From 8895d41e33cf184e8bbc4fdd5e5fe22e4a75f5b9 Mon Sep 17 00:00:00 2001
From: HenryTux <h.schoeller@fu-berlin.de>
Date: Tue, 9 Jul 2024 16:43:59 +0200
Subject: [PATCH] updated

---
 pyscripts/GetStartf.ipynb                     | 163 ++++--
 pyscripts/LIB/__pycache__/data.cpython-39.pyc | Bin 10735 -> 11015 bytes
 pyscripts/LIB/__pycache__/plot.cpython-39.pyc | Bin 35574 -> 35572 bytes
 pyscripts/LIB/calc.py                         |   4 +-
 pyscripts/LIB/data.py                         |  36 +-
 pyscripts/LIB/plot.py                         |  57 +-
 pyscripts/clusterfigs.py                      | 233 ++++++++-
 pyscripts/epsloglog.py                        | 470 +++++++++++++----
 pyscripts/hulldist.py                         | 414 ++++++++++-----
 pyscripts/k_pcompare.py                       | 227 +++++---
 pyscripts/sizevsdscatter.py                   |   2 +-
 pyscripts/syn_cond.py                         | 489 +++++++++++++-----
 pyscripts/wcb_plot.py                         |  14 +-
 shellscripts/calc_epsloglog.sh                |   8 +-
 shellscripts/test.sh                          |  14 +-
 15 files changed, 1564 insertions(+), 567 deletions(-)

