diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.ipynb b/applications/finance/portfolio_optimization/portfolio_optimization.ipynb
index 938fcaaba..5fff5f122 100644
--- a/applications/finance/portfolio_optimization/portfolio_optimization.ipynb
+++ b/applications/finance/portfolio_optimization/portfolio_optimization.ipynb
@@ -56,12 +56,6 @@
    "execution_count": 1,
    "id": "952d49b3-5dc6-41a1-8822-0622df536cf7",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:07.327339Z",
-     "iopub.status.busy": "2024-05-07T15:06:07.326871Z",
-     "iopub.status.idle": "2024-05-07T15:06:07.542423Z",
-     "shell.execute_reply": "2024-05-07T15:06:07.541631Z"
-    },
     "tags": []
    },
    "outputs": [],
@@ -77,7 +71,7 @@
    "source": [
     "# The Portfolio Optimization Problem Parameters\n",
     "\n",
-    "First we define the parameters of the optimization problem, which include the expected return vector, the covariance matrix, the total budget and the asset-specific budgets:"
+    "First we define the parameters of the optimization problem, which include the expected return vector, the covariance matrix and the total budget:"
    ]
   },
   {
@@ -85,12 +79,6 @@
    "execution_count": 2,
    "id": "6212e51c",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:07.547954Z",
-     "iopub.status.busy": "2024-05-07T15:06:07.546656Z",
-     "iopub.status.idle": "2024-05-07T15:06:07.552727Z",
-     "shell.execute_reply": "2024-05-07T15:06:07.552195Z"
-    },
     "pycharm": {
      "name": "#%%\n"
     },
@@ -99,17 +87,15 @@
    "outputs": [],
    "source": [
     "returns = np.array([3, 4, -1])\n",
-    "# fmt: off\n",
     "covariances = np.array(\n",
     "    [\n",
-    "        [ 0.9,  0.5, -0.7],\n",
-    "        [ 0.5,  0.9, -0.2],\n",
-    "        [-0.7, -0.2,  0.9],\n",
+    "        [0.9, 0.5, -0.7],\n",
+    "        [0.5, 0.9, -0.2],\n",
+    "        [-0.7, -0.2, 0.9],\n",
     "    ]\n",
     ")\n",
-    "# fmt: on\n",
-    "total_budget = 6\n",
-    "specific_budgets = [2, 2, 2]"
+    "\n",
+    "total_budget = 6"
    ]
   },
   {
@@ -127,12 +113,6 @@
    "execution_count": 3,
    "id": "42650f31-8efe-4ca9-8ed6-f5d9d440bee4",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:07.556775Z",
-     "iopub.status.busy": "2024-05-07T15:06:07.555783Z",
-     "iopub.status.idle": "2024-05-07T15:06:07.565348Z",
-     "shell.execute_reply": "2024-05-07T15:06:07.564551Z"
-    },
     "tags": []
    },
    "outputs": [],
@@ -141,14 +121,11 @@
     "num_assets = len(returns)\n",
     "\n",
     "# setting the variables\n",
-    "portfolio_model.w = pyo.Var(\n",
-    "    range(num_assets),\n",
-    "    domain=pyo.Integers,\n",
-    "    bounds=lambda _, idx: (0, specific_budgets[idx]),\n",
-    ")\n",
+    "portfolio_model.w = pyo.Var(range(num_assets), domain=pyo.Integers, bounds=(0, 6))\n",
+    "\n",
     "w_array = list(portfolio_model.w.values())\n",
     "\n",
-    "# setting the constraint\n",
+    "# global budget constraint\n",
     "portfolio_model.budget_rule = pyo.Constraint(expr=(sum(w_array) <= total_budget))\n",
     "\n",
     "# setting the expected return and risk\n",
@@ -163,102 +140,80 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c671eeac-5b61-4ab4-9e92-cfcb2ed8170b",
+   "id": "ea100320-dab7-4a4c-aed9-e08e3a70fb78",
    "metadata": {
     "tags": []
    },
    "source": [
     "## Setting Up the Classiq Problem Instance\n",
     "\n",
-    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
+    "In order to solve the Pyomo model defined above, we use the `CombinatorialProblem` python class. Under the hood it tranlates the Pyomo model to a quantum model of the QAOA algorithm, with cost hamiltonian translated from the Pyomo model. We can choose the number of layers for the QAOA ansatz using the argument `num_layers`, and the `penalty_factor`, which will be the coefficient of the constraints term in the cost hamiltonian."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "c044e30f-2b4f-41ef-9bc0-11b951cb88db",
+   "id": "9503e674-3194-44ad-b248-fffa6fc3a9b2",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:07.570439Z",
-     "iopub.status.busy": "2024-05-07T15:06:07.569306Z",
-     "iopub.status.idle": "2024-05-07T15:06:10.280055Z",
-     "shell.execute_reply": "2024-05-07T15:06:10.275821Z"
-    },
     "tags": []
    },
    "outputs": [],
    "source": [
     "from classiq import *\n",
-    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
+    "from classiq.applications.combinatorial_optimization import CombinatorialProblem\n",
     "\n",
-    "qaoa_config = QAOAConfig(num_layers=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dce2689a-d47f-42c0-9468-5faa4da21d20",
-   "metadata": {},
-   "source": [
-    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
+    "combi = CombinatorialProblem(pyo_model=portfolio_model, num_layers=3, penalty_factor=10)\n",
+    "\n",
+    "qmod = combi.get_model()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "7c4a6a91-aece-4a3b-9fa2-e7f76c90f296",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:10.287616Z",
-     "iopub.status.busy": "2024-05-07T15:06:10.287145Z",
-     "iopub.status.idle": "2024-05-07T15:06:10.292183Z",
-     "shell.execute_reply": "2024-05-07T15:06:10.291510Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "tags": []
-   },
+   "id": "86d783b7-820a-40a8-90af-aedb735b7678",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)"
+    "write_qmod(qmod, \"portfolio_optimization\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a78aeea0-246b-4a58-9d7f-94cded812348",
+   "id": "696f5c22-eb43-488a-a948-aa057c005bed",
    "metadata": {},
    "source": [
-    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
+    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
+    "\n",
+    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d3988443-adff-4196-981f-d22307de17c9",
+   "id": "25bc3abe-18a1-4e41-ab3d-084cd49d463b",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:10.297071Z",
-     "iopub.status.busy": "2024-05-07T15:06:10.295820Z",
-     "iopub.status.idle": "2024-05-07T15:06:12.171845Z",
-     "shell.execute_reply": "2024-05-07T15:06:12.171188Z"
-    },
     "pycharm": {
      "name": "#%%\n"
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Opening: https://nightly.platform.classiq.io/circuit/79e25a43-a916-476c-a108-022dfec85da8?version=0.62.0.dev9\n"
+     ]
+    }
+   ],
    "source": [
-    "qmod = construct_combinatorial_optimization_model(\n",
-    "    pyo_model=portfolio_model,\n",
-    "    qaoa_config=qaoa_config,\n",
-    "    optimizer_config=optimizer_config,\n",
-    ")"
+    "qprog = combi.get_qprog()\n",
+    "show(qprog)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0d64c135-ec3b-490e-ae60-2d72f0ff633c",
+   "id": "b06ce4de-2fce-4360-bade-6af4d2c0558d",
    "metadata": {},
    "source": [
     "We also set the quantum backend we want to execute on:"
@@ -267,111 +222,48 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "e0b0013d-5152-4221-a8d5-d35820c3a878",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:12.174681Z",
-     "iopub.status.busy": "2024-05-07T15:06:12.174351Z",
-     "iopub.status.idle": "2024-05-07T15:06:12.190978Z",
-     "shell.execute_reply": "2024-05-07T15:06:12.190335Z"
-    },
-    "tags": []
-   },
+   "id": "fcddad02-a283-4812-a22e-289da66dcae7",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from classiq.execution import ClassiqBackendPreferences\n",
+    "from classiq.execution import *\n",
     "\n",
-    "qmod = set_execution_preferences(\n",
-    "    qmod, backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\")\n",
+    "execution_preferences = ExecutionPreferences(\n",
+    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"simulator\"),\n",
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "804e8e99-2e63-43df-9168-f1cb8497bdad",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:12.193308Z",
-     "iopub.status.busy": "2024-05-07T15:06:12.192932Z",
-     "iopub.status.idle": "2024-05-07T15:06:12.333982Z",
-     "shell.execute_reply": "2024-05-07T15:06:12.333340Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "write_qmod(qmod, \"portfolio_optimization\")"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "b098aa8a-e47f-474a-b5a4-75f3b98d2628",
+   "id": "a22a913a-720f-4ca9-806f-3ae70a5ba57a",
    "metadata": {},
    "source": [
-    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
-    "\n",
-    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
+    "We now solve the problem by calling the `optimize` method of the `CombinatorialProblem` object. For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`maxiter`) and the $\\alpha$-parameter (`quantile`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "73cccd8c",
+   "execution_count": 8,
+   "id": "ff59d10e-c215-42a2-b232-df77fd775aff",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:12.336365Z",
-     "iopub.status.busy": "2024-05-07T15:06:12.336172Z",
-     "iopub.status.idle": "2024-05-07T15:06:14.856707Z",
-     "shell.execute_reply": "2024-05-07T15:06:14.855692Z"
-    },
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Opening: https://platform.classiq.io/circuit/5c17bd7f-2b65-4deb-94a5-6fb8708345d1?version=0.41.0.dev39%2B79c8fd0855\n"
+      "Optimization Progress: 61it [04:44,  4.67s/it]                                                                                                                                                                                                       \n"
      ]
     }
    ],
    "source": [
-    "qprog = synthesize(qmod)\n",
-    "show(qprog)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45f19792-d1ec-48b4-bc15-0fe881e48cd9",
-   "metadata": {},
-   "source": [
-    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c6607c43-7b33-44dd-9e38-b90c2db888e5",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:14.861747Z",
-     "iopub.status.busy": "2024-05-07T15:06:14.861264Z",
-     "iopub.status.idle": "2024-05-07T15:06:18.624172Z",
-     "shell.execute_reply": "2024-05-07T15:06:18.623365Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "result = execute(qprog).result_value()"
+    "optimized_params = combi.optimize(execution_preferences, maxiter=60, quantile=0.7)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "90c622a1-d8ae-47ac-a924-86d5b73bbd45",
+   "id": "2d0f1a15-90ac-44dc-ae1b-b3c1c62d3d92",
    "metadata": {},
    "source": [
     "We can check the convergence of the run:"
@@ -379,55 +271,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "7cff5dd0-312b-45b3-8af3-0fd6fa04b6e9",
+   "execution_count": 9,
+   "id": "858ea131-6109-47cc-ba00-55ba0e8f09f9",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:18.628597Z",
-     "iopub.status.busy": "2024-05-07T15:06:18.628037Z",
-     "iopub.status.idle": "2024-05-07T15:06:18.667398Z",
-     "shell.execute_reply": "2024-05-07T15:06:18.666685Z"
-    },
+    "scrolled": true,
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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7v//7NddcQ9eH3BQlXgckzaOPfexj0DwjBIxI81Lox/AqAeqf//mfqSaRyU2T2NUQ6E0EIlOI3hTOpDIE1osAbAcD0b6TECWJWwaBGT2mvdYQnh48eJBA2msUSnRcoikFahPPFcKAKuCG173udaSCGCADPNAVV/iGK8nJkxlc4quSjSYN60M/ZAvzEUgOWBVBM8THEROe+/Vf/3UGb7lFBSRDWB9/QhWUBbkiD6QCkdOBIBMccciWaOSM/ISTsyYkHF4nMrZX0A+KKXWkXhQHDxGfCLCaikFWcDPVZx6XUWtyQJVnpB1FnKIZqIdocXRW4FpgBECEIS23KL4kx48HFiScq47tw5FkhQNMSmeiFzGQlis9FRyFEpn6IhLh5Eyg2m0RAo+SloqDPCMTEDC8ri8Rdf/FL34xT0GbGoGw2nUTwoQ9vEsFyQFAYGUVGBzocCQZ4jFnCPQmAkbAvfleTKpzREBbbW3llTVRuWi7YQIaZRp9GAL6IXflPFhHS4JalKRpxwmEZUn43//7f8eImoFZ2nfyVAp50pOeBHMQmQyJiQeWolxlOPKHm7Hb0vzJnHy4kj/88apXvYoJY0yWmBiGgFH7UE95moihwpz/lZoiGE6zQjztN2gfArX7t3/7t6E6yJIB4e///u9HAKVMJGfeFz2eikDqSsAsZWbmlaywlCYaC5+0ywLf0xsgnNpxPUMtqCzcCU9j+waYgAOPklw7B+RJQbAvmQAIRePwkwTQkmypjhZEKuKTA7o4U8vo6HSYkJZOD70H9Gl93Zoh+ZgzBHoTASPg3nwvJtU5IkADTeOuo5dKxpqRUhFNv94qleKnjYaE0L1o8VW1gkdp3KEK4sA3EA80wFPCYQL0LZ7CHPAERAsrkwkF4VFjaSYgYWgClQOw4IUVkAep4C0cA7CQH7fouKoKq0hcER6HSIzWogUyQE1vQOmKpxQNG8E9dCAIZ5AW2yXiE46+COMireqUSAsbqWx6RWZKJBOuPIVxMTpjkpjOAePnzPLyiPqiSfOU+lJrHpEntSZ/ngIRV4WRcGpENLogOMgY7Z+nOMBEJIavqQIeniIJjqlxcAAxVGGiIQ91QTaqgweNmYLIE2G0FvouCIFNMZcDTFKRm74sREI8YqIEa1+BaFpT4BU5nKMuxI/v7NsQ6DkEbA64516JCXQ+CCjN0KyTCa08DTd+ZSkIgxDCoQGab/gSP9TFUzhPW3xC4BiIFuOs7/qu72KJC/OgcAMcQ1OuVKeEdPjwYSIzDky7T0OPZsngM0V85CMf0dJhC0yUCeEpMeESrjgMmJl2ffe7303a5z73uUob+ghJKJrkFPfqV78aaTH65UogzEocPBSE3ozAWBQTovXF9ph80GvpGVAcHQJKR3EnMmJTI62vSkIghtBQLEZMTMQyb4rhMUDBZ+T2vd/7vWir9957L5lDnFQWqQCBTKgCHopWSaBhPCRnBJuJXgZ+uYXFmQuHF4lMEsj1B37gByDv3/zN3+SpJqEuPKWOXFU1JxqPqB0yEI0itL7UgtlfCJgZa/zERxheB5XCg6i8PmpBclJp3wKPcj8eY19AMNfLCJgG3Mtvx2RbNwJwjFIFKWEjrv/xH//BMCltN7cojjTKz3rWs6AWhlVp8VFJlcNo8fHTyjPZqQ03i0pvvfXWa6+9FntjdE3if/azn4WBMJ4iN+IQH6JCe8NPudAG62SwW2ZaFwtq+I/ltmirEB4FQZAwK3zGI0rBgAj2hX4gDyKQIVekhbAhGOgQTY5lSGSIpovAVIokGF3/xE/8BMzNwDgmV4wqs6vXN7/5TeicCV1GYlUwOBs+QzwyRDDEI0MEwJMUhG02s9GsnYV9oWdiQmmk+j//5/9g98RTRstRKxmmpo6YKNNrIQ7qOGxKJtyiLhOfGv3xH/8xI+rIhgwUhMxIAhPjp6YIT/hb3vIWwAQZkAdAMsS+DGARFf4mJrKRP7fICVwUBBQE/vIv/zKRGRXH0ExfKJPW5Ma8Pl0ZjKvJBHM5NGzeI1DQpWBQndVlYEuG5gyBnkaAvzpzhsDFhAAtONWh+WbtrGpUNPr8EUIV2spjh0wEtDQadOLAlEn1iQYtJbdwBobQjJ0SjqoHDWC7xFO0vfe9731kjs2wRlZNFz9Du6yrIQmbe/AUJkOHIxyiVV2cmKhuZMhsqAZquOptxISENE+U7N/6rd8iH6gFJmMZMYPSkB9P6Q2g9TJMTY3geNYcw4VwLY8gSFbZIioh3IIGXIjSrGuHCEEAruippIWlmJBWAZJy77zzzp/8yZ/UmXKu3/qt3/r2t7+dJERAE2XkAL7nFtbkiqMWqKHUFOKE+VCgWaBMx0ULUqnAmXygVRxrot7whjfQK9K01AJLZjJxmcnwNWbYGF7RW9IQ7LxYHEUq+iiUjvk33SB9RD+AFdgsFKYidCOgYVRtLLP0aZKn3trVEOg1BGRTddoCc4bAxYEAXIKjpUZl1BrxC4fAoDcIGBqDRWisicMtFEI0PLAUflpwSAs/EUirbK1/sVALIZAfyUmC4xYKUZaioSdE+UbTMpeptA1hMMOKjkh8IqDdkuHTn/50CvrEJz6BEknOiTCIRzRuiaNFcNvpkE0FUNlUE2XwXNVcYhJChknpSbgWnWRFPqpwKziaijgApTknMTsTAhESMnUNZULqwKUhVJ+ElMUYgNYFG2zG8Blp5xFZceURw8VccVoc4WSu1VR4k0KJQK8I6EiInDxVMbhyi/DASBwKxWkVSEs4pfNI8+GWp8tyToowjyHQCwjYHHAvvAWTYcMQgE2hQJp1FEFt/WmF4QlmHAmEJJQg8dCaQyfEx0+zDp2ge6ErM3ILCSkP0coTjSZe5eMW/qCV11vYV/0kR12j6acsaIanyr5oyYSjjBINByXwCLMsLHUxHUKBJj4hmj+S4CcaV2iDQPzQLU4TUjrhSIhsGk5yHZVViiIhNcVROpE1PrXTeW6ekkpHksmZaFocXRNlKeQnZx5RZa4kJHLCZwomSWBfwoELvwoGsOSsvQfSoveDJAusicAjHKDh5xWoh+JIqMSJX28BildGOEUTAvuSBD91pGgVA4hwxMERRx/RFSAm0hKZF4Efp37i6K1dDYHeRMA04N58LybVOSIAE9AK0+hrelpqbZQJJ5ArBKZNPxHUAwPBDdzSaqviBXXhV6omjuphmg/co3OrxCE34vCUOBqZLSCw29LNPZiJ/Ou//mu2pGCMl/zJkClb9tD44Ac/iO7LMC+DtATCKHpVvkxkS8TTinClLlCOkgo8R3XUj0gQG/kkMckQ2ZQjk0DVWZNbPJAWV+1qkKGSXIKYxkQqeJFHCikz5VA+CRkWZu6WWlMQS3UZsv6Wb/kWhr4ZV2cQnkdMJKMHkwnD9ToRQNVIyBUkuZIh1SECw+nQbaf8CoLCSwQQRgwi4FGcCURgrkn/AH+noxZEINUyEDrjmN8Q2HoE+K2bMwQuJgRoeXX6k0rhxy2rHVSE/pfE4Sm3NPd4oC6uUNqyJNySD/SsdNuZVudutRR0TVamskIJnmBkmGMDVPuECaAE5o/5g2fYGdMhQshTC+XRyuJ4BPMhDzEpVAXrjAaZEYEQeF3DYTIVQ3mOWuBUYI2gxTGBuqxchqw1AqUQn0xwWqjKqU/JlttlknCLAOy6RX+C2kF4zODqHC3yaMJVr8hAbjC6PsXPe1HBNARgqSBQqwxaEfBcJgCR0Z65IjPxuWp8zcSuhkAvI2Aa8Nb3gUyCDUeAPznVrjpzhucgJIyAaMpRv2jH0SBpr3XsFHYhkLYbj4YQgafkgJrVqZ8RQhyequ6IPpdocmh+lEKIsgW5wcTQNmSMSKQiH9X/NAkhZKLS6hUJEYNAonUKn/jhGMROFEH4icgkSSIkHs2cW/iVWqu6nDxFNtJSNQrCITAhnQolyUmyrOIkVyWY6vMI6k3GirlNSiQa7AsaECoY6jg5gUSgmp2lqDwUDfvq0AIhpEoqyCOVHGQYvUBUjaC4kaG+LM0nyY1UKyXvjGN+Q6AXEDAC7oW3YDJsGAKQCs20ZgcXJp7Odl+pjkfa0Gs0WvaEAmGCJI7mQEMPReGHeBI6IQ4hUKmyUZIEqqD11/lOIqC0ofVqWrQ6uBB/4jQtt8rcGk5W3HLFQT84+AnegnU6OxYo7ko/kBwxIS2IhwzJREjVcVUShxyI4LIMySchS9ImjEihmj/RNDlZEUF5jkcE0gOganpVaTUHlV8pGRnIIekWdBKqJkFO4gMpWdFBAXDqSCAO6LTuyqAKO+Fad7IlB0rXt0yldE4BGRImJo6CQJ5JLbRcuxoCPYWAEXBPvQ4TZmMQUDIgLxpuWmHNVJt1WmRp5t26Wx7BT7TsBCoR6lV1VuLjeITrFItAGIV2XzlM4yQ0lhAeJAGFUBC0RyCskFByUijZkglEBbfBNHgQgCQkVLHx8wjXKQCBOOUnwimayKSliISZCNe5bQI1ecKCkJbOjKr8SlfksKya5EApyAM+WjqZ46dcFVWvWl9AII7mQP+D/MmQzhDhiV6rmXBFYKTVCiaBeMiQzDsrq6+jswqkQoYE7WURKE6R6cykswjzGwI9hUAvErD+USUwaXuhf4T8dfGnhSMOf640bfiJSThOmyT+OPFoW9AZTjTNRDMkB9oOtb4h2srmIBHAPIaAIWAIGAK9j8Cynm7vC9xzBKzsCLlClsw2qf1kgiN9dvxJlxw//XroFqe0rf3xJL4SMOSKI0O4mfkwMqdXzkAiJE0EFAWeahc+SWgeQ8AQMAQMgR5BgNabRh5haKjx02KrH/s7dnODNRieweZApb2AFKqeI2BFMBli4ha/EiT6rj6FQWFifQ0M68HTTGLxVEcOicNb0Zk24vAynM4sGwXA1pjDsJMfmwnwSHPjSiZK7UmIeQwBQ8AQMAR6BAGacfQlHftEJFWoIGOWvHPUN3umqpzLFLAeEf4MYrTXDp4h0mY+AlMYF0YESsrFsIKOD9Dr9BiPUH/x41QqeFSNYphwglwJhGVhX8KVd/VKuJIxs1NsLctmgeSMoxSygrm1S6V52tUQMAQMAUOgdxBIlFo8SEVzjQdNbNeuXWqHr6IqL3Tqb71ThVUl6TkNGOwAMaFDaBUGhY/BGoeHagC9Eid++JjBZ0gXZk00YLgWl2SiNSc5wxSQNB5InSREIHOKg/V1znhVjCzQEDAEDAFDoNcQYNiSHU9ZZQAj0LYn5n4XEAH33FZtDCbDi2AKKfK+4VElXWhSp34JwcySAWduNYSYatUJm/IaSKVpl/1cyFZZFgKGs7kluc4rkHBZZLs1BAwBQ8AQ6BEEaPZVEtWA8dPI05Iz28jKN5pxZV9t25NldT0i/BnE6DkCRov93d/9XU5TgWI5f+bHfuzHOGtMKwBrgi9kiRYLlf7RH/0RY8jcElM9aldFZN4H/KqpkjfHrUaGywnkqiHkmUTWJHY1BAwBQ8AQ6B0EEh0p8ahs8C6NP37YgSutesLQvSP8GSTpOQKGXD/+8Y9zgOhdd93FuWYou5xEhqEyow0QLfgCNCMMVElX4nNeKZvNPvTQQ+y8A/T0fZapvwkB8xTHyDPJYegk/AzobNWjtnnYVklg5RoChoAh0NsI0IZDEDrqSZOuHKzX3ha8LV3PGWEh2oc//GE6NYALWb7zne/kcO9vfOMbnODGI0KgWLDGDyvDwcwBwM0MJhMfD49QjokGW2st0W7RcWFuHBEIVCbW4Wj8hGvMLbqKTYG60Iv6QwQt7RlJnHiUfOmTOK19GwKGgCFwESOAZkWjjQZMo02rjh8P9aWp10c0/oTolOWFgkPPteZAqSP4kCU0TAeHQWm1amZiGKx5AYz7gy+rvljUy0Jhzkf7nu/5ni9+8Ysk1AEKokG6+g7wYLFFbqwVO3ToUI+9GGVfvSYUKzISFIX2mMQmjiFgCBgChsCGINBzBEy/ht4NKizdGa5vfOMbOeMMB4nqhrrYRcPHPIKeOe7tn/7pnz7ykY+wFPi7vuu74GNIGlxgce0cMT//a7/2a7t374absVbnpJoNQa0LmSjhRteUF/BiOt6N3DEubUPTXUDesjQEDAFDYGsQ6MVlSHAw7MuQ8s/+7M++//3vv+2223RXDRRfZohhViKAFkqtWkGjJbOj5NVXX/3yl7/8zW9+8zIgmfFlVgC2Zsiag9Bvvvlm4jNMoZmQ25ZaYKmWm+i6Eec6ou3g36hKEs3v5OVlVbVbQ8AQMAQuUgR0nJnKJY02LTmGt73Unq8b+p6bA9bxZ+rx+te//n3vex+zvzpHy0w7HkaY4Utd5sVrwMMIsxqgQ8APPPAAtE043Nw5MUAgqXhVnI6+boQ2NUHExPF0b1R2PDe8kpI3VTgrzBAwBAwBQ2ADEeg5AoZoUX9f9apXMaeLY6cxLK0I1Kl1NaeiKwQEUCxXCBvVFnUWK+jrrrsO6iUEfZdHMDFXcuOpsjV9JUJ6ycGpCKnMGrFvL4lnshgChoAhYAh0C4GeI2CI9ud+7ufe85733HLLLYw8Q656JIMOOP/gD/4gOzm/5S1vgYN/67d+C60Xxwjz2972tltvvZXxaiVdVZHhXZwihwYMKxPeLSDPNd9Yu+20wFIm1mtb602I+lyLsnSGgCFgCBgCPYRAzxEwbPrWt76VOV1dd4T+Ssi73vWuV7ziFcB25MgRRpJVD2ZC993vfjeGzajIT37ykz/zmc9cddVVxNEjwZWwuU1GpPGrjTSeHnHM9You7xyTu5CtHynEq2vDhEYdijiVfRsChoAhYAhcoAj0nBHWBuLY+5P2sG8NFdj3Gk0vn/GClpdNe2FTVjO7oekIDNWSAzdSbQS8gb8Qy8oQMAQuFAR6vz0/ByTbI5znkNiSnCcCsrLI947ONP/+H//lq7ffX2m0CPEzOTfJ3X41apPFfTvoPAu25IaAIWAIGAJbjYA16Vv5BhhSZgPTB46c/Mi/fPozn/9Sw0tzCwfXg4Rzt1I8K9sQMAQMAUOgewj03Bxw96ragznDtQuBN1tpnZypHJteSBeEj+Fe1kGLKtyxF5YSMoHmDAFDwBAwBC4OBEwD3sr3CKE2fW+xmfKLg+WGHLhVaXlV9OJO7hUBCZLP8uCtlN3KNgQMAUPAEDgvBIyAzwu+80wMqaZ9b6ZcD9L5UzOL3GbSnp+SUWj34e3YCzpPjC25IWAIGAI9ioANQW/xi8EQ+uT0vJ8tzMwvVmO+hX1XcxC08fFqwFiYIWAIGAIXIALWoG/xS+MFzC0spPtGa6li3a00Ct0xTjrpGwtHLH1TMhAdB9q3IWAIGAKGwAWMgBHwVr480GcIIuU1p71itTBarsrtSNYrhE3W+woHh6L1trxUUwjYXtZWviwr2xAwBAyBjUXA2vSNxXN9uUGxfGrNes3LVv1crS4c64d1Dy24YxgaLzzcEbC+Uiy2IWAIGAKGQA8iYAS8xS8FZuWoY3beYJvMctmxLHtzmLK7xa/FijcEDAFDoOsIGAF3HeIzFKCqbblabYV+Op2dL5dl2NmcIWAIGAKGwCWAgBHwFr9kGBcNGCH8THphYUHeh+/LwPQKKl4RsMWSW/GGgCFgCBgC54OAEfD5oLcxacuVWisMU+ns4kJFJnpZCOyc+EO3KFjv7WoIGAKGgCFwESFg64C3+GUyB1yt1lteEKb8xUqZW3FMAy9VeB0nmyWWomNXQ8AQMAQuBgRMA97it4iaW2u0miELjvxqg28cWq/73mLRrHhDwBAwBAyBLiJgBNxFcM8m6yMng1J/X8rPMAo9O7egSZr1JcuQziYfi2MIGAKGgCFwYSFgBLyV74sxZbTfRivw0xmfswibgQ5BZ7LZSKylA9FbKauVbQgYAoaAIbChCBgBbyic68+sVncEnEm3wqBWrzMbLKPP6awsBl7ueBLNES9/YveGgCFgCBgCFxoCRsBb/Mbg3GYjSKeyqL/1eoMlSOYMAUPAEDAELgUEjIC3+C1jeBV4YTafqzUalXqNJUim5G7xK7HiDQFDwBDYFASMgDcF5rULWVxcTKUyg4NDTP9WK3V9H6GcwWDOEDAEDAFD4GJGwAh4i9/u/NxiNpsdGhnxUuwFHa0DZl/oLRbLijcEDAFDwBDoMgJGwF0G+IzZtzxvttryM/ltfZlSWCvXG7Xo1KNkHTCqcBAdTSh7c+A1ZwgYAoaAIXAxIGAEvNFvEepc9ukoYdmTpuedqKZrQeZRo+kxb2qhUiOkjCW0xydxoS/HA8PEmVCPD06emMcQMAQMAUPggkXACHjLXh2MCs3O18IglR3KhYNp2Q8r2onDWWLF88AuYhhA3vi4mjMEDAFDwBC4CBAwAt7il1gtV3wvGOzrLxaLYRjOL3IY0rIJYHtHW/yOrHhDwBAwBLqBgDXu3UB1HXlWqovwbglXzKe9cHHRS6VS7ZMY4pOR1pGjRTUEDAFDwBC4EBAwAt7KtwT6jWrND1rFQgYNGOotl8O0GVpt5Tuxsg0BQ8AQ2CQEjIA3Cei1iqlVyxxF2FfwIGAGn1kWLAPQQTLdywsiwF7TWvhZuCFgCBgCFyoC1rJv8Zsrl2HcVn+/11fIyxzw/LyYWTEKbc4QMAQMAUPgokbAGvotfr31WiXth8WCl8/ngyCoVCpOoFTb5nmZSdYWy2vFGwKGgCFgCGwMAkbAG4PjI+YCuRKn0WxozMBtNjlf8VKsLwqaA2mvkGdHrCybYUkEtxdlvOKId2Sj0AqbXQ0BQ8AQuHgQMALepHe5bHGR3jabQq35TJprJpP2/JCjGWSxr+/DvqoER/KZHrxJL8qKMQQMAUNgkxAwAt4koLUYWWLknO9WGrkB56CQzxHaVyhijVWtVlcIZO9oBSQWYAgYAobAhY+ANe6b+g5V8dXxZwoulxdSXthXLKHf9vX1uRA3BO2l23PAmyqgFWYIGAKGgCGwSQgYAW8S0Eq9KbexRqsVbfVcXlhgrHmgr8hrGBzowwqaZUjLBHIzwfaalqFit4aAIWAIXPAIWMu+Ba9QDbIoWFb9+r7qvokG3Mm4YrgVu05/HGbfhoAhYAgYAhcqAkbAm/fmUHC1sMTDjK8fMgec5UGhUOBprVaDaPUTxSbULLA27y1ZSYaAIWAIbBICmU0q5xIqRjXVjv2cte5CpwG2V6GXaoUhY9DEq7Ukci4tplmFjNBsrRVyIiFxtWcU70pp/SQF0a6GgCFgCFw8CBgBb+y7dLqrU1gh2piKVYOFfUOvBeX6hUIRloWDJ+fKtUZ9x8RYzvMmhlOcOHhiao5HfKBc2NcdA0xM2ZdDydiU4Y19YZabIWAIGAJbhYCpVhuNfLSCVxTcVRyKsZCpsC8fNOB0Op3PprPwa0rOQQr8FFt18EgJ2HP7dZBAczP2XQVSCzIEDAFD4MJEwAh4U96bcK46GDitbEoYc8CMPhcKKMBeNuWxExaeckMImDjtRFHaVYOSZ+YxBAwBQ8AQuJAQMALu1ttaBVlnhOWmgaVQKHZeTmLwioU8kdkIK5fL+XIi4SpGV0Qw9bdbr8ryNQQMAUNgKxBYhSa2QoyLqMwQolyVKwNPTK/ENcNIwV2YLwdBs5DLQMYov5zHwIh0udxc9lba2fmqPGs2djUEDAFDwBC4gBFY1tRfwDXpFdEjtoyA5SuhT1l9JDepIBBdGC5dKC8yI1zMyxA0jj0pMYheuReHPrWrIWAIGAKGwMWEgBHwprxNp7nq8l94F0toCJhPpVpLpf1iIQsv8yYyGTFK1wORlKEx2doU+awQQ8AQMAQMgc1GwNr3DUccSJ2hc5Kxkm10K49YiQQN42k2g0wKK2hZYgQHcygSJF2v11cfaCYBTq9RbvZlCBgChoAhcKEi0HMErNs0wkONRoNrdD6ug5ddoviGn3Qv5SaH+cVudnZWvXNzc3j0WPvk6KE41mZ/LwM3lRYFt95swrh1NwQ9u7C4MDdzxYTXbAkB79q9I+2FVGFZws2W28ozBAwBQ8AQ6D4CPdfUz8/PU2vIlTU57JNcKpWgZA3ERgk/7IulEjSspAtD4x8aGtI4pOK2WCxqJtGor5yxK05PRFB/d66CZ6eOCq06x6BzFBy6ZUgIpDLlchnSsAsW11w6QwWRn0fETvLBI/mgNrMs2OywIkjtyxAwBAyBCxuBniNgqFTZF1yPHTvGlduBgQHVelFqWaujgWNjY3hgaEJYUAs9cwv1qqKMH56OziBKTuH1Y0LkcXdcwpoR568oBZE0zoLwarqvVEAmIWFnBS1Kf6WSZLIitQUYAoaAIWAIXCQI9BwBQ7e///u/f9111w0PD99www3f/d3fffDgQbRb2FQPq8eD7gvv8gbe9a537dixg3OEHvvYx376058mBPWxk631LSV6sLK4BnbzGm1CGRWR6KyO/rUzAMXOL3hhyi8V5CxCPtAwZEwSrWYsnj6M7+zbEDAEDAFD4GJBoOcIGGA/8YlP/PIv/zLXj33sY9Dtc57zHDiV8GQAeXBwkNsPfehDb3zjG3/mZ37mK1/5ymte85qXvOQlt912G/zNIwZyIV1mkdUD76pHmZgI3XQrIF2iz6YgWgL4zC40GHhmGyx0ZYywCOnv76eOEPDqevqSfLpZA8vbEDAEDAFDoPsIrGCL7hd55hJYivO3f/u3L33pS5/85Cc/6UlPesc73oEG/JnPfAYG1UFm4SdGccPwz/7sz6699loIeN++fT/8wz98/fXX/9Vf/RXRyF/3dMRDTBx5MnYNl+vw9ZkF6M5TR57OyJn8dXR6fqHspfw+N10N4/IplWTquhoPQWu07shjuRoChoAhYAhsMQI9R8AnT55E39XDcU+dOqU66549exhbVqg0hIne22+//RWveAWBDEczH/zc5z737/7u71AiFxYWsMyCd2FcrkRIFF/VjzWfTbqurbZWq9QoxaS103eFbdXKDBPuaMes5SISZ+3slke2e0PAEDAEDIGeRqDnjiPctm0bI8ZQJow7MTGBKvxt3/Zt+/fvB0VmguFmtFiewtCYaEHDcCqOW/RgIsBecLBCrsPOXIkGE5Ntslppc96JY1ZHmvQD5Ibx5yDjpbEig3urzSDws/ls1o0/y7RxPp3yg6DS9DkQCV2YcKctJ50kPGtQ8+bUx0oxBAwBQ8AQ2DgEksZ947I8v5yYuEVzxaHXvv71r2d+94Mf/CCBsKzOBMO+sCkhaoe1VmnwNyPPUPKb3vSmvXv3wsojIyOXXXbZWvE3KhyKJatIUZWvprvJhH6mFYB2kKovlLyAnZ9nF5uVIDvSX8R6m4lrKHnXeCqoV6YWWxAwLk1y1h3FJxLGplrumV0MAUPAEDAELnAEeo6Amb6FYqHbV7/61czyfv3rX8ccmhlc2JTRaXgXD5jjGR0dhYm51fHqAwcOwNCM6OoQtL4X0v78z//8/fffDx/jdF3TJryyeLA4Xv4bRicMwqa+1/S9GrhXmxzOkC7lRAMmPiGyI2XQrDZa0Wg7wfHqYWF1dWHPvbJYMvs2BAwBQ8AQWAcCvdiaQ6uvfe1rP/rRjz7wwAPwMRSLQgwrMzqt9Ez9CHn84x//53/+5/iZ8Z2cnPzXf/1X1izBvii7KMeJvZXOEJMJnE3O68CmS1GRgeVHbrsu5OnrK6m6TEg2J1ZjDJi33HFJbtS6S0JYtoaAIWAIGAJbjEDPETD087rXve4f/uEf3vOe96CzQpzHjx9Hc2UGF/djP/ZjP/mTP0kI9kqoyHffffdv/dZvofsSmTVI3/d936fbT8K1RE4W1OJnJhhFefv27VuDt+P9iP3dQmBIl6OQoFsdSNfXkM3KkcBO+LXFTFYVrx3FnhgChoAhYAj0PgI9R8Aw69vf/nYo9nnPe97VV1/Nxlg7d+783Oc+BxNjYHX48GFolUCQfeYzn/mrv/qrEPDNN9/MaqWPfOQjrEQiGo900VGyGEmmlNNpiI1st+CVxFp3dFIwBxz5ogHPzzsCzhd0xpiQnC8W3ej6scW3rKPaAoGtSEPAEDAEDIHuI9BzVtBUGQbSisO1UClKIdQLfTK2zEpf1GImepkkhlNRiHFE1ph4iKzG0mi9ST540DXhY9WPNfOuXlf2a5jEFS7tmMGdW4CA06VinkOQeEQEJoMH+wdm5xcXKyyuWiLgygyXPLYbQ8AQMAQMgQsNgZ5r2GFWMDx9+jRXBo2TqVxdUwQHY2mFH/blShw9jIGYjF1zC1WjQ+MhgmrD+OFs+BiPbh+NZ8scTCtH/Mo4crlSk60oi5yNJATMIcFc+/pFA15YEBCcYdZSSaOeydJAuzMEDAFDwBC4ABHoOQKGNSHL8fHxxcVFxRMDK1ReaBXVFnJCkSUCnArF4lF7Kxb46iIlFGXVmEkr1kyOd0mL7ssjTLQ24R3Box2u407ok1vBHG+tKauN9DQkgjCJ5qpWZhW4uZ2FxJdc3HqkdrD5DAFDwBAwBC5kBKRx7zUHsyISRyyoYInayhbQjCQTCEslaq7G0Vlh/LpNNISNHy7XrNCVdQhak2uSbl87iFeKYlpX5oBdh6CBtut5MzNz9CeGB/o5YpEnRRRhIWefDgRqPX7HwcteUAcvS3RzhoAhYAgYAhcqAsva9wu1Gj0jtzDrqs5PC6G2UGX9lB4JDAtDwIU8G2ZCzcLBQsPFPBPDlZpuxbE0J7V/NgpeiordGQKGgCFwgSJgBLxJLy46kliMsHw/lWKrjWYrgEz7imxQGcoAtGNWJrlR05nJXl0sW4O0Oi4WaggYAobAhYeAEfBGvzOZqWW7q9g5HxqurieSUWhMsj2/UvcaQSudzrL0iLAUpOxiumXBAQS8uipt6m+Mq30bAoaAIXChI2AEvHlvsBnKAmWGnSHXxUW+fWayHe0KZ/MmoFdmr3mwugZs7Lt578pKMgQMAUOg6wgYAXcd4qQAlk25hciC+UK5Ch1HhmaBHtjgwdC6EcdiRYywRAl2/JzkYB5DwBAwBAyBiwYBI+BNeJURyOwvghKrH3YOYa631N8Hw7YaNRYGE4lhal1MtWS5lHHwJrwiK8IQMAQMgU1HoBd3wtp0EDa0wE6+xA/fMsuLtuvmemWs2c9g4lxvcCJhs19OQsIay+esCaIVQm8on275qflqQ9RfHEZbLkMZfpbcolv3zC4bhoDAmww3uFfmLrwENY4D+kydQzTcbmXyzpLIbpwiWdvtnugbw6vv0Pq4iopdDQFDYDkC1josR+T87sGzE1LZ9lm2vfKDdOCVMl4z8Bq+h4nz9Hw536xuK4pxdCbfz8IkjiksVIOdpVzgZydrciJhnUMM4epAGnIuQtHoyR2bWZ6fqJbaIcBiME63dB88gjUDFSEhAN7wwkWvNuu1aouzC+wLM8tWa8zfN9hERbdLDbwW0wdBsyEbp7jXpBm4jJjrdxZ5BrQhYAgYAqsi0MkWq0awwHUjQFussOKRNl0czFlPeWwIIgcDs88kGnDOaxRcvJafoe1Oea2C1yqmaNH9cqx3RYuXXNseugXEiXql+dp1AxCIpgZ4Cc65d0Z3pxmweYqj5iDsG+xnp1MImPtMlj1e0Ill7MILZItT3e8FT/y65aG7WxqgwXY1BAwBQ8AhYAS8NT8E7JwxyEp2ulYhaMc1hKe03Lwb2TfaqWRcGcPWW41s141CAC5NPkmeoN1EfxXEGcOQ+YPJqnfLFx986PBcoulKhJQMSMtWa0lK8xgChoAhcHYIGAGfHU7nHatzF0waa8ysoFvdMjPJ209DyWIpLbtYt0N1TNR0qQSRjfPEoDI4wSdSYGMudad9+KLwNr1Kw7v19kMf+adPfPlrX+fVaDpRfN0YBduMq0xx0o2T0HIyBAyBixcBI+BNfbc6c0gzzcESNN9u2w30q7jd9j1OciKcI6Hq0dZYIp4bIt1UOS+hwuBSN7vuTKzcKLRjVy5yPJVM4ae8bD6d807OzB2fnLn/gQOyh6gDiBXb+p3VXVSWo8Zrjd/s8kd2bwgYAoZANFlpQGwqArTKaMBoupyiSMHaeivLZmV+UUzT2Yyypc07BBE19Jsq5KVSmKNIZ9umQ8vtemeyvJ2U1+A1yCT9fLmRLQ7MV6ppputDmZZvYFPnHDMHfFtnto2d+QwBQ+AsELBG4yxA6kIUzjuCgNkJKybZSM3FGJqznnhUrdab2PiY6zYCvIBIA4ZNI3MsLTPt0RNKBZCtL7ZXxyZnW6nsYk1WiLFlCulkCJq+kTvhyhlBd8rKX5b9cXUCYn5DwBBYjoC1EcsR2YR7lC5mDRl5lhZ8hSMQRwQ9zHjJ84Sul4TazXkgIJBGiqzzRP66OypDzOCyOQiY0JnFapjOzy1U8bNESQiYsYqU34r7SbzWJX9O3POxZWPn8XIsqSFwcSOwpMW4uKu6tbVjWlcFUK22Xq/Pzs7u2DFCE41rhs0Urbdr/EdGRhhzPn36dF8h13TGPdwSgWiBnJ9krssIuFeSY6BZ+DTlpXO8Ft7a0ZNTzTBdrsqQtDItfSS8aebtzRkChoAhsH4EjIDXj9k5pUgsrdB5acHVbpYZ35WOIWgClbDb5lnOl2SyMpWFnCMCQrfxX8FSbdVpubIGieOrnNbrtwK/lUrRFeINykfWIJHW3cmeGysdyVcGWoghYAgYAoJA3PQYGl1GgGndpARacPaC5rZUisIiZnWWuHogkkZQAuapLEQ11yUEBPYULOowbr+mqLRUmkflBsZYsisHMctsfuUoOekedcq1In3nQ/MbAoaAIdBGwJqLNhZd9fkcREi77Wi0zl6GtRoTvcXi8jJp6/v7+xlzXlxk60PZAcJdWQpjb2o5VhtzD8LCpnyBsINbqFhcpAE7//wi4/8eC5A8P72woDpvFN0910t7Ow6Xh72yDmzMawgYAisQsDZiBSRdC3AbO0jutZqMMDPULItXOp1r/zkQCQLWA5GUgGFf1YBXVbk6MzD/+hBwgJMEvZZNrTrTxiwsYfDq/LxsOsl7CXx/dt5NA8PYknxJqs4cnP/MT1dEtwBDwBC4lBCwBmLz3jbNty75rXIWsMeeG/kEffjV6VuyqoVDghMNWFQyWQUjLb1G2DxxL4WSHM1ygX0FZtiUi4Ct3/jYdEMizc8v8FIc56bnF8syJew+Eijhq04AXwoIWh0NAUPg3BFIKODcs7CUZ4mAm8qVuIw/6yLgzoRBbMWj+1PqHDARGPlUJ22944bOVOY/fwR04Fg04KXwar+HcQvImXXbjoCZRUgtVmpJRA08fxksB0PAELgEETAC3viXnrTOjjpBmG/RoiBgbrDAWgzSzUwhl/YZglY7Wr5bGPaQMvSKmcAPGpWWs7aV96O2uSl05E1xSKufdmmU7D6yCqotxZKbduQLyedeVfw3AM9GGjDBvJe0LD7iv89JwOWmvB/Yt5HOVZoy7oypOh9njMV2HO5cbelCRdPA+MwZAoaAIXBmBOLG58yx7Ok5IQBDuYaYUUoZyUxhiCX2s979U81admTbYKbfNeKtJouAc0GLNr+ZS3nbS6l82Dwx38IKi8lG2nxGPpkGxgXRpkvnJM1ZJUJeWIePcIkyrNaCA4mn56e58qk16vUqZ+V6AUcW8/gCdlhfNf2wCZXylwDQTVgWH0cweLVMiOFzEKYzZc976NRkGPqw8GJYODlf5SWyTWVYnic6i5HceiTlYIEO7BS66P1fwPiY6IaAIdBFBIyAuwKu06xizZfG2KdNFqj9UFrnSpCt+5lCJqUasErAvlg8JmE+FWS8sBak6rTfaMVOMVYy5ETgbhNezBx8U3ikz3Gz2KiXBkbR/VpeKpPNsSIWsS+GzTJD6XAk70u7HjJOwc4nAeFp3heDFuUGZNzK54tNP1tpuTGJsJHyoWx5oaQSp9jB2W3i5aE5Q8AQMARWR8AIeHVcuhrKHDAzi8lZhLLKV5Tc6F1wQgMhxKEdlwdsBKHNPN44TpfEYwy2JZRDsXwgewx/0QnF+dlCw/MPTc/fefAohJQricKXKzi1z0W4gC/x/hta07gijFYw/hCFYZTOzqBDg4xZeJWyaMDi4qd6F1/tbypGwr4NAUPgjAhYY3FGeLrwkLa7UqlAwLLcqCP/lLOQJqRYTCkBR3qVi5MsYepIsfFep8zBwfGMZoAiiBRy6A+CzXren3/wo//v/R86OS9j47I5ZmcFNl6cTckx4tKorPad+Pjv61tYmJ9n0+ft42O8mvnFBam3PASo+C9oFShU/TUleFPeoxViCFyACMTNxwUo+oUrMia1qFMJAasGTHvuxqdleyxC2Cwagtt8B99EjAHBsvWT2/0JcmE8/L6jlc/e9sAX73jgEOcCubHxzRevCyVGxs/Cp45O3Z+EnE/Iml8ZEnDMOj8zw4D8rp07hIBZFKwu1oCFpDV9F+SzLA0BQ+BiReCiGEK8cF6OLiVapgHTpsN5QsCyotQr5oWA2Sya4xvUulba9rit72pdKZ5PTMB4kQAqyjI1zWzn/YcnvdJ4Kd1X8bISB6Mxsfu90PtwoBu5pTVJYfOsBEz1Fxfmsr6/d+cOos4uYB6HY4KYP59lxunt3No+F9suhoAhYAgsQ2Bpm7Psod12BwG0W7jW7fkcFSCWPDLRK/Y7WGYJJQdi8qzqF4+iAWqNFyXqyhfKHDI4h+U2c8Ci3fF/zvM+/82755rZRm7w0InpRWRDQ2+JFVIU/UL8ktlf/ciXdHTcR6riC/jSB3FbZFUri7lUuG1cZgfKlZqrs9ugzKHlOk4XYv1NZkPAENhKBGh2zG02AuxDCQHnclDtmo4INOvJ+YNqfSVB3XcRo1KUlAYlMSss08IPHTk1XW1VW+lTM4sBx/TJ2p0L/PcjWmpEwG1fhLGMPwOF9nla9UbGD4fEBsurNbRfJMPUcr+aW/PBapEtzBAwBC5NBKyh2KT3nnAnOuPMzEypVMLsOSMEIIqvHD4L37GmVL69sbEx/KdP13XkuVZvuIhtS2kXqysXCtKy3DJgvCw+li1B7jiC4VXKy+TnK/VKQ06rF54Srr7Af0JxhfVbBpRlQkDqlckWuKObdORkWMhlC5n0zjEZnNA5YKm+DAJI70R3GHWp5BIBmNybxxAwBAyB1RC4wFvP1arUm2FKpbKbhtuKEtJlL2hpxDucxiGAcxrw65nB3CbhHXG75UU8+U3AtXLYbZblR2FG1sLedc+D07Nz4+Pj2Wz65MmT2ZSsjvUyaMEXvtPXwHWJJyWW3pBsKFbrKS8Y7CtweiQvEDRYIub6Hmm4F7rVdGdQiC98jKwGhoAhsPEIGAFvPKar5+hznLvMp0JrHDWIgiuHLrioCb8mHpYC84R2X02vkvDVc964ULgkWljDWLcPuXLH8l+xwLrt9jvLlcXrH311Pps6fuwwMetQEF98LmQXc2dcB9mzDL/8XUC9VFHmv+fm0HHHhvpBhEMkGZpeLGssKo+h1lqO1Gs/XCuRhRsChsAlg4AR8Oa9aqZ+tTAIGE7t74fexCm/0lQ79VhC4GYC2fxBCTgJl2fddPwakCkLCTHELDtz5Zt+mgVIUzXvxIkT/YX8k25+VNZrLM5Py9grWyBf4OyrWLZJUoy6IwcA3PBhmoApA7Ywg4DpPDFzj1XW3Lz0pQQi58hBoIjQaGeiT+1qCBgChsCqCBgBrwpLVwJ1Ghhtkl2uUKSKRSlFVE1Hs+KJbZoSAlY51AS6KzItzZSlrmmvmYZ3xNor00pl6mlZAfzQoXKtXtm7Y/yxl3mpxqLXqs8uCDmhBLfZa2lWF8Qdwi9lS+7YB0zCIFSOziACrDozM+uH4cjgIOEMTtAfQid2keTP54JG4IJ4TSakIXCxImAEvHlvVhXZatVjFw5mebNomHHhNOJKz+Lp1IBdhHY04cUuO1YWhS3ZEsQX6kXT5SiC2+++r1lvXHf1FUOeV8qyLrgxPSkMFO2h3GWJup19e8NrSgLhGGP6RepFAw5bzWG3D6VYz2Wyswvz3ZbK8jcEDIGLHgEj4M17xXAuLFupiG0zI5nQKh+lVGn2nU9bfN0OWuaAN086VxJjsK2622RSuIdtoCHgxbp31933IunNN93AAPWuiZGwWccSmAhQlCiCF43Tl9FRHQaaqeD87BxvZ2hA5gx4NYxe8GqIxRHOrQt8DKCjruY1BAyBzUbACHjDEQdSQVW/hEGFZrlrsedhLUhXGmHaD7Ailkg8cVc/CH235QX3ebTMDBpoZrGVgf9CMdty/zWfbnOyW/cKrYh260ydF1re4RPT/X7jxiuk07B9265ymC1zOJA7S1Fr4SpxAV+iGVyqx/izQ5h68U0d+czUAg5B6ivmWbg9kAlzHMPYYJtseieyBoloEjl6kfgEEpdHx/cFjI2JbggYAt1C4OJoP7uFznrzpRGmRXb2OdIEx62wM9UJ0CxbjbR3aLJczHjb+9OESvvuJhoz6VYu49Vr9YLvDXrezpK/UG9NNfPoWdVQZoYbtUWEYfuLrruMbA+SzhfqjRCj38XA+/rD3nwzfc148TJ6BlghDWyfCkcPTDfpKGQCzgW6gHVg5U7qK29NX5hcZXdoP2xgiY7FM48enm02+8b7S9k+z9s/lq9MHqk2wxpz+XJKM0u15BMxt7Bv8pLUn9x2/dVZAYaAIXBhIWCtwwa/L1WEhFqXM5OwLWoThkuY1OYzoRIw7btjMMagA4am4TOIrchaUz9bbaax2GKGkkBoWtI77WqDJe7MThiJta2ZRquZyXLjZVLefQdOVxvBDVddBvsS1N8/2soNTi0iGgIxYXwBE3BSdd6CvDGpsThnAY1WzBizhFeCNMczD5byDEH3pRp5v8nWKLxKfXf8CbmhenDgc4ZVSZq3XQ0BQ8AQiBAwAt7sn0IVKyzPYxeOJQW72cdkuRHbRMPFcmxwTArJoyWpunHjTLIrNXahkAlgBLjzjtvCoHnTTTdRGlOeE+NjGd+fnJ5piHAX4e9HmDh2ug8lr4wlZKOjAzCuWkFznlUcxb4NAUPAEDhHBC7CBvQckdjYZNF2hpqpO/AO7UpO15HtNQjtK5YkICl0KQFjagsB08oTX9+Q6Mad8ZOE3fCwHCqdhWwocnLKO3LgwZGBvssuu4yi2I9ifLiv1axPTU25mc/MxcHB7RcR4ckri8KqocfhGQw1DxdlX2jdQ1R3o1yRqhsvw/I0BAyBixYBI+ANfrXLGmUY1GlUbGUYFSTKkx+USrLXFZHd0+gk4GQdcH9/P4zLfh1xImhuE99UGGbzGdYgwcH33fdws1a+at/ufF76FExRj/RzBlKNhbBOcje9vcEQbmp2y+ew5f21oQb1uTlZNsaYhIoFAWMFTfU3VUorzBAwBC5GBNptzcVYu62pk2CqvLqkfKfkOg2YNS3Sjnc8ZWtoPXZHFyP19eVVA5bjCdX5mNkmN3Fgd745gomSak0h4NvuuKs/l7rpMVer9FgbjfZ7uTS7ac67rSiZFb3gf0IrK+D6PWIPnUl7k5OLwDwyNMwVrZg9UoyAu/O7s1wNgUsOgZWNzyUHQbcrrK25IyoZgq7WGnzpHLBowI5VhXedTwmYraAhYKYe208hYL3ptrgImk5DvUHGm6l7997/wGAxe8Oj94rRtqvDaMljRJqBdDbDYpJ4kzoF3a91XIKctaAOxZcXNDkzDeOOjIwQyLaUvDjm43UOWLRlc4aAIWAInCsCRsDnitxq6WiRY0CjZjxuzQmGqqRx1x2hsxl0x7ZbRq7YHuPcscHtOJvkE5qX5a0Q7uSsd+rUKRZN7d3mFiKLTB5D54P9pUajNVNuiJH2JonVpWIQHzPz5cu7XK8i1ZRxCQ+uhXEZsSCQ98t7o2/EQVUuTizVZvWN4vLs2xAwBC4GBGK+6KW6wEZMfypRoYW4ZlDkSybe1I6JEGxhiKmyHz16FI/aGGsIV9KSGy2mhizjuSTaxnriwtrjxy5/OdSPR9PT01RqYmJCG/S0i51F53U6plo7Q35DQ0MIz3wxxsYq/+YID2S1RhMCnml5t955X75QfPR+WYCkfMPSJGQbGexnTvTBg4ep0RIe2lgcNyk3akA95NWo03tM0HPZHP7pqdlsOlUqypw9dd+1Uw6z4hdIfF5NVP34BxbnYd+GgCFgCDwyAj1HwPATfHPrrbe+/OUvv+aaa1j18dGPflQpedDthk+d2NOR69/+7d/u2rVL7ZXgrRtuuGFycpJHNI6QN+zFyCHh5ObGcqWpHBgYeGRINiZG1DKTGT4OsHPH60rW2rHIqJK7dlmZDIt90LTWjtGlJ6kUGELAxbR3/0MHa83GY66+Em1duZajkFg+NVDMIdtCpUG0i8Ml7AsRa03TWdmQpBlKl041YOLw18KH3xXVZ/jdaPfiePtWC0NgqxBYMhC6VUJ0lstkG00eq1yuv/76l770pa985StRthgGxPiFaOglyqn4R0dH4drDhw/jZ2tlUnGlcdSNlAmEg4mMR01YYb6DBw9y21XnmnLRqFY6x8RSBZpvphJdTMI6Gv+ONNQF4cvlhj8ivY3Nc2Lt5S+wwUbWu+O+h9B4H3/91YAoo7EpL50K+cWMDQ9QhakF2Yxj9apunrjnXRJ7bbRXM7ffRTolE+EcIOkOhfQZkED9JQQo5N1Vq+Uqa4Ll5TF730523uJYBoaAIXDpINBzBAz0MOiLncMPATPOrOzLLSSa+OEnnGrAKMeQNBN1UDJMDIsTEyWSJGjPcB55crtv3z5CNtNBsFAUDXTaLQKmBUdCxOZMHcRgtSkRVnV0GuhM0Poz4rtqhK4GprLevSe8U1Ozu8cndg57KIPxkUGNjJ8bGxmm9NMz8xejEZaOWEQdC3RcXgE9uaGhAarMe+TDq6nJOqzWWAFSNmcIGAKGwDkiIDTQm47xZASDNaFPyJUrt8q+cCrTqBAttjBPeMITHve4x73oRS/65je/KRZDxeLw8DAxYS+uOHQ1ZWL8p0+fdmFdvTAxmMwNLikIJuYDASNSf1Ga8jMokErAdD42e5zTTTWjdH/hS3dlCn1XX301UnKrc9UogX7QHBsZINbklC4FXlLHi+aGCXxeFvtyQsD82IYGpDPHXwsfXg1vcHZ2Vv94HGAXTb2tIoaAIbB5CPQcAdOuae3hUbgK1Ra9FqfrdrSxQ7tFKYF63/nOdzIT/I53vGPPnj3PeMYzjh07RloaR67sXqT54OE8VwxnoHB9pOGbeU3UXLZyVAIuYdd0Rkcrj6Is459xtM1s6OGeW++4s69/6LrH3tCoi/ERYhBI3yIVNkdHhhBmcmqakKRqsZgX+nf0F5HUiz6Q04CjeikB82qkb+TCNvO9XOjgmvyGgCHQiUDPDUEz2YZ8NGqosDRzTAZDnKggBHJlZhQPVkIQKjFf/epXw6833njjU57ylPvvv/+tb33rX/zFX8CyJFHCxjT63e9+99vf/vYTJ06QcPMIWEx5RAVP2nF0J/zMpEo/QPfBcuqUVGw1h/xUX9h6taddDHPdl+Oz3ump6UJh8KqrRvpywr5xRZgx9UaHxEBsZo6Z4ovC0atYviGWbLpJ7fjdSR8oXWQEGurVd8GPkOozKqOVNwK+KH4EVglDYAsQ6DkNGAxQOGApPEq34+PjtHfwsd7qkCBj0dwSkytj0ajLj3nMY+69915iEgh7cSUHzKR/+qd/+s477yQOLeaDDz7YfYyZ2AVVt/+zo1habddwizw136u2mGBt0o9g+jeQdl5adonitycUCcn7rYzPiqAWNCBG1C4PF3MDagCbrvpxYssi4KMnaiJcfW7vgIw/hy3+OQ72MwzL9pXEGqtSKzMH7BxV088ZZDubOGdI3rVHvCxnhCXYul4Gr0HhpsoLoTfbaBX8xrALJAQ0cmm/nsrNN+Wtujcib1YTk4HeuHB3cc/sYggYAobASgR6ro1gWhfND5spZIVNuaIFEoIHTuUpVlfcKr9ymyi7X/va13bu3Ek0jcxV9WadFYaS8WA4TYSuOkdstOnZUPZNEi046wVpNrAI6tw+iB5eHNo92kc7zuNmQATMmzC2zfBxgglRUdt9EwPNxanpco2jG+Dger2ZSWfkeLx1O8lQEyWkq0ErrzB94GXo+9z14KHy/PxzH3fFOHwj1r9yJqIo66lS3csWs94g06Bea2rG9QucruhKaU9+J2XhiR5FJB0JoyJt9ZVhCaydeRtakQznM/MaZJilWcG4+e4pbzE9cOXO0hB1cG+UqYPd4/3lzNDDc8LXfujei4zQNBm3oW6uvkm1eu7vK5HMPIaAIbDlCPTcEDTDy3Aqq4FZMoR2i2p7xx13sG0FKi867ute9zrMTxlnZmj6z/7sz1j7u3//flTbX//1X7/vvvt+6Zd+CUCZ8WXOWNXlTcZXG9+EYVzrKxstCYWFsi9HpYUVU66QDnOEccavxJBGW/5DySJu1IAX00Lb9aZs98hTVp7yTHskEus8HLmpoyQy1auGODH8SuB97dY7ctn00x736GyzxeADfQWniCNMhjh0joYHipPT3uzcojfc5wQkAyojA9TOo/kluaqns6jOCFvmR6CWKrGIwA2fyAVptv7kt9Tw6plcX1oOaebtUEO4mlXQNT+zGKSJwMv19QxgSSvQ8r89lBFnZ9+GgCFgCKxEwHHEyuAtDUFbZUL3+c9/PmfQwqa/93u/9+QnP/ltb3sb070otQ888ABGWGNjY2jGr3nNax7/+Mc/7WlPO3LkCHz8rGc9i0dwNprxltZgZeERzm5knRacsxZWxlkSAgjQLSPnGqpqveih63YUveQt6z1X8kquGgiJwPezFe+Bhx/q6ytee90uNxcQpFkB7OJ7gWyPTGTGEkI/c2JyTtTipfmrgERLPusWecsTOF4FDSygQZ7On9Br7BhKYQBGZ0k0bM2OUWeyOLl9GwKGgCGgCPScBoxYqL/f8i3fgkkzLR23KLs09zR5NIVvectb1EoLY+lf/MVffMMb3kAgOnGySRYzxKTS1Uqb/46hnBVNLmyFg6fEw3IqrijoEqaUpr4VV41DJ0MzpJpuwwdK2BhHRgiUXCkFP9rbbMN78Oh0OpPdtm2bSCzWZJzUxHGIPjOgrGaWMPbiGBllmP3U1Hzo7XQCJZlJhAvSUauOl+f23PRkrVEqNTQwyMOkz8R+avwaeTVUEwssRqr5EKHTyW1Hbp2PzG8IGAKGgCLQi80l22iwcpdpYF1KBLkq+6IOKvui5qpHFUSNoHbOzBCjNG/t2wVT/SwVQ9pjNZ2FXLU1X9Zqd8bXPgTG3hqIjqVT2p1xzsZPqclHWCT6yGStjI0vvZJhOut97iu3ZkuFq6+5il4DirhkEDbQgOmssU0WOZDhyOBQmCqenmVMHUd1z1CVsxFzy+IoICuLV6WWviDWf/IDE6DEUfehgf6w2UpeDYGxBrzir0m6L+YMAUPAEFgdgRVNxurRNi9UaQbqpVFD+dDBZDyQrlpm6aoPKFnneomvVlrbt29X6mV0unN4cPNEX7Mk5Sc5CmmxLGoTOjpB+nFN+iopXRQxQEuenRsBJ8ljj9NouVHVVpRe9csVdmHs/gtfuw14H/fYx0C6KTbgwBDbaXiya5caZLe80eGRIJU7OY0GTJju5xVXKC5pxXfvkrRIRk0SJ5PzEsYvit/ewNAgE8LcawU6NeAgBEcGJ1zdUYWTHMTjTNKWBi15bjeGgCFwaSNAw9G7jrYPJtB9rLhCxpAQxIyfR/Ax/ISCAp/p9h1ojcSBqrfEAktxdLOHkd6jXNfZAqOyI79TKx8BdmcGvuRwJ6B4hDRrPF6eDIGw+ooMv5xHb52Z2OEFb3qxXOrL7788yi6s12BYsTiSjALINp/2JkaGQz97bHIeDRgGUhKSjBMZOm7U2xGQROoFj76lWBLlWCFb6aHML5ZTfoZl29rt07+WviKL3JqdnbwVI9BxbvZtCBgChsDaCPQcAetoHgxKkwfLYhSN8MzyQj9ySk+rBb8Swi0EDPUqLemINGRMHLd9o7aja9e7y09WFC8aEgzUYCssxnI56eiRBNDzgrWyJMQpBzxSuiXPSSjlJU4zisJ0YW8UJGthfdHRb739QN/Q8Pax4X7WPgnSrE9OC6/Ss5BTCnFiEjw8kGdeeGa+TBD6n1u/tLQsF7WjfP2lCQ7Rkx74ok4p9+mQU8Siuq5SsmsKt9qfU7npf+RyGd5Fq8EissjR/3CLiYWzSeti4nUfCTNnCBgChsAqCPRQa6jSQbrqUSbWWybh1ENTqApxEi3xkEr9TCGvUtFNDdLGNyqSG2E52XJDNqNGSCTEoOnMHDxcEMalw8FKH3WlImfCS04b4cKWGmRzXHKzyagB8nHKLWx6z/0PVav1Jz/+Bjo+OTXRow8kkoSyfArz8rDFMtmd47Iudma+AkE5JVgkq9Zk+08hLRGTJUlLuEdYqvccb6H9IpyPWW7kboTS25iZnaXzt3fPHjbf0JjUadugJGEOuNxgiD7X5uHeq51JZAgYAr2MQC+2ib2M1yPJ1km9nfTDml+hI9Vo6UPEZxucKT+0eTjYKfxnivaIz5aSdiSV698I4fhZDp4vECdX6mMC+N77H2zWqjdcfTksG5nIowj7mD+L/EzLS5KwyVLgYiHPGMVcJVqpzFM3FZpYJGm/Q6RzvCYeF0c8PeaWIqTChSl6JWw6DlCcckkV3FhL1KvgDSavBmZ26Ylif0099mJNHEOgtxGwJqO774emmY8jHmmla9VG6Kdkja+jpTPv2ICxNK18rSYJ8WysoKk0uz6h5TG3K3tPLtRlR4mDxxZPnjw5OtB39YTsBiXzvgjPDDETokozGElDuGEt53sjQ33w8eR0vRGKEkzczkGLWFqqzqeXnatkB7raS0APpjuCNu+nU241XNR5YqyZd5fPs0F2UK3KGH2EzDL2lWl1Biw68u1lDEw2Q8AQ2AoEjIA3GnVpcvlPM74EW1kuCs9VylxlGdJZFMuSKoagOw2hzyLRI0ZRqdi+KSWH7fly7DyfeksU2a/denshl7vpMY9GwU0zSi4bUWPt1sdpBRGRitxQSw16Ho0OJZxBw1WewW6dxynZzDF2MQH1OAmv1ktIcdpCtVHH1KAETrzUAIRQiKVK2jfSV8OjuHYyzW/OEDAEDIGzRGAJSZxlGot2JgR0SU5HDFpn2mUmW7myDhg1sb9PliGdmYOJrOubN5qAY8mELFMst4F6odlMUQj4m7ffjXnYU266oVUL+9JhwEmEsiAn1Qhk/BlNV1zQ8oJqNqyNjQ6EXpOVsloXHvocHyF6MBWOosaF6bdktTSkF+4QmU/k6CYhuquFPzc3z5QB3SB9U2JhoIPsMQF3LgUWMONM4u/ls+BxuH0bAoaAISAI9GCDeBG8mHaDvqwysCkEzOnGhENSYu4jy4FWcbTmNP3MO0qSVZ6vI2iV5Bg8h9JToGxGUGUgFQOxBe/YidOteuOqfWO5AGOrRkwpqXTWR0tuIKz8YNgYq5b2m+OjQ+jIk9NTpJV8+DG5ktycKNEJuABdzKLUk75FGPjsbKp/JNmMGOSjAXNbKhTx052Kowttr+7WfLB6dAs1BAyBSwcBbVsunfpuQk0j4nG2tO3i3JRgtK5Xdpc6Cw2YcU4IuHO9aTu78/ahzylJoglDwPgfOHCyXK1tnxgfHeSwowyrjnIFRpq9RpPBaKFTtoGWuqHlhk025hoZKKESCks5Ff+8JeqBDDrIkppiAo1M0eZrbiIcQyxd8su4NCMarOrWl92RrrMW+rAzxPyGgCFgCLQRMAJuY7FBPtk/yjFaC5/e0BJzih8jvY2gmvIahZQYGNOCS8MtQ8Hyv9MgCz/aFiTY8DKLrSzsiOkW9JcSdVlmItuiOvZzFyFRiZnQoVvppC84SoCGKvobV6d5Y+LrMoJGWE109+GpxWbq2v37+l1gfW5BhdINQaUuIi6Vy3upPuyQBvMZDsqdcwcmUhME5ghGcpdYcrYxVeQjxXHwsVtuKzdaouTTKw6hsKzKiGS64ZcTjJe1UGHFc2MkJ5bf7D3JW2JMmYf856wqhgcqQVrWXZE+3ifL5eEiSTyq72aPXRy7GAKGgCGwDAFpH81tHAI0xRnIiMlR4Vwa7aYwK5xUS6dOEJBqlHLN8bxHy83xBqwjdcwlu24QDUdLDoniH2acM1wsjO28+5Qc4NBA/RRqFRJ3HxkOFiJ2S4NJQihF8mmgpilDawRXunIzVyEHOJg1vLVyJhWg4ZIHDDPveR/63B3e4PZnPvGx4QJHMKVyIxP1JgPVHscisSFZPvCGYJMgqDc4THHXTCu/uz8/FJQPnpim6ILvZWVDrKovJy4GNTVVQgBHwE4EhrxlFy0Ru7cciAiPAp1buNzkhGZkXPC8yXIwEDYuH3QLsFznpimcKjW4fNeY36o/dGpRD6vKhkyWk0EgvRCJIZmxU1jL58dgf2KAYc4QMARWQcBah1VAOecgIRfUPWE1vM4HoypXOmpCiWJLTVpl18hLpEg3FRVKvO5W5lJpx0vZoOlny0FOiFOydhknVwkRl4QSoJ8ouCMCXqJJgMZAA8baqlWDEekCQBdTVa+Jp9m8bMfoUFE6A8Icuh0X/qCeDuss/xU7pHSBDgF9hJFSrhg2q81gBuGjAuBi9voSApOKaFl4VMYoVO57yQGA2HgLyCKn9FWQFEwWKq10szqWT2MyxzpoEd8t5ubtFDMy011tyVphbn3WczkIxK9Ig66Sei9V1WQxBAyBnkLACHjzXgfLWph5PfNG0Mn7wIOlz5nmgGnsnSMmH9f0S+svI9WO/vSpEGGHk8VQUcLQq1dZUIRqO+B5J+47Xaid3D2U3jmcS7H3JGQTeBwDrE4X+EoQzCsPpcThQTblTrM/9zzqM84NqDtfXEIsoQZGIkY3vf6F7OyBxdah/UNDgrCMQ4vjCwCYA8avh3G5YLsYAoaAIbBuBKJmZd3pLME6EQBoziqmHWf/6rNM2nnqjltFTDqyST7tbGALWAHqTXtMPOtSmugpA6pZL2DklGREE05EJecGL3txEO7LwOlDd3/Dq0w/+YZriMCCV+Z93cZPUSaYQUNBsm8HRwKj2DlW7yt4OXbnDoK5eSlSMmQuu4OGo8ROr3R+KbX3nFRGxF/q0Hc54YMhARaD8YQuCHq9dj645dgPQtorxNxEvsM4zoX0q2UbP7ZvQ8AQMARcS2wwbAICNPFoVBAwts1SnKyWjZy01e078SpZDvUPsBGHHiEcx+WbhzGTxak0PlyrH4ksVrvuGrCKqZz2KhmmoWVglQ/FuRS5EoZCzENDQXffc2ezuvj4m26QLFsBI66cOCXeVtjJxJI2zhv2ZnrY99PzC3VHN/qs7XX30cVRdmdAT/k7pJP6R7DOzi9gJ0c3SIIYcY6nc3mcnNa8em1XD+2pKpswhoAhsPUIxE351ktykUtAq60asI5eam1pqNdqqzGuoukPW0GtUpFoS+KRWfTiYs0YxRT2Te5c9sSSjTFqXrDoBRUx4HIHDMC+Yh+UKjVT2bKfXkx5h2bCB4+cLA2P7trjyIaR5azkz3/mNsUQS/Nj5yymQZnxZR8tR1NjI6MQ9OzCQsxgzrwriq1fyTUSOLnvFY/Mvi93VIcZXLo+7Pnc3y9T9m6AQd6Bm+LVnodPBKLJ25E8lldwZbbLi7F7Q8AQuLQRWN5qXNpodLf2tNdowGeeA1YJaLv9MNTDFnUVkCwYilp0Xpm8NWgxIuXoNFpYw300FB3X5eJ4V3bVENNc4WPshlJYUbEcat7Z+i563hfuOjDbyl3+mMfDM1GeMRKZNOOuyOIUQKe1h0ErB9s7IXZsG681WnPz0kXomHeWxGKzhBOZl2XpwnvnIsZTnS6CF2PuSrXG6Vt9fe2a6V8LV7eTiuwU3ZnYGbQlWckTjZ8EmccQMAQMgU4EZBbQ3OYg0Gg0mDjU4xQ7S1QyU3rlSqtNCI4ZVoZA9QClKL5Msgrf0rpLHJcmbuU7zyoU62aUs7Sf9VOyLkasqn0WR+VZ/cQYtK66gYBR7Li95+Fjfr7v2kc/NsrWJeAgIKywdHMrRqGFmuNhc0qEypFlaGiASi1WaqIIEoJuLRbQsVOvViwO68lvujcxik4+BOdkRarGoQss2lbnuhSyfhpMnHl40Gy2Yg2YSHG8nqyhCWUIGAI9iEDXCRgFDspB8+usPLwSGdZ2hl7UfniOBn1mZmbv3r1iTezw4JsPLLjM8bDVCsaGMzAfejMbZQxjXhW0IEThXhbyOlaj9Semy8EtdarWOKZHaNnPzJXr+VJuMfAKqRHO9eHUI+gUGUiiK4bh4FML3v0PLdz/0IOf+tSnmM3dv28CSZwwwpy8NHJWlxbrIz+o11LZLApxtRlmMz7a39joSL5YfODBhzzvShGDjaI4PNg5EfACoF7tOCx5AzrVOzcnBuX9g4PxCwrSGIXLBib8dL2RYb5D3qYgFTtidv7K9dXED+3bEDAEDIHlCHSdgBnEo6li23oYlzFV5V1si1YqgstFu+juEw04mdBVhqIR17Zbb7lqs04LnnWqlhwJLJtCLnHE0YQRe7D+FvaFm0NfNm3O5KDYBmctyALVHDHJlmyOnvZuv/vBBx4+dOsddy+UK1NTU8NDbLTRuGH//ivHZPePiD/jojqLSPpMfsC2IJks/FQsoaDXGiwhdvs+cQ5SUG2zEKGxi7xawziwZ75BzY0oxALxOmbna4DJdtwKL6ZonL3o9heRKXE+/ICDZksMwgXyaMTdZUD3iE5OJxfH+dq3IWAIGAIdCHSdgCmLhlutRpNyL0H2pcleOQcMLXWQVAKPtO+04lyZMKb7UmW9LgSsI8Cx+kvLr2mjK+PbOQiYTDKsOoI2plveTOA1s96xU95D9x277+47Dj380OzUdKvBfhqcGZidGBl89OU7v+UJ11++e3z7cGkPy5Vqiz4n3apS3RZHckUYR8AMdEPvLTbpIHBkdIg54PlKVQjHMZU8xMarnRYxLyAnRKwSs8c1cnMSg0rPUAR7ZovxuMOCb15NrVGtVL0AW3Jl5iXAyaw90TqguIBwMFENAUNgMxDYDAJGSULllTY/wwZCPoog49LLKHkz6rqlZdAQr7SC7myd237XxAMUIaiYc9WqGNsOpl3A8joQVwnDzxfwBZWGX2SuV8aZ/+Wzd911+PSnvnKHl86xo1OqXin4wa7x0f1XXL57187HPOYxE2PComNFj2FrWDfjVbw0G1lCGmv8KrQH4ERANj6jwznWK80vVuAhyYLFshF/xb2DTnlJ4KrWGbbVfmqr+C0RBDFZg0SfYmBoWB/w66UnJH4xZJO6s5ystjC7iA1biSwIWOF6rrIrJLQAQ8AQ2FIE1mhqN04mWmR4F0eW0DCtWNa5jSvhwsgJZYh1wEymRuuAO6SmRY+GkZNAMXgSuiOyI2COvRsgGpk4HdcxQEwdBEZNPQc2pHIMBzPR+9Bp7x8/ect9R0/2DU4wyHzV7iuu37/vMVfu3bM9O5iTfJR2KLeFcXTYbNRmM/BwWkkykoNstVCREPqRiXz212xlWCbsCmUvjlKpv1ytLix44WjEQkxBO6aKKxMLJ1JG/vhRj3yvJhWTJpAuK8GwGAcUqh4wyA9yzhaNX3NfsTTteYuL1XCCnbClZsudszlfHmj3hoAhYAjECHSdgOERVR3wOFseoWGU4M7lsLEwF/l3vBNWe5qVFp22m0/bJQ25UzeZNQc9xq4hYLfaKHkc0RkJo6B6i2lJPy8bMTPXe+DwqZOzizt37/o/b3hlKeMNprw+Rk1FzXUs7nnzFS/rtuTKZwhk4e+g10KNhbuXiNMWDB/EKtsks4OWjD/jyG14dOTY0ZOz82Ew7GMjhiMLZ5C9dj4ubU9cqAafpZJq2MzcAtZWA4NDHIPU0T/ijYmdN/0P+ka8GniajSkJ4QFueUeqJyppQhgChkCPIkBL0l0H0f7AD/zAv//7v+vgJMPRMPElyL6gHM8BLwdcW3yuK50CxZ7DS54uuZFEtP5siIGOhl8OQ/C8k9Ozfq7whMfduD3nXZ7ydjJfGzZL9bnc3MlMeTbTao4UPLYzhkHFVkvss7Jeus/zi26TrJWCwFKOpnSEmXONYC4nxtjYGG+WvkWFjAh1+0d3rohaLa+eChNOXUrBIl4yB5xsP9lirCB2xE9ejYC+hluZ7RoRLdgQMAQuRQS6TsDvf//7aaBf8IIXXH755b/2a792/PhxlKQuHTLflRcIzbgPF5paWIYxST40xkJA0no3ZY+LUCyBuYOEHKboQmnx8XHNMCSHqRKGs5xir/Y5xMiI1XKkNklaYsvgrvO6xb75dJhm9U/U9vMIJ0W6GO7OXVJMV+bIjNFkKQXxTswsLC7M9WeDYTdHma7Ne3wYFEUdziEQ+3IEAbtQOrPnesNrcDRDwKZXTigJFkc8fHqVezGuopao4hKBGhN7pI8VwvXZRsCSJ2rjh+kcByaxXxZR+KTdiYxsOB3nKfn0jpOKRIMCUiXZPiRDPXjRU4vlIJUaKWUZOaDa1C1A9XX4EJNBjGKafcU4rVmeEcG976SSRJGP/ELMGQKGgCGwBgKu2Vnj2YYEf/d3f/df/dVf3X777T/1Uz/1iU984lGPetSzn/1sFp7qSTK6zRPrKVGhKK7njpehBYVyHd3V2RWSOT+Uy0D2kCoTzFP0zVrVq7LXI2t/RJXEOcbKt7yiHIxAwyyLUryjTa+VHcpXqrsL7rh61vU2anmvlfMaGcxrhdlor/V1YNMjZZFs13A+21ycmmNfJqRw61DlEGEZCCZEG30t0W05CYYNjKH4uufIydFS/ppdY268O+Xl+7z8gOi4Ka5Fj905mC7OCpHw6cu6A32ZQWYdkc+pv2QsO3AgjfgiBnKdgyxHGUeMlcuKsdae4VR/rnHrgaOtfqm7rHyq1HI+B+R6ZZ9uCiPjbthahJdKyqd3HN0EwT7bCNjOWiRjxRfvEeFPVRqL1ca+kfSQ4E7/hHq7IQNfduZgMH/XcNbLlw5P8YsAJQ5JZprczX4TlT1P/CxdNN5RT1W3d4A3SQwBQwAEtMXvIhQ68nzllVf+xE/8xEc+8pE3vvGN99xzDwrx/v37//f//t+sEmasj8UeLLiEhntxaBqScY1oKi17SN328Ml/+vc7vvagLLyRYP1PG+7omLZbvBIo2o82we7bq0DAfraIpspDFyWD+iVKlaN3Fxj5nA2TY1wvnwkzYaPWlBPe43jyJParcqY5NmWzDV/GkuGP6YWaHzS2j/SJPOKgz4zwrlAvH4hWQrkoi+Npx4x/FRoYh7tM5FH7N0Pa8YGsH1SnyjXpZVBnkYx6Cfe46jNQrUHuqSDTWw6V170shzcM6qQDQDY/8bOcViFrqN3OmwE2DO7NSowcO5ygAfupaqAWZ9TRvRNXQS5k53LsrcqaNIaAIdBTCND6dNcx4EwB999//3vf+94PfvCDR48e/Y7v+I7Xvva1R44c+emf/umPfexjt912m45Ic+4bCjGU3F2BziF3Dldn6jorlkcPP/zwpz/95cddP3fzlU9hf6kM2h0VdI0wXIpvrexrqLRu8Wg7gsyqrslIoMZj7ZEoPpL10uy5awcwPsz8Kxdf1HS6NaMpf+e2be0I7YI30rdtfAJbpMnJSWrSgP3RnEWHTopYWsl2eBJhiz3O2FxkUA3Y/VqFfcGclb6yr5eTmaftmO6d657e7TEbosUvE69+trhuVrwhYAj0NgJdJ+C/+Iu/+Gfntm/f/uM//uMve9nLMNvBFOvGG298xjOegR7MoapYJxEIUL3IvrSqaHRshORa1Xy+cPLk6SNHj0VqnfIkz8QmiajiW9VhLQtRlfpKGgMd1u0riZq0piPv/lKRx+Dj1Cu8sutShwrakVY0afKWoePZBQ9Ui0PF4b6OCN3x8uIwc5+emSR7IWAYC6cGWjEcESh8xRTlIvXGRY3LnCxIrXeLiyGG+kNDI0lvMAg5orEtP97ECroH69QbyJoUhoAh8AgIdJ2Amfp96UtfiinWU57yFBbVIA48pEuBjx07xlMCk3B4TpcqPYLUm/YY3ZcRSJpbrItcoXv27Ennsqenp6A7sTPitCC2SOIhO0BJvDXd4qKM0cpB7i6Kq6kbS3Zmw0uSQaXCA0KlEt+XY++kOI0UF6LUz7XNzUTB+intTQobBuNjIzztqiP/oYFCNp2Zn5+nIGcwLCZaYQsDLaVi6S3EEsaE3FWZ1pu5wxOQkZteke7nxYptFsuxCDjL34eTnvfFq9Z3QI301VAUy5Dce6FjtHz4o9vgr7eiFt8QMAR6DYGuEzBDzdBtMnxH/fHTukG0TAwzJYyqQYRTp05Bw8wE9xpAYpDEuG46XcU+J5PZNjFaLPSdOj011/SwO8akWZw8YmvkmBtXqwMkKhpwKdGARQWWFl0SdZrLLmGrgb5lGrBEdh9Jh0+vUqAY9MoZRaQ/dnQqk07v2bkNbiC0q26AvTgKualFDKG9PcweYAxOr8Up9sslVDmkvr3knDzYn/H6Yr3dm52d5VfKr1EeRh0fkVkZWqKxD3apRKdrPiJg9yZ6qVomiyFgCPQ+At1un2X7JxyTu8yWnTx5kmVIUBF8ltg/u60MvImJiR5kX3l/bjkQU4PIDLcNFdBK+yv1BhssL9axq4LiHIZOkW2T54o3r5OFOnHIQ8hYrrToHRsnd7yMqNVnDhgmIC33LkjKS/Kmb8BNRBIy84qdENbX3sEjh9n+ZPeOHfKom4786SCwJxQK4uSUjAV4zYYIFKv1SIiLxOAr8nVTpnPKm3VcSTpeIjb5vG7dLTWUDbAixxFV2mWiHkwQA3+1ivk0zlUsemlxbFlu1k6bhJrHEDAEDAFFoN3udAkRxmwxcoZ4sLHavXv3ZZddxi1Mcc011/zCL/zCyMgItxCM0jDacJfEOLdsaVGbYashG1wwBSirbmho0WKzucLk1EwZw2bydaqn8jAjr2sVxLQ3j9iSUzlICVgjr0jTbrUZGyAOaWPljHFO4Vz5H3+iEiVM1rCC4NTUDEt6R4f7NVoUoTtfiJHPovkGs3OyLKsJRXEssBt/dqVzEae1Vn+PX6kFFlgQsIJP3yIRuNOvu6s23HlIJJFPRyXdYm8S6ifJwDyGgCFgCLQRiNrHdsBG+971rnft2rWLLTiYBv7Qhz70pje9iam1P/3TP331q1/9m7/5m29+85spkHaN8Wc4WJu8jRbhvPITrVJmY4VCMDLiOj62jcb3wYOHBkqy0gbznKZbSsQ+15ySu1ZhHPyHQTgdDo1RKIixt88W2XIvb6HdzHMTcqCRnOI7McHZFbXp2RkGuFnIRCufOEkj95JOmn4OxvNTNQa2GYI+frzZql+xd48qoEmSLnl2bduGsoh9O90RX2ZKowXNMRZI6ngovu+SGOeYLdtrur4RyfkdMnguAB47xu22bdu4alcpI6uf0Xq5MtkbYhmwd1eJmRTMv121uv53ROnmDAFD4CJDQJqVrrp3v/vdv/u7v/u93/u98Ctk9uIXv/jqq6/+4z/+449//OPXXXcdfAwTj46OIgONOHF03K+rIq0rc0e+kCynOUXa6y6Gdm+76+TklFvnyig0h9CzMYNbjQQjrkEzNNY05St6GMSWtruDfTGmFiol0I17R1Pmeusid8QlXRgwfyk1Ei6XQfKF0CvX6gw5FAuZyA5KHnfFIT0/oL5CkZNxFytsWM0otDu3vl0asskwQeTWACd+vBXfHSLRQ1IraJ0fUZt8frRtsbQHhB26e9H0w5oM2rQYHomiuJlk8ZPGvZW1fg7tLM1nCBgClywC2kp0sfqf+9znnvSkJ9FMwawM3jK7xgIktsSCax//+Md/5StfYayP4rmFn3qNfRUXwUiWIQU5t7nk/iuugiIPHRaFj3ZYtq+iCljJ0uhCgNpGr0CUUU1V9F2SJY87GVUeiMG1rHpC60Wf5OSher3J1hDMq7qYiCMSOVqQ8pyHsIyMlfre6SlZtjQ4ONDHObUxDSwpb0NveHmjQ4PNVmNqdkbEy0Ti4RUppSpReSsrHj3omS96PDqhr51Ffq6ItoSARVQxdBObabeqm9MYFysESl2XOnk1a/4alka1O0PAELg0EVjZcGwwDsz1/s7v/A76n+aLpdX73ve+G264Aa697777MIQeGhriEbcsXVXNY4MlOK/ssCuCC0Mfym0F7MQBXnt356E65llrrn1ls2Pa32atDm+eYTyB2gHCWRiaiZmPshb5QuoozXRfqjUpRp7EztGb7h6hQSlGyIlw4uR0vdGaGBtn40S3hVOcoAvfyIBUE2MjDMRjOSwdgDS7P7udG5PikCmqkfQQOquQRNlyDwdYuK02RBDm8VlVBe8ODDCwASUL0ktM3OPlwjA0T7ExjKooqdsOZMwZAoaAIXAGBM5AGWdItY5HsO+LXvSiz3zmM4997GNp1O6++262vvrLv/xLCIntsdgVi8YOW19ohuas08hlHWVsQlRphSPTp+F+MTuq1Stzc97EIAqRoxfIBXpWklyt6VUCZv57SWMttlUynMlnSSKXFSHol8yOV8sLcupdXqK5PkASOdYuHQJMTBLhyNHj4LzDmUCzIMhlKxTSPcdmWGQOAbsinP6OXLodlhhGR26JrHFg73wrAQNgo9F0O5PnXc+QF0ONGGaI3w8x3EA19/xiZ2ZSM/NznjdIRXgSOXwSXYYx3DuMgu3LEDAEDIFOBNrtY2foBvqf/vSns30je3Gg/2HNy+0dd9zxPd/zPfDKj/zIj7z97W+X7Q6yWW2+VdvYwNLPMysaUEdqogdJSyz78svpBePjYp5z+Nh00uZmOcbgjA4CpnuBBpwk0ejLbjta8Sg7kkCo8wsy0Oka9Cjc+WUOOErCxGRGuPygLLzOYfiGn6nrKHY3v8bHB1QRxAAbluUTD4t3s9SNyxt44deEYhlv0CHogQFXhpwiEb+l6FuZVfpGVHx+boF4Ufcijrhx0llOhoAhcNEi8Ai0cf715tyFd77znb/6q7/KxCSWQTrje+LECfy6HolhZ6xdoGHKUv/5F7qhOQiRaQscchCR2wvyst17Dh8/eODAAe+aEXbpwM7Izzv9lII7SbJDDqoGATNmubKJjtrudmQCpGOk4ahZgCYKtKy5lcB2p2lZXm6S+MjhY3Rotk+ME1ntt9sZb7RP6hp6jEBDYXPz87WW10yHGINlOWQoKcvhF8OyRPwkytZ6lIATLOkmMmHvZ/2SjECLE0PoFa+VAH7DEDDWZ/qmiAkzcyaSOUPAEDAEzgaBdmN+NrHPIQ7jzLoHhRKJ7lnIvtCwL6RCYwf74lHdtxf3gpamF5Qg3hAlng4LGvC2iZGalzt0apFn3MpTxqdpeWmJO1rq2JtBNay2WJrrFzNunnhpG03uWoakl3zkwCJy4kPmpVw6SOfKDd1jStlXB3bRxyE7JBKFTLL0ZRHw6Zn5gtcY5+BBEUwM3LrnKAIJmWzGLVQbVRZGR8qh1MiV7VZuyeA9cnJ1QnVPoPXmjJR8nKgcpwzPNnzMyEu1IJcPKiOam7x5IrmZgii+1IL6FFOtrNfi1QC7vlKtHtF1pkAzsKshYAgYAqsi0PUG8eUvf/kHPvABHX9GAjRdVGEVhRE83c0AT6dwsDK3XBkMxIM6wrXTPktaSuc0ApqlhlCKKpr6dCOuqVaLKcBsyCl+XpApNDm7l6OAr7piRzU3dvvBKcfH7IXEAtxUwEm9GSgIc6mgXGVCVsgpaHJccLbqecdnq7liaSgrs4U5GmipAebTWFmRZ0BM6MqZAjEUWvTCfAOja7gt9Mb7SrVU4dB8DaNojKqUAhzVBpxv6ARreKGDCAuswJsuNwr16UfvFK5jh8yuMgGW4QW/ifATO3akSkMPHJh2FkuiM4KMI2AZA+dwXKy4XYWV79xNb1x4T7LQG47lwKuUB47fPNFqFrZdNZDe6SRE8kwqW6tzsLHHAc9i8S6nYzXodlw1mm/OTs4HhVm31oqHsvWVL+P+rR7sbfQG4CaFIWAIJAjQTnbXMUz3B3/wBx/96EcxxYIvMW9hSyyYksXBqxYMg8LQqMJws45X65VhVViWhNAt6rIyN4GEaASe4lE/pZyFvfGq5S8JhCXTLO9x+g2ThJy77oWNvmxhbLhvseUXG+kmbW0KIhRjpzzcSMMrVwKJjlc26iA5PY5myPm/GaiXTGD0KJpEpRAOlZWt/MWnvCWZSA5wcJ5ziNPZast1laIlwm7q15fziV0cvnmQhypOz0ruo32FAaeiRblJKV1ylNBAqy8N9HunZlCCEQU+zojsyCZsJAC5SlK7XnNtkWQqHTHlhc01co2wMJRt9DladS+F1yk1wU/FZMKYw5Ew3UcDDoNyw4e2XVZE4QehpgJk5t6oIGHOEDAEDIFVEOg6AXPi77Of/WxGnlnyy9QvttCf//znr7jiilVkcUFQL06fsnsUi5SgbehWDGWcg3QTPwHKuHgIhIy5Qs8bwr5aHMVKE4wiGZXv57KZiQmhXaYKp+ebI8OCoUgn5zbgpB1PJMTDkwr6bBAUpFfhorgLzBzpi+0w14xHBUWh9GDw6TCANP9LnUvAIClUJ23/iRMwCDZi46KqiYrdfSfV8MZHR9IPH5memWuG25YIKR2LFUJ3X6h1lyCzvJGcOkKz7CfEj4rHDm3JW9+vzgEzgbLu4iyBIWAIGAJOTeouDN/85jdpoRhkhkfhTi3MLfNYvVxIlAckoQWUjRvZgTmTQS1WVlbFV1OqKkx85phpHykCMu409Vq9gPWEaptLs4tMWUahAx1J9dhkebi/0KiXp6bnrhgeFspVF4+Nu+ZaghCMK4PuKOicJbyUgNnEahXrHs1JtScS6/i87jMVUyrBghKCRaThvij86PETHA64c/tEFK55dfcqpzCODg/xOugw6RQwwkcVc6yGuCoPQGknpbsSrSd3BI5ESkfrwegs8rKwUdBskJzq8B4T9k2y59UQTvxNRDsp3DyGgCFwwSMg9NBtRzv1ta997c477+RoQnYtoLhl6kWnADKInE4r3cLZPIK2uYVoaeI1Ju1jMtdLZDbxUJbiKaZeTB6fgeA7yzobP+OooWz3qIptitk/NgKmwd23ayLtNY+enHSzfc5Eh+xkRlFaY1VtZf2ou11YYBbYY7mzKMAdtXCRo9a73b47H6HCr76cNUQi2SmMryUuSuhYWd4jQnLeFHBh4wY/S/Il8btz40YGxoeH2BJ7cmZGxdYX58pDMInB12YIs/4qgrADVi7afWJFHD+w8ZHRBEB+eOxH1im/vlZ+ePz8+El3Plq/CJbCEDAELlEEpOHuqmOtzvOe97wnPOEJbDwJNTIBzObPrABeq1DlTiVgrrpFJQlp6VSbhIm5hWZUG064FkrWwUAGBs9A8GuVe4ZwzkGKW1gGVFlmIvOx11y+K9VsHDh6AqWYljoUgsYOK+IZTUDDratLdV0pBEwpst+/c7TycaGrvwVnVy1dCqIl45xJmjit+3YswiNOewQWCFhCnf1VLPmS6Bt3E0k+NjqM7REnRKl4QSAj4eIw0hYT6GSzZMGqZx3GfMg2u9DWgBW9zhMJI752dUg04J6tkQlmCBgCvYzA6k3/Bkr8K7/yK/DQ9PQ0VyYyoUl24bj11lvXKkK5k8i6YAm61VHoJD68BRkrJ6KsaHwCYWsaRLiZUrgm8c/PA62y5kfaZcccjFdmORgH1K7YNRE2qgeOnZIjCCAat1QYMyun7EXaHrXQ0nWQXHsV1EgDEwJ2GbhMNDFe4U63OaIsNs1yV67WXLTl70sCZeWShDcCIWCynRgfRQ4MrLvMviKzurGRLMPpU7NzOkavb0cqQU9ExKMjEkERp+iV7wh81xnSN8IsBh5dmK5Sanh7O45YdpsDjpGwb0PAEDgXBJY36OeSxxnT/PM///Ob3vQmZtQmJiYgTkiIE4LZjfIMiWgBOcLhB3/wB3fu3MkRwu9973uJrIuJ8dAaqioMqbPI+KabbkKz5ISlP/mTP4Hb0P/UnSH/dT6iiRbywLpYWmnR52TWcNtQMWzWTs6WF1vCzX4otBqpyrFqqw03LEiHIOlGJKzsxGBmkQxXfQsMdItGTXWISQ6Uohm6hFzEujhyjt9g9pmZGeIMsDOVyxSz8ThG175dJ2Owj6Mj/MVqre425sqkODlCnNsem0qAXcemlF2T5dwyTmB0bzJa8MYPVV8jFVHY8SQdGn3RvBo89CnPrVxLZQgYApc4AtpOdhEExk7VnkXnBeFI5swYiD5DkSgWjNlyaBJLlRhh1uQEahLVbgmn4WNjyxe+8IVf/vKX3/CGN/zYj/0Yi52IA81rWQldJZyXWIGdofTlj2R1CtwnM5lOwZVGGEq8bHuqL5c6fGJSVuAS4qYIhU1ptn2f5TisUMplc03mjD3Z3B9hqAh55d2QMklEtZU23q3SSZp2yF5KQ/9loDvF8fY7dozreIAo9clYuEQR58iDxPIeT55kr7EsnzF3DlK9VpVtjDWKi9yVSyhdkqESBzeF1XqLvTgoUjsNyObEAy1k735X4JyqJ68Hx+reVovBDbwHDx7kd8JhwBwgqa+F6oRuNoHHOqygP63R0RydRRlxcXnYxRAwBAyBdSHQdQJGQ/3whz8MDaO2wouw49ve9jbmg9eSEn4lDoc0/NzP/RybeLAMCTImIaoGjR2pdJyZkeff+I3fQJn+//6//+/666//zu/8TiJzzDARiKxEq4oyIcoHeFAQua7HxSTi0qi2irZJQzyYxxC6WPeykwtCM0wMR9mu4EjCtdOwKv07ilomkRQKoQcssZXTkKAGpr1Xi6hdA2FfttnigKYpqjwxNgZt8HF9AqWXZflv5K2KVcSwrlhotsLZeSXdaMwZUAQXWWXbVDLbyLI3Ii9el2CkkLHcSzoNsvMVsKuay0NZBZzcUBueu7fM2IT+tKrxy98IiSwPQ8AQuFQQkOHNrrp3vOMdT3ziE2+55RZ4kR2hWZX0wAMPHD58eK1CE7LE+AgdVw9pUOpKHqFz4OeEpe///u9XzXh0dJTzHl7zmteQLXQVt49SO8qF/lXnZhh8rXJXD6dtFg1YVtnixScqkrsOpLyd48OH5mcOnQxuGGbIVzZqgAXZrwqnVtB4VBIEoNWm67AqH2rO2hWKWCqkIL8atNhUGc0fDbhalzlginDRkCdaMyOFSViA/TVG5tzQKeHKwHCWqWrSdJ/3KAFYRoaHTk/NnZquNoYLEUyufyBVTsbk8feYU9gTocoV4Ve6gBjMuT2v5ElEvlIT7NyjXxd+SJrpj1qlKj3DAXlqzhAwBAyBs0dgWftz9gnPNiazsxDD0572tGc+85mMxD7nOc9BkaWBWys9pIvTpyiONHBQLJxKoBox0T6y/IMIcDMKH4/wM0MMvTEuTREkIX/iE5NHXInGFU36nnvu0ZzXeYX4hPzIzinBMjXLXiG7t483vdSB45OE6yoVNx8qo9AoSE5HStYBi11PYpudmPPQsrtWvVMc4XFlcx05h4CJ1qi3ZNetJU7enUoFB8O2hw4fRYA9u3ZGnCsSiORddUpOvM6xkSEs1CZnZl3Fo2ohspOASy8O0yp6CMlKM1BC6Lm5Km+KX1ohrX0dB56+y8hLjdm5UipIrfmxcSuHRZozBAwBQ2CdCHRdA0YeiOeXfumXlBFp2iBLBpbXkhNlVwdsiUDrhqar7IsfD2lp76BkKJZsE6pemZuu3mFU9g//8A9/+7d/WzVpSHplzEcKibiEaDTS2nFgT2ba593bJ5r+wQPHTrIXsmbCkmGJxmaZrocB0SoR0zNQAta8iJBOrdoFERoQ57ax0BjMSwIamCyyllg6Hm0Xx9aegSwCJmcmL4kRTUm32J6abLrYzfJTae0YjI0M+5ns9OxiZH/l+Iz6uiozrCvCJgC269ADPn0poYOMSQp+YNrDQzQeSW8miiGz+ylnhceadH4KPHK9vYUyirM7q6oHamMiGAKGwAWDwGYQ8H333ccmlJOTk9ChaoE0cz/6oz+6Kkg0f0q6UCysA3VBKjJ46wYGtWUkkHzGxsbIQZVp2kGGeQlk9QgkrTnA0wxNM5fM0DQ5oAdjOM1BuauWu3qgrC6CPHyIlEaYT0whbMuf2rljm5/KHT0xKcod0YRsaZORVBtsx8SOaFG+HQE7w1omd5cScJynikBxtPqSA7XQIOq1sDAnu3n0yUriTqciYa6FB1TT6cHRUaEMYRPHeS63zhQb75flsyl/1G1bdnp6WkSPnbBu5DqD47De+EYy9+IENzpq/KISI0F5kxKsbzXaZSW669CAnXXCOfTtXM52MQQMgUsVga4T8Ec+8hEWFEGZqKRwJC0dZLljx461CBg1lxYQB0uh+UGcECrsBbuwMyVMxptSFr/hhhv+4R/+4Y1vfCNEC7XjZ6Cbp6SFunCkojhUahRubnlEhhv1ojnCaPu2cToLJyan6mzY5YDUocqVBEwVkCtRv5MIncJ0cFUUnEbJggBCj27H4uL84iJq1hIC7uS06dkKNFAY3BZvoSic4ck2kV13gIyWPTw8yCvjRXRWxImvXQGC+XRRFz/nejqMIqCYwuD3o4uAEbc99ozKy68pjcm6jGokZfFjxu9+ltIdNGcIGAKGwNkj0PUGEe3z9a9/PeovDIpGi/0Ubdwdd9yxlog6p0vkr371q6jOcDYjq2zcodSLzfP/+B//Q9O+9rWvZcXIm970JlYV//3f//3f/M3fvOpVr+IRjIvhFR7lOehB2VcfadqzvjLOmPdSOQgGhtV219Ee7XAw3seWHCGaaUUWHRGFo5OWZhyghKaY0K63MJqq97ErlEuMYJ0spWlI6lJn3Cm/WbRKyZH0EHA+wzZg5WqTWrnxXiIyD80xRCIVNwQeWazPBCExRxiydsqZUDfjpW2yWCrbRtyh+XLiISzFErExToNqNWfmK5zbJwc4utqohJ6f44RjCnQV3IiCNygP5AEewVlcqsapxnVeYtCflxcF5+LcG3M/Jjo0GLu7dUva56B2uQzgZ8p1jUt+ZBYtwpbEbrxaPOYMAUPAEFiBwEY2icqdFIHCp7Oz0CcGzz/zMz+DDodiwSNaLq6rqoAqGwPFeG655ZZv/dZvveaaa/D/wi/8AmuZWOlL/jDuqVOnYHHC2aODo5ZY+3vzzTe/+c1v/qM/+qMXv/jFhKOHqdV0UgohhJ+Dg0Xqfr6JQbG3mPcWc64xhgXlu9EaK3oTwwU/1XrwWL2eytBmy2nBHFSXyqrBFHXFSqvseYdOz5f8+s4BDjMUAs3m8izwdVKJPZcSACPTjivTzZTHJ+Vn/RZHBddLaW+slK3Mz1UzxVnOdQho8YEoaDVmi95iiVR12STkKyfnp4r9V+3bPejUZLgBIndTyRv5ipdhSGV4E6CRq1du2DvUrFUOHzuFOkyfA5SoTklOO254aUyKB4lGTV0dl2Wzlbetuhu8aLQaLX/e8x6aqaWD6o6hFJ2YnNvpRH46CJ3OpTLSh8hksPujk5fmaA6qs3Osr5XJH5tlAxLn2JIsZDu0gF8C9XWHU3YRfy3TroaAIXCBIiAN9UY5SJcRY646Lnf69GnOxXvlK1/5wQ9+kCuloMtiIgQrE22tQhlHZQbuJS95CZtIM1K9LNoHPvABGFpZnEye+tSn6q6WkJkyNySNQ+XV22XJ13tLq8r8Lu2ssAhaa9hHDoFPw9xKhy34bdf24UOnT52YmmteMd7EetaHktKMUdLoCtM4I2dyaIla1Sil3NIgGYkV0nLmPOwPJb0D10iTP0nklF9chiPtw4bnN1BoC7LzZlhtcu/l3doipEhTvMfINudDFOsZ79hCvZbNjw7KLHGOHJm0Fvp3iqfLsBsXykEk8OFk3IFMqlTI1UORCYUYsiJcqkD1/SK4ECJrm7Wu3ZDmnPJkcMNpu+wnkqYzMVejZ9QYLsnUvTvg0b0Z2ekTybXn5YqR6eBW1kvls4CcrrZkEVrb8VPhxGDJWObme63P0ZbTfIaAIbClCGwkAXfqnbCg3rKh1Vvf+tavf/3rL3jBC+BFwqFY5tie//znr1rxxP5F2ZeRZ5IQiDp79OhRtF5SYUvFrlI8ctZJYpAF3UL8xISVVdVeNfPzC3RtcZKF2Od4+/dd9vmv337w4OHg5vGQUeOYXXTuUDsKUJDq4kxA5+JJWY0g07SRL8l3FQ+T3FQQ3HxvQFhNnR4/zPpfZ2x9/MjRXCrNMQw093GurJbpevMP8YhimE7nU7mBvv7Ti83T097gSCxk/L0Uuzi0B76TFdvsLw5azI/wK8J27yxFw7KBzp+b4z/LFBbNEDAEDIEIgY0kYPgVkqBJ0vYI0ydIkfFnrui+LAdSHoJOmKOlmVv1JdACKnOjRhNNzZ6JiXmqsi8RsMYiRE2x9MptYmDFADUJk/BVSzn7wJg5ou+2BQ7E6Xv79uzymo2DRw773o1u/2OpFNXUZh0Chg4rdQmhW5CNybMFVTveFUDOTMDuKb0KCBhDNt/bTv4MX8txEOJY34yZmeRx+OAhVOYd27cRyq5Z7ODhHneXgCkk4fh0yh8ZGphcOH16cn7/yAAVVxFEDBkD6FHHuD/nW+lbQEgl4JGh4bMRlzrSNYSA+dmfTXyLYwgYAoZAJwKukewMOFe/2j3RGJEBDAp9oqdCiphTQcAsQ+IRfAPvMombrPRdWRrKMWlxPFKSJiGj1rojNNROBEKw6iICheJHFSaCZkVTCNVtFPtqnrSzQiIdBk1M0WqTvXMM5dQ/dXqKWU+acfdfNv3AKyTreJjGmRCgiDo77qlkedZOCTg5d1aW/dARQAYQTacx/0LJnjp5CsOnCbaBVlJ0zLsh4/BnFhM5BB/XnRoeGqTLcWpyWuov4YzCOtw0jsLjHvXOhR+svJCUzPdCwPyQeFnLfz9ayaiqS2SHgPmVqoXgkgd2YwgYAobAIyGwYQSslsaovyigUCCtGAuN8EMe8DFbYTz44IMEKiWcYS9oBKZFg2zwkFw5mEy4RQlOiFmVYHImQ7RkjUBk1GuV5JEqfrbPAchh5OZUXSI3w0uoz7zf6AA7QpdmWKVblUnCoK0gq42VJCiXpeeBhEJUODZzcHs+OG8U5h6seSEtkDoNWOKQm6N/rIHQf3NYbE3Ne61arZTJjI24PoBQBeJQDEr3hr3iVeWLCnAEPDI0yBD75PQMTBZXTEQhYXeFWFWyswuMsRQhsZBr1Op0GfpKIjCfuBZL8hLCjh3dQX51pgHHeNi3IWAIrAOBDWsYdb4TOlTKRMfF9grKRP1FT2UXaLiZOKwTJcI3vvGNM8hIJqqCMIgN8aDjamTdP4s8oVjiQM9knjyFloiMw8NA4hnyP/tHHa2waHLQq9CapJfxZ7S74Zw3MTRQqdWPT8sjdzKQI0ilSVcScioBc6c1oVmPGvEzjz+75FzoVQBd0soLAYtLccQfGWI6dORUvS+bH8rl0X/JXDf61BfhYnbrImW5VbEqEkPQrJc6PT0nKqWUKWi5EwkTATbs95bkeL6eds/Kq1ek88evq4AN9Fk4qq9zwLzis4huUQwBQ8AQWILAhjWIyRA0TRLsq/O4tGU0zfjhRaiXOJi3wKDJfO0SWdwN8SEb+JXBQB2pVlJPBpmhYSLQ5EFL5MNTkmgqWk/CoWT0kpU5n1sIjaxgJHawGaVP7ggkiP8Q3nBfsd4MTs9WnNonq1DkYQcBIxXiISoJOC9In57VNaZnYKTKijAJpRsgy12wsRKr68XAO8kWY4X8AGg42dzId+AIOBLmrIo7p0iMq4MG4pF6oIiRWbhQZtY7cqyYAjQEdl2XOLSXvhMjLMb12YM8bAX8WLHidq+4Q9Dl99EjDOt4ucmr6UhgXkPAEDAEHgGBDW6goUMKhDC02MSEiuFTSBEmZraMI4nQXNeSS5tynjLuqvmQJ/ytg8xJKtg38ZNEUxGNcK7Jo/P00Ooy0SrkITsqiJUszjXFzAKj2wnZXrVnV67Ud++BwzCwzMy6KXBac4WC1aAMp9MnQKcnMgTFyYLEkS07OocyJd0KB6tx3nvLm5gYp5VnNl1jaAWb5Wo23yesn/IOnjham1+8Zu9l5Iqr1lgKRNINw8HlusqF4vRTrUul9u7ZzST9sVNTcVSpIRI6l/Re4oe98Y14IiFLjlotNSSgj6gwure/ipTK2Zz0TDTmRviV8qtmITAjNe7dSpLAbQXD0UmrpLcgQ8AQMAQcAtrUbAAYCenCtTroyiJg7K1gREI4Iw9DaFgESkYV1sHkDSi161mo6ga1QmZRr0Igg3mFawOmpreNDLAC+MT0HPOtwtCOg+XbKYVcUI/wJ/hoOBHO0tGd4Ng7KDxRszSHTIZx0hQNPBZYxyenuNkzPqaUq2csaQ/gLEs552iy3DXucg32l1j+Wq3JrhQOhgDxOokX/3pGAM5ZqHUkRE4Rlb3JnDEz12K+EAUuy8YNVvODcC82fr+uv0VEpV59tCyd3RoChoAhsCoCEams+mxdgSi1jLLCDTp9i7oG0f7AD/wANEz4oUOHfv3Xf51ZTJRgVA1GodeV+ZZGViUGE2PhEq6RIiz+ZtZL79u5HTX34PETEHONjRljRBNLKx08V/MxKgIvRmqRtNZn5CMUSGdGhbYPaDrRSALRwOgBZPJc0bkZTDh0/Ggm9K/cs0e4ROaA/SBkS+38GXN3Uc/7EqAIsgsY239igz0q66PYjJJFOa5nx0BAQsCbIMu6K6MvVCQTO4M0P0v6iI84f6HvTbtBVJM325B9uFmTFHEz+fGUbDXOusWyBIaAIXBpIBDTxXnXFk0XhoBr0SEYI2V5xqtf/WqUNgZgmRX+zu/8zp07d7I3FhG4/b7v+77zLnBzMqBddhOurrRODsHf8tgLyds9MZJKZw8dP8UgbKIAQZ1q6QwjMj5JQ6yj8ZKD6qdnIz65OA5mnBNi0NXVZChmT44w+CZDFmCdmp4ZDYJ924a4hRIYsg8DLKRdauXksylu/XHIm0lfGaJ3R1cN5TK5bKZcrUen84UyfB/1NlRTXn8Rm5CCd8xL4UfLwjn2HGPBm2AYF5x45N0lLrbn5ym/55kyL7nqDRZ0dDqJ5QzUkjvzGAKGgCGwBIENI2ByhSRgGkgXdQ2i/Y3f+I1EmYCbO2dtzzAHvES6XriBOZzjiyZY6My1xIEPuWAHJSuRiv19x05Pzla9EVm93OEcfWKSDSw6MKB5sZCXc2UlnkRYmyF5xCBuCh0LgkvX62VGmyPHZhyMTbv1wNOBxzEN+XRmtF/ycwuQ5LxhvND06ucOx9mc/3c2zU9ISJaOFzWhnzE7tTg77/lFqSsPFC0tyPnPv8yNzEEAIz/mCFIpnWUfHR5J5ORR4idW9FPA54DmEQRMladPnRTD+53u9bscTffdyJdkeRkCFykCSf9+A+qnRsuMyMG+0LCyr85c6nwkbTTtFNE2Z3pyA6okrS//peEV71In23GEHrsBj09sb7BT5rEycZZFowWX9buJBuwe01ORBvwRHQTs4uXTshIJ3KqJ7RqqljOoRrJDx8ockwdtFDhISfJk4BfiA+xHLGADIiT6vJqGjQ4P0bk4PcWALE6GoOOKboo066+QiBg7CJg3hYECYcLKq7noxbkvjaZjG7xljS7bpMTONOAYCfs2BAyBVRDYSALWpUdaiPqZ/lSPmmXBu7AyLbU21quI02tBMpHnzkhwgjkOiYgEo1f3iNMUvB27dmbzhYcOHEpIRptpGRJwmyvRrOsccBL+yOwYR6VkdEwImNww00WglpyxlHaj0CLWgwcPhn56145tRFO9ut4Uomb108bZg0tBazqxteKcCbEAk12UU5mTp6fWjNxjD6IBCNcj0jng4cHBiH1jJuXb/Q6SzgT9Inmmz3mz7tUwFSBOH4mnHd09sIshYAgYAksR2DACpt1B8SVzxp+TIlg7xC2PGICFhNCG8auekcTpbQ86KMfldKKk9s+YYhEqlAft7Bws9WfSx07PLDAt7OrjThTEJ+t0OS2YzZuL6RB0hNAlu4BJWnyusSZz5U2XMta9hPbhAUfhjGwWOd/Oz5bd+XYEkodu+kHikycXGl5uZHyM+BTBAbXNGmZhbEjSfhFR1t34ClN69qJUhw26B3JBKj01T6URTSzS+CgTJb2Tbkhxznnqq2XCnCn8WVaeB15/LhXNJLTXcTG6LudW8a55g3x4D/IOPI6s8krpFk95NTVZm83LAXZ5OwyQrKVGn7O0ltAQMAQuJgS0/dmAGsGvODJapt1yq+E8QhvGr7tIbkCRm5EFdAupZZ2hkQ7wcrgeDSzrTqhtVqjQ967fMTLULN/18LFaWg7jw2VpgZtVP5Vj1vbYXCtM5fYO5NINj/MY2Bkql+FgBoaIOW0wDS25dwDhwl/iJXs+WHGBJRbPOS/o87yhdCs7OHFwUpbbcvxDi8afsW+vQaT7H5hOF3btuHwXpJcJa6nKwmDfMJTP2imJ3GWH1BnssaUiTcTet2eiFraOzy2wNVTQSJVCrw8pWPaNUZgcEtFbLAx6MCgKfJgpoMAeX2j5mdzuUeoj2DH9zlVfByhiT8aaMnb+IjCdzYNwLmyUPG+8EBTT3tFya9HtSpbJub5Zik1RZACC5OYMAUPAEFgVAWnxza2JgOy0QSsqWhxOG1PaX8x7Mz7HrtPGSvi2gUI2qM2VawvSajsnCq5MBnLbaGUarbCU9rBl5plQkDxlCVGQdE2SJzykSAoSiyZXHu0+fIAG3PAylZZs4JFJpSVbyUwIuFwNF8vB8MRITniDEDIX5sBJWV12igklQkz0CEpo+r43s1gVm3A5tN4JQXdBkFKhuizQerJHeJGM+XRgZKwChk1lCqkw75CLXmWUIcLTSaI+1MwNXwjKogH30U3ymuWm7AnqaihPJa1Wt+cqvR6ALK4hYAh0EwHaH3NnREA4WBxfyol6datx9Yk3NpFl/c3M3CxHMvA0Gj124wE0v2oWXiwW2FCSBGfZILvR6Sh/vhjMR2WWva+RBGKjiUel9DMy8lkuN2qLO7aVJHfKl4FTkbnN7hLeLecGXakV31Iwy88oV8+qcgC4cvHJjROwW4Kce74qJ8d/YObGfik59v1cmpkbjSAMVKMfgz5XhHWPNl3tveTx0kzszhAwBAyBZQhYi7EMkNVuYbqV7CGkIpFhxKEShxOXyuWFqRnhVzRbmQx0zTiEWWvK6iHW8uqUoIw0r9ORgkl0CJhWPkntCsmcnqm3atVSJj3CODUO4650hjhoYNDDpr3dZpNDhISdOEkXBX1melrqrKghDTpmiklToOoth2iJTLOz6MCyqJd9x3Du3Z5JWnkR7hXzaugtJVbQbirBJTyHN32mAu2ZIWAIXGwIbFoTfcECR0PrPkowHXjJrC1trLbg23bu4P706Tluo6FLHzMomf3EDI2JcN27muRsUqVYBBhSPSIqroknmp47CwFr5pKPj4VX6sTktB82do4PuRFeJ00miwzwB1ZAskVG9x2dDa+lnQ5vpD9TKhTYF0r24sDRFaFPILVI0YHoQK/7Yp1dCbwNIAXhmdl5XhNW+szTKy+vIm0HLSf0qsvtEgJOjBCp79mJYLEMAUPgEkVglUbmEkVirWrTNusnjuAmWQkU+yv2w6BN5vnevbs5I/Hw0SNRXFE+/Qbqb00W5BZzefZoxMnTuOVOPHHGZ/pGzaJlr1Tk2DsyEUJLySaUx05Mp4L6/j3bRAzsz0XLjt5pttt7cDhJIF7oNe6HeH1Zr1Qo0rOYm6vL6DQz2PFPrDcJibcBnji2bGP8eXCoX7oyq/QV5IVKfYDY9YqS6izTgJPwdb1fJ4JdDAFD4NJCIG4dL61an0ttZd1JO12AYssdTTGBECGnTWSyqYMHH0brEUUPAvZZnxNU62JsxVJRovE5N6UIhtA54EpdCFgpVss9fHLKa9b27ZBjGIQYmB72U3rmIepv0GIkuLt6mNRXi8bquV5zdlh5zN3hM86355GbQBWhRROOOwdy3xuOF5UQMBbOA6U+3h7AIZ2+siViEhS7hF9LJVm1pHPAeJLwhInjFPZtCBgChsASBIyAl8Cx+g0ttDbSrlHWONK8ihmz3DHJOzYynOYs+tOn47gCLG2x7g6Wz6kCjJKqqeWatNTtoLV9UBrxUYKVAsidgljydHq+6geNicECBIxOLMTvcYKhihtwNtHaWW7Mk1ifl4KChjuhD4O0NDtCV+puSSwqslAZl3jsfWMK3qBcFE8yUwbVfWMExFXyJ3gVx5wxr0Z3fOPxul7rKtlZkCFgCFwyCKzeplwy1T/3iqacrQ46EwgWs6x/zQTN1uHDh9VKqsGSVy9VyKePHz/O/iSqAVMYiRip1lIzWZYNremiw5Scygy5Mj3J+mmsi6HWgJMWdK8rtsE6dCysl6/cLdtgOW0c4khlnbk1XMAWlWsWsEEP2PIDh+EVYg339VH8FZftY+OzY8eOIQYKOLbRYrAdesVCXygnRPSWo0eESLwtVHb6N4xklGtYl4Pbkhlr7dHI2IMb+eBWd/6iMsPDMvaAgbq++nyh0HJdjSz70sTdk96qs0ljCBgCvYFA1xvo3qjm+UlBC70qbzh9lieQH7t1DA32M4nIJoyyUDQtBlY07lARZetKlZVY0+7HLfsaEqqKLcwtxzjqMiTUXOYhyXzO8+ZrQTGb2jYk871ORsrtLIcS+HTXSRV8xpflaApZF1sq5dKZ6blZwvlIHQVAqYnOnnZXmnXmnuDPm2JUo8BxTkAZh3ZAGXnDFdtbEaCHPTfE8tucIWAIGAJni0BHC3O2SS6xeMIb8oFFlFGi+qPcwCluYhgQmfvcMT6WSaePHD0uBOzsj1FfZeGQ7sS5TtiW6U7o0GKEVa7pGDZHICDMiRlvvtocLGZ39OuOIFJGm9QZf5bPOgteT/SIXEnioJBvtxIJff3EqZMqSU2ObJIBctDovkK+HuldXB2kBySOCQHh/v4+2T+T/oQAyafdnXE9m1X+XgjSTVjZqVtdD/YzIsnsyxAwBHoJgVUalF4SrwdkUQJO2uNIIlpsuA1uYatoIT8+u7ZNYG91/PhJ98wRX0o0YPQqVpdqug64URfP5KKpxFgDxtSW6eTFSlk4wfUHuB6f9mqtcGyoH/pHTESCJLR0uXP84TxdvDAI7zaYVEAEh9GhPsqbmpoiiE8T/pWZaVmO1dXewDlUMqFZGU6YmwPz4SHpSwmQS2zu2nmvJFf4mhEOwhfYkxvHUcHuZTgvAJgzBAwBQ2B1BDoYYfUIFurozqm/YOH4z12UGjnZ1ymZNLk7tm1n+cqJ06ckmmt4AVfPnygWGKJen+ts6MlscDALkUPnUdvuDmk4cqrcSmV3TozBAczBagHRV1TaZhGAasCyGtiDw4JWixPqYTWKZ7TciYS1Mf2T9YGwCbH1NTabHgKD+eCgK5PJhY4OElLrZ1U0qTIdLNLKkcDCv6vG2oSqWBGGgCFwgSFgBHzGF0bj6hhvlfZX21mxhZa2WjTgHdvwHD8mGjAPSYLr1IBdTmcsruNhpPq6UshwYEAmUKFzzZYS8Bw6etLzs3t273RLaeQJ/6V093FRZGq2I9eueOUIqKg7IlbQI4NSCqIu1EVakFF50myG1XVZ1l1BFbxe56hHOTqzT4cqIjnpTwiqSx0Vil1MtjrHr3txaJhmED+P49u3IWAIGAIdCBgBd4Cxhldnf5c/FDKhdZYGmmFYyHXH2BDGR0ePHiWIZp3HqIDlao1WWM9s1xw6aThq55dnLfed0bgtsa4YVZITlNj0v97QnagPHj3GQigIOBNz7mo5dTdMIHCm2iKCk3q4X4yisQOX43Ud+zZDGdKVVVJnqHB3xVwzd4SiCnQX6q0mBBwfRAj88mbP0ukcMNt/Eb+TdDv9Z5mVRTMEDIFLBwEj4Ed6144JuYCUXsUrnMxdWiYK3ZFIqEWj/SxN8Y/OLi44Ss6GHgpgtVb3w1RfoeiywTyLQ4yaLHORj8vQheN1o8gii4ziKlV1Wv0QKATspSDfOjtcuHMMT0/NQn7jwwNO2ZXEJESSKC8pguMP4xLk+cY7SgxC6iUbj3BMBIX1MyeablW8/KlKGrMkkbxV993WFkTuKQ5GHsRjkdSCl6uF2UK6VXQh7u3wckXeBD6N3Ikgk9v0KJhd4AioMJWuNFMy6u5mvFPSC6Oush2oOUPAEDAEVkVAmhhzZ0QARmwyw8rpgxzNC6dBqHCcrLjxM+xzxWNacFrhkRyj0KPh+M6vHAhlm+YgwOj34aOnMqlMXzYnpj1yYN2inM7gGn0ab9p4PjjHkpI3ClQ6pDhZy0QeXkbSsV6Yr6Hh8TCVOXVqsdRXaHmZuZZ3enKagdNrrhz3hAKFeRkyRbI8wpGtn/VSRWV6KaMLznFSWExzHnCG5cdovq2mVwy8R+0anPaG71+Q7UGoUonzhdiXs16VrkGvuaDFcMUdx6vzqYGrdw4NSH+C7kSOs5UFf3Eyru5qynQDnSdet/wY+EksVmWtMJy9dzhfa3inyiFWWOl0wQ9q6ZQskPYz8h7MGQKGgCGwKgLaxKz6yAJBQEZYndbmPLSmurBHpnghYDZrUFUHr5fzWQrcV/HTR6bmRe2RUwgYeGXjjVR/Qaca5UR3Nv/XHEUXdM0zTIzmJHtcCbWLcbVMPeossrMfhsZwbpOmVFNOtvc4fXZ6gXHo1NiQHMMgc87q5Bsy5kMhy9YER1E29gs+inU8McBmBws24xgpZdCAp2rSuxDrsABTaLiKWiJVDzl5gY5w5xqpZrrAocv0dcCTmfNIUH3dKjJ+0XjVueeyvEr6Rn1owH5WNWB5IKnitxzFty9DwBAwBJYjYAS8HJGzvFdGTaeFIZX+0Hd3bN+OBfChwwdkX4xACKnBLlBhUEJLEkdc/eitqIeP5OBhOc6AiKVCnr0da1UonNVH3slT85S4a8e4kACO0IQd4lIeOXtNuzFX+EnmplmEMzo6yvQnC3s6+dbt20lVesuhmyPQPPMG6VQ0Vb8MtTbpRpKnXK+IG02LR42wdDNLeQn644ii25chYAgYAqsj0HMN4upi9lQoDbT7oKjG20oK40GEl+3elQ6Do4ePoOvJoQiet1BebDYqxVxEtbq4VAk1CpKYS6vnbtttuDNxIqx/oASrYekDq7H6+Ojxk36rsXf7REeLr3wXv9M2Hy/Nf0PvsD+TNbO4WE7uR4dHmPzUpcAwshaYSruuwrLKbqgw688skg17MYYTOFgQBJ2A8T6UK9g3AtX1uUiiJbJKG09sBU0WvVXJ9cNiKQwBQ2AzEIgb680o68ItY5X2VLUgbWnR+YgBvezducNvtU6fOAn1hhk5JWm+UsVSh8UtDmhi8d2RWzzSGUHDExmF1mhRGCPZjJTS7g/09TGwOV8uk3k6AwGf4Byk3TvGZEhaxkITbZMcXGlkENFFF5FPCJi1vxTDhTW1IyNDHHYMARMi2KQYrsdWzc2Dd1GWc8nambZ509PTsOnQ0JBgGWcjHu7bGIrxXdTbcHEY/9C4MDf5oPFLCh0P0as+tqshYAgYAqshELfUqz2zMIfA6hBBvczG0szSBmP7xJXP+FhfNhXOzc7OVby677Eqpd5slArZwWgjLPIjt1UskaKGvAPxdrPPBKrLfLDfacCVis7xHjlxIh3U9+4YjzVPUsDBzuxWpenIravehHIidRENeHQUfKanpzr4i4qvjmRXZTtz5k48wX5mepbtMwfdNhwOPLFF73TSu3FBWlm9JjteoQFDwLEG7F5dBEpnHuY3BAwBQ2AJAj3XJi6RroduHH+4tjkRiiVAgaimQix8oJ/hnDcxNOi1mkdO1jgrcKHp1YOwWMhg2uNabyy2UFWl3RbcJYiGXdr2NR0Kl+hSwgf9hTyacKUmmuai5x07NZX1mpdtT0enEAqZuKZfzKc71LYobM0Szv8BKJBJxEmMOKc4nJGDj4L52dl23Zx+HAt4/mVuYA7yKuBOzlLuKxU6JZQHzrVrgV7fGSPGeckcsEZYGi3Oyb4NAUPAEGgjkDQy7SDzdSBwJnyEfln/qexLGrcfFgTMdhyn5xbLoVdueA3Wq9BoK+O69UtyJIHbPKtdSidHOqLuCMCEWPiU4Jw7pqcZhCxmqgTe7HylkGptG3R8LoQOTTgidKwQc0b83S5s431KwJovg7LZtMeUKJbd1WpVi6e6rqMi67V60CFUpV5DhWU/Dfc2OUdZXlWnc4IvC5PnWiFnoC67eXQmMb8hYAgYAmdGYJU25cwJLsWny1pjJm7d3G0+n00O9wXHrDM3umLPzmzKv//AkabvHZ32ssXS6HB/0NAFLDCvaMAdGNKAd3CkY199qoyFXpkqFr1mg+lTtnIs5vOzc/Po1pPz3mKtMTZYGua0XTha8mDpi35JjkoMErhsmrmj7I3y5uVg4yCdZy20OAafCyk2pBxiyvTkyVpd5rBTjRbrbWWqOhZM4279te5Uc+aAUYL37u1LRpWlIiJdx9uR21RaDNzlQEi5c+8S37YRKDtk29EqC7TpJ5FnMj+s8exqCBgChsAKBDrJYMVDC4AulDHW5g2aafk4pZa2edvwUKvZPDWziAUWp+PAOjTIhWjal4gCuM6VxttFL0MZmpI4bmsOfUTZYpSbz9LKtxbKVXI5PgMzZLaNDJAxltginfwXtkBf5msZb2hGm3BVHJB2dHQYTpqZmWEZtIjmy5pg8fSYw/YKvZUzNdBiYVV9mxi7yVLs9aDIFDI6NAZo4tQ2r8dqauIYAoZAryEgbb259SLguAQdVeyTY74TyoOAL9+9I2g0Dhw7RbM+X2fM1c9lvCztuqQB7Rhwx0XLCSlWtcmLFOLippzbUjFPcXMLzP96Dx2eDfz0vl0TaMbkCOk6MShNyiF5lPOmzURSXkdlkHbP7t2toHHs2DHEa7YC+AmpnJwiaw85TjEqy3bWnLhMV0bUW/CLxh/aYrYhbYct8eXzebi8XO7oOC15bjeGgCFgCCxHIOaD5eF234FAB7UQuvTORdOgkO0JvT07hjJ+6vipyQoEXGsEqXQhz6yoOtk2K3JCsNx1bq4UP4ppOL6Xxp/3NNjXh05ZrsnE6gMHj7bC9L6dO6L3J7lFcvGlH8k89sVZbd73rl07gmbr8KFDSMs5wOmMDFC7Cm+eDGdTEshNzy6ykRW7cHSYp6uky+UFToJWddhh0clQQ+hVI1igIWAIGALLEDACXgbIem87W2qxVR4b8PqLpbnF2skK07R1P50qFZgSbTsaceXFjtY93vahHSvxuQafwU0IeCDLkQacMoQeiYYdptM7tw27LYedkbRkG7FD9EVI9yeA42lQJ3AH328fnyAIDZhrJp1BpKjiLmJPXZgABuD+wYHoNcmWk9Kjid0KVOMHnd9KwHok8NLknbHMbwgYAoZAG4FOamiHmm8dCAixKL3IaDD2SONjI6GfOXjUm6vUgrTPDlZr5EbKNlfGcVa8EbGwkjL62YcjDDkKSYywZuazucL48GBWlWp53uaJKF8J3EQnZA91RSVOTHiMysJti7UaK3w0dAmvbaJoaxWlyLINFhZubKah0QA5nijo3HVjrTyicNOAHwEge2wIGAIrEFjR3K+IcckHANFylBzXOb7pRIdxTJmE9SYmJnLF0oMH5xcqVdry/v5SNBS8FiOuFa6ZQ8BOA+4rCAFXqzVMoBcq9WJf/9Bgf6bTbHfl1OWZc+4UfkP9gDA2ymKkfrZH1v2wWqzY2tAiNiozZnsX5suMHpdK0k+SN+tsytebvyxhSqV0O2jTgNeLnsU3BC5NBJZTy6WJwiPUOtbqkmgONaU+PfBVzWclHvOIowOFfC596vTpaqXO4ptsoY8lSU41DHIcXAgR8RFrKQyD29OO0ZtwZeHXFUuO+9McLAgxYErNiuKFeji96PnNal8OFVMSoV06BRMZJDeCuO3MLRF7wz0Iy2FHHCgkC4w4/VD35wwbzPeOMWZeyFWD3Kl5sWhqNVi9nKjH6xXETWZTinD4ma+r56yQr3rFprzcCAteayATYtFGEW4gPyNlYVAlNeLMC8FTIQVbZ3nH+5EfgEKNp5AO036r0vRlLbCg4fZHWV0cCzUEDAFDQBDQVqUXsajXWaIpZrMyQuicbnTQbDZ15we91d0wkgqQKvFvgIeWNXJJI0yzG33iZtk9oqV2q1jgnic95oqZqcOnTh1pVJtMAZeDvGuUadBZltTwUbBIITnzRZtPYx0xk7OgFS5Hky5QjGxCmfZyQ5WW5DCUYn1tq+IXv3z75EC6tncok81yLFKQ95pZmn3Wp6YY/44yFSpGJGGCqALd+WLamxU7Ictfa15q0ZcNOL2wlg9rQ+yMPTY0HQ4+MONXWmExL9uPtAd31yGNjjSs9yoFJIy7VmJ+K9jKHTldLjbLl/f7BXiUKshSIiVWUATSDK+DvpKusHJHL/Ni5KQNPSWaGhP7ip1Dc5MnZuuy/6g795i9R2Rqv7vwrwNGi2oIGAI9hwBNRG85CJUdlCBdxvQYvTx9+jRb5MPE8C4bHSBrJiPHu+MhhKuSsT4iIak2uD4UpZ9V8lX0nPorxCcLXvk/wfLcdFBemJk6Pe2n831D4yKoE9kL60LDTrmCFSInj8QRQg7ifA7QRW2m1ZeGHh+PaOXzGRatZk9MzXitysRwiSJDUdFa7DolzOvLUh/uicnH5cMaYQK66ZzCiNgip6uCfMPBnrd7fLjuF45MVXx6LERjU5G4pusTSEA5s+IbPxWclrskSLGNEHax8CPzbLmealbHSnpaE+PHbBcCaEtw6xCcRFJn6RtKcRwWKcZ3RTpgflhu+vxGXYlLki+Xye4NAUPAEFjWyvQCIEyksSJTT2ZlMnV8fBypIGB4NyFXJWDd/0/5GHufxcVFEuo6EFWdt6Q6NM/btg0gD2fjnDhxAlHHxoZorKXdF7J07bJrzh+5hY5tlpINIqgg2Rw+fJRRgN27d+MXiyHy5CKZL3WbYQK9pESqJTWLa7f/in1+GBw8eBDelWORV+zvuCTxGW5ElT+7j0ARgxxnqPdckSu5JpFAbnp2jvXao85sm0T8otzrSog7zuiM37watw4YBVgB0In7M6axh4aAIXBpI0BD1FtOB5ZhUEctsks+SzuUehMCRmJ9igcNGFUY9oWzsbnVk1mxqdmqWkE2+Sz2RwONemthbjZsttgYud2WK5HEwjmq4mH7efzEvRcGPQPHJ6ILi+NEQj+T5iRgTJp27t5NsqYkJRvq6zJz0doXVLXNomHlNld0xG6X7R1Ph81Dhw7R/wiYn17ZRWgLunG+1WAgd4JVwkTOJOLM3Fyj1RoZHdU3kWZP0cQOK4mER96BRllFWmy4+NVFy5BWeW5BhoAhYAgsR4DmqLeccifKBLojNEy7xvoQWFmHmuFaKJlbnqKpiFKVSin7ajWSseitqhUtNA31+MSEqumtZp2Z2rgZd8wU75VEoJgv4UigBLtSaMcE+pK49vUVqTUI5FiDNNYv6VRLFjsh8pAxcHVSohscjgM24xshXE2j77ERr5DLzkxNVlqMjrMl5WbIIPWOPwgTf2SMWgbyl18lMsu1GckvDaR1CF0wBE9XE5FYs1DPKl0liYKj/8dPN7FX0MB1LGPSBHY1BAyBSwmBzWoUzxpTHcdjePltb3vbjTfeSKP2qEc96rbbblNm5RYdl6flcvm3f/u3WXwJz+3du5crbmRkhLFftBAdqj3rMjcsorIv2V1+2RW02wWmq8Mgy1aUkZLrmClWBN3cYZzC6ak8ju/hEGm9Ue9VOGWB/lJRORYQ+oeEPDC2lVWrTFuKgreVDgljIcR4mIr0+d7Y8EDYahw+VVNuC2QTka66dhckLgZB+ICUehK/hngLTHCE6OfpAmdeuDQSQ7s1SwYPNLmLsdqFniI/XX578lJWi2BhhoAhYAgsQ2CLW+1l0nCLjktb9v/+3/9785vf/KpXverAgQPPetaznvrUp6Lm8pR1lphl4SHOjh07UIvZ7v+uu+46hc3xkSOam2rM6t+SKyRz5ZVXNmtVKBYOLiVmYTTrS4agadNF3VrNRe8FFV+pl4jQW18+xwbLhWIOw7Q+bHCZsJSIehrDqtrZWvmvVua5hUl/QviKDz6EdEPNGCTJ7e5to3SJOBsKe2NWI2VTHFe4Xkc2Z/+RzIVBOx0YiAmYou08eutGCObmyd7HoJyBZ2QjEjHcORlRNh0IMp7fcddZBJ2tAp3DNP3CJDi2bU8CzGMIGAKGwBIEaNp6y6n91Lve9a4XvOAFr3vd63bu3Pk7v/M7l19+OSFM8cK7apYFHxMTPZChP+IQCB9TE1WU1T5rqypGI71rW6FeXUy1qoVsWHQE4oRxlkSxWG5iV4khClrOHKIGi+MloYxBDzlYolljGLq/j4lmcfr+6Ig4vovyib/WZIs4wgZ/w766RhaNHE5GtrGh/nwue/jElBCwVGFlFTdSBiq8pIAIAA3T8eAYk7gnBGOiuWbkJCTXe0AhDtg1RJOAauR5RCmx3GIMRi0YHjGyRTAEDAFDAAS0Ae8hKJjjRI34+te//vKXvxyxmO+EZZ/2tKd95jOfYYQZpsFBvfCuzoZec801V1xxxUte8pJ77rmH+OhbLEaCtIiGRkIISjNaMsSc7MrUvdqCJmuj/BYE7I0P9BVS/mAu2+Rw2M4iId6IiGjcY27ojKB+OXeWAWbmwkNWRHOeA/cjA/1hs15ZmN27czspUa3hEzJLuZoq/WiOUX4x3azMfsNC4ilt6u5+TNCseLEk5uux1z4K8B8+NsnorpMxqfLZls+rVLWSFxrtM3W2STvjCRCNSiXaK4wRcoEpVQ+8ydMzDCDs2SUm5Tgol2l76T6sOTihEZdf2fkLUfm5isWZ6zlJbokx1/Lodm8IGAKGQO8RMKzJ8l/20xgdHWU4WkmXq5q3EAInKbMyN/yWt7zl3e9+91/+5V/S9j3mMY/B4JbGmglgdBGi8XqPHj3KVPF1111HINozI8Pdfuf5DBs3eCXY0W9lg2omrA2wQ4aSE626Y9+OidDlhKQkmghJRfCzLpUvMsmlvR2jQ8N9uct2boPhhCXojASNZkPWFm+NEwHF8e3LLCo10OFntynY0EA2nZpeqOoQ9HpZTbJ1tnh4eKFqBk8H68yK5tJehzCtuCDI5gvk0mjwIwr4fcC+/EbAr1mt9JfYgUO6OBwcmUsDbeTiyslvSToWcW8jft7+luTuSOBGx9ttPzafIWAIGAIrEHAty4rQLQxAh0CLRQAMrHTel5ZX97ei4dQmmPYX/xOf+MSf/MmffMYznoF+DAc/4QlPeN/73pcMPutY9K5du372Z3/29ttvJ1sa7uPHj3e1atIKO1tkdNN9O8cGcv5QIUNlQDluykU9Eg1J5Ii/xbPEyVNZ4OsmV9lQyXej0GBSyMxPneQzPtTHLmEMlLIXdFpGUCF92Q5CXZuBkqD4UXe+5Vck/9kRSwZvZfMQZKDwXeMF3ubJ6fnppqtkW7KzFUTySWW48qHnxTWTzTNlqyHLrmfKVMacmZbNNIMUhyPSYamwXwjnNR0+Ul2c2zY6jB8C5pOSKiRvRPYO4ZHUT17CmRw1pbvAmHaCuvafzpTGnhkChsAljMAjtCmbjwwDzoODg5T78MMPY2oEa+LQa1GIk20mUTUYfyYOLMvEMB4Mr9CS77zzTh4RmSTJomE8w8PDDFnTPqIod7tGaH+wL8LtmRgNKnNDRZp0t+OzKxkNiu+lQiRt/RLRIlGdHk87rm36EFp8LjU+VLz+6tJgmp0p0djcLHITQlmSjysiIYIlOW/oDb+f6CfkhtWRQWajVRRqPjzgDQ30TS1Up+Y9Z3+1RMizlCR5lfU6qn4LPXOthKvlruJBvX690axU69lCjreA+pvOefMN747bv1nMZa+98krpOSkHswaJSuh7il+VfEuxUWVXCsDD+ETCRiJf/N5WRrcQQ8AQMATWblC2ChtYFi32ec973t/93d8hA0PHjEh//vOfxxAa9sEP4+rwMh44Fd7FBJqYDz30ECTNI6KRiqFFrQJMhrqsfjXgUn9Xrq69ZlfCVt3bs20406ruGB1kSlhI2BEkDKEk4a7JXSQLqQnSaxTkJiPRgMkBah8bGazOT5VSLVb4SJ70JyiJgV+3Wmk1cki4IMqvu19OA9YqIB7y0BeZGButNoJjpx1/iZyu6uuRI8PGnoHYcOF09oEu1noycHH9VDqTqzVbZIMQNZDzvM9+8c5DDz786Csvf+y120pwtK4SRtKow+MQde9UshAPIavB7NiZHx4/P3ZAc+XJhYwSv3kMAUPAEFiGwOqtybJIm3mLuoNeiwXWxz72MZYCf+lLX/q93/s9yPWHfuiHEOMNb3gD8754oFUGnP/mb/6GeV82O3zZy15GKqymdZAZPyqymsCghcDHJIGGWdTU5boEXoPdnr2BnPctNz72ud/21CfccB0Tt27FUEQ8NONJk34GYUR5ghBc1ESRGhtmAji/c9tIteqMimFfWnhoOAPfnU2uZyjw3B9RsPsZUcH2Qh3k4oNYe/bsCdO5g4fd4L9qlussCiR43YwF5HLRbh76QtfKhnKXOz/FvC8j2PlSESnLTQGMTZs/8++fC1rNm2+6aSArotJVcijC9hHyy/NZ+55CkQoCTjbDWncWa2duTwwBQ+CiRKBtb9Ij1YMmGcr70R/9URTc97///T//8z//6Ec/+t/+7d/YbQNyZTb3+uuvR1Q46atf/ep//Md/KKe+8IUvfM973rN//36tBZox7EtrqLdYz9I4QsksZ9KQLl7RApuNdC572bbcd774+UUODfS8er2Sz6INtt0js6WjHWGxIMCkjJSMvI4M5f77K1/BOcAjBY/VTZxAK0c7MP6c1kVJ7fy3zAftxMPsysG8uFT6gfsfPuR5O85B/aV3QYYsa4I4Hz5wmNXe11577djoUNSdWU89SQJgczWPaXmo9t+/cP+hI8f4STzxpkfDvqjGDOnLWAJdKH1ZvCQlc1YjuVVVq1B7LACP+NXxk1ObbYLpNCQ9pziWfRsChoAh0Eag5wgYmtS5XoynsLFiOFobMqyoWOn7j//4j8wQEwFF+Z3vfCe0CtHiklTUTOyCMXnNZvHQAuKIxpVHiXbSBmDDfdlMs1yDgKHFbaVIC4RCk3LU564KPteoo0AgPr1KfGEex1muJ8GjUsZ7ymO3EUYa2S3bJ2MOOpCRWSLj1YyIIE6sds+BpzTxOq4UJyJQOGZKrmwuYsqEqbEMQY8g3rGTk8SRMxMj4c42/6AhvRmmuKfK3t/dcudnP/+5l3xH+ftf+izZYDsM6n6GGoKGlBjTJVlH/RtCxXJKUPEzKYyn89lsUKnm8oUFz/vEF75RzQ48+lFXXj4mfRjM153BFWcuK5Ad48cAm/SYnPySozj9lhVj9IAK6VYrnVloORs0ToDiLGiJHMd1Cbb8EnUqIkMzFi8LVq5OzphAunpRXaPAHpN/ywE0AQyBDUSgt1oHrRjkisOvJs3KnRhnEaL2WfoUWiUEnQP2xaOBeJgm1IT6lORQOH6cZqX+7lw5lC6d6e+nRR7KymIk9FT8uWxBd4sEblpqxHUaK5e8O8eeKNL481R8UYMoWUmqDHtJEyytJAmGAQEm5sNi1RSP8l5+wGUir5J4S3Jw7Wl3aqq5RhuJQK4ckasCIycVTIf1tNcA+H37SkHon5yanakSymsVOc/WhUGGYxnDoMIq3pL30fu9hwee8O7P3XW4HHitBa98iglwqJQ53YXZGtG8Wlk0WfpfHquBmvrWuXLYEXEy2SAon9re55UC79776194cKY6sPOHvvfbAHM46+WVcCHOYkkqIp0H159w4VCpsCkfcUHGC+SVhLIPqOt8eEN0NUpeI1c6uBAyuM0hGSnAb1HsZvSBnFSPfAEKlSYoz3lBA3WfEESUiXGGUhCc8Rugdh+JG/H0I+dsMQwBQ+AcEFhPa3gO2V9qSaSNllZbSQge4uMa7ag1l+eOI+NAnrtHDih96h4pcPqo/Y5o9GEwxrQ7skWJcR9HbKvl0OV3wES1c9KyO76P6sP8dBjAajk5G6ofTpzG5jjqHqxHJOldpWDQo2XvcNk71igemq793cf/VcrKy4QuH9huaDCPGttqwH1o3gEyONVOBhGI0HKYsmY6Vywysw7J/Ntnbmn6hRtvfopAGoYc6yGEKs6J7yqib05D46sWyF2g9eYeR/b0jYoZOC27GKSFxHAAE2Gj9z1xVaFSsp4rqNdqyF+ue3WIGK6VfqpUSOM4cfGaMwQMgW4h0G7cu1WC5XtxIwDFtV3HzykedYCDt4+NcCTD8VOn0ZLX16JDhKlsrRGyTcaJh2fSjZnLtvXtm5j43C1fuvWB463MYCOU7kga1mCOmCXRxT7RXNFiQz/tDoriibIvNJ4WrbUQZorffHjyi1/7yvah/Aueuo9w3DkPjST1oeZ9pRL9DAwAyTAZdHHZ99AF8zImL7wssLGzSZ6+AvPd6Rx9xo5310PymiiGwMWMgP3VXcxvdzPqFvNv9Etyt07tjB7AcHu2j7Jc6tjxk4x2rsvB1k3yyskQ/KH77sqHs0++/rLnPumJ5YXGh275ypSX4pQl8s9nWq3qglAvW3b4MizM7pzycfocJeJD5cv6+XqQKXvexz77hYrXeuK1V9ywTTTXc2ZfqUtcfbwsRuf0KjXCEhl45CaBJVrPOKY6ZCG1E6zaZNzZYxh/dpGOCgJ3VKZnBDZBDIGLGAEj4Iv45W5e1eKfES144o2UKrjz8h0T6bB15MQp9C0Z4jxrR2Q3piwJHrz7rnxj6nFXjD/9xmu2T2z/9B0Pf/KeSTlrWbiumU4x4g1fp2AUGQHG9ko+8ijlsTOG2/WKr7R394nw01+/fWhs6HlPun7QDeaftTgSkSwlV1zMVlpnrkP9A9j9RQSsAwBxHInfGy7enZrDtoNsIQtWoFbog315UfG76w1RTQpD4KJHwP7kLvpXvEkVjLkm/pbjGIQT+YXt2zmS9ZqciRSx41lLBDdUMKbyvOlF7/Dhw6Ml79pdhev3eU9/ylMmK+Ff/uMnoWf2tBKXy1TmphmIJknE8fhkY41W2sMcTEgZuyzU309++Y7JSmP37t1PvjpfbIkGvMwxenwWA8ir/OEMDPSFrYBt2yjZOeJgnb5KzGUlbuZtvc5LwKXCVBZgpyresTlBWF6UznzH+G2mVFaWIXBpItBbrcOl+Q4u9FrDNwnr0rK78WdmZCFgMVTia++2VN5vHj81NY+hzzpri74Jod5/ZG6h3rp82/juQa/P857ztJuuuurqu+47/M9fnqtgaywTwaliPocRL3qcOH7XyCSLh1nBC/vK4Pei7z08433263eNju54+pOfVIS1W5EtlUsjl7Og3riuSRpXFAWyE1YyBxxtzL1K3I5kW+EtFlyXw0/VWqmFpvcn7/3w2//kA/cf1dM8IvCQywku6G2FjFamIXCpIGAEfKm86e7Uk99P+ycUjc1qSdKEc5ai2D1P9KNoNqcXa7OyS9g6HHkUMpLk0MxCJZXfNjxccjrrrlHvqTfeONI//Hf/9K9132N90yL7a3BEcq2sA6kthFI20U0swxQj0fWsd8vXjh88dnpiZOIZN+6S8xz8inLzWcpECkdKseU3GcR1xrYplyHLlu57GpugnWXGmxQNPANMxnGsYMtnKqH35dvv+fqd9wOdhKIBy1trn+ohMc0ZAoZA1xBot55dK8IyvpgRUALSnxGNuLt1TbzjpmZTlvcww7h/z3ZOH7rvYB2TH3XspqLqpqzaXcORrQxkex6ztt7AxJX79g9mvaBVYy3WDzx7/+Wjo0empv704w8wsFwYnKjOzXAGJKuBg5asZK2h+VIaI8BikJWBaada3kc/+alCsfRd3/4drP0N62WPFUNtMpWCMMhSJzdn7ZAT7hocYkV6hm3XZquMiPuy7fSaNTvrrDc6Yppzs9xrYkzg7ofmg0w/n2/ec5hOTK0h4wQ4O1FRcbCrIdBtBIyAu43wpZA/vyKxOVYF0VUYbpXxXxwEzDkHE0N9YaZwcqbKEhh17KAC1eE/86gv2TLRe2qRxT3ZnRO7GT/Npimr2e953/PcZ/p+8IVv3HZ4wYNfC8OjoqCyd4cMSssnzXYuEHADa6w0mXz+G8dmFuf37Nhx87WFVM2tvXFD05FAZ/WFOGKExQh7XI+EwTnmENmkw6Bnf0jt5HNW+W5aJO3u8Gog2+OTs810sZEtHjo1RXU4pZEeCJKIObkIlFRx06SzggyBSwsB94d2aVXZarvxCCQsI222a7d1WwfYl0YdAmYlEkcyPHj0lBIS1sLJTt1n0IDJDB6dmfNOTE1zlsK+y7CmhhtSqbDe1/C+/eahK3aNHzxy8BOffRgCDtikhD1JmjL8LHRCYhELM6hCmE5NNr1PfuHz9fL8M5/8+DEiRqZI6/79OxV/aaqYZGHbAmcdhq1qVWzBxAJMa4sUPeMYIWDTDe2gPHD4eM3P1v3s/QeOwsfsHBrI1les14rFTTxxgH0bAobABiKwtCnZwIwtq0sAAUe1Wk8mDuOhZBnUpR2Xn5bTcUMmgPfuGA/8zL0HDmvsROtNPKuiRS4Q5bHJucXFxYH+0o4xiSX2Xb6MpBZD7/tf/Oz+jH/Lf3z1npPeTIOdO/NSspTNDUllZTDLhCu+97Xbj95//4OX7Rx7zlMnGMEeLHmhHKKMkOdIMu1kggKlirnzQF8fN0jL1an3fPeUC1gEXG/VERkOfvDw8fk6W31mD5+YZISAwCYbjDsEHYo9JbkJYwhchAgYAV+EL3ULqxQ13HKEkTAUlxRTskFj54RowIdOTApboZZyeoRzMlS9NlNBEhDDfQePZ4PW/p0TpGAimYFe2fnRD2rz1ec+evgp11x2enb2A//0ZWysGKZuMI7qB7lWMxfKjCb7eGCXNcMs8i1fai5WX/SMp41mvDr6su/V2UZLslrfnwAVPAM59XUQsERbX94k2AyHmZhwreedmJ6rtvxs39BcpXFiJqqXdonQ46WivWlLthkgWRmGwGYg0JMtxGZU3MrYGARiNqJJl3HXxDEuyyPWJMmqpLC+bWw4nS8uVloYKOEgXRp6bevPQMDkQPQ77n2wP5O64erLoHRCZN9iUW2DgYH0sOf9yHe9kAy/fOe9dx4NscZqZaH2INNsyCRtGLIHx4LnffXe2l13PXD59j3f8fRHof7K6cm+ly/1eSjn5+nadRbjYVYikR97cRAs/Yr20/MsZoOTA+OJOW++XM8PDE/suqzppQ8cPImwnR0jKZLDGc0ZAoZA1xAwAu4atJaxQ4CNmRntHRooFop9qVzh5MmyHjfJQ2Z/mQlOJoNXAoYWhrJ66z33D6TTN+2/TBgCxmSQlOU+oqLVWyePXrc9e/PNN9Yz2b/7+KdP1WRkVWLUOCYZ4pdh6BOh94+f/UKrmX7mtzxtLCOGz9hmyfYeISuc+Kz7TwAx9NMWmHvhdOaAC3iqOgncftxTvhCWRd6HDhyrt1o7d+++4lFXM7/+4MFDAh1vyw1IaN/ozNPzPVUrE8YQuBARWHfrcyFW0mTuEgJQDj8gt/OGlKC3WpYLV1thhqELLDEdyoWD2dbk9MxiXXRlZoqFBzBV8n3WvzgvSUXFdR88wqYQMCt382lvzw5O6hWTLlRbGRlNZRfnFzIjg81K67+97PH5oPaF//jq0VNNR8A6Hy3bK3I7u+B9/Ru3Dw0OPu2JV5NjiQMenWG0rBAWOVTes71SL/Rnp0K7JJpcrvKnVCRzzjxs+axOXntk/WzL6kI8wEv7mYJowCdO+GFz18TIvh2ldKsxOYmZGtBj4M0YgZp2y9HIXZDBsjQEDIEIAWk1zBkC54oAO0nRbjvWcxOqMJMcAQjfNqos2U2zYMjjjHpslr3H7errKx++78EDqX4hgBRH4rG2CBZkLS6UGJ1ByyG1ZS9k4LlRb7QYyf3iHY3te66c2Inu6vWjS9cXseMl+1ozKA1u9zKlxVr18pz3+pc/rzY7/d73/TWMUWH7yoFhcq6w2xNHL3zwU+lG7alPumlwUAgyrFbSQYvjkiiRtcJQ8Nk7MqeCSOLL6bkNyJu6ctpSS04FltHsvcMlNgM5OtOgAtKDaG9lffaFdDEm76nuDjCm1g/ee/f2vuxVEwPX7/Wy1ZkH7r2fp4jNHp9Inufw5HoznROF3pwhYAh0CQEj4C4Be+lkK1OyQkQy4ev0SegRyyoZeRYOQicNskLRl08MlILKqckp9uJQxvYzWdGAHVcR1TEWOhi5tQhPZdPkceDg0UaztXffZazuzZBT2stmUFzDXCbDLtCcfVTsKzLveu3OsSc//oYjx0585BNfYlPK6cVmq1AMC+mHjzUfvO/BvO8/6Qk3jBSdvt5C3RbiRQNmwasrdB0vC2k75aUi1F6vPCgyxO2hkwu7O2qnd7LeEtYhzDlERSoEQjyGIsLa/P4dQ3tGvL5cqlJrTFalIoFq7m5kwr3WcyjEkhgChsBZIWAEfFYwWaR1IwC5OcepOzpgu/+KfdjWHjx6LCInqABrqEDUWbwQm2NylwqCDCSEm/vvva/ZqF937bUaIZ0SHTtoYt2MjowGyy6KkuSKndlvf84zsX76ty9+/WTNm8sU5zxv3vM++aVvHD56/Ik333T9fg7h89icym1STZEBu0aSlytPJT2rq6PV1WOSlc4B65HALtJ6s189540KRRo+VGG25R09NR02qldfnh3xveHB/sVGeOQEb8bLCcAyOB86qDeqaMvHEDAEViLQWw3ESvks5MJDALMmSBUCdQuDMeeB+SC+PbsHqMvk1BRLiUTTchVjMa6yL3dqt+w4QraLILzW8k4eO05G+/YOOuKFlslMpo25MkzqTl8IgoYMCz/xxu1XX3Xlsan5v/nnb3glb9bz7p3yvnjr3cW+EtxMBD4tBotzedHVMf0KmzKY7MQ454smd7WVrFiGhGz0A6hgbzpgRLbjs95s3R8o5ncUZOh828R4PUwfOHqaKrCjdrtRkPfYvuvNGplUhsCFi4D9dV24764XJRcFMeY0dmWGZxlH5kfGRO9QwSvms9VWc2peOMDtSYmZjxuDdlWRIWzH2TT6gZcRnjiJmlofGehn3wwyybgYxGXHx1ajlZU9JxlPDrJhExYZSnvPe86zq2H2w5/6j/vL3hx7T94+ec/BY4+74bH7L2OWWZgmwxfz0tyxDIkNn5osM9aewHrAXMNwWgmYjBbKcsC9uBgKvdvyK10ZRshB/oFj1Wa2n91RGL3H5mrf3j1BOv/Q0ZNIKFPZ0sXhf8+dpehEs4shcPEgYAR88bzLra6JaLArHaqmNOZY33re+DjbcWQOHp8Wwy0JZaJXfoH8d3e+W/eCN+NnMuR2/wMPQZf79+7KO/1VMw9QYxkiFQ2Z4VRGklv5FIOpos4+5QnbH3XN1Shz7/nrbx73vI9/6rOtIHzW079lkE0iXQ6IgsZdkwVKGdGIVxdZy1n7ioAdzkmO/KLo95dEA+Y8BoQT+YSqe+1PTOS848AxLz/4qMtlb21ezVWX7w0yhQOOgHlNRCCSjBFE/aGO2prXEDAENg6BXmsdNq5mltNmIiBa7uqOVpwGPZcRIrps796Wn7r/4AGhUO55IDqp4yq5Q0+GtlKYcEEADOPe98DDqaD56KuvREuTIWieivGUqJfZLAlFw5YNP1IcgxQSp+h5L3zes668/LIv/McXP/qxEwcPH77h+uuuv3oA/i6wO0eI+ZdQC0cTymwnydxQtnjPziEcRQqzrkar1KaAFZauA9YMCeo1FzSZDL/34eMNL3vVZbvQgPO+d/ne8ZaXPnxyirXTspFo7KSjYs4QMAS6hsCa7WbXSrSML04ElJxifpI6iqmxsK8047AjZDQxMR6E/rGTU0KhSk6+0DN6bCdVoZ6iItdb3vETk0Gzvm/XNmhNCFgIGitdOEJOGq5VIFTSkX8znxUbK+LcfHVhYihfSgdf/uwn924bfvbTntiP4TRbUoaVZnm20ZBtkHH0ACRtZ6kSsk7nTmYgjfQlQulRaHei1ZAFxr3JXY2m9F84B6naCMaGB9JejV7L6LCcSTU9O1dlFZg7kKFn5V/nG7LohkBPI2AE3NOv50IQDhJbyWOYGUOj8CYHEcnpOvzOKuXwumuvRgM+dOykaMDUjXQynoyTcVFcNsuYaKrCwiPPm6t79zzwcMYPH7O/Dz6AXINKmTjJdon5YoEbl47MWnA8mi4q3Y++7BmlxsziiYf7w/ILn7Q912LDyZoX1jLFbEH0ZnFk2JIxaE2uYWd1JaFI3uE6/4RGhmXkdn5+HvmJGVerI/YWe1OZXPHwVLNcq+YLucsv20XfBYkGM96OHTvYR/Oeuw+yUQeSi/DycvTtuBu7GAKGwEYj0Nl6bHTelt+lh0Cbn+LRXV/UVpkLLeb8oYFSo9War9TY3yqiMRiU0xVEoeW7hX6GVhlkstDCwWP1TKGwbXSkwPZSYqrllGmBFBOtOLkOBTvGgDdgVOZ6Bz3vB1/yrG997L6Xf/vTMJDuo4TFBbd7luSAJEr2cAscHCux631VqPeMYEeVUNnIlsxz6YyMcjcTCdebcxfjg1PDS504PcdL2TE+IttmhyEXxN61fTxsNU9z7KNjX8aicUlfp4syWdaGwCWMAH965gyB80dASFHVJvJyHqU5bhgiFourQsYbHvLyueJirX5ysqNEGYUmtTT5TbGw8lHCGFy+7d6HvEz+in27S+jQQrEtXX2kaqujT1Io60W5oT5DwLv6vVe8+Mn/5UXf9l3PuG5UFtW0fNnRSWMK3yjlkOU56HeOmNrCkymfxPHnlM+jh3tl2eMLBbjzYRJryzzUl8nvBw4c8uuVq/fu4EhHsWZzVbj28t1Ysj148IjYx/FxxumcXbhlslrBhsAlgIAR8CXwkjexisI6rrh4H3/Zbor5RaU9aHDbzl3Mlh4/cSpq2qFnOZeXVEIEYcqHgPFBA3c98GCl3rh6/xWkEqpgsFQ395C55ejjlDdddiylkrJZKWe8JkluuHIXVyiZErxMQY4K5i6UdTgoyvq7J59lhOpkf4QLqZY68ojqTbYM5FJfdyIw3NxbBIaU9GweOHg01Vh81J4JasEKaqqDlPv3TaSD+oGDh0BeKyjMrJ+ltbU7Q8AQ2CgEjIA3CslLOh9tqBMyEzrSZUKgwsmBQkPR2O/ePfvYP/LIkWMxXlgviYrcbEEN7FKVZqEwGpgMQZ88XWu1rti3F76UvSzZnzKmM6U70V+xvma5KmU4XZOxXyY5hTZq1YG0n2rKJly1IB3Cvqmi5/OIvacb6bChRmGxDOf0HQvTmZg/Jz0SeGFBNhjpNQduLHw+enIyH1Qu3/b/t/cegHUdVf7/fb2oS1a1LHcnTiO9EhJICAQIJZRAQgsLbCihswTIwrJLWSAE2IX986OFmkCAkNBCSQ8pTpxix3Gae5OLrC49vf7/nDnvXT/bkizbek8v0oyfr+ZOPXNm7nznnGlVUJjxhbXuZtdxTUVmZ3dfr7lRiqGOKZ9ZZF5uxbD0WA5MFw5YAJ4uNVke5XBRJ68uNmTRl2fSuql09uzZLGTevGmreBjE9khnr2jGtCq3Ggh0b+7lttqRSFVlS5OuszJnNwOnRDLCsmKwvIs0y5ovack4+lj5xYV7bHtKxeXeXzDa7wF1zBnTcvS0LKUG7DPJQ2j6hl6l2o2tbrLeW/LKS8AchiV5i3FD6utUPiFxyHG6B4a5maq1PsorSnItQF2l01gT5UToLTtG3ErMC8NTSbPN23JgGnOgjHqHaczlGVg0bw43FZlkmxE4S2traWnmbMqdnHEFFOawVIDAHNchcm7AF0S0ffa5zaDmnLnzqpFVxQhyKk6LwJx7N6uMRQiW+5fUkUOih0aQn51UggS8A0MpJOueEVG9ykQ0ibAbWO4ykoOgcZi0D0A2R0mCOgfMlcCUApeyMtCzucuJjcTrq0JNNUKeEgmPqzxOS2MD559s3LKT+pC9SkCznJ1tjeWA5UCxODBp/U+xCLTplj8HzCkcCmZG5nRRzSyuysGcoB+trala9gT3DsaY+JXtsgRBW53xBNAPOx4OriIU+Llha6cvm5nTLDuAczKaqJpl9hYBmbT4aV6GPUZhKjmICUeZ+fX6oxXZVCoa5bxKp5LZ4JzhEEp+0uwBzINt/YTnZ/IVgDJv5l4HdN8eud0Yr7BPShVL8SYhBPJLZMhIf7n8oEB/5j3n1bk7NZL2VVeEOblTeGvi8IRDjdVhJ1i1pZdLIN3dYTKUscZywHKgSBw42C6oSGTYZJ+nHAD5BJUUi4Af/RngMV7MyHqDMk1rguG7uMnxJ4e7hmMcxZQKejOejDcY9bDKSi7f4fxJTzwrs8FrN25zBvpPP2oBS6eC5EAKPm4TZC2VHIEFcuCu+CEYkh8BaO6mTSNue5HnWMZLyIgJL+7egKQjSQWISHhD6kSZT2DWLHGFk0TIy9zGjoQtq5lYCz5nVlXaF96wbTeJl0AGJlNjkOzzP0OegKgBV0YCsp0X3zQqAeeJNVuz4bqli5Z40lnhrYkMqeFM9tRjlww6oXtXyMXAkaAzNNjvBNRfs7BPywHLgUnmgOmsJjlNm9yM4kCuCQkO5n/58uNV+BPAq3Sc5poqdqN29stKq9wBHBzRHBeRNOQLBD2+nqyzc3dvVTjQMUuOdjQYCZyr8IpDDuYNwpmsNON87hJe/otYzF8i8COwcccR6OUn6YjLwRkjR5rpXuIVyJu5XVhIwSHGEhwHbTD64NKehNDI3maGXGkzVgZHufl4U9odXX2xRKaluTHiQ2ZPKlvwCXrTDdXRjDcwkDDXZMAcjlCxxnLAcqCYHLDfWDG5a9PemwN09GiH29vaUh7Pxq3cMyj4LOIZFiNXqs5687ZU79DQrPr65oZDwMi9syzVm0E3mefWK4GZAzY5q3MRiRgrA/mwc1gMlvIGDssBn1u3bk4mRhbMn0tEtoplsnKzsprGxsag3zc8ONDNsSWMWnwyKZD3tH8tBywHJp8DFoAnn6c2xXE4gMg1d05rJutdv3kbwUBcBC7ERQA4mcymMqifnU3bdxKgva1JlM/lZUSqLjR5AnMCL/uVdRsSq6DzyuHC4MW2mzuZ8hQyWc6Kbwxr1DiCG5F2YNjp3r0r7Pe0NYeF8kwagTkf3KmtCtRVRdOJWOfOAaDaz8XJ1lgOWA4UkwP5DqSYedi0LQeUA6bTd2a3NDv+ACdCK0QhNYIOehqW3ytLmdZs2MyR0fPmtMtiqjLknZnP1o1PQh0glhcVobYyGuF9cHCw1IRDlf4kY9gpp5fobmyz1JlV4px/0p1JjLQ01Bj4FdW0Lk0nQtoontubG3zZ1Mat24x23YXmUhfF5mc5MEM4UI792wxh/QwspqBChu0u9T5/ePuu7oTunUX5zNZcOTILf07Ccp5Zsz6T9S2a3wEAPw9AQPBX9i4L9WYfMMA2NDwChrk64Kmo6wxzuHIQtyGOY1BYds5Jk1yMsXBuO5+9KJ9lhXiObDmEmyVy89rDvuy69RuRgIV+maK3XcRU1J7Nc2ZwwH5dM6Oey6OUtLaQ32msQ70Z7O4b7BtCTyv7d0EJ2bYjZ0x6e4eHN2/bUVldvWheNfrq8mugUGQU0eaKJ8NX0T+7dJo5YK/OAZuNTq5PkerACKuSf+FP88p4WVwuWCtoyhpo1P4+bxaUhdscFCYDhIzIyvzYt0UNLJzTAgBv2LhF1sfxE1nfGssBy4FiccB+YMXirE13fw7Q0aNiroo4AZ9vKDYyOJKKS/8v8mMQOTjjsH12d9/Q4HCssaGhPuokho33/glNuYs5eAsqhHoxcqcBiIWRbVKMJ+Q8zZIawUvzy+WaToGuADCvaJcBVETyXd19aJhbZ9XKu4Cyk0kl0ymRcx2z5rmlPhp0Ujt29+IvQayxHLAcKCYHLAAXk7s27f04QF8f8Trts5uDwfDaTZ3sEgYHODgymXB8ASflDd730GO1tfVz2lvAsShBy8woyhYQZQRQEShlpxO+TbUy8zowOCg66D2CcUGMybS6e38lUUNKbhwge7MBYKPkT7IHGACW+y3WJ2KDxx5RIYd0itZcTu4M+jn/RKLHU05HAweJsBTOea7TqKB1fls8rbEcsByYfA6UXQc3+UW0KZYPB+jzza29rQ31nmx6Z/dQzNCGWGymI52RrHd7Tz9enIEFRuwt05VLMQS48pJvniaDYPkXv9/PNHAyqRiWdy3yXygw3HWzETj2yLS60fA7zm42Xme99dUV7K02Z5toSLkkg2kA4nJ4KBuPWuqr0yzX6h7CJVlqMd4l3losB2YEBywAz4hqLqNCyklSzqI5zdlMct32LgCYjh5FqVwOz+LhtLN+WzfwdeT8xhwAlxHpY5CClGkme11Q5jhor9c/GJOtP2PEmURn+Jcze0YBkGRU0DkJ1iMYvHn7UDrrbW2Zxblg5rPXm47Yo5TTRyPEo3VYMKcFCXj9lu1Eyd8pmc/A/rUcsByYVA5YAJ5UdtrEDsgBg1XzWhu4tGj99t0qAavsRo/fHeO2gL6KSGBeq9GoHjC10gYA60SuNIOGvXMW7JORhHGNRCJer3dgYGDvMMV7E22y/vZgMBuMGNfk9MxC8/qtO5MZT0dbm6wtz4WmMsTG/C9/IB4AXtjRxtor1kvj4g3Y/qF4tWZTthwo+hyVZbHlwL4coFNvqvVFg94dvSOy2gdjxDXAYlNXrG/EaW2sqwEWnlf6T9BLdtoaGOMsDp/P19c/CIYV3+Qwlz9kxy+PwcjlLL5i85FQhePGLdtSjmdOq0yuZzh+DKMXSxHAnECGE9g8p7XJ8QW37uzmSBQLv8WvPpvDjOaA/cRmdPVPQeFBqqxTG3Xqaqv7RpI7+gwJGZbsyomUT67dlA1GFi6YB0iY6cspIPAgszTA52HdlUAahcMgAbOoeArO4jC57/swh3GAppu37eSk7fa2VnT7zLILKOtBWbpX2IA06ujGWXWRisq+oVhXTyGc75uqfbccsBw4fA5YAD58HtoUDo4DzDpWep3mhtrhZHbrThWBgTGZcFyxeo0vXHnkovmABEujRUR7XhgVdRV+kSPDzAF7OY2yxNSbsUCeXykYm5HF2cYMJ5zOnTtDwUhLaw1OeWfjl2eyOYoj21ATqaqqSqWz23fszqdl/1oOWA4UhQMWgIvCVpvo2ByQZbecMjyrqjqTSu/s6ZVbC/zcM+hlu+r6LVsDgdC8xjq9Bs8cIDF2SlPtI+Am0CuIhsQO/iFX4hD1pv1OajAjZ18Ygx4YOOT0C8E4/Ym7+4JFlMR6YMZeznsFMWnt9ZDtyKiNwdMMf+AqgxZoSHv8aTYiQ5dchJQh+8Gk0zc4FAwG66tFscwaMXO7FGz2kisuIhYT09zbUBP2+bPJXf0DVI2QZmjbK9/9X1xC9/eyLpYDlgNjcIBPr7xMLCbrcpLs4XCcREKO5h8aGlISESnU0t3drRYNgL2vr09d+vv7sWgiKU5DMHNdiCPo4UjTaOM0oH1OBQc8HDgpx04CFbOr6mc31K7dsJG67GcTkj/w5Jr1kcoKTzJxbL1Ti3404ySoOEG5MjSCl3LJLkbu1Q1xrwGQR3sFidqqvd5Ez854hrYos6vpYeDYbOrhSt7UcCbNxlwJxxNcll265r5eOY5TENokPcpzP4xjGTNwyyfMGqq4JzPoS6U46RPU54NJRyrkbqZEDCLB0ZVrOusaZ7XNbolKaM6xphoC2UAk6wl5A2F4HOSAFL8TT8uNSacsbqv3x1c8tZFPkVcoMkMH96moLB5i0595y7m4dmuxHLAcGJcDfIzlZZg/AzjZSalk7dq1iyUt8bjIEtFoFAuYWl9fD5QCuozoQWVguKamRhedBgIBXmUSjk4xFGItDBZiod/Eq7KyUpO1zynjQDbt8cnl9W2NsxIjQz29vfTyDKyArm07uhKJkYUdrQAa8EsV+8rxSlrFRyFRfsCPgUAaGmuecMLURDy+TKx3aGSAoSFrs3yhrD+SlssRPIwEo15f7pYnPj6aJ5ugAUBZrszBj3u+R0nYoJv5Kw9N3H3FQpg0GCxjlCQYLLIuGG6YmfEg3crZnpwHjff27q6R5EgLdy2YPHGWuLk0JdOAJ4MXd1JhWuoqPfGh3b1DVEpBpgVWCWWN5YDlwOFyIIdzh5vM5MUHUBVoAVcMd5RyrC7n6wKrvJIP8AysssIF0OWVwDwJA9xiAXpBYkKC0/TgCuS8iiqOswh27+ZpzVRywEiNVMa8jtmJeHJH1y5kLBCA59PPreOcpuOPPkLIK80e2kNhBMQqFLmWXCpMWrOPmeFFVYgGl83EhylmjwwvwrRRWmeYPdCi5k056Xgqk8r4AD3uf0L85/hKuQ9Z2ihHTMsfyUMz4DmGKfAhiv7yQXNbkH3s9pKTqzdu3JgYiS1esMCkbTLKhzR/pURsWtJYbe1zUpn0zq5dQxlnlmTirocryFGja3KAuWvUxX21FssBy4GxObDfFzV20NL4AKggKGh6/fXXt7S0gLLnnHPOPffco1CKOwaQVjD+3ve+RxhE5GOOOeauu+6CQnCaJSSgLyCtoIujWlBB24MFSlOJ4+SCDhpf/re3OIFQkCmDuEwCy109azZtc5Kx45bMlf4cLDB38Yhf2ZnRvxoGsxGEe24kjAT86eT6Z1ffvmxdtwjBzgC/jCNzqgLAfi6j8IfDwWDIz/GbwgwP6l22YqUTuet5QTFxzT+JcyBc20OSxjJnWyFRB2Ry3XG2bNnmzaTnz60hnP6UqXuiQVpKBkf8WlsjHBTa29M/0Z3MZOn+yq6yLEGWA+XLgcIPsFyoRFH8wx/+8PLLL//KV75y9913v+hFL7rgggt27NihE8NQiTIZGAZxr7766k984hPLly9///vff9FFFz3xxBPIxwQAaMFgNzx2DLFA63Ip5Eylg/l46gZdKcjT1NREffV2CS96Es7O3sGqkG9Os2ENkltWlhgdCHjKgI/oeBF3h0agFg0uADynZdaSeW2DO7f9/oZfvu99//m/P/rro2sGY14nHnEGQk6cBVreQCodYFbEy52BGU8mmU7AEzgis7EUWTTboz1zMMcARX+ApRykkTPElc9ZodDcv8ALifp64k5fT29NZaSlLuerQff5+GG4plQRcOpnzRpJJrq65GsSGTr3MxlrIPN0KXEtBZ7WajlgOXAADpSdChp6EXBvuOEGABjDa1tb2+23337TTTddccUVICs9OFAKoF533XULFy4EgAn/jne8A4n517/+9VVXXUUUAvDUCWC10LmgpnbXauFozdRwgInOdJqltyhG586du27b9i2bNx7bPve5zUzUe+e21NcUwAJiobnjYGooHTdXqAT+DNyZv6FomLd4LOmPBJa211168WtWr1n77NrNz67d8sxjjz7x8LKamuqFi+aeeMwxJx6zpK2KaxkRTlkHJYunkfZlmaCIwXljJm7N9K1JXTIyFmFbPoz+zb3K/LJ6QRnjAAFmuUzQBwc3bNrFhEx7u6zAwksp33ObohaEKLIyKwftHR3znlzXuXHjZmfh/L3zG/0tT2VOcB89kHW1HLAc2JsDBb3d3h5T9QZGolK+4447zjrrLKWB18WLF//xj38EUOkjkJlAU7TK991333vf+17CoLVuaGg4//zzAWmkZ6aHgWR00RodO1O/xAKV3TH+VJXO5gsHqESPX8Bh7uzZ/N24cQNg8fgz672hyiPmtsvyOWAAQJG7bPFx+3bjUZ4PQUcRW31ZNiCJEHzU3MaLzzv9/W974xeufM9lL3/xEe3NieTwY0+v/cWtd371ulu+fsODq7ucrYOy+oxdWewJ8sk0azydHRIlNZpg96cc0FeyUBg2TNiXL3DUwDMJ8eM8bcM5HzLsc+s20vjnt7dCmACwaJoxBQngkkn7mbQxcfHr6Ohg0mfdunUmpHngp4Y64SfRcynwR93E2RrLAcuBCXOg7CTg6urq7du3Q/9RRx3Fs7e3t7a2tr29ff369VoohGDQlGVZ9A66ZFpfEaeYPGYDki51VgAm+je+8Y3vfOc7WOj37RzwhBtG8QICOLJcOJVwZtVWOew33bWLHnzd5h3eYHh2Ux2Llejqc1178aiYhJR18KpLpyhH0hPwV0RYaOUMDsfZ81tZEaqNOE3tlfPaz3j5hWes70rd9fDjd97/6JNPrd+ypeef9y4/sqP9jBOOOPnohXNbg2Ef0BjwyLDEyLKQpzApmKa4pkuaeeWO5JziWT3Mk4i5+WMj40rx5I5ixwMAd+7cDZq2GuWCC6MSQowBeyKn0x6RyXOmsaGO74UvkSQYDGHMzcJ579HGAVSZciQfyP61HLAcOAAHyg6AXYzUlVP6xFH1yUzrsvxKBVmVg8cqH8EAZhZkfe5zn/vUpz5FOmitEa8Z2o8VxbqXiAMIWixl9/vnt0dqKyIrVj6Zct7w7PrNI0PxY45cAggoSIAZWSe5H2CUiMaJZiNCp4wWPEH2IUGsoGNdNIQuF1ewMG1E3Gqfc0yz/8hXnXzZK05+/Gln5bPP3vXAfas6N2/q7/z5n3/70tNPfOcbXtkQ9Adlf5YUn6bLbuEgi6NFKDUIK88xmYEm2wi8EoD/ACHisIfjNMxU8LpNW9iJt2hBh+wiGN0QmEgijcfNQu729kgo4Ovs7BxOoi2Xjcqs8U6MpEOMFJQcSUcKXgi7WmTjaB+WA5YDB+ZA2Y1ZkVx1qdRjjz0G+ayCRqjt6upCDka3jCYNMNZTdtFLA6h000jDhGSjBXCLTKwqaFwYwgPbRMERPTayte5cOjBXbIgiciDXa9Oh10SdaMib8Qae3spxFJ6aurqKKDgmwpa2y3KbMshjWx4JeRfIyxuVC40gysCWkQRlAdLwJhTbdCNmV89ZRzmvP3/JN798+ZXv/5fmtmYnFL5j2SP3rVyPOprfQNxJosr2+4OhyEhcp1E8abMPXviSBz9lYj5jkJBcGApIXvw3REkQ4vcnnVicTQGB+ooo2gVDjTzUaizmYVZgEVH4b84Bra2uYufyzl0jzCKTrSTHyR57jDgQ0puD4T0e1mY5YDkwQQ4UflETjFLcYIqg5513ngIwmYGyK1asuPDCC91jNABa3E888cSf/vSnWHSW97bbbrv44otBX4IxK0zfjcirPbiK0YQErXlaM6UcMJ25OWFjlt+pj/qz/uBt962KJ1Md7W3VVVEkOdYlqcLTKFGnlNj9Moc8I2sCP4JABuv4iAp/RmQFhs3Nx4qYoBpypG4FrmUPdKUzx+ecf0T0Sx+8+J1velNtQ9uXv3PdHc+lupGYQw5CJ5uyjAztT5MbOeVxV3Lc24h3LsPct4yLT7ZVg6heTtna3esMI7kGI011Bp8luFBLkibVXCyD7hlo5pAQqI0GndktjZwbsm79RgmJEExZfVItuVGAiUxaSoCkatJVi31aDlgOTIQD+c9vImFLEgYJGIn2sssuA1xZ2Lxy5cqvfe1rq1atuuSSSziT8g1veAPrnHV/0bve9a6tW7dec801yL4EZg8SYZB6IdPdgAQAY0ShZzYmuSuzSlIUm8nYHDDtDsVFQ3XUG6p6dPVzbHqZ2zE7Yrpzd4bRw6kWRmYbO6Ey8IFmIZsi7fs1gVdMdatMGfCJAMnZG/5U3Bsbqko7NY5T4TgXnjJ3yfyFvkjFL/7011XdAr3eoDPEkZWIoZEQICoblETHgxzK9PAo5cXNDAeMBCyLmwFtUBLE9CK8du4aiqfk8LiqgCy6xhgQ3S8ddN1mqRfnSlMMBqrcmwTyr92wnoSIwqy2npPFuDYXOZ8Q4aFh38Lvl4N1sBywHNiHA2UnETJ3i0TLBiSA84Mf/CBQyiEb999/v+4ZBUdZaQWOYmFz8H/9138xv/v5z3++tbWVZdLHHnusQiwwjNSL8llLq8unieKKwvtwwb6WkAOAhU+BhClJjoN+JDTc2RNjunJeR7uSIYufZS6Vfj03J1pC8iaSFUVQuBkDdAzOaUIcsCwTsVknkU16femgXJSQZSKVw7DiOwbrGxu6R5z3XPriXUPDy9dt+MmNf2q97FXzqxx/yBlMOFVBttJ5RoaSFRWR/NLlscgD63O5igrBIDJUQubGLdszHj/LGIWdBDLyu5uKOMry6RymemXleQ5NO9qa0SJt7tyOn+CriYhlz0ckyQkrFH0lO2ssBywHDoYDZQfArBZBgcy879ve9ra3v/3tKI1x4dgNxF/mcZGJUTJjURmX7b8YysseX5V9wW8QWnXUIK46apeBXVdyHQx/bNjJ5QC9ND+OPxZDXz+/vTm1bGfaG6wJp+e2V+Io1wLLyqPyNGOjjJJcAL1uARDogTgOWwaLjTDJwmOvMzxYwc1ELJaOCkcuf/Mr133ju2ueePTPfw6/4/XnI60yStHM/AEzdUv6ui7apAvr8HWRj3QlMO8mjtBi9M1Y1q7byDXA7JgH9NlrlGEUYDhvktnngeqfczFzpn12k9/n7ereza2KUeNKLtwXEfT5OLJLxHktrHlSYxpRsrbGcsByYGIccD+3iQUvfiiwFsRlJhj5FfTl4iNcwFRAF1RGO60LqfBFUIYcnqq1JhavhCG8kunOAYPE6oJUrRb7nFoO6LqekJNeOHdOjInKQLSupnp2tcBHmvvhjWhFrZVpbRWQhbXwtxdXTTD2W2V9SZ835Qd0014nFXBGfM6I41TVxb0erJ0DMaBxSa3z6bdefEJTzaMPPHjbXY+DeX5CgdRog4N+d0plr/TzL0qAwK/JMYfxKI3T0u7XbNjs9wXnzWuStm+UyPzFXT8JfeZTEowHQQkJSe3NTjQc7O3v6+vPxFjMbdJNcuXTGIZK42eN5YDlwMQ5UHYArPKuapKRa5m7QvZViRYRFkwFehGCgVIEZcrJE6mXhVoahq6KV2AYL/DblX2VIxaAJ94yihMSsYkml+uoOeWqdVa1P9ZTmelvjDL1aUQ4memUtcMyZVAcIg4jVfO9uIuZxkpI6eZpirJH6cu4A/zyR5xwJafJcBMDqFZfFRkejHH8yAuPaD3rhBd0x9J/vnf5409uJ21WqslFhaxpEKblFjnDQIU54yQUmHx4Y8+SeZE/Qmc8nRnkFOju3oCTaq9D+pXAGLwxEiL/NG6anqyh8GVkvRjz0+GQrzvh74p7kwm5QZKIVArJZIQDhgqx8Mu97bEYhTghxvhRbvcnkayxHJiZHDAfTzkVXeVXQBSidH8Rsq8SiJfqkFnnvGciyvi5+4vYa4QDAjRPV/DV6PZZBhwARQKJZJppQ04qo3eu8jrHN3prep+88NQlXJbLWqNsxo+gCPhkstK7K1qUAeUuCRDl/oS8wt9eL8bDqGa5EYElWJy04XeCAcFVj8MtSDRxUI2W2lwZwYLW+TWvPveol7x+ZVfyp7++cXdPDMcQ6l5ass/copQDzTwlskKaX8qsujKLD3M+zCsFWD+F6PzwNsfb0FKdHuzwOVG/k0klEW3JyGi4c6vDpDgebzLJzcL+VJIxghP0CPryW7p4caJh6e2rdsyKeIOZwZDD7m0P9GQ5uwtD3o43RQRZMZbklbGTIK5BXwCWkPySBT/jglwud0+wyZunQeIc3faP5cBM4wBdiTWWA6XkgFdHVwEZYsm0ZkdtYHFj8Ig5jTUhgQTWz9GJS8ecLvtTOMZhm8KyBMijNcOJ3C83rKCw/IAy/enrpZccc+qpJ/ennGv/33XD5hiNoZEEm6PjyQwox0pkFMvplOwXkuGJzArDLbnKV3AQwx8cTKbwcN3OZMrrnz+7EWURE++coo6vkraHQIkGbaThBUHlJZOCJOB/Vn3dYCbY2Z+QbiI1TNJMC0l+vJql6liMHTeo47cnceR0k1iu/HjljdAMBktEEzvvbv9aDsw4DlgAnnFVPrUFTsRjECCgZDrokNd566Vv/vjHPjJ3Tr04mh8dNxaZL8ivzp1amkuWOwqAxR7nI285ZfdQ9oku56d/ebgr6VQEUM5zB7bclIA0mfV6/Vm0B1hh4R7kzREp6GuMWUC+Zs0apN6lSxbjlETgHNsULlcklO41mjtvDou1Ozu3CVgapFXYZi2XgXmDoQVpKuIaL0FfIB+4lp8RyZHKjchOBMYPeKLqzi3HK0jDWi0HZhAH6OissRwoHQeM+IsMl0pw9AbLrdiJ1FzZ3lgRNjOYdOy5+eFsVs7qKN/l0EXhGEBVw7KGkczVV304G6j43T/uX7a6M+nzox42emuBNhF8OU9GsA4uyu5cFlnjlvuSVQkMTnMPkuNs3bgxFR+aP2/uAcnVOR13ZkcBeN6c2V4n0bW9c0QyRmWR23HkLpXek28uf4CZKlRpOCmjBBkoyJPZb36s7DZ08mD+W6E5R/gBKbQBLAemHwds659+dVrWJQIzoI/1cdFQAM3qyIistJL5YCMQ0xw5MYon8/czcM8Y+ORLx+bXeF8w27nkogtGMt5v/vg3a/ucmFmulUnE48OiP5CtxFz0m+XaTT2zRHAxN3DJV37GI8Jlb88u9MlNs+oJwJLEvOcofwuXK6o0TKBZNU5dRWior7uL65pDlYA9jqTCYR1i8kpm85J/CEXkDPorDFO1+kupwtxEZdAggfaVoPNp2L+WAzOEAxaAZ0hFl0kxM4ICabpmcFcmHCvCsjOGgw9lBhJFpaxPkh8TwXJDn8xxzqxe2s8yp0yiznFefWbrWScf7wtWfPJL39804rDHTo6HDsEhYQpXJijkAoogmeGSqoVzL7C4a9hJjcRqK4K1VXBUtAkSc1zjwjAjJAJSKfNam9j1u3bTtpTjT2ayrJ4yXUYuIeySNA8RzIWSrAwOOGiFnxlJ4S3z1XpkB7EkogTNRZTI1lgOzFgO8CFYYzlQQg74fFnW32Qzw0MDqURqeDhOE2RrC8Cs85m8ZjMp0T3z3z31sIQETmFWoBvLkINefyQ53OA4n3r72c0Nlb1J33evv3/joIPw6/Wh/c3CLqOfh1UyMSyYxmS5mS83YCzCJQC8ZuP2bDrR3txQHcxBr6DkGMbdoad7B3TLH8GPmNvGSuyn127iXoekXMMN2GbMEGpPQgaUReBmITVEDjveEcefcAJpFnKhuEZYFzw2eQtEC2ZT1/rbk4q1WQ7MPA6M/UXOPF7YEpeGA8NDg2RUUVkZCvqronTvAic82ZliJCW579lQIod4l4ak8sklCRMQdp10OD1S4ThXvv1NzXVVDzz86D+WrV/bKwd4AHLZdFx1z5xhAgAb0EXBu4dXOIKXW7fvBikb66rgLcumdNmxSqzjlDcfLrcOa3ZjVdiX3bxjNyK4SN0Sk/XpJM9LrveAJPYjmadQyGppfiDxCJcbKgyz70lgGKla6rkQg02C4maN5cAM5IAF4BlY6VNc5IqaGqHAqJfpfwVvRK7i7CcPWmcWFHEGmpLozx9qNsUUlyp7mMB9jJw/4wQqPL5wOOsc3+Z5z2vPaa0O/PbWu+55qqfXYFs2NSK3GyKSekQCFgMQw08RTsXEUyIBP/bkU0G/55Tjj8bFXV1l/Ed5uPO+qnwmBMvlQMujF7Ukh/vXb9ku+fmcRJKEsz7ukvR4E/HE0PBQCnmYHI3MDSwPOM6vbn/yf2+4bfOQ05UWJO7NgMR+NNiZLNuPqVn6nAy6caa0hWYzfBiFIOtkOTADOGABeAZUctkVEaDJCVP0xXus4qoIos3SBBPHmWNEOR8MoLyVZUreTDLiOKcvab7kwheNDPbdfu8Dq3Y6Q3AuWoM0OTg8FPLLtKuwTMRfwWLEZ2bXmcDlvW8w7s2kG6oigF7Qd0AIHoXJAHBd1KmpCA6nnN250yh1DhrROu0NhCPRCvB0OC157x52+jPOL37/4E1//sfv/nL7l679YWePADCneCEWg80c2RHPOPGRFEvs/Exmo/dgNYAuIxslc+tkOTD9OWABePrXcTmV0D2Mgoanx1Lu0wLNK8iLelN/IjDNKMNsrIFSQMscQdkQdi48c+nLT1u6e9OzX7v2J8s2Ob2OsysVCEbCABgALDKkjFXAXEfkUXN6ZXfC2d036PdlW2eJChrVwiEMZ4gyK+q0zqodSmQ278gNjsgFE5cNULLTaVdfDMl4gPur/M4vrv/bH/5ya0VV9cLFR+zo6v3iNf+7bHUPwrFIxllRShMyGAly5JY5LgvcZoG0Sc4+LAdmJAdmWu82Iyu5vAptmlzucONRKSNAQZhRg0xjR07gNEctA378TXB9kePUBp2PX/6yI5qjI0O93/nBz66/c0PCz1UNfo/ZnpQAw1KocwW2ZfGakYl39jr9wyM1FZGGaoXeDBu9DtaA3Ijgc1oahxKptVv7ELX9sgiMhLyhcARxFpeamsgullt7nG989xd/v+OOptqaK9/7rq9+7p1trc1sJPv/vveDf9y3gb3AEMaTMQJPCEqz8yyBZpocrLEcmLkcMD3dzC2+LfkUcEAWzR5IHJtImCkgvehZshot6A34PdmkL5sKhfwpj1fuREo7dY7zxY++81VnHx/r233dr2/+zm+Wx3wenXmVXbnMAac470KwjdsC+btl51Ay42ltaYrmFj6Zk8UOkn56B9TXc9uaUk5g3eYdohUnL4PkoL17lHMs61z5qf/duquvuq7+qg9fcUSLU+k4X73qbQvaWgN+7y1//POtd68Bd4mOKDyUcoYTaV+IGykiB2wGB0mvDW458DzjgAXg51mFPd/JVTGMp1r2L47r5Vr2DzPdXQyECpiajdFeERS5+KAmmL3iTede/vqXN9dV3PvAQx//758/vMXcwQk7COH1iPI5z7V1G7ciHy+Y20Ei/FBWH8IkMBHZNDZ/TpvXH2AdFgjKT/LwyLEfID2vj67edtW/XzvEQR1V9Z/59KeObY82+mTNFaLzpz/wupe88CxuFL3xtzfd8o/VW/tkqtjnl5lgM7+Qf0736rTlsxwYiwMWgMfijHWffA7Qa2snrk8Fi32emqsbDN+ZZczi8EzGm0jKlh3WDfPDDKW51TeEOPvmc5b+1/vfNLsh8tDGvm/97r67nujpGRC1LqwlvKqg0fSuXbfR5w/OndPBrDAfOXuv1cuEnPgj482m5rS1cQTI1h07kV/d6khkRJl874Orvv/j60ZSno5FSz/8ibfMniX3WVVkEzWeDBYGBG+8+IxLL3kDuuib//Cn2+98aOeQYHAo7Iuxmlque7b9z8TrwoachhywH8A0rNTnRZFsyxulmhTfWDAlW7KYbTWnSBnUM0Iw1xckUUG/YE7N1VdefsLSJZs2rP3JL3552wMPxUZY+lw/zKVJLI8ym5F2d+4IOU5TfQMoCOalPV6m3fWyo1HyHdOJi5mDtbXeSCAz1LsL7fEgQwF/dMQT4hLEv9zxCLn39PS8+IyTPv7+85sYKXC/YGzIScZC3kysb7DG51RzpNd5S9/6xtd5Uol/3nP3zTf/fWuPmQwO+HvS4YRH1nu7oD4mFXkPQupPSlTwY8BR+OoGM/Hw1E3SxuKOAPcOlM9B/uKjTzdIoaUwI+yFXiZe/iGp5IeRstvK0KDbrsRrL6MOmhRpQihjHX5YjFc+HTfB8SxaTJ77x9qLWs3uYJ+jJbt/RgUubgamLMo9l0R9NWU0DNlj24s/0/iFKR5rLAdKxAFUmi7uil50bDPBYGMn8Pz0gSmyPM0on/M3HjBhjnOGXbRej8cbqK2q5XVxtfP5S85+5Jm2n/7+5h/dfOuqZ9Z++ANv1xjIwd2Dji8WG+7cPH92IOKRnUshfzDhJNm9G5BpXJe7B+AS/eNgxuEywiWt1cN9u57lduGO3E7eG2564IF77swkMm943ate+YoXhU11kleYq42z0qs01FQSHWKCjnPRWQvqwpfd/Kdb//SnP3X39F32tjc21ojHQMZpZNs3rUK02nuI4cwQ7ifm+m+cUHjzk+smmHVmF1MiFQrLlYjsZsLojnGs2pFxmUc6mYmGZKo6mUpF/BxpykJrwI+RBG6kYoYhuWO5TKLmlExyJAsO7GKMAtnsjSKGUiRgwhbrXNgcPJO1Sy8+aqcUkglGgETOC+PEEpk9YNs0rziSCsvVOH0V9b0xOiQiCxIZSaL48OIDAWzcolCo8SuEhwmHgpnDzzTWuE8SMxRTAImCkScHsXi9AaMNkfRxUpCEXiWZVzO3YEIbX41sUsiVGqK5VFqX+6n7AZ7QIOTAc8aBnozfyx45cuenhqxhDT/OUaOWJEdelKB8mOn9l+JaYzlQOg5M8OOaYLDS0V2ynEYrOW7BXNdOV0Y/Jv33Uc2RmtC8uqZ3/d/PblyxfudnvnbDm974lqMWCp6t28qqrPjSxfODRhoGPzhCmt7N3C8MIE7UKGTQR7TUhOvCnhVPPtPecQTx/9/P7r//zr8ds7D9FS9+zXlnHh31cJ1GypvNBkJyRYT2+CCILwsASncKtWefNN+TfRmnfKxa/fSPrrv53R94bTTgREXIly6XQOy+ys1Sc5ET2GHQF5+MHPYlyQAPvmy6Kkw+6UQqHQkE6dtZAQ5Q4d014rAti4ibuuOPPPLwwoULX7B0NvqAak9UpqqZQo/HSR81PnZutTAgmeZqRy+6BhcQhVq2U+VLIIEE+vUKJymZQSaewhnzykMtkAefeZEyAcWQ7fX4lCEIvtzmLBjkd4KVhEwmshwoyky6z+dBP8+x54NDsUhFhKLGCAhk+6QeeWUjdQC9ProNLoH2eOVsFJQQI4lIWOrRpUFcjRkajlVHIwbK5JGGrdm03+vvHeyNRj0BfyCdTnDYWSAQYNUAiUEr8bSONLl0BrRMcxgOZeTsb4YInqyoYwhjssOmpZ/YMz4gw45gyMfqQrMSXvIzDExQ0nimIuj1cVFliiJywqopngkwQx4WgGdIRdtiTh8OcEKnOLBQkQAAOGhJREFUnlfVWFt5Sm3lv1/5wWv/57v9u7d/9zvffuvb3nnCiTVbdnXGnETb/MUqakTAs2w2hIzpinUTZgY9Ir8XHLP0rn8+MNLXu7vH+dUN/1j52MNL5nWce/bp5551NHO9BAggunlI3XTSe2u6cQSYeJ558kJfKPqzX/16/dpnvvql6z72icurK51MHIwYAoU8nB4C0NHH07FzBKkPdTsiYdaP8t0vuC4g4Uk66WFPciTEFckZYCbk9wTAUrS14bDz3E7nNzf/lWM7Q9GqwD+fa2xuOefkE157Zls0a3CLRddGrBQaw9xDnTMgOGvXkNTS2SQCaygQks3UgCRw4ZX/hOPiYuOCNAscuVHFwmmgXDxFQJgAoAlESfFBPfCVnFDKExnJE0TldopAwpyUEghxWRWboRk8UEVp5PpKUNPJJEZSQGxYLq7CPdU7nO2PRvsdp6fH2bGTk1dGwpFgRbTKD3zCKiPVs6gNKvWJpSYa6YUGs3QAiPZ7PH7mMzhhrbqWpxCXSUdwZbJCABTBE+BLc+grQO2lCmA36QILrLw3hZGCSw3LCxbEVBWjzesBHj5PxldBXOpHRlieVCCQ8TL1T/bwo4IkaZoY1vx7h0Fjc3I4Mj9jj5liGFfC3+lpGMcxuKRsjPj0pL3+/v6amhqe0Wh0H6/pyQJbqmnKgWTSLLzyBZLmICoEwR/8/Na7Hlpe29S0YMkR0YqKZfffc+F5L7noJSdwE1ItfXQ8ZbS14Aoo4ULIAbgDCAzQD/uc+1Zs+smvfldR19wzMJxMiSrxk1deMafJqSUludc3G+KkLbS+iVgYEVPWkdHl4ifzuwZhHM7SAiSwb9yRvubb3+0fjnOM1ievfO8JbYhaUpZskoTlg/UGTP9biOIkLYiW9tH5ZzmGE5oQmlBeRxOOvyvmrN/efcutdy5b+VTaG567+KiRZHrDls501mmsrvEP9b/x1a94ybmz0QHAs8qAgMngoFNTKSgDidw9xblhPqHUKKMhGz2p0M9/l1HGssdF4IReBZEtyaEiJrgblGhgrtxaRYrm2gxwEcP11/FEOhASydWbymSScZ8nHWReAb6AUfE4B4t5vCJ3pjPJwYE+QN1b1XDj/RvufOSpp5582uv31FbXxZMjg/0c/5kM+kNwl5FV4VOkVa8vEg23zJq1eOHco5csWtgRbozILADSOYMOQJ7M/DILTx3pOjhDnBCYNwnOGI2HKgBCmGDGHOpDFcgpaxFGDcSBzgM+4cngwPZQ0BcJkRpVZhQkxMQAysIJEoo7mZgQxwxDkuPV6sYC4GnZn1sAzmGzaQv2YTlQphyQ7t4MKN1Dmxk6i8YUEQsJKe6kgs5tDz793euuq29r6+nrRfn70fddccaSJvrcSiSXGFAlsCHHVOqE6gQKSsr0k3TVfSnnZa9+a/vCpezfJd8v/sfHm6tkwTPrvOiFfWnkNlDG4O4e0CIDgTVcBVSM9Imd364h50tf+TaCUH/vrs9c+e7G2mhjYyMT2PTBQBdSL7piQEg0xug+vV7KjkHoJ49YPBUO+TWdvoTz3Nptdz2w7MFHVoiUGwwfc9zx55z3ksXtope+96Fdf7j1b/0Z7+ZtnWxHfvFZZ1708tMWzxKaASElXmRE0iJXmfXNSdmAmOTHO5eDGOBHJEZnCz0QIwONvIE5w+CGKVo86cRiDrptxkbU1cL2sEJyQXDJuhrURvCVuXivnKBiRGzhoUkTh4FYMpb17RwYueuB5bfedd9gAqV0tqG2fs68jmgo0tPfi/49XBGJDQ6nskiumWSG/+lEOok94fj6RtJ+xGT2pMWHwfiaSGBOSwNnmZ103FGzm+rmtlZVmpxS8WQo4ENvQRiGCVJUj0fE6rwSGPUDFElLkf9uIQoVAHkujP2X1gaLqHqpWWkBqaA3K8pvM0TLjoyIahuRHz+R0jUjyXLUJC0Aj8qW8nWclhVWvuy2lBWZA/SS4IJqbnJZGWRLcagU8lbY2dCT6uzrv+6G659eszYU8F/z5f9aXB+m00dmCcj8JId1pBwcJgzALnDSGX/xG99f9dyGI48+7mNXvpnckVKBdgEwAY9MKjkiUCndt0KJEig9KYmQN0+2/4KLAC2rqZEbv/W/v3zo0cfqGpvmzpt/3LHHLFw4v6k+HA0LSmlnTGQs2vdrokQfNAuUegedjRu7ly1b9uijjw4PD9fWVp911lmvfMUL6yPS3UMbcfuGnWzUuX9t4q5ljz791JMcVOJNJWsrwq86/yWveOFCkLI+lKOfYUIA+mBQ1hmSJU+Sqf6Ms5ZFiO/jhomu/q6uLjY3o0gbSqQ37erjXJHBwcGhoSFu0aCCQDJwBR3bokVLTjrhxEWLGrh8REtUxdnaSSeMHtfDESuC3KTPj8Vlgv4+p7s3s3zFk3c/9NgzG7YNpz2hYPDYxR2nnXLCKacsQJU7YmBMdLoFRsciJKJMxqdnyNm0aYAiP/PMM52dnQnGBXASvbLfX1dbPWfOnMULFs6b1zF7dl19hfBK04NjJIJdSVXm82T2XPBStA688W6eYjuwga+DNDqm2AvCwkam8Fc++siuHTsWz5930jHH4puMDctgK8jllWYaoiC8a52W/bmVgK0E7LZwa3kecECvaATsoJX5tNRQwl8RoYfriWX8US/62MF0+oZf/ToYDFx2yRtngbYZJ4oABzDpZBM98YQBmCySyTgnUnuDgXuXr9rZM3TaWafVRqU/BXdBXw9ztE7aK+KjpJ8VkVXhby9O0rOzxDcQkLub+geTFZWCd90Dzm9vu/+Xdz3Rnw1lk4mAJ1VfFZnf3rJ0QXt7Y+3xS+fURpxKFi1lHZaSgfGcDra5L9VfW/HwM86jYOrqlV3bt82qqXjpOWe+9NxT2uudgd5May3I7gz391WF/T50257QbiOhbu9xbr75L8uXLwccQ6HQYGz4He94B1A0u81PKdSAuCDVtkGnPyZzrjt39+zesXvn7i72WYHxwG1axj8yn2UQVsYbcilkcgAJLufiDwJyfj/3TPjA41QqM5JM4FVfX79o0aJjjj5ucUdFR43D7ix4RG2EdKYXXM84T29K3/PwymWPr+7uJ0FPfV3VGaeedMEZR8wnPEUy+OpC3+7ewZqaSmRxaHYdCYM9mEGjm8h4Aqw3Szre4YyzZkvvhh09Dzy+esO23Rs6u4fS3mBFbSBa6QmEmbZfMre9ralh4bz2ubOdWTUyL8u4ijHQSMypCjmVZjrYBwan0fyzCk7W0eu6LWXa+E9mi6lxxhmMaSjjhi5nxTNrHn5i5boNG7gOq9Lvec35517y8jPrEbzjCdGhYBjDjdE+LQCPz+2y852WFVZ2XLYElYQDKDbRgmpWqjAUuwg+iCaeVCrtCQbo5nCImyedGf02Em8UDAMp06nU0JC/ksuL6KULO+3xqRf5ClwdSmT9IT/4BPQiwUCHK/uKYJ2b9CUpgWZoMF3pXinzMQJLKqXRKWMSaSfmc1YMOOxuWv3kc2ufeWr3jm3ZVDzi80SCnuRw3/w5rYvndXAO15GLF3Q0RYaTYEnfj/94/7Obd+3esaOjrfnC88+94JyOBjOti0pZSAIj0iM+2dSCRnjYiVRlPBUDCYeVVRiujfjnA4/d/eCD67d09g3FOhYuaZs31/GHegeH+voHe/r6Bvpj3AKZBetZkWQM8ElEXqUzCfgjoXBFVWVdTX1tfU1NVTUw314XrI746urqauqcCq48lnxECn9y7fC6DZtWr169ZWtn3CzARrxOJZJNs5qXLJh/4rFHHrmoiWO6u7qd+5c99sCjK7b3xLoHRjyB0JFLj37RC08/7qhQfUCwsE40DbCcKkMrTBpMG1NWGCkuPGUKQtbWMQYCwTiOFKQ0N0SyzMsXSXiDI4j1OPhEebBr0Fm7xXls1XNPPL12d98AbcGXGvF7mE6nhCnGUnXVkTZIrK9Z2NHWMquuo7W2sVqqm3JRtWr25F1Ix2h2+LCjz3l67a4HH3+MGt7RN5BiYltWRHvmz5kzb3bTy846bWmLj7qisNQdJZQVAMrEfHbu32nZn1sJ2ErAbgu3lucdB+gM5UAp0x3nYdW850qypy+TkDiaOcd8yFygcf4QReASXSJyTC4J0xcbIdj4CvryM6tyZT8nIncO4fOZ46BG8oU63vWJRecI9RV4eG79tlVPrdmwrfPptZsy/mCafLmSghuUkNgQbuPD/ZufOfHoJee+8KwXHDevyWCDoUQ14QIS/MxstBkWyLpsqGBdFajq46gwoGz3kLNtd+/Pf3PL5p27dvYOekIRf6QSpThrwAKZZL0Ta4gG6kHY+vqGWoC1DktlZWVbWz17iEJ+SZ9E3TKC7FpM5boWFbuWiPKieN+0pfupp54CjDd09gxkq3pGmLb1s8qJPVGJ2HA2MRxyRlqrwycevfhFp514xMJ65ZIOdKIysNDcDvwkBLHcrDUdnrirI5WoHKFSe4ec4YGRdc8+jUZ945Ytndt3ssIuxoRy1p9k3Xm0eiieGmFcF61qbG1tbGmvq2uoCHpmBZhX9tRV11bWVFVFveEKpyIozJdWYkZmmtczGxOPPLR85bNrNvZyk5bjiY/A2+aaquMWLTjt+GOWLpzdWJtjI+GJqz/SqTLyt0ls34cF4H05Uubv07LCypznlrxScsDsNpXlQigt0dyZ3TSmr6W7xYAMBn1YrGU6OFndaw7imDiNpE2nTTpyZBWJkLBJUvrc/C4aulAMACxQSBjzIk9CSucvxLgnWeRm+BQP8AAT08wgQ6JPpFTwWH/D7Gbe4Tz+1BbOGHluw5YdPYOsFm4MOx9+00vn14fbZzfVRKUHJx2eBnTFYnIUixp5RSUr7xJKi0D6iMJsSnpua2zN+k2DiVQwXFFZ29Awq6q5xmkPidypBnmT0YWIiWwdTiETs98ol0U+COgI5FFoycMs4uKvDEF0IAJ5+hOXrDPscR54duSZrX2rn127duPG3t5+rqs68ajFJy2dd85JrYjysEC1HNz2iKablUmK925241ugg9LtUwVQBh+EFfnisBoPk06x8joeDofZh0sUfsjK27qTG7ft2t4ztPK5Td1DiR19g72DyVg6G0954gxpEoNzqr3+jEx1Y1QTg86ddQkMU6qqqoLBILr6bdu28UTVH62uGkwlmW8+7dijjz9iwbxZTp3XlBHuwBeapcwwi86GRobFAvD49fs887UA/DyrMEvuQXIgLQcYiARMzyWIiCpRfqbX51X7XRSLppujg8OBLp4+fcJGAZgUA1mPgCs27dDldAvmfaXbxOTEXxCOF9O15oJJDN4lnoKABCC8uOVcxR9vfpCHXXPhlTVHGCLEHKerR7BwVrXTHBCI0lRkRja3MlxCajpq4UkKuIw4abNPSGIY3OFvzujdyRyVgR+B9cmCXJmFNEGgBI9C0N0nBYY8bGEyTNAY+pRQeQAWO3O1UIKhIH0Gb9CWsyx9qJuFy7HGhqraCqGNyDKEYekwtcpNVxkjr/qicN7EntAjvxZ9lMBMJYCaOl2t3lBFHpSdVWAy2jCXgLAtGpk1HBCv3pjTO+D09Q/3D8gqs1g8+dyGDYk4sxlDzIuz6CyRSDA5oj0tKQPJZAEMt7W1LV68eEF705nHt7JqTAtAGfkxEKEqOWcFe37kxNBQIRgeUL2jl3da9uejF3WU2rNOlgOWA2XHAenx6cgKjEJJ3sH4KSzknQ7hby4X7TGJLxCumme6cFxlZazIlwozmgF07Iv0Gth4E4k3ohFMk90TmE08iURFOBwBxPDjymE0k+E4/W8UXOBU69yZ1hkOxpKrGEkDAZoZUFmrJSIqKSsXQFDEV0KYZMQdS67LI3E2vxDFyK2UhvlysmNRFsE0ihRb/utDLJqO2IyBknySro9EIO1kKsnpItBECP2xOwkpGmo4tpNTwBrR3LYg4lZxDAZXVXG3I6lTNvBe8oN0Gd/IT2YN8rO8Otc77pMolH4fI2zhH6uuBAA5dQPKOKKDE8KVV+ZAUOII3xhb5SujOuJ0oBBoYhlfVOKJKmUJGajBAlTr5qu+vsGBgQFQGcG3SUyQLdeM9qppBtlUMpVNMFD0BxnciB7GpM9fY2UMydQ1PyMDyzqvfAYz4K8F4BlQybaI05QDdNQqreW6LO3SBALyJt+X0eOptdAzH2j8vxpDnvxXYDMYRdetbkCFHFaY68qN6wQfkKSSNJITUcADnmAqO31FNsMbMGbzKLPAYVwERZIpD2uOzaZhAuMIRYhvrEQDWkgCPSlAzLFcQgJbnkPGX5KStAV95CQmI7FzLATGXJKQyXq5ophIRpMvzhI8LVuvk9BGrBCSshpeTOLmjfxyWvecr/kjhfFz8LKBUZfzAW8Vi6qyg2jFs0Mpj58dVxXk4/fJyu2QxDG0Qh8A6WH9eoXhBwQCTpDDOOOATzKjqvP4mUsTR9JkgESxDfHIwazdgzqf3wvqsToutwRZ5srZU8x/OatSnPNJgZsemYPQ3x4GQFGFk65wfPWVZsN5IRtkRMa4DDVBwMNGarT5coX0UGIYubmmssokbcYWlItzzaT6ZCBVwN69UpuWL1S9NZYDlgPPSw7QD8qaYzW88KND3M9IMBw1JC+HZ/ZLQNBMYY4csOxNAr6qhTbk5bN2qTYOutSYa5uYVpSOXoASDJJV314uVZQUJGFnZHA4VFlDly40uEmAWNiVLHEEYxSrTNq4gDFE52wqQiHyyt9sPBbnHAofx5KI0tPDSq8UilgOSc4GOdQLQ6E4+MPPmZcmayPxE9YYdN+GZIE6ky9JasGFyjwt+PAKXsthlMb4RchDsx5nnbcJBcAje7PqWpapC2EyNGAIYYRzA1A+AV2FfEWm8Z/kKSMhZYb8ETyT7FnlLDKw+ggH5D9GjsUAVtMoklECO4GgTw70FtkVk+OdrLTHmHNRGPDBJaHbLBQHo4VXaNRjMZaNu69SfRLezH8IQVIHZI5GoJKz0tjvm6szyIBCUjXaBBNGQs4YYwF4xlS1Lej04wBdm+kbtTuli0UXK52+KSn9It0b/av0wDjxxEneD4oRJvQoUYywIinhlxN/9WWU1I0oJ+57yzcQxforVvEYf9lO5OajFsRfEAoMo2cnWLhSBEUy4+wm/nsAL0oo+Rs4ESSgNzd2sqK7J7RsfeJCJCz5vk4gjTuJ2cxj5F+DLmzgDXr8+MBO5RNPI3GSiBgubcDBaJT5o1kI8PuyGZG2Sd99SrpOYiTp83s4nUtechSZiBl2gongK2dPI12j9DaDALjg1iR4JOMqjquEtuBB7LuVvIwhthoDjpJ91h/GUVX6ri/usZEYvEXeDXElghjKLeGBWPNqHigUcgMOSslGIoYmwkzEZVE3mEoLRyiUcclmXbk5kUoaOMfHLPySQgXSUq5MQFQaEhW3dH7dAmlBBM+ZY/KNcuaU2JbUcmD6coDukx6tsIfNlRUn7dhcyyExwYUS6aaNaKX9NYlp8gRwLaPkYPwKyfMbCQwXkQUF9NgWyN0GEpXDoQEnI7g6g7ERZn+5oJAEpM+Xv1xmoEdImnxwEHTPEagDEfBGBiR0ckw8gsYy25pCUPO4x29w0ISrZQVPzRmRJEdi8sz9MQdFq13wqdAYvNbyFD7ZO5S7zclcgiRaa5H+GChkvZFcEj45hFHrK2kOyiYBWX2FupuBCNkR3M8M9b5ZFma/v72ggsRTicJCguQlJ30aQkgeL+iKmDsqNB0YgIswCy2EwWNchIWGBlIGceOJEdFOG6YBxYLBhPHklndhweCiCTIL7sK48IBZdqoOxQN/CWLQF3ogTH+SBQE08sx4WgCeGfVsSzktOUDf6HZX9NcGDOnCXIOLdJ8azHR5rtfELECaBiSlvFVtrixrBEpXrpZwxuTiid11U5+CdHIO8odbeExZ3PI4IblGN2cqI3rjcP6d1KU4hrw9OZmMzCsPXoyVv8EcCXLtoEnT0Cxp7Ykrb5ziRar7kkuoPcH29tzjLtFHN3pLc96PGG4Srt0Vzs1UgSFK6JCAbph8Agf4Ow5FJAfD3NwlIUXXfJLyVlDUQn+JK8MeL0eR5IPLX1z0lWFTobvaAeM9jqStMjZFyxstHUm4jXYv8vLBpvHfPU18GhfSFs1yYNpyYK8uTko5WkdoSl8Q8mC4MUaXKKkZL5Msj1HyPZhsxg87Cu05pwLy9g6UfysI4OaR93MdXMvYPm6QQstoiRf6j2YvzKLQvlfYAo8C615BDvZF0zm01CYz1t5p6VtRG8/BMqqU4Q+lAZWSPpuX5YDlgOWA5YDlwLTkgAXgaVmttlCWA5YDlgOWA+XOAQvA5V5Dlj7LAcsBywHLgWnJAQvA07JabaEsBywHLAcsB8qdAxaAy72GLH2WA5YDlgOWA9OSAxaAp2W12kJZDlgOWA5YDpQ7B6YtAHOSCye8wH4sbL3XetB9aVybxWGknMaCIxaeuv9cw+z/HBwcVEcNxh0ghVE0ncIA+6dQGhdL5yTyubBJuFU8agNw2T6JuU88KUvnxHl1OCG1DegVQKTDRX48U1zgUGZmGtNJN+4eganMlzPUzFZjLTWnabqfaplVy5jkTFsAdkGXHko7KSoPgzuvkUhEA3AdJrzh9sqxOEQUruPGt7u7W2tXoxTWtKZPGNcyVmrFc7d0Ti5v3ap0LaS/TwOgSeBI84D5k5v7xFNzyXMtls6Jc2/iIekuQN9oNKqfP3f+0GnoKZITT6QEIacxnbRwNYVsxEV7ctCXI7qoEWQqFasKg5WtfdoexOEewoKFfhNpmF5Sa+uhhx5asmQJH1JfXx9XV/ItYR8Lg7n5krumiUs18+zq6qJ2a2trqW8df2mn7Erbbr4lrnJL5+QynKbi1ikpU/u4IPHwkff29tJs6urqtLMjGEhcUSFn4ZbeWDpLw3MuG6ipqaGi+cDpK6j96urqnTt3Kh6XhoaJ5DJd6dRunNaOhS9OP0a+R16pAlwUdOmiUU6g45wIr8oiDCWZ3oYqwVBGakhRtr293WU9PSl2HUO5joUWV8uBo/bIs2bNcgNQ/WrH4tpd31JaLJ2Tzu3COi2sXG0A2hjIFC+YP+m5TzxBS+fEeXXIIVW6UrgFiQ85nWJHnAl0Fn6MlHf58uUoJ1wU41pi117+Frnqo9htokzSp6SMmDZs2ICwggiLNolqQ35FdlQl86h0MuNLHRMF/CY63e5TTz116qmn7tixA/Cm5yUFUgbCaRbYCxvHqAkWydHSObmM1TrlqXVK/VL7tIHm5mY0KEuXLmVUR+3TBmg/qEOmShKydE5uvY+VGsLuggUL6D0YvmNHAqYroN7h/1hRpsR9GtMJqzE63FQ7T2QqFagQfPkk6dWnhO2HnOm0BWCtqlH5Qj3x5dCf0qtqhVF5KgrvH576RquDmpoEiYKFuMTq6emhz1XRR/toQhJm/xRK42LpnHQ+a4VSp6ogofYZ5dDzMm5jTEabofYJg4UlBVNb9eSOsXROehtwE2TsxfeuVa9fPbNR6EJguxumHCwzjU56Y4rM96hdNN8jtUA1PV+QeCpVZ0Vtr6ZHynVJhRkxYmLyRvWH9KfUH5WH0ZorDKl2/cC0ayMW+g3wGMTF142CRe363D+RErhYOovB5MIKVTtVz9gLxC2cZ4L5hSGLQcn4aRbmbukcn1eH5st6EfoNenY6AV0CAvrSGLRnOLQ0ixFrptGJ4OTKTgyFqQ6GR88X9KUBTNtV0Nq4FZZ48qmona+IegJECcCQVqF0nC8BuYfelgB8ezzpdlVfTWqaII6F9nGSKqqXpbMY7C2sWddOA1D01SZB84D5xch94mm6tBHFtVs6J87AA4akr9C1V6AvnQZDdraflRv6UoqZQKfbwikv4q9+fXyMlB0wRqw6YG2WT4BpC8B8GxiVBni6dliPskIxVe088R2rSvjSkJIxjKpAbib8sFx99dV0wQy1aApERKdNFlO7H1RHElBCE9RGyfOaa65hwA6d0A+dhHHFo7HKW2x3+MnoR/lJXgzYqQv4SRUonuEIV5XOYhMzTvpwEl+EXW0bEAwbsX/mM59RDuNLS9BlfVP4zWvWsAsKtQ1AJOZzn/sc5NFcccTiNtRxilxsL3gFndCmGdE+6TH//d//XcmmVUCnetFIik3MOOnvM5zS75pVIB/96EeVjcpqxjfow8ZJp9heo9JJv/SlL32Jhfrkrqx2g+nUG09KoQUpNoWkr9+RmxHE0IvyShVfe+218Jbah059usHGt9CKMBqGj5EPE7vbeFzL+ImUie9UTluWCQvGJ4MmAoYBErQkLASmDdFidu/ejQ5K2wHfIe2J3oROxIX28ZOddF9twTRuhhckzgcGbazIaGhogGxoppmCcJCHl9sJTjoZB0wQOiGGz2bXrl2NjY0avpBvYJ4Obuig4ar7pR0w5ckNUEgSHS6JKyq4zQBmwkn9/qeKSC0yzQ9KUO0UckDrGsK0JYDTtGRabGGYUtohgA+EHKEHeGAqXXPXFqtE4qLNGIYrt0tJoeYFl0BWCIAkRoduE1Vf2qSOw3ilFEjD6l7651h00j4ZdTFc4KuHt3zpcHJ/eMMFmkvTD8BJviYIo/NRsFR2bdu2ra2tTe1TXu+lr0HN0QLwgTmv3YE2a0KDak1NTRqNvo+2TjsmDG1Iwe/AKRYnBE2ctq5NXBFX84EwumO6FfdVhY/iUHGAVKFEh6hYFL0YyjBKgL2gBb2G8pC+D9+pGs1oGaAQC2QoYW7B9ul5gRYaAMHcAFNlUR7CXsUz7aNxhDZ6Ydqqas6nijzaJB2xSwPwQMvUKtZWQQOmZeLCc2r56Q5k4ZULY9iVsXzskMp4QnuGqeKn0ub2OYV0KkkuqtE+acOFcAv9uKgpKv3KMc0CdkEDhjYJ2bRJvh36K+2yCFM46i0qVeWTuAXgCdWF4hkNiFZOB8erdnY6BNb+ovCjnVCikx1Ivzee+l3RmunFgDQ3H3pkGj3NvdDR9S2ZBQr3GQGwp4vtPS4BLid1xZzrXkoLVUzfAbs0UzosXHil+8AFwMAXNpreYypXMmrLhCQ0B65YSUOFNkhVTrpspNEyXiwlG928YBfEqBDM6MrdSltodwNPoYWKhk6+cT4fyGA0gAvMBCpwh+wyIXgsOqG5UNlAV7B9+/aWlhbaCaXgla+PMNqMtWjF5jZ8IwtyJ+v9x1U0DGjr7Ozs6OgoNiVlmL4F4ANUig5y9+m5dKTGE19Uf5yPw2lZJDSFI3caN5+TAgZ2/cAgidYPVYgdNHQ+P3XBrl3hAQpfBG8FDH26OEE+QBpgBoUUAfIwFKE0HcSopVSZTIVIV+cBY6FcOxEohFpesfCcKmGdyoVRsI6KRqoAHmAaSKx1rUWDbF4LXUYtclEdC6uSD4fuGKapLkSbKwEogrYBXFzBrqhU7Z84NLjDLHx1nK10Yoc8CNPPXLch7Z9CaVzGohOuamOgFDrwcrsCCNO2CvPVUTu30hCsufBZ8flAIa+Fnaq2AbcjLSVJU5vXmIuPppas8smdjw1ikBsYSGJBzmDykt6Wz48nzR1H0Jc+hU9i//FdyQrCF8XXpdnRl6kdkugvVPkMlmhZIHKq0Bfy9NvjCVTQR9AFwEnc6eMgDBcUvEAFxcGdnqJkDNwnI+1zlXU64wAzoYoap18jsOIZBaEIU4W+kOF2Z7COV0Q0WiO0QS1tEhdGD5CNi5KNy5QYrUoEHRon7NLxDd0uxFDjPAlAWTAUZKrQFzLIWtsedPLKd6QtgYkSOgElTNvtFE6oj0Onfl9aCvRzlKWw3mkG8FmbrpaOZ1ENVYxBKNeukp5HLWSqyhhgmIaqAVSMKSo95Za4BeAD1AithB4W6FJt8//93/+97GUvo5t46Utf+re//Y0WQ9MhCfoUHA+QVpG96Re0cdN3QDNjhe9973t8bO9617t0/o8AeLkfQJHJGT15COOTww+ooHf76le/euGFF9IptLa2siAWd/isyDG1/Lz77rtf8pKXzJ49GwbedNNNkEpfRjNg6eaZZ55JdS9atOiKK67gWDQ6aNg7emlL4vqnP/3pRS96EfQoqeRJl4cdIjml7wMf+IA2zte85jVr164tCUWjZMKA4Jvf/OZb3/pWPqjFixd/8IMfvO+++2ApSMxHBDDDzDlz5kDqRRddtHHjxlGSKIkTowHofO1rXwtUcPTVv/7rv/7jH/8gZ5Yp8OSbogHD2/PPPx+ou/XWW0tC1CiZjEWnBn322Wff+MY3zp07F10IzfW3v/0t413aML7wXC3YdVQ0SuqT56TZ/e53vzvjjDMYFrCo7bLLLvv1r39NR8SYhmZwyimnwNuTTz4ZVheOFSaPhLJOyQLwAaqHhgJuAQZ8b7/5zW++8pWvvO9973vkkUde/OIX85UiXvCh0rnQpvks3ZZ9gESL4K39ghIAqRBMp8Y2pOOOO47JFXo9CgKF+skRuAgkTChJmKnE0POyX+Ib3/gGG2bocL/73e9+//vf/8///E9SoRfmqdLGhBItQiD6gnPOOYfxFj0IycM6KGdkcOedd37yk59ctWrVT37ykwcffPC9730vzHQXFhWBkAMkCdaSO4PCL3/5ywwFeGWABc00AAYHjCFe8IIXMJhYv3497VaFuQOkWBxvavPmm2+m833sscd+9atfod74l3/5F7QytFUy/MIXvvDnP/+Z7+uBBx5Aw/S2t72tOFQcOFXGf3/9618hAO6BEzSDd7/73QywaK4MHMEzmsG3vvUtLHxQOvg+cKJFCDEWnWRFA7jqqqvWrFlzyy23LFu27NJLL4Xt9FQ68CUAjUQ7ihIAMBnxgQCxH/7whyHp/vvvR3PAYIshAiRt2bKFTuC555779re/TdUjKhSBVeWdJAyyZnwO0O0SgFZ73nnnvf3tb9fAfHsAG+1Gh5bMXuBOUxs/qeL56kelNPAFMkBm/Hv77bczVP/EJz5B94GXFgQidcRQPGImkjLdwcUXX/yhD31IiaE7e8Mb3vCOd7wDi0Z3SzSR1IoRhs6XZIHen//85276UAt7eYWNK1eu5ONmBZnrO4UWnV+47bbbaJlKBm31zW9+s9qhdsr5qY0QMrBs3rwZCLnnnnsgb9OmTdiRJpXy1atXw1UGOngpq7UIRKSMai/qc1Q6lRI+c5ADSR36GVL84he/4JtiGKH0lIY8t+yj0qmODGuuv/56Quonz5D3xz/+Ma9u29DiuN+am2YxLJqXUqIEQA/KOfKim3Kb5Y033sjAcZ+G6voWg7BySFNG99aMzwGdMaWfBc9ACBBXw7/85S9HtYJUQQujB6Fr1uH8+KkVyRecYDhJ4tCA/fOf/zzwdtppp0E2jVhlSqQiJRJLkcg4YLI6DKfpw7ejjjqKboKBMF8dQjCKsle96lUqcQJ+fKsHTK2oAbRmIRVxh4zofHnCOuQGVJHwWTWlUyhWavFpflQxkhmiMOylC8YdVICfKFFf8YpXoEg/8cQT//jHP9LhapTSP8laVRqgFK2R+qXzVcZywwH2o48+mgBw+8gjj2Q+4oknniAkzZWiQS3uqoQoNuVj0alkwORXvvKV6EVQm0MbQ3A6B+BE2yq+RMcUm0jSJ5dR+Qlv8ULfS9eEZAl5qBbgJOocYkGhxqUN41gCOvm0yYvqgxK+IGhGdQQYM2lC7sxDUa1YUEfjSEPV7ksJIxYGzivzS0DtFGShhbTPsThAM6VB82QRFo2b4TmvOpr77Gc/+8IXvpCIfIo8aWoEGyudYrsrDeRCX0YTpxdDlccrM2pIwOjQlAB8IdINXGyqRk1fuafPT3/60yCufoSQrQyEPLWodmHURErjSF3zTf7+978nOx28QzaAp+Sdfvrp6FGhVr1KQ9L+ubhcglTECDcArwwOvv71r3N9E7r9efPm0Re7vlNiUe7xRNtxzDHHQAPcQ45kK4FLDx/aBRdcgJ4f5vNNqbu2FjpiN1hRLfvTiQv0oEelxpUY2PvTn/4UaqGE9qC06ZelMl9RKdTE96cTd9T70IN4AIXAHkbVCS492nrdPsF1L54FhjBrw24oSAKDaZDKIpcGOit8OWYOGuAkrCaAspSnWopH3hSmPJVbGKmM8jc6ZtTRN82dZsRVdLRpRA3AjIZCa1aBkrEbr1NVInIna749Gi5zVEg/OkCGZlR8rOZFMKI7ZsgJWkD/VNFJvpo7T9Dihz/8ITNVXCiGxMNRf0iWH/nIR5SffHVTK1wqx2AsCEfHqvzkSWNgUH/22WfTAJjDhlo+4CnkpxKmBKj+gDbJSJGmy9wwwy+8WAqAvveGG25AIJ4SUmEgTKPS4RWr7Zjwo8ahBO6Bsqrt50n7xAV5iGCURYtDMOUwTYJEikr/WHRCOcMXZoWZTdcGDBnIwbp/HYKZ2qSpUBboLzaRZD0Wnbgz4cpkKm2ASfcTTjiBT4xROHMTLHeCn/QPSl4JiIROvh2+Ypoi2gIm19EUMm2H+Ev7RM9Bv6T9J2taTzrppPe///1Eoa6paL4yDK/6xDI9DaW1ZnwOqHxDS6IF/P3vfyewulx55ZXnnnuuxqXvoOmPn05pfFnhoi1V9XvYFcbInd4BUJlyOuGe0gDusnTIHd4iDaMzB4Px1R65NBwbPxf6LJYZw7fCYMysI8CpozuKLwxQYrsKwaigmUl16aHq/+d//gdOqhzJilMWMZSYsP2zgwxaJtp7pZO61klfmIlRR04oRHAnLsNHbR5aBLep7J/spLvsQydj63/7t38DYl3c0pV3LMZk1EvuEK86J+xuFUw6VfsnuA+dMGrFihVUPU8CIyfwZC0eo1vlHnRqIsrS/ROcdBdYR148NWUszDQxl6evzJEx+8DyeIazbtaQqtS6LtPVYueAFa3GezJGw5uZHrrdP/zhD8yuIXOAItdddx1r/fGiSenXSCsZL6Ei+zGZSg5vetObWGpIX/CXv/yFBSNAGuo++ji+AboPhucY7EWmZczkdR5Iv0bkBt08o6Hp2nBhjAx58JPOVwc9Y6ZVTA/GAZq8dgTMPmCBHrqJ173udc888wwyHM1g69atjOIhtZi0jJc2HMObxsmTfh/yoEfbIVX/+OOPQyT1TkfMEgHdSzNeckXzo9/nO3rnO9/Jrh5Wj0OJamWo6yOOOIIx4s9+9jOYjCNrtumU2TwDLTQJBCCKQxGKRtpeCY9FJ+MwlkOzhJutXKjBeMJtlu7/6Ec/Qg6mT4B4hGAdWWp17JXuZL+MRSeMgjB2rsNSRgY6+CYwTVdFSVUq0Jh55TnZdO2bHvmSI70lT5hDOySE+13z+YDEDMj++7//G+UH9a7xoQ2DnY4CUwI696W7ZO/TdWQxWeVSYVdT+8EPfkBbYU0s/S/TFdQR+ihalTvgRb0zWfkebDo0U6JAgLZX9iDxCm3I6HQc2FWmVAr5IA82/UkMzwpSTY1hLx0umj26MxRlS5YsQf9MN40vn+gUMhMCqHfQC5SlxtEzP/roozrbx2iG3g2CMQwmgF5X7plEFh1UUnAMUtnyS5/FNjkaJ7QBbGggaaLMU9Kvsd+avhjkO6iUJzcwEiT03HvvvYwUaZ90vlrXfD6XXHLJ8ccfj5r04YcfZrkQylL1oueFBm0Jim2TS9KoqY1Fp3YFSgaNgZENiwOgEPLgtiaFhQClabqj0smnDUkMBVDzMtXKyJt9Pnxluo5BiXR5Wxo6IYmthpyaoAOXr33tazQDVImMD6hr1gnSIaARoZUyjiGwSxVdGaTCUm0Go1bW891RFO7WjM8BPjz340dliuKUSSBmgpEvFfaI7n6B4ydVel9UZGwA1XyVSLd9l54YN0dGA7COnpdxDFNBSAzw81Of+pRqzJROAgBvbpQSW+644w66CRUg0BmAbW95y1vYyKgDcxUjCIABUaaWpWjIkRRV6qKrhaTLL78cdsFAlsKyvonmeuqpp6K80Z63xJzU7KhTCFNFERaVaBnRqi+ksvUW5QfSJMuMQWjcaSTaAHS8WJrGMBadyjrFYLVTClgKndpcAQn11RIV+zkWnYpVaDtYhMWyO8RKFAwsc3PpxALOFZu8fdJH88HwmlbK5AIjA7aTEIDxFjx0jX5ZCsManVYBq6cx+lLMqTw7wmW9tVgOWA5YDlgOWA7MNA7YOeCZVuO2vJYDlgOWA5YDZcEBC8BlUQ2WCMsBywHLAcuBmcYBC8AzrcZteS0HLAcsBywHyoIDFoDLohosEZYDlgOWA5YDM40DFoBnWo3b8loOWA5YDlgOlAUHLACXRTVYIiwHLAcsBywHZhoHLADPtBq35bUcsBywHLAcKAsOWAAui2qwRMw0DnA87wc+8AEtNUcNYOG4CZ4c5sCTHfrqxVMPxdQz/DhCwXV3z/PDxY1FRM4u0DCcY8DJlGrnlAPO61A7T8JwroX7qtF55XAinnoAgtLgUqInP+DlurjRrcVywHLg0DhgAfjQ+GZjWQ4cFge4M+Paa6/lhLWFCxdyvyzoy6lbHOysN+0At4qjoCxnYXLSlh50xfGH5MoJYjw55IgnIYFSjcXRUQAt53Zx3RCXxnPwEAcUE1hTO/nkkwmvWKtnF/PKTVk8ia5pcoShjgb08C+wX4cFpMzZVUAvaboAT0RrLAcsBw6HAyU64vxwSLRxLQemHwc4UFpFSSRLUA1DGbmiABETLORQRl6BPUVZPbgR5MOCI1ANTHIcJodRc3YjIYFJIJPAWDCc+MgBn7gD2IrZ2DUkWAvqq52MuCQOLxCaKKRPIiQLPJMFhIH9+Lq5YIc8EkSwVgpxscZywHLgkDlgJeBDZp2NaDlw6BzgAlTO6H7961/PSfRcgwpeIryCnSAxtylwFxCvXOb6nve8R6+IAfm4AJEbGznXHtTkYGrwm9sG58+fD2py1PPVV1+NuAxwcoHEZz/7WW41IAVeua4HBAV3ufRCIZ+LeLnXi4wAUU5gBncBXRCdq+bf/OY3c8Q/5+NzS9UnP/lJkBjxGrRGRkekJhh2Lk6w6HvoFW9jWg4UcMACcAEzrNVyoFQcAPDAVIANBOW2Je65Q6wEMrkjiGP0gWcuJGDWFndus0FCBS8JwJ02QCDu11xzDaIwwiiq7Keffvo//uM/uPuIq5Ag/9hjj73iiit4cmMPqQGxREHqVSX2tm3buACusbGRm5H++c9/AsZcqYmmGnAlCy6cB7O5yJLr4X75y1/qFYGEYbqae9S5suaRRx5BlU1qpeKTzcdyYDpzwKqgp3Pt2rKVLQfA1JaWFpTGSKWAMfIlsiZAeNVVV3Ed0Be+8AWVhgHa1772tbyCzc3NzQimH/rQh1wBFIGYAqJJ5pLEdevWAc+8cgeOBiA8k8e6eIqM9IZKLqJBwl62bJnem4SszAXSoDLX1BAXSfrjH/846mhQ9oYbbgCkuVgJIEeA5hZkaIAqJGOGDmXLWEuY5cDziAMWgJ9HlWVJnT4cYHoV3TKIiwYYjEQARUJF5cuqKKReoA54BpK5TBDFMgZYRQI+8sgjAVdegU8E1t/+9res5HryySdBRFZXkQgMwo5kjCALxpMmiRMecZmIBFi5ciX6baITjBTOOOMMsJlbY6GHoQBhiKhes2fP5pphKASVudIOMfrVr3712WefjQravVVw+tSHLYnlwFRwwKqgp4LrNs8ZzwHAVZdWIaSCsgjBGHARcRNxFpH07rvvRlq96667VDzVFVIIysigQCaxli9fjvb4ggsuQE5FM4ymGrjFHUNSBAOGsYOghFdsxhE8Zs6YAFoD6KKBZwCVYLgg6YLKGLLDBQuOQDJIfOONN7JGDImcS9TRRc/4CrQMsByYBA5YCXgSmGiTsBw4WA6Avoi5xALkkDhBSnARdDzppJNQJrMmC6Pu4CiyKYGRgMFRBUW8WGaFWMzsL7AKRrKYC5FXkwU7EYixg6NkQWAMiRCMlVw/+MEPmMQlDM+///3vWMgLOwE0cXTaQDIkkSDRA4EAvkxLY774xS8C86tWrZo7d+7BFtmGtxywHNiHA1YC3och9tVyoBQcQNsMspITS45ZtwzCsZYKqGMLLyroj3zkI0zToqP+wx/+8LGPfQz4JCThgVWNBZSykWnDhg233HILEa+//vqf/OQnSLoEQ7ZGfY0kjVis23wVVokIHqNAZm4YZfJTTz2F6MwcMHaWVeNLmkjGwDByMPQA+WiheWV51/e///0VK1awGgsLcjDhS8Ejm4flwHTngAXg6V7DtnxlyQHmXFUt/PWvfx2BEi0065ZBu6VLl950002sk0LcBP/wbW9vBxcBY6RSdNTYd+3aRZnYwgQ2szbq0ksv/eEPf8i6ZeAZd8KgxD7vvPPwYs74m9/8JlF0RhmAR3793e9+R3ZHHXXURRdddMopp3zrW98iFmdgIX8D1Ry+QTDwmFhbtmwBhsFjYP6ss846/fTTicuKMEglijWWA5YDh8kBD9/8YSZho1sOWA4cLAcQMUE4nY4F8Iiua6b4HlU1jQuACvixehkVMa8IzYCrZqSfLSERjlXAVV+epIOMizsA7wZAIAaDNS66ZbTTwK3mqwpnvHBRDTa0kSZJsSYLu8rcGldJ0hTUxT4tBywHDpkDFoAPmXU2ouXA4XIAzFMYxgIYkxywB/gBjbhjAGAcdcWWTgATkgDgInO3bvY6DYyXYqqu2FKw1OgEZrEVeIzsy4pogJzUCEBIXHjyCvoC24q4CuoALS6kqU+SIgp4DPwT3s3dWiwHLAcOjQP2Kzo0vtlYlgOTwAGwDTxDZgV9QT5SBBoBS5APFwwLsnDEwhNQBJIVLAkD6IKLnNeBF9O3PElBJWOepAB4g5e4K44yQwzW4sVT4ZPUsBOANEFfLGQBPYq+vOLIK4Ghk+wwpAmKW/SFOdZYDhw+B6wEfPg8tClYDhw0B8A85FpdNqWRgTfFUT17GewE54Be5FowDyxkFTQ6YQKrtrkwS1JzxWWVg3EhLomoDK2vroiMBRe8wFpVQavg66apyK1xkZiZfoYGfMmF4QKvbkhrsRywHDhkDlgJ+JBZZyNaDhw6B0BH0BcgBNJIBZADfYE9XjnyApAD/AjDK2FUPAX2VMBF/MUdwNa4WAhJeCRdkkJaJYoKqThix5EAurpKUyAKq7EAXbCcZHklFsE0MGGIiEHCxhHoxaiAzivhFZ6xW2M5YDlwOBywEvDhcM/GtRywHLAcsBywHDhEDlgJ+BAZZ6NZDlgOWA5YDlgOHA4HLAAfDvdsXMsBywHLAcsBy4FD5IAF4ENknI1mOWA5YDlgOWA5cDgcsAB8ONyzcS0HLAcsBywHLAcOkQMWgA+RcTaa5YDlgOWA5YDlwOFwwALw4XDPxrUcsBywHLAcsBw4RA5YAD5ExtlolgOWA5YDlgOWA4fDgf8fu778Z9b6LLkAAAAASUVORK5CYII=",
       "text/plain": [
-       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
+       "Text(0.5, 1.0, 'Cost convergence')"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "result.convergence_graph"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(combi.cost_trace)\n",
+    "plt.xlabel(\"Iterations\")\n",
+    "plt.ylabel(\"Cost\")\n",
+    "plt.title(\"Cost convergence\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23969de4-f4b9-4e55-a09d-df4cbe4eb3ac",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Optimization Results"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a5a26d5c-ffc0-40bc-9964-e9fb6e16f232",
+   "id": "9159388c-fe90-436d-b0aa-9cf6d6148c5f",
    "metadata": {},
    "source": [
-    "And print the optimization results:"
