diff --git a/examples/AdvancedFeatures/hybrid_continuous_species.ipynb b/examples/AdvancedFeatures/hybrid_continuous_species.ipynb
index 0e32a79ca..72ab4665a 100644
--- a/examples/AdvancedFeatures/hybrid_continuous_species.ipynb
+++ b/examples/AdvancedFeatures/hybrid_continuous_species.ipynb
@@ -26,10 +26,9 @@
    },
    "outputs": [],
    "source": [
-    "import sys, os\n",
+    "import sys, os, numpy\n",
     "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../../')))\n",
-    "import gillespy2\n",
-    "from gillespy2 import TauHybridSolver"
+    "import gillespy2\n"
    ]
   },
   {
@@ -92,7 +91,8 @@
     "            r2 = gillespy2.Reaction(name=\"r2\",reactants={A:1}, products={},\n",
     "                    rate=rate2)\n",
     "            \n",
-    "            self.add_reaction([r1, r2])"
+    "            self.add_reaction([r1, r2])\n",
+    "            self.timespan(numpy.linspace(0, 100, 101))"
    ]
   },
   {
@@ -102,7 +102,7 @@
     "pycharm": {
      "is_executing": false
     },
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [],
    "source": [
@@ -130,18 +130,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 782 ms, sys: 2.18 ms, total: 784 ms\n",
-      "Wall time: 780 ms\n"
+      "CPU times: user 984 ms, sys: 29.6 ms, total: 1.01 s\n",
+      "Wall time: 950 ms\n"
      ]
     }
    ],
    "source": [
-    "%time results = model.run(solver=TauHybridSolver)"
+    "%time results = model.run()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "pycharm": {
      "is_executing": false,
@@ -151,7 +151,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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oDQAAAABAB7NBnbWE1j6PyDhdjQYRETmaTZx/R5vWfVrT9t8x534DAHAehNYAAAAAAHQwQ2tz+eJa09rjmdbH8yq0vjRxxwGT1Uxre8Gky1po7XHLHAAQFkJrAAAAAAA6mO3qplEhyz/zt22sTeuDaSzxKpy26cf7LJRMrWa17/O8AQDhILQGAAAAAKBDmhmLGFua1j63jbVp3TQaRMRoWvdaxGiPB/H3sQMAwkJoDQAAAABAh7RhjrW9sNDnZYQnq6Z10xJGkapp3WcRo92sNoN9AADOg9AaAAAAAIAO5tgPM7S228V+N60zERE5nDWH1tq0XvR4HGuhNU1rAMBACK0BAAAAAOiQNsyxtsdoZLm/bePTPk3rqH/Tej2w9/exAwDCQmgNAAAAAECHtGfTOvW4aX0yXz6G1qZ1sgqt+yxiXH1PZskyWvC5ZQ4ACAuhNQAAAAAAHbKGmdZ209oemeGTcjxIS9M6iTYJrZd/R0Nwn+d5AwDCQmgNAAAAAECLPC8aly+GNCKjz3iQZDXT2n5cLvpY9d/r8zkAAPRBaA0AAAAAQIvUmlNda1pbs599XkZ4PF+G1kct40E0tLYb5C5203qRF1L0mIUNAEAXQmsAAAAAAFrYs5prM60X4TStT1ZN64MeoXWvpvUqzD8wmts+z/QGAISD0BoAAAAAgBb2nOqmUSEifi8jPNGmdY/xIHaD3EUfq9ncznJ/Q3sAQDgIrQEAAAAAaGG3hxcNSxlF/F5GqE3rw4Ga1vpYzdA6zfwN7QEA4SC0BgAAAACghd0ezlpC6xCa1q2hdbQMrYuie661PtZLEyO0pmkNABgAoTUAAAAAAC3s9rA5OmMttPY4tD3WpnXLeJBJXMUE9ugTmz7WaRLJRBvaHof2AIBwEFoDAAAAANDCbg+byxbt0NrnRYSnOtO6pWltZNZrj82mj3WaxDJN4tXH/A3tAQDhILQGAAAAAKCF3R42s1xtXa+Kxn43rVeh9UGPRYwifULr5WOdJJFMkv6zsAEA6EJoDQAAAABAC7s9bAbTGtJqEOzzIsKTHuNBaqF113iQ1fdlGtO0BgAMi9AaAAAAAIAWZaN4FeguHIsYy9Da46b1STkeZNL4d3QRo4jIomPUiY4HmRgzrQmtAQBDILQGAAAAAKCFtqkvTZaX0M7QevVnPi8iLJvWs+YoYKOmdbmIsWpa+/z4AQDhILQGAAAAAKCFtoe1TZ0XIsUq0NXQ+pI2rT1uGlfjQZqb1lEUlfO5u2ZaZ+Uixkim5Uxrfx8/ACAchNYAAAAAALTQMRjmAkMNdBdWC9vr0HquTevmmdYiIpN4vVHuMi8XMcYyWTWt5x7P9AYAhIPQGgAAAACAFrpw8NK0uoTWkSE6QkMD7awj6B1LURRl0/qoI7ReZdb9m9ZxNdOapjUAYAiE1gAAAAAAtCib1pMq7M2t8SAHU79nOs8X+drSyCZ9m9YaUE+YaQ0AGBihNQAAAAAALaqZ1o6mdTkexO+Z1qfz6rgOO0JrnWnd1RpPy5nWsUxWM619ffwAgLAQWgMAAAAA0EIbxZfMpnXe0LT2dDzIcZqJiMgkjmQ2aY8CdD61tsmbaEC9XMTo9+MHAISF0BoAAAAAgBbaKHbOtLZGbqSZn03jcgljR8taRCSOlq3pvjOtJ3EkU5rWAIABEVoDAAAAANBCg9hZEpejM7RpreG1zrtOPV1EqEsYDzuWMIpIuVSxK7TW78skics52CkzrQEAAxg1tF4sFvLKV75SHv/4x8vh4aE84QlPkFe96lVSdNyCBAAAAADArmTG7OZkFehqWK3h9SXPFzGWTeseoXXSM7TW78EsicumdUbTGgAwgMmYX/x1r3udvOlNb5K3vvWtcvPNN8vdd98tP/7jPy7XXXedvOQlLxnz0AAAAAAAEBGzURxJEkeSLooy0M3KRYzVTOeiKCRajdjwRdm07jEexA7mm5jfF51pnTLTGgAwgFFD6w9+8IPyAz/wA/LMZz5TREQe97jHydvf/nb50Ic+NOZhAQAAAABQSo2m9XIMRl6G1rqs8MAIg9NFIbOJX6H18Rma1n0XMU6SuFze6OtMbwBAWEYNrZ/61KfKm9/8ZvnkJz8pX/d1Xycf+chH5AMf+IC84Q1vcP79q1evytWrV8vfX7lyZVeHCgAAPJLnhbztz/9OPvfPJ+XH/vUTr5fv+Lrre33+l69m8vYP/b084+avlhsffbSloxzWez/+ecmLQr7n5q9u/Dtf/PJc/q+/vFd+4L//7+T6ay/t8OiG8YFP/aN88Xgu3/8tl8c+FACo0ZEX0yQqZ1ovCmumtRFaZ3kuMw9WSD3wpavyf/zFPXI8z+RvHviyiGzYtO4YdVKOTYkjmZbtbEJrAMD5jRpav/zlL5crV67ITTfdJEmSyGKxkFe/+tXy3Oc+1/n3X/va18ov/dIv7fgoAQCAb/7y7x+UX/z9j9c+9p/+38/KX//iM3p9/rv/+h/kf333f5VPfv5L8r/922/ZxiEOap7l8lN3/aVIIfLRX/yeWjBiuus/3yO//P98Uh48nsvPPuOmHR/l+b3orr+UK6ep/I//6ivl0Y+YjX04AFDSkReTuGoUL+yZ1pMqpPZlGeGdH/yM/Pqf/E3tY4+5pvtNzSTq2bTW70sSy2Q109qXxw4ACNuoofU73vEO+e3f/m2566675Oabb5YPf/jD8tKXvlQuX74sz3/+89f+/u233y4ve9nLyt9fuXJFbrzxxl0eMgAA8MBDJ6mIiHzVtZfk+550g9z5wb+TL51mki7ycqZmmyurz//y1cVWj3Mo6SKX+ep263SRN4bW+n354pfnOzu2oWSLvDz+43lGaA3AK2nZtI4ljupLCrVZfMlsWnuyjPCfHl6eD77tcY+WJ3/No2SaxPKDtzy28/P6zrQ2G+ga5vu6iBIAEJZRQ+uf/dmflZe//OXywz/8wyIi8qQnPUnuueceee1rX+sMrS9duiSXLoV3qysAABiWtri+5tFHcvv33SR3fvDvRGS5ZKpPaK2f39Ug84V5nG13XevjOpmHEcabdEGYSPtjBIAx1MLZuB5aaz49iZdLGhd50Rn27oo+t37Pzf9C/pd//S97f14507oztK5mfc80tOZJHAAwgFGHbB0fH0sc1w8hSRLJOckBAIAW1eKnSGZJXF5c9w1r9fPDCa2rXy9ajlkflxkAh8L82bU9RgAYg74pOEmi8pxjL2JM4kimqxEZc0+WEeryxaPZZn21vk3r8nwcV2H+3JOWOQAgbKM2rZ/97GfLq1/9avmar/kaufnmm+Wv/uqv5A1veIO84AUvGPOwAACA57TFNU1iiaJIDqeJPHw16x1aZ2VovbVDHFRhNq1bAl1tvB2H3rQmtAbgGXM8iB3o6n+TKJJpHMup5N40rU9Xz62Hs836anYw3yTN9U3kmPEgAIBBjRpa/+qv/qq88pWvlJ/6qZ+SL3zhC3L58mX5iZ/4CfmFX/iFMQ8LAAB4LjVuRxYROZytQuueDWNdHBVKNmpmBm23amt4cBpi07o2HiSQHwyAvWGOwVhrWudVC1uXEfoy01rfzD2cnq1p3RVa6/dllsRly9yXxw4ACNuoofW1114rb3zjG+WNb3zjmIcBAAACoxfJeivy4Wr5Vd+GsV5QF4Gk1mbzuG10RshN62PGgwDwWNkojtfHg+jdP3FULSNMPWkb63Pr4cy9wLdJossmO56PzbEp+kZyyhuPAIABjDrTGgAA4CzM27RFqtC6b8M46EWMLYcc8kzr0zmLGAH4qwpn4yrQLZvWy78ziaNyGWHqSdu4HA8y3TC0LoP59seRmgsqV03r1JN53gCAsBFaAwCA4JiLGEWqBlnfhnEa3Ezr6tet40FWoUrf2d4+MX92obyZAGB/6B06M3MRY2E1rWNjPIgn775VixjPGlq3/72sXMQYyzRezbQO5eQKAPAaoTUAAAiOXhDbTeu+DWMdoxHKZXXedxFjHm7TmkWMAHxWa1pbLWSdBDKJo3JslS/jQfS59WBbTWs9H0/iqmntScscABA2QmsAABCczLgdWaRqkJ3Ms16fr7NJw5lpXf26bSlWyDOtzXZ41+IvANi1zDnTWlb/rZrW+mZq5ktovaOm9TSu5nn78tgBAGEjtAYAAMEpG2+rW5EPytB6s6Z1KI1ecyRI2zFru22e5cEFvzStAfhMn19nE0fT2phpXbaNPRgPki1yma8ObuOZ1lF303qRF+WbqpMklplno1EAAGEjtAYAAMGxZ1ofleNB+l0olzOtA7mu3nQRo0j/pZS+qIfWIx4IADiYb5Y2Na2TqGpa+7CM8NQ4hsNNm9ZJfdmki3nOmSRR+UbynKY1AGAAhNYAACA4azOtNx0PUs60DuPCuvd4EOPPQhsRcsx4EAAey4w3S7WFnJVN6+VzVhJHXi0jPF6dE6NI5NJks0v/6jH2O+dM42qmdcZMawDAAAitAQBAcFJrpnUZWvddxLgKGjzIFHrpu4jRXPwVWtP6lPEgADymz6+zpApn9bnKDK19WkZ4Ol8ew9E0kWgVQvelCyVbl/8aj3Ga+DfPGwAQNkJrAAAQnMyaaa2zOvu2i/XzQ1nEaB5n20gTM0AIr2ldteRDGdsCYH+YY6libSGvziWLwgyt/Qluj9Pl8+qmo0FElkslRdqb1uYbpUkclUG3D/O8AQDhI7QGAADBWWtaTzdrWutiqkAy6/p4kB6LGEX6fy98cTKvjr3tMQLAGDS8ncTxWgt5sahC65lHTWtdTnyw4RJGEaNp3WOm9TSJJIoima5GkPjw2AEA4SO0BgAAwSkXYq0abUflTOu+TWsdDxJGOHqW8SDHPed7++IkNZrWgfxcAOwPDWJnk6hcxKhBdq1pvboDKPVg/pSeE4/O0bRuy5/tu57Ked4etMwBAOEjtAYAAMHRmdQ6P/Ngw6a1Bg0eZAq9mHdat7XeMuMvhjbT2nzDoe0xAsAYzIA2sVrIrpnWPiwj1HPi4Tma1ouWUR9pXr/ryad53gCA8BFaAwCA4Gh4oBfKR7OJiPSf45wGNtO63rRu/ntmu80ctxGCk9oixhEPBAAczJnWa01rDa0jv5YR6jnxTDOtV3O728Y1VefiVdM66Z6DDQBAX4TWAAAgOGV4oIsYZ8v/9m0XawMulMtqMzNY9JgvKhLgeBDjDYe2xwgAY6jmN1dNa32uysymtUfLCIdoWrcvYqyCfBHxKrAHAISP0BoAAATHvlA+nC6b1n1nWqd7MNM6uPEgtaZ1GD8XAPvDbBXboXVuhNblMsJs/OcxPQ/o3UibsEeguNhvIOt/54wHAQAMgNAaAAAER5tfeiuy3vq86XgQD4pwvSx6htbmTOu+3wtfmMdLaA3AN9qcnsSRJNboDHMR47RsKI9/gtHn1YMzNK3tESgu9rl46tE8bwBA+AitAQBAcMzbtEWqW597jwfJw2pam7O3m0ZnFEVRa1r3XUrpC/Nnx3gQAL5Jjaa13uWzWLgWMca1vz+mk3Km9eaX/Zs0rfVcPGE8CABgQITWAAAgOHpBrLciH23YtA7tgtrMDJpydjvo7TsqxRfmzy6Q9xIA7ImiKMrn2GkSlUsK1xYxxlEZaPvQNj4ZYDxIa9Naz8UaWns0zxsAED5CawAAEJyq3bW8QNZbn0/SRa2V3GQe2kzrvLtpbQcLITWti6KoHS9NawA+MVvTkyQuw9m8KKQoivKNxSSKZBpr03r84PbkPONBouoxNrHPxbOJPy1zAED4CK0BAEBwUqvdpU1rEZHTtDso0HZYKNmoeZyLhgDBXnwVUtP6apbX2tVNjxEAxmAG0NMkkthoIZtvsk3iuByVkXpwgtE7WMxzZF9J2RjvXv6rIf7EWFDZ5w1kAADaEFoDAIDg6Exqu2kt0q9hHPJM66YgwA4WjgNqWtsBO2EHAJ+Yz69Ts2mdF7W7XOJYvBoPorsCDs/RtG57E7E6F9dnWovQtgYAnB+hNQAACE5mLMQSWc7evLS6LbkrtK4tLAzkmrrWtG7IQeyA5DSgprX9M/Mg6wGAkjmjeRLXm9bmm5/LpnV3Q3lX9Ln18CxNa6M13cQ+F+tjF6kCbQAAzorQGgAABEcDBG27iVQX5SfzrPVzzQvwUJrW5nE2td7sW9H7LvEm9DIAACAASURBVKX0gX2sjAcB4BMdDzKJI4miqDYGY61pHfs0HmR5PjxT07pHaF1+X1Zh9ZSmNQBgQITWAAAgOGlWb3eJVBflJ/P2dpd5Ie1BptBL3mM8SJpZM60DGg9ymjIeBIC/1u7uiapAN7dnWusywmz8pvHJasfDWWZaT3qF1jrTOq59zvLPxn/8AICwEVoDAIDg6G3Hk2S9aX3c0bQ2b/MOpWldW1LYECDYt2KHtIhxrWkdyrsJAPaC3ShOViHtorCa1pHItBwdMn5oe3KOpnXcZzyItV/CbKH7MB4FABA2QmsAABCcdNHStO5oGJsX0oFk1rVwvSk/sG/FDqlpbR8rmTUAn2gwXe1RWH58saia1omODln9oQ/jMfS59eAsM62j/k1r81yswT5NawDAeRFaAwCA4OjSwWlcvZQ5Kmdad4XW1YV0KGMozMwgb2pahxxaW+34pscIAGOYZ/U9Cq6mtYa85SJGL5rWy/PAWcaDlDOtW86T2WL9ric9L2c8jwMAzonQGgAABKeco2lcKB/0bFrPF+Z4kC0c3BaYTbfmRYzhjgexf2YsYgTgk8amdV6Uz88a8paLGH1oWq/OA9texGi+gawzvTOa1gCAcyK0BgAAwUkdM62PypnWG4wHkfFDhT6K2niQ9kWM11yaiMgyCA6lSW4vzwxl1jiA/VDe3WPPtHaE1lNPxmMURSHHqzcED8/TtO6ziNE4F2sbfU5oDQA4J0JrAAAQlEVelLOozXaXNslOu2Za5+E1rXuNB1l9/JEHy9B6kRfBhAb28kzGgwDwybwcg7E850yMQFfvDKlCa20aj/s8djXLy3PleZrWbWM+qkWMRtPak8cPAAgfoTUAAAiK2V7T25BFRA5nq4ZxR9M6rS1iDOOiut8ixuX35dqDafmx03kYobX9RgOZNQCfZNbCwdgMre3xIJ40rc3n1bOE1hrMt72JWH1fjKa1RzO9AQBhI7QGAABBMVtfelEtUl2UH3c1rRfdAbBvzNC66VZtfVyHs6T8vhynmfPv+sYe6dJ2OzoA7FrVKNa51VULuWmm9diLCPV5dZbEZUN8E3HU3bSuxoNU/75+b3yY6Q0ACBuhNQAACIrObhap35J8OFv+uqtpbY7MCKVpbR5m40xrY+aqzi8NZRmjvYiRmdYAfDLPVuHsKpBNVoFuXhih9epjs4kfTWt9Xj2Ynu2SXxvTbc/H1SLG6g1kPS+P/fgBAOEjtAYAAEHRJYxRVDXbRESOeo4HyRYhzrTusYgx11AlLlvndhjsK/tnRmgNwCf27OakT9N65KaxPq/quXFT2rRuu/OFmdYAgG0itAYAAEGxZ4uqg55BrXmrcyjhqJkZNJXXsnJRWLhN61VG0vgYAWAM9nknMeY924sYfZlprc+rej7YlIbvbaG1czyIJ48fABA+QmsAABCUMjwwWtYiIkerC3N7PrItrY0HGfjgtqRP09oMVUJtWj9i1QgMZWwLgP0wN94UFDGb1vla07psGnsy0/osSxhFRFaZdXvTelGf9S0iMvVkpjcAIHyE1gAAIChVeFB/GaMX5qcbLGIMJRw1jzNvCALSfH2mdVeA7wsN1x9xaXncLGIE4JOmpvXCOR7Ej6axvhm4k6a1OdPak5neAIDwEVoDAICgZPl6s0tEjKA2a/38NMiZ1tWvF00zrbMqzNfWeVeA74uyaX1p2bRueowAMAb7vOMMraN603rs0Faf/4/OGFrr+8Jtz8ep401kDbtTZloDAM6J0BoAAAQlK5td7qb1SdoeFKQBzrRe1I7Z/Xf0VuxpHJXfi1Ca1nqc117S8SBjHg0A1M31TcHVeSeJmpvWviwi1OfVgzOOB0l6LJTU886stohxNTqFpjUA4JwIrQEAQFC02aW3IKvDnu3iLMCZ1r3GgxgLsQ5Xs6FDWcR4mlpN61Aq8AD2goaz5Uzr1X8XhWM8iIa2eTHqCKpyEeNZQ+tVMN/25m5qzfoWMZrWPI8DAM5pMvYBAAAAbKJqFLub1l3jQWozrSWMi2rz2r95EWN1+3ocLb83oSxi1Ebg0YzxIAD8o8+v2ijWQDdbOJrWxrkpy4u1UVa7crI6F559PEgVvjfJjDdL1YSmNQBgIDStAQBAUKrZze6mdVe7eB7kTOvqQBtnWpehdVyNSgmkaa3h+jWrRYyhLMgEsB/mi3rTWhcP5kVRhrplaG3cBTTmXGt9Xj37eJDVY2xdxLg67xiLGGeezPQGAISPpjUAAAiK3nJsz7TWNllXuzirhdZhhKO1pnXTeBDj+zKbhNW0PmE8CACP6XlDzzux0ULW80g5HsQ4N425jLC6g2V7TWs970wdTWsWMQIAzoumNQAACIo5BsOk7eJ0UbQ2vMwL8EAy6/pM66ZFjMb3JaRFjIu8KJecXXOwDK3JrAH4pFw4uHpDcGK0kMumdaSLGKtz05gjMk7PO9O6R9M6c8209mQRJQAgfITWAAAgKNreMptdItV4EJH2hrHd/gphFEVtPEjnIsaobNZ1LaX0gfmzumY107otJAGAXdM31jSsrjWtrSWNURT1ailvm75peXjGpvVkg5nW5vl4Wn4e40EAAOdDaA0AAIKiF8L2TOtZEouO1TxtaRjbzbcQ8tFeixjzaqb1wazfUkof6DFGURWusIgRgE+q805z0zqOjLZxrCMyRpxpfc7QWoP5tufjdFEP80WqAJvxIACA8yK0BgAAQTEXDpqiqBqL0d60tkNr/y+s89p4kIamdVY13o7K74P/TbfT+fIYD6dJdTu6/z8SAHtEG8Wz1Zuliatp7VxGON6TmZ4HzzrTWh9P246B1ArzzV+ziBEAcF6E1gAAICjlGIw4Wvuzw9V4ibZZzql1AR5CaG0eYlMOUIYHcVQ269oa5744TpdN68NpUjYVGQ8CwCfV+KXl5XP1BpvRtI7Nuc6rUNuHpvUZZ1rr8/EiLxrHaFVhvjEexIPHDgC4GAitAQBAUFwzNNXhbPmxtqa1fSEdQGZdC3G7woNJEpehtQbCPtNg5WCalKFPCG8kANgf9hgMc96zjs+YxOvLCH1oWh+ccxGjSPPdL+YuBTWJV4+dNx8BAOdEaA0AAIJizm62HU2XTeuTtqb12iLGAQ9uSxY9FjFW3xdjTEoATWvzFvYk6r4dHQB2TZ9fZ5PlecdsIS9WgbbZtPZhGaE+/x+t7kDalBla9znvKB9a5gCAi4HQGgAABGWeuRcxiki5gLA9tA5xpnX166alWKnRQA8qtDaWhWlGEsLPBMD+qMZSxbX/LvJC9H3Q2jLCyfhznfUNwbOOB+kTWqd6Po6rWMGHed4AgIuB0BoAAAQly+vhgUkXEB63jgexmtYDHtu2mCNBmvJc8/Z1XbzVNibFF2awErOIEYCHyufX1ZulevpZ5IUsVm1jvVNEpAqwxwxuj403BM/CDOEb3yxdPVlrSC9SfY9YxAgAOC9CawAAEBS95XjqaFr3WUCY5iE2rXuMBzGa1jrDtG0hpS+Oa01rxoMA8E/1/KozrY2m9eqUYjaTdXyV/SbpLpVvCJ4xtI6NEH7R8DjK87FjnveYjx0AcDEQWgMAgKCkrYsYNaxtXkC41rQOoAxmZrhNIbvZBNSm9dUsry1x9NGpOdN69SMN4Y0EAPsjXdR3KZRN68JoWsfrc53tN0l3ZZEX5SitozOOB+lqWi/yojw3TYzzsQ/zvAEAFwOhNQAACIpeCLtmWpeznNPmi2X7QjqEgNQ8xqbjNcemmM2608zvtrU2rQ+mVdM6hJ8JgP1RjV+qz7QuiuqNVHMRo/75WG1jczTUmZvWxuNxBdDm+A/zfDxlpjUAYCCE1gAAICitTetp9yzneRbiTOvq140LsVYBwmwSycGkCil8HxGiixiPGA8CwFP6pqCOBzHnV8+NfQKqWkY4TttYn1ejSOTS5OyX/PqYXKXpzHieniXMtAYADI/QGgAABCV1BASqXEDYNh4kxKZ1bjat3X9Hw/xJHEscR3IwXb7MO/E9tDYWMSYsYgTgIfvN0sRoFusYDnMG9NjBrT7vH04TiaL1c2Vf2rZ2jQfJzKa1Z/O8AQAXA6E1AAAIil4ITxxN64MeTWv7QjqI0Nqcad24iLE+NqVP69wHZriiuYfvc7gB7JfUen6tNa2z9TdSx15GaL4ZeB76mFyLGM3xH7V53vG487wBABcHoTUAAAiKNqVnjpnWR+Uixuag1m6+BZBZbzTTWltuR7OJiATUtJ5NqvEgIfxQAOyNzFrEaIa0GlqbHxt7GaEuIz7rPGuVtDwn62ObJlGtzU3TGgAwFEJrAAAQFJ1J7Wpa6wX6aUu7OMTQujAOsikHSK1QRceDhDLT+nAaMx4EgJfWxoPE6zOtE8eIjHngTWsdg7JwLWLMqpFUpunI87wBABcHoTUAAAiKtrtcM631Ar0tqM3yizkexJ71rU3rtgDfBxquHBlNa8aDAPCJ/fxqnn5cTWsdI5KNPNP6aKimteNhpEbT2jT2PG8AwMVBaA0AAIKSWY0302G5iLGtaR1eaL3oMx7E+r6EMtNab2M/mCXl0q8QfiYA9oc9fimKojKkvuoaD+LJTOuD8zatW8acNJ2LNcS23yAGAGBThNYAACAo9hgMkwa1be1iu/kWQj5aGw/S1bTWRYw95nv74CRdHvfRNDFafQH8UADsjeq8UwXTGuiW40Eif5YRlmOXztu01jcSXU1r65yjdFwIM60BAOc1GfsAAAAANqHtLftCWaRfUGu3v0IIrc3AoHMRYxxW0/rEWBimP1Ga1gB84nqzVEPqebZ8jk0SczyIH03rc48HaWta5+6Z1owHAQAMhaY1AAAIiqvxpvoEtTp/VIUQkOa18SDuv1N+XyY601pHpWTbPbhzMm9jj1nECMBDGj6bb5Zqm7qcaW00rWcjB7fatB5qPIjrPKmPbTapRwozFjECAAZCaA0AAIJSLcRafxmjywfbZlrbjbEwQuvq167RGUVRlLO69ftyUIbWfgcHenxHs4RFjAC8UxSFs1WszepyPEi83rS2dyjsyvFQixi1ae14HPZySjV2yxwAcHEQWgMAgKBUy5/O1rS2L6RDyEfNmdaFI2Q3g2z9vuj34jj1vGmt40Gmieid9yG8kQBgP5jB88w5HsQVWmvYO86bhrrX4fC8TWvdM+B4Tq7a59Z4kJHneQMALg5CawAAEJRUZze7FjHOqtDaFe6KuG5Z9j8gNUNcZ3hghNYaIGjD7tTjRYxFUdRmr8YtAQkAjMG8O2fiWsSYrTeOdbeAvUNhV47LRYznW2Glj9F1h49+X+w3kKc0rQEAAyG0BgAAQUk1IGgJrYtC5GrmbnnZt2uH0LSujwdZ/3MziNcA4SCARYxXs7x8bAe18SAjHhQAGMxzRltoHZuh9er8NB9rpvVQTeuW0Hqe6cgUO7TW5Y1F45vHAAD0QWgNAACCUra74ubxICJV06zp81UIoyjyjvEgZqiiDT9tWjd9H3xwagTqy/EgzUu/AGAMtTcFzZnWcX2m9cSj8SDmHSznMenVtLbGgxi/H2umNwDgYiC0BgAAQWmaoymyDBFmk+XHXQ1jc2HhqtQbRKu3qDWtXbNFV02/qGr7aYB/6nHTWgP1aRLJNInLn4nrMW7DPzx0Ii/67b+Uu//uizv5egDCo+ecJI5qbWoNrfWuHr1TRMRoG48U2uoy4vM2reO20LrcL1E/F5vjQuw3iQHgPP7Dez8lv/yeT4x9GNih8w25AgAA2DFd7jRxLGIUETmYxDLPcmdYa154z5JYrma5FIHNtHa1kHXOtxnkHwbQtLZvYa+a1rv5+n/0sfvl3X/9DzJNIrnlcY/ezRcFEBQNXhPr7p61mdbGOWmyamSnI82fupotn1svTc/XUSsXMbrGgyzcM60nMU1rAMO7mi3kjX/8SSkKkZ/4zn8p1x5Mxz4k7ABNawAAEBRtd80cTWsRI/h03s5shNarRnYIkyjMwMCVgWjT2hyZchjATOuyDbgK2DUg2dV4EG1INs0/BwB9OkoiK7SO6uNBzKa1PhWPNepIzxlx5H5zt69yprXjcejz95G17NEM913nYQA4i3RRlM/Hc1637Q1CawAAEBSdL9rUtE5WLa+spRkmInJpFVq3hQr3P3Qq/+W+h858rEPJO8aD6PdkOllvWp8E1LSOWlp926BLPdOR5s4C8J+eI+w1ChrO6ilk4ph3PVZoq1M57Hb4ptoWMerz94E1gsT8kq7zMACcRWoE1dzFsT8IrQEAQFD0haoZEJi0gN02g1Okamq3FeF+7C0fku//tT+TL3zp9IxHO4yiazyI43uiC7jCaFovm3q7XsSot+5z8QOgiZ5K7Nby+riQ6tfxyEtlq6B9e6H18dy97DGKIpbqAhhcmpuhNWWDfUFoDQAAgpI1zNFUGtx2LSxMku6L6vuvnMoiL+QfvzQ/1zGfV22mdetCrOp7ou03n5vWx+WysOXPrLylfkftPP3fA8vCADTR5187/51YoXV9PIiGvds9tiaLhnb4pjR8djWmdW/E4Wx92WPb5wHAWZjFE55b9gehNQAACIq2Y6cNM621bOyawWkuLIyke+mfBt9jt8XMY3Qdr2s5pc4Z9Tm01tBDjzWOun8mQ8poWgPooHe6xHZIbf3efP7VedfFWE3r1XPbeceDTFrGnBzPMxFZHw8iYuwnIFgCMJBaaE3Tem8QWgMAgKB0zbRua1rrPLxpHJUNtLZQQf+NsRsdZmjuCuPLprUxHiSERYzatNbQo23p1zbo/5a4+AHQRJ8e7FEbbU1r/eWunstsTSNNNqWf7zoHnsyX3xh7PIhI9b3Z1X4CABefuZeGssH+ILQGAADBWOTV5vBpw0xrzRGc40HyamGhXoy3vezVf2PsC28z93A118pFjMn6IsYsL7zdsn6S1meixjtu5+n3jYsfAE2a5kOvh9jrixjHOnfo17Xb4JuatIzROrUW6ZpixoMAGFjGTOu9RGgNAACCYb5I7Wpau8PdamGh5g1tAakvoXXeuYhxvX1uBgm+tq3t0EMzn12NY9GGOhc/AJrkDfOh7XOQ+T6qhtZjTZbSY04Galq7FzEux4O4ZlpPWMQIYGD1mda8btsXhNYAACAYZmureaZ1c8PLXFgY9ZifrLd2+xRauxdMVrO61WwSl8GBr3Ot7dDDnGm9i1mw+iYGbUAATfSpaJOmddQS9u5CU9C+qbYxHyd9mtbcxQJgICnjQfYSoTUAAAiGOXu4KbSetMxFNhcWds20zo1RJOOH1tWvXYdbjj2xEgrf51rrTFQNrc1W4C6+5fp9Y6Y1gCaNTWvrA+YpSZ/LxpppPdR4kLgttJ7XxzuZaFoDGJpZMOANsf1BaA0AAIKhS1iiqLr92lZeZDte0FaLGLtnWi86lh/uUtFxLGnZIK+/tDtYhQm+Nq1P0lXTuhwPYobWu2haM9MaQDvNSaLIDqnt31fPv30W/W5TUzt8U5OWO5dam9YtCxwB4CzSjJnW+4jQGgAABKMc79GwhFGkvWmtF9DTJDbGg7gvqs1m2WLk2Xn1Y+k301qkasBpOOwbu6lnZkC7aLenzLQG0KFsWlunnbXQ2giI2xrKu6Dnv4YbknrTx+ja/XC8ev4+cDWtk3EfP4CLJzWeT3jdtj8IrQEAQDCq2c3N7bGkZZaoGe7qv9B0TV0Pis9wsAPqHA/S0LQux4PM/XxxX4Yeq+M0Q6BdFBR1LAhtQABNinI8SPMMa5H681fSY2fCNuUNx7yppKVprYt0XeNB2s7DAHAW5ig3XrftD0JrAAAQDJ1J3TTPWqS6yO5aWKh5Q1PTOvOoad05HkRnddszrVdhgi489M1Jajetq+PfxUgW/RnT2AHQRE8Fa4sY18aDmE1r/dyRFjGuntLOHVq33JGkbzq6xoO0nYcB4CzMUW68btsfk7EPAAAAoC99kTpta1q3XCyXnx9HUhQ6dNT97+SeNq3bwvjGprW3ixjroUcttN7JeBBdxEiwAsBNzwV2/ru+iNEIrUduGuvXbdr90JfO6bZbjUVRVDOtXU1rQmsAA8uMAgmv2/YHTWsAABCMsindMtO6NbTeYKZ1bUv5yE1r+xjt5V5NYf6R94sY66FHfTwIM60BjK+xad2ymDH2ZDzIOYvW5Uxse6b11SwvRzi1Nq1HXmIM4OIwX6vxum1/EFoDAIBgNC0cNLWPBznbTOuxbvGuvn799/ZjS42xJ6YD35vWqd20rv5sFw09c6b1LkJyAOGp5kPXP243rc3fty0w3IW82G7T2nwjtH08CMESgGHUxoNwF8feILQGAADByIymdJO2hpc5RkOv5ZsCafPzx74N0Q5U7dfqWUPTWsOEY1+b1vN60zqKorIZuIvrEfMCiKU+AFyalhraM61jZ9N6rNB6+d/k3IsYl/+130TUNxxnSbz2Zuny8zS0PteXB4CS+Vo848llbxBaAwCAYPSaab26SHeFkObCQg0VmjKFhfHieOy5nHbwYf9eGyf22BQdD3LqedP6yJiJ2rb4a2jMRwTQpWk8SFvTWn851niMRTmHe5imtX0O1DdCD6buOCGJaFoDGBav2fYToTUAAAhGusFMa9dt2WmmoXdcNnqbxkKYYcPYczm7xoNkDWNTDjyfaV0FH1VovcsFZmbTek5rB4BD2bS2Tjv26A0z1B59PMhQixgj951Lp+UbjhP359G0BjCweWbMtOYNsb1BaA0AAILRNAbDpBfLrqZ1NV4kMhYxuv8dsyHmfdN69X2ZWbdpH02XgcKxh03rRV6UFyBm8KHB0C6a1uYiH241BeBSNIwHsQPhWtM6bj+/bFvTHO5N6Ruhi4W7aX04W59nLWKeh3leBTAM83V9mtG03heE1gAAIBhNCwdNrU1r4/M7Z1rn5q/Hnmld/72dA1SPy5ppPVt+n049bFqbyyEPHU3rXWQdGTOtAXTQ5yJ71MZa09ox03qsc8eiIWjfVNzQtLaX6NrK8zALbgEMxCwX8IbY/iC0BgAAwciMmdRNWpvWRlO7nGnd8bVExg+tu5rW1fel/tLucNVg9nERozmyxJyLOtZM65SmNQCHptZyW9M6KXcmjLyI8bzjQZoWMfZtWjN3FsBAzJFuKc8te4PQGgAABKMcgzFpaVq3hJ76+ZO4e6Z17lHT2v76dutNgwF7bIq24E48HA9yajT1zAajthV3MUc8rW2i5wIIwLqmRYxJS/Na/2isfQh6p9F5m9ZNixhP0kxE6kt0a5+3wzcfAeyH+iJGigb7gtAaAAAEo1rE2NK0TpobXmlejdGIOy6qay+OvRsPUv/AfFEtmDSVobWHTeummajl2JadLGKkaQ2gXdGzaW3+fuxFhIuG5ZGb0vdB15vWywd20DEeZOxzJ4CLo9605jXbviC0BgAAwcj6zLRumMG5/PxqYWFU3r7t/nfMMHsXAWqb9fEg9T9v+r5oC87HpnXTTNRkhwvMMm41BdBBn4u6ZlqbzWv9szHGgxRFUZ7X7Db4ppLE3bQ+ni+b1p0zrQmtAQykVjTguWVvEFoDAIBgaPvZHoNhqhpu6y0Mc2FhtYix4Wt5tKTPDq3XxoM0fF8OPB4PUoYeVtM62uECszRnqQ+Adn1nWtcXMS7/O8Z4EPOp89zjQSJ3Y1rHOzWOB4l39zwOYD/UXpfTtN4bhNYAACAYaTm7uaVp3XJbtrmwUC/lm8aDmGHD2HM57et+u71WjU1paFp7OB6kKfTY1SzURV7UWvY0rQG4VKF1c9PaHlkV7/DNN5v5NeNzLmKcxO7nYx3vxHgQALtSn2nNc8u+ILQGAADBMBcpNmm6yBYRSbNqYWEctd++bV74j33hbR+j/di0cTKxFzF6PB6kKfRIWn5+Q7LnIdLaAeDSFFqbQbUdDscd46e2yXzuPGdmXT4u+xx40rNpPfYbvgAujnlmjHTjDbG9QWgNAACCkS26x4OUF9nORYzVwsKumdZmaD32XE77y9vtPW0JzxoWMeooDp9o+9sOPTQX2nZD0Q6taVoDcNFynx1MtzWtxxyPYQbF9giTTU0aZlOfNuwkKL9uw1gRADgrs2mdZhQN9gWhNQAACIY5k7pJW9PaXFjYNdPap6Z11yLGtKNpfZrmowfvtqbQY1eLGO03NVJmWgNw6DPT2l54GI/YNB5ypnXcEL7rnTL2TgKl5yLfzjsAwlXfNcNrtn1BaA0AAIJhzqRuEpcNr/UXtObCwqgMrd0X1WZQPfYyKfvCf208SN4+01pE5DTza0RIU+gR72imtR1SMx8RgIs+FbXNtE6sNwzjjvPLNtVmWp8ztJ40hNYnHaF1TNMawMDMO+S4O25/EFoDAIBglGMwJt0zrV0jis2FhV0zrXOPQmv7EO3jaRqbcjCpAgXfljGeNDStNezZ9vfcDqmZaQ3ApbFpHTU3rZMRFzGa567zjgcpF0paJ6Gm52/VNFYEAM4qy2la7yNCawAAEIxqEWP3TOuF4wWtOUajDK0b/p1a03rkZVLr40Hqv58vdMFk/aVdHEdyaRXw+7aMsWzqebKIcU5oDcBBTwVRW9PaOidF5R0j2z02lyEXMU6aFjE27CQov27D5wHAWdG03k+E1gAAIBjmTOombU3rzFhYWI4HabioNi/8FyO/OLZDczuPzxpmWotUoYKvTWs79CjHg2w5Q7YveBgPAsCl10zrliWNu24b6/kiitaD9k0lDY1pHe900NG0HvsNXwAXRz20pmiwLwitAQBAMMqZ1C31saRn07qrCVdf+DJ209r+vXumtd20FqmazL41rcvQY6SZ1vatpdxqCsClKEPr/k1rc1zIruda65ezR5acRdLQmD4t33ScOD+vvOOJNwMBDKT2upznlr1BaA0AAIKR9mhal6G14/WsubCwa1GW2RAbY5mWSUOT2epx2+21trEpuijr2NemdcN4kG039OwLHm41BeCiee0moXVknKJ23TbWOdrnXcIo0t207pppTdMawFBS43mIpvX+ILQGAADBSBsWPSQsdQAAIABJREFUDpr6NK2nxkzrJovcv6a1jv+wA4SsYaa1SBVa+9a0PtXQY61pvfzvtm+pty94WMQIwCU3xm2YJn2b1jt+ailD6wGu9Kumdf1BlIsYG2Zaj7mIEsDFlGbV89DYr8uxO4TWAAAgGG3hrIpbLpZT4/M1UmhsWhufv+uZpDY9xmpJYf3PqzB+/ftyNF3evu3bTOuyqWfdXh43PMah2c1qmtYAXJqa1ubv7VEcsQfjQQZtWlsPoTO0XiXmhNYAhmK+eUbTen8QWgMAgGCkLQsHVbWIcf1iOdtgpnW9aT3ei+OiKMoQQkNp+7G1fV8OPF/EaN9e3vamw5DsZnXKTGsADkXDIkbz+dZuWsdjjgfRNzkHnWldPT8u8kLmq8Zj03gQff+U0BrAUJhpvZ8IrQEAQDDKhYMt9z3HbaG1sbCwc6a18fljFjrMw9MAoWhaxOj4vhxOlx/zbTzIScNMVA1a7Mc4tLRhxAoAmKpxGy1N65bxIMWOzx96TrOP9yz0cZjv6ZnnkiOa1gB2JKVpvZcIrQEAQDDKMRiT7qa1a96dubAwLsNR979TD63He3FshurThuVWWbmgcv37cjTzczxI0+3lmrtvfxEjM60BdGsaDzIx3iS0l+Caf3fXTWsdZzVAZu1sWh/PMxFZzvi+NHHHCTStAQyt1rTmuWVvEFoDAIBgVKFz80uYagZnc2g9TeJyqVZTo9cMGsYs4ZqvyyeO8SBFUZTtE+d4kKmfixiPG5rWuxoPsjbTmgsgAA55w3gQ8zS01sI2fr/r4HZh7UA4j2qxcfWx03k1GiRqGEFSNq13HNgDuLjMdjVN6/1BaA0AAIJRLWJsvhgvm2GOpNlc5LjJTGtfmtaTeL0dvsirmdcz1yLGVZP52LOm9ekqRLdvL08cj3Eb7Asecys9AKhqpnX/pvXy79c/f1f0dNUUKG+i2hFhNK3TZdO6aZ61iIieomlDAhiKWTYgtN4fhNYAACAY2oZtbVpH3U3rSRIZgYL736mH1uNdeBe1pvV6C9kMBSaO0FqDhVOPmtZFUZS3mNvjQaJdLWK03oggXAHgok8NdghsPt3agfbyz93jnLYtH3ARo2tHRLmPoGGetYhxxxPPqwAGYo5xYw/J/iC0BgAAwcgWzWMwVNIy09pcWBi3hNv2548ZWpuBh95ybR6z2TZxtf0Oy6Z1tq1D3Nh8kZdBkB186I+26ecylLXxILR2ADg0jQdJzKa145y0q1FHtnzI8SCOx1DuI2hrWq++N7wZCGAoqVXY2PVdLBgHoTUAAAiGBouuMRiqreFlNq07Z1obTdwxQ+vaIsZkPWg32ybTlqb1SepPKKszUUXWg4+2meRDsls6tHYAuDQtYjSbzK6mddey323R89UARWtnW7xf03r5X5rWAIZilwt4U2w/EFoDAIBgaLDoGoOhmprWRVGU7drleJCumdbmr0ccD2Icx8SxFEtfxMeRu1mnM6NPPJpprTNRp0m0FrRX40G2ewxrM61pWgNwKBqb1pHz1/bHgm5au8aD0LQGsGPm/hZF2WA/EFoDAIBgpHn/8SB2UGD+fpbEVdNaejStR7wFsbaIMXGMB8nbg3xtw52k/owH0QD9wBF6tM0kH9JaaE24AsBBn4vsmdbmecg1mkn/+rafy2xNzfCzmDjOp8ebNK25fR/AAFzFgjllg71AaA0AAIKhrYpp2yLGhvES9sLCTZrWY7Y5aqG147HpnO9pQ6tOg2Gvmtbz5qZevKOww24AZlz8AHBoCoHjjvEguxp1ZNOAeYCidbmIMS+qxrku9T1qDa3j2rEAwHk499Twum0vEFoDAIBgmOM9mmhT136Bay8s1H+hKVAwm9ZjtsX0YUSROwRJO0amHJWLGP0JrdtCj10tL9OLHc2a7MWMACDSvIjRbFe7zknJjkYd2XSO9BDjQczHqM/Jxy13yqim8zAAnIUZUOvrNp5f9gOhNQAACIYGz66Fg0rDA3sBVGotLNQGWVMebY4EGfOFcTVPNTIC3erPs1y/J+6AQtvMGhT7oC30SOL2BvxQ9H8P+v3Jcho7ANbpuSS2QmAzFHY1raMdjTqyDTkexHzMeh7Uu3bam9bNC5EBYFNzo2igy9jZRbIfCK0BAEAwylEYLU3ruKHhlVkLC8uZ1o1N6+rjY154VwGEOwhIs9XIlI6Z1j41rU96NK23/T3Xix0Nrbn4AeDSFAKbobVrprU+Je96RMbCeKPzvMzHlVvjQdoXMdK0BjAcczzgtAyteX7ZB4TWAAAgGF1LB0VEJqtZmmtNa+tzu2da+9G0NpeAaX5QX8TYvpxSg4UTj5rWZejRFlrvaKb1QRlac/EDYF3TeJBa09oVWkftd/Nsix7vEONBzODbHg/SFlq79i8AwFllxnhAfb3LTOv9QGgNAACC0bV0UKRa5NfUtNbP7ZppbX7+mMukzMCkHA9SW8TYvpzyaDYRET/HgxxOJ2t/pj/axZbDDm1Wa9ubix8ALsUZm9aR4/l6F8pxJgMsYnTNtD4p33Rcf/5WGuKPucQYwMWR5tV4QJrW+4XQGgAABGGRF2UrunWmtTatraBAQ0q7ad2UJ+SehNZmYOIanZEt+jWt00XhzQgMnYnqalrvahaqhillaM1t7AAcGpvWRojtajXrx3Y+HqRhBvdZJK7QunzTse08TNMawHDKgkYSleUTdpHsB0JrAAAQBDNwbQpoRao5onYImRoveEWqAKJpprV/TevIuaRQl9NMGprWB7Pq476MCCmbeo7QI3Y8xm3Q/z3peJB5xsUPgHXmiCZTkrSH1l3nmG0ZchGjOZbKbloftTWtG3ZLAMBZpMZr3QlN671CaA0AAIJgXvy2Na2TVXhrB81VS2P551HHTGuzIbbr27tN+jiiqAp0a/O29XFN3N+TWRKXgcqJJ8sY9ThcoYcdkGyLXuwc0rQG0KJxEaPZtHYExK7n610oZ1oPEFqLGI3xQmdaZyIicuC4U0bpG8tjLjEGcHGkxl2F+vziy92D2C5CawAAEARz5rBrfqjSC3U7KLAXFur1fONMa6PBsRixzWEGJq5FjHp7ZNOc7yiKqmWMnoTWOtP6wLHIq1petu1FjMvvm35vmGkNwKXosYgxcdzpknS8Mbot5d05A13pJ9Z86pN0tQ+gZREjTWsAQ9LnkmkSlztcmJm/HwitAQBAEMzbAF23Yiu9UG9sWsf1mda+N63NwKQKQarjSY2N6k20TXzsSWhd3V6+HnrEVqtvWzKrac1tpgBcdGyqPSO6Hlqvf17seL7ehXKm9VBNa+txnKya1q6dBGoyUsscwMWkreppEsl0smpaM9N6LxBaAwCAIOgL1lkSr80WNU0axoOk1sLCKm/onmk9ZltMv3QSR+XjNkvBmbFRvUnZtPZkpvVpOdPaEVo7HuM26P8eqkWMXPwAWFfNtK5/vL6IsXk+/2jjQQZYxGj+O5k109p1p4z9OYTWAIZQFjTiuHydT9N6PxBaAwCAIGQ9GsUiRtO6aAitY2umdUNWaV5sjzmX01wCprl0rWmd1Wd1u2gwe+pJaH3c0tTTsGPb40H0fw8anNO0BuDSNNM6jqMyyHY3rfXzdxxar85pbW/ubqJcAKyh9bz+hl/b54x5lxKAiyMzm9ZJVPsYLjZCawAAEIRyJnVHe0xD6aKoh832wsKumdYLb5rWxngQKzwQ6fd90UacP+NB6oGxSX8u227o6c+0Cq25+AGwrmmmtUj1vOucaR3Xx2rsyqJcxDjMv6ePrWxa65uONK0B7Eg1Cq9qWs953bYXCK0BAEAQytC5pVEsUr9le9GysLBrprUdVI/Vti6Mll/kOOY+3xffxoNo6OFq6u1qeVnZtJ5NRITbTAG4LcrQej0F1o8lLX+268lDxeDjQZb/XeSFFEXRupOg+hxCawDDyYyChpZPeN22HwitAQBAEKolLB2htVEvMy+Y59Z4Eb2eLxpmWtsh9Vht69wITDQYWdQWMdZndbtouKBh8djKmagt40G23U7Uix1mWgNo0zQeRKRqWruef/Ucs+sRGYuBx4OYeyKuZnn5/XA9f6vyXEVoDWAA+pptNonL8gmv2/YDoTUAAAhCn3BWxGpa18aD1EPvSHR2svvfWWtajzSbUw8jahoPYiynaXJQhtZ+NK11TIl7PMhuwo50bTxIsfU52gDCk7eMB4mtO3dMrufrXajGgwwTWpt7Isy9CIwHAbAr80XVtNbrAHaR7AdCawAAEAQNkTub1kaykLlmWif9ZlrbH/ehae06Zg3jZ5OWprXOtPZkPMjpvPn28mo8yJZD62z5fTPbgmPOLgfgp3KmtSO1LpvWjj9zjXPahep4h/n3zKa1vuG4XIbW/AUIrQEMKTNnWq+ee9hFsh8IrQEAQBDSRb9FjGZo3bawUJtxjU1rq8Ex1sW3uQTMOR4k725aH66C2VNPmtY6HsTV1Is73kwYit5Wah4D8xEB2PKWcRu6pNAVaLuer3dBz1Wu9vdZlGNO8mqe9UFLy1rECK25ewXAAMq9NElUjQfhNdteILQGAABBMFsWbczsoK1p3RWO2h8fK7TWIkkcRWUwYh5a1mNsiobWvixiLMeDOJrW8Y4aevZMa5HqjQ0AUG3jQfR05Hr6HWs8iH654RYxVs/JJy13yTR9DmOXAJxXaryG19fxvGbbD4TWAAAgCNq0nnXMtI6iyLnMz56JHXU1rXM/Qmt9DFFUNedqs7p7jE3RNvGxB03rfLXMS8TdtK5+dts9Dr3YOaBpDaBF+yLG5fNu4nj+7RpBtS35wE3rxBgP0naXTO1zjK/NhBAA51XdbVmNB+E1234gtAYAAEFIezatRdzzNO2FhZ1Na89C6ziKylafeSzzrLplssmRR01r8xiOZpO1Py9/LttexJgt//1Lk7j8msxHBGArWhcxLv/rWno41lznRTF0aL36d42m9aHjubv2Ocb5iLnWAM6rWqYela93M16z7QVCawAAEIQs7zfTWsSYJVobD1JfWLhp0zob6TZEPb4kjow53GbTumqfNNFW3IkHTWsztL40WT/meEeLGMvvWxKx1AdAo+pul+amteu81LU3YVvaxpmcReJYxHg47ViIHBFaAxhOVVyJyufdOU3rvUBoDQAAgmDPpG4zcTWtc7tp3R6O2hfaY43OMwOT2LHYq/q+NCcUOgLDi6b1XBd5xc7lZdVj3O5xmM17lvoAaNI2HkSfwtqfy8YZDzLYTOvVP5PlhZymzfsIap9jfG2WMQI4L7OgQdN6v4weWn/uc5+T5z3vefKYxzxGDg8P5UlPepLcfffdYx8WAADwzLzHwkGlAUJWGw9iz7RefrypBGaH1mM1ravAxAzaqz/vMzZFx3D4MNNag3PXaBCR3S0vK5v3SSzTVeN7rJ8xAH+1NZfbmtaucU67UC7vHSi01seYF2bTumM8iBla82YggHPSUsFsUi1itO+IxMXUfrbZsgcffFCe9rSnyXd913fJH/7hH8r1118vn/rUp+QrvuIrxjwsAADgobM0rfNaI1nn4dVnWhdNTWvr47tepmV/XXOmtRno9hmbcjhbfuKpB03rKvRwN/Vix89uG8rmvXGraUq4AsBijmiy6fNVW9O66RyzLUOPB9HJU5m5iLGraR3RtAYwnLK4Ekdl+YSRbvth1ND6da97ndx4443ylre8pfzY4x//+BGPCAAA+ErD2bYxGCp2jHtIrTEa5Uzrhn9jvWk9zoW3uQQsdo09KWd1t820Xr7k82Km9bw99NCgZdvtRLN5P+UCCEADfS5yz7SOav81uZ6vd0FDa9dyyLMom9bGeJCjhjcdVRxHEkXLwJ87WACcV2bcVVg2rSka7IVRx4P83u/9ntxyyy3ygz/4g/JVX/VV8uQnP1l+8zd/s/HvX716Va5cuVL7PwD++chn/1me8pr3yrv+6t6xDwXABWLOIO7ibFpbCws3nWlt/v69H/+8POU175UP/s0/9j38M9PrfXOmtXM8SNsixlVA/KkvPCyPe/m75XEvf7c853//862P4HApZ6I2hB6J4zGexc/8zoflWb/6fpln64HJIi/K9uQ0jo3WDhdAAOramstl09o573qY57JNtS2OPAt9jC/9nQ/L69/zCRHpblqLGOfhC5pZ//R/+iv5/l/7AG92AjtQFlfiqHxuCe3/9/6/ex6Up7zmvfIHH71v7EMJyqih9d/+7d/Km970JnniE58o73nPe+SFL3yhvOQlL5G3vvWtzr//2te+Vq677rry/2688cYdHzGAPv7ib/9JPn/lqvzxf/3C2IcC4AK5mi3DzrZGsdKwwGxH2wsL9XK+70xr8/fv++QD8vkrV+WDn/6nXsd+HmZgkjiC9qzHrO8nXP8Iuf7aS7WP/efPfFEePJ4Pfbid9Od4qeHnONR4kHf/9T/Ixz53Rf7+i8drf2Ze6EzN+YiBXQAB2D59KnIF0095/KPlmksTufnyI9f+TJ+Sdz1aSp/GhlrE+JTHP7r2+ySO5JbHdY/zrM7DF/N59Q8/dr989N6H5N4HT8Y+FODCK++WNF6zpYHNtP7Ap/5RPn/lqvzJf3tg7EMJyqjjQfI8l1tuuUVe85rXiIjIk5/8ZPnYxz4mv/EbvyHPf/7z1/7+7bffLi972cvK31+5coXgGvCQhgE+zE4FcHGcznWBX4+GV7J+W3a1yHHVtF5lpk3zRu0LbddIjl3M6syNwEQzk0VtpnU9jHe59mAqf/bv/o1cOU1FROTbXv1eyYtxRp7MO2aTa9BxnlvqF3lRNqxd5yLzcU/iSKYxS30AuLU1rV/xfd8gP/uMr3c+n40+HmSg0PpF3/Wv5HlP+VpJV+fES5NYrj2Ydn7eJI7kqlzcprW+DvBh7BZw0aXGTGt9vRta0UB3AlzUN/K2ZdTQ+oYbbpBv/MZvrH3sG77hG+R3f/d3nX//0qVLcunSJeefAfCHvhN6zIs4AAM67piFbEocwWc5D8+6nbspd9bXlJcmsVzNciu0Xv56Fw26+iLG9RayhrNdY1Nmk1i+8prl66hJEss8y0e5tbKrGV4umzzH9/bECKpd5yLzQmeaxCz1AdCoa9xG1xtwOx8PUs7gHu7fvO6oO6S2lbslLmBAY46YOqGkA2yduYxdyyehjXQ7mWciwizuTY06HuRpT3uafOITn6h97JOf/KR87dd+7UhHBGAI+uKUF3EAhnTSMQvZ5Gq46XOTjheJOmZa23/f9W/tYiZ0FZiIM7Sumtb9X9bNRlxik/VsWp8rtDaCate5SFv3+j0N9QIIwPaZd7tswjXOaRcWAy9iPCvXbomLwnyDkztLge0zl2eHOtNaX4+GdtxjGzW0/pmf+Rn5i7/4C3nNa14jn/70p+Wuu+6SN7/5zfKiF71ozMMCcE4aSHC7HIAh6XNKn9B64git7YWF1Uzr9QvqoijKoOKSK7Qum9YbPIAzqo8HWV9spa3htvEgNm0Wj9GAS/PqFk+X2PEYN1ULrVfNFlMZnK/+tzCNw7zVFMD2FS3jQdqU8/l3XLXWU9pQ40HOKimb1hcvtDYfE9c7wPaVBY3Y2EMS2F0ceuffRXxO3KZRQ+tv/dZvlXe9613y9re/Xb7pm75JXvWqV8kb3/hGee5znzvmYQE4Jw2GaFoDGJI+p/SZaV3ORS7M0Lo+lqLt1m0zoNZWsuvf2sWs0sKYT5o4H9cqjN+gaa3B/RjN4jTTkN19vBp0nGdeuHn+cZ2L7KWcoS71AbB9Z21aa2a8i90HpkU5HsSP0HrXM713wXyD85jrHWDr9HX3dBJVr9kCuzvulKb1mYw601pE5FnPepY861nPGvswAAxIn4iZaQ1gSBo+HvRpWpeLGKsXhnZQWZbQXKG1ETLoeJAsXw+tdznTOoqqY84do0qmG7TqpiPOcO5aHOl6jJs6NtrVJ/P1x1i2vVcXPpNAl/oA2D7zOXgTyUhNa1/Gg7h2S1wUc3M8CNc7wNZVixjjYF+zaTZCaL2ZUZvWAC4mDVB4EQdgSPpi72jW/Z572bQ2XheaL3hFqgDCFTzXmtar0LoeFO9wEePqMcRRVN1u7mhaTyf9X9aN2VLpaoYPMtO6tohxfTxIao1Uqb4fXEgAqDt703qcRYxnHWcytCS5uKG1uQ+CO0uB7TOLJ9NyxF1Yzy36XMEixs0QWgMYHONBAGyD3lZ3OOt++VLNtDaa1tbCwrZFjK7Q2tW03kXGmRsBRDUepPrzKow/w0zrMZrWHTO4Y8dj3JS5GMu1JCuz5ptXS324kABQV4XAZwutxxoPEo890/oCN60JrYHd0vFtkzguX7vNs7CKBjr/nlF0myG0BjA4DSSyvAjuZALAX8flIsYeTet4vWlth6WbzrSuNa1XF6zFDsKIwmj5rV6n175u1T7ZoGkdrwfxu2JeeLgMcUu9OZ7KNarKnI24/O/q+0HTGoClCoE3+zx9St71eBD9cr4sYryIoXVqvCHOIkZg+zJjL021iDGs55aqac1rzU0QWgMYXEr7AMAW6IXhYY9FjNqcNTeLz62xFHo973rJa15ku14c73IRYzVPNTLGnqzPtJ40NJdd9O/OR3jhXI3maBgP4hiBsikzRHAuYjS20C//G+atpgC2Lz9n03oXY6RMGpKPPh7kIofW5kxrrnWArdP/n5slcTUeJLDw94SZ1mdCaA1gcOYTMe0DAEPR8PGoR2idOILPsmm9+jPNH1xt6YVx0a8Br2uO9C5u+67mqYoztNY7Wpqayy4a3I8xV697PMjyv+cJOsyg2nUeShf1oH8S6CZ6ANtn3u2yCdcdP7uwOGPIPrRkdU7a9XiUXTDPnSyeB7YvM4onob5m09ejzLTeDKE1gMGZTTWa1gCGUjatp/1Da/OFYdmunfSYaV1UIyz0wrv+by1TiF1ci5stP31c5tfVxzXbYDzIbMSWSrWI0R2o6BzU83xvu5rWqTXTWgN02i8AbPkZFxvq39950/qMIfvQ9JR0Ee9gqRV0uNYBti417ip03U0ZAn2uSAM77rERWgMYnPlC7niejXgkAC6KPC/KF3sHfUJrRyBtLyyMW8JRDaiTODKWOq7Pkd7Fbc/lErDYvdgr6wiBXTSsHWMZTDnOpKEZHg2wvKxrprXd9p7EzLQG4KZPk9GGIbDrPLQLOh5k9JnW+vgvYGhNQQfYrXJ/SxyX4+VCalrPs7x83qBpvRlCawCDM5+ImfMGYAhXjaWum4wHMTNIe5Zy1YJb/3wNGZI4cgbF2pLYxW3PGowvZ1rXj888lrPMtB4jpK0WRzY0rQdYxGiee1znodRaXlleAF3AcAXA+Zy5aT3STOfyeMcOrfdlpjXjQYCtK1+3TaIg744z39wKKWz3AaE1gMHVZ1qHczIB4C/zxV6vpnV5sVw9B9mN5Ei0ab3+4jEzmmoTx5K+NCsaP3do5q3edqC7yIuyKT7dYKZ11VLZ/XP0vGMRo374XIsYO2Za28srQ13qA2D7zjzTumxaD31E7RYsYtw6ls4Du1XdLVk1rUNqLP//7L15tGxXWe79rqbq7NrJOYdDEhBDkw6ISYDgjaA0EoHQKMSA3xVpLmiIODLgI4FP47BBhiiGIK2idAPh3nG5Bm9AjXxXEGNi8o3oDWkhELiYaHKSkL45OdlN1Wq+P6reud41a65Vq521Vu3nN8YZe+86u6pmrV01m2c+83m3UqI15pplgGgNAGgc6VRDPAgAoAm4L9nlu4WOPJsWy3LCS5SfNxoK0drk/GXR08ZiXLr8lHNvdpuc+JZxWiculWUUYkyK6ZiwEw+Sdlr7S7weAIBuE1UsbNjEqZEqqJNCS8+0rt+Xd5UgFYUI0RqAtpGxbuq0YI+yoWU/AYNEOSBaAwAaJ0BxEgBAw7BDYVQgGoRIFGJMCc3pgoVOTqa1SbROOa1n4qYNLYLb5zlJVAnP06VoneVcNuF7y8tw5kVGZjyI9hqrsLkgHmSs5ZurjG8sJAAAGiwCl9WAnZyN0TbpTiHGneG0RhQiAO3Dpjjfc8WcLbZy4rEJ5Kk/RNGVA6I1AKBx5FEd07FsAAAoCzsU1gtEgxAJh1tOwcJCTmtDJMf0saK529oiEUycucJesr8tI1oPDEK8LXixn1WI0W2geNlmQae1rzKtndTtAADAVBWBvQZOjVRBxYMsPdN62r+uomgtHZ4w6ADQPspp7TrKfEK0nHlsFTYnyelzOK3LAdEaANA4EzitAQANwyLkWlGntZMWZeM4Tty1nGndgNPahhiRCCaJ0M7t4yKMjkOFYlMYFmvHS5g4T8J8pzVr2XWEDilam8YhFhyGc4UYsZAAAKSJVWHDcvdbejzIklf63MX3RVQqAww6ANgjjGI1Fx54bioOry9mA1nnK4pXczOvLSBaAwAaRy76kfMGAGiCDY4HKei05gmtLFjIDApkWstCjLq7mSjp52zMOWWeqqs5yFU19ZJqyjKdxXqetE7ikq/+HBsLCjFONNe938OiPgAAO3BfVDYj2llSIcaqGdxNw05r26K9DcYw6ABgDb1+ixSt+2I20Ot8IY6uOBCtAQCNEyDnDQDQMFscD1LQae1qTmvp9Br4Wqa14f686PddhzxN4A2jWLmzbSzGpctPF3SDBa7lLAZLzLSeaI53nSbiQbaEUD0Oo7nXqRflTApTYhEBAEjDm55O6XiQ2f2XFQ+ydNF6+nXVndZY6wDQLqk5vOemjBp9MRvom1ur2C+2BURrAEDjyOIkcFoDAJqAJ3trRZ3W2rHslEvDXZxpzZNgVzitw3j+sWwU2OJ5reM4c22eaNnMRVFFbJYwaeaJequZ1triYCtIi9G62C+L+gAAgCRxLpe7H5+MsV0orCuFGLlftV2I0gYy03oSxtjwBKBFAm0O77rJfLgvnz391B9yrYsD0RoA0DgoTgIAaJqNsk5rLYfaVLBQHd02zBtTTuvZY4WzX5TuCBuZdFIwYREiVA7yqk5rdo/bnzQvcocn17v6tdU3TOePZaYjStQ0J1vsAAAgAElEQVT16MkxUwCAPeKKIrDeX9siUvFWVp92DjUOr+BmoL7BifUOAO0hP288R1S1SHoi/up9BEwSxYFoDQBonIlwtG3BaQ0AaICtkpnWWe5oWbCQXRomF1wgjlcnIur0/2QfZ9Np7TpO4kLWxPgs13IWvorDsD9pHi/ItFZu8hpCj35ce2usOa2jdETJAJnWAIAMqmZE62OHLcKOZFr7bv1TM11FF8qw3gGgPfjzNvRcZTjp27xtXrTuh9jeBSBaAwAaRx43RzwIAKAJuC8ZDf1Cv8851OFsMsv9kszBc/MyrTnCwnPUwpud1rLoi5VCjAYBnZ93vCAfOoskDmN5TuvFmdbVHj+O4zln9cYk/bNeDNJHpjUAIAMWXctqwHkbo23SlXgQvbbEKqGfUoLTGoD2CLTi2fL7vpyQm48HWb1+sS0gWgMAGidARW0AQMNs1nRa50VSGDOthVDsam45OdG0GQ/iyHiQOO20HpY8Bz70l+dQ4Wub6bR206+xLOMwUqLN3tGAiOYXC0rsd/VM634sfgAA9lAicMlQa72/tkUSD9INp7XteBQbIB4EAHuwWcQXfVrfapHo89BJT8T2LgDRGgDQKGEUp9xxegcNAABV2CyZaa0vlk0FC/McvXw/3513Wkuh14aDLhUP4qafd5FrOQt+TcuYNE/C+cWHhDccql5bOe4cdshw7jYi6drRM637sfgBANgjrlqIcVmZ1hWd4U3jrrBorbs7sd4BoD3003HT7/t1Qm5jAqd1VSBaAwAaRR844DwAADTBpooHKVeIMRGt553WugAsCYVTzdOcv2PRz9lw0HH7PNdJHOT8upS4XjbTenkOFdPiQ8IvparQweOO7zq0e81P3abaEHE+ol7QB4sIAECaqnEbPHbYjnQO4bRuHV1wgmgNQHskc3gpWvdr3qbn3vdFbO8CEK0BAI2iu9T0XFEAAKgCOxTWCsaD6ItlU8FClWltclrnCMXSYWVjzildc44uWgfZsSd5KGfxEibNpsWHRDrgq7itN8QGB29yZFVt97VM62VcDwBAt6mcab0k0Zbb6y3Zau2tsGg9hkkHAGtMDKcK+zZv0+t8QbQuDkRrAECj6APH1gQdMgCgPmXjQeac1tH8hJe/M2VacxSI5zqqqCML38uMB9GdeyygZwnAWSzToWJafEik0FLl8ipX/sBTGehZiwXe3EiuB8YsAEBCHMeqHyrrtGajs2mMaRMeM5yuiNa2reYWmHNaQ7QGoDVMtVC4sHpfYt3mT/z1o91dAKI1AKBRUJgEANAGWyULMWY5rWXBQic303r6NeW0jtNRI/Lx2yQSearzbZmvqF4Evj7LqLquFh8ZkSZSGKoidvB7ZX3o0frQT92m2qBFlKiM754cMwUA2EF2QWWdy54aY3ZmIcZVdloj0xoAe5hqofg9y7SeP/HXj3Z3AYjWAIBG0SdxiAcBADQB9yWFM601cddUsJDnvuZM62SCrC+8pTvCTqb19KvrOOp4OosgdZ3WyygEEywQ2qWWXUXsYFf12sBTcTJZTuuBKsTIjh0sIgAACbKPL+20XnI8yJI165UWrfUNTn1jFADQHHrxbKL+ZVpnFQQHi4FoDQBolEmgT+Ii5fgAAICqbM6ihso6rVlgHiuXRrFMa76fjOTQc6Sz7ts0SZ5q0hbWVpXTuqQ6wYKxnsvZNnEcq+fMEtqlO7BSPIhwWo+G0+eYWyxEaeF8mSI+AKC7SJe0U3Ll7Oac5mkTFtrdZTutnVUWrXWTDkRrANqCP29DYXZYZm2WKsBpXR2I1gCARpko118yqGwFmMgBAOqxOXNaF820TsTddDyI7Jt017IkEqKmHjUykU5ry/EguoM8yYcuN6Vj8d72ZF9er6zikXXjQTZFIUaOB8laLHAb+nbMFABgB9kFlXVac7dsPx5k9vxdybReQdEamdYA2EMvnk2UzGMnPelfeG46UPPNfrS7C0C0BgA0Ck/iDt3lq9uQ8wYAqAsvCNcKOq292WQ2ifSYd/fmueCk01o/4i2FXhtihCzE6GoiiCmruwhDP+1Et4V8viyhXQpDVa7vpsg/5/eLPg5NNOc952tjEQEAkESpeJBy93WWlWkdJ+PXMvHc5YwzNuAxhDfSIVoD0B5BlJ1p3Ten9e61AREhjq4MEK0BAI2iju/4Lu3yp10MjswBAOrC/Uhxp/X0q3JHG3KUeT0fkynTOondmHNaC2HTRvxRbCjEGMfT2yeGrO4i+EsSaaWTOSvSRN5c5fpuKKe1nyko6M57tfjBIgIAIJBdUGmntYrHaLJFi+GxKqPWrTX0E0+rBAvxu9dmxX6x1gGgNfTi2fL7vpyQ49o83Gcgjq44EK0BAI3Ck7iB56qCaShOAgCoC/cjRQsxKqe1FqNR1GmdLPqdzEgOeVubcFscx9FcyCKbuaQ6sSyHipykF8m0rnKsXL1XBq7KQJ9zWkfpBZAs6GMqzAkA2JmkMq1LGpeXJdpym71lZ1q76bFzleB5wJ6ZaxJOawDaw2TQ6FPMRhTFtDVJ9xl9Edu7AERrAECjSGFofSYWwGkNAKjDJIzUpLRoIUbdaR0YChYqp7VhQc2LbN911CRZjxohslNgKx0PkhZ0g3C+jkARluVQ4edznWxBxXEckTde/jk2lSvfV5sc807rdBa4vH6rmL8KAKhGLLrIsk7rvLoJbSLHjGWyEwoxsmuSi0UDAJpnYnBa+95yarNUQdb32jOa9hl9ENu7AkRrAECjJDmhDq0h5w0A0ACyDynttFbFE+cLFuY6rcPEqeZqC2/b8SCJa06LzohjGhtiT4rA4r31eBBV4DJ/CurWyILljdK1gac2OfhYJqOOmrocD5K0ZxXzVwEA1UhnWleMB7EsWquTQssWrXdAIUbOp93UxhgAQHMkdWmE07pHmfnytN/uXci0LgtEawBAowSiui+KkwAAmoCzIl2neMFBFgsCzWkt71/Eae25jore0B+LyI6DLhauOelOjmLptC43pePftz1pVu1dcGzdqyFa85izPvTEOJR+nRPNaS0d+OMeuHYAAHaoU4iRT8bYTseIRB2EZbLKojVvwO4ZIR4EgLZRdWncead1HxzL3D/s8l0a+v1pd1fwl90AAMBqIXdCuRCjniUKAABl2BBxD05B55ieJWrKwyuSae05jooaiQzxIDYW4yxA6JnWYRSn6giUQYnW1gsxFnNa88uscn3Z8TYSTmvdBTfRXDvy+qE4DgCAkV1Q0fGH0U/p2ILHqs5kWq+gaB2oTOtZPAjWOgC0RmDMtO5PIcbNcVKXZ9CjWJOuAKc1AKBRxoEsxIiJHACgPuxQWCuYZ02ULJYDLdJDujTkcl53WyvR2nVV1Ag/1jhYVqZ1+rh3FKUjmcrAE3/bruJkYzN/CppsOpR/DvV+GXqZMVV6JfppDAz/HxYSAIApcQ3X8rJE21C1uRuidR+O75dlLh4EmdYAtAbPdeVpycGSCopXQZ0AHHiigGT3290VIFoDABqFBQnfdWg0mHYxGzgyBwCoQeK0Li5as4jLLmVTwcKUAKytqRPROinqqB5L/LKNeJBICBB6PIgec1GUgbskp7Xa2GwvHkS9XwYiHkTbPJ0YssDVUdMVFFgAANXg7qCKa5nvYoqgahO10dkRp7XtQpQ20AsxbmGtA0BrGOdsbn/mbKrWytBTrwHxIMWBaA0AaBTpXuNj2VtwWgMAasCLwVEJpzUv1rlPSgoAmkXrQk7rMC2Ay99rE+n0kxpEGMciq7ucODHw2QFn1+mRFMTMb6+KB6kgdqj3y1DGg+iiNW+wCtcOF6cM4H4BAEwJRTxTWdwlFWKMRLzVMtFrS6wSPJZxPIhe7BcA0ByJKa7fTuvRwBN1crrf7q4A0RoA0CgyN5bjQTYgWgMAarAhsuCKojt1TQULHTEL0tfUvMj2XWfOtS3dETa0CJ7XOo4z+0eqPUUzonWUQyWMrboA1camWywepErb5PtlJOJB5GOZnPf+kopTAgC6CwvAVUzLeXUT2qQrhRh5czJaQdF6Lh4Eax0AWiMxxck5W38cy5vixGgitne/3V0BojUAoFFkUTDlcMOROQBADTYrOK3nM60NorX4ff34slr0u0nxQ/2xiOw46CItn1QJ8lE6kqkMcuJvM2/VVEzHRJIFW/455PuF3zNRTLQtHNQTMVYxgx5VogcA2IG7+Cr50HpBYBvEcdyZeJBk7Fy9jUAeJ/aMOB5k9V4jAF3BZNDoYyHGtYGHuWYFIFoDABplItxrnCWKnDcAQB02Z8duSzmtNbEgKcSYFQ+Svn/Kaa25xeRRZxuCryzEOP2aHDnXCwoWRU78bU6cx4bNAxN8FL/K9d0Sjha50SHHInMlerhfAABp9E3DMnA3ZzMeRHaZy44H8WsU1O06vN7ZM3Naj8OoFzEFAPQRk/GEv+/DnE0VYhx6Sf0U9BeFgWgNAGiURBhylcCEnDcAQB02q8SDaE5r5UgWE96UaE1apnXImdb5Tmui9ots6aIJJ2tEUaxE4EXOZR0p3k8sKgpBwTiTWoUYZXag56pq8xwbEkaJE1HGlKijpquosAAAKhGpTOvy93Uc+05r2WdWEdqbRNWWWME+NVCFGAfqti3UQwCgFbgPGYi5K89j+zBnU+uYgadewyr2i20B0RoA0CjSvZbEg6BTBgBUh/uQKvEg7NQ1FSyU63ldU2BnnCcyrfXHUr/bsiChiyZS0DVldRdB/r5Nl4pp4WGC/7uKaK1vcqwNpq+VnS5y0yHltOacbwgPAIAZyUmXCk7rJWRay/FoQemA1lFjZ/eNkKXhiKlDdnlqbEauNQDtYIoH8XvotB4NfeG07n67uwJEawBAo6hMa+G03oTTGgBQA+5D1ks4rXWh2TThTYvWZiHac5x5AVxzR7QtSOiiiSzuldQRKCeoTB3k0+9tHmlO/g4LRGvtmhcljGKVXc2bHOuzosAsKMh4l4E377QObCpMAIBOE9coaujWiDmqihzKuuK0DlfQUcjj5lDW8IFoDUArmIpnD1Qhxu73LxvSaa2i6Lrf7q4A0RoA0CjjmVgw8JNMaxRiBADUoUohRpn7TJRMajMzrbW5oxKtXWcuamQcmIs2tkUsXN9EaUE3eV3lp3Qs4I8tTpyV03pRPIhbzaEoc6t543SkjUVyoWAuxIiFBABgSh2ntYpysphpLfOzvSUXYkw2j5fajMaREVM+Cs8D0DqmujR9Kmi4JTKtVbthkCgMRGsAQKOo3FjXpbUBZ1pjEgcAqM5GhUxrds3q7uhB0Uxrg2jNwoPutLYdDyKjMyZhNac1URLRYfNo5SQoVjjSrZhpLcebNX8mWmsuOL5mjpMWdfp01BQAYIek/61SiLF6Nn9VUvEgS3Zae85qOq31iKk1iNYAtIoqxOiL03E9yobeEAXlVf0URNEVxq96x/F4TPfccw9F2pvkyU9+cu1GAQD6SyAEFByXAwA0QS2n9Vw8iMi0Fr8/l2ltclrPJs26qNm2IMFTLX5NUggJajutQ6sT/kk073g3oYT5khsC7GZZG7jKkZ4UBeZ4EM7VTl8zFMcBAOhEDcSD2DTUxalCjPae14QerbUqpCKmXDc5WYr1DgCtIONHmUGPjAZcm2dt4KnXgCi64pQWrX/wgx/QmWeeSVdeeWXq9jiOyXEcCkN01gDsZGRuLE/ituA8AADUQC+sV4T54onzTuu8TOtAitaa8KDHR7StcSaiSTrTehoPMpvI++VF62UcrUw2Nos5rcOSGwKb6ghmMsXVxyJ2e+u52vzzuAcLIACAHbgLqhK1sYxMa/lcy44HWVnRWnNaj7DeAaBVVBSemLf5Pcq05nXMunRa96DdXaG0aP3Lv/zL5Ps+ffWrX6UnPOEJlY5KAQBWl8TB5iAeBADQCFWc1vpi2RSj4TgOOc5UlNC10Sg2OK1n/Zs+0SwrrJaFH571BxZC4jjd55YlKQZjMR7EsPAwodzkJef0stgNo49Fk4xc7cS1g4UEAGCKvmlYBtktR1GsTn+0idSHl71OV+OwxXgUG8iNXh/rHQBaJzAUU+9THZLNySweZOCJ05ur1S+2SWnR+vrrr6drrrmGjj/++DbaAwDoORPhZkQhRgBAE1RxWuuL5ayCha7jUBjHqSPVRMJp7ThzAqp+pK/1eBAtU1UK8qaJfFESZ7HFeJCSTuuy19b0XtGLZAUZOeB9OmoKALADb3xW0X+l0zmKY3LJhmidLty7TNRYtWJ9alIjwyHHcVCIEYCW4Tn8UMzblPGiByc55Nw01tYlYDGlVzgnnHAC3XfffW20BQCwAsh4kBEy3gAADbApqm4XRXdaqzw8Tajkn/Q5L2cp+54zV+xlPh7EjmjNGgSLJ2EcK8F5kXPZhMrVszhxTmJaFmRaz2ao5eNBEjcLk+SNTv8vawOD/84TZFoDAGZw917JaS2EY1tu41BsuC6blXVac8TUbAyBaA1Au0yi9GdOfm8z4q4q8hSgancPxPauUFq0vuCCC+i8886jyy67jO6//346cOBA6h8AYGcjBYn1wfQwRxDF2E0EAFSGN77WKsaDxKJgoe7wzXL0skDtOo4SHqKYZo+lO60LN6sSumjCr022ZVjBaa2cxRYnzqaFhwnPSV5jGTbH07+bdFqvaYJCVkSJOmqKiu4AgBmxtmlYBil029Jtk5M5dp4vj2QcXnJDGkYVFPbSxX63YNIBoBWCnEzrPkS6bU3mM6370O6uUDoe5KUvfSkREb3kJS9J3Y5CjAAAIilIOLQ2TESJjXFIe0flRRUAANgYzxfXW4R0mUVx+hSIhH9N1xPYbOu7idOaaFb8UBN523aRKaf1rOmekwgBQVTdab2MYjCmhYcJx6kmdmyM85zW0wdLXPea07pHR00BAHao47SW45CtYoQ8dnUiHkT146slzuibxSPEIQLQKqYi3sswXlRlQ5hvtmfGCETRFae0aH3ppZe20Q4AwIrADjXfc2noueQ60wn/1iSkvaPBklsHAOgjW1UKMXqa0MxOa20hr5zWUYbT2nXmjnjrTlxb8SAs5Kp4kChOxPgFzmUTvmf/aCUvLhY5w/V4l6KY3ivJ0e10PEhWpnUfjpoCAOxQx7ksu2Vr8SA1Ckc2TdV+vOvop3UQDwJAuySxbjLTetrBjntwOk4VlB96c0XBwWJKi9YvetGL2mgHAGBFkIKE4zi0PvTp4HaAitoAgMokTusSorXmcAuiBU5rbU3NuqXJaR1oE83WCzHOns4UD5IlwBZhoCqY25s4j4NiTuvK8SCG/HO9vkKQIfQPcGQTAKAR1RCBU/EglroVvQbCMll50VrPtMZaB4BWYIF34MtM63S9mS6zKdYxDy/hlGPfKS1aExE99NBD9LnPfY5uuukmIiI68cQT6cwzz6S9e/c22jgAQP/Q3QdrA48ObgeYyAEAKhHHsRIiq2RaE83c0RmxFFmZ1nyc2XOd9GNF85nWbS/IuW0s5HKbQ5FprYvxRVDxIBYFBRVnssAZLotNlkEdwTSI1vx/3AZd6EdxHACADndBteNBLDmt+eRPJ+JBVrQQo17YGYXnAWgXFQ/iGuJBOn46bhJGqs8YDTxRBL3b7e4SpVc4V199NR177LH00Y9+lB544AF64IEH6CMf+Qgde+yxdO2117bRRgBAj9AFlHXkvAEAarA1SZwIozJOayk0h9kFC7Myrfn3PdeZc23rR/ra1jgT0YS/JoUhJxkCbBGSCb/NTOv0Yj8LTxS/LINyWhvjQab/Nw7MQv8yMr4BAN2mTjyIvE/bJ3KS55l+RTxIeySb4NMxRC/2CwBolqQuzXwhxq7P2WS/MBKFGBFFV5zSTut3vetddPrpp9NnP/tZ8v3p3YMgoLPOOovOPfdcuvzyyxtvJACgPygHm6vlvMF9AACoQGqyV8Zp7dRzWit3s8FpPQnMv9sWeqa1J2I9+KkHFTKtl+FSySqIqZOVNb4IHmvkBse6Hg+SIfQPlyDiAwC6TR0R2HEcVdul7doHDAvEbpec1ismWutF4ZBpDUC7mOZtw57UIeG5p+tM26yi6HoQa9IVSovWV199dUqwJiLyfZ/OO+88OuWUUxptHACgf4z1iRyc1gCAGnDfMfTdUsedXdchx5m6lIMomsugZJJMa70QYxLJIYUHU6a1rXiQxGk9/Spd6Isyok1wHuDYptNaLTwWiNYVxQ6TaK274ExV6IkI7hcAwBxyA7MKnutQFMb24kG0OKll4olTQXEcq43XvqMLaLwxuoW1DgCtwAXQ5bzN74n4m+RZ++Q4TlL0uwcFJLtCaVvOnj176Lbbbpu7ff/+/bR79+5GGgUA6C9BRkXtjXGwtDYBAPrL5qzvKFOEkVEL5mg+g5KRURuSSBVunP4/i91BFM+Jmm07rVU8yEw04a/bQbJAXiQCm1hGPEjRwpH832Wv7QZXaB9Ip/XUaMGitakK/fRn/htjIQEAmBLXLGzoZIwxbdGlQoxyk3iV3NYqYspN6vcQEYrOA9ASEzUnl4UYE6d12aLdNlG1Vmb9BL8G1E8pTukVzute9zp661vfSl/60pdo//79tH//frrwwgvprLPOote//vVttBEA0CPmjszBfQAAqMHmeCoglokGYVSMRhRlFix0ldM6fV8WuVnU5rX3tBCjlmndssY577SeOaSFS6OaaM3XZwnxIAviTLJiWxaxNZ4XrfWYqqyIEr4eevwLAGDnwt19VZewVzHqqCpdigeR3bzNcaZtVEFh/VQpRGsAWoHn3QPRr0nzQ5c3xdgwMRpO+4uBiPgDxSgdD/KhD32IHMehN7/5zRQEU/fTYDCgs88+mz7wgQ803kAAQL/gomDsPuCJHNwHAIAq8CmNMkUYGZmnmVWw0MnKtI7S4ur0azR7rKQwVxzbyLROt5VFkO2ZaO041Y6u+0vIA9RP42SRxIOUe/wNQzyIHlOVlWmduF+wkAAATKnrXOb72RJVulSIUW5O2ipEaQO9oDBvjMKgA0DzRFGs+rWU01p8H0Qx+eWXCVZQ8SCDqfTK7eZaB13YYOw6pUXr4XBIH//4x+n888+nm2++mYiIjj32WFpfX2+8cQCA/qG7GVGcBABQh01D3ENRWMid5BQs5KmivqBWTuvZryvhQRR13OW7tDWJWs8qTUSTtOubndZVijASCae11Uzr6WsZLnCGexWd1qb3i755OsnItF5GYUoAQLeJtf63LCxI2BJt62ZwN8mqOq2TmCusdQBoG2kkkIYHaTwYh5GK3+ga3C+szeaist2TKKJdbjfb3SVKi9bM+vo6PeMZz2iyLQCAFYAncixIqOIkcFoDACqQFDCpLlrnFSxkIULXE3jhr5zWypWcCOC7fI+2JlHrx75VprUWD8KuripFGIlkHqA90ZqF9sVO6+nX0vEgk6TgDcOCwjiIUvEuekQJKroDAHTqOpc9y6K1igdZvmaddlqvkGgdaCex+Ng/RGsAmkeeBpSGB2nY6LLZgPuF9QGL1km7J2FMuyorsjuHQpfota99LX3hC1+gPXv20Gtf+9rc3/3KV77SSMMAAP0kyQrVCzFiIgcAKI9yKFRwUPgFChYuyrT2lNN6XgAf+skRvzYJtXxtT70uc0HBonA/bbMYjL7Yz8KtmAObxIMkjy83PDYnYWZRTlnUBwAAiBKxuWraRlax37bQT+YsEzk0rabTmqMQZ8V+sdYBoHHkaUA533Vdh1xn2rd2OR96U4s5lK+hy+3uEoVE671796ocxT179lQuRAEAWH30rFA9SxQAAMqwYSisVxST0KyL1lmZ1qESrTnTOu1uJprGg8jfbQtdNFGFGPlki18tHmSo4jAsxoOE5jxpHX6NZfXjJB4kmeLu8l2VP745DjPd3krExyICADCjrtNa9WW2Mq1n3VcX4kEcxyHPdSiM4pVyWuvFfHl+sj07zdOFaw/AqiCNBPpny/dcGgeRVfNFWTa1WivyNcAkUYxCovXnP/959f0XvvCFttoCAFgBVKa1q+W8wX0AAKhAEvdQ3WnNj2EqWMg6RKZorbmbpWjN7u/YUiFGbgO/hG2OB6mYaZ2ItPYmzfpiPwt1pL7kQkRfHBBNhZPRwKONcUib41BtrmbGg2ARAQCYoTKtq3WzSyjEyJuc3RBOPcehkOKVclrrm69yU31rEtIhOO8PQGNIQ5zerw1ch8bUbcfyhlZrxXEcGngOTcIYcXQFKT38vvjFL6aHHnpo7vYDBw7Qi1/84kYaBQDoL+z8YzFkHU5rAEANTCJkUVwt09pUsDDr6LYSrT1zJAdRIoq3XYhRLwQ2Fw/So0xr/TROFm6FQoxxHCfZgdr7RRbKYlFad6gPPPvXAwDQberGbXB/bSnSWo1HC/YFrcGv35ZobwMVMTUbQ3eJsQTrHQCaZRJkx8oN/O7P27YMtXlQ+LscpYezyy67jMbj8dztW1tbdMUVVzTSKABAfwm0QoxryLQGANRgwxD3UBQ909ok7ibG63JO64HnWFuMJ6LJ9Gd2mrBorUeeFGUZzmJ2Wi9qs3InllB6xmGk/hZ6BrqMqlJub/2Y6WxBtEqOQABAPdgIV9W5nEQd2YoHSY9dy2YVRWs9Ysp1HVobzIoxYr0DQKNMcswOfahFYoo55PnnuMNie5covAL81re+pb7/7ne/S3fddZf6OQxD+trXvkZHHnlks60DvefuA1u0b31YOW8TdI9HtwP63l2PqJ8fv2cXPXHfOhFNJ8o8J1U5b3BaA7BS3Hdwmw4Z+pWcz3k8uh3Q1iSkww7dlbp901BYryi609pUsNDJclort1pW8UPXmoOO28Zt9RxNjK9ciHE22RfHE6Moph8e2KIjHzPKvW/V9wG7YRa1ucq13Ronr0PPQOefv3X7Q/TDhzenbfDM8SAPb07omlsfJCKip//IbjoUR70BmOPOhzbp8XvWVj6/V980LAubA8ucGqmDPl4sG8/SiSQiotsf3KAjHzNq/bUnJ4ZkwV+ftibjVOK9tBwAACAASURBVIQYAKA+QY7ZoQ+xbqaC8nBal6PwLPzkk08mx5nmyJhiQEajEf3pn/5po40D/ebW+x+ln/nQZXTaCY+nT/+XU5bdHNAQv/DJK1OiNRHRP7zrp+lpj9+dEj7m4kHgPACg9xzYmtBPf/BSeurjDqW/fccLGn3sX/jklXTHg5v0r7/9klQe5GaNQox6prVpA1VlWgvVOopiJZbyglu5toXT2rFUYEs/ns5tGtd2Ws9Pmi/4+vfo0/98C/23M59DP/20I4z3e3hjQi+84FJ62o/spr99+/NLPWfe4kNS5dpuThIRX/9b81j0e3/7HXXbUHPt8H3+7Z6D9AufvJKIiI45/BD6p18/tXAbANgJXHvbg/TaP7+S3vSTT6Y/POMZy25Oq6ixoGo8CG+MWnIaJ0WEOyZat/z6//b6O+icC6+n3/7Z4+ltP31sq88VGE7ryAgqAEBzTMLsKDxVm6XD2dCbhngQFP4uR2HR+t///d8pjmM65phj6KqrrqIjjkgWMsPhkB73uMeR5zXrugL95pb7HqUoJrr53keX3RTQEHEc0/+5eypYP3HfiO59ZJu2g4huvucgPe3xu1PCB+e8rWESB8DKcM+BLdoYh3RLC/36zfcepEkY072PbKdEaz46V+XEjuvMu6Ozfkcup2U8RFL8UMvH9lzi+XP7mdbc1ulX1k6SeJBq4oRyqIjJ/g/uPkhERLfcezBTtL7z4U3anIT0b3c/Yvz/PEwONRP832WEjo1xQETmDY43/eRT6JHLblZ/qz1rA3rZiT+S+p0ff/I+et6xh9EdD21SGMV0+4ObdMt9j1Icx51xLQLQBf59NgbcfM/qz/HrFjZ0LW1uMnFNZ3jT2BKtb7b4nlQxV2JewvEgiEMEoFl4Tm7MtJ7d1uX4IdZA5MlExNGVo7Bo/ZSnPIWIiKIO72KAbsECZperuYJybAeROnb4v855Ib39i9fSFT+4T03Q5G7hQDmtp90MnNYA9B+eXDU9yYrjWC0C9Ura3K9UcROzk4Gd1nmZ1vLotvyenVSmx0oiLJbjtN7m2JOKTmueNI+DpP0s/Ob9jXl835iEpQVdlSe9QGhnd2KZa2taGDD/+ZQn0X8+5Um59z9kl0//41d/koimbvJnve8fVJuHfkcUIAA6QBiZ++tVJKwdD2KOoGqLsGbhyKbxLIn2PFbYcFyqzVfptMbJUgBaYZJjXuH+tcsxG2puKgwVwx4UkOwSlUP6vvvd79Jtt902V5Tx9NNPr90osBqwWN3lYHxQDpnTNhp4c0fh5N+aRRUclwNgdeBFZ9POYrmY1ccMdQy3gjDrFShY6NB8dnKe01q6tpMIi9JNK0Xi9COtLXUzreed1puTxWM3iwJxPL0eetHDPNQmhMExI3EqFC9L8s/rn/wbCJE6iCIalq9dDsDKwp/LnTDH5+Ggqgis4kEsZVrv1HgQtZFi4T2ZxBUk4wIXoEdhNQCaJa8Wiu/a7V+rsGGYm3K7IVoXo7Rofcstt9BrXvMa+va3v02O46hdTbW4CCFMgSmTHeTC2ClwpzvwHBp47lxedSCq+3KfgExrAFYHJVo3vPiUIrG+4DQ5moriaZnW5kKM069ywhsaRGtTPrYtMUIXTXQBvWqx46Eh03qTndY5E+n074elROugqNO6gjvR5GapijyGuhOEOQDKEOygOb6K26i4b2UaY9okrimyN42tQow8Vth4T5pODPkorAZAK+SZV7if63LMBq8b0pnW6C/KUHr4Peecc+joo4+me+65h9bX1+k73/kOXX755XTKKafQZZdd1kITQV/hBS8+jKuDXv1WHYWb3Z4UJpEZb8nv2CpCAwBoBylaNxmJIZ0G+tHeSR2ntZt2JJuc1irTWryclGjNQvHssWSxP8+Sw4P7ziQeZHr7OMh2nxTBVAhGnZzJjQeZ//2i8N93kWitYltKZVo36LQW7UPMGQBpIouu1mXDr7VqprUtpzHDz+PuMKd1ZNH9z2PCUMwpTDUiAAD1kaY4HTUP77DGwHNTafBAf1GO0k7rf/mXf6F/+qd/osMPP5xc1yXXdekFL3gBnX/++fTOd76TrrvuujbaCXoIL4JxTGp10KvfjgazvOqZaDE2VPeV4sF2EDUiJgAAloNcdEYxUcX6f3PIReYkyMq0ruO0zn4M3mMzOa0dJ1n4+0oAT44Fs4ZhazHOGkRSFJLztetlWqdE63E0d5vOuKJoPd3smH4/XNBmt4LQsdWg09pxppsSYRTDaQ2ABrvadsIcv248iGs7HmSHFmLkx7dx3J43deWGMW+Kj4PV/0wAYBOuu2IyaHD/0mWntYquS4nWPP/ubru7ROlVThiGtHv3biIiOvzww+nOO+8kommhxu9///vNtg70GlVUCx/GlUE/ej0aTrsQFQ8y+1tLMUJ20FzgCwDQT+Sis8kFqHSy6hNP7leqFGLUndYmcdeUaa0yQYVIoUeNDDynUoRFFfjxHc31nWR1V1MnEqdHvXiQokgxYZHQnkSvFH74uY3VugwMTnQAwA5zWjdViNFSN8KnoLyuxINYKsTIfycrmdZi85rhTeAui2cA9JHEaT0/b7R9kqUKmyoeJPELI9O6HKWd1ieddBLdcMMNdPTRR9Nzn/tc+uAHP0jD4ZA+85nP0DHHHNNGG0FPUfEgOPawMiRFrqZdB3e+fPvE4LT2XIeGvkvjIEIxRgB6TluitYyi0CdwKk6iUqb1dILLTmvTY6gYCum05kW/Oy9ab4tIDuWga3GyLGNYkqKQ05/HoihkFXzN6RHHsbGwrk66cGNF0XrB37OKO9F0BLMOA9elLYogQgCgoTKtd8CCu25GNE+J2850ZvhPsuPiQSw6rfn9LzeM1SbwDvhMAGCTPPNKL0TrHKf1Ttj4bYLSovXv/u7v0qOPPkpERO973/voVa96Fb3whS+kww47jL70pS813kDQXwI1eZhmn1bNggPdQeWFDqYdLQsDG5xprY7LpQeV9aFH4yBSDkUAQD8JDcJuE6Sc1nohxjpO69mwI4sn6jimTOvQIFprkRwDz7Vy7FvOw7k5nlaIscq1md4vvcjeDqJCxaykoL1Rwmkt/7aL2qzciSWu7aah2E0dfIgQABhR+cEdFgqagl9r1WWMjc1NSV1neNPYy7SefrWxyahMOmK9o28CAwCawWSKY9RJDkubgmWRZpBRqhAjnNZlKC1av/zlL1ffH3fccfS9732PHnjgAdq3bx9ESZBCDtphFC8sugS6z5bW6fKOYRIPYj6qPhp49BBNSokbAIDuEbTltJaFGHWndc5kdRF6pEcjTutJIhSz7trmZFm2S48HGdfI+57eL32cWW4sToLs15TOwC7htJ4J4a6TvrYmqhwpN7lZ6sAixE7I7QWgDDbzg5dN7UxrSzFSTGQYv5aJtUzr2N57Um2mi41wxEkB0A5ZpjiiZG0QdvRkP5/0JNJEa8QJlaKaNUfjsY99LARrMEdahMAHchVInNYcDzITrSfTDNSkEGO6a+FOuoy4AQDoHlFrorWIB9EeNynEWCfT2tw3ESVCsHxanvyaROstlY9tJx5EitZ6IUam6qYwi/gcMyI3Fic5C4BUpvWkeK0Cvl+RwpGmzYRFJG6W0p4MIwMu8IM5DAApkniQ1f9sRDUzom07AZNCwt1Ym9sqlBaJE75tozJ2ZSFGiFAAtEJeQXZXGRysNqkwMkJPGiqGPja5ylBoVv/a17628AN+5StfqdwYsFrI47STKKIRNeN8AstDP96yNue0Nh/jH2kxIgCAftJeIcZYfJ+ewJmyI4tiKp6ow2tOmR3NTfBznNa+61px0EmdgyfnuoOuaqZ14rSeviY5uc4To6SgvTkuPuFWC48CDsAqxcs2GnZas4sOtTkASGMzP3jZ8NhQsZtV94stidY8HnWmEGOFqKcqJIUY239Pjg0bsDjuD0A7THJiAn23ffNIHTZmxc2Hvpuau/O8HcbOYhQSrffu3dt2O8AKIt1yO8GJsRPYnHW86zNBIHFap4tu6sIQ/94WnNYA9Jr2CjEuzrSuIsxyIcbtnIKFKtNaPqeKsch2Wg+E07rNY89pp7Uza3P6d6rGgySZzdPnkKdh8oTatNO6TCHGMk7r8u7EraYzrVV1d8xhAJAop3VHhYIm4ZdY1blsY5yQsHjTmXgQx47TmrViG+/JwBBbNlCZ1hCtAWgS0+eNcS2d5KhK1rwUNVPKUUi0/vznP992O8AKki6shQ/kKqA7rZPYj6mYrQQJbaKsCjZCtAag17RXiFHGgzSYaa0KMWZHjJhiKKIcp3W6EOP8fZtGzsNZM9EddNULMaYX2VKAzhNq05nWJeJBMjY2TfBLKuOeYUfLWkOiNaq7A2CG+7wwiimKYiUcrCIsNld9idZF65qFI5uGx+62nZDREjKth958pjXGCwCaRZ24NGVau+UNDjbJOgGIOKFyNJJpDYAJueBFEaPVgI+BzxVinAkdk6xMa+33AAD9JOW0bnBhlqqBEJhF62qZ1jOndU48iEPzER/KaS1Fa4dF60REdy0cezY5refiQaqK1trxROm0zlv4Tyo6rbMipEyovPBSmdazMaqxQow47g2AibyNxlVDxYNUzbSe9de2NJWwZgZ307iWnNY2RWt+z8uNbd/DcX8A2mAcLHZahx2dp2UVCB8g07oUpSvVHH300bnHo2655ZZaDQKrwyTltMYAvgpwwS3ueFm83pjLtDbHg6AQIwD9JiVaN7gCT40X2sK2jNCpw3eRxRN1THmjvPhNL0i5qONMAHdddQ3anHNKdxo3R5+HFcmINqGOJ0bzhRjzxm15eqrMCZoyrvkkHqTww6sIqqbiQXDcGwAzcjMpCGPa1Uzt007CXXBV0bpK1FEduhYPYitzlucnNtacpqLCqnDvim/iAGCb5JRentPaapMKs6GdUmeSTGv0F0UoPcU499xzUz9PJhO67rrr6Gtf+xr9xm/8RmMNA/0nVVgLA/hKoO8W8tctzWk9V4hxCKc1AKtAkMq0bq5fT7n2tJmnydFUFE9zEpsyrVlQkHoCt8czxoMI4XXWpdmKB1FOa008qeq0TpzEMcVxrPry6W05TmvRqK0KmdamI546VYp3bWgbq3XB8U0AzAQ5dQhWjbpxGzZipCR1M7ibxlbmLF9eK05rtd5BpjUAbZNliiNK5sNNrkmaZCvLaY04oVKUFq3POecc4+1/9md/RldffXXtBoHVQR4XxFGp1UDlMg3ThRgnYUyTMFITUl0YGg2mXQ1EawD6TZQSrZt73LQAkn7gJpzWzNDPfgwpKIQGp5opRzqKp31amw4y2S5ugq7fVy3EKPM4wyhOO61zXlNVp3VeMR0dJfSUuLabY7OjpSqIBwHAjPxIrHo8SF2ntWfJacwk45eVp1uIrcxZ5bS2cJ1NG+GIBwGgHfKKeHP/2tVpml4PjEF/UY7GhrNXvvKV9OUvf7mphwMrQMppjQ/kSqA63tlu4ZrYNdychEqQ0AWU0XDa1SAeBIB+IxeDTZ6gGafyUZPv4zhONsMqCLP6BprJrZ1kJye3qUxQcX9Pe6yB5xjv2zSRylNNnHN60bOqhRjlAiCI4tTGYl4B5XQhxhJO64yNTRMqp7CKaN1YpjUWFQCYCHeQ0zoWfXAVbBdirJvB3TS2Mmd53LYSDzJ7/w996bRm52RH1TMAeoqKBzF0wolo3c3PXXYhRsQJlaEx0fqiiy6ixz72sU09HFgB5KIWhRhXg00tL3SX76pJ/NY4VMKTvhO6PvRT9wcA9BPp+m1yniUXeamijGLxWUWY1RftJpcG92Ey0zowONX0u/qem0yWW3SQ8UPL1zJfiLFiprV4nHEYpaI+xjkL/+qFGGcLjxzHO+NV2BDgtjSWae1ChADARMppveKfj6imCJwU7G2sSbmEHROtbWXO8hg+DqPUeN4GgclpPXudE8RJAdAoST2U/jmttzLmpQOYIkpROh7k2c9+diojK45juuuuu+jee++lP//zP2+0caDfTFJO6472JKAULAiszTpex3FoNPDo0XFIG+NspzU7sjcQDwJAr2mrVkGQMV7I56gSgaGLuSaXhinTOilkleO0doXT2kI8iBQg5uJBCjiXTciNgCCMaWMciJ+z/77y71LKaR1mu2V0Ehd7iUzrWVvWmsq05kUFRAgAUoSReaNxFambEc3DkK1Ma/5zdKUQo63M2VSh6CiuvJlbhLEh6oo3YyfBan8eALDNJCcmsDdO68x4kG62u2uUFq3POOOM1M+u69IRRxxBp556Kh1//PGNNQz0n1RGKRZ8K4FysQlBYDT06dFxOI0HicxFtvhIDJzWAPSblNO6wQV4Vg0E+X2RSAmdIk5rxyAoKKe1uLvJaV1FWC1LIpgkt82/rupZq44zFeyDMKLNcbFxO6jotE5yCQuI1iULMUZRTNszsaApp7WP494AGJHmsFWf40cqLqra/W2ME5K6cSZNY8sJGWnvSb+ZYcBIYtJJ3hQo3AtAO2SZ4ojIyonHOijDX2YhRswvi1BatH7ve9/bRjvAChKkhAd8IFcBU5ErzqveGIfi+E56UGEBYXMSEACgv6QyrRs80pbl4JaTuUpO67ns5/nHcAwxFJEhe3k+0zqJR2pzssxtyYsHqZppzfcdBxFNojjVR+eN26l4kDKFGKP5hX4W6toWFAC2gqQdTRVi5HauemYvAGXZSU5rU0RTGVzLx9e5z9RrHywLW05IuSkwCaPGTtzMPU8Uq/mCnGOgcC8A7ZDUQzGI1g5nQ3dznqZHqzKIEypHadGaiCgMQ/rrv/5ruummm4iI6IQTTqCf//mfJ9+v9HBgRZGDNhZ8q4EpL5Rd1FuTUP2ddUFiDU5rAFaC1PHbJp3WMsc6mHdaTx3B5RfgRQoWqkxrmndaS53ac+YFcF6MtxkPkggmyW26eFJF0Ff3dR0aEzutkz46X7QW8SAVnNZFRGuvpNN6Q7R9rSGLHS8qUJcDgDRyLFj1TE7ug6rGg3DfbctpHdUU2ZvGltPa1ntSngyT9RkGOO4PQCtw5I4x09prfx5eh6wC4b4yRaC/KEJplfk73/kOvfrVr6a7776bnv70pxMR0QUXXEBHHHEE/d3f/R2ddNJJjTcS9JN0MS18IFcBU17oaFZkMeW01oSixGmN9wEAfUbPjGyKVCSIwcFXVZTV+yJTLIVrclrHhiJL2n1917FSYMuYaa2/roqZ1kQ8cZ7231L4zdtslm74jTJO6zDbLaOT5IUXe+xNNT65jTkMWZDAxjsAacLUqZvVntsp53LFbsXG5qZExZl0TbRuWbRPRXy1+J6U44GMQ0yO+2O8AKBJVPyoKR6k607rCZ9ST8uuQxRiLEXpVc5ZZ51FJ510Et1+++107bXX0rXXXkv79++nZz7zmfS2t72tjTaCniI7Dxx96D9hFNN4ttMpdwtHg2k3sjkJExedr2Vas2g9RjwIAH2mLdE6yDiZk5WTX5S5GA3D4/C6Pk4teOePV5vysctGWFQhcfklt+niSZ2CUwN1pDlOuaYLx4OUclqXiQcpJ3SohUGDR8K5YGSTRUcBWAV2ltN6+rVyPEjJvqwuXYsHcS0VYpQP3+a6U46NcuzlzWOseQFolry5o295U7AsG5lOa8QJlaG00/r666+nq6++mvbt26du27dvH73//e+nn/iJn2i0caDfyEnsqrswdgJSmFgXu4X8/dY4TPJKtYmyKsRYQtwAAHSP1pzW0rVnyLSuU2hQYnoclWltiD5J5VUa8rHZ4RFbKMQoBYj5qJJ6mdZEU6F+S/TR+YUYk7/ROIgojOK5a20iq+6BCX5JRa9tkhvYXFSdDycMAEaCjD57Falb2NB0mqdNoo4VYvQtxYPYclqnC0TPZ1pjzQtAs6hTeqaIP2Uu6OY8bcsQrUok40G62e6uUXqV87SnPY3uvvvuudvvueceOu644xppFFgNspxzoJ/IrNNdwknNgvTGOFATOX1QYad1mWPkAIDuIZ1izcaDyEiQeQefaaJahCLiLgsK8tUkx8GzIzkGXhJD0WohxgLxIHVEa+X2iIrHg+gibtENSV5UFIkzcZQ7r1ym9dqg+rXQgRMGADOR4WTKqlI30xrxIHac1mE8P3dog6SgcLrWxhCZ1gC0QpYpjkg4rS2dZCnLxuyUuV4YFif5ylF6Zn/++efTO9/5Trrooovo9ttvp9tvv50uuugiOvfcc+mCCy6gAwcOqH9gZyN3vFDEqP/IQgJSMBmJvOosF50s1ggA6C824kFM9RBME9UizMWDGDOtp1/lywmVuJrttJ7Gg7TvIDO55uajSuoUYpwttIMoJT6PwyjT5awvyosW2eW/89Av4LRWR+oLPbRwszTntOZrA+ccAGmkUL3qc/y68SB8t50aD2KrEGOUiqxpP9Na33yFcxKAdhgXcVp39HPH9bxGmtN6gJN8pSg9s3/Vq15FRES/+Iu/qHYXeVHz6le/Wv3sOA6FIQSqnQznHxNhwbcKJIUE0p2uiv4YB+rvrOfG8pGYSRjTJIxquQIBAMsjNERoNEE6Tmr+2Lmek1+UuXgQU6b17GtscJHL+8+5m11H/X+r8SCz4VMKJnoXWjXzm0gcaY7iOfE5jGKjIK4fwywqWk8yFvsmyl7brNzAOqhFRUePnQKwLHai07pyIUbHrhOwrsjeNLac1rKbbvM9mWXQkaeWAADNofSFnEKMtjYFy8L1vObjQXCSrwylRetLL720jXaAFSSVS4oFX+/JKnK1Pkzyqnlhrw8q8kjM5iSEaA1AT2nNaR1lOa3nHc9l0IVmkwDrGgQFk2htclorB52FQoypeJAGndaqeFQYzcV8BFFMvkED1jeii8aDlMm0LnttuQ1rw+ZEa2SUAmAm3FGZ1tOvVUVg6/Egavyy8nQL8SzEaBFpxUFbfE+qovPaBVbxIAHWvAA0SZDxmSOSm2Ld/Nxl6ScDnMwoRWnR+kUvelEb7QArSGDIJQX9hTOZdKf12iDJq06KpqUHlV2+S64zdUFsjkPaszaw0GIAQNO0Fw8iBZB5B1/VjS5daB4aHic5NZbcZhKt5/OxHeGgq9S8QiSCSXKbLp7UKsToJxNnve7AJIzmcvimt6dfMI8Piwii4n/PsgsRXhisN+q07vaxUwCWRUogXPHPR5JpXe3+joUYKYlpo3OZeCXrE1TFlvtfbb5mbIqv+iYOALbhTSiTgaXrorU6Bag7rV2czChDpeC/hx56iD73uc/RTTfdREREJ554Ip155pm0d+/eRhsH+s0kRDzIKrGVsVM4Ek7rRGBKDyqO49Bo4NGj47DwMXIAQPcIWhKtxwsyras6iXWh2ZiHZ8i0DkyitaH4oXLQWSjE6Dh5bamTaT2973YQpWK9iLIX/nOZ1iWd1kXa6xk2E/LYzNhYrQO70Fc9sxeAsqSL3q3256Nu3AYPO7biQcKa7W0az1uGaN1ipnXG5mtyaqmb4hkAfSXIybT2Oy5aZ+knyMAvR2lrztVXX03HHnssffSjH6UHHniAHnjgAfrIRz5Cxx57LF177bVttBH0lEm4cya0O4GsnUKOB9mahGphb8orHc2KYxUVNwAA3SOy4LQ2jR1VncRzmdYGsdRRonXyvPx9qhCjnl/pOsJBZyEeRFwC3WximsgXhV/XI1uTuf/LGrurZlpnFbAy4ZTMKdwcm4vd1EG60AEACanTMSs+x+c+WB9PimKKoGoTHqc7UodRbUC2HRUpx+E2Nxqz8nUHiJMCoBXyDA9uh0XrSRipNY2eaa3ihNBfFKK00/pd73oXnX766fTZz36WfH969yAI6KyzzqJzzz2XLr/88sYbCfpJKqO0gx0JKMdmRpGrdDwI74TODyqjoat+DwDQT1JO6wYX4KkaCPKUDjuaKhYanHMkGx7H1YpKT9vAQnFejrRLnkHwbhqTy28uHqSGOsEbAo9sJREfQ8+lcRhljt08yd7lu7QdzGdhZ1HKaV3Sxb4xmTmtm4wH4ar0OL4JQIooNm80riJxzUKM1kXrmiJ709jK9JaXt82NxnFGFKIq3LvinwcAbJOVI0/Ubae1nBvrUXtJIcbutbuLlBatr7766pRgTUTk+z6dd955dMoppzTaONBf4jhOfQix69x/VF5ohtN6cxyqibIpN3Z9MO0ztuC0BqC3mIoVNsEkI9O6djxIIae1IdPa5LQ2RHK4FuJBYkM+qUlArwq/rgMzp/Vo4JHrEI3D7LGbBYHdawPaPrhd2Gk9yTniqaNiWwq+z7bG5jGqDj5ECACMpGoPrPimDr88p2YhRltLIR6b9ULEy8JTm38tO61jO+/J5MSQeX6BjFoAmoU/z0andclTeTbheanrTE0ekgEy8EtRepWzZ88euu222+Zu379/P+3evbuRRoH+o4sZWPD1nyynNf+8OQlzBYm1YeLIBgD0k7YyrYOMTOu8HLsimHKodZJ4kOS2cDaJTAnFhsdSDroW55zcLqmXNJppPbsmBzZnovXQU7EYWWM3/432jMrFPgU5xXR01JHPok7r2dhiKhxZlYFywmBRAYAkSm0urvYcPzRsHJah7AZcXepmcDeNb2Fzl8hecVAex4a6CDU7yRXH3XR9AtBX8qLlfEuZ+VXYENqJvunJrwXxc8UovQp83eteR29961vpS1/6Eu3fv5/2799PF154IZ111ln0+te/vo02gh6i76ZjF6n/ZGVaj4TTOs8VORpMuxtkWgPQX9rKtM6qgaDiJCo6xoqIu0khRvnapl8XOa29ksJqFSKT07qAGF8UJVpvJfEaSUEp89itROu1ARGVz7TWF/smvJIbAlmngeqARQUAZkwnYlaV2vEglkRbRsWDdES05tffdj8qL2+b78lJhtN6IMa1Vf9MAGCTcY6+4FqoLVMVnpeaaq34MEWUonQ8yIc+9CFyHIfe/OY3UxBMFziDwYDOPvts+sAHPtB4A0E/0QtgTILudSSgHFnVb0ci05oHDlNu7DoX4WadSQAAIABJREFUYhwHc/8HAOgHcgOyWdF63l1NlNRDqFyIsUCMhinTmp3WUvTWhWLfda046ExFteYKMdY4Bq4XYhwNPSV6ZIkMLFjtGU1F66InaNTGZoGM8rI5sFljVB0GOO4NgBHZ5636pk5d57Lt4+s8NndEs16K07rN92TWCTA5Dk/CqNFTPwDsZJLip6ZM6+ltXRStswx/RCjEWJbSovVwOKSPf/zjdP7559PNN99MRETHHnssra+vN9440F/0yQIWfP0na7eQf96ahLRr5qY2uRlVjAjiQQDoLXJu1WQ+ZZZrL6ibaa3dz+TYVqK1uI2b4OU4rX3PsVJgyySY6GJ8nYJbPOE/sDndUFwferQdTPvprLE7ybQuV6ugTEY569qFCzHmLA6qwgukVRflAChLqs9e8Tk+90FVRWDPUDehTbpWiJHHrrYzrVPFQVt8T2YVFJaCGsYMAJohimI1DzaJ1nxTF0XrPDMFb3pF8fQ1dqUGQVcpJVr/x3/8B33jG9+g8XhMp556Kj3jGc9oq12g5+jFmzB4958sQYALLG5OQjoknH5vcjOqGJHJai9uAFhl5KKwSXexHDMCg1uqKad13uNEC5zW+mMNRaZ1m0OcKR5EZuMNPbdygTAioqGfLsS4NvDUaRnT2B3HsRIEOB6kqNM6UM75AqK1OvJZ6KGTjdUG3W04vgmAGdlfrvocv7bTWhVitBwP0hERxFbmrK33ZNaJIc91yHGmmxOrvpEDgC3kZ8lkePDYad3BQoyqHthwXnKVr2USRbTLxcmMPAqL1pdeeim96lWvos3NzekdfZ/+4i/+gt70pje11jjQXybItF45sgSBtWGSVZ3lPpD3QzwIAP0lJSg3uAAdh+bFpsqxq7j4NrmjdRK3dHJbaFj06wKA77nqtlbjQVi0FuvjlAO8RhFGomTh/chW4rROHMbzY3cYxcoxWLYQ4yTniKeOV/JI+WYLTutF2d4A7FR2ZKZ1xdIB3F3bElV4yVVnM7NJbGXOhpbek8nm6/wbYuC5NA6ilS9OCoAt5JrAFD/aZaf1htJODH2Fmz6Zsat0/sXOovDw+573vIdOO+00uuOOO+j++++nX/3VX6XzzjuvzbaBHqMvdMfItO49LAjoRa44qzqOiQ7ORA9TXum6clojHgSAviLF2SYjMeSYMQ4jJRJkZUcWpUjBQv4V+XJ48ivd1fOitWOlwBY/dCoeRLyMOnnWRInofWBzlmk98NRten0KorRYpQoxFuzX8yrA65SNXmmjECO70Ns+1g5A30gLhKv9+TCddikDjx2xrUzrjhVitJU5Kx++zfdkkGPQ4Qgy04YvAKA8cgMq12ndwXnaltJO5hVp2X+s+mmlJii8Crzxxhvpj/7oj+gJT3gC7du3j/74j/+Y7rnnHrr//vvbbB/oKfoON5zW/YdFa72wiHReH5y5qE0TuTVRsBEA0E9kX95WpjVRMvnk5xtWdBPPOa1NmdYGQUGJ1l62aD0QhRjbnCwnearmeJCq0Sn6/bn/Hg09tUlgmkjL8X3PGhfYLeu0LhIPMv1a9NpuZIxRdfBzYlIA2Mmki96t9hy/rnPZltOY4c3lmkNDY9hyQkaW3pPjnM10XxVXw5gBQBPIz5JpDu9Z7l/LsMHzasO8VK4pTAYRkKbwcHbgwAE6/PDD1c/r6+s0Go3o4YcfbqVhoN/ogzUWfP1nY2LeLfRch4b+tCthzcckoozgtAag98j9xyYjMeY3OuPZ7c06rU0Zn3xLKg+zoNPaRoGtJE81uU22q348SPo1jAZe4hYzbDjL8Xw3O60Li9bF/56eW+7a5jlaqoJMawDMpETrDooFTRLVdC6bIqjaxLTRuUxsZc6mCzFacFob5hMDjBkANArPQweeY+zTeK7YRdGa63iZzBSO46j+AubOxZSa2X/961+nvXv3qp+jKKJLLrmEbrzxRnXb6aef3lzrQG/RRWoM3v2HBQHTbuFo4NE4yD++o+JB4LQGoLe05rQ2jBlrA08tDqsKs9KVkVWw0DEIColTLUe0dpN4kDYX46aj6W4LTmtmNEgyrU1uMR7PXYfokFkI30bReJAoe7GvU9ad2EYhRpXt3cHFEADLxFZ+cBeIDRuHZeAuts3aBxLutrsSD2Lr9ctxuE2n9aJM6+nzY8wAoAkWxcp1WrQeJ7ViTPiuS5MwRH9RgFKi9Vve8pa5237t135Nfe84DoUhBCkwXzUZC77+owQBQ8c7Gnj08CwPlcg8keNdRjitAegvcl7V5ARRFz1YLFVxEhUrYLkFHMmmTOtggWjNjg8bx75VETDRfHk56ovW6euyPkwyrU3uD3aw+Z6rJuJbBTcjeWI+8AtkWpfYEBgHkfqbNSla86bHJFhtUQ6AssjP5aqL1nWdy3w/W4UY6xaObBp2Wre9FpTDVZvPNcnZTFenc+CcBKARxgvMK54F80hV8rQTotlrmqz+GNoEhUXrCJ0vKAGc1qvHRo7TWt9BNGVO8e8g0xqA/hKKuUCbojW7pCY5jqYiyEluVsFCU8E/fm3yPunvp+3hZrVZYIsvs5MRVVK/EGP62q4NhdPaUERZHo1WtQomQaHn4oV8kTYn0SuLr63cDM1aHFRBXQfMgQFQRFGc3uRbcZdYZNg4LINnOR6Ex6+qhSObxlbmrBzDxy1uNKrNV5PTGnUQAGiUvM8bUTIf7uJnLk87IZqeACVCBn4ROrIHC1aNLNcc6C9bObuFelaTKa+UO+wtOK0B6C2ya2/S1aC7olisrhsP4hWI0eBfMRVilJnYJte2DQedSTBJt6XeVE4XkNcHXuIwNjmtRS419+ub42ICAYvgRTYhyhRi5Ngpz3UKFXksCo56AzCP3t+1mR/cBfjjX1UEZlHFWjyI4aTQMmHHd9uidSoepMWNRuW0NmZau6nfAQDUY1EBb/4cRn11WhP6iyJAtAatkOWaA/0kjmNVAdeUy6TfNswrxAinNQC9JeW0blDI00VBHjMSh0U9sYAoW/g2ZVqbndZJv8YLU+Wga3GIM7nm0pnW9YSJuUxr4bQ2ibWyKI6KBymZaV1kE8J1k7/LIrc1LwzWB16jxceSmJS4VTc9AH1CFx9XfY5fN27DKbEB1wRxTZG9aXwLhRjj2J77P6+gMEQoAJolUPNxcwfM/VwXo2h5bpyXaU3UzbZ3DYjWoBXmBAh8GHvNOIyUoGOqgKvvIJoECXbkIR4EgP4iF91NLUDjOFaZdYzKtF4wWV1EOoc6a8I7awfNvzZPPK/nzQvFngWHh0mAKPK6ijIvWvvq9ZkW3tItPVKxT0EhUXey4JinRL7eRQ/Nm6prDUaDEKWz1HFiDIAp86L1an826mZa2xgnJKE6ndMN0dqz4LTWH7rN/po3X4fGTGuczgGgSYJFTmvP7kmWMrDmYdJOiJLXtOobv00A0Rq0gnRiEWHHue9Id7Rpt1DPajJnWk8j9BEPAkB/SYnWDU0Q5ePoYwYXwKvqJnYLxIO4OU5reRcZNcIiug0HXSKYJLfJLrZ+pnX6/qOBpxbepoX/RLilWbSOYprbeDARlCisKa/3og2SRW6Wqgz8pA1tHjcHoE/on8cin/0+wx/9qiKwqW5Cm0TqdI6Vp1sIb/62OU7qj93mujPPaT3gfF2MFwA0QlKIsX9Oa9ZPMp3WyLQuDERr0Arj2YePd5YgWvcbPnrtu45R+JFOa991jG4UOK0B6D+hIfe5LnKiyWMGu5SSDdD2CjHyraZM65TTOuVunjmtLYgRfHm8jHzt+k5rLdN66OW6P1Rki+umNiyLRD8p53yheJDk+0XXd1Gxm6r4cFoDMIceDbXqAl3dQoymjdE2idRJoW6o1jYKMepjRJvvybxaGzwejzFeANAIiwox+pZrBpRBZVpnOq2RgV+U0iud/fv30+23365+vuqqq+jcc8+lz3zmM402DPQbHtBHmgAB+okSBDJ2CmVnnCVGrA2n3c3mJEQ2KAA9RYoVTS1A5WSN+xJ28+Y5morgFShYqLKTxZyRhXQvI5KDH0vmLrdFZDjq7RbI6i6KHr2yNvDUbaYCa3LBPvBcJXBvFjhFMylRWFO+3kX6w+aCMaoqUtDH8U0ApuhO61Wf49fNiOaxw1amtYoH6YpobeH166J1m5uMk5wTQz6O+wPQKPrpfR1PnW7o3ji0ucBQoQwiK77x2wSlV4FveMMb6NJLLyUiorvuuotOO+00uuqqq+h3fud36H3ve1/jDQT9hCewfBwCO0j9ZlGnK4WCrJ1QjgchItqa4P0AQB9pxWktFpc8Zsw7resXYjTlTxIlsRsy0zqK5p1qphxpG8e+Y4PLz2vUaa1lWg+83IX3OEy739cKnqIJo6RQVqF4EPGCF8WDLHKzVMVxnE4viABYBjajGLqAKaKpDNyV2YsH4efdOaK1zeKgeSeG8ooYAwDKo8wrGZtwvDnXZqHXqiwy/fFrwkm+xZRe6dx44430nOc8h4iI/uqv/opOOukkuvLKK+mLX/wifeELX2i6faCnsEtuNBMqsdjrN5wXWsRpnSWgpI6RI9cagF7SRiFGFjwch2iXn97o5KJ/TRRiXJSHJ4epYKFonS7EaKPAlJNyWif/X1XQz7r/+tCjIS+8jU7rtPud+/ZF8SBS2Br45QoxLhJ7Fm2s1oEXFeNgtYU5AIoyL1qv9hzfdNqlDMmJHEuiNceDdE20thCjxbS57gzC7NgyHk9XPecdAFtMFmRa+5ZPspRhkX6Cwq3FKb0KnEwmtGvXLiIi+sd//Ec6/fTTiYjo+OOPpx/+8IfNtg70Fv7wjQbI6lkFFuWFrmuZ1iY816Ghn0SEAAD6RxuFGNm1NHBd5VxSonVUPE7CREq0XpBpLQUF/t7PuD9/rxx0Fo49y+a7hqKQVdEXAqNh4rQ2CbXK/T5rEPf/i/p1KSIUKR4pf2XR9d1csDCoQ56AD8BOxKartQvw0FA1I9pVmc5NtSgf/vt0RLNOROsWhRl9jGhzkzHJ2J2/wIkItdqfCQBskfd5I5L9a9y5+FGem64PfOP/D5FpXZjSK50TTzyRPvWpT9EVV1xB3/jGN+gVr3gFERHdeeeddNhhhzXeQNBP+MM3UvEg3etIQHEWCQJrBZzWRNKRFzTYOgCALYIWRGuZkay7DvjrsGqmtSHSQ4cnvHKI4ueVmaCuIR/bRoEtfmz5/Oms7ppOa02IGQ2TTGtTzl6SMz6931pRp7UQEYpEmsi/3aLr21YhRiJklAKgM+e0XvENnbqFGD01xthyWs+etyOZ1r4Vp7VeiLG952IXtWnDmMdTbHIC0AyTnJMNRGkTRJc+dnEcK/2E63rp6EYdkE3pVeAFF1xAn/70p+nUU0+l17/+9fSsZz2LiIguvvhiFRsCwEQ5rZOdpS4e2wDFYDFiPUO0lnnVeUfVlSNvjM4ZgD4SteG0FpXBh1pRkjKF+0yk40EWZFqXcFpzP6ccHlYyrc0CepF86DzmnNYDLykOY3DGJTnj0/sVdVqza95xiokpMg5l0XuNj2BmjVF18JUTBnMYAIjmBblV39AxRTSVgbtoW+sgFQ/SEdHatSDkzhcHbd9pbZpTYLwAoFm438g6VSjNJV3SmraDSJlhpE4iSQwi3Wl3VzFfwRxOPfVUuu++++jAgQO0b98+dfvb3vY2Wl9fb7RxoL/wZEEuIIMoJr/59SSwwKIiVyOxg5iVOSXvvwGnNQC9pA2ndeKicNQEbqIKMdbMtC5QsNDJybROCcUG1zaLAm066CLDUe9UprXfXKb1wHNo4Lm5C2/OGedrMBoWc1oHYfp+RfBch8IoXpgFy07rtRZEa3bOwQkDwJQ5V+uKC3ShYTwog43NTUldZ3jT8IZvqzFaWvfcpmjMG7emE2ADHPcHoFECsUYw4XdUtJbFydcy6rgMcJKvMJVWgXEc0zXXXEOf/vSn6ZFHHiEiouFwCNEaKPiooBQ5MYD3l6T6rXmfSzrq87JK1TFyZFoD0EukWNFcPEgiTM9lWi+YrC7CVDxRR+VSS6d1ZHZScf/GIrpnwUFnjAdJOcDrOa2liMx9dN7CW+WMz9rA/f/CTGsWrUsoKZ7aUCiWaZ2VG1gHLhppikoBYCeii9SrXnQurhsPojY3m2pRPnVF9qbhdrTpJtTHiDbXnBOtGLEEIhQAzTLO+bwRpefDtjYGi8Dz0qEwgujgZEZxSs/ub731VnrFK15Bt912G21vb9Npp51Gu3fvpgsuuIC2t7fpU5/6VBvtBD0j0DKtifCB7DOq+u3A3OnKv3Oei269oCMPANBNUk7rhiaHstjiICPTuow7V+I4DrnOVPjNPFpoyrTOWPS7swfjhakjCsC0xaJCjFUFfUYK89xHD7SYFon+N+H+f2NRprX6Oxf/W/LLXBgPojZW6wn4JnzltMYcBgCipE9ynGm/uepHm00bh2VwC/ZjTRF1TLROZc5GcerUUlPMFQdt8VoHOQWi1WmxFf9MAGAL5bTO6De8jjqtuX5XXoHwATKtC1N6dn/OOefQKaecQg8++CCNRiN1+2te8xq65JJLGm0c6C/84Rv6rlp0Yte5v2wuKHIlb8/Lnh0VzD4FAHSPKIqNwm5dpAiqBMKGMq2JkkVk1mPwfDemeRe5fnJEOa05HsQgeDdNtCDTuowIbEKK+dyX6zEtEv1vwpuZW4syrSu45os6FBedBqqDvpECwE6H+/61WeZfGK12sXUp0lfBLXhipCm6VohRitRticn6pW3Vac0RWaZCjLO4Lll4GABQHe4zsswrXon6Jzbh+l15BcIHyLQuTOnZ/RVXXEFXXnklDYfD1O1HHXUU3XHHHY01DPSbpLDW1Dk3DiLsOveYRYLAekGndZJpDdEagL6hO6ubyqeUeXUqikHLtK7qtCaa5T+HOQULWVAQa0ye+OqLfp4cs+PDRlapcvm5UrRO/r9M3IYJKSJzH+/nHHGeaE5rLjCzqFaBjIEpilvQyb6o7kIdVGQN4kEAIKLk87g2cNVnbxLGNKyZr99V6jqteRxpM9NZwuNRG47mKqSc1i2NlfOFGNu71pMcpzVEKACaZZF5xXUddeqnS6I1z4nzCoTrkYggm9KrwCiKKAznBafbb7+ddu/e3UijQP9RR6dcVy2o4bTuL4sEgbWBFK0XO60XOfIAAN2jreO3nIcqxwuVaR2k85OrwCJpVsFCU6Z1kCVaz/o3FmxZf7UdD5LO6m4u05pd01xgyvQ31oviqFoF4/wxXjmtSwhb6lj9okzrBaeB6qBc53DOAUBEUrTeGXVrYsNplzLYLsRYN4O7aTwLTuv5+Ul778e82DKIUAA0S3JKL3uu61mI6isLaydreU5rnOQrTOmVzste9jL62Mc+pn52HIcOHjxI733ve+lnf/ZnG20c6C+BcFojZL7/bC7YLZS357nokGkNQH/RJ4PNOa2zxwt2NNVyWs/WywszrcVtLBRnx4NMv9oosMWPLQUTx3HUUfXa8SApp7WXum1sEGonKjqFndbFYp+Ua76E0zq5vgULMeY4WqqSJ+ADsBMxidarvOg2bRyWIYkHaapF+aiTQh3JtLaROauPEaaxqyn0jVsJRCgAmiU5pZfdn3Ef06VCjFsF5qV6JCLIpnQ8yIc//GF6+ctfTieccAJtbW3RG97wBvrBD35Ahx9+OP3lX/5lG20EPURWVkbIfP9Ru4UZHe+ooNOaFzgbcFoD0Dvmjt82NMkKRIG+gRZLUbcQIz/u9DEWZFpLp/Xs+fXj1Sw+KKe1jXiQiPNU59sSxnGpjGgTg1Sm9SweJOeIczCXac2bkfnxIFXyyZN4kPzf42OYeY6WqsA5B0AaFh6Hol9e5UU3d4N6H1wU7mJtxIPEcVy7vU1jI3N2fn7SZjxIdtQV1rwANIuKpPNznNYsWndosyiJVs1xWvt8kq877e4qpUXrJz7xiXTDDTfQhRdeSN/61rfo4MGD9Na3vpXe+MY3pgozgp2NPMqBXef+wx3velYhxoJO60TcgGgNQN/QJ4NNrQn1GghE00VhHMdq4VmnECMLn1mOZIfmXXD8fabTWsu0brPAVpKnmr7dcxwKKa4VnUJkdlrrmwcSPWe8aIHdSZVMa7fY9d2aTNvZhtPaxxwGgBQsEHquQ77rUBDFK/35iMTrrYJnMR5EPkWXCjG2nTmr75m0mmmd47RWcVI4mQNAI7CxJa9+Sxed1oXiQThCd4U3fZuiUpl13/fpTW96U9NtASuE6mA8B0WMVgA+4pK1W7jLd9WENG8nFPEgAPSXtpzWcpNTOXzDKOWUKhMpocOibpZbm01g6UzrmdNadzfzY836ORkPEsdxK862KCNPlX/M63OLIK8Lb0zmxXrxseuB5rReVGBXHaku0V6Vab1AAGCndZ6jpSp6zjoAO51QFKLzvalovcqfjzhj47AoavPNgpApx+muxIMQTdsSxHFrG7xyYyGM4lbXnHknwPI2fAEA5ZGn97NQonWHtCbWOvILMSJCtyiFROuLL76YXvnKV9JgMKCLL74493dPP/30RhoG+o10VKlKyvhA9pZFR1wcx6HRwKONcZi7E6oKdiEeBIDeoS+4m5obyrw6ebRWiiB1nNY8mc3qm1SmtXRaR+bn9bXHkg8ZRnGtdmaRVVQreV0NFmLUndaGP7IstCzvs6jArnLUl1B+vIJO9kXFgusg3f8AgCSux3Wmp2O2JtFKi9ZZG4dFsZlpLftKp97Q0CgeO/JbLsS4y3dpYxwqV3cbbvO8qCs1Xqzw5wEAmxSJlvOVaG2lSYUoUiDcxyZXYQqJ1meccQbddddd9LjHPY7OOOOMzN9zHIfCEGIUSGdeIg+y/xQRBNaHU9E6b1BZH067nEWOPABA99AXm405rUWmdTJexCnnQROidZZLQ+WNGpzWulPN1R5LZl63JUhk5ZN6Kvak3qLcE0e3E9E62/0hC2cSJfdZ6LSOFi88dJJ4kOzfiaJYxYO04bTGogKANMpp7SaRTqtcqJTHhqrG5aKbb00gh+VOOa1bdpvztR3ORGui6brTc5sfE/SILAmckwA0izqll2PQ4I3BLsVsbCw4pU6U1IWARraYQqJ1JN4AUYfeDKC7yIxSle+FD2Rv2SqwW7imHSs3MRpO/2+RIw8A0D30iIamuvTAMF4EUZQSCeu4iZUjOUMs5Uxr1hNkISvdpaUyrb10pjVRe4LEoniQvD63KAPXpXEYqT7ez4nE0I9qjgqeoJExMEVJCjFmX9utIHneNp3WOC0GwBTltJ5lWhOt9hw/Es7yKvDw1VaesyQVD9KRTGuiREBva3ODx8ldIn5qEkaNF+eNolj9HU31JPJOKQEAyjOJ0kYJE77aFLPSpEIUclrz+LnCm75N0aGDQ2CVSBVi9LHg6zu8W5iXy8T/l3f0ezSY7pMhHgSA/jEvWjefaT0U4wUvbj3XSTmay8J3zSpYqGday9epL/pZtGAR3bMgWocL40HqCxMswq9rTmvTuK2Lz3yfrQVO60mYvdDPQrnzcq6trJHQhmidLCo6tBoCYIlwn5RyWq/wHL+5eBALTmsZD9IdzZo8b/EGZB34YaVI3cZ7Uo4Dpg3jxKi1up8HAGySnN7PcVp3sKDhVgHtJCn03Z12d5XSovU73/lO+pM/+ZO52z/xiU/QueeeW7khH/jAB8hxnFqPAbpDoHahXVRGXQFYFMhzLLBYkOeiK3qMHADQPfRCjE0tPmUNhMS1lxT2KiNymuBFZFYBQD3TOs+pxuIuOz6kAbytxbgqApYloDfhtJ49RnJiZnGm9VwhxgWbkXnFq7JQGwo515bHk12+W2tzIwt13DuACAEAUbJh6Wl1CFaVKGPjsCjJ5ltTLcpG9pWdigcpcGqmDtL9zC+7jY1GKYQPDWPZTvg8AGATPZLOhF/A4GCbjQLaSVK4tTvt7iqlVzpf/vKX6fnPf/7c7c973vPooosuqtSIb37zm/TpT3+anvnMZ1a6P+geKn8olWmND2QfCaOYtoPp3zNvt5AF6byd0PWCBbsAAN1j3mndTJ+eHi+SOKlJBZHThKscyfmidRGndZIj7abuS9TesUQWIXSXX5KvXV+Y0AVovubjICceRCvEuFkw07rM31MJHTkLkSJuljoMcdwbgBSsx3musyMyfLPqChSF72YlHkQ8R1VneBvwWNqa01qMk4MW3c7BglobO+HkAQA2GSsDSwGndYc+d5tqbpqdxqxOZiAeZCGlV4L3338/7d27d+72PXv20H333Ve6AQcPHqQ3vvGN9NnPfpb27dtX+v6gm8jMS1RS7jdSYM4rJpCIHXnxIOy0DhpqHQDAFnOidUOOBp6s+Z6TyoOUYnYd9BxqHV7XK6d1nmit5WPbiAfh5szFgzTotOaJcxLzlF1cTRZaJkr69e0gyhUkknlB+XiQvEu7USA3sA47QZQDoAyhKFTrr/hpyjiWInC1x5DjRNyyE1B2wW2cPKlK66K1qEPRZvHcVDyI4fr6cFoD0ChBgbkjfxabWpc0gcq0HmbP0fkE6MRgEAFpChVilBx33HH0ta99jd7xjnekbv/7v/97OuaYY0o34O1vfzv93M/9HL30pS+lP/zDPyx9f9AscRzT1268i046ci896bHrmb93+4Mb9PXv3K12tveMfDr9WUcqUVNlXooiLTZ2v+56eIuuvvUBeuVJT7BWgOSSm+6mW+59VP387Cc/hk456rGtPd99B7fpypvvp5ef+Hja5bezSJfIKI+1nOdTTuucnVA+IvPw5oQ+e/ktc///+L1r9KpnPKFTE20AwJS2ndbTeJBEINQL/lUlcSRnOa2nX41Oa8csWnM75X+3NVnOylNdlNVdBl4MrA21eBDDuM1CtnJni83MT/3zzcYj00RE/3rL/bP2lokHWSx0bBao0F6HNgUQAPqIdFqbnKXX3fYgTcKYnnN0e3NhW6RE4IrOZbkeCaM4V3y586FNuu62h+iVJ/1Iai48DiK6+IY76cFHx8b7nXLUPnr2k/ci71WBAAAgAElEQVSp8aJLRRiJhGitjZOPbgd08Q130sGtxWaWZz5xLz33mMOM/8eP6zj8ngwzNxq/ffvD9Mj2hJ537OGF2h7HMX31Wz+kux7eoke2JkTEMSQG0dqFUQuAqhzcDuiSm+6mFx//ONq9NiCiZEM0a25JVKxodxH+475H6R9vulsZJR6zPqDTT/7RlN4SRTF99ds/pLsf3sp/rPun2lCeoQIRusUpLVq/+93vpne84x1077330otf/GIiIrrkkkvowx/+MH3sYx8r9VgXXnghXXvttfTNb36z0O9vb2/T9va2+vnAgQOlng8s5rr9D9HZX7yWXnDc4fTfz3pu5u+9529upEu/f2/qtnEQ0X/5qaOISCxqfeG0tvCB/IOvfpf+32//kD7/Kz79zNMf1/rz3Xr/o/TW/3p16ra1gUvX/97LGq9YzXz4H/4P/eVVt9HHf+lk+vmTj2zlOSTstF6UF7p3NCQiokN2Zb/uvaPB7DEjev//usn4O0ccuot+6ljzpBQAsDx4Mjj0XBqH+a7aMvDibui7It8tSuIkai6+D5mJmYdm9E288ORXE+Q4rfmY3yG7fHVf15kKG205reOMPFUWabktdTh09hjcRyu3mGHc5sgQlYPtezT0XRoHEf3x179f4LmKj428RsnbEGhbtB5AhAAghcy01p2lYRTTmz93FY3DiK77vdNyj0b3gSjltK4aDyJE6zjOXXy/9+Lv0De+ezf997c+l17w1ERU/fsbf0i//j9vyLzf7l0+Xf/el9XO326LxGmd7kf/x/++LXM9oDP0XbruPacZxzw2UXkupU5smXjL56+iR7YmdM17TqM9M2Esj6tvfZD+77+8LnVb1rg79Pm5u+P4BKAv/Ld/+Q/64Ne+T7/+sqfRO178VCKiQgYWHofqrkt+/X/eQFff+uDcY7/m2U9UP//rLffTO7X+II89o+w+Bif5ilN6JnHmmWfS9vY2vf/976c/+IM/ICKio446ij75yU/Sm9/85sKPs3//fjrnnHPoG9/4Bq2trRW6z/nnn0+///u/X7bJoAT3PTLdFLjrQP7u0X0Hpzv9P3XMYXTPI1t0872P0t0Hkg0FWUDLZr7X/Y9O23D/QbMToWn4NR+6y6eX/tjj6G+uv5O2JhFtjMPWROu7Z38bW69xLASlPH71hUfTnpFPZzw7W0g/Yvcu+v3TT6Tr9z80939X/OBeuu/gmB7IcJEAAJYLL8KG/lS0juKpoFo155NJMpIdESclCjHWdFr/Py97Ov349+6h5x9ndlXpmdaBaI/+2t512tPopCP30KlPP0Ld5rkORWHcXqZ1Rp7q7736RPrW/ofoxB/dU/s5fvfnTqBrbn2QTn7iY4goEWrjeLoIkOK9LLRMNHWy//H/9Uy6TNvINrE28OhXnn904Xax0z2vECMfwVwftCOOJRvvWFQAQJQIA57WZxNN498e2Z66Zh/enKyWaF1xKJL956K9TZ4D362tw/jnJz92nf7TU5I4zSiO6W+vv5Me2Q5ocxKqv02X8qyJpGidvv3+2es99ohD6Jmz8cfEX193B42DiB4dB2bRmh3mjqPGJtO6cxxE6hof2JwUEq3vmjkqDz90F71wtpFw2gmPN/5u3nMDAPK575HpZ1PqUBMtks5EU4VeH9iYPv8Ln3o43Xr/Bt32wEZK35Jte9zuXZnrCubIx4zoOTmn73/0MWt0xsk/Skcdfkitdu8EKs0kzj77bDr77LPp3nvvpdFoRIceemjpx7jmmmvonnvuoR//8R9Xt4VhSJdffjl94hOfoO3tbfK8tOj3W7/1W/Tud79b/XzgwAF60pOeVOUlgAx4MbqooBJ3IG//mePo//u3++jmf75ZuZ2IZKVX12q+F4sGto7x8mt+0mPX6WO/9Gy6+IY7KYrbfX7+29iqkBsULIZ2zBGH0m+98scWPt5bnncUvcVw++s/869038H7O5VHBQBI4D5n6LtEszncoqPORWA3lK+NF1UykE38p6fsSy3ydfjRea6bN0E++UmPoZOflF5YT8Xk2Ho8yIuedgS96GlHmO5Smhc89fCUq28gNiknYUSem8zHTFnjP3/yka2c/CmSg8pj4hriQQCwQpASrdOuVrkWWLSW6AOyW68qBEvX8yJRhf9/UytYvjmeXt8XPPVw+qPXPEO0L6aLb7iT4nh6vXkd1DnR2jG7n3l8e8mPPZ5++2ez1xB/d8OdFETZm8N83Rwncf+PDX22aa26CL7PM47cQx993cm5v4tMawCqo8aRcfL5UTpEgUKMdUVrNki88yVPpb+57g764v++bW4c4/7gWU96zML+YBEn/uhe+tgvPbvWY+wUKonWQRDQZZddRjfffDO94Q1vICKiO++8k/bs2VNYwH7JS15C3/72t1O3/cqv/Aodf/zx9Ju/+ZtzgjUR0a5du2jXrl1VmgwKwoPs1iR/oqmcVp6jCjfJ7ONEhHBSGaVtw89ryxGlHF4qB3R6RLrN5+fO0tbRM+mab5PkaA8megB0EZ44yly5RUedyzzutAZCUgDQ9HxtwPNgjuFQ8VYFbXVF3MB1yCrE2Cayv5+EUerkUFNZ40UoIlpvcDzIoJ32DHLyvQHYiZhcrdwvyAW+Lrz2kSbiQeT9FhlOwgzzEF/Lde0Up+M4NBp4tDEOaWsSdj7TWp/ic7+6qL2e60znBRlrBFmIMe+Er1zfFs2RTdZ6i2c7Qw9xUgBUhccR+TnlmLq8oux+U6K1mm8n+pauienaD7BD6bXmrbfeSq94xSvotttuo+3tbTrttNNo9+7ddMEFF9D29jZ96lOfKvQ4u3fvppNOOil12yGHHEKHHXbY3O3AHoE63rdAtBZOKw6Ylx9qmXmp8r0sDOA8P7HntJ4egeRrMHAdGrf8/NxZtlWBWycputWycKSO9rT6NACAiqSc1jOa6Id4kjrw0+PFJMp2PDeJyrTWxo+iz6sXcmwalWltUYSQ/b2+8JeFltsmq3iXZKuEoFAFJcohHgQAIspwWoer6bSWH/uq5mUpyC7SSbOd1rP1hkEoYdF6YxyqcatjmrW6BllOa73ocdb9s66ffBz9PSmR69uiZir+WxSJffQtRmICsGrwZ3ZjHIjbFhsllIZQcx4uo69Y29E1MR7X8gosguYprUKdc845dMopp9CDDz5Io9FI3f6a17yGLrnkkkYbB+wzEZPOOOeDn2SQumoCtZlyWidONZsLPnbp2trh5uMrI+G0bvv5efJkS7SeGI6Ct0GySwrVGoAuIjOtmWZE6+Q0h3TtBWKcaZMkHmT6fOMw2XQtAovJbXXJ3C6bp70911HPpxdjlIWWbbSDaIHTelxcUKgCj32TAGMTAESy6J0zl/kOp/U8rlaIMY9M0Tqn4Kxah4m1m81NziL4apxMv/5AFPXMI0v0ZpJ4kPyNxs2UaF3WaV1AtHazixgDAPKZGDY/TZF0Ok0VYpSFbEczI0SZvhi0R2lbyhVXXEFXXnklDYfD1O1HHXUU3XHHHbUac9lll9W6P6iPHOC3JlHmB1JmfqqdKENOmO85VvMg+SlsVWHlnUDltFZZZu09/4Zlp3VTxdAWkeRRtfo0AICKsFCRigdpoB8KokQkTmda29kwSwoxztpTMMefKSKs1oH7RNsZpYNZ3FWW07rtyCgiIm8mPuRmWk/aPaqpjppDhACAiNJOa2XWCDiLNFkLLDq12Qdk7FPVLk/eb2E8SGyOB9nIcfcljsBAfb/IuWwbnuPr4wmPb0VF66zrJ2NR8pzWfEKWqLzTuohIlRdNAgDIx7T5OS5gYHEbKsQoa8hw5FyZvhi0R2kVKooiCsP5Scjtt99Ou3fvbqRRYHnIAT7PIREI8YIH8S25ey2Odf//7L1bsGzdVd831q3P3lufvk8IWRICDArYSLKwsA1IoaKIGCIKJcYpSCUxyPCgmFRRJA/YJUSVqxTAFVyVkOISk6RIqIIXVGAlLi5lFwkhIGKwE0lIIIRQMBgwEsHo8p1Pe/fpXpc8dI85xxprzrXmXHPO1b26x+/lnLPP7nuvefnP//iPasF8L3TpLrVY2LLN8hKLle3CTmvtdhSntSBcMyaHbcx4EDpf1G1HxNFlool0prVvPMj4ZjqUjjg/lgTjP/jc7Svqh4AfwVgPh+0+7QZCV1CJCCEIAMxpzRywdO8w1R9nDdChZ+7BYZbpypWp3ge2TOuxce6WZK/ShoTnhM1p3Tg6rUv1PRsXrfMsG6167Td483Nau1Tz6Mak3WjFsiAIQ0wxU7VHpnVovy9qEqEVLBSu/QjL4L3jeOMb3wjf933fp/6dZRk899xz8Pa3vx3e9KY3RX1ywvJQsZXmCXGo+xYv6vvj6XXTdiob9BAPkt59jOBibSlHFC9LVi7BRI/fdZ36XEJzm1yhLsiUxOr8KwhCGvDarPJMCahRnNYqI5nOF9rhmzqGAl8LDql7z4O61KI13YwviU2s9RX1Q0Cn9ZjQcz+S9RoDLULIgaogAHCndX+Nf39pTmsyrocMwQWr6LGBc+r93uLus2Ra4+9ox/H855oCnL+4qOTqtJ5yUuLwnBOntWnf2cvKdVy/3PvEg5A3Xg46BcEPU281F6OEzrwPu+Y6UrGB8SBcD0sdSSeY8Y4H+d7v/V74qq/6KnjVq14F2+0Wvv7rvx4+/OEPw4te9CL48R//8RTPUVgQKraOOSSo+xYXS3gSTU+2qzJftLQWFzO7pTKtWclYlad1Wu+aVi14l4sH0S7IlMQ6JRUEIQ045uR5BkWeQdt0UQ7PtIM761WrqAOz1BZj1kjRPx7k8GeqMZl2M18Sm1hLGy2nBh9ibF542B97S6RyWmM+ai1zkyAAaNNEQVytOG5eXiNGnZUc4l7O8wygnZ4zbU7rBwen9QMVrc/NaW3JnEWn9dQhcTlhbNHfyfFIJ/r9dK0A9qnmoW7Qum1h4+8PFISrBeNBtobrdEyHcGna7UJDDv2UvrXvjxOSaX0avEXrz/qsz4L3ve998I53vAPe//73w3PPPQdvectb4Bu+4Rt6jRmFdUI3ZWMOiT1pVHW3sYvWZS9bbAGnNRMdUqOac3CndSLRnC5il2/EmHbhVSR2KwqCEAaOr+VRtKbNEkNAEbTM+4ecu4UOzLhT2nfMU7dPdFbanigepLQcwtJGy0s9h7F54WEhp7U01hKEA81xTCiKDDZtP4rh0hoxdpEODXH8do4H2ffdfboZ4HDrfkfiQfDuzy0exOaUbhzf36lqTHxf8ywDLM4yOZ37jRhdndbucwxdN+zrDmAz8suCIPQw9UZwWZPH6i1D45W0vmUbi0W0XhJv0RoAoCxLePOb3xz7uQhnAD2Vtjkkuq7Tm1bSiBEXp3SDe2istVweJC6kl2j6CGBwWifO76YbgKUzrVM3QyvEaS0IZw3Nnox5yETnExSoDw0AF2oCi5nWx3+7uDpMt0+XaX18nIVV66o8fhaWTOsl4kFszbsoizVilFJvQQAA5rTO+4c6l+q0Dh1+XefMesppvRnOhzckHgT3BlNxG0tjc0o3jnFTU07rlsyTOdgNRL2sXNdMaw+nNXWMy0GnIPiBOtT9voGuO8TN4rU9Vo3h0rTbBRyeiyxT4yo/fPUZD4R4zBKtP/ShD8EP/uAPwgc/+EEAAHjlK18J3/qt3wqveMUroj45YXmosGxzSFBRsZdpjU7r44CTZce8O0szpxTgQnq/kPD5wDLmeJlkqscDWN5pnboZWqw8KkEQ0kCzJ2MeMtVkjFERS612cW+Si9aHP3Gx6uskxl9L1WdAl6cvLFobnNZd1/UaLafG1ryLkjpfMHUFlSCsDVx/lnkGLVv33l+Y0zqWc3kqk1k/3vB9BCD7jcrutH7YNyerzJmisIzlDXFIjzHltG7I6y5GDjt78SCO6xeMB3A5GM2OBzl0DSMIghuoQ3UdwJO67fURGDOw4HI0ltO6yKnTmsWDKO1nlowqzMR7J/jOd74TXv3qV8O73/1ueM1rXgOvec1r4D3veQ984Rd+Ibzzne9M8RyFBaGnzjaHRN9JrZ3WT+oW2rZTAw66kzbl8pnWizutK8y0TtuwqdeYYCmnNXFBpkSc1oJw3iindZZFPWRSc0aZK3dv3RBxNPHuGxfFPB7EVZRFB12XWLRePB5ERXvp+Yw3Wk6NrXkXJXWpZkkOUgRB6Pc3qNg4QbNIL6IRI4oYoaK1wwEcgPl9BBiaZCi0t1BLPptzwrbGpwcgY0w5rWkDtbGqV7q3dXZaYzyI48GoHHQKwjx4xT/VnMYMLLGc1o0yidBxlcWDiNP6JHgfEbz1rW+F7/iO74Dv+q7v6v387W9/O7z1rW+Fr/u6r4v25ITloROszSGx62VW53BL8rq2daMWASjgqiZGS8SDtCg6LJxpvelnWu8SPT5dxC7lSF4s01qc1oJw1uDQn0d2Wu/JnKHmi7ZVPRZSx4Nkg0zr/sHrFNoBluDJwekaMerPQn/G9POuyvSi9ZRQAZB+A1GJACEIPajQiOMSjhNUFBxr6L4WdKZ12P1op/H47+H/D5zWY40YSRk7jW45J2xr/MZRZFdOdYvoTx3bYweNpqzcKXwbr1VFDtt9K3OGIHhCe6s97Jveune8EePhz9A9ienw6+EYVYJ7Bcm0Pg3eO46PfOQj8I3f+I2Dn7/5zW+Gj3zkI1GelHA66ObU5pCgJ9NVkcFNqS/a+12jNvwoNJjcWqmolWi9zEKBlyXr7MvLcVrzzzMVLo46QRBOR0Ocz7GangDoa74sciUQHkoDD+Nd6ioPlWmN8SDqoM4v0zpVZFN3Iqc1d1ACDBstp2aqJBxg3IEYA8m0FoQ+VGhUrtJjAy26Tr1nDrU1oitd4jRinJonakM2+K5u1TxpdFofy9Tvd020xpGxKSxCMv57SmSfOiinorVp7kLuaTyI45juO8fohtIyZwiCDzQH/n7XMKPkdKZ1qPGNjiN4vbfHqBL9vA7zWqpIOsGMtwr15V/+5fCud71r8PNf/uVfhte//vVRnpRwOugEb3NI1MRhkWUZ5HkGN9XxNGrXqAUXLhrU4mGByRsHq6U2l1vVAOqwYEy9uaWL2FRNvzjcOZ8K5ahb6HUJguAHFSqKiEItjeOgh2M43qXO0+eZ1ns1x7k9bvp4kMOfi2daG0qseaPl1OBDnNJpLaXegtAH12llnvX6EAD09w6YBbxmWlIuHoJrw17UbPrvo/672WmtHYGuzuWlsWXO4vvh2ojRJkqpBmrkIMVU9bqdFQ/iN8cs2ctJEC4Jusbc7rWmhJqTjVhOa1rZSK93Oh5vPTLuhXh4x4N8zdd8DXz7t387vPvd74bXve51AADwq7/6q/CTP/mT8J3f+Z3wUz/1U73fFdYFHSxsTmtT3ufdpoTtfgcPe50/hBt+/HNXL+e0XiI/G0C/Rziw8S7qsXk4YaZ16qZbyrkpbjZBOEvogWVRxDtkwjmjyvOekwIXiZhznYoM+mJCbZjjRm+Pm/HkmdYLx4MosVa/Lt5oOflzcMgp1KWaaZriiGtOEPpQN1rBBDrqruZZoGtEjb+B450SrSe2B7h/2Tcd7JsWqiJXY1yZZ6pPEAXHvu2u6TUkPCdsVTM4/7s2YrQ6rcnhgooHMTmtPfdRXdcpd7av03qpqEpBuBRqFlNbO8b14TUfYuijB2IYD1IVGeybDu53Dbzg7vD80P0tmdbL4r3C/5Zv+RYAAPihH/oh+KEf+iHj/wEcHEFNs/4ss2uDxoPYMq2pwIDQJiC4cEChYckNHw5WqTKlOTrn7PAaF3VaL5xpnToepBCntSCcNS3JyozptFbVOWXWW5jiRj11wz/eiNF1kYy4ZpXORQtEae7fhp67aTyI33sTylSO6b4hZfOpnNYoyi1w8C4Ia6DpHWD2170PFofwWonVU8Bljdt1XW8eedg3B9F6oprk5iimHuJBdCbrOWGrpmyJa9/l9ra9D12foLBvzLT2jAd5UrfKxe06x4zFkwiCYIfH1Lo2Ro8RMUrHJhyObqoC9k2txo1e1Ys4rRfFW7RuF3KwCqeBbsoePJzWt2TBhAslFBqWnLyV03qhhYIWrQ+XUuoy4r7TepnXiK9lrGtvDGJm5AqCEB+VPRk50xqrcMo8hyLPIM8OQgE6olJXefBM651npnXqJrKnyijVJc76dS0VF6WeQzH+PaOuuZtNmjlKueZk/SsIAKCvxyLPYMPWvXSdattHrIk2knMZz17HnIB8nHvYNfD0TTWZqXxHGzFiw+Szy7Q2V1PiusK1EaNNlGrJd3IsnqMvWk+P6fQ77BwPIk5rQZgFvSYfSJ+0Sad1Eb4Op2Mzjkd3mwIeb2s1DuD4kWUAjxZoRi5o5N0WelAh1C5aY1zE0Gm93TeD0uqlJu+u60gjrfQLhabtlNii40HSvla62FrqAF/HvSwUDyKitSCcJW0i0Ro3oRWbM3C8S+3q1WLC8fl4Np/NIrrOTcQqT/elNFQOLdWYF5lqcokRMgfxLK1oLY0YBeGAFq1zve5tDU7rSxCtj2vt0J4COh5kRLTuhqI1AMDD/hCzYhOt8ecPu+ZklTlT2Jzmbeu2x1CilEX0b8jnZJq7kAfPTGv8Pm+K3Hnew9eylLlIEC4Fes0+7GttlAw81HKBXq54f7fkQBCgn2+/dJ+Za8d5hf8rv/Ir8DM/8zO9n/3Yj/0YvPzlL4cXv/jF8M3f/M3w5MmT6E9QWJZ9b7CwNWIcOm+p05o3sapGTrxjQje1Szii6PuDYfybMq2rnC62msWc1suIFDHjBgRBiA8OawfRejpr2BUuEuOcQXM8U4KZ1lhWreJKHB8XDdmpmuPGcvr5slEHzqQRY+vnQg+lnDgcuV9gA1GSZtKpmm0KwprQojW5PlSm9aXFg8QZf3GNOzZl8nEO38uH3XiGqhKt9+cbD2Jb43s7rS2HhyoeJB/fd/ac1g7rlwfPPGsAUPEk0ohREPzoO61bYmqZyrQO1xDoGh7HK6yk505racK4PM4q1Hd913fBBz7wAfXvX//1X4e3vOUt8JVf+ZXwtre9DX76p38avud7vifJkxSWg54K2xsxosBA4kHISVTNSqvLhTKt6f0v4YjCZjO0RIQ7TuI/JhGtF9o7LyVSFBNl4IIgnBY8KDuI1vizsOu16zp1yHgypzUTnX0P6pSDLplo3X+cpTDFXe3r/qF0amzNu5CpsvkY0Ex1acYoCNotW+T5oBJhS0RB2z5iTcSKZ5oay0z/h3Mg7jesTutKG4d0I8YzE60tB92Nq9N6IhOcNiyulGgcwWlNDkZdMUVrCYIwDV1j3e/qgaZkw2V8naKXaX1c9t1W+fG5NL0/b6QJ4+I47zp+7dd+Db7iK75C/fsd73gHvPa1r4Uf/uEfhm/7tm+DH/iBH4Cf+ImfSPIkheXADSlAf+HZ+x1DqYZuxKhLOXAhWyXOeUaoYLDE6faWOB/Q4cUdJ7Hpx4Ms67ROLRwVEw23BEE4LQ1xcMVyWjetjnXSfRCOojU2Ykx8YIbjN74S18YvCC6WU+mZ3Ymc1ihM14YqJmy0nP45jM8LUw3KojwH8j0Q55wg9J3WPPOdCtVP6naxpuGpaCOJwPxw1PhYbHjh7j6r05pENJ7qkHMK20E3baA4fvvxeBUdi5KNxnP4NmKc47QeiycRBMFM23a98WG7b5xNJFNrRdfHR1Q8yEaPrQAA2504rU+Fswr18Y9/HF7ykpeof//iL/4ifPVXf7X695d8yZfAH/zBH8R9dsLi7HtO69r4O7VBxLwjpWncib1UHmRvY73AxvIeM+bIIrIylFPH5IF8Jks5kn0FnLlIprUgnDeqEWOWqUiM0EMmOm7rOePwp27EmPbADPfKuGDFQ0fXjOTU0UZ4t0vn55maKKv5fyGnta15F/KwwAaCrnXEOScITCAkxpSu6waRIGuPCNE9BcLux6Uih4usPEfVNs7RPRjtPXFO2A66a8fnWygh2ua01r83tu+ke1uXvdr9DKf1UmYtQbgkeLTrQVNyy7SeWiu6QIcWFQ9SHeJBuNM6pVFCMOM8Bb/kJS+B3/3d3wUAgN1uB+95z3vgda97nfr/x48fQ1VV8Z+hsCj9AHzzZKtLufXX50Y1AWlJnMQxMqOwn3jHhJ6QLVHCaypL1guVNI9PF/9L9fdQpTmJRQoRrQXhvFGb4SJTLtzQ65Vu6vicgeNd6kxrFBNQS+B9GSZvrxo5ps60Xlq0PrywHZnPeKPl1NiadyH4HUlZqkmd/qmqqARhTdRkjMS1Yd108KRugV+q6xetD3+GO60d4kF4I8Z932ltG+dwD9a0nXIEnpnRWjmt+f7MVWRXTuvJeBA9P+3YeN22HWz3w0PYMeZEUKU2MAnCJcKvx/tdM9CUbEytFV2gYzOOn7RfAP0zZSSdYMZZhXrTm94Eb3vb2+Bd73oXfMd3fAfc3d3B61//evX/73//++HzPu/zkjxJYTn6AfjjTmu6ab3DPLV9rZ3WxwEEN/67Ou3kvXSmtalcT5dTp4oHGTbESo0ScBKLFDGaKAiCkA7qtM4tpb7e90nGahwDUATBMRabGqWCl237CrPLZVonuXsrprgrX0E/lCkXO7rmUjqtsyybdPkJwjWhhcZ+9j3NC8bDnoeV51p3kQ4NcQwZmyYGmdbH8W2q+Rfdhzz3pO493rmATms+T7o7rcerdvE7mecZOUjp75OesH0od3aaeNj7zzGmaC1BEMbh1/bWUL1vI0bFI43iw8rGu6ofD7JEJJ1gpnT9xe/+7u+Gr/3ar4U3vOEN8NRTT8GP/uiPwmazUf//Iz/yI/DGN74xyZMUloNOsDZ3hMl5qzJ/ds0gPmSzUCNG6rTmp+spGHNapxLNe/EgC62FtICTWDgS0VoQzppWCZYRndbHTWOW6U2rcvjWWBaYWrTuZ1rrOcxPtE417cQSTXypDHP3vsZM64XjQSzfs+1CG4gyz6BpO3HOCQJQoTHX695WR4Nsihye9wvW8awAACAASURBVKiAj9/vL8ZpHTr8zmrEuOvHg9jGuao4fA77poNPPTn87lRG9NLg85nttJ7IBG9INjb2XOCPxWMv3ZzWhzHfp5rHFK0lCMI4/BDp3qAp2YjZiJGORajx4NixRPNvwYyzaP2iF70IfumXfgk++clPwlNPPQVF0f+wfvInfxKeeuqp6E9QWJa+09q80NwZXGj6om4GjRjLxEIu0ndaLyBam5zWqpz68hoxbsRpLQhXTU2cTLEOmfYkI5k3tEWWiqLAzfCucStHRKbKlmM9r6U1CByT6bpAlWou5OKbEq1VJ/fEG4hNkcOTupVMa0EA0jyPNmKsW533uSngblMeROuVO61jN2IcK1/n49w9y7S+3di37bdVAfumhk8dxZWleyBMgfM4b6To7bS2ZVr3GjGa4zn4AYrLIeSUy92EKVpLEIRxBtdrT1MaHx9iaAh4Wzp23pL4WwCq/ThLqEIkvN/xZ555xvjzF77whcFPRjg9/Uxrm9MaSzWI07qijRj7orYqHUwssjY90Tr9QuHecNqmOlYnenzalX2pA3zdBGEZt2NoYzdBENKAm/eD0zqOaG2K4uBVHa6O57nkSnTG5zSc40Zvn5k347HAsX5pp3VpaGblWqoZiynRWgkKqZ3W4pwTBAWOCUWea4GQ5CnfVgXcVIef369dtFZiaNj9FKp3grtovcXmXw4VJbebAp7d1iQeJOjpRie3Oa0N7kYT+Hpsc4GK0coza9UrP0BxOYTEClefah6ZLwTBn8H12mvEmN5pjUMzrVLR+tZhHNDaz5kNsFeAvONCD3rKtW/MpbDotKLOW3VR75pegxYAHSPSdWldtPS+UwvkALosmZ6+Y/Zqqrzp7W55p7UpwzwFeP/itBaE8wTHgjzPoh0y8R4IAEMXb/oDs+NfULT2dBO7OOhCOFU8yIZk1SKuTXFiMZUlvVSpZqkaa8n8JAgtjWIgAh01c6jYwAuJB4nXiNH+OwOn9fH93DoIJbgPe25b9x7vXMA53ua0nmq4XExEkjW0EaOj09plrzan2W8lmdaC4I3JaY3XkKvTOuSaawxVH1TfAqDajzitl0ZEa6EHv9hNbmstMgwzrR/2jc68Zk7rw23TCa1UMFjSaX1jaMSYamN734sHWWYxtJRI4dJZXRCE09EYnNahmzLT+MIPyJYae1CI2Xs6rQvm1I6NKk9feMVmEmr3tZvAEIup6JWlROtKfd/FOScINNKBVmTQ2Ly7Y/n06p3WKp4pULRGp/CY07oz78HuXeJBjv/3qaPTOl9ojHYlt6wZGuVkD3Nad71Ma/NejH8XXfaKeBuvRozHNcyulvlCEFwxaVC1Y1xfkY2vFV0wRfHR+NvDn4fx1ecQS4iDiNZCDy4qbw2LTR7/AaBPou53jcrwwgGGDjRJRWsaD9J2oyV4MXgwLGRSl4Q97JYXrfeOTRBCkW7bgnDe0M1lYXFN+YIiKB1f+FiTOh4E712L1m4Zekge6b2wEcvp50tpEGr3SzutLSXlyFKd3LWALyKEINDmeRWJAFRRCptC5cyvvxFjpHiQfDoexBZn4TLO3R7jWFQ8yJk6rbkw3yin9ficMtX8Wa1P8sx6yMi/iy79h+Y0+9VNjGW+EARX+CHPIdPazUQyVZXngimqiMbfHp7T4Tn6HGIJcRDRWujBN2Qmh4SpkyuWSWzJqRgKuPT3Ujqg+UImdRmvaSGjFu8JHrttO3hCBvSlsp9NhxQpwK9JKuFHEIQw9OYyi7JABNAiKB1fuCDq6nieS6YcGod/u3YrR1JXicRqBOZLZci09n1vQrE170JMh8cpSDm3C8LaqHuitXa10qZ1d9VliNZdpENDl3mCuwSVUOLQDBD3YbhvOzOjtfVwV4vNE7efiCSjvR9M/RgAhkYsF4ORqX/RFLZMbUEQ7Jic1spEMhkfFG4eMfWPuWMxV3MOsYQ4iGgt9DCF4HO004p2V9UNV3T+0OFnRZ6pUouUWdN8IZj6hNtUrlcldGPxz6JZaDGkRIrEtelTncEFQTgtNO8tltPaJILy6InUURT07ruu00K64+PiVBhSljiGFk2S3L0VVeLc63WxzCEmopt3mefUpZzWJgFfEK4VOhfQpryfeqJj81Rs4NF9vVZiOa1zdjhqgq9/cZ+hYpBGxjksVz/XeBBbpJiz03qi742KB8nNcxeAIR7EYf0yJ4JKeiAIgj/8EIlqSlNrzqmm3S6YDCI3lniQ1JF0whARrQVF13VqcLgbKeurDaUat8fsun6nV9pYK/0EvrTT2rRZThlxMRCtl3JaG5yQKVBO64VelyAIfmihIo/mtK5N88UgHmSZTGuAg0A812mdauyKlanqixZqaSNGN4EhFrp5l/n/Tb0lkjwPEoEgCNcOrbqhe4HHxyaAdxsqWq/7mlHxTIEiMN587KCX/x93Wo8JJbhve/zkPBsxqsxZLlo79myYcqo3RHBSjRAtRix8r1z2iS4ud05KA5MgXCp4PVINShsl3OJBQkRrvcfRP+MVQ0sZJYQhIloLCio+PH1TAUA/Q1n9nqFUQ3UJ3zXGDX+VOOsZYCiepF4saOfDMq+TfxaLNWJUn2dq0VqcbIJwztAFXYymJwAA+9Y+X9j+HRu6t2+7zttNrMqeEw1dsZx+vqj5jLywfe2X9x2Kbt417rRO3cm9lPlJEBQ9gZCMBY+3ewA4bOhVr5v9pTitw8Y8JaqMZVqzSWS7d3da3zKn9bllWpsOutu2U5VE05nW46IU7f1QlZZM6+P7iHtcl72ay3vPkca9guAPXi94fe7qVsWiblxF6wiNGOlYrw9f3cdiIQ0iWgsKKvI+fXt0ThtE653RaY2L08bYxGqJUikunqTeXJo2yylf58BpvZhovUzjrVgimCAIaaBChRITA8c6kwjKHRWpxx7qYG47EoHh6CYuUmdaG3L2lqDMh24x0yFDStR7a3lrdel22uezEeecIChaUrJNx4JnUbTeFDoL1LCPWBNd9HgQd6f1PW/EOOL2vWVl7OcWD6IixcjrpwLTlMieT4nWvcga814M30fc47qM5/q9dz8YlXgQQfAHr0e8PgH0QehUXN9U024XTE3PB40YZ1ReCHEQ0VpQ0MkVT7nujfEgQxcaLpaatlMLptLktE546szFk9SbS8w1uiEDF56up3jsOVlsMVCHFInLwfHuJdNaEM6ThggVZQRXA4CeE0zzBZI6mqiXaQ06JmtTujqtD3+maiLbRXL6+WJqJmWa/1NSqBzTqUzrxE7rIt3cLghrA8fIPMt6YsKzD8e8z6pQkT2mhu5rAof10Him3BKPQRk0IuM5qmNOayainJlmrZ3WZD6hAvTUFmNKlFINHTN71es9c1o7xYPMcVovUF0sCJcGXo/Pe1SqCkicUybjQSaadrtgagrLDwNVJJ2I1osjorWgoJPr828OG0CTQ0Jt6A1OawDzqdgSpbUDp3Vi8fNhf3i/7shrr8qEmdbHz2JzfIxUAgmnNjTeTAF+R5Z6XYIg+NEQoULnx4VtyvbqUGw4X9j+HRtbprXr47o02ApBiyZp7t+GyWnNGy2nRrvYzf8/p0nWHEppxCgICuW0zjPIiHBtigcx9cZZE7HimQqHGCk8BMZ1/sO+gbbtYHvcb4w6rZmoWpyZam0q36eitWsjRtseQX1OJGedi9IYt4J7XBcj1ZwM20qc1oLgDY2XxesN55QpDSKG01o1czU4rXd1C03bqTFEnNbLI6K1oMALvcgzFXlxb+j6bSqd3pS5WrQ+e2zEQje1tk7OMeEDVeoT7q1hs1wmdFqrxdYjXGwtm2k9dcoZSiFOa0E4a2gTPi1ah97nMH6Iu3insuxCCc60zoab8Zg0p3Jal8ON964eNs5MSTlxOLJUqaZklAqChu4XAPRYiev/XjzI6kXrw5+h4y/efCxGqjmOtbjOf9g1KtMVYHyc4/93bo0YTY0Uaw+ndT4hSrW9Rozmxrl4yPl8lWk9PWffzzgYlcocQfCHmuRQLMY5xTXzPo7TWo+dNAJ2u28k0/qEiGgtKGgW9Y1ySAwnXC1i9hdEaoB5wFMxImov4FLiA1VKgRxAN5ehC5kq4evEhdPzjovZpbKf9SHFMo0Yl8rqFgTBD50ZCfGc1vWw0SsXqdPHg5gzrV3dxPhedInGZBUPsvCKzSTUmg4ZUjKVY7rUBkIfvMv8JAh4kIZjH44HuP6/3RSDsuq10rZxDg1Nmc4cfF9xnf+wb+BTxDx0U9rHuZvqvEVrUyPF1sdpPfH+4V0Vmc5Z77r+492zTGuXfaJLnjinwupi2c8IgjN74rS+4ZrSRFzfVNNuF0wGkUelHpfud40aQ1JX9wlDRLQWFGqwyHN1Ym8q6zM1WgTQFzA2Yil7jbXS53vxTW3yRoy7Y7keWSimPF3Hz+KphZ3W+Fo2ZWKndeJmZoIghKHddfGc1ntDpjUXqVOL1nRv33UdiQfxdFqnasQYyenniykSQ5dvLu20Hr63bdvNEhTmoA+kxTknCE1rEa2P6/+7zeXFg4QOvy7NxvF9xXU+AMAn7ncAAHBT5aPNFbnTeqFzRWcKw1jec1pPvL9alJrItM6z3pqB7sceWKb11D6xaTtVXXTnEw9SitNaEHyhlfx3TFOqnA+15j9+Rw6+kDzXru9PPuzU74jTennObEoTTgltsIQbwAdTPAgpEafgbR6reJBhRuk+oSA5iAdJXMaL781dLx5kmAEa+/GeOmaxdV36/Oe27dQEkN5pnbbEXhCEMFrlrqOHTGFjnUkE5XPL1GI1lIHT2tNNjENjqrGrPVE8iDqEJZ/xfqG4KCQfmRdo2XzqDUTKKipBWBNd1w1Ea51prRsG6n3E2kXrw5/h8SDTB71KtL7RovWfPncQrafGOP7/5+a0NonWLXHsTzW6nCr/p9njdO6me8OHY4Xs07dH0Xpi/UIPXLziQRLuBQXhUqmJKZJrSlPmlShO69Z8QInPBcdiABGtT4GI1oKCbkbHHBJqUGHO21vWKZwKD0t0UubuhZQNMLquMzbnUDEoKRoxskxrgPQCLxUr0mdai9NaEM4ZFOx6TuvAMcjUI4EK2HkGo+6yGNB7p05r/3iQ2M/sgC5PT3P/NnA+wwgXAL0hWDrT2rQPoT03eGl8qufBM1IF4dqgSzQ8vMSxUuf/lqMVm2siXiPG/v2ZwPXvpshVdePHPuUmWt/wTOtzbcRocFoXDgL7lChFBXA6P+3rodMaGzHum2401gvnmCzrxwRMofe8sp8RBFdMOpTSlCbW42NrRVdaFnuF4HPBsXhT5IsZNwSNvOOCAhcCmyIfzaLTMSLmeBCk55wr0p86c6E45WM9qVu1cKcLxTLhQgU/C+rASC3w0teRuhxcRGtBOG/Ugi7Lol2vOG73GzFSATv9MoU60pq20w0nHce8LHE8CO6pT+W0rntO64UzrTO7UIFi2KMyH2wyYmOKShGEa4SOcygkmnrc3FT2fcSa6CJVuuDtxyokacQFiv5/iqL1hNOXx1ecmWZtPOhuW7NIZEJHRZn/X7sks6Nz+/BzetB4z+JB6O1MbEkM5JQTvP9c01cXC8Kl0WvEyMa7zZTTemSt6IpVtGZj8U0l8ukpkHddUOxJPMhY12/9e2anNVIVQ+dcSvczXwim3FzS98WYad220Zty8UxrgKVF62Wc1qkjTwRBmEdNNpixRGt0QdFDsWph0ZruRekc5RpL4uKgC+Fk8SCqxFm/rr2nCz34ORR2oWK7YEOcTcJ+FYKwJuiYj0IiHw9uK51pvV25aI0v10e0NDEWdYTg/5UkR/VjjqI1/38X9/KSjDqtHUTrqUxw1KqK7BA1opohkvlrqxoxatF6bF9qqqh1ASuRqctbEIRxTL3VkMlGrSNrRVfwtnys52Px3aYEYXlEtBYUe9J8aswhQU/CKIMBxiA8pMyZXtJpje9LVWR9gSU3d6yOwYPJaZ04HoR21k5dDl6q0j8RrQXhHGmNonXYfe4NruZ+lU76jTddoO7IJnOqWzlSODjoQtCiSZK7t2KK9aK9L5ZgLDsd52GfBllz0dViMj8J1w1dd/JMa+R2U6iN/f2+iW7iWBLayyEELbraf0c5rTPtNFRCSTUulPA92BriQXg2usvtbXsE/jmZKl/vWTwIwHjkE8aD+B6MVuq5imgtCK5Q8ySPfJtac8bos6OrSfs/52PxEkYJYYiI1oKC5niqTOuxeBC2guMDDI0PUXmQSzqtE4qfttN3mvMd+/GVaE1O+JrEG2iaXxrqMpnCxYUiCMLpoK4oXaob2ohxmGlt+3tKcLraNXrOc31sFQ+S2mm9sAhhEmp1nMtCovWIox/nRJ7lmoJygb4cgrAG6LqzsDmtN9pp3bTdqg97YjVixOHbJR6kyIfuvqlxju/Bzq4RoyFGy1aOb7z9RDUmzr84H+MahZpvcO/2NBGtx6py5zqtJU5KEPwx6VDIVHVfjOrPlhwaUgZVL9KE8SSIaC0o8LSZxoOMNWLkzooxp/WlZVo/7Mxlyb3mH5EfX8WDLOi0rlVThPSL3zLChCMIQjqoKypXQm3YfZpEUJvrOiX4ep5Qp7XjY6vNdKKhS2dap7l/GxWJu0LQib7UYcLYRuT+OCfytUcKVKm5zE/CldNzWqNAyCsvq6K3Pl5zM8ZomdZqnpgWrcs8H5akTwglvGQ9dc6/L8Z4kMZftLY3Yjz+HmsOSn8f40Ge96hU8+nYQSTu9XznGJw7d3LIKQjO7IlRbhgPMuG0jiFa41rbom+J0/q0iGgtKFyd1qbGWQCmTGt90W8WOHXmAm5KZ8eD2iz3F4n0PYn9WuniCcfT1AKvarq1gECRExfGmktJBeFSaUneps6PC9uU7VSmtR5jNgtnWgPo8QefT+FRXeLioAvhVJnWprgr2/yfClPzLgSzcpdwvZQiQggCAGgRMMv05p6vEW83BVSFjpEy7SXWAo7rocNv4VCRQxsx8uZfk5nW5+60NsR70ObOrre3LTl4U8eK7Tv3Tav2hbdVof5/bEzHvR53sU/BH1sQhGmUDlUanNalo9M6QD9oLOMRPpc/VZnWIlqfAhGtBQUVKG9HnNbKacW7hY8IuOUCTYwadt8py3hVWTIbVHsdqxM5rW+qQrnckjdibPUEkhp6iipmNkE4P2pSOpcbSn3n3eewsW+5cKY1AABgPEhtriQaI09cJdJ2cUQTX+h7j/MZzqvnEA9yb5mHU6BFCBGtheuGNrxDaP5/lgE8KnPIsky5g9fstI4WD+LQbFw7rWkjxicAMC1a31T9dfqZGa3VnEqd5l6NGCec1g07XOD7TvodPByqTAvL9zOd1ipOSjKtBcEZrUNlQ01pwjwXNR6EPZTOtD6MxUusOYUhIloLChqAr0TrEac1Lw/mp2I94WGB0lq+7tgnfKyxhQwOrLEfXz9mqQbU1KL1HAFnLrQcRyJCBOH8wAVdWWTR4nyUsyIfzheHny+baY1OrI2Hk9ilwdZcuq6LJpr40qscatGtht+B08eDPCwZD2Jo6iUI1wgKcYVlzL6tClWlgjnM2NBujehKl7D7yR3miYZU1QxK0ieEkizLer9zbvEguManB39ejRgnIsl4PraOBzn8HPezeXaY312EZYwT8Y0DwOthzVnugrA0dH15yw7h3BsxhsSDuGVai9P6NIhoLShqMlhgl+qxTOtNyS7qDRtgqPCwQBMjXqae8rG2I805Ur1W+piLO60XEChKEa0F4azpOa0jidYmEdSWb50SFQ9ybMTo87hajIg/btG7XFq07vVoOB5g4ga/WkgQoQcCPDbqYcl4kESH0YKwNpTTmowBdMym1yP+fbtqp3WkTGuHWL+e0/oojNBIiymouJq6ebov2mmtf0Zf7xTFRCQZ/5zwPnHueiDGnyzLnIRlW1XtFJsF+jgJwqVRE/PkMH7VLdPatFZ0xXaINmcsFuIjorWgoJvRm6MA/bBvBhe/EhkGGXb2AUbnQSZ0WrO1QcrFwr2lESMAbTqZxml9SzOtkzdiNEfBpIBOEqlflyAI/rRkg1mqUt048SD9+eL0mdY+TmKXBltzaQ1Nz5aiF3d1/JyWdlrTdQYXex6UC66/9kjBEgfvgrAGJp3WZF18p5zWaxatD3+GisCFwzzRy7Su+uOai9u357Q+L81azbHU2Uxf7xTaSWn+f16RpPZix//gcVIbh+qZ4HgQcVoLgjO4n9gUuarSQab2AkUE41vHxhCEj73SiPE0iGgtKPakESOecHUdwJO6Zb9nybQeNGIcCg9LOq2XaMQ46rSOnGVGHxMXY+kbMS7ntO5NOLLQE4Szg+ZPumzAXVAZdhan9VLZyfgoON/5OInxKaYYj+ldZguv2LIsU/EstJkVwHKfCz0b5wckY/NwbCpxzgkCAAxjGAD6fU/o9YgC4aobMUaPB7HPE3XPaT0ewWiCiinnFg+Ce0a6NfJxWutIMovTmrkkN+ygUR9y5sfnM92IcayqdvS5Yp5220pjeUFwBK9FmumPcKMkh453c800zUQ8iO3fwjKIaC0oqBh9Qxag3CFhi4zgJ9FlT3hYINOarTtSnnCPLWRUyVkd9/G35MQ/VhO0KfZkAkkNdRGK01oQzo+mG4rWoeOsdu4O5wuA6YVqLHD4OWen9dLxIABDx1i94EEmQP/z5+/vw0wX3KznkaiCShDWBq616bqQHvLdGZzWa27EaHPf+ZJPOIUB+sIrL493GefuzjgeRGdS6zHUJhKZmIoka9jhAh+zcd+G8ZelQ/WMrnD1q+bBw96uk7hDQXBFV3fng/HONR4EYP5aXFV+sIfiz0VE69MgorWgoJvRsshVJhdfbNqEzIHTOqfCQ7+LcwoGmdYJuzZjUxlzPIg+YY9F13VwTxqCxGqCNoUu3U8/VOSkFF06bgvC+UHz3opIOc616hY+nC8AFsy0zjHCyt9JrBx0CYatfqZ1/PufQs3dbT/TernPRf994LQeiemKTaoKKkFYG3gNUKGRjgc3hkzrVTutVYRF2P3gMnrMeUurmXiOsss4d3PGjRhpU118DxqP+WRq36Mc8Xk/0xq/ryoe5Pg+qiqikX3U3GoeWnmQ0qwlCJeE1qGG49+UkSSG07ozVBEBDDPtJR7kNIhoLShwU4oTPV6UfLFpc1rxi7g0ZJSmdD9zd27SeJDd4b0yDVybBK9133RqoXZTFb3FX0pMLsiUFAnFH0EQ5tN1XV+0jpZpPe60PlWmtc/j5gYHWSxO7bTmsRjKib6QA55W4LTsu3a/YDyIS9MuQbgGWoPTmgoK1JV2ewFOa56VPJfMoUKSRq/MKUmn7/2ZadbMCXn4E31MXk5ryzzbsvvic9eDclof3iNlMBoxU82t5uk1MZZIKUFwYq/igoZO66mK77G1oiu2jH1e9SKi9WkQ0VpQKDH6eEJsc0jY3LdjmdYbh8VBKFw8SflYD/uD0/rOFA+SoGET/QzuNkU0wWgK9Z1YSqBgzghBEM4DOtQUGcm0DhyDzJnW5nzrlPBMa5+DOhcH3VyoaH2Kau+KHcLSRjlLMOaeWdJp7SJwCMI1oJzWlngQej3ivmDdjRjdIyzGKCZEVwA9zh7iQfzdfXQfdopDzjH6Y/nhO9Qws9QYymltOThUgr8SrXHMPvz8gVXIVg6RT7Od1mR+lGaMguCGqrws88E1tyndndZzDX2N5YCSP5clIumEISJaCwolHhwvfFMWXdd1VvctX1D1ckkXyIPk4knKpo9jm2XlyIooKONnUOYZVEUerQnaFOqAolzIaa2EsEUeThAER+giMM8zJViEHpztSTkgYqrSSQ264PYznMQpewzQuzxlpjWuD+qFq2+yLFOOQT7H4+HxEk7rFBVUgrBGcN3Zy7Qm43QvHuS4Rt6u2GndsazkuaCYOrZsp8LroCTdpRHjCuJBAPQaXzmtHZ7rVEWTdkke/s0rfPm+rXLKtLZHQY5R5HrekoNOQXCD6lC+Tmu6Vpy7FlfxIOyh5jTFFeIjorWg0GL04WtxoxwStfodKlBw9y29iLOsv0Dh2WIpGDitE7qQ70dEa3W6X8d7rXzhFKsJ2hSnKgUXp7UgnBd0EVjmWbRcfdV4hYwxVe/vC2UnYyPG4/PxcRLrzXT0p9Vzb58yHgTn1/3CmdYA+rshTmtBOD247qRC41Q8yLqd1oc/QxsbZg6CCq59ZzutN+tyWqvX6/Bcp2IRuSOe9yHgcVIuBqOHfdu7jQ/KrCWZ1oLgBNWhbgaRs9Nrcrym50b16UaM3GnN40H8GrMKcRDRWlDorq19pzV1SFCRlLtv6WKJC9q8xDgFbdt3fyR1Wo+UjOlNfsR4EPZ4sZqgTYEiwVIl+kWxzOsSBMEPugikjRhDRWtT5c4pnNY809ovHgQddKmd1tHvfhLViLFujw20Dj9fKjIKQDvn+HctRFDwxSacC8K10Rid1iQexNSIccVO69jxIGPrW9y2FHk2EKl9ndYu7uUl6WfOHv/shvO/jelGjMfHUY0YD2M2zunbHROtHZzW24CD0WqBvaggXBK00fcwctYl9/54PzO1JtW/ILfrWwDitD4VIloLCiVQHq96k0NiT4RY7r6lrgC+AMF/7xJO3riQflSmjyJBId+Ua1SyHLUUj7dcpvWpnNYiDAjCOUFzJGkjxtDmg6YeCf2mjEvFUBz+1JnW7mOei4NuLvQ+Q51+c6iIW4y6jE/htB6I1scKpCXyBV1KyQXhGjC50egakbrQlPnlApzWoRpw7mA2aYjTepijOu3uW0sjRuW0btwPBKYiyfT38vBvJUoff/7A9lEuZqr7/bx4kMPj9xtBCoIwDl6LmyKHqsj7sYEOOgT+zlzjG8/FR/gaUzKtT4OI1oKCN8S6MTgkaOQFFxNuSuK0LmxO64Si9XFhgs875UIBhXyeOQeQxmnNHy9WE7Qp9qw5Z2qmyv8EQTgNPac1acQYy2ndb9xrbsqYEuW0Zr0dXEjZYyBWnupcqFhL59SlPhcAvw5nvgAAIABJREFU/dr5AQmuTUzzcGxcmnYJwjWAY37vcJFUXpqc1uuOBxnGocwhd5gzQ53WtKTeJXJjSXqZs8f31JSPbqOc2PdwR/yG7Tv5PqpyMFM97OZX88icIQh+7JRR7nBt0rWdk9P6+CtzjW88Fx/ha8wl1pzCEBGtBcXeEg/yQBabOBAUeTZwfeV5BjfV4SvFB5eqGD8hjwEXrVNGkYzFg+hy6oiNGHcnclq3/gJOCCJaC8J5gmNBlh3G+nii9TCOg7qcl6rywOlsVjwIOugSnJPykueloU2Ue/FgC4rW+By4WHG/s1c8xX8OkmktCAB6zKeiKI0L6mdaH9zBq44HOb7eUA1Yx/rZfwed1uXMTOu7M44HARiu8evW/UAgn6jEbMn+FGBY9cqd1qWDmSqkmkcf+Mp+RhBcqFlvNXWtGjQnE7a1oiu2KKg5Y7EQHxGtBUWtHG+Hi1Vl0dF4EHYKxuENLhDV8CLhhg8XMo+q+E5njhaRh+V6uvlGgkxrJlqnFndNebMpEdFaEM4THM5w4x3rWlXzTk6F6uXjQXimtY8oi4vp0KgUE7iIPkU0CEC/ifKeHFwsKaLbxIrtfrlGjLwhpSBcKw0TBwH6a0RjpvWqndaHP0MzrfHtGhNUGvJY9H0s8sxpLjznRowAw3UD70U0Bn7HbBVNDROcdB+Cw7zFG/dWE+afrutGDUquzzfmXlAQLplaVfz3dShXDWLqYGsKHI/42FkVeW+MuhOn9UkQ0VpQ7NXioZ9p/WBoxLixbOhtA8wSJ8442DwqMR5kAaf1Zvg+bBxy0rwfjzUQWU609s93DUFEa0E4T2qStUn/DBat26Gz2ZZvnRLutPYRrVPGg7QnjwfRB857wwHDEpgacNHnc1el7+SuKqjEaS1cOWbRWo8JNz2n9eHnq3ZaRxqDc4d5oiHz4Q0T/10OLm96QvfcZ5oO3sDZx2k91fOGN1GrLE5rvU8dj+94UrfqPmc1YsT7r2XOEAQXhjrUYW3nuuacatY6xVhlY+8wVpzWJ+EMpzThVNhOuO578SDjpdN4IXNRe0mnNUaULJFpfWt0Wsff3PLHKxI6+yjaBSlOa0G4ZpTTOrJobcq0LnKdfXmqTGsX55e+7eHPFD0GcIg/lWuObvxrQ5TLEpi+a1QEuzEcHseGiveCcM1wRytAf4141xNbD2vW+2PMwhrpLCXjvuRq3W7/Hdrk8lGZq7nFVSSh1Z+nqs4Zg4/ljYfTGm/bdfozoah4kAxF6/6Yfc+d1hNj+pbOMXMyrXOpzhEEH3CNuSlRhzpGzjr21Qrdl5jmNgTHjSwDeLRQny+hj7zrgoI3YlRdv8nEvav7eUOc243FaV2mb0iB7gVsCJnK1d20nXLjmTOt4y9UtEPgcN96YE67gcaytqWEo6XEeEEQ/OBOa+VoCLxW9+ywFME5ZqmxZ5hp7f642kEX/WlZM/aWoiSVQ6YDhiUoDN81rD7KM3vlV0yqBBVUgrBGjI0YyTV4uxk60rb79R72xIoHwbdoNB6EHA5nJCLENZ6iFymyItHa5blS96NJlGqYI75kY/aWOa1pk2ETuO+qimzWnCd9EATBDxVJyir+XU0kprWiDzoeZPh/+Fxcq16E+IhoLSj2LAD/ZsRpbXPeYpkuz7TW2WEJndYNc1oneiwq4puacyhnWsSSsO2+n6GtF37RHsLIfuKQIjbitBaE8wTFU7xG8yzOtcobryAVE8dTg4+i40HcH5eXPMcE7/JUa2TdRLnV8/85OK1JX4klNhCleh86o8tPEK4F6gZGepnWZF18Z4gZXBuxDg7x9i7xIDjmYXWlayNA+t6fqnnvGMVxb4iiUsPWFeO31b/DTUFd1+mqJIwHYftOnmmtKoAt8/b9bn6eNcBQNBcEYRzemB0rdVwPjUI1BL7PoeA4sETjb8GMiNaCgm9I7wxdv5XTylIacbPpn2AjS0zeONg8Suy0xoWMrURElZxFFDDwMW8GmdZpVeulRQoRrQXhPKlZGS9u+ILjQSwHoThnLHVghoLCk8a/usRFjJjLyZ3WKtqr0wcMC2daj8WDzCnbngPNVJRyb+Ga4XMBAHNaGxoxrjkeRDutw+7H5aAXty14EIqZ4K7jHH3vz9EMiF8TnEtUpIenaM3nWvqWqkaMLLOai9CTTutdWKPfjTitBcELnFtwPrFV79sIjgc5XqomIwQ+l6XWnMIQEa0FxbAs49hAhTqtJ/I+71iDC2SJLspLZVrTEjPTwFZOLITm8KCc1ly0jvYQRvh3IjWhTRQEQUgDd9fhkBByrTatdkcNnNZFv5lSalSmdd13ejjdFsu+E4jW3YkbMdIS592pMq0NYs/9rj8npoa+ZnHOCdeMSWike4I7SzxIisz/JVBjcOAg7NKwt2F9g7B61XWco793jvEguJfA96CeKVrzg0P6nupM675ozCtWS9KvwcQD+31fppzcgiBouq4bZNwrTck1HiSw6lE7rYf/h2OrOK1Ph4jWgsLWiJE6rfkpGOfW4rTGzEmMm0gBDlLotE4lWk+VjKnsy5iZ1uwx9cCc2GmN34lyIeFIRGtBOEv4YjKG05qO0XzOqE6WaX3MsfQ4qFsiHuRUTuuNqpJqlVi7RIY0xeSe4fmkqaHfw50454QrxiQ00upLk9MaAGBbrzMiBIWM0CFYNex1bMQIoKtXnTOtiaASKrKnAKdV/A55Oa3JB8APQOjcgI/B+xAMndbjBqeHXVg1TwoDkyBcKvTwCOcT3jR1imjxICantWd/ASE+IloLCt5kCbPUqNN6yml1U5kHmJLkYqaiYU7rVCW8U2XJKOrEFM3V4gmd1sUy4q46pBCntSBcNWozjZnW6LQOcBf3RWvznLFYpjU6rWe4iVM2YsT3/VSNX6gbrT6V09owL9wHlm77UvWc1iJCCNeLKfeTrhFvyDVJ18l0L7EmYh0cOsWDsMNhbL5+4zjO0ff7VAedYyindZvOaa3jQfRerOs63dCeCWG2ypnQap7NhCguCIKmtx/ITyNaj623cWyVeJDTIaK1oBgG4Buc1hNxEXeWTq80FzMVWrROnWl9yOazLWTKBAuVeyxTY07r1NmaS5eDq0W9NLoShLPC5rTuuqHryRU6RvM5AxetSzmt8eGxGuj8Mq2j37UT+DnXbavKnM8p03op10uWZep5SKa1cM3guF2QcaDXiJFck0Weqd4v92sVrds4YzCOH2ONXPnhMEZT3DmOc3dn3ogRnxKOoc2Is5GTZZl2qw9Ea/13fN1Vrg1M273ej92yfaottjK0mmcqfkQQBE1vP8B0qKUyrfFmprFT4kFOj4jWgqJmG1LV9duQaW0rD761ZFpPNbyIgY4HWSjT2jJwbdRrjbdQ2Q66XqcTSSjaWbeQ03ohB7kgCH7wzTTdZM49ZKKbRb5IVE7rpTOtWUyW220Pf6YYt/CtPZVrriIb7329bGNexCQWPxwPj5dyWgOkqaIShLWhS6j1z2gPAn7gp3OtVypaR2qG62LKGDqtjyXpjuNcVeTqtmeoWQ+c1g0egDjOKWVurqSlcy9+TNRpTc1XfJ865bSeHw+io7UEQRiH7gf4+Oda7R1qLBiLK/Idi4X4zOsuIFwkPNMaJ2ra9Vs5rSwLDFumNe/inAJcCD6q4mRav/f3Pw4/8n/93mDB8dFntwBgP32P9Vrf8c9/H37xt/8EAAA+9MePD495fH919rP99j/w8x+GD37kWfXvL/rsF8B/9obP83oOuJirFlr9upRPCoIwzf2uhr//j38L/uTxE/Wzv/aal8GbvvAzer/XdR3smlb1ArDBN9N0k9m0HczZ16nxpcgG5Xi4OV3K1YtD3JPj5tbncbWD7vDvpu3gv/m5D8EXf86nwVe88iW93/0nv/ER+Kn3/ZH63Rc99Qi+/atfAU89Mi/HTKX4S4Lz2c994KPwf//ex3o/WwpTA7OHheNBAA6C0JO6FeecEMT/9K5/Ac+/KeE//pI/e+qnMotGbeyJ0xrLuQ0TwV1VwCdgD2//qQ/AM7cVfNrzNvDWr/oCeMHdJuh5/OYfPQs/8f/8Afznf/Xz4dOfeuR9+1/+8L+GH//nv6/GlWduK/jbb/wC+DPP798XLkdDI5pc1u0Na/qI45vPOHe7KeDxtj7LeBD1HnT+TuvD7QGgGe4ROkMjRpynPvBHz8Lf/olfAwCATZmr+WQzEVupGzHOm2OqQAFNEC6Nf/juP4R/+jv/Gpq2g7rp4HNfdAd/541fAFmWGfcDyqjn6rQOrHoc61+Akbm3lUinp0LeeUHBM63vlDtCT+jotLJtWl/2ghsAAHjx8296P0dn9u6YLZYinzN2PMg/+IX/F/73D/5/1v//jGdujD8v8/GFkAtN28Hf/Ue/MVjs4GPq7GfzY/zBx+7hv/3ffrv3s3/8Gx+Fr/3LnzVYkI+BhxRLleiXssgThCj8wm/9CfzYr/zL3s/e+/ufGIjWb/2H74d/8hsfhZ//O28YjNuUhrnNek7rwHgQk0D84qcfwW9+5PDnIgQ5rfuHbe/7w0/Af/9//g78uRc/NRCt/97PfhD+8OMPvZ99yctfCF/zmpcZ77tWDnfnpxOVFx/ni9/703v4vT+97/1sKUxxWNvjWuRm4rAlJpsyB3giTmthPp+838Pf+9kPQlVk8B/+lc8+ywiHKXQOsf7Zi45jwmc8czv4/c94wS380Se38E9/50/Vz179smfg618bJtr/8Lv+Bfyv7/1X8PIXPQ++6cs+1/v2//XPfQje9wef6P3s81/8FPynr/83ej+LFdGkBJWR+VLPiYfffdlxzf8ZT9vnZs7LnrmF337yGD79qbBDgRTwNT4/DJ++fQ4A7WDNQf+N1xTOUx/71A5+4UMHAxDdt+E+dlebP4+t6l80b/LdHKt+n9QyXwjC/a6Gb3/n+wfX7le/+jPg1Z/5jI6oJYtdrSm5rTlDndZjh2j4XGzaj5AeEa0FhS3Tete0UDctlEWuhFib8/bf/4svg6ceVfCln/vC3s+pS+BJ3SYJsueNGG05Za48+3BwmP+NL/1seNXLnun9X5Vn8JWveonpZpMdqV142Ddq0P0v/9qroChy+OxPu4VXvPRpAJh2bKBD4HmbAt72plfCf/WzH4SHfQOPt3sv0XrpxlvKUSeitSAE8ex2DwAAr3jp8+Gr/sJL4ft//sO9qhnk3f/y4/D4SQ0f/uPnxkVrVmUz1hTJFZxPTBvWv/+1fxE++NFn4S999gtm3bcvKtMaRYMZmda44H324fDemzJc8Wf/xV/9fPi53/xj+K2PPlZRFyZUNNSJmr/8jS/9s/DC523g2e3hOZZ5Bl/xyhcv+hxM8wIeoG/K5UQ/1edjpdm8wunZ1ofvzr7pYLtv4HmWCotzpjU4rT/zBbfw43/rdcYN/ff/J18Ev/jbfwJtB/C/vOcP4b2//wl4fJyfQsBxdu59PT7e/m+9/uXw/j/8JPyz3/0YPN4Ox+JYEU0qrmJkb9Iy0eSb3/B58OrPfAb+7T//Z5wf53/8m38FPvrs1niAcGpyNpbz2LHJ22MUF3NS0n+jKeq1L38h/PA3frGqjgUA+LLP+3T19ymD0V4dYM8TrXG+WGssjiDE5LknNTRtB1kG8Hf/vVfB//CLvwN/8viJGnO5BgUA8IY//2L4n7/pi+GLHPcBoRqC7l8wHI/+oy/+bHjp0zfwb5IxRFiW9a2WhGTUzFVLheaHfQPPL3KyoTcvMKoih3/XIObesg7iKUVrLHMPdVqj8PvGV70U/p1XuG/SYzTfwE1xlgF805d9rqF0ftxpje/F3aMS/ubrPgf+u//jw/Cwb3q5bi6ELtp8kUZXghAHHEP+3EueD1/3lz8Lvv/nP2y8rnADPXXIpkrCs6FoPXuBiBt0w3zy0mdu4KULOhr4ItVnzOMNtnCTatoM4/v8H/ylz4QP/fFj+K2PPh6dK5ZuOMi5qQr461/0mSd5bMQ0L5yiKSQeiPvOo4KA8GaiaxStTU5rALBu5j/r0+7gG177OQAA8FsfeRbe+/ufiHIN4X3MvS+83V//os+ELMvgn/3ux4wCYyyndeXQ70a/t4fffepRCW/8Cy/1epzPfdHz4HNf9LyZzzItwU7r45duGA9y+JOuS7IsM+5HkWoi07pm1ce+3Bj6QgnCtfJADBhv+bdeDv/ovf8K/uTxE7JeHl5vRZ4NqhXHCM60xgNKw3h0UxXeY7EQF2nEKCh0acbhYn1U5irXBxd39UwRs8gzVSp1n2jDx53WdduNdumeAl2JvgJ7FaH5Bh3cTVEqUw1dbM1cfBdP+8ZvQRmKKbtUEAR/tNiZq4M00+YMfzZ1yNewzTQdEuY7rf3yLFPCh7iQRozoph57v6sid2rUdH+C7OZzw+SeWboKCADg7phpKCKEMJeeaL3S7xGuz+YcGMWsVsCx0VTR4nP7m6ogPXxGROvAdbBqIjgy3o81ArsEeESKv9Pa3PdG3Y/H20YbNZrYj1SCuXB3zL5NtecVhDXBM+JxTYtjLteg5hDqtNbxILOfgpAQEa0FBT9VzrJssMA0nYS5krq0VonWJOMyxO2MWd6+TTiUmyLALTzVAGQq+1kv4LCZQdm7X1dUHEy5lNN63PkgCIIbOM7ebUq1OcOeAhRcKDo7rY9jT5ZlwYdM/D5PSQbmRpAuqJLn49uA4+zO8J7SEkiM2Tpnp/U5YHRaL1wFBEDWMCJCCDPhTus1UrP1pQ+4po3x2tGhNzd+ga6zx56Xct8FHq5WqrePu9P60uBjuX+mtVm0bjv/7+RUlOO+9o8Ko9xuDrfbrvRwShBiQg8JAYbrqdDKBoAITmvPQzRhWUS0FhSmTSBfyO0CTsLuEpdK4QkZdUaHNENEp7Wvww3FjpBM6ymX99RpIl/43h7d576OFDWJLFSCjaeb4rQWhDDoApFev3yzh4LpfmKRxxsxApgb5PlwVqI1ewo+Dl7uHnuwOK27rlPvVZlrp/VYxik9fLhWCkNl0Z50ml8K7gwSBF/oWLnW7xGOc3OqHG4iXkMhTuum7WB3zMW/rYpRU00XOx5kLNP6jObEFPCDbtO6wuX2A9G67f+/C1MGI/ycNjPnGDQLrfU6F4SYbHd9M57WhPqZ1iFrOtNa0Yc5h1/CcohoLQAA20yTAYOXzNUzmlQhqV1KKtOadHoOypWe6XCrYmRaTzz21GmiLt9Ep/W8hiC7hUuw88BTUkEQDtAxhFZK8GsLS5Wn4ox4I0aACKV4Z7RBH2Za+8SD9BfKujKp/57S935T5JOZmgD6c0zRB2ItYOZ5Q76juomnOK2F9UAP5NcaDxLitI55DalM6xnvI338200x+rxw2DZF9flQOoz3V+O0xlgyz9eLwz1fx/iK3wDUYGT+POY0ZabIfCEIGhV1Z3Fah15vAHqtOH9PcvhTROvzRERrAQD6CwDqysOTsK0SreefPN8sJVpTgWam27ltOxUP4uu0jplpbYsHmXRaN/0Sl9tq3ol/vbCbzVb6JwiCH+heuNsUvcoYXgGCDmvXTOue0zrwkOmsRGu2GvIpUcTboiB1TxbhNI6FvsdlkZHGXNNOayw1vka0e0b/bOm5CYAc/q5UbBRODx0DHvb1CZ/JfHwjHSgxKy7xPubsKbCaMcsOewZdRTH8TGK570oVBzXitO7OZ05MgXJKH19n6/ldQqGZV2POaZZZTsy/oX0TUkdiCsKa4AYMXrlWB2bIA4RXf3Zq/J39FISEyMciAMBwM43cMqf1vp1/EsZLQWKjF9K5WhjNdTtva+LC8HS4qZLvCE7rqXgQX6e17+JJLdqWigcR0VoQoqDGkE3RE2C5OI0baFP+MqU2bC5Dr1clWp+BqyEo01rFgxz+TcdZ+t7QGJCyyLTTa+T907mr1xsPUhoOaXUV0IJOa4kHEQLpO63nGxtOiW/zPMpNJCGv67ogp/V2p6NBev179sPPRGdaz3yyR5ShZWS8N82zlwRvpFh7fpfw1/g6Zk4W7Wbi81A9nGbuf2LmtwvC2hk0YrRkWm8CemiZ1oo+zKnYEJZDRGsBAPqbaSpwcHf0vp5/8nybeAKnrr1qoiv0FHQR7B0PogTlkEzrCac1iiSW3CZevnk30+WOYspSza5EtBaEOODm+64qoMgzldlMx8Sm7QCHkKnKEFPWZjTR+gw26HyN6uPg5TmdNIaJHl7i/Alw2AireaqedlpfczyIKTYqRtMeX6TcWwiFfofX+j1quvnC6t3MpuCcXdOq+WOW03qvK5HonyZTTdvGETJwvG/aziqqmCqaLgleTRnLaY3fSZ8DcDzw3Fnm311gxm7q6mJBWBO6apCPueE905DQiNFLH3/XjojWAgCwzTSZoPmgEnLyzF3bsWlIWR0+v7kDFz7HR2Xu7SaJ4bRG0cMWTVKosjaL05rlz4Y6rZcqwS4yEa0FIQYPrJFsVQxdvfteRvD4NWfKngwWrc+oFJovUn0cvDzTms5x9ECYvod5njk576YOMK+Bkh0KAOhD4SXjQVJXiwmXDx0r1/o9apr543asyIQtcanPyrRmh4E3I6YaHHdCdQw6p9ia79KK0UuErxl889FtohS+nT77tXLCYFQHVvPEjMIRhLWj++wcDi5vuL4UIdPatFb0AW92DnsSYchlzoqCN3QzTZuNcHf0PiDja65w6gpdSE9llU2x3c8XCspAlzcAOZGszCXhUx1y+UJwrss9RmMEH4rjQn1u519BEA48sIMvVQFiEaqnDtlMWZuhh0zn1HQqxGmNT7/r+mXrAP2DxT1zkrjMFeoAU5zW7L1cXtwR55wQSnNBTutZjRgjVVzS28+5L16qrgVGUzxIXKc1gN1woqNXgh7qbOGitYoydJxvbeX/czKtpxohh/ZNSF1dLAhr4p71Z1FGRowHiWBEMK0VfcBxSYzW58mFTouCL3wzjaBoyjOH5pTkpj51piWLquxrpnDMu9z6sHHoEO78+JbmW1MOR94oZ67L3fa9SAV+rcRpLQhh8DHMVAFCBeypQ7ZaHQrqMYk3VfLFFDlyKrgg4TPH0effdv05rndIwObPqU0zgG4M5tsQ+JIoDd+z0CZZc7iTTGshELq2Wev3KKQRY6yKS9owcc59PbD5UTvATY0YD3+GN2K095ZArs5p3fgdCFid1rPiQcYPjfeBn4X+rq+zokIQYrJl/VlwPYWNrWMYEUxrRR8uvRHu2rnMWVHwxiZGo2iqGzHOPwlL7VKiDT2qwNM27lL0Qbm8AzKtp9x1rqJ1zkTrred7r+JglnZai2gtCEFs2abclPO/6wmqE5nWalOof6bHoXlj3TllWvOn4CPI0Oqkpu07rXeGgwGcI/Axxg4MHsRpTZp30fcyzAU3h7nzqCAgvUaMK/0ehTRiVELFmTitb7hovW+gY4JHN8PFa4KOVdZ4EHysC92d84NubTZye8E8ExuZ853cTEQ5hvRwAtD7x+2+nd0YThAuBTy8UZFMg0aMqC/NH/xMa0Uf5hx+CctxodOi4Mvekl2MJ2LbffhJWOpMa9rQoyoxKzSsEeMs0ToPz7TWTmtLPMiUaM0a5dxu/E/8u65T979YprU4rQUhCves/BnHpbrntCZxC86Z1nrs1xvIec9RidZnsEDMojmtu94cNxZpURpyxjnYUPOaRWvT92wfYYPjy604rYVAeo0YV/o9CnJak7UoF4d9oO/drm6914y8VwA+r7YDeMIa8ymndaBqnWXZ6EFlSxojn8OcmAIeKaYPrv1uzzNr50S4TBmM8OebmXMMnbO39TqvdUGIBUYv6Uimg76hTJERemjF2pPw/YBwHohoLQCAPbv4hpU3hTTmi+WwsEFznPXCMNBpPUMoMDkaYz/+lGjNy+61i8T9OdH3bvFMaxGtBSEI3miqKo/jEm0MaIkKMWHaXOYT49AU59SIkT8DH3cVFRjaruvNcXWvESNugrPen2PvPZarX3MjRpOjv277BwBLcCuZ1kIg7QWJ1nNEXJyP2m5+fB/A8Br0vSZ5s/OewMjuK1amNQARSg17E1rSfrHxIIVNtHZ7vTgXDONB+v/vAjUYmQ5QQnv60O/UWq91QYjFw/4YdceqWwamyAC9IbT6c844IizHZc6KgjcqAJ9dqLw5ScigkjIehG4Eijxzygod42HC6TxGGSHT+oG5QDhTDdB4gzOeHeUCFd0Xc1pPNJgUBGGatu2UW+xONWIcjkt7Q9yCDdPm0laq68p5xYPMd1rTmzZt19ug7k1O64I5rUfee1XGLqJ1zz0TcoA+l9TNpIXLh4pt9ys9/KgDnNZ0Tbs1ND10hVc7+F6TuufDYY1fFrly1PL71pnWc55pn0oJpcPXTufRC9Ws1Rofv0M6Q9bx9pZGjHMaqNG5w7SGwX3x3HiQPM/gUWn+TgnCtTHoI8Aq12w6lA+mtaIPcxq6CstxodOi4ItNjNbOoqPTup1fkstLQWJSM9F6qsHGFPfK6ez/OpXTOiDT2tlpbRF3ldP6uIJDweN+7x4PQsWtpVwf+Lk1AYK/IFw79GAQF4alwdW792jEaHRaBx4ynZVozYa4ymPM440YaQyTyc3OM63HYqymDjCvAZN7JoYrxxd1iL9SsVE4PVQc8zERnBMhuZ9Vkatxz2c9yuFuaF/RWhtT9Phxc1zv8+u7S+G0Noik9LtxsU5rJjqbGjy73D5OI0bSGNMkWmPfhIDPInWFsSCsBd4rjFeu6T4lp3Raz68iEtJzmbOi4I3NtaQ7aodnDuHiMMXkTfPNqNN6rmi9VUKBv9MaFzhdN9+BiKKHLVN7Kh5k4LSu/B1iVHRfys0WKoIJgtDfdN+U/UzrfvNFc761CZ1jSpzWRdgCkY9Tp4Rn2M2OB2k72JIYpn4jxv4m2GWe4u6Ua8R0SKvWIgt+d25mzKOCQKFrtrUefoSO2zEqFvht58aD0DU+/p3fN+4vYsSc4pi/qw1OazK+XahmPRCdfcVmm9MalyA+30m6rzFF1eyaMKc1gERBrW05AAAgAElEQVRKCQJic1pzfSnkepsy9E2Bw8Cl9hRYOxc6LQq+2E64bjfmk7B5jRiXcVqXeaZL4WeLxv08WB/ogDtXNJ9qvuXaiFFlWs/YJNTqs84Wa0oQGjcgCALNs86VY6AyZGn2nNYTwnNjcJvpTt3znmcbUGYem5BMazo87pqWHQwM32O872oiHqTrOl31c81Oa0McFs7t2HR5CWyiliC4QjfTa40M4D1TfInRlJ3f1qfJOL09XePz/Q6CU2MMp7WKLjQ5rU9Q3bg0ONejWO17AGJzWjfqYMFDtCbvsenQvo7g/JTmvYJw4IE1h6eVa13XRbnepqJTp8C5LcZYL8TnMmdFwRu+mUa405qXN/uQMg+ylwWXhceD8MHVBzrghjq9p5zWNlG+OT7usBGjf6Z1yKmnL6GN3QRBMMcL6c3ysJkdgHumNR0PysBSvPqMFoiDTGsP0SDLMpWB96knffGEvsd8UW6KbKE8qVtAjeuqndbFcF5Q89OCBx7imhNCoWPlWiMDQmOdYkQmhDZiNM2RtkqKNI0YJ5zWp58Sk8DX+L4HILa+N77Z2Phc8GFNn0eMvgm2gxBBuDbUQeHxmsDxtmk72DWt1qFCMq0Na0UfdDzI7KcgJEQ+FgEAqKu2/5XgGY64Ad/MyrRON3k3zGnt0uBqjIddv8utD1S0ntuMEbP+bKJ1aSmRQ/BhudPa57Rfl18vN0yI01oQwrk3xBvpgzwi/NVmR7CJxiAw6w3ovOeJC8QlD8Zs0HVykWfemXb4vnzqSX+M3Rtc15VyWo8fPlLx5KpF62z4PsVw5fiCn0HddsbyfkGYgo6Va3VfhorWNxGc1lxY9hXATb0C7izr5JjNuXCNa9qb0Pd1qerGpeFrfG+ntep70x9/5zok1V7RMAfjz0Jc73eVVOcIAgCNZOo7rQEOTXnrCH1KTGtFH5qAfg1CekS0FgDAfqJ8wxZxuvnRDKd1wjzIftftTOVcTgkxNnjDAB8OC87D3+c2Y3zYjceD5BMDM7p5uNP6Sd1ahW5ObXBWpkac1oIQDo0HQUz5yfs5TmuyuXRpJDgGLlLPzWk9x+mBY9dz3GlNDwnYJljljFsEUJyHNkW+aMPBc8OUY3qKSiC6HhDnnDAH6rRe63eIx8/5EsPAwm/rK4CbnNb4dy6A47ATQ0g2VTwhSrQ+g/kwFTkr3/c9ANFO6/7P8TPyXUugAWtvmINDejghNwkrjAVhTdyzTGvalPdh3yi9ZhMh09pV5+Cow69LLXVZOde7CxJ67NTkbHZab1WmNZZv+H91bhKW1nJBpRo5PXeBD66+qEztQKe3LZ4EN+qtpdkAbyZAHZeu7//e8p1IiRbBRLQWhLk87HH8IE5rdZBH3ao0KsTRaU0Wc2qBOLPpiXJan8MCkTyFOWNeoZzWfdG6d0hQ94XWcsppvR8ePlwjpjisU8xPVZGp57LWaAfhtFAfxVqFrDpQXI0RFchFat/7MjU7t1UkxnRajzXfDXWwrwG+xjcdhrvcnkeSzX3v9BxsigeJkGl9nLvvZb4Qrpiu68wHhWrMrWEXw2kdqCHMPfwSluG6d0KCwlaWwRumhGR83aXMtO76gspUVugU2wCnNX38OZnWtsGdwt0KnIZllD8izapcRetTlF/jJmiuCCYIgrlSQx/kERHV0pTRRG3YXBYGIdyH+oxcDT2n9Yz5TWVas4Zg9OAUN8bo7sI/bfPUgyHm5RoxHY6oRowLxldlWQZ3EaINhOuFO627Fa51VA7xTEdcjGx4fmjkn2k9nCNtzws/ohjzlCmmC7kG0TpnTkhTg2eX2/Mpc+7BAhqwTJ9HjGoenLu3Ml8IV0yvP8vGPOaG9ExDQo00OgZx9lMQEiKitQAAejNdsSuVDihd15F4EP+vDt7Xrmlni8k2mqYvqKgSvLlO54BGjPR5zMnUflK36rTPnml9eH120frwJy4E8zzzjmc5Rfl16CmpIAjmeCPTmNhzAU+MVaaGSaGHTL4uq5TQpzDnoM4eDzJ8j7XTerz3QkhM1SVhOhypA6LKQpBybyEEumbDBlRrI9xpHZ7ze88OB33vy1TNqI01/fuO2YhxrArTN995jVid1o7juM1pPfczUn0lRj6PEONOjPx2QVg7tv4stOomhhEh1EjTBUZfCWkR0VoAAHtWNQ4oXXcQU5W4PSfTOmEeJA/Px4XN3A2B6nI7Mx5kU9pz66agDhLb4+OYPum0JgOvbxfrfbO8oBSaRyUIgrmRrKn6g45PUweJpg116CHTOTnL6GaXH9763J7Hg5jiWFCsnsoED42puhT44UjXdbrT/MKitc7jrSd+UxCG8LFyu1ufaN0GHjZiZEJYpvXhfbM1T5y+/XGNT/YltgjDqI0YR+Io2isQTLgT0tTgeQyb09r3fhC1LuIieNtFOVSPkd8uCGvH1p+FGiNjGOWCjTQRDyiF+IhoLQDAcDON0M3yw64Jiox4VOaqQWF00ZqVK1ZloNPa0FnchzIg0xoX31WRWd/nSac1i0sB8G+EqQ8oFowHCWzsJgiC2aFrKoOlf58SnltDV+3QQ6ZzEq3pGnVOJVGhnNb98XVncFqjKF5NOa134rQGGB6ONG2nSk2XjAcBoPOozFGCP3wzvUYxC9dnc+My7iI4rTFu4YXP2xz+7RsPsuuL3gAjmdascjGEsTEf9wvnMB+mwjSWA4RnWuNl5fveVZZGjFTEDsnY1fOFHHIK14s2AjKNiYy5MSJJw400hz9FtD5PRLQWAEAvoDZssCiLXP3sYd+oDfick2eaBxm7tJZ33a4Cxc+pTOkpQjKtXR4bP6bGcppoKt+0LchtnCTTWolgiz2kIFwc9waxszLk/NcWAduESWCO5rQ+gwViFpxpbXNaE9G67R8OVxPzhG6oKaI1gP6+0O9bVS67jNXl3iJCCP7wsXKN3yN8CXMdqDEiE+6PYyOK1t6Z1oZqJNyfcAG8ixkPMjLmmw6GLw1+0O17cK36+bC9j6lRtAsqroVdl3RtxPfFPvhWuArCJbJVkav9/iy4tt0Sp/WcSn6ErxV9kXiQ80ZEawEAyGbacKHiyViMk7BUEzhf+ExlhU4R6nCbctCFPnYx4eQ2Ncq5tSzIbZwi0xoXpOK0FoT5mA6+dCPGYWNAgOkDtjHReu4CUcU6LeyWNdHLtJ7xfPD2z22ZaE3fbzZ/TvVeQDfg3JiqS4GXlNPv6tJ56FLuLYTAq1LW+D0Kd1qHX0MPzGntI4Dbmp3fWvLq8SOLoSXrKszhfHsNmda8mtJXbLYdlDdzGzFaDhHonByyB9LxB7KnEa4Xk5EGQF8f97tG7U3KCJnWoXuSCx6CV83pd4rCWaCbGg2/Eqr78b5RC425k7htURjKULSe73QGIMLxXKe1agYw32nNTyQpU7lNMZzWupR9uWFClf5JpLUgzMYUb1QanNa7mrqu3URrurnE8WX2AlGN27NuHpVepnXpP7/h3PMp5pzcGzKtq6I/T9kO6UIrfi4F3lyHCgpLVgIB+MdsCQKFi21r/B7hcDU/0zo8MoGL1j4CuK3ZuTUeJKLTWu9NhnPmOcVlpUKvGQ7/RpHINx6EH/50M13qpeXguBcPEiPTeoUVFYIQC9taljblrWNkWgeK1u3Mig1hGc5gqyicA3wzTaELuX2o0zpVPAgr6ahG3AwuuAjHY5hcjc6P7dAEcqos39Qo59bSZMZG6AHFHPSEI64EQZiLaQzR1R+0EaN7PEhtGFPU9Tq36YnapJ9+KUJHuTlOD7d4kL6ThOaMd4b3EDe61x4PUlqc1lm2vMAj5d5CCBfltJ4p4sa4hvC2n46itceeglYb9pzW1kaMhz9jTFMYNWE6qIzR+O/cyckav+s67waKuc1p3fb/35WNxeC0J1GYWcBhhcwXgkDimAZOax0/W1tian3ga0VfcFi55IimNXP6naJwFuBm2iRG40Lu8XavfjbXfesrnLqCIqcSrQNE433TqgXRXIebKT/WlXuDS5Iz1QCtNpwW+pZkhh5QzEGL1os9pCBcHPrQjTZiHDq8evnWEwdFY40Ym5mlEefktKYb0zmZejglfoo1YqSbYWz2hPdPH8d0AImf47XHg+jYqKNojeuVExx2pFrDCNfBMNN6fd8jHNLmHhjFiEy4V07rR4f78ngf8babIu9Vl95Z40GWdVpfssuPVlPSSyHUaT07HiQ37xV19XHYZxEjv10Q1o7NaU2b8qqeaQEbAr5W9MX3EE1YljPYKgrnAG6mTRM0now9JlmdofEgsSdwvog2lcK7Qp/bzWbeJRKSqb11KAmfdFobSu58SzLH3PepEKe1IIRjijcqDQ6vvUcjRlPeZjSn9RksEOkidY7TGl/Dc8xp3c8Q72+E6YGgKdfa5QDzGsD3C4WKU8xNiE3YEgQX+Fjp2mPknGhGeuC4cBsYmdC2HTw57lnQaX3v8T7qw8D+OH9jdVrHyzktDRVPyDU4rekan65FXIV6myg1t4Gaba+om8KFySQyXwiC7s/CndZ0zI1R3c3Xir6osV7U0bNEPhYBAPQCwORcQuHjWeK0ni1aJ3Ip4WCHwoF2Os8XjYs8m12mol2N80Xz8UaME07rZujY0BsFt+eky+NO4bSWUGtBmItyNZAxZGMYE3su4Imxqh0TrWder1oIP/1ShOrmc+a3nInWeH+14T3GjTB9nL3hoM7lAPMaGDitR3pwpOZGRAghAF6VssbvUagjODQyYVvr22Gm9dbjfdQ9H/rxf9T1R8G5LyQmAqly+96kiejoPlfomoFOed5Oa3b408z8jGzNkNWeOHCOkcocQQC4x3gQnmlNKhHqCH20Qp3WMatqhPicfqconAX7kQB8k9N67qBiWxSG0nKn9fH57UJE46qYvUjdlPbcuilcmm8VEwOzqbmJmhz2bu6WfaTyOB9EtBaEcO5HnNa9eBDqAp50WvcjmOjfQ5uenEM8CN0zzzmsRAEHM62fenSY6/pxLP2NMJ1Hx5zWYweY1wDO5/g9w+/iSZzW1eFz9XF2CgLCndZrjA0IdQTfBkYm0Nu94K4CAD9R0HSoS58Xvy/8yOLEg2AchclpPZxjLw3avJnuj1xfc24R/edm0SqDUWt2Woe63m/lkFMQlAGDVw3iv7f7ZlSHcoWvFX1R48gFj8Fr5gy2isI5wDfTFOW0fjg4rYs8m+2wsJXfhcIFlao0n5678BBBKDDlx7o/vvlEklIU42X5plwmfD2ujhR8T0OaIvgSGjcgCAJx6Bozrc3u6slMa0OOKd2AzkE30D39UqQXDzJj0czjQZ6+OYgppvcbP4s8z5RYbnK6x5iLLgH8euD3bF/3G1ouye0xMszH2SkIyCU0Ygx1BN95rkU5utFwrowwPgK4rdk5Xtv8vmLGg9icvQDhWeFrwOa0dn3NpWWPYKoEcwH3ihiRicTq6aNjGdd3nQtCLO4tY+6Nioyto1Q38LWiL5Jpfd6cfqconAX7kYxIXGBiPEjIyfNdokzrluWZqRK8WU7nadF4inJkYTr9+A7xIBNikckJ45snfhKndYaxJ4s9pCBcHKYs5MqQaV2zTOtu5LDI6LQuAkXrM3JaZz3R2v8J4c0xa/X5Nwcxpfceq0xrff/VSMapS9XNNaDcMx02YjyuV8rlNxahLlHhuuHVcWsUs9T6MjAmcG61gm40XPaceq7YegXcYiXoINP68GcMIaMqhofHSDNTeF0TVLTuOa0d31tbNCLODb4fkd4r8kaMcap57izfKUG4JkzN4QEA7khTXt6ofA58regDHVMueAheNWewVRTOAbWZNjiXblSm9UHMDTkFu52xwHQBhQHdiHF+I0TMfA5pfjW2MHV9fJdM66Y1C02mxa9vtlp9gtzQIuCwQRCEA7rR1FC03lsyrQHGc+DwZnRzqQ7PQhsxnoHTmm52qxkrVi40PH17dFr3DgmwekX/7pjz7sEirlwbBXPPxMg+nItN2BIEF3gW7xq/R6ENdEMjE2ij4Vvi1Bs7dKXYegXgv3d12zuI1c25IsSD5Pa9yVWJ1l3XE5pd31tbE3plXPKNB7HsFWP1TcDvVN12s/aDgnAJmJrDA/Sb8o7pUK7wtaIPdG6+5DF4zZx+pyicBWOnysppfYwHCXHe3iQqleILliDR2CD4+KIXpvOd3ndj8SBkQDWNzSYxyNeRotz3Cw7eWoxf7CEF4eLY7rQTDSlVI0YaV8HdRSOiNTqtyfhfksOzOWjxY9bNo0KHuTkHs9yF97TJaW3YCKvPxXBQF2MuugQKnmkdIftwLtJYSwgBx4ObyhxFce50XRec+4nX0JO6tTYTH4Nm/aPo0XbuPWxsvQLo4SC9vnWmtfdTHWCah5FmpvC6JpTo3Oh4EJ/Xq5zWlngQXzd8Zfk8cD4OzbS+2ei5fm3XuiDEwtpHgDTljVHdwNeKPlDzTYymu0J8RLQWAGD8VPk2otM6VTxIzRwKKBrPiefALrdhTmssxU+TqU03C6bB2VR2f+NZ1owOwSWd1loEE9VaEObQdZ0qu77tOa2HOftcKDU1h0IawwYzjyVan0E+SB4YD8KdYphp3YtjMWyEx5x3D4bDh2uEx2HtVDb48t+bu0CXqHDd4Mb4qUeH8SF21WFq6Fg/V7Sm49mcwx/qlKZznOs1aYtdelTmquKG3lcbmOFN2YzsDZr2dIdxS0FFZ1PkmOvtbY0Yfd3wtniusT5PPmyKXD3ntV3rghAL20EhjVuLcc2F9Nmh52DitD5PTr9TFM4CXDwYGzEeB5nHR6d1iPMWB6jYkzcvq1MCzQzx01Y66IPt9N4F2+BOmRStDQ3OfLPVYi3afAgVwQTh2tk3nbp++o0Yh5szvvHjzYgojVFwDbte1WHjGbgaqLNijtOD3wTjQXb1MI6l6mVa26uCJNP6AG+uo+em5b83qZpJC9cBfocx8x5NEmuhiVBC/ajU49+c64iukasiV+OA6309WIwpWZYZG+e1M/OSTaAgbXKF448uuQkYzvU1acToJVpnWvSmzO2PodZFbA2zi1TNQ79T4rQWrpWpSKbtrolyzRUBe5LegewFj8FrRkRrAQDGGzGi8IFO6xDnLc2fi8lQtA5xWk+LxlOUBlejKy5CBRWOTHmypgWcbxfrWI1IfAgVwQTh2qHXt8lpTR1eXpnWWH5Lxp7QQybceIaW4MaAPoM5Dl5rPAg5ODU1tzVljSMx5qJLoOTxICOH7KkRp7UQAn6Hn3qEJoJ1VZXFcFrnuVkcdoWvkX1jB8dil0zxPzEbMeom7SbROk4kxTmDc1/bBjqtLZnW0eJBIpp2QjPcBWHt2Pqz3NF4kDaC0zpEtO7Fg8x+CkJCRLQWAIBspg2bdVzEPd6GZ1qnyoPkonUZIdM6xN0WlGltaVhAoQuzZrShi/48b4/Zaq7vfYymCL7kgY3dBOHaweu7yLPegVNl2Czz8WlsvGraocAcy2kdo8FVKHmg05q/hudjPAiNYzE4rccyTm05gNeGaq5znBdM4v9S0AxGQfCFi9bblQlZMURrgLDriK/RfZ2sY83OdYzewVhDmzvGEK0rS7wFAHFan8F8mAq6xle9iGaI1nzNMVe0thmM9MFo+Geh973rqqoQhFjg2MwPCvHfn9o1xj2GL7TRqy8dWYJLPMh5IqK1AADjjY3wJOzJsXR8EyHTOvaJMx/soojGAULBpgzItHYQKpyd1mQBd1thOaqjaF0vn6+HjyVOa0GYh8rkr4pe5EVpcPTy8Wm0EWM3FJhDD5naCIvUWIQ3Yuz/++nbw3jbi2NRThIikOfmuaJpO9gdx+BrjwdRzXWa0zutb5moJQg+KNEa40FWJmTFKqEOiUzg8R6+TcbHmp3fMTGdvt44jRjNcRSHx7oCpzUZy+sZ879NtJ4braL6H/H+HiNGLl/0vnddVRWCEAscm3l/Frw2diSaMKSaX40PM6rce9FXYrU+S0S0FgDAvJlG+MnYWTqt2Sn7pjSXkLkQQ7TGRVgypzVZ5PHFFsDQeQ6gX4+rswffu5BDCl9CmigIgkBKn9n4VRnGJC+ndTPcYJaWUl1XcOw6B2cZfQ5z5jjuzFBO614cy3AjbMs4pXNkSFPgS6BghyP7+nSHHWoe3bfq0EUQXFGZ1hgPcu1O61mi9WGsxDnOt8n42BqfPy96iWcxnNYjlTWmCK5LIydVM+r1eryvNtG6U65tv+eDBqN93b+/schMX7h7XxCujXuLrmEag0M0hzLAaU3HFNGszxMRrQUA0KdcJucSPxkLOXlOVVqrnNZF32k9J9M6SjxIQKb2g+VEcvAY2IXboDM1hrI77iCZYh+pEYkPurN4vyxTEAQ3tKOBHzYOHb2DRowuTmuymlPX69xM6+PYdW7OsnlOa/0aNkUON9Ww2sc0ptr6L6BokmX9xmXXSMHy2LHBcogjZy50XbCt1yU4CqcHx1F0Wq9VtM6zMBE3pCn7PXNK+65tx3oFcGNN24sH8X6qA3QPg+HCfY7zeG3Q/gRz4gBskWRzBX9lMGIbKVV9HNNpLZFSwpViqyC/KYdjcIjmkAcYabouztwmpOO6d0KCQi+W7JnWSMjJc6qGFPzEPijT2tIwwAeVW2dSlCfQC+rxyzMfeQzTYhBP+2tSdj5GrXJDF3Ra09gTcbEJgjc2R0NlGBOHjRgdMq0LKlrPj0GijxcjKzQU+hzmiAb09jdVTiKqhpnW1Elic97RiptrX0Cj0xoPR3BuWrIKCKHX1doER+H0DBsxrus71HT2vYIPuim7/+vfMtHZd18xZkzhz4t6J6I0YlQVT8M5U2U8X/B4r/oTENHaR2i2RZKZDtVdsBmM8POpIhwYhxzQCMIlYKtuyfNMGTyQkEO7MsBIM3cMEZZDRGsBAGiDqOHFysXTIKd1QMfwMbhIO+ZmmCKm03pX+w+c+kQywGltWAxSEd5lo6TK4xZ0fVDReq4QJgjXjG1xaHL08o3zqNPakJOPG9C5Tmt8uHNwltGnMOegrl/VUhobLO4NB4FK3GbvYYx56FIomHvmFFVASJ5nyvm+NsFROD2DTOuVHXzg/BFqQA2JCuROaezX4npfY8YUno9NndYxmnOpediwcMfx7ZKbgNGD7llOa0vfG/yYfAV/dWjMndZtvP3PTcABjSCsnbppVfydaT1Lq8rLPAsyaaDgPEc/wJtccjzT2hHRWgAA82Ya4eJpyMmzcjHsm6jxD1ykVVmrM+I59IJ4XDQew7YQmsKn+RbP+eT3A9BfDFZFrv7tcmiwb+3fiVTQxXor8SCC4I1N7DRVnwybD9nHK9OGOtRpjY2nzmGT3o/3mOO01n+/3RTKBdyLYzE02jKJ2wA6/zKkt8Kl0JsX2i5qk6w5pGooLVw+OB5gpvWTel3Z6G0kp7W+hvxzfvkcN9dpzfv10J/pTOu4Oae2OCgAffh7DvNhKmjVTJDT2hYP4vnW4feYG4z0njj8s7hL1MtJENbAVH8WulcJba7N14o+tAZjjnBeiGgtAIB5M40MysxDmq8c76vrDov1WOiSRea0nhHPEcPhNrYwdXlsl8cvlONgpKELG3x93C1j7vtUiNNaEMKwNXLVcRVEtGbj09h4ZdpQK6f1zAMmfCrnsEmnQ+Wcgzo61t5WBam2GTqtq148iLkqSJzWGj4vnGJuoqRqKC1cPi1zWgOsKxu9nikOckKuIe6U9hUF9e2HxhR8XveGRoxR4kFGoguvwmlN+hPMy7TWmdgUXIP4uiRtBiNdzRMhHkQOOYUrBsdlW38WGg8SekgUoiHMPfgSlkNEawEA9GZ6YxhQhg29wkVrgLgTeNP0F3tzRWP6vEIyredmatP3hOc8cZTTeiQehC9+dV7ftLulNggsqaEnnGtyHwnCuWBrMlWxZnYAoEr2kLFDvlGn9YxxFuC8nNZZYKY1fQ23m0JX+7RUtDY1YjRnnNpiXq4RXoGDVUBLzk0UKfcW5oLjKBVM1/Q9wnVZqJgXkmnNndIxM615PEjXa8QYIx5kJNP6GkRrdFp33awMWTT420Rr/3gQ8xomZt8EEa2Fa2a709XjpugPOhdGdVp7mmnmHnwJyyGitQAAZDNtuFgflXmwC43eFhcBMV1KuPjBAaskAo1vDMlY6aArlcHV6PTYHs23CoMogvD3A7llC/Ix9iPu+1SI01oQwrBtyE2bM30wNR2n1BrGlLGIIhdsh2ungD6FOQtn7rQee7/pRlg3ghKntQ06BzXEaX2KTGsAEm0gTmvBExxHq0I3oFqTmFVbqvh8CXJas7HxpvITwHXfGHvjebPT2vupDihz83gPcCVOa9KfYM7rLabiQTzfO5vBKOb+R32nZL4QrpD7/THqzrKWpT8Pvd7KAA2hlUaMZ4+I1gIAjLtqsyzrZw4FDiq3CTZ8vElYRfL2xpqLmYjhcCsNrkanx967u7xxoWcyR9aNRbRWeX3TYrpqxLigmy3LMrUxEKe1IPiztYwhanPWtuogDw+8cFxwyrQ2NGLkG0hXzku07uf/e9+eO61HMsR7TuvcPFfYHPPXCP1sqNhxKqd1qobSwuWj12a5cpit6fBDj9lh96MMFHOc1izew/cQaaxvzS37TPqZ1hHjQQxzZjPTLbwmcK7vOn0tzGrEyA7KVRM1z/eutPTlqEf6PPlyF/BdF4S1M6Wp0J+Hruno9e+7L8FfP4f9iGBGRGsBAMybaUrMoPwUGz69kD48t6qkm1xPt7OHcGzDllM6BcZ2uLi8x5zWbWdeDJ57PAhA34khCIIfuCG/4fEgx7Gx6/R4iQd6uPm3jVdt20FnWNAVlnxJV2wVIaegX00U2IixGjZi7DpzA0GVfc3ee9vhwzVC57GWNCtesgqIooQtESEET2iMwRoPP3QO8enjQdAprd/H6XXtVLPzW+Z+1+4776dpZGOprAEgMYcnqiBZAirI43pjTiNGXhXWzjxM2ZQWp3XEvgncvS8I14Stzw7S15fiOa199yW2XmDC+SCitdDbTNsESnoSFivLLmZWxqEAACAASURBVInT+vjUyhhO66BGjNPl9sbHnuO0NpTm20o4fRwppvzVJcDXNVcIE4RrRo0hVd9FRq9jHB9w44xjsm28oq4mUyPG2aJ1owWcU5P1nNYzMq3J7e8MTmt6CFf1Mq3NmZo4D4XEVF0KOSv51Ifsp3Jax484E64DGomA8SBrErNUDnGo0zogHkQ5pY9znM+egv6OaZ3N3e849cUSMkplaLlSpzWZ++YcPuLeju975uRj0/vjn4fpgHkuNwHfdUFYO1O6xl1EfSmPIloHPQUhISJaC/3NtGWCjnkSluLUeeC0pgKNh9u5bTvi4giIB8GFkG88iEdJeGFxHADQZjm2eJDp916XYC8sWlsy6wRBmEaPIf2xnB5IopCKGzPcVNmqUui1eKlOa/oU5mxU6WL5pip6m+Gu63rjNF2Y6wPO/nt/vxOnNaUkh7Q6G/xUmdbitBbmQZvt4ffIpcfIuRDLaX0X0Jxuy9bJWgCfXuvj42XZoV8P54ZVI8Z23+E4Zqpq0u/t6efDVFBBHkXrOY0YeSXm7HgQy/yL/6ZVu3NZYwyQIMTifsKAQatCY4x95Uzjm6maVDgvRLQW2GbaEg/SG1QiOa0jbvhq5rTOskwNPD5O6ye1XriEOK2Vy66eF03iEw9iaoJmc1r7nPjXEZ0GPoy9LkEQxrFVilDRGq9tbDaEAoJtrGxtTuvAA6ZzyrTOQD+H0HiQu03RO+xr2k691wD9g0DbAedWGjH2wEOBuiUxKydyWotzTpgLdVqvMTYglhtt7jXUdZ1qaIfzlhbAp+NBppqdcwEc575Y5ueKxUZR5jYTXBN0rn8yo5pTOa25aD3ze2n7PJRpJ8L+Bw0EcsgpXCNTRsCY8bMAevz01RDmVmsIyyGitdDbTDtlWgeePOuIiukFpiutoaxuzNFgg2Y9h4gFOs/UU7T2cNeNxWi0FsfGnUeO4E7iQQRhddyrBWI/HqTIM7Xx3jctNCSnWovW5vGqtjqtww6Yzkm0pk9hM2PhTF/DbVX0DwnavtOaboSnnNamZmHXiHJat52OrjpVpvUKxUbhPKBjXoqovNTEc1ofxjXfa2jfdOo5oPB9MyMexLa+503z4seD6PVtxyMuDM2OLw06T85xWhdWp/W8tQTOIbvanGkdY/+DMTZrus4FIRZTukY/HiSe05ofbE3RRoq+EtIhH41g3UxTet1dA69o5bDY+Qm6Y2j3in5uvBGWC7ioeFTmQW6H0pJT6vr4LoL5mLhbW8QgvF+XclQUUeYIOCGERg4IwjWzHcnkr4irlwrUKh7E5rSmonVmEK0vwGlNN85zHLz09rck0xrgcAComk5lfSedzr5mmdbitO6h4rBIpvXSTYIRJWyJCCF4Qptkh+Q6n4pYbmCftSiFvld4Hz6HSFPVjPjz+6OpZq4YaqMf02URrS+4ESN9G+dkWhcWp/Xc72VlMRhp0TqG03p9DVcFIRauYy5AnDUdXSv60F7BoeHaEdFaUJNzkWfWCT/mSZhPM0BXTJ2jbVllY/g4ncdQQsRMp7VTpvWIYGTLir31cLfUJyrBDm3uJgjXDG62TWMYHROpaI2bftt4Nem0DhWtz2CRSJ/CHAcvF63p4W7ddNZNcKUac/Xfe13GLss0AC3kNL14kBM5rVnurSC4QqPbfGItzoVYucu3HlV/FBwXyzyDzTGTmrujx8Brdsr1h6YanNrixYPoO+Jj/jU0YqTRjSha+xwIqEgy5lJvZ5b22xoh47+rCIcVKfa8grAWfJzWMXpo0bWiD7H7Fwjxkd2Q4FRqG/MkTDcDjLdQNzmtx7p024jlblOOxtpv0LwfcUlyRkXrCae1y+IJBaylS7BLcVoLwmweRpqeVGRMpJs0XDROOa3zDHo5oMGi9Rk1YqSva84cx+NB8jxTrrK6aUnzQLNozd973XFd4kEA+vnpqknWiZzWPo3fBIFCGzHeMIF0DcR2WvsKeaY1unJaO9zX1jFfFfcnc8VQGyU7zKScU+VRSpRo3TS9fzvdFg/eLY0YfQV/XenEnNaqEX0Ep3Ulh5zC9TKlq9Cfx+ihNbfXjmrmeuHj75oR0VrQJ8ojk3PPaR3JYZHaaY0n5D6Z1krwieS09s203u7dnd62gbnrOuvi18eRsnf4XqQA5yxpxCgI/mz3/eaKlIqMS3golWWHOCSA6UxrvqCc26UbOadNOn0Kcxy8dK+M7706OCWRFvy+Ve+F1uy0Dp2LLoWcfNdUk6wTO63X5JAVzgNjI8aI/V1S05B4kxBuPdaiFBT+qOjsE78wZQyhrtiu07nTsaaontOajfnnNB+mBPcuIU5rWzyI79kCGowGjRhjZlqrOKnWO2dXENbOw0R/ltvYTuuZ+xJTbzThvBDRWrBupikxu7umaGJkclpXpX8zRN6VfC7VDJc3wEyn9aBMjvwOG3xvHEsyqfC9tDAgTmtBmI9pU4+UpAJEl7/mk1Up2l3X/3luKdV1JVapeQyok25WI0Zye3S5q74KTasjLbjwb3Faq7lIMq0BoH9AorNQT5tpLeXegi/Uae1jIjgXYkU6qabgR3HYFZNTGtfLT+ppUXAqgg/XyG13vL/IjRizLFNjmc1pfQ7zYUrUQW0z3LdNgfueWI0Yq9LitI5YzUP3c9t6Pde6IMRgSteIqS/B/8/eece7TZ3//y2vO7IDBEJCIIS9V1mhjDIKhVLKKHu1tNBCKfTXPWjponQvypcCBcres+y9V9i7EMiCQELIvMu2/Pvj3CMdy5IsyZLle33er1deufa1dWVbPuc5z/k8n4fGk9Y6Z9266KS1xnMxrVI9qKTTgMUP2wvOvs8ZGAXBr4lZGPIR/LRBKaMJUBLuNTCrt50NXboDlmSq71mzPa1lzKmT1hpNePxK8VSv/aKiJKrn/1/2UlpH9I4DsTHWSuV4VZ7WEeY4dbEsLT3U0mN7EVx97IJHeXJfiP4G7YC6QZK20tpuJq0TEJpwlJTEZGcCAo6kiUsNLF+76lEfBLcEiGqhVC+27a0jTKlKMBbLSiIjvrHGy5IiLuuVVke+vn6ptA7xctXrTt2giJq0tjbyy5WqzRN7Xdz4Z6Fatek5Q9NuWBuNHv1Z1Bg3jnyDl6CvHu1S6TKU0UlrjRU4FfyU1kpQ2OigErUBix9luWOvnJuXV6gfPXXKWIKiloWHIUzzrUBJa0egHbSMUlWna6W1RjM0MM2KZQ/iluxUx0TV/sdSBHt858oeJdKZiN5xzue0grJMTUpEUfCqz5eJD3VB7NU80EvlLi0DdNJaYG+QmKl7WncnEMNo2gNrLFWU1kNJsR/Xwl5NGodJ5LkppaW9FQRIWvv0fAAxpsiYt2egjAyF45yirJ43HknrVpgPk0S+PtseJLzSGqrjFdseJGwjRvvxakwi10D5XDxJNHmN6jlD027YzW/d8ypVjRhjGPvs3Eg40WDc/Qs08aOT1hplMe2ntFYaHMblaR1n0trFi8hLzeBHb50dwaBE8dOu+vtBPK09yuTU3cWojRirlNZNLsHORCzt0WjaHbX01E1prVaAWIuyrGF9xwfqKa0dc0QjntbquNUKyjL1FKJs1KlvTVchU3WcUtm7eaBVKl7jaT24+aDtQQC1h0OwmCVJkqgW07QHamJyKF5HcSWt89mMNfaFSdq7VRJlMgadgzF7vXVFEAu+TiVOTiKRYfe8cY/dh3vSRM73Mt4IM4xXKa2VtU70RoxKY0w1aa3Yp8WB7Ws9dL7rGk0cyDHbqz+LuoEYh4e8nbQO9zxrg7IF1iMad3TSWhOo4YS6Q9aouikJdYmbQiHnoWbwQwa8XjuCQZGBUKUSLqFTr2FB1d/IeDQkKfskrS2FmH/jH/U9a77SWietNZooqCoeV3sQJTldUmyhgtqDOBfTUb3joHrB2QrKMvW1RZnj1OfL8dvqbWCatqWFYxHsVREkm/zppLUgqyT3LauVlK6boWjroEkftVdIxjASqTpMmjhLqIPGoyp2jF49LsqYvd66Ikiz825FWGPG3IgR1OoaD6V1SrZHzcJWWovPIozSOuehtDbNaJ+Tejx1074YYyNGCG7NqNEMN3plc3jP5rfxVfKDvXEVpp8ZuNvMaloLnbTWeC6mVdQdsrgaMcaqtHbxgqtX8u6GtSMYk6c1hEua9/j40TrJWAOzj9LaaQ9iKXv8z6mk+LnF6eUXBK201miiIcfUjlzGVS2gKn/VRZl6vxteZcuNJK2rlNYtoCxTX1qUhWrGzR7E4/1WkbfVBXOlUqnrvdpuZK1NWsUXOGV7EJ2A0IRBHSZVpfVQuo7KEb2D3Yjy+r1i9KAN3v16Pridl/zM4oyDCx4blV6bw8MN+fpse5Dwz4XquMOs1K4Bg6CuZ9XPw7ZPi+ez6ByCG1QaTRz0+jSHh+qxOEoTdCdqrBgGs03G36GMTlprrMWy7KLsRneM5RtdARURYZABS5XSugF7kEYTBXmPkrN69HmoSNyQr890NBuQu4uGURvABV1sx60yCINdLq+T1hpNGOqpyHLWRp5Z5WltVaV4eVp7qOuiNjyB6gqRVlBag6K0jlASXN2IMVt1HNGI0T3Raiut7Xmqv2RayRKvksp2w01pnZYiMQmLM83wR02yCU/rwVh4CF1HMi4La8PgRncEy4Qejxg56HcySN+aLuVzsZOhgU+xLl5rE7tJ5/Bemjs3asO8XjVWUL9PMt8c9rrMZgxrw1qdg0sRzs0PvdGpaVfq2Z5WNWKM0dM6rNK6lRrDa9wZ3jOjJhBqmbgXXVVK6waT1gkoreVryFQlrcM3YuwN4HcXhKoStlBKa7EjGUTpbSmtHa9PjtNug79d1uxfjhlEfZ8UMuh0JuM1Go0/9fw65ZhQrPJYNqxmQ8WS+1hVqpe0NitUQn5f1U2pVujWLU8h47LZF+b5hmE3BlOTEyUPS4u8i7+pmsTR9iACSz1TqVhzXhyqnCh050VSq2RWQvet0LQvzuaz0vt+KCWy5GZjNoYNoyg2O30eSumg/uBB+tbI3/UMlK15LVZPa2UeVrHe22G+Ms86lNZh3tuMR9LaUklGeO8suxblePLnuJr9JrHu1WiGAvXyKl1VosjGv2+5TLQcglfDeU3rMMynRk0Q1OSFF9U7YS3oae2itI7SDNGtM3kU1CSMV3Mz978/6P0URGntMTDL3UW3QNBWtpg1XthVx0hRyebVYFKj0fhTT9FQyClKa0XVla+jTDA9SsJVVVPYr6vqQdlsCyI35HgZNWiWi+mufNZ6PerGqdci2O69YL+B8nPMZ43YFs1DHWteKFes6zQtpXVnwf5MhlLCUZMuzibZQ9EbPU6ldZSKBS+ldFB/8CB9a+Tv+hR7kDherySvVDyp2JvDw3vMl2O5TFqHHcfd+t400jDTin9clNZx2YN0DcGqCo0mDrz6EEgKObspbxzft4wSK4ahEqP1lSYZhvfMqAlEMUBJmroT1ugiOolA3c0LLu+ye16PMJ7SfhiG4elb50eY5lsZj+Sun9JaTWb1lbzff6tsL4WEie1HpZPWGk0Y6m26WQqvUrXS2m4M5f6dk2NYTdJaCTDDluJ5qbfTQk5/UdW7cu6p9uezNwO8NgLdmmDWU8y3I2oFjpXsSCm5U8hmrOtWJyE0QXE2ybaSo0PoGjLNWoFIVKIIWLw8qYP6Y8vvq5/tkrpGka83zn1Vr+a79uZwfH+rFZFjZ38EpTUofW+UTaBG/MDdGmOWPOy8omKp9/Ump6aNqFQqgfIq8ndxiDQiK621p3XLM8ynRk0Qgqhqq5PWjXpaJ6C0dgmk3ZIB9YhLaV3994MNnGrzrSB/301tAEqJi5s9SM4+rt9iO83ya6201miiUa/JlF0Gq/oCZxSLijpKa0cwV6W0DumS4OWTnRYGUh0d7Xzke6FaO8mk6oDSiNEZlBdcFsxxzkPDBduKRrGvSklpbRiGLvfWhMbZJNtqHlgsh7ZXSouS6R1fhiXKd8geG6vHUftY/tZ3gRIoyhrF8jmN0x7Ey9Pa2hwe3ktzS2lt+UZHVForayv59YkST+RdNu2jnpsXln2Nni80bUR/ybS+m74bhYXq5uWNYFmnhswh6KR165PqzHjWWWfxqU99ilGjRjFhwgQOOOAA3nzzzTRPqS2RgZNfglIt62h051k2dRwomTUJ16i4JUC81Ax+9A56SjfaiBEUVWPAbI7afCtIsiLrlbQ2zarfq2QyBp2KX58XaZZfa6W1RhONek2m8spGmt2I0bagKJY8lNZ1PK3FY8JlrcsxlpnHgTyNqOpdmcSpnivtjVOrEaPjPXRTudfbfGhH1OY6cavgojAUrR006eJski3jvLJZ8axyaTW8NjCjIOepMN+hXo85LqhqO0iz824lAZ6Ep3XexRIK4n1vWxmnPUjYDRD5/lQprRuxB3ERGJU87LyiEuVa12iGOuqGpF8862xe3ggy7g6bX2pk40vTHFJNWj/00EOcfPLJPPnkk9xzzz0Ui0X22msvVqxYkeZptR3WYtonQdkZY3dXNSEbl9raLWmdc+zmB8EqHYwhWZB3UdD5Ebb5lnfSWvzv9TkFaVjjlWBpBlF3STWadqdekyl1TJIJlHw2U3eDzQyQtA6ttG4x/zg57kRV78qXUd202N44texYck5P61qVu1dipp1R57uide2md+0k0ZtDM7yRX/Gsi5XQULmOLDVwDN89OU/FYQ/SGdTTulg/xndTWseZR855VDa1mmVWUjiT1mGT9JY9iPL+qT0ywmIp3wePV6lUrHVVbJ7WAe1rNJrhRND+LPL7EafSOmzSWjdibH1SXRHdeeedVbcvvvhiJkyYwIwZM9h5551TOqv2oxTAvzhOT+uOXAbDELtavQNlRnY0fhm6Jq0jKa2DN0KsR1h7kLDNt1SPTxW/Rowgmsx80lP0DZ68StmbgVX6N0TKZTWaVqGvTpMptemfujFVryrFqyRcXWxGVlq3SIRoN2KMaA+SqU1E5ZXFsKXccrxet/fe9rQe3mXiYZDvb7Fsl5vGocqJirYH0YSl5KiCkw2oSmaF3oEyY7ryaZ5eIMoxqoHVhodB8Wrq1R0wKdgTQGltN4g0G2rw54VXDwmvzeHhhrx2BiI2fLetEe37vJpFB0HOI3IOVj+XuKp55PUW5lrXaIY6QasGuyx7kMa/b16CvnokMdZr4qWlZDxLliwBYPz48a6/7+/vp7+/37q9dOnSppzXcMdrMa2SH/Q9LZYrDe88Sz/InoEy+/z14ZoBYmRnjr8cugWbTR5r3beiv8TxFz3D7htO4MRdptUc0021J5tgnffwO1z+1KxA5/bxigHAMcCWi/Dx27DgDVg8B5bMhaXzoHcx9C+F/mVglhGWqAbku6BzLH8qlZiXG8EjF9zEtZlVWdY1mdOP/CJrTFzVOvSHS/v4yiXP8NHSfmuADaryjtKIURxfTArH/vtp8tkMY7vznHPEVqy76ijrMSXLOiA9T+tyCIV8UGZ9vIJvXvk8J+48jX03mxj78etxy4vvc/Ydb1ibAt2FLGcftBnbrb1S6GPd8fIHnPvQO/ztsC1Za+URcZ+qZojw35c+4De3v06xbLKiX9gbeY0h+Sq7Cnuz0s1j85V5S/jWVc+zrK9kLbScY0omY1gbkJ/9ixjLJ43r4pIvb8voTv8EjJ20bo3ErJyGoo55hlRPKsmQLqPIhsYsJs+bQ2H5PH6a+x9bvT8A/zGh1AfFXtYa6OfmQj9mOQ+X/B26V2Ldni5OyQ7QVZoG87pgpWnQOabh1ziUkddeX9G+RtOwr5Ik0ZtDM7yxlNbKONqVz7Ksv5TodfTMe4s489ZXOXP/Tdh6zXENHcvPfi4scp668NF3ufqZOeQyBt/aY10O/dQUz+d4NmKUScE6m0h9IZqCXfH0LK5/bi5gN+qNg4IyD6tYSmvKsPB/sGgmLHoXFs+Gno/Fv95PoDwg1hyVMmTzUBgJhRHQNR5GT4TRk2DMGjBhAxi7VrwnHwMZh9I6aiNGdaPcr5dPPZxVsWocFJvSevD6vOqZ2dz+8ge+j91w4mj+fdynPL9j7yxYzmlXvcDJu63D3pusFsv5aTRhuW7GXC59chbnHbU1q43pdH1M0P4scswtxPB98+r3VY922TQcyrRM0to0TU477TSmT5/OJpts4vqYs846izPPPLPJZzb8GQioqt1o9TG8t3AFq452H5zCsPHqo3nmvU9YuHyg5ncfLevn7lc/rEpavzhnMU+/t4iFy/vdk9Yug836q40GhLIijI/Y1Pwi1v/oDnj5GZj3LCx4UwSJIZkO4htWAcrAcuC878O4qbDapjBle17vX4/X5vVhKk49G00cHej4Vodc00Np7THwbrz6GN5ZsIJPeoqAeL/ve+OjqqS17OqdRvm1lbROQGj98FsLeGnuEm54bm4qSesbnpvLvMW9Vffd/vIHkZLWN70wj5fmLuHBNz/iuJWnxnWKmiHGjc/Pq7mmNlrdfQyxy2ArVc1W3ayM7n51Pu8sqLbqWk8ZIyTrrzqKN+Yvs8byj5b18/zsxeyy3iq+522P2b4PaxprrTyCfNZgg9VqX2MQ1lu5i80yMznSeA6uPx/mPcufFr1LpqMCL4nH7JADlg7+G6QD2Fy+B++Knh7TgO/kgUXA+b8VvxuzBqy+JUzaCtbYDiZtA7lCpHMdisjERn/JnsvT2FSVWE306jR+02gkTqU1CFuLZf2lRK+j21/+gFfmLeXuV+c3nLRWeyE0ipyn1Bj92mfn+iatZdK5w2Gz1GnZ3vmLHazn+1SxyBi8r2hax1tvQrR5wQ274mnwXJe+D+8+zGn9NzK18DabXzYHyv0+Rwjzx7pE8nrytrDGtjBlexgzOZ5jRz0lR9I6aiNGtbhL/hxFJVkYvJbk+ahVT1F7XDipvqb8P9uPli1g5oLlVWsylQffXMDL85Zw0/PzdNJakxrXzZjDi3MW89jbCzloa/cxRW4yelV/SjZefTRPzPyYdWIYZ7Megr566EaMrU/LJK1PPvlkXnnlFR599FHPx/zwhz/k29/+tnV76dKlrLHGGs04vWFN0KZG15y4Pf0lkxEx2HlcfsL2vP3R8pr7L378Xa55dm6N6kQGtF7+1G6DzRHbTWHHaSvVT1iXB+ie/wyjZt/LyNn30bHkXbjd8ZjCSJiwIYxbSwR8oydB93joGA0doyCTBypQMaHYC32LKfd8wscfziG/dC7vv/cG4/pms7qxCD55V/x7/RZ2BZ7v6ObVzq2ZsuMhLJ+yG2uvESyg9FRaV/z9qP986BZ8Y7dpmCac8+Db/PelD2pKnPsCTjRJYJf2xK+0ltdCWuo4+fd/9LkNeH9xHxc//l7kc5GLx946izTN8EYufL+953rsseGqdBeynsr7nFUGqyqtDVuBrYwl8lo9eOvJfHn6VHJZg3VWGVlzzJtOns7MweT26Ve/wJsfLqNYqn9NyjE7rkVho0wa28XTP9qD0WFK9Fd8DG/dCW/fy3YzH+CWwifwnv3rDLC4MoLeMeuwuDCRh+dnmTJlbfbZdmNRkZPv4uO+Ct+56lm6MyXO+dJG0PMxL7zxP956+222GrWYdbLzYfmHsGSO+Pf6LeLghZGw5nRYZ3fYYD8YMynOt6PlkBsualIqzaS1LvfWhMXNwqAZ11Fvnfg5yrHi8Nvff/PV2XKNsSzrKzFj9if89KZX6sZDVmWoR28Av8bnlUolUIO9PTZalUe/vxtLe8VGQjZjsM6E2rkvKoVMmR0zr7D1q7fAszPg4/8BcCCISaMM5EfA+Kni39g1YeQEoaTuGgf5TjCykMkKQc3ACuhfDj0LYekHohJ00buw8C0o9cL7z4t/T58nTmDl9WDdvWDdPcUckm2uLY0zqRRWHW33vVE8rRuwrXH6TavXUFzCnd02mMBjP/gMSwbFQl4ce9HTLFjW7/s96B3c4NJVPpo06Q2wnu4J2CfsR5/bkJN2mcZKIzsaPi/bGjbcfCeXP1GqNTTNoSWS1qeccgq33XYbDz/8MJMneyfsOjo66Oho/ILWVGM1iKozOXfksnTkGvd6BrGz7aYGnDyuG6htptIzOCh6ea7aCZDq1+Bpm2CaMOcpeOlqeO0mUXInMbIwcXNYc0ehaJu4GYyZErrELgtMGPz5zKue56YX3ueXe67G0VOXwbznYNbjFN97nDGlFezY/wg88AhkcjB1F9j8cNhgXyh0ex7fqwRGvkdeA282Y7DBoAp99cGSHq9NgnolPUlgJ63jP7Z8XWl18JaT/DoTRlrJuqjnIpOOvVrp19bIhduaK3V7KqwlUlFUMu3Fey6TUZLZStJ6cEyYPK7L97id+az1+1Gduapz8j9vOU7VfWjTGDcigHK5bwm8dgu8egPMfEiUaEs6xsCUQRX05G341XM5LnhuGf9v8/XpKZY5d+47fHniVPbZYiPrKeayfh4wy2DCPzb9HIZh8Fjv2/z+jTc5dNoanH3wZuJvfvASvP8czH0WZj0mSsX/d5f4d8f3hJJu4wNg00NEgmOYIRMVMrlnGOmWcXZavrc6caAJRsklTrUV+8ldR/LYYfq71DtWHH1fANYYL2Lc5YPWVvW+T9Zmq+O7H6SHjbopW88Pf/K4bmhMlF6NaYpx+8WrOGvmLYwoLIW5g78zMjBxcy6fP5mn+tbk1KMOYZ0NNmt8ciyX4JP3YP6LMOdpseb54CWRzF74FjzxD5EI3+gLsOnBMGXHpkzIznE7tNI6W9vPR66DoogkuxxNPEtKvw8jRtXlpLFdTBrb5fuYUZ05Fizr9x0PLPGNnns0KRLkOvTqQeDEMIxYEtZgW92GVVqbuhFjy5Nq0rpSqfDNb36TG2+8kQcffJCpU3WJexoUW0jx1pV3V51In7piHaV13UXsioXw/KXw7EWwWPG5HrEKrPtZWO+zMG03oZ6OERlQL8uOgbW3hrV3hU9/m6see5sbbruNr632FvvknoMFr8M794l/hVEiCbHt10Ti3EHWm+aeYQAAIABJREFUo0NuuY7SWsWrmVTQ5glJYL+u+LPW8rpKSx2ndq7valBhZSWttdqirSmWgo/falmuulnp5mktx9wwY4A8zkCA5IhdEZL+vFOXSkUs+mdcDK/eKNRrktU2hfX2EYrnSdtA1g6rBl59BVg+aMfivjms3i6bFXJZw7IKsDYNO8fA1E+LfyASIB++DO88AG/eAXOehLlPi3/3nAHr7Q1bHi2UdJnmj+FJ4PS0TrMJIyjJRj3+agLiVhHY1YTND0tBGoMSIIgndBScalcvvJqE513mMK/nQhP98Je+DzMugRevEN7UwAjg48ooPpz4GTba9VChdu4ayx9/eQ+LzAFOHj8tnuRxNgcrryP+bXKQuK93Mcx8EP53N7x1l1Bnz7hI/Bs7BbY+TswdCW58OtXQYZWN8vnqBoVcBkXZyKxRWkdsEBkHQb4H8nc69tekSZDrsLc4GMs2MZeQ92h0W49GqjU0zSHVpPXJJ5/MFVdcwc0338yoUaOYP38+AGPGjKGry383UhMfQZXWzcDe8a5Wj8rbXgFpqV7Sev7L8Pg/hDpO+lMXRsFG+8NmX4K1Pp3o4t5ugFY9iA5UsjxfWZfbJ+zCPodvCR+/Ay9dAy9eKZLqz18q/k3ZAbY7ETbc3zpP2cCsXHEkrUP4MskST+euvlTvppK0zg5/pXV3IWftPEc9F3ktpfVaNK2BLGUNssCy1GimaSWW89kMBRdP6yiKunyIsjx7nAp8+OZT7INXroMnz4UPX7HvX2UDoUzb+EDRJNED1bu0aNlwOZPWdnKiWK6Qy0LvgHj/PCtdMkKZx8TNYafTRGLk9VvF3DHvWXjjNvFv7Jqw/TdgyyNj34htNjKxIT2t02zCCIqtgx5/NQHxasQIySagei3RR+NK66QEDU61qxdeTcKDJCrU3yVuLTT3WTFvvHYTmIPrmY7RsMmBnL94K3776jhOXWdDNtpgXespgcU3jdA1VohhNj5AKLHfexhevl7MH4tnw32/gAfOEmuj6d8Sc0zMNKq0tvveqEnr6O+dvXFUvc5MY2O0O8AmVq+1jtFVlpr06A2wnpaxbD17kDjJuaxngiAfru1BWpdUk9bnnnsuALvuumvV/RdddBHHHXdc80+oTSl6BIFpYAfw1YONvO1V7uEZsMx6Ah79k1AVSCZuAdt+VSQcfOw34sQrmVOzYbDSNNjth7DrD2DW4/DshfDazTD7CfFvpXVgp2/DZl+yGpiVy+5J6yCL+q7BZjROta+1MEnDHsSoDUjjIm2Fgrrg62xwsSorJLTaor1RGyrWwyqbK9vK31zWcC2tVqsCgpIPUKItacoCPSp9S+Cp8+Dpf8GKBeK+XBdscqBQok3+VKA6ZHuzUvEQdyyE1XG6aJp0kQ2vThm9utjU3O5E+Oh1eO7SQWXfLLjz+/DAb2DbE2CHU0QvhiFI1rIHida8K26aYeugGV64NWIMkqRqFDmWB7FtqkdNFUhMWInDgEprZ3ybs0rCvV+jGn8nItKpVODdh+Gh38EspT/TlB1hm+NF74FCN3NvfoUys2qSKmaz58RsDqZ9Rvz73O9Fgv3Zf8PcZ+CV68W/tXcTG6NTd4nmveH2Zx2vL6yyUT5f/agb2QS3k9bV68w0NkY7PapfVXqtilHdz0aTHr0BKpflfBGXnVQQ1Lg7DKnag8x5RvQqWG3TFP740CF1exBN+qRZCuXES71kBd0eyRA5OFnB0LwZQjEw80Fx28jARgeIRfvkreM/8TpYijun/7RsCuPc0TcMWGu6+Lf0AxFIPnM+fPw23PwNePC3bLTal8mwjqfSOkggKBstOhcKdZV+CZJkI0Zrkk1daZ1teLEqr3ndCKy9CTN+5xQ1mtqQyq2JVdCu31XHD9AMS2InrdPfLLXoXyaS1Y//HfoWi/tGT4btvgZbHSOaYIXAtl2pKApBh9Jaef3yMUF9AF2ZsCHs/Rv4zE9Exc6T/xTzxiN/FK9tuxOHZPJavpdSaV3IpXvdBE2yaTQSN3FFo5vXQZDjSRye1lJAErfSunvweAMlk7JZ8UzcesXMQTZM1YrMOL2KAdHf4IFfC99oEM3ZNz0YtjsJVt+i6qHWPOyYJ908z5tGoRu2OEL8e/8F4Xf9yg0w8wHxb82dYPefwpTtG/5TNUnriEprt0aMQSpMnXjZg6Qh5OoOMK8EaYCn0SRJpVKxRWA+a9ik7KT88Mq31KPpm4YAH74G9/8S3rxdjLHH3Rbb5uBwpCUaMWrSxavcLg1kc6OeYnXZk1W2ZZpUKpWagFOOTR2LZ8L9v4fXbxF3ZPKwxeEw/TTfMu6k8dr5C5RwGj0RPvNjmH6q8OJ+/O+wZDafWfJz7ihM5pEl34DKZtZAF0bB2OlhxyKVft1p2IMk2IjRKmcqll2voyQxzUqVerXRsmBtD6IBqhoq1kP1/VQbWrkt+OV3pasQfF6IprQOfPjkKJeEp+eDZ4kGhwArrw+7fE80qcrmIx3WanBpmlaCwjnPZjIGGUPMYXJ+CNpx3ZdCN3zqK7D18fDmf+HBs4UP9iN/hKcvgJ2/IxLYuaHR3DpTo7RuDU9r3QxLExQ5LrrZgyQ5j8fpad2bkHJOFUj0FsuM7HBfnpY8bJbc+jI48Wri2BDzX4F7fwZv3ytuZztg62OFtcaYya5PyXnYBUoBSurl6atvAQddIDY+H/8HPHeJUI7/+7Ow7l6w5y/E5mhE4kpaq40YzRC2iE66HfYgaa6Jg8wr8vus7UE0adFfMpFfP7++HmlUbUdVWpcb2PgKzeI5cP+v4KWrgYoQVo6fCqV+objWuKKT1hrbEzXtQAk7SerVGLBSsZtVqXSYy/lm7nomXXk3mEXAgM0PEzYb49Zqxqn7oiruVELt6HeMEonrbb8Kz1xA//2/Y/3SXNaf+yP4z52wz+9hwgahktbdXnYsA82faCS5JJXWg6+rbFYolisUcs275vtL9uvpLmQbbsBkNWLUSZO2pui0GPIhr3ha2xtmGXsRbVaszRzbyiaE0jpA4kDSMkrr/90Ld/8YFrwhbo+fJuaNTQ5quM+BVAOXFKV1zmWsz2UzDJRMBhzNVWNJDGUysOHnYf19hZrjwbOEP/c9P4VnLoA9zxRVSC2u7pDzQqt5Wmu1myYoZZdGT1Z1YVM8rWNIWsuN95hjw45cBsMQMX7vgHvSulKpWGOkt6e1X9I6uJVWXZYvgPvOhOcvAyqQycE2X4ZP/z8YtZrvUwsedoHlNJXWboxbC/b9g7AHeeh34rX+7254+z7xWnf7UaSKndiU1jE1YnRWO6TaiNGj+lVFfp/7iiamWUl/k0PTdqjrTr81aE8KuYTojRjF/4kmrYu98Nhf4dG/2A3dNzpAbBCuvK7/czU6aa3BdzHdbLwSeaoKpWSKZlWAiHBfvIo7M99nFWMJmAglwB5nwqobNems66M25FKx3vswQUe+C3b8JtcP7Mzie//ACfk7Kbz7MJy7I2x3EsYqxwLBgjdnAxJJGhONJJOg0lrdEe4dKDe1xFxVRVQpraMmrc3qBJemPQmjCnKzqyhkjZpmgIWcEWnjyh7n6geLVql2WuutxXPgju8LFTJA13ixCN/6uMjKaidyXB9QlO1umwuFwaS1/EwSKanMZGDD/WD9fYRtyP2/Ep7X1x4nfEs/9wdYeZ34/l7MyHlBvjdpV4Y1w9ZBM7xwExTY1W7JXUcy9vDqCROGhqyLfDAMg658lp6BsmdMVFbOv8ZmSdl49aIURzKyXBKbfQ/8BvqXiPs2OgB2PyNwNadb+XqlUgnVRL2pjJkM+/9NqMfvOUM0+X3mfHj5WtjjZ7DVcWJ+CYjTujB00tqoVVo3olJ3NkUvRlmXxUSQygv1d32lcigLN40mDqrW0kVvxb+M15pZtR1GPKOSuD3IG/+FO34AS2aL22tOh71+BZO2SubvDUP0SKexvtiFFvC09rJMUFUoxbIpFoyLZsJtp8PMB1nFgHfMiax04B8Yu8V+TT3nIKiKOxWrCWaE5Gm5cyy/Kx3GvLUP5dfdV4pA8slz2KXzWvbIHE1vZq+6x/BaeCfVIT4ISSqtVS/r3mKZMcSTnAqCfE87chmyGcPTTzwoTv9bTXsSRhUkfUBFY0B7s1JNAJRMkwKZSGOArFwIUpYnF5xNt3kol+Cpc+GBs6C4QijktjsJdv4udI2N9U+pDS7thbCb0rrao9OyB0li0zCThS2Pgo2/KKymHvmT8Cw9dwdho7Xzd1rSMsRWWrdGZVhXE5KNmuGFW9K6O99YHBAEaakTjz1IcrGhlbT2eC/UhLRTZBNkw7TYqEBn3gy45VRRqQIwcXNR4Thlu1CHsZIqSvWdmmtPe2zzZKVpcNjlotnknT8U78Ntp8OLV8Hn/xrYMsQZq0T3tHazBwl1KMC+luU6s+Rh5dUMglRe9DnENzpprWk2Q0FpHbaHg9z4in3PcOkHcMd34fVbxe3Rk2GvX4oYvNU2KFuc9KW1mtRpOJCLEa+FYJXSulgS5RX/3BFmPkgl18nZxcPYe+BsSuvs2dTzDYpXgzIrOIoQaUm1wYfZ1UQgedT1MH5tuvo+4oLCHzl10W9g+Ue+x/BqBpjkwqQeUmXibDAZB6pXerP94JzKVfnelswKA6Xwi0n5vdVJk/ZGbahYj3zOXuyVFFsoNZFadGyGhFHUhWmAIs+7qTnrj16HC/eAu38iEtZTdoCTHoXP/jr2hDWo6j9TWQjXjvXOhEtvM9QphRHCBuXkJ2GdPaA8AA//Ds7bGebOSO7vRqTVlNbNsHXQDC/c7B9kz4CkNp9LZdt2qNFGjJVKxVLYJZGE6PLosSJRk+7OxG4ugI9p5Hi72At3/xQu2EMkarvGwX5/hq8+EDphDe6qcFVF3vJ2D1N3hq89BHufDYWRovnk/+0k1OelgbpPdyrJgzSNr3q8JWxR7UGCN6B34lx3puppXec7IH5Xdv1Zo2kWvQGvQfm4hvqzhCRMQ3iVRsYQVyoVmHEJnLOdSFgbWSEMOeUZ2ORAnbCOQPpZSk3qlFrI01om8vpLprVzDvbAN8X4kBFXfl6UqJV6YerOmCc9zrnl/SmSa4nX4EbOY+evkQ0D+VqtErl19oCvP87r075CqZJhu56H7MHSAy+LijSaJ0hyLgFpXPQO2JNYs8u6ncrVTqXBXZRzkd9bnTRpb8I0l5LJ0YGSalfhUFqXzZqmoUEJkjiQmFYCpwlhSLkEj/5ZJGTffx46x8D+f4fjbm+ooVQ9VG89v4Vw3tGYq6k9BcavDUdeB4dcAiNWEd7eF+4h5thiX/J/PyA5K2kd3MM9STqb0EBPM7yw7B+qktaDSuuEriM1tgiymeiH2nwrKaU1eMdDqoo6mqe13cchMHOfhXOnw+N/g4oJmx4Cp8wQns4Rex64nasa77bqOqaKbA62PwlOfkr0SzBL8NDZcMHu8OFrvk91vr5syLHcmbSuVCqWUj1Kc3XndZemp3WnR58hFfX7oeN/TRoEvQZj7c8SEK98Sz1itQdZNh+uOBRuPVXYSK2+FZz4kOghU+hu/Phtik5aa1LdVXailjn1lZRBcaDE4dn7uKPwAwrvPwOFUSLpcMwtlMauZT2uVRUKeQ+PpUaCo4xLiRz5Ll5c/zS+MPBLZhemQe8iuPoouOlk6Ftacwy1mVRFUTanaQ+SSTRpXVJ+bm6w5yyTKmQz1uQYJfCUpaU6adLehGnmqiq8isq4bxiGrU4oV2qahgYlSOJAUnJJ4CTCJ7Pg4s/BvT8XauL19oZvPAVbHZO4zFu+p6Wy6TvWy/ctkUaMQTAM2PgAOPlpkZSpmKKa6YLd4aM3mnMOdZDqFzlWpl0Z1p1wslEz/HBVWifsja5en8UIFV0qapySRGzoVfknkZuhGaM2sRCk+ZY95wWYc8olePBsuHAvWPQOjJoIh18FB10AI1aq/3wf3JIqamVhYp6qSTBmMhx+BRx8kegLMf8l+NcuYv7wUDo65/xGldZqUWaU987ZS8m6TlJoEt3t0WdIJajKVaNJCrUSIIjSupm5hEJET2v58CgbX1W8djP8cwf4312QLcCev4QT7oXVNm3suBqdtNbg2Y07DToUb2drIOz9hO8u+RVn5S9khNFP36Qd4OuPiaSDYVTFRa2qUPAKqEuWn3gDSmtHcrdkVni1MpXfTj4HdjodMOCFy0T5nqPsW3qmmhWqElW2NUDzvdJybsn4GKhUKlULw7SV1rLxEEQLPKVqyrnhoGkvQjViVDytnU2p1OYlzqahQckrjR7rYbokcGLn5evEuDfnKegYDQecKxIPoycm9zcV1HHfVsT7eFqXHZ7Wzd407B4vkjKHXQHdK4tS+H/tAk+fX50ZSIHs4PvWMp7WuhGjJiRWszhlUVwvUdso6vVZarBPiByXCtlMIptG9ZqbypjH7W/bcaOPPUjQuVJudD74G6iUYZOD4BtPiCa2MZDP1CZVyuUhmrSWbHIgfONJsSlcHhCVOpcdCMs+rHlojbVLyNfrrMasSvhHSDip4h1Qqo9TUFrXm1dUux+/x2k0SdIXcC2dRtV2LsAGphuWPUjUqa3YC7eeBtccIwSDq20qbJSmnxq5KkdTTfpZSk3qxNJROyYyGaPasmLO0/B/O7NT6UkGKll+VTySuftfA+PWtJ6jBqkt13V7EK+AuthA4ibjcUw58BrZDtjj53D8HTB2CiyeBf/+LDzxTysBoe5+uk1CXYXmDxFZj2R8owyUzapmN81WyPW5eAQ7FR5hUC0Y+htUUGmGJpVKxdrcCTJ+55Sksu2FLe6zmjQq1iCyaWhQrKR4gOSIpbROYswe6IGbT4brvwL9S2HytsK7eosjmuojp24EON9vFfW9Lyse96k1WNpgX/j648JyqtQHt39HVOz0Lk7nfLAXEv2DFViFCM2L4yTpZKNm+FF2GasTV1qrSesGPa1ty6hkvntBldZuntRBmm8VgyQj3/gvnPdpe6PzwPPh4H8LH+uYsJIqqqd1g4nXlmDUqmJT+PN/g1yXaPD7f9Ph7XurHuZUWoettnL2vVGrMo0Il2Zn3kNpnaKntdd3oM8R6+uktSYNAtuDpKC0DrKB6YbpsqkcmAVvwvm7w4yLAEMIBk+4H1bdKPyxNJ7opLXGdzGdBmLSrtDx7Hlw0T6wZDZzWI2DBs7kgvK+lBwx6VBSWjsD6lIUj71BbKV19f3yb1iB4Jo7wImPwIb7g1mEu34IVx0BvYur/Gzdmnt0pZA0cesMHgdevt3Nwk09aS9YwzWFLJuVqgS8LhFsT9TvSJBS1oJi3zFQqlb+qmrfqJ7KTm9mP+SCM/Yx++N34MI94fnLxAp25++JjTtlo7NZ5JRktN9CWE1uJ12CH5hRq8IR18LevxUljm/cJlTXH7yYyulIpbX0tE57rldVoXFvsGqGJ2WXjTrbGz2ZxtBqbBC2MZWTpCvwugp1lNY+PWByAUrC5bzk2kehXIS7fixi474lMGkbsdG52ZdCvYYg5F16P8gEi2G0rs1hIAwDtj4WvvYgTNgYViyAyw6GB86yFisNK62zPvYgkZTW1VZP1uZIikprr5jeOU7oTVNNGlTNK0oloRPb6q55uYQgG5huRE5av3wd/GtX+OhV0Rfm6BuEYDBXCHccTV100lpjB4Ip+He5MS5X5G/5fzDh8Z+L5h4bH8iB5lm8XFkbcEn8KoF4q5bVySBrwDGwh/LYcyAHVi+ldVUg2DUWvvQf+NwfRALizdvh/N3go9drlD6q0i+NpImltI65HN25EGp2sOfmU2sri8ItJp0BglZbtCfqdRBMaW0nUe3NykzV/wNl075WQ37/cw5vZj/Kg4+Jdcx+/TYRPH74iggej7kZPvNj0TQqBQo5OznhV9Gkepyqi4GkFI2ByWRg+6/Dl+8S1TqfvAcX7AnPXdr0U3HmqdL3tLa/G7rSRROEkktlnbyO+nwarzVCn5pccCo+QpJ0qbdT8eqk6JNMLATytPZ4/oqF8J8vwBP/ELd3OCXRjU63pIoM44esytrJhA3gq/eJhpVU4KHfwhWHQM+imtcYWWntZg8SxdPa2YixmU2iHdjjgYfS2rFW0ElrTRoEFYHZArjmfZecPWKCIh8eeAwpF+HOH4qKzmIPTN0FTnoMpn0m1N/VBKc1spSaVGklexAWzeSC4g/YP/sEppGDfX5H5aALWVjssB7iHIhsn8AYDPQTQvWSVfHzOa1/TFkiV32/Z9m9YcC2X4Wv3A1j1oBFM+H83dk39zRgT0Lq5NPMjr8SS2ndYCmrE6dyodnqZLcyqagqK6cK3a9pi2b4oi7QA3laq3YVjgW8upCW12pnyO9/LsR3Vz4klqS1acIDv4GrjxR2IGtsL6pLpu7c+LEbQI7rA4oSxe1zKrgorbvy2daZzyZtBSc+DOvtA+V+uOUUEaybzRtDs445Mu3KMLViRm8aaoIgFfnqmCcTwM1QWsflaZ2UmKG74K8y9fOktiqFfF6j6xg8/xX4124w6zFhB3Lo5fDZXyeqkrOaHivnKs+7VYU3kch3wX5/hi+eJ+xC3r4X/rULE3pnVj2sUU9rVeASZcqU38HSoGBHNixNY03cWU9p7ajK7NFzjyYFapLWHtervdHZPOGIs0dMUEzTziXVRW50PvlPcfvT/w+OvlFUKGoSQyetNVYgF6UZYKzMegLO352p5iw+qozlhc9cCtudSH+5UlX+5UyIlF0WAq2GpbhzNk2UQXgEf067g7YjiW+9Hx5PXH1L0Rxg6i5QXMFZpT9weu46K/GpTj4dKfiGShVG7ErrlO1B3CbvqH6Wzsk4rFJbMzxQr4MgSTzLO7lcqSm1Vhf9PcVoyQnpMxwkOVKOa5HevwyuORoeOlvc3v4bcNxtTWu26IcaPNsVTS5K64ztcdoT0ZolcbrGiQaNu/5I3H7yn3DFl0QpfRNwXt5pV4ZlM4Z1vSeVcNQML0puSesmelqHbUxVc6yExyb5XnipTP08qa0xtFzxbExdctqLvH4bXLgXLJkN49eGE+6FDfdr6DUEIa/YdEnMsCq/ocTmh8EJ98C4qbB4Nl966SvsmnnB+nXYcvyMM2mtrKuiKNW7HBuQMn5Jw9Pa2RTSiXMd06eV1poUCFK5nFbVtmW/FNK2zbIHqTcGL3gTzv+M2OgsjBIbnbufoZstNgGdtNb4+sQ1jZeugf/sD72LeDu/Hvv1/5oPxmwJ1O44OxN2QyFprQbUKlZwFOHc7aR19f32++HzeY5YCY66QZRBAt/K3cCUh06DUn/qSj+7weTwsgdxUynVKwX0wnkd6aRJe6ImQYJ8V/M5OzHtHHss9Ve54to0NAhe45wboUvx3Fg8RyQd3rhN2B4dcC7sfRZk89GPGSP5KjsW74VwlZ94xA2DppDJwK7fh0MutpVzF+wpqnYSJpt1Kq3TD1+jjt+a9kQuit2U1n1FMxFvdDXOCas8c9KX8NgkN/TrKq1dYlt107bs8T5aY7ABPPR7UZlTXCEEHCfcB6us38jpBybn0vthWCqtVVbbFL56P6y5E4XyCi7M/55js3cB4V9z1qheI6ifd5QmavmsYZ1DX7HckG1jo9RrxOi8X/ez0aRBkMpldc3d3EaMwdchKuUgntYzHxQx7+JZMG4tMaY1YaNTI0g/6tekTsmjOUZTqFREg44bvgrlAdhwf85e9Y98xDhrwHMmG4uOgNRK0rZKKbULXuUqjWwYRFZaWwfIwWd/zT9HnUqpkmHV926BS79I39KFQDrWIFBb+hcXaSut5YJPfV8765TDeuFUsury9PbEthcKNvZZdhUlW/krqzxU9Zdb09BAxw9RlmcpraOO2x+8BBfsAR+9BiNXheNuhy2OiHashFDfU7/S9nyVp7XYgGo5pbXKxl+EL98BoybCwkHVybuPJPonnddJ2vYgUL9plkajIscAVVCgxgN9pfivoyqldYMxVdJVIPVU58UAfQHE49xfZ7FcoZN+Tvz4N/DAr8Sd254IR10P3eMbOfVQuCqtXTY0hh3d4+HoG3lj4hfIGhXOzF/CmbmLyBHuupefv2nZg9i/i9LE0jAMq39Hz0C5VpHfRLrzYuNGNG+ujaNqxDc69tekgHOj3u06VMVUzezPEtUeRBboeK5JZlwClx0E/UuEBeEJ98Mq6zVyqpqQ6KS1xncxnSjFPpGsfui34vb00+CQS8h1jgDsQdCZbCyW3JO0rdxx2y1IVW9H8U5zNiOR2I0Yg32ej4/Zj+OK36eYGwmzHmONG7/Amsb80AmruMgmlbROOdhzK63tjlga7GyopJV+7Ukx5Nitls05E95VntYuGyxhjh9KaR0l+fj2fXDRPrB8PkzYSKgd1vhU+OMkjOrx7TfWy/dtQPG0TmvTMDCrbwlffQBW3wp6P4FLD4AZFyf255wbM63Qg6OeKk6jUbESk8ql25lTrAkSuI6q7UEaU1onXQVSzxrBry+AuolV9LCnyvQu5KrCL9l2+QOQycHn/wqf+13TK3PcytfdmnQOS3IFHt3wZ5xVPByzYnBs7h4m3XF8KJupjENpHUfCv1MZy63rLIXPolNpWOf2PXDep2N/TRoEuQ5l09BmV20XPPIt9fDMJZkm3HMG3HoqmCXY9BDR5H3ESrGcryY4OmmtsRobNnURKE3sX752MHj8G+x5JmQyttrCxWMZalWm5SEQ7NlJa3dP6yh+4pbaIGgjRg8681keNTfl7u3/A2PWoHPJTG4o/IxtMm+FPqc4SCxpHbBxRFJIn2B1MyBq0sO5KNNKv/bE2UyxHjklMe3crFSbNEYtA/fanHMjstL6hSuEl/LAcljr03D8HTBmcrhjNAn1/Sj6lLbbn0t0lXsqjJ4Ix98OmxwkgvlbvyUqp2LuRwC1C4m0Pa1BUVrrxIEmACUX67ZMxrB6hyQxj6vHrFQai6tkTJ7Uhpod+/vbg7gpYNVx1bUR8Cez2Pfp49giM5Pl2TFwzC2w9XGNn3QEckpvCUk5ZNw+lMld5+qsAAAgAElEQVRlM5xX/jxfL55Gb6VA9+wH4MLPwuLZwZ6fqe57Y7930c/J3jApKd7pzZ9jCtmM9Trcvge1tgzaGlDTfMLYgzRbgJHzyLfUw7YHUe4c6IFrj4HH/ipu7/IDOPB8yHfGcaqakKQf9WtSp5HEaSQWzxY+pHOehI4xojRv62OtX9uJPBE41NiDOBsxDoGyOktx50g2FhuwZrHVBtXHlCVzQTch5IQyv1M0olk6fhNWMpbx+56fwKs3hj6vRsl6KMgbJW1Pa/n31Ak8atLauSjTJYLtSVh7IakcKppmjfLXatJY1QwwXMfvnHKMeoQetysVePj3cNPXRYJ0k4PF3NE1NtQ5NhN1I6Dk00Qsn7GVY27jREuT74KDLoRdvi9uP/RbuP27dmexmHDOka1gD2J5WutNQ00AvKzbkvRGdx6zEbV1r8vGe5x01omH/HrAZDKGlWyoKQv/8FW4cC/G9M5mbmVlzpn6T1hrenwnHpKcUlkjGQq9eeJCvsa7zE9xyMAZlEesCgteF4nrj96o+3xn3xsziBdtHewNE1PZHGn+Z2EYBt2DcZfb96DWlkE3Ydc0nzD2IM0WYHjlW+pRca5JeheLCsLXbxU9cw48H3b7IbTBxmKropPWGt/FdOwseAv+vTcsegfGTBEdpdfeteohtnpJDHjOnWTnQGT7BLbuQKKW3qs04p1mqQ0c43JYpXWVsn3Uajy20yXcXd6aAkW49nh47j+hz60RLKV1zGq9mp3hZtuDDF7Pqno1qlLPufDU5entSdhGrnIcqlSgv1Rdai2bNBYbaAaYD+ElVwqzSC+X4LbT4P5BH9Lpp4kAMtcR6vyajboR4GfloipDkm52lgiGAbv9CD73B8CAZ86H678CpYHY/oRTad0KjRg7I9o7adoTrybZSXqj18bP0eOqnoQ31LrrxEPFOslEaxxVX+Osx+HfwkpqYfc0Duw/k8Xda8Z41uHJK5U1kvZKWtvX/yuVtVl4+B2wygaw7H1h+zXvOd/n22ufwaT14NvYUNLa6i9Tsj6Xpgm5HHT6jAfO+3q10lqTAkGuw7SU1l75lnrIMdgwDFj+EVy8H8x5CjrHCDuQzb4U+7lqwpF+1K9JlUrFXkwnXm77wYsiIFk6D1ZeH75yl2u37i6HeqlWKeLu4TwUGjEOeHhaR1GNZT12E8PapXQ5fASXmx2cVDydB0buC1Tglm/CE+eEPr+oJGUPIq+jUZ1CxdBsdZyrp3VEpZ5OWmsgvNJaXezL77tMrKodt+1rNdycEKYszww6TpX64dpjB/2SDZEYHbSSanXkRoA6h7mN9Wqy31K5D6WktWTbr8JBF0AmD6/eAFceCv3LYzm0c35Po3TbiW7EqAmDl9LaGYPFiaxYlDh7woQh6Q01Z+zvxM/TGhQvU/ka37gdLv2iaJw1ZQeu2uQ8PmJc6lUabkmV9kpaV982Rk8SNl+rbwW9i+CSz/s29vVSWjfy3qlNQItmk9bEHvh5u8vYTK5j9IapJg1qrkM3e5CEG/d6oVY4hkGmHEb1zRfiyg9fhhETRJP3NXeM+zQ1EUg/6tekipoYTDSQm/WE2LXqWQgTtxAByujVXR9q73iXq/6XOAciS7HXAuXCXuRdPOyAhjYM7ORu9f1hG1M63+/eYhmTDNeu9m3Y8VTxoLt+BA/+NhGvUidJJa2l4milEQVxu9hchYJbMqorolLPqZbSnqrtSdhNL3WxX3bYCKmJU9uiIpw9iGU/EsjTWvzvO04Ve+GqI+GN2yDbAYdeJhKjQwQ5rqtfV1c/VsX72lK5DxV7ECebHgxHXA35bnjnftG7omdRw4d1JiTSaJLlJElbB83ww0tgkWRDz15HnOPVpDDQsRJOQlixqEdsVq9pfC6rCDmeuxSuPhJKfbDePnD0jSw3Rvk+v1nIjVr1s2ivpHXGcduA7vFw7C0wdWfRr+Kyg8Smgws5xxrB1Ys2JOpYLjc90mr26+ftLuMDax2jN0w1KVBzHfo0DW22AKMQ0dPaNCusbbzPnk8eY7sBfPlOWG2TJE5TEwGdtG5z1C91YoHc/+4dVDsshTWnw7G3+nZddSbynAk9Z+JXKvZaWWktFXc11iaDtwu56ErrslNpXQmptM5XL7xloNRZyMGev4DP/EQ88MGz4O6fJJ64Tq4Ro3ifxg9Oss1WJ/e5lEr5lQH6oZXWGqi/iHfiNibIhZmltDYrrk1Dg5DPhfC0rufn378cLj8E3r4Hcl0iEbrhfqHOJ23cNhNcPwPlve8dykpryTq7i3m+axzMe1aoVpa+39AhncmcllBaF7TSWhMct0aMAN355FSTNY3MQy7kVXqSTlorvsJu1OsBI8fRkS9cCLecAhUTtjhKbHbmuyzbh7SSkRK3xuzlIVAxGhfOodsa2ztGwRHXwgb7Qbkfrj4KXryq9vlGdSNGM6RQxw01Fpff07QU+X6VF/L7nNY6RqOB2uvQrTom6fnCi6rNyxBM6v8f1xR+wYi++bDyeiJhvdK0JE5RE5H0o35Nqqg7/YkEcq/eCFceBqVeWHcv0Tirc7TvU5zqJeek7Km0bmGFglp6X1GSvqVYlNaOxpQhPb67HSqfqt1Rw4Cdvwt7ny0e/MQ/4NZTwUwuUEosaT2o3hk/Qvjg9jW5gYlbEyOr4UpYpbWzEaMOXNuSYsh+BG5jgmUPIkvqSqrSOlywmQujtPZrntS3RCit3nsECiPh6Btg2m6hzqUV8FNVV9+nqNxT8gGMncnbwPF3wuhJsPBNuHhfWDIv8uFasRFjV4LJRs3ww/SwB6nXgLAR6ok+ohwrqQ01OxaNprTOZw2+kr2d1R7/mbhjx1PhC/+ArPieWsnIlK2l5FxbNu31QNsrrSX5TjjkEtj8CKiU4cYT4al/uT7ftgcZvD+ORozFcl0bmqTxq8CU99nrGD33aJqP8zp0u1bT6s/ilW/xZfaTfH/+d1jZWMonYzYSbgBjJiV4lpoo6KR1m6MGsLEHci9dC9d9GcwibHwgHHo55LvqPs2pPq1RijiSmeYQCPbUBbaajC02oPyQAVpN0jqkv5vX+12VNNn+JPjCOWBkRGPGG74qmqMlgPW6YlZ0y9dll9WlYw+ivq/SMzjsYtW5g6yTJu1J2E0vwzBqmgtJdbS8v2SakYPNMA1QPL33exYJS4k5T9oNUIaon5wzsZox3Mdl24OvYo0TnUM9aQ0wYQOhVhm7JiyaOZi4nhvpUK3YiDHq+K1pT7yU1l15cTsJm68a0UcM9iBJbaipyTq3ZEO9ePlo8yZ+mr9M3Nj5u6JSUElkNhJvx4m61pJq67ZKWjuSyzWvOZsT643tvi5u3/FdeOo85fHif7n2q2qgFhFVvFMK2Sskbvw2b2rXMXru0TSfINdhWkprr3yLJ+89Bpd+kRGVFTxlbsBj0y+CESsneIaaqKQf9WtSRZbLZYzGSqtqePk6uPFrojxvy6NFc6ZcIdBTnerTWqWIl9K6dS9nNfhRSwIb2dG3FMmO4L4c0i7F2fTDU02z5VFw8EWiydYr14vPN4HEtapCiRNrZ3hkoep2s7ASgWrSOqJSb6DkUFrrpHVbEqWRq3PBLpPG1YlT8b0OG2yGaYDi6r2/fIFowvT+89C9Ehx7m1DsDlGcG8Fei2A3T+vuoWwPojJ2Chz3X5G4/uRdkbhePCf0YZybG2knnkCJVXTiQBMAr0aM8jpKojm0MzYI25zK7VhhbaOCIjfqzEpt03Ko0wPmkT/xjeJ/AJi72amw24+rEtbi+ekqaCV5xQ5QChDCNlAfyjiT1K6J+kwG9j4Ldvq2uH3H9yzFda3S2v17FQa12sG6TlL6LPwqL9Jex2g0pbJpjc9+12FaVnd5j3yLK7OeEDaExR5e6tiaYwe+T7kwKuEz1ESldbN8mqYwYCkPYrwUXr0RblAS1p//G2SCD1rOJhTOHbwBxyBUjiFgSRo1EJVKF9OsWGVtUQJVT3uQkIoNr/e7y60J28YHCH9Ambi+6euxW4VkPBTkjdLj2BnuK5qWUiNpimXTmjyrGjFGLAuuUVrrpElbEmURXmuzIO1BbJV076B1Tthg0y7Lq58YqbF1Wr4ALtkPPnwFRq4qEp0TNwv191uNTMaoag7ltQhWFep9KalTEmXsGuLzHLcWfPJepMS100Ym7RJ/UKqUdOJAE4Cyh8Aiam+LIDiP2ZA9SMQGvUFR5xu3mEYKVmp6wDz8e7jvTAD+VDyYWZt9qyZhLZ6frlexJOejtI5VPNSi1CStvQQ2hgG7nwE7nS5u3/FdePr8GqW1V4PTMEhf+Z5imaLVpDpdexC3ecXZUF7H/ppmoyao/a5Dtz5OzUAVNPhWFs1+Ci4/GIorYO3d+N24M+ijoy2qXYYq6Uf9mlSRQZyzZDwyr90M131FeJFtceRgwjrcsa2S28EBz+nZ5VRam0NAaa0mleR7rg6mskQ/DM4O2pKwSetOT6W1xzmtvzcccjFkcvDyNXDzKdBAyakTGdDHnbTuszy4bMV/X6k5AZ86yavJKKfKPSg1ntY6adKWRCljVcciQ7GrkAnVYtm0ylLDJk5ti5H6311TVZb1LIJLD4AFb8Co1YWf3IQNQ/3tVkX9bLw+J8sL3DRte5DhorSWWInrqbB41mDienbgp9dUCLSA0tq54avR+OHVbC9qHBAEZ/wch9I6KeVcPpuxEspu74WVTFRj/QfPhvt/BcB/uo7mb+UDPV+j6/NTQE2ay3NtJ6W18zX6rlUMA3b/GUw/Tdy+/Tts/P51gB1nxGEPItedfQNla42Z1uaG1dfJVWld3VC+ZFYa+k5rNGGRY7NhwJiufNV9Kj1pKa0ztfmWGuY8I/rmDCyHqTvDYVfQj/hOufbZ0bQErZvl0zSFUshGXr68fqvwsK6UYbPDYP+/h05Yg22Z0ONQ/spAx5kQsRR7LTzOZBXFnQyIGvUTl4oMs0KV/1/YpHW3Y+EdSE2z4X5w0IVgZOHFKwabM8YTOMm3Iiml9bjuQs19SSPf04xRvUEUNelheTMOfsba1649keN3mDJWNWnt9nPRNCMnJ6yu3QHUfHLc7igvh8sOtBXWx946rDp2q5+NlyK+WuWerJoxVcZMrk1cfzIr0FNrlNYtUFrlbBqt0fhRtjYZq69lOw6I326tXvwc5VhJVoF0+ajOS05P6od+Bw/+Rvy8x8+5fuThg49zf41pJyMlhmFY8bk8V9/GxMMMVU2eMQIkmw0D9vi5aKwJTH/jNxyZvdd6z6xGjA0k/LsUW8p6DT+Txu87IMeIcSOav47RaKDa9kPGqa7XqmWJ2dxYNuOSb6li7gyx5hhYBmt9Gg6/Ggrd1jjSDmPwUCX9qF+TKr4ecWF443a49jgwS7Dpl+CAf4ayBFHpcuwyy4FvVGdu8JzdldZpqyfqkbOSQuJ81cA6yqaBqlZQE7w1Zfd16HIqrYM2Atv4ADjofNGc8flL4b/fhhiaJyaltO5VSpU6cs1toKVuBKgBeqfSeCiMVYn83srvhE6atCdFjySIH+pj1YRqVeI0YnLC8rQ2zbpdu81KhW76+OwL37Q9rI+5GVZeJ9TfbHXUKhqvZIm8v2SaqfkANo0xk0TievzaQmn9n/1h6Qd1n1Zra5P+wsK2dWhuU1/N0MQrMemMweKiUqnUxs+lGJTWSSatfSzTquywnjgHHvi1+MWev4CdTqdQp6dCMeUGeyp5x7laSusWGNeSRh3LA6/bDEN8zjucAsCv8/9mh09uBWx7kEZE6mqiWFbBpqV69xsP5H1juvLWGk/H/5pmooparAoFX0/r5o+3znyLxbzn4NIvQv9SWHM6HCES1tBezXCHKunP3JpUicXj7a274ZpjRMJ6k4PggHMjJ6yhtlSyzwq681XnLJFJ2hbPWdvl94OLBrXRTJTgSFUrqOoZuylJzI0Y3djkIDjg/wADZlwEt3+34cS1XE84G0w2ipo4brZCzquBker11R9iMSkVtvI7oZMm7UkUT+u8h12FlThVmwGGTE7IipFKpf6mU6bYy4X5PzBh8QvQOQaOvmnYWIKoqItyr4SE/EwGSpWmJIZSRyaupcf1pQfAio99n+Kcz1phk9qeO3V5tqY+XhYQltI65uuov2Ra4ZiMFWoW8QEpmxUGBmOUJJvEOhuxq8ik81YLb4G7fiTu3O0nMP1bgNJTweM1tkojRrDnSqfFRTuo/NTXGGoYNwzY61e8vtYxABy14E/wwhWW4KMRP/AuRUCS9nWinouT3oHB72Ahq+2pNKmgilpkZbxf09A0YllZ0Vy1STv/ZRFr9i+BKTvAEddAYYT16zg2vzTJkv7MrUmVgUYn51mPwzVHg1mEjb8IX/wXZBsrBZGJvdJgkCzLTkZ3eSitK0NDaS0VdzLhaJX2Z41IXmzqwsdUEry2XUpAT2tHKZqd3A040Wx+qFDWY8Az58M9ZwR7ngfSm7zcQMMgN+wJNONbfpcEPR7vqZrEDpN4los3+Z3QQWt7EqWMNVdlV1GreBKLttqmoUFQVcW+ZejFPk768Ax2yL5GMTcCjr5xyDdd9CJfpWz397QuKZ7Ww1ZpLRm9ulDWj1pdeJlfdiD0LfF8eE3SugUUibYqVG8aaurj1WyvO6HrSI0LpNLatVw6yLE8+nLETadPIq5kmuyfeZzPzjxL3LHjqbDzd6zf55SNVzfUmDttcg6ldcljQ2M4knOJOwJjGLy80Xf5d2lvcfvmUxgz6y6gwUaMisK/FKGCLU66faoNrH4j+WyiDVw1Gi/UakA5F7hdgz1Wb5zmW93llOpFAD5+By4djDEnbwtHXgsdI6ueYyWt22AMHqq0dpZPkzg1HnFh+OBFuOJQKPXBenvDgec3nLCG6sReb7FsDZCjOgaVIk6ldXloDDSWCqRcbQ8SNdmuqhWqlNYhywylsmWgZFI2K56qYF+2OAI+/1fx8+N/g0f/HPy5DmTgmZTSuquQS6wc14s+D/V6NmPYViUhzkV+b+V3ok8r/dqSKGWsOS9P65w4xrI+O3ES2h5EOQ/P5kDlIlx7HJv0zmBFpYOHtz0XJm0d6u8MJaoW6HWU1qVyJbWO66kwbi045iZhDfPBC3DFYTDQ4/pQZ9K6FdSSfoo4jcaJp9I6oXhEHq+QzVjxnDN+DnysAbv5VkeExuFB8WtKue4nj/Cn/D8xqMA2XxF2EYa6CWuPo27EZocYA1b5+uA8abZRaXqV0jrCy81kM/yydBSPjNgLKmXWe+Rb7Jh5pSGVeqeL0jqt66TTY15R7X66CtlEG7hqNF6olZh+VcuycigNAUZVvmXp+/CfA2DFR7DapoMJ61E1z5FLlnaodhmqpD9za1JFJjtDNwJc+LbYtZK+QIdcDNl8LOeUz2asoL53oGwNkFJVWnI0/CtXhoZCIe9oUlZsZMMAh9K6ytNaHDfowKtOKL3FsqcquC5bHwt7/lL8fO/PYcbF4Z4/SNbaIY0vaV02K5b9hro73CyFsl8DoyjnIt8b+Z0YKJuRFVSaoUspgkdnwSOJKueApX1FoLZpaBDURKJr4sA04aZvwFt3MGAUOKH4HZasPHwT1uDd+FJFfg4DZVNRp7RB0hpglfXhqBugYzTMHqzcKg3UPKwlk9Y+KiONxomX0jopxaQ8Xmc+U+WbHwVVXRelMjAonpYHMx/kiNk/I2eYvLnqvvC5P1QlrMGOiQe8lNYNxtxxIu0C5TwZthfNUKbK0zrCOJ7LGFTIcP7Y02HDz5MxBzg//0c2NN+KfE5VSuvBz6KQS0tp7d7crr9kWs3iuvLaHkSTDva8kg3UNDQNAYac7yorPhYJ6yWzYfw0EWt2jXV9juzD00jFhiZZ0o/6NakSKXG6ZK7wBepZCBM3h8OvhHxXrOelKpjkhDzaw9N6qHjBqYkJsFUfURffWS9P68F4PWgSX1XN9A6UPVXBgZh+Kux0uvj5ttPh1ZtCH0JOGGEaE9ZD3QXuLmTpznv7JiaBn094dwS1nvSWlN+JsM/XDA9s78WISmsXv2WptI6SnMhmDCuPUKO0rlSED+nL10Amx5/Hn8ET5sbDfpFe/R67j/VyDugZKFmL0lCVLkOd1bcQ/oK5Lnj7Xrjhq2BWj2fOhUQrJJ7keK4bYWmC4GUB4WcH0Ah21UbOGmM8K2DqEKrXSQO4qkxnPwVXHk6+MsCd5U/x6EY/dzVDtiz46jRibAV7EKddYNheNEMZ9TVGWbfJTZ9iJQMHXciiVXdkhNHPL5f/DD58LdI5qdUO0gc3LaW1V3M79XaV+EbPP5omoiqt1WvQ2Xw9UtV2TOSyBiPoZc07j4WFbwobumNugpETPJ9jbyo36yw1YdEfTZsTWqm3YuHgrtUcWGldsWvVOSb287IVTCVLeTa6SyTonCoKr5LLVsNqvGIlrRvrUG0YhlVa56q0DnjcTMao2rHvaXR3dPefwVbHQsWE60+Ad+4P9XQZ0MaptFZ3gTtyGTqbrJDr9VFPdkZSWovPeERHzkoSarVF+xFl46ta5VSreFraK5TWUX3o8l7NsB79Ezx1rvj5gHN5rrANMPwX6dVqdi97kFprlrawB1FZcwc47DLI5OG1m+DWU6ua+tYorVtgZSE/o2K5EjkZqGkfTA8lV1I2M2qFlxz3o9qDNKsCpNsZm334GlxxCBR7eK17G04tnkK+UHB9br5O7Jh2gz0V5+ch12LZFhjXkkYdy6Osf+RzymYFch28NP0cnjPXYVRlOVz6RVj0buhjqmugYkiLxbjxqryQtwvZDLms2ptH91TQNI/qRoz2fOC0qVSbhjab7kyJf+X/xIiFL0LXeJGwHjvF9znlytAQQLYzw3921Pgig7hCkMm5b6lolvTx/2D0ZNE8a8TKiZxXl+KTJAdCr0Yy5SFSVmf57ZnV5YCNBNBuCV65JglT4iLf7yW9RVvpF3WiMQzY78+w0RdEg86rjoK5zwZ+uvo5xqW2VtXjhmHQlQ/vI90IvX72IDLwDOVpbSuGoii1NcODKOXOXnYVcg5YKpXWhWjjkm2DpIzTMy6B+34hfv7sWbDZl+xxe5gHiOqGsNfnJBVdMmmdyxgtkVhpOuvsAQf/G4wMPH+Zfc3Qmo0YOx3WWhqNH14WEEnZlanq6Fwdv+cwx0qSquqFxXPgsoNE86w1tuOcCT9ngLynAtb2iXZ/jbbSPf2x1al8tzc0UjulpqFe/1HWbRlH35tibgTHD3yPWdk1Yfl8+M8XYOkHoY5ZpbROeXND2oM4xwNbuZoZfJyu9NE0H1VB7RcDqU1Dm0q5xBl9f2R69lVK+ZFw1PXChq4OUiPR6rmkdib9mVuTKsWgQVypH646QjRf7F5pcNdqjcTOSw5yy/pKlrJ6lLQHcSQyvXwCW42abuERSvudWMGbmrQeVOFmQxxXvt+LegZq7ot2YlnRmHPt3aC4Ai4/GD56PdBTsx4NJhvB6dNtB4XNUSj0+NmDyMAzxIK1qFRIaF/V9iVKTwI3dTWoiVOhtJYWOmGpSRy8fivcdpr4eadvww7fAOwF53APEFV1dT1Pa1vl3mYqa5WN9ofP/038/Oif4Kl/AbWKvFZI6nfkMla1U5jxW9OeeDXbS8qbVq3watTTulkNYuXYZ674WCSsl70PK68Ph19FT6UD8Gto67JhqhBHzB0XOetcnZ7W6Y9rSVPViDHCy3WKdcpmhSWM5Bdjfy2a+y6eJdYcfUsDH1O9rpcPbh6nNcd4VV70WusYEZs1u2JUo4Hq9XQ2Y1gWo6riX20a2lSldaUCt32L6aUn6a/keeXT/weTtgr0VNtqNskT1DTC8J8dNb4EUuqZJtx4Erz3CBRGCUuQlddN9Lxk4PqJkkQdPai0dpbhDhV7EKfSpRihiVrNMTNuSWvxfxSl9aIV/YAI6hsO2HIdcOhlMGkb6P1ELECWzKv7NDXZblbiSVo7vbUs38SB5pR09w14T95RmjCpShCvTuOa4U+UngRVSmsXqxA5LkWttKhKjrz7CFz3FWEVtOXRsPsZ1uOGSoVMowRpxOiswkmj23pLsdXRsNtPxM93fA9evalmU7oVEk+iakcnDjTBkAlj55hnbaLHPIerSQM5xsh+GGFRm28lSVchSwcDHPD6/7O9SI+6HrrH1/WkrufbHUfMHRfOc7Xnw9ROqWlUWZRFyFrL58tNIOmluzS/Ehx9E4yYAB++Alcf5drU143OnH1de3nPNwsvj3ur2kGKb3Tsr0kBZ9+rLhfFv9o0NHLVdhQePAuev4wyGU4unsrClbcN/FRT24O0PG0wPWr8KAXxRL3np/DqDZDJwaGXiqZJCSMn7Y+Xi4DDMFR7EIfSeogMNHnLwy4eT2uw1eXlSq3SOsxxne93bAuTjpFw5LWw8nqwdB5cfogo9fQhGaV1tU93d6G5wZ5vI8YI5yIXv/mMkVgTJ03rE6WMNZ+tTVS7HaM74hggF6HZD1+BKw+Hcj9ssB/s9xdQvtvtkrSuUrZ7elo73vt2VlpLdv4ObPNloAI3fI2OeU9U/boVEk9ge7/rxIGmHlLk7KW0LpnxeqPLTfnOfNYal6PGVH4WZ3EyIlfhH/m/s8aKl0W/nKOut6o67ZjZo2LFq5/CIHHE3HGRd3we7TIfgrMRY/jnZ5xKa3UNOH6qWHMURsK7D8HNJ9tfvDrHVJvSQ3pKa1WIoja3s76DzmShjv01TcSeC0Ts0+0iAnM2DW0KMy6Gh84G4PxRp3CvuXWo+dSrEkrTOrRG1K9JjYF6QdyT58IT/xA/f+GfMG23ppyXZVexYsC6bQWkQ1ZpPZi0tjytG/dNc1dah7dL6XS837EmTbrHw5HXwchV4aNX66of1AmjHFPSus+hULDLcZtkDxLA0zpM0rnKHiSh0mJN61OqozxzQ13w56vsQdx9VsOSyxpMYgFr3XkMDCyDNXeCgy6EbLXdSLss0oMorZ3vfRrd1lsOw4DP/UFseJT7GXnD0axnzLF+7dXUstlI73ettNbUw0tp3an0D4jzOonIOCMAACAASURBVFI36+tZZ9SjKaXelQq7z/w9e2ZnUDTycPhVsOpG1q/r9YGpaw8y+PxCLv2lr9MusF3mQ3A2YmxcaW2X9Q8ed/Ut4EuXCKHVy9fAfWcGOq7z2k6rb4J6HmpzO+c6QlsDatKgx7F5YtvUlGoeE0vVdhDeugtu+7b4eefv8dCofQHvDUw3hooAsp1Jf+bWpIrl8eYWxL12M9z5Q/Hz7j+DzQ9t2nnJHTw1ieosX5cMFU9rqwTcUlqHTzg5yfokraMordVNglgZt6aifnhYqB88rD+SSFrLHWCnQqEVlNZRlHqqN2OzX4umdQjck0DBS13tnAOiJq3HZXq4qPA78r0LYMJGcNjlkO+seVy7LNKrSqHrlLVLtNJ6kEwWDroA1tgeo38plxTOZiIfA62jtJbe77oZlqYeXk2yC9mMNQ7GeR2pZdxe8XNQepthD/LQ2Wz4/vWUKwYXTPgJrLlj1a/reVLXe42tpLR29n5ol8bE4FBaN9CIUW4CuTZQW2cP2P/v4ufH/mL1RvDDGZ+H6RUSJ17N7Zy+8l7e1xpNkjivQ7dqYaclZ6LMmwHXHgeVMmxxJOz2I6VnQAil9eA4opPWrUtrRP2a1LAbeTm+pLMeh+u/ClTgUyfATqc39by6Brsjf7zCtqsoWL6f1YNQ2v5jQbGT1pWq/xtZfLsmrSvhk/gy+JHvt0ykxsrEzYX6wcgOqh9+4fow9bTjSlr3KA2JgKb7kPb6eFpHCTyLiuJIK63blyiNpQoe6mrnAi3SxlVpgF/2/Zb1MvPo71pVbFR1jXV9aLss0gMprR2fX1s3YnSS74LDr8RceT0mGou4pPBbRrO8JTytQTfD0gSn7KG0TsobXfWhtu1BGvO0TmxD7blLhR8pcEbpeB4v7FDzkHqe1F7VmJJAdohNQq655BxuNyZO/9ySplppHX4cl/OlXB7IWKImlNjiCPiM0hvhtVt8j+ucd/O5dOYYr+Z2Tl95Hftr0sBaT/tch35r3lhZNBMu/xIUe2Da7vD5v4Jh1ORbgmBaAshEzlQTA/qjaXNcg8AFb9pepOvvC/v8ziUaSBanPYhQWrsPQkPFh0gGZwMOT+uGlNaGS9K6HD6JX2vHktDQsM4esP/fxM+P/gmeubDmIYZhuCbjG6FGoeDSOCJJ/Hado3hSF0t2A76kmjhpWp8ojaW8lNY1idOwSetKBW75JluUX2Z5pZOXdz0fxkz2fLhcpKdVgtss8lXvt/trLWRj2DAYznSPp3zEdXxQGc96mXmcl/8LeZpj7VQPOVfq8VdTD6tJtkts1hUhDqiHaukhLTGiKq2dzbdiZeaDcNtpALy9wUlcXt7DNTazYmaP2Fa+Rq9EhUzYt8KcYzVirPG0Tu2Umoa6UR2H0tqs+KwBP630Rrj+BJj1RO1jBnEmraNYl8SF2xrFWbHZ7N48Gg0o69nB66/TRXjlV10cGysWwmUHQc9CWxSXzQN2/qMYYpPWGkeGuZBmKNMG06PGjxql3rIP4bKDoW8xTP6UKM3NNH8BLZW+n6ie1ln3Qag0RJLWtfYg/k1lgpDNujRijODLJAOkT6xNggSU1pItj4JdfyR+vv078OYdNQ+xktYeFiJhcSoUupusjvP1tI6wWLUrJDLWa9JKv/YjSiNGdbzJ+SRUQyskHvgNvHQVZTJ8o/gtFo/awPfhMrEw3EvxclXK9qBK6wTH3yFKduwUjh/4HssqXeyQfY3C7ad7Wkw1EzlX6mZYmnr4CSzsiqv4NmPUxm3WIr5BT+vYx6YFb8LVx4BZgk0OZu4WwpfULZ6x4h4PT2q/REWlUlEs+dJf+jrL1+2kdfrnljRZZb6LpLS2PK3Fbd9kk+yNsP7nhBDrqsPh43dcjyutniRpVvO4VV70OhrK69hfkwaWitpnPe1s1hg7xV648jChtB47BY64FjpGWb+Wc4QUeAVhqFjNtjPDf3bU+FKV9BjoEYPAktkwfhocfjUUulM5L6ddRWc+a5WvF0sOpbXfLnsLYTWJsRoxNh5AuyqtB8foMGoSt/c7UXb5Hmx5NFRMuPZ4eP/5ql/L12XG5Wnt2PV12xlOEqfSW8UKPMPYg5RVpbVWW7QrdjPX4N/1vJentWMcCjUGPHcpPPw7AM4bfSoPm5vXLUOX43aaaqZmoL7fXmOy8z1IrNJlCJPJGLzJFE4unkqpkiHz0pXw8O/TPi0ludAaym9N6+LViBHUiqtoSWU37ESzd6ViUJzNt2Jh+QK4/GDoXwJrbA9fOMe3x0c9T2q/11hSYsm0vIpVLKV1TdI6tVNqGmpyOYqq0VmJKdc8htexMlnRDHrS1tD7CVx+CPQsqnlYpxKfZzOG9/GagJuYxVmxqassNWmgzitgzwlqVYA9XyQwoJkm3HgizH0GOsfCkdfDqFWrHmLZL4XIIWhP69anDaZHjR9WIy8DuOkkeP856BonvEhHrJTaeckAfklv0bptKROcSuvy0EhaOxuvRPGjdeLeiHFwYRRi4HV7vxPFMGC/Pwu7kFIvXHEYLJln/ToXYcLxw+mvFcWSI46/H5c9iOqHbge3OmnSblj2IKEaMaqJalXxFLEZ4Nv3wq3fEj/v/F0eHrl31bl5UW4T/zj1ffXaoKxVuWultRu5jMHD5ub8vPxlcccDv4aXrkn1nOxGuPElGzXDExnOuMWqSWyk9yoVXvYivjGldWyxYbFXqF4Xz4ZxU62GvVYizk1pXUcpnfdYI6jPhdawB7GV7057kGE+IVJ9/UdZt2Ud6wNbuOTzpEI3HH4VjFkDFr0D1xwDpYGqh3Qr8XnaPZLcet3IDS3b5lC8YN0EWNNMapLWLmN2n+MxsXL/L+G1myGTh8OugFXWq3mIM98SBG0P0voM/9lR44tMnO7+wb/sQeDQy2GlaameV6djoOsqZBVlgofSusUHmryjHDCKH60T96R1+CR+zfvdDE/VbB4OvghW2RCWz4crD4X+5YBdnhOXp7VTad3srtt2E6PaZFSUsmC5KCtkjaarxjWtQ0lR3Acl72FX4UycBgo2P3oDrjlOdO3e7DDY7ce2DVKd5Ij8bg9/pbX3eyxxzgFN6bg+BJEKmBsye8KOp4o7bz5ZNI5OCbsJkd401Pjjp7ROQrGvJprz/5+98w5zozrX+Dujstp1xd2YZsAG0zsk9GpMABPAuNBDC3BvCCQkhEAISW4IIQktQEJvtjEYMAmhE3oJYCB008GAK+uu1arM3D+OzsyZozOjadLMSOf3PDzIu9Ls0ZRTvvN+7xfQ07onTKW1pgFzTmeUcvcAfYaQ42ftPeJLdcY7OpYUy7XfkbUMiUPQOsP5b1cSso4Jg7CC1nTtp7m1ROw7DJg2C8j2Az5/DnjQajHFznn4OhPNRiRmoWsEcx1D1hPSHkTSTPixoFOQLWwqrUMWYLxxJ6mHBQCHXg1ssKvwbXy8xQ3tIqRJMvLStDmlio4j1Gex64LbyA8cOoFm0pXhg6hp207IT5A2CvjK5vWKyriBqjKCBq1rznejldaUXH8yiewzFFj4NnDfKYBWMVQOoQWtOX+tRhQ9csKpiJGfthQZhW0j0oolyYAGILwssNj+Jm2xrvBYDDDfTTaaiquA9XcjY4eimAoyQeCApeJGHdUCOKnZRe8BmpDpklDovZVWFWC/i4FxhwKVInDXNGDpx5G0SdozSdxC46aiwCS9j8JUTYbqaU2zxcLom/79W+Dd+6simTuBIWOMXxmb8IL5UL06ME5Ka9bbNBb2IJzy3SigHoOAeqOx2IMEUVpX7wfNixft8M2BSbcCigq8eSfw/OXGr9jN4qivg7C4XZFXuDZ3HSORAO4yl3k1dih89iyT1fkzYJuptm/l4y1ucL35JYmM6EduSaSMWvE6LsncQP6x+08cO4Fmwnd0nVnVTPfgAplJKcRoFpK0eloHmRzRgI8laO3D47v2fDcxaLLW+iTFJ9UBzHsIeOKi0JXWeSNorFb/39zJXp4b5FlMpbX7wZVV2DaigJMkGdRTnolgg9NZBxWwYx9QKZH02mWfAwPXB466HUhnLcevV7W7Ytg6tfY0xKnYpfHzGk9rGbQWQceFTEolcpjDrwdG7UB8SmdMAtZ82/Q2yWJYErc4Ka1pMDjM+yjPbNZngnpaU9V20L7JopS7Chi9u+XXNButt6zVzP/onNluk9bpO7LrhDgU2uLT1/0UUE8qqqqAfk1fQWta86Z6menldq1SH7MfMIHU4MCTFwPvzgFgnZ8HyYANA1FxO76gu9wwlTQbXdeZ9bT95olRNDSsueySD4FZx1QL9h4B7H2+49szNvEiJ5zsuyTxoLVXixJnvv0E0z4/H1mlgo+H7AvsfUHULTLgF+1d2bShTOB3zpwqsseJrDGh5pTWgexBwlFa8+noTQ+arLsTcNi15PWLV+P7+hMAwgtaFzh7js4mTvZ0Xa8poMJi7lJ7sAdhvB2l2qJ9Kfuw2LArDMj3Q7Z9gK4DD51L0muzfauZEmb9g6zL4Ei7pEOz18ZuIayqCtjuOhQ1YwtiKK3pfZvpBKbOJNXjuz8FZh0NlHub2qZmjiWSZEOnrsJCjA2w+WIzvAzRhE+ldaEYgnLu02cs9Q+wzbSat7DjDq86L9dRIzt9x3pFHJsN31bTLise7Ws09HsG87TmlNZeDrXTKcDOPySv7z8N+Hqu5d4LkgEbBqLidrY2h3LuL2kSvWXNcNSh81RhVkCYSus1S4koobACWHdnYOK1QJ11QxB7kBZfkiQaGbRuV3qWATOOQp/KSrypbYhnNvtNrIx8+I4ulzE9rXXdGsxMnNLaKMToXFTGDXTuXg5qD8J5LUeSnr7lkcBevwAA/Kx8Pb6jvmsEtoKS51Jbu6o+W2VNR7HcWFuNAqOgFp1XP57UVMWaSamMF6acuLYbfoq52nla84tl28nmqzcCc28BoABH3AQMG2f5tdvgSLlN/OMyDhsDLGxAOzR1SouRYpXWlL7DgKNnAx0DgC9fAh48x+JT2mgaYesgaU1okWxRYLIRm895xh6EZnP4LW6dp366fueGS+YBs45llHK/FL4tlzGfbXZOo+u6Me+p52ktGnvCmG+HiXE9uKB1HFTgzYAqyv1sWhue1tXLbKT1ez13438PjDkAKBeAmVMxVFts/Ip6jkcFtTLMW9Sr1oxNdu2ghSTwkUicYMcnuvYUZQVQu8rAQetSAbjraDOrc8oMIJOr+zE+3lIPnZkztrqQJsnEY/SWNBea2v3tx+hOD8MpxZ8C2T5Rt8oCr/JjlSKAdVJKJyxxVyikjUVDVWntsIDxekx6DnRdN1NcPHS8/PmOrBDYnj8HtjgSaVTwt8zlSC8Lx6eUVyjksmrN7xoFe3zRee30kRbM2kLIoEn74qeYq51dhStP60+fBh7+OXm9/8XAJgfWHt9lcETzoRJPIm4KMQLWlPem2jMlCGHQGgCGbgJMusX0KX3pr01rk7QHkbjFKTDZiKA1q3bLpIN6WleDEH7mhquXANMnAb31lXKKoghVphVNN/ai7Dyps2mqwBXZg3i30mokfPp62yqtfVyPNKe09p21paaAI28Ghm0OrF6EQ949G32Rt/yNqBAVi+czNlkRTG+DxTcSCWDeg5mUYvRh4qwAa9FQX+g68OCPgfkvE1ECU7C3Hny8pR6sEDLuAsh2prVXi5JadJ1UTP7sWSDbF9et/X9YgoGelHrNgFekdmVTloVqWaC0jrsXHD3HtEAZ/X8Q7zQ6dy9zE1/AWzCID5JEVghMUYCJ1+BddRMMUPLY+PGTSMG3gPAKhWxKNQamRgd76SCfTavitGAfQWdDNaRKpXU7U/KjtFbZIKrY3xqozb7At58Adx8P6BVgqynAd38kPr7Rz0mlNWDt3536ZDaYIoPWYmhQQhhQ2HhfYPwl5PVjFwLzHmlKm0RFiCQSEU6BSWMcD7MQIxO09lOYioXOTzzPDcu9xLZn+RfAWhu4UsqJlHvsnL++0ro2aF0sx2uTNM2lr7ed0poGrX2s2+hnNZ0Idehmhq81YEc/YnHWdzgGrf4IV2X+ihQqkSvyO6vCGlFxOzo3Y0Uw0p5K0gx4ARggtkjj19y+ePFq4L8zASUFHHUbESe4hI+31IPN6lZiHktqZ+Ixekuax4tXAW/cQRRJR96Cz1IbAojPRI4i8lhmJ/psQMRQ7MUs8M7DFygra94DTjXHpEprrrgj4C0YJFK2R0Ymh193/RLztaHIrfqcpJSWi4EOySsUWDVPo4O9RkEKm8GbtqNU0V0vKEuMt2NOMGGQtAf0efeywErbqKv5/tPSB/QsB2ZMBgrLgXV2BA650lYpxxecFaH53FxLInYe4jzseZCFGMVQVZ7tRu/OpwHbnwBAB+49CVj0XsPbJFLESSQinIrtGZvXIc1HKoz1WVcmxXh8ercR0HUd+aIP5RwVycz/D1HKTXOnlBNZprFzI7vxjg8Es9D5djYm6wT+epTbVGnt5/uyn6loevCA/8B1gakzUU7lsE/qTVyQvjPy9SQNTPcI7EHoM5hSFXRUbUzyHmriSCR+6RHUNhCtpQ1LTr9z2Q8fAx7/FXl94B+Ajfb29PEMF2+pB+soJ5XW8aW1V4sSK+8/CDx+EXk9/hJg7AGhBE4bAR/g68ymLB0J2xHR7xB/pbW1QFkpBI891UiTI8fSdH/BIP58R10IbFV6LZxU+inK6b7AF8+ThU8An9K8YNe3WQUM66XVsoO/28AH+9xKpV/74qe4VNrGrsLW01qrkADgtx8B/UcBk6c7KuXMfs5+sshurrW6f1xaFZ9vHvZ3kWW6xBx6r9ieR0UBDvoTsMHuQHE1MHMyKeLTQGQhXIlbKg4Ci7A3P9jjWJTWPrxvixXNsJ3zNDd8+TrgzelEJDPpFmDoWFcfEz1TbLDdbs5sjD2C7+jHSquR0OtRrI6TWpsUJqbQ9ZyfQDP7mYrOBK2DnLpR2+Odnf4IADgx/SgO7n04wMGC47a4XaePTE2JxC+82p+8rl801BNL5pE1B3QiQtjpFM+HSHPxlnqwWeoyZh1f4jF6SxrPwneA+04FoAM7nkwUSYhfcRIKnx7dmUlBURQjhZ3tiGhsJO4KBUNZQZXWleAee/Q7B1Za8/YgESv9UqqCD/V18d6uV5o+pS9c6ft4BVFKkzEpbKxCwVAo2Sz2sinVGCTdBj7Y51Yq/doXP/131hKoNj+nKIqlDzXu18d/BXz8BJDpAqbOBPoNdzy+U+CAwm6u+fG0TBLuCzEy514qrYXYelpb3pQBjrodWGs0sPzLUDJ1nJD2TBK3GEWyBYHJsL3R6bxDUYCOtOqoQq6HqPhWXT5+EnisWmzxgP8j9j0uMTbimbkZFaooir0Sjo5fouLaYcy3w4QW+uOV1u2i8kuFqLSmRdSCnrtVGx6ES0tTAACnrL6O2GhGhMgiJy9QuXbJ8UfSREQKanPsMvtr3/Yg+W6S1dm7Elh/V2DCZbZZnU7w8ZZ6sGuSuAsg25l4RSoljWHNUmDmVKC0BthwL+DAS41OoBSziRwllxZ7LKcFKY60InvcveAMZQX1tKZ+tAHS42nnaiitfabd19ixRKz0o5PPpSN3J/crADzxa+DDRz0fS9d1ZndYELQuNraASb0dZ0VRhKmAThSN51a1+InpAdTokuRhKu492IM4KH/Z43RmUsDrd5hF7Q67Dhi5tYvj1y/41U5K64xLT2v2fZEVwo05rgMdXYOIT2lHf+DLFwNn6jghlW4SN1iKZAtrW1TnACHdRwUmw4sVfPjxtBYV33Jk6cfA7BMBXQO2OQbY5XRPfy8nmJuVmDoedpgbprXf0c3nm0mGKyaotVvQWgmgtFasQWsn2x0vdGZSuK5yCO6v7IoUNODu44DuTwMdM0hbAHNcYe1+2LVETmb6SJqIaVFTWzhc5L/uKTOnUgLuOQFY9hkwcD3gqDuAdNZXO53qG4hgh4x26YOTSDxGb0njKBfJwLviS2DQhsCRtwApM63D9FGL162gqgpyTKdIOz4jIML0MLRPinvwI83t/JVC8OKm6wc6aSv7THERFb6MEjpolCs6SQ3a/kQAOjD7JJI65IFiRTMUTjlBWl2jveDc7DiLUgGdKBsbHqY3t67LCuLthK7rFm9zt6QdlL/0d9m0itT8l0mwDwD2+gWw+WEuj18/ONJOlbqt59vBHoQZg6Puf+NKqnqOXAXOhm5C5js0U4duvoRMV4bMp6TSTeJEvT5PVHgtCPmStZaG13Rpy7GKHlK9e5YDM6cAhRXAOjsBB//Fs1KuSzA3K7soOsxb8LHQNUMmHY/xxhwn6dydtK/Vx0OKUZ8gFE9r8jpw0DqbAqDgvNIp+CS7CdCzjAi+CisDHdd/W8xngF0bsPMD41mRm6aSJtBjjCu19iDCQoxeBBiPng989gyQ7QtMvQvoM9h3O41CjC43aStSaZ0I4hWplISLrgMPnwt88QKQ7Uc6ga5BlrfQBzobk4kci8jKISMIiFClddzU4jy8tYk5Cff/GNLNhkrFqtZQFW8VcDMp1TIRjFrpRzcgNF0nC54JfySpQsVVZEGU73Z9rAKj1hHbgzTY07pUm07FI0oFtEPTTMVWmrEHAaTaop1gN6i8qMcsyl8bpfWG6W5g1jGAVgI2OwzY42euj58VZMPwtFXQWmXPt0t7EBm0FkJPn+uxfsx+wPjfk9ePXegrU6ceORpslJkuEgfK9YLWmXCV1j1cGrdZINe/PUjdfqmm/sGdQLrD89/jVaaAO0/qtEOggo5HcRHo8EEVellafTykGEprHwGilK09SLA20fuuF1lcO+xioN9IYMkHxFpTa+7c2lyfkBuDPoPU7od/X1gFXCUSJ2j2S06wli5VdKM/E/mvO/LaLcAr1wNQgMOvB4ZvHqidThuYIqz2IIH+tKSBxGP0ljSGV28E5t4KQAGOvIkojzhKMZvIsYh28kQdkVmEI949DT+hNv1o/bebptbR0xFEOW/xSWPOfRTwBSaRzhKf0gHrkXS92ScCFXcKaao44lNbRcUjGoHImoRHtEizg110ZlIK0inV2BCRaov2ge0DPSmtmRkZ30+kVQVdKOBq5VIgv5TYgRx2nSeDfF5BJqKdip5YPK0dviwbjOHtsSQEqrT2NL7t/ENgu+NhZOosfj/UNrFjpcx0kdhh8fEXKq3DTfPn5x10Y9OP0rogKL4l5ImLSP2DdCcwZUbd+gd2dAo28d0Ujc84pIS7UWo3kzR3PZJSUD4s6BzfT5BeURRDvE+U1uGsAdn7e3XHUGDKdCDVAXz4MPDv3wY6tve2WNcEbF0eVpDUGbKtkETihNBqk3lN71M3616Dz58HHvopeb3PBcCm3wvcTqcNTBFU8Kd4FPxJmkv8IpWScPj0GeDhn5PX+/0aGDte+La4FSdhYe1BOnm1iEVpTQO18fsOLMYktdreUgjWLPQ7U7W5MXnzcUiRCjkqzO/FLD76DCGF4DJ9gE+fBh67wNWxeMURJedB3RyEHheptV7awi7IaBCePitSad0+sJsXXvrvDKPS4RfwWRW4PHMtxuhfAH2GkcBDtstTu9LGhlN9e5CUqrT8BJHdKGPPPQ9VqOcyauzrM0QFvV09ZYYpCnDQn4D1dyOZOjMmkzofIcH269IiRGJHPaW1KMU6CPy8w+sinkVUfKuGN2cCL15NXh92DbD2Np7/DkWUBVcq158vU+sPoad1zKwQM5zynU7r4r6OCQv6Pf1+X2ONwHjFBx032b48k1KBUdsDE68hP3j+cuCtewId3wt8cTs7ix7qLSzHHkkz6Knej+x9mE2phviEjjuuxgwAWPY5KZatlYEtjgR2/0ko7eTjLfUw6k20+Hok6cRj9JaES/dnwD3HA3oF2GoysOtZtm+lD3QQi4pGwe56d/JKa6YjMgO18e5s+HTAUjn4hgFVFtB1SCXAxJzdEWU3DKIgJQpaA8CILYDv/428/s91wOu31z1W3sZTuqtZ9iAuUmu9tKXMLDrpxN1rIUdJ8ikxqk5P9iCWQozWz52m3YXxqddQRIYErAes47ldIgsnnopuBq1bHbZ/d1qg0z476iyXOGMWYvQ4PqWzwOQ7gLVGA8u/IHU+ysXQ2pRNmxYhEokItki2aGHcyQWpgsLbkmVcZMDUO5atam7+q8A/f0Re73EusMURnv8Gi1kkmwlau7ABZItv8VY9Ycy3w4TPGq20mae1GqAQI/v5ckU3shiCBpzYOboxN9pqErBbtbbHP/4H+HpuoL/htS30GbCzW/CSpSmRBEV0HyqKYikkzBYNdZzP9q4invE93cDa2wIT/+q5/oEdfj2t4x5HanfiF6mUBMPoBJaRXeJDrnLsBGjHEsfdfZHy1yjEmESltU3hlWCe1pzS2qii7f1YdHHDp59FgW3QGgA2OxTY63zy+sFzgC9fdjwWm1bHEnY6rh154+/bD95mW+ovWNlFJz1PnSGrtCTxp8yolb1MtCxBVHYB//ZsHFsiSqK/9T8LWHdHX+0yFX0O9iCVcBaZScCitHbhxxp1lkucMYLWfgJPXYNIXY9sP1Ln4+GfkbofIWAG2Rpb1FeSXOoprXNG8EmzBLj9wm/W00V82YfS2jFbbOU3wKyjgUoR2OR75twsACLVOQ3uZh36UDZziJ870vm20+ebCa98p5elXYLW9PsHVVprOmsPEqxNmZTCbIwyB9vnV8DYCUC5AMycBqxcEOwPuYB/BvIChStg2oNIpbWkGeRtRFhmZkDF0m/bzmc1DbjvNGDxe0DfEUQkk+kMrZ1eN2m1kPoQSWOJx+gtCQdNIwUjlrxPOoHJ04FMzvEjYQROGwW1TMimVCPgK/JLTcoOGb9oKFWCq9wNT2teae3jmHQQikMRsJTiELQGiJpns4mkUNysY4Dl822PZe4MW4PGIjVPIzCV1vbXxEsAnfV2pJsLzSoqKYkPdLHrddFn8Vim/cTXc4EHzgQAXFc+BK8OOMB3u0wFWX2lddw3etB36QAAIABJREFUGsPA4iHu5MfKWf1IaqEBBS+ZBRaGbUrqe0AB5t5C6n6EgDmWSE9riZh6RbJZFXMY3ugFThFnzJ19BMRti0mXeoC7pgGrFwHDNgMO/7s/bzqOnGA+5MbKkJ338mnhZiHHeIw5piqcBq3bS2kdpBAjYK17o4W0BlQUhbHTYe5jVSXF4YaOA1YvJPd8qSfQ36oHX9yOf57598m5v6QZ2IvAzGwztt/usLPEe+p3wLx/Ec/4KTOA/muH2s60x03asLI1JI1Fro5aiad+B8x7iOkERtb9SDmEwGmjoJYJ7CI+K+iIzKrg8e5seGuTsot0x3rYelr76HjpoikOSj9DaW2nhFNVUiBu+JbAmiXAXVOB4hrhW00vOOs9bhT7aZI9iFOaFF8p3AnRM+tFqS1pDfz23ez706pClHIzpwHlAl7J7ITLypMD9QHG5pxDcIT2V3HfaAwD10przupHUgstxJjx4mnNM3Y8qfMBkLofnz4TuF1dRk0C2f9KxLCZMSLYgHAY95E57yH9CS0C60dpLbRY03Xggf8BvnkD6BxE6o109AvYagKd+7NzMzee1OwaoMh9TzPoHY+1TjZtHScrde6PVkOoaPaAobRmg9YhBJw6DbEUd6xcf2DqDKBzLeCb14F//Ci0TB2ndgAkEGjnad3VpIxRiQRwsts0LSrZzBzhHP/t2cBzfyavJ/4VWGf70NspspJ1IqxirpLGEo/RWxIcthM49GrXnUApxoUYRcpfkdI6KTtkdJJFLVloYRnfqjHUBneDWKXQyVAslNZO9iCUbB8yiewaAix8G5hzhnASWSiJg8aGF1yjldZ2KiWGLg9B56JAYRt2ESdJ/GEV915gF+1ZvVhVyi0Eho7DlQN+Bg1qoD6AV5CJoL+K+0ZjGFiC1o5FxMjv4rBpGFforR64mNquZ5F6H3qF1P/o/jTQ4XJS7SapQ72gZEpVDFVaGPcRn+FF+yFNrzOvEiBU1z1/OfDObEBNA0fdDqy1QeA2U+j4w87NqCe103jH9rVlLi3cqN8TkzHHKBRWae+gtd+N6xSrtA7RWkWotKYM2hCYdBugpIC37wZeuCLw37ODLW5XYAOBvNJaBq0lTaTHpsAimx1j578OwJLViV1/DGx1VEPaKbKSdSKsYq6SxiKD1q3AN28wncBZwNaTXX/UnMjF71agHR4bbEwbExVGaZ2QyZ6581cNWoegtDaDu6j+3/+5oPYZtsV2moiroDUADFwPmHwnoGaA9+YAz/6p5i12VYxNdVyDPa1tdqZZvKT4iRS2rJ+YpD0olv1ZAZmLdh3rPneuRSlXyvQFEKwPyLjwtC63kdLa1kOcg16XOGwaxhVDaR10k11RSL2PUduT+h8zpgCFlb4PR58XWQxLYocbcUWYAagebrOe7XvcLuSNY/EBs3kPA0/+hryecCkweveArbUi8uk1MxPtx7uUqhiBPl5RXoyZ0rrG07qNihMDwZXW7BqhEqLSusuw07E51oZ7knseAJ64mDwLDYAtbsf6BPNzs05BVoJE0ijs7kM2O6bHxkIEKxcAdx1NvOHHHgjs+6uGtdNNQXgWrc3636QSj9Fb4p9Vi4zUbow5ANj3Ik8fpw90oHTbBtFp2IPUVnRmVRRa0oLWtBBjCNYs5sSNXMcgAXxqn+GkCG4WroPWALD+d4DvVbMMnvod8P4/Lb+2Uyg0Sx1n5wEmaouboLMoO0KmCLYfhtLa47NOF+1nph5A/48fIEq5yXcAg0YbwcAgfYAbT2utnZTWzIawGz9WqbS2hw6VoQSeMjlio9ZvJLB0HnDvyYDmr//sbNIGqCS5uJmbdYU4J+EVcRkHv+d6WIpvLX6fPCvQgR1+AOx4cuC28og28c0aMM5jhp13d9ysEPmgiqEWjnnGaFjQ58Dvuo2eJ03XQy2iZjwvTkKunU4h9z508iwsfj/4H3ZoC+sTzM/NpNJa0kzsivKy2TFG0VB2zV3qIQV7Vy0g3vCH3wCojZvrimJFToRVzFXSWOIxekv8Ue6tdgLfAEM2AY640VMnoOu6WZwkhkrrLkNpzdqDVC02mIBIUhQKvLKCBnWCqMZ4pXWQ3cKuJCqtKdsfD+x0Gnl932nAoneNXxk7wzVK67Tl943CMVXKaIsHpbXA27FZRSUl8cEsLOWt706nFIxXX8W5mbvJDw76E7DBbuR31XsqSB8gsnDiCVMZFXfYQLWjPUiKelpH3//GFXp/hpbi328EMGU6kM4BHz1qqkc9IothSerhRlyRC3Hzg1fEsRuEXn2t6bEGYRUwcwpQXA1ssDsw4Y+B2ylCNJ9xWzSe9g0lrphlGPPtMDGyRqmIpd0KMVb7ct9Ba6Z2RpgqybpKa8qEPwLr70aehZlTgHx34L9t15aekr3SWmb5SJqJ3Xq2k7lXC/y9quvEA/7rucQTfupM4hHfQPh4Sz3C9MWXNA5Z8Sep6Drw4NnAV68CuYHVTmCAp0OwAcG4TORY6I5yZx2ldaWSjKA1DVjQoKObwjL1oGoDQ2kd4FyIzndU0O/VnS9ifnfe8jtVVbD2gBwUfnAZ/3tgyQfAZ8+QSeQpTwN9Bjt4wZHzvrKnVPM3wmRlT4n8PYfz6qSWKFU06DqQTVuVOVmmKrMf1biu6/hmRcFYTNuRUhWMFJ3vCOgpVmJjn7B0da+rTYIRA3LChfbCFQXjWnZmUxjSt6PmPYVShXgbCp7nss96BB1L38NfMtcCANZscxL67HCi8btMCGpfo+CXZj9ZrIRgjZQULJ7WdpXUmfflYnJ/xxH6HISqlhy1PXDoX4H7TiYepcM282SxBpj998IVhZqxRFGAUQM7a/pPTdPx9fIe498DujLon8v4/BLB4McYCWF1bxnL1hSNf48ckPOt8jeV1vafD7r5oWk6vlnRA10Huqvtpsdk54Ss6CNfLOPb1UU48e3qXqRRxkHzzgeWfU5s2SbdBqQac792CjbxaQ2Yetk5mbQKFCs1408Y8+0woX1YsaJhfnfe2OSN+zomLOjQH1RpvXBFAat7ibIzjDlqpyAzQdyADPFyv2Fv8kzcfRxw7P2hPhO0LfO781i8qtfyMwqd+y/vqV0ruSWbVjG8f87xPWzfAgD9OzMY0BnNeBUFy9YUjfvMiaH9OmKRreyHFfkSVhZKju9ZVaiqqHmldfXfC1YUDLWycR5euIJ4wNP6B4NGh9twAXy8RQS7vgrTF1/SOGTQOqm8fC3w5nRSEGLSrcDgjTwfgn2Y4+LzxkJ36az2ILUBkaSo9vidv1KISmt6LYMUpaTnOw5Ba3qu/v7Mp/j7M7VFsg7fdhT+Mnkb6w9TafIs3LAPsOwzMok8bo41tZWhs1rt+IOFq7D7H58K/0twOAVb7Raruq7j0L++gHyxjCfP2RPplGragwgKMXpRaJ07+y3MnvuVq/ce/531cfHELVwfuxG8+MlSHHvTK/j5gZvg1D2893dhcu/cr/CTe/7r6r1jhvXFoz/ewxJ4/vNj83D1vz+2vO+KydvgsG1HGf9eni9iz8uexk6jB+GG43aoOS595rNe+u7VS5C9Zxo6lF48V9kCm+79W/Rhfh2GPUjaRVpepY1Sodn+3SngQoMpceh/4wo9f6HPV7aaBCx+D3j+L8A//hcYvLGniva0/7363x/XPNcAMHGbtXHllG0tPzvl9tfw5AeLjX9nUyrmnLkrNlu7sQokHk3TcfBVz6NY0fDEOXvKRVuVr5blsf9fnrWMydusOxBzztzV1/HMeiP27+liUqz9cOodc/HE+4ssP6P9iaIoyKZUFCua0TcvW1PEHpc9ZQQinLgoPR2jlr0CZPoAU+8C+gz21UY3CO1BXHhaA2whYOv446aQYzOhQdHesmaZf7bL80c3b/yu2+h5OmP66+bPQjh1dJ7u6j7pM5g8CzftD3z+HPDIL4Dv1dbVCdqWs+560/yZTbDwna9XBlrHnLP/WPxo3zG2v+f7lkxKwf1n7IotRnkTyyWRJ99fhFNufw1uEn9HDsjhqZ/ulbjA9aufd2Pq9S+7to7i17N07PrbM5+Y78mkgHmPEO93oFr/YI9wGlwHGkOwW4esLJSw12VPY6t1BuDWE3dKTByp3YlfpFLijmxfUnxu/O+Bjfb2dQhWbRFHb9HdNh6C0UP64KAtRxg/E01Ie6uT0Vwm3rezGbTWoeu60ZkGWYBTZVSxeg6CFGLcc+xQbDC4CwdsPqL+mxvMfuOGY1CfLDozKct/9Pu+MX+5+INdpKAcsv2AL54HHv65bVGILdcZgE1H9Kv5G434b9zI/tjcIRhhp7QulDS8v2Alvvg2j+48UUOJ7hv63bykCL7+xTIA5B6yazc9369/aXO+m8hbX61ARdPxRgza8sZ8cu7SqmJ77mh/9NHi1VhdtAYFXv+yeu5TqtH3vsnd058sWYMVPSW8UX0vT9Gr0rrcC8w6BsqKr7A4sw6mr/8bDOnfZXnLgVuMwOghfbD7mKHujilAZOFU05Q2SoUe0JnBPpsOw0FbjnBcyOw7bhg2GNyFfccNa2LrksUBmw2v3p9Dwj/4PhcCYycAlV7grmnAym9cf3T/zYZjsNN4Jeiz5laf6460ClUhz8u736wI57t4IF+qYN6iVfhs6RoszzsrbtuJDxasQk+pAkUx55Zvzl9eNzPJjooLpW/QgspvMPdUZyaFdQd1YpcNzeAyv5D/eMlqi3LO7r/jsk/jxPSj5CCHXw8M39xX+9wiqtFB21xvkzZjE6wwis7HRKAzrF8Hdtt4iOU8b7veQGwwuE/9D7cAE7YYgQ2H9MF3NvK3+XHo1qPQJ2ueu1EDO/HdjYOPC3QOtKvbYw3fjDwTUIBXbwBeuzlwGyiHbL02+nWkje84rF8H9trUOj/Yet2BGDOsr++1CX2e7OaZlDe48apU0SMZr6Lgv/OXQ9PJfNXpXAJEabxoZSHiFnvnra9WoKzpdb9jZyaFrdYZgLHD+1k+v9+44RjS15wD9c+lMXX0GuDek9DI+gd2sJksul47Zn+xNI/uNUVjHWzYg8RjeJDYIJXWSWX744H1v0sUQT5hJ3VxmcixjBneD0/9dC/Lz0Q+RaaSNt63MzvZrmh6KEprQ2FbDVYGCVpvve5APH2uvw2QsNl33HC8fuH+NT9/5+sVOPjq541CD0KGjSP+7jOnAK/dhO1G9MdM7FjjBde3I41HftycXd962Hlas9+TLuBMb0fzGovSaetBn5t7f/hdbLmOWC3x0iffYuoNLzuf7yZB2xuHYme0DeeO3wSn7SlWfeu6jo3OfwiaTq4dm/pPP3/1tG3x8eLVuOzReTXnuKfO9y17qUeg68CD5wDzXwY6BmDYKXPwtyG1qpqJ24zCxG1GCQ7gnqwLpXU7peIpioKbT9ix7vt23XhIbPrfuDJhy5GYsOXIxhxcVYEjbgBu3B9Y8j4JXJ/4MJDprPvRfTYdjrmC8er9BSsx4crnhM8w/dm/f7oX/u9f7+GhtxdG4onN9jv5YgWN088mCzqn2mX0YNx0wg7Y7FckaFsoV4x6GF6ouFgUe6ltIYLeU0+csyfWHdRV83u6QUpVy/T940b2x8Nn7S4+6BcvArfdAmgA9r4AGHewr7Z5gZ3P6LoORVGEBahFGGsE3h7Ep51Wo1BVBXeevHPUzYiMI7ZfB0dsv47vz5+13xictZ+9MtgvB2+1Ng7eam1vH9r0e8A+FwD//i3w0LnA4DHAaJvnyQMn7TYaJ+3mbKUwoDODx8/Z0/ff+Od/v8H/znyj7rya7VsufeQDPPjWgljMxZsB/Z4n7zYavzhonO37dvjdE1i6ujeR56WnOg+YtP06+MMRW3n+/B5jh+K1C5g5UL6bWOc0uP6BHez6uKLpNf0+nffQsdYs5hqP8UEiJn6RSol7howhhok+oZ6oqpKc4IEZEDGVxVRlHPe0alYZW9b0UJQfhsK2GDxonQRcV8re5EBgv4sAAIcvvAq7qO/FOl3L8KTmvhe7eKWvaZYBe990+rAHMQtqOHhsGkVevBVuagRURR6HYmcFm2IkLIqi2BbIZCtwm37k1nPcw3xfkVLAU2Gpl68F3rwTUFRg0s1k7GgQaYGFE087Ka0lCaKjH8nU6RwEfPMG8MD/AIJnzy12GTD8vMWu/28GhaL5nMpiXiaFotnH59JmP+/3GrlRWgcpqKzrujFm2M11+Jow5jhk06blXwKzjgG0ErD594E9fuq5XX6g42pF042MHVEBahHUy7S2EGO8lNaSFmP3nwBbHAFoZWJN2P1Z1C1yhZssTb5vabfCw/X6VQpdSyXxvLj9jq6olIB7jm9K/QM7+HgLD7ueLlU0w/qlHSwLk4wcvdsYozBJgiZxrMUGYB0cYh+0ZgI0vWWNWcT47yR5hW25xYPWnpRIu/4Y2HISUqjg2swVGFJyn+7dbKhyiw86s4tX+jsnT2svQQezQKW9asz0yo6D0rq6Mx4DFUOeCTo70Wl3XZnq2mYqtPUc0++r66YFEkvJ7abXR48Dj11AXo//PbDxfs7vD4idpyiL4b3fov2UJMEMGk2KBalp4J3ZwPOX+z4U23+yG0/s+MX2AVEotPIlq9JaQqD9b2c2BVVVDIsQv+eoYii57N9Dxws/QQ92Y5nPKqPwmYo91WsvVI73rgZmTgXy3wIjtgImXhtIJOMFdlylmypuPamNwDwXqDA8seWYI2kEigJMvAZYe1ugp5tk6vSuirpVdXEz9vB9i8i+p5Wh39OuX6V0VeskJfG85F1+R1c8+kvgs2eJjW2D6x/YwfbzJYFVIXuNekoVc3yW40OsSU60UhI6hlIvQQ9p2qgIW510Mx1P3D2t2eASG1wMsmlAJ/d0wGn1YBD9vnR31BFFAQ69Gh9nxmKQshq7vvq/sZ1E2qkd2MUrVX6JFEP8fVCPckUzFExOgdc4KSp6inShHYe21Fdak9+LlRf0806qFfZeEE2C6SLesf9YMg+Y/QNA14DtjgN2/qFje8MgI7Bw4qH3sEzFk8SS0Uw665O/AeY97OswuWr/oOlWj3f6PCuK6T8MRKN05hdvEgLNfKHXJug18qK09hMYZ69dPaW1EbQuauL3axow53Rg0TtAn6HVOiG1diONIsPUeqCbKqbIxqU9CDf+lEKoISOROJLpBKbMAPoOJ4V97zvV9EKLKTkXQiC+b8m1W9DaRWYlgESfFyN7NKj4b+6twCt/J6+bUP/ADnZ9LBLQ8Gtrw9NaLklijRy92xjT4y05twFfZIXtaJWYB0BSqmIIVdhBLYindSensDWU1jE/F35hJw2uFo+ZTvy2z/lYpA9Ev5UfAfedFstJpN1iNS8IKIi8Gb2mlxfK9VVZgNUexG8BqrAw7EFiMCEsMEppJ6jywm4zoiubsrW8qRdMMrzN7WZZ+W7i6967Eljvu8BBf26KUi7txtNaD55lIpE0lB1PAnaoFhG692Rg0XueD8EuANnnmZ+3BFHYBkVkQSWx9tHk/8GukRsll5+MKQptV0datRUt8Cpk/jsaPPtH4P1/kGLvk6cDA/x7D/uFHxdNOyzn9Yrd+EM/n42Jp7WkRem/NglcpzqAeQ8Rn+sY46bP4fsWQ1HcJuNFj8uAbleMRD5ecSvEceTzF4B//YS83ucC4vUeESlVMQLQZZHSmpv3mEFrOT7EmeREKyWhI/LGjTtppiIsEHJKSxOg55rtMIOcfz61S3OpRkkq2ZRqDERug5fzK2vhtOI50NQsMO9fwFP/18AW+oMvPERh7xN6jUXejl4X1DT1mSr9bNvFpumWo52I8YUzoiTPKKWdyNmkXrITRNsNC8G1ZzGVY4JnvVIC7jkB6P4UGLAeMPkOIJ11bGtYZGwKYbGUZSqeJAlMuJQUESquJhtA+W5PH8+kVON5EPXlvIo3CnsOy+ZYDDYE4wK1a6LXJix7EKeNOrM2hXc7rh7GzsQOoxCjobS2fkcAwHsPAE9fQl4ffDmwXjTFAvlnwm0NmIwqldaSiFlnB2DiX8nr5/8CvHVPtO1xwM3Yw/ctRgZhm4wXeZcBXdc1l2KI2+9oy/IvgbuPJZ7umx8O7N6c+gdO0L6+JPK05qw3K7IQYyKQo3cbY9oMJOchrSkkE2bxgCZAJ9TsBCGI2jDH7eyWW7zjVRTFc4C2UKzgTX1jfLX7peQHz/0JeOfeRjXRF+xEgfUvLgjUtoZiKG1eY68FnKhPZL0MBTulYBT0xEhpbSrU7P3AAbHyolTRjOe0K5O2VboUBMpMlrJTpswjvwA+e4Z4yk27C+gzpN5XCg1aCEvXzUANTxh+/hJJw0lliL/1wPWB5V+QAluVkqdDiPpmPt3YU62GkLEojmLQt8aF2mvUeKW1XVFeN1Crjy6HuTCvQq5Je1/4NnB/1UJqlzOA7Y713I6w4MdFUS0PERmbQEVZelpLmslWR5G6OgDwwJnAV3OjbY8NdoIZFr5viTIzKArcZlbyNaaShG3WjRtq6h9c07T6B07QeItQac3Nx+it36rWqq2CDFq3MUZhkiQFrWknpFGlNS0kk4ygNV000A4zrSqBbE3oQqrAK61buOPNeVSl0cG4uPkkYNezyA/nnAF880ZD2ucHNjic53aAKfSeKVZqldZeJ0vUJ7JeupuqKoYSO+oiXfT795QqkVuV9HBKSTtM5YW42Fkuq9rez3b3AYUGvrN80PrVm4BXbwCgAIff0HRPOXY8sfO1rrR4wVhJC9E1CJg2i2wAff4c8PDPPX28U5BtkecUrl43HcPE0q4ELrYbhZ0a3u81KruYmwUpcEbvqZzDXJivN2BR161eQgIPpTyw4d7A/tHaGvCCDLd2humUOFAhqgUikTSUfX8FjD0QqPSSwowr41cMnvZrdgW/gdq+JcrMoChwm1mZ5PPidk1Tg6YBc34YWf0DJwyltcCqMM9t1stCjMlAjt5tjFGN26EwTNzgO6ECrxSJOXyqcNANA2OQrO6St0PavV1xOzssE459LwLGHACUC8DMacCqRQ1rpxdSqoJsuvZ7iV6XBZ7WXtPSvPiXBfHZDBP2XNhNrpuFW4870XWh5zGlKsimVNsNh3pes0WR8uyzZ4GHf0Ze73shsOlBbr5OqFgLoMigtaQFGDYOOOJGAArw2k3Aqze6/qiogB8/b4mygJOlXQlcbDeKsK+RUSTbRWZTT8mHPYiLMSljM3/um6oAs44BVswHBm0ETLoFSDlnETUa3vrOCDq7VFrzntZFwbxJImkoaooIB4aOA1YvJIHrUk/UrbLgJpuS71viVKC9GbgWqST4vPjOWn/mUuD9f0Za/8AOp6LwvC1aRRZiTATJiVZKQsetR1ycyHAqCrc7oHGB97QOeu7pIFnRdJQqutHxtmohRsAsbudm8ahpuhHg7MqmySTyiBuBIZsAq74BZh0NlAoNba9bugSqXN53C2CeW9bTunofFCuaMBWKx8uuelzUAyKlYhTouu66mji7qURhFXzE7sZFIUbB9y3zHp3dnxL7Aq0MbDkJ2O0cL18rNNg+za4YY6XFC8ZKWpBNJhDlHEDU1p895+pjNJValDlB+4cuQT/RLOpldLQrYV+jsouNuiCeqPQzTlmH6ZpMxQoAHft/egkw/2UgN4BkFXSu5fnvhw1fZNyoweNQgwNgfLs1XmntrpCjRBIquf5Efdo5iGR3PvA/gI0NRxSkU6qRrWfXt/F9S5CMkCTi2g5QsIZLCuY19rBZ+e4c4Jk/kNeHXBFZ/QM77DYwAW5tzVjjyDVJvJGjdxtTSqDygFeKuJmoxwl6rqmiKXDQOmvdJa9Ur2kqQdfUK14UT+yOtxGgzQ0gk8jcQOCrV4EHz47FJNJMPzYXWz0CdR59bjPpWqU1/xk73AZdAeZ8R6weKNRRHjeL3rJm3C5ug9YFblcfMDfa6HvKmm5RBNRTWpuLcAUorARmTAF6lgGjtgcOvToyT7mUqhh/2q4Yo7G5JmUNkiSx29lkQ0grk6JD3Z/V/UhnRpBBw2W6GAG6qAsxJlAh1ijCvkaam6B1gA1iN0q5mpowxQpOTT2IsQv+CSgpYNKtwJAxnv92I6gtxOjR05rLxjJFOnLMkTSZQaNJbQQ1DbwzmxRnjBG0yGw9pTXtW3jrnlbHrcgnyefFbfaowYK3gDmnk9e7nAlse0yDWuaftENReMvaulgBXXq1aj2wVkEGrduYGqVeAkhz6R6eO9qIoepYqhQN6j2dSSnGIqinVAHdUGzl3UIviid2YOpgFTqDNyILNCUF/HcG8NI1YTfTM+YiTex/TCdOZkEi8/t0pFUjUOgpaO3iuYmySBiLyGIj6nbUO3+ic8cXPLHbcLAGuWonXbTQVFbVgXtPApbOA/qtDUyZAWQ6XX+fRkD7ubpKaxm0liQJRSEbQmtvRzaIZk4Felc5fsQo4Cfov/g+IIo+VmRbIgn/GrlRWgex4sq7UVpX58/UKmPTlc/jvPRd5JcHXgJstI/nv9soOrnnpuRSKW1kY3J1L0qCWiASSdMYvTsw4Y/k9ZO/AT74V7TtYTBqI9n0O3zf0k5Kay+ZleZ5ida+0A9ehExYvaRqdZMnY8b+v2lw6/zhtA7h11rUvksOD/FGXp42hioX6nnExQmjE9KsSuukeFrTRQMNuAZVWiuKYgZxi2VU2qBCuhfFE7tDXuPzvdHewPjfk9ePXwh89Hio7fSKaFHMTiJrvB0ZxZCiKJ4KReU9PDdRFgmj6LoutNiIAtqObFqtG3QVFVnkVRvZlGr4qInsYMjr2nRDqiTb75vrgI8eA9I5YMp0oN8Ir18pdPjNRR4ZtJYklkwnec76jgCWvA/cdyopRmSDSH3F25pFacEUF9uluBH2NXKjtPZaZJql4GIj2qK0XvQeTv/2D1AVHV+OngzsdKrnv9lI+AwFI+hcRyltV3wriZmlkhZjx5OAHU8mr+89BVj0brTtqSIqFszC9y1RbrI2m0LJHNvrZlYa5yV546jrNWG5SLLMaP2DI2+OvP6BHU6Vao/xAAAgAElEQVTrEH6tpcnsz0Qgg9ZtTNHlJDBOJF1pTVUeNOAaxrln7RuMFJcW7njNCVb9iUHd3eOdTwO2Ow7QNWD2D4AlH4bWTq+ICnax35H3duQzJLwUAeFVZI7tEigFmw1ryRF1W7xYEjkprek9SXyt7dWY/GtKWdMxOfUUdlkwnfxg4jXAqO28fJWGYfiKSqW1pBXpX81oSHUA8x4C/v1b27fyBeWA2myLKIvdWvum5CnEGgXvYxr0GhlFsp0KMQYIBplBB/sAAt3oThWWAjMno1PvwYuVzfDZThdFZidlBz8muvWk5uveUOi/swnKLJW0IAf+Adhgd6C0Bpg5BVizNOoW1V078H1LHIQszUJoMWlDUs9LRdNRpLWfnL6jrgP/Ogf48iWgoz8w9a5Y1D+ww7STdbYHIbGT+uOzJHrk6N3GJLEwSVrgyQckx9OaFpEJqxAjYF1MtYXS2phg1V9g88WUalAU4KA/A+t9B+hdSSaR+e7Q2uoFkdqB/Y40gG2XIVFPLcHipYApVTxFUSSMwk8C49AWV0UsBWmU9Dqynxcp7OoVSFtnxWv4Xfpm8o+9fgFseaTbr9BwsmmaESOV1pIWZZ3tgYl/Ja+f/wvw9mzh20SbkXwfwlpD6U2ur1Cv4Gu7wl+jHHON/EB9/J3mZkEC424EHGlVRQZl7PnGT4DlX+JrZQTOKJ2FXEfO899rNHyGgltPaioM4QMVdpv9EklTSWWIv/Vao4HlX5Li2eVipE0y56nivo3vW9jNNU1r7njVbGh/7yaz0ssaLE5YAvNOsZQXrwbeuAOAQhTWQ8c2vnEB4ONFLHlu3kNvYxm0jjdy9G5jTJuB5NwG2YQrrWmgkXaYYQSX2bTVtlBae1AUu7KPSWeBo+4ABqwLdH8S2SRS9L1ERbJKNs+tqOifHd7UwmnXx20U/LWOQ1tcBa0F11Skcu/M2hdr438OAFj6MaZ9/ktklAo+HDYe2PPnHr9FY0nX8bQ2/F3lBFGSZLY6Ctj1x+T1A2cCX8+teYto44rvQ2i2lKabfsPNol7B13alJlDjYbNchFEk20UhxlJFt7VWssOc69jP59Mq8Lv0zRix/HWgoz9+kj4fy9HPGOPjBJ+hIKrlIcJQ13HBNLeFHCWShtM1iKhUs/2AL14AHvpppMXg662p+L6Fnfv2lls7O8eN7RJFtEGdBOj1VRSu9hPL+w8Cj/+KvB7/e2DM/k1qnX9ovEUknilw8x439l2S6ElOtFISOqUETuIMFUW1g3GTEhknqB0IHSTC2DBgF8XtoLTuqqMKYHFtg9F3KDBtFplEfv4c8ODZTZ9EioqbsN5oNYUYuXvHS8FEL4HXID6bYcH/7WjbUlVKu7JWsVdQ55jPd2Vq7UHsAtjIdwMzjkJXZRVe1zbGc+N+HbvU7nqe1nSCmCRrKolEyL6/AsYeCJQLwF1HAysXWH7t1Afw6dZA81OL7Qr/tjOi4lte5h0ijCLZTkFrm6K8bjA3ou3nwvssuweT009DgwoceQveL4+s/t34LQX5AFBJUMtDhL09SPJEOpIWZtimRK0KBXj9NuCV6yNrSj2FMN+3sONVq9dBcFPglpJYpTWTVaSI1hLfvAncdwoAHdjhB8Aupze3gT4x1yEipbV13mMUYpRLklgjR+82JomTuDQ3ITWDb8n4DvRc03aHEbRhd8kreuv7MokKW9nhxQYDwzcHJt0KKCrw5p3A85cHaaZnhIo8QRDTtPWxXmMvweUeD5s9XoLhjYJXLsShLW4C/qJ0b8MrlbUHEXlfiwLY5SLJBOj+BN+mh+PU4k+gZDp9fpPGkbEphkVx4+8qkSQCNQUcfgMwdFNg1QJg1tFAqcf4tTCDhpu3ZFKq0Z83u29jlcNJ8+JsFGwNBTouBy0+5kbJZVeU1w20XbZznQ8fw0ELrwMAPDv6LGDMfkxgPn6ij5wRAKraorlUWtsFKoqyEKMkbow9ANj/YvL6kfOAj56IpBn1vJj5vkVVFUOR2+rZOZ7sAD2sTeOEo4hp5TfENrOUBzbcG5jwx9iJZOxw9LTm6ge1Q+ykFUhGpE/SEJJYTTtj42ntRvUYB6gC2vC0rjMBdwM74aDBoHZQWrsKznq1jxmzHxmUAeDJi4F35/hqox86q2pb1q9ZVLyrZHONG6W0jkPKW409SAza4qbPyQkWAwVBn9XFTXZZpZ/xeVoE5fPngGw/XDvy91iKAXWVZ1FA701e7UbRXPi7SiSJIdcfmDqTFCX6ei7wz7OMTB2RQrcgUMVGVcSpwC3eJNZrUGMP4vP6lF0Ere2K8rrBURG4+APg3pOgQsPM8t54dfhkS/GtONrrmWMiaaOhtLZLX69iWFNpYqW1LMQoiRXf/RGwzdGkGPw9JwCL3m16E+p56Yv6liiLBzcTL/N9w0oxYefE9jsW1wAzJpPN+CGbEFFXKtP8BvqEjxexFLjNek0KaRKBHL3bGDtv3DjD75x5UYzGAUNpTe1B0iEorQVFMVrZl6nTw2SJBgo8Ferc6RRg5x+S1/efBnxV61PaCAxfY5sgAv25obTmFm8ipbYdpnqg/rPfySmeoiBe9iDeA/5CawDm83whnALnm9pTqphFUBQVOPJmfJHeAEA8C0vZ+YpSjErdLdxPSdqMQRsCk24DlBTw1iwjU0eUGSSyCIoqtThfkvYgPHTjmC2+FfT60I26ej7+fu24bDOAVi8GZkwCelfii37b4lflE1HWuOJbMQxa82OiXQFqHrqJWyrbeFrHcJNX0sYoCnDwFcD6uwHFVdUg4aKmNqFenyPqW0Rz21bEj9I6aedEVBwemgbcdyqw8C2gazCxz+wcGFEL/UFFMaWaDUzNUjuE2IOQ160cO2kF4rfalTQNO5uBOGOk/lU7obxXJW3E1NiDhKi0zjNK61bueL1MDHwX6hz/e2DMAcSndOYUYPl8z+30ikjRbK1wzHk7cveOF9sUw6LCxWZP0AJUYcAH4qNMv/NVxFJkDSAIWNFj899vi5XPM0VQLgHGHmAu4mMZtHZWWlfaICNE0oZsuCcw4VLy+smLgXfvF2YGiSyColJp9RQZxVHCFGKNQtTHB1UWUrVXqs58W1SU1w3CWgvFfHX+8iUwaEM8MPYPKCGNYkWzjKm5GNrr8XYsdN5Tb5PW3DA172td183PhzDnlkhCJZ0FJt8BDNoIWDEfuGsqeXabRL0sTVHf4kUkk2S8KK3ZPkuPsLCmV4S1n564CPjgQSCVBabMAAaNjqh1/rFTWosyd6WQJhnI0buNMW0GknMb0EAd7YQKHgJIcYAG3ekCNowNA3Yx1U5KazeTJbPglcf7Q02RIinDNgfWLCbqh8JKz231As0WEAU3ALJBQxZfYsWQF9sUOgnNeZmIRai0ZgtSAtFOlL1YEok2WETKDeN91evNqto3Vz7HWSsuBSmCchKw82kA2JoE8XvW03U8reUEUdKysJk6952GkSvfAiDOmmH7kKgK3rL9eqsHINwi6qODXp+KS6W1qCivG+imstFmTSOZYl/PJbY1R89GuWMtAGTsqFt8K2J4Oxa3doZpQaCiwmT8xHG8lEjQNQg4+h7TYur+08gz3ATqe1rX2ggZmSctvtHpKbOyek50ndRFSAo1tZ/m3ga8eBV5PfEaYL1dImpZMOwKwvP3uSzEmBySE62UhE4SPa35Iis0tdVVob0YQDcIaKcZxoZBjglWtpPSOmzv5ho6+pGUqD7DgMXvArN/AFQaF7gVFuxiBlc6ESrbKI68eE/TSWiXi/MSh0KMrBqQ/Dt6T2s3fY5