diff --git a/pyscripts/GetStartf.ipynb b/pyscripts/GetStartf.ipynb
index 723a267..8f0ae89 100644
--- a/pyscripts/GetStartf.ipynb
+++ b/pyscripts/GetStartf.ipynb
@@ -88,52 +88,52 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<xarray.DataArray 'pres' (time: 32, latitude: 361, longitude: 720)>\n",
-      "array([[[307.50943, 307.50943, 307.50943, ..., 307.50943, 307.50943,\n",
-      "         307.50943],\n",
-      "        [304.63217, 304.63217, 304.61862, ..., 304.6593 , 304.6593 ,\n",
-      "         304.63217],\n",
-      "        [280.94904, 280.86758, 280.8133 , ..., 281.2069 , 281.1119 ,\n",
-      "         281.03046],\n",
+      "<xarray.DataArray 'pres' (time: 4, latitude: 361, longitude: 720)>\n",
+      "array([[[416.23508, 416.23508, 416.23508, ..., 416.23508, 416.23508,\n",
+      "         416.23508],\n",
+      "        [414.1839 , 414.17355, 414.17355, ..., 414.215  , 414.20465,\n",
+      "         414.19424],\n",
+      "        [396.2    , 396.22073, 396.24146, ..., 396.16895, 396.1793 ,\n",
+      "         396.18964],\n",
       "        ...,\n",
-      "        [323.93155, 323.75513, 323.55154, ..., 324.51517, 324.31158,\n",
-      "         324.13516],\n",
-      "        [327.90817, 327.8539 , 327.79962, ..., 328.13892, 328.071  ,\n",
-      "         327.98962],\n",
-      "        [364.97333, 364.97333, 364.97333, ..., 364.97333, 364.97333,\n",
-      "         364.97333]],\n",
+      "        [308.89102, 308.82886, 308.76672, ..., 309.0775 , 309.01532,\n",
+      "         308.96353],\n",
+      "        [304.23965, 304.2086 , 304.1671 , ..., 304.3536 , 304.32254,\n",
+      "         304.2811 ],\n",
+      "        [298.52124, 298.52124, 298.52124, ..., 298.52124, 298.52124,\n",
+      "         298.52124]],\n",
       "\n",
-      "       [[279.53754, 279.53754, 279.53754, ..., 279.53754, 279.53754,\n",
-      "         279.53754],\n",
-      "        [296.54327, 296.54327, 296.54327, ..., 296.5161 , 296.5161 ,\n",
-      "         296.5161 ],\n",
-      "        [295.74252, 295.68823, 295.6475 , ..., 295.90536, 295.83752,\n",
-      "         295.78323],\n",
+      "       [[409.82263, 409.82263, 409.82263, ..., 409.82263, 409.82263,\n",
+      "         409.82263],\n",
+      "        [414.42218, 414.2875 , 414.14246, ..., 414.8676 , 414.72263,\n",
+      "         414.56723],\n",
+      "        [416.07968, 415.9036 , 415.7482 , ..., 416.64944, 416.46298,\n",
+      "         416.26617],\n",
       "...\n",
-      "        [312.7211 , 312.69394, 312.65323, ..., 312.82968, 312.78894,\n",
-      "         312.7618 ],\n",
-      "        [316.1141 , 316.07336, 316.04623, ..., 316.2091 , 316.18195,\n",
-      "         316.15482],\n",
-      "        [320.90503, 320.90503, 320.90503, ..., 320.90503, 320.90503,\n",
-      "         320.90503]],\n",
+      "        [319.2504 , 319.20898, 319.18826, ..., 319.38507, 319.32294,\n",
+      "         319.2815 ],\n",
+      "        [335.6908 , 335.67004, 335.67004, ..., 335.72183, 335.7115 ,\n",
+      "         335.7115 ],\n",
+      "        [317.3857 , 317.3857 , 317.3857 , ..., 317.3857 , 317.3857 ,\n",
+      "         317.3857 ]],\n",
       "\n",
-      "       [[316.7384 , 316.7384 , 316.7384 , ..., 316.7384 , 316.7384 ,\n",
-      "         316.7384 ],\n",
-      "        [302.74567, 302.70496, 302.69138, ..., 302.88138, 302.84067,\n",
-      "         302.79996],\n",
-      "        [283.44626, 283.41913, 283.40558, ..., 283.5413 , 283.50058,\n",
-      "         283.487  ],\n",
+      "       [[415.64462, 415.64462, 415.64462, ..., 415.64462, 415.64462,\n",
+      "         415.64462],\n",
+      "        [406.13464, 406.37292, 406.6112 , ..., 405.3888 , 405.63742,\n",
+      "         405.88605],\n",
+      "        [417.1053 , 417.4471 , 417.78897, ..., 416.03824, 416.40082,\n",
+      "         416.75305],\n",
       "        ...,\n",
-      "        [320.8643 , 320.78287, 320.70142, ..., 321.12216, 321.02716,\n",
-      "         320.9457 ],\n",
-      "        [325.28876, 325.20734, 325.13947, ..., 325.47876, 325.4245 ,\n",
-      "         325.34305],\n",
-      "        [329.94397, 329.94397, 329.94397, ..., 329.94397, 329.94397,\n",
-      "         329.94397]]], dtype=float32)\n",
+      "        [300.1684 , 300.1477 , 300.12698, ..., 300.25128, 300.22018,\n",
+      "         300.18912],\n",
+      "        [322.17175, 322.1303 , 322.09924, ..., 322.2857 , 322.24426,\n",
+      "         322.20285],\n",
+      "        [340.0624 , 340.0624 , 340.0624 , ..., 340.0624 , 340.0624 ,\n",
+      "         340.0624 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 0.0 0.5 1.0 1.5 ... 358.0 358.5 359.0 359.5\n",
       "  * latitude   (latitude) float32 90.0 89.5 89.0 88.5 ... -89.0 -89.5 -90.0\n",
-      "  * time       (time) datetime64[ns] 2017-01-23 ... 2017-01-30T18:00:00\n",
+      "  * time       (time) datetime64[ns] 2016-05-01 ... 2016-05-01T18:00:00\n",
       "Attributes:\n",
       "    units:      hPa\n",
       "    long_name:  Pressure\n"
@@ -144,7 +144,7 @@
     "# =======\n",
     "# get tropopause data\n",
     "troppath = \"/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/era5/tropopause/\"\n",
-    "mon = \"201701\"\n",
+    "mon = \"201605\"\n",
     "tropfile = troppath + mon + \"Tropopause.nc\"\n",
     "\n",
     "with xr.open_dataset(tropfile) as ds:\n",
@@ -170,23 +170,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "7acdb78c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<cartopy.mpl.geocollection.GeoQuadMesh at 0x7f0772458500>"
+       "<cartopy.mpl.geocollection.GeoQuadMesh at 0x7fade8cc4980>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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4/etfj9/93d/F5s2b8dGPfhT33nsvvv71r2vHPfLII5KoPPjggwAgqxDv3LlTjn/37t145zvfiY9+9KMYGxvDy172Mtx33334jd/4DVx22WV4wxveUHoNVcf1b//2b/jABz6AV7/61TjrrLPQ7Xbx5S9/GX/yJ3+CF7zgBXjlK19Zeq7p6Wn8xm/8Bn7zN38T09PTsljY+973Plx//fWadlq9ZpHd5jvf+Q5GR0cBAK973etKz2fi3e9+N/7iL/4Cr3jFK/Bbv/VbaDab+N3f/V10u128733vk+08z8Pv//7v4w1veAN+8id/Er/wC7+AxcVF/PZv/zYajQZ+/dd/Xes3CAJcc801WpXlqufyfR8f+tCH8NM//dP4+Z//ebzpTW/C/fffj1/5lV/Bi170Iqsa88k8V40aNdYXSfZZz/PXyLBeBQpq1Hgq4Nd//df59u3bued5HAC/8cYbOedpgalrrrlGtnMVdLrxxhs5AP43f/M32vaPfexjHAC/7bbbtO1f/OIX+fOf/3w+Pj7Om80mP/PMM/nrXvc6/vWvf/2EXB+F9773vRyA8yPugcCBAwf4W97yFj49Pc1brRZ/9rOfzb/2ta9Z/Yprpj5vfetbtbZRFPHf/d3f5eeccw4Pw5Bv27aN/8Iv/AKfmZmpfB1VxnX//ffzl7/85fy0007jzWaTt1otfvHFF/MPfOADWlG0Kvjwhz/MzzvvPN5oNPgZZ5zB3/ve9/J+v2+1K7q3FMqKhXHO+QMPPMCvu+46Pj4+zjudDn/hC1/Ib7/9drLtF7/4RX7llVfyVqvFJyYm+Kte9SqtoJk6TvUZX8m5/vIv/5JfcsklvNFo8K1bt/L/9J/+E5+fn1/Xc9WoUWN9IIqF7f3hFv7E49vW7bP3h1vqYmEZGOd1hESNGjVq1KhRo0aNE4e5uTlMTExg7w+3YGxs/cpUzc8n2H3BQczOzkqr9tMVtTtQjRo1atSoUaNGjZOCGBzxOubqX89zn2qoKwbXqFGjRo0aNWrUqEFg586dYIxZn3e+850A0uxq73vf+7B9+3a0221ce+21VlX6Xq+Hd73rXdi4cSNGRkbwqle9Co8//vh6XI6GmgTUqFGjRo0aNWrUOCmI+fp/hsFtt92G/fv3y8/XvvY1AMDrX/96AMCHPvQh/I//8T/wP//n/8Rtt92GrVu34kUvehHm5+dlHzfccAO+8IUv4NOf/jS++c1vYmFhAT/xEz9hFbw82ahjAmrUqFGjRo0aNWqcUIiYgO/fs3ndYwIu2XNoxTEBN9xwA/7+7/8e999/P4A0RfcNN9yAX/3VXwWQav23bNmCD37wg/j5n/95zM7OYtOmTfiLv/gLmcb4ySefxOmnn45//Md/tApjnkzUloAaNWrUqFGjRo0aNUrQ7/fxyU9+Em9/+9vBGMPDDz+MAwcO4MUvfrFs02w2cc011+Cmm24CANx+++0YDAZam+3bt+Oiiy6SbdYLdWBwjRo1atSoUaNGjZOCU6VOwNzcnLa92Ww6C2MKfPGLX8Tx48fxtre9DUBaGR4AtmzZorXbsmULHnnkEdmm0WhgamrKaiOOXy9UJgHdbhf9fv9EjqVGjRo1atSoUaPGKtBoNNBqtdZ7GKc8RCVygfe+971aIUIKf/Znf4aXvexlVtV5xpj2P+fc2maiSpsTjUokoNvtYnrDViwvzZ7o8dSoUaNGjRo1atRYIbZu3YqHH364JgIleOyxx7SYgDIrwCOPPIKvf/3r+PznPy+3bd26FUCq7d+2bZvcfujQIWkd2Lp1K/r9PmZmZjRrwKFDh/Cc5zxnTa5lpahEAvr9PpaXZnHVtb8OP8gfKqbGFBtshsUcMAgOS+gYZEaEanPGnO1RdF7iGBZz/RgAVJpYZrUpP8Y63myTDY8r4+S+p+83jmFJol+HOYygIJSDc+uecC/9Pwk9RB39J+cBy86ZnZv4LZKsjd/XDXgs4rIPb6Dv87p5xDsPGPylQX6ccg5u/n7qPU+yPj2P+C14+T6tY2bdF+s8or8ScJ/ptkz1kGFZvWjOjW0rDdd3xfkTtlf1Xpu/QyFWGklE2X89x/6yc6jjJd4x8hDufqcslOVLcJ1L2SzHozZV5wHqGvx8m/ae+PT8Id5f8Y5bf4s/lW0w50ixzzXfUm3NeYK6Heo516o9dW0g5i3tfmRtIr2Nek/V8TDjOeWe+reYL42+1LHzgn3Kec0+EmU8XuzeZx7LjX1JAPiK0V4dfxIy+D2utVXhDdz9ehGxNqhjdtxfuTaY74xjnuYF7755H9JjgKCb/2jcaNJ5csk6hHsM3rLbsyFpN+zTFLwf3uxidqDRZjCwGxdhEFmbeGRvY8wDj+3tABBFXfzLgb9Bv98/ZUlAAoaYnARO3vkBYHx8fKjA4I997GPYvHkzXvGKV8htu3btwtatW/G1r30Nl112GYBUZv6Xf/kXfPCDHwQAXHHFFQjDEF/72tfwhje8AQCwf/9+3HXXXfjQhz60Vpe1IgwVE+AHLQRh+lCRL0T2krOY5z1rk6tDQKdGwUELAi4BT/RvHKP1XyIEWCTAPKbAiU0XMMxFQB+UFOLVd0DlNUliL2qqwBb49CAc5IgzhqThwQMQ9IFoJL/hScjgxQBEl6Eu3HOm3NIQ8Hv5TUhaLJ/4A2hEIJn04HfzScpXBBeWXSwltGmCaYPp98G8x+K+OkgAD32wSPnRHIIbDxj926ttGDOEfeVvigQwVi5IFoyp0rHDHCeFKGP7ShwzXYs0cwtC2hhcfbnuadm5sr9dJED+tq57M4zAL9o6SQAh5DtIYhkJ4E1a2BctNEHQRQJEFy7hWb30IUgAV2QkSnFgnhNhhfOoY3T177gOrijwPCXjnibAN2mClW5QhucQxtX7mgRMU4po+5Tp2SIUvtoH4PcVgVz97dtKH5H9OvBAXIe+PQmyttn99hQ5MQmZto/F3O63JQR3QuBvA0FXH696PG8DfrZfIxAhTSC4Z/wOlvLKOgTguVIqPSY/B6CvTwL9M1voPL6o9Jt1PJrKMt5yzz5PH0jahlbYT9dmEtNNeMcW7B+q2RyOCFBKqEZDIwKMZW28BkkQ6kjPE4MkSfCxj30Mb33rWxEE+U1mjOGGG27A7/zO7+Dcc8/Fueeei9/5nd9Bp9PBT/3UTwEAJiYm8I53vAP/x//xf2DDhg2Ynp7Gu9/9blx88cX48R//8fW6JAArfFyG0eiDA2AFBICCay0qWMQZJXMM2T9nhDAohLmqBIDanyQaEWCcW9ohtS2QTqKu8bNBDB4aRMAxhiLtaBJmFgJfXzgBxULAbc0KYEzE1r5iCS5pBWA9OjeuJWw7wEM/vz+GwM19v7SPqprvoTTkAmXHqOMlLDcnBGuZB2wYjf1aQhWgK1hsTugYXMK84KXq++4SHFYJ9T0T7yg1D1bGMBYBBS7rhbO9IDTqbStIlS3mqaI2KmLZ3j0W0SegC6impjlueFabKvuo85iEIO1DKM6cXSBpMHgKWeDKqh03mEYkrP6buubfPLd6rPo7Ri2mC/xBvh0APEOuFWOKW8zaR/UnBPzcKkIMMJMdtP+R32/13or9ccOzLNYAsLRjBADQfnJZ3+EzJKMteAtd6xhvuWcRAfFeU+90MjUCb2bR2o4wrE4EHG2ZEDrjxNpOEoEaa46vf/3rePTRR/H2t7/d2vcrv/IrWF5exi/+4i9iZmYGV111Fb761a9ibGxMtvn93/99BEGAN7zhDVheXsYLX/hCfPzjH4fvO5S6JwmV6gSI3K7P+fH3IwhbtEA/pJC8ZgRAO09J/xUXN9ItqGQdL7IEAIpQoGp7NBOyPbHIa6D6M0kA0c4UlJJs0UqaPqK2vk9bYBPu1rj5DJ4yyZoLqLkgCmtA0vQRzOvmVxcRSDvO2qiauNDXXHcoK4G8ZtXjKrMG8NB3VgnRLBCmuw8xLm2/aaIWQmGB21rhPgqrLecxhJuQ00WHgssV6kSUH1khCVhTSwA1lhISQLkDaRpq1UqmLPIrcvvx6fdWnRudlgBivwpXH3oje7+lYPDsNuq1qHMI5YpjttH6VO9JkauK6iIj3FYK3IWSwNCCB/o+eZ5BcR+qm456nEakHBYec1+Ri5Ddp7ZLs1YA+m9k7aO0+D7Ltf6GKjHxaQIhQJGW1LKiXHdFvYgkAkaXFBEQ7177icw9yHR3IogAYFsEyPlE+ZskAsBwFgGqrTgHIceoRCDiffzz7F+sOAf+iYSQJb9z9xaMrmOdgIX5BM+88OApeY9ONob+FU5VAgAo2rBhy8FZ/RDCTFXNctFYKX9GQF635TZU5FJS4RpJbUUzneGDZX1f4iOdXBJ7ouE+kx8gJxMAEHV8JD6TCxJlIRDnjMZsP0sA4E03E04aAXjo56THIfzxRlAoGJKkSd1vXGMpfJZ/XBBxCOsc/V8Ij/isFHXdwVUjafpImj7idoCk4SFpeIhbPpLAK7WwFYGz/CMwrNWAe9lnyMc5Dpn8FPl7y/ZND3HTK2wbN5j8VOrT0V4VpONm8YVFLYaoxQqtoHGTyY9JANJxpB8XAQBySwbZf3YfKd/4JGDyQ8UJyA8xFcb01Awg1/6b441bjCQA4pj0XhH9GQoocT/l72PqSwrmZW/ASbkhCU2/4Pz45dM65LzNW/RNUN2FrDhIYo1OpkbIftAsuMkmwlD/Xz0H4UXAgtoHqMbKMNST4yUczFgEXASAxZz2z3dhlQSgtP2wJm7G1syML4XTmOuTT9GQkvxYbRycp+4uVH8l8CKOpCD4PQmZpckqdPtplK++g7GQ1soAiKZaMmCYN33NMpA0w1yDT7lpAYg7DXiDch8B3gjy58JnNoEaVrZaT6F+pTEEZgD1agX29VLiUNdPWC6clrmi6ybiDFRoygFN4y5cGk585mtN+BN/r1DpoQlbhNuF8zjNmplvL3KLEZAuNIPyexUrMRGUj7rZrqiNPga3C40gAl6Bh4UqMKvrn0lGpKsPsfYIDbZLQx+LWC1iHwBwX3chMs/Nvbxva595rDhng44xAHIi4BsKanGNAO3S5HI5EkTAXm/SNSdY5lp78TclQ3gx194LQW4FEaCey+WtbbQP5K5Bcq1pNcC6dsCwt9xzkgQKlmuQENybDaBXMdW6IAJUanaPWTLNU8k1KF7nwOD1PPephpXFBPB0ASkkAEC6IFcRFqj5eAVCiphs1QlwpX2l/RgCuAene4QUOkzNgHnuTHB3+pozQkg1+mJxbBMBxzUyzjW3gmAxkoHBXkxrheTpCt6TqO3JRSppevCUgKy47VmZOAR6G1oIlosF96QdFu9v5vuT0KeJQFV3kYJ7N1SbobMCneD2qz1O4GQI+9QYh3WXOgWgue9omWdsVx4Wrx1h4FSgJFAqzDv7E37aQ5KLEyHgy/aKNtpz+LGrmnyXEC8E4rjBiv3w1SlInc4tgZq2AJtuW65sPpq7k/GYF83NaT/2+ADkLlnEu5v74tN9uq5bIG7kLk1m/0moxwqo9xqw3cISX3cdUq0GUZtprlPy/GaMQ3atIv6DirVLAttNFUiJAAAtaBhwEwHW7dNEwPM0N1V53swi4M0a2YmGIQKc03ECrrjMIAAGdT2nGtWxYhuSF3M6u4u5cGREYGgXoCFQlLorz+axNueiiIAzMJgS2gvAirTVRUKoMyOMWMwTPS0pci2+SQR4wLT7xGKeuwEZl0AGFGcaLh4wiwjEJZaDaLIFzxEnELcDJ7GQ4+mE8PoVLAOhX661Dbziey5wAgVUlwWEbOt5lds6caJ8+p8iQvzJRKK4wMkUvgUpIlcE6rYP0a0aeCnGWEXbr7p7uIJSVWgCfgW36SraelWYdQn7mitL0fRaJRuWFiNC9SF8Vd3ncSVhKNunN3TvKnObKnWrEoanhn3vxbGJzDxk74+93JKgriVRixbSBx2GcMmOLRC/f1FAtAlh0TbP4/UTdDe30TqkBwzzZgjWIwJ0M3JgkQEHEXCiChFQ52CVCJR4NTC/dg2qUR2ryg7EuE4EnNlsXFmDXBhCAKlUS2A1qJDqkRTWXMcILZt571yafDGxGONINfwl/ugOeP2EdOUp8hEu00rFbc8pIAxGAy2YOGr70hrQm9YtA0nTt4hA0io+edwO5DOWNHySCFQOJNUCOh2//QnI7rOiLESrOZaqr7BSuAJn1yFGQL4vJcSY2kYF+GrHrIVgXhGaG55Iy6nKnCvV8hMBtMMQDtU/vIoQFrVtQbnQtaehtwXcc7yqOS6KS3NqzFW4vOxcr1bBO1cozBe4D4ljzfOKbXKflpfT7t8lzLPYbQGwjnFYFwS0NKjU8q64HKl9xyH9W5luQ+L5H3RogV/EPoRLdJpWCpylblZ+9vypa5JGBESSiczi7CIDJhHgvg8W22tPMtGxrQFAHiNAkQFq/grD6haEUxy1O9Cpg6FJgDkhC2HWSQBOoCAwVKrSlfTvcvEpguMYxrmzyFcpATBRRaA1hdOEa9rHYCFCNKrkulWHYFhNijRQSZCbuk2za9Tx83RuDU+bdAEgyoR7lRSoiEdDLc+/al3gYTUhNmn4YCXuCVq6UVcb3z8hz/IwAnwV0liItdDIn2itflkKTuqQkySccyXYsMzdhyz+VUGLXnksa+impVrohGBdScBXCMEwlgLdbae8fVEhrrxN/reTIA356BZq3kvc1YqEefl/kcAupiyKsxLCtdo/2a/YJ461MgDlf1MKH41AVF0Kfbqt6a5rug1RJCFq53ECKkEedNKD1WxEQG6REJYl9XeIQ4Zw0V5vupvbaB00hHXOwRsBWJ8o2KUQATFHCYu/SQacRKDMFVJFHAOBD0TF1m7OT3xsUo0fHQy1lAwrdLvdZMpOVD5bD00AVrv+ijEVvV+u6xW5hSP7YPIeSf9G4udR849XFH7EBEW52ogJ37XgFQUGk8JPBiFYuI7vTbn9/qOxUGr/KeLEzcwP5rk7IZKGj6ThtiDw0AcPvOLqy0h/g7XMST+s1n7orEVrBc+Dlt1ojbIcifvJT1D/pzJE1pu46clsP3HDk1rNtYSZCWhYDDqe/JRpWFUMm7VHy2hTyd3l5D0njOefQhQQcpbw9OMyTLny47v2K/uK6iYUxTsAutuVeQ7T1SevJg9rHS27N65nQGaaouIWzBI42f9Rm35PkgDoj+bbVVewJKTfAzNDEZAS0v4GpUqbmja6QetLWbdPzumU+28yaWQNEse1lGwdRQRAwFUsFDUBqDE8Vu88VhYoewJwsghAVZ/sQouBK6Wl4oJQdA4ZnCxzBCfVg15RLHSyQQL4jvF5bvMq95hz8h90PKevbm/StzQ2ct90iHCh3J8/HlFci5T7bQrzPPBI0sUDr9TCU1gnQLQZ0gd/KOF/hZxjmBgCDWspVJVk2KnhhjPAVzYYvk89n73qe1+9M+GSAeRCWxVLgV6Qq/w80m+7SlEwR/yKNi+Z1k0XeHmb0hoJlFtgZetx8Tlllh/CquBRVgFT629aDLL+vAHhHiT6jehjy5JJaF0Ja5inn1drYybxkBY0uk81MBnQXcL6owwBEYMSt/KaBuk40r+jtidTZasWqf6GNhpHCK297+vCOAAkgLfYQzJip93T3IOyZ0MQAcsq0GoCy3SdAuucnKfr9homFzjZSDhDshotxRqcv0aK1ZGAlRIAw91kGJxQAlA0LrVeQNG7JwRLl9DOeblmmQoMdsGVgUkEBVtxGwmiTv6z+90EcSvtwHwvWKJUNma6po4zfWGMm0zeuyTQF/3+qJdPvEblSCCNJyhC3A6Kg79LhM2kHZS6BCUVgoWrkAPrmGEF4YoEYCVjsbBaIX0dhHxybnHFbVB/i0OIdJ96TIAi+Mr9sPafKHcfCkU+5MMi6ghLXb6tShCvPF6LDShvHwv3DFeQruq5qAiAwxCCwjnChZWQhkr9Fih2lLmzSmE2uU1cX0FCDkpxo95Da44vSDPKeLpf/B6FxMMAGRztSGsqSYJxzSZ5EH2KuBGKVNjBxOlBIvg8WNIPitoeGvP2QzaYbCM8rqQQFeuxSgTUxIFFRMBcVxhDMjkC77hRVKzTBpaWrbYIAkCk/lSfK4IIMLZeuZtrPFWxchJwIkmo00fetZ3qY8hzMuXbPHYlgcEqMiKQl0hPKruYkIHBJpGomIqVr9DlQI6b05N7UpDR0yw0o2J5c4DGfP7jDUZ9aQ3ojwdaQTMzzZ48d9O3Yg3kvoZfKhio99FKCStQJUXoEFixAD/k/L5iywCFEyDway5OT12lloRaoEgKSGpsg0zPqJLy1f0+qtAnhLUqfvYUNI1/NsYqxEDmkK9iHSBWnMJUnVn7KpYEPQVrwVhWccurKhC1sRBWhKEVkRWzw2mKG9iCdVHQskcI/GI7SUAddQhImBp+S+Bn4FBTfar77PaAHYMm+s6DiY1zENfNuL7uADmZH0ymrkGNo4bW3vcBIi01RQR44IHDgydiCpT5QCMCYnsnc0cyrQJBQFcRfooSgTow+NTBykhAmavEaoSPlRKA1WQjMZ+HjAg43UaKrAGF9QKUyTkjAkX3iRRIlcJEhUTCME+rBCBYijRrgDfgmum+KoqE/7jp9vHtj3ryVvTHPI0IALS/pra/E8AjXH1UmBoxHnrSGlAtWNijnznHb+8qZEMefwKxmixD4nj1ueeKRWnNxnLqr1EnDep76RJShoXQ8gN59VmXG15pX0p2nzJCoBaOUufUMtcj1fXDRQhU8lCJEKhES32Pq9yGNRL083YORZXarmhcQ6aHLrIYADQRULX8LoG/KIi5qN6BeT/FfaOsF4lPyw7m/CrT6gb0/JT4DEkbCEXRsewUgxEP4WKSHZO3H4ymD2HjuP5wkTVoksRprVeJgOqimjQCsq9kcsRyDeLNAGiOgh1f0Bs3GnThsBo1VoHhScBaaOzKXG4MUMIYPakKV5yVDy3vC4Z5uAIRKMvXHyXaxOAUrJTA4CIXlSoWBSnERZy0BIjFUiMChuuPWivALBOvIgmZ9rtQi430S9Y5kcTC9hCtmXSyVH02ASBu+VJLkwQeSQSSwCG8ZygLKjbrKRS2rVpLgIKLSA5DZstc0ypgtaRB9rMW7klrMQ71faC08eJvrZgT0U64+5ygAl9VId+fVdZw6I8pgpaqEBiCHAiXCvWdrpLXX8wrlTIBqYTAcbuHdRcS4y1KI1rFRbVIe+9OJZrtd2izK51/pe9oQQYj02Kg7xte8y/2lRY4M93sqmj4Tdc704rgAxz5MWq9jUHbdj8djHhoLNAPQ38ykERACO3ReBPBXC8bnHKco2K3t9hDPNGGvpEhaQbwejrJ4D5DPD0C/1hqEeDNfIHlkwoREL8fRQSe4vEBNdYXw5EAh9uJ6Xe+IqyWAMh+VnJukAKp5YLiIAKFgaau++LaXlItmAxAc8mtRluVCPj9BFGbygXn/h2LNP9l2U2K9ncnveLfFCDrGggMRgL4vfJJMGnm7kGqZUCC0FiRhMJMD0lprgKvelzHScj8Y76fw2r2T1p2ItX1SiOUBeM9SbEJWrapArcOTbgS920l/uqucWREVk21GSleCEL7WQUiq4oq2AWO+ER6LOl3JTKgBiZXIQRCeC9ShBcEnaYNuNa2UCNf8hip40gc70NhUa+yx7QKWVFhvBPF9QkUoV8UsSyKMagg8FeyGKjclSAdVpxA9opJDX9itwcAFun/y2OIZ0VNLypAWaDlvskArcM9vY/xJoLjy3Zjzm1lTOjDX+oj7mTBC6qlQyECWrXr6RF4i/o5gYwIzBqxAz8CRCCGh3gdTcJVQo2eLlj1ryAW56GECnMuoEya3O0C5MQw7a0xVD2ubKXQJ1rr2pKSPszFpUAzLLWeBe++aUlgSW6mNHPzq4ucK0iLQqxVFtX3DUbcqQLjBstTlDraLG0JnQQgbniIpUBEt0mCNMVhUZpDHrDSBZr7SkpLZz9epbSjEh5OOAHgiktP5WMCL09L6uHEuu+Ie+qzk0KG1gs8YPlHSY8oUnmuZbKK3oSH3oSHpY0eehMMvYnhOo/a6WeYWgRJmH+quNPEIdPmDQtOa3H+p+mCU+U+utJSav2LTwG8mDuLrMm0okVjqXCOQhAEQP0m9yWcXku4YzuGU7ipFheWoHSNzWNk8o+239cVT+p+Htjt05PT/+tF69Kv/piH/hj9MPQnG9a2eKxlDNBQCIZ++sngL/U1AiCQNANLqcI9D/FY22pLZgLkPC0cZmIIK3aNGgKrempMwX8oQiDeAQcBSP+gZ1G7EApfGQGgJilrYiuYyZMKbcT4oAjtZRpipaowAHIicLoBFY1FFPQhAmm51LzYYxPmfFVjkwRMW8ipxVdoGbUJOIOqxTSxuCVAf9xHfzydULtT+cTaH/NLF/Ko7WuCv4sAJC1fXrdL0819r7QfmfN+rbBeefPXShD3lI/o82QQih8RiGdXrSuwWvTGGXrjw9UjEGRAflrVyEoSKJ8QpRbEoeoksPRTaB0QZKDQeqR8nB2hnAxEXH6sINyY03FCKpExjZHq/0XxYlz/OPswjyvc5yYClkZert/ph4oD0AqCSeVY1p6SbdW4AlXDH9ICvyuNqOu3jdq2sod7KWlWIWJYVCIgfsd4rJWSAcriT7juesu0Dz9Xcv2ra7lGBLLtfGpMOVA5bxjaZMD3ajJQYyisKkWoKwC4cmBwEQGQG9yacK0PhmqalSprjdDiCGU+dT0rCgweAh50m5XnpZNMUWCwWm3VqldAX7gXcZki1AKj3XjKFuzFzQwhkWYZAOZ2MYw8Se9TAwsHbWa5NQzKUol2fEmw4qZHugglQXl+f+6zUpM892jTs9ZGjf8oST+6YqwwLoCqeFsZzoDftXN5MUGSNMrXn9n7dfcEgrQS6T6p/kpdf1TBaI3RG08vQs22FWcuQOFi9RMORvO/hRUuILwcXIgVZag/hNtQrChWXa5DYl7RXZMcVlD1true/yq1BLROHd0Q7ioUtLgq5XxqYDQ1d9LZ7QxLsurWWaTwklp/0di9j6qyLN0lifWiyO9fcwMyfhvKdcoKJlbThRLvmZl2WrUiALACyl1uYmqKavX8ggi0j+gd9ScbaB7V3XRYwpGMtuAtZC+A+pwpKcFlvF1GBJK2bl3ggU8qL+OxNnzDNYhPjYEdm7PaIgztrEEnsEbTWoGvc50AXtcJkFg1ZeTUQmz8TYLy9XdNwhX8PdOGxadMjzE7r9Yu12QI7Tyxv+TlM0uJOyEsB5RrEIWiJEHSZciw2sRc+hb7XdNliMsJ0vTdLUo/t7SZYWljun/Q0fcdfF6MuV3pvsXt9jjLiMXCtpyvDkbyVShueZU02HFT0eg77mPS8Et935OwuIKqcL8xz+Gy3KxVYK4TnuNTFapV4kRYJ4QrgvoReIoqtDTXH6GVPoHoTTL5Wd7E0JsAehPVj49G8s9gFBiMFFva5HEt5ZNZC6ogbqafKpmPojaTHxcoVxIL2bNbeM4yywAAFuUfSqPvDdyuNUBxGlVpOaCUY9m+KgSg6j6h4S+sSUDAFc9BugABttuU4NeJTQaosYr9wrpktudMzx5FkQsVUYt2UfX7kNZnOdSY6xYB5b4koy16PkwSex3xPCsoON1OKTeAeNSoOcAY+AbjpRZWgI6y2D4FCECNUwvDWwIIzePQgswwBCCbQMjJwqXxLnMNMv01q1oRTBj3oiybj0SRoOl6iV3bPaONYg1w/S4yB3g3RtyiV08v4pZgLt2CiN9ieRPLx2PcgqPPdOf0m9/JMPI4va+oxgDgjgOQ+zvlqUDTImElloEKWYC471V2SataIwJAuSXMhdXWNlgrYd94Jsn9T1Oo75ewxg0UYbc5tzrr0WA8/dY06w4LnYloRDlm0d3OOk6RR8osDFrbCuMajKT3Jlygnxmt8JnD2qApMSq8r7p7jrEvpsmHaumgsh0JIqCmZVbdhvy+bjnRMq45zpmey0hTrBpCXEHRgL0eyvMSGYSy91Vm4jHWCC+ia0EIIkApe8hjhCGEcgNSrkUrMhfQ10hde9xkWspbtaZAf9xHc0Zfs/qTDTSPEYG7zRCsZz9sbBCDi/gAZb73ehGSpnGxqsyiXE882oS/qLsTCSLA5owXptMBFod4UWvUyDAcCfCU75L1yelyMQQBsIKfEuKFVoUkNf3fGhOBwgxAFcZeGVT/lMuVGhTs8uYxszZx7gxaZQMO7gjU457DHQNAb4qRkz4AHHtOH7xPn2/55bOIf5BOaIs7oBGBwSjQmKf7FFjcGqA1kz4IccjgZyb3pMEqpQ1MAq8wr3/iqhOggAflbkNa+2GE/2FAuLtUGcuaFRJTz71OQr1Mf6tZI7M/1LSEco6AvY3iKJogJUjwybnGpc1Z0LsiECbZ38MI5iqEO5BGDEqEddWFSNyjYIFsqh+XEYkqAn4/O4fLjVB33VCJEv1biHtWVM24yu8p3UoccwqLUWg9kMcRbYQQSs2fYtzqPjHXiD61QFnDpU29JlmgzOEik+6kx1mUShSgC4wJtx2SDBDKJXGMq5CciwjQ6UXpOhKa3JCdXsSlNeZtK3l/3EdjznANmmigMZs/UCxLUV1IBJp2QIyTCBBxBvFoE/6C4RoU+vbjFAbA5ARwfNY636mIuljYqYOVxwQUEIG1JACm0F/J/7MqKhIB9/U4xuGwBnA/m81irlkDXDEUUkhdhXAliYCQc8xaBRGXGnWLCPB8olRrBQCpxjLOzP/mBD5/bgyMpBMoaySSCFz44vvxw0ObZTv/4pwIyH5H4UR32issPFSWozru+PBKUokmzbwWgStF6FD1ASpYmdT4gsqC+QqfiRNGRNYQdK5/pcHJDpg2oBFi6WJW3G6tiUNfeW2EkDSMj74K4cYzzPGR8p6WEZKBYlWQJMJxrr4SA9moQDR6k5kgN0vf30iJYXCdU/VZd7m6JKE7lkHzrXeRBcN6qq5jqouQKXBLqwEVSyCE7Ya9LyHIgDneqgoMGSdgniZ7D50FxlyWkjUiAubcqroNif5UcM+WN1icVhgOlzKPA0UxpBIBsb0/kbLL5lH9YSKJgOfpFoEMSZjFAqi/tWkJUOY4lQiIvpINY/COZpqyULlpkxPAscOoUaMqVhUYTIElnNaMUHOrx+hMOSsReIkKucyc0CmBrAIRqBoYrGXzMYiAJADm6WWgon6OsuqzWmBwadpSenOSkQG/l1iuNWIR8XvcyuTTz8zy/jIkERCYP5teBZ+55+HCIR69MsL4D+nHMW4o2qWQkUSgO5VP2ImfFxGKW16pZSAJqwQL05YBp5md0uyYqCjLWm5mK9D2n/DYg5XiVB3XGiMS7j6d/Hqj7G81x3+ZBazwHGocjjB4VMjfL6AG/cpA0QrVecV51feg7Lxq/IBne1kAyAlB6CIZqnVgSrEOHKffOzFvNQoCqcVcR1VHpmoiWGktsymMum/UPGHOKaqyRd1nVXVXDvP6XCMCVWNQrLl0mFfRfG+NdVSmC3UI8KZSScBlRSgkAqh+zZwpz7ayLgw6jKwbQLkG+UR2PXAO3gjA+llbRZGhEoFEJQSUYoCDjBOIR5tWTEGyYQzeHGHGmxgHjtibTyXE3EM8TA7iNT//up36lMPKf4VhCnZJUyOhQTMmgoRwV4mpolayb279LTMHUMGmVSYL0i1AzDZC0C/pw9C4UkHBpGDmCgp2oaQdmeUh5uR9BlJrgKsQDmf5QmpiaSt3EoBLdz8i/75g8yFt3+gzj6C7LZ3c5i6wV87YTtecn3Ozj8Goh8Foei1mUFepZaDl5ZWQXcHCQXmKUAEeeulzXdSupA+tvxXk+F/pcSItJffowObq/Xjysy5pTp/i6E6nn+XNQH88/QxG009S8C64oObvF6kWh1l71ZoGwwiJMoizwjFJM/24fN37o/mn7J0GgO4kQ3fSXZ+kP8bkR3P3UvpWyZqJuMUQt5g9XlWxW5AWVTY3hUApnNIBwN6AZ4HHdl9en8PrcyNrUoGbUzbvJSFLyYUp0xvzlPY7mkkPxL88/Zi/uSu9qCuFKme0RYXF9HbXM+ZyU3X1T/3mLAb643lHggBEY8rLqBakawRkSm82iHUCgFRpSCqfCqw3KuJ2gMGWMbtto8JLUqNGhuErBqvfBrRJR2gGXBYABSKgSQimSejLkt0CcduHbxS3SkIPXt/MDaarJKRFwKXtd831Fdw4TFgWA8Nlg8VxahFQ6gCYGZXU42Wg1wpdgUihlQlNV4y4mU8WqonW7yZa2lAvyoN0wyWuTZa9CWAwlo7PX/QQj6QPx8T0AnZOzsh2UeIh8PQHp910qwtnzwZGH6P3JUpxL9WUq6I/5iFYSs+nWgaiEZ+sk6D1HxbHCwC6ZaAsMwmv0J9sO4TQzH1Pe0aHrgK8SgH9ZFoXuJ8TCvq5JsbkEduovilhkUovKs5b4G8NVHezGBaqpr7MV70qKP9yZ1vDJx1wC1oSWuKC4qZUEK2JqGNvU/3+1VN0FetAg8iuCAD98WxeI6wDYp5T05RqVV4VC6nm0iMek9Aen4rEZ86CY0X3yhWUq46VUp7IFJ9E7Jc6j1pKD3NNU4dpdpVp2uV2IfC70osyaHEHGolxWREU16oykumMETAqFYv5WXcNyvf3xwO0j+g/ZDTWQDBHBAubAdoAeODB60dIGukLo1n9zdg9QHMXEtcatwP4y5H8W2CwZQzhwdSEyBs+EFcw39WokWF4S0BRHICpTSAW38TBUqOOvpoI1pwogqpqERDpLan+LIHLIzSSnq39sDtS/iTyNRfC0d5VYE0eZmgRnBl+KhQeq+qHXGR1EPuofN1LW+ljJk6fBQDsOz5F7j9tctZJABYu62I5CxtYOD3f3puC1DSlA6PPvbQ5QNTJcqp3DBenkjSkceiVCoxx0y+1CAgMVz14COG/pALyicBaWQqsfhnT+xbvZWZREak2n0qI2h6itoflDflncVv6Wd6UpvGMCjTNw0CrtsqUz5Dwe/mnSupOAa+ffioRH/H+DjG+KpaEuJF+iobdm0w/rhWvP+Y+0WDEw2DEK5wnoxZLU08STeImcxZHTHyWWl4LBu86r+VKpLoIRWZbd/+CMAw69Lwiq4cT77z2rDBjO8XVtaQe6nZO3jtr3AqpIJ8NgpSYVhkxZvHemPLCoMMscu33E80iAKRz12CCriKs1RxR1gCvH9Hxfw6LgKmgiNuBRgDkmLeMPaUsAAkYEnjr+HlqrSknEmvilKVrv9OvIhcK0xVFaKSThr69P9VA1NEf7OXNTUkABAZjbturlUmI+i4B/YIWHOBM55kF4EYVVYWyqItBDFYT3KlZJvR8xmpQnBfRPpsAwAM3AeChezXb0Z7BXD91gJ5u52lA/M4AXiuC18q0HKfpQVcD2+KpQSzARSlFI8WyYT5ngE0QqGs3nzsKScDyRdMBHnqpcCsW1qLnMBOKue/JTykUYXBVrj2OmgfDHj/svqcDhFDam9Tz9CeN7KNU3R0aPP+4XCiK4HfTT7CYZg4KllEaVyPIgNfPyETfEBCNeAH5yY5xVdGVuewdOehVuFxwVEGqXzCXCBchVVutJUPoMPlx5aK3hH3lGVdrHpgCrHAxssYu3BWN+SQP7k1dh9R7ndd4ST/q/TStAKbFYNCmz5P+Q8/tlQR+KG5Ajt/SLhSat6eOcbWnyLBwh7OO4fY9YRwYjKpWnvxC+uMB+uOBNncNJlopGTAVej6zlUCMkes/ORcyQqHpWC+472GwsSCrRo0aDqyaBFACsiksCSKgbhdEQHVJAXIBLRrNtwsi0B9LV8TelD3bU0SA1H6YL1AFQaSqsOK0FjBG+gkW9l1Y3n31/gZC6LXcqQCpeTUr7noxl+bzthJ4xDgQTcaIJtO+jh8Yl/vOHzuInSNHsXPkKDmOmZk0dQhzVJs9fkmu0upN6/viVrkGcmlTkFuNCI1y1PFKLQRJwyslANzPF3ZKQ+UiB6QriogrGFLbz/3iQmYkVqg5LuzS5eu6xkhCL/8E6UfcY+4blgVBioSPO9FO/zh+G6T1KeJmsWZ4raBq+kXhsZXE06lCuhDshyEHghgMc4zXy4J+q5RO6WfEoaCtIA5FUGMfXOiPpR/XfRyMMAxGiom81PxTvtpC86+6ehrtBkpsFVeInohxGozaz5ckEMQ5XYW0gGJLryoAi7Vo0GYYGOeRwqhZ0C+DK2ubLAhmLokmQRDpWjlNKsyUoJIEOdqTsWQciEP9f4EkZHlfGQajjAwCdhVXi8YNq0CBrKESARcBkH/G3Bb+s/9NxdBg42il961GDYFVZwcy/diFcG/6xck0lMoDGrV9ctIfjPnWi9adDrWJpjcVojkz0LLaDMZChPODXHviytxiwpVuUZ10xHWqgcGqxorI8WsFLZs5+6kAIiHgmylYzfgCkR2oSOO8ApcRL+KICaFYaNlaMwm6U/m4o9F0TOGMj8GULiG85PS9AICmF6FHqDT7A/fjF20eAIN0HIu7Iow8rLc1sxKp6E56pcLCYNSXJCdpMHhEJc+46eU+uwyWyT5u+vBKrDq8SpYg0XaI2IHcmlWtOQCdVKyqkJjy9xrK+knoWde1Eh//kwU1EF34j6tZb+Is64/66FP+yUNDiwPJNq2AGKj1AXJ/8eJj1Ew+4txl1grNZ18VzolnR31vqRz5an9FSQOqZI2RdQyU+6C2V9OLuvz64wZzVgEWWZ/MwmXC0jAYYQh66tqpD7Y/xmQOe/VaByMewsX8hqiCvBkjJfalZFcfhyAiScjIbEbSokAI+GpGIjFniXaWZj0B/EQRwFWLAAgrrJhrxW9oxJWYBE88J6I/ca/ihvK7KZcQh/TvqdabEYg6eWwZkD/z0aiPYCFLH6q4ZUXjLQTzRLorypUqSqxg4XSH8T8H2CBJE09o2232wxKOaAMROHOKoa4TcOpgTdyBxKJsuvmoGQjkNpHLV0xOps9bZk7VJ2NPO0Zgaau9CvQ26uW2Ky+Oq7AIVNZ6Gv6CpRp9qe2oFl9gjYuKCRC5nTXXH30cfjf/3+8nTsGgyEXAdTt3jBzHgblxHJhLrQUjo/mEyRdD8IEPPnCr8WbP404CkAaXp3+7xtyd9BATebUFohEfScAKrQNx01OsC/QDljRsrbw7i0Wx1lEeH6wg484KrQra8Ws4XwrNPQ9Y6halaPOfDpCaas/+rAaqi4307V+BRlBo74eyEgzyT1k8gXBRqUIeZZ+O61DjGChiJP5WPxSiVvopIgzid6MQN5g1p6h9xWH+MQNjhdbdqs6ezWP9MUaSHRGnoK6Jwh1SuCyZ62Xi55+BkeUt1pdNSxhWIYia16ez+wTLqrJK7RPkXKKuRdpvwB3vharBV4gi92xiGDdQ6VkTbWLlnok4uKjjIep41vMTjRLV5jlHNEr8YFTBz8CRmprTf7OBcjOz41isEJSTVMSwxo8WVrb0UOZIxyJeyf9axLgSwVOqL7cK0bY3RWiYp/SXcMWLLDFhmdp7pyA+7HZH/05kgmDl+AKX8OgJUkAlsU6/VK0TkObQ7k2lfzdm8z79JQ9T2+YwtS1NxfHVx3fLfVubs5jLVGvP3PaodapkPvvNHBWGF84ZoLsxvXcLp+f3cHkTnKnmBJY2e+iPMvRH3at8NOIjyaxKSZMeA6VNUxG3fCnYlkG47lSx1qRCc7XnQnVzGTY9pzxmlcRBmqhFcO8w8QwnAHrQcWZGF6416iUqbkPrMk7h9hMgnWO93J1iJfCX00+4oPj2V7REmKTCG6CSm4H08S/Tb0SKz3pF4lDUVroTFfUlYiXMbRnUVKoqmCF0umI14pAhDungYHk6VyxBI/+YfQ86nvyYa6dwXaLiodT5LjauSR2jICZxM/2oBMC0ApgWVtMKIp6vYJmTz61KEPTjHIHBZvI/cc84/RtQxcDMdpYVwLTwhsxKhEGNmzOgP6GyEMUiUEIEtIKdLiJA6fAGiSVDsDh5yhEAUSdgPT81UgzvDuQp31TkvuoBI7SbiitLnnYM0AN3mFVVkcpXn2pN9W29qQDNmUgT3vpTDTRm9Le9sntQFYigXaqQWJmG3qjaO/Q5hnWHcFkwCkrCewNOVqJ05c9Omun45h6cxPjZx7V9U85qPyn8MKFlhoEH3nKrJLVKpAS4B+lvG3UYAiOVaHfKR2Oh+LeKOr5mGaEQE0RXRdLMNUYcsIvYqW0bXv4OVbQwJQFd2bgI3GfyXRh2PtQr4Q537LBIiYmxrerjvwq3IREXIzSDUVtxO8y0qGq6SiFoDFuNtSpU4V32rSoGK87kwp1HU7hWTWCljKEspajpAlN0jHBF0YJbS9q69qsCnqYVVqdPOa/SfQD5/SxyK9TSTGr+6ur7QQiPJfdbLWhlroFq5WKVrMRNphU4E0qy/iizCp9F7dzaTsY0ZPO+qfwx74VwlRREwFxLguVEe2+EokYI1JERhBx0OUlmxD02yZlZYVg8E/I5UgOis3Zhno9C61u9D/4gHYcgAmK8agV59dnpTwRoHLcflGi0gWDBYBycWxWENZmoBIxzcFI7yVc139V4+mK45d9sLXx0Ve2EeJ4JAd4SOLndNhdMMg01YZakTNVVLAKFWIWPtOUmNERFVxKM0Zo0McaqPuZUnme5LzuVYgEQVRzF76H6yQdLiZMA+Mvu6z3Yy4OEzxs5KP/eOLqAbi9Et0cEdC/4wFI6UbIuvdrPXpA/BEtb8/N7A5RmRFne4MlrcVkH+pO5ZSAmrFFJ0yu1MMUtn7SEUUgaRKAp8RzFzbRP+amQPlMNSi6zZlDQjl3DYFjZn2otyFKCikDfpypUSwOZ3SSzOKyVQkq48QRLmSvQELEHWraeuDwTEJC/Y16kvG8lxIdF2bgqTF8yCNjRVrUOuCCsA64+1MxDWkYig4zIFKxULIax3T4JSMuS5qJkuMAA6XNjEgChCDKtFWIOEAHJpkVdzUpkCt4mmVJJlOpO5CIAcmyJ7tYjhOVgOUGwnFiWWi+CjHcA8vk66HIyHbUXG9tZ3o8XEddBuOU35vV1Qfs7SYV/1QoStZhlAUhTJNt9DyaoxCQccUffzj1PS+utyUQlU6v4/b2B40U7CckYavzoYShLAJnusyECfpUHuwoBKADj+rwqS6arcwBRSGTQ8RAu6S+IahFwauecrjvE2FyBu1ZDVvhSstjOASwKpwBIA34rBIm6LApF95vMICFiBfqJJbiKSa99JMbyxvSmh/NAd0PeJjzuYTCZ3pDjx0Zwzfn3O8+/bz5N87NjwwwePzql7WN997jjFgcC9z2RGSYS+hqTkEmNUNS2J/jehOcM8hOIRvxc8xV6up8mUqtAVS2waiEobbsCgTgJvaEXBu3dXYNFRZIUVn2xe1rBJAZAPq+s4j6pQhtTBMvKw1qFJaOKe48AVQFYPbcrUFiFEFxdPvu6YFs8tqKxmwWm1O0C+n1XhFye33/KZYWybGi+8g7LR5kSwJwH1WsQ90L0rQZIizkuCW0LKqBbK7TfK7LXNRZzOdeabsONeW4pl7yIo7HA0c8qwauEI+hyi8gIFzSRSloQAH+Qu0E15tXxWJeTXhPhKWAGWlOWAHEfBhMhwtmB1g4A4k4Ir2ucNOa026jhTZGfw3TLSmgX7KcIEUjrBKzfQlDXCcgxtDuQahJTBROxnSw24rrfYlKMlZz0guHHXNOEiElJDdzxe9zWejQ9BD17BVsLAmAhIwLOuAAXERACt1pKXCjtlfsLD7oGxeivyKWoyNXHEpIT7swyxAYc3HALEu4QraM6EQCAZCqdBP/l3nNJIpC4VJ/tGGw2fxzVMfpzAZJGdt0RI4nA/JmMrDDs99z3QWB5oycXKVe2j+6GAMGyWzJKmp508ylzO4tb1YV/HnqVfLGTpi/bVSYWDQ+i+u0wBaK0PtahkBf3GJHvu/gCzAqmqvVSuHOJuUTNCiO2qa4lUnhyCHVrBU2gXEm1XrM/VQ4RYy77+YpIigPD3BePcMdwQbgYubIGeYIMFBiABQGhyAdgKJ8Sewf3DeuD6Qnqi/OogmP2Td0PIUjH+j0w548iouB6BykCIL4tQT3Jg4NVDT1gZ09TBWHKalQUn+X3E819ksUcjXmO/pgnr0egsZBYAj8ANObyVNWeQvDCefu39wdpalsT5n1j3J67hRuVICmNBTtLkInBRGi5IZP3w0NWp6cCEWCpkk5TtDIGFnG6kOJThAjUODWwImN0HNKmemoyKiMARduEBkB94YS5TkwWpr8jkBIB7f+RkC4otgICMHShrhLXIJYk9L0gKg+afZYFBctJw1l1OA9M1bIMxFwLmlQXgNaM+5zhLH1vDvXHsXdhG/YubAMAnDF6XO7rtHsYLKe/DZ+wbften2nfKpbOiGUwm4nBqPL8OITiuEkX6BFYnvZkFg4X+hOBM5BYO1fDK3VxiVo+4kaWg75ipdyqgcjaMcNUMjaPVYJ817Jqce7eZOf7T4zPqQzpXqK6e8id6afMn74qZFGvbirsVHHjsQeM4S0OImi5RNawgq+Z+xjKLUfbrxwnXIWGzRqkXmeZKxGA3N3PGLPIsGNuV8fjfDe48VEgXLGc98Dx+3oR14Rn8rTGMyeujbrnql++JE0NhqRhxx5Z5E1LY633LX5Dv5/A7+suQo35hLyGxpypAc+3e0Saz+Yst/4PDBlBnEfcN1OJxz1brqBclCiwiGuyhrhGbY5WyV7s+rFhvZtUtXYW8cIYsxo1yrBij1RTuBeTQSUfV5ePJPE+mL6HAKwCHuKFVSdJQQRUAdYiAqZwTEzMhWk4lcDdoWCmCjUmAiu1pCr4mGlCy7IDEcNXq01a+0Q8ADGxiH1jj+c3unkc2mTV3JdL5A/MbsR0Iw0KFt8qji2lkZUT0wvk0F2LobecP2S9LfbK2B+3NkkEXV6acaU37snKmRFBMJanfZmtw4XBqI+kwRC3cr996t2I2j7i0ENcpRpxmAnFipBcBh6mArsQrodxK0pCXxKG1Wb3UeMYYuLztIIiBMpnkSOdT1bhhqP6yVfNvCOHpBYlE8JvyeNlpjetci41TWX5oIr7FTEGRXEGkgw4rkWLLVCNruow1MeT69tVYVoeq8S1Fc0zZipT9XjXHFUU76Rut6wAygWplhTTmiSE7KjFSJKkxe8J64gkS7b1Q/xNVeo1YwhcgciNOZ4K/UZcQnNebx9m1tzmLJcfgaCXkgFrXWOE1SXRMxUKAlBk9TSF8cFYaFkAeOiREhdJBFwyxamtA6mMBB7idfwka5Md/0cCQ7sDqZo44RdoTTjZpEhaAahtWjYFylSpuHSI/LiGu4uIBVCtAOl+/UWMOgGCJTXVRuZiU7CAMc51QZ+xSv76hTAKfZimQe572uRgFl+z+qJKiYttCYcr3aN6H9Mx0Kuz3+OIDN9NoV1qzkCmDBU4besMAODWw2fiWZsesfpr+PRKxkMONqDHGiwx6YrhLXtI2vrkeXw3R+dJ+tjBSK4l8vvcyutNZaUw0Z3ytEXS1A6lLkPVnouo41cuDBZ3fCv2oAhJ6Mg/XXRMo5rbUXEfyrNzkherpOHJc4p5R/WZFYu6ED7NzCrqtkSdf4Ryw3AnOpGQedhF1hxVuBvSikAKcifwGobJOLWiTEouQiDukUNgV11CKMshoLgmEfeYe27hG8iFaTP9pF51V/m7hOCo7cjUmcY6KeZ6FttxDzJzlcMKJfarMQGAUWwLeZIO7jNLmFbnMpK8qONVrTpGDIEgAqZiwIv1OAHhESCIgCmgU67CLObw4zz7kVaVlxhz3GRozuo7ksC+djp2EBiMNxDO5TcwVdz48Pr2g6St/7Jgosud2D6n5hpUZwmqMQSGokOUxs412VdeaKhn3GXeMvPjZppsLXAoiwcQkwSV2SXqVOM+emAX17dVdUsoCg42tfhl5nXfFMIVbbBp0iyYCCzNhzopKxMUS7jm0qBWTmwfdQ/WP0Q75YZejAPLYziwPGbvaw9kRiAe2n0Xme57GxJEI+kxS9vtY/tj7nvhd4sJIJBmEIpa7v1LG330JqpJZ/0xn3wmTQxGA8QtH3GrvN+45aeuMmE164DmbrMCdyLZT8PXPicTws0kCe2iSCf2xPlH0+JnH+kCY2jJ1woi443fzTT9K+lEcUuyaiW4Dsk00FUz/AjXFi+mBV1tG2GBLUUBGWBxcX/SOuCAM3BUsZa4iEtR4LE8tuhaCfelMhcqOztd/jE1/UW/t0qOZAxeVrvAzNKnZb8qUWZoAcREUyoDoGrtV9f3xkJiFTLz+lxbm4RngOrSo47R73PrxSG9EAawrL3ewFgvhDXEkTZ5MN7AYLyh7XfNlyxObAF+CIsAi2hlYI0aRRh6iarip1w4oVP7iEnEJAJexEmfV8ptJTQCOIXQpU4EkgjIarzG+Qnh3dom3HmGKQzmaCtjDTixzYGiybeyJlhokEzlhtTa2P2I3M+dQ/m+5gwQdTiiTrrtwPe3yH2PL01hPpOiTx85bvW3tJyuPuEGIoILbgLgL3hpoDCyzEEGZs9hxQSAiCcREMJlkYDZm/CsFKNUIFt3OsBgxMdghBaU+5O+rEo5GPURt8sF6v5YdZIgEDeJNKQVIav8Kp+1gCSZnOefHyUQvr1rWZRMCufKZyUWnSrCs/PYstSgmbDqRcVVhbVroNqYAnLB/SuLMQCKyUDZ/eABnLUZRApPZyxSEWl1PRusZJ84t6F/UTX/ZnVdrYvsPsVNuwAYAM1yqq47XgxN8CxSMJnHqv+7iIDlDswzxY04v5HG2pzT/Z5dSJJ7jA66d2SlGnQ8eAOuj5EgrmrtFW171ffcKfBTEj+1reJ5TgGsd6GwulhYjhXdidzsRO2s0gHRVryoysQpiID2chq5fc1+xDaTHFBCS9w0BCipeVkDQaSIAKgWhpjTwr5oauwztT6VXEoIV6A0bzk9azjzEAPwCzLkjDxh97d9ZA4AMNtvW/s6jYEkANb4OMAyAV9dvLweK60DsLAzQXdT+vfyFnu/6QqkglqMVEQdOhc3heUNPnrjbiG9O+1jMFrtFexuCDAYC9CfCDAYrUASxnPiMRjzEVcIXhbgSl2BtczXX1Xr/HSECHYNF1NNf7Cs5MofEqrwO+w9l9plP6+MW/SuAbmGOw5TrfFQFYBd/u1KgHXheLn+IduUzBeFVgGCSLjqCJjnp+YJMX8XEgHoMROmvKK65CbKWmwSAJHRSvyW+QXk/brSYqoQz2DcYDYBkH3aAjCLubaey0xkWYV3q3aAMfcybqzjyp9+l2sEQPZBuOqo75DqQkwFl7OEfueo+ZNae02lkXj3BqO2iUh3n8xdckmY7siAvq6Lx6AsRrBGDQPDVwwWEBNRQr+kKii/RqpqIxsklqbS6yWWu4Lf46SG1hlImk0MSdODl7kLiRc4bgXwu2qMgHEdRZX4ROS/WtF3GKsAlSqUOtTz0ixCSiCyRVRcYyzI4KKlZjX68XtJPvFxPf6ieSxCbzp9dFrHOLrT9DmeeHIa28+ds7YHLMGxbhoUvHFiAUdmR/OdIxG8mWzC9DkQq2qubKKMGDiRJjRdjNPt0UiCYFF/buZ2MYw8YY+TxW6tnTx1wCprWrqTHhnQLrA87VlWLTMNX9rOL836IdtuDuUiWlbrQMVgxNcW36rnAzIBwiT0J0DI70/4ueAiBZhMmFIEHzOgXiuoJP42+jH/VtukO7NNxPW5kiOsNcSzQlanrWgIouK2qqKfee/FGY9vHSluv7wx/7td0lam1HSRHTGnV3FZEqkr1edAuVdyHSAUQoIIuOIFWIVxJD6doUkQATObjaqMUN9ZGbdCEAtBABKjkrbprmg9FxXHLZ4xQQTU4Fovytfdsmw5moAsfheeYGFuPxrNMTSb4+S4ZNyB8ht5ESezgtlpPt3tvQFIxY1ZdRjcJkbiWuOmB9+QH2T2H5WYhWKd0u/RYDREuKA/6EnDt5Ru7tShxI/oMUu2eCoQgWSdg3OTlZg8f0SxIhKgTUweS1/yAgIgvqn80WI7U7QEpmDKBjYRkAXEZJusU+VYr5dYvvvcZ/D6xS+JlqsfsIkA57TJvciCYAb5GH1KIiA2GWREEgHzlGpMgNKfMyjYGURcvN8MxG7OxljekK40KhFoH2BYuChf7W67fyeuPHcfAGA5DnG8l0oS060lSQQEBt2Cx9GhzgwWvbyGgAPzZyVoHE+fn8XToBGBsjz3VCl7qmjQoMMKtY3dKa+6QD/tlaYvBIClTX6ldioWt4UyeNnMslUGERNUNaC5Crobin7zNTvNyYd4LYVAR2hiyzTslU4jXBmGyLevjgvIBd+QTtJlYf6s/IcZe7j4/VnYkbV7tLjPKCMYZnCqgJ5+k9qvW1fTdg5LZ0Fu/TIy4HwmhSHbQQSAVFNPpbUE8sQEpjZcG6dpaRCafsqQzPJ1VttvKbnycZtBzX4/d29sGJl4rIxIsX3/BRbn9uPQk3fg0P470VueAQCMjp+G6U27Mb15N8Ymd4AxNco5Xc+5WN85IfCLeEBRm8UU+onf2JzLc9KQ3wP1+gBohcKAlAgES8YPPMQ8RRKB0NOJAOdgUQwe6EwuzyaYnzAdp26hWcuq7jV+9DE0CWAxt7TLVCYgltjCpBfThZvMDAGCCGgTS0YEVHYtiIDGuGOuV9sV/ytIGp5GBKyXUIAS2sU2DzoRcLn/mGY8tV2SAIq7D0scGgDRXeBJls8ZK3Tm0jIaCSKgWis0wkD3YRZsk8i2tY/mRADIJ9HRuxoaEQCAhp/OtJPNZUkE5L5GhMU5OvJWdQtSFzCvz3ItYp+RRCBpcCRZkHF/MicCAktbGFpH079V4pfmjyaHY59DIaIkORhh+UJLZZZQ0J2uljGoO12tKvHSpkApNV/eXjt2Syg1YDKv9pCkQYAS9E90oa2nAqTrouKOsmpioL6uRDaWIixtTb8H0/kgOo8Umxpmz0/bjt1f3G5xe/o98mTxGCJlagiW6DZVswl5lrCk7CuwCgAVyIAK0wOlIOuRcNnRXVTyv+MWkz7vpjAXN+lx8QBpsLY4h5INiCz2Jr6N8anZgExCkITMsjSKcVMulIP+IvY/disOP3kHFucPIAjb2Lj1Ymzaegn6vXkcO7wXTz5yEx598J8RNkYwten8lBRsPA9BmD4EbMAti4B6XnlZPNW6y99StQhk22RyC6nVNxQ7lJI9TtupcQZ+PwEPFJlDKDmJwl1aO/XeuIiAkTWIRen/SVOfP7lva/+1Z2VIxVCNpzeGIgEpAQCZcpJMZUZolU2NsjSrGSnP5AuvHC5cedQH3u8llqDKBolONjIioL6QgggIHzyTCHDGkPo5Jno/ZvrABKSG3iVwu6DWDMhThaF4AU9QPaqDICnynOZvx/OS5ioRYImj1DmA1lGOpc30dd755HZcebqtCvQYx/GldMIfGe9KIhCPxAhm3Y9mYbBfl5W6SBw/n6N9MB1rdwMkEUg7Lz5Wghe7YhTFHQCZ5o8DUZOofqugP5YL/IORcrclII1FGEY7Nb8jlAJTkX90EbobgxPqEmSiN6mQL/FImsINIQCR6RaN/wtDgsQ+xRXqRMQ5UMGKw2r7zb7igsw1LixdkD4Qav0PCvMZGUAnl0DCx+1jBNEAgP5EegNHH6FvYJzpBVzWAYEq74Sz4m4mwLoSAKjCcOyqRKzM01q8gKrYIgRuQE96IMYXt5hF2kV7k5QIUiGClK07UfRsmoqs7DwNwyIk8+SHzMrln8a0qRaptO3eO/4Ks8f2YcOWPdh57kswtek8eF4+p2857XLwJMbc8Udx7PBeHDu8F4ee+C7APGzd8Uycc8FPwvMDS7hmPP0thcJJy2xHuQFFhMsr9BSiUiGhyCCqNSduMjTmdQGdByz3PhBjq0oEWFpHIJwnXIPM9KEVs/3INN8Vq8WvN2LOEK9jgNh6nvtUw8pjAjIioD7gpmsPACkEa+bajAi4tJ60u5CyTVgKlBShFhEwyYYQ8FUNuK+7cEgiQLnhiPNXEbxNsiA7I643ScAL4gEstyDFGqC3I1x4OG15qQLKBYva3pxN0BvPswUJIjB6dwP9Z9H+Ba0gwoHF1NF4srMsiQAA8H7aVzQRkUTAi5VJW1nYvR4DPG5tVzEY54WZU5a2MnQOCg2hnZXI7zksIwp4gEIBPGmwwv0CUadau4Harl1MKEwsbgtWlElm4bRQCkdFsQ/DYmmTO0Be4KlgOVCzxsjaA+I5XQsXINOdYwX3ZGlbelDzWLX5oX9WmrnLD2M5tUVHC/LmAsA5aYFAb+9IYbO5Z6QP0/idtJQdZV6DLsuAqs0nA/uFgG7OC6oAWUIG0jbZ+SgyZaxR1hBU9xzlFKIvu2qv0rWxzwwANsehEtVSGETAi3VrTGtGv5+Jz/JK7IalRdzDxfkDmDlyP3Y/499j82mXOZ9P5vmYmN6Field2HX+y9BbmsGhA9/Hvvu/iqX5g9hz+U+j0RxLZQxTuI8JMsxptyGWcK1miIDfs109qWB0MgBZWENUQz9PrReamzKH7pmgtC8lAiJmsB/bKUUVrwJ1jV/LKu41nh5YOQkALVhTJjtPuLCokflRYmW68QY2kwYyNyKzbaRr+wURIMkGRUqyl9Jk39ynTXgaBBEQgbqqv77McgTFHF/cHxvEGhEwCUwpESisCUAQAZmJgNaSWH1QVh7ki29zLicCcrwAGreOWkSgm6kjt47MSyIg4DdiRH165Sp0zxH3JsmJgECw6CFuF9//4xdwNI8WLf489x8lCKcaMEfCNMsXIAlZpbZxsxpJEBiM5J2aRd/KsLDdz8iyTwtZQ2Bxi2LlykBl+fpRBe3Prn+vpk/hClKVLC1fkAr3jc4glxkPd5ztVQRZSt9oppgMCALRfCBvJ6wAKuYuy9XuE7fbku5A4RKhXYAcQC7Eu4LjKf9vFepxqsuIZpEeFNcDsOCyGCn/U1l6BJJQISBqILzehV7QbohXXBAj0wUoWNaLIgo3pcRnZDyR0Ho/se9baDTHsXHbxfl4Sp5HxoFWewpn7LoGk1O7cPf3/he+d9Mf4cLL34rRidNst94krfzODRcgwHYvBgBvkFhEIFX00MHF8rjseYianqxBpJIC6j0j5Rif6UlUMjiJgOGe7CIC1LpcE4Eaw2DF4dmaFl8U0iKqBnraNq5/U7n4zUqERFtZuMt4qSh/a9IH2ziveLlkdglNkyAuxOiDmrCH0YqaqckGOvu3zHrUi+0gGUWFwuj2dDMWKdWEub7d5Us7cpC+B7c+eqYkACaaYYRuN90XjOYTYtxOyDShKly+7l6fwetnAsGyPdblbRzdzenHhaXNzCksAKmZXPrLE8Kx5t9d8GhwH5ULS1UtjiXymCchK3VNUtGbYOhNpp+lzR6WNg8/RSxt9PLPpuyzwr5WBa58EmSue7m2T35EGsisrZl28mRZH+TYRH565TNsrIB8nnj1Z0ugvXkR7c2LaGxcRqMzAPOKbwAbHYCNDjRXIArdXX10d/VJAmDi+DMGOP6MgVOYjZvpx3VdScDSOcqwKguoaUpd8Hscfo/T/uLGvKMJ8MZvR4IU3tKPFqCqVLg2CYB2KsunjT63lSVKOU6Lx1BcsCIlTiEfl6FB5wDnHEeO/BCHnvgutp95teb+U1jTwRjn+OQZuPzqX0LYGMUdt/zfOLz/++mOmKcfM404JYib7wvT47G0tNMRJ99z0wIQNT3SKkC54JCKRIdwPhjT10XuMSt9OWfMqhrv9Gh5ChQMi+Gt+6dGiqHuhMz0Qwi7HuGiQglGVjosikDI+gDEy+VMwZntVxXkBRo2a/EwX1DPiAeoAlOr7jo8ofc7g0KpvMDmuYRpkDHtO/3HMQ7lONc5rWBQkUK0q28PFxP5fIwcyPts3DoKns1W339iu3bMeLOL5UE6AU6M6Y6/TLgFjdnSjxcpgps57JiBxe5J0Bsw+dRTLkMze4DuNHOmPQXSBdmsWqkiLVDj3C19T7mP/A2kTpdp9NRc5GWo2s5sHzeYZRqvguVpL/9sTD9rCfXarY8p3BYJXauEJATIfipKyPPSj8ivX+i2MfQAkD8LK6xEPJhIP4tXLoNv7yLZMlwAiB8WMxHPT/JPkCCOPER94mHc0pUf/7QlhCMDbSXiUf4czl7ax+ylfV0TrwruJb95lSq9RZWNgZwMmJDFzVZiwanwrFrvcXYMdZjz+lRdnZd/c08XIsW5ojYdg6FaPoRiT9QSYRxYnD+Iu27+M9xz68cxPr0L28+8mhhk9XE3W5O49Kr/iA2bL8AP7/gU9t3/NXCekO2tAmRcjBPpemV4KlAKJUrWMIvB+QNuVXs3FZvaviGJAPeYpsWPmz44Y9pazgZJce2PpwABqHFqYWh3IJnHV3X3ET6C3HbboXzoTTcUT814I89jBwHLdmqJbiLwh3IfKrwm0a/hZsMDX0boF0EjC6r/H1A9eDfLEsSiBFwUZxLdmN48sspx7hfogrwP3O4nz2JRLXbA6RaULSDBUoKoY19s87YR9K7U7fdLkduezsIEcLgFCeHaDCQX43MhWGByIfN6DElTb7y8NUkJAoDlrRztA/qFLpzG9ABiAqa2jNpfRYtP/VYkmPFd1jzJ2w4bFyUDEzOrQhqkPFwfGpRnSQo7qtYW9ranGiiNriD+Xj/dXzUtZ2H/nv5/VcvFlk1pDY+lfv4uDiI3g4yW1cILmYWwUVZJLPtKiidBv5lK9tEiPS8IV6HJ22hmVfYsappfVYtPuKWp84r6nqjBpCr0zHbF4zBhzmOcuffJ7cY8zBzb805pxYAUco3npT+qBweLYOAktOsDRN0lPLr3a9j/8E1otaew58q3YnrLHjDG0mnMVGwprkFlz2nAQuy56E14ZHQb9t3/FSzNH8Dui98IP2jYc784j7GOUalCrd8x+1NdB3OvgGwsy/lgBREwU4VS6ygVLCzTqpvbTFDbGO3alO576hCAhHtI1rFqb/KjVpl+FRg+O5DwOc0mKDMwUBABRgjCZi5hKw2oONasqGu2EyQiMf4nxiEgxqn5czPbMiGuTUASAcciw8zUoYCddcEkAur+RNmmQCMxajYmj9FuR8KNSLluiwgJ4VK6HBVoFJWsRuqko050Xo8W+gE9SFjFHY/uwHnbD5LHhGGM7kK2yI9EwGL6sCWtBMFCeh4qDaffL/DR5cUCAouBuJU9GyGXREDg2MWQ8QJWJiGkvvaNOfeE4g346oRlClVJQgY2pIbcixRhYpXriilYMb4yLfaPKqwc7uq/axBELIjBYKJa+8EgPcDzqz0wPM6yiAVqKXe7ndxf8jz57VzNn/RsyfX4nvw803c4CIsUMulrEAKx670U953SGAuLgMifb0IqGhzuQkUxIZrrIcv3uYiA2s7cTroHEZp/MWYxXlHHoJ/Vbxw5qN8kNTB4Zv9e3Hf7X4EnMXbueSlOO/O58Pz8pjEOWuBljnuvrDlSw84Ydu56PkZGt+CH3/807rjl/8YFz/gphI1Oas3g6eTGOQd4Ao60Dw6e9scTeEET4eiEdl/E7xhbgcGURt8eqpdlJbTkn5UQgax9NBYiEPEB2bak5cPr2u+OSQRERsGnSnagGqcOVhUY7A04qeWmgn5ZxO0Ji9qWVGxnaPslWTBiB0xSIQOIs01WalCPpeRGMeVxny7UpS0yggi46giUWQSiBBAWgJgD5qShTJDc9/Ly4mvE/p1aJME91ElHyTpkav/9LpeBpyoRaHxnBN3LUjvzfU9ukURgtNnDoVk9QFiFEMqj0ZwIyKHF6viUTBtU5ggDwSKTNQQoLJ6RxyP0NtiBwzPnM3QOpH/3x20ikDRyy0BR0J/Xr2gdAKoJ8kMSBCAXHFYqdMpnJzu3RjrWSDll+VyrfRuaRTNjhwpu/aG0LRqrYlDT+iMKDK0VZN8sP68kU0PmAh+MpD00zp5HE8BEp1vcPivcx4U7T0lcQGVrlCrI+MXs2GtmedIHtLA/d1b6PfGgspETfxPa/irwFUHeDGcSmuHBaN65ptFXcver51R91a3gVQf5kMHMpBsL3ZdKHuQ+8X6o2auyv5MQCIhgazUwWAYn+wy9Ywdx722fxPj0Tpx7+RvQbORzuHWPTSKQzRd6lisuvykLwaZNe9B69i/irts/gdu++X/aDQrB8KwX/jrC8SltK/ey+bcgZkr8hnHIpAuoSFUOYPVEwEA0FiJY1F/upOXD6xFusYMEccuoIeAxMrlKjRouDGcJIFNcQhNuWZFrECWgrGKbpe2n3IWIdJ2qWwSgEAFlopKFnYSvfehpgTnO2IQiVCACIkuQ5hYkhR0HEagKoh+W5PfLSQQU5DmV7YkuJwZcy0Ajjml9ry2JAAAsDlKt/+aJeZII8GYCOAQALTJOXeQL3IW8PspdszgsVyEVMxcmCOfcnSxuY2jOFvcvC9bIwkE2EfD6ihlbjRsoeuyGeCTNd6Aq/MHaWQlkn31CgBZC91NwPZM521elYik5h0IQgOHvUzLsAap7XlBg+TIFex9IIvp9MeMFXAHIfuZyFM/krkDBXH7s/Jnp99g+pS9D6ARsQkjC0cYf0HUWhP+8mrlIktEAWoVg8/zu2gV06lCzvZ6swXbvYYlNHCgCIDAYA8L5/P/mbGb1aDE05pW1sbuMH976CTTaEzjvqjejwfTiBZbSg/gtAH0t0TsAObeMjWzFFVe/C8dnHkp3M5Y29Dww6fvPskM9MJ8hjvu4+7aPY/74Y9jYmZRzqubO7CACJtGOQ4ZwQRfGPUJBST5DnLgoot4Sme3HZ4g7AfwlfUBSPngKYr2Dc+Onsp/pGmP4isEV3X20tuYkYAozw26jzkG4EBXm9rfMoywtKKZZBXTtqCACJAEYwk+/KDe7mi60tIIwQQScsRAFa77lTmUGFauCP5FSNFhKMBilhfXOIY7FLfYx+45OY9O47RDtBQniLv1Yxg2OQGT6IQRir08vqlo74lkIFjyy4rCK5a3FhOvYxUDziPsmL+zILQdFCLq8MCORAItsQbD4AGAlgbMia46vWF1WAr9vk4ens1uQzPoiiGA/FTLNNI2V+zMDpIHKNSB6g/R9i/qZ+11BYD2gGDszTX1Z9qBACPFKvAF1Di7iDJT+tILr46kq2n88TzWqBgnPnZ1+T9xPj0dPW104ZJIwmERAnQuDzLBiFfPK2lu/q2J9o2oXeAPDysTy9mkD9STZvsie//SiV/o+Vekj7s0g08WMPp7fAJUAsCjBA7d/Bv3lOVzy/Hch9JrpEmHK8R7KU23DJgBWumDjMWmEHWzacpHVj/wtzHEEDI3mGBZmn8DG7RfDi7iVLY2Kh5NWZqU/f8CRND1pCZDeAqosIX9XZa0UyU/UGjviuhUiIH6LwWiAcCHSrwvQiIAgAF6UIDHJwFq7n9b4kcaKlmFvkNgvryNVF+mjluQa9sJtJKO2t5N+fKbfvDCbEllz1OO54vsOwBJ6LfbNiXEr56uynSQVwggwpLa/LHtS/r/dTgppBdWFtZSvSjNTQxIs5TvVtKHhvW00mulifnhuVG4fa3fRz4R/v2Wv0KxfYLIVKRUBjbSJ7B0ueH0mXX48qn+exgm43IZmL4qwcCbHwpn0/qOXcixuZ1jcXi49ewNuBd252pW57VgpJodBdgxVNKcKZOrFOPselAtcK4WZ7tMcO5lJhBMfJTWnyPaifrQUo45+1woys1CQ+ognPpB46Wdo0pRlK1reArCYoXH2fOkhAOR9TLo+eALwkikoaMQIGnGpew/zEjAvKSUZSd9H0ved7lnRth6ibT3nczW/k8mPC3HIEJtueIaWmtLK+oPMGqasC6rg7ateVkp/cSP/WOtXlGmdTaXGICMDxDjkMVaGOfWf/M/E1zXbMi6C289VsJRnSFMJAAA8fv83cPTJu3DuM9+Ikc6mvD+HBlvfoAwtK+Kljd28Fm63EWO2rSoO+SPmGJ3cgYXZx9MhmK6+IhA4Uo9RO0iFfzUbXNL07NTjlFwS27IBMzwO0g7tNLSD0YBUuMWdwJJBqMyMNWpUxfCWALVCMFmMCxYr1qC+FFRQreKqUgjTUlBgJZBIAAZDm02NkTn8LgR5D314g7hc418BMkWqMlYWx+CeUjwsziwCxH0Zyi3IvEfqva563+Wgyl2ITLegOKtBxL8/DnbJnNy+nGUn2TC1gKMzo1ofSYPDX6ClH9X33HL/KfhpvEFuCqfchvzF3DLg9ZllJRhMxtIlIh6L4c/r6reZSyOwbtppd3OC1iF6/IMRhnCx/BkKlitaB4bV1hOC/jCCprZwrpGLkN/Tx1EUOHsqQIwvVuplCQErd3fQv1d7LhXD9Nnr5mrsMAvUFVaAUmSCO2+4T+gH+b7Y4QIE6EHHRYQgWszH5rVs5jvYmUvcwWN0wTKR6letfKuuCWIecNUbcUEQdrUIn4DftbXuKuKGYhlQBWPFKqD+rn4/dx209hFuSiyG7vajuhBFQGKMTY1fMKsyq3PU7P578ehdX8aO3S/Exi0XWtel+fkLJblwNS2xCjBjPdcs+1T2Om4QGMbstSj7e3RsO/Y/ejMSxnNnoYgjbhgxZhGsOYZ6v7xekrsLy/FnSkMzFpLKYkclMjG2MQ7ELR9+V3/uOUtrCPg90y2JsAicwkgAxOvo61nTphzDxQRE3DrCqem34gHoSYARLJh+6Yk+zfNUIQKwXzjVnUhMVlbwrXGdzsrCrsBgAoUWAKqKsKO676qIAPJJiqxULP5U7lkaR2B3HXQTRC16Iho5wDF3lj3+xX4DHiGxM58Dy+lJ4tFEEoEkKHeZ8CKQQrPQ+so2Zh7oXl4cjBL+vT5D0so6iJjlG720M5L3jLcSSQQEjl4ZoXEoAMDQPpRuo4hA1GZoLHAr4MyFYJnbWk0CKkkYZv7VXOK81cviLMqFhbWOLziloRBWASmErtQNKPtW76cVdFkA30sHMxgE8PwEEZGRR0PmsuO1i807ghCInzVykAIvaydcgRKqrgAALggBQQYAgJ2TuhayH+ZKhDDXM0gXEFXYV+dfKXyrlsQK91C1eKoZg9TzaJnLuBhPTnhVuIi8mPO0VKfZWE0iwDjAsvNHBDdiA4Bn7QVZ4YF+vwCgfYQj7ncxc+R+LOzbiyOP3YHJLefh9D0vBueMXPs5IzT6MdcFfNMCoP7L7P2cMadyUY1pU/tSBe8o6mLQX0KSRPD9EJwn6Hfn0Zs5juX+LPrLs5jatgftsU3aeWTWJkXg14KCAwafqHu0GiJgxgjELd86LwAnEVC/a9SoghMXulZBsyyzkigMnikTRSUiYG5bIREAaG2FnNCIVGdmXQGL6JQQASuWgXNwZfCSCEgLQR4jYAZfCSJQqT4CcY80OGoLsJhLbYNFpLKAK5MIhIscg8waMP5QTgQG941j4pIjAIAETBKB9kgfi8ez0pXtWBIBIM8UpGnS1GHHoB3ciIVJhTdAYRBnsOgVZhKKmxwI3SdY2jWQbkdFmNvJ0DpS2gzcU/J1V/i5h8kko5nKV5l5yrrnJ1PQX4WRzhz3MMXXVgo1s5WKYTXUAmI+jTq5drcKzxCF/bijTocAyybqZjsdYFmgsdD+x2qgPyFls4wUJF3VEqr0vZy+qEypLB428wecX5CSgcYtChkgst7omuZ8uxDWrIqzkm3ZfeXn4aRlQGbwMZcuYY2MHO2pzHvisk3rpcjcYyo2DCuCVPgMgEgxuiYB0JsGeJKgf+BJJDftxSNP7MXioX3gPEFrdBM277wSp1/w4/C4WIOEUshYF5W1SXPLZcb/sB8BkgDIf1AZQvB+Yt9NeHLft7Dzgpfj+IG9eOiev0d/eRbc8HHrLc5g12XXyfOY40oChmCRCAo2kQCoMl8wRqd/jbldCJQ6nFIQDWvNX0ck8JCsY2Dwep77VMPak4Ayd58MLn9d7X9BBCr2mbfByiwCgaHdN92CMiKgpguVRGCFrkHOoGbxv5IxSB6juEqYRMDyNywCUS/AMqka99I0O0ryRqRAc2nShHZq9vsbJREAgKV+ulqNTC7nRCBD0rRz+MuxFxIa9y6/W1BfAECwzMi0fPL4eT8lAAVIRsrzbh67CGjMFk/evUlWqbCUN+B5qkERfFolZRx3WKbKzqcsRrI+xRrOr35PNflD77/gstbS0syM6Uc7tRiTEei7VudXyanZ57AEgR9J1b5jO2eHOo4lDPBti62KZphLskUuRiyzPPAy0pBp/Pki3RdfSrc3puhUpwsXp2r2qZtz3xf1foln1WVtS3yCCMCw4vj29nCRoz+mrCkOgVDrW1EWaRl/ClwcXZmF1O3iWKEwMa0CwvoUdYBofh5H/+nvsHTfXsSLC/DCJsa3noPTr3o1Nk6eh9bItHad+XUINxh9O/cgC4vq23MLwlAEQLSpqNADgMNP3IkH7/kSTjvredh25tX4zo0fQmd0E3ac+3w025NotifQbE/hBzf9P+CJwcQMgdqL6KBgbf1WiI+lYFR/SzW1ubpdXD5BBNRzCwKQBF6u9VdSrNaoMQzWngRQPv5mkwoEQG53uQtpgqrDVahCFdz03IogkxEBNTDY1GZY7jcuUlLVQ8cgAlq/gO4aZFyXlY5NtVgYHEa9hvRg474phVpUK4xrfCwmch8jy6LgEDzHH+I4tsfeJwiACR5weMvpLKnWCkgXmWwcJnFBushSAnyaG5o8VTr2XrFVIJz1CzMJBcd9WXysCHPnJoXWgWMXMKnBpPJ3m+Ae4PerLwDhMs9zhFfQPAlI7dcaCbllBZuesii4P3KuW22MgFEhuCrxmDueBuc0OukLJFxxyiDO440Us49WI90fZIHC80u2T0rVc0KNQSBclfrzqZDfGCN8awDM7k6Pn/oBzUzVecoUWqVroMOSJgR5874L977+uDqH2scBqqWZfuYpK4Ja18OVQYhSbnixohhQptvowCE8+qd/hGRpERuvfAFGd+1GZ/tOeH6A8X3uF9FMEmFbOgylmnTVsXPri+2SCBDxe1T8nNxnCN4zR+7H3u9/Bpu3X4pde16Bxx/4BqL+Es679A1odaZlW+4BjPngcX6dcgycoztzEIcf/x42nX45OmObkTQ9yyJgZUskxpP2C5KskIosBxHwjSrFSeCl8YnagGoiUKM6Tow7UIGmfiWZNUh3IUtjXU4EqOCdvNy4ekK9G0kENB/SlAjkx3sAUVCsKiyNAjFZyMBhQ+tuERX1GlxEoOq4ZPoyJW5CmeDUsuzegMuS6ur2cCl3CQKA6XtyIjD/3Y0IL7I1k42RPnrHU+EhaSeSCKR9p99qpUsBNV825fcPpAugSQT8ZcUlQ825nSFYZrmrABEv0DgSSJchv8ssItA4EiDqcESjHDxzjeABt4jA3Hn6tbpw/HyGZmZEodwdTAiCMMz75w+4pdVfiYY7Je0sD/6rocFT3Nik18kqSNCwlhiR1WfQC9IUvUvFy4JI4Rt0IvR7IUZGcm08Ix6wiU6aTH9mvkP2ZxYDY64sQ6JroprxQEkrTBGM5TyRDdqH6e6TkJHaazGH+A7uoypN1HsfLmRkYMwQ5ny6PyouQZ7D9d4KkmDwI9OKICsZx0CkGFkH8RKe+NSfIRgZxdlv/y/wm7oFNm4yWV1XL3pGK/fMdVqsU8y4r664Fe7pxIFcn0EI2Sz/HTjn2PuDz8APmjj3gp+EHzEszR9CszOJZntKHi9qs3h+gJmD9+LxH3wF0zsuQXtsI449cRcOPPRtzB15KL1P/UWcc+lrAdjPiaxPVMXlmLJaZAXSuCarADDWeRZxJA0PXr8kC9dTICQg5h7idcwRvZ7nPtUwHAlwLeCEAE5pBsiJrKIfm8t6QBIBlYAQFgGykFmWP15MOqZgbdYMSLd5YHHBal0hONiEHJepCaCqCAPy3mnjLbudqjDGjXzGSrYgqkZBnsHIzmYAAH43kURAhUkEgFyYH9w1oRGBQSYUNCe7kggIxG0Ob96+QC9WXZNsIT4JaI2eSAkJQCvSIxCo5IDw4Wwe8+QC7A3sKsTtR0NN6+bCYJyDFxRhAoCZi4sLlZnoT7BKVgQBvwfb5WYISBP5CcroIwvBCQ0sdR7z2+rE3b9tcTT2K1Y3V3GzNYMgBeYjMSShFyQ5bgGNGQ+9XbTGXAVTn+Fe+YMg3H+EBcCFdivdv7iYu+i4qgEDAB8o51ZiBGThsg499woLR/QkTTpU10cTLiIAKJYB9bRCMZLo1k/1+WjMc4sIAO7YpqIK46rwbFoHZM5/I8OQmqmIe3lAcr8TY/9f/i/ES4s44+f+M1i7DSg/4cj+JDueyQrJ5JiSGIO+kVaIMXieD+YF8OEDvpeuYcg17RQRYAmn181sfZZ/g7ZEs2y9P+v8l+O+uz6PO275Y1xw+X/AttOfhUNPfBczh/ZiessFAMsVVTsvegX2P/gtPPnAv+GxvV+D5wVIkgjjG8/CeVf+B8wefgDHD90HAFIAFwQiWFYsCNmzEPMYhw/dhcefuBn93izGxnZgbPJ0jI3vwNj4DgRoyt/CGrspRxFxByYRkNkK8dQgADVOLaydJaAiEah6bFWY51AnGAlh3lOZNlXa29BumBYATdAWSnvlBVwLaAQlIwLcy4JxlSrCkqyYxIB0K0Kh8GAWNpEBX7FerEwrwKbAi2ifTJevJgBsuItjZjdh7XCMkwccXiaQDMY4wnlBfkrcgvp5+kaVCMRtwFPSUZqTZ7CkaM8IchDOscKA0dYhr3B/fh6GuF0s2c2flcjFcjDuJgLHLoS8L4VVizMIDZ8PIgi/7Fiiyu9q/OBJYi72PYUWNnthR6qlXGWxtbxD5Vz+8DUY/EMpI43HYvSXfTQ3Lhe2F1YrYbEqIqq9fv6SjLTdZENOb45MQLKdyBy0oLx8SpAwW8yOn8wl6VCpMdI8PcsYdNu43NY6mnclhH2zWneeLU0ZCxE8bK4XIpd81LR/aIsIiHtQQASs8zL9O7+QrC23LZ9qXQJzPjr6pb/F0r4HsOMtP49OOzWViHlu7FEzxU/ej4wD4AkOPf49PHrv19BdOmZfhNGB5wVgnp+SA+bD8wJ4no+wMYpLLv8ZBEz5nV1EAEC3exwHDnwPve5xnLv7VWCBftEs4diy/XKMjm3H3Xd8Et/91h/h3AtfjbHJM/DYg9/A9NYL8tNEHGPTZ2Bs+gwkcYTjh+/H4vEnsWHbReiMb0nb+CEO7rsFvZnDaI9syI81yGK/P48nnrgVTzx5K/r9eUxNnY1Nmy7G/Pzj2PfQPyOJ+wAYpjeeh0su/5mS+5XBlQzFvDWhnS2oRo0qGI4EuDT56t9GhWAhmDmtAMaxKwF1DpKACKuAUJhTGYiKxgiAShea9k1IKkMIL1aWIDV/v9ouUqoOiv8zYuBMWwqUEgGtKXVfPOVbBrFx0jLgdxNEbTuLUNC1qzUKdB8YR/PsNEddEMSIsuqirBkDC1kl02YiiUA6zvSbcgvy+ooZv0vncU+aORGQxw0KTO9IyUGRlrwxy0qF4eYRr9A6wBIgbnH0GiitZAykFgKvW3zSYxcytDI3iPbRTPivsGb4Pfu9XImVQLgjPVXy/59ISIuV+b8gDEPMG1pwqpzXsn0FQe8C/aW0kRfwStMvy7Tw/mRxrqFuXz+5R6rd1QYFnXUywX6eviCebW9sygmNr7gULe1OX/Lpb+UqcS1+RrGMmtZfQCdaWpC4I8hV9G3OdSJWYDCqb1ez92hpQ4uUaA6Ll7oOxsYco1pI5x+4B8e+901se9HrML71XK1d+xBH1GIyA5maicyLODjnOHbgHuzb+09Ymj+IDVsvxK49PwHmKQojngBxgiSJwHmcfkfZdxKDZ9+97iwO7v8eugtHMDq2zXGxQBwPcOTIPdi//3bMHHtAaus7I5tw+pnPTc9pKPk6E1tx+Y+9C/ff9QXc+/3PYHLDOTh+9AEcfvL7CMMOBv1FDAaLGJ/ehc70dnh+gOmtF2DDpt3i9gMAJjaeDcY8HJu5D6eNXA1AIZCBh4Vjj+Lxx7+Ng4e+D8Y8bNt6GU7b8RyMjG7R7sdi9wgeeeh/49ix+9MxUr+vWbvHsY2yWFFpQ09VJGBI1nHiX89zn2pYlSWA0rprufUzSDcBI+uNc5uSfUZ1zyncJoNhdRcVkggw/TjuOQJfjXy7rnSh3GeA74OpFoEVEgB1m8zLH8fgvpIyb2AQAUXQ1tKWmpOJIAKUVxGVrgz578k9Y6fUPtEa3GBZIQKKqdzv50Rg4kGOY3bNGYn+sluKidoc4SJhfeDINVZETEASuNwAirOtFAn/4bytZdOOXQDgpVr/vA4BLCLQOOYVFhkSGIwn4AFHfxqlqUePXpogWKwutQ86DOESzyroDul7kiHo2iTyRLhgFgnL8tF3aE3pg/R/8wra+jf303NHuvv0icEQ5N06VLhN+SkR7m2o1pEXpgfGy+7lQRTyYl7arjWRxwX4hE//claojCf0D8DFc8wBPvDhNW1hhrfzbd6S/cL15tKXpzOVk4Hl+fyFWjg9/Z54ML8P0oWHA2Z2Gtkmm4Y0jb14FowgV/V5U+c69fkXVYVjIoe/ENytGgKqZly1BqlCf5hvswigWAKi9Hk4dse30N56Oqaf8RywXl5ArH0ov5aoxdA6nv+WLAFmjj6ER+75R8zPPIqJjefgGc94PcanzrDXFFUhRyimxD1bXj6Gg/u/h95gAVqZyATgnGN+/nHs3387Dh28E1HUxcTEmTh/96uxeesleOiBr+Dhh76GTVsuQqs1aSmvvIgDQRPnP+ONmJjehfvv/lswL8De73xSG0urM43LX/BueL5tUQCAqLsAL2hiae4gkpAhWErJzeGDd+GJx27C3OyjaLWmcPZZL8a2bc9EGLbNWwDGPIy2N6PVmYY/m0/8TiJggtT+00QAJ6hCe40fTayJO5DUxGuVgzOrgJpBIBNWVeFablPbZdpt8lhiW5FFoZRtIxu34T5kFTSJDY0olS40zIjAKgmAes68QFeiCeKSCBCaePPeaXESZspVsR86EbDcrFTi4RKkKGtO9q8aKAzki9T03SCJQKs5IEkA9zm8HuFGVOBHC6S+/ZTQFrfd2YLKUjAGi1A0iDYR8Lu28Ktm6BBoHitORQoA/pKHuFP+YHV3RNk3ct/pCqDym5chWOZWddy1gHQ3EFYD8Uyp51ihYLyW0GKEDKIATyhIsndrFecpIlDDBBCLNLTLO6pJCV4jS9OZeGlhPELwFujOphLtyPSSsw0AxCLDjzplE4G8omgYC+hnXj5vnv0giMrIyYC+cUIwt+tB5HOphDI0V3VhKXgSc7nfT7XqeR9M2Wdo65XD46ZCBFQrrGaJyL6ZruwQJFX+b9ze5PAMFh7ei+0vfr18frwe0JzVx99YyOfs5UOPYd89X8bxQ/dhdHIHLnr2z2JqU2ZBKCAAcr+yTSVNjcYYAKDfX5D3cbA0hwMH7sCB/bdjcfEgms1xbD/t2di27XJ0OpvkmnnWOS/G4cN34/57/w4XP+On01MRRCAJGLadcRXGJk/H3d/5XxgMFrBzz8ux+fQr0F+exXe/8fvY/9A3cdq51+rD5sD8zGO4+5Y/R9gYwWnnXINoYQ6PPXoL9j9yM/r9eUxOn42LL3ozNm7YrVlCRMplUxmSDHrw/YZ1nnUsnlvjaY4VkwBTSPYGiR0JP0jsoNxBYk0a3iCxXhavH1uBqWR/EXFeImi1jAhIDbmwbnjM1gxZKTVhY4gsQZXzsksh3SACcQIeOCpsWqlNmf530bnF+UwiUODjn+c6zu9RsJwg6tgLsd/jlh8uACwe62Bik50MnzcTeItuAaRIECoqBOYSrlw5wgX8HnShz7gUv1tsGRAIF8rbNRXrQBERiDsJUFDQTMXhKzgaMx4AhkbqgSW/iyByf6vB1MMi6OaE28yowoXg/KOYuEEhi2CQBaKG9em3oOQY56waKUirVqcuZ5jLHsAddL59Dc3MKljw26v1PUYmC+INBsKF0XDLAbT3iWXPhFpJmCkFBEUhsYQIEu6M59cU3TVh7Q+6uoCed0ps0uqx5PfZzOsP2M+vcKfpjxJzYaaAiAmjZ9zUMwi5agYAyAPJrbU160tYGAbA4XtugRc2MHHuZbJduAwkGTlqHVMUWzzBg7f9NQ7v+w7aY5ux+1lvwcbNF4JRxJwYl5qsgiqu5fsh/KCFXncWhw/djYOPfwdHj94LgGHTpj04+5yXYXr6XDDmpeulsm6FrIlzz3sl7v7Bp3D40N3YtJk2K4t1a3R8O6543g24/67P4aEf/C2W5g7gtHOvxbYzn41H7/vfCBojGAyWMOgtYNBbQH95DseP3I+xqTNx4eVvwb57v4KDT9wOBg9bTrscp2+5CqOjW53XDuikhPMEvd6cRQKAlRMBpzXgFEedHejUwYpIgDewXXkAh5BI5esnBCc6UDexiACZ7adCKq5K/VuVD1MioAnTRgYiq2YAAAQeUFK6uyoBcOXil+44UZwTgWHiKlQiUOU4GSxsFFgjrAgqgqWcCHgRlznpwwUufWPHHmaYuTxdrWYPj2pEQGQISUZiiwjEjVTDT6Lg9nIGcqGPW7lVgCICmvBLuGl4/eLzCvhdOK0pjbnUShIss0Kf7sYxH1EWUJw0i5+1wXQE5nMsbEKhNtdE1BYuXJUPkfB7vBIRWmvI90rxgFP/NwkIAMtH3+lvvWajpLEWAdBWsHbF3yA5nGUsaWUFidoF7EQSGpZaPR3BwoszmVtElLkFElW1hRDPCY2+CmmVMFwp5N+Z1cuboB/W6Kx0opj8F90cKAR08ayrKCooVhSU7RLoGot5P2a2IJn22AzsVVJ7qlCTHKhuheo8rFos/DQmFTyJceyeWzB5/uUIvSYQ2alK4waTcQ0HHrgJh/d9B2dd+hps3fks+HGB8OQiAGI3A+mK2WiM4uEHvgIAGBs7Deec9xPYsvUZCH06u1PeH8OmjRdiw8bzcf+9X8LU9DkIgqYueMtnNT1x0Gxj9+X/AROPnIV9934FBx65BePTuwBw3H/H38APWggbIwibo2g0R3HuM16HTaddCgwiHHjsVmw749nYed5L0ELLdnNyrKOccxw9uhcPP/Q1LCzsx5k7ryWupfBSC1GU0apGjTIMTQIEAQBygZB071FfECG4a+5CgMjtK48V/vnqNiGoJ0R/RKVdLaOD4S6kTg4yoEv076pr4CrPrf5r1gxYY2hZgTitjac08WT9AK1jnQiQqUIBm8QJJLBLpJeQCS/mWnEqkTZ06ruhJAIC45NLmD2seYqmwwsBfyk9LmorREC9VEJI11yCFCIat/IFk6ohYPVtIqEXNyAjDuIWF9yaYKmawFYWVKyfnINXCCwGgGOXxGgcSwfQPpITtaoYtnptFYRL4n3N6xUIV6rcenDy7OhWtdRMCIvaKbGLOupztHbj0ognhhMYpGXAA9gAGIxUO04I7oUxFAKikrdKRqlHp8A9TZABzQpAEBGencPrKkGoCglJskxCnU15btzuUv7CLGWK24mH8mPihq6xNqHOV74ibKkCu8i0Jcde8s6rSpAqv6eqlMgrgUP/fcQlJLRFyBsAs4/sxWBxFtMXPltuj8OcCIh3Pm4w9BaO4tHv/wO2nnU1tp11dXZSTlpsyN9bWfNlHJ/4mZX2p53xHPSWjmHr9it0zTqgJaFQIZVPjOHcc38St97y+7jze3+GPRf9e7Tb02kcmrlOskwuCRi273wONp9xJY48+X0cePQWxFEPftjB1jOeia1nXIXO2Cbt0CgbyMT0WWghdX3TiqERCjXOOY7NPICHH/4a5uYfx+TkLlz2zJ/H5OROgCAqq8FTjQjE8BBXmlxO3PlrpBiKBHi9GDAEEK+faWhUPzy5Tc1iQ2wjgojJbQXHahkBxAupbUssi4AJFtvuSIBOeErBDKmTSnGmwFklWDYgtAquHP2x7iakdUPVO3BWaTW1/NkflDUnsY9JKyLa/bpqBzRnEww69gHzc22MjaeS/cSmBUkEeDOBl7kCxB0uiUDcTIVoF9QUnyYRkJmCiMDgsmDhQmuDZy/EZO2MqFwD3JitluklOO4jaWbWgZKaA/3NEbgH9LYCrF99FeqPpb+jyBtOmfirwjMKmKla7PWwIpwQZLdWCG7ilZaXuhp3IKY/T6xCX6rlzM9ia7qbiyaq/FzweR7AS0EI+YTGfyXgWQ0BFhJBwtTzrbgW9ftZRrEof6ga9ypWAKEAcLxXrnS1VOpOIE8zar4P+bNtz6vhgp42tCwuyIwlogiA3GWkNRaE9cgPb0Z78w50Np+utY9DoDWjrBOc4+Gb/hpBcwRnXvhy/dqoW2Msf1LI91jqxgtiX4YdZzwn3U4FZnuURUEfQHtkCpdecT3u+cGncdvNf4jzL3gNNm9/hj7favJE6o7q+yG2nH4Ftpx+BRbnD+Lgo7fi4KPfwRMP/ismNpyFXRf+BMYmd2SXJ6zZRrXgwL4+cI7Z+cfxwINfxuzsPoyPn45Ln/F2TG48J3ejyq53mGrtZXiqEYEapwaGp0OODAqqZj3flmjfrm0aiy7YJo/Vtunmf22MSoXbMliBwInb/911rMxqJK6t5O66FhryfsRcczHKNVfZ4lNAWNQS6eq3ejw1BmZaX5S+1Gszg7W1brJ2fjcfnxdzaWIf3Z9PqhN3hTIryPxcvmC3JrsyC07S1idhsRhHhOWY8XzBcQnzlMYsbqP4t0vgJHhxO198i4JmkxBOEuF300C9YCHPJFJERppH/dIMQGzBB5a99IZUFNKWNzD0JhmWNjMsb0g/K4E34OknSoURFlUTWNcS4llgcfrxIg4vSl39xN/qc3mywP3sORECC8eK/Y5kX0z5VEDzmIfmMQ/hrIfGwaDw+WBBkn7iEjIgPj0fLGaOFNFwX68i5PPYA489JF1CShbvIlFFGABGRnsYGe1pBGDi4bxtoGbhUQkVkRzClQVIryHAHAql9B0w54LGAgc7Mo+9f/N/4rGv/RUWn3gInPN0/iFeafEbJzzC7APfx76/+zPc/cfvQe/QAdlGnTO8QU4A+gvHMffoPZi+6Nlk32qQ8tG9N2Pu0AM4+4rXw2u1q5H97JmzfmsjUYcLpFUvsddJTXmWXcfExBm48tn/CdObzsc9d/0V9t79WcRRP6tSrh9Pua2OjG3BWRe+Ele96D04/7I3IRos465v/ykW5w5kl5CtP5YSh1DWAbj33i+g15vFJRe/FVdc9h8xPXUOGWdmzTeMlVrTnVjpcTWe1lhZYHCmGTZ94dPAXb0pi6ptE9UEte3ENkkeDOuBpQk3Mv4UBbXmfdvuSKUuNQY4Y/rau1qLAIxJMErSmANgKHccK4uSeYx0mdInSS2WosizyHF/XbECJoSLy9jtLcxfkQf0iWqkzY3L6B0xStq3OIIl4np58WITLNGkIQmLtfJ5ilhYv2nchHzeeOAWcvuT7jgG7rszFakI59L75XeL4wYAIJjzrSrGLgwmEjSOe+huyNKaDoHuBg/BUnoe4We9llopP+tTeqypLhFQtKTG47ASUztZkRj2qyWImbi94rjeBqwakpwat7CKRYiE+uyWINyfvoyD7QUPY0vcpOz/XrHphnIDsUD9VkqsgMwU1LXPxZT6If5m+wXrb0iP3fQdVRObfgc9IFLTdVIKaS0uLPtiuq++mZGHGc+F6Edqf7OvA/d8A/3jR5D0e5jZexsak5swvedZmLrgmQjGJuTxicfRPfg4Zn54G47v/R7i7iLam08HmIcjd30TO655na0sUO7pkQdvgReEmDzvcgB6BiJRwyBuAPzgDB658++wedezMLn1fBnLVYkgc+PapfWASLRBHqush9r91NdJyi02CFrYc9GbcGDDubj/h1/C7MwjuOAZ/x5j46fJl1ct9EatV54fYvOOyzC9ZTe+f9Mf466b/xSXPesX0GhNZEPkSAIPftc08+rra6ezCb3eLDZuOF9vFtkxfl6c9lnUXykESWl4T4kUoQlnSNYxJdJ6nvtUw8qzAwmzmLkyJrAXmqrb1O1JSVsjQNfMnJO2QSERoLI5kBOV0Y+1WxAHWb/AB4v1cuJOrT8IImD46qepxtQA5iSvG5BVFQbEhMu1GgIa1IBmyi3IEeSbWkU8eQ4xiWk+kTDaO7RhVFag0f0x5s60F/aFhRbCpj2jJWFeNCzq5EQgbgF+tv5TQWheLycbKhGgcvbn58oFc+qe8bBY0FzeyuUxnsPtpj9REOCsIFwodxlozKTZhHygtNCYt+ylbhUMpfUGTCxuZZmGkSGokFimCCzhVmab/G/xvD51Jm3xW4rfiitxDNLNaYULNWURqhwjohzPWTqlxQWxl8GhLNWmIFobCkiByOuvCujZi6FmADIrEKswMwUxKnMQAJ5ZKlTBX4WoRDw6bj+YoppvuGxYfgniRwaPm25A2eVqwbVijmV68LD47VOhL+1w0F3AwXu/hc17nofTLn8pZuYfxrG7b8HBW76CA9/+MsZ27sbUnivRn5vBzD23onf0AILOGKYufBYm91yJkfGt2H/Ll3Hkzn/FaVe+EgizVGLG7e23OY7dfQtGTz8PXqMp3aDiJtA6plhGlhLc+93Pwgub2LXnJzQLgEkEXNYQee2mEqBoHVV/X0Yr3tR1koyLyxRw27ZfifGpnfjhnX+J7938UZx1/stw2hk/livPxJiphCIZiQnCNi666h2481sfxfe/82e49Kr/mJ6DJ/CixFr7zJpJ42M78NDRveBxBKbWH6B0VlQwscuiQhED4/ikUfu716iOoZ4WpgjcEuKhVF/axPgeZhtslxKqjXZucVyF1Jyib1dJeKu9DPwp7td8kYWQLs83DKunxmG4GKkkAwbJsHwUHdB9sEusJKqbV6wENSlaDcZz64se8J33ry4M6iI7/kh+PSN3tmRqwEEvn0DDqR4g3IKMjDjiN4ypAk4c8vkxNe2ulIqqZpASspJGSgBc6G3k6G4q/s2Xt3IMxgqbwO/lnyI0ZhhtFaH6XPCdApQLcTONpVjeCPQmhjpUIiVRudvNerjfnCqIm/onyT4rger2MWzKUX85/Vj+5gTYbAg2GxYqRNBK0k+Jpo37XBf6qTYRS2MQVAFRdUPiDOAMTPXRUCoZxwlDnDAs7hsvPI8aF+DF6cesPlsG7mXzqRnA6hnkLxs2i1NXtP17vwGAYeuF1yBp+pjYeA52XfMfcMHPvh/bn/8aREvzePQfPoGDN/0Dmhu24szrrsf5P/tfsfXfvRKtjVvBPWD6omcjifqYeeC7+TUpz9KgAwAc4fg05h66Cw989o+w8PBecM7h99IaIf0Ox8IDd2PvjX+C4/v34qwrXwe/rTNEFwEQ16mCcWNtiEvWUbM/VypqxpwEQMVIZxMue/Y7sf2MZ+PBvX+Pu773CfS7hnnT093UpPUi+260xnHxs65HHPfwg+/8eXYd+YLBA2a5jQm3w/HR05AkAywuHs7rEIm1UbuPjNiWfauKD5mS1fRHeuooR2qcmhjaElDoyqOCkkOJbXnWANvvz2LILutB0XFiaIZFIP1DtwpYE1lVAUXMb2q13ipjU7ZXAeMc3CqykO1zFRPTxkloVzz979I0hdnpvUGCRO1fjkN1H8r9JVUrAIu5PFZ1vxp7NMbsWelq2fleG0uX5erxJEtNF27qYnBYL7UZtzkConpwWsDOfSnBEqwKveriFrVgabmLNPH9cV6JVi9vi6Ulw+pjMitChmLBP8hqDPjL5S4i4ZyHuJmp4arK/kzJdDOkC8r8aZ70tRYBxOHi2gr7IhuLCC4WgoHUvls1CIwOjAVfBZPWB7kh/V+xlKn74zUpuZj1KfrKhPlhioEJUFrZMoSLetv+VMHvNWc8EGOEeULJ7CNOzyP74ZNEoMjSKoR8VWDTLA7Z91ROANqjysuTHT91t30SlQDoOf8pCyc9Ps3X23CtlJuFS4/yrAy6izj0w29h67nPBRvXs6AFYRsbL3wOpi99DnozhxG0R+C3Oto9EISvMTaFsTMvwJEffhsbL7haViKOmyq5Y9hw2b/D6LkXYP6+u/DI5/4E7S1nYPMVL0R/7hiO/uCb6M8excj06Tj3x96MDZv3AEjfK7+vX7jreeKencqUBwxeT70R9LFyn0r4KFdcKk7CUaDSZwHO3vMqTG48F/d9/2/w3W/9Ac67+A0Y33AGOE/0T5LAD5poNNPfgXEACUe7swEXXfkOfP/m/yc9l+qSRMVJZGve2Oh2AMD8whMYMzMeIVsDCctEYrgKcY+Ip/kREPyTdc4OlNTZgSRWGBOAlYQUW7B8/Ci/v2GIgCxyRRxHmSKtysGEsKBOTAShMCGIgNAYWFWOS1yD5LhMEJmBLMG/ChGoAJcPf1qcjFjIA9vfUy4Uxj3Oc1jrJEK0n3goJwICgZ+gT+Sn5iGXLjbRSE4EnGk+M/g9W/j3e0b1TgNJ4BYCohElGw/xfMaj6YGx2E+gP8HhDcS1pERAqxqaoUrMgEDjOLOu0wXG02JkSQg0jlc7JglTIiKEjt4kg1disSgdh2kVFAQ7KRcW1wviNxJxILIAVPY8CQ3/wM52WxlSEBAhUeLfCjO4RQoqTAmN4+kZuhsrkLf5TJIeKzZDCOsep4KKxc+bCPJGCeGCoTgyoWWTSGcsZ+5dxQog1haV2FMkULOKSO0teUo6JsBwZzGtzuL+H9j7LwCAbRdcA3+Ry6rd5jzUnMrSVRIEQGDDRVdj39//GeZnH0OnlWf+iVtA78gBHPjK57H06AMAGDb9u5di43NfgiM3fQ2P/OPHwDwfUzsvwebn/geMbjoTDSM1sKqoKSOUpiKJDRKtCn3hsZm2XC/MptxLU6dFraPEpg2bL8Dlz70B937/M7jrO39WOIbR8e2Y3rIHGzZdgNGJ7WDMw+j4Nlx0+Vvx/dv/DEGQvsyCAGgxhIoL7+zcIwCAdjsNEKLq61juSMxOoS0ygK1FDZEaNSgMRwISngeiUr77oIVcyl+f0n4XbdP6NAUtMQZl8iaPG4IIFFoBzH4MDQY8ZBIfDfMeFcYEODvJiECkC+ZkbETFfsVEUxTMq9UsUOMDtMkQ5GTs9elUodyjx+U93kLzvDkAQKMRybR/3lQf/Gg6GScNrvnauxbrogVI7PP79gKchHAKtlFHITURs9JyxmNxcRYV6H77SZgTAatdwTgENOtAiZ94sOCl6UQHjE63SMDvK0LACoXx3nh6w0TwsNTox/mi+qMKQRZkULOfCvHiGWgdHf7aTWGwCimwCk8VWHqax/SJbnlrgTQyn52csgyYaVVKIMkA4TJUJhB1l7OH/2BuMZy+K99PWTylO6F6LyoMWbwP2j3lXJkX9POKcw36izhw37ewcefl6A+WEB06BC9owj97h2yv/S7KbdCqCGfnHzvzAoSjkzjyw5twxpY3AgB6YQ+H//dXcey2f0E4OY3TX389lp98FIf/9csYO+9inP666zE4fhRjS2NodHKypBYi8xRh15qjxRomLB9i+vega/+hEiSQUMmF+ftw306eQSarsFyUmPTxb7bGcfGz3oGZw/chjvtpBWLfB2MewDww5qHfncOxgz/EEw9/E4/e/3U0muOY3rwbGzdegNGJ03DJFe9AFHWx/9Fb0evPod9fwPj46di65VJ4xvP06OPfwtjodkyOn5kPz6H9pwOFTYvAjxYRSLiHZB2r9q7nuU81DEUCzBdxGFeeQuG04rlJIqC+GEqQrPO4ikSgFA4iIHz3eeiDDWInmRiaCChjdFYRluPP73WVrEjpMfb/uTuEvQBI1wiVCBRZVbLj1ZoBXqRMdso5Ogc5jl2Ubu7dNy6JwPjoMmaOp9WO2IaeJAICcct2C+JesZ900LWtAgJUSjcg04AHjusEEI/GpdrWsuJacSvtO80CVNw26Jb7dIfzzEpdWgZh3vf71bTHKkTcgNBuiu9hCpBVgRcjfW6yVH1irJzppIK73IQKSI0MFpWPqK4NXWvCsrwlC3jN4l78bvrdOlq9D1ndWQi1FSxBpnUpbtHtAKB9ML1xYqwkMsuAFApH8xdQtQIUaf1lGxFA7BIeBUkgrBDhfHrQ2CN5/74S5E8909LVS1UouRQLRjagdBt9Lea5jj78PSRRD4ceuBmHHrhZbt968fOx45k/ge6Uw8XF+K2EwM48D5OXPRtHbv7f6L3gVVjYdy8O/vPfIl5exMZrXoKp51wLLwjR2bMH7a078MTffQr7/uLDOP01bwe2TQCzwkrCZb/ibzkErr4vqraasASHnoxN09YWYv21rAuMgfuKIo5l62Oiv9PqfTGhxXTwTIiGh+nNu/NGVu0bji3bL0OSxJib2YejB+/B0UM/xIHHbjUHiEY4gjDs4Iknb8Fjj30T5+x6KTZMnQMAWFg8iGPHH8CF579OqwsAAF6U2JmACsZeo8aJxvAxAQVuO+Q2o0YA9zxb4+8ot+06twS1Dq0REZBCaZHM4tB4S3geELtNAqVEoACaBcCV/k9UR1aJACHQO4uHKQG9mq8rRUJcw2aO84pYgUjXegxG0ouYvguSCABAq5FKzVOTi5IICMRNO1WoWuhL1WwJ+N3cTUN1D6KsAfISfUWbF+WCpRxHm5fm4PeXvUI/b69n92s3yq6nQvrVcL6aIAiklgh/mWEwmhYoGwrCG2qFwa0mhMCRmsJ5GnBnZvNaZ99YTUsKSG2geNaE5r+7ynsig8eze9yYq36s6U5WJZOQOCaiguwztA6JFyH96m5yP/feXPpAxyNKxjTFMsAGWV8FgcKsaz/ompUg689r2C+XmAdUa6D6jpuCtXbeCgTArBMghFV1zlGVBSwBpk+7GJ7nw2914HfGEDZHcPiR7+DAXf+C6bMuR7N5WhbQ64awWiRB+t5NXnIVDn/zq3jw4/8XouPHMLL7Imx5yXUIJ6eV8QHtSy7Crun/Hx77/J/j4U/8Ac5/y68DE2PoHNR/Q7VKsawDxGkhVb1u+Z6GnmURIMm2g1xw30jXXVE4Tutt5Ou5rL9hHq4WwVTO43k+pqbOxtTU2Tj7/J/A0uIhLC8eQSsYRaM5jkZjFJ7nw+snmJ17DPc/9GXccdfHMT11Ds7Z9VI8/uS30WyMYfPGi0jZxiQCpkUdoJVLloVELK3c3lajRlWsKCaAdNuh8vcT2XqodgBWTgQoVCECFMwxVJHHxeQiTaEeWJLkVpLMIuA6h5MIUGSAuEd66lAYgcIOla8qkJtaF7OpSSAyqBNZmuLRvrfOCsI9jrhFtHfc78VDI2jtOG7vmOqDH9elmqidVw+mKv4yJXGJ11OIQNetAU0aiiBBaf7bvLBCr6jIK1x90vuttwnnWKlAH7fLswQJTbgXlbuGBIsMcQNgQ7gECZjjH9ZSMHeGJ1Npto6n5w4XMs3h2hoL1h2CUCWK0AbkAnlviBSfANAXnhuCFMxXP1Zq/YXsU/CMyLS12XmiEWdTtA6nD0ARGQhmMzIwpjw8qmXAEPR5k1o/0vaJSrYVciGsg40Z+4EU1jJrThIElrgX0pphaauzL4IkcI/B7xe7MTU7E9h83tXy/6jFsH36pTj+2D145ObP4dzrfgnhkqcRAZWsqG5LvY3ZH2MTGLvkMnQf24ftb74eI+fvSZ+xJTGu/Bhv+0YEjQ7QTuA3W2jMckQtZtX4SHzAd2R0o67bLCqm+fQXLL9VyEUuIDusLYRiT+vTRQTMsagEjzGMjG7BaGczed6J8dNxxTN+FkeO/hD3P/xPuPW7HwFjDGed+UJ4XkCugZSgziJupfZ0EQFTLrEyNdndn3KIwRCvY4DXep77VMMa5rXIYOTvH6pdRSJQCUVEoOgUYgwV0sLlxxj/M6ZtlERArQRcQASKz6UcyzlYHOc1AwCdCEQxeJCnKtWDkAwyoUzWVf0PWZwHHjszHynnVTMdqG5BLObyN2ksJOiPptsn7mc4emWqTjv6+CQ2ZERganIRx44RUZaZgBB1ciIgdwX5IqppngDnrJn4uTaPIhRJyN2aVcbBsmwovMHB+oz0+Q8WWWkGmCR0ayQF/CoWBLVtRZcggUrFnqjjMreLfqbN7o+VX+9qweIshxZjeeaygVhQU8FAugPJHP66m5AcP8uelyHv1zAQMS1xRsRE+lufKIpFYZAJ5zJbUUZmO/srnFtkralwKpG1qijAWbgLdTcXkIGZ9EGNxhXLABEL4y2ng6LchXgWR8EUMsDm8xegOZN+t47ax6r57lVSLe5FUYIAoNo7oGr9yf1GylC/x+HDx45/91o88KWP4viD38foxZfKucl3FAEzC9NNv/3fI5z1wBiTc1PUsV0KZ775DSwdeARnvO2X0FrM71vUYmjM54M206UWIc0Ml1+zDJ71bXchE2Xkwvzfcj9yxCzoVoYq10Bty0mItHYMEjk3MMawacMF2DB1Hp44cBuOHN2L07Zemd8HdQ2U5DGRtXcAZNZd3UJAWTC4T9Tm0cWNGjWGwtqQAMq9ZyXHqdsKhGIq6JcOBHb0VebGMwwBcI3NSBdqufo4iABTx6ySBrEt+18LBi4iAspkUxYfoFYV1iZz4jg5mVXIQMSSPOOBWgTF6yeIm0Iag/xNmrMxljanj+aG2wJJBIoQjSQI5m1JRhXe44Zt+lcLiAmoLkGUKxH33YW4uAd5gTxIJBEw4XfdwnA4r6S6dLyhcSMVYFiM0gUgzDINeVF5uk8epARqWEHdbM+whoKzfE90dwN54XK3mAPW6LzqEISLg/L8inNL4Qf5/LPiyr4KoiyrlHj8hLWmdbDajZ3flbtwAMDoPveNsYKFC1aGMLM8iN6kZUJB+4DyXgPobbAf0sax9DoGY26NP6Bb0Cxk7dhivnPsISr5gHGYzxAuKWMilAJUTJCWRlRLDaocpwppXNmvNidShgLAhtGzcHDzWTi891spCcgQhzkRUElK1OYIltPOB2MJGBiiydStT4UghsEi0Dv4JI5+48uYvvpaTEzuQg9Acyazxi3lc71lzVBdbFT51dT+e3nq3nybmwiUkQzS7YWwEJgWbgERE1CGIgKgntfrKZYnlrfxPB+nb382Tt/+bDvGjgj2VZVoAl6UILZSe0Nzv9UKlakEpyYCNVaAtSEBVTT/w6KCVaAwragZlMpYeVyA2O44F4DqGnsoRCAjA8JVyByXeQ69E5o4VMoKJAOJFa1D2X11/JZVA4ztA9MvM/WZmJT9nkIEsvMAQOdQJImAwPG5DibHDRX/+EBmJYnGYkkE4lau/XK6BRm3S3UJIjMFNeCeaMM8FR5V7tEbMHi9zC3IwWmqVA2Om+VBxV6m9fbj6oK4KyuRC/m1DnUYid6EMPNnmYOWnvppMGRl3ux3gKcHWHc3OA8txfIO/QHqPFJtGl/O0pW3D5a3HabWgHB3Kip81zySdtCftPeFs/qLGI0p87nqSZlZBJK2/XwI9x9hAQB0K4CoUSKuI+jmGXy0WAGCvGmZa8R8pv4EiiCmFkOsQqQtqySAzedcjQdv+hR6xw6iOb1FH5vSdnFHeq6ozS13vriTwF+yJd+YRdj/t3+FcHoTtl3xUrm9N8Uw+oRiAYh0rb6qkLIUQqaAz9P7bVYMJjX4DDq5MAR+896bRMACsbZVclXkxLTNubWNRRzc92TxTMZhrZkUmRCFEs0CYGo8AOCQL6jiaAFRQ2EIi8d6o84OdOpg7e7Eat14qhxP+MqzJLG3cW6/IKSAXXFoBskoHOKw96GoP8bS4GJHe2lpEMJ+ScXkXHCrZrkpek+siZFyBRqSGLoEjdH7A/jjqQr/+FzqJDsxuZQXETIyg8h0f4SPfxLC+t1VlwAqEw/34ZxYk4AjacfaebWxDBj8xeLXzBuU+/pXcc/yBtU1+EJI9fsWX6HbK5lyVqpp97vZ/WXpJxop9jM/UfAGCbwBhzfgqRtG7+Sr0BrH00+wkArlzRmg84RXWkWXwvLpUfo5Y4DlM0oYIoDuxuyzAehOlzZPwZWPA+F8+il6VptH009Rxi5/mcFf1i1lSajMe2GSfoh71ZjlaMxyjQDETcMFr0tfhFk5mPrbgirTxVy6v1lWAgKCoJvv3/h5lyBojWD2O9/WT8XTe8uSnAAAAI8iHP3S53H8q/8MHsfyfHFH/yG8PnDsX76O3qH9OONFb4IX5BcsLAGArtmnqiBrRdtksD6zng9BvORhDLoiSLO+cEvgp+IwzOKWlPuemW60EMaYJSlRrpmzdI1T3XC47xkWQSZ/G617Y4xp9WCmK/+y9VOsoZplRdQEMn8Cz76/tSWgxkqwcksApVGm3G8quPdIVLUoVIwnMLXXpLbctAgYZrVhBVkA0johhfTAA6LELaSXaec9D3BYEMhMPZ7dDglPtd+irXkP1bbKdksbZI5TTFxq8S/FRKpqP+QECF0T78UccTahJUEeXNY+GuP42elK2b69g+UrlrLL44gBTE4v4vgxXYqMRhOEc4Q7QKBkC1KKickYBaqAWN/t1sG9NCUpAGDgWVmBWMzAHEWNZP89OCfuoJvdn4IxxM3MvYmVC//+choE7fequarERLXkyjC1pWuk2JdF8jKCnxcRMt570V78uOKxVlx6TPjdRDuHjBUQr4s4h8xMJE6if1eNy3Ch82R6Yhk8nH33N1T3z4p3LiNsRvAARPvKK5SZVglVm25BSa3KQQvzInZAgMoyFFawHgjttibUjdvpRlv73TddJQD5b5k/C3qVYPc2L3bETYhHzNBwqznzte1Zf+Y7KNoPOgweAmw490ocvf82bOtfR7YPFhmiEY6k38fBT30M3XseBJIEy3fdg41veRPCTWm0sCAC0Z2PYOab/xuLe+/G9PNfjHDX6TJgWBCAQYehOUfk99fWA3ETacFUC6rlynqjKOUSKkbA9N83USDYO4NlV2K4dgQKW0U/40S3EnBuXf9q4EUcSVO/UMbtZzAJme56VROBGkNidUuWS3gdNuiXSs1ZJZNPlfEo/vGAgwhY40E2ydnnqKrp54wNNwetxE2HcA1inAMxYFYnlIdEBGkoOZ+V718K8oSLkCI0aendmH08kC8S/iAnAkA+/skHcyIAANPjhoQhMBIDC+njPBjPiUAS5nEAplsQy9w0TKiEwBvoC3AS5MKJ32U5ERDweJ7b3ACLc42/SxNapCEVoLILucDi6pp70SerMAb74BUcY6A/KoT87Jno6ULgUyFrkCx6Zro1iJz4Q2YCEhBubjzgwHLefzJS8iBsT5lcPBciHgWaB8unfEEKqtQnEC5zRWk2xTNPxRmIfcJqR7mvCfcfNQiYD/IXV/TR2Z/ebK1Al1rcUoGogSHPoZ5XCPZcby85pSMjkJjv1HthpgaV24kUwwAQZpWDm+ObEHWXwDkHY0xzTxS/DzuyjAN//f+i/9h+bP7//wxYEOLon34GT37wf2D6tddh9KpnYvmuezD79W+g9/A+NDZtwZZX/3tMXfBMAGnA8MgT+UU2FjiZ6jP9h9lZaAh3IfO+Atmzavix61mDlPWJIgJlc4sgJqr1v+p8pCj9LCJnPjM+g9fXL06NCQAIIkS4nVpBwTCUaNm4vH6iZQyq7MHyFJgnY6xvhp4TnJviKYWVkQBKuC9xRXGCEnyLyELVrEMqDCJgHyP6V061BjEOSejDG8TumABrHAWkCnBr7uWglclUWEE4T+MkFOLjyv9fNTuTOvGpE5oXGROZPADkpOwqeuaKPeD724BBAtqjPSzPZRLEaCSJQDSaSF9jKiA4CTNrgPJseH26dgCgCwksslNqcg+Aq+hRkhd+cqGK8F9Fiy8rXleoIWASnDKopGklLkFelF1nNz9v3ACiVrr4l8U6/ChAppoVtQRE7EBmdelVdc/J4C/oUjPfVmy+GYyL9zb93yyup2J5U/ZH1qR1xN2v0PYXxbVI95eCFViSaupd6mXZtpTnemRfvoR5RpB81FGtwFkbxdWC0uy7/LmrgHuMdDVyxVdYRCA7NFzk4K0g3cATMKQD9fvA4ra0TTQ/h8f/6o8RH5vDll/9WTTOPAMAsPW9N2Dm01/C0b/8a8x86R+QLCyitessbH3bO9DZvRtNJXlCuAD0s3gcNR6ASvUJGAK6alUxXG/V+gKaFcfwY0+zBhn3xow1qkoAgNR1ZiVJPZh9DZTCwYvsh6OKYmIoImCkCfX6eRY9cT5z7k0azArErlGjKlZGAlxafkpTXebzfiJABQJnwl6hFSATVovSmQ2VzhO2RWDFRIBCkuQxAzEHwFPXI7kfRt0AD1QBMU1r73ALysdvH8Pi3BKhajSqVBD2eok0e6amZ/syRw5wHL5c39YOBzi+mEoe7fFuTgSQL7qDicQKOpTCvwKW5AKBWjtA7o9prZ28FCKfeboDMhjYBa/vXue8KJ3wqwT4VjFFe/285kEV4T9SgquHBWfpmL2MOJj3fDXIAzodd064C4ndlLuQ9N9m+TYAXGQcEUKMzDySbXekFF2rTEiN46J/MXZgcXv149n+9D2Q8sqO4h8w6oh3OL2eIiK2vDn9bh12txGZgsTzRbkWRZmHUtEzkTTF/bbn4uah9GVU0wCPPq5bLdU6JKpliwo4zV06iG0wtPll6XyzecJF6tXzqu+g+u4mIUOYER5EMRDmD1dzBujPHsVjf/fH4P0BtvyXn0fjtC0IO3305xvw2k1s+JnXo/2MPejedS8mLnoWWmfuBAAEC0zOc81ZfVyDTp4tSWS7MscFwMq0Y7oLyWvwCfKTvW8yYDhL1StTiapTdZUlcK0q63JjPVMJiBiqeKG8nGjk7fLrLyy8qV0fswOFqaFRyrMfASJQBwafOli5O1CR0F/FvYeK4hduJlWIQwVNvSWwZ776RUK8iPq34hpWQFhETIBVMKwMrnvoaKsRmygBAi+/xpICYqrPc1UiQMHMVkSZJLWCMEq/Xi9B1PHz47ImXsTRm0i3b/ouJBEI/PS+To4sSyIg0YmBBVsiozIEIT8V6fPr9d0FxLivByta/fboSYYlSjyCqxpphGoh+zxLHVgiYFdNkSfSoUYjtl93lbGkJxvyOAWDkfT+RG0PjTme+SgLIZVXFsTWA3LxZ67vFWbXAtA5kH5ndwKLp1df7L2H0/cjzJ6R/mTxsVWek+UtuhDUmCmwJmTJbQS5UdGfSDsQAj+L7X5k/QAqCDjrs7FQ8B4qwrha9IpKqSi118Q9YInd3gSlPbf7SduYwcoCItjTy+ZpbzECJlM/oKgFdI8cwL7P/THQDrH1Pf8RwaZphJ10YmuMpUQAADqXX4iRCy5JjwPPKzxniNq51UaNCWgs6FKsnZFHvRb1wiiFUd5GPS4JmeYyxIMKGnyzsi8lPA9rBTCacy+vHyHBUsWWORY7Mw+xzYBmgZfbdCKgugDlRS0TLT6grgpcYy2xupgASkAUAqkaXZ/lsC/LXiPbmzn1q55b3Sf+FEK/T2wzD6OCb4d0lXFBEIGq98AeHOFypWQF0ohAwVjVAmKF1+TYp05artzPZAVhpuwjJvBgKc6JAPIFtTkbSyIAAIcPTmDbdlu96DdjxMvp48xHY7CMCMQd7nR54AFI/3evVyD8B7xQ6yviAXgz0YgASxTf6Cww2fTt18bicGHzKhQFE1WRWRULAkvPW9UtSMYYrFLzLbMhZY/PWuTVP9WhVk5lyJQeyfDXPvI4/W4tbS8XgppHcxeM3lSRxTP7QwSyFsQyiLSfJnFUjxF1AoKldLBR2z630PqLQmCqH3/jkFCxEwMQU3QBgdGr3mZ/MIfmliC0ajvKiqAKgJoVQJ1Hud5GWA3Uvv0eR9xkYBkJ4EmMcImjO82wtP8RPPKF/xf+9AS2/PzPIQjG4XdycwjzOJoT6YvVP6CUGoY+1wilQdQGRp4seGZUq4nxyGmWPuPatHaO30R1GQKgadgtqOuFS3O+QjcgPdORnRKURxEWlo9gce4AFhYPII66OPP0a9Bsjhs1IyqcnysargJo9XPEtp7uFkThqWYNqHFqYCgSQGrQXcJ4tl0VTqUrTIEVQP2/EhEA9PO7hFqjgrBJBEpz9DvGWQSzYBg86AFqLiHcdQ4zS5B6iCACIoNKSQB0bu7lpHZeG6N5LsUcqbsF5aXP1fvrtL64nh3Hzz5xP8PMlcD+J6ckEQiDGIuLqcTut6OcCDS4MzUnD3JtvFoMzJkBJNsXS42lIVwzLud2HuiBwSxmefBjk04FyhI4M+gIP3wWAUmJlrZKwDCL8wrERQWhBKLRXLhbSeaLxE/93sU9luM7kdos6dsjBkE/UCyBRWbklGJqMMV2UVW2tXYXIO6NIG4CMZFVpwidJ8T7lv4vffodaGW5+8U5e1PutuK5pYR3gWg0E+IZtGJfQrhPul3MPvgAOrt3g2VLj7ACqAjmPFkoDQAaR/MfqZ1ZRcSz2Ji3hfuk4f5tNE12SRrQsufdLBoHGNZGVYGkbs7maC+iNet+j0tFDU9iJB7H3C3fxmM3fxGN03dgy89dD7+dPhxRP20XtgxthhIf1TyaaZZ9O2ajP8bkPdSsAObPosivmkuTb1hAhrDUD0UEyrDSY5ktwHMGRMvz+OHdn8XMsQfAs5e+0RgD5zEOHvoB9ux+PTZMn1vJ+mOm9dYs5iAUYwzw+wliLSiYkJl82/qQNBjYUyC2KuYe4nV0yVnPc59qGNoSQArnDpcfSgiVWWzUACGH0FuJCBSc3+q7hAhUcX2xjxEnotsLIsDiWPt/xRYGlQgYx7Ik0SsHA6TmjMU2QaD8G0mwXNDQzJIiVahiztTqK6gTncPdSM0QlPi5abY5G2N+h/6o7j84ifHJVAs2MtKVRAAAkKXmjEcSjQhwLxe41DShVFVgAT1YmEkiAAAIOHggfgs4BVtK+LdySpsEEangz8V2D/ASmgjEjTzA1AXGqwn9SZCOazBWrXiZ7F/Ec3A3kRoGQpATLle+CPpkmRDFIf331yNrkNC4STLI8oWaC02/EEpXaG814zFEf1TKTQrtQ9nQsvtDVfZVIbIBFQUni+q0wmol6zyYxfGy/XGTY3D0KGa/+U3M33YreLeLbe/4eXTOPT/VSC8z9CfzB18E0Lf3Z5Y8xZogrgdIK9tqdQSUeyy3U8+F0NybAr6RAlOPCeDab2vCzA6TjkGZ+5S+RNVYzhP0+wtotMfJ8QTZix4NlvDYN76EmYfuwMbdV2PrC6/DYjtTdGxKJxXmcUR9H0EjvfD+QnbTRiM0H8lvoPk+i8rP/TGG0f1UyW/jQrmt2deE+CHX0pTsGOuzOLeKhFfz/18xEdDHujh3AHd97xOIkwHOOfdlGBnbhrH2FoRhB/3uAu7Z+ze48wefwM4zrsGuM18Av0g7QynRXETAcBXy+wmilmId73M5L+apjI3UpQkQV8n+V6NGhhUtT5TvvhAqC4V21WMle3jLNOskEaicxYZoQ9UTMC0JVYmAJgTDSQTgM3A/AOu7osWMMQ2TLtQkAoMYPAskY5yDw5iYM6gBvKQQNUxMwFCB0uIPfdyifLpGBJSaAWOPRzkRyDSLc8c7kghIEFlF4mYeROgS+M3tajwAFSwstJss8nIiIMAA1i2PCXCBcbifJWSBvWVvrgyedZvkgdwtYdhMQSJoOT1J9eMEWJweF3SzaxEC84+ggsbKlCK/BZEY7gaqwlzUcbczISr7sgQYFJQPaB7RhldIChrHgb7iViQthJxj+eEHMPtv/4aF++6G12mjc/5uLN55B7ym7mvX3p/+6H3CPaktgpCVXSIGwKzlkG5TlSJpO1LLrygxVG2uOReq9U4STYGkNKIUAEoaZQ3Zvw999/M4+sj38MzX/jYYM5IXBABj6cXd/49/jDgZYNfzfxpjl1wGABjbB8xdmTJEpsx3Ud9H0s+FxsZj5QQAANrHEhmjoBXOM9Y0eW+5cV+LMtplEG2FgkCcJy2UZR4L+34aCrzC2IAhkMfypBbto4f34p7v/yVanQ249NKfQyeY0No3WqO49KK34JHH/g0P7vsajs8+gj3nvxadcNLuvMBd2axhBMDaRskvXp8jbuvPC/ft6sw1alTFqmIChICuPqzUtqLjV35yQlCuqlXXct0TgcAUyIkOpURAI0oNhQiUCderSBfKoiQnAmJiofz7Y25pJGTf5v+ucxGXQE1w6bg4uFXlMFsMBokkAumG9EslAgAw9oMG5i+1fWr8IEZ/MVv0WjHQzTSJTY4gYog6ejYRgA4WFu4yQKqJNWMDWAK7NoDYxwC4agTwCpl2PDjdeYQvvpekwo/qU6wiCQktpwMsQWnMgH5A9jXkesOysQpryFoV1DEhUy+aecPN8SrWO6vgGNfbiPdXCrcnQMsmBU3kc2LV3yXMXLXE+FyxLCYac+n3oELFZpEatLtR3y7enWbmVuQNgCTq48jj38Xxb/8b+gf2o7lpK7a+/HWYPvsKLD25D4t33gF/lGYgnf1pPxRByTPX2Ptyd8Z8G+WjLYV+UQ+DG+0MVxY9IJY557q0QT421/MtSMShh2/FoQdvBgDEgx7CIDftyOxCQTqXhZ0JXPhjP43k7M2yzWAUaP+whe6FumSfzDXSuQ85AeBe/ozIMUe5NWnsCdUKQ2jixS3R3FWMFKLqmqT+n8GsvswMF5qgG2sabz8LxJXuMKJ5zMutBENYA7Q4D87x2L5/xUP3fRkbN1+A3Re9EaHX0GsPZOdjzMPOM67BxPgZuHvvZ3DTrf8Xpid2Yevmy7B5wx4EfpNe203lo4MIJMqa7Pf0+AAepAK/VS34KQYOhmQd6wTwdTz3qYZV1rekBXmncF+Wr59AoWWBSk1KpQcVMM9dJc0pYL28Wt9FRIBlpCghFhrx9wqCcwvbCnKjWARA+D7LIRCmSbJ7R/YgK5BKqeqqTXAiw1qFCcyLuTSbqxh9Msbxs3yM3dGURGBhoYUgzBa+kX5OBBhPK/mS15IL2yoREK43lFacRUDs8IdmRbePFbjViDWTuQUHV9pUuT/TaMYhrfHU2qrxAFVShHZSq0V/IiVQZVmIBMrGMQziRi5gArpmNQmyNHs4ccTChNTqJ5lAEqtuQLqGOi7wTS9DXoQp/apqpRFEU7yTZhVsE+GC/n9UQCJEalDGgT5R6TeJIzz06T9E9/B+jJ95Abb+1E+is/NcMMbgLQPRcnqysdlRed7ehN2PcEsa1o2KEvzVZ5El+lxFEgCtv2wcoTn/Z30bwqzqbpieT3lWs7lw4dhjeOi7n0dncjuWjj+JuLuIcLQtj0fMweMIo1M7cP6/ezsmtp4LFjaAIxxzO/VxNO9po7cnnVySOVFJzEfjsJJS9Lg9boHWTD4+X61vYKxpnMFQnHHS7YlaO2Wganb9fje9qULQ9Xvp/0E3Jt1hYqPujBcZawOHbiWgCICZWUgcmmUvSpII99/1eRx48nacseta7DrnxfC4IOOKu41IH5yteVOTu3D1ZTfg0NG7sP/QHbjn/s9h74NfwuYNe3DalmdianynPo4K6zlZzbyXIBrRF3BzHU3C3BpQKUi5Ro0MqyYBQ0MQASv/sG09qBQP4AjoNQtkFRKAom1FBKAI6jyVCdAiTaiVMnQYIiCIQ5V7o47XNKcqmQrKiACZRtTYr2bJIN0blN/cU7UdCvFSC6Ook33i21q41kNN9M9LF8Bo4EsiAEAWFUKYaEQgCXNttBpEmwRuAdcbFKUJ5WBE6kIBf9l9T8u06ZXy/kfZdXB68Ug8Reld4bH1+4rgVaE9E4+QuBae/Tks0U9S4TUJUq6ahKkwWqWA2qkMkZEmdwPKBAsvJW3DQFqsTENaicVAWp+EHFNSsTgUhLWERAjhUiUDR27/BrpHDuDc1/4njGw5M3V7O5THIkRLC2BBCC9syLlA9EO9Y4I8F9VtSSiLo9DIq4dxfV+VBAgUyt6j3NqgWIAzK0LUX8K93/4EOpPbsOuyV+Ouf/5DRL0lYAToR4s4/PBtOPTgLeBJjMtf/l8wefqFWt+jj3Ms7GDaeZr3tLG8I5/72k/kD4Rp+VQhCMCgw9A6ZrszktfLue0u5Zh7AOiZamIuNfwA4EWJJayaFXJZzBHEuZVA3FMv4vbvTlkJTFchwkowiJdx920fx9zc49h90Ruxdftl1vPGfQZmpAnlLB1HEDSxfcsV2L7lCix3Z3Dg8J3Yf/gOHDh8Jy44+zqctuUKfQymJV2t1yMMWmYhMQ8IlmNE7fy3pdbY5P9j7z+jLDmuM1H0izTH1ynvu9p7g0ajGx4gAJKgAUHQiV4kRSORorw0V/fNzFtvtGbdx/tm1pWZEWc0kiiJFCk6EXQgCBIESIDwptHoRntvqst1+Tp1bGbG+xEZmRGRkeecqgaJpqb3WtVdlSYi0kXsb5tv2wRm+ZdkDblMuZoYfOXILx8EAPFMKE2GETXNXR8UyFri+MR+BGkaAIgJsPVE54mIO67ZcCXxWIc53AKPAAcC3N0tTjb1aEWFiV50i9djCYqNc/a7MxxPcnvyNs1yCAQ41SUgA42ar1QkjqcDIBC2H73nXpLCKrCGIkm6MY/IcEIFSBcW5KX9GhAukYEARUAN6qY9GQhQweugeFC4uMn4nIHgvseEAgVjM+OvS5UAbDTxajsZwCyxY5djeacmu/4wxOaX5JINkJDyv/i7mpS4TE7/ZiUAOML7DaAh/asqHMgSNGc5F9+tZkKOOIhoBAZqM5dw6dmH0bPzDmR7VynHsHtbmRhBsrULRPPcucLP12aZZYf9p63i6ipAS5RlGETVkB7R2ir2oa2oq/GAiWE0c5dOoVqcxdY7PgPDZA9rduw4Rk88ianhA6Aeeyn6NtwOIKQLBcJnmxumWBgK2zSqQPY0e5Bq3pJVDHNGxLGLHgA2cEANg1PPYfuIVC2e3wMdEFCpKs2KJ63DRtWfPwXPMd+urh1W2dVXoheHRtk1SLqdLqFYAQKnDn0fhcIYrt3zm2htW8XaUPIQiUsj5+nexXSqHWuG7sTqFXfg2Okf4Mip78E0bfR1XSMMVOdxinq+iauEx4IBgVou/MjVAmSUsBor+BVgB7oqV45cEXBI+uCCeMxlWGo0FnLieTJNZ9ODahIAaCZK9Xc+aVKh8iMIAepVL1ZFpZvT5UPofgeiXge1aSXhq+FQxK6FECRxwVuSS1IXUqZh6QCA3Kj+WbqugWrJN6+mo6sxpy+U+9CNBcEzVRl9jCoJ8gEM1cpPKOAXNtJVEDZ9JUcN63CT7MdJL59FhnfvmY0tlTwPoNl8AC/BjluKckpcpuQajq/Mkcu36pc6DbgJdn0Bbzlh7wk1iPTcgmNMsmxlnriU/fgsTobrh5O4FMSjMPh+l8Jw5J/LEcOV265nBY+c64Q/hLMp1b1G+VnV806ZFfZjlaPKLqUU55/+Nux0Cwa33w2rRLVAtjh8Gi2962BWGJDVKfdGTQYAcrhJnWvxwh/+t7qvYbicRN+8fAAQKTjlS626CIAgle2AZbMQoAuHfoTC9HkM7XgzOlbsgGmnsWLr3aFRpEJl5iMPyJ+jyJ+L3mOeuyH+bhXlsVtlwEmHG1Iz/IYhspbJFKjCvTBI5B5Iz1AHAAAWaln1AgAAhN+ZKOJ+dh6FUQlvMPFkr4Ja3Vk9NyIGq5UxNXYY4yMvYf2W+9Daukp6v6ifvCuNzTeABVWnY+YWAmDTmnvQ370Th07cj0vTR/0d8XNRM/NGnNcrmO98cdJXhFr3b04uXryIX//1X0dnZycymQyuvfZa7N27N9hPKcWf/dmfYWBgAOl0GnfeeScOHToktVGpVPB7v/d76OrqQjabxX333Yfh4eFf9qVIckW8LU2HATXKD6hz3JKAwDJCfmLP0yF/qXaAEXtc4/6XAAQ8T/u0I67PJQxDy10sLpbqZA5IXiCZWk+0btWfENuPCY2MpwKGDDMRLhTUQGxOgGchmPC5IuzZqKtkcEBgz2vaNDlpueZ6/RVDx/muUmmqAMGz0dBC30i59iwfHBjNKf1GjSu7jY8F2C0Tfy6HrpMvsPzHs4ByO4HzKnLy/zLErFKYVQqjSmHUWJiVUWPv9XK80MRjYIBQGv7eBDgQFeNmngsHBcQHPHHfA78+/o7Mnn4Z8xePYejmd8O0mTmaAyI+zmpxDpX5SeT618n9iQqc315wrkJ9KF6TJIoSqAKA8DiqNRSpx4UUo+zYpuZEX4lWw4CC3wkLB7ISaRBiwLSSWLfnfdhy529h59v+FG0DWzA9fBArtt6NhC1TPlklCqtEI9edEwp9JXy2n9SkDAY8v6K4WWUAgIuTJiEAQBREBdspIutMpIKucGwsAIBv3VbvpfI8RI8APCop8UbFld57s+ZF32udjqB+Ky5F1SnixKFvo6N7M/r6dmkAkMabTZswdHEjJjGwZf070dW+Ga8c+wam505Hj1UkAqyE+8wBgFmSJ2bdfKJbl6808Sh5zX+WIjMzM7j11lth2zYeeughHD58GH/+53+Otra24Jj/+l//K/7iL/4Cn//85/HCCy+gr68Pd999NxYWQiquP/zDP8R3vvMdfP3rX8eTTz6JQqGAe++9F677KibSLVFelXCgV8O9X7cmQKOwmbj4/kbZ+E0mKusLXTU+LzjfNEDckIUokhMQaVsT7tQsZafCfCQVEFNj/zm3v08XKvGccyU55jr17vfQNa9VOnhegIeAZUWtQMxFCgsS2rJKFE6aIHsRmNnq9zeaBukPw4JoRa/tukkaVCyVmIEMAK5cOyAYR7V+ciVJxD9HznkePak+2HKyjZmEeD5AnDRruQ9cyk0q79XWsHiYyqzUrPBvPXj2l2c8/5USTo3IF++wGBmROPGbkQiAb7D4Swn8TcxfXCGkBjA/fAznfvZVWMkM7EwedroFdjoPO5PH+Cs/Q9uqHWhfsVXTCPtvYfwMACDbv1berYRFiRKhVtWNjUS36ULViBLKoTsPaAx+5cJY8edEnkUAAkIFv2fNDUFu0vl9DyCV60Tf+luUdsLfraIHJyMz5uRGKKotYV+cPhVgAFoUkQY5PRXSglql+A9QMs4ISqlujmfF90gQWqcCAOncCHUqjQA2lvxaf4GWwkqDMFeNAu9RaZ45deQBuG4Nm7a8KwxP04yLmiTCZkQNogdCKisSMbFl3X14cu+f49T5R9HRti56jtRudBupeXBT8kRvltxIorA4Nrx2+uS/Wfkv/+W/YGhoCP/0T/8UbFu9enXwO6UUf/VXf4X/+B//I9797ncDAL70pS+ht7cXX/3qV/HpT38ac3Nz+Id/+Ad8+ctfxhvf+EYAwFe+8hUMDQ3hkUcewZvf/OZf6jVxWbJdinheSKup7msynIQfqx5Pdaw+WmTvNRe6orP4RCi/mjtPEiMMgQkbVsKHdB+04AEImXv4asZ940T+XzemGCo23bEN2ZWCsUWPa6go0OiCqIqu3XrblyLth6OdGqZOA6BBFUUnI4+5roU05pqMKglyArSnVWI+qzphMW6KgQ1Oj6jmIDRjxechPo2O5Rz9jbwI1H83qcGoJJespIqWZdrgXjcptZwhK1hqWJAggZvcIP43i+gPb4aPj/9w4dZIj0aOCa2nSwvbiRNuseXeA7PWuAicKEQII2p4LPe41LH4B1KtYfjJ+5HItiE/sBFWMoNqYRaz5w9i5KUfAcTAyhveGVqSFS8bABTGTiPZ2oNESkMrBMZ2YtT04VSG64djacYpXYe6T+M1EfOZ4s4DQq+cpARrAIA0Th1tJsK1QAUB/P6Mn34Gs2PHsHLH22ASK3IeEK5bVtGTPR8USM5TJOepBADYscJwhMJ16an4uUuKMVe/J9EKrrw30n00iQwAPNkgp/tW4liGVGVbTdAFGBBoWNtHOK1YuISJiy9h3ca3IplqFfrVnNisfqDZ5rpV7D/6VRjExKY1b4sdIzXC+x65N6peYRuRXAFC9fPfVWks8/Pz0k+lEqUfB4Dvf//72LNnD9773veip6cHu3btwt///d8H+8+cOYOxsTG86U1vCrYlk0nccccdePrppwEAe/fuRa1Wk44ZGBjA9u3bg2NeC1m2J4AECoJa+S66vZ6nQJcMHHgFGn1szSTMKtSZQJMegTiqUYUvWTcG0coXWtMIaMKEUV0CTI+hP224TzwfigeAT2pSQpHvERDYGZr2llEa3hMheUpaMByhMqTAEhRsj3mOKlsEF6vooZqTt7vTSRitzIxvZmpwizZAKAyft58mKEhVXND1lyN5CCikxd6sANVWfzGeM+G0ioHAAGIKhFEjnqnDs+pb7Z10Y2rOpoqHQQ9MdVIbHcf4g99CbWYag7/5Wdgdnc2dCAS1DJYjxAG7j/y15q+szf+/shc5w5VDR0jwTyjLoQ0V39VmqUJDOl//v0a5Efyeaw6bOPwEKgvT2PrOP0GmrY8dLh7neqzglTgdCQCAEoLC2Cnk+pgXgCvLOqWFX6v2Xa0zxnqJ6vXAp5hcKVr0iQcpWdPQsc/wYZlykS2xIKJ43wMQQMI+p8/tx+m930bv+lvQObBdboMDFkUxtErsYrmnlItZpnD90DmHW/mLct2F5CyFkySw/PGKXoB68wNX0iP5ABpwz2lAI2MnRPIIiO2ytjSGKJNI3mJCAaJZFww3ZAwilK0tXuBtRrCdEmBs+AVYVhq9/ddJ1wFAjkgQ1m6JHlsnynrsOGXsP/Y1FIrjuG7rx5DPDYTnN9BZWPVguVaAk4uf4DlZxxIjXF5TcWHAfQ2j0XnfQ0ND0vb/9J/+E/7sz/4scvzp06fxN3/zN/jjP/5j/If/8B/w/PPP4/d///eRTCbx0Y9+FGNjYwCA3t5e6bze3l6cO3cOADA2NoZEIoH29vbIMfz810KWBAKIRyN883HFtuLAgC7sJwhbiXSoCX3RKb/qfnV7XKEsBRgAymIZoeeMbmOTVHTowX6F6tHzgUAQDmQYzBugY/fRNijs5+eKScNiO5RKycfSPebAh2roVJcqIgtDXHiHCLRUIGCTyNj5c4gAAeV5t5wlmNvkc0/PJQIgQCwPqJrwrBAIBEOxoyAgrnYAG0MY05+YIwEQYB0hnH1TngQEKAGskv5ZUqMxXWOtxWcmSshAgI+VNAoJIgjveR1FyHD88Tg1TD/5KKaffBRmtgXOwhymH/kRet/3Yen4alvImGQV0TCEoq5Qbsm6jDZ+hcSohcpkWOCr+YuPowpV80tUUb0DsUmNCniolRcwuu8RdG++Gen2PkEx9f+nABACgIDmkOeCmgROeRHlmTH0XvN6qa8Ihao0DvnvegqOrLwra0s9AMCZlRSrNv+mArazBp4So6rPGxBBK/GAWrWIZLot+Htu/AROPPdVdA7txNqd7wzCUtS24kIzRa54HotvlikqreH7pAIALk6SIDUrh+iIRo96se+qwsl/t0qyUUT19tYFAAjvdxAeqlZ/FtZFnYHIcClUz03Em+G6GL+4F70Du2Ca3N0D+RhCot+LIQMYtYglGyRBpbKAC6PPYHjseXjUxa6tH0Vri6xo1l3jdSQZmpo5ZpXCUeYNQv1rvlo9uGm5cOEC8vl88HcyqY/99TwPe/bswec+9zkAwK5du3Do0CH8zd/8DT760Y8Gx6nMZ5RSLRvaUo/5RcqrRxEa82KrIKEeaFg2EBC3q8fUUW51oET1EgTjEofLvQQaiz87R1ZqRAUTYJMbNS0Y5RhzNL+X9cJ9RI8AIWFYkSqex+6BS0FcV2YoEoRfpzoRx0q98QlWLKmtGCAQLGYxz0G0BPF+05MuFlaya2k9ZgZAAAC8qn+NGRcoitzKYZy+kxaKeNFwf0Sh9RdGsxRN7iU1AiQRWRWZtYr3QyNAIFZhI/Bp3vSeCjfROIRHAgYNFCdqMKWSmkDxzAmM+db/zttej+rsFAqHD6DttjuCMCMvgbpgQtsPZefwGGJqoSnmmkbiJomchKiAnUBxEx4N0b3T/H1Xt+vG5ysGwTQj5NBcjgR5ArwdgiBeu1kRmW+a8QoRN+R817GOcBl58SEQw8Dgrrc0rm2hVeYpFkdZUmS+d602FKQea0+UplJsG5BDUoT5o95YqQIyYjwY4hji2lMTYYMibRqPj1MtItvKLMKF6WEcfeqLyHevw4brP8A8Kbr2leToYM3hRpIalcYvMv/EAQAASM5pknQB9r0ohjBxfePATWd59kwSjLceANCFq0nPzmucC0AJolXmNSKtHQBmJo6gVi2gf3CP35DmHEql9SnYFmk3vC/F0hTOXXwSoxP7QIiBwb7rsbL/FqQSeTQSNr+q+hCVQI5ZdqXcAGr4xdSEY36VPAHLSc59tfsHgHw+L4GAOOnv78fWrXLO05YtW3D//fcDAPr6mId0bGwM/f39wTETExOBd6Cvrw/VahUzMzOSN2BiYgK33CLnAv0y5dW3v9Xjutf9LSp+viLbVG6BYcgKvq7fJqzbRJNfQByZVpR4XsSKwbaHv6uWzIglK+BDFqwJKWG1jrs/9UQ8pt61CgAh9EBED5PAUDMxzuI9X45S10xStpBArErL+aj24BZj4iVc1g6n+ASE0AqdblhHkbKKBG4mBnRxS1pCsSSR0Iq7nIq6Trr+uIwamnoG1IB0vZ7jYPT7X8eFf/4bWNkcVn/mT2C1tWPhwEvouffXkOpb0fQYA6YlP6RnuVV8zap/vsPum2f7BcSSBE6SLJlL/xcuNPyJ5D4sIU9KFLNCYVZYnLxZpTBrNFDAGolIz9oMNWvAxqOMtXhpGJPHn8fgrjfDSgqMNXHDIMKPIAtjp5DItSORaw/i+w3RCo/o74AA3vg9VQEAUDfuP5LfIYxdzV8IznVDgKwbiyjn9v0A5w4+FO1X7dJxcXb/AyjNjSGd70Fp4RKOPPH3yOR7semWj8EwhDwAoR+JalQYt6gwitZoDgDMKpUAgFVioNJNEiTnPCTnlHh9cay+d1hl7aEGIu+fTAvK2vQ0ibS6tVPaH2Goi7bBdtAIHaaaI6DNNxDekdGLLyKXH0SuZSD67NU8RUPZxot+evJ9uTj2Ip5+6a8wMXUYa4buxG17/h02rnpLCAAagWd+zxuIWXal/AGAAQHxnrhXeMjkr6rceuutOHbsmLTt+PHjWLVqFQBgzZo16Ovrw09+8pNgf7VaxeOPPx4o+Lt374Zt29Ixo6OjOHjw4GsKAl695VRnhVdFDHsBtMfEeQpihYfE1BtPM6KxvqtsOkFxLSnJCbEeAd2YqGWCOAKVpRhO1EyOgzheTfuxXbsuqGmGvxtqXBe3LHkNrTCxz1pZqINDvKhbNnqMHkhpp0bNdWYumlhczTQe6hgsHAhg4WsOAJMGQACIV45Ur40oZiksVKaKYbnwFvUAJJigNWE9cUCIv0tuSs/C4yUQ0HnWNaj4Vvw4r8D4I9/F/MG96LvnvWi97kZUxi5i4sFvo/W6m9B63Q0NcQUvUGTo86liJaiirckJ+jcnQtJ/kFiM8JEspT6EqPQ1myPB3/XASh2Dkw0HcGsVVBamUC5MYvSVnyLV1ovuTTdrj49VehXlfWH0JHK9CiuQn5we/B3Mgf5/OiMF/y51c0ydF5Wfp20zZm4ylIJ8avue52Li5DNwnQpaOlejs29LsE/0AlQLszjxzFdQmD6PVTvvQ8+a6/HKI/8ddiKLzbd9EpYZhh/IjEVhiJEUTirmKfjvguFSKUeq3Bkeo+YiiWtUEO7ksfh516miUphFtTSLSmkWleIsKsUZVP3/CQhaO9ehvWsD2rrWwU5k2TMWFHGz6kH0BAdhPAJzj3iNqkghumIumXhflLw88ThKSHQd8rupVOcxNXkMGzbfF+lXa3j0oOQIUDk/ACzJ+NiZB9HffS02r307CzHSvYuRMekoSOX1Xw15ogaRWPMARIqMAVeBwC9C/uiP/gi33HILPve5z+F973sfnn/+efzd3/0d/u7v/g4AQAjBH/7hH+Jzn/scNmzYgA0bNuBzn/scMpkMPvShDwEAWltb8clPfhJ/8id/gs7OTnR0dODf/bt/hx07dgRsQa+FLA0ExL1bmvjzukWtmln064XEqHFXXLmtByB041Hb1pxPanIIDbdqUCuOokuv0ErH+ECAW+UlylAdS9AymZC0wpPgavGhQQDilfZmaiHoHpkXndD5ePjEL923IFmZwtO41rk7On3Jw/wadlL2rBUAAQBADFUotSig5Amwkqv+fiUsSByXPc+AgFUw4PTWAEpgWFHuZsMPO6pHEwogkpTO4/PriZv0lRRbDxDMGgs5YsmN8e3MHXgRsy89jb573ov2XTejMn0Jw1//ByR7+9Hz1nfJw3XALPCEjc/J+pbmkr5t7bUuzyjeoG0lJIf3wRUc4VC+S1LoglA11RNXR7Gk0L/LyxTiJ52KLeoWd1WMmjJX+HNAI3Bg+l4j16li7MSTKM+Oo7IwicrCFGqlkNPaTGaw4c7fgAEz9tGJIUw6qRbnUJoeQf+2u0Lrf/B/zBoBQVHloEDU+3jICdc9m3y/gn5VC3BEsZTHEOGQd4HFS2fhOhVkWvtxcu83kHvjnyCRapHu/ezoUZx85qswrAS23fVZtHStxtnDD6G0MIFtb/1DJBLZ4FgVAOgkeCeo7CVwUmF8erFXIUzwDQ/UdUHOX8JscQbV0hyqi7OoluaYsl+eQ6U0B7cmf8x2qgWpVDuS6Tbk+gbguTXMXjqJsfPPASDI5fvR1rUB7e3r0Nq+GglXRE2yws+UddlCpirUEd3AL+oVNQ7JibMAAwyS8UpzC8cuvgSDGOjp2xmOUXevIx4pOR+AJ0d71MWhE/cjlchj89p74wGA39dyxKhqKELLHmq5cJuYFA34QOwKFw8GvNcwMXipfV9//fX4zne+g3//7/89/vN//s9Ys2YN/uqv/gof/nCYL/enf/qnKJVK+OxnP4uZmRnceOONePjhh9HSEjKi/eVf/iUsy8L73vc+lEolvOENb8AXv/hFmGYT9H+/IHl1Hes8/ly0zOuUeR0QaEZJr3c+4Me0az4AXd/1wm+UY3QKM3FcUMvUF+tYYgVerdRjAlqmxOZdKP0Rh4LGsZg0A+IES5C0WWPZASBZg4NYU6Efo0q1yZNsAifInwmBAIDQC5B0JSBAaKgIuCkKs0wk7mw1DlQEA+JiZBX8pNhxG05vDZ5jMiCQdEHmmZnVS9EACKgSq5iLLn/NY/ISiPVSAEy5a4ZKlBpAZfQiRn/0r2jdcT3arr0J1ZkpnP/K38BIpjD4wU/BsOyAb3pJoZuCQuaZywt9ioyXsHZ5GBBf8F5TRrxAgfUv2QgVNL79cpKdQ0U3vMhmKVoNIWyjHpgozozg4t4HkW7tQ7pzAPn+jUi2dCLV0oVkvgtWMhskrOl4+SWJseDPDx8FQJAf2MgAru58zh5X54FGEn7FMCJN+E7EayDqozwPJAAwJAQbjZygvEDa6DFYySy23vYp7H/kL3Fq779i8y0fB/EIqOfi/OGHMXL4UbT1b8a6mz4IO5nF3OwFjB76KVZc8xZk2gdQ9T0ytkLtKa09PMFaZSryRSykR6mHyvwsqjOXUJ2dRHVqApXZSVTmJlGbmwKl4U2zE1kk0m1IpluR71qLZKoViXQrElm2LZFug2FajJqVv4v+/5XSLGamTmF26iQmLr6E4dOPgxAT69a9GSuHbovc7+BczTqmJtwSz4sY2GKBgFiMrcFkQCnF2MiL6O7dAduv1szyhRSiEq364AHUC/I2+Ptz9sLjWCiMYM81vwnTSuqrE9cZlraegUo8YpCIR0D3TfMq4wBzfF+VV1/uvfde3HvvvbH7CSH4sz/7My27EJdUKoW//uu/xl//9V//Aka4PLl8EKAqp44XDXHgk5a4/VXyFERCCZo9t97fQGTMWiDgapTqOmFR3EvAw4EkD4CO1WgpQKCZ/YAU4hSZhASWIGniaRaEEOX3peIWfqkUEWVBdYPy8diLHmrZcLs9Y8LpVi1OgOEn6oqKPf9dAgIQ9jlRr0CsksAfX74WAAEAgMHCiKgpJCIrEhuC5FujtQnLvrhJxl5ULxzCqMn5D7X5WZz/1heQ7OxF35vfA2d+Bue+8j9hmDZWfvizsJMtWnpsnVRbKYwKAVLs+hoVOYsT4rGxwYVfTGh57VxJErFiN2AsayQqM1AzYURqHLeoQKRbWcLawLbXo2vNbuGY+PZEC70uwV2N9Z8bPops1xCSFgtSD+lBdW3ToG12THROjFS05eQCQviO5MVTGWPURFtl/gsZwvhkJLYV/j47dgxtvZtgZ1qxbvd7cfTpf8LY2WfRMbgNJ579F8xPnsXQznswsPlOEGLAc2o4/czXkG1fgf5td0lUr7UcCYBA8B0LDDVs3VD2+1IuTOHiwZ9gceoCyoUpUNefyAwDiXwnkm3d6OzZgtTaLqRaupC12pFM5WH4zDiiBVk1XgXF2kwiVX9PptvQt3IP+lbsBqUU5blxjAw/h5MnfwhQBEBAW1RLDbl11bnakNnmoHlXAmBKw0JhiFGqfZmbPYNScRIbt7wrsi7p2IBEOX36YYyN7sP2ze9De+tq1t7iRZy58BhWr3gdY/+htPGax2xW8qXEAAHV02FUPTiZ8INT10NKIk1flavSlCwNBOhecJ2CqIQ41N2+VK+AuM3z2HfXyJWyVEu6p/xeBwhEYuiXEtcPRKsHq2Pl3pXLEZWJyaXBdcSyIQGy21WK/ddco+6SNZNi3EStjQ1fwr1MTwBzG1hn1iUbTjfTmKjlgdRMeAkaAgECmNWYdutM5FqL47wF0hkTEO8f66YZEJAYicCUkDiaUG41jWMmUZmC1OM4649Z9UGODwTccgkXvvF3IMTA0Hs/BWLbuPi1r4AQglUf/G1YucZMCSwUiTakOFWFVyamGtD1aokaTgYPoVWVH6OGewBRt4IQkx1hHHqVRcwToEvwIITjp02fI3oJbDuNZK4Ti9PDEggIuPq5ThgzvXIlMY7tinou5keOo2/z62LHHvmeFIu9rrJvXC0B1dPAFWdDp+BxG48PStRr4OF2QVvC+1otz2Nx9iL6N94BAGgf3IbedTfj3P4HMHzoYYAY2HrXp5HvCSvEnjvyI1Zr4b4/Ak1Gb1gtR5BYEAEKUypVYwj/zp1aGRdO/QyjR38OK5lBy8YdaGu/GYm2biTau5HIt4MYJlouhi+5VdaE4/hdStZlItNfGo6vlHNLt0AHbVYpsrlerN/0dphmEidP/RCWlcZA/27fSKZ8Z40AAJe48NEG64G6jnAr/+TEYSSTrWhrXxs5x3C8yNrD23HdGkYuPg9KPew78I9Yv/bNGOy7