXGB90cV+nf/Bsxr1xP/wAFHv8Agqdp+v3SjX9Y+EH7IWsWFpiZhPpOiaV4p07UbmGTyxCs\nVre6xp0Uis6O8t5mJJFjmaP+P/Dnhyrn/Glenjcbj8u/svD4nG4avl1aFDFQxWHxWEoKl7SpSrR5\nHGvVdSLptVIxcJe45Rl+6cVZw8syClPD0MLi6eNqUsNVpYqEqtB0qtGpVbcITpy5k6UOSSmnFvmT\n5lFr5b/4JV/8HSHxG+CeoaZ+z1/wUri+Ifxn+EGlRS2fhX9tfTfhx4wh+I3hnSLWJfsS/HfwpNo9\ntdeNNLsrWJ1v/H+hRXHjSAIt14gsfGtzPe65bf3n/Dr4jeAfi94F8KfE/wCFvjLw18Qvh1460Sx8\nSeDvG3g/WLHxB4Y8TaDqUQmsdV0bWNNmuLK/s7iM/LLBM4V1eKQJLG6L8H/8Fjf+UT3/AAUi/wCz\nJf2lf/VS+Ka/Cf8A4Mv/ABV4p1n/AIJq/HPw5q+pajqHhfwb+2F4xs/A0N7cGW10jTda+FHwi17W\n9K0aKRBLa6a3iW91TWJYo3Nm+saxqs8MaXct+8v9g04unCFOVSdWUYJOrUUFOo4pJzn7OFOmpSvd\nqEIRve0UtD8Jk1KUpKMYJybUI83LFO7tHnlKXKtlzSk9rtvUb/wcvfsl+D/gRdfAX/grv8G/D9n4\nT+Kfwv8AjR8Pfhl+1Dd6JKdJtviv8F/H848LaRqfiuytbUxaz4m8La+NE8L2Gqy3FndT+HfFE9pq\nc2qR6D4et9O+pP8Ag1BJP/BKTLfeP7VX7SBOeDk+KNOzkDpzXqv/AAdE/wBi/wDDkj9rn+2v7Xz/\nAGv+zz/Y/wDY/wBj3/2//wANJ/CX+yft/wBs4/srz8/bvs3+meXj7P8APXk//Bp6HH/BKBBJnzB+\n1P8AtHCTPXePE2m7s++7OfenyxVRzS96UVGT7qDbjf052Nzk6ag2+WM3KK7OaSl9/Iv6ueg/8HUR\n2/8ABET9qtu6+LP2az+X7S3wnP8ASvkr/g0n1yfxP8AP+CjHiO6ULc69/wAFGvibrE6rkqsupeAP\nh9eSKpPOA0xAz2FfWv8AwdQrv/4Ii/tVIOreLP2alH/Av2l/hOP618pf8GmPh678I/Av/gpD4Vvx\ntvfDf/BSH4p6Fdj0uNJ8CeALGbsv8cDEfKPoKbfvxXeM3f0cP8/wEvgl/ij+Uz9jv+C1H/KJH/go\n3/2Z58df/UD1ev4JbjQdYf8A4J56b4csorZ7Wz/YLfxxFe7TtntL74AT6nqFszquTc2kVnLgYAEj\nDcxBJr/QI/4K1eAfG/xU/wCCY/7eHw2+GvhLxD48+IHjr9lv4xeFvBvgvwnpN7rviXxR4j1rwdqd\njpOiaHo+nQ3F9qOpaheTRW9ra20MkssjgBcZI/zwrzxF+1z+yn+wJ8RfAv7V/wDwT1/bC+EvhPTP\ngN4n+Dmn/Gvx78KNf8NeA9E1vxv4Cn+GvhK01rV9dtdOi0+yvdf1DTrGwQvJPLLdx28MLzOkbcuL\npSqexcY8zhUUr6e6rq7132OzBVadNV1Uklz01GKezd/S11e93a1urP8AQQ/4Isf8ok/+Ccv/AGZ7\n8C//AFBNIr8jf+DqaKzn+F//AAS/g1FJZbCX/gpz8HY72OAZme1fwb48W4WIZXMhiLBBuHzY5HWv\n1y/4Isf8ok/+Ccv/AGZ78C//AFBNIr80f+Dm74OftDfFX4KfsI67+zp+z78Xf2jfEPwX/bs8BfGH\nxT4K+DfgrWfG3iO28J+EvA/jiS5v7m00iyvBYWlxfz2GlQ3l8I7X7bf28TMdxA63qmrX0enfyOOD\ntKLbslJO/azWp+DvxDm+Gng5ptU0fW9S0+8igZ7fStWaGOCchiAG8oyS7Oo4f+HpX6Vf8Glnx5t9\nPtP+Cgv7DMt0k9l8IfjjpH7Q3wzdb2CaH/hB/jppA0vWtD0yB5FvUs/DGr+CNFv7siKS3TUfGsvm\nSRTzbZvxM+Lvx0+KkPjLwn8Nv2jv2QP2hf2VfGnxE0XxTqvw/tPjv8P7jwZH4vsPB8FvdeIpdAe+\nhtZ9QXSkvbQXrWtu6W0l5apKyGZM/f3/AAauQR2//BTn/goqkSLGr/s7/CSXYpyoMninTCcHJOP5\new4q/Z0IU4OnpN3U16JdHZ3v18/Q0rzU6jas7/aWz++z/Bei6/3O/E74geHvhN8NviF8VPF13HYe\nFPhn4H8WfEDxPfTM6RWXh7wZoOoeI9au5XjinkWO203TbmZ2jgmdVQlYpGAQ/wAC3/BLO+1/xr8H\nPi5+1/48tYoPiX+3L+0L8X/2ivEoW9l1B7Sy8S+MdXh0jQxd3KrcS2GkXa642leaiEaffwlIokZY\n1/tV/wCCjf8Ayj0/bw/7Mz/ag/8AVIeOK/i4/wCCdOqTR/sNfst21vGFji+FXh9XYAhdzyXTyE7R\njc7MzEkfMdxPJNELc0bq6Uk38mt9H/XUim2pab6fi0vI/RMahJ5UrGcEj59pPAxkkdcj/wCx7V5J\n8Rfjung6yW3s3STUJG8iJVb5hI5CLgZBzuYHPPI/CuV8a+L7nSoppQ7xShGVohkfLsIV+SCNwGff\nOeBivmrQLG68eeK0nuiXhSdXBfOB5b7uQeOq57e/Y1tXxrp0W1HmlFvlb9dG1rfX79V6dUKjg1Ju\n6dk02320em2q0Xo9j7I+EHjvxF4kBk8UvtnvJN9ozj5lTIdVOQARtJHG7nOCea+S/wBsK4bRfH8E\nlpJHDPqNlGokyVdnY3BJPByp5zgD27171ruo/wDCNiF7NhGbARDcoxkg47dOn1yT618CfF/xdqPx\nQ+KFrBOzCLTzHDvyRgK8q5UgY745/px8lneIpwwFZ12lXqQbSkn7t+WyS722t8vNZk6NSmoygkmk\n24rvyq+muj00/wCAtX4eeHNais7rVrqxW8UIZTcDJBPy9SdnP4Y5PPcfSn7H2oTXXxU1pbmAxFoi\n20jO0CWBSB1POPz5xzXl+oCPwvoUWm2upDzXiCTo5UZGM4GBySwBHt+dejfsoXUVn8Qbl+AzxBGk\n5LNl4jjOPxzx2681+T5dBxxeHryk1eppzJreUVbXy2X331PnMDTprHUlTlKT5nvduy5NvRfK212t\nf1p0C/8AMeSKORpVZj05CY46hevrn1Gc18U/8FLPiNaeF/CPgPRtNhW88QXytDGke0yG3kunEiS4\nZTsJKZ2q3GPSvsXwRARJPMQVQPuz1xxnPPsD7D8q/FT/AIKL+IfEPiX46+GPD/h6+gW2020lW5SV\nwGBaa0kBVDG6ggOcYK1/RmVyp08DKvOKlKFBatL+VW+e/mvy+rzGMJUPZ6xdaLUrJa2tr22+e5+M\n2oX2jaZrWr6ffRw2V5cahc3bygcxyXM0kzp8xbktKRzkcd65XW7Xw9rEqXup2+I7TAiuI8hpwAAu\n/wDeBQDsGdoHBxz27bxbHpvhrVdSnlsRquotcyqxm3YFwzv842tgKrYwu3btxkHNeW3fjrS7i5Fv\nqlrFY3IIQQnKxsTgKVBPLdMYA5Ix1xX4ZxFXlVzPETTcYuc7XaS+JNX1dk9OqPjKdStQcqNNSkoN\nqS1S5fds1dW7+m19Ef2R+MfG0wUaXpwF5HGhErLjceSB90tk9O/PY4zWT4Wg82QXMybIWKsfMAyH\nyOQH6nbnuc+nNcboMIu7h7+Q4E7kYck4BGQdp5H9M8V6rBbSWuh3E5KEtxbbQAQccE+nO7PUe9ft\nMtWmkn19Vpa+3nvd6ep+mTvpZtenyt3ON8Xw3DC7uoACsY2cg8DYD0w2OPU/l28WnvTAf3yjduJG\nOR169SAc17RftLJCLWd133PLNnAwcrgY46fj1znivnnxQXgv3tSjjBO1grEAbh0OBn9PbiutwXLC\nSjG1o3srvaK6O9tLWfa5zuona81putXZ6dnp2++9zy7422UOsfCP4lWsjH974c1V0P3ip/s+6b5e\neASeT7dD2/mk8BSTQnVLVIzJJFqV7bxhEDtiO6mjJPBZeFyeu3nGK/pe8Q2gv/DHjGykkadbjQdT\niwxIQ7rSXrklcgHHfHt1r+aXwrdQaV4y8W2ibpBaeJdfQIu4gbNSu1wTyM/L/hmuOrbmuvK+2itv\nZbvy39DCvJulKzXdWad3dWXXVa9tvI9rs/JggmkuE2CFEZhuyXlGcjYWAGGHb16DBqxo+oSNdC+t\nbSeMKY3aRULIwV88knGB1PYZ71zt1qSTW88piMLuSoiy2GDnAbJUDvn+vauv8D+TboRcO88VzG6p\nEAdqOUbq4JyMkcEYA9KzPLak91otNEna6vsu+76HzN+1vEthrvhrxaBuGu2ESStsC7ZQtxO4+XAU\ngEccHt9fl+18aLDZJBDKFAUNtZiGzlRyDk4wMcnnqRX1l+1W4v8AwXpbSlCNA1aeL5VUmNFgjiCs\nwwQAX/iyOeueB+dtxNp1xO3l3HlsuV2hsY/Dd+eQB0pN2+5v7iueUdE9NrXfSyXXS3L36n3d+yp4\n20+T9tv/AIJ832qX1jpulaN+3T+ynrOqapf3MFlp+nafYfGDw1Je39/eXEkdtaWVnbmSe5uriSKC\nCFHkldI0Zh/r4/8ADXP7KP8A0c5+z1/4en4b/wDzS1/iZHw3e61pyQrbre2qjzB5nKOQWAYYByRk\ngEEfeqW1+Dt5cLAkfg03Mt0SICn29fNJYqAm04J3A4xnPAwKLp9V