IedEjUDUY1676veg1HIZluDH7J+TQWy2CcqlRBKXosjBVFBhpeTZK63bw3pe0MeN/D4ydAFR6sf1LZ2JdZZFwMzJn2bhqfmEr3ju/rOm2z2w7IZo3BC2yVakGoOrNzURFed3Qw6shn7wYeP8fgJoBJk8HBm9kKs80TTgOxYkuTpxQtskw4zGyfJiAH7t5Gkc7LYkEADB4I/Ksqhny7P77N035s/WKzNb0Laids7YqBS9Ka2a8SNIGsGUT/dNnSO0cANjzPGCroyJsWTDo2ogXz/D3eU+p4tq+SxItcvRuY2jqdpIKk/ATUhrMiuvEmydb9bCmA2G9ojJuMJQ5bdLxelFaB1biD1wXmHYXkO4EPn4cePR8f8dxAb9IK1U0I7hHIcU2xd6OnR4KOIkmoXZEpQBk4f20o1R3OBa84mDPL/WprlE1oFYlny9WkEMvbsj+GSOVbnyOUTVFUMox3nTMpsXFsCjt0E9J2hg1BRxxIzBya2R7u3Fr5o9IFVcYvxYXtvKnsA1Cb1kDbzufND/ORiBKhw96feheQL2gNV+U1y2WQPvcW4EXriC/mHgNsMGuAKwZMEa2VUwFH/wmgTHvqVMHhgYqioynNWsVIpXWklizwa7AxL+S189fDrx+R8P/ZD2/fuEmniAztBXxMt9PqYox902SAp1e3w3wNXD3sYBWBrY4EtjrvIhbFgwab+FtCvl7tqdYMcbnOGYdSUySE62UhI7pEZech9Twq6um3tgWn4kpdEJt2IOEoDRkC/i59U1MMl4UT6KCV54ZtT1w+N/J61f+Dvznev/HcoD3pGYHVjqO9pQqtgVUfXl9u/Jljt67jj7nxnmIQVu8FGYBzHMu8hPnr31PoRdXZ67G1uqn6Nb74kycV1MEhW5oxHERbiqtxfYgZkaInIJIWpSOvsDUWSj2WRsbqQtw0ZpLgHIRgLgP8VKTICzYrBzatzbbUzuOOBUe86tGp0rretklfu246Jg4aNELwINVpdxevwC2nmy8h601EHelNb9Z7taTWqS0Zq1C6im1JZLI2XoKsMfPyOsHfwx88lRD/1y9LE2RnVUUmUFR4CWzEqhvtRJHeooVDMQqnLngl0BhBbDOTmSzM+EBXENpzYu/+PUkK/iTw0OskZenjTGqaSdoEmcoRTQduq4LFTFxhm4QGIUYQwg4sYOkW9/EJONF8SQqeOWLzSYC+15EXj/yc+DDR4MdTwCvtqUTyJSqoG+Hqaav52ntyh7ER0XsKCen9Jys1ZWttiU6f23TJ71+EUtWeUHPn5luaPa7FgsWXcc6L/8a+6deRwFZnFz8KT4qDa05tt3mRRwQ+YqymN77TWuSRNJ8+o/Ewu/dilV6J7bX3wH++SPoNtYMXmoShAX9W9mUij5topxzg6gWBvvazzmice56Pv5+C5z1lCoYq8zHiEdOBfQKsNVkYM+fW97DbiZ6SXuPAvo89JY1aMxGQb1NWlHxLaq0UxVZR0GSEPY+H9jiCKJ6nXUssPCdhv2pemOPeLxqfmZQFHgpvM6+L0nnpbdnDW7K/glDi18DA9cjhRczuaibFZi0TUH4npr1ZHvUA2sF5JKxjYlzIS87qDK5XNFQrJiprXGdePPwAaYw/PUMWwimAm4rd7yePK39FmIUsdvZwDbHALoG3HMCKZYSIp1c0JkNLLMToXJdpXX9gK6XiVgcqoTTaz2oTzY2bXGb3dHFKefMwi5m0Ntyjl+4AqM+ngFNV/CHznPwuj4WxYpWM/EqxdjTOmNTAIUildaSdkEduSXOLP0IZV0F/jsTlacvNcbpnEW55n7TMSxMFZlqKr0TtNhuFKI+PpNSjHmVn2vkWmnNFeV1Q6miYa1KN27OXga1uApY77vAoVfXKOVMexDNU4GxKGDnbD2lim0tDx5R8S2j/oPcJZUkBUUBDrsOWH83oLgKmD4JWPFVQ/6UU99fqmiGQCLqzKAo8CLwYd+XmPOiVTB+3gXYXv0IPal+wLR7gL61IpkkkmHGOxYjK6m6ntR1c7yV9iDxRo7gbUwSJ3K0rZoOrOk1B4W4Trx5+AVLKPYgrNK6HYLWguJ2dnhN7XJEUYBDrgA22gco5YHpRwHdnwY/bhU+rYxVe3UxGxMlm8Wv2wJObPGtpCitC3zQuhRdsbCCQHXihHldqQ+/KNWSvN56+ePAE78GAPy2fAzeG7iX+Xe5auRll8qzKDALoNh4WkultaRN6Mqm8ay2NS4snwgASD/7B0xKPQ2A9whtftDY3LxMx6LgblwQbeoqimL6TftRWlfnKmqdRbGRMeXhb/SsWobbspdiHWUptEEbA1OmA+mOmvdljUKMergb+g0gl7YGrem8p954R8ceth4IDXgnqX6PRIJ0BzDlTmDopsCqb4A7jwR6lof+Z+j6QrQZx44HnYLMoFYfL0wrxfqZleR9CTovug48ch42WfYMevU0/jHuT8CwTaNuVWiY4hmxPcigqtIaANb0ElFRK2eptwJyBG9j7BSbcYa1RFhVKAEgHVNSvgPfzjDazU4eqC9TPTVPkqGTAl0nqaNO5JlFeSikMsBRtwMjtgLyS4E7jwDWLA3l0GYxFDJ4soFl1t/RTnHkdkHNFt9y4/UdB6U1vY6DDaV19PYgXj3uRNfVeE82he+o7+LkpZcBAN5f/1jcUpmAgZ0ZQyzHK+hLLpVnUZBJ1QYOWOjP6wVwJJKkQ5/zmZV9UfzOWQCAS9I3Yv/0G5bxv14xrEbA1jZIohdno7AL6OYCXCMqKHCttHb7N8q9yN17LMapX2KxPhDKMbOBrkHCt7IqZK8ZQ81GVRXkqr65qwtlUH1CPU9qUZaPYYUYww1eicSRzrWAo2cDfUcAS94HZh0DlHvD/RMOfQ7dPFMV66ZPu4wXXjNSzPMS3RrFNS9cCbxCajSdXToD3UN2jLhB4WJuYIqV1n1zaeOeXl0NWrdw6KQliN9qV9I0yi6VC3GCHTRX9sS7+rkI/lyHEXDKMUExGtBsZd8+dvJQb2HXkEKdHf3IJHLgekRpPeMooLgm8GHN9HCrIrcra7UHoYsxXjXkdoefVVO4OS9dVRsLvwWowoBP54pSxeBVocZfF5GKb8iaT/D3zOVIowxsNhEvbPRj4z10M6JQ5JTWMe6/MzZecpRKG2yuSSQA0JE2++kV3zkfqzY5EmlFw5Xpq4CvTIupKDJa2NTnRCnEGoxdhlaQa0SD1vXmZp7S7jUNmHM6svNfwGo9h9P186AMGm37dtYeJO5Ka8AUG6ysClSA+oFnMyXc3DB1W8RRIoklA9cFjr4HyPYDPn8OmHMGefZDgtZXEfU5bNCWtU6Ig5ilGYhq0DiRmHH0rXuAJ0idptlDz8BD2i5Gcc1WwVyHiJXWnVlz3kOV1q0cO2kFWusOlXgiiRM5NshBJ7JxrX4ugg9ShxFwMgvwaW2htBYVt7NDVFApFPoNB465j6ggvp4LzP4BUAm2s06vI/UvZhfO5mK5zChsrdfYrVKP/t5thkKOmaxFVaSL97SOsliYqCiNE6wiRdP02oDIym+w7XOnoL+SxzupccD3r0e+VPUQZCZVea74ZJwzZdI2aXmUSqX1bYwkEoAsgowNybKGL3a7FM9UtkIXeoEZk4ClHwOo9b5vBuz46KUmQquTF2wssv/2E6gpu1Rae0q7f/xC4J17oatp/LB0Nr7IjnF8u1kTRo+9pzVgtm1Vwbwn6413ouJbSazfI5FYGLkVMPl2QE0D78wGnrw4tEN3OhRVtLPH8OO9n0ToeMjWoHHCc6ZMFHz6DDDndPJ6lzPxUJ/DAYSYkRwT0oINTMC6EUOvF7WblfYg8SZ+q11J0zAKeSVoIscGOag9SJwn3Tz8giWMDQN2Z7dd0u7dpmA1NAV2yBhg2t1AOgd8+Ajwr3OAOh7bTrCqrp5ShZkspZhCKZqtwtZtIS+v5ySbUgMVoAoDXmkdVTvYtrhOF2SeT9bOpiubAvLdwJ1HoCO/AJ9oI3Fu+jwgk2OuUdq2SI5RkyCGgd96ntZUaS0LMUraAXZDsVBRcXrpx5inbgTkvwXuPBxYtSiSwlZsJhJfCLidscvQCnKNNJf1RlwrGF+6BnjprwCAz3e7DM9rW9ZVAxoqZE33XJshCqg9yCpWaV3n/ImsqYoJXOtIJDVstA9wKHnm8cIVwH+uD+WwtJ8TFfw2NzZtMjvjHJwNAVo/x3NmZVzPy8J3iMWMVgI2/z5wwO+Mtrqxi0wSdKyoKcTIjO90/FslldaJQK4Y25gkqg8URTHaS+1B3BZIiAPZNKe0TodXiDFfLLdFIUaAVTw5p8gVbBRTobHuTsARNwGKCrx+G/DMpb4P1ZFWDT+tnmLFsqhk1VdlmwwJtyowr2nBiqJErh7Ic0HrUiU6qxJjM8Gr0rpUsQQ7cnoBmDEZWPweyl3DcXzp51hc7kPeyywU7K5rnJXWtJ+z87SuyEKMkjaC7wPyyOHX/X4NrDUaWP4FMP1I9FcLAJrbx1qU1hF4ascV87xY55ZdWXOu5ZWy26C1m8D427OBR88nr/e7GN+sdyhpXx01IKtCjrunNcDYg1Tn+opS//zRQEWxzCqt6Ua/HHAkCWebqcA+F5DXD/+M9AUBYddHfL9jrEMy4r6wp9TamTlUGOV2DRkkG6fhdH9G6jD1rgTW3xU47G+Aqhpq+a4YjwV+MNchvKe1eU0Ne1XD07q1YydJR47gbUwpoRM52t6VhtI6Oe3nA431isq4wVRIaUYgq9WD1m4WdrquG4NxQ30bxx0MHEQK6OHpS3yrH9jgMFFam7vfrLLceG65DRA2XU93UHyb9hbuN3uiVg/QifPgPh3Gz6LyjKOe424X++wklgY7+qY1pGYfD3z1CpAbgCWH3YWv9GHGNe8RpK/x3zfOxaXsFA4UM2idnL5bIvELW2SXPuO9HYOBY+4FuoYAC9/Cd1/7MTIoN1XpzAYuo/DUjit2AV232UwiNN2b0tp28+CTf5up3TudBux6lmulnKFCZu1BYiz6oOebzvUzqmrx1RVhKq3ZQoy68XmJJPHs/lNgp1MB6MD9pwEfPhbocB1p1Sj4zff/dn2LXQZgq+F1cy+KjClXrFoI3HEYsHohMGwzYMp0IJMDYIq74lzfwA9mxqfY0zqXEXhax285JWGQI3gbY3rsJes2oAGRlT3VoHWCOlo+wBRGwIkdTNdUg2ItH7R2oXgqVXQjONbwYp07ngzseR55/fC5wH9n+ToMayXRw+x+sz83gtbcNabv0XVYbCh4jEmoh3MSdUCD/t0BnRljUlGIYLJcrmhGqrFrT2smaF0oVaBAw2Xp64CPnwAyXcDRs5EeuTl5T3XDgfURFBV20XXdmIjFcdMxzQRHREiltaSdMOtOsBk0aWDwRqTAVqYPhix+EZdnrkFvsdi0domyeaLoV+OGnXVGkM1bo0h2naAre6/UMP8V4K6jgUoR2GwicOAlgKK4Vsqxm4lmMCa+nTA933Su72a+nBGMPUm0QpRIbFEU4MBLgS2PArQycPdxwBcvBTgcU3eBy16161tMpXU0GY/NwmtmqqeaBM0i3w3c8X1g2efAWhsAx95P6jFVYQsTthKi+gaAmaHNzntW97ZH7CTpxHe2Imk4Zspcsh5SU2ntrUBCHODPNV+Y0Q9s0Hp19ZzE0ec2TNwontiFZVN8G/c6D9j5h+T1nNOBDx7yfAhWZdUjSN1e01sBdVzg7x2LJ7bDotquyJQTUae8iQuGNb8tFnsPt57W1f4pX6og31vGRenbMQEvkII6R90BrLuTZbJYKGnCQiHs960wthtxVI8ZBb9sKtxLpbWkncgxz3Ce3zQctR0w+Q5oagYHp/6D01deBdg8N2FDN31zNv1Mu1JzjaoEKT7mtkh2zu46LHwbmH4kUMoTb9vDbwBU60ZDvaCDUZhK04yxPM7Ft0yltft5rVkEuLYQYxjzbYkkFqgqcNi1wJjxQLmHWM0tfNv34cy+zSoEsutb3NYVSjpeldadcVOgF9cAM44CFr8H9B0BHDsH6DfC8pYkFOX1A4238DaF1NKmk7EHWVM9B9IeJN7IEbyNKSZ0IkcnpbSieJJ2B2vtQYJ3kKqqoKNqFbGqTXyZ3Phv0slGWlWao0ZVFGD8JcDW0wC9AtxzAvDZs54OYaodrEHrLmPxxhQk4jZAMinVGKSddvntikw5EXXKWw+jfDMqnUfQFvo3VQXGM1cPWsCmp1jBoNcuxwnpx6BBAb7/d2DMfuQ9XBFOqwKSfF92g4ZNd4ujeoyOKcVyHaV1i/dTEglg3fTrEW0abrwv5u99NSq6ggnlJ4lfcYCivm5hFUeijI52RXiNmH/7UaMbRbLdelqzf+PbT4hSrrACWHcXYPKdQNq0ynJbZyHLqJCTUHyLnm/DHsTFPI5u4rKBCrvsNIkk0aQywKRbgfW+A/SuAO44nPQVPrDLIrHrW9phvChVNGOu7T6zMrr1SQ3lXpKZ89WrQG4gUVgPGl3ztiQU5fUDjbcUy7zSurYQI12TtHrsJOkkK1opCZWkKq1pR0QnskkqHsBPusMKphopLlSRkrBr6hU3dhVeC+aFgqoCh14NbHowUOkFZk4Fvp7r+uN0wpMvViw7/PQ70I0aQKywdaOWs1ORObaLKfbZbCqabkw6yLlQq22JIGjNTHbqeWtS6LnbZsHdWOe/VwIA/tb1Q2DLI433pFTFKBpCfG9NBaRIeVdilJhxfNZNhYON0tqlv6tE0gqwmUF2yq3SJgfjZ6XTyD/+cx2pj9BgDMUR62kdF4VYhNilS+dcbJbbUdHcKa27+KDHiq+A2ycCa5YAI7YEps0Csn249rqrs2CqkJl6HzGeP+e4eY+b+bJIaV3S4mulJZEEItsFTL0LGL4lsGYx8S1eucDzYez6f7u+pR0yc/xlVsbkvGgV4L5TgU+fMmwIMXyzmrfpum6ulWM8FvjBrG9gFQCIMncpcoiIN/LytDHlhE7kaHAniZ7WtfYg4QRt+CBuq+8WuvGW9JrWFRqpNHDETcDoPYDialKtefEHrj5K/SV7ShVLgJROmOg9D4g3m8yinPXPix97kGYWCaOwE0eiOq9VHje7LV76nM5sGpNTT+GwBVcAAP5SOhJP9T+09n2WwBargDSV2hTWrzOW9iB1PK3LmgxaS9oHdiFr55GZy6Rwr7YHflM5gfzgmUuBF69uaOVY+EsAACAASURBVLtEFlSxUIhFTI9NunSQ2g4Vl32eJXi0eglw+2HAivnA4I2BY+4HOgcK2usu6MBuJhZs1ORxopOb93jxtC5VdKMgdVl6Wktamc6BwLH3AYM2BJZ/SQLXa5Z6O4RN/2/Xt7hZayQd2kcqPjIrIz0vug48eDbw3hxAzZCii+vuKHxrsaIZlpNxzrrxQ8bG07ogEIRR6mVCSaIlfqtdSdMwipMk7CE1CjEm0R6kQUpr/hwkrbimV9x4LEea8pTJAVNmAKO2B3qWkUmki7Q9Q2XFKK3ZYhErGaW1aPFbo9IS4HaByxJEYRYUqgKgE8do2+I9aL3ZogdxSfpGAMCHGx6PqyrfF6o2urJsYMvs20TXlE7CUqoSy0mWSO3GorlUHUokrQAbELDrQ+hzfnPpAGh7X0h++NgFwGu3NKxdcakVEDfssrSC1HZwHbSu/o1MaSX0O78PfPsRMGBd4LgHgL5DhZ9xuxFN54WlslmIseFFqgPAz3tc2YMwgWl6zssxLloskYRC32FVv+KRwJIPSHZGvtv1x+36f7u+hYpHShXddp6XdIz6P54yK81s2UjQdeChc4HXbwMUFTjiRlIDwQZ2LGs1pXWa2cBkcVJat7rgL+nIEbyNSepEjnZEq6r2IEnqaHlPvbCCNrW7haEcNrYYlgmO9iARL8o6+pGUrKHjgFULgNsOAbo/dfwIq+Ri209/vsrwdlSEkyjbIk4MftTCXQEUZkGh1cypJUe0bfGo3n/rHuzy9oVQFR2P9ZmIV8acA0ARBhdYhR2bJZDL1AZKijHfcKTqb7vFjFt/V4mkFbB7tkXvAYCenc8Cdj2L/OPBs4G37mlIu0QWVFJpTYrhAg1SWtdZFHdmU+iHPG7LXgpl4dtAn6EkGDVgHdvPuJ3rGJuJmu6rIHOzyXHzHneFGM3JLx1n4j5eSiShsNb6wPH/BPoOBxa9A9x+qOvAtV3/b9e35LLmc9aqY4a/zMoIbbZ0HXjkPODVGwAowMRrgM0Pc/wI/Y6ZVJNqPzURGm/h1yHsvIcf/2SdnXjTWneoxBOlhKbMUSWFYQ+SoKB1w5TW3DmQSmv7YkpNpWsQcPw/gCGbACu/Bm49BOj+zPbt7MTRVDikjZ8babI219fdedEsf8sNRipgFD7SnNIjDm2h3uOOvHs/cP+pUKBjenlf/L3PaSiU7b1HLdeeLcIpWEzEfcMxbVO1myKV1pJ2gn2G7TKAchkmCFDWgP0uBnb4AQAduP/UhgSuC4JsnlZO93ZDuaIZQU7+GgUpsuXWx7+zshq3Z/+AbdWPoeXWIsWzhmzs+Bm3SmuzEKPmqyBzs+ni5j2uPK2Z80uvo1G/x2WKv0SSWIaMIYHrPkOBhW+TAq49y+t+zK7/t+tbsinV6MuimIs3A18Cn6jGUV0HHv0l8J+/kX8fejWwzbS6H7OzwmoFaH/Pr0MKzNqa34yRMet4I0fwNsaoqB3TwIcdtL2re5NnD8L7EIdnD2INoiXsknqG9f+1w8+EoyH0HUYmkUPGAiu/Am492DZwLfY+VY2f03verniqqQSzL5hIf+elgCk9h1Hag9DJRZRFIY20skydB+z9fwKzTwJ0DQs3PBIXlE9EvqQxaWm1QW87NaaoQA4tcBjXDUfTV9S5EKNMxZO0A6ylEd+fURRFsT7rigIc9Gdg22MBXSOB6//OCrVdrIrOTZZOO+BUfCuIhYrmxh6ksAKp6UdgW/VjLNP7Ysnhd5Pii3UouJzrUNGEpgNrqvdhnJXWbuc9LOycmm7uGvV75CappB0YuglZc3QNBha8Cdx5OFBY4fgRu/7frm9hx6tWHTP8BHQjOSe6Djx+IfDyNeTfh1wJbHesq4/6sTxMCmkbpXWeua7895Z1duJNi4e2JE6YE7lk3Qa0I6KbZ0naIeSD1OEVYrQeN5Wwa+oVGvRztMGI0w5yv+FkEjl4DAlc33YIsOzzmrexxU3MwGXa8Dul97zdZodpJWHvMWdXCMyJIGnRQbFTWtNihdG0xUFpPe8R4J4TAb0CbDUZ83f7A3So3DW1V1qvKpQND7YumwJppZgrrc0CKGKlNU2Vj2vQXSIJE9bSiC2yWvM+/llXVeCQq4DtjqsGrk8D3pwZWrvY/oytp9DOmMWsa4tvBVHR1S0+W1gB3HE48PVrWI6+OLp4PlYNHOfq2HmXcx22v9UTUHyLjn103sNnKopIqQroKaYKazpeuvm8RNISDBsHHPcPoHMt4Ou5pCB8YaXt2+2yNJ36lla3lOpxEJnY0fRzouvAE782izZ/7y/A9ie4/njBzZomoYgKwuu6bhG01diDyKB1rJEjeBtTriQzcMAHauKsFOHhz7Ub5Ygb+AGn1X2Z3ARRnVStkdBvBHDCgyRwvWI+sQpZ9oXlLayKmA0ud2bdbXaYxfzsVch+dtaDFKAKCr/5YKoRm6+0rrsR8sG/gLuPBbQSsPnhwMRr0dmRBWC9pk6e1t35ovEz1s+cvaaGPUhMJ1hGwS8bexAatJZKa0k7wPpcOhXCFardVBU4+MrqQlQH5pwOvDkjlHax/VmUG5Nxgj0nfN2IIGp0x0KMTMAanWvhR5mL8Z6+geu/43YjWiRQicWmvg21tnfuxgujAJdRiJFmlcrxRtJGjNiCFHDNDQS+ehWYPgnoXSV8q13/79S3tLrSOm+ITNyHykSZkQ1D14F//xZ44Qry74P+BOx4kqdDRF77qYGYNoWmwKm3rBkbtqJCjG4LbkqiQQat2xRd183iJAmbyPHtjbNShIf3Ig7Le5ofcFp9t5AGcR29m31MOBqOEbjeGFjxJbEKYQLXZnBDExbjo9jdN55sU3ykvEWptKbnQOTxHFVbLLw9G5h1LFApAuMOBQ6/HkiluYCVwwKg+rPuNSRoTZV+5rk3J15m3x2je5vBKPhVtrEHkZ7WkjaCLaZq9CEOfUDNuKaqwPcuNz2u55wBvDE9cLtMxZHa3MV2jHGyFQtSZMs2aM0FrHHcP/BVx8ae/o7bMV0kmohrtg5QK8Zw21a6mWsqrWkhxvh+V4mkIYzcGjhuDtAxAJj/MnD7RGFxRrv+3zE70MV6I8l4LrzOvLdY0Yz+pyHoOvDERcBzfyb/PvBSYKdTPB8mluvkkKD9fZFZh7D3N7EHaS/BX9JpvbtU4ooKo4DLxnjSKoKfeHrx5o0a/lyHtWBotxSXzkz9gkixTXvqNwI4/kFg0EYkcH3zgcCSeQDYgKxVlVu7eLPxtHYR0PVjmxKl32meUyfHqS0Gc28D7j3ZsATBkbcAqQwAa8A/7xD0psf8dnXR+JyiKIzKnVVax3vD0Sj4pTkHrVu9n5JIAHMM6ilVzD7EoQ8Q1iRQVeJxvcNJAHTggTOB1+8I1C42G4kdOzSbDIl2wCkTKciGqVGIkV0U57tJoTQmYI2RW3lOMXertOY3CeOuruMzzNwqpdNcTQWquI5zgF4iaRhrb0sC19Qq5NaDgVWLLG+xs1x0I7RoWaW14fvv3R4EaKCwRqsAD54NvHAl+ff4S4BdfujrUOY6M2br5BAw1yHmfIauwbJpUkiUXwfLJUm8kSN4m8I+xHFV69mRTVt7lSQVEGiUPQjf8bZ6MMjNZCnWaU/9RwIn/AsYuimw6hsSuP76daOta3orFlUvf33tFl9uzou/itj1NwkaBV8IJkqltbAozUvXAv/8EQCdKCEP+xuQMieAtL2lio5VhZLlZyz02nev6a3+DXKMnOD70v47rhuOaYGXHEtdf1eJpIVgVWwFhyBA3ZoEqgp878/AjqcA0IF//A/w0jW+2lTRdEOBxBck6rXJkGgHnNR1QdToNT7+K78BbplAgkhMwNrP33Hraa0oimXOGXdrvZoMM7dKayNoLe1BJBIAwKjtgBMeAvoOBxa/S/qe5fONX1OlLa+advS0bnFLKZrd6GUN2ZFWQfclG3JeKiXgvlOBubcAUEjRxe+c4ftwjtmjCSctqK3Di7b4MVCVa5JYE88Vr6ThsNVUk5aiXaO0jvnEm4UPWoe1YdBuFXDdFETiC/jFjv4jgRMfBtbeDujpBm47BOuumAsAWN5TMt7WlU0hx6Vu2d03VL0Xuj1IVjyhbQbmJCNdbUt0KYmWCY+uA89eBjz6C/LL7/4vKYLC9U/sZHBZvmR+noPep930PdVzbiqtzT67FHOltWEPYqO01nQZtJa0D6xy1mlccrUhpyjAQZcBu5xJ/v3o+cATF5uV9VzC9p9dnLdjqwYh3GBu6tYqz9jrqHs83xYf/28/AW4aDyz5APj/9u48Pqr6+v/4686WhRCQLRgWESrggqgoVC1uUECtikrFre7WKtat9mv7q1s3bWsXq7VCK+K+r1UUv4i49CuKBbUgihuCCCFsgYRsk5n7++PmTmYms2eZe5P38/HIQ5ncmXwyc/O59557Puf0LIfzF0QC1vE/JxPZrCqLPn92cj1rSFQeJLPjRXwj4KBL+/eItKuyfaxrjl5DYdsXVrLMls+B5IkpqeaWyHVYF820zuUa0jCMluuwZDefcxWsg8fPhpVPgccHM+Zm1XQxkVR9dtwu+jrEPl7Xx32mrUqrqjyIoylo3U1F33ly25K5VjWtHX7iHS2+EU573TDItWGNWyVqThcvlzIYna64D5z7Lxg2ERprGP9/FzPZsyySbQvW/m0YscuY0pUHSZlpncNyMDtgnM+SHHYQN5/NX1qyTjxWx+7XfmN946j/B9/9NSQ44SnweSJLzlqyqJNnrdjbFNtB+gSNJyMX4Q6t0WnPc8GmJJnWzUF3Ba2lO4ies1KtAMq4yaxhwNTfwqQbrX//+8/wwpXWsuEM2eMwmmvnez0GAZ+n+Xud3+TWKWLm+DjRx+D6YHYBCTtoXbJtFdw71SoN1mcEXPgKDBid8OdkeozLNNMaYs+fHdOkOonW57WZHe/ib5qqprVIs74j4IIFVkP4neth3jSoWBlZ0Rc/96eaW5I9p6uINE3OMqAbuQ5LVOYrVw3VViPNTxeArxBOfxT2O7XNL5tLEpNb2Nchptly/I3fn1tnWnfiACVr+ni6KfskzjDcFziID/y6abJtXR6kYzKtu/oSl0waItnfc3yjzoKecNZTMOp4vOFGZvv/wndqFlrfag4mQOzBNdlNiYzel2BsEDgTbWlA1VbxmR75HouPJo75/JaWjt1TfgNHXZcwYA3E3HDYFlWvOp4dQLC3sffbRNl9Tl/unKhrdzS7OpWyGqQ7KI66uLdLbyS6aZhVhq1hwMSfWMuDDQ8svx+ePBeC9RmNqT7qYtUwYo8xXbWxVibqUmQWtiUbPRQ2GW98zPD5M2HXZhi4P1zwCvQe2mrbbD4H0zSzKvkVXVLK6c234n+fTDOlW26aWn9rdpKOU4+XIp2q1yAr43rgGGsuuu84dt/xXyC24Xe6ucXOKK7L8gaeW+Qa0G33a5TabXD/ifDVWxDoCWc/AyOntMtLZ9oPwY2ijxd2ScL4/bl1TWsdI5zM2Wcs0mHc3JjEH1fT2k0NBOLf7/Y6iY6/W9hdMq1TZTvZDRdc0ajTXwinPcDWb52Czwhzq+fvXOB9OeZEojAm0zpJTesMasxFN9/KVD7rSMdnJuZzLOH6Hcz1/5GR65+2AkXf+4tVFiQN+73elWIpnv3ZRbbxx55Uhc2WWrP2/O3UzLH4mqLx7GC2226YiuQi0QqYVDVCs1pFMu48+P794A3Axy/AwzOgfmfapyXKoMvnKhanSLVCy+MxKMgxG/2w0Hs8EPgd3mA17HE4nPcilPRPuG02Dc6i649nEnjwxdS0dva5c/xnkGkPh5abps3lQcL2TV5nHi9FOl1Jf6sh/JAJUL+Dg944h2M978as8kk3t7QEZ7tmpnWqpryptKX3QSvb1lgrczYsh6I+cN4LMOzwtr9us1x/RzeInu/tRM1I9rx9fRWf8KegtaPpCN5NRTL1XBg0iA/U2BcRbhB/0txeNa3jlxp39YnXPuA0hsKRfTleqoZXjuT1sXnSX5jXNBWAG/0Pcj33Qqj1ErW0QeskJ0vxzbcy1a4nYVmKz3YozNdYdnzDzzZezZHe/9LkLYKZD1uNFzMQn9WesDRA3DaJMgHszLvI/O3Qua+lpmiSTOvmhxW0lu4g0d97ovOWnBtb7XOitVonUGJlY93/PajelPIpiRow5XOed4p0jaly6qnw3lz+GP4DhUaQmmFT4OynobBX0s0Ls9gP0t0IiRd9/uz00noBnycmASPjTOvITdPYTGu3NZ0X6VBFveEHz8LIaXhDDdwd+CvTa5+K9EfI9CZrV+2BUJ9rpnV7vS/r3oV7JsGWT6G0OTu+/MC2vWacLl0eJGq+t48B8ZnW0Y0zQdckTqcjeDcVdPFJXHR2cpHf66pSGF6PETNBKtM6N9EB3GQnBnY9MdcErYHiQIBfNp3D74KnAzAj/DI8OhPqd8SWB8mxpnX0e5VN443oZevhcHYNqNoqvlFIXrIBK1bAPZPZo2kNm81eLD/mIRh9XMZPt+tTR/6dMNPaF/dvaxuf1xPJMLN/58hyZ4f+ndvHlWCSfUWZ1tKdxP+9JztvaWm6msPcNvxIK3u3uB9s/BD+eYz13yTsTOHosbXU4uyaQYhM1KZYDQMtK2AyOv6EmuDl62D+NfgI81ToCDYfdw/4i1I+LZv9wD6mB6JKiaXij8m0dv65UXQwJfOa1tZ29nGyKezscloieRPoAac/wrb9zgfgsuAD8OLVEGpKO7dksyLEjXLOtG6P92Xl03D/CVC7FXYfCxctatX7oD3UJTgP6Cqi4y32apv4FWbx/aK6esKf27kvYintIujwmqipRAfa3RSQtEXX5G6v5f2t6jJ18WBQ9N3RZBd2rmjEGKcw4AEMZodO5JLGq6inAD5/FeZOZYixObJdsv2mpWxK6vcEsluhEP0eRi8Z7AzxmQD2kuZOq7v6+atWl/XqDaz1DOHkxl/RVHZAVi8RX1c91VLLRP8ubK49ar8XjXZjKYfO33YwPZgg09o0zZaa1l18nhIBK+Mn+kZysvOWwrYErcHKwrrwf1uabN07DVb9K+Gm9QlqlUaOH100CJGJRO9LtIw/o/od1g3nd2cDcHv4dK4NXoLX6087hmwy3uOXO6cTc/7sgnOj6GNnptcr9vHHDlY3Njm7nJZIXnm87Dzqt9wcPIewacCyefDIadTXbAeSzxNdPdM612vINr0vpglv/hGeugBCDTDqeCvDunT37F8rA+lWFrldpL+BnWmd4EZEdMBelyTOpiN4NxVZLufCk7jo7EI3nHTHiw40tddNg/gLrK4eDDIMI6oJSOITA7vetZvuIEfXmHwlPJ6b+t4GJQNh88fcsvUqDjI+BZLvN/bzk70n0SdhRhZ3lKNPaDq7U3htXAfvTs20/s88ePg0aKyBYRP5UeAW1pv9c6hxl755bKJszJbvNX+ukUxrO2jtzPnbHld0125b9L/ViFG6i0TB4XiRLN62BAH6joCLXoURx0CwFp74Abx5W2TJty1hTesunjmXido0QeDiTLLRt62BuVOsG56+IjjtAf4eng4YeDM45ytKcxyPVteY3XmO32VJH8UxQevsalo3KtNaJCPFAS/3haZxSdM1mP5i+GIR5U+fzO5sTb7qJI9N0TtDS1Pe7ObJ+PP1jDU1wvOz4LVfW/8+9HKY+aCVDd9B0q0scrv4UoXxK3ch9vq2q8dO3M6ZV7zS4SKNSXzu+wN120l3PHv8Po+RVeAwlfgLrK5eHgTSX2Cnu/h0ovixbigeDRe/BgPH0CtcxaOB33Ki5+3kNa0DsWUk4uV6EuaNakDV2VkVdmfy+MYZdcEQptlBpUqaGuHFa+DFq8AMwf6nw9nPsLmpKGYMmYpveJUw0zrus0+0bN9+7+0GU5k2pups0cGB+GzrUNRnlkkAR6QrSBQcbrWNXS+5rUGAot5w5pMw4UfWv1/7DTx9EQTrIpskyjjq6plzmbCDwEk/o3TZ6GvftuqQbv4Eeu4OF7wM+5wUKauVyY26bG7MZnueE1NezwXnzzHlQTI83vkj5UFia1qrEaNIYvaKhoWhcTT+4EUoKaNo+yc8X3ADh3hWJ35OFz9e5JppndP7smsLPHQKfPCw1eT9+D/B1N+Cp2Pn6FzrdruFL64pfKLM8thMa12TOJmO4N1US01U9+0C0Seubrw7aF80tOfS/vigWHeYeOMDefHiGy64gddjEPDF7d+9BsH5C1hZcjgFRpA7An9j+pbZkQaN0dJlaLVlKVhODajaQX3cnfHoz7NDSpVUb7Jqyf1nLmDA0dfDybPBF2i5Sx9Xfzqd2JppiYPN8ftpqgZpkZ4EDr05FdMARZnWIjHnKsnOW7LJsE3L64Njfw/fux08Plj5FNx3PFRXAC3zeKJxdfYc7ySJ3pdoKT+j5Q/A/Sc21yE9wLrh3Nw4y54HMznlzuZzyPY8x+eylYoxDagzPN7542paBx1eTksk36Lngrp+Y+CiRdT0Hs0Ao4o/110PS//ZarVOzhnFLpHrNWTWGejfLId/HGU1UQ6UwJlPwCEXZfUzc+XG6+RsRDKtmxM1EyVuxVyfOfSaSizui1hKu2hy8UlcdKaIG+sw2SVZ2vOGQbfMtE5R9zEcNiPlQdxwYRatKEGwkoISHht+C7ObTgDgmK2PwYPToaYy4XMbm8KtyjJA4uZbmcqqAVU7shtq2pkg0e9Pu49l7RL4x5Hw9TtQUApnPg5H/hQMA9M0I0vCCwPZ/e3G1k/zJVxhkUmmda1byoNEzT9N8ZnW0UHrbjBPiUDsuUqy85YOKX108Pnwg2ehaDf4ZhnMOQLWvBX5GYUJMsBVHiTVZ5RgNVOwDp6bBf/6MYSDsPeJzXVIywFimhdnUpKvMLIfpC/FlW02oNuSPnLJtLaPP/aKUvuGgRvLIYp0Br/XE7m2rm0MQe8hvH3kI7wQ+jY+QvDStfDcZdBYG3mOvbKzy2da59iIMaP35f2HrN4TO76GPs2lvfb6btZjzVWiMmFdiT3nN8XXtE6y8k2JNM6mI3g3FWnk5cKTOLdlisTzdUCmdXwQrTvcLUwVtK5vannMbXeQkx1MCwMBftd0Bpc2XkmDp8i6Kz/nCPjq3wmfm+iEKV2TqVTa3CQsR5Hl2s2/W3Q2erudLIfD8O+/NGciboR+o+DixTByamSThqZwJNEk18YskD5gFfl3ogZpQTvT2tk1OqOD0Y0KWotkVNM6XSPdnO15BFy0CAbsAzWb4IET2e/LezAIJ5ybumoQIhPxjX/jtfqMtn4B93wXPnjIWtY96Ub4/v0QKI48pynL1SUtQY/0K4myzZRzW9JHbE3r7DKtg80rsZx+vBRxgvj5v8Ys4MfBH/Nor4utue3DR+Cfx0DlJ7Hbd9GbnOmOBclk9L407rJuAjw/q7nh4nHww8UwYO+cx5uL+hwD827R0t8gNtM60UpWUCNGp3NfxFLaRaQ8iM99u4A/vnyCy9ilAdqzvl7A64kEgLpDljWkvpsdnQVV6HPXPhK7bMnX6vGXwxP45+i5VmC1eqNVymLxrRBqojCq4V+iLK223FXPqAFVB6hLkB3esvyuHZpC7toCj54Or97cXL96prWsu9+34sYRdSMky/cvs9IAcUHrFBmQQYdnjhmGEZnn7GONTeVBpDvKZg7okEznviOswPXYM8EMc/javzPPfxv9jR2txtVVgxCZSNSoKZpdHqS2MQQfPg5zjoRNK6C4H/zgOZj4k1Y1QMJZ1vHP5viWbWDF77JM6+jgQraNGO2bBUHVtBZJK37+t+YWg8V9T7fmtpIy2Pwx/PNoeP/hvK2+7Aymaba5PEjS92XTR1Y5ELt+9dHXw8yHobBXW4ack9o05bDcLv46pKXxZPS1dcv/d4eEPzfTEbybinTTduEfaHRZDTdnWrfnCbRhGJH3ortMuqkyresiS589rns/Ypdrt+wjhVEnFTt67GkFVg84C8wwvPE7uP97GDu+jmoU1TpLK9flbpBBA6oOEHPimLDGcxtrWn+2EP5+KHz2CngL4IQ74OQ5UFDSalN7HAGvJ+uyHMnu6kcr8HmIjuGmapBml9xwciPdSOAgSdDaMLrPXCWSsOxTkm06LNM5UAzT/w4n3knQCHCU90N+uPJs+PSV2J/fBYMQmcok07qEWiZ9fD08+0NorIahh8GP3oLhRyZ8TtaZ1lnsB9ke030uO3+OLQ+SZaZ187HHzeUQRTpL/LwTM7cMPxJ+9G8YfhQEa+H5yxj+xhX0oqZL9kBoj5WVrd6XcBje/YeVrb7lU6tR77kvWCUI85SAUpegTFhX0nIdYh0DWlYbR11bR/3uWv3pbApad1ORRl4uPInzuaz7eTz7oqG933v7vegumdbFKRoitTRTyq5hnhPEZuRF3Q2Or+1YUGIFIE75JwR6wrol8PfDmOl7AzATvi+JmlBkKibDrJM0NIWxr/djgriRTIYcM60ba+Gln8LDM2BXJfTf27oJMO5cSBJUqG1DwD9Rfep4hmHEfMapMsuDLmikG19X1BYynd1EUqQjFEXN5cnmgE7JdDYMOOgc/jr8H3wcHkJx03Z45DSYfy09vUGg81fTOEm6eX5U3Qe8HPg5+25ZAIYXjv4FnPdipH51ItmWRCrKYj9Ilxkez++y8+eY8iAZHu/8cYEK+6aBMq1Fkos/x281t5QMgLOfgWOuB8NL6Rf/4pWC6zigcXlextuRatuwsjLh9cmOb+Chk+Hln0JTPXzru9ZNgGHfaZfx5iIUNiPN7N14rZwJO95ir05tWW3cehUzqDyI0+kI3k1FMq1deBIXneXohkyRePYJdXsHbSI1f7vJkvvCFEvT3NxcIqaOdZL61jErJPY/DS55AwaPh8Zqbjb/zj3+P9JYtaHVa7flrrrdgKoz651GZyokzLTOZSxr3oK7D4Ol/7D+PeFSq5bcwP0yGksu+1Qm9Wzjt0vUuM3+fYMuyBxryXaLDVrbmdeebjJPiUDLDanuLAAAIABJREFU/Gn9f+ryIHXBEKZpJtymvazx7sH0xl/z0dCzrAfe+yenvPN9vu1Z1a0zrZP2fWjcBS/9DzNXXcoQz2a2+QdazRaP/B/wpD4mZB20zuL4VpugRmcqbjt/jl5hlunxLj5Q0dhk9/DRMUckmcg5fvP8n3Bu8XjhiJ/CRQtp2m0EA43tzPXegvmvK6F+R6vXdKu2rKyMmb9NEz54BO4+FL58HXxFcNwf4cwnoEe/9h52VpJdX3UldrzF7m+QaGVSUUzQWscIJ3NfxFLaRbDJvdlu0QE7N9Zh8nVATWtoeS8yqZnYFRQHkl/Y5VqLzAmKkmTbRmfqtTqJ6jsCLlgAk39JIz4me99n76cnwXtzrSVpzXJtLBL9nM5cCpjsxDGnsdTvhPk/gfu/B9vXQOkgOPtpOPZ34C/KeCy5zDnJbj7ESxbcbsnAbM4cc0GNzmTlQcLKtJZuKLaGYuqgdShsRlZTdJT6xhANBPhozM/hB89CryH0rFvPY4HfcHrlX6CuqkN/vlNFsgujMrH4fFHzjc45ADzSdAy3DrsXhk7I6DWjg9aZTHv2/hEMma1u+iUdb6aZ1h6XZVr7U5z3JGEfe4KRTGv3JumIdBb7GFUfVx4k4dwyaBwNF77OA03fBcBYfh/cNQE+ealTxtrR2lJK0X6/Suu/gQenw3OXWgH9QeOsMlLjL85bOZBo0dfO0f2QuhL7mGEfA+oTXMdFX58paO1sXXMvlbSCLj6Jix5zoQtOuuP5O6CmNbRMvN0l07plCW3rEhF1XSTTOlkd1IT7jscL37mKa3r/lQ/Dw/EFq2H+NXDvVKvxB9lf4MaOq/PLg9RG1SaPHUsWDWBME1Y8BX87BN67x3ps3Plw2Tvwrck5jKVjyoNAfKDa1+rxumBzeZDI/O3cv/VkmdZ2AEf1rKU7iVk5kSxoHbVNR2c7R+azgBdGHAOXvs264acDMHnXi9Z8+d8noIMzvp3ENM2W7MKAB6or4Mnz4aFTYPtXUDqIxYfM4f81XcT2poKMX9e+Uef1GBgZnJ9F7yvpsq2zPddxWyPG6NqjgQyPdy3Nt9xzk1ck3+JXr6abWwqLe3Jj0/mc0fgLQr33tBrDP3YGPH42VK3rnEF3kLZcQxZ7mrjU+y/u2HZpc3Z1IUy6CS74X+i3VzuPNHfRv2MmxyU3imRah+LLgyRODlNNa2fTEbybcvNJXExNaxcGJe33vN1rWttB624y6aZaQtuWjOJ8K0qSlRtT2zHFvlNZNIKTG3/Fyv3/HwRKYP1SmD0R5l8Lddta/Yxsx9WZ5UFaguyx9dYyHsumVXD/CfD0hVBTAX2GwznPwwm3Q2FpjmPJ/b2L//9W2yUIVFuPx9Y4tedvnwOyNZLxRzIcEjdiVKa1dCcxNeqTzAF+rycyt3f0PBtZOWKPpbCUr779G85o/AVfewZbtf6fudiaPytWdOhYnMJuvhUgSK/ld1uB+4+eAcNjlZGa9S47B00EslvlY8+BmZ6bRTflTdf4uGVVWWY1SaPPO93QfCtmhVmGxztfXKDCDeW0RPItfvVqurnF6zEo8HlYEt6XirNfg8Ovsur8f/yCNXe+/jsI1nXO4NtZzisrP32FA+cfx3X+xyikEYZNhEvfhonXgNdZdaPbsnrULfxxmdaJVmEXKdPaNZx7xSsdys0ncW7LFIlnB2vau4lapDxINwkGpcq2bUvTvHxLdjCNvsBMFfArDngJ42HV0DNh1lLY+0QwQ/DeP7nyo9O4wPsyPXyplxwne13o+AzAaMnKvKQdy86N8PzlMPtw+OotK9Ph6Ovh0iVW9/OcxtKUcCyZSHbzodV20XVvEzaebA5auyDTOtKIMT7T2swugCPSFSS7GRmvJdstxyazGUpW23FJeF8uKPwLTLrRqr/51VvWTc/nZsHO1n0SupK6hiameZayMPBTChbfDA07ofxAuHixVUaqoGdOn0/YDlpneEEc3ZQ33WqibPt3xJ4/OyuIkkj075VtTWv7OBlpxOjgm7wi+RZJBmme2zKZWyIJFWE/fPeXVvmLYROtZoOv3wp3HgzvPwxhd/VJsOf3jG/sbfwQHjwFHjmNwp1fUWn25ibP5XDuC1b5Rgdqy+pRt2i5DrGOAYky6GNqWusQ4Wj6eLqpoAsy9ZKJDti5cbL1dVCmdWF3C1qnqGvcFTOtYy/ekv/dxrwvvQbBzAetE6ey/SgO13Cj/0HOWjodlt0PocwvvOOzfTtDssaRScdSuw0W/QruOBDefxDMsBW0n7UUjvwp+AvbMJZwwrFkIuNM6zSfvb1fN9o9CRy8UsYXKQ8Sm2ltZ4l3l3lKBGJLgqSaQzprRYv9+oUJ5qadTR6Y+BOY9S7sewpgwgcPwR0HwcIbYdeWDh1bpzNN+OxVejw4hdmB29nDUwklA+Gkv8NFr0H5AZFNWz6fzG/8NuWwuqQokNl+0NI4MsMsZI+7ViqmLYuWQEvzLWVai2Qqfs7JZG5pCXQ3z4dl+1rXGzPutfrG7FwPz18Gdx8On8x3TbmppA154239wiohNecI+GIRePzsOOgyjm74E081TQQHZ+62pW63W/iiyhQ2hcI0Nh8Lkl2TdZfyqm7l3Cte6VB2rTcnZ+ol43NZpkg8+z1v74CTnZnTXYJBqYKodqaAGzPxi5MEqjMtD5IwA33PI+CSN7mv79VsNPtQUl8BL1wBdx0C/5kHwfq047LHUtuJ5UFqk5TkKGpuzhQZS81mWHgT/GU/eOtP0FQHQybAhQutoP1ue7TDWHLfp2LqU6d4fqI61tE/sy4u09rJJTb8kUaMscGdSH1XnRxKN1Ic8/ec/Lyls1a0JJpbWx07dtsDvj8PLnwVhnzbmlf/769w+xgreF1T2aFj7HCmCZ8ttPo+PHwqgU0fUGsWMIdT4cfL4MCzWqVetXw+md/wzaWOf6Z9G1pu0Gd2Luz3RfWEcUHzrdjznkzLgzQHKsJhTLOlqakbyyGKdJb4OSeTuaXlOVHzoWHAfs1z6Hd/BYW9YfPH8NiZVvB65dOOz7xOdu0RUbESnroQ/nawVUIKYMz34fKlNBx9E7soojYYwnRwkN5ePerG6+RMtfQ3MGNuACdb/dpVa3t3Fe6L+Em7CIbdexIXiBqzGzJF4tnveaZNZTJV1E0zrRNd1EWyYl14MC5Mlm2b4cVbS+ZD3Pvi8fJKwTRubRjL04d8zH5f3APbvoQXr4LFt8C3fwQHnQc9+iZ+3TxkWtcnyZi3Mz967vgU/nWX1TCsqTnwPnB/OOpnMOq4ds1ySDaWTGSaaW1/9gGfJ+bvuDAu+9LOVg74nDt/+5NlWttL5V14w1QkV8lWzcSL/1vvKInms6Srl4YcAhcsgE9fgTd+Bxvet4LX79wNY06DQy+zMuzcoqnBas779p1WMAXAV8iWvX/AtPcOwldaxiUFJQmfmsvnk0sd/1QryaJlWwrN3zwGtzTfiimLluExwx8VqAhF9VRwY5KOSGeJn3MymVtSrgzyF8HhV8JB58D/3QFL/wGVH8FTF0Cf38L4H8IBZ2bdX6YzJFqJRDgMXy6Gd+fAZ6+0PL7XVJh0AwwcA0BRfRCw7ok2NIUduyK8LatH3aKlv0E48pkahtU3whaTad1N4idupaB1N9Xk4uVyMY0YXRiUtEuytHdplkjQ2gUXIu3BvjuaqjxIsoZXThadhZcoCw5SZ+mne18aCFCx94Xs970rYPn9sOQu2PmNVVbj9d/BPtPh4Atg6Ldjgr6pXrejJKxpHaxjbNWrPOx/hMM//qjl8fKD4MjrYOTUDlmSl6y+diYyrWltfy9+G3ufsMcQWe7s4PJOdoDGzgq3ZVvfVaQrSHYDMl5nZFqbppmwCZP9/8GQSTAUjr05ahgwapo1v362EN78A6x/zyob8sFDVh3TA38A+5xoBSucaMvn1jHvg0egtrnESaAEDjoXDvsxa7YVsOW9JQxv588nt0xrX0Y/pz7B55iKfe7gluy6mEzrDI93kVU+4XBMI2Anl9MSybf4uS2TuSWj64Ki3WDyTXD4FfDuP+Cdv8O2L2DBdfDar2Hs6XDAWVb/AIecF8Y0Xt+1xUqMee8ea9wAGLDvdPjO1bD72JjnRgdB64MhxwaFu0Mjxpb+Bib1zUH64rgbttHnY7oucTZHBK3vuusubrvtNioqKhg7dix33nkn48ePz/ewurQmF2daR2dLuDPT2i4P0s6Z1t2tPEjKTOvcm+blW3T9uGQZuv4Un3Hq9yUq8FpQAofOsrIdVjwF795tNRNZ8YT1tdue1hK/MTNgwN6d1iAsmv079PCZ8MViWPUcrHyWKQ07wAthPHj2OQG+fZlVDqQDTziybXgVLXo/TFnPtnm7VpnlcdnzkRqpDr7pGJ3tFi2Sad1N5ikRyKKufYa1jNuiMRSOBFKT1dquC4YSnx8aBoycYn19vdS66fnxv6yGjV+9BS/9FPY72br5OWwiePN8mVG9CVY9by3hXrek5fGe5TDhEhh3HhT1BqC2YjOQ2Rzd8ZnW1nufrhyXfTzO9LhkHzOcGkiJF3uzPrtGjPbNl5bHdcwRSaYw7tohk7kl/jkpFe0GR11nXXd8+Cgs/SdsWW0Fg9+7B/qNsgLY+5yU9+aFTbU7OcHzNj/6Zhn86T0IN1/3FJTC2DOsY0eSMfq8HgJeD42hMLWNIXoXd+LAs1CX5bHDjaLLFNYGE8cFov+tmLWz5T1o/fjjj3PNNdcwe/ZsJkyYwO23387UqVNZvXo1AwYMyPfwuqzGJufXRE0mOrvQjXcI7RPv9r5hYL8X3SUYlOrisS1ZsfkWXT8uviSI32sQDJmpGzHGZeVGS7jkzeuHA86wvr5ZDv+516o5t30NvPVH66vvt9i333eY6BnIjsZOuqFYU8mQb+bzJ/8Cjv3iA/i0uuVbReXcU/1tNu45g9+fdnynDCfbjLZoyRosJtuudTmU2H096IKeBPY81xhf01pBa+mGEmU0J2LP/x2ZaW1nHFk/r2UsBT4PHgPCJtQ3higt9Kd+oSHjra+qr63s5Q8egqp1sOw+66uoj1WmacTRMPwo6NGvI36dWOEwVPzXaor1+SJY+zbQfOPM8MBeU6zM6r2mtAqo16WrYwoUN38+CbPRkwg11zX1ZHFFbK+uqU9X0zrr8iCerLbPt9hGjBkGraOWhEeXp3Jjko5IZymOO8/MZG6Jf05GCkpg/MVwyEWw5g1Y/oDVpHHLalj0S+ur/2gYdSyMmASDD2lTE/WMmCZs/Ry+eA0+XcBFX76FLxAE+7Kj/ECrzMmY06zxp1EU8NJYF+7wMl9t4ebr5Ey1XIeYSfdnlQdxj7wHrf/85z9z8cUXc/755wMwe/Zs5s+fz7333svPfvazPI+u67KXbLvxJC66kYwbJ1v7PW/vgJM98brxRkQuUtVYbktWbL7Zv5fHiK3fDlawORhqSt2IsTlDK9X7kvSifNBB1texv4fVL1vB688WwtbPGbz1cx4MQLDKB/ccaGU2DxoHA/aGvt+ygt+5CtZB5cew8QPY8IGVwbf5Y6YAeIEQUNzXCoCM+T6vVg3n9if+y3fMTgiCNLPfu1wy1Lweg4DPQ2NTOHXAys60TpIJYI/BDY2l0mdaO3fsIu0t4xtXGTbgaws748jvNWLmEMMwKPJ72dUYyu7n9x5iZdAd8VP46k346Fn4+AWo3dpSPgSgbAwMPtg6xpQfBP32Al9B7r+IacKuzVC5Ctb/p/lrqfVzow06GPY7BfY9GUrLk75cXZJMrGiFUSuhkmajxwmFsy/HV5ThyqaWZmkZBq2bx+CWhI9Me3lEi26+ZZdC9BgKSIikEr+iL5O5JWkPnUwYhnUzc/hRUL/DWhGz4ilY+3+w+RPr699/AW/AClwPPsSqGz1wDPQZkfsqHtOEmk2waSVs+sgqc7XuHetY0swHrAmXsXHI8Rx28mXWsSoLRX4vO+qCndoDKFtuvk7OVMt1SLglaJ0kKQiyu7EsnS+vQevGxkaWLVvGz3/+88hjHo+HyZMns2TJkhTPlNUV1XxSsTPn539RuQtwdqZeMv4EDcrcxJ5E27u+nv1eZFM30c3sA091QxPPf/BNzPe+2rorZhs3ic62jW+UVOT3Ul3flLoRY/MB+Kutu1q9LzUNwZifkVSgh1UWZMwM62TyyzfY9uF8gp8soMyosk7y1r8X2Txs+NjVYwh1RQOpLyqjvrA/Tb4SmnzFhLyFYIJhNuExm/AHqwk0VhFo2E5R3UZ67FpHUX1lwmGs9Y/gf+tG0WP/EzhzxkzwWOMuXFkBwIaqula/Y0dZs6V5n8rxYr/I76UxTVOWpJnWzf9ubArz3PvfsH1XI+Dsmtb2sWX5uu30KGj5fVZttI5bDo63i7S76L/71HOA9YexfN12+n4Q6JCxbK5uSDqOooAVtH55ZQXlvXPJbtsLhvwPxqBr6Lv1P5RtepP+lUvotXM1bFphfS2bB4CJh9ricnaV7EFdYRkNBX1oKOhLk6+EsMdP2OsHE7yheryhevxNNRTUb6awfjNFdRWU1KwhEGx9HtzkLWJz/29TOeA7bCo7groeg6xvfGkCyY8X7365Len7Ygt4W7LRn3//G0qL0t+s/WKzdezIpl6mPYZl66rYrUfiMYdNM3IDM/PyIJ6Y13e66N8r03Nm++ZAxc56XlqxMavninRXdqmoip31PPv++ozmFvt8+P11VW0/F/dMgrGT8O29k7LKtxhY8Tr9tiylsH6zFche+3+RTcOGj7qi3aktLqeuaHcaA70I+ktp8pdgYlgBcdPEF6rFH6zBF6ymsL6S4toNFNduwN9U3erHhzwBtu82lk0Dj+DeytE8/XUxPxk+isOyDFhDy/uycNUmvthck/t70oFWfrMDcOd1cqbsa+VPKqojZbqSXV8BOPiSSshz0HrLli2EQiHKyspiHi8rK+OTTz5ptX1DQwMNDQ2Rf+/cmXvQ1u0Wrqrgj//7aZtfxy0nrtEK7OCs4Z5skWj2e17oa9+x92xeyhvdFbcrKymwpq/GpjBXPvZB4m0K876YJGs9m8ecaOw9C31UVjdQ6E/+Gdv7wUcbdrbP+1LYC/Y5kZqyyRzx4QkMNSoZZ3zKwZ5P2cezlr2M9ZRQT8+aNfSsWZP568bZZpawMrwnK809+TA8nKXh0Wyvt7qK/7T/qEjAGqC0efxfbtmV9HfsKPZ+l62ehT521AXpWZA8yJHssy8OeO1zcK56vOX3TbUf5Js9zz21bD1PLVvf6vsF7Tz/iTiZ/bed7rzFnr9f/O9GXvzvxo4dU4K5rGehny01jfx+Qetz8OwVAlOAKfRjB+M9H7O/50vGGl+yr2cNpUYdPWrX06O29fyQqbBpsM4cwApzT94P78UH4RGsMIcT3OWDrwA2N39lLtH7YjMMg56FfnbUBbnh+Y+SbpdIIItzM3t/eeHDDbzw4YaU2xpG5jdT7Qv0VL+jkxT5vXg9BqGwSWGG75/9O35eWcPNL6wCyPi5It2VfV69fnsdVz/+IZB+brGPV/NXbGT+ivY8Xg0CzgLOZJhRwQTPJ+xnrGFfz1eMNr6mmAZ61H5Nj9qvc3r1kGmwxtydT8yhfBQextLwKFaYw2ms9cfc18z1GtKev/+66LOcnt+Z3HidnCn7OuS1Typ57RMrOar19ZUvciM6fnWzOIur9tRbb72VX/7yl/kehiMM3q2Yw7/Vt02v0bsowPH7795OI+o8vYr8XD15JD0KvI5eHp/MKQcOYv22Wk4fP6RdX3fiXv047eDBHL9/8uWvXUnfkgKunLQX/1m7LeH3B/Qs5MiR/Tt5VG03qqwn5x02jDGDerX63jXfHcXbX2xh7ODeSZ9/xMj+nHLgIDZV1yf8/rihu9GvJPsl2UP6FPHDI0bw0YZ+VLIPLwEvAZgmfUObGdj0DbuFttAntIXe4e0UhOsoNOsoMBsI4yGMl7DhodboQbW3lGpPKVXevlT4yqn0llPj6RnTBWOf5v+WFvo56YDYffrgYX2YefAQ1lfVZv17tEX/kgKOGpVbr4Vrp4xi+brt7FtemnSbSaPLmDFuMKccNCjm8UK/l59NG82bn7UEX/bo2yPhPuIU5x46jKraIA1NrZdHegyD8w8f1vmDEsmT3sUBrp48kuJA6vOW0w8ZwrpttR3e8NbA4NRxg1o9fs13R/L4e19jYiZ4Vlv0ZQfDeQt4C8A06RXezsCmbxjYtIHeoa2UhnfQK7SdArMevxnEa1rvQdAI0OApoN4oosrbhypPH7Z7+7LRN5gK/2CCRktGehHQlq4LBT4v5x42LOU2Pzt2NC/+N3UgOZ7HMDhrwtCMtz9zwlDWb6+LlCxJ5Yi9+mecgHLM6AHMGDeYGeMGZzyWfPJ4DH5x3N5U1TbSN8PzlsNG9OPUgwZTsbMu8tjxY7rHebFIrsYO7s2ZE4aytnmlKqSfW047eAhrtuzq4ONVP9azH+uBBYBhhtgttJUBoU30a6qgT2grxeEaepg1FIVrMTAxmo9fdUYRdZ5i6owebPf2Zat3AFt8/an07U7QaJlPCoFD4n5q7+IAx4/JLUby42P24oElXxE22/s42r56Fvg55UB3HAtyccqBg1hdsZOaBmv/9Ho8/HDi8JhtvB6DXxy/D9X1QXoXd8zqNmkfhmnm7y+qsbGR4uJinnrqKaZPnx55/Nxzz6Wqqornn38+ZvtEmdZDhgxhx44dlJYmDwSIiIiIiIiIiIiISP7s3LmTXr16ZRTLzWuaaiAQYNy4cSxatCjyWDgcZtGiRRx66KGtti8oKKC0tDTmS0RERERERERERES6jryXB7nmmms499xzOfjggxk/fjy33347u3bt4vzzz8/30ERERERERERERESkk+U9aD1z5kw2b97MjTfeSEVFBQcccAALFixo1ZxRRERERERERERERLq+vNa0bqts6qCIiIiIiIiIiIiISH64pqa1iIiIiIiIiIiIiEg0Ba1FRERERERERERExDEUtBYRERERERERERERx1DQWkREREREREREREQcQ0FrEREREREREREREXEMBa1FRERERERERERExDEUtBYRERERERERERERx1DQWkREREREREREREQcQ0FrEREREREREREREXEMBa1FRERERERERERExDEUtBYRERERERERERERx1DQWkREREREREREREQcQ0FrEREREREREREREXEMBa1FRERERERERERExDEUtBYRERERERERERERx1DQWkREREREREREREQcQ0FrEREREREREREREXEMBa1FRERERERERERExDEUtBYRERERERERERERx1DQWkREREREREREREQcQ0FrEREREREREREREXEMBa1FRERERERERERExDEUtBYRERERERERERERx1DQWkREREREREREREQcQ0FrEREREREREREREXEMBa1FRERERERERERExDEUtBYRERERERERERERx1DQWkREREREREREREQcw5fvAbSFaZoA7Ny5M88jEREREREREREREZFk7BiuHdNNxdVB6+rqagCGDBmS55GIiIiIiIiIiIiISDrV1dX06tUr5TaGmUlo26HC4TAbNmygZ8+eGIaR7+F0up07dzJkyBC+/vprSktL8z0c6Sa030m+aN+TfNB+J/mifU/yQfud5Iv2PckH7XeSL9153zNNk+rqasrLy/F4UletdnWmtcfjYfDgwfkeRt6VlpZ2u51c8k/7neSL9j3JB+13ki/a9yQftN9Jvmjfk3zQfif50l33vXQZ1jY1YhQRERERERERERERx1DQWkREREREREREREQcw3vzzTffnO9BSO68Xi9HHXUUPp+rK72Iy2i/k3zRvif5oP1O8kX7nuSD9jvJF+17kg/a7yRftO+l5+pGjCIiIiIiIiIiIiLStag8iIiIiIiIiIiIiIg4hoLWIiIiIiIiIiIiIuIYClqLiIiIiIiIiIiIiGMoaC0iIiIiIiIiIiIijqGgtYPdddddDBs2jMLCQiZMmMDSpUtTbv/kk08yevRoCgsLGTNmDC+99FInjVS6iltvvZVDDjmEnj17MmDAAKZPn87q1atTPue+++7DMIyYr8LCwk4asXQVN998c6v9aPTo0SmfozlP2mrYsGGt9jvDMJg1a1bC7TXfSa7efPNNTjjhBMrLyzEMg+eeey7m+6ZpcuONN7L77rtTVFTE5MmT+eyzz9K+brbnitK9pNrvgsEg1113HWPGjKFHjx6Ul5dzzjnnsGHDhpSvmcvxWrqfdHPeeeed12o/mjZtWtrX1ZwnqaTb7xKd8xmGwW233Zb0NTXnSTqZxFDq6+uZNWsWffv2paSkhFNPPZVNmzalfN1czw27GgWtHerxxx/nmmuu4aabbmL58uWMHTuWqVOnUllZmXD7t99+mzPOOIMLL7yQ999/n+nTpzN9+nRWrlzZySMXN3vjjTeYNWsW77zzDgsXLiQYDDJlyhR27dqV8nmlpaVs3Lgx8rV27dpOGrF0Jfvuu2/MfvTvf/876baa86Q9vPfeezH73MKFCwH4/ve/n/Q5mu8kF7t27WLs2LHcddddCb//hz/8gTvuuIPZs2fz7rvv0qNHD6ZOnUp9fX3S18z2XFG6n1T7XW1tLcuXL+eGG25g+fLlPPPMM6xevZoTTzwx7etmc7yW7indnAcwbdq0mP3o0UcfTfmamvMknXT7XfT+tnHjRu69914Mw+DUU09N+bqa8ySVTGIoV199NS+88AJPPvkkb7zxBhs2bOCUU05J+bq5nBt2SaY40vjx481Zs2ZF/h0Khczy8nLz1ltvTbj9aaedZh5//PExj02YMMG85JJLOnSc0rVVVlaagPnGG28k3WbevHlmr169OnFU0hXddNNN5tixYzPeXnOedIQrr7zSHDFihBkOhxN+X/OdtAfAfPbZZyP/DofD5sCBA83bbrst8lhVVZVZUFBgPvroo0lfJ9tzRene4ve7RJYuXWrnC+00AAAK6klEQVQC5tq1a5Nuk+3xWiTRvnfuueeaJ510UlavozlPspHJnHfSSSeZxxxzTMptNOdJtuJjKFVVVabf7zeffPLJyDYff/yxCZhLlixJ+Bq5nht2Rcq0dqDGxkaWLVvG5MmTI495PB4mT57MkiVLEj5nyZIlMdsDTJ06Nen2IpnYsWMHAH369Em5XU1NDXvssQdDhgzhpJNO4qOPPuqM4UkX89lnn1FeXs7w4cM566yzWLduXdJtNedJe2tsbOShhx7iggsuwDCMpNtpvpP2tmbNGioqKmLmtF69ejFhwoSkc1ou54oi6ezYsQPDMOjdu3fK7bI5Xosk8/rrrzNgwABGjRrFpZdeytatW5NuqzlP2tumTZuYP38+F154YdptNedJNuJjKMuWLSMYDMbMX6NHj2bo0KFJ569czg27KgWtHWjLli2EQiHKyspiHi8rK6OioiLhcyoqKrLaXiSdcDjMVVddxeGHH85+++2XdLtRo0Zx77338vzzz/PQQw8RDoc57LDDWL9+fSeOVtxuwoQJ3HfffSxYsIC7776bNWvWMHHiRKqrqxNurzlP2ttzzz1HVVUV5513XtJtNN9JR7DnrWzmtFzOFUVSqa+v57rrruOMM86gtLQ06XbZHq9FEpk2bRoPPPAAixYt4ve//z1vvPEGxx57LKFQKOH2mvOkvd1///307NkzbYkGzXmSjUQxlIqKCgKBQKsbwunie/Y2mT6nq/LlewAi4kyzZs1i5cqVaWt2HXrooRx66KGRfx922GHsvffezJkzh1//+tcdPUzpIo499tjI/++///5MmDCBPfbYgyeeeCKjDAiRtpo7dy7HHnss5eXlSbfRfCciXVEwGOS0007DNE3uvvvulNvqeC3t4fTTT4/8/5gxY9h///0ZMWIEr7/+OpMmTcrjyKS7uPfeeznrrLPSNtTWnCfZyDSGIplTprUD9evXD6/X26qb6KZNmxg4cGDC5wwcODCr7UVSufzyy3nxxRdZvHgxgwcPzuq5fr+fAw88kM8//7yDRifdQe/evRk5cmTS/UhznrSntWvX8uqrr3LRRRdl9TzNd9Ie7Hkrmzktl3NFkUTsgPXatWtZuHBhyizrRNIdr0UyMXz4cPr165d0P9KcJ+3prbfeYvXq1Vmf94HmPEkuWQxl4MCBNDY2UlVVFbN9uvievU2mz+mqFLR2oEAgwLhx41i0aFHksXA4zKJFi2IyvKIdeuihMdsDLFy4MOn2IomYpsnll1/Os88+y2uvvcaee+6Z9WuEQiFWrFjB7rvv3gEjlO6ipqaGL774Iul+pDlP2tO8efMYMGAAxx9/fFbP03wn7WHPPfdk4MCBMXPazp07effdd5POabmcK4rEswPWn332Ga+++ip9+/bN+jXSHa9FMrF+/Xq2bt2adD/SnCftae7cuYwbN46xY8dm/VzNeRIvXQxl3Lhx+P3+mPlr9erVrFu3Lun8lcu5YVflvfnmm2/O9yCktdLSUm644QaGDBlCQUEBN9xwAx988AFz586lpKSEc845h6VLl0YKsw8aNIjrr7+eHj160KdPH/72t7/x+OOPM3fuXAYMGJDn30bcYtasWTz88MM89dRTlJeXU1NTQ01NDV6vF7/fD9Bq3/vVr35FQ0MDhmGwZs0arr32Wt59913mzJlD//798/nriItce+21FBQUALBq1Sp+9KMfUVlZyezZs+nRo4fmPOkw4XCY8847j7PPPpspU6bEfE/znbSXmpoaVq1aRUVFBXPmzGHChAkUFRXR2NhI7969CYVC3HLLLeyzzz40NjZyxRVXUFtby5133onPZ1XzmzRpEtXV1YwfPx5If64okmq/Ky4uZsaMGSxbtoynn36a4uLiyHlfIBDA6/UCrfe7dMdrEUi973m9Xn7xi19QWlpKU1MTy5Yt48ILL6SkpIQ//elPmvMkZ6n2u169egFW4O+CCy7g5z//OQcffHCr19CcJ9lKF0MpLCxkw4YN/O1vf+OAAw5g27ZtXHLJJQwZMoSbbrop8jqjR49m0KBB7L333hiGkdG5YbdgimPdeeed5tChQ81AIGCOHz/efOeddyLfO/LII81zzz03ZvsnnnjCHDlypBkIBMx9993XnD9/fiePWNwOSPg1b968yDbx+95VV10V2U/LysrM4447zly+fHnnD15cbebMmebuu+9uBgIBc9CgQebMmTPNzz//PPJ9zXnSUV555RUTMFevXt3qe5rvpL0sXrw44fHV3r/C4bB5ww03mGVlZWZBQYE5adKkVvvkHnvsYd50000xj6U6VxRJtd+tWbMm6Xnf4sWLI68Rv9+lO16LmGbqfa+2ttacMmWK2b9/f9Pv95t77LGHefHFF5sVFRUxr6E5T7KV7lhrmqY5Z84cs6ioyKyqqkr4GprzJFuZxFDq6urMyy67zNxtt93M4uJi8+STTzY3btzY6nWin5PJuWF3YJimaXZcSFxEREREREREREREJHOqaS0iIiIiIiIiIiIijqGgtYiIiIiIiIiIiIg4hoLWIiIiIiIiIiIiIuIYClqLiIiIiIiIiIiIiGMoaC0iIiIiIiIiIiIijqGgtYiIiIiIiIiIiIg4hoLWIiIiIiIiIiIiIuIYClqLiIiIiHSS119/HcMwqKqqyvdQREREREQcS0FrEREREZF2YBhGyq+bb76Zww47jI0bN9KrV698D1dERERExLEM0zTNfA9CRERERMTtKioqIv//+OOPc+ONN7J69erIYyUlJZSUlORjaCIiIiIirqJMaxERERGRdjBw4MDIV69evTAMI+axkpKSVuVB7rvvPnr37s2LL77IqFGjKC4uZsaMGdTW1nL//fczbNgwdtttN6644gpCoVDkZzU0NHDttdcyaNAgevTowYQJE3j99dfz9JuLiIiIiLQvX74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\n",
       "text/plain": [
        "<Figure size 1800x1000 with 1 Axes>"
       ]
@@ -167,11 +167,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -196,7 +192,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/examples/DataVisualization/Live Output with C Solver.ipynb b/examples/DataVisualization/Live Output with C Solver.ipynb
new file mode 100644
index 000000000..3d09b50dd
--- /dev/null
+++ b/examples/DataVisualization/Live Output with C Solver.ipynb	
@@ -0,0 +1,265 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LiveOutput during solver runtime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "import numpy\n",
+    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../../')))\n",
+    "import gillespy2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**From:** Vilar, José M. G. et al. “Mechanisms of noise-resistance in genetic oscillators.” PNAS, vol. 99 no. 9, 2002, pp. 5988-5992., doi.org/10.1073/pnas.092133899."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Instantiation\n",
+    "\n",
+    "Model must include rates, species, and reactions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class VilarOscillator(gillespy2.Model):\n",
+    "    def __init__(self, parameter_values=None):\n",
+    "        gillespy2.Model.__init__(self, name=\"VilarOscillator\")\n",
+    "        self.volume = 1\n",
+    "\n",
+    "        # Parameters\n",
+    "        alphaA = gillespy2.Parameter(name=\"alphaA\", expression=50)\n",
+    "        alphaA_prime = gillespy2.Parameter(name=\"alphaA_prime\", expression=500)\n",
+    "        alphaR = gillespy2.Parameter(name=\"alphaR\", expression=0.01)\n",
+    "        alphaR_prime = gillespy2.Parameter(name=\"alphaR_prime\", expression=50)\n",
+    "        betaA = gillespy2.Parameter(name=\"betaA\", expression=50)\n",
+    "        betaR = gillespy2.Parameter(name=\"betaR\", expression=5)\n",
+    "        deltaMA = gillespy2.Parameter(name=\"deltaMA\", expression=10)\n",
+    "        deltaMR = gillespy2.Parameter(name=\"deltaMR\", expression=0.5)\n",
+    "        deltaA = gillespy2.Parameter(name=\"deltaA\", expression=1)\n",
+    "        deltaR = gillespy2.Parameter(name=\"deltaR\", expression=0.2)\n",
+    "        gammaA = gillespy2.Parameter(name=\"gammaA\", expression=1)\n",
+    "        gammaR = gillespy2.Parameter(name=\"gammaR\", expression=1)\n",
+    "        gammaC = gillespy2.Parameter(name=\"gammaC\", expression=2)\n",
+    "        thetaA = gillespy2.Parameter(name=\"thetaA\", expression=50)\n",
+    "        thetaR = gillespy2.Parameter(name=\"thetaR\", expression=100)\n",
+    "        \n",
+    "        self.add_parameter([alphaA, alphaA_prime, alphaR, alphaR_prime, betaA, betaR,\n",
+    "                            deltaMA, deltaMR, deltaA, deltaR, gammaA, gammaR, gammaC,\n",
+    "                            thetaA, thetaR])\n",
+    "\n",
+    "        # Species\n",
+    "        Da = gillespy2.Species(name=\"Da\", initial_value=1, mode=\"discrete\")\n",
+    "        Da_prime = gillespy2.Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\")\n",
+    "        Ma = gillespy2.Species(name=\"Ma\", initial_value=0, mode=\"discrete\")\n",
+    "        Dr = gillespy2.Species(name=\"Dr\", initial_value=1, mode=\"discrete\")\n",
+    "        Dr_prime = gillespy2.Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\")\n",
+    "        Mr = gillespy2.Species(name=\"Mr\", initial_value=0, mode=\"discrete\")\n",
+    "        C = gillespy2.Species(name=\"C\", initial_value=0, mode=\"discrete\")\n",
+    "        A = gillespy2.Species(name=\"A\", initial_value=0, mode=\"discrete\")\n",
+    "        R = gillespy2.Species(name=\"R\", initial_value=0, mode=\"discrete\")\n",
+    "        \n",
+    "        self.add_species([Da, Da_prime, Ma, Dr, Dr_prime, Mr, C, A, R])\n",
+    "\n",
+    "        # Reactions\n",
+    "        r1 = gillespy2.Reaction(name=\"r1\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=\"gammaC\")\n",
+    "        r2 = gillespy2.Reaction(name=\"r2\", reactants={'A': 1}, products={}, rate=\"deltaA\")\n",
+    "        r3 = gillespy2.Reaction(name=\"r3\", reactants={'C': 1}, products={'R': 1}, rate=\"deltaA\")\n",
+    "        r4 = gillespy2.Reaction(name=\"r4\", reactants={'R': 1}, products={}, rate=\"deltaR\")\n",
+    "        r5 = gillespy2.Reaction(name=\"r5\", reactants={'A': 1, 'Da': 1}, products={'Da_prime': 1}, rate=\"gammaA\")\n",
+    "        r6 = gillespy2.Reaction(name=\"r6\", reactants={'Da_prime': 1}, products={'A': 1, 'Da': 1}, rate=\"thetaA\")\n",
+    "        r7 = gillespy2.Reaction(name=\"r7\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=\"alphaA\")\n",
+    "        r8 = gillespy2.Reaction(name=\"r8\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=\"alphaA_prime\")\n",
+    "        r9 = gillespy2.Reaction(name=\"r9\", reactants={'Ma': 1}, products={}, rate=\"deltaMA\")\n",
+    "        r10 = gillespy2.Reaction(name=\"r10\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=\"betaA\")\n",
+    "        r11 = gillespy2.Reaction(name=\"r11\", reactants={'A': 1, 'Dr': 1}, products={'Dr_prime': 1}, rate=\"gammaR\")\n",
+    "        r12 = gillespy2.Reaction(name=\"r12\", reactants={'Dr_prime': 1}, products={'A': 1, 'Dr': 1}, rate=\"thetaR\")\n",
+    "        r13 = gillespy2.Reaction(name=\"r13\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=\"alphaR\")\n",
+    "        r14 = gillespy2.Reaction(name=\"r14\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=\"alphaR_prime\")\n",
+    "        r15 = gillespy2.Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=\"deltaMR\")\n",
+    "        r16 = gillespy2.Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=\"betaR\")\n",
+    "        \n",
+    "        self.add_reaction([r1, r2, r3, r4, r5, r6, r7, r8, r9,\n",
+    "                           r10, r11, r12, r13, r14, r15, r16])\n",
+    "\n",
+    "        # Timespan\n",
+    "        self.timespan(numpy.linspace(0,1000,1001))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = VilarOscillator()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run Model with live_output\n",
+    "\n",
+    "In this example, live_output is set to \"graph\" so that we may view the simulation progress as it runs. We will also be making use of the pause and resume feature so in this example, we set t=200 which specifies the point at which the solver ends. By default, if t is not set, the solver will run for the entire timespan. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from gillespy2 import SSACSolver\n",
+    "\n",
+    "ssa_solver = SSACSolver(model=model, variable=True)\n",
+    "results1 = model.run(solver=ssa_solver,live_output=\"graph\",t=200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Resume the simulation\n",
+    "\n",
+    "Using the results from the previous simulation, we will run the simulation to t=300 using the basicTauLeapingSolver. For this simulation, we will instead use live_output = \"text\" so that we may view exact species population data. Additionally, we pass a dictionary to live_output_options with the interval set to 2 seconds clock time. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time      |A         |C         |Da        |Da_prime  |Dr        |Dr_prime  |Ma        |Mr        |R         |\n",
+      "100.0     |0.0       |417.0     |1.0       |0.0       |1.0       |0.0       |0.0       |22.0      |1635.0    |\n"
+     ]
+    }
+   ],
+   "source": [
+    "from gillespy2 import TauLeapingCSolver\n",
+    "\n",
+    "solver = TauLeapingCSolver(model=model, variable=True)\n",
+    "results2 = model.run(solver=solver, live_output=\"text\", live_output_options={\"interval\":2}, resume=results1, t=300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finish the simulation\n",
+    "\n",
+    "Lastly, we will finish the remainder of the timespan using the NumpyODE solver. The ODE solver is very fast and so we set live_output = \"progress\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "progress = 100 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "results3 = model.run(solver=ssa_solver, live_output=\"progress\", live_output_options={\"interval\":0.2}, resume=results2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### One last note\n",
+    "By default, live_output = \"progress\" or \"graph\" animate by clearing the IPython display. This can also result in error messages and warnings being cleared. To disable this behavior, set live_output_options={\"clear_output\":False}."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results3.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/DataVisualization/LiveOutput.ipynb b/examples/DataVisualization/Live Output with Python Solvers.ipynb
similarity index 100%
rename from examples/DataVisualization/LiveOutput.ipynb
rename to examples/DataVisualization/Live Output with Python Solvers.ipynb
diff --git a/gillespy2/__version__.py b/gillespy2/__version__.py
index 612e979af..38a796ba0 100644
--- a/gillespy2/__version__.py
+++ b/gillespy2/__version__.py
@@ -24,7 +24,7 @@
 # =============================================================================
 