HoeOfQu5XB/WrLhTAfeQ1w/11VOBANGHPKmi83SYZQ9OWvYQ6IrtXYniUgL3NWQHei37vtLk1ckJ4AqiTnluxqLXKOynQTgPT3qtyyoUBwSaAQc6ICAm8MQBgQitmL9bSQ5u3L/XcPKLlZjz6uUESECAJ0PztuoBAW1jgBgiwdoUgIB4bzVUoFJYkFAARhyLXaSoZYHWEyQAAsacDTen0TT9Z+AmqAQEJGu8MpFzr4AoiRmC0oD+5fbSLqw5/afl2awfV09HHFj/46Shslfne6Oug+FvfxG1hTms/ujvw8rnMXdwL0oXz2LVB38bdr4tsmYRnudnAKTJcCMAcDIM8Oi8LEuRkMaSVS0mDkCy8nvHKTZ/UUo6lwhbDRAq7+q3DrD6F8sQlV2omUqgOgt5o2fllhbhOVV4Tv1qdGpirXqfDXW/P5bC+Fm4tTLa+zbHWlql+6i5zHrFw9SiY8SjEaAn9aWENEmgWZ1bEP3meR9zo8cAELT1bQy2r7rmXixMnoWdzmP9TR+AnWoJrmdm9gzGD/0cK/a8DdaKfnCHjr0oALJFBuR03PPiOuLWypi8+ArOH/4RnGoRA1vvROtdb4Bh+xOKcHp2XA8A1PcpAuIohWciKCgW3qQoUOaKJyEEa1ffjdmpk5iaPobBnl1Cf4jWBGjAGKQNG9W804bjhd6A4P2MvmuV0gwy2R6Jj90QGQClkNTw/KnJI3CcEm64/g8wNvYSTpx+CBcuPoNqrYAbd34WhmktzcAIsGek3EsVCIjrLn9ekaRgzfziWQTWwr8BN+pV+aXJ0sOBYpRpapqACdmqbRjsw3Qu46VsIlQHEPrlE0JcuFEzogMvChAAAJoIb58KBHgSVMh1LzcXSQ62DHbOEvMgwg7rgBzVo8Inu0ZAANxbEAMEpH4QDxKh2RfjbZF4qoW++KKgW6xFyQ4TLKxl55gFSwIC1JKrBgNsUeFhFrrQnxC4MaXAqgA1ny0xPWJIQMBKOagt6M3jrKpo+LdZUYAA0SsdABry6rtJpohRxLdBPQ8j3/8qShdOY+UHP4NkVy+8agUTP30ALZuuQXbVBtYXJ1WqoGE146D/DDMAEEpYrsRi43NEYdXBIXmIjFpzfV/Jok0s5db1pUxL3JNB4X8TzdYCUJpRFOmze78Lz61hcNsbwzj5OoDDaBAWR9zQ+g4AcyNHYSWzyHYwmtmI9V/1tAhhepG+lSRosR9WFEoYhyc0rQATdh0KsOLhL0JyqY6qk2+bHT+ObPsg7FQuGLtpJXHN3X8MteDXzKUTOPn4l5DrXoWOG+6Urqnawg7KjomJSAjfEYPAdSpYmDyLuUsnMXfpFAozwwD10LFqJ4Z23Qt3bVjNWzRWcADgWUCiEL/+BgBAt6arVvw6CctmmRmqKPVgW2lQ0wgUfRbqJxqQoM3fI54HLxF9+HL+R9STbDhRFh01h6RcmUU2F/K364BCpBYApRgdeRGt+ZXIZXuxft1bkc8P4dix72Dj6rcim+nxD1TWxDre5HB/YyMacT14yvpslj3UWvRrdlB8r26rV+WqyLKsZTaIKdco90F4ixCrB8vQ5wpw919coSudNPI2OJ7cdz2WoXoIvgkvBqk6WiAg0aDViVOMtE8I+4m7H3ETB98WR5eq3gMxxMjzgJhwKqPqhpPy5SQqN6nwBJYY4Z5xl7WUVBxzHziXfsvpEAgATPkPQoESIRDwLMB041l2WP/CIuS/E3YxBAIA4FVMWBnWgN1SjQABI8bQSqi8wHGgEZzXwILOGYDihIdHGA7F8GPfwvyJ/Vjxzo8hs5KFKUw+/SjcchE9r7+vbt6BVnwth1rLWHL8kBfOsvGLkFDJY1oVo5tVLZtAbKiPGP7wC5BIcirqK+CqiCE7zXpnRGvj9IVXMHnuJay78YNIZtq042KVnGM8iboK68r4ZkeOoq1vE3SFsOLCgQABlHMFXplP9fUCtMPQeEio9H+0Cq9cgEkNJaLUw+z4MfSui9KmqlU/x8++gLPP/ita+tZj/es+CuoZrIaGcG3JOQHM1Chct4aFqbOYmzrFlP7p80yxTrYg370OXetvQL5vPVL5bhR7SeiJ4tO0BeSEECB70ZNCBcXnKXoARG8KICiUBqKc/eq3Ifzq1EqwrLQ/JkPOCSDyeSoQYMBBDU9FRNRjpN8NYd0Q+iqX59DZxWhcubJPTSUXQPFYl8uzmJ45ic2b3hUc0tO1Dd2dW2C6zX+r4cCVv5V1LBIWpLIg6hKCHRqZu9zka8e606xcKcXCrsoSQYCarBIHBnjMo/iBBdtqGk0jTnltVnRAAAD45KD+DUSVWFHRg/K7F38Mqfrc9D4YMKq+ZV+4V0bNk/4mrhd4M6hpgriuPBkt5X4sJZFa+Tt4fvU8Ao5QuVDsyyDS5H/ZWUkeAGUIQUyysIhJlivhulPTLgqD0WswSiaoRaWcAFbSXj9gXZKu6C1QJTFjoJIDnKIdAAEAcJMUiTl237yEDAQ8G/Xvl6dRSgXR5Sao4yU1iuLkRZRnxrFw7ihmj+3F4Js+gPzGa+B5HuYOvojp5x5D5813IdHRUWcwfp8+RWijvqXL8GeXAMxchk4dsKQkgIpfI4G/IPxemZUmv4Uldy7/SRtZ+pbbDVdahGuIrS4tnqdOqep4KcX4yadhWDZaOlfDTKRx+sVvoX1wO7rWXLfkMYkSxtTL+6uleRRnRzCw6U6pJoAujl+3nbetywmoRy1aL59M903pKEgDYCo2T9mxCwsX4VSLaOvbLL0DIk2jZ3oYPvBjXDz0KLo33IRVN7wbbosVXC9neeIAoFCbwuyRFzF36RQWZs6Bei6sZBb57nVYfe070Nq9HukWFsrC2YCKvdwgI9+77FioICfmhXo0RL4nYvK9mA9ADQKrIoTJ1JR7IeQDQFiz3KQBq+Si5pQE9p3omiPVEBDmcBGcBExCAFy3iiOnvodSeQb9Pdeir+saWFYKxJUr6hI3WmWYAwHPc1CrFpBM5iPWfqopbsa9MWNj+2AYFnp6dkjXQojhhzmK1v8mvAGNvOVAJFndcKIUoeo1UigJ700WFbwqVwVYhidAZ/kJCmqpIS9BlVwBDHBPgZrs2qxXIM5Cr9vuUlnx53/Xs2LrkpfVtjXHqF4BHkITWJ74364ASPh98RClKtNZ2+MSqEXLfjP3sRmmIEDPFASEExmf6JrVu4RjdYnA4hhYUrKy31GeJ2/W35S7GAKB1ARBqd93RzsE1KIyS5BFJSDAk2cBhFZxoSvVW2CVgEqM7pxsraBazqDa6gVAgLcbLAwN7lskIVjxGqhWI+4VoDb7f+zFH2HiOVaZ0Mrm0f/696Bt6/UoHDqIsWceRGVqHPnNO9F5yxtix2A4AHXlBMnY8SYoagkKuAS1HGAXCBKzjc+T+/MVKSGEg+cVxOZQXIESPDdfiZV0ySXmCQTx9gTgYQQNPQYKOC/OjuDMvu+Em4gBM5HG2t3vAYEA5us0S1wqKI3R/WaVStb92TFGDdrWu4md79UPYdLF9+u2y9cZDwZUS3+jfBGdQqyjRZ0dPQbTTqGlY2U4dn8eN1wKN0lwbu/3MXb8SazcdS/6ttyJWkv4oTop9n9qhp0zO3wEpx//MggxkO9ah1XX3It8z3pkWntBiOHnCbBz3CTzii72hQNyU+HYWs5r1gvB6h2IehgNr9Gshe+XIYABUETmXmqFCjShgJM04LpVXLjwFObnh5FvWYF8ywq0pvpgWcJACeA5VThOGckE41CX8sEoAEpRdUvYf+TLWFgcQ3vLKhw99QCOn3kI2XQ3KDzYVgaJVB7pdAcGV98Gm2aiANggINSEaSZRqymV0yB4lqS5loKCYmR0L3p6dsCy/MlHV5VaOCfaOCL3Wlc0TKYEVQ7XvPZWyUMtKwMDz/RDsn5FhFID3uVwKL8K/V8VJq9u1G2Mgk4JkRTnoLiX7sNpxgrO+1GfI5+kRKu6mpCmoytVx96MV0AjpOpIFvXAK8AV7aorT6QiEIAABMTFrR6Vaj1ZSiVlQwME6oUdiRPZUgCAcm4sEODx/0oRFIBZe92kr8grVkJqsMV1YYjtSI8aARDgInkEACmOVlL0Y6y9nhEuAMlpGQiwaK6Y5yIqWuIhimKvPW+Jhp3ChROYeO4R9Oy5G92774Jlp+BWKzj9zc+jOHIG2ZUbMHDvB5HuXymFkvCk5dixCGJUOVgi8BJLtzxJgAiNlbTLFQaSZM8BhSaRlP5ix0JqVPpmCAAYpCmLf3COqIwgaoVX5dK5vbCSWVz7lj/F4sxFFKbPI9+9FolUi4wXRJIyCJSbyrsQgGUNJSfAFPfZkaPIdQzBTmaD+xlULlbyD6TrUcJ9gjaVuHxR6in8TT3LOh4CsX03YWB27BhaezaA+BXXRQusZxFMnHgOY8efxOrr343ejbfCswCrxI4pt4eDryWBS6/8HBef+T46+rZgw54PwrJTDIQoXhPPCpX4Qr9guFCAsZMkQTJvgieHEiIplqrnJjC6UNmyLQEAwH8haOAJCLy0FgnyNQgh2LH745gdO4b5hWGcPf8zuG4VAEE23YV8bgXyuUGUytMYvbQPnufi1mv/QAYC/npQrsxi35F/RrW2iN1bP4HWlhUoV+YxOv0yyuU5gBDUaouolGYxOXUEIyMvYOPWd6Ore0vk/ZlbGIbrVrBYGA0vR9UL+PX5Ui7OoFyeRnfXPcLNi6ESjc3j02/WnR+l6ha8ARUXnsAo5VkEZsWTwn60DFhX5ao0IUukCG3yRdMp6JrtEes3385BgluvMhKfhTRjUpTrpsfYhMW/Ua4AD62R4lgFi7vqnYgUN9NaFJoAAMtMhCYuBVwlHEhgSQir+dbJbbgM0QIBIf5ft4gbNVo37rHlQggEGnQOgERi8eMUYM/ylV/FSm9O2TBWMisTpUQCAm6Kwp7Tj6WhMSIOOPhiuFGlyCkVcOFH/4LsinXovvXNMKkBeMDC+aMojpzB6vs+hdyGrYF136iiaapPs8KAQrPhlMVBisQMgZMBEnN+pVo0f75WuPMrwQCYUQH4qs+N7GYljJPVcc037IIrk/Us7rp8gctYhyWLPzTW27rnhmEzqsWceh4mz+9D18pdsJNZtPVtRFvvRv/Y+u1yetnYfqv6OYF6LmYnTmBg3W3161coCb1hgnD8SWr4jqysY+lGCanx+KRhAKhVFrEwdQ5rd78n0o5nEyxMnsWZl76NnvU3BwBAJ0bBwYUn78fU0ecwsOlOrN7yVj/EJDSySHkehM2/C4PyA3NSgOXX5UiP+9uSBJnJcN30TAggTFDyXSrlPkjhUCIAUO+nug4o+9s716O9cz0AwFysYrF4CfOFi5hfGMb8/DDGJg/AMpPo774Oo5dewuEz30dvxzZYZgomMVF2FlGuzuHi+AsgxMD12z6FTKYbAJBK5rGm/3VybDwhKNJFHDtyPw6+/CX0DlyH/qEbkG9ZAUopzpz8MYbPPQUAsCw/kUvneVfeh2SqFQQGqrUC4oSFEumsnrGnhPvF+9bEmm1UXDhZ+YUyK550L6hNroYCXZUly6tcMVj5PY5lRxAdLVewz4+XryvcWq5+SDwZWZQmkn0jUs8rEHNMkPcghDyRqhPWM/CBQHCcD1q01Y6B6PXVS8zlxy4xxyLIC/AAIAQqhIaxlhJLUJMSTLaiIqtOglyU51Mv7EG1hAB65d0oE7g5JWfFCK2Znk1h1GR3bKxFXiOJGUaH6Z3PBEAAAIz2CsgYc4HXWmUgQA1or594Qt8x/dVTqjxCcfHhb4B6Lla89cMghhFU/S1cPIlEaxfya7aCVJj1tpkwH4Ap/42YeoyyAS9JAQOgJoWTba5tVYhHg7hYrgjxsCA3gUjeyBUlXIeLAW1Lbk40JPj/1KsADIRWa0LZsXPjJ1ErL6B7VTT2P/D60HhAEFh5lXCZIFynFvYHsHbmp8/DrZXQ3htSg6oVedX4ft23rmVXEvdJYRX68TcSXaVgbdK2STB5/HkQw0DH4HYAslJdKc7i2NNfQq5zJVbteSfbX6MBjWMtS2BWAbdQwKmffBGL4+ew7vr3o2f19RFnX9y3JoYAVlvZ/04KaDkXHpOeciO1JijRFI3zk2LF98m1CeyCmhAF+T77RA1SKGKCRDwHhAKEGMhle5HL9mKwm71/nlMDCIEJAy3pHhw5+wNMzhyTzrWtLPLZfmxb9y7mJVC5/x0KKsT/Z0gW23d/HOMX9+LUsQcxPvISiGHBttKoOSWs3fRWrFh1G0yHyOsLUViERPpQYiGd6cTU5DEYhoXurm2wrKScTOxb8LVehWaAgJgH4UXfQakismlEC4TpALhNYJSvfCDggsB9DcubvZZ9X2ny6oEAQtgCrX4QTSreXFlUrcKxXgGVrUYn9YBAnKciTpo5Rz2Gx+r7CjmvZwAgkiAdJEk1SxEaVz+B/x4ndShDJaYgwWMh0Z9SighfdFxXAV2e/2xFxobI4iL872HZi7pd8FDLGbAXgcIK1ohZMODmPFCTwqj4ybpKjL8EDHxue8nir5nY4yzarsPuIe0rB0Cg0TliPLWWEUOwgGoTHD1gcfgkFk4dwtA7PwE71xqOx6IoDJ9EbnBdQwYgz0JYpbgBO1FQkKxMmgYU7CJCxdNNEtaPGVVU/s2K+k6puUt1xFCVuTo5BoZDMXl2L1K5brS0rKj7SXGFVhcbL4pZo3VDl4hLMTN2FFYii1z7Cu1+3XytVhKul/8SeAw0MfuxovtmFC+C2kdARmAz6suxM8+ic/AaJOyslExaIw6OPfVFEMPAhts/JnHHGzWKShtruDQzhjMP/gO8WgVb7/gM8l1r/L7ZsV4i/kIK/UYwzkp7uD01BdRyQP6cup6E90X8rtTQMdELalU8vTWZIvJ+RnKSkkYABPh9c9MWrEV5EjEsO8iLG+jZhYGeXfDcGhy3DNerIWHnYBp2Q+s4qXkBEHCTJgwP6FuxB70Du1BYGMPswlmU5yYwsPJmZHO9jG7VZMajWFHWxpaWQYyPv4zJqSM4k/opdmz/EFpaBiIegGUBgWYc1R7VUoQ6Yj6Aso4SJ8KBdlWuSl1ZHgiop2zyUBzVch6XzItwX6RirCBar0AjykxAT08aZ8xuFOqj0uLplDU1wVZjkY+1+ANR5b5ega56oGEpgALhNYlARSrUJQIBpaz7UkQCAmJFYhFAcUVAogoVrIf+AqTLGQB48SiC3DAJgAB8y6GX9AIgwMUzAcOTgQFXglWl27NZmAQgL7QAQM9mQIfK2uv2koBRjJ4DgvqKOUW89d8LLcQA4NXYwDL9q6Txzh15GZWZcQy87p3aZqwiS7wlbshcEidmGSBW816E0oALq2DArLAaAna8d31pwh+rwBbE3xXbZImqgHBvRfM8FbYFv/vHi13wP3T33wyrrb4qVKKu0D+fXpsABiIocBUl0nWqmBo5iBUb7gAhTLkLFLQGCcpGzYsFAipgoEJoZrk4g9HTT6N7xc4oNSi/x4H1X389EkVkEwq/zpof9qk/VidxfRCXYmbqJMqLU1i/5/0SQHFN4PTz/4ri/Bi23f27fu2AsBMnRWCWKaYnjuDcj7+MRL4DW97wGSRzHXABWMXGFjIOAACg1Btuz4Qh7rIiym1jFBEKXrHOiuszDVGLUYkGbalAIOY91AEBNTnVyViwinWsCZTCMCwkjFxku0SfqQsbrXlwcuFERFwKL20hm1qBbLdfm6ImK8Vu0pCAADWIXLhM6Hfzpndi7dq7MTN9EkePfQfj4/vR0jIQfac81F9vpYPrXKMKRAmRi6EB+irKFBIIaeQtvCpXRZTLS5GOscRTy5BLgPNtVkx3TY6CmmaooMYeRKOKt2UgwkYUJzGHieFK8dUvad39gO8BUKsnLqWYWj3GIDGpWN3XoJ3ImI3ocdIxMYqPrmS7ul2mg9Nfe3AvvTBuWGcNlKjRaGgZzY6G7boZTzonHAcAT2MZazCXq4pydpiEAOWCbPl3sx7MssZta8YoLdJBMZtjQSzPO3HZ8zMAt1rG6OPfQ37tDrSs3BQe6rAQm2aq+VIfHDdbR8AsEXg2hZtePlsF8ShTcLlXiLL+mx3zL1UMEv5wIcLPMoW4NPhpph2zSmFWaZAcOj16CJ5TQffKaCiQWfWCH7WwEv/mzRplP1Wq5SMXxwlKQamH4y99HZadwuqt98BwKftxqFZBMlz2o4YGBdets97X26e+blwn1p3j0dg5TDyOf2vjp59BJt+LfPtq6dixY49j8vxLWHvj+6SiaMRDQOc5cegJnHnwH5AbXI9td/8ukrkOqBLnBSh2GzAc9s6LACA5E/7eMswunJpEtgp78lwbzJkODQAAEPX4NQMAAETC3ogHmUXO3+dkZFsjNY3oWqWKB3mdpNFwWU+hznRTRnSOV8AuofIYI+++3xcIYJoJpFPtmJ07h1SqHWtWvzG0I5iGHL4JNPReRETLauVvF0OTHBb7zwFAXW/Gr4iwz4+8hj+v9R24cmRpIKCR1V0RDgREQECVEt/SSJYABhr1rY2JXwoQUDwHEatcg++wHhAAEFr+VADQjDWhkbLfLBBQ9ofxu1QpoiLQxtUaT0D1PDqxUufRqNasIClQWKwCrviqfKxV1CngNKxOanHvDgT3v2YQMY/F8G9HZkTux1u04cVUD45lVqLqoqLsjsklCPaXmBeCCsBq4ukfw62U0ff6d/rvcfz5QbceO85LNrb4G1V2vFEDrAKJLYwW1w/gAyIzvL5fNFPQL11IzM9SmnAo+3Fp8Hs9MasUk+deQr59FTKJjrqhCYbDFHWjVl8xi1PmAabMXzz+OOanzmLjde+HFXDFh/tFhV+6Npfq50tVUY/7XWyL3xsBAATiUvajtqNpS4x7L1fnMTV6GH1rbpYKglUq8zh78Ifo33QHulYzoKWGU106+gwuPPdddF/zOqx+68eBbGgksBc9XykVDCQk/Cl2h5NiMSx2GwAANxUCAIA9H66YSkUIlZoujhBXzgGAkzSYp0gEAPXmGsmiLffnJaLKeAQIWMpEpIbVcnHld454HryUGQAAxqNvSLHyOiBAqDyveImQvjuyRhL5fmUzPajVFmEYwjVQGrW4Rwx0iApFRMlXRf0WdPky9YAA9whflavSjCzPE7CUarFANPzFrPMRNAsEbDPqWSBEVvQ9LwoGjOV7BYLQlJix6+guJVGGEgEAnre0e9ussr8Ej4BEWSlV6gx3kJhwIMnaX2+SUyZfrTUGmvsHfThAnNgFdn5yMqr5SrSWPJxGCY4TgYE6obuJqLKavUBglgmsE74CZLADnDQDHW4y3oofZ2Wntq8g67zAZjgup7iI0Z9/H5kV62C3tAEAypdGMPXSE+i56W4k8u11Y7ntAmPasRfijwnG6rFk4aVUGC4OULgpoNzVHBBpJGZF9gh4CRYbXcsBlVaCYreBas6AmyRwk4wBihoE1CDwTPZDTUbN6ZnhPmqQQAkLL1j58SVy3HLkMsCB5ClQxKmVMH3pOFraVwJgCqJZi7fKcwmUdVWf4cp8jQY/oizMXsC5Yz/G0No70N661gcUmjFzMMP3iTlC4o8odaz2kePEfnTVYNXfNecC7DtluTEEY2eehWGY6B66Tto/PXoIADC49Q2S4s2Vw/lTB3H+mfvRueNW9N96H2yfmthNhOE3boIE1xz3LnEAQDzZA5A/58Gz/TBGARRGqiD799tJGQEAII7sAeD5AIHwXzX3fbnvvBYIeAjXHPF3Pgxl/VaJKdykGXkXVYCs4883K8rkxRVzQkCp8E4CMK0EPM9hbGGKAc6zotvCwUY3Nft9E5+lSgcAdNt5OBhxKUbOP91cJ6+heH6dgNfy56owWT5FaFw8PqV+EqjK3OKxMuGNCmLpRMkdkLqzDKZMi2NROfLFYlpxxzTqm/dnkrpeAJV6LIhrVye3pSTvNitxTEnivjr9BYq747EQKiCS7BzkDoi0p9Ar/bFgScwLCAqXUTnekYcI+NuJF7P48PlasCAbDkW5XX5wZtkIwlPEQmHEL/8eFAgLxsj+5knCgQgucCcJWBV/8fe3pS4B5W7hcH/hr+Uo7AKJJCSH8d9y/56tKGJKvoonVu91KS4+/HVQx8HQWz/MYrEdipFH7keyvQudu+/Q3DhIydD1xMmGYzOr0C9uihgVAlQIvBRdWtKwNEA/3MtlCY2GcK+uJJHeS034REOvoCoGWXKegdqHaSbQ1bcNF08/AYBg9Za3wiS+9VRQmrQxxlAUqZj8H96n69VwdN/XkM33Y9XGu6NtxNVvuRwAxcdUj6p1qR4lzgrk3xNqEBQmz+PisZ+hf91toXfDn2umRg4i37MOVjITAACel7EwcQYnn/wy2lbvwNBN74JZCbtJzjEFrx79ajVvwKwCC6vCbVYx9My1ndQvQmKVdTXR3qx6AS2o+E0mCko+QJ1nHwcAIrlTQqJw0FQDrzdfE0Naaj0AMCouannZy2rUlOrSNSqHigpsOxEAIIxlZGwvjp94gBUJM1MoliZRKIzANP3+lG9TH04U3VQvFyByui5Xplq/cjChALUNVBdmcOHsE7HHXZWrosqS4NBS6CF1ya9axbeBa0wSJXE0GJdlRKvdqvkDOrSuegWaHUuD20BFhRq+xU4EBs2G/Ijj0knc9nrJwnHH6ShaA9Yg/3SPxoYDNXVNjURsWscGorp5eVExzixCwgk0NcMaS8wBTo7tN0uq50g/jCBUSJ1zNV4BJxndBgCJCQukrH9R6imx1Ix6JETxrOgiMb3vCSycPoQVb/4g7JY2EApUpsZQHDmD3tvvZWwl/HyDKQCNDCHWov9LE588cRmYMBwWfqXLgajfAPtxkwSeRRqO7VdNqO91UH+WLDzvQIn91gmBgS3Xfhhrt9yLkTNP4pVn/hbVSjQrO7DM14n514bRCHL60AOolOaweecH5JAJ9XxVdFZ/Nngmap6FKuJ44rwIvD3Vs1NPKAMAtcoijr7wZeTaBrF661t4WREAQNUtYX7iJDoHtkthbYZLUZ4exfHH/gHZnlVY9foPw4ABahAk5yiSc6ICKXQpvPPVPPtjccD/rmoMAEiXLoA3XoxM4vp3ZKWYewHNqhcB5XJ4UJMAQLmnRH0/fPpRz2LJrRwAsFChcC2mNjMMquujqD8QpzE1tQow7MVo4hDxqAwACABKUa0sYGz8ZRw++i0cP/EAWloGMTd3DtPTx2EQE31912Hb1g9I94VRGcets3WHykS33hrx87LOI2D4if7id3v61MMwjSYLv1yVq4JlsANR02ChITFeAEkMAmqYIE4TcQPcUq3qmLqPwtNv5y4xKSxFZRXSoXDVK9CMh6IBkxCg8QpoGA7qjkNHg1qvKFgznpo6wr01jc4PmII0SnHznSHGLQ8J7AVWIZV1CbL3IGDdERZqLpmLBMVBpTN+OwWvQJxVnhqC69xvP8LyI0hiBqh0Ava8gVo+fJ6eFTILaYWzIiltUxH8UkhMSaXxYYw98QA6d9+BlnVbg3OK4+cBANnBtazNOE+KIGbV9w7Ms3jjOHEyCGL/690HVWpZwF5EEMIgWkeXK2YlBE58ERUrrfLq0rxKNEtyZfsNhwaeoODZB//4i6uIxQMLM/ulUVXlRiJWa/1FCCEEK9bcjlx+AAee+ztcuvgyBlffGn/CMrLlpsYPY/TCc1i/7V3I5HrCHc02xd+dgHFNoxypFIyqQlSvr6Uo/wifCfVcHNv7VbhuDZuv/0gIbihTwGeGj4BSD+2D29k36e+uLM7i6GN/j0S2Deve8HFGd+mLxD7Gv3VPBv1OmlXgXVglD1Y8N3+WXbBnkaAysBjqJ+ZF00mlbgABAABJREFUqTUDPJt5GDi5Af8WnJQRrREg3hd/7mGDFnYQBTh4VHoeZs2Tn5/H6s5wYxLxaOjNDzqLPlCj4sBLWuE1lBy4acu/Bj0AMMuuZD03Kq707tSK89h/+F8wv3ABAJDL9mFo8GasWnUXLEspxyxesxo61gxFqGZdktrQ6DPE9SSKUKPqSUnNjLCBBtc0OXYIE8N7sW7jvTh1/AfxnV0B4oHAew25+l/Lvq80WRZFqNPCZhCrEMYS8A8SAMxiTUr8oZYeCPBzzBL7aPlHbpSaoP+oAwSC/cE2U/ZM6BRpw2is+DcSTdiSDgjUb4NrMNy8pCjjcQCFezHiQESTQigNwnwCUCAyBQWuWsGiHSjGsju3IVBqAgg0BE6QlSlRIbVKFJVW5VyPRJUuIiiDujECEp+24SCMGVabj/mi4qzj1ABg6hlveEhSnHgGxcUf/guSXf3oed3bAvBCqYfJvY8ht2oTbDsDWucZeBZ7lg3rB9gMMLnpUKGuJ3aBFfty4+7rUoQKYUEWCe7Va82RoS7cIUh4FRoXFd1GyrmqFKvH+99Pvn0VliwxXTu1Ei6cegwXzz6Jjp4t6F95o9JnzLmilV+n8Fvh/AKE35e6vW5fy3zlRCPD+WOPYHbiBLbf8ikkM23SWIhHMTX8CnIdq5BMt7J9HsXC5BmceeFbADGw/s2/CSuZDobFQ248W/7W+HzhpMO+FweIUMxQONYG2k6EF2oXvIiSr5MgVEkorsgBPxc1rp51qIRoNnFfeSw7wAAAF2oSEOFvzzYkqzy1jEjtHFWMigMnF1q5zZIjFQ0zax6MqvyCmGVXnqMFC/6JMw+hVJ7G1k2/ho6ODUgkFJpSUShi9QMtRajuUBUIUH3F7bDhKEUoCwtSHrhHMT97AUdf+iq6+nagZ+XuKx4EXJUrRy6rWJiTs2EVahIAAIByL4udTE6FZs9KBzMtJmei5j83bQVAAAC8tMWAQCTsxy+0VW3Cs6AooNQwoiFKcQp2M2Cgngai9q0AgaakXs7Ccr0JTYoa7x/NC/BjM8XaAeL5ouIeAwR090Stkhg9KXotOg8BJaEFODvmYbHPgLVIUOnwgv2c/SOsCxAFAhGFIyaG3rPC7YYjAwGzZDC6zBSVgIC6cKu5B9QMgZZOQWe5AEBtYRZdN98Nwwo7nTt1AJXpcQze/f7oif51NRNyk5jzrf41ZsWPE2pCsp4vJWHYsxm4Erne3QRhIUZLjaW/0kR9lZd7OX6yctBso/sifEOe42Dk7NOw7Axy+QH9cfVAhoovPAej557FuZOPwvNqWLH2DgytvVNizYmcqzbv9xtR+MUpx9Qr/NziHVFa63gSmhFxDpmePI4Lxx7Fqs1vQlv3BojsaNQAatUi5saPYcW2N4FSD9PjRzBy+KcoTJ1Dqr0PG+7+TSSyrWwOpEBC4OAXk3G5gu/ZIbBdWBk//+UuMNrR9KQmqZXTulb116/OkbxvU6guG+HQN4mkuErvoKjvWnLFYOLRyLdLlDBSHS2t5BGo5/FW2uVAQAUAAFvLCBBWvRfj+YkJ07DR131NWCRTGE/9uH0SYa1jRrglvH9NhvkyBiTBI+DIzETF8jQOvfhPyLUOYPPO98PzljABX5X/7WVJIMDJ2bCUWkilPqbwJ+ai2lGlM4HkVBXVfGjOqLQnkZypRIFDDwMJiVnWTq2NuePs+ShooAmzOSCgJKTxUBeiJg0D0dwAUUHlh4uKUxPhQNKYuQV9KWBgKcp7vdAhtc0mwoZigQClII4rU7wt1/KmAwIa6wihYVEwHVCQ3KT+LrNCAyBgF4FqG5CcNgIgEFyWWjmYhMp4EHZDQsVWPd6I2W4X5MqeQX9WfBiJZ0NSmBbPnkRt+hJya7cgkW2LFjEiBHa+HU5hTrCYApN7f47cyo3IDqyW3lGjifeVh8A04uKvtlEhxKZxu6LUcn7RsVfRlB+0JVhG3STg+YZD/vwMJ7SyGlUSJE8SN3yWRPhfDAHiv77adQoCC3OTU0OzYUS16iIO7/0y5qfPYdO174sW71qiOLUy9j39eZQWJ9E3tAerNrwJyVS+8YkBq5p+zJ4pbw/eZSE8TtwOMMVTS5PaDLBRjpXapRRnDz2I1q41WLHxLn8AzHvomQwEHXv2n0EMCwYxsf/hP0dpfhwtXWuw4fWfROuKzSDEgOvPqXZJoFcWw3R8kMPnqGBIFcDzI1FqLez41hPhMSoACOZKQiQFXvRS8mMMh0oeB4AVDONAIAIAgj9kgEYNAooQpJOaXA1eoq/UGHq4eLYBQ1nHIx4BZX2yClXJG8D696JsVkqIkOGHJYky0LcHo+MvYXr2NDrb10cNUHWAAGeOUqm05dM91JwiErbgYaCQQLrWIKP0q80HcCjcpIFadRGHnvsHWFYa23Z/DIZpg6pFVa9AcSmBe9nUapfX/1VhsmRPQKnXV9YXwpWwMDuM4sI4Onq3IFOSs44WB9nx9kL4Yi6uyMCplmCMzyKRyEnxd9U2OwACAFDLJ2HPVyK8wm6W9WOWmqA34VWMfQm8ApKi7+mBgDiXqCFIKhDwNNsUadorsNzQpOWwL6nn1wMRvGkOBNQJS5w0+f2ox+4kMQX5/4nWfb+NuOrAQSiSF1pH+AJIPKDSxjrPjrBEO74fgJwPAP8cyFZ5MelPtHATNxyvKobLhp2ckYEAtShIlUgx/eweRJUjt1zCyLe+BLfEMnQzqzZg5Xs+AZKUY1UNO4Hawpy0zUylQP3wOx7LW09h5FSjxGXFxSrT40gNRUNHzDKBk6Vw081qq37c/+XOt4JFmXHa+4mR/jC8JkIiXk3RuvBpiN8I1Vg6m2mXNytaXJsASjoLbXFhHAdf+CJcp4xrbvottHauaW4QdcKQRs49jXJpGtfd/gfItQ7EK9qaUB9VsY9sr+cAVMFv8P3KnoRlC0Hw8GYnT2BxfhTbb/ktBpqIfD9O770f85NnYCcyOLv/+2jv34K1u38NmZVrw8NM9vytmJwXkRhAVAIXe31lvQJUutiA0mME1Ra2v/VMDAAAS/gFke8FoXICsWcyIKIWJnNTBIk5oW0N/WSQyCt6mUwi0W9Sg8Aqhe3wb0DObwrne+JQvYdeEA4IaIKpKtQyYJYduClBddEYtlQFPWJY8oC27BCymR6Mju9FZ8eGxvlNhES+a2oSGXD475JHXbxy/JuYnDmGazZ9EN3tm8L9arMxQCAuGZo/A8+t4fALX4JTK2Hnbb+LhJV97WMkr8qvnCw7HKjaYsEdn8DBF7+GwuwwAIAYJtoHtqFn1R60ZgdRKc+i6MyhsjiNyuIsqvNTKFdmUVmcgeuELgXTSiGZbkMy3Ypkug12lv2epXkkU62wW9tgmjYS81GF303bWiCght/FegXUpGFV4hT9OGlq0Va8Ak26Phs3HGP5vwwhrstYlmISl2CZQXJSwyJhjbwn6kPzhP99RTkWCCDqJgWA5KwXAAEAMCtErhEQ9OVPrIJFP87iG8Tqq+ONAwXNVOVVrKSX9v4UnlPFmk//Kcpjwxj74bcw9uh30X/P+wMAMnvwBZRGz2PwutultnKrN2P85w/Aq1Vg2Elt+8HCwz+LahnDP/kG5s8cAnUdrHnPbyO3cgPseWZRT1aAWh2jr5MNPQOGAyw19CUI/xAUYdfyw4IaFMa6EkV8T0VvAvGA+elzyLUOwDAbc6dGGFmAuveWEmBm4hiO7P0XJNOt2HHT7yGd6ajbrthFaXEKpw59D+XiNJKZdqQyHewn2YrhMz9H/9CNDAAAUoXawCrPreuBhTp+vMsxxunAbMTD4MljkQCJquDyqcHfPHzyMeTaVqC1a13kuOGjP8XE+RdBiIFcxxBW7rgHmVZG4s+XEbGauERb7HsBRADAq5sTDyj0h+MSAUCcUN8rq9KAiominolgTRDnRRUIiCFB/FwVQDAKabkr9VnoAAA/TgUCYuiOCgSobcIoyu5FUnXgZYR8gLIDN20LbZBI9XkdWJdAEiFoyfajXA2NKHWBAKXRd03H8gMPh09+G5MzR5HPrcCBY1/Djo3vQ0/n1vAcydIf058gKkUopR6OvfwNFGaHseOWTyOd6wRiQsGuylWpJ0sCAbWMGZzguTUcOPJVuLUStt70cWRbBzF58WWMX9iLo0/9o3SeYSWQzLQjmWlHvmsNkiuvQzLTDjuZRaVWQLU4i9r8DKqlORRmh1EeOwinsii1kUjmsX7bO9Fjr8R08TxmZ8+gWLwEx63Cdavw3CpcpwLXq8JxGZl5X+c1WDN4B7LpzrAh1SvA2YPqxtljyRb/ZiTiFVBDeupZ8Zdj5V+i6OLtpf7BOJfdpOlbm5VQHd3kprtvoqIQQW/K6TEeAVKjSrGb6DFWkXHeG1UNEDBoAAS4RGoE0BirIw1DSeL6jJyiLia+VDo9uFNzmH/8cbTdeifMVX1o7ekDqg5GH/omWlZvQcuma1CZmsDow/ejbdv1aNu6m902f2y51Zsw9rPvonDxFPKrQ8ag+bNHQF0XrWu3Y+7UQSyOnUHf7W8DIQbGnnoQC2ePoO+mezB95DlMv/wUWgY26C/KFzfj51jEcM3rhCdGehb7MctounZfM0Ic/3XibEEEAAmVL88GCHemUID4z1wKB3JI+Dz9e0o8IDVz+W726cnjOPTMF9AztBsbd/u0g0tlGxKt58o7dPHMUzh98Pvo6NmETbs/BMtOSe+GTqhJQD0XF888iXNHH4adzKGzdyvKpRnMTZ3BxIW9cN0qDNPG0Pq7wr4F8WwS/QYU639cAbKlgIGoEia3EVh/dblFwjb+XoiyMDuM2cmT2Lzn11mOgz+HUAO4dGEfzh/+EUw7hXSuGxtv/lhAvctDeqRcoGrYj1kJPZ1hkm7YebGLhaiV+uUb5CZDBq2WCx48S7ZEE4+BzSAshwMxjwYAQye8b04tyrYZEqBQjSlx+QBe0pA892xcei+Q7nz2N5XXMw/wUgkYZQEIKPO55AngfTaxJqpeWM9zIpWApb601NnyNapeh6Onf4CxyVewY8N70d2xFQdP/iteOf4NbN/wXvR2bQ/aXQpNsBoSVJgfweTIAWy67oPIdzCvrZcgUsjZlSyvdcGuq8XCQlkSCKi0GQF/+OkD30dpbhzb3vx7yLYPwi5S9G25E31b7sTi7EWUF5klKZlth5XIgBCiT/QSRmAXPbh26OqqluZQLc6hUprF9PkDOPzSP+OwrzEmEnm05PqRTORgmAlYZhKmmYDlmTDNJBy3ggtjz2F0cj/6uzgY6GIdqV4BPymIqDShItuOGtLSVHw1d4fWsejogIDYfz3r/nKBQL28AHWsHgATdT0ghkMFq5YABOLOEYBAcO06i+FygBaFEAPrK/JVilIP25a6RFDuZrEbPBE4UsBL5/VQ8wS4+L97pgwEjBjLOTWFthSFptrK/ph55CcgiSTaX/d6tr2NIr/rBkw8/kMsnDqM3IZtuPjAV2C3tKLvDe9m7D4IF9tERw/slnYUzh5jIIAA1KMYfvSbcApzaN9yPajnYvbYSyAGQcvqLZg68BT673wnune+DsQ2MfL491ArzMHOtUrjT04RVPOAl4k+GGdhHouHXkG6fyWSfYMghlEX0DUrQZiXG74vQViQyeK1zQqzwtarsXC5wvnbRcAgfkfis5Ssq754bg2nDnwXiXQrJi7sRXvvZnSvuDbYH2Eb8gBKKYoLY6Cei1z7ikib4jkzo0dw+uD3MLj2dqzZ+jZ2/3VtK0MrzI3gxP5voTB3EQNrb8WqLW+GZYRhZ5RSOE4R1PNgp1vgKZW++XsnKo66cKg44LsUmlmxP916Qk2Z/YsSALptQHgfuBfg1ONIZTrQObA9BAAEmJ88g+MvfgOJdCtcp4oNN38kFgAQTzYcmFUa+QY8O1wLF3uEe+YQUIsNKjFD/PbDwmBxHjHPlPMBdADAcChq2fAlIFTJB2gSAFDTzwfwx2KWXCmhuB4AkJ6BJh8ARDbQeakEjEo4MRulGmrtaaERTW2LJQIB16uBUPmcRgQVWm+UDwRct4qRiZfQ37MLvV07AADbN7wXh09+GweP/ysoPPR1XVPf6Mj7qUMR6tRKAKKMX16CgFTqX/9VuSqiLHnJLLcbmD/wIsbPPos1N/wasu2DwXYAsAsU2bZBZNsGGyoARs2DUQOcNDvXs0iwIBimjVSuC+ksU9y7h3Zh8uSLoNRDW8dapDKdsMtepA+ucJslBysHbsHI+F6cHXkCo5MH0Ne5HblML1yPeQ8ctwpQijX9tyOT6gzdkvWqDy9DMW1Ec9kUc1Hcvibi95sfqDr5+4t9zQW1TXmC9cegYwqSrrdB+FQEBAkWPWoS6Vw+OcdZEIkPWHQejPRECAT489MxAgWJvgkwLnwv3voff1HsP3ueAQGjJlcHFelGARZKwy3V1HVRfGk/Wm67Fe4aA5hmjc2/shdusYD2a2/G7CsvoDw+jLUf+gOYiWRkLIQQZAbXoDh2ARRA4ewRuOUynMIcOq+5DTNHXoBXq4AYJi698FNMH3gGmf7V6Lz2NoAC7Zv3YPTJBzFz5AX0XP9GWGUGXjyFFECU6ugoxr7wBThzswAAM9eCtptfh/abbodhN1m8RlQ2+C8x7EhXurgpIj0X163g7P7volKcwa7X/zHOH30YJ1++Hy0dq5BKt0XOLy6M49LF/Zi8eAClwgRMK4Wb3vz/gWFaKBUuYWJ4H9p7NqKlczUAwHMdnDr0fbR2rcfq7feC1Jl8gzoKbg0Xjj6K4VOPIZ3rxs7bfwctHSvZMUAwHxAPsBPZyPdGjXiFK1Ci1GRJHm6iKIuRQoBNgAKRa7/RuY1yDsqL05gceQXrdtwXJFBTApQKkzjy7BeRTLeiUpzG5ls/gVSWhVd5CTYXuUJUlyHkCnEPgDg+yQPQHf7Oq4wTh8BeCI9vPeuBWlFQKSqyZo2FAHly2pwErjyLBOMRQ4HcVFhrgLcbe5+EOdWzCGwhL9BNGlI4EBAPAAA+RxtSCA8L6RFDk+Rz3FwywkgXCXN05ARgHfOU+DxaW4Zw+vyjOHP+Z1g9dGfAcqUDAtI1xIAN00xgRd8NGBnfi/VDdzMjJTGxbf17QIiBg8e/BUop+nuubcjSpk0IrnpwMyZcfwExiRxSSLz6S9OVIh4IvNcwOfdqnYBQlp4YPDuOU/vvR9uWPcjccjMwQaWJrZYjsAtR6wdovCXIKnkBEADCyUv6uImB3jXXs999q4WTDmc9q+xKijajHQWG+m/CYO8ejEy8hHMjT2Jq7iRMI8G8BmYS5cocpuZP4frNn0A62a5XvNUPvk6ia5w09ArolPl6in2z9QN0xzbbZtw4RauJq1Rz5Ep8IyAggCkJCNCwDe154jvBFzmCwNVv1ihcm8WvcsXaLlLUMmx/udO3JFcIvGTUPChZ9PkjUaz/OlBA+HGKwmo4PqBw9FZqN+UrCi4bb/nUGXilEnJ3sFAct6MGY4Lg0uM/Qsvma5Dq6seF7/wTWjftQqZ/VWTCD5htiAFCCGYOPY+LD3+djSWRwsDr3oGunbfjwiNfR7K9G4adxPSh5zB49/uZ8kMBM5lGumcQldmJaMJyqYhKYQbWehYLbdQI3LSHsS/+I5BKYMXv/0d447MoHNiHqUcfwtzzT2HgAx9HanAovNUmWL2BGoLCYVYFwKuo7BvV8H7z0CC2I8ZbIL72VJ57MqOXN5aZkSM4s/fbqJUXsHrPu5Do6cXqtvdg/qG/wLF9X8eWN3wGiRJTOCcv7selkf0ozo/BtJLo6NuGgbW34tSB72B0+HkUZoYxcX4viGHi/PFH0N6zCau2vBkzEydQLs5gyw0fAyFEUeKj88Lc1BmcfPlbKC9OY2jTG7Fi410wiL6UdT1lf6nzhdjmcpKndRLxzgV9o6FGxN+Li6eeAKiH0TPPYOTM0/DcGjy3BqdWRiLdimppFv3rb0P7AAuv44q0SGUqeYI0AEAUDgAIBUpCjTUOAFrPyidKbD9G+L8lWH3FY/hYGEgR1sWk7BURwUUIqmQFOJIfgOg7ZZYVABAD9gAoCbuG9H5QWwYGXtqOJMjqgIBYyZ640XUs7jmsWXEnAOD0uUdRrsxi0/r72Hegnq97j5R3m3sD1q64E6MT+3Bm+GfYvPbt/qEGtq5/FwgxcOjE/ag5JbS3r4VtZ2BbaSkkSbr3yvrKQy89l3lIDNOKXJtYUOyqXJVGsiQQcOLpr6B47gQSrZ0YeMN7QAjB3Dr2UuaGw4+h2BO+hJlLMjTlyr2h8AbbPpcyd2WKfMeMR1mcKEgABLi4CW6RFt2JbFEzysCKvhsw1Ht95JrK1Xm8ePgf8eLRL2LP5o8jk5DDH6TwHHUxWwYTUMPiV0sJ8WlGaVcs90tpUxwroRQ00PBjgAClUCnQAmkABCLD0ACIetWDxYS4cFwhEGANaDoiDBDIneuTeYnoFfDHTkTQAkgsQm5Gc138WhLQrizFEwdgduSRWDsIz48Hnn3sMTiFOfTc9mlMvvRzuKVF9N52jzQu/1KCS/RcB9XZSVx85Jto334jsgNrYVgJENNEsq0b63/t94KTem99K8xESqoqbCbTqC3Og1IaWMfmj+7H2I+/DXdxAflrr0f7u98OZFnCQ2LlCtRGx2G1t8PMdoJ6HopHj8BZmIdTFEyblyHUAihImJQNliNCaWixdgPu9VelSwBAsZ8PgIQgK3h0RKpS3HYqfKGrpXmc2/d9TJ1/Ga29G7D1rk8j1cI8m1Yig/U3fxCHH/1fOPHUl1FZnEFxehiGlUDb4FYM7noLuls2MMo/MCX19P7vwk62YO2O+9C76gZMjx/B+SM/xsuP/3cQYmBg7a3I5vsi4w847gngVoo4d/hHGD37DFraV2HXXR9DJt8b7Af0ilugmBC9QiXG2Ecs80KyqSiekDwsze/8e2rSyyl9g/zQuKlO/Fb9351qEePnXwAAuE4FnQM7YJg2DNNiz6NvM17+yf+DXNdq4eQoCQEXUwzHEOZTN8H+FtfHcmc4X1hFeWz8fvHYfU5TGvQjrHVxeReicGMdJUo+gE0kXn/VAh0X5uVkTakWEHsH5HMbJtKqniKTgJpmECrkJfxQXTFMSKlNY1bcJdF7ivsJIVg7dBdSiVYcOfVdVCoL2L71A7DM5LIqeVOTwEYGq1e8DqfOPYL1q+6GZTGGRAIDW9a9A8SwcPzMg8AZ4RqMBHLZXqxZ9Xp0tK0HBMWfuB68pBwW5FA/TCphwzOUnLRf9foqV+WXKksCAW6xgM6770H+2j0wKjJVYWFFFAwAwPxK9jLnz4Urg5tgSru9GF1NjFo0qYkDAsl1pk76vng2K7suKt9eyoRR1psZU4k89mz5OF48/I/Ye+yL2LP1E0ibLUI/gsKrW5QUr4Bq8dfVB9ABgWUVFONjqQcc1BAevq3JegEAAF5BWK0doI6DPxRfIW+m2i8MYYLnWqyiWAeXorQnxfY2wkL+IpGaJCh3yffKS1IYFSIp+bqkYNaOrHRr+/dvkVlUgECEJoOtkG7WA02wRkv7j8HqaIO3WAI1sig88SJmHv4x2l53J6xcHlPP/hQdO29Boo0lu8eFylDXgVNcQG7VJgy+/tdADFO7qBkOYBupyOuTHVyLsSd/gFPf+Z/ovf5uTB16BnMn96Nl3XbkVm7A+DM/QuG/HEbnffchfcu1yN16AyY+/wUM/1+fg1tcBC1XkFqzFv2f+C2k2nub8lE7KZZQSQRPTFARlcYnX19JMrvOAAVlIZMPfxfEMND3ng8ht3M3Ukfkm5DvWYfBbW/AyNHH0D64FQNbX4/WFVtgWix8yqWA6z+YtTvfgXJhCr2rboDpswp1D+5E58B2XBreh5nxYxjaerdfUVmjxFMP4+f34tyhB+G6DtbsfAcGVt+irR1AzZAJRnfPuVIYH6OuP09KxlWU9TDvQ5lr+e8x81s0Jlx7WGS7GK40evY5UOrBsjPo6NuGNTvvk8Zc80kqiMEUMQ4wxXCOIDRHMF4F10JDb3mpywiOLXWH4xEBAF8rKWFeTC78HpkSs07M9Qpi1iiqufh8gGYBAGM+IrD886xFF27KDL0A6lwfrIUyeKDinM+3qcXMEmZkvzo2seKwNo5ft6bFrHkDvdchmWjBgWNfx0v7v4Cd2z+KZKIFEWnCA09NgpbcACg81NxyAAIA9g5tWv92rFxxK6q1RTi1ImpOCTWniInJQ3j54JfQll+FtavvRlvHWujEM4nvCSAgvveO07/+qggFeU1Dcuhr2PeVJksCASs/9Nug7eyFrmSB5HT0GF7xMDUpb59fFQUDtSwDAqrlSFvCHOGkKxWJ0bh8qUVAQSRvA0fSZjlq4k0lW7F7y28wIHDki7hu80eRsQSPgPqhN+EViCqs5BcLBOpJnMKvAzU6pd3xAiAQWBmMsJ2gGI6mgnAzQEBiV/CfZ1xyVtCeZoKnRFZQPIsABKj6CXGpKd/y5rEQFu1Y1NAfCiFsCUExrTgrUUAJ6AGecCs8m4YHCEAgCAeqGqAJD7mbr8Xcgz/H+d//f2BYJtyFIrK3XIeON74FlaPD8KpltO64PgRLMWJYCaS6+rHybR8DMU35WAK5EJZGuve8HqmuAYz+/Hs4/b3/BTOVxco3fwQtW69lOQfX7cTYo9/Fpa99DdYPH0Jmzy7k73kD4HkghonM+k1I969hyakxxcQ8G0G1ZVf/OC5LeGFDzkQE+N4EPusZwqMg8u9BXHedPAideNUKxh66H/OvvIj8NXvQfc87YGZYPP3U9tASy5W3QbwZgzvuBvETTePCZFvWbEWOsIgpxwCS834ipmGiZ+Ue9Kzaw85HaCmlhCmAhdlhnNr/HSzMnEf30C6s3n4v7Ewe1A8bYpWz9S+DmyCx+3jhKSdJgvGoScJ1qxtrrtWzNCxDQKxXQA1/keLQ+TvFT9V5KAnB2NlnYNkp1CqLyLaHVZWDa/Hj1AzDCgBAGO4SNiV6AESlVwQAXNg8FH2/xPWRAwBdom4Q7iP0r8a987+dpBFWRhdWfDdFJENchPFJ0MNF6lMnRZCaEvIBUqZcIAyQnpPh0Gh9CIVRpy4TnUZEABC00ajivE6EdaSzfQP2bP8U9h35Z7y472+xdfOvoS2/0g+TjHmPY7abhg/kvVpknTIoQSbdiYzIWghgaOAWTM0cx+lzj+ClA19AR/sGrF1zN/L5Fcz6L4Q/0ypj6woqdRONInRVrkoTsrSKwWlGFMOl4tNP6yqGlruiQAAAZtfLYGCxNxxCbrQxobquSiRzB0cVc882GBAQNrspSwsE0sk27N7yG9h75It45sD/wPoVb8TK7j2hpUxdhJpwVTfyCjTDHvRLEU3CL6CMSwQCQBimQ2mQ5BXIUidiKABInM/qWLq01YMdpWgYgMSiFwCBwGJXMgIgwMOBtAXB+BgEqkg2XtXNrd8eWFRrJAQCYKCAM4GI0vmBO9Fx73WYfeh5UI8isWkbEmtXgkxZKBfYB5Vo51mEkN5t4jPdUgKsuPv9AAEMS+DSJtCGcohCXATKccvqzWhZsQEL544j0zsEK50LWFSzTh6r3vxRLNx0B+ZfegHzTz2D1IbV6PndTwAegVElIGWfySJHYZaJzMC0DOGhPpz0hngA5RSfHkAcwORkXu6rwxTkpAXliiB4uJQgDFMzABgUlYsjmPzCl1FbmEP/Oz6E/LV75HA0jUztMkCDOSZ8Pl37659XaQ0/DDGxU5RatYjThx7C+MlnkW7txfbbP4PW7nVaoOFkWHtWKdoWZ5Vxk0ByTj/fFbtMpKeiiplnk8CzG7TNm1C8AZFKwdo4f71XQJsMbNQHulz619yC2cmTqJbn0dq93j83BGye51+Xb52WYrZ9ZV98t/k2tRowv85yh7BJsAm0XAjns0SBXTyP5w+MC7UQbIlgSZf4SjygJuTaebZ8P8wKmyvVqrcS6KA0ch2mwj4jeiaC6yRyLQBdEuxSgQDxaCSMWHeMtCY0stwrXtyWXD9uuObTePnIl/HS/r9HMtGKnq6t6Onajtb8UH1AIIhpsMmKx+5Hxxm9H8Qw0NW5GZ0dGzExeRinzz2CF1/6n8jnV8KyUyCWDcOwYBgWioVx1odwbV7SgKGCsatyVRrIkpZJswogFd3uJtg+9aUu+0BXdHVymV9lSFYPAJhfyYaTPy8r6XEJxYCyUOiAgBWy1wTj5V4B0ZpACDKpDty047M4eeEnOHb+hxibOoCtq9+OlnRvfB/8b12SKz+kSa8AEQHGctl+VJAibhPH3KgZ3TF1QoZ4XgBXvLXiA4c4j4d2uyYnABC8QlRQGoRFJKAJrfHFj6LcEXqpyl1xg2Rt80Wd04cuKxSFhwWVWCJscJ2mDwIFSkAAMFPsvSe5PLo++HpQj6Ay7xf7sihqU5dgteQZI5D46nqI3HTDsgOlUi7UE1WuDAegHiLsIgBATBP51Vv8P/z7InznqaFVSA2tgtHfjtmHHgatOSCmDS/twbMojOrSk9TcFEvsFS25VzKtM6UUC08+jenvfB9Wbw/6f+ePYPd2o2y6gMEeTGJiaYhkeiuBUQEAgrZT9eeCakto+U8UKKjnYuLcizj3yg9BqYtVu96Onk23gtgWKv4zThR8MGNE2+KKJ7fuAwwAAECx2/eqzbJ9jsh208Maz1zy9G3nDD1gofrnG5/w29gAw49rhoBkcNNdGNx0V5ivIymNAPX879JgCaNi8TcgqhQH4xeU3JJ/3ypt4X6r5HdB5TBae9GTvJpsTLKyzecjHa0sF/F7DjwYBEE4T3hSuN6whG3BiyCACBEg1nImUtMi8kFEIkq+ovhGvETqHFbjeYLRNTw8R16TIuuWouSzjgmkAnJKPlkq2YYbd/4OZgrnMDF5CBOTB3Fh5BkkEi1YM3QnVgzcGB2HsmYbNrvhridbSGPnMXFtIwZ6u7ejp2srRib3Y3rqODzPgefUUEMZnuuAUorOgR2RZhgQuPI9Ah59jdmBXsO+rzRZsq2MFy9x5ZSAoEiPp2EDdDIMCKjJenPr2BeRHpe3z6+0mgYC2vANjYReAdFNa0aAgG2lsGXV29HXcQ2OnP0+nj38t1jddxvW9N0Ky0xqWmbnwUTdhJymwmJUIABcHhgQXYX14v81XgBpvJKVyWPVljWTGfEom3MVi86yQp/Ew3VJxVK/irVWMwYASE2HQICdx37nOQFsrH6XYu2AOONKnIeYQGsBjl0AMi4MK4oyqiUbxCUw51lj1elLsDuYFyDwWvDLEbwCoZIS/h2hd/TrFcR5BqTqvQZirzW4hDUbMVN9EJWT55Haqo9l1XfE5gzqhwUtJxmvGbF9Q0QkNIgXETMRuDkbWe8BoDYzjcX9L6M6OorKyAhqY2Noue0WtL/r7SDJaFZypc8BCEUQgk8orEvNZS8XBkjgbSVOqMCr4jk1XLj4PMZfeQzVwjTaN+zB4E33Iuvm2HUJx5bbjCA0JLHAw07k9kpdBtxEuB8AvFoVxUvDqPUNgGSZRcguyOcVe4zgfUvN+m3795QDFhUMxD33el6BWIahJXhWVe+Drm/uCSBKhWfD0SiyvnDPCjVJMOdwAEC8cB0FgNxFChDALmgs6twD4DALd6RCsHi4BgCYVU+uD+CxEC+euxACbRkwEQ/SeaoibRfqW0X4fdEBgWhysHCeQyPrQqzyL/xOxYqD6i1SSSN074xyDCEG2lvXoL11DTauvQdz8xcwMr4Xx049gMXiBDasu0fLIsSFhwPVnAbxhPXeVdNEf/9u9PfvljY7WSEsSNyxXF3hqvxvLct2mCfmw9+r+fB3o+oDAdWgy8GB5j0t9UaBQGGADS03ooTuGCTyEXMg0IhyjtFrERAxV8B38UqFSwygvWUVbtr22zgz+gTOjD6Bc2NPoyO/Bj2tm9DdtglJOxftQKlGrErE4q8bo7r/crwCUud1gEBMOFBEeRfPURVzf7tRddk9VVz9bCf07fJ96hooHudBikULFm0xfInnA8RUFZa6cwAvGd0GQAoLMvzFWkx25HkB9URlJooDADTpW7scUwICtXL4abp5F+a8ier0JJI9/UHSshq+xBqE1ionHSIAZ521lTjNKcJWiSnuThZI9A/AyGYx/+jPkVgzACMddRk6aQqzGjLsNDNWnQTGBBqOnbjhvSAUIDU2Nhbv3tz1BCI+2wAIsY3VyUuYfeynWHjxRRDbRmKgH6k1q9Fx3z3IbNvKAGCT3Thdwtzma8HNAINqSxiqkpyncCpFTBx9CuNHn4RTWUT76p1Ye/dvIN3DiotVjHjgAACljrCORcInchKNOeV21p+9CMwcfRkXfvZ1gBCkugeQGViDzNBatLavhZ3NQ5VSF0FyJto3zweLSMw7ob6nofLuz1V87r9MACCyIHHx/BfLFCYgnichAWy/b54rAQClTvZ7tTVsT/SMZ8diAJ0djoP3pQKAOO57UdyEkA8gxfgT2SOgGIbcFGMMEr0RTsZAeqImtGFEQ4GASHiRPGjFSx4UnAvXnIjEGa34n0rJce360gwdrYZhDmCAoK1tNdraVqO1ZQjHTj6AUnka2ze/H5adFg8MxmZZaZhmEq8c+zp6OrZgoHc3OtrWoa41K7ge0VAlhzep1dmJG823+FWQqxWDrxxZEgiwigBNylYMAEjOsv958hD/m0tFmAC5dYMqPXMaPjWPoDBgITfiyJYiHRDgycBSHGJ4jBSOYxsgNU9q00uYESBgwMK6wbsw0LULEzOHcWn2GA6ffxA4/wO0ZldgZc8N6G/fLk9QJhsHcbiWJS9OQVGwX1QeQEzSrFbigIDnhYHX9YQDAUVBl5iC/F8litG4oWsWAHESXGryGCXhe8ZrBRAHqPihQNYigZOlAfhQGYG0C6uYbNjEukJNFt4S2W7TgBFIat7R33ezTGBYNub3vwharqD7xruR7h0MrlNk1NFJw6qs1AcWse5qhAqa6CXguw0DXe96Ny796zcx8p//El0ffz9S69bBS3kgSrG0ZoQr+kbNB/h+36/V3F0dG8PMTx9BYf/LMHM5dNx7L1puvglG2keShAoUm2yscAhLvk02H6fr9NbC96pqIHci/sbVCrMYOfA4pg8+A+p5aN9yA7qvuxPZJHvBxddzwZiFR2tItvYEVd91Umlnyr62vxxgtDPGlJ6b3oTq3DQKZ49g+uUnMQzAbu1EbsU6tK/bhdzgBsCnbQ7C8KZl6zNXlgNlVPFgRRl9lP3SPtK0F0mtgBvZLu0HqOuHA5kmiEdDA4EjAAF/juIADQAqrez3Wi78PsW1UwQAohdAZMfjSp6lhHioFaBVT7nhyvkA1IJ031jdEpk8g3g08GAEYxHmRHvRg5M1YS36idI1jVHLvxdyEbCoxyRaLC5G+df9HrQbnQzEfLsl59qpc5onvx/V6iI86mLlittwfvhJvLj/77Fz+0eQTrULJ7F11DRt3LLnjzF26WWMjO3FvsNfQirZiv6e69Dfd114TiScSTMsj0rc/2bFg5NRrBrC/XFSVxXcq9K8LNkTwBcQVYkHWF6AqwkHSl9i/1f8996oAfANCm5antB0CcU8eTg3FmpoUriJyCGcMGBUvcgkY/hKeXCe7/4UjwtiD5Vy5ulkG1b13YJVfbegWlvE5NwJjE0dwCtnvg3LSKK7dUNE6aaWEQIBQG/NEOMA47wE9SZHiRkA0UmzXl6A7jjFWxDh6heATDAB80t0KWCSsGqwWkQMshK/FBYkCQjw+6RLrHJoYCkJwgic8F3lykdyMgQCxFd8PZvCqInvlH8PbGZVZn+E/4lPW8f8KYa+mWUGBKhNZZe7zwgUnOcREEO+L+a8CbPMTlr5kd/G3IEXMfXkozj9z3+Otu03YMWbP6C9Z80IL3RExYJH4n1dgqU+NUlgbt6FzO+sxOj3vorxP/9b9Pzep5DesrH+iTS8f24iDMMyS8u9qsYShAaZgEurOPvdL8DIZ5HZuAXZ9ZthtoYW7crIMKYfewSLBw/AamtD5zvfhZYbb4Bh203FmwMAqYgmW+H5xjBUiVLqY8fb834lWZPNs055ESe+/hegnouuna9D587bYWeYgu74YIlSivLZU7h04AnMnT2IZEcvNv36n6LaGoaLWJr7XBWcnCoQTnay/Kh07xC6b30zAKBWmENp+AyKw6excO4YZg49DzvXhvYte9C+9fogkb3STpCaUkM9GOUh8SgiNQh0Hpk600ZcUr5uGxXmM3G7BAD8MDieXD87cgS5DKvDILH/JKIeAO49cTS1QoDGAKAeqxKNM0D4czDgc/87lFWu5kIQTc4X5nzPIjCqNCiE5vjnehaQnAvH6GRNJGaFtZjIYVkRBd+T+2mqSFwT+R6qor/kmjzBgew/j7oolWaQynbCMBSKUurh4NGvY2b2dLBtsTiOF/f9L+zZ9RktEEgkslg5eCtW9t2C+cIwRsb34vzI0zhz4Wdob1uLoYGb0d25BSycSVmr65BraL3cJAy7akT+cFWuiijLDgfiypX6wgVWJM17mpwJgUBwvB9PKoYU8WM4eOBS6LOQG3P0i6/gyvP8sJ84V6Vk/eFxpcIEEvEKCJKwsxjouhYDHTuw7+TX8crZb+PGTZ9CNuVnQYsLi8nGEfEKBAcoXgLdxFcvHKgh80GMgq9LHta0EQnHQbjIxI0nCAcK2lOO4Y+kWWOFuODpwokCYOIrPkSgJxTAA1/QUrMU5TbGVFPLaTwPQlgJt4JJRcLEY3WPxR8WT0jmwpmBIoDBYv4RYmje1Vk/uSzF2HWIaaFt101o33I9zv7L/0Dl0tJL2TazQBguewWasd4Tj1U55TlCdkcn+n73Mzj3x/8vOJNTkeN57P3lxP9z4AbDt9QaAGwByDhCIjcVnimJejtmDr+IxYunkHaGMP7dbwCgSPYMILt+MyqXxrB44jDs9k70vvP9yF+zG7AtwAHA2yGAm1nmqlsyZE0111z2OSXA2LMPwXMdbPr1/xN2rlVSCr1aFTMnXsLUy0+gPDmKZEcvUl0DoF60fScb/h5Xc4LtZP/ZLW0gVgKV6Qm01lj1XMtqRXLDtWjbcC0opShdPIeZIy9gcv+TmHjhEbRvvxFDb3g/AKDSxpVcID2pm++gVXDVBPc4iXgLiHAelY/RtsePMcN+sy0DGNh8F86//CBsI42+1WFiqJsgoJSi2hbOeTXhnoqVwrkBLSNcNwcAam0cUbgXQDIg8HHy14ez5rkUrmAJFo0goLJlP6jzQIiURGxUKap5OY9AFFPDIEUNEhjaeJsRIENkD4FWmlD+pT9jCCa0p/Lk55j950eexslzD4MQE9lMD1pyfchl+5HL9mF27ixmZs9g146PI5Vsw0JhFIXFUZTK06C0wTWZBPnWIeRbh7Bh3T1+ovGzOHD4q7jrtj+TqgXHiVHz4KSEcLSKBy8peHpI0/aaK0KuJgZfOXJZJHqEGwN0Cp1vRTQU+tDUNPtftDYBLMegmpcXolJ3FAiUOk2/HV0VGxlB85jFiCvSpdJiwysdSuFDphGG7miEEAM71rwbzx39e7x8+hu4cdMnWeIwV7xFi7rqFVClUfhOHCNRM+cuJUFYbV9jbeHWpnoTqgQE4qzJGqYgnkQWmzch5iEEC3v0GkSaUH6cVaYBEOBiF4gEBII4eSHWnr/jkdh5dXiywS2s3GsBbjp6LV7KC+LMAYB6hgQEvNkEnIlLcOfmkVm1PhgL8QC3VkN5Yhjdt701PN+so1Tztb6eFZXn1dRbg/mFxVkieVu1GgOEqTRTwhIUrgFGG9oky1ItL88dAUPTMoFDnMyd2I/cyo1Y/Z5Po1otoHD+OBZPH8Xc/udhZnLof9eH0bL9WsD26SE1bRilkOIzEOFGOpkmB73INU/WkVnSI+XixAVMHXwGA697B+xcawBGqvMzmH75KUwfehZuuYSWtVvR97p3ILNmA0Z+8k2UL42y0JQYqlbxHVDXSA6sqrUSzFQGlWk5iYt7FNwEQbZ/NbJ9q9Gz+w049uX/O/JO8XeA05ymZvzE25jbRJT3t1kwEPEASFb++o2ISfeEEKzccQ+8agWnXrofTrUI6nlYWLiIxakLgGViy6/9HzDsBCqtoZLN8yoMJ8wtylwSAICfE+FZRJonxfWqHgAIfheLfQlgIjCGKDk+noVoPoBwc2o5I6L4V1sMpCeE2gBJI1IbQE0CVv+O1nhQQ4kafCc6Q1QToa8NPQQUoKAYGd+LzraN6OzciMLiGAqLoxi/9Ao8nx1q1dAd6GhfD0qATLoTvd3b64836DBcd00zgf6+3bDtLPYf+jJqtSKSyXz0m1N0GV0YrFHxJMB3Va7KcmRJIMBwAMPUUAnGWHd5IRRdHG+ioAECc+z/mrBd5FQWAUG5IwYMKK5GzzIAS19cRFVmuFcgYDbwcwd0Qn0moV3rP4jnDv89Dp77HnaueS8r3hETvyiBirhwnbgJrR5bEI/hF9toRtlvJE1OsJGwISAIM2HtIB4ImAoQ4Em+4jZlwQ5Dg8KJk79jYsXpIPRISdJV6T4ZJajvReKUoI4AAERQwK8HCMIP4pQSboEzS0QCAjTFkxCIDAQWbVCPte9Mz2Dsv/9PuItFDP7+76MlNxRc58LJg6COg9bN14aDouF4JNHE78sXH16P/iIanC9Ictr/dsvswzfS6fonCH0ECp4RZRH7RYqVzsIpMneklcmhddt1aN12XehhABhYvZw+ihI6AAA42cbeA+49MioE1ASqbUBywsPFx+5HqrMPndfcCgAojp7Dpb0/xfypgzASSbRvvxEd196KZN7PDSDMO2DYTCvlCfFSaIhygWIyNXEBz3Ewte9JTLzwE1DPQ8uKjcE+Ucxy6BUaf/ZHMOwkBve8jcXCe9AmaFfzRhhuogIGnVcg7pvjc4Hyf7ifRLbXAxai19h0WdVmz6ni3MEfwrRTyHasQMfKazB27ElMHnkGrbffAcC3/AtrHvd2J2cFAFD0AKKEdlBIhiizSqNgTL0fwVoVHmi4FI5oJVbuuS4fwHAoKoI3g4c4cRYhq0hRy5kBK5DhstBLEuRFyIacsG8iGcCoQeQ1l5C6xrZAxENcKusaddapphKNAcwtXECxPIXN6+5DR+vasOAe9VAsTaFcmkZ7x/qY9y7GkFbnb9tm7qKqDwLiohuorVfy1QRhgD1TsxI1el6Vq1JPluUJMFwNEIBvAdEoFSKnuyiciUKNm+STZk0BCQwQEGTHw4Zcf7KzivVNjBE6UGmAodJMDVYBNyjKIhTIUq35lBBkU13Ysebd2Hfqazg99nOs678j2n7AxOIDgXpW/KV6BcTz6onOkyACizhvgSqm5hy1K8tnXBIqCHOlPhLHyRfaJURURBgTRAu1eI2R88JkveQ0K3bHufrFnIDAI2AJ3i4gfK9VLxLfJ3bpvzZWiRWcAgBPiAGXFJmyEBYCwCuXMfE3/whi2kh09eDSV76K7Cf/CCZhStz8kZeRGViDZKY9Xjut8wglxS3uVaNR8KQTw2FDFymAnZkZti+XjRzvpT1fqWWKrZiIvVQxav5t8w0TPEQIZmgLIG74PHmV5CBJ0wWS6XYUR87BLHowTSO4Xio+EsP/2wj38ftWa1ne2K1F2ZXvZptbuCdPPo/S+Hmsee/vghgmShPDOP2tzyPR2omBu96N1i17YKaYFu5Z4TssggAuniXfHy78nTdrAHUp5k4dwNhTP0B1YRqdW25C3w1vQSLVAuLnVgTviN+OVQIKF09g5tiLWHnH+2Clc8G+4Hn7l2v4Ckul1UBytvlJQAUCnLFLW+uCyoaECJMQpQIIJdIxxA33wTSx7vr3Y3DP22CnciDEQLmdoELKGD/wU7TcdDO7x/78ICZYp6bDaxOt8OJ18D654q27jkBERc+Uc6RY1WUaKPJcxG/NcKg017tJA1YpfBZBNwkiPZdazkRyTsgHUMBERCIhsJpDVAOZKs28FsJ6FJtvJmyX8toAjE7sQyrRivb8aoCQwJtMiIFsphvZTLcyZn0iepzHRpUEBwHOYrxnSzF+mTW5avCvsngg8F7DAKbXsu8rTZbtS7LK4Q8ASCHNmvtr1NjiQgQLa9CWppgYwCZRS0Ozu9hL/P3hVxbJlgcC6ygXN2EEP3x/OGZ50J4OgXMlQ+yCEHS3bcT6gbtwavRxnLj4KKg4CakTGCEy8w6lS1PgNWNdljQCGsL/waRqRMeiCwcijlh7IVxYRRq4RonBUqKZkKMQ2a+4deU2xHGy/8WKp9wSSpzQY8DZO1RrdKAk1B21f4zm1YmLG+d98w4opbj0D1+BOz2LgY9+Cn0f+AhqM1OYeOQB1k5pEYUzR5kXQPMIGzEA1b0Aws6v2wbxfwRFWJXC/v0w83kkVgywLFWDAiaVQFAz4iVYWJCbYs/DE6zzyyreppH8ys2oFWYw8fJjyzrfLvg/i+x/a9H/KRBYhea/U6tgsJ95E/asCXsuOp+5xUVMPP4g2rbsQXbFWrhuBed+/BUkO/qw7iP/Dh07b2WF5NTz0oDnVkHSCT27kv9eGMrcXBw7h1P3fx7nH/oSkm3d2PT+/wNDd743SEDmYtRoZJ67uPchWOkWdK3arVfiSAgAuFTaDFTaBKML3x0zRxKPsh9hV8TLF4BB38MbzCWI5IKJbarnB+w/rQaMtjzclBEk/3be/kY4xUXMHHg2+CaSc77n3JEBgFhYTPRoLgUAMC59f5tIkOGFIUCeGQ294zUyglwAACAkMKQBQDWnAAeFltTSVKRV10pqEumZsb9Rd+7RMf3AQzwA0G2vt6bo8hP4+KiHsalXkM30gAqDlO6TrrsGn3e9/bbNLJ+1moaKyyDaxGDPlNkPdWMUn+VVuSrNyJLeGK4gqQwDdjEM/eFC3HDS0QlxZIukVWQWpICtQvgGdECg0kpQGFAmH4sEP9J2zcfo2YZEu6UTahl6d5wKBAwDa/pux8bBN+LM+FN4+fQ34biV6HlSG2ojGqVW3K5OcHHnc2mGGUgnjcKSlPbilHkRCIjH6NgcYt2X4lpV59gIfSwQenKM8B3kC5rhhBZc7o0KwoGU95V4Gmt1g9upWlZdHhPukLrHAUDt5VMoHz6G3vd9GMmePiR7+tB511swu/dpeIVFFI4eBChFfuPOpY0n5hgeA143F2AJuntixsXiSy+j5ZpdSBSai+vxLIB6bnivqR5I/SKkZXADene9AaPP/RCV2UuNT1iiWItE/lkCOLAWDViLBswq85pM/+iHoK6L3tvvBQCMPPZd1BZmMfS2X4dhWaAWC/Xx7Oh77FUrIIlEMCcz2sZonwRAbW4aFx78Mk5+47/Bq5ax7u2fxrp7fwuZ1j4Ql+VqBOFxXHmtUZg1CqvCAH73llvgVoo48ZMvgC4swqzSyDsWV8ujmjf072MTFl6AAQHDFUGEcH1K3oua9M+/BfGbcFIEbgKo5MPxVtrYtloWSLR3IdHehcrYRRCXAQCdmEJ8f4QNBjIAUK+fg3MiKH3qOudZzCPgScx5cjuqUi8KBwDEpTBqNDi2liWwKl4AACJx6DqLuOhB9yArtdyQIEhDo1Bcjli9vwGm/PM1g7MUqWsQIVjRdwOmZ0/h2X2fx9TMCXYNTcxBywECxfI0Dhz5GgDAtjLhmJSxAexZeCaRnqlR9QLPpNbg9CsABHhi8Gv5c1WYLPltsYoamjFfOBBQLf1S9VFFdAuRVYp6B8yyvl8OBFQmIGoqYCBmkgmAQJ1JKC4uTx6giVUDt2PXug9geuEMnj/2TyhVZrWeg0DUj55SFt8vSiOLfT1lf7lAIE5ilEFpgpaSZ/VgoR6tW9w2dRzRIkEIrGK6mP+wc/ZfcibarBr6EtCECnmfsYxAOqCZACqd0ROoQbULp1E0MPfUk0j09SOzeWuwvW3zLoBSFM6dQHV+BlY2HxZmqnOrGiX6NmLnEUNn4oTnIRDK7nvhwgm4xQJy114Xe46bovCSjP/cox4mHvgWTvzf/wGj3/86ShfP1e9QEcNllJl2if0fFHwjzHPg2cqPJf9QA+jdczeIYWD+9CGYVdaOWQEs/lMK5ySryIweVhGxc1ozYi8Q2AsMENgLjRsqD1/AzMvPIr/5WngmxdyxfZg59Bz673pnQNspimezMEvu0fJqVXiVCqrTU/AcdpPUYnNupYTRJx7A8S/9/7B48RRW3PV+bHrvn6BlaFNwjMiRr6WCBJunu9buxoY3/RaKU8M4+sO/Rnl+kvHKU4paqYDFiQuYu3AEDnVkpdVvo5ozUM0Z0XmLW5jjtgt/M8t/nZsqGRkEq7/gEeNkAuUOA06GwMmQgOHISwAgQHX2EqrTE8it3RppNznr+RWCZQ8AC2EMxyxadNVE6GB7DADwLBJ4AFSlkM9polKvhkyZVSp5AFSmIruoPGdOvCBY9z0rukZSS0kwNoj8zRDIXmF1rTPk/bFAIM5joDMuKdsoISCEYMOqN+HGnZ9FIpHDvsNfwv6jX0WpPN0cnal4STpAJLxPoxMv47kX/zuKxUlcs/0j6OjcoDnB11/8H9VjpubILXWMV+WqiLJsdiCukEfCJurF+BLoJ2Uq7BfEKrKFTDL01qJ9FntYnE7LcLRzMXkpTjgQMCqi9TraDkU8dSiXro4tuDHxSew79XU8e+wL2LnmvehoWSVNUg2rAqvFuhrF6ddjC2qWGpSf1xSvMtH3FYw/BE5GxYWX5CsRtJN1kCfAL5kzc9S7ZLEmAB+CyKjgj0uk6OMLpV2gqAmLHq++G7YtKJNcURLf0QZzLkW4GNsLBLUWCohsQA4BNSmIS0AqodWzNjWF4pHD6H73r4EQArPkK0X5diS6erF45ijs1g6AenKBMOnGsP6Jb02PxHvz+PhG60aj10BQAERZOHcMdmsHkgMrwqbKBkuGpjIApK6L8W98GYWjB9F67Q1YPHUMc/ufR+u1NyDRP4DCUZYA3fe29yHT1gcvwazQatjK5YphJ9AyuAHz5w6jd+edTZ/HDRUUiCg3XNS8pti2BO8AodE8qerkOACK2QPPYvbAswCA1o3Xon37jb7SoG+XK3xGOo3Cof0oHNoPADBzLbDzbbBzbbDz7TCsBGb2PwOvVkP37tej+7q7YJnJwIukzR0QK2hzoeG+1p712PK2P8CJR7+AIz/4K9iZPKqFGXhOSPuU6V6JdXd/AnbGB7VmGDNvuH7xKgpYGlpKrWjCewBIoTdqmKiaUCyKmyDSXAGw8DTxXiwc3A9iJdCydkvgWQQNufVF63vEeIEQAFAjPpwpSAAWjB1AOKfx8wFo8wHEhF31OTopArtI5VyAJEFqxhOOMYKk4KBvm4TXRph3vV5+QJz3KVY8ea3Uhp7q2OsaiUslTwWXXLYXu7d/EuPTB3Hi9EN4+oW/RGf7BvT370FX56amqDzZmNT3K1yDp2dOwrJSuPH6P4BlRUP32PHRTYZL4cYlCTdguroqV6WeXBZFKBCCgUYJhFzUSVkSiigQ8MODagLRiOGGQED0DiyssBgQ0CrwcjydjknFS5owKm5dBaleDQEuuWwvbtz6m9h/8pvYe/LL2DJ0D1Z0yJbRWBrM4ABFYW/A6R85VzzeMGQPQ73k45h90mTbDGtQzQs9KBKKQ8PwkmDB0r0iShJxYEUU3h2+MEpUoZq/4YU86TwJVq0RAISLpvrOBJO937f6JKxF1j5neVHZgHgRKUoAOC4mv/FNGJkscruuk5i1iAfk1mzG/JGX0bHrVlD/WaqW3FhvWyOGoGYMSQLzUz1xKyXY6Rakpglq+ajCIUrp9EkUDh9gCml7J3rveTfcuXmMP/xd4JW9SK/ZgOrkBKaffQyZtyy/KFozkl+5FcNPfwdupQQz2SSrURPCSQ7EYlS6CtKqWIuQlNO2rXuQ//c74I7PoDY7Ba+wiNYNOxkbmX8MZ6PShQgM/sZvw52ZRm12Fs78LNzpGdTmZ+HMzqBw+iicxQXk1+9A/41vgZ1rq6usqZZJ1qn+2EyuG1ve9gcY2fcjgBhI5NqRzLQjmWuHW6vg9JP/gqPf/QusvfsTyHavBMDYhQLrM8fNfvXbpsGAKCSaxCmyUQHh8/ESrGAWwGgxAcBJhSGpYj0bADAqjGa2Zc0WpMph4nVynrH/GGKMPx+HMGdLHgAvHIc0XtHS2wAA8O1ionBIfRwq7ZQwRZ9LLUuk+ZG4QCVvsOsA8xYExjJhHfX8omTh3zIQ8BIyPXaEPYjX6RHukc5ar8pSik1GRP0+KALjEQHQ17kDnR0bMT5xACPje3Hw8Fdh2xn09exCf/9u5LKy502nS6jJ6FzaWldjbPxlfW2BuCVZS8NNf6XpQV/rkJyr4UChLAkEmDUKg1BtLGcckwg15Ek0ON5neGk29tcu+UBAOd7xF9SEv9gurAgvqeWCbC7k1YSDsWk+XmobbE5QC42JDEKmAXD6vBjLR8LKYPfGj+DY+Ydw+PwPsFAcx6bBN8Eg4U2KsAXpZLlsQbrjeHuNpF6fXBlUjhErHgcJwCIQgGy9r9s29CApmAwFIGG48vvY6H3iCx2fA7iiDggAwKcJhSdY5hQPQOS9iekvWIgLJqhfDIpUjTAh2Jep73wP5dOnMfgbn0GiFrUQ5VZvwvQLj6MycwlQFxA+Ng2IjhNK6ivoQdMUfvhVE8e6gOfUYFiasuEasfq6kN64Ge7sDKaf+im8cgnJnn503X435o/uR/nCWXiVMqqTEw2vBUBQ5Cxg4/LDfaSxU/nZ8XNb1mwBnrwfcyPH0Lb+WnY94vN2hdfd1VdGb1bMMqQXJq6qrCjEA2yahN3Th0x7X6jYaQ8OQ524GJYFs6sHic6eoD0xER6AXGQtztME+d2JcNYjqsgmzAxW73k326e8R1vv+UOcePyLOP6Dz2Plbe9D1/rdAIBamkTCUAAGBgIg0GwoZIzoAAD/3/EV5FIX+99JIeD95+eaJYBSD5XpCWTaBpn1fyH8qAIAEDMWFQAEv+sAAIEUV+/aJAJkABkYqN+3mkTKQ5TKHYZ0jKh0VvIGMpOCh1wFgOqfjW47ibpSJSBAacRQ1Kjyb9OVgZvVmSmFjQQG+6/HYP/1KCyOY3T8JYxOvIwLF59CvmUF+nqvRS7Th3SqHclkHiANGicEIEB7xzoAFLNzZ9DdtTX+cJdqQ6yCIcatowRNrwFX5aoAy6YIjQECHounBdgiKU5OOiAgTkpugjR8eS1/wXI0hrpqLgQCXOZXssvLnxcozXyllASJo+KkKVg0FMDADogq3NytyK0douJqGCa2rHobcpleHD3/EBbLl7Bz9XthW2m5TUKiuQBSJ68SEGh0bBMWfgCxVmExdrPRpCwp+ary6inHiFYV3reo4PjvY1AXQKEQZduE99HfFRSoE/Zx6zpfQD1Tz0QTAEiNB4BfUy2v2VwzmM5u0QAILDz1DBaeeBrd73ovUhvXw/PrFBg1BI1nVqwFsSwsnDgoXVNseFIdJS4YiyZcaClCKFCZm4RXqyLV3ovC6GkUx84h3TUQPdi/VvG67Y4ODHzyt1hdhhpQPH8G048/jEs/+yES3b3IX3s9Zp97Apl1G+GkQ2DmWYy+kjj+a3AZRkEuiXwHUh19mD2xLwAB9cSsahQeIiqUzfcd5D/557oxUQKS+O8eoYAbl38deMbCeVkVHiqnEjtIzVBB6ReVUqoHk/XeKaIAykSmFVve9FmcfeZbOPf4V1GeHsHg7reBGAZqWf89WZQfsJM25GJXgDyviSFngqFHFwIUhM0I+yq+B0C0+nMiAQ6a+DMjMDB4430Yfup+tHetR9caBmLMcngTxPlIpB4V7wkQNSyIHgAqsP6I1yYVQIuZcmU2IEjfi+gNAFhokAgM7aKHWoYgIYAbxv0vrJWmbM3nRTrDsRuy9V9TPJMaSjVhQ7lHyppS15PO3y9Ps423p1CEwot6njl9dS7biw1r34q1a9+EqeljGBndi+MnHwS/kYQYSCZbkU62o6d7G1YM3BQdk992Ot2BVKoNM7On0d29NXbuooRIdSoA9hxF63/Es31V+b8qy5BlhwOZCsuAqyQTcdYFkaWCLW7sOEux8vD21FhGXb4A515XE5z4pJ2Yl7fPr7SQP+/IFkBNIbCgRoAjAwTVXcnyDDzpA65XFXioew+yqS7sP/VNPHv8C7h2zXvRku6TD2oUstNIQa93/lKsZDH9RJT6RqFBopLuVxAOrBeakCC+UPKKwaroFXthG9/FFXPhUJ4XQGj4fiUKLBkuZDnRuM8b1AmgRPozHJf/tz2vAAGHAV1qUxCfjWjxmZcw9c1vI3/rrcjfeLNwz5S+PBfEsuGVS2jZsIMNp4ESLxU5o6hrpdNZx6MNQlY6XBcnv/Xf4ZQKIIYJ6rlI9wyh98Y3g7hAYgYgroFansK16mvq1AQyQ2uQ+8CnUZufgdWSB7VNdN19Lwz7l1M9rHvXXbjw6Ncwd/oVtK7dcVltxZEnAI2VfFEJo0ZjQEE8PzySIggJUsURitXZGnYizw6/ExEwiNbmepSJOiYevj1S8EoBAoZpY82tH0CmYwAX9j6A0swY1tzx67BSzFhSzRlIFOQX3Un760gpDG9Rw36kQoLKuDy/TgUgK8LcA1BpF/ryPTU8d8heCPfZi0D3lltQHjmLM8/9KzJt/ci0hyA4SPIVaUc5Sw00lnVRGnnK+fWJeqCiOMZZ7sVrthcpSp1iPgCkfABe2Th2GBQR41IECKhhQHXWS52oa4K2CrBqmNTdu7prH+oq0iZM9HRuQ0/nNrhuFeXKLErlGZTLsyhXZrFQGMWxkw8gl+1HW+sqtj5pjKW53ADm5y9o+4+scW5977nh0Egi96+CXA0HunJkSSCAK/a6iYnTm6mWBVEBEyUoFqSAiaAdDRgQQ47MchCRA4BRtXGp5qNAoDDILjWSPKxRYj3LCIAAoLgrhfEDkJOOTZZ7IE1u/uTY0bIaN27+FF4+9Q08c+zvMdhxLdb13YFUIh9lR4hL5G1E3xl3/lKlEeCIofa7OPkSjl14GAYxYZpJWGYSqWQrOts3orN3C5Jo1U9ovqJLfGuMnH+gHBthWxKehRuddPk2XsHYrIZJc9zCbxUAx0/gFBPeA0WOasYBYZ8OAwkF9YhH4GU0ZvlCFZe+/jWk1qxB5zveGQAjroSJVsxLTz0Mr8wSZFo374rcg9inRcN2GgGBeqI7d+HCMTilAobe8EF41TIS+U60rNoCYhA0zP+jAHHC2GURzNj59sDDIgIA6rkY+fH9qFwaBa1WYWfbsPKu98NsybNn6c8RpusrzxCUYsN/VFIoWdin59TQtuYazK0+gOHH/hXZ/jWsyBWJXvfl1ihQrfKxlnzeX03+vZGHx7NQN0fLTbF2jBjvQOANU+fsILmWBvdQx7zFRVSAdZ451qb/t0HQt+0OpNt6cernX8HRH/w3rH/jJ5BsZ/HXtSyJeAQAoJYhIRAwBJzOx8W9IUISa8Clb8tKNvc8VIUyCDxUkD8DuxC2yfM9CCFYdeOvoTg7iuM//yKuvfMPQBL6vBIp7EdTj0CUYP6g/G+N1Zd/19yTKVTkDfLvFG8NNSBVVq9lCawyDZiQDIflQ2THHOkcqQ2LRNbuywUC1DQkb4CuiGQkRDRmLWpGtB7rBkCAi2kmkM30IJvpCU+lHl58+e9w9MR3cf3u34kkElNCUFq8hKmpY1i96k5/EBoDGxednlXxGuYCxFHvXpWropPlhQPVSQY2KzQCBKjpL+gaRg+JYUDqgwb7RYnLPeChRtxqwy05iVn5OJ48LHkBYoAALMAs119xOfuQRIGpWjn8yTGTbMdNm38Tw5N7cWr05xideQWrem7C6p5bkDCSkeNjx/dqhgfFicJQJDH4iP37/88vjuDQ2QcAUJiGDdNMwDAslCtzOHb6B6Cnv49UqgMEgEddpNIdWLfmTWhrWyX322gS1oQi6ZR/XchakPzrhQt+ch6o5MP9qkjKRD0woLoDlPONoikBAaNGQJJJ5G+/HfNPPonSiePID23RWpArkxOYfukJENMCMUy0rA1jSeO8ARKjkbovxmoricajosrM8ZeQbO9Fx8Y9IITUPdaeM+Emoy73pcj0Cz/H7P7n0Lp9N0wrhfkTB3D8/v+GNff9JjKtfY0biJGFiydw9if/DKdchJlMw60UcfGJ72DVmz6iPV5XlZaLqJA1K7zSOkW8JV/tAwDLTYjxKvAxuBkKIoIIIReF90XccG4WAU6goAvzdsDUYujn88gYeYighhCCU9CK3uLWwc3Y+rY/wImf/iMOf/cv0L3lZvTtuAt2Jh+GBxUF6z+AWtaQEobDatRRpT+whPsKL/dgS94WGhoFxHvBDUvU84DpIkqlApxyAXSuAKeyiEzHICZPvYCT++7Hlut/XbqHYjtqeI7u+xXfsUhdAX+MooiFwtjfiudB6J+LUaNScTarHIaXJBY81LJG4AXgoY8h+xONAgPBaBGcU08arVG6/IEGbEFLlUgFe/VvdW3RjZl7dYiBzRvegRde+p84f+FJpuh7lOUQAqCU4viJB5BM5rFy5evYNg3QqZe7oEs2NhwKR6wNcBkhnlflfz+5LHagQCFXvguzSmFWmZVGlKBok27xiLOo1qLuLuKyQ3WFyDitKJdqWxQIlDpNACYyY8LqqFJ2+uKmzKaAAIVcPl2NWeTxhwaAlT03oL/9GpydeBrnJp7F8ORLWNf3Oqzo3A2z6cxOjVdgCcp/1VnEweEfYL40hrbMCrRnV6Ijtwq5ZHfAONIUpagvCTuL/o7tWCxPoViZQqkyg1JlBgAriLKi7wY4cGAYJmCamJo8hpf2/S26u7dh04Z3IJEIV10p9pN7YcQh+EBAXQSCyVp4l8I8AXkBtBdpoFQATNGhpqxUi9ZK4sXrr8EdV9asIDFylqDaRoEaYfl9giLW+fa3w7l0CRP//GUkf/P3kezpk4GAB8y+8hzgeSCpJFrWboORSCgdC316wnYNMJEWZnW/f9/qeQzcchmjT30f1flpLF48jd7r3xS+L4JwD6A9DxCXBBbVekJN/9E67LoNAHDDJNf5Ay+iZfu1GHj7h2BWgZ7r7sLZ730BJ7/539C57WZ0b7kFyXxX4454f5RiYv/PMPLcg2gZ3IC2DdehPDOG8sw4SKNEv7oNC7+rt6bB562yPTViXTOcMJRHZR3iRercvAtqUSQm4l0Onq0vygj4wJkitOJyy7Qw/6reDWZ9jr6gukrf4nrgWUAq342t9/wBxg4/jvEjP8elI0+ja9NN6LvmLiSybYr1n1uzffagMg3a8Swita3G//PQH891UC0U4JQXUUYB7vkCarVFOKUC3OIi3MUC3MVFeIUCnOIi3HIR0Q/LgJ3IIJPvRTLZ4o9NeztZ5dcYuk62X/5bmtd4d+o3SpjhQxd6JR4j9aOsq9wTIIYC1bJKKBaBXK9ACbWihsxrr7IFsXyCOt4B05DfG5PUZwuKyU8Lx9vAoIaodyFyvvinLhJCOL8l24ehoVtx9tzP0NOzA5l0Z7BvcvIIpqdPYPuOj8BIJJpOZSKenCRsVGmQxA7UN9RcqXI1HOjKEUJpY61xfn4era2t2PWh/y8sO6WlJdQlXQEMCKjWKnFiNitKSJDO+ih+85EqidGFki9Oru+RFT/a9KUwjhSADASC/lSrCzveKvnsLq4yCQpi1Dxp0tK5HInrBmi9XJ3HqbHHcHF6P9KJNmzofz368pujukIjy7863jqPdWZxGPvPfxuUuhho34HZ4gjmShdBqYe+1q24ZuidTLFTAIbEzsP71eUPUIqaU0SxMo3F6jTOjj4JSj3s3v1ZWOmMf4yHsbH9OHX8QaRS7bju2k/BNJlyGwkdEMGUMAb+OzUQJHyLVIzUIFBp9KgZvkOB+z8XAkcWoia/o7y6ddCSGiYh9MkLdIkKUq0FqOUo3LTAHOIQliDrAV65jJH/8Xl4lQrW/P6/h1UzJeurUyxg7NHvYP7IPqx832fQsmqj0HnYZ7Dgq783sPhHPAP8G1S/RQKMPPsgJvc9jvzabTCTWfTf8BbYKdlsyiyX4T1wsuxH9AQwZhp2/Zy7nW/nVYP5e8ZBwPCX/heMVBpD7/hYUBjMLZcx/vzDmDn8HNxKGS1DmzBw/VuR6R5iz5oXy1JCK1D1cOEnX8fs8RfRe+0bMLD7rYBpyIqScB/4dQH1Lf065Sx6kHyvgr7U40nYFw/pMBWAKF0jL4iWAJysoIilXSkh2yiZSE754JjnjLjh74bIGqS8H3EVZwmN5kHI9QNoZF8cvzl/bygBnEoJE0efxPihx+E5VXRtvBE9O1+PZEuHlBwsJvZz1htprXApaqUFLBbHUZ4dR2mO/V+eGYdTVhglABDLgpnJwczmYGaysBNZWOkcEmYOVioLK5VF0sjCSuWQcTNsXSRhAqxoPZbZb8I+grw6kZBCUMxlC7R879Tr5seIf8cBADXUVswHMFwgMS/kAxTlonCUyO+ASGsatiEOEFJoLYBILoC2CrwKIMVjNDllESCwFC+BBhhw633sKXV2u24Vz77016hWC8jnh9CaH0K+bRVOHH8AmUw3rtn1G1HDSZ3roYRow57EiAs3EZ7g1Mp44bv/b8zNzSGf17BTvIbCdcm7f/hp2NnLoFi7TKktVvGTe/72irxHv2xZuieAIuIGBMKJKWKJcEMmBScT9QJE3IrCokMNRBZRNZbUcAEegOwmFOtUKQQCXEpdAEDQcoF1UuoOrWPpS7W6k4eTNgMgEDceahDAICH7kDZpNkziTSXy2LbyPqzsvhEnRh/FgXP341SyEwPt16C/dRvSidbGE1odi73jVTE6exCX5k8gl+qBQUycnngKrZkB7Fz5LqRs9gG4Xg1nJ5/DyfHH0dWyFoMd18pdLCExmBCChJ1Fws6i1VyJtpaVeO7Q3+HQ4a9j5zUfA2wThBjo69uJ4sIYzp//Oc6cfRTr171VbqcBPg0AluTOld/BCIOCsq3q66/cg6StEyCs31qdjvcpfANikTK+3SwZIRDwZEDl1aows1kQf4OYlGxlcljx9o/Aef07YWVboh4A4XtZlvJf75oEqRZmMbnvcXRfdyf6brmHKfAewgWszmtqLQLEIWF9jyXG1RMK2K1tqIyPMgDo/xiZFAZuvw/9N74Fs8dexsS+n+LcT7+KLe//P+u2d2nvT1GZHsXqN34MHWt2AtA6VrRiuIryfhlGJTHcjCAEUHEShOjEhEWGDftegKyrfS5uiuUEUENmBjKE95940flarbshdCV9M9HxhBZZNVlWBQOGE+ZJWMk0Bnbejd4tt2Pi2FMYO/g4Lh17Fp0br0ffzjcime8M8sioyYwLi84sKlPjKM9NoDQ7htL8BMqz43CrLKeGGCaSbd1ItfWhc8U6JPKdIK1MyUcXU/qJnQDxb5y9COl/ICS2sBe9YBUljnA9VAMEBEWWUXEqBiVLPl7r6VOAFXHl+6clCqjzfjo+FauoUFbzBrJj4QfKxiIDFbECsjpXeKZSO0DNsYvkApAIEIjk4ZmKRV8HBGIkoKxW15MGHoLIcUS+z5Hv1N9vWkns2vlJTFw6iLm5cxgd3Ytz5x8HISZ27vq4UNtD6M9UwJAHreLPzotuM6seK6ynXNaVKhSA9xrSGf0K3KJfmiw7HKguM4lgURQlSHjULHL1qpvqFjvdR8iTAkXPA7dwqVWGF4ZCIMCllmMdRdgQhAXMExC3KQACdTw69iFAmIgsAxAmwpZ0L65b+yHMLJzF8NRLODX+BE6M/Qwd2VUYaN+B3tYtsMx4WhFKKWYK5wACpOw8PM/Fham9GJk5AMeroC0zhIsz+1F1FrG66yZs6LszqFlAKcXY7GGcvfQsUnZLhLlIolILO2zK2kJcikyqAzs2vR/7Dn0Jp07/GOvXvRXUIjh14ke4cP4JtLetRU/P9qBNXSJhRLgbmFuWNTUIgnYE5giuPPGJNFEIgQBP5g3eRSooNU0uOmpcrFox1qtU/YU7wcCgS3Hp/m/BKxQw9OFPRyxEhhMqvFa2Rdon0R8CEU+Ftg6GZlu4028kBu3Mnz4I6jro2H5Lw3tBKcXimWNYnBqG2d4GK98Go7sNZmcbiBU/7RBh7iBu+CyoCVit7SgcP6w9z7AS6Nx8A8xECmd//EVU5qeQaO/UHltbXMDE3kfRueVGtK/d+aqvCNocDcU71Oj8AFTW8zp47H0lTugF0ImR8OepBECmonMItQB4ehrRoOJwTD6XWZVvXnCNRNMeiRIssOuIfu8qQYSZTKH/mjegZ/NtmDjxDMYP/AxTx19Ax4bdSLX2oDI9htIsU/x5RWLDSiDV2oNUWy/yq7Yi1d6LVHsvkvlOEMMMaj3w9ULMC+CP0CqyZyHST0sAgI+X57DFKG5cwZNqA2gAAKF12tDcO749dr7UKoxydWAnTeTE/EIdZRlRAK+GBLHx1NcPdEnAdZmSGokaFkRpZN2ihLC6PHHni1XjXU+Th6Fn+2EnyNszyXasGrodGLodlFKUyzNwDReZbHfsJVAznvaU1Q2IXxOvVg2+KsuVJbMDSX8Lbnxq6BGqVaFB7KXaTkSJr+NNAGIsXxqFxaywiV2c1I1adJGcX8NObD2pgAEhISocBIlMhmJlWgAh9Rs/jk8Y4mQv5gqIE4p/THvLarS3rMZWp4zx2SMYmX0FB4d/gMMXH0JLug+t6QG0ZgbQ27oFps8+UKrO4vDFhzC5cEoan22mMdR5HYY6rkM60QZKKRyvAtsMg4fLtQUcvvhDXFo4iYH2a7B54E3SfhC5AJg60Tbrdu1sW4+Na96K42d+iGy2D2YigQvnn8Cmze/CYP/1/g2lEhDQLWDiWAILj24RVN4LKcGL47AihZNhCyD3GHEgENQJqGfdBOtDN217roPyzATKR0ZRnBtFeXoU1bExODMz4amJBEgiAa9QQN/7PwKrpwsQ+hIXW7WWgU4JVxMA1aS9utJo/SVA69rtGHn6AUwdeBL9t9wb7HLKi5g++jxyvWuQ7hvC/PmjGHvxYZQmLsBIpOBVQ1Oz1daOwd/5AyQyeWnM4lwSJ3ZbB9zFArxaFQairmTPAnIrNwKGgQtP3o+urTcj27sac+MnMHf6FbjlRax8xycxue9xgBD07r5bvjcEIQjyf4i/jYOnpSb9ihK8R9xy3gTzKQcEhod4pUpo300CTi7+QDfnIDluS+cE+/xbyqu0i8IZ3ojyLQRKekXUAsP2VCDAFdxI1eGYucSsClZqCph2Ev1b70TPxltw6eSzGD/wM8ydfQWptj6kOwfQsW4Xkh19SLX1IpFrYyFeosGXh4r6a4lns/sq5lPwejf8Wu3F8B3hDEV8fVCpsoN+NDSdjQAAIHhalGZVJqFo7L9mDoz55j0rZFoqdfnWYwNIzId9VHMGEguyti8ljVtECmOC4tVQE4k9S84NABoDAW014Wa9Aeq6BegBQCxQof6a0eCDj1n/JNpWQpBOd7Dtse3wE4VNVH6mKtnFr3rV4KtyZchlJQZLQuMXSR0QAJgbWke/GZeYyKz6Gl5cneWSAkYF8ATDlxQvKUxoc+tJBAionPHsD2FWE4QloNWZrJTkJm2hEz42vz/TSmGgaxcGO3aiVJ3DxNwxzBUvYrJwCuenXsDkwinsWPkOnLv0PE6OPwbbTGHXqvcim+pCqToHz3PQ+f9n78+jZTnOOlH0F5GZVbXnffaZR+kMmqzRtmzZ8oRH2WAbG4OxMTTzhYamL7eb2+u97ndXe71L8y780fRt032bSzMbcAMGG2M8G4/Ck+RJsmbp6Mzz2XPtqsrMeH9kRsQXX0Rk1T7nyJJ897dW7V2VGRkRGRkZ8Y2/b3K/ERSqbgnD4CtV4uiFe/HIqc9AiAR75p6L6c4O5LkrJBimfNSsjIwslGqBvTtfjMXuKTz08N8iSVrYsvVG7Nz9ArsQS7axRDTStN4Qio3nEqThOpUVJA1UqJbHAq5jFGffITYN6E+lSpz8xw/g/LfvNi5f6fQsst07MP68W9HasR1IU6heH2qtD6z0kW2aw+StzzXjIAdxbbKZw2QDFBjOIAZpVMWbZoAmZ7H1ua/E2Xs/jbQziZlrbkVnbBMuPPBlnPjS31dFkxSqyDGx6wD2v/UXMXbgGuTpAPniPNZWL+D0+/8CZ/7ivdj9U78IOXIKzzpOo44ZGSzMQ+rMt3n9utV9TFod7Hv5j+DMNz+LJz72h+b6se17sXb+NE5//kPIpmahinxkAdYZCuX+B5NV1+MaJAfw5ndjJuK6TR0zESurc1AUEyVk207gsldN/sFUiXRFRoXbMgNojIDTBYIo5PS7Xt+5ZUD3MSQMaEHAjFlEC5r0lOfHnqQt7Lzu5dh6U4WyEgpO95B5Ut12/b+WhXQcEFUy6ZgI6irFBQC/PVFbfIYIAI5QMroAwJF33NiT0QUATQ4wQgkMJoWxAmSrpcOU67XHrDNUiKvXYs2wOkk2qSBQx23J/oi+gOs1DJQAh68WTfFx/DEWKoA8V8YFgWA+ndG6qvvWpMEPIgFFErUCQLKmkI81o7Q9U2gjMPiZQ5csBMR8eqNa/hIQ9UZQtuBgVIcEgca2A4hBtE8UXUELAjw4eTBdtZeuVvUs7RPQ9sBND1F/SJfB0v6NIVcfh+pgHicLIiHqy+5oKJh2Q0mJsdYMrtryAgAvQKkKfOPI+3Fy/j4sdc9iuXca+zbfjmt2vsq4C020iRtEYAHs5yv4+pN/jfnVY9g2dS1W+xdx/MI3cByAgsJkZxu2Th3C1ulDmBnfUzFrl5BgDHA18EIIXHftD2J19Sy63Qu47oa31Js31e5EVOuUJMJCSaH8TYBpyEK+1NmKm2fCY6ibtNRUUCkLHP/Y+zD/0L3YfsddmNxzCK3tOzHYPYaiTtakkipAU9aMGmemZM18OChFXLgOCEdNcTohaowXaHjUW5/3SvQunsGpL/0Dznztk7jhXf8OFx65B5O7DmLXC38ASycfxfiOqzG595DB6k+SFrBjGyaSbdjx9p/A8T/8v3D6/X+B7W/8YchWwD1FuwAJoHviKJYe/jaWH7kPvbOnKl9t2QyZs/n6O7D52juwtngGq2eOYmzfAaSbNuH8t76Ik596P/a9+WegVImLD30N2258eWNd6yWOT79eMsxy/cxD2dE1aSZepYCKWBVULgGpoAY+I2PiM4jQKXM7J5tcOmLnipbvIlQd9wOHKTPDGV2+R3P3IF0u7fu5aXT/6H+TL4DuDbm7L+h3z+GtSxdIwukTsQLoNUYz2Y7/f1FJis54sURg6xEAzDnlMorDLFU6BqE/TWBBu248wGBSYPwM2f+Yf7+SgGBAHg55SD5MeBni+8+zEXsKsyYEPAl/LwqVHVVhoss5bkIKKg0P8rr5yrLen3gsSISCsQBrw/MGbNAGNdGVswQw4q5ClIwGjPJ+TcqBkKmsUDaIkwsEokLRoEl4aBZOuvDn48oIApoW91UQonMPhdN+lokAkmplSNaKxkya3OQZJC0QaGGAWQX0+e7aPL555G+w0D0BQEChwB2HfhqzE3v8+gC7IJKFsDdYwlef+HP081Xs3nQrTlz8NiY6W/CiQz+NsdYszi89gbNLj+H4xW/iibN3I006uHbHq7B3y/Ntn8jzHORdJDKDTOK+DVQQyIoEz73t51AUPWQ1LKjnD6rnRmDRC2E688VR0flCLQQmK7EVBNKuMtlHKbqGgQXV87Luk0J4Qy7LHMc+/F4sPn4f9r3+xzF77XNRppaJS7rCCAIAUGbKCAJANT9DQckm5iEYiFaXT+z7I/IIw1AiOJ6GFPseKZumbVx91z/Do+//zyiLAo/93f+FwdI8rnrFOzC+bS8mdlxdMf+RfWn8wCFs/+F34swH/xprRw9jx9t+HOO7r4KDaFTT0sP34dj7/wDJ2AQmD92Aza+4CxMHr0OadMKVA8YqJIDKH3xmWwUXCWBy//UAgO7pI0g7E7j42Dew9ZaXu9dy7TGzKF02qTijG6J0zZZrCgSWvRoVaDy8Fgmdsbkvka4QNBid80XC4Pa7F7p9p/91bA23KGioSYreA2XjBYIWCBYvELMGJ32iANJWPJ6bpr4Px92KtO/dF+C5OQEW0ILuVdlqtaY6SbAEAKWMprvyR6/7Rhhm+p5qpt8T7mmfGgQAWkYzjiqybjpBu8yiwgUonpCtWneYpTsRrsW2VE7fy4Qx9tJFFPJgQJu0PiJwmu5poXXmSjD+tLlBCdWKv3zB9zgiiIiBb1kIxbMNrd9c7P5Muwr5U8bVbdD3Il2R6dIE6QloH+uIz6Ly/Sqjk14zYlyo0ElhmDDABQFTv3SZst5c9eaPnXIrvnBdFhUETF2JRJEASY86TLraCI4YxKksc5yZfxCd1jSmOtuRSK2ig1mUziw+jPuO/B1KVQAQOLT9Zdi/9U7IxH2ERZljcfUEAECIBFJISJEiTVooij7uPfw/MCh7aKVjOHHxW9i/7SU4uPWlFX4/gB2zz8GOTTdCKYXF7kkcOf81fOf4P0DKBLvnbquYbqWwsHIMR85+BacvPoB2awrX730Dtm26vnGsNGVFgqQ16fKdMQSH4ICBwJTW/xqC48KLdLhqymzzPAFm/tXaLVWWWD11BEtPPoDFR7+F/vw5XPXGn8L0gZscjaNGHlJJbQ2gcI1E8+r1WQQYgKYNIbLROYGmjpozfk0TrV08g5VThwEAaWcSh970SxjfvLtReND9AICZm2/H2K6rcOqv/wxHf/892Pzy12Lzy14DQVRuRXcFJz/6V5g8+Bzs+eGfhkjtOQ2XqWrZWdVGPKGAplTF3VNHAABnv/xJdLbsxI4X3rWu+w4KCERoVIhj7YcopElvZPSJxj4WZKwVGvmEAjrhwcgnSyRd4QihmpriApRkQnFN1KLgtKOFAaZJ1/do4Em1u0jIRShAcuAHSWqBg7qd0jgwoSy8qnGjUpW7TyjfgRZUjCAjLIOsBQCHuSYCgGmfbgnUEkGgjW0B+3VYJmFOomQuIlz5oVxmP11zA4MBl/kfTEi0lqg5ekgnpGCw2Kx/Qy73YgPSIbEBADzLc1MfL8VVklIkC32Q9B5EuyNEfL/Ric88yY2WceeKKBvaR9xy9UyiDXegZw5dkhDg+QcSMgxUUBsZSB+v1PDgy0D9IQq6CclwUDDga2e7O0pPEFjeXV04dXRg7sH0hTRVtJNKENCLUUALH0MM6g2W8a3H/7qqHxKTY9swM7EbMxO7MT2+CyfPfxOHz/wTtk5fg25vHjMTe3Bw+8ucdtYGizh67h4cPX8vBsWq14amVHZQlH2000m86NDPYJohAdnshwIz47tw0/ibkcgM9x39EAABJRSOnP0qlrqnMN6ew6Hdr8KFpcP4xmPvw7bZ63H9vjeg05oJti3yAiqNcDm1wGPMvw2LpqI5C0C0hoL9hv1vmHthmZBkoKqMoXWZZK0KEHR9dmstYc1EF70ulp74DpYefwDLTz6IYm0VSWcCk1dfh12veTsmd+6v2mNuR0XHfyGMOweJPRAKrjtYLQjEgkiD2lt9jjBsXLNqzPSR4LqmNbJ77jgAYHrfc7D7RW9GZ3ZbtKwOuOTttzZvxd6f/RWc/tBf4fxnPobWpi2Yuen5puzpT34QKh9gxxt+uHL/4egfl0Cy1cb4rqux+fmvwMyBm5EUslFouBTKO3bqar/6oVYYQsaSo/zkX045gpwWzWDcTaBaZdSTz/jrBwSXMnPhlzWFkkPSawB/PhZtF1KS1mXeAcMkR1xD2VpKYX7p+0rjz0SpkPSY9lvVwBEtXQ/pg9be63gAopxKVytIz7RnXxbdLsVoN/0zLlVsD9D7X8jCouuMoCg55ekt1bEIitcp7Hm6B3pAHex5JWvVukitHdSKUd0gm1TMjYffg0qZm0+TC3Ag9s5zGxoWNAxccebftu3+FgqNTDkXaoZCX+c+kx+0BOtu9phL0AZ/u0HroMuyBDT6jBKGzD8J31oQMxWvc0JrmFCA+XvWiyD3sS1TQOQCqjaXr15lOzD1oB2epb2ZEQRsYwJlS5hAqOrFdTMiBoOEAUdz0ulswqapq7HSPYs9W2/HWn8BF5eP4Ni5e6oqIHHd7tdh79zz8alv/yaETPDwqX/EWDaDVjqOU/PfwemFByFlit1zt2H3plsgZYrV3kXMrx7H8tppLK+dw1p/HnnZw4FtL8HBbS8z2v8oXrKokLJv2P0GFOUA9x39OwDAlulrcM2uV2Hz9EEIKXH19jtxev47eOjoR/HF+34H+7bdASEk+vkqBvkq+oOV6nvRxea563DdoTdBouVrVJrQHmJaZn1c97lU1QZFNWGBuIC87GH+5IM4f/J+5INVyFYHYrwDMdaGbHcg2x0k7TZk2kGStlEsL2Hh4W9i5cmHocoCnW17MHfrSzB19Q0Y27EPQqOQsPlukmQx0lpYTwAIjMMwaEkvgFnF30sHKYVbGmpBqkkAECUwt/82bPrZG02grlO9gIE0HdbvfGUJq489jPb2nZi49kbTv3xlCQvf/homr70J6eR0cyUALj7wtSr+YnLWwBXS+9eJ1Kb3PwfT+59THSzrx8UtIyIwLor9vxRi46771URJz934Y0yAhpHVQgNNFOa0rQREL1CJ1vCTuADa12g/I/MkpNTRyhkPUjTx56p+V73swgFBIMR8pT1lLMCirJhawGrDRVlZbOj85MhNup9Uq0oFAHuvDNJYCN89lLss8i4TRUXVBxUedzYnmxjPUBk5APqT9XNYcxVmeUegPU8EnABYRyMNi+njp3nsG3MR4rkEAHgKxEZB4opr/0tr7WGkBOy+Yy6IP58QHKoclFHgjai1wFz87OP6NywBzxxatxDgL0yKJPfyB5ZqZdzrENxEqFYripDCr9NVs+M6UZnOBlum1mRdZsBgikjnRBDQtHKVwgqATfdXFes8Aq1Fd4crW9JBRPAWsHrzatLuHNr9atz78Htx+NQXsGvzrbjt4I+iLSewuHYCrXQSk2NboZTCVVtfiIXVkzh58T6sDRYAAOOtTbhu12uxe+5WrA2WcHrhAZyefwBLa6cBCEx2tmDTxD7MbL0Dmyf2Y6LD8NOHpFYXQuCmvW/C3NR+zE7txXh7zl5Xn9+x6UZsnj6ER098GkfPfhWJbKOVjqOVjaPdmsbU+A6INMPRU1/G8sop3HLju9AenzWa/97aAs6ffxjnzz2EIu/h4IHXYnp6r9tPMmdCJtlg7gg2Xy4evR+nn/wK5k8/BFXkGNu0C+3JOeRrKyiWz6MY9FD211D2eyj7PVoTxvfsx45XvBnTh25GNjnLGqr6RzfvQQ3rn/SEIwjIftUhh1nS7xBh6GOuIRoiVGPPO7kMzLvovz8xP2sP7YiVownIgOp5i7Tlb+yjMMmqqr9YW8OJ9/4eAGD3j/08knbHXJ9OTGH7a34Qpz/5dzjxwfdi5xvfCZlmNm8AqUeVCsc/+ZeQrTb23fXjmNp33QiduDSi2clNdt7M1caPnV5HhVzhOWT8bAxU+Hy6UiFcZUsCa9u4Cr+ec50SoieRdDVTTsrU86fMXLcmR2gJrLVOvgoti9d9pLFYolThOR+1Kgs/zwVj/GJAFMnAt5AkPcv4ikIhKRiTrKGe66ELCgBK31+EYSttPIDpmzENBS8x9VZuboQRpoIAnxsioNzg1j42JhRNivZf++trtCDtClSmggU/B55TGT/PE6RxHPwm64ASgMpcWFEPQpRbjYTwApMd62EZOM7vh+0p0UDdELIPFwRocV4vvxeecDOAVMTPKyKUGLS7DdqgdZJQargTtk71/Py3/wekmbVRVz6i/uXUX5NOTH/RimiszPl4n5r8vC1Sgz2Vj7tWAA0HmRN3oGLKTf4lu3YX2XS/QOeiXTFai7m7CCitOarqMEJAQLoXRekvVqiCbI+d+SqOnP4S+vkKts/cgKu2vRgzk7sdCDxRKEAplGWBtcES8rKHM4sP4fTCA1heO4tEtrB1+hpsn7kBW6YOIuUa26ZHzrMj0tgGmisg5mPQgCJUthIsLp/ANx/8M+RFD+32NISUKMsC3dVzAARmZvahKPpYXj6F3bvvwMEDr0Oa1nNOuYuySlyNv5K1xs1A1ul+A6UAjj38aRz5zkcxObcPc1fdgk17b0ZncjPKTCAfExjUwqJxwyhKlP0+yt4aJFJkY5OmH6Y/CZunNXOej8PkqSjaqNzOSrtJikJr5OC4AMlaI+tttrWG3SIR2TIyr77bumHdUABwhg1gDJ2+JwHyDiFMyl5L3Y1o8KpKqnevTKv3TFsGTBVrazj2F7+P3snj2Pcz/xLtbTucemW/qnPxoW/h+N+/F53te7H3h34a6cSkHYOyvu8cePx9/xmrJw9DyAQ3/Ny7kWUTrjY7qRl2qvIoK+x9hwkYYglQcrgQAFlxYrJfMdJJ7ZkXihWI5kZQIIoVvx80DkEzdkpWfbHrWsXwl5kCOoSRKiprQLImTPxASr0H9etfMAZ+2FrMBRo951Q1p7U7kGOhoXkwGoShaP4YfpydV3wuC/e44M8eVkBJSFAzFwCAau1xssQDYeFkmADAhG8niDQkALD1LkRc+OHBv4MJO6mUcK0zsgDSVXfx0YJA1Lqo71vPHdPv8HVm39frlacgdH83Kc8cIUEwKzzPMhwhGqjLXY48Yvubl8BL8vOk7nUELIuidJj8UH2hYOIiE8gHa/jy3/9vWFhYwPT0cEvqd5M0L/nyD/0S0ol48tOnmvKVHj73pv/6jByj7zZdljuQ1pzwxVn7a3LJVPHgJU58I2mgUCZUcy4Ue1D6UJAAkHYF8jHlCAAAoFKFYipHslQN0ep2YHW7xNyDtZaE+IFSbUY+lhhBoKpIeQsHADd3QF1Vlo5h/66X46ptL8KJ89/A4VN34yuP/AE62Qy2zVyHbZuuw+zkVZAKWOqewumFB3Bq/gGs9i8glW1sm7kW1+x4JTZPHqjyAwxj1DnDH4hjoP13YDlj9xU5rp/99OQuvPCWf46jZ7+CQg2gVAkFYPrgazG3+Rq05BjKssDx41/CE49/HGfP3o9rDn0/tm27BQhmLoa/wRKoUCUApUoc/vbf48TjX8Ce57wOe258LYQQhik0iYBWYQQBAEAikbQ7SNodiBwWGaiepzrREGcaTaBkv0ZsmSghSgHUHkOViwLrszaPR7SjIcQjjRcfJMqYhcYICL5vNLYiRDz4M0R5zfgXqlbHkudWrK7g+Ht/D/2zp7H7XT+P8ekdwFr4PqavuwXp1C/j6Pt/H4ff+x7se/vPIdvsZ9wc33U1Vk8ehioLFGuryLKJSuPM/f0DWmzD+EtbRFsaDAiBaGCARiXhCk3DyHneDaRdgSD8PBeGtOtZRLDj7mQOSlXpnov2E/DvS7loONwVSPfHe05sXsYs9zRTLY9FawKriCW91Me1ABBy/wHsWiZKlwn0UHTquhsTzPG5xeMB+PPX9zeivzovp7M807iAoiWQEatHPi4dQaDIBNJATIchFhjsWTS8tSwQ+Ksa7pneD7MElZl0rQmp9CFFvUpo2+6ANWbeDayjNDbFIya8hNx9BLtZGiAtBq4g4PE0eRgq/dlAG+5Azxy6IuhAQTcD4QZWFWyyRoNjRtz8gkReUlEqY+qmwkhIEBAKSBcT5NP+bkcFAQC4cL0VBEyzAs7iXaYSSCWStVqtRBcD+iKzJGKaEUxkir1bb8eeLc/DhYXHcWbhYZxeeABHzn0FWTIGBYW8WEOadLB9+jpcv/t12Dy5H1InBuPByTHiDL/+za7xsJp1kOaIGYMN8k+deCWbmsGBqTpba+IG+SoAEgn27n0Jtm69CY8+8iHc/53/gUce/QiStAUpU0iRQiYppMwgZYpNmw9i5547IEXm970o8PA3/gpnj30dB259K7Zf+xLnfrXvcLKmUHSaxqrqnBOoq/wNzjDodQxKf7YWlKWqBAHAg6TVFAyOpxsPnd8MutS7JlRV0/5GtYdssxP5cCZ4MFULXEWB5ccfwvx37sHSI/dh9nkvwrY3vBUAkC8u4Nif/i6KpSVc9c5fxti2Pc2VAhjfdRX2/8T/jCN/9Xt44k/+M7a/7i1IxichRQIhE0gkSMYnTfkyH0FKuQQq2vZTtqrnCcCOE2Xyn0pqYCjT5ep/2QbKTf4D0wxysmYfrtH2M0aY3ktjXAAQFWoMbC0RBEJwykE3EzqvQ3sC1/LXxxR7HzwGXD+2ULC2QjCAmV5HmWq9p5kEcrUrUNO74sWDKHd8BdOqB+uICXNEEKKMqcxhYZDhx+rw7M2ekDRMAF5Hjp8gSbgB6Cxp5LpiATwBMpCYkwFL8HOe9YD+DGayF+75CMl+gZJBjYbiA5yaByXKdtgvNDQPmuDKN2iDQnTJQoAXOOT4AgbKKwXuztHcAFkw+SIU0ujRc1xaH1Qf7aMtc0AuVd/1MYAIAtK9t7JdorsbGDtevYzdOQm9G0+dsJwbX5yKTmoFAVNI+YsYRT6RlXZC5CWEkNg8cxCbZw7ievV6LK2cxJmFh3Bu6THsnnsu9szdCikCCwTX9DeRl2nSXQS1piQqCAwhs9jW5atkMGU04QoAIwx1OjO4+aZ34dyFh7Ew/yRKlaMsc/u/zJHna3j04X/AkcOfxb7934ede14IkVU+EvnaKh74+p9j4dxjuO72H8OWfbcBLOMih5ENzl3GcGsBwNlwAkPd21QdFKUwTGPa1ZPaXiP7/rVOmZo5cyAa4SJ2NNXRyPw3+Z0OQezqT9v6BwsXce6ez2Dhga+j6C6jvWUHWnNb0D36BJIe0L94Dkff99+gyhJXv+tfoL15e2Pd5n4E0J7ajP3v+hUc/cAf4cQH/6zxOvUUCQGjUJnBmBEqVyGBNAPGzqyzopBgZxpxy8SeX7YgUbSAspPb8jUVEyVkXyDp1Uwsc53S9abEHYhn5ObHQn0uExtzYKBN63fP+N8rWzZoEWj4HbVCiMB5qusggg6HK9XkWAF0H2vG2tH81vPfMF+MqVeR56Rhbulvzow334+rgHFjAYQTH6WSyuVnME4EA9ZW0XJd1oqObw3gUN4Ow+sJX36uAOd+eXxAAzPsxygxDXsAUnSoIOBUABbUyyda8z4qB6VzjSOAsezIXBDga7pKqmSkMariaoZYgb4byogN+p6hSxIClHA1IqHFK5bVN4jnHtDwjNYRBJmcGOZ8tuQy/QAge4DsCQxmqrLJCsEqn8oh+nb17u4ujCBgymQCyASStRohSIoaEo/EBOj+NUGcMaaaBxcLITA9uQvTk7twqHxl8P48GkFLH71OKd9Uqp95ScqFqN6guLalwoNWniBA3YzMPRHI0M2br8PmzdfZ47q+Oitzd+UcnnzsU3j0wb/HkSc+i32HXoXJ2T148Ot/jnywhhtf9DOY3X6t6wJPHqMWBLQvrXbjMZsvDdbVb4ze9IhLgqb+jEUEEgOYbK6asaX1JRHmPQixW9g+0U03VgetizNxMcFgFG12PuEzNRe+/gVcuPfzmLz6Omx7+RvR3r4LF+75HM587sM4/5XP4vyXPw3Z7uDqH/tlZNObnGs180FdkfjtJ2MTuOrHfgmD1UWgLKAGBVAWwKD6Xq518eQHfx8n7/4wrv6Bn0aajrkwsYpUKgDIahpzv2tR/yll9ax5dvNLof5kxVAb40FICx2hpmcFwARL67lbMjdbuZIYwAPVcZmLwSSQdAEkEZjQNHzvIUQl852NZ5m5sQXVPakIPGhAEIgRV/YEflNXnJh2m853bQUYSQBgz6RMhZsQy2HKh95NtS9Si0uTwAOYtZcLOnRfFqV1TazcsUgMCWOMvfdeVUkUs1VbpmTMNgJJHkd1mxu6d7GgYh4vATS0Fajau99QxnlNPMCXWSai7pW6eK6c8iWDkJX9AkUnrN0PCQDJWoF8LFxeFM9OlyClBNTT6JLzdLb9TKMrlywssABoQcCDClRxRj2G9hAjURIeO4EbOBrQAKQr1QcAenP2eLZgBQHT/1ogUJk9nk8AS/ur71NP2LJFRxpBAACKtrSCACW+gFCXII5eoDMJAzDZhPW5y/VR5t3iz6MeO2eh1IJKrH1iRYgusIARBDx0Hz4XmhZq1HNEKYxNbMH1t/wo9h14JZ58/NN49P4PAlCYnNmNW170P6E1VT1omiug+k2ybZoMwtanvcJ/rjbIMvUZc5X62sRBHWPEEYG0K5DOFOxk9q2ZJxpMGIWpJJQMEHQLkjqvASNaX0gwaHJBGEyGBRNNW172OqyeehKrx59A2V+DEAKdHXug8hxnPvthTOy/Drve8Hak45UUvp6kWqaPQqA1NmMFL0UCqQHsf+sv4PCH/gCP/tV7cOCNP4/2xKbmCkckrTiogoBVGDkJo69ZVWVYlyBAKdROMqgEl2QV6MduOxdAogw6ldtI/X7UPyURegUPoCZ9CmaxZmVN/oASDqOs94WYdStKep4XVvgJ7Rs6h0bUogyrVdaxLrEgYKGIS+s6+IfYnDBCSMR9JMqjRLT/pi2GWiNyOG6OSd8i9ikGZ1pkbpBwCAlwXeRZlRusHajHeUR3Ft99jDHq66X1XtskCLC6ZL/0AobpFC8z4cYR0mYCnhOiUJ5g4Zy/XPesDfp/FK1PCBiy+AX9IZUbTOjB28UsoBFhIBgsWVOV3EZF4w84Q5ktAxmAtVoYSFfs+cE0WQwHAipTyOaZFaADLO1NMHU08gInAvl4NcTpKrW5Iy4IaCqZEMNdfCSgQLQlI/rnA3EBTJ9r1JhQQcAUYqbU0GbBIdIC7kAhoc1pP4pYU9372NQ2XH/bO7Dv0Ktw8dwj2L7/DiRJVk0xY52o662Zdzq/0jWFvCOMtl8z4hqSkzLmWiigWn0NRStzjYxj70XmwmiVBdWA6ncmxqALeO+IHMQZ8ljSvVEyj4aoaKPR7UvJqq8yaWPfD/88jv7NH+DJ9/8edr/xXZi65kbs/8n/Be0t29Ea1CrIEZl/SdFV9BQfcs3EnoM4+KP/Eoc/8Ht45P3/Jw58/89jfMvu0Rq8AiR7BNKyZkDLBAjr8Aitl7eq52LUFWipWmcHk4QBI/DHZaeE6MvKCgAfBcgyqBHGmfY35BZIGfGBrQuAkxXY9I3sG0Z7r+xvt7B7XFImX9l3kVoABKgg4A+2tqTp5Fj0no0FQGe9j2Hzq0DMg4AnbNP6dWIwh1EOafzZHhBk/ml5eo7t8NT1UVtkALtu6MBhTfmYcNyCgm49jisOmzPDEp9RYnMtlBDMcxlah2AS2tv4eduXgDXAq1C536O8TAkl3VUgW84bGflGt2kOJ1o2z4EN2qAmWo/uCkCDhkKfH1ajWp+kmvSsTz8nmSPovpAMlO+/iOqlD2kiOhf8OrIFgdZFezOyK1G0ldHujp+0ZZf2Dt3mK2aJ+fI5L7qEz1wjoFH3XGyo3Xl93ISbdVc5wlKTBt4s1pH2uBsRzIZcldeBTnpBVyS5lxMkrO8tNC5R/GZgfHIbdh18KZIkq+9N1w23XVKFZgQMsg/LIqrb19CX/Hqj7ayvG0yHTTWy51oedJ95fYZqK5u+D+5Coa93IEFhy+jPukn6DIRTt/TfddlqY98P/Swm9hzAsb/9Qzz2u/8/dB95CGJhBM6/RqEJ3cd6qDO3HYd+5F8iG5/Gox/4HSweedAIWfSZlikMbChNblamlctEPllZADiIwKVQ2a6YLyMwXiFFnSzquR1Zc1uLAmmX5D9J3IbzSWUEAIfxTeD5rFMK5mmJ3JOTDMwoduiaVf/nlq9gw3VRVk6Uyj2mFEShbLt1jha9/mjhRpT2va+SNfnjAKUs5KYQ4TEhbZepcN7ZIKyp8NckU5UU9XpIrMDkuwMTDPIOkjJCVe+ufn9lXiVPswnUlMsA06/Czyi8LhoS8xdct4mlqRmdZ8gEWWe3PWz+UaCznQuE++GnyxKituCLvNm8pV1k9YfHEiS9MtpO1RYazz/TqIR42j8bVNG6hQBOTQx9mYnoixm6TjP1+sMp7aogekPMj1nWG4HnphQQBLIloHXRr4MKArbvQJfFNRZtgf6MKwyUqfCCeBoFgRgNWfwaGfYhZZsY/pEEAcDrX3Ax1QJGKv0kKYQR4O2GNG+KbHbVF/Zbjynthi7DLEy0L/pZaf9oiwOu++LfFiVdt05CR+dd0hVIl0LaJH0xORRiIPQxxgRwiMRh1CgY1JaJ0KpQtIC8ZmYLbs2rKe0CWdHC/jf+PA6+7V9gfOd+nP7yx/HgH/zvmH/4625TRfO73kSiXieoK5A5p4BsYhoH3/rLmNx1EI//w3/H+Qe+tL4GGBUdhXysTgKlBEQhIPPqP0r7f+T+l7DuV1dAINCChRYKuLU1WZUQeThbcDEGQITndqnzH4wiDITmrNb+p/a7ZX7te08ZYmr5CbnDRV1UlLIfek5j1Ss4Vg6gdk1SymDNGzhTkvdBuwLFmCuaO0K3oxl57m5H+64taNV3YZn/UBv6neVISNJeT4m73xaZqKFkBembcOafFvw0UUEgH2exYVwhk4hqzdWulevY47w9JqTsIc/Rdz/jUhY/77cX2/OCyb/onBohjsEIgIFbbhIE5Jp/TvYLqFSYj3MuoOTcoA26FFqfO9CweRdDpdHzlx2WTrZAv7pspTrP8w0kPeUlQKEmzKIlhjJFFKu9sPnP0Lro+9R2zlaVabQXTd3tVYbQ8TN2Z+nPJGgtsJwD3KWJISUpqsUpIzuuqYwNomFq5fBrm6qNICg4i6W+Dy4ABKBFjRsPDeSVNiDYy/hba8gM6smw/ujytA+EtBk+FBBrk/vA02ibmADNBAh7XYg0A6vrymvNsRyIKlETLCSoSuEgsVC/akc44betx4S5TdBrQjClw4JKTfEG//Si7Z4Lufxx/34hBCZ2H8DE7gMovu+tOP6Zv8GRj/0ZhBKYPXTb6Bo7zTjVCBqKm70bKGm1sf8NP41jn/tbHP3MX2L+yftx1ff/JK5QGJRHksCoGvcuxvg1kcMgjjI+DeOQrNVWjZAVoxBAokzGYId0fznjpBPiMUbezC8mAChp57mZ4yQQuGpHIRRHZjTlTW5I67B4OnEwBrLUHuSJsHQsUBT7vS7LGW3n/gI0FGY1UN75LbkQIZxyigtyyt03uZDnu10294fmDqgECbJ/E8FN96VpCnvoPaFMwk2xA2y99JQITY17fWWdHfburcftNpGNSlLZL6OxCBxKFKiChoumWAC+pz6DaSNPwDOHLssSEMfqDQ+wgdALnM66ynHjSUnGRp5kBqgEhHRNOeVM+Z5yUr4Po6QHtJbt79Y80FqoPpTaFwPagwRY2cm5fFhGrB4jHRtAMxyO5BZFqw5tfuv0BWwyeY5sUeCzJlZnLCiuRkDw2gtoxZ3TIZPrqIyBIhsWyMZB6tRzSW/wwwIU+eZT1MgsNPBSM1uqdsWQteabMwyKauADVoGo9p/6X5fueXNcxb/HXD76kyxxGiM5qFwpOPKLqbu+zzQZw75XvhOz19yGJz/+Xlx86Gvh8poB1czmZSq6lACETLDn5W/D5hvvxNIT9+OBP/zf0V+aDzJYOtvuYLoS5PKxyvoxzPozjERegxF063iQEeT0kNbao0BAMheQ0tWq/bJVa1FTZVyC8skSZWoVIU6gOlyBhgawB4UV5c9TRQLptYuXxbFXTh0x7HxbiH6/BAFA2XdV5sqMrxEAajQX3b8ic7X4Hk69gGfBCgoAntWVnx9+D1RJoRUYXADQdXFffanjnrTff6Ect0/9PGOIRNwtKB+XzlgY1yfdT/IsOWKNtoyM4p7oWatD8WN0XDjjG7IOUL5DwFouuPb/KXanEUUJkRfm45CqmP+QAACsY3/eoA1aB12WENBkxizawnlRvQQlDQmIspXS0wRkqyWSvgoKBOmaCsYMpN2wMBB7maggoKk9XwUQmzKLIhijoAWB1iJNvekuUBV8qPuCU/9Mo8lnfuiNmPq0PKEmN5+h8GyjtDHELMsXZpqaHSDaeC/Qax19JBpJhwp3k+PE3cHoBm4EgAhzq9szAYh6EyTwoqZenam1LmMYZjnCRpggyCSE3hlZxAWW9QYED8YqASBESb+CukwaoPhF6QtHQkrse/U7sem65+PIJ/8cT378vci7q6DuCOshfb8GGUwxZpW2LQT2vvyHsf3216FYXcIjf/gfsHz4ofU1qF1+TKVWMysHWB+EaM2MOMGsTWOgMHSctOVBw4VySpcrdyAeEwBUMQ8erj3s/OMQqh4jRxl4xrjreAvHQgKyNrH3N+gKFHvHI2QEKHZ9FRPiKpZECReNRrlJLanVkB5z2qMCfcxvnPTNPx+/l5jri8dI06lZ2rW3ZJmgYwHKTZS3RXSfDyJDke9lKz6Wft4E102nZHse3wO9viQCwTiKkJ986HfMnz50rGlv4vugFM69eXEIZekLKfR8rqJ7eZO1ZIM2aFRalxBAtQqcNBSoBweK8DFNTQuRKFRwoqfdEnJQOlp1IBw8DFRCQraqHMxj277bRrZaZRXOVtxyWUBASLpu4KAogMFEgsGEnxWQEhcEgJrpDbnemAICkLL6RCi0WFwR7UGoLw31DhMATDUxS0jgsHMfMS0hItrMwDHzu372VBBIu6HOsroCt0Tx+s2GW5cLZQkOzX2OWASg0tJGguDX408f08KVWWXFGIyFr5O5ZfxD12vGv/Fdlgn2vfIduOo178LSkw/goff9Fs5+83PoL11s7rRmRGvGbmQceUY7X/B6XP3Gn4NSwJG//F2c+9KnL62iCMm+BTAQeRzMIEQmaHXYnk6FgcgyoOMCVChuYyCrjxKQawHtaojfoe5p3KLEBIFQOaBG0grNjYCQYeomx5qsfZTpd64jY8XfEaNIorFJodijxHcrDQUlD/UbF2J0YVwLidzCG0EGMpr4xH5ESYKZAQcm1QjMsXeV1c/vv7IG2OMciY8zs2UrblXhMQ6eEJH5goBjCaEB1EOUUI2MPidedljdsJaJYLzEsLYIyX7hxm4wYYfHAnD+SBQq+B4/00jnCXg6PxtU0aUlC4tkWdSkE5KMStr3H0AQkcSZ+IKfq1YzvWBIYikIBSZXlgGFAQt2CvnTac1tWd9PSgSBwQz5PuELDQaBJrELFoULdbCcGQzauvGYAXDsfgqt2QQJysuui3RfiWCihHBgA5WU4JCojrlThtunMQHBvhGtod3glLeomky73MUgQGUS/q01pR4DQB9bbXovOrDJmerrmgQA2i+L9GHvLeRmIOtA0GAm7YhGKUb0XZaosOZD/Qxf7I9JiGgA96Zrn4+JXQdx/IsfwMm7P4QTX/gAxrbtxZZbXoq5619gtcJY333E24QZl+kDz8G1P/X/wmN//ts489m/R/fkk9jzlp8CElFn+K1dFqWdWBqO85L6oH3Q6/ZVgqFZmM21ZcPcB0yeAYfpIN+TNXI/ocs7JVrnuVVSf4HLyJMxdJh1PTaKKYc070S10NI9FkKncebaCBaAUOCwOccZ/4EW9pkAoGCCLstMePEAmrGj2v7GOT9Ec8xjlKovrHjI3cdrh5T31i2ujbbroK6X30PMsgBUgABU+C4nJVKiUMvbwsCrAqOtCU7f+FwjVGauX72zV0rRKMh5eWhYWc5AOzkm+LonXIGwKTbJ60fq5j9QUkb34pDVXxSKCf3+fT1bYgE26JlHl+cO1GAZaKK0qyBz5WIq03NF3MUhZgLjVgGg0viE0ISAyr0oVD4UYxAy96crrj90sgZ0t0h0t/jCBSeVSRTjVtrxJP6SfHSZQHItv6PuzyYcZOOCxP4Pq3OoaVVTiEGFu8gpKUiwod714ZxvNEGH9lt9jI67YTK41sReo6/T7mMlE2S9TSHAHOd1gLnIreYvWfU3dNmgVQ/1zzH3k2POWNB4AHrrIaZDn9OMmYfP3bzJNfrsj7AXtSZncfXrfwo3/sz/F/te+y6ofIDTX/3k8At1EwxZyCSzYhrgYNuzm3HtP383Ojv2Yu3MCZz+1AegVLNfhA3gVlWyMFm5ksgBPJjiweI8Tn/8Azjx4fdBhQLbE3fch8UFaStBVIDX9x2LC+hWfdRxAUiVmSv5VMVcOBmntUAq4gJg0O2KCfq6L3RsdAC6dX+EJwDoWIigm1TAx95xIdLQnyZYvxq7qAAAVwAArABA3wHDQPOcLd4g2He96pe1EPguPK62dxhCkEfK33/LRBgBwAM9aFoHIkJI7DdXlnDFnVdfID6Al4/FC/h1SReJKGlyV/KFgtg+6mnwpVveY+wDz6npGapUROMQRKkqK0e9N/KswU1rRMxbYoM2aFRalyUgxhhQdx/fXOpuJtw1KFuxJ0MvjzbdNqE1aKKCAC2vBQGOKJQSAYHCHuqkUU7dZKOklo6kZwNCTdlCIZ9IkK5Uu1EswVkxniIhScS85CiAZ1GoGpBAAxJQDFln3WWHCQCmkkoD72nzmeuSMhuUm9XSSzgU0UQGNVlsY42S0WRWX2Shqg1TwHxP+tWzNQG+ddIvjxkxvs32nA0KtgJEsmYZCo1GpQRq64dlYAQIMyPgBGlSn2qaI0CflzkbK3rLwi8fwycH6kDYwHFHq3oZzD9vN2mPYdO1z8fCY99CMYgEYWjm8AoqumSa4sBP/i+4+I27cfLj78dgdQk73vpjQHZpyEGiBPLVZZz7x49g4VtfAWQCNehj040vxPieAyPV4aGUNJYN9KGwTLcOdKaUrkiUbYWytnLIvjTJEYtOHSBM5gtQv2801iI2dyQ5L+x3jVdPXcUoVr+j7dfvwjA/dWYp5fEGXODXiiQjAJTK3qf2necCQMjSrer1mVi1PIstydZrYEU9pDw7eKFYDB64H1MUeIy31FYMdr10fzvXNDD/fC8uMndv6k8JZCTeLh+XTvxdJURGFgt+T2yceL+4ZSNxYJ2ls+83wpGy/VUlgedD+xQ4Fn0eAQHEF5Zp2836V5mX0XtJ+qU3Ju61z3yhYAMd6JlD67cEKPbhp0X8HAC0FsNBvECdsTHyUoYmtucLSs8FtJVJrwFRqO8u+klPIVtRjquS7YtF1ajKAoPJ6kMpnwibSQri68jRAJQQjlbA3A+X9htiA3Q9o9KViBsYhuhgcI6dDZzcUwjZhgsCTnvxjdOU1wwHY2LoWGr3DB0srAVF83wpQ8T7hDq5FBEaNGmEIM1QGBQWBcvYkns2TDpNBibssZBVzLxHpGyTxi9EeadCAco7/jkZale434eifQirkYyV6y2eR3tmi3vZKAzhEKLBqya/AlmbZp9/J3b/8E9h+ZH7cOwvfhe9hbNWIisFPJSaBlr6zjcx//V/wuaXvBbX/It/j9bcNlz46ueQ1skOY8hNboet9jq0fmmibjux/slBdb3nDlTflxYA0lW3XuedorC1sXEggrm+B2c+a+z9lAkAuk0uYIy4I1Hl0jABwPhJcwYQcQFAf1cJqXeIZUK37bq3CO87fWdi65i5RAtI5EMFANdH3r/eIba2hmCFo5cO8XXnsQFBawBZp4KBvDFi54qWRJlKEzxcZjZWIMSM8ziCRqvLqHEDgbqdakK8UYNlQc/RmEWkQrCC5yGgSfZLyIGqPs8CIWCDnjl0We5AmmLMRtJT0aQWjUFz2lzMGN9koOJuQrFgp4hAkvTD6EHBJGUxwSGQCFVJYHmn3UkE2dgpFSzoKQgLJtliWzJmO7RgSZCNmS00jraFL9qBepyLh7TbQF7yGAH7jKm/Z2GPmb6GGHxel9c/UoBbVkL44zXjrRkmHRhscgVEUDBisTEOc07ro10cMcjXuA6R+pL+CC5FQ7R+OjMu4I+JHDQjAA2LOzBBipHVRSmFtQuncPprn8DDf/kfsXbuBNqbtjU0GOhCjcpiEgyW5LOOPXDq+pux55/9Igbnz+HIf/xNnP3A+5EvLbn9laggXmtm2AgohJEa270PADCx7xCSzhjmnv9yLD76bfQXzrOOk69DrHVUoG0U1IUda568KulV41O23bbyySpD7mDCXccc9yowJYunwUVUUFHCzmcjCCQiLAwxJrtR8OL49iMIAKZu6TJgZVoJRFoAKNNA2wo2T0KIhq2FQ1yImhRZQIj5pueEV84DPqBucmDf9XkjhPidcBH+3PHrT0oUmTACQG9aOgxsmTTAgo7i9jQi2k/QVYcKTk6d7P4S6XyGdkk1P7OhaF8RCno6KNWYLCzpl87n2URPd1DwRmCwpSsiBADNE19LqJyy1QamfoifW0xTFtUeNlgvuPSc9BXSnnLchQAEBYFY1lMqCNi+KQeeUgnhICCUrQRlu/o4XeeL3Kh++Q1EF3wHO3pdlWjtCtP6k3tSmYz3L4j2ofzvXLMWgGFbF9XXO8+irsPkCuAMMmOEvOQ7NAiU/DYB45T54xpMIKzdYcKEc4wej7wLcdO17aOfTTt8jWkvUqdGBmuKEdIa5RNf+AAe+ovfwpl7P43WzBbse91PYPNNd8abLd33bL0ZhofR2FUHcNW/+n9j7q7vx/I37sWR3/oPuPCJj6LsBaT8UP8U0NmyG7IzhtXHH0HSA+aufz6SVgfnvvmF6HXah9swLSNSSLjS66Ee/zJ1NcbpsqiE3cBCXXQC6ybXTnNhXLjlgv0kEJVVhmeddZnUw7XsjZYS15KwLgEAhDklmdytIECa0cKNqbehT6Rfzu9Q9uKamphIwzSzuBzu/+8IAALB7yEKAhzQuI2QkBVhqPm7Piw+sMwEmjILjyRMmbYa/Pu9euH2XwSO6WvXuZdQJd9IeX9oW4lw5iKlpnwBgB83sEEbdKm0bidYJ0U6xwOnvu+BjSpbjvvuJURICPq8xt4vuiGRfugXMhZL4Pmlo3I54uW1IDAYrxlH4vLBUZCUrFyCNJxomQr0Zqshbs/bC40/ek1lJr3AZpVJKEjIXnWdWewcE67mSJW7gMaQB5j/P0flcX7rdiQQMmnbe/Zh3Kpy8Jl3pay2xaSBH7ZrwW7ItKj2Iw0ECwbnhLKbKwAb7EfK6ZiAvIbKlIPKOkADiLmfrddXUreBlaVjQf/TTYiMlcfoK/e44+MfYYhDfXSCkws3wM9D6aDXiPA5gDCasfPanaI+3z1/Eue+/QVsf8HrsO35r4ZMI/A1NV2OSxBl/ow2UjNYSfVsVVIzgEkLm175Sky/8A7M/+OnMf/ZT2PhS3dj5sUvQTI+Xo1PIoEkwcSh69Ca2OS2JSUm9h3CypFHgBfdhSRpY+6mF+P8t+/GzuffhSQL+Fs1UDBzNLkvlfgBmgAMSlUIZa067/oqayHVBAIHAiTNHCMMZIVHb68N9RFlNbaysAK3CQ7mMQEI/HY6EhYArCAwugBA/wMwic0ctxzhvluhTNmxdTZo0RhBUWNchLSFsdR9Dtfh1E/XJbb+BQEBqHxC1wFPwRS4lhzLx4UDvtGblWjPu/v8eplje3FAuIqMY9lyUYTKTLprKmGavTg4Ac9iPCqJQrlrbUMMSLAMO9/E+ENWbj8xSnpFlSRuQ0DYoHXQpUXC1dQUdMQx+XlAcBJxEzJ1cSaysSPxUzwxFGW+g4JAxAKRrSojCJiyuRUKcoKxTgWBGFUbuYUpi0GGlu3UCAJVo/C1xlQYML+VLY/ANaYfKvg76n4wStr0+vkpjvNMza2BoDkTOMwFhPp2Rgm+ogG3VlOuKu2TYbire6hcFip4NTlQKDOBwUQt7JEAX6fN2LCQjVtJP9dAyHLlwTsSZBPdllAs+Jc8Wk8AaPC7dxuGGYuihfC7xvvKBIEyrcrEmosJFaf+6R/QmprDtttfA5GmVVcuYf+VlDHV078BAnFUSsYnsPn734SZO1+KC5/8GOY//xmgKCqkH6WAssQ5KTFx3XOw6XkvweRV1wCiajhpj6F78qipa/MtL8HZez+DB//i/0A2MYt0bBLp2CRmr74ZM1ffWBVq2K+NhaBJGIg8AKEFxsSPCygTQObCCABJ12WqvTrrPoQ0yKEcMDSeQD+nMhE2Qy+JaRgtVmKdAkAd/GuSk2l3Fg3XnNRBtGRcmvzydf+8eRVYwzwBILJW8rpCWZ+bULr8oF7dXryMF8/DBb6ISyWFF60Kus+9yQDAhacyca2NHD5zvRDZKtxllIlwrPtlKuPeBUPWuhDF6vLgOpUKoq+Z8qVCmcVHUAkfytTpg7eHXmaK8+8Sqac5MHjDHcjS+tCBGsbNZoOMNNTVjF3gWpLh1T2hGw5cE9K2jEAGf1u/qMoGKvNU50YDrLPBDmDcmvIxVrZ0kYKUBFa2Vzc7cboBySeIV1y5CGiJnjLPoigrZq8eHOdauoAmwoXJDAkPDeRYBUKWhhAaEIX/HJblGACksO1I22YIsSgGj2oWbCIABQUBc18wFgH9XS/cg/qZaouAYRIIExTdfGHb45CyDtQnZebJNYDLtBhGh0KC1v0P+r0TJmaUdyLvRN7FhoDUkOaZUiyxlKaVE09g8fD92HfXT0Ckoy09QZeR7wKls5uw7Ufega0/+qNGuBIlgKUelu+5B/NfvRvH/ux3kW3ajE23vRjju/dj/v6vYftLv9/0tzW1Cfvf+HNYOf4Y8tVl5N1lrJ45igsPfgUzV9+IPS95K1qTc/a5jsL8hJ6tvozFA5ig9F49n1tu/flYpawIopcFGKFgIH6t2ffOSWu5osHu3N3MjGuMRhQAtPDiuvjZaqgAYNplsUBuu3CtILD3yds2p4cIAN7aIeAy7SQImfdft9cEmUwF75C232XG3TVWSeGvTZF+cxqMC0fp15uVaC3axspURINVlQhP6Sg1WQPYHlGmrpJIMFdcnp9nqMWiEaa3gVEP5a6J7GeiUL47E6EylUi6BFGQxwr0imapbIM2iNH63YGGaEyGMSGeVsGpHOEVgW4yPPCJLnQBhs8xk9JzgQQbhgkL+HvzYxq6lEOJBiFDc4XBZIJsubD9dJhWFdTYl6n0THtaIBBFLSDwRGBUEOD3zYSHUN6AUWkYGpBXfpjbT+2KYASBBrg6v3Llfg8IAo7fLLW61OWKOsV9slb5SBcsey6F2DRm+4AAwF2Hgu46uix5F0KCgqkrJBSwfnlCT0SDXGbh90EWsHOPXduEOmIsAoRZ96hmdrrnTwBCYObQLfEKrzDD77pkwLjKlC0bx1D1T0uHpKz0OyE7Hcy+8CWYecGd6B0+jPmv3o2zn/sIVFEgm96Euee9zLmX6X3XY2bP9fY5KYWFJ76FY1/8AB74H7+F7c99NWb334rO9FbrK93AGCuB6j2OWX1qNx3jqkMCwGVfoGwpyNy/UJREyNPzGw3PVQXWSVqurk/nUpG5Mox6CBGI5gmwbbgMvU3YpceICwjKqd++p8LU4THSwkIB62scoSjxn0UlHNi+BfcgBgVK2zN9Ycf093gsD7kP1h+nLbIGeJYFp1/Ca9upl/5mezMdM6ASBEIxc0ClXHO08lwgyQRLCMo7Ej8XE1SD/WAuQ6JP51dACGjYE4PuYbperrkPuutGhAClzB4doqQXD4gy/dkQAjZoHXRZ7kAxTT13BeKLluTmP1plg+kMcF/yURkFbhamx3V1Xpr1gCCQFj7jH8opIAorCNCEYlQQMKSq+ICYG5KSAqr2E5R9e61KpBEEALja8xFMqjF0IM8dKOAnPIzKFrFcKKL1oJomzVzQbqyzraECJz9P2yPfaVBw3hHGtcsmCQpVHqm/PgeFsOVFl9FCT0H6VF9HNf1BlyBdvHS/ey4BnJmPbA6mHgko8k4MY/4b3z/KTxPNOJTCYHUJrclZ/5r1Mv46aJEKVevVLA4jEkMicmGCqWUuMLFzPybetB/5q38Qi/fdi7Hte6sYh4bAZSEEZg7eism91+HUVz6Gk/d8DCe/+hFkEzOY2nkI6fg08u4SBt0lTO08iB23viZeV4OSQ+bV8bwWAIoxZSwBSirInvBcFp14F0Wer4CDFkSFVMUCah1XoLyaczKPMPn0PkKCD1nH9Px2szBbK6qD/x4Q+O27Iby4Hs+tNSAE83eNku92ExAAnD7RsvAobHGs72OItYBaA4z1I7Dv2Qt446zfMQop3Aj1ZqUDr52PSaRdGisQr9pjsNcBfNG0J/C1oWxJ1xWJlw3N1ZjVSgg3roD3oWE/lnnZmAhUpQLpSjjVuMjVaEhLzzBSGMqiPOXtb1BFlycE1EThBIMbUpP/P0neMgztx6tbv3TryLLomEtZO5V/ntaUx6uRBYCCaNhIbMBgwi2bdoHuXIKxCzpxmCKCh+13kyBgul9r24V2E5KyYiT175BVAFjX2xZMHjZqMDBcAQCAwXIWSrmbK9V4E9cj64IEqxVrgNnTzEA86Uv9r7b8aA2iMX/X55OeQtEWKOr4TR4T4GwKTBA1AcPCfqfuceY6voEwJhaoGSddT+3CJdn63xgMHHoVAtpF4wKhN02y8SogKozR67z2ExikotAG2pqeAwAMFi6EhYAIOdroK7h6F2tr6C9dQLGyjLGDByDk+pdDoYCsM4ktz3t5NX4F4hYgQkmrgz0v+UHsvP0uLJ96AsvHH8HSiUdRnD6MbHwKMm3hxFf/Aa3JOcwdfF71bJsYp0igcNKvLFvUFUj2qslQtH13IHpf5j9ljPnSUAsbjvZRWFcgk+BOhnNO0JgAj/HirkAcgUv7/vN1k+Dx67bpcaqJdaA1uVacMdvOezgkERhtP6j9p8fZfQ/V+AfeZ24N0HUUHdc9sWy5kLBlxoAIIkKlPkfnoCzcskVbOGNGhYAQjCcV3Iq2aw3gypdLtQx6617CXIRII1XcQJjLj6MORjqm4O2jjgU6L11hkYyPHJTN6IhSuApAtugn3SsMn7ZB39N0SUJA0wvJcfc5uRCZo9cLuIyVQzrwk1GT1UD7kXuBwQPlxwZEBBHtO87r1UIRzUJMBYEoBRYak4GSarpSaRj/4O8aRlPorMIhYaAhRsCxEtQmdIrX7MQLNGgweJ2iHm8e+KshSn3rBK+DbZa6S1wQ0DELEZ/3StPkP3v9LNNu5S/tbOIR4dG0DyIQcLeaEPOkmW8qAAzYOQCSu78x/20j1GomjNRn+sDImbOU2Wl4/4J+0/qymLXEFKj+DZYuAgBkuxkpRzOel+sSRJlAlQAlCpz56mewcvgh9M6fQbZlC9aeeBwAsP3H/hkmb7mt7gCqvACXqWAT+h4YQ0kpaXUws+8GzOy7gXQcQKlw+DN/hiNf+CuMb9lj8ig0Wr50MyHtciGgUgVZZ7HOll1LQMjiOorwFWOilazWx6pt5dbJ2wnNaS0A1OOn/feNCxDpl8NQxgQA2Dp1n+g6b+6D9q/huZm2QiAGARqqwRe270BtaWMBtVyBxevgyHb6neUAB0XHFVIpAlqQGjTr/Dcdv7VNEu1Fe2AwmSBdpdYADuxA9zgBwRADY+sBP9fkIsTPeS5CDiJUPJahKsAXdTTPF36ObrMsUaqXbDMRjieAU03Bsgs/nSr2DXrW0TodPdZPjqbfO4fmlyZWZ+iaUkEUqlGCNpsy7x8vVzTUpQDZdxeNpKccmDRNsgAGU/Z3slaiaEkUrUsbdsdPPmFJRAKaQq6xDyYWG7lx9/5CeP4qk+4YSxZ8xrX9/Hji3l9I8282d+EfozkEPGaJbDg+Nr7tY1q7shl/Vy50jCio8mBfbj2g/wE/mDjUz1BdOsiZ1xciJzMwbbsBe99AaAaobDVoDXVXSTvzj3wTrdmt6GzeGSncXBevdz0WgsHKIg7/9X/D2c/+A+TYGGaefwem73wpdv/yryCZnEL/1Mn4xapiomXhr1mjAhNo685QFBpNUmDvS98GVeS48Oi93mmTFyAY71SdD6JbZUBCXIGM4BljAsmc125q+hNCpPHcaAg0qHfPVEjSVekyQriMfhIWAMzvwLvf7HJSuXZ5ePyRdwvw3wOtcdfrsofCxHNnCLc9bz2XJE6l7n9JLDy0PzwIlruk9sm+059x+8V/80z3zrzxgpPdecx/03vwxqshlqFqVzp7mspENFkWwNoV8N4xTXTODqOyndhcBjEPA54DYgQKou2Vyv3QPuclxMD9rKvuZziVEE/7Z4MqWp8lIDBuDvM7JKtldKPm2ktN9MVgL6RQ5Bh/gTjaDg8ArpkH6/9qU71z7XDMDCj7CmWLl1XIloHBpHt8MAVkbhLSSjNOA0MVzEInchVknnX/PMFFCqvV5wHGQfhQ2GN0IQ7dK6+XWgUo054KcIuB8dUOavmZ1ioWWGzyCwgzfxw8bFOfy0CY8wLO3DFadlYmpOkOxYY49YaOk74EswIzjanjYqDrLd3qQ/EAzr2Y+4bjE6xpMBFnVtfrWtSbq328c6vpda4T9avOZP+yyLH4+Lex+ZaXQgiuARxCIaZvnXTsY3+B7pnj2Pdj/xxj1xxCmVVMysoD96PorgJcYH4G0NLxR6DKAjMHbmyEfzUZmtlxkQP5OJCP+wNWZkC6qivw53lII+4rUFwG1/wvtYtkNQlE0SAA1N+9NqEZ9ZrxL6rfRmBn10TdY0JQxMS/PgRUEaqLujU5607qXuMxvomFwDSaekdQEmbsuPY/FE8AEF9/IRwQijJ1oarzDlDW5znj35tzFQ/9GRfWuGyRtYnt3Z7Vh94vG8vunEBn3pYYTErHGkAtHKbumopMGAAOoAogduaIg4zndqTJcsCpcg0NF67iBtyNcdRkhZfFnI/Q92cj879BzzxatztQ04uVrHHVpc98h9xbAPgBOlwKpy/8iC4otN3g8cAGQJk3z8e2FhaMoKCUWSipKTZbVp4gkPYU1jZXw905bzm/GFoS9c3nY1W1X4+jRglKE4icrE7c3Sfkw6oZH+02RGFFY8IE4DL7XsfZOS0IaGhW6g4Uw9Dm/qP6NxckGxZ+wyCHtDnCLcMDg4uWRXmKCgJep1kTgTguBxNdxIUEWpWTNVjfG0ESoq5DIZ9wE+RMyplrQ0JfhNnsz4bnKVAzQnmkPgB5dwVHPvZeFP01zF773HAhp0IrqF82CQASyNeWUfbW0D1/HNn1+4FMYvmB7+D0n/4RJq6/AZu+79VEQFSOYHVJVAdZ07m2Xjp73+cxsX0/xrfu9eoIPSMROSdKoMwURMFPwKAJAe7zVYK4ohHh23HJFIH1ywQFW+bdIyosR9zbzDFJrBUa/SfEKOv7occchYStmMZ+Bed0Q4xRaC0oUyus6H2A3oeTmCz2DlEXH77Gsbb0+XzCXWf6s9V/HpdWjFFlmntf6ZLtEBUCiswVfIbFyBUN7oJrswKt5QiTzRn9VESFXV4WifCDcS9lzWDPumhJxzrM13KVCBZczfrgFF5PP4ac5mhOyv1uLCijmiefRlJKPK39fDaM0XeLLiswmEKChcaU+tP5mMdDKudSPiEqMAQxdde5gce0SbG4gXCSseqj3S2aTNE6y6AgDLZxE3BQLgJBuoxUIisEjkEJldodSuSFDwlKtJ0mXgCohIGQIABEYwcqzXzF8DtZDgPj5dy78d2FYyEwkK3CHV9vXlEBQbuaxYZIwIsXsT7MytHC2WzBwggAIbhXU28D8UBefczxTW2wEuhb0po6B0KRI3vUTK7vH2wZFsqoKQ3p6SGxkK9EYMjHrXaTM3z9GYWkKxwELEpKAmWvh8f++j3IuyvY/+ZfQGduR6Cg2/aoRF0S+DPh8+bqt/0CTn3pIzj7sb/D/FfvRrp5DsXKCsZveA62v/MnIJI0PI/qsarQgWBcbbQ7TiPGPSXa1xHKrp47juWTj+Hq1/6zaJkyqf57jKmq5m0+DhTjCmVWN1oC6YpNEmaqUkAp3euBmilnwbjRYFcitFXZqC3D5gb7+vcRtfBopUtqXTCb5q3bP6a0oGVqVyCbByQkUbmCAEXDKqlSQteZ2DVcyXpKRp41R7+JBQxTcvz9RfVsgWpsqDtPf9pdWwbTJeSguracySGX7GQppwqACAGrO4D2eXLtGJD29P2xNSvwzpnucYUDo/60dGIHi5Zw6nYChluu9r8qaw84MVPS3buGxhyO6NJTthPPu0DQzL2NkKa8zZGaHI0uVejZoA0idEXQgQAEfeIpNW2WHD2oCdLMe7FzrWEOVBxaqBpcjKpr9I5kz4WY/qrfdZVEC5KuEb9r08dKGwLAMY3GsIb9LIrkfiJUZhKS+A1qgUBbB4w2XWvyJEHtASpBQH/XrjlaMNC/tQbfQQNyORBzrmGzN+eZIOAcp2NumF32DKTwBIGgGbgWBEKBbIBtK+8IpF3lCAKeT2xsc6v7wPH+qUBgAocpLKi+lQJOoLBB3iC+1F7AsXMPMFp8nnvA+BG3/PkWY1TKDObeZV6D/tSrRX9LDtGTSNZErWUUSMl9KgGgdrU4+fkPob90Edf86L9CZ26739A6NrFhmvAmSscnsfMNP4LpO+7Ehbs/jbK/hrFrrsHcXa+vUIEudTOtteme9naIy4B5bkCw7cUjD0CkGWb33+xfK8Pfdb1acKUCgMgF0hW7BqRr4XYpQ26CcplFINY24CIAxRCBHKY/JgCQY5U1Tvgoc1zzb47rE0Sbyxh44wo0LAkhqZP6mzehZMXqc/ocC8QOtF20q4sGNCs98fkHKquAqSsB8gnicpMpYLx6EOWU+0D6uwZIz9oNrMsEgX5WzxUACUucRikZuK5QXBDoTwoHRZAKAfmYQEYsBZ7SjTDgZSKQ0N+Z8OdYhNbjIuRcJ4UnBJQt6fASHPbcgZ1tUGhWDbDfMW2//r2hxN6gK0jrEgJEAQsFdxkTUb8wcT9lzdgHGO/IuVguAChY7UBgwY9fp8zmU5Vr0DCUVUO6bJkIpLW2LWem2Wy5RD6eIF3VKjZbtxMoxgWBAHHXqjKrboILA01uQo35BaiFIHS+ierN3sQ5FMrrr247Jgg4FLMuaEGAkINsEtLECBhkClX3Ne8IYw3QgoBmpqzbkM+IOv6sIyQKk0XdJmH2zfVaSMjrfmtGMY9sXvreagHAOUW09kXHzllt2g9ZK8x91cKpApykQIOZAkgVlFRAu0SBShCobqy+J9LPpScewIVv3Y3d3/c2dOa2Ry1u3m2xei6bVJ0Arg1kV+/GtoM/XrVTCgPrCc3g6TEfos0cSvV4cIEsSIFNvzO3AyofYLCyWOVXgNu/MoF55vpZ02BMrSkWChADgaRbXZgtWabOXKfjP/QygLDAZWNuSF/Jd5u1t1rjzdzm76Fuq0EA0O3qeepbT92+xbSx3vvKXYGI+6IfC+BqoWnAbvU7AGsJsqd49fn3GgQ4qL/TmLPetFU6DWbgrOH6WZt+dlxJX3UKO/yJAqhb2MDtpAMXCjjrStEOxwGZojlzSaT3kwAgdfdmpaM4HEwK5zftBw+CLhiij+SuwkOgtm3/wnNGtxmdlwGegGcndpB+mtyHgNF5KeKGHCKRN2cbfiZRqQTE0+iSU264Axm65Gi4tKu8Td+peATpfFjEflMa79g5imDh0ZC04EFtVGBBEapaeDiufwxNiGbw1JSPB5xLlXI3DlktIE2JRABfICozaQQCALXLEHnUknxQMePmI6X5DgAqS5w+qCwBReKgSAmeLz/1hU2EbY8KOwFkoOp4VXcTg+AQX88po6KLaCaUBQqXqXVd0JucCbDTGzpB6Aj99jL5Fu55wDIH+lDoHaFZUWkZ534JU9WkHR9MuDEBXhuMQsgaAJBPKOTTYVOenttlVo+JAFRZonf+NI5//H2YvPp6zN18Z7hB2L55aD+jUEhjVgtrVIi6VBIlIApRf1id66xfo8IMQ1PS9U7svBoAsHyygjClcR9limjsRplVQZ4FCwYWJdBaJAIAxXhnAgBtz7NwcOa7fmZ6buv56ggAnALvpF9Gryl2XR0mAJj5ywQAF71GuM8hhiZWt+MJVx2BoiWMe06ZufVRxr1Mq3IOIhBpp2DAEmVLoGwJDCaqT5FVzH9vuj6f1QIAUMW5jBMBQFXMvxYAZFEx/6pTP4hEVR/9fSCMAJDP5pADImRMw9kf8vHw3B0qzJfu3pfXgrj+UBpM8D2suW5KeVuYseYwqZwpbkIRMvsa3d9Mf2R8L25CEtL1Np0bBqjSVL9gnw16Sujd7353BWhBPjt2WNdWpRTe/e53Y9euXRgbG8P3fd/34f7773fq6PV6+JVf+RVs2bIFExMTePOb34xjx459t2/FoysCiUEhM4OkGcVLwK8VZfxaUSrn452nzEWIItf47dhPqLxkrhDZqvKyJgNAa0mhN2OHXA5UhQTENP7RxdVhehREUdrAYAJVp4kKAgBcQUBTIlwLCXU1SHh9iaOxpzEIJvnWMNKbJVlUy0y6WiPHP1nB+PYbCwKc/4aaFkFjbYE3l/Rz1YKADuo27l7aXibYb6b5M4x54f6uLnK7ExIAuP81UN9jwF/bCYhO/A3aBAcyxtrEGTCtY4yh5K5ttrNhhnb15JN48Hf+P3j0D38Tqiyx57U/ChENIm9WAvCyz0TS2nnKUA+LE6BCSkwoSMcmMb5tL47d/QHMP3m/vY6QdtcJAgvUTJtgml7tsubEiOh+0zJ6vFmwcGyuUKuWSSDGLQewx4LacrZPaGuar0llTD3rE9fgazAHJQWU9u1vghMmfS7T6l7yjjAwnAOiec87FQOrGfjqGsuM5mPu+bJVCRFaACgz91rN8AM20Ne0NVkLJVn1kQWqQN/6I3sCZabMR/SJImgggOXEfnKB9GJqPpRoMjE93vR+HaGKjV2T8o+/6/1pK/AAwNqcjDLznJQUZv/w9qm2jMKKhpRFsXndFNsGYAhzHhYmhp7TbUfuzbueg68MgUnfoEunG2+8ESdPnjSfb3/72+bcb/3Wb+E//sf/iN/5nd/BV7/6VezYsQOvfe1rsbRkYSF/9Vd/FX/7t3+L973vffjCF76A5eVlvPGNb0RRPL3J3a5YTAClpoVA9jk3FJnkgM/4M025D1lHTbOBekObUiDRWJN2NRbQRd0mTNUkGyPVbPRmJNoLbByU3qzsby8DsO575L6rTc52WOSqXkw1U1s6goCTHZEjAzUwMi5yEWfaq+aUEPDgRWF/Oy5AWrMduDevjTrjsrM4EqjYEAqO6VptjjfaQmE3G93GYLz6rV2DONY6tQA4Ambdd54p2Ek+o28z4CbBLQkyh9P3ysIVgFolScIE6k06EBAMNZp1Tvc9qrFulRClCM4PJYDz934e6cQkdvzgP8P41r3I2hPGP35oUjFTESl3mdotJSwj199UQrUqnxUxEJVANBBVcCFlqPUEkqN01qeSyrR6jjSMveM/TMb1wPf/PI784/vwxEd+H1tufRl2vORNkMJfsjk0JCU5qFwTQ8HbKmHMMyJKk8h7aU4rO9dEUVlJDYwkvY4JAOawQFiJQ96TMiWJm+p3ywvQ1adrpt9FJLNrsH7/nT0iwNTq90gzqRQBpz9plT/5OJADDuQmVQzl40CyUNfFXHc00b2DMv9l2907qEU5BAbgUGLHVHaTyo2vpmwhca4vUzjJ48rUdcmh45J3bMBwddK7HdvugAiaum5yDzTOgGv/8zFX+HPhyN2yRYuVpQHCqe/TH6OmuIGmWAUADow1wPgRDaRhzrHKI8ktq3YRZPaD9CyBDb1EnfAVbX+9lKapo/23dSn8p//0n/Dv/t2/ww/90A8BAP74j/8Y27dvx5//+Z/jF37hF7CwsIDf//3fx5/+6Z/iNa95DQDgve99L/bu3YtPfvKTuOuuuy7rfi6HLskSQFGBhpHQG0Ls/KB8amZEk/WBaUeBBk1/2SBdK/dlFGWlTU4ClhFRVpoPTWm3RNmSVYARK+c1I4SLox/zCeS+8amAItYAz00oTRyhQGXWbUilsnINktKUpZqPMpNWmxjAWXYEF3pPpf3tlCFMH2VEvKBYXYxkIbX3717ThF4BwIPUzMdqAaDWhBVMCx6KN3HM5FqI07xJCCaUHROBRF1cSNDXUOGLtsu1nhrBJtrvwDXVF9J31qeyUwK1q8Hg5FnMf/hTKPsV1yP7VXvFWhdLD38Ls7fcgcn91yMdm/DqDZKyn0sJ3HOqom4yl7uklAJBYeQS6tfWmpDVxmmyTg6lkiqY+eof+FnsetlbcP7bd+OhP/kNHP/s32Dp2MMoC39yFR1gMKNQtgPrT15BQGpm3ckw3djxcBlqZTXvnbYEhAKJ6fscmA8hpU2ZkutK5bxbRttvflf3ZOZPrTAyKGO1gEItCFXFpA7ShTKtfmsBoD9VjW8+QT7EHYf65ecTlUsWPd+bsQIAD+rtz1TXDKarj0Z20h/tahcSAEQhHHcluSYd1x9RCMhuDRJRCmQLCbIF/btyEdMfUcK4BdEkcsNw8fl8blQ2sHW8aFnLBgCsbRboT9uPU7YtvLkTc7Mr2g1WhZDekcxn996E83FuJZMoU/fjXRu1Mvgfv1PDLQbrKrdBl0WPPPIIdu3ahf379+Md73gHHn+8ctV84okncOrUKbzuda8zZdvtNl7xilfg7rvvBgDcc889GAwGTpldu3bhpptuMmWeLlpfYHBgM+AMbzTYl2uM10lDgwqbGD+O70+v4eXp7XjnuORQa5EL5ZntdIApYJlLoBIEWousHmkZYiWEE2tANb9lKiFzzg2HNQRO9t1MOhkHPSShRDpWAY34Y5LctOymUf2W9W9lhQotcFFUJaq55sGr9W/aXx4gbSAza/QeKnCpOnBLL7BB5pE/XzJHaK4HiCo3AFDlc8jbAvlYtRGWYBswZ2Zqxl+j86Ce555JHczfXyCMEpQH5jjXqHImVISZy6JTlTFaWu3WFhorKkwp24cSBdbOn0K5WKC1dxfQVlj6x3/C0ie/iO6Xvoldb/9JYFOF+rP44DehigIzNz6/6p5o5v0vNQCYBsg+raQtG2QeAGHhj5MWQJsYpjIFIAS23PZyTOw5hAv3/RMWH7sP57/5BchWBxMHr8fEc27E5IHrkWDCMP+iBGS/GqB0BQakoDoJ8HiW6gdhgpQ9FqJw7JT9X2n2bXv6fxDek+oBWCLEar6y/SUA98s12iZYV7v7kHKihGUKI8y/pnzcCgBAxagbBUHt107vqT9LLL/aihhQNgGVIED7XbTt+lC2AjkdCJUJoFJ3nMoW+d1zF4J0ObzxcqhYOXD7RNciDhGat+Gg/TQq+nJW7yDu888tEP0pdxzGzlf3qYUzijLUREWbYf9TpKmApSvmcTDMGlBm0lFuCSdwPB487FY6fHFrQlB8NggDz5Q8AYuLi87xdruNdtvHBb/jjjvwJ3/yJ7j22mtx+vRp/Pqv/zruvPNO3H///Th16hQAYPt2F/1u+/btePLJJwEAp06dQqvVwqZNm7wy+vqni9btDhTydafEobKcc1xDfglzYCRhICqIBKA+FaLoQZ7ZuqkvNZoQOWu+aZcSfT5bUehPVTtxa8nnApryAyghTD8dd55hRO+bMu+onhmPHwDgIRT5+NZ0paysAW7QrbKMP7EA2H7ALau/B+BBLWKULgPXvad00UOs2091vZMrQLh1KKk34Ao5JJ+w7kByAOTUJYgzM5oB1IKIdqUiLlXBvAF6wyXz1SCh6Po4JJ+yQiFl1J0xSZimUZDnxlyV+HjQ4/Pf/grOf+2z6F04AxQFICXm3vVWdL92P7oPPoSxa69DPj+PJ//bb6M1twUoSwwW5zGx/zpkU7PmZRkmCDzVpFKgGAP60wplp8TYXBelEhispShWU4g1CdWrgn9l4TLwIwXyhtqss/cKBSAmdNWksf6brEcqAdo7dmH3trdhl/ohdM+dwOLh+7H4+P049f4/A6REZ/9+bPmRH0Fr61YjALTP2zmUDNx3xuQFIAxQSAAYtt4aC2o9X2WuHAHZ3kRk3pH3Sfvq00BglTBXDuUqXELMEI0h0GsATYgmSltH6B2iiR6pAABYwRoAQvk5itSOuXe/OrMzNeoyAUa7oKnEFQSohatsu3uNytwJJgfCQMO25qXnJpQxoZC6MXGLOI93I5c5VKZuWQNxrMsXvuWV1kutHD3CJ3XOuWV5ALGjfOOxddyfniqRsjj6XhNv2uwuVP2PWk6GoetdBvMem1Mb1Ex79+51fv/7f//v8e53v9sr94Y3vMF8v/nmm/HiF78YBw8exB//8R/jRS96EQB4sW9KqXg83DrKPNX0lMQEaJJDIC6H+unFYgPQvLHS8+sSNkvlLm4jwpAaqElC1GWqP1NvODzhCqoxCPn+hwQBw1QbX3vSvyHaO0ra5UcLEdYv3mrizXeq6Sdlm+q1v0lZKgyMQir8nVqUNCRaxXwLL9CbxwfQ3zZZmDB1yhzozQgka9VmrwWBYVYsvukbTVb9rHg/DLNP2o3VSet1xkPAQYjRdfWZq4FpM5B5OGQ9K9a6mH/wXpz6xPsxdc1NmLn9xUh3bsPJP/tDXPjT96N91VXY/Pa3YfK5z4McAAuf+gzU4gogJYSQmL35BV7bRghp2v9on0a1DtB7EewZXYKFIdYvo9kmfdMC3CjuS04iLgEfhpGQSkl7jIqsCiRNNu3G5qt2Y/MrXocuFrDy4Hdw4VMfw+JHP4VtP/pOAEBSZ3+VtTuHFz+lb0XZ37YT9t45UUEh6AoUWod4fcpnWiyakDIChS3LrISEqY+5r/G4oMoCoNsTbh/o/G8LyAGwNmeP5eMukwq49+2cK922qQsTHYsolVZqDsXQaGuPSuDErAgFiNw2VMWCVL9l7lsmJYP69BQb8C0D1P0KqLT5VGgtMtc60EQ8VsBMDTbOa1vc9yUl97E2JzBxyo6BzqegiecuKlrS4SVSnsw0xtw3CQXM1987n8k41PflMH+ha59OJ/tnKR09ehTT0zYaP2QFCNHExARuvvlmPPLII3jLW94CoNL279y505Q5c+aMsQ7s2LED/X4fFy9edKwBZ86cwZ13NqPnPdV0RYWA4Uw9+R4qGkjUBYzA8LMsu081hZjCpmBi6mPeWlKOpqk9T6/z+07dhAxRQSDWR+oWVCIsaDA3IC+YzknJPkSLMSpRTblul/XNsQIEqGJEaiZe91HHK8RgYEkAuBHmEisM6Gea9BSKthUEjBWHuNQ4tzNgDL7OsFkz7kkf0HEOoc2EBwBXfQ3cc+kGBdN4AIWqjsEk6mytts4qpoVXBkABRb+Hi/d9GSvHH8Ng4QL6CxdQ9irucdNtd2LHXW9D2a7GJv3ZfwE1liDdu71WJQtICWy/8/VBJs+M8whTZj1xAMYV6OlVnhgyzwFk+RriQ001szF3IJ3xOSYw5BPV+RQzmH7xi5AvLWLh85/BzgtvQ1a0vPXJBHuH4kPgz2tPaGDCGVWyWKFW+a5ADXPDiWnhsJ6JZTC1hcBCgsJYvcz9cQULmx+VBl44v/n9GfSfiSqQ2mikuc87eyY01gBwQSKieWhgz3NG296nu2g4Gn8WtC7XpOMilC0Iw2Rniy7AQbLmtimZuw6Uq6jwLOIRjT4fc35vsskaQI7LAUE3gzveq9tF9J3obhEYO+cKBZwnodfmY9INJl5zbzS2pzdZAwB3ngGAIC8Oj5+73BioZys9U9yBpqenHSFgVOr1enjggQfwspe9DPv378eOHTvwiU98As997nMBAP1+H5/97Gfxm7/5mwCA5z//+ciyDJ/4xCfw9re/HQBw8uRJ3Hffffit3/qtK3RXl0brEgKSrgKoL99lPkOePMpUOwTPv8lvXyirlvdcf4RoZNaHkcfIRYQWJe1CRX0fQ37iVFveJESF3IOC6EGcPCQOdtPMTcj1uSV9U4HxBGDiAERAYDEWi0i/DBNQMbie9YM3x2+TJnRjWYFpYLFmks1vQeoTVhDQmiQtCBTEjcswGXSzZww+f77md92OFgi8eUc1bFzIoH6sepwS/zoddKgT+pWZ3cgNWlCdqKxYWcaFr38BF+79Aor+Gib2HERn515M33Absuk5tDZtQWf7HshSQPSquto7d6NsK5QA2melM8aURFH1q2lzW1cswGVskkpWvtaDCaCYLJFODtBuDZBIhRWh0FeAQopSSMi1amMwQdW1lns9eOWm3TqgVWvamzT/OumXEkSLSuZHPlbVV6Z2PpWZjheotbxdgZkbn4eLn/wYlh/9Djbtvw1J3xXCHM27sMMqyDFz31yb762x9rxQWlhR1uIg3OuD1gHhHjd7gYPqYseOCgCAz4gqUZfXc55p+Q1CUMqucXzVFXqztSAwVTPzRAlAhQzeL0Ha0uVN2RxmLZEFgMLtBxdonJdHKuecKOEw+3LN3kCVFZqUzYF0tS7Xd/vEmXvPDYgiA9H2ueWDadCLVkNCMS5YxhRZrNxgwm2zQzIar+wQjiW1u0U414+fcSvTieI0UWtB0XGTkNFzVFCtDojwd7ByqGLoqDXAmeNDBIp10ZVS1G2QR7/2a7+GN73pTdi3bx/OnDmDX//1X8fi4iJ+8id/EkII/Oqv/ip+4zd+A9dccw2uueYa/MZv/AbGx8fxYz/2YwCAmZkZ/OzP/iz+9b/+19i8eTPm5ubwa7/2a7j55psNWtDTRZdlCZA8uIaeWw/0qZMtdn2TuElgGMrkN1gmTFbAgEYsVG9M41O0hMlAC7gmS26upEx0I2MfIqJ1qyohx0ckx02IuWI5gbSAe35YG1TzX4aPU0HA6TtlKkxH3WM6MFvkKvxs9DOrBYWQIKA35WRQmbU1Pr7s14xA6fbHmNPr3+mq++zTrtsHLTCgHi7j86+ZiMKfV0GkKDa/KDKIyGF84EXNaEhdd1ng9Kf+DqvHDqN37iQgJOZuvgNzt38f2lObLENEx6VmhOWgYqZb8yIsxGA4Yz90s1uPYOA0bK+jKBxXzC9WM8WSdI8zy0PICBJ137grhqa8XT2/kLIAsEgzHhQkgPH2Voxt34vFb92LTftvc7Tx5nlR9zF6sbJzrcmdxzlPv/NrTQEyh9l6RN3YKJUJAUaomXbjbsdcgELzsEzh3Dtg164yAWJuQEAF4JD0KhcUXRdKdy8T7LvWthtGlG5lZA4mfdv/UOA/dVeCElAE3pO/FzzQN2UBvnztcZqiMV3MeqH7G7J0UJLkXoDmd00w9ygRua206+YkyZardUxTQdpb2+wKQhPHbQNLVylMHba/V7dJZMu2LI9p5C5ENNC4aLuwo05MIwPBoBREy6PnE1fYuCzGfYPp/67QsWPH8M53vhPnzp3D1q1b8aIXvQhf+tKXcNVVVwEA/s2/+Tfodrv4pV/6JVy8eBF33HEHPv7xj2Nqyvrn/vZv/zbSNMXb3/52dLtdvPrVr8Yf/dEfIUliprXvDl0xd6ChrkDDtPu6Ho4q4Z2nUjQzuzH0GPfkEE41cjrIvJBjHvSXsH2kmnMqCGhqLZYYTFQraLZiV5thAoCIoAeFUA5crGx2nQR0FIQ7dmSR4tqF0O9RiLkAOcdRneOWBOMCExEEzDPQKENCRIU0214lCJgAQQL9Jgd1YHD9nLQAIMqqee1mIwdwxjqptW3a/M034aQPN0gZZGOlx0jfbWAjhQUVzjVm09aoIqkbVEc373Nf/BQu3PMFzN70Aszc8DzM3PR8ZJ1Jn/muGV7TTH0uW4EXyGwK0Z9NzH5ofl4q8/9MICpw8OfaQGULUQQpwLr6KMkYsnr8kl4tmLHcALPXPQ+nvvD3UIurQKeSGLgbg0dUACDCTVAgDa11hbVWee5ySq+HdR2qnm4sHiAoCFB/bUdLzu6FKSu437oGbaCIQIIIAkLZ3CBApXV23LWIu15IeHPiebhygrv6EAsAFSCqe/brNtflwjC96YpwrssW3Hro2uNp7Xm9igk13NpB7o2PScGtE+Q+NaCC1x7rT1P/+PxO+sBgimrT7bmV3SooGANAd7ub/2Aw7jL2HO68aDMBq2sLly3pPF8OdBITdgB4icvEsASr3+NUKgHxNLoDlets+33ve1/jeSEE3v3udweDijV1Oh285z3vwXve8551tf1U01OTLGzAJngwIVj9P/YujKDJbmKWFdf48vMxLfM6iZoJfRzhCkJVZ4fUqCGAHwxqfdOHLw4UMWNUi4EoXY29UArQrjSAg3EsCmUWLZErN8FYGfptBQlejwcLGiMmDFBXJyWEJ/y5ZerjsIIAJc8/msdzFAoSFd523hHGnG3cgWoNu6xdXaqD1UcLAHUHfAGgB8t4iCpYXrHEMI5FIMIUe6hA1IJQb6S9zewapTBYWULvySM498WPY8uLX4NtL3uDYbyUbhtuHymVqR1fx497WIB3ZFrq+1sv839JJnNRx2jU77oqgUGeQGQFpFCQWYmiLFBKVQ2iBGS/YhJCwf6jUIipERF3oGIMFoEoIhSYBFcBATpbsJarpA9sOvhcnPz832HhsW9i7qYX+/2o51koRwC3BDgCKbUOkHVbKCsAaAHCCIGkHuPDX89bPs/4nKpcjIg/Nxc+IgkmPThRYgmgLje6PS0YJH1grRaei7FAoL4iTC29L9ZOVSmsEM37XecxsBp/ZtVxlATCfT65MAnfRA7P7YcH5DqQrUxr3xgUXJNRdOjfbEySHnNpYi5Oob4AtWIl4mKXrrnCULbo5l+gpBKgGLcdl137gixdrZB2wxt7d5vAOAkmzjvNQsFg0n3xqLJOZ3nWJDjfY074lgFKG3ECG/R00iULAcOQfxxqYGzXFfQ7LI13Q/3rghQleL4hTX9MQKFaL9cUbAUB08RAoTdbFWrPh4NzDdFFWm8wHDpPU0Dbaiv3+x3KceAE7WoNRhn+raFFhaI5A9yyxhpB+xxDC6o3UScJG70hYjHgGXQp9GH0+dVZHQU7JwqFfFIaa0DSrwKDdZCfFgQMIxwRICmDY7K01mX1OyOggFK4WlDDLLF7qoU3oVRltRF27HRbvU3VxqrKEmuHD2P5vm9i9dGHkZ8/D5VXu3dn1z7MvfK11UiSIOeyBSe5WEhb695g8zsr6BxT7DhlEhtoqDWH9cfp13quHYGMm4Qe91qLLFS1bw8VhlC7kzVokzXlY/GxSdYqxowyRZrxy1aq+uX4NMa37cPysUcwd9OL7Xhw9xfG/DuWAEJBQY0xwWWihSblHOeuQFRw1eW8dzOAEmTOCYBaHYcGZwp3v+BwryVZ8/KxigGlVjQg/qxU6gp21LJQFSC/FaIaf5PcjC5vjNnmGvWUKB08pLnIXBRUecFI8qBm2p6Cvxc5naHrVLyY0xdWnV5Xg8TKti4KDCbDRcuxEhgng3fMorwsHhTIlsLX9WYFOhfC9zgYF0h78fsfTEhHKHC6znMC8P623Fw99F49K1zTevksjQN4NmYM/l6ldScLC5qKFeC6hTw1I+xAbQWYFI+ZpecaTHU8YM1ps+lWGq7TwcFa65GPCd+3taakr4wZMumVjLG9hLEMCQKUAaALe52ESzPaPOuhLQdfGHH8S+kAu+07qENceOFoQRrJyASzKrctHmwceF7aN9zTYFLrkPLni2b4tSCgtWjmeO0La5CEVt220zU4mk7HWuFgnVflZMEY/oIw/FSzSQQOPnf6U8Da6RNY/PY9WLz/68gX55HMTGPsxudg+sUvRmtqDq2pOaQ7twEqRZnXQ1dWQg53BwDCTDS1CJhxV2Q8ENjg6fjHaJQykT49E8gwt2RdbIqHKtowzzLqOqSIsAmXwWotwrUcsbwGZZFDtsdNPbo/Fh6TXMsFAK4kUPZcUCgk1zsMvL42tM42CAI8+NKF+RTuMf2OG0FDeeWFsky3fm8o8w9FMv52KlhV7YIScmdxmPnMLeP49IONFwsw9wJ9idKAnktX3evSWtgDEE4qSOuU7DezvFDLAb/XhGi0qdDC0X2SnnICbWN7pV5DzXXMGkAvkwUcSN10xXd7M+cWEuRT4Zeov6cH9GxnsyX7cFZ3iLgrXkfEFSCAcd8NkcpE1Bow1MovXcvPZXGpz4JkYRv0zKFLsgTIQnkbxSVTg3+5xyxS4gI4P9/EoDe9IwrR5GGAXehGjQUIZkdUFo6Omx+NyZwzvUPIExxiMhkXECjEJmAyEgcFKj7G3I+fMhHe+BCtXOL2tcr+q7xz+reXHwHVd63V94INiSDgEAsMps/SxAHUDH9RK5KMINCvNYD173TVrT/rWuECog4wY4IAJUfDH2H4KfXzLs5++3PoL1+EUgVUWQKqRHfhDHrnTyEZm8Dkzbeh8/Jb0b76KggpkXQlZL9KHCQGAiWApN6MdfCwZuKBiiGSObw5Z3IlMO1rmcFhFO3NwTDFMcZg1LwBlMleN9XXthYq5I9iNcMagLxduQMlSQmVCRQDCZVVyEdVO5XrBffbHpU0co/ufwwdSNWBqkIBZeCZp8swOP8Fg6+WAwTHZLCygNbEjGvZ0X2hgd+EPC0uXZIDgoBgz1yUymE0qWAQwp83zZD31Rxz3IBEsDwPvrWukfqYsOeUZc61AEDXYxqMqjSiFl3H6u9Fx+8rKIKOI4C47QL1+DHYUHNOWauDKIgFEdVzphYJykyH3KootKfXxpCAX6BSSDluQDwoOABgYOpigg7tAxeanDoHcVciTq1F913IaV6U5RSYCAsFKwdyCIKkNPW4/b42J5yxaC3Zh5y3faEgI8HDgwnpxAakNEPwUGsV43eiCUKb63m2WgM26JlBlx8TwJgtb/Gh2uIhiB2aAQX8F2TkJFMBKhssBM0XuhrzkanW8up2NUIQAC84uL3gWgE0hQLmqj7ZrzGIUO847zqDBTX/KSRowE3ICVIdBZ7UXGi/8uvp/6YsyaFzpo+Uca5RG6JuYCQw2ByqtVkUKtRAvBLYwbI2XadduzArAWSrMBu/qgUAKhBUGn86Bq7A4GmIlDuu5x7+Mo599e+hihxjW3ZDCAlICZFIjG3bgx0v/QGIV1wL1ZGORaXMlMFbLzMFORAYTFX/ZQ4DezqYqjZigyikYyI69eajg0IV2YBVYH6Khg1L6fsKnLtcYkLg5cb5OFXngKhdSQyCkr6XQJK3EGmmsykOIu3CaNZDms+0W10bwsPX910WOYruMrLxGcO8BQNO6+dA3cBCjL8XxE2Dg7VLWWge0HoizyKu0YfjShFC0uH3oqSLKCQKq6HW62ihf9fMqWbq0x48FxPOHGtrQdEmyarqd8HJtkuExsZ+1/dKmWIvmRdn2GPjAXf8nSSDSfh4Vb8LB504yDfxZwq4gjGtN1mDk4/AuYb1m+dXoZyIZ3HoMkUaEQLa5xLkE7bvBRUCWiVESm5kzXZu6UCJhMQRTByzxfpTwrWUsCBercAzzZDz+bh0xpJaBjzffzTQ9zhjX7kDPX0Wi+/hoV03XfHAYFG6QaUO8YFvmAND0YYa6ClJGNYkEBAGkBIVBHjZqp+s/EB5Lz8Num2iUFKxEPPM63NgQbnFIBSLwOq8JDjTur/8OTU+N0+zVWvQiSBgNYFWkxjzPa6+KKNtNBaAlnUH4ljZsgRQb9aiZgozCs8nrGXHWBtq1w1ufXAEAee+/P6euPejGJvbif2v/AmkU9MuukqdE6C7BhRJiTJT1bNKlZ1GqYDIgcFUWW2wdWdlDgymK3z3EsLAieaTMH7EStjXtOjAjjPVQKYAdCAi7Pfq5BDG/1m8GFOfbkWEr+aLqrlGtfuc4UpX4ASyUnJQpeBq0+WghEgzLB55AJuvvR1CSOOvbqwAev7lPrPvPCd6jgtXxMpQAQPYftFAYK+uBsa40uiT3yJcziHdb5akTFsHtCBQubJZhCA6rkVWBY+XbbdO00TKNM8dG/xvLGlUgCotA+tZksiYOO4xRPPPzwHNzGIM21+3T0knQ7TtKPdcn/4mCiG2JjkJtvpuHoMSfE2335tyCCRrjNGnbkil+1qlK1aI8+o5l6EYI6VnyPe5PhQ1uXXtg13Z4z6HiRP2e9kSTgcS5vLDA4TpOJaZcHMENFkGGlyGRomlsoWfxYvqBn3XaYhu3qdR0GsM1YtH0DUD1SKk4wwuixruonInucQ2FPkw0nU6G0CgnEqBtKeCAUbtBYW8IzzNQsinTyhlPleCfLcb6QbSJcINECZ9KhPhBNrx3xR2U2tPFREiRhHSvHsNPGMTjFgo86mO605rLoHdN3luZWKvL1ObeEYjXFGUDYBsgKUbpAfl532QOnmSYtea/od/8//Z+Czak3PIxl0BAKhzGnAEjVbdEMkwqjdGqmGjkHvm2KRCmdSMktDXhjfdMkNjUN/Qd27Iecd95VLlegWgBNrnBZKlBOVCC/3FNvq9FEVewXWJVEFlZeUS1FK2bf1ZT3P12GnLQROl3Qq+MBYboF1BvCReQKVBZ32TWQtXv/pdWHjsWzj+Tx/ygmGD83CYAKD/03upnwt1YdNWAfNbr/kBAcCMC2szaClusCRUbkhE6KbrTCKcHBplIsyzAaqxLTTyks5FoG8nqY5pAUAn+dOffNxfF0y/JJkDOi8B2f9kTph8QRhP4Z/TTYaoKbkX3ZuMxaf+SPKhFEKro/ubFvj8xJ7udWlXmb4NCxbWVqRQ2aRfMf8y4Fngle0J50NJLWRQuTQfSvnOHvJtffOhtLKrgovVH0pFJpxPjMpUWGja0PlMQCXux71Z4X6aaCMOYIMukZ4SiNAYNSH9CB0QClw2oxvSilcnyPf1NjGKNlP4zDWloiWMCwFnntK1EiolWgOSGfZSrSIxFxoA/qJjtGhMG8F98RmZOuq2rGuNdcOiQcG6D/acbhf2t4Dxk3csBqQvUUGCaEY9RiTxA4N5YGNJAoFpAKeJC6jdCwxCTNPaq7WiikwfxlA5dXANe31te2pTFQvA5oweMx2gPNhSAlntnJ0qINeaT7KTK4GyraBayiIUFUAxplC0faamGKvKQbunqVozygVgpqEOkYbevJJBwJ6ble7DFdoThcaJVwBqBk8nDmvKBEz7JPL63otAfBAqTbSZAiGNnwrPNWc863OzV9+CPS95C4598W+RTc9i622vsOtILRDx5+C0F3rX9f0z5p3i+RtLV8BFzLkV/ly4EiVgHQtdL+r3VzItq2a6dNIw6gYExYJ3aaAv99dXVvA1ga2h+Q5UrlE08JqScMea54eoNPC2rOPuCfaOsbHl8QHDiCP6aQHB9kU4Gcq5RtkKMq4rkRxY4cprk7n28BwCtN9cMClYTAWvV5MXHthzH0IZiRPg1Nvbh1i2jbYWbD2DKWb1bdgTmxh/n+9R6zr/vUJKiafZHWhDaNK0LiFg2LhVzJHL+EXLqiFlGbrCpVATQ35ZxIJpbYN+0ZjfsByw+ID5uopU+G5UsKg9NG7CNEtcckYWoHQ1kv2HvxA5iU5i1XM3Jgmy8TGc/8SW5bEPVKNorhNwBQHAjFEUKrXuU5B5MgyL3cx05ki9aemFXJfVaBwmIdiaW5cxARPBwYyXgM/s07JcM8sEgaQ1hrXFc057mpK+zWcgVxOU47CCwEAYpkqUAkoqE9inBCBkxeAbxrRmbitXFeX3DcTnlzFrZQYkgeBgh4YweEOJCfFXLCvwJVLJXEFGMdlr3/+YBcXJRcFomFuVflZbb3oZ+ivzOPGFDyKbmMH0DbfZ2A5S3us3cfPRx0M++KY9Kgjq7+QZcxc4eix6b/Qem8rVZWmG4bK28un5rOFLaa4FDYsLVFp4quVVKQCyXss+ic9Q7vEyY0w4EfBirlyA7w7jJRuj90q/c404R2JqImn92ssk0IfY+skVFjT2oFTMLdEWTLsKOc3Ee4kJUZ17JMKzdWms21u1a2CQCGOPaSa5N6why1eXaJ+1BRwhgFBvSjoAHwlPQNaRrrBKvpepuzc2JRvcyCewQU8FrdsS0Ai1SUiUcNF1GrTZfLMZJeumQ5yhjbUzon89J77R23aVJwg0BUJzX0FKeYcsNsvxAYhpwEfx/w9SiWZ3qkj8Q9Um7KIkahcgyuASptdBBGLCYungTVsrAY2poG0K5QpLWhAwQcK6nzoBW4DxsJYHotWqYwSKVp1cCNXYGNN/7eJBF3yj1RJWWDBtMA0/ZYpC8QAx6i2dR3t6s3lWmpnRAgn1W5arCcqZEujL+j4VRCkge/XzSZTtR1+Y+pyxSV1hzrQTmCdGy1z7Qif0/hHk49yTT/FGJlTNWPeB1qJAsZagbEkUExIqKyGMlQRAooCBqF2C4jCCTaQFRa35jzGDVDlgxogzgWWEYa4tMjEfeyWAnS/6AfRXF3DkE3+G/ROTmNx9qCpCNNK6LkHfWfpu6z7Rd5kKrjUTLpRymdm6HkX60xTY6tx75Hwo0Fa/lzTuqkzrfhH/fwrf6WS2zWpGX7vTheYiO6YDgbn7TND1ignrthI3oJgLPk3afx43QWF9PfdCGrQbsVoFFXC0fcdS4PaL/paDQL4Zc45ZwCIKPpEjKtinPGaAVtd3hYBsSTgBytrFDwCwmFVKEk0dd4BViw14bHNssJD3mVAAsPiLtEFxdTmkeYMN16ANWgddli6tyd/fI4Gw31uwXjGy7/il0LA+DI0hiAkFvFjNiIYWWu2vGoJG42M66rhF+xFA+XE7Qz5N9TQ9a+Xep+frqeAzGPCFDDfBj4CHD06+q1QYBoXfo/EV1n0SMBacMhXOQll0bNxI3qbXVpugmQtlJQA4+OpEg+m5DFBtqe4Gv87pNCyTZZg1gbX5M+jMbKu6wLKeaveCbAnIFmuBSWNiUwZUa4qLqoxBxiD3InsEKs9RQwb6SqvmjC6535AmufHdEnDGYRhp7TP1wb+iGjJB5kH9fz2Ci+xXzyfp+Qyd00zJ+k206cEgW/2dlDN1KQCpxN5XvwMTuw7gyQ//IdbOnwq6Yjka+BCjPoRpF0rVwgC/IVZ8yD5B4z/ovmKu4x/lH6OMPvX/1wy6EXxyGw9QtmqBTN87vQ9hn5sRulMm6LO6+f2LvPqUCSAG9mOUAAoO4hRUfWv6vRTsXKgdvQZqxpSt5V5iT7YvaQAD/THFAsnI6Nonc/+YpozFBoRi6AA0QvDqeJhQ/elqdV4LU9lyHUMz8N30siUZjRlwaLnZZLG6LX4u74jGPX1U4nECTfRs9mhRz4DPBlW0PiGggZEfFgRESUmxPgHiEgL0hlIi7CdC60Y1aWD6mwKE5UChPynQn+TMOu+PGnkslBDOZlCmMpgIjD9PKoDFMjQ3PbOhPoyX8vaxrMAjXxYQSirNtuZgBPJxya5xr6WBh7QewGr3Qj7czv/I8eh1NeX5Go584S8xWF3A2OY9hiGlpBkSrbVOurUg0K+Z/TUJuaa5q+pfuiRrptaOa7pUX1cKU04OBETuPmw5sOg2IaKBsfS+jPb6cogJB0+3OVwlRJjP4CHFcDICRBNjPWychH8+NI9kDqQqxVXf/1PIJmfxxN/93xgsz3vXeXEzABaOfAd5v+v2kwkf5hkrty6vr+sgA8FKr+cCIRNWKBNuP3WiP4KKJAsyN5OK+aeubaK0DCSFQuVMt3mG9Ue3Y85RQZ4y4gHIT4cxlux2I9xKExCFvzYoIwDoGAkrXIgoY+4IA+vgnOQgHBTM3Y0M3G5g/28SGJJenNHnlhm3Xw2dXpMV8x8RAOic8ZQdMt7X/qSIX+e1MTrjv65g4Q3aoBHoklnrMhPOh9P6rAQjTmrJPl6nENVoh/po2ycffqpp4ZNx5CHqFuD1JRXBhQwA5KBE2RIGdiw6joFxaLKeaLSdMpNQqXD9/IHg4kPbNVr5uv4KEcj/hK4dlWILKq8zqkU39bgFuDBAj9ENr0xFhehjNF2uIJCuKcsgczSNS2T4Y8cXzx7Gd/76t3Dhsa9j751vw8yBm0z/Y4JAa776n65UkyKp/9M5ki1rZr+672xFIF2pj1H3AS1MkP6Z+cqekdBxBkwDZrTYnLlzCkWO83PrnUv1OqB93WVRQQtmK5W7QDafIF1IK6tJL6mEnaJGuardoTxhZgTS7lMyh8m90HR7huELnNTBrJ5WPdangACRJWM48KafBwA8/qHfQ9HrkopIwbrO5ZOP4fGP/ncc+cxfQPH4IsoIaka8DrYPWhGGCWl8zRWR4/wa1iezLhA3QwM5Sph5mVvBIF112xEFjNZe5FWwNmXuzUfHc/C9qP542nKyzmshcd3Cq3Cv43U4awLpg46RGEn1ycpF1+KA4BHT1stCGQEg7apoX2JuPoAVyoLWgC5cRCV6biUsLABAspw4H4dy4X4YcYHTOZeE98LwjQn3E6Eyk9E9turPOhWqzxDSgcFP52eDKrpi+nUNhxWNjKcTvkELb4LLLvMZ6aDSJtzd2DxoOte4iEcWWyWF2Vy8+soqKLVoD79hrc33NPoBoSgGJ0rHpMwYtCdxr3H6z38nwi1DnqfKpPNsVUqeNRe2yG9PG8W77uQ/8G7LqS+0WIYEgYoxqMeCxBNoCD2KnkHhPz3BTj/3WL8Yw89dhLziEjj1jY8jaY/hOW/7N9hy00uqBGGcJwu8vZ2z1f+s1vZTSruaIyIHibm+KuOXo2Z3SnIQn9MW5Sl8j1FqEMijpO/nCqwbsfodZmcdLkEGJnZYEyJcZ9GKBD1q5pUx5XQeajeabGIWB974P2GwPI/DH/0jlIX70EwdEjh5z0eRjk1i4fB9uPjIPVClQtHXUfD1h87jUvnzkt5LiNEPPSf9m78Xod/sugpkQgtgJGCVxLPofmkkG5VU1jMDncnmN63T1l2fY37yRggmH73e65gEaomk1zrj5gTaNpzj7kPC/U3HhlLBmG3HBYgIGqG12Pk0MP51FihAKSS9IXChsbmg7D7F92/jchlYPzPC+HPmP10RkH37oZSsSSSr9kOpt6WIu3ytgwaT3NQTJ7ovP93gBxv0vU/rmmIj+7spNfIEHrZBjhwf0GAhUGlDroBLheAS9bX6Q9trsBCUqYi6BSV9hcGExGBi+MAFNQOBy4RyMfTNcX7fgU3ZRf+oF/ema3gfM8E2NsLIp8L7bb6zuWMYyYAgwLUg9HuZCN/cnCuIvE4uVI+J4zPLmCqhAtqkJiGQUSwmQJ/zBKz6votBD+Nb9qI1uYlVCGfc6VjpfmaL+nflFpGuCMvY15Ss2aRHmjT6hePy1PMZTS0UGOFB35uG0wzQSO6CEUaYkqL3f4mv7pWkClNeQSVq+D0yJtqrq3YrCrmiFK2aieNMf0go0RYB8iw6m3fg6jf8NFZOPI6jn34flCq992zp+CNYPvEY9r3i7dh06Hk48rm/xLf+6N/iW3/4bzF/+D5b0Gi9lR2DmK86YN18QnOD3s8w5p8LFmScnPWQ7APU9cRcS+qkAi51RSkzO/fNh7nJ2JwE7NZLOAy2x+yTNSuY0VmX07k6hjGOIYFKr4GZ6/ZTBU4H1vLLeZciTDnf/7JVFYw9ACrXrBjjKwfx62Q/3n66Yp8vF/Jkv7KApsQSqilZlZB9+6G0srNKGKc/TdSfkFEtfZNLFKcyE438jVPvU6EE2aDvabqkEBautZaD+AqyLtOnUs4kvlSfX44SM/qFl9besL5E3VuITy3vZ2V2JnChibBFWaCXk90RCqquzGP8yW+VuJjQTeZLF/FImYXYWARikKnmGlsHh/Skvzk8qnOd0fhRDpg24qL8ON+FLVPdkLDxATWqkGFoeP3ESqEEDMSmOVe4mNlB32h9nNXrwKFqRqk+r/IcIk2tSwhgGEhRApCEIVZEy1i3nS5XmX/T5WoDoRtrugpTrwAgu/V5CQfJI12tGFNRWkbHbKaMyXSCJUkGWX8ggKfiPeNknkEJSN3/stpDRR3ToHoCZSJQjCuoVFmGrBRQiUKphIN2NIzK1MZLaNeRGJmhC8ifRc0QhVwZypaN//CCiVnlGrFp6ehDKAc9zBy4Bfte8y48+fE/QTY5i10vfiMgan/xgcLJr34UY1v3YPqqGzGx4yDa05shszYuPHIPzj/4Jczuv8nvLPnO39cYBdd4Lthxa0LoeiIECEWys2uXnbosnb+876FYFQ0LWqawLmXw5zWt0xGGeUAtrf8KzX9tYXDqpeci7x+9pkzdORZ1tVwHyQEct8Ckr6LeASJ3XYGcUg39aOojdw2i9acM4pMiCKVdV2GVE4S0fFI5/WlfvDROuz8pK7co09f4jTg8DNCIrvispIDQ9l1vf4MAXKFkYVVcQF1h9wqOLtPaeKRf2obNtkyFq6m/UrkD+MLunIufdBlzwjwWwIAEqbaWqpsK5Q3Qmi/AX0goXB5VGniQZKVbKOo2FYFA1Qw0AAtFGmPgCeY+LW/KUitARseEYVEHe0grUvBiS3T+An1MKSehmZKwkKCECReAQc3QpIUmgcrCYMa+hhmVhQtJWuU4oH2xN+H7k7o3WBYDJElmme0A00XrpcnNdO6ARDP3ZXUq6VZl9H1plwjDKAGArI9Jey1kVSdn7OQARqMVdPugDK5EXDCI0YhLifb714LVldaGUUhW2yg8DfCl1u2NjeZlWwCUy9jo59boYqH7J4Ciu4InP/YnKHpdbLrudux+xduw66U/iBNf+CBak7OYu+GF6C9ewPLxR7Fy6gkceMPPQUAgbY9h5wveAACQWQvH7v4ABt1lZJ3JuuN2rYkyM4yxp0IrvX8qCJtL6rL0XKhui5JVv3cMspaPG1DNZZMcLPA+OtZu0l+j/NBrJmNiIVz/fw82VZN+rwJE103vXOJeRwUBlcBLtqWpaAkHspIy0SpFNJdNVSD2bF3Fh3NJ9EezYOy8T4EMxuSXc46Oc8HeSWrtzMfccyZbc+B8uiJQdNz9LET9KaC9QMoxjwVfuRbZZ0PKwgjj70GMbjC0G3QZtC4hoOgIoCW8ZCOUlASKtn1j5GoTh+4yeY1BdE1JsNbj1DRMsLgEaozop/1mC0RCLCglq8PLG6Bfes5AUy00Y+QdvH0SR+AkHKuZvHj/w4cp02ww+ul5IiCIgdt3R6ghGo9h7mNUyx6Py1DeOBshpQRKPTfr64sW8Z2vNfs6PoBbSkz7Sjnp7CtXgVpA0LkJ9LOo+2PuLaAJNExHfU6VBQarC0g6E7ZNNsWMNUD3u1ZWWQsHjPmfCjemGVFtkJoJ012SfcaMSdhMybDjLwd1u9QqAVj3lLo5wTW6seVgxHeYBxo/nQhBmqHRz6/JakDntp47tOtaKxmCA9UCYnH+Ii7c/2W0pzdj87UvcC2J/Jq6vflHv4Git4ZdL3srTn3pw+ieP4mDb/tl9Jfncfxzf4Pjn/sbc+nU3uswvfcGcoPVv00Hb8Oxuz+Ai4/ei623vHxIQqP60gYLBdfyK3KexxN48QWsLQ5GQIlnq3UsV8xNR5TWvUMOXC1xFAkrhRfb41pN2W/av8SdP9zCQAUOGXtnmradpNlCH72OWQMcgcHZOzl3T5RPAyZIXYJWv2gLllnYFkx6ytkvFXlWSc99ro4loE7UZ9oYh0PcZaiIuPus7FEYOxOxbtAM90OoAqIYjZkvMolkMIpP5ZVXgmzQ9zZdkiVgbZOdZZ2LzQsNXYRjmgpD9OUZNfMto/XkFqig2bTaTfnnom00VRrXXmmYNsBneBXTWjnEhYxIn11XFlfAoJtR0SICwcBdtLyEYzLcZ67RcTQTtD7qLlMo715ErqylgCT/ilHQ3aZhrhgrAxM4IYAic92AtCuMTlJGBR2ZK8tsjzLFSAZl6j4E1PMg0mWVCCydeRJFfw3Te66L1F3/q90cKOPlVFuj1GhffdqPZA2GidfXJz0YBBNdT7IGzyXIWAXIPdEcCUFt5jCmMEJGU7ze5YBooYWCCdJUA9s/NQDQBsRKjXDWUYDU86Fer+QQTSnr6yjuQGVqBTuKYANYhp/e72BpASc/9TdYevR+CCmhihz50iJ23Pbq+O0X1XNLx6YAKMweug2Tuw7i0b95D4587L246gd+CmNbdwEA2pOb0ZqaQzY+XQmscOdnMj6J2f034/gXP4CLj34Dmw7dhk1X34psYqa+H9Ho2mCsTyNo/HV5fj0lG+hbSRDOex1TatRjHbPeOBZHvU5H5jN1d6H3FZrzTYqNmALMsQzxsQgJfjWVDdYApxx7XlFrAEdZa1r7AsqXEKVrymHMS3qDTetAIBmmJtl3r9WJEwHfpY7yI8mq+yxLZimgJEq4ScdIg92tAp1z8WvdikYst56y3NowDJnomUBPN0LPhqRkaF1CwGBCeJkHezPuYNLU2tkKYyjp+04ZcCAYoDtU6zsKNW0unAjjMDIRM54f/CPiTL+0mg4OkSZKhf5UVVlriXWGWgSk1TBzlyFODmNNBQQiEBgteE1aSxFi6oxLDXcJKmoffKG178rd/Onmo58vEQRMHV7yL/Kd3SrNPky1/qZNPde4oEeZfB0kPBB1UCC1Augv8BkW2gYnIgjo68vUvUZvSr2l8+gtnkdn804sHLkf6dgUxrfsCQaUOdo5vanVfdPMOdWCGitB3W7Sq6d4XY/MYS0K9Zho7b9hmKW72RqXJ4p9T9/vmokKjQtlAoOk72UE64CzRlDN+RUkM/8VPI3t0L6JiFBUk373tYWDkyiBtTMnsPTItzF96Bbsed07cP6rn8XJL38YaWscW254sS1LmbT62YzNbgcA9C6exuSeQ7jqDT+JJz7033Hqix/Crpe/1ZkD0XsAsO+V78T01Tdh/tGv48Q/fQgn/ulDuPHH/rdKcABjLDXTT+MDhLusevM69g6F+qPsO6/HKDRXeDyAEnCEBFG6GmsP4pY9M+NqVLDrpB0/lRCGOvTuEu0/ZVDLzJ1b9FqPuafvvxyN8S9abntFJhxLtNtRd41ufC4joLbJPJ5NOO0qFC1SB3mnypY7JlRQ4bkHaNxBkzVAMqG7aejkwO0zFQKaYE0p5R2BpN+gpHKSYyKIYhcu67oJN3pKbNAGNdC6LQFLV7kvxtiZhsLKap2T/hBTFtnAgowDYfSaSDgLmNtXjo3f2J1IXxoD3qhmJ8Co2oBa/zqb1IU1IGysQEZdq5hWvUlL77nHsEBWTbptAyFK6qf9os/AC/TNpO0f4C3+Zlx5AKFjSQB4DIMiG7fzXferUEag0bEEXlCxdj0yTEr1wx07ZRZ/w+QyBjd0P6YNAdAAZC+mgghEJWHgj37pg1g4cr8pt/naFwKSuC4JGC2xbotDR5bM7Ue77GjBhTInNS9lAp2NvCKsUGDmkYLxuefafyokGAGhgHEF4lrlKEM86h5WM/vCaINHvO4pIsMkKdGYsEgltq/Bcg0KiKmrr8fsDbdj4ZFvIV+Yx44X3oXu2eM4/+CXjRBA4Wep61lrZguETHD6a58ARIU6BVXi3Dc+j223vw7p2ITpH2Dnu2L9ka0W5q57PuaufT6Wjj+Kx/7uv2KQryJDJQQUgx7OPPhlLB97CPu/78ch22O1ValeB4SohUwrKMTuNzqGVMChbhd8DtBA4FoQKMlvU189F7XmmAbFh+ZoLBDYK8+09Y6VQdrfZeZe56zFjGl3FGikqSYGvcyEwzgXLTQypLaPIrrPjrofNlldaExG6Dp6jlZfpsIVCsheL3PlMdTm3IA9g0jbadftc8EsA1wouBTqT0pkK/ahN9WoUiY5NwUSN1hKnokUAqj6bre/QRVddmBwf8p+by01lx3V/7/RvaPB5Oi5sjRo6T2hgzJrlKlteksb3Fa0D3m4o1aTwWMBIGAShUm2YFcILjWj3uDvyTXxXvPk3kP3F9oEXO0zY+wzCY8S0YhoYCwJBP0oXFA5gga1QJjvyr1ely9bdMd3GXAneJeZpPX5kPmbb25cC+mg/VBtHgtELDMaNwCINMP4lj3Yfttr0Js/g9lDt7rDoF2IakY877iMCIcaNL7z+hE4QgrqOciOwzKBpphm/kn8AWA3VqppFQMyT3jwo66QfR+maaQxAOtyC6LzVbdRWnlYlHYsVO2eI3vC+uYXorqHdaADATAZlZv6abSIsTKEoQcAIQR2v/pt6J45hif/4Y9x7dt+FZsO3IonP/1n6C/Poz0x69yrYxXKEux7zbtw6isfwWN/+18BAO257dj+wtchnZhw0G+qxuDWRZ6P/p20Kq5ZFTkG+SrOf+uLOHvf55CvVWbgtaXzGG/vqRheEFdEAQeJC3DXmlDgsENO34T5bd4zGSkrA79rKhPff12E5mlgiWv0/+eWX+ZCZL6T2ICCBxNrS3ECJMwC5aBxMZfNmNXJYxKdNb3ZGjSKlabJXYiDPNAHkq651gCnbTaOjiWuiRdg/IWzRuf2d7LGrDpkHW0tADmJG6BxIv1ZBdmz7XPLgGN1aQ1xl6P95PtN02V8Tq4XeGGDNghXCB1Ik8wtfGgSwMGnFISJuwJlo3U0ZQzmRIuuo71oEBVcZlyRBjgUoOMvmQqszVZlO/OBlZ24BlHG3HG5oceZ1qk5toHuVJHNQ2+S1EWJCg8jDHkQ/YgyHjrpWABaNFTeOU/7GSAv6FZZi4fWYFPUHg8Gs/4tivq5kfbK1I3D0MF+StQB9nVBUSoUbYHW5CwWjz6IzsxWzFxzS3W6dkEp2lRoqQPW9G8QCwC5L30TQlTMObUIVG5ZIJx+1TehGUflloWyzL5xCSLlDCMp7LiZQTSdYs+1wbdZj6kAez6hcsoKXKLup4QvFF028b6OMrcloDJYixInE1jvt6EFHv1fZm3s+4GfxGN/8Z9w5NN/jn0v+1EImWDh8Lex7caXIV9bwYXHv4G5a283TLqm2Wtuw8w1t2D11JNIp2fQmp5z+8D80XlfnGclAGTVtnH6nk9g6ejDUGWBzdfdgemrnoPHP/J7KMTAsUY4KDZGEKDn3dgcD643ME88plz6x23sQF0HsSDqxFllC55bkGAur0bIzQkz3zB3jYKF+pvT+nkQMEH4oUJn0bF9KVpwXHHLDI6WPzYdVeoGCI+8f3JXRqdSUowz9048GKLWgJHyhoSabrJ4N5DvWhMuJ3txV590RSCfGG0AubtSjMrEfSZNytGiLd1neak5jjZogwitSwjobgWSpgBWQnLg5hNoQhRCqUbaVIHRFwEvqy6nURn9SxQIGmkI82OKkbaLlkDRqga/vcgzrVRihWaMqQacLhqO3/0QzURJoTpV5DpeBXPpcYgxhDSWwOlXg4bYcecKdD90rdmQIxoy6uZE3YU0k6sZ/yqoWyP/MK2fZmTqTXMwVtUp86rcYLL+DWXM3bIOVM3rsttvfy2Wjj+MRz76uzj0tl9BZ2IzlLTaJ1Uz2iboTWux2TSXhNlyB89eJ3OX2acae814G5QhIiwmAzixBN67Uf/WDHxIq2sgS5V/fjBRa9KbMPY1Q1qS308jCQUgrxDTmtZDzVjIgL849Z/3L9SFKv/+fa95Jw5/5I9wLGkBQiDvLqN78RQe+9jvo790Hucf/BIO3PUzaE1vqpqphTcBiYmd+72Abj2Hou3T90fL+lk1CZeOPYItN78EW257BdKJKQwuXAAAFMWgCrAn9+og4DDBrprDwnx3YqlCQmDkN7e+Oe+HcIVlUbB8APq4Hq+QYARYtzj448Y1/OYStst67qDUBaXtMvcUGShP3H1UeF/C/YoGEmfC2R/0ujSUAu+1OcVgkZ1lv2HriEGA8hgoSp6VgAnRo7oKcvQl+jNdjqMEUVreC0wdGa29/pR04iU9+G5Co8RFadI8T/l0L4ojkHqaA4Of1qDkZxhdliWA4iSHEttQoszWqKax4R0g378bQjF1WeE+63Rh5Isy2eQ9po1sNlx7GdNIKCmgagFL9oh/YSCglsK10kCwgkCQyl7ZKCDkbWEyHPPrYmPiIf7Q31zjqa8R/jGHmR9yHSUnDgCagUcw8Jn+Lokg4CIHKVIPCcgugd6srFAv6uDIoi0qGLvUCgBJv9p0F1efxLEP/Q8kaQtJNgY5MYakNY6k1UFn51507/syTn/tE9j3qneYbKMahrPQbTo3CuN7LwmTVcGWsnENWIIcZkux+s04gJ1gddT/jRtMrUT0HgtlQOu28g4coStEclA/l3XsbRwekvq46/sRAsCg6qvU7laSjFdh73MkUjX2eEA400QZQskz/er+6efAzokCmDlwC3a88A049ZWPYHL3NRjbvBsPffD/RGtqDgfv+jkc/eLf4KG/+W1cfddPYXLXAfP8lIDrFqPcNr13jwkplLKZORx4yz/H2LY9SFtjNvC3Vj6oYuAw+oaR1/UIeEnweJ4Mp83Yd15Wz8WAW1AlCLlxNJw5N/OCIfNIng+Ats2tJGxPci27flu6DsfXnbortRv2VtKWx8BH5myRcYuCiEOJjojWx4PaHcs9s4Zq4j78lDz3IFp36taJiBA2jJx9t0GYaKKyrSCuQJwAJcdKZg6GO1Sm0hWeNgwDG3QJdMXcgbJla2IFAEmxe5kVwGgBTWG6mim/7Ii0HnjQRopAcDptBbTXth8ADQ51zjUECEPYwC3uI9mftr9by+RE4o9dTOAosnCfi7GqoN4M9G96jAoCmgaT9gaybmkEhGTNrmLc3Ufj9GvhhTMfTcHhlBxLgbAMrQluLpSrmavdDCiKUZnauA2tjalcSVxEh0r7LiD7FdLRYMJWvDZnOZB0TWF5p1U9Jj1VB+PB/C+FQu/8KYzv2g+MtzDorqB38SzKtS6KQR+bbnoRtr349VbjXzOmzmYK68+vmfdEa/fr3xR6kscCaOKbt0YR0mNqyiB8vQ7SpYym0MdpMfasoGoGnQgQnNY2VfOcY3d7FBI2eD8vlwhjriTABdIgyZrh5LERtNp6vJwAbNi5rK8zfHvNKO947mswPrcH/cVzeOKTf4yZq56Dq7/vXUhaHUxs24fHP/nHePTv/iv2vPyHsOXGO5uZo1G0rOzdBIDJvdc48w8A5h/5OoRMMLZtb+X2lllmSwfEGhQdIkQHKdTngHKFn3NcgOq+a7ccLQCYuA8mMDiMOEUVqpn0WECpJ1BRN6CWO8Cmf3ADTRXLFWCO83sm/S2aBARCZeb6pHOkoFh7bkB/PFjYrySs1W/y00/XXP7BLcieEz3X8H5RCkLx6ioG63AfbGhr8Wpg8mhdbFwgWx1tvMpENLoPUWEpH3NRncQIgd4btEFNtD4hQG/6qBfzETVzkgVpRqHJoBkTvaIP4wSbzvmMZ6yOGOPpaNX5puQskI29jAcnC/j+r7BlYxCi3Tnb4MQZu7JWbirkvknwKUcdcrJpKluea4Vo0rLBOHmGbPHpTdtyZWorT3sl8nH726AgtaVjxeBjo/srSe4AExxMznHkGfO9Lk+ZjSIT4JCdnna7UJXHCbluUC/oZUugu9k20lpSGExWAvBgEljdXlWWrlbZJ3skn8bak4exMn8Ms7e+CK25bcimN2H3W37cMnq6W5qB132rmazgHKsZYOO3X1/maHsZM8818dT/X/dD1xGCrfQECqKFdSw1vKsi8B6w960/W/2X/Tjzr+Mqnglk8gEU1bsaY2gpUxjCY+fCkxNcrWDdp/TxRGB27w04/I9/hrQzgQOv+WmIGkkq7Uzi0A/8Io7d/QEc++xf48IDX8H4jqswtmMfZg7cDNlikFLsuY0Ug0H9/evyqihw7hufx8wNz4ecmwbyOug2s0qgMq0++jdH8ApanQKWCSDAtDEGj2p3aUBn0Ya3bxmGWLrPx11PyHcq1AasZA5qVylQtgnTNhKOv1vOcQsdkfHnLk3OOTmKFMvuk/XDs1JFNP5OnhjAGSc5UEwRZk+mXeW4E/NgbsdazAEIwlW661MTetGau+c2uR2PHJewjjVrWLwUpbIlHWWlH4D9DCXPd+9paH+DAFxBSwBdaIdp75pcMij5UJeX0DFGTdH365kXTXCg0aQpwr3fgvqjC0QtBBXKRkUcE7o3Ywt3zrschiSwrIJ0uOBB0tQqQIQ1fl+uNonuum45uoFreNPqeusDDwAyIa5KfeXCkGrBhOCP8+BgLthU7jz6uxvXwF2yYtpxAMhrS0jaLbGwn1g7VhX6FSIiWovAhRvshVntO5qsVYgS6ap9jmULOHXPx7Hy+IO4cM/n0Zqew+Kj38KObhfJ2JiPolOPnfmtGfX6q8xtOc0Q6HuhmleAMVmwx4bOdQYJ6sRb1IeDm03Dptjke1504htz0apzagSEknhjYSHG0eKrWpGY13MzQ4UKlCijsRYhl52mZkXN9Ec2YpaJe2UAAQAASURBVH1/hpGjdXOmXxeRMAGiNmAemLvhDlx49B4sn3wMU7uvMcKiEAn2vuxtmNxzEAtP3IfFJx/AuW99HsUr3oott77M7xTnCTnjrYVMLaQQ9yI9j+Yf+Qby5QVsuf0V1Tto3l/G+JPkdvqcSurkdfW96jlM8fGNK42+/4adS2uUaRnNmIfcSbhgZt6dwt3XHMsiWa8pcYUNtwS4ZVUUdpKi9ag0zvgXbTu2XECg/eNABTFqEh6ccg3vMs/G7IxAg5WO723OzwYlnPOuNb2rDXynl106Qq0F4UCHNq2jg/HKOmyav8RgaE68n9GM0hu0QSPQFUUH0qT9nw1118HoNxCHlosXbNC+PwXkJq4Jm39D5Cy2TNPkuqPY+svMjm3GEon1p20lLR48TJhnuobkbXeApJOAxP7OmTUlJS4/eVuGrRacAdNmdc0cUyadjKHMlaPtd/rI0I4c8zjFiaaMDRG+TCZgYQWBMhVA3d4asbLMH6o62lqs3LEWDhHN/taqPtkTGExUQa0AgFoQGBDoXLFWonv8MGZufgEGCxewfPghAMDid76OLTfdacbGMB/6HhRznxD1hk+tA4RJ9xj9wHnNGDvl2cbsCUhkHM056V9Lg33NYa6J4/XpAOQczmo0mAaK8SowjyYg9CiwtAhc2feeQ2Q6aC6xbsnqXYgxcCpFxYAWvrCthX7q412m1X3JOuYACpjceRCd2e04+8DdmNp1jfN8lARmD92GmWtvg1IlvvVf/leIpGG5b1IOK/jKEsJQKSicu/czmNh/HVrbd0IlFdylXt/KlmXwipZ1jdNUsKRQgwnrt17Cjp9m7jXTE3UfQSVUckHBuOIQjb9+nxyGOfOv0RR1U1RWYJA5kI+HzWIqjSvJnERjrG7nZ4OlgJITnN1ARas5oZWhBsSgEJCDJsmsATx4O9oc3cK4cEUz/zYpHq7AOpCujhYcDLjrPlC5OQXLTQovmeqVIJUIqHJDy71Bo9MlCwHrgeqimucmVyAwP+6mqewIBA2xAI2uQLzOEa0CTeeolSMYKxA5F0NHANiGSRajwZQwi87YObfC3ia7SrYvxHeCtGsfXtEwVpTpL5klIVspgueKtnSgYi3Gv89A6YBbQDOvmmnn5ch3RRiNhLgapRxyUP9XNvgX1fOgTH9rSaE3U31vL4DAwQm0FoFirPqddAUg67bacPGiU1Q5vgijm587h7K3hulrbsHM3udg4eFv4OTnPoju6aMQz7FaNcMc1AJB0rfMvyh9VB+TGbP+rRN/aTK/la2junGEBQEzsBFLibK+/Hr8KfqS8c9XtsrQq2K0sYGlQA5q1JbIMiGKGglLikbN3neDEsKQx+4HsFropFf9p6492s8/6A+uGWllx7HMrNZcCIEtN96JY3d/EP3uAlrjM46gopnZsl91NG2N+Y1QClgEQgKKLts9fhgXH74Xy08+hP7Fs7jqh3/B+tGn1buhn0+fZMRVafWMk27dvzbQbwPJaj1eHTja3aJdCz91WZXb796Y1e0ZVC32PtgfZByJZU1r4HVMjpeNmL0ndB3j8JEqIQqVgpkRAmTcwgJt8cDfmHKpyd/fKVeDF9gOhss1MffUauOfHN4HTpV7ULgjyRpBRgOc/haZ28dGS8ao60VD/9MVuw8L8OfONV/k63rWqibLx/cAbSQLe+bQFbEEJF0faSFGcgDzoif9MhqYxemKZcNzcIwbLBSjvrwNAkiTFcCBwWvQNnltU4GLXLe6td79AUyecKWy5d1WjTJx0hUIqL8+YJ+NHLh19GaJW8xyaRhp7s5FA28T6vMvhStwEHciIxyUPmIEZey1tsoJ/q3b00HLVBjQdepnpAUBmsuiRwKuFw5W31sLwPwNVV9lr8KG7m2mwgwgBgIqU4B0TfqtRVumTKu6yolZZDObcf6rn8Hsrudg9rrnYvrQzRWEKHHfMQxHSRA89LGaQdNabr3RG006S/zFNf6eWxC3EFBtd4R7NwwPEXBKvQErtx8xQYAGbuo2Za0NrzSobjk9lsBo2U4p8UBnTwgnVg1R2vIiF8ZNZBTXCEOyGqMyQdAdSI+VCGiCacI10z8wRqvuc9Gqx2sAzF1zO058+cM49+CXsOt5d9nBFpZp7PcqbjtpjbkPhAuAXDHBh7sWVlQKrJx4Ao+//78inZzB5P7rseNVb8HEgevMPqDdfszzZm5QZQqUU268AGrGqmzBy5ir4WMBAMSyoOdGMEhYC6tk7mtBDHC17g6kZ4MlgENWUgZQlEAxQVww+/5LFMSrj/CQXJMfUyIVHWZdiDH0zNIQ259UirBFAkBj/oAmigULcyGLUNZVTnxFwa+jPMelJsoa8VZG9VhY26rQujgat9+ftMHDI1tjUO1jo+Yh2KANGkZPiTsQXVDTVTc4s5FGNBOurzOR+nnTTe2tQ4KPMfCNlgUS6OShQTANN6VYApqFA3ZVnX7SHfTFq+0ON33UXemXd9nr0i4JPj7tllu82j7gyWMlUGtoqEWA++9XDKMWMJSxCBWZ6zeJnFgcIrCy1F2Iw6KGfDsHE7LuX4mV7XbQVq6zZbJFoBhXSFYF+jP2eNmuMkNqTZ5KFJKeQFHDw6lMOa5QOqGQHFRxAgAg0xamD9yE81//LAYLF9Ca3QyZpMatQQeWVpXA0e5rZs+MKwhjIOz1jv8+1/iT4MUQs09952ngZ1DTL+wrlY/Xm3EJYwFwfIWZVpmTzoKs8y54zICqtL2ijGs3naRTVg5+6kj3V6HRHahMa3/wgGleM5mitBpuU32ddE4MYMZMz2fNMNtAfiBRY9h06Hk4950vYvOh29Ga2ewJIMVqpWKXHWIJ4EwzW5tDAoAuO1iYx5Mf/iOM7bwK+3/oF6HGUnNO9oF8gtRTukw1TaqnpF3DdHyAcX/TLkVkrjuoO9TnnMQmcKJ7TtJrCDjP4fp6s5ivUP18nSmmCvO8VaIgyCCrxCoLVKqYcEkVImEXHiV9YToGiuD0iblexd6PEPqbqbvBGhAjz4JEz/FgYe+cc8R8y1bdYOEiwr14VmY61iPeh8zRGHdCKV0RGEyPKk2MVuyyqMlV+plCxBr6tLW/QQCukDvQqJQMlLMhNCUQ07ju1ff1txWiJlcdSo0afM6kR7Qz1Uny3dl04y9pmQpnw+X1aU1WwUzhzsZLFr1zN9ubmXnU7eC5G+3jn33MHeSFQ/b7ym5bbuYxt46L1xF3mgX7fep4gdVt9rqx87ZT1KogyZxI13yGniJF8flgsvtK+2zTnnLcmrpb7PflXcRFar5C8AGAtAss768qLcaBZFW4iFGlMMxaumrVqEVbYeykdOoEKn5Ku7yUQmHhwW/g/Nc/h+3Pew06U5uhSle7S7WBArUgoUqsnHgCFx/9OrLOJHa+4PXV+diGpjW8TXOQaIEddx92zlPfB6brYAo2tkICKKymMcg0MOsAZVQdq0APNv9IoF1ruVBQ35UdNU6C5EWIal8ze9MhzXDRdrXTNKDWCfJUNdPPhIYyA3Y+7y4sn3gMD3/oPTj0xn+Ozqbtpj/d+TM48o/vg0izKlNwk5aY4ON71qP6GZV5H4f/4Q8hZIKrfuAnIdO0yrWA2kVC1K4bxG1RDoggQIRMfZ+8H85YMG2+OTXMV5/81mOmBQBalwNoMSBuHk37g7DtJ6sC/e2Wa6cuQOVYiWSFBnux/pt1XrH2hFuO9NURhiP7Yt5x99aYtZ1Dh1JqsgasVyAAmoOFm55toyUugkjEr/PmSiygOXfrabzNEcdgZZfA1FEyJ54CtSuH4d6gDVoPXZEpSRn71pL9PTSB2KgoQesxP1Jzasq00UyTThlOqlniC6MjPDTlDmiIk2hy8XEWWzKWDooPY6xEaX1aixbxpx33g5M0XbgZ0KvXzMNuJ068HNCr6vSj7qq5dpXdUdZ22nPTD7rTZ/GaEtOPVOdP3ml3udmHhEHZmXnCXdVX6vomTpZY3WpX4LHzhQM7Su+dLqTt+RJrc/Y6+gw6F0v0ZnU5OBr+7jZb4WAakAOBMlNQUlWbbc2wWaYfDua/7MEx+2qmX7tvFBcXceahr+HCA19Bb/4MZg/eih0vfD2giKZPAmvLFzD/4NfQ2bwLE9v3YbCyiIuP3IuLj30Dg5V5pGNTyLtLaE1txubrX4BGigmPCB8PaTi9YGEmNGh0JF1WaPeU2pphXILofCdN68RnigkaZe0GJUTNkJb+xq7EOlWSyrVs0Ps2DC4qjS7Ny6DjL3TG5+qiIU2J6r7K1EeI0VS2VC3IC2d8jHsPU4pYNyi3LFTtLlbXkc7O4Jo3/zIe/fB/wyMf+i/Y9cLvx8yBW7Fw+D4c+/z7kU3O4tof+pdIxyeD92KsPSyrr0oAnoxp/vFvo3vmKA69818hHZ+yt6Fd1lC/A32XwU56VnPOY4JEAU8oBHyNuCj8Z+lYwwg51wprcbEKhPpc6cYWNLt+2q9FhzzATuEW6UYY/4TtC5Gp7FgYEn8cQrSeYOEYc+/tS7G2JI25albSRS00TfthwyveFCxMib6DnvUnoFwLkV7PgBr2uSE4mMaFjZR1WTc9qjJVjDZ3NmiD1ktPiTuQpqTHcYAbSLlMubuwNM94LSQMTRbm2FPjxSgSTtpzVzjqosIRbZxydJNbhyXDyWRIhB9+bx5joNsqgGyp+j6YAvJJ4qNKgtMu3mZXqonH3WmwcLtVN6YnXZNDe0vXfF+8zTY89khVjgoCms69yEqDgwm7Os88Ye9vZac07jMAMH+wGsDx01WZKuYBGD+r0N0iyHVEcDgLrG2uvnfOA+dutf2gPv1JV0CUAqoO7k261ByvzO9iTDmaVz2uWhCgG1JWC2FlPsCJL/4dzt/3TxBSYvbALdj70rdhctdBCCUdVCKUwMJDX8fJr33UGa+0M4HZA7dh7sBzMbH9ahz5/F/i6Of/GhOb92BsbufwADOuyW84HnN1AMgGVV9HBQBNxVit/U9Q+eWW4fnuZGEl/XCsAuydTFcsQ95E2iogMISJu0wyAb0jQJWWLWXcnTgV7Urzm6y5wkBZM/icafFcOlBnfiXH0ulpHHzrL+H4Z96PI5/9S+CzfwkAmLv+hdj9srdCttuVq5knWOmbY/9DJIDu2WNozWzG2PY9DkqUkrUmnSwXVfyXbsiOhShQ5RHQXkQlHGFDkH7QoF2eaIx+D2V3FtSSUrrXUFcPrrxxGDMyHnRuJWsCg5mqUjGfQWy2C0U5sGtcFAaUu8tQ+VYyl5jcWjyda5LRgoDztjtXLuUVqSxDl8d5Nrlsxc4lA+W5wWpKKZ5/4VvHNZVsrY7GOwAjM9c0OHgYeQh8T7U//2U+p+8GKSWgnkZUh6ez7WcaXZIQ4GXba8XLOtcRpjlZK0eWgikDPNQqMOL8zzu2TscnnRHFuU8iPpOA9XfXRC0QaoglwWLi2+s4SkKTlSGzfDl6bBNuX6gu7M2VKMcp12r70b2l68pHhW2s3Ns1PFrZczmI1kS1A/VXMuS3rJjJ1N2lDAxpcXjCuWbtartr9WdT6Ac2eVRgeQ/pXi0QrG4XRhAALDZ/+6Kuw56jWZXpcTkQlsmoDRSVptIKAkAlDFDtfmuRCActGH9/UVToQeZcrf1eWz6Pwx/7I6xdPI3dL/wBbL7uDqStKtJVt2KCv2TlclQO+sgmZnHdm38FK2eOIMnaFeZ7mpiNa++dP4SVs0fx+Cf+ANf94K8i7Uysj9mlFoKQgEANTrVQwN9NIwDU1+fj1XdZWNcB2SOMjG4LNaSj/l1frzMelwEtvRwAIMxf7J7E06kOI/epJIKBwADTeDOGrWhX9XAYwcoSxcpqa0GPHUONEiWAdGwS+17/E9h5/vVYPXsUSWcC01fdYBF7asHTMO6M8Vewz12y90WX6549hs7W3Tb+Qtl7NBDAdFfRjH8g7kOvY/y/+e74jOjBYYw+sQ6IkmmKhRs/Qy0emkq9BI2wF/E4EEVyAKilFsSkz5F7AjG/pwaNuDlO+paPVVrpYH2EQnPINOtYVa1LEEcMKrPRhIxRyXn+SiHmFiuYWw6ldE05lmC+71IaFWlwVKloVGVeb5Prtte+EC+bjwkjoA0mBNJVorRrWN68mLsN2qBLpCtuCaBa6QZr3XeHyDsymCR+6Ozl6U/GdwAnQRaT6Jv8Feni5OB9M+Y+9qKXRMvD3Qua4hBahDntEk1FtmA5lf42VxVSdO00SMZyCBnulEhL9FerzrTG7e6QtAuUdUCclApZagemc+28+b5wdtKpr9xk61hsE3/9cwm626s+jJ0WuEiSculn2ttkBQFzKlHG2iG09qwOwku7AvmYcjSOsl+7Zuhx6LgxF1RjlGkUEyIIVG1WLnDzT96HJz/750g6E7j+Tf8S45t3VygOuTK5CYwAWGvVhKqEgCRtoTW5Ce3x2apOYX08lRCQaQuHXvvTeOCD/wlPfPpPcej1Pw8hk7ggELN4aQGAauJDU1+45bUblRYQKHSqTuiUrlZlTdUKNqkZna+aGdSaXt2MIrxmwDoh8xEUAOQ+uTBSUoSlmsGsYkmq56kSG6is3YAMIzoCk6CThOnkV8Ey2k2SMWd5B7VlwPafu1QaRQFh7nS3ijY5JiRaW7ajPbfd1C8KonVXAMhvxyJDqNSBp/TVUwrdsyew5fbvc+aPEuSeuaUpxjhpzbeKlGUWJRqsHpX96mcasxw5mZtLsleFtP0RC0AsoZTKSmtMme0DF6h51u0DXVeiQBI00VmqHHcTZ35xJn2EVyRkWTLn2oKdG15hk0tQyB0vVi7W6qjMd9Jz90oH8Sl1EaUcywAVPhv60UTpihuX10gbvPsGPYNoXUJAlUGRzeBIxkPAdZ1Ju8pN8NHUTm7zBcQCly6HONPfuMjEGKqmc2xIojjTiGt/3DIiGuikmKaOUuec/b5qeQK0ztnH3t/p7iLqAjElbOqjWLGrquzY3Wtt3koY6QRx9+lmGHSrayZmuuiutYLlilUm2aR2YHqb7QNZ3W2LJF3h+OJ2d9hzlilXjtuTFgYAKwhozWveIYwX3KRUVJOjBbIyqzSkyZI9J5cLHL/nIzj17U9j5qqbsP8l70DarrIAa02Nzk1gNhiyMZZFHzJtOVodw+wK+709tRkHXvUTeOSjv4fjX/kw9rzozf4Gu55XRZG5Q5kPhgzEXYDyScq1VYW0m5RhBhTRIJK5yTdb844PUMmngVgBWVhtb5TRKKsKlRTe/VwxUux/U1FZMyAR5qdsuT7Eel4UHYudb+pK/bEsA0wc1/w64yX8wEydc0KlkVsKzImy30fZW0OZ9ysIUMowU0GP+vwrtw5qOaD3DuUqPvg9hLI/mzoa3El4kKljtY4w+qMcB4B0PsFgq+0wTdJEldx8TYqRUCxWge619CHROCW2hsWoaBHYYUaX4hmhRPx9HLk+LtQPc+fVxeg6wgW+LFyusRtD4ho0aTdQU3/DWjAg6+T89cDUYaIUXA5dUVE+Lo01YNQYDaC2DGhe6dkiZDxb+vk9TpdtCaB+eNmy9ZMbtjA5mvKmBGLeheR7E4oPZbqG0KiJzxxGvEGKEYWFgGsyVzYRjQ1YDwRyDGp07Kz93qUCwXG7avaYQCBPt80Q59MFcJGsgBO2V8U8mQRj9vjKCav5z7asIV/mtqFaa5+4D0oH5ZYtxcZcGS1fmTJGimr4yW0Ubdd83logQgE5XhJ0FJ0wyJQjjFllIq/N54nCk1/4a5x79CvYc/sbsfXWVyLNAQ3zSRF4HGGWbKBqMIBMIzYzxtBO77oWe170Zhz7pw9gYvvV2LT/lubr+PwnAqMnPIRcgGbc84MJgEP/aEQk3R5HrwHq5xGoX+aA0sypCpSPMX3rYMifUqqZ/Zh22AZX+6aNsl11nsajANX6qRPHUShQwGXyi4wxdYq5b9Taee5iQ/M0ALAubnCPm+/CfhedNra++DU4+6VPYuLAdZjYd8i0xQO5Hcac1iPJMdTv2ghuKyEKzQ/tsuPg4Tf5/yfkukBwsi1c/ZO5GxQsiPKCvhtqZgAs2ve6KeGWM4/JWiip0o0o2/qzCumy/U2nn2NZYMHCXrv6GlYmLlDZgOD10KhCQfR9b4AN5eSs/U1uyiPehswRDfT3qkwubUGKrh+BcqKB/3kqdB8b9L1P6xICkv7oDGmy5r6EaTdeVhZw/eafAl+3UQUCushRbRtfCGhiDy44OBYPUi4W4FRVQhhD9nLTLMsFFyqaqiRMLSVqIVibI8ePuzdJtb7ZeTtVivES6Xz1O591VTFy3pYrSdyBOjxulDSDuRzpgi3nmNuJcKHjGbT7SbJGGHjSbNGx8ysfY9r9RVJ3G44Q4bj70CRC9fUqBZaOPoSTX/x7HHj9z6I1MWtSvauyxNEvvg/nn7gXB17yDmw+eLtjmqeBiiH8++pkZQko+msoBj0kWSgFKswzLhOBLTe9DMsnH8ORz/8VJnZcjWxi2q2Tt6GJMl+Bd6FM3HdEoyppMkFwqqqkfQHe3NMuQbSdZGAFIX0vnvm95pFlH5b559P8UpMBkXYUrKUCsBp7rdUWqm6nrOfGOgL6VQqDe69C7nRC1dpTzvSTua0I498KwIFqbb+2HATQgziMJNVy6ufgoSW53XRzQ5BCSgBbXnoXVo4/jhP/8D4c+sV/CyQM9zGiORclc0nSxi6d5I4EjgfzTAypX7cBwAvcFoVdv+l37x5pXVxui7jqyIsZyrkGrOsAeShBtK+5cNZNTWWnBIpwh7hl2HF1AYLlnLozEVXEVe5B69uPm8AGRq5D5z6pybFikczCXkIxsseajOuBOjyiwtOIsYpJd/TgYEqDSYFs+enWYDy9tBEY/MyhUQGqDIlcmE91QNlPA5nNtk5+pbNJDn3hBPk01c9MiaJU5tNEsrCfJkrWFGRuP41l+8p8nLYGbh3JwH7cziPad6EUZKGCCAOxMaWQh5zaF+yHU7oszIdStiSRrAkkawLphRTJkkSyJJFdSJCsSCQrVQfSxcR8KI0dzZAtCmR14G22JMynfTpFtiyQkTbTFf/hy4H9tOYBMag+7Qv2OFC57+hPtlxtLlWMgPvc9XEA5vl0Tx3DEx/9I6xeOI75h+81YylKYPH4Qzj/+D24+o63ozW+CQvHH8T8kftw4Ymv49xjX8X5x++BKgvnGoOOQjTZm666FWuLZ3H/+/8PXHzim+FXiRoRhMDel/8IRCJx5LPvgyKuQ1EKaDWd4ECWddUTAGrZRI9V+7zfL4PsVB+TA6upNjkA9Bzk070BAUj2fVcWj8wS9F3aWEdaj8JllFRBIaHoqEogIHOjaPnazJBWkvu2O0yuqOMeGENOyXGpUPADeAlTJ4TElpfdhcHCBXRPHgEUkC8v4vgH34sn3/s7UCUJbGTwomZu12hA5n0b2PdPFDCZmk1chjMA7D4U+eh62TWOsJewdZI9CiXsh+499JqkK8xzVFK5fSDj6HSbMfa0HcXqd4j2I1Xmo7Mqly2gP+veX/R6QkW74VyDBj223TfxVJUrl7Af56Q93ggNquJtU0p6qpo/ORpRgFQy+t5Pn69ZyyMoaKa/WhAdZa2QDc+/8TrhjuuI7W3QBlG6ooHBo5rNvOso2lDDCymUcsUW+hI2mNTyMRlF3aGUdkdnIqggEMt+CLiWAE+LT+uj2n7SR0W1ZIDzxHSQH+Av/jF3Jcrs8iyUrcXgJehPuwgm9DmnFF6T9K110T6ovOPi7VPqnLXH8zHmE00uoVYArn2hTAvdIBwUDaoRZdqyjCBiyPo+uyvn8dhHfg9j09uQdiZw4YlvYNcNrzTl5o8/gNbEHLrzJ3H4S/8jfG9TWzG5ZR8uHvk2Ttz/j5BphrQ9gX0veAta49NQEsjGpnDoVT+Lsw9+AY//45+i/aY5TGzZ69VFA8rTziT2veIdePwjv4fDn3ovpnYdwtiWXRjbtBMyrSFjiLWhuml4m4PG+DflVBVwTRkY86xrVJnOuaqsInVnSzCChRJWM+0kAcsJ06UZylrbR5NRmTiBYcpVXc8l8v06ELjMcEkJfDhT2djH0KlaEOC+4sVYlbWaEkdpiaEHUfcgjgfP/Z6dd6YERJ9oy1X1DJ1nT9xmJnccQDI+icUHv4nemZM4/Zm/r4qtdbH88P2Yuv5m245OIlevVaGATHOMCMlcaHGYTCoU0+cv3LoMalHt6qfjAYRiLhgjCNCiHNFtg/ZnagAs0wWb3hTic5ebZkzdZK2cKSBXmakm8LUkLkFNcQGXRAJxRrhhTOl+2ag7HPHd9voQ2/uGMO603KjMOO1/+6Jw86g0uQfx8fl/tmFgg55GesryBNAArNaCy8QkDTNeFNbUJwduevEmrb5owNSnVLSFI2g01knO0e+c6XeDOu1xrh1JeuEVqGy5K46kLkRjbiVUY+pobBSCJnWPwWlYnCVzK9DUmrffBzMuc01jQuhxajKnKD4Fc9eh/aHIRhTxhLs4OMyNZk4CC7cTJ0DuzYHBS4UXyFjmAzz68d9DkrVxzat/DkunHsNjn/sTLJ15HCvnj+H8E/di5fxRbL/+5egtn8f09kM48OJ3QKQZZJqie/E0vvOx/wwBiby3iie+9JdoT25GknVw8clvYsv+5yHdfR2OfuVDOPPgF5G2J3DdG34JveWLePLuv8YNb/yfHTcLz41MADP7bsCuF70ZFx7+CuYf/yagSkAIXP2qH8emQ88diUF2cLUViQGor6VMopJA+xwqP/i6SLriXg9Ru7AQph6w85I+Izmw74cAYf41Fnxonmot4JD7MkyjcOtq1CIKGO20kyRsRLLaZsqhjnCdDholfSvGbK4KGhfgvZ8CLmQoixPgSDQhQYCSE2uhNev1bSQDu5ZISExfezMufOWzAIDZm1+Iba96E46+/w9w4cufxfTBm837TwN6TV/IEPGkXrRvIXcc4z42QhwXR4eJ4f8Hf+s6Qsf567iQQU3n4fOUIW+Vbs4AWjmxDonStxxUJ1S8nw6a0GjBwpTKlnD2HUpFy7oEUUjR9VAMwMALfo3ssx5saBN/Tec4VypGxm9UFyaZj640oG0v71OYemI0FX1vRqK1FOY9vmdohHX8KW9/gwCsUwjgmoRChhexoeRAJ4x+3VORL+BSiAd5xshJ5d1ggZB9u2oVbZeTpQwrhyh1rRH2OA1G9oK+IknMOANNYzhywixSt6HBlOtz78yNCFQbFSiKtsvE0LHMiEDhJFBLGeNAvifMF5oSZfSpRcDkiCDnF089grXFs7jxjf8aWWcSM3tugExbeOBj/wVCJpjdfQN2/v/Ze+84OY4yb/xb3T15d2d3dle72iBplXO0LWc5Z2ODwRgDJmPCgTlz8MLdvb/j3uN4D+494DAchznAgDE44ZzlnC1LVrCtnKXd1eY8sbt+f/R091M9XbM9syvZBj2fz3ymp6e6qrq6uuoJ3+d5Fp4NPZNC985XMGXOyQjFauy2sklzUAKRKrRvWQtDz2HOWZ8GU1S8cfBNJAeO4NCmx5Aa7ELLqsvQs+s17Fz7K0xb/X7sevLX6Nr+EqYsPB2eRMaoYdlZaFh2FoxcFr3bXsGhF++BGo56lnVTNmJ+W/Hg3XkAGGECuWomXwPLyxqKKfAVCLkpou1HHvZnmalpuYwp3LsjBHErjCecTdl61n43wmJRZCZElkChEM22Z/v53BOlmPU1bjvDW2T5CgiWNmoByJ82XO+QO/yj8L4wRysu3JclrOUgWNRUV7IvlTBAicWnItPfi/qTz0NFk+kgXHvCGhy69xakDh9AuHWaWQ+J7gSIcDA7iAGcNmTjRp8rJ30WK8j/JJmJLUHXbRktpu31E3pXTSomTj9PBolCJjgLR3UgJVNNi416ao51JjgLC/2kEdUSHMGB0tC9VNFSCvmJv1/UcuJTC84MLvAKshVAyZbAmPsU7oVcFCU4B5dNZa5Z9FnoIcVcc42jsQAep79UmjRLAIVrAP5fSnPzp6uKT+69RE1dqUQx9zam0SbXf5QkzAoroj2hm5HbPyBLLAGKzh34DxkzN4ZT0P65Fi9ZqFEhl4Hr2QUkmn9BIHDFSLbCbWZjLgsB2RhohmDdpe2nzAB1kHRHf6D9pvemetyPoPGHS2PEnQ1x6PB2BCsSiFZPNZkhLYjpq67EWH87KqfMRCAQQffedejZtwF1bSegdfGFsJPfcI70cA8UNQBFC+DI9hfRtPhcBKNVyKVNyerQxocRrqrHwktuQDTRhJq2Zdj28E1o3/gYamefiMPrH0b1jCUIRuOiFUCytitaAMOHdyJc04DK5rmeZagzov2sDMBQCuNbK3mHOqaYZUIDZtsc5i2qeQdgysRbjKfNvOfnMYOZzRfMEgry1xrczqNgCwouqJBlITDrZILgTy0Cpfh4WRpmruahQAGiRS1BcWDlA5DCBqy5RSwSUuKO5tdLGKBrq9sqBhBBIF9MD+UTiFn/B0RBwCYi2Hgl7AKIBjZvqbCExlhNM2a97wuOdcAAapoXoytei951z2DalOuE4WRUCAAZN/LM3WMyXsQeIRcEvVT1PhbKlKH5LxAcCLOlDmtOMkadAUHvDUq0bhSZcILm3Ht/5GGjaIhuLyqIKkWpSFWyaELuiDVS4ZhY+Msl2o6a4a59yrVP2seiJbVoVL8iFgoZqUnTuu2HaP16iEGlSUp9tmdFnXNO+LvuOB0nGZUWHSjtwO1K8YoXkrQUS9ntvo4aGnS5KbSAvPl1jwZoPgJMfv1+qyumkU8Znv8Jyc9kECEUZ+5lWpFiGnVqZhY0/MQiQBdFmlk3F3b5FlCBgAoarsgoXCYQ0JCJYYc5Mpke58FQzShz35vH8xto34bqxnlQuMn5KgbHcNce9Ox9HUd2vGC2rYUw+5RrUTd9Zb4ygOcrSw33IlRZh4GDb4IbOupnrzaZGubcyMzTP4pooskco4oE5pz3OWx/5Gd5TT5D99YXMXX1pYWd86D0UC8G972F1jM+COYBhbOfu+IwwczSuhKHZc4cbT4zzKEJjsBm0BmI1pnCfwCB2VUzZmFLY6taic/yGxjPlzfhfi5BIWuVLbwPi/H3qyew7snq73jX2fUTBp4rMMetmPMgc9rhisc6ZUi0vB5khYRU0o6jZIEgEECB74Wd6C5fzM6BkX8u1IorJLtzCylkvOxTLngFhVfRCCwKU1C3/Ey0P3cfcr19CFST0GO6WCe1+AjMvgQGNB4JllC637gyGPtm/n20q2RckXxkuQCIlZyHucC4U58QEUom4UiLaHmllo0ioUJl96mH5AnFikUTEp/dsdNIu63FUnLveT7nml+hIDjkWFWZ7v+9L6Byh86P0uFdQe90R98Tg4QPfOADJV/z3//935gyZYrv8uVbApiLuSrCyFJSM2LSMEaPSwkDKHMQdlEuqthQAso4TxoJfgPO6cKwkDIViTgZqcWAFwkpStOLZ0nyM5PpJxCiIN1wvOtywxsExZMMU8nl9QlYcVKX5hIg6Pyh4xUYczpjBJjgG0CJWk2UIrAgQdgh90mvsRjW1HAv0sM9iC+/zH5m3OAY6tyJhtmnoHn+ueCGDi0YhRqOiPUyIJseRe/+jahrW4nefRtROaUNwWjcHAoiBFjOv9Z9RxNNaDvzo9j15K8QrZ+GwYNbRSGgyJrV/eYLUEMRJOasKvhPJ86QHOYGZWnrmZEX5vN9Fyw2iiPYWTAdtwbagqpxxuw6bObAEhqI5tfSpCqEARWtCdyBBAlWgXde3WWFEbWcgYtmSPV4Vswwb9YvM2GEuMsZ3vltCxyuuix/G4ux16Pi75zF2OWFG8Eq6GIUi2kc3cIU9SVIzD8JR155FN2bnkfz6VcIZQRtck78TSFCDI6CQIDxkbEtsFgQx18l5/J3EQqKP4v6CVhl3Eofuj7K/BGoQKCIk0Joxs98YFwUMGifyXGmVkdgwBysTFyEatJ26D1bViPzmLngQcfovWP+3vFSHHaF60jdxfbUstsql893vV/l3Jsf36/j9N6le++9F1dffTUiEX/mpttuuw0jIyPHSAgoVuno+GW8SMlx0YHYrwOSYDJm5b0UHloq5z9vRr+oP4BfxqVI1kSW4w7PzZzmjKDYsEwgACCP/ywTDlwLEZcJaUWsIVKnPbqPkX5ZsBAvoj4RCk3a5VrMZZuxwLgWPFdSLn/9UPt2MKYg3jDbZjoyw73IJAdRPXUBQtFq53K6seaPD29+DADHlLmnYstD/4EZJ7zfaV7VkJixHA3zz/BkFuMzFqJ27kno270BXM8hMzqAYKwaMuKcY2D3RnRveRYNy8+FEggWanLz2n7OHNgPh7kBGZojHFgCgMWsW5YZS1gQoBeE6TNhQSakR82ITK7gu2EJCblC7bOZHIwXCAVKllgUCm7eOWTwqanjDiOvB00YjRFw7rkUZ2DDIyOwF2rDSZjFvc8LNyGeclsA9DAXEuQJUYPyfclFHIuAFXqStifMWdWZ94AYScaCf9H+UQsJjT7FyX/p/m4wNYDR9t3C/QIejFUxxbdMqe72caJrf5GIP9QiBB+MPyW/TqM0OznLqeAk46+gERb2K8k+oTMXJEhyDMjvQablLwYJmkRiOi+aG6dYwjKnkBgcpFhb1t6pZkRrAJ0XSk6EJbnnuLx+8dh3RDFS59Asjop9pWugucreFYqQSaW81fUdbf89Qj/5yU98M/V33XVXyfWXnCzMen+EyAvFMvPB9UL6iOowHpXiLS9sGpThLmKq9JvBuADnX8RZWaZVK2oxkPTRjOJgltPDYgXaGL3eOcxGxbqkCcgMwkd6xZy2++njPFzPW2YM0cU+yzT3lGjo1QInYAmjJYuMYjIx5kWDHdtRUTcdmhYGuDl/Brt2A4yhsr5NiJjjbjOXSeLIrlfQsvRCHN78GBhTkJi2lDDQDLNP/7gzXqywry0nXY7eHesAAEMHt6Fu/smem1M2OYJ9a3+HkfZdqJm9ElNPulgMk8pMhjDfjGgtY/nf3PlpMWjFYsRbpGTh4PPzbVkQINsnwLWpUwbSEjwUGiKUaFgta1ZRxquYBoyXIBi4rlMsuIsC2wnYywoiXGYxrQoZUFKn1/PzZCxdAhzjpkXAZvw5KwgPagRQIFzZz4/lYXJ5TS9XAV11IH1cNSPJ0CzD1E8AEJkfwQLiFs4Y0L/zDRxc+0eEaxsx/aJPiM/WRRRSBABwJfESBASaOMr13gmJuzIOFLEsx19Xu36gIkxndrhXJc1E6BEVgokQwDXuWuOoZFakbxYVCYkpRhUizK4mn8Oy8ciFmB04oVhUoAJrq4T8arr9MP1A4X4iuw93nhHpEBuuPc8nm1GQNKxMBpNL/A5LIUNlQtLK4/TepqeffhqJRGL8gnl65JFH0NzcXFIbk2IJKCfCAJBnROn6NwkCQklUxkvnF4/s18nIL4xKVp8s7Cggwp8oxKZAIKC5DIiFwB2bXOiPz34L5GPsiiZgkQkRLobeIgHuUwALIhYGiifNZaCFYsJ1w737EK1ughZ0THJem4WeTQHcQO++N5Ac6sKc069DIFxR2OE8Y0gZISuikxqOIVw9Bbn0GIYOvG0KAR506IW7kezrwMyLP4f4tAXOveeZS0sAsDSfBbkVOMxNT3GcuMHN+3Jnqi0gBnAwMNMRwpknRNtvlaPjI5TRhZ+C4FBgKaDnPUjA/YMXDRFcLun5kJyWgOA5lzlzkke5u+DiesfDvQvJ3EIcSsqR8ExHYOZcSxl/iM7DXDEZYxpVKBdz/AVoRCbOTGaRMvqGRt4Pyvi7mHvOgKF9W6GGo5h11VegaAHn7yKPw64v/34a1GFYKChWRaMN0bktdSQmx0IEKZ9a/mIkrEtU+CVCDUsrQIBqIUg5meBBSXXVLe2AQ2bEIO8KZUsxhQcVNHMs9uSJEHdNSB/k18ozWUT3jXScIThUOrMvdZA/Tn9xtGbNmpLKn366JKpgETo6eQLIpNSS5b1kapYDRCNAzaZFFYRkIdCDijRaT+GFkuNil/tZvOHSQLusBULYU7e21sKk+9SMuClAYUJRhZwnAkFMrFsjEQuo5kcnmFSqHdRDooaI3gOFdtEyhsakjHtRiAA8yrmYEUWyUYlhXV3ZKfN1Z8YGATCkhnuEa0f7D6Mi4Urg5TEvDN3kfpODnZh18keQaF0sFiD3QP1iqOAFBkTqWzHSvhPDh3bAMHJQVPE17d+zCQN7NmHGuR+3BQAb+28QJifPjOoakBsZhharzGvtHT6VhoJlgO04bUNz8gyv9ZsRBt9Ls2hrpPNj5LXJUtO9fZ2lgXcza65xs8k1T3zBNfL3owdNrbMeIgmySghYYPWpUJNPJ6ys347muFhZ23k7T0bYEKFAIW7HnLeYd5pjgUbO4qooCNgWjvx/Ofpf3ufBStrHVTihYhWxHfe7VzljPga2v45cahTByurCe/S4BoANe6GQR+Ea6zpymlqXhQzJBZZV8i1bZ+BN3DUXpYKbZAFTUwycaOYNOuepxt41H2RzmToRM+ognGHg+WhEeqUOJeksLjL4FSV3WNlySLZWKzkHEsRc1h5hrwgwJxyw3/fZnUn8KDLzSglhQoX9Ps28cz6MQ+kqRdi/1VL8Jd8LxOFX73r02n8PUzKZRDYrbsBVVVWS0sWpNCEg/+AKMliWEENXpqk51tKsX6sDhR4VzWbsDiPqM/+BryRnxfwGZI5p7ihDSadgLkIEghHCwLuaoZGGqHBA4UM0y7LbGU8G2RHOq+K4ujde+5jUK8OSFtP2C0SiQiX7O3Bk9ysY7NyJ1FAXACDRugSmsxpg6FkkBzvRMPNkz7GmbapaEIwpmL7yCtS3rRKeZwFzUkSrHa1rwcDuN8ANHam+TkTrW+z/c8kRHHr+bsTblqB69nKB2becy3JWqgAOpLuP4MhLD2N45xZoFVWY97FvgQXDJtyFMJpesA1bUGF54YIwmQVCFHeeo+N4zG2Bi0KuzEqc/7wW5AKmyzrvc/OwBQrm/xr7OtVkdrniPLdxowMpRRgXtxDk5STs0b8CQYBAg0xGnwvJp9yYfiPo/OYsDw3KO+TmNJe1ICzChKiQkCXRumhISKtr1nOvbJsPMAXDe99C7bLT5EoSDytCsXGwztP1iFommU7+kwlfHvUJxx4MflHnUMm9UXgQANGhl+YPcMuBknaYKmmoyHzmsmvo5a5Ecn5IDzInGIDCpLDco7qX+3y+RbH/BYVL74aiF8mDUAKVE5bUTW4n/mNp2ThOx47GxsbwzW9+E3fccQd6e3sL/tf18iTFyYEDZb2PS65DYJq8tcvjUTlQAKZzuXBScm3lk2AloIeu+Mp+FllmwLaCuCMiUIGA3qDuylwsZNYl2mrKxFPHLxr9p1ioUiE/AY3k4xIIaFIvaaZTCazHSkZl/5Y8yM6dL6Jr1yuonb4cLYvOR7xhNgLhyvxFwNhAJzg3EKtuFrWZtAv5+wnE4lj1oe9CU4s7ydBEahaD3PH6I4jWtiLethjR+hZww7yZkc49yKXHkOrrwEjHbgwf3gmmKGhZcxWgMHCIOGg39r997R3IDg2g7uTz0fPKE+hatxZTT7sMzBBzKbihO264k4Xft36rruyihT4AVkV5Zt+LYbByKwjX5XlEBaJGkJNh99ImH2WyfAOKkTsCjnOx61riJDzehu0lCDCSS8AIEEEgbxEQNKyWYJAXiKhgp4fIO8ZExj9X4VgHDMIwci0vQFC/hPy3Eoki1jwTQ3vfQmL5abbwaLXnyTizwnEQ7jc/nhbTbyucuAuiKNN4FxPM/MyhIhYDwdlYptASHErpYusKH0mciJksrGSxuSKZSLzYDi+zbBDKhRkCyfEHajIUen6sFgXkd4OmhpcsFxJqFr2M5pwoJ3oPAJW8r6l6IHKk9Dr0EBP24+P010ff+MY38PTTT+O//uu/cN111+FnP/sZDh8+jF/84hf4t3/7t7LrLcsSUHZjKTdD61NbnnMzC2WY12qODvLJpjIiCAkMW5EFhiY/MYokXJEuxDoXuyFohJwfStbpkBFwRRmiTJ+PZyHDlZoaJO//hIhB5D5Fx2Em1/ALDTma1mKbUtOic9Czdz20UAXqpq8AYMJ/ssMDSDQtwmj/YYApiFY3jd8mTGsAH6dNSoZmCis9W19BLrkWLadeiVC83qwrGMbhl+4170HVEK2fhobl5yI+bzm0WJX9PtqGACXPJOVPsAxHqrsD9Seeg/rV50HVGbrWP4nahScjWFMnDpfFbHowRxZUhwHeAoBbo+h6Pswo4uRLBAEhQ6h1D9Y9SqwCnPnTfFlljbyWW7cjA3Eo+agu7rCZMpJFr3HDRQT8PO2IR7/Ga88Jw8nAAxDyXRgBcg95Zp55rC2WlcPT+pO3BtiWA0WEFOUqiCBA6rdgOZYVoWL2InQ9/xBy2TTUYMh5nB4O9TKmiiJdDJf2X4D+yJw4J5FXKgjj6INxlpGSZq5EZpI9Q4hyRDoQcbIP8wAHo4nC6Lhq3gOQrQQ0kqDxaCm4/GqiC8LQFqNyOlvGPJAK8uMRDXE9Wpg801cVruiIhTBgn/zSe0FW8LtwH83232P0wAMP4He/+x3OOussfPrTn8YZZ5yB2bNnY/r06fjDH/6Aj370o2XVWxZnPFkOQnSSl6v5F+orwQog5Crw0D5710/K+QxfWqABldVf7J4LoEv5Oikz71ow/M5xIS8B0Y7IIkG4TaCyZGSy827tvK+IQfQ8Gc9i11MSxsbFoIZiCbQsuxgHNjyAcCSB/va3MNRthjecd8onMDbYjkhFHVTVAo/Tev21KcTldkHnbKy9oiIQrcShl+4BAMQaZqB+2VlgAMI1jQhV1YEpZkUWI2Rhv7lhoH/bBlS2zoUaz+MCOWCMjcDIpBCqroeaBqasOAd9W19D+4v3o+3iT9tzhIaRZDyvPbSmGBEAOADNzg1gli0QAFxkPZ+izK5gASsUBMSyxdsTipaxzlvRdqyMwJZfgzTHRl4gKMD5WyRgliVz2q8gQLhjQRBgpiBAQ1RyFQIUqKDPpJzTEQgJ9CgciqsOdJ0rJpNjQ4RIRuLYosXgzz2A9ifvQPMlH4URUQTYia08J3PMGgNK9jrDTQHE7q/snWP+BDhKUhiS17wj7diHdNr6KANAWOeFRGG0PYoh56zAZ2Bc4rChR+nGHLQ+ZwH2s+HbiefGa0ZhjoKqiGWFjoGakcOH3dHo/DxPWR6JUsjvNUqusI/l0Lvewfo4vSupr68PbW1tAEz8f19fHwDTGfiLX/xi2fVOunpcgAWUGwJUFyViVq6A4FpL7TqKrKmKkIDK/+IrxMMuwx+AUoEwI6B3CBNMtPjMELX9wkbv1ihYVbhOC6FRabQRai3IOeagAmuBBPIjaP+o8EUFJJ8C3GT6kVjXT51zGnr3bsD+TfejsnYG5p7ycfQc2Ihdr9+Butbl0HUfXnNEIDE1rNbmyArKWWTNcc4ApiiombUKoapagAOJBSc5ycUoisDyu8jH7s+ODODgw3/A2KHdCNZMwcwPfxlarBJKBuCBKJRACOn+LmAGoASCmHrq5Tjw+O8x0r4bFU2zCm6DE7iI4GvATAHAhmZxIiDYWmpyzx5zWxoj390HN3PGXMfvgKbLhsoUC4fojmxjWwIKO+zW7ttjU4Th4wovFAToO2dph0tlhNzLg/DuO8cUPmT9tihTZTJ4gXAtmq76ONrv+h2gKqhcsgLh6gYEq2uFtqx67ft2dcliFK08Dp7kLEXelVi35zovWAilgmlh+YK6uKQunbkcVmUKDfIsaYMpBTzkDLTQRUl/WU6enVawDEjW2WLOwe9BpalJpN9KCbH9BWhwmRAgoT42vrLEi3JhJoTypvt4KWHSj9NfBs2cORP79u3D9OnTsXDhQtxxxx046aST8MADD6C6urrseo8yRkYkygBqY7ysF4zpYghAvww3pZzLgVVkkn3W5/cSSWSgYlYL3+UM7wXCTTI4kV+nYiVDYUJEIMh6V2AEFEGTTzU6hmTcBEuQZAMumwhz6pWvgTEV80//DLKpYcTysJ+qupl4/YF/hmFkkRkbgK5nHWtACSRYnCTzRM+mwTkHN3TULhBDgnKiERU0XSqQUw3svfVnMHJZtFx8LTqfexB77/w52j78ZQTVGJiqoqJlNkb27QBWnA8AqJ6zHF3r16Jrw5OoaJpla7tpWw5zanUceYaHgXHzvbUyz9qRg1xOvxSqw1zMkvT5Fn0nimu0xyPOnIhA2QpAj3AbOsUNgOUde7k6vl+TE4ffW/VIw266+0ov8bJkMTdWvKBuIggwnrcIEMbfpeWnjsIF65SMoSQOse5nB9J3QSBizjUVS5ah0bgWnXf/AYNvrUegphaz/uYfzD8DcqbanfjLD0ZcVL54lym43s/aLZtbhvif0H9pjHlvSyTLwsbsK2lFjCJDF0o6H8IEEhQU/UMoyYTJVD1HuMe8hoaSLbje77vlY7tkOofbr01aHS12jEKGlxL5h5IQ7CJpriulUjbGhOAck4HRKpag7d1CnHvqiY5p++81+tSnPoVNmzZhzZo1+Pa3v41LL70UN910E3K5HH74wx+WXe/E4UAFmxj5q5RNWhbqq4SHJYQHDZQnwstgHMUkb4p5NqOaEEZbcgNFJfky3mEBnqQ4Vbh9CKTt0j7rHJDAcRTJZkqFA5UIDbrLAUuIKkVhQhI/iqLwIcmtSBOUFXnzg+FKBPMOwYwD6dF+AEBl7Qx073sdqeFuU0AgjDF1XBaIMrPkMBeyOChAN7Lo37ERg3u3YOjQNnA9h0BFdeG9EOYuG+BgFn6eA8n2/cgM9GLGNX+DysaZiNS3Ys+dP8P+O3+BWVd+EWoogsqWuWh/8X7oGQujzTBl5dk48MRtGOtvR6SuqYBB5Na9MefY0hJyZmqnLNgIAwqgW24IkGCVYt5lzD85aDQhGY0rJDKI1oMSSQ8TBUVe2yw4dRLiCpfnDci/LF73D7iELfIMqCDgKfwoXByDPBSIM/PYcRTmeagOs2FLUkbZvY6T91HxCI9ZUA933gMOjlzSBJ8r0RimXvspOQTE/VvC+Ethg2Uwh758tsj9eN2rd8Xex6L10yNErEe/hGUrq4AFfNwoeUjMJVD5Idk7l40yO6x0MQfVYkKYdNpNorDhpoKMxeXU4U4gNglUrmJLfIeYaLl5L3K1x6kk+tu//Vv7+Oyzz8a2bdvw+uuvY9asWVi2bFnZ9R5VS4B7gS4npJY71BeEmPT+J77okOVieH1QUdPxZJNMa1WMJDIPtQJwlyZc3HBLGAe7PtIONVsWwIesix1Ox8xA6V2XG6dsH/p0uPYFExqnzGj/ITCmoKZhPgA4QoBng86hYGlxa1FJ+Z5tL+PwS/ciUtuEqSdcjOoZi02HYLcAnP+dHRnEttu+j1BNPeILV6F2xRkY2r4JWqwK0eYZYDkgUtOAtg9cj713/Rf23H8zZl5xPYJVteCGjtzYEJSw6XAcn7MCgVceQfeGZzDtgmvNpjSxXdtpj4vZaK3Qnfb7YMCJxMS5v7GHtxbc/MNDEKDvnkzwO8rvpqFxO6wqDctpt6/CZPr1vFbeQwCgZN+7pfF1a5h1Bihyh2r3+NFyRpCLMCEiSNoCnv2nR7+IoGuVsas34Kw1dlggUp4DA88/jZ7HHkR0znxM/einoWiaWWWJ76WvLLRFxkEcT+/zxa4plSx4HADBuVfIeOxqhEvW3aLvkXsxsUgiXBjxHNgozR0gm5TOIQ0ZW1LfZMQlfS6oXKIB8kmmY2/pD1GEz/q7RtGBHE3COAnzyP2+64Ll/S+I0afrzDvV/nuEDMPAf/zHf+Dee+9FNpvFeeedh//v//v/MG3aNEybNm3C9ZenLjfIp0yyYlFzlWQOphq8cupkzP74rc+Km80VS+Bw+lVOnxjn9qd4wSIfSX2+6yYkjElBX52PvJ/M+RRUDs+X2UpY5f6IZbj9KWiSXkPaKDgvvedCDbS7v0LfqGN4vl49l4GiBvPWAYZcZqzk9qgTsJAQDEBi9ioEYtVQgxFMWbLGjggkXB+GDYvo3bsBXM9Bq4yj8+l7MXpgFwZ3bEbVnKXQMor5LnIgVtOMme/7AlL9R3Do2buQGuwCGIOSzw0ABjBNRd2KMzGwYwMyyWEzYg4ZUsfZ1Xk/rJXCEiq9oDle82i8fbzgGXnNMx9M27j8Qv6ejKAp8BhBmNp2xfw2NJN5NoLjv1tGgMMIOAw6jRbENV66ACB7Bw1JBV6kkjUUAM87OEMxrQO+HV1pP6w1npvvCNNhO0nbx/S3bpaNzVuEUHMrxnZuQ3rfXs/3X+gCd31IeeE9dZfzGjfXOkrXc/ujuD5eZcrU/lp9UnJ+10HJ+ZRiPn/rIyF6HwKlFEA1nM8xIN8OthkurMO+6narK33uzbI5445wVrRtMsaGKn7KoVS96XhtfWRzoBQqN6HocXr30/e//31861vfQiwWw9SpU/HDH/4QX/3qVyet/gm7vSi689FS/ha+8YgrzP64GWThP5801qAJjFo5Cz293ndkIJ9WhsILyUfCaAOYsCAmb9+b8ecqEzcdySIsZ8I5ZGBAa3Gmm3Q55Ps6KhBQhoJzGLksRvsPgXMd6bEBAByhilpnDhbrow/BjjNAC8cw45yPYaRzDzrfeEL4j0btsZiUwW1voHLWIrRe8Ulo0Up0vfwYciMDiM9dKtwTV4DI1FZMPe0yDGx/A9Gp06CFYzjw1B9hcN3uc2qgC0ogCEXTbOHAxnWTxFcGSZql5PNpCOEeKcNVhOktYNiY5B0uIuBSCFGx9tyY/FIVikbAZDqsNULmUMg1k9l2h2T0nPtwjUGesbOYW+t4UqIfMdc3TMHF6oDb50CqCGDkveQcyQP70P34g9j7k3/D7h/8E7oeugfpw4cFYZ0ZQKiuAY1XXG1WoWqe7xndN2R7hnvfkK0rbkbY0MgnL7DpYRMaZX30oPOh54V6iwgPfsitaLA/kvtTMgzMcD5iZ8iHUsAwhU7LCqBy50MvDxrOR7L/+cbFk3Xcshh6zSHpPuC3GYUV3//yVBBFqMg+44esTMSl7kN0XJWsWIfw/EuIYsU155OpUmAEmP1xC7PH6S+TbrnlFtx00014/PHHcd999+Hee+/F7373O6kVsVQqCQ5UlhkwT+5JSjWjNBtt2fWXkSQMcC2EtIrJGd/JJdInwXm4HC1AgcWBNMO8j6UwJQrXcmv2fXRNysy5HO2KhQe0DyXPTRYGlkby0XMZ9LW/icPbn0ZypBszll+Bod69AIBwpRhX3yLKIHo5vx187X4Mte+AGo5CC8eghmPQQjGooQjGug4AYOjbsQ5TV11ox0RnBpCNiPfE9RzUcASMKaicuRD9b74KLVqJWONM20GTaqYCFXEAHMOde9B80bXYf+8vsfPW/4eGMy5FuKkV/ZtfQeNZ74Majpj8KEMh1CcvlNlMvrUps7zVjGj/KWNebJ2wYT5WKFArkhCNKMS5K6yr0wffUK9JVoxxDQ7cx+t/Gc6bkzGxpbv8fx5Mrv2/LWjJMeTWNfaYKFx4mbgKMX48EQ5o+FAhrwDz6BfnOHL/HRha/yrUaAyxeYughMMY3vIGBl59HhULl6H+/MsQrMlHADKAdHsHACBU1yhXAsluiz56ia+Ym7k0QtzzGgGPL4ueIzxSD+bbg4RoL8K6LLmWVGsxiACg6kxkvmkFsnELGkDaW4NFBVKV+BEYKad8siWHcIe5cGUrgcCwpB2/JGNGfO7J4/r4WP9NEJvPDP8RgqR1uPahyWC+aUTF4ChHJvZXoNWfiHQ4We2/R2j//v247LLL7N8XXnghOOdob29Hc3PzhOsv+ZVQaLSb8rIUFxB13lFTXMqEliKE0I1HJc6o7jjtMlKyYj+YsKCVLiGUE8XIvHD8ZmUWh6IRGWQMPSAs6qIPgYsxs4i2rzKBISzLv2GSSJifdKMmggoDMNi9G0f2vYre9rdg6BlU1bVh6dlfBQsEsGXtT1DTtBjBihqBiRqPrHkWSTThyNvPA9xAIFoFJRAG17PIpccQqqpF88mXoWb2SkcLDxRosjkDIo3TkOw4CCUHVM5YgP43X0XV7CWAqpi3puQ1Rpyjd/1zOPLM/YjNmo+aladBqYhgRs3fovvJB3Dgvl8jWNsAgCPSNM3J7Ew3t7xGlyuwMeBK1mEqrf8Yz/OrdgQahvG0b6JQyUVGIX99KUItheLIyMhrcXVLAxzm4Iqlxcy3x7hnVJ8CUnnRRcnLkVfQ6roFAOtyeo313uSZfy/fErkVwN0/55urjhCjh7jNyHIFAM0xoDk5B7gCDL70IobWv4r6y69C9apT7HwV9edfjuEtG9C99iHs+9n3UXPyGtSecS6UUBiZni5oVdVQQ0SaLTY1ZIy/+zc51gnjr4e9K6f+G0L0HbgZdrOcERL9KYR1g8b1p8KFLPyjz2ksBNOgwRJIe4ZL+JS5BVBSNSIEhGQShT+izsG5iILA2Pj1McPx5/OL1S9ILPhOKeZkirAyyZ13oRQ4kowKsh7nqzRKgREep3c9ZTIZRCLOOsoYQzAYRDrtI5mHD5pUx+CjnQSDZd0CQumTPRcRr6GOnEKyq2IvKY3X68pmLO6/Pl90v4xyqbdruPpANRh07NzKe4vRcIcKFZyMyeV0cacCgVZk46JzZSIangIBxvtYJkAaho43X7gZ4VgCLfPPQV3rCoRjCWT1FLY8+RMEI1WYdco1ohDoxbhJ+lU79wQEqhPYu/a3yI4NARiCGoogWJlAIFqF1EAXRo/sRXXbMgFmIzDkHIhOnY6Bt16HkcugYvo8RBqnIb7iZBhBk4nIhjgyvV3offFJDG15HYlTz0H92ZdAgQKuAOHmFrR8/AsY3b0d3U89aNYdDTsOwO5hI86eStpkpBkzT1sOapw52YStMS4aIcRrvIj2XwoRktVFNef0Mte8tYQVv2Rp3pnBHIbcBzmwHuZE8LFxVa7+0u9xhMvxBIAC6wv94WJm7GmscOgaTGZfMRl/xYoARUKLZocG0P3wvag+5QxUrz7NnC95ppdpKqpWnIiKhUvR98JT6H/xaQxufA01p6xBpucIlHDEW7jx6Bsg17BSxpj6bOgVhHuOuDRSObMCPuLOQEXqpaepskOI5kM7SNY/WSQesTWpoky6V0rOK0nV9O3w/JMoNSTWjkA0i2zKGcjJhI9IrR+Qa+9luWOKkbvPUgd3v/sk1V2lxezUfolacVgOyJWRKdhNwnjqYhu6j4Sex+kvj/73//7fiEaj9u9MJoN//dd/RTwet8+VGya0NDjQJDH5brMchTGUzQ8WaN+cF0Rk6H0yGar4sv01Yu6kz9s91hJzLrVQcJVJmWfhevqHX+iHVQ/dWAo2DO/K9Fwa4AZmLLoEiVYHX7/79TuRTQ1jybk3IKCG7c1ZYE48mDHAUsY6f1Y0zcKCD38byd7DyI4MIjM6gOzIADJjgxg+tAMDezYhunCpLWgYJI+FdU+RhmkANzDWfQix5pmYde3XkFUz6H/rNYzu24mx/TuRGx4CCwQx9QMfRdXSVQAXfQqZzlAxez5is+Yi092FcG2j3W/3hmrfC2DDRbgKsIxpuWMGt7GztkMp54UMaYnkpUmXEs/3j2q7GRMj2EwGKTQEsLcVwD7kLG9JIeWIk6swPm5tIxVm8jdGoUBlY6tpO26sOIGPUM2iHjXAMgzDG18HU1UkLrjYSZhmCxImk6uEQqg792LEV56MnicfQu/Tj4LncohMnynkubBICj0kz4wyPpT51aPORFXjYnarcNgx+aZS+QqiWei95IXyspK4OigqK1zaf+6Ut94ZIyCWkyk7/LwXaprZe6M6ymAQC4cgBMjqSitA2Gw0mwwgEPFOBHDM9jNBQChjHy5nztNw1y6YpO8qKHwULtjnJI+dTjJu/7XQuAFJjkH77xU688wzsX37duHcqaeeij179ti/WZlweKAMS4CQAv4oDKQ7CgAnZmpBQpiI0611aLja82ldkZkrFZ2Xrq131SFvU17IF9RoEhcut7ZWho6QO24SZiMg2TzL6phzSMOpFas3PdYPAFA1h1EY7t2HvkObMfukaxGpLIza41TsbUWy8wHA2TC0UASVU2cLl+shoH/7ehx8/A/IDvchGK8tuA+LwrWNAGNI9nciMmMmlCzQ8djdGNr8OsKNzahcuhLRmXMQaZ0JJRQyq/Bg7s1uK7YA4H1f5NCKDsTMjdHGmSvM1ETlZTvFyI9HuYKAa36PB3sRhAVWnFkw8s56etjU9hkBbjO/3GBgqpmv1bYuFLNmuGP003v1MsNbY+76tv9j4n0I3+779KBxx5nnw60W8SsQIhvRe1M4htevQ3TpErBYCHZWWwkjrdXWYOoHPwYjm0Xq0H6oFZWe5QQsP2H2DZeDtY3zF+YjA6pN5t/IKojXOFG7KkLOAt7HHa3ZmEKEAB95UiiM0crBYBcrcV77gQ0VjZxEr6F7IRUO3QoPWV0+Om8EnFwuehjQkuNeUrAf+lIWUqEwV7jvS0kGyy1j37UCHdhVTAKMhubBoYy9Hp4E3wuI70s6wKAlCwUrYwIM4XF699EzzzxzVOs/enkCPEKhWSTgHsuUgMtmuIuQkLxKwJMTq0ApwgddnMvxnxC0Z3TRdzPhdHPwV7WvrMSClm7yNDeyMuU4bTGJps2Pg15yuBtbX/4NwrEEYolWW1N14K3HEalqQN205eblRZwT7b7DgJ5NQQ1G5FArcjoX4hjcuRGHn/0zgjX1GNq3FTVLVkNVAyIDnL9GzyQBzqFWVIArwNDIAQxtXocpl16F+ImnOE3ozphYUSmsY314CAObXsPIzrfQcO6ViDRPLz5WVh15RlTJEFhQmpvH3KXhZkxqdZEytBIBwGvc3OelgqZb2C9FG5hnAjnB5EuLUgtAQSeKtEG0/oKzoS0ITIKGhUJWrPwF1l8yCwM5Th/aj2xXNxIfej9yMQNKNt/JIIeSNgsKWYmtKkIBRGbNNsfQOlnEmZIy/1xiAeDEyTUSdTitWNCxBsSDTmD7Yc2VFt7j/twCjzBeMqW1j8ciY4oNzbVeyaA/ZDx1zfu8UWydJ300dO+CxSyak0niWlCoHBm/gtI7xwzRr0gKQy2lH7QK+kyyJoM/qeRegyVKtvc0Wdbjd7L94wSgRCFA0QE2ydnzlKwYKYhKz1rSmLDpLR0Xb5GaBgXe2SeTztzagzIWUCGjcBkOw74Y+HJJwuRS8r1pUMGhCCPneex3WMp8mUcHO/DWCzdDC8aw6PTPQQuajjdDPXsx2LUDc0+9DkxRCiMUEcbN6u9o32Hsfuk2pPo7oWhBBCqqEayoQSAahxaOQgtGoYaiUCNRqKEIUoPd6Nr0NLLD/WCqhkx/Nzqf/DNgGKhbeabTnOLcXmagBwAQqKsDV4H+l58FAHQ9dDe6HrpbuLfKRSvQ9P6PQs06E3Vw/avoeOxOMEWFFqvEoT//Bm2fvBFaZZXTiPsZWcypxehbsCDFFAqZpfXPw4JgOIy+NEqTWxAYb/568dbSeZlndCf5lSi2CTuRf2QXS+ojYyRzOPcrUEsjMzEuWidkkBzXJRZkKLlzO1gggNDCWabfABEcKH5ayXgzXG6mV1hbCCxJTORIzgediwMVDrMfCjpCgEoiVUQ15/zcRA929JkRveItgxg8FC/oQ6HVp0S4SpEH5AdKKWqhSRkh+AZhnIlQpGSZ7ejMskwYK0oGdfiW9DedAEJ9kv5KKBt1nIO5ykoOh63kXNl8yyEfe1VJRIOJpFwhU8tQUBkaEBxwflPe5mj4T1pBHo5NVojjdKxJ13XccsstePLJJ9HV1QXDEJ/0U089VVa9E7ME0E3Frfn3AQspuTmJ9qLghSqjPZYTnYQVyvyVk6mPOww607kvZt1dRnDq9dsFOhYlWgU4Y772QemzpcfFLAeSjVBWRiAK+XFnRfUjUHFgZOAQ3nrhZoSiCSw6/XMIhGL22HcffAMAkMskMTZ0BH3tWzDcsx8zT/oQArG4XY2hAtzQ0fHW0zi8+XGEq6dgxpkfQTYziszIADIj/UgNdEJPJ6Gnx5BLjxX0T4tWIj5/JSrnLMLhR/+EkX3bULvKEQLo8xtLWUJALYwAkDjzfETb5pjjZAAMDFAY9KFhdD/9MLrjNWhY44QV05OjAOeY84V/BDjH7l//O3pffRr1p5wHJRgy47m7GTbmjK35fgBQgcCoJRzktf625jyvFWYs72dQokXA/agsJq3YXCDWAMY5uEdhO+a/ZjKZ3Iqjzpl5bLA8Y87NMecQICAFfYKHkKMQhtt1qex+Bedm5rqgBHJHU5NqsalQYDk9w0oIV9hmaOZ08GwWmUPtCE1vkTqm5iocK4ERgu1gDEB8HjIIC2X8iVWAOrm6g0h50ZFkBZpjg95/0q7L1hw/w+4SZOg8oYokIeKPxJLoh4ml9aijihgFSeYoTAU/QenllM+1pqC2j6/CLg+TLzltlBb5y+wAL0/TJtRB+mBBGidAbn6DQqYUlwvGZFtZ3DkBjOOOwn81dMMNN+CWW27BpZdeisWLF0/ID4DS0YMDUVLEBTJHtEhuj3xD9PWaVBqdWp5ZQdBUuRcxocqJvYTlaPiPplVAhIl4w5H8+QC4fk+gm7JoHYpwnl5QWEf7rhegBsJYfMb1tgXAotaF50PX09jz+p3m5UwB5wbSI72CEJAe7cfu52/FSM9+NC49B1NXXABF1QRBUiDDQJaloaeTGDmwA4dfug8sEER83nKEp7aAKSqC1bUuJsM5zvT1QK2sAguFwDgQrm9EuL4RI2+9iVT7AaiRKILxWkTnTEe6qwN9Lz2FmqWn2LHbYzPmAM9wZPq7EW2ZCSUUBtd17Prlv4HrOto+/lWEJH4C1sZpabyNQN4iADNBoGUtUHLjaKa9SLLJi6FEIYyLxVQX3cyJAFOOaoznhQSZlr/g3jgT5xqxrgiXu7XPHOIawiyBvPQ+exH1YRC6aAk9LmLcea3Cs2dBqYhhbMNmhKa3iHPTlVmZE4GPangFDbFkboshQEn5tAKEzY0jm9QQiJhS/+hYCJGwuVEMjEVQGxu1L8kRU28mS6NPeAsjcD9Dq6uC0OAIT274kwBBlFiTqbJCgDzKFCjuOhXXb7syiaBqeJdhLkGPCn7saGKCKE1iMwV7MVWy0GO/e2kRCPNkwHCocJyNMYQHSAhXsm9oKY5c+Bg9j3eKOJu8Ra7c9t9j9Kc//Ql33HEHLrnkkkmttyQhwP3cjvYw0gg9atooAiMo7w2lvgmcalxSXIANuaV7aX0uRsWpwFXQB1PihgmJjLdMw+o6T7VRPvwGCgQKKUyk9AXW7lKQmnO4v0lUxOLkVUZ6Pt+WGghCC4QFAcBiRILhKsw+9Vo0zjsd2dQIFFXD1qdvhkrKjvQewI5nfw2maJh/4ZcRbZnp3QVyb3pUgYIIlEgEVQ0nIzxnDg49+Hvsuf0mBCprkB3sQ+yc99n9oDhTZgC5wX4wRUVudBiBiOlwmRsbweE7f22WCQTBswQfvWw1AtU19u9QQzPUcBRdzz2MilkLkB3oBVM16MlRMFXD4QdvQ9vHbgBTrRAwhffBmRlKz8qequStZ4xzgVHncKwAMkFA0I5b8yk/lwrWZy8t+ngWpGIWhFKJ1un+i6Oww24m312Pm2g0I148OZgfkkWtAmAzwO7nYoZDNU8oubwlBxqiS5dg9PWNiF94DpRwWMxEK2Pq6Trox0LM4cCAckwQMAQLABkXhRyHNYfLHiEapkzawXP4ikDGuA2/MS1ZpU0ggWmUWAUoiUnCio/beO1ByGFA1vGsApa3GJhO8KVJxHTcchG3I6q/60vlt9w5BWTW5kmB1Ph5P8chbYz8cA2P7yzMZZIepMcMSg7QjzpndpzeCQoGg5g9e/b4BUukybMEFJl3BY5jRzscFnkRJ8NEJotfrWZ4WUJR2YnDrD74YLwn2kbxyktbZGQmYCPorx6Z9r9c7UwgGEM2M+YJ7bAW7YraaQCA/sNvAQAU1Vxtew9swp6X/ohIoglzzv4UApFKKyS5R8dJt11a62B1Ldo+8hV0vfgoUt0daL3iEwg1tzgh5F1zLr5yNUZ3bsX+m36AKRdeicp5ixEMViAQTyDWNgeNl1wNfWQY6d4uGJk0KuYsALMCmHOAqQqa338d2h/8E7qeeQg1J5wBI5tGIJ5AfNEq9Lz0BIZ3b0HV/OUFTKwQ+pTCCzL53wYz3wULSuMaBvc4WwxoAYPglSjMi6HNX8/p/0z+XnDNGU+uwMS3qzzfVwbO8p0eT6/g1uJ7NlbkenK+GHM0XpZgz2t44TjbTTL5sRjKtFBrXLn6RIy88hoO/f2/ovriC1B14RmFNwOIwoEqas4FBtXdaYtoZCCqoSbHmupsJDURh/MKEjPzkVEnIpGeopMV3qRCjLpDScJkypx7Bcaf7nFU/5IRj6mwLyilqPZfsAxIJinx/2EF3LH3XJIJbsXoqDmmllFvYRhoOrkn3lEh4WhKPC4np0AxKriXIuFUhcAqRxE5cZzeXfT1r38d//mf/4mf/vSnkwYFAo4VHKgI0cmvjYmmZBo1wp1xrxxyx+KlUvRk5CqQausnYeEsFfZTTsQg4Rq6ntL7cuG8RVO+RHsjKS+1nPgwsReQjIHz6IcWjCGbHrWFGVkcac4ApppSwaaHfoBYbQtGuvchMX05Zpx5DRQtgBwVZNzMF7nXkfY90NNJRKdOh1pRYXZNVTFlzaWe5YVb40B02kzM+NI30fXAXei49w/oAEMwUYfsYB8iM2YDCoNaVYVoVZU0uk5s5lzM/Pw3kDpyGLFpszG8fQsGNr6MnpeeQOXcJYjOnF+AainA0xJG03b8ZGLflQwcQVGYt/A8du7fhwBg9YlYJzwZPOb8x3SUtdLZUCCXJcDW/tu4pPx5K8mbhNEvyBNASbAGFO+TwOxTxr3AgsLpV2lkMPBcDmNvbgULBcHTGRijo1DSjDxrJmD4BZiNHyFGEJIJ408iAakBZwEIkmOF3FSKhNCJh5PY250PtZsmL7bGHaaeJm4DpLAP6bjRMZclAyuybsmi4zGJskmol/aVOv1SeFZGEYWyEp+/HnT225zPMKHuEKsyfyC/JNP+u9eNcuY2fTbCMOtczAcwyRp1LSV2VpgjR9li8K4ja219J9t/D9AHPvAB4fdTTz2FRx55BIsWLUIgIE6aP//5z2W1URociDilWBuaRaJpuay+jNO2Sxqm2iIZHrxMckctUKROXmUstIaciZ+w9l6SEbhYvTLGX0bC2NBnUmTRp0y2LAxoOWHQpNeMw2xmM6NQA04IwWIRqOJT52LpZd/EQMc2DHbuRMvySzBl5dmmr8B4/VMAPZ3EoZfuxcBb6+zzjedc6TgAM7G8d0XmlxatQNNVn0C6qx2p9oNIdbUjOmMWqhatEIvLNMwcUMMRxKabJsXY7PmoWrQSFbPmo2rRKlO74GYyvawZ3MwibOUPUNIkgZhHtmPavrR/jHlYBpz2KXPtXCMpRy0Xk7GPu9u1KqXMoJUQjCZQgyMY0N+WRUEIDepqh+lMZIw92irsF1zCsGwieJ92n08fPIjBJ9ai8pRTED//HAQSifzmTU1n9Hq6NkgEAsm7SZ2AFc2pVCPHqXQAsYjJnQ6kIggQR6AsiZmZGc6/20EDSFLplPRVhveWQYAkL7sf2A/g2qOoMkrzLuMnKp6aciIEqUkRRuUrQdYE342yoMESIYvlygsRLXt/FMFCI1rsS3ZOhvlsRFQBEXrcIW8n2UrgJnovBXAjVnQrPk7vMaIZgQHg/e9//6S3cWwsAZRX1gCVYOwNmYPYJJFKIvsIjjkVYrmJts2OoWf+UQ0RatdLjv2MTYGQ5hzTCAYCCfH0vcfPl3OywUti9kb6D6KiprXoNenRfuzdcC/qZ56EROtihBINaFi0BgCgK8ybJyD16UFg+OAOHHrydujpMTRdeDW0WCUO/PlXUGOVBY6tRWNN081SYQg3NiPc2CyPVCOZim6hSdECaL7yY8XbJYy2oABQC89zhUFLObAgmkCMUlEojIcgYLfFnHsTIEqMzAdOyrnmMFfyddnWA25+K0wcs3E08e4ybqZcycHJLstd17jqZlQrTYUCBgGnLwv36VvQIWNnOiMXEdozGQw8/iS0vE9J/OyzEKhJOG0SSIqgB5HkgnE74tr3SKMFGQwsb1UwsirUkMkpZ7OqkAk4R+LejyQdQT6bJg0mBW0IOSZCFYXV8MJxdRNl0IV4/UGCCWdEs0/H20VCOGqyJ+WoQECHzO/aRp8LdUam5yU7/lgzR/Rw+XuJobqUZUdnWzJfA1X8PaH6dAh7lfCcJ3lPT9UwxI4QQY3mG3CHTD3OyB8nAL/5zW+OehulCQEMjjblGASjpY7BVPOvZrj4H4lAMSk5dig0KC06CTMqeU8CROlokR+rwoQFCKpyIEw5zZjrXkhlkYX8JNGRaXDoXBCZMzfHxcA5x0j/QVTWzkB/x1YEQjEEKmsQDJtYYs6Awc4d2PXirchlUhg+shvRxI0IVNeSemjjKDhvZDM4/MqD6N30AmItszH10i8jGE+g/bE7oFVUIVTfgN71zyHZfRg1J5xqJu2SwBMKktv4Zfisvnkdu7sts6JINkdbCOAmPtY6ZgbsBGLghClkrHiSvSLz0LYAUE0/vQlLAHBrwfNlTQFB3rS8YZdgIC2HPPzB/Gl/50xcPzOYaf2jiccoI+zqN9PhnT3V1R+3hcVXqF1ZgjAXDb34MgafeNI5oTGxHsHySt5nndxnlsCGdCbAU+xQlbrb98Gb8zWI8JPMEsffYZIQLEMw8RInWcFyI2P2ZQK0at4TYM5xVYbFLnH/ocw6DUChuxhdOTxIcq+0jO5n0fBJk+CTZV+vi/v4hNE3VEOuMdGyMslCiVC3y+pCh0VL4ZiStea968lDIXLM2z9OACbTElBEkyYwpO4FaZKTjxXE6CZMKLUKZCdBg07DetGgCywrj2Qk0SGXRUfLCuAmOVM+/rXZmLyQFDfqY4gEJzqKQqCRnFw4VbNqDjUQQX/H2+jveNsspqhYftm3EYpVo/3tp3Fw08OIN8zB9NUfxLa1/41dz/8ecy74HLRwrGifMqP96Nu5Ab1vv4xscghTz74SiRWngwcUGNkMBja9AgDY8+v/B6aqUGOVGNm+BS0fvx7hadPHv2kFGNn+FvRUEuGprQjW1oMpiny8mOSY/iaaZJkvAS0DiM+dCsha0mTIOeA4CgMFWHh5lK8iG5iEETLcAiA7Rhshg8AMW9GSzB9m4zaDxvOMGIUw5hlgxgG4tJHiWupikrzG0PrPg0p1MDYyWQw99Qxiq1aAhUMY27AJSiwq1qOIzKfQDYnjqgxfLdRDmFidOLomUzRUFjkcdSaflPEnJGjgJUnUqEBXjAQsvx+/JYniwA2JkdU5HlQQkCsPZDOA16fBuh1BqlSmPhdmAs5dnMPjC1x+qeDeZUqYckn2bCYhu3DZRPpUEDKUTl3L4mj9dTxT2F8UrVy5Ek8++SRqamp8lT/99NNx++23o7m52Xcb77hjsCxTr5YWM/OKWuDJF+O4BLdOnYnVjEvrXE4/pLhUl8a8nKzCsgXA7+Il0QgLRSR1CQ7dBP5TzKmYkiz2ttA2fUaSePwCA0DnlsXoMgUrL/02DD2LbHoUmcwQ3n7y5+jZtx5T55+JgxsfQl3bKsxc/WEgoGL2mR/Hjqd+ibfv/xFmnX0dYvXTPBnMnrdfwsEX7gbTNFS3LUHtGRcglJhi/68Egqg/42JkBntQtXA5oq0zoQc4Dv3+Zhy69Wa0fPKLCDe15G+isN8Wdd5/B/TRYfOvYAjhxmZUzluCxMlrCsfCo5+ccxy69ReItrahbs2FGDu0F0YqiUhrG9RQpPACQPQNIMw4MxxhTIAIQZwnCkkgVgD7kkC83HUU5JrwUB7Y/gA8/+U1RRgKJ5j1U4a1F8oSiYlEvrEiJ9nOmtwpZlOOidGVrP84y+di4Gbfc973V0Ay/yTXaS/fmeTOXRhY+zRyfX0wRkfR8JlPITyrDck334I+NIzI/LmoWH0i+NVXeUei8IFXEWBDlCmVXUokSpqYjGUk9UuVEz6wNAovVEjZ15A2qOyT8z4+GlZxNSnPWOtHIIAk6hN3CW/HQo3kl3meqOBerJ13SjvuDqlK82ZwWU6ZvwY6bgnwRRs3bsSmTZuQSCR8l0+nS4OolC0EFIv/fLSpUDNAFrmCEC3Ob0XI/DvxF1AGSXLj4ycaKWgyNf7SMIpSgUv+bCmzr5PjYungLUHAHalJRjJGUAZVKar9JtcyJYBgoBoBpRqxRAsGOrejacl5CFdNgaKFgIDJkFTUTcOCK2/E7rW3YNvDP8W89/8tIrVNQn2GnkPHhscRn70Ured8GEa1qbnkrr7Vn3q+7TTGDICHgKkfvg57f/Sv6HnyYbR87PMFfU0daUf/S88gUJNAqLkVPJdF4szzEJ0xB+n2g+hf9yL6N7yEmlMKhQAvSnccwtjeHRjbuwPDO95CuvOQ/d/0T30VkZYZhRe55F5r7M1Ms+axlT8AgICz9YqY44cKQ+blz7stfTyfjTtvaeBwGF9LKCibASCad6dfYvItaBxKlqwDGrdhI17+A+I4MMgsAEV5bCKQySA+BTkASBu5gUF0/fp30BI1iC5YgNTOnei5889o/ru/RWTufITnzkHPbXdAHx5B1blnwYtEA4UPplumrRSEAAot8j52TyC7OSFQBIEZuWP/S/wZZCRkBJasWaXufbJ8AGpanPdUCJBmIPZBSlKBEco3NKIBMaeyUjXH2aiC4KhzkfTdkj+ykonpLqdhGepgsnlqLlc8CfuPbkZROlZUMOZ0Pug4NpLdcTpmdO6554L7VASXEzr0qFgCimmuqTbYUNkx8S3w7odzTCOKABCgJpTRVTMTl3QEDb/LMc4+z/nEnX8lkYJ89ZFzDPTuhqoFEQpVIRitAlPM1VCI9BMobTcqJ+pP0WzNdsXe1/qxgPTseR3D3Xsx86QPYbTvEDKj/VC1oFCPrmeRTQ4hWFmLQEW8YJFN9h1GbmwINQtOgBIJi5BpMpeEkLQhgBsGjtx7OxQtgPpzL3VCUuYpOzSAQ3+4GYwx8F056M+amVG1WBVibXMQa5uDwU3rEEzUj3+jeRrc/DrUaAXiK0/GyLYtaPrgJ2Dksui89zaMHdyDcOsMs6DMRC6BAtFNWsvmBWRu+oTYoToljsKA+GwLtP6WQMFR4BhY0FdXXzz+Lo0ow20dutsLEEGAm4IAFQzsqjgKHBGLRZ2xhSqXNn+86FcFRNc6w0D3bX8E0zQ0Xn891FgM6YMH0f7j/8TQCy8hfuYZaPzC59H/8KPov/8hpHbtRsVJJyA8dw7UChMOxzkXNhuhD5KIRjItdclLmyvGqmD5o4wZGX+ZRUIgSbQgyviXDAHysd5xReyT4JhKA2jIQkhKmO2yAs3JYEoTjFrnVgrJsPrlBNsrRn7es2NO9JUoAnM6VlDf4/Tupr1795Z8TUtLS0nl33E4kIyKviDFwoX6jStfBrmZD7owK0RjxKjJPycmQGLFHCQl5MvJl5oW/QpWkjHt792BN9f9SigYDFUgGI4jGIkjFIkjXjcT8eYFUDUTUyrbpPzEixbDrjqHMsgPUESg8MgkPTrQjqEjuxGMxhGMxhGKVCMQrsTYUCf2rrsb9W0noLppId584icIxxvQvOwiux9jfYexY+0voYVjmH3pF0W/gHyZaH0LQjVT0P3Gs6iYuVC4CbdQkhsdgRYzQ1Plhgcxtms7Gq+4BuHG5oKxGHj9RfBsBtO/8i1o0UrkBvuR7upEZNpMu0z1qlPR9eg9GNu/C9EZ8myCenIMXY/di6FNryNxxnmoP+cS1J97CTJ9PTj4u58j3DzNtCYwofuC5l+IUW+ITKrFeBc4MXpt8m5BIB8e1Bov2o47r4TbobZgLVBd1of830agCMPsnkteBT2sAvS+qAXAbI9DITAWaR4Mom0UogF5te9ZmYQkNzv0zLNI7dyJqXkBAABCra2oXL0aA48+hooVK6BWVCBx6SUITWtF/0OPoPuWWwHGoEQj4JkMuMEx9WtfRmj6tKJ9teBN43abCgsS4ZNLMhVLhdUJMP6yMKHC3lLqMu6uUna9DH5EIxL5iCmvuCFlwv356EcxklwjvIsT5GEVnUvXf1kgA7MP5K/JVjBSBYgrDCj9TTMIZyoYIr3HgfoFxCdiop2k9t8DNH369KPeRmlCANFUilkMPcrZf5bTraNDappCg5zzepG4vpORREwgQTk2vlUAKF0T48Yayq6XaRv6jmxFKFKDRSd+AqnMIDLJIaRTg0inhpBJDWKgeyc69r4EZUMA1VPnI9G6FPHWhdAC/m2iMg1/0XdTtuELDFbhvQ52bMeBTQ+56lLAFAWRynrMOOED2P3KH5FNDmHB+V+CogWQTg2he/vLaN/4OKL1rWg84WKMde1HMF6HUFUCCrUWqCoazrwcB+77FYb3bUVs/kKnHSt8pp5D58P3YfC1F1F98hmou+ByqNEK+/7tuug+qmlgWgBaZRWYAQSqEwhUi9jA6pNOw/BbG9H5wJ2Y+ZVvew6bnkpi3y/+A0YqicYrrkHVshMBAKnOwzh0681QwmE0fegTYKrHDCdCAQ0Byci92ZmDAahjjkCg5Jz3h+YVKcrDkjLuuVBgIXA7FZOwpdRJ2FBLVA5IsUcoYIAE7aXm0hYXCBfOeT+QDpn2H3z8jMJ2ZCLAfE4c0MfGMPD0U6hYfRLC8+YK5WsuuRijmzah76GHUf/hqwEOxBYvQWzxEuQGB5DcsQP60DBYKIC++x5EZv9BUQjwAQspyAFhkcSRV5qTRShEivjA+Ev7SdZcynD78VWStuU+72OMpIImnVdCKNYyuHjqtCvjT0m1uTArwLaP24Q7Yg65RwFWJYP2TAJJ3x83FdHOl5NT4GiQsF+6l2mXlfI9wt8ep3cJHXNLgOjw6v6vyHV04melxY4qUbOmluau7MZOOUH77cY2Hy2lgMCgFVlNx2E+OOfo7dqKRMMCxOJNiKqOlzm939FML/oObEbfwc3Y9fIfwF7T0Dj3dDQtOhdqOEo6Q8ZIoiGWYWQLNkeLiSLj62cONS04G9VT5+PIrpfQvW89jFwGAEfDrJPRuOAsKIEgqhrnou/Qm9j76p0IhCvQt38TmKKicfl54KqCPY/8EnS1DUSrTIEgXodwohFVK1cj1jobh5+8A5UHliA8ZSpC9U0INDWC57I4fNdvkTy8H1WrVmNw3UtIHtyH2rMvBAsEkBsadBhtMkZqrAL62CiG39yI6Kz5UMOFQhZTFISbp2Foy3pwcE9MYN8LT0IfGULrJ78MNRzF2O5tyPR2o+fpRxFI1KHlY5+3rROyMTTbIuMsCZFHtZTUkV7Ncud5uq1DluCWT1bmVa/A4INszoTZBxzhwc4HAMk7x/Oad1lyKDhWiWJkl8kPGtcAll+f9CCHKnFqdfdFqDDP4Cs5Mp4c0qRbvrTtCoc+NABjdAxMUTCyYT2UigqosUqolRVQYzHUXHwxeu+5B1Unn4zwNIfBD1RVI3DCSWZTCjD0/IvIHumW344fJsRPGR+RjShmX/QjkFwgQG+8mWG/CcC8O4SJKcHoWuij7WPF8B1Lnz/P9n1GqCsn6eRkEM1BFBwWG6Z9d+cCENa448z7cXoHqPQ8Ae+yiapmueiIK8kZoIcVIUQoJfoi+nVYLZekGEiJrwDgshiUAScS2y+udU+OdiGd7Ediyryi9YQr69C06Bw0LToH6ZE+HNn3Go689Sy6dr+KxqXnoGHBGVC0gHRDlI6zjAH1w+gU+S9aPRVtJ1yF1mWX4sjOF3Bw86OoapiDUEUNAKBh7qkIVtdhz3O3Qg2E0XTiZaiZuQyHXrkfA3s3omHl+ahbeAoyg31ID/UgPdSL9EgPUv2d6N+5Ad1bnkf9aRdidN92jB3chf6NL9ucMtMCUEJhtHzqS4hMb0P8xFPReftv0X7r/wAA1FjU81mE22YhWN+Ajjt+B6gqotNnITZ3AYINUxGoroEaq0TmSAf6X3kWAGDoWSiBQrPWwOsvges6DvzqJ2SgFMTmzEfTVR+DEhKFC2FeUsafFpI5bNM9TnCqc45VAStOLGCuawWFvKVJZR4WADdEiIbNy5/TI7A14k6FHjdmVe0ZWqnwlBfJoD/0t4D5pwnDUCRprUxwlvWLiWOrNTUifvEFGHzyGQy//IqrLIMSDgOco++hh9D0hS+S/8T+R+bMwdALL0KtqkL83LPBFEWEZEr6LOtbySQw7z4Yf6nVUNI1Wc4SmTGCua5xaWbLJqpLIoov4T59QIPMi2RtiEzpZBHLuWBLVBlErBfHKqRlgV+CxOeuGAxZp/djADkSTE15hxSTbtKDeFfnL7LI8u96J9s/TiYdFUuAbxNaEdP6u8WRR2BwqJOnOzoS1R5R07Jgrp74TfnB/gsLnBDRwPu5cM6RGuvH8NBhdLdvhKIEEK8z8eWUedNJErBshNRbU4sprRejZuVpOPL6Ezi84RF0bXsRjSddhCktq8xY9i4SErDJ7kPYjEscO4nwoQXCiNWaWs6hrl1QI1HEaqdBUTVUNc/Fsmu+Az2bRtfuV7Dtnh/CyGXQdv4nUD1zGQAgGK1GxVQTk29l+c0M9uLgM3ei/dE/Ib74REz/2N8gl0oieWA3xroPItvXC60yjoGXn8PQhlehxatRc/rZCDU0IzS1GSwUKOirnkph6PWXUXPmOQg3T8PYzm0Y3f42ep54EFwv5HbUyirpO9Py8euR7e+Fkc0ABkewvhHhpmZPgaEo0elDGG/KzNKQobZ1jAOKR4ZhwKe2U5JYCyg0jRvET4HDgfopWXlEyAJiZmOyWPh2/V6WAm4yP5Y/gBGQMwglM4t+jAouCATnHKPrNmD4pVeQ6+2DPjgkCF5Va85EZM5s6MPD0IdGoI+MIFBfV3BPtM91V34AajSG/oceRmrvXtRfey3USocjmhDjL4PGyCAsfqqkZSQJw6QJ+lwCsV2Oydetkp+ln3sg74ngKzDCkIuR9V4ikPshWtwImtGKPMvJBJ7SmiuJZIoJN7SuIODHu4CEZ+KGSUn6qOhc2G/pfqlkxAzzx3MDHKeJ0LvWMbgoSTRiAEQ4BM1u+G54UbjIiMtCh/qNDFBu4jFdz2Cgbw8GBvZieOgwRobakcua3kzBUBVaF5wHFg7CAJCLOqsUZfx1ogWxNqhArAota65C/dIz0fHKwzj41J/QnXgGzSe/D1Wt810bmbfFxtdzmoC5nStAOD4F1U0L0L1nHTq3Pw+maqion4HKxplgqoYjbz+HXGoMtfNOQuOK8xCsIjh8D013IFGLaddcj4HNr+HIU/dhZPdb0JOOd5gSCkGrqoZaFUdusB+jO7dBHxkGU1VULT8RNWvOQSBRa/dPTybRfssvkOloB3/5eaiVVUicfT6aPn09kMkhNzSI7EAf9KEhBOumINPXi+GNr2Ng3YuoXLwMgbjT31xfH/pfeQ7Db24UB44x1Jy8BlMueF/RsbIvcTPfNARofhyUrHhsCQHamOPkTfN/KBLLXDEq0NqR/gmQIWa2r+TM70K/pfEnjheTL8T25+QE49IEVH7IdmaGCWuxsN40gzDTmaBFtZtSXExSPsZ+5lA7+v50D9K79iG8eB4qTjsBam0NtNoaaDUJDNz/GEY3b0bNlZeCqapYh6DUoI0BjClIXHgxwtNnoOu2P6D9hz9C8ze/bloSAIhO3/RaH+YLGeMvtXZIjkutR3LeCLiyA0+E05WsWxOFsEgTpcksSqWut2WSVMhlE7zho0hUkQGI2n+9iMtbcNg5DoyJ9+SOVnacAArvfcfafw/SwMAA7rrrLuzevRvf+MY3kEgksGHDBjQ0NJSUIIxS2dNTDAsn/+/dYiYrRsLLPSb+RzX5QnQgl3mRvuiifwA5zk3uzJMlG6HwIYuxGBvtRl/3NvT17MBA/15wI4dgqAqV8RY0TzsVsbpWVFS3IBiuLDmJie4knQTTgUB9PaZd/gmMde7HkWcfxO6Hb0b90jVoXXEplLxKWJYkjpKg/ZdsXLJINMUoFK3GvLM+A4NxjPUfxtCRPRjq3o0jbz8PPZtG7bwT0bDyfIQqE+JiRdrKErcHIwgwMNQsW410Tyf6N7wIFgxhxqduAKur9sTy68kxDL72EgZeeQ7Db29G65e/Bq3OFASO3HErsj3daP7CV8C0APqfWYvu++9GoKkJ0Wlt0OpqodXVgqey6Lr/TgxvfB2hxmb0PvUIeh5/AE0f+TQq5i+GkU6j494/InX4AOovfB9CtQ1QIlHoYyNov/MWwDAKx1ZijRM0WJSBoREzaJI9CSxIlo+CmocNAucpgAVJyBIIWL6cEcgf8/zGDhLBY7zX0IYMEUFAwlR6wSmsaEFWmFAziZhHO+4EazKGVoaFIxfQ5FqGMYqBe57A8JMvI9BYi4ZvfRqRxbOFezDSKiovOA1j39uIsf3bEVlswv9Y1jG7WNYMIQkaQVKFWluhhCOmA7uhOoLWRBAmMoa9REiPqMF35QrwuJ6rjoadayLG20dXxaVnIhZtiSBD17zAsLhnFWNOPUmwTjvHqVog3ONdrhgU6phQOVunIGu4KpiAIumdIO7i1HgRS7qfvfU4vfdo8+bNOO+88xCPx7Fv3z587nOfQyKRwD333IP9+/fjd7/7XVn1HnUZ1T15Rccs8S/K9CkSrXEBFWMM3mUvN3cLDuRY6uzqc/ErFhqtt3sb3txwC5iiobqmDTPnXIRE3VxEYvW25UQPOxVkKoj2P0a0/4TZFwQd0kdqqq6OTUP84i+i663ncei1B5Ds3I+ZZ12HYCwu9I9J/Dhkm6BUIPATvolqoVSGaF0ronWtqA+eBc4NGNkMlIj3rjqewJEdGcLAplcQSNQhNzSAsd7DqGye4glhUSqjqDn3PFSedgoO//Q/0fH7X6H5azfCSKcxtn0b6q66CsE2M97vlGs+gmxvN3ruuwutX77RjuIztHEdhjdvQMNVH0HV0hOQ7jiEA//9IzAtgM77bsfQhlftNsNNrYi2zkSmvxfdj98HruuoWrzSHD8JUyAxqomaLVpGkxwLuQUIVI6+1z7muRBFShb+kOeZ/bw1gAHIVhCrQQkCACAKAJ7JvygxTCyMpMQRVpr7gj6UoPkijL6+Bf1/uBdGKoPER85H/ftOBAtoAESuNhPQMNqxH1AURObWQaswtTV6ynlwPEelOufQusfU3j3I9feh5Wtfh6oFHP/urJMZWTFE7bQD7fLCUkE4J/hCuWQfG6rFYC+k3DX+xeBcThnJ8dHQFJaq/S/SfSbJXwAZwm8y90K/YyO8l5I13n3Ju2zPliYpAxAYo8ckgVpBdnO5z4EsAVlB28fpr55uvPFGfPKTn8QPfvADVFZW2ucvvvhiXHvttWXX+1czzdxaeKrpoQuPOwazItE6UyZEzXDhOl2CX1UnwSfAD7k1+VrAVFsvO/GziNe0AQCMIBN4omyFc0PZCuf6HGH8/STfchNjDA2Lz0Ssfhr2PPU7vH3/DzHrtI+iZsoc0mFS3kfIOin50FYJxwXOrQoUNSzVxsnqMcAx9OZ6HHnyPrBAAI2XX43eF57Ekbv+gP7n1iJx7kWoWLDE9o2g7SpVUdS+/0p0/s//INPXjeyRLoBzKJUxcBXghoHkru0wsllkOzuR6etBcEoDAGB4yxuIzpqDylUnAjqQPHwQUBSEm1vRcedvEZnWhuSBvdCqEzj4m59Ci9cgNzwENRrDtI9cj2jjNJOBMoDMQB8GN7+KTE8XMoN9gK6jasEKVC86EYGKKiEaECW6UcneI5klgOL3ZZpeX4+dOiAHieUAQC6aZ5K0PESJZGw2wwjJ6y0QALzKEybUrnc8KhHbLxU+qdCgZtD/p8cw9MgLqDx5ARo/exECdVUIB7OwBACFdD6tZdH+9GuoOmkOqprDdpmRrNMYtS6IkBPzO9Bqmp+zg/0INjdJhUkZ/GNcho/EiXVDnmRrhizij0A+lAvyPpVY3g9JLCcTFkSk0aTkYyQzOr0TqB1F954rxXwOZZZGQFS0CYy5S3mk+3W2Pk7H6RjSunXr8Itf/KLgfHNzMzo7O8uutyQhgMaglWkDC34XM1UVmK1k3FcJnXwXk8D86QDoQiQUdA6LOcRKcZYuqow3gykahgcP20LAREkQbohAReNJU8eyePUMLL3gb7Hr5T9g+1M3o23FlWicfVp5bU9kasiYKw/SMykwpkFRtcLyDDByWYzs2YrejS9ibP9OVC1aibpLr4BWUYnmGZ9Bsn0/etc+is4//hbh1ulo/syXoAQCQiQVrgJcywsHUQ1qUx20KfXo+s1vzWZCIfB0GqGZbWj8yhcRaJoCDo7krl1I7duDKVd9GBwcQ6+/gp7HHkBk+kxkB/pgpFIIJOqQPLAXrR/7PNI9XUju2QWtMo6aE06DqgRNOIphoPPhOzHw5jqooRDCU1oQqWuCkcug+8XH0P3Co6iasxR1J52DSH1zwaAL4e/ymydnJFeA7jD7gSRsQVLJUN8A8lwI421o9Fi+aQtE+qaHCTOaA7KJQviTc538/fG0AMg0uVzspw0xobkKZAKPTD0smNoKtf/GWAo9378Fye0HUPfJi9HywRV5C18OAcWbE85u3oHUvi7M+OwaqKpTJhBx1MrptDPQAnQrz2hrtQmoVVVI79+H2KJFcgudEL5YAmH0EQ5UBk8pmZH3Uz89LYP90B8TgGD4qh8Q+idYysnxO4kEYQaXMuh+fQ+KJQJ7rxBXxPdFCK4RcN1Ukf2IKlLcykmBl3BBrrWxvxCG6TgJFA6HMTQ0VHB++/btqK+vL7ve96QloJhWgJNVhG44bmaamu+YTrRfRfwbBBFFiI3uxnuQPtBIQe+Qf4SiaKiMt2BwaD+mhs8EAKTj4opDYT9S7b8E9kOZfSoQCPCuHKAGK7DgjM9h12t/xKGtT44vBMg0Udz7GQtPWCIgyXHXYrucc+y876fQMylMW/NhVDbNAZjJNI8c3oX+XRswtGMzjHQKocYWtFzzWVTMXgg97PQjNG06mj59PcZ27UD7b36BwQ2vovqU013hNTlyA30AY9Dq42Cqiqbv/B2Sb21FZt9hGOkUtHgCWk019LExDG14HcOvvIr0nj0INjUjtmQpBl54Br0PP4CqVatRd/H7kOvtBWDChaKz5iGQqEewdgribYuR7DyErsfuBc9koEZi0CqqMLDlVUw55ULUnXAWVNV5+PyMJPq2voaeTc9j960/RPPZH0I40YBYc96apDgMnsBwK854sqyzgRl0blBojyTjt8gHE8GJMtoSXxyrLq6YloB0AwfL5R1ux9H+Uz8AKaSEO/dIMf9c5aLWvMgcMzvtnKfO1EqawQgWdpTWpwbMwcp0dSD59j7UXXYCWj+0EkHVGURGBlHNCwT96/Zix7/ci+oV0zDlpFbkCFQoQDjKniQZ0CQVCJw6QzNmYHjDBrBIGPGTT3Ocg+ltk2cq3JGwtviA7kySZnoi18ojH01+/Vb2a48mxLWRwoGo8CXMPW84Troxh3A7NeeV2OGjSALkSXbzENeDYlp8Ia8PtVQWCQsq7HOjYrHgKPcsBxRPQHqcjlM5dMUVV+D//J//gzvuuAOAibI4cOAAvvWtb+Gqq64qu96ShABDA1j+CvW480nJZAToaiOqRoSoRoIZVKYmKkIKXRTN48q6NnQdXo+xBANjDOm4S3g6munWCTFFQaJlKXoOvIH02CBC0bhYQLYJSTClRwVDyoFUbweSve0Ixeux68Gfo27hqUA4gIHtbyA3OoRAdS0Sq85AfMFKaM0NTtc9mL7onLmoWL4C/c+sRXjGDATaHC9+rnKk2w9DrakGQgwcBnJ9A+i/60HkjlAvPYdC01sx5bOfQsXshWCKgkxXJ8LTZqDhAx8GAGhTW9B83fXIDQ+heuEqMM6Q7e9Dx71/xNjB3QhU1SBQVYNk+35kBkyBITHvBGhKSNx4tQimLFmD+oWn461b/wWHn7oDajiGJZ/+F7PvQdiJsej8ob4jUj8BIhBLNaGUSmROjIA5T5gOJKfAdGzVOJBjJkadszwUqhB3Lias40UTinmSwK16Y35LYRSVtAIjlM85kWHgYfNYDZjfsYUtaLn6RBy6Yx0qm2No/eCqgqRxmb5RDLx5AAMbD6H94TdRf+I0rPjni6GGsjDgPPSk4nBHg8QqkBlyzlMhp/qC89D/wIPof/QxGKNJ1F52mXmvpG2pj5PhJRVBDrkp8WWXJiE8GoyupGtSq+V4wuEkU7H1XerHIjsuQsKzLoNHOOpr+18BuZOVvRuJ4Z2BmNH232v0//7f/8Mll1yCKVOmIJlMYs2aNejs7MQpp5yCf/3Xfy273rItAVSSLgijJwsv5/4tM+mOR4Jml2i6slyE2OiSjQUujTXprxESywmRjmTm4gIzHrko/e54IUPROLJjQzByGaiB0PgX5En2vKjmQ8Ba0o2XPifiHFhRb8bp7963DvEps6EGwtACUQTClcI1R985T74U9O9+A2oogvkf+gZ6t76M9lcfAgsGEZ+7HNXzVyE0fZrjVO1jRam5+CKkb74Zh37yQ0SXLkb1xRcg2NwEfWwIoy+/jqoL14DlmbrhJ56BMTKK2s99CFpdNdR4DVg4BDAFSk4FC4fNtpMmytvIZsCCQXvsGYDYbCvai3mu9+Wnke7pRMsVn0DlnMXQdBWccwy8/TqGd78FLRY3Nd8e2VIVrmLe5V9B747X0PnGE0BGB1NU0GiEMn8ZwQeAZkOWJBsrmSHyAR+w7oMZDNlqvaCcjf1nTn0lzz2JWV+wTBKmV1q9NHQmeS/yh9mRIGIJ0zsx8OGL0MYz2PuLZzH8wts44YaTMGVZA96+bw+2/f4NjBwcBABUtMYx98NLsOT6E6FoBoAM4oGkXfcoUWEeGXKcz2RMa6i1CY1f+jy6fvsHpPbucXJGyLT/kozgzM9zL5EplVoOJrKuyPpZIlPvm9n10Z6wFuulvkDvDaLBI9wKSFnekeJY/yKaBzJHNbJ/q669nLZFY/oD7miB9GGJTdFnn3MZ0XQagc7Fqakp5zhYiBA5Tn+BVFVVhRdeeAFPPfUUNmzYAMMwsHLlSpx33nkTqvc9CQd6L5FoFqQLhbgaCBFyytAY0igDetg5Tlcq4Jyj84X1iLXNQ6YpXNAvzeEBzJ7RRVYSMaPkKCikTCgYR6RyCg6++SgOkiI1TYsw56SPQA2UGvPOozk2/rFh5DCwbwu4njOtJ4oCxhQwztC/6w1Uz1gKFtBQt/QMJBauhh5TwfIhTnWF3JIMMULOa/W1aP7WNzGyfgMGnngc7d//IaIrl4JFFEBVEH/fyVBDOnIDIxh5Zh2qr1iD+DnLzbYyZEKkVIyu2wh9cAjBmkYkt27D6JYtqDrxZO9+cBPCNLx1M6oXnoD43GX2eQaGmoUnonbOieY5nTLM+THSc+jf/Qb69mzE0OHtULQgMv3dGOs+gFwmiZpZKxGIVgqQMLejrn1eEjFIiostAf5B26GbfabGuZbTOr14JamW2ruvlPwkJuKkAV+JpiR1uqMdAgBTFay+8STMWNOCjT95BY9+/iHMOKUB+189gtlnN2P+38xH26oEKurNdyvLB5xmyOCGcg5HqRFfAQHC5sHIh2bOwOimzTCyWSjBgD9GWFhDvC0mAkm11BLFgR/G38/aNUlqQ1+MPxFEC8rJrqGPiQgEAq9MG9dde0/Ou9hEoyVJI975vZ4cH7cKHKe/ZsrlcgiHw9i4cSPOOeccnHPOOZNWd2mOwSo8NT1urDv97Q7vKTXLul/yMhYN9+ZRLBupgFcn/aUxl7Wki3ERtN/kvFuz6KOvx5LGuvZjrPsgWtd89p3uik1Lz78R2fQI9GwSuWwKqeFu7Nt4H958+meYf9qnEIolxq/EL0k20sxwH/Y9fas3VwWYzLGF1w4EoWve09IwdOijI8iNDCE7NgR9eAi54WEoFRGEZ85GsKHRtISoCipOOQGRNUsx+soGDD74JPSeftR84EyEEkHEo6M4dPczYJqClg8ug1ZpanlHUw6H2/vcFvT+6i5A04BcDiwUQs3lF6PmxDWeGyXjQLqrA/roMCpnLXQ03ZRx8LDcMYODc479T/8B/fs2oaKhDU3LL0A2OYyt9/wHuGFedOiV+zD7vM+gcuYiuw5qZROsAlQ4oBkwJZa98bS3UgdhyQuYjeuwHI+ZxHzjFXrS7CRp148T6zgkxPmWQV4kY8GJgJLJOAtUSg8gvnw6zvyfaeDPvYqnb9qKBWfU4dM/XgBFZYgqowBMYHOKO5JSlnRmiKgjdakgVAh3Cs9qA3Qdqf17EZk3xx9jDu/zsrVVypRKjktlPGVMehE4+nF6h0iliQapvxCNhOuy9Ao+AR5J97yoqGUhTOsT/xOsgdQq4K6DKC9yFfI6NFf+olC/cxzpExkdPvYewGrTKDPvVPvvIdI0DdOnT4euT/6zPW4JOAok4KMpNIjLpR4qiAjOiDIsvMshOhsljEEVPQY6X3gegZpahBfPRy7fN2pOdKeHp9okqWZWFl+bHlPrhsvhU1U0qJFqIFINAIjXtqGyZhq2vvRrbHnqp1h5ybehKiXGahOYfcpYklsg9xOqmYJ5V34NB1+4C2PdB1E9awWaTrwEaigCxhSowbAAYbHqN3I55LJJaLFKdDxyJwY2viI+W8agRmPQU0lA16HEKhCeOxPhObMQXjAPgZlxVJ69ChVnLIN64E1ULJ0BJZRBODeE7oc2oOXKZUjUMQDmQ9INBs45+h7dgN7fPo6KNSci8akrkX7jEFLbdkAfHkH3vXcitngJYgsXiQPBgUClOca54SGHyfew9rizOPfseBX9+zZhysIzMdpzAO1vPAqmapi65Fw0LloDRQ1i59O/xv4X78DSln9yoFyK9+YoOOP5sAp4kR70LuDH8uOLZLyBwPj6r7/UDLViGcJIU61qmuTxSDqD2jnqwHeWXLgYHz5/EZrCAxgGA3QgQFS+I0TbMZhz0n8fHK2xj1NJiXcjZXDy3dKaGxFomIKe225H41e/hGBNrVNoAlAfKUzIw5fDfeyHfJWXwVYnAjGaLHgSilhoaRkBMiNOWl9hmaXvhfwF8J0JnsseODld5PLjdJyONf3f//t/8fd///e44YYb8OMf/xgAwDnHP//zP+Pmm29Gf38/Vq9ejZ/97GdYtMhRkKXTafzd3/0d/vjHPyKZTOLcc8/Ff/3Xf6GlpWXcNv/xH/8R3/72t3HrrbcikZg8JelxIeBdQhRTKLeWEMiPa3/OkNj+2SrnfCo7hKFtm1B34eVQDMVm+KhmwS0EiJhm0rxsoxaEAG/Ig5/NNhpvxOxVV+Ot5/4b6ZE+RKsaxr9IQjIzutvROjqlFXOvvAE9W19Gx2sPYfuhHZi25mpUz1hSWA+AzGAfDtzzK2T6e9Bw/pVId3UgPG0Gas44B1pFFdSaKqixCjBVRQ5ppPbtQ2rXLiT37kLfXfcBnKP6ijWouepssKiGqadPg/lQ0siu2wV9LIMFV81FKORIaWldw6FbX8Dh37+IynNOQN2nLgPTgP7Nb2L4uZeg1SYA3cDI5k2Y9o1vQQ1VkxsG1HAMgZo6dL/6BBiA+JzlYEQlZT0nrjA7Nney/wgOvHIvorUt6Hr7OVRNnYu20z6C6paF0EIOWLVh/hnYsfZmDB3YiuqWhQDM8J8WKUGJQCCxEHBJRBBr/mhpbgsCUqiExD/B9EbLH5eoUBEFScucQgtICsO7jCDoy/IM0DqJYEWjfhrDzqAeCTgvvhUxpSvmnKsOOri/gYzD+A+mHYGgY9Apb2Tog0FRYoqChr+5Hp3/+V84ctPPMfUrX4KWMAUK+s75wf7LnGn9MPt+knCVDE3xse6VysgLfSjGIPvhfn0IAYJVyadjMPOZ4Evsi+ThHafjRClvkX1H2y+T1q1bh5tvvhlLly4Vzv/gBz/AD3/4Q9xyyy2YO3cuvvvd7+L888/H9u3b7eReX/va1/DAAw/gT3/6E2pra/H1r38dl112GdavXw9VLZ7t9Cc/+Ql27dqFpqYmTJ8+HbFYTPh/w4YNZd1PaUIA2UQphIBqlQFRo1iQwlqqUXD9LAsO5NJwSKJ0AGIfiV+cAAcyNNeCKXNAcvVdhmkVHIZdN+gOMTZZNLjuJTBNQ9XKk45OA5NMFgwoPTaAaOWUkq6VM/7e5+1TioL6eScjPdCF7jefx8CeTbYQQJ/5aMd+HLz7f6AEQ6hcuBydj9wJMIbK5SegYr4p7VuMLgfAQkFE5s9FZP5cxGM6jFQaQ0++gIEHnkBy83Y0fePDiDc5E+ngxj2It1ZgThuHwnrt85piYM+OA5h6+gzUfPVSmFxDBvrwEMKz29Bw4+eBIxkc/P6/ofueuxFtnYV0ZzvSnYeR7etFy/s+hoazL0fno3fi4CN/QPtzD2DK4jNQt+BkaGFzIeGcIz3YhZFDOzHUsRND7TuhBkIY6z2E5uUXYeqS88AYM98H651gQLxxLuJN87HvpTvQuupyJKYvg0KWFQoNkoXlEx2JiQVnnCzaSsaJRFTAgzOrPg4oZntKWoGRj6xjJZ8SGTlvXJ+Q8VYGI5DAX4T6ZeGC6XnJuiLTpFKIg06iGnTkw3t2hR3rgBZ0Ko9GnAeTSjsPJj3s1MGGStse1Jo4Gm74Io78+L/Q8bP/xtS/uwFqLDqhaGNSqNRRICEIhMwXyofA5sc/5K+OigkUXPpDStTKTAVkUQhy1UX6oOTkPihCjH+aBNRlhTTcCgb6H9nrcy6eQihHYnS4YdXU4dcd8Sc47Ew+JSO+YHqR3ELHaWI0MjKCj370o/jlL3+J7373u/Z5zjl+/OMf4x/+4R/wgQ98AADw29/+Fg0NDbjttttw/fXXY3BwEL/61a/w+9//3nbmvfXWW9Ha2oq1a9fiwgsvLNr2lVdeeVTu6bgl4BiSmF1VXDWEWOmGdzkhygBEgcVroTEyaQy+9jLiS09EQI0gQOIcC46ciqvNEjcxPwJbsf2bXh+MxAEwpMf6peX9kKy9sZ5D0DMpBCtqoFXGoaga0kO92P/UHzDatR+NK85H46oLHAaQ1NPz8hPQYpWYfu2XwWpiqJi/CJ333w41GvXW2NFzGodSEUT1Feeg9byp2P7Pd2PwT49hyr+vtotsax9C7YwKxLQ0VKIerNDSyHQPo2HlVIQCjrSoDw1BrY6DqxxaNILEJZeg5847kNy5E6GGRoSbW5Hp7sLAlnVgqgY9Yz50fWwYHa89giMb1mLamVejsr4Ne578HUa79+X7bSYAqKifi9RQN5qWnu99TxxgYGg7+WrseflP2PPibTi44UHMP+8LiMRNK45gFSDWLmrJkkUTkmdSLRTu1awjEKgZM1MwZ0BgmCFdl2f8LQbehf2ejARFUsZfp4w/ORYiupBjiTKACgeU+aBO/UZKcf4fzCegUx0uJlPnVJ7tcKw5jDBEWsZbIMlFJC+5sFZwBOLVmHL9Z9D+vX9Hes9eRJcsEoQoOsCyexVI8iL70fj7gbP4Ck9bIokhZiWFvNFlvjWUsjVaOhb0PnNip/xAO2V1FcjNsrCv8D5NkkIfp+N0zMmdfCsUCiEUkkdP/PKXv4xLL70U5513niAE7N27F52dnbjggguEutasWYOXXnoJ119/PdavX49sNiuUaWpqwuLFi/HSSy+NKwT80z/9U6m354vKFwKETc+l1S4SOUaW9MlNxRhLMQyl67zEhM5dbTHiWEQZlYLkQNRqTy0BRTYhGQb9aMbf96LutQ/ByKRRc8pZk1KfLxP8BOtSFBXhWAJH9ryMeP0sRCrqitbjN2tyZmwQh9Y9gL69b9ALoEUrYWRS0CIVmHvZ36CioU3Eq1vCADcwdmgvak9aAy0ag86AyoVLEZs9H4YMp04EOyXscDx10xNIXb0Ku3/xHIy++YjVmwyZkdERSwQRVrIIEbVQJUYwdGAIyz44E4GoKcnpmRwy+w4hfuV8MI0DDKhcfRKiC+YjpFSBKQp6n3kCMAxoFXH0b3gRzWd/EFUzFqDjxQcxuGszYg3Tse/J34NpAXBdB1M1VDbOQiBahd6d6xCubsBQ+w5kjRQCqillerFzwVgc8875PA5tfAgdbz0NPTUKVmmWVAnjaxDB05D5BPiAolHBWAZdMzSzg9b/PMAhBKd2v+fUIdjdoGxaCb473ucpsy8cUx8gWZAFmTZa9S6j5q8VjBXkWCVOxErGuwzVTArRrUa9B0GnwkFeSRGoNv0Bxja/hfDsWVAiRENBnaoV7/ET55hLYvMiHzChCZFMmPCDmZ9I/ZAjzv6S4ulLn5nhPVc4A7jqvekUTSSqMu9yLhSG6EBM63N33Dl0WwmIm40YTt3FA2hEMRfuEwciNEiS/rmuE+7/vUjvEjhQa2urcPqf/umf8J3vfMfzkj/96U/YsGED1q1bV/BfZ2cnAKChQYQwNzQ0YP/+/XaZYDCImpqagjLW9e8EHbcEHEMq5gBpSNKtC4yNy09W8AtwpREfO7gHA+teQOPZVyAargWSova/YOGVMVyevSqdCk3AMsAuw5yTrsWO127DprU/xIwll6Fh5ikFyY/8kmHoOPL2c2jf+AQULYBpa65BrGEGMiMDyI70IzPSDzCGhkVroAbzjC5tKj+u6e5OGOkkIq1t5iaUP8/CQSgSszAPEMiZld21oxfBmhTaLp2Lvb95Edv/vAOnfHExAKAyoWG0P42QkkW9NmxfO9g1ACNnYP5c4EjA5N66Nx0Cz2QRWTrXHlzGGLR4HGyUIX2kHb3PPg6Ao3/DC6iatwy1808CU1W0nnsNMgM9GGnfDTUYgZ5NIRyfglnnfxrhqjqMdh9A7851iNSbC+TYQAeq6tvIjTlf1ua0//V70LX9RQBA+1tPoaJ+BpoWnAUG4ntANJAyOJ30HZE9flcZCwqkjQGZajMKCNMZeJCbDCiHyaQzDg7mzEtLgcAhMKruaB4yhpMqD0TneO97ljH+cnx2/pTilJExf7I6tBHvMkKkIsl5GoJV7BfzKB9EzaUXYeCxtRh78y1UX3Q+Kk89GelDhzH6+htIbtuO+o9/BOHm6QX3Z/7wbktGvqIPlVinH38FqTafFvHTn6NFkntmWbFxma+FCHfyvpECxrjQSHecjgIpaefhFORpOsbKxvcyHTx4EFVVjh+UzApw8OBB3HDDDXj88ccR9siKbpGbT+Gcj8u7+CkDAIqiFC1XbuSgvzwhQKIVKDrGMiYErhdMkuDIjeUT1l6JpvJokpHLouOh2xFpnoHEyjOcfhVzZPMDiRA2C2+Mp2yTLIBdFKHK2ulYfv6N2Lf5QezZeA/6Ot7CzBVXITxO2FAvZnLPs7eif/9mVDbNxcxzroMSNbXu4WrR30CMvFRY58jBnYCiItI8vegGLkAf8sfcMDC27i303fcSktsP4oDKUD23HsHKILbcvRunfG4+1ICKeCKArh3DCLMcQsyZVK1NQFVtAOtu34cF/2c5GGMYDeXVykYaSsAQ5xYHuh78M6DriDa3oeHMyxBtbgPL++4oqoZZF30W/bveQC41ClXRMGXRmVA0U8oc7thtjlG8HgCQS7sSSXgw51VNczDaewihigT0TBKHNj+GoSO7sWDNZ+zcCkL0IcIcc+orwJw6BYdScOceqYZ6DMjm/aOUbF4DlxfSDI2Da/lnMo5lkXGIlgKrKTdcIf9DyujnxP75So5oeJwTOkEsUj6SstHzdG3yY5WUnVfJFBDapVYEclx72gWoWnQi+p58DH1334f++x4Ez+agVlVBHxtFavcehFunk4rgeSyzBh3TbKMygUBWptR13ic0SLB6S9oT54H3QNI5EenxYOTfJeRH2w8UavwtcsNn5e+LWM7wsAS7+3Cc/jKoqqpKEAJktH79enR1dWHVqlX2OV3X8dxzz+GnP/0ptm/fDsDU9k+dOtUu09XVZVsHGhsbkclk0N/fL1gDurq6cOqpp47bh3vuuUf4nc1m8cYbb+C3v/0t/vmf/3nc62VUthBQDEdbDK/phg7JyBfm8z1GRWFDMm2yLNKJu768EKhn0jh03y3IDvZj2pWfhqorZaVvL5uKbNSyZ29wHR27X0RtyzKEotUAAFULYdbKq5BoWoTdr9+BDY/+X4SiCVTWTkdt82LUTF0ERVHl2uL8ce2cEzHW347h9h3Y+8ytaFh1Pioa2wr6Kna08Hho5xbE2uZCCZoqUWlISvqMNI7k27vQ97s/I9fVi+i8Zkz7Xx9APe9G9xsdSHaNINmbwvChIdTNrML0pZV44baDGO5OoqHJ6VxLdRrXf6cJ//6V/Wi+YCvmnt+K+pOieLIqhrF1byIyt6UAKlC5YCkSp5yF6qZFpvbAZX7VwhWoX2wKiApxsuOMIZs0rRD7nvkDACBa0ygMjxfTUD1jCWpal9i/hw/twLanf4lDbz+J1sUmBlJLczupXbifI1NpHis5x6dFDzpwwlzUwbwbGrOdAZnOxQ3eYpDzx0bAfA5qhiEbMhwoUBEyNA5meMMsSoZe5MuraW/GS5FkK5cy+BJIgjRPAiHBekjnabagaGE/ZbBO2mfq2+CKWBaK1WDq+65B9ZlnYWTLRoTbZiG4qA0d//5jZLt7xHwLkpCfEyIZemgie0uJVgShCxImXmrkcu8XkromSn7CnkojLU0gQtJ7nWhEvywJ1OJ2/tVI4JTQgDhIgRFiLR51p0Imxy5BRQwLK9ap5N79pgDG31m+rtS2zz33XGzZskU496lPfQrz58/H//pf/wszZ85EY2MjnnjiCaxYsQIAkMlk8Oyzz+L73/8+AGDVqlUIBAJ44okncPXVVwMAOjo68Oabb+IHP/jBuH244oorCs598IMfxKJFi3D77bfjM5/5TGk3lae/PEuAX5JEFHBH6fHD7OVUF9RG4lx1NDUuzAByYyPYf8//IN13BNPf/zmEqxvAZFADiJpZISKCDKM9wf7LGKhcJoV9mx/Aoa1rMXPlVahrXW7/V9M4H8sv/AYGjuzAcM8+DPXswfZXfo9AqAJTZpyEKbNWI1xRW1C/dRyfthCV0xegf+8mdL7xBHbedxNiDTNQ2TIXsYaZiDVMhxoQTYDu+8yODGHs0F40XvZhT220iGnnwrHe3w9j1ORkk7s70ffgOlSeVItln1+BipY1mJrrRLgqCMDAkjMTYAqw7dlutF1T7VQDA6dfXInHT41h0+07sOzCBkABKk6aj7HX3kL9deeLcp4B1Jx0ptnNEdibid+Qrc2r34fK5rlof+1BBKJxBGM10rIya1tV01w0zDsNR3a8hJaF59rWADFJH3kHqYVVMrYFWup8OdsawEwmVg+b76cRyEOBAq4dh8Gc15xYFzgzNe7Ia/8VFAhO1qXMcFsHyP8yaIqsjGRN0Sk23wdESqaxFPwHqDAjsxbInETptRIGVqHBBsgf4UQjwmsuAgDoOkegtg56Vy8Eovfo4VhbDOJwNJx7SyXpc5fBkCSCiN81VgbZFK6nUa0kyQGLWYSPBZNW7H65yw9Aeo3EzM+LcDcy68FxOk5eVFlZicWLFwvnYrEYamtr7fNf+9rX8L3vfQ9z5szBnDlz8L3vfQ/RaBTXXnstACAej+Mzn/kMvv71r6O2thaJRAJ/93d/hyVLltjRgsqh1atX43Of+1zZ1x91IaCY9rugrCSqghXOr1QSGIh3QRK9gj74YbBd0AIu2Sxz6VHsvf2nyCXH0PahLyPSMH7yCRkJWkgJkycuohINv+Yv9GkgFEMoWoNsegQ7Xr0VfYffRM3UBfkGmanJZgyJpkVoaFuNbGYUvYc2oXPPSzi8/SnEG+aitnUZQvVTEYk32Nh+u9+KgsSsFaiZuQwDB99G37ZX0f3mC+hc/zjAFERrmxFrmA4wBXomhZyehJFOQc8kkcskoQSCgMJQsWCRM/6SeawkFehR88GwIQ0Va1YhdsYKxMd2Y2jzAQxtOYCdd2/F3od24tLfX4lwo6M+DdeE0LYiji3P9uKcD9eL98AYTrkwjpv/pQPR1CDCFRqqTlmAobXrkTvYAYY5TtkJbOSMczAA1S3zUd04F4aeg5JnkG2SMbEuZiwxbSmObHseg917EG80+ydkKpbBgzyYXve6oejcDsNnQYgM1dz41