94VKMqkueMoapaO6d9Oye9/v\n6an9AP8AwcveP/B/jv8A4LJfE3xT8PfGnhjxn4auv2efgfp8XiLwd4g0rxNok93b6TMlzaRarol3\ne2MlxbSKUuIknMkDgrKqkEV9L/8ABpR8U/h58OP+CkX7S+tfE/4heCvh9peo/sXXul6bqvjvxZoX\nhOx1C+i+NXwlnNjaXuv3+n21zeC3hln+ywSPOIIpJdgjRiv85Fn+zJ8UPD/h5/FM3gC50LRJHit0\nvXhvFVp5N5gMrXEA4yjnlzkKOAc1HD8ArLW7+E+L7+yhkeKNkhVo9xjUK5RJI5YX+YOScZIJ7GvO\nxea4XBuManO5O91FXl0SSjp59S3Rl7FU9b8zk39nRtaaX672bd9F0P8AZr/4a5/ZR/6Oc/Z6/wDD\n0/Df/wCaWv8AKO/4Jp/tZeKv+CcP7Tfwv/4KA+E9E1X4geC/DvxB+MHw7+O3gPwtPpR1rxf8BPHH\nieZtSn8PXGoh7KfU9K1GG18WaJG9xp1nqGseH9F0+81zSNN1K71G3+SdI+AHwRjmjiTTbS/v4XAa\nC7u78pKc4+4dQCsGIOMKQemDXsdlaQeG4INC0u1j0zR4AyQ6baqy2wjkdmZRGSQyuzsWLZLBjuJG\nMeBj+JYWovB0qjqU60as1VShGVNRlGpTbjKTTlGbs7Wi0nq1Y5400ubmu7xsrJ3TvBp69tvTtdM/\n1f8A9kf9vn9jb9vr4d2Pjv8AZf8Ajt8O/i5ourWCHV/C1lq1nb+O/DMlzaCa60Lx78N9Wa28XeFd\nThgkZbrT9e0W2S4gzc2j3mnSw3Uvr/w8+E/7O/7LXg3VdH+Ffw7+Dv7PXw/utZ1nxXrWneB/C/g7\n4W+Epte1WS61fXNe1K20Wy0TSH1C6K3V5e39yplFvC5aRba3Cx/48XiD4VfDPUdSj1SLwzLomvgv\nNFrvhq+1Lw/exSyI4keJtMuILeORo2eN3EO5lkYOSG4r+K/Btt4yitI/iD44+K/xFtrH5LPTvHvx\nM8V+KrCziRg6rbQX1/8AuVDhSEQhQVVsZrpo8WZdVUU6WJjVaX7vlpyV3bRS9pG613lGN+yM3CST\ndm0rX0fW3RX7n9hn/Bx3/wAFmv2dP2j/AIG61/wTL/Yu8f6Z8dfF/wAUvGHhZP2ifir8Pr99V+Ff\nwy+H3gPxZY+Jr7wta+NbB/7E8ZeKvEPiLQdGhvU8PXGt+HNI0C11aG/1JNfutOtbb+eyz1zSfCfh\n21uLK5RLXT7GCyslIVkWC1iWGFDuYjCxxAbuc4A61+f2lXMHguCTTfCOlWOk6MUxMllbLC0rg8G5\nlKtPcMABh55XYeuMV6p4V1rWNS0eKK9RZ7RshYnfJAJbPBUk9z+lfLcRYmrm0qcuT2VDDqSpQcry\nk6ko806lvdu1CMUk5KNn7zburp1fZK8U3e121bbou1tb+Z/YN/wac/Ev4J/Dr9mz9t/4i/FL4s/C\nf4eeLfip+2z4ntbay8a/EzwT4b1rUfCngrwB4Ml0a5XSNY8QWmpQ2EGqeL/EUFrcT6fbR3LCZreW\n5jTMf7Uf8Fcv2t/2eZf+CXX/AAUFs/BX7R/wY1Lxbq37IPx80Lw7p3hf4y+B5vEV/q2v/DjX9Fs7\nPRbfSfEM2pXeo3Mt+sNtZWMT3N7I4tYl3TCv8zC5+B3wl1rVJbzXPCGl311eu888ry39t+/kbLM5\ns7y3UsxJYgjk5OPTY0v9nb4JW1rdX9n4A0iS4tiCks11qtwsbYZspFcajLAxG3Kl4zjHbdX0OE4o\nwcaFGgsNiuelRpU7JUnFuEIx0ftebltrdxTt0JpwlWqKMfim7676vd9f8/U/qQ/4NB/iz8LP2evD\n37d3wl+KXxJ8A/DFLr/hjn4q+HYvHviXRvBy6vJ8RfhF4vufEcun6n4j1CxtNTNpeadYRzWNrK09\nk0hmaIQXSFP6eP8AgoR8WP2Xv2gv2EP2yvghpf7SnwFvNZ+Kv7MHxz8C6Daaf8ZfhtLfXPiHxF8N\nvEen+H4LSM6xqLPcS6zNYpEsVlczlyBbxGfy8fxVr+yJ+zB8Yvht4I1j4kfB3wp4k1qLwnpml2+t\nzzaxpt9Db6dptpZaXarJo+p6d5kVnBDFbwRuzKsUSxkMoxXwt/wxR+ybonjK40/UPhLo5gsrqd0Q\n6p4odXijkcqDDLrro3lIoOGD7s5bkCujF8TYbL8HSr1sPiqzcFeNBUpSukrtudWGj7/gevRyTEYm\nryUqtGKk7p1HNcqckkmowlrrffZdLnMfstfFzxVD+z98KU1ew22dl4P07TLK8uchXttHD6LaqJGQ\n5It9Ni2ruYDIAAxgfS/7Hv7dusf8E5f+CiPgH9uaXwr4g8ZfBHxL8Orz4B/tVaR4RtLnVdf034ba\n9rmma1p/jfSdMS8srS81bwnrmg6BqltBfSCO+sNJ1LRIJbO51mG8h5nX/D/hTTLGTwf4J0xrfwtp\ntov9k6fZ28rx6Z88swhQl5pViaUjKO5HXJ5NfSP7NXgrw1oPg/VdQ15LC9uPEVvLZ6ra6lFBJD/Z\nzoC9tJbXAkikRxJLG0bx4ZWIYHkV+OZBXp4PiGWcZfTjTlXxWKc6FWXIp4fFVZTdGTjezjGUXGXv\nJThFu6un9/mWF9tlbwGKqSlGlQw0Y1afvKNajCC9qou11Jxkmm03Gb1XT+/X9mn9sn9kH9vD4YL4\n1/Zz+M/wx+OngPxHY/2XrWlaVqen3+oWY1fSPtd54W8c+BtVWLXtA1N9JuJU1Xw34n0Wzu1txdRX\nVoYkmA9D8B/Dz9nX9kX4Uw+Efh14S+Ef7OHwV8JHVNTi0Pw3pnhT4XfDrw8b2S51fWNQa2tY9G0D\nTjO4ur+/u5BFlI5JppPLiJT/ADdfDn/BN79mH4sfFrU9esvBeoeGdNkkmu4df8AeINf8Garpd5dN\nLHPJpr6RqCafbB4ZDFtFh5ZjmlTZtc19Qaj/AMEnv2XNX1Wy1X4k33xv+OyWn2f7HB8YvjR468W2\n9n9le5kgjijt7/SWEETXl7tjld41+3XhVAbiUn9yo55hasb+zrKdruCUJpOydlNTs99L2bXS+h+d\nyyjEJ+5UozjupKUk7abxcbp6rRN+p9zf8Fqf+Clvwp/4Kpap8Ov+CY/7CGvR/Gr4at8YPAvxF/a5\n/aK8KwzX/wAJdK8GfDy+XxDpXgHwX4paFdL8Z3N/r39m+INT8S6DdXGhQ3Xh3TNA0fUNZudT1gaP\n9lf8GwXxw+Cfwo/4JlXng34nfGX4UfD3xZY/tUftHS3Xhvxt8RvB/hbXYYJfFVisFzJpWuazY362\n1wYpDBO0AimCMY3YA4+bfg78Hfhv8IvBtr4S+F3gzw34B8PwbFi0zw3pVvYRysiMqzXksKLc6hc/\nMQbu/mublwSGmIr418df8E4P2I/G3jW+1vx3+zz4P1XXNdv5rq61Aah4r0eS7urmXfNc3EWia/p0\nLySyMzu7RZLEkYyauGYpzc5UpqEoRUIppyu7tyd2kr3S0b2XzHllR04xUoxqKTlPmuk00uVKybVr\nPfe72tY/ZT/g5l+PfwK+Jv8AwRu/aX8G/Dn41/CPx74t1Txf+zb/AGb4Y8G/EnwZ4m1++W3/AGlP\nhRPcvaaPoutXuoXCW0CPPcPFbusEKPLKVRWYeSf8G5/xi+Efwl07/gqtpHxW+Kvw0+Gur6v/AMFR\nPjjrWmaZ488d+FvCF/qGlXXhvwWYdRsbPxBqunXF1YuSYxcwRyQeajxb96Mo/Obw7/wS/wD2Evh7\nrNl4g8O/s2+C7PWrGWC7sbq61Xxfq6QyxFZYp44NY8SX9pvicq4byM5AyDgAbPxY/YD/AGLPjL4v\nufHnxP8AgF4S8U+K9c1CS/1/VXv/ABNpF7q1zcy+ZdXV1LoWu6YJZ5Szs0rRtliWxkc7rHU3JS5K\niXK001Hmu3HpzWsrPrr5AsrrcjXPDmcou/vWSSkmm+W925LRJrTXU/tH/wCGuf2Uf+jnP2ev/D0/\nDf8A+aWv58P+Don4/wDwH+Iv/BGj9onwr8Pvjb8IvHXie88cfs/T2nh3wd8SfBnibXbqCz+OHgWe\n7mt9J0XWr2/mhtbdHmuZY7dkghVpJWVFLD+UX9s//gkx8GPhl4qHir4Y+A9J034Zazbh7e3uNb16\n4GkagqB7i0Nxfazc3GxZ5lgh8+csRHjBJIr5w+FX7L/7P2k6j4Whv/hp4dutWu9T1O2uLue81a9S\nWOGCPytsM+pPbjBdyrRxhsnBY4GNHi4KHMoVJeUVFv5+9prpqclTB1KUnGcoq2l1zWb00V0r7/gf\n6Gv/AAR3/aY/Zv8ACn/BKz/gnx4b8UftA/BHw34i0X9kn4IafrOg6/8AFfwHo+taTf2/gbSEnsdT\n0vUNft76wu4G+Wa2uoIpo24dFPFfpH/w1z+yj/0c5+z1/wCHp+G//wA0tfwdfCz/AIJc/sIeMPCZ\n17xf+zx4W1XWbye5d71Nc8a6cAqRLNn7HpfieytFBJcllgAwTzwMeLfEn/gmz+wvp9tJdeH/ANn/\nAMLQQwTtG5t/E3jqV2UAnJEniuYgcrgAjNVSxVOrblUldJu9vd20dm9U7r7u454KrCmqjlCzSfLe\nSkrpNacvZ9z7x/4Oc/ip8Nvif+2v/wAExdS+FvxG8C/Eaz0D4cftfRa5d+AvGHh/xbb6TJe6R8Ph\naQ6nP4e1G/jsZboqzW8