From ce35245b98857721408f89e808e642c9d1be2a92 Mon Sep 17 00:00:00 2001
From: mdmaas <martin.d.maas@gmail.com>
Date: Fri, 29 May 2026 17:34:28 -0300
Subject: [PATCH 1/4] fix(palace): remove implicit stack capping and align PDK
 stack defaults

---
 nbs/_palace_electrostatic.ipynb    | 318 +++++++++++++++++++++++++----
 nbs/_palace_electrostatic.py       | 156 +++++++++++---
 nbs/palace_inductor.ipynb          | 243 +++-------------------
 nbs/palace_inductor.py             |  12 +-
 src/gsim/common/stack/__init__.py  |   4 +
 src/gsim/common/stack/extractor.py | 176 +++++++++++++---
 src/gsim/palace/base.py            |   5 +-
 src/gsim/palace/mesh/geometry.py   |  47 +++--
 src/gsim/palace/models/mesh.py     |   2 +-
 tests/palace/test_mesh.py          |  51 ++++-
 tests/palace/test_sim_classes.py   |  90 ++++++++
 tests/palace/test_stack.py         | 110 ++++++++++
 12 files changed, 896 insertions(+), 318 deletions(-)

diff --git a/nbs/_palace_electrostatic.ipynb b/nbs/_palace_electrostatic.ipynb
index 8f824570..c656d983 100644
--- a/nbs/_palace_electrostatic.ipynb
+++ b/nbs/_palace_electrostatic.ipynb
@@ -28,10 +28,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "0b8b2971",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ports: [('MINUS', (-5.86, 0.0)), ('PLUS', (4.5, 0.0))]\n"
+     ]
+    },
+    {
+     "ename": "PermissionError",
+     "evalue": "[Errno 13] Permission denied: '/tmp/gdsfactory/layer_properties.lyp'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mPermissionError\u001b[39m                           Traceback (most recent call last)",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/gsim/.venv/lib/python3.12/site-packages/kfactory/kcell.py:890\u001b[39m, in \u001b[36mProtoTKCell._ipython_display_\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    885\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_ipython_display_\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    886\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Display a cell in a Jupyter Cell.\u001b[39;00m\n\u001b[32m    887\u001b[39m \n\u001b[32m    888\u001b[39m \u001b[33;03m    Usage: Pass the kcell variable as an argument in the cell at the end\u001b[39;00m\n\u001b[32m    889\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m890\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/gsim/.venv/lib/python3.12/site-packages/gdsfactory/component.py:1199\u001b[39m, in \u001b[36mComponent.plot\u001b[39m\u001b[34m(self, lyrdb, display_type, show_labels, show_ruler, pixel_buffer_options, return_fig)\u001b[39m\n\u001b[32m   1197\u001b[39m     lyp_path = pathlib.Path(layer_views)\n\u001b[32m   1198\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1199\u001b[39m     \u001b[43mlayer_views\u001b[49m\u001b[43m.\u001b[49m\u001b[43mto_lyp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlyp_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1201\u001b[39m layout_view = lay.LayoutView()\n\u001b[32m   1202\u001b[39m cell_view_index = layout_view.create_layout(\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/gsim/.venv/lib/python3.12/site-packages/gdsfactory/technology/layer_views.py:1065\u001b[39m, in \u001b[36mLayerViews.to_lyp\u001b[39m\u001b[34m(self, filepath, overwrite)\u001b[39m\n\u001b[32m   1062\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m ls \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.custom_line_styles.values():\n\u001b[32m   1063\u001b[39m     root.append(ls.to_klayout_xml())\n\u001b[32m-> \u001b[39m\u001b[32m1065\u001b[39m \u001b[43mfilepath\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwrite_bytes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmake_pretty_xml\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1066\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m filepath\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/pathlib.py:1038\u001b[39m, in \u001b[36mPath.write_bytes\u001b[39m\u001b[34m(self, data)\u001b[39m\n\u001b[32m   1036\u001b[39m \u001b[38;5;66;03m# type-check for the buffer interface before truncating the file\u001b[39;00m\n\u001b[32m   1037\u001b[39m view = \u001b[38;5;28mmemoryview\u001b[39m(data)\n\u001b[32m-> \u001b[39m\u001b[32m1038\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mwb\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m   1039\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m f.write(view)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/pathlib.py:1015\u001b[39m, in \u001b[36mPath.open\u001b[39m\u001b[34m(self, mode, buffering, encoding, errors, newline)\u001b[39m\n\u001b[32m   1013\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m   1014\u001b[39m     encoding = io.text_encoding(encoding)\n\u001b[32m-> \u001b[39m\u001b[32m1015\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffering\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[31mPermissionError\u001b[39m: [Errno 13] Permission denied: '/tmp/gdsfactory/layer_properties.lyp'"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Component(name=cmim_W10_L10_LMMetal5drawing_LMMIMdrawing_LVVmimdrawing_98194961$2$1, ports=['MINUS', 'PLUS'], pins=[], instances=['rectangle_gdsfactorypcomponentspshapesprectangle_S11p92_59dd5848_0_0', 'rectangle_gdsfactorypcomponentspshapesprectangle_S10p72_f76ae31b_0_0', 'rectangle_gdsfactorypcomponentspshapesprectangle_S10_10_ef94ac30_0_0', 'via_array_VTVmim_C10_R10_VS0p42_VS0p94_VE0p42_LCContdra_14498da7_-4440_-4440', 'rectangle_gdsfactorypcomponentspshapesprectangle_S11p92_316b73f6_0_0', 'rectangle_gdsfactorypcomponentspshapesprectangle_S11p92_2d7918b4_0_0', 'rectangle_gdsfactorypcomponentspshapesprectangle_S11p92_ff69e054_0_0', 'rectangle_gdsfactorypcomponentspshapesprectangle_S11p92_81cf13d4_0_0', 'rectangle_gdsfactorypcomponentspshapesprectangle_S11p92_3e735432_0_0', 'rectangle_gdsfactorypcomponentspshapesprectangle_S1_2_L_313130ad_-5460_0', 'rectangle_gdsfactorypcomponentspshapesprectangle_S1_2_L_21edbbcb_4500_0'], locked=False, kcl=DEFAULT)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from ihp import PDK, cells\n",
     "\n",
@@ -44,7 +77,10 @@
     "#   -> 10x10 Vmim vias (0.42 um, pitch 0.94 um) -> TopMetal1 (top plate, PLUS)\n",
     "c = cells.cmim(width=cap_width, length=cap_length).copy()\n",
     "print(\"Ports:\", [(p.name, tuple(p.center)) for p in c.ports])\n",
-    "c.plot()"
+    "\n",
+    "cc = c.copy()\n",
+    "cc.draw_ports()\n",
+    "cc"
    ]
   },
   {
@@ -57,10 +93,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "115539f6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation: PASSED\n"
+     ]
+    }
+   ],
    "source": [
     "from gsim.palace import ElectrostaticSim\n",
     "\n",
@@ -68,7 +112,8 @@
     "\n",
     "sim.set_output_dir(\"./palace-sim-electrostatic\")\n",
     "sim.set_geometry(c)\n",
-    "sim.set_stack(air_above=100.0, substrate_thickness=2.0)\n",
+    "sim.set_stack(substrate_thickness=2.0)\n",
+    "sim.set_airbox(margin_x=5, margin_y=5, z_above=5, z_below=5)\n",
     "\n",
     "# Metal5 = bottom plate (MINUS), TopMetal1 = top plate (PLUS)\n",
     "sim.add_terminal(\"T1\", layer=\"metal5\")\n",
@@ -89,25 +134,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "4fae1dec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Mesh Summary\n",
+       "========================================\n",
+       "Dimensions: 31.9 x 31.9 x 20.2 µm\n",
+       "Nodes:      26,689\n",
+       "Elements:   203,560\n",
+       "Tetrahedra: 150,804\n",
+       "Edge length: 0.06 - 15.96 µm\n",
+       "Quality:    0.610 (min: 0.002)\n",
+       "SICN:       0.669 (all valid)\n",
+       "----------------------------------------\n",
+       "Volumes (3):\n",
+       "  - vmim [1]\n",
+       "  - sio2 [2]\n",
+       "  - air [3]\n",
+       "Surfaces (7):\n",
+       "  - metal5_xy [4]\n",
+       "  - metal5_z [5]\n",
+       "  - topmetal1_xy [6]\n",
+       "  - topmetal1_z [7]\n",
+       "  - sio2__vmim [8]\n",
+       "  - air__sio2 [9]\n",
+       "  - air__None [10]\n",
+       "----------------------------------------\n",
+       "Mesh:   palace-sim-electrostatic/palace.msh"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Vmim vias are 0.42 um with 0.52 um gaps; MIM dielectric is 0.19 um thick\n",
-    "sim.mesh(preset=\"fine\", margin=20, refined_mesh_size=0.1, merge_via_distance=0)"
+    "sim.mesh(preset=\"fine\", refined_mesh_size=0.1, merge_via_distance=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "8b3073de",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13cd220370864fe1818fbc373211b82e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:41833/index.html?ui=P_0x77330dfa7d10_4&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# sim.plot_mesh(show_groups=[\"metal\", \"topmetal\", \"via\", \"dielectric\", \"SiO2__vmim\"])\n",
     "\n",
-    "sim.plot_mesh(show_groups=[\"metal5\", \"topmetal1\", \"vmim\", \"SiO2__vmim\"])"
+    "# sim.plot_mesh(show_groups=[\"metal5\", \"topmetal1\", \"vmim\", \"SiO2__vmim\"])\n",
+    "\n",
+    "sim.plot_mesh(\n",
+    "    style=\"solid\",\n",
+    "    transparent_groups=[\"air__None\", \"sio2__None\", \"air__sio2\", \"air__passive\"],\n",
+    "    interactive=True,\n",
+    ")"
    ]
   },
   {
@@ -119,26 +226,86 @@
     "\n",
     "For the MIM capacitor: C = epsilon_0 * epsilon_r * A / d\n",
     "\n",
-    "The MIM dielectric (SiO2, eps_r ~ 4.1) is 0.19 um thick between Metal5 top (z=5.38) and the Vmim bottom (z=5.58). The MIM drawing is 10.72 x 10.72 um. The Vmim vias (tungsten, conductive) extend the TopMetal1 terminal down to the MIM dielectric surface."
+    "In this estimate, epsilon_r is read from the active PDK-derived stack material table for SiO2, the MIM drawing dimensions are read from the geometry `MIMdrawing` polygon bounding box, and we report two spacings:\n",
+    "- d_topmetal1 = topmetal1_bottom - metal5_top\n",
+    "- d_vmim = vmim_bottom - metal5_top"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "b2ec945b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "eps_r(SiO2) = 4.1\n",
+      "MIM drawing from geometry: 10.720 x 10.720 um\n",
+      "Analytical (d=topmetal1_bottom-metal5_top=0.850 um): 4.9 fF\n",
+      "Analytical (d=vmim_bottom-metal5_top=0.200 um):      20.9 fF\n"
+     ]
+    }
+   ],
    "source": [
     "import scipy.constants as const\n",
     "\n",
-    "# MIM dielectric area (MIM drawing: 10.72 x 10.72 um)\n",
-    "eps_r = 4.1  # SiO2 permittivity from IHP stack\n",
-    "A_mim = (10.72e-6) * (10.72e-6)  # m^2\n",
-    "d_mim = (5.58 - 5.38) * 1e-6  # m (Metal5 top to Vmim bottom = MIM dielectric)\n",
+    "# Pull SiO2 epsilon_r and plate spacings from the active stack derived from the current PDK.\n",
+    "stack = sim._resolve_stack()\n",
+    "sio2_props = stack.materials.get(\"sio2\") or stack.materials.get(\"SiO2\")\n",
+    "if sio2_props is None or sio2_props.get(\"permittivity\") is None:\n",
+    "    raise ValueError(\"Could not find SiO2 permittivity in active stack materials\")\n",
+    "eps_r = float(sio2_props[\"permittivity\"])\n",
+    "\n",
+    "metal5 = stack.layers.get(\"metal5\")\n",
+    "topmetal1 = stack.layers.get(\"topmetal1\")\n",
+    "vmim = stack.layers.get(\"vmim\")\n",
+    "if metal5 is None or topmetal1 is None or vmim is None:\n",
+    "    raise ValueError(\"Could not find metal5/topmetal1/vmim in active stack layers\")\n",
+    "\n",
+    "metal5_top = metal5.zmin + metal5.thickness\n",
+    "topmetal1_bottom = topmetal1.zmin\n",
+    "vmim_bottom = vmim.zmin\n",
+    "\n",
+    "d_topmetal1_um = topmetal1_bottom - metal5_top\n",
+    "d_vmim_um = vmim_bottom - metal5_top\n",
+    "if d_topmetal1_um <= 0:\n",
+    "    raise ValueError(f\"Non-physical topmetal1 spacing: {d_topmetal1_um} um\")\n",
+    "if d_vmim_um <= 0:\n",
+    "    raise ValueError(f\"Non-physical vmim spacing: {d_vmim_um} um\")\n",
+    "\n",
+    "d_topmetal1 = d_topmetal1_um * 1e-6  # m\n",
+    "d_vmim = d_vmim_um * 1e-6  # m\n",
+    "\n",
+    "# Read MIM drawing dimensions from geometry.\n",
+    "geom_component = sim.geometry.component\n",
+    "mim_polys = geom_component.get_polygons(by=\"name\").get(\"MIMdrawing\", [])\n",
+    "if not mim_polys:\n",
+    "    raise ValueError(\"Could not find MIMdrawing polygons in geometry\")\n",
+    "\n",
+    "dbu = geom_component.kcl.dbu\n",
+    "left = min(poly.bbox().left for poly in mim_polys)\n",
+    "right = max(poly.bbox().right for poly in mim_polys)\n",
+    "bottom = min(poly.bbox().bottom for poly in mim_polys)\n",
+    "top = max(poly.bbox().top for poly in mim_polys)\n",
+    "\n",
+    "mim_width_um = (right - left) * dbu\n",
+    "mim_length_um = (top - bottom) * dbu\n",
+    "A_mim = (mim_width_um * 1e-6) * (mim_length_um * 1e-6)  # m^2\n",
     "\n",
-    "C_analytical = const.epsilon_0 * eps_r * A_mim / d_mim\n",
-    "print(f\"Analytical capacitance: {C_analytical * 1e15:.1f} fF\")\n",
-    "print(f\"  A_mim = 10.72 x 10.72 um^2, d = {d_mim * 1e6:.2f} um, eps_r = {eps_r}\")"
+    "C_analytical_topmetal1 = const.epsilon_0 * eps_r * A_mim / d_topmetal1\n",
+    "C_analytical_vmim = const.epsilon_0 * eps_r * A_mim / d_vmim\n",
+    "C_analytical = C_analytical_topmetal1\n",
+    "\n",
+    "print(f\"eps_r(SiO2) = {eps_r}\")\n",
+    "print(f\"MIM drawing from geometry: {mim_width_um:.3f} x {mim_length_um:.3f} um\")\n",
+    "print(\n",
+    "    f\"Analytical (d=topmetal1_bottom-metal5_top={d_topmetal1_um:.3f} um): {C_analytical_topmetal1 * 1e15:.1f} fF\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Analytical (d=vmim_bottom-metal5_top={d_vmim_um:.3f} um):      {C_analytical_vmim * 1e15:.1f} fF\"\n",
+    ")"
    ]
   },
   {
@@ -171,10 +338,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "0130be42",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Capacitance matrix (F):\n",
+      "  C[1,1] = +1.4403e-14 F  (+14.403 fF)\n",
+      "  C[1,2] = -1.4403e-14 F  (-14.403 fF)\n",
+      "  C[2,1] = -1.4403e-14 F  (-14.403 fF)\n",
+      "  C[2,2] = +1.4403e-14 F  (+14.403 fF)\n",
+      "\n",
+      "Mutual capacitance C_m[1,2] = 14.403 fF\n",
+      "\n",
+      "Stored electric energy: 2.7130e-03 J (excitation 1), 2.7130e-03 J (excitation 2)\n"
+     ]
+    }
+   ],
    "source": [
     "import csv\n",
     "from pathlib import Path\n",
@@ -222,24 +405,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "cc69ea26",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Palace result:                                14.403 fF\n",
+      "Analytical (d=topmetal1_bottom-metal5_top):   4.9 fF\n",
+      "Analytical (d=vmim_bottom-metal5_top):        20.9 fF\n",
+      "Ratio Palace / topmetal1-based analytical:    2.935\n",
+      "Ratio Palace / vmim-based analytical:         0.690\n",
+      "Using A_mim = 10.720 x 10.720 um^2 from MIMdrawing\n",
+      "d_topmetal1 = 0.850 um, d_vmim = 0.200 um\n"
+     ]
+    }
+   ],
    "source": [
     "import scipy.constants as const\n",
     "\n",
     "C_palace = abs(C_matrix[0, 1])\n",
     "\n",
-    "eps_r = 4.1\n",
-    "A_mim = (10.72e-6) * (10.72e-6)\n",
-    "d_mim = (5.58 - 5.38) * 1e-6\n",
+    "stack = sim._resolve_stack()\n",
+    "sio2_props = stack.materials.get(\"sio2\") or stack.materials.get(\"SiO2\")\n",
+    "if sio2_props is None or sio2_props.get(\"permittivity\") is None:\n",
+    "    raise ValueError(\"Could not find SiO2 permittivity in active stack materials\")\n",
+    "eps_r = float(sio2_props[\"permittivity\"])\n",
+    "\n",
+    "metal5 = stack.layers.get(\"metal5\")\n",
+    "topmetal1 = stack.layers.get(\"topmetal1\")\n",
+    "vmim = stack.layers.get(\"vmim\")\n",
+    "if metal5 is None or topmetal1 is None or vmim is None:\n",
+    "    raise ValueError(\"Could not find metal5/topmetal1/vmim in active stack layers\")\n",
+    "\n",
+    "metal5_top = metal5.zmin + metal5.thickness\n",
+    "topmetal1_bottom = topmetal1.zmin\n",
+    "vmim_bottom = vmim.zmin\n",
     "\n",
-    "C_analytical = const.epsilon_0 * eps_r * A_mim / d_mim\n",
+    "d_topmetal1_um = topmetal1_bottom - metal5_top\n",
+    "d_vmim_um = vmim_bottom - metal5_top\n",
+    "if d_topmetal1_um <= 0:\n",
+    "    raise ValueError(f\"Non-physical topmetal1 spacing: {d_topmetal1_um} um\")\n",
+    "if d_vmim_um <= 0:\n",
+    "    raise ValueError(f\"Non-physical vmim spacing: {d_vmim_um} um\")\n",
     "\n",
-    "print(f\"Palace result:       {C_palace * 1e15:.3f} fF\")\n",
-    "print(f\"Analytical (C=eA/d): {C_analytical * 1e15:.1f} fF\")\n",
-    "print(f\"Ratio:               {C_palace / C_analytical:.3f}\")"
+    "d_topmetal1 = d_topmetal1_um * 1e-6\n",
+    "d_vmim = d_vmim_um * 1e-6\n",
+    "\n",
+    "geom_component = sim.geometry.component\n",
+    "mim_polys = geom_component.get_polygons(by=\"name\").get(\"MIMdrawing\", [])\n",
+    "if not mim_polys:\n",
+    "    raise ValueError(\"Could not find MIMdrawing polygons in geometry\")\n",
+    "\n",
+    "dbu = geom_component.kcl.dbu\n",
+    "left = min(poly.bbox().left for poly in mim_polys)\n",
+    "right = max(poly.bbox().right for poly in mim_polys)\n",
+    "bottom = min(poly.bbox().bottom for poly in mim_polys)\n",
+    "top = max(poly.bbox().top for poly in mim_polys)\n",
+    "\n",
+    "mim_width_um = (right - left) * dbu\n",
+    "mim_length_um = (top - bottom) * dbu\n",
+    "A_mim = (mim_width_um * 1e-6) * (mim_length_um * 1e-6)\n",
+    "\n",
+    "C_analytical_topmetal1 = const.epsilon_0 * eps_r * A_mim / d_topmetal1\n",
+    "C_analytical_vmim = const.epsilon_0 * eps_r * A_mim / d_vmim\n",
+    "C_analytical = C_analytical_topmetal1\n",
+    "\n",
+    "print(f\"Palace result:                                {C_palace * 1e15:.3f} fF\")\n",
+    "print(\n",
+    "    f\"Analytical (d=topmetal1_bottom-metal5_top):   {C_analytical_topmetal1 * 1e15:.1f} fF\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Analytical (d=vmim_bottom-metal5_top):        {C_analytical_vmim * 1e15:.1f} fF\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Ratio Palace / topmetal1-based analytical:    {C_palace / C_analytical_topmetal1:.3f}\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Ratio Palace / vmim-based analytical:         {C_palace / C_analytical_vmim:.3f}\"\n",
+    ")\n",
+    "print(f\"Using A_mim = {mim_width_um:.3f} x {mim_length_um:.3f} um^2 from MIMdrawing\")\n",
+    "print(f\"d_topmetal1 = {d_topmetal1_um:.3f} um, d_vmim = {d_vmim_um:.3f} um\")"
    ]
   },
   {
@@ -253,7 +501,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "gsim",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -267,7 +515,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/nbs/_palace_electrostatic.py b/nbs/_palace_electrostatic.py
index 1a97b2d4..653defda 100644
--- a/nbs/_palace_electrostatic.py
+++ b/nbs/_palace_electrostatic.py
@@ -7,7 +7,7 @@
 #       format_version: '1.3'
 #       jupytext_version: 1.19.2
 #   kernelspec:
-#     display_name: gsim
+#     display_name: .venv
 #     language: python
 #     name: python3
 # ---
@@ -40,7 +40,10 @@
 #   -> 10x10 Vmim vias (0.42 um, pitch 0.94 um) -> TopMetal1 (top plate, PLUS)
 c = cells.cmim(width=cap_width, length=cap_length).copy()
 print("Ports:", [(p.name, tuple(p.center)) for p in c.ports])
-c.plot()
+
+cc = c.copy()
+cc.draw_ports()
+cc
 
 # %% [markdown]
 # ### Configure ElectrostaticSim
@@ -52,7 +55,8 @@
 
 sim.set_output_dir("./palace-sim-electrostatic")
 sim.set_geometry(c)
-sim.set_stack(air_above=100.0, substrate_thickness=2.0)
+sim.set_stack(substrate_thickness=2.0)
+sim.set_airbox(margin_x=5, margin_y=5, z_above=5, z_below=5)
 
 # Metal5 = bottom plate (MINUS), TopMetal1 = top plate (PLUS)
 sim.add_terminal("T1", layer="metal5")
@@ -67,31 +71,86 @@
 
 # %%
 # Vmim vias are 0.42 um with 0.52 um gaps; MIM dielectric is 0.19 um thick
-sim.mesh(preset="fine", margin=20, refined_mesh_size=0.1, merge_via_distance=0)
+sim.mesh(preset="fine", refined_mesh_size=0.1, merge_via_distance=0)
 
 # %%
 # sim.plot_mesh(show_groups=["metal", "topmetal", "via", "dielectric", "SiO2__vmim"])
 
-sim.plot_mesh(show_groups=["metal5", "topmetal1", "vmim", "SiO2__vmim"])
+# sim.plot_mesh(show_groups=["metal5", "topmetal1", "vmim", "SiO2__vmim"])
+
+sim.plot_mesh(
+    style="solid",
+    transparent_groups=["air__None", "sio2__None", "air__sio2", "air__passive"],
+    interactive=True,
+)
 
 # %% [markdown]
 # ### Analytical estimate
 #
 # For the MIM capacitor: C = epsilon_0 * epsilon_r * A / d
 #
-# The MIM dielectric (SiO2, eps_r ~ 4.1) is 0.19 um thick between Metal5 top (z=5.38) and the Vmim bottom (z=5.58). The MIM drawing is 10.72 x 10.72 um. The Vmim vias (tungsten, conductive) extend the TopMetal1 terminal down to the MIM dielectric surface.
+# In this estimate, epsilon_r is read from the active PDK-derived stack material table for SiO2, the MIM drawing dimensions are read from the geometry `MIMdrawing` polygon bounding box, and we report two spacings:
+# - d_topmetal1 = topmetal1_bottom - metal5_top
+# - d_vmim = vmim_bottom - metal5_top
 
 # %%
 import scipy.constants as const
 
-# MIM dielectric area (MIM drawing: 10.72 x 10.72 um)
-eps_r = 4.1  # SiO2 permittivity from IHP stack
-A_mim = (10.72e-6) * (10.72e-6)  # m^2
-d_mim = (5.58 - 5.38) * 1e-6  # m (Metal5 top to Vmim bottom = MIM dielectric)
-
-C_analytical = const.epsilon_0 * eps_r * A_mim / d_mim
-print(f"Analytical capacitance: {C_analytical * 1e15:.1f} fF")
-print(f"  A_mim = 10.72 x 10.72 um^2, d = {d_mim * 1e6:.2f} um, eps_r = {eps_r}")
+# Pull SiO2 epsilon_r and plate spacings from the active stack derived from the current PDK.
+stack = sim._resolve_stack()
+sio2_props = stack.materials.get("sio2") or stack.materials.get("SiO2")
+if sio2_props is None or sio2_props.get("permittivity") is None:
+    raise ValueError("Could not find SiO2 permittivity in active stack materials")
+eps_r = float(sio2_props["permittivity"])
+
+metal5 = stack.layers.get("metal5")
+topmetal1 = stack.layers.get("topmetal1")
+vmim = stack.layers.get("vmim")
+if metal5 is None or topmetal1 is None or vmim is None:
+    raise ValueError("Could not find metal5/topmetal1/vmim in active stack layers")
+
+metal5_top = metal5.zmin + metal5.thickness
+topmetal1_bottom = topmetal1.zmin
+vmim_bottom = vmim.zmin
+
+d_topmetal1_um = topmetal1_bottom - metal5_top
+d_vmim_um = vmim_bottom - metal5_top
+if d_topmetal1_um <= 0:
+    raise ValueError(f"Non-physical topmetal1 spacing: {d_topmetal1_um} um")
+if d_vmim_um <= 0:
+    raise ValueError(f"Non-physical vmim spacing: {d_vmim_um} um")
+
+d_topmetal1 = d_topmetal1_um * 1e-6  # m
+d_vmim = d_vmim_um * 1e-6  # m
+
+# Read MIM drawing dimensions from geometry.
+geom_component = sim.geometry.component
+mim_polys = geom_component.get_polygons(by="name").get("MIMdrawing", [])
+if not mim_polys:
+    raise ValueError("Could not find MIMdrawing polygons in geometry")
+
+dbu = geom_component.kcl.dbu
+left = min(poly.bbox().left for poly in mim_polys)
+right = max(poly.bbox().right for poly in mim_polys)
+bottom = min(poly.bbox().bottom for poly in mim_polys)
+top = max(poly.bbox().top for poly in mim_polys)
+
+mim_width_um = (right - left) * dbu
+mim_length_um = (top - bottom) * dbu
+A_mim = (mim_width_um * 1e-6) * (mim_length_um * 1e-6)  # m^2
+
+C_analytical_topmetal1 = const.epsilon_0 * eps_r * A_mim / d_topmetal1
+C_analytical_vmim = const.epsilon_0 * eps_r * A_mim / d_vmim
+C_analytical = C_analytical_topmetal1
+
+print(f"eps_r(SiO2) = {eps_r}")
+print(f"MIM drawing from geometry: {mim_width_um:.3f} x {mim_length_um:.3f} um")
+print(
+    f"Analytical (d=topmetal1_bottom-metal5_top={d_topmetal1_um:.3f} um): {C_analytical_topmetal1 * 1e15:.1f} fF"
+)
+print(
+    f"Analytical (d=vmim_bottom-metal5_top={d_vmim_um:.3f} um):      {C_analytical_vmim * 1e15:.1f} fF"
+)
 
 # %% [markdown]
 # ### Run on cloud
@@ -148,14 +207,65 @@ def read_palace_csv(path):
 
 C_palace = abs(C_matrix[0, 1])
 
-eps_r = 4.1
-A_mim = (10.72e-6) * (10.72e-6)
-d_mim = (5.58 - 5.38) * 1e-6
-
-C_analytical = const.epsilon_0 * eps_r * A_mim / d_mim
-
-print(f"Palace result:       {C_palace * 1e15:.3f} fF")
-print(f"Analytical (C=eA/d): {C_analytical * 1e15:.1f} fF")
-print(f"Ratio:               {C_palace / C_analytical:.3f}")
+stack = sim._resolve_stack()
+sio2_props = stack.materials.get("sio2") or stack.materials.get("SiO2")
+if sio2_props is None or sio2_props.get("permittivity") is None:
+    raise ValueError("Could not find SiO2 permittivity in active stack materials")
+eps_r = float(sio2_props["permittivity"])
+
+metal5 = stack.layers.get("metal5")
+topmetal1 = stack.layers.get("topmetal1")
+vmim = stack.layers.get("vmim")
+if metal5 is None or topmetal1 is None or vmim is None:
+    raise ValueError("Could not find metal5/topmetal1/vmim in active stack layers")
+
+metal5_top = metal5.zmin + metal5.thickness
+topmetal1_bottom = topmetal1.zmin
+vmim_bottom = vmim.zmin
+
+d_topmetal1_um = topmetal1_bottom - metal5_top
+d_vmim_um = vmim_bottom - metal5_top
+if d_topmetal1_um <= 0:
+    raise ValueError(f"Non-physical topmetal1 spacing: {d_topmetal1_um} um")
+if d_vmim_um <= 0:
+    raise ValueError(f"Non-physical vmim spacing: {d_vmim_um} um")
+
+d_topmetal1 = d_topmetal1_um * 1e-6
+d_vmim = d_vmim_um * 1e-6
+
+geom_component = sim.geometry.component
+mim_polys = geom_component.get_polygons(by="name").get("MIMdrawing", [])
+if not mim_polys:
+    raise ValueError("Could not find MIMdrawing polygons in geometry")
+
+dbu = geom_component.kcl.dbu
+left = min(poly.bbox().left for poly in mim_polys)
+right = max(poly.bbox().right for poly in mim_polys)
+bottom = min(poly.bbox().bottom for poly in mim_polys)
+top = max(poly.bbox().top for poly in mim_polys)
+
+mim_width_um = (right - left) * dbu
+mim_length_um = (top - bottom) * dbu
+A_mim = (mim_width_um * 1e-6) * (mim_length_um * 1e-6)
+
+C_analytical_topmetal1 = const.epsilon_0 * eps_r * A_mim / d_topmetal1
+C_analytical_vmim = const.epsilon_0 * eps_r * A_mim / d_vmim
+C_analytical = C_analytical_topmetal1
+
+print(f"Palace result:                                {C_palace * 1e15:.3f} fF")
+print(
+    f"Analytical (d=topmetal1_bottom-metal5_top):   {C_analytical_topmetal1 * 1e15:.1f} fF"
+)
+print(
+    f"Analytical (d=vmim_bottom-metal5_top):        {C_analytical_vmim * 1e15:.1f} fF"
+)
+print(
+    f"Ratio Palace / topmetal1-based analytical:    {C_palace / C_analytical_topmetal1:.3f}"
+)
+print(
+    f"Ratio Palace / vmim-based analytical:         {C_palace / C_analytical_vmim:.3f}"
+)
+print(f"Using A_mim = {mim_width_um:.3f} x {mim_length_um:.3f} um^2 from MIMdrawing")
+print(f"d_topmetal1 = {d_topmetal1_um:.3f} um, d_vmim = {d_vmim_um:.3f} um")
 
