diff --git a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
index 3c5dbbb4d3a9cde91eda73ef33680e7b2d206b3d..7f2d02452310afd30559b173c230b90510106fb9 100644
--- a/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
+++ b/.ipynb_checkpoints/multi-area-model-checkpoint.ipynb
@@ -291,7 +291,16 @@
    "execution_count": 6,
    "id": "6e4bed8d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2479643048.py, line 20)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn [6], line 20\u001b[0;36m\u001b[0m\n\u001b[0;31m    'replace_cc_input_source': replace_cc_input_source\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "# Decide the parameter replace_cc_input_source\n",
     "complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
@@ -311,7 +320,7 @@
     "    'fac_nu_ext_TH': 1.2, # Increase the external input to 2/3E and 5E in area TH\n",
     "    'fac_nu_ext_5E': 1.125, # Increase the external Poisson indegree onto 5E\n",
     "    'fac_nu_ext_6E': 1.41666667, # Increase the external Poisson indegree onto 6E\n",
-    "    'av_indegree_V1': 3950. # Adjust the average indegree in V1 based on monkey data\n",
+    "    'av_indegree_V1': 3950., # Adjust the average indegree in V1 based on monkey data\n",
     "    'replace_cc_input_source': replace_cc_input_source\n",
     "}\n",
     "\n",
@@ -382,19 +391,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ab25f9f8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%capture captured\n",
     "M = MultiAreaModel(network_params, \n",
@@ -414,19 +414,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6a7ddf0e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 0\n",
-      "Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "p, r = M.theory.integrate_siegert()\n",
     "print(\"Mean-field theory predicts an average \"\n",
@@ -451,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6316ac24",
    "metadata": {},
    "outputs": [],
@@ -467,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "8408d463-557b-481b-afc1-5fbbbd67306d",
    "metadata": {},
    "outputs": [],
@@ -484,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "445a722a",
    "metadata": {},
    "outputs": [],
@@ -501,53 +492,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "05512922-26e5-425f-90a4-0df7c2279ccf",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 7864\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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th2NZ8ff4AYC7Pw18l6gQvheYbtFxDgbH96v7PTc4KGRmXYlm4mwF/JxoNtD2RNPbOzTSp9WJvmH8NSv/3Z1GNADTWB3QkYLdSBqxRvz/4/WsZ0tWfn0JVff326mhQDOeo6b+Puv2p/+8KFd8vSF9G8gWL0u6vY8mml2zJ9E3zl8CrxMNbOwZD8huQ/0DHU091idY8Xf2m3j5j4mm1x9HNOjxhUXHKegc397k66uZHUg0hf9tol1ndiT6vUyn8dfzum3qfVbeprrT9DbV2Lb7hTe+20udvix/PS7U0DbQ1POcxEIa2c6LfFl0fQnRoPNXCgYpQ987V9UHrPg7OxbAzH5MtNvk40Tv6zuwfHCivj4W/22sQTQwXLxNvBzf3iteT3O3u0LNeX7r/l4b+puuuz3p60vd38H1rPyYD2DVX1tFck3H6BCpDDOJCrbTG7h9YtF1L7xi0cExDyaarn1ZwfItG2jP61k2k2hmxgUN3KfujArDgJvc/fyC9XRt4D5NmQGsHu+j31AxPgtYq57laxE9jvoGmNIwk2gKdEMHhax7PmYAqR10zN3nm9m9RLvI/JZowONDdy/+pjnEP4hOD3ow0bEXJhHN1GmOWdT/jVvd72hm0fL6trUQi4kGnAo1p3gcRVRIF7YLgLvfTXQ8na5EH1IvAP5r0YEK6/abX+FgfEV2Jirudy/8NjH+Zq8xs4k+HF8J3FRfoLFZJETPdZKDANf9ToYDE+q5fV6CNhpT9+GgsWMMNPc5akjdB5U1WfExrVlPtqH715ctXpZoe3f32WY2jmjWxhyiKepuZk8S7YoylGhQq76BjqacTHQ8gzrT4nVWEx1j4Bdmth7R7j1/IfrAdw7JXl+HAe+7+/C6BWbWjiZm57B8m/o6K3/gLLy9sfs3tO0mfc34lOUfNAsl3QaaY3XgjRK2D9H7TvFMxbp1p+VAVhxg/Cj+fxjRbhh1x9LAzNZvpJ3i39XMuK3DG8hPKlhPc7a7VVVXLzRUU9Sdvarw9eXDgkzxtlW3nf+CaHCoWFOz7UQqmgY6RPJlMfV/G/Rfom/nPol3eQnVgaiIXlq0fHhAG/8l+jAyoYkZFp3rWc8PAtZT6FGib3dPAIoPuFrnaeAMMxvg7pMgmnZLdJCw1zw6iGIp/Jfow0IPd3+ikdyjwDAzO9DdRzWQqftQ3YlkHypvBo626GjxB7N8Vk1jFrPih6GvuPsHZvYo0QEDhwC/b+IDdGOeBr5rZrsWDb58j2iw7u1mttuQj1l5IGn/0EbcfSZNfPiKPzg+aGYbEB1zoRfRrgeTgBPMbIS71/chrO4b9K/+Liw6C9HBTaxvvpk9SzTLYWwzfifvAO3MbJ0mdkN6nmi728jdbwxcBzT8ulVnfWBygtcNCHyOGvEG0cynw1nxdJ/FB15uyAvAt8ysS93uKxYdkHlXlg9iQtj2/hRwJNFAx5UFy3oTHRdncuEueEm5e/FAd32Zj4G/mtlRLP97SfL62plot4FCxxC9nzTmMaJBunXd/bGm+lePd4gOTrsqXiT6HXau233FojNn7ErzZ5ItoYFtPX7f6c/ymZal8jHwHTNrXzcjwcz2oIHX9yIN9r+Qu49v4KbORLseFQp5f/8v0YG6q939nUZyzd3uVtVEolkZw4hmYQBgZrsQDcT+NV70EtH2fTjR4GGd4teXiUTvD1u4+18QkSAa6BDJl7eIvmE7heibg0VxwfE3og/uz5rZ34jePLsQnf1hd3dv6gPTHDN7EfiZmX1K9E3ecTT+LXSx3xBNL33GzK4gevNejaho3sDdj4tz/wWONbPxRNOWvw3sErCewn4/ZWb3AJfEHzKeJNqPdw+iY0iMJnpuhgOPmdlviYqwU4nOHBP8gTegb6PN7Haib/ovIXpuaokObPct4Bx3f5doF54TgdvN7M9EBVI3orOuXBoXe2/Fzf7MzP4D1Lj7GBr2ONGHreuJCsJbGsnWeQs41cyOIJqWPK/oA9JVREeGX0qyMwY0ZCTRzKN/m9m5RLt0HEW0u9TJCaech7gD+FW8rheJZtgcmVbjZvZ7om/pniJ6ztch+lA6zt2nx5kziI7u/6SZXU00vXozogPr/ZZoIGEucGW8jXYhOtr/DKIzQDTm/4jO5vCImV1P9AGtN9FuDlXu/vNG7vtM/P8ONLJrjbvPNbOz4v71Idq3fQ7R68PXiGYfNHaK2oZet+rsWNCXhqzKc1TfY5odv1aea2bziD7Ub090dqMkzifaZelRM7uIaNbQ71h5avpIkm/vTxKdVaIf8cwNj87IMgHYmwZm7TSXRWe7eYDoWBPVRL/LrYAb43UneX39L3BI/Fw+CGxLtP3Pbmzd8eDpBcAVZjaQaEBoEdFAwL5Ex7RpbPbKM0QD2G1WYdD1fKJZLI+Y2cVEA/6/JvodNrfNt4D9zey/RDNVprl73cDXIKLttqltfVXdAZwE3GDR6WTXJ3qdmJPgvo31P4n/AueY2S+J3vP2InqOk7qVaGDkCTP7K9HuW+2JjmNxENFBahfQzO1uVbl7jZn9BrjGolM/30L0OvhHogMg/zPOTTSz24Dfx7sP1p115VtF7bmZ/YjojFDtiY7pMYPoPWUXoi+vLinlYxJp1dI8sqkuuuiy6hcaPutKfWe8mMSKR0nvQnQQzS/jNiYV3LYa0Yf6j4i+lfmCaFeSMxpbd8FtA4g+wMyL73sF0UCAU3DGgIb6Gt+2DtHpUKfGffiU6Ju7owsyvYkKsS/jy61EHzCKj0Q+kgRHeSca0D2X6JvzJUQfIh8mPlNBnBkI3EdU6C0i+sC7X1E751H/mU1W6AfLj9i+TwO/1wEFy9oQfch5PV7vnPjnC4lmetTluhLNuvi44Hm7m/gI80TfUl0Z/15qic9kQiNnBonbc+D5Bn7Xxc/3WvHzNi++bXTRfaqIvgH/V8C2vtJZV+LlfYlmncwg+rb/jcJtpKlttYF1NXTWlY5Esys+jR/bnUQf7Bvd3hp7bova359oN55P48cymWiAqV9Rru5AktXx5XUKzvAT3/4a0T78HxAV7ecVP3/UfxaZzYj+pr6I+zCF6APstxI8by8B/yxaNrSB5/Jb8WOYG/fzfaJBr80LMvWddaWx163+8TZ9QIK+hjxH5xctq2+br4q30c/iNkcTHX9mpee4kW3utfg5/5BoF5EVtqOk23uc60Y0kPhZ0fLL6tsWCXgtaqD/F8T9n0P0tz2eojOs0MTrK9Fr3PlEg3wLiAYstibBGT7i5ccQvR7PJ/q7eJvovaepMzptFrf3taLlI2ngDD/FfYqX7Ut0hpvC3+G9RLP9iredE5r6OyGaDfIq0ev9CttR/Dx+StF7TD39bOj3Wu9jo/7t/WSiD94LiQYJt03yO2mo/yR/P+5EtKvjdKLX2weJBlqKn4vzqOf9Nr6tY3z7O/HvZRbRQMF5LD+DXKLtroTP79FEr+GLiWb63Qz0Lcp0jp+LWUTb9gMsP6tQ8d/yzvFz9WX83E8iek3fuSAzGp11RRddVrjUneZQRESk2cxsX6JvvffxxnfFkVbEzIYTfZDu6wVnn2jB9Z8DnAJs6OnP5pEcM7PRRMdpOCHFNrsSDeA95O5JZ/ckbfst4B53/3Wa7YqIVCoNdIiISLOZ2YZE+8L/DVjs7tuWuUuSovi4AeOBG9z94hZed0eib9F/7u6p7pYh+WdmuxLtpreRuzfrzFVm9neiGQ/TiHYZOp1oZsD27p7aQUPN7GCi2U8buvvstNoVEalkOr2siIisil8T7dK0mOj0wZIj8SyK44imf7e0AUSzSZp16mOpbB4d3PWnRAeBbK6ORLvwPAqMINqFZp80BzlinYh2V5qdcrsiIhVLMzpEREREREREJDc0o0NEREREREREckMDHSIiIiIiIiKSGxroEBEREREREZHc0ECHiIiIiIiIiOSGBjpEREREREREJDc00CEiIiIiIiIiuaGBDhERERERERHJDQ10iIiIiIiIiEhuaKBDRERERERERHJDAx0iIiIiIiIikhsa6BARERERERGR3NBAh4iIiIiIiIjkhgY6RERERERERCQ3NNAhIiIiIiIiIrmhgQ4RERERERERyQ0NdIiIiIiIiIhIbmigQ0RERERERERyo+QDHWY2yczczIbG10fH14eXet319GV4vO7RLb3uEAXP2SEJ8+fF+ZGl7ZmIiIi0hHLWS0mZ2dC4j7MD7rNCXSgiIlIKTQ50FLwhFV+GtED/REREpMIU1B61ZlYdX7/LzHYsd99EREQk+9oGZB8EPii4Pj3lvoiIiIgUeoio3tgV+C5wqJl9z93/Vd5uiYiISJaF7LpyvbufUXCZWt8uEwUzPgY0t1Nm9j0ze8vMFprZLDN7wcx2i29ra2anm9mbZrbAzD43s9/Et+1rZq+Z2RwzW2pmH5vZ75pY127x9NAvzWyamd1gZr0aydc9vrPN7CMzmx3/vLuZTYyvX16QNzM7yczGm9l8M3vfzM43s44FmVPNbLKZTTezs+pZZ1szO8vM3o7beMvMTmzOcysiItKKXO/uxwFbAHcQfUFztZl1BjCzAWb2LzP7NH4ff6pu1oeZbR+/X78ZX98mvv5SfH2P+Por8fWR8fWrzWxUXGO80dgMVjNrb2bXmtlnZrY4fi9/oOD2dczsxrgeWRS/j28f3/bXeKbKonhdLza2O0doLWDLd9d93cwuiWfGvGVmW5vZH+Ja6UMz+3rBffqY2XVm9omZzY37tF/B7T3M7M74tteBbepZ77pmdoeZTY1rokfNbFBD/RQRESmFkIGO483s0rpLqTpkZp2AkcB6wK1E3+Z0BzaMI78DLgU2AO4BngY2jW9bG5hBVAzdDHQDfmNmwxpY1yDgCWBb4L/Au8APgH+ZmTXR1Z8BLwA9gL8AdwMvAh2AH5vZPnHuFOAaoD9wJ1GRdi5wWdyHocCVQD/gUeDoOFvoD8CFgAH/AroCI8zs2Cb6KCIi0uq5+zKi93+A1YFdzawL8CRwGNH795PAUOBJM9sQGAvMBTY3s57ALvH9t45rjbrrTxet7mRgGfARsCXw90a69n3gBKLa43rgVaLZJ8SDMU/GmUVEdcmXRO/3AOsDL8X3ewrYkaj+6NbAuppbC2wZt/02sFm8rsOIapb1gRvi/rYBHgCOjx/P/UT10UNmtmvc1uXA4cCc+LH+tnBFBY/5cOAN4DGi38lTZta7iX6KiIikJmTXlQOKrp+RRgeKBk1eJnqTrQK+AO4D3nL3D82sKh58+EmcPcrd743baBcvuym+3zZAL6JdbbYD9iIa/Ch2CtAeeA34PL7sBOwJDATeaaTrP3P3W8xsF6JBmRvd/Wwz6wp8G9gaeBw4Lc6f7u43mtlWwDjgBDM7nWhgA2Ckux9vZqsDnxEPQsWPua6N54mKtnFEgyGnADc20kcREZG8+Ljg5zWA/Yk+qH8IDHV3N7N7gUOA4939l2b2HPBNovf2XYkGRDYh+uBf9+G9eKDjYXc/1Mz2JPrQvjWAme0AfK8gdwVQV3+MJ/py5i2i92mAbwEbA58CW7v7griduvucQDTgMAB4D1gA9CYamHi+sEOrWAvMB/aJH/NTRF/Q7AJMjttZ28z6ED2XOwHVwO7uPt/MZhDVez8ysxeBui+Ovufuz5rZa0SDH3X2J/piaiowMV72SbzsMODqRvopIiKSmpCBjkPd/b7GAmZW1Yw+nF7w843ufpuZnUL0LcGouN0pwDHABKJvMCD6JgIAd18a//gP4KR61tGngXUPiP/fMb4U2ojGBzrejv+fTTTQUfeGPi/+v0vROurydW22ISpQ1o6vTwRw91lmNhNYK17em+WP+Qf19FFERKQSrFfw8xfEAxDARHf3+Od3irKjiQY6diEa2LgO+CGwG7AzUAs8W7Se1+L/Z8f/172fb86KNct9RF+wDAUOJhoEcOBxMzuUaOAAYHzdIAdENYtFu8iOB/rW8zjrq1lWpRaY5O4LbcUzo0x095qCyatdWF6vTHb3+fHPhc9nb6Ivh2B5zfNu0brq2libFZ+rJP0UERFJzaqeXrbujbB7/H/wPpjubgWX4fHiG919baLpnacD6wC/JppKWR1nvhqYMLO6AZsj4v+HE80K+UddpIHVT4r/v6SwH8AG7v5gE12vaeJ68Trqdq8ZGP9fS/RtytTC5fGMjsJjhMxg+fM8uKCPbYhmq4iIiORa/D5ft5vELOA5lr+/blKwu2nde2zd7I+62RrfJfpy4TmiGRHDid5rX3f32UWrWxb/74UL3X1kUc0yGljm7kcQ1UGbEc3k3JdoZudH8V23jHeVKXwsuxMNckwn+mKjA8sHVuqrWValFlipPnH3+mqWSfH//eNdUGDF53MGsKRo+SYNtPEq0Kagn6sBf2yinyIiIqkJmdFRn7pvPb5lZn8lmqaZhs/NbDQwjWgKJ8DseFrq5cAvgVvN7B6ix1BLtAvI50RTMn8CfB04tIn1jABOBE43sw2I3sQ3I/rmZ1UHgepcSTS99TIz+xrRbjQQHWBtkZndRrQ/7HCLDlA6iILfS/yYrwTOBh4zs1FE3+rsRFTADU+pnyIiIllzvJkdRDQbYxOiQYgfuvsCM3uI6IP1hkTHgJhB9L6/kPi4E0QfuKuJvmyoITomxmDgO/Hto1exf0ea2TnAmHg9X9UsRMcAe49o95XXzKzumGKXENUrEM3e+BvRcce60oAWqgXGED0/OwLPmtkE4EiiAZ+r4hkgdxLNsL3NzB4j2h2l0MNEuxJtCzxnZm8A6xLNevkWq/58i4iIJLJKH+bd/XGifTMXEhUXV6TRKaKDV21DNACwBdEBSX8W3/Zb4KdE35QcBuzN8qmTJxBNs9yc6ECk1zTR/9eJ9lt9BtiDaNppN6KDi6blKuBUopkbRxINyvyZeEqnuz9JNDDzKdH02nuI9mct9CvgHKJvsY4mGiyZSHRwUxERkbzan2i2ZgfgLmDXulPLxrtX7E30vrkp0fv508De7v5+nFlGNIsDotkb1QXXYeXjc4SaSPQlybeIapYlwPnAg/HuKnsTHYS0M3As0bFFprn7C0QzHL4kmgFyO8tneDakpLWAu9cCBwH/jPt5KNEXWge5+//i2E+IDr7eE9iBqJ4pbKPud3I70QDHsUSzP25h+e4uIiIiJWfLd2sVEREREREREWnd0to9Q0RERERERESk7DTQISIiIiIiIiK5oYEOEREREREREckNDXSIiIiIiIiISG5ooENEREREREREckMDHSIiIiIiIiKSGxroEBEREREREZHc0ECHiIiIiIiIiOSGBjpEREREREREJDc00CEiIiIiIiIiudG23B0QERGRbDKzLsBVwBJgtLvfWuYuiYiISI6lVXtoRoeIiEgFMbMbzOwLM3uzaPl+ZjbRzN43s5/Hi78N3O3uJwIHtXhnRUREpNUrR+2hgQ4REZHKMhLYr3CBmVUBVwLfBDYHjjSzzYF1gMlxrKYF+ygiIiL5MZIWrj2064qIiEiZfGO/wT5zRnVq7b366kcTgEUFi0a4+4jCjLs/Y2YDiu66A/C+u38IYGZ3AAcDU4gKjnHoyxEREZFcSLP+yGrtoYEOERGRMpk5o5qXxvwhtfba2tGL3H27Ztx1bZZ/ewJRkbEjcDlwhZntD4xKoYsiIiJSZmnWH1mtPTTQISIiUiaOU1ubiT1CrJ5l7u7zgR+0dGdERESkdDJSf5S09tBAh4iISNk47svSbLCHmY0ARrl7yLcgU4D+BdfXAaal2TERERHJilTrj0zWHhroSFGvXp183f7dE+fbLJyTOLt0RlVYZzwsPndh56B8t44Lg/I1Ncn736nb/KC2F1WH9d29vsHDhlUv6RCU79JuSfK2l7YPanvNQesG5UNNe2Ny06ECtQHZ1TsuajpUwCxsI14Q+HuqspDewzJPvovgvKVhuxOu03NeUH7u/LBtvk3YJk/7qqVB+UXLkm/Hi2vDOvPpkulB+f4degfle3ZZEJQfP2vBDHfvE3SnljXH3U9qxv1eATY2s/WBqcAw4Hup9kxKolevTr7uuj0S59ssmB3U/uLP2wX2KLmOGw0Mys+c8HHgGsLeR1brMTdxds7c5PUewGqbDwjKhwp9bnptsV6JepI9MydMCrxH8vep1v48Lvnw7cTZ9htsVsKewHuvfRKU71QVVk+sM7h/06HYh+PC6uENhiRvG2DJB+8E5cfPnq/aoxk00JGidft35+knj0qc7/z6A4mzn13bM6gvtTVhH7QeH79VUH6PgclfGAGqq7skzm469OWgtt97JmyXsGUBgy4A//tk/aD89n2nJM6+OC3shfGMMVcF5UP9et2fBOUX1SR/kzli4HtBbXdsn3zACODVyQOC8j07hA28TF/UKXH2mc/DBiIu3H90UP6xV8O2+fZVYVMTN1g9bHDhrS/6Js5Omp/8eQQ47+Org/Jnr/edoPwh244Jyve//dXQT1qNc3Bv2amjZnY7MBTobWZTgN+6+/VmdhrwCFAF3ODuE1q0Y9Is667bg6ef+n7ifOfX/h3U/geXrB3apcQGjno6KD9yixOC8lWBA+aH7v9I4ux/H9k7qO3DxtwYlA8V+twMH3NdiXqSPTcPOj4oXxPwhVhrfx4/OWKHxNl17wyrz0Pt3+20oPzm3cPq+YvGXJY4e3jPsHr4rjGXB+UnHbZTUH79e15Kt/aAFq8/ylF7aKBDRESkTBynNt1dV5pep/uRDSx/GHi4RTsjIiIiLa6l649y1B4Vfao4MxttZt8oWnaGmV1lZv81s9lm9mC5+iciIhKoh5mNMLMDy90RqZ9qDxERyZlM1h6VPqPjdqJ9gQrnKg4DzgLaA52Bk8vQLxERqQipH4y0ufvJSstR7SEiImWWav2Rydqj0gc67gbON7MO7r7YzAYA/YD/ubub2dBydk5ERPIu9YEOyT7VHiIiUmb5rz8qetcVd58JvAzsFy8aBtzp7oHnLBEREcmETE4fleVUe4iISM5ksvao9BkdsHwK6f3x/8eVtzsiIlIx3PFa7bpSgVR7iIhI+aRbf2Sy9tBAB9wHXGJm2wCd3H1smfsjIiKVJOdTR6Ve96HaQ0REyinn9UdF77oC4O7VwGjgBqJvWERERERKRrWHiIhIaWlGR+R24N9E00cBMLNngU2BrmY2BTje3R9p4P4iIiLNkP+DgUmDVHuIiEiZ5L/+0EAH4O73Ala0bPcydUdERCqGQ+3SNBvsYWYjgFHuPirNhiVdqj1ERKR8Uq0/Mll7aKAjRW0Wz6PTxMcT5xdsdVDibFXbJ4P68v6kDYPyPTssDspXV3cJyy/slDxcG7ZH1cx53cP6srhjUL5T27DRzj1feD1x9ldrrxfUdlVVt6B8G2sflP/pWkcH5RfUJM++PHXdoLYH9/ksKP/m7LBtsleHsO3gjS+rEmera2qD2l68MKwvE+aEPdb2gTspdm8f9nrwn2nJ+/Pp4kVBbW/X+aig/OOftgvKf/LMrkF5eDUw3+IyeUAwKZ02C+fQacJDifMhtQdAhw7PJ86+/uFGQW3v022foPwtg8Pe72cu6BqUb9NhSeLs3R/3DGr7sKA0VLUJe52vqZ0fuIbKccyb1wflb9j8xBL1JHuWLQ17zyylh+ZdEZQ/pPuPS9QTWBhYx4UacPeLYXcwazpTXpmsPTTQISIiUibu+Z86KiIiItlSCfVHRR+M1MxGm9k3ipadYWYPm9kLZjbBzN4wsyPK1UcREckzh9pl6V0k81R7iIhI+aVYf2RUpc/oqDuPfeGBvoYB5wDT3P09M+sHvGpmj7j77DL0UURERPJDtYeIiEiJVfSMDuBu4AAz6wBgZgOAfsAz7v4egLtPA74A+pSrkyIiklOe+oyOHmY2wswOLPdDkwap9hARkfJKs/7IaO1R0TM63H2mmb0M7AfcT/SNyp3u7nUZM9sBaA98UJ5eiohIrqW7j2wmDwgmy6n2EBGRTEiv/shk7VHpMzpg+RRS4v9vr7vBzPoCNwM/cPfSHn5XREREKoVqDxERkRKq6BkdsfuAS8xsG6CTu48FMLPuwEPAr9w98BxAIiIiTTMcy/CBvKRk7kO1h4iIlEkl1B8VP9Dh7tVmNhq4gfgbFTNrD9wL3OTu/ypj90REJM/q9pGViqLaQ0REyqoC6g/tuhK5HdgKuCO+fjiwBzDczMbFlyHl6pyIiIjkjmoPERGREqn4GR0A7n4vYAXXbwFuKV+PRESkMqT+jUoPMxsBjHL3UWk2LOlS7SEiIuWTav2RydpDAx0psi6b027nMYnzNX/olbzxy48P6svQnhcG5at/0S8o367bgqB8h1/OTpxd/JceQW3v8t3/BOUXTV4jKE+bsGPBnXDxJ4mzXxw3KKjtc2+YF5QPFdqfpYvbJ86ufevY0O4E2a2krZfWwt/1Dsr/5a6XStSTyLRjtg7K3/HK7U2HcuIiuzzlFh3TWVdkFXz+UTcu/f7QxPljv/ZoUPur33BQ4uwRvW8KavvTEzsF5dt1XRiUP/CKA4Ly+y7skDh707nXB7X9yp7vB+WnHrNRUN6sXVB+1DaHB+UPHHtXUD7EnYO/F5T/cF63oPwXi6qC8r07Jt8ONu52SFDbp/XtG5Rfo+OioPzf3w97rA8On544e956Pw5qe/xsazpUYNKyOUH576wZth1cstGpibPbrh72PP41oG2AZR723JRGqvVHJmsP7boiIiIiIiIiIrmhGR0iIiLl4kBtTbl7ISIiIpWkAuqPip7RYWajzewbRcvOMLOHzWxhfCCwt8zsajOr6OdKRERKITq9W1oXyT7VHiIiUn7p1R9ZVelvoLcDw4qWDQP+DHzg7kOAwcDmwCEt2jMRERHJI9UeIiIiJVbpu67cDZxvZh3cfbGZDQD6AVPqAu6+zMyeB8KOCiUiItIkz/3UUVmJag8RESmz/NcfFT2jw91nAi8D+8WLhgF3Eu21BICZdQb2Bsa3eAdFRCTfPD69W1oXyTzVHiIiUnZp1h8ZVdEDHbHCKaTD4usAG5rZOOA54CF3DzuHqYiISMvrYWYjzOzAcndEGqXaQ0RE8iKTtUel77oCcB9wiZltA3Ry97HxNNK6/WRFRERKxtKdOprJc9nLSu5DtYeIiJRRivVHJmuPih/ocPdqMxsN3MDyb1RERERKz/O/j6ysTLWHiIiUVQXUH9p1JXI7sBVwR7k7IiIiIhVBtYeIiEiJVPyMDgB3vxewguuTgEFl65CIiFSMlHddkVZCtYeIiJRT3usPDXSkqHbq6yz41VqJ80uruyfPnjMqqC9zLwiK02mdWUH5xZ/0DsufuW7ibO3SXkFt97zsw6D8/B9tHJTvsv6nQfkQa9zwZsnabo4O3ecH5dstXVyinlSWz94I2ybXD2z/vYN3D8r3WjtsO5BVkf+po1Jaa6w+m1MOvzdx/oNXw8ZS2h35YuLsFzO+H9R2p3/+LSj/xgNDg/K3HPJyUH7Cy0MSZw8bG/Y6+fjXwvI9BkwLyoc6cOxdJW0/xA4bTwzKD17UMSj/7dFrBOXXnLN64uz7Cx4KavvxT08Oyq/VKflnBYCXFl0XlH/h5YMTZx+fviio7W/3axeUP2/DsG3+l88PDMrXBuzIsGsfbzpU4InPrOlQgWUe1n5p5L/+0K4rIiIiIiIiIpIbmtEhIiJSJuae+6mjIiIiki2VUH9U9EBHfMTzP7v7IwXLziA6ONhgoApoB/zd3a8uRx9FRCTncl5oyIpUe4iISCbkvP6o9F1XbgeGFS0bBowEdonPZb8j8HMz69eyXRMREZEcUu0hIiJSYhU9owO4GzjfzDq4+2IzGwD0A55x/+ooMR3QgJCIiJRCBUwdlZWo9hARkfKqgPqjot9E3X0m8DKwX7xoGHCnu7uZ9TezN4DJwAXuXtrDX4uISGWqrUnvAj3MbISZHVjuhyX1U+0hIiKZkPPao6IHOmKFU0iHxddx98nuPhjYCDjWzNYsU/9ERESSmuPuJ7l72DnJpaWp9hARkbzIZO2hgQ64D9jbzLYBOrn72MIb429TJgC7l6FvIiKSa47V1qZ2kVbjPlR7iIhI2aRXf2RVxQ90uHs1MBq4gfgbFTNbx8w6xT+vBuwKTCxXH0VEJKectHddkVZAtYeIiJRVmvVHRlX6wUjr3A78m+XTSDcD/mpmDhhwsbuPL1fnREREJHdUe4iIiJSIBjoAd7+XqKiou/4Y0bnsRURESsgz/W2IlI5qDxERKZ/81x8a6EiR11SxZE7XxPmapVXJG6+1pjMFOl53VVB+4Qk/Ccq3vfimoHzXCz5LnJ1zxvpBbYeq6rA0KN/+zOoS9QQW/6VHUH7hp72C8j0v+zAoH/rctGm3LChfSjNO3Dwo3/vat0rUk3Dr3/NCSduvqgrbf7Jdl4Ul6onUxzy7+7dK9tXWtAmqPbp0WRDU/py53RJnO1z996C2F51yVlC+/+v/DspvdmeHoPwf+/dLnO1buzio7ateDXu///tDzwXlQ1X/ondQ/stJayfO9r/99aC2l9WEfRzpsfrsoPw6HlgftG+XOPvCdvs1HSpww9sBtT+wZkdvOlSga8ewGvrfH/dJnO3bPqwvnywI++zy8Yw1gvKdqsKOwNCtXfL+LKkJe19eq2PY73VZ2FMJYS/bieW9/qj4Y3SIiIiIiIiISH5oRoeIiEi5eP6njoqIiEjGVED9kfsZHWY22sy+UbTsDDP7p5m9ambjzGyCmf2wKNPHzJaa2ckt22MREakotbXpXSQTVHuIiEjm5bz2yP1AB9FRzYcVLRsGjAR2cfchwI7Az82scOfM7wIvAke2QB9FREQkP1R7iIiIlFElDHTcDRxgZh0AzGwA0A94xt3rjiTVgZWfiyOBnwHrmFnyIzCJiIgk5a4ZHfmk2kNERLIrzfojo3I/0OHuM4GXgbrDIg8D7nR3N7P+ZvYGMBm4wN2nAZhZf2Atd38ZuAs4ogxdFxGRCmC1NaldJBtUe4iISNblvfbI/UBHrHAK6bD4Ou4+2d0HAxsBx5rZmgWZu+Kf70BTSEVERCSMag8REZEyqZSzrtwHXGJm2wCd3H1s4Y3uPs3MJgC7E003PRJY08yOiiP9zGxjd3+vJTstIiJ555me9imr5D5Ue4iISCblv/6oiBkd7l4NjAZuIP5GxczWMbNO8c+rAbsCE81sINDF3dd29wHuPgD4MysfVExERGTVODpGR06p9hARkcxKs/7IqIoY6IjdDmxFNB0UYDPgJTN7HXgauNjdxxN9o3Jv0X3vQVNIRUREJIxqDxERkTKolF1XcPd7ASu4/hgwuJ7cefUsewPYvJT9ExGRSpT/qaOVTLWHiIhkU/7rj4oZ6GgJy5a2ZcaUtRLn27dfkjjbuee8oL7MeWe9oHy7394dlF/2u+8H5Rf/qWfirLVJnm2ONu2WBeVn/3ijoHz1zJ6Js+06hP2eFi/oGJRP3pPIknmdg/I1S9olznYL7Euozn1nBOU/+s7OQfn173khcfberY8JavvQ124Oyofqudb0oPzi2WG/rXnHbJ042+/m14Lavnazk4LyJ749Iihfdg5k+Ijlkn3LFrfns4/XSZx/edIGQe3vv3Py174vJ4S13eHntwflO10WNsFlcr+w14Px/+ubOLvx5xsGtf2z3V8Myv9f7bZB+dc/DKtVXnxycdOhAguXtg/pTVDb8+Z1DcrPmRv2HrVR1+S1CkDvjp44u8UO44La3njKukH5Ph3Dfk9bzNgpKL9xt+TvP7OXhu0IcPIOLwfl3/go7G/q8AFzgvLvBmw3s5dUBbW9YcDzCFC7fPw7mVlh8UQqoP6opF1XRERERERERCTnNKNDRESkTAzHcj51VERERLKlEuqPip3RYWZuZjcXXG9rZtPN7EEz+4GZjYsvS8xsfPzzX8rZZxERySGddaViqPYQEZHMyHntUckzOuYDg8ysk7svBPYFpgK4+z+BfwKY2SRgT3cPOwCAiIhIy+thZiOAUe4+qtydkZWo9hARkbzJZO1RyQMdAP8B9gfuJjqF2+3A7mXtkYiIVI6689inZ467hx3BVVqaag8RESmvdOuPTNYeFbvrSuwOYJiZdSQ63dtLZe6PiIhUFNeuK5VHtYeIiJRZivVHRlX0QEd8jvoBRN+oPFze3oiIiEjeqfYQEREpvYoe6Ig9AFxMNHVURESk5ThQ6+ld4v1kzezAMj8yaZxqDxERKZ8064+M1h6VfowOgBuI9isab2ZDy9wXERGRVZHJ/WRlJao9REQkLzJZe1T8QIe7TwEuK3c/RESkQmV4/1YpDdUeIiJSdjmvPyp2oMPdu9azbDQwumjZgJbpkYiIVB5Pu9DI5CneJKLaQ0REsiHV+iOTtUfFDnSUQocNN2Wje/+XOD/79A0SZ3te9mFQXxb8aq2gfOfzPwvK19zWJSg//9QfJc52uvXyoLZDtV/jy6B815/ND8r3DEpny+pXvVvuLjRb599/EZRfv0T9ADj0tZtL2Hq4Tn3CtvmaRR2C8r0vmBKUD9Gr46KStZ1TmZw+KqUzY2Enrh+3ZeL8T3Z9Maj9PtdNSJyt/kW/oLa7/nlaUL7mpk5B+fln/zQoP6TzVYmzt41+MKjthb/rHZTv9NsZQfkBQenScm4tdxdWsG1JWw+rWc+6qETdiA0vbfNB5p3VPyi/feew9/trn90tKP/bSX9PnL1i4A+D2j5t4tVB+VC/sStK2n4KMll7aKBDRESkXOoOBiYiIiLSUiqg/qios66Y2Wgz+0bRsjPM7Kr45+5mNtVs+bBZfHvnlu6riIhUCK9N7yKZo9pDREQyKee1R0UNdBCdxm1Y0bJhLD+92x+Ap4tuPwNQsSEiIiLNodpDRESkhVXaQMfdwAFm1gHAzAYA/YD/mdm2wJrAo3VhM/tJfPtTZvZUy3dXRETyLaVz2Gf8XPYVTrWHiIhkTP5rj4oa6HD3mcDLwH7xomHAnYABfwXOKspfDkwD9nT3PVuwqyIiUgnq9pFNr9iY4+4nZemo55VOtYeIiGROmvVHRmuPihroiBVOIa2bOnoq8LC7Ty5br0RERCSvVHuIiIi0oEo868p9wCVmtg3Qyd3HmtnPgN3N7FSgK9DezKrd/efl7KiIiFSAnB/1XADVHiIikjU5rz8qbqDD3avNbDRwA/GBwNz9qLrbzWw4sF1BoTEP6AaEndBcRESkCe6ZPmC5pES1h4iIZEkl1B+VuOsKREXGVsAdCbIjgP/ogGAiItIKZPKAYAKo9hARkXzKZO1RcTM6ANz9XqKDgNV320hgZMH1vwN/b5GOiYhI5Ul36ugcdz8pzQYlHao9REQkU9KrPzJZe1TkQEepLPt4AtNP2CJxfvW9Pk+c/fwHg4P6smj+ekH5iTscEZTffKPNgvJzbx2TOLvOzacGtT3/9A2C8hPG7hOUn3rTsUH5DlU1ibOT53cNavvH714flA9186BjgvJLaqoSZ4/87n1BbS9b0DEo/9rz2wXlt//6s0H5j18ZlDg76Yu1gtreZbcXg/ILZvYIyn/+6dZB+SVL2wXlH9vgtMTZDbtVB7X9xKerBeUHfnPvoHxVwN9rSTiQ86mjUlpr9ZjLz7/5eOL86ntNCmp/6lHbJM4uXrh+UNv3bXlcUL5n+28H5T84/8ug/A9eTl6nzzku+XsCwBo3hO2FdN3mJwblQ96PAU6deHVQvjWrPmedoPy8T3snzva/9eOgticfFVaf971pXFD+6k1PDsrvvO5HibMfTA+rbR6fFvb3esvcx4LyN246Oygf4qUZHYLyyaugyGWbnBJ4jxKogPqjUnddEREREREREZEc0owOERGRcsr5NyoiIiKSQTmvPyp6RoeZjTazbxQtO8PMrop/7m5mU83sivL0UEREcs9TvEjmqfYQEZFMyHntUdEDHURHQB9WtGxYvBzgD8DTLdojERGR5svkkc9lBao9REQkTzJZe1T6rit3A+ebWQd3X2xmA4B+wP/MbFtgTeC/QNhRDkVERJJw8Np6T8TRXJk88rmsQLWHiIiUV7r1RyZrj4qe0eHuM4GXgf3iRcOAO4lO//ZX4KwydU1ERCpFbYoXyTzVHiIikgk5rz0qeqAjVjiFtG7q6KnAw+4+uWy9EhERkbxS7SEiIlJClb7rCsB9wCVmtg3Qyd3HmtnPgN3N7FSgK9DezKrd/efl7KiIiORQuruuSOtwH6o9RESknHJef1T8QIe7V5vZaOAG4gOBuftRdbeb2XBgOxUaIiKSuvSP0SGtgGoPEREpqwqoP7TrSuR2YCvgjnJ3RERERCqCag8REZESqfgZHQDufi/RQcDqu20kMLIl+yMiIpXCcj91VOqn2kNERMon//WHBjpS1Ha9Lehz3ZjE+WX/6JA4u3rgCXva7fxSUH69sOYzZe7vwjbjXXteWKKelN5pXF3uLqyCa0va+h4lbR02LVG2OboH5tcqSS+W276EbR9RwrabxUpQFHiqbfYwsxHAKHcflWbDkk2fz+nOpY/unTj/2y2uC2p/jVMXJ8623218UNvRyWey491NT02c/fM73wpq26xdaHeCuC8tafulVOrnJlT7tslPI1FTWx3U9sB/LQnK9753/6D8R9WPBuW7frxB4uzCJa8EtR363IT628TdgvLHdNw4cfa41cL+vrt03DAoX1ubkb/X9OqPTNYeGugQERHJj0yey15ERERyK5O1hwY6REREyqUCDgYmIiIiGVMB9UeuD0ZqZqPN7BtFy84ws6vMbDcze9nM3okvJ8W3n2tm4+JLTcHPPynPoxARkVyrbZPeRTJB9YeIiGRezmuPvM/ouB0YBjxSsGwYcBZwG3BIfO763sAjZjbV3f8I/BEgPn/9kBbus4iIiLRuqj9ERETKKLtDMOm4GzjAzDoAmNkAoB/wdWCku48FcPcZwNmAzlcvIiItx+Ojnqd1kaxQ/SEiItmVZv2RUbke6HD3mUSH9N4vXjQMuBPYAni1KD4mXi4iItJi3C21i2SD6g8REcm6vNceuR7oiNVNHyX+/3ai89Z7Pdn6lomIiIiEUv0hIiJSJpUw0HEfsLeZbQN0iqeLTgC2K8ptC7zVwn0TEZFKp4OR5tV9qP4QEZGsynntkfeDkeLu1WY2GriB6NsUgCuBl8zs3+4+zsx6ARcAvy9TN0VEpAJ5BZzerVKp/hARkayqhPoj9wMdsduBfxNPIXX3T83saOBaM+tGNJX0UncfVcY+ioiISL6o/hARESmDihjocPd7iYqJwmXPANs3cb+upeyXiIhUutSPWN7DzEYAo/ThufxUf4iISDalWn9ksvaoiIGOlrL0o7eYetQ2ifO9t2qfvO37ZgX1ZeLv9gnKd+kyPyj/8af9gvJDn78ncbbmzu5Bbc/61+ZBea47Oyg+5agxQfkn3huYOLvvJm8Htb3ZQ88F5UON2Stsu1m0JPk23Lvn7KC2B+z8elD+7ad2DMpXtakNyi9e2i5xdtKsPkFtf32vp4PyY1/cNii/dp8vgvIfBf59z17cMXF2SW1VUNtXfRC27+evtwh7LevcfnFQvhRSPmL5HHc/Kc0GJdv6dKvmlN2eT5z3pWF/g0semJM4+9o+ewW13bVr2N/rRvf+Lyg/qOuwpkMFLr9wROLsKweHvV/O+fKnQfmPj3gtKH9Q9x8H5f+w08Sg/FaPPhqUD3H5xicE5dsHvn+v3aU6KL/X/o8lzt5x9/Cgts/6eGxQ/sN5DwXlz1svbDsYvv0ribMb3PNRUNtP73JAUP7vE/oH5b+25pKg/DMT30uc/XaPsOdx/qIPgvK3D/5BUP5740cG5ZNKsf7IZO2R3aOHiIiIiIiIiIgE0owOERGRcnEyfcRyERERyaEKqD9y8+jMzM3s5oLrbc1supk9GF9f08weNLPXzewtM3vYzLY0s3HxZZaZfRT//Hg97Y80s8Na8jGJiEj+ea2ldpGWpdpDRERaq7zXHnma0TEfGGRmndx9IbAvMLXg9t8Dj7n7ZQBmNtjdxwND4usjgQfd/e4W7bWIiIi0Vqo9REREMig3Mzpi/wH2j38+kuXnrQfoC0ypu+LubzTWkEWuiL+BeQhYI+3OiohIpTPc07tIWaj2EBGRVib/tUfeBjruAIaZWUdgMPBSwW1XAteb2VNmdq6ZNXVagUOBgcCWwInALqXosIiIVLC6fWTTukg5qPYQEZHWJc36I6Oy27NmiL8pGUD0jcrDRbc9AmwAXAtsCrxmZo2dA3IP4HZ3r3H3acCTJem0iIiItFqqPURERLInVwMdsQeAi1lx6igA7j7L3W9z92OAV4gKisZ4CfonIiLyFR2MNBdUe4iISKuS99ojjwMdNwC/jw/29RUz28vMOsc/dwM2BD5ppJ1niKaiVplZX2DPUnVYREQqk4OO0ZEPqj1ERKTVSLP+yKo8nXUFAHefAlxWz03bAleY2TKiAZ7r3P2VRpq6F9gLGA+8Czyddl9FRESk9VPtISIiki25Gehw9671LBsNjI5/vgi4qJH7Dy+67sBpafZRRERkBW6ZPpCXNE61h4iItEoVUH/kZqAjC9q0raHbmrMS5xdObex4ZEUCN8RNvvF8UL79TxcE5fv9qWdQPsT8V8LaHnD3i6XpSGydW88Oyv+454WJs4sv7BbUtj+4NChfaov+uFribMdzvyxhT2DIL0vafJDtStz+0BK3v87vegflO/12Rol6AkeXrOVmsvSnaGZ5/1bJvqq2y+i2xszE+TnvrBfUfpuq2sTZzff7X1Db3X7eIyg//f8GBOVrAl+Nn7jioMTZK94JO/Pvh+s0tsfSyp6fsnVQftRqfwvK37lr2HNv1i4oX0rzfh5QPwOnjPhuUP7J645MnL1xTtg2f2qvXYPyVwz8YVD+1lmfBeWXvrxD4uyP1toxqO1fv7YoKP/v7z8S1v69+wXlz133J4mzG3UL+9z18/7J2wao9e5B+VLJe/2R72EcEREREREREakomtEhIiJSRlk+kJeIiIjkU97rj4qd0WFmNWY2zszeNLN/FRwVvTr+f4CZLYwzb5nZ1WZWsc+XiIiUQN0+smldWoiZHWJm15rZ/Wb29RZbcSun2kNERDIhzfqjhYTWHpX85rnQ3Ye4+yBgCVDfTnAfuPsQYDCwOXBIy3VPREQkfWZ2g5l9YWZvFi3fz8wmmtn7Zvbzxtpw9/vc/URgOHBECbubN6o9RESk4pSj9tCuK5FniQqKern7MjN7Htio5bokIiKVoAwHAxsJXAHcVLfAzKqAK4F9gSnAK2b2AFAF/Lno/se5+xfxz7+K7yfhVHuIiEjZtHD9MZIWrj0qfqDDzNoC3wT+20imM7A38JuW6peIiOSf0/L7yLr7M2Y2oGjxDsD77v4hgJndARzs7n8GDihuw8wM+AvwH3cfW+Iu545qDxERKaeWrj/KUXtU8kBHJzMbF//8LHB9PZkN44wD97v7f1qobyIiIs3R28zGFFwf4e4jEtxvbWBywfUpQGPnEvwxsA/Qw8w2cverw7takVR7iIhI3mSy9qjkgY6F8T6wjfkgQUZERKR5PPWpozPcfbtm3K++TnhDYXe/HLi8GeupdKo9RESk/NKtPzJZe1TyQIeIiEiZGe6ZOC74FKB/wfV1gGll6ouIiIiUVCbqj5LWHmV/dCIiIpKaHmY2wswODLzfK8DGZra+mbUHhgEPpN89ERERyZlM1h4VO6PD3bs2ttzdJwGDWrJPIiJSgdLddWWOu5/UWMDMbgeGEu1TOwX4rbtfb2anAY8QHe38BnefkGbHRLWHiIhkSHr1RyZrj4od6CiF2mVVzJ/RM3F+6eL2ibM1y6qC+tKmw5Kg/KL/GxCUXzKvX1B+1veHJM72vemToLZDff6DBs/mV6+eG4f1Z+6pybNL3uvfdKhAm8s6BeVrqjsG5Rd9sVpQfun85NtBWE/CfXx4Y8cuWtl6d71Uop7AtZs1+lq/khPfTnK8ppYz58O1g/JhW6UUK8NZV45sYPnDwMMt2hlZZbXLqpg/s2fi/LzZ3YPab9OmNnG2fY/qoLaf2z1sTGfia2F/K9dtvTQo//6snomzG3QNmxR99gaLgvJtLxkZlJ8z56yg/KfDnw7KL7rgjcTZxYG1xGfvDgjKj3m6T1C+tsE9/us3fXHy7CM7rhnU9mGvvBeUn/RJ2LGIf9d1+6D8UzN6J84+v+C6oLYv3TigIAZeHTskKL9+12VB+bM+SH4m9OP7nB7U9vXTS3vIqousNO238FlXWrz20K4rIiIi+dHc6aMiIiIizZHJ2qOiZ3SYWQ0wnuh5eBs41t0XmNk6wJXA5kSDQQ8CZ7l72DQJERGRxqR/1pUmp49Kean2EBGRsku3/shk7VHpMzoWuvsQdx8ELAF+aGYG/Bu4z903BjYBugJ/LGM/RUQkhzw+6nlaF2kVVHuIiEhZpVl/ZFV2e9byngU2AvYCFrn7PwHcvQb4KXCcmXUuY/9ERESaksnpo9Ig1R4iItLaZbL2qOhdV+qYWVvgm8B/gS2AVwtvd/e5ZvYJUTGS/GhMIiIiTdCuK5VJtYeIiJSTdl3Jt05mNg4YA3wCXA8YUN/xmRtaLiIi0jweHfU8rYu0Cqo9RESkvFKsP7Kq0md0LHT3IYULzGwC8J2iZd2B/sAHLdc1ERERySHVHiIiIiVW6TM66vME0NnMvg9gZlXAX4GR7r6grD0TEZHc0YwOQbWHiIi0sLzXHhroKOLuDhwKfNfM3gPeBRYBvyxrx0REJJe81lK7kNEDgknjVHuIiEhLy3vtUdG7rrh71waWTwYy9YsSERFJIJMHBJPlVHuIiEjOZLL2qOiBjrQtWdKejz/unzg/+GsvJc4uW9AxqC/PPb1rUH6z9T4KyvdYc2ZQfsmiDomzNTd1Cmp7/M17BOX7b7g4KP/lxPWC8ktOHp042/OaA4LafuvwcUH5ULW1YZO85i9Kvl2+sdN3g9redMOw3dI/nhr2e5q4wxFB+bEz+iTOrt9tSVDbc/9vQFhfXtw2KD9nUdjf1H6HTwrK/2a9nyTO1gQeVnGtjkuD8u/Oax+U/97GYa99aas7j71Icy1a3JG339socX7bHV9tOlQgpP44Y+ShQW3/fJfXgvJLloT9fS8MzB912s2Js59edGJQ26t1mxeU//KDdYLyS37wVFC+98g9g/KXbj0kcbZjVW1Q26e/+4+g/NYdNwzKL1j8QFD+30OGJc5u9+TDQW1PCkqHm179SlD+48N3LFFP4Iz3rgrKL/jVWkH5d/91SFA+RIcSvy3v2+WU0q4ggUqoPzTQISIiUi6e+ullRURERBpXAfVHvodxRERERERERKSiVPRAh5nVmNk4M5tgZq+b2f+ZWZv4tqFmNsfMXjOzt83st+Xur4iI5E/KZ13J5AHBZDnVHiIikgV5rz0qfdeVr85lb2ZrALcBPYC6wuJZdz/AzLoA48zsQXcP27lVRESkESmfmi2TBwSTFaj2EBGRskux/shk7VHRMzoKufsXwEnAaWZmRbfNB14Fwo5+JCIiItIA1R4iIiKloYGOAu7+IdFzskbhcjPrBewETChHv0REJKc8nXPYF5zLXloZ1R4iItLiUqw/sqrSd12pT+Fva3czew2oBf7i7io2REQkNU7qu65I66TaQ0REWkwl1B+ZGOgws52B44FNgJVO2O7uO7RQPzYAaoAvgM2I95NtiXWLiIhIy1HtISIikl9l33XFzPYFngHWAXYDpgPVwFZAL+DNFupHH+Bq4Ap395ZYp4iIiHub1C5k9MjnWaPaQ0REKl3ea48szOj4PXAZcA6wFPi1u481s/WAR4DRJVx3JzMbB7QDlgE3A5eUcH0iIiIrqNVZV8pBtYeIiFS0FOuPTNYeWRjo2Bz4FdG+qA50AXD3j83sPOB3wE2lWLG7VzVy22hKW+iIiIhIeaj2EBERybEsDHQsAtq4u5vZp0SnUXs2vm0u0bTSVqHLwI3Y6elRifMzTt4scbb3NW8H9eWbQWlYfGG3oHyHs+cF5VcLSocZ8v0SNt4MNff2Spyt6vlyUNvr33V2UL57zwuD8qEW/SH5Y+3465kl7AmsXdLW4eslbj/E0BK3v+BXawXlf//x5SXqSQZZygfu8mwfsTzHclN7LK6p4qO5PRPnB83tGtT+Y2O2S5zdY835QW2vucHkoPzzz+0UlJ+/tH1Q/sl/HpI4axa2p9FnX64elH/707BN8NAz/xWUv3v3gUH5U15do+lQ7LrtqoPa/vfWYYXcx8d0CMpf8PCpQfkXpifPVg86Pqjtaz9scJyzXs8sGBGUH9jt20H5rap2TJy9K6hluGurY4PyOw5cLyjfs/2SoHyIZSXekfCx+f8IyptdnX4nKqD+yMJAx+vAQOAx4AngF2Y2FVhCNLV0fBn7JiIiUjKVcNTzjFLtISIiFasS6o+yH4wUuJTouQb4JTCfaP/Yp4jOKf+j8nRLREREcupSVHuIiIjkVtkHOtz9YXe/Mv55KrAt0bcsQ4CN3P3VtNZlZm5mNxdcb2tm083swfj6cDO7Iv75PDObambjzOxNMzsorX6IiIjUcbfULpJMS9YeoPpDRESyJ++1RxZ2XfmKmRnQF/jI3ZeVYBXzgUFm1sndFwL7AlMbyf/N3S82s82AZ81sDXevLUG/RESkQmW5SKgELVB7gOoPERHJmLzXH2Wf0QFgZt8ys5eIDg42GRgcL7/WzI5OeXX/AfaPfz4SuL2pO7j720SngOudcl9ERETSlMlz2WdRC9ceoPpDRETyKZO1R9kHOszs+8ADwDvASUDh0NK7QNjhjJt2BzDMzDoSFTUvJejjjkSnoAs4DrOIiEgT3Kj1NqldiM9l7+7JTwFWgcpQe4DqDxERyYoU6w8yWntkYdeVc4GL3P0XZlYF/LPgtgnAmWmuzN3fMLMBRN+mPNxE/KfxtzrzgCPcvcQnGxIRkUrikPvTu2VUi9YeoPpDRESyoxLqjywMdKxHdHq3+iwCupdgnQ8AFwNDgV6N5P7m7heXYP0iIiJSPuWoPUD1h4iISIvIwkDHZGBr4Ml6btsOeL8E67yBaIrNeDMbWoL2RUREEsn7wcAyqhy1B6j+EBGRjMh7/ZGFgY7rgd+a2efAffEyM7O9gbOB36e9QnefAlyWdrsiIiKh8l5oZFSL1x6g+kNERLIj7/VHFgY6LgD6AzcCNfGy54Eq4Bp3vzytFbl713qWjQZGxz+PBEbGP5+X1npFREQkU1qs9gDVHyIiIi3NsnJ8KzPbENib6BRqs4An3f3d8vYqzHbbbedjxowpdzdEvjJ39tlB+e49LyxRT0Tywcxedfft0mpvi+7d/baddkqrOYY89liq/cs71R4i6Zs76/+C8t1Xv6REPRHJh7RrD0i3/shq7VHWGR3xKdbmEB1R/D7gg3L2R0REpCU5lvupo1mj2kNERCpdJdQfbcq5cndfBHwBLCtnP0RERKQyqPYQERHJv7IOdMSuAX5iZu3SbNTM3MxuLrje1symm9mD8fXh8fXXzOw9M3vEzHYpyI80s4/MbJyZjTWzndPsn4iICEQHA0vrIomVpPYA1R8iItI65L32yMLBSHsCg4BJZvYE8DlQeOAQd/dzmtHufGCQmXVy94XAvsDUosyd7n4agJntCfzbzPZ097fj289y97vN7OtERdHgZvRDRESkQbXpFgk9zGwEMMrdR6XZcM70pDS1B6j+EBGRViDF+iOTtUcWBjq+AyyOf969ntsdaG6x8R9gf+Bu4Ejg9gbWgbs/Ff+CTgJ+WnTzM8BGzeyDiIhIS5nj7ieVuxOtQClrD1D9ISIilSOTtUfZBzrcff0SNn8H8Jt4uuhg4AYaKDRiY4GT61l+IDA+/e6JiEily/K0z7wqce0Bqj9ERCTj8l5/lH2go5Tc/Q0zG0D0bcrDCe5S/Nu+yMx+BUwHjk+5eyIiUuHc819oVCLVHyIikmWVUH9kYqDDzAzYFdgE6Fh8u7tftQrNPwBcDAwFejWR3Rp4u+D6We5+9yqsW0RERDKoxLUHqP4QEREpm7IPdJjZmsATwOZE+8TWDS0VHhRsVYqNG4j2GxpvZkMb6cfXiPaP3XMV1iUiIhLA0j4YqSTQArUHqP4QEZHMyn/9UfaBDuCvwBygPzAZ2JHo6OdHA98nOphXs7n7FOCyBm4+wsx2AzoDHwHfKTjiuYiISMnlfepoRpW09gDVHyIikm15rz+yMNDxNeB04NP4urn7J8CfzKwN0Tcq3wht1N271rNsNDA6/nkkMLKR+w8PXaeIiIi0CiWpPUD1h4iISBZkYaCjJzDd3WvNbC6wRsFtz7Nqp3fLtLf23ytxto3VBrW9Wp9ZQfkO3auD8qGWLVhp9+cGLZi7Uo3YqHXvfDkoX33OOkH5UItmd0uc7X1N2Bd4X/5o46D80oUdgvJcEhZvf+W1ibMdz/0yrPFA00/YIijf57oJJeoJ/Gf7I4Py33zl9hL1JPLewY2d7GFla281MSg/ZdymibObPPBMUNu3bHlcUP7o8TcE5bMg79+oZFRPclJ7TBv/Ceet9+PE+Z3WnB7Ufrs2NYmzAzf4KKjt2po2QflO3eYH5T/5aN2g/A0TNkmcvfKzhibr1G/qUdsE5Tt0XRiUn/Jh2GMd8tgjQflpx2ydODtrelOHpFnRunf9Mijf5o83BeW7XjAlKB/io+/sHJRf/54XStSTyKlrnh6Uv+rzsO04xP3bHB2U322HV4LyR968d1D+0fnJ90Y8o1/Y83jptNI9j6WU9/oj7B2mND4C+sY/TwCOKrjtQCDsE7uIiEgr4UCtW2oXSUy1h4iIVKw064+sysKMjoeArwN3AecD95vZFGApsC6t6FsVERERaRVUe4iIiORY2Qc63P0XBT//x8x2AQ4FOgGPuft/0lyfmY0G/uzujxQsO4Oo4FnX3QfFR0e/H/iQ6JRzd7j779Lsh4iICBVwHvssUu0hIiIVrQLqj7IPdBRz9zHAmBKu4nZgGFC4c+Iw4CzgHwXLnnX3A8ysCzDOzB5091dL2C8REak42Z72WSlUe4iISGXJf/2RhWN0tLS7gQPMrAOAmQ0A+gH1HqnI3ecDrwIbtlQHRUREJFdUe4iIiLSgihvocPeZwMvAfvGiYcCdRMdkWYmZ9QJ2IjpYmYiISGoccCy1i2STag8REcmSNOuPrMrcristpG4K6f3x//Wdv3B3M3sNqAX+4u4qNkREJHV530dWvqLaQ0REMiPv9UelDnTcB1xiZtsAndx9bDyNtNCz7n5Ai/dMRERE8ug+VHuIiIi0iLLvumJm34+naNZ32+pm9v201+nu1cBo4Aaib1hERETKIq3z2Of9oGJpUu0hIiKVLu+1R9kHOoB/0vDBttaPby+F24GtgDtK1L6IiEgTDPf0LpKYag8REalg+a89srDrSmPPTi9gbilW6u73Fq7b3ScBg+KfRxN96yIiIiL5o9pDREQkx8oy0GFmBwMHFyz6tZlNL4p1BHYHXmmxjrWw2prkE2pqvCqoba8t7eha7dKwTadmWfJ8zbKwxxoq9LnxmrD+LF3YIXF2wW/WCGw7LL9kUcegfI+rrwnKL/7RyYmzYT0J1+e67Byzb2ltFibLLVcTuA1XdV4clC/laP6SwL63Nu5ketpnnuS19jCgfVW9J2+pV5v6T/TSoDmLOyXOhtQ1EP7a8dbbA4PyC5Ykfz8GGLLawsRZs3ZBbb970E5B+VCdOyXvO8CcM9YPys/8YqPE2c9mrxbU9oA/jwzK1547PCgf+rsyS74du9cGtd22bZ+gfM+OA4Ly6/rmQfl2bevdg69ey2rCxn8v3fiEoPyegbXH6u3Dfq9tq5Jvl2f2Oyao7aqqHkF5fFlYvgQqof4o14yONYAtC65vCKxVlFkCPAqc31KdEhERaWlZnvaZM6o9REREYnmvP8oy0OHu1wLXApjZU8Cp7v52S/bBzKrdvWvRsvOAane/2MxGAl8D5hCd5u1H7v5CS/ZRRERE0pGF2iNet+oPERGREiv7/Gp337Ou0LBIPzPLwrFDAM5y9yHAz4GwOf0iIiIJ1GKpXYAeZjbCzA4s9+PKsozXHqD6Q0RESizvtUfZBzoAzOxbZvYSsAiYDAyOl19rZkeXtXORZ4DkOyeKiIgk4JD2WVfmuPtJ7j6qzA8t81pB7QGqP0REpATSrD/IaO1R9oGO+Fz1DwDvACex4pHQ3wWOL0e/ihwIjC93J0RERGTVtZLaA1R/iIiINEvZBzqAc4GL3P1Y4Jai2yYAYYcPTtdFZjaOqAjKStEjIiK5YdR6ehcyOn00g7Jce4DqDxERKan81x5Z2B91PeCxBm5bBHRvwb4UO8vd7y7j+kVEJOdSPur5HHc/Kc0GcyrLtQeo/hARkRJLsf7IZO2RhRkdk4GtG7htO+D9FuyLiIiI5J9qDxERkRzLwoyO64HfmtnnwH3xMjOzvYGzgd+XaL2dzWxKwfVLSrQeERGRejnR+UOlxZWr9gDVHyIiUmaVUH9kYaDjAqA/cCNQEy97HqgCrnH3y0uxUndvdDaLuw8vxXpFRES+4qnvutLDzEYAo7J29POMKUvtAao/REQkA9KtPzJZe5R9oMPdHfiRmV0C7AP0AmYBT7r7u2XtXImZeeJsvw0+CWp79atK+9R9+aONg/Ide8xLnG3feWFQ20su7hqU73ZRdVA+VCl37O5cwrab4829v5k4u/E9Zwe13b3nhaHdCbLoj6sF5a0q+bj3Qa/OCWp77pnrBuVrl4a9dK+7Tdg2/86TOwble/eZEZQPcdxb1wblZ/5wYFDea6qC8q1AJveTzZo81R6La4z35ybfjndas6bpUIE9tn4lcbb3NW8HtR2q7VHbBOU7dA2