Vy8TzLG7Rq6xtjzr/AINmfiT8N/hZ/wAFH/28tY+JvxF8D/D/AEzX/wBn\nD4Rw6VqfjvxX4f8ACFlqd5F4nsTcWdhda9qFhBeXUCwSvJBBJJMsSGV0C5Nfkh4j+AnwG+D+ofb/\nAIZ/DrR/CWoeVPEL21uNVvLsJMjxyxifVNRvpVWSNmRwrgbSRjFb/wAN/wBlD4TfHe8n1j4j/D/Q\nvFpsoUjlnvLvUbK6WFNhjTz9KvbK4CqrYCmThT74qatZU6qlaU1y8to6tXad7NpLs3pvZnPTip3g\nnFNrm5m9ldaL11ei+e9v9BL/AIKFftT/ALMOsfsC/txaTpP7R3wG1TVdU/Y//aX07TNM074v/D29\n1DUdQvvgv41trKxsbO28Qy3N3eXdzLHb2trbxyTzzyJFEjyOqn+QX9gZrzRf2Mf2a7W6jaC4j+FH\nhpjC24ELc28lzCzA4ILRSo3IHLAAkYrzv4d/8E1/2HbazXWbn9nfwpPfW0n7mS91nxjeRI4zh2tb\nvxJNaS4KBh5tu4weRgkH62t4NL8P6bZ6BoFrbaVo2jWVvpuk6bZqsNnZWFjCLe1tLaFThYYIY0jQ\nZ4VQOtd2HcZtOS5YtK6nZPWzVrPXf+tDohR5FNylB+7ZNar7LvqvPTa6ab0ZyPxC1m41C6e2ViWc\nFSMZ44GD19e+Mc16H8J9DfTdOfUZIwDCksmSvUlScEn6c9zn0ryPTrWXWvEDhiZB9oCKPvDcX246\ncHPbOK+lr+BdG0O00CzYHUb0J5iR8yASBNoAXJyNx4wPXJ4rKUVKrBe+4x1k/suzVtdrJLyWv3TF\nN9LpXb0eys9fwt5306HEX733irWPIihd45ZHjCwoWBJYhWIA9WBHf5a9P+HX7FHh1ry/8c+LL+6V\nZk+0/ZBEoKkszcEXEfTd6EHHHv8ASfwH+CIs7KHXtXtjIrR+c5mVhtwgf5Qe+SBkYB/DNdf8YfiD\nZWGlHw/pKpC5zAWhUbscAodozjjqT7DgVy4rKaOYVPaV05U4WjyLRS+G1rdLJ6+fyO+MKUqTdSm3\nHR3a1s7La+zS36b310+CPFf7OvgzxJ4llGi6nqMVq0zIdwJEfXBIa5Ze4GCcVmz/AAW/4Z+QfFK3\n1ufUbfS5gbqx8uM+ZGq7zlRIxPJB4GOOvp9I+CdKnmaSVQUnI35fPz8j5hu6nr05wBmsf4+3DH4S\neLreaHd5cDsWIJBIEQ4XaRxu6ge9cE+GssrQdWNJ05Uk5Rs1q4cr1Xr+Gmhy1MNh8OliqUUpX1dt\nY/ClotV0ejut9hmj/tm/DHTPDyrfw6hY3M8P2gu1puUYVlY5e4VcZ46fyr8NviX8Ux8UPjxqvi6G\nSVrOPU0itJS7KptQIkfC7imcoOOepyTXVfGLxdZ6D4LlvXkij822lt23sqtGSHIAJPyEBcgDb68V\n8Z+FL6SGc3JkDq8wuFCtu4c+YMsvqDx/e5rysszbHVniKDio4enUVOHMrqUU4p3v5dL2s7ii3ioz\nvJv2aupN6LbRaba26/Ox5h8Z/EEukeP/ABDYTwPL5bWd1aqq9fOgWbcQB0ywOTu9/SvNnj03xN4b\nN7qlqbW+ivU+yyKjKxKzHywzBo8rlVznPHOD1r2f9o7wtrEvjzQfFGlWStZ6noyC7mkclCbaCyh+\n40ZQnAk/unjHTdXkniL7XNYW1nYW6MIgks/kgKDIu1iPkXuyntngc189xThaftOela9SG0bpXvG/\nzve29zysTSUKdSUbczvdxTbe2mmytr+Nz+xCxsIw9taQyopZV6MD83JGRn0GPT6CuyurWV3jsIrg\nAKQJAp3Rjr/Duwp56f8A6x4xofi7wlGZNQm1NmW3O/5pGQ7eAuN4Hdvy7V2Ph748/Biz1SO01zxB\nZxNcuIo3lYFi59QAW6DIPqD61+v4VYapVtGqp6J2Tu9ldtbb7X63R9nKEoQ99OLl8N+j01t1/wCG\ntuy1rFgz6jFEkoPlLk+oxzk/N156/n3rz7xfoUSwm9YiaXG04XqPfGTwB3/OvUNQ1LQ9T1O4vtMv\nYJra4cNbPG8ZDxFVwPlJxnHQ4bIPOKyJra21CGWJ3G0HAHHOc/X8f517E6FOUIqMouS91W+V0991\npfbv3PMaadmrPz8z5UewhuLXX4RLEoOkal8jMuXIhkwoGRkk8AbTzX8r93BqUXxR+ISWqiMW/i7x\nEDHjBIOr3+Djg4bGfT07Ef1i61pdvpetTI0BaCdJUIAILFwABnHzA5Axggd6/mH+L2iP4K/aC+JW\nlOVQXuqXGqQR4GVTULq8usFs/wAKSKM4x7dq+fr0nRm000t7W0tprpfS/wBxKd7+Ta+4yvtt9LJC\nl6sglYqkaxL8jBcfe2cY553Hj69PXdGsru4gtjue3t48tNIildpKd2XHVgO+Bn358etfEWnwXUcF\n64BjywkwD1HYn029M967rQvFtpqM0mm2mouEJPAWQpxnqBxngYAHXntWCaauthnF/GLwk2o+AvEx\nEv2nF1JcqCSWKtJbjccliWIGTz74xX5xyeBo7m4jitUlN3qEscUYV2UB3dfl2444B6EY4Ffpp8Sb\nybR/COrI7M6Xe5FkYMBy8fRW6DODgZH5V4b+z78OD44+JeiQTI02n6bepf3Z2syKih129x99k4Oc\nHnaKnnjdq60Wv4LTy3v66aHK4Sc5c21m7rrqu36rz0se/wDhn9nFPD3gnQvtVopvnsI5LhSN2CzS\nN83BOcEcZxg9uK9Ss/Aq6fo+h3UFqiTaPdJI37sfdWSWRQxH+8vB79iBX3DPoun3EW1okCGJYo0b\nACBFCAbcYH3cjaD15zUcXgGCbSbyNI0cMGKrtBycccev40ewi3zN72klzJJ7NbdNLr+rZx+KPTVa\n/Pc+kfEPhO3+Kf7N97oNnBYpLc6B/aNiUgiVlvdPtJYVAxtJdpZGIxhs+pr+fHxN4TlttVuYNaEl\nu+mz3lqJIyVYyW0pi+YAg43R4AYg4654r+h79n/VIdO8IXWn6zPHGdAvEwkrrEj2MnnTzphzyCNo\nPGPzr8kP27vB6/Df4j3eoaRF9o0LxUkeq2IiG6JXlSOa7CSKCn+tueQB1ySM8V83xBgbxWKjFpc3\nKnbS146u/R9LefTRevTcXFWd7Qs9r6qL3W9/N+Vj4HS9tLf7RcWn7u4tJyDcyrhTtYgEFu4I/vdM\ndua3LDxpqDXKG9t/tVq4+W54DI2DyflYhAcAAnpuz1OONub6z1bTLu0WEWUsZe4lbARmKEy/wqpP\nQ5HPJ+lect4lvbSGO0WVWgebywApJKB1GQSw2gA87hkcH2rwKmCTp8rteUNXo5X0t809bq9mtLq9\n+SKlFWktb9tNbbKyslf9T6kh1qK6+SXawI3IwPRW4OMEZ454bHtU0OmWEl0t19ofK5xbSFlQkAkc\nu2D7YX2HfHh1nruow3mgaNoWj+IfF/iXXJbiHR/DfhPRL/xLrupHT7R7+7i0/R9Ljnvbpreyinup\nxBC/k20E08m2KGRl9Kuh8abmRW/4Zi/aVi2rgNH8DfHGd/OTgaYOOR0A5zjg8cdDh+vJRq0aNSSv\nZTS0TTSbV10ae1lf0IlKCaTlFNK1r7p2s7N9tevTe2s97fWpuWsZrAJFLIUeSJAyKOuSyJj9Rn1w\na6WCaPTrEfZHzBEN7OjchTk7evXnB9x15ry7WdU+Jfhawk1fxR8Bvjt4a0K3K/bNa8TfCHxho+m2\n5fdtFxqF3pywRbsHaHYZwdorltJ+LHhTXVmk0TWIJY0gL3NgUkgnA3LuZLedIZH2jO8ojqoPUDAO\n9TJ8ZCK9tTq8kUrtxly2urtvo7337/eo8lS0YuPS0U1dt22V76O177632PWZvEjf2lHBbzSFLxS7\nklw0DqQi5JyVBA3Y+WvXNC1T7Pod20sqkOACQw5O1wO5POeO36V83eG9T1rxW9/q3gX4RfGL4kWW\nm302jajqXgD4ceI/Fuk2WqxQ29zLYXOoaVZz2kN9Hb3NrcPaSSLcpb3UEzII5o2ftLm+8c+H/DGo\nXevfAP8AaO8OaVp0Nxqes6/4i+DXjHTtC0fS7RJrm9v9S1K4slgsbGzt1ee6u7lo7eCCNpJXREY1\nrSyLER5Jxw9WSunzWbvF8vvJbtW2S87dWb4d0KNVydWCbSi1dXT0T2e69b6u97H9JPww8y4+Hnwx\njAb7PPaacZUBbaystnkkEYw28k9h/ujj4z/agv8Aw9rXx/0/4eeDNQhsNSvo9KTUL8zpa21iZFjF\nxulLxRMxzKXVpFJK4brX1V8KPGOi3Hwk+HWq6Nfx6hYr8PrPXLeTiMO91oljf2KsCW2s4cbkJ46D\ngV+Ft18SNb179pPxff6x8OPjH8QB4b1s3HiXT/hF4I1rxhq2k6dqd1eXGgNenSI2TT01G2gneyN5\nLAtytvP5Hm+TJt9WrgoVsBUpewnXquLUIRV5J2Sd09rWs7/meqsU6E6dRVYwhzJuUpWVm0/821uz\n+kL4OfB+w+Eng/yIr74aeLbvVdstzeeINT8Lz3UshKSPDENQuJ5YUBcgFWKqG4GBivOfGTwfELxe\n3w/07wbo3h+4Hmtdar4b1fT59OCqkrqf+JZFFAAWhPHm4G4A9q/MzxR8e/AGraPFY6b+y/8A8FC7\nS9iklZLsfCDxVCkasFEYEcbsWPBDEMoA6E5xXD/CD9ovxR4O8eaDo114B/aA+Gtt