 # %%
diff --git a/nbs/palace_inductor.ipynb b/nbs/palace_inductor.ipynb
index 595d68e2..3a8f2cc7 100644
--- a/nbs/palace_inductor.ipynb
+++ b/nbs/palace_inductor.ipynb
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "52509b1f",
    "metadata": {
     "execution": {
@@ -62,18 +62,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import gdsfactory as gf\n",
     "from ihp import PDK\n",
@@ -158,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "57e3ef84",
    "metadata": {
     "execution": {
@@ -176,15 +165,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation: PASSED\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from gsim.palace import DrivenSim\n",
     "\n",
@@ -198,7 +179,7 @@
     "sim.set_geometry(cc)\n",
     "\n",
     "# Configure layer stack from active PDK\n",
-    "sim.set_stack(substrate_thickness=180.0, air_above=200.0, include_substrate=True)\n",
+    "sim.set_stack(substrate_thickness=180.0, include_substrate=True)\n",
     "\n",
     "# Configure ports\n",
     "sim.add_port(\n",
@@ -217,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "eca89cc6",
    "metadata": {
     "execution": {
@@ -235,63 +216,16 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Small conductor feature detected (2.100 um) may be under-resolved by refined_mesh_size=5.000 um. Pass auto_size=True to scale the mesh down.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Mesh Summary\n",
-       "========================================\n",
-       "Dimensions: 262.6 x 262.6 x 396.3 µm\n",
-       "Nodes:      10,223\n",
-       "Elements:   73,943\n",
-       "Tetrahedra: 52,077\n",
-       "Edge length: 0.40 - 185.69 µm\n",
-       "Quality:    0.538 (min: 0.003)\n",
-       "SICN:       0.578 (all valid)\n",
-       "----------------------------------------\n",
-       "Volumes (4):\n",
-       "  - silicon [1]\n",
-       "  - SiO2 [2]\n",
-       "  - passive [3]\n",
-       "  - air [4]\n",
-       "Surfaces (13):\n",
-       "  - metal1_xy [5]\n",
-       "  - metal1_z [6]\n",
-       "  - topmetal2_xy [7]\n",
-       "  - topmetal2_z [8]\n",
-       "  - P1 [9]\n",
-       "  - P2 [10]\n",
-       "  - silicon__None [11]\n",
-       "  - SiO2__silicon [12]\n",
-       "  - SiO2__None [13]\n",
-       "  - SiO2__passive [14]\n",
-       "  - passive__None [15]\n",
-       "  - air__passive [16]\n",
-       "  - air__None [17]\n",
-       "----------------------------------------\n",
-       "Mesh:   palace-sim-inductor-guardring/palace.msh"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Generate mesh (presets: \"coarse\", \"default\", \"fine\")\n",
-    "sim.mesh(preset=\"default\", margin=50, refined_mesh_size=1.5)"
+    "sim.set_airbox(margin_x=5, margin_y=5, z_above=5, z_below=5)\n",
+    "sim.mesh(preset=\"default\", refined_mesh_size=1.5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "6a4d41a5",
    "metadata": {
     "execution": {
@@ -309,29 +243,14 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/jpeg": 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JuHw5aJMkROrRIwbIpOjo9YMDAXBkiYFwyqidoyEwGgKjITAaAqMhMBoCoyEwGgKjIYA9BNAWiyIrImPhKLJ2stni4iCtqanQGyP+/CHMILgEFGQiAwOkK/jqFYRHgLxxg4ACukn/+QO6dBFi3fz5DGfPQpjoZGQk6Cycz58ZVq+GSs2YATqKRl+fwcYGKjJK0RGMdgjpGNijVo2GwGgIjIbAaAiMhsBoCIyGwCggLwQwF4sim9PZicyjE9vICGTRwYMMzMzEIvh5myCduLGaGkju0CEQiR//+AG6mRC/GrrJdnQwnD8PWiwaHAzqGycmgo5gxbSdh4chPBwkPGcOiPz1iwGyfHR0enCAwGiHcIACftTa0RAYDYHREBgNgdEQGA2B0RAYDQEiQwDrYlFkvWTfQoFsCKlsX1/QrsJbt9DPR8E0Jy+PQVWVIT0dUwa7CGQi8fZt0GX02FXARGfOBM2twXgDSV+9ytDcDHLAlCmgw1ElJBguX2ZoaQGJYGLIqtGjR0G92dWrGV69Al1HER2NqXBUhA5gtENIh0AetWI0BEZDYDQERkNgNARGQ2A0BEZDgNwQwLNYFNlINzeGhw+RBWjOVlRkgFxWERnJ8PEjTusOHmSYMoXhzh3QgSs4FaFKmJlBF09mZ4M6S6iSCN7DhwzV1QjuALL+/gVd5PjrF+gKwbAw0JJXyCEx7e2gOUNMh5mbM+jogITnzgXdWMjAwJCQwMDFBRIZxXQHox1Cugf5qIWjITAaAqMhMBoCoyEwGgKjITAaAiSFwIMHDMQgksykiuLOTgZxcYZ79xi8vUFdPkwzd+0CXR3x/z9DSgqDnR2mPE6RyZMZODkZ3r5lcHRkOHMGi7LTp0E3cHz9ysDOjkWWzkK9vaBpUgEBBkg/kIGBITCQISQEunD0928szoFMEs6YwXD8OOiA1qwsLGpGhegCcN+hSRfrRy0ZDYHREBgNgdEQGA2B0RAYDYHREBgNAXwhIC+PT3Zg5WRkGDZsYPDyYjh6lEFXlyE/H9TrMzBg+P2b4coV0L0Lmzcz/P/P4OyM6CkR6WADA4Zly0AzkNeuMVhaMmRmMoSFMairgzYrQpaSTp4M6m75+zPw8jIsWUKkqTRRdvMmQ309yOS+PgbkWz2mTGHYtw90BUVbG1QBSBEMx8QwlJczfPkC4ru5gZbUglijeADA6AzhAAT6qJWjITAaAqMhMBoCoyEwGgKjITAaAsMkBCwsGE6eBPXZfvxg6OwETRVKSzMoKIAmBjdtgs59bdsGunKdVA8HBDAcOQJaWvnnD8PkyQy2tqCLHISFGSwsGPr7Qb3BxESG5csH+GL6f/8YkpMZfvxgcHVlSExE8aK4OMidDAwMra0Mly6hSDEwgO57DAqCCo4eJzOgYHSGcECDf9Ty0RAYDYHREBgNgdEQGA2B0RAYDYGhHgKqqgzHjjFs28awahWoC/f8OWgeT06OwcWFIS2NQUuLfP+ZmIC6Uhs3MqxbB1pa+eIF6IJ7CQkGa2tQ78vREWQy5KoGS0sQm/548mTQ7CgPD+ggGUzb4+JAXdYdO0BbBE+dQu+7KiiAdMjLg3rRINYoHhjA+P///4GxedTW0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgZEZAr9/M8jLMzx/ztDRAVo7OjIDYXCA0SWjgyMeRl0xGgKjITAaAqMhMBoCoyEwGgKjITAaAiMnBNavB/UG2dlBK05Hjq8HJRhdMjooo2XUUaMhMBoCoyEwGgKjITAaAqMhMBoCo2BAQoCRkVhrWVhAx+cQqxpV3dSpIH5EBIOICIgxigcOjHYIBy7sR20eDYHREBgNgdEQGA2B0RAYDYHREBgNgcEWAg4OxLqImZlYlWjqrl5lOHQIJDZ6nMwgAKMdwkEQCaNOGA2B0RAYDYHREBgNgdEQGA2B0RAYDYFBEgL799PcIZDpQVNTBlNTmts1agEhMNohJBRCo/KjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCVAwBDg6GlBSG0FAqGjlqFNlg9JRRsoNuVONoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAgMbTB6yujQjr9R14+GwGgIjIbAaAiMhsBoCIyGwGgIDNEQePoUdHE9IyPD06dU88HDhzjNXLoUKsXICGWkplLN3lGDhi4Y7RAO3bgbdfloCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAhQBEY7hBQF36jm0RAYDYHREBgNgdEQGA2B0RAYDYHREBgSIRAVxfDnDxRlZQ0JJ486kh5g9FAZeoTyqB2jITAaAqMhMBoCoyEwGgKjITAaAqMhMLAhwMjIAL8ngml0VmhgI2MwgdG0MJhiY9QtoyEwGgKjITAaAqMhMBoCoyEwGgKjITAKRkOAjmC0Q0jHwB61ajQERkNgNARGQ2A0BEZDYDQERkNgIEIA+aiVR48Y0tMZFBQYODgYxMUZHBwYFi1i+P8f4azz5xlSUxlUVBi4uBhERBisrBh6exm+fUMowGT9+cOwdCnoGgUFBQZOTgYpKQYbG4a6OobHjzHVMsTGQs90kZGBysrIQEUwr+X7949hzRqG6GgGZWWQewQEGDQ1Qe4/cACqNzERpDczE8qlMzVzJsh2+Ck1mAxFRTq7aNQ6ksHoklGSg2xUw2gIjIbAaAiMhsBoCIyGwGgIjIbAEA2B48cZkpMZPn2COv/VK4ZXrxgOHmRYv55h+XJQF7G/n6GsDLTRDqLi+3eG48dBaPJkhq1bGbS1IcIo5KFDIDPv3EEIPn/O8Pw5w9GjDG1tDDk5DF1dDGxsCFniWWfOMCQkMFy9itDx/TvDx48MN24wzJrFEBjIMHMmQmpwshgZB6e7Rl2FAKMzhIiwGGWNhsBoCIyGwGgIjIbAaAiMhsBoCAzvEIiLY/j8maGwENQJfP6cYf9+hpISBiYmhg0bGIqLGZYsYSgqAvUGY2IY1q0Dze8dP87Q2cnAzc3w8CGDszOoM4YWPkuXgsTv3AHNJXZ0MBw4AOoKXr0K0h4czPD3L8PEiQzW1oguKAMDw8KFICv+/GF48ABq2IMHUJETJ6AiDAwMmzcz2NqCeoOsrAzZ2QwrVzLcuQPqCi5bBup/MjGBOrH29iDrEHrozkpLg7ocflwNhPHiBWj2lYGBYfT0msEPRmcIB38cjbpwNARGQ2A0BEZDYDQERkNgNARGQwAlBDo7GcrLUUSI5Hz/DuqqBQZClUtIgJaMenszuLszTJvGMHcuaAFkVxeolwhRISPDYGHB4OXF4OTE8PIlw6RJDLW1EBkQefw4Q1ISqEcUHQ3SzscHEmRgYJCQYNDSAs3gnTrF4OPDcOYMQ0wMw8aNIMMZGED9T4g6FlhLnIUFcdwLROrKFYbISIYfPxj09UEdSH19iDCIVFcHSSUmMiQmMly/DkIg0QHCyAfVIDshKwsUXDY2oA42svgoexCC0RnCQRgpo04aDYHREBgNgdEQGA2B0RAYDYHREMAZAp2dDBUVDLt341SAR8LTE9RPQ1Pg4ADq1zEwMPz8yeDhgegNwpXp6DBANulNmgQXA207zMlh+PUL1JlcsIAB3htEqGBgMDNj2LYNNMG4eTPDsmXIMgTYBQUMX7+COpZ794L6hJiqra0Zdu5k4OHBlBl4kQULQL1ubm5QV3b0ONPBD0Y7hIM/jkZdOBoCoyEwGgKjITAaAqMhMBoCoyEADYHdu0G9QQYGBjc3cvqEMTFQc9CoiAioQFQUlIFGBQWBBN68YXj0CMRgYGA4dIjh3DnQdN+sWQzwuT6oHBJlYsKQmwviz5gBIonB588z7N0LUjh7NoOwMIiBFSsqMnR3Y5UZSMEHDxjy8kAO6O1lUFICMUbxIAejHcJBHkGjzhsNgdEQGA2B0RAYDYHREBgNgdEQgIbAw4egfiCUA+4TwtlEMlRUsCuUlYWKwxlQPoyCnwj65g1UaMsWEMPFhUFODsTAg9PTQf3GI0cYnj3DowohBTFZVxe03BQhio2VmIivL4pNB23F/v1jgOzS9PAAHYVKW8tGTacSGO0QUikgR40ZDYHREBgNgdEQGA2B0RAYDYHREKBxCKxYgW4BqQtHJSXRTYDwRUUhNIOQEJSBRgkLgzp1DAwM379DZe7dAzF27QLtDMS8bgFZRFGR4d8/kOLbt0EkQXz9OkiJtTWIxI/Z2bEffIpfF+1ku7oYDh9mEBQE7caknS2jJlMXjHYIqRueo6aNhsBoCIyGwGgIjIbAaAiMhsBoCNAkBOCLRZFNJ3XhKK4tbXBxOAPZFggbTQr/zYQQLWgk/LoLNHE07tu3IAFBQRBJEOOa8ySokeoKLlxgqK8HmTp1KugmRhBrFA8FADvbaCi4ddSNoyEwGgKjITAaAqMhMBoCoyEwGgIjMwTQFosiB4KbG+h8F2QR+rDFxUH2pKYyTJ8OYhCD0bqUuLRAuoKvXuGSRxG/cQOFO1CcHz9AJ6n++sUQEgI6BHWgnDFqLxlgdIaQjEAb1TIaAqMhMBoCoyEwGgKjITAaAqMhQNcQwFwsimx9Zycyj05sIyOQRQcPMjAzE4uIvKVdTQ1k8qFDIBI//vEDdDMhfjX0ka2oAF2ZKC7OQPzZOfRx2KgtBMFoh5BgEI0qGA2B0RAYDYHREBgNgdEQGA2B0RAYyBDAulgU2UFk30KBbAipbF9f0K7CW7cYTp0ioDUvj0FVlYRDVnx8QAbevg26jB7Ewo1nzmT4+xe3NL1k9u4F3dDIwMCA/1hUejln1B7SwGiHkLTwGlU9GgKjITAaAqMhMBoCoyEwGgKjIUDPEMCzWBTZGW5uDA8fIgvQnK2oyAC5rCIykuHjR5zWHTzIMGUKw507DNHRONWgSZiZMdjYgMSysxnwLBx9+JChuhqkbGDx+/cMCQmgVbtJSQy+vgPrllHbyQGjewjJCbVRPaMhMBoCoyEwGgKjITAaAqMhMBoCdAuBBw/oZhVpFnV2gi4MvHePwdubYcECBszzXXbtYggOBnWWUlIY7OxIMHzyZAYrK4a3bxkcHUHXu5uYoOs9fRq0Z+/rVwZ2doafP9Fl6cnPymJ48oRBXp6hv5+e1o7aRTUw2iGkWlCOGjQaAqMhMBoCoyEwGgKjITAaAqMhQPUQkJenupFUM1BGhmHDBgYvL4ajRxl0dRny80G9PgMDht+/Ga5cYZg1i2HzZlBv0NmZYdo00iw1MGBYtgw0A3ntGoOlJUNmJkNYGIO6OmizImQp6eTJDH/+MPj7M/DyMixZQprhVFS9bBnDihWgizfmz2fg46OiwaNG0Q+MLhmlX1iP2jQaAqMhMBoCoyEwGgKjITAaAqMhMMxCwMKC4eRJUJ/txw+Gzk7QVKG0NIOCAuhO+U2bQD2lrCyGbdsYWFlJ9ndAAMORIww6OqCO3+TJDLa2DGJiDMLCDBYWoLm4P38YEhMZli8f4Ivply8H+ev/fwYnJ5BnkW9fhLMnTgSpGcWDFozOEA7aqBl12GgIjIbAaAiMhsBoCIyGwGgIjIbAEAgBVVWGY8dAvb5Vq0BduOfPQfN4cnIMLi4MaWkMWlrke8HEhOHSJYaNGxnWrWM4fpzhxQvQBfcSEgzW1qDeoKMjyGTIbkNLSxB7FI+GABmA8f///2RoG9UyGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEw1MHoktFRMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAiMUjC4ZHaERP+rt0RAYDYHREBgNgdEQGA2B0RAYDYHREMAMAUZGTDHsIiwsoONzsMuNig4dMNohHDpxNerS0RAYDYHREBgNgdEQGA2B0RAYDYHREKBxCDg4EGsBMzOxKkfVDWYwuodwMMfOqNtGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkOAhmB0DyENA3fU6NEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBjMYLRDOJhjZ9RtoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhQEMw2iGkYeCOGj0aAqMhMBoCoyEwGgKjITAaAqMhMHhC4Onnp4yNjIyNjE8/Px08rhpsLqFFKD38+BBXyL/7/q7tcJvNPBvJXknOVk6liUoJGxIuvLgw2IJlGIPRQ2WGceSOem00BEZDYDQERkNgNARGQ2A0BEZDYDQEBm8IbLy5MXpt9NffX+FOvP/h/v0P95deXlpgUdDk2MTJwgmXGmXQCIx2CGkUsKPGjobAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCOAMgXXX14WtDvv7/6+1rHWpdam5tDkLE8uVV1eWXFoy7/y8nmM9b7+9nec/D6f+UQkqgdElo1QKyFFjRsFoCIyGwGgIjIbAaAiMhsBoCIyGwGgIEBcC335/y9ue9/f/33j9+IOJB/3V/SV4JES4RBwUHOb4zdkUuYmRgXH+hfkbbmwgzrxRVeSD0Q4h+WE3qnM0BEZDYDQERkNgNARGQ2A0BEZDYDQERkOAjBCYf2H+089PpXilpvtMZ2ZEv9DQR80nxyyHgYGh+VAzGYaPaiEJjC4ZJSm4RhWPhsBoCIyGwGgIjIbAaAiMhsBoCAyxEIhdH7vk0hJkR8v0yUC4JlImp1NPQ9gQ8u//v2uvrV1/Y/3JJydffHnBzsIuzSvtqOgYpRtlKWMJUYNMvv3+tmJPBQMDQ5drlyCH4IcfH5ZfWb766uo77+68/PpSiFNIRUjFS9Ur0SBRgkcCWSMDA8PDjw8VJigwMDA8KXry99/f1sOtO+/sfPHlBT8Hv6aIZpJhUqx+LCMDI0TX+Rfnp52etv/+/mefn3GxcqkJqwVrBWeaZHKxckEUYJJ//v1ZeXXlhhsbTj89/fLrS0EOQSVBJSdFp1TjVFk+WTT1JIXSv///1l1ft/7G+hNPTjz//JyNmU2SV9JO3i5SJ9JBAXSxfeLGxAUXFmSYZEz3no5mEYS7885OBgaGYM1gXLsEg7WCJ5+afO75uU8/P/Gx80F0EUlOPjU5b3seDxvPxYyLSoJKmLrcl7jvurvLRMrEQd6h53iPgoDCvfx78KDGVG89z/rY42NZpllTvaZiyg51kdEO4VCPwVH3j4bAaAiMhsBoCIyGwGgIjIbAaAhQJwROPj2ZuCHx+pvrcOO+//n+4ceHq6+vTjk1xV/df47fHBEuEbgsAwPDl19f5pybw8DA0ODQcPzx8ZRNKc+/PIcrePHlxYsvL448OtJ6qLXarrrCpgJrr+P44+PJm5I//fwE0fjq66tXX18dfHhw/Y31y4OXc7Bw9J/oL9td9uffH4iC73++H39y/PiT45NPTt4avVVbVBsijkweengoeVPynXd34ILPvzx//uX50cdH2w635ZjldLl2sTGzwWWJZ5x5diZhQ8LV11fhWr7/+f7x58cbb27MOjsrUCNwpu9MuBQuxoMPDxgYGFSFVXEpUBNWg0i9+vqK1A5hjlnOxhsb997fm7gx8UDCAbQwn3V21q67uzhYOBYFLmJmZO453vPgw4O99/a6KLlAbEQjL7y4cOzxMQYGhizTLDSp4cFl/P////DwyagvRkNgNARGQ2A0BEZDYDQERkNgNARGQwAzBP79//efAdTiffLpCWRG7kHBAxk+6CQhfL3i+hvrI9dE/vz7k4uVK9cs11LW0kDC4PPPz2efn91ya8uaa2sYGBgUBBQOJx6G60We5ZvpMzNra9bf/3+NJY3j9OOMJI1UhFRuvr15/vn5uefnXnl1hYGBIVQrdEXICiZG6KYt+AwhJwvnjz8/CiwKAjQC1ITVbry5sfX21r7jff/+/8syzbKUsYxdH8vAwBCjFxOkGWQqZfrk05NDDw81HWz6+vurOLf4zdyb/Oz8yB5fenlpwoaEP//+iHCJlFiVWMhYqAurv/v+7ubbm0svLV17fS0DA4OJlMneuL3wvhaRobT51uaw1WE//vxgZWJNM06zk7czljL+8+/Puefn9t7bO//C/H///2mKaMrxy+28uxM+Qwj36ZOiJ9K80gwMDGuurfn486OdvJ2qEPY+4f4H+50WOjEwMHyo+IDmO2Sf4mI/+vhId7rup5+f+t37CywK4MoefHigN13v86/PEzwm5JvnMzAwOCxwOPjwYLh2+IqQFXBlyIzUzalzzs1xUHDYH78fWXzYsEc7hMMmKkc9MhoCoyEwGgKjITAaAqMhMBoCIyUEOtd0loeUk+rbp5+fQhaLwrslcBPOvzhvPdf6+5/vNnI2CwIWKAsqw6UgjLXX16ZvTn/7/a2xpPHxlOOsTKwQcXhXh4WJhYmRqdGhsdS6FN7JhKj59fdX86Hm9sPtf///LbMu63TphIjD9TIwMKwLXxeoEQgRh5AHHhxwX+L+6+8vdmb2X39/dbl2lViVQKQg5JVXV5wWOr3+9rrJsanWrhYiyMDAcPzJcYcFDr/+/orWjZ7mPQ3e5YMrOPX0lM8yn9ffXvuq+W6M3Ig2gYYnlK68umIxx+Lr76/64voLAxfqi+vDzYQwjj4+mrgh8fa72xAung4hRAEeMn9H/qSTk9SE1W7m3MSjDI/UggsLEjcmcrJwXsi4AJlv/M/w33mh8/4H+x0VHPfG74V4fMmlJbHrY9mZ2Z8WPxXmFEYz8MOPD9J90t9+f1sdujpEKwRNdnhwoeMTw8Mzo74YDYHREBgNgdEQGA2B0RAYDYHREBj2IdC5prPiasXus7up6NO87Xnf/3xXFFDcEbMDszcI2e22JAi0EfHs87PzzmO5C+HPvz/drt0VNhVovUEGBgY2ZrZmx+ZyG1APtvdY77XX19Bc7qniidYbBM1cKTgkGSYxMDD8/PvTQ8UDrTfIwMCgI6aTaZrJwMAw6eQkuIH/Gf7nbMv59feXu7L7goAFmL1BBgYGM2mzbdHbuFm5N9/avOzyMrhegoyCHQVff3+V4JHYG78XszfIwMBgLWu9M3YnDxsPQaPwKzj59OTUU6DdennmefhV4pFNMEjwV/f//ud7/Ib4v///MjAwTD01df+D/XzsfAsCFkB6gwwMDCFaIQIcAj///kTbaAqSY2BYcGHBt9/fpHmlAzQCICLDjxztEA6/OB310WgIjIbAaAiMhsBoCIyGwGgIDNsQ2H12d8VV0CEublvcqNUnPPX01JFHRxgYGOb4zeFm5cYVdh4qHpE6kQwMDBNPTMRU46jgmGueiykOF6mzr9MS1fr7/2/v8V64IIQRoxcDYaCREToREJEo3SgIA40M0gxiYGB48+3No4+PIFKHHh469/wcEyPTLN9ZLEw4jwsxkTKBuHbGmRkQjQTJ8y/O772/l4GBYbbvbMyZNLh2RQHFbtduOJcMxosvLyLXRP79/9dBwSHDJIMME+BaZvrOFOESOfHkRM+xnrvv75bvAfXJJ3lOkuOXg6vhYOGAhP/cc3PhghDGf4b/08+ADsVJN0nHE5gQxUOXHO0QDt24G3X5aAiMhsBoCIyGwGgIjIbAaAiMrBB4+OKh2xY3uJ+R2XBBMhibb21mYGBQFFB0UgRtWsNjQppxGgMDw/U31x9+fIimDNeBMXBl7MzsiQaJDAwMW25tgexphEupCKnA2cgM+Fmgsvzoh4JClMF3M7759gYisuXWFgYGBhclF+Q+D0QKjUw3TmdiZDry6Mizz8/QpLByISbriun6qPlgVQAXTDRMJLv79OzzM+dFzvc/3FcUUFwevBxzuhVuCzEMcW7xGT6gHm/9/vrglcHffn/zV/eP149H05tqlMrAwHD51eVTT08hS+2+u/vW21usTKwQBchSw4mNc9hgOHly1C+jITAaAqNgNARGQ2A0BEZDYDQEhkEIrDiCfuzH7rO7XY1dKfTarbe3GBgYDCUNCZqjJ64HUXPr7S15fnkIG0JayVpBGHhIiBWvvr568+2NKJcoXKUkryScjcwQ5YaqEeIUQhaHs4U5hZkYmf79//f9z3eI4L339xgYGHbd3cXYCL2vAiKOh7z97rYUrxQeBRCp669Bh69ay1lDuHhIdmZ2bVHtiy8v4lGDVerOuzuui10ffHigKKB4IOEA5kUdWHXhFwzWDI7WjV56eenFlxdFuURn+c7CVK8nrmcqZXr62em55+eaSZvBFUw7PQ20WlgrmCougRs72BijM4SDLUZG3TMaAqMhMBoCoyEwGgKjITAaAqMhgCUE4ItFkeWosnD0w48PDAwMWC+sQ7aLgYFBiFMIsloSPiMHUcDCxILnPkCIGgYGBnVhdQgbTTv83FGILJyEi8MZcCk4A03q2+9vcCkiGfDrLvCrf/v9LQMDgyCHIH5lEFlcc54QWazk5VeXbefbPvjwQFVI9WDiQYIznFgNwSoImddlYGAI0AgQ4xbDqibVGDRJuPzy8q+/v0IUPPr4CDIpmm2aDREZruToDOFwjdlRf42GwGgIjIbAaAiMhsBoCIyGwPAJAbTFosgec9vi9t8YdKsEsiBJbMjVgrffQs/GxKP3y68v776/w+wX/fn35/a72/D+Hi4T7r6/C5GC9CohbOqS4tziDAygNZDTfUCb34gxHK1LiUsLpCv46usrXAqQxW+8uYHMJcg++fSk5xLP9z/eG0gY7IzZiavbRtAcTAV//v0p2AG9dmL+hfnpJunGksaYyiJ1Iot2Fn3+9Xn11dUJBgkMDAwzzsz4+/+vvri+jZwNpvrhJDI6QzicYnPUL6MhMBoCoyEwGgKjITAaAqMhMDxDAHOxKLI/O9dAL3JAFiSerSGiwcDAQMwSxyuvrkC2/ykLod9LceHFBYI2nnt+joGBQZhTGL4WlKAWUhUYSRoxMDAcfHiQmZGZSAQ/bxO/XZCbGw49PIRfGQMDw48/P0jqEO69v9dlkcv7H+9t5GwOJBygYm+QgYGh40jH+RfnRblEgzWD//z7k7gh8dffX5he4GHjCdcOBx0sdG4OAwPDr7+/5p4HnTGTbTbMpwcZGBhGO4SY6WFUZDQERkNgNARGQ2A0BEZDYDQERkNgEIUA1sWiyO6j8BYKb1VvBgaGBx8e7H9A4Obx5VeWQxaXYl6nPv30dEhfEdlhyOzf/34vuriIgYHBV92XyD4YsnYi2b7qvkyMTLfe3kI7HwVTe972PNXJqulb0jGlsIpAzpK5/e72yqsrsSqAC848OxNyzQNcBA9j482N3ku9v/z64qniuSt2Fxl30OMx/Orrq82HmhkYGKZ4TZntN1uCR+Lyq8sth1qwaoGsGj36+OiNNzdWX1v96usrAQ6BaN1orIqHk+Boh3A4xeaoX0ZDYDQERkNgNARGQ2A0BEZDYLiFAJ7Fosheddvi9vAF+smfyArwsI0kjezl7RkYGFI2peDZg3fm2RnI5Xg5ZjmYph18eHD6aXyrNNsOt114cYGJkanYshhTO7VEFAUUIZdVRK6N/PjzIy5jDz48OOXUlDvv7hDf4TGTNoMsnszemo1n4ejDjw+r91bjshdNfPGlxSGrQn7+/RmhE7ExciMnCyeaAkq4f///TdqY9OvvrwCNgDDtMEEOwWneoENi2o+0n39xHtNkc2lzHTEdBgaGuefnQiI6wSCBmK2hmEYNLZHRDuHQiq9R146GwGgIjIbAaAiMhsBoCIyGwIgLgQfpD4hBlITLBI8JnCyc997f817mff/DfUyjjjw6Ero69O//v1qiWpkmoOvg0dQwMzIX7yruP9H/7/8/NKk///60Hm6FTEwVWxZDeh1oaqjI7XTpFOcWB/llqfedd3cwTd51d5fPMp//DP9TjFLs5O0wFeASmew5mZOF8+33t44LHc88O4Op7PSz026L3b7+/srOzI4piyYy9fTU+PXxf/79yTDJWBq0lJWJFU0BhdzeY72nnp4S4BCA9AMZGBgCNQJDtEIgC0d///uNaT7keokZZ2Ycf3KckYExyzQLU83wExk9VGb4xemoj0ZDYDQERkNgNARGQ2A0BEZDYPiEgLwEyu0ONPKYgYTB0uClkWsiDzw4oDtNN98i30LGQl9c/9//f+een9t9b/ess7P+/f8nwSOxIWIDBwsHpjOmeE3J2ZZTtLNozbU1sXqxhpKGyoLKt9/dPv/8/JxzcyBTUuHa4R0uHZh6qSsiwyezIWKD11Kvo4+P6k7XzTfPt5O3M5Aw+P3v95VXV2adnbX55ub/DP+dFZ3hPSUiHWAgYbAseFnEmohrr69ZzrXMNMkM0w5TF1FnZmQGLSW9snLyqcl//v3xV/fnZeddcmkJHmPbDrdV7wNNJFbZVrU6teJRSZ7Uzbc36w/UMzAw9Ln3SfIgbvWY4jVl3/19F19ebDvcVm8PUoAMYvRiyveUf/n1hYGBwU3ZDXNhMLLiYcMe7RAOm6gc9choCIyGwGgIjIbAaAiMhsBoCIyGAPkhEKgReCjxUOLGxGuvr7UdbsM0yF7efknQEvhd8GgKfNV9ZfllUzalHHt87NjjY2iyPGw8NXY1ZdZltNs9iGyjhYzFydST8evjjz853nm0s/MoyqE7TIxMmSaZ/e79ZEzKBWgEHEk6krgx8cqrK5NPTZ58ajKyvQwMDIkGiVO9p2ZtxTe39vf/X0hvkIGBoe1wG9bQhhv7peoLNys3nEsM49//f8kbk3/8+eGq5JpokIisRZxbvN+9P35DfOuh1kCNQPjFkhA1QpxCQZpByy4vY2BgGAnHyUB8PdohhITDKDkaAqMhMBoCo2A0BEZDYDQERkNgpIeAmbTZ5czL62+s33Bjw/HHx19+ffnv/z9JHklbedsInQh3ZXf8AeSt6n0t+9qSS0vWXFtz++3tt9/fCnEKqQmreal6JRkmId9Ej98cqsiqCqkeSz627fa2VVdXHXl05PmX58yMzHL8ci5KLmnGaVqiWmTbYiJlcinz0sYbG9ddX3f8yfEXX15A5k6tZa0TDRMdFRwZGBgguw0tZSzJtoUSjZNPTT76+CgPG89sv9mY5sTpxy2/snzHnR0JGxJOpZ5iYULpECkIKDAwMMjzy0OOGsLUPvxEGP//p+jaluEXIqM+Gg2B0RAYDYHREBgNgdEQGA2B0RAYDQEiQ+Dhx4cKE0BdiCdFT6R5pYnUNapscIbA73+/5fvln3953uHSUW5dPjgdSXVXjR4qQ/UgHTVwNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYOiFwPrr659/ec7OzJ5smDz0XE+ui1FmSMk1ZFTfaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgI0CoEGBsZiTSahYnldy2WE0SJ0T719FQGBoYInQgRLhFi1A8PNaMdwuERj6O+GA2B0RAYDYHREBgNgdEQGA2B0RAYDYFhGwIOCg5E+o2ZkZlIlWjKrr6+eujhoRF1nAwkBEY7hJBwGCVHQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQEBmkI7I/fT2uXQS6jN5UyNZUypbVdg8r80Q7hoIqOUceMhsBoCIyGwGgIjIbAaAiMhsBoCAylEOBh40kxSmFgYOBi5RpK7h51K0YIcLBwpBilhGqFYsgMc4HRU0aHeQSPem80BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNARwgdEZQlwhMyo+GgKjITAaAqMhMBoCoyEwGgKjITC8QuDbU4YNMiAvBTxh4Bq9IgIUElgwLULp60OGjaDLORgwQ/7XO4bbMxiebWP4fJfh9wcGTkkGMTsG9QIGQQMsbhsVogEY7RDSIFBHjRwNgdEQGA2B0RAYBaMhMBoCoyEwGgKjIUAwBJ5sZDgWzfDnK0Lhl/sMX+4z3F/KoFHAoNfEwMyJkBpl0QaMdghpE66jpo6GwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAjgCYHH6xiOhDH8/8sgas2gWcogYs7AyMLw4QrDgyUMd+cxXO9h+PmWwWIeHgNGpagCRi+mp0owjhoyGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhQHQI/PnGcDYP1BtUimdwOcgg48/AIcHALsIg7sBgPofBfhMDAyPDvfkMTzYQbeKoQjLBaIeQzIAb1TYaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyFAZgjcm8/w7SkDpxSD6XQGzJsDpX0Y1HJAJl9pBpGjmJZgdMkoLUN31OzREBgNgdEQGA2B0RAYDYHREBgNgQEPgWOxoFWIyM6AHC3DwMAgZMLgcRpZBjRn9Xgtw+P1DG9PMnx/wcDMzsApzSDuyKAQxSBiiaISwvn5luFCBYhp2MXAJsjw6wPDw+UMj1YzfL7D8OMlA5sQA68Kg5QXg3IiaAYMpA4JIx+18v8vw9VWhuc7QZay8TPwaTIoJzEoxoImyiA63p9nuDWN4eV+hu/PGFi4GHjVGGSDGVQzQWyIAkzy/x+GhytBk2xvT4MdI8jAo8Qg7sSgksrAJYuunLRQ+sfweB3Dk/UMb04wfH/OwMQGPQxGPhI0xcfAwHAikeHeAgbVDFB/D90mMP/5ThAlG4xzl6BcMMOtyQzvzjH8/sTAygdSTDy+M5PhVAY+5dwKDP738SkYSWC0QziSYnvUr6MhMBoCoyEwGgKjITAaAqMhMBoCeELg7UmG44kMn64jlPz9DurjfbzKcGsKaFmj+RzQskaENAPDny8Md+eABHQbGN4cZziZAuoggfhg/OMFw48XDK+PMFxtZdCuZtCuQHTwwPJQAqQxGdTzgfB/vGL48Yrh1UFQv9R6OQMzB8ONfobzZQz//0DkGf5+Z/h5HGTdrckMDlsZ+LWh4sjUq0MMJ5NB/VK44PfnILe9PspwtQ00/2bYBerIwWWJZ7w7w3A8geHjVYSOv98Zfn9k+HSD4c4sBtlABrOZCClcrK8PQDJ8qiASK+ZVgwr/eEVyhxCqEzfFyIhbbsSB0XsIR1yUj3p4NARGQ2A0BEZDYDQERkNgNARGVgj8/8fA8B/k5W9PoJcf+D9g4JIBiTAwINYrPl7PcDSS4d9P0JybWi5oPlDQgOH3Z4Z3ZxmebWF4tAaknluBwfUwQi8DAwN8ls9sJsPpLNAEo5Axg2Icg5ARaG7w002Gd+cZ7s0FnZXCwMAgF8pgvYKBEbZpC66XmZPh7w/QuZoyAQx8agwfbzA828pwo4/h/z8G1SwGUUuGY7Eg2xViGGSDGIRNGb49YXh1iOFKE+h8Tg5xBt+bDKz8IAVw/GApqM/2/w+o+6pZwiBiwcCnzvDzHcOnmwwPljI8XgtSKGTC4LwX0dciMpSebgadBPP3BwMTK4NKGuiKCCFjhn9/GN6fY3ixF7Tr7/8/0PQmtxxothM+Qwj3KfzaiUdrQH1IMTsGXhx9wpf7GfY6gdwZ+gHddyBR/Pg/KOgwlfx8w7BNHzRZatjNoFmCKT8ywWiHcGTG+6ivR0NgNARGQ2A0BEZDYDQERkNgKIfAwU4G+3KSPYDnhr335xl2WYNm3kRtGCwXMPAooxv+eC3DqXTQuZdCxgxux0HdIYgKeFeHkQXU09NrBB2YibYp7t8vhivNDFfbQd1FrTIGg06IVkRnkoGBwXYdaG4NKgGmXh5g2O/O8O8XAxM7iDTsQu/DfLgC6jL9fA26nkGnFqwHTLw5zrDHAaRFIZrBdBqiyweWBBFvTzEc8GH4+ZpB2pfBfiP6pCWeUPpwhWGXBagXKqjPYLGQQVAfZBoyfn0UtFj0822oGJ4OIVQFbupsPsPNSaCVsb43cSsiUeZwMGilq6gN6BgbeLd8xAPY+MSID4jRABgNgdEQGA2B0RAYDYHREBgNgdEQGBohcLCT4WkFw83d1HTtmTxQb5BHkcFxB5beIAMDaMOe1RKQje/OMtzDdhfC/z8Mht0MWhWIKUeQajBmYmPQa2bQAvdgr/cyfLwGFkUipDzRe4MMDKDNeMpJIEX/fjJIeaD3BhkYGAR0QHsIGRhAHSeQOgj+z3AmB9QblHQH9Wyx7r4TNmNw3MbAws3wdDPDg2UQbUSR5wpAvUEOCQanvVh6gwwMoAskHHcysPAQZRoeRW9PMtyaCpJXzwORVMH3FoB6gyzcDJYLQV13qpg5LMBoh3BYROOoJ0ZDYDQERkNgNARGQ2AUjIbACAmBm7tBvUEGBoazblTrE749Bdrmx8AAuvCAhRtnQEp6MMhHgmRvTASRaFjckUE9F00Mhatbx8CvBZokvNGLIs7AwKAQgy4C4ctHQGjQkTZQFiolGwTi/3zD8PURiMHAAFpK+u4cqMNjNgt0rR9UFIMSMmFQA7v29gwMORwC78+DFoWCQmk2A7swDkUMDDyKoI4xTmkiJH68AK3d/f8X1CVWxXs2DBGGQZV8fQC65YKBgcGoF3SyDlR0lAKB0Q4hKBRG8WgIjIbAaAiMhsBoCIyGwGgIjIbAEAiBtw9B/UC4Q8+6wZkUMZ5uBmnnUQSdwAli4cYqaSC5T9dBqz1BLCSshePAGLgSJnYGpUQQ7+kW6J5GEAeMeVXAFAYBPwsUzkBTAt8J+fMNVAZkOAODhAsDtxxUBBelmg7qN74+Ajq2FJcaZHGIyQK6DNI+yMJY2MqJ+PqiWDQgCX1/xrDXmeHLfVDH0no5lulWJLXEMv//YzgeB9oOKunBoJJOrK4RA0ZPGR0xUT3q0dEQGA2B0RAYDYHREBgNgdEQGOohcGUFug9u7mZQd0UXJJX/6RZIh6AhiMSPBfWg8p9vMXDLQ9kQStQKQuMjIVb8eMXw8w0DuyhCJackgo3MgqthE0IWRrDZhUGduv//QOtdIaJf7oHo57sYlhF9kObn26D7AEHa8OKP4MNXRa3xKgJLMrEzCGgzvL8I5pBCfL7DsM+V4esDUG/Q+QCWizpIMQyh9noXw6vDoEtBLOYiBEdZMDA6QwgLiVF6NARGQ2A0BEZDYDQERkNgNARGQ2AwhwB8sSiyI6mycPT3B5CRPEogEj9mE4KuloTPyEHUM7IwMHNBmPhIPnWoLJp2BhxtcvjBJ3AGVD8yhar3zzdkOaLYvz8RpezXW5AyNkEQSRDz4JjzxKPxw2WG3bag3iCvKujQF4IznHiMQpZ6f4HhUj1IwHQqUf3ekQdGZwhHXpyP+ng0BEZDYDQERkNgNARGQ2A0BIZcCKAtFkV2/1k3BnXwrRLIgiSx2UVAyuFnY4I4OPCfL6DLGxgYQNNNyEr+/2H4fBt0tQOyICb7y12oGBvuPXhQFeRSnOIgnSqpOG+EB0mjYny9TSSVkK7gj1dIQriZn27glsMm8/Ykw35Phl/vGQQNGBx3MnCIYVNEutjfHwzHYkBH7MiFQPd/km7GsAeoIwrD3rujHhwNgdEQGA2B0RAYDYHREBgNgdEQGIohgLlYFNkXB2EXOSALEs/m0wCpJWaJ44cr0O1/mPdSvL8AMgQ/fncOJM8uzMCBtF4UJEQ9LGgEMuvlQdDuO0ZmokgG4haXQm6Kf3UIZD5+/PcH6JJ6/GqQZV/sZdjrAuoNgi6EOEC13iADA8OFCoaPVxk4xBlMiT47Z+SB0Q7hyIvzUR+PhsBoCIyGwGgIjIbAaAiMhsDQCgGsi0WRvUDhLRRS3iDDvj5geLkfxMCDHy4HSfIoYblO/fZ0aF8RpAIb/veb4f4ikIS0L/rVfyBRKmFpX9Cuws+3GN6eImDimTyGzaqgyxUJqINJQ86S+Xyb4eFKmBAO+s5M0GGqOCTRhZ9sZDjgzfDnC4OUJ4PTLtLvoEc3D8F/sRd6IYc53mNRRzwY7RCO+CQwGgCjITAaAqMhMBoCoyEwGgKjITCYQwDPYlFkZ591Y3j7EFmABLaQEYOYPUj9yRQGPHvw3p2BXo6nlgNSjIZfHWQA9QnRRJG4V9sY3l8A9dY0ipFEqc3kUWSAXFZxNJLh90ecpr86yHBrCsPnOwwK0TjVoEkImzGI2oDEzmQz4Fk4+vUhw8VqkDJi8P3FDIdDGP79BLnZbiMDMycxmohS8+s9w4kEUBddOYkB1AMnStPIBKN7CEdmvI/6ejQERkNgNARGQ2A0BEZDYDQEhk4IuD+guVuNJzDssmL4co/hoDeD+TzQKZdoVr4+wnAsFjTxxa8FvQ4eTQEjM8O5Yoa/PxnU80G9PmTZ/38YrnUyXGkBiWkUgy6UB7Fohg06QRcGfrkHmnmzWMCAeafF810Mh4PBnaUUBjE7EtxhMhkUSj/fMux1BF3vLmSCrvftadCevT9fGZjYQd08dGlU/q2pDGdyQc5QzWAwmYoeaKhqSeadzmL49gR0EqxRP8l6RxgY7RCOsAgf9e5oCIyGwGgIjIbAaAiMhsAoGFohIIx6uwONHC9owGC1FHQf+ssDDNt0QZ06EQsGAX0Ghn8M784xvNjNcGcWw/9/oIsQ7DYwMHNgcYXJFIYzOQznihgerWFQjGUQMmTgUQadNPP+PMOdOQzvz4O0yIczGHSAGDTFXDIMdhsYDngxvD4K9YuYHeiwln+/GT5eAXnkyWZQN0zCmcF0GmkOETRgsF7GcCSC4eM1hp2WoI6xfBjoKB1GZpBPH65kuDmZ4f8fBhl/BhZehgdL8Bl+tQ06kahdxaDfik8lGXIPljE8XAFal2sxn4GVjwwDRhQY7RCOqOge9exoCIyGwGgIjIbAaAiMhsBoCIyGAI4QkA1kcD3EcCIR1Nu52oZFkZg9g9USBvhd8GgqpH0ZuGUZTqQwvDkGQmiyLDwMOjUMWmWgXgqaFC24IhYM7icZjsczvDkOmpy8hnroDiMTg0omg3E/AxMryZbLBDC4HQGF0ocrDLcmgxCaEUqJDKZTGU5noQmjcP//hfYGGRgYrraBEIo0KifsCwMLN6oQIR5kqyfDf4a9TjiVGk8AdftHAQPDaIdwNBWMhsBoCIyGwGgIjIbAaAiMhsBoCIyGADgEhM0YvC4zPFnP8HgDqCv14yVohpBDkkHMFrTJTdIdrAg3IeXN4HMNNDP2aA1oxuznWwY2IQY+NQYpLwblJAb4LfO4DaCmDK8qg9sxhmfbGB6tYnh1hOHHc9CJo1xyDBIuDCppDPxa5NslZMLgdYnhyUaGx+tAofT9BTiUJBhErRmUEhnEHUEmQ3YbiliC2KN4cAPG//8pu7ZlcHtv1HWjITAaAqMhMBoCoyEwGgKjITAaAqMhQMMQ+PqQYaMCyPyAJwxc0iDGKB4NgSEFRk8ZHVLRNerY0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQoB4YXTJKvbAcNWk0BEZDYDQERkNgNARGQ2A0BEZDYDQERkOAFiGwjJFYUxlZGCJ/E6t4FIzuIRxNA6MhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKDPQTEHYh1ISMzsSpH1YHB6AwhOBhGidEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYFBGwLO+wet04Y6GO0QDvUYHHX/aAiMhsBoCIyGwGgIjIbAaAiMhsDAhQALD4NyCsh6Fi4QOYpHQ2CogdFTRodajI26dzQERkNgNARGQ2A0BEZDYDQERkNgFIyGwGgIUAmMzhBSKSBHjRkNgdEQGA2B0RAYDYHREBgNgdEQGMQhsNh18b0991AcyMjAysXKzscuqCio7K6sH6cvoCCAogDGubz08rqYdTAeiDZKMfKd7QtijeLREBjiYLRDOMQjcNT5oyEwGgKjITAaAqMhMBoCoyEwGgLkhcB/ht9ff//++vvL8y+Pjz0+0HBAL1rPd5YvC+doC5m8AB3VNSTBaHIfktE26ujREBgNgdEQGA2B0RAYDYHREBgNATJCwKHRwa7ajoGB4f////9+//vz48/XV1/f3HhzY8ONCwsvXFpy6dfXX+HrwtFM1o3S1YnQgQhuz9t+etppCHuUHA2BYQBGL6YfBpE46oXREBgNgdEQGA2B0RAYDYHREBgNAaJCgImZiZGZkZGZkYmFiYWThUOQQ1hdWN1f3X++f8jyEAYGhhvrb9zZcQfdLEYGiC4QyUT0hXjopozyR0NgMILRDuFgjJVRN42GwGgIjIbAaAiMhsBoCIyGwGgI0DkEtMO1ld2VGRgYLi+7TGerR60bDYEBBKMdwgEM/FGrR0NgNARGQ2A0BEZDYDQERkNgNAQGUQioeqoyMDC8uPBiELlp1CmjIUBjMNohpHEAjxo/GgKjITAaAqMhMBoCoyEwGgKjITBEQoBbjJuBgeHrq69DxL2jzhwNASqA0Q4hFQJx1IjREBgNgdEQGA2B0RAYDYHREBgNgWEQAm9uvGFgYGDnYx8Gfhn1wmgIEAlGO4REBtSostEQGA2B0RAYDYHREBgNgdEQGA2BwRICazrX0MIpj489ZmBgENUSpYXho2aOgsEJRjuEgzNeRl01GgKjITAaAqMhMBoCoyEwGgKjIYA9BNZ0rrlacfXs7rPYpckVPTP9DOTmeu1QbXLNGNU3GgJDD4zeQzj04mzUxaMhMBoCoyEwGgKjITAaAqMhMGJD4Ozus1crrjIwMGxx28Kwi8HY1ZiSoPj3+9/3d99fXHhxbc218/POMzAwSJtL60RCrxykxORRvaMhMFTAaIdwqMTUqDtHQ2A0BEZDYDQERkNgNARGQ2Ckh8CLhy9A/UBYMGxx22L8n7QO4b6afftq9sEMQKeV3ZWDlwUzjt40iB4wo/zhDEY7hMM5dkf9NhoCoyEwGgKjITAaAqMhMBoCwykEjqw4guads7vPUjJJyMjEyMrNyi/HL2MuY5ZrJmEggWb+KHc0BIY9GO0QDvsoHvXgaAiMhsBoCIyGwGgIjIbAaAgMhxCALxZF9gxowpCUhaMOjQ521XZwExiZGeHsUcZoCIxMMHqozMiM91Ffj4bAaAiMhsBoCIyGwGgIjIbAUAoBtMWiyE4H9QmR+XjZTMxMjMyMcIRX7ajkaAiMCDDaIRwR0TzqydEQGA2B0RAYDYHREBgNgdEQGNIhgLlYFNk7NLqFAtmKUfZoCAxXMNohHK4xO+qv0RAYDYHREBgNgdEQGA2B0RAYJiGAdbEost9ocQsFsvmj7NEQGMZgtEM4jCN31GujITAaAqMhMBoCoyEwGgKjITDkQwDPYlFkv21x2/Li4QtkkVH2aAiMhgAxYPRQGWJCaVTNaAiMhsBoCIyGwGgIjIbAaAiMhsCAhUD6g/QBs3vU4tEQGO5gtEM43GN41H+jITAaAqMhMBoCoyEwGgKjITCUQ0BCfvQqiKEcf6NuH/RgdMnooI+iUQeOhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIYAbcBoh5A24Tpq6mgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCAx6wPj///9B78hRB46GwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCFAfjM4QUj9MR00cDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQGA2BIQFGO4RDIppGHTkaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgLUB6MdQuqH6aiJoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMCTAaIdwSETTqCNHQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkOA+mC0QzgKRkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYISC0Q7hCI34UW+PhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjILRDuFoGhgNgdEQGA2B0RAYDYHREBgNgdEQGP4h4MrAwIiKmBgYeBgYpBgYrBkYmhgYHuAOg3cMDG0MDDYMDJIMDJwMDEoMDAkMDBdwqx+VGQ2BIQRG7yEcQpE16tTREBgNgdEQGA2B0RAYDYHREBgNATJDwJWBYQ9erYwMDNEMDLPAXT5khRvB4l+RhcBsFgaGAnBPkhPMHSVGQ2CIgtEZwiEacaPOHg2B0RAYDYHREBgNgdEQGA2B0RAgOQQaGRj+gNFvBoZvDAzvGBhuMDBsAM/4MTAwLAH3/ZANXcfAEMzA8BU8i7iBgeE5A8NrBob9DAzJDAx/GRh6GBiykVWPskdDYAiC0RnCIRhpo04eDYHREBgNgdEQGA2B0RAYDYHRECAxBCAzhC0MDNU4NK5kYIgAS21nYPAAM74xMKgxMDxlYIhnYJjLwMAMFoQTWxgY/BgY/jMwrGdgCICLjjJGQ2CogdEZwqEWY6PuHQ2B0RAYDYHREBgNgdEQGA2B0RCgQQiEMzC4g41dBiYZGBjmg3uDUgwM0zF6gwwMDD4MDDlglc1gcpQYDYEhCkY7hEM04kadPRoCoyEwGgKjITAaAqMhMBoCoyFA5RDwBJsHPy1mJ5gbjLGrECwMIoJBBMM5BoZPYMYoMRoCQxGMdgiHYqyNunk0BEZDYDQERkNgNARGQ2A0BEZDgPohIAY28hWYZICdO6oK42LSajAhuBaYwCg9GgJDBrAMGZeOOnQ0BEbBaAiMhsBoCIyGwGgIjIbAaAjQMgRugA3nA5MMDAx1DAwfGRjsYFxMGqKegYFBFFNuVGQ0BIYIGO0QDpGIGnXmaAiMhsBoCIyGwGgIjIbAaAiMhgAsBDoZGMphbCrSx8BmaYFJBgaGEBgDF70BLKHGwMAPZowSoyEwFMHoktGhGGujbh4NgdEQGA2B0RAYDYHREBgNgZEbAp0MDBUMDLupHQDTYRcVhhJn8kkGhqlglXlgcpQYDYEhCkY7hEM04kadPRoCoyEwGgKjITAaAqMhMBoCIzEEdoN7gwwMDG7U6BP+ZmB4ycCwk4EhFXZkqDkDQyQR4foCrOwvA4MDA0MGEepHlYyGwKAFo/cQDtqoGXXYaAiMhsBoCIyGwGgIjIbAaAiMhgBKCDxkYFBAEQBdA4gqgJMHuYcQpzRYwp2BYRkDgxCYjYd4xsDgysBwjYFBkYHhGAODBB6lo1KjITDowegM4aCPolEHjobAaAiMhsBoCIyGwGgIjIbAaAiAQ2AFmEQmKFw4ysTAwMvAoM3AkMTAcJ6BYQcRvcE7DAzWsN7ggdHeIHJkjLKHJhg9VGZoxtuoq0dDYDQERkNgNARGQ2A0BEZDYISFAHyxKLK/3RgYdoHn65AF8bAbGRiqkaSZkdjEMC+Dl6q+YGBQZWDYy8AgS4yeUTWjITC4wWiHcHDHz6jrRkNgNARGQ2A0BEZDYDQERkNgNAQYGB6Ce2JYQ8KNgYSFo8wMDKR2AuGWnmRg8GRgeM/AYADedgi5tBAuO8oYDYEhCkaXjA7RiBt19mgIjIbAaAiMhsBoCIyGwGgIjKAQwFwsiuz5TmQObdh7GRhcwL1BGwaGAwwMo71B2gTzqKkDAEY7hAMQ6KNWjobAKBgNgdEQGA2B0RAYDYHRECA+BLAuFkXWTotbKJDN38jA4M3A8AU8Q7hr9NZB5KAZZQ99MNohHPpxOOqD0RAYDYHREBgNgdEQGA2B0RAYviGAZ7EosqfdwMtKkUWoxV4MvqH+JwNDBAPDRgYGTmqZO2rOaAgMDjC6h3BwxMOoK0ZDYDQERkNgNARGQ2A0BEZDYDQEcITAAxzidBCeysCQC96jmAG+hn50LoUOYT5qBZ3BaIeQzgE+at1oCIyGwGgIjIbAaAiMhsBoCIyGAAkhIE+CWiorbYMdSVrFwNBKZbNHjRsNgcECRoc5BktMjLpjNARGQ2A0BEZDYDQERkNgNARGQ2DwhMBfWG+QgYGhjYGBES/6OnjcPeqS0RAgEYx2CEkMsFHloyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMFwA4////4eLX0b9MRoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhQAIYnSEkIbBGlY6GwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBwAqMdwuEUm6N+GQ2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNARLAaIeQhMAaVToaAqMhMBoCo2A0BEZDYDQERkNgNARGQ2A0BIYTGO0QDqfYHPXLaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIkABGO4QkBNao0tEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBhOYLRDOJxic9QvoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhQAIY7RCSEFijSkdDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A4gdEO4XCKzVG/jIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGAAlgtENIQmCNKh0NgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHhBEY7hMMpNkf9MhoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAiSA0Q4hCYE1qnQ0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNASGExjtEA6n2Bz1y2gIjIbAaAiMhsAoGA2B0RAYDYHREBgNgdEQIAGMdghJCKxRpaMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITCcwGiHcDjF5qhfRkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDgAQw2iEkIbBGlY6GwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBwAqMdwuEUm6N+GQ2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNARLAaIeQhMAaVToaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgLDCYx2CIdTbI76ZTQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEgAox1CEgJrVOloCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAgMJzDaIRxOsTnql9EQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHRECABjHYISQisUaWjITAaAqMhMBoCoyEwGgKjYDQERkNgNARGQ2A4gdEO4XCKzVG/jIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGAAlgtENIQmCNKh0NgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHhBEY7hMMpNkf9MhoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAiSA0Q4hCYE1qnQ0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNASGExjtEA6n2Bz1y2gIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCJAARjuEJATWqNLREBgNgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYTmC0QzicYnPUL6MhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjIUACGO0QkhBYo0pHQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgOIHRDuFwis1Rv4yGwGgIjIbAaAiMhsBoCIyC0RAYDYHREBgNARLAaIeQhMAaVToaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgLDCYx2CIdTbI76ZTQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEgAox1CEgJrVOloCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAgMJzDaIRxOsTnql9EQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHRECABjHYISQisUaWjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwnMBoh3A4xeaoX0ZDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQ4AEMNohJCGwRpWOhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAcAKjHcLhFJujfhkNgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDQESwGiHkITAGlU6GgKjITAaAqMhMBoCoyEwGgKjITAKRkNgNASGExjtEA6n2Bz1y2gIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCJAARjuEJATWqNLREBgNgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYTmC0QzicYnPUL6MhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjIUACGO0QkhBYo0pHQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgOIHRDuFwis1Rv4yGwGgIjIbAaAiQHAIPH34gWc+ohtEQGA2B0RAYDYHREBgugPH////DxS+j/hgNgdEQGA2B0RAYDQFiQ2D37mcMDAz73E7pMlx53OEeEaEqLy9ArOZRdaMhMBoCoyEwGgKjITBcwGiHcLjE5Kg/RkNgNARGQ2A0BIgLgd27n4kzvLnrNpOBgUGeYdprhoQfDAxsDAyCHU6SEb6j3ULiQnFU1WgIjIbAaAiMhsAwAaNLRodJRI56YzQERkNgNARGQ4BgCOze/Wz37mdubo8PuB2QZJgmyTBNrKNDjmGBUYeGEsOCWxUPFRSOd3aeHl1ESjAkRxWMhsBoCIyGwGgIDBswOkM4bKJy1COjITAaAqMhMBoCOENg9+5nUgxvvrrp/wErYQSTDAwM3GDGXzD5iCFBkmEBAwODAAPD+o5To4tIwaEySoyGwGgIjIbAKBjmYLRDOMwjeNR7oyEwGgKjITDCQ+Di7kvXzn04VPFuBoPEboay3wy68h1yXysqGBgYWBgYuBgYfjIwvGVgYGZgYGVgEOroeFVxQ5VhgSTD4hyG3zYdkhYRFqOLSEd4Ehr1/mgIjIbAaAgMbzDaIRze8Tvqu9EQGA2B0RAYuSEAXh36aDbDBkOGzvsMWRIM0yCzfx8YGEQ7Or6B+4TiHR1vKio+MDBAJgm5GRiYGBggh63xd3QwVFQsYujj7bAZnS0cuclo1OejITAaAqMhMNzB6B7C4R7Do/4bDYHREBgNgZEXArt3P5vKOOmi25o0hhfXGFQ4GBgUwb1B+Y4OPvDE4E8Ghq8MDMoPHvAYGSns2iXV0SHb0SEGW0EK6goyMDBUVPAxMJh3aD6puLZZYcrmzh2jewtHXlIa9fFoCIyGwGgIDH8wOkM4/ON41IejITAaAqMhMHJCADIryMDAcJHBkq2j4wV4GpANfIgoFywU3sMYDAwMQgwM7B0dH8HKIMJcMC4bA8MP8FJSBgYGDtDKUltThqKODunR2UJIQI2SoyEwGgKjITAaAsMDjHYIh0c8jvpiNARGQ2A0BEZ6COze/Wy726lQhsDHDNkMDP+VGaaxMTB8Ac8HMjMwsIMXhX5HDSRIF/ELAwMjGMEl+Ts6OCsqXoI7hAzgzqQQWG4XQ7Ecw1tFhgU/OhZ9MLJ2dVUCC48SoyEwGgKjITAaAqMhMIQByxB2+6jTR0NgNARGQ2A0BEZDgIHhwu5L093uzWKQLGR4w8/A8JthqlBHx78KBj4GhjfgDiE76I7Bjj8VFSK7dkEC7K2bGyeExcDA0tEBYzLwGxkxMDD8OneOHXzAzFvwzCEnA8MHcI/RnKFXDLSxkGFlxZNWhjMMDNcv7ZLUdQVpgZswyhgNgdEQGA2B0RAYDYGhBUb3EA6t+Bp17WgIjIbAaAiMhgAiBHbvfsbIeILFTd+JYc9+BgtzhvNfwQeHfq+o4AQt8gSdIyrU0cHR0fEZzOVgYOBkYPgG6w0K79ql8v+/Znk5pB/IwMDw8dw5XjU1PvAOw28VFYq7dsH7jf8ZGPg6Ot5XVPxkYDBjOHeKIfwUgw+7m3EN4/zdu+8h3DTKGg2B0RAYDYHREBgNgSEFRpeMDqnoGnXsaAiMhsBoCIyGADgELu6+tNftwC0GOQYGhiKGwM8MDBwdHZ8qKljAXT4m8GrPn2CVcIIFvBXwNwMDF/iIUS6kiUFQVxA8GQjZLviHgeEXbPcgRDvkmJnvFRWMDAwfwZOHzAwMzOBTZ1oZutRBYhz2u7xGZwshwTVKjobAaAiMhsBoCAwhMNohHEKRNerU0RAYDYHREBgNAQbIsTGFDFfiGVIhfbN/FRWsDAzfwKs6/zIwiDAwfAd32yCBJdDRwcbAALlk4j8Dwz/wVCFECpmEdPl+VlQIMDC8BM8QsoINZEVSxANaj8rwjoFBuKODg4HhT0XFVwYG/o6OGxU39BkWzGaY0cog09EhNnrqDFKYjTJHQ2A0BEZDYDQEBjsY7RAO9hgadd9oCIyGwGgIjIYAJAR27372w60VfDAMIxvDf0WGad9gF0X8Aff0+Do6+I2MmM6d44uIYJeXf717NwMDwws3NwbwiTLSu3Z9O3fua0UFB7jTBjETjfy1YsWvigquBw8erFjxtqJCuKMDQipERPDJy3/avfvHuXMcERH3FBQ4wJ3Gv+BOIz/40JpbDCE3GeTlGN5yMDCc78ga7Raihe0odzQERkNgNARGQ2BwgtEO4eCMl1FXjYbAaAiMhsBoCCBCADIryMDAsImhTIrhMDt4tee7igoR8LExHLAVnoJgcWHwetEPFRUSu3ZBeoMSu3Zxq6lxycszMDC82b37lZub2K5dIq6uCAvArC+7d39xcxN68IANrPLTw4cPVqwAy0AJCSMjhnPnmIyMXoE7mb/AE4aQw9m4wVZ/rKj4x5DAwPDFgsGfgYFn9I4KaMCNUqMhMBoCoyEwGgKDGIx2CAdx5Iw6bTQERkNgNARGfAjs3v1sm9upCIbAe+DLJIw65FgYGD6AT3YR7+jgrqh4BO7+icAuD2QC7/37wcCgBO4Nyu7aJYrR8XvY2fm1okLhwQNIFxESxr8ePnynoMCzaxcPqvpPDx9+uHXroZsbM/jAUkawdQwMDJClpEJge7nBM5DsHR2s4I2IPxkY9jBUyzHc1GBYs5Chj6/DZnS2EBLIo+RoCIyGwGgIjIbAIASjp4wOwkgZddJoCIyGwGgIjPQQ2L372ePdu9MZN7i5PWJgeMPGwKDEMNV6V8DTioqHFRXfwMHDwsDwiYHhP/gUGRYGBiZwh02UgUEAPGF4w82NuaPjspvbxc7OTw8ffnr4EKwJRMiXlzMwMLxGmv2D9AbZOjrQeoMvdu/+duvWazc3iY4OdpBWUD+QG9bn5OroeFZR8QO8X/EveEvhZ/CMJf+uXeYMraYdJrwgF/6pqLjZo7B+ZeeOhw8/gM0YJUZDYDQERkNgNARGQ2AQgdEZwkEUGaNOGQ2B0RAYDYHRENi9+xkDA4Ob2+OdDKXcDId/g4/0/Aw+NuYHuCcmCp6U+8vAIMbA8BZ8pstH8LycaEcHG5jBumsXu5rafdgmQAYGBsg+QEjYKoJ3A357+PCBggJ84ei7zs5fFRWQxaIvwDsPP58796migg98cimElAbfYcirpnZXQeEP+NwaiNveVVQIgS+y/wTq/jEwg60Rhk0hfgRzPzAkCDDcNWMoGl1ECg6PUWI0BEZDYDQERkNgEIHRDuEgioxRp4yGwGgIjIbASA6B3buf6TJcPeC2YR8DaHdfJkMgK3jCjYGBQa4DdK38WwaGPwwMIh0dX8AdP9BRLh0dbEZGkq6uvx4+/Aye8eONiIDsAISEJGTB5/tz5yBXzKvs2nUHvP1PuKODm4HhS0WFzK5dHAwM79zcGDo6fjIwfKqokN6166mbG6QTyNfRwWtkxKOmxgPeVQgx8+vDh3cVFHjBJ9P8BQu9WrGCB7Z4lQPcg/1QUQG5uEIQ3H2VYWC4wxCyisEMckGFeYe8WISvvLwAWPcoMRoCoyEwGgKjITAaAgMJRjuEAxn6o3aPhsBoCIyGwGgIMDCAbpKAzArOYzipx5D/CXyvw9uKCg7wXfAM4O7fDwYG7o6OF2C2UEeHQkTEn1u33ri5qf3/j6s3iBa2T3bvvgvuDYp2dLwDm8MAnmb8BO5nMoHt+gwWh1xBoQZeWYpmCIT79eHDV+D+p1hEBLe8/LvOTgYGhn9GRi/d3MR37Xrp5vaHgeEzeBUrMwMDZErzD3gyE3JBxUmG6nAGk9HZQkhgjpKjITAaAqMhMBoCAwtGO4QDG/6jto+GwGgIjIbAiA6B3buffTp3R6rC/jFDFuRSeCWGaZBllmzgk1rEwetCOTo63oO7av9hofUfPBHHBF6uyQa+WpDdyOgXTJaBgeHTuXMQHmRBKQO4Pwa5gB7S3/tYUcHLwMAG7g1+Avc2GcDH1TAwMAiAjwz9ALYRzgWZYGQEMZNPTe3rrVtfzp1jZmAQMDL64Ob2BeLaXbueg/uckJlD/o6ObxUV/8FLSf+C17uyMDCIgo24wRAiybCGn4Fhfcep0SNnwEEySoyGwGgIjIbAaAgMDBjtEA5MuI/aOhoCoyEwGgIjPAR27362xu3ULAZJBob/uxnKBBgOc3R0fK+okGRgeAzuO/0Dk1IMDD/AZ7cwMDAIgruFgh0dTAwMkG4eF3gW7gd4cg8enhApeK8PzoAogHBBE3rgDiED+DrBb+BuIeTqCHi3EN4V/FBRAe8iojF+gDuNX8FGM4MdIwTuTEJ6ngzgBa4MFRX3wX6R6+j4V1HxAXwQDl9Hx6uKGyoMCyQZFjMw8Kzo4PCO0ERemAo2cpQYDYHREBgNgdEQGA0BmoPRDuEoGA2B0RAYDYHREKBrCEAuFZzKsMGcofMzA8NLhixZhmnc4Dv9OME3vL8Bd5kYGRhUdu366eYm/f//c/BBL4/c3CBHY/ODN+YxgGfb/jAwQLpY/2FzgHyweTwGBgZeNTWI39D6Wq87O39WVPCBz4lhAJ8Q88zNjbejQwV1mehn2Nmkn27dgpjzETbx+KGiArSJETw5+Rd80CgDeJIQooyZgUFh1y5+sO1vFRQkHzx4vmLFP/BtGT/AvmMD+5SBgQEyVfiBIeE9g/L9DvfR2UJIAI6SoyEwGgKjITAaAnQDox1CugX1qEWjITAaAqMhMNJDYPfuZ1fc1txkkGNgYAhk2C3CMI0D3LUT6uj4XVHBAr7i7wdSIHEwMPwDI0gPEDK5B5kAlNm164ubGx8DA9uuXW/AJ4JK79olgXqFIJJJCObPhw9fKyiwd3SwGxnxubp+Anc1+VxdX+7e/Rnc2ZMEbwtEaMDB+rR7989z535UVDB1dIAOQe3ogMwTQg4dhWiCsisqPoIPHYX0ZhkYGBgZGLgZGF6Apw3FwDOKXBUVJxmKDzPIqTN80u+Qlxs9cgYSgqPkaAiMhsBoCIyGAO3BaIeQ9mE8asNoCIyGwGgIjPgQgMwKFjJciWdI/QRe/PmnooIRPL0mAL7agYWBgQ80WwgKKXFwH+kneDXmT/B8Gjf4tE/IjB9kru/Xw4fvwffIc7u6fnn48Dn4iBdeIyP8fUJIL46BgQHSG2RgYIB3CBkYGF6CXcnb0UGwT/hp9+6Pbm4cHR2i4BnFt7t3v3Zzk3/wgBN8GOmnhw8/3rr1Hty9ZAL74j3IW6CtiV8rKiDdQiHwtORriDiY/APeuwg5dWY2wwzeDiMjI2FXVyWw5CgxGgKjITAaAqMhMBoCtAKjHUJaheyouaMhMBoCoyEwGgKQE0Rvuq0RZ7jJwMD4hEHNiSH/OwMDBzho2GErLQU7OqSMjH6eO8cIW+35Cnw0i9iuXZCzYbiNjMRQZ//egw/2FAT3x8CGMdzu7ITcHKiKJAiRgpDw3iBfRAQ7uOeG1iEE7ScE9y0/V1RI7doljmojxBDQjfadnT8qKvh37eJDUnCDkZG7o0MW1erPu3f/AncLvxkZ3XNzg28vlI+I+H/r1odz5ziMjN66ubGDV73+BlsgBCYfMoTcZFCXY3j6lkFZZlfUaLcQHCqjxGgIjIbAaAiMhgBNwGiHkCbBOmroaAiMhsBoCIyGAHi+7REDA8N2hjIhhsMPGbI0O+TeV1QIg5eGfgNvooOcC8oInh7kAU8DwruCwrDu1n1w3w+5T/h19+4vbm6CDx4gXznIwMDwArzs81NFhfqDB5CJRHgsYO0NYnYI4X1C0GykkRFan/Dnw4evFBQ4YMtN4YYzMDBAJglFd+2CO/vXw4dvwGtThWG9xMe7d98Dd3QZwEtG2cGrYXnBE6SQ3iBEnAU8QfqxouI7QwIHA4M5QxgDw79duzRHu4XIAT7KHg2B0RAYDYHREKAWgCxdoZZpo+aMhsBoCIyGwGgIjIYA6F7BUsYNXG7SyxiWLOn49YtBl52BwaADtHVQmIFBiIHhG3jVKOQmBjbw/jpu8JUPl93c3jMw/O7o+MbA8Al2oIsiuEP1ws3tK1jk18OHX9zceHbtQusNMjAwSLi68hoZ8XV03FRQeAHeHAiJjE+7d38C98REy8vhc4MQKUySW14ecrTMMze3O+C+KEQNyJAVK7D2BhkYGIRdXUV37Xrt5vYWbC9mb/DLw4csDAxqu3ZxMTBwgY+fYQVPDD4G7zDk6ejg7+hgAdsEWSvLx8CgwrBAc1fUFIazUxguSbgpL2Bs2b37HljJKDEaAqMhMBoCoyEwGgJUA6MdQqoF5ahBoyEwGgKjITAaAhd2X0pn3ODm9kiL4dFnhmytDlnJCnv5DrlPDAwvKiqEjIxYGBg+MTCIgS/oE+roYAZ3jdjB9wH+Al8tCJkzfH/u3DkFhQOMjAcYGS91drIYGf1hYLipoPD14cOvK1awdXRww+YP0cJcwtVVMiKCr6Pj87lzkD7h687OT25ufLt2Qfb7oanHxVUpL5cCn0EK6RNCJhh/VFTwRUQgrxRF1i7s6srd0fHazQ3SG/zMwPDLyOhWZ+dpRsbTjIzPVqx44ub2xM0NcjQOO3jd7F+w94U7Op5XVDwBn08D2W3IA146+4yB4aabm2sHqzlDFR+oG8ns5naTkXHrys4dDx9+QLZ6lD0aAqMhMBoCoyEwGgJkg9Elo2QH3ajG0RAYDYHREBgNAUQI7N79bJfbqQiGwDsMWeIM0yB9vP/gY2OYwcdpMoDXhT4H9/1EwIeL8oPXiIKm19TUXisoSP//D5kVhJ/IAjmlE0J+AZ/OwsHA8At8SifCYjAL0ssCM6HEp4oKaKero+NnRQUneB0mVA5MQW6nYDx3DrSnEdwBA60gBW/5A8uDiM8VFWxgx/8Fn23DgWoI5LxTkDokzAVeCPoB3On9WFEB2TcIUSkD7mHyqqlBlrM+YGQU3LWLUU3tPvhEnLdgD8qBXfsNvKpWEBxKDGAH8DMw3GZIkGRYwMDAwM/AsAh86szoHRVIAT/KHA2B0RAYDYHRECATjHYIyQy4UW2jITAaAqMhMBoCkBC4uPvSPrcDNxnk9BkeOzPkfWZggOyLYwffF/+qooK/o+NdRYUEuCt4F6znLwOD8q5dsrBZvs/gKxxEwEtDwfIg4svDh5/BJ69AuoKQ9aVS4Gvr+Ts6GEFKEBjS40LwGRh4YXsUP4JvOBQGX2ePrOBDRQUXWM0f8KQlI7ifidyxZGNg+Aq+DIOLgQFyNeJfZP2waw/hYqwMDL8rKljBXceX4J6wcEfH24oKZgYGxV27BNTU+GAn2UC0vOzs5DAy4ocFwpvdu1+C17X+Bl9w/x/sKsGODgEjo99ubu8YGL4yMIh2dPypqBBlYGhl6FJn+MjAwCHaYWIRYSEvLwAxc5QcDYHREBgNgdEQGA0BUgFkwwKpukbVj4bAKBgNgdEQGA0B0F5BN7dHsxk2aDB8NmHIFwRdJwgKlv8gAoR/VVRApsWEwZf18e7apa+m9gA8Ifb+3Lm74IM3FSIifp07xw6eo4NcQP/53LmP4G4kpJsHuYRQqKODjYGBASz+oaJCADxZJxsRAbKGgUEGxoBwP65YwcbA8KOi4h+4F/qTgeE1+L4HYXD/ioGB4Y2bmwhYKQsDwx9Y1w50tg3YGa/c3NgYGPh27frKwMDDwMAN7tfxd3T8Z2CQRLUIbAaIeLdiBQu45ym0a9dbNzdOBgYesC5hsDvfnzv3/ty5txUVEK4i2BAOI6Mf587xu7rCu4IMDAziu3Zxqal9uXXr67lzfysqBMCHr0L2HP5lYPhYUcHBwPCEgSG349+NiqcaDAsuVKQoVPzo6JAenS0ERcMoHg2B0RAYDYHRECAdjM4Qkh5mozpGQ2A0BEZDYGSHwO7dz0QZ3jx0m3mbQf02g5w/w24FhmmfwJNyHOAZPEiHEDQpB76/4T94HSabkREv+M5ABgaGj7duPQD3BhkYGEArQsH3TzCDO2aQriBIDawzCV9j+QZ8xItIefnnhw+vKCgwgC/uk4mI4EWdeUO+JPDd7t1v3dzEHzx4uGLFO/CaTClw15QdPDcovGsXMzgqPzMwfDx3jhE8NfcDPMsntmvXKzc3sV27fru58e7axePqegd2rQXkyBmwPhDxfvfud+CZPQYGBqFduwRdXZ91dnIaGQmCp/4+PXz44RboTkJIb/BtRYXKrl133NyEwbso2UEGQDF/R4cYuKMI4X8H9wkhC0TfVVTw7tr1HGwLE3hJ6j8GBuGODvaKikMM1a8YfjAwcMgxPJXscBIbvdEeEnyj5GgIjIbAaAiMhgDRYLRDSHRQjSocDYHREBgNgREfArt3P2NgYHBzezyP4aQeQz4reC2oUEfHF3AnihUcPt/BpGBHx3vwBjx28JmiYDEQAVmTCe/1va+ogNy89xIkycDKwCCPutEOLAwlnjIyiiJdNfFs9+6H4D6SQEeHJmy56WuMSwJvMzIK79ol5Op6u7PzO7hPCJlkA+1d7OjgBB31KSXA8AziJPaKipfgxZlsDAy6Dx5wMDC8VVCQ/A/p4YJurv8M7tzCb65/v3v393PnOMHzipAeIAMDw3twL1QFpgvqeti5qfdXrPgBdgY3eJHqTwYGyNbED2BByMwnA/jMVYhGLvB+Qswj4LgZGD6DtxfK7Nr1xM2NkSFhA4NhK4Pc6GwhJNxGydEQGA2B0RAYDQEiwWiHkMiAGlU2GgKjITAaAiM6BHbvfibF8OaO20whhmkvGLLlGKaygRdb/gP3Sf4yMEiDN9pB+jYM4IWakG7Pb/CtEsyw81EggQjpFjIwMDBWVPAxMPzt6LgNPn8FNFvY0aEQEYG23Y6BgeHXw4evFRSQO4QMDAyfHz58Al6A+qGiQqKj40dFBQeYFAD3KiGHxHytqGAEL+aE7JEQ6uj4VVHxmYGBpaNjVwVnGYMZA8P7HIYXrgyHFBkWiDIwfOno+AH21Dvwbj3QfYkdHfzgLh/IxnPnPoENVNi16yO4O8rZ0QGfD4T4joGB4Q64FwrvIsLFX4MvpWBgYIBsF+Tt6PhaUQGa4AOvLEXuB0LmPzkrKr6BA5YJPHsJWXEK8hd4T+YdsLmQSU4JBoZHDCHfGRgkGdbwMzCs7zg1uogUHDyjxGgIjIbAaAiMhgABMNohJBBAo9KjITAaAqMhMMJDYPfuZ2IMbwzcvjMw/N/FUPabQZeXYZoc+HST1+A5Lmbw4TFcFRWcu3b9ZGCAbMBjAE988RkZfTl37nNFBVdHhzJsEg8enr8ePnynoMDW0SEElnq8e/f7c+cgCzuFMLqF8PWicO0MDAxfHj78cuvWYzc3DvAS0I8MDN/BJ6+Aem7gCTfRjg5WcI/rM/iMFiHwlCY7A8N5holZoK4gxLD34N7Wv5UMZwUZ7gowLICcgiMEPh4GckbOO9iuxS/g3iA/aF4Ret6pDLYDY5BXjULseA0+M0Z81y5IV1B81y5R8JrSN7t3v3JzU3jwgAt14SsDA8Ozzk5G8GLR++BggThepKNDENw7/ePm9h7cjfwL7lS/AceFYEfHq4obHAxfTBn8GRh4VnRweEdoQg41hThjlBwNgdEQGA2B0RAYDQE0MNohRAuQUe5oCIyGwGgIjIYANAQgC0Rvuk0yZ+j8CRaDTLJxgtlsDAzvwZ0Q0OY3I6Mfbm4c4H13DAwMYrAJOhFwn+f17t0v3NwkYF0gsG4Q8YKREd4bBPHB+NPDhw9g+/0YkA4jfdPZyRcRwSYvD+kEfgLP1PF1dHyqqOAD76ZjAC/1fA++l0K4o+MD+PwVKbCCf2CTv4MXrz7tOBgNukviDgODKvigUykGBobdu++5ud1lYHjAwCDT0SGiWTFNg2EBLwMDY0fHU3DH8hd4nhOybpOVgUGUgeEt+DDVO7BzYiBbBAWNjCCniSKvGoV0BSEHxrx0c4N3BcGOAhHXGEFnpmqhLjH9snv3Z9j2RdCU4MOHD8FzodBdl2AvQyYY/4DMYPgEJkHziuADTn+AuXygCyr6+DpsRmcLweExSoyGwGgIjIbAaAhgAaMdQiyBMio0GgKjITAaAiM8BC7svjTN7d5sBkkGhv/5DFfiGFJ5Ozp+MTC8r6iQBx8AwwTeIgi5KI8J3C1kA8/OcYFP1+Q3MoJ0BeHBiNknfNfZ+auiQghpTyBcMaT/AzmMFNL/Ee3oYAIvB30L7v5BOoEgZeCDZ0AdJwYGoQcP7igo/Ab3+kDzkwwMLODOIWQV6/uKitMME4sYVBkYQDN/u3bJubqCuoLIloK7hTcZGFiqGJ59YvjnynBIEjxbyFBR8QHc22QBTz9ygfdGsoJ7v2wdHV8YGCC9QQjJAOaKdXSwVFSwgbujLOCzQyEWQWYFIWw4+e3hwwcKCtwdHfLgmVLQzCdqbxCu8tPDh5cUFCALR79XVPwBL4VlZmCA3Hb4AbyTkx28GpaBgeE/+CbG/QzFSQwGDAw8hzs+y44eOQMPylHGaAiMhsBoCIyGAAyMdghhITFKj4bAaAiMhsBoCIDmyp65uT2ax7BBj6HzIwPDS4YsrQ45yPV6Yrt2/T93jqmi4gV4DgpyYfo/8GpLIXA37DO4AwZZ2QjpoggYGfGrqYGupJeXR+4Tftm9+4ubGw/49E54qH95+BC01PPWLYgI5PIJCJsf3B/7CuGAbWRgYBAE32vPycDwFryXjwG8TZEZfAgnZH6MhYGBE7zR8RCoKwjaK8jAwLhrlx5mVxBmMIjevfuBm9tu8PXvvDkML/wYDqkxLHgP9jITWPQFSBWoDwY5Skego4PLyOgD+KoMSJgwgNevcoLD5DvYASzg6UTQkaGwAGFgYEA+HBWycFRs1y4RV9dfDx++VVBgh62khVzFwcDA8ATsTcj2S8j9E5AbFBkYGCCXNEKOTgVdrggOmZ9ubowdHYxGRqfdloF6tgwMC0evswfH3SgxGgKjITAaAqMhgAwg61+QRUbZoyEwGgKjITAaAiMxBHbvfjaNcdIat1NpDC+uMVjwgi82EGeYJmhk9JWB4Qt4c+CnioonDAxcHR3M4GNX+MC3vcvs2sUIngST2bUL3huEbAX8cO7ceQWFhytWHGRkfHHu3HdwrwZyFcQn8CGZtzo7b3V2nmZkPM3I+GzFimcrVnw+d+6Jmxu888Pf0cEPvtCCjYGBGxwtgh0d/8AXVPwDO+kt+NL25wwMzysqeMB76tgYGDgg2xo7OnYzTDRh2FbEYA7uCur//++BvzfIwMDg6qrw/3/qrl0mDAxMUxhkDjLo9TDM+9yxCHKsCx8DAxcDgwj47gom8C2FPyoqvp479wV8KI5QR4doRwcf2Kkc4K4pC7jj+gc8uQq6S+PcuYsKCo9WrHi0YsURRsYjjIxXOzuvdnaCjrHp6AD1xTs7HygofAYfJAMJFkhX8ImbG7wryMDAANq42NEhAL544y84pv6C+59MHR1PwdcVPnVze8LA8Lii4ombm3qHBqj/ycBg3iHypOL4ZoUpKzt3PHz4ARyco8RoCIyGwGgIjIbASAejM4QjPQWM+n80BEbBaAjs3g3qiTAwMNxksHwGXn/4rqKCHzzvBLlvgR98dCc3+I5BoY6O7xUVkMsheNTUeOTlMc///ASe6/t469YH8CUNH8Eb7d5XVDCDe1OM4A14kGk0yEQiqKcE3qoHmkMDz25BIoVXTe3zihUM4INVnrm5fQXPE0KkBMF9P+6Kil/gjuVv8FLVv+DlmuzgDuQuhomloGNjvjMwvN+1y4JgPxBiLBoJmy2UY2D4B5ktVGBY8BHc2/xUUcENnjB8Bd5eKLRr12s3N8i9guzg9aJcHR1/jYxeuLlxdnSIgy8Y/Hjr1kdwgLwHexbihfcVFYLg7iUDuBsJmiMFhzwDuNv5ERxoMuA9mfD7GCGO/PHw4XMFBUXwzkNIgN9fseIn2FUM4PnMn+AeKWT/pGBHx8+Kit/gyL1RccOMYYEkw/rRCyogITlKjobAaAiMhsAIB6MdwhGeAEa9PxoCoyEwokNg9+5n39xaf4HCgPEJg5obQ/43cC+CA9zJ+cvAwA5eh8nOwMAHUsPwCXxKJ5jJoAw+kgV+IYQ0uGcCkfry8OFn8MpPyLJP+NQWKwODBPgomh/g6SzIQTXM4AkuOWxXTSAfrPJu9+43bm6sHR3fwLNtnOAtgj8ZGJR27Xp37twP8PUM38BGXWGwjIEeGyO9a5eyq6scxFVkk+C9hbfBHU/uHIYX4R0sHBVxvOBuJ++uXS/B04P8HR3/GRggt1P8OneOEbY9EnmhLJoDnu3eDQkfyF3zTAwMIuA1t7/BuzT5Ozpeg2cdIVdxMDAwKIB7lcgXctxnZBTctUsAfHLPG/AppqBudkeHgJHRIzc3gY6OlxUVvLCe82vwfYag5bvgbvlDhpCbDOpyDE/fMijL7IpydVVCc94odzQERkNgNARGQ2CEgNEO4QiJ6FFvjobAaAiMhgBKCMBnBXczlAkyHL7HkKXZIccInkT6At56J9zR8buiQvLBg/+3bv0Cz2vxRoAO+YScAgox611FhfKuXZxg2d/guxAgPRx4DxCZAZm4YwCfPfO+ouIr+F4KyBEskFNSIIeyKMK6Pci9QYh1txgZRXbt+sfAAJln+wk+teUxePrxLfg8lecdB8NAXcHvDAzfd+0yIG9WEGIXGvnw4YcVKy6Ce52g7tvGDkaBipWcDAsgU5oMFRX/QPstGdgYGLQfPPgDPg4UcpcGAwMDWp/wBfgqwidubvAjUiFn5IBumAAfjXNbQQG0LRDckYP0BiHhfNfNDb4iVxC8F/H9ihX8RkZ/YLcaioMnEkFTi66uXx8+fAl2BqeRESsDw283ty/gk1f/giOXG7zs9mNFxU+GBPAdFaEMDNy7dmmOdgvR4n2UOxoCoyEwGgIjAYx2CEdCLI/6cTQERkNgNAQQIbB797NdbqeCGAIfMWQ/ZlDTYrgpzTCNB7woVICB4Ql47o4BPNXGBL587zd4fSPfrl3giUSoOQ/BB5xAThllA2tR3LULeZ8bP2zlJ3yhI/KxopCbGMR37WIHHzlzf8UKSG/wLdgu4Y4ONgaGr+DVkpzgftFHsDgDeGrrB/hoTch+QlbwItIvDAzPOg7CZgUVdu1SomJXEOphMAXuFt6uqHgHmS2EXFCh36HBXlHB0NFxD+xIyLJY+Pk3AmD3fwb3V5nAc3QC4KNHIb1ByFpQCVdX5FN2vj18eF9BgQd26ChkOSjkzFV4/xDSM+QGn14DOUEH7EAGno4OyCwl5Hibz+BLICF9+68MDJK7dj0HR9x/8BWLQuC7Kz4xMOxiqJZjuKnIsOYMQ/PobCEkJEfJ0RAYDYHREBg5YLRDOHLietSnoyEwGgIjPQQu7r5k4PaNgYFhHsMpSYZbHAxThTo6voC7MULgZYrIAcQB5jCCew4c4IWj8Ok+6LQYWME/8HV/T8Hb3kQ6OoSMjOA9QLA8lEDuDUKE4H1C+E0MkJ7PLQUFHvCJNV/AvS4G8OJVyHY7NvBE3E8GBibwYkhIp+gqw8Qc6F7BP7t2Sbq66kDMpx0J7haeB193z1LN8IyB4Z8ZwyEN8MEtHysqZBkYXjMwcHZ0/AUH3Vdw8LKC19y+A8+OMjMwKOzaBdl+CXHkr4cP3ykoIJ+5+mb37q/geVf4RRQQlaCbNh6CzmK9qaAA6RIzgDuZP8Cny8DVQBgfwP15BgYGxoqKt+AgBV13CJEDk2zgs4JEwWmAH9w5PMmQEs7gysDAvaKD2SLCQl5eAKxwlBgNgdEQGA2B0RAYzmC0QzicY3fUb6MhMBoCoyEACYHdu59tdzsVxRD4HcxnBZ/D+R3c1+Lp6PhaUSEOPtYScmUf5GSUT+CeHuRgkl/gUz3BWhn4wEtDIezPbm58DAw8u3bdB/deIHcGCnV0yKNuCESe/oJohJAPOju/VFQoPXjAJS8PWju6e/dr8PwV6EhMcBf0Dbif8xF87x83+LYJLrD4f3CfcFcFJ/jYmFcMDAy7dpnQaFYQ4lRMEtwtvF1R8RC86RJ0QYUrwyF98CLSL+BOGkQLL3jqFbJz7ysDw3/whOH7igrFXbtkwHv/IL1BNtglExBdDAwMkD2B4uCLKOCCEPFXsFASA68RfeXmxtHRwQ6LF8h6WoiWDxUVkuBe6DswXwB8PCykW/irooILvMwVMvHLDZ59vccQIsmwBtThB99ozzt6oz043EaJ0RAYDYHREBjeYLRDOLzjd9R3oyEwGgIjPQSe7N7dCjqeE3QTQzxD4B/w1erfQJvHQDdJQI4MFenoYKyo4Ny16wu4X8dnZPQG3OUQ2bXrLwPDCzc3iV27xMC9F+TQROvJfHr48CF405oA+FI+BgYGSLcQV28QYtQlRkaejg4eIyN4J4e7o0M0IuL7rVsvwdfo/YDdRM8NnnmDXD+4u4KzCDQrCLqYfdcuBVdXRYhp9Cdh3cJ34O7qmxyGH66w2UIO8IWNkO6WQEcHD/is1B8MDPfd3CATnpBuIdO5cz8rKqQePGAD94qRvYDWJ4TcVQhRALmxEMKGiCOLQMRBB5bCLrj/iGTvB3B3lI+B4ee5c3fAE5iQ2eB/4MsSQefKVlTwMTBIMsxlYGDLYfht0yE5OlsID9JRxmgIjIbAaAgMPzDaIRx+cTrqo9EQGA2B0RAAhQDk2JjtDGX8DIf5OjogVx2AugHgoyw5wAsav4FXEkqAO4RsHR2cSF1BYVgP8NXu3Zh9QrTeIMg+MEbuFj4GX53HXFHBBJsZAythgG8IBM1EdXT8BS++/AKeroT0lEDKKio4GRhegbcIMoMPFOUG71Q8Dr1i/jsDw9Ndu5zoPCsIchg2DO4W3q2ouAA5eTSH4YU/wyF5hgVfwVOFzODbKX6B3c8HniGEmPENfLfHJ/DhOh/AHTOIOGTbIYT9BbaR8hd4FyIDAwPWjt/Dzs6vFRUKsLlWiN7PsN4gLywqn+zeDemOfgRPD/6FqAOT/OCpV0YG0Lk4kCniGwwJkgwLPjAkCDLcNWUoGr2jAhxOo8RoCIyGwGgIDEMw2iEchpE66qXREBgNgZEcAg8ffn5/6/40t3sMDAyzGSQuMFj+AW9p+8PAIGVk9B087fazouIXuGcC6Ztxga+t+wMONfGODhHUBZ8MDAyYfULMPYEMDAzw2yYg18r/ZmCQZ2AAuQN8pR4T2PyPFRWQvYiQuwdFwZ29H+C1i5Ab81gZGATB6r9WVHwBb3L7Dr49D7xAVBXc4/o9sLOCYH9gIcDdQsiRM0w1DFc+MAhAZgu/VVRIgKc3/zIw/AHPJEI2RoKOA2VgeAE2CXkTIKRzCOkWsjMw/AD3jR8wMDCCwwE0Rwo+YhT5/olvDx++XrHia0WFFuzyD8zeICR2IMfA/gN3/BjBQwOQPirEUpDhYKeCYgHcRRQDcW1NGdJyGN6pM3yS6zDRH91bCI6yUWI0BEZDYDQEhg0Y7RAOm6gc9choCIyGwEgHu3c/Y2BgcHN7NI/hlB5DPgu4y/cZfDPEKzc3xQcP3ioofAH3Af6CexeQq/NAnZOKCkYGhr8dHY/BDNAxJLC+hwCs74HcJ4T3BkGXwmO7b1AGfOIoOwODMLhDKAS+5OBDRYVIR4cs+FYJSFT9BC8xZQPf3v4ZfKoNJ/jYzF/geUsO8O47VgaGqx0Ho6GXSbzbtctykMwKQryASYK7hXcrKs4xMECvs4/rYOGviGME96/4wNOeXLDpUOaKCmbYSl1RpJBhYGC4o6AAmaljAF8U8buj4yl4FhFyRQdoyyX4vg34/YS/bt165eYGmT+E9wa/gN0H6QRC5gAhvfGPFRWCHR3fwbeMcIHTCVghaF0xR0XFTQiHgUGqo+M/+ABVSDf+f0XFeYaUSAbv0dlCWAiN0qMhMBoCoyEwHMBoh3A4xOKoH0ZDYDQERngI7N79TJfh6kG3DY8Z1G8zyKV0/GCqiPwMDhQmcOeKgYFBsqPjFfiic2awuHJ5OZhm+PXw4RsFBb5du3jACwshR33eX7HiHbj7ATmABEKygHtr/OBFnr/BiyHhvQtIZwOyBJQXfLqJpKvr5927f547xxYRgXVv4dvOTtCl9gwMvBERr1as+MXA8KWi4g94wpAPPGEo2NEB2yt4h4GBOlfMQ7xMBxLcLQTNFlYz3G5lUM5heOHKcEi/Q4OhogIyZcoG7uZxMDDIPHjwcsUKSKQogSMFsicQ4khIB+9FZyeHkZGAq+unhw8/3Lr14dw5yP0Tb8G9/btubsIdHW/BR+8wMDCIgy+Q/A2e9YX0zOGxA7nlAn4MLNoFiaAzgcCRwhIR8XzFClZwjPxjYID0GP9XVPAyMLzpWKRfwTiF4QYDA4d5h7xohO/oSaSQmBolR0NgNARGQ2DogtEO4dCNu1GXj4bAaAiMhgADbFbw8TyGk5IMN6U65BgYGL5XVHCDz5aE9NAYwF077o6Ot+AA42FgEDAyEgJ3/yDnjvw8d04E3BUByyOITw8ffr516wH4gBlR8AQXP/gqCMhNgJArK5gYGOTBZ11KwgyE638D7l1ATIbvLRQyMnro5iYIng8Eba4DT099BTv4Kfjyw4/gXXMXGSZmgY6Nof4V83Dn0YEB7hYirrNfyXBWiuEuO/gkUlYGBglQ/6oD4oz34DNducAnvrKB13NCuoIQ2Q+7d/84d04CNY4gXfcHK1Z8AIcYJDrYwefHfoLFPmSZLhMDg/aDBxCjkBeaMjAwIPcJIftChR48+Hzr1vdz575WVPwH3ybC2dHBycDwE3wQESP4EsUfHYvkKuJmM0xsYZAbnS2EBOwoORoCoyEwGgJDF4x2CIdu3I26fDQERkNgRIfA7t3PpBjefHbTf8yQxQCeu1NmmPYZPO/0nYFBDLzeUv7BA1YGBsgddx/A50wKdHT8B8/8KO3aBbkP/ZObmwjSEZeQnWagjuK5c5AdhvBQ5gfb8hO86PQ90kUUkNkq+NpFeJfjKSOjKNhkiJnwhYvsFRWfwKfFQI60gTheCNzV/MTAcJZhIvgyiTvg6/podcU83FN0YIC7hbcrKj6Ae8Hc0OvsGRawMzDcBc/fMoE3FvKC2d/AB/C8BS8HFTQyElBT45OX/7B793s3N0XY/kBIeMLjCD4B+BfcyfwF7g3+Bp9V8x68NJSBgQGTwQAeF+BTU3u1YsX3igqJXbuYwbfY/wB3AhnA2x1Fd+36eu7cJ/DK0j/gZa584A4hJwODKAPDdobqrww/GBg43Bha13eciohQHZ0tpENyGrViNARGQ2A0BKgORjuEVA/SUQNHQ2A0BAYgBN6e371ytZudfoecXQSfJOhSuwFwBL2shBwfCt4G+P8qg+VlhiyrXQEvwNNuj8FuUN6167mbGy/4RBY5BoZ3DAyy4AkiXnn5J7t3fzh3jpWB4S14wkccfNEfI3iRJ+QkGLABUIK/owOy/hPSZ/vl5sbW0cEREXFPQQFy8d172NrFd7C1i5DL4oXA836sFRWQCSUByOwiWPBnRYUoA8Mf8BJH0BZEBgaVXbvenzv3tqLiBsPEbNCsICMDw/9duwRcXTWg7hgWFLhbCL3OnoHhTzXDC3OGQ+odGp/A92r8r6jgYWBg6ej4UFHxmYGBFRw+kO2CH8Cdw/8VFVzgy+4h2/kgR/LAu4KQtaDs4BsshB88eL5ixZeKCuFdu7jU1D6Bl5hC+oSgM2PA580KdnRAbgeBdBTfg8+tYQP3yZlh20choQ6ZsIWwv8CkmCsqXoOnMSErXWVBd1TMAl9oIj3aLYSE1Sg5GgKjITAaAkMIjHYIh1BkjTp1NARGQwA9BN6e3/3yN8PxtW7SXAwbPzFkm3cw/Gf4K2+kb+GKrnRY8HfvfrbL7VQkQ+BthmwGhv+PGdRdGfK5waeDcIJvlhcDbxSEHMrCy8AAum28o+MBeMkoJABAN0CA56MgXAHwpNVv8Kme8L4ffI8ZRA1op9/Dh+8UFOCXp7/evfulm5v4rl2i4GWi8LWLoE1o4G2Kv8AXKvxmYPgA7rZCjg+FLF/kBdsO2Tj3raKCs6PjOwPDrgrOMlBX8BUDA9uuXXqD/NgYeLCQwQB3C+9WVNxnYHjPwCCTw/AivINFtCKOh4GBsaOD0cjotZubUEfHE/AqUIj5fxkYeMC3xn8DR5ZwR8dn8EmtkPiCr9T9ArtkArIX9Cr4gkfRiAgu1OsNIZEF7yUygE8xhdxDyM7AADqVCDxDCLnqgxm2HvjnuXMfwPOE38GJSgis8hd4wyc7eFL6PtKN9qOzhZCIGyVHQ2A0BEZDYKiA0Q7hUImpUXeOhsBoCKCHwMUTu89scGNgYJj/lEGPj2H6f4YMJoYZP0EzF/tsOowdhtVU4cXdl6a73ZvJIMnAwHCLweIFODAYwYsMOcHsn2ASTnCAF/j9YGDg7ujgw5gABE0oubl97ehgrqgQfvDg461bd93clHftkgV38OCGwBkvGBnZOjp4IiLgl6ej9QnhKt/u3v0avOeQDXzqzAewxH8GBsGOjj/gU0lA9y50dDwDL2V8X1FxBnSvoCp449ufXbtUBvCKebBL6UTAuoVvGBiYNnb8fVRxxpyhFnJCD2TD53vIzC0Dwy/w3kIGcNfrGwMDA/i8VgEjIxnUmELrDTIwMEAuovhSUaENW2iK6TdIJEJWh0rv2vXBze0TrM8JWbP6FjyUAJmo/AeOMm4jI5Zz534xMNyrqGAHLytlBvf5ecFLkV9V3FBhWCDJsJiBgaejQzo7QowHtTuK6YZRkdEQGA2B0RAYDYEBB6MdwgGPglEHjIbAaAiQEwJPzuy+v8Ht+W+G0+8YmJgYujgYrD8yHGVkKGRjOMfMkG/a8ftGxR/tDh/XId8thCwQvcRgeZ0hW4Jh6guGLHmGaSzgMOPr6PgHXmr4BHzToEJExO9bt76BjwPh6+iQKi+/Cz7WBXJeKKgTyMAAmf37snv3r3PnQN2MiIgH4LsfFCIicHUL4ZdMwHuDYMtB55G8dHNTgl2GDu8KMjAwiO7axQpW9JmB4aGbG6TPAJmGYgJPQH1nYEC6Yv7Hrl36w3hWEBwSWAhYt/ACeP6PbzLDed8OVsgGy48MDHzgiV9WBgbhXbvYwT3/j+fOfYQZ876iQnHXLki38NfDh28VFNg7OoRQT5359vDhfQUFno4OeVRxyFkyL8H9dtCppLt2caupccnLv9u9+62bm/CuXSxqagwMDPfBCQNyv4XKrl13wOolGBg+gd3ABD749TlYEHIILQd4JvMnA8PLjtkcFakMDAxiDAwrRvcWgoNrlBgNgdEQGA2BwQxGO4SDOXZG3TYaAqMhgDMEThzYfXO5mxAPw///DHeYGH5rdWw+WHEUcj4j+KySDquOirMVi7kZDIyH6sbCC+BZwVngWcG9DIt+MjBId8gJMDD8r6h4BT6Qk5eBgd3I6LObGz94lu+5mxsPAwNkZSak5yCza9e3c+d4jYzEkCaUfj18+AXc1ofP+H16+BBXtxBXbxByDf09BQVQu3/XrjfgjgEDeEJSNCLiE/iYSkYGBkhf9D8D6II7HvBKSNhlEoP6inmcyY4GEuBuIeiCCrDZfyYznHfrAPWmf4JPXn0AjmjQqt2KCtGODmFw2N53cxMEb8iEdAsZwVsH4UfOgM2BEm927/4K7vlD+oTfHj78eusWclcQsugXqpqB4TYjo/CuXfATaBkYGCBLTCGdQ0EGhr/gs3G+gad8GRgYINfZC4BPmuEDL1pmAM3Qgw6efePmJgraW7g4h+E3AwND2i59XVcjuEWjjNEQGA2B0RAYDYHBA0Y7hIMnLkZdMhoCoyGAMwTent99/fY5PVvEdN+zxw/3blyh+LLi+B0GMX4GRRGGl78ZBPU7zv5jED9TseANA8c3hp0yDJuMOy6fqeBgYXAO3zWENhZCZgVvMVg+Y2D4D7qUL0uOYRofAwN7Rwc7A8OjigomcBfrJ5jkB0/asIPn5Z65uUnv2vXm3DmpiAj4ar1Xu3eDum2wPuE78LQhm5ERZLMZPNAh3ULI1XaQE0EFwWtKf4DthShDPncUssSRpaLiI/j0GlZYXxR01wV4X9kr8FGikHNHeBkY/jEwHAQtEDUDG3Vn1y6nETgrCPY7FgLcLYRcZy/DwPAP1C1kqAVN5YHnBkH3c4APd/0GDka4flYGBn7wmaLM4NNo4OIC4O4ihMsCPlRWqKOD3cjo57lzX8CrQOH7PyFq4CR8khC5TwiR/bR79wc3N46OjjfgM0shgozgpa2QbqEEA8Nr8CLSX2A5UXDS/QE+jfZhxQ1BBgZThpBduzRdXZXA8qPEaAiMhsBoCIyGwGABox3CwRITo+4YDYHREMAaAtdO7X6+ze03I+jMmChOBkFd6HTfs8cPO1IVLJQYvv5iuP2SwdqnQ1mA4frFimOvGCZAVrCxMYBmJv4xzLfpeMnAcP9KRRQHg5L/LhnTQX3ezMXdl/a5HbjJALpOMKXjB3NF5E9ww5oNvB7vO3hjHh+s9Q+5dJ4N3AqXAU/TScKmd251dvIZGUnAOoHwPiGu5YUMDAzPwf1GSFfwL/iGCT4Ghifg82YgK1QZGBj4jIw+gWecIKdcioLXf4K7rKD+HgN446IgWMs78BkkP8GdVeVdu1a6XQdfJvGBgUEQvFdRCmt0j3BBcLcQMlv4mYHh94oOQaUKT/6ODsjRrG/AEf0HvGcPEtSQvvcncEr/Ae6bQQLwQ0UFvE/4HdwDhOz3eweevtMDHzkLvx0EogVOYk4SguYJwb1BgV27+MAp6vPDh4/Bk8wQXbIREbzy8m86O/8bGT0/d+4XeLMo5HoSPvABs2IMDCcZisMZtHIYfqszvFbfFTXaLYQE3Sg5GgKjITAaAoMBjHYIB0MsjLphNARGQwBLCIC6gtvdDr5gaAZddcZg/ZMhWIDh5k+GmQwMjxN2MUmozStWuPeaIduJYeUpBlUxBhb9jn+XK1TFGT5+Z2BnYXD/xbDXpOP0d4bLJyt0bTsY/jFwXq4Q0e9gYGLwcUbMNGKxeICEILOC8xhOSTLckumQZQAvz/sHXoTJZ2T0+9w5poqKewwM+g8e8MnLvwSr5u3oYKmo+AGeI2IFTwpBlmhCbn1gAl80xwO+yI6BgeFeZ6d4RMRPcDsestkMfp0d5MIJyCZDiO8/nzv3o6KCC3yYJCN4Vkqko0MIfDgNL3iDGQMDw88VK/6D16y+cHP7DD6MhBPcjYScccoBvgiRlYHhasfB6Ao2sBvf79qlNcwuk4AEF3VJcLfwXkXFHQYG0MTqPIbz1uDZQsjmPT4GhlfgCVhW8LpN5oqKvx0doAs8KirEd+3igcXOp1u3Xri5gY4ABe9F5ARfKcEITiTwA2OEOzoUIyJA/Xyko18wJwlfdXb+qKiA9wbhnn26e/cD2OJVfiMjdvBhM5/B/c8/4PnMP2Cl/B0dfysqmME9Q0jn9idDAmS2UE1NaPTqQnAgjRKjITAaAqMhMJBgtEM4kKE/avdoCIyGAK4QuHZs95WNbvu+MFz5DD1BlOE3QwYLA+c/hrOMDO1WHX9uVvz9z3DgBoOGBMPrzwxvvzA8ec/AwcoQYc4g6LHr9mq33+wM4e9AxqdxMsz6DZpZy+JisLXoiDxXcdmig4GBQW7Q3Fh4cfelm24znzCo32KQ02d45MSQ/52BgQ88t8YJvjWOF3zRPCMDw18jo8fgM1qYGBgkOjo4jIw+ublxw2YFQb4FY8i+L8jVAh8rKv7Cro/7Bp66YQffcQeaXIJdM8irpvb51i3I3CDk7kFeNbUfK1awGxn9V1ODbC+EXGSnBJ7dAx00Au4k8O3axQi+2fwXeF2iINi1fKA1rqBjRXdXcBaBLpN4z8DAsWuXsqsraNoT7MBRgnAIgLuFoNnCaobXrQyCoEWkHazsDAz/KioegHUzg2+A4ABfMvlixQrI0lyJiAhueXn48aHiu3ZBLggBHfp67hxnRMSHW7cgtz4Kd3QIGhndcXODHCLKwMAA7xxC+oSq////fPjwJfjOSQ4jI8jcINhmhi8PHzIwMHy+dQs0UXzuHBN4MyEruM/JCl7jCllODJpaBF9A8gG8qFgY3DXlZ2A4wVD8FWQQBwMDh3mHvFiE72i3EBQeo3g0BEZDYDQEBgiMdggHKOBHrR0NgdEQwBsCJw7s/rPXTUCn497JisPfGUTMQVN8FccqGNgY7L4wHGIHa2ZjWM/GcO0pw/OPDOZKDP95GHbeZQhVYHj1iYFRt0P1VQUjI2iJ6ZTTFbo/GdQjdrnNccviYpjGwDDBoOP6hQoGBgY7y46omHKwWQNDgOf5Hs1jOGXAkM8K7k2xgbcF/gNPqDGA29agWTjwUszf4BWDf8GrNFnAk0d/wV0CUfAKQAYGBl6keR64f748fPgMPCvIwMDAWFHxCXzpOURWGDzj98zNDd4JhG87ZGBgeNPZyQe7ZwKyvRDino8VoPk+ZrBjIL3Kr+DjT16CRVjAbr7IMDEL1BX8zsDwfdcug9G9gpAAJ4MEdwsvVlSArpxgYPgHmS18Bw7kvwwMCgwMb8DJBtIVh3TGIGe6wud7IcfG/Hj48IWCggLSFRSfHj6EHBUDuVsCco4opHP4taKCD3xxBCt4og8yPfgZvFQYsokU3t8DdQgrKvjBa5iFGRjegTt+kHQCcQx/Rwcb+ByaTwwMirt2vXJz4wANzoDOUL1RcUOfYcFshomtDHIdHaM32pOROka1jIbAaAiMhgB1wGiHkDrhOGrKaAiMhgB1QwByiKiFa8euzRUKIgw8xh0XzlT8YmB4Cjoig0HaqMNczchxvdshHoZ//xgefGdgZWJ4y8Jw8haDuBhDgCjDjeeg00c17Dre/2T4ebdi3xcGZiaGP/8YZjGDelodFh28PxjmXapI52JQ5x6YjYW7dz/77tYKXlPHyMLwX4Nh2hvwOsDfDAyC4J6VwK5doEvDwUs3xXbt+nzuHLeR0T8Ghs/nzn2qqBDu6OCC7CUzMnoFbqlDZvAYwMsIIXHBb2QE2fv33M3tD9hwQXCTnbuj4yW4EQ86/JGBAbKAkIGBQQF19eBTRkZpcP8BsrIUPn8IuRfhKwMD+DBXBkjfXAgyO9TRsauCE7xX8A74VoLRWUFIVFBKgruFt8Edq/cMDNyTGc6bM9RyMDCIMjA8Z2D4CT7Ohwl8VyE/eD/hE7CFkDlAAdia4QeMjIK7dvGDNwGC5REEZPEnhM8M7m1yg1PXawYGUfAq0y/gBANRAOkTQsYCQMMQ4GEF0BUm5849r6hgAKsXMDK6Cz54Vrijgx88q/mvo+M3eDXpD/AK5P/gEQ0+BobtDNVfGUCLwuUYnv7vCDeIsBidLYSE8yg5GgKjITAaAnQDox1CugX1qEWjITAaAiSEwIkDuz/fO/fkZMXjdwzvvzIkx3e8u1xx5gfDsrcM3P8ZDrGAm8B/wSQDg9EfhmgxBjM2hqO3GR5zMyjzMfB+ZnjxicFGlYHVZde1JW7qkgxCuh2v/jGsPFUh+Z5h51+G3/8ZzvIxzDPtePWf4f7ZCjvLDvpsLHz4ELQ2083tEQPD+0sMXm87lqsYCX9yc+Pr6HhYUcHb0cEJbnm/qKjg7ugQMDL66ObG1NHBaWT0GXy7IG9Hhzh4TeDPhw9fg9fyiWBcMff54cMvt259AauX3LXr07lz78ENcQFwh+E5OMwkwBcYwDePQZaYQpaGQvqHAkZG39zcGDo6PoPd86migg98tQDoLFMGht/gLiUf+BoJBvC9CF8qKi4yTMxGzArKubrqkBDfo0qJCAFwt/A8eNMoEwPDn/kMl3QYqiDxwgc+eEYI3BsE7SdkYGABLwyGzPhB45SBgdvICDIKAFrwee4cWtcOwuXv6GCsqGAFd9s+MjB8AMfvB/ClFwwMDLJIp9eiOfl1Zyc7eGUpfD4ZtLQYfE8mO3iLKTM47UF0cYLN/wM+gxQ08AGaA7W1ZIgF90BHZwshgTRKjobAaAiMhgCdwGiHkE4BPWrNaAiMhgBJIXDiwO6FrW6Wygw8HAwvPjKI8jJ8UemQeFzxmYnhyQeGZ0wMotYdRgpGtdPcimUZOj4wJLAx/GJjkGVh+P6b4fdf0HUHq78zuEoxmOl0LFhQEW/FoBK46/RSt19sDPu+MKizMxT9Yegw7Kg4VDHPrkNGw2jZarc4DgZRfegRpiQ5lUjFu3c/A3XA3KQfMWQxMDA+YVCzY8j/B24ic4In7iBN8H/giwS4wesAOcGTP78ZGOTAh4Vwy8vfBx8MwyUv//PhwzcKCqIPHqBdFv9y9+7nbm68HR08sHmbp+CLKH6cO8daUfGzo+MHA4N0RMTLFSs+VFRANijCJxUFwFr41NTerFjxv6LiJ7gz8A/sPcjdhpCugkBHB09ExOsVK75UVIju2vX63LldFZxloK7gKwYGhl27TEYXiILDjFYEuFt4t6LiPvjaP9kpDOeDd5lD4pe9o4MDHLlfKypYwIe4QFZvgqbvwOf9/AEvGGZhYPgI7udDnAjvB/IaGb13c+MC9yrFwIfWMnd0fALPIUNUQrqXaDPJECnQdkHwKbXIWw3vdnb+Ag9GgJYOg3uAAh0dkG7hp4oKIQaG92DNouCzkdg7OuYx2FZU3JvGcEOP4emRjqyICNXR2UJwCI0SoyEwGgKjIUBbMNohpG34jpo+GgKjIUBeCDx7/PDaNIWlxxmizBn2XAcdInrqPoOmJIOaBMOvPwz32RmYfjP81OkA7SpkAV/L8JchjZ3hyjvTY0Kn9/IzPPrGoMbH8Eilg+1cxZaLDMa6DH6xu84sc1O26Lh3ooKDlcHzB8N+i46TPxgqjlYwMDN0WHTcOF2x4A/oVP7LdlTuFkK6gm5ujxkY/m9nKFPs8JYEXy/+4dw5RvCJL+zgVXY/wE1nBgYGmV27boCX24GuIwf3GIU6OuQjIiD3BFxmZNT9///T7t2f3dwgSzoZGBhegtviz93cJMELTRkYGCD9QAYGBnHwEsFfDx9+XrFCuLz82e7dn86dk4mI+AY+EQRyYT1khvDTrVuv3dy4wRHGzsDwFtwdZQTP4byvqJABnxX5CbTkloEH3F9lYGC4yDAxl0EVvO3xz65dKq6uimDdowTNQwDWLXwDtunPXIbzuuCTSCHHePIxMLwAr/mEbPb7VlHBDkraILUvwR0zyGpSyIGx8I2jH3fv/gFefsxhZMTv6vp+9+53bm7K4GXDkEk/yG5DyJwwyCzwMmNIsoR0CH+eOycKnrJ+C06QEDUM4B7m63Pn4JdhfKioUNq167ubm/CuXVxqaq8VFJg6OpjB0+CfwLlAmoFBgmHW6GwhPABHGaMhMBoCoyFAUzDaIaRp8I4aPhoCoyFAZgg8e/xwW5PC0dsMWpIMn38yqIgxiJl07DxQYSfKcP4h6Bp6VXGGS+8Yvv5m+GfXwf6dYcm+CmN+0JrIsz8YyoRAbeG73xnsBBjefGZY8oChpnLXh41uC44yJNky/FPpUNIyOr4GdISpvXnHobMVCqYdFXsqEtgZIuw6lpypyDPp4GRhEFAyovzGwt27n0kxvLnlNvMnA8M+BtfQXWaybtIM4N4Ud0eHQkTErxUrvjEwCEdE/Lh1C7RwdNeu125u/xkYxHbtEnZ1hfTTICd/vKuoEAJfEsDCwPByxQoekBdBh46C7g8E9wOfublJ7dqF1g+Eh/7bzk5e2AkxXx4+fLJiBR9shyGkT/hu9+434F6oMLhL+e/cuU8MDM8rKhTBZv5mYBAGny8COUvmEwPDGegV868YGN7u2uU+Ois4IADWLbzAwMDFwMAH2VsoBu7JfwA76C/4+krIPk8ecJf+I3j0ASzJIAcbZQCdDbN793s3N86ODv6ICA7Y6UR3GRmFdu0SBI8pQLQg9wyVd+36AO5AQjeg3roF6RBCeoOvwMkJkpLhej/eunXPzU0IfAgNO/go3f+g6XwGAfCCZOjyV/D2wlsdsy9W3JBjeKvBsGB9x6nR2UJIGI6SoyEwGgKjIUALMNohpEWojpo5GgKjIUCFECjwZBThZdCRYrj/hkFLiuEDE2hJ59vjFSseMij9ZQg2Yfij1PHvFmhj4a0fDKAuF+gYTdARnCa/GIz4QPMLK/gYpFgYvvxgkDDquHmuYudpBi1jhvCIXY+/MPzZ4yasB7p8Yu7BitcMDEoMDPf+MNz8xXAGdK4/2PHMDL38DGEhZF5kf2H3pdcMIpBZwT0MZXwMh7+ATWVnYFDatYuRgeH9uXOg0yArKr6Aj4H5B16iCe8KgtUiiC8PH34AT+g9ADeyWcFTcr8ZQEc1fq2owNMPhBgBnx6EcCHkM/AcDgsDw1fwcTVv3NxEdu36Bm7fi0REfFBQ+NPR8ZuB4VNFBWQV60uwO99XVIC7gqOzgpBQHBQkuFsIuqAC7Jp3kxnumjPU8oOXjMoyMFwDi0KmBH+7uYk/ePDx1i1IQoKsFpYEb1UFsTGOnHnW2clpZITcIQQbBiIwe4asDAzfKyogt1+AT0sCzSSDlIJXqEIYcBKyFFmcgeEpeGiDESYh0NHxDZwXhDo6ICfZ8FVUSDIsBh96Orq3EBZMo/RoCIyGwGgIUBWMdgipGpyjho2GwGgIUC8E0pwZtdUYFLkYLj5m+MjP8JmNwYmbgfc/w+FbDD9EGay4GGRMOxhkja4vdeMRYhBgYLjOyPDvP8PDVwxHfzBESDIs/8QQx8rAyMjwm53h7z8GSWaGz+8ZBFw7xK9U3H0FO4P0BwP/o4pXfxj+aHeIqhg1LnYzYAHda5+ftGvvvXMGjAwsVyrEDTp4bSLkpeWJ9Nnu3c92uZ2KYAj8DLqOL/sxg5onQz4jA8Mf8MzMl4oKSFuZBTznxg5utTOAjxWFiDMwgGZLINu64Db+h7PAM0Gc4EWyb0CdX4Y/oGlRBvldu/hhh0kiqYUykacHIUKQg0OfgC8uZwbvFYRsD4PI/q+oYARva/wL5rODLf3NwHAAOiv4nYHh6a5dTqOzguDgGSwEuFt4t6LiAgODFAPDv8kM5307WBkqKt4zMECSGTd4HOEjuGsFd/SvigrQlkIwnws8BQ1fAgo6txZp1ShYCQoBSUUfzp37Al7nycoAyoaQpaoC4NQOUY28XxEuAmFIMzDcA7MEOzog+1Q/VFRIMzBALlr8CzaQl4HhOkOIJMMa0LmmDAyjs4XgABslRkNgNARGQ4CaYLRDSM3QHDVrNARGQ4CKIfDk9O5ja9zevGJY/5UhSpAhiRN0UmHVbwZ5doazcetnvXi09lL+57cMn78zyFp38PEyvL5Use8Tw8xfYCewgW9FYGJIZWeY/Y8hnZfBiZGB6QeDCC8DEyPDtWegyUB/eQajnAfLqhUs3To+X634rdXhdKRiuUXHkRMVU3+Cd1z9Ak9eMDCk8zPkmBLeWHhx96UZbvdmMEgygBaILvoMas5OYwJvAmQGt8V/gp3GDm6RfwUvAf1cUfGbgUF016634P1UkD1dDOD+HlgtlIDcAA5Z0im7a9djNzdO8DUPH8EnQEIVMTBAlpVC1+8xMEBa9r/Ax8+IPHgAOnwS6fZ5yMGhPysquMD7zf6D3ckEPtKGDdxB/Q0+tER51667bm5nGCaWgvYK8jIw/N61S0FNTURenhdu7yhj8ITAw4cfV6y4VVHxDuykP5Dr7CFdMvYK0MUtn8Cp6y/4GhIOsCLQPRMdHfcqKlR27YLfWQ+/pB5t1ehz8MQy/A4S0FrTigqZXbueuLmJgfeXQs6JYQSPa/CDjymCp2qwbSACvmvxKSMj74MHj2D3ZH4AL1H+4+b2HjweIQ8+OvUnOL/8Z2B4VXFDhWEBfLYwO0IMbg7I0FE8GgKjITAaAqMhQC4Y7RCSG3Kj+kZDYDQEaB8Cn54/vLh7xfNrFcLqHQ+YGX59Zjjw7JHnm2nKogzP/jCwMjB8/sBw/zXDw7cM1oEdQvcrGP/q8HBfef6X4dA/BoNvDAs+MBjxg7piaswMjH9Bs4W3fjLkOnSsXlmh7dNhoWN0Zqnb9ssM4WYMWiG7Nixy0+ZleKIGmircuN7NVKdjz+EKTQ4Gq4hdc9a65Zl0PLhUIarf4RiF5SJ7yP3yFxksbzFkSzBMfcGQJccw7Tf4XEe+jo5/4FlBoY4OqfLyXw8fPlNQYAGvi2NkYODq6JAGH8LxvLNTEGnvFmbQvgc3xN+Bl4wK7dr1n4HhrZub6v//n8CrST+cOwe5WuAtuFl/F7xNC9o/BJ9bA+kmC8LmbT6BG/GgwyfBa0R/GBl9BTN+gVfrcTAwcIIX7LF3dGyB3iv4nYHh1q5dvgwMDKMTg5ixM9hEwLOF9yoqzjIwyDAw/FvRwWxY4QnaSdjR8aai4j84ZX4Cb+GT3rWLiYHh+7lz3BER91esgKQiyHURwuCdfhwMDFIdHSzgE18+gpPNEzc3yFk1kH4mL7jXJ+nq+oKRUejBg58MDE9gHTx+IyMppP2HaKH06+HDTytWQO5NebJ79303N0j6/A4eK/kGnnVkAO9cZWQAZXY+8N7Ilx2z31fslGRYI8bAsGJ0byFamI5yR0NgNARGQ4AsMNohJCvYRjWNhsBoCNAxBD49f3h5z4oNKysuczDs1IhKPb4syZbhnuok5kt5nL8ZNl9g8AnvUBZg+PKb4f0PhqeH1yhLnzn/naEYvGmvw7qj4mQFw0/Qqs3vHAyzWBhWcDGIgXcsCel13D1RsfwBg70cg1/MrsU9blYqoG2KDAwMU05UiIOn85p+gSfO/oGmCjssOyrOVqzkY7AKQmws3L372Rq3U7PAs4J7GBb9ZGCQ7ZDjZGDgqABtTfzIwCDd0SEXEfFpxYqP4Pu+IXdzMzAwiOzaJeTqepuRURV8iiOeDuH73bvfurmBncyAfMLHbUZGmQcPOGHnf4COeXz4kIGBAXKdIAMDAzcDw6+KCj4GhifgxaUM4OMlGcAegjTo+Y2MPoHvOfwCa+izglWygY+9gewVLGT40g9yrAIDA/toV5COqZ4KVoG7hZC9hZ+rGT5LMDx162D9WFEhCx6P+NXR8RN8pQQPOKkIgQ8T+nzu3DvYPkAGcGLgBa9Jfg2ei4YsDZUBq+RVU0OeoPv18OE7BQUJcGJmYGB4tnv3R/AQw4eKCp0HD3iRUincY8gdQojgE/DJRrzgNcy/wcfbQsRBs9ngeWxm8My9UEcHAzhhQ2YLV3RweEdoIjsGomuUHA2B0RAYDYHRECASjHYIiQyoUWWjITAaAgMZAg+fPVs7IU+GYe0+rfZ/iyt1HAIY3m3I/8zQysWg8p9BzLLj2R+GH+cqfn9mOHcgVMdmtaYU6CyZH7/kDnI+0vzBsOgdgzEfqC056y9DJwvDi68MMrwM0iygGwt/fmJQcun4d7HiyG0GKQEGdduO9z8Y+B5VvIZsLFQ12rjOjeELw4ffDF7mHdHXKtb+ZxA0A51G80fK083x20UGy+uwWUGDDrmf4GkNYfAt85zg0/b/grthQuDeIOgCbvAshyms3QzvEH4ATwAKoE6nQLqCkGM/QWd+oMq+A2sRQhWERNLb3btfubmJ7dr1CLyQ7zGoP8vwH3wIDWhjWEWFQAfo1vtvbm7vwKtD/8CcxwZeSbgHsVfw3a5d2gwMfKNdQUjADkUS3C28V1FxB3x4J2i20KLC8ycDw2twpDOD+3uc4D4YE3idJ9yP3yoq+MEcRtBibdAN9cjXn4BlEMS7zk4GBgYh8HQ3RPQz+DBbBgYGyG0TMhERaN3Cz7t3/zx3DjJDCNHyfvfu7+Bu5CNwPkJeOy3PwHAH7OBfoCl/Bj7wknCIrg8MCe8YlCMYDHft0nR1VYIIjpKjITAaAqMhMBoCxIPRDiHxYTWqcjQERkNgwELg4bNnK/bulxVmZznQ+endmTPsDAmiDJavbUOfHPYQZ+DgZWBjYdj7ncHuH8PtF6C7Cvk4GX6rdvy8XMHABbqJ/t9/UFty9h8Ghh8MKRyg7tEcRoYeHgblXwzcbAyfGBhEWBj23WA4y8NQLM0g5r3r0Bw3K3fQxsIlXxiufWLIcuiIPFjBwALu2/1jiGFh4PnCoCrNIPMtm/faf/F9076BW9UM4N4UEwMDG3hl5o+KCsjt3pwMDH87Op5XVEh0dIgZGbGqqV1WUNAFT5vAO3WYHULIGlEGBoZ3bm7IE4PwOHgHnjmETDDCBSFdQQbwvsRX4EsFuTs6fhkZfTp3jt/I6OO5c9IREQwMDA8VFPjAO76+g2eBfoO7qUq7dq1yu14CumL+DgOD9K5do7OC8HAd8gxwt/B2RcUHcCL+s6KDWanCk7+j42tFhSA49iGbDhnBZ8lKR0S8X7ECdJgteDnoz3PnfoL7hO9g/TSlXbtkkUYiINODQg8esGHMBMK7haAjYVBXkH4Gj2jwgs358fDhUwUFUA7q6BACr52+0dn5AWydQEeHqJHRTze3zx0d7yoqIPseQdcqdnT8A59fKsrAcJIhJYLBkoGBJ4fha9oufV1XoyEfYaMeGA2B0RAYDQE6AsixXnS0cNSq0RAYDYHRECArBCpePH0srxL+UpcrYKWTbtbffwxHLbzdnTpsk3bd/Mqw9xODMycDPyfDgzcMz1gZXv5n0NM32nmFgfM3gwcbQwQ/aHpwOT+D2yeGOT/ATeL/DI/fMNz9z3D5F8PubwyvfzOI8TJEunUoB+46Mtft0hOG1xcrlAJ2eTIwtEgyiDMz7I/b5cjE0MXNkMrMIPKGgYOdQU2n4/e3qZ9Npj1OY2B2YoBcqsbV0cHBwCAZEaFeXi7W0cFw7pzMrl1Cu3YxVlTo7drFbWT05Ny5/wwMVv//P16x4hp4UgUzMN7v3n2HkfGdmxtkx6Dy//9Yz/2HzA1CupSgTV+7d19nZHzl5ia6a5coeFGf1v//LGDTpVxdZSIiHoHXnT5bseLHrVvs4AkWzo4OCfAWMmYGhqcdB+XceEsYdBkY3u3aZbJrl7qrq+LoxCA4/IYDIS8vUF5u+uCBKTiFfo2o+G/GsOU8g754RwdoS2FHB+ScT0bwhN79FSu+VlR8h/THXF05jIzYGRh0y8sNHzwQ6ugA3SJ47txBRsbH4B4dPHQwe4MMDAy88vKa5eX84I7lx3PnjjMyfgYvbGZgYPgJngwETVnv3v1uxQohcE6RKi/nAPcqNcrL5cGJ81NFxV03t2dgh7HAJrS/go/G+QS+c+UXA4NFh8ophuRTDOF+DId+uk1mZNy6ezfk+FK460YZoyEwGgKjITAaAjjB6AwhzqAZlRgNgdEQGFQhwFhQ3BEVzPDufcXx07ty0sTu37m/3P7QZ4Z+RoY1XAx3DEF7BWu/MNhJM3z4xvCOmeHXOwZ2VgZRXoZ7rxjcs3Zd3eL25AfDxe8MXMwMMxgYUhgYzr9niBBjKP3BkMLDEAM6cAN0a8WLXwyf3jGs/c6QKA/aK1hf6hZnzaAWuOvMMrcdDAz/3mUxcE+b/Rs0x8jwlyGalUHuI0O7KMMSDgaZpwyCeh1CRkaM5849ragQ6+j4Ad6L9R+2XRASmJDb2yAHgTIyMFxWUBBlYFD9///Hw4fvV6zgMDKCHDcKUYy1HwiRgpCQ3iDoAEbwYTOQfiBogyJ41uXXw4evFRREYeeLMjAwQO6ZgFwzAB8OPMUwMQ80K/iegYFj1y5lBgaW0X4gJHiHKwmfLaxieNnGIDiZ4bxrByvEsz8rKtjBWwrfgWe5IQn1/61b793cFGDrnD89fPgQdmzMu4oKpV27WM6dY0ddLwoxDZmETxXCV5D+XLGCFXyaEaTnydrRwQruN0KOMAXNKILd8AF8CQobeAH2b6RFrR8qKjjBh828BY32MAiBb3ARA80WFoczaOUw/FZneG2/y2t0thA5FkbZoyEwGgKjIYAVjHYIsQbLqOBoCIyGwKALAcaC4l1ZqW4P7zA8eMLw/GWHljbD95/RGmIf7p37/odh98GKWzcZfvEx+MgyvONlOPGZQf89g7ICw8UvDFYGHTxKRg/XuInzgQ4affmX4dAvBr5XDHv/g88gZWE495khlJ/h5VeGb+wMjlwM798wiEoyuOU9ODlRYckxBjt1hi8/srhfMygbTXv1h+HVQ4bTfxi4FBh0XjEcZ2PwfslwVIxBl4vh22eG/4IMpq8YuJeBZgshSzFBF6mBG7Wg1q2REZ+aGiRY/8OOflGIiHgHbltLREQ8VVCAHOyBvEb0C2w6BXLzBET7J/DUykdw250BfP8E5DZwFvAB/RA1oCNkwNcM/AEfzvEJPNvDyMAgBF4p+h7cpT3NMLEA1BX8zsDwfdcupdG9gvCgGwkMcLfwYkXFN/AxQ39mMJx3YKhlAK2tZuBgYLgNDgLIpSOguzHBS0nBBy2BJcBrNSEsbnBa+gjhoJLItxFCZCCrQCFsDrBdrODzbEEZBJxTICeXQhTA2fxGRi/AR5v+Aq9whsiC1pdWVHxlYHgF48uCT/RlAPcM+Ts6foKOzE0wZQjp6BCLiFCVlxeAKRylR0NgNARGQ2A0BFDAaIcQJThGOaMhMBoCgzYEGAuKH7TUr3j6qGLG/P/9vbvv3HTbvv1/bsGn5w/vHlhxdEfFFR6G7e/kQj49CjdjMH/AkPiBIT244/mNir+fGa4+ZdCQZHj1mcExddftNW6/2LBvLPzDCVpfyv6D4TszAxszaGPh/CMMVzUZOoUYnl5h+CzMoMHF8ImN4R4bw4OnDL8FGGZAriv8BmrYdv1gWMXKECnGYPeNgVujQ1QNtG2PHbxtDx6kyK1hyIEZ/8AnZDCAJzqYwcvhGMB3UaBdTA83AcKA3FMPWpIK7vJ9AfcJRTo6PoN7fZBWOD94id0XBoav4P2NouDWtkZ5+ZvOTgYjo8Vu14tAXUHIXsHRWUFIuI5EEtwthOwtfMDAILW2gzmo3OMJI6MM+EaTqwoKkFNnpBgYnjMwcIPXl0LuHuTv6GBjYPgOPu3zE/hyCHZwGkMOREiChyRIuDgzA8Mn8OSeAAPDS/DS5b+gDASSZwWfeyRgZAS5uhD54NBvDx++Ag+diEVEcIHXlMI3Lv5gYLihoCAE7gF+BxnDwAfuE8oyMBxiKA5nMGBgYJjM8EC0w8QiwmK0WwgOoVFiNARGQ2A0BFDAaIcQJThGOaMhMBoCgzYEOhcvrRDlfWDjoFDb+KClYcWLxxVT5v7v74U4+NPzh48OrbhxZNrxe49UlNRZ//Ade376AZOclswjJ0aGwy8ZBIUZVBgZpE07zuyusPXseHamgo+DYcUPBq5vDApGHYUnK1LYGOYwMeT9ZJDlZbjzGzRrYsvG8Pkdg+BXhv/8DDfeMfzhZtDjZ9AN2HVoqZumFMPz1wz/3zLMEWNwZ2e4/Y2B/S3DCX4GTy4GDkYGDmYG9W8MZjG7RG1dIc5DJr88fPgU3LT9CF4Lxw9uTH8FnXcDUsXIwCACPgIUxEHC8NlFiNjPW7cYzp1jNzL6du7cq4qKt2BR0PLUDtCy1Y9ubjK7dr12c/vBwADpEzKArw1gYGC4WsEZBeoKQmYF5RgYhEYXiIIDb0QT4G7h+YqKH6B0z/BnHsN52w5WyKwyX0fH/4oKXgaGtwwMHB0dfxkYeI2Mfpw7B1nnCTpZlIHhT0fHb/CcofKDB5jhCJncfgpe1QyZyv4Hvh7xM3gf4HNwb/AvA4PSrl33kO/PZGAQMDLiB0+q84F7gN9Q+4SQc03/gVeZMjAwvHRz+wc+HQfiAJmOjk8VFewdHa/AoyQcIClbU4aijg7p0dlCSBCNkqMhMBoCoyEAB6MdQnhQjDJGQ2A0BAZ1CHQuXsogLRahq79ix+4KKcEHFrYKNQ3wDiEDA8Ojh49ObFn97VLJOy4Gzp/q3z7eXM/HwPWOQfkdg48+w5M/DFLMDGfug6YKWVkYPip1OFsa3V3vdhG8sdDDqiPqQoXxcwYTAQZldoayHwzJ4I2FjAzQjYU37zNc5GUIlWDQUuuYsLAi1orh6zOGf0IMoI2FPxlAuwqZwWfV/GWYb9shrWbkttptDweDuEGHTjjoIvsvDx9+vnULvjkKHtD8DAxSu3b9ATdn+Xft+nruHPj2RKi8IvhEUEhrGCoEpn49fPh5xQp2cFOYx9X1FiOj7IMHb27d+gDeyvWqooKHAdSrZDcyYmJgeOHmJvvgwYMVK3ZBr5g/xgA6oH90VhAclKMEUgiAu4V3KyreMTDcZWCQncZwPmiX+c9z59gqKnh27QJNRJ8797miggm8RpSVgUFs1y4+NbV3CgpCDx58vHXrC3glsyLSzROgfhr44Jmnbm584B4aA/j42p9ubqALTnbteunmJrJrFwu41wfZl/gOfCs9hAQdsYvKFQHvOWQB9xU/g6/Q/MnA8LWi4i/YF+K7dj0BdzvZwF1NyCpWMfBsoSR4b+FhBnF1ho8MDBzyHSZ6o7OF4EAbJUZDYDQERgEDA8Noh3A0GYyGwGgIDI0QAHUI+bgZ2Fkrzp5nkBZ/4O2vMHHS/5YWuOsfPXy0dsXa8IiwHVUpfELXfv59tOU5g61Lxx9WBqELFe95Gfg0OwQuVvzlYLjwhSE2etehk+cE71eIGnX8vlEB2lj4m4HvJcNeBgYjPobZjAyurxjkRBlYwEevOHIxfHzHICzBoKLbcftYxdVnDNKCDKo2HcxyRn/2uL36w8ByieGSFMNLQQb1PwyFHxh2pe06d+fc95MVauYdL85XKD1ikF4HdyYD/F5vNgaGLytW8EREfFmx4i+4dwe2jUHY1fXTQ9AV8/dXrHhbUSEMXokH7xx+3r37F3hukIGBgQd8eAzkaJk/DAzP3dw4wYfZ/GdgeM8AOmbjXUUFGwPDg46D0SAadLn9rl38o3sFEZExysIIAXC38HJFxW0GBtCV9fMYzpsw1P4A70RlYGBgr6h4z8DABE6TChERf8Bz3ZDrB1/v3g3vE359+PDLrVtP3dykd+2C9AZBJ44aGYm7ukKWevLs2sXj6voOfBO9yK5dkCNzIW6BJP6P4AEOBgYGeOdQwMgIPoXIBd4KywbWALqnHjw/Cd+I+L2iQgDcqRUDzxCCzxhmgHjhRsUNDYYFFxhSwhm8R2cLweE3SoyGwGgIjILRDuFoGhgNgdEQGCIhsPvESZBLFWXc1q/fZWZ57tWLiu9fHpjbyktJgcTB2JLRKqMjRclI5fmt01/PrPzDdPosB4OWQcfF3RVp2gwWDxiKXjEUN+46vMbtyzuGp+8ZDGQZrj7N8neXe3ij4jMjaGOhMgtD2R+GVEaG2V8ZchkZpHkZKpgZ9nMyMDEyPP3JIPeL4ek/hlWPGczUGEy/Mhw6wyClyyDIxcDCDNpY+P8lwx0exMbCWDYGs8cMuSoMizkYFL4ziGh0yPhEIG+LesHIKPTgwZcVK+Dt6ddubuK7dgmDu3lgDzF8evjwPrjN/Ra88k2uo+NDRQUveLvUD4gKBtAZNszgi8X5Ojp+VVTwd3T8qaiArBR9Bu0KvmJgYNu1S4+BgWF0gSgs2EZpfCEA7hZeA3cLQcfWQu8tZADdCP+lo+MlODVyg4/3/Ip60AvkqJgf4IWgkN6g9K5dPGpq3OBln5DeIFtHByTNMzAw3GJk5OrokEGdV0Rz2aeHD+H9w58VFVzgha0/wKMezGDbmcHWQXqGshGgXPaQkZGho+MDA8Mf8JjIN/A23X/gPYp8FRWHGKrDGTRyGN6pM3wy75AXi/Ad3VuIFuaj3NEQGA2BEQVGZwhHVHSPenY0BIZwCOw+cfLcx3cMKvJGrz8zMDCcE+Wt2Lz9QVgkWocwoSPWK8InTCE8oyPF0Er+1qokSbOseZsrDBQZ9A06Ni6p6JNmyGZl0P7G8IWfYeUXhioehusHsqTMp7EyMfBwMPzkYNj7g0H1H8PKNwxn2RnS2RlmMoLuqJjzjyGdGdQOtWFj+PKOQeQrw19+hmtvGQREGdh/MRgtYLiYCFqM+uYNw/e3DLGyDMuYGPZ/AW8sFGDw4mRgB28slDDu8HGO4JOUB817dHZC5gZ5IiLgF7hdYWSUBF8hiNwnhMTZnc7OX+BW+G8Ghnfg1q0IeJYGdF9GRcVn8BZEHnBLXbij421FxVWGibmIvYKaDAzso11BSEiOksSHALhbeBu8iJSBAby3UAd8Eik/eB2mCPi8orfgY5D+gQcmIOfNiDAw3Af30IQ7OtRRe3rvwMlYAnaDBSgjYJskxOrCN7t3vwKvCAVNNnZ0fAcn+0+wzbGgqzhh0+mQY1H/g1eTQk5p+lBRwQVes8oOdjNkYTbsJNIQU4bo0dlCrGE+KjgaAqMhMEIAZIHSCPHsqDdHQ2A0BIZ2CFS8fxMhIQvqDX58Z8TD3yEigeYfVgYWDSPNTSs2cjFwfmH49v4H82OZAnkNI1tBhqtfGMRYGT6xM9gyM7y6z3DvNYPaB4YuUYZvPxn+qU6TFmDgYGNg38Tw4D1DDDfD3/8MoSIMGbygg2SM3zMw/2dYysMwk4GB9Q+DDAeDphSDsAqDqAjDw7cMy/4x8IoyPI1huPCY4fc/BtYfDHIaDOl/GfZ/Z5j9j2GKCMMZVoambwxSRh1Mmh3RZytOT1K4srLzzeHdbEZGkPWi8N4gAwODOHhXFZqn3uzefY2R8WdFheiuXazgeyNEwWd7PK+ogKwIZevoEO7o4GZg+AJuEO+q4HRiOJ7LoMDA8GrXLvVduwxGr5hHC9JRLpEhIA+9zt68o0Oko4MviUHZjGHLSYZm0OJMBgZOBgaeXbv+gftdjAwMfEZGP2BXoQiCk+irioornZ2QJaAMDAxfdu/+VVEhhHrwjJCrq8iuXW9gPT2sDoNkAUhvUGzXLq3//2XLy7k6OngZGBz//1eIiBAAr7iGLK5+W1EhYGT0qaLiI7gj+5GB4SXo/gmGf+BNvnzgayrYOjq+gRej8oFGUnhyGG5zV2zcrDDldOfihw8/YHXDqOBoCIyGwGgIDGMwOkM4jCN31GujITCsQmD3iZNuxw4xcLAz/PjJoCj3wMZxxYGDRrJyrhbmcH/2d/YHRwTvX7FnZcXKmQ/mbF2xZWvF5s3/tzEwMFxZ2fnpWsXJuww3njOc+z1Jlf8GA++05bwMFV8ZTGQY9nxnEP3HoPWK4epvBkM50AJRPk6Gy28ZLrMxPHrOoMbM8FOIYQ4jg/EnBgNe8MZCZgZ38IziJ2YG6fcMj1kY9l1nMFFg4P/B8Psig6Qvw6s/DJIKHZNPVViJgc43LPzAYMLAoMnHoPSXAbKx0PgRg2XfA+TeIMQXr3fvhiwchdwt8crN7T/4hAxx8MwhGwPDl3PnmI2MHrm5MYMX7P1mYGDr6HgPnjy8xDAxh0EVfJPFn127VEZnBSFBOkpSJQQePvy4YsWdioo3YNNAJ5HCZwvZKypegi84YWNg0H3w4JOCguiDBz8ZGB6tWPG+okIQPJUtYWT0x80NebEo2BwQ8f3hw8cKCmg7CUESDAzIs4Jiu3aJIK2mxrP/8OWKFTwVFc/A+wwh5jCD71GEXF34HjzHrgLuhbKCOoQM/B0do3sLIQE1CkZDYDQERiYY7RCOzHgf9fVoCAy9EHj47NmKvfvLY6MZD+/uePGBgZer4v2bXYoaaB1CHgYuHSOdIrfS4/+PZTAmXWS4cfz/MQYGht27n33vaP0nM+2XBGivoOwjhquiDO+/MYh9Y3DjYOBQYeD5w3DwFoMlP8M/YYZ/TxnU8nY92uj2mZHh0U+G//8ZyhgYUv4wzPnG0MXH8JeJYc13hm5hBmZGhiffGQzMO/Zvq9j4k8FZFrqxUFqXQYCLgZWZ4Qc7w9MXDCWs4NBmA925FsvGEGrY4XetYjEHgxwbg5L/LhlTlKspvjx8+GLFik/gsxwhZyeCDo8Br9ADLX5jYPgE3jQl1NHxtaJCpKPjd0XFKwaGJx0Ho0DHxrxiYHi7a5f76F5BcIiPEtQPAfAi0rsVFRfASy9FJjOcN2eolWVg+NLRAdk3+K6iQhSWSuHWczIwcDMwvGFg4AR3DhUwjs/F7N3Bu4Ji4KEQ5K4g3FiILtkHDzjBGxQR4p2dPysquB88+HTrFuQcGoiUXETE9xUrOIyMGNXU7igocIDnDL+CD03lYGAQY2C4w5BwgUEYPKTCYdYhLz66txAScKPkaAiMhsBwB6NLRod7DI/6bzQEhlcIdN69tktcluHnrwhbOwYxUUzP/Wb4Y+lqHdYRGs8YVfsAtLZt9+5njIwn3Nwev92nwbEoS1OuI8euQ06GgfUTg/E7BikWhpOMDCHvGXa9Y1AUZrghyHDuD4MgP8PeErcXHxl+fWLwsOyQ4wLdGc/1g8H4F0PZZ4bKr6Cb4Je9Zziu1HHoB8OkwxUMXAyVCgw+jxmEbDv+STN8Zmb4/46BfwLDh1cMxtIMj9N2rTDsYGBjWCbEwMHAcI2FweQbw86vDPdUOg5dP7dsSefTw7uf7959q7PzNCPjPQUFrooKVvCBGV/BVxT+AV8xz9fRIczAIABuWHOA+4TfGBgeVVTsZZhox7AtCnTw/uddu3R37XJ3dZUa3S6ImTBGRagSAuBFpMYPHoR0dEgzMLzPZVAzY9iyhqH5Pfjslo/ge+o5wF0soY4OfjDiBG95/cfAINnR8a6iQtDI6JyCwoMVKw4wMl7q7Hy8e/enhw8hN098A19c8Wb37jfgyyrEdu3C0xsEXYEInjD8fusWste+7N79s6JC+MEDPnl5GVdXu///BYyM3oGn0B+tWPGwouKlm9uHFSvYwL1B8IWjDIzgDPWcgUGsQ8OtQ9Stg9eFofZHxSYFhQOdnadHF5EiB+8oezQERkNgWILRGcJhGa2jnhoNgeEZAowFxbsqi9T+MiqsWt7h6xnBKQCZM4T7dt/ufQ1uDSwMzH8Y/sZ0RKsa2TS75agw5M9mkGRg+H+q4wFTRSQr+IhO7iiGo5ygY2McdDuu7qzYIc6g+IhBk5PBjY3hpwLDw3cMT94xyMgw/PvKkDzr/4QcRiMBhmOfQA3HBwwMv1kZLjxjMORnUGFnqPjDkPOPIVgINIv46jeDKAvDwmMMi3QZdnAzcGl3LF9SEWjE8OU5g7Asw5kfDDe+wW4sZAStVMv5xsD5k6FbkWEPBwP7FwZhjQ4xNSOGc+d+gf3zHXynPORwUQ4GBlFw5xB0gih4y9afjg7YvYKjs4Lg8Bol6B4C4NnC2xUVZxkY5BkY/k1mOO/WwcoOPgX3PrgD9ht8cwU7+NQZZnAvkTMi4gH41Fz4ZRKgc2UqKiDHhHLBVH4Bj4mgrRHF6j/IJKEI7OKKXw8fvlVQ4AXfaYGm/kZn5/uKChYGBi4GhpfgM2/EOjq+gE/lZWBg+Al2w1fw7Rr/Kip4GRj4du2ado7xScU1DfBJpKKjs4VoATrKHQ2B0RAYRmC0QziMInPUK6MhMNxDgLGgmEFNvkNJueLju11mlgzvP567eqM8Nhru73279zEwMGioqZcpFH1k+PyfwYSX4Vogw2lRhkeQY1f+VFSo////gpHxU0fHx10VP7VAWnc+Ybj5H3QGi8JPhnXiDHvZGaq/MRhKgzYWOjMwiBt37NpQYSTHwMjIwMfJ8EKrY9/JikfPGbSYGb4KMcz5x5DExHDxC4MJH6iNCd9YqKzbcetYxYEbDMbyDEI/GN5eZFD0Znj7j4H7EsMiCQZLMQaZ9wwb/zHc+MNwhpuhngG0sfDQyQqTHwwREbtAzVMGhrdubvy7dn04d+5bRQUjeDbjG3gf1PuKiksME7NH9wqCom4UD3wIwLqF78BOeTeZ4a45Qy0/+EhPGQaGa2BR4Y4ODgYGhooK6QcPPsMm9B6CD5JhBitgAZP84F1/nxgYeGHbYiH7DwWMjPjAl9fzoS4NBWtigPQJ1cAnl77r7ATNHMKONn0Onmn8fO7cR3DH7yO4s8oIPmPmM2hvL8gAZtDEYAc7+IIKEfANGa8VFL4wMPCDr7nnA48f/WQAnUTKwMC6a5emq6sSSNsoHg2B0RAYDYFhBEY7hMMoMke9MhoCwz0EGAuK/0/o7bx0puL69Q5+IZB32djKnRB78Pbt3nfj3PXPFUeOMHx+xPBMmiGHk2FjLMMFUYZHDOCjWXjAB7F8A7P5OzqYGRj+8TC84GV4frXij3bHrx8Me09W/HrLIMTMEOTd8elOBWRjoZ4Mw88/DFrGHU/WVXDqMnxiZHgM3lhYirSxUNKko/9oRY8QAzMTw8tfDDI/GM5+Ytj0kyGZn0GCmeHIGQYxXQZ+TgY2FtDGwmcvGIrhGwt/M8QyMoQYdpw8XaEswKDCySCi3MHyCnRk6B/wrKDSrl3P3Nx4GBj+dHTsruAsgl4mcX7XrojRvYKgNDCKB0EIgLuF9yoq9jAw6EJmC33Bs4U3wd0tyHG4/OATPkU6OpjA98jzg/cTMoNPx/0H3pIoysAAucTiBbjnBvGWgJHRB/BSUgYGhvewI2oEYF1EBvB6VMiZNGwMDJ/d3Nh37frJwADpBMqAO3WQSzL4Ozp4wYeRcjIwvHNz+wy2HXJZBQP47grQbCFoVIfhM/i6wi8VFRDtouAO6mwGC+6KzQwMHNwMHDK7oka7hZDYGSVHQ2A0BIYHGO0QDo94HPXFaAiMiBCALBllYGBw6+j739/befNKxZYd/4tLIJ7fvftZvtsGXYa9QQzv33QEr6l44spw9jvDWxuGN7q75rx0c2NiYOBlYGDu6HhWUaEN3mL0/eHDtytWyJSXf3r+8NGhFRcPVux+xWDu1PGfleH2vooJggxN3xnEGBk+8TF8ZWSw+MrwVLuD+VoFCxODHAvDcx6GiE8MuT8Yjv5gOMcOuoXN6BODIR8D53uG38IMDEwM2r8ZtAUZxFQ7nt+qOHaHgUeYQfkLg4lex5abFRqSDEyfGF5qdYTdqoDcWMjwnuE8H4MGO4PuRwYZt47XByvsHzD82cfwA7R7kUEIsUD0OwMD5ARRZldXOYjHR8nREBgkIQA+ifQW+LoH0FLQyQx33TpAIx8fKyog58o8ZWAQ6Oj4AD4P6Tt4AafErl1fzp37Au7psVRU8D148BK8ppTDyIhbTe3TrVuQ3iDkHF1B8MwhZNoQ3jl8X1HBAcpwDOzgVdZC4BOYIJ1ABgYGGfCZNJLg3YaQUPrx8OEzBQWhXbsEXF0hV2LcX7ECdFkFAwMf2ATIgNE/sJn8HR2M4AsPmcGrSX+CZhdDTBl8GBhERmcLIeE5So6GwGgIDAMw2iEcBpE46oXREBgpIdC5eCmDplK5iSWoZ1heCOoZdvb/n9C7e/ezv26t3xkYnjKwLGc448Tw5x6DIgcDmxjD430M3zMYPpsyXOMAzw0KgTcX3ersfFtRIbtrl4yr6y1GRshis7ednXeOVlwSA20s1DPsuH2g4ggPg9IjBh1uhkDPjle3Kx69Y3j8jkFamoHzGYP8C4ZTegzIGwt/wTYWqoI3Fmb/YwgBbyx8Cd5YuOgYwyIDhu3sDH9eMmx/weBvwPBDuUPgUQV8Y6EXI8M2ZtDGwtRvDFw/GSYqMuzlYOD6wiCg0bElh7MUOiv4dNcup9FZwZGS3IesP2GzhecZGKSQZwsZOjo+gGf5mBgYBEG5lUEYfL+8UEeHeETEH3A/UKi8/NvDh5A+oXhEBBfqGlFI/w3eS/xSUSEA3gf4j4FBHOkcJsgyVAPwhYdYV5neY2SEdAjhYfyss/NbBehoJsid9X/BuxnfV1RIdXQwVVQ8hqnjY2CQ6OhYU/GagYFBjuHtWwbl0dlCWNiM0qMhMBoCQxiMdgiHcOSNOn00BEZaCHQuXw5aLKqnx/DrN4OyXLm5NWhX4cTQEwyLeRmmfWFgOMGQ+7dDkYuBc2XFSlsGbnYGThaGn14Mm1nAa0QZGBj+gdeJ8RsZfT137h14WRo3+Ix5NgYGdiOjX+fOveJheHOjQsCmY9nyCt6XDLyCDKyMDGtlGAyeM4hyMyjJMxz7wGDFziBm3LFjXYWqOOh6CdCNhS8ZLnExPH7OoMjMMIsbtIgtkYlh/n+GdPBZGcEfGRgkGT4xMyjrgDYWrrzL4CXNIPidgdWog+t1BWRj4QVZhid8DO6fGNbCNhY2MDBwf2W4w83A8COL4YFpcIU9A8PorOBIS/JD2L94ZgtlwP3AZ2DPCYAn9JjBs3O/OzoUwTdSsDAw4OoTfnn48MutW5/OnfsEW9LJA758BXLXxTcGBqaOjrcVFcq7dt11c4NcVc/AwIB20cWH3bvfubkpgbcdvt+9+62bG1dHB6eRkaCr61fwvS8M4HNHJSIiXq9Y8R/WIZTt6PgBmiEEbXH8WFHxgSFBkOELZLawo0MsIkJVXl4A7KdRYjQERkNgNASGGBjtEA6xCBt17mgIjOQQ2H3iJAMDg5qCvMLR/QwPnzDISDL8/lsZw1/UcfMugzxnReRDBrnJDGreDHxSDC9eMPxU7EhRZvjICDkKv6NDqLz888OHDAwMH2/d+nju3FfwQYj/wKcO/gTfQsbQ0cEG3mX0j4Ph/v1zz65WCPrv2jHZ7bIQg+BT0Ami9qwMTIrQGwsFxRhUeRh4TjN85mGAbCyE31iY/Idh7jeGTl4GSdOOCUcruoVAd9m//MUg9YNhx2OGbSIMFUwMoswM584y8CJtLPyn1RF9uAIUv2ygvYOxjAwM3xKu/7kbKH3Yz6xDQMkI7cZCkMpRPBoCgzsEYLOFZxkYZOCzhWzgRZh/wZN7/OBeFi8oyTOwdnQ8qKiA9OI+V1SId3R8rqjQePCAS17+Bfh4mCdubnzg42ogJHxFKBt4k+E/8EDP+4oKpQcPIGeZQrYIwjuHb2GGK0REvAGvGv0PPr0JtIdw1y5B2LJSeJ+Qx8iIi4Hh57lzDEZGL8FH4PwFL+H+z8AgtWvXLzc33o6O+gpOddAWZQ59hqfHOrJGu4WDOz2Oum4UjIYAdjDaIcQeLqOioyEwGgKDMAQgHUIGZVm3s2cY+t5OUL5S4GU9ufOUy9FOFvDU3z8GhoUVq+xA68fOfYXNCrIxMHCCG3BsamqMDAyQQw6fuLlBzrRgrKgQ2rXrNZgrCTuc8MvDh59v3fp469yr5xVP3jPsfsXwgoHB5TfDFV6GBbwMzT8Y9AQYbjIwfP3P4G3aMWN2hY8Bw9cfDAYOHVfPVkR8Zcj9znD0J3hjIQuD0VfQxkLm9wyMSBsLuR8wfBJgOPyCgZGPwZqDQXwWw+F4Bh1p8MbC9wy7xRlUv2bd/vJt9g8GE4EFGuwMzqYdbEwMLy5VhIWjX2Q/CKNp1EmjIYAWAuBu4W3I3sIchmeuDIc0OjQgJ3/+AC/L/FxRwQ7u/v0Az+9BTv6EXB7IzsDwm4FBEXw8DOSUF8i2QAlY/+3Xw4fvFBSEHjxgk5d/s3v3V3DnUB6clyFLTCGdQ0hvEDJ5+OHcuT8VFUzgaUAGBgZucOmB5uYv4AEjFvABv4wMDOK7dr10cxMHn/DEwMDAA+7H/gZvLASNMYEUJ2xgMGxjkOvokB7tFqIF5ih3NARGQ2CQg9EO4SCPoFHnjYbAaAggQqBz9o7Hb878UpZnu/3IVlAogo2R4f2nExu3MB89/BN84xkb+KzCv7Dm3YeKCm7wBOBvBgbIofj/wIYxgZtx/OA1ol8qKkR27frt5vYCfNwFI+z8Q8iBhLxqak+f3To0143bruPDL4b7hyoOcIE2FhrwMUQX7bqz3u3iE4Y3DAxyAgyCLxlMindtWuyGvLHwJyvDxWegDqEKO0PlP4bsvwzBgiAXvPzNIMLMsPgYwyJDhgVsDDwvGPa/YvDVZzh0M0tf7tsT9gXQGwsZwOtZvzB02Haw/2EovF6xg41B2qhDzi6CT1IeZNAoHg2BoRMC4G7hvYqKawwMPDkM78M6WAwj7H6sWPG5ooKxo+Mv+IJN/o6OV+ArIv5CciI4/35gYPjIwCDS0SFkZATvB8L9jXbVxJvduyE9NxFYjxGuEt4//FNRwQ6+doIJtG8XdM4NXA0y41NFhTgD6ODT7wygg50YYXKfKyq4wN3U9+BTSdnA2VScgaGNYYY6w20GBg4DhqdHR2cLYcE1So+GwGgIDH4w2iEc/HE06sLREBjpIbB7N2i3kZvbI4bAOwxuH1LvPZxtbMCgpcxw73HR+u06nz75Zaa/Ai/f+gDeFvixooINvK2IC7y+6ze4uSbx4AEP7IAKyMLRJwoKn8BBK8TA8JyBQbSjQ8DI6IGbm86DB7wwlWB5BsgZpAwMDKtWVIi+YBAXZLjLx1AlxGDyiSGOmUFKEnRjoQsDYmOhBhvDN16Gmy8ZTnMyPHnBoMDMMJsbNE1p9JFBn5eBjQlkqtd/BnYeBnYWBj7djnUrKySkGDh/M3z6lsVg4Kf7zoONmUHYc9fGhW6P+RjCjTrm7qu48Z0hxL3jx8kKVfOOQycr7Cw7fJxHu4WgkBzFQysEHj58s2LF1YqKlwwM3DkMryCzhZBJNjFwx+8H+Ap70HWCoN24DOwdHd8qKr6CR3neVVQogc+CQvbyC0ZGyPQgXBBPnxCyY5ABfGbMb/Ds38eKCjHwSaRw7RDGp3PnOBgYOCoq7oLdwwfb6ygAdgkbAwNTRcUj2ByjELh/yAeW+lhRIQvaNTwLXKiMzhZCgnOUHA2B0RAY1GC0Qzioo2fUcaMhMMJDYPfuZ9IMb267zfzBwPCEQYPH+ul1tbc/lWVZRIX+MDC6HTl25C+Tx8OHfEcPS4ObdP/OnfsHPk2U08jolZub8K5dIq6ub3fvfu/mpgI+QIKBgQGyGekpeEeQ9K5dn93cJHft+qemBllXphAR8WTFCn4jI2nM6YXnD/fkKjyQZdAN3bVnqttFAQb++wziPAwOLAz/FRAbC0UZGfSuMbzjYfgDvrEQc2OhGDPDASYGVxYGWVaGM18YpFlAkxQn/jBI8TEYMDE8kOhguFKhbN3x6VLFLw6GP5odh09VzPjJkMrBMPsPQ+xfBmerjvd/GM5erSi36GBgYNAJLx/hiWTU+0MxBMCzhbcrKu4wMPBBuoX6HRrsFRWvwBN3oGWc4GWZDAwMYhERzxUUuHftegVeCwq6zgKpW/hl9+4vbm4SsNwNDwqsfcL34I2I38+dg5wfw8DA8Hr37i9gYxXBS0zh2iEMyNwjS0TEI/AJqPAbEeUiIv7fuvXezY1r1y4ONbX7CgoCDAygg0fBy9T5wQfkLOw4yl2xjoGBQ53h6feOcP0Ii9EjZyChOkqOhsBoCAxCMNohHISRMuqk0RAYDQEG2Kzg43kMJ/UZ8u8yZCswTH1vbevu550mL6U3d9k9bY2UCRNAl5vt2vXi3DnIXWSM4BVcDAwM3xgYIJeV8RsZ8aupPVJQgJwi+NTNTXrXrl/nzvEwMAiVl/96+PDLihVCsLbgp4cPId1CZvCcgGxEBPJU4bvOzl8VFV/ndTy9W/HsA8Pulwwmjh28rAwHDlfM5WGo/sJgLsJwA7yx0O4bw9JbDJCNhYosDE/ANxbmfGc4Bt5YmM7OMJOJIekfw7z3WenC0xiYGJwZGER4GUT1O/78Y1i7quILO4OrLIN+7oObe1cw3al49YeBX61jw/WKEKOOF78YnvxhWHOiAr6x8NDJipqE0Y2Fo1lmSIYArFv4joHhXw7DC/hs4c+KChEGUG6CHDDDVlEhumsXG/hawvtubpCs/b6iQnHXLqZzoEtA4VkYORSQ+4Q/Hj58t2LFN9BOPwaZBw84kJYAvMbdJ0See/z08CFat5C1okICPA71wc1NYNeul+CO5SfwYtSvYHdAbkT8z5CwcXRvIThARonREBgNgUELRjuEgzZqRh02GgIjNAR2734mzvDmm5v+c4YsSBCoMEz7CWY9t7b18/Pedve29Ky5vxlAh78zV1QwgTcdsYB3DzIxMHxlYBBlYFD+/x+yLvTjrVsv3dxAS786OrhhG5DedXbyRESwycvDGWDjoQSkW/i+ooIZfJQFZKrwXWcnG3jPoVB5+ZeHD6/vXHHhVMVZdgbxVww/uBj2sTAoPWcwEWDwcOl4fbsCvrFQ6CWD6D+GC+IMRoIMxz4x/P/P8ICB4cxv0MI4UwEGKy6GvT8YmDgZ4thAVp//zuBk3tE3rWKxLsMOqw6WXwz3PzHIPK34pdKhxM+w63QFYmMhG6jLG8PKEJewq2qRWwE/g/7oxkJQEI7iIRkC4G7hPfBsIXQRqT7Dgm/gfb+QPhV4wTVoGzBkTSbEk98qKljBd8dDFn5DBCHrOSFsBvDw0BfwjkQmBtC19fCJQbgCCANrnxByVg3a3CNyt/CJmxs7eErwF9giyCFVH8H7lr+CMijDX7A4HwPDG4aQmwzycgxvOUEblZ3EInxHZwshIT9KjobAaAgMEjDaIRwkETHqjNEQGA0B0Kwg+HaxjwwM744zeAl0dPAYGXEwMHx1c/sB7s69cXN7YW3LefQw5Og/DvBBMkzg4/6EOzq4GBhYwJeYvVZQeMgAOuxPALxwVAy8cPQqI6Porl1irq7wTuAX8PoxHoyloZCY+PTw4QUFhf8MDBCTQZuOYNeRQRT8c2LgTuv4cKRiz3MGoacMCvEdNw9VlAmANhZG/GdQkIVuLJTR6Ni0s0JNnMHYqePVhYpV77J+sU57/IJhD3MCA/cCBk4Gl5cMMqIM7OCNhXaWHUKXKjhYGH7/Y3j3leHGcwYVMQYpsw5Vp4gTPQpifAxMah0ndlSUijOsYWPY8JZBx6Kj4mJFwz8GVfOOF+crJIxHNxZCImeUHHohAO4WQk4iRcwWQk4iZQff/vcZ3Pv6A14TDvEeP3gLH0tHB2Q3IAMDwwfwVfUQWQbwKTVcYPWvwStRhcB7/NDuJIQoxuwTQtaLos09Qm5BfOTmxgTef8gBvi0DMh/IAO5z8nd0sDEwfKmo+MXAwNbRIQh20kfwcJUwaBfijdGTSCEBPkqOhsBoCAwqMNohHFTRMeqY0RAYoSGwe/ez726tP8C+38fgGsiwW61D7gcDw3vwJIAQeLidA9zAgtwTCGmQgTppDAwMHR3vwYf+sYNbe6CzQ93cOHftYmVg+HDu3CPwzWOKERG/b9166uamBF7ixePqClksygbuQEIuomBgYPgMXvT1EbyujAF8EulfBgbI2fcC4LlHsAMZZHft4lVTgxxR8+n5w0u7V5w6X6Ebumv/dLcLfAy89xmkeBmsWBiY5GEbC0UZxJgY9l5bL8Jzx0239BMjw/3vDHImHVFnK5L+MMwD31goysxwEGlj4b//DDJ/GU7+ZmBkYXDjZpDx37VvpptP9q69s91khRg2/GZg/M0w4yd4AoIRurHwBQPDlYsVCRwM4gajx5BCImqUHHohAO4WXqyoeAbZWxjWwSJeEcfHwPCrowOyTIAZ3An8Cc6kIHHwTfSgosDIiFtNDZSLb92CePu1mxsrmMXGwPCZgYG5o+Md7MJ6ITAbrX+I3CeETA/Cz6qBbDxGuwWRD9z3+1FR8YWB4RMDg8quXXfc3CBrXL9UVPAwMHB2dDBXVHwCdxoZwJOZXAwMtxlCwI7ikWdYwNCxSGh0thAcHKPEaAiMhsDAgtEO4cCG/6jtoyEw0kNg9+5noONDGRjnMZySYrghsSud+dx2yLkRkBF3FvC0wH9wOPF3dLwGNwQVdu0SUFN7tWIFKwMDr5HR33PnWMFdOwYGhscKCqwdHd/B6hkYGBQiIj7cuvUIfIQMKwMDP7j5CDkG8DG4y8cANgGinFdNDblzCOkZ8nd0/K+oYAGt+wLdhyYE7n8ywPYoQi6GeHOp4ociw6t7DDv+MCh8YODgZbjFAzpZtOYLgwwLwycBhm//GeS/JWy7xOCpu0DHvoOTheH1f4aVJytYPjIcR9pYmP2dwdy+4+ipCuSNhb/BGwt/cDI4SjEY5D04uW2F4KOK138ZtP12rVnjpmLSwfCbIeZAhfsPBlExBmcj8I2F5yucw3fpW7hC/DVKjobA0AoBcLcQfbaQgYHhZ0WFAAPoGngGBgbdBw++KCiIPHjw4dYtyMEw4hER3ODNgd8fInYMCu/axQi+fV7swQMGBgbIJuF3FRWQPiHkznoIW6ijgxU8lQe6px7SizMy+gRetMDX0fGpogJCyoAHleC3X9xgZOTu6GA1MoLsIWRgYHhbUaGya9dzNzdu8KpXUfBA1ZeKCkbwFRpMYO73iorvDLbWDLGjJ5EOrZQ56trREBiuYLRDOFxjdtRfoyEw2ENg9+5nd9zWiIAueGfcy+ASyLCbj2GaAPjuh8/nzkE7Y+Am1DtwEwrSuwOtCrt16/25c28rKjjAp/mxMTDwgptoDAwM/8+d+1xR8RHs9b+g7TqgtWSQJhozA4NyR8czcENQkIGBJyLiF+ySeuSJQf6ODvgNhL9WrOCJiPh569Z3sHtYOjq+gE2WB3c+GRkYbikowHc3fWBg+JbM8IyB4Sw7g/BrBgZuhl1sDEqPGSyEGf78aZERfPbr37SHbxlkBRlsknYxMzJsWuRmJMhw9BOov/uAgeEXG8OlZwymAgzyLAzX/zEwczDEsTP8Z2C48J3B0ayjfzpoY+F2qw4xJoZzrxgYrlTwcjCwszDcY2O4/g00YzLnN3hh3DeGGDaGuPhdbivcFnEwyLMxGGQ/GL2xEBxpo8TQCwFwtxAxW+jKcEifYcEb0KG8IL/wgW96YAMXDp8qKkTBfTbZXbtYGBi+nzvHwMDAaWQkBFsQfoeRUXjXLkEYl4GBAX4nIWT4CTJbyAy+/BAyyvMVPAEv1NHxBdwV5DMy4oGtCwBZD8PfHz58qKDA3dEhCz6bCmLs/RUreMHLXF+AlQl3dHyoqOAD9zZ5Ojq+gucPQSced8w+XXGDgYFDjuGpZMfo3kJwYI0SoyEwGgIDAUY7hAMR6qN2jobAyA4ByKxgR4eAWcUUHoapjxmyxBimcYFvC4Qs6/oLPhhGGLysC9LZ+3Du3IeKit/gPt5X8HEvoCWd4O4iY0cHs5ERqIXn5ia1axcjtkbbu927H7m5fQUHOzcDw3fw/h9mBgZ5cE+SVw200gyyBBSshOHXw4dvFRSEHzz4CV5+9hvcxPzPwCBTXv7p4cOH4DPo/4PXqYImDcCNyD/gNaWMTgxMWiAzTr9jOP2V4d777ii50iJehtT/DBqfGeRloBsLNcJ2rehxM1ZgULEAbSx8odtx71TFoScMzp4dFUcrErkYLn5lCORmUDKD7gwUv1LBzMSgHLjr0Hy3O68YDOQYHr9lyFn4f2Ico6Ecw+X3DK9fMLxSZFBnYPihAbqLYs3xCjdOBhXjjveXRjcWgqJjFA/dEAB3C9FnCz9WVDAxMAgxMDwGXxkvYGT0GLwKgB+cu38yMEh0dIhERPCBJwwZGBgg1w/C755BDo0vDx9+vnXroZsbM3jJAGjyECwN6hCCe5sgETAD6+ZD0HjT7t2v3dxEd+0ShnU4v+ze/dnNjWXXLnbYfTZvwUsb2MB9QibQGA4DxDrIBYwfGBIEGb6YMkR3dEgbGQm7uiqBnTBKjIbAaAiMhgCdwGiHkE4BPWrNaAiMhgADA+jYmNtua0QZbj5h0LjJIBvIsFu9Q+4DeK5PdNeud25uH8DBBDoeBtxmYgRPGILFGCDdtn/gRV9vwTsDhcA9tFuMjNIPHvwD99x4wQ0ySAsPtJsIPFHwEWy+CLjv9+vcOV4GhqcVFR8YGPg6OiDmKIJn/OBtR8j2QqHy8s/gU2cgZt5mZPzPwKD2///b3btfuLlBtu99A1+VJmBk9NLNTaCjg9/IaNc59pgKtnSnu7oye+6yLhC36pD7x3DodMW7xwwyfAy+Hh1vb1fw/GE4eJNBUJSB5QeDb/aun38ZHm90+8TIcO87g7xJR9TJikQGhvnfGBLYGJRZGWoZGLL+MoQIMpz/xvDvP8OXJwwfBRmOMDI0CjDIBexa3u3mH9rx6y+DADvDlBMVDoYdW49VLPkLvs/7J8M8m46PvxkKr1fsGd1YCElDo+SQDQFwtxAxWxjWwSJXEcfe0fEBPBwDOeJFANzd+sPAwIm0nY+BgUExIuLbrVtfwBeTQiYJn4OzNmQlAj/orJcK+BmhYh0d7OCzi7+B9zB/BHc48W8+ROsTQoaTeHftghxY9Xz37g/gJQbM4GWokINwPoDLN2bYOTRcFRXsHR2tFZzqDK/AdzByyOyKGu0WDtnUOurw0RAYemC0Qzj04mzUxaMhMBRDADIryMDAsI2hTIzhMDt4Jx4/+Og/YfAJoqBRfDc3kV27QN2Zc+fYjIz+qak9XLFCwMhIFtzNQ/b15927fzAwPAdPG7KDDwL9W1HBCG7YgWYOkZZ9MjAwQFZ+soHnCiBHjP4Br/b8wcCg/eDBfwaG++AZv7fgTqaUkdHPc+dkkHqDXx4+hGxNZOvo+AyepWQGLfNkkNq164Ob2x/w1AEDA8OuCs4SBlXwNCfIpbt2KWsK3Pxw79zr7wzLt1fEZ+y6cu/c00MVzTwMdQwMUt8ZznKDrqT3Nu24+JqB6WoFZGOhgJKR7BK3ircMYrYdRScrElgZFrAwZH1jsHDoeHK24uEfBnsmBgk+0I2Fv/8xHN5eIWLewX67QjUEtAb1+Wa3d/8ZmNQ7Xl6ouMLGYKfbEbW/wuQfQ4hbh8DJCl5T6GRjVMzoRfagCBrFQzEEwN1ClNlCSYYFkB4dHwMDLwMD165dL93cPjMwsIIzJmQ73303NxZw14sZfNDL54oKmV27nri5wTuBkG2BkJOi4IeLvt69+yt4REkIPGCEf/OhIPhOmt/g+xIZz50DnX9jZATpcMLtgiyDF+/o+FVR8QhcagkaGd1xcxMAnzfzGzw/Cdkn+ZMhxJTBh4FBZNcuzdFu4VBMqKNuHg2BIQdGO4RDLspGHTwaAkMsBHbvfvbFrfU+g/otBjm7DiFlhmciDA//MzD8rajgBG/7+QBuDwl3dIiCV36C7+RjgAyug3pi4PF1+YgIJtiWPwYGhtfgOTpI34+hooK1o+MXA4N0RATysk9IMEF6gJDeIPI9E6/BUwRvz53jMzKSgnU4X+/efQ+8cowJvPAMckQhxBwOcHPzAwPDB/DOIgbwWRC/Kyq4Ozo2VnAWM5iBdZxkYHDYtUvO1VUKogtyBun1Q6AbC4Ojd12d7lbIz5B0lcFGlcHICbS288KBiodvGSQkGXRMOyTUjFbPd7MUYVj2nuHbHwY9TobS/wxG7xlMuRlkWRhkjDsSzlcs5WSQYAJtLHSz6Fi+vELEp+Pr9QpPk47zrxgYr1QYOnU8OlXBycaw/heDOiNozepyRnDn9S/DrqRdVbPdVPkZMngYlPxHL7KHxM8oOSRDANwtRJktNI+we6OgwNjRwRsR8XLFiq/gCTfIVaKQUuI1+KZ7dvDRUD/AM+hy4CUDkE4gPBTQDheF9wkVwPsDIcoguwSR+4cQcQYGhu8VFWzgnY1/wWUapKfKxMAA6XBKwsoZyIZGZjW1+ytW/K2o4AcfgvobbAo/eH6St6NjNoPFk4pT6gwfGRg41EdnC8GBM0qMhsBoCNAOjHYIaRe2oyaPhsBIDwH4rOBpBkvujo6/4PB4B24AgZZ1dXSwMzD8AK/Oel9R8Q98qaAEeFz/P7hnCFnzCRlWZwDv+uPr6GCqqODbhbj1Ad7Hu9bZyW9kBLlEHmwPiIDLguYJHz78smIF8q1ikD7hbwaGT+fOaZSX/3r48JmCwg/Ysq4nYIZoR4ewkREvA8Nn8GTgh4qKb+C9ggwMDIwMDGcZJpZCZwX/MDCw7Nql4OqqCLIYFX96/vDRoRUMDAx7tlQc/8yQWbTr261zM09WiLMwmP5mEBJBbCxc3uNmogDSzMfBwKDXUboXdHYov2NHxZGKJm7Q1GLmVwZ5PgZWJgZD044PJysEuUEbCy+udFtzmsHPkIGdBXQM6aE9K8TvVDDqdLz7wXD/RsVfbdAxpAwMDGuOV7jzMPiH77p9B3Tkho9rxOh5M6CwHsVDMwTA3UKU2UKNDg2IV9grKt6Dz54RBA85cTIwcDIw/AFPEn5hYBDYtesDeOpPDmmTIUQjfHoQwgWNPcHmCZH7hHBZ0Nblhw8/3rr1DLyDkQF8tBMzuIP3D3YQ8fuKCkFwsSZgZMQH3q78bsWKPxUVKv//P+vs/F5RIbRr1yuweyDG/quoYAYvduDv6LhRcUOfYcFshhmtDDIdHWIREaqjN9pDQmmUHA2B0RCgLhjtEFI3PEdNGw2B0RAAhcCF3Zeuu83cz+A6m0Fy1y5ZOTfp7+C5NSHw9fGQ5hoz+AYIPiOjj+CjQUED+eBZNsjJMZCzFiBnfsJPAeVnYHjPwMDR0aEIbskh9/cYGBhudnb+ZWCQjYjgBa8ORZNFnioEORGMLzMyKj548HTFCjYGhm/gu6R/g/cWclZUyP8HXXXxaPfup25uvOCVZqIREXcUFAQ7OnhgewXB7kWfFQQbjIX49PzhpiaF618Y+Bw75P8zHDldcfc9gw4Tg49Hx5tb0I2FAqIM4swM1gm7WJgYTi1xY+Bk2PeFQdG44/6Fipm/QS3cePDGwjsMDEzsoGNIIRsL5f8xnH/PcJ2fIYibwTp4V2OpW4gpg6B+x5vzFZcZGZ78YVBlZij9DG4OfwPNb+5KAh1DuoSDQd949MZCLDE1KjSEQgDcLbxXUXGHgYE7h+EV6CTSDg32ioqXoOwC8gdvRwcD+EAXyV27mBgYPp479wkkzCBgZITWLfyye/cXNzf43YNgVSAC6zwhSAKMX+3e/dzNTRJ8zwQD+NSrb+BhI8iqeEhXEN4nhDO+gU/G+gXKjiBTIItXQcNe4EXpb8ALSr9XVEiCJwy3M1R/ZfgBXo7OYd4hLzZ6dSEozEbxaAiMhgA1wWiHkJqhOWrWaAiMhgBkVnAewyk9hnzwlQoMwh0dvOC7/j6dO/ebgUGMgeFNRcVf8CA6ZJ6NETL7x8DACu40/gKvthKE7QME3RMIHlZnAB8b8w/Mvr9iBTO4kfcX1jmEhPw5RkYO8OEu/GA+fN0pvHOIfN7MR/Dtgozg0x04GBi+gNd6MYE1ioJ7nkxglzCBpxd+gzc9CnZ07K7gLEKZFVR2dZUDayKKeHJ694d75958Z9i6r0LDuOMvD8PzIxUNrAxF/xk0/jOc4gTdW+ht2vHwE8On8xWszAwG9h0CSkZLb557dLBCxaSj6ERFPCsDBzfD/RcMtsIMzEwMD/8wqP9lMBBmeMPAoKzbcetoxc3nDGqSDKLGHWYeEStrFPhFGP5odzw5XHGfn8FOv2PzgQozs46l5ytSOBgsInbtXelmaNph7Dg6W0hU9I0qGpwhAO4W3q6oeMfA8C+H4YUrwyGNDg1eI6M3bm5CDKCbbSD3xX+rqBBlYBB/8ODTLdDVNaD+G1K38JuCAltHB/IiArhnsfYJIV1BBgbQoBekTygGXhT6dvfuV25uYuBDRyHrS0ETibduQfqf78HdPIaKimdg0wXAO5NZwWwGBgYRcEH0noFBEXwuDjd4ypEbXBiedlsGni2c2Mog19EhPTpbCAuzUXo0BEZDgApgtENIhUAcNWI0BEZDgIGB4eLuS3vdDtxiAPWOkjt+8FVE/gYvlHoHPiAedFQM7NrAn+CzZGTAe3h41NR+37r11c2Nb9euXwwMr9zctP7/f7p798dz57SQ9u3AT/6EBDWEywI+7OH+ihWQ82AEjYx+nDsnYGT0G2yOyoMHX8B9SHbwgk/Q4X3gYxt4jYyQz5MQYWB4Dl7/ycrAwA128H/w+tU34BMLecF9QtDNZgwMBxgm4torCHEVkSRkBemFAxXnfjOIW3VwHazI42UIu8fgLceg59Bx7XzF748MD98ysPEzqPIwBE/4v6WE8RYDw63voI2FnMwMsxgZjD8znGVh6OBl+PUXtI50KQ9oY+GLX6ClpBdvMXwRZvjNyOAjCDqGdO9MNxtP0MZCLjaGdX8Z1P8xfP/DUA2eJ2T4yxDDwmBs1FF4vWI3B4NG0OjGQiIjcFTZIA0BcLcQZbZQA3QqE+hqCjHwIkzQTQ8MDJ/AOf0DeEQJ4hNW8Hr1vwwMjOASALRaAbZqHbLIk4GB4dWKFd8rKsR37RJ1dUXuCkJMgHQFIWzIoaPwPiFcEM543dn5s6Lie0fHE7AbhGHHHYOutbh16+e5c+wREdfBd5wygFe6/geTkONPT3ds8qv4mMPwTp3hk0WHvOjobCE8WEcZoyEwGgIUgNEO4SgYDYHREKAoBHbvBo10u7k9msNwSorhJg/DNNAF8QwM/OCD/r6AWzygBha4QcYFXlLFxMAgBe7svdm9+6ObG19HhyiYy8DA8AZ81ouIq+vnhw8fr1gBX/+JvOAT0htEG8j/9PDhh1u3noB38vAygI5iYAAvuxIwMvrl5sa7axebmtrnW7fg5/5BJh7/w5pfj1esYKio0Pr//9Pu3Z/OnXtRUSHR0fGdgYHDyOj9uXO7KjhfMMj1gvwE2StI2qwg1vCFdAsFlIwuXTy3cXdFXMaulYvdGFgYvjMzGP9kEBNjOPqBQZubwTZ016GT54TuVzAyMkA2Fq48W6H/huGzLWhj4SKnjgmHKsIZGMR5GThZQaeP8jAwfLxcwaTXwfOPYePqCmUxBnE+hheyHRYmRk83u7HqdSzdWSEvwMBm0sHwk+HKiQpj847TVyvqI3Y9vXfu/OmKlLLRi+yxRteo4JAJAXC3EDpbWM3wwgw8W/ixAnSxxN+KCm7wuTL/wH1CBvCgz9eKij/glQufwUvAuWB9QoiHP1RUCMBEPlRUsINFv4NXDfCCL8VB6wqC5UHEo87OrxUVCg8ecIJXsIOEwBjSGxR98IAdLP7p4UPI+TSQJay/Kip4YYtdGcC32nyqqOAFL1P/CTlJC1yQ8nd0/KyokOnoEKpQH50tBIfrKDEaAqMhQBEY7RBSFHyjmkdDYCSHwO7dz3QZrta5fZ3NIMnA8P9oxwPBish/4MYTe0XFD/A04B8GBs0HDxgYGPjk5Z+Du3+Su3b9PneOAXx3HwcDg+iuXXzgdVbwkLzGyKgF3r8H6RPyGxlxnjvHExEBOSmUgYEBuXMI0fUC3I186uYmvWvXTzc3ho6ObwwMrysqWBkYxDo6fldUfAQf8MBrZIR8qOCX3bt/gu+3gNw0eIqRkbej429FBRv4IApucK/yOMPEIhwniEKsppB8cnr3gXlu178wtHEwrLToOHKmQsmo4/GGioDwjtc3QRsLX31muPGcQVGEwSZpFzszw4Xlbr/YYBsLz1bMZGBg+AaaxIxnZfjPwLCIhWEZD8O/3wzP/zDocTAsO8GwUIOhmYchKmzXvhPnGK9UyAozcLAw3PjCcJaBQY2R4Y9uR8WxCtAN2X8ZOqw6GJgYWK9XuBmNbiykMFZHtQ98CIC7hYjZQtC9hQzP2CsqeHbtuu3m9h98ZBQLuFj4x8Dwu6JCkIHhBdjVvB0dnOCNzWAeiIDMJbKDN+GCOmkMDN86Ol6Cb6mBrE1gYGBQAO9qBqmG4e8PH74Gn3eqCS7NIMKg8SbwagjkQu8LePDrHfiE0r9gW/6DC094RxQ0T1hR8R3cURTt6GAGX70ozsBwgqE4nMEAMlso2mFiEWExeuQMJJxHydEQGA0BUsFoh5DUEBtVPxoCoyEAul+egYHBze3xLQaLx+DwgGzR4WFg4Aa3rsQePHivoMAF3qvzANz8AqsC7ZD5A96zJ7Nr1/9z59iNjJAbRhA18ElCCPd+Z+eHigqVBw8gR8Ug9wbh/UA+8Ki5NHgNKvKNFP/AbazX4MYfxDTIGQ8CRkYcDAygpVkMDF/B04BP3dz+MDAIMjCApjvBU4u7EJdJUG1WEOIGTBKysVDOLmJplcKB96BjSK8vcMtiYZjAw8D/leHSPwZOTgZfsw5eRaOd093E+Rj0wRsLD98+d+pohZVRx73/DNdPVihzMWz8xpDNzaBg1vH6YsUzpQ6texUfmRjc8x4c7lU484BBTYLh1SeGkJYHx3sU/nGCNhbu2VOhK8HwC3wMqZGG0eweN3ZFhjyTjvuXKv5od9D/GNJX53a/uws6ARUSRBd+MkixMIgxQ3gMaFzQKr6/CFlMLpr6C6C74RgMIFM8DFhMu/CDQYqZQQy2nQuknQnBBZn/G4V74RuDFCtCBE09Ghek/R+DGGSLKgPDhR8MmmpG+uauUL+NUrQJAXC3ED5beMOM4RLk3sJPFRUyDAy3wZaKgfchf1mxgj0i4tUK0GnA3EZGyPN+r3fvfgleesDAwCC+a9dXNzfQTTmwdZ7wjYjInUPIENhb2AmlcuAVEMi9QfhmZuTl65BrKtjAE4MfKyrACRbsRAbQkNtbBgb2jo5/DAwQizgqKhjA5R5ktvAng60pQ9HobCE0vEap0RAYDQESwWiHkMQAG1U+GgIjOwR2736mwXC1xe3rLAYJBgaGWwyWfzs6fjIwfKmoEOro+AM+qJMNvJxJAbxKE3J54H1wc4qVgUEcfNXERwYGroqKb+A1mZDzQtECFT5JCFkdyhoR8XjFCn4jI34Ghi/nzv0H3/j8CbwYFXQSKGxnIOiaCvDV9kKurm927/5z7hw3eD8hAwPDM/Cp7iAT1NQerFjxG9zY4gVvHfwAtltx164f5879BV9puLaCsxQ6K3iLgUEf+V5BsFqaEJAVpGtWVfzmYmhjZ2j5z3CGi+HHU4YgIQYrt47rFyr+fGK49ZKBl4NBThi0sXBXOeOVv6CNhTMhzUYWsGd+MqRzMvz8y/D3P8NiDoa93AyQjYVMPxiW3WFgUmSI5WBwKHmwatUK6ScVsiYdd05UXGJg4GVm+P6T4epPhmUM4M46E0OHZcfqwxVRYgxuRh064fS4yP7t+d2vdrnte8fw4SdDVOSuk7fPRR6rmGHakXG64nPdgy17QM303XsrnrIydKXsUpJT27JnReTRCuc/DCkOHT7OEbi4VhwMZdUg7ZFHK6K/MizlZfhcC+IyMDBs2VWh69whz8IA187AyuDxhUGZjSEtYZeSglpAkwLrDwR36p4VB/ZV7PjLsNypQ0vd6MGtc9NPVjgYdjw+TEyQOwABAABJREFUU5GWsIuBiaF8kRvDJwZlLpB29v8MGqvcfN4xyHOCuEoKalP3rNBjYPgITq+RhytA1yD8ZTgowGCQPbpGlyYZCtlQcLfwXkXFKQYGWeSTSK+Dj2xhAK///Ac+vljAyOiVmxsv+GAqMVdXtK6gKHgtw8vOTg4jI34w+xN4pfqHc+cgnbS3FRXKu3bddXODbwtkZWD4VVEhs2sXKwPDGze3XwwM/Lt2YXYCIccpQ9YvfHv48NWKFdzgTYwQGxkYGF4wMrLs2vUMbJE8eNUDpOASBPcJxRgYToJnCxkYGHIYfoOmQ0f3FiKngFH2aAiMhgAhMNohJBRCo/KjITAaAuAQgO0VvHiNwesSQ5YEwzShjg62igpG8AJLmV273p87xwpenMkKPjX0C1jXX9i56gLgUzrlOjo4wT0XzoiID+ATX+6C+4qiHR2C4AYQWBPDMzc3HvDS05/gC6YhVxFygc/f+wleBSoAVgxpP0G0MDAwwI8SBfUMwQf9Kf///w68mlTI1fXTw4ePVqwAuQ28j+gPuP33nYHhXUXFF7CThDo6diG6gjSfFYQ7G5kB6RYKKBm9/8WwdJabqlEHPw/IcXvPVzgyMJz/wLCbhcGbnyEmYtelC+d+XKoQ52NgZGQQ0uuYcbaC+yXonNb+fwzz+Bn2szBof2PQ5WH4wgTaWPj8G4PYnQom/Q55JaNTi92+qnYI3Kt49p7BOX3X7dVu9xgZdEN3nVzm9pidQUmn48rRiuS4XSVL3ZI4GGIqHuyYoKAeuEvfglZzWW/P725b6Mbwj0FMv+Pe2YpZ4C4Tw18G378M1tYdFacrOkw7Kk5UMDAwuP9m2MkK6qyClrkyMDj/ZnC166g4VcEAvuASyj1ZwfAPFKIdlh27D1Xs5QSnxX8gXRBuhxl4lSxY+142kHjFcZDh8407lpytUPjDEJ68y22um/Nfhr1cDNOMOzYdqdgBmdZjZ/D4w+Bn3vGJkaHiaAUDO4PHbwY/CyjXmYFhLwdD8lcGVbuOiqMVTn8Y9vEwzDDu2HKkYgsjyD0MHKCDQQr+M2hadlw6XaFm1nFgX0Vm1i7JR+cu/WHwcRo95RUcSrQkwN1CLLOFHysqZBkYnjAwSMDuJ/wMHmxiBx87zAa+Vh7eMQNdh7N793s3NwWkhaAQV0MOFIVsCIT0D4WNjB6Ayzdm8JUR4MXdILUC4F4c5CBltEIMJM3AgNYn/PXw4TsFBQmwjW93734DNvMDeCDsQ0WF9oMHXxUUfnZ0sIGPy3rOkKAHvrqQr8No9CRSSHiOkqMhMBoCBMFoh5BgEI0qGA2BkR4CF3Zfmu52j4GBYRaDRAnDixSGwE/gxUuC4Lv7PjIwyILXan4+d467ouIzeND9FQODzoMHPLBTE56uWPEefO+WIAPDO9CWNwb4vVuQwOUDn90C2h8Iboop7Nr13s1NpKPjDbhD+Bl80ILkrl2P3dxkHjyAHyvKwMAAn2CEzCVCTpp509nJbmQEmTnjVlN7t2KFdHk55Cz4z+BrBn+DD4r4zsDAb2T0zs1N+sGDmQoby6CzgucZGKzoMysI8Tsm+eT07lNL3Y7eZpD17zh0usKUiaGKkaHuG4OVJMMHRobf/xj4/zKsP8tgrcogZdGhqWt0c43bR0bQxkLobCE7dGMhAyNDHAvDIgaGXgEGrX8MV38xmHAz7LrK8E2YQYiLwdKoQ9M5YnW1gpYUAwcrw+cfDEt+MwRwMtz9Cj6GlI0h+i/D0v/gLtPZist21N9Y+Pb87q8Pz335xTBrb8WRnwzGvAxKxh2cfxkOn67QZmFQZWd4ogs6Gmf9sQqW/wwx5h1P2Rgg/UOG/wzivxiWnK7Yy87QYdrB8J/BWtWoYb7bXnaGaSYdWScrHtQ9SK5TcLXtMFI36pvnVpS46+nlc0tOVZSn7Dp38xxU+ymwdjOQdp4/DDlnKhiYwGuLGRmmWHTknAN3RA9XePxjKErd5bbUDdQvPVTh8R/K3RW369zVcwcOV/jZdGSdq+iw7GD4xQDhfmJhqDhf0WEAOvXH5z9DXvIut+VuHcYdDP8YNJgZ7pyq+GPVEWsfkVOlYCfBoB286+HNc3LXKqRGt25i5gQaiIC7hfcqKs4yMEghzxa+A3fSIDfN/AEv0fwGHjP6DFuiqQg+05gPXKY9YGQU3LULMkkIdyN8FSjk5CrISVrvKiq4wKcrvwerA5U54KEuBgaG9+CyDrKIHbJ4AXTSFdh80NZgpHlCTgaGX+CVEZCuoMiuXexqak9XrPgILse+gbcX/gJvPhQC+0KUgWE2w8Q2BqHR2UJwqI8SoyEwGgKEwWiHkHAYjaoYDYERGwKQSwVPdTxgqoi8w5DFwMCg2SHHWFEhCj4Y5jt44SUjAwNLRwcjA4OIkdEP8HkJvK6uNzs7IXcMQib3IDdMMJ47x2Nk9BF8NzT8+FC0sH0KPuTzXUWFEPh0+F8MDKIdHaIMDGxGRrzghaCgq7pgi7UYGBjgnUPQ+aVGRvxqaq8VFEQfPICcQPNm9+6/DAzvwCu1GMCtfQZws4kXfOC7IHjS8jLDxGwK7hVEcz8VuVdWdl47V+FR8GBFrcJf4w49DaO7q9yOMjI48TA8fcbwlpFhJztDKSdD+OT/1QGMFsoMimYdf/4ziCgbyS53K37GIOrSce1khS0nQyorQ+c3BkN+BnYWhldKHR+OVfCLMOz9yeDNxPBPreP+kQpFUYY38h3uVkaHVrod+s6gaNzBdrmi6D9Dh04HAwODjbKRzSa3UyYdnCwgz/HaRMhLy4NYFOC353e//MMwd6lb32eQKYFMDOuZwFN5DAwdFh1njlc4CzNIgK3TMOh4dKbiG5jNwMCgYQBaovkVicvAwHDjAmiKDyIL4XL9YYBo0TDouHGhAkJCFDw+W/EVtilRwwDkQWTtN86BjQIr0NDvQOOCDD9XoWHUceNihYY+WC8GF+QfZga4LITL8YtBwbTjxnmQ4X8YGW59Ytj2keEoG3j2/DdDhzXIy8JsILV2lh0+LqOzhaCgoCl++PDjihW3IPcWVjOA9hZqdGhATiL9CL6rkA18BcWnioo34B19gkZGd5AWggowMMCXozMwMEAON4YMckEMAU0kgvt7/OBlFGywa1f/gHuDyF1BUL8RfIYN5M56ZKnPFRWC4CNk/oNPuoKUpZBxLmS7PoAd/AzsTsjMJCdse+FzhgTw1YUzeEdnC2mankYNHw2BoQ9GO4RDPw5HfTAaAjQIgd27n611O8XAwDCTQXIew0kDhvwv4Jk9JnAjlh98Fst3sL2QEe4P4Ek8yOF4DOBFVqAm0blzfEZGUuD+25fdu3+dOweZwYMfHyrt6oo2rA7ZS/PZzU0KPB8o/eDBawWFt+CheshRfk8UFCBnkIIthxKvd+/+cO7c+4oKDrDDIEv8IHJc4AswHoM3xzEzMAiB16z+rKj4DV4gCrtXcOBnBSGuRSMhK0gvHarY+JDBKKjj7UnQiaD7vzIEfGcwU2K48p/BO2DXiUVuN58zCPMw+OXsurfe7dV/BiadjoMnKmb9AhvGDj5KHx4iHAx7eUFLTFmYGJ6frzhwg+GbKIMaB4NbzC4OSbWNdQoaUgyfvjN8YmE49o8hx6xj5f6Klt+gKI9hYlgCXpmZzs+QY0rRbOG1U7u/PDjHfLPC5CXD4eBd1x+eu34Z1FMS1+34c6hCU4xBU5Dh+QcGLu0OcVWj93fPPTxV8fYr6HBUEydQH+zZuYrPPxhefmLQlwV7kIHhwRsGZiYGfi7wtRxgLiMjAxMjg6wQSMHH7wx3XzGI8mLnMoDV83GCVApxg8j3XxnefGFQFQexv/5k+PqT4c8/hr//ENrvvWYQ4UFw775mEONlkBEEqWdgYDhzn0FRjIGLDXQLCGjG+yvoSklJfgZ5c9CZQCcXuvFzMvBxMTz8xnDnA8NeNgaxtwzruBmKmRlyGx+s2LPi7pGKMGEGCUOKQhjqlFGKUAigzRaGdbCoGEnwgK8t5d21i0VN7b6CAntHxy/wwnIGUD4AFSAfweUMGwPDawYGEfC18jK7dkFmBSHFF2iiT00Nsj7i8+7dn93cRB48+Hzr1hvwDmdhcHkIWtb+8CGokLx16yN4hzNkzhBSnEI6h/zgjuIv8NqKz7ASTGHXrq/nzkG6nbzglfOsYAd/YGB4Dz4NSwB8BulvBgZe8GyhLAPD6Y41fqAzSnlWdHB4R2hCHEYobEblR0NgNARGFhjtEI6s+B717WgIEAwByKzgbIYNhgyd98B7BQU7Ov5VVPwGL7b8B74U63tFBduuXbKuru93737k5sbd0fGtArQySgK2TBRuyzPwFj4+cJNFErwHBt4DfATeCQPZSIPchIJvBfywe/cnNzcJ8HQffH8OF3ipFXtHB+gSZ/BtFqAzY8AnkTKCZ/8gx4SKgI+FYD937nNFxc+Ojl8VFa9Ae/Gg7rrOMPEe9F5B0I1iu3ZR4V5BqNE0oJ4+evj++IrXfxkkFIzmL3N7+ITBx6vjxekKFj4GDSaG1WcY5qsztP1nCE3c9fUPw+HZbhpSDO/+MagadLxnYCg7WGH3i6H/H8McfoY/TAyzfzA08jBwsjC8YWB4/YVBmRN0YeO2nwxhbKCejJUXaKb32x8GXl6G15cqwt8wmPxkMBJgmPUP1M8+nbrr1KVzx29W5IFnCwWUjGRMSdtY+Pb87gub3VyeMoA28h2tYICc5/mPoZCTQVy/Q+ROhZUAw0+5jveXK848YDBy7Xh1puLff4bH7xgcMnYxMDBYOLhePrJ7z0w3KcuO+4cq3LJ3Gdm6vnj8cFq+QkAhSIGRLcg9dUGMBm4dNw9UVK4A7UdcNb2ThZEBmcvAwHDvIFT7mUO77109d+9gRUAeyAQNK9cV0zrPb6+IKNslo6R2bseKMzsrNJ06GBkZAtNBh+vUBzPqu3bcOlBRATMcNE19qMIta5ehreuZQ7s3TXALKNx1dqFbUOMDYWn5Ewd237967vWpClEeht9/GYxjd51b7CbIzSBh3PHtWsWZHwy/tTqena+Y9A280voPQxQjg555x/2zoB7y6GwhDTITFiNhs4VvGBgYOjpEjCqmiTMsgE/EsVdUvAAXHYzgPXvMDAw/KypYwEXNG/DK+Q8VFSIdHTIREZgdrV8PH75RUODdtQtysc1b8PY/kV274H1CTNd8BvcSIR3Fb+fO/QLfnfMZtDuY4TsDA+SORMg0IGSADHKc6QvwigyJ8vKXnZ3fwdODvxkYPlVUKO7a9QZ8C+tpt2WSDAs+MCS8Z1C+3+E+urcQM+RHRUZDYISD0Q7hCE8Ao2A0BBAhcGH3JUO3b+DGD8MehkX8DNPYwQucJBkYnlZUsHR08DMw8BkZ/Tp3ji8i4uWtW8/d3DjADSNm8GnsL8GdN4RxDAyfwcfGMJw79wN82MxvsBx8EF3S1fXp7t0fz51DXj4K7w3+evjw84oV7yoqVMHdSLBWKPEGPB/4DdwyYwBfdPGDgQE0Ig6+9Z4vIuIfA8OjFSu4wW1s3oiIH7dugdwG3ngD2yv4ioHhLQOD5sDuFYT6hzgKcjXF5vUVh/4yOHh2iJ6oSGZi8HjDkC3PIG/WcfkPA8fpiitPGPg4GaQEGcxidt1d7/biH8Oj7wwP/4JWgYLOQeEAX2QGOeOEAXTMxXYuBmF9UM/kwVvwPJsUgx4Lg1/f/zkTOyUfV/ByMHz5yfDsD8NpJgbb/wyx7xmMfzOc5WPoMOkAHcTCzNDLz5BSQNQhmddO7X6+zY1Xq2P/NwYjdaPXd89FH6/YFQPam9f4hcFKgUGak+H1Z4adVxisfTrk+RjWraow9eyQ0TA6PMdN2aaDT9lo1xS3wAJQn+3apXN2vhEvHtzaPsWtauX/VdM7lbSNTOxcOyIY3bJ3/fvPcO/qubDMcghXSkHt0JYVYZnl5w7vZmBgkFJQm5avkDXxwfMHt3ZMdatc8b8uiBHCBR01yczw8Pq59z9BuzTfnqzQdOjQd4uAaIeY9u8/w/2r50Izy8+DTZNEMu3htXMWXhHT8hX8CnYxgUP4w91zkmpG89vdCqY9uLBzxePjFToOHX//MezdWBEQ2rFjQ8XLzwxGcgwc/AzqgbuOXjs3/1RFiAnoQJpqNgb7+F3n7pwTuVBhbt5BRsebuAQ1qgolBGCzhecZGCRyGF6FdbBwVMSBSjwGhgfgguVTRQUTA4ME+AwtCdjlE4zgu+k/gKf45FCvIoT0Btk7OoTBd05ALHsCvq1eDuO2eogsMvl29+7vYGM5jYy41NRuKSiwMTD8ht11gXacKScoN4PuE+Xp6PgOLqsh60s5wHu2GcAHcUG6sj8ZQkwZ/BkYeHIY3qft0td1NUK2dJQ9GgKjITBiwWiHcMRG/ajHR0MAEQKQWcFTDJZfGRhegvcKmnbIfQEvVRJgAM0mgc5CALctGBkYxMA3trOC+2CM4GVUkNF0PgYG1o6OPwhTQb3Hz25uvAwMPLt2PTp3jt/ISAa2XAquCmX5KHg6kcfVFdIbFAafBMPAwIA2oP4KfLsXF/iAeNAOHAaGfx0dTysqQKv8OkDn0Ah2dEAc/4+BgQ28UZCFgeEcw8Qc6LEx3xkYWHft0nZ1lYI7Y0gwPj1/eGn3imdXK9QDd7H+Y1g2203RoOPL9YpPbKBln4zfGdbcY7gnyVDDx2BX8mDfphXstyv+/mP4ysygE7Jr+iq3e68ZnO077p+pMGZjOMzCwP2dIZCPgYOV4cxPhtevGAwlGPb9YJBiYvA27Zg8s8JLn+HZewZj147n1yv+aHZEH66IZAKtdZz5HXRn9oOWB+t3rvh+rcLXFLSMU84O57Y3yH0SWk8YLN4xBDh1VJysYGAB9Us7rEEaTZ5WSLEz/FfqePmV4e6RCo3oXTeWunGwMjx6y8DJzsDIAGJ4BHf8Z2A4tLlCgh90ieKhzSvCs0CTdetndv77D7pgIywTxD1/ePeOqW7ffzFkTXwgISsP4Srbd9j5REjIyr988nB+icLvvwyeubtM7EATiR0RjL//Mqg7dXgFRjAwMJzavkJW0+j0YjdBw44jWyrUJRgsE3Y9uHZOTtNISlHt9PYVJ3dW/PjNEF2yS0pRjYGBYUqBgpJ9hyA7g6UXSPuKGoXnHxksPDsgXIhpb+6c276uwsyzg4mRQUzNiIeFYc8stxvPQctQ7dUZrjxlkOQHLS619ei4cLbi1W+G37qgDiEoKbIxGH9i0OBm0IXNFtYk7iJ1PhZkzigmMQTQZgutK6bxMyz4ATaEHbxm/iOYrfvgwc8VK9iNjBjU1B6Bry4UMDJC6xa+7ez8WVEhhTqe9f3hw0cKCtwdHTJIvUSwkQgCMpEI4cOnE5GvNIQvl2CAHXgjYGT0y83tI2gin4ENvDCe18joqZsbZG0/S0fHX7BKMbChkKX+zxkSNBgWLGKYZ7/L3tVVCSwzSoyGwGgIjFww2iEcuXE/6vPREGBgAF0x7+b2CDIruJ1hkQjDNEhz4R8DgxC4yweZ1oOEFfjYC9Ah/8zgXYIv3dzEd+364OYGmcR71tkpFBHBATslD3IPxGc3Nx7Yiqkn4PlAbWwtoae7d/88d07MyIjH1fUzeLchfFj9CiOjMOwUU8iwN+SchhfgJawMDAwfwIc3QKYKP4EPofleUSEF7gd+BZ/l8KzjYHQFGwPDK7CHpIfQrCAk2NFIyMbCuycrjtxikPbreHkOdDxmx1+G7LcM/sqgjYUquh3MlyrWnGGwVWOwTdzFwczw4d65KScrNDQ6frKAr0xgYGBgYTD+xqDNzfDvPwM3CwPnXwYPfgZpo45rFyp+vGe4/4ZBXpZBWrfDNal897xO5tsVYgYdVw9X7GdnCDfp+P2HYfHBiiXMDIlsDI4mHUdPVcxkYMA8hhRyn4QWO4OwZseKUxUPfzKcYGfw+8jwlouhz73j81XQqkhhFobNFxgcs3YxPD7Ho2h0Z7Xb688MglygLZHcOh23jlS8/cIQWbjrwoVz/29UiPCghQTDo3cM/JwgBJH4BG6583FAeAyffjC8/8ogL4ydy8AAWowK2WcIUfH8N+hgG9bfDN9+gbYIfvzO8J2JQQJ2kf2jtwyC3Axwwx++BWmCG84A7rvKwexiYABtxfzKxPDnK6jLLcoLUszAwPD9N8OLj6AtiL//Mnz4xvBXs0P2RQWL865js90M5Rje/Qf1gff+Zbj/mMHJFdQ5rGFn4LHsqDhbsZidQY6dQcl/tFsIDUmaUpizheIMz9jBRyi/YmBQ37ULcpmEMAPDf/Aaiv/gdeyQ7X9vwTeyShkZfQdvHYScboXsWkh/D97TQ5YCdfDAg2IQQbSBMMg5yWIYK04/PXz4YMUKsYqKW+B9hhC9zAwMyrt28TAwvHdz+9jR8bsCVAIygw/Wkty169O5cwwVFXygw1TnMjDwMDBw79qlOdothATdKDkaAiMTwI8aGJneH/X1aAiM3BDYvftZKeMGITfp5QxLlnT83LVL1nhXAOTueIMHD0TAHULQsaIPHpj//y/d0cHDALpegg3UlQBNu707d07n/3/k67nQgvIL+DQFeG8QdNKMq6tcRMQRRkb4Phm4Fn4w69G5c6/Bk5W/jIxudXaeZmQ8zcjI1tHxDbx0SioiwvT/fx2w2wR37RIFn7+nEhGh1tGhs2uXLPiAB8mOjhcVFcwdHdcrKt4xMFximOjIcDy6AnTUKAMD565dlv//Wwy5iUFw2CAIPkl5nfByx9IH1qoM+jwMGbUPdBgY+s06DG07nn4EdWl+XKm49ozhmQjD7Z8Ml1a4/fjLsGtzRSg7g9TdCkM2hr1OHWlCDMY/GM4yM2iyMtixM3xlYfjBwMAKrg2e/AV1YDSUGI7/Zfh6ueLwos69Gyo+/WC4+5FB27ZDjZnh/NkK90MVS0BnyzLM/86w/1hFZdUDoz8M1y9WPDq04spK0Omyb8/vvt7JuHyl24FPDHJ+uwKPVaxkZDjBxGDxEdQpd2JjuHyhwsBvlzALA5NKh2PWrte3zr0+V8H58pxj4q6Hbxk0JBk+fmfYs7/iOjOoz3Zuudu//wx7nzMcecnQ+4rhp0aHrM+uU18ZJt1jYNTsYNWEcqueMGx+yHD2AQNE9upHhi0PGXZ+ZfgqD1Ww5QHDqfcI7rIbDCfeIbgWLxkCvzLse8Fw9AUDk1bH0/cMZ/8w3FTrWPKC4ZdaB5Nqx+G3IMMXv2D4qQYy8PJThp0/GI48ZbivCOKeecBw4gPDzNsgLkTB1G8Ml58znH/EcOkdqBO4+CvDmXcMux8z/P7DwMnG8Bkc2gwMDAuPMOyZ6vbmM8jLKgYdmkYdNkwMWrzg2SgmhpbfDBUnKozfMez4yHBcsaNlvtuyRZ2fnoMOI0EkiFEWtUNAXl6gvNzowQPPjg6xKQwSdhUikyvEHjMksHV08DIwfD13DjQiBu59fQWPRjGCJ+UYwYNToOGIior3bqCLeq6vWPFo927IhB7cjcKuriLgrX1vkfp+kK7gTUbGN25ukLsl0HqDkIUSYrt2vXJzQ9PIJy+vAb4Sw+DBA+MHD4Q7OoQ7OhR27Xp77tx1NzfQdYXgw2b+gB0M2g587hwTeKn/JwaGQx2sGxlWn2LwkXFTrmSc39l5+uFDyI33cPeOMkZDYDQERgQYnSEcEdE86snREEAOAXCf6xFomVzHA+aKyM8MDP/BSLij429FhdSuXR/d3ITAI99sERE3FRS4wBtU2Ds6PlRUiO/axa2mxiUvD5nuk4uI+LRihVBEBKe8/Htw+0YQvCgUszeI7ICrnZ2Q5aPwA2Zeu7n9BB8H+hN8dh8zA4PKrl28amqQW7+uMzJqgldevQNfCS3Y0QE5rfTLw4d3FRSEOzpAZ58aGX0Gb1b8Cb6//iliVpCBgUFo1y6lod4PRA5AOBuysfDtD4btayv2ijJU2oJu1fvJzPB6T4W4NIOoXgfvP4b3PxkObqowkGPQCt/1YoubuEHHlJMV8n8Y5E06og9UgC4qZGSIBZvIBb7OQZyZwce8g4OF4dnZikO3GEwVGV7+YVAyBM0WTohl1JNlYNXoeHMedF17Zviu178Y3Oa7MTAxpLOBdirePwuaLSxiYPCI3LVqldu5rwzn2BnmmnfIqht9un9O4CZoVpD9LwOPVoeKY8S5KQrM6h3WseXx9owRZgx/2Rneq3RcXVehosVw7DbDLV6GEvuOW7srXvAwnPrNoPaBwdq348PJCgHLjhTwVfI1rxjsSnatnAu61H4uO8NKg47XrAznj1U4WHbEnqnI+8ig5tLBDzpWhiH2TEX0d1BXWde549/Oiip+Buc/DNpfGKQ8oNwOi44It4jrO1Z8YmB48ZfhwtaKryIMFhwMcVUPzj245TbbLecrwzVRBsUfDA4WHbHHKnK/MlwVYlD4zeBo0fGUiWH3wQrtfwxS7h1/t1Ywu4KuH5xp2yH7n+ExIwPjL4YdByrY+RhM2Rh+/WLgNOsQ/Mmg8rqCy3vXoxWgfqCyOMO2xwwxabuurXDj4WDQz32wvlZBU4rh7T9QlOz7y3DmFcNZJoZoFoZw245r/xlAs4UcDAYmo8eQgsKHDhg2W3iPgeF3DsMPyN5CsY4O9oqKX+ADSD+BD9xiAq+ygHTzhcBF0C/wTfevwYNZkGNgBI2MBGBl2k1G0E5TdXCxBukNwv2C2RWES0FUvnJzQ5snfAc+VAZSKjIwMEDmDN+CD/riBK8j/QNe2w8p1T+AZwuZwEfUQE4rfV5xQ4lhgRzDXPC2a56ODunRU2eQw3yUPRoCIwGMdghHQiyP+nE0BKAhsHv3s39ure9APMZ/HWFiRirybtL/Ozq4wRtg/oP3mfwHDx6DVhGBtnoxQC4v+A2+5EoLdbXn54cPH61Y8buiQm7XLiHwiaMMDAyCrq54eoPwHuAjNzfIzXOCDAygU/jU1BjAJ9BAtiN+Z2C4Cz6DVADcLwUNusOOkJHp6PjEwMBuZMTIwPDp3LmvFRX/wc0v8NVxDMIdHVcZLENAy6O+g+9b4Ny1S31YdgVBcQjGn54/vLN/xdl9FWfZGHjBOzgfPmXQkWSQ5AQdMMPPwHDoJsMPLgZhXoawpF2MDAybprjpyzK8+M3wR6fj7cWKYiYG42cMZzkYGjgZJFkY0lkZlrAwyLIynPnF8Pgjg9ZfkNS3/wzhnAwC3rte3DwnzcNw+QBoi92yrwx2hh3RRyqMfjGc4wbFgclvBsWPDKvVGGaodmScrQBtZvoPOoHm/LEKaUEGLwEGKTbQIaKLFlf8+cfgqQva/qdq0fHvbsWrTwzvvzHImHZcPVvx/T1o5k3wB4OjR8fXExW32RhkGBkOfWTwsu1Iu1jBwMyw17Tj/n+GfXtAexGXcTF0mHcw7Kzgce64ubdiMh/DroRde6a6vWRk8LLqCL8AUp/0ncHRquPW9opmUVCjeLEhaO/i6T0VV3kY9rIzLDbq2H+wQs2h48/RijOsDBv+MEy06LiwrWK+JOjKe7YTFSf+MdiZddzYXyHj2lFxuKLsB8NLZga+vwyThUDpKwm8olr6C0OzLGh1cuJvULfAwr7j5fEKNkuQRULg/PPqTIWaVceT0xWO0bsmTXYzk2I4cZ7BPrjj/+UKVXGG538YJFhA5/f8+gOahOTnYlAQYZA36/jCBDrrtesJQ6gjaPko6MyQf6CpXQ0uBh4WBjU20FGlo5cWgjMBzQnw3sI74LOKQSeROjHc4K+I492166mb23/wSNZL8GL1bxUV0rt2/Xdz+9PR8aoCdIvg04qKX+AJOrgTIZ3DL+AVm1LgxfCQKUHQDasYy0HhupAZj8DdP24jI3jX8QUjoxD4NGa4sle7d78AF6GgA0vB883gEQZQIgINnzEwCDMw/Ono4DUy+uDmxsPAwL9r13s10xUrrvFWrFNjePuEQfnl6GGk8NAcZYyGwAgAox3CERDJo14cDQHEXkGG2wyWj0BbX5aLMDz8UlHBDp4b5APfWAUJJ8hGQS7wljvwXjOQMG9Hx38GhjcVFcq7dgmoqfHKI64mh08V/gH36CCXYgnDWifPwdOGyHc3Q+7O+nzu3D8Ghv8VFR9ArXQGDlijCtL3g5xuygBe4wS6sKui4iuSsk/gQ//4wE0ZXvA9YAwdHS8rKs4wTCwBHRtzjIHBEHxszPCcFQTFBwaGbCwUUDJaP9Xt8AeG1Ipdbxa7RX1lKGBi0GdleC/A4BS869YSNzadjruHKizcO+Q0jD7cO3fpL8POYxVC3xkkrEBdjlgWhsVsDLF/GJSYwLfYf2MwYWPQtwdtLPz4g+HBfQYn/44vlytEfHcpKKvdmK7w9h+Dtt+uqZPdHgkxtETu6p3mtoQLHE//GcqYGSwtO5afrfBkYmBmZDCRAHV1/v5nUI/YtarLzS2o4//Nik1vGULkGR58Z1BlYdh6iaFLkmEWJwObQcfP7wxXN1XoyzJ8FWAIDtv16s65PZsrvnEwCAqDuAwMDPvnuX34yvBPnEHfqENFxejmlXMHt1TIyTJ8ZQUpuHH53NFtFdIyUO6dO+eObqhQEmF4xQWSZWBgmN/tJsDJ8E8Myl0+x43tB4OACINzxK47t89t218h+pWBTZYhOATEfX2i4tZLBnEpBo8oEPfM/oqv7xmkZaHci6crPjxmUBNnsE0AnYC6dpnb56cMckIMcg4dKspGd26fO7irgucPg6gEg1M4SPvDQxXH7jDc42do0GZovc4QLMDwkY/hxjcGJx4GISbQ+tJTXxkmTH5wrEeBiYlBwn/Xm81uglyg3jsDA0P4L4bUNwyzWRjm23UIMzH4Ha+IZmVI5GEQNxidLcTID7QRgM0WvmVguJvDwO7KcEijQ+NnRQUP+CRSBnDaF+3o+F9RIfPgwddbt36ApwfFIyL+MDB8uHXr/blzbysqxMCFrRCY5Acf0AWaK2RgkN61i08NdF4RZFkEfh9cZ2Tk7ugQBS/N+PXw4TsFBQnYZCNyV1Bi1y4xV9dPDx8+RD35ho+B4W9FhdCuXZxqaq8UFDg6Ov6CV1h8Aq+Afc+QIMDwBXIY6ehsIf6IGJUdDYFhA0Y7hMMmKkc9MhoCWEJg927QtXzf3VrBM2+MTxjUAju+v62ogKwg+snAoMDAwP/gwbMVK6TA1zN8cnPj27XrnZsbO/icAQYGBnZwGwU0zAy7QPldRQWkBQM5RIHfyOiZm9sv8NEEoh0dXysq2MArqSAHwMiAh8Ah1wx+vnULcn0zaD8hWJz13DnIqX2CDAzw9U43GRnhK6lAS6Q6O9+CF2X9BB2BwPAIdMgl6OwN8F3iDBzg5tftjoPBiFnB77t2WQ7vWUEsMQ0WenJ695llbveYQLfbcZ2p4DHr2LGmQlOCwUSAQcGs49DhijNXGPhlGfS4GALqH2xrVRDmZvjEyHD3O2j56MEzFbMYQe3TWDYGJRaGF98YZvIzLOVg0DPqeP2P4cGZij8fGM5yMkgyMXhbdFx8zcB2o0LHruPhqQouNoZ1PxmcTTrCDlV0WHVUnKnI+sNw8RODr3uHIzcD792K77Idz85VGETsunzh3J71Ff//MxjLM+jLgW6TF3PbteniuW9nKxg1QYtdpe5UvGVnUOBkkHPfxQ1evwr2Fhbi518GdsikMBbJQS107fK5dzcqdIw7Tu2t+GHYocHHcOF0hXbIro83zv04UwE5lub9NwYO7Y4Viyu0pUGH4vCwM3CzM5z5wbDsJcNZ8IBNNBvD0j8Mxt8ZNLgYvGw6XpyrMDTvMHbAedbroA6Roea43bsfuLldh7g6h+FFWgcLe0XcO3Bv8C+4jPrT0QE5mgtyv/wP8LQhvOjjB696+FpRwQteiv8NNLXMADqd69w5iPr34F4ZpHQVMDKC9BIh9w1CLAWdTvTw4esVK75WVGj+/w9fL4rZFYSrh6wjRe4W/gBPUf5jYPgDPmkGZD74Rg3pXbt+ubnxdHTUVwioM9xmYOBwY2hd33FqdBEpcmCOskdDYPiB0Q7h8IvTUR+NhgAoBCBdQTe3xwwM/28wWF5gyLba5f8efHPgOwYGHfAN8o/BS49uV1TIgW+W5wGvEf0P7trx7doF2Q0IMgsDP9q9+66bmwj4dviPt269cXMDrSkFX+33D9wwUti1i19NDXIPIaQlBLl+kFdNjQc2u/jr4cNPK1bwRUR8WbEC1G1lYNABL0mFHJkg7Or6Enz06Btwc+qNm5tYRwcn+Jz3KwoKvOA7MAQ7Oi4zWEZVsBUz7OllsB5ps4IY0QIVeHJ697HVbnJ6Hbv2VFz9wBAZ2HHiSMUHZgZrRoYfvxi4nTtUwTvrLr1h+Hi6wtazgxPc9br0l+H0oQo/uw6n3RWxrAzc/xhmcDA0/GdgYwZ1vdh/MshxMiiadew6U6H8l2HDOYZQUwYmRgZez138bAzvd7i9+cPwRbqDj4+h+1hFj03H3fMVRmIM154xCBp0iKga/Xh47ueNCq2gXTuXu71/yaAuyfDwP0PKJ5CDpzAxcJl0yKgZnZ/iZijPYBS+i0tKjVMcMQUNUjS8MGQ698y+Cl7zDmEOUIew8BND8AvQzR+SvAw8bAy8/xkO3QIdRRMe28HLCvL8s68MPPcqIEfRPPnLcO4Pw42XoNW88+1AC1MTT1ak/mEIFx2dLQSFFX3w7t333Nxugs/YAs0WhnWwiDM8+1lRIcrA8LWj4xl4JhB00nJFhQgDwzfwlTz/GBggw2Sc4ENo3lVUCIN7g8zg7dmQZRGQPiGyF+AicAYDAwPongnw9CMLuLj+xsDwrqICsiiUt6ODywjldkHQmaJgEyG2g5kM/xgYIFcKMYKWOYPEmGDnNn+tAB3DBdlw+BHso9kME9sY5DpG9xaCwmkUj4bA8ASjHcLhGa+jvhrJIbB79zM9hqsH3TYwMDDsY3C16xAyqbD/De6n/WBgYOro+FNRwQG+T5nt3Lmf4DbKL/C+QUYGhvcMDOIdHQwVFT/Aw9j84IYFIwMDL2yeEBKwkCWjj3bv/nLunIiR0Qs3Ny4GBk7Q9BKo6fMTvNTzL9hG0Y4OPiMjafBJMxC9cPJNZyekN8gTEcEmL//p4cOLCgrKu3Z9P3fuF9hVkBMa+MAu+QY+CEFw165nbm784GWitzoORo3OCsJDE5UB2lh4YMXtoxUnfzOIm3VIn614atxxc0mFrwHDL1YGLeOOM/sqbr9k+MHLYCPMoBex69ZaN3YWhos/GC58YwAdLcPMMPMvaHignZvh519QtzCRgyGBC3Ttwe07567vrHjyl+EdP4MTI4NZ7K7dx85x366QFWL4/5/hr3bHvWsVVjwM15+Bro6Yc4ihf8WDewdWfPvN8PFWhRr4MNmffxgWH2Mw8ep4y8GQe77isFkHu4rRpU1uAeGgVZfChqBLAlF9Mzx5kG7htXMV2sYdBw9V7LvHoCXHYGYJCo2PN8+9PwbaqCmk23H9aMXH7wxHbzN46TGYRO+6t8GNlZlh2SeGM++hs4WgvZp/QZeIwGcLnSN36ZuPlGAc2MQBni3cwcAAGr/IYXiRu0uf0c0YsreQr6PjE3gakJeB4QP4BBdOIyMx8F7rt+DdfTzg1Q38Dx68Bq/nFI2I4AIPlkFOJf0EXoEPudsQPmcI6RNC7jz8WFHBBN4cyApeNMHMwACZe0QLEMixMWiCoBUf4J7eO9BOWAYB8LlcHyoqIHu2eSoq7oPLcGbwjfacFRVbGKpBxQEDhxzDU8kOJ7EIX3l5AUwzR0VGQ2A0BIYuGO0QDt24G3X5aAighwBsVvDmPIazBgylHzqWM1dEQu5RgzQaBMBtFAYG0PAwB/hEOfFdu76AD+cU3bXrs5ubwv//bzo7GYyMICtFIY0S0EJC8Gg0A3jDIeTqPzZwfw/iAsjItBADww9wb1MRPP3IADvsDrTss6ICdBI6+Gx0+A6Zp4yMnB0dkN4gZKvhE3BnjwG8ovUTuJXDAHYkC9guHgaGF2D7zjFMLGQwA29yBN3vNuyPjQF7mhwC0uV4/YeB8Q/oGNJ9ogzx3AzHvjL42HV8PVRxjZXhOy+D9z8G3fBds1rdpAQZNCVBtpjmPeDvUah8ycDv2HH/bIUkC8PmrwzCHxmi5UBTgq/+Mvz4zGAhxvD2H4NlyK4rq91O3GWQEWJQl2C4/RK0lJePk+G7asefSxXK1h1PvzCwMjMIPKpgYWQQ42P4rNhx4kQF5z+GbS8Z3rMwaH4DxaufPOgMFTG3XSOnKwgKZRiGxNH6NRW8rh3a2kYfV7mFfmEIecHgLsXAwQealf36EzpbWNWx68IyN4OoXXevnmO9UwE5hvTJX4Zln0E3ZOxK2nV7mdtnkw7QNRV/GaIEGcLCdo3eZQ8LZtrSSLOFb3IZfrgxHJJkWMDX0cHGwMBWUcG7axeTmtqLFSs+gUe12BgYhMHrL76AL1wVKi//Bl7/ycDAAO8TYnXup4cPn61Y8R7WD/wHXnHKA56jvAfWwMzAwArKUig9Qz7woB5YngF5XI9HXv4pI+MH2FILSKku1NHBzsDAWlHxnoGBoaND0MjopZsbG7g3+x88SMfIkLCBwbB1dLYQEqCj5GgIDCMAXio0jPwz6pXREBiZIbB79zNdhqsf3TY8ZlDPYBCWZLgqALqMOBJSl7NUVPxkYJDates/A4PMgwdPFBQYGBjEwEfkvXFzE921S+H//x8PH/7p6Pj1EHTFmQhsQg8yE8jAwCAFE2FgYHi1e/cf8InqX86dg0wDMlVUsIJ7a2zg8ebnK1b8YGCQB3f/9MCrQBnKyyEnocNjR9LI6BN4+8pdsGNkdu36fO6czK5d7OANLY/B843fwOPTf2F6flZUXGGYmA3qCn5PZbgxG3SZxDA/QRTmdTJpyI2FT07vPrLQjZmP4QwbQ61RhxwDw70fDL++M7gKMsiZdHz/w3B0gdvzjwwGcgzKgaA5un1dCivZGPbxMzw+VcHNztDwGzwMwA+6dOHXP4b5TAzpAgzmDAwieh3H17gx/WT4+pPh0S8G9tcMT98zsLEwXHzMIPmyQkOS4ctvhg9nK+6+YoiyYPit0fFE2kicmUHuIsMr2Q5PdQZ3v4g9kxTU2Rg0g0ZoVxASqZA4krOLuDBV4S9Ph37krpZ5bhf+MzxkYjDT6+BXN3q40E1JlMFQi+HL/XNSJh0bJ7tdfQqaLWRnYVA273hyrsKWgSHGvMNtvls0K4MuA4PbRwZPcN+SgYHhUA2jfOYDeWnQ/BXEulGSFiHg6qr0/78SeLbw/WQGickMYR0dWRERqmwrZn4G2/dEQYG/o0OQgeETeF29GHjBxa9z53jAhSSXvLxoRMTrFSter1iB2SeEDJZBFt6DyknYTOD3igpBBtAm6u8VFUIMDIzgRR+QGUXJiAj4uBvYfuwEe0eHnJGRtqvrk92777u5CXZ0/AMX4P8YGBR37Xp97txX8AGkvxgYvoAPc4ZctmHeEcRQ8fFJxfHNFTvNO+RHZwuxB+6o6GgIDDUwOkM41GJs1L2jIYAaArBLBRlnM5yUZripuyvg47lzDOBNIB8ZGPQfPPi1YsW3ioqfHR0/GBikjIxeubnxdXRwGBm9dnPjAO82EQN39n6AD1ZnZmDgAy/gRLUExHsFPi/0hZubBPg8mOdubpJghoCa2msFBfaOjncMDFIRETzy8q937/5w7twPkCbQzhbIXhQBIyN+NdC9gp8ePnyzYgVjRcVHcPMIIvsZPOzNwcAgC14gymhk9MjNjRN8NsNb8BELzzsOxlSwlTCc62HQZGBg2LVL2dVVDmzDKEFUCFxZ2XntbMUfnQ7m0xVr7zHcl2Ao5mQ48IfBiY/h52eGc4wMfrYdP89XCOt3rFtZ4aXPIKgD2px26S/D4UMVOkwMX4w7GC9W/GVl2P+SIZSDQUkUZOnbfwwH7zGEqTEsfsXgYdSh+Kbi2QeGvdcYfA0Y3n5h+MkBut9S4B/DlPsMu0VAMxfrBRh+aHZ8+sqgyM4g87pixM4KgsIOG35yevfl1W6HbjKIe3cIsDJ8O1vx2bjjxsKKACOGnywMEmwMt16C1o4GRHWIgE/j/fyb4dlJ8MUV4Mstwn8yRH9muPGVoT1v18IVbvERu9wWuaVzMORYjh5Dii24aSOGNFv4J4fhBeTeQv6ODsj2wifgLhwDeKknF3jykA3cOWRgYIAsHGVmYJAvL0frBEKXToDn6CDHdLExMHwFF+Bi5eUvwVvBOSMiHqxYASktIT6DLMrA0zN809nJbmTECy7/GRgY4N3Cn+DS+C94IAhy2AzEwF8VFaDFGpDzUcE++skQYsoQPbq3EBI+o+RoCAxpMNohHNLRN+r4ER0Cu3c/u+S25hYDqF80i0FiD8NiPoZpXxkYlMFHFIB6U+CVPwIdHQLg9sdn8I1YkI15UrALryDdPDFXV0iH8E9FhTTs+HJ44EIOr5PYtQvSG4R0BSHdSAYGhjednaC2DvieiTOMjCZg7ZcZGXXBDMjCUdCWlVu3Ppw79wt8RDsjAwM3A8NT8GEG3B0d/EZG/xkYvp879wd8CwUrAwM7eOXS54oK2V27LjJou7k9Ao9cvwRfl6U/Mk8QhUcH2QzI6sRzBypufGbgd+2wYmC485dh2p6KOFYGDUkGCcOO578ZPpys2HqJITW5Q0HL6MkWt/f/GbyewRqGrOApXfCBNBA3zBFh4PvGwM/JcI+JQctr19MVboLcoN2DurIMz6U7jMyMvm53O/CUYc8nUEyrSjPkmnccPVNhJcTAYr9L1WJ0kxskFNFJ0JlAa9z+aHewnqmY857B36Lj+aUKM6sOOXWjU3Pc+EVAlxZ+/Qm6t/DcQ9DBM2n2DK8/MyiZd+w6W3HjFeheCpCJTKAbGkW+MHzmYRDjYNADLwbitYkYnS0EBQ7tMbhbuBd8mQ4v6CTSXfrS53b/YmD4b2T08dy5z+C1o9zgPds84CEwuIu+g89wZgOf8sUP7nrBz2SGn8j1Ezx496OiQg5cxn7cvfuDm5s8mA0pbx+ANyVCthoyMDAo4Jgw/Awe44N3CCFuuNfZ+R7sBkiHkBfmvE8VFTIMDHfBisAn3TJIdnS8rrhhyuCcw/BOneGTfoe83OjeQnD4jBKjITAUwWiHcCjG2qibR3oIQGYFT3Y8eFtxhJthqmBHxztwC4MHfH38j4oKho6OL+BFPgzgrtdvBoav4MY8LwODyq5dj86dEzAykoENDF9iZNT7//8jeEMLB9KA8deHD7/eugXpBDIwMED6gaC1pjCNkGgA7TlkYBABLw39At7lolZe/hrc1BBFUvl29+43bm4iu3Z9OneOg4Hhe0XFc/BRe5/BV0rwg/cK8oE9wgeeeHx/7tyuCk4GhmePGXQnMkiNzgpCQptyEtIt3HW8QsWg4+SJituPGfSkGH4wMbxmYogSYth9jeELF4O7KoMYM8O7rwyPfzJM/8CQ7dSReLpimXnHizMVS94wxDEw2KgxcLExfP/FsPc6AxMvg6wQw/efDMxKHbw8DPf2VCg7d2zYUBFlyPDwE8O1zwwe8buWVrsF2DIoczNIeYzoBaJERh8kjq4dqbjwnsExc9ePO+dOHa8QNun4s79CUISBlx0xW2gT2OFpbXRltZtJ/oM5xQoWyohLC2u+MWz/xBDq3vHlXIVdxC63hW7p3Aw55qOzhURGAhWUgbuFN8HDcR9zGL5C7i2EnPLCDl7G/xm8nfsP2Cp2cBkIOkEUfKvhXwYGkY4ObXC5CpaHEpDeIAMDA39EBDv4EBoGBoaHjIwCu3bxI5W3yEv0IdOGmBOGn8HHOEOKbtBO7927X4FPuwHt3AYXxRAr4V1WPnChDVkEK9zRwQj2wn/wEtYbFTf0GRbMZpjB22E0ekEFJNxGydEQGFpgtEM4tOJr1LUjPQQu7r40ww10gsAMBslChudpDIF/wFf/CYJJBvChAmwdHbxGRrxqak8VFCDLO/ldXR91dv6oqPje0fEWfL4LZJ5QHjxyDJkkZGdg+OzmJgae6IOIfAWPZEvs2gXpCsKnBJHj4NfDh68VFETBuiDimH3Ct7t3fz93jtPIiJGB4a2bm/CuXf/OneOLiHgNvtiQFTR7BFpZKtjRwQLuKH5nYDjEMJGBgaEItCXqMwMD665do7OCkNClGvnk9O4P985t21ix7x+Do2eHwpkKoYhd526ce7q6Ql2V4R4zQx+k3ccAPrPiP7jdCrZ8jgiDATeDgs+uOZ1u1qoMa84wpNfumrfULTVm16R6tz//GHjYGYS4Gbg5GGQ8Ou4eqrCP3XVloxvbbwa32F180sP8PglwCFGNgHYLz1VAZgtXPWKw9uyQvlohEL5LjJnh5lI3Bm4GLUNQB+/L01sMDAydtW6eIR3/b4B2fp75wXADfIs9yDXM4NnCzwzTr1S0OnRIj84WggKFfhjcLTyXw/B4CoMGZBGptpHELzc39l277ru58XZ0/AWXe+DrRUAHfjJUVLyHzcu9q6hQ2rVLFtbTw9obZGBgeNnZyWFkhNwhhHjv08OHH8BLM0D9TCMjyA5D+IQhcofw7e7dX8+d4wafQCMMtg5SkvMZGX06dw6yF+ApIyPbrl3samo3FRS4wceQ/gLfVPEBfMcGe0XF6Y41fhXfGRh4VnRweEdowm8YgjhmlBwNgdEQGMxgtEM4mGNn1G2jIYAIAcis4GyGI4YMpfcZsiQYpoFOpQOfEacEnlJjAh/x8oOBQQjczfu4e/d7NzdB8LDxJ/B8HR+4moesKboPXlP0rqJCqKNDEXwl/Qc3N6GOjn/gOyQgq0P/MzBAdgli7QpCXIY8PQgRAV29BZ4nlImIeLliBbeR0ddz5xgZGL5VVHB1dAiDD1F4o6DACT7ShgE8pfkXvJaVGdwG2lXBqclw05shBNy3/b1rl4KrqyLc5FEGFUPg0/OHF3eveH6lYt8XBi+zjlMnKhgYGBwlGf5/Z9jynuHOZwYHSYbSHwy9nAy3fjPo/2cIS9z1apcbk3LHoR0VmtG7PmxxE+dnWHqcITa248DhCgGNDvFHFR1vGdS+MuhLM7D/ZmDQ7WA9WxGQBTqohoGBYWQeIkp5fEG6hecPVFz/jDRbaNjx51CFthTownrQ9rM/oCXVh24xTNz+vyea8epThlAThi/MIMshlxYyfAHtLQx17zDSMGL4xwCaLeQc3VsICh+6YXC3EDJb+K+a4YUZ+CRSfvCxz5ClmL8YGCCTeC8VFOQePPjJwAC5R54BfMegUEeHuJERA/i0Z+S5QYj70VaNQgSRSfiEIXwdqYCRERN4sQYjeGM5aC1JRwe3kRGkNwjXewu8QRG0k9DI6K+bG/euXX8YGF65uXF3dPxnYHhbUQG5YJb13Lk34C2Rp92WSTIs+MCQ8I5BOYLBcNcuTVdXJbhpo4zREBgNgUELRjuEgzZqRh02GgLQELiw+5Kh2zdQi4/h+x6G1XwMCyBXGCsYGT1zc/vMwPCFgUGYgUH9wYNv4Dm3z+CdK/xGRm/d3KQfPHipoCCwaxe8NwjpsEGMfgTrFoI6ZuB7ppgZGGTAU4Iyu3bh6QdCtEM2EKIdQgM5DuEJeOkR5Pp4RgbwsDdMz++Kii/g0WV+8LHsXysq+MBeOIAyKyi2a5fc6F5BWJjRkIb0Nz5fq/jGzODylKHDrKPiaAXo9HoG0CJj618MOrwMpvwMVgIMlx4z2JQ9uH9gBTszw+crFdeeMYjyMlx+wlA2/cGhLSt+MDOIcjKw/2WYtbEiRZNBOu3BkloFfSEGy5jRNaJUiD5INF07V/H5B8ODvww3njDc52QoF2UQCN/F8uAc8/WK1/8YDp1nCCvb9ezGOQNDo/UT3dQkGHg5QNd+LPvK8O896C77sxygo306zDtEkGYLBZSMRm+noEIMEWcEuFt4l4HhDAODQQ7DC1eGQ/odGuwVFdcZGEQ7OpjBx8YIgS90hdzlAzeVnYGBHSzOCR7CA/XQYOtFIWowV41CxJFJSLfwA/iYLlHwolBRBgb4UgBu8JAcsnoI+1NFBeRORR5wQQ0eZ2Dgge1vhKj5D64+2MG7ABgYGH6AVpOGmDF4MjDwMDBwX9olqetqBFE5So6GwGgIDE4w2iEcnPEy6qrREACFwO7dz3a5nYpgCPzMwPCSIeExg2Fwx/dfFRXMHR384Ar4SUUFF3g13xdQ651BEDzG/AM84fYR3IDgZGCAzLyBjnUBtwNA5qJibgYGLrD2r+BOJ+QiePhVyKDNKuADQiGa4BdRMDAw/Hr48NOKFSLl5V8ePvx86xb8YHQGBobP586xMTD8rah4CdYm2NHBCGawgfc6fgLf1MwPOpMANCt1nWEif4dcdAX42nLQCaKjs4LgwKILce3U7ufb3X79Ybj+nWHHU4bSvF1uS9xmCzD8+8/wQ6lD+HqFCDeDODvDydcML58wFEx+sKNNgY2FQYSHYcM5Bnt10KEmqXNB58xA1qB++sVwbHuFsxYDByvD6CGiVI9ASLdwx6aK3T8ZSgp3fVjpdk+/g4GJoeJkxXpOhpsvQIt1mbgZ7jxgsPPtUNI2urTcjU2ng+12xWcmhie/QL3Bc38YboDzJHy28PXdcy8uVoSFj15aSPXowmkguFt4Hbwa+181ww17hksCDAsgR8iwMjAIga9Y5QcvJYUs2f5UAdpOzQE+lllw16734DUXENMVwGsu+OTlsa4ahZTMEJWQ8hlUnoM7fqDlIWCJj+Bln6A8DObiIj5VVIgzMLwBb0dnBu9vhKv8VFHBC7429i1YiBl8a8UPN7eXHYveV2ySZFjDz8CwiGGe/S770dlCcAiNEqMhMBjBaIdwMMbKqJtGQwCyQJSBgXFNx0/dCvvz4DWi8I2CXODeIFoosYPvKf4FPqLzG7jvx8LAIAJeTQpRibXKf+fmJrNr1zPwXRSfKir+d3QgdwVBrQfwsXjvKyoEwc0IZFkecAfyI1iK18jo87lzvEZGkLlBmV27foC3x7xgYPhQUaG4a9cjNzdRcKNBdteuJ25uoGP0GBh2VXBKdRhEV3wAN3VURmcFITFFH/Lt+d2vdrlpPWboMOy4f6Zi5l/wKt0fDIEMDArcDIxMDKKfGczFQXfNf2BiuKnacW1eRUFGx4tzFSK8DH/tdp1b6Obt39E/s2LCdmjK+vT84ePDK24crXBIGJ0VpGEcPjm9+95Gt79aHV9+M5w6X8Fj0LFvS0Vd9q47K9yUxRie/2Q4/okhybHjyuEKAcOOlYsrrFUZ9Bw6Pl+pYGVmWPaV4f4TBifPjopjFQzMDLvid/VMcNslwXDOsuPz1Qol/9FuIQ0jDs1ocLfwNgPDoxwG0LZOV4ZDpruifoGLTUjP6lNFBU9HBycDw9eKCrFdu/6cO/cTfDApZB/424oKlV277oKX+r8Fd8n4GBggJ3Ih9/0gJTPkJBs4G1Swd3Twga8j+glzFg94AyGMh4VmdHO7ARZmBi/xYGZgUN61ix98bcZL8HWyX8B1BGj1aUXFV/CNsvwdHQ8rbugyLJBkmAuZLdy1S1NNTUheXgBs0igxGgKjITBYwGiHcLDExKg7RkMAEgK7dz/b7nYqjCHwMUM2A8P/JwzqAR3f34HvFWQGt9h5wXcvsKmp8cjLv+ns5IuI+Hzr1is3N7Fdu/4xMLxzc/sFvjVe1NX1NiOjKuwscojhyOQH8CZDLvB1UpA1pdcYGcV27YLcSv/5IeiG+o/gNagQXfA+Ib+R0S/wBRIcDAxPYFcSQE6pYWBg0H3wAKL+14oVbyoq+MHdP9CxdeA2BBt4nRIrA8M5hokmHWIWoEFn0NrEXbvUR+8VhIQbHchrp3YfX+uma9whbh0RXa/ga9vBeqlCNwS02a97kpsVO4OeBIMoK8OFR6ArJcR4GbZ8Y5j6g8H1A4MrO4OVCsPV9wy/PjHIW3eIPK24I9rh7B8hJTt69Tkd4g3Fiiend7++c+7svoqjtxmeiTDsEmDIf8oQbsbA5bXr1gY3CWaGN18YTt5jePmJITy2gxd8YsnPvwz/b1W8/YeYLVz6gaHfsuMnJ4OzmtH7fwyv751jYGDwcY7gkxyNUJTQph0HfJ39VchsYUeHiHXFNLEOjY/gET0O8IFbr8BrPXjAtwcxVFTI////5eHDx7DV/sygeV+Q69gZGHjBt97/Bq8TAQmBF6BC+n68sM4e/O6KL7t3f3Fzk/j//8Xu3Z/AWxMZYBfJQvSikb8ePvyyYoVQeTlk3SnaKTV/b9165+YmtGsXs5rafQUFIQaG5+DTRz9WVKh0dPxiYLjNIHWx4qYcw00F0EFUza873EcPI0UL4VHuaAgMLBjtEA5s+I/aPhoCiBCAzQoynADfJ8HDMFVp1643bm4c4K0j0h0dfyoqBHft+uLmxrxrl4Sr66+HD58oKPxkYIB0BSH3Q7x1c5N/8IATvL3kaWenUEQEhI2whoHhx8OHH1as4DAyYmJg+HHunBjsZPM34GPHtXD3IT8/fPh6xYpfFRVs4LMQBMAdQgYGBsjkIajlYWR0381NEOzUvwyguy7YGBhkd+36eu7cz4qKP+AtKE86Dj6vuFDMoMAA2qkmODoriBw1tGZDZgW3vmZY+w40XXuCG9zk/Afq1ndYdry6XOElxCDFDlrtuWO229kHDPpyDPwiDIaZD1bsWOHIy3D/UMWTvwxBCbuurnL7o9Zx+2AFIwNDVOeD0Q4hrSMOq/nQY4GuVvzR7njzk+HClgp9RYbQmF1X/zC8uXvu6aGKt98YfnAxFKfsOr7Y7eN30KX23voMxlG77m1wu/iP4f03hlMvwLOFRysYGBl2Je3qnuC2W4LhsnUHAwODnN1otxBrqNNEEDZb+ISBgSuH4QfkOnsx8Aa/v+B7ZcXAQ2mi4MX2ArCJOMi831fwglIG8Mr/N+BVG//AfUK5iAhc53z+evjwnYICz65dPOCTxiAHikI8xmdkJAEWhHDhJLxDCBHB7BayVFSwMzCI7tr10c2NqaPjCwPDJ3C3FrR0BexgiGu5KyqmMhS1MxgygPYhSo92CyHhOUqOhsCAg9EO4YBHwagDRkOAYffuZ1/dWsG3UTE+ZlCT6DDQrrDn6+j4C94u+Ah8mTvoAsBdu9gZGBjPnftjZPTj3Lnv4HVE/xgYXri5SezaBbn07y34QFFhcI3+DswWArPhofxh9+4f584JRET8ugU6qh75sBkGBgbkSUK4Fgjj7e7dr93cRMFrUF+D1zWxMDD8BvcAhcALTSHdwj9g1zKCmzI/wX1CYfBdF+wMDDcZJsrsCnFzuwk28M+uXdqjx8aAg4IexNvzu899Z3i00U3Xf1ffYrfkuF2TJ7s5O3WsPlkRJcKgzgFygxQHw62XDPuuMYRX7Pq2023bRQYXLYZtfxjirDp471fc+szw5D0Du1rHW14G9csVN3U7ZK5WnLrGUD5rtEMICr2BwpCNhR9/MsxdXxFZuotnp5vVY9CVdiFPGJK0GDhZGRgZGV59Yrj+jOH+G4aI2I7Plyr0I3ddunFO9E7Fi9+g2cJbDAwzf4B6Ev2WHdr6RqCihpFh70o3Q5MOY8fRbiH9Inb37mtubvfB9xYyQGYLZTs0XoFP8nxXUcHNwMDHwPAC7BzISV28Rkav3dykd+2CLNf/f+4cg5HRC9h033vwWn34fbNgfVDiHfjsUCHYUCBEFHmqUA1VCnQUGfiiWjQtkG4h5J5DFvAsJyt4OzoD+HQZyDwnO1icBXwZ0g83NzYGhj8PXq9YcYu7Yh0DA4cbQ+v6jlOj3UJIFIySoyEwgGC0QziAgT9q9UgPAchRLG5ujxgYGHYwlCl1eD9mkFczEv547hxTRQVkGwmkl8VUUcG3a9dDNzfIxgtG8PYMRvDsDrwrCA/Nm4yM6uBZPrQOIaQrCJkY/ODmBlkmCtcFYWCdJETrCoru2iXs6vrj4cOPK1aIg1cQQU4r/VJRwd3RwcvAIABenvT53LlPFRV/wb3BZx0HGRgYYioegc9D5d21S2m0KwgJcDqQb8/vZmBguLvJzfwdw65Q0GWDFScrGJhADTeL7wxi/xn8lBmseEGzgnevnfv7n+G/jNHKDjdzJYYzDxjirBnYWUBScy6d4zlTwWXcsXxrhTYzQ7hPBxMbg7IqqPMgbOhKB1+MWoE/BCDdwmvnKpgZGZ78Y1j8kUHxMYOLEgOHecfrSxUy3xkugU+TDHDrEFQ2OjjX7chtBltVBnUJ0OlBZ34zfPzO8OIzw9nfDGfZGRKYGRb8ZzD6wVAuCbJTyxh01eHoIlJQWNAFw2YLPzMw/IbPFvJ3dLBXVPB2dLwCu+EjmP0HfEYX6P4hcJkM2UHAJi//6eFDSJkMVssgB76ICMKGHAb2TkFBCOnyWLgUZKqQD7wVXOPBA+QJxi/g4UXIjCJcPYTxcvfup+Bd6J8rKphBAwsMP8EbBCDLWSU7On5XVIjs2sXGwPDJze13Rwc72HwZ8PqXnwwhpgz+o7OFkJAcJUdDYADBaIdwAAN/1OqRGwK7dz/TZbi6323DfgbX2aDb/mQNGa4+dXNjAC+tkTIyYj537je4W8XCwPDz3LlH4LU3jOBdIu/BS4M4GBhYOjqegG+Zh180DAnQJ52dwuCVosgdwg/g6pxDTQ3rxCBEI4R8A1YJ2UmItSsIUQbpEP4DOxJyhsEf8HUXn8AH3giBPfKvouJOx0F+I5XRWUFIoNGfvHl696wFblrcDBx/GGL+MvTpdBSdr+gw73h1rSLcuOPztQopToYjzxm8ih9wszAc6lGwKX7w/+Wtvllu2owMLz4xSEgwRH8FLShl+M+wko3hvmFHxbGKzq+gmUN5n9GTY+gfnwRshHQLP16t2H+dgce1Q5WD4dSJiqZfDPlfGNxloZcWvvrM8EGp4+j6ipSkji9XKpSCdnGxMBxd7fbkF+gAoZsMDD//MTgZdkzYXRHmBIpukCgTw430XerGoz1/AuFPRWnw3sLrEAM7OkQCjZgZ3IzZOzp+gncG/mdgkGNguAta9w26vVARfNborxUrkC8BgnQL34PrDsGODni3EOv0IMQiCIl89yB8+ei7zk6eiAg28GYEiLKX4JoCVG0xMEiD75WVhu0Y/8PAwAmuAiAq34JXkzKDCxL+jo4v4DnPjxUVYgwMvB0dsxlsn1QcVWf4yAAavpAXi/AdPXIGEm6j5GgI0BOMdgjpGdqjdo2GAGh1KAMDg5vb4/kMJyUYbrEzTP0P3u/BzcDwvaJCfNcuRgYG5nPnwGu+QHsw4AeK8nd0/KuokN616wcDwz03Nw5YdStoZPQBvEYI3i2Erxr9/vDhuxWgq+EhN9QzMTDgmhhEjhjIJKHIrl2vwbcPs8OuLRbbtQt+9gBoNwj4AIM/4OuzQNsIwYO+XAwM38DHnL6rqHjacfBZxYVS0CHqKgwM38HHxkghWzTKpkMIvN7SeeYtw879FRNZQcsIGb4zVDAzOCXuerTdzYqH4dsvhq8/Gd7JdmxeUeEEXl5oHw+6fZ6BgeHnH4a91xju/Aa58TofgzQrQ6ZlBxsHA+/9itH7JECBMojx5xcPHx5c8fovwx8Fo8+r3GY+Z2D4zZAiw3DPpMOMleH1sYoLn0Cur+h48Pnprdd3zl14xaD2puLbLwY2FoYzvxm+/2SQM+yYsKfiHAdDGhvDrP8MFXwMRTYdoj7lIG2jmI4hAJstZGJguJvDwO7KcEijQwNkP/j61mvgukMQvG6fhQF07RBzRwfkAkN+8FAdAwPDh3Pn3ldUQA4CFuzokDAy+uzmJoVtehBkLAxDpgohPCnwXkTkDiFkShDSCWQA9wYZGBjEwXsTvj58+Ap84M1vBgYOcN0EOROVCTx5+Bp8J+1bMFth165vbm6cHR2QNbE3Km7oMyyYzTCxlUGuo2N0byEk7EfJ0RCgHxjtENIvrEdtGuEhsHv3Mw2Gqy1uX2cxSDIw/D/d8YCpIpIFfFQAA3jolAl8bwQXuEMF2X0hDL6qWBhcH0Mu/eMAn/AJ2cjxCXwQ6P0VKyBbOCAX/Ql3dIgZGf05d04GvAnkLiOj8K5dAq6un8Cjucg7Br+AtX8G7ySE3BzIAB57Bm3/AN839Q98up0kUj+QD9zI+HTunLSR0VM3t2+gdibDD1gdL9zRwQU+If0qeIFodMVfcIy/27XLcnSBKDgoBoB4vaXz2BeGgMMVc806ks9X9H1nKPrHUMHNYMDOoOu/6+12t3dfGIT9QOeLftjqxs8JumDw2DsGbu0OjhsV+84xTJZlmGzWcXVHhRYP6HxRhdFZwQGIQzKthNxO8fE7w7qzDBpxHV9OVTCwMLT8ZGj+y2AhxvCOgUGCBbS98M9fhocSHeqCDJ8uV9x6yWCjyrDqNcO5jwx6vxgE/jB8FWcwsOwwUTbifX1utENIZkxQrA08W7ibgUGGgYEhh+FFXAeLWEXcTwYGXvA+AmbweZ5/wb2+b+CqhAc2XAi3+UNFBRP4PFJ+8BFlv2AS8PNpIEeCQYT5wDdJfL516wt4qJGBgYHPyOi/mxsTeAM52pQgpB8I0Qgn74P3KDIwMCiCq6FPDx/+WbHiLQMDZEkL5PybXxUV78EahDs6PoPnDPkqKk53bPKr+MjAwDCZ4YFoh4lFhMXobCE4kEaJ0RCgORjtENI8iEctGA2B3bufMTAw7HI76sVw8BXDf3GGaUIdHV/BhwSIgs8DgHT/mMFXEr9lYODq6NAE16OQKT5pMPtNZycjAwO7kRHWXRyfHj5E7hlKMTBwgGcUfzEwSD8AbeH/UVEhALYL3utDixe+jg7QvF9HB1sF6OjId+B2A+S6CCbwCQEI9eCJyldubszg/YE/GRi4we0PQSOjlW6gNU6lDLoMDJzgWUFlV1c5hMZRFt1DYFktY/Qzhol8DAzaHfkXK2bzMnxW7JC5W2Fj3bF4WYUoDwMrC4Np3K45rW4eegwPmBiSW/53LuhUv13xQ6vj2OKK4rkP9kxWsBJkYLXfpWIxumKQ7vFHsYVXj+6+usntj3bHr38M385XbHwBGsVJlmbYx8KQY9Hx7lIFExPD4VsMfnm77l0F3TlhIslw9HDFXbWOisMVHfYdFWcqcpkZzn1i6NZgsKyEzDNR7KZRA8gKAfBs4U3wES1/chheQE4i5QeXvX/BR3kxd3Q8Ay8QBR1IhmTFV7AgE/iwGkHwQV8cHR0/wAo+VFRA+oT84PtmwWII4gNYIwN4ZJANfEY05HohyM52zINnEDoZGND6hPAJxue7d38F31rEAL5TURA2pSlhZPTVzY1t167TbsskGRacZGj2ZaidzTCDt8No9MgZ5IAdZY+GAI3AaIeQRgE7auxoCIBC4OLuS9Pd7jEw/J7JIMLA8Ocmg9tTBgZp8KwgD3gFH1NHBwsDA6+R0c9z5xgqKlh37Xpx7twH8M5ALXA/EHKX4K+HD98oKPDBTgkHGY0NP9+9+8O5cx8rKoTA6zkhc4YM4IFhiHJ4gw7SjIAM/UKkIH2/n25ukA7ex4oKdvCtxxBZ4Y4OyJJUiEv+MTCIPnjALi9/G9xN/cvAcJbBErxAVBes/umuXU6js4LgoBhg4vEExplnGK79ZFgvCHbJb4aEnwxe0gw6IgyffjDce8VwhRHUQ3ATZbj2lCGLg4HhH4h05Wbg9t51dqGbtTiDVtDoXkFw0A1ZArKxkIGB4cSeCkadDjkTo4OL3RiYGHhNOjy0jJgfntu8ruLmC4YgY9ABQnq2HbN3VkgZd6SeqACN6nxmmGjRwXGpwlOZQbYAXn4M2bAY+g4HzxaeAm8nB80WhnWwaBlJfANvIhAtL//68OFz8IpNBgYGyYiIL7duPXdzk9y167mbG0hk166/bm4/wH3IDxUVUrt2caupfQIvEvkImwwErTKF9QMFOjpYwYs+2MD2vWFgYAPrhYQifHcifHYRckk9L2yf4Stw3w90aG1ExM8VK3giIj7duvX13Lmv4POxOdTUnqxY8R5c2b2vqOBlYHgN7uwKgdfFfKyokGVgkGRYDLaLZ0UHx+hsITgoRonREKAVGO0Q0ipkR80d4SEAuVRwNsMGQ4bOewxZEgzTBMHXSPwB38vHwMBw081NAnz+ylcGBnEGBr6ODlbYWXCfHj68oKDAzMCg9+DBCwUF1f//33R2shsZ8YI3acAD9gv4lFLIak/IHCNEih9s2tuKii/geULIhg3lXbsEwHfZQ9Rgkr8ePvy8YgUbeFEoL9Kl9sgHi4t1dDBWVPwCGws3YU8F53+GZ3kM7uB7BX/v2qXg6qoIlx1lDGwIPJ7AeF6iw/9whfUvBilOhgiDjuBjFUulGeT/M/CwM8w7wuBTt4txt9vHtwwzX4JOrXV271C/VcHJArqNICB8tCs4sLFHTdvhlxbu+8LgbdZx8lwFr0EH3zeG06srtKUZzJVAx40yM4FOkHnwh2H9S4Z0mw7XixVtnxjW/WTw4mVI0RjtEFIzOig0C2m28H02w1cXhkOSDAuQzWQDc74wMCjv2vUK3CcUB19d+3nFCuHy8le7d0OWg4pHRHDD+m9gHVDi68OHkM4kA2iMCDSI+aWiArzzFHSCKPiGJMT1s1A9MAq5owg5d5QFfBAaZE7yL/gAUlAfEty3hCxLAXULwTOQjGBByLwlV0UFD2zC8ANDwnsG5fuj19nDAnmUHg0BqgMWqps4auBoCIzwELiw+5IhaHsd6FaIawwqpgwMGh1yDAwdAuADV14yMLw4d+5HRYVERwdDRYUk+NQ4PiOjh+fOMYCHSyGrcZR27Xp/7twlBQVuBobP4O1/XxgYIAd/fwYP5UJ6gJCJPl4jI95du3jBGz8YwMO9PK6ufxkYBBgYOI2MeDs6RCIi7q9YccvNTbijA3IeHR9qI+Az+I4peG8QEoPfHz7klJfnk5cXNzL6AR675ayoeA0+1+4beEfKaYaJDAwMeaAFogoMDD9Gj42BhNvgIX++enj4LUP05Yq09wzikgzN/xlWn6mYI85gxc9w4RHDU6MOBoYKxp1u5z6DlpGV5u66feHcswMVCqoMsl67TBgYRu+TGDxRSblL+CTlbePKPz2P0Dq0goGB4fs/hrc/QUMA8xUZyt6CjpO5o9Shx8Zw61iFqQwDg1qH6/4KBg6G418ZhP8xNP1hUH/DEPzqIbuYPOUuGTWB8hBwdVX6/18JPFt4hoHhQyBDWEdHliXDDTGGZ5DLHr6AF5H+Y2B4c+4cU0cHJ6R2gFksBhtbfLliBY+REZzLwMAAOTv0y7lzn8HzhJK7dvGoqf1csUL2wYMP4Pk9yIwft7z8Z/Au9I/gGgcywYjcFYRYJQM+d5QFfE/SW/BaFeGODkEjo6/glSz8HR0y4H2JvLt2cTEwsLq5fQBr+ws+sPoj+JoKDVAXsUOsouIkQ3FFxU3QaWWjR86AQ2mUGA0B6oLRGULqhueoaSM6BHbvfrbL7VQEQ+AdhmwGBoa/HWE+EfJPFBTYOzrYwQHzA3wvH4+R0dtz5wSMjH66uQl1dLDBpv4gp7x8vHXrE3jVKFgH6Lo4PgaGT+DtH/zgu30ZGBh4ccz1QXqMPK6ukD7kIzc3pQcP3q5YATlg5tPDhx9u3boDXjsEqZUF1NT45OUhin+eOweZhIQ44+2KFb9AzUXQSaeQfSNSu3b9dHN7D6qeGXZVcDIzMBSBuoIgZ47OCoJCYfDhn68eXpqucOgpQwkDOC7/MBRxMGhxMxibdbw6VXHmIeiI0cL6XRlz3TKMOv6rGz3a5KbCwqATMjoxOPjikqou+vT84ZkdK+4erTjLzqBo3PFuQ4WsBkNw+K57nxguLXLjl2Z48Y2h+Ct469gPhkw2hsSoXQy73fQyH4x2CKkaD9QxDDZbyMTA8C+b4UZshwhPRRxPRwdLRcW3jo7vYEsgB4+JdXQIMjDwgu+oAA0KgI+N4QGvChFzdX25ezfa+lL4gTEvGBkh9xa+Ao9Ofj13TgzH1CJoxQpaR7GighU8jMjMwPCvowNy1Bjk6FFBIyNIHQTa2b57N+QQbEY1ta8rVnyHHTnD19EhbGT0C7y98Oq5FxcrbpoztPIzMIxeZw+O2FFiNASoBkY7hFQLylGDRnIIQBaIghvdDDcZLJ6BF+rcdXODDNYKgZeGMoBXWkJWyDAwMHwDH739E1uoMYLXfPKBzwdnZ2Bg7ei4V1GhsGuXDGxYF5sm6Pwhj6vr285OSCcTcqPgOzc3NfA99XBdkBNo3oFPpeMGT/1BKmmIAtANyAwMvysqPoCvGGZkYBDv6FArL//58OGnFSsuGMW+PnfnYMXLWaClQyq7dsmN7hWEhNsgJCEdwh3vGKR4GK79Zjj4gSHUAnSz3FJpBoGvDFu+M3hxMLyW77h3viLSq4Px3uh9EoMwDmnoJMjeQgElo8nNbvJeHe/3VXxnYxDhZhATZoh+ztBh23H/aoUaA8OtbwyXPzPkSzJ4xOzi0x89WIiGMUKJ0eBu4V0GhjMMDAaFDC/yHwSyKAjy7dr1X03t2YoV78BX2PODO2ZfwfUL8l5BUC0DlpIF7zaU3LUL3hWEX2QvAatE4DsDuVGnFjEd/2737jdublwdHVzgPucbN7fP4OtzIb1BuHo4lwN8wg3kJJvf4P2Knyoq+MD32n8Azy4KgU8++9GxSK4iDnJBBQMD665dmq6uSnDTRhmjITAaAuSB0Q4heeE2qms0BKAhsHv3s99urV9BPMYnDGoSHQZ6FfbM4GNj2MCbJbgYGLgZGBg6OjjAlSLohjfwrnrBjo5vFRUsYHHIxe6QCUD47N+vhw/fKSjwgA+S+fzw4SUFBYGODm3wSTMg21AxfG7wbWcnL9L1wVcZGfk7OiD31CPreA0+fuYn+KTTV+BLhFnBk5Cgs2QYGPgYGP6A0XMGBhXw4tW3FRXPOw4qVay0YggBm/MHvEB09ARRcGAMVuLTxd3tC9w6vjKcdOlYebKi7xc4RTIwPFJj2H+DwcSp49J3hj8XKwQYGSxjRmcFB2ss0thdT07vXtbv9oGZQVwMZNO3pwwxFgzLeEGnjDKwMaSzg9LMTPA1BR9zRzuEoCAazBjcLbwOHuX7V8nwIrCDhbEiDrJuk6ejQ6CiAnTgNegYKdCudQbwHPB38NJQfgaG9x0dnyoqdB88QNtQ8Ovhwy8rVkDuOoL4HX7ZIJ4+4bvdu7+dO/etokJk1y4h8FDmE/BdFFxGRkKurpDlKu/PnYP3BgXBJ6v9AA9EsjIwyMOuO/oIPm/mDwPoopS/4G6hWEcHV0XFo47ZFyseMDD8kGN4+5ZBWWZX1Gi3EBI7o+RoCJAHRjuE5IXbqK7REABdMe/m9qijQ8CB4QJzReR9hizNDrk/FRUc4O7fl4oKSDUszMDA29HxChxgP8GyP8C1GqhnyMDAw8AgAN7+x4O6qY+BgeEdeKIPfsnE54cPH4GPj5OLiIAf4wY2FTo3yKam9kZBQQT1xuE34DFaMViVzMDA8Bp88hs3+Lp5RvDZcT86Op6C2wTMDAwau3YxgteOMjAwPHVzY+3o+MIAWiAq1WFwsOLWbAZp8N1XSqOzgpCQH+Tkp4u7r251eyzTEX4EtB+MATzM3mHXYfe84ttnBl4OEBq9ZX6QRyJ9nPfk9O4P984xMTIc21XhrsNww2yX2wI3BlbwiSJ/GZbZdajcrVAPGO0Q0ic2KLUF3C28zcDwBHyHkYpWxTTTXVGgTQEVFYy7dkGOk/kL7jXyGRl9OncOdAJNRcVdpOuF5GEnnGHWRBDH4ekTfn/48O2KFd8qKrg6OiC7FSBaGBgYbjGCjr7GXLHy4dat9+fO8VZU/GNgeAlOdAzgM8oEOzr4wNdRsDIw3GVggOx0eOPm9p8BdNcRE/ji3J8MCRwMX/Z2FFZUvB+dLYQH9ShjNARIBaMdQlJDbFT9aAiAuoJf3VrvM6jfYgBNkfky7JZnmPYbPNrK1dHxvqLiOwPoBDYBIyMhNbXXCgqiDx58vnXrlZub2K5d/xkYXrq5iYNrZW4jI8j2PGHwACpyyKL1BuFST3fvvu/mhrx8FDI3+J+B4de5c8LY5g8hk4Qy5eWvd++GWP0NPEUpBu6IvlJQkP3//0Zn53vwmiJ28EAsJ2zO8EgFp9yuEDe3m2AHfL++S0bDVQ/MHiWGQAi83tK5cU8Ft0lH1IkKBmbQiuZdYbvc5rkdU2H49I7BIHyXuPHoCsAhEI/0cSJkBemZfRXq8gxWtxk6bDqMtI3O3T7H8Ifh2dmKCCkGbe/RDiF9ooI6tuzefcXN7SG43/evo0PEieEGf0Uc765dT8EdKn4Ghqfgq4/EOzpUysufMDKKPnjw6tate+Cd7RAXQLqF8CsEIYLIJNplg6DeI3hiEHT1PHgmEFkxRPaNmxt8zhBZ9tfDh28VFFh27Xp+DnQfpqCR0XswQyEi4v+tWx/c3CQePPjJwHBHQYEffEEFRK9gRwdHRYXwrl2VbtfVGR4xMHDIMTzl3FU7OlsICZ9RcjQEiAejHULiw2pU5WgIgLqCbm6PGBjeX2DwYgH3/SCHgjKB73jgAq8CZQPv+vtQUSHY0SHKADqU5ScDA3JXUATW/Xu9e/crNzeJXbvQOoSQPh58bhAt3OHLR+UiIhjBJ7xBbgeDX0oBORUGruvFihWfwHfNMzAwiIOPIAcdEwc+242dgeGFm9tXBgY5cCuBu6ODqaLiF3i965OOgy8qLhQzKDAwiIGvmFcfnRWEB+lQYVxY22m4DdQV7LDsYGBiqDhTwfCXoYKbIUGU4dE7BtcOSMIZKr4ZdSc9wMFqRgl+hgVPGDo+MjBwgNaR74rZde7muYpjFec9OwyCy+nhiFE7qBcCsNnCzwwMb3IY2F0ZDml0aPyqqBABz7nJ7tr1AXyGGS94EBNy0PTHc+fegdeMMIDnDH9VVIiC6wvIeWZoTnsDXrfCwMCgWF4O2TTIwMCAtcsH0QhRg6ngHXhBKWRhKuSuI4h6CMleUSGxa9dX8Nmnv2E1LCO4s8sI3t0AOXD7I2hjfMJGBos2BpmODrHRG+0hoTdKjoYAMWC0Q0hMKI2qGQ0Bhgu7L01zuzebQZKBgWHXLlkxN+lf4OU47ysqpMD9qCfgGULJXbs+njv3vqICcqMLC3invtKuXe/As4LwriAkQF+DT2x74+amCduvzwC+W+LXuXOQehGiDJP88vDhFQUFyA4QLvA+e2Q1kKoRdG4buFLnAR9pA7qTEHaWAFRxRQUnA8MrsCxkbwYL+PyYnRWc/B0G0RWQg2be7dql7eqqAdUySg2pEHi9pXPTroo3eh0VpypAy//ABw5WCDIkiDPcEezwTh1t3A+p6KSLY7fO7lR5X3FDpiPgADjN/GPoMOmQY2UQvVehb9Ih6jOaZugSDVS15OHDDytW3KuoOJXDwDmFQSyH4UVcB4ssw7P34CWXzKBdoqCK7QsDA3tHB+SOQYj93ysquEBjAqA98MjiEFkICTkDhhVsyF9wJaiKsQsRohJOIm8mhAhCpgeFUfc7fHr48OOtW3fd3EArWsH7LH6AzeeH3VzP39HxAXy7xh9wPcvf0fEfvOHwTMcaf9AyHYYcht82HZKjN9pDAnmUHA0B/GC0Q4g/fEZlR0MAOit4k8HyLOgyCdCkiveDsh+3bkFO6BZQU/sCHiL9b2R0G3yjAzM4zP6Cr+LlAq/A/AIWwXoHINok4Zfdu7+4uUEO+AZrQhDPwb1H+PEzoC0WFRXfGED7woQ6OiC7KRCqwSz+jg428Clt7yoqnjIw/AfXqazgkWAGsPjXiorfoCvoQO0AUEcX1ENkKAVdJsEJnhVUdnUFrYkFGzZKDL0QeL2lc/eZiug3YJezgI+S+MdQwc7gJMjwS2q0QwgOllECNQSeb+r8dKNC4zrDrtRdbqvcGP6CCwgGhjk8DH5uox1C1MAaUjxwt/B2RcU7cBfvTQ7Dj7AOFnEG0CkzPysqRBlALD7w8CJ3R8d38A4CBvAUHGQfAQN4HwGk+wf3twD4FiJO8KHZ7AwMzB0dkMvr4UfFKICvuEA7pQa+yRC+mRB5ehBu+PPduyH1HWS8kg88dgm5zBCiRqyjQ8DI6JObGzf44DRmBoY3Cgq8u3YxqqldWHGIoyLuOUOCKcOChR2nGBgYRmcLIYE2So6GAC4w2iHEFTKj4iM9BB4+/Pz+1n3wFfMMHR0CgRWaT8BBAjl1kwG8FpSTAbQw5VNFBQO4HoWcIgMhGRgYpMADrk/BuhgYGCA3QQmDa1DQAhvYrv3LjIwsDAya//8jHysKmip8+PAzeEXoE3A/E2IsZPbvY0WFaEcHa0UFe0fHTdAiGQbFXbukYStRYRYyvO3sBPVfGRhAM5YdHZ8ZGN6BT7UR7OiAHH7DD1sItOsc+7OKC+CuIOg0mV27nEYXiMKDcegyjrcxWj1k0P/BEOnYUXGhguEfaBi/ggu0ZJRPo0PSb3S2Z+jGLa1c/nxT5/OLFTe+M0Q/BR/rwQaaIaw4WsHAwHBMhcGyElKi0Mr2UXNpHQLgbuHdioqb4KNbeHIY3qft0pc+txt0vYSR0TM3N17w/Ns/BgYB8LHYfGpqn1as+AhzlgTq9YOfwefHsII7hJBu5NeODkhv8G1FhTJ4PSqk7hMGd97gtw4iLxz9snv3Zzc3yPTgF3CtB6nyGBgY+MEVK+T87W/gevALuAJ9W1EBqYjfVlRA7viVBDv7R0XFZ/BCmE+wQ91kGBgkGeaCN0vydIzeaA+Lx1F6NAQwwWiHEDNMRkVGegjs3g0aNH3itkaI4dYPUGD8/9sRbsFwXDIi4t2tWw/A1RLkgkE+Boa3DAwi4CoKskRTBtzFkgRfDf/JzU0UtgbmE/iu3vsrVkC6hfDKTLijg4uB4Rd4wyFTRcUn8CGekGFReN8P0hVkYGCAGA65l+JNZydoWBds/rXOznfgG5x0YIfK/Hr48I2CAltHB+Su+aednVxGRr8YGJ6Db4X6V1HB1NHxEmzp7gpOJYabAaDLJFgZGH6PXjEPivDhgh9PYNz6hOGjJnjJKAN4hpCB4bx3x/uzFVxaHeYxox3C4RLT1PPHpSO7325z+6TTEXCwArSogJ2B4RtDBR/Dxw8MlfoMsgWjHULqhfXAmQTuFl6rqLjHwMBWzfD+IwMbZG8h6OzrigoBBtDxLL/Ap3oKgXeSipSXv9y9G3I8KXKf8GlnJ+Q0Ueny8ve7d79zcxPatUsQfKsEAwPDA3B9BxkDFTAy+gA+JAbSXWQAn7D9raJCateun+D7CXl37Xri5gbpAUKqPEh9Jwkb6ISbz6ymxsDAAKlMIYazgXukjODJbEFwHxIiLmJkxHTu3FUGKch19hwMDNcZmu93uI/OFg5c0hu1efCC0Q7h4I2bUZfRPwQgXUE3t8cMDP/nMZySZLjFwTBVedeuZ25u3ODmNDes7we6YxC8B+8B2JVCHR0yERHIV0c8ZWRk7+gQgfXQwKqgBOReeEi9KGJkBOlhsjEw/ALv4P9SUQHvCkJGRhkYGOCVIsSIXw8fvlZQQDb/6e7dD9zc/jEw6D94wMHA8HnFCgYjo9dubvy7dn0GnykK2WWhBD5URgg8iHuAYSIDA0MRaIHoZwaGT6OzgpCwHU7k4wmM7VcZprODzhftMOuoOFnB8JOhQpAhUYSB33n0iNHhFNVU8wukQ7jrK0PHD9BBoxVnKxjAqwDr2RmS1Uc7hFQL58FgELhbeJuB4UtFxWsGBt4chheuDIfMOjTeVlS8YmCQAFd2PysqeDs6voOdCxkG/QyefGNlYHgLHl7kRDpNFNJnU0baEg9aSgMeDH0A3lgBqfUYwNOPD9zcmBkY+BkYvoAnK5nA1d9HsOGYVR7Yfoa74FsrkM3/9PDhbQUFXrD0F/Ba2J8MDNK7dt13cxPs6OBkYPgAvv3iS0XFj45F4hVxFxiKwxkMGBhGZwvBQTZKjIYAEhjtECIFxihzBIfA7t3PpBjefHbTf8yQxcAAumI+oOP7G3Df7HtFBWdHxw8w+xWY/F9RIdLR8Rl82vVHBgZIjw75KojPu3cjTw8ihytkSQwDAwNkVQx/R8fPigo+cKX4FqwOsl9CGXwnBNrWC7A8iECeHgTxwfjZ7t2fwOfZMIG3C0IOiPsIdjAbeMEqZ0fHK/AxM9dAXcFn2QzuYH0Mo7OCkHAYZuTPVw/XTlTofMJwiQvUIWSAHHP0l+GYLAMfA4OI62iHcJhFOHW88/Ls7g973c5+gC0ZZWLosOioOFqRxMbgLMwQnPeAXUyeOjaNmjI4QgDSLayoeAPuT/2rZrjh3iHCAbvOXoSB4TsDwz/wgk/IFB9oyBI0bczwE9xpVEIa9IR0CCGThFg99+Xhw8crVrwDb1D8y8DADZ4n/AjeHMgFPt1aCNwLxbrzkIGBAdP8d0i3XPxhYIDMYb4Ed2LfV1QIgavC12CnMDMwKHd0XKu4cRN0my6DHMNTDYYF6ztOjc4WgoNnlBgF0DbCaECMhsDIDQHkWcGrDAwSDNMUwTsfvlVUsII33Snt2vUcfDXTPwYG0///P+/ezQBeFyq8axevqysvAwP/gwePVqx44Ob2saNDG1w7fnJzY+/oYAPfNQ/vAWIuBP187hykN/gTvFuHg4HhK3iVDgMDwy03N8iiFwYGBrTa8dfDhz/Buwch5kMOm4EstmFiYFAE39H0FXzq2vOKCrGOjr+gjWMMPGCRMwwTpToMLla8nMXwh4GBc9cuudG9gsM46RvzMzDcY+iwAy8Z/Q1KBx22HZx8DE9PVfCLgpZdDWO/j3qNvBD4/Jvh5gsGRVGGDvuOihMVDFwMFUcrOqw7+K5XKHKTZ+SorkEdAvLyAuXlphERH1asuF1R8aGV4UdrhUgOwzxXt2Wmu3Yxurnx7tr16ty5J25uTOAuIzd4Eekf8ETcy3Pn9jEyws9LE3R1ZQAfqS2IOkmIXEkxg/fzQZaG8oHnBn8zMEh2dDytqBAC9wmVd+06p6AAYaP1DwVdXd8xMLxzc4OYD9mIyAU+UYYTXNuC9t6fOyfe0cFjZKTz///bzk5QbxYc/JzgUVHFjg558NmqzxkS+BgYKipuVlQ8Hd1bCA6hUWKkg9EZwpGeAkay/3fvfnbbbY0ww819DK6zGCR27ZKTdpNmBy+PYayoEO/oeF5R8RG8UlR51y4eBobv587xgffZ/zp3js3ICH7vHyQMn+ze/fHcuY8VFeq7dr12c+Po6PgFrnggyz4hHTbIPkOICOTSCAawUQzgIdKXbm7a//9/evjww61bH86dg+w2VN616y5q5/DXihVvKirg5suArxCEbLoQBZ/K/RtcrULmGxlgPcwjFZySHQbB0Msk/uzapT56gigk4oYr+fPVw0vTFc58Z8iCnHLLCD4d5EjFUikGkR8MWtkPZORGp3qGa+ST768njx6+Wqhw4zvDY+WOissVHcYdFYcrGP4w1LMz2AsyGIeP3k1PftgOfp1os4UdHSLWFdM4GBbwdXR8Ah9M/amiQpiBgRdcxfwEDz4Kd3QIGhndgVVSX8FbECV37QJvv0cshIGsBYXUg7xGRpCd8C8YGYUePPgEPjvtK7gq5FZTgywufQfuHzIwMAgaGd0FD8hCRL6BF9RALpxgxXbh4deHD1+Cl6fyGBkxg838Ab5ulwE0IAbaFcvAAKrsf507x8vAMJvBtqLi3mSGGwwMHOYd8mIRvvLyAoM/mkZdOBoCtACjHUJahOqomYM9BHbvfga+X57xJoPFJoZJbh3fIJXNl4oKQXCfioGBgaOjg8HI6B64KoL45y94oQuIDV7WAmKAu3wQBoRkB8/1MXR0fAWv1YT0ANHOg4Go/PXw4ZcVKyAXAUPuoL/KyCi+axfyXYXI9/NCDm176ebGzsDwGbwGkAE8XssEPoqNgYFB2sjoi5sbD/jQ7TsKCpK7dnGoqd1fsWIX+DKJ2wxcs0FLZXh37VIanRWERMHwJj9d3N2+0K3jG8yXjNBEc0yOgfc/g2ry6No/WMiM0kgh8PPVw6szFX4yM1jdBItyg9vR4EvoKvgYKhNGO4TDH8C6he/AVc3vjg4VrYpppruiGMCLPCG7Ib7cuvXGzU1w165fsE0TQuDTXCCnxfxlYGDp6PgG3hAI7wSi7Qz8sns3/MbdN+BLlb6eOycaEcEFm+uD3EP4HnwUDaSChvQPX7m5cYDXrIJTJTQ6IBdgQDifwTsj+MEbPX6Da0mIuBT4OABecPXNzMDwB3Y8+I+ORRwVcbKg80jXj84WQsJqlByBYLRDOAIjfUR7+eLuS9Pd7jEwMMwE3cT7fx/DYskOuRcVFRLgIU82BgZhBoaPkN4gAwOfkdEncG0EWqIJ3o3ABK7zIIe+QMIRrgYy48dYUfEeXIsygafm0E6agWgBHfCN0RtkYGB4s3s3ZJIQrgzO+PTw4esVK35VVEC29b+EOAZ8vvZ7cOXHC672foEvumAA3zf4raLiXsdBASMVNzdIy+77rl0Go11BeJAOe8ani7u3rHaLfsbQYQ3aAwY6veEdaPlo1OeKk/cYfGtGO4TDPgmQ48Gfrx5OL1YINmZYxgdeMgo+7arDooP1N4Pu8wrzwNEOITmhOhT1gLuFFysq7oLXeIJOYQkwYmZzM/7JwAA5qwy0QR18ES6kMwbigi8wZGdg4GRgeAnefCgC20aBGQKQ6UHIxgdI9cfAwIDWJ0TW9enhw2crVvwGD8t+A9dxHOANERA1H8DnbEPYoPsqjIyeuLmJge8B/gauixnBB8xAnPqpooKfgeEp2BAxcJ0u2dHxuuKGKYMzAwPoOvuwDha50dlCeGiOMkYGGO0Qjox4HvUlA/R++RMdD1gqIu8xZEkwTGNgYBCB3KULG8hkBO0hZHiCFFwcsAlDBnAf7BcDgxh4AyGSEgZe8CnYv27dgq///MDAADnlTMDISAZ2ajZcC+bcIFwKc5IQdInT7t2vwHddiO3a9QV8Mtunjg54/fenooIHPPfzE3wyzXcG0J2Eq92ug08QFWJgUBm9Yh4evCOK8XpL55VjFaufMcjbgi8hZIEOqh9TYbh+jyFp9uj9ASMqOZDg2QmxjGKyoCWjDKwMFecrQDub/zBk/mNIVGRQDxjtEJIQksNAKbhbeA187OgvBoZ3OQzsYR0sHBVx/B0doAsAKyrgo5Myu3a9Bc/dcYHvuf3AwMAInouDTO7Jw+7dhYTJl927v7i5SaBuNcQ6TwhRD6kHv4PHZzmNjP4zMHw6d46ZgYHDyIgLXP8yMDBAru2FqP947hwHAwNnRcUd8FApH7jGZAZXlJChWx7wNg1GWJeSo6LiJ3ibPQMDw3OGBA2GBYsYZvB2GI0eOQMJz1FyJIDRDuFIiOWR7seLuy8ZuH2D1AWFDM9TGALfMjAIdnTwgocMv4PZkKO0v7u5sTEwSINrqbe7d78Gd8NEwT1AYVfXt+BlLcIYHTysM36fHj68qKAgCK4R4dcDYlWJHD1ok4RvkbqCEGX/wDWiaHn50927n4BP1v4Avsrp37lzvxkYflRUXOo4+KLiAvgyCQYGhne7dlmOzgpCgm6kkRfWdm7YW8Gn31F8toLhLyj570ra5bbY7ZE6w/4bDHEzRjuEIy1FEOvf/VWMKmIMcufB6jkZdsXucpvnxsDAkMjK0OnTIeozen0lOGRGEgHuFt6uqDibw8A5hUEsh+FFWAeLNgPowt474M0RHysqIIcZc3V0KJaXf9q9+8e5cxwREQ/B2/nglxDCu4UvGBl5du2C7JVADkhcfcK3u3e/AVfHIrt2QapgXCrhpr3r7GRgYGCJiHiE6gY5cNf0ISMjQ0fHB/BJM0rgHZLfGRj4wQxGBgYx0PbCia0MQuBjUFkv7ZLUdTWCmzzKGA2BYQlGO4TDMlpHPQUNgd27n211OxXFEPiTgeEFQzYDw3/LXQFv3dz4Ojr+g1dagub9Ojp+g1eHiqipQS73YwTf4MfAwADpCoLOZQF3AiHdM01wdxFqAZjCM+N3BVwnva+o0H/wgE9eHo9KsEkgAjJJyMjAAJ8VBF3lBN5c8fPWLchxNW/Ajuft6IAsIv0H7nbuBu8VLGJQYABVY6NXzIMCcyTj11s6+/ZUdHwGhwEX+JrL36BLCBPEGf78Y9CuGO0QgkNmlMAIgfX5jAbyDLOeMICun2MBzZt0WHQwMDJ8uFZRZD/aIcQIrxEjAOsWvmNgYOroYHlS8QD5OnshBoab4MWZDAwM38FXPkg+AF3T++nWrXfgcUx4t1DSyOgPxvQgPBTRenqYXUFcKuHiEAbyktRPDx+idQv5GBj+V1TI////Yffud25uvxkY3jMwyO3a9Qk8tfirooJ3167TbsskGRYwMIDu6VnEMM9hl72rqxLE8FFyNASGHxjtEA6/OB31ESgE4MfGdHTw61dM/gO6AHcaZL6OqaLiDwPksmXQPCH83BfJjg4G8LoRzK4gyEQwvs7IKAYboQQLMBDs4z3ZvfsD+HpAmY6O3+Bq5jP4IG+IdsgeRQj7I7ibxw9eyvIZcjgNRAI8csnAwPC7ouIHWJYfvFWDmYFBFLz1EfWKedZdu/RHZwVhITdy6ddbOmevq2C27mBgYqi4UMHACN7T84dhlyyDsHqHUeToPM/ITRv4fX5ySeev+xV298E3T5yrYPjJ0GHRcfN8hS8Pg4no6N30+ANv+MuCu4V3KyouMDDwwq+z12dY8JeB4XNHBxv4riZm8KjkJ/AF8AzglTjwcGFlYOACr17/BRaCbOoDM0EEP/gcb8h1gj8qKsR27foD7kxyGhlBJgZBipAwWu8RLgOpl4XAt0DBBdG6hd/B1zJ9AVe7rOBBVUgVzAzu1v4Ea+M1MrritkybYcHijt0VFe8YGLh37dIc7RaCw2aUGG5gtEM43GJ01D+7dz/74tb6g4FhP4PrbND50nL6DFefu7kpgo+3/uTmJtHRcb+i4h8DA1dHx3vwFfMfwIe18MKOk+EBB6Iyal0CFmNAWzUKqXWQTwqFKIOTz8GrTB+Bb3DiZWB4y8Ag1NHBBJOG7MKH8UA0LwPD54oKJtA2BgZm8FpWiOKPFRWQKyU+gE/6Fu3oeA2uxsBXzDNkM+iCNDOMXjEPCYZREhQCjycwbnnKwKPTEXe0AsT/BxpjqOBiSBBn4NPskPQd7RCCQmUUY4bAww2d/+5VzHrA0PGRgYGFocOqo+J0RYdpx8vdFaEGDJajc8uYQTbyRMDdwtvg6+xZGBj+VDPcMGO4JMmwAHIBEiu4Q/gGHCy/GBggvT7QLmZwtSXKAFpsyg9emwNWAiX4jYw+grt/oOsEK0ArXphBS3sYIFfyot3HC9UDPowN8zQayHpRtA4hRMuz3bsfgi/J+FhRwQ02n5mBQQzcIWQG911ZwQcH/AXfaQFaQwQewH2nZrhixS2einUMDBzcDBzSu6JGu4WQ8Bwlhw2AtDaHjXdGPTKiQ2D37meMjCfc3B5pM0x7wqARwRC4l8FS7NxiDvCm8x+3bompqQmBQ0hq1y5e8KHY7AwM/EZGzKBmD2i08w94R8HDioovDAwXOzsvdnZ+evgQrANKCLu6QlZy4toN+OXhw+e7dz/fvfs0I+NpRkYGBtBFTIIdHaLgLYv/wEe4vwNXigwMDLK7dvEZGcERaLjVyEgEXDMJgK8xfFlR8QpcL8qADxTlA+9mlAHvgz/DMPFZx8GLDJLZoAWinLt2qf//7+jqqgh16Cg14kPgyTeGR78Znv0Bpzl28IF64EHvT99HfNCMBgDeEPj8k+HVJwZdPtAIAgMHaHq5w6xDg4Ohf/SGNrzhNqIkIdfZP3hg2dEhxMDA0srww58hbBHDvNNuy2R27RIEzw3yd3Twd3RwgMseSAnE1dEhAa7FmMDbNFjB9S+/kREEwXuDP8G1Hqj6ZGBQhFSIRkYPVqx4sGLFfkbGS+B6GV41Qy5q4jYyer1ixTdwff3r4cNfFRU8ERHIMQKvl7+cOwfpYbKCpYXBtf/ziopfDAzPKyq+MzAoP3jwl4FBetcudgYGNnAH9SsDA/ut884V1sG73M0ZWj0Yas+de8HIuLWz8/TDhx/AxowSoyEw5AHLkPfBqAdGQ4CB4cLuS3vcDtxmkGNgkNy1S/apW5Zvx7efDB2Qa4vOKCgwMzD8cHPjB18IwQA+UxRyiswTN7cnbm4yu3b9cHNj7+jQBVch91esgFwKr7Jr1xkFBdAewo4ORbAUn7w8d0fH2927edXU4LcIfmZg+Lx792fwrfSQGykgl/B+PncOYjgDAwPruXM/GRj0HzyAnDTzm4EBsrUdHnufd+/+xcDw2c1N5MGDHwwMMhERChER1xQUOMBXHX5kYGCoqBDs6PgCvj3p1a6npQw3GUC3zAuMXjEPD8NRBjwEfr56KMTCIMcCWhq9K3mX2yo3BlYGhj8MHR8YQBdZwtWNMkZDACME/kobMVxnuPwF1FLusOioOAtab3zjL4P9BwYZDgzVowIjOATA3ULjiIgPK1YIVFS8mcIgNYUhrOOcgDVDgix4JOpjRQUHA8Oviorv4J6hgJGRgJral44OtYiIuwoKvB0dLAwMYuAt+gwMDFJgBug4N3CQiu7a9RF8kAxk8+Hbigrhjg7hjg4BI6OzCgrCHR0QEQYGBoWIiF+3bkH6hKIREf9u3WLr6IBcaAFZpAOpiOEkf0cHr5GR2v//dxkZQWeldnRAlgX9ZWBQ2bXr/ooVDB0dj8+d+w2eQvzMAGpYPAEfPfDEzU0UvCyWgQG06/BJxd3NFTtHb7QHR9coMeTB6JLRIR+FI9wDkL2C+xgWf2JgVOyQhZxwzQeuiriMjB6CL2kAHTXGwCDOwHAfvHuBG3zvkAx4nFISXAN93r37k5ub6IMHkCoEEqSfHj4EVQxgDqR/CBlW/AK+woiNgYF3164n4OpKZteuz+BxRF7w3UeQyoaBAXQdBQ/4jt1fDx++VlDg27WLF2wd5KQZBqQ+Iag3CDZBuLwcNMe4YgUjA8OXigrJXbs+nDv3vwK0SOsveKD0MoNlVAUbA8MdBgaF0SvmwZEzSmAJgZ+vHh6aprD9BnhW5zeoZc/wH3T++lIxBmMeBlG3XUIGrli0jQqNhgADw8uzu48vdvvGxRD9FDxJCD6tv8O8Q/h8hbwQg13W6A2Wo6kEC4AtIv0A3nvxO4fhR1gHizjDs5/gi5E+g/tvoP35HR2CDAy84KM+74MPXeM2MoL0CUFdQXCVKgrbqA858UVo1y4BcNUJmRV8AD41FN4bhHcXQZVyR8e/igq+jg5mBoZX4JU4/ODqHkIiV/oQD7wHnygjtGuXoKsrxHDIcLAweMO/AAPoypVvoJE0kHIm8GZ+ASOjz25uzDBjf1ZUSHd0CFeoj95oDwqjUTyUwWiHcCjH3sh2O+SKefD98gynOu4zVUR+ZmCQAp+28h+8++4V+DhsXiMjhnPnfoJPi/kMOjAPdJCMFur+wKeMjOwdHSKogvDQhdQTl8FThZB74TnBJ5L9B/fJvp47B+oigkccGRgYID1MuF4I401nJ6jaQDqeFH7SjDB43PQ32HngBX2gk68hOxk+g5vxoAHUjg5GBoYT0K7gd/C9gkqurhoQw0fJ0RDADIGfrx6unaQQ/QIkA9oDdq6C4QuoQ/gqqOPKsQpZv10qFqMdQlDgjGLMEHh5dvevw25vZTt2fmKoOFXBwA6+4fQ/qHO4VIIheLRDiBlkoyKwEAB3Cy8+qbg3hUGKgeFfDsOLtA4WUYZn78FLXZhBiYiBjYHhH3gDv2BHx2dwt00EXIF+QO0NQoy8x8gI7xBCRODkp4cPP9y69eHcubcVFaLgHhozeMyXA7xHUQgsAlqV2tEhGxEBGZyF64Uz7oJ3digj1c4MDAz3Ojt/gw8agNTCvB0dkNpZwMjok5vbP/B996IdHV8qKmQ7Og5VvA5nMAAbyLOig8MiwkJefnR1NTg8RokhBUaXjA6p6Bp1LPh+eQYGhu1up6IZAp0ZsqMZpr5gyPpXATpBlKWiQsDI6D347LNP4HWhoAsb3Nykdu3iAc8H8rq6XunsfFtRcQV1dg50rjR4RShyAEOWmkAWgjIwMCjBpgFZKkArNXnBfcIP4Gm9L+A96BLgnYrIJkDYvx4+/FlRwQ6e34OIMID3VLAwMHAwMPyrqPgAWcgKXqzyFbzuFLSIBdwb/APeOriugrOUwQys9/uuXcqurnJg9igxGgL4QsCYj0HvHsMlDtANhB3mHQz/GCoOVOw+UcHzg4FDSg2fzlG5kR0Cn34x/PzOcO18RcVbhg7rjoqTFR3WHUYaRq93umkwM/x8fotdTH5kh9Co73GGAHgRqf3DCH2ZFbcrKt7JdOjrVdzMYRADX1DR8a6iQoyB4Q0DwzfwDoiP4FWgXyoq/lVUPAEf/S3c0cGqpvbp4UM+8OIaBgYGoV273rm5CaB22CDWM4G7l6JGRj8ZQJ1MyJnbwuBpPUFw/1Nh16734Bt6zygoCHV0CBoZ8YOrabjhcPPf794tCJ6E/P7w4SPw4K8IeJby1e7dX86d4zECnXbDaWT04dw5bvBB3wIdHS/AXdnXFRXmHR0bK/ZCLqjgr2CYXdHH22EzeqM9JI5GySEERmcIh1BkjXSn7t4NugbXze0xA8P/QoYX8Pvlf1RUSIJH7P6Dl3nI7trFwsDw/dw56fJy+FJMNiMjyHJN0Ckvu3ffB49EKu7aJePqCp8e/PLw4edbt0AKwLJoC0EZwMtFGMCnkjIwMDyqqOAEjxrCF67AGZAtDaBOJrhKg0wPij548BbV8Cdubpq7dn1xc3sBrmB4wEfFfKioEOjoEDcy+ujmJvngwZYVD6NBC0S/MzDc2rXLd/QyiZGeB4j2/6eLu8+udBM07tj5gaHiGHiS5w9I8zFFhh+fGawKRlf9gUJjFGMNgZ+vHt6eq/D5P4PVTVCLu8O2AzRP+JdhuUmH+L0K4/BdfPqj08tYQ25UECUEwLOF9yoqjjEwCDAw8OQwvE/bpS/qZsy2a9cv8HFrfOCL4JkYGETAy0y5OzpYjIzeg4dZIQYpgleWok0SQsZqn7i5wXfsQ1aE8nd0sFaA6kvOXbveurmxdHR8Bt+BIQQejRVEMhmytQTSP+STl4dPEr7dvfs72HbkWy6+Pnz4ErxIlQe8tPVdZ+dfI6PvDAzP3Nw4wR3RH+ATdP6Du6CyDAySDHPBwjyji0ghkThKDhUw2iEcKjE1ot25e/czaYY3t9xm/mRgeMygIdWhH1Vudw18JeAP8DWyLysq2Do6noBH7IQ7OsSMjP6CxSFTc7wREWzy8l/A549BwvHjrVuP3NwEwLvDf1dUQO7uhtcukK4g5E4I0IpT2EJQyCUTvyoqJP7/f7l793M3N8ldu8TBew/gC1cYGBiUd+26Cz7VGmIXK3g56C/wmaKfz53jNTKCzDqKdXSwg539x8jouZsbA2yLhQp4UPNIBWcOaFbwDwPD6BXzkIAcJUkIgU8Xd7cvcGNgYOj4zNBh01FxooLhO3jJaHDHkd0VXtWjHUISAnOkKf356uHBXgVNSQa5a6DpZQYWBtAG1J+ghQ0VvAyVcaMdwpGWIijy78OHb1asuFVRcY+Bga2a4f1HBjY3hkPwCypkdu36Cz7R7QfYkhcVFZ/A1wC+raiAVIVvwVsQORgYhHftegYbq0U7HoZXTY1HXh6yVx9yFsBt8EJQVfC8ImTTB2TnIaQrCDltDtLzfAc+9oaBgUEctsRUdNcuTvBEIthFDLzy8vA+IQO4aws6dBR8X+KHc+eYIYrAWUQuIuKdgsLPB+97FFaqMzyWY3iqwbBgfcep0dlCWCCN0oMajHYIB3X0jDoOeVbwBoPlBYZszQ5ZBvCwHye42vhTUcG3a9cncIfqPbhDyABewMkFXn+CHICQ/h5chA+8GYAdvNlAYdeuL+C1mpjnwcDVgyzt7PxVUSEEO3sG0ic0AFc5cGWfHj6EVDwMDAw/wcfP/AEvLmViYPgHapBDFcrt2vULXAuKgDurFxkZJXftEgP3LWcqbCwDdQVfMTB82rXLaXRWEBpkoxQpIfB6S+eaAxVZ70B6OuxBd9NDlowulWb4/Zohonm0QwgKmVGMNQR+vnq4p0tBSIjB6iFohnBXzK5zt8+BJgkZGHaJMpj7j3YIsQbbqCC+EADPFt4Gn5X2GnKdfXgHi015LGQFjTS4Gr3GyPiHgUEUfJTaR/DlwBATOcE7A78wMDCCL4tiYmDAPB6GgYEBvtgHVFnv3v3WzU141y4h8EJQiDkQErN/+LWiQoQBNGLGDb7tCaIMTn6AtSsgIgLgpgXkSgzQOiDwJCfkosVvYJW/IYuJGBhuVNzQZ1gwm2FiK4Pc6GwhJPRGycEMRjuEgzl2RrTbdu9+dsttzS8GhpugM58ZgneZyblJc8EO9mQDd66EwH0tDvCCEEhgsYO7YT9BzRjQxe4MSFIM4BUdEGWg9Z/g7Xk/wVsXBMGXxWviOFQGouXL7t1f3Nx4du3iQapgLoA7cuJIIhDFb3bvhlxXyAo+nfoDeJydH7aNnpuB4Td46yAHWARyCCpoqWrHwTDQgpfvDAx/du1SGb1UEBKYoyQZIfB6S+eNExWfVDuu/GGoOFvRYQbaCcbwl+GRFsPrzwxGtf/JMHNUy8gJgd0VjBoSDHLnwVuZOUHTHx3mHcFqRo83uelYdYj6lI+coBj1KRVDANIthF9n39EhYlkxTaJD4yO4K8UPXjXzAXz2mwD4mJnP5879qqhgAQ/y/mZg+N3R8Q/sGnnwUlIwE0pAjgqHdCwhQu9w9wkhCiDkvc5ONvCexm8MDLwdHR/AFxJCpEDzgahNiN/gQd7HYGlh8GFvoD6hkdGnc+c+gXuV78AnAjCD14xKgr2zsOPok4qj6gwfGRg4Ri+oAIfcKDFIwWiHcJBGzEh2FuQmCQYGxp0Mi78x/BdmAB0Y876iQoKB4TX4cJenbm6gkbyKCl4Ghp+w8hq0OhQ8uPjEDXTR2n8GBo6ODlHUo2Lg54x92b0bdLP8uXM8ERE/GRggdwPyGxnJYHTtIBHx6+HDdwoKbB0dQqidRsgkIWThKAMDwxuwsZCuoBj4GJsf4Nss7q5YAdkcyM3A8LeighncD2QFD0lCerOjl0lAwnmUpFYIXFjbabi9AmoaGzipsTB0WHZEfao484AhcMJohxAaNqMU1hBYlctoqcywjB+82JgFvLwBcswiG8N5tw6DoNEOIdZgGxUkKgRg3cIPDAznGRh0QSeR7tJnP7f7Z0WFKAPDQ5gZAh0dnOA7DLk6OniMjD64uX0EV/eQeyYYGBjg3ULIYlH4xU4wAxhuMzJydXQIRURwgvfzw8XhjHe7d387d+5bRYXsgwf/GRjgewVFYS2Bz0g7TUBthlu3Prm5PQEfY87AwPABfCgOfLaQFzy+/BN8/PgHsLHs4Csr+Ds6blTcUGBY8JMhxIwhenS2EB7+o4xBBUY7hIMqOka6Y3bvfrba7dRsBklwQPw/3vGApSISMo2mBF7f/wF8waAo+GpaATW15woKiv//Qy4vEgV3wF67uUGuMILM0WmBF6KATUMQX3bv/gW+iEIYtviTgYHhamfne/C5Z9qoXT6Itneoi0UhghAS0ieErGBhYGBA7g3yqqm9VlAQffDg/a1bH8+d+15R8ZuBgRO86IUdvA2d28ho/zl28L2CkMsk5FxddSDGjpKjIUBJCDyewFh/nWH+b9ApkaAD+P4xVByuYPjNcEOb4cRdhviZox1CSkJ3+OtdlcsoJMTg+oKhwwq83piBwUjFyG2Z23QeBm9xBtmC0fQz/NMArX0I7haer6j4AV7uA72ggr0i7j+4rmdkYOAHn0fKwMCg8+ABOwPDSwUFgV27GNTUHoKPeEHuFv4Ci2BeHPX94cMnCgpcHR3SWKv13bvfuLlxdXTIwGS/IZ0fA+8TIofDu85OHvAujyfgo+kEwR1U+JXCTxkZ+Xbt+q+mdk1BgROsjZ2BgbUDdLYqf0cHW0XFa4YEMwZnsAzoggrvCE34IDVYcJQYDYGBBKMdwoEM/VG74SEAmRXczbD4MwMjeCT6v9eDsp+3br10c9P5/x/ehXsNO3lFuKODH7zwErLlgIGBAd4VhJt5jZFR4cEDLtShQYhRoMOmYXUAXP2T3bs/njv3vqJC/8EDXiRdWBeLMjAwwE8lfe3m9gd8UijkFhcecCXBb2T079y5zxUV38AbHj6dO/efgYGxouIlbHBxdwVnEWivIAP4XsHRyyTg8TDKoEIIPJ7AuO86g4BdR8Be8BGjzAwdph0fLlUkiDP8lu/QCR+d4aFCIA9jIx5u6Pxxq2LBE4aObwygU0bPVTD8Y9hg3fH1UIWt2miHcBjHPL29Bu4Wgi6o6OgQrag4lcPADrnO/j8DA39FxTXwbUzvwTcN8jIwiHZ0sINPc3kIPl1GENx1BK27YWD439GhCF4QhHylBK7NhN8fPnwMu1sCc5Ph/c5OBgYGHiMjtD4hZKGQBNIoM1q3UAC0tprhHXj5qyR4hPor+MxSBgYG6YiItwoKwuDDxu+ce8FREfeBIeE9g/L9DvfRI2foneZG7cMBIC1YHJKjwqMhQPsQgHQFwZvsGB8wqOsz5H8BH9R5FlxeCzAwXGFkhGwbYO/oYGdg4AY76Qf4ZLDX4Hm2p25u0rt2aSAV02AlDGK7dn27dQu5Q/gOfEE8O8bKT4h66HrRjo6LCgoK4BspQEtEHj784ubG1tEB2ToI7wRCzrzmNTJ64uam0NHxGnw5ITe4KwjaoAi+N+I/A8M38J1It93cmBkYhMBbDtR27Vrmdh3cFfzOwPB01y5tV1cLiANGydEQoFYIPPnOcJCJQR18MiQDEygVVhyqqOBj+PSdATJ0TS2LRs0ZliHw+SfD9+8MCbG