rJ4Ys7JA4u/j45FmA9mc+G5TPmi1LlG2Os9c5PSg/Z85ZQfnu3f8UlA/x+Q8GB+Unf9wnKL/dkzcG5V8aekDibI+uYbVE/8H3BeX3vXT/oPwNe70elL/jwS8TZ49aPWwbu2WLbwfll9WGHT3i+xNuCMqXQSZrj7IPdNRx9w+AD8rdDxERkZZUm+6MDgmg2kNERCpV3uuPsg90mNkejdxcC8wFJrr74hbqkoiISItx8l1oZJFqDxERqXR5rz/KPtABjCY6HkodK7oOsMjMrgP+z92D5lyaWQ0wnuixvg0c6+4LzKwa2Bm4OY6uC8yJLzPcfZ/QByIiIlJmmdxPNoNGo9pDREQkDZmsPbIw0LEP0dHPHwYeAKYDfYCDgW8BPwM2A34JVAPnBra/0N2HAJjZrcAPiY9w7u7jgbrbRgIP6rz1IiLSUhxLe+poJveTzSDVHiIiUrFSrj8yWXtkYaDjNOBGdz+vaPkjZnYeMNzdDzSztsBwwouNQs8CiY4CZGZ3xv16OL4+kmiU6p5VWL+IiMgKapMfl1rSo9pDREQqWt7rj7BDvpbG14H/NXDbc8Ce8c/PAH2bu5K4WPkm0VTSJO4Ajojv2x7Ym+ibHxEREWndVHuIiIjkWBYGOmYBBzVw20Hx7RCdWTPsnI2RTmY2DhgDfEI0VTWJ/wB7mVkHoiLlGXcPO0+ZiIhIExxL7SKJqfYQEZGKlvfaIwu7rlwIXG5mA4BRrLyf7I/j3J5A8pO5L/fVfrIh3H2RmY0GvkH07crtzVi3iIhIg5zUT++WyQOCZZBqDxERqVgp1x+ZrD3KPtDh7leY2VTgF8CVRH1aBowDvu3u98XRPwNLWrh7dwAnANsR7aMrIiKSZZk8IFjWqPYQERFJTSZrj7IOdJhZO2AH4CV338HM2hB9ozLd3WsLs+4+owxdfBS4CXjA3Vu60BERkbxz8JwfDCxrVHuIiEjFq4D6o9wzOmqAJ4mmiU6LC4zP01yBu3dNstzdh9eTWQr0SrM/IiIihWozvH9rTqn2EBGRipf3+qOsAx3uXmtm7wFrlrMf5dK9x9zE2fbdFpSwJ+E69/8iKG/tliXO1i5qH9R2m+5Lg/KSnm2f+E/J2p47++ygfPeeFwblO577ZVC+lLpf/ElJ2196WaegfO8+YV9id+k9OyhfSr2unljaFVyX76KgEuSt9mhj0LEqeX61LtVB7bfvNj+wR6XTa9OPg/LWIWxCTM3cLomzbbqq9iiXC6dcVtL2Q+qP0NpjzX++EZYPSofbcfSDJWt7yd86B+Uv2T5svLl337DPIiHaBL7VH/nGP0vTkdj37YaStp9X5Z7RAdG56S8ws/HunvT0ayIiIq2eA57uwUglGdUeIiJSsSqh/sjCQMeviKZojosPDPY50XP/FXffoRwdExERKS3TWVfKQ7WHiIhUsFTrj0zWHlkY6HgzvpSEmdUA44ke69vAse6+wMyqgZ2Bm+PousCc+DLD3fcpamck8KC7312qvoqIiKyiTB75PINUe4iIiKQjk7VH2Qc63P0HJV7FV+eyN7NbgR8Cl8TrHg/U3TYSFRMiItLCcn7Q80xS7SEiIpUu7/VH2Qc6WtizwOAkQTMz4O/AXsBHkPPD0oqISItzSHvXFcke1R4iIpIplVB/ZGKgw8wGAEcDmwAdi29398NTWEdb4JvAfxPe5VBgILAl0UGP3wJ0yFsREZEcUO0hIiKSX2Uf6DCzbYGngclExcYbQA9gADAFeH8VV9HJzMbFPz8LXJ/wfnsAt7t7DTDNzJ5cxX6IiIispLbcHahAqj1ERKTS5b3+KPtAB3ARcA9wHLAUON7dx5rZLsDtQNgJqlf21X6yzZD3XZdERKTM8n56t4xS7SEiIhUt7/VHm3J3gOiAXLexfFCpI4C7Pw/8DvhLebrFM8AwM6sys77AnmXqh4iIiKRrCKo9REREcisLAx0OLHF3B74A1iu4bTKwcVl6BfcC7xGdHu4fRFNcRUREUuMeHQwsrQvxuezN7MByP7aMU+0hIiIVK836g4zWHmXZdcXM9gDGuns10YG2NgSeAl4AfmpmY4AlwNnAB6uyLnfvmmS5uw8vuu7AaauybhERkaakvJ9CJs9lnwWqPURERJZLsf7IZO1RrmN0PAXsDLwMjGD5Nym/BB4F3omvzwcOa/HeNdOi9yfy7kF7JM536py87a4XTGlGj5KbdeomQfluA5cE5WsXJ9/UOp77ZVDbWTPle9slzla1XRbUdt+bxgX2JsycM9YPytcsaZc4u/pV7wa13b1n2C7yc2efXdL2Q7y2z35B+a0fT3pChubxZWGT97r2Cfsb7NBrblA+xJM7h70F7PXC3SXqieRALmuPPp0XcurWExLnzcJK2+4XfxLapcSmHrVNUL7PNouC8r44+XsUwE13H5w4+8N3rglqu9TeO3j3oHzHTmHPZf87XgnKhwjdDhYv7BSU3+DfzwXlQ+qDuXN/GdZ29z8F5UNdt/mJQfkT3rq2RD0BX9g+KN93zS+C8h1XL13t0TOs65JR5Rro+OrIJ+5+c8HPb5vZZsAuRPvLvujuYVu9iIhIK5L389hniGoPERGRWN7rjyycdWUF8ZTSR8vdDxERkVJz8n96t9ZAtYeIiFSSSqg/yjnQ8S0z2zRJ0N1vCmnYonmZt7j7MfH1tsCnwEvufoCZrUl0Tvv+QDtgEnAOUPcNz7rAnPgyAzgBeNDdB4X0Q0RERDJFtYeIiEgFKOdAx28S5hwIKjaI9q8dZGad3H0hsC8wteD23wOPuftlAGY22N3HE51uDjMbSVRc3B1fHxC4fhERkQQs9+exzxjVHiIiIhVQf5Tz9LJ7At0SXLo3s/3/APvHPx8J3F5wW1/gq6N7uvsbzVyHiIjIKqlN8SJNUu0hIiJC/muPcg50LHT3+UkuzWz/DmCYmXUEBgMvFdx2JXC9mT1lZueaWb9VfTAiIiKSeao9REREKkA5BzpKKv6mZADRNyoPF932CLABcC2wKfCamfVp6T6KiEhlc8DdUrtIean2EBGR1iDN+iOrcjvQEXsAuJgVp44C4O6z3P22+KBhrwB7tHTnREREaj29i2SCag8REcm8vNceZRnocPc27v5yC6zqBuD38cG+vmJme5lZ5/jnbsCGwCct0B8REZFS6mFmI8zswHJ3JGtUe4iIiJREJmuPcp51peTcfQpwWT03bQtcYWbLiAZ7rnP3V1q0cyIiIkTTR1M0x91PSrdJCaHaQ0REWoMU649M1h65HOhw9671LBsNjI5/vgi4qJH7Dy+6PgnQeexFRCRV7lCb4f1bJTnVHiIi0lpUQv2Ry4GOcvlwXnsOe3KdxPmfr98hcXa7g8J24x3++CZB+cHdvhmU/2JR2MmE7p17deLs/Oo1gtpePLNHUH6d68P6/u0uBwTlb5n1euLsyWucGtR28mexee7479eD8ktqk+/9dszpGwS13anvzKD8uZcND8r/8e2zg/IdRvwjcbZ9ux2C2p5x4uZB+e6bTwrK33fTd4PyywJ+rwAPTU1+Js4fPx42q3HWwtWD8suuS/66CvC/G8L+vkWy5p15S9nl6WmJ89/tum9Q+//3zb0TZ7f4zxNBba9969igfKhhq50elL/hxysdVqVBM384OqjtXldPDMqH2vj+Z0vafimtc9v4pkMF3JcG5WedGlYTdw6oP7r/OqxWmTs7rPYYt/97QflFNWsG5UMs/F3voHyn384Oyq8XlIbHdjw8KB/yyvdhdU1Q20suXmmcu1Htz6wOykvzaKBDRESkjLJ8DnoRERHJp7zXHxroEBERKaMsn5pNRERE8inv9UfeTy+Lma1lZneY2Qdm9paZPWxmm5jZm2b2DTMbF1+qzWxi/PNN5e63iIiItE6qPURERMor1zM6zMyAe4Eb3X1YvGwIsCaAuz8CPBIvHw2c6e5jytJZERGpOE7+p45WGtUeIiKSdZVQf+R9RseewFJ3/+oYju4+Dpjc1B3N7CUz26Lg+mgz27YkvRQRkYrlnt5FMkG1h4iIZF7ea4+8D3QMAl5t5n3vAA4HMLO+QD93b25bIiIiUhlUe4iIiJRZ3gc6VsVdQN05GQ8H/lXGvoiISE7VYqldpNVT7SEiIi0i77VHro/RAUwADmvOHd19qpnNNLPBwBHAyan2TEREKp4DtRme9inNotpDREQyrRLqj7zP6HgS6GBmJ9YtMLPtgfUS3v8O4Gygh7uPL0H/REREJF9Ue4iIiJRZrgc63N2BQ4F941O8TQDOA6YlbOJuYBjRVFIREZHU6WCk+aLaQ0REWoO81x5533UFd59GfGCvIoOKckPrue/nVMBzJCIi5ZLt/VuleVR7iIhItuW//jDP8jBMK7Pddtv5mDFjyt2NVm/x2J2C8h22ebFEPRFZbu7ssxNnu/e8sIQ9kXIys1fdfbu02uvXYU0/Ya0j0mqOP3zy91T7J9nXuaqXb9Lxm4nz/dv0DGr/VZLvPTN3adJJK5FaXxqU799+m6B8l9ouQfkXnn0vcXbDr3UOaru79w7KT655PSi/cMkXQfnuHQcE5auXfJo4W1O7MKjt3p22aDpUoJaw7aanrR2U713TK3H2HV4Jant72y0of/fkjkH5XmvcHJTfs8N3mw7FZjAvqO1ZbWYE5TvQKSh/bJ9+QflHPku+3cyx+UFtd/WwvtcS9vn7hYXXp/7enmb9kdXaI9e7roiIiGRairuttPT3FmbWxcxeNbMDWnbNIiIiskoqoPbI9UCHma1lZnfE+8i+ZWYPm9kmZvammX3DzMbFl2ozmxj/fFM97Yw2s8yNUomISOvmQG2KlyTM7AYz+8LM3ixavl/8Xvi+mf08QVPnoONIrES1h4iIZF2a9UcS5ag9crsPqJkZcC9wo7sPi5cNAdYEcPdHgEfi5aOBM91d+52IiEjejQSuAL76cG1mVcCVwL7AFOAVM3sAqAL+XHT/44DBwFtA2LzqnFPtISIiUq+RtHDtkduBDmBPYKm7X123wN3HmdmApu5oZp2AfwKbA29D4E5jIiIiCbX0eezd/Zl63gt3AN539w8BzOwO4GB3/zOw0vRQM9sT6EL0PrnQzB5296Rf7OSZag8REWkVWrL+KEftkeeBjkHAq8287ynAAncfbGaDgbHpdUtERGS5lOuM3mZWOENghLuPSHC/tYHJBdenADs2FHb3cwHMbDgwQ4McX1HtISIirUKK9Ucma488D3Ssij2AywHc/Q0ze6PM/REREUliRjOPfF7fOeaarIHcfWQz1iX1U+0hIiKtUSZrjzwPdEwADluF++u8uyIiUlIO1HomzmM/BehfcH0dIOxcoQKqPUREpBXISP1R0tojz2ddeRLoYGYn1i0ws+2B9RLc9xngqPg+g4gOfCIiIpK6lE8v28PMRpjZgYHdeAXY2MzWN7P2wDDggZQfaiVQ7SEiIq1C3muP3A50uLsDhwL7xqd4mwCcR7JRon8AXeNpo2cDL5esoyIiIumZ4+4nufuohgJmdjvwAjDQzKaY2fHuvgw4jeiMIG8Dd7n7hJbpcn6o9hARkQqUydojz7uu4O7TgMPruWlQUW5o0fWFRCNKIiIiJdXSR/F09yMbWP4w8HALdyd3VHuIiEhr0JL1Rzlqj1wPdLS0T8d/wh/XPy1x/usDPkqcnTy7V1Bfpi3oHJRfUhM2uWfnflOC8rMXdkmc3WCtDkFt3zsh+XMO8P1dnwvKT3h346B8h7ZLE2c/re4R1PbqHRcE5Xt3mxuU79I5rP0lS9onzt47cbOgtvddd1JQfub8bkH5R6b2Ccqv1yX57/XEMWcHtf3M3lOD8o9O6x2UP2RAWPtr9JgdlN9guzcTZ8eO3imo7Z3+kLxtgDOP2j8o36VteQ+J4Hw17TMtPcxsBDCqsW9WJD+6tGnLTl2T1whbr7YoqP3D226UOPvJ/C2D2v5sUVVQ/jvrh9UeD0xaPSh/6Xe3TZzdxsLqpiu+9VJQ/pJn9wvKh07RDv2As6gmeXaNjmEvajWBr4EHbfBhUP7sl9cMyndpk/zj0VUbJN9mAP7+/pKgfPfVrwrKz/wiKM5R6yXvz8e1bwW1fUy33YLya3UM2MiAb270blD+/KnJ8/NuC6uf2x42LyifBSnXH5msPTTQISIikh9z3P2kcndCREREKkYmaw8NdIiIiJRRS++6IiIiIpL3+qOiBjrMrAYYX7DoEGAAcD9QuB/Jme7+eH15d59U4m6KiEilcKjVCUVzTbWHiIhkTgXUHxU10AEsdPchhQvMbADwrLsfkCQvIiKSYZncT7bCqfYQEZE8y2TtUWkDHSIiIpnh8SVFmdxPVkRERLIj5fojk7VHpQ10dDKzcfHPH7n7ofHPuxcsB/iOu3/QSF5ERCQVeZ86Kqo9REQke/Jef1TaQEdD00E1fVRERERKQbWHiIhICws97baIiIikyD29C/F+smZ2YJkfloiIiGRY3muPSpvRISIikhlO6qd3y+R+siIiIpIdKdcfmaw9NKMjsruZjSu4HFbuDomIiEiuqfYQEREpkYqa0eHuXetZNhrokTQvIiKSprwfDKzSqfYQEZEsynv9UVEDHaXWd8t1OXfMFYnzU763XeLsptu9EdSX6R+sG5Rv33FxUL5n/8+C8l0vmJI4O/OHA4Pa/vmop4PyofqVtPXW7d2D9kic/fVHfy9hT8Lt+au1gvKdz0++zT+24+FBbe/xxICg/Nf+eHtQvl33BUH5KWM3C8pXT+udOLvFVhOC2r7vp7sG5S+ddllQfslfuwTl/3RmUDyRnNcZUmK1DvOWJs/vvuF7Qe2vs9n7ibOfvTsgqG2zsK2/z4CpQflzxtU7ltSgf+85Lnnbt44NajvUpSVtvXV7YY+DgvJPL7imRD0Jt/dxg0ra/tD+04Pyo6f2TZz182YFtd2u651B+Q/HhD03nTsvDMq/uf/aibOHHxe2jblfHpRf9MfVgvKdfjU7KJ9U3usP7boiIiKSH5k8IJiIiIjkViZrD83oEBERKRMn9amjmTwgmIiIiGRHyvVHJmuPih/oMLNewBPx1bWAGqBu3tdWwOsF8UPcfVLL9U5ERHJt+anZpIKo9hARkbKqgPqj4gc63H0mMATAzM4Dqt394vh6tbsPKVvnREREJHdUe4iIiJRWxQ90iIiIlFOK57EXERERSSTv9YcGOhrXyczGxT9/5O6HlrMzIiKSLyU4Roe0fqo9RESkpCqh/tBAR+MWavqoiIi0Ij3MbAQwyt1Hlbsz0iyqPUREpDXJZO2hgQ4REZEySvkLlUwe+VxERESyJcX6I5O1hwY6REREyijvU0dFREQke/Jef7QpdwdERERERERERNKiGR0F3P28outdy9QVERGpEHk/j700TrWHiIiUQ97rDw10lNE6t40pWdvdStZy6S2a16XcXZCENnngmcTZxReGbZU1c8O2g87nfxaU77DJnKD8sus6JM7u+9LioLYX/6lnWP7csN0gO/a8MCi/UVC6tA4rcfvL5oS+3ixIdf1O/k/vJqU1u3YuoxY+ljh/60Nvlawv3UvWcvOsd+NPgvK1NVUl6omkaednHgjKL/5Lj6D8kpnJt+RuF00Oanu1rT4Iys/84cCg/Nj5E4PyU4/aJnG22z+ODGq7c/c/BeUHBaXhsR0PD8rv+9JdibMLu/4osDdhvnhj48B7vJJ6Hyqh/tCuKyIiIiIiIiKSG5rRISIiUka1eZ87KiIiIpmT9/oj9zM6zOxQM3Mz2zS+PsDM3jSzb5jZuPhSbWYT459vKnefRUSkcniKF+Jz2ZvZgS35GGRFqj1ERCTr8l57VMKMjiOB/wHDgPPqFrr7I8AjAGY2GjjT3Ut30AwREZHSy+S57CuQag8REakUmaw9cj2jw8y6ArsCxxMVG0nv95KZbVFwfbSZbVuCLoqISAVzj85jn9ZFyk+1h4iIZF2a9UdW5XqgAzgE+K+7vwvMMrOkhxa+AzgcwMz6Av3c/dXSdFFERCqXp/pPMuEQVHuIiEim5b/2yPtAx5FEhQPx/0nPi3QX8N3458OBf6XcLxEREckn1R4iIiJllttjdJhZL2AvYJCZOVBFdLyUq5q6r7tPNbOZZjYYOAI4uaSdFRGRiuRke9qnhFHtISIirUEl1B95ntFxGHCTu6/n7gPcvT/wEbBOwvvfAZwN9HD38aXqpIiIVLbaFC9Sdqo9RESkVch77ZHngY4jgXuLlt0D/DLh/e8mOojYXWl2SkRERHJLtYeIiEgG5HbXFXcfWs+yy4HLE2Y/J8fPj4iIZIN7zueOVhDVHiIi0lrkvf7Qm2kZvXPA0MTZXmtND2p75md9gvIdOy4Kyvfe+JOgfO3S5Jtah65dgtoONeeM9YPyPS79qEQ9gQnf3Dsov8V/nihRTyKTh20flF+6pH3i7Ab/nhfanSBLLu4alK+ZH7addfrtjMTZ8d/YN6jtDXdM/jwCdO95YVB+7uyzg/KzTx4dlO+zafK/kU6/C3st+99uhwTld/vffUH5zr//IijPHyws3wQn29M+JftWr+rOId2Sv+bctuUPgtrfe7vkJ375YtqaQW137VodlO+9wZSg/HXHVQXlF80Oex8J8fkPBgfl1/znGyXqSeT+bY4Oyh889pYS9QTG7PWtoPyCxR2C8ns8NycoH9L64gu7BbW9cPLaQfleV08Myof+XvfYOXlt1r37n4LaDq09xh8Y9lh33uuVoHyIYzZYULK2Ada98+WwO9yVbu0BlVF/5HnXFRERERERERGpMJrRISIiUkZ5nzoqIiIi2ZP3+qOiBjrMrAYoPIr5IcBC4HqgP9AOmAScA9wcZ9YF5sSXGe6+Twt1V0REKkDKU0d7mNkIYJS7j0q3aWkO1R4iIpJFKdYfmaw9KmqgA1jo7kMKF5jZNcBj7n5ZfH1wfEq3IfH1kcCD7n53y3ZVREQk2Bx3P6ncnZAVqPYQEZE8y2TtUWkDHfXpCzxad8XdS3v0JxERkZgDtTmfOir1Uu0hIiJlUwn1R6UdjLSTmY2LL3Xnub8SuN7MnjKzc82sXzk7KCIilcVT/CeZpNpDREQyJ++1R6XN6Fhp+qi7P2JmGwD7Ad8EXjOzQe4edg5EERERkZWp9hAREWlhlTajo17uPsvdb3P3Y4BXgD3K3ScREakMtSlepPVQ7SEiIuWU99qj4gc6zGwvM+sc/9wN2BD4pLy9EhGRSuA4tSlepHVQ7SEiIuWUZv2RVZW260p9tgWuMLNlRAM/17n7K2Xuk4iIiOSXag8REZESqqiBDnfvWs+yi4CLGrnP8FL2SUREKpjn/6jnlU61h4iIZE4F1B8VNdBRahNe+5BBXYclzv9grc0TZzu9F7YH1I/evT4of0DXk4PyR07cLCh/xkdvJ84+v2/YZnnvZmGnbT72pM+D8pMO2yko37l7deJsj9U6BrX92bFbBeXbdVoUlO+1yZdB+dqlyX9XU763XVDb1XNX+mzQqC5dw7ZJdwvKv7fTdxNnN91wZlDbyxaEbQfTjtk6KD/98z5B+fXvGhqUtyvfS5yd8M29g9oesO6coPxtW/4gKD/uy+5B+VLI8hHLJfvaGvTqkHwb+mR+l6D21/xn8jPfrhnUcrheXbYNyvdos1ZQ/vZtk78vHN/ttKC27/5F2J5J/90heT0JsFaPsPfvrTYIe2197+DdE2fbtVsa1PZGW34RlF+2qENQ/pMjdgjKf/5F8vfMdm13CWr7izk9g/JfD0rD4pqqoPySBZ0SZ0Pfv1/4eIOg/OEvbhGUb3PhS0H5J3c+LHF27W41QW23VnmvPyr+GB0iIiIiIiIikh8VP9BhZgMLzm8/zszmmtkZ5e6XiIjkn4MORlqBVHuIiEg5pVl/ZFXF77ri7hOBIQBmVgVMBe4tZ59ERKRyZLlIkNJQ7SEiIuWW9/qj4md0FNkb+MDdPzazE83sFTN73czuqTsNnIiIiEiKVHuIiIikTAMdKxoG3B7//G93397dtwLeBo4vX7dERCSfPNV/0iqp9hARkRaW/9qj4nddqWNm7YGDgF/EiwaZ2flAT6Ar8EiZuiYiIjlVt4+sVCbVHiIiUg6VUH9oRsdy3wTGunvduUdHAqe5+5bA74Cw8z+KiIiINE61h4iISAloRsdyR7J86ihAN+BTM2sHHEV0oDAREZH0GNRabbl7IeWj2kNERFpeBdQfGugA4oN97QucXLD418BLwMfAeKLiQ0REJFV5nzoq9VPtISIi5ZT3+kMDHYC7LwB6FS37B/CP8vRIRERE8ky1h4iISOlooENERKRMouOV53vqqIiIiGRLJdQfGuhI0dodjN9v3D5xfmntgsTZl2f0COpLny7bBuV3W6O0U5f267Bj4uz/3p0f1Pajn4Ydq+24fmGb/Zj3Bwblr3mvc+Ls6ZvODWr7wLF3BeW36fy9oPxte00Kyi9blvy5HDVxs6C2t1/j86ZDBbZcY0ZQ/q7ndgvK9+9SnTg7a0avpkMF2rZfFpT/10s7BeX32fDdoHzH664Kyi865aTE2YG9rgxq+7NntgzK13jYMba/P2hCUP6iEhwxIe9TR6W0OlTVslG3hYnzUxdk5/ima3bdJSg/hB2C8uNqXw7KT/oy+evNqHlXBLVdc+dNQflDxz0flF+45JOg/EHdfxyUf2Du34PyIe7f5uig/OKaqqD84a/fGJS/f5NTEme/u3PY72nIY6U9kdKYmT2D8ptM6Zs4G9r3BXvuH5Rv//e7g/JLzjwxKL/H2pclzk55YuugtkPdPeTYkrafVN7rD511RUREJD96mNkIMzuw3B0RERGRipDJ2iOXMzrM7Fzge0ANUAt8CaxGdE76PsBHcfRU4E9AX2ARUA0c5+4TW7rPIiJSmVI+6vkcd08+xUZSo9pDRERakxTrj0zWHrkb6DCznYEDgG3cfbGZ9Qbau/s0MxsKnOnuBxTkAY5y9zFmdhJwEXBQy/dcREQqTbSHbL73ka0Eqj1ERKQ1qYT6I4+7rvQFZrj7YgB3n+Hu0xLe9xlgIwAzm2RmF5jZy/FloxL1V0REJC2ZnD5aAVR7iIhIpcpk7ZHHgY5Hgf5m9q6ZXWVmXwu474FE562vM9fddwCuAC5NsY8iIiIA1Kb4j3j6qLuPKvfjqjCqPUREpFXJe+2Ru4EOd68GtgVOAqYDd5rZ8CbudquZjQN2Bc4sWH57wf87p9tTERGRaPJoWhcpD9UeIiLSuqRXf2RV7o7RAeDuNcBoYLSZjQeOBUY2cpej3H1MfU018LOIiIjIV1R7iIiIZEfuZnSY2UAz27hg0RDg42Y2d0TB/y+sSr9ERESKOdFRz9O6kNH9ZPNOtYeIiLQmadYfZLT2yOOMjq7A382sJ7AMeJ9oKmlzdDCzl4gGhI5Mp3siIiJ1Uj/qeSZP8VYBVHuIiEgrkmr9kcnaI3cDHe7+KrBLA7eNJppWWrhsaCPNXenuv0urbyIiIpI/qj1ERESyJXcDHeX02aI2XDKxQ0nafm7hVSVpt845H7wYlP/Nej8Jyt88K3n/31w0LKjtWW2mBuW/fHydoPxh424Myn/X2iXOLnx7eFDb7kuD8qG+1fVHQfmlnnwk+KgBi4La7tx+cVD+y+m9gvJrd54flB/Yb0ri7NiP1w9qe9cec4PyW/aaHpTvtcaMoPyHo7cLym+4/UOJswuGnR7UdpvnHgvKH7jn00H5ieM3DcqXglNT7i5IKzZ7ifHglI6J8306Zmev5c+rny9p+xt02z8o3z3wfSfEx3duGZRfuOS5EvUk8sDcv5e0/RAHj72lpO3v2+WUoPxxGyavVz7+uH9Q230DakSALbp8Jyj/ZvUdQflHdzii6VDsrLXD3r9P2XF2UP7+uw4Oyn97t/uD8guOTN7/6vvHBrX96feHBOXvnxxyYq7SyXv9oYGOBrj7gHL3QURE8s3T33Wlh5mNAEZl7TRv0jTVHiIi0hJSrj8yWXtooENERCQ/MrmfrIiIiORWJmuP3A50mNm5wPeAGqAW+BJYjeiAYX2Aj+LoqcCfgL7AIqAaOM7dJ7Z0n0VEpPKkPKNDyki1h4iItBZ5rz9yOdBhZjsDBwDbuPtiM+sNtHf3aWY2FDjT3Q8oyEN8PnszOwm4CDio5XsuIiKVxXO/j2ylUO0hIiKtR/7rj+wckSpdfYEZ7r4YwN1nuPu0hPd9BtjIzAaY2bNmNja+1Hs0dRERERFUe4iIiGRGXgc6HgX6m9m7ZnaVmYUc2vZAYDzwBbCvu28DHAFcXoJ+iohIBXOiqaNp/SM+IJiZHVjmh1aJVHuIiEirkGb9QUZrj1zuuuLu1Wa2LbA7sCdwp5n93N1HNnK3W81sITAJ+DHQDrjCzIYQ7Wu7SUk7LSIiFcnT3Uc2kwcEqwSqPUREpDVJsf7IZO2Ry4EOAHevAUYDo81sPHAsMLKRuxzl7mPqrpjZecDnwFZEM1+Sn1RbREREKo5qDxERkWzI5UCHmQ0Eat39vXjREODjwGZ6AFPcvdbMjgWqUuyiiIgI0fcp+T4YWKVQ7SEiIq1H/uuPXA50EJ3G7e9m1hNYBrwPhE6nuQq4x8y+CzwFzE+1hyIiUvGc1HddkfJR7SEiIq1CJdQfuRzocPdXgXqPVO7uo4mmlRYuG1pP7j1gcMGiX6TWQREREckV1R4iIiLZkcuBjnLp1rYNX+vdKXF+r36fJs4eN2HPoL7s03GzoPwaXXcKyre3zkH5qjZdE2d36Lp6UNv3zH8nKP/6+C2C8r/rHPaF3E/7npo4u2OfOUFt79f1R0H57VcPm/X8tz3fDMovW5b8JeTpDzcOanv1HmHPzXvT1gnKr9F1blC+a/fqxNnDL382qO1/nLxvUH7+srDfa5/uYc9lv/U/CcrPu9MTZ2de8kJQ22veGPbczPnhU0H5WyZuGJRPn1PrqU4d7WFmI4BR7j4qzYYlm9qY0bEq+Un09lor7LXvOz1+kjg7vyZsW64yC8qv2SHstW+7qrC/7w/mLkmcPaT7j4PaXqPjdkH5T7udFpRfXBv2zewh64Tl/zUlebbGw9r+Wu+OQfmDN3o/KL9Tr42C8oP6Jm//l88PDGr7V/1PCcpv0+vLoHzNv3oG5R+Zemzi7PTAIwbd+8ZWQfn9N30rKH//6WGfXdq1SXq2b9jzyW2C2n77kLC+d8zETomp1h+ZrD000CEiIlJGOuuKiIiItLS8n3Ul+VcAIiIiIiIiIiIZp4EOwMyqzOw1M3uwYNmPzWyimU0wswvL2T8REckrx6lJ7SKti+oPEREpj/Tqj6zSriuR04G3ge4AZrYncDAw2N0Xm9ka5eyciIjkkwO1gfuzS66o/hARkRZXCfVHxc/oMLN1gP2B6woWnwL8xd0XA7j7F+Xom4iIiOST6g8REZHSqfiBDuBS4GxY4WgsmwC7m9lLZva0mW1flp6JiEjOOU5tahdpVS5F9YeIiJRFevVHVlX0ritmdgDwhbu/amZDC25qC6wG7ARsD9xlZhu4e/JzKIqIiDTFwdM9vay0Aqo/RESkrCqg/qj0GR27AgeZ2STgDmAvM7sFmAL82yMvE33b0rt83RQREUmkh5mNMLMDy90RaZTqDxERyYtM1h4VPdDh7r9w93XcfQAwDHjS3Y8G7gP2AjCzTYD2wIxy9VNERPIpmjia3j/ic9m7+6hyPzZpmOoPEREppzTrDzJae1T0riuNuAG4wczeBJYAx2raqIiIlILn/KjnEkT1h4iItIi81x8a6Ii5+2hgdPzzEuDocvZHRERE8k/1h4iISPpMXxSkZ7vttvMxY8aUuxsiuTD/l32D8l3+9GlQftmIDkH5kB393r1316Cm19vq7aB86GMN9dLQA4LyO45+MHF22Q0dg9p+/dY9g/Ib37NlUJ7z7wiK9/jr5FfdfbuwlTSsqk1H79xhvbSao3rRu6n2T7KvTZsO3rFd8tfLBYsnla4zGfPtHj8Oyl+412uJsxvd+7/Q7kgrMffMdRNnu1/8SVDbSy7uGpRfMHmNoPzLL4SdqGnwZu8kzq514+tBbYd6bMfDg/JLasK+r//6Mfcmzj5686FBbe/+5ICg/LTvPR+U3+yhp1N/b0+z/shq7aEZHSIiImWU96mjIiIikj15rz8q+mCkIiIiIiIiIpIvuR/oMLNDzczNbNP4+gAze9PMvmFm4+JLtZlNjH++qZ42RptZ5qbjiIhIaxcd9zyti2SDag8REcm29OqPrKqEXVeOBP5HdPq28+oWuvsjwCMQFRPAme6uA2yIiEiLccC9ptzdkPSp9hARkcyqhPoj1zM6zKwrsCtwPFGxkfR+nczsDjN7w8zuBDqVqo8iIiKSH6o9REREyi/vMzoOAf7r7u+a2Swz2waYleB+pwAL3H2wmQ0GxpaykyIiUqk89wcDq0CHoNpDREQyLf/1R65ndBBNHa07d+Ad8fUk9gBuAXD3N4A30u+aiIgIOkZH/qj2EBGRzMt77ZHbGR1m1gvYCxhkZg5UEe2OdFXCJrxUfRMRESmRHmY2Ahjl7qPK3ZlKo9pDREQqUCZrj9wOdACHATe5+8l1C8zsaWCdBPd9BjgKeMrMBgGDS9NFERGpaJ76eeznuPtJaTYoQVR7iIhI9qVbf2Sy9sjzritHAvcWLbsH+GWC+/4D6GpmbwBnAy+n3DcRERF0etncUe0hIiKtgE4v22q5+9B6ll0OXN5U1t0XEnCkdBERERHVHiIiItmQ24GO1uDLH22cOLvale+VsCfZUn1Okhm+y3W9YEqJeiLl1OVPn5a0/bYnLS5Z20vu/EZQvuNW84Pyi//UMyjf4Zezg/IbDvwgKD/vrP6Js207dwtqe/6ijkH57j0vDMrPO6c6KM9f/xGWb0IlnMdeSstoQ9s2nRPnZ5y8WVD7va95O7RLmfH4kieC8r3XXVSinkhr0v3iT0rWdvszw95z2ge2X7N90mMfR/p84/3E2UV/6BXUdsdfzwzKD978nbD2e8wLyi/+bLXE2ekLk7+mAnTv/qegvF0fuJfHWk+H5ROohPpDAx0iIiJl45DhaZ8iIiKSR/mvPyp2oCM+MnrdUP9aQA0wPb6+ibt3LsgOB7Zz99NatJMiIiKSG6o9REREWkbFDnS4+0xgCICZnQdUu/vF8fXAucwiIiLNk/JZVyTDVHuIiEhW5L3+qNiBDhERkfLzTB+xXERERPIo//WHBjrq18nMxhVcXx14oEx9ERERkfxT7SEiIpISDXTUb6G7D6m7UrefbNl6IyIiOZbvb1QkMdUeIiLSgvJdf2igQ0REpJxyvo+siIiIZFDO64825e6AiIiIiIiIiEhaNKNDRESkbPJ/MDARERHJmvzXHxroANz9vKLrXYuujwRGtlyPRESkcuS70JD6qfYQEZHyynf9oV1XRERERERERCQ3zN3L3YfcMLPpwMfl7oeIiJTMeu7eJ63GzNq4pTi50ln6qruX/EwdZtYG+APQHRjj7jeWep1SP9UeIiK5l2rtAenWH1mtPbTrSorS3gBFRCTvHKdlv3AwsxuAA4Av3H1QwfL9gMuAKuA6d/9LI80cDKwNzAKmlLC70gTVHiIiEq5l649y1B4a6BAREaksI4ErgJvqFphZFXAlsC9R8fCKmT1AVHj8uej+xwEDgRfc/Rozuxt4ogX6LSIiIq3TSFq49tBAh4iISPk8Ast6p9heRzMbU3B9hLuPKAy4+zNmNqDofjsA77v7hwBm/9/euYfLUpTn/vfCRvdGRQFRiARBUEAQQfFCvIWbUTygoIJ4EEgwahK8Hs2jeCKiOQa8INf4CChCohtjFEQwAiIXhYACG9hcRSAgBJCLBBFBwe/8UTXsXr27Z7pmrVlrZu339zz9rOnqr6q+mZ7V9U5dvtJJwBsj4p9IIzBTkHQb8Pt8+thMOW+MMcaYWWEm9cdYag93dBhjjDFzRES8bq59yDwL+GXl/DbgZX3svwMcKelVwPmjdMwYY4wxM8uY6I+Rag93dBhjjDFGDWmti3cj4iFgv9G5Y4wxxph5zki1h7eXNcYYY8xtwJ9WztcF/nuOfDHGGGPM/Gek2sMdHcYYY4z5GfBcSRtIegLwNuDUOfbJGGOMMfOXkWoPd3QYY8wcI2m9ufbBrDhIWgz8J7CxpNsk7RcRjwL7A2cA1wL/FhFXz6WfxhhjRoe1h5lN5kJ7KGL29s8100PSm4CNgKURccYcu4Okz0TEAWPgx/4RcdQs1PMmCj9/SQtzngBujIiHW+y2AjYEro6IazuW/fSIuKeLbQnD+DJEHbsC34+IR0ZRfqEvW0XEkhGUu15E3NrR9rKIeFF+/e2IeHPHfJ19l3RmRLy2i+24I2nNiLh3hso6kv7rQd83E/UYM8mMk/6w9uiUp5P2yLZFbf4ka49cj/XHMjtrj0JmSn9Ye8wO7uiYJSTtGBFnNaSvBqwVETfW0reIiCsr5/8MbAZcCGwPfC8iPj2gzrUBIuJOSWsBrwKu79pTJmlDYE/gbRGxecP1xx+QXZF0dkRsPyitIV+rsBnSj936XY+I79Tsiz5/SQuAz5D2fL6FNHtqXeB44OMR8YeK7SeAvYBLSZGG/ykiju1T9s7AV4FHSVsr7R4RF/axfxlwDElALAX2i4hrWmyLfCktv5LneGA7UsTkk4Azcq9u3a70Pi2lueFQMo8tGuo4B1gH+BZwUtv/h6S+37GIuKxm31lASFoSEVvVXw+iq++l5VbyLCQFfdoMWNhLj4i/arF/DnA4sA3wR1LP/Qd724Y12L8cOBLYFHgCad/030bEahWbg4HPR8Q9krYG/i2XvQqwd0ScVyuz9D7tUzk9CDiwZn9Cv/KMGWemqz1yWmn7N2+0R7Zr1B+Trj2yfec2fz5oj5xvoP6YDe2R8w1sw0vbtJynk/4YV+2R83XWH6PQHtmus/6w9hhTIsLHLBzArQ1pu5MCrlwOXA28pHLtsprtVcDK+fWqwKUD6ns3cDPwX8DfABeTGqjrSY1BW751gA8APwUeJv3jvaDF9gpgdWCNpqNmuzCn1/OsD1xbsz2idhwJ3N87b/Djsn6fRYvvx+fjdODXwLfzcR/wnQb70s//i8BxwFMqaauRGuXDa7ZXA6vm12sCPxtQ9pXAJvn1y4DzBthfAuwIPBF4K6lRb7Mt8qW0/Fq+VYBdgK+TBNlxM3Cfnt3v6OPL2sD7gAtIgun/Ntick4//BP6Q3/el+fVPGuyXNL1uqf+yptcdP8eBvme7m4Dd2o6WPN8CPg3cCOwDnFn//tbsLwLeQdrRawFJuF484LuzEbCEJDT+Evh/NZultXvwkvz6ecAl071PbffMh4/5cDBN7ZHTOrd/TLD2yPad9UfpszrnGRvtka91bvOZJ9oj5+2rP4a4T0Npj5y3bxvOEG0aHfUHY6o9cr7O+oMRaI9s11l/DHOfutwjH9M75tyB+XSQgqc0Hd8j9RTW7S8H1smvXwpc1/unr3/p6w+gQQ+k/NBZldRgPAisndNXBy5vsP9r4EfAz4F/BLYAbh5QxyP5AXZzw3FTzfb9Ob2e5wpg/5rtbcC/Anvnh9s+wN291w1+PAo80HD8BnhgwHs4rXcP8vk6NDdipZ//DeQZU7X0lYEbammX9jufAV8625f6Mow/NdtVgJ1J+2LfPQP36cyudbfU8wLgX4Df97E5iYoABzYHvtbvc+lwj/5Y+c5Wv88Dv8NdfQfuJf3gOL7h+GpLniX575WV+/WjPj4sJyyAi/rYX1ItP7++sGZzHbCgqSwqImTY+zTsd9eHj3E5GKH2yGklbcjEao9s31l/MOHaI6d3bvOH8GVstUe2H6g/Cu7TtLRHLmNQG965TaOj/mBMtUfOtyT/Hag/GIH2yGnF+qPkPg373fXR/ViAmUleRepFfLCWLpKYqLMgIu4AiIifStoWOE3Suiw/BW4TSb3ppAI2zOdt0+IejbTX8EOSboyIO3M9v5ZULxvgaFIv5Nsj4hKAFrsq10TH6WgRcThwuKT3RsSRA8yfD3wKeB3wkYi4XdKB0T6Na2lXPxpYv3cPMneRemrrlH7+EfnpVUt8rOFz3VBSL8KwaudExC41+2dI+lDbeUQcWrN/Wm0a5pTzmDoFs9SX0vJTwdLrSJGVtwXOJY1A7d5Qdo+u92mtPmU0ImlTYA/gLaQG+STg//TJsklELO2dRMRVkrZssHuhpAdIn+Oi/BqWfWeqUySvGOY7XOj7LdGy5KQPvWnO90vaHLiTNBLaxjmSPpr9iOzb6ZLWAIiI+2r2DylF2b5c0meBO4An1WyOBr6fp5D+QNJhJGG6PekHWxtd75Mxk84otQeUtX+TrD2gTH9MuvaAsjZ/4rUHFOuPkWmP7EtJG17SpnXVH+OqPaBMf4xCe8Bw+sPaY4xwR8fMchHwUNTWjANIur7B/gFJG0ZeIxsRd0j6c+AU0pq0KueQ1l3eTp/gNRUek7RKpPWYb6j4sZDm3Xb+hDT171BJzyStQ1ulQz1FVIVG27rXiHgA+ICkFwP/Kun0Fp9ngnMlnQEsJn2ubyN91nU2LSz3Gkl7R8SJ1URJe5F6iKu8sXb++QFlHws8pc95nfNIoxZN50F6aA/rS2n5PfYlNUjvjm4Bwbrep6f2W1vbInyOz+W+NiK67N19raTjSKN+QfqBsVzgtIhYuUNZj5sX2FYp8V1DlH+MpNWBfyCNED85v25jj/z33bX0vyK9x+fU0vci/W/vD3yQtJf6lLXEEXGkpKuA95AE5oL897ukEeA2Ot0nSb8lrTcHWHVAh5Qx48gotQeU6Y+J1R7Zbrb0xzhoDyhr8+eD9oAy/TFK7QFlbXinNi3X11V/jKv2gDL9MePaA4bWH9YeY4SDkc4gko4GvhERF3S0Px04OCJ+XEtfhRTk6euVtPeTHrDrAN8EFkfE5X3K/ippSthPaunPAjaNiB/2ybturmtP0hTUk5tEgaR9I+Jrg95ntj2inkRaT3ciTI0uLOko0ud4oSQBfwtsExF7tZR9QER8posfLfl3BV6dT8+PiJOHLatS5rNIjezvSGv0AngJsAjYNSJub8m3FkBE3D1dH8YRTSP6dpf7JOleUgPU1LBGdVRB0tciYt8h/FhIWnv+uC/Al6IW1V7SdhHxo/x6g4i4uXJtt6rwkXQbUB8Nqzo+5dowvkvaPCKuqpyvmd/DrRFxac32faT/+1+W1FHgy3NJQrYXRO7Dbf8TLflPjIi9B9h0vU9LpjEqa8ycM0rtkdM7649J1h7ZvrP+mE/aI+e1/mjON6PaI9sP04Z3atOybSf9MW7aI18fmf6YrvbIZfTVH9Ye44U7OmaQITojiuxznmfnPG8jBdlaTIp4/PPplt1S38akyOcHNVw7tSHL41SnGuaH6bmkYEK9huDzwIez7QkV22E+l9cDHyNNOw3gGuCQiPh+i31RgyfpN7ncnu+9f5y+Pa+StiONkIm0ZdrZDTYCPgG8N9utRForeWREfKrBvi7cplAXbjnPxsC7gE1y0rXAMQ3fmyvreWtlN+1a8qEm20qeQyu2RQ/2Ie5T5yj4JbbZvqiB19So51Pqaji/A/gSLSMf9f+/Ut9zntOAj0aaRrkOcBkpWNaGpO/CYRXb/wF+SwoCthj4Vj/xWyoeJP2Y9EPjfFJAuG0ionE0rOU5sx1pXf9yU5qnc5+MmURmQ3vkfAP1xyRrj2H8n2Ttke06649J1h7ZvrP+GKX2KLUfsnOhk/4YN+2R7Tvrj1Fqj2zfWX9Ye4wn7ugYAV07IwbYL46IGwbUsxUpwM8W0TJNrcQXFW4JJ+lu4Je5zIupPShj6rZLq5HWvT6DZeteb4qI+nSyYt8lvZM0rezvSQ9PgK2Bg0mRtI9pKHukPam5R/c9pKjOS4GvRMP2qdn2g8BOwLt6ve5KW2V9CfhBRHyxZr9Pv7obhNs2pBGeY0gNjICtSEHgdouIiyq2p9NninJE3NLg/4H93VkmliTdRBaYLcb1LdtKO0ZKhMx1pJHDtga+vhVYqZB53Je6Xw3npWUX+Z7zXB0Rm+XXB5DWke4t6SnABVUhKWkJ8GJgB9KU0F1Io4OLSYHYflMru1Q8XB4RW1bOW9+/pMtIPx6OY5ngX0x6Lkx5zgwqq6X8ohEtY8aV2dIeOW9f/THJ2qOr/5OuPbJ9Z/0xydoj23fWH6PUHtm+cxs+ZOdCJ/0xbtqj5x8d9ccotUfvOh31h7XHeOKOjhHTpTOixF5pamkvkNL2pDWJiyPilOmULendwEdJ/8SHkNYwXg28AvhsRHylobyVSVt77UmKlH569qXfftovJvW+nk6KeL7+IL87+H4N8MqoBRtSmh73k4hYbo3rED+4q+LhStLU3H7i4ZukQEo/Bl4P/FdEfKDFdgmwY0TcU0tfixTJe1qiSNJ/kEaYzq2lv4bUy/76StqMjMZVyvtATJ0pUDq9s/Q+lSzP+A3wsz6+bFezH7pjpN+ISj4vFUlFvuc8jzfwks4Gjo2Ik+rXWvxbhfQ93hPYISLWaiu7KX+DL/XP8uvA23vnNZG3EmnnhJ1IP1Qu7/dDZYj7VDSiZcwkMNPaI9sMpT8mWXv083/StUe2H5n+GCftkdNKlraOTHvk653b8CE7F7rO6Bgr7dHib6v+GKX2yPad9Ye1x3jijo4RUCoGuthL6jXqbyDtM38ScEpE/HYmfJG0lLQv+iLSnuIbRRpdWR04p/4gaqjnidm/zwGfilp0cxXG3Sj0/domQdHv2hA/uOvi4ZaIeH8f35dGxAvy6wXAT9sevpKuiojNu15TwbTdbP/ziGiKEo6k6yNi44b0opHBNiTdGhHrVc5Le7xL71PJ8oyRNvCS7ieNMog0Qnl+7xJJHK9esV2jLpYH+FLke87zPdL07dtIon2DiLhf0iLSVmubVWxby5e0KCJ+V0srFQ9Nwdx6tImldYEvkqLe71L9XtXsSu+Tp4+aecEotEe2K9Yfk6w9uvo/6doj23TWH5OsPXJayXKRkWmPbN+5DR+yc+F+OuiPcdMeg+qo64/Z0B4530D9Ye0xnnjXlRmkRQy8q00MFNofAHyDtP5s4EOp1BfKt4Tr1fPEXMeepG2fjqA50vUNwBdyA9DrrT96hnx/QNILI+KKWjkvJO0F3kTpdlfPr4iHr2S/+tHbFouIeDTpq1Z+X3htG/pM222g7TOAtA5yOSJNEz0EOETLRrMOBEp2EqHBt0G+1im9TxvEslGVvwTOisoUSeCwwvqr/KKtEWyhGkW+HkF+ynmJ0JgG+5GmcO8A7BER9+f0l5MiqFfZgxbqnRyZO5g6BfPOynmQ1rRW+VhUpi13ISJuA94q6Q3AA31MS+9T6XfSmLFixNoDCvTHJGuPIfyfdO0BZfpjkrVHW1obk6w9oKP+GEPtAWX6Y+TaI9fbRX9Ye4whntExg+Sewm8A3+7YGVFkP2JfLiGNcvxB0rr5n7o3bfLiiHhhQ54TgM2B/yD1uF9Vt2nI02Xda6nvr8j2xzM1yvg+wF5Ri/6e8xT1Std7Xgf1xEp6jGUNuUijVQ/l1xGVAGI12ynFAAsjYspWeyqctivpVySx1lT+7hHxzIY8Qy+RqpVTn9GxWZufLflL71PJ8owdI+KsEfqyXkTc2tW+hFLfZ8Gfl5eIh1GOZAxxn4pGtIwZN6w9ZkZ7lPo/6dqjwX7KJWr6Y5K1R07rrD9GqT1yWuc2fMhZFCNp7609+pZt7TGGuKPDAKAhtoST9EeWNZDVL1Jjg9qQv2gNcZ9yjiKJlr8gRT5fibTG9+je6FBDntL1lEXiYbbQgGm72aZzALGW0axBU5R7UeGXuwQsiogFDba9nuy+EeSH6BgpWZ6xtMXv5NjyAbJKO0aq62C/HRHL7dE+LH18F/DHlh8Ho/Rn6ECtM80wQswYMzdYe1h7ZNuRao+a/UD9MUrtke07648hOxdG0t5be/Qt29pjDPHSFdPjCuBztemdl0fapqlxq6aIWKm0kpbe+ukG3LmBNBXvT0iNY5cgVgdLalxPKWm59ZTTEUOjQN2n7S4XCX0ARUukcvlP6Vp4iW3mIqXpy506RiibIrkb8EzSVNwqzwb+u8GXQ9VnKnW9Y6TiM0DfCP9D8L8a0gSsS7qHTYzSn9IpmBuoz3rvqK31LqTtPrUKMWPMnGHtYe0BI9YeQ9iPUntAmf4o1R49P3vMZHtv7dGOtccY4hkdZgpdp3cOUe5QwVQL6yjZzq5ou6txonTabr+HOkz7wV6ECiPIj9iX04ADIuLKWvrWwIERsXMt/bn0ESYR8YuafWvU85lE0pak4Fu7AzeTplwf1WA3Mn+0LPBZI/XvmKQbgHf2sT9vGr48uymZLMQiYqdhyzbGjAZrD2uPUTOp+qNUe+Q8I9cf1h7L+WLtMYa4o8O0MlPTO3NZI1sT3FJfX99VuJ5ynCidtivpbvoEEJvOg70UlUeQL91ar7OwUv9o849Hrq+klXaM9KYcV6cbwwxMOZb0PJKo3hO4lzQS+uGIaGpoZ8OfIvEwyumjtXq2pIMQM8aMD9Ye48kka4/sT2f9MUrtke07649S7ZGvjaS9t/bo7NeWWHuMBV66YqYwoumdRMS20y1jEIW+/1LSe0nrKV8E/CCXsQhYpSXPWDDEtN21WRZA7O0MCCA2YkojyJ/AMmGyE7AZaU/zNkqiwi/sc21RQ9r6daEBEBGXSFq/IX2UU46vI30mO/dGcyR9sF+GEfvzYKFo/bWktSOvY5e0N/Bm0vaSn5zOD5IWIabZeAYZY4bD2sPaYxYo0R+j1B5Qpj+KtEe+Nqr23tqjBWuP8aR4naOZn0jaUSko2G3Au4DvAxtGxB5RGO16thnS9/1IDde+dFtPObFExGMR8YOI2If0/n4BnJvF1mwzZeu7DvbPj4i9IuLLwFtI+8H3Y23SOtHNgcNJIuueiDivoTH8maS/rhcgaT9SBP06pR0jo+TNpG3UzpF0rKTtKV+rOpP8WtLavRNJe0v6rqQjJK3RYP808taFkl4NHAycCPwPcMw0fbmO9GNj54h4ZaQgeY9Ns0xjzAiw9rD2mEVK9McotQeU6Q9rj3asPUxfvHTFALM/vXMmmWTfZwstH0DsVNJUzMZgbyP0o3Tru6Kt9Wp5+0aFl/RM4GRSo9cTFlsDTwB2jVrUfEmLgR9FxLG19P2A10ZE697vo0LSk4A3kd7ndqRRqJMj4sxZ9uMyYIeIuC+Lh5OA9wJbknZOeEvNvjp9+2jg7oj4ZP3akL7sShpV+TPSaOlJwHERscGwZRpjRsMkt9+T7PtsMS7aI/vSWX+MUntkm876w9qjrx/WHqYv7ugwKySl6yknGRUGEBsnSjtGcp4iYSVpW9LnA3B1RPyoxa6oY2S2yaMXbyWNEm43y3UXiQdJVwFbRsSjkq4D3hUR5/euta1dLvRpLISYMcb0sPaYDGZDe+Q8A/WHtUffuq09TF/c0WFWSMYtSNYoUWEAsUlmNoRV146RFYlS8SDp46R1z/cA6wEvioiQtBFwQkS8Yob9mzMhZowxPaw90iWsPYapw9qjhrWHGYQ7OswKiaSVWRYkawvmPkiWmQFWJGE1TgwjHiS9HFgHODPyVo85mNeTI+Ky2fPeGGNmB2uP+Ym1x9xg7WEG4Y4Os8LTZT2lMaY/Fg/GGNMdaw9jpo+1h+mHOzrMCss4BckyxhhjzPzH2sMYY2YHd3SYFZJJDpJljDHGmMnD2sMYY2YPd3SYFRKvpzTGGGPMbGLtYYwxs4c7OowxxhhjjDHGGDNvWGmuHTDGGGOMMcYYY4yZKdzRYYwxxhhjjDHGmHmDOzqMGTMk7SvpUkm/kfRrSUskHTrXfhljjDFmfmLtYYyZbzhGhzFjhKSPAZ8GPgucAywEXgzsFREbzaVvxhhjjJl/WHsYY+Yj7ugwZoyQdDtwSkT8XS1dMUv/rJIWRsTDs1FXVyQtiojfzbUfxhhjzHzD2qMZaw9jJhsvXTFmvHgacGc9sS40JD1d0gmS7pX0kKRzJW1dswlJ+9fSPinpnsr5vtnupbmM3wEfyde2kPQ9SfdLelDSTyXtWMm7hqQvS7pL0sOSLpT0sn5vTtKTJB0l6frs982Sjpa0Ws0uJH1I0mGS7gaW5vSFkj4r6ZeSHpF0haSdann3lvQTSffl6bfn1D8bY4wxxjzO07D2sPYwZp6xYK4dMMZM4TLgvZJuBU6LiHtb7E4BNgI+DNxDEgjnSNoqIn4xRL2LgS8BBwH3S9oEuAC4HngPcC+wNfCnAJKeCPyQJI4+AvwK+Bvgh5KeGxHLCabMqsDKwMeBu3N5Hwe+BfxFzfYjwPnAO1jWKfvvwEuBA4Ebgd2BUyVtHRGXZ5v1gRPz9ScAbwfOl7R5RNxU9rEYY4wx8x5rj2VYexgzX4gIHz58jMkBbAHcBATwR+Bq4FPAahWb1+Xrr6mkPYnUeH+5khbA/rXyPwncUznfN9u9v2a3GLgNWNTi537A74HnVtIWkBr4zxW83wXAK7IP69V8X1Kz3b7+vnP6+cC3WspfKddxHfCJub6/Pnz48OHDx7gd1h5TfF9Ss7X28OFjQg8vXTFmjIiIK4FNgV2AfwYE/ANwiaQnZ7OXAndHxHmVfL8FTgNeOWTVp9fOtwO+Ge1rU3cALgVulrRAUm922Hmk0ZdWJL1DKZr7g8AfgJ/kS88b4NMOpKm1F/TqzPWeXa1T0qaSTpZ0F/BYrmPjhvKNMcaYFR5rj74+WXsYM6F46YoxY0ZEPAJ8Lx9I2g84jjSScTiwDnBXQ9a7gDWGrLZe3prAHX3snw68nNSQ17mxLZOkXUlTO78EHADcR3o/J5OivPfz6enA2i11PpbLfwpwZs77IeAW4GHS51cv3xhjjDFYe/TxydrDmAnFHR3GjDkR8RVJnwU2yUl3AM9oMH0mqfHu8QhpnWiVNjFSj6p+L0kEtHEfcAlpbWydR/rkeytwcUT8bS9B0ms6+nQfcDvwpj7lbwOsC+wYEddV6nhqnzzGGGOMqWDtMaVOaw9jJhAvXTFmjJC0nIiQtBbwVJaNMlwMPEPSqys2qwJvYNlUTEjrXDet2KxEmhbahbOB3SW1jUScTQpIdmtEXFI7lvYpdxHLi5H/XeDT2sCDDXVeUimfah2S/owUJMwYY4wxNaw9Bvpk7WHMBOIZHcaMF0slfZc0BfJXwLNJ0c0fAk4AiIgzJF0AfFPSR0kjIB8mNbSfq5R1MvB3kpaQgoy9E5iylVofDgJ+RooY/oVcx1bAvRHxVdIU0PcA50r6fC5/TdIa3jsj4ost5Z4FHC3p4yTRtBMp0FcXzgLOAM6SdAgpWNpqwJbAwoj4GHAR8CBwbB6JWpcUBO32jnUYY4wxKxrWHu1Yexgzobijw5jx4lPAG4EjSFM97wQuBPaIiJsrdrsCXwAOI63//CmwXUzd3u0g0jTTfyRFKT8KuAqYsr99ExFxvaRXAgeT1pgCXENa20pEPCxp2+zvQaSpq7/Kfpzap+gvA88B3p/9Pou0BdtFHXwKSbtlHz4ArEeaUno5cGS2uUvSW4HPA98FbiCJor8fVL4xxhizgmLt0e6TtYcxE4oi6kvRjDHGGGOMMcYYYyYTx+gwxhhjjDHGGGPMvMEdHcYYY4wxxhhjjJk3uKPDGGOMMcYYY4wx8wZ3dBhjjDHGGGOMMWbe4I4OY4wxxhhjjDHGzBvc0WGMMcYYY4wxxph5gzs6jDHGGGOMMcYYM29wR4cxxhhjjDHGGGPmDf8fb7RZOa8EVKAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 1080x476.769 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "visualize_interareal_connectivity(M)"
    ]
@@ -582,38 +530,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "15778e9c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prepared simulation in 0.00 seconds.\n",
-      "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1515.25 MB\n",
-      "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1541.84 MB\n",
-      "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1571.05 MB\n",
-      "Created areas and internal connections in 0.21 seconds.\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "expected str, bytes or os.PathLike object, not NoneType",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [13], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# %%capture captured\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m M\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39msimulate()\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/simulation.py:300\u001b[0m, in \u001b[0;36mSimulation.simulate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_network_local \u001b[38;5;241m=\u001b[39m t2 \u001b[38;5;241m-\u001b[39m t1\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreated areas and internal connections in \u001b[39m\u001b[38;5;132;01m{0:.2f}\u001b[39;00m\u001b[38;5;124m seconds.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m    298\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_network_local))\n\u001b[0;32m--> 300\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcortico_cortical_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    301\u001b[0m t3 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m    302\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnetwork_memory \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmemory()\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/simulation.py:224\u001b[0m, in \u001b[0;36mSimulation.cortico_cortical_input\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m replace_non_simulated_areas \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhet_poisson_stat\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    223\u001b[0m     fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnetwork\u001b[38;5;241m.\u001b[39mparams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mconnection_params\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreplace_cc_input_source\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m--> 224\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    225\u001b[0m         non_simulated_cc_input \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m    226\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m replace_non_simulated_areas \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhom_poisson_stat\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
-      "\u001b[0;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not NoneType"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# %%capture captured\n",
     "# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\n",
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 3c5dbbb4d3a9cde91eda73ef33680e7b2d206b3d..7f2d02452310afd30559b173c230b90510106fb9 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -291,7 +291,16 @@
    "execution_count": 6,
    "id": "6e4bed8d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2479643048.py, line 20)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn [6], line 20\u001b[0;36m\u001b[0m\n\u001b[0;31m    'replace_cc_input_source': replace_cc_input_source\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "# Decide the parameter replace_cc_input_source\n",