 
-__version__      = '1.6.6'
+__version__      = '1.6.7'
 
 __title__        = 'GillesPy2'
 __description__  = 'Python interface for Gillespie-style biochemical simulations'
diff --git a/gillespy2/core/assignmentrule.py b/gillespy2/core/assignmentrule.py
index bbf4e79fe..99131fc45 100644
--- a/gillespy2/core/assignmentrule.py
+++ b/gillespy2/core/assignmentrule.py
@@ -16,6 +16,8 @@
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 """
 
+import uuid
+
 from gillespy2.core.sortableobject import SortableObject
 from gillespy2.core.jsonify import Jsonify
 
@@ -36,9 +38,12 @@ class AssignmentRule(SortableObject, Jsonify):
     """
 
     def __init__(self, variable=None, formula=None, name=None):
+        if name in (None, ""):
+            self.name = f'ar{uuid.uuid4()}'.replace('-', '_')
+        else:
+            self.name = name
         self.variable = variable
         self.formula = formula
-        self.name = name
 
     def __str__(self):
         return self.variable + ': ' + self.formula
diff --git a/gillespy2/core/events.py b/gillespy2/core/events.py
index c7c3f0a2e..9954a510d 100644
--- a/gillespy2/core/events.py
+++ b/gillespy2/core/events.py
@@ -16,6 +16,8 @@
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 """
 
+import uuid
+
 from gillespy2.core.gillespyError import *
 from gillespy2.core.jsonify import Jsonify
 
@@ -39,7 +41,12 @@ class EventAssignment(Jsonify):
 
     """
 
-    def __init__(self, variable=None, expression=None):
+    def __init__(self, name=None, variable=None, expression=None):
+
+        if name in (None, ""):
+            self.name = f'evn{uuid.uuid4()}'.replace('-', '_')
+        else:
+            self.name = name
 
         self.variable = variable
         self.expression = expression
diff --git a/gillespy2/core/functiondefinition.py b/gillespy2/core/functiondefinition.py
index fd18efd32..fb48e266e 100644
--- a/gillespy2/core/functiondefinition.py
+++ b/gillespy2/core/functiondefinition.py
@@ -16,6 +16,8 @@
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 """
 
+import uuid
+
 from gillespy2.core.sortableobject import SortableObject
 from gillespy2.core.jsonify import Jsonify
 
@@ -38,7 +40,10 @@ def __init__(self, name="", function=None, args=[]):
         if function is None:
             raise TypeError("Function string provided for FunctionDefinition cannot be None")
 
-        self.name = name
+        if name in (None, ""):
+            self.name = f'fd{uuid.uuid4()}'.replace('-', '_')
+        else:
+            self.name = name
         self.function_string = function
         self.args = args
 
diff --git a/gillespy2/core/gillespySolver.py b/gillespy2/core/gillespySolver.py
index 4e9f6b0f2..5926d341f 100644
--- a/gillespy2/core/gillespySolver.py
+++ b/gillespy2/core/gillespySolver.py
@@ -61,19 +61,16 @@ def get_solver_settings(self):
 
         raise SimulationError("This abstract solver class cannot be used directly")
 
-    def get_increment(self, model, increment):
+    def get_increment(self, increment):
         """
         Set the default increment value if it was not provided
 
-        :param model: The model on which the tspan can be found.
-        :type model: gillespy.Model
-
         :param increment: The current value of increment.
         :type increment: int
         """
         if increment is None:
-            return model.tspan[-1] - model.tspan[-2]
-        if model.user_set_tspan:
+            return self.model.tspan[-1] - self.model.tspan[-2]
+        if self.model.user_set_tspan:
             raise  SimulationError(
                 """
                 Failed while preparing to run the model. Both increment and timespan are set.
diff --git a/gillespy2/core/raterule.py b/gillespy2/core/raterule.py
index 3b30f5e01..87d02d2dc 100644
--- a/gillespy2/core/raterule.py
+++ b/gillespy2/core/raterule.py
@@ -16,6 +16,8 @@
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 """
 
+import uuid
+
 from gillespy2.core.sortableobject import SortableObject
 from gillespy2.core.jsonify import Jsonify
 
@@ -34,9 +36,12 @@ class RateRule(SortableObject, Jsonify):
     """
 
     def __init__(self, variable=None, formula='', name=None):
+        if name in (None, ""):
+            self.name = f'rr{uuid.uuid4()}'.replace('-', '_')
+        else:
+            self.name = name
         self.formula = formula
         self.variable = variable
-        self.name = name
 
     def __str__(self):
         try:
diff --git a/gillespy2/core/reaction.py b/gillespy2/core/reaction.py
index c2add42c4..9caba420e 100644
--- a/gillespy2/core/reaction.py
+++ b/gillespy2/core/reaction.py
@@ -77,7 +77,10 @@ def __init__(self, name="", reactants={}, products={}, propensity_function=None,
         """
 
         # Metadata
-        self.name = name
+        if name in (None, ""):
+            self.name = f'rxn{uuid.uuid4()}'.replace('-', '_')
+        else:
+            self.name = name
         self.annotation = ""
 
         # We might use this flag in the future to automatically generate
diff --git a/gillespy2/solvers/cpp/build/build_engine.py b/gillespy2/solvers/cpp/build/build_engine.py
index 91389f016..5a9423a59 100644
--- a/gillespy2/solvers/cpp/build/build_engine.py
+++ b/gillespy2/solvers/cpp/build/build_engine.py
@@ -110,14 +110,14 @@ def prepare(self, model: "Union[Model, template_gen.SanitizedModel]", variable=F
 
         # If a raw GillesPy2 model was provided, convert it to a sanitized model.
         if isinstance(model, gillespy2.Model):
-            model = template_gen.SanitizedModel(model)
+            model = template_gen.SanitizedModel(model, variable=variable)
         elif not isinstance(model, template_gen.SanitizedModel):
             raise TypeError(f"Build engine expected gillespy2.Model or SanitizedModel type: received {type(model)}")
 
         # Build the template and write it to the temp directory and remove the sample template_definitions header.
         template_file = self.template_dir.joinpath(self.template_definitions_name)
         template_file.unlink()
-        template_gen.write_definitions(str(template_file), model.get_template(variable=variable))
+        template_gen.write_definitions(str(template_file), model.get_template())
         custom_definitions = model.get_options()
         if custom_definitions is not None:
             options_file = self.template_dir.joinpath(self.template_options_name)
diff --git a/gillespy2/solvers/cpp/build/template_gen.py b/gillespy2/solvers/cpp/build/template_gen.py
index f09fee9b4..164977ee9 100644
--- a/gillespy2/solvers/cpp/build/template_gen.py
+++ b/gillespy2/solvers/cpp/build/template_gen.py
@@ -34,23 +34,32 @@ class SanitizedModel:
     :type model: gillespy2.Model
     """
     reserved_names = {
-        "vol": "V",
         "t": "t",
     }
 
-    def __init__(self, model: Model):
+    def __init__(self, model: Model, variable=False):
         self.model = model
+        self.variable = variable
 
         self.species: "OrderedDict[str, Species]" = OrderedDict()
         self.species_names = model.sanitized_species_names()
-        for species_name, sanitized_name in self.species_names.items():
+        self.species_id: "OrderedDict[str, int]" = OrderedDict()
+        for spec_id, spec_entry in enumerate(self.species_names.items()):
+            species_name, sanitized_name = spec_entry
             self.species[sanitized_name] = model.get_species(species_name)
+            self.species_id[species_name] = spec_id
 
         self.parameters: "OrderedDict[str, Parameter]" = OrderedDict()
-        self.parameter_names = model.sanitized_parameter_names()
+        self.parameter_names: "OrderedDict[str, str]" = OrderedDict()
+        self.parameter_names["vol"] = "P[0]" if variable else "C[0]"
+        self.parameter_id: "OrderedDict[str, int]" = OrderedDict()
+        for param_id, param_name in enumerate(model.listOfParameters.keys(), start=1):
+            if param_name not in self.parameter_names:
+                self.parameter_names[param_name] = f"P[{param_id}]" if variable else f"C[{param_id}]"
+                self.parameter_id[param_name] = param_id
         for parameter_name, sanitized_name in self.parameter_names.items():
             self.parameters[sanitized_name] = model.get_parameter(parameter_name) \
-                if parameter_name != "vol" else Parameter(name="V", expression=model.volume)
+                if parameter_name != "vol" else Parameter(name=sanitized_name, expression=str(model.volume))
 
         base_namespace = {
             # ORDER IS IMPORTANT HERE!
@@ -153,7 +162,7 @@ def use_rate_rule(self, rate_rule: "RateRule") -> "SanitizedModel":
                 log.warning(f"Could not sanitize rate rule formula expression: {rate_rule.formula}")
         return self
 
-    def get_template(self, variable=False) -> "dict[str, str]":
+    def get_template(self) -> "dict[str, str]":
         """
         Creates a dictionary of C++ macro definitions from the given model.
         The keys of the dictionary contain the name of the macro definition.
@@ -168,7 +177,7 @@ def get_template(self, variable=False) -> "dict[str, str]":
         results = dict({})
 
         # Get definitions for variables
-        parameter_definitions = template_def_variables(self, variable)
+        parameter_definitions = template_def_variables(self, self.variable)
         results.update(parameter_definitions)
 
         # Get definitions for species
@@ -332,11 +341,15 @@ def template_def_variables(model: SanitizedModel, variable=False) -> "dict[str,
     parameter_type = "VARIABLE" if variable else "CONSTANT"
     # Parameter entries, parsed and formatted
     parameter_set = []
-    for param_name, parameter in model.parameters.items():
-        parameter_set.append(f"{parameter_type}({param_name},{parameter.expression})")
+    for param_id, parameter in enumerate(model.parameters.values()):
+        parameter_set.append(f"{parameter_type}({param_id},{parameter.expression})")
 
     return {
-        "GPY_PARAMETER_VALUES": " ".join(parameter_set)
+        "GPY_PARAMETER_VALUES": " ".join(parameter_set),
+        # Currently assumes all variable or all constant.
+        # For partially variable models, modify to compute these two separately.
+        "GPY_PARAMETER_NUM_VARIABLES": str(len(parameter_set)) if variable else "0",
+        "GPY_PARAMETER_NUM_CONSTANTS": str(len(parameter_set)) if not variable else "0",
     }
 
 
diff --git a/gillespy2/solvers/cpp/c_base/model.cpp b/gillespy2/solvers/cpp/c_base/model.cpp
index 56db1420e..fd6a1418d 100644
--- a/gillespy2/solvers/cpp/c_base/model.cpp
+++ b/gillespy2/solvers/cpp/c_base/model.cpp
@@ -21,6 +21,11 @@
 
 namespace Gillespy {
 
+	int Reaction::s_num_constants;
+	int Reaction::s_num_variables;
+	std::shared_ptr<double> Reaction::s_variables;
+	std::shared_ptr<const double> Reaction::s_constants;
+
 	template <typename PType>
 	Model<PType>::Model(
 		std::vector<std::string> species_names,
diff --git a/gillespy2/solvers/cpp/c_base/model.h b/gillespy2/solvers/cpp/c_base/model.h
index 8b936a2a3..f28e4196c 100644
--- a/gillespy2/solvers/cpp/c_base/model.h
+++ b/gillespy2/solvers/cpp/c_base/model.h
@@ -18,13 +18,17 @@
 
 #pragma once
 
+#include "template.h"
+
 #include <cmath>
 #include <memory>
 #include <string>
 #include <vector>
 #include <iostream>
 
-namespace Gillespy {
+namespace Gillespy
+{
+	typedef unsigned int ReactionId;
 
 	template <typename PType>
 	struct Species {
@@ -48,6 +52,59 @@ namespace Gillespy {
 
 		// List of reactions who's propensities will change when this reaction fires.
 		std::unique_ptr<int[]> species_change;
+
+		inline static double propensity(
+				ReactionId reaction_id,
+				double *state,
+				double *parameters,
+				const double *constants)
+		{
+			return map_propensity(reaction_id, state, parameters, constants);
+		}
+
+		inline static double propensity(
+				ReactionId reaction_id,
+				unsigned int *state,
+				double *parameters,
+				const double *constants)
+		{
+			return map_propensity(reaction_id, state, parameters, constants);
+		}
+
+		inline static double propensity(
+				ReactionId reaction_id,
+				int *state,
+				double *parameters,
+				const double *constants)
+		{
+			return map_propensity(reaction_id, state, parameters, constants);
+		}
+
+		inline static double propensity(ReactionId reaction_id, double *state)
+		{
+			return map_propensity(reaction_id, state, s_variables.get(), s_constants.get());
+		}
+
+		inline static double propensity(ReactionId reaction_id, int *state)
+		{
+			return map_propensity(reaction_id, state, s_variables.get(), s_constants.get());
+		}
+
+		inline static double propensity(ReactionId reaction_id, unsigned int *state)
+		{
+			return map_propensity(reaction_id, state, s_variables.get(), s_constants.get());
+		}
+
+		inline static void load_parameters()
+		{
+			s_variables = std::shared_ptr<double>(get_variables(&s_num_variables));
+			s_constants = std::shared_ptr<const double>(get_constants(&s_num_constants));
+		}
+
+		static int s_num_variables;
+		static int s_num_constants;
+		static std::shared_ptr<double> s_variables;
+		static std::shared_ptr<const double> s_constants;
 	};
 
 	template <typename PType>
@@ -67,15 +124,6 @@ namespace Gillespy {
 		);
 	};
 
-	class IPropensityFunction {
-	public:
-		virtual double evaluate(unsigned int reaction_number, unsigned int *state) = 0;
-		virtual double TauEvaluate(unsigned int reaction_number, const int *S) = 0;
-		virtual double ODEEvaluate(int reaction_number, const std::vector<double> &S) = 0;
-
-		virtual ~IPropensityFunction() {};
-	};
-
 	template <typename PType>
 	struct Simulation {
 		int random_seed;
@@ -95,8 +143,6 @@ namespace Gillespy {
 
 		Model<PType> *model;
 
-		IPropensityFunction *propensity_function;
-
 		template <class T> friend std::ostream &operator << (std::ostream &os, const Simulation<T> &simulation);
 
 		// output_results_buffer: Writes the contents of the entire simulation trajectory.
diff --git a/gillespy2/solvers/cpp/c_base/ode_cpp_solver/ODESimulation.cpp b/gillespy2/solvers/cpp/c_base/ode_cpp_solver/ODESimulation.cpp
index 2a99cea10..499510c32 100644
--- a/gillespy2/solvers/cpp/c_base/ode_cpp_solver/ODESimulation.cpp
+++ b/gillespy2/solvers/cpp/c_base/ode_cpp_solver/ODESimulation.cpp
@@ -39,25 +39,6 @@ unsigned int number_timesteps = 0;
 double end_time = 100.0;
 double increment = 0;
 
-class PropensityFunction : public IPropensityFunction
-{
-public:
-	double evaluate(unsigned int reaction_number, unsigned int *S)
-	{
-		return 1.0;
-	}
-
-	double TauEvaluate(unsigned int reaction_number, const int *S)
-	{
-		return 1.0;
-	}
-
-	double ODEEvaluate(int reaction_number, const std::vector<double> &S)
-	{
-		return map_ode_propensity(reaction_number, S);
-	}
-};
-
 int main(int argc, char *argv[]) {
 	ArgParser parser = ArgParser(argc, argv);
 
@@ -72,6 +53,7 @@ int main(int argc, char *argv[]) {
 	number_timesteps = parser.timesteps;
 	increment = parser.increment;
 
+	Reaction::load_parameters();
 	Model<double> model(species_names, species_populations, reaction_names);
 	add_reactions(model);
 
@@ -80,7 +62,6 @@ int main(int argc, char *argv[]) {
 		random_seed = time(NULL);
 	}
 
-	IPropensityFunction *propensity_function = new PropensityFunction();
 	Simulation<double> simulation;
 
 	simulation.model = &model;
@@ -88,7 +69,6 @@ int main(int argc, char *argv[]) {
 	simulation.random_seed = random_seed;
 	simulation.number_timesteps = number_timesteps;
 	simulation.number_trajectories = number_trajectories;
-	simulation.propensity_function = propensity_function;
 	simulation.current_time = 0.0;
 	simulation.output_interval = parser.output_interval;
 
@@ -98,7 +78,5 @@ int main(int argc, char *argv[]) {
 	ODESolver(&simulation, increment);
 	simulation.output_buffer_final(std::cout);
 
-	delete propensity_function;
-
 	return 0;
 }
diff --git a/gillespy2/solvers/cpp/c_base/ode_cpp_solver/ODESolver.cpp b/gillespy2/solvers/cpp/c_base/ode_cpp_solver/ODESolver.cpp
index 0d589ec1a..33016c3c6 100644
--- a/gillespy2/solvers/cpp/c_base/ode_cpp_solver/ODESolver.cpp
+++ b/gillespy2/solvers/cpp/c_base/ode_cpp_solver/ODESolver.cpp
@@ -165,20 +165,16 @@ namespace Gillespy
 		for (sunindextype reaction_index = 0; reaction_index < number_reactions; reaction_index++)
 		{
 			// Calculate propensity for each reaction at the current state.
-			propensity.push_back(simulation->propensity_function->ODEEvaluate((int)reaction_index, current_state));
+			propensity.push_back(Reaction::propensity(reaction_index, ydata));
 
 			for (sunindextype species_index = 0; species_index < number_species; species_index++)
 			{
-				// If the species is a product of this reaction, add the propensity function.
-				if (model->reactions[reaction_index].species_change[species_index] > 0)
+				// If the species is a product (positive) or reactant (negativ) of this reaction,
+                // add the propensity function multiplied by the species_change value (e.g -2 for 
+                // a bi-molecular reaction reactants). 
+				if (model->reactions[reaction_index].species_change[species_index] != 0)
 				{
-					dydata[species_index] += propensity[reaction_index];
-				}
-
-				// If the species is a reactant, subtract the propensity function.
-				else if (model->reactions[reaction_index].species_change[species_index] < 0)
-				{
-					dydata[species_index] -= propensity[reaction_index];
+					dydata[species_index] += propensity[reaction_index] * model->reactions[reaction_index].species_change[species_index];
 				}
 			}
 		}
diff --git a/gillespy2/solvers/cpp/c_base/ssa_cpp_solver/SSASimulation.cpp b/gillespy2/solvers/cpp/c_base/ssa_cpp_solver/SSASimulation.cpp
index 672e28929..1150714e6 100644
--- a/gillespy2/solvers/cpp/c_base/ssa_cpp_solver/SSASimulation.cpp
+++ b/gillespy2/solvers/cpp/c_base/ssa_cpp_solver/SSASimulation.cpp
@@ -38,25 +38,6 @@ unsigned int number_timesteps = 0;
 
 double end_time = 0;
 
-class PropensityFunction : public IPropensityFunction
-{
-public:
-	double evaluate(unsigned int reaction_number, unsigned int *S)
-	{
-		return map_propensity(reaction_number, S);
-	}
-
-	double TauEvaluate(unsigned int reaction_number, const int *S)
-	{
-		return 1.0;
-	}
-
-	double ODEEvaluate(int reaction_number, const std::vector <double> &S)
-	{
-		return 1.0;
-	}
-};
-
 int main(int argc, char *argv[])
 {
 	//Parse command line arguments
@@ -71,7 +52,8 @@ int main(int argc, char *argv[])
 	end_time = parser.end;
 	number_trajectories = parser.trajectories;
 	number_timesteps = parser.timesteps;
-	
+
+	Reaction::load_parameters();
 	Model<unsigned int> model(species_names, species_populations, reaction_names);
 	add_reactions(model);
 	
@@ -79,8 +61,7 @@ int main(int argc, char *argv[])
 	{
 		random_seed = time(NULL);
 	}
-	
-	IPropensityFunction *propensity_function = new PropensityFunction();
+
 	Simulation<unsigned int> simulation;
 	simulation.output_interval = parser.output_interval;
 	simulation.model = &model;
@@ -88,12 +69,10 @@ int main(int argc, char *argv[])
 	simulation.random_seed = random_seed;
 	simulation.number_timesteps = number_timesteps;
 	simulation.number_trajectories = number_trajectories;
-	simulation.propensity_function = propensity_function;
 	
 	init_simulation(&model, simulation);
 	ssa_direct(&simulation);
 	simulation.output_buffer_final(std::cout);
-	
-	delete propensity_function;
+
 	return 0;
 }
diff --git a/gillespy2/solvers/cpp/c_base/ssa_cpp_solver/SSASolver.cpp b/gillespy2/solvers/cpp/c_base/ssa_cpp_solver/SSASolver.cpp
index fecd0f277..bdf4a8386 100644
--- a/gillespy2/solvers/cpp/c_base/ssa_cpp_solver/SSASolver.cpp
+++ b/gillespy2/solvers/cpp/c_base/ssa_cpp_solver/SSASolver.cpp
@@ -80,7 +80,7 @@ namespace Gillespy
 				//calculate initial propensities
 				for (unsigned int reaction_number = 0; reaction_number < ((simulation->model)->number_reactions); reaction_number++)
 				{
-					propensity_values[reaction_number] = (simulation->propensity_function)->evaluate(reaction_number, simulation->current_state);
+					propensity_values[reaction_number] = Reaction::propensity(reaction_number, simulation->current_state);
 				}
 
 				double propensity_sum;
@@ -126,7 +126,7 @@ namespace Gillespy
 							//Recalculate needed propensities
 							for (unsigned int &affected_reaction : reaction.affected_reactions)
 							{
-								propensity_values[affected_reaction] = (simulation->propensity_function)->evaluate(affected_reaction, simulation->current_state);
+								propensity_values[affected_reaction] = Reaction::propensity(affected_reaction, simulation->current_state);
 							}
 
 							break;
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.cpp b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.cpp
index ef488a1e4..606598013 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.cpp
@@ -18,260 +18,599 @@
 
 #include "HybridModel.h"
 
-namespace Gillespy::TauHybrid
-{
+#include <cstring>
 
-	HybridReaction::HybridReaction()
-		: mode(SimulationState::DISCRETE),
-		  base_reaction(nullptr)
+namespace Gillespy
+{
+	namespace TauHybrid
 	{
-		// Empty constructor body
-	}
+		Event::Event(int event_id, bool use_trigger_state, bool use_persist)
+				: m_event_id(event_id),
+				  m_use_trigger_state(use_trigger_state),
+				  m_use_persist(use_persist)
+		{}
+
+		EventExecution Event::get_execution(double t,
+											const double *state, int num_state) const
+		{
+			return m_use_trigger_state
+				   ? EventExecution(m_event_id, t, state, num_state,
+									Reaction::s_variables.get(), Reaction::s_num_variables)
+				   : EventExecution(m_event_id, t);
+		}
 
-	HybridSpecies::HybridSpecies()
-		: user_mode(SimulationState::DYNAMIC),
-		  partition_mode(SimulationState::DISCRETE),
-		  switch_tol(0.03),
-		  switch_min(0)
-	{
-		// Empty constructor body
-	}
 
-	HybridSimulation::HybridSimulation()
-		: Simulation<double>()
-	{
-		// Empty constructor body
-	}
+		EventExecution::EventExecution(int event_id, double t)
+				: m_execution_time(t),
+				  m_event_id(event_id)
+		{
+			use_assignments();
+		}
 
-	HybridSimulation::HybridSimulation(const Model<double> &model)
-	    : Simulation<double>(),
-		  species_state(model.number_species),
-		  reaction_state(model.number_reactions)
-    {
-		for (int spec_i = 0; spec_i < model.number_species; ++spec_i)
+		EventExecution::EventExecution(int event_id, double t,
+		   const double *state, int num_state,
+		   const double *variables, int num_variables)
+			   : m_execution_time(t),
+			     m_event_id(event_id),
+			     m_num_state(num_state),
+			     m_state(new double[num_state]),
+			     m_num_variables(num_variables),
+			     m_variables(new double[num_variables])
 		{
-			species_state[spec_i].base_species = &model.species[spec_i];
+			std::memcpy(m_state, state, sizeof(double) * num_state);
+			std::memcpy(m_variables, variables, sizeof(double) * num_variables);
+			use_assignments();
 		}
 
-		for (int rxn_i = 0; rxn_i < model.number_reactions; ++rxn_i)
+		EventExecution::EventExecution(const EventExecution &old_event)
+				: m_num_state(old_event.m_num_state),
+				  m_num_variables(old_event.m_num_variables),
+				  m_execution_time(old_event.m_execution_time),
+				  m_event_id(old_event.m_event_id),
+				  m_assignments(old_event.m_assignments)
 		{
-			reaction_state[rxn_i].base_reaction = &model.reactions[rxn_i];
+			if (old_event.m_state != nullptr && m_num_state > 0)
+			{
+				m_state = new double[m_num_state];
+				std::memcpy(m_state, old_event.m_state, sizeof(double) * m_num_state);
+			}
+
+			if (old_event.m_variables != nullptr && m_num_variables > 0)
+			{
+				m_variables = new double[m_num_variables];
+				std::memcpy(m_variables, old_event.m_variables, sizeof(double) * m_num_variables);
+			}
 		}
-    }
 
+		EventExecution::EventExecution(EventExecution &&old_event) noexcept
+				: m_state(old_event.m_state),
+				  m_num_state(old_event.m_num_state),
+				  m_variables(old_event.m_variables),
+				  m_num_variables(old_event.m_num_variables),
+				  m_execution_time(old_event.m_execution_time),
+				  m_event_id(old_event.m_event_id),
+				  m_assignments(std::move(old_event.m_assignments))
+		{
+			old_event.m_num_state = 0;
+			old_event.m_state = nullptr;
+			old_event.m_num_variables = 0;
+			old_event.m_variables = nullptr;
+		}
 
-	double DifferentialEquation::evaluate(
-		const double t,
-		double *ode_state,
-		int *ssa_state)
-	{
-		double sum =  0.0;
+		EventExecution &EventExecution::operator=(const EventExecution &old_event)
+		{
+			m_execution_time = old_event.m_execution_time;
+			m_assignments = old_event.m_assignments;
+
+			if (this != &old_event)
+			{
+				if (old_event.m_state != nullptr)
+				{
+					// If the containers are not of equal size, then we cannot reuse heap data.
+					if (m_num_state != old_event.m_num_state)
+					{
+						delete[] m_state;
+						m_num_state = old_event.m_num_state;
+						m_state = new double[m_num_state];
+					}
+					std::memcpy(m_state, old_event.m_state, sizeof(double) * m_num_state);
+				}
 
-		for (auto &rate_rule : rate_rules)
+				if (old_event.m_variables != nullptr)
+				{
+					if (m_num_variables != old_event.m_num_variables)
+					{
+						delete[] m_variables;
+						m_num_variables = old_event.m_num_variables;
+						m_variables = new double[m_num_variables];
+					}
+					std::memcpy(m_variables, old_event.m_variables, sizeof(double) * m_num_variables);
+				}
+			}
+
+			return *this;
+		}
+
+		EventExecution &EventExecution::operator=(EventExecution &&old_event) noexcept
 		{
-			sum += rate_rule(t, ode_state);
+			m_execution_time = old_event.m_execution_time;
+			m_assignments = std::move(old_event.m_assignments);
+
+			if (this != &old_event)
+			{
+				m_num_state = old_event.m_num_state;
+				m_state = old_event.m_state;
+				old_event.m_num_state = 0;
+				old_event.m_state = nullptr;
+
+				m_num_variables = old_event.m_num_variables;
+				m_variables = old_event.m_variables;
+				old_event.m_num_variables = 0;
+				old_event.m_variables = nullptr;
+			}
+
+			return *this;
 		}
 
-		for (auto &formula : formulas)
+		EventExecution::~EventExecution()
 		{
-			sum += formula(ode_state, ssa_state);
+			delete[] m_state;
+			delete[] m_variables;
 		}
 
-		return sum;
-	}
+		void EventExecution::execute(double t, EventOutput output) const
+		{
+			for (int assign_id : m_assignments)
+			{
+				Event::assign(assign_id, t, output);
+			}
+		}
+
+		void EventExecution::execute(double t, double *state)
+		{
+			if (m_state == nullptr || m_variables == nullptr)
+			{
+				execute(t, EventOutput {
+					state,
+					Reaction::s_variables.get(),
+					state,
+					Reaction::s_variables.get(),
+					Reaction::s_constants.get()
+				});
+			}
+			else
+			{
+				execute(t, EventOutput {
+					state,
+					Reaction::s_variables.get(),
+					m_state,
+					m_variables,
+					Reaction::s_constants.get()
+				});
+			}
+		}
 
+		bool EventExecution::operator<(const EventExecution &rhs) const
+		{
+			return m_execution_time < rhs.m_execution_time;
+		}
 
-	void create_differential_equations(
-		std::vector<HybridSpecies> &species,
-		std::vector<HybridReaction> &reactions)
-	{
-		// For now, differential equations are generated from scratch.
-		// It may be more efficient to determine which formulas need to change.
-		// Until then, the compound formulas in every species are cleared.
-		for (HybridSpecies &spec : species) {
-			spec.diff_equation.formulas.clear();
+		bool EventExecution::operator>(const EventExecution &rhs) const
+		{
+			return m_execution_time > rhs.m_execution_time;
+		}
+
+
+		HybridReaction::HybridReaction()
+				: mode(SimulationState::DISCRETE),
+				  base_reaction(nullptr)
+		{
+			// Empty constructor body
+		}
+
+		HybridSpecies::HybridSpecies()
+				: user_mode(SimulationState::DYNAMIC),
+				  partition_mode(SimulationState::DISCRETE),
+				  switch_tol(0.03),
+				  switch_min(0)
+		{
+			// Empty constructor body
+		}
+
+		HybridSimulation::HybridSimulation()
+				: Simulation<double>()
+		{
+			// Empty constructor body
+		}
+
+		HybridSimulation::HybridSimulation(const Model<double> &model)
+				: Simulation<double>(),
+				  species_state(model.number_species),
+				  reaction_state(model.number_reactions)
+		{
+			for (int spec_i = 0; spec_i < model.number_species; ++spec_i)
+			{
+				species_state[spec_i].base_species = &model.species[spec_i];
+			}
+
+			for (int rxn_i = 0; rxn_i < model.number_reactions; ++rxn_i)
+			{
+				reaction_state[rxn_i].base_reaction = &model.reactions[rxn_i];
+			}
 		}
 
-		for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i) {
-			HybridReaction rxn = reactions[rxn_i];
-			if (rxn.mode == SimulationState::DISCRETE) {
-				continue;
+
+		double DifferentialEquation::evaluate(
+				const double t,
+				double *ode_state,
+				int *ssa_state)
+		{
+			double sum = 0.0;
+
+			for (auto &rate_rule : rate_rules)
+			{
+				sum += rate_rule(t, ode_state, Reaction::s_variables.get(), Reaction::s_constants.get());
 			}
 
-			for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
-				// A species change of 0 indicates that this species is not a dependency for this reaction.
-				if (rxn.base_reaction->species_change[spec_i] == 0) {
+			for (auto &formula : formulas)
+			{
+				sum += formula(ode_state, ssa_state);
+			}
+
+			return sum;
+		}
+
+
+		void create_differential_equations(
+				std::vector<HybridSpecies> &species,
+				std::vector<HybridReaction> &reactions)
+		{
+			// For now, differential equations are generated from scratch.
+			// It may be more efficient to determine which formulas need to change.
+			// Until then, the compound formulas in every species are cleared.
+			for (HybridSpecies &spec : species)
+			{
+				spec.diff_equation.formulas.clear();
+			}
+
+			for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i)
+			{
+				HybridReaction rxn = reactions[rxn_i];
+				if (rxn.mode == SimulationState::DISCRETE)
+				{
 					continue;
 				}
 
-				HybridSpecies &spec = species[spec_i];
-				auto &formula_set = spec.diff_equation.formulas;
-				int spec_diff = rxn.base_reaction->species_change[spec_i];
-
-				switch (spec.partition_mode) {
-				case SimulationState::CONTINUOUS:
-					formula_set.push_back([rxn_i, spec_diff](
-						double *ode_state,
-						int *ssa_state)
+				for (int spec_i = 0; spec_i < species.size(); ++spec_i)
+				{
+					// A species change of 0 indicates that this species is not a dependency for this reaction.
+					if (rxn.base_reaction->species_change[spec_i] == 0)
 					{
-						return spec_diff * HybridReaction::ode_propensity(rxn_i, ode_state);
-					});
-					break;
+						continue;
+					}
 
-				case SimulationState::DISCRETE:
-					formula_set.push_back([rxn_i, spec_diff](
-						double *ode_state,
-						int *ssa_state)
-					{
-						return spec_diff * HybridReaction::ssa_propensity(rxn_i, ssa_state);
-					});
-					break;
+					HybridSpecies &spec = species[spec_i];
+					auto &formula_set = spec.diff_equation.formulas;
+					int spec_diff = rxn.base_reaction->species_change[spec_i];
 
-				default:
-					break;
+					switch (spec.partition_mode)
+					{
+					case SimulationState::CONTINUOUS:
+						formula_set.push_back([rxn_i, spec_diff](
+								double *ode_state,
+								int *ssa_state)
+											  {
+												  return spec_diff * Reaction::propensity(rxn_i, ode_state);
+											  });
+						break;
+
+					case SimulationState::DISCRETE:
+						formula_set.push_back([rxn_i, spec_diff](
+								double *ode_state,
+								int *ssa_state)
+											  {
+												  return spec_diff * Reaction::propensity(rxn_i, ssa_state);
+											  });
+						break;
+
+					default:
+						break;
+					}
 				}
 			}
 		}
-	}
 
-	// Helper method to flag reactions that can be processed deterministically (continuous change)
-	// without exceeding the user-supplied tolerance
-	std::set<int> flag_det_rxns(
-		std::vector<HybridReaction> &reactions,
-		std::vector<HybridSpecies> &species)
-	{
-		int num_reactions = reactions.size();
-		int num_species = species.size();
-		std::set<int> det_rxns;
-
-		for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i) {
-			// start with the assumption that reaction is determinstic
-			HybridReaction &rxn = reactions[rxn_i];
-			rxn.mode = SimulationState::CONTINUOUS;
-
-			// iterate through the dependent species of this reaction
-			// Loop breaks if we've already determined that it is to be marked as discrete.
-			for (int spec_i = 0; spec_i < num_species && rxn.mode == SimulationState::CONTINUOUS; ++spec_i) {
-				// Reaction has a dependency on a species if its dx is positive or negative.
-				// Any species with "dependency" change of 0 is by definition not a dependency.
-				if (rxn.base_reaction->species_change[spec_i] == 0) {
-					continue;
-				}
+		// Helper method to flag reactions that can be processed deterministically (continuous change)
+		// without exceeding the user-supplied tolerance
+		std::set<int> flag_det_rxns(
+				std::vector<HybridReaction> &reactions,
+				std::vector<HybridSpecies> &species)
+		{
+			int num_reactions = reactions.size();
+			int num_species = species.size();
+			std::set<int> det_rxns;
+
+			for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i)
+			{
+				// start with the assumption that reaction is determinstic
+				HybridReaction &rxn = reactions[rxn_i];
+				rxn.mode = SimulationState::CONTINUOUS;
+
+				// iterate through the dependent species of this reaction
+				// Loop breaks if we've already determined that it is to be marked as discrete.
+				for (int spec_i = 0; spec_i < num_species && rxn.mode == SimulationState::CONTINUOUS; ++spec_i)
+				{
+					// Reaction has a dependency on a species if its dx is positive or negative.
+					// Any species with "dependency" change of 0 is by definition not a dependency.
+					if (rxn.base_reaction->species_change[spec_i] == 0)
+					{
+						continue;
+					}
 
-				// if any of the dependencies are set by the user as discrete OR
-				// have been set as dynamic and has not been flagged as deterministic,
-				// allow it to be modelled discretely
-				if (species[spec_i].user_mode == SimulationState::DYNAMIC) {
-					rxn.mode = species[spec_i].partition_mode;
+					// if any of the dependencies are set by the user as discrete OR
+					// have been set as dynamic and has not been flagged as deterministic,
+					// allow it to be modelled discretely
+					if (species[spec_i].user_mode == SimulationState::DYNAMIC)
+					{
+						rxn.mode = species[spec_i].partition_mode;
+					} else
+					{
+						rxn.mode = species[spec_i].user_mode;
+					}
 				}
-				else {
-					rxn.mode = species[spec_i].user_mode;
+
+				if (rxn.mode == SimulationState::CONTINUOUS)
+				{
+					det_rxns.insert(rxn_i);
 				}
 			}
 
-			if (rxn.mode == SimulationState::CONTINUOUS) {
-				det_rxns.insert(rxn_i);
-			}
+			return det_rxns;
 		}
 
-		return det_rxns;
-	}
+		void partition_species(
+				std::vector<HybridReaction> &reactions,
+				std::vector<HybridSpecies> &species,
+				const std::vector<double> &propensity_values,
+				std::vector<double> &curr_state,
+				double tau_step,
+				const TauArgs<double> &tauArgs)
+		{
+			// coefficient of variance- key:species id, value: cv
+			std::map<int, double> cv;
+			// means
+			std::map<int, double> means;
+			// standard deviation
+			std::map<int, double> sd;
+
+			// Initialize means and sd's
+			for (int spec_i = 0; spec_i < species.size(); ++spec_i)
+			{
+				HybridSpecies &spec = species[spec_i];
 
-	void partition_species(
-		std::vector<HybridReaction> &reactions,
-		std::vector<HybridSpecies> &species,
-		const std::vector<double> &propensity_values, 
-		std::vector<double> &curr_state, 
-		double tau_step, 
-		const TauArgs<double> &tauArgs)
-	{
-		// coefficient of variance- key:species id, value: cv
-		std::map<int, double> cv;
-		// means
-		std::map<int, double> means;
-		// standard deviation
-		std::map<int, double> sd;
-
-		// Initialize means and sd's
-		for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
-			HybridSpecies &spec = species[spec_i];
-
-			if (spec.user_mode == SimulationState::DYNAMIC) {
-				means.insert({ spec_i, curr_state[spec_i] });
-				sd.insert({ spec_i, 0 });
+				if (spec.user_mode == SimulationState::DYNAMIC)
+				{
+					means.insert({spec_i, curr_state[spec_i]});
+					sd.insert({spec_i, 0});
+				}
 			}
-		}
 
-		// calculate means and standard deviations for dynamic-mode species involved in reactions
-		for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i) {
-			HybridReaction &rxn = reactions[rxn_i];
+			// calculate means and standard deviations for dynamic-mode species involved in reactions
+			for (int rxn_i = 0; rxn_i < reactions.size(); ++rxn_i)
+			{
+				HybridReaction &rxn = reactions[rxn_i];
 
-			for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
-				// Only dynamic species whose mean/SD is requested are to be considered.
-				if (means.count(spec_i) <= 0) {
+				for (int spec_i = 0; spec_i < species.size(); ++spec_i)
+				{
+					// Only dynamic species whose mean/SD is requested are to be considered.
+					if (means.count(spec_i) <= 0)
+					{
+						continue;
+					}
+					// Selected species is either a reactant or a product, depending on whether
+					//   dx is positive or negative.
+					// 0-dx species are not dependencies of this reaction, so dx == 0 is ignored.
+					int spec_dx = rxn.base_reaction->species_change[spec_i];
+					if (spec_dx < 0)
+					{
+						// Selected species is a reactant.
+						means[spec_i] -= (tau_step * propensity_values[rxn_i] * spec_dx);
+						sd[spec_i] += (tau_step * propensity_values[rxn_i] * std::pow(spec_dx, 2));
+					} else if (spec_dx > 0)
+					{
+						// Selected species is a product.
+						HybridSpecies &product = species[spec_i];
+						means[spec_i] += (tau_step * propensity_values[rxn_i] * spec_dx);
+						sd[spec_i] += (tau_step * propensity_values[rxn_i] * std::pow(spec_dx, 2));
+					}
+				}
+			}
+
+			// calculate coefficient of variation using means and sd
+			for (int spec_i = 0; spec_i < species.size(); ++spec_i)
+			{
+				if (means.count(spec_i) <= 0)
+				{
 					continue;
 				}
-				// Selected species is either a reactant or a product, depending on whether
-				//   dx is positive or negative.
-				// 0-dx species are not dependencies of this reaction, so dx == 0 is ignored.
-				int spec_dx = rxn.base_reaction->species_change[spec_i];
-				if (spec_dx < 0) {
-					// Selected species is a reactant.
-					means[spec_i] -= (tau_step * propensity_values[rxn_i] * spec_dx);
-					sd[spec_i] += (tau_step * propensity_values[rxn_i] * std::pow(spec_dx, 2));
+
+				HybridSpecies &spec = species[spec_i];
+				if (spec.switch_min == 0)
+				{
+					// (default value means switch  min not set, use switch tol)
+					if (means[spec_i] > 0)
+					{
+						cv[spec_i] = (sd[spec_i] / means[spec_i]);
+					} else
+					{
+						cv[spec_i] = 1;
+					}
+
+					spec.partition_mode = cv[spec_i] < spec.switch_tol
+										  ? SimulationState::CONTINUOUS
+										  : SimulationState::DISCRETE;
+				} else
+				{
+					spec.partition_mode = means[spec_i] > spec.switch_min
+										  ? SimulationState::CONTINUOUS
+										  : SimulationState::DISCRETE;
+				}
+			}
+		}
+
+		void update_species_state(
+				std::vector<HybridSpecies> &species,
+				std::vector<double> &current_state)
+		{
+			for (int spec_i = 0; spec_i < species.size(); ++spec_i)
+			{
+				switch (species[spec_i].partition_mode)
+				{
+				case SimulationState::DISCRETE:
+					current_state[spec_i] = std::floor(current_state[spec_i]);
+					break;
+				default:
+					break;
 				}
-				else if (spec_dx > 0) {
-					// Selected species is a product.
-					HybridSpecies &product = species[spec_i];
-					means[spec_i] += (tau_step * propensity_values[rxn_i] * spec_dx);
-					sd[spec_i] += (tau_step * propensity_values[rxn_i] * std::pow(spec_dx, 2));
+			}
+		}
+
+		EventList::EventList()
+		{
+			Event::use_events(m_events);
+
+			for (auto &event : m_events)
+			{
+				// With the below implementation, it is impossible for an event to fire at t=t[0].
+				m_trigger_state.insert({
+					event.get_event_id(),
+					event.get_initial_value(),
+				});
+			}
+		}
+
+		bool EventList::evaluate_triggers(double *event_state, double t)
+		{
+			for (auto &event: m_events)
+			{
+				if (event.trigger(t, event_state) != m_trigger_state.at(event.get_event_id()))
+				{
+					m_trigger_pool.insert(event.get_event_id());
 				}
 			}
+
+			return has_active_events();
 		}
 
-		// calculate coefficient of variation using means and sd
-		for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
-			if (means.count(spec_i) <= 0) {
-				continue;
+		bool EventList::evaluate(double *event_state, int output_size, double t, const std::set<int> &events_found)
+		{
+			if (m_events.empty())
+			{
+				return has_active_events();
 			}
 
-			HybridSpecies &spec = species[spec_i];
-			if (spec.switch_min == 0)
+			auto compare = [t, event_state](EventExecution &lhs, EventExecution &rhs) -> bool
 			{
-				// (default value means switch  min not set, use switch tol)
-				if (means[spec_i] > 0) {
-					cv[spec_i] = (sd[spec_i] /means[spec_i]);
+				return lhs.priority(t, event_state) < rhs.priority(t, event_state);
+			};
+			std::priority_queue<EventExecution, std::vector<EventExecution>, decltype(compare)>
+					trigger_queue(compare);
+
+			// Step 1: Identify any fired event triggers.
+			for (auto &event : m_events)
+			{
+				bool trigger = event.trigger(t, event_state);
+				if (m_trigger_state.at(event.get_event_id()) != trigger)
+				{
+					double delay = event.delay(t, event_state);
+					EventExecution execution = event.get_execution(t + delay, event_state, output_size);
+
+					// Update trigger state to prevent repeated firings.
+					m_trigger_state.find(event.get_event_id())->second = trigger;
+					if (delay <= 0)
+					{
+						// Immediately put EventExecution on "triggered" pile
+						trigger_queue.push(execution);
+					}
+					else if (event.is_persistent())
+					{
+						// Put EventExecution on "delayed" pile
+						m_delay_queue.push(execution);
+					}
+					else
+					{
+						// Search the volatile queue to see if it is already present.
+						// If it is, the event has "double-fired" and must be erased.
+						auto vol_iter = m_volatile_queue.begin();
+						while (vol_iter != m_volatile_queue.end()
+							   && vol_iter->get_event_id() != event.get_event_id())
+						{
+							++vol_iter;
+						}
+
+						if (vol_iter == m_volatile_queue.end())
+						{
+							// No match found; this is a new delay trigger, and is therefore valid.
+							// Delayed, but must be re-checked on every iteration.
+							m_volatile_queue.push_back(execution);
+						}
+						else
+						{
+							// Match found; this is an existing trigger, discard.
+							m_volatile_queue.erase(vol_iter);
+							m_trigger_pool.erase(event.get_event_id());
+							m_trigger_state.at(event.get_event_id()) = !m_trigger_state.at(event.get_event_id());
+						}
+					}
 				}
-				else {
-					cv[spec_i] = 1;
+			}
+
+			// Step 2: Process delayed, non-persistent executions that are now ready to fire.
+			// Both the volatile and non-volatile queue are processed in a similar manner.
+			for (auto vol_event = m_volatile_queue.begin(); vol_event != m_volatile_queue.end(); ++vol_event)
+			{
+				// Execution objects in the volatile queue must remain True until execution.
+				// Remove any execution objects which transitioned to False before execution.
+				if (vol_event->get_execution_time() < t)
+				{
+					trigger_queue.push(*vol_event);
+					vol_event = m_volatile_queue.erase(vol_event);
 				}
+			}
 