488WaX4M8Oa7\n8ZPh14k8Nadqeuay850/Rk1rVvKs/wC0rqCC8mgtBO88kNtdTIjR28rr+bf6s5rRxTrf2dioUab5\nudwfLGKs3e20VZ620s76H1n+sGVyw1OhGtSnOUUmuf3nLRJe91b21v8Ap++/wB8ASeDtOvtNnmhv\nDEfJ+0RFGaQq0fLOjuWPy5PzEH0r6KmtWaE2kZ2ykgOWBxt3E5Knue+T05PFeOfB64n0fTrrT9Vk\nS5udv2ia4JEYXPlh2ZnZ8AbTkl8ADJOOnz14+/4KTfADw58SpvhL8LfDnxf/AGrfjRb2l/cP8Lv2\nXfh3qvxZ8RW/9mSJHei9/sdhCYLRpIxeXGnHVEs/MiW4WN3VD9/lOE9rRioxcpJLm5U97K/M+na7\nto3r1Pnq9bCwbm5xpxcne8knfTTXV2V7JJv1vp+kej6bHaWhydxgCuADyQADgc+p7H9K4rXrCOXx\nRZTzKWjMRkQcgAq6ALuOSWORg/lnpXyNe/tI/wDBQfQdM/4SrxH/AMEWf299P8EJG1zPqGheFovE\n3jCGwht57qa5uvh3pukp4gtnjt7aZpILh4mWQQ27Os1zAj8T4B/4KN/Af4n/ABOk+FesWvxF+BHx\nm0e+t7HVvg7+0R4KvfhX8QbG8mhS4+xSaPrFxMiXWxlaKzuLm3v7gPG0Nm4kUH3o4WtSjrTlbRKz\nUrLzs3bd31/I4frmFqzap1ot6JJqUW2klaKa1+9v1P0Oij82J2uQxC/LGWO3Cfd4yR2A/wA4rktf\nuLewuLTyYTLLLHKsZGNqM21RnaCFbPTtkcV5n+0d+1l8J/2b9I8D/wDCdad451/WPiLc6tp3g3wv\n8NvCF7418XeIrzw/p1vqetR6ZoGnyR3d01jYXMd3ctGSIrdJZpCkUbuPiO5/4KkfDK5MkL/spft4\nBIpI5LW5/wCGZtednKMGYOh1cNGMgdGYHHI54caNWSvGEmu9tOn+f59mX9ZoUm4VKsIyTT5XJJ6p\nW0b076201Ps/x74H0b4v/Dvxn8L/ABNqDQjXNOvrjRLhLhoprDXbS3uJ7CNZQ6yRJc3/ANmjYBkV\nlGCCDX86+ueHtc+D3xG8G+B/EfmW+qaDr8+nzNcOS1yrNBaLdLJIQZI5yjMjhnVhzk19Z3f7f+qQ\n/GSTxNY/s3/tqXnw+1C3g+0WFx+zl4sttT0+9gRmWe3g+3TwOhncOdk0bbUzjd1yf2t9S+H37XHh\nqx/aZ+DMPjPQdX+CviHSPCfxl8HeL/Cmo+F/F+iTCC1m02TVPDk7NfWlxOHurmZpUeTbHucYG4zS\nqVsNXl7WlUjQqwUXNxfLGT5Uk+ib6J7vuTU+rYxc0K1KpWhrGkpJymk4ttLrpq10s3ZH7kfD/wAO\nz6N8H9D1tJo4Y7rR5J2d3ADwyWQ3MhJxvKljxk9PY18R+ItT0280CWXRrlriBZpVlZm3kzCMMVck\nnkDaSCc9xWH8Mf28vgvrfwj8P+F9e8U6tFBpvh1bN0i0rVZGNwtoYy2+O3YbHO1cDPAHJAr4/tv2\nivhFpmk69D/wk2qxWk3iG7NjbjRdWDiF7eJVuHfyAChG4Z2ryMkmurATjSqv2jab2bV000rX9bp/\nhpqeVjqdWo4pRbdknHa3wpvureXXsfOfxp1W7n8WWdksqsJL3ypUB+6mHPIGcHOCAduTlfSvZP2X\nfFU9v428Y6DFcottaW4zD5qqx/c2TFhGWyQCxGdp54avF/Fvib4ESXcniuTxte6ndQSPNDYRWWoR\nNK+w7UdnR0+8f4lxwB1rxL9nnVNa8UfHHVdZ0vVZNOsLuXEkKmUefZ5tkKSBHQFjsXll+6B8vQ1W\n2MbbTha6a2s+WydtLdt3dHH9Waje65rXb2aen5a9X5H9FB1a00jwjFOLmINcwkhRKhJJ3jgBiScj\n0OK4GbU7d9MluJnYzqGdCpORjcUHB6Hcp+nYV43Za5DqE9rZ318Layso0jjWWXYkj4Us3zMBgsW+\n8PzrvdeEMNvYwWbx3H2tUwIsSgqNqZ3JuAOG9c/TJNei3ZJ6Nbb6Jafkmn2t9xFROKcX8Vl7t7N6\nKy7aXTXW/wAr+r/CvSSiS67cwtOd0n2aKFDIZJpGfy8hA2Tv2g5HAPevtr4V/B2O6mPjfxfdRWy5\nM8EF0yosUSkyIhErqqEKVGNo6dPTy34JXXh34f8AhH7Z4psorhyn26BZwjHIDTRgear8ncMAdzx6\nVx/iv40eKfiXrT6R4dWfTNCDiMpbhlV41baQpiCBcquM7T0+YnFddGph+TlqSurpu1v7u77X6fPz\nNcPNQhed23a1tdbLfpZXvbW9vkvvrxV8TdPtPDF3Y+G7u3220bxb4jGoPAUYKPz0GcV8fabb3+v6\ngt/ftJcSPJu/eFzGw69yUyfz4xXH61rR8GeFrt5Ibi4NtAZJo23NIzbcku5Q55XIJByQc1wvgL9q\n3wINLhttUtpre5jZRIMligAUZ+WA5Oc59R6DNGaZtg8PGjSk3SjJJuon2ttbRpL56a6m/t3V927S\n0flbT3VfTW2qem/kfaukaebcLNHF5ZRQu1R64PAAwRn04659K8Z/aNuobL4V+MHmkWGI2EpMkpCK\nG/d8bnwFyB3NZkf7VPwyitiYNW8piu7bOjkj5eh3Iv48YxnnrXw7+2B+0KPHnwr8T6T4CmOslbeU\n6mllGxe2QgKSVQFsgFOAAfmJycVOHxWV1cPKVLFKVScHaDeib5Vezvbt5+ZhiXzUm4vdO8Y63ba1\n0S2t+i8/xW/bJ+JsQ8I2WmWl5unuNVWM+TKCjR7blTnYdpyQOee31o8HahaT+H9Li8uZb7ybOWac\nSuFeMRAkAYxkBgG5PA5r4L+P/imTU9Z0rQFFy0ulbmuhIzkrcGaRwHUhSCFlxz+PPT66+F2sC68O\n6LOxB3Woi3kZGVOwg5GBgKR97g85r42GHdJVUk1arKTdt03Gz1Wr++25nlyajKErqE1ZyT0i7rdv\nrot7o+mPi5pGt+K/h9oN9oRDS6TLHbXLmdUHkzyFm3ZblVSLBB+X8q+YZDf+HQtrq+ntLLOq+TPb\nkyoDIoA3mJJAAC6k7j65x0r7AttVFr4A1QIPtUMTRzPEOMhUnZ1wRznJ7fXPfAupvDXiew0yOLTU\nsLmWK2ExkCPhFSMPIpWNCvGW6njFfJcTSqQjQklJxUtZK61XLv8Ajo9Ld9D5jNcbWwWKnGnGUqMW\n7S1d0mmr3vfTT5bd+K8E/tn/ABG8Q6lb+FLuWOeTVEEaPao6ybWBGeZmP8B9Dz64r6E8P6PrGoa3\nZajr5uJlW4SYLO2QowTnDk464wPmI98V49omjfs/+E/EkF34T0C4fxLagR20jm02xSqHPCiFHKYL\nchuCevNdvq/xu03RWQ6/LLCZ0LRQxMAyKDtAIw/93bxnAr76naM06PLR5rc7TTuly2++z1/zaf6v\nXwWOlZSpyk0lbvZ2V29X136aXbPvLwV8W7vR7praC4nkhQLHCkk2+GNVVQWQcBQDngH1B619NaR8\nTbsxxzvODEwV5MsCOncFjwCcde/rX4ly/tR+BdDIMlveorHIclfmZsndnZ6dfwr2XwX+2V8N9S06\nWznu3tZSNqvcMQoGCOvl9OnQnnP4e9h8U48sudVE+WMry12ir6vrp0387Hl4nC16Tip0pKTV7JdH\nbVW7/LXrbVfrPrXiuw162imgkQXkLxyDaeWVSCw/HA78+/Br+YT9taS6i/aa8UXMdyUe6sbAiNNy\n/MLduw7nPfmvvTxT+2Honh93uNL8RWuza4hIeUjdjAUEKByePYY+lflF8WfiHL8RvjNZeJLiT7TL\nqT20JkO5kIiMaIcE5x83AB4+lbYytGtGPK1JKLty6rZW/wA++q7HL9XqqLmqcrNXbs9ber3t97af\nkT+DPBXinxrrNtavHJbwkhfNc4BQnBZiWAAA+b5sfjzX3l4C+Cvw98MxSxa/qYGoJGkxkhmB+dTu\nKErHISTtx948H0r55TxHf+HriWOzk+zu1pEga3YqPmRlJ65/iqpp3jW6W4YXU7zyyOWMjuDIz8t1\nyABn8OtfOyxEqMpxvG/MtJK9r21u9Fo2rdehEaVeSvGDtd2T00VtPLy79bO9vrL4heEfh58QPAms\neH9Oje21NIWk067Z0y5Ro25AgV/uRucFh19eK439mrwponw50G7u7W4TWPEN9MYJkEMge1AEcmC0\nijoU/hZh83XPNeR6N8QJotQZjdMPnCFNzbdrMFaMgccqSvOAAe2cV+jfwyuPhxZaDo13f+FItIl1\nS3SV9Zto4jBNK24GWZYzLLuwvzHaOMd8VrSTxF/Zrmk99LfytrXffdbdO4pJxclJWkl7ytbW3f8A\nLsP0nQ9c1aZLr7WX85g4t/nGwHA2csF7Doc/zr2nw/4f1O0kRbv/AFOBgdVPA+9tJ565zz69K0I/\nC63lob7wpf2d3bBd6PYsEnCgd0kKuSD22e204FcxLq3jLwnfWt3fFtV0NrhUvoplkMlsm4l+XaNA\nAqk7gD17mvTdOdKKjKLjyxjfrZtJJX6Xb0W33HI2lJpu+rSs76tq1tnuv6s2aV94fSTVZLS/vpdP\n0nUontriW2dkBE21Q2VDHKrkAgHr0zXn37WnwTi8Y/AeZtNnN3rXw9kt7q21G4/eXFzpV2Jb2VSS\nFl2xQW8aZPAXHAG2voC+0+LWtNF3AgEcca3sEYHXCmRlO3Hy7iQMEfjW5Y2NtqkGsWniLUY207xp\n4duvD1lpbbjDFcPYnS1mKMpG9ZJAykMMHPy54rPFwlisOqNRe7FScXvtbluum2+um2p1YeXIlzXW\n1r62Wnpp179ne9v5Y9d8N31hczRvOBM8XmNtB2iORCzcgnop5XJPYivGL+Jre4e0nR28xm+zzLGz\nbWfJyoAJGMg+4xnnivuX4q/Djxf4K8deKfDV7pLXtrp2rXy2d20YLGwnu5haxpudT5fkKigbeFwO\n1fO2s/bdFuWu7nQYG+yiR7ZJINyBlU44VycggegyM+oHxqqUo1I03NKUbrlab0UklZ697O+rS2bs\ndU5w5E+a6klayb0dl+fpLprfT9DP+CDFndWP/BaP/gnBFOZnWT4ifGGRZJQ672HwI8eAqquAfkzk\nn/b5yTmv9b6v8jH/AIIM+NNX8W/8Fwv+CeqalGIIdP8AH/xXa1gVHRI2u/gV8RhOEDE8D7NED/uj\nNf651fVYGKhhaai7r33f1qS2seJWVqs1ayVrLy5UeR+Hfjl8CviB8Q/iF8EvCvxb+FnjL4q/DOO3\nh+KXwm0Txv4V13x94HttW07Sr+1PjbwNZ6lc+INBsdR07xBo8sM2saZbWl1FqlrGsjtOEP8AN5/w\ncr/8Ej/2bfjx+xD8cv2z/h78NPCXw3/at/Zj8HXfxgX4keC9B07w7ffEnwD4MCX3j/wd8Sk0qOyt\n/FcUPg2PVNZ8N63qsN54g0TVtE0/TrDUYdC1HWNOvfzs/ZQv72z/AOD0T9sm3tbqe3g1X4b+L9P1\nKKGRkjvrKP8AZ0+CGqR2l0qkCaBNR02wvVjfKi5s7eYDfEpH9R3/AAWN/wCUT3/BSL/syX9pX/1U\nvimuppNNNJpqzT2aMk7O63WqP5//APgy0vV1H9hj9r6/RBGl5+2hqt0kY5CLP8G/hXKqDGOFDhR7\nAV+9P/Baj/lEj/wUb/7M8+Ov/qCavX4C/wDBk8AP2BP2rQOn/DYd3/6pT4T/AMq/fr/gtR/yiR/4\nKN/9mefHX/1A9XoirRilskl32QN3bb3erP4Cf2V7u41D9lv4XWmm6gtzcXHgXwbbXEyuWNoYdCsY\nXt3IbIZDuRhkD92eK/oQ/wCDU74fy3nxY/4KxfHeZZfs2o/GX4K/s/aW4MZtjcfBXwn4vn8RFFNo\nJlmlbxb4fmkKXzRyB1MtnE6RSyfyg/sa/ELRNG8A+FfDFjql1YQz+FPDNzc211J+5OpHR7aS9a2V\nEACPdPI2Sxbkc5XNf2x/8GmHg2ax/wCCZnj74xXsRF9+0n+2T+0D8WzcGfzDPZWknhX4bRKYlu50\ngMWpeAtX+9a6dPL5nmSW0sJtru48fLZRlXxPK/gspLqnKTst9b8j/pnp41yVGhFx5VL3o9OblhFN\n/fM/p8r+bj/g6s8B/wBt/wDBJ7XvizZ6e1z4g/Zm/aJ/Z2+Nnh++h043t1pNx/wn9r8ML68S4SWO\nTTrX+zfiTdC8uSlzA0YSGe3USJeWn7R/Ff452/gf9pj9k34GtdTRXfx3Hx41SG1hks0F1bfCPwDp\nmtXT3STTJezWVvL4lsmKWcU0a30mnyXXlhIWPzT/AMFpPhavxk/4JO/8FBvAo07+1bqT9lj4r+Ld\nKsMWGZte+Gvh25+JPh1kbUitpHJBrvhLTriOZnjnhkiWa0kjvEgkX2Gk001dNNNPZp6NP1PNTaaa\n0ad0+zR/GD+1LrPxQ+K93+z3+yP+zrrLaT8VP26/ix4c+Dvh3xVBtlj8L+CNRNlfeOfGMqpc2sj2\nGg+HNQj1fUha3kFxLolvqUVo5nlhB/ue/YP/AOCfP7MX/BOn4JaD8FP2bvh9pHh23tbGzbxt4/uL\nCym+I3xX8UR20MWp+M/iL4qEI1LXtZ1S4iM8dtLONJ0O2MOkaBYabpFnaWUP8G//AAS88Uf8LA/4\nKOf8EANcu7h9Q+2eH/2iHuJ3ldjJqugfsntYXTO4Ks0tvrGl3BkDFgXV45N6lg3+lFXm5Th44fCK\nKVpOpV531bhUlBX9IxWnTy2OzH1fa4htO8eWm4dkpwjN2+cj4n8Mf8FFf2M/GX7ZPjL/AIJ/eG/j\nbo+pftb+AfDC+LvFPwkTw/4xgutP0kWFpq91Enii58PQ+C73V9P0bUdJ1q+0Ky8RT6xBpGrWOo/Y\nntjcSW/iv/BUb/glj+zp/wAFMvgJ4q8FfEHwZoGlfG/R/D+rXfwI+Pum6TbW/wASPhX47jghudFu\n9O8QW0+m6hqHhm/1DT9Os/E3hPU9RbQtY00b3httTstK1TTv5Fv2O7m6t/8Ag8o+NLwXd1CdU+Lv\n7XOlaksVxNGl/ptl+zNdX9pYXiI4W6s7e+tLO7htpxJDFc2dpOiLLbQun+hXXpnFt+D/AFR/mzf8\nE2fjX8QPiv8A8FDf+CHPh/4rwXlp8Vfgv8e/27Pgj8U4L11e6/4Tr4Z/Bi10W+S+aP5ZL62spbGK\n6kJZnuWuJSzGTcf9Jmv82T9j6yj8Jf8ABxr8MvhnHcpex+Ev+Cyn/BWPV4buOyWwRoPGHwk0a7S1\njtlnuViiszpjQoizMvBlVIhN5a/6TdRTioR5UrJSm0uylOUkl5Wenka1589Tmbu3Topvu40YRb+9\nPXruFf5+9kn9qf8ABUH/AILd+G9RhjvNC1z9pzw5Z39hcxia3ngn8I6zbXEkcbfL5tubiCUkq3zL\nFgcYP58f8FUU/Zv8Rf8ABaT/AIKmaB+0p8UPEHgQaf4l+Ek3wolg+I/iTwfpUOqv8BfCt5rVsbHS\nrlbW7W+vl0QN5scaRT3EkhlVrmR6v/8ABNtfh98IfF/2PwJrWneJPB/xVH/E21221q4177d4k0+z\nmuLgahqdzc3Es15H9sgBDsW3TBm3b8t5WbYiKw9SlyT5lUo2k4PkbUoVfdaeunuvazuumvt5Jg5O\nvSxbq0HBRq/uue9a9pU1eFlZX1u5Waa76ddpfwE8TfCz4zT/AA5sfh7oOr+BdSuZNX0HUb3SEup7\nrRpTM8Nis/moDJHDaksPLGBJzGMmvctem+BWseKz8PPFnwN0v4e25VoV8Uy6baJbSE7wQTb25dD8\nm4b2GCwxz1/UOysrTXtTs9OCxwax4Ymlv/D2oOAHcSxmGewLjkxxWiSbVBj5f7xHA+Yv2ktJ0TxT\nZa1/a0FoZ5hK7xSBd9jcjDhlGTtPyhe55z6itMsowxitfmko82u+iWj669PJff6GZzjhPeivi29X\nbtqtbfd1sfjv+07+yr4B0Wzm1n4ZappzwASSx20bLibAYhERViAPTGR2/EfKn7L9neaF4q1q9ubd\normzkaCRSMDeBA4C59RggAnj+LIr3Dxbeat/a50SO4lWK1uZEtSr/wCjyYBAJTOSgBIGccg+1aHw\nk8Mpc+Jrye4nhgt3u0e+VuCxCRqexBJUA9PT3ZalB2cWop2td26OyvfrtZXd99jwnOKVnJJy1Wqa\n6eW/V6aXufZnw1+GF98TbufVtZ1aXT9Ds0MkoEjRDC7SwVjuBOAcYU9BzxivoTwH4ZivvFUel6Zd\nzXGjaMymS8mZpU8uEqZAzYXsGxxggZ574elXFqPDdpomhMLeznKRTS2/3pmbcMOFByrbjnKkcc8Y\nr0/w5YXOjQHw14Wje41TVggv7lEbMMcq4KlyFyTFLuIBYccjPB1w+HqK/PVfLouVxu2mo2u+qS2v\nq1sctVc1RSVmlLvppy/0n08jrPE+san431m38NaKHbT7Py7Rmt9580x7YiSFPAJQ9uB96vq3wB8M\noPBuhx32opDbssJneSULvf5NwGAd+fvdVPXnrSfCj4d6L8PdLjvtXjibVZ1Nw9y+0spkHmNnjIPz\n8ZHUe2a8m/aD+PdrpsMujaLM99eyRlI7eF8kEqEAwQowD7/QV6dPD04Nc0Xbkurqzukra2ulZ29V\n16F9k3s7662Tt07afM8p/aC+J1jp2h+LJre5SJI7V4UbeFDMWkT13Hhh9OhwK/Izwh40fUNQConm\nCaQ73kIO8kE5BOOCSP616/8AtDt40uvB0F5qpntk1m7YC1LHBTfA5yNzDkP68V8kaBLdaFc2sAsp\nysTgi4CqQgxjbgkYCj8OT7V+c8ZY6dSnTw8YOnOnKyld3t7qu9Nul/8AgWSf5rfZ/wCa1/M+hfFE\nzwWtzfXGyHCyLGiEDBxwcqTgHO3nHPtXd/s/a74e8BfCX4veM/E8EdzJPpt1dwRXIG10C2aBSZEI\nxuGeSOBXjeoRaj4ofSNPtFllXVL5LZstkbNhky3AGPlb055xnr0/7eV3ofwE/ZFmsRFBDr3jYHSb\nIoyCXyGtnmLhVO7BktmBbp7GvK4QoVsVioRq1JctOUW92pWcVdWvpZfdb53eHJNXs5JX+Vv8v1Z+\nAereK18bfEPxhr88KRjV9YuZbOFCNkESssKKoHAH7sMMYODX2V8KJJ/+EMCRQzT/AGW6WMrFG8oR\nZfOckhFYgDqQPpX5l+FdSktdRl3s4Cy7huPLGQhyF6A/ex35647/AKIfs5+KPHGn3DvpcUn9kSSR\ntOk8UzW0hAwPuMoYlAwxwTzxX6VmE4xlUoRgl7kbT6u3Ls1ta1/zvdDo1oxp+zk0+ZxUlbVaR108\n07+VlsfdPwmtL/4h39h8PLC2na98Q3MVq7xxSRR2lsWEEtxPvXpGsoc/Mpxkj0HO/E3w7r/wL8ea\nz4O8UQT30VvDbHTdYjG238mWHcYxuLu2xDEjbCBkdMZFfV/wZ+IUfgWSPxDfeENI0m6uQTJrSQpF\nd3CgbTBB/pDSqJhlSXj2BgCSoxXpX7YPg1fix8C9O+LWiCE63pmpW9vfWkKht2m3FzGm6UDJ8xbW\nJix34yCQuMivDnl0cww1ehU5WuWUlzLXm91q29nZ6a32OLOMLRnhZTpTUu6lbTRPay66+W2t2fXM\nn/BMr4SjUrnxHFNqVpPES6rttgAzcDkwk9HJ69q818W/8E0Phbrl1Be3viO9ikiBIiX7MSrbmZQQ\n1sQPvEcHv9a6Dx1+3LrvjXX4YfBsEtnp6TlJ0g+7Ih34JBaTBBK45GMZzmur0j4szGzn1nXtXMRK\nhzbSSIHHygt8u1eOoPz9evavpoYfLNFy2fLbR36xV276b91rp0R9lLNMxUub2jd4rSzbtpre3d20\nt00sfOXif/gmN4D1bTkU+I3ibzFEDbI8oqhlxxaNySMnp+NeA+Lv+CYugS2stpba08MUKHE4UK7Y\n56i36NnnjHQ193Xv7V2g6NE8eo6lYWlsNxhe6mKkkHjZ8xGTzjPQ47Gvm3xt+2x4GsreWX/hIoZp\nVkLCOCVGLcscAEqcY+XvnpW0qGU01+8moKNmkpK8vh00elra3++9yIY7MKzs4uo731i9NY2V9NLJ\nab66efwT4+/4J4Wum6Z5MGuljb5dZX3ZJj6Lxb/xcdVHp8vSvjD4gfBu1+F93pmoXl4t5LbSiOJQ\nDkFHjVWyUTByB36d8E19+/ED9u/Rp7dofsU86OzlGULl1DHHHnDhgcsAP0r88/i18a9O+KG8aZBJ\nDLas00iydEMZD7V/ePnlG4J5rjlicvT5cPWi1azXNeV7LZ37dOu+upFVZpKcbqMKSguZ2bja6u9v\n8vMfo11deKjq17alp/sceZEXJeKNGfHOMcAEDnqPy4u71K4tJWkjfEisB8+QVJyMHhTwCF78n8Dv\nfCHxho/h1ry/vgsK30LW1wHx5bna6hjz1BkJ6DAPXiuN8eajb3WqXF1pUsc9vLIXSKLkIrMSAcY5\n9cnucnNeLiWp15Sgk4u1mt7WSu+/eyu9vUITUUlN3aSu42u9N7bK2if6F6w8Qy/2hBblwXeaPzF5\n2tlxuIH0PUj9K/S7Tfjp4d+Gfg3w5aXem3+tmTR45bq2iSOS2hTMqkyFmjkUZUDhu4r82Phpoban\nrFte6rDLBbM2YnKjDcEg/NkjtXvPxH+LWt/D7RY9K8Py2yQalb/2Vey3EcTl7PJl+QtFJsJkjHQr\nx39evBqouWdJNzvayTja9uWTezv5+XfXy8RLkqOcruL2as+2t1rZL13aPZm/bS8H6ZLFq/gyPVfD\nupoxd4LZ4hZXDAnIdWklkIyBkDA4Hpx694T/AOChll4wt7jQfEHgObW3a2eS+vrNFaSOFMI9y3nX\nir8oZcnZxkZXrX5Z6fd/bmigsdGa8IUySYiUlt7lmb5WHGW46enFd/Y3/jjwxc/2noOlyaSZ7ZrW\n+KwIUltJSjPH+9SUYYImeFO4Dkd/Rr1MXKOtNuSsmtW7pLX/AC/4DvMVh2nJyim0pJpvR3j/AJ7L\nTR36n7X+Af2p/g34hsbGC28VafpsgL201leyskqfOVMbhInUFMeWfm4PAOOa9r0XW/DWuzoljfaZ\nqKWM327TbuOYskRZvPY9QwG7YenZs4zx/OBE+gazqH2G20y4j1y6uAiRQO6y3F3O25jEgmAJaRvR\ncM2AMV1938Ude+F+hQ6F4Wu9Z0TxTa3Dvqk167Kz2ssrP5ADtMNpgOxf3aHaBhuc1yyxc6aUJxs2\nmpX05XdXutNbaLvfruXGEp2cFdPdvqkvkrWd/wAVc+8P24rrU/DHxHsNatJYzpet6VAbl4Duhmnt\nrWHzEH8W4yykMCRhj97vXwpB438MeI9RgsdY0mCyspMwzXUikEyuNjO2XYDLfMPl7jtmvSIfiLef\nFT4R2niHXvEj3etaNrT2aabMYZI5oZ71beYsTGJQwji4AYDPoea4iTwv4c19xIJbe3mtdry2YOC7\n5wcdyQVLY3Ae2OnyWLpR9vKSdpSS5H56db6b211t2sd0oUYUKMm09VdJXejjrrprqt9beh9y/wDB\nHnwj4Y8O/wDBcL/gmzfeGLyK4ttU8f8AxoSWKEALEbL4FeOTG+Nin979qk6k8xngd/8AVjr/ACnP\n+CO2iRaV/wAFvv8AgmrNa3Aktbrx58ZY0twxb7NJa/Arxz5nUbsyidAcluYuoGBX+rHX1WURnHL6\nEanxr2nNrf8A5ezt+FlrqeHjZRliqrgrRfJZelOC6eaufCfw3/4Jt/si/Cn9tH4v/wDBQTwh8O7+\nD9qv446D/wAIx8QPiLqHi/xTqdvdaEbTwtp7afpHhW71V/C2gpJYeCvDFpcS6VpFtcXSaVHLczTX\nM1zPN8r/APBwB+0L8Mv2e/8Agkd+21d/EbX7PS7v4t/A7x18BPh3ozToNW8WfEP4xaBfeB/D+k6J\nY4a4v5NP/te48R6wII2Wx8O6Lq+pXLw29pJKv8p3/BVH/grP/wAFW/h5/wAFTv24/wBnv4D/ALcX\niL4K/Bb4MeLfhpo3g7wfpnwZ+BviyPSdP8VfCDwj4hv4oNa8S+BZ/EbO+s3Op332i+1i/uhLfsIZ\noIreGNfwl+LZ/aD/AGr/AIhQ/EX9qj9oX4s/tQ+MNJS6Hhq/+JHiK+vtI8Pm8Fn9rTw14UjuH0Hw\nzbXBs7Qz2Wg2tjZzm0tDNbyGCILz47P8uwFWph6tWTxNNRvRjTne84RnH33FU7OMot2m2k9VfQyh\nSlNq1knfVtbJ2em9/kf15f8ABlRbS2f7Bv7WtpOpSa2/bK1C3lQjBWSH4L/CmN1PuGUg+4r98v8A\ngtR/yiR/4KN/9mefHX/1A9Xr8Pv+DOu0msP2Sv26bG4jMU9n+3p4xtpojwY5YPhd8NY3jIPIKMpX\nHtX7g/8ABaj/AJRI/wDBRv8A7M8+Ov8A6ger161GftKVKp/PThPTb3op6feZyVm0tk2l8mf5Rbay\n3g34Y+B9ZsWFhqGn/CrT9WhmVtv2mRPCtleKCcnLlwqgAcljnHFf6h3/AAbx/CaP4N/8EZf2DPDo\niMVx4o+Et58XLti9w/nSfGvxp4o+LFvNi4ubry86d4wsY9kLRW+I98VtbhzEv+SJ4p8cXWq/DbT7\nCaaVl03wxoek2yZULHHHaWFiy5ADFXhU8EsAeOBmv9sP9i34UWnwI/Y+/ZZ+C1lb/Zbf4Vfs8/Bv\n4f8AkeZJNsm8KfD3w/o10fMkjhY77qzmkwILeNN2yK2t4lSCPjwFOnFV5w3nUtJ21birrvp77t8z\ntxtRzdKN04wprls+6in+MfnY/Cj/AIKK/Gg6D/wcY/8ABC34aWF3FbXNv8Ov2xdR11PMtpZZ9J+L\nfw18QeFdIje0nkxb+ZqPw1v7e2vTH5zpcahBYM1wjhf6QviB4Ts/HngPxt4G1GGG40/xn4R8SeE7\n+3uGZLeez8RaNe6PdQzukcrrDLBeOkrJFIyozFY3ICn8Qf2x/wDgjH43/ak/4K4fsff8FPdH/amt\n/AWlfsraR8LNE/4Uff8AwtufFjeKLHwP8QfHni7xTHpnjOb4gabp/hdvGuj+OJtCklj8FajcabcW\na6os1zcG3Fp+9legcJ/lyf8ABDvWntf+Cj3/AARi+EmshIvG3wN+MH/BQz4d+MbRJHlW21Gw+Efj\nWVI1d0jYpH5k1vGSiFkgBKKSQP8AUbr/ADNf2MPh3efBf/g7S0n4PXqyIfDf7eX7d/jLTke1nsx/\nwj/xa+AnxD8f+GnitrgCSO3l0LVLGa2IM0UtvJHLBdXkDx3U3+mVWNCPLCSs1+9rPXtKrOSt5NNN\nDcnLV9lH5RSivwSP807xNYftReGf+Dhr/goD+0F+x5rfwd0X43/Ar9oX4ijRm+Ouk+Kta+HtzpXx\nT+GQ8C60mo2Pg+SHWJLmLTJ7uSxeO4hSK78mZxMkbQSfqj4u/wCCkP8AwcW+EvD+r+IJPEH/AAS5\n1tdHtZLubS9E+Hn7Qc2rXMUSlmFnDfapYW0khxtVZbuFSxAZ1HI/KXR/2Zvg3+2n/wAHV37T/wCy\nt+0R4f1Xxh8EviX8c/2iNW8ZeE9J8YeL/A8+rXvgT4Baj4q8MSvr3gjW/D+vwLp+t2FvdGGDUo4b\nlBJb3MctvLLG/wDVEP8Ag1j/AOCJQII/ZZ8Xggggj9pf9pjII5B/5Kz2olCs580a0Yw/kdLmey+1\nzxa1TdrdTpjUwsYwVTDTqTS96Ua7p819VZezmlZNK/kfyI/8ExvHnjb4kf8ABdP9hj4i/FhvDcnx\nj+Mn7Y37Z3xz+KD+DtNv9I8KW/ij4j/Au8fUNO0DTtUvL/ULPTLO+0i+Npb3V5dzQwTJHJdXDqz1\n/qDV/l4f8E7vhb4A+D//AAcnfBn4dfBq1msPgl8Mf+Ch37d/we+FumXOr69r1xpHhr4afCfxHpdp\npM2seKLvUPEOozWFrd2ttPdaxfXWoTXEUz3UrSszN/qH1pC/KuZ3lrd2td3ey6LsZVnF1G4R5IuN\nO0W3Jpezju2k3d638z5f8c/sQfsW/E7xXrPjz4lfshfsvfEPxx4juIrrxD4z8c/AH4UeLPFeu3UF\nrBYwXOs+Itf8J6hq+qXENla21pFNfXk8kdrbwW6MIoY0X