+    "We can also examine the statistics of the algorithm. The optimization is always defined as a minimzation problem, so the positive maximization objective was tranlated to a negative minimization one by the Pyomo to qmod translator."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93232ede-dfc9-4eba-8270-e6039af36c38",
+   "metadata": {},
+   "source": [
+    "In order to get samples with the optimized parameters, we call the `sample` method:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "9e5789ba-f1a5-4108-a0fa-a1b90870da1f",
+   "execution_count": 10,
+   "id": "f0fb79e2-719a-42f6-a9bd-18a67d490aad",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:18.670930Z",
-     "iopub.status.busy": "2024-05-07T15:06:18.670670Z",
-     "iopub.status.idle": "2024-05-07T15:06:19.843274Z",
-     "shell.execute_reply": "2024-05-07T15:06:19.842574Z"
-    },
-    "tags": []
+    "scrolled": true
    },
    "outputs": [
     {
@@ -451,144 +363,102 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>solution</th>\n",
        "      <th>probability</th>\n",
        "      <th>cost</th>\n",
-       "      <th>solution</th>\n",
-       "      <th>count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>0.003</td>\n",
-       "      <td>-4.8</td>\n",
-       "      <td>[1, 2, 1]</td>\n",
-       "      <td>3</td>\n",
+       "      <th>1022</th>\n",
+       "      <td>{'w': [1, 2, 0], 'budget_rule_slack_var': [1, ...</td>\n",
+       "      <td>0.000488</td>\n",
+       "      <td>-4.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>141</th>\n",
-       "      <td>0.003</td>\n",
-       "      <td>-4.8</td>\n",
-       "      <td>[1, 2, 1]</td>\n",
-       "      <td>3</td>\n",
+       "      <th>847</th>\n",
+       "      <td>{'w': [2, 2, 2], 'budget_rule_slack_var': [0, ...</td>\n",
+       "      <td>0.000488</td>\n",
+       "      <td>-4.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>0.006</td>\n",
-       "      <td>-4.8</td>\n",
-       "      <td>[2, 1, 1]</td>\n",
-       "      <td>6</td>\n",
+       "      <th>367</th>\n",
+       "      <td>{'w': [0, 3, 0], 'budget_rule_slack_var': [1, ...</td>\n",
+       "      <td>0.000977</td>\n",
+       "      <td>-3.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.010</td>\n",
-       "      <td>-4.8</td>\n",
-       "      <td>[2, 1, 1]</td>\n",
-       "      <td>10</td>\n",
+       "      <th>101</th>\n",
+       "      <td>{'w': [1, 3, 1], 'budget_rule_slack_var': [1, ...</td>\n",
+       "      <td>0.001465</td>\n",
+       "      <td>-3.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>210</th>\n",
-       "      <td>0.002</td>\n",
-       "      <td>-4.8</td>\n",
-       "      <td>[1, 2, 1]</td>\n",
-       "      <td>2</td>\n",
+       "      <th>1023</th>\n",
+       "      <td>{'w': [2, 1, 0], 'budget_rule_slack_var': [0, ...</td>\n",
+       "      <td>0.000488</td>\n",
+       "      <td>-3.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     probability  cost   solution  count\n",
-       "98         0.003  -4.8  [1, 2, 1]      3\n",
-       "141        0.003  -4.8  [1, 2, 1]      3\n",
-       "35         0.006  -4.8  [2, 1, 1]      6\n",
-       "5          0.010  -4.8  [2, 1, 1]     10\n",
-       "210        0.002  -4.8  [1, 2, 1]      2"
+       "                                               solution  probability  cost\n",
+       "1022  {'w': [1, 2, 0], 'budget_rule_slack_var': [1, ...     0.000488  -4.5\n",
+       "847   {'w': [2, 2, 2], 'budget_rule_slack_var': [0, ...     0.000488  -4.4\n",
+       "367   {'w': [0, 3, 0], 'budget_rule_slack_var': [1, ...     0.000977  -3.9\n",
+       "101   {'w': [1, 3, 1], 'budget_rule_slack_var': [1, ...     0.001465  -3.7\n",
+       "1023  {'w': [2, 1, 0], 'budget_rule_slack_var': [0, ...     0.000488  -3.5"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
-    "from classiq.applications.combinatorial_optimization import (\n",
-    "    get_optimization_solution_from_pyo,\n",
-    ")\n",
-    "\n",
-    "solution = get_optimization_solution_from_pyo(\n",
-    "    portfolio_model, vqe_result=result, penalty_energy=qaoa_config.penalty_energy\n",
-    ")\n",
-    "optimization_result = pd.DataFrame.from_records(solution)\n",
-    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)"
+    "optimization_result = combi.sample(optimized_params)\n",
+    "optimization_result.sort_values(by=\"cost\").head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac53b57d-8168-498e-ab04-e8dcbea1cfc1",
+   "metadata": {},
+   "source": [
+    "We will also want to compare the optimized results to uniformly sampled results:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "05449034-19e5-4b5d-80ea-7b2ba7ccd877",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:19.845777Z",
-     "iopub.status.busy": "2024-05-07T15:06:19.845472Z",
-     "iopub.status.idle": "2024-05-07T15:06:19.849615Z",
-     "shell.execute_reply": "2024-05-07T15:06:19.848912Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x = [2, 1, 1] , cost = -4.800000000000001\n"
-     ]
-    }
-   ],
+   "execution_count": 11,
+   "id": "843cff7f-5230-4855-a1dc-13cbe386fa40",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "idx = optimization_result.cost.idxmin()\n",
-    "print(\n",
-    "    \"x =\", optimization_result.solution[idx], \", cost =\", optimization_result.cost[idx]\n",
-    ")"
+    "uniform_result = combi.sample_uniform()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b170b0ef-1e3f-4680-a7d4-82b4e537d785",
+   "id": "9c96cee5-1621-41b1-ace7-b3b59104acb4",
    "metadata": {},
    "source": [
-    "And the histogram:"
+    "And compare the histograms:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "f48034c6-8000-4bcf-83b0-e3a76a38d9c7",
+   "execution_count": 12,
+   "id": "b6eaa101-6f7a-4497-a208-0b6a2d33416a",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:19.851995Z",
-     "iopub.status.busy": "2024-05-07T15:06:19.851643Z",
-     "iopub.status.idle": "2024-05-07T15:06:20.087635Z",
-     "shell.execute_reply": "2024-05-07T15:06:20.086888Z"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -598,7 +468,52 @@
     }
    ],
    "source": [
-    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
+    "optimization_result[\"cost\"].plot(\n",
+    "    kind=\"hist\",\n",
+    "    bins=40,\n",
+    "    edgecolor=\"black\",\n",
+    "    weights=optimization_result[\"probability\"],\n",
+    "    alpha=0.6,\n",
+    "    label=\"optimized\",\n",
+    ")\n",
+    "uniform_result[\"cost\"].plot(\n",
+    "    kind=\"hist\",\n",
+    "    bins=40,\n",
+    "    edgecolor=\"black\",\n",
+    "    weights=uniform_result[\"probability\"],\n",
+    "    alpha=0.6,\n",
+    "    label=\"uniform\",\n",
+    ")\n",
+    "plt.legend()\n",
+    "plt.ylabel(\"Probability\", fontsize=16)\n",
+    "plt.xlabel(\"cost\", fontsize=16)\n",
+    "plt.tick_params(axis=\"both\", labelsize=14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "05449034-19e5-4b5d-80ea-7b2ba7ccd877",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = [3, 1, 2] , cost = -4.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_solution = optimization_result.loc[optimization_result.cost.idxmin()]\n",
+    "print(\n",
+    "    \"x =\",\n",
+    "    best_solution.solution[\"w\"],\n",
+    "    \", cost =\",\n",
+    "    best_solution.cost,\n",
+    ")"
    ]
   },
   {
@@ -611,15 +526,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 43,
    "id": "324c9a09",
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-05-07T15:06:20.092527Z",
-     "iopub.status.busy": "2024-05-07T15:06:20.091284Z",
-     "iopub.status.idle": "2024-05-07T15:06:20.178692Z",
-     "shell.execute_reply": "2024-05-07T15:06:20.177959Z"
-    },
     "pycharm": {
      "name": "#%%\n"
     },
@@ -634,20 +543,21 @@
       "\n",
       "  Variables:\n",
       "    w : Size=3, Index=w_index\n",
-      "        Key : Lower : Value              : Upper : Fixed : Stale : Domain\n",
-      "          0 :     0 : 1.9999999999999998 :     2 : False : False : Integers\n",
-      "          1 :     0 : 1.0000000000000002 :     2 : False : False : Integers\n",
-      "          2 :     0 : 1.0000000000000002 :     2 : False : False : Integers\n",
+      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
+      "          0 :     0 :   2.0 :     6 : False : False : Integers\n",
+      "          1 :     0 :   1.0 :     6 : False : False : Integers\n",
+      "          2 :     0 :   1.0 :     6 : False : False : Integers\n",
       "\n",
       "  Objectives:\n",
       "    cost : Size=1, Index=None, Active=True\n",
       "        Key  : Active : Value\n",
-      "        None :   True :  -4.8\n",
+      "        None :   True : -4.800000000000001\n",
       "\n",
       "  Constraints:\n",
       "    budget_rule : Size=1\n",
       "        Key  : Lower : Body : Upper\n",
-      "        None :  None :  4.0 :   6.0\n"
+      "        None :  None :  4.0 :   6.0\n",
+      "Classical solution: [2, 1, 1]\n"
      ]
     }
    ],
@@ -657,7 +567,11 @@
     "solver = SolverFactory(\"couenne\")\n",
     "solver.solve(portfolio_model)\n",
     "\n",
-    "portfolio_model.display()"
+    "portfolio_model.display()\n",
+    "classical_solution = [\n",
+    "    round(pyo.value(portfolio_model.w[i])) for i in range(len(portfolio_model.w))\n",
+    "]\n",
+    "print(\"Classical solution:\", classical_solution)"
    ]
   },
   {
@@ -704,7 +618,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   },
   "vscode": {
    "interpreter": {
diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.qmod b/applications/finance/portfolio_optimization/portfolio_optimization.qmod
index f5008b88e..363c48bc6 100644
--- a/applications/finance/portfolio_optimization/portfolio_optimization.qmod
+++ b/applications/finance/portfolio_optimization/portfolio_optimization.qmod
@@ -1,667 +1,17 @@
-hamiltonian: PauliTerm[] = [
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=7.25
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z
-    ],
-    coefficient=0.8
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I
-    ],
-    coefficient=0.8
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=0.8
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=0.8
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=-0.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=-0.5
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=-0.5
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=-0.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=-0.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z
-    ],
-    coefficient=0.65
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z
-    ],
-    coefficient=0.65
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I
-    ],
-    coefficient=0.65
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I
-    ],
-    coefficient=0.65
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=0.9
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=0.9
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=0.9
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=0.9
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z
-    ],
-    coefficient=1.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I
-    ],
-    coefficient=1.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=1.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=1.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=1.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=1.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::Z,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=2.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z
-    ],
-    coefficient=2.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I
-    ],
-    coefficient=2.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=2.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=2.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=2.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=2.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::Z
-    ],
-    coefficient=1.25
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z
-    ],
-    coefficient=1.25
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::Z,
-      Pauli::I
-    ],
-    coefficient=1.25
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I
-    ],
-    coefficient=1.25
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=3.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z
-    ],
-    coefficient=3.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I
-    ],
-    coefficient=3.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=3.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=3.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=3.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=3.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::Z,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=6.0
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::Z
-    ],
-    coefficient=1.45
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=1.45
-  },
-  PauliTerm {
-    pauli=[
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::Z,
-      Pauli::Z,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I,
-      Pauli::I
-    ],
-    coefficient=1.45
-  }
-];
-
-qfunc main(params_list: real[2], output target: qbit[9]) {
-  allocate(target.len, target);
-  qaoa_penalty(target.len, params_list, hamiltonian, target);
+qstruct QAOAVars {
+  w: qnum<3, False, 0>[3];
+  budget_rule_slack_var: qbit[3];
 }
 
-cscope ```
-vqe_result = vqe(
-hamiltonian=hamiltonian,
-maximize=False,
-initial_point=[0.0, 0.2239532619279455],
-optimizer=Optimizer.COBYLA,
-max_iteration=60,
-tolerance=0.0,
-step_size=0.0,
-skip_compute_variance=False,
-alpha_cvar=0.7
-)
 
-save({"vqe_result": vqe_result, "hamiltonian": hamiltonian})
-```
+
+qfunc main(params: real[6], output v: QAOAVars) {
+  allocate(v.size, v);
+  hadamard_transform(v);
+  repeat (i: 3) {
+    phase (-(((((((v.w[0] * (((0.9 * v.w[0]) + (0.5 * v.w[1])) - (0.7 * v.w[2]))) - (3 * v.w[0])) + (v.w[1] * (((0.5 * v.w[0]) + (0.9 * v.w[1])) - (0.2 * v.w[2])))) - (4 * v.w[1])) + (v.w[2] * ((((-0.7) * v.w[0]) - (0.2 * v.w[1])) + (0.9 * v.w[2])))) + v.w[2]) + (360.0 * ((((((((0.1667 * v.budget_rule_slack_var[0]) + (0.3333 * v.budget_rule_slack_var[1])) + (0.5 * v.budget_rule_slack_var[2])) + (0.1667 * v.w[0])) + (0.1667 * v.w[1])) + (0.1667 * v.w[2])) - 1) ** 2))), params[i]);
+    apply_to_all(lambda(q) {
+      RX(params[3 + i], q);
+    }, v);
+  }
+}
diff --git a/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json b/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json
index 0967ef424..cf711f93e 100644
--- a/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json
+++ b/applications/finance/portfolio_optimization/portfolio_optimization.synthesis_options.json
@@ -1 +1,43 @@
-{}
+{
+  "constraints": {
+    "max_gate_count": {},
+    "optimization_parameter": "no_opt"
+  },
+  "preferences": {
+    "machine_precision": 8,
+    "custom_hardware_settings": {
+      "basis_gates": [
+        "tdg",
+        "z",
+        "u2",
+        "id",
+        "s",
+        "rz",
+        "sx",
+        "sdg",
+        "cy",
+        "x",
+        "cz",
+        "u",
+        "y",
+        "t",
+        "u1",
+        "rx",
+        "h",
+        "p",
+        "cx",
+        "r",
+        "ry",
+        "sxdg"
+      ],
+      "is_symmetric_connectivity": true
+    },
+    "debug_mode": true,
+    "synthesize_all_separately": false,
+    "output_format": ["qasm"],
+    "pretty_qasm": true,
+    "transpilation_option": "auto optimize",
+    "timeout_seconds": 300,
+    "random_seed": 3453328217
+  }
+}
diff --git a/tests/resources/timeouts.yaml b/tests/resources/timeouts.yaml
index 93113b94e..49a59efc6 100644
--- a/tests/resources/timeouts.yaml
+++ b/tests/resources/timeouts.yaml
@@ -1,3 +1,4 @@
+
 3_sat_grover.ipynb: 36
 3_sat_grover.qmod: 48
 3_sat_grover_large.qmod: 10
@@ -324,4 +325,4 @@ whitebox_fuzzing.ipynb: 720
 whitebox_fuzzing.qmod: 720
 X.qmod: 10
 Yasir_Mansour_HW3_VQE.ipynb: 30
-Yasir_Mansour_HW4_molecule_eigensolver.ipynb: 600
+Yasir_Mansour_HW4_molecule_eigensolver.ipynb: 600
\ No newline at end of file