-				spec.partition_mode = cv[spec_i] < spec.switch_tol
-					? SimulationState::CONTINUOUS
-					: SimulationState::DISCRETE;
+			// Step 3: Process delayed executions, which includes both persistent triggers
+			// and non-persistent triggers whose execution time has arrived.
+			while (!m_delay_queue.empty())
+			{
+				auto &event = m_delay_queue.top();
+				if (event.get_execution_time() >= t)
+				{
+					// Delay queue is sorted in chronological order.
+					// As soon as we hit a time that is beyond the current time,
+					//  there is no use in continuing through the queue.
+					break;
+				}
+				trigger_queue.push(event);
+				m_delay_queue.pop();
 			}
-			else
+
+			// Step 4: Process any pending triggers, unconditionally.
+			while (!trigger_queue.empty())
 			{
-				spec.partition_mode = means[spec_i] > spec.switch_min
-					? SimulationState::CONTINUOUS
-					: SimulationState::DISCRETE;
+				auto event = trigger_queue.top();
+
+				event.execute(t, event_state);
+				trigger_queue.pop();
+				m_trigger_pool.erase(event.get_event_id());
 			}
-		}
-	}
 
-	void update_species_state(
-		std::vector<HybridSpecies> &species,
-		std::vector<double> &current_state)
-	{
-		for (int spec_i = 0; spec_i < species.size(); ++spec_i) {
-			switch (species[spec_i].partition_mode) {
-			case SimulationState::DISCRETE:
-				current_state[spec_i] = std::floor(current_state[spec_i]);
-				break;
-			default:
-				break;
+			// Step 5: Update any trigger states to reflect the new trigger value.
+			for (auto &event : m_events)
+			{
+				m_trigger_state.find(event.get_event_id())->second = event.trigger(t, event_state);
 			}
+
+			return has_active_events();
 		}
 	}
-
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.h b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.h
index 180ba99d1..b21fd4f45 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.h
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/HybridModel.h
@@ -18,113 +18,249 @@
 
 #pragma once
 
-#include <functional>
 #include "model.h"
 #include "tau.h"
 
+#include <vector>
+#include <queue>
+#include <list>
+
 #define GPY_HYBRID_ABSTOL 1e-8
 #define GPY_HYBRID_RELTOL 1e-8
 
-namespace Gillespy::TauHybrid
+namespace Gillespy
 {
+	namespace TauHybrid
+	{
+		class EventExecution;
+		class Event;
 
-	typedef int ReactionId;
+		typedef int ReactionId;
+		template <typename T>
+		using DelayedExecutionQueue = std::priority_queue<EventExecution, std::vector<EventExecution>, T>;
 
-	/* Gillespy::TauHybrid::DiffEquation
-	 * A vector containing evaluable functions, which accept integrator state and return propensities.
-	 * 
-	 * The vector is understood to be an arbitrarily sized collection of propensity evaluations,
-	 *   each weighted by some individual, constant factor.
-	 * The sum of evaulations of all collected functions is interpreted to be the dydt of that state.
-	 */
-	struct DifferentialEquation
-	{
-	public:
-		std::vector<std::function<double(double*, int*)>> formulas;
-		std::vector<std::function<double(double, double*)>> rate_rules;
-		double evaluate(double t, double *ode_state, int *ssa_state);
-	};
+		struct EventOutput
+		{
+			double *species_out;
+			double *variable_out;
+			const double *species;
+			const double *variables;
+			const double *constants;
+		};
 
-	enum SimulationState : unsigned int
-	{
-		CONTINUOUS = 0,
-		DISCRETE = 1,
-		DYNAMIC = 2
-	};
+		class EventList
+		{
+		public:
+			EventList();
+			bool evaluate_triggers(double *event_state, double t);
+			bool evaluate(double *output, int output_size, double t, const std::set<int> &events_found);
+			inline bool has_active_events() const
+			{
+				return !m_trigger_pool.empty();
+			}
 
-	struct HybridSpecies
-	{
-		Species<double> *base_species;
+		private:
+			std::vector<Event> m_events;
+			std::set<int> m_trigger_pool;
+			std::map<int, bool> m_trigger_state;
+			DelayedExecutionQueue<std::greater<EventExecution>> m_delay_queue;
+			std::list<EventExecution> m_volatile_queue;
+		};
 
-		// allows the user to specify if a species' population should definitely be modeled continuously or 
-		// discretely
-		// CONTINUOUS or DISCRETE
-		// otherwise, mode will be determined by the program (DYNAMIC)
-		// if no choice is made, DYNAMIC will be assumed 
-		SimulationState  user_mode;
+		class Event
+		{
+		public:
+			friend class EventExecution;
 
-		// during simulation execution, a species will fall into either of the two categories, CONTINUOUS or DISCRETE
-		// this is pre-determined only if the user_mode specifies CONTINUOUS or DISCRETE.
-		// otherwise, if DYNAMIC is specified, partition_mode will be continually calculated throughout the simulation
-		// according to standard deviation and coefficient of variance.
-		SimulationState partition_mode;
+			inline bool trigger(double t, const double *state) const
+			{
+				return Event::trigger(m_event_id, t, state, Reaction::s_variables.get(), Reaction::s_constants.get());
+			}
 
-		// Tolerance level for considering a dynamic species deterministically, value is compared
-		// to an estimated sd/mean population of a species after a given time step.
-		//  This value will be used if a switch_min is not provided. The default value is 0.03
-		double switch_tol = 0.03;
+			inline double delay(double t, const double *state) const
+			{
+				return Event::delay(m_event_id, t, state, Reaction::s_variables.get(), Reaction::s_constants.get());
+			}
 
-		//Minimum population value at which species will be represented as continuous. 
-		// If a value is given, switch_min will be used instead of switch_tol.
-		unsigned int switch_min = 0;
+			inline bool get_initial_value() const
+			{
+				return Event::initial_value(m_event_id);
+			}
 
-		DifferentialEquation diff_equation;
+			inline bool is_persistent() const { return m_use_persist; }
+			inline bool get_event_id() const { return m_event_id; }
 
-		// Boundary condition species are not directly updated by reactions, while standard ones are.
-		// If `boundary_condition` is true, then reactants are not consumed, and products are not produced.
-		bool boundary_condition = false;
+			EventExecution get_execution(double t,
+					const double *state, int num_state) const;
+			static void use_events(std::vector<Event> &events);
 
-		HybridSpecies();
-	};
+		private:
+			int m_event_id;
+			bool m_use_trigger_state;
+			bool m_use_persist;
 
-	struct HybridReaction
-	{
-		Reaction *base_reaction;
-		SimulationState mode;
+			explicit Event(int event_id, bool use_trigger_state, bool use_persist);
 
-		HybridReaction();
+			static bool trigger(
+					int event_id, double t,
+					const double *state,
+					const double *variables,
+					const double *constants);
+			static double delay(
+					int event_id, double t,
+					const double *state,
+					const double *variables,
+					const double *constants);
+			static double priority(
+					int event_id, double t,
+					const double *state,
+					const double *variables,
+					const double *constants);
+			static void assign(int event_id, double t, EventOutput output);
+			static bool initial_value(int event_id);
+		};
 
-		static double ode_propensity(ReactionId reaction_number, double *state);
-		static double ssa_propensity(ReactionId reaction_number, int *state);
-	};
+		class EventExecution
+		{
+		public:
 
-	struct HybridSimulation : Simulation<double>
-	{
-		std::vector<HybridSpecies> species_state;
-		std::vector<HybridReaction> reaction_state;
+			friend class Event;
+			~EventExecution();
+			EventExecution(const EventExecution&);
+			EventExecution(EventExecution&&) noexcept;
+			EventExecution &operator=(const EventExecution&);
+			EventExecution &operator=(EventExecution&&) noexcept;
+
+			void execute(double t, EventOutput output) const;
+			void execute(double t, double *state);
+			inline double priority(double t, const double *state) const
+			{
+				return Event::priority(m_event_id, t, state, Reaction::s_variables.get(), Reaction::s_constants.get());
+			}
+			inline bool trigger(double t, const double *state) const
+			{
+				return Event::trigger(m_event_id, t, state, Reaction::s_variables.get(), Reaction::s_constants.get());
+			}
+
+			inline double get_execution_time() const { return m_execution_time; }
+			inline int get_event_id() const { return m_event_id; }
+
+			bool operator<(const EventExecution &rhs) const;
+			bool operator>(const EventExecution &rhs) const;
+
+		private:
+			double m_execution_time;
+			int m_event_id;
+
+			int m_num_state = 0;
+			double *m_state = nullptr;
+			
+			int m_num_variables = 0;
+			double *m_variables = nullptr;
+
+			std::vector<int> m_assignments;
+			void use_assignments();
+
+			EventExecution(int event_id, double t);
+			EventExecution(int event_id, double t,
+						   const double *state, int num_state,
+						   const double *variables, int num_variables);
+		};
+
+		/* Gillespy::TauHybrid::DiffEquation
+		 * A vector containing evaluable functions, which accept integrator state and return propensities.
+		 *
+		 * The vector is understood to be an arbitrarily sized collection of propensity evaluations,
+		 *   each weighted by some individual, constant factor.
+		 * The sum of evaluations of all collected functions is interpreted to be the dy/dt of that state.
+		 */
+		struct DifferentialEquation
+		{
+		public:
+			std::vector<std::function<double(double *, int *)>> formulas;
+			std::vector<std::function<double(double, double *, double *, const double *)>> rate_rules;
+
+			double evaluate(double t, double *ode_state, int *ssa_state);
+		};
+
+		enum SimulationState : unsigned int
+		{
+			CONTINUOUS = 0,
+			DISCRETE = 1,
+			DYNAMIC = 2
+		};
+
+		struct HybridSpecies
+		{
+			Species<double> *base_species;
+
+			// allows the user to specify if a species' population should definitely be modeled continuously or
+			// discretely
+			// CONTINUOUS or DISCRETE
+			// otherwise, mode will be determined by the program (DYNAMIC)
+			// if no choice is made, DYNAMIC will be assumed
+			SimulationState user_mode;
+
+			// during simulation execution, a species will fall into either of the two categories, CONTINUOUS or DISCRETE
+			// this is pre-determined only if the user_mode specifies CONTINUOUS or DISCRETE.
+			// otherwise, if DYNAMIC is specified, partition_mode will be continually calculated throughout the simulation
+			// according to standard deviation and coefficient of variance.
+			SimulationState partition_mode;
+
+			// Tolerance level for considering a dynamic species deterministically, value is compared
+			// to an estimated sd/mean population of a species after a given time step.
+			//  This value will be used if a switch_min is not provided. The default value is 0.03
+			double switch_tol = 0.03;
+
+			//Minimum population value at which species will be represented as continuous.
+			// If a value is given, switch_min will be used instead of switch_tol.
+			unsigned int switch_min = 0;
+
+			DifferentialEquation diff_equation;
+
+			// Boundary condition species are not directly updated by reactions, while standard ones are.
+			// If `boundary_condition` is true, then reactants are not consumed, and products are not produced.
+			bool boundary_condition = false;
+
+			HybridSpecies();
+		};
+
+		struct HybridReaction
+		{
+			Reaction *base_reaction;
+			SimulationState mode;
+
+			HybridReaction();
+		};
+
+		struct HybridSimulation : Simulation<double>
+		{
+			std::vector<HybridSpecies> species_state;
+			std::vector<HybridReaction> reaction_state;
 
-		HybridSimulation();
-		HybridSimulation(const Model<double> &model);
-	};
+			HybridSimulation();
 
-	std::set<int> flag_det_rxns(
-		std::vector<HybridReaction> &reactions,
-		std::vector<HybridSpecies> &species);
+			HybridSimulation(const Model<double> &model);
+		};
 
-	void partition_species(
-		std::vector<HybridReaction> &reactions,
-		std::vector<HybridSpecies> &species,
-		const std::vector<double> &propensity_values,
-		std::vector<double> &curr_state,
-		double tau_step,
-		const TauArgs<double> &TauArgs);
+		std::set<int> flag_det_rxns(
+				std::vector<HybridReaction> &reactions,
+				std::vector<HybridSpecies> &species);
 
-	void update_species_state(
-		std::vector<HybridSpecies> &species,
-		std::vector<double> &current_state);
+		void partition_species(
+				std::vector<HybridReaction> &reactions,
+				std::vector<HybridSpecies> &species,
+				const std::vector<double> &propensity_values,
+				std::vector<double> &curr_state,
+				double tau_step,
+				const TauArgs<double> &TauArgs);
 
-	void create_differential_equations(
-		std::vector<HybridSpecies> &species,
-		std::vector<HybridReaction> &reactions);
+		void update_species_state(
+				std::vector<HybridSpecies> &species,
+				std::vector<double> &current_state);
 
+		void create_differential_equations(
+				std::vector<HybridSpecies> &species,
+				std::vector<HybridReaction> &reactions);
+	}
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSimulation.cpp b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSimulation.cpp
index bdd198042..f3ba18c28 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSimulation.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSimulation.cpp
@@ -37,33 +37,6 @@ bool seed_time = true;
 double increment = 0;
 double tau_tol = 0.03;
 
-class PropensityFunction : public IPropensityFunction
-{
-public:
-
-	double ODEEvaluate(int reaction_number, const std::vector <double> &S){
-		return map_ode_propensity(reaction_number, S);
-	}
-	double TauEvaluate(unsigned int reaction_number, const int *S) {
-		return map_propensity(reaction_number, S);
-	}
-	double evaluate(unsigned int reaction_number, unsigned int* S){return 1.0;}
-};
-
-double Gillespy::TauHybrid::HybridReaction::ode_propensity(
-	ReactionId reaction_number,
-	double *state)
-{
-	return map_ode_propensity(reaction_number, state);
-}
-
-double Gillespy::TauHybrid::HybridReaction::ssa_propensity(
-	ReactionId reaction_number,
-	int *state)
-{
-	return map_propensity(reaction_number, state);
-}
-
 int main(int argc, char* argv[])
 {
 	ArgParser parser(argc, argv);
@@ -75,13 +48,13 @@ int main(int argc, char* argv[])
 	number_timesteps = parser.timesteps;
 	tau_tol = parser.tau_tol;
 
+	Reaction::load_parameters();
 	Model<double> model(species_names, species_populations, reaction_names);
 	add_reactions(model);
 
 	if(seed_time){
 		random_seed = time(NULL);
 	}
-	IPropensityFunction *propFun = new PropensityFunction();
 	//Simulation INIT
 	TauHybrid::HybridSimulation simulation(model);
 	simulation.model = &model;
@@ -89,14 +62,15 @@ int main(int argc, char* argv[])
 	simulation.random_seed = random_seed;
 	simulation.number_timesteps = number_timesteps;
 	simulation.number_trajectories = number_trajectories;
-	simulation.propensity_function = propFun;
 	simulation.output_interval = parser.output_interval;
 	init_simulation(&model, simulation);
 	Gillespy::TauHybrid::map_species_modes(simulation.species_state);
 	Gillespy::TauHybrid::map_rate_rules(simulation.species_state);
-	// Perform ODE  //
-	TauHybrid::TauHybridCSolver(&simulation, tau_tol);
+
+	std::vector<Gillespy::TauHybrid::Event> events;
+	Gillespy::TauHybrid::Event::use_events(events);
+
+	TauHybrid::TauHybridCSolver(&simulation, events, tau_tol);
 	simulation.output_results_buffer(std::cout);
-	delete propFun;
 	return 0;
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.cpp b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.cpp
index bf07ebfc8..e28b9f320 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.cpp
@@ -19,6 +19,8 @@
 #include <iostream>
 #include <csignal> //Included for timeout signal handling
 #include <random>
+#include <queue>
+#include <list>
 #include "cvode.h" // prototypes for CVODE fcts., consts.
 #include "nvector_serial.h"  // access to serial N_Vector
 #include "sunlinsol_spgmr.h"  //access to SPGMR SUNLinearSolver
@@ -30,271 +32,335 @@
 #include "integrator.h"
 #include "tau.h"
 
-namespace Gillespy::TauHybrid
+namespace Gillespy
 {
-	bool interrupted = false;
-
-	void signalHandler(int signum)
+	namespace TauHybrid
 	{
-		interrupted = true;
-	}
 
-	void TauHybridCSolver(HybridSimulation *simulation, const double tau_tol)
-	{
-		if (simulation == NULL)
-		{
-			return;
-		}
+		bool interrupted = false;
 
-		Model<double> &model = *(simulation->model);
-		int num_species = model.number_species;
-		int num_reactions = model.number_reactions;
-		int num_trajectories = simulation->number_trajectories;
-		std::unique_ptr<Species<double>[]> &species = model.species;
-		double increment = simulation->timeline[1] - simulation->timeline[0];
-
-		URNGenerator urn(simulation->random_seed);
-		// The contents of y0 are "stolen" by the integrator.
-		// Do not attempt to directly use y0 after being passed to sol!
-		N_Vector y0 = init_model_vector(model, urn);
-		N_Vector y;
-		if (num_trajectories > 0)
+		void signalHandler(int signum)
 		{
-			y = init_model_vector(model, urn);
+			interrupted = true;
 		}
-		else
-		{
-			y = y0;
-		}
-		Integrator sol(simulation, y, GPY_HYBRID_RELTOL, GPY_HYBRID_ABSTOL);
-
-		// Tau selector initialization. Used to select a valid tau step.
-		TauArgs<double> tau_args = initialize(model, tau_tol);
 
-		// Simulate for each trajectory
-		for (int traj = 0; traj < num_trajectories; traj++)
+		void TauHybridCSolver(HybridSimulation *simulation, std::vector<Event> &events, const double tau_tol)
 		{
-			if (traj > 0)
+			if (simulation == NULL)
 			{
-				sol.reinitialize(y0);
+				return;
 			}
 
-			// Initialize each species with their respective user modes.
-			for (int spec_i = 0; spec_i < num_species; ++spec_i)
+			Model<double> &model = *(simulation->model);
+			int num_species = model.number_species;
+			int num_reactions = model.number_reactions;
+			int num_trajectories = simulation->number_trajectories;
+			std::unique_ptr<Species<double>[]> &species = model.species;
+			double increment = simulation->timeline[1] - simulation->timeline[0];
+
+			URNGenerator urn(simulation->random_seed);
+			// The contents of y0 are "stolen" by the integrator.
+			// Do not attempt to directly use y0 after being passed to sol!
+			N_Vector y0 = init_model_vector(model, urn);
+			N_Vector y;
+			if (num_trajectories > 0)
 			{
-				HybridSpecies *spec = &simulation->species_state[spec_i];
-				spec->partition_mode = spec->user_mode == SimulationState::DYNAMIC
-					? SimulationState::DISCRETE
-					: spec->user_mode;
-				simulation->trajectories[traj][0][spec_i] = spec->base_species->initial_population;
+				y = init_model_vector(model, urn);
 			}
-
-			// Population/concentration state values for each species.
-			// TODO: change back double -> hybrid_state, once we figure out how that works
-			std::vector<double> current_state(num_species);
-			std::vector<int> current_populations(num_species);
-
-			// Initialize the species population for the trajectory.
-			for (int spec_i = 0; spec_i < num_species; ++spec_i)
+			else
 			{
-				current_state[spec_i] = species[spec_i].initial_population;
-				current_populations[spec_i] = species[spec_i].initial_population;
+				y = y0;
 			}
+			Integrator sol(simulation, y, GPY_HYBRID_RELTOL, GPY_HYBRID_ABSTOL);
 
-			// SIMULATION STEP LOOP
-			int save_idx = 1;
-			double next_time;
-			double tau_step = 0.0;
-			double save_time = simulation->timeline[save_idx];
-
-			// Temporary array to store changes to dependent species.
-			// Should be 0-initialized each time it's used.
-			int *population_changes = new int[num_species];
-			simulation->current_time = 0;
-
-			// An invalid simulation state indicates that an unrecoverable error has occurred,
-			//   and the trajectory should terminate early.
-			bool invalid_state = false;
-			// This is a temporary fix. Ideally, invalid state should allow for integrator options change.
-			// For now, a "guard" is put in place to prevent potentially infinite loops from occurring.
-			unsigned int integration_guard = 1000;
-
-			while (integration_guard > 0 && simulation->current_time < simulation->end_time)
+			// Tau selector initialization. Used to select a valid tau step.
+			TauArgs<double> tau_args = initialize(model, tau_tol);
+
+			// Simulate for each trajectory
+			for (int traj = 0; traj < num_trajectories; traj++)
 			{
-				// Compute current propensity values based on existing state.
-				for (int rxn_i = 0; rxn_i < num_reactions; ++rxn_i)
+				if (traj > 0)
+				{
+					sol.reinitialize(y0);
+				}
+
+				// Population/concentration state values for each species.
+				// TODO: change back double -> hybrid_state, once we figure out how that works
+				EventList event_list;
+				std::vector<double> current_state(num_species);
+				std::vector<int> current_populations(num_species);
+
+				// Initialize the species population for the trajectory.
+				for (int spec_i = 0; spec_i < num_species; ++spec_i)
+				{
+					current_state[spec_i] = species[spec_i].initial_population;
+					current_populations[spec_i] = species[spec_i].initial_population;
+				}
+
+				// Check for initial event triggers at t=0 (based on initial_value of trigger)
+				std::set<int> event_roots;
+				std::set<unsigned int> rxn_roots;
+				if (event_list.evaluate_triggers(current_state.data(), simulation->current_time))
+				{
+					double *event_state = N_VGetArrayPointer(sol.y);
+					event_list.evaluate(current_state.data(), num_species, simulation->current_time, event_roots);
+					std::copy(current_state.begin(), current_state.end(), event_state);
+					sol.refresh_state();
+				}
+
+				// Initialize each species with their respective user modes.
+				for (int spec_i = 0; spec_i < num_species; ++spec_i)
 				{
-					HybridReaction &rxn = simulation->reaction_state[rxn_i];
-					double propensity = 0.0;
-					switch (rxn.mode)
+					HybridSpecies *spec = &simulation->species_state[spec_i];
+					spec->partition_mode = spec->user_mode == SimulationState::DYNAMIC
+										   ? SimulationState::DISCRETE
+										   : spec->user_mode;
+					simulation->trajectories[traj][0][spec_i] = current_state[spec_i];
+				}
+
+				// SIMULATION STEP LOOP
+				int save_idx = 1;
+				double next_time;
+				double tau_step = 0.0;
+				double save_time = simulation->timeline[save_idx];
+
+				// Temporary array to store changes to dependent species.
+				// Should be 0-initialized each time it's used.
+				int *population_changes = new int[num_species];
+				simulation->current_time = 0;
+
+				// An invalid simulation state indicates that an unrecoverable error has occurred,
+				//   and the trajectory should terminate early.
+				bool invalid_state = false;
+				// This is a temporary fix. Ideally, invalid state should allow for integrator options change.
+				// For now, a "guard" is put in place to prevent potentially infinite loops from occurring.
+				unsigned int integration_guard = 1000;
+
+				while (integration_guard > 0 && simulation->current_time < simulation->end_time)
+				{
+					// Compute current propensity values based on existing state.
+					for (unsigned int rxn_i = 0; rxn_i < num_reactions; ++rxn_i)
 					{
+						HybridReaction &rxn = simulation->reaction_state[rxn_i];
+						double propensity = 0.0;
+						switch (rxn.mode)
+						{
 						case SimulationState::CONTINUOUS:
-							propensity = HybridReaction::ode_propensity(rxn_i, &current_state[0]);
+							propensity = Reaction::propensity(rxn_i, current_state.data());
 							break;
 						case SimulationState::DISCRETE:
-							propensity = HybridReaction::ssa_propensity(rxn_i, &current_populations[0]);
+							propensity = Reaction::propensity(rxn_i, current_populations.data());
 							break;
 						default:
 							break;
+						}
+						sol.data.propensities[rxn_i] = propensity;
 					}
-					sol.data.propensities[rxn_i] = propensity;
-				}
 
-				// Expected tau step is determined.
-				tau_step = select(
-					model,
-					tau_args,
-					tau_tol,
-					simulation->current_time,
-					save_time,
-					sol.data.propensities,
-					current_populations
-				);
-				partition_species(
-					simulation->reaction_state,
-					simulation->species_state,
-					sol.data.propensities,
-					current_state,
-					tau_step,
-					tau_args
-				);
-				flag_det_rxns(
-					simulation->reaction_state,
-					simulation->species_state
-				);
-				update_species_state(simulation->species_state, current_state);
-				create_differential_equations(simulation->species_state, simulation->reaction_state);
-
-				// Determine what the next time point is.
-				// This will become current_time on the next iteration.
-				// If a retry with a smaller tau_step is deemed necessary, this will change.
-				next_time = simulation->current_time + tau_step;
-
-				// The integration loop continues until a valid solution is found.
-				// Any invalid Tau steps (which cause negative populations) are discarded.
-				sol.save_state();
-				do {
-					// Integration Step
-					// For deterministic reactions, the concentrations are updated directly.
-					// For stochastic reactions, integration updates the rxn_offsets vector.
-					IntegrationResults result = sol.integrate(&next_time);
-					if (sol.status == IntegrationStatus::BAD_STEP_SIZE)
-					{
-						invalid_state = true;
-						// Breaking early causes `invalid_state` to remain set,
-						//   resulting in an early termination of the trajectory.
-						break;
-					}
+					// Expected tau step is determined.
+					tau_step = select(
+							model,
+							tau_args,
+							tau_tol,
+							simulation->current_time,
+							save_time,
+							sol.data.propensities,
+							current_populations
+					);
+					partition_species(
+							simulation->reaction_state,
+							simulation->species_state,
+							sol.data.propensities,
+							current_state,
+							tau_step,
+							tau_args
+					);
+					flag_det_rxns(
+							simulation->reaction_state,
+							simulation->species_state
+					);
+					update_species_state(simulation->species_state, current_state);
+					create_differential_equations(simulation->species_state, simulation->reaction_state);
 
-					// The integrator has, at this point, been validated.
-					// Any errors beyond this point is assumed to be a stochastic state failure.
-					invalid_state = false;
+					// Determine what the next time point is.
+					// This will become current_time on the next iteration.
+					// If a retry with a smaller tau_step is deemed necessary, this will change.
+					next_time = simulation->current_time + tau_step;
 
-					// 0-initialize our population_changes array.
-					for (int p_i = 0; p_i < num_species; ++p_i)
-					{
-						population_changes[p_i] = 0;
-					}
+					// The integration loop continues until a valid solution is found.
+					// Any invalid Tau steps (which cause negative populations) are discarded.
+					sol.save_state();
 
-					// Start with the species concentration as a baseline value.
-					// Stochastic reactions will update populations relative to their concentrations.
-					for (int spec_i = 0; spec_i < num_species; ++spec_i)
+					do
 					{
-						current_state[spec_i] = result.concentrations[spec_i];
-					}
+						invalid_state = false;
 
-					// The newly-updated reaction_states vector may need to be reconciled now.
-					// A positive reaction_state means reactions have potentially fired.
-					// NOTE: it is possible for a population to swing negative, where a smaller Tau is needed.
-					for (int rxn_i = 0; rxn_i < num_reactions; ++rxn_i)
-					{
-						// Temporary variable for the reaction's state.
-						// Does not get updated unless the changes are deemed valid.
-						double rxn_state = result.reactions[rxn_i];
+						// Integration Step
+						// For deterministic reactions, the concentrations are updated directly.
+						// For stochastic reactions, integration updates the rxn_offsets vector.
+						IntegrationResults result = sol.integrate(&next_time, event_roots, rxn_roots);
+						if (sol.status == IntegrationStatus::BAD_STEP_SIZE)
+						{
+							invalid_state = true;
+							// Breaking early causes `invalid_state` to remain set,
+							//   resulting in an early termination of the trajectory.
+							break;
+						}
+
+						// The integrator has, at this point, been validated.
+						// Any errors beyond this point is assumed to be a stochastic state failure.
 
-						switch (simulation->reaction_state[rxn_i].mode)
+						// 0-initialize our population_changes array.
+						for (int p_i = 0; p_i < num_species; ++p_i)
 						{
-						case SimulationState::DISCRETE:
-							while (rxn_state >= 0) {
+							population_changes[p_i] = 0;
+						}
+
+						// Start with the species concentration as a baseline value.
+						// Stochastic reactions will update populations relative to their concentrations.
+						for (int spec_i = 0; spec_i < num_species; ++spec_i)
+						{
+							current_state[spec_i] = result.concentrations[spec_i];
+						}
+
+						if (!rxn_roots.empty())
+						{
+							// "Direct" roots found; these are executed manually
+							for (unsigned int rxn_i : rxn_roots)
+							{
 								// "Fire" a reaction by recording changes in dependent species.
 								// If a negative value is detected, break without saving changes.
-								for (int spec_i = 0; spec_i < num_species; ++spec_i) {
-									population_changes[spec_i] +=
-										model.reactions[rxn_i].species_change[spec_i];
-									if (current_state[spec_i] + population_changes[spec_i] < 0) {
-										invalid_state = true;
-									}
+								for (int spec_i = 0; spec_i < num_species; ++spec_i)
+								{
+									// Unlike the Tau-leaping version of reaction firings,
+									// it is not possible to have a negative state occur in direct reactions.
+									population_changes[spec_i] += model.reactions[rxn_i].species_change[spec_i];
+									result.reactions[rxn_i] = log(urn.next());
 								}
+							}
+							rxn_roots.clear();
+						}
+						else
+						{
+							// The newly-updated reaction_states vector may need to be reconciled now.
+							// A positive reaction_state means reactions have potentially fired.
+							// NOTE: it is possible for a population to swing negative, where a smaller Tau is needed.
+							for (int rxn_i = 0; rxn_i < num_reactions; ++rxn_i)
+							{
+								// Temporary variable for the reaction's state.
+								// Does not get updated unless the changes are deemed valid.
+								double rxn_state = result.reactions[rxn_i];
 
-								rxn_state += log(urn.next());
+								switch (simulation->reaction_state[rxn_i].mode)
+								{
+								case SimulationState::DISCRETE:
+									while (rxn_state >= 0)
+									{
+										// "Fire" a reaction by recording changes in dependent species.
+										// If a negative value is detected, break without saving changes.
+										for (int spec_i = 0; spec_i < num_species; ++spec_i)
+										{
+											population_changes[spec_i] +=
+													model.reactions[rxn_i].species_change[spec_i];
+											if (current_state[spec_i] + population_changes[spec_i] < 0)
+											{
+												invalid_state = true;
+											}
+										}
+
+										rxn_state += log(urn.next());
+									}
+									result.reactions[rxn_i] = rxn_state;
+									break;
+
+								case SimulationState::CONTINUOUS:
+								default:
+									break;
+								}
 							}
-							result.reactions[rxn_i] = rxn_state;
-							break;
+						}
 
-						case SimulationState::CONTINUOUS:
-						default:
-							break;
+						// Positive reaction state means a negative population was detected.
+						// Only update state with the given population changes if valid.
+						if (invalid_state)
+						{
+							sol.restore_state();
+							tau_step /= 2;
+							next_time = simulation->current_time + tau_step;
 						}
-					}
+						else
+						{
+							// "Permanently" update the rxn_state and populations.
+							for (int p_i = 0; p_i < num_species; ++p_i)
+							{
+								if (!simulation->species_state[p_i].boundary_condition)
+								{
+									// Boundary conditions are not modified directly by reactions.
+									// As such, population dx in stochastic regime is not considered.
+									// For deterministic species, their effective dy/dt should always be 0.
+									current_state[p_i] += population_changes[p_i];
+									result.concentrations[p_i] = current_state[p_i];
+								}
+								current_populations[p_i] = (int) current_state[p_i];
+							}
+						}
+					} while (invalid_state);
+
+					// Invalid state after the do-while loop implies that an unrecoverable error has occurred.
+					// While prior results are considered usable, the current integration results are not.
+					// Calling `continue` with an invalid state will discard the results and terminate the trajectory.
+					integration_guard = invalid_state
+										? integration_guard - 1
+										: 1000;
 
-					// Positive reaction state means a negative population was detected.
-					// Only update state with the given population changes if valid.
-					if (invalid_state)
+					// ===== <EVENT HANDLING> =====
+					if (!event_list.has_active_events())
 					{
-						sol.restore_state();
-						tau_step /= 2;
-						next_time = simulation->current_time + tau_step;
+						if (event_list.evaluate_triggers(N_VGetArrayPointer(sol.y), next_time))
+						{
+							sol.restore_state();
+							sol.use_events(events, simulation->reaction_state);
+							sol.enable_root_finder();
+							continue;
+						}
 					}
 					else
 					{
-						// "Permanently" update the rxn_state and populations.
-						for (int p_i = 0; p_i < num_species; ++p_i)
+						double *event_state = N_VGetArrayPointer(sol.y);
+						if (!event_list.evaluate(event_state, num_species, next_time, event_roots))
 						{
-							if (!simulation->species_state[p_i].boundary_condition)
-							{
-								// Boundary conditions are not modified directly by reactions.
-								// As such, population dx in stochastic regime is not considered.
-								// For deterministic species, their effective dy/dt should always be 0.
-								current_state[p_i] += population_changes[p_i];
-								result.concentrations[p_i] = current_state[p_i];
-							}
-							current_populations[p_i] = (int) current_state[p_i];
+							sol.disable_root_finder();
 						}
+						std::copy(event_state, event_state + num_species, current_state.begin());
 					}
-				} while (invalid_state);
+					// ===== </EVENT HANDLING> =====
 
-				// Invalid state after the do-while loop implies that an unrecoverable error has occurred.
-				// While prior results are considered usable, the current integration results are not.
-				// Calling `continue` with an invalid state will discard the results and terminate the trajectory.
-				integration_guard = invalid_state
-					? integration_guard - 1
-					: 1000;
+					// Output the results for this time step.
+					sol.refresh_state();
+					simulation->current_time = next_time;
 
-				// Output the results for this time step.
-				sol.refresh_state();
-				simulation->current_time = next_time;
-
-				// Seek forward, writing out any values on the timeline which are on current timestep range.
-				while (save_idx < simulation->number_timesteps && save_time <= next_time)
-				{
-					for (int spec_i = 0; spec_i < num_species; ++spec_i)
+					// Seek forward, writing out any values on the timeline which are on current timestep range.
+					while (save_idx < simulation->number_timesteps && save_time <= next_time)
 					{
-						simulation->trajectories[traj][save_idx][spec_i] = current_state[spec_i];
+						for (int spec_i = 0; spec_i < num_species; ++spec_i)
+						{
+							simulation->trajectories[traj][save_idx][spec_i] = current_state[spec_i];
+						}
+						save_time = simulation->timeline[++save_idx];
 					}
-					save_time = simulation->timeline[++save_idx];
 				}
-			}
 
-			if (integration_guard == 0)
-			{
-				std::cerr
-					<< "[Trajectory #" << traj << "] "
-					<< "Integration guard triggered; problem space too stiff at t="
-					<< simulation->current_time << std::endl;
-			}
+				if (integration_guard == 0)
+				{
+					std::cerr
+							<< "[Trajectory #" << traj << "] "
+							<< "Integration guard triggered; problem space too stiff at t="
+							<< simulation->current_time << std::endl;
+				}
 
-			// End of trajectory
-			delete[] population_changes;
+				// End of trajectory
+				delete[] population_changes;
+			}
 		}
 	}
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.h b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.h
index d45f3d7d7..319ceb59a 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.h
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/TauHybridSolver.h
@@ -19,8 +19,12 @@
 #pragma once
 
 #include "HybridModel.h"
+#include <vector>
 
-namespace Gillespy::TauHybrid
+namespace Gillespy
 {
-    void TauHybridCSolver(HybridSimulation* simulation, const double tau_tol);
+	namespace TauHybrid
+	{
+    	void TauHybridCSolver(HybridSimulation* simulation, std::vector<Event> &events, double tau_tol);
+	}
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.cpp b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.cpp
index ba42a0427..691a4acb4 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.cpp
@@ -19,6 +19,10 @@
 #include "hybrid_template.h"
 #include "template_params.h"
 
+// , cannot be overridden, so we can't use it as a delimiter
+// Use a separate macro to represent a delimiter
+#define AND ,
+
 namespace Gillespy
 {
 	namespace TauHybrid
@@ -45,10 +49,151 @@ namespace Gillespy
 
 		void map_rate_rules(std::vector<HybridSpecies> &species)
 		{
-			#define RATE_RULE(spec_id, rate_rule) species[spec_id].diff_equation.rate_rules.push_back([](double t, double *S) { return (rate_rule); });
+			#define RATE_RULE(spec_id, rate_rule) species[spec_id].diff_equation.rate_rules.push_back(\
+			[](double t, double *S, double *P, const double *C){ return (rate_rule); });
 			GPY_RATE_RULES
 			#undef RATE_RULE
 		}
 
+
+		bool Event::initial_value(int event_id)
+		{
+			#define INIT_TRUE (1)
+			#define INIT_FALSE (0)
+			#define EVENT(event_id, targets, trigger, delay, priority, use_trigger, use_perist, init) \
+			case event_id: return (bool) init;
+
+			switch (event_id)
+			{
+			GPY_HYBRID_EVENTS
+
+			default:
+				return false;
+			}
+
+			#undef EVENT
+			#undef INIT_FALSE
+			#undef INIT_TRUE
+		}
+
+
+		bool Event::trigger(
+				int event_id, double t,
+				const double *S,
+				const double *P,
+				const double *C)
+		{
+			#define EVENT(event_id, targets, trigger, delay, priority, use_trigger, use_persist, init) \
+			case event_id: return (bool) (trigger);
+
+			switch (event_id)
+			{
+			GPY_HYBRID_EVENTS
+
+			default:
+				return false;
+			}
+
+			#undef EVENT
+		}
+
+		double Event::delay(
+				int event_id, double t,
+				const double *S,
+				const double *P,
+				const double *C)
+		{
+			#define EVENT(event_id, targets, trigger, delay, priority, use_trigger, use_persist, init) \
+			case event_id: return static_cast<double>(delay);
+
+			switch (event_id)
+			{
+				GPY_HYBRID_EVENTS
+
+				default:
+					return false;
+			}
+
+			#undef EVENT
+		}
+
+		double Event::priority(
+				int event_id, double t,
+				const double *S,
+				const double *P,
+				const double *C)
+		{
+			#define EVENT(event_id, targets, trigger, delay, priority, use_trigger, use_persist, init) \
+			case event_id: return static_cast<double>(priority);
+
+			switch (event_id)
+			{
+				GPY_HYBRID_EVENTS
+
+				default:
+					return false;
+			}
+
+			#undef EVENT
+		}
+
+		void Event::use_events(std::vector<Event> &events)
+		{
+			events.clear();
+			events.reserve(GPY_HYBRID_NUM_EVENTS);
+
+			#define USE_TRIGGER true
+			#define USE_EVAL false
+			#define PERSISTENT true
+			#define IRREGULAR false
+			#define EVENT(event_id, targets, trigger, delay, priority, use_trigger, use_persist, init) \
+			events.emplace_back(Event(event_id, use_trigger, use_persist));
+			GPY_HYBRID_EVENTS
+			#undef EVENT
+			#undef IRREGULAR
+			#undef PERSISTENT
+			#undef USE_EVAL
+			#undef USE_TRIGGER
+		}
+
+		void EventExecution::use_assignments()
+		{
+			m_assignments.clear();
+			#define EVENT(event_id, targets, trigger, delay, priority, use_trigger, use_persist, init) \
+			case event_id: m_assignments = std::vector<int>(targets); break;
+
+			switch (m_event_id)
+			{
+			GPY_HYBRID_EVENTS
+
+			default:
+				return;
+			}
+
+			#undef EVENT
+		}
+
+		void Event::assign(int assign_id, double t, EventOutput output)
+		{
+			const double *S = output.species;
+			const double *P = output.variables;
+			const double *C = output.constants;
+
+			#define SPECIES_ASSIGNMENT(id, spec_id, expr) \
+			case id: output.species_out[spec_id] = expr; break;
+			#define VARIABLE_ASSIGNMENT(id, var_id, expr) \
+			case id: output.variable_out[var_id] = expr; break;
+
+			switch (assign_id)
+			{
+			GPY_HYBRID_EVENT_ASSIGNMENTS
+
+			default:
+				return;
+			}
+
+			#undef VARIABLE_ASSIGNMENT
+			#undef SPECIES_ASSIGNMENT
+		}
 	}
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.h b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.h
index 92f891184..a853eb305 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.h
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/hybrid_template.h
@@ -24,10 +24,26 @@
 #include "HybridModel.h"
 #include "template_defaults.h"
 
-namespace Gillespy::TauHybrid
+#include <functional>
+#include <initializer_list>
+
+#ifndef GPY_HYBRID_EVENTS
+#define GPY_HYBRID_EVENTS
+#define GPY_HYBRID_NUM_EVENTS 0
+#endif
+
+#ifndef GPY_HYBRID_EVENT_ASSIGNMENTS
+#define GPY_HYBRID_EVENT_ASSIGNMENTS
+#define GPY_HYBRID_NUM_EVENT_ASSIGNMENTS 0
+#endif
+
+namespace Gillespy
 {
+	namespace TauHybrid
+	{
 
-	void map_species_modes(std::vector<HybridSpecies> &species);
-	void map_rate_rules(std::vector<HybridSpecies> &species);
+		void map_species_modes(std::vector<HybridSpecies> &species);
+		void map_rate_rules(std::vector<HybridSpecies> &species);
 
+	}
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/integrator.cpp b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/integrator.cpp
index 19bff198d..615ec0b1d 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/integrator.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/integrator.cpp
@@ -123,6 +123,7 @@ Integrator::~Integrator()
 	N_VDestroy_Serial(y);
 	CVodeFree(&cvode_mem);
 	SUNLinSolFree_SPGMR(solver);
+	delete[] m_roots;
 }
 
 IntegrationResults Integrator::integrate(double *t)
@@ -131,13 +132,91 @@ IntegrationResults Integrator::integrate(double *t)
 	{
 		return { nullptr, nullptr };
 	}
+	*t = this->t;
 
 	return {
-		NV_DATA_S(y), // NV_DATA_S instead?
+		NV_DATA_S(y),
 		NV_DATA_S(y) + num_species
 	};
 }
 
+IntegrationResults Integrator::integrate(double *t, std::set<int> &event_roots, std::set<unsigned int> &reaction_roots)
+{
+	IntegrationResults results = integrate(t);
+	unsigned long long num_triggers = data.active_triggers.size();
+	unsigned long long num_rxn_roots = data.active_reaction_ids.size();
+	unsigned long long root_size = data.active_triggers.size() + data.active_reaction_ids.size();
+	int *root_results = new int[root_size];
+
+	if (validate(this, CVodeGetRootInfo(cvode_mem, root_results)))
+	{
+		unsigned long long root_id;
+		for (root_id = 0; root_id < num_triggers; ++root_id)
+		{
+			if (root_results[root_id] != 0)
+			{
+				event_roots.insert((int) root_id);
+			}
+		}
+
+		for (; root_id < num_rxn_roots; ++root_id)
+		{
+			if (root_results[root_id] != 0)
+			{
+				reaction_roots.insert(data.active_reaction_ids[root_id]);
+			}
+		}
+	}
+
+	delete[] root_results;
+	return results;
+}
+
+void Integrator::use_events(const std::vector<Event> &events)
+{
+	data.active_triggers.clear();
+	for (auto &event : events)
+	{
+		data.active_triggers.emplace_back([event](double t, const double *state) -> double {
+			return event.trigger(t, state) ? 1.0 : -1.0;
+		});
+	}
+}
+
+void Integrator::use_reactions(const std::vector<HybridReaction> &reactions)
+{
+	data.active_reaction_ids.clear();
+	for (auto &reaction : reactions)
+	{
+		if (reaction.mode == SimulationState::DISCRETE)
+		{
+			// Reaction root-finder should only be used on discrete-valued reactions.
+			// The required IDs are placed into a reference vector and are mapped back out
+			// when the caller of integrate() retrieves them.
+			data.active_reaction_ids.push_back(reaction.base_reaction->id);
+		}
+	}
+}
+
+void Integrator::use_events(const std::vector<Event> &events, const std::vector<HybridReaction> &reactions)
+{
+	use_events(events);
+	use_reactions(reactions);
+}
+
+bool Integrator::enable_root_finder()
+{
+	unsigned long long root_fn_size = data.active_triggers.size() + data.active_reaction_ids.size();
+	return validate(this, CVodeRootInit(cvode_mem, (int) root_fn_size, rootfn));
+}
+
+bool Integrator::disable_root_finder()
+{
+	data.active_triggers.clear();
+	data.active_reaction_ids.clear();
+	return validate(this, CVodeRootInit(cvode_mem, 0, NULL));
+}
+
 
 URNGenerator::URNGenerator()
 	: uniform(0, 1) {}
@@ -243,18 +322,18 @@ int Gillespy::TauHybrid::rhs(realtype t, N_Vector y, N_Vector ydot, void *user_d
 		}
 		else
 		{
-			dydt[spec_i] = (*species)[spec_i].diff_equation.evaluate(t, Y, &populations[0]);
+			dydt[spec_i] = (*species)[spec_i].diff_equation.evaluate(t, Y, populations.data());
 		}
 	}
 
 	// Process deterministic propensity state
 	// These updates get written directly to the integrator's concentration state
-	for (int rxn_i = 0; rxn_i < num_reactions; ++rxn_i)
+	for (unsigned int rxn_i = 0; rxn_i < num_reactions; ++rxn_i)
 	{
 		switch ((*reactions)[rxn_i].mode) {
 		case SimulationState::DISCRETE:
 			// Process stochastic reaction state by updating the root offset for each reaction.
-			propensity = HybridReaction::ssa_propensity(rxn_i, &populations[0]);
+			propensity = Reaction::propensity(rxn_i, populations.data());
 			dydt_offsets[rxn_i] = propensity;
 			propensities[rxn_i] = propensity;
 			break;
@@ -269,6 +348,30 @@ int Gillespy::TauHybrid::rhs(realtype t, N_Vector y, N_Vector ydot, void *user_d
 	return 0;
 };
 
+int Gillespy::TauHybrid::rootfn(realtype t, N_Vector y, realtype *gout, void *user_data)
+{
+	IntegratorData &data = *static_cast<IntegratorData*>(user_data);
+	unsigned long long num_triggers = data.active_triggers.size();
+	unsigned long long num_reactions = data.active_reaction_ids.size();
+	realtype *y_t = N_VGetArrayPointer(y);
+	realtype *rxn_t = y_t + data.species_state->size();
+	realtype *rxn_out = gout + num_triggers;
+
+	unsigned long long trigger_id;
+	for (trigger_id = 0; trigger_id < num_triggers; ++trigger_id)
+	{
+		gout[trigger_id] = data.active_triggers[trigger_id](t, y_t);
+	}
+
+	unsigned long long rxn_id;
+	for (rxn_id = 0; rxn_id < num_reactions; ++rxn_id)
+	{
+		rxn_out[rxn_id] = rxn_t[data.active_reaction_ids[rxn_id]];
+	}
+
+	return 0;
+}
+
 
 static bool validate(Integrator *integrator, int retcode)
 {
diff --git a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/integrator.h b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/integrator.h
index 968bd582c..a0ce3884c 100644
--- a/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/integrator.h
+++ b/gillespy2/solvers/cpp/c_base/tau_hybrid_cpp_solver/integrator.h
@@ -26,8 +26,10 @@
 #include <vector>
 #include <random>
 
-namespace Gillespy::TauHybrid
+namespace Gillespy
 {
+	namespace TauHybrid
+	{
 
 	/* IntegratorStatus: represents the runtime state of the integrator.
 	 * OK indicates that no errors have occurred.
@@ -49,6 +51,13 @@ namespace Gillespy::TauHybrid
 		HybridSimulation *simulation;
 		std::vector<HybridSpecies> *species_state;
 		std::vector<HybridReaction> *reaction_state;
+		std::vector<Event> *events = nullptr;
+		std::vector<std::function<double(double, const double*)>> active_triggers;
+		// Container representing the rootfinder-enabled reactions.
+		// Each integer at index i represents the reaction id corresponding to rootfinder element i.
+		// In `rootfn`, this means that gout[i] is the "output" of reaction active_reaction_ids[i].
+		// This is used to map the internal reaction number to the actual reaction id.
+		std::vector<unsigned int> active_reaction_ids;
 
 		std::vector<double> concentrations;
 		std::vector<int> populations;
@@ -84,6 +93,7 @@ namespace Gillespy::TauHybrid
 		SUNLinearSolver solver;
 		int num_species;
 		int num_reactions;
+		int *m_roots = nullptr;
 	public:
 		// status: check for errors before using the results.
 		IntegrationStatus status;
@@ -114,7 +124,38 @@ namespace Gillespy::TauHybrid
 
 		void reinitialize(N_Vector y_reset);
 
+		/// @brief Make events available to root-finder during integration.
+		/// The root-finder itself is not activated until enable_root_finder() is called.
+		///
+		/// @param events List of event objects to make available to the root-finder.
+		/// The trigger functions of all given events are added as root-finder targets.
+		void use_events(const std::vector<Event> &events);
+
+		/// @brief Make events and reactions available to root-finder during integration.
+		/// The root-finder itself is not activated until enable_root_finder() is called.
+		///
+		/// @param events List of event objects to make available to the root-finder.
+		/// @param reactions List of reaction objects to make available to the root-finder.
+		void use_events(const std::vector<Event> &events, const std::vector<HybridReaction> &reactions);
+
+		/// @brief Make reactions available to root-finder during integration.
+		/// The root-finder itself is not activated until enable_root_finder() is called.
+		///
+		/// @param reactions List of reaction objects to make available to the root-finder.
+		void use_reactions(const std::vector<HybridReaction> &reactions);
+
+		/// @brief Installs a CVODE root-finder onto the integrator.
+		/// Any events or reactions provided by previous calls to use_events() or use_reactions()
+		/// will cause the integrator to return early, which the integrate() method will indicate.
+		bool enable_root_finder();
+
+		/// @brief Removes the CVODE root-finder from the integrator.
+		/// Early returns on root-finder events no longer happen,
+		/// and the underlying SBML event data and reaction data are removed.
+		bool disable_root_finder();
+
 		IntegrationResults integrate(double *t);
+		IntegrationResults integrate(double *t, std::set<int> &event_roots, std::set<unsigned int> &reaction_roots);
 		IntegratorData data;
 
 		Integrator(HybridSimulation *simulation, N_Vector y0, double reltol, double abstol);
@@ -136,5 +177,7 @@ namespace Gillespy::TauHybrid
 	N_Vector init_model_vector(Model<double> &model, URNGenerator urn);
 
 	int rhs(realtype t, N_Vector y, N_Vector ydot, void *user_data);
+	int rootfn(realtype t, N_Vector y, realtype *gout, void *user_data);
 
+	}
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_leaping_cpp_solver/TauLeapingSimulation.cpp b/gillespy2/solvers/cpp/c_base/tau_leaping_cpp_solver/TauLeapingSimulation.cpp
index 31544a486..12bbeaf42 100644
--- a/gillespy2/solvers/cpp/c_base/tau_leaping_cpp_solver/TauLeapingSimulation.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_leaping_cpp_solver/TauLeapingSimulation.cpp
@@ -39,25 +39,6 @@ unsigned int number_timesteps = 0;
 double end_time = 0;
 double tau_tol = 0.03;
 
-class PropensityFunction : public IPropensityFunction
-{
-public:
-	double TauEvaluate(unsigned int reaction_number, const int *S)
-	{
-		return map_propensity(reaction_number, S);
-	}
-
-	double evaluate(unsigned int reaction_number, unsigned int *state)
-	{
-		return 1.0;
-	}
-
-	double ODEEvaluate(int reaction_number, const std::vector<double> &S)
-	{
-		return 1.0;
-	}
-};
-
 int main(int argc, char* argv[]){
 	ArgParser parser = ArgParser(argc, argv);
 
@@ -73,14 +54,13 @@ int main(int argc, char* argv[]){
 	number_timesteps = parser.timesteps;
 	tau_tol = parser.tau_tol;
 
+	Reaction::load_parameters();
 	Model<unsigned int> model(species_names, species_populations, reaction_names);
 	add_reactions(model);
 
 	if(seed_time) {
 		random_seed = time(NULL);
 	}
-
-	IPropensityFunction *propFun = new PropensityFunction();
 	Simulation<unsigned int> simulation;
 
 	simulation.model = &model;
@@ -88,13 +68,11 @@ int main(int argc, char* argv[]){
 	simulation.random_seed = random_seed;
 	simulation.number_timesteps = number_timesteps;
 	simulation.number_trajectories = number_trajectories;
-	simulation.propensity_function = propFun;
 	simulation.output_interval = parser.output_interval;
 
 	init_simulation(&model, simulation);
 	tau_leaper(&simulation, tau_tol);
 	simulation.output_buffer_final(std::cout);
 
-	delete propFun;
 	return 0;
 }
diff --git a/gillespy2/solvers/cpp/c_base/tau_leaping_cpp_solver/TauLeapingSolver.cpp b/gillespy2/solvers/cpp/c_base/tau_leaping_cpp_solver/TauLeapingSolver.cpp
index f84e19af0..2a150c3b2 100644
--- a/gillespy2/solvers/cpp/c_base/tau_leaping_cpp_solver/TauLeapingSolver.cpp
+++ b/gillespy2/solvers/cpp/c_base/tau_leaping_cpp_solver/TauLeapingSolver.cpp
@@ -148,7 +148,7 @@ namespace Gillespy
 					//calculate propensities for each step
 					for (unsigned int reaction_number = 0; reaction_number < simulation->model->number_reactions; reaction_number++)
 					{
-						propensity_values[reaction_number] = simulation->propensity_function->TauEvaluate(reaction_number, &current_state[0]);
+						propensity_values[reaction_number] = Reaction::propensity(reaction_number, current_state.data());
 					}
 
 					tau_step = select(*(simulation->model), tau_args, tau_tol, simulation->current_time, save_time, propensity_values, current_state);
diff --git a/gillespy2/solvers/cpp/c_base/template/template.cpp b/gillespy2/solvers/cpp/c_base/template/template.cpp
index 50c00132e..19418c342 100644
--- a/gillespy2/solvers/cpp/c_base/template/template.cpp
+++ b/gillespy2/solvers/cpp/c_base/template/template.cpp
@@ -29,6 +29,9 @@
 
 namespace Gillespy
 {
+	static double param_overrides[GPY_PARAMETER_NUM_VARIABLES];
+	static bool param_override_mask[GPY_PARAMETER_NUM_VARIABLES];
+
 	double populations[GPY_NUM_SPECIES] = GPY_INIT_POPULATIONS;
 	std::vector<double> species_populations(
 		populations,
@@ -57,39 +60,35 @@ namespace Gillespy
 		r_names,
 		r_names + sizeof(r_names) / sizeof(std::string));
 
-	#define VARIABLE(name, value) double name = value;
-	#define CONSTANT(name, value) const double name = value;
-	GPY_PARAMETER_VALUES
-	#undef CONSTANT
-	#undef VARIABLE
-
-	double map_propensity(int reaction_id, const int *S)
+	double *get_variables(int *num_variables)
 	{
-		switch (reaction_id)
-		{
-			#define PROPENSITY(id, func) case(id): return(func);
-			GPY_PROPENSITIES
-			#undef PROPENSITY
+		double *variables = new double[GPY_PARAMETER_NUM_VARIABLES];
 
-			default:
-				return -1.0;
-		}
+		#define CONSTANT(id, value)
+		#define VARIABLE(id, value) variables[id] = param_override_mask[id] \
+			? param_overrides[id] \
+			: value; (*num_variables)++;
+		GPY_PARAMETER_VALUES
+		#undef VARIABLE
+		#undef CONSTANT
+
+		return variables;
 	}
 
-	double map_propensity(int reaction_id, unsigned int *S)
+	double *get_constants(int *num_constants)
 	{
-		switch (reaction_id)
-		{
-			#define PROPENSITY(id, func) case(id): return(func);
-			GPY_PROPENSITIES
-			#undef PROPENSITY
+		double *constants = new double[GPY_PARAMETER_NUM_CONSTANTS];
 
-			default:
-				return -1.0;
-		}
+		#define VARIABLE(id, value)
+		#define CONSTANT(id, value) constants[id] = value; (*num_constants)++;
+		GPY_PARAMETER_VALUES
+		#undef CONSTANT
+		#undef VARIABLE
+
+		return constants;
 	}
 
-	double map_propensity(int reaction_id, int *S)
+	double map_propensity(unsigned int reaction_id, unsigned int *S, double *P, const double *C)
 	{
 		switch (reaction_id)
 		{
@@ -102,12 +101,12 @@ namespace Gillespy
 		}
 	}
 
-	double map_ode_propensity(int reaction_id, const std::vector<double> &S)
+	double map_propensity(unsigned int reaction_id, int *S, double *P, const double *C)
 	{
 		switch (reaction_id)
 		{
 			#define PROPENSITY(id, func) case(id): return(func);
-			GPY_ODE_PROPENSITIES
+			GPY_PROPENSITIES
 			#undef PROPENSITY
 
 			default:
@@ -115,7 +114,7 @@ namespace Gillespy
 		}
 	}
 
-	double map_ode_propensity(int reaction_id, double *S)
+	double map_propensity(unsigned int reaction_id, double *S, double *P, const double *C)
 	{
 		switch (reaction_id)
 		{
@@ -130,8 +129,10 @@ namespace Gillespy
 
 	void map_variable_parameters(std::stringstream &stream)
 	{
-		#define VARIABLE(name, value) stream >> (name);
-		#define CONSTANT(name, value)
+		#define VARIABLE(id, value) \
+		stream >> param_overrides[id]; \
+		param_override_mask[id] = true;
+		#define CONSTANT(id, value)
 		GPY_PARAMETER_VALUES
 		#undef CONSTANT
 		#undef VARIABLE
diff --git a/gillespy2/solvers/cpp/c_base/template/template.h b/gillespy2/solvers/cpp/c_base/template/template.h
index 286d16ef4..d64436515 100644
--- a/gillespy2/solvers/cpp/c_base/template/template.h
+++ b/gillespy2/solvers/cpp/c_base/template/template.h
@@ -22,23 +22,28 @@
  * Includes functions for loading and defining simulation parameters.
  */
 
-#include "model.h"
+#include <sstream>
+#include <vector>
 
 namespace Gillespy
 {
+	template <typename PType>
+	struct Model;
+
 	extern std::vector<double> species_populations;
 	extern std::vector<std::string> species_names;
 	extern std::vector<std::string> reaction_names;
 
-	double map_propensity(int reaction_id, const int *state);
-	double map_propensity(int reaction_id, unsigned int *S);
-	double map_propensity(int reaction_id, int *S);
-	double map_ode_propensity(int reaction_id, const std::vector<double> &state);
-	double map_ode_propensity(int reaction_id, double *S);
+	double map_propensity(unsigned int reaction_id, int *state, double *parameters, const double *constants);
+	double map_propensity(unsigned int reaction_id, unsigned int *S, double *parameters, const double *constants);
+	double map_propensity(unsigned int reaction_id, double *S, double *parameters, const double *constants);
 
 	template <typename T>
 	void add_reactions(Model<T> &model);
 
+	double *get_variables(int *num_variables);
+	double *get_constants(int *num_constants);
+
 	void map_variable_parameters(std::stringstream &stream);
 	void map_variable_populations(std::stringstream &stream);
 
diff --git a/gillespy2/solvers/cpp/c_base/template/template_defaults.h b/gillespy2/solvers/cpp/c_base/template/template_defaults.h
index f8278d95c..1e43bd1b7 100644
--- a/gillespy2/solvers/cpp/c_base/template/template_defaults.h
+++ b/gillespy2/solvers/cpp/c_base/template/template_defaults.h
@@ -34,6 +34,8 @@
 
 #ifndef GPY_PARAMETER_VALUES
 #define GPY_PARAMETER_VALUES
+#define GPY_PARAMETER_NUM_VARIABLES 0
+#define GPY_PARAMETER_NUM_CONSTANTS 0
 #endif
 
 #ifndef GPY_INIT_POPULATIONS
diff --git a/gillespy2/solvers/cpp/c_base/template/template_params.h b/gillespy2/solvers/cpp/c_base/template/template_params.h
index aa9b699ec..f6152278c 100644
--- a/gillespy2/solvers/cpp/c_base/template/template_params.h
+++ b/gillespy2/solvers/cpp/c_base/template/template_params.h
@@ -41,12 +41,3 @@
 #endif
 
 using namespace std;
-
-namespace Gillespy
-{
-    #define VARIABLE(name, value) extern double name;
-    #define CONSTANT(name, value) extern const double name;
-    GPY_PARAMETER_VALUES
-    #undef CONSTANT
-    #undef VARIABLE
-}
diff --git a/gillespy2/solvers/cpp/c_solver.py b/gillespy2/solvers/cpp/c_solver.py
index 05b7fd16c..cb4595c83 100644
--- a/gillespy2/solvers/cpp/c_solver.py
+++ b/gillespy2/solvers/cpp/c_solver.py
@@ -80,17 +80,8 @@ def __init__(self, model: Model = None, output_directory: str = None, delete_dir
         self.output_directory = output_directory
         self.delete_directory = delete_directory
 
-        if self.model is None:
-            return
-
-        self._build(model, self.target, variable, False)
-        self.species_mappings = self.model.sanitized_species_names()
-        self.species = list(self.species_mappings.keys())
-        self.parameter_mappings = self.model.sanitized_parameter_names()
-        self.parameters = list(self.parameter_mappings.keys())
-        self.reactions = list(self.model.listOfReactions.keys())
-        self.result = []
-        self.rc = 0
+        if self.model is not None:
+            self._set_model()
 
     def __del__(self):
         if self.build_engine is None:
@@ -312,6 +303,19 @@ def _make_resume_data(self, time_stopped: int, simulation_data: numpy.ndarray, t
 
         return simulation_data
 
+    def _set_model(self, model=None):
+        if model is not None:
+            self.model = model
+
+        self._build(self.model, self.target, self.variable, False)
+        self.species_mappings = self.model.sanitized_species_names()
+        self.species = list(self.species_mappings.keys())
+        self.parameter_mappings = self.model.sanitized_parameter_names()
+        self.parameters = list(self.parameter_mappings.keys())
+        self.reactions = list(self.model.listOfReactions.keys())
+        self.result = []
+        self.rc = 0
+
     def _update_resume_data(self, resume: Results, simulation_data: "list[dict[str, numpy.ndarray]]", time_stopped: int):
         """
         Modify the simulation output to continue from a previous Results object.
diff --git a/gillespy2/solvers/cpp/ode_c_solver.py b/gillespy2/solvers/cpp/ode_c_solver.py
index 5d67dccea..43a59a776 100644
--- a/gillespy2/solvers/cpp/ode_c_solver.py
+++ b/gillespy2/solvers/cpp/ode_c_solver.py
@@ -15,6 +15,7 @@
 You should have received a copy of the GNU General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 """
+import numpy as np
 
 from gillespy2.solvers.cpp.c_decoder import BasicSimDecoder
 from gillespy2.solvers.utilities import solverutils as cutils
@@ -37,10 +38,17 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
             increment: int = None, seed: int = None, debug: bool = False, profile: bool = False, variables={},
             resume=None, live_output: str = None, live_output_options: dict = {}, **kwargs):
 
-        if self is None or self.model is None:
+        if self is None:
             self = ODECSolver(model, resume=resume)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self._set_model(model=model)
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal ODECSolver.model.")
+        self.model.resolve_parameters()
 
-        increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
         # Validate parameters prior to running the model.
         self._validate_type(variables, dict, "'variables' argument must be a dictionary.")
@@ -48,10 +56,10 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         self._validate_resume(t, resume)
         self._validate_kwargs(**kwargs)
         self._validate_sbml_features({
-            "Rate Rules": len(model.listOfRateRules),
-            "Assignment Rules": len(model.listOfAssignmentRules),
-            "Events": len(model.listOfEvents),
-            "Function Definitions": len(model.listOfFunctionDefinitions)
+            "Rate Rules": len(self.model.listOfRateRules),
+            "Assignment Rules": len(self.model.listOfAssignmentRules),
+            "Events": len(self.model.listOfEvents),
+            "Function Definitions": len(self.model.listOfFunctionDefinitions)
         })
 
         if resume is not None:
@@ -68,8 +76,8 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         }
 
         if self.variable:
-            populations = cutils.update_species_init_values(model.listOfSpecies, self.species, variables, resume)
-            parameter_values = cutils.change_param_values(model.listOfParameters, self.parameters, model.volume, variables)
+            populations = cutils.update_species_init_values(self.model.listOfSpecies, self.species, variables, resume)
+            parameter_values = cutils.change_param_values(self.model.listOfParameters, self.parameters, self.model.volume, variables)
 
             args.update({
                 "init_pop": populations,
@@ -85,7 +93,7 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         if live_output is not None:
             live_output_options['type'] = live_output
             display_args = {
-                "model": model, "number_of_trajectories": number_of_trajectories, "timeline": np.linspace(0, t, number_timesteps),
+                "model": self.model, "number_of_trajectories": number_of_trajectories, "timeline": np.linspace(0, t, number_timesteps),
                 "live_output_options": live_output_options, "resume": bool(resume)
             }
         else:
@@ -94,7 +102,7 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         args = self._make_args(args)
         decoder = BasicSimDecoder.create_default(number_of_trajectories, number_timesteps, len(self.model.listOfSpecies))
 
-        sim_exec = self._build(model, self.target, self.variable, False)
+        sim_exec = self._build(self.model, self.target, self.variable, False)
         sim_status = self._run(sim_exec, args, decoder, timeout, display_args)
 
         if sim_status == SimulationReturnCode.FAILED:
diff --git a/gillespy2/solvers/cpp/ssa_c_solver.py b/gillespy2/solvers/cpp/ssa_c_solver.py
index 4a2f1ae31..34867e40e 100644
--- a/gillespy2/solvers/cpp/ssa_c_solver.py
+++ b/gillespy2/solvers/cpp/ssa_c_solver.py
@@ -39,10 +39,17 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
             increment: int = None, seed: int = None, debug: bool = False, profile: bool = False, variables={},
             resume=None, live_output: str = None, live_output_options: dict = {}, **kwargs):
 
-        if self is None or self.model is None:
+        if self is None:
             self = SSACSolver(model, resume=resume)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self._set_model(model=model)
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal SSACSolver.model.")
+        self.model.resolve_parameters()
 
-        increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
         # Validate parameters prior to running the model.
         self._validate_type(variables, dict, "'variables' argument must be a dictionary.")
@@ -50,10 +57,10 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         self._validate_resume(t, resume)
         self._validate_kwargs(**kwargs)
         self._validate_sbml_features({
-            "Rate Rules": len(model.listOfRateRules),
-            "Assignment Rules": len(model.listOfAssignmentRules),
-            "Events": len(model.listOfEvents),
-            "Function Definitions": len(model.listOfFunctionDefinitions)
+            "Rate Rules": len(self.model.listOfRateRules),
+            "Assignment Rules": len(self.model.listOfAssignmentRules),
+            "Events": len(self.model.listOfEvents),
+            "Function Definitions": len(self.model.listOfFunctionDefinitions)
         })
 
         if resume is not None:
@@ -69,8 +76,8 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         }
 
         if self.variable:
-            populations = cutils.update_species_init_values(model.listOfSpecies, self.species, variables, resume)
-            parameter_values = cutils.change_param_values(model.listOfParameters, self.parameters, model.volume, variables)
+            populations = cutils.update_species_init_values(self.model.listOfSpecies, self.species, variables, resume)
+            parameter_values = cutils.change_param_values(self.model.listOfParameters, self.parameters, self.model.volume, variables)
 
             args.update({
                 "init_pop": populations,
@@ -86,7 +93,7 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         if live_output is not None:
             live_output_options['type'] = live_output
             display_args = {
-                "model": model, "number_of_trajectories": number_of_trajectories, "timeline": np.linspace(0, t, number_timesteps),
+                "model": self.model, "number_of_trajectories": number_of_trajectories, "timeline": np.linspace(0, t, number_timesteps),
                 "live_output_options": live_output_options, "resume": bool(resume)
             }
         else:
@@ -95,7 +102,7 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         args = self._make_args(args)
         decoder = IterativeSimDecoder.create_default(number_of_trajectories, number_timesteps, len(self.model.listOfSpecies))
 
-        sim_exec = self._build(model, self.target, self.variable, False)
+        sim_exec = self._build(self.model, self.target, self.variable, False)
         sim_status = self._run(sim_exec, args, decoder, timeout, display_args)
 
         if sim_status == SimulationReturnCode.FAILED:
diff --git a/gillespy2/solvers/cpp/tau_hybrid_c_solver.py b/gillespy2/solvers/cpp/tau_hybrid_c_solver.py
index b656c36fb..f3d41a02f 100644
--- a/gillespy2/solvers/cpp/tau_hybrid_c_solver.py
+++ b/gillespy2/solvers/cpp/tau_hybrid_c_solver.py
@@ -13,15 +13,15 @@ class TauHybridCSolver(GillesPySolver, CSolver):
     target = "hybrid"
 
     @classmethod
-    def __create_options(cls, model: "SanitizedModel") -> "SanitizedModel":
+    def __create_options(cls, sanitized_model: "SanitizedModel") -> "SanitizedModel":
         """
         Populate the given list of species modes into a set of template macro definitions.
         Generated options are specific to the Tau Hybrid solver,
         and get passed as custom definitions to the build engine.
 
-        :param model: Sanitized model containing sanitized species definitions.
+        :param sanitized_model: Sanitized model containing sanitized species definitions.
         The GPY_HYBRID_SPECIES_MODES option will be set as an option for the model.
-        :type model: SanitizedModel
+        :type sanitized_model: SanitizedModel
 
         :returns: Pass-through of sanitized model object.
         :rtype: SanitizedModel
@@ -37,9 +37,27 @@ def __create_options(cls, model: "SanitizedModel") -> "SanitizedModel":
             # When species.boundary_condition == True
             "BOUNDARY",
         ]
+        trigger_mode_types = [
+            # When event.use_values_from_trigger_time == False
+            "USE_EVAL",
+            # When event.use_values_from_trigger_time == True
+            "USE_TRIGGER",
+        ]
+        persist_types = [
+            # When event.trigger.persistent == False
+            "IRREGULAR",
+            # When event.trigger.persistent == True
+            "PERSISTENT",
+        ]
+        initial_value_types = [
+            # When event.trigger.initial_value == False
+            "INIT_FALSE",
+            # When event.trigger.initial_value == True
+            "INIT_TRUE",
+        ]
 
         species_mode_list = []
-        for spec_id, species in enumerate(model.species.values()):
+        for spec_id, species in enumerate(sanitized_model.species.values()):
             mode_keyword = species_mode_map.get(species.mode, species_mode_map["dynamic"])
             # Casting a bool to an int evaluates: False -> 0, and True -> 1
             # Explicit cast to bool for safety, in case boundary_condition is given weird values
@@ -48,12 +66,72 @@ def __create_options(cls, model: "SanitizedModel") -> "SanitizedModel":
             entry = f"SPECIES_MODE({spec_id},{species.switch_min},{mode_keyword},{boundary_keyword})"
             species_mode_list.append(entry)
 
-        model.options["GPY_HYBRID_SPECIES_MODES"] = " ".join(species_mode_list)
-        return model
+        # EVENT(event_id, {targets}, trigger, delay, priority, use_trigger, use_persist)
+        event_list = []
+        # [SPECIES/VARIABLE]_ASSIGNMENT(assign_id, target_id, expr)
+        event_assignment_list = []
+        assign_id = 0
+        for event_id, event in enumerate(sanitized_model.model.listOfEvents.values()):
+            trigger = sanitized_model.expr.getexpr_cpp(event.trigger.expression)
+            delay = sanitized_model.expr.getexpr_cpp(event.delay) \
+                if event.delay is not None else "0"
+            priority = sanitized_model.expr.getexpr_cpp(event.priority) \
+                if event.priority is not None else "0"
+            use_trigger = trigger_mode_types[int(bool(event.use_values_from_trigger_time))]
+            use_persist = persist_types[int(bool(event.trigger.persistent))]
+            initial_value = initial_value_types[int(bool(event.trigger.value or False))]
+
+            assignments: "list[str]" = []
+            for assign in event.assignments:
+                variable = assign.variable
+                expression = sanitized_model.expr.getexpr_cpp(assign.expression)
+
+                if isinstance(variable, str):
+                    if variable in sanitized_model.model.listOfSpecies:
+                        variable = sanitized_model.model.listOfSpecies.get(variable)
+                    elif variable in sanitized_model.model.listOfParameters:
+                        variable = sanitized_model.model.listOfParameters.get(variable)
+                    else:
+                        raise ValueError(f"Invalid event assignment {assign}: received name {variable} "
+                                         f"Must match the name of a valid Species or Parameter.")
+
+                if isinstance(variable, gillespy2.Species):
+                    assign_str = f"SPECIES_ASSIGNMENT(" \
+                                 f"{assign_id},{sanitized_model.species_id.get(variable.name)},{expression})"
+                elif isinstance(variable, gillespy2.Parameter):
+                    assign_str = f"VARIABLE_ASSIGNMENT(" \
+                                 f"{assign_id},{sanitized_model.parameter_id.get(variable.name)},{expression})"
+                else:
+                    raise ValueError(f"Invalid event assignment {assign}: received variable of type {type(variable)} "
+                                     f"Must be of type str, Species, or Parameter")
+                assignments.append(str(assign_id))
+                event_assignment_list.append(assign_str)
+                assign_id += 1
+            assignments: "str" = " AND ".join(assignments)
+            event_list.append(
+                f"EVENT("
+                f"{event_id},"
+                f"{{{assignments}}},"
+                f"{trigger},"
+                f"{delay},"
+                f"{priority},"
+                f"{use_trigger},"
+                f"{use_persist},"
+                f"{initial_value}"
+                f")"
+            )
+
+        sanitized_model.options["GPY_HYBRID_SPECIES_MODES"] = " ".join(species_mode_list)
+        sanitized_model.options["GPY_HYBRID_EVENTS"] = " ".join(event_list)
+        sanitized_model.options["GPY_HYBRID_NUM_EVENTS"] = str(len(event_list))
+        sanitized_model.options["GPY_HYBRID_EVENT_ASSIGNMENTS"] = " ".join(event_assignment_list)
+        sanitized_model.options["GPY_HYBRID_NUM_EVENT_ASSIGNMENTS"] = str(len(event_assignment_list))
+        return sanitized_model
 
     def _build(self, model: "Union[Model, SanitizedModel]", simulation_name: str, variable: bool, debug: bool = False,
                custom_definitions=None) -> str:
-        sanitized_model = TauHybridCSolver.__create_options(SanitizedModel(model))
+        variable = variable or len(model.listOfEvents) > 0
+        sanitized_model = TauHybridCSolver.__create_options(SanitizedModel(model, variable=variable))
         for rate_rule in model.listOfRateRules.values():
             sanitized_model.use_rate_rule(rate_rule)
         return super()._build(sanitized_model, simulation_name, variable, debug)
@@ -68,10 +146,17 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
             increment: int = None, seed: int = None, debug: bool = False, profile: bool = False, variables={}, 
             resume=None, live_output: str = None, live_output_options: dict = {}, tau_step: int = .03, tau_tol=0.03, **kwargs):
 
-        if self is None or self.model is None:
+        if self is None:
             self = TauHybridCSolver(model, resume=resume)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self._set_model(model=model)
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal TauHybridCSolver.model.")
+        self.model.resolve_parameters()
 
-        increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
         # Validate parameters prior to running the model.
         self._validate_type(variables, dict, "'variables' argument must be a dictionary.")
@@ -79,9 +164,8 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         self._validate_resume(t, resume)
         self._validate_kwargs(**kwargs)
         self._validate_sbml_features({
-            "Assignment Rules": len(model.listOfAssignmentRules),
-            "Events": len(model.listOfEvents),
-            "Function Definitions": len(model.listOfFunctionDefinitions)
+            "Assignment Rules": len(self.model.listOfAssignmentRules),
+            "Function Definitions": len(self.model.listOfFunctionDefinitions)
         })
 
         if resume is not None:
@@ -98,8 +182,8 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         }
 
         if self.variable:
-            populations = cutils.update_species_init_values(model.listOfSpecies, self.species, variables, resume)
-            parameter_values = cutils.change_param_values(model.listOfParameters, self.parameters, model.volume, variables)
+            populations = cutils.update_species_init_values(self.model.listOfSpecies, self.species, variables, resume)
+            parameter_values = cutils.change_param_values(self.model.listOfParameters, self.parameters, self.model.volume, variables)
 
             args.update({
                 "init_pop": populations,
@@ -115,7 +199,7 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         if live_output is not None:
             live_output_options['type'] = live_output
             display_args = {
-                "model": model, "number_of_trajectories": number_of_trajectories, "timeline": np.linspace(0, t, number_timesteps),
+                "model": self.model, "number_of_trajectories": number_of_trajectories, "timeline": np.linspace(0, t, number_timesteps),
                 "live_output_options": live_output_options, "resume": bool(resume)
             }
         else:
@@ -124,7 +208,7 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         args = self._make_args(args)
         decoder = IterativeSimDecoder.create_default(number_of_trajectories, number_timesteps, len(self.model.listOfSpecies))
 
-        sim_exec = self._build(model, self.target, self.variable, False)
+        sim_exec = self._build(self.model, self.target, self.variable, False)
         sim_status = self._run(sim_exec, args, decoder, timeout, display_args)
 
         if sim_status == SimulationReturnCode.FAILED:
diff --git a/gillespy2/solvers/cpp/tau_leaping_c_solver.py b/gillespy2/solvers/cpp/tau_leaping_c_solver.py
index a188c47e2..c56a98ea9 100644
--- a/gillespy2/solvers/cpp/tau_leaping_c_solver.py
+++ b/gillespy2/solvers/cpp/tau_leaping_c_solver.py
@@ -15,6 +15,7 @@
 You should have received a copy of the GNU General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 """
+import numpy as np
 
 from gillespy2.solvers.cpp.c_decoder import IterativeSimDecoder
 from gillespy2.solvers.utilities import solverutils as cutils
@@ -37,10 +38,17 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
             increment: int = None, seed: int = None, debug: bool = False, profile: bool = False, variables={}, 
             resume=None, live_output: str = None, live_output_options: dict = {}, tau_tol=0.03, **kwargs):
 
-        if self is None or self.model is None:
+        if self is None:
             self = TauLeapingCSolver(model, resume=resume)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self._set_model(model=model)
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal TauLeapingCSolver.model.")
+        self.model.resolve_parameters()
 
-        increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
         # Validate parameters prior to running the model.
         self._validate_type(variables, dict, "'variables' argument must be a dictionary.")
@@ -48,10 +56,10 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         self._validate_resume(t, resume)
         self._validate_kwargs(**kwargs)
         self._validate_sbml_features({
-            "Rate Rules": len(model.listOfRateRules),
-            "Assignment Rules": len(model.listOfAssignmentRules),
-            "Events": len(model.listOfEvents),
-            "Function Definitions": len(model.listOfFunctionDefinitions)
+            "Rate Rules": len(self.model.listOfRateRules),
+            "Assignment Rules": len(self.model.listOfAssignmentRules),
+            "Events": len(self.model.listOfEvents),
+            "Function Definitions": len(self.model.listOfFunctionDefinitions)
         })
 
         if resume is not None:
@@ -68,8 +76,8 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         }
 
         if self.variable:
-            populations = cutils.update_species_init_values(model.listOfSpecies, self.species, variables, resume)
-            parameter_values = cutils.change_param_values(model.listOfParameters, self.parameters, model.volume, variables)
+            populations = cutils.update_species_init_values(self.model.listOfSpecies, self.species, variables, resume)
+            parameter_values = cutils.change_param_values(self.model.listOfParameters, self.parameters, self.model.volume, variables)
 
             args.update({
                 "init_pop": populations,
@@ -85,7 +93,7 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         if live_output is not None:
             live_output_options['type'] = live_output
             display_args = {
-                "model": model, "number_of_trajectories": number_of_trajectories, "timeline": np.linspace(0, t, number_timesteps),
+                "model": self.model, "number_of_trajectories": number_of_trajectories, "timeline": np.linspace(0, t, number_timesteps),
                 "live_output_options": live_output_options, "resume": bool(resume)
             }
         else:
@@ -94,7 +102,7 @@ def run(self=None, model: Model = None, t: int = 20, number_of_trajectories: int
         args = self._make_args(args)
         decoder = IterativeSimDecoder.create_default(number_of_trajectories, number_timesteps, len(self.model.listOfSpecies))
 
-        sim_exec = self._build(model, self.target, self.variable, False)
+        sim_exec = self._build(self.model, self.target, self.variable, False)
         sim_status = self._run(sim_exec, args, decoder, timeout, display_args)
 
         if sim_status == SimulationReturnCode.FAILED:
diff --git a/gillespy2/solvers/numpy/CLE_solver.py b/gillespy2/solvers/numpy/CLE_solver.py
index c58ffcfef..511cfbbf5 100644
--- a/gillespy2/solvers/numpy/CLE_solver.py
+++ b/gillespy2/solvers/numpy/CLE_solver.py
@@ -105,154 +105,161 @@ def get_solver_settings(self):
     def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=None,
             debug=False, profile=False,  live_output=None, live_output_options={},
             timeout=None, resume=None, tau_tol=0.03, **kwargs):
-            """
-            Function calling simulation of the model.
-            This is typically called by the run function in GillesPy2 model objects
-            and will inherit those parameters which are passed with the model
-            as the arguments this run function.
+        """
+        Function calling simulation of the model.
+        This is typically called by the run function in GillesPy2 model objects
+        and will inherit those parameters which are passed with the model
+        as the arguments this run function.
 