TSQifO8RYA7FRQbjDzuh2sc3GAOPEjCJNlVGc7YKGlxnOT4anJI7lUIoSrg8SWmCB/PWSZEFThRW2UklglpLHsqKFjPSvVOOKhHACXDoEYqkOnudvXTvAElS8bQfU+W74vuPQaAt/WmKPmAe/gR7Hw5CfvgO4tp1n0JC0eLt/UxnsVDf5LTdE6+C6wHbr8fwdpG9rmcCwpOnX/puAdGxHIR4vBLnX0BAFSL/9ckl3goWI55+5NM3/zmN5FMJvGlL33JThb2+OOP2zkCAOBHP/oRNE3D1VdfbScLu+WWW8bNESCjZDKJm266yVeyMRmVJARwhbwgRRa9d8OLXQoFxpwOZ9Lim+hOymVRAS6ROtd54H3NP8rrn4xodYNbNyA90IM5n/gmwvEpJAusU4YyXQCkmkE/JPN10INMTOkudFhiDwdQkZiGzNgAGmedhr0b70HPoY3j9IBBC0ZRVT8Lei6NPa/fBeuGameuwoyzri3sH1NQPWMxqmcsBucG0n1dGO3ci5HOvRg6tB2MKVCCYSjRCNRoDMGaOiS7DyPV3Y7Y9DkIqjHo+ftWss7cYAakcC6mcDCFYbhqNlquqgWuWoF01xA2f/E3ePlfn0fw++dherQfANCVqcK8NQ1Y+/PdODIaQU3UVNEezNaiOdCP5WdWwci1Y+8rXVh+QT3yEH6oioFI2yBGd8fzN0z6UqJAIGp/FShKEHAJn4I8UASONTZgRjzIppxd0W0ls36H+zjScbMCpjuOqLmIY2XTQ0yMapNvnytmrhI9brarB01m0wgZphDA8sIAM4/djpEAADXPiOZNAZbTsK2AcPHh44WlVNPOeKgpMjaGc5yLQgiTSYUAyviLWbuJ4EQhNMW0u+Odt+tzBAJFd+Y00x2tKu1vgVxILE7Wc9WDjqXBqiN75AiC9VOg5MORGiHvey3VwdF39l8P68IxURL5eRclZYpFs5E2R8JEUad8MTu9c+zO93MsmLSCCD6SfUWwChS5f4rvdzP30n3p3UJFrBPMtXdTCJAbDnScjg0988wzwm/GGL7zne9IowsBQDgcxk033YSbbrqp5PZqamoEx2DOOYaHhxGNRnHrrbeWXJ9F71o4kMz0Px6Jmmvyw605eSdeHLewVE4cZloduaehnZtR0ToHoZopUq2duz2jyIJpXzOZm6OHM7Fh6Bjs2onM2ABGBg6jrnUZ6lqXwTCshjnAOXQYyGXGkEuNIJsaQTY9go6dz4FzA0vO+Qr0XAa9fdux+9nfIhCpGlfDx5iCcG0jIjWNqFtwitkSGc9sPox6x9P3INl1COGp00yG08c8FJIO0TCh+ZuONFTi5H9cg+e++QS2370NTR9rssvUtlUgm9Qx1JtFPCI21jgthGCIYfTIKGJKFebvexKHEpWItE0BY9we00wNR6jPvDYXczRTesjZ6MUoM8yG6XDmzBN3/gibKXFpuK25IwiDASAxcwW6dryIgxsfQrxxDgKRShgBZ9NSM1x4NorO7LppNBoKwdLDDlNNhQWuAoFRIJVAHgqEPM688D3nASOviconP/N6qCwPFeIMtiHELQjQHABpjzokwpjA3ORX4FyU22NNNfNKzrlWyTJ7jrqx+/b5zPhMokyLLFglAFiBnYS2SHmpQEDulSaL46q5caU721E95ywbDkaFAEj8BGTCplKioOCHFOLc7RY6/UQZKjW62oTDfcqsOnQsc86TO2oCjzsMt9QS4G31oxnLjwbpJIGXkL2chgR1ReATkoW5A8eQrob7nGO3xeA4HafJoB/96EcCT6soCurr67F69eqCsKOl0FERAopFxGFCDG3xhVdJvH533N1jRQUp1f2YeeHaWH1sFGWTR325sRGMHt6D5nM/CMbl8cgNtfRFVpr4hhznogxassQb5UD7rudxcPuTyGVGEa6ox/SFF4IxcyBVVREsBwoDAsEo9HAVeva9joGunUiP9qG6YR4YBzQ1iL4dryEYrUbT0vOhZJ0FXMl5O5IWdMnDFyKXHgM4hxoOm9fJpiXzZmS0gPeO23TGDMy5aiE2/ORVzDvxPNTPrQYAHHprCNGaICobo1DYGOmOAc45shmOSBgIMB1bnhtA5cp544cXk1oGCoUy97F9jmiwC+shp0kZNRzBnPM/j813/Qv6Dr+JKXNPKbzWZTXzet+MgDmnqTBgHVs+ABYcKDAMpGtNfwDkmXgYHPBr6WLI+wtY7XNPQaAgnwgh6gjojhxGccpu+I8X8850Zg+7kGBMkn8AXMLkCVpVyTFtXwJTEiBF3tVL1zwlB2SHBmCkkgjXN9kMNrXMCBmZx39lfZEvnwHZmiAIqU45v4y0TNHjK9mXDClCeXt5XqOSifrraGn3f5PXTjEyZBGBZFnFXc9NEILeIf/YinZxcmhJEs7THe9f8554LCsWdO/b7mSj7zn6C4QDHW365Cc/eVTqfddaAiaDRM0kEzHIdK0hL1isQ8fo1PLwWX7ITzZJIfxorshGnaeh3abXetWsJQX/Me5ePLzbETYfH4tnUY2VsJF5M5oAkBzpQi4ziumLLkbz3LPBGBv33ew9uBF73vgzwrFaNM07G40zTcYyl02hv2MrWk+4HGowlO+jbIAl9+FRPDPQA3AOJRQuyol45RYAXNo4QsmhHDKjORgZHYfWd6N2jinJp0dyMHQDg50p1Lc6DyLIcnjizwPgHKitA5Ldozi8K4n6VVXg+cZ5JofB55/D2PZtiM9YjMQJZ4AxRb4Z+oEHlajVdAuKwYo4tFAM2fSIjd1nOsgL6NQaGjKQjZnWgOAgkM1HD8rz4XaduQiB2DBT25yNmvPZCJjhLPUQTHiPBQHSHQmOB8QB4ZohtwoQY4IlCNjjmf/PbQUoeDfy5XIR2NCiTI1hQzR4gIsMH82nQJl9opWW5hagzcqsDxLHf5lPgqBplggEglVAMmeULGAMmWapQCBqWwl0kqVYJ7k1BLMDvUeJpdSPRl3qQC0p44fXKni/fAjI0hCtMu25T57PkIyfIsnRMWFmSHKvBcyuTPtPmf5JZszcUXoo0cg+ghO+2z/gXcpru/d1+7zBJ30cj9O7gx599FFUVFTg9NNPBwD87Gc/wy9/+UssXLgQP/vZz8q2BhzzCMGKzu2PmhY/tnTI4Whdi2lf4dIcHGWywlox7tEl0ldOPpPdLlwfbnD0v7UOseZZCIQrTDyu7nz8WlToNZZWkqsm3ML6WD4hFrM1UWpbeiUSjQtxcOsTGOzeVViAc/vDDBPuUt+6EsFINZiiYtqiCxGMmDF+tUAYlYnpGOzY6Yy97CMhen+cAQY40r0mrl0Jh4Xn6snoWR+F2x+FOR+DMxicoW/9ATx+3Z04/NxerPzWWVhw9XxkuYosV3HOF2YhFNVwxzc2QMmmEVay0PQ0HvlDL374zQ5cek0cJ59XhXiNglMvjqP79udw4D8fQGo4h2xHF/oefgjG6BiOPHkf9v/pFxjYsg4DuzYh1dtpaonpXMpTLj2GDb//NgYObi36vLyuLRxE52ONUyBahXRq0B57I8BsLL/l3GuNnRWm02YiqMaICAJ6xPnNmRlKlGv58KCaCZ1Rk97LG8sxW5MvxFK3btJ9P+ScIwCY59wCAI0SJlgB8hYGrjiMBte4o/m2BJoUszH1oX4GRTc10MFBUwiwPir5KLnxP2rW+Vh1KuSdZ7rJyFsfJe18aBlhnaPrBPnAddv2a2cAoZqpYFoAqQP77bbovQjzR+H2Bwqcj3sh9nqv/bz7sjXbx1ohEOlzMQuB9VyLfYT13Q+RcUk2cfAA7E+plfl6v48icU38GPSjMvIh881V7mgSff/CvSYEyPpEu3L2JzCiCx/6fCebNzhOfz30jW98A0NDQwCALVu24MYbb8Qll1yCPXv24MYbbyy73pJem8CIk5qeSvp6iJlMfJ6KZRE8puS16FtEnXDcWd4mUzQq9rLLNCeua4otGIPb38BYxz60XXm9c3mx8SZ1UROjO7GKRRlXHg0a+UCW9VVGbrO8oqqYt/rj2PrKLdj68m+w8LTPIl43s2idR/a8jExyALNWfajgvyltq7F7/Z1Ij/QhVJGQ90mCY3BrCTP93TAyJlenjAMHEiEW3g8gN5bF/l89i87730C0sQLx2bXY8Yc3oI4OY+l1ZpixWE0QH/mP5fjlJ17FjWesx9wTq7DttSGMDebw/o9W4hv/Uo0ezqEFgf91Uys+9x/noPvXDyH59n5MufJjUKIxxOYtxNQzrkD7Y3eg/aE/5u+HofGECzB16Xl2uM5schiH1z+MZF8HjFwGB16+C9Wt/7vwffW4HTuMJghzmP9yM4LBihpkhnsLNKzucKucAWragW6F+zhS1SSrLycMtEIsAvnfgRFAr3WYASNohgiFDjNjsF9yz4+8IEC1qUBxAcCuipljlYlbUhGQq8kBxP9BgBZx5rxXHKKG3SDt5C+hlkIl59w7o34C5JgZkAYroG0J0Yno/QhYaudYmPIyTTMDFE1DdOoMDO96C5VtCxGIJ6AEnYroWOiqAX1gELn+AUSmTgPLR9CQae1LxgxN5Fo/1RcLyUstBLL9hkuO6WmfRmtGrC1C5upjEOfffX9+nXzLISPg/SDdSisKo+LvIIRIcPgtYjUXEie6fS985l56N9F7LU/Au4H27t2LhQsXAgDuvvtuXH755fje976HDRs24JJLLim73nctHMitwab+AsXoaD7cggXCB7aWq4W44JJJYnLNZdLoeP5+VM1egspp8+xyelB0yPNTL93QM05EK9sy4Hm5LMSpsLlSBqfw4SiqhvknfwJbX/o13n7xfzBj8aVonHmKCWNxFR8dPoJ9Wx5C4+zT0dC2uqCuutZl2LvpPhxa9wDa1nwUgLe5Qhpqjxzreg6HH74NWqwSudFhKJGwWVbGLFBmSuXgnCOz/zCMXd3I9Y8gNzCCfc9vQrpnGOHmGqS6BlE9fwoClSGs++l6zLp8DsLVYaSNABqWTsEX/nw21t+1D12bu3HWR6bio5dnMG9RAIwZyGadB/Lzr+/FV2d9Bd3/dTsO//dPAACZzk4Ez7sU81r+EYaeg5FJY+D1F9D5+uMY2LMZ1dOXQAtF0bHhcQBAqNLMs5AZHcBw515UNrSJscRlt+wDfgEA4fgU9O15Q3A0NlTYfgZ6kEGxInfk54tl9Qr3O1GDAJNRtZ2BYTrhqRmzHq4AgSFAr3dp+BlcEYEYeIjgdNOKXa7gO2+VVFwRhWwBIP/lFgCE2Pv5vokes6ZQQPsVGFRsrTTgYiLdz4MIRlRo8Iz8wsS6VI/1iCuOb4GhArCOAzQ6ExEUFNJPIhAKgkjOm8+umrkQHc/eh12//jeAMQQqqxGoqUOwphastgq5vj6ke48g29UFnjIbn/q1v0G4bYbZV5qh+Sgw8jLBwo9vgdS3xn1M6yrRb0BGBoW5xXShw+75Oy6RuVoszPNk7rcyyBAgKhaESHyT7OTsri8wRo+dmw0OiwWpEvSdJGbw96RQcJzGp2AwiLExc0KuXbsW1113HQAgkUjYFoJyaPKEAJlGFS4mmMxPissD5NroySK6yAhOUCkDuTDlaOF9XC7RsZGFwKObh8sPQObU2P3aE9BTY5h6xhWFG4mPCEEyxi0Td0MknEO6mRREzihSbyY1hENbn8Ro/yHEalowbcEFCATNEDyqGsCCUz6FfVsexJ5N96L74EbMXvkhRCunCG3v2/wAgpE4Ziy5TNikrOeqBEKYccaHsfe527Dj0Z9jxoWfRiBS4d1JAIaeA1dV27GW9rvzqT8j1dWOxguvQsfDt3v7BJDfaopBjzjQjvS2PTjy/V/mB4pBq65AYGoCIQC5oSQW/N8PY87JNehefwidL+5HelRHMK5g71gdpkX6UDOjCuf93VLUBczsvd0AlismXGd+sAsb005cYK22Ho3f/iJGX34DwbEKRGfOsZESqqpBjWiYesKFqGyZi+7Nz6L7zeehZ5JIzF6FlhPfh0CkApwb2HrPf2D3U79GvGUhZpz4fqgBM8yFFKfs+mGVUw0zOpBFwYoaZMeGCi0pxAJghpaFEJ3Iqi88YFoEZG3rwTzzqQJQgOCQOSdS9UVe3oxiNj8OF8NyrKCIIwDwQs6QFzJN6TpuozL0eA6w8xC4uMH8jTEAgUHnL23MxfjINMwyBrREUiQ/aHZgI+hqI19O6Cdx5qZhVmtXnImqOUuRGehFergHmYEeZPp7kTxyELldQwjEa8CNLHgqDRYMIvGh9yPYNh3cQxhzOif/LfN9kjL1svXaj8Dhd9xl5Uq0qAoQGM1f4zJBxW8yQXnHyKEwhq53V5P/VyrpYef64j4AznEBZG2SeeZi9yQ4+Lq1/cccnP0O0juNiXoP4rFOP/103HjjjTjttNPw2muv4fbbbwcA7Nix49jlCQgNcqh5s/pkYMIni2TOYQX/Tbgh8acssc6xeJn1TAo9bzyL+lXnIlSVMJVLMq2IhFGgplOa9jzcLWegZA6D4w3u7vV3Yah7D8KxWnTvfx09B9/AtAUXorHtFDDGoKpBzFr+AdS1LMOuDXdh41M/wryTPobapkUAgP7ObRjo3IZ5p3wCiqp5WhUAoKZtGQKxauxe+2vsuPfHmHnxZxGpaRTvwdDR+cZadLzxBELxOlTPXo6a2SvAEnEkuw5hZO9WDGx8BVMv+TAC1QnzGsbtsJr2ZuPi4SwYEBvSEJo7DcEZzdCHRzH7pi9AjYZw8Ht/wvBb+7Hsfz6LSGstMvoYdMV8kbIZjlx+4mTJIBtkMgXI5N6bJsnEkioYVFScsBrhI/lr9cL5UNHYhorGNrC0jlxqBIFolQNJYQpmn/VJdO98FV07XkJ2bAhzzvkMFFUzeVuPd4xxgHOOsYEOqGoAwapaMKVw8nNw0+mbMTBwG3dthyklwoBZMRlfD1IyYv4OBoexsPw5TKsAA1cYstWl2/iVDLP7Q/MCUFgF4GEBsBjlvECQmpJPHJdPKsaYOU84Ny0QBVpnwiwLTL1O6rcYa3pMIVkG9xc9hD5TCYRHeJoShlGa3Vji2MvAEKyoQbCiBjFltn0+lw/L2/fac+haez+C01ox5bqPQm2sE5u3DDcueJYgcFjPToE4j+glMoHAx7omq1JaqAxGU+Zg7Yt/IbCq0EFnsw71u8uV3q+jRUKIWkkIT/d/k8HAU6t5qN+pMDTkitBDoDuykNpHi4S8AC5NP/3NdH48b8BfKP30pz/Fl770Jdx11134+c9/jubmZgDAI488gosuuqjseo85HCgbdb3QFPbjmrtH08RWsJBKmEpKfjPgFrQli8IzAamE6zlwXUekvsWbaShx6LKV4m8au1usa/xOy9quqm/DwlM+jUxqGPvfegR7Nt2DSLQWNQ3z7OehBaJgiqmdVxRzenLOsf/tx1BV14bE1EXjPquKKdMx/303YNfaX2HHvT9B2/mfQFXrPDNG+UAX9j/9R4x1H8KUJWcilxxB9+bncOT1x53+awHUnXgOqleshpHNQAmFMfzmRoQamwrakm3ILKCh7kvXoON/34Qjv3oULV99H/ShUYSbaxBsqAHnDDmuwFDNe8xlTMdhAMhJ8FdZ0tjhdLXTVs77HfJi7HLJEYy170Nq4AhqZi5HOJawi4Rq6tF6wmWIN8/HjrU3Y+Od37H9B+wysQTqZ56I2mnLoIWiyKWG8ebD/wEACFfVY9bpH0WsttVsP9/m0IGtiNSaC5ahMhvTyhkDY9ZxHsZmJZZi4kxzWwOsMJxCTG6Wv2cNwsXasGI6sFrA9fx/ephDTSl2ewIMyDWJKfNvCQVqapx3wSUg8mrT1CFEF8o/mMCgap8LDpC+j9GyHtARqSVAIsQzyXorYY7FDM+kCF3TqDJEEgJVkWiZhfozpoDetfZ+VK5chfqrPgSmqtCp1U+AgnjfQMGdE6HSE2rljmQjsQRMRKHkCzpXglLFIkH7TSMD5Urt4dEjMSu2a/+nz7OM7V4qgAJSa9BkkNsHUkzo5xwWhOQ+zp8fpzJp2rRpePDBBwvO/+hHP5pQvWULAW5mmCbiKNiA3iXrkWzjKpeOqhPROGOmaKYq1Mh5eCMWqYtuopbmDTCjHaRqJZfnRAbIPpYlHSJl9KC5YAZDleht34KBIztRPWUOZq/4ELoPvoHkSDdqGuYBAEYGDmHzsz9DpKIOy86+AdGqBgDAYM9ujA4cwoLTPjt+TPz836HKBOZf+hXsfvb32P3wzVACIRjZNMA5QvF6zL3iK4hNmQ4AaFayGD6wHXomiWBzC0K1U8AUFToDWDCIqkUrMbRxHWrPuahQ0y2DDigcwaZaJD5xOXpvvhv60CgSl5yIw/95HzrvW4/Gq1YjZyjof8uMPqRFnFcxa1Dtv/Oi7cvV2McbulvtY8qM5io4ggPm70wcCA3k+6YCI/t3YtfDvwC4Aaao6NzyNGae9XFUNc91Oq4Alc2zMfeiL2C4Y49zOq+hHunZj32v/xn71t0NLRRFKJYAmIJgpBKKGsTbj/wEiRkrEEs0I1zXjGR/B4YOb8P0M69xxok8Q0N1EpWBkQyzTBS6OTMtkem4A89hupkgLBcjcnDe14Blxo8Wonol+CLEOKCkFNiJw+i1HgKA2/cnVU/8DurSgMHADZia7JRq5yMIDKj2PZoHKHz/eWH95TBNUuEg3yDjXFSF0+JEeLEsIFwV+0GFMj9WAUpKDsgMDwGGgcoFy6DmNCBnCmtOnfSY7DmSEKK+tS7UokL641WmWE1+88pMiChTLcloLxSXZAjWkhPrxmTvf34TeE10XN33HRp0KgwPOBPAnaW3WEbfsshVnSAck7aVnCuk8WT3450gavF8p9p/j9GGDRsQCASwZIkZCv6+++7Db37zGyxcuBDf+c53EAz6WAw8qCQhQE1zaPkNOxc5ChPRLTvIHKY0CJEsJp1ofGXdxUyUMXlkfgBchTRSh7yyfNG8BtnIZp1zJUZ6cGdApBuKjMHww3jkIq7kYYyhdfFFSI31460Xf4nWBeehdf55CEWqkUoO2MxPf+c2qGoAS8/+KlTVXPk55zi84xlE41NR3TDXo7U8/CG/MAaHODJV5rEaDGPmxZ9B77bXYGRSULUQ1FAUVdMWQNWcm2fBAKpmm9F5LCaGA/bziK9YjYENL2Fg3YuIn3gqBCcNOiDucJMAKs5YiUhCw5HfPIbhH94DtTKC4a3tqDMUDO/tw+5fvoDmK5ejotkJw0QhQNvHpmJetAMAsCc9BeH8jv7Zthfw72+ebzY7awT6TseU4xVRBgCObH4akcRUtF3wKQSUEPY+cyt2Pn4zmk64FA1LxTwNsaZZqGwwIzX179uM0SP7YOSyCFbUYNoJpg+KnksjPdILNRBCMFqDtpM+iI6tz6D/8NvoO7AJXM8BjKFu1omoa11hWnDyDYhOgKJ1gDLdekBkxkKD5vOlfgOBESBTAbg1qVYUHQp180uKFV6UQExsa1XSQwCwIAX5v9IJDmYwgWm1ckYYKTXPZ3PR+sCBcI957PmaWXChPMk005z8EqACRd5d68mbVo78sUI6QiBR1G+Dqw7jzFWXczFpl/5WvNarfLnc4AAAIByosSOR5WKEISRRnsR11cfCxMQ1yTmG9zEhSzAFCiFgpQpjpeYCoPtPNupdBoC4/pDwuL4Tm00mYyRRPAlWgTIhNVQokNVREIxjkuHLUt8+uCxUR4FNYjpRLrgsDczgwv/H6S+Hrr/+enzrW9/CkiVLsGfPHlxzzTV4//vfjzvvvBNjY2P48Y9/XFa9xwQO5BYYCtJv50mdoGbCi2RmWNMRcfJWPSsGsHNiohUWaUcBGFOgaEHw7PhhgNz4YGtRDg0ABFUiasFSpW0ihdoJcWyD4UosPOOzOPz2WhzY+gSGevaCMQXp5IBdZmTgMGLVzVCVALoPvIHe9s0Y6t2HbHoEc074CBSMn0zMTUxRUbfwlML7kFk0PHw7Qs0tiC1YjO6H7kHfs2sx9brPINwyzSwjeeZ8JAClMgPGgOjq+ag4YS72/f2vkTnci1R7H4xsDm9+9xGEp1Zj+ufW4PBoAFOjphNwV7oSNcFRuy6ddGp/us4+TvdG7GMaGMQrsk9qoBvDB7dh+pprEK5IgOkcsy/4LNo3PIr2dQ9irOcgpq35MNSgo8rVFQYlncO+F26HogURiFTB0DPo2v4iACAYS6CyYSbqZp6Euukr0HtgEzq3P49QLIG2E65CtHoq1GgMoYoEwAFrv2Lc3LwErC935qmQ2I/lMwaTTT04JCoiODPhQVwRrVsWaUnLZO9gjGg8eE6/3WRJg4ybMfzdigodjuY+r93KVsKOqsUMBmXqmJnQjTMwlcNgjkZb69cc3DqxBDCevycKX2FimYJu5seW+gZ4arh1yOERlDfm3gy3EK6QQoFcQoD9nxXLHhCCHnhBm7LDAwCAUKTatAzETf8Mi4wwDXNELtW9Hp4/hlu2VPvB3tN5KWR01l3XyxYuSRu+k4MJoW+p9lhWMWmaCE5e4W1LITczWo62Wqb9p/4/NIpVKRRwllOEBpw5RCP+mA2UXndRKiZ4UwE9N/HxO05/2bRjxw4sX74cAHDnnXfizDPPxG233YYXX3wR11xzzbERAmhqezXDhURdiuKexGX1R0puRlQaiu1dSILw4dJsTiT7N9MCMLKZku9fGs5TQnSjMzRnw8hFHcxyNiYutOKN8fwpBa0LzkdlXRt2vHYbsulhxOtm2v0fHTyMuibT1HVg66PQs2k0tK1GvH4W4vVz/N+fEFMZnsdCyDnmfWwzUIxh6rWfQvstv0By/x4oaqCgDACoIwr0qkIOPDUWhDLYidTOw6g8eQFGNuzE3n+7F6mDPVj+049BDZn1pXTndaR+Af05R5X9Wt90p+IMEdQoDpXMLz1kPq+et1+CGo6hpm2FyXArDICKphMvRbS+Ffue/SO23vUDtJz5QVS3LrSvH+1vh55NYfYFn0VlfZupWU+NYOTIXoy078Fw1x707lmPw5sfQ3q0DzWtS2FkU9jz6u2IVE3B3DM+aW+EakYeUMCtdTZcGb7dYW8ZNzfwbJQ5zLzilMk58tGEScnCkytzZw62BZYcYORPqtNH8xmdTc4+lwqAqRwwGJRhExLE8zce7XDasHwePJUYLosALSegfUif3eKzV5Qwd7AiGc5ZFkVGtg5JIUIeTHKwyvRRGT24B1WzzKAA1jPNVBvE6smcsKQZ5rmO+YWryAR5HwnHizLrNL/FREhYn8iOPdZEbrBSHodayAvwDuyV8mzBzrEeZL6Ze2oZcUejmkziRCgp0PYfZWW7AAEqyAtAj9/lzI+EjucJKJ045zAM8+GvXbsWl112GQCgtbUVPT09Zdf7juQJoAwYxedxVZTM/cQqnwiJi3/p3HjRvAHlMPcy7T/3rk/RAjByksW/jPatxEsAiiZxEeAmZbxM1fWzccLFfw/kDKh534ZcZgzpsX7E4s1g3MwMfHjX82iZew7UgMR0JO0gOZZApOhmpGYA3WJUKBQs61ithl55EWO7d6DxmusQapxqV6VkmOBELWMo+h58BWplFPGzlmH45a1I7u5A6w2XIzKzEZb1Nkd8AXRS0YjucFF9Y46qW9FhRwHJRTkCw+ZxqhZIv7IZPZueR8u5H0Y0Wof0YA9iU6ZBVTQgv3FYTnrxWUsxr7EFh567E3sf/h9Uz16BltOuRCBSiaGu3WBqAJGGaSZ+X+fQIhWonrEEtc2mwDbUsRMH33gYLcsuQqJtJRhjSPYexs4Xb8WWx/4Ts07/GGpaFtpjagkCSo5YA4gAYJGhmYKADQ3KCzQOc8XtON40lwCQn8t5uSInjxIrJYvxFDLIWgxojpwj5ylMRgGgT0+Cc8v5mUHPKmCKAW4oNnSDaxzQmSkAeDD3ZiGPcy6LgB3ByDpviBp3gflyMfFejDy9F9vhGvl6COMhaCxpvWSNoMydTPi2DqNN0xFpnI7eDc8gPnNRQRhiltf4i5meWcF4jEt+1keZQDSJVl5f+4TgWDt+cbXLHzZYxshS4ds3s+t6b8sh0SeAHFOdi0+eQCN7mVvbHxyZXA5eYMSLaf7dTsJHI4og5wVCw3H6y6ATTjgB3/3ud3Heeefh2Wefxc9//nMAZhKxhoaGsuudNCFAS4kTL0sgQJOplbOILlRFF9JJtqq5E02VoxGQWUlK1uhzY3xH2XHaoVp+anYtMA+X2oxsIcqfVphm4jS5c1pRNHTuexWJhgUYG+oc995kIfT8xO9T096RJRQqEBBKdRxGqKkFlUuWF94SbcMjW7A+msTAUxuRuPwUxE6Yh/k3fwnBxhowhcHgzgMwSGepX0B3ugKZPPc0u6YHr+6dYf5RnwbrcDprPbN0dycOPn4buKFj950/wayLP4dApBLJ3sOiC0OGIxc2NarBeAJtl38e/TvWo/35+7D1T99HsCqBZM9hVLXMBwtoThhaiynPO/FWNs/BwuYbbEgcBxBNNGPxRTdg14t/wO7nf4dFl/wtIvGGAsdJJesIAizvM0AZS0MzBQ+rzVw+LnhgzLAFd86A4HBhLgGz0jzMkOUtmYrz7Z4b41ovGcCsx0WFFqJJ54b5Mz09A0VnMAAoCsAYNy0AAMAM8MGAkzBNM7l3CxZU1GGTMs5uCGKR9cPOpZHjcjgObUbybjGD8NuKw9iYUZ/y55mjwDE0F+PmSvqU6u5AquswkNXBDR1KIITEklNw+Ik/YWjv2whOnQoWCEIJBIEsyelBIuHIkkcVw21bnXWHGZV7/Poo44eKCQClthH0kfQFrnufqFWi2Jj6uEb0CSgHMiT/T50M3p6+X1QYoiE6XWtU2U1lnQ4rbidkQn9V+QOOk5R+/OMf46Mf/Sjuvfde/MM//ANmzzbDK99111049dRTy653QkKAMHEnidmmmYELomFMdvxbCZNZcdjASLPPN48OgURLUW5eivGu47oOpjlqErpY2FFWrL5Jho6WcTMfQog/CsXws6l4wIEAgOe1yW4KBKNYfNrn8dYrv8H6J3+AbHoE81dfB1ULlTx+Qlg23b0JSUzTMtM/A7hhIDc8WKRB51DwN8wfDz7wHHjOQM1FJ5pMTN0UGNwAdGBEDyESNB9C71gMNRFTtd2ZrEQi5KSrHMg4krSRcbgejTAxLAfo6SQO3fUbBKsSmHnJZ7Hv8d9h5/0/Q7imAdmxYRFvD9emyhhqFp6AyhnzcOSlx2DkMqhdfCpqpi2BhZnXQ0xwkjTyCYq4wsx31yqnAAoLY9YZH8dbD/8Iu1+4FYsu+hoYU0wBLGgx/oCa5WLsb+LobQkFjIux77NRBYGkk3MAMMOIWnNzrH7ypH/rvS54hzjsaESWQoBxIFlngGcZDKhQoCMYzkLXFQAGdCjQhwNgGgdy5vWxg6otAATy76BXKEurTRG37/TRukbRuZxJcVkbbAuCyxogdzr2/iFoQ1VmRwBS4FgCchEnBKz1vA898Duke4+IXcyvafvv+x/hfLBxKqbd8A3zegFiRJk17/dbtk4JfLcrbKgUJiijiWxPLodZWVZzMSSoN9dLE6qFCUrA8o0ZjyYKdXHDfPzUJ2j+XYYMqqyRJah0E1VKqqnJx+7ItP/u6Fu+8nWM15bb+Zc6/tJbey8JC6415x1p/z1GS5cuxZYtWwrO//u//ztUtfzEFccGDuQacI1omQscc8ogdyxegdEj52l6+1Ko2GZQ1isu0/iUagnIZc3EWbQuujbReeHDjFoQUcGHRt0v6bkMkkNHEKlqgMaI4ELqraptw9IzvoS3X/kNmuechdq8f4CfGNuK7vioBEa4mEmSjIMQTWIch2DAtLZ03X8nxnZtR8NVJMwljXaSZtAjrjS3APSUilzHAQw++AJqPngO1Ooq20Bi+OAqMmRnHEgRc1pGsf0BcjEDgSHzOF3F0fXrPyI3MoxZ1/4tgpEEZr/vS9j3xO8wtP/tfKcMcNW5QUGLmmf+1IpKNF3wQUGoNVwOoGDWWDIT66+Z8CIrKhRX8jh+BFE360QceuNh6OkklLDp36BmuAlHysNRVHcCMFfCK/dmyjhHLs8cqG7tPIBoN7cTh41NKcNaZj1OeM85IetpnpE2NNOKpOQFAA4dFTUp0ydANWODZkY1gJlMOtM4ogc0x6JotUMFAC+IEBctMnafPZhym5ktomWmydC86nKvoXYdsshmFC5E/XMoZjs/77SKuCMEMAWKppnhfJlihvQFEIzXIjPYi6o5SxEYAlL13IEFBbgJqbLao5p9P+tsMe27JVQRoVcbcxU5SsyEDYHzsEp6lifCTzFsfalWAbfmf6KWb6r95+VwHxTGSfgIbez/Z+8/wy05zvNQ9K0OK6+dc5ycMYMZ5EQAQxAgCBAgSIk5gTJlUnpkBVs+8jm+1zq+x+dcK1zbtGVbEmlSFClSJAiAESABIuc4ATMAJsedc1ixu+v+qK7uquqw19qzAQLU/uZZs9fqrq7U3VXf+8Xa5yqK3gq7f9G2X9ImrCb0WqVl0MzMDO655x4cP34cf/zHf4yWlhYcPnwYnZ2dXvKweumCQIAotQfkB9s2fe4iNUVRaql/IzYKCucqNqeoEi+UuVejlSxHei/H+fUZzuWCD7taxtzp1zF7Yj+Kk8MsQZhjwbGZ6tyxKsxJtU5mXRybVvX7RmxZ+q8LoEBijGvoezXjM4ST00dx6rnvo7w4Bd1Ioq3vYgxsvRmJdEOgsmxDFy59359ExmQWpZQq8xIZ1jRCWuKIYxKY4fkjr2HmhafQcu1ezO57EfMHXkHnhz+Bhosv9auxAEc0SeB1LRpAlu3C1LIx/tf3ItHfiaYPvsdzAI0jERxUbL9TC6UkiiV2c4zGMpwhwTfAvU/zhw9i/vhrGLj980g1tYOUAN1IYM3eT+HA1/8PVn9pEUYq5/kDGCU/hCYFGGPHmVBXqk8JAN2PKuOY8KLfAL4knGpAJU98iaMGaCULo288CaLreP2R/4FqcR5E07Hrzv8dmm0yzQGlqBbmUTg/BKRM5LvXs3ppbZlvOVYKgFiX0hMuINAJqM6c83mQA8f962UcjmmOEoSvmAKjziSVrJJUb8GdRwoNQKHAxksMCmoDyWFmEsTnNTEr1BdRv3eI+oekhFt2MKeBJ+lX35UIbYNdLeHkC/fCtkoApWjs3oyuzdeG1wvIIUN5pCOlDKD4BrjzuPbDX4JdKaI0OYLy5AgWTr6BueOvYd1Hfw9OYRGTrz2H+VOH0bzranRcfrNnjkWqPgjwIg9VSLQ0NAIELMe8hZPIhC4nYk0kxTyDov+RpIEsRyVtEcqIOQJKwefkQqhurYlC4r4jmg9zP6BaSPRp0wVNgORb+BbYy0sM/XKl8WK/Au+6qNmm7y6JfxTRtw5A19r+u40OHDiA9773vWhqasKpU6fwxS9+ES0tLbjvvvtw+vRpfPOb31xWvXXnCViJ5y8xL98BMfueCCychAatUj8cl0JvQVmUlqOeEy6xU/JisxyKVLVrgFMsYvb0Ycyc2I+5s2+A2hbSbX1o6N8CzTBBdANE10F0A5phomn9xdHt1HizojbqKOdgqc+6z3yFLdiV4hyOPPK3yLetwdrdd2F+8jSG3nwUqWwL+jbvDa+fEPkldUFUcX4cRNORyDaBEDY42SZalLQIhwVwV5wawfiBxzF37k0QMwE9kYSWTKH5mhuQ27AVALBwcB8KJ46icPwIoGno+uinkd+5O9CeHFpUeG7LZSw+fwALj7+Aytkx9P5fXwYxdFC6tDpmtpRGxmQ676qto+xGDMqlypif8Rl/U0hWRRyAOjYmf/kz5AY2oWntTsBmzK5eAfREGq1br8Tk68/BckGAmOVSVMVzUEg1P1mXV46DL57nSmOqezvFTA0cjTHTetk9ngAqKQ0NPRvhODaMbA6lmTEsnD8GaBrmxo9j6MBDKEwNwSr7oaUGr/0oGge2Y/TgoyBEQ+vWq5HMs6gxUcmuuGkOP72iQQWWAAa8LS6d1yoAHSzCcTTYBNA1B8WS6eYJYI7BiTHTAxUOgNSUPBYJ3Lr1euY+rmSaUH+tC42IFSPdjxwjBRYmz2Ly1Mto6NoIUIrTL90HQjR0bvLtTgNyBxqsyjGU49xPwISUQRipNLK9a5HtXYvmbZfjxHe/gnO/+C42ffKP0LBuO+xyESSVAnEfQDFRm5OVe8JNg3hZ3tlI86oaqGYGl7exjGevlhwBxW5hjWmpRF5UazQgfm45AGY5/gBiQtF6pPaSOZAA9Je7D0v7rzgOxe5/OSTa+auRe+T8AReAwKJMgHidK2CCtErvPPqjP/oj3H333fizP/sz5PN+XqBbb70Vn/zkJ5dd74qaA2kCA69qCcyFC38wZVtvRZIhqRlX4CVYiSqiwb1/3H2Jy7MTGHnuZ5g9fhDUsZHpGED3Zbeiad1OJBtaQ6/xSE1AFtoZteElOo/ozSwuPbtKhclzoNSBpplYmDgN3TABSj0mPm6xFe/v4swQ9j36XwDqQNNNpHPtSDd0INvUh56N13n1eX1X5sSxqjj+2Dcxe/YwjGwDmjfuAShFlZZRGjuPoR/8PQa//C+RaG5FaeQ8Gi+7Gpn1G6Gl0khv3OgPMQoECNFKxv78aygfO4PURZvQ9a8/i+TansDYqpYO02ATvFhOIJv0jV0toeJiVRCRlTQQbgaU882AnARQPTWCysQY2u68OVTl33/Nb6C5bzuSje1e3/lHLzPpG9X9EJe8C9SB70gL+GYvSf/eOQaglQE7za6vNADJaXcsjRraPv0pzzb53LP3w1yYwvmDP8fwwV8imW9F+7ZrkWvoRrqpByOvP4bTT38f2vM/BEBACMHIwUfRsv4S9F/zERiGaDOE0GePa4u4E6u9DMkFsSEBpTDiyf4ocfddClRzFHaDBd0msN3s0pS6IZQNB46lQRtzE+EZACwgNS2MB+FjAmWntaqrHQmRFoaZCPnnY8ZB5HLlwgwAYPN7vgCiGyxD9Ev3oXXwYhjJjFRfgA9VJM88NCnLDC2cs4CxVx7BzMkDSLX1IN3Rh0zvGqTae9B36ydx7O//EsNP/xQ9N9wFPZEGdSgqc5Mojp1D+VgRDRddAs0wURU6IEYMkh3QSajZDzO3ckGDRWoLFVqvxFvROtSSwFgyn4mLBsT7PltjJiwa8X0lSDTdi/C7iiOR0ZcAQo39FM2KE/MKQllh6b+azdcxlrHAxIT2jBR0rNI/aXrxxRfx13/914Hjvb29GBkZWXa99YMASdq68g+r6iW/Em1IDOEFmg0BUNC3XK7ekGpWpYSxFx/CxL4nYGTy6L7ydjSt34lErjnUERFAYLOXzZrC+1YzRW1SIZLJ0MuV49nWfnRsvgblqVGMnXoBldIc2gcvQff6a1n5GiIsUerg+L57kc61Yc1Ft6O4MI7i3BgW54YxcXYfGloG0NC6NnITN0oUxQTF3NARNK/fjd5bP+H5UlQzgF0q4sRX/xxjD96P7t/4DCqTY2i69nrktu8MTIkMPIXNoKTBzrCBpPfsRPnYGeRvuAzJbZu9m2JbGnRDiAgh3GBJ4yvYKZXK/gZPKppnBqF2LN3cjVRnL6ZeeBwdnRcHQZGmIb92u/c+GUWKcoMgUXWbER1duTaA0CC45Pb7msXKVHsozAUCxwCsLIWVBRKzrH47BSz2AalxoLowi8rMBIZnfoney25H544bANdHwShTDF52F0AICNHRtee9ILqJyaMv4vxLP0Nx8jzWv+9uJPMCKHb54TgmV69QFxhQgDBmjwMbxyBwTO7TwP5SYwnmX4wsRNw+aP4ckZIOx3028tkSqpYOGDYsS4czwbgdJ0GhVQjS426dimkb/+3Z0QuAhzMJqoaAXSiXBTizGz0eCXwQoFqag2YkmdaRELSvuwxjR59FeWESRiIdlDRSvx4JHEh9EAVEBNMn92P42Z8gN7AZxeHTmD70AkAdtF32XnRe/X50XXsbhh//IahtoTw9jtL4edjlotfQ1JO/ROtv3IVU+xa/G0K0HCpoAqQcCc7Si2JA+PFWmM2ojsA17FFOhCOwSnXnBRAWH0kTvIy90koHAapXn+ibJeCWSmNMolChD4lp+ZQqaKyFZJPdleFfouz+VyyqTxQP9G42C6JYeSBab/vvMkqlUpibmwscf/PNN9He3r7set+2PAHJORWd+19Vx95aSbzOEdXhFSppAy7U9lGvMtOKeklibhUmijoOpg4/j9FnH4BdraDjspvQvudGGERMjxhTubiYKdKmWq6J7LNqhiOouCVGUImkEUVmOo81l30YAGPGHduCpht1JTkZP/sq5qdOY8e1X0Jj+3qgi5ntUMfG8z/5U8yOH0dD61opdBuL8e/feC2dROPai1CcHAIMXdqMHasMu7iIZEcXyuPDAKVIdveGbv7E8edBZPzZAZfxu/laVE6ewsTf3oPuvk7ogy3+9cIEi9/LloGkwXZgSxBd59MljI81uj8s6GMCKBCbdjR03XgXTn33v2Hi9MtoH7wsWlrrXeT7reglFk+fEqYV4CYuLC68DwgAf/zUDW1pZygcg8JO+9F5qEFRSgHmrO4xtFQDMms2YO7IfmT616Pphr3AtN9ONUNgFjSsufzDXp8dA+jYdi3y3etx7OGv4/Uf/mes3/s55Hs2uBfyyXC/kuWZYyxJnLlWiQN/FyjpZQInQZFqKCOVZLYLpmEDlo7CRJqZURkUxCLIjPh1BMC3CADc9kPDCLoaAm/e+bUUS4KD0GESFt7VscpYnB1GtqUXZq4ZAFAuzCLb2i8j1hjTA9FPwHsWNaA8PYbTj38XTet2YfDmz4IQAseqYuzwExh55mcojpxB/62fwsKZo5g//SbS7b1o33Mj0u29SHb1wS4XMPTIvRj56leR2bEDrXfeCbJO9i+ae/wJLL7wCppuez8yW32gIDFO4hoWMwY+j8ai8F1gWvWKUsEytjO+bosS8VKbbP5ChezAkrxDcApOjfnfzQW54Ntmgy1pS2vbfG3BD0DMgixGOoojvRQxuDqk6mGO9awP8gZXa8QfKcToEv1YiShCq/TrTXfeeSf+/b//9/je974HACCE4MyZM/iTP/kTfOQjH1l2vReEJR1Tkz7UIN7HXHRgFP3PcolqxPs4OvER5DIXNM2m0oc4/qf2Pvkfu8Y8VjyyAnGAhTNHcfS7f4nzj3wfucHN2PyZP0HnFbewWNhiO0T+1NsOseXfUjnqf6B+aikXVUY6IX8oAYhhwHKqnsmBOs4wKhdnoOkmcs39cvWajoa2tZgdPx6YK0oYg8s/ANC6+XKUpkdx4p6/wuzxgyhOjWDy5Sdx9p7/BS2RRMvV70Xp9ElA02B2dUb2iWrU/+j+h7hmIZpG0Hr3hwDbxvQ/PiBda1V9JFWuyhjcAfE+88WU90FR9z7EJtCq7EN1/35U80BD+zo0btiFkad+Crtajp1TSggSs8yUiH+oDjemoysVN5nE30qx55zqgJ2icEwKJ+XAzjqotthwUg6LdZ+xQXM2M8uggFbRfIm2w/qoJ5muP9u3Tk5A5T4jlZxslqFZ7JNu7sbWO/4AmdZeHHnwrzH+xrOh4/KY3zremeDkAMYCM5PSqvEgVwQcmuX+zjHuvSLc6/kpZkbDGZzsOY6uIf+F/5tSisriDOaHjqE6PSmfFhlYG4KWwAclhKpOkUoTwjsrvrv57o3QE2lMndkPEMBM5UA0HZXCjDu3xPtEvfuB4+5z6lgVnHjk72BmGjBw/cegUWYmRBImOi55L9bd9WWUJ4dx7B/+E1p2Xo3OK2+BmW2A41iwKkXYc7NIZpqw7kNfQtfHPovy6TM492d/htmfPILq6DioZcGeK2Dmxz+HNT2Dsf/xVYz+z6+iMjoG6jiwF+dQHjqHyuhwYL5DP7WSO75lZa2NaY+/h+V+xetdWOhIFd4nql/Gov89bu8U97Y4Cltvl3rfHMP/2Cn/E0fE9j961f9kxpfHAETth/WQVrW9D7Ec/1NdfqWEUu8DW/ms0ioB+Iu/+AuMj4+jo6MDxWIR119/PTZs2IB8Po//8B/+w7LrrVsTQJWoPMt9maKkEnaCQKuGSzxi61OYeNEHM6C9jvIZEA4HcgUsh6EQtR2u2cTwsz/F2Cu/RKZrEBs+9vvIdA1K5RwjZIOvo51AX2n4Yam4cr308wIkSdU0gelGCLKrJUyfPYzJcwcxM/w6HLuKNRfdjt6N18tth7TX0bsbZw4/iMmhA+gYuFSSyja2b8CZQw/AsassUpIkhZK/5wc3Y+2tX8DwK7/AmR99nbWh6UgNrkXX+z4NkjQx/fxTyG3fBV1PRDoESqHtJNMw1mFq2Zj46r2gABpuvgpOVYOe8G+kJMUTJlXUAFQrfiN6UwV0LAmtTBjYEMIgis5xmg30XHk73viH/y/GX3sCXbvf5zXmMdyEeMcoAdJjQKGbdUqrumY+/DzPassl/zo8x0tPKpni3p7uRRUN0AF9XoNeIdAqDGAQl5lfPH4EANCwdgcDL242X6Pkz3E1S2Auyg+bZgEJPYONN38Rp5+5B2efvQ/Na3fBSGSwJBEBWFDqMqeuxsiioBWu6fCjB8UlJRK1QRxwEMqAEtcMlQsmkpkqShUDxYWkO2cOSEVD5qzrK6D5YF19OfUyMHb4aZx7+l4AQCLXgu0f+d+g6QaLJhT2MgtMt2iOE/reChoUFQjoREdz33bMnDuE/os/AEI0mOlGVIozft1KvZF+QkQuN3boKZRmxrD1jj+EYaRkabwN5Ls3YMMn/whnfvpNL09AsrkDdrkIqzDvlc10r0HD7suw5u4/wPQLT2Dqhw9h9r6fM2CSTgGg6P4//wjlwycxc99PMfR//znbCBy/wfZPfBL5Sy6R/AFiHcojF9Clj9cKSGMztYtZku3wCtVcBxdEyj7iJVS0YoQLwnNgCWGarUw0QBJNg8SIa0uRaLpUjxDPI3F/VphtVfpfM4lBSRzxZqyQpP/dbAIk0gUIcles/XcZNTQ04KmnnsIjjzyCV155BY7jYM+ePbjpppsuqN631BxIYuap/FLZieU9zVFqNSnB0FJ1iNmGzfpfTrUHUdF+VJo7eQhjr/wSXZffivYrbgpGwQkjEvGdRhyPOxfImhn+vRaHZgm46MKCLDAfVmkRMydew9Tpg5gbOgLqWMg296Fv202olhZx6uBPYNtVDGz2H+IwhiWVbUFj+waMnnqRgQCBGtvXw3EszE2eQlPHRmkTNcoUlmsSxLUB+Q07kFu/HYuVcVgLc0h398NpYkza/MFXYc1MoelTn/dizHskRP+RHBArBA7PEWBpgG5h4qvfQ3H/m+j4g08hvYOZrchJxBTAKjTEgUAyVUFh1teRJxb890WUrtopV+0PFuoz5bSibce1GN33CFq3Xgkzk3evod41AOAk/DYlxoNL9NzxOib77oVl1cB8ISiAjHDTdco0FRWCxIzGIghVAHOegYtKE5NG5tdsxczhF5Fq75WYTzspJwKq5liD5oK4hgCGraP3klsxefRFzJw6gLZNV0qx6ympETjXQw7LbeKY8P0FOBAlCDp8lnQg68ChBNUyiwpEdQoCIHtS9+bRSzIGoT74EbYKY2cBAP1X3oWzz92HysI0Mrl2by5UH6RawuUGiLerlLcqRRjJnPc7mW1CZXFGZmzj6hWfd7ePjg4kMo0sM7Cmy1HcBFv9RKYJ6z/8u1gcOYVUayeMNOuHXSyiPD2G0tQIZo/tx8gD94DoOrLbd6L9Ux+Hns/BmppGZW4CibV90BuzyFy8DVoug8rps9CMFPTGBhiNDZh//BlMfP97MNrbkOlZE9r3ADirZauooQxVk4EJ18hJs4RCIgAoa26Wad7PpW+KuG/qwnumWcsLFVpL7haVROFJzRp04V2uNVRoFIn8SD1ERJv/lc87pkQlqi0scih5a/y7kMNdpViyLAupVAr79u3D3r17sXdveGTF5VDdIEB9kaSkTKYmZ6BV4/zXSPQCBfBAiGRAil5QYz8iNgAnyaKhLN0JeItyZWEGZx75LhoGt6Hzkpsu3AYwZOMOpVrXg4iwY1G5D6LaYYz/fsycPID5oWMApch1rEHfng+gvXMHklk31KNNUS3OYeLsPvRvvSnaodD93jlwOY68/A8oLowjnfOdYLKN3Ujl2jB68nk0dW4K+AJwBtcsUFTyvu1ForkDic4OrwylFNNPPYr0ho1I9vd59uV+RcRLSqRVheRggAQQ5n72OAovHkT7734S2Us2e4MhQhnb0WDo7N2oWjp04Zxl+ROszQj2/8qrJErVdCWLZteemzD1xgsYfeUh9F3D/DEcg0iOdF70H0VaSSzAyVC/jKYwCdz5MsHD4oBJJavEAwB62Xf006pMym+ep6g0ELSuuRhtX7oYpMBAiwhoHFNOzEQJyzuQmKfS85E0G9DQtQFTx15B26YroRL3Y+CPUr0aLEIBcxGgZaYZoXq4ZsCcddvT2ZpAHDYXWhlIdlRgOxoDfBqBBgfpw2mpf9CUMIfUtwGfOf0apo6+CAAYeuUBgGhIuLb5Xj8vhCFRlx/hvaPUwfzYSXRtvtYfa6YRleJc5DXetSogEotToGVgJ86lf4I3HvjvGLz6I2hacxFLnCY+3wbT0OV61svJoZJpZLoGkekaRMuOK1BdmMH0Gy9j8s0XsbD/FRiNTchcdQnSl+5A9cwQxv/bN1E6fBS0XIGWTqPpAzcjs30riK4j+bFeVCcnMPb1r6Prs19AamAwdroC4SjrXb5VbbT7W8yOW2mQ32saJxV3tYGJcf/BNGeVdmp87qNCytZCjiGvTar2taY6xHwrwv6SnCKRdYh8CM9HUy9F2f0TmwYsHiJJ3KtEyb/7fi+rXwITL/EJvy5aACDcfPhtbv/dRIZhYHBwELa98g5vdYEAo2DDMGxYmfpTFDuKCY68WBA5m17dtfOKhO81ViIlUCk6sNLLiScYf5o6Nk4/8i1ouoGBGz8OAhIE63UMev7cEVilRTSv3113V+WOxR8vz03iyP3/BWa2EU1rd6Jp3U4k2jq9YuKLVC3MYezAY5g4/Awc20K+ZwP6r/4wWnq2I5FpACAv1lQnoKAwU760MbQr7ry09O6AfiCFsTMvYXD7rf45oqF7/bU4eeBHKBVnkMw0SddLGSrF6BRiouUKxcwLT6A8dA7d/+y3Zam/ZAYkDFiUxAllKqfOIrV9PbKXbwO14ZkBOTaRIgOJJJkBlVnHnLIO5C2Yk+y3naJSfHT+3GoWc+Tl2gDHAPR0Fm3br8bEoWfQe/kd0LTga24UKapZZgaRGgeK7m2lGlgG5ExwlaYamLlPzvLF17YGsqBDLxHoRZY9WC8yJlqzqPduEwdITVNYaeL6qlAkXJ6yysEZhRedyBsnZeZBAJDgWgECtK7bg5NPfw/W3AwS2abaGA4pIQ9lN85VSxPHZSwsxog7UUwAde2rBeJRfDQAWFtCIsk4OY1Q5l8BB6n9bkY2FwAYIdFQuDbNgYMzT37PO25XSjDSeeYY7GZrVmlFNjWXqS/OjMCuFJDvWO/Vm0jlUZg6L5n5iNeIFGbSx04AmmZi221/gFPP34Pjv/wGmtftRv81d8Fw1wHJHJIANALoEBtIpJvQufu9aLp+L0pDpzGz/wXMPvY05h78JUAIkusH0XDrXqQ2r8Piky9j6t4fYf7p59D64TuR3rQZnb91N0b++1/j/F/9F2S370DL+29FqqU7cnr4M2Yu+uNflnQ6Zq3n0vJilw3NiuDorbgK2J/EgiLEeguZHr6uVjMk2sxXjAbUJBzP0Fg/Cv4sZM8vbwDLzRci8SPLBduSYE3U3Cjr6kqYCoWEhV6lXy/6t//23+Lf/Jt/g29961toaWlZ+oIaacXNgcSFx05dOHQN2KsrL0zNMXXF0KZi+DiHetlTVapVZbkUjbzyEBZGTmLDHb/rqbaD/autroWh4zj+s78FdRzoZgoNA1tXppMKOVYFJx/6BjQziWRjO0b3PYLhFx9AsrkDjet2onH9TmSbe1FdnMHYvkcx8fpzIJqBjh3vQdtF18FM+8ksqCuBrmYIDCGKQ7U0z7IGqxRyO3TDRHvfboydfgkD226RQmB2DF6CM4cfxMjxpzGw41ZMnn0NVoVxWXaKINHQgnRbLxwj611jFJg9ulMuYfzBH2HupefQeN31SG/YLDP+EnAQQYzIUALEPWdNziEx2OnZ70at71TRodsuEDCTFipjaW8KtIiQoI7JTG04qbbAzRsuweirv8Tc2TfQPLCdXS6asYCb/bC/qQnmG8Bt/kGo79RLBWkdd+KlALVY3gIOAJIzrIhRYACAOfRRXh07V6RSEiniAIlZpqXhzueUsGzZs8cPoKF/C8x0noGBDOu4UaJoGrgI5NkfYOrUPnRtv0GK4rLi5DBthlOFlDNBJGoA5RaHKUZsDYZhgxAKcjiPhJvXic+BWQheL5rTFSeGYBUZqkvmWzF4zUdx5MH/gZlzr6NlcOeFbfLC/Q89B2Bu9DiTxLf70nEzlUO1NO87Xrv1qPOumomE3RMz14gN7/0Cpk68gjPP34/D3/8zDFzzETSv2QnAzycgms1EtqEBRokg17IGuRvXoOljH0L59GkkurtBu32v09SatcjfcBWmvvdDjPz3v0HbJz6K/JWXo/f3/xCLr7yCqV/8HOf+63/B2n/9/4aezgTarEVIU2teFWl9ickJIK439bYJyO9cvRSn0Yl7z6LGFtvWMriQqOzgxKGRi67IfC83ImGkzf8F0GpegFWqhb7yla/g2LFj6OnpweDgILLZrHT+lVdeWVa9ywIByRn/Dazm5SqsZG1vvrpAiC+1uPg4CSIlIauVuL1tTWVFH4FarxG+O6avxrVNFr2A0/zQMYy88hC6L7kFuZ513nHNkhnMKKmNaPdbnBnFiQf/F7Jda6HpBk498m1s/sgfsWyqF7qOKOvmuafvQ2l6FJvu+n1k2nrhWFXMnzuC6VMHMPna0xh7+WGYuSZYhXloZhLdu25C+7ZrYSSZyUMty2OlNI9sc58rkQ2XvovUsfZyjJx8Fq8/+w1UijPYcs0XkMw0Q0+k0bH2coyeeA6LU+cwO3Y09Hoz34xUdx+SXb1IdfWhai1i4qGfwikV0fYbv4mGK1zTElFLpdje0iQbGbGIbxoDgLgpUO2pWZiXbPLMf0T7f+oQb55tSmSn4KoOx40mo1UE+38l74PnSAo5U61juJFsXOYs3dyFVHMXZk4d9EAAr8Obj4KfK8Bz+iVuOxaRwtpqFQI7azNwQwFqEZCKBnOGFUrOMKkoz/7t9dtlBL3flJkv2ab/XBMKJGeZuRCnmaOv4tyjTBreuG4X2rZeiXzvRhBosBMEJJFBU/82TJ54FV3bb0CAOANK4xmlMOKCBuKwMVEtmlExC2wsPMqJUzKg5yqoHM9DswgT/OtMEEgBpv1Qnm/RBAoAdNfGu3P79Rg7/CROPfnd2P5Kkkr13REl91FmH8o1pdlRpBraoRkm4/UdCiOZg10pwrEtEMOQ6lkyFG1Yn0HQuu4S5Hs24syz9+LEL/8OG2/5beQGt4iFfIpZlzmAqmaBRCWBRPdGAEBZUZ0n+nvR+UdfxvS378PEd78PJEw07NiD3KWXIrlmDc7+x/8HxVMnkdu6PbCARQYJEMFJDeN2TMjre9S4RFOgpNwZ8nY4BcMfm5g8MFAmImdONSubeNFlOP9K2YEFk0ez4ERr6gBvL6mHb5Ak/yJAqMfhOMKkNlDncimm/lX69ac777yT+ZGuMNVpDmTBMCzQGjPkqSZAqsS95hdDZBC15S1wslpasXOOmFhLCTyi2l5HkttWtbiAU7/8FnLd69F58U3BWPtRl4eMsTQ9huM//RuYuUasveVugDp48wf/CSd/8Q1suvP3WGScCyFBQjj5xvOYfON5DNzwcWRaexkTZ5poWLsdDeu2g9o2FoaOY/bUa0hkmtG29SroiVRt0hVxoyjNI5HKu2OWgUAYZZt60dC2DjOjbwJEAzGT3n3sWn8Nho88icLcCLbe+M+RWbeJ1es4WHBYptHi2HkUps5h6oXH4ZSYpiC7Yxfabv0g9C7Z3tpL/OQQOAKzL23OoopXp254wgWYbQ0sQyyCEn8xcZEjJAVzqjpz+APgZG0YUwa0qgwCAcbQcsBppZhpAt9sVcdQp1qGkeJSzfBnPDEPlFrZeJPTBOVWztGxdpgjrOvYqlPmI+CaI3gAYIqV9QAAd3ilvgScj8OTTrpmFFwrwIEAAJQbCKjjcxCzJ/Zj9sR+JPItaN6wBy3rL0F1YQbzYyehaUu/UA6XKFOubZCfM+Iy6LWGFdXLroZEARdahcBZ0IHRrGcuRl2b6dSkC7QEMxqt4s+NV3eFItPSg0s+/+cgREO+awPmh4/CqVaQ71wb6AsHfaFCABrxHZAZdyr3I5FpQqUwK5XnZntWeREJvdG9mKMA4tfJ69UQCQ7E59TMNGDd3s/i6M//Bqee/C62tf8x9HTW11rxa6KSaSnj4r4o1QZm1ga4fkyeZo6g5TfvglOtYuLvvwP90wayF+2E0doKo7EJxVPHkd22XWLoRZMfbnZXF6nPlPvbSsvjEk0UpeADwv/ajOADMKNJdUeahIlLlhXSn6h+iqd4P+vgZfkaWm6WI/mIJGo7tErtDI4mOdPWdo3s3yYy/U4kDxCoQ7X7Xw7VmGdjuUTst8J7eZV+1fSnf/qnb0m9F2wOlJiR9XKkQWZGq5naAIPKQK6EJGM5i0NyxkaxjU1L85sU05trXBxC2hp56eewCvMYuOP3QDQtUE7iRUKaoY6NuVOHMHHwGSycOwIz34z1H/iiJ21f+77P48gPv4JzT9+Lgfd8rKZ+BtpS+l2YOIezT/0ArVuuROvmy0P7RnQd+f5NyPdv8iO6xN2vEMkZpRS2VYZuLhEoWqyGEGy//kt49cH/iHzrIBJmxut+KteKrTf/DlL5dpjpPKoeANWRyHUg0daBxm17AACVDIU1O42qUUaiq4sVs2UnPcn0R5T46/53TbTxp4A9PQtQCq25EZT6AEBOECaOh8K2dNhVxjGRkv+uiPHpvctd0wtd2FC5tEx17quUF1g0mfYBF2Ah4HDG4/PzzdoxAHOWoNoggm43D0LKYXW7AEBfMGBnKDLnCPQy0yoAkMxDPBMYKgMAb1wOdbUC/qRQAiTmKVp3XI1szzrMnTyE6TdeRHlmHLm+DZg49BRGX30YANDQuwlrr/ukZLbE61hp0ssA0SGZA4kmPsYiYCwQ2GniCQ94ucwIPEad8+ucKfJDlwK6GE2NaCAO0NS/DU3927z4/5FL2jLHLDL/fCypfDvsShFWaRGmG4KVm+2VF6eRyDTKlShgAJSCOEQCPGHEgQKBhjXXfRyH7/sLnHnmB1h342eYL4njlwsLd8r90jmJzHoVgF5wQXXe8UJqajYTOLR98qOgloXRb38LXZ+/G7kN25BevxHzr76E3K49SHf3xU+c0p4KCKPzcwTnAACKHf7A7LaqnNk4QvLPKmB/ErOIXdfDrlm2SYxIUsCN6H5KAKdGeZWckK22vsraEAFgVGl0aHBA3oyXE25U7YekTXAA7cKjIa4mFPunSYVCAX/8x3+M+++/H9VqFTfddBO+8pWvoK2tbUXqr+vJ5Ek2tLLtfcTwWUs2VqHSRxc+dfXDpt4HDrxPPSq3qCQ3ccQTtzgmk25EEU+20rRxN4hh4OzTP4BjRRgxhvTHmp/FyAs/x+vf/L9w6oFvwKlWMLD3k9j68T9BItsM7syYae9D/zUfweQbz2Pi8DNSHbHjo8KH+J+5s2/i+M/+BqnmLvRdc5d3PJBoLCrhiliveJj4Hx47mhAC3UzCtoSkVkISoigJyezYMZQWJ9Gx/ip2ne5/ct3rYOTy0jGqs81ETFQDk8Boa0G6qVu6p2LiLyQc70NM6n0AAuJKxSklIBq8z8yPnwRJJ5Fc28MAgPuhjuYNiVICx/E/1qIBWtFAKxq0MvE+0hy7Unnv/XNj7usl15Ey5H4WJ1h4yUxbX1AiTCEBgOSMOy+u2YtRYPHyeYhCmnJNoAw2B/oC29GzZ5lkXQQAImm2m0DL8ftIbAoXIXnlwt7/5DzQaHah89KbMPC+TwMA8mu2Yc1tv4V0Rz/6b/oENtz8Rcn3hI+PzxnrFOpmkPUqhV5lDovEjpZiAgyEmYuusyjgXUMo0HACyJ8U+iUAIq9vQnsiqQAw8FsFPOoULrGuBZhRQQuSyrcCAErzE975dFM3jGQG0+dec/tDg+uMeF8plfoqrUUha0Qi24SBqz6M6RP7MHniVf8ehqxl0tojPM/8uGMyjQD/8OR63L+GBWzS0PnRTyKzZQtG/+4bKBw7gtZbb4fR3Izzf/vfUDx1wp8bMYGWsF6GzSklcjlqyOufSNKapBDRKDMpVO+jTfxPoAPsY87737l2zqu3hn0vFkQL4xejsAXqqDHpGHTqffQy8T7Nh2t7aUOzaEd13aLep+ZrhCReF+QD4Dj+Z4k9rpa+vOuJvgM+7xL6d//u3+Eb3/gGbrvtNnz84x/HQw89hC9/+csrVv+KOwYb8zKzS4kM+61anYUFhp5qpK6X3b8w5lyN75/orKWaA0m8lSjtNhjjkOtZh3W3/hZO/OyrOPnw36H7sluRaGjxJPlMKuVe4ziYHzqGyYNPY/bkIWi6geZNe9C64xpkW3ojG27bfAUK42dx9ql7MHfmdfReeQdSje1S8aBztfLbtjH00gMY2/cI8n2bMbj3k8weOGKORKmXFOtbaEevUs8cjOpy9Ce++OlmClbVF/eIklWVuJRp9ORzSOc7kG9d49YllvG/G0UxJKhMUWp3KSSfQ4AkT4QFEEHqL4b75B0uvXEK8w+/iNa7b4eez7kMefhgHNcJ2LE0kIQDzLGHzE5TJGa0UBt2qvuSRzsJL7KO3w+xMIsxryfSSObboOYIAGFSQO5oy7ME89wI1HABkUk9LQh3fEZRh5NykD/K1AeJueD7yaXWxAXmGmXRdkIT+riHeMg/W8hfAMLyBDRm+5DtWYezD/0DHIu9hNmudXDSOhwARmHptYEn9WK3hQSAEWeMqIMl1wZCg1FhiMPeZ8cEcmcQusF7zycHa9QFQKqmLQR0i+8GcfsoOekq9dZCUe93Ks8kTOXZcTS0DABgCcRaBnZh8tSrLIGYmjBA0Pp4p6gbalF8XcSGFBvn1nV7MHPmEM4+/QPkutb5UZ8ixiNpBSDPG78/VgZSVC1R2wddR+enP4uRb3wdw9/8X+j5/G+jZe/NGP7m11AeG0F6zTrJfEZ0xA8OJobccnZS7iONko6r67N7jSGaAs0papAa7rmuyqHEa5YpZBYT/HGqZpR7I6zLlRZHOB7faR6FKzUTz3zzdYXUkQdA2rekSEDLi9MvMeY88lgNJLYVABl19OPXAhisUijde++9+NrXvoaPf/zjAIBPf/rTuOaaa2DbNnS9/kidKq0ICBDTZRMATo2JwPSSKjpcxkqkOuDUqj4TN6Ya4wLHZRENkFtdvn8T1t3yWzjx869h7sxhAICWSCGRa2afhmboyTRmju9HeWYcqZYu9F73IbRsuhR6gnsbRvcdAPqv+Qjy3Rtw7vkf4fV7/gwdO96D7l03+dcDkqpWXKBL85M49cu/R2HiHHquvB0du26QIu9EtRlWV72UbuzCyPFn0Ni+AY2dG4NNKs9DtbyIqfOHMLDzNhD3nFhGr/gRZAJ1iVF+BGZfKxPYOW53AMX0R9gcRPt/afMlqJwfxehXvofkhj403HSFUE54FoV50nQH1XmfGzHKwr2xg0ygZwphCSHv+DFx73GrKU+NYfr4PmTa+9nrIUj+fGDGmAIrza4z55h2ixqAlXU7kLGY0I/bU0+wcFmpMQI7CaTH3ShAotkGfJAoagA0W/bDEe3HRdIrVAYCAECAvj0fwMj+R6DnGzB1+DnP1wGURYAiNh8njYwcciGkVanro+GaUYW8IsZiMHyoF0nJkI9RQIqU5Z2Kedcl/xDOeC+xNtRLlDBwbqbyKC2MS+faBvdg7OizmB87gcb29dHtis+6LSaaEwAB8cGNyIAOXvURHLr/z3HkJ3+Fji1XIz+4DcmmNpAQ3w8JLPMwlQuzqEzNI9XR6znRcZNFqsPLts2yPlMQ3UDXZz+Pka99FUPf/FtoZgLpdRvReKmQf0LEO+53Q80bUCPxd6TcJGuYpGhj4hid2vYw6ZoLfAYk23nVzKnGPAA1R1YS3a2W428RQ9GmQU7sfr8iDHWU3X89AEPkZWr0w1ylXz86e/YsrrvuOu/35ZdfDsMwMDQ0hP7+/guuvz4QoKjxayFzXtala1V/Ma81Eg8gv5jSS2oj+oVWD9fYdVGq2b6PYvxi1tE4p+S4hbehbzN2fOrfoTw7jsriNCrz06gssM/C0AlYxXnk+jai78aPIdu9FoSQgCQl0keCAgQEzesuRuPANozufwQjBx7B1LGX0bP7FuR7NiGRa5ajGRGgODWE2VOvYfTg4zBSGWy64/eQ7RisSSIkLqg1O2oL9VopxrCvf8+nceyJb+Lwk3+L9Vd8DO1rLgHs8GsAYOLMKwAo2gf3CAxnCNPI+xaRF8CcJ6i0iAus8GyJjmqu/X91bAoT33sIjR+8Dsk1PUzQ44KC0punMfJn34LR0oDOP/wk8/1wbx5VIgA5lgbHTQZGSkJSMBcEhDntiZuwZvs+AFRn51TGemz/Yxh6/qcwM43o2X2Ld5yZKTAmjEtDPTMFNxmWUQCK3WzMtMECKGE+EBSg8yZowkHmHOt3alIBAC6z6+UvsOGuF2xMUQAgLIoXNw+yk8S7Nte5Futu/gKOPfg3LGZ+IoVqYR7F8fMoTpxHdXYSbVuvQba5x881IACluvwE3HFolPp9j7qen4t4/z1zLYXMghKcIOx65VigHkUhtdQYa/WZoARINbSjOD/hOewTCuTbBpHINmPy1KtocHMIxDGJvI+EIhBy0sMCFFJuCCOZwaabfxvD+x/G+Zd/Bvrij0F0A6mmTqRbupFu60Xr5iugJ1IoTg7j3NP3Its1iEphDovDJ1GZmwQAdFx9KzqueJ8ECG1dfX/YRBhOAj2f/i2c/7u/QXV8DF2/8UnPh6sWZlaKcx/zLISZAwFAucP2wElcO3EUtf/EAoI626E6osdWazhQPeQhCKEwgBxGsT4AcYy+XVs/ImmFfG9XIsxoPSbZ7xSq1xT7rWj/3UK2bSORSEjHDMOAZcXYqdZBK24OBMjZ9wDASdSoslBs+mt2XIpizJ0lnIGUsjU1JaZyF6TQdlIxERDXGB0wUlkYqSyyWCNJEaMy8MaZxkSRZiTQs+cWtG68DOdf/AlOP/19AICeSCPd0oNMay9AKWbOHEJlYQqamULzuovRd+UHoSfSjNmLqFuOYqGYZ0WolTWLek6fVCeB+2kk0ti8959h3w/+P5gZfgPtay8JRE8S2x079SKaerfByOQiJVJalXpZrMXj5iJQaRTHIDL+csQHYrqMMCXQdIrFp/dh8dkDWHz+NTTefi2aPrwXetLE4kuvY+wr30VyfR86/uWnYeSYmZccFlSIAOQCAFrRgKQDfZ79tlMU5gIJmgG5zJNeZtJM0eRBYr5dssslDD3/U7RsvBQDV97lRYxiZj7Ed2p166UaY4qqbj6MagNgLGqodruiU435PcD1AUiN6XASQGaYMeqcoeLMjAoAwpi/sDCGnGFX771eZkx4NU1ACcHQSz/D/PkjSDS04vA3/k9YBWYTpZlJ6IkUJo++jDXXfhSt6/ZAJZFJjmTqazSlIRQAD5sYwfxEaQ3NRVGY4YMnTp6Ts3J/vTnk5dXrYsxmwvqvgjKV2U03dWF+7IQU3QlEQ+vgbowfew6Dl3wIOgkOMiqKD4vAxFEZkQCJlGuAMv+Dddd/BtVqEcWp8yhODaMwM4zi1DCmT+zDxKGnMXjDJ3Dmye/DLhdQnB5BIteExoFtSA+uQ2liGGPPPACSTKD1iusBuNmohT1T3TI0M4m+u38HTqUEPcPib4vvY8AUKI7csUQBQHVuAEEQkbb9+1jQI58vsd7EXAywiwIHy3V+dduxYvwBxHer3OJ/r7TaUn/EZGj5E/5As8OOLNgRhH5i8i+t7AA1MvrLGq8oeFzKWiBKOKqEv661PWha3QLXVfr1JEopPv/5zyOZ9BNXlUolfOlLX5JyBdx7773Lqn/5IEB9PmuUKmgVBSCIkUGWYw5UB6lqvihzoUBo06S4GMQ0UOu7voKavTBJfDLfgnV7P4vK4iyKk+dRmDyPwtQQZs4cAnVsFnFkcAdy3euh6YY874p00buvRBl71FjFa+L67ZZZmD6PamkebWv3eMxZGCOxOH0ehZkh9F10i3vcb0SzZKaLAyu9JId5leZdTMRT1oGMzyFoujwJxUPHkd69CamNA5i+91EsvnAIuSt3YOaHTyB72Ta0/+5vQkuYkgZAvN6pupGhNAeYSXjToytmQAHJL2eqOU9usbEFbHtdmjv9Oqhjo+vi94GYCW8ToRqRHRvFudBYrPtCF/NJcFIOUHLnw9IYIwIgNaHBnGdO1iIAYP4F/v30tAOC9F9lZCPNndznSwUDRoli5vxhjO77pXesZfOlSHf0Id3Wi0RjK2BZOPvYPTj5+LehG0k0923HipL7XBNXuxFYOwTGL4xq8VvwqhKBEteahEmkVdASARA8kySRoVfbFI9TBgLGjz4Hx7aga4b3LLWt2YPhw49gdvhNtPRsD5iASSErleg+fvXi+0W8c6rgwzTTMDs3oKFzg/v8EpRmx3Hykb/HkR/9VxDdwOa7/hBpIcuvnQTo2p0oT41j9NEfItHchvyG7VIfqbqW8edT00EycgIekURTIOkVj+MN3XPVbEz22ghTIAAeWtEX4zcNMUqVV+0y8uuoJAlb4sZZ655W4xZfc9+Xk9lXst+v8RrIoKIuvwGxbD2MvXidJDxxfj3Mg1YxTk30uc99LnDs05/+9IrVXxcIIA71EHU9L4EYt5bWENdbbM8jkTEkpGa7vZqTuQjVZUarKHSGxzETnUcXBygy56JENcrvqHc2rlNRQ9Qgi7IizAoS2UYkMo1o7N8W3664J6u5DGoxf6pRgio9M24FE8eeh5lpRGPX5tC+nXr5fkyfPwSiGTBSOTT2bnGBD5WAgAjcojYucw6oNLvfp3VU2nzGX9dVzsutt1xB6ehZtH76/Wh8/1XIXrEdE39zP2bufxwNN1+B1s/f5poOKABTiN5BLY0BDcgZgO1khAYA8KIAAYCjOAKHxl2nwPTxV5Bu7WPJ4wg8+3VvPkQtjc38AQCW6MoDAEmb5T9wAYCx6DP/RpFJ59VEYF6dLgDwmD9hdVE1GHEUBgYcx0Lr4MVoW385Grs2otKoy5JmI4GB934CMyf2o1iaQj5NmFbHgR/JpoakYcsB6FHaPECW/AMCIy46CbsUG8M9Clyrx1SAoPwO0xiIIVb5/Uw3doFSB6X5CWSaujyGJNPcjXRjF0aPPoPmnm2yg3BcPyPOiRGDJHMTVRPqsELpfBs23/EvMLLvIaSbuyUAQHVg8dwJjL38EObPvIlEczsSmUYGUsXEVYbyjHGthHrv49ZmD/T635fS3vLnpNxGPVOkSruP6EmN2pzELKmZmeYUCUDiSNx+DdmxX3w3qxkSqQGKe580ISSyHuNjIfuDNxvTAAEAAElEQVQo1LbvS+bDlhOP1Hi5OkxzAo7AbzNJJkDinNTg27hK7y76+te//pbWv3LmQMvUDARs+mp9hiVhrXxdJEBR3/FarZRy8grK408HuiQufoheeGu2n9QU6dVKrjXK/EmOwxLgEsrF+AKEOagGSDhuJwjs2VlMHn8ZXRfdCCJ4uXNJYaUwi9Gjz6ChaxMotdHVf52UHErKuCzOqRglqOwzu8FyfqftkgGDawMo8TIAl4+cBiwbmR1rQQhFsq8NPX/6BVTOjcHs6wpm8HMH71hCQy4AIBUCalAYglQvzhFYq/pAQC2j2pOPH3oas6cPYeCGT0hmKgG7c5dx5xGsih3CeQ4AHAKUNQ8AmAvMX0CzqZ+/QAAAjMGmgX56ScO4RoNPr3gPFOZTJBFMtPRfhJaBnd6YzEXq1eNHJqGgtg2ihy9rntRaYDzZgZC5WoLsFBDHsKnMv9SPkHbiwpDWE/EncF3Y7xCNgdqndBPLnzF+7Dn07boVeiLpaRP6Lr4VRx//OsZPvICO9VewNSpi7sT7H9AIEPgMFCFytLQwsOA+d7qmo3fP+z0fF0op5oeOYnjfQ1gcOo5USxf6P/AZNGzcBaJpXkI6AICqORX3jQgm3FisY83mGiElM7AqBIpyBPYqKGnx99w9F5aBeilarilQnKaez48lKFIqjZBBrpAMLDmhoZqL6p/w3Y7mCSQfAHuJqD7uc1ZPeNBl2/1HSf6XihoUda6OaEOx9azSKoVQXSCAEiKHtFpGIouASU6NK9hyPfYlhlYNVRlDlYaYBU98xwVG1MrK6thIVWqctCyGorQacn+CtvciRTHrUfdSBgRKmEexmThtgtCe2P7wwUdANB0dW69TNlm2uY8efw5EN7Hhmk/BSDBOXtyIxGv0KvXiVutl5nwcNk6xA+akgWprvHNN8bUT0BtzSPR3+GurpiE5wJgkyUe9IgyaALTM7ZIotKLmbYKis6IU054fc4GfBwBcMyDR7EakueGjOPf0fWjbfi1aN1/mM7Uh0mYOAOwEA0daGai0UDiuIzAcACUd+qIGY9EHAKZozsKZfx6q0n3epGgnmj8GEcyq/eHx5T0b8RBGg9cRmm2bsHCwpcUpnH7iHwHqIKmEyF0JstJ80PHlzvzsm7CK80hkm5HItaCpfxsyrX2R6d4DggIBqAWCAyxjCYwUHsQAAgBIJLLo2no9Rt94EhMnXkb/7lvRselqgALN/TvQvu4ynHrlR8h3bUA60xqq4fEY+hBnYVXST8EYHc9RXNU0wgeLvC2HUMyeOYyRfQ+hMHYGmfZ+rL3lC8ht2gbomt8WZCCi+iMAQfv9QFx/97tRrAMsuuWqOXkdFU3GwgKxqderpkBRDuLmIlX25xr7KdZT49icWiXOMcV4NKDcOb+jifmQTruH9PISoUI5o19dpkT/7aBlZguWtBTi/v5utQpytde/0vZXCcBb5BgMQN7oRWRfcWJDiC7XBCgKhNQa/jOOSELZqYsxb57K7EdQIFoKPx6jnZAYKbULylz5ddcICKR2FM1ADQulqhkQbcRVHwsAqCzMYPzIs+jeeROMVIYJ+cTrKxbGjjyLtvWXQk/7mYElExBHifkvzonQpFFyQQEBEjMElSZF9Byi2aW2Bk13UHztBNI71kpMnGTz70n+WSQgRwACpOx3SKsQDwQ4BlN/hzFOYnIqOyGHzVMdWokDlGcncOrnf4dcz3r0XX1nEAAoc0F1HwBwfwmnwW3Q7a++qMGcI8iMssNhAIBL8LlkLXQsigbAGwOiGRTP3DAE7HnaBvj5BCh1MPbGszj/4k9gpLJYf/uXkO/byNrxhKmsbC12+VYmJNPtEpdpC1UMv/pzpBo7MH1yH7LtAyjNjWPm3GEM7/8FUk2dWHP1byLXsda/iIZrClXBgcqYeoxwjZtYTeVVQODS4J470LX5Opx59cc4/eL9aFmzG4bJwPjApR/C7OhxnHj2u9i298teRtTlZHr3fRF81YwUyUjU2FB2z6dPH8TwgYdRnBpCtnMN1n/gt5Hv2wxCCMuj5fbDTgjXq2aUarQbD4gq/YswE2IhYyMGpRzndZQ6HTnRlxREQFhzI6pNzqgVK826cyjmtIkNI6s2RMOPi3NQzREpIpIcfjmk0y4Z8yvLsdb8DixHon8hAXfebmAhmV9p8v60Squ0BF0QCFAZ71o1A6I0XrdtiUmP98BfRieVfqn9ENsTyzUfKWN8J/PGzu9PYn5XOXA9ABR6HKRHI8yDQiRJYaQ6E4qLmy0uqqbvFEqhmBBc4HsfjBgSMWfqBhK3oYQQb2Nk/8PQjAQ6dvhaAHGxnjq7H1Z5AR1bro3dlFWJlCeBriobEgGqC3NYOHoIVoqi8cqr3QuEazkT4G7GVCOonB9H9sodcJR43eKGTW0C6oa+FRl/aBRaOUYDAGU+FaZZq7INNoyJBgWsagknHvwajGQGa9/3WcDQ2XDUDZwDA92PkGRlACftsChJRc3bSFIjbBwcACQWqF+HIKEWTYDCzHykePYARLMPvwyNZLy4NisMQAJM81NamMKJ5/8Rc2PH0bblKvRe8UFo6VTkMsGl+Uslz6uF9BJ7R6jt4MTj38bM6dfAszr1XXo78l3rQW0bc0NHcfThv8Xxx76JXR/9d2xs/H4q60Mt/VhOX8PGG8lAcUbbLZPMNqN/zwcxdXo/Zs4eRtu6SwAAeiKF9Vd9HK8//D8w9Obj6Nl2YyDca5SdeLytPZGk9ioVJ4dw6rl7sDh+Gvmejdh06+8g27vej9QWImEU+yWuc1HfRTJKfn/NxZi+C2uhI0fzC47DNQVyGq1QEFIrmXPKgRqur8sUKG4rdvcs0dSymkekxs9ckIM0iD5OcXk9ojTPmkVlgYjoICzKLGL8AVY8H8BStMwcAYEoRavmPqu0gvTWaQJEUheeGiXz9SxYsVqCmNWs5oVA2CFogwUyFzF1tVanXi5cZ/mRoGIXSNEhMQoQ1GMCFWnXHyKNrIuU6Z8/dxSTbzyP7kvfryQ088vMjZ1AurkbqeaOAGMrhQS1qQcEAmDB/Tt9+EVMHngKpeGz4Lu1nkghf/EeJMZNVFvY5DnzJkjWAnUz+tq2Bi2TglOoMEZffAZEaZ7NxXzsr1ZRNQB+vz0JHZGZJA4AiM2An1lApMMwJUwaevrhb6GyOItNH/59L6yh1z9xvtxnrdLA/pabOYPtSg3dfptz7KLMCCvHAQDA7rnjmlJwZ1tAAACcCRI1BSHvL2fuQ22+w47bzBHZMQmq5UVUCjPQNAMOtfHmk18HKMWWvb+Nxu7NzPlzkaKSW/lNUqvCj9TjEqUUZ569FzOnD2L93s8hkWvF3NCbyLavYePXdDT2bUHfpR/E0P5fxJr+qMRNrbzfyvxEvofiPQg7TeS/kaZCAhBIZZqRbR3A9NkDHggAgIbO9ejeej3OHXgQTT1bkW3skuenBsGvLCgJdpz3z7arOLfvAYy+/iRSDe3Y9P4vo6F7AwDAcSMMAQz4E0ELIJoQRQEBwJ024XvYfEiHbHl8JGLey40I1ZBKdQOgVvhNC0QFilh7Ewu0doAY9QzFPTc1+s/FlTMK7G9qArDT4WUCsf7jniHKyznxwLLWPV7ycVOY8HcQ4y0JWy9U+vcrotU8Ae8cqi860FIvk4hY1VO1Zu+1fFMUqpH6JBe1kKoSXgGS0qI3AYkZ4WSENkCVmtmCDbvYP1W9KlanVcJP8ERSoRfFSZ6U/l0QCXWJJkFjB5/A+Wd/hHzPBrTvuE56LljSINap4vQQ0i098rgEBlOeCN6OojkBUBw/j/O/+C5yg5vRc/snkV2/BaMP3Yfx+7+PVO8AEq1tgLABcwDASc9lYM0W4VT1wHyJQIBUNI/5F++LGN4TEO4Lr0vz/+oF/zpiy/dQ3VxHX/kl5k6/jrUf+C2kWjpZGeIzOGIIUDvhP18cAECn0CziJSlKD7O/CTceOrMvlplPngGYOFRyaA4DjPw+hjL3FNCU598bd5hElhBoFnDggb9EteSLEI1kFjve/wdIZpul4jwRFw+JSjXiazKWYkzdvqnHwmhx9CQm3nwWXTvfi5a+i0AJkG3uCY6JaDIjIjCVcQ7RgT4I99fzDwl7t5cAAlK16j3mdShAoHVgJ84eeBB2tQzdTHrl+nbdiulzh3D65fuwZe+XoElOkVGNCm3E9Mtj7O0qjj72vzA/dgJ9u29Fx473QHOdvx2dBNqRniEHnt29tH0ReOus6MQbac4S1lcRUAl1OyrfzmUEHY58byPmh1TDH9LkFAkFhurvgJN5rBlteF2iOV41I+eFcWo0/xFNkgLniuxvcm4JO38vTLITH0K8RjYhMqrPUvvdMm35l0X15CcQ908eNnSF+ZtV+vWmCzLSI5RKn5qJZx6uIQMxD0vqhSet8TqpjhhJOHd2Vp2eA+UsIn/c0IthTAXV/U8c2Un/I5KTkD9S3UK7dspvx1bLEf8TScRH5J6UM+pTCwnlwzQYlfkpnH/mfrRuvgzrP/BFaGaCzRGRP5Q6KE6PINMshP9TxuHoxPtEjZVUKYYfvQ/J5g4M3vFbaNp+KcxUDt03/yZIIomJn9wPYgPJSR1aVWOfaRO0onkfks3AWSyy2Pll+UPKmvdRAYBWJSwcqMY2Zf6RHDH5dwoQ13xJrwJmMSih85gHAsyfP4KRFx9E56U3oWHdNumZcHT/mbQyzBeCA4Bq1o3QQQA4BPqiBq1MPACQGacwSlSKbCPOK2P+ZQAAyH0lDvWk/WIUnrDyHFSoFCWl2XLN3TBTTJ3R3L0VO9//LwMAYCkSGV4RKNQrUNMrFIaWQaqhHeNvPIvi7CgWxs/AcXzkptkAKlUsjJ4ECJGez6i1I4xJVE2rwhL1xdp9i+VC5la8x9J7JBxrHtwJaluYGXodlcUZUNf0SdMNDF5yB+ZGj2Pq7IF4E8gwwMPXnqicLbaFo0/8HRbGTmLTe7+I7h17PQAQGIcQBUg1s/OcjYXzkjQyZJ3Tqv59Mgrxa2pg3IQJhKAJHwjrd17h1MU1EIh+Jt1+JmbDZiBIUWYy9ZJjEDgGQbmReHtOpan26/Wi/4kjHmI43MdI4AVs/xMgib+Av8bWKFCsSdhZJw9yIRQ71ncrLcVvvB2fVQJQrzkQpcFYuhGLd6xPgBWfyVfMK7BUf/zvSl/iwLMUQ5jCiUi8Iarvmw6amLko3DYnTrqoSkqimHKViZcWQdEGXlMY7DBJuXwYji7Y2eqIjB4UZysdZb5RqzoZAMxsE/RkGma2EUQM9SlpMQgqs5NwrArSbT2KoyQrOHv2MAqT59B1yc2sWccHXJrtS6pmjryCwvkTWPuRL0HTdaDCNAXV6UnYCwtI963xI/EIibuoG1mkOjmFyvFzyN9wtR/TWhyw4iegVf0weKIUX7MUqT4fE2euq/499aR4AtMvlq8uzuL0Q99Crm8jOq64xZcG825xAOAy/hxgcntcrcoACtXdqD+u43FqmlXAk/SovhZs82XjiLMx52CSS9Nl0y1EEwdGoecoQAhyLf1o6tyEqaHXsHbPh5FIN/jjpr5iXMo2ukJklFhD5cVpmOkGjB55FmdeuB8ABdF0vHb/nwEAunfdjL5dtwAORXFuHMef+haKM6NYc+VH/OHUKP0XzY88sxaFqfOOuVl4mcaGeuEzOYAUzbWi7N9F8u6pWy6Za0W6qQvHn/x7AMCayz6Czk1XgzgUzd1b0dS7DWde+TGaerZAN3ypBoE/Bu9A3L2Gz7A7toVjT34Tc8NHsenGL6Cha4MUOYjq8lpOXYdiCgZ8VSVJmLMxNfy5V4UxXl+kwbB3IAoIOKIjMvxy5Q45W27gQpGE0MXxZi7sj7lI6waxQDQosBNK3P8YYZa3vyl1iVHyjIK8v4kgVtRa6Iq0v2amV8wobC1hQsTJpm9tZJ2lTIiWqVmQNOfJ2nMurdIqRdFb5hNAHAdUi4sCpJQVnYNjGXj5d812kDG4QrLtFBiV1kNlTG4XNjTRDtx0AFdtW2p3kBr3K4lz9BVJZfxrpagxq0laJEfUC5RaqOZLYju1mA5pLtjKdW/A/Plj6L5MrBzS/SlMDQEAUq298qKnAVZpEace+w5SzZ3ouvRmoSNCdRRwrAqGn/oxGtfvQq5/k3/ecjD88+8h2dKBtstv8KJcJOZ8JkAva6imHEx9+/vQMhk0730fO15kHHeU86MY2QcAiIWgah4CaOGqbvc+6VX27IjmRGqYxTOPfx/QNAzc/CmWpEyQIHLww7VHnPGnRH4WeMZTc4E5O+plFwBUwwEA37RjAYAQ5jOM2fIlrvLmKJkj8OvClg1KMT10COOnX8L6yz6GZLY5kqfSqsoZLr112/LzCvhkLtLAM6SOc370ON586K/Rtv4yaIaJRLYJ69/zGRx77Buwq2VkWnowfOBhjL/5LOzyIih1kMy3Ydut/wLZ5t5QJpADowDIdoJMhOwk6TL+/Jj7m8VL94FAECDAC5NZCxDw5oIAg5fehbOv/gSOXcX4iRfRufEqr+ya3Xdg/8/+AkOHHkX/rvdLdQSaUQ6w54SNgzOBDrVx/OlvYXboDWy84W409mwGaOgUsvLcLIgE751ovqKa7njj8wqwP5qS+yAgYPG0YyGdiZpXfjwdBATerY7wDUhOLnWz2J/lJAUL8y/h72AlR6Q648x/wtZSTtw0KLFAY5PreWaEVRnYiP3ThBCgxA76QnhBF5YSJr7VZjNR5kZLgY8LzU/wDvJfWKV3PtUPAtQHVItalleIVOa11gd8CWlTzc3XWodQzsoBhhDaUVw4pRjREYx1sBO19Y+a0VqCyOtjmHhCwyVhcX2SNks7GN0l37sB55/9EexKmSUgCqHC9BCMdB5GNh9gzIZe+Ans8iJyXWuCfXUZPeIAi0MnYRXm0HH5+6Ry0y8/heLIWaz7+O/BqBqSzzoRNqfFZ19C8ehR9Hz2i0hU06A03knbOyduVopUX83EzAGnYwJJNdKHwGTw52fu7BHMnzqMgfd/ls2NW46bAQGQo3a490+zWT4A1inme2AWWft6mfqMqEaCQkkXAIQms1IZf6oAALGoJ1muAeyHvLvV8iKOv3wPmru3oX3wUm9uanVYFCkQKrSG96Q4M4Kjj34deiKD8WPPA5TCzDQi1zGI7R/8Q5x+/j5Mnz4AM9OI5v4dSDd2wkzl0Ni9GbqZkuoS7frVY3GMPlWZIiofF/0wWP3UZ7B5PUIsfg94Ef/+BMxahGMN3RuwvesPMHnqVRx/6lsozU8glW8DAKTybejeej2GX38M7WsvQarBz9VQdzABCpx58X7MnDuMDdd/Do19W9lrJdTjqOE94f8OTeTGf4vjE7QKdiq6j8aCf11cYjQ76ddXaQrpg0i8jrAy7jl9PvzhTs5E90Gqpg7zkbh9jq8tlSZZQCHuZeraqIcH02Nl3ec+sYQ/gGT+VodgMLq+t5hXeYvJSbw9cVzeDlp1DH7nUP0KsTquqMdnQLR70yzBB2DJNup/oJYdYovIn7i2a/ULiGzK9j8qUBDtJkNjWAtST68/gt9DzYleYkiNZx6wJRb7IDDn+d5NoI6NxZFTkf0uTQ0j3dLt1c3/zo+exOQbzwMAsp1rg50ShjV39k0Y2QakWn2/gsr8NEafeQAtu65GpnstY8SFj1F0nYtn5jDxwA/RsPNS5NdsdU1oWB38u/oBmCSMawMCZj3iGF3Sq64PwKK70Tp+eaqxZEiFmWFM7HsCJ3/6NZz+2deR6V6Dxo272HmDXRcKAMCeSw4AjCL7cACgl5n9vyiJVhkBvcJMgMJDlLoMJ7ffjQEAvC/iJixKmQNRqYhcBgBO7v8hqGNj/SW/IeVsEN+Tt8rW066WcOSXf4tEtgk77vxXyDR3I93cjQ3XfxYAYKYbsOGGz2HT+34bupHA2JFnUZobR0PXJhkAcMDkhEtsw9Y78VioCR7C1yA1BK2kMQBjrIhNA+AtsJYJ7zC/N03926EZSUycflUq2rttLxLpBhx88D9j+PXH4NjxifgAFXT4+QEI0UF0A5nWPgDsXXDsamidkuOkMA5Cg+uvumZHCUACkcYitAIB37CQda/csYR43u0Qdfz3UFvQg/VJHWKfQGZqigt+D/iaEiax535q1bx8XPJ7UkizhU9MuTh/gJrHtRyfwaXKEoKahI9i23wtF9b0mq6rB7hpmvdZlf6v0nLpVwctly3hp3WUFaqncgIskfSKA8dkq7gakUiMbZw5ZaCwJnxjqzWtvGYFzYW8c3HxkhWb8nqj96gRg6L8BwLXxYxLZPojQRhn6KiDhZGTmDr+MgCgMH4GDQObwztBEPAZoI6Ns09+3zuW7VoTkBiLY1o4exT5/k2MYbQBaMDwo/dBMxPovPo2r0ni3g9vPi2K0QfuAdF0dL33Tn+zstgc8nsUZ9cdGhLQvWf8N38OjEJwQyxMncfoq49gYegorOICiKYj070G7XtuRPPOKwHDl9jzZ4mbAHnZeTU2Nr3k15+c4ZyPe0yV/MJnFv1oRh5XFq12V5l4dV4UACAdFyhKGjk98gYmzr6KDZd+DIlUw5J8QOBZdP0FPOABAISEZ2EOeZ6qlQIqizNYe/EtMFM5bP/gvwy2aQNNnZvRcNu/wugbT+DcgZ+jMHUeW2/+Xb+M489lLVSz07TyHniCA/e4l9lZ0TYw+3kKAuI/C8K7zKXvEhAAoJkJNA9chMmTr6DnovdBAwEFhW4kseOW38e5gz/HmX0/xdix5zG45w409W5l7zCloFYVdqUIq1qEnswwv46QMfbtvAWTp1/Faz/6C5YArFoCdWwkss3Y/pF/DQ2JwDVhNvninEmmQPw7QWAt5esr1WSNrmjaFKbd4M9TLGVifAPEgcB//tKjBHGLtBgVKE6iH9Ak8ePCvmil5ZDSceY/vE+JGTmAheoPFdV3NRwoRH+AWqMCKpF0aikXxpRfcM6At0LLYGir/qur9JbThYMABzVrB+TwXIhkygGFkaq1fiea0a+HbCGjcfu+KsYvjlgJheFUmhwkZiI6qmzSmho6TZGqe4djBGm1aD4c1TwoqpyyeakmPVSyqRUWamECqLCZiteXJkcwcexFTB9/FdXFGZi5ZnTu3ovWrVdG9sdI5VCcHJLmbOzg4yjNjCLbuQZWuQAjnQ321d24q4V5lCbOo3339d65hbPHMHfiNQzc+lmYZlr2VXQl2QBgjU9g/uhr6Ln5ozD1LIs9L5Zz50RkKt3J8M6JRGhwIxUlYR6zLWgAxl97EvPnj6B1+5XI9W1EpmcNNCPhSfr5++Ao2iYJHDoMABgl9tN0Y/5rXGIeQh4AqMq/pQRfLkMfJjmOch73D5Doc94J+adtlXHi1XvR2L7BNwMSJIK1rg1hVKv9dDLXglzHOkyeeAVtGy73uxpyvaYbaOq/COf2P4h85/pIABRHkXOjrCO8DxLD7/bJM/lxwasKCKS2qOtH4L7RHliAAAS8C9jB1rW7MXniJSxOnkGubdC9lsJIZbHmsg+jY+NVOP3y/Xjz8a8hkW2GYzPmnwrRkxKZJuz+0L9165UHpifT2HTjb2H6zEFoyRQLS6rpOPP8fRg99CS6d72XTYkefKbCHH1Fh2KvvDtHYTHrVf8qwE/UKFfC/liuCZKju0mzYhl9r2YEbqg4EKlD7M9SpkCS701ElaLWzzFIEAS5c1pulPegKOEV4O9p3N8otO+zfkNmwQmW8wQQVNmDBEbfofFSK7HcStBymPt6BHR1+ApQU3wo6+nQO5AuUFu1Iu2vEoC6owNh6Qe8rqx44nfl5a7Vsz6ubOAFjgMdNLJcNSdUKfhA0IwNrRAe5aaa8yOvADIzHucYFcX4hzEc3jlxgY/LESBQXA4G1TZYMp9Sjot1SMz/4jSGX/k5po6+BCOVQdO6i9G8YQ+ynYMgevxqZ2TyqJ6b99qqLs5h5OVfoH3HtZgfOoZc19rIvhIKTB58CkTT0dC7yZub+ZOvw8g2oHHDLrez4mT41ybyzdDTOZQnRiKd/sRwnYHTbl3ePdaCe7pm+ZumYzCnOc/sSQNKM2PID2xG95W3eYy/F3tctP9XzM2Iw54zbo+rWew7j/jjgR0igzZZIieMI5ARHD4AoPUBAKqRmoCrWsfIsWdQKc1j+3W/LZkBeW0KfJQYAWulqWPDFTjxzHdQmZ1EMtcaWY5SitMv3otEuhG9296LamkBx57+NjrWX47WwYvjGyER72SYRkABp5zhVzUC4nk/kpJQjoMDrhVw7zuPshMFBBq6NyLV0I43H/kq1l39MTT37XA1CqyRTFM3tuz9EqbPHcLCxEnoZhqGmYaRSEM3Uzi7/wEgInAE72uudQC51gHpfhZnRjFy4BG0b74CRionXScGWgiL9OMPRp478a9o128UlGepKs+bx0hzkB+iRaq01yD5dy/g+Qy0eT2e0eX9K9X2Qi313nGQU82SwP7DGX9LzkUYFGTV0Z5aTnUCls5Z0e+D6CCMOHCwUlL6FcgVQCjTvUXScoUakqnlhQtCV+mfDq2MOVCND11dD6ek5pPbiJP+qZtoZCSdqA0XgJ16K2OHhUjnQ6ROALxoC2EUAAU1rHPUiK4zoMoWNzTVJ0Fi/mSAYJUWMLLvYYwffhp6Io3+q+9C65YrQYzaHzUjnYNVXIADCkIIhl/+BYiuo33XDRg/+BTaL3pP5LWLI6cx9soj6Ni9F2bGNx1ZOH8Mub4NnukDN5kBIPld6MRA89ZLMX34BXRdczs0wwg3F+FMcMTzpdmMOZA0O1Vfmig+A9wMw2M4CMHs8QMYbn4AHVfcDOi6JO33ciuIc1b0xwIAiTlB8u/215PuCoDDi/wj2OKqtv2SXTkNL6OSeK3YHm8rrGwYjZ99Ba29O5DKt0cXUkjyo+FthwgYJB+WsHqE57x5cCf0F+/D+PEX0Lfr1siysyNHMTd8BBvfczcotfHmY1/F4uRZLEycRqatH+lcW7AhQbMiHw8/FnBOpcH1QDQ9A1UAoCYcA7xcCVxLQN2GPSAohuV0n1Wi69j6gX+Bk09/F0cf+zq6t+/FwMW3sfLcX4QQtPTvQEv/Dmmezuz/GQqzw9h4zWfc4AMha7t434T3rPvimzBx7AWM7P8leq/6UE1zxkEzf19FUx47iaBgS1z73Gt1RcId5YNhZUOYYH5degnVkxhkw/2eHqptL4rTGovEQseKzKx8XpT2R5kCmfOymWysGasjlovZpFaCT68ZfNRYsB7wUIf0X9RsxOYmEkKXU11bOTDzTqBVTcA7hpYPAmrkk+tj/JXfKwFoRROEOHMh4SVOzFioNIVPDXFiOhXX35hFQg6XKk9DnMlCZDp6oQKVuSfqBhAhzZXNkqgU5ScsRKZdLWPs4OMYPfAoAKBr9/vQcdH10M1keP8i2iKUaQKobcGpllEtzGHy9efQc+XtKE2PAqDICpoAkWZPvobTD38L6fY+dF7yXq9/VrWI4vg5tG6/SgYwyrxDAyh1kG7rwUSpgPnjr6Fp48VsmviYOVMbJlUFfCZLGBefIw8AVBhzIdrwikz+ujt+G0PP/QRjzz+E7OBmZAbWeWW85FZCvz3Jvyud486CIgDwxqzcV16HaBIl9d0bZwwACJGwenOhMHF8jOK8iOVFWpwdRmFuBAM7gkw370fN0bsukHQjgdZ1l2D0zafRvv4KJHMtoYy7Y7GbkG3pxakX70Vheghbbv4dnHzmu3jtZ/8/ZJt7sebSu5DhmYWjNvYlpP+ymF4+HupL4HinJUAA5Td1o856v0HZmuf6VIlNGskMNtx4N86++CMMH3oEvRe/Hzp0CQgA8vMycuwZDB1+BAO7P4iWwV2sriX8vOaGj2L86POwqgVY5QJAKcZefxpdl97K1hciM6+hWgD4AEb1heFzU835fdUqQeGHZG6nrgEC8TKlzmgtANH9hZ4qe0pUNKXUVHhdAVLnM0JzbiXlvUGcw0qTLDBSQYbhZjc3hSzn6rtoFP12k7NO7aZ7cQzaW8kIxz2H7wQGXOJl3ur4pqv0T4XecsdgQmkkEFAReSxgUDQDsam0A/nhlyYVIIjfO15xMHIF+63P6rAb/ZVTXKytvANjXuSQ/a9OUgjTiJBFXmTOamT8pePqkMXfIigw5BTwce1Ii3aUu4MOzBzZh7PP3ge7XEDbtmvQufsmmOkcuCPhkm2J0mICGGkWesIqzGP4+QdgZhvRtuMaTL7+HIimI9nUHthwxl97Cuefug+Na3Zg8H2fgmb4tgHFiSGAUgw/82NM7H8CRjoHI5P3/2Zy0BNpLAwdx+zxA7AW59jxlKADD2Os+CmReQ6zzweTljm6H4GIj5WdBGjVwvzYCWR610BLJZFq7wEIQbKzx6tXBCK8cZ6Bk9eZWPAZ+7B+e1J92y+nSpGDNr0ROQDESYDwW2nTY/ojjofNLSXA+Ll9MBIZNHVuChYI6WckuFgBIg7Qf9H7MXPuMI49+ffY9r7fBQnJXMuzXC/OjyDbsQYTJ1/G4uQ5bLnpy5g89QrO7XsA5w7+Ahuv/3y87b/Sdi3lAHmOPdt+5ZjHfAKeBoCDNQ4IuB8BAwRK7gEI5QlBItcMTTdBdJ3xUQ4AQuBYVVilBSQzTQCA6bOv4dRL96Fr83Xo2qJo80ICN/D7O3nyFcycO4yG3k3IZJvQ0LUBicY26Hz++bMpzpMKzqVJcosIpj7c5FOcL9G0UnQWFoGAI2QpXppccG44oKKXMeA1SuZMrx9xZJSC8fHlpkRpc3QxriUpNwZNgbgpol6SHX+5n1EcJeZpMF+OO29qyE/p+Yxx7tWsED+CEIqV9NcSqadeWqFMvlQXTIsjEphG0qoJ0Cotk5YPAup46JbteV+rZqCecGAxDkPiudREBaW2iGxeQkIXx6TQquKK5n+t5pnqNORUgFmSzHSUcUbZ+IdFtQgjx1DDPAoSBXGzUKMORUjr2XX+97FDT8DMNmLTXb+PZL4lvBN1kJlmO/LoKw9j9sR+DOz9BDTDBHUcEN2QbMMpdTD07E8wvv8xtO98D3quvgNE06T+ZjoH0X/TJ1Gdn4FVnIdVXIBVmEdxYghWcQF2qQCAwsw1oWnjLjSu34VM9xrJd0EEKqoZkMcUgd0rR3lstIoQYpTfB1diW1ocx/gbz2H6jRdhFRfQsH4HBj74eRTOnUCqow9aJhU+99QHlVzDwDUAUf4jnpRTUMsvVVb0AeAUCwAgM/1h2qWokKDiNZQ6mDj7Klr7doIYptf8kvbGxAVMXh+Di0ZUlKOlyEhmsPHaz+LwQ/8NZ/f9FIOX3Cmdd3TAbGiGZiRRnB5G90V7sTB+GiOHH0P31uuR79kI7HsArWt3B/vkmmnU6zchjyMoyQyLlMROuD4aPHcABwwuIOABAbhpFX/GNdtl1jUfCGi6Cce2cG7fAyxaENUwefJlnD/wC5QXp9DYvRktfRfh1Cv3o6XvIgzs+SB7h3lfA5nf5XtkphuY1mHv5925ksfjGL6/iZUSnjvqmrTw6mOcg6UpEp9Z3Te14zk31DKe71I+RkiTq0oSf0IoAwIAiJprxy2WORfPCC4V5lQlyhOqRRBn2sMcm7mWMTHrgwNWqdgh5Zo4EyVP4+JE8hJhgEAVYixJIbwB3/8uOCLQckjabwVzn6QeD1DUd+RdTmKY3l9V+6vEqD4QoBGglhjz9YT/lNTG8iYWpxkgNo3XBnjlHBlVR22KS/VTIH1Rg50Nt8WxshTGIgk7Fexb1IZRo+lQgGp8sONMglTHX05aVbYRlcxKjATMbOPSAECV3EbMjZFhmoCpN19E+873oHnTJe4F7oZBWASgxZFTmHrjBcydOozeaz6EtouZdFGdBk030Lrp0sh2KbVhlQow0rnanm/xWv5Xl/9C0QIArgOwG8LTIQ7G9j+KoRcfgJ5IonnrpUg2deD8Yz/A5MFnURg7i9zgpgCD7Dl7unbheoXnKmCSwdD42wITL2b/jSLJB0AcJGfOwqSsgiaH/SZwtBDQ7YYaDQXj7jUAMD92GuXCNNr6ZYbZi8nOAUWNttArRbm2AQzsvh2nX/4h8p3r0DxwkXSeEA3p5i4UZobd3wSUOhg9/iymTu5DqrFTuoZLv0M1eSFalVDnVu/+MCAh/fUaUtUwVP4uJhNzmWjNkiXgmsUYQHbvCIjLgLdtuBxWaQFDBx7CzJnXQClFaXYUzf0XoWf7Xoy++TROvngP8u1rseGqT4AQJa65Gn5Web7MTB7V4hwopQHncC/niTg8V9DsOfmGMOwAgpJ9ThoFdc0fudmLpwkV/DGoFrKWuOXKbb4pEMmzBUBl9kUg4F8fvoinJ6I16nIFS2wCHLBkSewawLuVmAv6BvB3LiwHQ2Szcd0Sco5EkWbFnFypSEC1kgPwmxtn6cDPe7TUvalD+B8lyFulVaqXVs4cqJ4H8Vf50NbRdlxItOWg8dhFTjXJCLG79w9E9CNGWi/7CRBoYti1iLFoNpUTi0WsdVoyCbsakyJS7MNS+xgB9FQG7btuQL5vAxoGt3nXUurAqVZw+Nv/NyqzEwAAM9eENbd+Do3rdgarihiXJynkjCTRYWbyQQZXlfiLkm3hOzedoKJpgMugA+y4qBGqVhZx+vHvYO70YXTs3ouOq24BSZigBJg7/TqmX3selalxJK+4Ue6DA4nR1ywfAADhZmT1AAAxnKUcR9z/TQm8eN5SZChNYO4Fpo6GlSWuVNLTngQloZMjh2CmG5DrXssYrwiSGBQSbGuliRKgY8t1mBs7gRPPfBf95UW0r7/My2vh2BaqhTmkGjtACZDrXIeFidM4/dy9AHWw7tpPgRDNf9aEeeVhNjkTL94Dx5BBYGT/XGmvdy/ryb/C/4pJuxRGmQhAgD2LFETT0bvjfWjq344zL9wPzTCx9tqPI9c6AOIAbRuvxMLoCeSaeqDppvTuhHZFCZRgphtAHRt2uQA9nQ25gI8dfkhj4T0E2HMimvaIoXmtjLzGUo1peHkFnNklIXH4PQ1E1j9XaY5e6PWEDeoIEmARVCzEBeX3mT09JjKPfAGJ3e/4uEot8WBa8zSN0aBHsyCBAnF/EX0DgLdWCrtiDPEyk43V6gNJDa12Z+IlqlQBwSooWKV6aPkg4G1g+pdE2TXmEpDq0AmI7V8oM8rLzDNQ48tczciOVGryLsk2UjXLiTgXG7FCNSOKAAWxFLHo835YpQIKo2cZsyYyz7VUrTIDHlNO0HvNHYF+ZDr6ke0cQLq9D9mutch2rYHZ0BxZf4DRiGM8SMR5lwkPM4Eh8GP1e2UhSP4Vh10AqCzO4siPvwK7Wsba2/4Z8usYyOGWArn12zD88D0AgFRnrwQAABkA6BXfvlazgs9FGAAAgEAYWcgAQLyezUswURjgg0lK/PlzTJcRV549ftzTVIjSVBdoSmF0nSrMZJYxzNz0O0LToZL0DteQpCzseFg0If8nwdqrPoZTL/wAp577PkZffwKb9v4zmI0tmBs6isriNDq3XgsAaN94Odo3Xg7HrqKyOItEQ6vrDMsr87+zqD1KKFU+d+44uCaJA05i+/dKZHQ5AxjlZBpHnMHn9RDbBxOUwA9FqnGAwNbNbGMPtrz/d6S6mNaGIN+1npVT2gpzvlULzY8eh2YkAYOr2+C+k/JcUbDnw4FskseBovjcqyaS/LuVod6zqReJBDJ1S3l3FCGBZguhgR0iR/sRiGj+i6oJ4l8bANzrs6d8jjozRiP3N/HehO6zAhCIBV4Gry9eWOWFQBbNrLy+sL/JGUc2GRKoFn80v62VZWaJHcNP1NpWPVGAVlDy/2tHwt70K2t/lQDUnSeALpuhr6uNFXZy0ao2HDM8eHjcJimaG7XvA8YvDi/nJB1o5XCTIysLGIsIPRfGjEVRTRu5uymFRrBA/OIeRWJkIHUjckBx7ol74FhldF19u9AQ3pKXLN+3Cfk+xUl0Cami1x9ePAo0xQAYNdqPFNLVC+vpnnIZfm76o5fkzWBh7AQqC9PY+vE/gdne4dWnOYBtAtn1W4CHAWgakm2d7LwAALi0n5kB0UCyMWlslDFong9DxLMX+mxxxj/EwVAEb47BJKXEUiS4Yc828WO5hzHzYpx33UiFapc4mPAb+tWs5kYijXU3fBpNJ7bjxBPfQqk4DbOxxXNqd2x/8JQAxDCRbHTDg4rSf+rPG1GO8/dOTMJHADgJIbyxe1/thF+XF2FMIwLYiJcKcxIdwKX7L2gIJCdiyEDAC88qgDxC+XtC/PXJ64t/Px0d0rNKdWBx4hzG3ngafZd+ELqZ8juqPtMuQOTMvZPwQaVkxihoCRwD3rMLAFaO+nXDL+M58rshlqnOx+DOvdAtSRvnEJDGeLE90RxPM0B11nD2BOfIhXJ8DajKx9mFIXumYm5lm/IaEZurxp231GRtGYNDT7lzpseEBQ3kABABVcWJrL9mf4Cwa2tZL96KNWVJMy1/sFR8b0PLCl9FredbwDut0q8/veXRgVaUFMmLSGEOXtKlAkNPdR1aJZz7Jg6F42YMzoxWUOj0ORNi+3UY8zqsvBAlSFhg7RSFXhJVdP45KyPYmSrngn2JPideHxbuzu9z9HW1Eq9fq8jh96aPvoyZY/swcMtnkGhoBuKktLWsTTH3N45iQ0VyZiTkfMAOWSWBOROTdQHCJkqFci4jbJR8LQDxpHAukHJTEJuNLV4uAS/5lwYkGluQbOtiDJdpArbPWPC/HFRKAECdOxEAhIwvmGciOAFcCxBw8OXSWuKDIMcMPns8n0GY1kHVnnCqlhZQWpgENA22VY6XZKuOtJ60fOU3QtU00DP9KMxA001k2wcBAOnGToAQFGeGkOsc9M1+VOLaE1Ui5h4TnV09sKC7GX0deMn+PE2Kl/XXZ8K53wgFBwhE1vgsAQxUzQjXEBAQHwhI0XNcICCuxUTsv79OE4eErtc+AALgODjz7A+QbupCx45r5XIa8Rk6/kcnrkZM9hdzDF8zwM2qvD4rORfEvnuAwvDBPTUA8JC64nXuu1huoXL2daHPRlLYLwiF46r/iOagOi9HE5Ce+4D6hP3hWjh2LPo+2gl2DR9XpYEEtcQRJPqCxQlbapHwh5bhJnw2DfaDP6Zx5n1x5yST15hy70Tzmai5XmqPfDsEtav0a0XvDBAQ4pjFiVAqedHXjHSXKCctLDXW2XaQYuIigbkXQIGToNAqIqfkf61mmT2l33Zcv2rqSiTFmUxoqgRFXSuWYKQBJoG2E4BdLeL84z9ApmsNmjZc7LUtXhPvDOYW49JLYcOTTAIUaV/NFCH9907T6HMeo68yKRok5sZvgDET3FbXAwCOAgA0gFps96U6gaP7AEDMOtp+w22AZYOIAMC9pxwAcAmbFN9fkI4yiarL5HmbqVCOz0GEE79o/iT6QYDIkl61rFi/B8DiMvgSgFKKydP7MH7qJcyOHAVP4KCG4Kw1L4CY1CzqmVGjB1EufFMVDUu0Vy3Ogzo2xt98Fh1br4VmmEg1tKM4PRzROWU+1bbCzpFwUGCbjOEW77Xog8G1MY4Jz5+A3ULiM5nL0RCAQswOzDtNHIDqzI9I9CXgDrSipsIx5LWOvweluQmUZsfQOLgNM+fewOLEGWy+9Xc8nwtQDnr894ozeMRmmjHH9O3QrTSBuciS/bJ5VABtmGkc7x/8jN5iHzXHB4VU9+em3OR+4ftCZ8m7nUbCDjgBa+4+V5lOsT1PWTApAXJD8aFAY9cxqSDvI/H9JuCHRuWkh7h2adWQd1joqrlI5czlNfCgNWcCVq+L0QK8LbbwIisSGr2I35Da+0J13QcsNQVeUdp6N5Iq/PhVtL9KAFYaBASY+TrKrjSpqrG3QUsWy9xHmf0s82EMZPGNkvgrDpmEyv2shanSKxR2QtTVAsQwkR/cgtmj+3H8vr9Czw0fRrqtR+oHt1f2fkeMNRjGMOK7ekypO2osS0WtkObSrYMDAGnzE7UKyp4tAgAxE68IAKABySZmEnLku3+BjituQm5gM0hTTmqnYd12AIL9v6IBkACANBilX2LkLQfBa1SJsNhXhcLmKJSWAH4k5N5VCrM49uy3kWnqxpo9H0K2rQ+LU+dYFBmFwpyIf5XUu+cDAKU4+8L9mB85jjXXfAzp5m4UpgQQsEQfRa1IZPhUFRQIZi0+gx0ECtyfwNMSGO6ttpXrPMAa8b4IEkgOHj3zMIeCUOJJ1jVQj1l3uPTfDdsqStrVyDyF6WEc+fn/hFVexI7f/N+RyDQAAGxq+X1UACerS87+zs1ZeD+tjAsEcn64TwCw0qweqjPtLSc7RUEsAifBjukV4tvMKw6w6pyJDLZKUjQg5ZngSSjzbxrB+PpC3bWQ+ByEnnf752l13bLJGdkkT9o7BEf+MOJ9U52AL1QqvRJagLeFljLFqTHqIaDu2fHzvkqrdKFUFwiYWxxCS+O66AIaopn7epj+Zdq1qSZBolmAVnE8M596yI6xiVyuKU+c028cqXacouOndDxOWhJmO+5SwHQhRopKDAMDt30O86ffxPBj9+Hod/4SrbuuQfelt0JPpv36IjsScy6KapEwiRqFJTQbahQS7ztBIBZ2VLQmz15YA8wFsQ5ls3evz/atx6bf/COcf+7HOPvAtwEARr4RyfYuJNu70Xb5XhiZnJ9XQDUBsqkU3URqQwEAvl+A0m8lLK4Y8SeMYjN9UiF2epS0MMDwQNKoJDKNSOXbkW3uQ9fGqwEA+eYBqYq49lWTr7cq+kjY86TpBvovvxO5znU49dR3ceiHfw67UkQy1yL1LZRIsE7RdEYFBYBgN+9e79nPu9ozUcIvaTsolU1hhBwAEDUCxI1QhAhwHgK8Pe2Aa+Lj2c3bFJp7geNqkEStAH8OOFAYevVB6Ik006y8/gx6L70NycZ2TB1/FQ39W1gEKh3eXkIFgCEjFLcN282hUXBNdCjxilQaGCCw0hGg1xBAQZJIEvKwsKKSsMMG7I5qqAyMEApNo3AkdYQ8r9wESSQxuzgQotVV+++WtVIklpFW90xPmFGKAdvCmmIuBp2Wedtx/gCBfrwbpNoO4gWb9YyBPz+1sCSC5D8WQCia3ncykZC17e1uf5UY1QUCDp34Ea7b/Qe1X7Bcab9NZbWYtADJ9p5Baah4Ljraj8woUymXgHhNbqiKhR4fCTiCxCggNhNWaTvFnEL99vzvVkrOuhj3QAbClEZIyaVIQ5q88AeixkQkBKt18eCbJwDkBzcj++l/hcl9T2LsuV9g5vWXYeab4dkmUgpKXW6UApqZRKq1G+nWHmTb+pDtXheI/a3ORyQzH8KUqPWERR8CwgGA91eV9CnmSoDPhHPmQK8EJ4+HzQTAwnsa7H6ag31YM/hllMszKJw9gdkDL2HxxBtYPHUMDZt2IWEyHT1/RrivAd9UQ829lgAAEM6xSmvTkoUxSERg+sX6I+OHK1lXA/0GQevALowceRprL/0INMEMSHJyr5FEUBMW1Sn0uAJY6gUUzYMXIdPag/Mv/wzJhnZ0bLmm9g5z4qAgZInh0VjU+bMT8M3G3Gs5UJDfcyLce1aOGpD9BpRJkpY33i/1nXNt4zkQ0CwWNcpPNkY9W30vu66rFeA+IxzElBemkO/ZAM1IYOLN55Bp7UFlYQbpZvYieM+V8u4Sh82PVmUmUvw9cXQ2L3qVlUssUFRyrPOJOR8IVLOAVnbBSgI+kNCZRsBOM+5PqwiPi/IsU51pDLz7AsCxWIfNdDXw/GluG6UJdyGlQOMhwzML1CwgPU4RBrpUks2bSGAdlzQ9wctBnHgnYDspaxZUbQefB70cJpEi7njkc2KfddX851dtvx+XX4MLVOox99G02PJEDCIQkoVcKvtuAEur9K6iukDApds+/xZ1I57imPlYKSVkhp5Y1NtA7IwBveBzU5KUpWzDSfqrXKXRP5k/CcyvDWf8nST1NhNAZtbUKEFxmoLY/ATqBsy7odi2qpFvItsTF3dF4u3ZgBMWiUaMzCJJvgwDbZfdiMatezD56pOg5TJbSAkB/8cWVgK7uIji5DBmjr4C6tjYdNfvI9M16Lcft+ktUy0qbliqxF+t25v7kHn2ounwTa/CynmbnydlFebJslCaH0eirROEaJ663UkCup5DeWwYiyePINXZh57bPo5MUw+LAsSdfj2TICZxC40RHgIAJNMB8TxR7mGdAECqk4eKDHnWoq4NBQIAWvt34fyhhzE7egTNPdvi+yQC/bdb7R9GbneS+Vasu+Ezy5LERQFdL+SqEA2IAr5PAGWMImd47aRvDuPZ5AvMvK8pcH+LsfVd/xLP3t/VCgRMuPj1VP7NAQF3WNVswE4QHwhU3ey+NrxQmOIzWFmcRVOuCS3r92DstSdw8rFvo2ndLgze8AkGdhx/HJ5GRFizRCDAmU5Hh/feUJ0BgWrWBwLEgsDYAaU2QC9osDPsIDV8IKBVNI9RJhQgyrvInYetDIB5tpDorUxKwLMFJxKW5xBcHMsGUKYXmWeKhoZLZeBuiQdM0AJ4a5JGUM0r/a2G/w6rnz+fRol6IC+OYrUAcZrqGECwXH+AJRnnOKZfElAtf63x/FccB3GWDkRIisZMsOIWaHluas1RsEqrJFJdIMDUU8GDFxA3N/DQSgwojX8BVoDExU4v27AFxl80HWo6bmFmvT9VPJSbV48tMiX+cSst26CKUSPspOyEJZn6KNI2ERSITGCA8Q+x/w81F4pZy4gt9yWKOTELilMZBfSmRnTcyEKFGmW/3+I4iQMUxs/h2H3/DQ2D25Du7I9sK4xJ98IpxjGaYcw937ztcEbfuy4CZPF59hx1q4rkS2SsHBvzQ8cwffxVzJw+CLtchJHOIb/xIjRu2gW9oxVzL72Kmf3Pozo7g/Zrb0HHnr0gui4DAFdKq1nUi98dFjWkZgAQMTcqhUr/JV8Cpbzu9zXqerFNPv9inclcK0A0lBenJUY/buNnZltcZO8fAxCaOG25FDmepXiCGpYvr26lLpsz/x7z7qUSg5NkzJ14n+0UgU3dkL4uM8yAgm9K4pkJQY6q5CW7E8OL8hj8YeZBKvAT+68AAu5PRDwzNgYEuGaAv8e2XYVdXoSZa0KisRWde94Lohvo2n0TnKTmv++CU7EXMtX9zZ2gqQ5QVypPDWbaYid80G4u+OOppgmSM6zrlUYgNeEDAR79jRpMeOSFECWAOUekiENGUX5OjEUNlptV3rE1aLqQG0DzIwOJL7Sq4fPN+KL3wihzMr8S9sdKST9BLDmXgvSeCesgD1MrEjdF0su17dFx73BACyBeFwd24t69C5GWxwECcR2o2T8jxK/JcWLPR5VdMtiJl+jxHSAYWYoieJO3tf1VAlAnCCAOBbEpbGrj/MQraG3YgEyqRSmzBCpYLmMvmfnEbczxC5MYlcBOGdBLwsorXGcsWLBy/vRYAv7JnNNQ6BNDW/hfA+FBherVZGFxNvOiL4Ko2leT3MSZAUWZDoUChBpui6cNCCmrai/E3+L36vwMTvzsq0i2dKL//Z8CdG1Z76Mo/WN9U+KBRzC7XlQP3qcwkMQ3X2VT1hwIicDcBZcnHyIOFoZPYOrkPsycOgCrtIhkQxvatl2DXNdazI4fw+zR/Zg+8Cy7zkwgv3kHen/zbuRyPd4YOPNKbL9uMYGPBEyk+0slySiPGhQKAMLutSrtjSPOBBIumYdkxiBVK0RUCthRC8z+3NBxgDpo7Noo9Zca8vNWk+SfBJ9HXl/smEK+1+I4HyjLcUlEV3kW7rDN2tO2qZJ7MLMMwJ9HxhQT2GnxGFsDrDSTvHuhLCmr2/NhUZlF+HXyZ0eNIBRqHhQ6EcJfV1PGQ5my9Us2ESI20wIAQCLbBKoDXVd9QNZ0CpGGxN9cQyCa8hHbZ3CJwwCSXmJzYpRkAGQWKappNq+pKYpSC0Fqwn32dFapk6CgCQonzTqklTRUGyjMOf/h4AKfap56D4KxqIG2svOOrSGZ8iU4mia/mC37dNnviCrPXg1CMS+fS0QxIpgw2klfc5GYl+36o8xJjVJI0jL3GeagM4o887qYUKIBLYB4rl4tAO/nhQQgUedbzJkimis7y2e8xXFRnQAi/6SrdqlCG291YJVV+idDdUcHmpo7iZeO/B0AYPvau5BJtSzN+EfQUo4u0jmHxjovRpK6aAVskqNfXlEi3vKGhaktgq2yIP1XHaJjVaURU2Unwo8DQeZJYvxjzIBiw43WuGYFAFcEg0QcWbIkhdc02QZkV8o49eOvgWgaBj70W0A6ARvxm0dsP8XHw2VCvWRUVbk/sXWEMNaeY64IuNxyWoU9m9zkoDQ/gTcf+O+oFmaRyDWjddPlaF63G6mOXhBCYKWBzOataLn1dhQnz6M6NYl8/2boyRSLJgTmP6IpjD/gSt2I3L7YTwASAPDKLQUAwpg5guAmH/LKSXHoFfIzBlP5WdGIAAaCN3Vm9E0kss1INHfEbnCOIvmvNWLKW0FLmSKK5CwR+s/h0bdCnnfVVtvzExAYXS+TsA4/hKbObq4DQDTTIk4NTL37e9lAwOus30eAOQozG35BI6AD1twMAOYkLo7JC0frglvu3Ow5NSMEIPCwu64vj15hQECruiYy4NJ75jBsFilAGWOcmmLAqdBOkBpjZQt9FKRCQN1oQXbegj5voNpA4ZgOzFnWIPeT8nwK+ks82i2MhKtVcOejcqQRxDU5ajmgSf41iTlmMsKjG/lzKYA67z6R0D2MA8Yo4kDAq8Zt204QL2JUGPlJy4JtRkaWWgYtWwsQVlwJJbtsEqX2Cs/DJfpL8kJKUIY4UoEPjVlDRF6Jg9dVWqVaqD7H4FM/wuj0Ye93d/OOZQOAC6VazUFI1QE1/YLEjunvEhhjOSFAo2L2AzKTqjkhcel5OZ2Z13AKMv7K4saliIr9dZyfQC2xqMW6ASAxD1QEO1OtwsbEN28xygXVHQzd9y1UZiYw+Nnfg5lrkMavl30GXq/IklxVyh/ZZxEUuHVFmVsQKtvEOrqSfAtivH/hOi9uv28aMHn0RTh2BZvv+H1k2gdANJ+hq2Z8symaJEj19CHd1cfqn2fHeVQhSdpP/DZETYA6djnfhds/RYocCQDE70RkurjEmXr1UJe516rULUskkKyaonGmVrMUCSbxGXnRWXBm6A009W4BhHprYfBV0y5ui77SFJSA1nANiWamOIWZW3jXR4SaZFJ1uR3Rzh/wv3PwxX1KxIu85F+c2Rfq51F3wkKJLpfZIw57Tph/AABQEAsgCYJqYQYAkMg1+Qm6bMD2ohcJTD4Bcy52/Ri4L5PowO8pIwhbVygBNL4WaezdttIERtH3teHvu2MCmXGKQrurIRjXUGp3GBBIsRfRzrPFgpQ0VBvdBy5nAYusEa2pwqxqhDm3XEdhfX8OyDOTI29u3CrMRa7SCU6w6uTv5Y4gxHe6jqCwfUgvRwQ0cB8EZvaztOBN3UeWilz0ltNK2PEvpXnhgIAHvVgu8QSHxtKMu6Q1eJfb/69GB3rnUF0gYGzmCNZ2XouTo09h17qPQguJ4w0guIDV+sCyIAzCdXV0TjQXUlWpYhNJHVo5nEvQC1XYGdEOJ6YDMQ+RuChWc3LoSOnhU6IsUOVuEIuBA8B3egtrI65fkTG/Y0gTpGy8vJcoh5sEuXXwKBteUVFYIoxn+oUnMX/8EPo++s+Q6uhRBSmSJiRgWmTKIEACBXHPCOfTxeg0im2/V6cl+1sQW4w3Lk+YpIlxHEyffQ2N/duR7RyU+gz4AMDK+n0H2AbsJIDkFG8DknSdZ4PVqtH32osfLphfiGMMBQD8e4j0XwQAzByCuJoI/5idJMFIHxpjEAMZQQm8/BIadyJWzp1/9UGUZsdQnp9A4yUflC53DPmaOEAtXRfGPL9Ne2Yt2Yq5dJtKqhIIjLt6AeL7H6GVCTMB5CE6Nctn6llyLyqBCdW8xgtBqgKBWrQBanddR2G9zNrWKhTVuVkYiQxM24TtmjxSnUX2sU2/D/yZ9p5rDUzazvsXBlDcstx5GvB9sqw064e3/hAWlctOMCDgGASFLgYE7CRQSTmeFpjq1AMF4IqvrA8ERD6yUkggkfEXGHMenqOuGnShFgo1/XG/W8n4PAFRTrtSLhhepRCUI04IRoX9AfCfnzCNgd+Pt0GAKErPa8hqLFFYJLWl2lC19m+Rfb5Y77sdEKzSr5bqAgEdTZtRKDOuJZtqk08u1yZONQmqMcSnWjZAYlQgWw4rKmUkVNSpjoDIjUUbVtbnKESGPT1KUOz0r+PJXoCQ8KAC82Kbbuxq4bffAGSmJ8YMSDouliNLMEsRt4k72oltRWZ6VXm9uM1BA0qjQxh/5CdovuI9yG7Z5q2TUaZLNKafgMtgq7eeKn9D6uXzx5l96Zxg6iP+5hJudYyOY2Pq1KsYOfAISjOj6L3CZ2A9AJBhz0HZtQumOoWtA4lZAqPgPyPePDiyJC00EhCEayiVAAAg33vu6Bxqky+YWAA+U6ECB8cg/rNBuGTVLSs4AwMKww/5eYryGRg5+CgS2UY09m9Dtn8joogS4V3hksoaQcFbSqSOTXipYnHMvgDaWDhORL7rWlVYU737TADHf158aT9BGFiQmuamKrwunhGYOxiHAIE4QQwAz1xMsxkQqM5PI5Fp8iTTmg1YSaZR0qsuA++Cf69uzvzzJdtTZQS7xH+LeRbsJAdGREpy5YVwpgwEZEbY8flBIDHGtkxzASj0OHCyQnjHqgZiOgwI8GMUwGgKaKmiUmBIg1sNmfNA40nKnLdDmOWl5lCkqKAKTAPk+4LoFUUjFAHi9CoV1g3KtCR19CUMhNTjDxCa1DDsuphzlJBIZjmWOb8Axl109A33VRCdL5ZoR5SULeFA7JdDzU7Lv1K6QAXKirS/SgDqBAGLxQkslEaRMhtgWUVQxwnEePfKliZxfvJV9LTsRC7T6Z8IU7PVkiq7TlIZf1GVZqcN6EVhoRZeMGOhCisn255w5qbxhIPZdcLLKDh3OSkKreSfk8KD5gBD0AZIUm0lGo+4+DmGDCak8YnmF+6G7km8lUU4dkGtUYMiEo/wIV7HyVxkcbe9vpSrGL7vW0i0dqB9721SPVLoQiGcIas0uv3ABsMZYBdcOYbAQAckM4rEXzzPgYCyWXlSegeglGL86HMYPvgIKgtTaOjfhoHrPopc11qvvFYFyo3se7XBDVuY8sPHSgBAUfGzOSAwlLCjgbELAEANXeoXFqTKBJIZmWprzctwkzQxpKSXLVWwR446ziT8Sn9EoMDBANfIGAbaN1+Fzp03ApBvV6xPC/wIOmG0ZBjFeiisqnqXrBo0VmFlxPsgklZ1yweOBzvLTLJ8CTqBAAxF4BGSS0B8RjwTIccvr0YaEvvsmRlFrStu/ZpFUSnOIpliL41ZoLCTBEYZsEDZ3xSz52dRuZigxTYBOK75IDclLPkRgghcjZvh/yYWpOy/0NhvK80mhoMBK8Ucfbl5DtUIMsMEhW6/+5khDYUewJzTUO5iiw+takiMmah0sN+5YyaKnRTaFHvwmw8RTyuYGXNANeILhdQM3xHMtGpCFhtSGv67oJo7Su99iFmab//P+yfX5xcMtklJuKkRp7ckKlBYcTE0Z2ShEJ5khUkFJKxTdbRZDyB4N0QHWqV3DNUFAnYOfhijs4dxfnIfXjjydWSSrehtvRjdLTuRSshBiCfnT+DU6DM4NfoMCDTccPEfeyFG61Jfic8zpXU5B6uONIEIA+5vAhppkxemIrUy7K85q6Ha6L+cceFBo1TUrJ/Cd7DFOkzqL0fLUCI4qOXDpHsXsDhwsMLHoVepG6ecScXEcKFiX8Yf+ymqkxMY/K0/gGaY8ZHduPBS2KRU51bR1EUksU3O5Ptxr4VyERubfz7I1Ig29jPn38DpZ+9B85pdWHfz3ci09kpFOQNsFIFiJ2P+AUArE88sTKuyey7F6XbHptnUt8NWmGgfMCj3XeifF0ddfLMFCakECsRkZgSB76qtL9XDn2Nuv83LedcogEIkz+5fM+A4YQ87a48S4o+/DlvjWsxyAk2uwObJtTh8HqKTFUICVIF6eIbYENMgNQSspxkRAADXsOoV6r9XNPhcUX7cle5LjL0IyIVnRw7T62oYeF/UcbrhLSMBgXtNpTCLXEu/63PimugkCRLzFE6CuInzmETaShEPRBtFysBBhYEHDhQcMEBAXdMiB66DsBukgJsZcW0CT5xmpTnTyH6LjGxylk2KUaQodrBymSEN1RyQHGFMfnocKHQCiTFf0qNqjY1F3zwwVtst3gMo313iSeS83wIzL0rzQ6tX6vPWdlVbUA9F2OPXGxUoKqLYBRGv03KCDHitmgH1uriQomo1cZoIdZw1zv+qKdAqXSjVBQLSZh7ru67Hus73YGrhFIam9uH48OM4OvQIcql2ONSCZVdgO2XYjs/hUDiYnDuO8Zk3sWPtXUtGBRIprqwq7a8nnF9UwhLHsXD8xKNoaOxHc/tmmPM2qnmfS+cAQL6INWynKfSi3wmRyVNNhOLMgKKi/QT8AqTwoIqdu6rmFRlH5bhkBkR9xiWQGTJKKq30xVxkkrSFk29i6sUn0HnTnUi39QC2O8zwfSJAUYy+Z26iB813vLKOYk4TohnhTKUIqKIAAKeJo88j3dKDtXs/A0KIdJoDgKq7wXMAAMrU8XaKZeYE/H5L5l8uAPD6Id4XBQB40YCU/nFmndetAivvGPEBgFdeaFOqR6nf75T/O9QOHyHzCYBS6mkQiW7AcarB8LYhRHXimSUB4UD57SbiMCFCLeQ5VZN4cCCd8aTl0etbuPSfX4yg+ZDyvntNcRAZIeGXuiU43KtAICB0oD4Q8PqjAIJycQat6YtcIMIYY73sJh2rMCCQmHM1BCXG7KemqWu6IzDY7rnEnJufwJX8GwX2VzTFFIGAJuYPYbcJVoZ4/eXPWnKW+Q+kx6jXXrlJw2KPX29mlAEBwM87kB4lyJ2nsNyIPHLiSBq9d6lAIIyUtT5M6BEwZ1QdiSPWG/W5C32fPRAa3y9OdWsB+P4fmsQsRMKOGpjj5Ur/lWhNAak850cojTXLWbJ/fIpUMFCrWdA7nUL2rre9/VUCsIwQocRxQAC0ZQfRlh1Etff9GJk5jPniCDQtAUNPwNCS0I0E0slmDE3ux3xxFIXiJIanDqJcXcSlmz+7/B6roUIV5jZOAhjlEMxOEq+6kdPP4az1CMxEDp29e9C08wqkm7oAAI3HKWbXC204wfY4k2pl2ObjdTXuvY9gsIMOwX5Bbt7hSc4V9XHUd5VqNQMKRGQS6jQKSsi5iQUM/ew7yA5uQttF18EBAnH3/cIxvwOSev87Z6Sl5Fr8HGemRRt5XTD1kTbNCADA++Ier5bmMXvmEHqvvDOQ34AS1wyoyS2bZ9J/J+E/A+YcYzpMgQHwmnEl+gEwJ45ZBQAA1EhRYQBAHIvKgEoRV+rZE0PKhjKUwm+qERSmzuPIj/8K6eZO5DrXgdoWqG3BM0lw+8EiuCwBCgy536LfgqiFu1ByEiQYRpaGSPNCSLMEO/yI+dWrzC4+av6JwriLdYeWD1nmJE1NFGMpaAZ42aWAgJdTQGMXE4cibIMVI02pgMB2qrAqi545kFeP203uPAzA0xBwxt9wBSscKNgJHxR4icqE7NueKZAr4NCr8JKMaRYbj236TsR2yo0gJGQ4NsrwmHkASM4wdYK5SFFucjUEo0B6guUgSE0AZsEB1X0zP9H0UZ1jTVmzRHIMEs7AEASk91ER9BxhjWCNKH/VqtXjQqAAlWnnY9IrHH2E1+lVFQsI4q/12wwHAl49Uedq0fwtZcITk8xLzSXwtpD7Pq7SKtVK9YEAxwkgU1NPob91T2hxqhNUrSKGpw6ga91HcWz4UUzNn8CZsefR332lUFBB5XXYzsUmDot58QLZi92/um5i64a7cOCNb0PTTYycewnnTj6BbNsA2jZejpY1F8MxfXWAXiK+xBdBR70oxldd3KOk/+o5FltbAD7qGANMc/0rggQeLFlqJGoHuFMfJx57mlKKc499H9S20HfLJ0CI5s2DZ7dvynPlmAKYURhbiWFXo/rYQWfYMFAAuAzZUosxb0vdIAkweewlQNPQun5PKENnpxmDUGwDnCS7UKsyIJCcAEyVMRUk2vyeBsJbUnmzDZXEqVL4kL5RUULlXhPKlKrjDqMQyaBkBuQy/BQIhAs0klk41RKsShGTx1+CVVqAZgTjv1ICVDME0Hw/hcRCoFgkBTR2tQBwKkRVoSKzX3u7dsJ9P5e4jlDK8k1w34ywMpIvj4tHSXBOvfIiGBQYeDVMsPiOSOBPZP5BIOUFEMGDCO5EIEDgCVMIDw2rRtai8LQMHBBUirMAgESmKXRcookV1xBIUWv4mCqsgzzajm0yIMBDjjoGAwQ8TCkToLiOs0LEMa4hcHRAd3wTIcDNPmzKIZsBBgQckyA5477H7vyagsOxnJWX+PsapTUJYtQcGXbwtfH2Ne4zEhtFTpL8c+CEyOcxtBr3HoQBUglE1iRkegu5V655rF6g+pA/z2okQ/HcUqTkFboQWo0StEoXSnVrAgKkOtwo6qrWhnXQiIGz4y9ix5oP4bVT9+ONMw8gaTago22bX1Dd2EIYe++Bt2l0QgwxKpBiSqRVohcAEbW3d2xDV+FyjJ1/FTuv/hJKhWkMjb2M08/9AGde+CGyr21DZuNmpDduhtnUHFSDIlwaJ4YAFWPoAzLz5xiCuQjgp7zndYu22noQePjllDldgrETnU4ljUodazNxgKnDz2P2xEEM3vZ5JNONgO2q20WfahUshUTxUW3Sw8J7qvXw36ESf3UcceNSp86hGD/6PJoGL4KWyQaK22lmBsSZT63CJLsmzwXgAgDJMddtxwunJ9j3i8wWN9GQpanKnHEwHMrY+5I76kqcfZt1//pQ+2MackwhHiKUwndC5cdtw4/7r5dZHPh0Wx+SjW0YeP9nUClMQ8vlUU6ycLORRFgoWm6mxPudnIm55i2mSt4FDQ7cGPjx0ktulqPZCDB9fK0idrSNOHFoJC+1lA+RSGruBuKGgQ1qjkKAQBS5z6uYuIv7GPA6KoVZFBcn0NiyFtA0DxBUFmcAAIlUg19fSHtsrkUBCH9BfJ8Dnp2YmUj5IVC5eZRm85CjLuOtyUCAA2q9yvog+rsAQvbhpAsE+LulCcCqFosNUfDljlU0O1oWqXMWwoBLZntUWDfi6glriudvEI+5dYvmWQy0KuXEZy++GeGi+gDCsv176rlOfL/i7nnYe6gtcZ5TROTEdzuJsodfVfurxKh+EFDnS2KSFNZ3XY+jw4/g8i1fQDrZjGJ5GgdP/gDXZfuQNH2PUhph77bkC61saFSSTvsrkJM0oJWFqEAiQChacNL+dKzdeQdmp05i35P/FfnmATRu2YPuS96P+fNHMDlyEGP3fR+gFGZ7OzKbNiO9cRPS69bDsNMQKZDkKcLG3jP7ocrvkHq4NiBsk1eTNoWXEfoT47zJSY1gJDEvimNbZXwM55++H61brkBL/06IPp+64mMgggJiRc8NwDZHL2Sqy3Bogppf7i8NAoiQSDVRNqsqUQ1YGD2F8uw4+q/9iM/kuv3lpgGe9Nk97wGAeVeqWPHrY1/c/vJ7GbJpS8BMWbm8qFCK3S+X8quSUi8ZmDh+oS6pPbGMKPnlUn7NdTwU69CBasI31XBsC9WFWZjNrXASzCfC0YFEVzcWTx4FIQSJhhY4rrS/3OIDmcQMlibiXqPsKJSsLDgoNwHchtXTDizB8ItUzbpSZ0HjIxKxue01jYz0EroGCv1RKSxhWiBKGJHjyYdKo+MyBauMvy4ylf6DYxUXcfbIIxg++SyoYyGZbkb34BXoHLwciUQOZiILgGDs5PNYt/1232E9Agiojs7eWLgTstsHrUKZY7CrsTTKLKoZ9wtwDBGwsHeQZ/gFBRyTSInLONkpZp7E33vi+hSIyRml74pWxn//qQ88aPB5Ep8FLzEY70OIFoD3u9bQuZoSgEC6LkaTJQYXYNeFFCSKhoBrDCpOaDmv0TiKKRrrdFsrzxJXLkzyX895TmFlagUEq7RKbwHVBwJsd8eJC+kp2ehTUE3DYMdVGJ05jMOnfoy1Xdfi8Okfw3EsnB1/ERt6bly63TAnHrEdKzq6j5PQ5IVH0FwQAFTXpbKcUiXg4hv+AFMjhzB+bh/OP/9jUMdBtncd2jZdDu3jn0RleAjFo0ewePgwZp9+CtA0pAbXILdmM3KDm5Dq7oed1qALZiCRsfeBwMImaQdiQIEc/51t2oENO6ys2nYIcy86lnIGLRjWlJV1bAunHvsWzGwjeq/+ENuQeGxvgUH1mF+lnkD/hH6rORPCAICXYVcFEGEAAMKxmLWfakC1MIczz92HRL4Vud4N3nFQ5gANuKZABT/qh15km3hy2v2tAgCXcdVcc4WoPobZ3Xpd58oOTZlfZXyBTNRxr68m3wOqwTP5YtFGGACgOlBJsuNmwWX6dYAaQLmRZTCdPvAyhh/8R6Q7+tG843LomSyq5QJmD72E1utvQak1mlkvtbF+cga/JlAgEPfNCIy3Bs2QyFgvJzpJqcWNtOOwuqLyPVgZJlUWSWISw7QxiDkW098lBQZga6wYTjRg4xxm3sGBgPtei6F+LbuEoTcfx9CxJ0EA9G+8EY3tGzB2+kWcOfIwzhx5GHtu+JfI5ruwbscHceK1HyGb70LnwKW+j5Y6DwThwEV1Qnb7wP0AuEmRrCEkHhAQtRdioivHJNArioDCDpplch8CEWTLoMB1dBZCrHoaTyXfBhAe9tNzwlfOie2qjrxqhDWVosyQOKCqmdyiS/nxiGU11R+gjkg7NQMC1VIhLtLPUrTUWhDlzBtFIeZy0rWaVjOAWTUFWqXl0oWbA9Xw8GlEw/aBD+K5N7+KUnkGmWQrCuVJnB1/CWu7roWuMbGGhOBtyI41AXMhZYNSzVeEn1pJ5J6VF0946Yz5Cqy8n7qWpJJoXbMHrWv2wKoUMLR4GDNHXsH5x34APE6Q3rQJud270fLB2+FML6J49AgKx97E1DOPYuLRB6CnM+i8+S60bLjEb07Mdsv76pKqDfBUzrw/IjMoXqfLgCGSCQ65Vj1PIyT+rCBiafjlB1CcGsamD/0L6KbvORfF2KvkbY7KMDkAIKrtv3BbPQAQtVAvY42kGlCcGcWxX/wtKHWw/tYvghBNSLDFNl2e+dMxAK3s9yExDy8yCe8zG5DbJdtnQAJ9FBnSCNvqKHU8Z9S9NsW6FYm5XCnbTHzbaIFJdJl+1eHWMRjT6xhBcx5iustLOoGhR+71OpxZsxGt170XVANKrX4/whymOVWagseicmi8nVRpgC81dT9xkthiOzM70azocppF/fCoALsvcJ0fAwwPazugcYw4zuoLRmJi5UQBDmTbfaVe+WKlbeow5v/1R+DYVXSvvwa9m25EwswClKKxdS0sq4z56TNIJNnL0zt4DQpzIzh64AfI5DuRb+5nEaPcIfvMPmTgToS55+CFupo110yNWP71kklmlV8ESYuhWZAypHP/AVVTybQKChAQnLg1i/pj4LcyLgnjBZAIXsLumdjvwLU0CB7YhUsDgbDnKypbcKgTsDcvwY6pAonoTqgXRhSL8wdYSgvwVtFSoUqXw9wvIzzy204h+9fb3v4qAVgJEBC2UCiqOUoI8ukurO28BidHnsZg55U4NfoMqlYBQ+P70N92Ka8MECX6F/DyaTFmQKLfgmo+ZAvaAKNgw8qwFdtIZNC4+XI07rkc1uI85o4cwPSJVzH+ne9gwjCQ2bQVrTfdiqbLrwG1bVSPn8H0i09h6IffRuWqMXRedQsIYXUHnIIVNax0TlRimMzpzbNVla4TGDYiXyc676kUt8hqin2yhJ0Un4b5c29i9MCj6Ln8dmRb+2VTciH8HrGVjdgRJNU0uJ5zAMAZfkqEjL/KxhEYy1Jr4RIagIWREzj2y/8FM9OI9R/4IhK5ZgkAAAy46SU/TwLvp+ky/nrR31zF+65ZbJPUFCmgaOoghVlU+6dIZb17QVxJvTh24l8jMUuQAYPocCpK+vm9pgaTsPP7Zyyy7zwZU8nNjMzzIegNjMHr/NDHYOQb4FAboBSmlgI0LTD9PLRqrbbRdir6XGQkqmWQGPXKIxGkLUHlRvi+AyHAjThAJeeCAzvI6MdRzZJ/0WRmKeISd9dPRAzpCUQAAcBjzksLUzhz8AGAOmjr342OtVfATGZZtQ7B7MQJTA4fxKaLPwbdSHhAd82mWzBy5gXMTZ9Bvrnf0zCquQak+VG+E3fs/hromvnorjmNxRl4dolediMI2ZCk9KJQhbqZg5lJl79feADAY/rl+QPgj0EUQC/xbKuSfsck3jgDWgABdKgO4KFhe2mwPPcJEU0P/fJLAwHPOTgkOVhYlDAgCBDki4R1qFYgIJIHLH4FtjVLmfssRR7wfxcw8qv0a0H1gQDN/agLQ40P7LrO6zA68zqGJg8AANKJJpwcfQq9LRdD09yuhJgTib+97xUbTsJf5QKqxTDymBwZbIhtJKYrqDT72gBx0c2fdTDfr8HI5tGy+xqk3381rJlpLOw/gPnnnsXZ//Gf0fHBDyO/+zJku9ci88E1SHX2Yuyxn6I8OYre2z4BLZGUQtWp5CSYUzBfOL0U9l5n/a8yo882yygzoEjGQtGaxEmqVPMdzhxapUWceuI7yPdsROfOG7y+iTbo0m8upRMYXpHURV8EO14isBDHX7+C8P57FMcHudfOnj6E4499E9mOQay95QswkmkpWRJxfOaQZ3b2HONcxt8o+GPzQJAHamiIw5xbJmwzDhuGzu65L+1XwXj4+Lh5D2NQeF1AVWH6ATczK4JSd8dg9vhSCFxKMfPSU5h77VX0fOJuGDnm6GnPzyHR2sa6R2RsH7XBX6i0NOx61bmVev9BOigdWsZebGVZPZ6WJ2KMpRbGFBMrKmxsjJNwmDQ+BJjI9ughz1OMYEA0bREdx1VQoPYj1diG3bf+a4wefw5jJ1/AxNlX0TF4GTZe8lFQAsyMHwUAmEnZwX703Msgmo72nl0CEKGS3f9SJGoTWRQklwl3mXcOKEQnXi+UqC0zz77jMLyIT1QLl1qL42dmkL5EgzhUBqXK2ixpX3VZ62cnSKRTtgQ6wIUGwTr5vIi+SGq+iMj5VU3CFIZWZdI900HBBJdpbcMAgrsO1cKs12IqFDFPrI2wZ3+Z4uB3mA0/NbTIvEfvVPKEXL/C9leJUT0YVSYe4izMri2qMc3A9oE7ULEWkUu1Y8/aj6NUncepsWeim3Ec7xPWDnPooksvJGHqRkJYRA5LvtYxifcxivI5O+l/EjMERlMzmq6/Hr1/+EfIX7Qbo/d+F6P3fAcVvQxCCNov3Yv+D92NxZNv4NQ//BWqczPBfiiAINRWWVjgOXOhSoXCmGmPEVElzML35dg9A8zxszg9jFNPfheObWHwhk942g4gfMPwvof1x3We9B1mucTc/7DjFw4AQhch99pycRYnnvgHNPRvwfrb/jn0tAwAqCsV5wmG+BxrVQYAjBL7EGFDFudZq1JPWhk2H4yBXOKdcqO5eCFahQhA6jxQzc18LZR3TKCaI17/+LNkpYGKYN7Ez1cbfMdnLvkHYRoQLpEfe+BejD1wH8oj5zH+wP3QWhgIqC5Gh/2JC5H5biDHZMDdTrqAKUY7wclKu2ZEEWQnlzch0jNdy3ugrhf8/eO/Xe2hd0wUQCwBpJMNbRjcdTv23P7/wuDO2zF2+kXMjB8DAPRt3ovmzi04/OLfYXL0MDPTcWwMnX4GHT0XI5nISe0td8Pmmi0vvC7fAygFMxmi3jG9Qr3swJo6B/DnwHvXo3J5uOP3+k8RrpWKuz8hvhjeqaXmIqYs77uXME19Btx5qZXECGNhEZECx1x+IdSEiMjjFPscPEiiASGf8zB+QNPYp14pu6Z84spFUdw4VkLqrxP/s0qrVAfVpwlQYhvX+vCKoTqbsn3Y2X8n0okm5JJtWNdxDY6PPI6W/Fo0Zfsj66CUYr44gqn5k0iaDchnOpHROwAS/uZJ5j9h9YX0nTsGJ2aqqDT5hvtiKvaGUxRza/zfxHIlH1oS7R/9GDJrN2D0x99H6fwZDNz2WaTae9CwYQcSn/g9nL7/azjxjb9Ebv1WZAbWITO4HmZzG8ueKqy9diI62ZG6KEpSP64NiFA3qw51USRqA3iIPU7V+Rmceuk+FGdGUF6YdJ8DDev3fg7JVKOUO001/VHbEBfNYEx/92+I3T+ACwYAUdc6hOLMM/dAM0wMXP9xz67ds7f37O7dyzhj7/bX09pQmcHlDq56mfpOzVQ4x/sgAIDAeSDA7AfCupKgJNMxmJ2/YxJoFZdxNV0tTrPb74LM9Fca2Xdd8ElxdMDJBZkaQl1QlMkAmobGS6/GzLOPw2hsYnXnG/25iJh70RnWIdHOtCLFxVVfSUmPlIeCa7BUk6slrhcjxYSRk2DgQK+QQHJAfl8pCEhAVSFLgFWK0grEkiBR5U6rnuOsjZqBG9UA3TDRvfl6TJzdjzOHHkTDDb8LTTex5crP4cgL38bhF74BTTNANAO2VULP2mu8fovma6L2IW7e1TF613mmRYqGg8ITEFHdzzHA/DK4xozXEdSO8DkRn2G9QqOfdXF+lwACdsKvQwUCWjVkHN4P+bgqcBDnJZaW0ALwfonCjsB5LQiYWFlX6xjqJxAuWIw1DYp7rqMY8DDA42qpL5jC6hH7sULMelRAlFVapVrpwn0CaiTRlKe75SLvBVzf+R5MLpzCgVP34qrN/xymLofYLJSncW78ZYxOH0axMg2NGHAo25UNPY2mhgE05wfQlB9EY7IbmsY5IOUlV1464jiSGZAYGYgTV8Wbiw6qWf+8KO1TbeObtl6CTEc/zt/7TZz41n9G194PoXnnVUh19GL9p/4Q4688isLpY5g99DJAKYxcAzKD69F40WXID2yJiOLhf3dMIkVgCDgFhySbqoVC40XzMQpJwWaG3sDM2UPo3PYepBs7kGrsQLKlE0Yq684HlUCTaPrEzYm8DUuVpkUAAV4v7ycQsoHVCADirqUaMH18H2bPHMba930eejbjHff+UnmzE2P76xWBKUBwPjkzKG6MIgDwtTZqToDwzZhJajmykR0QHZN4kYs4MLETvt29yNxTAyi7YEA0a6KaEPaUwtvUKE8ep6jfW69/HxaPHELh+JtIDazF9FOPItHeidTAGo9JYh9X+lqOvml2EgDxTZ2MYn2bZijDElLOB1HB78vWkJnwJOhLhmukDAA4iXBHZxobiU35yRmyOgFQ1JoTYBxF/xXl3kd2UQOIQzCw4/14/cm/xdjpl9AxsAeaZmDT5Z/G5Ln9sCoFOFYViUQO+cY+vz0RCEBg5MPAsdimAho87Rpl6xhxfIDsjdU18/GAgAFPS8BMhIjHhIrX6m7Y0dD5IC6zrjDlkl8D8effM/0Be1f5eW++3b961QclUhlh/F57HMTxObR94V2cz9FSJOYJCM0FIJqx2RTU3RMCIUIBvz+i9jMGCLCy0X0LC6cbmlVYFGiKJNZ9ITz2UoAibu4vJIrRu4GWsU6tePurBGAlQUAdmgGRNKJh59oP49k3/hpHhh/C9sEPevXMF0bw0tG/ByhFR/NWdDXfhub8GljUwnxhGNPzpzEzfxrHzz0Gx6miIdODSzd/Doae9PskUkz/9IIFO+NPR5gtbiXPjmXPA4u9whiEjd5OAYnWDgze/fsYe+hHGH7oHiyeOYaemz8KI5tHx/vuYOVKRRTPnkTh9DEsnngT577/NWy4+0+QaGLeleIix5OF8UVSDRcqjtMxlHMIkf5dwAJTmh9HItuMgcvu8Nusw37bt08WuiMwzoDARBM5Qo3q+CYxAVFMiTp25RpJAm1bOPvc/WhatwuNG3cCEMamMOveZsQTmFkhdQrXMXMmH8iojrtSlCC4z5/wTkWZ+bBNnh10DJbdlNh+6FInARS507LbR8dkUmeAgQFvjBpgZ1zgJkp7Y6SWIiNCDAOdH/oYzvzP/4T22+5CeeQ8Gq65FtS1t5ZIA6ysKzElFPpi/G5rZYKrtl4nMHgryMv6ywFODSuqucDKOXq49NROMuafR7WphSiom4G5gEQqDzPTCDPdgGSqAbqRiLwuTrMoVC59957/OCCgSMIbuzahqWsLjr/8PZw++BM0d29Fa9cOtPbthA7d70eYlgM+4+yBYyHqjroW+CZRBBSCVo36JkJcK8A1aAEgEEjmSOHA9cHRZZMWT/BBWaIsL6N6hXrgnDXq//bmT5nDWrU1S0ryxXrV+1QL4x9jkuSXUZrk/gAl+aEKjIlrmcLyCygUm3gtionn55TjoUBArIeVWrJPEi0FElbAZ0C1Wlh2IrRVWqUQqg8EqDaDlELNELwcyugNGGi/AmfGX8DWgdugQcPs4hBePvYtpBPNuGTTp5EwMl75hGagNb8Wrfm1AADHsTA9fxr7jv8j9h//Hnav/zg0EuYZGM802EKyMKPowEr7Y+MAwCublF9EUapJbEAnJrpv/giyAxsw/NN/xPG//0v03fFZJAeYyZOeSiO3cRvy67fBubaM43/9HzHy+I8w+MG73fplaW0gpJ94G3Tm+CbGivdsWOMAgEIqcytuglwKV56fQKqhTTIZCjoMK9oAkXkP0TiEAQDR9GdJiWzUbaXx55lU2j85O3wEVmkBnZffAiAcAEh9EgCNBGRCJKgBCZa671B5s+QaAGmzDxmHnfClelaaoMozFlddW3WX/3NMV2tlipGEWH4DVWIV6ZSrzKe1uICFNw6i6dKrvLlKdvdBS6XglMtY+yd/CpJJwUlR2Lor/S+EaNwIYOXkm6wXl15X7BBg4F1fWBmAIPqoeNGriMD419CMZin1RBAPzRqpPVAYa06UUpx7+ScYOfQYNCMBx5JtqTQjiUS6AY1dG7Hmsg9HM5lK/ZFMtug4XAMQ4NqALdd9AQtT5zB9/hCmzr+G8dMvo3PNFdiw+zcA+H4yUWEsJfOgiMhBgfj2PNkZNyVRrmfj8YEAoYADHwiA+sBOr/gaAs+chScTs6ibB4B4SeFCHZrF33w8lCUg43NvK5gtTCsjzYlIKvMvHK8pMlQIUaW+sPVBjVbn5y2gch1q3fweqDb8cYwuCXkRwt4NQsIdZuOcjFfKFEhtUored2Frk3f9qv3/Kq0A1QcCHAArFeNYQertjRtxYuQJzC6ehaGl8PLRv0c21YY9Gz4F05BNhNSFVYOG1vxaXLzuo3jl+D/g8OmfYPvgHczWPmoBBpa0pxNV8ZlxB4V2zbOVTk0SlFoFRlXKKOmr9hu27EK6vQ/nfvRNnP7Of8fa3/0TmPlGqR0tkUTH3tsx9MNvYeHMEeQGNrE6JW0AgV7xGX110Y2MskKWWPyVOZGS5SjSMAAozU0g37k+uj4+JhcIMJtbSEnCvHB8grOdSGIkhzAJYVTW2wApEjaV+AbE65s+sR+p5k4k27v8dTyKAVc1GpzZUfcmBdQEylB5/GqiH4/v5hs+5x8cH6RYaR8IACw6DWckjILLXGp+IjNwR3HeVj0bn9DfmZeexuRjP0d2yzbojY2MKTYIkj19KA+fg5ZKyXyITmE1+i+KFsPo22n5odAq9W14VlbkZqPLec6vlssEuhFi4DLty9lmjQJ/75imII64CZmjR/eTM9thkb8IBc7v/wVGDj2GgUvvRNfW98CulFApzMEqzqFSmEWlMItzBx5EcW4s8HwGksO5TLhtlTFxdj/GTr2AcmGaOfwTDYRoIJr7lzBHS/FYtqUfLf0XIdfa714DAQhoyLcMIN8ygIGLbsXQ64/gzKEH0b/5vUhmmrEsICAw2t46Fxa9jlKJ6ZauB/vOJPSECTUgaAwseBoAviYSm3r3jgUwcLMqCwDB76/cH8eUBQZWKj4CEF8zIgGncq3nzCwAIimK2AqSaI7KtNDBG6cmTfP6qXDtNfuswN+bpaAeESA5lmpNUBYXAGWJ4CjEcRAQQPI9bglGnq6AkPUdS6sKjXcEraxPQL0mQULZxmQ30okmHDn3MNqbNsFyKtg+eAf2n/w++tr2oKtlR3Q9mgY4Dlob1mHH4J04eOo+pJKNS2Yj1qo2HNNHNUtF5eAAQBqCE36NuLEmmlox+PEv4djf/D+YePin6L7rk4FyjVt2Y/rlpzH86P3Y+Kl/BaJpIdqA6DZUbQAgMg3xi1TNZNsozU+gfeMVrNoYbYDUPhAU3NjyXxCZ+Q+VhvINW9wQ4ySRvIjKyNMgALCJhdnTr6Ht4ve4HVArCf8dyMkgbmQu86NXIpy1w6S6IWu+GvFFzLnAAUA1x5KWaVVBkijY9Hs2u0LfAtoIYMm5FO/p4rHXAQDl0WFkGv2XI9HXi8XXDnrS/ah3xEk5DJDQ8IghUtmEAAw5A2z96iVhegnwNAPC3zgiDkCq8vvDiYN7qoEJXDjT6hI3aXDg/P/Z++8oTY7zPhT+VXX3Gyfn2Z3ZvNhdhEVOBAgwACQIMUeJEkXKClRwkC1d+372Pdc659q6Nj/bkizJtBVISqQk5pzEABJEzjkvNufZnTzzpu6q74/qil39vjOzIAH4m+ecd3e6u7q6qrq66vk9EYsnXsTUc/dg5uBjmLj0VoztuQHgQBCVUO4tgfSMAJzjzMFHwFmCyYtuASDi+L9w19+i3DOKnqFt6BnehlLXEABgafooTu+/D2eOPIIkbqJv9DyMbLkK4AycM3CW/g8OzhNwzsUxT8CSFqYOPIATz/4YUbkHGy+4CWM7X2MzZ8bfozuvw7Hnf4yj+27H9r3vFP1LGcYVA4Ec5tiU9IsTHiAA5xmGhkD5L5kaA8NUSMXWZ1rIorQRiT1XVTtMAZQhBBHvNZ8BVppG7kjbfd+vS8aevCJnYPd2p7xvfXL90uQ+GhrZsH0OwqaQzTQBPVenfhV6dC17XjtQsNJrfnWdp9wqKcjRbKzTOp0DvfSOwSuxw/fYpBNCsHfre3D/859M7Tg57n7m4wCAoZ7OkmdJ4wMXod6axwvHfogoKGPz6DXZZ5tSGcMh2I0K5KplXTKZm7YOgFSY/4zc8Bac+O4X0HfldShv3Cyen276hBCMvuHtOPi3f4L5F59A786LxTOMoUqKwil4JdqAjETalQBaa1Y2HrnrNAcAzaVZcBaj1D3sl1o5QMCMc26axHjbmy5u7jgq85g8gQjJ9qfTPQoAGBF/Fg4+B9aso2/nJdoB1pC62xW49XmAAPMAHaNtGU2C2V4DrLjlVSjPErGSa7W6deha2oToA9PXlbmSyfzIWOnmKuCaK5nv2fi7tbyI+rEjAIDG1AlUdu9W1wrDI5ibngZvxUA/AQ/SSpfaLDfOe1yJUy4PPVyNM14vFSkNgcM0rsgcyMgC3rFs+g0Fbfo//eIjOPbQd9BcOItizzA2X/NejO64NvcZp1+4G0FUAkuEs9DM0SexPHsC4MCZQw8DnCMqdSMsVFCbP4VCuRfjO2/AyJYrUaoOiEpybKlNgM0pwDnDwpkDOP3i/Tj44JcBQjC241p1XYYrbtbmETeX0b/hQpw6cB8mdt+EYqELMnfFaoCA2R4rz4xr8iPry5MYS2AhmX0DCKg6U0GL0M5khV5ZTUt2gigTqyA15zMEClbQgZbun1hP9PM6Rfuhia3lyCuXS+0Eeh6BAQuM0KNm0VQ4pY5DKcH38Ak+XzXfvMtpm3jHznlT0s55JhiFdS1T4Rql/3Idz/mGJThp6/QP+LUAQZtxeRWRcth/GZ+/ToJWnyzMJeaZ6XkqrA4Tt7e8Ebs2vBnPHvuOOkcIxeTwVf66chKWbRm9Dq24hueO/iMS1sK28ddmb1dOTDGSkmn8ruuJljlaFX0ca7cERVLdL513VTWuiWMA9F5yNaYfvgun/vEr2Pwr/1zF1JdMYnV0M6qbdmDqwdvQu20vSBomrl295gLPA6jwjTpSCLeP5X3OZmBumHnM8+yJZwFCUR3KD+Vq1i/6lkbk4EIazlynOlNq5kYLMjZH8KzUNNPWdBO3TWr8C62b+Xdm/2Mo9o+iODIm9jhul83EAfdJEmH2O/2fiRCdNOb5AIDYYT1lGVvTIxh/2WYJAOIup2NER+KQNs4ArLCeLjNLYljmWu2IBwDnHHOP3CuYx/4BNE6dSHMHcPCIozF1AuHgIFANoJEIgGoCJOn7aHZQc5uanrWQ77UrhtX4ZiRTFfA0Tr5OJNWOEe1EhXn5gBU0lYmltRNzJtty9P6vo9gzjK2v/Xl0DW8F5cTPcKTf/qZL3oqDD38Vz/zof6F3fBeSZh3dg1twwet/C3GrjsWzhzA/tR/N5Tlsueit6BvbpfN9+HKzGGuHtsVP+xpQ9IxsR/fQNoSFMg4+8GUEURHDmy8TZk+shaOP/SOOP3+7VffsmRcwMnEpIJ1vVwsE5H7gAQLuPW0BprFuSSBgCkncUKOrtbOXgiVORAQvUak+J4masfwdgU0ec+5zkM5g5NUIoleg2XcFP3pf5dY56nEC1n4Vco1YBYObU6aT9F8x3qxzWatNedc6gIG2ieWAFY0xAL/ZkJVZma47DK/TmmjtmoCVhtHq9FE6H8Dk0BWoNWcwvXgQleIAJoYuQ0BD/8eStxAQgp0b3oiARth3/DYkSQM7Nr5R+AjAj8ClU3BQZ0hKukOtiv676zCwuEnf49r7WqE8C/Z1TgECitGb34nDf/c/sPDYw+i78Iq0PZppHLrqDTj0xb/A4tEX0D15XlqZricuEkvNmkRERNDxSrbWtii4ZgqSkZ0+8gR6xnYgKnblmuNQn8Tf2pTTcuamQAymOUdjYWXV9EmpjWM1p+SxqdUw6pGbV4IY8/ufxNBlN6gumWH1ZB0uEHD9FazIR1Qz3sI2N2VAzH7K/chwQJZl8gBAy2D6JQCIKzrzr3RQJx4NkRmByPWrsJwPc8aXc47l/c9j6offRuP4EXRfeiUIIWicPG6Vqx84gOL5m0G6WuBND3KLU+nnKqenAC9acgr68m96Kvyk9CFYoYTLfb8uueYikljcRGt5HhsvuxXdI9u8fgIudQ1vxoU3/3NMH3kcRx7/DuoLZzBxwZsAAGFUQt/oLvSN7uoYRCAjRIBYk11nXJEDhGDzZW9H0qrjxXs+i2J1AN3DW/DMj/4Ci2cPY/KCN6NndDsIKAgNUO3dAICk4WNXDgTAeHqfaK9kfHPvWUW0FiF5l8AhCwQya52zFgrhhz42NctmOFCLXKY/PWeBDdePy3kvLvA5F7IEMTDW74SroAQ0kU7TdsNZQCwhT7vwnjwkSjvgjfQkj33r1xopN1rQaqmTX0EnJv+lMBWCIdD43zGk6Dr91GhtIKDTQtrOJMgT7sqctIQQ7Nr4Jl1gVU6LxuJHCLaP34iAFvD8se8j5i3snrxFOLEZzluAHRUIsCUbpZkE9X7Db8AxEaItvTC1k9oHDWGq0TW5A927LsbpH38TPbv2gkYFVZZzjq5Nu1Aa3oipB29Dz0YBAiSgMONJy+yWot/GM82hN53lzGHKWSPMxERS0iUX23h5AfOn9mHLVe9RDHIGCBhScDepjvobWcCiTC1y2tgOAOQteBnTGqMe09mZB8DifmEK1LPzEtuR1WAkMmYEjlTRAgDyeiAc5axrxj0+B2eX8UuK+m8JAFrdOsQnDzQAUBJsIxlZHtOjTBo8UkiTpDMyJ8CJz38Gi088gtLGTZj4ld9GefsOzNxzOxaeeAQMCQgoWL2O5tHj6H7TVaAhA4kSsEQ0kLVyogoQ6eC5is2LQ/sEuBqW9H8WZe5aG2WYY+i5sMr9tiNTxtMqc8o1FqYBAMXuwRU8TEspCSEY3HQx+jdeiNljT6NndEebNnik/9KZPHNOO+cSEGFuIec4pdh21ftw5tCjWJo+gq6hzVg4cxCbL307xne9Vr8vqUWQDCeF8TA7u7Y1flYmYB16M9fkZSX8nvmNZ7QdjoaBZJOPyTkh86rIdrBCVmKciQaWUtC0j03THvHwbNhRdSnhuXPypXYMlntQUOdISlrab+ZGYHlaAI9ZkAkEFOUw2DK866poNWFDzWeulqlegUbDu2+Z5Ts5BMt1/dXqN7wGIdBL/vx1ArBKEEASrjz6rUmcYTR4vtf7GvMJeO9dwSKwZfRahEEBTx/+FurNOVyw+e0oRFXwUDMk0UILre4IcSr1dzeRRp/+u3QGqA/p44x5jiGBzbPZ7z3/Uiw89xiS+QUEaV6AuRefxJGvfgIbb3o/hq94PY585zNYPn0UlZEJ/3NMrYM0N1Eqaj02rkSs40ZgDq+xwMwcfRIA0D+Z46Dtkdio+9ON0epDHnAx2mgx/57FzpqDZrvl/YYDrZvp1wQCs/seQ3FgFKXhMattBJ52E1i29u51M1SoAEOOw7acW46WQpYHstqUZjdBqzstH2gAwCI9cDQWEXQsR2vjmb7oI2ZIWat/OXMkGhwEKEVrYQ7Ns1MobdmKwsgYeKuF05//BwSDveBxHeAcpV1brHtZi4LHBKTVftfKmkylzNQapP60qbUG9hxzwEMKckDFQIl3ln47q34qUlODlRcnLP1EnKHJ5M/gQGP+LIAVggAP0SDAwORFon54QGIeAHDJ1QpIJpkSrUlLAAQBCKUA4yJ0KecISymaTRlulYmYQAAJI6SnkL5zpXHJlfAn4tmmE+2qyRAwZGPbOwykcjQmthYNGgBk6xf3JEU9L3MFMmr+c3WcHx3I3RexxombUzdg1U/jLNCQQECSO/5yTKjHPIaFVJsEmc/yzMXcrMErfd955jvt+Ip2eXU65Nxpa37UwZpC+Q60i2LIOlxfp3XqQGuePYRz65ctQPQvhzghnVVX7bQOKwQTE0OX45JtH8Ds4hHc/dTHMTW3D6Spd1fGYgUArEcXCFiBoPuYy4Hrn8weLOyJbcmtS9LOs3b4AMLuPkS9A+rawr4nAADHfvB5lEcnEPUM4MwjP1YmHCzVQMjxdp9jM9j5K6I0VzB/ee+Kxvra/On9qPRvRFTutupaKVmmMx6mX2eThdiE1wAAuEy+RGE5uyoNQCT+lgCAB0B94Qzmnn8Uvbsv0++VGnucb/N1mAy3faYpB3UAgNVeV1tCbADQqhALAMQVPb8kAPCZn1gaAONvnzRXMmIZHxbjXUgauvlWbPrdf4Py1m04/fUv4NCffgytuRlU9+5F6+wUlh9+DIt3P4xo4wiijW2YVJb+OFYsniQJARKivrNXFKV9oSw7jl5Kx3slfXGHp7lwFiQIEVW6/Tek9FI5vln1OADN/LudkIKkk4g1RdzkICpmNXoSpBOodUhr84hdVt7r+vtIhjnvHTjP9F4zSAkTPNF91DHn3vXJJJM5tvqSA2jy7d09bTDusezPnXX2JQ8PmtYf1vRDgjq3jlVRBxSZGmZJLIeRlaZdbuhkL0BI+YkVmcNQpOt4G97F2yCufz5qc++K29dJC9A2lGn7W9dpbfTxj38ce/fuRU9PD3p6enDttdfiO9/Rvqucc/zBH/wBNmzYgHK5jNe97nV46qmnrDoajQb+2T/7ZxgaGkK1WsXb3/52HD169GfdlQytDgQkzP61ozj/+oo/1JeQRvp24TV7fhPd1XE88sJn8Oyhb6FRZDg69wR+dO//g5kXHrHKs4LdvlaP/plhOwH/Rp7MzmH6vtuxfOIQuKnGTCByAWzeCUI0U1M7cQQ9aUSg+qnjGLzgGswdfNJK/OMuWIRx9WMh1MKsbFc9DKJJanE1tAamSl5m0ASASv846nOnweLYvtdHxgZntSGvLY4UXzkCE7tNol1EmSwBKeNPNRMhI+SwSJhnyZ80DREOrFAOrid++GWElW4MXXGDbiP3PNfsK/cw/g5Dz0MHABjjoupzGSiuTdFaFYJGrzY/k07pLORKKq6khS0C2iDZ8TWFVG470v9NrQsPkbFPVxFL0v4VhoYx+gsfwsQ//z1Ew0OY+vLn0ZqfxcD73oGNH/s32PyX/x4bP/bPhVN7M0C8HIE12icXIYyAJJ3DhNr3wAITAiyLesDS30tEag6nP2KeW02bPQB4pRQ3ljC97yFM73sQxa4B7bi7Qso193KrabMu55mXWQw/49aaIzNac86QxGLhDKOy3SYHCFjtyDDisO/tBATc8W4HBOQtxP+3bJcZbEDZwefUJ0P4yvU0LmbrNMdLmXm6TD0Ab5CDlOnM/XbOYc75nuszL3X3GBrzjAlQXiQc7vgR8JBkzvnaYV/wnGrHxK+GVgII2t17jpQr5U9BDI98KoRzfuxPnXzCyJ/1bzU0MTGB//Sf/hMefPBBPPjgg3jDG96Ad7zjHYrR/9jHPob/9t/+G/7sz/4MDzzwAMbGxnDzzTdjYWFB1fG7v/u7+MpXvoLPfvazuPPOO7G4uIi3vvWtSJKXV6q1+ozBayXLkcnxA8hB9bltWOPHVYy6cMmeD+HoqfvxwsHvYurO51BvziMqVPHCc1/HJVt2ISoIbos2OViBKIfMngMc81v1c6kRDs11Al56/mkc/e4/IKktAQDCrj707LgQPTv3otg/gsbUCQxf/nplOpQ062icPYWhy1+H5eMHUD9zAv17rsDJe76N+UPPom/bXlFOJgxL54w69gkzOryqdlEjMgwuAXo37MaRR76FxakD6N6wM7/idq/GYazd8lry5jyf+jdaxfgbcfHNY9pysh6nz5bn5p9+DIsHn8Wmd/0qSLGow8fJfdiU1KUMMzHKuDa93AiTR2KhAg/M6E2+sZH1pJu4a8feMgS+LNTMP61r5kLORctvwSehTa+bkjhpKmWaS6lY9QRqTK1kVhwoTG7A6Ed/DYtPPYapT/49Tv5//wwDH3oHet50tXLA152gQEyUaU87h17B3AtAo8Z2DQkKLYab63Nep0M1bsQrpVwLKTt362R+ecpyAvywBIdu+wfM7HsU4AzlwQmMX3pztpzExZlnyo/K5MzhnR/tIiFlnIKJ51xatwgNDBAQzE69gKRVR6lrCHGzBkBoAjLPNAUQTt0ZB2FDSEA4BBAwmdPULEmZ6ch74PxtnjPPu+TZc9xvX7YrKWiGPC6TzDph3i80rXr8gqYZ1tPT3nOgtuZEq6gD0N9H0ODWtUxuBg/5zIK8vgAAvDb81O/n5iNOxEeRW74dgF8Nr9OOJ2kXPUhSsIIy7UjN53OsZ51y6W1ve5t1/B//43/Exz/+cdx77704//zz8cd//Mf4d//u3+Hd7343AOBv/uZvMDo6ir//+7/HRz/6UczNzeGv//qv8elPfxo33XQTAOAzn/kMJicn8YMf/ABvfvObf+Z9kvSS5QlwGfsMZSIarGKyptFWrLpWS6mKLYgZJseuxkDPVjyz/+sYmrwEG7Zfj4du+684/Ng3sf3K96tb4lK2PyaTZmoAaAvgSYzTP/4Wpu+/Hd3bzsfGmz+A+vQpLOx7AnMvPIHpR+8ECUUF1UnNSNdOHgHAUR7bhNLQOOpnjqPUM4zS0EbM7n9UgQD3mUB2I5bUabFUm0L6TvQxMk7BhHGU+8YQlroxe+I5DQLMTRTG3yvZbHJASB4AkOY9bkIy18bfNANS52S0Q+O5Sb2Gk7d9Fd07LkT3jgtg2iZbTTTb6ZRxnfoIS+Nlp5oimtgRTqx+mzyLdJJL53Wri6DZbZj+SFMwJrgJ6RBrmpNkmCYjwhBnHM2ZM2jNz6E6uU19C5zq+Wy+c06BpCQqpC1iAQAWcbC4juUnn8bSg49g+dnnAMZQuvA8FLZOgCcULM3uS6LsLks4dJjQlRAXgMrnQ/KSmzisgSTjLUGHl/nPu5dDrG2epGGSjt/zDczsexQbr3kb+rZfjGKpDwDAOde+BDCeaTDI6hkAQAg4dPQc3z2rIa/gIXWWVe8lSXDosW+ia2ATBsYvwOzUCwBEMjOTTCZamtLJc3n/y787AgE5FsY9mTXK6b+XYTb2L3ndCo1JpPOvBi1m3ZzYAomg5W+L/9ntgelKmeI1AYEcEyhJbqIw2ZZOYECUsUGoNAkyfQ/zGFuVMXgFybNUW3iW7zinxGKZB7WpQ74/Sv1WEkrz1X7cOjoydzDBfsWQKaR5uZ4PYH5+3jpdLBZRLLax6QaQJAm+8IUvYGlpCddeey0OHDiAkydP4k1v0gFtisUibrzxRtx999346Ec/ioceegitVssqs2HDBlx44YW4++67X2UgwPy4XQTsqvyc6y5IMCfzT9U8yGNjVx4Yx2UDHwUAtEohtp5/K/Y9/mWMbLoCvUPbAAClGYZ6v77XldKa2oD64hROfOHTqJ8+gdE3vgNDe28AIQRd1W50Te7A2OveidqpI5h/4XGAc0Rdvere5uwUAKDYP4zS0LgoA6Bvx8U4/eAPwBtNFUUoKRLL7tIMEaockXOcgjOUWeD1n5lNJ6Do3XAe5o4/h8nL3+rfRNfA/Mt2us+0ws1JCb/BsJrvwmSwlWSb2M9ynXen7vgOWLOO8RvfpRaEDACA0S9TckoMBtuR3NFEABFTSpbJASAfwHU/hbmP4M6a3drXxOyncDInlkYiwwylbeacY+HZxzH3yAOoHzuktFLFDRMY+bl3o7Jhi25fCJF4C6kzrayGaDDA5pZRO7wfi48/huUnnwRvNlHYugn9730rKlftRThQTYPdy7EkQDPMJh/zkQlgyMqmEQBYmYgNRrzj81ZJLBJApG1CwA7UybE1ewNw9pn7MPX4TzBx/bswfIHIdcK5PfcsTVX6HOUY60qfaVooI111wIs3ikqHdjvlaQKcOvoolmaP4cLX/Q4IIYZPQCl7uwEEtMOwLchQx3KOdWJqDSAgKe+ejs7EbdZKwDAfTdveqhDr3bjCC6stPDUDctYSs23iD4MxzlSG9kzVStdnDxE5ZwxGlRrR6VgAbwQgGtthQxXjbr3TbH8BgEUUtJUjRMicxIoZSpmAi/jyG62V2vEuPhDjmvjkMPV5pkAqiRj1A6R1WjlNTto5j/79v//3+IM/+ANv2SeeeALXXnst6vU6urq68JWvfAXnn38+7r77bgDA6OioVX50dBSHDh0CAJw8eRKFQgH9/f2ZMidPnnyJerM2Wj0ISBgQ+CcniZl/4hpo22X2V8z8u9oA9xtud40x68NjpeyKPLL9apw69jD2PfpFXPr6fwUaiKGp9+v2laaA+rC+JykBQR2Yf+JhnPrmFxBUurD1Q/8c5bFJseaaGR8JQWVsEypjm8QJ49sNqz2ivuUllAbHceahHyNp1tG3/WKcvPfbmD3+DAY2X6xDhKaZgxWZ60AmsoHTUYsxttW3Ih+A+bd9rXfjLpzd/xBa9XlE5Z7ceq3Hd5JyyXIeAGBJ9c34/g4Yk4yP+0xpmiPvlRL65txZTD9yF8aufyvK5X6grgGG1RVu/2852Dp949QwBUp0xCbXvliZJMkQgpxjfu4gas8fQfG8HSgOj+sWEMF4WlGmmG6fO56y/trRQzjz3a+jduQAypu2oe+K61DetBkIA0x97xs48pf/HUFvH0obJ1DasAmFTZMobN4I2lNFUuYgywzNE8dRP3wY9cMHUT98CPHUGQBANDqKvpvfgPJVlyAaGwAPUsAptRMe6b8aQ5Jmbe1gr88DrsxLSGuNnIvD+Lkgzm6XwUekxyxKly1ig/3V0kptT0mSYqh0Hi8dP4BjP/4iBs+/BkMXXO8UBkywYzL/loTNvJbD+ObVZTEv7ZgsZ71p1RcwP3MI82cP4vSRhzAwfgF6hrYCTJg9AgCPW4BH2OaCGpjtcNpgagQUudoAQAGBdqSEECsEaCzQz/aNazszICANIy0/cS7CCIsGdH62S5YWYBXMsCjvMJE+htVk/l2hXjqu0rQnY8qEFAgEyDDKcn1Udcm9JuHq82RRGjUos4cZ7VHn4O+7AoweSwWKlfv0tIsK1Imk5jVPii8d4TtI+b280grCka5TPh05cgQ9PZqXaacF2LVrFx599FHMzs7iS1/6Ej784Q/j9ttvV9ddM1jOedY01qGVlPlp00tmDpRLHaIDrZncD7jDx8wizUXSemIBARYSEBBsv+Q9ePSH/w0nTjyA0Z2vAQB0nWBYHM9B5JzjzA++g7N3/QA9F1yG0Vvfi4gaUq523TMWrahboMPW/Iz+e3Eepf4RlIc2YnbfYxicvNi+3fjmWURUhkYpLctspqrR4pzavNKFXS3gUpJONUiQjEl1UACYpZkT6K30qHasVMXsM4mxJUXI/K1MkuRzCKzwo2Fd39OqCIaNtjSACIUAXJnWRMsci8eOApxj8LwrM22Uqu1MwjMHACjzAqN9LDScxjnS7Mh68E0AUJs5ibMHH8bMvofRmpsW38lPOKKBQXTtuQiV3eejNLkZNIgQ1D1tku/QeLfx7AzOfP9bWHj8YRRGxjHxix9Fdccua1wnf/tfYvH5pwRzf+wIpu+8DbwuHhAODCDo7UHz2HHwZhOgFMWNG1HZtQvFW96EwpZNCEYHjEXL3ngIA1APxOvNs/snEFoHOd86MPlcOryrE+L3iosQtBYymFjzHSX1ZRz65idRGduMja99dyrxQy6Dl2H+V3CNm1JEoiWyPE3yvBInbc4Y6ktnMT99EPNnD2B++iBqi0KrWSj1om94JzZfeKvKy1LpHkUQlfDYtz6GDee/HqO7b0AQSls33R43UVpWIwDbR2AtlCMZ12uU0R5Dm8BCcd4EIXFJX2+VScZvwfxuVR6ZtLxPgm61x2MeqTV+7QU+VphmA3Rk+gljLH2hMs3ncO7ViPGAWKY5bi4eayzTUy4QQN55w5wno6FyntuOcoEAoP3BVGHPOPiur9IOv5P50TklMPO9r1corcU596V+PgAV7WclVCgUsGOHyK9yxRVX4IEHHsCf/Mmf4N/8m38DQEj7x8fHVfnTp08r7cDY2BiazSZmZmYsbcDp06fxmte85qXo0prp3EGAaxLUKbzVz4ocjQRtJQoIzC8dx/4Dd6K2dAabd96MwdL5YOUAlb4xDE1ejGNP3YbhbVcpbUDDiHao4lDXE5z++ucx/8gDGH7j2zBwzetEtB8jcpDPHty3uRZ6NAggVLQxqFZAONC34xKcevD7iNFEiIJa6OMiQdjQ0WdYQatoz8UpWLbb93dzeQ4AUOzSk9hr6+5KWk0yrvkk/tKsyS1rHitG213PU4lttMSt+qNljmhZA4Hm9BkEUQmVegXNsnDqpk1tgiM3uUSFZRXPIo7jMFwmhKcJ4xryPi3BlOWSiODEg9/ByYe/D1oso2f3xei69DKUJjajdmgfFp95AvOPPoiZu34MEgQoTmxCZdN29F5xDaL+AdvMiQCcM7ClZczecwdm7voRaLGE0be9D72XXg1iqMdYJB2LCaoXXITKxSLfA6MMrekzaBw5iubBI0jm51G96EIUt2xGtGkCtBCpMRBmRzyNTJHOtYQobYA5Dsruv4O0jRfsi6SxsvXDFyaQp+0EJy8tSEjHXH3PPDs1V1zVCja+2eceQVxfxs5bPgQShVkGBZKJh2iMO/xB2kYv8w8xPgYzqMCsR5pqaa/AsDRzTDD8Zw9ifvoAWo1FAASVnjH0Du3A5K6b0DOwBaVKukYQYd1NEo7uwc247Jb/D4489wMcffL7OPnC3Zi8+C0Y2nqFAJam30Lq15J1EpYZirNtzWgBgIwWwCewaOcD4NUSyNCgBIjLVAhLjOuWaZNBYaO9wCSTeMxdl4OshqETEMrU6emjOnTr8jC4CgB4pP7cSQwmHcTdjuRlEobxXbGQeHMKmFmbzee2Sx5mZazPiehjApYMqOikBcgDAisQgOYGRcnLNSFpBT4X6/TTJc45Go0Gtm7dirGxMXz/+9/HpZdeCgBoNpu4/fbb8Z//838GAFx++eWIogjf//738f73C7/TEydO4Mknn8THPvaxl60PwGpBgHQ6cVSslgkQYxkg8JLZ+7smQT6S1z3tmKkdwcHDP8bZ6edRKg2gVB3A0w//LQZG9mDLZe9EqTqAid034dHv/1dMHXgA3ddcBwAonwRqY0ZFSw0c/9zfYunF51Desh3J8iKW9z2LyuQ2sELRMh/Ihphz/mZAUCyDRkU0l2YRhCUABEGhAkaBvu0X48S938L84Wcw4GoD3NjccmOgOdIRQxpkmelIib+0XZcbByGgiWbK6wtTACEodA/kc0E5560QmtwAAI5llnyWlMiz0I68JOuPlkX/WlWCMP07NO6PaoIDkmY3QZMjaIroHY35KRS7h0AIUXXTmIMu6pjehAktgwQOxJV+SebAiLIj72ORDvVnvqMkIjjzzD04+fD3MXLtLRh4zRvAekSDeQhUdu9BZfceDL/tvWicPI76of2oHdyP2QfvxvTdP0J1+y7wOEZSXwar15DUlsHqIjkXCUP0X3sjBq9/I2ixJBwRDS2Feg8RwCMOEqfgixJEI8OIRoZBLrs0LSS4ARPgsTbfHYk9DzLHKeQKKJBm+w+YFwRTy9OQn4B2hF4NWRqktBkZ/w4lUU27nErNXxIBFbfrFw9sf4ucQzNPP4juLbuF35ALoIgBdtxHmsy/55qleSCASszlk4hLsCCZLQK8+MiXcergfaA0RFf/JMY2XYXuwS3oGdiMKCznMrjKoTfhiIpVbLnsHRg773ocePDL2H/v59AzugPFar/aXxQQkBqBNmZJq9EGtGXAPUDAPU4iYgEBZfaTApNWhaj2me0y6zb/thhmQ9PorpVWO9IxyACBdpqbdv3itkmoz6G8k2Q5E4TCFAhB99PU+rpaUlWXEymIRdKGn/uzCZttMxnjFTpL+0gBAkJA8px4fUAA6DhWmWfJ+zoJSwKa36dXgeT/1U7/9t/+W7zlLW/B5OQkFhYW8NnPfhY//vGP8d3vfheEEPzu7/4u/vAP/xA7d+7Ezp078Yd/+IeoVCr44Ac/CADo7e3Fr/7qr+L3fu/3MDg4iIGBAfz+7/8+LrroIhUt6OWitWsCzEg/eb4A8nqnyEFOWZfaZyc2KKcJc83TeP7gdzAzfwDVygjO3/0+jIxchOZACdPHn8T+J76GR7//XzCx+43YcN6NGJq4GEefvQ27rrxaaQOSgmgXTxIc/9v/iebJk6ju3I3lfc+hdWYK0/f8CP2XX4exN7/H6RBgRnfgcKIpAAAhiHr60ZqfBsq9CEplkWUTQKl7CJWRTTj54PfQO7kHAQpq8YyLQrokj1mkpeEZNWmH4c9EJ5Fq4lT6mfAWzu5/CKWeETUmefb+rgOsu5n6AIBpT2reL5n0yJOEBoAGAHVTMpTe2xL2trY6nqO+cAblriHQhKO4wNFKY3nTGKCLHHFFLujC58MHTOTYmM+kpk9AkDpsp5QUCOYOP40jd34Jg5dcj76bblbzWmlCJCNAKcrjEyiPT6D/mhvAmg3M3PMT1A6+CFopIxwcQFAqg5YqCMri//LkZkS9/UrzpKImRQArcNUu0W4OLk0fXYY4ve4lkiOBNUne64kARCiASmI/rN4h/mfKuJMEwkwlkWP2yt34VqzmTseScKgIQY2Z06idPITJW39ZFeMpg6CAjEM8zdLrWztZaH9TZmQgFgig5ZramFovntrZz53ah1MH78P49uuxdc+toEG4sn6m0mYpaCAJBwVBqWsQld5RLM+dQKHS5+mTp90uwHHn4hp9AmT99vMd6bVcszLSctGGVoVamj+XwrqWuhPukTabVXqAgC8LsWuTn73eARy0Ib0eZ4GKLuS0xTP/Mn1x/NCSSPZBjwkP/UIsbyjRHNNE71wA2jLMnNKMw7DkazJgoF1I0BzqGAhFmW7l1KHMol65a9+KieMlkracw/NXQadOncKHPvQhnDhxAr29vdi7dy+++93v4uabRbjmf/2v/zVqtRp++7d/GzMzM7j66qvxve99D93dOsb3H/3RHyEMQ7z//e9HrVbDG9/4RnzqU59CEKwhBvZLSGsHAW0chNdCa7aDa9MEaf5z9MgDmFs4gr3n/TwGxy4AqxTAAUR1jsGNF6Fv9DwcfuZ7OPz09zB1+GGM7bgOZ44+hplnHkD39dcCAArzBM0ejtaZM2gcPoSB69+I6Tt/iNFb34vC0AiO/O3/SCVn8Ni05rdRLtSlgTEsHHgG3Zt3IyhVtZlPAGy68f147it/giN3fAlbXvMBG4CtVOKhxKHpfdze6GjshD9lOrID5wz77/wHLJ05gvPe/JviXBvpkhnOT51zs0Z6AEBgRJywwq+mf1vO0Hl9hJDaa61Itlih1IPZE89hefYEKn3jiGpcbUaEAdEiR6tKVDuCxGDUuWD2fRmbeaBBixkeNI44pp97EEfv+hK6t+3B4FvfKZJpCaWParOsR1ea1lUoYuB1NwO4GSp6iiQHNDGjnSYA4CEHD5GJqJMZQlfq6KMOnz2nHCTkgDQTqufdQIACA4+plvavQFZAGHRyNAkyPWDzpSJOoJyEz8kkaAUagZlnHwItlNC79Xz9vDzTJpIyE3laAeOZGebfswmbjJyWwovzjcYcCKE48eKdOHP0UfQObUfv0Hb0DW5DuTosTCEZvO+BM4a4VUNjeRq12gwatRnUlqcxffRx9G08X5kC6X45DKU8NpnOPA2AXNNyklrZUnB434U0u5HEIpJ5dlzWWgEZSlrZczuSebVu5cSTz0QDSrU28plKgp7TXlEJcueBddwB7FiaMp+k3gEW7nvwMfWuM35ePgFXcKVCJzPdllyAIPcXbiRopNl5Y/c1C1x8QEA8l8KXwG3FdA75jXLrW6efGf31X/912+uEEPzBH/xBbmQhACiVSvjTP/1T/Omf/ulL3Lpzo5fOMdh1pFnFhO8EANpqElwTIaMcbcZghRADvdtw7NQD6K5uAKsU7PuJSF6zde/bMLL5Crz46Jdx4JGvACA49fAP0HXtlSCp5DtoEJBFweXFy/MIe/rQtetCHPyL/4rKph0Yu+kdqtoM82lIsdyFlDBg9Jo344W/+y84++Q96J44z7q1PLABk699Lw7/6B9QHd2K0W1Xq2vSN0BvGDpMaEYSZDJNgHpPmcRRVC++jHAceujrmDn0OLa//sPoGt+aD6KdjRLwMP+G/afabKClWVb40wJREn5vBlDfxiefmzNdOAW2XvEePH3bx/HMj/8SF7zxn6LUNYCgyY1Nh6OwwNGqiMbSmAOxiMok66AtWDH2ZdtYZGglOLB45jAO3/cVLJ8+jJ5dl2DkfR8AChRJqNsqsqo6zJ4DqvSBfQ00PZXosWUhwCND6hTqgc7Y7yOHmcp7vlOGUw4ipf8eJ18SMNDeGCR9SFyLMmVU2RYBbRHlLExbQKv3HEL5GXNdWZSk807F2DfOczkniXF/Oya8A63acTVmmH3mIfTtvBg0LPjLmPygl+kjqUmQ8c7TdSCP+WcRSRktu91Ki5AAI5uvwODGizA/dQDzZ17E/OkX8eLjXwU4w/DGS7Hr0p9XDuOS2W7VFvDU/Z9CbXEKSaw9+GlQQLE6gK6BTRg777q2QyLXs3a26l4beSkJbsPkyoH0CmnSckmJqO9T+WOlZj+cpGuCte6ldvFSsOEKLjjPrP3WZcm05zD1WedfEyw5ZdoIaoAsAFBhUj3PtjWF9jfh1QAjy9QHLS1syTxfagFyHH1ZmEYKMuoWf2TBSgaotMkTkAcEBJBrAzZYZ75F39QeBMr68oQrK8m5sE7rtFY6JxBgMXiODf5q/ADOySM+U5n9ofGIYmDoPJAXAkzNP4/J7kHElRBxJfvFVXvHsfstv4MzLz6IIw9/A/HcDOaefBjdV1+lyrBm6vUZihiCM/f9BLzVxIb3/JJy6gWQWUgzG7axSDEKlPpGMXbdW1E7fQTj17/DuocHwOB5V2Lp5AEcvevLqAxNoLtno9FnzxjkDU9mI3CP7QInnv0xTj9zBzZd8x70bb0op9Kc0zkAwNWShOlGSVt2RQoA5DFhxvgABhNjvgYzEVNaPiyUsft1v46nfvBnePb2v8CFr/sdRKVuBAm3GPvCIlNSPsIEQJEaAh5IiTBHq7aIeGkBcW0ByeICWssLSBbnUZ8/g7nDT6E8uAFbPvDbKFwgIgvE5bS/seHAyo3Nqw2QMU1HrKFIpYdmQiLLZMYFEh4yzYgyUUQo15J9X8MCDhKJzvCGX73JEsFl8Ka4TgqdGfziNFVJ0UQ2YaDZ1/G2nzlJSbvl5LgS/GIwUIsnDqC5MI3+3dmoVTLKT16dwh/AdPjVayqnKWfsMv+hOK2+GwqhKeLca7MdhEX0j+9G/9guYSLYrOPMkYex7/GvoKt3Iya236BAFGFAfXkai7NHsHH7Deju34RipR+lygCCclXkDQiIobHIMkgZIQbxgJ90nfVqIgOSYV6tezxMFwsNCX8q7VdaESJ8kNQ3yu36AMPO3wXrsuhqtjniNwNy68ozUVoLWYDKZ+vfpl4lPBIvVJzzAAEWkkw1Kt+NrMuTDIyFwgnbBQncMJOxBG1ex+LUvMfRNPn4FWsv9ACINSUaa5frgQGEMfDIs36m960kK/OrgjzCiJ/589cJwGpBQEAtE6BMGDDTTGUVfgBrpjzkbIYDbSYICyX0927BsRP3o6s6htLgednAM+lmU1wEhndchb7JC3DiyR+CDvZa5VhTaAJoEAGcIerpBYtj0FKlwwKppWrigQA4QJXqEhi+5EbdHg/jO/Gad2F56igOfP9TuODWf4mwWAFgS8wBvfD6pDqm0695rBlpHRa0NncKRx/8Jsb2vgHDF+SEsVoBALBsQp1+SQAgJWacOkmZXEa/TXZVFeHEaoe/ncVCN86/4Tfw5G1/hmfu+Cuc/7rfQhSWEDRtRjqscyQFzfiHdRsIHPvRl3H28busumlUQlTpRlTpxuRr34vuK69Bq5+CQyffEswFFzbusm1US9lc6Zor7YQxb2TffVJ+Tp26VrL4mU7BnFjhPnnIQSjT354j/ScBR6GnCRqIBjZzJP+0RoEaBW0SBHUC2gIagyuT+hennbGQEn+qjwGg2ZtTwWooBUYmE+5qs4j8eyUk34HRTknTzz2IQs+ATuRmMjSeoWlnZqhMhXLa5su8rfKEpGaN6loIIMm2ISiUMLb1WtSXpnHgmW+ju28CvYPb1PMIEQ8ZnrgUXX2G0EKaTDKuIrtk+pJKaDVAyYISKwqPK4WWTt4SEGSEHZ4x8dj9a0CRJgBjeqykZlC0NyvECJrWoeFsC++77SSQ8ZIRqch8hpv8bCVaAB/5gEAmSIKHMmE+nfejcgtQHYEpicSzAmePyGQWjrSDnZtQzDdPVk05w76mRGN5EYuMOrgTwIS0TKdBAl6wP1Y3gd46rdO50OpAQB7ilZuNJyIPgLaJvNrFy21LXhVuyti2kgwQ2LTrTXj+yS/h4cf/CtX9YxjbeT2GJy9FEBYym9vyMAHQhaEJIZE3on6C18URjSJwxlAa3wSwBI1Tx0E3bEpNcfxNdhlg6o6Fx9bSTD9Pwwjb3vhhPPvV/4YXfvI3GN99A7qHtyIsVjIJxNpKnFyJm4chAABargAgKHUPpW3ySD48r8mKNiHratkLlwlazM1CAoDcTamND42lCfD03wJgAEpdg9hzw6/jqdv+HMee+j62XPw2bfPtOBPHqfNwUhAMa1IClk8fxdnH78LwlTehd9sFCCvdKPEu0LCgxqjeTxQzGnenGoCmbI9himVs3jp6iNF2nxBeMhPOO2A5X3WeeYrF1JpEAQTmpLQvE8oQVMXJpOl/MUGUIK4LIECj9nY15RNUmH4YvsNrZeQ51TkieAANFEymHtBMuWTo0/8lqFirY2W2Qe0vs7iJ2RcfxcjeGxAkVCyTucxIPqBTYCUPnFP7PrOfylzIbJcRqtFixgxGdvP5t2Bh9gieffjvcdmN/xJRoQogFZQAOPr8bdi440Z09U9qkyHpQ9PiwubeQ3nmk25ko0xfifG/FwCQzF7GImqdkxpA+Tz5/Ss/nyJpu8bKxIH54SNhAQF1nH7PplS9o6+LEZlGzPE2Y7NScoCVGbhhpfexMM1Z4+57zjt3+yiFLoQDgTH3FOA2yJdZWOdScBes7Kk1U+qsv2ryaLwUIMgxF6LNVMPqCR2aNYV6dYCCjJP/y/D8dRJ07p697Zh12v4J7dRoHVVsmdB5/nYkpRBJKcRAeRJXXP+vsPeKX0OpMoAXH/kSHvzOf8CBJ76J5fo0ACHpaVUIqifdRUX/kqQBEAISRADjKGzYAFCK2tGDaWGnWYn+mQyfKZ2S190VLhMClAHFnkFsfcMvozF/Bi/86BN4+PP/N5785n/Fofu/grNHHkOzviCGJyR6U3GHKhEbhPyRGPZxIrI8RuVudI1swczhJ1ek1eFSrepoHvLMfETYTi3ty0jEiPGD83da3vzphsh+2j/3OgBU+zag2DWIhLXUcyXTqIoHRIUXBYRTMGccJ+74GooDoxi75s2ojG1GqTwAVDQASAoEUcqISgDAKUdSsjUAPEjt+A0GTUofXbKkibJ91sabngvFO/SZf/likZumQF4KOVBKQMLszhdECcrddZSqTRRLOel1FwOwmQLCE0WUj65Q9sCB4gxQmAeiJSBcFmFbXQnry00cyGXYO96bfnOzB58EazXQv/MKfznqeXcmpcx87nMoyWUQfO9ezEliHQPp/FKJklKGiwY47/JfAGMJnn/0c+DpZKxWh7Flz61YmDuKx+74Mzx+x58jbhm+ATKsrg8bGg7K5rHZZtuZ2Plf9ds+Zkb4R3VOSpbTc0mp/bYo7drNb8tc4wL3EyA5gMF9Hca3uBLKhp8mmb+5ASjUfSvUAuSC0E4Bvdwxj4hg+p3xMscsb71z3wULhBmZFW40omCFbCd4SNVPP8jfZsK5+vkLeM514p7y6mrH2+TkMVDUwTH5nLUg6/T/d3TOjsFueFDXDrwddfIFWJVJkYOik1K2a4QQ9EzsQs/ELtSXpnH84N04dfB+HH/hJxjaejk2X/52hIWKKl8f9jym2QQJQhBKwVkCGkXo3nEBpu/9Mfr3XgMepZqFHCmWuxlkpCS+DdFx2OvdcB4u+sD/hebCNJaOvYiFU/sxe+wZnHruTgBAuXsEW/a+Df3je4RdpDmEqaSJJlzZ5AJa+i3fH02EZLB74w6ceuJ2/S5I+t4yqnn/e5JSfrlhSQDgZp302fPbBYz2A/kSKZ7DVJhFHB+CuLmMsFi2zImUWUnar7goNq5WlzieO/wUlo6+iC1v/zUQGqjwoLQpxkL6FjR7gGgRiLsFAAAAkhAkJY6gpscfhAsTidjMCJqdP2Y/pVQbSP9P4+rz0AiLKk2EnJCdKm4/c94b4dnNLeRKI0ACDpra8jNP/P6EUSQtiua8cGyNuv0ce+UY1Voz2cSSt6iXaEvf52OwGn0rr6sTsQJUCFjlm8CRicy0ElLSbcD6LqeeuANdG7aj1DesTc2l4KCdBNN3LcWXPsC4EjJNSTgFGIQ2gEXavtsyuyAExXIvzrvs/Xj63k/i2P47MLHtBhBCMbnjdZjYfgNmpp7DMw9+Bkee/yG2XvBzar22zavsNVxJss3+pf11TV2UWRDs8+Z1KRjR5Ykd+x9AXKGpzb+W4Kvwnx5yBRw+asuY5YGWFQbXUIIk02xKvXdi9VWVN8fO1JpKzYnESWoOyD2BW/dl1llTMOG0yypjPD9oMCRFveBIDQGneq9IShTgQFC3K2MRscafFfXmEdTtxvGQWmE+uQFk3fejIhF5nMq9zsIvtW9Ap+vyPeTwWutAYJ1WQ6tPFtbhfCZnwEulTjfJk44cAFhBd4c2EmtRAGypQqk6gO17fg5bdt6MU0cexMFnvou5k89j29XvR9+G3RkAIO0gi8Pj4HELjVPHwZoNNE+exPDNb8X+j38MZ+/9EYZf++bMptzOzEcupiouN4HNWGS0ASnzRgiKPYOoFAcxvE04LjeX57B87EWcPvgAnrnrE9h04VswsfN1IObqbEm47bpdAMcox9yRZ9A1uiV7r2N7mQFs3N40CNd2/76082Z78qJNWJuXjzluJ2Dx+Vql0yFp1hAUyqoOrYnQAABIHQUToFY7i5O3fx3VTeehun0PGElzEkgzHy4Sk9WGxP2NARHSMilzFeeeJCJ8J0mMF86I8gnI62smUpDRD56G5VQ+oBIAEAhGHgAcxp1TAySagCAwEnzJ4TB8DoIwQaHIVNQf7hFfBosh6IkIBQDNgc4hdoK6ftg5mTIAKM5CmbqoRGdUAFsuf6n2xfQjMIlTkQE6b76uhCzznJy1cOnkQSyfOoRtt/yqArHUnIdunUFaF/eMkWSyVgscjLZmmEIKMKL9BGTEHmZmiCUEA6N7sHH7DTj49HfQO7AVPb2TYK0mavUZBEERA6N7cPzFOzG66UpUukcUoyvqIFaI4nZhQX2Rgtz1yDX5UZJjg9FNisTKStyqEKWhJQxodkkbcCiBlemoGzayidYyWoBzCXjBs06glumMw3S70Z3UNfdbkmPgi0Tkzo1Oxx1IRVxzctaQFgdPrwUNBu5I9wFhkmWajcr9m1MgrKWCiIgAlIA27Y8rKQVeIAAAZIV+BPlgwLMIr5U6gQFpKuSG0QWyWoFVCGBfdnLB/cvx/HUCcK7RgeSkjBngJgtbIfPvagM6Sv59Ez29R4YElRSXbc6PNhhYkVrhKwMUsGHrazA4dj6efv5LeO7Hf4X+C6/GWN870JosgUnmKQSCOkFl+3noufxqzD/2MAr9gzjymf+JngsvQ3lsI87eexv6916FqKffkhK6UpOMmY/LH2X8JvTfasGS0tOCMI8gHCiWe1HccRkGJy7Gkaf+EYef/DaWZo9j52XvQxAYIQeJ2BhM20wZTYEHRDkNLh3fj+UzR7Htll9Xm5+3LQ65yWtMXwUfQ5Vnw54hZwPyOdW5dv/mNZ82gbMESdxAGJVVGTOHAgtEm5vdBJwlOP3UHTh193cQVrow/vp3gXIhXUwKAgQQJT0kCJeBpQlRbdyViHEPOYJlU3OWmkMxKW3zd91rIpBKbDlEvTIsqOX06d4XSfUHsa8T45oBBkjg4zRtiuMAzWXBwRWqfnOgyrEAQQNtQ+G5xFMzbdUa47290ogDol/cPNEZyJAEmHrsdhR7h9GzaU/7Z9D24IibrzQjiHBPGGXS9cA6lm03zovvSGgDWKQFI2abNp9/C+bPHsAT9/wlKA3Qai5l2jl98mkBAtJ2Sel1O/t3d92z+0KsMkxGBUoBghnxB7AdeuXa3KraWoJmF7H6LXwmso0z25xZ26RJE9Hl2s0HF7jkmW/ljRMLbCDgAgNuXvc5hrcBAm6UprwM6lZ9znfqRgAisQYCgJb8s0C/o7gkmN9w2a4sLlMFBAAocyBOBKgABBAAPFqBSC9AUjsgxh7wSfPbStYD4jfR6eQ/6fUTcQfRLkMMQODlk2Q71nMIrNMqaE0goCOjnrdJt3EQltTRBCjhNhDwlE3KMq6/rWoE/ItnXKQIigO48Mpfw+Ed9+PkT76OxcPPY+RXPoLixIRufrrwjd78DtT27wMtFFEa3Yj5px9DPD8DADj74B0Ye8Pb20qxGRVOwUr6L7UBjtOdpX51wpWZlDExIhSbLnwLKn0bsO+Bz+KJ209j9zUfQanSnxkD2uLWpigZyOnDj+HQnZ9HeWADeiZ32RJa09zJAHHEiYEN5Ev/LTMWp3+EcUhVtjo2ypgmTq46m4f2uLrkAq5WImyUw0JF1Sk3e6UpKBI0ThzHobs+h9qpoxi4/HqMvPZWRNAZw2gszFnCmgAAgNAAhMtAfSRRdZOYgBU4aNN4BgEQcAR1mzlR7XUYSinhVn+n9fKIawAQcK0JkEy9rIAAvJia9zSpeH5qqsQDDiJzDDiMAGcEhHJwBsTNAEHZedkLEfjxIorL4rAxmL8ZmQ7QAJQ51YpoldLIl4RMpsoETmtsC004OCOoL57F7IHHMXn9e0BIFh1Js7w8kx6T8fOR+l595NZpMP88dXzMRMkhAIzwoTIKjLxOaYjdV30Ix/fdgbBQQanUh2K5H4ViVwo0OMrdHjtL2QRPCMRM8kWzLbDXP9fkx3X4deP6A2nYTwPwyO83C4A0hU4QhjwpvGpzyuDnXldzygYCKyUl2DIFHjzLjOeaF3ranjleYeQdd20H9PvzAQHCOBJHYBc07H1JhvRmARAtiQ7EZSoAgmMmlBSpAgKABgMk5lauAQD5ZkLwAwIvBek7W2M+kbYWFnnRhRTA7MCLrdM6daA1gYBOjDphDFbYq3N3P14xSQAgKWgwtHr0OVeKEhsggRCCyhuvxeSl5+HkZ/8Wxz/+5xj90C+je8v5ALQanBZLGHvnB3HkU3+G7l17sfFdv4x4cR71Y0dQHBoD4GHsnWgCLjPqLsyZVPDOYmxeV9oAuTFHgqkamrgY5a5hPHv3J/HYj/4EOy//APpHd4EwmjoZptLnFsBkRIZ6Cwcf/Tqmnr0b/Vsvxubr3wfiRnxSElmjUQazYTluEpKxm5Wbg6v+Ne8BjI3Nifed51An25YJFZojQeZUmAIBUOZACgCke1KLxjjxwPdx6vHbUOwbxpZf/GeobNwCQK/5Ucr0kkQ7DzcGxLlmLwdtULASU1lxSUIUeFF8eQKwiIM6ITeFn4BxTPzghodcJNkK3LFOs/cC3nCeKCdAPd/TjydE3U+MUKEsoVg+K/xnin31zH1BHagcI/Y8b7NfqfwMHNa4rInZT++XdZhDYkYIYmH6iwR4ZKEYWz3v0u8JZM3OyCp/AACkZjzmPJx68g4EhTIGzksdgonRfiOYgEsykVfe+Iikf9l1Rrcrxx8ryyPbfTEZ7nSdccNBlkp92Hbh20R1sq8SyAd6QFyBjErAJ+3hCRHz18PEmrbtiuF3ohuZ9v5W29sANxkFyBwPYQ6kT4V5mcszEnSHiTNMlwBkGWqj35l6AT1m8jncuNamPdKhXFkdOuPky7GQe0yz6/lKfE5c+30eERDTmbqmJyrrSiX4jVQz7UQZblWpAgIAEHvMhKTwzwQDQDbpGACdDTi2+7VaW39fboNzJjkf2oCBVyMQaOuE/TN6/joJeukyBrsmQR0Y/zW9hBybN1bU3QjqsXIKlguBNANSTTM+UppwxEWK2rC43nUUWJwYxMZf+22c/OyncfKTnwB7+3vRe/k14ob01srkVgxc90ZM/eS7qG7djfLYBLp2XmAtmBmzH0tipaXjcrM3o+SYUhfCAAI7gY+7OOdJaKp9G7D3jb+L5+/9NJ655xMolHsxtGEvBjZfgq6BScXg0xbHUm0KL9z5adTnTmPTde/F0K5rLNWpKRWjzoIp205jpy8JzzLlyErbTS2Ia5qg+ms6v/n6y52/VyCllQneTu2/F5XRzQhQUEz2/Il9OHjvF9FcmsbIlTeh/4abQMPQkvjQREQLCpdl2wRDWZgHFidTJrLINBAxHXTd9nGiTKPMLMRKK9BJBR+KBGQ84ID8X5KpGWg432nF6JCb9ZcDvJXa0payHGWwGICc6EI1tfrIS+Sl7PDTR69I8v8KNPvJJYJVOwpzzjHz4iMY3HUVaJSTIdglmssPiDopbKdgg1RYRjeaVrs2Sp+GDID2Mybe/meYXWTmrso5o75bwRBbmYzT714xtCaTa1UGLzMel0gGCMRlW3OQ6Y/M5p2W8QIAV0DhPhv+Y9UfS2DBVblMWGB3XeS6/1ZzHNMeM2eCCcDazlWnHxkNQg7fmQGtRj0sIumel+5hEQFtZhsRLSZodQU6SEVLAkgoHqBVFYKswqJ9v2smJHkAwoEgFuuXSjrmBrcw/RcsIESEKc5qmO12Gp218D45z84AAEfYuE7r1IlWDwIM0w83yYVJGQdhwJqc5+wLYJSnzQTMSKjhmgDpZ6T/B7YkXQIAVS7kIGEBYx/+CM587Ss4/dXPI5mZxeDr3gyRjl6UG7rxTVh64Rkc//rfYeLdHwYtlEALRQRBAYQGwp5c8k3MI0kLIeyk5XOpbTbDqS1hy0qO9J+tAsDnFlCbP4Xa3EnU5k+Dt2JQGoIEIar9E6h0jyJJmpg6+giOv3gHolI3+sZ2oW98N1iriQOPfhWFSi/O/7l/gcrABsRm/G65N+Uw4pzYTnGcGpEzjGpMu1pzw7AikqSbVe4m1S4+uAsEzENjI5PMdtQ7gO3X/xIO3PM5PPP9P8eON3wEQVDEkQe/gTP77kfX6FZseuc/QbhxVNQRih+gGX/ChCkQbYpIMgCQRED5JMHSZu3xnZHMUh3FxwYHaaQg45yMq271VRfXpugB1//ztA5Dgg/CQcqxGKrYo+FJ967WkeOoP38A4egwovERhL194MuhABcA0JV17qAtoHQaapzakXJule1frTDLkY7+tOM+JwWBobi5hqWmOJkQtO0o1eRwQtCcPYu4toCuDTvyizMRmEk6NPuIFYzne8YhrgIkJl5/k7gk8l64mkc32k6e46mpDQhayDA4UhOSIaN+1wRIS/rNeSvvIdb3z6Uphvk8rrWM8lialphtl47ALiDIo7DO85lfo952JjeZ+1wwpRyiudrjrAhK5vNJ/nejxscxDzL/7px/QFYm24q2Um4vADCeJ/e2pEARNJkGAIRoYVFK0RJDs0dPeDn3gxZXIVqB1HcjvV5YEPXFZQoWAoUFx0yoEiBY1h+BNi/1vCxjvKw4/uq6hx9xmfuVmHZ14nvaXH81agAUpQD2ZX3+OgE4x2RhGbOfPJKTNUBunNtV+wK4IUErhjagxZBEDmPvCjiN465jMRY3hqilpqrhEkFc5SBBgKF3vQeF6gDO/PBbaM3NYuxt7wMhgdj8SYgN7/xFHPzrP8L+v/yY3Z8gBC0UQaMCaFREUKpg4pZfRKGn32aWA1sqyimwePQFHL//2yj3j6EyNIHq4CTKfeOgQQhGOOLaAurTJ1GbO4X6zCnU5k6hNnMSSVNwpYQGKHUPgQYF8CQGZwlYEqNZnwcBMDR5GXqGtqI2fxLTp5/D1MEHAQBDW6/ApmvfDVRKSCAYC8XMEY8kOiXJXMjNz1IXewCAxcwYzEDGOdGQ/qv6E71xiOv++eQNUacYsLQ9KaPQv+NSFPtHsO+2T+Lpb/yxuJ/F2HTde9G/9xoQQtGAw9hyIC5D5QEgTF9PUvU1D4VD7PIGpssY0nnXtALgai4I22tRgDY1J+UzD5HjRJpAUk6fIcEABZAQ5eBrRfMx6+IAT0FH/fEXMPXxvwFvxRr0l4oYeN870HXtlQhqFOEZ4Q/R6s6Tdon+S1BEO5jTWGZAL8ECrZhPBxQq86s4/e5IymCH0iFRXE/KAtixSIyn9OFYNRFYYMekpdMHAQDVkc2597TTqCqtUc54xSUgXppHfGoOlf6NYAFV32p9SESskmZOzBCM8IDYphGSaTTDCRNAJuySQADQwgGLKQ50aNFMlnmHaJwNFJCVQqfPdRyA9bjYzHziKFlahsmPbGecZgPWDGs6FjLvickQG8y3pZFwgIAcqzyywneuBAgQox2++jwaAfe4nUmUBOPZEKF2X0xtZX624/bAXDDgLj9h11OYl0mygGafEf6zlYa3lr4H6aVmN1FAQBxLMyGC0qyoK6kEQMIR1laA2j1jpDug9yFd3kFNq6U1+oOs0zqdC7105kBA1iQoh6m30m6fI5qVpkBBLbGci4IWQ1wxsgancfFNpyQzikrN46smNn6C/te/EWFPL05+7XOIF+aw8d0fRlAUQc2Lw2PY9jv/FsmZs2CtBlizCdZsgNcbiGtLOPvwT9Can0FpZKPITsy1KZCU3jXr81g8/Dx6JncjLHdhZt8jqM9PgSUtnHnhAYAzEBqg2DOM1vKcsmMnNECpdwTl3lH0jp+HUu8ouqqjKHYNgqZmLiZD3mos4dT+e3Dixbtx+tD9GN16DS6+9ffRWJpBq76ArsFNaKYOWDpJDywmQLaZpGFbXIffjL1oer+SzOVIi0yVt3fzUBuSrSY3tTpKmmgs0JpxEedcMyRxDaj2b8Set/4LHLjzswijEiavfAfIcC8AwUwFDSCWXwuXfRUMRli36wI0IGAhUDpN0RhybcMMkGNE45FRV7RNvLCJlWZW1lhJUk69AGkRkBYBqya6j5SDx2Iuk0JihfPkMVG+Cgg5lu9/Amf+6h9Q3rMTQ7/yS2BzS2idPIX5u+7E7Le+h+rVl0NypuESUJjRAWjbSSVZAavbG+UrNI4tx/lXGEmmxzJb6+C0u3T6IIq9wwhLVZGwr40DsCQi54HLdBnfpgRetaljOPjF/4mkvoSgWEbXxh0o7jkPXdv3oIQBda9k4pfHCKIF4dxuPdNhzpRdvgEEAO30ac0DyUhRosyCJBBw61UAwZT1uI6tSsLtl8SyyGbmWcFmrHzSfjcbMG1pib8UTpkRznz+CNaYGPXnnc/rDzHXOWYDAXEB2TUTyF1X856XR3laOT2nBaAzmf+VOhor8CCdhFNbfpJwlRk4L3pRYTZRQECu4T7A2OwWSfFMnwEAqPcFCggAOnIgTTgCw3zIp+kwBZ0W75JDygIiybF7VzkKcrQHwNrAwM/Q93Kd/vehc08W5mgDeNQ+neBKPiKL2kiNTF8Ak5I0XJgbgizwJHapDYr2dh3hWJzUZV3JX+9FVyCs9uD4Fz6Fw5/+c0x+4NcRdveAxiI0JyYFwyg3h+WjB3D2258Dj1sYvf5WDF/6epBAj41aCFsNvPjNv0D97HGAUPRsPA+1mZPo23Qhtl7/AbC4heWZ41ieOoLazCkUqr0o946i1D+GUteAsmk3NzbTzlI6ZBEOFApVTO6+CRt3vg7H9v0Eh5/6Dka2XIXq2CZEvQNgEDavKjqGJHNTC7SUTG7ipvTftylIACAl5FIayQzJoh4Yh5FyTWikpMrcXALHftaToZNTHW3I3TBle0phN3bd9Ou6bXWORp+4Ka7Yz5SST8K0Y7aoXICCVpfeoFgERHPioa1eZkkVfeYSPARICzBFciyCcCheIQNMlwIk3Y60iwN8yfCyKxvXE4Ll+x7HmU/+HSqXX4yhX/l5EAQIohKKPUMokh4c+V9/jPi+fQgv2Z15Hgu0c68EQD67f+0oajSrA2Pi9sF8DxlHYuSDkXMhVuQgTACyoKHBb66/RiciwOLpQ6iObGkLFKS/EI0BzrJzV7UvhFjN0+klAUChpx+D7/9l1I7sx9KLz2PuO1/BFPkqtn7kX6HSM4653QzBkqi0sOBU6mNaJdgwv0FCUqd2HVIXMN5rG6mzXXcnBCTq8ToFR2kBgyGXwQ6UT4DUABjtSUr2M9XaaTDbvnwAlq17YK+FGcm5vG5qCkwG3vnbBwTc6HB58yCPMs9o46+2WtO6dhoIXwQnIBslKClRKxmY1vjqeVGYFVHW6gNGoA/HTFKOk+s8DAggQDhQnLY/1qQcIqh5PmB3zq6Wd4EGELn35kn/X82mPisgwlc/z17q56+ToJdME8DLkfc8id3Vxp70uSZAciFxs0iaqLyZgJu+AL7U4ebiZ9roQwMASXHFOhS2spKRIUB123mY/Mg/xbG//0sc/NSfYNN7fx2l4THjAeK/5uxZHPz7P0d5bBKb3vb7KA2Oqusm80AS4PidX0Vz/ix2vO230Zg+hZkXH0FraQ494ztFG8IIXcOb0T2w2W5cmzUiKRAETQ7GEjSXZ9GaPYtmbQ6N2hya9Tk0lmdRWzwDADhz5GGUJ0TdiklP8we40nK96dkLuJX+3XjdMlIOYEts3Dj2FmPhITfDr0WmVMxhBH2MobtBu4sBje3kYEEDaPSn95pfS8vtq+HfwYFoQd8niUVAsEyRVHMchY0+sQJAG7qBnEIn7zKddw0tgFtHsBio60mXAwgSAIsBUNKdaB47DnCOsK8PtBGIrNjp/cWNkyiMbcD8ffdgYGSPYuJbi/NY3P8UunbvBY2qqi5ONCjo6JztYeTXSj4AoBgRdSI9ls7sDEAs5A3RspircQsgjKBV5eAFbmlrVkssTNcdo39Jq4Ha2eMY2nVN7n2dxkOa6zHn3demjuHAFz+OqG8AGz/0mwiqFVR27MTg69+MpFbDkb/4E5z8xy9h8P/+KIJGVmizOAGUpwgix+lSvSPPtwZChC9V044WZH2HhjZA9UEyz9yeJi5IyIRnNSgpGM+QGoDILhg7zH6cCjrMbzisuU662s9Jmcq4fjmKYbf7YrXZvE6QGT+X7H6T7Hh3kvq74Ool4CdXE2o0737AntNJJLS4pmaAcBHIwySpxZdUmo4tIGAKZOKyvq9VpalQxh7sxkDoBQK0yUCStcb5dMj1jWkHBtK4/6sNlrIeJnSdXgp6SUCAFwDIMI9RANJyknW08yPoIOFwNQ9x1VgMYq7NTgK9yJuLprlhls8mqA0GWJwQZcMl4USn6nMlmQSoDG3Alo/8Cxz57F9g/yf/C3p2X4yBy18rwkami3X95FGAM2x+x68iLHcZ7XP7AkSVHnCWoNDVj96R7RjZ/RokrQZoWLAXfhlyVJ5LpUFygW8szWBm/yNozJ9FY/EsGovTaCzNwPRkDAsVFEo9KJR70T+6C+VdN2Jg44UI6hzNbnshyWw0DuMkbYhpS0tfTCdmCQCs8G6G2huAvei5G5fjiCjHgCZ6I+3kGOyT3pk25z4mSzIUUhsSV1JTIGNegKURgRyziaSYZr1NqTijmYZlAysGSxRJhenswaZdraklCM3OGH9GNhhwMy0rkgCBCM0Ak1GAlBMpAakFqmzf224BLVcw+7VvoXn4GMbf/yGU0KX61n/xtTj1va+gtTCL2qljmHnyXizsfwbgDOWn78emD/4maEEgKJ4kWHj2Scw/8RCinn70XXI1SiMbsBoiab9fLVKbpJgmjAM0ODUcoE1anjoCcIbqqAb3Egh3YtqClpgrLvMPAJxzHPzaXyHqG8CmD/4mSFlINuQ6GJTK6PvQz+H0H38SXc/sQ2XbLiRVrQ2YuyBG8aRYU1tdGgi4YTLV83wYlsPyA+AEIrEdgwYCjIOk2jnJYMv1myTpeicZb5PBNk3CuKF9Mupx1xLlE5ACmGY30cKYtM4gZRRlHZnMv/AAbQjAoyK2pWt0xjfAAwo6AYEMyfI5DP9qnOtXI/Vv5/Ds83vIApSspJuHsDRnbvhQVqReIABorXJpOgYoQW3AfilhzdD4pFVKAEhjrt5rYyAEbXEUZ+1NWQYYoS1mtTuXQW/nLOyJ1GMJMTNO9Dl7Ywd6tYYJXadXBq0JBLCS42nVIYGXCwR8k1ZN+gSd1cIAmEwIVk9UMhBAfOi+6EB5C5UEAOp+Y/E3Hd5MRi3q6cOWj/wLzD56H6YfuAMHn/7vKI1vwuAVr0XPjovROHMKQbmKoKsLSDyOs3LdIcDIJW/A2efux7F7vo7tN/8KCOMIolQU7aojXcGc0fSZQ4/j6MPfUsfF7iFsvfSdKHUNoVgdQKHci8jzullAwEMgqnHlMGduxIqZCbPOnXJsqKG6BZcbo3ayVO2VAEBJ740N1NlQpNQtE5rQVWt3AAJe7QKx65XMlNzk45JofyvFbyxMpfLQTEfQhGWHDSCN9W84Scp6KVA+DdREklTQGAjPpuZBPVK9IsJ7AgArOJ3y2cI7IIG7zAC3zwMAXQ7SdtkcA+ESUBD03vQ6FCY34swnPoMjf/ZHmHjPR9DVO4nCAlDYehlOB1/H85/4f8HjFkojGzF287sQjYzg6Oc/gSNf/RRGb3035h59AHOP3IdkcQGlDZOoHT2ImQfvQGnDJgxe/Tr07LnEBnFrEL5ltAemJPinRK0eBtoiIEyHruREfBsIsyC/HS2dPgQaFVEaHO/IDJrmNySBX1CSOqa3lhcQL8xh7M3vQVCuiKnDxD2tbg5WYmg+dhwIAoTDg2AlBlqnKO+ZRaMZAtPC16k2DJSngGYvQbTA9RpG7LH3mfS48dZNExjCkaMR4EakHHdvQPa7BjLx420pPgcIUdpN2dZmD0nXKCNEs7HmEA5vFKWMs7IjrfcBAV/b8zQFXvIJRnLGXfXRZ5rjroc545lHGa2qAfBUu9r2Qy74xj1GUkcg1dxwbbIrQ3rTpgMGDEEfAJSnpW0pV9qBsMYRNDgavdkPJYlsgNfo04lFC86zMvPU5FkSnv0OXf8NQJQhxBsQRfFAPrDQJkdBbmTF1VsrvXxkCONetuevE4A1ggBab2aAACvYVdHWynd2d6KThGfDi5rPchOCpUBAqgxdXwB3gVoa1fdXTgopbZ4GQNoA2w0AaFjEwBU3oP+y67G07xlMP/gTHPvG3+FU1zdAoyKKA4LjczcUl1EIgyI2Xv12HLzt05g/+hz6xs6zrrdTwVJjsxnfcyP6xndj9vATmD70GJZnjqM6vg3dVS2CZrCl9QAsE5eoxtHoNm2A7HZICY6sg4XZuNnK/l86gsr133Xkc0guZILx16DCNRXyZQ8FbEZDMfnq2boPLkkAIBkKqcHggZD0N7uNMUiAwGDCJCgy20QdJs3clMunRRIxBZ5aQPEsQaNfj6EwQUvHsMx1ZBlGUkaPZxewdJwtQESRg5JgmSApZ+M4Bp+vg1a70De8C9WP/isc+7u/xNHP/TV2/8YfAACCYhljr7kV9bkp9F90NSqjk2CRmAcbP/BPcOzv/wIH/vT/BS0U0bP3CvRecS1KoxtA6jEWXngaJ775Dzj2lb9FddtuBIVSOvBQfhGqpcR+x6uhPDMvmMORBxS5uD9siWhAgpkjaEXAOYELmvIHcv5xYPnUQVRGNok8HZ6+ZsBNDoU1wdhIJrU5I2K0FgaG1bPN6Dis2cTcd36M7tdfjcqOKiIZ3sqgxpjQBiyPAuUz6X2mFs6RJHPjG884VmbMPw0goE4iM75KCOGQsvd3dy6PxNnVlDR77YcwD3DzaR2SQj6TK8ELD2wgwImdSdl6Rp503RzbnPkm781l+L2NhLMWZ4GPOLCf6/NbUJ9KmtPBFeLYz812IussbIfBdn0FWGriy4lO/CXH1d1TXDOh4px+aTTWoCCJ/Pkemr0hCnNupIssYFUMfQqu7Q7mfLBSsJkXYtUDBIB8MLAu+V+nl4rWbA4kgYB0BHbzArgmQC7lqbuUFsD1BTDqpo044xTMXO2B+41wXXf1dIKlEb1DWKYe0NJcxeTJhVd+pMYHSyhF944L0L3jAtSnTmL6oZ9g7qmH0LP1fCtxlqt+hrEI92+7BGeeugtH7/kqet75e6BpCFKrO27mVcknc11npWcUlQtHMbzjGjzyxX+PxvwUqn3jQtUq163UjEcu6rTJwQpEMe9RLWsaZAGPdMGW/VH1GdEtYlMDYIx7RtpPJeMA67qbETkjETOYbnWP085OITVNBiMjUTQiIxXmxZ+tdI5IB2BZL40BeKJUSAdN16+idAZo9hoaphgoTYnGNgZ1WdoUzumtbuGQKp9FGuLvuGKK5mxHdm5oEgjT48siV/pKlPnS1Le+hfnb7wAtlRANDqPUNwI0YpRHJq17hi67UWTZLQAtI1RgdetObPzgbyCem0H3BRcrsyBwgBdDdF24F9VnHkHj9Al9LYfU/EDK26kLawcHPwtiqWMuC8X7JcY3ahLnHEsnD2HwfO0PoNYFqd3osMfTlpg7JhNCYqAxPQUQgmJ10Asgak88A15roPvN13rrDXqbiBcKqG/QZkGifQQJhfVdWgydsSZJxs6M5GX1n9hjkwEWThhRF7y54EA9x8nIqwvAHk9ZDxEMIY2FE7GbuMon4ZbnTPBDmGivDo/qgI3AAzYsTYL4UdfhOq88Qcf5oQRi0m7cACmyjoyGBsaz3frTdpnBHDJRjdqZxviAoGxrGtJV7i0yD4AbyCMpUisDMGmJv5XfBAQQIAlHs8c2EwYEKDC1A3Ieme+92RsirDFhDmQ2P89m3+QJfOSOQzswIOtoAwbWk4Gt00tNq4wxYJMvElCw3ESwnB8UnKdOMO5CyQPS1gzIdTBOSoH1U2EiQyK0AO7G66l7eUz8wqwwLGP64pMEmXGSAaDSN4aJN74fe37r/8HYdW/VdblmAq7WkQGT17wT9bnTOP3MXe0BAJDpm2KaOEdjaQasUQMNItQXz3rLu6pkM3pPEmn7WE7ETy38sdzgnfqofb+OJy3AhbTr1eWNYwKRgCtnJnJCwAKSSTLGojb3pPbS7jNZqPuuMtg6kSVU34wxowlQnNcAIGiKCECyLI3FcVhL540hjZb3KCfUGCidzbaZxkD5VPp3eg9tAcVpAwAk+nxhzv+t0AQIa/qaCunaIgiWDZBuOLoGNYKBvdeBlkrgSYLi8Cias2fBWk0MXnCtApre8abp+wuAys6d6LniKi+T35qfxcKzT6D/yuuAgFjZg1dKPsdQOUctf4qfFlEOVmaIqyJaU1K0JborpebCNOL6IqqjW9T870hcrzd5kYjCBhCfnkKhZ0Bktk4pMNay5fseQ2HTBKLRIQBAq6XLsRznZ9fJFjD6azJ3VJe1v1dHjOwQcb413zW1ZhHnfFp3nj28/O6aPXYBy4G0LM+lbQ/1catqCzDMv80IJ8r/wQAgptmKJSRw53Ban2uW6F7PI/fbtMaeaAGPC446ORh7TS3b3r+6D9DtpzlegAAD8qfOFSnisk+yYx8W5v0fSXGOoXw2K0lwnx1XQ8TVEMzIN8TbxfFnxm8lFBD9c4kav7zreXROHN3PjuS383L+1knQ6jQBhFgferBYR9JV0sdtmH8AGcbfqrpDEhlJSeqETJtMqQpV/WH2fmUewGxbQtNRExBAwNyMpelLnv2my6ibJkQ0KohjIxQmTYx7UjRv2tJXBjdieOe1OPHwP2Jo06WIKt1pvfbmotWvKXMoHaYCglPP3oHDD31NtSNuiORhSsqlGOBUbZ++j7DG0XA2SXdTlYwHTezkQHJ8EkearjYeY0xdFb81hqaEg2i/BHks61T9J8KXQUUhofY74tQQahnv0CxjaY/kn1w490p/BpMpCesGA8BSvwCq71NdiWFpE4IGVKx/ea4wJ/5sVQ0wkQg77IahKaAtYbIG2NGGSCxMieS99UHdVpKIawDQ7OPKgZjGQDCT+iL02qtgpTKCTbf8Mg5+7S9RCLox8aEPImik7ywtIzUAbn9NUjbycizSfsw9ch9oFKFn75X2DVKTw3Wdq4k64qM8UxriHPC0rOn0KiPd0JgjWk79ZRYJ6oNG39dASREqB8SiTBJmOAXndybbD5fCmijHIiCuLYLQANywrQ8XxULfDBuoPfMMet/+ZgBAqx4iKsVYPtYNdMcIS+Jlhd1CG1CcgZ+pITn+D6l0XGpCpEZASs07RztK8wfw9N0ov6Qc5quNtBmAMutpdRHnWLyHpKQd+VtV8XdcIgjrHI1eI5Fa6riaFIi93pA0uo3sn5nnQK6Lxj0sFOt2IPMQ8CwjnUSp2ZWrhXDKy7FkwcoYGm3mRJQTtjpvfHs+IGC10QME7PtJx3dt+xek56RmOZRrlV1BxmzICSkqKoNgmtM9UQIBK19QWke0yNDqsgfffYY6XwxAG8ZGYN7m+z58ZkLtqNP8NvqUacO6VmCdzpFWhxtjvy6e1hqgtQbQIbxWJ493xZg6YENS4kQhkk5DnApVrJuoymUmlocD9aueMOotpdlBHUjkLmQk0T8lkZIOglK1KB2BnZHNgAbno6YxMHHJLQAhOPrItwEO2KE3uaXOJYxn6wjF+Jz3hl/H+W/6p5i46E1GA+znq+hNKWNcSKOAsAIRoEElBRM/VrDbzCKbuTaZZWVX7zrTGVFsTA2D7oDdTjd5krjH1iKx0MgcSYl1DCJ+pqTMp1kwAYCkoOHxBYF+33nHsj4zpKqpEXBJmmSbDHzprDBDMoGlMBsSP0nKsTER/gbybwUgmkDpdPY7CprCBKlylFjXu7fsxvhr344zD/0ICw89aDvumSFeo/R7KaTAoI0oQWoJWKsBEkVqjrYjHkCDA0jGvONtrwhKyum4pGPDg+z4LJ/SScJM8tnA+4hwnq4HyGgFBnZdicbMaSzsexKFORGhStLic0+BxzGqV1yM0v4iwpMF8IN2XGSZUbprn91o08ndS85aqaL2BMQBWMbfVp/S/9M1zseMmfW4vkBWxJu0jlYX0aFA07ksnf1Vdu+K+AFiTgNQuUFMXwoZYUYJN9zAFlLYZMa3lxJ+RxIvJdt575sZY219d6aQivj/ds1ibU2nrsOVfLfTqnrPS+ad2+V4zvtVmY/zhAfONyK1yLY2hVhaqaRErQShqq7IbnS4nKifSdEiQ2Ehy0X7NA2sGGSFjO0Y8NUy5zk8j6KVaA1eTcRfAb91ArCWqeMAAVrzcEqAAATyZxDhXP1M4iH1OgOzQqh+FhqX92UyR+qfXJiSAkFSIBk1YPWEXvhVf1r6p6p0GH3zvHWv6wSc6J9lGx7bm4h8VliqYuLSW3Fm3/2YO/RUbptobJuHSHvKcq/IR1Co9KJrZKtmuDjPRrfwmHewgrOxGZJZEe1Hb6gkyZrc0MQIDVqwr7EgLW/y3gyezcL+Ozf8pSxLoOxxdd+IDR5SkGg6HytTIKUdQUYT5DPTMBkQ85yv/a7a0XIANChoiDlBnFjyYc2fvbU4rX0VlLkPswGCJNoUZkbVE0LLoM6nn23QEuclGBnaewP691yFo7d9HkunDwkJdgd9Iacp09uGv+/eezmSpUUsHHxWmHKZWoUVkmWSYXznwMqkoedKQY2CBxxJV4LGWIxWF1chZVdixw8AnHPMH34WXRu2IzFA1EoAgGT+21Hf8A50j+/A1E++A57GJJfRUOafegSVsS3omxny3lu+v4riw1WUHrGBgWKGTWZT+oNweNtEuPM9mWY7HkbRimJlJCPUFdqMtO8+b5uhwXKzxylTyv4ty8i5HJfED9B9tvKfeBgzkvCsJNtZW1Xegpy1Q66XgDPvnTVVnSf+tvhIAzp33XTKeaT+qzpeJXDPW3MzgMXpJytS9VNlIpoBAwCsaENyzQgbXP0kxWXqHU9WWCFSVw3h4pfnEOxSCgZyw6jnAQXm/L9O67RCWht+TJj6kaabhCjL+LejPOZffgxulKGkSK2fVKMpUw9n8U2cxVcCgaUN4ielvZLJzmgDPPb5Shsgz8Xix6hdzrdImpI7TkgmF8HotqvRt/F8vHj3P6C+eDZzPaOGN7pbqYqIRLW5U2n9gKlmtPrmoGEWERQW7LI+xziT6TSl3UlB/GjCLebOjGduMko+k488+243QY9PQ2BtlIHhj0ClBC/1IwiccsY5FgizDXMjsjZj+UxDu6Ajo/g3QRM0mNIy39zIAzpumFpJ4bJTlAmNQHHGlgBTIypN6QwQpQDCDJUnn92qzYGWyuBJjNP3fU+MQZSamlQEQ+SLT6/qCe2fpNL4RhRGxzH/6INGYV03iwCe/l4K4sR5J9wAWXIeyjHnUI6O5vwKGhzlKZ6RqK+FpA/B0vIJNOfPonv3RSvoBFYksSKcgyRc+fJsuPwtqE+fxPyhpwEIU8fg1DKW9z2Hvp2XqPvCZfHS+56h6H3Yj8gKixxhDblaIdUG0+nUEwlMOe6qE2lZz5wXDrbZurVvgQEorIdIkC/+lw6f9QEPAEjN/UywILUE8lxsgAQdPcw+BjST6kamko6tykQxD0wTow7nfcs+WloAujLQmFljzHs8PgReMzy3Pa7gyJOk0Crf4dhHPjDgagVcwY9ZzqpLggFT6NZkloOxSa4gLy4HiMuB5RvACgF4sAbWqR0QyAkF6jWh7qQ1WJdyr9MqaHUzOWfykZhZv5WQcgReYcrspBoiqYYZExgguzCJk+LnmggBgvk3KZMUzLhfqZ1b4pdnKy+JUXvD5NSR5Kf305inETTsY0Iotl/7CwgLFbxw+98giYUdiTQHkupmyYSbsa4LURVRqRvHHvsujj32XdRmTsIyryJExWM2+2mqWAsLWmsgo2eoviZCG+BuFmrTLBOdqAUGE0aczc2V9jsMsAkcFLMd6vukWtt19HWZfMl4aRW4bqti/NNz5vOkg7MJXiwTI6N+U+Nhttsk0yRE9VP2yaSUAVV9MBklguzGT4RpTztTI8XomxoJLvwbrDZyjmO3fRHPfuI/YOap+zB4+Y0Yv/m93npZBMRljqQofu1IjhErAL2XXIml555EsuzxxFc3GO831RjAA7BescTFe1ImhpHNwC08/wRooYjqxE7v7XGlDbPoPIewrEZVvGdxLuIFFfRg9uAT4Iyhb/vFqmjlBNDzor2gyfWkPMVRmvG/20zSQ8eUJ9PUPAmxozXzPstZm81wpP46xR9y3rcMpYZcl93M8EnBdhQG9LEJEiRwUBqBonEu8jPSUros9wkemgAkFVAE9v9u3xRjmwE9+k+vRhxQ3727psi2rIi4/Q6zjsjOOza0PYAtCPEd5z7WpxVwzZ1kXZawi/jXC4evcHMQqPOJn29ISo6JnAQYeRoYX/VJG61AXsTEPIa/Exh4BdPL7RS87his6dy2VqXqsmc7iRkQBPrn3uZ+NHLyy/qc1NpJ1f74XCCQiU3tHC5uCKyfjM4imZPMYuPcTzwgQUnCjREMWrZ0VYR5zC54rqrYPS6QMna95sOoL07hwCNfzmyuGem8cbz9yvej0rcBJ5+7E0/9438Hi7ONd1XTUmImmd9okVttTiK9gdKWbVrE0/B3JvMP2BJYE0ypY0PaLxk+6z7jb+VDYEQRkhuRJcmPzE3Z6bPRZiXNc/phmWylUmHLqdgTVjQuCqdCbf8rzskwqZaWQbbJBA2FrFmM7JfsuwV+omzflHOeV6oKr2SIE0DG6Jfj0JqfAThD/4VXY/TGtyIc6Edc1oDLSwRgBQ4W8UwIUpcKW7eITML7nrKYqxVR2lblL2BqWX7G+yCJCVBKkPTEiKscjX7u1Zr5aOGFJ9G17XwVvadV0SYnrUr7ezkhUP4meW3jwNnnH0Sh2o/u8e3q/Mz+R9E1vg1RVy9K0xzVY/77izNC+2ESVYIH7pcy8mybFGObM2/yTIEy5QyhSrtITD6zO7nOSeffZq+W9gP627S+a8+8TAo6jLT89sxyci4rrXPOZyA1xoDxzVXszst1NBvJRwMB7vgz5QGAvPGy1lhHuJJZA9tQxyhG7ja/QlMuq4xPK+BoAdT+6JoY5wABK+R1k6lQo/oBEtVmb09KoV8LkGeznxcxKA8MeIBAFuh77ns1+gWs0yuC1j5t8lBrFIjQoSx/p8ow7Z76kkqkfnmI3bo9DQ3KQ6I+XukLUD3hCQnmLPZSoho0DclWavbTyRcANJtmPptdl6ufOpfa9uvIP1C+B9W+Ddhy0VsxdfBBNJZnxXWJYpnelAkz7E850D+2B7uu/hB2v/43wOIGludO5o6ZSZJBZZHQFriS4rhkS0dMO+a4ZJs1Kcbcfc2GVEpKx12HU1U0laq79vYus66k/ca9ZqZiFtrH8tkZJpRAfQ0q8kaqJZDPi8uC4Zc5AySjn1REjH8LaJQM+2IDpLgSR9XXgv5bticp+pkSTgyNhgGmTPDg1i9BDHeAj6yPFwg2vfNXMXLtLTj7yB048Nk/R3NWxzKV5kBt7fhTQJCUxM+k5RdfwIm/+StEg0Mob9sBIJWklrRENS75Kl0ZWaZaxJH4EH2+rXOYPEcEo5EUxFoSNjgK80C0QFA+tubUKmjMT6N+6hi691yIuKLnUdt+SdO3FTBMhAMsbmHmwKMY2HE5CBGTibMEiyf2oW/ThQiXdafNdSxa4CjmSP5FWePb72SKkrZXAYGI6HtcyaWp7QIAc30z9gmfZNatwySXATSZf0DP/cRn8pN+o+qbdL4peY/5LbnSfXN+ufHupQmquseJqKvXAoKkpDvHKVF+WdrHIkcD4B47w+e+Q9eMRt3uES5YfgSeUMwZHylXY2Hm/1mDVkCBGx/TzW1/DE6Rw2zbh6QlwIBrTixAd/b+XN+AgKyOq8oBA3m+k4ryzq8DgXVaJa1uykhJvc9+TTL/K7zHCwSMemjD1jnTJrNyDGhpDxWhQp3qXF8ACQTqQ+KnnGwb4ucuim5sfpHJFhlnVlfaL48lBU2OoJnta0alnpHucwxOXAwAmD+zPxupyF2YTEkYB7rKowAhWJ7xi/yUNiBdpKNl7o0HrtqbQEfaMPwBYmOToi2HkXcYzoxNvil5koxaaJ9zHXhNrYGP4ZU2/GYWVVdjYNatnq06oZ8DAM0+PWckNQZ0iFkZWSQuA7Ux8ZNMQlIClseB+ogh2Y8EQ6KilBjPa3WnEkejPSywnRNNZjcuZcEMR6rRKTjXHM2O1647oBh5zZuw5YO/g3hpHvs/+V8x/+TDdv0hByvxFdnvJ2WOpMwx8/DdOP6J/4Xiho2Y+O1/gah/oM096a8o6u/kcPxKI8Kgoofx0DYZW3z+SZAgQHX7ntz74wrQ6AOaXVnmMPMsrr9DSbOHn0TSqmNwxxUAhBS/Pn0SnCWoDE7Y9ydCKBEt56zFprZF3WRfz/bfrktFxgk6AwFdsa5HhUgOnLoN4Gv+71Jk+M24CQaTsqhPMf9FjlZXek6aOBrgIfEBeMPBnbkaAegxcjPUqqSDEowUbaDCCjpbed56l2Hcc5ZvM1M1d9YW69gDBHRCNOe8a7LkagXywIg6zmoFuE+rYZbxOdA7xanhs+ar35pDPDtf5b2+JHFC42qA4UKQb9VAsTrp/GrzDMhnubzYq8Ux2BXGvBy/dQJwrrgxDNTP6wvQJiuwD8V7QQSApBggKWavuXkCAIDEHCTmCGvZ9piMHKAjpHjJqNqNFgSIBSUj7XeOXebfjBYEIjZo+VMqXKl9oARRsYpyzyjmp/bbi6pnQiufAil9Cgso94xgaTpH7w/YG3oAFBbFmEkGUiW4Mp2gcxaZuJxKuV1sYizcZoi63NjThuTWbJtrW22BDXnddfrtACh8knPTPrjZp8/Xx2Ms7ojRSPnXVn+CpW0eDVOJoT7GVAhNAGAhR31Y/NRzikBtRMxJZqi8WSg0CK4jIw8MLYLDlOUy/MQDfiRRYzwcqkxsxZbf+D1Udu3BiS9/Bse/8Q9gjXqmHKdcmAIVxc+lZHkZZ7/0FZz9whfR/ZprMfo7vwYMlZFUV79TqVCjMsqUGUZ0BZLEl5IKZwPwRgBSYEhGmki6E2E21UFCvvD8E6hs2YmgqMXPkomU4KcdmQ7veXRm3wPoGt6CStewYnprZ8UaUBnQzlC+EJyS2pkb0dheD5iMi59zH+Gw1nrte0P8jKIpYJFmSDnmnp3CUoY1UdAcVzkmSSUNLx2KMs0+3fg4BQetlAk3wVgr/S4TV3Mnm+aJIKTa08jbDw1gIsFH0f6/aQIEZ+3LIzcSWx7JddXMHWDdr8bbNsPJhPB0tQJtAKQ49jv3tgMCZntV+ZSfyCSaYwDxaZAywMH/Xrzhn4FMqFAWUbCI5tvu53BZuVL+VToQ64bkX1qndcqjtem25WRPGGDYx5GY2cw954AR6io3GZiMIRwzK1KQj/E366EtW3rtqvLCGsP8Fr1ilk8LxkstVBS52TfldRcoWGY/FACznSw5MdS/qfmBuzgBYmNzk6qQBDi1/16cPfo4qv0T6OrdiHLXMObP7Fd1uxEo5Hmr3oAgiRuIoqrWBMjFw4yxr5zZDObUkSxbACDWyXTk5hM0RWIdVWeo7/M5AMoyvn7IzUZvSv5NztwEdXZi45yU+IWiSstUyagbsE0yZDhOyazL6Cn1cVG4OFQDhoDGVCr+LzDUN4kGhFMRWEn8zQKgMcgQLdirPw+A+iBHtEi0dC/kqA8BxVmSKdvsFYme1BhQWzIZGOFDOdXMCm0iI7E181hYzyFQq4C54QWlMsbe90tY2Lkbp7/xZdQOvYjh978f5Z1+h1Yex1g+dRyNg4fRPHgYjQOHEU+dASjF4Hvfg57rX2O97KQqjfztZ4dLa+PoeaoMVFmFmfE3pBQvPZ8CZ06gIwRBzBuacHBOtIQ2IgiaHGGNo/sg0OwmqI2uvn3x8iKWj+zH2Fvea4U3XYkfQVJMhQMdyjaWZzF//HlsucZ26F4+ewzFniEEhZL+Lj3DLOeGD1QpzaUrfGb2OibamE2aZRILdF/UvR5tgDTFyHV6NYs7aymNU61h+h6jJfGtx91MtZOHHCQmiPtioEWRlDmCNNt2YyhBuBAgKQrzHSmVD+tCWxem9ck1w0ygKCkpkDRHiPYZCdMITnGZIKzZghNpehqXYTF0rWq69hZFVmhAzAn5PB60mRvpOMo9TyZM85ZrQ+6cMNdwmUxNm+nADo5B9Ddn1ZXOD8u+3wAcor225N56fpKr/BDUjpl2tAI04cLKwMdyeJJ58ZBkTYcAcEpBfKbQcsxdjb+MbugCiISnvk8OP2Xeu8IEq69UWnfOfWXQ6kFADtpVk9hxlrXi3TrX8upTWYFbzArNBbQBEuk1KTWSWQIrpxmWRwwg4jzfXDytzUxGc0gXNNfmX5JrO+/af+YBAOuZckMMAMZjzJ16HgtT+8GYRihJs44wKrVlHDjnWJg+iFOHH8TZo48hiRsY3Xq1vXhwnlXFUpGJl4UExXmdPVgy0YDDcBoLZVIU/gOxk2zNcvyTUXaMOPic6NCViYepd58jzWFMMyppdiM1MKbELjDOyfPSQVA6+ZkAsDHE0AAQ1NO+T9ahXvl8JABASsXhZTTmDGPimCDuj0Fr1IooFHeJztA6sRj5Vo9gNliowWKjX/wdLeiyLBK+BoDOCyCJhQDrBqJF2H4BRPTXGzGIaJDnmqMpPw3zXRCCrquuRHH7Vpz+0udw4n/+T3Rddw163/g6JAsLiKfOonHkKBqHDqN59JjIIRIEKExsQPn8XShsuQmlnVsRDg4A7bRu8nmJbh9hADMY32ix8/2vJFJANPWNWdj3NMCB7p0XdryXB1AO40EzJ3qZLGs44J964scIohIGtlxilalNn0Clb4N456bAIAVCiplqx/yrBxrlfEtxuoZynr1urs+i7SQDIvKeRZscrJCWlyEtOTLMqazLXIsKs0KrZ2qrJFMe9yRijkUMaFGw8XSizkeIuwUQaPUy0JoY6Lgk1vxmrwjRG5c1EEgiwaS72eaTggCSdg4QjrhMEC1ytKq6E7QFJFLL5Vnn46JYN0migUdHIOliKGPcTId2FhBLKu6Or8t0WveGxNr7OjrKO0BA3efMBTlH8kj6nURLCWCACdpkmUhCnXJsAMbeTByNBPG3gwcUxAmMInmeXDDg1ZilY+tek3u3j3fCqx8IrNPLT6sHAQ4T2TZ8FQCSJOBmhCB3Mjv1ZbICO0BATvo8+3UeEJE/wKHaSHp/KglRTL6zWHGS3XR9AMCN0e5Tr+fa/aeSBbXYGQvhyI5rcfKFuxAVujC67WrEjWXQsIAgLFoMtKVB4ECrVcMTd/wPLM+fRLHcj/GdN2Bk8xUoVQeABlcOZaKCLBAw1brFeY76gNwlxH9JSTDRNNFhTc13YAEAAzxYvhXmEFGAOU5k0tSmdEar3QGtjbGk4HXdrqRka2ziaiqtWzbPccRVzeQ3+82XLv6LxsUNSazn63D/AtAPzCxpmwIXAAAAqVMtUTbfTUyEiQCHnvccSErcq4VqdXOEy8Q2UyIczd60jQtEm1QFQKNf/G2BBCrGRLYnqMNmBEhqJiQZLBmxxIiFrhgmThANDmH8N38T8/fdg+mvfxOLd92rqgpHBlHYOonqtZeguHEzCpPjIIXImAAAKAerSK44fWZ9dZaISUlLwzmg83UYUvyfFRWnkWYqDZAMNYFygrhFEXcBhVmq5qYkzhLM3H87Kpu3I+zqzgpdjXfDwvYSMq1pI+l4pKYry/OYeu4ejO+9CUFBzE+p+eBJDBrZKr6gqQGEZfYh54wnHLLb1o7E9foqpcWZIj4mz2EMaSwEFLSVmimme0AGAKTPMs8XFjmaXSZzSIQJm5yPjKj7opEa4qYtCo5Hm6BzIVhZAAEecLSqQNAgiCtijZG2/OGiltbzEFZUOU6yQxctc8RFIFqygUBQRyaJpQQWUnAihVNm9nrAZgpNJj7D0JvXjKWQBcTay3xAwFz3pa08bUFlQhahq/U+obQAhqAiEzLao9m2tALmfomc751xGwgk3MiBICXudt36XgCZACA8o5FQx6bzcUDT6462IA8MeICAYuTbLYvyHleQmWox1mmd1kJrD3WRmgER7pmAq7SNYyXN8VhMv2JSGJhhJpQHACSKD5cTpQkANABQ5VwpaLoJmI5jhDnZWo1HnhMAkMc5Gz2lAbZc9k48f8cnse+BA+r8kae/h1KlH8VKP0qVAfQN70Tf8A51PQgiRAUh4u4b2YkNO1+LMMoaGWdUr6mkQaiiiZLKRwscrS57nF3bSWmOFZe1xsOKxGNsEACUvawci0af+L8wpwEAoAFAs1c/T5rmAJqhl6YjcZUDVaAwTZSU36S4mmqHuhnibsGYSwpGxEumVJQZ6F7O3L/cjFCMYjRaoQUA6GyY9kvcS5uiXslAy+fI/rMC1D7hbt5We8vGJsxMwA00ewRIcJ2cFUiY12UBwSxI22ZAz13zurQ7djdWwXCmamlQ9Fx/Hcp7z0fz2HEEI/0IRvpAK4K5JJTr/jhZjjPEBSMmuESoMQmX1uaiRCTA4lCmEe74pI8V/IYrbTOloiljTZgAxkI7IgoEDY7CIke9f3Ub7uxD96Jx+gS2/MrvZkK+rtQkiEXIOAGbdPLx20CDCCMXXJ8VEtAAnDHt45NThzLRU/+0J8HkpdJ9h5EDNPOnpPMRERYOSTakqixjmQe5QCAiCJraX8lnumGu5TxII5ela3FxmqAxwMGL2QEPem3VGelpgc/bAqm4O0GwbM/RVpfeJ+KuFAik2gKfiZA1NhCAIS5CJWqUYCBaAlrp8k1isXYGLbvP4XLKpDpalzzpsA8IEAdUSMrMIceMyD12+8pCkonSk6d1FxUiM3+0GZFkwFfwrTCOoM6U3b7cs9x+i/psAOobN6/jsPKdsO+XGYpJwgHzPKXiO3c1BpJPWu2y97+D7X9OgJmf6fPXCcBaQYATJ1cBASXpdCTNOZNcaghIMwE3Qm7RFvM6/Sobdt/H6jCoc1v0/eGiWKCV416QXZxdBih0GBnLHCZdnEzHX530y6nXE/XHS8Yi2De+C1e89z8iXphDrT6NxtIMGkvTaC1Mo748g9lTz+PMscdwxc3/p7qd0hAXXvcbOHngXrz4xNdQWziNC1//O7r9DY647Hqx2W0xzXJYSLREKi3WqpBMJBHprGqGvsyzOzXLmJJuycACQGswRgsAXdbzgZUZmmXhkCkZelWncdwc4KANCQyYAgThMlG2wCZJAFCtCDVCOdIvb6mRMrfpjjF/UhoFpwx/CgAEOCEqJCZtEdAWUfNJMLsASQiCGjKhM2lLR7pyE28JZsjZxAgQd+lysr+yjHRmjhYy3RX3VrMgFkgZ/jb5AFgomCda7UF5sxB98mbKebuNrsRi05NtX+68zJBEjKFi4BiAyAZ/rzbiRPgCnPnRt9Fz2VUobtoErFBjISW/MhxsxiwnJRYRtObnMPXcPRi75CYEhTJkTgVl2kgofCGbze8016m3zfArBpIaZj4pQyo0hvbcbAsESCrZ59wCAmYbpICItpAJVevavOc5dZoCAFEpBwygHRYSxM0ApUoTqDRRmy+B9cagc2IOJxWGYJkiKXIEcq0xTIJaPeLbk0DAjr4j3qMrAAqa+rsztQJRTQOBwpzosxuyWkmruTBFVeaIcvwJPNp32CAv3Xt4AEtb4757F3gJDQ/UEiCBqmkiRBOurlu5Y4z6LY24cV2Wsc2Dsu81Wsp+VCTmlgNv3lz2AQEAGeffPMo1V6IEbr4BmV/ApzVYC3FCvM7FuQ7H67ROHlodCAio+HnMSbyMPtXI2GXauSeJGGCYpTiqPVf671ctAwsTgrsszNsRVjJxkSM/M6RCyqULY+gw/5LcyD++TWfFAMBDlAYo9A6g0KtF5EGDY2nuOF589MtoLM9k7iEgaNbnAc4wPHFJ5npYYxYQcBcw6bTW7DKdOuw6TCDAQoJwGagP2mXc7LqskB1/SY1xjcZIkt5QYmAlBjodgZWNqB0Gw8+KDM0iQBuGhqicgJUBtMQ5XhabQ6sMkGY6F7ts9CcBgEkSACycsoOLF6b05xKXuWZQOZRDIQ8NQMkFk640IInQXmiHOmOOcG2qlBS16luOidQ2WMQFyKAtxzSCcKVNUY62hjS85TGrsqoNecdsojRMQAp6803q+WFKSCUGN5yAUVv5siMdIFWIPsNPgAf+9v+sqDBDUCtGQHdL/BKKuEWU9obEwJnbvgNwjqGbfq5tXWZSt1wbeYNYAJCUITr1mNQCvNauUworoiJqsyfBkhg0MO32eGpS5YnUA9Eewh1AL5lznzRSCjJcJjO9Zvs/EQUEAGIwgsQGAsgCgSQVENEku64okyBqj2HpjB0djiQEPOAIShpdMUZAKUdXdx1xIp5R7qmjNl8ChhvAjFgXFBAocaX9M4GApKQAhLH8O/UL8Ei8Ae3IDAgzIWm+aQIB2tRAoDCvkzaKXDFIwZc9Ju0cgV1JvArHmiNxV8EFUvMsJchx+uRqBTL1OeU7mgcZ+5SruXABQFhLlISdxCLOvrQscM2SRH3pgxwTHWXuQ2FrzzOJO/1aAXGvXHR5pjwC4nVcbutY7Hsn62ZA63SOtDb9u4cyYdwobXudJPbH6wt9lxSo+lmSphxULwGASY0B8bPVqB0AgCznLOqEizBvbqg3FwCocJ1G0htiSESUPbPBLGZUodRevBdmDuOp+z+JR2/7IzTr89i89632eHGOI8//CEee/yG2nH8rxrddp8ORWn1ynZiI9TenmsnnIbx1tCr6XcVleyxVBlyjaa2qbePaGGJo9TILAITVGEFPCyjp9rEBfZ0HHEk1QVJNwKQ6PxC2vaySgKUMPyIOVBIFAACAhOKcCQDCYmLh2HozwsxSWdn+SwBQmArVT9UXC9t80TBjYMzNzjhvZgq15onPVMAAAwoUcaExCJpESR9V/VyMDSjP2HlwKkyqWt1+jpIHwkk5ruRznCzkYmyjfB00DRhKPQ30DCyhp38ZPf2ej0tVSIBiAl5g4BEDjzh4Ye2SKxkaVoUnJPrbUeFgje9LJU2T15hxnaYmQDIXiQzXmzKwkumJloHeFzpvvvUTRzH30L0YfP0tCLu0TRZP26SS0q0g+ZYK9eo8trU0hzPP3oORi25EUCin4RLtMhsuugn1+dM49vh39ck20kJpDpU9ny9hZ6mNvm0ykq5/PvxKbcGO9kvwt8utQ2Y5z8SFJ/ZaL9eyuCwi+gBAOCO+ZWLMaZ5+azQQ58JAX+tO5zPtT02GKJCkTv9S0wcicjwAtvmdPGdSXk4DU0sQpHsMbXIU5wzNXxPqWGqHlJZoBZ+RCaysZ5sR7TzX22UdVveZWg9PVnM3XGgmnCi1r9v3Z7+3sGFPxLCWpG23G0/djMAc8O25oH4pepa595fNDW2ax6jnJBbjlNoBVczndqBXixbATOb4cv3WSdDafQJ82gDzmLEMEHCJlfRqbTrhSLWZGwI0z2aw0SfqKc4zNHr0M007c1Gv30wiw/wvmX0S//kSfol228euf0AW/DgV5GyQkmpLZ3DggS9h/uQLKPWMYNu1v4CR8UtAqeizcJhjmJ3ah0PPfAeTu27CxHmvt9vkSId8GoFMG1S8a7vtcsMxQ1ICAghIUxR5LWjYITiTko6YQwYbyg8rTWyKpCF12fIGAhSY2qCBdLxN+3N1AcpUx7T5JpEGjzyV7kUVwX0nLYqFVhmRIQ1snCmLQDbElvxbgM0YH/X+pJOlNEvwRN9R7YdR1nz/xgYd1G3nYLVocaF1cM2K3ILuhtrq1mMWLtqMIg9E4iTVdp/JSpGJfT7sbJBKCEdPn/ioFuY6BL+XbShwICbglKcSbgmEVnT7K4445zj13S+jMDSK/iuuE+dS8NE2LLFBLBLv0RvpKaUzT98NEoQY3vtaWwtlUHVwEhsveQuOPvJt9I7vRu/ojkwZmoYsNinPeTRDPul2KnH1aYGTgt9ePGP6QXQbfJu2jBjk0wiY94d1EYkHAConRAI/UqfgpQQsoaABQ1jUA9eMAxRCeyCr1QaWlooIBhtIZsTCl1QZgiUKVtQaARlAIa7qvSau6CzBLEiBixGyWfbRBW92dnkhfY9qGliJcNQ2c+2CMLW2GyZB7li681EBK1fYlr5TdV+SbbOU0Cs/qMAxCSJ2vRlfgQ52/5nyqTAqWrA7QTi32ktbabJR1yLBMKmRe7XPz7GtGS9gay6kM7K593vCjHYiTmnaj3WOdZ1+erR2EAAoIKA+mIQLdCvJAQIqsk/qCOxuEIRxME/CMG6GCXWYagkAJC1u0n+bqlAVYtDZsNoCAIiybthP0XbzwMf8G1U4/gKm/apwQLTvkeHfeEAwc/JZzJ98Adtf80H0b7sUhFIkAOiyVgc3eRMvPPJ59A5tx6bdN2fa2pZyFvnCAkdt0F4ITbtksbkK21dldsVgmZGYIKE5osXeQdlesDkDWMvkSqHtdBtUN1FKuqVdb2SMeUwEE1nQOwRpUqAJoGK8jAZFq1EETbUCbK6AxlyKdAJRn5QUqnqk07MMrynt/Y1iNNbvlROobLrUcObjgQj/FzgWSAogGJJATtLN2TXFSMk0HfKRtteWJ+T/HHE3zzg46rbwjhl6g0gMAA0k6MqXile6G2CMgKW7fmO+Qxpck1gqaaUAKBE28hRWqMuXEySUzkKYGpwtojEeg5RjsCpBowrUfvgo6kcOYuKXfwskCFZkAaxMWFzmqw0lSQtRuRtB0Q+2pAnV+J7XYe74s9h/99/jolt/H2FBlJdx/X2OwIRx5UidJ+Vs1Rdx+KFvYOHkPlSHNqFneBu6x3ag2j0CYkxeuRb7Iw6RlHl0uVPtJwCYjC9XkWhMIJCYkuWUGZXPK80Ih24WCdOgpUmAzEbgfS0EpkaAExDC0WiGCFJtQJxQhAFDqdxEoxEh6E+BABEagWCJCrO8JgG4WPfCmtAIRAvZ793qp9xD85xujf0qL9lYu8g/1ntl6bHnHeTbl2eBgKmB9gGBTs67vqhO8lnq2JHWun4BMhFc/kOM/y3g4jFNJiQrrPMFPGlHBJYzcG6z2pgWuUJVYgBFAO3zHqzTOq2RztkcqOOH4ti3mZGAAL0gSlOUjAe9kydAgYyAAAFRWW7ntlHMbaMZMx/CsjHGo2X9kxQuZQFA0OKdAQDaAwDZBouBzpgPZe+RC1XX0GbR5p4BEANQmQzooUe+jrhVw85L3w9CaMaUCJzrNhICEJHwyytNl/UHQHHW7pdk6s0F2fS7SMp2mMmkINphAoCouwnaTpq8GIrfcgCk9v4kJuLXpDokZ0JAZJhJeS4myqFY+gDQFlFOfbI+WguAqRLYnFZzhAsBwtlQAQAaA6bKWGUIjTiSEs9I4r0OtVRn0tVAQJjfSBMcK5tywb1fX8szIQiawhE5j3wOpZxIx2mWr1EAgNQZOO/ZABCFCXq6aqiWG+guiV87CsqxAlvS8fNcVLMyM7Q0A2HS3IeK8zJrtGV6QI1zHoZESuAJ5/pbJcJ8RUS74m2ZkNrTz2PqK19C9/kXo7rNk1xNtsWYX52Y/iT1q5HflKSwWEHcsBc9FmRNKwml2PaaX0ASN3Hg/i9akWF81Ml/iXOOqRcfwBNf/8+YPfoU+iYvQKs2j8OPfANPfuu/4OGv/Qe0GnpBFXbruk7ZPit0MQwmyc1l4pipyHWZcG06IwrKegyTlPScaVZTPZIO4lL2ww1odmCasc3pBn1aPSMzYDPDrE0GQTAFIVIb4ZIVojUlK8Z9jqTfJNd3QxwT9bf9wLSMYaYo2kFyQXXGv60D+M5EbloBP+2OQ9bkyK7EzQOgLzgdNr4zANo8N/N8kv2bGT+zKe79TgjSTqS+Lw/jL3//WxN/BfzWCcC5aAKk880KETMrpXaYCVPmPpJ8UiYV69/VLgCZ47ltdn3hslDByiRSMiqDGUvd8hFwpf/wJP1yjjm1VdrCscq8np52VIJWshwjeQ6B2Bjl4hAuc8QViq6+jaBhAUsnD6B7ZKtaTOMCR+34IUwdeghT++/H1qveh2B4ELxu1E89KkmDZMIal0zVenGWo9FnSC9M4XtJj6mZRdU0IWoNxmpljHr8DCJbSG8wInfQBECSlZARZjsxkjpVmT/VvcsBeMht5tgEAABonYDWA8Q9IiEQ4LetNRkJbmoeGITpSioF59Q0pyFKayGi7vAMo84pEFd0hBFxIxfx/dN6Aifyj9Sy+Bh7WT+LsqubNFUAAOZh+n33mERCJiRz6f/tKKAMvWXtrXt20ROz1SWOVLWE1EZfOG2SNhqGVyot3f8oznzyc6js2Imxd/68cP52smevJCSoLMtDIA6Ra0IUFMtIGjVwzkFAsiZmBpUq/dh06Vtx4L4voHHJ21Cs9NmPk5LiDlSfn8LB+76I+ZP7MLjlUmy64h2IysIQPombOPzAV3H2wCMIgoKIBuQJoQykgg6eXQ85JV5gKE1JpJlL0OIW2AladoQzoH2YzsoxiqUtgpvljIBQjjAwnN0TiiBgSosFAMViC42GeEjQ20SSChKSMkNQS02D0u82KYg9qNmj18k8wCul/+ba60rnpSmRGWVHRV0K9DmQLIOc0Rb41hAZ+pJkpfBmaFt1PkEmgIClAQ+Q2SNVG3NIaq6sY5NpI8QCfSwgKMz5JB08y2A72afzgEAuA+5GWcrrA8kB0XkmQS+hc++6o/A6rZZesozBueFBoQGAqiIFAsp2zvk4M8m+Ep75qHhEMbdF1OtGAgI0AJDkZlwFDIfWHJWrsMc1jo1uuRtbJuQoQ+aDz/oHOMeZyAMAaICesZ04+dwd6Nu6F0mrjukDj2D64GNoLs0gKvdg44U3Y3jH1QAhaTp6pm0uZbxmQpRdqTUGOUDAJAkEzARDFqPfJYBUXHWkYT16NS/2CcaQMYLESMjDl1LTMINR9cWnzmOcSEJUpA9JtEWAFlERdUiLIEjNjXgoAIDq/3wgAJxMmOX5IjKReSwnRG5tajxECkB0XTzkSIxcAmoTp9IWX1wL6voaD6C0BUHd3L3tMXbBRTswAEBlPgVElBOLCFRb8ogEHCyhIJQjiQmiMF8kyDhBf1V8ZM1YDMbZuRWAAkkBBxKRHI0TDkpEzHcW6e/75dzyaAvoOiKiYzXrFPP/eB+mv/Q1VK+/FIPv+3k0gwCFudVUmE6lHOdblzgFgkIF4Ays1UAQlbzltB06MDBxEQ7c90XMnXweI9uuMirLfw7hANI1eu7Ec3j+x59AodyD897w6+jbsNsqG4QFLM+eQM/ELrBqIRNUwIz7LrWSSYGrtYVTkVTMYkQN5skFAuq5aULEoCn+1+ZHBElBC3XKZzhqQ7ZwoXW2hGjQDjMVhgliR/qfMIqAMoRRgrgp5rMJBNR4p9q/YJmg1WVk9ZbXcxKnuVFrONWmtl7GNN232jrtpqDAlxDOpAxQc8uZe1+ahEv5jdVqmD3wGPq2XIywWO5s52/stz4ycxaYJkKEaa1PJvt0Ok40ZkZfuaURAfQem+vEK8vlCTd936XTF/0Mz3dMstYOK6JVmAK9GoBAXoCBn+Xz10nQOWcMBpBFx9LOsZBK/2MGHtqFXLs8VjTyBBiZ/qj1oevyEgBIKsxnQ1WCC7tdRcb9mehARIfIlORzBrak/YFdzs0uKNtsboRe5zcj/Jlk0gkDwmWGuEKx7ar34env/xme/NrHwFmCsFhB/+aLMbDlUnSPbkXAnLF11wDjfZlAQGeU5UhKthTedbSTzok6VJ9gxsxwk5I5jXstHbMCAAAUAOCLWmRHTEaWyDExTpkfLDekhFxfd6XGIrKLo0lIsupuU8pkZz32bIrWjek9BkNuhvOMDYm77B+nNuNNm1Qz3UQAAjPuv3W+dHRc9AABAABJREFUTjKbt4ghzxE0Sea81Ix4Q4umFCzTLBBQjTP6lLNKUMoRJxSLtSLCgKHejDDY5VGrpbRYL4AzAhoysJhamp+1kJSwE5ouOTIBm8HAWAxR4nx7hjmQuynwAGDQ4VyVhDUi6XONb5xzzHzj25j7/m3o/bnr0feBW4D59iF/kqJhcgasSMLY6hHgRzqfBk0oX4CksWyDAGIzU5LCUhXVwQkBArZflZ3XJvPnMILLZ47ghdv/Bj2jO7Djhg8jCAuZ+5rLc1g6cxhb93xQXZKJo5KIZECBZO4zuRDcdcADBABjPeNcrGNFogCBL649pwIILI+IjlUPBljakiA+VUY0vow4CRAGCaJQ/JqtEJRwME4sM6GwECNuhmI9KyfAUqC0AS6ZbWh2ERQWs8IfuXfkOjnnxKOXkvKsX0C+hn7FfjTO+7d8FYxuHr39C5jd9yiOR1/D0IXXYXjvjYgq3VnNODeBnfEMpy9AOmbcbmtGEBiQjEMw4BkLBzDmnRP38szfvnHMaAwkWHPNmTs4Oq+IOgEACj84Wad1WgGtzRyIczuElUdNJgGAOnYTjEkn4dQR2E3u4WanlVQbFsyjaabS6E/rdGIiWwAAWQctSZZ9L7FtTM0F3JdgzGynb5H2SsLMY+cekW7dkFQvM5BiF/a87jdx8vk70Du2E9XJ81R0oMymCiApUQR1phYmYQ6lo3+QJKsR8LVbJZ7hIolN08kgbAMAEZO+vsEeJBMAMEYQFBLE05pZITHJMG65JDtgAgQ5fL5zPMfG1WH8eMDTTcdmnP1ZJu17VdNKTHSh4XwIARfagiaFFcIzEKE3SYMaDCtHUpYaA2qNh5kvgTYMkyhiJyCztAawNQU+LYfpICxtm61+BRyIKUjAwVsBSJAf2iYMEszVSiq8YjnKscPIPIS0lQy+komzBCe+8wXMPXE/+t/5VvS971pwTlR34oqdT4LTNOPtSpOGEQgNQQ6mkCAgbtRQ6ELnceQcvWPn4fQL94BzZjnv6jLZU/WFM3juR3+Fcu8Idr72l0FDx4El7eLM0SdBCEXvxB51SfgHEb1+SobekW5Ls00p0c+QY04hGTkaczs2Pk8jAhmCjSQS5kNynamcFkCANoHu5wMsbmNonaggGl9GvVZA1C3iQxeiGM1WiGqpibqU/lMmtAJhorWaVQEEWKRN/5IKR5C++2YvtFbI944sSXsWCOSZVAGGdoTb3zjhei/JOPi2qdsWuth7qiuomjn4OGb3PYqN179ThKt98i5MPf4TjFx4AzZcfiugItzYbbX67QrFjGOlxZLHhnmXBAA+zYrSOEnmnKX7n8HQE8ZTsNlZK6DbR6y/O9nuZwBAxl/BI1TtRD6m39L+vPI1Aev0yqHVOwanKtzM5GcQkYDSH2mJL5cVQrBC6F34fJGAACgnYRepSwAgqTjLFQAwqXRG/CQFzfwwe66DX9DIHsufyfDLXAC2NFp/9Do3gLnCeY49pDUDwgwqrDEUq33Ysvdt6B/ZjYK0b2/HSOQ41pnt12V1P60yhuSUBfZYJQXtTC0dWltdHMG83oWqg8sIwwRhmIClkvrWYkFF8LGydxp/cuKRhq0QAKiiLgfvoC9OuciCm5oRsVCY0EgzGmu8Ai7i2adfCysyJGXxAwQAAATDLPMWyPvks3gpseoT523mXrcXOheCZxjynHk5EUnM8oi2/P4EqlnLFOFigHAxh+OkHKwZgDUDJPX28oOAcDTjEM04xEK9iEbcXjKeIcm0GOZRPOQ6xv8rgFjcxP4ffAqzTz2IsXd+ECN73wA2n2aa7msC1RXGAjVI5jFg0cr2ctItAtHHrfzcDNJpVNbXO34e4uYylmaOi+vO2mU3CGjVF/Dcj/4KQaGE3Tf8KoLQifBEAM4YTr9wL449+l10bzgPYUm0q+WYGsrv2tUEq+s+czziHwszcpBcC11trrlmy4hCcs2unNbXuvanE+1ZIdlYWNDRlqTJW6kQq/+rJbGhFCrpxlKn6ruWawhp2WF+zczoVj+UBlkcS0d3STYTKu+xmXNOsmPn7p804RnhWjtwYVdmH5IEiOtLOHr7l9Cz5QIMXfRabLj2bTj/Q/8XRve+Hqce/xEO/eSz4Cy7SWU11XbEIfd9+xJ9Bk6OABY5mhI5Zu4ey+19D0C+M64Ph3o0BXnCqzwAkAEdki/waW8y+ZeM/ymyHNyrwBQIAF52p+BXqdDpp0Gr0wR08gdwQoKygrsqQXzwhVT676jk8uz0mj2iHlO6s7gxVZ827agqJvMP5DP/MokMIBYciwFOm+EmUAFgxTxWxTtFDmi3EOVQxj/AjUhkHMt2qpwKnAs7WGnCQ/XYZmJPmwl70sgn5jnCbGe7sMbR6E0l5uniHM0Dyxt0vcF8iNJW4YRRLTbV/6dP9OlnFRhIyhhadujckFyZJimq0RA20+bwuJIRIhhykmixkqiLpyZCXLU/8yYMu2zRmXRsi+nKYb6HspD+I7Z9EggBUGQq7j0AkQtBJkJrGWZKhKtEZ6ZWAABYgem52LSl/9LBV5oPWZqBsp6jYc3dcAQYkOOXuBGJUgpTc5bWQD4j21qO0vZxNAD0DuSbAi3XBOPIWjrqkzIHWqUogrZgZWFWYEG+gsQYRgLlbKgyDkv+wGQ2PHbLEnDQONUyppekqdCxu7+J+WPPY9stv4po7x6shliUMjNNCCfNFW5KcSX9JktAUAOCNJ1sUq9lynJCAMoza0fX4GbQsIgTz9yO8R3Xodq/ETTIxoUlDGi1lvHs7Z9AEjdwwc3/FFGpSw9zq4Ha3CnUZk/g9Av3YOnsEQxuvwIbr3prrqZRRmwKOiiJZIZdFjrhG2VGYWpoBVJpquvYLPcL1xfKl7MDAPqfolic0McLC2Ww2QJ6JrRDWVepidgxv6RRApZGKuNFBtKgQqKdXpeOwaZ/iDSRcvc8W6OhmULTtIsFxvqXStIz8fo95i9uvW01AOqkWal+TtxYxuHbPweexJi88X0gaTvDUhUbrngLSv1jOPijvwNnMbbe+EEQai+2psRfmv66Jk+uKY0cM3VcoKBNUUCCAq/ZlCttd0Nv+ogb/zvFcv0zVhAiVFVPCNzMwNK6YiUahnVap5eKzi1PQM7mzYqps6fHF0ACAOucwby6H6YEAJIk8++SXGDlYhlaUXLse0wAAGSl/+COhCRdCCxnJ+Iw6sRZwKSUwXWQylkIzXt8FDQZkoLUAHDQVCpvbm60xS1pUMbOFlnVqWT680LPcQKV0l76DYQNoOXJhAkAZFyY/zTqBQz0LvrLSHv1AsvahRM7KoyIisNBzXIUNjMuzzlMgIgw40jMUkdetWn6rCEo1zbxXDv6wZTmyzYGXPxkmFLFVHKgwL39E+dd+zkCXuAZ/whZFyvxrKkRtFbAjABkbrZxmWeBgEESKHrBAOGIZvT3Go+04dwIMDejnX5Hhj2e+D5iJAvqXgXUmD+LM8/cgw2Xvhm9m/agTY5kNMdboLOhfkeU6/mzQsoNEVssA5Ri8fg+9G/ZC6DzWAY0xIbdN+L4Mz/C2cOPgNAA3YObsfXy96DSM6qYr6WZY3ju7k8haTWw5/UfRalLOF0tnj6A/fd/AbW5U2mNBNWhSex58z9FdXyrEDoYz5MJoyRDrvoknTsdMx/5/KRIbLtyuV7Kj0ze5+wZyicgEWaMrYoGAiy0GazSLEe9jyAyMFS4v4x4Ww1sVn8UMlcAAISUIWYUXZUGFpcFuKXdLbAFG0ip3AGw31+zm6CwkN1z8nwx9HhpIGDuHZ0AgI8ygS0Sz/6jJOXpOhwALIkx9eTdOPHo98GTGJvf8AuIqjoqh3xfA9svBaEBDt72aexPPo2tr/8l0EBGStB9yIAgh9F390wWEkRLelNlBYpwyaMSN+eUcq52+sg44Bsr9/sxAVceGQ7H3shAq00U5gEJ4jnp/7714NWiBVinVxStCQRYMXSdySgBgCQJBGS8f1f6b2UEBgDOwYzoQJL5nd+cMsBxVu3pRuBwVcLyme2Yf+XwKqVOqYRF12E/IxvJxzn2qJwy/gCrZHyCJjMiKslzHEmBGMepzbG0d1+BY5KbXVZumOaCaToOszThlYyBzQOgfIqgfoneSTeNCIeMxYYotFgvotJfQ23KQA8EOuGXZH6pYMJJTNqCItfpkRMAQRZ0ZSJVmKH0DGIykpBZtphK+t2GkJTxN3ZtIh1wE8P2n0BnLDa0EgBU5l2eEKt+HnFVjrR0XZwCPDUdonXno0tBAgBQBygIZoqrfmUcj9OxCI3gKLEn7xQnQDAVqS4nQ+1FuaenesQYyyzNXW3S3uaQ1ACRBDoU6ytknzv+4HcQlqoYueC1CBoc3UeARh9Bs48CtSLIhhrCcgvoaaAx54/ak0uGdqJjURpg7DW34uSd30RU7cXY3jf6y0mAnDJHExe9CRvOfyNq0yewcPYgTr14D5784Z9i5zW/iP7xPTh98AHsf+hLqPSM4fw3/BaK1QGRBXnf3Tj08NdRHZjAtms+gHL/OEp9o6CRYJgVv5T6fOnQl6JTJpOnogH5IjxaTqISLEjhi9mxrKZLmjdKAUm0LICA6ycg14bSLFf5CrqOAosTQPWhMha2iwVm/mgPeibmMTNdxfCQSAMsgUCp2MJiGnksDwjIUL+NfqA4AwsAyL0pk6CPawZZSYa5I9AgaM+YGs9YCbXzG+AUWJo6jP0/+gyai9MY3HU1xq+6BVGlB0jfsesz179lL+hNH8H+H3wK+3/4N9j2hl8GDaPM3uqSOUeUtiD1F5EAwBVmeZlvT0hOUwsoKkpB5ErWlRxw5vYlEz5cPdxfXrXVPJQAIM/pd4XhSl+pdK75YV6K56+ToFWDgIzNWjoZuZT+M2Y7DQOZhF+soI99qemDGkNS1mUkAJAkgYAyd0kZQnNxdSVnnaT/MuycS9lEK54yRshI6xiOtJ97mFKqr1kmMO5iJR8hJWe+53uOfQDAWkCJjm5kJu6xnktSiVqVWOrqoCHMEwARuSTYX0ayraYAAAB0FRs4OSckRY16BNqdZupddHa9iAtGWT4/NI4pV1F4aJoEzIrLb5iVqPE0zHkU4y8ZSdN0hxEFACTTT2sUXNrku7b5kkGnGuXJjLmEcoAm4Ikz+bjoQ1bCRISZkKzHAAok4ECQCIdi9zazTeZ1Dis0KHWiBpEkDeuXNiXPPyBMsVyco+0BAYKzkewCAIANtWfyW9MlkCYRfKghBWdtohflkZyD1NxIpC+OZHZNG/e1LvhSQZmuL/JZS2eOYubFhzH52vcJB9k11s+K6bdcFN+StMtvl5xNNS2RAA8YufwN4LUGTtz7LQRBESPnX2/1gefwOJQGqA5NoGtgAsNbrsC++/4ez975CfSO7MTcqecxvPUqbLvsXaBBhCRpYf8DX8SZgw9h7LzrMXnpW5Vkl1OSHYJUU+ruFzRl5pPI8WnKiaEuHUFZSLyR4jLrcw4zV1jUAEAy1TzNVEwTrrQHtMnRsx9o9BN0vxhgYXuC0qkAzVP9wI46ps50Y3hoATML4uMol5ro6qpjcbEEVg/FOtaAyl0SNPLXam0SZFyXUndXQu1lHLOn3PHuZFYK2O/B2nccpvfk4z8CoRTnv+v3URwat+to2mXlu+/ddAEmr3svDt/xORx/9AeYuOItqu3CpEvfI3NqAKmG29ibgKz2gkXE0gK0lcJnknI5e1wug+/cm1dOlvFJ/N1zBN4wobkRndw9zTy3Tut0jrSqqZTnRc8d6T8vBNZP2nSyAgUrUK/U3Meszm8KMb8p9Eb1cW39XfWqiCmsfzocJhORczwfa8aOcjUAABCMvJ9n9JeXx47GIS9dujz2RRTS93O1cLbTALBIcwbSNpoaC67KlmwMSbRkPCdlwKIlAQDUffu1GLkUxiiFMbYMTqNR13OEJwSknG9rziMmfqXEjqjDARYx24eApNJzx+SHR1xnpoVgNlmBWQCAR1w54JqMtXLWNc6RgIGk5gDEeD4NOYICU+dIwEELCWhB6u1lpUQPNJBFd5SDRO7kSNvQxkmY51zT/dNZi62qU2ZTMBy+m0W0J/nrRMFUQf1WQzQmoDFR+R5+WmRKniSzTWPBAMkY+ivVMpx+7HYUegYxuPuqzoVTSnpitIZa4n2EsELLroiMtvto7MpbMLz3Rhy968uYevou8DbSX8XgGRRGJex6zUewYdfrsXDmALZd/l5sv/x9oEEEzhK8cNencfbIY9hx7Qex5fJ3IqBO7hfHn8J3zU3AmHH+z9lfpJTc1CashJQ02Wib1BKbzLAKR220rzgj/h58RDeyuE9odKYfH1bnanUx35lh3se7spx2bKTHaFVNbllru/S53C61JbU/q7myOgDgr1SvD7Xp4+id2IPSoAEAGHTiscxexbF4cj+OP/AtFLr6MbD9Uu3joDJDO49z5kRSsAuYgioJAFxrAq85lCcaVUYareU6He/NJeJPdGcVcQGAFeyizc3tuLVXmymQ4Qz9sv3WCcA5+ARYIUDTrL7KEdhja2dK/wEoNShxbEQlzW+ymxY0bDMFTsUmLhdrUan4Ly9DZFB3w8h4JATeCBkdmH94AIO5AMuxkJIs2U8ZBQii767GQC3m6XG7xcWVKEmnOlmfbqhe1EXYPn+lOh+A7ShcnOdodttjVjkBLKf7QnMwwYEXxrFnzxF1faZWwcjAAk5Pdwtpt+95lNsMP2A708r429JcqGWAmJALv4EGVUw+D7mQdje1pB8R11l/ZTskE220ixQMw1tAM/8BF6Ey07I0rTcsxICMkNSw7V5zN3RT5Cb7TTlIwWmfapMx4VLHQ1kPL2iwRmpZnxvChNRZhitUZDCWmdB9DkWL2U271e2/IThdsOoHgKS7g02abGtsxOY3AfEraM2mQYigUBbOjoZ0OqgDvS8QLI8BjUaApBGgb3AJpBeozazSJAgOOMvpP20CjAIhJdh49dvBkwRH7voSZl58FJPXvQeVvlGrjnbvmVCKzXtvxaYL3iz6BoBzhhcf/AJmTzyLXTf8E/SN7/ZKQ1ViPA+jooQULjhI1zVp6pHpv9NWHgBJYJsPyTLtwl9KplCaAxUWmeUwLEJpZu8tTWdDKffdU8LCZrvc4oFeoL8FhEz4+tTEOkRigqQozIEKM/Y9rSqxhCqAYNKV07IhFc5lDI330EkD4KN2AMD0AwAA1mygMX8W5aEN1vXMfsmhQm9OPXsvjtzzZVRHNmPrGz+sskqzkGRAo2XCGohgH0FqopgU7CzBSZGgsGA/mEWppkiZwa5CK5CTM8Ab0KNTkjHpG+AkM9N1OMeyGykPpYoZ1hSWbwBFVmv2agMA6/SKotUrlQICBAQksVeQTCSg9GNIigGSYuC1ayMeu7mlDRGWNkSWZL/ZLX6uL4AFAADIyCcuhXUuJEDmx5KGOlWHaUjPzGZyrgBAHpsLQsIzZbJahzbP4LAXmBTZqnOpRIPKuNjmra4PhyNFoU3PGJj3EyBazI5J5YQAAACAUoJnDojNYqambUpGBhbsPpVjkHKsI+hEXNnQE1OyzyHCcJrnZChPw5xESfXNc5KpNjPoRkw4+UoAEDCQQqJ+QMrwh0zZ7pMUXIRRgqgUIyoJTiQsaI6EACgUY/9C7+O8KCxthaqHcPXc7EVuOyjLBwOCyXOuqZC1MQEr8FzJv9RIiSRr/kdbxIFonqAwJ37tiHAgnKcI6gRBXcRml7/VkPndUZkYDIYkMm2X7Adt5QsEOj7LGAeWRgmSCYxKvaNozJ4GIwwsIkgK+Y71bYlysBIDK3IkJY64wjNrnPe2ltbkWG0mBJPXvRs73vJRtGrzeObL/wVH7/s64qTRvn3uWqMAAMehx76JqYMPYcfVvyAAgFlOLjdt6nZDUirfFGeOKRDhhA5V5maO9SBXEWUcjYKBrbWZqTwpwi0DWiMQLXu0b7A/16qIpIrylPGYA0K0n5ywHWjIUhaEm3OwKfhgFBa5eoYcC1cKboWtzPvEeBYAuOQL/9pRAwD7vdZmTgDgKA9ssOrNo+Uzx3D4ri9g8LyrsPPW31IAwFe3DEqgQjCnx4mDm+OiMzcMM2M5z7jKYSDHzd7nRaGsJJiwFUiH8wAASX/O/Twg3jpdc2lFSdYcyOscDKh+5da1Tuu0QlrdDPLEdubFALwYeCdrUnRWNcWQcPFzGOGlDfZKX1jgatE0qTgjfqaUJs+e1nUS9i0KLmK37eGzz1DH3MPbuSFEU6bEvceyV+bIgAQTIMnyGdMhNw8B7OtA1lTIJ/m3gAAhlqkV4UKrEDjgwAQCcsPtfSqwGFQJBACg1gpRa4Xo6bPjqHBOQApMO9AC1t+563LAM6YwPBRMlXWuwFQcf9FwyYVwIGDiJ59LufgZjDkNuJL4W48PGApFvbvLoUkSiiBiCORzLImXMWHUPOXWeaJsVqDMipRpkeGPQMsxqMekiiQEiJgXc8jIQzSGsinPiw2/KiaarM50SD2GCAbe/P00KGikGXbrIsRttCR+hQWGwgJHYYEjWuZqnrfLpQAApb4RsLiJ1uLcqtrRNbSMpDtG0hO3zeTsJcmcroB565nYhT3v/j+w4fJbcPqZu3DikX/01pf7/tNnLc0cwYnnf4LJC96EoU2XZoqxkHjj/bfLOyDNsHyUB4BcoYQslxRz5popH1EAOTvebuZedY/B7Mn1s/uQLtt9SPxfvkfb+NCZbIhVM9dHbCRWzGTSBSwmfzUZZjtFAvKZaXkBQJ6wKD2/PH0cIBSlvrE2z9J7+umn70RU7cWma9+lQKVVb/q3G5Usdhj/pGSDo7hIrHw1PKKdnTxzNSn2Hta2bE7W4Ow545a4jammh3l3Iymu+D5COgLBVxoR/vL/1knQOcFI7jD5EgiwYgBWDPyOto70f3m8oH7m5rA4QbA4QTItLM7YxxnVYkqKcW0npUoTfnWcECT7DO89lnQe7aX5yF5Hwq06iNs2AxzIiUwN/lAcZxtGHcCVCwSMhSRoZtsbpipZGXUnXHa0BgTofdhY1RnByeP9qLX07h5Qjv40njzPUTkQykCiJKvxlPbxkkkPuTonmSpSYEAlASqJTkoWMqCYiB+QMTsSz8xK3Gj6HBowBKH4AQIAqDKEg6YDlTgOwVHZw0FLqZHvfA4FIfPWxaWfASPekJO0QUBaxA476tbdJpGeqqdl/FYgwQ+XifjVSNvwpC4Rlkq5Y5KaBUkx8CtrgyulJjb1mVOZa2GNo3QW6H2oCDpTAGMEhSjGtq3Zsu2IB/avHdEEKsGUFBTQIMTYJTeh2DMM9v9j78+jLTnKO1H0F5m55zPPNc9VKqkklWYhgSRAIMCAMdjYl/aAp6a7sbttd1+/u7rvetfr9mr7tu97trvpfnTbxuAJA7YxYGwGDYAAoVklqUqlKtU8nFNVZx73lJnx/siMyBgzc59TQtL1+dbaZ58dGRkRmRkZ8f2+MRC0VRRGhkYTXACo9m5EqdqP5fnx5Fg8f03Mp3WtFTRVUrGFR+LAQk146CTf5pC2SV9qcAaVxL2GMZUi6A0FTQNbO72V5Lv/aPR/15loUMVpB+UTpbjvGGwrkbpEICASz8wujTnHnNfMq0Sm1rK2WkC/+K09RwqszI6j3DcSOYOb9jZhf/Eby5g59SyG998N4rh8XCqwsJGYhoE9D78aXU95LuYxPHVupP+2ksk+3GYzLoFLIn1L1QjMpkAmSuFPVDMwMY+AiSJH93XNwDp1RquaMWG5iLBcNJr4hKr0Pyau2oznb3OggOZAgatn+YD8CACoVFyMPqKKkDHx6qKlppxPtVPl44u/mTQjfgF55l+hnlH6n/LS25Cn6gBsbVv8hl4nDcTYwIJ6j1RiiZVM5WLbXJomPLLeZ4tJJJ4QmLscqXNcgfnuFxJLEUIjxj/+MGJScB4WvBBEn4ofAQBGXghSCiS7eeKGcEqBZFZDHMXMhggf5RoIoaBhBADEMhMFgcMzIotEHIpit6hWib8poncnlMuJE70fTiGEo5gDEUJRrLRRjMEAVfpzVxy4BlMEIGLKSzOEv0PaONtR4qnCEuClBbwXSAQGaURJBEacNuHM/WqJgwSmEaNJOQsEYLvGtVLoxmZBDlDo7gdxPTRnrwCIQTEhqzY9Cr3YFKgYmb0EpexzSGwORTPMh4LmCrxSlQN3kTRwoTB3juth83XvxMyFF7E0cx4qGaOcmerYlhpqXmOisVkYHYs5o8QUGkx6RBIBAAPAxQUqlDFuWBxP9C1KoZk5at+RZFClyTiUtQAA2l1RvdJM0h8zbdGivwna7FQgwK8xZR23HNKi4liOsfsXkgDz546ga3SHvS+Bpo4/AQAY3neH0JbakT7/xKSfoSODAUCP8Bd6BKSdTMJVAwHAKLA0EtWfCyXEfK9Nzy/eb6T74cBoZWFtI+5Tuz4WUnYdCKxTB9TxbAnL5ugfYdFFWHQ1z3dTOu7mgKw6ZUBgfifB/E7dvlbd1IOyzsQDCSiQ1MrxxsYdXeM6JtKjKZiBgjyY9MUjM1JAHtWUCkLE8QcURJDskzCJ7JMFFkQgwKPFGJgFRpQI0TWEtkS1Oju/54UCqDAVGBAAgJAShJSgb2BZYqyZOY40ZpfCKfsghYRbcDwKl5nCxMyy44VwCsyeP+TtOYUQTiGUJP3MwVe6NmH3Y2OiIYHfdOE3XV4WhgTttot2HBucS/+JPH7eHwWKXS0UWZx89VkrzDxj/r1CgELJR6Ek2060Gx7oXBFYKEQfRAAAANxlB4VpD5ULHspXzA/RaQHlaaA0F300CgFvuQNAQAGnGWev1ZPWGomBAad9dcCBiUrzFOW5EOW5EJWZEJXpAJXpAKW5MDYDClFcClFYClBYCjTTIOZHZDJRIo6Dcu+wUROQRht757F31wSqA3WE1QBBLeg4RCp1zZJtExFK4TeX4ZYSvxzVfp46SNVCDW+7BZWeEbzy+F/iyqkn0VqZxwtf+z3MTxxHUIjBkQmIGKTFQPbY9YRVglbAJHV1ErMgMfOsEURY2mCMOWPw3RZFcUmXcNmchwGgeikpY0BAJBEgtmONgIkZZ/eSS88lPzblG0ldVp9/MwdVwYxMy6uSAs74vw4wd+4I2isLGL7mTUbQoe6bzYUpAMDipdNSuRTilV9n9M0AgPp0fAUQi9GC3HqQXIswDqpGj8ppKkPCHFYBlvOs/RjKbPVNvBKrp5kBWebCG4bo6+CzTgA6DRFa9DTbf8b8S2UFJ/kIdm5B0UFgyfA3v9Mwm2NpqegXwMwR1NBhWgzhOB56Wh1GTNovOq05QlxwVkdy5g3j3x1sasx2PmsjtGkIpHKfyuCgnYyfjY2lVAeihcd0/W6LWtXyQLJ5iGPW/CwQ+W+om2TvYRnsTU/0IBQaqjcLKFbbKFbbcojNgtkZF4Bkn8+AgCg150BAABMMDGibpxJ+lAbEGL2Ihg5aK0VJ2h/6Dpp13Q64Wm6hVtVj2votl49Nojh/gFsIufmRSIHvWM1USzMOyhMyB8Zi/Lt1oHKJoOscQXnKfD7xo0R7hZXoYyJvRf+kkVuP7e/j0LwdURgDgiAO3+kjBgqrd/B9tajcFzkHqxQWCLx69BzKlwnmJ7swP9mFthYPM5tCL9FAZDkMO34sWSUJMxH6bdDAh1dKbNdZRJ60NUhaZxwHe+78GZS7h3DqyS/g2a/8J6zMjWP86Les55ty60X/QHfk5SdBl0pz8x+LNFRg9JnjsOonkMbUme6BKOkHBMZZLLZoGiqW94zlCmgzp+AF3R8OSBO+GIBACumJLE11TCeKnSbjmTz6GGoj21Ad3JQdhYgAW970Y+jbeh1OPfynuPzid+Tw9oG+Z6raLKKU+SX5uQRFokf6U2i1QACwzBnLIqwx7cQSlcjaGdEsKkSLiXxtIDNq0Tqtk43WECI0WslF9SUzBVKTXoSeozvsLgfway5md8fZFptAKKJ+5cVodUcSTJWSyApJSC6u7o2lHTxygKMvfqq0X3WaIhQ6898hrSbLqSbFF9KSc1MlGv3mdQ2mUU4rlBZEFhYvajMqY1mHreN35OcMgCfXEetEjtxyO72HC5g/EHFwxHcwd6EXAFAaTrjJ5nIkBnKLgrS/EIKGBJ7AOBfKPgI/ni+xtN9xQwRCjG7HDQEXCGMJvapZEImQRCOQxPkmCOM+iBOCMp10CPgrEafhCOP0W1FdrxSgWo5EirViC7Vi9P/kfBevQ0OSZAoWxuwWk2sRiV1Xeym6P4VpDy4Av5pcU2EBKCw4XJrZ6tWvMwqlG/3frunHowFFzLvR1lohbwV8k1Id+VQSgQAPTdt5tEwA4M7KQOKfAEQAhoUMbFdfXVU4Y05KfSNYnDjJzXKYJHPVYMWjUS4Lj0RhbQsRAEojEkZALnTNzrZ+MzK582JNgM3Ej7dneVVqfRuw/y2/hPriFC6feRyLU2ewdOUMgnYTbiFatEXfLJUJSvUVsGgLpMhDah01yhuNQjwGJd0nSpa2UyszaAozykKKuk0hgg1JgBS7l16TcvOeyhRQHwKKcwStPqolCxP9b3hoVPFymEbWYHJilBJT+XwrCfc6DQCw+8BMdZpzV7A4/gq23fsRHQAYkmABgOMVsOOtP41i9z/iwpNfQWNxGlve9AE4Qsg3MRwqGxPnI1ikKC+a3+W5qB8trKoSKlMMC8q05dZQoRlkmg/WEKHivY0tIUy8hpFEc19BkyV+6/M/R7uvc3qtnXPXHYMTWhUIoIrkn4RAUFHKYiDgCB7ygZIrgAEA3q4YOaCYbPJs4Wx3AwXBNEhl2KlLtEzA2qYQv5wdm/qoGxaRX/JOGX05kzBbQYj8Oy6TFjFLGnR2LFpcZSaftCjCYmK36LSp1eY2aoc1nrKBIwECYh0RCDDGpP9QAXMHBAa/7qB9rguFrUscAKjEmH/R9t3zAnhegLboaOyG3FHXj5lmx6Fw4snBzHUch3Lm3W+5mn2/44Wc+Wf3gAaOvuA6FKHvyNoHL0Ro0CAEoYOB7hVcmZZDXEXmSAFn+qli/Oo3XQCuBIoK09E1l2Yi+35Az+hLgsjuGJBzaohUWEr+V6XLhAJeE/z5Z9mmUySxvIGczD2JgYFh+mUBijxUmQ7kd569WoypFOa0BKgDygNFkYDCEd4hP365RYBU7huFX1+E31yB69lSK+vkEIqtA7No9S5gfC5CbM2FEtDsTFMgZky3kd+IgLZXqlrzA+RhVNiaWekewraD78XKyjRe+PLvYP7iy+jdezD1nNDVNZuUyOU8y7P42JTxJsy3noxJ1RSwLMPGPCgKEGDtcjMcpvlkPgCNCJi5rej5i+E8Qzf6TZ3IBNP3ZCDgNkgClquJFi0oE7gNQTAl3Aerb0WW7b9lOdeiA5maUSXzwhiWpy4AALo37LJ0YG6TEAebbn8f/GYdU0e/j55Ne9G3/XrZpyyQ1yAS6muSp2gT2zWC5ktHcfro19Dbvx0DQ/vQ278DnhMJaAKEaNfn0VieRbMxh+7uTajVRozaEXOSRDtQZADAmNmXQBMQRkDRpPGxAJOA6hGCVsGsripc8Tr9k6WOQEBYcBF6npQMjCUBI4HOWDqWEFkLO6LdtLQANHuAwMKwhEWZaWHEMk+KSWbYYs+Z/Lhr0wu3Klt/TTKv/DZJDjIoSrZiZ/CjfvSxJjaQ0cDUeMQkMCxk4kYcmz/xBG6UcvAkATUV9AiMFCO3SeFXYslF3GdhSXXgBgYOuZg5GMCpJ+1LACAgCOoxo9sTrfyuE2oGawUvQMGLduJmKzHHYWCA2eqLZb7A3BNCUSj5XDovXp9TCBC2XQkESRRrFRwhsy9j5D0vRKNZQLnUxnKriLIXTcwgJBjsX0IxHvPEVK90XiCM13EZAAAwWwSHADVdfOstRx8To05obN5ju464jpR0ybBxmJj1NE2B2wCg+JTkcXKNBhCbMilMe1afrxWV+0YAAI25K6jVtmvHHZ+iMkVQmSpgaQtwqasHALBr1GIzYiCW+I5lUrYl1WIUhX1lLyng1yOJiegTwI5FzHSKlozamYlqdRDV/k2YPfeCBAIk6SeTJpsYRNtaaQECjFFmduxiObsekUKXxNoRhSlTEjhxqbe45Lng2h0uNIm1p4V6ojF1gsRRnJFXj9bCZn90PGBjIwk4ZyQCARPlShYGaPdMLON7YHy/cuX/YBSPu3fzNSCOi9nTz2P0+vusYzDtszMnn8X08SfRu+0AejZfIz8vIgAvH5L5j8qg+5UkLGh1MsD5i4fQqM+i3VrCxbPfB3E8dHWNodVaRLOxIN2Q0dGDOLDvJ+KGlX2yEyCQpQVhp4raiBR/wVQNRQqoM0eW+3+AamCdXjNatTkQQoqwrEj/YyBgm/yM+RfJBgCYhFHd+LTU857BjlNh0NkLR1SzICZ4F6X6OUJ72VRJnQABE3PPx0v0a5BUxhQaU88WM1G7kKgTKZzYCVgEak4rlHIERJLgEH5JUdsKs8RmRyuaYRBK4dWjxVt8dgOHXMzti8bhLRFgKeIQW5sFkaZD0VwqotojiJhjYsw/ABTdAMVKgMV6mWsCPDeA50Z1WrG2oFjwUSxEg6g3ovlHHMp9DNoNT7q3TiFA2HLl+x2DETUiDxCZHRVj591ynDdgsJpEPlpolDkAKDgBto5E3MCF6T6pHbriob3iRZGPBCpNOSieEvxqyhHzD8S+KyuWOZcCAEwkatXS5rDbSo5nMeeUCOFEmSlQXlAg9tlM2vHqiSM6801xWtQeM/5VIOoAxcFhAATNuUvoGt0eCQILiQQZq8l5UIpE0GGFwmmRfKFRSfxMFFOflelxnPnu51Hs6kex1heHBrWbDmURXzNp5PfQt+VaXH75e6BhkMSB52YkMsDgUn2FuZGYfFu4UAtYsM3RoCCHcw4LRNd8CkweW7OlqDzsGTLQ4ejrNZBoAUQSJdmF5cj8TtSWtWtRudOOQUlIdVDDujdJnHOSbk6aUjl+LqbQoE61ht5tBzB9/EmMHLgXRNKisGzLifkKy747+dJjOP/9v8XAnluw7S0/CeK6chRa9Vr9pMxfWcLUM9+BRwro3rwP1eEt8CsOahPRBJ+fOYWRTTdh9973YmX5CmanjmNp8RL6y3tQLvehXOxFudKPZ5/6JKqVQaUjBQhyYaFch91DSpLfeU2vqEOs/ocasXmZIXSMBmQqkwupA2PUxtcd2cKw/jD7XycAHYIAtrBzCbKgEeB1DEz0ylgs+Z8P0eyNzl0ZiQ8aXixX4f88IeqIKBVQmX9GJomZNi6DCpQY4uerdDVsydIAQHRc+S1kQxTPFTcIEdSI/WhOekxjw6RcsalQGrAh7ZjBsUonIilZuyJv/l6dahmJ+44RLG1Kfjs+UD5TRGN7i0vaQQlW5iuo9prDzRTdhNvprjSw0ipy5p8dL7oBWkECUj03RHetgaV6SSrzai3UVxJulhDALQUIWolfAABjRCEgAgbMQbhckg3Ct3bNAl3A8bkRqfzKYheKcabhetsFXYleQ2/OBebiqENlitKUvFM6PuAsyc+B/y/eZtOzpDnnrlLPGqOeAG472Tzz+r06LSRjjfsJVwEMVCosx89JMX/QpMVUrENlpjSuFMV5j/6PkoxRBOUkIQ8tAo5XRLG7Hw2Dc3AeopRgQ+8CFlulyPej2uY+J5nnetG4bJL6+Qsv49TDf4ZyzxB2v+MX4bo52o2XBMl0JwRWlidx7vDXsOm6+1HrS5L/9W26FuMvPIiV8dOobd6NsCj7Q0g27DHTrkqiRa2BOA6+Jxi0AjYmzHQvQi9ixoKyk2iKYyGVaG7EAIBqp87qiwxDlv+USlnSdw5SRKGDxS/AyIRa3vWoIeW48FsVWKmhnylJwq4O7r0dJ7/xR1iZuYDq8BYZZLny3hF6BAtnXsL57/8N+ncexLZ7fgrt5XnMnXkBQbuJ3p03oNI/Jl0n658GAWae/D4uPfkNfs8vPfF1FHuHsO8nfgNAASv+PBorM+gb3AlCCGpdo6h1jfJxM2rV5+H7DXTVxnJJ59NMsSSyadGMTLs8d7Rj4kluHPJzDfzFuhnQOq2GOtYEhMXOZhoDAIxK8yFm9xjs3ogceYQ6CfOvMvV+haA8kw532TmJ9D9eCHiSj4QZ5yE3TarVmPIy/3m0AdKipAGAeLxMGiFJLeyDIJTyBSpqQBiPQcKjjlk85jVC+GU5E2NkY2vqOLnYQp3Kdt00ARm8iAC1cWB5owLUBADASAQCnhsak4u5DkV3uckTkokAgZEXawpcQtFbbWB+pczLCl6AQk/Ux+JipJZy3BBOJfYzaLoS829y2wAip+WVWNMwWF2OAACALreFmwcju9oXZzfgymKSMWjlIgsXIs/l4hxBYVm+Z+K9Sp2LEpMLcyjAuA7rwRp5hkYSOh7GL4WXdJgJhbCZBvl4WjiC2VHkrxOXx9dsdWZ+jancP4rGvBkEBIVEmlicB/BkDUvbQoyXewEAg7VlzDxzBuMPvYKen/0AnKLlZhGAejRmUgmCYrovQEADnHzoM+ge24ldb/057rirNcsEBClS+JAGOPHEZ7E0cx6z4y9hxy0fxMjO2+G0KWqDm+GVuzA7cRSVnbv1cZSjwTttxSwnXusdX16XRE2Dcbxpc94k0ImFImGsGWDMdlggQIFwzaiVDAy3aFbDwAJjHsU8BYXFyH9NXAuZT0BhGRpJ127YOyRAJdZR1m2baZAERFKAgNSnAIa6N+wGcVwsnD+K6vAWTXOj+m+UBzeg3D+GubOHcezLf4CVqQtR0jC3gIlnvo7ywBj6dt+E/l0HUeobBqHA3IVjGH/0S2jOXsHQNXdi9O53wy1WsDxxCqe++seY+f7D6LnmXViYOg0A6O3eZh54TEvLUfjeWlcEOKhDtPDl0YGEJ+BaAS1/hsA3MK2H+kwAoR0RSRM9iiBry3UER2K2AML6DhgpzYdhndYpB3UEAoKSm/uEZl8cwzyWogHA/PbYSbOtMxRq6EFPEQIzpr48y7z3oC+aKnOrSf9V1RmRwmjaqFPpf14goDoti4sHoVRS6xFKAdHpN6AgoAg9R5JKRJu7vfNoE6M8pjYArgERowh5jRABMwuK23ebegg+6ZpIEg2GAwYCvvmK96Q2DtQFAXn5VMSsNHYIHM6Ki5WVLvRsXkjOKyXHGSgghKJajMSQfuwI7LkhZ/RbgQtXeIi91QaWm0XJvIhSoKurjhVBU0AcikIl8R/gt5XQBAyEhJsWAcCWgVnjvTm+OIySl9RjAKBy2QGzlWl3UxTnVM2aWfjJGLhUin1jUjPOUkjO9KrmRiRR0pvpNCwwepKKusNImU47ymfg1SmPDFJYkaX+/g/RFIgRdYBS/wjmTx8Gje3Pme0+dc3PTCR/qYFj//nraM0so1kHRn7lx6NM2DUfqLtRMqAizYwQFHWYACbiuHBcF11ju8wAgMTjSzUPie7z+PHvYGnmAva/9WOYPnsIp578AhYnT2HbbR8EaROU+0awePYocPf7ACQOuc0+wBO0uUwiDwiOtZqG0jCOeI1fjfZVvcbQY+MQ6hjMcCRtgMjcxcyWGHefKj4BAGIzSFlgwvYuKTJQfK+0PUrY05g5kCb8SdGIqNdGAvm3SQBk0+Kx/qePPwkahujdcSBp34HEsFJhXhW7+7Hvg7+OS09/A83FaQzfcC96t14Hx/GwMP4yZk8cwpVnH8alJ7+GytBmeJUaFs8fQ23jTmx94DfQ3R2pigMX6Nq8Bxv33IPx49/B6K43cdOz8fM/wLZd9yvmSclcWZ6fgOsWUa4OSNcsARjOxMuMtMm/0cRsm54NF+KpZtE2gOc6UV3Rf9IE8sTfmjYodih+A5m4rEcHev3Q6n0CUuLSMgDAyG1QzFwjrzQMCDCmkb2cIvOvLrAcADBShxAvSqqTMG9PUgEnjC/XBKRJyFmZGKqTN8waTdR/LJKF2A5PWJbC/AMwxw1m/6shTf1QAgYAODhI2mf/COepQIBGYEBkAt1mKKm+CU3C5vHrhXxtrB9X0Rw4bUWNToDKJFAfFq8FqL4SnbSyKdq9nJaDpVN9AIDR/YnUtexFHGnDT+ZawQ1QcAO0BTMgh1CUPV8qc50QPZUG6u3oXHZ7w9DhJj3NWLNAQwI3jgTEwo7y/goBUAh47gMGADbX5hEKk/f4YnSRx05vgI28JaCwqAMAQGFSTDG/RXt+5Z1xggg8iufZzBkIjRPI0fR6kq0/Ogj5ySKhCMzIWlXYlERjZmYbYrQVGwNpcnAXyxHSWMKbcDluM6nvE4LQAyqjWzH5/HfQXp5HqdTT0bhPf+p7aK+0MfoL78TlP/kmSluG0fXu+3OdS8IoaIK3pN8/QgjKfWNozE7wurnvscDUrSxcwfkj38DGffeid3QPekf3oGdoB04987dYnD6HoN1Au7GIoevfzE9n4WkdJaJLUIzmUmGFAsJcFc1wjJSyUavSZ5PghUcosuxyTBClhpQW7xlnCIm8Dqr5XpjzcKNfvz4xoh1j/vn4hHfcZtJpvgHm4tRQoCJPShQAEDOpKiAIWw1MPPdN9O+9BZXB2BxMHGfcHwNOPDBHqYANd79XSrZHXaB3+wH0bj+AsN3C/IWjmHvlEJoLU9h+/8+ib9eN8JqCvKAJlOZCbLzmrbhy+km8+NB/wdYdb8XmbW/G2ZMPw2/Xseua94IIoUdDUFwZP4SzZ7+Fnp6tIMSJTdGSm2H0QckDBADu88D+T6M8QID1QT1HBgIArFoBGwhc1wis0ypodSDAAAB8IUSo20ykyPM73LhMlx6qyYRU6T9bRCtTscpMkW4AupRTDQlnAgJa2FCPaP4AaQCA/S+GGIwaMjD3yuZhkxxwSgEAUggyR16wVHL8UAYCqrkQEiCQhoqZDaxYx2tQ+BWZs0icqNLbAmRtQmUy+m4Km6fbALpPuljeknRKHYpLx4Yxtm/SCgAAoOT6KLnRzrPSLmrHxWRllULUzkpLN8Uoxc7EjWZ0jBAK10vCjhbiEKaqH4BIF5Z7AfTCc+SHWjkpoCMSMXMiWZ9HfI/duEuVuRHN36Ljpk1MTvYWWCT/rB4bCwd+BhKjCHHnvpzAgAgAha0HbB1gyZVer9S9eQ8AgoULxzG861ZzJRqZA1EXqI47CMf7MNM6hUtffQGDP/duDL7/NoT1Fib/6mGQoTHUbjugNxHzoNSjuTIrVwY2YPnymVQmmrfNmF3Jl4Jg/sorAAW2HHiAr8PD229FrX8zTh/6OxQHt2Lk1vtRHB5By2KuxZI8miK8AclabtKaEoPTbRZpQMAADJhUPignDBorY++T4yd7T5SnwCAFNkjPTdoxyYy1FIW85NoEJGZKqoakI/CmElEEPzYcoVrlir9jRvPy0e8iaK5gw63vSsanalkKqpBB0PjEsf55s/Ee7hSK6Nt1I/p23Zi8/8zRPxZGleZY5LUyDrz9V3Hh8Ddw8ujfx9fl4OK5x+D7Dey77kMgjot2axnHj/wdpq4cxsjYQezd8/6oqmByQ0IhOaZqsy+a/TDBmmEOiUBAvoHK3s7WYofIe7fFhIdJ86X93PT8spaAdTCwTh1QZyCApV0XkGjAEoQpi5bbDDFzjd3OFQAPx8Y2fRNCZwCAn6o6l8VqWW6qILzYasSTJAGNoMZjX6JGQKkfXZ9hJRUl9BbpInvhjSpdQLYtzAsAtHGwE+RiBgSyQgFK57Dwq6JGoKU7+Hr1UAMCauQP9kwk0y8qm4gB0TMsTwGNId0pHIgAACMTAGDEmH9G1UJLBgLChGgGsaSfElRihn+5KTs9uG6IWrWJlXpSzpj+Vlt/daZXIm5oc20+BgDAiZOJ9J948gMuzUXfou0811RZpKMMAJAAYO4PRkd4hdk3MS0kjPw4GKVF+xHbaleyNxkjMMgfTh+FOkWhHvmUJKYk8vuZadbyKlOh1IXK8CYsXjyGod23JUkJ40XAxNzSIMD0X/0Nils2ofeBOwCEGPrwPaifnsTMZ76E8r59cMoOEDP8YSGb8WfvGbsXlf6x2IQjACH6g6eOQUopVaAIwzYcrwBHcCqmDlDtHcP+t/9LzgTZ3BPqI1H2aJWYT4MN6DpCkid5TPG3ssZoPlUx00ZCy/0n0fvmBMk9CD0Sv0OKcEhxCmYkge94L2zXlPVxGfBN4EgxUTK9txx8dErxWMSIN2K4bBOpTK74O3AoLj//LQxd+yaUegYgJm7ThGvKb5ZDAYiAgBiVipJYI+In56rhUsuzoTSucnUA+6//Seza9R7MXDmKqctHMDv9Ci6PP4tKdRBdPZtw7PDfgNIQ197wEYyM3hBfRAYSNgABVQNgM++V+QjKr804tw0CQ6PJEZR3U21LbcdQx5Zh+3VFFLmEFK9q/+sEYC3mQBQI1BCh8QK2uDnaOApLQLtLrGAYgLJRsMWkPMu4oWjSi/aUmsmDKowleoguU4KZtDi+gKB5sDDgfMOheplUzwIAkn70sWiMuyhNEIGYWM2gJlTbUaUYRvMmJNIYdk1aVAwKeCshfBYaNE5UBsgS6OjcmMEUhsKAgMjsigCABEDXmaidxZ1scaQ4/XLEVG/YncRbn1+JxM6b+uZhIxEAEEJR9tqot4tSWVe5iaVGJM5jYUeLboBiVzRJGzHjXyu1uG8CMyniY5mr4tG53di5cVIq7zqRvGphIQEAoEmIRwCanb+4iZtsd0lIU03o1DbS/AMkm+WUeoUVKs35Vle+TcdbScbBvltd9vp5KIn+YwDwFikol7ZS817Ar0bcqEIlP0k70mh1b96LmZefBKUhSI74gdNPfhvty1ew4d/9G4RTNawAoF0+en/ifVh6+v/G4sPfR++PvC2zHadlz6NS6R8DDQM0FqZQ7R0FS9rFrjmNmIlKGPhwHHl7YM/M8WXAWFxMpP5hweJAHjPKhSWKoJCAWX49hpCKXLCUJv+wAAG5UtS/uC4zJjUoEiOTrPnGMJ8AQxQdUZtWWAHaVSCoJuMIKhEgUve6qBF5PMbjORkWo81/DpCs+UWwPYwQOG4BjuhbIoxHBSsRuEyOs2tic49rXArybylxmuQHlzyzQj1ej0tdGNtyG8a23Iaw2cDszAnUukbx/NN/jDBo47a7fh3lcl90PgPlggOu1o9NQCZqBYhelrSlC/iMQCBHP1KxKwslASTafxG4MJagHYIW1sMDrVPntKpZE5RdDQAwYgBAJL8afaTEKivRx8S0cAAQkxoNI3mBo49qm5kVozdSCdJstRrsAIAfzxI0mDYkA6lSAaNzr0PkBcPUlihsICSSPKkaBsM1GeNgt2Vmj5nziH14K6G2wLH7r0YdUkmS/tDIdrawKEvBCQV6Tjo8cVJ0noMrh0dw5fAIBwC+7+Ls1ADOTg3wenW/AEKolB2Y/R9QB8XYUZeVFdwA/bUV9NciL3Ux0hClQMnzJcdkEwAAAPdiGWef2oKzT20BIAOA0lxkAuW0ZdtgIGbWfUjqcz7u2F9GPK4+MyeImCuVwUraoJHpQfyxEo3eIbeVUQ/Ru1dYjpx2C0sUxYXok4eIH9+PKYrKFEV1MkT3xQCVmU6yGr221L1lH/z6EuozE/ZKsXSWnJ7G5Pe+gf4770F3bYtUxRvsQ/d9d2DhG48iXFHtImPmigC0QDPzM5T7oogojdlLmQwkA0taaMzQl7QAacyk24yen6rFYzlgrGZd8fptZH4Rz1d1LbetnwqTDwhatRBGzZpm/x4LLyTTN8E8RDWdCgqEn8N8ZFo9kPKqsD3GJMRSr9vuoGsuzyJjPgjFXEocj3pdhBDUxrZj+dIZeWyKho83ZzKRUuaq6j+krnXUiYQM/Lh67fE+6QQUnlfC8Mh1qNSGsPe6DyIM2zh7+hFQYT9qtZYwP38O1JUHmy8cqEmCYDEFUsx+AX1Pt9VflUMvUXgBREDgjeIczByDX8vPOkXUGQgguvSfUX3IQ33Ik15gQDcBoI4eCYi9kAwYMHMDpxUluUpNhsR+ErM0SbsEEwPMJoWYSCugmu+AcQwS0w1NtZo25iwyAoE8Qlcqn0tUO0NkAwF+Lcp5riG0nlenWs4G9VnYmFOnTeX74kSRRbyGsIkHQN/R2LekkUzZwjxB+FyvlBHYb7k4OT6Mk+OJx3HL97DSLnLToEDYARgQKAgMfxA6qBXFKETR99xSFVPzXZia7+IAYPZSN/+oVJ0Aeo/IAEAk6poXIyeIcwJwZl89ng50SRg9JxEQG0GeTxNAnEJuk0qfLCI0UueXZ8LMUL42KiyHKCyHcNvJGKPsrlE87XZrGcsLl6QN/7Wg2tgOOF4RC+PHEkd7YV3gcdApxbnv/Q28ajcG73vA2FbPe+8DfB8L33w0ijRkMy0wkBNQrvXzqt3wKt2oz16y1g89XfsnEXEQ+E3AtwMyElJut81IdIJ1Wjpj2LZpjZS1LnXtNaz9xiYz3hN+vhCrnmk7GdMbFMwO8iZnYykQAktut5Qk96MGRhoAD6EsggJVY5ZnHkh7jxoWVOnTdA6gMMcUqI3uwPLkOdBAtcNVzjOBBNdwzPBb1ByzRICSabESwU8LrEGBgaG92HvdhzBx4UmcPfMIAKDVWsSzz/5PPPvM/8Ds7CkYybDH6lp5cewpLJNFom+rpycE1Rn7TBLeYRbdzxgKdZ3WyUIdmQP5JReesjHVh8xNNPuib6a2BhLm32T7rwIDNY6zMatiXEXNIiyhYSmhlhkASOTmCxuqmuHo2R4N56SodgmlESRTupZ8BvIQN3WgHZ0rqjzTmAMRbKmOp16dwhfsxdUIC2K8e54ynkbPLygQDZK6TXmz7TvqYnFHLLWfJ3GfAJ7sQuv2JR7KEwDCpQIuLA1jZOssL6u3Cqi3ClzSDwCNmJlnICAIo0FMLiR2Kr1VWTLrX6pgHrGYsxqdVz1bABC11e6KAAAQmWuoJhvUTTZCEgKuzdwntEjzEG3yolNtVKi/V5EpEE2XfFF548uqKwKBPJl6K1OhZKrAGJRG/+rU15SGOPL4n2Bp9jwKpS7UejagUOqG367Db6+A0hDX3PrTKNcGkohZVJjjfK4L182znib9kFAGqNF8JBzMUhdw4KF7815ceuabCP02xq67F05RFnfSMMDlF76NxYvHsP19vwSnlJhXFOYcBLFjOikMoPstd2Pxwe+j96574Han20oVlmEFCpW+MdTnEhDAQsUas5FLg43AQf/Yflw4+iDmr5xA78Z9WjXHN8+p4nz0afXIAqB2F3sXbKL85DnxMQvPzGa2qDUT3w/TNTLnX9FEhZ8nmLGw/Uk1YWSkSrdFfzi3kUi7XWVPY/2Ia6DjQwPWPGrSKng5yffNBiLjfUhKpEcEx2ghclxt43ZQv4361AVUR7dlmr5yMmgJKBFePVc2jwo9grICKMV7YDWXQjKGsU23oNmYw5kTD8JzS5i4+DQCv4meni146cjncPstv4JisYvPpTAMMD17HD3dW1AqyA4cJKAAIck6nXathvEkAiz2j2DCI/YTGoCCLclYKK9VnN4IPgDr9LqlVfsEtHpiyawhgyIDAIycls7MqE5LPJlKLFEOi3pCF23TIQbJtOY0E73MNmmntNiyaBFuFMPfKNVXmyHJcTWTa14pnsSoZwABSmIJoel6NHW4KNmPTYNUJgeQQoWycmmzFTIWi8TC4qVREoFDPVeWCLltmslQkhDoORnVqQ/Jm0jxyS74B6OCcCliqrx5FzMvDmHg+inUY0ZraaGMpYVolx4eisSWy80idwruUxj+1uUKJmOGvzDcgH+pwo9Vx6MY/6INdHkKqNoFsFYbZ9HRl7rQ5oAk0TRZiglJ8bS2Dc6BtrCJkv9ABo9OqOwwnBZByESV6UCTdOZR00+cegxLsxew++CH0FiaxvLiZTRXZuAVq6gvTYKGARwvw2bmKtLWt/4ULj/zEC4/9xCmXnoMG295Fwb33o6QUswcfwqXn3sYraUZDF/7ZvRtvha4xJhiBy0lsmjv/W/F4vd/gPmHv4WBD7xP7yyMGE1rgreYKv1jWLjwcvIsU+rzdUsIWdnVvxnlriFMnnsWfaN7IxvllIg1XgPwLlGr03h5xlCYIhThVWKgJoKByA7bPBZjCF1+TuRbRl3CgYDNV0aTVnMfAnFskaBF1XaLQICf78navaQj4d94L+IJ/rJeJ8P9U69f2ofENpW2VVDEgEBleDOI62H50hlUR7fpQzAIwEx7Iv+OD/O127DfSeMQ1r3QBdyGChRkhnnrzrehWZ/HiWNfhVeo4qZbP4YiKeHJpz6BI0e/gIM3fBR+2ML4+FM4f/ExNFsLKJf6cPO1H0W1MihcA5Oqy4IsEoQG06KcfgF5NQU02W81vwAgST6mmgO9kRQAIU0PdvLD6H+dAKwSBDAAwIgBgVZPNCmLS4nDH0fvORasgmJSwoCAuCDzcGJZzL/UT/oDpw60EKGamtQkQdOY7nQgwEJo6gt1zOSnvMSqaZC28KRsGNK5ChAA9JwBgAEIACA+1YCASswsSNIIiNL+FGISMQ4GwkStLjIxJASqVxJnRCBiXmrPRAz64j4f3nwyaWZeHEJl3zxn/gGAzBQxNRMt+pVtMRiYrWB5Nmqj2N1C63LC8JevuMCVGvzB6CFFAAAoLggXoE5JEXAJIQ8JBYggTZbOIUjfFC3Jllj/JKDS/LIxbVLSJFMdCqMddRp5Daq9h3k0BWq/3MEwJkIJv7et5TmcPfp1jO24E2Pb75T6W5q/iEPf/q/YccP7UCyt0eO4A/JKVWx60/sxdODNuPTE13Due3+Nyy9+G6HfRnt5Hv07b8Sud/4CKgMbrY+W+j5ChHBrNfTcdw8WHv4Weu+9F25fT5REDLEUuZkBlONQwNXeMVw5+j2EQVuy7RcpLCASkBhMbwghGN56My4e/w52+i24KBqZDmbDbQKf3kok3ClPw7g+dcK0qFoBU3+OGm40lqiatGEkoHADRThhYr7jMhZBSNWsimFBmaaBRauLzNei6ywIWvDcFI8lrwRa2muYkE19tML1adJt9dodwHE8VEe2YHniDIZvvFcfouBPwBOIKWNSr1mLAOfAaDbI93gWHCQGABqDLUYEBMGe/T+KUrkXQ8P70V2OzEKvu/Yncej5T+PQC5/BwuIFhKGPseEbMDZ8I46d+iqeevGPcNO1P4uero3aOEgQzx0ODPIBASBh8rOCkCQnKHyIF7+f0vUKdU3mTG8kMLBOrzl1pI9vd7lod7lG6Q0DAIyKSwb1nbD4EJps9IWlyKnQN8RZ1pgkJz8AMDmAaA5kNPpQZTOQ0D9rR33fTBJdQ3/iN7sGjVaj+hVTjXdCJlW55jOQ7WPBmHvVHIvQxLkrTRvCTFokorHDsBIlgjnScskdSUxtxDacAOh9Sca23jJB+9m+pL2ZRJxXnHUQHOrlzD8AFC8WgZcTJrJ8JZqEpVmg54SDnhP6A2QbrlGibZKWAZKjLrsmdr0sIpNo18/eJ2br7LZpuu9KHkaeWp7DVSK3GfmLuI3I0djkU9IJnXzhS3C9ErZf+27uIxB6BIFLceL5L6LaO4ax3XcjdCPJYcS8kdRoRyKREHxNANh7T3hZ5KBN+TNw/GSDLnUPYMd9H8H+H/11VHpH0b1xN/Z/6Dex420/i8qAzlwwal26hHP/6T9h7m/+Hm6DoPct94J4Bcw/+HDmeL0V6EwCAK/cBVCKoKXH3E1jRJOkTwRDW29G6DcxM35YqZTMFxPDUahT1C5TlGcoui7oz9trmgQq0eRPY3i1KEA8WpFu+y++S1p9i7O7kWmGvAcxgUpYIEn0NDXhlzLegmAWZLpfTJOem3mLhUn8f5jHLTPKyrdQx2Riy68tjPxeli+d1vxvUvMMKGXa1ikUmACA+oxM5sESEBSYYcdxsX3X29HVnbxzAwO7sXPnO7C4NI7NG+7A3bf9W1y770MY7N2FWw/8Eirlfjxz5E8wM6/7DiRAJ4eg0WLTb5L4a/fLZC7EzYksHdMMU891WqcMWv30ieemXybwy0Sy9YxStBtCd0JfrFQbUQYETBF8GBOhqx8NaNi0mCtSek1y6xGpP2pKpJUDCKT1y8ukkGx01U8iT0xgQhOnQU6h8n+YLLy2zYiEFE4rhNMKtfvCgIBYLjmJU1iZQM6ACodYPVW6ZUoAxFTLjEl22kD3GYLuMwTeclTZrQPVF8uovphoA4qz0U33loCuIxEwKF6MvstTQM8rLnpe0blHtxU5+TJHX1XyruZFiMZmZ9gpYzItZDQjkBpA+jzMyXezJEm5mJFYyrmamObsPnBgTcCZ9jSaHn8RM5eOYOf1PwqvUJGOXTnzFJZmz2PnTR8EcTIaEu+XQ666TW11aDN23f/z2H7vR1DpH+XlhEbzn9AorCYJgda585j4b/8/BAsLaJ4/DwBwKxX0vvWtWHjscbQujkttF5aiNaq4mH7fQz9afB2vmCtePCAwsfHtqHQNoW90H84ffRBhGEfSsgBOxgQVViydxKepkd6S452DQ1P0ICK8b+bgB/JvDrDbhjqW98qohbC8n0VLojRGpnCqfM0zTUuTRsXA7Jvs503zhYeOZbb3HtG0wrWx7fCXF3Duob/E1Es/wNLFE2itzEuggJmnpYI4KFGSSDoA0J2WzUx/dNB8jpgwc8fme3HPm/4Ddu98QMrwXSzUcPN1H0Vv9xY899Kf4fL0EbtgUUkAaq5k4EdScnJYm1HPMfERLOPwGw0I0NfBZ50ArCVPAHQb4MISRbNPLnPacUZBJVoBcwRmGRNF0hZvojtPacRUvxnMP6AsslQpi+PxJ3bRemIxcVzGoYiMakooMOooEvgM+0jJL0D4nZc0Z+EgaYvXESVZAYXLbPoFUx4SAqQZIizJK4/b1n0EvDqV5glLYqXOHVOGUDFJWdYi59UjB3QJeFKgciX6l5kOeQ2g+2TUWHNAztbbdaSIVp/crtOOAAEQSQTT8lVIofa4ylYfqxyFSb43KkjgEYJsCxebv6H820o5p0wnjnCAeC+E68mrAucnEMX0j3LNHaHAxVe+g97h3RjceL10Wru5jLOH/xHD225Fz9COzvq82iRJntOd7NtL8zj/Z59EcWgE3btux8KTP4gZK4Le++7F0nPPYPIvP4cN/+5fg3ge3Eb+BxIG0UR1CpaEjUCSOMuQV4XRthvei+cf/D1cOvkDbNzzltQ+2fMSyWtSeM3kPuSZUzwIhEmaH5dbc7eEVF+WDQIf0fnWbUXrFks8yQBTKJmkUe6PJZLJyZjteYVlw7Wl+DHYnIH5vTDdO+P6YqgmaKRt/jeStDo2G6UO0L31Gmy8+0cx9eJ3MffKc3wfcwpFFHuHUOwfRnVsK4ZuuhfEcWTHWMF3g5t2xtdZmTYAAINGx20m9aiTnuFevUYgAgKOH7VB2HqrmMF5bgkH9/0zHDn5Rbx47PNo7XgPNo/drgkUbNGBOkocpp1sEoyZGBnhO0OwuE7rlJdWBQLEkGY8lFpcxuxARVJNDdRIQAwIMCdD08sTlEgmEOgIAPACfdHUbPZNicWyhAHULDEy1SEhNTL/Nia/U+bfei6NNzeHqX4T6ZmUUCyWYAZFR7o3jggE4vvjBFQGB5TGUYPkc0UgIIYC1TfU6CCzLZeSZymz12nBzHyTSCIn1nf8CCC0hbnqNoHK5fh0VwYUqkOvGFZQjbPNu00xseH31/IoUyX/MeBN22TECCHWZsTrsdVbg5DcZq6W5aNjomZ7CYsz57Drlh8H9UgkzInbP3voa/BbK2g3l3Dowd9Du7kEt1CC65XgeWVsueYd6BtYAzigNEkEFo+dSGrF+Lo6vKzpZ74DANj80x/D0umXMddoIFxegVvrQlh2MfhzP4VLv/sJzH/9IfS99105xpmMIfRbIK4HQhwzdrQ4qKt1ar0bMLrjdlw4+iCGt92CQrHKwY3jC8yllEU3kpZ6lvU6kzkiJNbU6pVS57NtXtneEZNZZNuQMZZlpQ8SIQcHDLFDf1hIkiJ6dQB16HHzDeNwm0oVEQhYmL209kRzSds9FgGFCvZNzryO62HkxnsxcuO98OGjNT+N1vwUWrOTaM5NorEwhUvf+yrai3PYcO8HQOLnJwIBlUrzadKu5No4AIjLrPNG8AswCfkASJl4TVF7HMfDgd0/jmKhhmOn/wHnJ57A1k13YcPwTRqYNgH8CKBQuc+0NTrF0V4jtY2UufFGAAMEna+XV7v/dYqoYxAQKpGAnADWiBBiSEDqKsy/KjFS0oZbXx5qPy6i8Uzm39QWZ4DFhVguUzMrmihU/AmSH4aFCugIAKyJLIujntGYxlERHGkcbiuU1KskoHBXAgQC009CCrceIKi40ibu1UOpHmikJQhEjQDVQ4hGbcZ1BSdT5tgHCABBkJZr4SAFEtPXM1MiOTSkvHHxYySJDqIy6VkAQJqbklqbVdDHZzXfYHPIZrZlkJymMXzqpp8rJvkaQhgazewy5vvcpZcBAP1j+5XTKKYvPA/iemi3ltE1sAXFSi+CoAm/uYLJM0+hf8O16BnZmZismRgl8R1IS/KzRmotz2Pm4mEsnH0JixeOY+jW++CWKmheGodbrcGpJKi0tHUzet9zP+b/8SFUrr+WR2cpT8fDbJp6iCj0W3DFCEmxsMMqUWbnecn9Yd9brnsAk+cP4cLRh7DjxvdHUtSUxxW9h/YKRvtptjbabKpzglntmEHDaGK+TJHOxAhCLMQqq0cCGu9rsUCMSf+XEuZfBApA/M4wQKGsH6FH4rwd6oVDXiNMEuB4HqdFBjKGyEVyTFpvxbpCn6EXhcQtD4yiPDAK7EjA3/QLj2H84b9Boacfwzffp/UPRHXF5GAsYhLvVxS0Ed33L3NdMkUBVLRGIhAQz+NrKnGwb/uPYHToepwbfwwvn/x7nDz7EDZvuAObN78JBdcecIDvlbE1gThmY79g84pI7wvxqfyMTNfN1upAB63rtE6dUEcgICwAjphOO15IvSaFL0YBMUxaVfrPFpfCcsIYqZuDpvZVeVULEFA3oDQAkFycAkIcaC+tpj419K/aU5oiGYjXREIqmQGtifkXmXzbMfV/ILlfSnxiEoSyvTQFnHYcoYFpDyiF1wjgl13putx6gKAsgoMICACAH5eTkMJbofCrjgZKIqkaSZz5AsBZoWhX5cWUAwQRTITCvqlI/22ka3/S66qggf9vi2XtCPfMYpe8Wudc7qR5lXBjFqOon2Duv5O42lBNLWi8OcZlMxMvRQx+WU7KRgjBLe/93+G4hUh1L7wDS7MXMHnmKXQNyNl5f5jE32ca2ekf/dLvwW+toGvDLoze8z4MHLwLIYCVE8dQ3b0PxJEnXu8Db0P9hZcw/aefR+/P/QYcz27eAwBBu4nW0izqc5fhuMV4DCnPgjC7dLNJEHWAYrkHfWP7cOnkY9hyw3vgESGTsNA2CWL5gsElg5kKWeeobd1L0QqkkZR1nJn+iJHqDMRyHzDGiuXwkLSTNNYWGNYHt6FLh522GYTw5mKQoJnEUvN7KJlzMum45mQNo9Sc0BjUqHsWy9kSS7dNGgzJdyFuX1xbB2+4C+3FOVx69CsodPWib+9N8Xh1IFCaSwoYEGBrOqvPAYDAUGdRWgJMxoTzfkSmXLmfANDXvRV9+7ZipTGDcxM/wNmL38OlqRdw+80fh1tQ4r+ayCFRW2KYbgMQ4Ay8mhvAAvj4OMWf60BgndZAqzIHsjJI8Tz0GoAvvCeM0VclLaw8adcsYWKLt+g/wM0xYIpCJEuVssAEYH9BJbUhoG1G4iKnAgCeOTGWwqm2gtKYDKBDH6TQrgQslN/qApIWuUBaeAz9W85Vnba9RoCgKDD9NGH6JU1BQFFYDuBXkjJvJeS/JSAR2+qKz7ewQgXn8ejbCQBnmaJdI5JWxQkoEMhhKtUNWCSxn0AI58nrE/l8rlFgdQUHZ6tUXTmmAoD0RE4GSaFaRRSuZk2nHPuGZp7QARklwp1mxAQQBj7mLh/Hpv1vlXxTuDSsUI7uK2RJ49L0OYA4qPVt6rhPG4kSUq8ehUZ2HPZeE2uoQACYP/8S/MYSrvnJ30R5YAztOD9RCMCtdaE1M4mQhAirAAqxDTNcDP7sT+LS//VfMPno1zD6tvdr7RIKnP3B32L29PPwm4khes/YHut1sLUqKys6pRTnDn8NMxdewKb998N1C9o8YCFvbaFm4wuxdWDv3DZXLGuxlagOsNNMMQiNpLEaQ+/r5oocMIiCCcG3wNan+F6pmdpNoE020zEngeR7keW2sLHzPlKi+Yh9mpyX1bwTlAAjd70b7cU5nP/HP8fEt7+EQs8Air2DKPYOoNQziGLPALrIAGi1D+KEUPc9LXqTY4mXLw3AtKmn/zZrBeR61fIArtn5XmzZ9CY8+ex/x6kzD2PvrvdkB+Rg+EJYDzL3dzVcL0Xii5LW1RsRAFDaMbC/6v2vE4BVggAj8lR+MiAgMvpMlSpFjWGn2wRByuIdFvQX15hN0BZXP+XZW4GAuJgbsvlp4coAfdMzgQexurpAqJunyuSb6or9UOhSFFXdnvdFyMm4sfTuYUE2D/LqAfyKa10EnQAoLkXntqtJX1FUHCozfpTCi6MeqknqCstJsiLG3LA22jWiSexdIfmPOH+isJ1yrgNmesHignOTHV94RKoEjdgZbSc2J0gjCUisYtHK7dibpXpWq8eO81bwm3FunnCQIi1MnkToN9G36drMuuIzWJo9j2rfBjiFIigEiSezCw7BfVk4idciCAizMmnnoblzh1EZ3ITywBhCL5pTfjViqPrvfAsu/vkf4cL/9/9G5YZr0fv+t8MpxhGrNo5h4J3vxvQ/fhU9+25EsWdbsq5SgIJi+uQz6Bnbjf7tN6LUNYBSbQCFSrc+iCzexSExg0oRIsTZJ7+Iy6cex7Yb34+Ne+9J6tlALutGMIHhZVQA0rF0P/8cJfwZXBWNF02Ydakb0ak/JpPzL9cuMMDA7gdLTBYk7bC6pkACRlAvCnHi/3UttJBA0gLUczP78fhMiR+N51qCbxGHYNM7fhJdO/ajNXMF7fkZNBemsXLxFNpL83xwhDgodvWjVO1DwavBK1ZRKEXftfIwuvu3oFCsJc+5HeLMiYdw8dxjCMM2QIHrb/0F9A3sVO6JMB/Z66EAJiNTLu7p4vlMQwmgXOiB6xURhm3Jh05cE6zgLEUwIN0/MXu5aC2gBASR2jatvasQtKzTP11adXQgFQiYIr6okn4A+QFAPPFVu0FAZtbFSAeira+x3auyeRhecnYfKGQmwiIB0GLy2wCA+n/O8aj9a/Vtacn5AElyvlUaZxkOITypmLjwefVEU0BCoLAcawnKspaguBidwzUDQoQiSaJPwR0PubNeCBTjOecrGtvCMuVMPWNQGLMfjYPwMpUYADDV5+MRIkqpm6mkfSCG5y9u+gppGiMDMaljmu2/LR64XjG1K716CLjC+LIySJuIukm/KlPo+MDC5Cm4hTKqvRvyt0kplqbPont4Z3blV4mYVJaZdaxMXUD3pr08qZRItT37Mfrzv4jFJ5/AwoPfQvXWAyhtT8yYBrffiqXBJzH73UfQ/8DPS+f6zWWE7SYGdt2Cge03yr4xYHPILvmmDonALUnejTAMcPKxz2Lm/AvYdduHMbL99qS+VaofX7cP4zzi5kIGYBD9sM8dbjImNWhfx0RbfH0c+QCdyOQ5sW+BCAhEwCCa73DprYWBZ+Wi1oqNNy2yT9QPNTKbNkBhvNeGSw9V8EuE6xTAUpQwzdAHe/aei759N8FR1rzCdAut5Vm0FmbQXJxGc2kG7flZ+K0VNJan0W4tR/M4iBbYcm0Q3X1b0NO9BcuLE7h08Wls2noXZqaOodVcQqU6IHRApb50s0Q7EEhujmEuCevyxJVDaLWWsXXT3fJpsUaccsGG/mxUkyTpmM2Ux2AGlUcrsE7r1CmtKUSo1lgMBMQoPmyz8wQnNmlRM5mbKC+RDQho4cTUCC5EYaJEJkmRfqohQlm52E4qAAB0h0KHgLTllTlV/d4pSFmtSisPAACkZ6JGC+LlQkZEKdylT7XcB5pjcUjhrUQ7tWYytCSbDDHbf78iSwKJD7g+lRzWI5MjxO3Gm7gPDjBEbQMAuO0kzwEDD04bKCqRiRgx8yMAsi8MonnC2hJ9Gtj1pjoxKqFF84LYLHMd46bNjqVoI9SEcXkk/k7bJNpcHTgAIuaje3QnLh59GIvTZ9A9skNO5Kc48DOavXgY9YUr2Hrje1fVbxpF9yX6n0WF8UuKas9ArZV5FGq91uNebw+8/v7o/w2DIC0HNAiw9NVv4+xDXwMANGeuoLkwg2qpP2l3cQYAUOoe7MxZm0TPxXTO4pVTmDn3PHbf8REM7bxZM7USKUuanyXtTvNZMOZYSVmPxfalvAe2d0N13nWVsQlAgAEpk2YgD2m+CTGFLonzGuh1jSFFxeuOr82UF8A8iJRjacKIINGCpoUZBSABAAAoz1JQr4By7wjKvSOREGjJEB60FaKxMoPFmXNYnDuHxZlzmBp/ERQU+67/MMqVflw89xh2X/sBlMp98QBMgi7DBawiiEF0HgUlFGcvfg8jQ9ehWh2KypTIZxqDbplzzAlYDcWdh3j7Kc/pjaIFIKb97Yfc/zpFtEqfAPtkVMN4Or6+QNliPau2kex8E0WLZLaazRQtQKujNuEQ3UwgAwBwZphLwwRwI0RzESUFqv1fR/Rq2LQpqk3jQuMAFESTYFCXyAutSY1MkvPU5+I2o+hBtggKjEQnYMaUOAGFw8KQKra53ooOELi2oUzgikmCRPDAwuAK2UhV8yMSRtlRgSjJnQROAjmikTbXxXdHnaJZG5YCYNdCJttlY5exxJ+r0i1aDxtJgD0+JSjkO7d3bA/K3UO4/Mr30T2yI2OgQNBu4MyzX0Lfxv3o27if3yNuBx//lgC5JG2FJIXjuSpIvntlo1LPEJqL01JZOw420uoJcOWTn0V74jLcvh441QpaJ8Yx9ZefR+vCOPquvx1zLz0LQimmjnwPW29+Hx9LcyFqs1wbzBwDi2IjhrtUiToA9SIutzqyNbW9VK0A0yxk1LG2nRLGNCt6nNYOZOabm+3QaB0RnZtVICCSJrll0n5VSWGR2tpAt3GfU99zG4MuXjNJr6uSNgeEc6UQ4CWlGlHWDgsYYU7AoibKCAACgBCCSm0QldogxkZujMYX+gj8Fhy3gGce+y/o6d+OjVvusF6PMc8CcwZW+IUs52BGV6aOoN6YxoH9Hxb6oZp2itB4bogO6IbIe9FYMtYSHuXQbGLEzX2lC31jAIB1en1RxyBAU6MqC44o1UmTTqQBANaOVZ0rhW9UGMr4JU4kFelAwfoiugkQiBi2hJlX1XcSM0wMJiWKww8lhCcviQqswzPTqwEAhKy1VlW5GOFTi4JEADdecLnzW8IAS1qCgBo3KbcZSqY9zGRI1Ag4ARWk8I7EyHn1kDPqYsQcl5sNCZcbRCZCIa+ftMP8CAIhwiIrAyLNgOQg3KYothE5Jgt9s4hGgG6exEl4Xwh0yXaWxCIPU8o27PRK0Zd4P/OYTKjauLwSUpYoL3STJE1Rg0zAIIA54mB0z90499zfo33zIryuntS2z7/4TfjNFWy/5cei5ECvsdSH3cdy7wias5ehZZUG0B6fQHviMvp+4kdQ3LMN83//EOa/+ggKI0PY8i/+DXq6tqKnbxvOffevMXXkMWy6/p3w3BLCMMDy5Fm4xQq8YkXvnCTghZuMAfCbdVw+9Cj8xgqo30YY+ijUerH54Lvghi7nJ8J2Q74W7leB7PuaorkyOxFT8GhANrJJWAWtglRutIcnmlZOjXKkJa1Tos5J2gAV0BNATQKYBmiM+RSMGhCTqYmhQcO53LSHimUiAxtfvxo6NTCvXez+cGbWMI7yjBxFyaoB4P4j0XNxhORgjuPBKXqYnnwZ9ZUpbN31NhDCNpjse6GbTaUAAX4Skr2QUpy5+F0M9O5CT/fm5Dx260LK1yyevddV93vzOm73CVHGbGLuFe3UOq3TaqkjEEBdko1gAWPMYn5MkfY7il0kAD1Jk3R+9jjT8gd01JarRCUgJPOFi+xNk7ZVUySJ+QdWx6Bk2fSvpj3xZ0amUyBZmDSJlwIA+P+qtMpCjhKVg4RRNCFA8R9oUxTaAQLVZCjON6FKmVlegaBEhEg8FG5cn0l7ucNvQHkOATVbsijhd4TY8zZzoyhhGqT7HBQh3RPGoKXe9bSDeadDTi0CC6fH/WxyOv86ATTpV5hT4s+H5QBsoDT+0zW2E5SGaNTnUOvu4dehvsPLMxdx6fh3seWGd6PcNfCqAQAxygsQMxvKuqeuFZW+USxcPGZsb+mZ5+DUquh++91ovnIa819+ED133Y0Nb/lRlOoe0AQG992B8z/4CkK/hfNPRt/z519C0G6gb8t1coMpt7y1PI8T3/gjNBenUewZhOMVQBwPs688A39xHu2lWSxePg3iuHAKpfhaCGhRF9Zo/dmk82kS6lhoEzHKVF6PUs5j67rIoKftT3LMft3BWDNLEsbN1nUAXBsg+nZIzJgBFJjuiwqgm8szWJw4gcHdt4Elp2NjylyPU65bBOaMITWZ9lFD+FATAAhKyr4t9B06sjlQlgZAC5fcMtwoQtA/tBd9g7tx8uWvord/OyqVAb2eehqP248kBLeQq4WESchQo3MwgJn5k1hcHsdN133Ubr8vAAFGTDNOwCT6bEwZg07RSNiI5xp4IxHFayuceY0FQ68nWnWIUC1WOpu7KYy1uoFoCZcMjlGJHaXMjEf1dQ0AoCBvJSynaYxpYRWplzi6chIcfeTxi1JUfZHXAMDrhQxJejQgIGoBRF8BZcM2RjAgSJi2HC8fV9WrJkONOPqQYHrlrZgdjD3uTCybCLlNc1IgJgHXj1F+jDk2p5sIURSXYul/RXxB5HpuCwgK7Bzw6zGZLkjELsW25qvltvvN3oUctsQsAsqqyCEGYECl55WHglYkkfZKcjryZA0ioGGIU0//DSo9I9hwzb3GCDUAQIUAAvx2seGJa4oYwxv55m4alftGEDRX0K4vAbUk6VBzQxPLzz6L6h3Xg7geiju2wSmXo4+XLNF+fRnUb6I2sh1Txx9HpX8DRve/Bf1bDqDany8Man3uMo4/9IegBLjmff8GpZHE2frst/4KsyeeRc+Gvdh210+gb+t1KBW6ECJlXc/Le5h4O/YcUqZC4myr9MnWesbMi5paC0Oc5R+QNW5xz2POxQwIMM21UROmgBjVLyx0ATTbeOXhT6E+dwmNxSlsvvk9SX/CuVbzEJglzqbxGJ2ADZxAIGir2FiYWZDqpycmAgsdoDJDpXO9ul0IZ8qlI+WuAeA4Lq49+M/w3A/+Ow4/8xncdOe/guclCEUDdIJGOmoA2X4BhGgCjLPj30V3bSMGenfxdk0Mt7bvceGJE+XbYfXE6ER5zT7VW2cCHOu0TqukVTsGmxJn2cx/VGZdRPviBGYJQrToKgb7fO0FUqsQHXSYxqiqno3RBVS7SUdXWRu9/kXg4StRHdaKRA0LZdTuKhtOAwJ5+DXLOtRJ3Hq1bxs5anQPmmQpln0CIsdj0elYYuoLcg4CMRKRNgd9Cs+PKocsylFA+eYmZUMGgBigsGtmpkLSmFsw9xUCxuyXCogVHQdNzHxeNTEDNJIpUNq+ElrmcAebEXvXHScyCYIfh/8NqeaIDQBBO1LLOG4BS5Nn0TWyTauzOHkay9Pn4RYqePlbf4hK9wiGt9+GruF0u/ZXm9j9LfWPAQAW/cuoFbtQH4nKmyfOIpiZR+1NB6P6pSK6brkFC088gf6dB1GoRgx+fWYcALD1TR9CwSujVIukoSRjSWHmYEuXTuOVRz6FYrUPux/4ZRRrvRJftPXeD2PbnR8EKZUSk0bjvCJcMJNqjpKiFUjzFSBhjuRHJmZ+FRpSVUIrhjE1O+Xq74bjwxBq1KxNtTF+5576EpqL0yhUezFz5nlsvvk9EgMvalyN4UENl20zhdLKDJoCLqAQTKOCstyPCH6ABAgwAMDGlgYAxH6ZkCeqoD/LQqGKAzf/HJ59/L/j6Aufw4GbfhaEOMm+ImhtogEi2bv4mpliDqSst/NLFzAzfwrX7/1J7sMl+v7xvA2uo/cnXpvrRH3yZxa3kTMJmjTXc5ho5g67+xoSodQKZn9Y/a9TRB1bk3kNyj+MCisUhRWqOQUDOnOsqvvYcTFFONs03VYYfQQbe7dF4baoJGV321SuE/9WGX6nRWVJn6gO5APKxzzJEWqoLDFS7QF98TfJBgCC5Nz4m5cTaKpzNbqPWieNDC+G0Ywqb0QGKku5XxUSxuLWkwcuxoL36iG8eigxucSnPJmZWB+ITHzYOawurxdGm5W4YREKeI0Q3koQlQsMBRtjYSlAYcmMkkXNgolIkC2xp67OiCQNQGbMMp4FCWPtSNvwfqRREL9fOfpIpfh8SuKPA7RjEHDumS/j6Nf+KxYmTmindQ/vwO67fxpj+94Mr9yF2YmjeOXxPwelWSK3NRJNnlHoEoQuQVAgmslFqXcIIA7qE2el8nApSqdOCskD7L33Pri1Gk5/6vdx6dtfgTPbQH3yAhyvhGr/GEpdGeYQyv2fO38Exx78H6j2b8T+Bz6OohCliI2duC5QVTxAxSYtseQlyjosrAm8LJS/TecAyXzQxpWmFUs5Jq7zSSIuS33WXgaxPYdQGVSngaXZE89h8vjj6Nt2Pdor89h08AF7VCA+TvNg+HVwMzX2m5gBgCfXA3StAAliACCMWT2HUWlBLpQAANOoGe6FBACAZF1X9ppq1wiuPfjPMDN5DGdPfUtrR1uvDP4K1CHSdZgA50pjBoePfwHVyhBGBsz5SYx7Wqg/G2MERFjepbSQ3KbnZxDGrtM6dUIdgQCVyfca1Bj3H4icOgvLIQcLxaUQxaXQ6ATlGuwAWeIp/rtNtXqOb2b+pTrMSVM8N6ByMg5uF5hUyQsEJDMJaliEtHMoUhn8vMfU4+Ixx8D4rwUIqAxqaLlOav5/LRFVTBTFw6faWCMn4BBu7FimhudkzDu7HhJEGgQRPIhO3ewc43VQCqeVSPpF6bgKEsTxFRcDFBfNHL2Y1j4pY/3lBAN55m3cjtuimmmCkUKaMPg5QQFj9ohPo0+4tnkQtCIQMHP2eRSrvTj/1JdB4wgdDCjAczC47SC2HHgAe+/6Gey566fRXJ7F/OVXlPcoej+4qUUs/RQ/LLIH0yhySR6lCGMHeOqa1y4bOa6Hgd23YPoHj8CvJ5l9Kwf3w9swjPnPPQx3xUHloofe+iD2/NS/xYbb3oXJI9/DkS/+Z8ycPoTKwIbEMVKgtM1/6pUnceKRz6Bv037svf+X4RUr8JpRaFzb+s38OPwSQejKpiFSv8w3IsMh3BgSkCbfWkZfMaGbepppzeMnEmlcmWMV13x1ycwAJUTaQ1gjspmMw68jAfrcnyoAli+dxZnHvoCB7QdRn72E2vA29O+6SRk4G788QEmglLZ32bS0qvaCJmXis/JrhvYkJjr6LscaAAYiTHmCTIyzKzgCG+exss4PDO3Flh334vzpb6PZXOiY8dWcq8UkXQCWVi7j6cN/DBCCm/b/nPS+qQK+qEwYv+AcbO5cBwJick1eJ8f+nfigvFoStnX6p0AdgQBRklmc91Gc91FYlA37GfMvUlFxCGJAgNUVGSZvJUgkqjExpkpcLFi5FEmgFcJp6Su30dlIJfUdozlMIkKZoee2qazfNpWZSjW+c4a0n7okWVQIiT5MopMGDqxlqwQClCb5DkTG2mACxZg+qZzShJGS2oUuMe5AiqwxbgI5kvReB49qOyJ4UM/RJPVC5B7bnCOhDjBEYqBYJcePNRErIXda1ogzITqjksfOWu4vAgJuMzQ+O5U4cx9aGLssYs83nitOKwFnzZU5LM2cw8rcJTSXpiUJPgMBY9fei96N+9FcmsnsqmtwGyq9Y7hy8vEOB/nqUd+PvRchCTF+6O/hbFqBs2kFpAj0fegdqL98DI2Tp3ldx/UwdtP92P/h/xcq/RtQn76I6oBg+59Dej1x+Fs48/3PY3j37dh1z8/CdSzcvEKU6P4u0nEnAgrUJXbH7zVoBQDD+kLlb3W8SUWVgZLHrZ1rAzAqOBGX7zQgoJBJy0cCYHn6Al558A9RHdiErpHtqM+OY+ttPxpFtBL6iAJyKBpf6OtamuZDi5IjRjaKSdMAUMAXAk5pJjzxvSxPA6U5+ZgJAEjjjEnd06N+sheVrTvvg+MUcPbkw9FYSL7zRDIx6vPLF/D04U+hWKjh1ut+GZVSnzZmYxjrQL8Ocf/OorDgmMFA3rF3kn379UDh6+CzTgBW6RNQnNeDGotlfs0VyttxmdyVChTcRmh1BJXK4njy6jGREXNaIcKiIwEEpnqTkh8ViDnUlsjvEcOmY1h8pQWSEI0x1BZhIbqO8bcUdtQgBRDtQpVzxXGt2m7WkFmYtMPE/hGQGHs1mRjxKULPkcbEJN1hwbGaD0n/G6VCgqQ2g1L9CnzDoklh1Aqpx/SIVYmWKlAXcggOzarfABKA3OoS/BZi4Cj6L/iGc5P+YRyXdB1598eYQTe2YaxPjWAgT3hRkS5ffAbHD38RNEzWkf4N12LPvR8FcRzUejdicPstGNxxC4784+9jyy3v48ySjQghGN11J84+9xW06gsolns6GlOnFHpJ9DCmkWRSYeYT4nV1o+/HHsDsX30Ffe+6GaWdEVNfvfU6uP19WH7hRfTct0tqt1IZwO53/BKWJk6i0jeK5tIMXFJAodJtHQvxKc4/91VMHP02Nh64HxtveldquFS3RYFWErrUxrwwRtEU155F38kbQc5YHgNMiSEV3nftPGGNU9dq6beyFpqCW8gXI3Qhhgw1rD0m+38p34Chbn3uMl755v9EqXcYO+/9Gbz097+PgZ03o2t4m7wMmt4j9V0z7VHCdfK+Y78CDfRQPWQtALSrepn6bEsz4rGMvD2KEEz2AYjGwTUnohDO8J57hQqGx67HwuxZALqzdV4SgwNMz53AC0c/i67aGA5e89MoeJVkbGz8Ul6AZIxsXzQ5DocFJ0mimLH3RnujIHRKW+MkZ/g3EgJYp9cTdQwCsgAAAHjLAfyaywFAVObDr3nwlpO6QSlajdxGIkJhTpdAwtiLZVF9OwDgZYqEwWhzrS7cYYb0XzxPXOxoshCL16I5AHHJnZ3Bp4To+hlLZAPTuRIQsK03KnMvbKJyg7okjsQRjlTH3Gjxk5l+xw+ljMKmcbHQiur1SOVicro0Ejf8LBIZV4VBMPbNjgVUcIRTOgoTyZYqHY3mRnRMi4xDgeJidEyKKCSQF7ebBgai8aYeThxy2/KctBGJJfcmsJd6XkiNz0u9ZzQMcPL4P+Lime9hdMtt2LT9brRJG/XFKzjx3N/gwovfwJYb342BrTdgYOsNOP6dT6PcNYix3XfDCeQ5q75nADC042acff6rmDz9NDbtf5t9wCYne6gbPTHeAx4ZJudq2nXfHZj/6sNYevIoBwHEcVAb2Yb2K+dQPBCCEAeukGWdEIKeDbvRmJ/ES3//+wjbLXSNbEf/1uvRv/V6lLoG+LOilOL0E3+NyZNPYOutH8DYNW8xmLokTr1Fn6YmbgsLEXgIvXRmS3QYTjpCLgDKk3ZZnHFBU+Z2Sh95GWQePtQCmk2AweQkLEcQEv6Py0ksTJh44UG4xQr2vuOf49KL30LYbmDzze+R6mZl6DblO7BdX1qZCQBIoUFDAE4yvxkQKE/Fv4V7TB2C8qxps6JSXZOAT51bUbS0lHtAKRzXk68/RxQgdZ8MPYKpy0fw4rHPY6B3J27Y91Nw3aJFUGUHArwsiKMJCueHhWhvJGoOIVP78ZjS/MSSznIK9dZpnSzUEQjw6gFcL+SRUDzB1IEx9IxEAMDPX5bBgtsMtBeNS/FNkv12UhZ6cuhHxlxITrgGBlRE6U4z5NJZaXHkC1ryU4t8xICAMH4RAAAGSbS6qIl9hoYyqT9oi5uoCehEFWqrqy5ochI2M9MvgYkglIGVaALEykMKpxXdJypIzUlAk5jK4uYXCgwoj8yQARAs0jp+2AA2jNoUpW/NPIHNNcOzYUx2WFDikVPAjR2Og4qsESE+RXFBBMQGCVgzlJ6RFpUIyAeCWNW2bKaVxeSrzH1HiWqIvNG3Got46fDnMTd7Cruu/VFs3HEXCImcansGt6MVrODcC/+Aav9GDG47GLfhgLgFECdfVjKvWMXglhtx5eQT2HjNfSDEEZwnBcC9Skniaoi4Lko7NqF5cgL+ZCRtLM46qGzcjoVvfRlH/vB/R2V4E6oDm9GzZT/6xvZEQ2w3ceKRz6BY7cXYdfdh9txhXHj2H3D+6a+gOrgZW295P3pGdmHiyCOYPPkEdr7ppzC06zbeby7pokDchj1DK0ACAK4caUuSGMf8kDnDL9XMKPl9it+/VYVANDFHDCxYQIM1e7ZBKyhqJpgpCjVoldX/QYAw8DF//ihGr7sHXqmKyeM/QBj6OPaNT6LUNYja6DZsuPmBpBPDeqZdkzg8yzvJQUXcng20MgDAw4IKvxmVZpU+Y+ZeAwAGBjWvH01mbgQaotVcRLO5iFKpO/ceSClFq72M+vIUVpavYGn5Ei5cfBwjQ9fhwO4PwXHYxM7VnDZ3uaBDeDZ8ry44iWltMiDj/LcBAS1Rp+fo1gmvc1qPDvT6oY6jAwGRtNNTbJ3dZvTbbfhwGz6XGAPgZiBq+MYoxJz+MIySfeXFMTFkmi260dFS/u00Q6MjVS7mRmiquNCWxui0Q/7h1b2Y6WAfaSDQQYFiHyuZ1oj/m1KTM7v1kGofUz3221SPhMJ9U5l+gUhIQfwwevbi+bH0Sy1jz1l6JqHF/4BSOH6oM+k5fBCkaxauRb2fattq3yYiNCWCjqNIt5RqrhidSF3saZQ5U9VoyZ0THi3LSLEE1WlRzT/F2mRIuRlSloqdBFGbbis0zvc0mp58GU/94A+wvHQJB2/6BWzdeCe8+HoLS5Ff0PaxN2N4w4049YPPYWU2Co85svsO1OcmcOxbf4zJk0/Cb65k9jW6+040l6axcFmPKPRqkNeItEVOG6gPEdSHCOb2h5jbH6K1swGnFKC4YyOapy+CCnNu4OY3Y+tPfAwjt7wdXqULc6cO4dQ3PgUEIRbOH8XRr/5XtJZmsOttH8XQvjuw5/5fxMGf+j+x856fhuN6ePmbn8SJ7/45Lhz6GjZd/w4M77iVvwd5pIpum0V5MzgMi7jcJfANYVxFoo7OZGZpGpNySAkjpWEY/GDE8aVFD6K2dT2NwZaEIvIY5bp68jF+SClfnDgRJXjbdj0AYN8D/wLbbv8g+rYcQBC0MHHoQQStRrIeqePjAMTAIJqu3xAZSDO3QpQDIFCCQwWCT4AWiUbB4XkAAA8MIozHFB0tj0Z+07a7QWmIZx7/BBbmzmWChqXFCTz33B/je9/9P/H97/0nPPvc/8TLx7+Eqamj2LrpLhy45sMghQyfGSVHUWa4T9PzKDh6pB9DoAtAjyBkBcQOyRU+dJ1WR7/zO7+D2267Dd3d3RgZGcEHPvABHDsmJ36klOK3fuu3sHHjRlQqFdx33304cuSIVKfZbOJXf/VXMTQ0hFqthve///24cOHCD/NSNFoVCADMKN1tyJJ+4odm51G1TGDWrIyqxMhRiQE1Mq4xU2di9BhDF3oEoUckFR0rE1821UyIOYMyJqm4kOxYJkbIaYeSGQRz8rVSKP8vgSeNWdUZ+WTcKfdePUc6L9nwUh31wmQBV0EDW9S5A11AtTJAAXxiG8I9FHMrcGbZwKTbQIL423Yt/LgKvlKAguqTIZEQn9rmcEsohVcPIg1bO4RrYaCzwEAY2x5nRQ9yYzDgrYT5QAFlz03310kjDgjiZyJSELRx/OiX8eJzn0F372bc9qZfQ//ALmM7hBDsveHHUakN45VvfxrefBNDPXuw++CPg7abOPX4F/Dc3/wfOPbQH2Jp4pTVUblraDsqPaO4vBoH4ZDGOT5gDVEZFOLoOSmmJyqVdmxGuLiM9vhEcr2ui64d+zBy69ux820/h633/i+gQRsv/8Mn8MqDfwyvXMW+93wclb5Rfo5bLGNwx0245oGPY8vB92D2/Ivo33IAmw68o6PLVKOqqcQcyLNNVHRTOOm47RAXWuiHOGhPA6WmQ+y9JIbwmET5Zs2kRDvqROslakFECn0fl1/6Lkrdg6j0R4naqoObMbz/Lmy54/3YcDB6bmG7kd6nqJhlY7YAAH4KWzoNfK6UGCyux6MCKX0V54Xf8X2qzOTQAKhzzLFIurlJVvqc7OregFvu/FWUK/147qn/iVOvfAONxpxWLwwDnDnzLTz99H9Hu7WErVvvxYEDH8Htt/1r3HvPb+GuN/0m9ux8N48CRF1ilMxnadBISM15AlQzMiUaURalheXVQIFDEBbzaUlfU6Kvg08H9J3vfAcf//jH8fjjj+PBBx+E7/t45zvfieXlJMrb7/7u7+L3fu/38N/+23/DU089hbGxMbzjHe/A4uIir/Nrv/Zr+Lu/+zt87nOfw/e+9z0sLS3hve99L4IgR9bOV4lWnSwMgO4IZHFUUZ211DLAwLCGBsdNjakNtReTKBF7or6yETZp08g8QyDqEolRokSPZqDZMTJTl/hFDCryC6n6A2hjU01+VDtnVp1A8UswAytTduUo2yWk30B0vXJYTWhklMJRGjvCKecLAICR4yvqfZowyOqc4f4HamZmX1C9Kz4QPLszAzGU8oRHmhkQK1fbz9h8VqtKFO+ziSghgoRJ7TOyo5VMgSquMSMoy9yZGbYxpJHGOqcAiYdW7dA/gAGBpaUJHDnyedQbM9i7533YtPFOEEJS12PHKaBQqCAImtEmTQjGtt+BkV13oNlaxMyFF3H51ON45ft/hoPv/d/gFvQ49wQEo3vuwpln/g5HHvpvGN1zNwa23ADiiC9BdC0/rAgbvd3XY7Z/EItf+Aa2fugXk1DGbfB3vTK4EcRxEbTr2PW2n0fvtuuMzr2UAA4cbLzubRjaeSsKxaoxjKh6Th7A4rYBtKnVdIQlrCMhELgWh2HBRt5KJnOg2LmWmQWJY+A29szfUlxSxGtLi6cv1QO/r3lt6Y1mh1RuW9zv2q0VnPzWZ7B05Sx23vfTiWO70I5bjGxvgnYzt08AHx+7lri9tLwAIgXF+J/Y9h+whAWluhkQkE8DkIw5eWYmjb9+AjU/w7iPYrkHB2/9ZZw+8U1cPPcYzp3+NgaH9mHTxjswMLgX9ZVpHD3611hYuICt2+7Fjh1v5+Y+onBJzOybADjBpMwyj0QzMG6SLJbFp4UuMWtWBTNZuWEi3cfQi4CJmuhMpdBzAMM7uE5ro69//evS709/+tMYGRnBM888g3vuuQeUUvzBH/wB/sN/+A/44Ac/CAD40z/9U4yOjuKzn/0sPvaxj2F+fh6f+tSn8Od//ue4//77AQB/8Rd/gS1btuChhx7CAw888EO/LqBDEKA5QpqI6Nl0qbIgimWpTYmLqMFuOXIYkyMzqPbjAJIXTHjhvBUfflW4fDcyrWBh8URTDXbdhVjiH8b+D4XZxHOPFl0OAIAIDPi9RaSRozC5KjMsa0SUky0gSgNm1JRdWWDaFXDAFzt1UXIs91PRIEh9mGyQRaZf0bYYn51U30nKVGmKiEV9S7ZOy+bEGW8TKFsF2YCH2l+aHi4z1CchcBshwpq5guNTwFdicJuYChoDLqq/X6lkMoNKOW9i4hkcO/5lVCpDuPWWj6Orlkizo2R7McBQzruycBRz0ydx3R0/H9nzC8eKlR6M7bkbfZv349A//C7GX/42tlxvXkhHd78JhXIXLr/yGE784C+xcW4C2699d/Z1roH8MkFhEVjcDniLDpob2kDTBWk5IJ6Lwbc9gEt/+1k0JsdRHdionV+odOG6D/1vKFZ74TguZy5ZRluTPXix0mNkwmS/FHs4QTHUotewA1ZCabZknBCEhQwJvrHtZH/QnIWFqGWRI6+dyTfuL2x/UhxVOeMurGGZEYTEruNx2RyFG0vTeOWhP0a7sYS9D3wM3aM7je14XgQCfL8JBmdTnZvFNSueD0zApZ7DNADivhqUIM8hAQioVFgSqnkR4KtMhfIYLeunKPGnTnrY7qzIQJrAy/Wwa897sH3723Dl0vO4eOFxvPDCn6JU6kO7vYRSqRc33/Ix9PZuk5vRIvI5YCaxSeOW+SXKD0zRoQxlYaxhcE1SXzHYiKDBUkFIWgSmoOhkRuVap6tD8/OROmxgYAAAcPr0aVy6dAnvfOc7eZ1SqYR7770Xjz32GD72sY/hmWeeQbvdlups3LgRBw4cwGOPPfbGAAFAxBBLzrdqGm7OlMkTdbUSNtvCImXsjV84OWZzLHFWzIhE4kBA2OhcNaswIibFW0ngtdMM4K4opk8t+cVWAYDKDDqKzwSU61FJlb6a4hKbzk1CXurPxRixx3ivo3tCXceufRCfk7Q5xuUKYBCl9ibwQD3F6TgESBhKGhzCcjVAAAisDZvKVby8HBqjPJRmFmQ9ltO0wGhWICRE8urMGTvqp22JLhSBApp6/QC4M7QECvI6ZtpMhhyCc+e/i97ebbjhwM/CdfPFqg+CFk699FUMDl6D4Z59oM0kMpUDgMZmJzW3H5t2vQXjR7+N0V13oljt1doixMHglhsxuOVGnH76bzF19jls2/+ufNclN8SZFC6pjb94pvNYe5gWZx8AmhMXQQpFuGVV7Jo4T7pxZmDW19V2aOtkXSYB4MZJ0tIAQKQdIEatAOszy7ws1b7eAmKsUc54BSL/HwuQKOzj6cSHwNZvEPo49vVPgjgO9v/Iv0a5Z9gyQMCJNVlhqyFHpjKFXeXXmwCBUDI7je6JSYsjM9qQ1gXfEBq0EFs0hIVIW1W7nJ/bTJKkIfYFM9xrCum+WiMDmfaneChuoYyNm2/Hhg23YXHxAsbHn0KhUMX27W+Lov1kkXAP03xopCRdonaZaaEhCypVQBOUXO5DKZEadZCdozwf5uMhrgVB0RDg5PVMFh+IH2r/ABYWFqTiUqmEUsmeNT06leI3fuM38OY3vxkHDhwAAFy6dAkAMDo6KtUdHR3F2bNneZ1isYj+/n6tDjv/taCrMmW46stRFlkLWZMMGSaG0a/AcK5xEc9jx6wwOIWFNgpKFKPSVIMz/d5iE95iEyTFhqsTAABAc5AmQaj5PIj/y8fkzUF0wpaZfOXeUuXDzrfYvQNQHHsFPwz1NlPlWzlXldpLY+Tl8jWqpPkgMF8MzbzK3K/x91qIQvdnEShPNITUOgxfpTURUhSWAxSWs+0L2fPLpBiskdjvgwSGe5zaEQEoUCr2wnWK8IiX27/g4viTaLUWsWf3j2TW3bz3rXDcIs4f+hqIH0mQmY8ET24WfwY2X4/WyiyW5y5Ktv5yZCx93uUK2ZdC/tQslh57BjNf+DKWjr+E2ce/i+E33Y9iV2+0eYeA24g+eYmP08BsZFEnAMDG0EcNJW2lhUnNMk9b9Rgscznr+qwar1grkLfcvJdFX6ELzJ59Aa3lWex++y8kAMDWdSXyxA1inwAxJ0oq8LI8c5MDMBBFAJKcgOPT/a74J2Os69FHpOpUqJ1nM7/SEjOK5rWqFldhdDXK0nDxIRD09GzBNdd8ELt2vSsTAJj6SrPDTxsT8akhih+0+xKUXM64S5TDdyUxNcrWUqxTOm3ZsgW9vb388zu/8zuZ5/zKr/wKXnjhBfzVX/2VdkzNXUMpzcxnk6fOq0kdaQKIn4RxVCl3/HDbPqo6qhKiRRhKzFdiSTFkZlEtIwDPwieFF2VlPkVxtoVWf7RIFAQH38Kyj3bNQ2kq2ZG9RSFwNwASBKBu9s7GkrQASRQlU/hSojj5Gn0eBBMizX5fZfKBZPGwStYSSbWV+VeuQ0u7Llyf1r/YniC5ksgQ311yAha0S8xHg98/A6MmaQsouGbHmnhHLc6SlmfdW/W42By73zYuIIQ5VKrEoKZcD6J74tUV8GzpzvHD7M3X1EeW1FWhSqUf8/PnhDEqN03VHDoE8wvnUKuNoFodzEzw6BUqGN1xBy6dfCxzLN0ju+AWK5gZP4zePZsy66+VgvkFXPjMJ9GavAwAIF4Bi/73UOwfxuDN93XcHrOt7zRbc95nxedASn1uJpMHRxrC68qN2TuiTApqY74NWgExbKetfc1kNdYkh57FdjvlXhht74VtYfLlx9A9thuVvrHEP0KVVbBklvGWHLTMjsHsnnNwIMpo8mjsaBLyUyST9F9k/pn5T9cE23+EeRjKEnGmZVEBANMASOZNhrXTbLaYAgCU+5AnV4DWvKPP0TxJ70ztAGatgFwx6isoOnBV3wj1elLKKSFRHqXXUqr+Bqbz58+jp6eH/87SAvzqr/4qvvKVr+DRRx/F5s2befnY2BiASNq/YcMGXn7lyhWuHRgbG0Or1cLs7KykDbhy5Qruuuuuq3I9q6G1Y0YifLRjysJgitIAmF9wQ0hGiWFmoSiVc9QyW7QecYEqzrYkAMBIBACkHUgLC3VdDQD4vUWrHwChVFIB2sKXqoy9es3iMYlEibzYbIqkP4vpt11HnnIxOpPtmFZm0ywIZj/smmwRdwCLxJZSTcPCyrX2pePQ76ftmEgZ+7FRWyMOzcl+JiwyE4tWZe1LvLcZxKX9affE2EfyMVGlPIB6fUYKiWlvLDK5CYIWKuUB7jDtBOnhS5srs6j0jGQ27zgu+jdei+nxw9ljySDqROYRjh9/t4GgQLBcn8bs1MuYungIk9/8Ktpzs9jwkY9i57//j9j6kX8Bp1zBhrd/EI5nl8Mws5ugECfh6nCjF0ND5gIA8Rqd3iYSrZT6Kikg3uS0Hp0YMXqm0JViP8bEYZzxNJ1E+Xcn0aySPrOlsFJ9Yrivyl5Ynx7H0pXTGN5/l3Q9ieaESEKLiee+CRAH1dGtdmdkwxiNzsyG+28CAGIYUNa+09Tr1S4rTLLYvjI3NQCgRAay+qR0CgB4gfJ7FZyNaFIqOVyrXYlzj/kVmEx4c2oOg6Kj5VmyOXaHSkZ6nkhVDDjyBggVKu4Xr9UHAHp6eqSPDQRQSvErv/Ir+OIXv4hHHnkEO3bskI7v2LEDY2NjePDBB3lZq9XCd77zHc7g33LLLSgUClKdiYkJHD58+DUFAWuKDmSMX6y9nAYpCbPfN73cJimMWKT2wYRWJsdhgeFjL7gj2O6HJQ9OI2H+g1rEwLsLgsNvwY0AAD8pKlMpywnYjU2MWIIsLtFn2gtJIyCfG/kMyEwwt7MHUk1eckv6FQckK4nnqQ6/pjZs7cb1qeNoYzFqFgAzw606fcdtc42BJy+azLcAMKh8UySHHZkOmTQEaVJVwTEZAN/ApHsgSr1MRKFpSUz9END842LzTOym0/2FRsmgKuUBBGEL7fYyisWuXKc6jocw1G1ALo0/h/rKFKrdo6h1j6HcNQTHcbE8cx59I3u5Tb4aOlFM5jSw8QCmzjyDlZVJVLqGE34vbQ4IFBZgdP4L2g2cf/5ruPLy9yHe6P677kPXtTcAAEpbdmDvr/9HuG22FkR1CisASOwPsMp9vBOToE60OflARFJPjQTEtAHU049pfWVE4hEz8QKQ7a5zCzNkibR4fSySiy0GfCfvwOTxx1GodKNv2wHtGJPcL06cQNCswy2WcfnQI9hw+3tQGdQdxRktT57DzPGn0ZiZgFMswy1W4JWq8MpVuOUuFCpd8Lp7UKj2otDVx00NGu05TD35KBbPvAzQMLpVhKLQP4jRd34AxcEIQJtyNHRfZO+U7Hvn+FS+94QYhG7snYIskFIl7zl9j9LMr9ZK1HXi6xP5CcOenOYwnjEuzdHb4CvA1n7Z3yMJYWrkkwiJwvN2YE64Tvno4x//OD772c/iy1/+Mrq7u7kNf29vLyqVCggh+LVf+zX89m//Nvbs2YM9e/bgt3/7t1GtVvGRj3yE1/3FX/xF/Nt/+28xODiIgYEB/Lt/9+9w/fXX82hBrwV1Zg5kMtuISYr0IjoOGzL5RgdWAQAsvzUm0gAwIimwIp1oyKudu9zSnYLbyo7lEpAwBBXCC4ZlF04z4FGDWIx7xvC7go8BaYfS/SO+bvKjMc4mMx/AKKntxLxH64/9b2LibeepmTnDeJAusbcrOfwarh8pQMBGITU7H7NnYbDxVE2HpLEC9ufS6bF4LNExy/ghPLtU24P0dgiN7fctWbLltlRAkJ9xtP22UaUcObjWGzMoFmRHWFskJMfx0PZlg+QwDPDKy18CDUOEYTsetotKbRj15Sls6Xt7rvH0je2D4xYwPXEEm/fcl+8iMmj2wks4++Tfwm+tYMut70PthhvhFstojZVAHIebVxAfIHDyhc40kOQsTmCNFLQWYpoW1fnZPCD7odAFnCCWmqumNvGYeYQemxMuTaTOWsIukzReJXVNY02n3K/QNSf/ouoaw8pVJULMJLrlKvzmCpYnz6FrNJEeMoAx+dJjOP/YF+EWinC8IkAI/Poirrz4KIpd/Sh29aNVX8DcieewfOkMWkuzABUnQPpDdwollPqG4dW6sXTuOEihgN49B+EWS9Ha5wCLxw7j9B/9fzB077swdMu9gJKNu/uCvEYzIODGib8YoIqk/8oeq+YGiIesJQdLW6PE0zMAAN/3VhklJ4lQZwgZKnZpcw4Ww4MKJr/sf1EgYdIeMI2AqNmlhET7l3idLtHuGcvPsaoM2z9sep04BuelT37ykwCA++67Tyr/9Kc/jY9+9KMAgN/8zd9EvV7Hv/pX/wqzs7O444478M1vfhPd3d28/u///u/D8zx8+MMfRr1ex9vf/nZ85jOfgZvDrPzVos41ARkAAACPICQlyGJRhdhL4DmJeQtDvR7RyqR4+AYJq9F23RBfXmuX/WZ1jDF8hf9DSE7EDAiE5eThOc1ADlfa1s00mG285BQlSvVVxlmkjHlrZfqzGFSVTC8Iuwx1o9YAQEwBTa9rKrdoFhjgkiLWmK5DzBGh7j2qf0FMViCgjqsTzYlNmsyYbslMTj+dawbSnNNygAo2FmLSlhjbTDb11VAaM1Yu9wEA6vUZ9HZvydVeGLRBQz/ZmEGwOH8Rgd/ETbf/S1S6hrG8dBnLy5exvHgZ5a4B9A/vzdW26xXRN7oPU+MvrhkEtOsLOPfElzBz7nn0brwG22//ELBpEADQ6gO8tjlBU15iDFOqc66FuFNiXgl5EEPQnHyEGvqZEbMZpw4QOPLY+TG2dLLXwsBsMcfWtCzBrzYxhs1quiQWM2ASr30bbrwfi5dO4NQjf4prfvTXUejqi6rREBef/CquvPht9Gy5BgvnX0ZlcCNK/aNYOHcUraVZ0ECJQOcWUBkYQ/fGvRjYdxsqgxsACoShD39lCa3GAvyVBbRXFuE3lhC2W3CqFTRnr6C1OIvRu38EAwfuhFsuw2dmQQ4wfM+7MPmdr2PykX/A4pHnsfE9P4nyyEYgjAFANGBp3VIj/JhMMG3JCLW6Vu1mZwDgapMxZKixoh0I8DKWm0c12yGWeV90OO/A9wEB870hGP3/B1EeM1ZCCH7rt34Lv/Vbv2WtUy6X8YlPfAKf+MQnruLo1kadaQKCyP44FL3aYyaDKrZqRgZG5Yk8R1cJGso0CW/8MqQBADZe6soOxqBUZvhtDmsqA2uQUokAAEicjxmaZxGFuMmPJAFXJN2GJGcSo9cBAJDbMDCveST9ItMfKuUqOLKRCTjYwIQ4Nk2zowA4A0AAEoAFWEACYDQfYpuSCQxkhiTkmpeUY2nMt4WZFxOH8TJTUjOKXFJ/0UE66cM+LOO8WEMEg4vjTwAgqFbt4RFFmpk7hamZl7F313ul8tnZk3DdIrq7N8NxXRT7dqBvIIq5zq+nJQsB2HeI5HkQCgxvuwXHfvCnWJw9j75q5ORlM5FRy/l9DkK89I//FaHfwu43fQT9O28GIQQGk+pcRMJoHkoZyjuJSmRbQ2zVfQhMf3rsf2Zmxd4TYyQgBgpiRtik5QhdArh6okWpTsrOxIGB6XST9pT9TskpYCtnIIeKmgEhvKh2jqrNcj3seuvP4aWv/D5OPvyZyDeAUsyfO4L5s0ew+c4PoGf7tXjpc7+Nvl03Yfi6u6NrdCj8+hLaS3Nozk+hNrYTpWqvLuQgkcas0NuHQm+f1HfoQZoP1IkiAkmPJAScQhHDD7wfXdfdgEtf+TzO/MUncMMH/j0KZcVsL76HbkPODWDS4FsBgEkzADPjvBri++AqnIMBRUJPk/muRTkSzYHUiDDcHFnQADCtn3idXCuQDQSSjs1aqlw5nNZpnQy0KsdgPjHFMI1t+xtHCYm82D29OxPTkgflZgGAtDKJHOh3QU0bbnGYc1cScyLRDtJV8ggQP9TGm2nzbvsf0BxrcwMAsTxNHacy/abjoXzMFt6SUME0JavdtDGH0AFbCO4Mrmo+SBgm4ME4Jt1BWHUiTyJY6I61avKl1PCgeZxyKVIZNkIpHD/UwstKFFKQdpj6Lsptyp9MWqX6dmHxAk6dfRjbtrwFPV2JrbMpDCcA+EETR49/EX0927F54x3SsdnZk+jt3wnHWbv6dGDjdShVB3DxxHeMvgd5aGn6HFrLs9j3lo9iaPvNuUO9sdClbjviBbyGzkDmIlESn6Nr5rzstsy23yZymxRePf3Zs3bTKCxECdTSiLqChkA93yPG3AscoKUxkZYIQZlhRIn+PyWWvkxFIVCodGPn/R9Fc34SZx/9HM599/NYmjiBne/4efTvuwUnv/7HKHYPoG9H5DMSepFUsVDtRnV4C/p334RiV68MjAQG0nS/TCBKDAnK3ruwkDhwVzZvx9aP/gpAgUuHvxW3k1yU16BRXhJxGGsBAAppa6TKXGcAXG3/Me3tOShvyFDdT8Cw/wWGIBgh1Zl4x8zIB2XFEVgxKwKS81arwX0tSA3b/Fp81imi1TsG54y+oDK7oedojIzJ0YWXGWz7kx/x5DcxRqFeD6qUQHy/WJlL9PNSyF1p647C6sKomkultWsSwK7Vzv9VoLQxqZkYgVgqIqpMKU2y6krZnYVOHEMZMzNSH3lAdT8EgAMBzaSIUhA/APVc7RyH+xGYnIqpPGaTPwpgnDsiqIjGlAIErSpycXwZUv9OzMCovjF1QtZESUELR17+a3TVxrBz69tytXXy1DfRai3hpuvjLME0uSmB37gqAAAACHGwefe9OPnC3+H7l15Cd+9W9PVvR2//DvQM7MiV1Gx+/GW4xQq6BrcCANq16Ea0YyEqY8YYg0xyMt4qOQFgsn/PemZZjJeJXCGUo4N0CS2zC0+Lq05dIHDNG28Q2zE7sXmQMd6/SyJGV2WcYkm0MTSmIq3XmK5VCk6t89xvot1YQrF7IAKCFLHUNjpeG9mGG37mP/Jy6hHQoI0TX/2fCBrL2POBf41CtVtn3mMtsDX6jyoXUc+P+/Mr0MgEHroXahg+cA8uv/BtjF13LwqVHoQeQXFJFo4kPgAydQwA4vElv+0aGyB55pkAQOocVydkaFaoWyBec0U1XvwVUMG/Jp6TojaB+REIyVjZMVvyMlMmaULzCVHXaZ0YdQwCGBPuBonZC1u4ieAcC4DnFVDDWuWmNAAQHze+lKoE2LRAmIakvjyq3beJqYrzGXCGcS0AwEBXJaKPTUVuo4xFM08+AY2oJXoQEmmSnj/AMgBbuckPIa7PtQKqSY0fyNmG+XhhdyqmSXjSTIc227MR5q7Wf9oxsU5b1jCkmx2lS9lsZHPazUsnTn0djeYcbr/543Cc7OVmdu40Lkw8jr07fwTVyqA8FkqxZcub8dJLn8fCzFn0DGyLx8jAPZvnkMqDlE1xw443obt/CxYvn8L87GmMn38CZ089gqGR63DdTT/DuT7u7McuIe5j/sLL6B3dC3KVgAkjtrmvJkGZGlo4i9LMclRym5FDaOgSuEj3deAOywYKikAAe1ZhwD6fxZj+vB92jAMDACBG4GHPKMwqyMWtlXmMv/ggxq59K8rdyZwMAx/zE8cwfeY5zJ0/gjBooVjtxfC+u7HxBtlBnYQUcBxh/oQ48/BfYmXqAva891+hODgsLWvydQiSe+ZobTJPtbxegSHqIXtuIiNamYy++3Zcj8vPPYjF5UsYqPRIAICRY0p4m9cHQCUGjPj9pxzEWesLREzrvk3I1wFJzsFMdiQAAdU5OImMFRrXbQkIsLGH+rOksTmg+A6HXuQcTJR7HBZk06DQI8AqBQ3r9E+TOvQJCKUz3GagMfgsSo4YDchphwgLjhw6jL0wohSfIWRTPQUAmM7TmP9OSZRCpDnosjLRHt0PNSaWhwNl0uM1AABj/6Zx85OJXt6Bg+BqbSq18dmcanNGBDI5A6c6CKs+ByaNgXgcydziC7eI9WJmWzoujo8z6zZ1cQZYMPWvHBMX+TRGP294y6ihDupCBAP5z5uePoaL409g7673olYdkfrVYsxTisWlCRx95Yvo69mmmQExGh25HmfPfhunTz2IGwd+KXMM1AH3Y3KCRPIsXld3z2b0ljdi87Y3AyHF80//UWa7ANBuLGF55gJGd3ce45nZynMNAbu/bL3rgPdfrQanEydgJ6CKMblOzGQojE12AkPUZGaXnmb6xrUKah0mFXfj98m0LLtxsUVrYAQAKddfn7+CY4/8IVrLs1i8chrXPvCrWJ69iOkzz2Lm7PMIWnVU+saw8fr7Ue4Zwsnv/gWWJ8/YG4zpyvPfwvyZw9j5rl9AZdM2Y52wAL5+cbtxEpsLsfcnvidWAGDICxAWIa9xAiNKXWDh3FE4hRK6xnagMG8wsTXsC5kAgCD1meumPubKxvDZgMz0h9DLAH1PM+1xShlbk6WkpAaNszbOONGn5iBsm4MCiX5HapAIWkiAAC8zaARe95RmjvzD6n+dAHSqCTCoahmDL5IpSYYaOzhyaDRI9rVQooIUl7dvkPRfrYea5hApMp+xCVDyksqaANFROgorl96taF4j/s41RhtDZirvFAgA2kJpyhpsHV+aX0IGEMiTP0C9b7yMmRpZHYTjbxMYMEQjko8bxq2AATmUoaA5MPjF5Gmft5OW/Zj3K4/bJsXvpG6n1PbrOHr8i+ju2mRl6AGg6S/j0uTzmLj8LJaWL6FU7MH+vR+0StaDoIWeni2YmHgaC/Pn0dObL9JQXiKEwPcbqNSyHZgXJo4DoOjbsE87FsYMMGPyVWZ/bYMU2spgrqTTQvad4wQaMxaKRNJEXoMC0KWcIrHrT5tfQQEAiC5lZpLilHNtzsKMeeZmWb58zHQOu6dL0+dw/JE/QqHcjX1v/+d45dE/xbN/+3+ABj6KtX6M7L0Lg9tvQrU/yhB68fBDAIDNN/9Ict1BJL0X39fQb+PKC9/B0P43oXvndcbroYad2ZZjIcv+H0gYfTYv1XlTvZzcj9lTz6F323WozevvoJYoi1IjAOhEE5V3zdFNX5SC0LBH2ICAo/yGpYw1I5oxW/ZficHnpsp65mApjCjfY6kxepvRVLpAolwWYih2EoHnNxwYWKfXnDr3CWC21wIxIMAmKwkChEXGJIsIOprxYsIuUULKbZ2lcFsKAAgsqkK1LM2ExyQFtTGrBkm66ANAVSdi15AF07J4GW3n8zDo4rWuJmKL7V4xMjlYdQoETG3nNGdKazctY7FpTCaQIB0LFCl9iukQP49nk9SfsxaKVB2PH0rzQTU1Ev0ObHbWYh3eTspGmtukh1Kdyeh0fsX1g6AFAgeLSxfx2FO/h9HhGzA6fAO6qiOgNMTM9HGMX3kWU7PHABAMDV6DXdvfgYH+3XCI8H6RSEswt3AGExPP4MrkYYShj4GhfSiVe+NKscaG37e1IZlWawnFkhwZRc1+67Yo5i+8jGrfRpRKPWhVo+tudeOqUcQsgEu/eXleDL8aX+c8bVMlbGnKFGHaAZNDL0gk7aaEIIAldKRy7dpQmCOxhZG3hRgNiun3p75wGS9/85OoDmzE3rf+IrxSFbvv/SjmLx5F/7Yb0D20XXICb9RnMfHiwxi55s2o9I1x4KLOGxIAM8efhl9fxvCN92r9MiadiIypyshCADemrMAiAIjHYdIIsHvEzIAAoDFzCY2ZS9i+74GkiVjSnFcDIEUrY1JqmyyoUwAQj9kUBYiV5QICUH6bNAKQy0MvSiQmMeXa3hV9i9J+4sdMvyhwMkVDYmu6If6/ZPLDop0JvgKhoBVYp3XqhNaWMVggLRFXK9BDGhoSdrEwWnJUlpihMwEA/iMFCJhMYNQy7QIs7SmkOgFzdWEKYyg1mcXwd2rnz07rgJHObN90LwyqVJbUSpP6ZGkmOnFY7YDygAer07KJ4Q/Mi3LUkN1uPwIXOecD1xDofTg+lbVPaSZFOSTMJETn9167p/meV7nUi7vu/F8xO3caVyZfwMWJJ3H2/HdQrQ6j3V5Bu72M7toG7NnxboyO3JBkETY8w7m503juhU+hUh7A9u1vw9iGm1Cs9pmHy0KrsmGy14n5LVF5DnLBRVxvefkK2q0lFIvdqZdLaYj5S8cwvOO2XPcjjVg8fc6UrlKaxxiCTpKPWaXhxsr5xuDE+WC8upn5d1vRt8lcCEjs3tNyBNiYR25Lr/huiKT2q9rfO0FkBhQGLex+y8/AK1VBCdC7cS96Nwp5KIRzLjz9VTheCRsPvjO6BmVnZUxf6IS48sK30bvjAEp9w0AsoWdj4tNWZPLdxAZfzLFAXWjMq9H+33Kfy1PqGIHZk4fgemX0jSbaLZP9PwA9fCUsGoCrBQD472whVSYQEMlmPqS0nRW9SDokSfvTtAL6fTCFFrWZ/Jicht8QQCAFGP7Q+l8nAJ2CAILopTDYzkcTV5jNVJ70gCDpVxM2GUIa2sI7Zo/RwuDnPZcxi8zcJ1YBhsXkVvGIM8L1pkZ6Uc5LcxaV/s85bpOUW5SKa6YyecgGBFQKzVqBVLMmEzD7IZFVk2BzTgZ0MGA1EzLY9NvAoVrPkswsqWB2cjRSHkafRvUk/4qsON3xGKi6BlirOxjo34WB/l3Yu/t9mJk9icnJw/C8MjaM3ozurg25LqdWGwFAsHXrvdi4+XY+/KtNS4uX8Pwzf4xKbRjDY9en1l2eG0e7uYS+sWsy23ViplfaxOMY+qEXBVnoiKjlO9e5NGEoMh53JwCBhIAbmvNpMPLqFKgDgRImlDFsjKEJCwRhwexQamUcmdbAsd8O1i5710MvAl4MqIj9FSs9AIB2ewUF0mfsj11r0G5g5swhbLntR9FamsX09CEsT57F8uQ5VAc3Y8e9H4nG7gLzp4+gOT+JrW/7X4Rxmcfrl81hV4MC4CiaAi7pVy4+jEGBymyWp5P/GeAoT/pYOHYIAxuvhRNHxjI6ABuYf8AMAKxrljpPLGaqGgDIGZUwOjc76zwlBES8aRlAAIDkMJzavylpmMITRfXidiXTVAo40sh4vgrtGRfksrRIXeu0TiqtXhNgemGZV7zIg8STXpL0xy9H6gttWBQ0+zibWcxa/AMIkey2qedozB11HH0RE9WVEJg/Hg5MXEUMDFReO3/TkC0mLx1rB9LGw0hVUwt9mcZhNRt6DQBAHkozHzI5FUvnpoA8zXzH6quQAQY6oZjRz9uebmKUF4SKP8zzxnE8DA3uw/CAbj+f2iYhKJa60d+3ExMTT6Ovbyuq1RGQ+J3s1Nk+jV587jMoFKq48bZfRqFYS607N3EMrldCT/9WtKsEJAAag5Ha3q/GQ++UubeQaEvc8blsPerk1e/AmirN1IOR16DwGlRzynZbkQQ80gxQ+CViZWB4lJy0vmx41yHWa/LLZnDtdkeaoHZ9EehX25PHQYPoQZ9/+ivx2u6gMrABXrmG2dOHsPWuD4FUIy79yvPfQm1sB7pGtoNCl9wzbKZqK7h2xHAdfkUYTwyGGCgQx8mAgAgAGFWmAsxPnUJ98Qq2XR/5NLzaAIDb0LN3WNnzcwMA7lRvNgelhBjXbLZ25Ar9aQIC2pqeFzCbw+6aQoYynxI2zqgiNH6jE9+g1wPZ8gr9MPtfp4jWkCcg/lZfLsO9NUv687wtOYFAjvPyEjf3YcygGjHGtNFQ+X/N5Mdm9sPGrjKXq2Rs0iQfuRx5TeY6FqbfFDnIanf/BqRUMGCb+4xymHSltk+QPX/ZcUm6nPLslbwBJq2Fdp6a1Xq1/PZVAOibN9+Fw0f+Ek88+V9QKNTQ178j+vTuRK02AsIGx15fwVkvKlCGFMrfANDdswlLixPwvAofNwkB6hF4TYqgSOA2KZbmxzHxyqPoG7smV9jTTikJPcjex/znisCD+RNknxSHN6Q0+xnbhB9iFQYMaJxkTJnjboMiKBN4KyG8FcCv2uciDwPagaMpHwdjDtUhx3uICXAExcg/oUBiTUB9QT5XNd2ggFuqYuzG++EWy6gNb0NtaDMcr4jG/BUc+dv/jIXJU+jddi2WLp3G8uUz2PnAL2hMPmOImeSeDTosJNqAUPB9CJ1IG+AbzH/8qpkBL82wzoRvCpTGV3D6pW9i4pXvota3GX0je43M7KuiAdCOE5hylmQBAEaRGbE8j/NkI+Z8RcqyKMbwByAJMsVEcmryOqn/uH3HD42+SybzIBEIJBWhT2wS97m6vIfr9E+UOjQHiieyygxKDi+hNbpKx2Q632J+knmeWkUM78nMe0R7f6I4+NrWEG2xUqTBWeNU76VrYZhyAoM8KlDJvEU1QRIphem3hem0JRF7TelV0LIAeHXAgA1kZkj0U/0QVBKdnxllJZgRgAljNDtOVU/N5iJ5pGfDQ/txz5v/35hfOIvZudOYmXkFr1w5DAAoFrtx260fR6nU09l4FNq+6x14+gd/gMvjz2BDbHak0sLUaRz9wadRqg1g58Ef66h9LpVXAMhaInqwNjqJ9U8VM4M8/UcS2/S6WbbIbjvSBritEG4rw2yBwhptSDKbMDCbUvhMpc3EbEjRmrpxPgkOGuV3STUZUvMRbLrl3Up7BMX+ERRqfTj/3b/Bhe87aC/Po9Q3gu6d1xqxmSkiEBsbB3fx+MNivPwwhjMej5oUjJVzAKBQ8/nDOHro79BuLGLrgXdj45574BLd29gW69+YMTiw7EGq8pv9VjTjan4J4luYc+OzV8bDlmDFDMdoQuvp1gnqPqZaMPB5JO2H0NZ8BkyocGGOH8ZaqlVIV0ThBhuCJTHeOq1TGnUuxsrlgBiuWprNyQIA+GEbk9khAOC/O8yyJy0ENpOfLDJFJLCd14ENvQoEJGAiLtqK5EOO6qAsYinMvSkyz1UDADZGei3OxXmdr1n1tHp5TCdUsGdISsfvr5Y4LPk3F6PPHJbT+jNRRpI7jWIHXNU0InVseUg0DVDIdYvo7d2Oy5dfwPLyFQBApTqEkeEDKBSqq+8zpq7uMQyNHMDZk49gbNOtIDEzRGmI2SvHcP7Mo5ifPInugW3Yf/cvwivKHBc3A2KMeUv+vdbwq9QhEbNPdEYplUjMmFDkf1ckkz6krmeJ5sJWgXIJL8tGrJKqFfBLBibNjcCLTSpMgphH1iSpcXKzuG/RWZmXKfO90ZqPxtXbB79COBPO/BZMfgSMWKhGQgg23PouLF44hkJXP4pdfejZeR0IcRLFcuzkq/kFxM/LmGW5bC4XHYC5nb/B9IcxjvOPPYrTh76E3pE9uO4t/xzlriHZzCluwxH9lYR7bwMAgLIHGaacMS8AEbT88Ri55F0VNhqAhjQvBOaYH4+BQBpAkaX7Sr1Qr5OcLwjUUtqXnX51TYEpZCg3hbMFsDBc6+uebBYSP8z+1wnAakCASzJto19tAJC7GaaREJhzbu/PXuiCqBEwMGIm3wdlATCa/KhSYttvKOWmuuJYOgQCacBEjKev9sOZUsfJxdxbsxkD8pg7KVe1FDbtSh4fCxPluJ82p+rM7MZpDF9K1KG0xGFSvZDa76VYj1JAjTKUwxSIAUQrOEkbm/p4rtIG5ftNvHD4z7CwcAHbt78NQ8PXoqtqjudv28hZOZecc3ALLM1fxPzsaRTLPQCl8Nt1XLr4LMYvPI760hV09W/BNbf9DAY2HQAhDvxydE+afa/CDsxcqxhf0UnEHybIZefmWTs72BTF50ndDMljxvvltinc+UDzF5A7TAeW1nsTM0duk8KFbv5TWgjhK07KjPluL80BAApdfVqzIihQSe1jaO/tGNx3uyblZyYeWhShGBRoZi6xNkCL/sM0Gwbn4tKseYy1ywGmx4/g9KEvY8PuN2P7De8HIeYY8xrIiYFAGgDgQ6NmMxzbeqDtq2rfMRAgBqBhAoaRkILoZcoz0nw8nDhjb4qvQSYQSCETM0+4U71QbsgdkJUnZp3WqVNanUFrWsa8vAAgi+ljlLKBGbUB8YsoMjrUcSIzJdEGz9EZG+oQffyqs5KmbswYszp+2/UwSUeKqRXvrwMgIP22mQwwoOA4OlBQtTp5mHiVBEmgVEap3qcIqNT20yQtNpCQxShnAQhuW24AUICk3pWO8ZB+Kc8qBUyTINTniq0t0S4gi1e3OHBnEY+GITjT5aarIHVpt1fw/AufwfLKJA7e+PPo7d9x1doGgPm5s3jxmU+jWh3CNdf/FI6/9He4MvE8KA0wOHYAe274ELqHd4Aw+/k1ErP9ddqJ02daPPwsCt2IKaFeBlOujuMq12XReSJNhT15EaERAEijKAkZNEadMWA2cMPurU3rAADFJflkt0kRlAgKy9E5reVIE1Cs9kZ9xky4KrHnmoF4jCS2x+bmObGDrsnJ1mb+I+URidsJPUi7NZPSq34PDFwwDQDPNBxT7XKAxZnzOP7UX2Jw43XYfsP74FBilCbb7p8xX4Bhb+kIALDTmQbEsleROPws/62sS8kYKf/m44i/RHt8UwhO0xi1KEKA5iMQNU6gacRM16zs7dxUUAQIBudg8frU68rj+7BO66TSqkAAJRGzrNkVrwYAmH4zyiHBsgIBtZ7ihEM92QEYSBgbvshpDHGHAADQGK5cNuZq2SochrWsylKbAtMvPkOTGVcWU25i4k1jtTHmYp82Cb9tvmSBhE7PM4b4tEf8ARUyAVuzDKcz3LkBA5Cet4BRaNfWGPsWqKNIOzzufnxuh+Z0cr/qwOSCZnMRh174E7Rai7j5pl9Cd/cms001NXAyYjv1OazMz4KAwAkJCHHg+FGip5df/AK6uzfi+ps/irOnv43JSy9g6663YWzTrSh0x4xgzvuTZQakMmarIRqHGO20LcpNC7LraiGd04T1JP04r8dMIFJ8GArLIQrLQFt1GI4fL09QZenPZr8OAIUVisIK5RJ4r0HhlwlKC9G1sj7by3NwSzW4bhFUapOB4Pj+e0Bg8W3wq7DuYVpGXz9qy2QyZmWcAz4cOK3IHKgcJ/4SwQqbI7XLARrLMzj6gz9BrXcD9tz2ETgSJxq1lwaeONMbr8s2XxRz2GpLmzmBKN9/2WtuNQuT9zGmNRV5AJb9NygKwkICmMyh+G8xKAkDDyIQYF3GQEYqE4jzAoZwodH4dU2BDQiwcYllq3Gi/6ETxaqsO65q/+sEYBUgQAr/GEvYV838Z5EqGU8ZU2K2IKrT4uOebBbEAQCQaA5c8brSN6moUn4AoP7PfudmulSHYbF/A8OrjV2sqzL9Ktkk6kzarGqBbEy8KmHP0hR0esw6nrgcSHEmM5wHJPcjDQhZTGkywYKmTjYAjsAwh00UKPH912p+p46RSfs7MDTlc45EO6DEzKxhfPXGLA49/ycIgjZuuvljqNVGtPU7DAMcPfZFzM+dwf4DH0Zf33aEvo/FxYuYXzyPhblzmF84h1Zj3tpP/+AeHLjxZ+B6RSzOn0ff4G5s3fd2ADrzwqS+jdgMyE+PJroq0pyIV6vxIJA1DDmaiRje9IrcQdYCEBgDxRiraD4YpKSI6jh+FBHGnQ80kxrGqBdWGOOmzyeRcTUx0m7LztxWZvQ+/fk5FGu9KC7G4KBm7tNXTUu8iKEPFAddpu1h0XxUflFL6CUykgYy5Q/oPkvRjjNXO20ZCFQnfCwvTODYk38B1ytj/5t+Hq6r2xBZpf9tfX2zgc+ONIxqd4JQQdzHtPlvEsgRkmgGDPsY8UNNGCgPRoiSxZpVxkFdgsgnRygzRbAiFgGl2mXs9KybB+nz2BglCApoWGdu16lD6ggEmB1qXiUAwKgDIGBK8qGaBZmiq+iZjdUFJunDGvIzBQDYKDcQYA7DWeYuEDQAOeqm+h6Y6gGaM7GxflZ5JyT2bfKnEMdjM73KOk+lNLMjETjZzIvEY5Z7kJpTQJl/WXMkl3nPKhhxExDuOCIQVNV1TmAIYGVlCs89/ykQ4uCWm/85SpUBtNt1eF4ZJG4nDAO8dPQLmJw8glptFM89/Yfo7tmIpcVLoDSA43jo7tmMkc03oad/G0q9IwAQZTRHCNIKAFDUujaAuC5CGmJx4QK27Lyv4+tcC+lSvlU0wsFbIpnMI2VNk5zb6qaZREljJ/a6Ide8mvsvrIRoVx0UVkIUVnSzICBOQJY2XgOzDERJwkIPHFiofZZQwdT8JBpL0yh3DfLjbityCk7rVzLzifevsBAz5aow22ICxrMqx3w68xVwG/H5ot8HAWoT+nhIi2J59hxWnn8ex8ZfRGN5GsVyDw7c8y9RYBm6AQ44bOZZRAzxzZltw9qgCU8ggUOeF4D9Vp3b1W03ZsBT/c3EcaoJvCjVpOoqEHBbYaQNEMfOtM+C6WMi5IibNvgEiPV4DgIDEJD2fRYyNHZe5/3DrCnIBQTeALSeJ+D1Q51pAow2fsQolbTZ6ku/gdUxiiamzpDNVY3xL75QNlW9DQDwn3nQfQfXlAkExPGlgQ8HsgmQqa4mMRf+tzHEnTi8mmgtz9kEALLqdXLcBoIYZY05y0chD6W1gfxStauZNCu1D+aUF78YomN9x6T53yT/Li9fwXPP/TG8QgU3HfwFOE4BRw5/FpOTh1Eu96O/fzf6B3Zj6sqLmJw6iusOfASDg9fg/PnvYmnpEsZGb0JP71bU+jbCcTwEsRMv22gZwyPGPqcA6ivTCPwmuns3r/66BFKz7nLJ/lVKJNYpif12ElbUUU0eUupkZ56OHWgt3Ts+5bb2pQVdSg8AxaXoZNWxlrdhYf7FPhxf/h16BE6bojQfYPPuezF+6vuYOPYodtzyYygsU7RrBKX5SEvS6orG5K1Q+FWCQjyedpc+VsfgSwDEz8LA0BUXDQ7AiOz82watU/f5kN8jbznA3OJZLB15HjPjL6JZn4NXrGFww3XYeeOPoXdkFxyi37RcAICPW97vO9FqMyCQmNwiU+uRnGyvZ3MMZt8ig+y0QoSCGZBjyBlBHdmfhRoAqymJnynxWCYQEMYByM7lJA7qIK6zTgAgjDJrS+e3zbkv1mmd0qhznwAlTBhgXgjSnHbTUHd0snKeqA1wDOWu3p7k9Et0ab9JZZwFAABBgstoFRoArc28jpoqU5zmgKwCrrRxpfFxIuBaLQDIM4Y0Mmh4ckVXyktZYCCLsvwgsp7rVWbgV5UpejVjcCDZ31LxGa/RQW1m5hW02ksYGNyLxeXLOPbyFxEEbeze8yNo1GcxM3MCExNPgRAXBw58BEPD1wIAtu24L2qAg/zOxrE4fwEA0JUCApo90URp9cZdxQye04x/exETutaQoCJFjr+RBLijnABMuNjhOyHWz0rsmDD/eRo2F7PcBdyh03KNXj1KQGaTejJ/AS0PQKwVSbsWUSOyOHsegd9Az8guFJajm1FYBoKSfJGl+RCleaDVLZczcxwNjMSMrOoozCT9xUW53G1H2oDSnHAtlMKfX0RraRrOqUnMLU2jsTyNxtI0GguTCForKJS6MbjxAAY33YDewR0gjpv47/CGovGYTIA0h1dW3sk8Mj0iatBO2Rj7nPPcpO1Sx8mAQBK+NwICbB5JzsJMbmXQpBk1AEpZ6Dk6EFDNith6a9hXpURizJSpHSZAgEU4aydAgF9XB1q9dVonYDUggFIjIs8ticxjUmRiGG0bjGEz0KL+5FCTsWgWvN9VAIC1klGiYooaZOg7NUdBmnlPXkZFrWcDbaKtuwkEquVZ4C9tPLb7vxpAcDUYtrxM/9XoSjV/S7nOVYGCtRARNnDed8yc5VRZb958FwhxcOLk13Dp0rPo69+F/ft/AuVyL6/TbM6DUopyuS85UbUIUJcWtlmHidTN8SkopZieOoZzp76FSm0IhXJNihzC6r6qJL4KbsIYdqw1WA3gZsxGnvdFYOSMElglSklWnoQ8QI0EFIVlPfoJM+thkYRs5jVWB1aFgVtZvIIrF5/F1PgL6O7fitH+66xjKs2bb1akLYio2UuM5czHwG0BQRGoTEXH/Nim321G2oDq5RDN5TlcPP19NOcn0VyYQnNxBtRv8bYK5W6UuwZR7h7C4OBe9A3vQffAVsBNbkaUrIpp6aM57TYZ56isDxZG0pQEjK0l2t5lAQB5yaRtSJxtk/lNTGWWOUwCWdhI/BBUuEc8eZfoH9ghEOBmQCYnYoOWAEA2EGDjFYEAq9eO7oto5fCGAAIUqxcKXq3+1wlApyBAYt4A1eZP9YjnKbwhMyK5JZMmpoo7ACcvA+sjFGP+53WyhLJoEF3TEdW5yhoA1QkzjXE0LGpS8i6Tg2+etgwmVFbKsjenNB0AiHWZg7IJ/Knn26TrQD7GXTUdU895NSIUrPF+qvaiANLHSUhiVpAlPXPAHX47jSKhOTevJkON+lwtTRBCsHnzm9DXvxOLi+MYHTsIQhQpbKnXfHKHNDtzEqdPfBMLc2fR278dOw+8/6q0ayLiqwVrbE/E/gaGJZWYvKOT8zLqJdmQKUT7aVs9FaRpGVmJeZ4yyS7LPmzqQzRxMWp+Dev8lYvP4fwrj6C7fyt2X/9B7nsitdsMUWnqoLA0H6A0D7S7ZCRSmYouttWtt1WbsDse95wJcPnK8zj95N+CuA4qw1vQtWEXNmy8FaXeIZRqgyjXBkDKJe7bIN3PGIyZrtNthMa557RC+V7Ga7sJRFlNXK4iALCebwK6JpNjk0CMED4fnGaAsBQnBeQhOamUrdoUgcsGBKTwriYnYjdKVpbLZCig2rwl7VDmadhyKvgPrGcMXqdOaHV5AhipL7vh5VdfDMA84c3tm7UGmqpXQe9AfoljdL7ykrP2eRhEKi8onQKAtIWJyjkN8lBq8q6sRG6MDCZU0u94bNJv0zG13HTMVMeQk0Crm9MhLBfZNAdX0WRDopxOurnAscjkA+mMvjrvU+pSReUMdAYMkroxIC2sIdC9hbpqo6h1jXV83uzMSYyPP4Gd+9+LkqA9EGlh7jzOHPs65qZPoKtnE66/5RfQP7gHKK7iOmLnTzc2CUpid0ffjsr8r5KiBExxex0CCKZRyDLvSU5QfqedlhNI6BoiO5kcipmWwQmodW5HzL89ZnqapHRw7Fqcf+VhbLvmAXT1blQ6jxxJxXaYJqk8GxiBRvcFnyeVU4kBAJGYj0Hp3DKOP/8lTJ5/BgNbD2LH7R9EONCF2uVoIokCL3fFLCEgNGYkuTIu2secpirQooBDuH+MTZMTtalnh5c7NZTlnW7WIAomUMCEYMq8I0w6KfzWNOFU0n6IQICXMSDAgWw+IGAKD2rKQiw5EbMMwsJ9TZKg6QDW5gRsCzm6TuuURmsDASrZzD0IZOk6kbUEgDDpRcdeg+MNW/wkdaGBmemUjPG2HSHkGLsWQ1ZEuaEckgipgU4dq4Tz1Ug14thsNvw2ptfmr6HWUevb6mZRpyDwaqgOXw2GP01TIRzPZPZdkkjW0xh3QgBPlralMnVudptS+1mRslKIO6az11EEeq9iEhvT/VxpTOHKlRcxO3sK+274MPo37Y/rAssLEzj70jcwfeUlVLtGcd3Bn8bgyHWAkkCIEVuHmGlHsz8qD8rCGF4FjZLqVEwdgGQ4vJqIqBrbzBOuUh2xekB5XgOpnMaMHJvDTFNoeKbcBMmWSCqgcIXY+SrlMZPo6tuMcnUQkxcPoX94j67tVts0jKWwFKDd5aK4KNtxFRcpWt0ElakAlSk5Rj1zPK5MB1h45QwOPfc5+K1l7Lnlp9C/9xaUFykw7nNzJ6cdIiw4EigBhPtpuVS3EQqgILo2U0hYBgSMWhjLWryWRFW5M9Gb+lWuVzO7MZj5qgyz0wwQVGR2SI1mRB1EPIEwj0w8ilUYKvTPxqmaVlNCEAQtAISHcGVRlKTISpbMwU6b4jWKOdAZUXp19vS19L9OAK4mCMhy9mUOxdJLJdvRAQkaFic3qydKP9iLLsX9XYvzqYlCRepBqWRekhm6LGssCmNos7dMpAId2P2nMbw2hj2N6VfJBgBWE2s/r49AXlqNz0MeyhobY2Bsx+PxSGY0mg27nck3AkVqBtDGNtXhZt3fsAOtXRoRcHvcJMFNPLZXKaxdtToMACiWe3D4mU9j09w9GN1yK86/8ggmLz6PcqUf19zwkxjZcBCEJYB6VUZiJ864KJLDtRBniFa5FlInQ6KfNUbG1AiMZhrTzk9JC9+etYyGMdOqLl8CI0tSwIF4/wkhGN58EOOnvo/dB34MjmvYItVrFMbBhEnFhYAf9xoh/HIS6tTkt1BYDuAtBjh76hGcO/4wuge24sCbP4ZybQDuYnIDWIhIJwCcINRBlR+tLlIkvJiZdZv6jXTa1HxflOeWtg7kiQbV0csVa22TvCXZ53cEBFiRAgTcus+BQMLcC3MzLjNlCraZXWlk0BSIZRQhnnniE6jXZ9HTswV9/TvQO7ADvb3b4HpF6xwHskHyOq2Tia4OCMjj7AuYEbKhnmlRCQ2hCFMTf7AuTU5pFkZN0gYoWgrN7t4BNMi9BhCS5ryp5SZIYTA1UhnXnGYq+gAFSZ06FuYPoIIUG0NsSzAWArAmAIvJZieflitgLVGNbL4Jnf629ZPD8sQkRTLWywADct9xfcYwAvocN3aS0nan2iDAygCvNeRptTYEANi++x2or0zj9PGv4+LJR1Es92L3DT+GDWO3wnH0rOFXm4whQdfA9CcmPYY2cl4GN6fplDnLGm/OtqLIODrTrnWX1Z7teMygRwxxSvx0y/kjmw7i/PGHMXPlZQxtOJB9nnAfbRohNR8BkMSoLywHaDUXceSZP8Pi/AVs2/cObNn9VtCiZwzd6bSpUbPmtBIbcpHBdRuxv4DALLIEbSrZBA6mNaWjdzRjrmUKG0TtBWDcazOBgKlZxbTGrfugDpG0AiaTnKsGBJSyudnTWFmexJZt96Ben8b4hSdw9vQjIMRBV8+mCBT070Bv/3Z4hUo8PssetU7rlIOuDggQGD7xZdEWRPHFFR0D2cKjSCu5yktk9vM4jBr65y9ymklLPH715TYxR9bFJS8QsNiFA4YFNw8AUNs1Mfy231ngwNa/zTQJSM3cG907JhEmQv2YLAnACKVAEJ9jKAcsG1MnQOAqmiDlTd6VP7JWvjrGpD15znMdqW5HDuesPiHJs3iV/C1SNR4xFUo9cN0iVlamsGXnvegd243lhUsY3nADHLfAQ0lG40wkfHJ70XcrDglaH0rAWLs7HstVtvk3Uehmx723EkmyoFJTyEoDSWYHeZYyi5NvWt30Ollo13JezNwSW04DJr3PAA+12ihqPRswOX4oAgECqABgtsc22Wmz81T5EZPmtymcdoAw8PHSs3+OZn0WN979L9HbszU6txnqzL5yb9i+5hjCfHor0YtodpoOk/eFj9OisVkrAGB9ZFVhQCBUftuoEyBg0iowFkR4dqZ7RS1zJg8Q4CF6xTGRqG896hBw6eLTqFSHsHPvu0EIQQiK+tIk5mZPYX72NC5PHML5M48CIOjqHkNv/w509WxCudKPcqUPJTFS2uuZYhOn17T/dQJwNc2BqL4IGu3sTYuHQ4wMQ2Qzl0PCmgEAUs8VFhIpKyCX7HQAACxjyTUOdmgtzrBp9vxiWZbtfxqJ1y4CPlMMZMT3T42+xCvSZGMVTITEc1i5pBkRQtWZIlRoYxBBgkkqKEVM0g+zsWrjN11TFrG+roYPbd6pwYbXKcBhj2SVRqZqVmT5HV/7DhAELSwvXwaIg+6uDQARQiISgkp1GPXlSQBAV+8mdPVu6kzy/UMi0SyIxyh3YGQgc7XDC/LfYyk5UtbcYK/CGu8lZ9qZv4AbL7y2Yaf1x4I42PoS1yeLZoAksgmMbLwJZ49/E77fQIEYMnex8Yigw9anylgGNNKGEIBSihMvfQmL8xdw8PZ/gZ6urfJlxk6sRM3FoDjzquYrqq9AdCx2qvbNx+R7YXcAzsoMrB1bA2nrvMqQ5wUC4jMyLeEGjUBQ8WRgG1At8EIaEJD8CUgy303jDKiP2amTmLz8Irbtuh8kBu8EBNXuEdS6RrBpy52glKJRn8Hc7GnMzZ7G9OTLuHjuMfFKUCx16xe4TutkoVWBAK4yVOMLG6QhKhDg6kpDmZFUhlVlxnICgFTbY0LkbLtAtLiracjx6gCAzIgLnbSv9mNiXtO0KOoxdl9cIgEAxqhTNTFKGCaaW1fhdEXTIUWjQIIA1HU1vwcSxByoEn6UpDHlwnE18lImM2ABOdI18MYytFFpTJjYjymiA5UBzlqc7rSmHYDthDYGxnwilb+BzgAQayY294oih6liOfM5YRhgZuY4lhYnsLQ0geWlS1ipT4NxBIVCFf39uzEwsAcDg3tRrPaiWhvGSgwCXkuiiBI+sQRRXGtgMunppF2DlLFjSpOMd9qUwNQDGWuucE4q5ZIgp9xCGs9xSwVb/8ObbsTpl/8RM+NHMLrpZvkclnhK0TjYyqVzfZk5nDj3OC6dfwr7Dvw4evq2avUdFunIcC+dpjnMp+YsLIQKFZn9rH1nzRoA27OzzDneX1oOGGN7OYCA5+h7vNpMIPsius0AfjlbWmMDAqYxiRSGPmanT2Dy0ouYvnIEvt9ArWsUY5tu1UPn8uWRoFIdRKU6iA1xvSBooVmfQ6Mxh0Z9FvWVKVw4893Mcb+WRMQ9/DXqf50i6hgESIuIEkoMSAcCIrNvKrMSAwI5zGI6BgDxmPX2IwaSM6V5VZSpHXWgAQByXS8nxuzmkfLb2mJmOsz8Slw0VQDA/vcF+35Fa0KCwAzWLD4B1vrsHAN4kH4bQq0SUz4C3p+uNeDHcmkP4r6NTHwCYDQwpPYV32erjwulCnhJeWnSpHKm6h6R35lO53YqKF89cFHfh8nLL+Clo1+A51XQ1TWGgcE92Fq7B121MYTUx8zMK5ieeQUvv/xFABS1rlEAQKu1tOoxmKjVF4+PMfRxziZt3YmHH64m2ZdAPDKQF/XFQlKGbmdaAkYsi23oklyZh3ObBglOvqmPncQMWtbYr8YendKGVUofU7HQDccpoN1U5g+b7raEWm2Dr4MizWc0N3USJ1/6CjZtvRtjm25NxsZMfAz+ALZjaVJ+dlyjECCguU0lrQCgk9ecCN9pz1gBAlrCsBxkAgLqWqp1G0ddYvPCawhAIIWPMAGBLNPgQ0/8DywuXEClNoyNW+/C8Nj1qHWNwZSbwnRNbJ65bhHVrhFUu0YAAL7feN2DgHV6/VBHIICE+RcMlYzmPp20lZM5MWoeRJ8FZVMzRhAQFw5HlyAYgUBeW/28lMcBk0tOHP2YqR11UWLXrkj5YVQXW3ZtlRG3HVO1OZ2UM0ph6PnxtDZN+RgMmguiAA2uimdMuuqP0A7k46qJUg4wQCgFidsJ0+Lth8mzyOMY3ynxDKBtgXNdjcMZIYlAQJX2r4JCGo3nzXf/e8GhN2m2r287dux5J1qtZczMnsTs9HHMTB8HpSEoQtDYVMgxCQk8eW1g60e7K7q/KyNEKn9V6WpI96FLHcXITNljSGdeJMobiSTno2cmOTb/ApUBYpTYflsaZuA4QzPAzIqW5i8gDNvoGdiRnG+juG0jEGSaESFbLaFAozmPo8/9JXr7d2DXvvfE9aM6jJGXTBrjd0k7xvum+Z8v0LFNtBEAZD1TldHX5qR8XNtX82oE8s7VnPUcnyIsJoP1GgGoSxBkCHM6BQKUhhjZcBDXXP+TqYy/dh5Z+/qwTuvEqCMQIC0ETPIT0CSVtSAlWU3SirxmQ1mmREYtA9W1FkY1svLSkjDUnFRXZbevSuiz6tp+q1L+vA696u/A8j9SGH6xjTTNjHosNeqPoVztRyzLMkWxmRwBCUhgYMCkuRCPq8w824ANfgrScYvmgISBdE5YNLx+lMJpRfYiWcm3iB9Kz8rYHq+cAEeSpuEwURrQy0spTKikOrdpbXh5+jiKxRpGN9yI0Q03glKKwAlBiPO6cgUwaQboGn1EKIna6BSocCfFMB9zwdvPyfwnwR3M2uHQBeCSzHFnjosikt7awpHawIXF5n5u+iRcr4Tuno35pM8q75fBkI+f+h4oDXDd9R8BcQRfFuv4qd33wCLlB2AW2l2VCGod1GMALO14TDYgYPUJyFiX+JwWTSvTogbF64zTChHGeRzYOuk2AgSCeZDpfbH6QRrGWe0aQaM+1xEAEPvWhp4Cnl93dDX2lLX2v04AVmEORA3286QdalLJToGA+uKYNgVbWS4tAyHaSxKpUIV2lYXGFBZUWzw6mUx5gEAnAKMj+0x7O1yyLJo/qeelMf1pY84R9UcqF0k0uTExrGmmKGlag6yoN7ZcB3EfRj8F8biqOTARpXCaSagWqjLwlIK0fD2PgA0YCO2F5YK935hEyaH4PmdqF8TxKCA7amwNuxAhEKX70qG4M0rzc7mEEHOc96tIHFAJZg4sKRZRGbGrlcWHTXcXEWOLnMx//GzCYmROEnrZIRT5qXmqic8sS+IO87rdSZ+5IhdlaAZICC3iDgDMz5xGT/92EGJ531Iwqb3PaP0PWy1MjD+NsY23olCsRb5VBuYxzWY/tx+ZyuCb5smrBQAggMy0YWYBAVv9vNYBpv5NjLmydolAgBEDAtyBX2yb+0rmAwLV2jBmpo7nuoYs4jkC1nnbdeqQVuETEGpqOmbKoJox5AUCxg3MJglWy2AHAkkFQa0aAwEpjJeJuTch7bUAAGUcRsoCADa7/6y2UiTakn2/DQCYxpLmQJvGkNiOpUmuWH+2Y2nmSIBdY8DIdi/T6lAKBAJXZ3CCFk1qMv0C8kr/memRrR4hcJp+MsaYwkoxtV0AgB9G2gVRatapyRHTwvCO4+81SrqZtCwMfbhuMekrOhj9NJj8adoOYaPW6sTtsd/N3qgS8wOIjkV13MarL3Jj0n1nleCBaxfYbeqknU7Mh/I2ySN+pdfjx9P4wFUOTcqoa2Lghb3EsQGAVfYLAKAUk5dfgN9ewcYtd8bH4uhIwjzOyiLfkYMuAwJrAAB8PJ0CAEu5zpTDDARMYzbMyywpuBi6M8mxkUMD6Yfa2k3aIagADhKnXbG/6Hqk6xT6ozREfWUKgd9AGPpwnHysmOk61Xu5DgTWqRPqaIeXmERmmiMwDE5b32VEFWaU2VFm+lMBgPh/GkNqawcwvtxaODTNHCaWkio2mdYx5qFV2lXz89Ls/lUSzZn8MA7Bp0t9jUy/+DEdA3SVe4hksVbBoUWKZS0PqDReaz+dUJrqsRMTrbQ2wjBVw0CCINEgpBBpB7I9vqlObDLktPzo+WZEvQAhcBptOPVW9N3IF3Sekti8IqCrv/e8sej+cZM8QrS1II1KpT4ABI8/8fs4ffrhq+7w+8Mm6kSf0Is+7PeqKZa4Und1bbGQpLnOizO/E5XJSRtejihU1CUIV2FGqo4rcyxZGoYQKFcG0KjPZp9HLMcE+3/12MXzP0D/4F5Uq0PW8dqigVGH5IsUlkcw08Ecoc7aAIAoOTcdV0ndHzjosQAA8Vst5204RF/v04AWA4PNZD0O3cRcSKsvChwY2yIhg6gwDAO8/MLncXn8EPbs/wBcogMA01wzXecbluFXeY3X4rNOAFbt5huRiZGzAQGTuU8mAEgrM43HpIIzVrQz95r0i1hU5msxe1Apa1IqfVFC+EejMORtaWZbJiBjAllpx9LGqTrMdvC/iazH1woGruYCYNIQpN6jMLv/eM51kv69k/okoHDqbf7phDiAu4o+yRx0S3NO/vT1bcOdd/w6RkYO4Oy5R/HYD34XLx//EpZXXvsQoDZizDgl0ScoAqDx91rbjttkzP+q28ntFxJ9OrI5ti2/YfLNtBxpJp2hG/sO2CikRuZwLVSu9KNRn0nyD6jNM4YstDNipvKF+fNYXLiATVvu1O6PZPcudsU0BMZEVikPJO0d7RQAdEDGOUJ08MLnMK8Tf1k1u4ZmLYx/GsOsRS80vAPqfHSaAUhb3nQygQArE7FBGODIc3+GycuHce2NH8HGzbdbx5nG7L9hmf91et3R6g1mQ1ij5DgtX3JSjMIt0vTQhvG58m/hf/XdspjEGDMDm8jk22DaSCi1OxLljaLRifOuYt+uxrmXqoqSZyaNtrWrlqUx9mnHUpjvPJlc854jHreen2VCZG30KgC4V6MNi3akoyYNIDzVxIhSbopkHVde4mp2cfe6emC5Wh3Cvj0/ip3b34GLFx/HhYuPY3ziKQwO7ceO7W9HV/9mqU8xwgenmBERmUq2SbPIQc04Q3CzP/rtLQPN/uia3GYsCWS3TJL0renyMimy5UdHtvz83FjjwMadmTmYRqEuHdXnwUbqcVt9HlAC1pCLzHQjdAngWtZlwM74x6Yla2GUypV+BH4TfruOQrGaNC0CoTS5jWVs4+cfR6nch8Hha3gZTy6lMtvMRIYY1lDBfKbjsNVrBAA2B3LrHFGDWXQ61lC4n8L5ecGXcazxns6YfdGO3wxIo2tQnYOdNkVYUC7c5KAej7nVWsbc7GlQGmL83A8QtBsYGbsRrldMlf6b6A0NBF5rafy6JoBTZ+wTV8klRdLiJJoGxYyFnOk1R9QZ/ls9nuMcdZwpxHMDMLWtBQDwf/NKzDoZiw0AxP/nBgCGtjoyX0pTk2WY+vCQkjkAQNpGZdNuZNq+vhYAQGxL/HR63FY/hVK1QLZmc5gYyZ1E80B9xp0Sn7+SAzHWzCwXClVs3/ZW3HXn/4pr9n0Q9ZVpPPfcH2Jh/vzaGn4NiLqQTHhEzcHVaDN0I+Y/97kkQ+puoFzMCEW+iELMbMxmJkNy3J+rsL+XqwMAgOWlS1r/aWQN1UmB+vI0rlx6Hpu23AlC5IXLKm239Ze2nItrgyN8O8i9XtrWGJs5Ty4AYClTQVXm2naV1nBTPqM85nBuI5DGIeZs+P+3997RkVz3meh3qzoDjZwGYRBmMAnAZIZhFCmSoswgWpJFrbQyLcle2ZJtcSWv/XzOe+f5nX1PPva+J8le7TqsbFGioi2JVKIkSqKYh+TkGUzCBEwAMBjk3EB3V933R4W+VXUrdKOBGWrud04dNCrceyvd+n2/aL4LTNvs8xCNlWHP3X+JjV3vBQjB6RPfx/EjXy/sBAQEioD8jfocOZ66sPvAwoMfAfBabxNczboAHn07g4A5k4qfUM3Zx9lRHgTAvlkmeU3apt82AhAAP00/R+h3c/Xh/c+DHwHg7ZdX8Nv1AD8hPigZCNJVAYK5GT+gMkse/a1UlcV8YgNYSFIIjQ27sPumT6GkpAHHDj11XVQINqG/vzTkXGdo5osBQ3DPNxbAIAvGc+nn9kGyeuYrncgFIioBSZ+kaPnZXfumPudG4dlXIPLAoKS0AfFEDY4fehqT4+fMNviNuyhQdMzNXsGJY9/Gm6//fwiF45bCYAWBq4Unlr8W5Bsf4jJH+fn554UAyg4AuSKdRayYnhd4c7ZEHFWZpQx1FneTCZcgh8JxrGm6Cdt3/wG6tn0YE+N9GLj4GhQlP9dMAYFiIK/pgRICGpIsGUNM4YDnmsNqHw3tYpZZZ/hysi+K3wfDlSBQ50ciqNCi2lxOVpoA+OzvsDrYzsthBWDH58gBrVo/UEFdfTjp6twEwYK1+DaseCnv1SIVRRT2V7TvAsdICcmRRXZZSZhCno30yxJkOYKt255AOFKCIwf/BUuLMys2DCmrLaZQyWjwLcG5xUssAyDXh0EelCjJO5BWDRFkYwRUBjIledx7NWC8CeUsflAopLT7jpJCvS0NPv0EEfx5+8hyGDtu/RRKyxpxdP+XMXhpr3cj9jYpxdTEeRw9+BXs3/t3mJ66gPUbHsItt/8XRCKlebWVaxSBLQCFKlAcx5naebf9C+pGAxM8rf3w3t0kAoarbDGmWD+ljfHTGGMeZIQXDG8PFK+p60Ldmu04e/pH2PvS/4O+k89idmZQ2/ib7LHC+36s9iIAYBkxATQkOYOAORYBklE4NQQUp6tLPg88BXfCMCuUsuOxHKdPHmweXzZVKCHehbJs7XD7cSFEzsEy18pvf0s2JQ8CwG63xwgYY+P9tvXhGKqLdp7nr89Lb1dIAPCKWgG87l8h7fhZktza510Xn7EEIkq8cRVCsII+nyxk532nKsc9ocgIhxPYuvNjOPTWP+DYwa9gx55PAVIuAtcwyZvaS3ac+juk6L69CzXa38Uajpl/BWEWQ2T7UjUtf779LyvLjn68lKVQZX6VZTuMYmN5gUMsJEU/X8MP3N4mAcwaBMtAkLGGIwls3fUxnD39E5w98SyWFqfQvvHduaFwdUMqxkZO4NKFlzA7fRklpQ3Y1P046hq2ailHvYzDxL3dfFGIQiWoBcC7Y+L8zuQzfxj3l+dZwHGzKkr1XI7cQiWOFt92HvKSAiXKT4tueT719rk+/4RgS/cH0dZxH4YH92P4ygEMXX4Dpck1aGi8CfUN2xCOlizn7AQEPLEsg7Qalq1EwCUYlWQUa3CiqgmrdosC4JyI7OvNyYHyTYQmEfAJuqSS80NiWjTYfe0B0H4CpIc23DGx8XwujWti7Ov1Afaq9JvPR8DnI++Wt9r9o7E8ASTw8cUSKPP9UPGON1AoIbDvG9SVrBCtf74CQpFIk0UZUAjB8EEsVoHuHU/g4JtfwuXzL6Kl64GitV0sZKO530Zw7nKFGFbgL7StnBU1jwJiavHvIaARAW5FVLu7hQe8rkM+Qi2RZKzf8ihi8QqcP/UTRGMVaGzdY20PFFRVMTJ4EJcuvIzUwijKK9vRs+P3UFWz0axx4SWwXq+VXrkuWG7BvcZz4Lbd0ThPXrD9bw8M5rRfbCJgyBVsuzkrBCxEjldQTEqrDtmEqNbjcoPX/iQSNejofBBt6+7HxPhpXBncj7N9P8a5vh+jqnoD6tbsQE3d5lyNFAbp9DwWU5NIljU6Yk0EBPywbK9Ukwi4EAADJhFgtdpZjQjY00Y6BH7bb3MdL6MC4EsAAF37p2sdHO0bE4ItANqhhQ/q18i24aVVZ4UkQpxCvqK4buPCaN8rJiCgWSyvQNSVdusBih8QvFwiYLThJ9wGFX6LvR+7/zLP042wLwcWzfUySEcy2Yjmjrtx6dwLqF67DYlkfZFGGADG62bweDAWR2OmLYIZWjUKQhsyV9Z1VwfGTu7F+Mk3AEJAJBmp8UE0b/8tNHXc4X2g8ZjlkYrTFKD8bh+BZpF1cbXIR+vPywwTVMB2EyYJBZrb78LS4jTOnvgBBi+8CkVJQ1HSUJUMKFWQLF+L2elLqKnbgk3dv4PyirWOtt36DAzme7XicBuXm6DPU4YFnGu8ircBLt+TFSICvLgHbnpYkpv/WCKQK0iWk01MUm0jELzzlSQZNbVbUFO7BemlWYxcPYqR4SM4eexbkOQIauu6UNewHZXV6zA12Y8rA/swNnIclCoIh0tQVbsR4XDC2fB1hpWMMQvav4CGvEiAlFX4R/gQABOcDyDJqo5UcdwbZLxI9n2Zl81S+dNNAAasmi7i8mHj+MTn5cLDgVfQq9MXk+YEXRXWQlO8c+JZMcyNzPpCH36P0vOsULjcl2tF3ICCaMGDCttu+7sRreWQgSCkwm+c+aAQC4MbVismgn0u9S5b178TY1eO4ezh76Hnjj+0aMeMOYJVHhjzQTahpwat0daHUgTpau29C03rlkzjNWLmwXyE8EJBCbQgYxlc4cELBmkYOfIShl77AcrauhCOlWL81Jvado+AREKhuTf4aOGJSjUZlb3txXgGgug67FriPOFFFFhNeMemhxCNlSO9NAtJjkDWl/7TP0U2swAA2NzzQVNby2vTECpXWvNvt94GTd7gWRU4D0Ffq2HBD4wNAmP85reaR4CCWhwC9+myPkAcgJTWKq2rYca7gaekNIhAgGFHokk0r70dzWtvR2phHFeHD2Nk+DCuXjkEQmRQqiBRUouOzgeRLGvCxHgfxkZOIJ2e929cQEBHXiSAyhJ3YqFhORcE7PFSGoKsUYbbnJiyOS04m7vf1IyzQrtCuUSAffm8xuFbHMw4limz7rAS+IAnyPpNwub52wIfAWhjYcMvXIRwc3xevv92METDdx/2f861WTV2XWyLpxdJWGmNfgFxAYFRLEJgoJiCvdH8CghDkhxGZ897cfTNf8bwhTexpn2P/0HXAEpc+8vm7CeKdmmkPDK6sjCtBIBF0Bg59GsM7f0R6nbei4ZbH4KcJZAjcYyffAON3fdi8sIJXDnzCkKRBMLhEsRKqrGm8w6EgiQsyeM5Y4VCL0GYKyjbhCdJMZQP/n267eOoKushpBMiobn9rtwKVcXwwD6oahbxkhqkFsawtDiDeGmNx2B0bbPb98nrUub5OuerUOF/t3ICPRduBCAI7L7yfsKx0S67j4e/fVHAO2/DGMKRhaSMapFFpLTqSPRB7IlIAiCeqEZbxzvR2n4v5maHMDFxBuUVbSivaDXdzSqqOtDR+SCy2UW8+sJf5dX+qoMn56x2/wIACnQH4gZyhmVn0SG34xUFsAUG2wt3GeuCvCxUIs6gYF6/KiwWAr/aAJCcpMF3LAEIgMMKwLgB8bTplixLfu4EfgTATXvCCPa+uJaR9YUQAC8rSZBj3eBhbfJsJ1+rQ7FRqMafUsd7e72ionod6ltvxoXjz6GqYQti0bJV7d+eHYjVKBvbfAt2BYASBRDVyAPAFDFjMHb8dY0A7L4fDTc/CEIIlBgQXdMM5egilpR5DF98EwvTw4gna7EwOYTh2ddQWtmMyvK2ZY8xkOabgqvgMduwJEco/vsRWDuv7zM/cwVnep/BzNRF1DfuQtPaPZgYPYXF9AzicCEBHu+aqxBbpFP1s9Jas+PBPM98LRaB3Gm8oBMBU8HoVqjT7GD5F8iedUgbhxvpsf3LiZUziIDhxsbKJmxdnUKs3oQQJMuakCxrWhEFisCNh4JIANfESKnVImDATVBSVatAYfjb29YR8C0HgCY82836bkTAkYecuKgdWGFRdTlXFyyXAGjjtFUx9inyZL8H3N+OgzyIAGAV8u0ynz3hUBFcgNi2VgxeY/Qb/3KCar36K6pmvVC7exEFqkIL6vnB0LwxwkAuRzmx7APA9C1v7XkIE8MncP7Ys9h80+/6dpNJaMelK5l3Lqr9JnoFLcpxPzIVlCt0+tRlllYCpiGdHzqPaEUdGm5+EGpMG6SUBUoa20HkEPpf+iYWJgZR134z1m57N0haxf6f/FeMD/WaJICGiZbKMwCZNoU+6hKzxe5rZG1yiwcIoIQxBHjNr9y/T7+2LDAEYr1NJb2Ei2d/iYH+V5BI1GDbzZ9ARVUHlGwaAJBenOY0WuB4isx13ObqYs27XnEPXCLAdSPmHW8bt/HpLiYBADzdm8x+HQ3o32B2m0ohLSmgMuMaZDzHbNifCxEITBAYsiYgUCjyVutxcxGzvu5sFiBC+L8NGAIvK/hy1hFF4QcG86wHLh8NR5YDL5McIVxh1wvLcQEy9w9CAGyTCL/hAJMjIe6TnWT764NifEQCtRFkPMZ5rQShKLaGPh+T6GpYB1agD/O+ymTVg8HCkQTat70H40O9mBg+qY1Hci4r9rzkgUyptigxbVFlfQn7H+sGo62Sjo1YmhpBOjtr2R5JVqLtkY9jdvgcsqlZJGtaAWhuL9XN2zB87jVcvvw6qJ/QrwJGDvt8ir5xBUNj/lZosIDggP0G0Uaz+1j2138vpiax/5XPY+jCa2jrvB+7bv80Kqo6AGguaKFQHEuL07lnyeu58rUG+o93uXCtPM4Vdn2+Zfm8Pra5xl4x2AKjWJjv9zeP/r2OyadyM+cZYVOOE4XzUNqSjTi6YSwFvng7EwCjRtS1XAQAFFAszLmSo1kIy8E/qry8/PZ1KgCOwA8AUjonKFOZeLoEmUTAVhvAATciUQRBwTRzhrwvvacFQOJMEn6a/3w/Rm7Dc6li7FYwbFVRbI29H4IIzkEKlBjtsEuxUay2iznGVagCWtO0DaFICWanLq94X74g1sUQ0jPJwps0Cl2pIY0wZONaGlI2FWmyfTNACGbPn3Acn1y7EZ0P/AHK13Yh3roOlABKBGjd+jDqO/bgwqFncerAN5DNLC5jkJrrpZ8gbhRS4gn/zkKQ8BaUAz6n+WTtGb70FrKZFHbd+RmsXX8vSEgz0cxOD+LwW/+IbDaFUDim3ZM8n+3VShHqWVkYwOTEWQxdftP830qG3L6LnJXGc27fJ+C8wRtfTulo9BFEW+4zZruFgf1rX+/bl3OVHxHwgshgI7AaWF52IAItWNj+oKsAlSR+Sk2vB9v+ctj91LMqwBGepbQCJR7wVGwM0PGi+ZifeSZVr3z5XAsGpc5EB6zlw88FyD5Gt2BWu2mTd/3d7gfPVcuyHa6TWSEuQiuRdrJoKOZkbFwzLw640tegCO5IjmJ/Kwn7I8u6APo+NxTZTAqR6DIkbRZ+ClzjsrgJFMuEasQT5BE4HIqXIlpZh9ToICoBZPUMgrIu14e71mN94zrtn5R2PSU5hPYdjyGerEX/oWcQiSbRufHRXNaXgJo0LQgy2H5Bnkdf9yCqu4VyHs+gfv/crD6UYnToCGoauhFP1gAqRXppFhdO/xzDA/uRKK1Fz+6Po7J2g2+7hBVibRrxVSlK53KdJ8b7cOzQVxFPVKOx5RZzTNaDA9ynPJ9518Bg3vfF8eHUrqGnu5FtzIHOyRiL17nYx8uMx7JKUS2uQfmoXguNHRAQCIq8YwKkjAI1bK18aCECrLkrHyLAe+F56zhEgMoEUlqBGnF3kuWlGsuXAPCOCeIm5DgmZMuyFKRKsdG/HwGw7GwbmxcBMIQ6e6yGsa2IRMArzsJTqPMToFfC1z4oeATWDSspP7sRwqDHFBo0zB7LPD/m/VxF27WZp58AmfQCQFWEo6X6Nl0TykwVhlCgGHV44jkJm4RUS5urgWyJtkhL2v+swG+sCwrjnJTsEqSSBFRnrSFPSLLmj5RenEFGyiBEffyT8rjN9irOrvsFnZep/3zMIwJBhO+56UGk5sewrus9UNUsBs+/iktnfwVCJKzf/CjWrL0FROJ/fzzPz+17yBMwVwoEmBw/i97DXwOlKkKhGACPcefjN6+fhxvx4R5P+ffcN5GHF/Qx512XIcg++RABn5gAbmIQNyLgR1KuZ6yUxTuf/gUAFFosjPc8yhJIZhlpYwJmpyGqCqRV0EhI7zc3GDci4JhQCGdCodQ3XaavdpuTS59HACzt2V/4rOo/UbJjDoogBMD+mwVLEgwQArCXm73OUk7wc43T8CAKnhqQoGTgWqLYGZQKDi4s8FoUm0gxBM0kBpahrczXLL04BwCIxIpkCSgCWB9oQyhXI9rFyJYC8uIyr4WkuQdZVskhLAxeAFVVeLHQbJwglKJQw1r2otq2m6CqWVw88iMceumL2Lj1cZTZC2FJCBwLYK/R4rqfIawX+viqDHHzEFoDad5lgisX30AkVg5KVRx46fNILUygce2taO28D+FIies4vWoF+J8EVowIUEqxMD+C8bFTGB87hempi6isWg+JSFBpfvlpc2lE3XaA9Tw8vm88QX9ZsW9sn37biilUB/yG39BEQOC6QH4xARLRU3apVhcgqi08P3eu24Cbj7rPaCwuM+msb0pQ7RhwNOKw+q/bX9ZCAmI5ufR9SQOPAJj/OM/N8gHlaXyDCm6EaEK9sbBDCknm4hiuJIHqfrDcwjHGMfaSDUysBm/C8/T/dMPbI1Nl/shH+C4kCHo5BEmSNG1akV2BLDU+2HMqQsBuZkkLhg0Xyx3ID0bMjDEnSrD67S+7RnsOSjh4ew3vej8WLp7F2Ou/sKw3g6NdQAhBw7rbsOPuJyGH4zj8xj/gwpnnoQSQ+o3zzq3Ibz50HVPAAPN8gpQBYHFhAv19P8OBV7+I+dmrMPzaM+kFjAweQm3jVpzY/1VEYmXYdeeTWN/1Ho0AuMBL6+xWVM3bahD4VNz7pRTnz/wMb776t9i39wu4cO6XCMlRbNj0HnRv/whACKanLuL0ie9jZuqSe1C4Q0jNaxDWfw03KZYASEa7wRrmxyUQ61/edo5skBfx8tqXva+GO1CeCUd8IQiAwDKxrE8SUVSHQEBDkiNrD9ctCOCzZReLAO94KZWBGs+Zp+1WAMtHwOjLrhDnaaMDuLj4mp1t2m+HUJ0vQbCkR8xNXkRVrRMZ75oa+9v3BUyLCklnHWNk7yV7n2koZK1gzLYZcnEdgm618bi2gI8F4HpFPsLGSpCXQgV7nvtUoW1dR6SMjRnIsJYAQkyhmRUaDCFY0bwgQMKMskGXbmhI19SHcpp6s1LwCj2umWTuXkgZrROZmeLkPGJ1S9s2oOauBzD68vOINrcgsWUL1AgQmvM+zijIlSipxbbbP4nLZ17ApTO/wsTIKVTXbkIsXoVYogqJaBUi0STY6syBoLuKeBERKmkxCEGEeneNac4aYGius9klzE1dxuz0AKYmzmFy7AzkUAQEEi6d/SU27fgPIETC8OW3QKEinqgGpSo2bv8gYvEKa3yZoe1mrDzOwbms9zwhW7vLRHppFpcuvIja+q3o3PwYKio7IIdy39DOzY9h6PIbuDp0EFcG3kKipA7dO59APFHtHJqtIq5hfaEhZp2RztuwFrmSCucqx3c5D4uCZR9Lm4bVYpW+Mbwx6sY4t+9c4DSuLqewWkHmy8M1dgdaNV+76x/L00sR4ph0CaVavnC7EOwWH2AIpyx4FWldXniDCHjFA1j645jZ7LUBvMB9GW0fMF8CYB+WwpwjwM2OZC8Db7YtSdZJw+6uY7FWSJbzNwiA+dtxHyTQiD52ltjJBFTOHWsJZCbEKqkoNtOyj8vV244AsPCpqVAwVvOacLVjy5swg1jsVgqZpVlIchhyKOq/8zUAlbVrSxQgm9B+q2Htr7S0jOvGkhz9d/Wd9yM1eBED3/4y4u2dqNxzF8qbNnMFd0Kh1Sdg4xEkGa0b70dVzUZcOP1TXBl4C+mlXNpRSY6grmErmlpuQ7Ks0X1orELD/mzYXHcsvuRwzt15JSFg2p4cO4uTh7+ObCYFWY6gtLwZG7rei9rG7Ri9cgR9vd9Fdt8iNm77AIYu7EVt43bMzQwhXlKrEQBAy25lJwKuJ80ZDrEKpWyMhCV2gbDXwXEZnF15KKrm54cBAO3rH0AioRc1Y0hGLFaOjs53oX39/ZicOIfjh5/G6PAxrO14h60P/a+NCNgzftEQ0YgAzZ2XY7xMGlAzRWbQIp1egcHLQSHEy4UAWoKCjXUrZAF4exAAgesJeZEAklVzR9gEf8dk7EcE7MIphwg4ffk9iACHBFh8VU0yAfMltRYgcY43EPwIgMyM2a6xB5y1DnjpUY22eC5CbgQAcHxQLTUcbNCIinYyJJ21uAlROSfYkyWnv6hZJI43scmykwj4xBLkzsdlsNeR1tkTPoTHRD5uXAYKDaxyc4HzO6bIRIQSwryH+roVSBmaWZxbPVcgPxR4etmpSUw9/0uQSAQVW3Yj2tAEYrsfhoXDK2sQIRKaP/BxzJ46iol9L2Hom/+C0Ypq1G69E2TzzYgvaURJiRCEltyfj7KKFmy9+T8BAGh6CYupSaRSE5ibGcSVgX0YHtyP8oo21DXuQGlyDUpK6yGHonm75wTL7c/3IdespTZhWsfQpTdw5uQPUFm9Dh2bHkaitA6S/rJSAjS03IRIrAynj34H+178WyhKGk1r9+Dk4W+iqm6TtTE7ETAHAe35NkiOavv+GK+hi1Cvyu4ZmPKJwbBjfu4qJCmEeLzK1igszychEqqqO1GabMT87BXLrvb7YhAB1gLAtktlwlgEcufFPQev8+ISiPzmQEtqUL85LQgR4FlqmHvKm9MKUYpYFIC/CQRABAZfN8jbEkAyikWDbK7nXVQ3IhDUz89eMlyxqKUAAGpCi6wzSnU72uCSCc7kUQQCYK8w7KflgkIt+7u50XD7U12uOdud4eqTVR0EwCBNUlpxugFFQjk3INs50KjMLdJGw/z1AKxEgKdlNiw/nLiK3ID5TbvCz8UliLY7qEbcj5QEJS3FcMsJiuvd4sIhzo6sLhLJFRPSH28lbn3O00szywoKlvTsQGoodz+MjDshRRuQGobpGmS4tZiuQsvE9CsvY+KnP4UUiwKUYvq1lxGpa0DZtt0o6+xGuLIaalQOnDWIyDLKunagfNMOpAYvYGLfKxh69Ye4+tYvsPndf4xYeZ1vG6xQJ8sRlJTWo6S0HjW1m7G2/R6Mj57A4KW9OHPiWRjSUCxehdLSBpQk16A8uRbl5WvNLDS5wfk/k75znkcblKo4d+o5DF58FY1rb8O6zQ/nMvowhJQSoKpuI3be8ST6jv47QIBQOIHFhQlU1nT6jtG3/oXMJw6GgMwVHF0IjRd4luP5uatIlNT7u23pgm1JsgFTE+etVZk5Y9PeV/1bZgRlu9wqNwITWPtvthN8f+51K4AIcLP9SRxrFClc2++ZWtylzbcVARC4rpC/OxAhGhHw0CqbYAU8djXvIedlnwFDBOzaZFWFWmr9iPCIQKBUY0UiACxcJwBj4rGTI0L4BN9LQLZr/ZhraHH1sQn5rNVEjciO60EJMe8vLxbDaM8R+8FmPrITAsNFiG3PkqHI0Y0VbteBB17QMq9P3j7FyEQRdFxB9l2toN9iYTXrBwRAZnEO4VipmV/fENIlRkjPJrSVqQbdDedqFFWbxwAAk7OJVRurATWuvSPzfScw/sMfIHn3bah+4CGQcBipvj7Mv7Ef47/+GcZ+8WNAkhCprEHjg48j0dTObY8oub9satR4UxuamtrQOPwI+p/9R5z99Vex+aE/hRyOmiRGlQnkDHUqZFwgERm1dT2oq+2GomQwvzCKubkrmJ8dxvzcMAYv78XFzK8AENQ29GDjlvdCDse8GzXcW6TcueQDQoGMsoiTR7+NibE+rN/8HjS13sZNFcoiGitDz80fBwAMXXwDIBLKatY58/2z1gA3AmDs41ORVrMc678Nrbk+B+SEbcYawHFBcXMdBYCF+RGUlLoQPUPgZdqqqOzA0OU3MHT5TTQ23+LQQnsVW2MJg+EWJLlUgXZY0PNx8/KBp4CcBxFgC63ZXa7s4+Umu8jDAuB6/jxjiyAAAstAfiSAdQHyIwJ2AS8IEQC4rkHaB8h2fCgEspgFjeVOwRRuPfwK8zUfchFUznETWnks3wja5V4T5ypTyObGKFgPUEqsycHtWYiMyYkoKjfmwByfDbwgcN9t7NjcgouBwNfB9fhC9yvEXcarD54rWD7jMZpxBLitgsBfLGsBm9LVdj0ox3pXTGSW5lBa2bKifeQDUwg3ZMY0gRLX/2ECktX5BUw8/T3Euzah8gOPQVrUDkxs3oyytVugLKaQvnAJ6ckxTO57FSOv/BRtH/yk1rQuLAcVDiLJCrT+1u/h7He+iIuv/TvW3f7hPE+Kv1qWw0iWNSFZ1sTsSpFaGMPk+FmcP/szHHzzf6Cz670oTTYi5EcGCgSlFEf3fRmzMwPY1PM46pt2ArBq1v2u1eT4GZRVrPUeo4uAb7YtEydxMI5xc/2RCV9zTgiozP+eUT09s/24+bkRzM0No6Z2i7M5472kzFxDgdr6HjS17EHfyWcRiSRRU587lkrW7xXJqKBhyUK42TG4fXsDp5C2ERTWtcgNRRGQuYI3z1rjTr4KcQFyEAsXC9HbEiqF68Sxav0LACvlXR04rZeb1oQZlqotZmpKWH+TRU1lZYkJIMG0VgAKswLw4HYlXSZwyMT0FyX2YF4fWIRrzrXOlkWhJLSMD3YCQGUCNSpz++IFMJnbXDS8XkHPngHRlObuMzfzAXGuZ9OasuNZCdeWIO5E7OI2Fi9/evtxAX3vSRDtVZFhpHKlMsmlwWSXonZGrb+X4T+aWZxFOFZapIG5g4b0RdYWNcqs05d8MPHtH0JNZ1D14fc7/P8BQI7FEd+0ERU3347qux/AwqWzSE0MQckj/jk8py2gQGm8AWvvfBwTFw5hpO8112MI1TW8us96vkIIIQSJklo0rd2DXbf8MQCCI2/9E/a++H9j4MIrZspSz2xBhJ9WWBsf7zmhKCtvAZFCOH38ezh19DuYmbpsno8rJAJIBJRQTI+fQ0Wt0xXISIPKE/IcKVLt23k+9EBO6HQ7ljtf6m26XLfRq704+NaXEItVor5xp2Xb1OhZKErapSuC9RsfQU3dFpw49i1MT17U+3GOQY1IHJc97a+Udn4IiUJ9v9O+xTg9XK8CP5tecws7RQeYg4IkDskHpqUhn3ip693VU+C6wrI+3ySjWDPDeH2sXXryJAJ260EoZCEABhypQd1806mtv2K4AQXZphMZbXC28+VpCDyEQfu5qfEw1FgIqm4RyZblpACDCBhgP1QmEbBD//DxQCWJSwY8awt4bNP680xSntvO249T5+CaoNha+TwIwbLGYCcyhQYA246llGJk6jTeOvWv2Hf6KVwe2Yd0Zj7/dt3Am2f0/k2hjGha58zSHCKxstx2SVuMnP1qCNZj9CUkqwjJKsJhBeGwgmjLHNQotSxKBOZSnNOiSJ3ow9g/fh0LbxxE5QcfhVxbBhqmyJYprkJlcvNWhJLlmNz/qmvbUkZb5EUgpC92VK3bgfpNd+LSgR9idvyiZZvmlpL/OXkJTomSWuze86fYceunUNe0A+dO/Rip+TFOI7qyhOYhDFn4tYTOze/Bnrv/Em3r7sPUxHkceuNLOLj3v2Npcdq3qdmpAWQzKURiSVBqWGDdBW4vEmNaHhzunfq7o9dUYQmCWQFbcpINswK2RDjbAEpVnD/7cxw/+nVUVW/Ezps/iWi0zNxn9MoxHD78ZcxMXzbX5TLy6X+IhM3dH0SyrAnHDn8Vc4vMPdLvjRrhn7CUpmb8CHsu+fj/s5YJ58b8iSi/k2DjydsCa5zvMr4RbhYAz9oIAgIBkZ8E5fLikowS7CXKlwjYBEdekRg1EQbJqqZw7EUALP0FLEVv6TPI1fLSjPLqHxgVgrkDIM592ebiViFfjVkJUjYRQjYum4sFVCvS5BZMTUMu2R6IZjFwsxq4CvtsoLQ9K5NL4TITfoL+cgTYIAjafiE+/x4wte+rOLEbRI9KUk4gCWjKppRiZPIk3jj5Tzh89lsANB/x0xefw8uH/18cPPU1DI4eQiabWslTMDE/NQRKVUTi5avSX6FQYiqUmIqpH/wEI3/3ZWSGr6Lqdx9D6d07Ah1PZBmVO2/HdO9+ZBdyZItQLVuQV8YgO1p2PIySqhacfPXLuNr3Kkgm66mVz3VmXfw0tKYQEw4jWbkWa9beCgBQsi4Rzi7uF/b/jXXjo6cwMz1g2R6OlGBtxztw0x2fQTxRg4W5UVCVc3Hseho5jHCkFGePfh9v/fJzOHvsGUyO9EHlHOtd70BbVJm/v6tlwGMbJVrcBg+ZbApHDz+FS/0voqPz3diy9UMIMaly0+k59J1+FrIcQVlSqwKtygSqTKznQbRrsGX3EwiF4zh38ofWMdjj8PTPlJR2kRfyFIiJSl0JQFHBa49nBC6w34KO49xaV9LzdiIAVL32iwCAfFOEUqplmuEIem7rHeDEBwBwjxEISUBWtWwz/O5Um6Y7CAHw3I/dxzYWQiloPqowXhyEEVBrxCzYKwS7uZ3o190PS1Wav2p4LoNswnlrlYgEOa06rocaliBl9GxAtlto5nkGHBMSlSVr5WjzGMnVZYnNLMKFERPi5WLjab51uY7FwgpMtKx1xe9DUdiHJI/r5wZPzy6KkamTOH/lZcymrqIy2YbdG55AVVk7AGApu4CrkydwdaIXJ87/ACfJj5CIVSEaSSISLdP+RpKIxsoQjVagrLQREtEkpUK1fKqaxdn9/4ZE+RpUNGwsrJFlQomxgYL6X0MAtJ3X9C9exMxPX0blf3gYyftvBzEqbAfsq+ymWzH26vOYOvIG6na/M++xyhm9p0gIG+/6KC4d/gnOH/kBrpzfi/atj6C6eoPrsVTydqsx5msvAVnS644oakZfoe3vOVcY/dueb1VV0Hvk66BqFnUN29C+/gHEE9UgFFhcmsGZE89gaXEKPbs/jliiyv0a6z77JWVrcMsD/ztmJi9i7MoxjF85jisX9qKh9RZ0bnsfcoGj9hMHckG+PufA2W4GY9tz7+u+9ipUEEeOam3+m5sbxvGDX0M2u4ituz6GqmrdlckYD6XoO/ksMpl51Nb1QJbDrmQCANSohLCaQE39FoxfPWmOwSAn9sxFUsZ6VQ3/faP4nP376OZL7+rzb8/QQ3xcuzgwUxOz3fJcLfX7WxBscYGu5+mR+ccyFJddtAxFBY5R4IZGQcXCVpsIBF3nbNC5KogwzW2KkODZaQBXAmC0JWV4GigPImBDpjIOAJD1mAiDAABApjTsOnkqEQnykvMaGBYBvlCvT/R26wnNCbCWuAbVJtjaMjtZsgjxLDK2ombWgwPc9+UQgWIIzMtAYK0/zx2Gt54HnlUj3/MkBJSquDqpCf9ziyOoSnbgpo0fRWWy1bJrJFyClrqb0NJwExbTMxidOIn5pQksZWaRWpzA9MxFLKVnoarasxwKxVFTtQm1NZtRVdUJWY5YM40YbgqsYsD2vA+cfgELM8PoeeDTkKRg01x8JHdNxltKtHWxnK90Kq7HH+mZrtS4CmlRe5ZpmPNhD6iBn9u7D1PP/gTlj9yLsgfucN1PSmt9KBFAtinMQyWlKOvaickDr6Fmx92mUM0DW2lY4qQyDUdLsO6WD6Cx7Tb0H/0hTrz2ZVTWbcK6LVpOfcOKSiWCTGoGhMiIhPlZlIK678RLaiBJYcxMX0JZTbu/QGM87oQ45vnUwhiomkVj860YGz2Bt177PBpbbkE2k8LI8FFIUghbtv9HVFR1aE1Rd+FK6wMgkFBe3Y7y6nZ0dD2C3je+jPTSnHacjwDq6xokM8cbxMHYFmICfPVg4kx2AZfO/ApDF/ZCDkURi1eaSzRRBVAV/X0/QzxRg603/QHiiSpHnYKR4SMYG+kFANTVdkPlPL+GYG26+kgE4WgS6bRWIE6NWIOPzTSinDld4s7zsKS65hYe5V64oK477vfV8/niEQFbZqe8Ktv7EAFPWYZ9FjwIgLH9bUMElhHnVbT+BQAso2JwYIHfDXkQATNdpS44q0b+ez0bARcBCYA91ZdvyW4eEbALYB4EwBiHa38cAdbiMkSpSQAAQImFHFp/GiYWq4XECP3spM4L1jLcfOxkQEtHx5ynnQ94ZRGSZQcRAOCoq5A7YHmTf9Fe8EBEM4++VsNc6xfQzAE1SEGer/PFkTfRN/A8qpMd2NL6MCp0lwJH2wxikTK0NNxiPi8GwaSUIqMuYSE1hvHxUxgdO4nhkUOQpBAqKzoQCZdAVVVQqoBChSRFEI4kEA5rSyhWinA4AWRKkc2kMHD6V2jaci9KKpsAVtDVh6NEc+OSzcJYy78/ysIC5g4dwtLFi5DjCcilScjJUsjJJOTSJKRy7TcJa+/sfG8vxr/5XZTefgvKf/t+QK8gTDOG4EUhLVhd+bJzsxj86ldQccudKO/KuQxV3XwXpnsP4MK3/ydaHv1dhJMVkDLMqeeZXrO0ogk9t/8hxoeOof/4T3Dgpc9jTese1NR1Y2r8DCZGT2NuZhCyHMX6TY+goXGXGcRsBMsGEkoIIEFGWVUbpsfOoXnd3a67BhG6w9FShEJxpFITuPn2z2Lw0mu41P8SwuEEOjY8iIbmmxAKx90bAPhxUbpARgjRSZa7Qomt9uuAHrvi9n66WVYUNYOh/tdw6cwLAChaOt4BSQ5jMTWBxdQkxkZOYCk1BUoV1K3Zjg3d74MsR2D4zRttLsyP4szpH+pDkVG5ZrOjL9bHnx1LJFYGJbuELDKQYc86p311TNKjEw4uAQD47rHUowq0x3zm9jzwiICj2BmPLLBEwPyu24gAp6aDKzlwIQI8DwcHPCwR9v7fttmCBK4ZCiYBAJ8I5EUO8rQI0LDsTJXIIQK58uvsC6vmXkIjtsv2AvLgeCndCIDx2z7hydacwo7c+oSTX9gQjFXVSVxs41mqyk3E8qLq0EgSFaBhCSSjOiY/NSJxiQAAi6uPI0Wdh6bdUhXa0p6NCHBSxgIupCgf2OMN3Lb5tZ8vAShyPAD3sAK09QVr/ANYFipKtNSbzbW7UVEaPA0nL7icEIJwOI7ycAsqki1Y13Y/FlLjGB0/ifGJPiykJkCIBIlIAJGQzixgbm4I6cw8stlULmBTR6KsAU1b7gs8puWAqioWTp3B7L59WOjtBVVVRJubsbS4BGVuFurCgvN84zHIZaXIjk8hsXMzqn7/PSAS4z/iAnVpCcNPfRlLA5cxTYiFBMSrG9H24T/GwDNP4dzXPo+1D/0ukmvWBz4PySx4RiClqVYjQAVqmraiqmEzrpx5BZfOvoChC68hFE6gsmYDmltvx/joSZw+/l1EoklU1QZzveJZCMqrOzBw7iVQVQEhMqBSS5pJe0pKC5jmIpESbNn2IRw98K+41P9rdHQ+iJa2uwGJgBCJ+/6ZgmCAV1MTtCRQqljW+Rbz8njvvY6nVMXI0BFcOPUzLC1Oo3HtrVi74T5EoqXa94bl/aqKbGYR4UhCm++Z66WqWVzqfxEXz/8asVgFGtfeikvnXsC5Uz/G+s2PmhYzr7S9kYiWaSuzNAc5VGXenyBuWwByaT09DPLctKAu9z1YVWnWeui/T26w1LneIAKGYYUhAm4WjdyxcBCBoMhHuJcK9HYQuDGxLBIArC4R4ObfDUm57yaxugWYbNsulEp8kyVvDI6xeoFDAFh4EQ3exEElCcR2cTJVugZLpRYCAABKTLJoXVjhnYYlEI7A72oVoMzkxnMtcslvTSjNnbdivRfW1K8u5IO97wVotT3h1941MBEWHPRb6LVZjjXCOFb/W5FsQWWyDf3Dr6CuYhM3lWVQ8ITCRLwarc13oLWZ7yJjBCxTSpEhGWQy80jJi8imF1BatdbTJaYYyIyMYeHNtzD36iEoE9MIr6lH+XvehdLdOyGX5zKw0GwWyvw8lJlZqPNzUGZmoczMQZmbBQmHUfHYPSABslzRbBbDX38K6dERVNx6F6befAWZ+RlE4rm+Eo2t6Pi9z2DwB0+j/7v/iDV3PIKa7Xd53hsp459hRZLDaFl/D+pbbsLS/CSSyUYYVWcnxvogy1Eky5v54w7oElResw4XT/8c85NDSFbw2zLbZIVmjmWgqroTHRvejfN9z6EkuQZ1a7Z5tuUHT59+xvrhW4zQvo7ROFuKgTHndP7Ucxg89zKqG7rQfevHkSit07ZxMjYRSdIIgG1enp68iNPHv4vFhXE0d9yNtevvRYhEQEBw8dyvUF3fheqajVwCYI5FpYhEterb6aVZxEqqAGjfDmrRkMNSMViVieW7ZM/v71sYbBkEwLIvz83Htg/7LLi2r1rbIayrlXlsMCLAwrUasFcCPZs1Qs6oKFKx8pWFqBNw3SCvr2Q+BS+KTQRcCYBlBWccvHiCQggAD+w+QVOeyRxrgUt/Utr6OpsEAOCaq9m83byMIGAITv4AACyLSURBVO4Cfy4mQMqojklX1V02HNUeKfMRopxrzSED5nFu25DTFBclCLaQ/YJqz1dB+59X23lplowHZXldd6y5Ewf6nsbk3EUzEHg1sxkBmoUwBBmhUAzhRO6EssZl4ZyjymRbMdxkJMbPXslqLjil0VxMwHxMy6yiyBQzz7+Oia/9GCQRQ8mtW1F6882ItLVowrZiI/+hEEIV5QhVlJvuPgBA9d8kxL9v8pysj0trb+qNV5E6cxpNH/0jxOoaMfXWq5g71YuqHbdZjguVJNH23k/g6mvP4corP8DshRNouuf9iCVrTW1/IZV3AS2+I1JRYlEwzExdhKIsYe9Lf43SZCPKypuRLG9BsrwF8ZJquNRCt4ICZaVNCIXjuDq430IC7EWnggjdlADNbXdibnYIJ49+C6NXj6Ft/f0oKa03hUFP4V/X+Lr6lBMCahMGueMwtcT8MYK4u0yZPvlKBolkA7bc9IRrW+a4XJTA507/BKn5Uey64z+jpKxBa1+hmJw4h0RJLSqr1lnb0QXiUCr3oCgRKUcC5qeAqlbIi/wOiWoVqB1EIOSTIMI80IUABLj2FngpfSz++VYCxq7z7wPBiEAeCJKZyyACckZYAATyR37ZgRQK2DNNsi4whmbO0GrlE0jrQQR4cAtMZseh7WjTVBdaHMzLDchtf55GG+ATAbZ91UkA7FiqZDIjUbsFJDd58D4KFjcgu4tQWIKU5ksIaoi4ln23V3O0QCaAcVxAorAqFXGDoogWCbeia0WBKRyxH7UCrAR5nG88WgmAyerihgIqZl7PWDo7gEhbIxr+j09AioRBF/K3OpiXWSE5xqL/dXomUEy99CKS23Yhsa4TRAESbeswd/KYgwQAAJFkNO55BMnGTgz++rvo+8Z/Q/2u+1G/9Z5AFhLJDP7NkX+HMK5j9x2fxdzMEGanL2F2ehATY30YvPQ6AECWoygta0KyvAnllW2oqtkISdbmLlPQ0oUsSQqhueNuXOz7BZo77kYsXmm9BgGEZss1IASbun8HldXrcfHcr7D/tS+irnE72ja8y9G29eT9n1VCJMxND+Ly6V+hak0XEsn6XDyEz/GmYBfw1YzEypFenNH6LYAAAEA6PYuWjnegJNlgrrs6fAgzkxewdffvc4PnQ/OK47sXCicQjZVjduwCGqq7LdZiv/N2IwLcOcpDU2t+6+yafTdNvz3ezO7OajsukIXBtS94EwEvrb593zymzMhMBoo9DbiAQAAsy17uENAVagbxmigCEeDBEPjNtJusH5yia5r9CICbkE61ickU2IIQAFaYte/Pm6zY+AT7/pzrxVoBLAQAsE06tq55H26V5rT7tlRuhFIzhsAOQpFLW8eL2XBzH6K5GgGu2h+GGHE/Ch5xCEWDH7ErtP8iacbdUssFOsb+TAVVGvmQlun5QQBAeUlTwAbZtpnsIqZFqQhWl1WAMjOHUF0VpEjYsY1G2JQp+l+VFQSoZVMQZCcmoMzOILl1h7mudPNWjPz0GdCZBcgxLTuPGQisN55s3YRNj/85hg88j+H9P8dU30G03PU7SNZ15IaoD1eVAdkltzsPptUxFEZ5ZSvKK1vNbZn0AuZmBjE7M4DZ6UGMXe3FwIVXEArHUbdmOxqadqO0rMnhptTUejsG+19F/6nn0Nn9PoRCMUtfXHgE2hJJRkPTbtQ17sCVy2/h7MkfACDYtO1xZzseRNV0cTHGue4uUKrict8LuHjyZ4iVVKOqoQtVTV0oq2p3nJfpvsRLBcpqnm3zHCVAKBxDNrMARUkjJDEuoHq2IGN89mtitDMycBCZpVmmQyCbXUT/yedQW9+Dyupc3IgRYycv6Iog5vskp1UoEQlVVZ2YmDjjOA8pq0JlM7/5aNCJqn8H7TonL+2/HXah3i7QK9RxzXnrvFyFXK1BbsTHLZNQEK2+sa+hjwlghQjPaQpDOaW8fYiAyA503aBgEuBatj2jWImA24PviLwvbBzutQGo+35emgA2WFjluDT5CSMc8mFxb7E/fJzrIy16uAFxYBHk7XyGwMxNLilwTLBGajgpY9XI2ImAJUWjV65/mns2HOfLpA7lBQ+bH2FXa8MqEAE3rGS/BQvoMLNA5WXhIiRn0SvgvNj7Oz03iHikAuFI6ep4eFreW+Y502G4waVLc7FCKjvLUdtfMFYz25QAALFQzsIhh7Qbo2YlqDNzCDdUQzJcecoyUBb1i6r4zBEFID18BQAQrqkFCEBDQElrJ6CqSA1dQmnHJtdjpXAEjbc+jMrOnRh46bs488MvoapzNxp77kOsvLag8bA+3zyEIwlU1nSisqbTXLcwN4LhwQO4OnQQQ5f2IlFaj4amXWhsuRVSWHO1kkMRtG14F870fg9jV0+gqmYDatdsRU19j8WCkcu+45GBiJmrJRICkbT709i6h9kn4LnakKxai823PAFVyWBq9Cwmho9jdOAQhs69jNqWHVi3/X2Q9cJcZsVfuGuZ3dI7To704cKJ51BW2apZUHj6EZf5QlUVnD/5YwwO7EVd006sXf9OfX+Ki32/QDa7iHUbH7IcI6dVIK1av+86ESBZilBWQWVVJ64M7cfS4jSisXKLj7+dCNgtxIY1wD3/P3+16/6Acw7Tv+fGnMgSOONa2Ukde1wQGPfKQQRc4xc4dYbcFKR2ixbVXHiVqD+LkFMKsk69hICAKwpPEerhq28SAT8CYPwu1EXHC2ZuZQ5RCBgUSiXJ0k6gCcKFAADgB0DZyIcfAViszmmCiEp9CQALVQYktw9GmDi0gGyQmD2o2JLr34WIWc7XfgtcsghZrAa856JYrjkF+NEvqx/k4QqUh0XMbFu2fbSDgJc9KE/MzA+ivNQ7iPM3EcrMPOSykhVpW15gpEZo73GsvQMkHMbcscOouud+AEB2fg4AEEpW+LZJFCBR0YiND30KY6ffxNDBn2PizAFUtvagoeselNY407taUITXJFFah46N70b7hndhcuwMhgf3o7/v5xgfPYWtuz+uuaRQYE3Lzais6cTY8DGMXjmKU0e+jYaW89jQ/T6YRbl449GVBG5++JfPv4hQOIH04oyWgUjy1poG8scOhVG5ZjMq12xGxw4Vo5cP49yh72FuchCbbvkI4hUNrscuzk9gdOAQ5FAU4WgpIpFSRKJJLcVpOIbRgcM4c+jfUFHbic27P6LFIRBdkPUQig1Xz+nJCxgc2It4ogad696tpQtVVExN9mPw0utoX/8AYrEK0x1LdskURxRV09brc4UWP0AwMXEWjXU7QUAtFY0NIsC7fkbcGjvr5KzLxk5WZY8nAeCNVwXsDwhP6OcSAb+2uSSMOjNX2awBgG6BMOZp41LbiADPsmDcT3nJSgSi41rBDzWaE+NCE/OA4lJ1W0CAg+VnB3ILfglCAICca4yP4OJIoxkUvHbtbkAcwdwhsKngx0OwY/MgAOZ+bJ82MiSlrH7VS3U5IUPKqhYCoLXPfA0DXBJCkSv85QjyBZSwxA8uUgE1JPFTj+lCu2USN/tjz9HpduQniJvtup5QEQT5oNaFYmcqyufjY4sXCbz/MsfsF1tAqYqZhSuoq3TmGQ/S5tsZJBwCzQTPw0Ey7L1grqtqEF7v6yInEijdsQtTb76GyrvuBZFlZCZGARBEKqst+xouQbxCYIRIqN20B9Xrd2PizH4MH3sRJ5/7OyQb1iNRuQbp2Umk56eQTs2gZk0P2rsfApXDBcdSTY6fxczUJWSzi1AyKWSzi1DVLGobt2PT9g9hZqIfR976J4yPnEBtw1bzuFi8Es3td6G5/S5cOv9rXDzzCxAQtG16UKsHYUNOmCOuhKVzy2O4eP4FnDj0NKKxCjS27kFd43ZE4xX5nxhP+UUk1K3didLKZpx+82kcefHv0bHjvahr3Z0bJwGU9CIGTr+AobOvgBAJlKqgqu1mEQmgKupadmH99vdDkuQcKXSJzQCsAnNFRTs2bXkfzp55Dm/u/TyqKjsxNdWPdHoWJaX1aG7Tsm6Z8WEWt1I9sx6neGRUSiCZbMTk2Bk01u10bFdNa7jh0wLY49YsY3ZT9lAPi4ELvOIieNscRIBjDeAFCzvaUVSHAG8R+tl19mdHJwJBsmgZRMAgAAAgLWWhRkMaAXi7gOIauwNdu66vN+SXHcgl6MbiPw9oufF52YG8sq54EAG7AOJVets+Toe22e4GxANvvRHoywm+5KUhNYpuaf9whCj7RLOYtZAKlgAAsJpYjSFlGa05Z7J0y49MCcx6AlLGKrw7iAD7004EWGJmcxHyyhTkeS3sRIqZRF0FkXyF3eUIx6z23CXN6Yr6rxcaV8wL+s0DvPctgzRUmtW0iSsAMz+8i6+2WQ+kCEW+8oVcVYnM1WkoS4xmwCwBy84/xeuz/PY7MPvWG5jrPYZkz3ZkRkcRLquAFA5bMhsFgRQKo3bjHtSuuwWTl47h6vGXMD3Yh2hJBUoqmlBW047hc3sxM3EBm27+j0jEqv0btYFSipNHvglVySISLYUciiEUjkFVFZw6/E0MVbyG1nXvNEZkHmfP2d/YehuWUlMYGToEAOjseW/OzYTAYfE0+7cJblW1G1FVuxGz04MYvPQaLvQ9j/7TP0U0VoGyylbUNG5F7ZoeRzu58fg/Z1QiiJfXo+feP0X/oWdwdv93MDPWj/btj0GGhKsX3sKlkz+Hkk2jecM9aOrUCn4p2SVklmaRWZpDZnFOy8MvR1DbvMM39a6Zq9+erpkQrGnYjeqaTTh/5ueYmx9GfdNOVFavR3lFG8JZb+2bY55mvn+VFR24MnwQlFIQos39NEQ0AhAApluQy5yer/C/HAQiAl7ZiCg197FboexEwD0NKH+9tKQ4+o0NzYJGrT4/oZEZIPQ2iQcQuK6QtyXA4uJh95+XJEtwlYUIeBEAA5w5yS3w0avSr2t9gKACn34sNzDYTniMvhmttIUAALD7RdrHTWwuQLyJYq5Je+kjc4bvpd3qwCcDfpkO1LBWGIjdnyUcbDpRQq0TmkXoV2E1f7oJ7GymIB4kiS9cq/x77cByrAP5kgO/ugduH41iJAiyu9R5jcMFBWcR0pHNatqoUCjGtz6shJsfD+wpG4GSbLAh4WznDC08x/wzqvlzjzMuP6qinaQcVhGqrkDm6tgyBp0nKEG0oRGxjnWYePF5hMrKkJ4YRaSqAJ9+xs1IkiRUtW1DTfM2c7MxH9Q37cKpfU/j8K//DhU16xGNlSMaK0NJaQOq6jZx5zUWmfQcMul5bNnxEdQ0dFu2TY6dxflTP8KxA/8KQAt+BfjuGaFQFJ3dv41ESS3OnfwxklWtqG/epRVXCyAs2hUhyfImbNz6AXRsegjTE/2YmbqAqfHzOHngaSTu/ixKkvXc43lwKzImhyJYv/sDSNa0o//wM5ibvAxQFQszV1G3dhfWdr0b0Xi53oF2/qFwDPHSWuuzaVE8McTXSLHJ3EvT5mpk3dG1+FGSwKbN7zOb1Fw/eVZd53lYTza3MR6vQiYzD0oVEBICiK6ksSltqJyzzNjrAzjbh6fFIMjc7rCS8Pz8uUK+BxHwCDo2+sy1oxEbi7usfh3cMx16EAAbwuOatp8sZUwiIE3rxQizytuHCIjA4OsGBYkj1MWFgufjTbIqXxjirQtIAOz7cGsBLIMAWNrgZAZi+wxU4ZY6LQe83waW6hOgUm5yMQgAAKRLPdJ0wqlR0DREjFbV1h2hOauAQzCi1Awc5rqDhCTXWhBUlpxkSG+TysS75gTPFYttghBzcYXbtqDrDY2/m7BthyTlFg7ycoMhRPuYGks+8BmHG9hr6nttdciy5p6Wzjgr4mpj0RYaknJtGgLT6ivviwqpJAFlfGrZ7ZAM0VyFVOgmcuSuD+cWVj/4EGg2g4Evfwmzfcd8SYCU1RZjDsjnOSytaML2uz+N+taboChpTI2fxeVzL6J3/1cw0P+K57FUAubmtGBmNi2lgcqa9dh5+6exoeu9KKtsR6Ks3ts/WyJobL8Ndc070Xfk33DiwNNIL825769fQ682I+ES1NZ3Y93Gh7F9zycRiZXj8rkXrefhcbmohxVCGwNBfdtN6Ln3T0GpilCkBNvu+TQ6d38Q0Xh5bm52mc7VEDHrt3D7dxEe/fLvy0v2D63Hzsb3xPbcJBJ1AICF1Ljr8ZSprGtZByYFrX1+c/ue5vHc5lKwOpWV3HVweU48YgddLRhGMLLdddaF2LhV97UQAL1PgwCYfS1x0jJnCywAInDDoqCYAFPzzXlhWe2/qTG31RFg93fLDhRYM2n372fXuf3vBttE4xcYzBWMCbFqVKhtmw1SRgEMQTqrYqne6u9qn5wkBUgntZWRWRcffd0MbndFICrMTEHa+JnDwgQkzZkswQ8atnQZ4lcjBjQyYPqVurj6cCfUANWFAZtlyo4gVoEgH5d8XWi89vf76Hm1V6DbU0Eaf7c4BGZ9RCpBSawGk7MXsKbG6UbhC8Oa55vO9/piDPMH+zD7wl6U3LLddR/Tx19xriNq4ecTa21D66f/Eqn+s5h66UWU1LVB1l2BjFiAQguB5bTMmjVAlTUNdUf3I1r7erv9J57D+ZM/RjhSgvrGnaAh4sx8o1LMz12FJIURS1Tx+5MkNLTegobWW4KNT5KxafvjqK7fgjNHv4cDL30enT3vRU19l21H/vGWKsM2SFIIzW13ov/0c2jb8ABiiUrHfGnRyrtkR3JkiyEEJeVrsOOB/2Ju1xrwPlfVRcD3gxsBIIqqzcV+mnjueueG0pIGECJjcuIsSktzlhMjjaibewuhRnu5cZixX8bzR2zPEk9B4zGPcV3wPCwCbkTRCDDmpRi170clznpdFjItUfo9MMAjADztPwBErs5yFW6mFYCF0HIL5IG8SYBFiGBfRlbQyKpQI5ymbeZC8ziXCrq+Aoud4QcN8uSBzSggFxYYbBHyecNgT53qBIAF5yWf7tC0raEUdVQBTiclKxGwBE8753TVy1JIc+lCAVhchACtWqQBhymZ2ioOW7YxfpIu2hPPAGBqe2YKeVauQTBqYI3rMgX8fMGtG1CI37reTkVZG6ZnLxc0lsDIJ3bHmI4YYYcXM8AKCsZHniW68UFtZWawCnMbNOlaiipIHTuDq1/4FuI9G1H1u+8DzXLuA/HzqygMkl68OJSWsDifxvyF01i8chnx+mbEquq9D+YNEYyF0CPY1I62jQ8inZlH39F/x5XLbyEaL0d1Qzfq1my17Dc/O4xovAJUVUGMua0I8kntmh6UV7Wh7+h3ceLA19DQfBPWbXnETMnpBUMgU5QMRgYOIJ2eRTaTQiab0rIGURVD/a+jo+sh9zZchEaTXPAETsuOzlUGOTGEf1YINoo0KmHNvBFiNPk0TEAy1KndR04BY97rLBO8ajyifvfDZXs4HEdt9WYMXnkLLY17LN8uKUOhRDnvnJuuhkNc/AJx3b71ns+wq2bfe34ItD5Lua88yaigzLfTIAIsAZAXFSgx2Z0AjMzpfVjjLMkixxIgS866C9cjVBUF54UvWv8CQJ7uQK5FnOwadJmAKJwnkefaQKnpNuDY5DmRupvqfNfZYX8euJppa1t2lx7HWCWSWwDnBME5NSUeQmghFx9gEAAAyMY5E5VCkUkQc7F0b7v8dgJg9Tt1jkWNSFAjzptCdIHfNFNb2tGKkJlaLAtJ0ywrxmKH4SJkMQ/z7p2Le0w+bizLRb5uM+bzHfRt47kiBRxLQddA4ix5gE21aBmHy71+uyJ1/BxGvvA04l3rUPOJD4MU2f9WShNIaQKSzS1SRltY7f7CwAUMPvM0StdvQagkiQvf/2dk5qb4jRouRkDOjTFfwxaFtQq5LGFDz3vRtvFdiMUrkJobw6kD38DY8HHr+cghpOZH8fqv/i8cP/A1DF/e5+3C4wWbBjsSLkXXzifQ2fM+jFw5goOv/j1mp2xklDl3OxYXJnDmxLO4ePaXmBg9hcX5CUhSCHWNO1BZkyueZc6jvPeRnap8rilRnZlu2Dhyr+OzcWJaf+2Q0tRduOZk9rHAM/c++NfO8OWmFE1rbsbCwiimpi/k/ON5cw/HrVE1i3y6WC6CkEWOX7/XdtdmOJp93v+uBMPNusP7fC1mEZpPO9ZHh/nvRXjU5gKkkwcuARAQKADLzw5kMCr9Y2/N5qKAyvqH0o0AsOAEBrtqeYNo/Qu1CsgFBAYz43WUnbfnEAZ07bl2baSMAiXO5PpdyGK82+oWJGWBbExrN7TozKpAVCAblxBKqQ4CYEegCVaHGpEswcGWbWGPbSGSq2HA42ZudQLAuAkFqC7MPX6ZAa/5thMoYBkoTHtfyDGSTfu9XKWHhxy/uDSFWKQseFuc8+Fp5a83LPb1Y/SLX0N0Uztq/+RDms9MnpCYNKEka7gG5bZ7nfuVr/8r0sPDoEoW2ekpxFva0fTbHwGZWMC57/w9+r//z9jw6B8jFNXnjWVq3HMWAr6VjkgyWtbfAwBQoeLkgW/g1KFvYuttf4RkRTMgE6zv/m00tu7B+NWTGL96An3HvgdCJOy+8zOIl9QUNjBGcCWEYE3Lzaio6sDJw9/C4b3/Ext63o+Gxl3a2D1endKSOnRt/zBOHP02orEKdO34COSIrSijUQBLBdSIe2Ou/uTLsEwbmnCe8icblRBaUh3WWhZuBICb097sFMGsAzoqKzoQj1dj8Mo+VCXbQBQKJap908yc9i5KG+53Q79e+XyfcqlE8ziG14wevOuq6Vf41h2i2Aqrca6hlFahRiTITAIQI60nAISmUgCA8GQKmcrcM2gQALsMRFJp/nchJAtXIIG8kfcn11pJULX85gV7EkUJRgBcRlSwm0fQl8H+0rMkhhMYTGUJbGAjL32p71hs/6phq0Zxpj2O8DxFeF7/CNlSSBtkIDdOZluco2UncAQIs9vYIGQ7tMBhyfxtP4+cRcB+HeAZOAzAW0uswluTHDBwtlDrgP2YIFr2lbRGLEvjv0xNv9t4VAALi+OIRcqX3yBszyBjDeE9t+zzbGi4l0P4HGOhKhZHr2Di0GsY+8dvYPSL/4JoWytqP/oxkFQJiEpyi6IvaSm36AG/Uja3LAfZ6SkoC/Mo23ETmt//UbQ9+gcIZyMIlVWg4+FPIDs3g/M//ReoWaeW0f9ktUWVNYtD0JgCY94gkoSNOz+IkmQDjr/1FSwuTALQhPSSsjVY23kvdtz2Key+67OgVMH8/NX8rBGGVtqF+MdLarDj5j9CbcM2nDn+LBZTk9z9JIWaCwDU1Hdj2+7fx+z0AI7s+1/m94yo1FItnTskkps7ufB6N81vB2eMuuJDiREHAVCYeT9rqx7LWm3tvuaOZBCsBYANlKU0ULalXEcETWtuxshYL9IZZ456+/kZgcxuiqOCBFivY/Jsz9MlldMej2gRyq9tEJp3au6lJWchj/CkTghsFgBzrjdqk9jPzbBKXgPX14LAWJSu2SIAYDnZgexaXD0o2NmDxF/v5e5gmJ2X4xsdxJ3CgwAAyAUGm/nKOak/bXDsE+AcpHQW8oI2Scy0W7VRBhFgEUpp2hxjsZxCmkKJECi65srxAVCoqWG3b3MEQLHfCnuWCmabPYOFY/IPS87jzT7dXYTYfTyRBxlYlWJV+Qrcqz1xs+5Gebge2TE5cx6ppUnUVW0GCCeOZpWRjeXItOHCYvhaU6IJucY7w5IIY93C/CiunH4ZZ156Cn3//f/E+a/8Nwz/6hmoV2dQuecdqP3ERyFFwh4jWBnIi0D1nnuhLqZQ1t6N5MYeyNGYuT1WVY/2h38fC2MDuHr4196NUeviGSfEO5zkFssY5TC23Px7kOUwju39Z6QXZxzHxpO1kKQQlhb4QrrrOF1A9DzzRKGQJBmdW96DcDiOvuPfB2U0ynJG5RZBVJUMxq72QlGWEA4ntArsHhZGw6XHKzubtqPzXXJYJZj/7f7/y4FbthkveAYL2wUmw+qpX4s1ddsBAMOjRwEAsu7XbpASv0xFuRiFIhKAYgl5KocU6e2yBICXKZD9bcT+2UmGPDGP8JVpR7eRwSn+eOykw+jD7pb4diECAtcFCssOpKpWU6ctKNjU/jJCAbd4mHEsL8Ann5eYeEwkbiZZ+1zJ+vzzBBpOYDCQm8SIyqkPwIHd1UVK57QBGhGwkgBW60+oRgAsIHqu/wx1ZPBRIoxLjg0kS83UoGz79uBlFhZtky0QTY3kLo7sEuBEw5Knhs245jw3IfZ+uLkR+QUQm235uO8E9vNn4fftXemJ2RgPLwNGEBTw0bw09DpKE/WoLGt3buSd7nX8caJUxakf/R1UJYOS2lbUdN+G0sZ1KKlvw2KLFpuzFF2ZYDKjPgFLwu2EvHTTVoQrazC+9wU0v+8JRxslDW1I1LZgaZqpXUBhCfgNlGyBA1XO5Xf3ctWIxJLo2fOfcOS1f8CxN/4Xtt72hwhH9DoLMgGhQDRe6dTU5zkkN41tKBRD55bfRu/BpzB6+QAaGne6trG0NIOj+/8FqfkxrFv/W2huvwOE2JQZSq4aupxSoEY95nefZ5tKWgYlL5LhRQCMe6hGcnVd0kkZkVkFcsqpUTar3LJCLCHO7bzh+LkykZy/SyRcgtJEPebmh7XxhZ0ZiIxCYsY3z8vFFPAYlwfM53y5wr9x7q7VjY0AAbs13pkKnVfU1HimZKa6L0mlQePaHEMWtFRfJJ0BZRQOxEj9aa+j83apC2DHtdbGC0uAibxVdxYBzEWDSLJq8DzlLgJ6QVpb3v5BCYDfMfqH0E1r4lrymw0gZisHhoiFAADAYn0C8bEs4mPaervbj5c2SQ3zzh2WQF3242n4P5pBT/bTopz+zM4ovz8dSlQ2F8eQdKuAm2UA8Nf8Bwo4DWgdKApWONFAoeMsSnCui9VgPjWGsak+rG3YA7+KptyxebigsdtXK0ZgaWYcSjqF9fd9DBt/65NYc/O7kWzeACkc8T+YA2mJQFrSXYJSEqSUhNA8YRYgNA/IqWDtEUlC9a33YPbUUWRmpqx96bUAwrFSZBdmczUHCoSWGrOwZy5WUoWePX+AzNIcet/4MrIZ7QQXFyZw6tC3kJofA6X5vzCmK4+HZlnKUNRWbER9/Xac6fsxFhenLNvZuTuTnkdqfhTVNZvQ0uIkAEaf3KJaAGRduSJl/S0Drt8G8K0qBkIpai48RKey7i4s8NHwwzrn2387jvUQmkoStZhPjbrWNODVMzAtBUY/y5yLi1mZm9hz7RtDZDXxlqQX1LkOAElnIXFy+cuTzrSeJOV04yPpDH88xnwe5utwC313BW5M5JcdiFeAizM5UEkC7H6JPCtAAAF9WUQgCAFwaYOoau58bS+V6+TqoTmxv5hSRgUNy6B6PMCirT6AQQRYhBfYmdq6TcpY3YAcWvwQn4wAnAmUnd/sl585dy8iAGiaEF6GIa1h6l7shtJgQnwAV6KVihlwRb5Caz59L8Ov37xORThXSggGrr6FSLgE9bVbV9zNyiQDfq5LLnEihpBsbTS3SFmKuct9AIB4VaN2TDq3RCf1ZUIyF3lWNhdpQV8WJXNZCZSu3wyAIn1pUDvdtLYYCMWTyKRmA7dnukvJOfcOtSDbsBWJZB223PJRzE0P4urlAwCAE/u+iqmxc1jf/Rg6uh4O1I4hYPsJ2VKGWiye6zsfRigUxdFDTyGTcbIsKaOiPFqPTZvfj7HR4+g//wvnPhzh2rB+SmkKKU0Rng8QN2AjACrzbHq5YkWnvAMzolPO74PRp/aDc80CaD89NfBsAgzG6p2I12BufhiU2S4vauN3m+PlJcW10GZgK4DpmhRw/yBNKkZciLVRrgXb4xqTNP/+SDP688ix2BpWAEu/LsW/qItboiAAAvkirym/qaMCO27aCAA4fugCuna06r8vomtHm/b78MXc+iOX0LWzFb1HtdRtXdvX4vgRPY0bpeja1mJp//iRy4AKdG3PrT9++BKIfiy7zoCx/vjhS5bfoNR5DAW6tuXWmesJ0LXD2U6vMVaJWNrqPToAEKB7a4u5X7d+Lsa5GtvMdfq72bW1xRx/99YWHD+k/c4mQpY+DgxcQZ0u0G9f34SjpwaBSmDHukYc7hsCCLC9UxNYjpzShILtG5tw5KT2W8pSbN3cZLZ3rHfQMun2dDHbTgzCjp4tTblt7FxHrdssfpAK0M2029s7gJ7uZvQeGwDJquju0a/RMf0a9TSb99y4fsePaNeja1uLto2daJln4/jhy5bfxiC7t7U4nwXL8db1XdvX6r+tE3rX9lZzP/szZjwzlv315+r4kUvo2rHW2q/ZJudZPXKZGQN//179XLV9W3LHbcv95h535DK67c/7kUuW48zftv7Nd1P/WLH7Hjt8CaGSVtxbfye29exwjLv3qPN9MGC8F4awcvzoZfO58EuzyF509rkzBCuWcFqsbvpvi7sN09f05BheHwYaHv4Y3nPfbr2D3A6K7n6/78ogdrQ36X0xApG+6/7BIexuatTHxwxbLxJ28OIgdjfr7wcjU0j6vgcu5t41QoGdbdq+B/sHsbOtCZRSvHDpA2jf0o51PRvNLGCHzwxiR0cTziQfxKW+E3jnno1gcfjMYO7KUWD7en3e6BuyzCHbN2j9GYTp6IlBbN2sbdfmAWb+YOYAY545dmIQPVuacOzEICo2tKEs9CgaWm9HJBrHWPsWtK7bhdq6dm2f44O5NtjfJwYtrkfGfNJ7fBDd3bn9WUUMUYHu7mb09g6gu7sZANDc/L9hfOwgSpNXUFbWDYkwEjfV5p7e3nKsbWlAKnUBsfAI6uvX5drU9zHG1N3VhN5ebZ6kEtDTpW07eio3rqOnh8A+o5QAWzc3mfPztk1N5nUl0Obno/rca1zDI6eHQLIU2zY34sjpIWzd0my2d+jsEMIL2k2Xyqhlrj1+9LLNKkAd820OxNzG227Z1nvZnBm7e1rQ2zug/e5u1vqkFMmym9CyNgHgPFrX7kBXT7N+LbR3r8fyTRg04xa6tzSh91iuPWMcBEB3t3N87PkYv48f1b4Rju+tPmhzHmK/0fp82c3IH71HLoOoFF3bctdba2sARIW53jIHH7psXE6rzHLwIgjNyRTGcWQpg67f2mxp//iBC+Z71bWrTfvf+H3wIrp2tZn7mb8PXcSWXe04cbBf23dnG44f0o6jhCCdWQRO4vqGamhhrmX/AgBAKPVXDywuLqK9vR3Dw8OrMSYBAQEBAQEBAYEC0NDQgP7+fsRiMf+dVxEzMzMoLy/HfVUfRUgqzNWyGMiqafxy4iuYnp5GWVkeKa5/AxHIEhCLxdDf3490uoD0cwICAgICAgICAquCSCRy3REAgesTgd2BYrGYeKgEBAQEBAQEBAQKBqVqQQkCitm/gIbrtD6ngICAgICAgICAgMBKQZAAAQEBAQEBAQEBgRsMRUgIJyAgICAgICAgIBAAlF7bDD2iWJgJYQkQEBAQEBAQEBAQuMEgLAECAgICAgICAgKrA3qN6wQIS4AJYQkQEBAQEBAQEBAQuMEgSICAgICAgICAgICAC15++WU88sgjaGxsBCEEzz77rGU7pRR/9Vd/hcbGRsTjcbzjHe/A8ePHLfssLS3hT/7kT1BTU4OSkhI8+uijGBgYWMWzcEKQAAEBAQEBAQEBgdWBql77JU/Mz89j27Zt+NKXvsTd/rd/+7f4/Oc/jy996UvYt28fGhoacP/992N2dtbc58knn8QzzzyDb3/723j11VcxNzeHhx9+GIqiFHwplwtCqXCOEhAQEBAQEBAQWDnMzMygvLwc70x+GCESuWbjyNI0fjX7DUxPT6OsrCzv4wkheOaZZ/DYY48B0KwAjY2NePLJJ/EXf/EXADStf319Pf7mb/4Gn/jEJzA9PY3a2lo8/fTTePzxxwEAQ0NDaGlpwXPPPYd3vetdRTu/fCAsAQICAgICAgICAjcUZmZmLMvS0lJB7fT392N4eBgPPPCAuS4ajeLuu+/G66+/DgA4cOAAMpmMZZ/GxkZ0d3eb+1wLCBIgICAgICAgICCwOqD02i8AWlpaUF5ebi5//dd/XdDpDA8PAwDq6+st6+vr681tw8PDiEQiqKysdN3nWkCkCBUQEBAQEBAQELihcPnyZYs7UDQaXVZ7hBDL/5RSxzo7guyzkhAkQEBAQEBAQEBAYFVAVRWU5B+cW7T+qdZ3WVlZQTEBdjQ0NADQtP1r1qwx14+MjJjWgYaGBqTTaUxOTlqsASMjI7jtttuWPYZCIdyBBAQEBAQEBAQEBApAe3s7Ghoa8Itf/MJcl06n8dJLL5kC/q5duxAOhy37XLlyBb29vdeUBAhLgICAgICAgICAgIAL5ubmcPbsWfP//v5+HD58GFVVVVi7di2efPJJfO5zn0NnZyc6Ozvxuc99DolEAh/60IcAAOXl5fj4xz+Oz372s6iurkZVVRX+7M/+DD09Pbjvvvuu1WkJEiAgICAgICAgILBKoBTANcxOX0Bm/P379+Oee+4x///MZz4DAHjiiSfw1FNP4c///M+RSqXwyU9+EpOTk7jlllvw/PPPI5lMmsd84QtfQCgUwgc+8AGkUim8853vxFNPPQVZlpd/TgVC1AkQEBAQEBAQEBBYURh1Au6NP37N6wS8kPpOwXUCfpMgYgIEBAQEBAQEBAQEbjAIdyABAQEBAQEBAYHVgUoB8vZyB/pNhbAECAgICAgICAgICNxgEJYAAQEBAQEBAQGB1QGlAK5dnQBhCchBWAIEBAQEBAQEBAQEbjAIEiAgICAgICAgICBwg0G4AwkICAgICAgICKwKqEpBr2FgsMiMn4OwBAgICAgICAgICAjcYBAkQEBAQEBAQEBAQOAGg3AHEhAQEBAQEBAQWB1QFdc2O9A17Ps6g7AECAgICAgICAgICNxgECRAQEBAQEBAQEBA4AaDcAcSEBAQEBAQEBBYFYjsQNcPhCVAQEBAQEBAQEBA4AaDsAQICAgICAgICAisDkRg8HUDYQkQEBAQEBAQEBAQuMEgLAECAgICAgICAgKrgiwywDV0y88ic+06v84gSICAgICAgICAgMCKIhKJoKGhAa8OP3eth4KGhgZEIpFrPYxrDkJFmLSAgICAgICAgMAKY3FxEel0+loPA5FIBLFY7FoP45pDkAABAQEBAQEBAQGBGwwiMFhAQEBAQEBAQEDgBoMgAQICAgICAgICAgI3GAQJEBAQEBAQEBAQELjBIEiAgICAgICAgICAwA0GQQIEBAQEBAQEBAQEbjAIEiAgICAgICAgICBwg0GQAAEBAQEBAQEBAYEbDP8/NDoT16eP/7EAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x900 with 2 Axes>"
       ]
@@ -208,10 +208,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "c21f69b1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'pres' (time: 4, latitude: 361, longitude: 720)>\n",
+      "array([[[416.23508, 416.23508, 416.23508, ..., 416.23508, 416.23508,\n",
+      "         416.23508],\n",
+      "        [414.1839 , 414.17355, 414.17355, ..., 414.215  , 414.20465,\n",
+      "         414.19424],\n",
+      "        [396.2    , 396.22073, 396.24146, ..., 396.16895, 396.1793 ,\n",
+      "         396.18964],\n",
+      "        ...,\n",
+      "        [308.89102, 308.82886, 308.76672, ..., 309.0775 , 309.01532,\n",
+      "         308.96353],\n",
+      "        [304.23965, 304.2086 , 304.1671 , ..., 304.3536 , 304.32254,\n",
+      "         304.2811 ],\n",
+      "        [298.52124, 298.52124, 298.52124, ..., 298.52124, 298.52124,\n",
+      "         298.52124]],\n",
+      "\n",
+      "       [[409.82263, 409.82263, 409.82263, ..., 409.82263, 409.82263,\n",
+      "         409.82263],\n",
+      "        [414.42218, 414.2875 , 414.14246, ..., 414.8676 , 414.72263,\n",
+      "         414.56723],\n",
+      "        [416.07968, 415.9036 , 415.7482 , ..., 416.64944, 416.46298,\n",
+      "         416.26617],\n",
+      "...\n",
+      "        [319.2504 , 319.20898, 319.18826, ..., 319.38507, 319.32294,\n",
+      "         319.2815 ],\n",
+      "        [335.6908 , 335.67004, 335.67004, ..., 335.72183, 335.7115 ,\n",
+      "         335.7115 ],\n",
+      "        [317.3857 , 317.3857 , 317.3857 , ..., 317.3857 , 317.3857 ,\n",
+      "         317.3857 ]],\n",
+      "\n",
+      "       [[415.64462, 415.64462, 415.64462, ..., 415.64462, 415.64462,\n",
+      "         415.64462],\n",
+      "        [406.13464, 406.37292, 406.6112 , ..., 405.3888 , 405.63742,\n",
+      "         405.88605],\n",
+      "        [417.1053 , 417.4471 , 417.78897, ..., 416.03824, 416.40082,\n",
+      "         416.75305],\n",
+      "        ...,\n",
+      "        [300.1684 , 300.1477 , 300.12698, ..., 300.25128, 300.22018,\n",
+      "         300.18912],\n",
+      "        [322.17175, 322.1303 , 322.09924, ..., 322.2857 , 322.24426,\n",
+      "         322.20285],\n",
+      "        [340.0624 , 340.0624 , 340.0624 , ..., 340.0624 , 340.0624 ,\n",
+      "         340.0624 ]]], dtype=float32)\n",
+      "Coordinates:\n",
+      "  * latitude   (latitude) float32 90.0 89.5 89.0 88.5 ... -89.0 -89.5 -90.0\n",
+      "  * time       (time) datetime64[ns] 2016-05-01 ... 2016-05-01T18:00:00\n",
+      "  * longitude  (longitude) float32 0.0 0.5 1.0 1.5 2.0 ... -2.0 -1.5 -1.0 -0.5\n",
+      "Attributes:\n",
+      "    units:      hPa\n",
+      "    long_name:  Pressure\n"
+     ]
+    }
+   ],
    "source": [
     "ptrop = ptrop.assign_coords(longitude=(((ptrop.longitude + 180) % 360) - 180))\n",
     "print(ptrop)"
@@ -219,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "id": "experimental-allah",
    "metadata": {},
    "outputs": [],
@@ -262,7 +318,7 @@
     "lat = np.arange(-90,90.5,dy)\n",
     "\n",
     "# relative amount of noise added to regular grid\n",
-    "noise_fac = 0.25\n",
+    "noise_fac = 0\n",
     "\n",
     "# define hemisphere / region\n",
     "hemi = 'NH' ; ind_lat = np.where((lat>=0) & (lat<=90)) #only NH\n",
@@ -270,8 +326,8 @@
     "# ind_lon = np.where((lon>=-30) & (lon<=20)) #to select the relevant block only: 2010\n",
     "# ind_lon = np.where((lon>=-20) & (lon<=60)) #to select the relevant block only: 2003\n",
     "# ind_lon = np.where((lon>=-60) & (lon<=0)) # 1981\n",
-    "# ind_lon = np.where((lon>=-148) & (lon<=-82)) # 2016\n",
-    "ind_lon = np.where((lon>=-17) & (lon<=84)) # 2017\n",
+    "ind_lon = np.where((lon>=-148) & (lon<=-82)) # 2016\n",
+    "# ind_lon = np.where((lon>=-17) & (lon<=84)) # 2017\n",
     "\n",
     "lat = lat[ind_lat]; lon = lon[ind_lon]        \n",
     "lons, lats = np.meshgrid(lon, lat) #for flagbuffer\n",
@@ -281,7 +337,7 @@
     "nlev = np.size(plev)\n",
     "\n",
     "# =======\n",
-    "yy = 2017 \n",
+    "yy = 2016\n",
     "\n",
     "#create output directory \"startf\"\n",
     "if not os.path.exists(outpath  + 'era5/startf/' + str(yy)):\n",
@@ -311,7 +367,8 @@
     "# for ii in range(548,575): #2010\n",
     "# for ii in range(1234,1259): #2003\n",
     "# for ii in range(30, 58): #1981\n",
-    "for ii in range(90, 120): #2017    \n",
+    "#for ii in range(90, 120): #2017    \n",
+    "for ii in range(477, 501): #2016\n",
     "            \n",
     "    flag_tmp = flag[ii,:,:] # one timestep\n",
     "\n",
@@ -427,7 +484,7 @@
     "            # select only tropospheric start points\n",
     "            out = out[ptrop.sel(longitude=xr.DataArray(lon_array), latitude=xr.DataArray(lat_array), time=datetime.strptime(str(date_list[ii]),'%Y-%m-%d %H:%M:%S'), method='nearest') < plev[kk]]\n",
     "            output = np.r_[output, out]# stack r_wise and c_wise. wanted form is lon lat plev\n",
-    "        np.savetxt(outpath  + 'era5/startf/' + str(yy) + \"/\" + date_list[ii].strftime('startf_%Y%m%d_%H_') , output, fmt='%.2f', delimiter='\\t') "
+    "        np.savetxt(outpath  + 'era5/startf/' + str(yy) + \"/\" + date_list[ii].strftime('startf_%Y%m%d_%H_reg') , output, fmt='%.2f', delimiter='\\t') "
    ]
   },
   {
@@ -1046,7 +1103,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.12.0"
   }
  },
  "nbformat": 4,
diff --git a/pyscripts/LIB/__pycache__/data.cpython-39.pyc b/pyscripts/LIB/__pycache__/data.cpython-39.pyc
index 5a3b317adb236413ccea5ea60a8486b1cf8c0b3d..eb81590d528d8a0d63d7a0e1ab95f62f31ce8b0a 100644
GIT binary patch
delta 2139
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diff --git a/pyscripts/LIB/__pycache__/plot.cpython-39.pyc b/pyscripts/LIB/__pycache__/plot.cpython-39.pyc
index 659ea44619b41dd9af9edc82d97e8087a8533592..e453abe8985c4c6311671afb8bc47680949d4cfa 100644
GIT binary patch
delta 47
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F004;y5Ay&3