/M98F6lo/gP9sX9uDwXoum6b4b8LeFv\n+Cj37V2l+DtC0SxtdH0Pw5o2j/FK4s7HRdC0qwit7DStK0/T7WCwsNOsbeGzsbSGG3t4YYY0VfrD\n/grd+2f/AMFAdG/4Kz/t/wDwx+Fn7e/7WfwZ+F/wp8ffCfTvB/gH4b/GTxh4f8JaHaeKPgz4T17U\nbbS9FtdVjsdNhn1db2+8mzhjja6v7qUxlnr8ttLin0YnW38U+JfFPi/xJ448RfEzx74x8W6j/afi\nTxZ428Y3cWp+J/EGsakwWW+1LWdSE17eXc4ee5uriaeWR5JGY+fmMqc6MqLa5+aLtorbNavbSS++\n3U9TK6NanUjieX924SjF31d2o7J3Vmnv1WzP6p7TV/7Ss9C1yxZkaS2tr8Txff3yKTOjHLLgoOV7\ngk5r84f+ChWqeNfBV3afEbw9Jcnwd4rU2uqmLPl6dqf765lEuMKo2CFPl38MOtfVH7Lnxo8HePvh\nl4a0mDXNPfxLYafDDfaXJKDeO6QIHITB4z5mcEY2nsK9X8e/Dzwt8afBXin4XeIFhFlrtrcpp1yy\ng/2drChZllDHOxmW3WHhXHzkY7nycNi3gZSXMo3aTs97qL02dtEtPz39/E0Y4v2UKkfeSTS3vs03\n57X/AEP5e/8AhMvEOrXF7exO88tnE0odDkr8w55x2fqM9eO1eU+H/j14m0zXr4rbi5Q3YWVNzDBV\nVXOPMUDAB/i+lfVqfB7Vfhf4r8feEPEEoS+8NXU2nahLcLtUW263dZ0JRQR+9jTlce1ewfDX9gv4\ne+IrOTxBcapqN9d6o3n20dtHbNEjSDO8MVViAwZepx6cmumDxGIUpUm5Nrmi7tq7aaTtrrba3y6H\ng4zDKFlytTTTSat2Wj93Ta/pc81+FP7YfiXwvNayXGhJqFgLhZGtpn5kIbkJ/pKrnlh8xx8x+YV9\n6eEv+CoXg/RYwZ/hLHaXYHz31sqefLKowN7vqTjbnPCqDgd+teY/B7/gmVaah8R7HQviBreo6X4Z\n10SxaXKIoFZJZJ4khYb4gu7Jc8Fz6cYrn/iL/wAE8Nc8K+O/EHh3w14zb7DYSKlrNdpbjzIXTcu7\nZZsMhSqjAzzy2enDWxWcYOP1mpUXJTkl2S0jZO6s1prrt9x5uJi8PDmnpzJNO+iemnd7rbzPbvE3\n/BS7TNeglksNM1O0FywZy7wYjxlWRQLh+Bkocg9Bn3Phz+0z8BdXurnxB4s8QSHWXRtlrfuhgjnZ\nXCKdseflchR8x6fSvke9/YV+Ilpay3kfizTpRAJCInJEbHDFjkWanLEZzuHPI2ivIZP2YfjJf3N3\nZadpsF7BYxs6zW4uCJnUOXC4iXLAoTjgcgcCvOlxviabXtOWSXxJcuy5b/dfRLpe/Y82NeUpxjaK\nTaTd7aNrVLtq7W0ba101/TL40aXY/Hf4Oat8TvBHj2wi0vwReC3vtFtXk8yJGmt7RZwHtSoBY9fM\nyQh49fzftdch0q2Mtz4pS8klB8uN3BDKV+UBRCvXrn5f0r6I+AXw/wDHvhv4E/GzwjqWgaqdW1SK\nJbewjWRvtEsd+8rGNWcFjxnj0z3xXwbF8Bfja8Zkj+HXi0iGIPIRZyFIlXOXYmTKr15Az071xZvO\nOe4JY2nCV3OKtFa29za3RbXfbXo17mIwtSEaNWEXKMoxU2ltdRvddXrt6X7v7z/Zn1B/FPiKa01W\ne1jh0+Q3tmZHAQZxFlSTkHaWxjt3rlP+CqvwuPj74YeBNY03X4ZtT0QNPa6NBMJYriNRfjc8YQsp\nDSEcOvAPXINfE2m6F8TPDMssx0rxVprF2tXeKEoGkUByqMWJ44GCM4zzXpvh/wAG/E3XdJudd8R6\nP4nvPDVhYyNPqd9E72dmobO8kttBG8fwYw1bcO4qeV6LDyc21FNxabT5bPyT669DlnCckrQcUurW\nrWmva/o1fs7H5O/DnwDd+KfFFpZX9mbd9LkBuAFOyQRuAQ3LH+LocfSv0u8AeNZPA2nHw3Hp1lPa\n2zxXEMTKwZriFTsZyHHyfO24ZBGR3zXzn8IfEOkaV8RdegjggvJZ5riO3hk4FxJ5kaqEC7SPulW2\nkElT1OK9L1Kwvbt7+8tFIvFuCr2vIkQSb2XywOoCgD5j2B96+hxtTFSm61WEoRqRUoKzsk0rrfTz\ntfv0sehToUOSDTV6iV00rt+7e7W1tdNPM9WufiNrmq3327V755ZEb/R7bcPs9rEp+VIRgMPlCht5\nYEr19foDwz+0Tq9p8P8AxR4Vu7pptIutPuJPs7vlRJDbXDowBYAfM2Rwe3XNfBwuLy3Hk3kE0cqg\nNmUYQfUknr7e/Si/1C4m0HVIdNMjXL2U6FUOOTC47djz+tc1HGzgpWvzbLmXp122evzsu0YzC03R\ncHpCXR6rTS/k227Jan6PWfx+sfDjyWvg7w7bz+aCF1K8DqR0w6/65CcqOuDknFeXeI/H/wARPGF7\nPcXGsSQpKzf6PaFRGqkjjasSDjHUj1/Dufh58GvG3jZgnhHwjqupLhQLp7K4SzAJUAiZIZUCnf1x\njmvvbwP/AME4viNrsFnfeKNT0zw3bXIjZ7eCaGa5RTncGimigYYAOQzZHryRXjvHZlWnGNChUUZN\nqU1GWifKnfayv3Xb0P0qOFyvArlqOM72Vt7Npf8AB+/W3T8mb7w9qGqeXLqdzdzsGyXkllVcg9wG\nCkY9umayrjwrYzsIYLKa/mXCgRoZXLnBACl8sfYjPXNf0Y+Ff+CfXwN8HrBfeKNR1TxLdwujywFH\nggcgZbAgvyChIx/qzzzXrSfDz4GeGrea20jwLo9uPLKwTzxJcXG8LtR8TI5JB65k645r1cLkOY4q\nPPUr2fMrqcnfpok3dK36O6Ma+YYHD2VClGTlG7fLZ3Vt9NL23etvuf8ALDr37OnxO8TWynQfhx4l\nvnkUeUY9IlKlXHyshXcCDwemenOK8z1j9mj4wfBO3uvFfjj4VavDo1/aS26STWV2scUt3C0Ucs5K\nxiMiSVWAywOCK/sI+EPj+yZ9c8H32jaGt7orPcaTfR6ZY4mtJFlnEUjC2VQyKYoxy5GM8Hk+zyeB\nfCPj7wtrWleMNI0/VNP1azvTcW01nbv5JSCba0GUzGVLFk2bOQuCpUY9Cnw1CnKMninGSTe7avGz\n1ab6LT7tjy6+azrUpUvYxXNeziteiS8tt+y0P8+e1e9tbu6jvLSe1hnup5YbeeMpHsd2YeUxyW2r\ngg8duDWy93aQldsYdw2SCc5HGAQ3BHHbr7cV9Xft73/hzwl8XtY+HmiaPbadp/h/XNSGn3KRLHO9\ns9w8axSALkokcAUFnfOe+Ofi+OVH2XCbmjchQfvc8DgZ44OOT8vp3rph9XpRVOd6jjJK6dotx5X1\nfTRW2d/S3ydaNWFWUYLR2dmm106rzdvV6avT17SvE9xAbSO1h3ZYIVQHEeRtz8ozxnv/ADr6S8Hf\nDbxD8V7a+0Hw7ZWOr63PbieOC9JDRhnjH7jy4pnLHGNpHPJz6fIWk3EcEgaSTykSQlVCnLngAZJB\nHJI47DqK+5f2UJ9T1b43eB7G0M5y6S3MdvNJCTbmO6A80wkbv3gXcr8YA4xXfzwjTXsYtNq7s0rv\n3bPT8e3Xoa0cPOSUqsW4y6LXa2lrK1769fwZ6F4O/Yw+Olh9pvE8JaW6zWTQhJJbtTE3mKc5Wz4I\n2jHzZAPPXn0Of9iz48a7ptjp76Bo0Utll5F+13ivcIZXfDf6HnjzFXjPC/Wv1ChfWTCVt5ZgUlMZ\nxI4+XGeTwM5znPrXp2k6jGYVjvWnF4iBGlR3G/PzDkOB0xnj1x0NY/WMdpeTajay0WmisrvXtq7v\nXyt1LC4D3W6M76JJb393V7Wsrt/mfjlP+xf8dPBGn6d4ktfhvpuoahoE/wDaNpeWRvbiYzW8jTRx\nun2Ty3DEAHcG4T0LLXY/H34eeCvFSeFfiR4g8HrpGvXGiQ6T4r02C1MaxX9jbWlhDPLHvj8sSGGa\nZ22x9Sdp7ftJZarcGyVZPNeEKyLEQ0gZTn7ytwQfYE7T09eCsfAuh22sanrVxY2OpDVdsd1p1/FF\ncQojKyExxTJIkblWJ3Rop3csTiuHFNyk3L4r3d37z2v5L8V5XO/CYalJunFxV/dUXvZtK6emu3p3\n6L+U7xb4UvfBN9r/AId8HTw/8Iz4vuIb22u438yHTdShkln2O5VxEDc3Cq3zNwhwvGCyy8FX8lrY\nedcSy63Aggvbm2dminkZVjWaLBVJAJNzktGMKQeOa/o5+PP7CfgT4qaffeIfh9bjw34jgsjPHYLG\nBpl/cpCXeBGkk2QyTyR+WpigDBpAVXIAr8VPEvh7xb4N8ZRaBrHhfUNO1zw/cNY3enx2U00E0Ct9\nmF3HK8cbT+cqSuCYyo4ZW5JrzsQ6DTnNwhyxUlzSSvZpXSWz0v56HNWy/EYWbp8iqUpWUFa7TbVr\n3b0b/N7O57x/wR28OS+G/wDgtn/wTNtrm5mubyf4g/G6a4aXoB/worxkI9uDgEkyFgFX24xX+q9X\n+T98LvH/AMY/2Xf2wP2VP21/hN8Cbr4yal+z3r/jzWG+Ht/rcngm28Ry+MPAmpeC/Jm8RHTNabT0\ntF1h9RVk027aVrQWpWFbg3EX9Bdt/wAHSX/BQi6IEX/BILQQCcbpv2pVgUcZ5M3wzQDj1r0cBmmW\nwwdJSx+Eg4uaaqV6VOSbqSduWUk9mtba7njYnLMwVaSjg8TNSUWpU6NScW3GN0pRi1dN2a3Pwe/4\nLAxapN/wWz/4Kc/2c4CwePPgncXEZOBIq/AnwSqjIVjkbmI7c9DmvjL4deMb1r28kNuY4LMmOW7J\nJWHAUnO7K55yd3X06gfQn7Qmg/tUftqftnftIftmeMPhJoXwH1L9oPXfB+rXfw7/AOE+tvF1toI8\nK+BND8F+TD4ig0rT/wC0UuF0JdQLzadYvA169sqzLB9okr+Cv2SvFOi6L4h07xF4u8LaYdY3TLM2\ns2xdJSsabQZI4tqkJkkMMHnFfBcSYzA18fXq4aUa7fs4urTnGUJctGlHmjJNppfDdaOS08/ZwXDm\nOq4anUlSdKTUm41YuE1+8dk4ySavGzadnZ9Nb/1ff8Ggk/2r9mD9vy53b/tH/BQPx5NvGPn834a/\nDh93HHzbs8cc8V/Tf+1N+z14S/az/Zw+OH7Mnj3V/EegeC/jx8MfGHws8T634Qn0y18UaTovjLRr\nrRb7UNBuNZ0vW9Ji1S1gummtH1DSdQtBKqia1lQla/z2v+CYf/BRr9qz/gj/AOBfj18Hvhp+yh8J\n/wBp/wAOfF749ax8ZofG2rftS+D/AIWXOnHU/C/hvwzHoo0K/wBH1eW4VYvDgv2vZbm3kL3jW5s0\nFuJpvvXxT/wdu/tfeDJEi8Qf8EyPhNblzgNZ/tn6HrESnOP3kuj/AA+v44xk8GRlBHIOMmvv8HnG\nVPCYZPMcFCSw9HmhPFUYTi1TgpRlGU1JOLdmraM8Srk+aQqTi8vxrtOSUo4Ws4y10cWoNNNNNWb0\ndzzn9vz/AINVv2Of2Qvgl4F+Jvwq+PP7UXjvxbrn7T/7I3wWsPCnxV8S/Cm+8BXmnfHL9o34a/Cb\nVTq9l4e+Eehai6WeleLLmeyEV+ttFdxQSXVrfWsctlP/AH2Qww20MVvbwxW9vbxRwwQQxpFDDDEg\njihiijCpHFGiqkcaKqIihVAAAr+DH4jf8HA/7a37ZngrwXpMH/BLj4T6n4f8DfGj4KfG9Ibf9tLw\nfdtqWrfBT4jaB8TvD+mXmnXng60njtLzW/DNgl1I8UhjiVi1rcIWif1bxR/wdP8A7efg6YQa/wD8\nEnfA1tKzhFS3/a6sNRdmLBQFXTfhzdsckjnGOetdEc2yhX5cyy9L4nbF4dLXq/3i+/y8hf2RmzSf\n9m5g1sn9UxDXov3ffofpD/wUy/4L/wDiH/gn/wD8FLP2fv2B9O/Zx8L+PvDPxg0f4B6p4o+MuvfF\nDU/C0ngGP40/F3xJ8Nbu4bwpB4K1O11e08L6bott4hDHxLYDUXuZrGWbTlhNyP6XK/yaf27vjt+0\n5/wUW/a08V/tva5+zZJ8HviV/YPwI0r4c+AtJ8XSfEDTtFuvgv4itfEKXZ1/UPD2mWTHWbqKW4lt\nJ9GS0tnndJI73zJ3l/o8tf8Ag5v/AOCj19k2X/BHjQ7hN21ZF/alWNW5wCBN8Mo2we2QDzXPHiPI\nnOrB5vlsHTmo+/jMPBSThCXNByqJTV5ON4trmTW6Oh8O54oUpxynMaiqwckqeDr1HG05R5ZqFNuE\nmkpJSSdpJ21Pz3/4KYeL9D/YJ/4O3fgZ+0t4olj0nwL8Q/EX7M3xC8U6ve3cVnp+geD/AIlfDq4/\nZO8b+Kb6+1DVBb22l6LHoet65qM9xLptpb29pcRfZTDGk13/AKLsM0NzDFcW80Vxb3EUc0E8MiSw\nzQyoJIpopYyySRSIyvHIjMjowZSQQa/zDv8Agolqn7X/APwWA/aU8J/Hv4o/sGWf7Ol1ov7Pt38B\n9UtNN+Jem/EkeJdPh8Ya1418M6895aeE9E1HT9T0PUfEGpQwrMNRge2NssDWGy5+1fpf+wl/wUn/\nAOC437BPw58O/Bj4q/smwf8ABQf4HeAdJ0Pw54E1i18Zr8Pvj54Q8I6TIlnBodz4ju9I1xfiHaaP\noaR2GjQ6z4VuteiNtZw3fjKexjjtoeaHFfD88XUwn9q4JSUYThW+sUnhqnMknCOIUvY+0i1rBzTa\nlHlvrbaXC2fRwsMX/ZeNcHKcZ0vq9VV6fJa05UHFVfZyT0moNKzvZWv+pH7P3/BEf47fCr/g4Q+O\nv/BUrX/Fvwwv/wBnTxdZfEDxP8PdGh17xFd/FaHx78TfhxoHgbW7LUtATwvpXh3TtK02S48aNbX7\neItYZ9Jk0iD7FPqM89/p/wDSr458ceEPhn4M8V/ET4geI9J8IeBvA3h7V/Ffi/xTrt3HY6N4e8Oa\nDYz6lrGsaneSkJb2dhY209zPIckJGQqs5VT/ADTN/wAHHXxVvNIjt9C/4Iqf8FKbzx3JNPB/Y+se\nDdH0HwKrq10tqW+IzwX+yKYLYtcTyeFY4bYXF2Y3ulsomvvzI/bG1f8A4Laf8Fh/Dp+GPxl8GeEP\n+Cfn7H2rxWU/ib4G+BvGw8b/ABK+KSQXdvqEOnfE7xnbG0Op6TbX9nBcxeHItK8G6MrRwS6xoWvX\nlvb3EPXjOIMmwNGVevmOF5UrxhSrU61Wp2VOlSlKc29LWVtbtpanNhMhzjG1o0aGXYvmbUXOrQqU\nqUNlepVqRjCCXm79Em7I/Ij/AIJNaxrHxN/4Lj/sTftLzW1xpvhb9qb9uj/goH8cPBulXyiK/t9J\n8Z+AfFmq+fd2yKI4JJReQ2zCNijPZPtARVLf6mNf53XgD/gmL+13+w3+0n+x5+1h+zL8NdJ/aV8R\nfsw698QL9fg14j+JmhfC3TtS07xZ8P7zwTaw2ninXjc2WneQ2rXOo3BgsbiS5ksooZEjFwbmL9tD\n/wAFd/8Agt4F3H/giX8MguSAW/4KD/BpSSD/AHTp279K4sq4nyrHYOGIq43C4Sc6tf8AcYrEUqNW\nEVXqKneNScW+anySTStZpHo5rwvm+Fxbo0MvxuLpQo4aKxGGwtetSnNYakqvLKnCS0q88bN3utj0\nL9sv/g2M/ZK/bR/aq+Nf7Wviz9pv9sn4Z+OPjvrPhvXPGHhr4U+NvhjoXg6C98L+D9D8GacNOtdV\n+FGtaw0K2GhpcganrGoyxXd7e+RNHDKsS/yG/tQ/se/A79hb/gpF+1F+xlffFz4vePvht8IvBXwP\n8SeDPE/xV1fQfEHjbUL/AOIPw90Lxhr1rf3eiaP4T0o21pe669vYJaaVC62UEIuZJ598z/1Yt/wV\n1/4LeInmP/wRL+GKpjIJ/wCChHwa5GM8D+zs/pX5Ua3+w5+01+3Z+2x+0d+29+2P8APD/wCzp4g+\nMfh34O+GfDfwq8MfFTQfi9baOnw48F6b4Nvb6fxV4dFpbTNqUeg2N8YZbOD7PJdy20bTJALiTl4h\nznLVlOLqYPF4TFYtKj7Knha9GriJS9vST5VTlKWkOZtvTlTT0Ozh3h/O55rhIYjLsxw+F5qjrTxG\nGxFGhGKpVGuaVSEYXc+Wy3cmrJ3Pyk8DeNfg34H8Z6drvgr/AISi8v8ATrgvbtBEwhuPlZRDIkeo\nFCCCR/q2yW9cEfrn8H/ixrPxGEupX3gu/wDDdtcyLcx39xHdRjJKEMRK5Rd2MDbgc9KraZ+zb8Lf\ngJ8X5vBnirwxazW0sMM1lf3sflxb3lnyEdiVLKsYP3ieR2r7Y1jS/A+neEppPD0tktnGgCx2iwnY\nqlDjchyAF7g/418ZleLxeZyiq/7lXjpO6k9U7Pe9rWs+3zP0V5Ngqca14ydeipSumtb20Wmqfb5H\n5G/8FTvhVfaz8PPDvxq+HVmba80W5Sz+KAtEKm7tUWe4fUZSgfKl5LGEsREuVAzng+7fsY/EjwH4\ng8FeDY5YreO/g0+CGZZAm1pRI2SSXyew6fezmvtC20Xwz8RvCeseCb+Oz1Pw34z019H1HeUdbYuy\nzCRyNwSUNBGuSy8MBnsfzb8IfCyf4GeL9a8J3EM1u3h2/kWwIVljmtX23aTI3yiRR54TI3DggHnF\nfpmUVo0pOi7uSjGEb7N2jrd3fba127d2fnOcqME6jjJxUnZtbWcVbdO73v8Aij9ffEfhK21MeH9Y\n06e1M1hc293C8RVfKCMWC5Reh+UnOMYHfp+cfxft/E/i/wCJXiAaZdeQsKQxOY25MkVuigE7CfmK\nknn370sHxb8YWlyttFfzi1WQFVd22AA9CTnaDk9BgfnXxTqP7ROt6P8AEjxAuoNhDOp3r8xk+Rf9\nlcn5sHr69avihU3lTpUnatL35Xd7Oy1aX5Wd1a99j4XM69XFQiqMZSUZK66fZW6626W3211PofS/\nAPxBu7n7HfytLbGT5hvf5kDfeOEAyF45P616oPBtl4a0oxiNjqRDmGNNzM00g+UE7skbiuQQQB2O\nePL/AAf8fxrUkTi0nEIQeZdCIgBgoyCANpBOTyQc9sV3et/HTwpaSxz3SlJoI1LySIuwfKMMAz7R\nyCeFz071+HYnB46dXljRlOLTs4qTWlrO2nfyflex5dKlilOMnHlSackr6x91vTra9/L8T9Gf2QPh\nr4c1jwLda34j0+0PiC21C5P2WWFD5sG+IIZUYKW4EhwykZ6DkV9nzeFfCOqQW+nWvhvQIBKPLvEW\nwtQWjC5wxEG4cg5weo69cfi78Cv21PCvhXTfGE02qx/Z2d/KXdGDvM7krCnm5ycqPlweR07+feM/\n26dX8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