-            :param model: GillesPy2 model object to simulate
-            :type model: gillespy2.Model
+        :param model: GillesPy2 model object to simulate
+        :type model: gillespy2.Model
 
-            :param t: Simulation run time
-            :type t: int
+        :param t: Simulation run time
+        :type t: int
 
-            :param number_of_trajectories: Number of trajectories to simulate
-            :type number_of_trajectories: int
+        :param number_of_trajectories: Number of trajectories to simulate
+        :type number_of_trajectories: int
 
-            :param increment: Save point increment for recording data
-            :type increment: float
+        :param increment: Save point increment for recording data
+        :type increment: float
 
-            :param seed: The random seed for the simulation. Optional, defaults to None
-            :type seed: int
+        :param seed: The random seed for the simulation. Optional, defaults to None
+        :type seed: int
 
-            :param debug: Set to True to provide additional debug information about the simulation
-            :type debug: bool
+        :param debug: Set to True to provide additional debug information about the simulation
+        :type debug: bool
 
-            :param profile: Set to True to provide information about step size (tau) taken at each step.
-            :type profile: bool
+        :param profile: Set to True to provide information about step size (tau) taken at each step.
+        :type profile: bool
 
-            :param live_output: The type of output to be displayed by solver. Can be "progress", "text", or "graph".
-            :type live_output: str
+        :param live_output: The type of output to be displayed by solver. Can be "progress", "text", or "graph".
+        :type live_output: str
 
-            :param live_output_options: COntains options for live_output. By default {"interval":1}. "interval"
-                specifies seconds between displaying. "clear_output" specifies if display should be refreshed with each
-                display.
-            :type live_output_options: dict
+        :param live_output_options: COntains options for live_output. By default {"interval":1}. "interval"
+            specifies seconds between displaying. "clear_output" specifies if display should be refreshed with each
+            display.
+        :type live_output_options: dict
 
-            :param timeout:
-            :param resume:
-            :param tau_tol:
-            :param kwargs:
+        :param timeout:
+        :param resume:
+        :param tau_tol:
+        :param kwargs:
 
-            :returns:
-            """
+        :returns:
+        """
 
-            if isinstance(self, type):
-                self = CLESolver(model=model, debug=debug, profile=profile)
+        if isinstance(self, type):
+            self = CLESolver(model=model, debug=debug, profile=profile)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self.model = model
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal CLESolver.model.")
+        self.model.resolve_parameters()
 
-            increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
-            self.stop_event = Event()
-            self.pause_event = Event()
+        self.stop_event = Event()
+        self.pause_event = Event()
 
-            if timeout is not None and timeout <= 0:
-                timeout = None
-            if len(kwargs) > 0:
-                for key in kwargs:
-                    log.warning('Unsupported keyword argument to {0} solver: {1}'.format(self.name, key))
+        if timeout is not None and timeout <= 0:
+            timeout = None
+        if len(kwargs) > 0:
+            for key in kwargs:
+                log.warning('Unsupported keyword argument to {0} solver: {1}'.format(self.name, key))
 
-            # create numpy array for timeline
-            if resume is not None:
-                # start where we last left off if resuming a simulatio
-                lastT = resume['time'][-1]
-                step = lastT - resume['time'][-2]
-                timeline = np.arange(lastT, t+step, step)
+        # create numpy array for timeline
+        if resume is not None:
+            # start where we last left off if resuming a simulatio
+            lastT = resume['time'][-1]
+            step = lastT - resume['time'][-2]
+            timeline = np.arange(lastT, t+step, step)
+        else:
+            timeline = np.linspace(0, t, int(round(t / increment + 1)))
+
+        species = list(self.model._listOfSpecies.keys())
+        trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(self.model, number_of_trajectories,
+                                                                                   timeline, species, resume=resume)
+
+        # total_time and curr_state are list of len 1 so that __run receives reference
+        if resume is not None:
+            total_time = [resume['time'][-1]]
+        else:
+            total_time = [0]
+
+        curr_state = [None]
+        live_grapher = [None]
+
+        sim_thread = Thread(target=self.___run, args=(curr_state, total_time, timeline, trajectory_base, tmpSpecies,
+                                                      live_grapher,), kwargs={'t': t,
+                                                                              'number_of_trajectories':
+                                                                                  number_of_trajectories,
+                                                                              'increment': increment, 'seed': seed,
+                                                                              'debug': debug, 'resume': resume,
+                                                                              'timeout': timeout, 'tau_tol': tau_tol
+                                                                              })
+        try:
+            time = 0
+            sim_thread.start()
+            if live_output is not None:
+                import gillespy2.core.liveGraphing
+                live_output_options['type'] = live_output
+                gillespy2.core.liveGraphing.valid_graph_params(
+                    live_output_options)
+                if resume is not None:
+                    resumeTest = True  # If resuming, relay this information to live_grapher
+                else:
+                    resumeTest = False
+                live_grapher[
+                    0] = gillespy2.core.liveGraphing.LiveDisplayer(self.model,
+                                                                   timeline,
+                                                                   number_of_trajectories,
+                                                                   live_output_options,
+                                                                   resume=resumeTest)
+                display_timer = gillespy2.core.liveGraphing.RepeatTimer(
+                    live_output_options['interval'],
+                    live_grapher[0].display, args=(curr_state,
+                                                   total_time,
+                                                   trajectory_base,
+                                                   live_output
+                                                   )
+                    )
+                display_timer.start()
+
+            if timeout is not None:
+                while sim_thread.is_alive():
+                    sim_thread.join(.1)
+                    time += .1
+                    if time >= timeout:
+                        break
             else:
-                timeline = np.linspace(0, t, int(round(t / increment + 1)))
-
-            species = list(model._listOfSpecies.keys())
-            trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(model, number_of_trajectories,
-                                                                                       timeline, species, resume=resume)
+                while sim_thread.is_alive():
+                    sim_thread.join(.1)
 
-            # total_time and curr_state are list of len 1 so that __run receives reference
-            if resume is not None:
-                total_time = [resume['time'][-1]]
-            else:
-                total_time = [0]
-
-            curr_state = [None]
-            live_grapher = [None]
-
-            sim_thread = Thread(target=self.___run, args=(model, curr_state, total_time, timeline, trajectory_base, tmpSpecies,
-                                                          live_grapher,), kwargs={'t': t,
-                                                                                  'number_of_trajectories':
-                                                                                      number_of_trajectories,
-                                                                                  'increment': increment, 'seed': seed,
-                                                                                  'debug': debug, 'resume': resume,
-                                                                                  'timeout': timeout, 'tau_tol': tau_tol
-                                                                                  })
-            try:
-                time = 0
-                sim_thread.start()
-                if live_output is not None:
-                    import gillespy2.core.liveGraphing
-                    live_output_options['type'] = live_output
-                    gillespy2.core.liveGraphing.valid_graph_params(
-                        live_output_options)
-                    if resume is not None:
-                        resumeTest = True  # If resuming, relay this information to live_grapher
-                    else:
-                        resumeTest = False
-                    live_grapher[
-                        0] = gillespy2.core.liveGraphing.LiveDisplayer(model,
-                                                                       timeline,
-                                                                       number_of_trajectories,
-                                                                       live_output_options,
-                                                                       resume=resumeTest)
-                    display_timer = gillespy2.core.liveGraphing.RepeatTimer(
-                        live_output_options['interval'],
-                        live_grapher[0].display, args=(curr_state,
-                                                       total_time,
-                                                       trajectory_base,
-                                                       live_output
-                                                       )
-                        )
-                    display_timer.start()
-
-                if timeout is not None:
-                    while sim_thread.is_alive():
-                        sim_thread.join(.1)
-                        time += .1
-                        if time >= timeout:
-                            break
-                else:
-                    while sim_thread.is_alive():
-                        sim_thread.join(.1)
-
-                if live_grapher[0] is not None:
-                    display_timer.cancel()
-                self.stop_event.set()
-                while self.result is None:
-                    pass
-            except KeyboardInterrupt:
-                if live_output:
-                    display_timer.pause = True
-                    display_timer.cancel()
-                self.pause_event.set()
-                while self.result is None:
-                    pass
-            if hasattr(self, 'has_raised_exception'):
-                raise self.has_raised_exception
-
-            return Results.build_from_solver_results(self, live_output_options)
-
-    def ___run(self, model, curr_state,total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
+            if live_grapher[0] is not None:
+                display_timer.cancel()
+            self.stop_event.set()
+            while self.result is None:
+                pass
+        except KeyboardInterrupt:
+            if live_output:
+                display_timer.pause = True
+                display_timer.cancel()
+            self.pause_event.set()
+            while self.result is None:
+                pass
+        if hasattr(self, 'has_raised_exception'):
+            raise self.has_raised_exception
+
+        return Results.build_from_solver_results(self, live_output_options)
+
+    def ___run(self, curr_state,total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
                number_of_trajectories=1, increment=0.05, seed=None, debug=False, profile=False, show_labels=True,
                timeout=None, resume=None, tau_tol=0.03, **kwargs):
 
         try:
-            self.__run(model, curr_state, total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t, number_of_trajectories,
+            self.__run(curr_state, total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t, number_of_trajectories,
                        increment, seed, debug, profile, timeout, resume, tau_tol, **kwargs)
 
         except Exception as e:
@@ -260,7 +267,7 @@ def ___run(self, model, curr_state,total_time, timeline, trajectory_base, tmpSpe
             self.result = []
             return [], -1
 
-    def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
+    def __run(self, curr_state, total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
               number_of_trajectories=1, increment=0.05, seed=None, debug=False, profile=False, timeout=None,
               resume=None, tau_tol=0.03, **kwargs):
 
@@ -268,7 +275,7 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
         # how species and time are initialized to 0
         timeStopped = 0
         if resume is not None:
-            if resume[0].model != model:
+            if resume[0].model != self.model:
                 raise ModelError('When resuming, one must not alter the model being resumed.')
             if t < resume['time'][-1]:
                 raise ExecutionError(
@@ -278,7 +285,7 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
             print("t = ", t)
             print("increment = ", increment)
 
-        species_mappings, species, parameter_mappings, number_species = nputils.numpy_initialization(model)
+        species_mappings, species, parameter_mappings, number_species = nputils.numpy_initialization(self.model)
 
         if seed is not None:
             if not isinstance(seed, int):
@@ -301,7 +308,7 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
             if live_grapher[0] is not None:
                 live_grapher[0].increment_trajectory(trajectory_num)
 
-            start_state = [0] * (len(model.listOfReactions) + len(model.listOfRateRules))
+            start_state = [0] * (len(self.model.listOfReactions) + len(self.model.listOfRateRules))
             propensities = {}
             curr_state[0] = {}
 
@@ -312,35 +319,35 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
                 save_time = 0
                 curr_time = [0]
 
-            curr_state[0]['vol'] = model.volume
+            curr_state[0]['vol'] = self.model.volume
             data = {'time': timeline}
             steps_taken = []
             steps_rejected = 0
             entry_count = 0
             trajectory = trajectory_base[trajectory_num]
 
-            HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, critical_threshold = Tau.initialize(model, tau_tol)
+            HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, critical_threshold = Tau.initialize(self.model, tau_tol)
 
             if resume is not None:
-                for spec in model.listOfSpecies:
+                for spec in self.model.listOfSpecies:
                     curr_state[0][spec] = tmpSpecies[spec]
             else:
-                for spec in model.listOfSpecies:
-                    curr_state[0][spec] = model.listOfSpecies[spec].initial_value
+                for spec in self.model.listOfSpecies:
+                    curr_state[0][spec] = self.model.listOfSpecies[spec].initial_value
 
-            for param in model.listOfParameters:
-                curr_state[0][param] = model.listOfParameters[param].value
+            for param in self.model.listOfParameters:
+                curr_state[0][param] = self.model.listOfParameters[param].value
 
-            for i, rxn in enumerate(model.listOfReactions):
+            for i, rxn in enumerate(self.model.listOfReactions):
                 # set reactions to uniform random number and add to start_state
                 start_state[i] = (math.log(random.uniform(0, 1)))
                 if debug:
                     print("Setting Random number ",
-                          start_state[i], " for ", model.listOfReactions[rxn].name)
+                          start_state[i], " for ", self.model.listOfReactions[rxn].name)
 
             compiled_propensities = {}
-            for i, r in enumerate(model.listOfReactions):
-                compiled_propensities[r] = compile(model.listOfReactions[r].ode_propensity_function, '<string>', 'eval')
+            for i, r in enumerate(self.model.listOfReactions):
+                compiled_propensities[r] = compile(self.model.listOfReactions[r].ode_propensity_function, '<string>', 'eval')
 
             timestep = 0
             
@@ -364,12 +371,12 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
 
                     propensity_sum = 0
 
-                    for i, r in enumerate(model.listOfReactions):
+                    for i, r in enumerate(self.model.listOfReactions):
                         propensities[r] = eval(compiled_propensities[r], curr_state[0])
                         propensity_sum += propensities[r]
 
                     tau_args = [HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, tau_tol, critical_threshold,
-                                model, propensities, curr_state[0], curr_time[0], save_time]
+                                self.model, propensities, curr_state[0], curr_time[0], save_time]
 
                     tau_step = Tau.select(*tau_args)
 
@@ -386,19 +393,19 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
 
                         reactions, curr_state[0], curr_time[0] = self.__get_reactions(
                             tau_step, curr_state[0], curr_time[0], save_time,
-                            propensities, model.listOfReactions)
+                            propensities, self.model.listOfReactions)
 
                         # Update curr_state with the result of the CLE step
                         species_modified = {}
-                        for i, rxn in enumerate(model.listOfReactions):
+                        for i, rxn in enumerate(self.model.listOfReactions):
                             if reactions[rxn] > 0:
-                                for reactant in model.listOfReactions[rxn].reactants:
+                                for reactant in self.model.listOfReactions[rxn].reactants:
                                     species_modified[reactant.name] = True
-                                    curr_state[0][reactant.name] -= model.listOfReactions[
+                                    curr_state[0][reactant.name] -= self.model.listOfReactions[
                                         rxn].reactants[reactant] * reactions[rxn]
-                                for product in model.listOfReactions[rxn].products:
+                                for product in self.model.listOfReactions[rxn].products:
                                     species_modified[product.name] = True
-                                    curr_state[0][product.name] += model.listOfReactions[
+                                    curr_state[0][product.name] += self.model.listOfReactions[
                                         rxn].products[product] * reactions[rxn]
                         neg_state = False
                         for spec in species_modified:
diff --git a/gillespy2/solvers/numpy/ode_solver.py b/gillespy2/solvers/numpy/ode_solver.py
index 60656b0a9..acd5efc58 100644
--- a/gillespy2/solvers/numpy/ode_solver.py
+++ b/gillespy2/solvers/numpy/ode_solver.py
@@ -109,8 +109,15 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, show_l
         """
         if isinstance(self, type):
             self = ODESolver(model=model)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self.model = model
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal OSESolver.model.")
+        self.model.resolve_parameters()
 
-        increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
         self.stop_event = Event()
         self.pause_event = Event()
@@ -132,8 +139,8 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, show_l
         else:
             timeline = np.linspace(0, t, int(round(t / increment + 1)))
 
-        species = list(model._listOfSpecies.keys())
-        trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(model, number_of_trajectories,
+        species = list(self.model._listOfSpecies.keys())
+        trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(self.model, number_of_trajectories,
                                                                                    timeline, species, resume=resume)
 
         # curr_time and curr_state are list of len 1 so that __run receives reference
@@ -144,7 +151,7 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, show_l
         curr_state = [None]
         live_grapher = [None]
 
-        sim_thread = Thread(target=self.___run, args=(model, curr_state, curr_time, timeline, trajectory_base,
+        sim_thread = Thread(target=self.___run, args=(curr_state, curr_time, timeline, trajectory_base,
                                                       tmpSpecies, live_grapher,), kwargs={'t': t,
                                                                                           'number_of_trajectories':
                                                                                               number_of_trajectories,
@@ -167,7 +174,7 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, show_l
                     resumeTest = True  # If resuming, relay this information to live_grapher
                 else:
                     resumeTest = False
-                live_grapher[0] = gillespy2.core.liveGraphing.LiveDisplayer(model, timeline, number_of_trajectories,
+                live_grapher[0] = gillespy2.core.liveGraphing.LiveDisplayer(self.model, timeline, number_of_trajectories,
                                                                             live_output_options, resume=resumeTest)
                 display_timer = gillespy2.core.liveGraphing.RepeatTimer(live_output_options['interval'],
                                                                         live_grapher[0].display,
@@ -202,11 +209,11 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, show_l
         
         return Results.build_from_solver_results(self, live_output_options)
 
-    def ___run(self, model, curr_state, curr_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
+    def ___run(self, curr_state, curr_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
                number_of_trajectories=1, increment=0.05, timeout=None, show_labels=True, integrator='lsoda',
                integrator_options={}, resume=None, **kwargs):
         try:
-            self.__run(model, curr_state, curr_time, timeline, trajectory_base, tmpSpecies, live_grapher, t,
+            self.__run(curr_state, curr_time, timeline, trajectory_base, tmpSpecies, live_grapher, t,
                        number_of_trajectories, increment, timeout, show_labels, integrator, integrator_options, resume,
                        **kwargs)
         except Exception as e:
@@ -214,13 +221,13 @@ def ___run(self, model, curr_state, curr_time, timeline, trajectory_base, tmpSpe
             self.result = []
             return [], -1
 
-    def __run(self, model, curr_state, curr_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
+    def __run(self, curr_state, curr_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
               number_of_trajectories=1, increment=0.05, timeout=None, show_labels=True, integrator='lsoda',
               integrator_options={}, resume=None, **kwargs):
 
         timeStopped = 0
         if resume is not None:
-            if resume[0].model != model:
+            if resume[0].model != self.model:
                 raise gillespyError.ModelError('When resuming, one must not alter the model being resumed.')
             if t < resume['time'][-1]:
                 raise gillespyError.ExecutionError(
@@ -229,13 +236,13 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, tmpSpec
 
         # compile reaction propensity functions for eval
         c_prop = OrderedDict()
-        for r_name, reaction in model.listOfReactions.items():
+        for r_name, reaction in self.model.listOfReactions.items():
             c_prop[r_name] = compile(reaction.ode_propensity_function, '<string>', 'eval')
 
         result = trajectory_base[0]
         entry_count = 0
 
-        y0 = [0] * len(model.listOfSpecies)
+        y0 = [0] * len(self.model.listOfSpecies)
 
         curr_state[0] = OrderedDict()
 
@@ -244,14 +251,16 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, tmpSpec
                 curr_state[0][s] = tmpSpecies[s]
                 y0[i] = tmpSpecies[s]
         else:
-            for i, s in enumerate(model.listOfSpecies.values()):
+            for i, s in enumerate(self.model.listOfSpecies.values()):
                 curr_state[0][s.name] = s.initial_value
                 y0[i] = s.initial_value
 
-        for p_name, param in model.listOfParameters.items():
+        for p_name, param in self.model.listOfParameters.items():
             curr_state[0][p_name] = param.value
+        if 'vol' not in curr_state[0]:
+            curr_state[0]['vol'] = 1.0
         rhs = ode(ODESolver.__f).set_integrator(integrator, **integrator_options)
-        rhs.set_initial_value(y0, curr_time[0]).set_f_params(curr_state, model, c_prop)
+        rhs.set_initial_value(y0, curr_time[0]).set_f_params(curr_state, self.model, c_prop)
 
         while entry_count < timeline.size - 1:
             if self.stop_event.is_set():
@@ -265,14 +274,14 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, tmpSpec
             entry_count += 1
             y0 = rhs.integrate(int_time)
             curr_time[0] += increment
-            for i, spec in enumerate(model.listOfSpecies):
+            for i, spec in enumerate(self.model.listOfSpecies):
                 curr_state[0][spec] = y0[i]
                 result[entry_count][i+1] = curr_state[0][spec]
 
         results_as_dict = {
             'time': timeline
         }
-        for i, species in enumerate(model.listOfSpecies):
+        for i, species in enumerate(self.model.listOfSpecies):
             results_as_dict[species] = result[:, i+1]
         results = [results_as_dict] * number_of_trajectories
 
diff --git a/gillespy2/solvers/numpy/ssa_solver.py b/gillespy2/solvers/numpy/ssa_solver.py
index 20c909546..5056b6fba 100644
--- a/gillespy2/solvers/numpy/ssa_solver.py
+++ b/gillespy2/solvers/numpy/ssa_solver.py
@@ -73,8 +73,15 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
 
         if isinstance(self, type):
             self = NumPySSASolver(model=model)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self.model = model
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal NumPySSASolver.model.")
+        self.model.resolve_parameters()
 
-        increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
         self.stop_event = Event()
         self.pause_event = Event()
@@ -94,9 +101,9 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
         else:
             timeline = np.linspace(0, t, int(round(t / increment + 1)))
 
-        species = list(model._listOfSpecies.keys())
+        species = list(self.model._listOfSpecies.keys())
 
-        trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(model, number_of_trajectories,
+        trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(self.model, number_of_trajectories,
                                                                                    timeline, species, resume=resume)
 
         # curr_time and curr_state are list of len 1 so that __run receives reference
@@ -108,7 +115,7 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
         curr_state = [None]
         live_grapher = [None]
 
-        sim_thread = Thread(target=self.___run, args=(model, curr_state, total_time, timeline, trajectory_base,
+        sim_thread = Thread(target=self.___run, args=(curr_state, total_time, timeline, trajectory_base,
                                                       live_grapher,), kwargs={'t': t, 'number_of_trajectories':
                                                                               number_of_trajectories,
                                                                               'increment': increment,
@@ -127,7 +134,7 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
                     resumeTest = True  # If resuming, relay this information to live_grapher
                 else:
                     resumeTest = False
-                live_grapher[0] = gillespy2.core.liveGraphing.LiveDisplayer(model, timeline, number_of_trajectories,
+                live_grapher[0] = gillespy2.core.liveGraphing.LiveDisplayer(self.model, timeline, number_of_trajectories,
                                                                              live_output_options,resume = resumeTest)
                 display_timer = gillespy2.core.liveGraphing.RepeatTimer(live_output_options['interval'],
                                                                         live_grapher[0].display, args=(curr_state,
@@ -165,19 +172,19 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
 
         return Results.build_from_solver_results(self, live_output_options)
 
-    def ___run(self, model, curr_state, total_time, timeline, trajectory_base, live_grapher, t=20,
+    def ___run(self, curr_state, total_time, timeline, trajectory_base, live_grapher, t=20,
                number_of_trajectories=1, increment=0.05, seed=None, debug=False, show_labels=True, resume=None,
                timeout=None):
 
         try:
-            self.__run(model, curr_state, total_time, timeline, trajectory_base, live_grapher, t, number_of_trajectories,
+            self.__run(curr_state, total_time, timeline, trajectory_base, live_grapher, t, number_of_trajectories,
                        increment, seed, debug, show_labels, resume, timeout)
         except Exception as e:
             self.has_raised_exception = e
             self.result = []
             return [], -1
 
-    def __run(self, model, curr_state, total_time, timeline, trajectory_base, live_grapher, t=20,
+    def __run(self, curr_state, total_time, timeline, trajectory_base, live_grapher, t=20,
               number_of_trajectories=1, increment=0.05, seed=None, debug=False, show_labels=True,
               resume=None,  timeout=None):
 
@@ -186,7 +193,7 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, live_g
         timeStopped = 0
 
         if resume is not None:
-            if resume[0].model != model:
+            if resume[0].model != self.model:
                 raise gillespyError.ModelError('When resuming, one must not alter the model being resumed.')
             if t < resume['time'][-1]:
                 raise gillespyError.ExecutionError(
@@ -195,18 +202,18 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, live_g
 
         random.seed(seed)
 
-        species_mappings, species, parameter_mappings, number_species = nputils.numpy_initialization(model)
+        species_mappings, species, parameter_mappings, number_species = nputils.numpy_initialization(self.model)
 
         # create dictionary of all constant parameters for propensity evaluation
-        parameters = {'V': model.volume}
-        for paramName, param in model.listOfParameters.items():
+        parameters = {'V': self.model.volume}
+        for paramName, param in self.model.listOfParameters.items():
             parameters[parameter_mappings[paramName]] = param.value
 
         # create mapping of reaction dictionary to array indices
-        reactions = list(model.listOfReactions.keys())
+        reactions = list(self.model.listOfReactions.keys())
 
         # create mapping of reactions, and which reactions depend on their reactants/products
-        dependent_rxns = nputils.dependency_grapher(model, reactions)
+        dependent_rxns = nputils.dependency_grapher(self.model, reactions)
         number_reactions = len(reactions)
         propensity_functions = {}
 
@@ -217,11 +224,11 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, live_g
         for i, reaction in enumerate(reactions):
             # replace all references to species with array indices
             for j, spec in enumerate(species):
-                species_changes[i][j] = model.listOfReactions[reaction].products.get(model.listOfSpecies[spec], 0) \
-                                        - model.listOfReactions[reaction].reactants.get(model.listOfSpecies[spec], 0)
+                species_changes[i][j] = self.model.listOfReactions[reaction].products.get(self.model.listOfSpecies[spec], 0) \
+                                        - self.model.listOfReactions[reaction].reactants.get(self.model.listOfSpecies[spec], 0)
                 if debug:
                     print('species_changes: {0},i={1}, j={2}... {3}'.format(species, i, j, species_changes[i][j]))
-            propensity_functions[reaction] = [eval('lambda S:' + model.listOfReactions[reaction].
+            propensity_functions[reaction] = [eval('lambda S:' + self.model.listOfReactions[reaction].
                                                    sanitized_propensity_function(species_mappings, parameter_mappings),
                                                    parameters), i]
         if debug:
@@ -251,11 +258,11 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, live_g
             else:
                 curr_time = [0]
 
-            for spec in model.listOfSpecies:
+            for spec in self.model.listOfSpecies:
                 if resume is not None:
                     curr_state[0][spec] = resume[spec][-1]
                 else:
-                    curr_state[0][spec] = model.listOfSpecies[spec].initial_value
+                    curr_state[0][spec] = self.model.listOfSpecies[spec].initial_value
 
             propensity_sums = np.zeros(number_reactions)
             # calculate initial propensity sums
@@ -311,7 +318,7 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, live_g
                         print('if <=0, fire: ', cumulative_sum)
                     if cumulative_sum <= 0:
 
-                        for i,spec in enumerate(model.listOfSpecies):
+                        for i,spec in enumerate(self.model.listOfSpecies):
                             curr_state[0][spec] += species_changes[potential_reaction][i]
 
                         reacName = reactions[potential_reaction]
diff --git a/gillespy2/solvers/numpy/tau_hybrid_solver.py b/gillespy2/solvers/numpy/tau_hybrid_solver.py
index 57572a63c..921e9becc 100644
--- a/gillespy2/solvers/numpy/tau_hybrid_solver.py
+++ b/gillespy2/solvers/numpy/tau_hybrid_solver.py
@@ -75,7 +75,7 @@ def __init__(self, model=None):
         rc = 0
         self.model = model
 
-    def __toggle_reactions(self, model, all_compiled, deterministic_reactions, dependencies, 
+    def __toggle_reactions(self, all_compiled, deterministic_reactions, dependencies, 
                             curr_state, det_spec, rr_sets):
         """
         Helper method which is used to convert reaction channels into
@@ -112,10 +112,10 @@ def __toggle_reactions(self, model, all_compiled, deterministic_reactions, depen
             return rr_sets[deterministic_reactions]
         else:
         # Otherwise, this is a new determinstic reaction set that must be compiled
-            return self.__create_diff_eqs(deterministic_reactions, model,
+            return self.__create_diff_eqs(deterministic_reactions,
                                             dependencies, rr_sets)
 
-    def __create_diff_eqs(self, comb, model, dependencies, rr_sets):
+    def __create_diff_eqs(self, comb, dependencies, rr_sets):
         """
         Helper method used to convert stochastic reaction descriptions into
         differential equations, used dynamically throught the simulation.
@@ -125,51 +125,51 @@ def __create_diff_eqs(self, comb, model, dependencies, rr_sets):
 
         # Initialize sample dict
         rr_vars = {}
-        for n, rr in model.listOfRateRules.items():
+        for n, rr in self.model.listOfRateRules.items():
             rr_vars[rr.variable] = n
-        for spec in model.listOfSpecies:
+        for spec in self.model.listOfSpecies:
             if spec in rr_vars.keys():
-                diff_eqs[model.listOfSpecies[spec]] = model.listOfRateRules[rr_vars[spec]].formula
+                diff_eqs[self.model.listOfSpecies[spec]] = self.model.listOfRateRules[rr_vars[spec]].formula
             else:
-                diff_eqs[model.listOfSpecies[spec]] = '0'
+                diff_eqs[self.model.listOfSpecies[spec]] = '0'
 
         # loop through each det reaction and concatenate it's diff eq for each species
         for reaction in comb:
             factor = {dep: 0 for dep in dependencies[reaction]}
 
-            for key, value in model.listOfReactions[reaction].reactants.items():
+            for key, value in self.model.listOfReactions[reaction].reactants.items():
                 if not key.constant and not key.boundary_condition:
                     factor[key.name] -= value
-            for key, value in model.listOfReactions[reaction].products.items():
+            for key, value in self.model.listOfReactions[reaction].products.items():
                 if not key.constant and not key.boundary_condition:
                     factor[key.name] += value
 
             for dep in dependencies[reaction]:
                 if factor[dep] != 0:
-                    if model.listOfSpecies[dep].mode == 'continuous':
-                        diff_eqs[model.listOfSpecies[dep]] += ' + {0}*({1})'.format(factor[dep],
-                                                               model.listOfReactions[reaction].ode_propensity_function)
+                    if self.model.listOfSpecies[dep].mode == 'continuous':
+                        diff_eqs[self.model.listOfSpecies[dep]] += ' + {0}*({1})'.format(factor[dep],
+                                                               self.model.listOfReactions[reaction].ode_propensity_function)
                     else:
-                        diff_eqs[model.listOfSpecies[dep]] += ' + {0}*({1})'.format(factor[dep],
-                                                               model.listOfReactions[reaction].propensity_function)
+                        diff_eqs[self.model.listOfSpecies[dep]] += ' + {0}*({1})'.format(factor[dep],
+                                                               self.model.listOfReactions[reaction].propensity_function)
 
-        for spec in model.listOfSpecies:
-            if diff_eqs[model.listOfSpecies[spec]] == '0':
-                del diff_eqs[model.listOfSpecies[spec]]
+        for spec in self.model.listOfSpecies:
+            if diff_eqs[self.model.listOfSpecies[spec]] == '0':
+                del diff_eqs[self.model.listOfSpecies[spec]]
         # create a dictionary of compiled gillespy2 rate rules
         for spec, rate in diff_eqs.items():
             rate_rules[spec] = compile(gillespy2.RateRule(spec, rate).formula, '<string>', 'eval')
         rr_sets[comb] = rate_rules # save values
         return rate_rules
 
-    def __flag_det_reactions(self, model, det_spec, det_rxn, dependencies):
+    def __flag_det_reactions(self, det_spec, det_rxn, dependencies):
         """
         Helper method used to flag reactions that can be processed
         deterministically without exceeding the user-supplied tolerance.
         """
         # Determine if each rxn would be deterministic apart from other reactions
         prev_state = det_rxn.copy()
-        for rxn in model.listOfReactions:
+        for rxn in self.model.listOfReactions:
             # assume it is deterministic
             det_rxn[rxn] = True
             # iterate through the dependent species of this reaction
@@ -177,10 +177,10 @@ def __flag_det_reactions(self, model, det_spec, det_rxn, dependencies):
                 # if any of the dependencies are discrete or (dynamic AND the 
                 # species itself has not been flagged as deterministic)
                 # then allow it to be modelled discretely
-                if model.listOfSpecies[species].mode == 'discrete':
+                if self.model.listOfSpecies[species].mode == 'discrete':
                     det_rxn[rxn] = False
                     break
-                if model.listOfSpecies[species].mode == 'dynamic' and det_spec[species] == False:
+                if self.model.listOfSpecies[species].mode == 'dynamic' and det_spec[species] == False:
                     det_rxn[rxn] = False
                     break
 
@@ -197,15 +197,15 @@ def __calculate_statistics(self, *switch_args):
         Calculates Mean, Standard Deviation, and Coefficient of Variance for each
         dynamic species, then set if species can be represented determistically
         """
-        model, propensities, curr_state, tau_step, det_spec = switch_args
+        propensities, curr_state, tau_step, det_spec = switch_args
 
         CV = OrderedDict()
         mn = {species: curr_state[species] for species, value in
-              model.listOfSpecies.items() if value.mode == 'dynamic'}
+              self.model.listOfSpecies.items() if value.mode == 'dynamic'}
         sd = {species: 0 for species, value in
-              model.listOfSpecies.items() if value.mode == 'dynamic'}
+              self.model.listOfSpecies.items() if value.mode == 'dynamic'}
 
-        for r, rxn in model.listOfReactions.items():
+        for r, rxn in self.model.listOfReactions.items():
             for reactant in rxn.reactants:
                 if reactant.mode == 'dynamic':
                     mn[reactant.name] -= (tau_step * propensities[r] * rxn.reactants[reactant])
@@ -217,7 +217,7 @@ def __calculate_statistics(self, *switch_args):
 
         # Get coefficient of variance for each dynamic species
         for species in mn:
-            sref = model.listOfSpecies[species]
+            sref = self.model.listOfSpecies[species]
             if sref.switch_min == 0:
                 if mn[species] > 0:
                     CV[species] = sd[species] / mn[species]
@@ -260,7 +260,7 @@ def __f(t, y, curr_state, species, reactions, rate_rules, propensities,
 
         return state_change
 
-    def __find_event_time(self, sol, model, start, end, index, depth):
+    def __find_event_time(self, sol, start, end, index, depth):
         """
         Helper method providing binary search implementation for locating
         precise event times.
@@ -268,19 +268,19 @@ def __find_event_time(self, sol, model, start, end, index, depth):
         dense_range = np.linspace(start, end, 3)
         mid = dense_range[1]
         if start >= mid or mid >= end or depth == 20: return end
-        solutions = np.diff(sol.sol(dense_range)[-len(model.listOfEvents) + index])
+        solutions = np.diff(sol.sol(dense_range)[-len(self.model.listOfEvents) + index])
         bool_res = [x > 0 for x in solutions]
 
         if bool_res[0]:  # event before mid
             depth += 1
-            return self.__find_event_time(sol, model, dense_range[0],
+            return self.__find_event_time(sol, dense_range[0],
                                           dense_range[1], index, depth)
         else:  # event after mid
             depth += 1
-            return self.__find_event_time(sol, model, dense_range[1],
+            return self.__find_event_time(sol, dense_range[1],
                                           dense_range[2], index, depth)
 
-    def __detect_events(self, event_sensitivity, sol, model, delayed_events,
+    def __detect_events(self, event_sensitivity, sol, delayed_events,
                         trigger_states, curr_time, curr_state):
         """
         Helper method to locate precise time of event firing.  This method
@@ -291,8 +291,8 @@ def __detect_events(self, event_sensitivity, sol, model, delayed_events,
         event_times = {}
         dense_range = np.linspace(sol.t[0], sol.t[-1], len(sol.t) * event_sensitivity)
         solutions = np.diff(sol.sol(dense_range))
-        for i, e in enumerate(model.listOfEvents.values()):
-            bool_res = [x > 0 for x in solutions[i - len(model.listOfEvents)]]
+        for i, e in enumerate(self.model.listOfEvents.values()):
+            bool_res = [x > 0 for x in solutions[i - len(self.model.listOfEvents)]]
             curr_state[e.name] = bool_res[-1]
             # Search for changes from False to True in event, record first time
             for y in range(1, len(dense_range) - 1):
@@ -308,7 +308,7 @@ def __detect_events(self, event_sensitivity, sol, model, delayed_events,
                         curr_state[e.name] = False
                 # IF triggered from false to true, refine search
                 elif bool_res[y] and dense_range[y] != curr_time and bool_res[y - 1] == 0:
-                    event_time = self.__find_event_time(sol, model, dense_range[y - 1],
+                    event_time = self.__find_event_time(sol, dense_range[y - 1],
                                                         dense_range[y + 1], i, 0)
                     if event_time in event_times:
                         event_times[event_time].append(e)
@@ -344,7 +344,7 @@ def __get_next_step(self, event_times, reaction_times, delayed_events,
                         next_delayed_event)
         return next_step[curr_time], curr_time
 
-    def __process_queued_events(self, model, event_queue, trigger_states,
+    def __process_queued_events(self, event_queue, trigger_states,
                                 curr_state):
         """
         Helper method which processes the events queue. Method is primarily for
@@ -356,7 +356,7 @@ def __process_queued_events(self, model, event_queue, trigger_states,
         pre_assignment_state = curr_state.copy()
         while event_queue:
             # Get events in priority order
-            fired_event = model.listOfEvents[heapq.heappop(event_queue)[1]]
+            fired_event = self.model.listOfEvents[heapq.heappop(event_queue)[1]]
             events_processed.append(fired_event)
             if fired_event.name in trigger_states:
                 assignment_state = trigger_states[fired_event.name]
@@ -391,7 +391,7 @@ def __handle_event(self, event, curr_state, curr_time, event_queue,
             else:
                 trigger_states[event.name] = curr_state
 
-    def __check_t0_events(self, model, initial_state):
+    def __check_t0_events(self, initial_state):
         """
         Helper method for firing events who reach a trigger condition at start
         of simulation, time == 0.
@@ -399,7 +399,7 @@ def __check_t0_events(self, model, initial_state):
         # Check Event State at t==0
         species_modified_by_events = []
         t0_delayed_events = {}
-        for e in model.listOfEvents.values():
+        for e in self.model.listOfEvents.values():
             if not e.trigger.value:
                 t0_firing = eval(e.trigger.expression, {**eval_globals, **initial_state})
                 if t0_firing:
@@ -412,7 +412,7 @@ def __check_t0_events(self, model, initial_state):
                         t0_delayed_events[e.name] = execution_time
         return t0_delayed_events, species_modified_by_events
 
-    def __update_stochastic_rxn_states(self, model, compiled_reactions, curr_state):
+    def __update_stochastic_rxn_states(self, compiled_reactions, curr_state):
         """
         Helper method for updating the state of stochastic reactions.
         """
@@ -425,15 +425,15 @@ def __update_stochastic_rxn_states(self, model, compiled_reactions, curr_state):
                 rxn_count[rxn] += 1
                 curr_state[rxn] += math.log(random.uniform(0, 1))
             if rxn_count[rxn]:
-                for reactant in model.listOfReactions[rxn].reactants:
+                for reactant in self.model.listOfReactions[rxn].reactants:
                     species_modified[reactant.name] = True
-                    curr_state[reactant.name] -= model.listOfReactions[rxn].reactants[reactant] * rxn_count[rxn]
-                for product in model.listOfReactions[rxn].products:
+                    curr_state[reactant.name] -= self.model.listOfReactions[rxn].reactants[reactant] * rxn_count[rxn]
+                for product in self.model.listOfReactions[rxn].products:
                     species_modified[product.name] = True
-                    curr_state[product.name] += model.listOfReactions[rxn].products[product] * rxn_count[rxn]
+                    curr_state[product.name] += self.model.listOfReactions[rxn].products[product] * rxn_count[rxn]
         return species_modified
 
-    def __integrate(self, integrator, integrator_options, curr_state, y0, model, curr_time,
+    def __integrate(self, integrator, integrator_options, curr_state, y0, curr_time,
                     propensities, y_map, compiled_reactions,
                     active_rr, event_queue,
                     delayed_events, trigger_states,
@@ -445,18 +445,18 @@ def __integrate(self, integrator, integrator_options, curr_state, y0, model, cur
         updated and returned to __simulate along with curr_time and the
         solution object. 
         """
-        max_step_size = model.tspan[1] - model.tspan[0] / 100
+        max_step_size = self.model.tspan[1] - self.model.tspan[0] / 100
 
         from functools import partial
-        events = model.listOfEvents.values()
+        events = self.model.listOfEvents.values()
         dense_output = False 
-        int_args = [curr_state, model.listOfSpecies, model.listOfReactions,
-                    model.listOfRateRules,
+        int_args = [curr_state, self.model.listOfSpecies, self.model.listOfReactions,
+                    self.model.listOfRateRules,
                     propensities, y_map,
                     compiled_reactions,
                     active_rr,
                     events,
-                    model.listOfAssignmentRules]
+                    self.model.listOfAssignmentRules]
         rhs = lambda t, y: TauHybridSolver.__f(t, y, *int_args)
         if 'min_step' in integrator_options:
             tau_step = max(integrator_options['min_step'], tau_step)
@@ -481,14 +481,14 @@ def __integrate(self, integrator, integrator_options, curr_state, y0, model, cur
             # ODE processes.  This will update all species whose mode is set to
             # 'continuous', as well as 'dynamic' mode species which have been
             # flagged as deterministic.
-            for spec_name, species in model.listOfSpecies.items():
+            for spec_name, species in self.model.listOfSpecies.items():
                 if not species.constant:
                     curr_state[spec_name] = sol.y[y_map[spec_name]]
 
         # Search for precise event times
         '''
         if len(model.listOfEvents):
-            event_times = self.__detect_events(event_sensitivity, sol, model, delayed_events,
+            event_times = self.__detect_events(event_sensitivity, sol, delayed_events,
                                                trigger_states, curr_time, curr_state)
         else:
             event_times = {}
@@ -504,7 +504,7 @@ def __integrate(self, integrator, integrator_options, curr_state, y0, model, cur
         reaction_times = []
         next_step, curr_time = self.__get_next_step(event_times, reaction_times,
                                                     delayed_events,
-                                                    model.tspan[-1], next_tau)
+                                                    self.model.tspan[-1], next_tau)
         curr_state['t'] = curr_time
 
         # Stochastic Reactions are also fired through a root-finding method
@@ -539,11 +539,11 @@ def __integrate(self, integrator, integrator_options, curr_state, y0, model, cur
         # Priority order.
         elif next_step == 'delay':
             event = heapq.heappop(delayed_events)
-            heapq.heappush(event_queue, (eval(model.listOfEvents[event[1]].priority), event[1]))
+            heapq.heappush(event_queue, (eval(self.model.listOfEvents[event[1]].priority), event[1]))
 
         return sol, curr_time
 
-    def __simulate(self, integrator, integrator_options, curr_state, y0, model, curr_time,
+    def __simulate(self, integrator, integrator_options, curr_state, y0, curr_time,
                    propensities, species, parameters, compiled_reactions,
                    active_rr, y_map, trajectory, save_times,
                    delayed_events, trigger_states, event_sensitivity,
@@ -578,7 +578,7 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, model, curr
             if loop_count > 100:
                 raise Exception("Loop over __integrate() exceeded loop count")
             sol, curr_time = self.__integrate(integrator, integrator_options, curr_state,
-                                              y0, model, curr_time, propensities, y_map,
+                                              y0, curr_time, propensities, y_map,
                                               compiled_reactions,
                                               active_rr,
                                               event_queue,
@@ -588,8 +588,7 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, model, curr
                                               tau_step,
                                               pure_ode)
 
-            species_modified = self.__update_stochastic_rxn_states(model,
-                                                                   compiled_reactions, curr_state)
+            species_modified = self.__update_stochastic_rxn_states(compiled_reactions, curr_state)
 
             # Occasionally, a tau step can result in an overly-aggressive
             # forward step and cause a species population to fall below 0,
@@ -616,31 +615,31 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, model, curr
             if time > curr_time:
                 break
             # if a solution is given for it
-            trajectory_index = np.where(model.tspan == time)[0][0]
+            trajectory_index = np.where(self.model.tspan == time)[0][0]
             assignment_state = curr_state.copy()
             for s in range(len(species)):
                 # Get ODE Solutions
                 trajectory[trajectory_index][s + 1] = sol.y[s]
                 # Update Assignment Rules for all processed time points
-                if len(model.listOfAssignmentRules):
+                if len(self.model.listOfAssignmentRules):
                     # Copy ODE state for assignments
                     assignment_state[species[s]] = sol.y[s]
             assignment_state['t'] = time
-            for ar in model.listOfAssignmentRules.values():
+            for ar in self.model.listOfAssignmentRules.values():
                 assignment_value = eval(ar.formula, {**eval_globals, **assignment_state})
                 assignment_state[ar.variable] = assignment_value
                 trajectory[trajectory_index][species.index(ar.variable) + 1] = assignment_value
             num_saves += 1
         save_times = save_times[num_saves:]  # remove completed save times
 
-        events_processed = self.__process_queued_events(model, event_queue, trigger_states, curr_state)
+        events_processed = self.__process_queued_events(event_queue, trigger_states, curr_state)
 
         # Finally, perform a final check on events after all non-ODE assignment
         # changes have been carried out on model.
         event_cycle = True
         while event_cycle:
             event_cycle = False
-            for i, e in enumerate(model.listOfEvents.values()):
+            for i, e in enumerate(self.model.listOfEvents.values()):
                 triggered = eval(e.trigger.expression, {**eval_globals, **curr_state})
                 if triggered and not curr_state[e.name]:
                     curr_state[e.name] = True
@@ -650,7 +649,7 @@ def __simulate(self, integrator, integrator_options, curr_state, y0, model, curr
                 elif not triggered:
                     curr_state[e.name] = False
 
-        events_processed = self.__process_queued_events(model, event_queue, trigger_states, curr_state)
+        events_processed = self.__process_queued_events(event_queue, trigger_states, curr_state)
 
         return sol, curr_state, curr_time, save_times
 
@@ -677,18 +676,18 @@ def __set_recommended_ode_defaults(self, integrator_options):
         if 'max_step' not in integrator_options:
             integrator_options['max_step'] = 0.25
 
-    def __compile_all(self, model):
+    def __compile_all(self):
         """
         Compile all run-time evaluables to enhance performance.
         """
         compiled_reactions = OrderedDict()
-        for i, r in enumerate(model.listOfReactions):
-            compiled_reactions[r] = compile(model.listOfReactions[r].propensity_function, '<string>',
+        for i, r in enumerate(self.model.listOfReactions):
+            compiled_reactions[r] = compile(self.model.listOfReactions[r].propensity_function, '<string>',
                                             'eval')
         compiled_rate_rules = OrderedDict()
-        for i, rr in enumerate(model.listOfRateRules.values()):
+        for i, rr in enumerate(self.model.listOfRateRules.values()):
             if isinstance(rr.variable, str):
-                compiled_rate_rules[model.listOfSpecies[rr.variable]] = compile(
+                compiled_rate_rules[self.model.listOfSpecies[rr.variable]] = compile(
                                                             rr.formula, '<string>', 'eval')
             else:
                 compiled_rate_rules[rr.variable] = compile(rr.formula, '<string>', 'eval')
@@ -698,37 +697,37 @@ def __compile_all(self, model):
 
         return compiled_reactions, compiled_rate_rules, compiled_inactive_reactions, compiled_propensities
 
-    def __initialize_state(self, model, curr_state, debug):
+    def __initialize_state(self, curr_state, debug):
         """
         Initialize curr_state for each trajectory.
         """
 
         # intialize parameters to current state
-        for p in model.listOfParameters:
-            curr_state[p] = model.listOfParameters[p].value
+        for p in self.model.listOfParameters:
+            curr_state[p] = self.model.listOfParameters[p].value
 
         # initialize species population state
-        for s in model.listOfSpecies:
-            curr_state[s] = model.listOfSpecies[s].initial_value
+        for s in self.model.listOfSpecies:
+            curr_state[s] = self.model.listOfSpecies[s].initial_value
 
         # Set reactions to uniform random number
-        for i, r in enumerate(model.listOfReactions):
+        for i, r in enumerate(self.model.listOfReactions):
             curr_state[r] = math.log(random.uniform(0, 1))
             if debug:
-                print("Setting Random number ", curr_state[r], " for ", model.listOfReactions[r].name)
+                print("Setting Random number ", curr_state[r], " for ", self.model.listOfReactions[r].name)
 
         # Initialize event last-fired times to 0
-        for e_name in model.listOfEvents:
+        for e_name in self.model.listOfEvents:
             curr_state[e_name] = 0
 
-        sanitized_species = model.sanitized_species_names()
-        sanitized_parameters = model.sanitized_parameter_names()
-        for fd in model.listOfFunctionDefinitions.values():
+        sanitized_species = self.model.sanitized_species_names()
+        sanitized_parameters = self.model.sanitized_parameter_names()
+        for fd in self.model.listOfFunctionDefinitions.values():
             sanitized_function = fd.sanitized_function(sanitized_species, sanitized_parameters)
             curr_state[fd.name] = eval(f"lambda {', '.join(fd.args)}: {sanitized_function}", eval_globals)
 
-        for ar in model.listOfAssignmentRules.values():
-            if ar.variable in model.listOfSpecies:
+        for ar in self.model.listOfAssignmentRules.values():
+            if ar.variable in self.model.listOfSpecies:
                 continue
             curr_state[ar.variable] = ar.formula
 
@@ -821,18 +820,25 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
 
         if isinstance(self, type):
             self = TauHybridSolver(model=model)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self.model = model
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal TauHybridSolver.model.")
+        self.model.resolve_parameters()
 
-        increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
         if timeout is not None and timeout > 0:
-            for i, s in enumerate(list(model._listOfSpecies.keys())):
+            for i, s in enumerate(list(self.model._listOfSpecies.keys())):
                 # Solve_ivp doesn't return any results until it's finished solving so timing out early only slows
                 # the solver.
-                if model.listOfSpecies[s].mode == 'continuous':
+                if self.model.listOfSpecies[s].mode == 'continuous':
                     timeout = 0
                     log.warning('timeouts not supported by continuous species.')
                     break
-                elif model.listOfSpecies[s].mode == 'dynamic':
+                elif self.model.listOfSpecies[s].mode == 'dynamic':
                     log.warning('timeouts not fully supported by dynamic species. If timeout is triggered during'
                                 ' integration, total solve time could be longer than expected.')
                     break
@@ -850,21 +856,21 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
             print("t = ", t)
             print("increment = ", increment)
 
-        if len(model.listOfEvents):
+        if len(self.model.listOfEvents):
             self.__set_recommended_ode_defaults(integrator_options)
         self.__set_seed(seed)
 
-        species = list(model._listOfSpecies.keys())
+        species = list(self.model._listOfSpecies.keys())
         number_species = len(species)
 
         initial_state = OrderedDict()
-        self.__initialize_state(model, initial_state, debug)
-        initial_state['vol'] = model.volume
+        self.__initialize_state(initial_state, debug)
+        initial_state['vol'] = self.model.volume
         initial_state['t'] = 0
 
         # create numpy array for timeline
         timeline = np.linspace(0, t, int(round(t / increment + 1)))
-        model.tspan = timeline
+        self.model.tspan = timeline
 
         # create numpy matrix to mark all state data of time and species
         trajectory_base = np.zeros((number_of_trajectories, timeline.size, number_species + 1))
@@ -875,10 +881,10 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
         # copy initial populations to base
         spec_modes = ['continuous', 'dynamic', 'discrete', None]
         for i, s in enumerate(species):
-            if model.listOfSpecies[s].mode is None:
-                model.listOfSpecies[s].mode = 'dynamic'
+            if self.model.listOfSpecies[s].mode is None:
+                self.model.listOfSpecies[s].mode = 'dynamic'
 
-            if model.listOfSpecies[s].mode not in spec_modes:
+            if self.model.listOfSpecies[s].mode not in spec_modes:
                 raise SpeciesError('Species mode can only be \'continuous\', \'dynamic\',\'discrete\', or '
                                    '\'unspecified(default to dynamic)\'.')
             trajectory_base[:, 0, i + 1] = initial_state[s]
@@ -889,7 +895,7 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
         live_grapher = [None]
 
         sim_thread = threading.Thread(target=self.___run,
-                                      args=(model, curr_state, curr_time, timeline, trajectory_base, initial_state,
+                                      args=(curr_state, curr_time, timeline, trajectory_base, initial_state,
                                             live_grapher,), kwargs={'t': t,
                                                                     'number_of_trajectories': number_of_trajectories,
                                                                     'increment': increment, 'seed': seed,
@@ -908,14 +914,14 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
                 gillespy2.core.liveGraphing.valid_graph_params(live_output_options)
 
                 if live_output_options['type'] == "graph":
-                    for i, s in enumerate(list(model._listOfSpecies.keys())):
+                    for i, s in enumerate(list(self.model._listOfSpecies.keys())):
 
-                        if model.listOfSpecies[s].mode == 'continuous':
+                        if self.model.listOfSpecies[s].mode == 'continuous':
                             log.warning('display \"type\" = \"graph\" not recommended with continuous species. '
                                         'Try display \"type\" = \"text\" or \"progress\".')
                             break
 
-                live_grapher[0] = gillespy2.core.liveGraphing.LiveDisplayer(model, timeline, number_of_trajectories,
+                live_grapher[0] = gillespy2.core.liveGraphing.LiveDisplayer(self.model, timeline, number_of_trajectories,
                                                                             live_output_options)
                 display_timer = gillespy2.core.liveGraphing.RepeatTimer(live_output_options['interval'],
                                                                         live_grapher[0].display,
@@ -936,12 +942,12 @@ def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=N
         
         return Results.build_from_solver_results(self, live_output_options)
 
-    def ___run(self, model, curr_state, curr_time, timeline, trajectory_base, initial_state, live_grapher, t=20,
+    def ___run(self, curr_state, curr_time, timeline, trajectory_base, initial_state, live_grapher, t=20,
                number_of_trajectories=1, increment=0.05, seed=None,
                debug=False, profile=False, tau_tol=0.03, event_sensitivity=100, integrator='LSODA',
                integrator_options={}, **kwargs):
         try:
-            self.__run(model, curr_state, curr_time, timeline, trajectory_base, initial_state, live_grapher, t,
+            self.__run(curr_state, curr_time, timeline, trajectory_base, initial_state, live_grapher, t,
                        number_of_trajectories, increment, seed, debug,
                        profile, tau_tol, event_sensitivity, integrator,
                        integrator_options, **kwargs)
@@ -950,30 +956,30 @@ def ___run(self, model, curr_state, curr_time, timeline, trajectory_base, initia
             self.result = []
             return [], -1
 
-    def __run(self, model, curr_state, curr_time, timeline, trajectory_base, initial_state, live_grapher, t=20,
+    def __run(self, curr_state, curr_time, timeline, trajectory_base, initial_state, live_grapher, t=20,
               number_of_trajectories=1, increment=0.05, seed=None,
               debug=False, profile=False,
               tau_tol=0.03, event_sensitivity=100, integrator='LSODA',
               integrator_options={}, **kwargs):
 
         # create mapping of species dictionary to array indices
-        species_mappings = model._listOfSpecies
+        species_mappings = self.model._listOfSpecies
         species = list(species_mappings.keys())
-        parameter_mappings = model._listOfParameters
+        parameter_mappings = self.model._listOfParameters
         parameters = list(parameter_mappings.keys())
         number_species = len(species)
 
-        t0_delayed_events, species_modified_by_events = self.__check_t0_events(model, initial_state)
+        t0_delayed_events, species_modified_by_events = self.__check_t0_events(initial_state)
 
         # Create deterministic tracking data structures
-        det_spec = {species: True for (species, value) in model.listOfSpecies.items() if value.mode == 'dynamic'}
-        det_rxn = {rxn: False for (rxn, value) in model.listOfReactions.items()}
+        det_spec = {species: True for (species, value) in self.model.listOfSpecies.items() if value.mode == 'dynamic'}
+        det_rxn = {rxn: False for (rxn, value) in self.model.listOfReactions.items()}
 
         # Determine if entire simulation is ODE or Stochastic, in order to
         # avoid unnecessary calculations during simulation
         pure_ode = True
         pure_stochastic = True
-        for spec in model.listOfSpecies.values():
+        for spec in self.model.listOfSpecies.values():
             if spec.mode != 'discrete':
                 pure_stochastic = False
             if spec.mode != 'continuous':
@@ -990,10 +996,10 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, initial
         # If considering deterministic changes, create dependency data
         # structure for creating diff eqs later
         if not pure_stochastic:
-            for reaction in model.listOfReactions:
+            for reaction in self.model.listOfReactions:
                 dependencies[reaction] = set()
-                [dependencies[reaction].add(reactant.name) for reactant in model.listOfReactions[reaction].reactants]
-                [dependencies[reaction].add(product.name) for product in model.listOfReactions[reaction].products]
+                [dependencies[reaction].add(reactant.name) for reactant in self.model.listOfReactions[reaction].reactants]
+                [dependencies[reaction].add(product.name) for product in self.model.listOfReactions[reaction].products]
 
         # Main trajectory loop
         for trajectory_num in range(number_of_trajectories):
@@ -1013,7 +1019,7 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, initial
             curr_state[0] = initial_state.copy()
             curr_time[0] = 0  # Current Simulation Time
 
-            end_time = model.tspan[-1]  # End of Simulation time
+            end_time = self.model.tspan[-1]  # End of Simulation time
             entry_pos = 1
             data = OrderedDict()  # Dictionary for results
             data['time'] = timeline  # All time entries
@@ -1021,17 +1027,17 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, initial
 
             # Record Highest Order reactant for each reaction and set error tolerance
             if not pure_ode:
-                HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, critical_threshold = Tau.initialize(model, tau_tol)
+                HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, critical_threshold = Tau.initialize(self.model, tau_tol)
 
             # One-time compilations to reduce time spent with eval
             compiled_reactions, compiled_rate_rules, compiled_inactive_reactions, compiled_propensities = \
-                self.__compile_all(model)
+                self.__compile_all()
             all_compiled = OrderedDict()
             all_compiled['rxns'] = compiled_reactions
             all_compiled['inactive_rxns'] = compiled_inactive_reactions
             all_compiled['rules'] = compiled_rate_rules
 
-            save_times = np.copy(model.tspan)
+            save_times = np.copy(self.model.tspan)
             delayed_events = []
             trigger_states = {}
 
@@ -1041,20 +1047,20 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, initial
             for ename, etime in t0_delayed_events.items():
                 curr_state[0][ename] = True
                 heapq.heappush(delayed_events, (etime, ename))
-                if model.listOfEvents[ename].use_values_from_trigger_time:
+                if self.model.listOfEvents[ename].use_values_from_trigger_time:
                     trigger_states[ename] = curr_state[0].copy()
                 else:
                     trigger_states[ename] = curr_state[0]
 
             # Each save step
-            while curr_time[0] < model.tspan[-1]:
+            while curr_time[0] < self.model.tspan[-1]:
 
                 if self.stop_event.is_set():
                     self.rc = 33
                     break
                 # Get current propensities
                 if not pure_ode:
-                    for i, r in enumerate(model.listOfReactions):
+                    for i, r in enumerate(self.model.listOfReactions):
                         try:
                             propensities[r] = eval(compiled_propensities[r], eval_globals, curr_state[0])
                         except Exception as e:
@@ -1063,19 +1069,19 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, initial
                 # Calculate Tau statistics and select a good tau step
                 if not pure_ode:
                     tau_args = [HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, tau_tol, critical_threshold,
-                                model, propensities, curr_state[0], curr_time[0], save_times[0]]
+                                self.model, propensities, curr_state[0], curr_time[0], save_times[0]]
                 tau_step = save_times[-1] - curr_time[0] if pure_ode else Tau.select(*tau_args)
 
                 # Process switching if used
                 if not pure_stochastic and not pure_ode:
-                    switch_args = [model, propensities, curr_state[0], tau_step, det_spec]
+                    switch_args = [propensities, curr_state[0], tau_step, det_spec]
                     sd, CV = self.__calculate_statistics(*switch_args)
 
                 # Calculate sd and CV for hybrid switching and flag deterministic reactions
                 if pure_stochastic:
                     deterministic_reactions = frozenset()  # Empty if non-det
                 else:
-                    deterministic_reactions = self.__flag_det_reactions(model, det_spec, det_rxn, dependencies)
+                    deterministic_reactions = self.__flag_det_reactions(det_spec, det_rxn, dependencies)
 
                 if debug:
                     print('mean: {0}'.format(mu_i))
@@ -1089,16 +1095,16 @@ def __run(self, model, curr_state, curr_time, timeline, trajectory_base, initial
                 if pure_stochastic:
                     active_rr = rr_sets[frozenset()]
                 else:
-                    active_rr = self.__toggle_reactions(model, all_compiled, deterministic_reactions, 
+                    active_rr = self.__toggle_reactions(all_compiled, deterministic_reactions, 
                                                         dependencies, curr_state[0], det_spec, rr_sets)
 
                 # Create integration initial state vector
                 y0, y_map = self.__map_state(species, parameters,
-                                             compiled_reactions, model.listOfEvents, curr_state[0])
+                                             compiled_reactions, self.model.listOfEvents, curr_state[0])
 
                 # Run simulation to next step
                 sol, curr_state[0], curr_time[0], save_times = self.__simulate(integrator, integrator_options,
-                                                                               curr_state[0], y0, model, curr_time[0],
+                                                                               curr_state[0], y0, curr_time[0],
                                                                                propensities, species,
                                                                                parameters, compiled_reactions,
                                                                                active_rr, y_map,
diff --git a/gillespy2/solvers/numpy/tau_leaping_solver.py b/gillespy2/solvers/numpy/tau_leaping_solver.py
index 2ccd82d53..09e510af7 100644
--- a/gillespy2/solvers/numpy/tau_leaping_solver.py
+++ b/gillespy2/solvers/numpy/tau_leaping_solver.py
@@ -92,154 +92,161 @@ def get_solver_settings(self):
     def run(self, model=None, t=20, number_of_trajectories=1, increment=None, seed=None,
             debug=False, profile=False,  live_output=None, live_output_options={},
             timeout=None, resume=None, tau_tol=0.03, **kwargs):
-            """
-            Function calling simulation of the model.
-            This is typically called by the run function in GillesPy2 model objects
-            and will inherit those parameters which are passed with the model
-            as the arguments this run function.
+        """
+        Function calling simulation of the model.
+        This is typically called by the run function in GillesPy2 model objects
+        and will inherit those parameters which are passed with the model
+        as the arguments this run function.
 
-            :param model: GillesPy2 model object to simulate
-            :type model: gillespy2.Model
+        :param model: GillesPy2 model object to simulate
+        :type model: gillespy2.Model
 
-            :param t: Simulation run time
-            :type t: int
+        :param t: Simulation run time
+        :type t: int
 
-            :param number_of_trajectories: Number of trajectories to simulate
-            :type number_of_trajectories: int
+        :param number_of_trajectories: Number of trajectories to simulate
+        :type number_of_trajectories: int
 
-            :param increment: Save point increment for recording data
-            :type increment: float
+        :param increment: Save point increment for recording data
+        :type increment: float
 
-            :param seed: The random seed for the simulation. Optional, defaults to None
-            :type seed: int
+        :param seed: The random seed for the simulation. Optional, defaults to None
+        :type seed: int
 
-            :param debug: Set to True to provide additional debug information about the simulation
-            :type debug: bool
+        :param debug: Set to True to provide additional debug information about the simulation
+        :type debug: bool
 
-            :param profile: Set to True to provide information about step size (tau) taken at each step.
-            :type profile: bool
+        :param profile: Set to True to provide information about step size (tau) taken at each step.
+        :type profile: bool
 
-            :param live_output: The type of output to be displayed by solver. Can be "progress", "text", or "graph".
-            :type live_output: str
+        :param live_output: The type of output to be displayed by solver. Can be "progress", "text", or "graph".
+        :type live_output: str
 
-            :param live_output_options: COntains options for live_output. By default {"interval":1}. "interval"
-                specifies seconds between displaying. "clear_output" specifies if display should be refreshed with each
-                display.
-            :type live_output_options: dict
+        :param live_output_options: COntains options for live_output. By default {"interval":1}. "interval"
+            specifies seconds between displaying. "clear_output" specifies if display should be refreshed with each
+            display.
+        :type live_output_options: dict
 
-            :param timeout:
-            :param resume:
-            :param tau_tol:
-            :param kwargs:
+        :param timeout:
+        :param resume:
+        :param tau_tol:
+        :param kwargs:
 
-            :returns:
-            """
+        :returns:
+        """
 
-            if isinstance(self, type):
-                self = TauLeapingSolver(model=model, debug=debug, profile=profile)
+        if isinstance(self, type):
+            self = TauLeapingSolver(model=model, debug=debug, profile=profile)
+        if self.model is None:
+            if model is None:
+                raise SimulationError("A model is required to run the simulation.")
+            self.model = model
+        if model is not None and model.get_json_hash() != self.model.get_json_hash():
+            raise SimulationError("Model must equal TauLeapingSolver.model.")
+        self.model.resolve_parameters()
 
-            increment = self.get_increment(model=model, increment=increment)
+        increment = self.get_increment(increment=increment)
 
-            self.stop_event = Event()
-            self.pause_event = Event()
+        self.stop_event = Event()
+        self.pause_event = Event()
 
-            if timeout is not None and timeout <= 0:
-                timeout = None
-            if len(kwargs) > 0:
-                for key in kwargs:
-                    log.warning('Unsupported keyword argument to {0} solver: {1}'.format(self.name, key))
+        if timeout is not None and timeout <= 0:
+            timeout = None
+        if len(kwargs) > 0:
+            for key in kwargs:
+                log.warning('Unsupported keyword argument to {0} solver: {1}'.format(self.name, key))
 
-            # create numpy array for timeline
-            if resume is not None:
-                # start where we last left off if resuming a simulatio
-                lastT = resume['time'][-1]
-                step = lastT - resume['time'][-2]
-                timeline = np.arange(lastT, t+step, step)
+        # create numpy array for timeline
+        if resume is not None:
+            # start where we last left off if resuming a simulatio
+            lastT = resume['time'][-1]
+            step = lastT - resume['time'][-2]
+            timeline = np.arange(lastT, t+step, step)
+        else:
+            timeline = np.linspace(0, t, int(round(t / increment + 1)))
+
+        species = list(self.model._listOfSpecies.keys())
+        trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(self.model, number_of_trajectories,
+                                                                                   timeline, species, resume=resume)
+
+        # total_time and curr_state are list of len 1 so that __run receives reference
+        if resume is not None:
+            total_time = [resume['time'][-1]]
+        else:
+            total_time = [0]
+
+        curr_state = [None]
+        live_grapher = [None]
+
+        sim_thread = Thread(target=self.___run, args=(curr_state, total_time, timeline, trajectory_base, tmpSpecies,
+                                                      live_grapher,), kwargs={'t': t,
+                                                                              'number_of_trajectories':
+                                                                                  number_of_trajectories,
+                                                                              'increment': increment, 'seed': seed,
+                                                                              'debug': debug, 'resume': resume,
+                                                                              'timeout': timeout, 'tau_tol': tau_tol
+                                                                              })
+        try:
+            time = 0
+            sim_thread.start()
+            if live_output is not None:
+                import gillespy2.core.liveGraphing
+                live_output_options['type'] = live_output
+                gillespy2.core.liveGraphing.valid_graph_params(
+                    live_output_options)
+                if resume is not None:
+                    resumeTest = True  # If resuming, relay this information to live_grapher
+                else:
+                    resumeTest = False
+                live_grapher[
+                    0] = gillespy2.core.liveGraphing.LiveDisplayer(self.model,
+                                                                   timeline,
+                                                                   number_of_trajectories,
+                                                                   live_output_options,
+                                                                   resume=resumeTest)
+                display_timer = gillespy2.core.liveGraphing.RepeatTimer(
+                    live_output_options['interval'],
+                    live_grapher[0].display, args=(curr_state,
+                                                   total_time,
+                                                   trajectory_base,
+                                                   live_output
+                                                   )
+                    )
+                display_timer.start()
+
+            if timeout is not None:
+                while sim_thread.is_alive():
+                    sim_thread.join(.1)
+                    time += .1
+                    if time >= timeout:
+                        break
             else:
-                timeline = np.linspace(0, t, int(round(t / increment + 1)))
-
-            species = list(model._listOfSpecies.keys())
-            trajectory_base, tmpSpecies = nputils.numpy_trajectory_base_initialization(model, number_of_trajectories,
-                                                                                       timeline, species, resume=resume)
+                while sim_thread.is_alive():
+                    sim_thread.join(.1)
 
-            # total_time and curr_state are list of len 1 so that __run receives reference
-            if resume is not None:
-                total_time = [resume['time'][-1]]
-            else:
-                total_time = [0]
-
-            curr_state = [None]
-            live_grapher = [None]
-
-            sim_thread = Thread(target=self.___run, args=(model, curr_state, total_time, timeline, trajectory_base, tmpSpecies,
-                                                          live_grapher,), kwargs={'t': t,
-                                                                                  'number_of_trajectories':
-                                                                                      number_of_trajectories,
-                                                                                  'increment': increment, 'seed': seed,
-                                                                                  'debug': debug, 'resume': resume,
-                                                                                  'timeout': timeout, 'tau_tol': tau_tol
-                                                                                  })
-            try:
-                time = 0
-                sim_thread.start()
-                if live_output is not None:
-                    import gillespy2.core.liveGraphing
-                    live_output_options['type'] = live_output
-                    gillespy2.core.liveGraphing.valid_graph_params(
-                        live_output_options)
-                    if resume is not None:
-                        resumeTest = True  # If resuming, relay this information to live_grapher
-                    else:
-                        resumeTest = False
-                    live_grapher[
-                        0] = gillespy2.core.liveGraphing.LiveDisplayer(model,
-                                                                       timeline,
-                                                                       number_of_trajectories,
-                                                                       live_output_options,
-                                                                       resume=resumeTest)
-                    display_timer = gillespy2.core.liveGraphing.RepeatTimer(
-                        live_output_options['interval'],
-                        live_grapher[0].display, args=(curr_state,
-                                                       total_time,
-                                                       trajectory_base,
-                                                       live_output
-                                                       )
-                        )
-                    display_timer.start()
-
-                if timeout is not None:
-                    while sim_thread.is_alive():
-                        sim_thread.join(.1)
-                        time += .1
-                        if time >= timeout:
-                            break
-                else:
-                    while sim_thread.is_alive():
-                        sim_thread.join(.1)
-
-                if live_grapher[0] is not None:
-                    display_timer.cancel()
-                self.stop_event.set()
-                while self.result is None:
-                    pass
-            except KeyboardInterrupt:
-                if live_output:
-                    display_timer.pause = True
-                    display_timer.cancel()
-                self.pause_event.set()
-                while self.result is None:
-                    pass
-            if hasattr(self, 'has_raised_exception'):
-                raise self.has_raised_exception
-
-            return Results.build_from_solver_results(self, live_output_options)
-
-    def ___run(self, model, curr_state,total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
+            if live_grapher[0] is not None:
+                display_timer.cancel()
+            self.stop_event.set()
+            while self.result is None:
+                pass
+        except KeyboardInterrupt:
+            if live_output:
+                display_timer.pause = True
+                display_timer.cancel()
+            self.pause_event.set()
+            while self.result is None:
+                pass
+        if hasattr(self, 'has_raised_exception'):
+            raise self.has_raised_exception
+
+        return Results.build_from_solver_results(self, live_output_options)
+
+    def ___run(self, curr_state,total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
                number_of_trajectories=1, increment=0.05, seed=None, debug=False, profile=False, show_labels=True,
                timeout=None, resume=None, tau_tol=0.03, **kwargs):
 
         try:
-            self.__run(model, curr_state, total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t, number_of_trajectories,
+            self.__run(curr_state, total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t, number_of_trajectories,
                        increment, seed, debug, profile, timeout, resume, tau_tol, **kwargs)
 
         except Exception as e:
@@ -247,7 +254,7 @@ def ___run(self, model, curr_state,total_time, timeline, trajectory_base, tmpSpe
             self.result = []
             return [], -1
 
-    def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
+    def __run(self, curr_state, total_time, timeline, trajectory_base, tmpSpecies, live_grapher, t=20,
               number_of_trajectories=1, increment=0.05, seed=None, debug=False, profile=False, timeout=None,
               resume=None, tau_tol=0.03, **kwargs):
 
@@ -255,7 +262,7 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
         # how species and time are initialized to 0
         timeStopped = 0
         if resume is not None:
-            if resume[0].model != model:
+            if resume[0].model != self.model:
                 raise ModelError('When resuming, one must not alter the model being resumed.')
             if t < resume['time'][-1]:
                 raise ExecutionError(
@@ -265,7 +272,7 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
             print("t = ", t)
             print("increment = ", increment)
 
-        species_mappings, species, parameter_mappings, number_species = nputils.numpy_initialization(model)
+        species_mappings, species, parameter_mappings, number_species = nputils.numpy_initialization(self.model)
 
         if seed is not None:
             if not isinstance(seed, int):
@@ -288,7 +295,7 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
             if live_grapher[0] is not None:
                 live_grapher[0].increment_trajectory(trajectory_num)
 
-            start_state = [0] * (len(model.listOfReactions) + len(model.listOfRateRules))
+            start_state = [0] * (len(self.model.listOfReactions) + len(self.model.listOfRateRules))
             propensities = {}
             curr_state[0] = {}
 
@@ -299,35 +306,35 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
                 save_time = 0
                 curr_time = [0]
 
-            curr_state[0]['vol'] = model.volume
+            curr_state[0]['vol'] = self.model.volume
             data = {'time': timeline}
             steps_taken = []
             steps_rejected = 0
             entry_count = 0
             trajectory = trajectory_base[trajectory_num]
 
-            HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, critical_threshold = Tau.initialize(model, tau_tol)
+            HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, critical_threshold = Tau.initialize(self.model, tau_tol)
 
             if resume is not None:
-                for spec in model.listOfSpecies:
+                for spec in self.model.listOfSpecies:
                     curr_state[0][spec] = tmpSpecies[spec]
             else:
-                for spec in model.listOfSpecies:
-                    curr_state[0][spec] = model.listOfSpecies[spec].initial_value
+                for spec in self.model.listOfSpecies:
+                    curr_state[0][spec] = self.model.listOfSpecies[spec].initial_value
 
-            for param in model.listOfParameters:
-                curr_state[0][param] = model.listOfParameters[param].value
+            for param in self.model.listOfParameters:
+                curr_state[0][param] = self.model.listOfParameters[param].value
 
-            for i, rxn in enumerate(model.listOfReactions):
+            for i, rxn in enumerate(self.model.listOfReactions):
                 # set reactions to uniform random number and add to start_state
                 start_state[i] = (math.log(random.uniform(0, 1)))
                 if debug:
                     print("Setting Random number ",
-                          start_state[i], " for ", model.listOfReactions[rxn].name)
+                          start_state[i], " for ", self.model.listOfReactions[rxn].name)
 
             compiled_propensities = {}
-            for i, r in enumerate(model.listOfReactions):
-                compiled_propensities[r] = compile(model.listOfReactions[r].propensity_function, '<string>', 'eval')
+            for i, r in enumerate(self.model.listOfReactions):
+                compiled_propensities[r] = compile(self.model.listOfReactions[r].propensity_function, '<string>', 'eval')
 
             timestep = 0
             
@@ -351,12 +358,12 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
 
                     propensity_sum = 0
 
-                    for i, r in enumerate(model.listOfReactions):
+                    for i, r in enumerate(self.model.listOfReactions):
                         propensities[r] = eval(compiled_propensities[r], curr_state[0])
                         propensity_sum += propensities[r]
 
                     tau_args = [HOR, reactants, mu_i, sigma_i, g_i, epsilon_i, tau_tol, critical_threshold,
-                                model, propensities, curr_state[0], curr_time[0], save_time]
+                                self.model, propensities, curr_state[0], curr_time[0], save_time]
 
                     tau_step = Tau.select(*tau_args)
 
@@ -373,19 +380,19 @@ def __run(self, model, curr_state, total_time, timeline, trajectory_base, tmpSpe
 
                         reactions, curr_state[0], curr_time[0] = self.__get_reactions(
                             tau_step, curr_state[0], curr_time[0], save_time,
-                            propensities, model.listOfReactions)
+                            propensities, self.model.listOfReactions)
 
                         # Update curr_state with the result of the SSA reaction that fired
                         species_modified = {}
-                        for i, rxn in enumerate(model.listOfReactions):
+                        for i, rxn in enumerate(self.model.listOfReactions):
                             if reactions[rxn] > 0:
-                                for reactant in model.listOfReactions[rxn].reactants:
+                                for reactant in self.model.listOfReactions[rxn].reactants:
                                     species_modified[reactant.name] = True
-                                    curr_state[0][reactant.name] -= model.listOfReactions[
+                                    curr_state[0][reactant.name] -= self.model.listOfReactions[
                                         rxn].reactants[reactant] * reactions[rxn]
-                                for product in model.listOfReactions[rxn].products:
+                                for product in self.model.listOfReactions[rxn].products:
                                     species_modified[product.name] = True
-                                    curr_state[0][product.name] += model.listOfReactions[
+                                    curr_state[0][product.name] += self.model.listOfReactions[
                                         rxn].products[product] * reactions[rxn]
                         neg_state = False
                         for spec in species_modified:
diff --git a/test/test_hybrid_c_events.py b/test/test_hybrid_c_events.py
new file mode 100644
index 000000000..d2317f41d
--- /dev/null
+++ b/test/test_hybrid_c_events.py
@@ -0,0 +1,128 @@
+import unittest
+import gillespy2
+from gillespy2 import TauHybridCSolver
+import numpy as np
+
+class EventFeatures(unittest.TestCase):
+    class BaseEventModel(gillespy2.Model):
+        def __init__(self, s1, s2, rate):
+            super().__init__(name="BasicEventModel")
+
+            self.s1 = gillespy2.Species(name="S1", initial_value=s1, mode="continuous")
+            self.s2 = gillespy2.Species(name="S2", initial_value=s2, mode="continuous")
+            self.rate = gillespy2.Parameter(name="k1", expression=rate)
+            self.add_species([self.s1, self.s2])
+            self.add_parameter([self.rate])
+            self.add_reaction([
+                gillespy2.Reaction(name="r1", rate=self.rate,
+                                   reactants={self.s1: 1},
+                                   products={self.s2: 1})
+            ])
+
+    def test_event_with_time_trigger(self):
+        model = EventFeatures.BaseEventModel(s1=0, s2=0, rate=0.0)
+        event = gillespy2.Event(name="ev1", assignments=[
+            gillespy2.EventAssignment(variable=model.s1, expression="100.0"),
+            gillespy2.EventAssignment(variable=model.rate, expression="1.0")
+        ], trigger=gillespy2.EventTrigger(expression="t>5"))
+        model.add_event(event)
+
+        solver = TauHybridCSolver(model=model)
+        result = model.run(solver=solver)[0]
+        s1, s2 = result["S1"][-1], result["S2"][-1]
+
+        self.assertGreater(s2, s1, "Expected S2 > S1")
+        self.assertGreater(s1, 0.0, "Expected S1 > 0")
+        self.assertAlmostEqual(s1 + s2, 100.0, places=1)
+
+    def test_event_with_species_trigger(self):
+        model = EventFeatures.BaseEventModel(s1=100, s2=0, rate=10.0)
+        event = gillespy2.Event(name="ev1", assignments=[
+            gillespy2.EventAssignment(variable=model.s1, expression="100.0"),
+            gillespy2.EventAssignment(variable=model.rate, expression="0.0")
+        ], trigger=gillespy2.EventTrigger(expression="S1<90"))
+        model.add_event(event)
+
+        solver = TauHybridCSolver(model=model)
+        result = model.run(solver=solver)[0]
+        s1, s2 = result["S1"][-1], result["S2"][-1]
+
+        self.assertEqual(s1, 100, "Expected S1 == 100 (trigger set S1 to 100 and rate to 0")
+        self.assertGreater(s2, 0, "Expected S2 > 0")
+        self.assertFalse(np.any(result["S1"] <= 90.0), "Expected S1 > 90 for entire simulation")
+
+    def test_delay_trigger_persistent(self):
+        model = EventFeatures.BaseEventModel(s1=100, s2=0, rate=1.0)
+        event1 = gillespy2.Event(name="ev1", assignments=[
+            gillespy2.EventAssignment(variable=model.s1, expression="0"),
+            gillespy2.EventAssignment(variable=model.s2, expression="0"),
+            gillespy2.EventAssignment(variable=model.rate, expression="0.0")
+        ], trigger=gillespy2.EventTrigger(expression="S1<60 and S2<S1", persistent=False), delay="t+1.0")
+        event2 = gillespy2.Event(name="ev2", assignments=[
+            gillespy2.EventAssignment(variable=model.s1, expression="200"),
+            gillespy2.EventAssignment(variable=model.s2, expression="200"),
+            gillespy2.EventAssignment(variable=model.rate, expression="0.0"),
+        ], trigger=gillespy2.EventTrigger(expression="S2>90 and t<3.5", persistent=True), delay="1.0")
+        model.add_event([event1, event2])
+
+        solver = TauHybridCSolver(model=model)
+        result = model.run(solver=solver)[0]
+        s1, s2 = result["S1"][-1], result["S2"][-1]
+
+        # If delay is working correctly:
+        # * event1 is never triggered. event1 sets everything to 0.
+        #   If event1 fires, event2 can never fire.
+        # * event2 is triggered, setting everything to 100 (and rate to 0).
+        self.assertNotIn(0, [s1, s2], "Non-persistent event fired unexpectedly")
+        self.assertEqual(s1, 200, "Persistent event failed to fire")
+        self.assertEqual(s2, 200, "Persistent event failed to fire")
+
+    def test_trigger_priorities(self):
+        model = EventFeatures.BaseEventModel(s1=100, s2=0, rate=1.0)
+        event1 = gillespy2.Event(name="ev1", assignments=[
+            gillespy2.EventAssignment(variable=model.s1, expression="100"),
+            gillespy2.EventAssignment(variable=model.s2, expression="100"),
+        ], trigger=gillespy2.EventTrigger(expression="S1 < 50"), priority="2*t*S1")
+        event2 = gillespy2.Event(name="ev2", assignments=[
+            gillespy2.EventAssignment(variable=model.s1, expression="0"),
+        ], trigger=gillespy2.EventTrigger(expression="S1 < 50"), priority="t*S1")
+        model.add_event([event1, event2])
+
+        solver = TauHybridCSolver(model=model)
+        result = model.run(solver=solver)[0]
+        s1, s2 = result["S1"][-1], result["S2"][-1]
+
+        # If priority is working correctly, event2 should ALWAYS fire before event1.
+        # Proper result is S1 = 0, S2 = 100, so no further reactions are possible.
+        self.assertEqual(s1, 0,   "Events fired in an incorrect order")
+        self.assertEqual(s2, 100, "Events fired in an incorrect order")
+
+    def test_use_values_from_trigger_time(self):
+        model = EventFeatures.BaseEventModel(s1=100, s2=0, rate=1.0)
+        event = gillespy2.Event(name="ev1", assignments=[
+            gillespy2.EventAssignment(variable=model.s1, expression="S2"),
+            gillespy2.EventAssignment(variable=model.rate, expression="0.0"),
+        ], trigger=gillespy2.EventTrigger(expression="S1 < 60"), delay="1.5", use_values_from_trigger_time=True)
+        model.add_event(event)
+
+        solver = TauHybridCSolver(model=model)
+        result = model.run(solver=solver)[0]
+        s1, s2 = result["S1"][-1], result["S2"][-1]
+
+        self.assertGreater(s2, s1, "Event assignment did not assign values from trigger time")
+
+    def test_initial_values(self):
+        model = EventFeatures.BaseEventModel(s1=0, s2=100.0, rate=1.0)
+
+        event = gillespy2.Event(name="ev1", assignments=[
+            gillespy2.EventAssignment(variable=model.s1, expression="S2/2"),
+        ], trigger=gillespy2.EventTrigger(expression="S1==0", initial_value=False))
+        model.add_event(event)
+
+        solver = TauHybridCSolver(model=model)
+        result = model.run(solver=solver)
+
+        s1, s2 = result["S1"][-1], result["S2"][-1]
+        self.assertAlmostEqual(s1 + s2, 150.0, places=1, msg="Event assignment assigned incorrect value")
+        self.assertGreater(s2, 100, "Event with initial condition did not fire")
+        self.assertEqual(result["S1"][0], 50, "Event assignment with initial condition failed to fire at t=0")
diff --git a/test/test_ode_solver.py b/test/test_ode_solver.py
index 1c04f93df..b99e2a61d 100644
--- a/test/test_ode_solver.py
+++ b/test/test_ode_solver.py
@@ -17,6 +17,8 @@
 """
 
 import unittest
+import sys
+sys.path.append("..")
 import numpy as np
 import gillespy2
 from example_models import Example, ExampleNoTspan
@@ -77,6 +79,24 @@ def test_run_example__with_tspan_and_increment(self):
             results = ODESolver.run(model=model, increment=0.2)
 
 
+    def test_stoch3(self):
+        class StochTestModel(gillespy2.Model):
+            def __init__(self, parameter_values=None):
+                gillespy2.Model.__init__(self, name='StochTest1')
+                A = gillespy2.Species(name='A', initial_value=10)
+                B = gillespy2.Species(name='B', initial_value=0)
+                self.add_species([A, B])
+                k = gillespy2.Parameter(name='k', expression=10)
+                self.add_parameter([k])
+                r = gillespy2.Reaction(name='r', reactants={A: 1}, products={B:1},
+                    propensity_function="k*A/vol") # testing if 'vol' is a pre-set variable
+                self.add_reaction([r])
+                self.timespan(np.linspace(0, 100, 101))
+        model = StochTestModel()
+        result = model.run(solver=ODESolver)
+        sys.stderr.write(f"\ntest_shoch3(): B={result['B'][-1]}\n\n")
+        self.assertGreater(result['B'][-1], 5)
 
 if __name__ == '__main__':
-    unittest.main()
+    #unittest.main()
+    TestBasicODESolver().test_stoch3()