     "complete_area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd', 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd', 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp', 'STPa', '46', 'AITd', 'TH']\n",
@@ -311,7 +320,7 @@
     "    'fac_nu_ext_TH': 1.2, # Increase the external input to 2/3E and 5E in area TH\n",
     "    'fac_nu_ext_5E': 1.125, # Increase the external Poisson indegree onto 5E\n",
     "    'fac_nu_ext_6E': 1.41666667, # Increase the external Poisson indegree onto 6E\n",
-    "    'av_indegree_V1': 3950. # Adjust the average indegree in V1 based on monkey data\n",
+    "    'av_indegree_V1': 3950., # Adjust the average indegree in V1 based on monkey data\n",
     "    'replace_cc_input_source': replace_cc_input_source\n",
     "}\n",
     "\n",
@@ -382,19 +391,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "ab25f9f8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%capture captured\n",
     "M = MultiAreaModel(network_params, \n",
@@ -414,19 +414,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "6a7ddf0e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 0\n",
-      "Mean-field theory predicts an average firing rate of 29.588 spikes/s across all populations.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "p, r = M.theory.integrate_siegert()\n",
     "print(\"Mean-field theory predicts an average \"\n",
@@ -451,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6316ac24",
    "metadata": {},
    "outputs": [],
@@ -467,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "8408d463-557b-481b-afc1-5fbbbd67306d",
    "metadata": {},
    "outputs": [],
@@ -484,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "445a722a",
    "metadata": {},
    "outputs": [],
@@ -501,53 +492,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "05512922-26e5-425f-90a4-0df7c2279ccf",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing network from dictionary.\n",
-      "RAND_DATA_LABEL 7864\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Error in library(\"aod\") : there is no package called ‘aod’\n",
-      "Execution halted\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No R installation or IndexError, taking hard-coded SLN fit parameters.\n",
-      "\n",
-      "\n",
-      "========================================\n",
-      "Customized parameters\n",
-      "--------------------\n",
-      "{}\n",
-      "========================================\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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th2NZ8ff4AYC7Pw18l6gQvheYbtFxDgbH96v7PTc4KGRmXYlm4mwF/JxoNtD2RNPbOzTSp9WJvmH8NSv/3Z1GNADTWB3QkYLdSBqxRvz/4/WsZ0tWfn0JVff326mhQDOeo6b+Puv2p/+8KFd8vSF9G8gWL0u6vY8mml2zJ9E3zl8CrxMNbOwZD8huQ/0DHU091idY8Xf2m3j5j4mm1x9HNOjxhUXHKegc397k66uZHUg0hf9tol1ndiT6vUyn8dfzum3qfVbeprrT9DbV2Lb7hTe+20udvix/PS7U0DbQ1POcxEIa2c6LfFl0fQnRoPNXCgYpQ987V9UHrPg7OxbAzH5MtNvk40Tv6zuwfHCivj4W/22sQTQwXLxNvBzf3iteT3O3u0LNeX7r/l4b+puuuz3p60vd38H1rPyYD2DVX1tFck3H6BCpDDOJCrbTG7h9YtF1L7xi0cExDyaarn1ZwfItG2jP61k2k2hmxgUN3KfujArDgJvc/fyC9XRt4D5NmQGsHu+j31AxPgtYq57laxE9jvoGmNIwk2gKdEMHhax7PmYAqR10zN3nm9m9RLvI/JZowONDdy/+pjnEP4hOD3ow0bEXJhHN1GmOWdT/jVvd72hm0fL6trUQi4kGnAo1p3gcRVRIF7YLgLvfTXQ8na5EH1IvAP5r0YEK6/abX+FgfEV2Jirudy/8NjH+Zq8xs4k+HF8J3FRfoLFZJETPdZKDANf9ToYDE+q5fV6CNhpT9+GgsWMMNPc5akjdB5U1WfExrVlPtqH715ctXpZoe3f32WY2jmjWxhyiKepuZk8S7YoylGhQq76BjqacTHQ8gzrT4nVWEx1j4Bdmth7R7j1/IfrAdw7JXl+HAe+7+/C6BWbWjiZm57B8m/o6K3/gLLy9sfs3tO0mfc34lOUfNAsl3QaaY3XgjRK2D9H7TvFMxbp1p+VAVhxg/Cj+fxjRbhh1x9LAzNZvpJ3i39XMuK3DG8hPKlhPc7a7VVVXLzRUU9Sdvarw9eXDgkzxtlW3nf+CaHCoWFOz7UQqmgY6RPJlMfV/G/Rfom/nPol3eQnVgaiIXlq0fHhAG/8l+jAyoYkZFp3rWc8PAtZT6FGib3dPAIoPuFrnaeAMMxvg7pMgmnZLdJCw1zw6iGIp/Jfow0IPd3+ikdyjwDAzO9DdRzWQqftQ3YlkHypvBo626GjxB7N8Vk1jFrPih6GvuPsHZvYo0QEDhwC/b+IDdGOeBr5rZrsWDb58j2iw7u1mttuQj1l5IGn/0EbcfSZNfPiKPzg+aGYbEB1zoRfRrgeTgBPMbIS71/chrO4b9K/+Liw6C9HBTaxvvpk9SzTLYWwzfifvAO3MbJ0mdkN6nmi728jdbwxcBzT8ulVnfWBygtcNCHyOGvEG0cynw1nxdJ/FB15uyAvAt8ysS93uKxYdkHlXlg9iQtj2/hRwJNFAx5UFy3oTHRdncuEueEm5e/FAd32Zj4G/mtlRLP97SfL62plot4FCxxC9nzTmMaJBunXd/bGm+lePd4gOTrsqXiT6HXau233FojNn7ErzZ5ItoYFtPX7f6c/ymZal8jHwHTNrXzcjwcz2oIHX9yIN9r+Qu49v4KbORLseFQp5f/8v0YG6q939nUZyzd3uVtVEolkZw4hmYQBgZrsQDcT+NV70EtH2fTjR4GGd4teXiUTvD1u4+18QkSAa6BDJl7eIvmE7heibg0VxwfE3og/uz5rZ34jePLsQnf1hd3dv6gPTHDN7EfiZmX1K9E3ecTT+LXSx3xBNL33GzK4gevNejaho3sDdj4tz/wWONbPxRNOWvw3sErCewn4/ZWb3AJfEHzKeJNqPdw+iY0iMJnpuhgOPmdlviYqwU4nOHBP8gTegb6PN7Haib/ovIXpuaokObPct4Bx3f5doF54TgdvN7M9EBVI3orOuXBoXe2/Fzf7MzP4D1Lj7GBr2ONGHreuJCsJbGsnWeQs41cyOIJqWPK/oA9JVREeGX0qyMwY0ZCTRzKN/m9m5RLt0HEW0u9TJCaech7gD+FW8rheJZtgcmVbjZvZ7om/pniJ6ztch+lA6zt2nx5kziI7u/6SZXU00vXozogPr/ZZoIGEucGW8jXYhOtr/DKIzQDTm/4jO5vCImV1P9AGtN9FuDlXu/vNG7vtM/P8ONLJrjbvPNbOz4v71Idq3fQ7R68PXiGYfNHaK2oZet+rsWNCXhqzKc1TfY5odv1aea2bziD7Ub090dqMkzifaZelRM7uIaNbQ71h5avpIkm/vTxKdVaIf8cwNj87IMgHYmwZm7TSXRWe7eYDoWBPVRL/LrYAb43UneX39L3BI/Fw+CGxLtP3Pbmzd8eDpBcAVZjaQaEBoEdFAwL5Ex7RpbPbKM0QD2G1WYdD1fKJZLI+Y2cVEA/6/JvodNrfNt4D9zey/RDNVprl73cDXIKLttqltfVXdAZwE3GDR6WTXJ3qdmJPgvo31P4n/AueY2S+J3vP2InqOk7qVaGDkCTP7K9HuW+2JjmNxENFBahfQzO1uVbl7jZn9BrjGolM/30L0OvhHogMg/zPOTTSz24Dfx7sP1p115VtF7bmZ/YjojFDtiY7pMYPoPWUXoi+vLinlYxJp1dI8sqkuuuiy6hcaPutKfWe8mMSKR0nvQnQQzS/jNiYV3LYa0Yf6j4i+lfmCaFeSMxpbd8FtA4g+wMyL73sF0UCAU3DGgIb6Gt+2DtHpUKfGffiU6Ju7owsyvYkKsS/jy61EHzCKj0Q+kgRHeSca0D2X6JvzJUQfIh8mPlNBnBkI3EdU6C0i+sC7X1E751H/mU1W6AfLj9i+TwO/1wEFy9oQfch5PV7vnPjnC4lmetTluhLNuvi44Hm7m/gI80TfUl0Z/15qic9kQiNnBonbc+D5Bn7Xxc/3WvHzNi++bXTRfaqIvgH/V8C2vtJZV+LlfYlmncwg+rb/jcJtpKlttYF1NXTWlY5Esys+jR/bnUQf7Bvd3hp7bova359oN55P48cymWiAqV9Rru5AktXx5XUKzvAT3/4a0T78HxAV7ecVP3/UfxaZzYj+pr6I+zCF6APstxI8by8B/yxaNrSB5/Jb8WOYG/fzfaJBr80LMvWddaWx163+8TZ9QIK+hjxH5xctq2+br4q30c/iNkcTHX9mpee4kW3utfg5/5BoF5EVtqOk23uc60Y0kPhZ0fLL6tsWCXgtaqD/F8T9n0P0tz2eojOs0MTrK9Fr3PlEg3wLiAYstibBGT7i5ccQvR7PJ/q7eJvovaepMzptFrf3taLlI2ngDD/FfYqX7Ut0hpvC3+G9RLP9iredE5r6OyGaDfIq0ev9CttR/Dx+StF7TD39bOj3Wu9jo/7t/WSiD94LiQYJt03yO2mo/yR/P+5EtKvjdKLX2weJBlqKn4vzqOf9Nr6tY3z7O/HvZRbRQMF5LD+DXKLtroTP79FEr+GLiWb63Qz0Lcp0jp+LWUTb9gMsP6tQ8d/yzvFz9WX83E8iek3fuSAzGp11RRddVrjUneZQRESk2cxsX6JvvffxxnfFkVbEzIYTfZDu6wVnn2jB9Z8DnAJs6OnP5pEcM7PRRMdpOCHFNrsSDeA95O5JZ/ckbfst4B53/3Wa7YqIVCoNdIiISLOZ2YZE+8L/DVjs7tuWuUuSovi4AeOBG9z94hZed0eib9F/7u6p7pYh+WdmuxLtpreRuzfrzFVm9neiGQ/TiHYZOp1oZsD27p7aQUPN7GCi2U8buvvstNoVEalkOr2siIisil8T7dK0mOj0wZIj8SyK44imf7e0AUSzSZp16mOpbB4d3PWnRAeBbK6ORLvwPAqMINqFZp80BzlinYh2V5qdcrsiIhVLMzpEREREREREJDc0o0NEREREREREckMDHSIiIiIiIiKSGxroEBEREREREZHc0ECHiIiIiIiIiOSGBjpEREREREREJDc00CEiIiIiIiIiuaGBDhERERERERHJDQ10iIiIiIiIiEhuaKBDRERERERERHJDAx0iIiIiIiIikhsa6BARERERERGR3NBAh4iIiIiIiIjkhgY6RERERERERCQ3NNAhIiIiIiIiIrmhgQ4RERERERERyQ0NdIiIiIiIiIhIbmigQ0RERERERERyo+QDHWY2yczczIbG10fH14eXet319GV4vO7RLb3uEAXP2SEJ8+fF+ZGl7ZmIiIi0hHLWS0mZ2dC4j7MD7rNCXSgiIlIKTQ50FLwhFV+GtED/REREpMIU1B61ZlYdX7/LzHYsd99EREQk+9oGZB8EPii4Pj3lvoiIiIgUeoio3tgV+C5wqJl9z93/Vd5uiYiISJaF7LpyvbufUXCZWt8uEwUzPgY0t1Nm9j0ze8vMFprZLDN7wcx2i29ra2anm9mbZrbAzD43s9/Et+1rZq+Z2RwzW2pmH5vZ75pY127x9NAvzWyamd1gZr0aydc9vrPN7CMzmx3/vLuZTYyvX16QNzM7yczGm9l8M3vfzM43s44FmVPNbLKZTTezs+pZZ1szO8vM3o7beMvMTmzOcysiItKKXO/uxwFbAHcQfUFztZl1BjCzAWb2LzP7NH4ff6pu1oeZbR+/X78ZX98mvv5SfH2P+Por8fWR8fWrzWxUXGO80dgMVjNrb2bXmtlnZrY4fi9/oOD2dczsxrgeWRS/j28f3/bXeKbKonhdLza2O0doLWDLd9d93cwuiWfGvGVmW5vZH+Ja6UMz+3rBffqY2XVm9omZzY37tF/B7T3M7M74tteBbepZ77pmdoeZTY1rokfNbFBD/RQRESmFkIGO483s0rpLqTpkZp2AkcB6wK1E3+Z0BzaMI78DLgU2AO4BngY2jW9bG5hBVAzdDHQDfmNmwxpY1yDgCWBb4L/Au8APgH+ZmTXR1Z8BLwA9gL8AdwMvAh2AH5vZPnHuFOAaoD9wJ1GRdi5wWdyHocCVQD/gUeDoOFvoD8CFgAH/AroCI8zs2Cb6KCIi0uq5+zKi93+A1YFdzawL8CRwGNH795PAUOBJM9sQGAvMBTY3s57ALvH9t45rjbrrTxet7mRgGfARsCXw90a69n3gBKLa43rgVaLZJ8SDMU/GmUVEdcmXRO/3AOsDL8X3ewrYkaj+6NbAuppbC2wZt/02sFm8rsOIapb1gRvi/rYBHgCOjx/P/UT10UNmtmvc1uXA4cCc+LH+tnBFBY/5cOAN4DGi38lTZta7iX6KiIikJmTXlQOKrp+RRgeKBk1eJnqTrQK+AO4D3nL3D82sKh58+EmcPcrd743baBcvuym+3zZAL6JdbbYD9iIa/Ch2CtAeeA34PL7sBOwJDATeaaTrP3P3W8xsF6JBmRvd/Wwz6wp8G9gaeBw4Lc6f7u43mtlWwDjgBDM7nWhgA2Ckux9vZqsDnxEPQsWPua6N54mKtnFEgyGnADc20kcREZG8+Ljg5zWA/Yk+qH8IDHV3N7N7gUOA4939l2b2HPBNovf2XYkGRDYh+uBf9+G9eKDjYXc/1Mz2JPrQvjWAme0AfK8gdwVQV3+MJ/py5i2i92mAbwEbA58CW7v7griduvucQDTgMAB4D1gA9CYamHi+sEOrWAvMB/aJH/NTRF/Q7AJMjttZ28z6ED2XOwHVwO7uPt/MZhDVez8ysxeBui+Ovufuz5rZa0SDH3X2J/piaiowMV72SbzsMODqRvopIiKSmpCBjkPd/b7GAmZW1Yw+nF7w843ufpuZnUL0LcGouN0pwDHABKJvMCD6JgIAd18a//gP4KR61tGngXUPiP/fMb4U2ojGBzrejv+fTTTQUfeGPi/+v0vROurydW22ISpQ1o6vTwRw91lmNhNYK17em+WP+Qf19FFERKQSrFfw8xfEAxDARHf3+Od3irKjiQY6diEa2LgO+CGwG7AzUAs8W7Se1+L/Z8f/172fb86KNct9RF+wDAUOJhoEcOBxMzuUaOAAYHzdIAdENYtFu8iOB/rW8zjrq1lWpRaY5O4LbcUzo0x095qCyatdWF6vTHb3+fHPhc9nb6Ivh2B5zfNu0brq2libFZ+rJP0UERFJzaqeXrbujbB7/H/wPpjubgWX4fHiG919baLpnacD6wC/JppKWR1nvhqYMLO6AZsj4v+HE80K+UddpIHVT4r/v6SwH8AG7v5gE12vaeJ68Trqdq8ZGP9fS/RtytTC5fGMjsJjhMxg+fM8uKCPbYhmq4iIiORa/D5ft5vELOA5lr+/blKwu2nde2zd7I+62RrfJfpy4TmiGRHDid5rX3f32UWrWxb/74UL3X1kUc0yGljm7kcQ1UGbEc3k3JdoZudH8V23jHeVKXwsuxMNckwn+mKjA8sHVuqrWValFlipPnH3+mqWSfH//eNdUGDF53MGsKRo+SYNtPEq0Kagn6sBf2yinyIiIqkJmdFRn7pvPb5lZn8lmqaZhs/NbDQwjWgKJ8DseFrq5cAvgVvN7B6ix1BLtAvI50RTMn8CfB04tIn1jABOBE43sw2I3sQ3I/rmZ1UHgepcSTS99TIz+xrRbjQQHWBtkZndRrQ/7HCLDlA6iILfS/yYrwTOBh4zs1FE3+rsRFTADU+pnyIiIllzvJkdRDQbYxOiQYgfuvsCM3uI6IP1hkTHgJhB9L6/kPi4E0QfuKuJvmyoITomxmDgO/Hto1exf0ea2TnAmHg9X9UsRMcAe49o95XXzKzumGKXENUrEM3e+BvRcce60oAWqgXGED0/OwLPmtkE4EiiAZ+r4hkgdxLNsL3NzB4j2h2l0MNEuxJtCzxnZm8A6xLNevkWq/58i4iIJLJKH+bd/XGifTMXEhUXV6TRKaKDV21DNACwBdEBSX8W3/Zb4KdE35QcBuzN8qmTJxBNs9yc6ECk1zTR/9eJ9lt9BtiDaNppN6KDi6blKuBUopkbRxINyvyZeEqnuz9JNDDzKdH02nuI9mct9CvgHKJvsY4mGiyZSHRwUxERkbzan2i2ZgfgLmDXulPLxrtX7E30vrkp0fv508De7v5+nFlGNIsDotkb1QXXYeXjc4SaSPQlybeIapYlwPnAg/HuKnsTHYS0M3As0bFFprn7C0QzHL4kmgFyO8tneDakpLWAu9cCBwH/jPt5KNEXWge5+//i2E+IDr7eE9iBqJ4pbKPud3I70QDHsUSzP25h+e4uIiIiJWfLd2sVEREREREREWnd0to9Q0RERERERESk7DTQISIiIiIiIiK5oYEOEREREREREckNDXSIiIiIiIiISG5ooENEREREREREckMDHSIiIiIiIiKSGxroEBEREREREZHc0ECHiIiIiIiIiOSGBjpEREREREREJDc00CEiIiIiIiIiudG23B0QERGRbDKzLsBVwBJgtLvfWuYuiYiISI6lVXtoRoeIiEgFMbMbzOwLM3uzaPl+ZjbRzN43s5/Hi78N3O3uJwIHtXhnRUREpNUrR+2hgQ4REZHKMhLYr3CBmVUBVwLfBDYHjjSzzYF1gMlxrKYF+ygiIiL5MZIWrj2064qIiEiZfGO/wT5zRnVq7b366kcTgEUFi0a4+4jCjLs/Y2YDiu66A/C+u38IYGZ3AAcDU4gKjnHoyxEREZFcSLP+yGrtoYEOERGRMpk5o5qXxvwhtfba2tGL3H27Ztx1bZZ/ewJRkbEjcDlwhZntD4xKoYsiIiJSZmnWH1mtPTTQISIiUiaOU1ubiT1CrJ5l7u7zgR+0dGdERESkdDJSf5S09tBAh4iISNk47svSbLCHmY0ARrl7yLcgU4D+BdfXAaal2TERERHJilTrj0zWHhroSFGvXp183f7dE+fbLJyTOLt0RlVYZzwsPndh56B8t44Lg/I1Ncn736nb/KC2F1WH9d29vsHDhlUv6RCU79JuSfK2l7YPanvNQesG5UNNe2Ny06ECtQHZ1TsuajpUwCxsI14Q+HuqspDewzJPvovgvKVhuxOu03NeUH7u/LBtvk3YJk/7qqVB+UXLkm/Hi2vDOvPpkulB+f4degfle3ZZEJQfP2vBDHfvE3SnljXH3U9qxv1eATY2s/WBqcAw4Hup9kxKolevTr7uuj0S59ssmB3U/uLP2wX2KLmOGw0Mys+c8HHgGsLeR1brMTdxds7c5PUewGqbDwjKhwp9bnptsV6JepI9MydMCrxH8vep1v48Lvnw7cTZ9htsVsKewHuvfRKU71QVVk+sM7h/06HYh+PC6uENhiRvG2DJB+8E5cfPnq/aoxk00JGidft35+knj0qc7/z6A4mzn13bM6gvtTVhH7QeH79VUH6PgclfGAGqq7skzm469OWgtt97JmyXsGUBgy4A//tk/aD89n2nJM6+OC3shfGMMVcF5UP9et2fBOUX1SR/kzli4HtBbXdsn3zACODVyQOC8j07hA28TF/UKXH2mc/DBiIu3H90UP6xV8O2+fZVYVMTN1g9bHDhrS/6Js5Omp/8eQQ47+Org/Jnr/edoPwh244Jyve//dXQT1qNc3Bv2amjZnY7MBTobWZTgN+6+/VmdhrwCFAF3ODuE1q0Y9Is667bg6ef+n7ifOfX/h3U/geXrB3apcQGjno6KD9yixOC8lWBA+aH7v9I4ux/H9k7qO3DxtwYlA8V+twMH3NdiXqSPTcPOj4oXxPwhVhrfx4/OWKHxNl17wyrz0Pt3+20oPzm3cPq+YvGXJY4e3jPsHr4rjGXB+UnHbZTUH79e15Kt/aAFq8/ylF7aKBDRESkTBynNt1dV5pep/uRDSx/GHi4RTsjIiIiLa6l649y1B4Vfao4MxttZt8oWnaGmV1lZv81s9lm9mC5+iciIhKoh5mNMLMDy90RqZ9qDxERyZlM1h6VPqPjdqJ9gQrnKg4DzgLaA52Bk8vQLxERqQipH4y0ufvJSstR7SEiImWWav2Rydqj0gc67gbON7MO7r7YzAYA/YD/ubub2dBydk5ERPIu9YEOyT7VHiIiUmb5rz8qetcVd58JvAzsFy8aBtzp7oHnLBEREcmETE4fleVUe4iISM5ksvao9BkdsHwK6f3x/8eVtzsiIlIx3PFa7bpSgVR7iIhI+aRbf2Sy9tBAB9wHXGJm2wCd3H1smfsjIiKVJOdTR6Ve96HaQ0REyinn9UdF77oC4O7VwGjgBqJvWERERERKRrWHiIhIaWlGR+R24N9E00cBMLNngU2BrmY2BTje3R9p4P4iIiLNkP+DgUmDVHuIiEiZ5L/+0EAH4O73Ala0bPcydUdERCqGQ+3SNBvsYWYjgFHuPirNhiVdqj1ERKR8Uq0/Mll7aKAjRW0Wz6PTxMcT5xdsdVDibFXbJ4P68v6kDYPyPTssDspXV3cJyy/slDxcG7ZH1cx53cP6srhjUL5T27DRzj1feD1x9ldrrxfUdlVVt6B8G2sflP/pWkcH5RfUJM++PHXdoLYH9/ksKP/m7LBtsleHsO3gjS+rEmera2qD2l68MKwvE+aEPdb2gTspdm8f9nrwn2nJ+/Pp4kVBbW/X+aig/OOftgvKf/LMrkF5eDUw3+IyeUAwKZ02C+fQacJDifMhtQdAhw7PJ86+/uFGQW3v022foPwtg8Pe72cu6BqUb9NhSeLs3R/3DGr7sKA0VLUJe52vqZ0fuIbKccyb1wflb9j8xBL1JHuWLQ17zyylh+ZdEZQ/pPuPS9QTWBhYx4UacPeLYXcwazpTXpmsPTTQISIiUibu+Z86KiIiItlSCfVHRR+M1MxGm9k3ipadYWYPm9kLZjbBzN4wsyPK1UcREckzh9pl6V0k81R7iIhI+aVYf2RUpc/oqDuPfeGBvoYB5wDT3P09M+sHvGpmj7j77DL0UURERPJDtYeIiEiJVfSMDuBu4AAz6wBgZgOAfsAz7v4egLtPA74A+pSrkyIiklOe+oyOHmY2wswOLPdDkwap9hARkfJKs/7IaO1R0TM63H2mmb0M7AfcT/SNyp3u7nUZM9sBaA98UJ5eiohIrqW7j2wmDwgmy6n2EBGRTEiv/shk7VHpMzpg+RRS4v9vr7vBzPoCNwM/cPfSHn5XREREKoVqDxERkRKq6BkdsfuAS8xsG6CTu48FMLPuwEPAr9w98BxAIiIiTTMcy/CBvKRk7kO1h4iIlEkl1B8VP9Dh7tVmNhq4gfgbFTNrD9wL3OTu/ypj90REJM/q9pGViqLaQ0REyqoC6g/tuhK5HdgKuCO+fjiwBzDczMbFlyHl6pyIiIjkjmoPERGREqn4GR0A7n4vYAXXbwFuKV+PRESkMqT+jUoPMxsBjHL3UWk2LOlS7SEiIuWTav2RydpDAx0psi6b027nMYnzNX/olbzxy48P6svQnhcG5at/0S8o367bgqB8h1/OTpxd/JceQW3v8t3/BOUXTV4jKE+bsGPBnXDxJ4mzXxw3KKjtc2+YF5QPFdqfpYvbJ86ufevY0O4E2a2krZfWwt/1Dsr/5a6XStSTyLRjtg7K3/HK7U2HcuIiuzzlFh3TWVdkFXz+UTcu/f7QxPljv/ZoUPur33BQ4uwRvW8KavvTEzsF5dt1XRiUP/CKA4Ly+y7skDh707nXB7X9yp7vB+WnHrNRUN6sXVB+1DaHB+UPHHtXUD7EnYO/F5T/cF63oPwXi6qC8r07Jt8ONu52SFDbp/XtG5Rfo+OioPzf3w97rA8On544e956Pw5qe/xsazpUYNKyOUH576wZth1cstGpibPbrh72PP41oG2AZR723JRGqvVHJmsP7boiIiIiIiIiIrmhGR0iIiLl4kBtTbl7ISIiIpWkAuqPip7RYWajzewbRcvOMLOHzWxhfCCwt8zsajOr6OdKRERKITq9W1oXyT7VHiIiUn7p1R9ZVelvoLcDw4qWDQP+DHzg7kOAwcDmwCEt2jMRERHJI9UeIiIiJVbpu67cDZxvZh3cfbGZDQD6AVPqAu6+zMyeB8KOCiUiItIkz/3UUVmJag8RESmz/NcfFT2jw91nAi8D+8WLhgF3Eu21BICZdQb2Bsa3eAdFRCTfPD69W1oXyTzVHiIiUnZp1h8ZVdEDHbHCKaTD4usAG5rZOOA54CF3DzuHqYiISMvrYWYjzOzAcndEGqXaQ0RE8iKTtUel77oCcB9wiZltA3Ry97HxNNK6/WRFRERKxtKdOprJc9nLSu5DtYeIiJRRivVHJmuPih/ocPdqMxsN3MDyb1RERERKz/O/j6ysTLWHiIiUVQXUH9p1JXI7sBVwR7k7IiIiIhVBtYeIiEiJVPyMDgB3vxewguuTgEFl65CIiFSMlHddkVZCtYeIiJRT3usPDXSkqHbq6yz41VqJ80uruyfPnjMqqC9zLwiK02mdWUH5xZ/0DsufuW7ibO3SXkFt97zsw6D8/B9tHJTvsv6nQfkQa9zwZsnabo4O3ecH5dstXVyinlSWz94I2ybXD2z/vYN3D8r3WjtsO5BVkf+po1Jaa6w+m1MOvzdx/oNXw8ZS2h35YuLsFzO+H9R2p3/+LSj/xgNDg/K3HPJyUH7Cy0MSZw8bG/Y6+fjXwvI9BkwLyoc6cOxdJW0/xA4bTwzKD17UMSj/7dFrBOXXnLN64uz7Cx4KavvxT08Oyq/VKflnBYCXFl0XlH/h5YMTZx+fviio7W/3axeUP2/DsG3+l88PDMrXBuzIsGsfbzpU4InPrOlQgWUe1n5p5L/+0K4rIiIiIiIiIpIbmtEhIiJSJuae+6mjIiIiki2VUH9U9EBHfMTzP7v7IwXLziA6ONhgoApoB/zd3a8uRx9FRCTncl5oyIpUe4iISCbkvP6o9F1XbgeGFS0bBowEdonPZb8j8HMz69eyXRMREZEcUu0hIiJSYhU9owO4GzjfzDq4+2IzGwD0A55x/+ooMR3QgJCIiJRCBUwdlZWo9hARkfKqgPqjot9E3X0m8DKwX7xoGHCnu7uZ9TezN4DJwAXuXtrDX4uISGWqrUnvAj3MbISZHVjuhyX1U+0hIiKZkPPao6IHOmKFU0iHxddx98nuPhjYCDjWzNYsU/9ERESSmuPuJ7l72DnJpaWp9hARkbzIZO2hgQ64D9jbzLYBOrn72MIb429TJgC7l6FvIiKSa47V1qZ2kVbjPlR7iIhI2aRXf2RVxQ90uHs1MBq4gfgbFTNbx8w6xT+vBuwKTCxXH0VEJKectHddkVZAtYeIiJRVmvVHRlX6wUjr3A78m+XTSDcD/mpmDhhwsbuPL1fnREREJHdUe4iIiJSIBjoAd7+XqKiou/4Y0bnsRURESsgz/W2IlI5qDxERKZ/81x8a6EiR11SxZE7XxPmapVXJG6+1pjMFOl53VVB+4Qk/Ccq3vfimoHzXCz5LnJ1zxvpBbYeq6rA0KN/+zOoS9QQW/6VHUH7hp72C8j0v+zAoH/rctGm3LChfSjNO3Dwo3/vat0rUk3Dr3/NCSduvqgrbf7Jdl4Ul6onUxzy7+7dK9tXWtAmqPbp0WRDU/py53RJnO1z996C2F51yVlC+/+v/DspvdmeHoPwf+/dLnO1buzio7ateDXu///tDzwXlQ1X/ondQ/stJayfO9r/99aC2l9WEfRzpsfrsoPw6HlgftG+XOPvCdvs1HSpww9sBtT+wZkdvOlSga8ewGvrfH/dJnO3bPqwvnywI++zy8Yw1gvKdqsKOwNCtXfL+LKkJe19eq2PY73VZ2FMJYS/bieW9/qj4Y3SIiIiIiIiISH5oRoeIiEi5eP6njoqIiEjGVED9kfsZHWY22sy+UbTsDDP7p5m9ambjzGyCmf2wKNPHzJaa2ckt22MREakotbXpXSQTVHuIiEjm5bz2yP1AB9FRzYcVLRsGjAR2cfchwI7Az82scOfM7wIvAke2QB9FREQkP1R7iIiIlFElDHTcDRxgZh0AzGwA0A94xt3rjiTVgZWfiyOBnwHrmFnyIzCJiIgk5a4ZHfmk2kNERLIrzfojo3I/0OHuM4GXgbrDIg8D7nR3N7P+ZvYGMBm4wN2nAZhZf2Atd38ZuAs4ogxdFxGRCmC1NaldJBtUe4iISNblvfbI/UBHrHAK6bD4Ou4+2d0HAxsBx5rZmgWZu+Kf70BTSEVERCSMag8REZEyqZSzrtwHXGJm2wCd3H1s4Y3uPs3MJgC7E003PRJY08yOiiP9zGxjd3+vJTstIiJ555me9imr5D5Ue4iISCblv/6oiBkd7l4NjAZuIP5GxczWMbNO8c+rAbsCE81sINDF3dd29wHuPgD4MysfVExERGTVODpGR06p9hARkcxKs/7IqIoY6IjdDmxFNB0UYDPgJTN7HXgauNjdxxN9o3Jv0X3vQVNIRUREJIxqDxERkTKolF1XcPd7ASu4/hgwuJ7cefUsewPYvJT9ExGRSpT/qaOVTLWHiIhkU/7rj4oZ6GgJy5a2ZcaUtRLn27dfkjjbuee8oL7MeWe9oHy7394dlF/2u+8H5Rf/qWfirLVJnm2ONu2WBeVn/3ijoHz1zJ6Js+06hP2eFi/oGJRP3pPIknmdg/I1S9olznYL7Euozn1nBOU/+s7OQfn173khcfberY8JavvQ124Oyofqudb0oPzi2WG/rXnHbJ042+/m14Lavnazk4LyJ749Iihfdg5k+Ijlkn3LFrfns4/XSZx/edIGQe3vv3Py174vJ4S13eHntwflO10WNsFlcr+w14Px/+ubOLvx5xsGtf2z3V8Myv9f7bZB+dc/DKtVXnxycdOhAguXtg/pTVDb8+Z1DcrPmRv2HrVR1+S1CkDvjp44u8UO44La3njKukH5Ph3Dfk9bzNgpKL9xt+TvP7OXhu0IcPIOLwfl3/go7G/q8AFzgvLvBmw3s5dUBbW9YcDzCFC7fPw7mVlh8UQqoP6opF1XRERERERERCTnNKNDRESkTAzHcj51VERERLKlEuqPip3RYWZuZjcXXG9rZtPN7EEz+4GZjYsvS8xsfPzzX8rZZxERySGddaViqPYQEZHMyHntUckzOuYDg8ysk7svBPYFpgK4+z+BfwKY2SRgT3cPOwCAiIhIy+thZiOAUe4+qtydkZWo9hARkbzJZO1RyQMdAP8B9gfuJjqF2+3A7mXtkYiIVI6689inZ467hx3BVVqaag8RESmvdOuPTNYeFbvrSuwOYJiZdSQ63dtLZe6PiIhUFNeuK5VHtYeIiJRZivVHRlX0QEd8jvoBRN+oPFze3oiIiEjeqfYQEREpvYoe6Ig9AFxMNHVURESk5ThQ6+ld4v1kzezAMj8yaZxqDxERKZ8064+M1h6VfowOgBuI9isab2ZDy9wXERGRVZHJ/WRlJao9REQkLzJZe1T8QIe7TwEuK3c/RESkQmV4/1YpDdUeIiJSdjmvPyp2oMPdu9azbDQwumjZgJbpkYiIVB5Pu9DI5CneJKLaQ0REsiHV+iOTtUfFDnSUQocNN2Wje/+XOD/79A0SZ3te9mFQXxb8aq2gfOfzPwvK19zWJSg//9QfJc52uvXyoLZDtV/jy6B815/ND8r3DEpny+pXvVvuLjRb599/EZRfv0T9ADj0tZtL2Hq4Tn3CtvmaRR2C8r0vmBKUD9Gr46KStZ1TmZw+KqUzY2Enrh+3ZeL8T3Z9Maj9PtdNSJyt/kW/oLa7/nlaUL7mpk5B+fln/zQoP6TzVYmzt41+MKjthb/rHZTv9NsZQfkBQenScm4tdxdWsG1JWw+rWc+6qETdiA0vbfNB5p3VPyi/feew9/trn90tKP/bSX9PnL1i4A+D2j5t4tVB+VC/sStK2n4KMll7aKBDRESkXOoOBiYiIiLSUiqg/qios66Y2Wgz+0bRsjPM7Kr45+5mNtVs+bBZfHvnlu6riIhUCK9N7yKZo9pDREQyKee1R0UNdBCdxm1Y0bJhLD+92x+Ap4tuPwNQsSEiIiLNodpDRESkhVXaQMfdwAFm1gHAzAYA/YD/mdm2wJrAo3VhM/tJfPtTZvZUy3dXRETyLaVz2Gf8XPYVTrWHiIhkTP5rj4oa6HD3mcDLwH7xomHAnYABfwXOKspfDkwD9nT3PVuwqyIiUgnq9pFNr9iY4+4nZemo55VOtYeIiGROmvVHRmuPihroiBVOIa2bOnoq8LC7Ty5br0RERCSvVHuIiIi0oEo868p9wCVmtg3Qyd3HmtnPgN3N7FSgK9DezKrd/efl7KiIiFSAnB/1XADVHiIikjU5rz8qbqDD3avNbDRwA/GBwNz9qLrbzWw4sF1BoTEP6AaEndBcRESkCe6ZPmC5pES1h4iIZEkl1B+VuOsKREXGVsAdCbIjgP/ogGAiItIKZPKAYAKo9hARkXzKZO1RcTM6ANz9XqKDgNV320hgZMH1vwN/b5GOiYhI5Ul36ugcdz8pzQYlHao9REQkU9KrPzJZe1TkQEepLPt4AtNP2CJxfvW9Pk+c/fwHg4P6smj+ekH5iTscEZTffKPNgvJzbx2TOLvOzacGtT3/9A2C8hPG7hOUn3rTsUH5DlU1ibOT53cNavvH714flA9186BjgvJLaqoSZ4/87n1BbS9b0DEo/9rz2wXlt//6s0H5j18ZlDg76Yu1gtreZbcXg/ILZvYIyn/+6dZB+SVL2wXlH9vgtMTZDbtVB7X9xKerBeUHfnPvoHxVwN9rSTiQ86mjUlpr9ZjLz7/5eOL86ntNCmp/6lHbJM4uXrh+UNv3bXlcUL5n+28H5T84/8ug/A9eTl6nzzku+XsCwBo3hO2FdN3mJwblQ96PAU6deHVQvjWrPmedoPy8T3snzva/9eOgticfFVaf971pXFD+6k1PDsrvvO5HibMfTA+rbR6fFvb3esvcx4LyN246Oygf4qUZHYLyyaugyGWbnBJ4jxKogPqjUnddEREREREREZEc0owOERGRcsr5NyoiIiKSQTmvPyp6RoeZjTazbxQtO8PMrop/7m5mU83sivL0UEREcs9TvEjmqfYQEZFMyHntUdEDHURHQB9WtGxYvBzgD8DTLdojERGR5svkkc9lBao9REQkTzJZe1T6rit3A+ebWQd3X2xmA4B+wP/MbFtgTeC/QNhRDkVERJJw8Np6T8TRXJk88rmsQLWHiIiUV7r1RyZrj4qe0eHuM4GXgf3iRcOAO4lO//ZX4KwydU1ERCpFbYoXyTzVHiIikgk5rz0qeqAjVjiFtG7q6KnAw+4+uWy9EhERkbxS7SEiIlJClb7rCsB9wCVmtg3Qyd3HmtnPgN3N7FSgK9DezKrd/efl7KiIiORQuruuSOtwH6o9RESknHJef1T8QIe7V5vZaOAG4gOBuftRdbeb2XBgOxUaIiKSuvSP0SGtgGoPEREpqwqoP7TrSuR2YCvgjnJ3RERERCqCag8REZESqfgZHQDufi/RQcDqu20kMLIl+yMiIpXCcj91VOqn2kNERMon//WHBjpS1Ha9Lehz3ZjE+WX/6JA4u3rgCXva7fxSUH69sOYzZe7vwjbjXXteWKKelN5pXF3uLqyCa0va+h4lbR02LVG2OboH5tcqSS+W276EbR9RwrabxUpQFHiqbfYwsxHAKHcflWbDkk2fz+nOpY/unTj/2y2uC2p/jVMXJ8623218UNvRyWey491NT02c/fM73wpq26xdaHeCuC8tafulVOrnJlT7tslPI1FTWx3U9sB/LQnK9753/6D8R9WPBuW7frxB4uzCJa8EtR363IT628TdgvLHdNw4cfa41cL+vrt03DAoX1ubkb/X9OqPTNYeGugQERHJj0yey15ERERyK5O1hwY6REREyqUCDgYmIiIiGVMB9UeuD0ZqZqPN7BtFy84ws6vMbDcze9nM3okvJ8W3n2tm4+JLTcHPPynPoxARkVyrbZPeRTJB9YeIiGRezmuPvM/ouB0YBjxSsGwYcBZwG3BIfO763sAjZjbV3f8I/BEgPn/9kBbus4iIiLRuqj9ERETKKLtDMOm4GzjAzDoAmNkAoB/wdWCku48FcPcZwNmAzlcvIiItx+Ojnqd1kaxQ/SEiItmVZv2RUbke6HD3mUSH9N4vXjQMuBPYAni1KD4mXi4iItJi3C21i2SD6g8REcm6vNceuR7oiNVNHyX+/3ai89Z7Pdn6lomIiIiEUv0hIiJSJpUw0HEfsLeZbQN0iqeLTgC2K8ptC7zVwn0TEZFKp4OR5tV9qP4QEZGsynntkfeDkeLu1WY2GriB6NsUgCuBl8zs3+4+zsx6ARcAvy9TN0VEpAJ5BZzerVKp/hARkayqhPoj9wMdsduBfxNPIXX3T83saOBaM+tGNJX0UncfVcY+ioiISL6o/hARESmDihjocPd7iYqJwmXPANs3cb+upeyXiIhUutSPWN7DzEYAo/ThufxUf4iISDalWn9ksvaoiIGOlrL0o7eYetQ2ifO9t2qfvO37ZgX1ZeLv9gnKd+kyPyj/8af9gvJDn78ncbbmzu5Bbc/61+ZBea47Oyg+5agxQfkn3huYOLvvJm8Htb3ZQ88F5UON2Stsu1m0JPk23Lvn7KC2B+z8elD+7ad2DMpXtakNyi9e2i5xdtKsPkFtf32vp4PyY1/cNii/dp8vgvIfBf59z17cMXF2SW1VUNtXfRC27+evtwh7LevcfnFQvhRSPmL5HHc/Kc0GJdv6dKvmlN2eT5z3pWF/g0semJM4+9o+ewW13bVr2N/rRvf+Lyg/qOuwpkMFLr9wROLsKweHvV/O+fKnQfmPj3gtKH9Q9x8H5f+w08Sg/FaPPhqUD3H5xicE5dsHvn+v3aU6KL/X/o8lzt5x9/Cgts/6eGxQ/sN5DwXlz1svbDsYvv0ribMb3PNRUNtP73JAUP7vE/oH5b+25pKg/DMT30uc/XaPsOdx/qIPgvK3D/5BUP5740cG5ZNKsf7IZO2R3aOHiIiIiIiIiIgE0owOERGRcnEyfcRyERERyaEKqD9y8+jMzM3s5oLrbc1supk9GF9f08weNLPXzewtM3vYzLY0s3HxZZaZfRT//Hg97Y80s8Na8jGJiEj+ea2ldpGWpdpDRERaq7zXHnma0TEfGGRmndx9IbAvMLXg9t8Dj7n7ZQBmNtjdxwND4usjgQfd/e4W7bWIiIi0Vqo9REREMig3Mzpi/wH2j38+kuXnrQfoC0ypu+LubzTWkEWuiL+BeQhYI+3OiohIpTPc07tIWaj2EBGRVib/tUfeBjruAIaZWUdgMPBSwW1XAteb2VNmdq6ZNXVagUOBgcCWwInALqXosIiIVLC6fWTTukg5qPYQEZHWJc36I6Oy27NmiL8pGUD0jcrDRbc9AmwAXAtsCrxmZo2dA3IP4HZ3r3H3acCTJem0iIiItFqqPURERLInVwMdsQeAi1lx6igA7j7L3W9z92OAV4gKisZ4CfonIiLyFR2MNBdUe4iISKuS99ojjwMdNwC/jw/29RUz28vMOsc/dwM2BD5ppJ1niKaiVplZX2DPUnVYREQqk4OO0ZEPqj1ERKTVSLP+yKo8nXUFAHefAlxWz03bAleY2TKiAZ7r3P2VRpq6F9gLGA+8Czyddl9FRESk9VPtISIiki25Gehw9671LBsNjI5/vgi4qJH7Dy+67sBpafZRRERkBW6ZPpCXNE61h4iItEoVUH/kZqAjC9q0raHbmrMS5xdObex4ZEUCN8RNvvF8UL79TxcE5fv9qWdQPsT8V8LaHnD3i6XpSGydW88Oyv+454WJs4sv7BbUtj+4NChfaov+uFribMdzvyxhT2DIL0vafJDtStz+0BK3v87vegflO/12Rol6AkeXrOVmsvSnaGZ5/1bJvqq2y+i2xszE+TnvrBfUfpuq2sTZzff7X1Db3X7eIyg//f8GBOVrAl+Nn7jioMTZK94JO/Pvh+s0tsfSyp6fsnVQftRqfwvK37lr2HNv1i4oX0rzfh5QPwOnjPhuUP7J645MnL1xTtg2f2qvXYPyVwz8YVD+1lmfBeWXvrxD4uyP1toxqO1fv7YoKP/v7z8S1v69+wXlz133J4mzG3UL+9z18/7J2wao9e5B+VLJe/2R72EcEREREREREakomtEhIiJSRlk+kJeIiIjkU97rj4qd0WFmNWY2zszeNLN/FRwVvTr+f4CZLYwzb5nZ1WZWsc+XiIiUQN0+smldWoiZHWJm15rZ/Wb29RZbcSun2kNERDIhzfqjhYTWHpX85rnQ3Ye4+yBgCVDfTnAfuPsQYDCwOXBIy3VPREQkfWZ2g5l9YWZvFi3fz8wmmtn7Zvbzxtpw9/vc/URgOHBECbubN6o9RESk4pSj9tCuK5FniQqKern7MjN7Htio5bokIiKVoAwHAxsJXAHcVLfAzKqAK4F9gSnAK2b2AFAF/Lno/se5+xfxz7+K7yfhVHuIiEjZtHD9MZIWrj0qfqDDzNoC3wT+20imM7A38JuW6peIiOSf0/L7yLr7M2Y2oGjxDsD77v4hgJndARzs7n8GDihuw8wM+AvwH3cfW+Iu545qDxERKaeWrj/KUXtU8kBHJzMbF//8LHB9PZkN44wD97v7f1qobyIiIs3R28zGFFwf4e4jEtxvbWBywfUpQGPnEvwxsA/Qw8w2cverw7takVR7iIhI3mSy9qjkgY6F8T6wjfkgQUZERKR5PPWpozPcfbtm3K++TnhDYXe/HLi8GeupdKo9RESk/NKtPzJZe1TyQIeIiEiZGe6ZOC74FKB/wfV1gGll6ouIiIiUVCbqj5LWHmV/dCIiIpKaHmY2wswODLzfK8DGZra+mbUHhgEPpN89ERERyZlM1h4VO6PD3bs2ttzdJwGDWrJPIiJSgdLddWWOu5/UWMDMbgeGEu1TOwX4rbtfb2anAY8QHe38BnefkGbHRLWHiIhkSHr1RyZrj4od6CiF2mVVzJ/RM3F+6eL2ibM1y6qC+tKmw5Kg/KL/GxCUXzKvX1B+1veHJM72vemToLZDff6DBs/mV6+eG4f1Z+6pybNL3uvfdKhAm8s6BeVrqjsG5Rd9sVpQfun85NtBWE/CfXx4Y8cuWtl6d71Uop7AtZs1+lq/khPfTnK8ppYz58O1g/JhW6UUK8NZV45sYPnDwMMt2hlZZbXLqpg/s2fi/LzZ3YPab9OmNnG2fY/qoLaf2z1sTGfia2F/K9dtvTQo//6snomzG3QNmxR99gaLgvJtLxkZlJ8z56yg/KfDnw7KL7rgjcTZxYG1xGfvDgjKj3m6T1C+tsE9/us3fXHy7CM7rhnU9mGvvBeUn/RJ2LGIf9d1+6D8UzN6J84+v+C6oLYv3TigIAZeHTskKL9+12VB+bM+SH4m9OP7nB7U9vXTS3vIqousNO238FlXWrz20K4rIiIi+dHc6aMiIiIizZHJ2qOiZ3SYWQ0wnuh5eBs41t0XmNk6wJXA5kSDQQ8CZ7l72DQJERGRxqR/1pUmp49Kean2EBGRsku3/shk7VHpMzoWuvsQdx8ELAF+aGYG/Bu4z903BjYBugJ/LGM/RUQkhzw+6nlaF2kVVHuIiEhZpVl/ZFV2e9byngU2AvYCFrn7PwHcvQb4KXCcmXUuY/9ERESaksnpo9Ig1R4iItLaZbL2qOhdV+qYWVvgm8B/gS2AVwtvd/e5ZvYJUTGS/GhMIiIiTdCuK5VJtYeIiJSTdl3Jt05mNg4YA3wCXA8YUN/xmRtaLiIi0jweHfU8rYu0Cqo9RESkvFKsP7Kq0md0LHT3IYULzGwC8J2iZd2B/sAHLdc1ERERySHVHiIiIiVW6TM66vME0NnMvg9gZlXAX4GR7r6grD0TEZHc0YwOQbWHiIi0sLzXHhroKOLuDhwKfNfM3gPeBRYBvyxrx0REJJe81lK7kNEDgknjVHuIiEhLy3vtUdG7rrh71waWTwYy9YsSERFJIJMHBJPlVHuIiEjOZLL2qOiBjrQtWdKejz/unzg/+GsvJc4uW9AxqC/PPb1rUH6z9T4KyvdYc2ZQfsmiDomzNTd1Cmp7/M17BOX7b7g4KP/lxPWC8ktOHp042/OaA4LafuvwcUH5ULW1YZO85i9Kvl2+sdN3g9redMOw3dI/nhr2e5q4wxFB+bEz+iTOrt9tSVDbc/9vQFhfXtw2KD9nUdjf1H6HTwrK/2a9nyTO1gQeVnGtjkuD8u/Oax+U/97GYa99aas7j71Icy1a3JG339socX7bHV9tOlQgpP44Y+ShQW3/fJfXgvJLloT9fS8MzB912s2Js59edGJQ26t1mxeU//KDdYLyS37wVFC+98g9g/KXbj0kcbZjVW1Q26e/+4+g/NYdNwzKL1j8QFD+30OGJc5u9+TDQW1PCkqHm179SlD+48N3LFFP4Iz3rgrKL/jVWkH5d/91SFA+RIcSvy3v2+WU0q4ggUqoPzTQISIiUi6e+ullRURERBpXAfVHvodxRERERERERKSiVPRAh5nVmNk4M5tgZq+b2f+ZWZv4tqFmNsfMXjOzt83st+Xur4iI5E/KZ13J5AHBZDnVHiIikgV5rz0qfdeVr85lb2ZrALcBPYC6wuJZdz/AzLoA48zsQXcP27lVRESkESmfmi2TBwSTFaj2EBGRskux/shk7VHRMzoKufsXwEnAaWZmRbfNB14Fwo5+JCIiItIA1R4iIiKloYGOAu7+IdFzskbhcjPrBewETChHv0REJKc8nXPYF5zLXloZ1R4iItLiUqw/sqrSd12pT+Fva3czew2oBf7i7io2REQkNU7qu65I66TaQ0REWkwl1B+ZGOgws52B44FNgJVO2O7uO7RQPzYAaoAvgM2I95NtiXWLiIhIy1HtISIikl9l33XFzPYFngHWAXYDpgPVwFZAL+DNFupHH+Bq4Ap395ZYp4iIiHub1C5k9MjnWaPaQ0REKl3ea48szOj4PXAZcA6wFPi1u481s/WAR4DRJVx3JzMbB7QDlgE3A5eUcH0iIiIrqNVZV8pBtYeIiFS0FOuPTNYeWRjo2Bz4FdG+qA50AXD3j83sPOB3wE2lWLG7VzVy22hKW+iIiIhIeaj2EBERybEsDHQsAtq4u5vZp0SnUXs2vm0u0bTSVqHLwI3Y6elRifMzTt4scbb3NW8H9eWbQWlYfGG3oHyHs+cF5VcLSocZ8v0SNt4MNff2Spyt6vlyUNvr33V2UL57zwuD8qEW/SH5Y+3465kl7AmsXdLW4eslbj/E0BK3v+BXawXlf//x5SXqSQZZygfu8mwfsTzHclN7LK6p4qO5PRPnB83tGtT+Y2O2S5zdY835QW2vucHkoPzzz+0UlJ+/tH1Q/sl/HpI4axa2p9FnX64elH/707BN8NAz/xWUv3v3gUH5U15do+lQ7LrtqoPa/vfWYYXcx8d0CMpf8PCpQfkXpifPVg86Pqjtaz9scJyzXs8sGBGUH9jt20H5rap2TJy9K6hluGurY4PyOw5cLyjfs/2SoHyIZSXekfCx+f8IyptdnX4nKqD+yMJAx+vAQOAx4AngF2Y2FVhCNLV0fBn7JiIiUjKVcNTzjFLtISIiFasS6o+yH4wUuJTouQb4JTCfaP/Yp4jOKf+j8nRLREREcupSVHuIiIjkVtkHOtz9YXe/Mv55KrAt0bcsQ4CN3P3VtNZlZm5mNxdcb2tm083swfj6cDO7Iv75PDObambjzOxNMzsorX6IiIjUcbfULpJMS9YeoPpDRESyJ++1RxZ2XfmKmRnQF/jI3ZeVYBXzgUFm1sndFwL7AlMbyf/N3S82s82AZ81sDXevLUG/RESkQmW5SKgELVB7gOoPERHJmLzXH2Wf0QFgZt8ys5eIDg42GRgcL7/WzI5OeXX/AfaPfz4SuL2pO7j720SngOudcl9ERETSlMlz2WdRC9ceoPpDRETyKZO1R9kHOszs+8ADwDvASUDh0NK7QNjhjJt2BzDMzDoSFTUvJejjjkSnoAs4DrOIiEgT3Kj1NqldiM9l7+7JTwFWgcpQe4DqDxERyYoU6w8yWntkYdeVc4GL3P0XZlYF/LPgtgnAmWmuzN3fMLMBRN+mPNxE/KfxtzrzgCPcvcQnGxIRkUrikPvTu2VUi9YeoPpDRESyoxLqjywMdKxHdHq3+iwCupdgnQ8AFwNDgV6N5P7m7heXYP0iIiJSPuWoPUD1h4iISIvIwkDHZGBr4Ml6btsOeL8E67yBaIrNeDMbWoL2RUREEsn7wcAyqhy1B6j+EBGRjMh7/ZGFgY7rgd+a2efAffEyM7O9gbOB36e9QnefAlyWdrsiIiKh8l5oZFSL1x6g+kNERLIj7/VHFgY6LgD6AzcCNfGy54Eq4Bp3vzytFbl713qWjQZGxz+PBEbGP5+X1npFREQkU1qs9gDVHyIiIi3NsnJ8KzPbENib6BRqs4An3f3d8vYqzHbbbedjxowpdzdEvjJ39tlB+e49LyxRT0Tywcxedfft0mpvi+7d/baddkqrOYY89liq/cs71R4i6Zs76/+C8t1Xv6REPRHJh7RrD0i3/shq7VHWGR3xKdbmEB1R/D7gg3L2R0REpCU5lvupo1mj2kNERCpdJdQfbcq5cndfBHwBLCtnP0RERKQyqPYQERHJv7IOdMSuAX5iZu3SbNTM3MxuLrje1symm9mD8fXh8fXXzOw9M3vEzHYpyI80s4/MbJyZjTWzndPsn4iICEQHA0vrIomVpPYA1R8iItI65L32yMLBSHsCg4BJZvYE8DlQeOAQd/dzmtHufGCQmXVy94XAvsDUosyd7n4agJntCfzbzPZ097fj289y97vN7OtERdHgZvRDRESkQbXpFgk9zGwEMMrdR6XZcM70pDS1B6j+EBGRViDF+iOTtUcWBjq+AyyOf969ntsdaG6x8R9gf+Bu4Ejg9gbWgbs/Ff+CTgJ+WnTzM8BGzeyDiIhIS5nj7ieVuxOtQClrD1D9ISIilSOTtUfZBzrcff0SNn8H8Jt4uuhg4AYaKDRiY4GT61l+IDA+/e6JiEily/K0z7wqce0Bqj9ERCTj8l5/lH2go5Tc/Q0zG0D0bcrDCe5S/Nu+yMx+BUwHjk+5eyIiUuHc819oVCLVHyIikmWVUH9kYqDDzAzYFdgE6Fh8u7tftQrNPwBcDAwFejWR3Rp4u+D6We5+9yqsW0RERDKoxLUHqP4QEREpm7IPdJjZmsATwOZE+8TWDS0VHhRsVYqNG4j2GxpvZkMb6cfXiPaP3XMV1iUiIhLA0j4YqSTQArUHqP4QEZHMyn/9UfaBDuCvwBygPzAZ2JHo6OdHA98nOphXs7n7FOCyBm4+wsx2AzoDHwHfKTjiuYiISMnlfepoRpW09gDVHyIikm15rz+yMNDxNeB04NP4urn7J8CfzKwN0Tcq3wht1N271rNsNDA6/nkkMLKR+w8PXaeIiIi0CiWpPUD1h4iISBZkYaCjJzDd3WvNbC6wRsFtz7Nqp3fLtLf23ytxto3VBrW9Wp9ZQfkO3auD8qGWLVhp9+cGLZi7Uo3YqHXvfDkoX33OOkH5UItmd0uc7X1N2Bd4X/5o46D80oUdgvJcEhZvf+W1ibMdz/0yrPFA00/YIijf57oJJeoJ/Gf7I4Py33zl9hL1JPLewY2d7GFla281MSg/ZdymibObPPBMUNu3bHlcUP7o8TcE5bMg79+oZFRPclJ7TBv/Ceet9+PE+Z3WnB7Ufrs2NYmzAzf4KKjt2po2QflO3eYH5T/5aN2g/A0TNkmcvfKzhibr1G/qUdsE5Tt0XRiUn/Jh2GMd8tgjQflpx2ydODtrelOHpFnRunf9Mijf5o83BeW7XjAlKB/io+/sHJRf/54XStSTyKlrnh6Uv+rzsO04xP3bHB2U322HV4LyR968d1D+0fnJ90Y8o1/Y83jptNI9j6WU9/oj7B2mND4C+sY/TwCOKrjtQCDsE7uIiEgr4UCtW2oXSUy1h4iIVKw064+sysKMjoeArwN3AecD95vZFGApsC6t6FsVERERaRVUe4iIiORY2Qc63P0XBT//x8x2AQ4FOgGPuft/0lyfmY0G/uzujxQsO4Oo4FnX3QfFR0e/H/iQ6JRzd7j779Lsh4iICBVwHvssUu0hIiIVrQLqj7IPdBRz9zHAmBKu4nZgGFC4c+Iw4CzgHwXLnnX3A8ysCzDOzB5091dL2C8REak42Z72WSlUe4iISGXJf/2RhWN0tLS7gQPMrAOAmQ0A+gH1HqnI3ecDrwIbtlQHRUREJFdUe4iIiLSgihvocPeZwMvAfvGiYcCdRMdkWYmZ9QJ2IjpYmYiISGoccCy1i2STag8REcmSNOuPrMrcristpG4K6f3x//Wdv3B3M3sNqAX+4u4qNkREJHV530dWvqLaQ0REMiPv9UelDnTcB1xiZtsAndx9bDyNtNCz7n5Ai/dMRERE8ug+VHuIiIi0iLLvumJm34+naNZ32+pm9v201+nu1cBo4Aaib1hERETKIq3z2Of9oGJpUu0hIiKVLu+1R9kHOoB/0vDBttaPby+F24GtgDtK1L6IiEgTDPf0LpKYag8REalg+a89srDrSmPPTi9gbilW6u73Fq7b3ScBg+KfRxN96yIiIiL5o9pDREQkx8oy0GFmBwMHFyz6tZlNL4p1BHYHXmmxjrWw2prkE2pqvCqoba8t7eha7dKwTadmWfJ8zbKwxxoq9LnxmrD+LF3YIXF2wW/WCGw7LL9kUcegfI+rrwnKL/7RyYmzYT0J1+e67Byzb2ltFibLLVcTuA1XdV4clC/laP6SwL63Nu5ketpnnuS19jCgfVW9J2+pV5v6T/TSoDmLOyXOhtQ1EP7a8dbbA4PyC5Ykfz8GGLLawsRZs3ZBbb970E5B+VCdOyXvO8CcM9YPys/8YqPE2c9mrxbU9oA/jwzK1547PCgf+rsyS74du9cGtd22bZ+gfM+OA4Ly6/rmQfl2bevdg69ey2rCxn8v3fiEoPyegbXH6u3Dfq9tq5Jvl2f2Oyao7aqqHkF5fFlYvgQqof4o14yONYAtC65vCKxVlFkCPAqc31KdEhERaWlZnvaZM6o9REREYnmvP8oy0OHu1wLXApjZU8Cp7v52S/bBzKrdvWvRsvOAane/2MxGAl8D5hCd5u1H7v5CS/ZRRERE0pGF2iNet+oPERGREiv7/Gp337Ou0LBIPzPLwrFDAM5y9yHAz4GwOf0iIiIJ1GKpXYAeZjbCzA4s9+PKsozXHqD6Q0RESizvtUfZBzoAzOxbZvYSsAiYDAyOl19rZkeXtXORZ4DkOyeKiIgk4JD2WVfmuPtJ7j6qzA8t81pB7QGqP0REpATSrD/IaO1R9oGO+Fz1DwDvACex4pHQ3wWOL0e/ihwIjC93J0RERGTVtZLaA1R/iIiINEvZBzqAc4GL3P1Y4Jai2yYAYYcPTtdFZjaOqAjKStEjIiK5YdR6ehcyOn00g7Jce4DqDxERKan81x5Z2B91PeCxBm5bBHRvwb4UO8vd7y7j+kVEJOdSPur5HHc/Kc0GcyrLtQeo/hARkRJLsf7IZO2RhRkdk4GtG7htO+D9FuyLiIiI5J9qDxERkRzLwoyO64HfmtnnwH3xMjOzvYGzgd+XaL2dzWxKwfVLSrQeERGRejnR+UOlxZWr9gDVHyIiUmaVUH9kYaDjAqA/cCNQEy97HqgCrnH3y0uxUndvdDaLuw8vxXpFRES+4qnvutLDzEYAo7J29POMKUvtAao/REQkA9