diff --git a/pyscripts/LIB/calc.py b/pyscripts/LIB/calc.py
index bf88306..d5610d5 100644
--- a/pyscripts/LIB/calc.py
+++ b/pyscripts/LIB/calc.py
@@ -603,11 +603,11 @@ def distances(dataX, dataY, r, m, dataZ=None, sphere=True,
 
             vdist = dataZ[idx[i]] - dataZ[i]
 
-            dist = np.sqrt(np.power(hdist[i], 2) + k * np.power(vdist, 2))
+            dist = np.sqrt(np.power(hdist[i], 2) + np.power(k * vdist, 2))
 
             # Now with the custom distance
             valid = np.where(dist < r)[0]
-            x.extend([i for _ in range(len(valid))])
+            x.extend([i] * len(valid))
             y.extend(idx[i][valid])
             v.extend(dist[valid])
 
diff --git a/pyscripts/LIB/data.py b/pyscripts/LIB/data.py
index b8f7eda..c142685 100644
--- a/pyscripts/LIB/data.py
+++ b/pyscripts/LIB/data.py
@@ -120,24 +120,38 @@ def retrieve_dat(date, path, level):
     c = cdsapi.Client()
 
     if level=="upper":
-        if not exists(path + "plevdat" + date.strftime('/%Y/%m-%d-%H.nc')):
+        if not exists(path + "plevdat" + date.strftime('/%Y/%m-%d.nc')):
             makedirs(path + "plevdat" + date.strftime('/%Y'), exist_ok=True)
             c.retrieve("reanalysis-era5-pressure-levels", {
                 'product_type': 'reanalysis',
                 'variable': ['potential_vorticity','u_component_of_wind',
-                'v_component_of_wind'],
+                'v_component_of_wind', 'geopotential', 'vertical_velocity'],
                 'pressure_level': [
-                    '125', '150', '175',
-                    '200', '225', '250',
-                    '300', '350', '400',
-                    '450', '500',
+                    '100', '125', '150',
+                    '175', '200', '225',
+                    '250', '300', '350',
+                    '400', '450', '500',
+                    '550', '600', '650',
+                    '700', '750', '775',
+                    '800', '825', '850',
+                    '875', '900', '925',
+                    '950', '975', '1000'
                 ],
                 "date": date.strftime('%Y-%m-%d'),
-                "time": date.strftime('%H:00:00'),
+                'time': [
+                    '00:00', '01:00', '02:00',
+                    '03:00', '04:00', '05:00',
+                    '06:00', '07:00', '08:00',
+                    '09:00', '10:00', '11:00',
+                    '12:00', '13:00', '14:00',
+                    '15:00', '16:00', '17:00',
+                    '18:00', '19:00', '20:00',
+                    '21:00', '22:00', '23:00',
+                ],
                 'format' : 'netcdf',
                 'grid'    : '0.5/0.5',
                 'area': [90, -180, 0, 180],
-            }, path + "plevdat" + date.strftime('/%Y/%m-%d-%H.nc'))
+            }, path + "plevdat" + date.strftime('/%Y/%m-%d.nc'))
     elif level=="surf":
         if not exists(path + "surfdat" + date.strftime('/%Y/%m-%d-%H.nc')):
             makedirs(path + "surfdat" + date.strftime('/%Y'), exist_ok=True)
@@ -189,17 +203,17 @@ def io_eigen(d_path, m, Tmin, Tmax, x_list, y_list, v_list, epsilon, bounds,
         eigenvectors.
 
     '''
-    if (not exists(d_path + str(epsilon)) or force_calc):
+    if (not exists(d_path + str(int(epsilon))) or force_calc):
         print("calculating E")
         vals, vecs = cc.eigen(m, Tmin, Tmax, x_list, y_list, v_list,
                               epsilon, bounds, N_k, d_path,
                               x, y, z, customMetric,
                               k, z_coord_type)
-        with open(d_path + str(epsilon), "wb") as f:
+        with open(d_path + str(int(epsilon)), "wb") as f:
             pickle.dump(list((vals, vecs)), f)
     else:
         print("loading E")
-        with open(d_path + str(epsilon), "rb") as f:
+        with open(d_path + str(int(epsilon)), "rb") as f:
             vals, vecs = pickle.load(f)
     return vals, vecs
 
diff --git a/pyscripts/LIB/plot.py b/pyscripts/LIB/plot.py
index 45c9217..a3b1feb 100644
--- a/pyscripts/LIB/plot.py
+++ b/pyscripts/LIB/plot.py
@@ -9,6 +9,7 @@ import numpy as np
 import pandas as pd
 import seaborn as sb
 from matplotlib import pyplot as plt
+import cmcrameri.cm as cmc
 from sys import path
 path.append("../wp21/pyscripts/LIB")
 import calc as cc
@@ -85,12 +86,15 @@ def plot_spectra(D, r, eps, X, Y, Z, k, ax, bounds, customMetric, d_path,
     ax.grid(True, which='both', linestyle="-", linewidth=0.5, zorder = 1)
 
     # Modify legend to be more compact
-    ax.legend(loc="lower left", bbox_to_anchor=(0.01,-0.01), reverse = True,
-              ncols = 3, frameon = False,
-              title="$\epsilon [\mathrm{km}^2]$", borderpad = 0.5,
-              labelspacing = 0.25,
-              handletextpad = 0.5, borderaxespad = 0, columnspacing = 0.8,
-              handleheight = 0.1, handlelength = 0.1, shadow = False)
+    legend = ax.legend(loc="lower left", bbox_to_anchor=(0.01, -0.01),
+                       reverse=True,
+                       ncols=3, frameon=False,
+                       title="$\epsilon$ $[\mathrm{km}^2]$", borderpad=0.5,
+                       labelspacing=0.25,
+                       handletextpad=0.5, borderaxespad=0, columnspacing=0.8,
+                       handleheight=0.1, handlelength=0.1, shadow=False)
+    legend.get_title().set_ha('left')
+    legend._legend_box.align = "left"
     # Tight layout for making everything compact
     # plt.tight_layout()
 
@@ -410,7 +414,7 @@ def plot_set_comb(trajs, cmap, norm, date, dth=None, kclustL=None):
         cbar = fig.colorbar(h4, ax=ax, orientation='horizontal',
                             fraction=0.1, pad=0.05)
 
-        cbar.set_label('pressure [hPa]',size=9)
+        cbar.set_label('$p$ [hPa]',size=9)
         cbar.ax.tick_params(labelsize=9)
 
         if kclustL is None:
@@ -517,7 +521,7 @@ def plot_set_sep(trajs, cmap, norm, dth, date):
         cbar = fig.colorbar(h4, ax=ax.ravel().tolist(),
                             orientation='horizontal', fraction=0.1, pad=0.05)
 
-        cbar.set_label('pressure [hPa]',size=9)
+        cbar.set_label('$p$ [hPa]',size=9)
         cbar.ax.tick_params(labelsize=9)
 
         savefig("im/sets/" + date.strftime('set_%Y%m%d_%H') +
@@ -644,7 +648,7 @@ def plot_set_sep4(trajs, cmap, norm, dth, date, kclustL):
         cbar = fig.colorbar(h4, ax=ax.ravel().tolist(),
                             orientation='horizontal', fraction=0.1, pad=0.05)
 
-        cbar.set_label('pressure [hPa]',size=9)
+        cbar.set_label('$p$ [hPa]',size=9)
         cbar.ax.tick_params(labelsize=9)
 
         savefig("im/sets4/" + date.strftime('set4_%Y%m%d_%H') +
@@ -677,7 +681,7 @@ def plot_traj(trajs, cmap, norm):
     from cartopy.crs import Stereographic, PlateCarree
     from numpy import vstack, nonzero, abs, diff, split
 
-    fig, ax = subplots(1, 1, figsize=(3.5, 2),
+    fig, ax = subplots(1, 1, figsize=(3.25, 2),
                        subplot_kw={'projection': Stereographic(
                             central_latitude=90.0, true_scale_latitude=50.0,
                             central_longitude=-120)})
@@ -725,7 +729,7 @@ def plot_traj(trajs, cmap, norm):
     cbar = fig.colorbar(h4, ax=ax, orientation='horizontal',
                         fraction=0.1, pad=0.05)
 
-    cbar.set_label('pressure [hPa]')#,size=14)
+    cbar.set_label('$p$ [hPa]')#,size=14)
     # cbar.ax.tick_params(labelsize=14)
     tight_layout()
 
@@ -814,7 +818,7 @@ def plot_traj_sep(trajs, cmap, norm, dth):
     cbar = fig.colorbar(h4, ax=ax.ravel().tolist(), orientation='horizontal',
                         fraction=0.1, pad=0.05)
 
-    cbar.set_label('pressure [hPa]',size=9)
+    cbar.set_label('$p$ [hPa]',size=9)
     cbar.ax.tick_params(labelsize=9)
 
     return fig, ax
@@ -922,7 +926,7 @@ def plot_traj_sep4(trajs, cmap, norm, dth, kclustL):
     cbar = fig.colorbar(h4, ax=ax.ravel().tolist(), orientation='horizontal',
                         fraction=0.1, pad=0.05)
 
-    cbar.set_label('pressure [hPa]',size=9)
+    cbar.set_label('$p$ [hPa]',size=9)
     cbar.ax.tick_params(labelsize=9)
 
     return fig, ax
@@ -1015,7 +1019,7 @@ def plot_set_comb3d(trajs, cmap, norm, date, dth=None, kclustL=None):
         cbar = fig.colorbar(mapper, ax=ax, orientation='horizontal',
                             fraction=0.1, pad=0.05)
 
-        cbar.set_label('pressure [hPa]',size=9)
+        cbar.set_label('$p$ [hPa]',size=9)
         cbar.ax.tick_params(labelsize=9)
 
         if kclustL is None:
@@ -1051,6 +1055,7 @@ def plot_wrapper(trajs, dth, date_0, plotCase, kclustL=None):
     from numpy import array
     from matplotlib.colors import from_levels_and_colors
     from matplotlib.pyplot import savefig
+    from matplotlib.colors import Normalize
 
     # colormap for trajectories
 
@@ -1077,6 +1082,8 @@ def plot_wrapper(trajs, dth, date_0, plotCase, kclustL=None):
     # 17 levels
     # convert RGB values to range between 0 - 1
     colors = array(colors)/255
+
+    colors = cmc.glasgow_r(np.linspace(0, 1, len(levels)+1))
     # creat colormap
     cmap, norm = from_levels_and_colors(levels, colors, extend='both')
 
@@ -1229,7 +1236,8 @@ def plot_set_hist(data7, data3, kclustL=None):
     return fig, ax, ax2
 
 
-def plot_spaghetti(ax, dat, alpha, colors=None, labels=None, inv=None):
+def plot_spaghetti(ax, dat, alpha, colors=None, labels=None, inv=None,
+                   text_coords=None):
     '''
     Do a spaghetti plot
 
@@ -1249,17 +1257,28 @@ def plot_spaghetti(ax, dat, alpha, colors=None, labels=None, inv=None):
     None.
 
     '''
-    if not(inv is None):
+    if not (inv is None):
         plot_dat = pd.DataFrame(dat)
         plot_dat['id'] = inv
         plot_dat = plot_dat.melt(id_vars=['id']).rename(
-            columns={'variable':'t'})
+            columns={'variable': 't'})
         plot_dat['t'] = plot_dat['t'] - 72
         sb.lineplot(x='t', y='value', hue='id', palette=colors,
-                    estimator="median", errorbar=('pi',50), data=plot_dat,
+                    estimator="median", errorbar=('pi', 50), data=plot_dat,
                     ax=ax)
+        if not (text_coords is None):
+            medians = plot_dat.groupby(['id', 't']).median().reset_index()
+            for i in np.unique(inv):
+                medianLine = medians[medians['id'] == i]
+                lastPoint = medianLine.iloc[text_coords[i][0]]
+                ax.annotate(f'{i}', xy=(lastPoint['t'],
+                                            lastPoint['value']),
+                            xytext=text_coords[i][1],
+                            textcoords='offset points',
+                            color=colors[i],
+                            arrowprops=dict(arrowstyle="-", color=colors[i]))
         ax.grid()
-        ax.set_xlabel("$t$")
+        ax.set_xlabel("$t$ [h]")
         ax.set_xlim([plot_dat['t'].iloc[0], plot_dat['t'].iloc[-1]])
 
     else:
diff --git a/pyscripts/clusterfigs.py b/pyscripts/clusterfigs.py
index 23dfafb..9e741c1 100644
--- a/pyscripts/clusterfigs.py
+++ b/pyscripts/clusterfigs.py
@@ -5,19 +5,33 @@ Created on Wed Jan  3 15:07:16 2024
 
 @author: schoelleh96
 """
+
+locale.setlocale(locale.LC_TIME, 'en_US')
+
+plt.rcParams['text.usetex'] = True
+plt.rcParams['pgf.texsystem'] = "pdflatex"
+plt.rcParams['text.latex.preamble'] = (
+    r'\usepackage{amsmath,amsfonts,amssymb,cmbright,standalone}')
+
 fig, ax = plt.subplots(1,1, figsize=(3., 2.))
 pp.plot_spectra(D, 3*np.sqrt(epsilon_opt), epsilon_opt, lon, lat, p, k_p,
                 ax, boundscs, True, path + "spnalp" + str(alpha),
                 t_sel, "p")
 plt.savefig("/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/im/" +
-            "clustersnapshots/spectrum20170126_18.pdf", dpi=300, bbox_inches="tight")
+            "clustersnapshots/spectrum20170126_18.pdf", dpi=300,
+            bbox_inches="tight")
 
+# %%
 
 plt.rcParams['font.size'] = 20
 
+c = kclust.labels_
 fig = plt.figure()
 ax3d = fig.add_axes([0,0,1,1], projection="3d")
 X, Y, Z = Xcs, Ycs, Zcs
+X, Y, Z = X[c!=0], Y[c!=0], Z[c!=0]
+c = c[c!=0]
+c[c==5] = 0
 minxy = np.min([np.min(X), np.min(Y)])
 maxxy = np.max([np.max(X), np.max(Y)])
 ax3d.set_xlim((minxy, maxxy))
@@ -25,27 +39,39 @@ ax3d.set_ylim((minxy, maxxy))
 # ax3d.set_xlim((np.min(X), np.max(X)))
 # ax3d.set_ylim((np.min(Y), np.max(Y)))
 ax3d.set_zlim((np.min(Z), np.max(Z)))
+
+set1_colors = plt.get_cmap('Set1').colors
+custom_colors = [set1_colors[i] for i in [0, 1, 2, 3, 4]]
+from matplotlib.colors import ListedColormap
+custom_cmap = ListedColormap(custom_colors)
+
 points = ax3d.scatter(X[:,plot_0],Y[:,plot_0],Z[:,plot_0],c=c,
-                      cmap="Set1", alpha=0.25)
-points = ax3d.scatter(X[:,t_i],Y[:,t_i],Z[:,t_i],c=c, cmap="Set1",
-                      alpha=0.2)
-points = ax3d.scatter(X[c!=2,t_i],Y[c!=2,t_i],Z[c!=2,t_i],c=c[c!=2], cmap="Set1",
-                      alpha=0.2)
-for i, c_i in enumerate(np.unique(c)):
+                       cmap=custom_cmap,
+                      # cmap="Set1",
+                      alpha=0.2, s=16)
+points = ax3d.scatter(X[:,t_i],Y[:,t_i],Z[:,t_i],c=c,
+                       cmap=custom_cmap,
+                      # cmap="Set1",
+                      alpha=0.2, s=16)
+# points = ax3d.scatter(X[c!=0,t_i],Y[c!=0,t_i],Z[c!=0,t_i],c=c,
+#                       cmap=custom_cmap,
+#                       #cmap="Set1",
+#                       alpha=0.2, s=16)
+for i, c_i in enumerate(np.unique(c)):#[c!=2])):
     mean_x = np.mean(X[c==c_i, :], axis=0)
     mean_y = np.mean(Y[c==c_i, :], axis=0)
     mean_z = np.mean(Z[c==c_i, :], axis=0)
     ax3d.plot(mean_x, mean_y, mean_z, color=points.to_rgba(c_i))
-xb, yb, _ = cc.coord_trans(block[1], block[2], block[1], "None", 1,
+xb, yb, _ = cc.coord_trans(B[1], B[2], B[1], "None", 1,
                            proj="stereo")
-ax3d.contour(xb, yb, block[0][int(72/6),:,:], [0.5], zdir="z",
-             offset=1050,
+ax3d.contour(xb, yb, B[0][int(72/6),:,:], [0.5], zdir="z",
+             offset=1000,
              colors="k")
 coast = coasts
 for lon, lat in coast:
     xc, yc, _ = cc.coord_trans(lon, lat, lon, "None", 1,
                                proj="stereo")
-    z = [1050] * len(xc)
+    z = [1000] * len(xc)
     ax3d.plot(xc, yc, z, color='grey')
     # This will connect the points as line segments
 
@@ -60,7 +86,7 @@ for lon in longitudes:
     lons = np.full(lats.shape, lon)
     xc, yc, _ = cc.coord_trans(lons, lats, lons, "None", 1,
                                proj="stereo")
-    z = np.full(xc.shape, 1050)
+    z = np.full(xc.shape, 1000)
     ax3d.plot(xc, yc, z, linestyle='--', color='grey')
 
 # Plot latitudes (parallels)
@@ -71,13 +97,13 @@ for lat in latitudes:
     lats = np.full(lons.shape, lat)
     xc, yc, _ = cc.coord_trans(lons, lats, lons, "None", 1,
                                proj="stereo")
-    z = np.full(xc.shape, 1050)
+    z = np.full(xc.shape, 1000)
     ax3d.plot(xc, yc, z, linestyle='--', color='grey')
 ax3d.invert_zaxis()
 for lon, lat in coast:
     xc, yc, _ = cc.coord_trans(lon, lat, lon, "None", 1,
                                proj="stereo")
-    z = [1050] * len(xc)
+    z = [1000] * len(xc)
     ax3d.plot(xc, yc, z, color='grey')
     # This will connect the points as line segments
 
@@ -87,7 +113,7 @@ ax3d.set_yticks([])
 ax3d.xaxis.label.set_visible(False)
 ax3d.yaxis.label.set_visible(False)
 ax3d.zaxis.labelpad = 15  # Increase the value to move the label further away
-ax3d.set_zlabel("pressure [hPa]")
+ax3d.set_zlabel("$p$ [hPa]")
 ax3d.xaxis._axinfo['grid'].update({'visible': False})
 ax3d.yaxis._axinfo['grid'].update({'visible': False})
 ax3d.xaxis.pane.fill = False
@@ -100,18 +126,181 @@ ax3d.spines['right'].set_visible(False)
 ax3d.spines['bottom'].set_visible(False)
 ax3d.spines['top'].set_visible(False)
 
+# 20160502
+# text_coords = {0: [-3000, 3200, 800], 1: [-500, 3500, 900],
+#                2: [-1300, 3600, 200],
+#                3: [2000, 4400, 900], 4: [-3700, 4600, 400],
+#                5: [-4000, 4400, 500]}
+
+# # 20160504
+# text_coords = {0: [2000, 4200, 180], 1: [3500, 4000, 380],
+#                 2: [1000, -3000, 550],
+#                 3: [1000, -1000, 450]}
+
+# 20160126
+text_coords = {0: [-3500, -1000, 50], 1: [-4500, -6000, 900],
+                2: [-4000, -6000, 400],
+                3: [500, -2000, 450], 4: [-1500, -2000, 500]}
+
+for i in np.unique(invclean):
+    x, y, z = text_coords[i]
+    ax3d.text(x, y, z, f'{i}', fontsize = 20,
+              color=colors[i], bbox=dict(facecolor='white',
+                                         edgecolor='black',
+                                         boxstyle='round,pad=0.1',
+                                         alpha=None))
+
 # ax3d.zaxis._axinfo['label']['space_factor'] = 5
 
 plt.savefig("/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/" +
             "im/clustersnapshots/20170126_18_3d.pdf")
 
-dat = dat[inv!=2, :]
-inv = inv[inv !=2]
+# %%
+
+plt.rcParams['font.size'] = base_font_size
+
+datclean = dat[:, :]
+invclean = inv[:]
 plt.rcParams['font.size'] = 10
-fig, ax = plt.subplots(1,1, figsize=(3.4, 2.4))
-pp.plot_spaghetti(ax, dat, alpha_spag, colors=colors, inv=inv)
+fig, ax = plt.subplots(1,1, figsize=(3., 2.4))
+# #theta
+# text_coords = {0: [-25, (12, -17)], 1: [-25, (0, 12)], 2: [-25, (4, 12)],
+#                 3: [-10, (5, -15)], 4: [-16, (0, -14)],
+#                 5: [-30, (-10, -10)]}
+# q
+# text_coords = {0: [-29, (10, 20)], 1: [-50, (10, 0)], 2: [-55, (0, 20)],
+#                3: [-45, (0, -9)], 4: [-30, (-5, 20)],
+#                5: [-18, (-15, 5)]}
+
+# #u
+# text_coords = {0: [-121, (12, 17)], 1: [-35, (-30, 30)],
+#                2: [-85, (-10, 20)],
+#                 3: [-20, (0, -30)]}
+
+# #v
+# text_coords = {0: [-22, (-20, 25)], 1: [-22, (-20, 25)],
+#                2: [-95, (-10, 20)],
+#                 3: [-20, (0, -30)]}
+
+#theta2017
+text_coords = {0: [-65, (-5, 5)], 1: [-40, (20, -25)],
+               2: [-55, (0, -15)],
+                3: [-2, (-5, -30)],
+                4: [-10, (-5, -40)]}
+
+pp.plot_spaghetti(ax, datclean, alpha_spag, colors=colors, inv=invclean,
+                  text_coords=text_coords)
 ax.legend_.remove()
-ax.set_ylabel("$\\Theta$[K]")
-plt.tight_layout()
+ax.set_ylabel("$\\theta$ [K]")
+# ax.set_ylabel("$q$ [g kg\\textsuperscript{-1}]")
+# ax.set_ylabel("$v$ [m s\\textsuperscript{-1}]")
+
+# ax.set_ylabel("$v$ [m s\\textsuperscript{-1}]")
+# plt.savefig("/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/im/" +
+#             "clustersnapshots/20160502_00_theta.pdf", bbox_inches="tight")
+# plt.savefig("/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/im/" +
+#             "clustersnapshots/20160504_00_v.pdf", bbox_inches="tight")
 plt.savefig("/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/im/" +
-            "clustersnapshots/20170126_18_Theta.pdf")
+            "clustersnapshots/20170126_18_theta.pdf", bbox_inches="tight")
+
+
+# %%
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.lines as mlines
+import pandas as pd
+
+def createPolarPlotWithSpecialMarkers(trajs, inv, cmap, text_coords=None):
+    """
+    Create a polar plot showing the temporal development of median U and V
+    velocity components, with special markers at the first timestep and then
+    every 24 timesteps. Each marker type appears only once in the legend.
+
+    Parameters:
+    - trajs: Dictionary containing 'U' and 'V' keys with ndarray of shape (m, n_t).
+    - inv: An ndarray of size m indicating cluster assignment for each trajectory.
+    - colors: A dictionary mapping cluster labels to color strings.
+    """
+    plt.figure(figsize=(3.4,3.4))
+    ax = plt.subplot(111, polar=True)
+    legend_handles = []
+    # Plot lines and special markers for each cluster
+    for cluster in np.unique(inv):
+        idx = np.where(inv == cluster)[0]
+        color = cmap(cluster)
+
+        # Calculate median U and V for the cluster
+        median_u = np.median(trajs['U'][idx,:], axis=0)
+        median_v = np.median(trajs['V'][idx,:], axis=0)
+
+        # Convert to polar coordinates
+        angles = np.arctan2(median_u, median_v)
+        radii = np.sqrt(median_u**2 + median_v**2)
+
+        # Plot the trajectory line for the cluster
+        ax.plot(angles, radii, label=f'Cluster {cluster}', color=color)
+        markers = ['o', 's', '^', '*', 'v', 'X', 'p', 'h']
+        # Plot special markers
+        for i in range(len(angles)):
+            if i % 24 == 0:
+                marker = markers[int(i/24)]
+                ax.plot(angles[i], radii[i], marker=marker, markersize=8,
+                        linestyle='None', color=color)
+                if cluster == np.unique(inv)[0]:  # Add legend entry only for the first cluster
+                    legend_handles.append(mlines.Line2D([], [], color='k',
+                                                    marker=marker,
+                                                    markersize=10,
+                                                    label=f'${i-72}$',
+                                                    linestyle='None'))
+        if not (text_coords is None):
+            angle_i = angles[text_coords[cluster][0]]
+            radius_i = radii[text_coords[cluster][0]]
+            ax.annotate(f'{cluster}', xy=(angle_i, radius_i),
+                                    xytext=text_coords[cluster][1],
+                                    textcoords='offset points',
+                                    color=color,
+                                    arrowprops=dict(arrowstyle="-",
+                                                    color=color))
+
+    plt.legend(handles=legend_handles, loc='best', title="$t$ [h]",
+               borderpad = .6, framealpha=1,
+               labelspacing = 0.25, handletextpad = .75, borderaxespad = 0,
+               handleheight = 1, handlelength = .2)
+
+    # Example wind speeds to label and their angles (just for demonstration)
+    wind_speeds = [10, 20, 30, 40]  # Example wind speeds
+    angles_for_labels = 4* [0]  # Example angles where you want to place labels
+    for wind_speed, angle in zip(wind_speeds, angles_for_labels):
+            label = f"${wind_speed}$" + " $\\mathrm{ms}^{-1}$"  # LaTeX-style formatting
+            ax.annotate(label, xy=(angle, wind_speed), xytext=(angle, wind_speed),
+                        textcoords='data', ha='center', va='center',
+                        arrowprops=dict(arrowstyle="->", connectionstyle="arc3"))
+    # Adjust plot appearance
+    ax.set_theta_zero_location('N')
+    ax.set_theta_direction(-1)
+    ax.set_yticklabels([])
+
+    # Assuming `ax` is your polar axes and you want to label specific angles
+    # Example angles: 0, π/2, π, 3π/2
+    angles_rad = np.linspace(0,2*np.pi, 8, endpoint=False)
+    angle_labels = [f'${d}^{{\circ}}$' for d in np.linspace(0, 360, 8, endpoint=False).astype(int)]
+    ax.set_xticks(angles_rad)  # Set the angles where you want the labels
+    ax.set_xticklabels(angle_labels)  # Set the LaTeX formatted labels
+    plt.savefig("/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/im/" +
+                "clustersnapshots/20170126_18_uvpolar.pdf")
+
+# %%
+
+mapping = {0: 3, 1: 2, 2: 1, 3: 0, 4: 4, 5:5}
+
+# Apply the mapping using list comprehension
+invclean = np.array([mapping[label] for label in inv])
+
+text_coords = {0: [-58, (-15, 5)], 1: [-5, (0, -15)],
+               2: [-2, (-15, -15)],
+                3: [-35, (-15, 00)],
+                4: [-10, (-4, -40)]}
+
+createPolarPlotWithSpecialMarkers(trajs[inv!=5, :][:, t_sel], invclean, custom_cmap,
+                                  text_coords=text_coords)
+
diff --git a/pyscripts/epsloglog.py b/pyscripts/epsloglog.py
index 87c2800..35441a9 100644
--- a/pyscripts/epsloglog.py
+++ b/pyscripts/epsloglog.py
@@ -9,7 +9,7 @@ Created on Wed Jun 28 12:43:24 2023
 # %% library imports
 
 # This will move the console to the right working directory.
-from os.path import  dirname, abspath, exists
+from os.path import  dirname, abspath, exists, join
 from os import chdir
 chdir(dirname(abspath(__file__)))
 
@@ -21,13 +21,16 @@ import matplotlib.pyplot as plt
 from datetime import datetime, timedelta
 import pandas as pd
 import seaborn as sb
-from matplotlib.colors import LogNorm
+from matplotlib.colors import LogNorm, BoundaryNorm
 import matplotlib.ticker as ticker
 import pickle
+import cmcrameri.cm as cmc
+import locale
+import matplotlib.colors as mcolors
 
 # %% Initials
 
-if argv[0]=='':
+if argv[0] == '':
     date_0 = "20160502_00"
     trapath = ("../era5/traj/2016/")
 else:
@@ -39,32 +42,53 @@ else:
 # only every nth trajectory
 n = 1
 
-YEAR="2017"
+YEAR = "2017"
 # Initialize dates
 if YEAR == "2016":
-    dates = [datetime(2016, 4, 30, 0), datetime(2016,5,2,0),
-              datetime(2016,5,4,0)]
+    dates = [datetime(2016, 4, 30, 0), datetime(2016, 5, 2, 0),
+             datetime(2016, 5, 4, 0)]
     dates = [datetime(2016, 4, 29, 6) + timedelta(hours=i)
-              for i in range(0, 144, 6)]
+             for i in range(0, 144, 6)]
     # dates = [datetime(2016,5,6,0), datetime(2016,5,7,0)]
+    trapath = ("../era5/traj/2016/")
 elif YEAR == "2017":
     dates = [datetime(2017, 1, 23, 18) + timedelta(hours=i)
-              for i in range(0, 28*6, 6)]
-
+             for i in range(0, 28*6, 6)]
+    trapath = ("../era5/traj/2017/")
 
 # k_p_var = cc.calc_k(U/1000, V/1000, Omg/100)
 k_p = 15
 
-base_font_size = 10 # pt
+base_font_size = 10  # pt
+
+locale.setlocale(locale.LC_TIME, 'en_US')
 
 plt.rcParams['text.usetex'] = True
-plt.rcParams['font.family'] = 'sans-serif'
-plt.rcParams['font.sans-serif'] = ['Helvetica']
+plt.rcParams['pgf.texsystem'] = "pdflatex"
+plt.rcParams['text.latex.preamble'] = (
+    r'\usepackage{amsmath,amsfonts,amssymb,cmbright,standalone}')
 plt.rcParams['font.size'] = base_font_size
-plt.rcParams['text.latex.preamble'] = r'\usepackage{amsmath,amsfonts,amssymb}'
+
 # %% functions
 
-def calc_Sums(trapath, date_0):
+def get_NN(dataX, dataY, dataZ, r, m, k=15):
+    from sklearn.neighbors import BallTree
+
+    dd = np.array([np.deg2rad(dataX), np.deg2rad(dataY)]).T
+    BT = BallTree(dd, metric='haversine')
+    idx, dist = BT.query_radius(dd, r=r / 6371, return_distance=True)
+    dist = dist * 6371
+    # each element in idx/hdist corresponds to a point whose NN has
+    # been queried
+
+    for i in range(m):
+        vdist = dataZ[idx[i]] - dataZ[i]
+
+        dist[i] = np.sqrt(np.power(dist[i], 2) + np.power(k * vdist, 2))
+
+    return idx, dist
+
+def calc_Sums(trapath, date_0, NN=False):
 
     trajs = np.load(trapath + "traj_" + date_0 + ".npy")
 
@@ -86,36 +110,61 @@ def calc_Sums(trapath, date_0):
         # Parallelization only works if there arent too many traj,
         # crashes otherwise (RAM probably)
         # D = delayed(cc.distances)(Lon[:,t], Lat[:,t], 3*np.sqrt(1e10),
-        D = cc.distances(Lon[:,t], Lat[:,t], 3*np.sqrt(1e10),
-                                  Lon.shape[0], dataZ=P[:,t],
-                                  customMetric=True, k=k_p)
+        # D = cc.distances(Lon[:, t], Lat[:, t], 3*np.sqrt(1e10),
+        #                  Npoints, dataZ=P[:, t],
+        #                  customMetric=True, k=k_p)
         # D = dd.io_dist(path, 1e5, Lon[:,[t]], Lat[:,[t]], P[:,[t]], True,k_p,
         #                 "p", [t], force_calc=False)
 
-        for e in epsilon_opt:
-            # vals = delayed(np.exp)(-np.square(D[2])/e)
-            vals = np.exp(-np.square(D[2])/e)
-
-            # df = delayed(pd.DataFrame)({'eps' : e,
-            df = pd.DataFrame({'eps' : e,
-                                          't' : t,
-                                          'date' : date_0,
-                                          'sums' : ((2*vals.sum() +
-                                                     Lon.shape[0])
-                                                    / (Lon.shape[0]**2)),
-                                          'm' : Npoints},
-                                        index = [i])
+        # Do NN search
+        if NN:
+            eps = 1e5
+            at_least_5_NN = False
+            while not (at_least_5_NN):
+                D = get_NN(Lon[:, t], Lat[:, t], P[:, t], 3*np.sqrt(eps),
+                           Npoints, k=k_p)
+                at_least_5_NN = True
+                for d in D[0]:
+                    if d.shape[0] < 6:
+                        print(d.shape[0])
+                        at_least_5_NN = False
+                        break
+                eps = eps * 10
+                print(eps)
+            mean = np.mean([np.mean(np.sort(arr)[1:6]) for arr in D[1]])
+            df = pd.DataFrame({'t': t,
+                               'date': date_0,
+                               'NNdist': mean},
+                               index = [i])
             Sum_list.append(df)
-            i +=1
+            i += 1
+        else:
+            for e in epsilon_opt:
+                # vals = delayed(np.exp)(-np.square(D[2])/e)
+                vals = np.exp(-np.square(D[2])/e)
+
+                # df = delayed(pd.DataFrame)({'eps' : e,
+                df = pd.DataFrame({'eps': e,
+                                   't': t,
+                                   'date': date_0,
+                                   'sums': ((2*vals.sum() +
+                                             Npoints)
+                                            / (Npoints**2)),  # sums are normalized
+                                   'm': Npoints,
+                                   'dist': D[2].mean()},
+                                  index=[i])
+                Sum_list.append(df)
+                i += 1
 
     # Sum_list = compute(*Sum_list)
     Sums = pd.concat(Sum_list, ignore_index=True)
 
     return Sums
 
+
 def calc_dd(group):
     leps = np.log(group["eps"]).reset_index(drop=True)
-    lS = np.log(group["sums"]).reset_index(drop=True)
+    lS = np.log(group["sums"] * np.square(group["m"])).reset_index(drop=True)
 
     # This is the derivative of log(S(eps) with respect to log(eps):
     dlS = lS.diff().dropna() / leps.diff().dropna()
@@ -124,13 +173,23 @@ def calc_dd(group):
     e_max = np.argmax(dlS)
     d = 2 * dlS[e_max]
 
-    density = ((lS[e_max] - np.log(group["m"].reset_index(drop=True)[0]))/d -
-               (leps[e_max] + np.log(2*np.pi))/2)
+    density = ((np.log(np.pi) + leps[e_max])/2 +
+               (np.log(group["m"].reset_index(drop=True)[0]) - lS[e_max])/d)
     rho = density * d
-    return pd.Series({"dimension": d, "density": density, "rho" : rho})
+
+    # meandist = np.mean(- np.log((group["sums"] * np.square(group["m"])
+    #                              - group["m"]) / 2) *
+    #                    (2 * group["eps"] / (group["m"] * (group["m"] - 1))))
+    meandist = np.mean(np.sqrt(-np.log(group["sums"]) * group["eps"]))
+    dist = np.mean(group["dist"])
+    NNdist = np.mean(group["NNdist"])
+    return pd.Series({"dimension": d, "density": density, "rho": rho,
+                      "e_max": np.exp(leps[e_max]), "L": np.exp(density),
+                      "meandist": meandist, "dist": dist, "NNdist": NNdist})
+
 
 def plot_epsloglog(Sums, name, in3d=False):
-    fig = plt.figure(figsize=(7.2,2.8))
+    fig = plt.figure(figsize=(6.5,3.25))
     if in3d:
         ax = fig.add_subplot(111, projection='3d')
         if name == "all":
@@ -173,21 +232,30 @@ def plot_epsloglog(Sums, name, in3d=False):
 
         plt.savefig("../im/epsloglog/3d" + name + ".png", bbox_inches="tight")
 
-    else :
+    else:
         ax = fig.add_subplot(111)
-        if name=="all":
+        if name == "all":
             sb.lineplot(data=Sums, x="eps", y="sums", hue="date", marker=".",
                         dashes=False, legend=True)
         else:
-            palette = sb.diverging_palette(240, 10, n=len(Sums['t'].unique()),
-                                           center="light")
-            sb.lineplot(data=Sums, x="eps", y="sums", hue="t", #marker=".",
-                        dashes=False, legend=True, palette=palette)
+            colors = [cmc.managua(i/len(Sums['t'].unique()))
+                      for i in range(len(Sums['t'].unique()))]
+
+            # Use the generated colors in your Seaborn plot
+            palette = sb.color_palette(colors)
+            # palette = sb.color_palette("icefire",
+            #                             n_colors=len(Sums['t'].unique()),
+            #                             as_cmap=False)
+            # palette = sb.diverging_palette(240, 10, n=len(Sums['t'].unique()),
+            #                                 center="light")
+            sb.lineplot(data=Sums, x="eps", y="sums", hue="t",  # marker=".",
+                        dashes=False, legend=True, palette=palette,
+                        linewidth=.5)
         evaluated_x_values = Sums['eps'].unique()
-        sb.rugplot(x=evaluated_x_values, height=0.02, color='green')
+        sb.rugplot(x=evaluated_x_values, height=0.025, color='green')
         # plt.setp(plt.gca().get_lines(), markersize=2)
         ax.set_xlabel("$\\epsilon$ [km\\textsuperscript{2}]")
-        ax.set_ylabel("$S_{\\epsilon, t}/m^2$")
+        ax.set_ylabel("$S_{t}(\\epsilon) m^{-2}$ [--]")
         handles, labels = plt.gca().get_legend_handles_labels()
         desired_labels = [Sums['t'].min(), Sums['t'].quantile(0.25),
                           Sums['t'].median(), Sums['t'].quantile(0.75),
@@ -198,10 +266,13 @@ def plot_epsloglog(Sums, name, in3d=False):
         desired_labels = [r'${}$'.format(d) for d in desired_labels]
         # Add a legend outside of the plot
         legend = plt.legend(filtered_handles, desired_labels)
-        legend.set_title("$t$")
+        legend.set_title("$t$ [h]")
+        for legobj in legend.legend_handles:
+            legobj.set_linewidth(2)  # Thicker lines in the legend
         plt.xscale('log')
         plt.yscale('log')
-        plt.savefig("../im/epsloglog/" + name + ".pdf", bbox_inches="tight")
+        plt.savefig("../im/epsloglog/" + name + ".pdf",
+                    bbox_inches="tight")
 
 def plot_epsloglog_heat(Sums, name):
     fig = plt.figure(figsize=(5.6, 4))
@@ -256,45 +327,135 @@ def plot_epsloglog_heat(Sums, name):
 
 # %% execution
 
-if date_0 == "all":
-    Sum_list = list()
-    for date in dates:
-        Sums = calc_Sums(trapath, date.strftime('%Y%m%d_%H'))
-        # Sums = Sums.groupby(['eps', 'date'])['sums'].mean().reset_index()
-        Sum_list.append(Sums)
-    Sums = pd.concat(Sum_list, ignore_index=True)
+# time.sleep(60*3600)
+
+
+def load_or_calc_data(date_0, trapath, dat_path, force_calc=False):
+    """
+    Load or calculate data based on existence and the force_calc flag.
+
+    Parameters
+    ----------
+        date_0 (str): The date identifier or 'all' for processing all dates.
+        trapath (str): The path to the trajectory data.
+        dat_path (str): The directory path for storing/loading the pickle files
+        force_calc (bool): If True, force the recalculation of data.
+    """
+    def calc_and_save(date):
+        """Helper function to calculate and save data."""
+        sums = calc_Sums(trapath, date)
+        filename = join(dat_path, f"{date}S.pkl")
+        with open(filename, "wb") as f:
+            print("Saving data to:", filename)
+            pickle.dump(sums, f)
+        return sums
+
+    if date_0 == "all":
+        Sum_list = []
+        for date in dates:
+            filename = join(dat_path, f"{date.strftime('%Y%m%d_%H')}S.pkl")
+            print(filename)
+            print(exists(filename))
+            if not force_calc and exists(filename):
+                with open(filename, "rb") as f:
+                    S = pickle.load(f)
+            else:
+                S = calc_and_save(date.strftime('%Y%m%d_%H'))
+            Sum_list.append(S)
+        Sums = pd.concat(Sum_list, ignore_index=True)
+    else:
+        filename = join(dat_path, f"{date_0}S.pkl")
+        if not force_calc and exists(filename):
+            with open(filename, "rb") as f:
+                Sums = pickle.load(f)
+        else:
+            Sums = calc_and_save(date_0)
 
-else:
-    Sums = calc_Sums(trapath, date_0)
+    return Sums
 
-with open("pickle/" + date_0 + "S.pkl", "wb") as f:
-    pickle.dump(Sums, f)
+def loadOrCalcData(date_0, trapath, datPath, forceCalc=False, calcNN=False):
+    def calcAndSave(date):
+        sums = calc_Sums(trapath, date, NN=calcNN)
+        filename = join(datPath, f"{date}S.pkl")
+        if exists(filename) and not forceCalc:
+            with open(filename, "rb") as f:
+                existingSums = pickle.load(f)
+            sums = pd.merge(existingSums, sums, on=['t', 'date'])
+        with open(filename, "wb") as f:
+            print("Saving data to:", filename)
+            pickle.dump(sums, f)
+        return sums
+
+    if date_0 == "all":
+        SumList = []
+        for date in dates:
+            filename = join(datPath, f"{date.strftime('%Y%m%d_%H')}S.pkl")
+            print(filename)
+            print(exists(filename))
+            if not forceCalc and exists(filename) and not calcNN:
+                with open(filename, "rb") as f:
+                    S = pickle.load(f)
+            else:
+                S = calcAndSave(date.strftime('%Y%m%d_%H'))
+            SumList.append(S)
+        Sums = pd.concat(SumList, ignore_index=True)
+    else:
+        filename = join(datPath, f"{date_0}S.pkl")
+        if not forceCalc and exists(filename) and not calcNN:
+            with open(filename, "rb") as f:
+                Sums = pickle.load(f)
+        else:
+            Sums = calcAndSave(date_0)
 
-# %% load if present
+    return Sums
 
-if date_0 == "all":
+# Example usage:
+Sums = load_or_calc_data(date_0, trapath, 'pickle/', force_calc=False)
 
-    Sum_list = list()
-    for date in dates:
-        if exists("pickle/" + date.strftime('%Y%m%d_%H') + "S.pkl"):
-            with open("pickle/" + date.strftime('%Y%m%d_%H')
-                      + "S.pkl", "rb") as f:
-                S = pickle.load(f)
-                # plot_epsloglog(S, date.strftime('%Y%m%d_%H'))
-        Sum_list.append(S)
-    Sums = pd.concat(Sum_list, ignore_index=True)
 
-else:
-    with open("pickle/" + date_0 + "S.pkl", "rb") as f:
-        Sums = pickle.load(f)
+# if date_0 == "all":
+#     Sum_list = list()
+#     for date in dates:
+#         Sums = calc_Sums(trapath, date.strftime('%Y%m%d_%H'))
+#         # Sums = Sums.groupby(['eps', 'date'])['sums'].mean().reset_index()
+#         Sum_list.append(Sums)
+#     Sums = pd.concat(Sum_list, ignore_index=True)
+
+# else:
+#     Sums = calc_Sums(trapath, date_0)
+
+# with open("pickle/" + date_0 + "S.pkl", "wb") as f:
+#     print("saving" + date_0)
+#     pickle.dump(Sums, f)
+
+# # exit()
+# # %% load if present
+
+# if date_0 == "all":
+
+#     Sum_list = list()
+#     for date in dates:
+#         if exists("pickle/" + date.strftime('%Y%m%d_%H') + "S.pkl"):
+#             with open("pickle/" + date.strftime('%Y%m%d_%H')
+#                       + "S.pkl", "rb") as f:
+#                 S = pickle.load(f)
+#                 # plot_epsloglog(S, date.strftime('%Y%m%d_%H'))
+#         Sum_list.append(S)
+#     Sums = pd.concat(Sum_list, ignore_index=True)
+
+# else:
+#     with open("pickle/" + date_0 + "S.pkl", "rb") as f:
+#         Sums = pickle.load(f)
 
 # exit()
+
+
 Sums['t'] = Sums['t']-72
 # %%
 
 def plot_dd(Densities, name, var):
 
-    fig = plt.figure(figsize=(3.4, 2.4))
+    fig = plt.figure(figsize=(3.2, 3.4))
     ax = fig.add_subplot(111)
     def draw_contours(ax, data, levels):
             nrows, ncols = data.shape
@@ -320,48 +481,147 @@ def plot_dd(Densities, name, var):
 
     # Create the heatmap
     data = Densities.pivot(index="date", columns="t",
-                             values=var)
-    cax = sb.heatmap(data, annot=False, cmap="YlGnBu", ax=ax, antialiased=True,
-                     linewidths=0, rasterized=True)
-    cbar = cax.collections[0].colorbar
-    dates = data.index.tolist()
-
-    if name=="all2016":
-        ax.set_yticks(np.arange(3.5, len(dates), 4))
-        ax.set_yticklabels(
-            [datetime.strptime(d, "%Y%m%d_%H").strftime("%m-%d")
-                            for d in dates[3::4]])
-    elif name=="all2017":
-        ax.set_yticks(np.arange(1.5, len(dates), 4))
-        ax.set_yticklabels(
-            [datetime.strptime(d, "%Y%m%d_%H").strftime("%m-%d")
-                            for d in dates[1::4]])
-    if var=="dimension":
+                           values=var)
+    if var == "e_max":
+        unique_values = np.unique(data)
+        num_unique_values = len(unique_values)
+
+        discrete_cmap = mcolors.ListedColormap(
+            cmc.batlow(np.linspace(0, 1, num_unique_values)))
+
+        # Create BoundaryNorm for discrete intervals
+        boundaries = np.append(unique_values, unique_values[-1] + 1)
+        norm = BoundaryNorm(boundaries=boundaries, ncolors=discrete_cmap.N,
+                            clip=True)
+
+        # Generate the heatmap with discrete color mapping
+        cax = sb.heatmap(data, annot=False, cmap=discrete_cmap, ax=ax,
+                         antialiased=True, norm=norm, linewidths=0,
+                         linecolor='white', rasterized=True,
+                         cbar_kws={"orientation": "horizontal"})
+
+        # Adjust the colorbar to show discrete ticks
+        cbar = cax.collections[0].colorbar
+        tick_locs = 0.5 * (boundaries[:-1] + boundaries[1:])  # Midpoints of the intervals
+        cbar.set_ticks(tick_locs)
+        cbar.set_ticklabels([f'{v:.0e}'.replace('e+0', 'e') for v in
+                             unique_values])
+        cbar.ax.tick_params(labelrotation=45)
+
+    else:
+        cax = sb.heatmap(data, annot=False, cmap=cmc.batlow, ax=ax,
+                         antialiased=True, linewidths=0, linecolor='k',
+                         rasterized=True,
+                         cbar_kws={"orientation": "horizontal"})
+        cbar = cax.collections[0].colorbar
+    ax.set_yticks([])  # Remove y-axis ticks
+    ax.set_yticklabels([])  # Remove y-axis tick labels
+
+    if var == "dimension":
         cbar.set_label('$d$')
-    elif var=="density":
-        # cbar.ax.set_xlabel(r'$\frac{1}{d} \log \left( \frac{m}{\mathrm{vol}
-        # \left( \mathbb{M}_t \right) } \right)$',
-        #                   rotation=0, ha="right")
-        ax.set_yticklabels([])
+        # ax.text(-0.1, 1.05, '(a)', transform=ax.transAxes, fontsize=10,
+        #         va='top')
+    elif var == "density":
+        # ax.text(-0.1, 1.05, '(b)', transform=ax.transAxes, fontsize=10,
+        #         va='top')
         cbar.set_label('$\\ell$')
-    elif var=="rho":
-        ax.set_yticklabels([])
+    elif var == "L":
+        # ax.text(-0.1, 1.05, '(b)', transform=ax.transAxes, fontsize=10,
+        #         va='top')
+        cbar.set_label('$\\ell$ [km]')
+    elif var in ["meandist", "dist", "NNdist"]:
+        # ax.text(-0.1, 1.05, '(b)', transform=ax.transAxes, fontsize=10,
+        #         va='top')
+        if var == "meandist":
+            cbar.set_label('$\\overline{\\mathrm{dist}^{\\ast}}$ [km]')
+        elif var == "NNdist":
+            cbar.set_label(
+                '$\\overline{\\mathrm{dist}_{\\mathrm{NN},5}}$ [km]')
+        else:
+            cbar.set_label('$\\overline{\\mathrm{dist}}$ [km]')
+    elif var == "rho":
         cbar.set_label('$\\rho$')
+    elif var == "e_max":
+        cbar.set_label('$\\epsilon^{\\ast}$ [km\\textsuperscript{2}]')
+
+    # dates = data.index.tolist()
+
+    # y_labels = [datetime.strptime(date, "%Y%m%d_%H").strftime("%d %B") if
+    #             date[-2:] == '00' else '' for date in dates]
+
+    # ax.set_yticks(np.arange(0, len(dates)))
+    # tick_positions = [i + .5 for i, label in enumerate(y_labels) if label]
+    # ax.set_yticks(tick_positions)
+    # if not (var in ["density", "L"]):
+    #     ax.set_yticklabels([label for label in y_labels if label])
+    # if name == "all2016":
+    #     ax.set_yticks(np.arange(3.5, len(dates), 4))
+    #     if var == "dimension":
+    #         ax.set_yticklabels(
+    #             [datetime.strptime(d, "%Y%m%d_%H").strftime("%d %B")
+    #             for d in dates[3::4]])
+    # elif name == "all2017":
+    #     ax.set_yticks(np.arange(1.5, len(dates), 4))
+    #     if var == "dimension":
+    #         ax.set_yticklabels(
+    #             [datetime.strptime(d, "%Y%m%d_%H").strftime("%d %B")
+    #              for d in dates[1::4]])
 
     t = data.columns.tolist()
     ax.set_xticks([0.5, int(len(t)/2 + 0.5) - 0.5, len(t)-0.5])
     tick_labels = [r'${}$'.format(i) for i in
-                   [t[0],t[int(len(t)/2)], t[-1]]]
+                   [t[0], t[int(len(t)/2)], t[-1]]]
     ax.set_xticklabels(tick_labels, rotation=0)
 
     ax.set_xlabel('')
 
-    plt.figtext(0.25, 0.075, r"$t \rightarrow$", ha="center", va="center",
+    plt.figtext(0.25, 0.3, r"$t$ [h] $\rightarrow$", ha="center", va="center",
                 fontsize=base_font_size)
     ax.set_ylabel('')
     # Overlay contours
-    contour_levels = cax.collections[0].colorbar.get_ticks()
-    draw_contours(ax, data, contour_levels)
+    # contour_levels = cax.collections[0].colorbar.get_ticks()
+    # draw_contours(ax, data, contour_levels)
+
+    # # Adding stair-case lines
+    # numRows = len(data.index)
+    # numCols = len(data.columns)
+
+    # for row in range(4, 2*numRows+4, 4):
+    #     if row % 4 == 0:  # Ensure it's the 00UTC row
+    #         for col in range(0, numCols, 6):
+    #             x = [col + .5, col + 6.5, col + 6.5]
+    #             y = [row - col/6 - .5, row - col/6 - .5, row - col/6 - 1.5]
+    #             ax.plot(x, y, color="w", linewidth=.25)
+
+    # Adding diagonal lines with a slope of 1/6
+    numRows = len(data.index)
+    numCols = len(data.columns)
+
+    curr_date = dates[0] - timedelta(hours=(numCols-1)/2)
+    i = 0  # iterates through rows
+    while i + 1 < numRows + numCols/6:
+        if curr_date.hour == 0:
+            if i + 1 > numRows:
+                x_start = (i - numRows + .5)*6 + .5
+                y_start = numRows
+            else:
+                x_start = 0
+                y_start = i + .5 + 1/12
+            if i*6 > numCols:
+                x_end = numCols
+                y_end = ((i*6 - numCols + 1)/6 + .5 - 1/12)
+            else:
+                x_end = (i + .5 + 1/12)*6
+                y_end = 0
+
+            ax.plot([x_start, x_end], [y_start, y_end], color="w",
+                    linewidth=.25)
+            ax.text(x_end, y_end, curr_date.strftime("%d %b"), rotation=45,
+                    horizontalalignment="left", verticalalignment="bottom")
+        i += 1
+        curr_date = curr_date + timedelta(hours=6)
+
+    ax.plot([numCols/2, numCols/2], [0, numRows], color="w", linewidth=.25)
 
     plt.savefig("../im/epsloglog/" + var + name + ".pdf", bbox_inches="tight")
 
@@ -377,6 +637,11 @@ Densities = Sums.groupby(["date", "t"]).apply(calc_dd).reset_index()
 f1 = plot_dd(Densities, date_0 + YEAR, "density")
 f2 = plot_dd(Densities, date_0 + YEAR, "dimension")
 f3 = plot_dd(Densities, date_0 + YEAR, "rho")
+f4 = plot_dd(Densities, date_0 + YEAR, "e_max")
+f5 = plot_dd(Densities, date_0 + YEAR, "L")
+f6 = plot_dd(Densities, date_0 + YEAR, "meandist")
+f7 = plot_dd(Densities, date_0 + YEAR, "dist")
+f8 = plot_dd(Densities, date_0 + YEAR, "NNdist")
 
 # %%
 
@@ -426,3 +691,8 @@ def plot_3d_surface(Densities, name, var):
 # Example usage
 # plot_3d_surface(Densities, 'example', 'density')
 
+# %% Investigate why l not 105km
+
+
+data = np.genfromtxt("../era5/startf/2016/disregard/startf_20160502_00_reg",
+                     delimiter='\t', names=['lon', 'lat', 'p'])
diff --git a/pyscripts/hulldist.py b/pyscripts/hulldist.py
index 6d53210..9eec69e 100644
--- a/pyscripts/hulldist.py
+++ b/pyscripts/hulldist.py
@@ -15,176 +15,318 @@ chdir(dirname(abspath(__file__)))
 
 from sys import argv, path
 import numpy as np
-path.append("../wp21/pyscripts/LIB")
+path.append("./LIB")
 import plot as pp
 import calc as cc
 import data as dd
 import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import cmcrameri.cm as cmc
 from datetime import datetime
 from sklearn.neighbors import BallTree
 from scipy.sparse import csc_matrix
 import pandas as pd
 import seaborn as sb
 import time
+from matplotlib import use
+import locale
+# use('agg')
+plt.rcParams["axes.grid"] = False
+locale.setlocale(locale.LC_TIME, 'en_US')
+
+plt.rcParams['text.usetex'] = True
+plt.rcParams['pgf.texsystem'] = "pdflatex"
+plt.rcParams['text.latex.preamble'] = (
+    r'\usepackage{amsmath,amsfonts,amssymb,cmbright,standalone}')
+plt.rcParams['font.size'] = 10
 
 # %% Get the data
 
-Mins = pd.DataFrame(columns=['t', 'dist', 'bound'])
 
-calcNN = False
-date_0 = datetime(2016,5,1,6)
+def curate_data(data, bins):
+    """
+    Prepare histogram data and calculate statistics for each array in data.
 
-# only every nth trajectory
-n = 1
+    Parameters
+    ----------
+        data (list of np.ndarray): List of numpy arrays,
+        bins (np.ndarray): Array of bin edges for histograms.
 
-path = '../../csstandalone/'
+    Returns
+    -------
+        tuple: A tuple containing the histogram matrix and a list of statistics
+    """
+    hist_data = np.zeros((len(data), len(bins) - 1))
+    stats = []
 
-fname = path + date_0.strftime('traj_%Y%m%d_%H') + ".npy"
-path = (path + date_0.strftime("dat_%Y%m%d_%H") + "/"
-        + str(n) + "/")
+    for i, array in enumerate(data):
+        hist, _ = np.histogram(array, bins=bins)
+        hist_data[i, :] = hist
 
-if not exists(path):
-    makedirs(path)
+        # Calculate statistics
+        #  mean_val = np.mean(array)
+        median_val = np.median(array)
+        q1, q3 = np.percentile(array, [25, 75])
+        #  p5, p95 = np.percentile(array, [5, 95])
 
+        # Convert stats to bin indices
+        stats.append((np.digitize([median_val, q1, q3], # , p5, p95],
+                                  bins) - 1))
 
-trajs = np.load(fname)
-t_sel = np.arange(0,trajs.shape[1])
+    return hist_data, stats
 
-Lon = trajs['lon'][::n]
-Lat = trajs['lat'][::n]
-P = trajs['p'][::n]
-U = trajs['U'][::n]
-V = trajs['V'][::n]
-Omg = trajs['OMEGA'][::n]
-T = trajs['T'][::n]
 
-k_p = cc.calc_k(U/1000, V/1000, Omg/100)
-H = cc.ptoh(P, 1013.25, 8.435) # standard atmosphere
-k_h = cc.calc_k(U, V, cc.omg2w(Omg/100, T, P))
+dates = [datetime(2016, 5, 2, 0), datetime(2016, 5, 4, 0),
+         datetime(2017, 1, 26, 18)]
 
-# %% Calculate distances
+Mins_list = list()
 
-def unnorm(x_sd, x):
-    return (x_sd*x.std(axis=0) + x.mean(axis=0))
+for date_0 in dates:
 
-eps = 1e6
-r = 3*np.sqrt(eps)
+    Mins = pd.DataFrame(columns=['date', 't', 'dist', 'bound'])
 
-xun, yun, zun = cc.coord_trans(Lon, Lat, P, "None", proj="stereo", scaling = 1)
-x, y, z = cc.norm(xun), cc.norm(yun), cc.norm(zun)
+    calcNN = False
+    # date_0 = datetime(2017, 1, 26, 18)
 
-# # Test inverse operation:
-# xin = unnorm(x, xun)
-# yin = unnorm(y, yun)
 
-# lonin, latin = cc.project(xun, yun, Inverse=True)
-# lonin[lonin<0] +=360
-# print(np.allclose(lon, lonin), np.allclose(lat, latin)): True True
+    # only every nth trajectory
+    n = 1
 
-# get the boundary
-bounds, hulls = dd.io_bounds(path + "stereop" + "norm" ,
-                             "opt. $\\alpha$", x, y, z, None)
+    trapath = date_0.strftime("../era5/traj/%Y/")
 
-# get the distances
+    path = '../../csstandalone/'
 
-D = dd.io_dist(path, eps, Lon, Lat, P, True, k_p, "p", t_sel)
+    path = (path + date_0.strftime("dat_%Y%m%d_%H") + "/"
+            + str(n) + "/")
 
-x_list = list()
-y_list = list()
-v_list = list()
+    if not exists(path):
+        makedirs(path)
 
-d_list = list()
+    trajs = np.load(trapath + "traj_" + date_0.strftime('%Y%m%d_%H') + ".npy")
 
-# calculate boundary point distances
-for j in range(Lon.shape[1]):
-    print("t = {}".format(j))
-    if calcNN:
-        # select only boundary points
-        lon_t = Lon[bounds[:,j] == 1,j]
-        lat_t = Lat[bounds[:,j] == 1,j]
-        dat = np.array([np.deg2rad(lon_t), np.deg2rad(lat_t)]).T
-        BT = BallTree(dat, metric='haversine')
-        idx, hdist = BT.query_radius(dat, r=r / 6371,
-                                     return_distance=True)
-        hdist = hdist * 6371
-
-        x_t = list()
-        y_t = list()
-        v_t = list()
-
-        p_t = P[bounds[:,j] == 1,j]
-
-        for i in range(lon_t.shape[0]):
-            # for each point get nearest neighbors
-            hdist[i] = hdist[i][idx[i] > i]
-            idx[i] = idx[i][idx[i] > i]
-
-            vdist = p_t[idx[i]] - p_t[i]
-
-            dist = np.sqrt(np.power(hdist[i], 2) + k_p * np.power(vdist, 2))
-
-            valid = np.where(dist < r)[0]
-            x_t.extend([i for _ in range(len(valid))])
-            y_t.extend(idx[i][valid])
-            v_t.extend(dist[valid])
-
-        x_t = np.asarray(x_t)
-        y_t = np.asarray(y_t)
-        v_t = np.asarray(v_t)
-
-        x_list.append(np.int_(x_t))
-        y_list.append(np.int_(y_t))
-        v_list.append(v_t)
-    else:
-        # array of size n_edges x 2 x 3
-        edges_t = hulls[j].vertices[hulls[j].edges]
-
-        # Legacy: too slow
-
-        # array of size n_points x 3
-        # points_t = np.array([x[:,j], y[:,j], z[:,j]]).T
-        # def find_point_idx(pt):
-            # return np.where([np.allclose(pt, r) for r in points_t])[0][0]
-        # Since (edges_t.reshape(-1,3).reshape(-1, 2, 3) ==
-        # edges_t).flatten().all() is True, we can flatten and reshape
-        # start = time.time()
-        # p_idx_t = np.apply_along_axis(find_point_idx, 1,
-                                      # edges_t.reshape(-1,3)).reshape(-1, 2)
-        # diff = time.time() - start
-        # print(diff) # 167s
-
-        # Note that (points_t[p_idx_t.reshape(-1), :].reshape(-1, 2, 3) ==
-        # edges_t).all() is True.
-        # We now simply apply the indices to the points in Lon/Lat/P
-        # points_llp = np.array([Lon[:,j], Lat[:,j], P[:,j]]).T
-        # points_llp_bound = points_llp[p_idx_t.reshape(-1), :].reshape(-1, 2, 3)
-
-        # start = time.time()
-
-        xbd = edges_t.reshape(-1,3)[:,0]
-        ybd = edges_t.reshape(-1,3)[:,1]
-        pbd = edges_t.reshape(-1,3)[:,2]
-        xbdin = unnorm(xbd, xun[:,j])
-        ybdin = unnorm(ybd, yun[:,j])
-        pbdin = unnorm(pbd, zun[:,j])
-
-        lonbd, latbd = cc.project(xbdin, ybdin, Inverse=True)
-        lonbd[lonbd<0] +=360
-        points_llp_bound = np.array((lonbd, latbd, pbdin)).T.reshape(-1, 2, 3)
-        # diff = time.time() - start
-
-        # print(diff) # 0.032s
-
-
-        # Now we can calculate the edgelength in custom metric units
-        dist_bound = cc.adj_dist_vec(
-            np.array([points_llp_bound[:,0,1], points_llp_bound[:,0,0],
-                      points_llp_bound[:,0,2]]),
-            np.array([points_llp_bound[:,1,1], points_llp_bound[:,1,0],
-                      points_llp_bound[:,1,2]]), k_p)
-        d_list.append(dist_bound)
-
-# D_bound = (x_list, y_list, v_list)
+    t_sel = np.arange(0, trajs.shape[1])
+
+    Lon = trajs['lon'][::n]
+    Lat = trajs['lat'][::n]
+    P = trajs['p'][::n]
+    U = trajs['U'][::n]
+    V = trajs['V'][::n]
+    Omg = trajs['OMEGA'][::n]
+    T = trajs['T'][::n]
+
+    k_p = cc.calc_k(U/1000, V/1000, Omg/100)
+    H = cc.ptoh(P, 1013.25, 8.435)  # standard atmosphere
+    k_h = cc.calc_k(U, V, cc.omg2w(Omg/100, T, P))
+
+    k_p = 15
+
+    # Calculate distances
+
+
+    def unnorm(x_sd, x):
+        return (x_sd*x.std(axis=0) + x.mean(axis=0))
+
+
+    eps = 1e6
+    r = 3*np.sqrt(eps)
+    alpha = 1e-3
+
+    # xun, yun, zun = cc.coord_trans(Lon, Lat, P, "None", proj="stereo", scaling = 1)
+    # x, y, z = cc.norm(xun), cc.norm(yun), cc.norm(zun)
+
+    x, y, z = cc.coord_trans(Lon, Lat, P, "None", k_p, proj="stereo")
+
+    # # Test inverse operation:
+    # xin = unnorm(x, xun)
+    # yin = unnorm(y, yun)
+
+    # lonin, latin = cc.project(xun, yun, Inverse=True)
+    # lonin[lonin<0] +=360
+    # print(np.allclose(lon, lonin), np.allclose(lat, latin)): True True
+
+    # get the boundary
+    bounds, hulls = dd.io_bounds(path + "stereop",
+                                 "$\\alpha$", x, y, z, alpha, force_calc=False)
+
+    # get the distances
+
+    # D = dd.io_dist(path, eps, Lon, Lat, P, True, k_p, "p", t_sel)
+
+    x_list = list()
+    y_list = list()
+    v_list = list()
+
+    d_list = list()
+
+    # calculate boundary point distances
+    for j in range(Lon.shape[1]):
+        print("t = {}".format(j))
+        if calcNN:
+            # select only boundary points
+            lon_t = Lon[bounds[:, j] == 1, j]
+            lat_t = Lat[bounds[:, j] == 1, j]
+            dat = np.array([np.deg2rad(lon_t), np.deg2rad(lat_t)]).T
+            BT = BallTree(dat, metric='haversine')
+            idx, hdist = BT.query_radius(dat, r=r / 6371,
+                                         return_distance=True)
+            hdist = hdist * 6371
+
+            x_t = list()
+            y_t = list()
+            v_t = list()
+
+            p_t = P[bounds[:,j] == 1,j]
+
+            for i in range(lon_t.shape[0]):
+                # for each point get nearest neighbors
+                hdist[i] = hdist[i][idx[i] > i]
+                idx[i] = idx[i][idx[i] > i]
+
+                vdist = p_t[idx[i]] - p_t[i]
+
+                dist = np.sqrt(np.power(hdist[i], 2) + k_p * np.power(vdist, 2))
+
+                valid = np.where(dist < r)[0]
+                x_t.extend([i for _ in range(len(valid))])
+                y_t.extend(idx[i][valid])
+                v_t.extend(dist[valid])
+
+            x_t = np.asarray(x_t)
+            y_t = np.asarray(y_t)
+            v_t = np.asarray(v_t)
+
+            x_list.append(np.int_(x_t))
+            y_list.append(np.int_(y_t))
+            v_list.append(v_t)
+        else:
+            # Legacy: too slow
+
+            # array of size n_points x 3
+            # points_t = np.array([x[:,j], y[:,j], z[:,j]]).T
+            # def find_point_idx(pt):
+                # return np.where([np.allclose(pt, r) for r in points_t])[0][0]
+            # Since (edges_t.reshape(-1,3).reshape(-1, 2, 3) ==
+            # edges_t).flatten().all() is True, we can flatten and reshape
+            # start = time.time()
+            # p_idx_t = np.apply_along_axis(find_point_idx, 1,
+                                          # edges_t.reshape(-1,3)).reshape(-1, 2)
+            # diff = time.time() - start
+            # print(diff) # 167s
+
+            # Note that (points_t[p_idx_t.reshape(-1), :].reshape(-1, 2, 3) ==
+            # edges_t).all() is True.
+            # We now simply apply the indices to the points in Lon/Lat/P
+            # points_llp = np.array([Lon[:,j], Lat[:,j], P[:,j]]).T
+            # points_llp_bound = points_llp[p_idx_t.reshape(-1), :].reshape(-1, 2, 3)
+
+            # start = time.time()
+
+            # array of size n_edges x 2 x 3
+            # edges_t = hulls[j].vertices[hulls[j].edges_unique]
+
+            # edge_lengths = np.linalg.norm(edges_t[:, 0] - edges_t[:, 1], axis=1)
+
+            edge_lengths = hulls[j].edges_unique_length
+
+            # xbd = edges_t.reshape(-1, 3)[:, 0]
+            # ybd = edges_t.reshape(-1, 3)[:, 1]
+            # pbd = edges_t.reshape(-1, 3)[:, 2]
+            # xbdin = unnorm(xbd, xun[:,j])
+            # ybdin = unnorm(ybd, yun[:,j])
+            # pbdin = unnorm(pbd, zun[:,j])
+
+            # lonbd, latbd = cc.project(xbd, ybd, Inverse=True)
+            # lonbd[lonbd < 0] += 360
+            # points_llp_bound = np.array((lonbd, latbd, pbd)).T.reshape(-1, 2, 3)
+            # diff = time.time() - start
+
+            # print(diff) # 0.032s
+
+            # Now we can calculate the edgelength in custom metric units
+            # dist_bound = cc.adj_dist_vec(
+                # np.array([points_llp_bound[:, 0, 1], points_llp_bound[:, 0, 0],
+                          # points_llp_bound[:, 0, 2]]),
+                # np.array([points_llp_bound[:, 1, 1], points_llp_bound[:, 1, 0],
+                          # points_llp_bound[:, 1, 2]]), 1)
+            # use k=1, bc p already scaled
+            d_list.append(edge_lengths)
+            #        d_list.append(dist_bound)
+
+    # D_bound = (x_list, y_list, v_list)
+
+    # 2D Histogram: heatmap
+
+
+    def plot_heatmap(hist_data, stats, bins, date_0, t_sel):
+        plt.figure(figsize=(2, 2))
+        ax = sb.heatmap(hist_data, cmap=cmc.hawaii, rasterized=True,
+                        linecolor="none", linewidths=0,
+                        cbar_kws={"orientation": "horizontal",
+                                  "pad": 0.05})
+
+        ax.grid(False)
+
+        plt.xlabel('Edge length [km]')
+        ax.yaxis.set_major_locator(
+                ticker.FixedLocator(t_sel + t_sel.max()))
+        if date_0 == datetime(2016, 5, 2, 0):
+            print("true")
+            plt.ylabel('$t$ [h]')
+            ax.set_yticklabels([f"{t.astype(int)}" for t in t_sel])
+            from matplotlib.lines import Line2D
+            legend_elements = [Line2D([0], [0], color="black", lw=2,
+                                      label=('$P_{25},$ \n $P_{50}$, \n $P_{75}$'))]
+            ax.legend(handles=legend_elements, loc='right')
+        else:
+            plt.ylabel('')
+            # ax.set_yticks([])  # Remove y-axis ticks
+            ax.set_yticklabels([])  # Remove y-axis tick labels
+
+        # X-axis ticks and labels
+        mid_bins = 0.5 * (bins[:-1] + bins[1:])  # Mid-points of the bins
+        tick_spacing = len(bins) // 5  # Defining tick spacing for better visibility
+
+        # Defining the tick positions
+        tick_positions = bins[::tick_spacing]/bins.max() * (len(bins) - 1)
+        # Select ticks at intervals
+        # Ensure the number of tick labels matches the number of tick positions
+        tick_labels = [f"{int(bins[i])}"
+                       for i in range(0, len(bins), tick_spacing)]
+
+        # Setting the ticks and labels on the x-axis
+        ax.xaxis.set_major_locator(ticker.FixedLocator(tick_positions))  # Set ticks
+        ax.set_xticklabels(tick_labels, rotation=45)  # Set labels
+        ax.set_xlabel("Edge Length [km]")
+        ax.xaxis.set_ticks_position('top')
+        ax.xaxis.set_label_position('top')
+
+        # Add lines for statistics
+        colors = ["k", "k", "k", "k"]  # , "k", "k"]
+        for index, stat in enumerate(stats):
+            for i, val in enumerate(stat):
+                plt.axvline(x=val, ymin=index / len(hist_data),
+                            ymax=(index + 1) / len(hist_data), color=colors[i],
+                            linewidth=1)
+
+        # Adding a colorbar label
+        cbar = ax.collections[0].colorbar
+        cbar.set_label('Frequency [-]')
+
+        plt.show()
+        plt.savefig(f"../im/bounds/bounds_{date_0.strftime('%Y%m%d_%H')}.pdf",
+                    bbox_inches="tight")
+
+
+    bins = np.linspace(0, 1e3, 51)  # Define bins suitable for your data
+
+    # Data curation
+    hist_data, stats = curate_data(d_list, bins)
+
+    # Plotting
+    print(date_0)
+    plot_heatmap(hist_data, stats, bins, date_0, t_sel[::24]-72)
+
+# exit()
 
 # %% Violinplot: distance in km vs t; diff by points in boundary and not
 
diff --git a/pyscripts/k_pcompare.py b/pyscripts/k_pcompare.py
index 72386b2..d82d1b7 100644
--- a/pyscripts/k_pcompare.py
+++ b/pyscripts/k_pcompare.py
@@ -18,28 +18,32 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sb
 import matplotlib.dates as mdates
+from matplotlib.ticker import MaxNLocator
+import cmcrameri.cm as cmc
+import locale
 
 # %% Initials
 
-YEAR="2016"
+YEAR = "2016"
 # Initialize dates
 if YEAR == "2016":
     dates = [datetime(2016, 4, 29, 6) + timedelta(hours=i)
-              for i in range(0, 144, 6)]
+             for i in range(0, 144, 6)]
 elif YEAR == "2017":
-    dates = [datetime(2017, 1, 23, 18) + timedelta(hours=i)
-              for i in range(0, 28*6, 6)]
+    dates = [datetime(2017, 1, 23, 12) + timedelta(hours=i)
+             for i in range(0, 28*6, 6)]
 
 
 trapath = "../era5/traj/" + YEAR + "/"
 
-base_font_size = 10 # pt
+base_font_size = 10  # pt
+locale.setlocale(locale.LC_TIME, 'en_US')
 
 plt.rcParams['text.usetex'] = True
-plt.rcParams['font.family'] = 'sans-serif'
-plt.rcParams['font.sans-serif'] = ['Helvetica']
+plt.rcParams['pgf.texsystem'] = "pdflatex"
+plt.rcParams['text.latex.preamble'] = (
+    r'\usepackage{amsmath,amsfonts,amssymb,cmbright,standalone}')
 plt.rcParams['font.size'] = base_font_size
-plt.rcParams['text.latex.preamble'] = r'\usepackage{amsmath,amsfonts,amssymb}'
 
 # %% functions
 
@@ -54,22 +58,25 @@ def calc_k(trapath, date_0):
 
     Omg = trajs['OMEGA']
 
-    df = pd.DataFrame({'date' : date_0,
-                       't' : np.arange(-72, 73),
-                       'U' : U.mean(axis=0),
-                       'V' : V.mean(axis=0),
-                       'Uh' : U_h.mean(axis=0),
-                       'Omg' : Omg.mean(axis=0),
-                       'Omg_abs' : np.abs(Omg).mean(axis=0),
-                       'k' : ((U_h.mean(axis=0)/1000)/(
+    df = pd.DataFrame({'date': date_0,
+                       'm': U.shape[0],
+                       't': np.arange(-72, 73),
+                       'U': U.mean(axis=0),
+                       'V': V.mean(axis=0),
+                       'Uh': U_h.mean(axis=0),
+                       'Omg': Omg.mean(axis=0),
+                       'Omg_abs': np.abs(Omg).mean(axis=0),
+                       'k': ((U_h.mean(axis=0)/1000)/(
                            (np.abs(Omg).mean(axis=0))/100))})
 
     return df
 
 # %% Loop-de-loop
 
+
 k_list = list()
 for date in dates:
+    print(date)
     k_list.append(calc_k(trapath, date.strftime('%Y%m%d_%H')))
 
 k_df = pd.concat(k_list, ignore_index=True)
@@ -78,54 +85,89 @@ k_df = pd.concat(k_list, ignore_index=True)
 
 def plot_k(k_df, name, var):
 
-    fig = plt.figure(figsize=(3.4, 2.4))
+    fig = plt.figure(figsize=(3.2, 3.4))
     ax = fig.add_subplot(111)
 
     # Create the heatmap
     data = k_df.pivot(index="date", columns="t",
-                             values=var)
-    cax = sb.heatmap(data, annot=False, cmap="YlGnBu", ax=ax, antialiased=True,
-                     linewidths=0, rasterized=True)
+                      values=var)
+    if var in ["U", "V", "Omg"]:
+        cax = sb.heatmap(data, annot=False, cmap=cmc.vik, ax=ax,
+                         antialiased=True, center=0,
+                         linewidths=0, rasterized=True,
+                         cbar_kws={"orientation": "horizontal"})
+    else:
+        cax = sb.heatmap(data, annot=False, cmap=cmc.batlow, ax=ax,
+                         antialiased=True,
+                         linewidths=0, rasterized=True,
+                         cbar_kws={"orientation": "horizontal"})
     cbar = cax.collections[0].colorbar
-    dates = data.index.tolist()
-
-    if name=="2016":
-        ax.set_yticks(np.arange(3.5, len(dates), 4))
-        ax.set_yticklabels(
-            [datetime.strptime(d, "%Y%m%d_%H").strftime("%m-%d")
-                            for d in dates[3::4]])
-    elif name=="2017":
-        ax.set_yticks(np.arange(1.5, len(dates), 4))
-        ax.set_yticklabels(
-            [datetime.strptime(d, "%Y%m%d_%H").strftime("%m-%d")
-                            for d in dates[1::4]])
-    if var=="U":
-        cbar.set_label('$u$')
-    elif var=="V":
-        ax.set_yticklabels([])
-        cbar.set_label('$v$')
-    elif var=="Uh":
-        ax.set_yticklabels([])
-        cbar.set_label('$u_h$')
-    elif var=="Omg":
-        ax.set_yticklabels([])
-        cbar.set_label('$\\omega$')
-    elif var=="k":
-        ax.set_yticklabels([])
-        cbar.set_label('$k$')
+    ax.set_yticks([])  # Remove y-axis ticks
+    ax.set_yticklabels([])  # Remove y-axis tick labels
+
+    # if name == "2016":
+    #     ax.set_yticks(np.arange(3.5, len(dates), 4))
+    #     ax.set_yticklabels(
+    #         [datetime.strptime(d, "%Y%m%d_%H").strftime("%d %B")
+    #          for d in dates[3::4]])
+    # elif name == "2017":
+    #     ax.set_yticks(np.arange(1.5, len(dates), 4))
+    #     ax.set_yticklabels(
+    #         [datetime.strptime(d, "%Y%m%d_%H").strftime("%d %B")
+    #          for d in dates[1::4]])
+    if var == "U":
+        cbar.set_label('$\\overline{u}$ [m s\\textsuperscript{-1}]')
+    elif var == "V":
+        cbar.set_label('$\\overline{v}$ [m s\\textsuperscript{-1}]')
+    elif var == "Uh":
+        # ax.set_yticklabels([])
+        cbar.set_label('$\\overline{u_h}$ [m s\\textsuperscript{-1}]')
+    elif var == "Omg":
+        cbar.set_label('$\\overline{\\omega}$ [Pa s\\textsuperscript{-1}]')
+    elif var == "k":
+        cbar.set_label('$\\kappa$ [km hPa\\textsuperscript{-1}]')
 
     t = data.columns.tolist()
     ax.set_xticks([0.5, int(len(t)/2 + 0.5) - 0.5, len(t)-0.5])
     tick_labels = [r'${}$'.format(i) for i in
-                   [t[0],t[int(len(t)/2)], t[-1]]]
+                   [t[0], t[int(len(t)/2)], t[-1]]]
     ax.set_xticklabels(tick_labels, rotation=0)
 
     ax.set_xlabel('')
 
-    plt.figtext(0.25, 0.075, r"$t \rightarrow$", ha="center", va="center",
+    plt.figtext(0.25, 0.3, r"$t$ [h] $\rightarrow$", ha="center", va="center",
                 fontsize=base_font_size)
     ax.set_ylabel('')
-    plt.title(name)
+    # plt.title(name)
+
+    numRows = len(data.index)
+    numCols = len(data.columns)
+
+    curr_date = dates[0] - timedelta(hours=(numCols-1)/2)
+    i = 0  # iterates through rows
+    while i + 1 < numRows + numCols/6:
+        if curr_date.hour == 0:
+            if i + 1 > numRows:
+                x_start = (i - numRows + .5)*6 + .5
+                y_start = numRows
+            else:
+                x_start = 0
+                y_start = i + .5 + 1/12
+            if i*6 > numCols:
+                x_end = numCols
+                y_end = ((i*6 - numCols + 1)/6 + .5 - 1/12)
+            else:
+                x_end = (i + .5 + 1/12)*6
+                y_end = 0
+
+            ax.plot([x_start, x_end], [y_start, y_end], color="w",
+                    linewidth=.25)
+            ax.text(x_end, y_end, curr_date.strftime("%d %b"), rotation=45,
+                    horizontalalignment="left", verticalalignment="bottom")
+        i += 1
+        curr_date = curr_date + timedelta(hours=6)
+
+    ax.plot([numCols/2, numCols/2], [0, numRows], color="w", linewidth=.25)
 
     plt.savefig("../im/kpm/" + var + name + ".pdf", bbox_inches="tight")
 
@@ -133,49 +175,67 @@ def plot_k(k_df, name, var):
 
 def plot_kmean(k_df, name):
 
-    fig, ax1 = plt.subplots(figsize=(4, 2))
-
-    color = 'tab:blue'
-    ax1.set_xlabel('Date')
-    ax1.set_ylabel('$k$', color=color)
-    ax1.plot(k_df['date'], k_df['k'], color=color,
-             marker='s', linestyle='-', label='k')
-    ax1.tick_params(axis='y', labelcolor=color)
-    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))
-    locator = mdates.AutoDateLocator(minticks=5, maxticks=6)  # Adjust maxticks as needed
+    colors = ['#ff7f0e', '#0071bc', '#008080', '#76b7b2']
+    # Blue, Orange, Butter Yellow, Sky Blue
+    fig, ax1 = plt.subplots(figsize=(3.25, 2))
+
+    # m
+    ax1.set_xlabel('Initialization date')
+    ax1.set_ylabel('$m$ [--]', color=colors[0])
+    ax1.plot(k_df['date'], k_df['m'], color=colors[0],
+             marker='s', linestyle='-', label='$m$')
+    ax1.tick_params(axis='y', labelcolor=colors[0])
+    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d %b'))
+    # locator = mdates.AutoDateLocator(minticks=5, maxticks=5)
+    locator = MaxNLocator(nbins=3)
+    # Adjust maxticks as needed
     ax1.xaxis.set_major_locator(locator)
-    # ax1.plot(k_df['date'], k_df['k_after'], color=color,
-    #          marker='o', linestyle='-', label='k after')
-    # ax1.set_ylabel('$k$ [km/hPa]', color=color)
-
-    ax2 = ax1.twinx()
-    color = 'tab:red'
-    ax2.set_ylabel('$u_h$ [m/s]', color=color)
-    ax2.plot(k_df['date'], k_df['Uh'], color=color,
-             marker='*', linestyle='-', label='uh')
-    ax2.tick_params(axis='y', labelcolor=color)
-    # ax2.spines['right'].set_position(('outward', 5))  # Offset the second Y-axis spine
-    # Ensure ticks are at integer values and labels are formatted as integers
-    # ax2.yaxis.set_major_locator(MaxNLocator(nbins='auto', integer=True))
-    # ax2.yaxis.set_major_formatter(FormatStrFormatter('%d'))
-
-    ax3 = ax1.twinx()
-    color = 'tab:purple'
-    ax3.set_ylabel('$\\omega$ [P/s]', color=color)
-    ax3.plot(k_df['date'], k_df['Omg_abs'], color=color,
-             marker='*', linestyle='-', label='omg')
-    ax3.tick_params(axis='y', labelcolor=color)
-    ax3.spines['right'].set_position(('outward', 35))  # Offset the third Y-axis spine
+    # ax1.tick_params(axis='x', rotation=15)
+    # ax1.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
+
+    # k
+    ax4 = ax1.twinx()
+    ax4.set_ylabel('$\\kappa$ [km hPa\\textsuperscript{-1}]', color=colors[1])
+    ax4.plot(k_df['date'], k_df['k'], color=colors[1],
+             marker='o', linestyle='-', label='$\\kappa$')
+    ax4.tick_params(axis='y', labelcolor=colors[1])
+
+    # # u_h
+    # ax2 = ax1.twinx()
+    # ax2.set_ylabel('$\\overline{u_{\\mathrm{h}}}$ [m s\\textsuperscript{-1}]',
+    #                color=colors[2])
+    # ax2.plot(k_df['date'], k_df['Uh'], color=colors[2],
+    #          marker='*', linestyle='-', label='$\\overline{u_{\\mathrm{h}}}$')
+    # ax2.tick_params(axis='y', labelcolor=colors[2])
+    # ax2.spines['right'].set_position(('outward', 35))
+    # # Ensure ticks are at integer values and labels are formatted as integers
+    # # ax2.yaxis.set_major_locator(MaxNLocator(nbins='auto', integer=True))
+    # # ax2.yaxis.set_major_formatter(FormatStrFormatter('%d'))
+
+    # ax3 = ax1.twinx()
+    # ax3.set_ylabel(
+    #     '$\\overline{ \\vert  \\omega \\vert }$ [Pa s\\textsuperscript{-1}]',
+    #     color=colors[3])
+    # ax3.plot(k_df['date'], k_df['Omg_abs'], color=colors[3],
+    #          marker='*', linestyle='-',
+    #          label='$\\vert \\overline{\\omega} \\vert$')
+    # ax3.tick_params(axis='y', labelcolor=colors[3])
+    # ax3.spines['right'].set_position(('outward', 70))
+
+    if YEAR == "2017":
+        ax4.set_yticks([14, 16, 18, 20])
+        # ax3.set_yticks([0.1, 0.15])
 
     fig.tight_layout()
     plt.show()
 
     plt.savefig("/net/scratch/schoelleh96/WP2/WP2.1/" +
-                "LAGRANTO/wp21/im/kpm/k_decomposed" + str(YEAR) + ".pdf",
+                "LAGRANTO/wp21/im/kpm/k_m" + str(YEAR) + ".pdf",
                 bbox_inches="tight")
 
 # %% Application
 
+
 for var in ["U", "V", "Uh", "Omg", "k"]:
     plot_k(k_df, YEAR, var)
 
@@ -186,4 +246,5 @@ columnsToAverage = k_df.columns.difference(['date', 't'])
 averagedDf = k_df.groupby('date')[columnsToAverage].mean().reset_index()
 averagedDf['k_after'] = (averagedDf['Uh']/1000)/(averagedDf['Omg']/100)
 
-plot_kmean(averagedDf, YEAR)
\ No newline at end of file
+averagedDf["date"] = pd.to_datetime(averagedDf["date"], format='%Y%m%d_%H')
+plot_kmean(averagedDf, YEAR)
diff --git a/pyscripts/sizevsdscatter.py b/pyscripts/sizevsdscatter.py
index 813b358..44607af 100644
--- a/pyscripts/sizevsdscatter.py
+++ b/pyscripts/sizevsdscatter.py
@@ -168,7 +168,7 @@ ax[0].set_ylabel("$m$")
 norm = plt.Normalize(vmin=df['t'].min(), vmax=df['t'].max())
 sm = plt.cm.ScalarMappable(cmap='viridis', norm=norm)
 sm._A = []  # ScalarMappable needs a dummy array attribute '_A' to work with colorbar
-fig.colorbar(sm, ax=ax, orientation='vertical', label='$t$')
+fig.colorbar(sm, ax=ax, orientation='vertical', label='$t$[h]')
 
 plt.show()
 plt.savefig("/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/im/" +
diff --git a/pyscripts/syn_cond.py b/pyscripts/syn_cond.py
index 3692f38..d17e047 100644
--- a/pyscripts/syn_cond.py
+++ b/pyscripts/syn_cond.py
@@ -12,19 +12,24 @@ from datetime import datetime, timedelta
 from os import chdir
 from os.path import dirname, abspath
 from sys import path
+import locale
 
 # Change working directory to script location
 chdir(dirname(abspath(__file__)))
-
+locale.setlocale(locale.LC_TIME, 'en_US')
+# %%
 # Import third-party libraries
+import pandas as pd
 import xarray as xr
 import numpy as np
+import scipy as sc
 import matplotlib.pyplot as plt
 import matplotlib as mpl
 import cartopy.crs as ccrs
 import cartopy.util as cutil
 from cdo import Cdo
 from matplotlib.colors import Normalize
+import matplotlib.patches as mpatches
 
 # Import local libraries
 path.append("./LIB")
@@ -34,67 +39,222 @@ import data as dd
 # mpl.rc('font',**{'family':'serif','serif':['cmr10']})
 # mpl.rc('text', usetex=True)
 
-base_font_size = 10 # pt
+base_font_size = 10  # pt
 
 plt.rcParams['text.usetex'] = True
-plt.rcParams['font.family'] = 'sans-serif'
-plt.rcParams['font.sans-serif'] = ['Helvetica']
-plt.rcParams['font.size'] = base_font_size
+plt.rcParams['pgf.texsystem'] = "pdflatex"
+plt.rcParams['text.latex.preamble'] = (
+    r'\usepackage{amsmath,amsfonts,amssymb,cmbright,standalone}')
 
 # Define constants
 YEAR = "2017"
-multiple_subplots = True
-extent = [-60, 120, 30, 90]
-central_longitude = 30
+multiple_subplots = False
+var = "pv"
+vecCol = "b"
+plev = "surf"
+levels = "150,200,250,300,350,400,450,500"
 
 # Initialize dates
 if YEAR == "2016":
-    dates = [datetime(2016, 4, 30, 0), datetime(2016,5,2,0),
-              datetime(2016,5,4,0)]
-    # dates = [datetime(2016, 4, 29, 6) + timedelta(hours=i)
-    #           for i in range(0, 144, 6)]
+    # dates = [datetime(2016, 4, 30, 0), datetime(2016, 5, 2, 0),
+    #          datetime(2016, 5, 4, 0)]
+    dates = [datetime(2016, 4, 29, 6) + timedelta(hours=i)
+             for i in range(0, 144, 6)]
     # dates = [datetime(2016,5,6,0), datetime(2016,5,7,0)]
+    # dates = pd.date_range(start='2016-04-29-06', end='2016-05-05-00',
+    #                       freq='6H')
+    extent = [-210, -30, 30, 90]
+    central_longitude = -120
+    p_levels = np.arange(980, 1005, 5)
 elif YEAR == "2017":
-    dates = [datetime(2017, 1, 25, 0) + timedelta(hours=i)
-              for i in range(0, 3*48, 48)]
+    # dates = [datetime(2017, 1, 25, 0) + timedelta(hours=i)
+    #          for i in range(0, 3*48, 48)]
+    dates = [datetime(2017, 1, 23, 12) + timedelta(hours=i)
+              for i in range(0, 29*6, 6)]
+    extent = [-60, 120, 30, 90]
+    central_longitude = 30
+    p_levels = np.arange(970, 995, 5)
 
 data_path = ("/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/" +
-            "era5/")
+             "era5/")
 
 era5_path = "/daten/erafive/arch/data/dkrz-mirror/ml00_1H/" + YEAR
 
 cdo = Cdo()
 
 # %%
+
+
 def process_datasets(dates, data_path, cdo):
     """
     Process datasets for the given dates and data path.
 
-    Parameters:
+    Parameters
+    ----------
     dates (list): List of dates to process datasets for.
     data_path (str): Path to the data.
     cdo (Cdo): Cdo object for processing datasets.
 
-    Returns:
+    Returns
+    -------
     xarray.Dataset: Concatenated dataset along the time dimension.
     """
+
+    def filterW(field, n):
+        """
+        Apply a convolution filter to an xarray DataArray using.
+
+        Parameters
+        ----------
+        - field (xarray.DataArray): 2D data to filter.
+        - n (int): Number of times to apply the filter.
+
+        Returns
+        -------
+        - xarray.DataArray: Filtered data.
+        """
+        weights = np.array([[0, 0, 1, 0, 0],
+                            [0, 2, 4, 2, 0],
+                            [1, 4, 8, 4, 1],
+                            [0, 2, 4, 2, 0],
+                            [0, 0, 1, 0, 0]])
+        weights = weights / np.sum(weights)  # Ensure normalization of weights.
+
+        # Define a wrapper function for scipy's ndimage.convolve
+        # that we can apply directly to the xarray DataArray.
+        def convolve(array):
+            return sc.ndimage.convolve(array, weights, mode='wrap')
+
+        # Apply the convolution n times.
+        filtered_field = field.copy()
+
+        for _ in range(n):
+            filtered_field = xr.apply_ufunc(
+                convolve,  # function to apply
+                filtered_field,  # input DataArray
+                input_core_dims=[['latitude', 'longitude']],
+                output_core_dims=[['latitude', 'longitude']],
+                vectorize=True,  # automatically vectorize
+                dask="parallelized",  # enable parallelized computation
+                output_dtypes=[field.dtype]  # specify output data type
+            )
+
+        return filtered_field
+
+    def calculate_gradients(data, dim_name, spacing):
+        # Get the dimension index for the specified dimension name
+        axis = data.dims.index(dim_name)
+
+        # Calculate gradient using numpy.gradient for second-order accuracy
+        grad = np.gradient(data.values, axis=axis, edge_order=2) / spacing
+
+        # Convert the numpy array back to xarray DataArray,
+        # preserving coords, dims, and attrs
+        grad_da = xr.DataArray(grad, dims=data.dims, coords=data.coords,
+                               attrs=data.attrs)
+
+        return grad_da
+
+    def calc_Wk(data):
+        g = 9.80665  # in m/s^2
+        R = 6370*1000.  # in m
+
+        n = 0  # Number of filter applications
+
+        # Apply the filter
+        u_filtered = filterW(data['u'], n)  # in m/s
+        v_filtered = filterW(data['v'], n)
+
+        # Geopotential height
+        z_g = data['z'] / g  # in m
+
+        lat = data['latitude']  # in °
+        lon = data['longitude']
+
+        # Calculate distances for gradients, considering the shape
+        dx = (2 * np.pi * R * np.cos(np.deg2rad(lat)) *
+              np.abs(lon[1] - lon[0]) / 360).values  # also in m
+        dx = np.tile(dx[:, np.newaxis], (1, lon.shape[0]))
+        dy = (2 * np.pi * R * np.abs(lat[1] - lat[0]) / 360).values
+
+        # Now calculate gradients with correctly broadcasted dx and dy
+        dudx = calculate_gradients(u_filtered.isel(time=0), 'longitude',
+                                   dx)
+        dudy = -calculate_gradients(u_filtered.isel(time=0), 'latitude',
+                                    dy)
+        dvdx = calculate_gradients(v_filtered.isel(time=0), 'longitude',
+                                   dx)
+        dvdy = -calculate_gradients(v_filtered.isel(time=0), 'latitude',
+                                    dy)
+
+        # Calculate vorticity and other quantities
+        vort = dvdx - dudy
+        divh = dudx + dvdy
+        def1 = dudx - dvdy
+        def2 = dudy + dvdx
+        strain = np.sqrt(divh**2 + def1**2 + def2**2)
+        Wk = vort / strain
+        C2 = xr.where(np.abs(Wk) > 1, vort, 0)
+
+        data['dx'] = xr.DataArray(dx.T[np.newaxis, :], dims=data.dims,
+                                  coords=data.coords, attrs=data.attrs)
+        data['dy'] = xr.DataArray(dy*np.ones((dx.T[np.newaxis, :]).shape),
+                                  dims=data.dims, coords=data.coords,
+                                  attrs=data.attrs)
+        data['dudx'] = dudx
+        data['dudy'] = dudy
+        data['dvdx'] = dvdx
+        data['dvdy'] = dvdy
+        data['divh'] = divh
+        data['def1'] = def1
+        data['def2'] = def2
+        data['strain'] = strain
+        data['z_g'] = z_g
+        data['C2'] = C2
+        data['Wk'] = Wk
+        data['vort'] = vort
+        data['u_f'] = u_filtered
+        data['v_f'] = v_filtered
+        return data
+
     datasets = []
     for date in dates:
         # Retrieve and process pv data
-        dd.retrieve_dat(date, data_path, level="upper")
-        ds = cdo.vertmean(input=(data_path +
-                                 date.strftime('plevdat/%Y/%m-%d-%H.nc')),
-                          returnXDataset=['pv', 'u', 'v'])
+        # dd.retrieve_dat(date, data_path, level="upper")
+        ds = cdo.vertmean(
+            input=cdo.sellevel(
+                levels, input=cdo.selhour(
+                    date.strftime('%H'), input=(
+                        data_path + date.strftime('plevdat/%Y/%m-%d.nc')),
+                    returnXDataset=True), returnXDataset=True),
+            returnXDataset=['pv', 'u', 'v', 'z', 'w'])
+
+        # TODO: turn cdo into xarray
+        # # Open the dataset
+        # ds = xr.open_dataset(data_path+date.strftime('plevdat/%Y/%m-%d.nc'))
+        # ds.mean(dim="level")
+        # # Select the specific hour
+        # ds_hour = ds.sel(time=ds.time.dt.hour == date.strftime('%H'))
+        # ds_hour.mean(dim="level")
+        # # Select specific levels
+        # ds_levels = ds_hour.sel(level=levels)
+        # ds_levels.mean(dim="level")
+        # # Compute the vertical mean
+        # ds_vertmean = ds_levels.mean(dim='level')
+        # ds = xr.open_dataset(data_path+date.strftime('plevdat/%Y/%m-%d.nc'))
+        # ds_selected = ds.sel(
+        # time=ds.time.dt.hour == date.strftime('%H')).sel(level=levels)
+        # dss = ds_selected.mean(dim='level')
+
+        # calculate W_k
+        # ds = calc_Wk(ds)
 
         # Retrieve and process wind data
-        dd.retrieve_dat(date, data_path, level="surf")
+        # dd.retrieve_dat(date, data_path, level="surf")
         psl = xr.open_dataarray(data_path +
                                 date.strftime('surfdat/%Y/%m-%d-%H.nc'))
         block = xr.open_dataarray(data_path +
                                   date.strftime('../Blocks/%Y.nc'))
-        # block.loc[dict(
-        #     longitude=block.coords['longitude'][block.coords['longitude']
-        #                                         > -90])] = 0
 
         # Append processed data to the datasets list
         ds['psl'] = psl
@@ -107,14 +267,17 @@ def process_datasets(dates, data_path, cdo):
 
     return xr.concat(datasets, dim="time")
 
+
 def adjust_dataset_attributes(dataset):
     """
     Adjust the attributes and scale of the data in the given dataset.
 
-    Parameters:
+    Parameters
+    ----------
     dataset (xarray.Dataset): The dataset to adjust.
 
-    Returns:
+    Returns
+    -------
     xarray.Dataset: The adjusted dataset.
     """
     dataset.pv.attrs["units"] = "pvu"
@@ -123,6 +286,7 @@ def adjust_dataset_attributes(dataset):
     dataset.psl.data = dataset.psl.data * 1e-2
     return dataset
 
+
 # Process datasets
 DS = process_datasets(dates, data_path, cdo)
 
@@ -130,28 +294,19 @@ DS = process_datasets(dates, data_path, cdo)
 DS = adjust_dataset_attributes(DS)
 
 # %%
+
+
 def initialize_projection():
     """
     Initialize a stereographic projection for plotting.
 
-    Returns:
+    Returns
+    -------
     cartopy.crs.Stereographic: The initialized projection.
     """
     return ccrs.Stereographic(central_latitude=90.0, true_scale_latitude=50.0,
                               central_longitude=central_longitude)
 
-def initialize_color_dict():
-    """
-    Initialize a color dictionary for color mapping.
-
-    Returns:
-    dict: The initialized color dictionary.
-    """
-    color_dict = dict()
-    for c in ['red', 'green', 'blue']:
-        color_dict[c] =  [(0.,1.,1.),(0.1, 1., 1.),(0.4, 0.85, 0.85),
-                          (1., 0., 0.)]
-    return color_dict
 
 # Define the PiecewiseNorm class for piecewise normalization of colors
 class PiecewiseNorm(Normalize):
@@ -163,28 +318,56 @@ class PiecewiseNorm(Normalize):
     def __call__(self, value, clip=None):
         return np.ma.masked_array(np.interp(value, self._levels, self._normed))
 
-def create_custom_colormap():
-    """
-    Create a custom colormap for plotting.
 
-    Returns:
-    matplotlib.colors.LinearSegmentedColormap: The custom colormap.
-    """
-    color_dict = {
-        'red': [(0., 1., 1.), (0.1, 1., 1.), (0.4, 0.85, 0.85), (1., 0., 0.)],
-        'green': [(0., 1., 1.), (0.1, 1., 1.), (0.4, 0.85, 0.85), (1., 0., 0.)],
-        'blue': [(0., 1., 1.), (0.1, 1., 1.), (0.4, 0.85, 0.85), (1., 0., 0.)]
-    }
-    return mpl.colors.LinearSegmentedColormap('Custom cmap', color_dict)
+def create_cmap_and_norm(var="pv"):
+    if var == "pv":
+        color_dict = dict()
+        for c in ['red', 'green', 'blue']:
+            color_dict[c] = [(0., 1., 1.), (0.1, 1., 1.), (0.4, 0.85, 0.85),
+                             (1., 0., 0.)]
+        levels = [0, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 10]
+
+    elif var == "Wk":
+        color_dict = {
+            'red': [
+                (0.0, 0.0, 0.0),  # Start with blue
+                (0.45, 1.0, 1.0),  # Transition to white before -1
+                (0.5, 1.0, 1.0),  # White at -1
+                (0.55, 1.0, 1.0),  # Stay white just after -1
+                (1.0, 1.0, 1.0),  # End with red
+            ],
+            'green': [
+                (0.0, 0.0, 0.0),  # Blue has no green
+                (0.45, 1.0, 1.0),  # Transition to white before -1
+                (0.5, 1.0, 1.0),  # White at -1
+                (0.55, 1.0, 1.0),  # Stay white just after -1
+                (1.0, 0.0, 0.0),  # Red has no green
+            ],
+            'blue': [
+                (0.0, 1.0, 1.0),  # Start with blue
+                (0.45, 1.0, 1.0),  # Transition to white before -1
+                (0.5, 1.0, 1.0),  # White at -1
+                (0.55, 1.0, 1.0),  # Stay white just after -1
+                (1.0, 0.0, 0.0),  # End with no blue
+            ]
+        }
+        levels = np.linspace(-6, 6, 13)
+
+    custom_cmap = mpl.colors.LinearSegmentedColormap('Custom cmap', color_dict)
+    norm = PiecewiseNorm(levels)
+    return custom_cmap, norm, levels
+
 
 def add_cyclic_points(dataset):
     """
     Add cyclic points to the given dataset for plotting.
 
-    Parameters:
+    Parameters
+    ----------
     dataset (xarray.Dataset): The dataset to add cyclic points to.
 
-    Returns:
+    Returns
+    -------
     xarray.Dataset: The dataset with cyclic points added.
     """
     def cyclic_wrapper(x, dim="longitude"):
@@ -200,12 +383,14 @@ def add_cyclic_points(dataset):
         )
     return dataset.map(cyclic_wrapper, keep_attrs=True)
 
+
 def plot_data(dataset, custom_cmap, norm, levels, projection,
-              multiple_subplots=True):
+              multiple_subplots=True, var="pv"):
     """
     Plot the data from the given dataset with specified settings.
 
-    Parameters:
+    Parameters
+    ----------
     dataset (xarray.Dataset): The dataset containing the data to plot.
     custom_cmap (matplotlib.colors.Colormap): The custom colormap for plotting.
     norm (matplotlib.colors.Normalize): The normalization for color mapping.
@@ -214,7 +399,8 @@ def plot_data(dataset, custom_cmap, norm, levels, projection,
     multiple_subplots (bool): Whether to create multiple subplots or individual
     plots for each time instance.
 
-    Returns:
+    Returns
+    -------
     plots[Plots]: A Plots object with figures, axes and contourSets.
     """
     class Plots:
@@ -231,31 +417,33 @@ def plot_data(dataset, custom_cmap, norm, levels, projection,
 
     if multiple_subplots:
         # Plot pv data with contourf
-        figure = dataset.pv.plot.contourf(
+        figure = dataset[var].plot.contourf(
             x="longitude", y="latitude", col="time", col_wrap=3,
             cmap=custom_cmap,
-            norm=norm, levels=levels, subplot_kws=dict(projection=projection),
+            norm=norm, levels=levels,
+            subplot_kws=dict(projection=projection),
             transform=ccrs.PlateCarree(),
             cbar_kwargs=dict(
-                orientation='horizontal', location='bottom', extend='neither',
+                orientation='horizontal', location='bottom',
+                extend='neither',
                 ticks=levels, fraction=0.1, shrink=0.5, pad=0.05
             ),
-            figsize=(8, 6)
+            figsize=(7, 5)
         )
+        if var == "pv":
+            # Add 2 pvu contour line
+            for i, ax in enumerate(figure.axs.flat):
+                dataset.pv.isel(time=i).plot.contour(
+                    x="longitude", y="latitude", levels=[2], colors='k', ax=ax,
+                    transform=ccrs.PlateCarree())
 
-        # Add 2 pvu contour line
-        for i, ax in enumerate(figure.axs.flat):
-            dataset.pv.isel(time=i).plot.contour(
-                x="longitude", y="latitude", levels=[2], colors='k', ax=ax,
-                transform=ccrs.PlateCarree()
-            )
         plots.append(figure, None, None)
     else:
         for i in range(len(dataset.time)):
             fig, ax = plt.subplots(subplot_kw=dict(projection=projection),
-                                    figsize=(10, 8))
+                                   figsize=(8, 6))
             # Implementation for individual plots for each time instance
-            ContourSet = dataset.isel(time=i).pv.plot.contourf(
+            ContourSet = dataset.isel(time=i)[var].plot.contourf(
                 x="longitude", y="latitude", cmap=custom_cmap,
                 norm=norm, levels=levels,
                 ax=ax,
@@ -263,24 +451,26 @@ def plot_data(dataset, custom_cmap, norm, levels, projection,
                 cbar_kwargs=dict(
                     orientation='horizontal', location='bottom',
                     extend='neither',
-                    ticks=levels, fraction=0.1, shrink=0.75, pad=0.05))
+                    ticks=levels, fraction=0.1, shrink=0.5, pad=0.05))
 
             # Add 2 pvu contour line
-
-            dataset.pv.isel(time=i).plot.contour(
-                x="longitude", y="latitude", levels=[2], colors='k',
-                ax=ax,
-                transform=ccrs.PlateCarree()
-            )
+            if var == "pv":
+                dataset.pv.isel(time=i).plot.contour(
+                    x="longitude", y="latitude", levels=[2], colors='k',
+                    ax=ax,
+                    transform=ccrs.PlateCarree()
+                )
             plots.append(fig, ax, ContourSet)
 
     return plots
 
-def add_quiver_to_plot(dataset, plots, multiple_subplots=True):
+
+def add_quiver_to_plot(dataset, plots, multiple_subplots=True, col="b"):
     """
     Add quiver arrows representing wind vectors to the plot.
 
-    Parameters:
+    Parameters
+    ----------
     dataset (xarray.Dataset): The dataset containing wind vector data.
     plots (Plots): The figures to add quiver arrows to.
     multiple_subplots (bool): Whether to create multiple subplots or individual
@@ -293,13 +483,13 @@ def add_quiver_to_plot(dataset, plots, multiple_subplots=True):
                                   latitude=slice(None, None, 10),
                                   time=i).plot.quiver(
                 x="longitude", y="latitude", u="u", v="v",
-                scale=1000, color="b", ax=ax,
+                scale=1000, color=col, ax=ax,
                 transform=ccrs.PlateCarree(), add_guide=False, width=0.003
             )
         plt.quiverkey(
             quiver, X=0.1, Y=0.05, U=40,
-            label="$40  \\frac{\mathrm{m}}{\mathrm{s}}$",
-            coordinates='figure'#, fontproperties=dict(size=12)
+            label="$40$ ms\\textsuperscript{-1}",
+            coordinates='figure'  # , fontproperties=dict(size=12)
         )
     else:
         for i, figure in enumerate(plots.figs):
@@ -309,59 +499,90 @@ def add_quiver_to_plot(dataset, plots, multiple_subplots=True):
                                   latitude=slice(None, None, 10),
                                   time=i).plot.quiver(
                                       x="longitude", y="latitude",
-                                      u="u", v="v", scale=1000, color="b",
+                                      u="u", v="v", scale=1000, color=col,
                                       ax=ax, transform=ccrs.PlateCarree(),
                                       add_guide=False, width=0.003
             )
             ax.quiverkey(
                 quiver, X=0.1, Y=0.1, U=40, label="$40  \\frac{m}{s}$",
-                coordinates='figure'#, fontproperties=dict(size=12)
+                coordinates='figure'  # , fontproperties=dict(size=12)
             )
 
-def add_surface_pressure_contours(dataset, plots, multiple_subplots=True):
+
+def add_pressure_contours(dataset, plots, levels=np.arange(980, 1005, 5),
+                          multiple_subplots=True, plev="surf"):
     """
     Add pressure contours to plot.
 
-    Parameters:
+    Parameters
+    ----------
     dataset (xarray.Dataset): The dataset containing wind vector data.
     plots (Plots): The figures to add p contours to.
     multiple_subplots (bool): Whether to create multiple subplots or individual
     plots for each time instance.
     """
-    if multiple_subplots:
-        # Loop over the axes in the FacetGrid object
-        for i, ax in enumerate(plots.figs[0].axs.flat):
-            dataset.psl.isel(time=i).plot.contour(
-                    x="longitude", y="latitude",
-                    levels=np.arange(980, 1005, 5), colors='y',
-                    ax=ax, transform=ccrs.PlateCarree()
-                )
-    else:
-        for i, figure in enumerate(plots.figs):
-            ax = plots.ax[i]
-            dataset.psl.isel(time=i).plot.contour(
-                    x="longitude", y="latitude",
-                    levels=np.arange(980, 1005, 5),
-                    colors='y', ax=ax, transform=ccrs.PlateCarree()
-                )
+    if plev == "surf":
+        if multiple_subplots:
+            # Loop over the axes in the FacetGrid object
+            for i, ax in enumerate(plots.figs[0].axs.flat):
+                dataset.psl.isel(time=i).plot.contour(
+                        x="longitude", y="latitude",
+                        levels=levels, colors='y',
+                        linestyles="solid", linewidths=1.5,
+                        ax=ax, transform=ccrs.PlateCarree()
+                    )
+        else:
+            for i, figure in enumerate(plots.figs):
+                ax = plots.ax[i]
+                dataset.psl.isel(time=i).plot.contour(
+                        x="longitude", y="latitude",
+                        levels=levels,
+                        colors='y', ax=ax, transform=ccrs.PlateCarree()
+                    )
+    elif plev == "upper":
+        if multiple_subplots:
+            # Loop over the axes in the FacetGrid object
+            for i, ax in enumerate(plots.figs[0].axs.flat):
+                contour_set = dataset.z_g.isel(time=i).plot.contour(
+                        x="longitude", y="latitude", colors='y',
+                        ax=ax, transform=ccrs.PlateCarree()
+                    )
+                ax.clabel(contour_set, inline=True, fontsize=8, fmt='%1.1f')
+        else:
+            for i, figure in enumerate(plots.figs):
+                ax = plots.ax[i]
+                contour_set = dataset.z_g.isel(time=i).plot.contour(
+                        x="longitude", y="latitude",
+                        colors='y', ax=ax, transform=ccrs.PlateCarree()
+                    )
+                ax.clabel(contour_set, inline=True, fontsize=8) # , fmt='%1.1f')
+
 
 def add_block_contours(dataset, plots, multiple_subplots=True):
     """
     Add block contours to plot.
 
-    Parameters:
+    Parameters
+    ----------
     dataset (xarray.Dataset): The dataset containing wind vector data.
     plots (Plots): The figures to add p contours to.
     multiple_subplots (bool): Whether to create multiple subplots or individual
     plots for each time instance.
     """
+    handles = [mpatches.Patch(color=color, label=f'{label}')
+               for label, color in
+               zip(["Blocking", "Surface Cyclone", "Tropopause"],
+                   ["magenta", "yellow", "black"])]
     if multiple_subplots:
         # Loop over the axes in the FacetGrid object
         for i, ax in enumerate(plots.figs[0].axs.flat):
             dataset.block.isel(time=i).plot.contour(
                 x="longitude", y="latitude", levels=[0.5], colors='magenta',
-                ax=ax, transform=ccrs.PlateCarree(), linewidths=2
+                ax=ax, transform=ccrs.PlateCarree(), linewidths=2,
+                linestyles="solid"
             )
+        plots.figs[0].fig.legend(handles=handles, loc="lower right",
+                                 title="Contour Lines", frameon=False)
     else:
         for i, figure in enumerate(plots.figs):
             ax = plots.ax[i]
@@ -369,35 +590,47 @@ def add_block_contours(dataset, plots, multiple_subplots=True):
                 x="longitude", y="latitude", levels=[0.5], colors='magenta',
                 ax=ax, transform=ccrs.PlateCarree(), linewidths=3
             )
+            figure.legend(handles=handles, loc="lower right",
+                          title="Contour Lines", frameon=False)
+
 
 def set_subplot_titles(dataset, plots, multiple_subplots=True):
     """
     Set the title for each subplot in the figure based on the time coordinate.
 
-    Parameters:
+    Parameters
+    ----------
     dataset (xarray.Dataset): The dataset containing time coordinate data.
     plots (Plots): The figures to add titles to.
     multiple_subplots (bool): Whether to create multiple subplots or individual
     plots for each time instance.
     """
+    def get_prefix(index):
+        return f"({chr(ord('a') + index)}) "
+
     if multiple_subplots:
         for i, ax in enumerate(plots.figs[0].axs.flat):
+            # Generate title with prefix
+            prefix = get_prefix(i)
             title = dataset.coords['time'].values[i].astype(
-            'datetime64[s]').item().strftime('%Y-%m-%d')#'-%H:%M')
-            ax.set_title(title)
+                'datetime64[s]').item().strftime('%d %B')  # '-%H:%M')
+            full_title = f"{prefix}{title}"
+            ax.set_title(full_title)
     else:
         for i, figure in enumerate(plots.figs):
             ax = plots.ax[i]
             title = dataset.coords['time'].values[i].astype(
                 'datetime64[s]').item().strftime('%Y-%m-%d-%H:%M')
-            ax.set_title(title)
+            full_title = f"{title}"
+            ax.set_title(full_title)
+
 
 def add_geographical_features(plots, multiple_subplots=True):
     """
-    Add geographical features to the figure, including coastlines, gridlines,
-    and setting the extent of the plot.
+    Add geographical features to the figure.
 
-    Parameters:
+    Parameters
+    ----------
     plots (Plots): The figure to add geographical features to.
     multiple_subplots (bool): Whether to create multiple subplots or individual
     plots for each time instance.
@@ -407,18 +640,20 @@ def add_geographical_features(plots, multiple_subplots=True):
         plots.figs[0].map(lambda: plt.gca().gridlines(linestyle='--',
                                                       alpha=0.5))
         plots.figs[0].map(lambda: plt.gca().set_extent(extent,
-                                                crs=ccrs.PlateCarree()))
+                                                       crs=ccrs.PlateCarree()))
     else:
         for ax in plots.ax:
             ax.coastlines()
             ax.gridlines(linestyle='--', alpha=0.5)
             ax.set_extent(extent, crs=ccrs.PlateCarree())
 
+
 def set_circular_boundary(plots, multiple_subplots=True):
     """
     Set a circular boundary for the figure.
 
-    Parameters:
+    Parameters
+    ----------
     plots (Plots): The figure to add circular bounrary to.
     multiple_subplots (bool): Whether to create multiple subplots or individual
     plots for each time instance.
@@ -445,7 +680,8 @@ def save_plot(plots, filepath, multiple_subplots=True):
     """
     Save the figure as an image file to the specified filepath.
 
-    Parameters:
+    Parameters
+    ----------
     plots (Plots): The figure to add circular boundary to.
     filepath (str): The filepath to save the image file to.
     multiple_subplots (bool): Whether to create multiple subplots or individual
@@ -456,28 +692,28 @@ def save_plot(plots, filepath, multiple_subplots=True):
     else:
         for i, figure in enumerate(plots.figs):
             figure.tight_layout()
-            figure.figure.savefig(f"{filepath}_{i:02}.pdf", bbox_inches='tight',
-                        dpi=300)
+            figure.figure.savefig(
+                f"{filepath}_{i:02}.png", bbox_inches='tight', dpi=300)
 
 # %%
 
+
 # Initialize projection and color dictionary
 Proj = initialize_projection()
-cdict = initialize_color_dict()
 
-# Create custom colormap and add cyclic points to the dataset
-custom_cmap = create_custom_colormap()
+# DS = DS.sel(latitude=DS.latitude[(DS.latitude <= 80)])
+# cyclic points to the dataset
 DS = add_cyclic_points(DS)
 
 # Plot the data
-custom_cmap = mpl.colors.LinearSegmentedColormap('Custom cmap', cdict)
-levels = [0, 0.5, 1, 1.5, 2, 3, 4, 5, 6, 7, 8, 10]
-norm = PiecewiseNorm(levels)
-Plots = plot_data(DS, custom_cmap, norm, levels, Proj,
-                   multiple_subplots=multiple_subplots)
+custom_cmap, norm, clevels = create_cmap_and_norm(var=var)
+Plots = plot_data(DS, custom_cmap, norm, clevels, Proj,
+                  multiple_subplots=multiple_subplots, var=var)
 # Add features to the plot(s)
-add_quiver_to_plot(DS, Plots, multiple_subplots=multiple_subplots)
-add_surface_pressure_contours(DS, Plots, multiple_subplots=multiple_subplots)
+add_quiver_to_plot(DS, Plots, multiple_subplots=multiple_subplots, col=vecCol)
+add_pressure_contours(DS, Plots, levels=p_levels,
+                      multiple_subplots=multiple_subplots,
+                      plev=plev)
 add_block_contours(DS, Plots, multiple_subplots=multiple_subplots)
 
 # Set subplot titles, add geographical features, set circular boundary,
@@ -486,6 +722,5 @@ set_subplot_titles(DS, Plots, multiple_subplots=multiple_subplots)
 add_geographical_features(Plots, multiple_subplots=multiple_subplots)
 # set_circular_boundary(Plots, multiple_subplots=multiple_subplots)
 
-
 save_plot(Plots, "/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/im/syn/syn"
-          + YEAR, multiple_subplots=multiple_subplots)
+          + var + YEAR, multiple_subplots=multiple_subplots)
diff --git a/pyscripts/wcb_plot.py b/pyscripts/wcb_plot.py
index 38312a4..25cd0f0 100644
--- a/pyscripts/wcb_plot.py
+++ b/pyscripts/wcb_plot.py
@@ -27,7 +27,7 @@ wcbs = [f for f in os.listdir(wcb_path) if
         os.path.isfile(os.path.join(wcb_path, f))]
 wcbs.sort()
 
-YEAR=2017
+YEAR = 2016
 
 def get_data(wcbs, YEAR):
     i = 0
@@ -112,10 +112,10 @@ def plot_wcb(wcb_df, df1):
 
         df1.loc[df1['date'] == id_df['t'].iloc[-1], 'm'] += id_df['m'].iloc[-1]
 
-    ax.set_ylabel("$\\Theta$[K]")
+    ax.set_ylabel("$\\theta$ [K]")
     ax.set_xlabel("Date")
-    ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))
-    locator = mdates.AutoDateLocator(minticks=5, maxticks=6)  # Adjust maxticks as needed
+    ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %b'))
+    locator = mdates.AutoDateLocator(minticks=4, maxticks=6)  # Adjust maxticks as needed
     ax.xaxis.set_major_locator(locator)
     df1['ratio'] = df1['m']/df1['n_t']
 
@@ -124,8 +124,8 @@ def plot_wcb(wcb_df, df1):
     ax2 = ax.twinx()
 
     # Set labels for the secondary y-axis
-    ax2.set_ylabel('$\\sum_{\\Delta \\Theta > 5 \\mathrm{K}}m_i / \\sum m_i$')
-
+    # ax2.set_ylabel('$\\sum_{\\Delta \\Theta > 5 \\mathrm{K}}m_i / \\sum m_i$')
+    ax2.set_ylabel("WCB fraction [-]")
     # Creating a custom color gradient line at the bottom of the plot
     y_min = ax.get_ylim()[0]  # Get the minimum y-value of the plot
     n_segments = 500  # Number of line segments
@@ -160,7 +160,7 @@ def plot_wcb(wcb_df, df1):
                   handletextpad = 0.3, borderaxespad = 0.1,
                   handleheight = 0)
     elif YEAR==2017:
-        ax.legend(handles=legend_lines, title='$m$', loc="lower left",
+        ax.legend(handles=legend_lines, title='$m_i$', loc="lower left",
                   bbox_to_anchor=(.45, .05), labelspacing = 0,
                   handletextpad = 0.3, borderaxespad = 0.1,
                   handleheight = 0)
diff --git a/shellscripts/calc_epsloglog.sh b/shellscripts/calc_epsloglog.sh
index 7590fc3..535ed64 100755
--- a/shellscripts/calc_epsloglog.sh
+++ b/shellscripts/calc_epsloglog.sh
@@ -1,7 +1,11 @@
 #!/bin/bash
 
-#SBATCH --array=10-22
+#SBATCH --array=1-25
 
+# 28 for 2017
+
+eval "$(conda shell.bash hook)"
+conda activate /home/schoelleh96/miniforge3/envs/wp21
 
 PYPATH=/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/pyscripts/epsloglog.py
 TRAPATH=/net/scratch/schoelleh96/WP2/WP2.1/LAGRANTO/wp21/era5/traj/2016/
@@ -10,6 +14,8 @@ cd ${TRAPATH}
 Trafiles=`ls -v traj*`
 CurrTraF=`echo $Trafiles | cut --delimiter " " --fields $SLURM_ARRAY_TASK_ID`
 
+echo $CurrTraF
+
 filename=${CurrTraF##*/}
 Y1=${filename:5:4}
 M1=${filename:9:2}
diff --git a/shellscripts/test.sh b/shellscripts/test.sh
index 6e4b859..afefc1a 100755
--- a/shellscripts/test.sh
+++ b/shellscripts/test.sh
@@ -47,19 +47,23 @@ H1=${filename:16:2}
 #	rm -r ${PFAD}/cache/${Y1}${M1}/${D2}
 #    fi
 
-source /home/schoelleh96/Applications/envs/wp21env/bin/activate
 
-python ${PYPFAD}/epsloglog.py ${PFAD}/traj/${year}/ ${Y1}${M1}${D1}_${H1}
+eval "$(conda shell.bash hook)"
+conda activate /home/schoelleh96/miniforge3/envs/wp21
+
+#source /home/schoelleh96/Applications/envs/wp21env/bin/activate
+
+#python ${PYPFAD}/epsloglog.py ${PFAD}/traj/${year}/ ${Y1}${M1}${D1}_${H1}
     
-sbatch --wrap "$CSPFAD/calc_bound.sh $PFAD/traj/$year/  $Y1 $M1 $D1 $H1"
+#sbatch --wrap "$CSPFAD/calc_bound.sh $PFAD/traj/$year/  $Y1 $M1 $D1 $H1"
 
 #python ${CSPFAD}/calc_dist.py ${PFAD}/traj/${year}/  ${Y1} ${M1} ${D1} ${H1}
 
-python ${PYPFAD}/dist_plot.py ${PFAD}/traj/${year}/ ${Y1}${M1}${D1}_${H1} "100"
+#python ${PYPFAD}/dist_plot.py ${PFAD}/traj/${year}/ ${Y1}${M1}${D1}_${H1} "100"
 
 python ${CSPFAD}/calc_E.py ${PFAD}/traj/${year}/ ${Y1} ${M1} ${D1} ${H1}
 
-python ${PYPFAD}/BoundR_plot.py ${PFAD}/traj/${year}/ ${Y1}${M1}${D1}_${H1}
+#python ${PYPFAD}/BoundR_plot.py ${PFAD}/traj/${year}/ ${Y1}${M1}${D1}_${H1}
 
 #n_p=$(wc -l < ${PFAD}/startf/${year}/${CurrStartf})
 
-- 
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