KtPzJZe5R9oMPdHfiRmV0C7AP0AmYBT7r7u2XtXImZeeJsvw0+CWp79atK+9R9+aONg/Ide8xLnG3feWFQ20su7hqU73ZRdVA+VCl37O5cwrab4829v5k4u/E9Zwe13b3nhaHdCbLoj6sF5a0q+bj3Qa/OCWp77pnrBuVrl4a9dK+7Tdg2/86TOwble/eZEZQPcdxb1wblZ/5wYFDea6qC8q1AJveTzZo81R6La4z35ybfjndas6bpUIE9tn4lcbb3NW8HtR2q7VHbBOU7dA2rJ4Ys7JA4u/j45FmA9mc+G5TPmi1LlG2Os9c5PSg/Z85ZQfnu3f8UlA/x+Q8GB+Unf9wnKL/dkzcG5V8aekDibI+uYbVE/8H3BeX3vXT/oPwNe70elL/jwS8TZ49aPWwbu2WLbwfll9WGHT3i+xNuCMqXQSZrj7IPdNRx9w+AD8rdDxERkZZUm+6MDgmg2kNERCpV3uuPsg90mNkejdxcC8wFJrr74hbqkoiISItx8l1oZJFqDxERqXR5rz/KPtABjCY6HkodK7oOsMjMrgP+z92D5lyaWQ0wnuixvg0c6+4LzKwa2Bm4OY6uC8yJLzPcfZ/QByIiIlJmmdxPNoNGo9pDREQkDZmsPbIw0LEP0dHPHwYeAKYDfYCDgW8BPwM2A34JVAPnBra/0N2HAJjZrcAPiY9w7u7jgbrbRgIP6rz1IiLSUhxLe+poJveTzSDVHiIiUrFSrj8yWXtkYaDjNOBGdz+vaPkjZnYeMNzdDzSztsBwwouNQs8CiY4CZGZ3xv16OL4+kmiU6p5VWL+IiMgKapMfl1rSo9pDREQqWt7rj7BDvpbG14H/NXDbc8Ce8c/PAH2bu5K4WPkm0VTSJO4Ajojv2x7Ym+ibHxEREWndVHuIiIjkWBYGOmYBBzVw20Hx7RCdWTPsnI2RTmY2DhgDfEI0VTWJ/wB7mVkHoiLlGXcPO0+ZiIhIExxL7SKJqfYQEZGKlvfaIwu7rlwIXG5mA4BRrLyf7I/j3J5A8pO5L/fVfrIh3H2RmY0GvkH07crtzVi3iIhIg5zUT++WyQOCZZBqDxERqVgp1x+ZrD3KPtDh7leY2VTgF8CVRH1aBowDvu3u98XRPwNLWrh7dwAnANsR7aMrIiKSZZk8IFjWqPYQERFJTSZrj7IOdJhZO2AH4CV338HM2hB9ozLd3WsLs+4+owxdfBS4CXjA3Vu60BERkbxz8JwfDCxrVHuIiEjFq4D6o9wzOmqAJ4mmiU6LC4zP01yBu3dNstzdh9eTWQr0SrM/IiIihWozvH9rTqn2EBGRipf3+qOsAx3uXmtm7wFrlrMf5dK9x9zE2fbdFpSwJ+E69/8iKG/tliXO1i5qH9R2m+5Lg/KSnm2f+E/J2p47++ygfPeeFwblO577ZVC+lLpf/ElJ2196WaegfO8+YV9id+k9OyhfSr2unljaFVyX76KgEuSt9mhj0LEqeX61LtVB7bfvNj+wR6XTa9OPg/LWIWxCTM3cLomzbbqq9iiXC6dcVtL2Q+qP0NpjzX++EZYPSofbcfSDJWt7yd86B+Uv2T5svLl337DPIiHaBL7VH/nGP0vTkdj37YaStp9X5Z7RAdG56S8ws/HunvT0ayIiIq2eA57uwUglGdUeIiJSsSqh/sjCQMeviKZojosPDPY50XP/FXffoRwdExERKS3TWVfKQ7WHiIhUsFTrj0zWHlkY6HgzvpSEmdUA44ke69vAse6+wMyqgZ2Bm+PousCc+DLD3fcpamck8KC7312qvoqIiKyiTB75PINUe4iIiKQjk7VH2Qc63P0HJV7FV+eyN7NbgR8Cl8TrHg/U3TYSFRMiItLCcn7Q80xS7SEiIpUu7/VH2Qc6WtizwOAkQTMz4O/AXsBHkPPD0oqISItzSHvXFcke1R4iIpIplVB/ZGKgw8wGAEcDmwAdi29398NTWEdb4JvAfxPe5VBgILAl0UGP3wJ0yFsREZEcUO0hIiKSX2Uf6DCzbYGngclExcYbQA9gADAFeH8VV9HJzMbFPz8LXJ/wfnsAt7t7DTDNzJ5cxX6IiIispLbcHahAqj1ERKTS5b3+KPtAB3ARcA9wHLAUON7dx5rZLsDtQNgJqlf21X6yzZD3XZdERKTM8n56t4xS7SEiIhUt7/VHm3J3gOiAXLexfFCpI4C7Pw/8DvhLebrFM8AwM6sys77AnmXqh4iIiKRrCKo9REREcisLAx0OLHF3B74A1iu4bTKwcVl6BfcC7xGdHu4fRFNcRUREUuMeHQwsrQvxuezN7MByP7aMU+0hIiIVK836g4zWHmXZdcXM9gDGuns10YG2NgSeAl4AfmpmY4AlwNnAB6uyLnfvmmS5uw8vuu7AaauybhERkaakvJ9CJs9lnwWqPURERJZLsf7IZO1RrmN0PAXsDLwMjGD5Nym/BB4F3omvzwcOa/HeNdOi9yfy7kF7JM536py87a4XTGlGj5KbdeomQfluA5cE5WsXJ9/UOp77ZVDbWTPle9slzla1XRbUdt+bxgX2JsycM9YPytcsaZc4u/pV7wa13b1n2C7yc2efXdL2Q7y2z35B+a0fT3pChubxZWGT97r2Cfsb7NBrblA+xJM7h70F7PXC3SXqieRALmuPPp0XcurWExLnzcJK2+4XfxLapcSmHrVNUL7PNouC8r44+XsUwE13H5w4+8N3rglqu9TeO3j3oHzHTmHPZf87XgnKhwjdDhYv7BSU3+DfzwXlQ+qDuXN/GdZ29z8F5UNdt/mJQfkT3rq2RD0BX9g+KN93zS+C8h1XL13t0TOs65JR5Rro+OrIJ+5+c8HPb5vZZsAuRPvLvujuYVu9iIhIK5L389hniGoPERGRWN7rjyycdWUF8ZTSR8vdDxERkVJz8n96t9ZAtYeIiFSSSqg/yjnQ8S0z2zRJ0N1vCmnYonmZt7j7MfH1tsCnwEvufoCZrUl0Tvv+QDtgEnAOUPcNz7rAnPgyAzgBeNDdB4X0Q0RERDJFtYeIiEgFKOdAx28S5hwIKjaI9q8dZGad3H0hsC8wteD23wOPuftlAGY22N3HE51uDjMbSVRc3B1fHxC4fhERkQQs9+exzxjVHiIiIhVQf5Tz9LJ7At0SXLo3s/3/APvHPx8J3F5wW1/gq6N7uvsbzVyHiIjIKqlN8SJNUu0hIiJC/muPcg50LHT3+UkuzWz/DmCYmXUEBgMvFdx2JXC9mT1lZueaWb9VfTAiIiKSeao9REREKkA5BzpKKv6mZADRNyoPF932CLABcC2wKfCamfVp6T6KiEhlc8DdUrtIean2EBGR1iDN+iOrcjvQEXsAuJgVp44C4O6z3P22+KBhrwB7tHTnREREaj29i2SCag8REcm8vNceZRnocPc27v5yC6zqBuD38cG+vmJme5lZ5/jnbsCGwCct0B8REZFS6mFmI8zswHJ3JGtUe4iIiJREJmuPcp51peTcfQpwWT03bQtcYWbLiAZ7rnP3V1q0cyIiIkTTR1M0x91PSrdJCaHaQ0REWoMU649M1h65HOhw9671LBsNjI5/vgi4qJH7Dy+6PgnQeexFRCRV7lCb4f1bJTnVHiIi0lpUQv2Ry4GOcvlwXnsOe3KdxPmfr98hcXa7g8J24x3++CZB+cHdvhmU/2JR2MmE7p17deLs/Oo1gtpePLNHUH6d68P6/u0uBwTlb5n1euLsyWucGtR28mexee7479eD8ktqk+/9dszpGwS13anvzKD8uZcND8r/8e2zg/IdRvwjcbZ9ux2C2p5x4uZB+e6bTwrK33fTd4PyywJ+rwAPTU1+Js4fPx42q3HWwtWD8suuS/66CvC/G8L+vkWy5p15S9nl6WmJ89/tum9Q+//3zb0TZ7f4zxNBba9969igfKhhq50elL/hxysdVqVBM384OqjtXldPDMqH2vj+Z0vafimtc9v4pkMF3JcG5WedGlYTdw6oP7r/OqxWmTs7rPYYt/97QflFNWsG5UMs/F3voHyn384Oyq8XlIbHdjw8KB/yyvdhdU1Q20suXmmcu1Htz6wOykvzaKBDRESkjLJ8DnoRERHJp7zXHxroEBERKaMsn5pNRERE8inv9UfeTy+Lma1lZneY2Qdm9paZPWxmm5jZm2b2DTMbF1+qzWxi/PNN5e63iIiItE6qPURERMor1zM6zMyAe4Eb3X1YvGwIsCaAuz8CPBIvHw2c6e5jytJZERGpOE7+p45WGtUeIiKSdZVQf+R9RseewFJ3/+oYju4+Dpjc1B3N7CUz26Lg+mgz27YkvRQRkYrlnt5FMkG1h4iIZF7ea4+8D3QMAl5t5n3vAA4HMLO+QD93b25bIiIiUhlUe4iIiJRZ3gc6VsVdQN05GQ8H/lXGvoiISE7VYqldpNVT7SEiIi0i77VHro/RAUwADmvOHd19qpnNNLPBwBHAyan2TEREKp4DtRme9inNotpDREQyrRLqj7zP6HgS6GBmJ9YtMLPtgfUS3v8O4Gygh7uPL0H/REREJF9Ue4iIiJRZrgc63N2BQ4F941O8TQDOA6YlbOJuYBjRVFIREZHU6WCk+aLaQ0REWoO81x5533UFd59GfGCvIoOKckPrue/nVMBzJCIi5ZLt/VuleVR7iIhItuW//jDP8jBMK7Pddtv5mDFjyt2NVm/x2J2C8h22ebFEPRFZbu7ssxNnu/e8sIQ9kXIys1fdfbu02uvXYU0/Ya0j0mqOP3zy91T7J9nXuaqXb9Lxm4nz/dv0DGr/VZLvPTN3adJJK5FaXxqU799+m6B8l9ouQfkXnn0vcXbDr3UOaru79w7KT655PSi/cMkXQfnuHQcE5auXfJo4W1O7MKjt3p22aDpUoJaw7aanrR2U713TK3H2HV4Jant72y0of/fkjkH5XmvcHJTfs8N3mw7FZjAvqO1ZbWYE5TvQKSh/bJ9+QflHPku+3cyx+UFtd/WwvtcS9vn7hYXXp/7enmb9kdXaI9e7roiIiGRairuttPT3FmbWxcxeNbMDWnbNIiIiskoqoPbI9UCHma1lZnfE+8i+ZWYPm9kmZvammX3DzMbFl2ozmxj/fFM97Yw2s8yNUomISOvmQG2KlyTM7AYz+8LM3ixavl/8Xvi+mf08QVPnoONIrES1h4iIZF2a9UcS5ag9crsPqJkZcC9wo7sPi5cNAdYEcPdHgEfi5aOBM91d+52IiEjejQSuAL76cG1mVcCVwL7AFOAVM3sAqAL+XHT/44DBwFtA2LzqnFPtISIiUq+RtHDtkduBDmBPYKm7X123wN3HmdmApu5oZp2AfwKbA29D4E5jIiIiCbX0eezd/Zl63gt3AN539w8BzOwO4GB3/zOw0vRQM9sT6EL0PrnQzB5296Rf7OSZag8REWkVWrL+KEftkeeBjkHAq8287ynAAncfbGaDgbHpdUtERGS5lOuM3mZWOENghLuPSHC/tYHJBdenADs2FHb3cwHMbDgwQ4McX1HtISIirUKK9Ucma488D3Ssij2AywHc/Q0ze6PM/REREUliRjOPfF7fOeaarIHcfWQz1iX1U+0hIiKtUSZrjzwPdEwADluF++u8uyIiUlIO1HomzmM/BehfcH0dIOxcoQKqPUREpBXISP1R0tojz2ddeRLoYGYn1i0ws+2B9RLc9xngqPg+g4gOfCIiIpK6lE8v28PMRpjZgYHdeAXY2MzWN7P2wDDggZQfaiVQ7SEiIq1C3muP3A50uLsDhwL7xqd4mwCcR7JRon8AXeNpo2cDL5esoyIiIumZ4+4nufuohgJmdjvwAjDQzKaY2fHuvgw4jeiMIG8Dd7n7hJbpcn6o9hARkQqUydojz7uu4O7TgMPruWlQUW5o0fWFRCNKIiIiJdXSR/F09yMbWP4w8HALdyd3VHuIiEhr0JL1Rzlqj1wPdLS0T8d/wh/XPy1x/usDPkqcnTy7V1Bfpi3oHJRfUhM2uWfnflOC8rMXdkmc3WCtDkFt3zsh+XMO8P1dnwvKT3h346B8h7ZLE2c/re4R1PbqHRcE5Xt3mxuU79I5rP0lS9onzt47cbOgtvddd1JQfub8bkH5R6b2Ccqv1yX57/XEMWcHtf3M3lOD8o9O6x2UP2RAWPtr9JgdlN9guzcTZ8eO3imo7Z3+kLxtgDOP2j8o36VteQ+J4Hw17TMtPcxsBDCqsW9WJD+6tGnLTl2T1whbr7YoqP3D226UOPvJ/C2D2v5sUVVQ/jvrh9UeD0xaPSh/6Xe3TZzdxsLqpiu+9VJQ/pJn9wvKh07RDv2As6gmeXaNjmEvajWBr4EHbfBhUP7sl9cMyndpk/zj0VUbJN9mAP7+/pKgfPfVrwrKz/wiKM5R6yXvz8e1bwW1fUy33YLya3UM2MiAb270blD+/KnJ8/NuC6uf2x42LyifBSnXH5msPTTQISIikh9z3P2kcndCREREKkYmaw8NdIiIiJRRS++6IiIiIpL3+qOiBjrMrAYYX7DoEGAAcD9QuB/Jme7+eH15d59U4m6KiEilcKjVCUVzTbWHiIhkTgXUHxU10AEsdPchhQvMbADwrLsfkCQvIiKSYZncT7bCqfYQEZE8y2TtUWkDHSIiIpnh8SVFmdxPVkRERLIj5fojk7VHpQ10dDKzcfHPH7n7ofHPuxcsB/iOu3/QSF5ERCQVeZ86Kqo9REQke/Jef1TaQEdD00E1fVRERERKQbWHiIhICws97baIiIikyD29C/F+smZ2YJkfloiIiGRY3muPSpvRISIikhlO6qd3y+R+siIiIpIdKdcfmaw9NKMjsruZjSu4HFbuDomIiEiuqfYQEREpkYqa0eHuXetZNhrokTQvIiKSprwfDKzSqfYQEZEsynv9UVEDHaXWd8t1OXfMFYnzU763XeLsptu9EdSX6R+sG5Rv33FxUL5n/8+C8l0vmJI4O/OHA4Pa/vmop4PyofqVtPXW7d2D9kic/fVHfy9hT8Lt+au1gvKdz0++zT+24+FBbe/xxICg/Nf+eHtQvl33BUH5KWM3C8pXT+udOLvFVhOC2r7vp7sG5S+ddllQfslfuwTl/3RmUDyRnNcZUmK1DvOWJs/vvuF7Qe2vs9n7ibOfvTsgqG2zsK2/z4CpQflzxtU7ltSgf+85Lnnbt44NajvUpSVtvXV7YY+DgvJPL7imRD0Jt/dxg0ra/tD+04Pyo6f2TZz182YFtd2u651B+Q/HhD03nTsvDMq/uf/aibOHHxe2jblfHpRf9MfVgvKdfjU7KJ9U3usP7boiIiKSH5k8IJiIiIjkViZrD83oEBERKRMn9amjmTwgmIiIiGRHyvVHJmuPih/oMLNewBPx1bWAGqBu3tdWwOsF8UPcfVLL9U5ERHJt+anZpIKo9hARkbKqgPqj4gc63H0mMATAzM4Dqt394vh6tbsPKVvnREREJHdUe4iIiJRWxQ90iIiIlFOK57EXERERSSTv9YcGOhrXyczGxT9/5O6HlrMzIiKSLyU4Roe0fqo9RESkpCqh/tBAR+MWavqoiIi0Ij3MbAQwyt1Hlbsz0iyqPUREpDXJZO2hgQ4REZEySvkLlUwe+VxERESyJcX6I5O1hwY6REREyijvU0dFREQke/Jef7QpdwdERERERERERNKiGR0F3P28outdy9QVERGpEHk/j700TrWHiIiUQ97rDw10lNE6t40pWdvdStZy6S2a16XcXZCENnngmcTZxReGbZU1c8O2g87nfxaU77DJnKD8sus6JM7u+9LioLYX/6lnWP7csN0gO/a8MCi/UVC6tA4rcfvL5oS+3ixIdf1O/k/vJqU1u3YuoxY+ljh/60Nvlawv3UvWcvOsd+NPgvK1NVUl6omkaednHgjKL/5Lj6D8kpnJt+RuF00Oanu1rT4Iys/84cCg/Nj5E4PyU4/aJnG22z+ODGq7c/c/BeUHBaXhsR0PD8rv+9JdibMLu/4osDdhvnhj48B7vJJ6Hyqh/tCuKyIiIiIiIiKSG5rRISIiUka1eZ87KiIiIpmT9/oj9zM6zOxQM3Mz2zS+PsDM3jSzb5jZuPhSbWYT459vKnefRUSkcniKF+Jz2ZvZgS35GGRFqj1ERCTr8l57VMKMjiOB/wHDgPPqFrr7I8AjAGY2GjjT3Ut30AwREZHSy+S57CuQag8REakUmaw9cj2jw8y6ArsCxxMVG0nv95KZbVFwfbSZbVuCLoqISAVzj85jn9ZFyk+1h4iIZF2a9UdW5XqgAzgE+K+7vwvMMrOkhxa+AzgcwMz6Av3c/dXSdFFERCqXp/pPMuEQVHuIiEim5b/2yPtAx5FEhQPx/0nPi3QX8N3458OBf6XcLxEREckn1R4iIiJllttjdJhZL2AvYJCZOVBFdLyUq5q6r7tPNbOZZjYYOAI4uaSdFRGRiuRke9qnhFHtISIirUEl1B95ntFxGHCTu6/n7gPcvT/wEbBOwvvfAZwN9HD38aXqpIiIVLbaFC9Sdqo9RESkVch77ZHngY4jgXuLlt0D/DLh/e8mOojYXWl2SkRERHJLtYeIiEgG5HbXFXcfWs+yy4HLE2Y/J8fPj4iIZIN7zueOVhDVHiIi0lrkvf7Qm2kZvXPA0MTZXmtND2p75md9gvIdOy4Kyvfe+JOgfO3S5Jtah65dgtoONeeM9YPyPS79qEQ9gQnf3Dsov8V/nihRTyKTh20flF+6pH3i7Ab/nhfanSBLLu4alK+ZH7addfrtjMTZ8d/YN6jtDXdM/jwCdO95YVB+7uyzg/KzTx4dlO+zafK/kU6/C3st+99uhwTld/vffUH5zr//IijPHyws3wQn29M+JftWr+rOId2Sv+bctuUPgtrfe7vkJ375YtqaQW137VodlO+9wZSg/HXHVQXlF80Oex8J8fkPBgfl1/znGyXqSeT+bY4Oyh889pYS9QTG7PWtoPyCxR2C8ns8NycoH9L64gu7BbW9cPLaQfleV08Myof+XvfYOXlt1r37n4LaDq09xh8Y9lh33uuVoHyIYzZYULK2Ada98+WwO9yVbu0BlVF/5HnXFRERERERERGpMJrRISIiUkZ5nzoqIiIi2ZP3+qOiBjrMrAYoPIr5IcBC4HqgP9AOmAScA9wcZ9YF5sSXGe6+Twt1V0REKkDKU0d7mNkIYJS7j0q3aWkO1R4iIpJFKdYfmaw9KmqgA1jo7kMKF5jZNcBj7n5ZfH1wfEq3IfH1kcCD7n53y3ZVREQk2Bx3P6ncnZAVqPYQEZE8y2TtUWkDHfXpCzxad8XdS3v0JxERkZgDtTmfOir1Uu0hIiJlUwn1R6UdjLSTmY2LL3Xnub8SuN7MnjKzc82sXzk7KCIilcVT/CeZpNpDREQyJ++1R6XN6Fhp+qi7P2JmGwD7Ad8EXjOzQe4edg5EERERkZWp9hAREWlhlTajo17uPsvdb3P3Y4BXgD3K3ScREakMtSlepPVQ7SEiIuWU99qj4gc6zGwvM+sc/9wN2BD4pLy9EhGRSuA4tSlepHVQ7SEiIuWUZv2RVZW260p9tgWuMLNlRAM/17n7K2Xuk4iIiOSXag8REZESqqiBDnfvWs+yi4CLGrnP8FL2SUREKpjn/6jnlU61h4iIZE4F1B8VNdBRahNe+5BBXYclzv9grc0TZzu9F7YH1I/evT4of0DXk4PyR07cLCh/xkdvJ84+v2/YZnnvZmGnbT72pM+D8pMO2yko37l7deJsj9U6BrX92bFbBeXbdVoUlO+1yZdB+dqlyX9XU763XVDb1XNX+mzQqC5dw7ZJdwvKv7fTdxNnN91wZlDbyxaEbQfTjtk6KD/98z5B+fXvGhqUtyvfS5yd8M29g9oesO6coPxtW/4gKD/uy+5B+VLI8hHLJfvaGvTqkHwb+mR+l6D21/xn8jPfrhnUcrheXbYNyvdos1ZQ/vZtk78vHN/ttKC27/5F2J5J/90heT0JsFaPsPfvrTYIe2197+DdE2fbtVsa1PZGW34RlF+2qENQ/pMjdgjKf/5F8vfMdm13CWr7izk9g/JfD0rD4pqqoPySBZ0SZ0Pfv1/4eIOg/OEvbhGUb3PhS0H5J3c+LHF27W41QW23VnmvPyr+GB0iIiIiIiIikh8VP9BhZgMLzm8/zszmmtkZ5e6XiIjkn4MORlqBVHuIiEg5pVl/ZFXF77ri7hOBIQBmVgVMBe4tZ59ERKRyZLlIkNJQ7SEiIuWW9/qj4md0FNkb+MDdPzazE83sFTN73czuqTsNnIiIiEiKVHuIiIikTAMdKxoG3B7//G93397dtwLeBo4vX7dERCSfPNV/0iqp9hARkRaW/9qj4nddqWNm7YGDgF/EiwaZ2flAT6Ar8EiZuiYiIjlVt4+sVCbVHiIiUg6VUH9oRsdy3wTGunvduUdHAqe5+5bA74Cw8z+KiIiINE61h4iISAloRsdyR7J86ihAN+BTM2sHHEV0oDAREZH0GNRabbl7IeWj2kNERFpeBdQfGugA4oN97QucXLD418BLwMfAeKLiQ0REJFV5nzoq9VPtISIi5ZT3+kMDHYC7LwB6FS37B/CP8vRIRERE8ky1h4iISOlooENERKRMouOV53vqqIiIiGRLJdQfGuhI0dodjN9v3D5xfmntgsTZl2f0COpLny7bBuV3W6O0U5f267Bj4uz/3p0f1Pajn4Ydq+24fmGb/Zj3Bwblr3mvc+Ls6ZvODWr7wLF3BeW36fy9oPxte00Kyi9blvy5HDVxs6C2t1/j86ZDBbZcY0ZQ/q7ndgvK9+9SnTg7a0avpkMF2rZfFpT/10s7BeX32fDdoHzH664Kyi865aTE2YG9rgxq+7NntgzK13jYMba/P2hCUP6iEhwxIe9TR6W0OlTVslG3hYnzUxdk5/ima3bdJSg/hB2C8uNqXw7KT/oy+evNqHlXBLVdc+dNQflDxz0flF+45JOg/EHdfxyUf2Du34PyIe7f5uig/OKaqqD84a/fGJS/f5NTEme/u3PY72nIY6U9kdKYmT2D8ptM6Zs4G9r3BXvuH5Rv//e7g/JLzjwxKL/H2pclzk55YuugtkPdPeTYkrafVN7rD511RUREJD96mNkIMzuw3B0RERGRipDJ2iOXMzrM7Fzge0ANUAt8CaxGdE76PsBHcfRU4E9AX2ARUA0c5+4TW7rPIiJSmVI+6vkcd08+xUZSo9pDRERakxTrj0zWHrkb6DCznYEDgG3cfbGZ9Qbau/s0MxsKnOnuBxTkAY5y9zFmdhJwEXBQy/dcREQqTbSHbL73ka0Eqj1ERKQ1qYT6I4+7rvQFZrj7YgB3n+Hu0xLe9xlgIwAzm2RmF5jZy/FloxL1V0REJC2ZnD5aAVR7iIhIpcpk7ZHHgY5Hgf5m9q6ZXWVmXwu474FE562vM9fddwCuAC5NsY8iIiIA1Kb4j3j6qLuPKvfjqjCqPUREpFXJe+2Ru4EOd68GtgVOAqYDd5rZ8CbudquZjQN2Bc4sWH57wf87p9tTERGRaPJoWhcpD9UeIiLSuqRXf2RV7o7RAeDuNcBoYLSZjQeOBUY2cpej3H1MfU018LOIiIjIV1R7iIiIZEfuZnSY2UAz27hg0RDg42Y2d0TB/y+sSr9ERESKOdFRz9O6kNH9ZPNOtYeIiLQmadYfZLT2yOOMjq7A382sJ7AMeJ9oKmlzdDCzl4gGhI5Mp3siIiJ1Uj/qeSZP8VYBVHuIiEgrkmr9kcnaI3cDHe7+KrBLA7eNJppWWrhsaCPNXenuv0urbyIiIpI/qj1ERESyJXcDHeX02aI2XDKxQ0nafm7hVSVpt845H7wYlP/Nej8Jyt88K3n/31w0LKjtWW2mBuW/fHydoPxh424Myn/X2iXOLnx7eFDb7kuD8qG+1fVHQfmlnnwk+KgBi4La7tx+cVD+y+m9gvJrd54flB/Yb0ri7NiP1w9qe9cec4PyW/aaHpTvtcaMoPyHo7cLym+4/UOJswuGnR7UdpvnHgvKH7jn00H5ieM3DcqXglNT7i5IKzZ7ifHglI6J8306Zmev5c+rny9p+xt02z8o3z3wfSfEx3duGZRfuOS5EvUk8sDcv5e0/RAHj72lpO3v2+WUoPxxGyavVz7+uH9Q230DakSALbp8Jyj/ZvUdQflHdzii6VDsrLXD3r9P2XF2UP7+uw4Oyn97t/uD8guOTN7/6vvHBrX96feHBOXvnxxyYq7SyXv9oYGOBrj7gHL3QURE8s3T33Wlh5mNAEZl7TRv0jTVHiIi0hJSrj8yWXtooENERCQ/MrmfrIiIiORWJmuP3A50mNm5wPeAGqAW+BJYjeiAYX2Aj+LoqcCfgL7AIqAaOM7dJ7Z0n0VEpPKkPKNDyki1h4iItBZ5rz9yOdBhZjsDBwDbuPtiM+sNtHf3aWY2FDjT3Q8oyEN8PnszOwm4CDio5XsuIiKVxXO/j2ylUO0hIiKtR/7rj+wckSpdfYEZ7r4YwN1nuPu0hPd9BtjIzAaY2bNmNja+1Hs0dRERERFUe4iIiGRGXgc6HgX6m9m7ZnaVmYUc2vZAYDzwBbCvu28DHAFcXoJ+iohIBXOiqaNp/SM+IJiZHVjmh1aJVHuIiEirkGb9QUZrj1zuuuLu1Wa2LbA7sCdwp5n93N1HNnK3W81sITAJ+DHQDrjCzIYQ7Wu7SUk7LSIiFcnT3Uc2kwcEqwSqPUREpDVJsf7IZO2Ry4EOAHevAUYDo81sPHAsMLKRuxzl7mPqrpjZecDnwFZEM1+Sn1RbREREKo5qDxERkWzI5UCHmQ0Eat39vXjREODjwGZ6AFPcvdbMjgWqUuyiiIgI0fcp+T4YWKVQ7SEiIq1H/uuPXA50EJ3G7e9m1hNYBrwPhE6nuQq4x8y+CzwFzE+1hyIiUvGc1HddkfJR7SEiIq1CJdQfuRzocPdXgXqPVO7uo4mmlRYuG1pP7j1gcMGiX6TWQREREckV1R4iIiLZkcuBjnLp1rYNX+vdKXF+r36fJs4eN2HPoL7s03GzoPwaXXcKyre3zkH5qjZdE2d36Lp6UNv3zH8nKP/6+C2C8r/rHPaF3E/7npo4u2OfOUFt79f1R0H57VcPm/X8tz3fDMovW5b8JeTpDzcOanv1HmHPzXvT1gnKr9F1blC+a/fqxNnDL382qO1/nLxvUH7+srDfa5/uYc9lv/U/CcrPu9MTZ2de8kJQ22veGPbczPnhU0H5WyZuGJRPn1PrqU4d7WFmI4BR7j4qzYYlm9qY0bEq+Un09lor7LXvOz1+kjg7vyZsW64yC8qv2SHstW+7qrC/7w/mLkmcPaT7j4PaXqPjdkH5T7udFpRfXBv2zewh64Tl/zUlebbGw9r+Wu+OQfmDN3o/KL9Tr42C8oP6Jm//l88PDGr7V/1PCcpv0+vLoHzNv3oG5R+Zemzi7PTAIwbd+8ZWQfn9N30rKH//6WGfXdq1SXq2b9jzyW2C2n77kLC+d8zETomp1h+ZrD000CEiIlJGOuuKiIiItLS8n3Ul+VcAIiIiIiIiIiIZp4EOwMyqzOw1M3uwYNmPzWyimU0wswvL2T8REckrx6lJ7SKti+oPEREpj/Tqj6zSriuR04G3ge4AZrYncDAw2N0Xm9ka5eyciIjkkwO1gfuzS66o/hARkRZXCfVHxc/oMLN1gP2B6woWnwL8xd0XA7j7F+Xom4iIiOST6g8REZHSqfiBDuBS4GxY4WgsmwC7m9lLZva0mW1flp6JiEjOOU5tahdpVS5F9YeIiJRFevVHVlX0ritmdgDwhbu/amZDC25qC6wG7ARsD9xlZhu4e/JzKIqIiDTFwdM9vay0Aqo/RESkrCqg/qj0GR27AgeZ2STgDmAvM7sFmAL82yMvE33b0rt83RQREUmkh5mNMLMDy90RaZTqDxERyYtM1h4VPdDh7r9w93XcfQAwDHjS3Y8G7gP2AjCzTYD2wIxy9VNERPIpmjia3j/ic9m7+6hyPzZpmOoPEREppzTrDzJae1T0riuNuAG4wczeBJYAx2raqIiIlILn/KjnEkT1h4iItIi81x8a6Ii5+2hgdPzzEuDocvZHRERE8k/1h4iISPpMXxSkZ7vttvMxY8aUuxsiuTD/l32D8l3+9GlQftmIDkH5kB393r1316Cm19vq7aB86GMN9dLQA4LyO45+MHF22Q0dg9p+/dY9g/Ib37NlUJ7z7wiK9/jr5FfdfbuwlTSsqk1H79xhvbSao3rRu6n2T7KvTZsO3rFd8tfLBYsnla4zGfPtHj8Oyl+412uJsxvd+7/Q7kgrMffMdRNnu1/8SVDbSy7uGpRfMHmNoPzLL4SdqGnwZu8kzq514+tBbYd6bMfDg/JLasK+r//6Mfcmzj5686FBbe/+5ICg/LTvPR+U3+yhp1N/b0+z/shq7aEZHSIiImWU96mjIiIikj15rz8q+mCkIiIiIiIiIpIvuR/oMLNDzczNbNP4+gAze9PMvmFm4+JLtZlNjH++qZ42RptZ5qbjiIhIaxcd9zyti2SDag8REcm29OqPrKqEXVeOBP5HdPq28+oWuvsjwCMQFRPAme6uA2yIiEiLccC9ptzdkPSp9hARkcyqhPoj1zM6zKwrsCtwPFGxkfR+nczsDjN7w8zuBDqVqo8iIiKSH6o9REREyi/vMzoOAf7r7u+a2Swz2waYleB+pwAL3H2wmQ0GxpaykyIiUqk89wcDq0CHoNpDREQyLf/1R65ndBBNHa07d+Ad8fUk9gBuAXD3N4A30u+aiIgIOkZH/qj2EBGRzMt77ZHbGR1m1gvYCxhkZg5UEe2OdFXCJrxUfRMRESmRHmY2Ahjl7qPK3ZlKo9pDREQqUCZrj9wOdACHATe5+8l1C8zsaWCdBPd9BjgKeMrMBgGDS9NFERGpaJ76eeznuPtJaTYoQVR7iIhI9qVbf2Sy9sjzritHAvcWLbsH+GWC+/4D6GpmbwBnAy+n3DcRERF0etncUe0hIiKtgE4v22q5+9B6ll0OXN5U1t0XEnCkdBERERHVHiIiItmQ24GO1uDLH22cOLvale+VsCfZUn1Okhm+y3W9YEqJeiLl1OVPn5a0/bYnLS5Z20vu/EZQvuNW84Pyi//UMyjf4Zezg/IbDvwgKD/vrP6Js207dwtqe/6ijkH57j0vDMrPO6c6KM9f/xGWb0IlnMdeSstoQ9s2nRPnZ5y8WVD7va95O7RLmfH4kieC8r3XXVSinkhr0v3iT0rWdvszw95z2ge2X7N90mMfR/p84/3E2UV/6BXUdsdfzwzKD978nbD2e8wLyi/+bLXE2ekLk7+mAnTv/qegvF0fuJfHWk+H5ROohPpDAx0iIiJl45DhaZ8iIiKSR/mvPyp2oCM+MnrdUP9aQA0wPb6+ibt3LsgOB7Zz99NatJMiIiKSG6o9REREWkbFDnS4+0xgCICZnQdUu/vF8fXAucwiIiLNk/JZVyTDVHuIiEhW5L3+qNiBDhERkfLzTB+xXERERPIo//WHBjrq18nMxhVcXx14oEx9ERERkfxT7SEiIpISDXTUb6G7D6m7UrefbNl6IyIiOZbvb1QkMdUeIiLSgvJdf2igQ0REpJxyvo+siIiIZFDO64825e6AiIiIiIiIiEhaNKNDRESkbPJ/MDARERHJmvzXHxroANz9vKLrXYuujwRGtlyPRESkcuS70JD6qfYQEZHyynf9oV1XRERERERERCQ3zN3L3YfcMLPpwMfl7oeIiJTMeu7eJ63GzNq4pTi50ln6qruX/EwdZtYG+APQHRjj7jeWep1SP9UeIiK5l2rtAenWH1mtPbTrSorS3gBFRCTvHKdlv3AwsxuAA4Av3H1QwfL9gMuAKuA6d/9LI80cDKwNzAKmlLC70gTVHiIiEq5l649y1B4a6BAREaksI4ErgJvqFphZFXAlsC9R8fCKmT1AVHj8uej+xwEDgRfc/Rozuxt4ogX6LSIiIq3TSFq49tBAh4iISPk8Ast6p9heRzMbU3B9hLuPKAy4+zNmNqDofjsA77v7hwBm/9/euYfLUpTn/vfCRvdGRQFRiARBUEAQQfFCvIWbUTygoIJ4EEgwahK8Hs2jeCKiOQa8INf4CChCohtjFEQwAiIXhYACG9hcRSAgBJCLBBFBwe/8UTXsXr27Z7pmrVlrZu339zz9rOnqr6q+mZ7V9U5dvtJJwBsj4p9IIzBTkHQb8Pt8+thMOW+MMcaYWWEm9cdYag93dBhjjDFzRES8bq59yDwL+GXl/DbgZX3svwMcKelVwPmjdMwYY4wxM8uY6I+Rag93dBhjjDFGDWmti3cj4iFgv9G5Y4wxxph5zki1h7eXNcYYY8xtwJ9WztcF/nuOfDHGGGPM/Gek2sMdHcYYY4z5GfBcSRtIegLwNuDUOfbJGGOMMfOXkWoPd3QYY8wcI2m9ufbBrDhIWgz8J7CxpNsk7RcRjwL7A2cA1wL/FhFXz6WfxhhjRoe1h5lN5kJ7KGL29s8100PSm4CNgKURccYcu4Okz0TEAWPgx/4RcdQs1PMmCj9/SQtzngBujIiHW+y2AjYEro6IazuW/fSIuKeLbQnD+DJEHbsC34+IR0ZRfqEvW0XEkhGUu15E3NrR9rKIeFF+/e2IeHPHfJ19l3RmRLy2i+24I2nNiLh3hso6kv7rQd83E/UYM8mMk/6w9uiUp5P2yLZFbf4ka49cj/XHMjtrj0JmSn9Ye8wO7uiYJSTtGBFnNaSvBqwVETfW0reIiCsr5/8MbAZcCGwPfC8iPj2gzrUBIuJOSWsBrwKu79pTJmlDYE/gbRGxecP1xx+QXZF0dkRsPyitIV+rsBnSj936XY+I79Tsiz5/SQuAz5D2fL6FNHtqXeB44OMR8YeK7SeAvYBLSZGG/ykiju1T9s7AV4FHSVsr7R4RF/axfxlwDElALAX2i4hrWmyLfCktv5LneGA7UsTkk4Azcq9u3a70Pi2lueFQMo8tGuo4B1gH+BZwUtv/h6S+37GIuKxm31lASFoSEVvVXw+iq++l5VbyLCQFfdoMWNhLj4i/arF/DnA4sA3wR1LP/Qd724Y12L8cOBLYFHgCad/030bEahWbg4HPR8Q9krYG/i2XvQqwd0ScVyuz9D7tUzk9CDiwZn9Cv/KMGWemqz1yWmn7N2+0R7Zr1B+Trj2yfec2fz5oj5xvoP6YDe2R8w1sw0vbtJynk/4YV+2R83XWH6PQHtmus/6w9hhTIsLHLBzArQ1pu5MCrlwOXA28pHLtsprtVcDK+fWqwKUD6ns3cDPwX8DfABeTGqjrSY1BW751gA8APwUeJv3jvaDF9gpgdWCNpqNmuzCn1/OsD1xbsz2idhwJ3N87b/Djsn6fRYvvx+fjdODXwLfzcR/wnQb70s//i8BxwFMqaauRGuXDa7ZXA6vm12sCPxtQ9pXAJvn1y4DzBthfAuwIPBF4K6lRb7Mt8qW0/Fq+VYBdgK+TBNlxM3Cfnt3v6OPL2sD7gAtIgun/Ntick4//BP6Q3/el+fVPGuyXNL1uqf+yptcdP8eBvme7m4Dd2o6WPN8CPg3cCOwDnFn//tbsLwLeQdrRawFJuF484LuzEbCEJDT+Evh/NZultXvwkvz6ecAl071PbffMh4/5cDBN7ZHTOrd/TLD2yPad9UfpszrnGRvtka91bvOZJ9oj5+2rP4a4T0Npj5y3bxvOEG0aHfUHY6o9cr7O+oMRaI9s11l/DHOfutwjH9M75tyB+XSQgqc0Hd8j9RTW7S8H1smvXwpc1/unr3/p6w+gQQ+k/NBZldRgPAisndNXBy5vsP9r4EfAz4F/BLYAbh5QxyP5AXZzw3FTzfb9Ob2e5wpg/5rtbcC/Anvnh9s+wN291w1+PAo80HD8BnhgwHs4rXcP8vk6NDdipZ//DeQZU7X0lYEbammX9jufAV8625f6Mow/NdtVgJ1J+2LfPQP36cyudbfU8wLgX4Df97E5iYoABzYHvtbvc+lwj/5Y+c5Wv88Dv8NdfQfuJf3gOL7h+GpLniX575WV+/WjPj4sJyyAi/rYX1ItP7++sGZzHbCgqSwqImTY+zTsd9eHj3E5GKH2yGklbcjEao9s31l/MOHaI6d3bvOH8GVstUe2H6g/Cu7TtLRHLmNQG965TaOj/mBMtUfOtyT/Hag/GIH2yGnF+qPkPg373fXR/ViAmUleRepFfLCWLpKYqLMgIu4AiIifStoWOE3Suiw/BW4TSb3ppAI2zOdt0+IejbTX8EOSboyIO3M9v5ZULxvgaFIv5Nsj4hKAFrsq10TH6WgRcThwuKT3RsSRA8yfD3wKeB3wkYi4XdKB0T6Na2lXPxpYv3cPMneRemrrlH7+EfnpVUt8rOFz3VBSL8KwaudExC41+2dI+lDbeUQcWrN/Wm0a5pTzmDoFs9SX0vJTwdLrSJGVtwXOJY1A7d5Qdo+u92mtPmU0ImlTYA/gLaQG+STg//TJsklELO2dRMRVkrZssHuhpAdIn+Oi/BqWfWeqUySvGOY7XOj7LdGy5KQPvWnO90vaHLiTNBLaxjmSPpr9iOzb6ZLWAIiI+2r2DylF2b5c0meBO4An1WyOBr6fp5D+QNJhJGG6PekHWxtd75Mxk84otQeUtX+TrD2gTH9MuvaAsjZ/4rUHFOuPkWmP7EtJG17SpnXVH+OqPaBMf4xCe8Bw+sPaY4xwR8fMchHwUNTWjANIur7B/gFJG0ZeIxsRd0j6c+AU0pq0KueQ1l3eTp/gNRUek7RKpPWYb6j4sZDm3Xb+hDT171BJzyStQ1ulQz1FVIVG27rXiHgA+ICkFwP/Kun0Fp9ngnMlnQEsJn2ubyN91nU2LSz3Gkl7R8SJ1URJe5F6iKu8sXb++QFlHws8pc95nfNIoxZN50F6aA/rS2n5PfYlNUjvjm4Bwbrep6f2W1vbInyOz+W+NiK67N19raTjSKN+QfqBsVzgtIhYuUNZj5sX2FYp8V1DlH+MpNWBfyCNED85v25jj/z33bX0vyK9x+fU0vci/W/vD3yQtJf6lLXEEXGkpKuA95AE5oL897ukEeA2Ot0nSb8lrTcHWHVAh5Qx48gotQeU6Y+J1R7Zbrb0xzhoDyhr8+eD9oAy/TFK7QFlbXinNi3X11V/jKv2gDL9MePaA4bWH9YeY4SDkc4gko4GvhERF3S0Px04OCJ+XEtfhRTk6euVtPeTHrDrAN8EFkfE5X3K/ippSthPaunPAjaNiB/2ybturmtP0hTUk5tEgaR9I+Jrg95ntj2inkRaT3ciTI0uLOko0ud4oSQBfwtsExF7tZR9QER8posfLfl3BV6dT8+PiJOHLatS5rNIjezvSGv0AngJsAjYNSJub8m3FkBE3D1dH8YRTSP6dpf7JOleUgPU1LBGdVRB0tciYt8h/FhIWnv+uC/Al6IW1V7SdhHxo/x6g4i4uXJtt6rwkXQbUB8Nqzo+5dowvkvaPCKuqpyvmd/DrRFxac32faT/+1+W1FHgy3NJQrYXRO7Dbf8TLflPjIi9B9h0vU9LpjEqa8ycM0rtkdM7649J1h7ZvrP+mE/aI+e1/mjON6PaI9sP04Z3atOybSf9MW7aI18fmf6YrvbIZfTVH9Ye44U7OmaQITojiuxznmfnPG8jBdlaTIp4/PPplt1S38akyOcHNVw7tSHL41SnGuaH6bmkYEK9huDzwIez7QkV22E+l9cDHyNNOw3gGuCQiPh+i31RgyfpN7ncnu+9f5y+Pa+StiONkIm0ZdrZDTYCPgG8N9utRForeWREfKrBvi7cplAXbjnPxsC7gE1y0rXAMQ3fmyvreWtlN+1a8qEm20qeQyu2RQ/2Ie5T5yj4JbbZvqiB19So51Pqaji/A/gSLSMf9f+/Ut9zntOAj0aaRrkOcBkpWNaGpO/CYRXb/wF+SwoCthj4Vj/xWyoeJP2Y9EPjfFJAuG0ionE0rOU5sx1pXf9yU5qnc5+MmURmQ3vkfAP1xyRrj2H8n2Ttke06649J1h7ZvrP+GKX2KLUfsnOhk/4YN+2R7Tvrj1Fqj2zfWX9Ye4wn7ugYAV07IwbYL46IGwbUsxUpwM8W0TJNrcQXFW4JJ+lu4Je5zIupPShj6rZLq5HWvT6DZeteb4qI+nSyYt8lvZM0rezvSQ9PgK2Bg0mRtI9pKHukPam5R/c9pKjOS4GvRMP2qdn2g8BOwLt6ve5KW2V9CfhBRHyxZr9Pv7obhNs2pBGeY0gNjICtSEHgdouIiyq2p9NninJE3NLg/4H93VkmliTdRBaYLcb1LdtKO0ZKhMx1pJHDtga+vhVYqZB53Je6Xw3npWUX+Z7zXB0Rm+XXB5DWke4t6SnABVUhKWkJ8GJgB9KU0F1Io4OLSYHYflMru1Q8XB4RW1bOW9+/pMtIPx6OY5ngX0x6Lkx5zgwqq6X8ohEtY8aV2dIeOW9f/THJ2qOr/5OuPbJ9Z/0xydoj23fWH6PUHtm+cxs+ZOdCJ/0xbtqj5x8d9ccotUfvOh31h7XHeOKOjhHTpTOixF5pamkvkNL2pDWJiyPilOmULendwEdJ/8SHkNYwXg28AvhsRHylobyVSVt77UmKlH569qXfftovJvW+nk6KeL7+IL87+H4N8MqoBRtSmh73k4hYbo3rED+4q+LhStLU3H7i4ZukQEo/Bl4P/FdEfKDFdgmwY0TcU0tfixTJe1qiSNJ/kEaYzq2lv4bUy/76StqMjMZVyvtATJ0pUDq9s/Q+lSzP+A3wsz6+bFezH7pjpN+ISj4vFUlFvuc8jzfwks4Gjo2Ik+rXWvxbhfQ93hPYISLWaiu7KX+DL/XP8uvA23vnNZG3EmnnhJ1IP1Qu7/dDZYj7VDSiZcwkMNPaI9sMpT8mWXv083/StUe2H5n+GCftkdNKlraOTHvk653b8CE7F7rO6Bgr7dHib6v+GKX2yPad9Ye1x3jijo4RUCoGuthL6jXqbyDtM38ScEpE/HYmfJG0lLQv+iLSnuIbRRpdWR04p/4gaqjnidm/zwGfilp0cxXG3Sj0/domQdHv2hA/uOvi4ZaIeH8f35dGxAvy6wXAT9sevpKuiojNu15TwbTdbP/ziGiKEo6k6yNi44b0opHBNiTdGhHrVc5Le7xL71PJ8oyRNvCS7ieNMog0Qnl+7xJJHK9esV2jLpYH+FLke87zPdL07dtIon2DiLhf0iLSVmubVWxby5e0KCJ+V0srFQ9Nwdx6tImldYEvkqLe71L9XtXsSu+Tp4+aecEotEe2K9Yfk6w9uvo/6doj23TWH5OsPXJayXKRkWmPbN+5DR+yc+F+OuiPcdMeg+qo64/Z0B4530D9Ye0xnnjXlRmkRQy8q00MFNofAHyDtP5s4EOp1BfKt4Tr1fPEXMeepG2fjqA50vUNwBdyA9DrrT96hnx/QNILI+KKWjkvJO0F3kTpdlfPr4iHr2S/+tHbFouIeDTpq1Z+X3htG/pM222g7TOAtA5yOSJNEz0EOETLRrMOBEp2EqHBt0G+1im9TxvEslGVvwTOisoUSeCwwvqr/KKtEWyhGkW+HkF+ynmJ0JgG+5GmcO8A7BER9+f0l5MiqFfZgxbqnRyZO5g6BfPOynmQ1rRW+VhUpi13ISJuA94q6Q3AA31MS+9T6XfSmLFixNoDCvTHJGuPIfyfdO0BZfpjkrVHW1obk6w9oKP+GEPtAWX6Y+TaI9fbRX9Ye4whntExg+Sewm8A3+7YGVFkP2JfLiGNcvxB0rr5n7o3bfLiiHhhQ54TgM2B/yD1uF9Vt2nI02Xda6nvr8j2xzM1yvg+wF5Ri/6e8xT1Std7Xgf1xEp6jGUNuUijVQ/l1xGVAGI12ynFAAsjYspWeyqctivpVySx1lT+7hHxzIY8Qy+RqpVTn9GxWZufLflL71PJ8owdI+KsEfqyXkTc2tW+hFLfZ8Gfl5eIh1GOZAxxn4pGtIwZN6w9ZkZ7lPo/6dqjwX7KJWr6Y5K1R07rrD9GqT1yWuc2fMhZFCNp7609+pZt7TGGuKPDAKAhtoST9EeWNZDVL1Jjg9qQv2gNcZ9yjiKJlr8gRT5fibTG9+je6FBDntL1lEXiYbbQgGm72aZzALGW0axBU5R7UeGXuwQsiogFDba9nuy+EeSH6BgpWZ6xtMXv5NjyAbJKO0aq62C/HRHL7dE+LH18F/DHlh8Ho/Rn6ECtM80wQswYMzdYe1h7ZNuRao+a/UD9MUrtke07648hOxdG0t5be/Qt29pjDPHSFdPjCuBztemdl0fapqlxq6aIWKm0kpbe+ukG3LmBNBXvT0iNY5cgVgdLalxPKWm59ZTTEUOjQN2n7S4XCX0ARUukcvlP6Vp4iW3mIqXpy506RiibIrkb8EzSVNwqzwb+u8GXQ9VnKnW9Y6TiM0DfCP9D8L8a0gSsS7qHTYzSn9IpmBuoz3rvqK31LqTtPrUKMWPMnGHtYe0BI9YeQ9iPUntAmf4o1R49P3vMZHtv7dGOtccY4hkdZgpdp3cOUe5QwVQL6yjZzq5ou6txonTabr+HOkz7wV6ECiPIj9iX04ADIuLKWvrWwIERsXMt/bn0ESYR8YuafWvU85lE0pak4Fu7AzeTplwf1WA3Mn+0LPBZI/XvmKQbgHf2sT9vGr48uymZLMQiYqdhyzbGjAZrD2uPUTOp+qNUe+Q8I9cf1h7L+WLtMYa4o8O0MlPTO3NZI1sT3FJfX99VuJ5ynCidtivpbvoEEJvOg70UlUeQL91ar7OwUv9o849Hrq+klXaM9KYcV6cbwwxMOZb0PJKo3hO4lzQS+uGIaGpoZ8OfIvEwyumjtXq2pIMQM8aMD9Ye48kka4/sT2f9MUrtke07649S7ZGvjaS9t/bo7NeWWHuMBV66YqYwoumdRMS20y1jEIW+/1LSe0nrKV8E/CCXsQhYpSXPWDDEtN21WRZA7O0MCCA2YkojyJ/AMmGyE7AZaU/zNkqiwi/sc21RQ9r6daEBEBGXSFq/IX2UU46vI30mO/dGcyR9sF+GEfvzYKFo/bWktSOvY5e0N/Bm0vaSn5zOD5IWIabZeAYZY4bD2sPaYxYo0R+j1B5Qpj+KtEe+Nqr23tqjBWuP8aR4naOZn0jaUSko2G3Au4DvAxtGxB5RGO16thnS9/1IDde+dFtPObFExGMR8YOI2If0/n4BnJvF1mwzZeu7DvbPj4i9IuLLwFtI+8H3Y23SOtHNgcNJIuueiDivoTH8maS/rhcgaT9SBP06pR0jo+TNpG3UzpF0rKTtKV+rOpP8WtLavRNJe0v6rqQjJK3RYP808taFkl4NHAycCPwPcMw0fbmO9GNj54h4ZaQgeY9Ns0xjzAiw9rD2mEVK9McotQeU6Q9rj3asPUxfvHTFALM/vXMmmWTfZwstH0DsVNJUzMZgbyP0o3Tru6Kt9Wp5+0aFl/RM4GRSo9cTFlsDTwB2jVrUfEmLgR9FxLG19P2A10ZE697vo0LSk4A3kd7ndqRRqJMj4sxZ9uMyYIeIuC+Lh5OA9wJbknZOeEvNvjp9+2jg7oj4ZP3akL7sShpV+TPSaOlJwHERscGwZRpjRsMkt9+T7PtsMS7aI/vSWX+MUntkm876w9qjrx/WHqYv7ugwKySl6yknGRUGEBsnSjtGcp4iYSVpW9LnA3B1RPyoxa6oY2S2yaMXbyWNEm43y3UXiQdJVwFbRsSjkq4D3hUR5/euta1dLvRpLISYMcb0sPaYDGZDe+Q8A/WHtUffuq09TF/c0WFWSMYtSNYoUWEAsUlmNoRV146RFYlS8SDp46R1z/cA6wEvioiQtBFwQkS8Yob9mzMhZowxPaw90iWsPYapw9qjhrWHGYQ7OswKiaSVWRYkawvmPkiWmQFWJGE1TgwjHiS9HFgHODPyVo85mNeTI+Ky2fPeGGNmB2uP+Ym1x9xg7WEG4Y4Os8LTZT2lMaY/Fg/GGNMdaw9jpo+1h+mHOzrMCss4BckyxhhjzPzH2sMYY2YHd3SYFZJJDpJljDHGmMnD2sMYY2YPd3SYFRKvpzTGGGPMbGLtYYwxs4c7OowxxhhjjDHGGDNvWGmuHTDGGGOMMcYYY4yZKdzRYYwxxhhjjDHGmHmDOzqMGTMk7SvpUkm/kfRrSUskHTrXfhljjDFmfmLtYYyZbzhGhzFjhKSPAZ8GPgucAywEXgzsFREbzaVvxhhjjJl/WHsYY+Yj7ugwZoyQdDtwSkT8XS1dMUv/rJIWRsTDs1FXVyQtiojfzbUfxhhjzHzD2qMZaw9jJhsvXTFmvHgacGc9sS40JD1d0gmS7pX0kKRzJW1dswlJ+9fSPinpnsr5vtnupbmM3wEfyde2kPQ9SfdLelDSTyXtWMm7hqQvS7pL0sOSLpT0sn5vTtKTJB0l6frs982Sjpa0Ws0uJH1I0mGS7gaW5vSFkj4r6ZeSHpF0haSdann3lvQTSffl6bfn1D8bY4wxxjzO07D2sPYwZp6xYK4dMMZM4TLgvZJuBU6LiHtb7E4BNgI+DNxDEgjnSNoqIn4xRL2LgS8BBwH3S9oEuAC4HngPcC+wNfCnAJKeCPyQJI4+AvwK+Bvgh5KeGxHLCabMqsDKwMeBu3N5Hwe+BfxFzfYjwPnAO1jWKfvvwEuBA4Ebgd2BUyVtHRGXZ5v1gRPz9ScAbwfOl7R5RNxU9rEYY4wx8x5rj2VYexgzX4gIHz58jMkBbAHcBATwR+Bq4FPAahWb1+Xrr6mkPYnUeH+5khbA/rXyPwncUznfN9u9v2a3GLgNWNTi537A74HnVtIWkBr4zxW83wXAK7IP69V8X1Kz3b7+vnP6+cC3WspfKddxHfCJub6/Pnz48OHDx7gd1h5TfF9Ss7X28OFjQg8vXTFmjIiIK4FNgV2AfwYE/ANwiaQnZ7OXAndHxHmVfL8FTgNeOWTVp9fOtwO+Ge1rU3cALgVulrRAUm922Hmk0ZdWJL1DKZr7g8AfgJ/kS88b4NMOpKm1F/TqzPWeXa1T0qaSTpZ0F/BYrmPjhvKNMcaYFR5rj74+WXsYM6F46YoxY0ZEPAJ8Lx9I2g84jjSScTiwDnBXQ9a7gDWGrLZe3prAHX3snw68nNSQ17mxLZOkXUlTO78EHADcR3o/J5OivPfz6enA2i11PpbLfwpwZs77IeAW4GHS51cv3xhjjDFYe/TxydrDmAnFHR3GjDkR8RVJnwU2yUl3AM9oMH0mqfHu8QhpnWiVNjFSj6p+L0kEtHEfcAlpbWydR/rkeytwcUT8bS9B0ms6+nQfcDvwpj7lbwOsC+wYEddV6nhqnzzGGGOMqWDtMaVOaw9jJhAvXTFmjJC0nIiQtBbwVJaNMlwMPEPSqys2qwJvYNlUTEjrXDet2KxEmhbahbOB3SW1jUScTQpIdmtEXFI7lvYpdxHLi5H/XeDT2sCDDXVeUimfah2S/owUJMwYY4wxNaw9Bvpk7WHMBOIZHcaMF0slfZc0BfJXwLNJ0c0fAk4AiIgzJF0AfFPSR0kjIB8mNbSfq5R1MvB3kpaQgoy9E5iylVofDgJ+RooY/oVcx1bAvRHxVdIU0PcA50r6fC5/TdIa3jsj4ost5Z4FHC3p4yTRtBMp0FcXzgLOAM6SdAgpWNpqwJbAwoj4GHAR8CBwbB6JWpcUBO32jnUYY4wxKxrWHu1Yexgzobijw5jx4lPAG4EjSFM97wQuBPaIiJsrdrsCXwAOI63//CmwXUzd3u0g0jTTfyRFKT8KuAqYsr99ExFxvaRXAgeT1pgCXENa20pEPCxp2+zvQaSpq7/Kfpzap+gvA88B3p/9Pou0BdtFHXwKSbtlHz4ArEeaUno5cGS2uUvSW4HPA98FbiCJor8fVL4xxhizgmLt0e6TtYcxE4oi6kvRjDHGGGOMMcYYYyYTx+gwxhhjjDHGGGPMvMEdHcYYY4wxxhhjjJk3uKPDGGOMMcYYY4wx8wZ3dBhjjDHGGGOMMWbe4I4OY4wxxhhjjDHGzBvc0WGMMcYYY4wxxph5gzs6jDHGGGOMMcYYM29wR4cxxhhjjDHGGGPmDf8fb7RZOa8EVKAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 1080x476.769 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "visualize_interareal_connectivity(M)"
    ]
@@ -582,38 +530,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "15778e9c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prepared simulation in 0.00 seconds.\n",
-      "Rank 0: created area V1 with 0 local nodes\n",
-      "Memory after V1 : 1515.25 MB\n",
-      "Rank 0: created area V2 with 0 local nodes\n",
-      "Memory after V2 : 1541.84 MB\n",
-      "Rank 0: created area VP with 0 local nodes\n",
-      "Memory after VP : 1571.05 MB\n",
-      "Created areas and internal connections in 0.21 seconds.\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "expected str, bytes or os.PathLike object, not NoneType",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [13], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# %%capture captured\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m M\u001b[38;5;241m.\u001b[39msimulation\u001b[38;5;241m.\u001b[39msimulate()\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/simulation.py:300\u001b[0m, in \u001b[0;36mSimulation.simulate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_network_local \u001b[38;5;241m=\u001b[39m t2 \u001b[38;5;241m-\u001b[39m t1\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreated areas and internal connections in \u001b[39m\u001b[38;5;132;01m{0:.2f}\u001b[39;00m\u001b[38;5;124m seconds.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m    298\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_network_local))\n\u001b[0;32m--> 300\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcortico_cortical_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    301\u001b[0m t3 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m    302\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnetwork_memory \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmemory()\n",
-      "File \u001b[0;32m~/MAM2EBRAINS/multiarea_model/simulation.py:224\u001b[0m, in \u001b[0;36mSimulation.cortico_cortical_input\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m replace_non_simulated_areas \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhet_poisson_stat\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    223\u001b[0m     fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnetwork\u001b[38;5;241m.\u001b[39mparams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mconnection_params\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreplace_cc_input_source\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m--> 224\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    225\u001b[0m         non_simulated_cc_input \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m    226\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m replace_non_simulated_areas \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhom_poisson_stat\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
-      "\u001b[0;31mTypeError\u001b[0m: expected str, bytes or os.PathLike object, not NoneType"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# %%capture captured\n",
     "# run the simulation, depending on the model parameter and downscale ratio, the running time varies largely.\n",