7Oma7geaWmUGboW98ZVj2AXTDT9Sw9POopwYiBMD3FppGRHxYseIe2H4muwqRHIY/fgyHdBkY1HftegCuPXnB5wL8rqj4Dd5wAZ+a4wPv1vvDwMBhZHRGQUEYPGQsjNQ5FHJ1/d7R8f3cuXcMDJC+3zvYxCCXkRFEBGwvglAsL7/f2fnl3Lkv584pIg0cf1mxgg1WxUNUy7i6yvz//2T37odubkwMDL/Aa6t/g+Xugo8xZ2Bg+AzuH34DZSCGJwoKMrt2cVTEyezaJebmdpKhuKLiZkXF044OaSMjYVdXJbDWUWI0BAYGMA2MtaO2joYA+Ir5AsYNn9xalzMsSWV4vmuXbMAuTW4GBuMHDyDXv4qCl5JA2q/wi91ld+0CnQUK3mUOuY72f0fHRTe3i52dkNPDkIMWsoATIvIOPOzHu2sX2j5AiCyElHF1lYuIAI07njv3BLwh8MuKFT/B28Sf7959mpHxGXhpCuTM648VFU/c3EQ7On5WVHB0dPCARytBC12MjEBbBzs6OMCGMoPPHOMBH1+2iWGihNufIgYj8Kygwv//wa6uGmBVo8RoCFAtBH6+esjwn2H+L4aKoxW7EnYxsDJ0OHR0WHbocjL8/ks1W0YNGsYh8PIbKKksWOnGwAyaIWTgZNiVuqvibIWXLIMiFwMogQ1jz496je4hAO4WGj14ECHTocXAwDSFQeZcR1wrw7yzbstEOzqEwJf0CoJd9Ql8+iifkRErA8N/cF+LG1zVvj93ThjcWxMGX0B/RkHh/ooV+xgZL3Z28kZEvK+oeAueVIT3BoUjIrD2BsGWMMD7gZDZQojgr4oKHvAkJITLwMDwfPduEAI3A1jAZ+GwgQ9EFejogJw7ygA+qAZ08ExFxT/wMqKHbm6QDSbsDAwBD3J37TKbzHCGu2KjuJvyfMaW3bshvWK4DaOM0RCgHxidIaRfWI/aBA8B2Kzg93yGDwoM0z50LI+sCGRwY3jW0fGfgeHdihVMFRWK4LOnf4Jn3iD7A+Ha+cDziTy7dgmqqd1fsQJyI7wgeICQAXwXBeQ2WxFX11fgg15EXF0hvUE2IyPIRB/cKDgDfkvhl1u3hIyMHri5fQL3A9+CLt9iEGRgeOHmJgc+4/QjeAkoAwODKXhO8ufDh68rKkALQm7dAmkBO1gAfGe95IMHP8Ajgn8YGFa6XS8FLRD93tHx08hIafQyCXjIjzJoEQKibAzrxBhuqXS4zXVj4APbwMpw+SeDpQjDj9FhQHB4jBJ4QkBE1YjvPYPuH4YOjQ6IMrdlbh0WHSLnK0TgN69BJEbJ0RCgUgiAu4WQ2cK7FRUXGBh4pzCE5VS8COtg0QZf1MS3axcj+IKoZ25uQuATRyV37RJ0dX1UUaFoZMQHvreJgYHh/ooVyJ3D8woKLOAJxquMjN/ADG4joz8MDPADY5B3iMC9olhe/nr37i/nzt3v7FQsL/8FPl2GDbyX5Dl4sBgyLoxMMoANfwU+1k40IuLzrVsQ0/h37WICtyiEwF3Wm25uMmDHf1qxQqCiIhjcrvjBkMDJ8PDcuRdubtd37dIcnS2EBN0oSU8wuoeQnqE9ahfD7t3P3ri17mdwmM0gwMDAsmuXspib/HcGBi5wQfm2okKBgeEpeNvAGzc38V27RF1dbzEyqoG7XpDgw7oJ8NPDh/fBc3eQC2fvgMcChTs6uMHDdb8qKqATjODtBxBzPoEX90NOuwYt8gQ7ADSSBz5CGqSmooKVgeEl+Bgb0Lwf0koVASMjPvDFtXzy8q87O38zMDAZGT11c+Pr6PgO1vUXbNo7MPtpx8FQpMskGBjYR3uDoOAdxTQLgZ+vHq6drDD7GoOHN/i2iX+glUwd1h3u/Az8Dyv+Wu1SsXClmeWjBg+HELhzYjfzMbeP8h2gs2r/gpfl/WfoMO+4eaoiRorBOvMBu5j8cPDnqB8Gawg8fPgRfJ39G/DNJ39WMpwVZLgrwLAAUlmzV1S8AB8kDnG+IAMDBwODWHk5fE8HAwPDQ/BaU4j6bxUVkP0mb2C6IOd+Q7TDbxqET+vxg5sKfGpqkHNHv1RUSIJXAPGCO28yYJIfLAKZ7pOEnUoKshe8FonbyEgEJvjr4cMvK1YIlZc/3r37rpubREcHI/hyC0iz5GNFhSj4yJnPu05sOPejouIxAwPbVIZbqruiRruFkAgaJekDRjuE9AnnUVtAXUE3t0cnOx68qTj8ERwe3g/K+OTlX+3e/dzNTf///08PH/5ZseJjRcVn8MWv8F18Tzo7hWHHRmPtDYINgxKfHj6EDAcKgQtrZvBc4k8GBhFwDw10GT2sHwi5px5ZBGoEA4NYR8d/cP3xm4HhLdgxjOCJRwYGhvewqUuI4l8VFYwMDF/Am90FwH1CZnD1Bdqu0NFxicEyGtQVPNbR4WZkJMTAwDDaFYSE2yhJ0xD4+erh40UKbXcZ5jOCrpqIcIxQaFBg+M2wVJrBVJDhm+UufZvRDiFNY2DIG37nxO43293u/WeIfgUqQ3fF7jp341zFiYqlYgzS7AyGfqN30w/5KB4SHgAfOXO3ouIdA8MZBgaDjg4RzYppkgwLxBgYfnV0fGNgeAvuqr2rqOAEr9gUht1e+LGiAlLFfwMPyzKA5+44YMeW/gav5IT0A+GdQHiA8BsZfQS3E+Cdxm8VFdzgxgDoOLqOjs9gw2UiInCdEfoQtU8I7xAyMDC8Be9g/AnKVSALFR884JGXf9vZyWZkxOvq+mL37jNuyyQZFogyMMxmmNHCINPRITZ66gwopEYx7cFoh5D2YTzibbiw+5Kh2zdIAVjE8DyeIfANUpgId3T8rqiQ27WLBVwEsxkZ/VdTe7RiBfwSiHfg5RlCrq64eoPwQcHP4MUkoB2GRkafz517B95sIMDA8Bw8y8cGrhIglQQfePwP4gpe8FwfvGT/DL6UgoGBgc3ICLI7/CsDw13wlCMj+BZESFXxq6KCC3xmDAcDwwfw2TaQlasCRkaf3Nye73rq5vYIfPvb91275MC72aUg1o2SoyFA6xD4dHH30x1u+z8wqPrvOnf1nJGmkdtSN4afoH2369kYPCtHp3doHQND3vyXZ3ef3OB28BND3yfQovldSbvclrt1GHf8OVsRKM0g4znaIRzyUTyEPADrFr4Cnyrwr6NDxLlimvSuKMgcIE9Hx5+KCh4GhmeglRCgS3YEwd1C0Pnd585BFjhzGxlBtvR/Y2D4Am5piEdEcMuDZrnhC0cZGBg+gRd5Qqp4SPjA+5N84JFfZvBST4jUu4oKyBJQyIohtNNNkfuEkHuPeVxdIb1B7o4O4YiIr7duQc4gFTIyeu7mxrcL1GgArVHq6PgEvo/xYscav4rvYLtAd1RYRFjIywuAuaPEaAjQBIx2CGkSrKOGQkJg9+5nO9xORTIEfgGtvcx6zKCe9sD/mYKCxv//T3fvfgDeBgDptkGu6/lZUSG+axdkNeYn8N19DOA1n/xGRmwMDL/A5TgbbGk+pPsHWRACOe0T0hV8Au68Qdjf3NzYGBjeg6cHIU5SiIhAK7gh4hDyM7jzCWHzurrC+6KfHj58AF6S+rGiQryj40dFhTT4UOnX4FuMXlZUfAFPIb6tqDjLMBG8V/A9AwPHrl3qo7OCkMAcJekZAp8u7u6b6dbIAD7YDmIxI0OHRUfF5YqjvAx//jEo+e+SMR2dJIQEzSiJHgJvz+9+tcvtx28Go1cMHXodFcdAd04w/AedLlNxtKJCkKEyfrRDiB5oo3xahwC4W3i7ouIDA8NHBoY3ObALKj5WVIiBZ/yegV3wHnxZvIiR0TM3N1bwWK1oRASXvDz8CsFX4Fr+y7lzPEZGYrBVnWCtKARk7RLkVBgeI6Ofbm4MHR2QxU3wriADAwP8OkTIbRNC4EuSIW2Y9+A2A7eREdO5c8zgQeqvFRUiu3axq6lBthd+PXfuE/iwmV9gm5lgV1Kpl5e/ZGTkfvDg/IpD7yv2STIsEGBguNGx/RqD8OhsITioRgmagNEOIU2CddRQ2LExDFd3cTG56V9kyBJnmCYEvpOdBzxxBumwQTp1wgwMf0A9RgbIWdKQucH34APEGMBDghwMDLzgBSE/wCd28oLLVlBJDWZA9gFClvIzMDAgr+Z/DV65IVpeDunRvQWbKdzRgbVbCOkN/jp3DrJ4AzS++PDhuxUr+MFdUIhT/4KnGf+BjvEHjUTK7tr189y5XxUVWv//L+08FANaIPqdgeHdrl3aDAx8owtERzPCgITA63O7969w2/eZYSZ8ZRIb+IQ7ToayvwyGNh0HTlTYWXb4OEfwSYLGyAfEkaOWDsIQeHt+94Hr544drxAz6OC4WrH6HcNRdnARDFkswcgwR4RBgYNBTKNDd/Qqy0EYfyPASeBu4b2Kir0MDJIMDDw5DO9LHgSyKwiy7dr1k4HhI/juXzYG0EFaz8D3QAiDe2gCamp/VqyA3CnPwMDwCnxaDA94oRBmnxAi+7miAnKXILeaGre8/Kfduz+6ucnCjjOAHGn+6datD+BxangXEd4/BF2BCFutyg9uvTCA2znwIWxIi4K/o+NrRQUTAwMb+CRVSAT+rABtRYH0EoU6Om5U3DBgWDCboasF7OWODunRbiEkoEZJ6oLRDiF1w3Okm7Z7N2iQ7ptbK7gsY3zCoCbRYaBTYQ+5vedbRcVPBtA1aPzgi/sgJSM7pJO2a9c/NTV48foevM36fUUF5HAwZvDSf2bwVr0vYPWQ/iSoT6imBl/tiRb6Px8+fKWgIPbgATt4ZQhoQQh4og9rtxCyUvRnRYXIgwe/GBggA3i/zp17A579E+voYGZggMxDsoLnXSDbwSE27qrgLGVQBXdaf+/apTB6bAwkWEbJgQqBEwd2/9nnxs7McP8rQ/gbhg6bjorjFQwsoAVVFRwMv78xXP7C8FOQYYo96ADJ0UvqByqaBpu9b8/vZmBgaFvupqDWwXGxIg1yq88fUGO2w66DgYlB5HaFnWHHu8sV/x12WTiMzjAPtggcQe55+PAN+MiZewwMbNUM7z8ysLkyHNLo0IBsHWQH19qQo90EjIwegxcNgU6xA1/+JA++OoKZgQFyYAwDA4MS7LJByOQhA3hZKVp3EdIhRG5OoAU3pIv4CDwrCOkfMoFWWzN8BZ9H8BO8tUS4o4ONgeEjeDMLvA3DysDw9dy5DxUVzOCD60AnpYM32LwALzv6UFGhvGvXXze3hx2LLlbcZGD4IcfwVoNhwfqOU6PdQrQoGOVSCEY7hBQG4Kh2aAhAuoJubjcZGBiuMji9ZWD407FclOEhB/iyINCBYODlEJ/By+6ZGRhkwEUw2m0Q8NOcGcBXOzxxc/vDwCAHWSACvtgdckM91vk9qDuQKEiHED6kB5eBzxZCTqZWiIj4umLFJ3A/8xNYEaSkfuLmJgveR/4VvHmAAVy4Gz548GrFip8VFbIPHny8dQv1Mgm90QWi4PAbJQY4BE4c2H1zhZtLyq6D186JXK248puh+DPosCPrvwyCbxm28DKscOmYfqiiOXbX4/vnXpyvCIsaXUE6wFE2sNZfO7X7+Fo3XeOOn5JGNQvdvGxBh9MGfWJ4ycvga9ZRcbRijhCDKhsDh16HEDPD0V0V6pGjHcKBjbFR20EhAJ4tvF1RsYeBQZmBgTeHAXxBhZHELzc3oQcP7oL7Zl8qKnjAp3oyMTD8Z2Dg2rXrHmyvCuTWNSHwaK80eFMf6GpBcO9RctcuzJnDx4yM/Lt28eFeZQpyExi/3L37kZubIHg3IOSMXm7w8enCSNOADAwMkPuxGMCXY0H2E8rD2kXvwCcUMID2fTNArl7kAe98+VhR8ZwhwYxhgSTDYvB62NHZQnCIjxLUAKMdQmqE4sg2Y/fuZxoMV1vcPsxiEGFgYNi1S0HaTYm9owO0G7qiggO8VO01OIhYGBj0Hzzgk5e/ycio/v8/pDf4i4HhP2zlJ+Q0Z5lduyCrKWR27fro5sbLwCD64MEPBoYH4CsHwSaBCFwrP0FyYAxfLwrmoROfHj68Ar6eiA1c8kLWgsru2vUVfDINAwODGFgHR0fHc/DBp3zg4hhycszniooX6JdJMLu6yoF1jBKjITDAIXDiwO4dk91ctRkmnWOI9uv4f73iJyvDH92OIxcq7H4wvDLoyD9XAVql/Z+h36xj6fGKSDEGN6MOObvRFaQDHHH0t/7t+d1fH55bfbRi3XtQSe3n1FFxpoLhL8Mam46fFyqOMDFw/mfQ4mAQ+M/Ao9SxYH1FsRnD1ksM7jmjHUL6x9WojdhDANYtRL+gAnItBD94z78wAwMj+DwY0FaUjg7IDN67igox8KQcN3i09wN4+RJvR4cauFeGaRn+FgUDA8PXhw+/3LoFuYAKtCIJfNYAj5rar1u33rq5MXd0fAIfiyrc0fG2okJl1673585BWhQMDAzc4N0oDAwM8uXlLxgZhR48eH/r1jPwIQWfwGtKIe6R2rXr17lzvKAzSG0rKkATpJMZbol2mIweOQMJn1GSEjDaIaQk9Ea6Xtis4OMbDBbPwYEBOXSLC7zTmg0s8ouB4ee5c/+NjO6BB94gpzYzgpfIs4BvhoAc+wlWC1pKAdkKyAteCPrr4cPXCgrsHR0iSAU0ZH4PeTM3rglDXON5L3bv/gzezC0N3gH4u6LiI2hVFMMv8CQkZM2JopHRGzc30V27eFxd70I2IkZEfLh16/25c1cYLMF7BUcvk4BE2ig56ELgxIHdS9rczJUY+Mw7/vxnOHa2Ii5+F/P9cxuPV9R8Zwj8zODg0rHuWMXXrwzRDh3a+kYHl7v5mnZwszL8lTfSH72icNDFJ60cdO3U7i8PzkkqGk1b5OYUvWvaZLd3nKCZZBMeBiv9jr1HK4yFGFzNOl78Ydizq8IpYdftdW7f3zM8eMPgVzjaIaRVpIyaS14IgLuFdysq3oE7gCw5DC9cGQ6Z7oriVVP7AZ4qFCkvf7t790s3N/aODi4jI8jhc//AO2QFwBNx/zs67oEPpGFgYFAALytFO38OsmoUc80RAwPDS3CjAtIJhIwd8xoZiSPNJb7fvfutm5vMgwcc8vKQxaX3wQPckGVKgkZGkM4hG3iZKDN4DJqBgUEafPQoX0fHf3CgMIFJIQaGNxUVvLt2nQZfUHGSoTmEoVaSYf3o3kJw8IwS5IPRDiH5YTeSde7e/QzcpfoIXoXBcIPB8id4Au0d+FKg9xUVfAwMn8EBhNzfAwsw/K4AHb3yB7ShiUEEdsErr5ERpBMIUQMh33R2/qyoEH3wgA22CRAiDip2kXYDQgTRJgwh60XhK/6/wIbuIIXsZ/BG8E8VFULg08kegm+J5QYPHHIyMCiAB/C+gU+OEQKv+mBnYJDatesqg/boZRKQ0B4lB3MInDiw+/ZKN2mzjtdnKz5wMrB8YHhj03FgW0WuHAMPO8P91wwbvzOo8TFY6nXcYGEAnSHJAN6zwsBg/IchSowhpeDB6GEzgzl+KXfb2/O7L2xxc3nG0GECWhEK2l/KzNBh1mH0n2HP+QqX6F3Kz879vF1x7zXDxLsMzt4dAscrmCUYOH4wqPjuerPZTTVkl7rl6B5CyuNh1AQqhwC4W3ixouIbuKMHuqAiwIj5v5sxB3j34EfwlCBDRcVLBgZZWNuDGdwg+Qt2yCdwM0bAyOiumxukqwbpHEJ6hpBGBfKqUVxTgpALLcBGQglIh1B41y5BpF4ipCUDGWiGTBWyMTD8qaj4Cz54D3LGHlQ/mIJcnMgL3snCBhu/ZmBgkAUdsLMYrISno0M6O0IM18EKYDWjxGgIYAejHULs4TIqiisEIMeHXmCw/MDA8IIhm4Hhv+eDsjcrVjCCT/T6VlHB1dHxC7xS9CsDAwe4eIWcAgq5QIITfCLLD7DpEuCbG5APBQULQwms04NQOSQK14Th687OHxUVgg8eIC/hgBxP+gm2kIONgeG1goLogwfvwMs8pHft4mRg+AHe3s0A7tCyg7uISrt2rXK7XsJgBrb2+65dygwMLKMniIJDY5QYjCFw4sDuu6vcGBgY1rxj8FBgEP7GsPU9g41Jh62Z0ef75x6frmD4z7CDgWHmd4ZebobiXwwdRh0Xj1akxe8qWeqWxMFgZwU6bGZ0BelgjFqK3fT95cNvz24xMDAsm+e24CfDuvoHM9oUBMw7Xl2rgIzhfWNlsGNnMDfvePqF4d9/hsmrKriEGXwFGASFGO6Ld7w8VaHEyeBQ+UBKdvR8WoojY9QA2oQAuFuIckEF5MgZkG3gI15+gFsmfEZG3AwMH93cxB48+HHr1ns3N8FduxjV1CBXTIEukQcf6AI5RxTSM/yooADpEBKcEgTZhYpx9Qnhqj49fPhixQruioqHYKH/4E4tI5gNWhIFdjOo9cLA8AA8kA3pQ0oZGf05d+42g9TFiodyDBclGdYwMiTs7cgaPXIGFnKjNLFgtENIbEiNqoN0BSFTCbsZFv1iYORmmKq0a9dzNzdOBgbWjo7vFRVSu3Y9A+/Y/l9RwbNrFwfsznd4UQi6y+HcOU4jo7fnzqGtqUALYcj0oDTslGc0WTQupFsIOT4Usg+bA9zzZGJg4Ovo4Dcygizr5zUy4gEvRoVoh1sBWUT6s6LiKwMDZ0cHP1jvr4oKmQcPtqx4GA2a0Ry9TAISZqPkEAiBZ48fbqxT4ONk+GXY8YOVQeB0xbmHDAf4GEzYGIxYGLSidz1b7ybEzcDOwqDov+vgzXOgc+3+MVRvqzjLxzDfsuPlPwbQqaRsDI8TRg+bGQLRTbwT357f3bbQjeEvg5hBx4UTFStYGSL+MbhadlhoG22+cS5aw+jyRjcFHoZbLxjYtDuenKjYx8BgbtrB84NBlofhKRPDtd0VLk4d949W+DeNdgiJD/VRlQMTAuBuIcoFFWm79KXP7eaJiLgPXkT6E3yM+WcGBk3w0QYPGBkFd+3ih83gQRZ2QjqHbysq1HbtegDe9sIFbh5AdseAume7dvGA76UgxpN3GEH9OxXcrZo34P0p7BERj8EuhJgpGxHBKy//+SGkn8jwSUHhW0fHE/DqVgHw3pZ/4HYOZLfLw4obggxfTBn8R4+cgYTeKEk8GO0QEh9WI1fl7t3PbrutEWK4uY/BdTaDxK5dcoYMVz+eOycaEfFAQYEDvBn6OXgBgyB43fzLzs7vFRUK4FLvPfgSWEjYvXVzgy+Z+Prw4S0FBaldu5DX2UOUgWbndu/+5ObGt2sXL6x0hkvhYbzYvfvDuXOQW324GRhegTuvguBd4//BZ5yCCmMGBn4jIz41tZ/gfd6gA6bBK/U/nzvHDl6az9PR8Qx8FOrhCs4clMskRo+NwRP2o1KDKASePX64d+MKAXaGZycq7v1gUBdlkOdkuPCD4Y9hh8LpCm2bjt7pFV56DJImHf9vVzzW6Dh0skKWgaHmJ3gnzSeG+fYdrs4R/q0KBfwMcmwMBtmjK0gHUeSS5xTIXfOvPjM4fGaYqtbxmYuh4nQFAztD0S+weZwM4RYdvHcrGJU6bhyrWH+OoTKnY9fBiu8mHSLXKvR5GC48YvjGzCDDwcCu0/Ho2GiHEBxoo8RQCAGsF1SY7op64ubGzsDAw8DAsmvXB/DhLpwMDAIMDDIPHkAunQK1Q8BbSyBLnCB3ZQmCtbwGn3zOwsBgAO5JEh8M8JFxtIWjEBM+g5s98D0ynx8+xOwWMjAwPGVkhDSNPj18eEdBgRO8twW+cZEd7OazRoFubhcmM1xiYOAw75AXi/CVlxeA2DJKjoYALjDaIcQVMqPioBCAzQp+v8XgtJFhkjFDHmgyoaMDdHsEuAfIAb7aFdKteujmJr9rl7Sr60vwKJd4eTmkN/gWPK4G7wqCzAXjl2DTDcH9RrAAlCBysShUNQPDC/B+bshY3VM3N+ldu165ufEzMDwCq2AEu5OBgeF9RYUgeLbw47lzvysquBkYXoL3MSqB920LdnSwMzD8Ba8UBd8raMbA8Kqjg83IaPQyCXA4jhJDJwSePX7YEK/wSJDBXY7BwLTjzisGntsVWrYdNy5WfP7J8P0tw/N/DDq8DKZxuzhZGI4tdGPgYjj+jsFdgGHDH4bMiF3ds91ufGU4K8JQ+5fBNmbX5dVuEsajt9gPnejHcOnuY7t3LHfziNh1c6lbLhNDn0HH6/MVMYm75q5x82dhuCLRwf6oQp6JQSNk1/2r564cqHjzn0HNrIP7boWyGAOLSsfJPRUftTsuX61w4mOY9IzB5A1D2azRGUKMUB4VGMQhAJ4tvF1RcZaBAXRuAOSCChWGZ/8YGN4yMHwA33sMulmqouIdbOwYMuEG99MP8FrTvwwM/AwM7xgY/nV0wE+CEe7oUMR2CA1cLzLjWWfnt4oKzElCSLMH0tNDVv909+4H4BssIIKyERE/V6xgNzJiUVN7t2LFt4oK4V273oA7gXxGRp/OnfsL3rbzE3zb4Y+ORRwVcTIMDFLgI2eMjIRdXZUg5oySoyGACUY7hJhhMioCCoHdu5+tcTs1C1T6fWdg4LzF4PQXvC2bmQF0Kw47AwMXAwPkMgkO8DJLAXBh9L6iQmHXrt/gg7y+gm/24+ro4DQywjoexsDAcJ6REXOSELKSEz5OBnINBsZ1SIw0eF8iI7h85IyIuKigINjRwcjAIAcurz/dugXpDULMYwPfd/8HfK4XqE8LvkwiGLpA9M+uXSqjV8xDAmqUHHIh8On5wwtTFV7+Znjyk+H8OYaY1I6zpyse/GOwYWX48YnhLAfDTAaGVcwM7pUP8iMVAowY/v9n4GBlWPqDIcmy4/jxiurfoMGS6H8MLtYdH34xFF6vOMTDoOg3uoJ0iCUEyMSg1jPQrCAnF0PS8QoGDtC1qgv5GTSsOyYeqUjnYRDlZXj1mWHbRYaypl0nlrot/cKgb9rBuKeCWYqh5AvDPA6GQz8Z/lxl8LVi4GVnUOBmYLHfpTp6FO0QSwijzgWFAKxbCL2goorhRUAHC0NFHOToO9CRM+Dr40E3ZoG7hXzg8W5eNbU34MuHGRhAx+D9AK9d+q+mBj8mVNDI6A7SITTw2wVBVmJgXJOEkMWiyAeqI2t9unv3x3Pn+I2MPp479wN8aB8r+Lg7iJMg13RBNsswgL0gCj7CnZmBQayjg62iQgp0aSHo3Pcchq9pu/R1XY2QDR9lj4YABECOsYWwR8lRAAqB3bufMTKeUHSTdmLYvZ/BaQXD1o8PlH6BV5P9AQ+SKe3a9Q/cIeQAF5qcHR0CRkYfzp37x8DA39Hx1M3tKwPDQ/CxyCwdHUIREbh6gwzgozufubm9RFpW+uvhw58VFewdHZgnizKAJwNf7N59lpHx+YoVn8+dk961iw9sI6+RkWREhPH//xKurhKwVaZ88vK24OnHdxUVD1esAF1Ky8DAXlHBA171IQfuOqru2gW5FGhXBac1w/HgikcdHTy7dqnv2qXt6qo4enIMKEGM4iEYAnyS8nYt/zWNOhQZGaTlGdyPVBhyMMxiYnir3sHFzuDEBTpORsqkY1GFgrUKAyMDg0LQLp3QXe7sDCuPVTz4zLAHfBB7fPyul39AC687jDtYmBhu3z63f1nnp+fQrSxDMFRGkJOvndq9t5Hx7u1zbyQ6Iv8zfOZhSDpdsStxFwMzwx45honPGS6fqigUYtAO3vXDatf7rwza0gxrp7jx6XY4izL4vanQkWbgVO5g4GIQY2XQMOl4Lszw/QODWeAuzfL/o73BEZSMhpdX5eUFystNHzyw7OgQYmBg4etgM6sQ2ddx6kbFDdDNw+BVQqK7dvF3dMiBGzn/GRgE1NQ+gHuDIrt2qf//L+zqyge+rYpPXl6/vNzp/3/FiIj3585BjiR9W1EhaGR0f8WKfYyMFzs7IbsQ0YJQ0NVVeNeut25ukPVTEFlIs4cPPGwNEUEjpV1dBYyMHru5MYHXMf0EN7FYOjrYOzog92fIgJ0NutYLvCTqBXieEHLBxncGhme7xA91/DzFEO7KcEjIzZiRcevu3ffQrBjljoLRGcLRNIAIAfASzkcMDIylDM/TGAKfgEsWBgaG/+B7UUG9O/AN8p8qKvjB+wYFHjwAnZt869aHc+e+gecD2cHirKAxaIZPMO2gu1bBJR3k7GaEfWAW2sJRyPQg2lkyL8A9RshyUDjJ19GBdkgM2DwQ8ZCRUQBpd/iT3bufu7lxgC5eZuDq6PhWUSG5axe/mtrrFSv4jYzOjV4mAQqzUTw8Q+DT84ePDq0QUDL6859h+Qo3MfkOWV6Go2cq+FkYNNkZVp1mWKDKUMPBEBcNmvbpiWY0lGP4/IOBkZEh4A/DZeuOjv0VS/9AQyaTg0HepIP1WoWb8egt9tAwGZwU6PCY5W4HXjKE2YFvlQCXfY3/GEI1GbReMCz/DXK1pABoVrBz8//DCzsXHamY85eh8zuDkybDxe8MP78wmEXtmlzupmrKEBW2i5+N4evDc9zyRsKGo1dNgIJuFA+DEADPFqJcUOHFcEOU4RlPRMTTFSs+gDeY/Kio+M3AwN3RwcbAoAS7DBmy0w+tiQJqCIEPfYEcV8PAwIC2oBSt8YN2ugye6cHn4MbPEzc3+DTmXwYGUXBj5m9HB2SxqwBsJpMVfHD6OwYGBvCRpAJGRq/c3NgYGNjB04n84CsahRkYpjJ0tTPIMzBw79qlObqIdBgkZmqB0RlCaoXk0DZn9+5nmxmzL7qtBU0HMPy3BI2fMTj8/y9hZMQOLhm/g/fgvTt3TjIiwuj/f96ODo6ODj55eT55eTYGhr/glaLsDAyS4JsbBDo6QL1H8J0N7yoqBIyMzisoPFyx4iAj4+XOzse7dyOPnEEOlYFMEn7evftnRQXfrl2Q0Hyxe/ftzs6z4IO54P1AyKJQ4///VcvLJVxdMe/b+QkulyEHnL7evfv17t3v3dwUdu2CNGshvcGP5849WrFiYwWniBuvm9s9BgbQZRK7dhm4uuqMzgpCAn+UHB4hwCcprxNeDloRff+cwi+GHbsr3E9W6LMxFDMw7HrDEGbKcNmlQ9Oy48Iyt42zOjlYGd59ZVAN2aVs3tHLxnD5FwPXL4Y0boYOu44Oiw6GPwxGakbrPjHI2UU8OrTiyendwyOIhpMv3p7ffb2T8fWdcxw/GGLsOoy0jEBrRFkZ5kgw3GBi2CTcMYeXQV+WQc264+ZzBmEehhU5jJ8uV3zlZHiTu8tcmWHmA4YU8BVuV66fMw/r4HvN8HE36P4SuYDy0d7gcEono34Bzxbaw2cLGRiY9SpYWivE9ik0MILve2BgYOAFo3cVFV8YGC7BZvzYwWen/wI3M5CDEdIc0i8vV4yIgEwbvgUfBIo2Zwhp/AiDGzmQSUJIswd5sejz3buf7959mpHxNCPj53Pnnri5yezaBTnbRmbXLvldu0CNLrDdoB06DAw33Nxeg5dosYNvbFbYtUuvvFwhIuKVmxsvA8Mf8H7C/wwMvEZGeuA1U/G7nDs6ZBgYvt51m7eAsaWz8/TDhx/A5o0SIxqMzhCO6OhnYGCAzApWMDxOZAg7x2ArznBYedcuMTW1JwoKort2/QBvxvtRUSGwa9fjc+cg57KoRkS8VFBg6OjgMDJ67uYmCS7a4Iyfbm4cHR2isOG0Tw8fPgQfoPwOfHQnPLjhXPmIiOfgvdHyu3Z9d3Nj6uhghM1DSu/a9Rl8OwWkN8jAwABfDgo3B5Px8+HDFwoKXGBXgVaZurlJ7Nr1ws2NB3wWDid4J+SuCs4i0L2C3xkYbu3a5cvAwDDaD8QMyVGR4RQCn54/vLh7xfOrFR4FD149vLV4uZucUoc0D8PTSxW87Ay8/0GzhQIqDCJMDGFJoBGZA3Pc+IUZfn5i+MnBcJqBobvswYOHt/TnujH8A48agU5gYFhqO3rYzGBJI2/P796wzi3lKcg9kcwMy5kZGP4zpPxmKPbsmH22wuAvg9B/Bh52hsufGKIqQSs71tQqfOFluHgeNCigJMpw8wODsEbHnHkVfw0ZMo06ZJwiDvUqOEfvkjIZnRUEBekoHsYhAJ4tvFdRcSyH4d8UBrkchvdhHSyyDM/YKipYd+16Dm4FQfbFQG4j/KyggHn6C9bw+fTwIa6thizgDThyDx68Bpv2Bawfsv6TH7wDUGbXLsjEIC94AlASthcGNBu5e/dHNzf+Bw8erFgB6XbCnSfIwMBYUSHz//+Tzs6vFRUiu3axq6k9BbfBGBgY+IyM/rm5iT548P7Wrefg0/5+MthuZAhpZ5Dr6JAevbpwhIPRDuHITQBfHj6cr7AR7P/Pqgx35BgWvIMt8mRmYPgDXgIquGsX5LZWsDKGj+A+IQeopcEgC76BUBJGioFLq58PH75SUBB78AAyUgXRBSc/PXz4Eby+FNIbfFdRobRr1z03N2YGBjYGBsjpycxg1ZAzlD+B3cAAvhYCLEwU8Q18szza3LcEuH/45dy5JxU3nBnSGRi+MzD8GT02hqgAHVU0jEIAsoL0ydmK8x8Yzj1jiAvtOHW6ouU3Q8svBjFmBm6HjutnKqxYGX6pdrw+XcHOwmDo0PHiQoXrV4YeVoaS7wx9XAyX7zCo+3acWVdxX4Yhkxt0gzkDA2jOkE9y9KbyAUsob8/v/nj/3OaTFVufMJRm73Jb6NZh3VFxrILhO8Mal46QcxUHuBl4tTuen6248pRBz7nj342Kpb8YnHkYDHU7tu6uqONhmPyfgduw4/t3hq9HK/yCOv7drRBz2zU6KzhgMTpqMd1DAHxBxdWKisdgm3nmMty2YiiDrFf6cO4cpNHCwMDwDrxlhhl8dAJYJVEExATQHj/wYk6IOZxgrZC1S4KEOoFgtVDiE6RDuGsXH7jdBbmHGXR5Pfg0B4aKin/gWwi/VlSIgls+8Cs0GBgYflRU/GRggDS0GBgYuBkYHjKE3AKtIOVgYODQ2BXn6ioHtWaUGmFgtEM4wiIcybuMjHOzGZjCOljEGJ7xVFR86eiAnAvKDL6N5yO418cBK7wg+n5WVIDKDAaGF+C7WcXAZSKkKwhR8Bp84QR8ehAiiJW8Dlb5Cbw78R8Dgxh4qzR4dJuBBXxWDVqPDqshWAXZKipeMzD8ZGAQ6ehAN6SiYjHD5DYGxY4OeSMjodFZQawBOCo47EPgyendxxa5MRl28GsYfVjhds+k48a8igAjhp8sDFrGHdePVmy7xKCjziDHzhA66f/9k7tPb3B78p2h+DNDJgfDpU8MR0Hn1YHaFNGMDLrmHffPVjAwMNSM3mI/EOkGtF1wIWja9vtfhukMoIIv5Q9DnGPHMWYGU2aGO3sqOPkYNAUZeDQ7NELLV+QwXv7N8OQGg6cug5JDx+XzFRI/QTPDWqkd8icqLCse7Jmk8PYxQ3LZaFdwIOJy1M5BEAIPH35QUDjPwMA5k2GDc4cgeL8twlnvwdN6/8C7+L6jto4QiohgfQa3fH4zMMgyMDyHbAgEFaggnXrgoxnQ9hyCJFDxY0ZGfliHECLz6eHDJytWsIBH8/8yMEAOcYDcvQxRwMDA8L2iQhB8bcZHsHUiHR2/KioEGBh+dHTcqHi0h8FVtkOyvNwcrn6UMaIAeoN5RHl+hHt21y7zqQwS9hUiRyt+fwOPG/F3dDAzQLacMHCAl1bCg+hnRcXfigr+jo7f4HKEk4GBr6PjB7ggg6uBDD4hc5HZXx4+RF4ZL2BkxAzu+Cnt2iUDU8cK7hb+Afc2X1ZUvIPNEMLkCdPs4NNQhcAl9R/wRfNwPezgw5r9Gc4yMLBUVDyWAPVq4ZKjjNEQGEEhIGPq6lH1QIObQfLxOYOoXX8OV9gEdjDpd1z+xjDlUMXLjwwBUR1/rDsuMTGcWtr57BvDpzcMmqwMl906BD4yuDEzpHODThnusOhY+otBnJGhsuqBomXHgdVuV1aOnkFKv1QE2S544MY5MYOOuOhdDKwMu2J27UrYde4nw9sbFa+uVDw9VyEtyCDEzHDuIcMbEaPHp3e/YWTgs+pwMQY58vKFir0fGfh0O1Ibdj3dXiFt2sHz+lZA5K6y5f+FRw+PAYXQKB6JIfDhFuQC43e6HdqfKyp+IDVCvoGPKGcFz6oxgI9OIDWAPldUfK6o+AruDf6DmQOZphPt6ODv6JDftevhihXwAxcugzcuQrYdotnFv2vXR/CaT7j4j1u3OMBdPgbwZYkgFzIwvK2o+IDkBR4Ghl8MDHwMDILgfZJ/wP3GHwwMRyo4AxlipzJIGBnxww0cZYw0MDpDONJiHMW/sKXzb+cznPTcZQ7ZB/gHXMpI//9/ipGRv6NDBLx+HTS2dO7c14oK3o6OfxUVog8evLx16y7s7h2FiAg+eXnM9aKQA7Igl+Twgw8FhWyPhov8q6gQ6ehgB1vx0s1N5sGDXwwMkHO63sJKMcitrwQHzCAegx9S+mj37g/gpf8MDAySRkZfweyfFRWM4IUZkBJzfcep0UXzkHAbJUdmCHx6/vBon8KhWwy7BRjqnTpOna3gMepg3l1hKAe6r/nzTwZd447psyu89Bh49TtkNYz+Pzl3+HBF1h+GpZYdDAwMj/8wVByp6LDpYGBl4D1VYR2za+9KN0PTDmPHiNEVpDRNUbuP7n602c0l98GuTSvSLldM4GIo+ArqEH66f+7YqYq+L6BTZFzNOrjlja5dOvf0dMX2y6C9gpzGHTysDN8vVGy7yCAU1sF9qoLbsCPpYEXnL9D5ooq+oxODNI20UcMHdQg8fPj5zIrzihX238BD1aBllhUVoAVTHR08RkZvwO0i0EEGu3aJurqinWSO32Pwa5Old+16CjYHcjaekJraawUFll27Ppw7xwweDf8FXpIKGdFGLAFFEpSHHdgOaW5BJglfgc9Rh7hBEnyC+ksFhY/gCwkFwYPjoFX9ERHfV6z4UVEh8eDBYwUFBgaGbzCpXRWcbxi4Ohl0d+2SHV02NZLBaIdwJMc+1O+dnfsrKl7kMfwoehDIvWImaF3orl2/GBg+nTvHWFHxBnzQFhv4SlZhV1e0RaGXOjshPTfhjg5BBoZfFRVc4JNg4CdifQb3xHiNjCC7pWXAK9p51dR45OU/g89ThjiC19X1DiOjzIMHHOBjsiBDYpCeIeT4ZgYGBvz3vULMecrIyN7RATmwC7Kw/ldFBSd4ypELVvzxGhkxnjv3s6JiNkNbK4Pmrl16oycvQ0JvlByZIfDk9O4P987J2UXsbFd4rdmhr2H0bL3b3r8MTtwMj54y7P7FICLBEMAIusX+8o1bn7a5cbMzPFHrEFU2en3n3KHTFTP/MzB8BQ04R7MyxMftclvptpiDwSF09BZ7mqSmt+d3X9js1vCYIcy04xszQ8WpCtB+oJ8MRdwMHuG7ntw69+9mhZUgw73XDF+VO5wtjHbPdYv8y+D6isGZjcFEk+HBDwbuTwwywbua5rolCTD41z54fXzF6JUSNImqUUOHTghATtcrYnjhxdCn2OH9DHy4Oj+43cLJwAA5i46DgUEFdlrex927P7i5yYMP7cTjyxe7d0OaQJ8qKiC9Qeldu+Bn4/16+PC1goL0//8vwcogh8eIu7pC2j/IBy5AOoegS+fBDSrohsaKCi4GBsjiT9DA965d8P07rzo7f1RUyP3//wQ8Ms4KXgL2DXyFhqCR0Z9z5/5WVAjv2vWQQdzQ7Rv4lHVJV1cNPB4ZlRoJYHTJ6EiIZQKgvNxx1y7zSQwSMxU2bqr4/YyB4e25c5/BXSbQDTYMDG8YGJ4xMDw9d+41+FqI3wwM8PWfokZGoOKSAXSw1YeKircMDJDiD3JNKqQTCCnmTP//N/3/X9LVVdIVdFcEaCrv3LlP4NEyXvDGaGRXIp/g/P7cubcVFW8rKs4oKOC575WBgQFyEjT8dtfft26xV1TwMjB8Bp8h8x485chrZPQZfDMPb0dH6oPUyQxHbrrNO925GNn2UfZoCIyoEJAxdYXcJMHDznB6ZcU3dgYO4w5RZob7Gh0cLAy9IR3elh0cph1H+xTmNLoxa3ZI+uySvV0h+eSctobRl/8MvRwMj7N37UraxfAXdABph1kH+w9Q+I2uIAWFAvUwZI0oAwMDi3zHEXbQ2v0txyoCvzLs5GWYI8rQ9xVkkyUPQ0A46K75zz8Ytq6oeH3n3BtGhjkiDBPNGOSFGc4odOz9xvDmL8PRu+ckHzPwcjC8Pr5C1DJidI0oKOxG8UgNgRe7d791a01leOGxy0y1w5uFgUGqo4PfyOjjuXOcDAw8DAxfKipYwBtknsBGsSH3Wn2EcdFC7sXu3S927z7LyAiZD4T0BhkYGIz//4f3BpG1iLu6SkZEfAa3u+50djIzMPDJy8u6uuqWl9v//y8fESEfEfHh3Dnkk2kEjIz+gK+U+A5u4XxiYHh17tyVzs4n4Gu9foJN/7R7Nyt4mP5rRQXonjAGBnEjo/fnzn1mYHjCwLDC7bqh2wNwb1BhtDc4ChgYGEZnCEeTATQEwMtHL1ZUXO/oMHaqMOPv6GAD759m2bXrupubKHh6jbmi4iMDA+SCVFDPCjxYxQ4ucVjBs3CvQK0UBnmkaUCo6UgU5F5XdrCBkKk80ObDhw+fKCio4B5vgxzfDLnv9Q54qaqgkZGAmhryUlL47a5vd+9+4+bG3dHBCV6MCj5qgeGhm5v8rl18DAzPwL1QBvDaUZlduz67uZ1nSIlgCHjwwFpeXgDJpaPM0RAYWSFw/+Tux5vdnqh3mKoaLVrmJqnZocQGvpeCg4H3H8MXpY45+yqU+Rmi7ToUtYzOLnNjYWa4/pvh3DeGpX9hAQVqgIBmC6F8dobLdh2QixChIqMUWSEAOTym7zNMMwt4hRmM12HVIXILNDH4RqLDNr788MLOHScrTr8EzQr6B3UcvVDx/SODmHbHGwaGf9cqoiof3Dmy4vvFCp2Q0TWisBAcpUdkCEAmBuczbLDvEPwM3nEnDL6eSgJ8YB4TA8M7NzehXbuY1dQewW5ueF9Robhrlwx41SgDAwPaJOGL3bsht2RBuoKQpaFYO4GQ8H7KyCj64AEbeGHU14cPn8NskYyI4AYLQpQhk5D5w7sKCrzg5tYX8D5AwY6O/wwM/JBTRsGqv4N3Kn5jYIA03rjABcafiorv4Auin3UcjK74wMDA+uCBjbw8L1jHKDHSwWiHcKSnADT/d3YerKh43NGh7mXEzO5m/AN0ah2oBwjq/oH7UZ/Ax8lAVoRCThwVBi+LF2JgYO/oeAW+v15h1y5pjEk/iEVoy0QhgsR0CCEq4d1CyN07EEHIUlLI6gtGcGkuAu6Rfj537hPs2OXP4Lso2BhAl6j9Z2DgAh+fA7K3okIUNFqWYMbgzMDAc2mXnK6rEcTYUXI0BEZmCFxZ2cnAwAA5azSqbdeHFW5hfxjS/zLo/GDQdu9wOlWx+h+DhnXHhjUVEgIMfByg+82VAnZ9YmCYvsLNzrhDVMHIbY7b6bRdogpq1V0KeaYdP65XKPmPriAlMzW9Pb/71S63V58ZHD4wHA7edevRueSTFbtid326C9ouaMzKwP6HQUuK4eEbhg/fGbZfZmjp2fXg2jm7cxUnrTvuHar4yg+6a375bwbhiF1Lqt08dRlck0H3TI7OCpIZH6PahksIXNh9ydDtOwMDw+mO+/wVkZ8ZGFjA49QC4JWiwuC1UWzgZZZ8sJHlB25uAmA178GHc/IxMAjs2sXv6voCPFUI7wRC+oR4+oHwIETuEEIEX4KNgtzALI6jHfUeNKXpJrxrFxMDw3s3t6cMDAq7dkFuBePv6HgPPg2eH7yaVLCj43FFhXBHx1vw+D7o1JmO5TYVCrCJQUWIpaPkKBidIRxNA1hCYPfue25u1xkYvq7o4DNkuAjZ/scPvsXhV0XFz46O1+D7A+E637q5sTIwsDIwQAa6nu7e/cDNDWufENIb/HnuHLuREdoy0R+EZgjh1kEYkJ4hZPsiRIQfNi0BudUHdGwM+BgbyBJWiBou8LmjnxgYBDo6PlRUKO/axXTu3OeKClBXFrygVIyBYcXoSTOQwBolR3AIfHr+8MyOFXv2VThm7lJgYli0zM05ZNeZmW7aUgyfWRgUdDpuHqnY9IghQJ7BLG4XCyPDjTVunxkZjr9l+M4OCjVF446KoxUMbAwd5h2XT1RArqawsxy9xR4UOMTjt+d3H7h27tjJCjH9jgsnKgysO1btqzjHwdBh0fHqIuiqj76vDD28DI5mHRI2EdubQAdFXPzIoMPLoC7JIMbLcPMTw6sXDH+MO47cqsg07PihbPRirZsQM4NF9OjEIPGRMKpyGIbAw4efFRSuXmCwfA+7e/kPuL/EDJ49A5dhDPzgm7d+g9eOQg6og1zhANl7IgjudzGBF0x9qaiQAw+Lw+9MJqYrCAlWzA4hAwMDfEvh53Pn4LsWIeoZGBjgvUFBV9ePu3e/d3Nj7uiALx1gYGCQjYj4Bp5p/MHA8KGi4hd4sB6yC3GV2/VSBlUGBo5du9RHz4+BB+koAwJGZwgh4TBKooQAePno4YoKlhyGF1W7ZHgZGN66uf1kYOAFHxjDUFHxHnxiMjsDg/CuXUKurshHuTAwMED6hIIdHVqwHdgMDAyQ9Zw/wSeUQhZIIFtJaocQrvdGZ+cn8Pp+UQaG+2BRXvCQHgMDA2QaE77AlYGB4YubG19Hx0uwsrfgI6QZGBhkdu2CnH36y83tNYOtKUMaAwPP6Ekz4EAaJUZ0CEAOm+FWMNq+1G3/Q4ZA344PFysEzDpYLlf8/Mxw7h/Dd14G51+gw2ZWr1oh8rDi/3+GL0wMGsG7Pt8/t/JUhZ1Zx+PvDJdPVoTbdHCoGS1c5ZbIwSBuMLqClKhE9fb87td3z2keqJiq2WFqaNS32M3AooPhH8Or8xXJMbu4pdVeHl3Be7/i1WcGZuddmgIMJ5a6Lf3FkBi+6/wUN2ZJhpJfDJP/MpjH7ppS6Waqx+Bi1/H/3uhd80SF/Kii4R0CkGWihQwvIhkCxTo67oOHhp+4uXGBt+RB/C69a9d3Nze2jg4G8E5CiCCkKwiZIWRhYPgEHkSWYGB4AJYW7eiQAh+3DuYRS2DtEDIwMHx9+PCWgoIUuMWFvHz0WWfnN/B5MIKwyUNIn1Dh///PDx8+XrHiK7hn+xV8MT0DA8O/igoe8GpSsYiIWQobSxl0GRjePXjgNrpMlNgYGkmAZSR5dtSvxIaAvLxAebmvkdE9NzemKW5vNjLsUgSfMvr53DnI4BkLA8NrsGHsDAys4BUO8KNcQINkrq4M4AUM1zo7tcrLkTcNSuPeJQg2jyji68OHX27dgqwIld61i6Wj4wu4aGZnYPgGPmr5A/i0aIMHD5B3GIIG3sCLPfTKyx93dgqCrXrHwADvDYI6hx3ehxj+X6w4w+62cVNHpH6E2eiuQnA4jRIjMQRkTF35ZNQeHVrB9p1BlZfhMQuDEiODmVPE+m8MwhcqLPkZrEJ3nb92bmOTwp1nDP/FGOyTd3EyM3y4d+7WP4aZvxjUTld4ROxiYOzwO1DR/7uD4Q8Dv9+uv4wMfYWMzuG79C1cR2KYEuHna6d2P9/uxqvZ8eIbQ+Q/hs/cDGaL3TrMO4xUjB5udnvJwPDlwTnG/W6svxk43Xbdnu22t9fNUZxB066jgJuB94jbL2mGh0od83gZVP4x7Lt27rkQA9s3BlEVI+HQ/0RYPqpkNASGcwhc2X3ps9vMFQyMfzvChBhAd+cIMjC8P3eOC3S1KugydwYGBvUHD0CnoO/a9fPcORFXV8T+l/LyLw8fPl2x4ktFBR+4LcEBDioh8Fj5XTe3X2AuAwMD5CIuGI8cmlte3vD//zudoNX7z1esgPQJn4G5wrt2wXuDcKM/7t4N2uADHhz/z8DAxgA68l1s164PHR3fGBh2VXCWVDxnYDDbtUvc1dURrmuUMRoCyGB0hhA5NEbZ6CEAXj66n4FBZkXHf6UKT7GODtChnRUV3Lt23XRzEwKfy8KCNK4GWpYALihBCxvAZdMf8EpOpV27OBgY0JaJIltGzAwhpB/4FDzLB9rTCD7SBnJ+F9O5cz8qKmT//4dcNQFyBmx3NXLR/LKzk8PICLLwA2L783OgfYbsDAwcYL9ALkuE3Pe6iGFyO4MyeGWFEkTxKDkaAiMzBD49f/jo0IpdxytUDTtOnai48YjBQIrhBxODIAeDJhvDoVsM4r4db69UxEXsUrVwnZPKqCjC8PYvw53vDMwGHffPgu+lYAKP1jAxdFh3MDAxsF6vcDPqkLMbva4QJUF9f/nw9fEV8nsqOgxBTdWLJyuWMzJ0WIDW3xZyMXiG7zq0xE2Bn0GFheGvcoeGrtHdq+cun6nI/s0Qd50h15nh4neG7x+gs4IyBgxxEbtEOBhe7XITcxtdI4oSzqOcERgCkIlBBob3pxm8+Do6IOdwvquoYGNgEO7o+A8+h/MXAwOkacFrZMTNwPDJzQ0+hI18gQSkN8hrZCTh6vqYkZGho+M9LEAFjEBnEECWmBLTM8Q1QwgzD7R89D8Dw1M3N2ZwQ4sJfAICfAEUXBkH+KxRvo4O8PHDDN/B12Y8Bq8XfQo6P4adgeHOrl2Oo8tE4SE2ysAEozOEmGEyKoIIAVdXpQcPhFasuBtR8aaj41QEw77PDAyvGBgY3NyUwIsZxMGF1P+Ojn9gTXxGRp/A/TRmBgZQDxB8sNVvBoZnYEGGc+cgB8CA1aIT8HsC0SSQ+4Gg6UfwYv1PDAzSu3bxqKlBVti/PneOA9wR5ZOX1ysvh3QLITsMIaRwR4dCRMR38MX0bysqOMHdv1/gDQCgwr2i4mdFBT9Y8B/41JyfFRUWHSoMFYxubrdXdNwKL/dAc9UodzQERk4I8EnK64SXCygZfbh37uUfhhvMDGZJuz4sdxMI3bVtnpubGsPnuxWBZQ9OdSi8uNVx9xUDPyeDdvgurSfnBJSMDrF1cB6rSLbr+CtvtOPKOSM1o9tXzl3+y+DOyPDo0AoBJSMZ09GpQlBSent+99eH555frFhm0SGiDGpWVpwD3TUv/ODcRUZQb9CIi0FGgYFRqePQjgqWlxWvzoJ0qUbvOvnk3D2WilOiHVmZoPNFr9w491yYQesVw+fdbiK+uzTLRycGQQE1ikdyCFzYfWm12z0GBsmjHR8ZQNtvGb5VVPyAbSDkq6i4wcAgBL7/nRncKvh77twL8IFz8NNiQO0E8DJRSMODB3YEKP+uXR/d3PTAS58gDQ9IOL8Fr0R9AN7Lh79n+PPWLcxNNKAdLg8ffr5164mbG39Hhwh4PO0P+JyYLxUVIh0dsuCT8yDNLdaKiu/g+6JZwKcAglacgjdAsjEwfNn1NNrtcUcHf0SE3+gyUUjUjJK4wOgMIa6QGRVHCYHdux8cdNvSyqC6kWGF6a6oz+fOfayoEAMrYezoeANepy4ZEXFTQYERPCUotWvXSzc3AQaGX2D0AqxSZdeu95CeIcYt8+937/5+7pwU0p5DtH4gr5ERfG4Q7eitnw8fvlJQEHvwgB1WRoNtAxHwAlrAyOiZmxvkKiFu8HwgM3gsEHJTIh8Dg+CuXVxqaj/AxffPiorfHR3s4KPGOEAlrO3ejt6ICNXR5aOgMB3FIzgEPj1/eHH3itPnKoo/M0zlYDj2m+HnUwYDcQYJXtCa0tufGe48YHgrwiDPzBCWBDrK8uRCNzZehn1fGGZCBuQhQQdazwS6sRDEY2bo5WVIKX7AJykP4o5IDLpSYrFb30eo5ztsOypOVjD8hnIZGEAXCSpwMrx5w+BaCdqvtKxNYf9DhnCvDu67FUqioMNjnrxnOH2V4bc6gxULQ+4vkHpb/12ySmqc4iM3VBHBN8oawSEAmRjcwlDGz3CYFzxqLGpk9MrNDcqOiHi1YgVPRcUr8HEyQmAFwkZGD8FnqnMyMHxhYGBnYBAEi0OmBNHC8tPu3R/d3NCaH5CbISC9QUjPENec4ZvOTrQz9r4g9QM/VlRAzjiQZWB4Dx58h2wO/Ac+LUako4PPyIjx3Ll/FRVM4GYYaGUWuJf7rqICfLEEGwMD465dsqMTg2ixNsrFCkZnCLEGy6ggegi4uiow7PJpdTvlzxC24txf74gIEXBP71NFBa+REU9Hx/eKihvgs7Ygp3q+dXOTAZehPBERdxQU9HbtYlNTu79iBWS+TmXXrvvgrhcDRs8QcuYypO8Hnw+EDM4ZgQfh0F3GwPDz1i0GBgbM3iAD+HZXCfDa0ffg8p0RPG/5iYGBhYHhVUXFKwbQlYn8amq/b936ee7cazc3xo6OTxUVPxkYZIyMPp87pwy+pfAxA0NFxc2KiqejJ81gBv6oyIgKAT5Jedu4ckVNI5ONbhyaHUo/GOZurPjBwyBm2GF/oGIOP4OSKIOuU4fwf4Zrq9yEDDquPmOwUmHIse8IvVDh3P7/4sndBkvdlpp2PAbvtrF5WCGT8aCzX+HiNAVBnZG4gvT7y4ffnt16tNWN6QfDrpRdUo/PXfrFIKJsdJKrY8KeigKXDrOw8oM1jLzaHS/PVZx7yODy8taJpW7cph1R1gxivxgOvGPw+cYwmYmB27Tj+csK588MLkEd10YPjxlReXLUs7hDANwbfFzI8EKU4TAbA8NncBPl9blz78BsyG4XMSOj/x0dShERouB+IDMDw9eODibwlkJuBobv4OHjFxUV+hhHEkCs5XN1/QhuhCC3QCCHF0AWKylERCD3DCFszDlDrP1AGfA0oP6DB+8UFMTA7O/nzj0Cj78r7Np1183tI3hi8AcDgwS4GQbaLMPAIBgRcYXBMrriLwPD0127tEd7g5CYGiUJgtEZQoJBNKoAEQLg00fvVlS8mcxwWo7hrkaHBmTBPSP4TJcn4I6WWEeHTETET3B/j83IiMfV9Q341BkR2KFYyDdGQOYMP1RUQC6N+MnAIAdeiYpnPhDhGhjrNXintSjS7CJE5tXu3c/d3CR37YKTvOBbCpmQxgIhKn+Ar3D9Cr7CFT5zCNkwCW64MtxmkLpYcdONofV8x3aLCIvRqUJIuI2SIzkErqzsvHak4vhPBgnzDqWLFQLhux6dOSfysOInC4OoQcebExWflDsuH68wFWIwj9vFzMjweLPbme8Mt74zqLExSBh2PP7DUHG0IkOI4fxbhkhBkJYX5yukTDoM2BCBeuE3gxQjgxjSuOWFnwxSzCgir/6gcMnR8otBignVkJ8MBpDjs2BuQbeFSlpOn6rQ4mL48ouBQaOj4EIF6JpUBoZdsbsebXZLeQOa6DPiYeBU7xCzjlhbr3DkFoO1CsMvdobI1F0XNrmx/GZovcVQmr/r7XI34chdO1a6ib9mSC4b3S4Ii7NRegSHwMOHn8+sOM9UsfIXA6P6rjSec9sZwJdjQbbVCXd0CIJXAP0HTwAyw04mhwTYR/D+EZ6KCt5du+6D1zQJgIeVMXtxEPW4WiAQWQiJdc7wLfgyQ4GOjlfgLX/w+UBIP1AS1mR6B27hCJWXvwUvpAKNdEdEXFVQgBxpww7usn6DWAO+CHopw6qZDJIdHTwREcqjy0RhATNKEwajHULCYTSqAi0Edu9+5Oa2gYFBbiPDRkmGBQIMDLwdHYwVFTcZGJjBSrkYGFg7On5WVKg+AC1w+nXr1is3Ny3w/B5kGAysiuGhG2ifNAO4ROZiYPgKXp7xF2wIMwODFo4BOYheOIm5XvTrw4dfb92CdAIZGBjgDDFw8XqRkVFy1y4xV9dPDx9+vHXrw7lz7yoqZMFX0P4Ge+Qt+IRSjV27Pri5MYB3FUJOmnnj5sbHwDCboa2VQXN0qhAe/qOMkRwCkMNmXv9h+PKH4dLBigtvGDRkQQubBDkYZP4xsOt0BFyt2GLWIcbIIKBsdHKhm6QA6LCZPzodospGl1e7Ff8ErS+foN8hzsFw5nBFLxeD9WcGA06GjlpQubFl34pd+ypu/2TIdu3wcYtgYGDYsm/F+18MG/dXJDiDRO49uqW/1K3DoOPx6Yq0+F1K8mpb9q3YvKfi3V8GZW6GjjqoIW9/Mhw6UhFoD9Wy/ea5zXsrDLgYoFr2rhBWMuqd5Zbg1OHjGnHv0a3t189dP1DBw82QFgs2c++KyOMVHcYd8iwMPq5gZ+xdsWV3xVtGhnhStLz6wbDqYEWOK8gWkF/2rpiyr4LxH4MVK4NL/K7aOW4nuRiKmBm0uBjkfHddWu2mzMUgxsqw9QJD8fQH1/as+M3KcOonA+POCm1phhfsDHs/MFj9Z9j0ikGDmSHUq+PW9QpXiw5ueaPR6+ZHcn4c9TskBMATg48YGO5cYIgVgx2A966iQgh8ILn8rl2PwW0PyKkBfxkYhMGHk0Mqel41NdARo7t3f3ZzkwI3WuAbTxgYGCBLnCBHEkCmARkYGCCrRmXBiiEOwEM+3b37E7jVARn44mcArQ1/D97yxwAqDhn4OzoEjIz41NRAHT/wLpgXjIxCDx78YmD4fOvWN/AxeP/AU5eQcxz+V1Swge9V/srA8KTjYBTolJzru3a5j04M4omFUSmsYLRDiDVYRgUJhMDDh28UFHYwMPCs6OAwqvD8wcDwA7wVm9fI6Iub2z9w10581677bm6C4CKYG1zq/YWpgSwrZWBgEDYyeuPmxgHuE74Dr5KHXBsIsR5SgjMwMMhFgNph8PIXIgshkTuEr8BTkV/Ad83DJwYh/UCIYgYGBsi0ocqDB9zgoha0dRtc9PPu2nXv3Lm3FRWyYAf/Bi+I5WFgEAPfUsjBwCANnl38ueuEutvtHIavNh3yoyfNwEN1lDGSQ+DJ6d2XV7mBzhr16Xh7oSIudte6q+ee76zwVWJ4y8jgkfdgT6fC818MZ64xhJsxWBU+ODVRQSN419Lr52SZGV6cr9j9jEH8KYOILoOccYfgVwZ1IyPzuW67kna5LXWbCD5s8ztknImRoeJ0xa7oXctnuqk7dDD8Z6g4UxH9mcHaoWPj0Yqd4PNLK85URH1isHHo2HSkYgczQ4dNR8XpiimGHZwsDDuPVRhZdFScqegw79i8r+IoKzjG2EDd1w6Ljv2HKxgYGRxtOypOVTj/Ygi06th8tGInK8Ou+F1ui92iv0BtgShgYGCAiGw6WuEA1uL0iyHIqmPLUZCluxJBWqI+MyRk7Tp38xwDIwNoQyADw0Rj0MGhIL8wMVScqtgVu+vF2XPvORien6tgfMrwhoOBV5HBSqfjzk+GFCOjrw/P/fjLcHJPxZ5rDJ66DExMDGGT/s+tYVRhYFjyjEHFrsOKkeHOP4blWyucWRmcNRkUfUcnBsEROkqM+BB4tHt3i9vX2QySV3Zx/nHTF+joeApeY8kAvozhf0XFJ3DlLgKu6GV27fp17twv8M1+QuAhYwYGhl/g9g3vrl3I56J/evjwA3gEGbQsE3XCENII4d+1iw9mAnIkwAfBIQcWQFo4H8FO+gIejH4Lds9X8E7F/wwM8NlI0IZA8NorDvDhfAzgzYGQ2cufFRVcHR2PwV1cSDPpS0XF111P3dxuMjAw7Nql7Ooqh+yGUfZoCBADkNbiEKN8VM1oCIBDQF5e5MEDnxUrrkVUPMhhmOfPcEh7V9QTNzdGBgYR8NbBf+D7/UAnslRUCIGLM+6ODtAZWRUVH8Hdwh/g61O/d3Twd3RAhsoYKiokOjpEIyI+gYtdSDH3rqJCadeuCwoKQkjrPJH7h59WrADdGHHr1nMFBUgnkLejQxK82l4f24idmKvrl46Or7duQTqEvx4+/Ozmxt7RwQMeT2MGF8dfwPsHfjAwMHd0vD93Tv/Bgy8rVvw0MgKdi3HuwCmGCrGOjtcVSxkr/j54YD26fBScIkaJkRsC4OsKH8geWnHtbMWN/wwXlrmxa3c8/cpw8zPDBwaGHX0KfGwMT+8zPJNlYGVmePf4lpz/rqML3SwFGIQ9d7363qHxpcLPr2P12YoQXaOcXrdMo10MbAxui90YfjCsPlQRatdhpG7kttINdBPDN4ZzN86Z2oHuZ8+6VOH8g2EpC4MuI4O/dQfDyYqK8xUMXxn0HDpUNI2kDzF0OHRUnKhg+MHwhYlBTc0oRW2X20q3sL8MFQcqJtp0LPWO2LVrxTsWhggn0GATAxOo27bzQkX0F4b4zF1uC90YWBncGRjO3T2HEGFnYDhZ4fyPIdC8Q03TCKSGnWHH+YqoLwwJmaATdHacAi1vOHf3HKQ3CFLAwNBh1RHKyJAas2vaZLcNfKApgGAmUC/03NVzN3dXlMZ0bDLr8NU2mrvWTV2xw0HbSGuX24mlDDy6oE7sF1aG39oM2y8xRJgzXO9kFP7PcPk9w6+7DGK2DBdPVXw16QhXYbD136VmOXpM68jNfaM+h4cAZGJwH0NzAsPhSAaGv+c6vjAwiBkZSXd0sBkZfTt3jg086PwL3LP6zcDA3tHBraYm6er6lIHhO3hjIaRP+HnFCvaODuTeIGSyjk9eXg68tgiyD1DAyAjO+MnAwA9zB7wHCDl7D94DlAFvh4H06CCLQpXBmwMhE5UMDAz8RkYP3Nw+MDAIdHS8By8iFezo4AafhgrapsjA8Bx8lddfmAIesI1C4BbUWob1fW77OjpMyss1wMKjxGgIkAxGZwhJDrJRDcghsHv3FTe3xwwMDIc63hgZSTwHn93yEXxzA2R7ISt4iIsJfEmOPPi+17e7d79xcxMB99k+gKcQf1VUSPz/j7bVELQS4+FDBgaGR+DtiMj9w3vgKxDfVYA6Zl8rKv6De5i84HNEeTs6pCIiIJ09ZHcisyGThJCFo+86O39WVPwDr7gQBzvppZub+K5dHGpqT8D2fqyoYAUP4MmDS3MG8Nmkv8DenM2woBU0g6ju6jp6USFyAI+yR2gIQFaQPj5TcegWw24xhnpbUM/t2h+GL2cqZH8z8Dh1+DhFHOtXEDfs6J9ekWrP8PYLg4pFx5TjFSxsDH9+Msz8zhD4mUGWm+ERJ8OEygcXd61Ycaji8Q+GHKeO5RcrJlY+YPjHsGLHClDjjJFh7dmKNN0Oc98IiMitoxUPOBmWVj64/PCW22y35L8MT/kZVN4zhGXuOnbzHAMDQ4RHhEKvwvXQXatmue36xSDKxiDLyiBt12GkbOS2yG1X3C7hvwxVy9z+/GEoTwT17kDrF/4yGK92i/7AsJSLYVfcLlcr1+Rkxp//GCxtOnJOV+yK2yXyl2HqArfv/xgSUndBDBH5y2C0yi3qA7SLyMDAoKagtmL3CpH/DCknKgr/MyTH79p7/dzTcxUSfxjWfmYATVQyMxTxMfQxMaz9y6Bo0fHhSsW8QwzVeR23T1RYxezauNbNQKtj656KOg7w4THGHcu3VKS4QM8XHb1gcITmtFFvY4QAuDf4uITheTxDIAMDwz/wwDRktu1LRQUXeBETG7grxWpk9B68aBNypigDA4O4kdGfc+e+VVSo/v8PmR4UefAA61UQcGsh60g/gJs3ouDRajYGBh7wrCO8B4jM4AXfTwhZjwo3BL458BYjI1dHx7OKCm6ws99XVCjs2sWnpvbr1q0/bm5/OjpegCcDWcAuh2j/Ah5AZ2BgOAk6P4aNgeEz+DTR0d4gJHhGSXLAaIeQnFAb1YMcAg8fflBQOMnAwDCV4bQbQ+3/jg7IkTDsDAyQESzejg7h8vKHnZ1/GBj+VFRAuoKQPuFvNzc28N5nNnn5N7t3w7caIpsPZ0O2ZV9SUGAGjbMzMIHH5FgYGF6DZyAFwCUpA/g4ZtCiDhiXH1wQQ1bkQ4zilZeH9AmVd+165eb2FbyhnNPI6KUbaLHrX7AiyHaCz+fOfa2o4GNgAHVMQaP/DGIdHczguc2fDAwvOxa9rzikwTBn9KQZcJiNEqMhAAqBJ6d3b5/pdpaNQdEYdNjMPf2OG4sr/AwZfrEwaJl0XDxVwfaLYeslhkAjBpPoXXzSaod6FER5QRrpj5//ZpCErB2lv92oNnKzM0h67Pr6h+HKKreFNxlcxRgYuRl0rTuuXKwQ+8Gw+gyDVnwH95kKbtOOfzcruL8wWMfvkh29vxE1DEd5IzMEHj78PFFhbyhD4GOGLNEOWwWGh1/BKyq/MzB8hFXWwuBa/gMsgN6DO3LwCzohR8uwMTAo7Nr1A7xiSLi8/At4PJoBvHMPog9y6R/ypfCQLt8/BobfFRW84KbIF3A7gYEB1FQQMDJC6wFCzIGTkM2BbPLy78C7Xd64uUk+eAAZAYeokQDfKiFUXn4PfK7MO/B9g3wdHW8rKlTAOyGPMUwsY9AFLxNVBy9ygugbJUdDgBww2iEkJ9RG9aCFAPj00YsVFc9yGX64MRwy2RXFzcDwC3w815uKCj7I3FpFxTcGBg7w8TMiu3YJu7pChscgJ5FCDLzGyCi2axf8PFKIIIR8Di4xIZe0QkT+VVRwgrdWf2Vg4ARP8SH3Az9UVMC7iJBeIpwLGdXjBXcjf4DNgoztgZkMkInNj+AKgx/c/fvLwPAd3L0UBO94ZAKX+CJgT4FOIqyomD160gwk7EbJ0RAAhwBkqvDa2QppnY7VWyuuszBku3ecOlrxmpHBiZfh8h2Gj5wM1kIMnKwMG88z8HIwyAgxaDh2CKkYnZjjxsHK8OM3g55zBzcrw60jFeKmHdf2VyjadgiwM5h5gpd3MjBsX7+CmZFBkIPB1AMk8uz+rSXdblxsDGbuHRA1x7etOLG9QoKfIaIFdLQMA1gLCyPDx3OgBQWSZh3fLlf81ehgY2LQ1jeSVACd3/Ds/q0LF87dO1iRMwGq5fi2FZ9/MSDbu239itsHKiw9oLbA7Q0u2AUx5Pi2FUe3VnCyMWT3Qw15ev/WlYvnvpyvePaBwcarQ0zN6My5cxzMDG/PVDx6w6Dh0vHzYoWwScf+DRVGXh1yLyokDDqmrKlQZWOwV2c4/52BQa3jy4mKLa8YNFhAh8ewczIoqxpxSY1eMAhOZ6PEiA8B8MTgoykMG80ZOgQ6OiAVOihUwO0NZgYG6V2grXVC4FPrGMBLMRnAu/VAasA7RO6B1/tAziCF3JAK2bMM6exBlOEiIe0EUPcPdF8xw18G0GEKkCHv9+AjCYQ7OhRxnIDw6+HDdwoKErCNLe9gK6eEXF0/P3z4aMWK7+AWzjfw0lYG8I1ZDOCD3DmMjHjU1E6vOL6ygmM2A1NHh1h5uQUuF46Kj4YA8WC0Q0h8WI2qJBACu3ffg+xpXtHBaFDh+QmsHLJuno+B4VNFxRdwocnd0aEQEcF06xakxyiEdF0E2qpReCeQgYEBcj0rhASV1BUVkM2Kf42MXri56cJK1c+wUT3QilPw/YQfwf1SSJ8Q0mPkYmD4VVHxGzzN+BzcuwO7FHT/rDx41SjyqN4r8MicWHn5/c7Of+ANkH/AWv6BhwB5GUBXG0FOmpnMcEO0w2b0UgpIYI6SoyHw6fnDo30Kp+8z1IsybDQHHanCqWpUNdUtg4tBxabj8W8G1ksV7LwMRy4xfP3J4BXWoaJttKrPLaBg17s75/Zvqggv2SUoqzY1VyG4aNflZW5P3zNUrQSN6Z85tPv+1XMqOka7prmVLweJrJreycDAoKpj9PDauYD0cgYGhrogRr8C0F0XDAwMhrauLx4/PLRlhZae0ZbJbnHdD/ZuXMEP3rjMzMggpWFkaAvag7dqeqedT8TzB6BLTSFapuUrNK373xHB6J69y9DW9cyh3fcg9k51q1gBtVdJ24iZERTVcC1ZEx8sKFWAaGFgYKgJZJTkZ1Cw7ji7syKl/8HnJ7fmtrnZ+HYoaxttnuzmlbPrw91z61dWMDEyeOsz/FLuuH6qgoGBgfMXwxxGhrNsDAcsO+7+Y3j9n4Fxd4XT6OExoJAexaMhAA2Bx+DzY2YxSFzZxfXbTV8QvOryNwODFHjc+Q1YlWBHh1p5+afdu3+eOwe/mwqy2ujTrVsP3dyYwC0BJgbQnS8SDAzvGBjYOjqYwS0WsAGgYz8hDD7waiPQthE10BASAwMDD+xouueMjMIPHjwHbzN5WVHBDp7Eg1xuccfNTRg8oIx26zJkQBy5/QPvE3Koqb1aseJLRcUf8Gl8oEWtu3bBr7b/VFHxvONgWAUTA8P3XbtUXF0VIc4bJUdDgEIw2iGkMABHtaOEAGz56P15DG+sGGoZIdc2MDBIgc/y4omIOAle8MkPLnz/goe+IEs6Iab8OHfue0WFEHjyDXInz+dz5+BTdpB92JKurl9270buTF5mZJTYtUsU2wFfEGPh5Dvwic98RkafwAeLMTIwfANPLf5lYIDciPi2ogIypAc/0fQRI6P4gwffwH1LBgaGd25uTOBRRp0HD96tWAE6ZgZ8xz1ojLCj43XFVlOGotFLKeABPgpGQ+DJ6d3HVrv90elgPl3xwKDj1pIKAU0GNx6GBwwMJ24xZIR23DlaIWDYoaFndG6p27dfDAH1D05sW/HzL6irJqukdnTLiqAMUB9vy+xOKQ0jKUW16fkKmRMeSMjKnz+8m4GBQVJBDdJzY2BggHTebl85x8jAEJoJ0tURwVix4v+q6Z1cLAwKWka//zHcvXLuxJYKM5+OsMzy/Qs6bxyssEgCze8d2rIiDFWLnU+EhKz8ucO7d011S+h+MC1fIWsiwt6//xnuXT0H1+KevevOlXP/GRjCMstBWqa5JXY/eHTv1uNr5yDuXz+zU0nb6N9/hi2T3Gy8O+Q0jc4vc/v6k8HEqePG0YobXxnMRRmW32DwCu6QuVOx/wnDjdsM0W27Xi9zY7cEbSwMCB89R3Q0M42GADQEIBOD+xjKOBgO/wQfEvOlokJx166Xbm7yu3Z9BS/7hHQIIRr4GBj+V1TIwAaO4WPNkANd4CRfRcUzcLMEoksuIoJXXh7CxkNCGiSQrt1D8Agyt5GRCPj4mfvgLiLkvgrkNoagkdFfNzfGjg52WCcTNIQN3qLCwMDACN6OyMDAIB0RcVtBgRvc5PjCwADpWB6o4MxnEALPfY4uE8UTLaNSJIPRDiHJQTaqgWAIdHbur6j4lsPwKm2XviLD2y/nzv0Ezw1CztdiAK/G/AlesSm2axd8Bu8rWJyfgeEV+IJ4XvC9sZC9fMhTdpDCF3QEM2xq8fXu3ciThLicB1+mz8DAILJrl5Cr621GRtX//yGDhfdXrID0BgWNjO6A15AId3RIGhl9dHOD9PogxrKCh/p+VFSA1pHC6iHILCg72P2zGSa2Mkh0dCiWl5tCtIySoyEwwkMAsoL00ZmKw7cY3Et26SipXdi5QvR5RcNFBk/nDu6LFR+/M4jwMLz5wvDpO4O2NAMHKwOnVseRbRV//zHI2XXE5oC6dgwMDG3hjExMDEp2oL4cJEg7Ihh//mHwzttlYgea5Tt/ePf2KW4/fjNkTXogIQNqyZ0/DJpOvHeowiN7l441VM2WSW6pfaCuHQMDw41juzdMAnVEm9aBJv0YGBggWv78B3XtILacP7x7yyQ3z1yoLZCe588/DOn9UENAPcCpWAz5/Y8hPAvq+CVTOt+crPjwjcHOp+Pz5YqffxiO3WYwkGNQs+k4s7tClJfh3i8GMZMOxRcVH94yOJQ/WLVhhQgjA9fdCj3b0QsGIfEwSo6GACgEwL1B9PNjBMEHrnwBn2oO2ZwCWRYkCK6yP1RU8DMwcHV0/AJvLIQsNYKToHEl8IDyC0ZGif//n+ze/fHcOX4jI0jjhGC3EDI9CD+EBrlPCHIuAwNmG+OVmxsPrKnDBFEEI7krKj4wMEC88LYCdKugUEcHK3h16xMG8Wlu92Yz8IMHwHVgOkbp0RCgDhjtEFInHEdNQQsB+PLRyQxn7RlqITvuQHN94L4WG/jQUUjpzMDA8BV8kid3R8cvcKkN2Xwj3NGhAevywQ3/At5J+MXNDb7yHiKFf5IQshKDAVwfcBkZQY6WZmBguM3IKPPgASfSEOCnhw+Rh/REwVsCmGDDdRC7INcZ/a6oeA7ji3V0cDEw/AR3CMEnzWzSYFgzetIMLHhG6dEQAIXA/ZO7L65w2/GTYeZPhjlMDJc5GCz+M2x9zsDzicFWjSH6J0PgFwaBHwxOSgyqfAwfvzN8/cXAxcbAzwnSC8GfvjN8+MYgLwzhgchPPxjef0UXAR1DygGSheDH7xhkhSBMEIlVC1UMQbP3E3g/Ex+SS778YLj/lkFOCOSpvR8ZzrwGLQhbz8MwmZ3h3xeGq18Z3KQZjjIwaH5jSGNgmCPCoMLMoBMyOjEIirVRPBoCDAwMu3c/2+52Kgx8foxmhxwb+DQXFvD6TGbwVg5QLQ8JqY4OyOKjj+fOfaiogNx1/At8tAzkVBj4giOIcsj1g19WrIBM9EF28UGugoB0DmXAPUa4YjjjC/geY0nY3CNEHHIaAmjoGUPXi927P4BbQSAbQWNLoBNi2MDLVhV27fp57hx7RcULWJPjO7gf+AF8tfJxhomlDAoMDEK7dsmNXjMICedRkroAbWyCuoaPmjZyQ8DVVenBA0sGhie5DMazGOY9cXOD9Aaldu0S6eiQevBAYteuLxUVwkZGvyoq5Hbtkty1C3QyGHi1hvyuXf9AN3UxPAF3/+CBCOkN/jp3TugBpM8Il2GQ2LXrBayQRYgyMLzbvfsWI2iLj8iuXaCTbCIi4L1BUM0BHjtEVs8nL69fXq5fXq4YEaHX0SEELqa/wBaFghp8RkZ8RkZ/wRWMQAdoFztoqrCi4i142wBDRwdHRZx+hwk7A8PlivMKCgd2776HbP4oezQERmwIKJq7OpU9yLHvuOzWIW/ZERu6695rBkVehpcSDJx6HSs5GWIdO9IKdt37yfDyEwOLZseu5ww3nzNIpj44KNvx2XWXZNqDLY8ZWLQ7Zt9jOCDbIZH64LfrrvfyHaphu2bfBYmYN/yHiJx9AOJ+ct0lkfZg2S0GJq0OiAKJ1AefPHZteQQ2BKzlk9uu6scM7+U72HQ6zsl0mDf8/+S+a/IDBkGvXWcfMEBMOKDYsewm2JB7DCAT3HdZvGHY8hBkyG1pqJYzch3v5TvgWizeMJyR6/gg33H6PlTLAcWOP667dj0C7ZYUM+j4/INBnoUhyb4jyrFjLjODomHH1V8MujwMP/4wxITsUrDqeJO9KyB8l33rf2FD0KzmiE0zox4fDQF4CEAmBpUZTnIyMOh2yAkYGf1iYPjf0fGzArSdDnJonMyuXXzga6gg83uQKT4O8FksfxgY/oNHn6UiIkz//5d0dZXE6K3B7eKVl9cuL5eLiICY8PHcuSOMjMgnFMBV/jp3jhd89ABchIGBQeHBg6/gwwsgxyLApd7u3v373DnRXbtEd+1iZ2DgZWCArEvi7ejg7uh4fe4cK/jCDPUHDyBTgpwMDJCtg+ehvcFPu3YpjfYG4eE5yqAuGJ0hpG54jpqGHgKdnacrKt51dAh5GTGzuxmzd3T8BK/ZgJTRL8DL4n9XVMju2iWgpvZaQUEUfP/Pp4cPLyooCHZ0CBgZQUbmfj18+AW8HJ8nIgK+NgPZMrRJQsisIOS8L8gVF8hdQYhGyCJSTPFfDx++VlBgB6/v/6+m9hBs77uKCsMHDyB7C5FPmvkLnhj8Bj7vVLCjA1LKf66o+LnrxGS3Q+oMH+U6bPQjLEbvr4eE+Sg5GgKfnj+8tHvF8m0VZX0PnsxQ6L3PUJa86zMnw72ZbtwcDJbxu9ZcPfdge8V1HgY+JgajvwyNLKDB/6gvoAbdMi6Gjq8MFdwMDOwMea8YrnIxsDIx6P1i6OIE7bypfcvQLMSQ/AsUxnM5GZz/MGh/YZjECxPhZYh+D5JaysXQ/oWB0aOj4lRF3lsGNZeO84crGBgZ5oIOR2aofQczhJFhLgeD808G7e9gQ8DnUM1lZogGT/0t5QAZUikEuqU07zPDVU4GhX8gN8xlB+2QrnvP0CTIkPgbJDKfBWRp2TcGBaeOYwdBZ8boOHdkuEdsbFS495PBOnUX7y+GnUvcQt06+C0jVlUqWKsyKPuNTgyCAm0Uj4YAAwPDw4efH63YuhQ0z8eQu0vpj5u+QEfH84oKno4OFvBFxJ8YGHjBU4LSrq7I58dA7rJiAE8MikZEPAdX5Z8rKlQfPMC8rBjSxoDMECIHO2S2ENLDZGBgQF5B+uvhw7cKCmjTgxC93x4+fL1iBTd4iyDk4PS3u3e/Bg9bi4JPWf+we/d7NzfBXbuY1NQuKiiIdnR8BV+SATkKlQXsIxYGhjcM8rYVCuDzYxRGz4+BhO0oSSMw2iGkUcCOGosIAfDy0dsMDB/nMdzSYaiFbAv8BS4ZH4O34f0BT8RxMzD8qKjg37WLVw16hNdFBQXICdGQ23hAh3rh6A0yMDDAdxJ+f/jwO+wMGFxdQYjjsHYIP4OPI4Ns9eaFjSA+3r37A+wqW93y8keMjKzg4lsYPDT4+dw5ViOjx2AfCYB3S4JbjKDTyf5VVLxisLUfPWkGEuKj5GgIwELgyendH+6dY2RkOLOv4pdhx+WLFQy/GOytO44frxC16ni3rkLIr6PiSMVymw4tDaPDd86JfmO4Dz4aVPofg7am0bG750S/M9wH3yIo/Q/UY9xxrMLFtOMlC+gO+vNbVzxnZ7ixu0LNvuMLE0hkeYkCr0cHmpYFZyrS9DoE9UF30y827jhwssLNuMPOJ+Ly41tui9zaPzAwenUofmV4zcagpmnE9Z9h00w3OYcO0Z8MCC0nKmwsOnTUjd6zMLgtclts3MHAyMD+m+EVWAsDA8PpiW6mBbuE/zBMneumZt9hpG50pNetSZghC7w12cW+4/jpijufGPx5GWzdOn7erhi9ax6WQEbp0RAAhQB4YvDRDIaNhgwdXODjVSCtAsh182wMDCwdHa+Qbpnih50f82b3bsg0HeSIF5BZDAwvwcuOvpw7JxERgdYnhCxB4oFV+hD1cPLJ7t0P3NwgOxLh3cJ3nZ3I92bBFUMYkD4h6NBgI6Ov4ANjuDs6RMG3UDAwMHy6desd+Opj5ENNGRlAFxhC7ksUMDJa7Xa9iEGVgeH3rl0mo9cMQkJ1lKQdGO0Q0i5sR01GhAD4osLDFRUsOQwvqnbJ/IKd1PIXfAwpMwX6A5QAAQAASURBVPiKecgF8TxGoCNA4Toh2wuZwHsO4YKQxSFwLmSrAAMDw3M3NwHwHN038KydCPjkGLgyTAZmh/AzuLaAqIT3BiHcTw8fQqYKWRgYILOCbGDHg7YZgJee8hoZQcb/GMGH1nw+d+4f2BnsDAxKDIvBZ1mPnjQDCctRcjQEQCEAOWzmzL6K818YVAUYRCxAV1PIMTDsPlihZtjhGxxx78GtWQvc1BgYPr5hCC/eJaWoBhFR+sfw7QNDWCFCROYDg5J9h1cgVIvSX4Yn3xk8nTosvRAiXz8whBchtCj9ZfjwluGdLIONaYePC+g+wykFCs8ZGP6KQ0VObV+xZU+FOBeDvD3IYT4uEae2rzi6reKHKIOuFULLt18ohkzKV3jFxGDhjdCyYx/IEGlbqMiJbSs+/WYQ4WAQUDeatcBNn5Xh41sGaUEGt5TRWUFQqhjFoyEADwFwb/AxA8P/swyWf8CiXODaFlTtVlR8YmBghHEhd82DlTBwggaIQEwBcAeMC+mYAAakPiGPkZE4UvfvXWcnrvVHILPAGPm8GdB1hRUVHOCVRKAOHniNKFgViIBfUcgOWtnA8ImBgRXp/kPI9pNXbm6C4KNlvsIcLA0eaObp6GCF9gZ1GRjePXjgJi/PCzJ0FI+GAC3BaIeQlqE7ajZqCICXj37o6GDRrFikyLAActIMqGQEn8jyBbzqElLoQ26JZQHvJBQFrcli+MkAuoEV1TwQD76Q42dFBRf48tmPoNVkDJDbDiErPEHqsOHvDx++W7FCGnZ0DaQ3CNrVbWSE1huEn1LNAC64GRkY3oMZKrt2QcYgIaW/ELg7ygxeJfIffGHGz3Pn+CIifjEwLFSYrc7w9AmDsuODnNHlo9hiY1RshIbAs8cP7+5fIYp0eMyPPwwc4GWW8BD59IuBDzw9iEvky28GHvBsIS4FDAwMaGq+/Gb49x/FWDQFoEYehr1oajAdhqYAZO8vBh5Ux3/9zcCN5Noffxlk1Y1GtwvC426UMRoCu3c/u+W2Vorhxh0GjfQHfswMDC/BCz7/MjCIR0R8unXrt5vbf/DCUciEIQN4nRED+GZgHnDL4T+sr6gAnpRDbgy83L37y7lzPOCaGt4nJKZD+Bx8AOlH8K5FVnAfjxF8aSFmfEFutwIdQVdR8QN8pvo7sCImsC4wk0EUdKoU6C577o6ObwwMkINSGRgYrlc8imDwYWAQ3LVLfHSZKCSsRkk6gNEOIR0CedQKRAiAl12cY2DgXtHxX6nCE/mK+d9GRs/d3LjA+wafu7lJgldjfnJz4waPAr6sqFCCnSXzGbYiFHRc2LlzPysqOMETcD/AJe8P8KJN5V273sMWeYI2eUdEINcHEAd9f/jwiYKCKvh8MMhK0Z8VFZBNjF8ePoTY8gS8EBRysQToXJxz535XVPxiAA34CYNXqkC6iPDthTcZGdX//4eYBjlp5k1FBV9HB5+REdu5c3sq7oQzeI9eVAgJ/1FyNARGQ2A0BEZDYDQE0EIAMjG4jWGxDMPUf+B+3VvwUeSc4D0mPEZGYq6ukCsiQCuDwJ20zxUVkGsk/jOAOlqgHcdgjQJGRpBzWSBWwDuHXx8+fAHuYcLnCV8wMgqBjzCAqISTkOHgz+fOQe5GfuLmxg9euSoGHsJ+D9oyzMDCwKAAbrHAdUHur/9569ZP8N3FnysqPoNXt0LuJIRcavXLzU0Udp39b/BVE+8qKp51HIyu+MrAcH3XrojRZaLI4TnKpjUY7RDSOoRHzUcPAfDy0bsVFW86OoRSjT68Afe4GMDLLznAg3yfGRiUdu167uYmBN4yzt7RIVRe/gq8mFMMaYEHqGDdvRuyS5CBgeGNmxtPRwfoRlfwcOCDFSveVVQIdXS8q6jA0zmEXEX4BnyZ7C8GBkbwkCGkxIdchAjpCkJuUOQ1MuJVU/tz69b3c+e4IyLgF1QgX13IVFHBCF4i8pOBAVI/QUh+8JFiPLt2TT3H+KTiblgHl1yE7+hUIXriGOWPhsAoGA2B0RAYqSHw8OHnNoW9DAwMSh0aTgwX+CoiPzMwQK7/hUygQXqGUh0dv8EnDjxxc4PWsOAmhAz4DIIfK1awGxn9V1P7cOvWh3PnICd5vgW3BCCdQ4iIiJHRM9i2QOXycngPE9LJBE02onYCQQPQ4G4npLP3Y8WKHwwM8P1+H86dk8MYd34LbloIl5ffZmTk6uj4DF7uxMDAwAFeFfUNfNoNaBFBRYVoRwe3kdFlBm03t5sMDAwPHpiMLhMdqZlgwMBoh3DAgn6EW9zZebai4j4DA++hjlfiDM9AN/YYGX12c/sA3i74m4HB+MGDPytW/KyoEAYP2r3avRsybQjpE77dvfv7uXOc4P4bpE8o7Or6DFz4SsGWgIJWfD18CCpbcXcOOSoq/jEwcHR0vK6ogJx2A6lg4MOBkKKfB2kTwnOwLZIwW5CvLuRhYPhVUfEFfHuhILh+EgYPUipFRDxXUACdIgEW5O/oYK+omM3QZ7/L39VVaYSnhFHvj4bAaAiMhsBoCIyGAHhi8OJlBq9/4JFcIXCvD3KOC7uR0QPw2LEouA7lAy/C/Ag+pRwyccerpgavpt90drKj7vuAXA3/YMUKSFeQAXy/3103N2HwHYYs4KNc/lVUCOzaBa/6IePCaJ1AuBWg7iJ4kJrX1RV+KDokBuHdwl8PH75RUBABN2DegVssoL0w4JVQDOCbMFgZGH6Bl0QxMDCAz48xY2B4v2uX/ujEICQkR0k6g9EOIZ0DfNQ6RAiAl4+eYmDgncxwNniXOeTcUfaOjrvg0TKGioqXDAxc4JsJBYyM+NTUXq5YwW9kBNrJDTbjO/jYLtDtgrBpw2ednZxGRoIwLlgVgoBXCR8rKkTASz54wROSX8H9N4Vdu565uTGDp/UwO4EIUxgYnoNtEUC1BXKiNCu4duEA92kZGBjkd+26Dx6A5KqoeMLAIArebABad+rmxsfAMIVhYiuDREeHYkSE6uhUIXIIj7JHQ2A0BEZDYDQERlQIgHuDj0oYXqQyBLJ0dHyB7an7VlHBA94SAumb8YOPXRFnYLgPDp1/DAyKu3ZB7qYCC4AIyJYNEdigLUgICcNbApBth8zge7DAF9kwvAZvPhQD9znlwUtAkfuZSGaAmGi2PNm9G1Ldg+TAt1P8Bi9JZY+IgGw/gVyV/AM8K/gd3MhhA1v9v6NjdwVnEQPo/JhduyxHe4OQABwl6Q9Qd+7T3/5RG0dwCLi6Kjx4ILBixd3cCtOv54S8wB0zfnDHiR0cLKIMoAJaHLwH4CG4t/YHfGwMOwMDG/gkTxnw9j+wWsLEV/DOw58VFeIdHbxGRl8YGHjAmiAbvj+cO8cF7s69PndOISLiH1iKGALSFYRcNfvazU15166nbm5iHR2cYGcLdnQwgc8aZQZvH//LwPDr3Dl28BbE7F2aEm4H5Sq276tQltkVNTpVSExoj6oZDYHREBgNgdEQGE4hsHv3s99urV8ZGFYxMFjuCvjoBjqQk4eB4ambGwd4dSULeIEle0eHDLiTBll3oxsR8Qjc4/pw7hzW5Zq4gohPXv757t2iRkbwOUDoyXbg084ZOjp+MTBwdnS8Ap8ayq2m9unhQ8wDCDANl3F1lfn//wn4hioWBoarCgpc4E2GDOCtK5BdJ58rKnjBw9wM4B7vO/DFg/cZ1ItAd1QJRkSMLhPFDNdREfoBJvpZNWrTaAhghIC8vEB5ufGuXZoVFRf0GLb86FgEOa6THdyJEtm1S7Sj44mb2w/whnJB8MGe7OBe4lMGhifnzkGG+jBMRQg83737+e7dpxkZTzMyMjAwQJaDQhhi4OlB7o4ODvDyVNCOxIoKYfD6jbMKCg9WrLjU2XmpsxOPFW93774BNlZ01y7IhRMa//8Lu7pK7Nr1vaKCjYFBp7xcLSJCkIFBoKODF7x2lI2B4R34sGxe8NIUc4ZWmw4NboYLbm6HOztPI9w9yhoNgdEQGA2B0RAYDYHhHgKQiUEGhstqDNMsdwV8OHfuf0fH54qKNxUVv8HzdZK7dont2sXBwCAbESHp6iqspvazooIXvFtPp7xcDnxkAAMDw6MVK67gra+RGwOfz52DNAYg7Q1J8PHgPB0dfB0dfysq3oKbH28rKgSNjO6vWHF/xYqLnZ0XsRn+E6wSHkXPd+9mZmD4DhYUho04/2RgeF5R8Q28xlULfDDeH7ACyPkxVgzHoyu+7NqlXl5uMLppEB6So4wBAaMdwgEJ9lFLUULA1VVu1y5zBgYWuwqRD7vu8jEw/Ozo+MTA8MDNTcDISAg8LigGvkwCcjS9cEeHcEeHoJHRGQWFfYyM8JL6e0UFp5oapNy/1dkJ6QR+PndOZtcufnDHEjK+KBURoQzuoQnv2iVWXs7T0fHr1i298nLH//8VIiI+nDsHGYB8W1EhYGT0YMWK/YyMlzo7H+3eDekcfq+o+HLu3Fvw/gG0riDEV2LgPiGE/QU8hMloZCRhZMQArgb+gjuikMNL+cF7Dr12pXd0SFdU3FzZuePhQ8gedYjuUXI0BEZDYDQERkNgNASGYQhc2H0pnXGDm9ujjg4Bs121LAwMkEWV7ysquMFnsAl3dLB1dLw8d+4X2PffwWt8Pq9Ywd7RwQbb1c8nL69TXi5gZAS5hxDSLfwIPnscdFjLw4fPd++GNwYgnUCwYaAj3xgYGEz//1crLxc2MmLr6JAqL+c2MmJjYNDftUsxIsLkwYP34ANpIOohnUNIewPe5IBIQZockPYGZNbxM3jqj72jg4WBQbaj4y94XesvBobjCgofwaegixkZ/d71NLriLwPDu127DEaXiUJCcpQcWDC6h3Bgw3/UdkQIgE8fvVdR8TaH4Vl4B5MYwzM+8KIO0Y4OVvBND7wdHcLl5bcYGdX+/4cf5cIMnjZ8B55C5AQvOoWcCwoxF7JOA3I6KHw7+Jfdu3+BV4MIgfcYvNm9+6Wbmzbq6lNI3w95DzrEQNCoIYQFI5XBY34wHgMvuKJ6tXv3C/ApqZARRCbwkKfErl0vwEehfqqokNy1ixu8fPRNRQVkthDkVAaGRaMnzcCDcpQxGgKjITAaAqMhMBxDYPfuZ7vcTkUwBH4G3yTBAF5I+QV8hidoS4WbG9uuXc/B1TTk3ogvFRV8DAwiHR3/Kiogx7RAQuUL+Nw40BEv4ANFIYIfKioEwPsyIAfSQAQhZ88wMDBIou7/Z2BgQL6B8O3u3a/c3MR27RKGKfv08OGHW7cgnUPISDQDAwOUC74rgpGBgZEBtOjzY0WFHPiYUx55eYiZT8Ajwu8qKr6D94xAtN9xc1vBsJ4f5B2FiAjl0YlBSASNkgMORjuEAx4Fow5ACYHdu6+5uV1hYOA93PFGxUgC0mtiA58myqim9vnWrTdubuzg5ZeQxR784OG3f+Bb6f8yMPwA7zgXBO8SxLodHK03CLH7KiOj+K5dIrAKACIIJyH1wYdz536BNwD8A5fskIOw4WrgjA/gaUAIlwvcQUWehYcsHP0E7r6CDh0FHzzzGVztsTMwsFdUbGcoTmIwGz1pBhKAo+RoCIyGwGgIjIbAcAqBhw8/r1jxtKLiQwnDlXiGVC7wbjrIlRKgOb2KCiFwvckBruUh836C4EPgmMGrhCD1JjxAQFcZgznwi+DfgxsD/AwMT8DiorgbA2B5hl8PH75TUJBAGhF+BD5InNvICN4nhKiEnFsOuW7qS0WFMLgT+xY84wep2UEdxYoKSC/0R0WFKniw+OWKFZCT8MQjIv4yMMxU2FjGYAa+ZtDS1VUDbvIoYzQEBhyMdggHPApGHYAeArt333Nzu83A8G9FB6NBhScj+MgvBvAIHENFxX8Gho+QXXlGRp/B18Rzgu8p+gxeZ/IIbBjmsWNgYQasvUHQHYbg9Z+4OoSg7YXgCw9BZ0aDF4G8qajgBFdXDAwMyD1AAZggAwODOAPDQ/AVFEzgo7EhDoCTbBUVr8AVCQO4kpPftYvRzY2ho+MnA8MjBuGLFQ+4GThGT5qBB9coYzQERkNgNARGQ2Coh8Du3c9uu60RZbj1hEEt+YH/YwUFVgYGzl27HoOvlGADb7STZmD4glSTQrz8Dbwt/w8Dg2BHxw+IEJiEjAuDmaA5OgiDowJ0s/t78HjrfTc3XO0BiOJ34O4fZLkQRAS0IxEsiLVPCGkPfAMP4zJVVHwEN0gYwNfTwxsAPAwMnysqfoLPSIf3Ej9XVDzpOBgGWibKsGuXgqurIty6UcZoCAwGMNohHAyxMOoG9BCALR99kMPw0Y/hkM6uKNA0Gmzy7Tu4nOU3Mvpz7hwvePLwnZsbE/hkME7YyWMM4DNdkM39Au71/Tp3jiciAr4DAaLg28OH9xUU0FaNQqTegruCIuDDzSAi/xkYMJeYQqQ+w5av/F6x4lNFBRv4Cg2IFOS4GgibgYHhj5vbj46O5xUVAuCjTbnBGwshFyFCfPqKwdaWIbmjQ6u83BSua5QxGgKjITAaAqNgNASGYgiAz4+5uI2hXa3D+zvMA98qKljANzF8BtfvIh0dLBUVTEgVLgMDwys3N8jB46DpRPBorBjsLBn4ThCYedAZP6EHD36AT5qBi8OvB4SLMDAgFKM1CRgYGK4zMoqBnYE2Twg5Vxx0rdSDB+8UFBjB5x1AzHxfUaHz4ME9BQVe8NVTf8CikAVNDAwMcyrUJ4K6uibl5aMTg+CgGSUGGRjtEA6yCBl1DlII7N59xc3tMQMDw6GON6oMz3iMjL6cO/cTvJ7kI3g+kAk8VchpZCTg6voSPKQnDt4WeAXMZgDfBQQ5MPrXw4eQ810we4MQCx92dnIbGSFPEiJ3Bd+4uSFfeIh/iemX3bu/uLnx7NrFA76y9iF4FwGo/gAfjMbAwPDz4cOXCgriDx78ZGB4u2LFX3BF+BbcJwRNhIK2FnQwMDDMZjCuqHiRw/C794ETZnUFcfYoORoCoyEwGgKjITAaAoM5BHbvfrbW7dRMBoldu+Sk3aTZOzo+gA/elAB3/96Bd2EIdHRogKvvJ4yMog8esIN340N29DEwMMA39UH2/OPZ4gFZBwSf8fv08CHkdgpI+KB1C7FOD0JUfn/48DW47haNiOAEO+b7w4dvVqz4WlEhCtthCL+K8Nnu3Z/OnWNiYHgPPk7mFwOD3K5dX8+d4zcy+nju3CkGyyjQueOfd+2SdHXVgZg/So6GwGADox3CwRYjo+5BCYGHDz8oKJxkYGCYzHDanKEWPocmwsBwH7xagwG8IFMxIuK9goIErBYBXSwBuyVWwMhITE0Nf28QsmoUPu8HOUEU4g60riBUENs5NBApSG3EwMAAr5AYGBgeg+8mege+j0i3vPzT7t0f3Nx4d+166+YmDB6DZGBgeHnu3MeKCtVdu+CbJH+Cb63lBZ8047jLf/SiQkgIj5KjITAaAqMhMBoCQyUEdu9+dtdtkhFD50/wgs8v4F12P8Anq311c+Ps6HiH5BPZiIiPCgp8u3bxubq+3b37K3hlJtrSzYfgAV+0AVy4GS8YGYUePEAbQsW8NR4yUoxVMdwoSEvg67lzohER327dgiwT5ULaW/h59+5Pbm7M4Er8iZvbf/DpMpwMDPy7dn0Au/x9RcUZhongS+cZdu1SHz1NFB62o4xBCEY7hIMwUkadhBIC4OWjFysqnnV0qGhWTJNkWMDPwMDHwMCza9dZ8BXwoDm3igpGyCWwMK0C4G3ooF1/4Ntm/3R0/KyogKwAgSjhVVODMCAkr7w8ZN4PdF8hA8Mb8JYG5FlBiDI4CVGMPKMI2hCPen4pXDFkMzpkqvBdRYVSR8cP8AYDYXCf8Dd4wQxkLwQHeEshA3jbJH9Hh6SR0VM3t2MMKckMrqMnzSCH5yh7NARGQ2A0BEZDYDCHwMOHnxUUrjIwMExn2GDE0CkAvvMdfmqoBAPDU7DrIWfGMIB7Uwzgm+iFOjogt/lxd3TAJ+jAaqEErj4h5gkxUA1gCnJrPMQBDAwMEkZGf9zcJP7/hx9VCjmtFKwWSnw9d+4vA8M38KWIkLNhIAtBodIMDNwMDF8ZGH4xMLCBhSTALY2vDAxfGBheMGTtZXCdycDU0SFWXm4Blh8lRkNg8ILRDuHgjZtRlyGHAPikmZsMDAybOhjVKzwZwDu5IROGb9zc+MDlLxv4xFG4LlZwOc4HPhj6KwOD1K5dH8GDdnAFyIwP4KPJ/oIPelHctevbuXNK4OUryGqQ2W/AOxKRO4RY5waRtTwHzxN+qajgZGB4C5YQ6+iA1DEMDAy84DNy/oA7itzgg9E+urmJgGsX0QcPDt76uclthwbDJ7tdXrquRmDdo8RoCIyGwGgIjIbAaAgMxhC4sPvSdbeZ+xlcQ3eZWan9vqGgIACuoCGXCv4En9L5DXZ4DD/4LIAHbm4s4KM7f4IrYgYGBl3wQZ2Q2Tw0T2LtE+JZAgrR/unhw8sKCpCrhhkqKt6Dr62CH1XKwMAAP60Uov5jRYVYRwdjRcVbcJODlYFBCTwf+AncluADHx7zA3x+AeSom3fgJaOCHR3XGCyDK5gYGL7v2qUyen4MJDBHyUEORjuEgzyCRp2HCAHY8tHP8xhu6DDUQu4b/Am+nugn+AzS5xUVkFsBX61Y8a2iAnLvnzC4Q/iZgQFyjDXcOOTTQSU7On5VVEBG/pg6Ol6A13ZCVngqgDevY62QrjIyKj54wAXeXQA5seYLeLgRbgUDA8NzcL8Rcg09xMGgi+nBtct/BgYe8FYKMXClKGBkxKum9gt8966QqytkcwLo+BlwTfnEzY2vo4O9omI2Qx9vh01EhKq8vACyRaPs0RAYDYHREBgNgdEQGAwhAL5mcFscQ+o/8Klpwh0dokZG38GdKAYGBuXycsiuflZw9frp1q0nbm6MsF30zOBb6V8yMMjCFl5CfIRZF0PW6TAwMEBGZiHTg5jrRSHa4XUxpPv3EXy/xQfwwiKhjo4v4J38EJUQBRA2n5HRD/CRdb/AM4GfwOegMoO7hQJqah8VFBg6Or4zMLAyMDytqOABV+WCRkYr3a6/ZODqYVAeXSYKCcZRckiA0Q7hkIimUUciQqCzc39FxbcchldhHSzaRhIM586xgQ+b+W9k9By8ghRycxGkN8gHLqk5d+165OYmuWuXGMZNg+9gh4h+At9gwcTA8BVcpkN6g4JGRnfB98tDrEerkOCThJDeIPz8UnjFw8DAAOkEQmb/QFfPg6f+/p87x2xk9OLcubewSggyYKkYEfEH3CH8B5P6DDbhiZsbyBw3N14GhtMda/xAl9zy7NqlN7qrEBIvo+RoCIyGwGgIjIbAYAgB8FGioOufTnU84K+IZOno+MzAALlm8C34Al7lXbs+nzsnysAAqrjBLoYMd4I2VoAvpv9x7hwPA8M3I6MP4HoQUjnC13mCdTBA6mIWBobXK1Zwg8dMRVxdMacHketifvBGDJldu564uUHWFv0Dn/X9Ezwr+PHcObmICF7w8C7ECgj56+HDNwoKIg8ePFRQYGBg+A5uHkAc8xd8E4bsgwevV6zgNzL6Cm6KvGQQn+52bxZoFyGXq6s5xJBRcjQEhgQY7RAOiWgadSRKCMCXj85jOKvDUAs6mRPc0frs5vYXtHCfQRw8HsnBwPAZfHYLr6vrq927n7u56SNdPvv94cPv4N4X6EQZ8CGiXGpq71askIKtFP308OED8CFjDAwM7yoqlHftQuscvlqx4if48tkvK1Z8BjvwFbiDB+q8ge/D+Aw+JwZS9/CqqcEPyH7W2SkUEcEBrns+PXwIueiWgYHhbUUFP3gHxWfw6amfKirkwLWXzK5dP8+dYwffY/HTyOjOuRfvKzZpMKxZ33Fq9FIKcMCPEqMhMBoCoyEwGgIDHALXdl+67TbzBwMjA8N/zwdlj8DLRH+B72B4W1Eh3NEhYmT0ALw/X5iB4RP4KE7I8hm+jg4+IyMJ8Ijtr4cPXysoSMMq608PH364dQtX5/BzRYU4rJP2x81N6MGDt+BqHWIspPsHISEVMQMDgyTYFgYGhqeMjHy7dn1kYHjg5ga5TR5yMjlyt/AZIyN7R4dwefm73bshh8CxqKldU1AA3X4MXin6FRzk78En5Ryq4MxmAG3o2LVLenSZKDhgRomhBEY7hEMptkbdCg8B8PLRXQwM76oZ2OM6XoEm38C3NTwH7woQAO/N+we+5U8a1sG7yMgInyR8t3v3t3PnvoH7byK7dgmBa4j3u3d/P3cO3iGE2/UJfLsgZueQjYGBB7zg5D/4AtzPeDuBcNMYGBjuMjJKP3gA6RBCxCF13mtwTckBNvM7+EA2yGynIHhF61+wUkhvk4+B4WdFhQLDrI4Og9Hlo+CAGSVGQ2A0BEZDYDQEBiYE4BOD+xjK+BgOi+3a9R68QPQdeE8HD3h2DjJ0+7qighe8KPQF2KVCHR3qsDoaLAC6GxC5QwgRhJCQihKzc/gZPFn3B3wsjTxsFBXeJ0TuBELMgZBvOjvZjYx4wbX/k927P8JuiYB3Cz/v3v3ZzU0K1jW9zsj4B3x+DAO4dmYEH1Lwi4GBFdwjvQy6W+IDAwPrrl36o6eJQkJ4lBxagGVoOXfUtaMhAAkBeXmB///DOjtPV1S8+1jBkLvrLN+53Z/Bu/K+gesG9oqKrwwM7xkY7jEyQgb/2Ds6nri5sYDvdQDtOti1iwvWFYSYiYuEbCDUA1daT3fvZu3oeOrmxg1ejCoEHuZ8ycBww82NmYHhNQODwq5darD6A5eBmOJ/bt36CT4aB3QsakfHb/BwI2TQkYGBQYCB4QPYcFGwF0B7DisqeDs6dhnZb3Lbsblip/3oSTOYYToqMhoCoyEwGgKjgPYhcHH3pbVu99IZGIJ3mUm7Hebq6Hjs5vaTgUGqo0MAfPzmL/BaUMiSGTYGBmHYmTFy4JPenu7eLQ3ulRF0KZ+8PJ+8vJyrK0N5+dPdux+4uX0DnwoDuqgQPA/5gYHhOrgu/nDunPqDB5C6m6CxoM0drq78amqQGwv5jYwerVjBwcDwBzxkfKuzE3IGOH9HB0SEtaPjEfigASZwM+CvkacByCHfd+3SHZ0YJCa0R9UMTsA0OJ016qrRECAmBMrLTXftUp3CIKXudnNNxW82I6NX4IqHD3x5Azv45glB8NgkCwPDJ/CCzHtubt87OljAQ3qQiUFiLHq+e/fz3btPMzJ+PXdOwMhIqKNDedcuAXBvkAV8EaIw+Pw03o6Ot+fO7WdkvNTZ+enhQ8jUIn7z3+3efZsRdNWF8K5dwrt2qf7/rwDueYoaGdn+/y8XESEXEcFYUfEfXO39Ax+m+rGi4jUDw92KCgE39dyO724MtdfdFnZ2nn748AN+u0ZlR0NgNARGQ2A0BEZDgFoh8PDh587OG6fdlqUyBOZ03ORwk34PPl5FqKMDtA+wooKro4Ozo4MDbJ9URITp//8a4FM6ZR480HnwAHLu98dz5651dn4Gr8QBK8RHINfFoh0dQh0d/B0dbOAa/yv4hFKJjg7FXbueV1TcX7HiYmfnRXBdjM9EmByvvLx2eblcRMQTcJfyb0XFZ/AOlF/gI8ohWxC5Ojp4wSuDhDo6BI2M3jAw7GCYaOD2iQF0mqj6aG8QFpaj9JAEox3CIRlto46Gh4Crq9KDB+YMDF9yGYzb3J7I7Nr169w5BvB2O24jI9Vdu35UVIh2dAgYGYkwMPwDVxtvKyoeVFRccHPDX1V8efjw+e7dtzo7T4M7bJ/PnZMB12RP3NzYGRgYzp0T2bVLDiyiV16uEBFhDD4gG7JTQsDI6KyCwoMVKy51dkI6h3AHIzPegc8ghdxGCLrLHjZKKrlr13M3t5e7d/PJyzOCd0SoP3hg8ODBj4oKMfCqmN/gw2ZAh6GBD7kGL125qaBwYPfue8jmj7JHQ2A0BEZDYDQERkOAFiGwe/czBYWrFRUfBDssWBgYOMELKbk7OkTAB2L/AA9fgla4gA994WBg4JGX//Xw4Sc3N75du9jk5Xnl5bXKy2XBB40yMDA8XrECV7cQ3gk8zcj4+dw5yClrkFk7XiMjSD+TAXzyp+yuXZwMDI/c3CBH0bytqBA0Mrq/YsU+RsaLnZ2Pdu/GM0oLseWGgoL8rl2fwRUrO/iawb8MDG/OnXsCblSIgl3LxsAgZmT0/ty5MwwTSxl0GRgejJ4mSosENmomncHoHkI6B/iodbQKAcjy0Y4OIWeGG+wVcT/BXSZIzfHazU0YvPRfeNeuZ25ukg8eQM5xETQyugPeticMvvT9C/gIGUZw7QU/iAyyMxDOBV1nf+vWL/B5YgwMDDyurk86O4UjIjjBJ8RA/IZ8Gs1b8Gk08D0PkLPRXisocHR0QHYwMjAwCGNbufoSfAqOwf//T8Gb2hmMjF6Cncre0cEIvvtIfNcuSL8U5IyIiJMrDl2suCnH8FSyw8m0PBbiklFyNARGQ2A0BEZDYDQEqB4CkPNjbjNoSHXoB0TIvwbXnuxGRn8YGH66gRZQfgRvrYfsJGQG969U/v9/09kJ2q8BXgWD7KSnSFv42BkYOCoq+B88+HzrFmSVKeRUGAiJeTYMAwNo2+GXFSuEystfg8dYv5w7x2NkxK6mxsDAcH/FCsgoLeSkU8jhbZAaHzLY+gXsDkhTAVLRfwSvM2J0c2MCn0/OysDA0tHxB+msVHYGhv8dy20rFMATgwqjE4PgIBwlhjwY7RAO+Sgc9QA8BMCnj55hYGCYwnAreJc5pIj/CT7Dmg3czeN1db3JyCj34AGnvPynhw/PKyiwMDAIgZePfqwAXSL7h4FBEe+WdPgNE2xGRjzgCT3ILB/W1aeQwcgHsAoJUhXxMTCwMDD8ZmDg7uj4WlHBDbYdtNse7EKIX0DdTgaG2woKQuDjZCA33orv2vUVvE1fLCLiloKCSEfHj4oK9o6OnwwMkArs87lznypubGAwbGVQfvDAevSiQkhgjpKjITAaAqMhMBoC1AoB2Pkx728yeP0A11/vwPv2eTo6/oO7Uh/d3Ph37frBwAA5lxtyexMHeEvh74oKvl272NXUPoNXvsCd9OncOcjNwALgu3nZwQO4AmDDGWAVHK6zYRgYGL7s3v3r3DkhcD/z9e7dkA4hAwODKLiOBl1ogXSaN+hIGPD83s+KCk7wNOZ/8NwmaFNJR4dMRARkJhPSw3wF62FyGBm9hl2DcYXBMgZ0RsHvXbtMRs+PgUfiKGOog9EO4VCPwVH3o4TAw4cfVqw4XFHBksPwYvL/xM/gegJ039G5cwzgHtd7Nzfmjo5P4M3ikLHGz+fO/QJXDMwMDC/B9ZAIeIkp/HBquAXw3iBPRAQbbEoQT4cQrhFSIX24dQtyjigz+HLb/+A+IbwrCNlNAeHC2ZwVFU/By1whN94iG/gfvJNQFHyeKqTK5Gdg4ANdVLjJr+IjAwPPpV1yuq6gI7CRdY2yR0NgNARGQ2A0BEZDgLwQuLj7koHbdwYGhlMd9/krIj+Dj3KBDKqyMTB8rajgYmBghHXk4Fa8B9ewkKk2iCCfkdEn8PgmhItM/quoYAQfCAfqucF6aMgKMNnvOjuRK+VvDx++XLGCB1zjw/uEEF2QUdr74Cuj+MGDsy/Blzz9h0gzMEBO9hbv6PheUSG0axe/mtoLsFHfwbOOYq6uSzsPxVT8ZWB49+CBm7w8L0zfKD0aAkMejHYIh3wUjnoAMwQgy0cZGN7tYtglwLAA1MuCXVQImqYDL2X5Ar4kEH6b0CfwWdg/Ozog03GCRkaysMFFiPlYe4MMDAzfHz58rKBA8GTRd7t3vwGv+WRgYGAD90hFwPsPQX051HoRMlAKqgvBXcFHDAxM4HoLeb8vZG/DE4jLwOfNaO3a9Q5iPvgQHZCXGRhudmzXi7AYnSqEhdMoPRoCoyEwGgKjIUBOCDx8+HnFiqdeFZrvYctqWBkYfoNXqfwFdwX/ggc6ZRgYnqEazw4+aJQBXE/9ZGCA1LCgGgqp38gH7rwxMDD8PHeOraLif0fHRwYGfiMjyFUQBM8gResQMjAw4OkTvtm9G7L/AjQDWVHxENwh/AA+uQ0yVcjGwMBUUfGTgYEd1h5gAHcULzBMzGVQZWAQ3LVLfHSZKGokj/KGAxjtEA6HWBz1A2YIgJeP7mdgkJrHcNa6g/VjRQUHeAKNgYHhX0fHC/C9DuLgSuj7uXPfwYeh/aqokHjw4NWtW5BtDwwMDJCrJiArUkAbFc6dQx6GhFt6i5ERT4cQ0hWEd/8gi0svMTJK7NolhtrnhBsIsuvhw3cKCmwdHV+NjO6Be3pKu3bB+6g/wVf3su/a9RzcmeRiYPgBHlWFXIjEa2TECD5ZR5JhFgOD6K5deq6uSsiGj7JHQ2A0BEZDYDQERkOAyBCALBMtYnjhzdCn0OH9jYFBwMjow7lzf8FbLT4wMEDWqvAaGf0Grxflg1Vtb8BLLiG2MDEwvHFzg1/8CxFEI9+CNxkKl5d/fvjwMXhfIqQzpvfgAfJ98Wi6XjAySmDc9oS1TwjvDYrv2sWnpvZaQYF9167bbm6gziG4C/r73Lk/FRWcHR2vKyp4wOtXIXcC76/gzGVQYGD4tGuX0+gyUbTwH+UODzB6D+HwiMdRX6CHAPj0UaEVK24nVZjmVDzrffDg3YoVP8GqfkMm3CoqrjMwKO/axWlkJPX//8+HD19UVLDLy8vKy/OrqUGuoT/AyGj04AETeLfDLxy9QQYGBq6Oju8PHyKfKwO2BzR5+B2sVwR8+SFyRSixa9cLNzcG3H1CyAk3PBERQvLy/A8ePFyx4p6b24eODvmICD55+U8rVrB3dIi6ujIyMLwE3/j0FTydyMrAwGtk9NnNTaSjg6Gj40FEoILCjltu89512FiMThVCYmWUHA2B0RAYDYHRECA6BF7t3r3a7SsDg6THLjPVc96gKo+B4QN4LJIHfKMgaK872DRxNbWPYAYDA8Ob3btfgYcyQZcE7tolAu4ivgH3CYUwOm8QTb8ePvxZUSECPq8bcgYppFuosGsXpHMoGxGB2S389fAhG9JkI8QokCPl5cUjIiBrR1/v3g2pKxkYGMRhjoGoZGdgsPz//9nu3Z/PnXvq5sYG9hEXA8N38PUSLAwMRg8ebFzxMJfhLwMDw4MHjqPLRCHhNkoOPzA6Qzj84nTURyghAL699nY1wzMzhgOmu6I+g6sxKSOjt25ud2EKhcH35zIzMAhERMA3uz90cxME3xH/D1w3KO3ahbYbAaabAes2wne7d387dw5ylChyVxCu69Xu3S/c3PSwVY1fdu/+4ubGs2sX5NwaiJbH4ElPyOHaHKDzYyoga2/EYUtPn7i5Se7aJaim9mXFil8VFQxg8Sdubj86FilWxMkwrH/wwGF0+SgkMEfJ0RAYDYHREBgNAfwAMjH4gMHyQ0cH5KxOxYiIBwoKgh0dIhERf27d+uTmxgZbqAI5xhO09Q68qhJuMqQrCOHC18tAVspABOEkfHoQLgJhQO6gVwDfYs9vZIS2gvTXw4eQA2AgitHIl+Ce3o+Kit/gs9xAh3J3dPCDFwdBZggZOjq4jIyeuLnxgXdbcIHPERAF154fzp3bXcFZBF0mKjc6MYgWtqPcYQZGO4TDLEJHvYMlBMB9wlMMDLyTGc66dbCyMzCwg+8U+m5k9MDNTaij4wt4QelPBgbejg74Zncu8AYJJgaG5wwMIh0d3xgY4DUiH+xEGYhlaB1CSJ3HBa5mkG8XhChGJrEuHP0FWywKOTMNWf2nhw+vKygwMDCwgkcxGcBTnZAajgG81fBzRQUrmIF264YQA4MMw2IGBp6ODunyclNkM0fZoyEwGgKjITAaAqMhgBYC4N7g43KG55kMge/BR8VAunygPRQVFaCL/iBns+3a9encOchZLJCN7nwMDG/B02vSqHNxEPOfdHZ+q6iQBZ/1DRGBkJ937watbXnwAH5gG0QcTl4DryaFrCBV2LUL3i1819kJP/Qbrvg5eKkq/BoJfvD+xo8MDHK7dv1nYIBsTQQd3lZR8Q98gA0/+BQZ/o4OHvChpr8YGFg7OsC9Qcg1g+6jvUF42I4yhiuA5N/h6rtRf42GACgEXF0VHjxw6+gQy2UwXc/g9LjixmsGhrsVFV/PnVPatetLRYUwuGfFBK4JHoNXufyuqOA1MvoG0s2ghNQbRL7l9tPDh5AjyzjV1L6BJx7f7d79BFxpoe0YBBuDhYAuHEWVgS8WRRVmeLl79z0FBQXw9YP/wbcAs4B7eDzg8U5+I6N/DAzCDAx/wNUbT0cH5NYN0FHdHR3sHR0PHvh0dIgJV2w82bn44cMPaIaPckdDYDQERkNgNARGQ4CBgWH37mc9jJPfubUu7fiZ+cCZAXwbk2JExHvwvQug6xzA3SfOjg7Gjg7IYaGCHR2CHR284A4V5DInxo6OZ+fOYd4FLwy+2/0tbH8gPMA/u7nxgi+sh4ugMeC32CuAp++Qb7FnA185yMDAALlc/jQjIwMDA6QGhNzJxNPRwQ4+PObTuXOPwFX8Qze3HxUVfODlP3zgOwY5Ozp+MTDcqaj429Hxj4FhK4N/EYNuR4fggwcho71BtLgY5Q5LMDpDOCyjddRT2EMAPFV4joGBexfDCh3w8tGPFRWQ2xp4du265eYmDe5uvXNz4wTvSeA0Mvro5iawaxc/eP/Dp4cPkW+5vePmBholBdclH8F6QYebgZeJYl0jitVNaJOEmItFX4JHOp+Dl4OCKjww4+u5c58rKsR37foHNhQ00snAwAGe+XwHPjWHAbzpXw7sbLSr7ecytPUzaI6eNAMOuVFiNARGQ2A0BEZDABECkInBNIbL4R08IgwPBY2M+BgYPri5yYN3N7zavfs5uEPFDN61/g9cUSKfEcoKvmnwPrjrCKkfIUYrgHe/Q9iQRTTIteTbzs6fFRVSYCsgavCQkBWkgmCrGRgYOCsqOHbtQr7Cnh+8+FMG3G+UBFeCn3fv/uTmxgiezISYLB0R8QPcKWU0MnrlBtoi+RV2euoVBsvoCjYGhj+7dimNdgUhwTVKjgQw2iEcCbE86kdECID7hKDloys6GL0jNBlu3fp17twvBob/Rkag7YUVFW8ZGJR27fp37pxgRASHvPxDRkaJBw/YUdeIIl9q/xF8wxIHeF4OMuHODZmyA49ZYu6ARzgFzILsJFR+8IBbXh5tsehLcNUruWsXpDcIIcXB1RsDA8NFRkbejg4l8FW8YJMYIHWqyIMHL27dguz4f1tRIQO+LPgHeC6R28jo67lzzxmEl1e8Z2Dgs+mQtxw9aQYSdqPkaAiMhsBoCIzsEHj48PPxFecPV7xjYGAI7fjBVBEJuWDwXUWFIAMDf0cHZF0oaNXJrl0sDAxv3dyEd+1C3g34BrJABlYrPdq9G1ITQY4kZWBggHcLb4En8SCnc/96+PCNgoII7sWi8Gj58vAhAwMDZJ//Yzc3RnCn9A/4cnlmsCIm8BXzfEZGUrCKEizMAOkQSv////nhw6crVnyuqGAGX4PBBL5z+Ne5cz8rKsR27WJTU9uy4mE0+JrBXbu0XV01INpHydEQGAlgtEM4EmJ51I8oIQC+vP5uRcWbjg4RzYopkgwLIDfUv3Fz42NgeADebMDAwCDc0SFpZPTdzY17167fMAMgw5CQO5R4wSOLrODVmz/Bm/o+g2sjyO1JEB3vwfdbQNhy4KUyDAwMaL3E+52d3EZGYq6u7zo7f1VU/O7oYDMygnT/4FOCoLPRUGs4yEitJOycUkidyt7RIQyujD89fHgNvPX/e0XFH4j14D4hjAmiWSsq5jD0Oe3yH72UAhQco3g0BEZDYDQERmoI7N79bLPbqTiGQB7YzBs8JCAdwk8MDJwdHRwMDGrgKoaBAXSa2ls3NxnYbkBIp0sUo1/36eFDyKndDOB9+JC6lYeB4U9FBWSSENdZMl8ePoT0/ZCrXbirGBgY/jEwcIA3AX5gYPgMnt8DLRUFq4DXvAJGRnxqar9u3frp5ib64MG7W7eeurnxgu+T4AJfyvQKst2jAjQnuIBh/SxQz1cwIkJ59DRRcECOEiMIjHYIR1Bkj3oVOQTAU4V7GRikDnW84aiIAx2NDR5uZN6169m5c2wQpRUVH8A9Q9BdDuCL7CFdx8/gqwtBXcGOjm8VFULg48s+gOtL0CpTcAUDr5AgDH7wATaQVS4QEVCv0siIX03txYoVXyoqRDs6flRUfGFgwDUlCHERMgnpE+qDl9nApwfZ5OVf7t795dw5HnCv8gf4BJr/sF4uRDs7eLfhLwaGOQxdHQzyHR2KoyfNQEJmlBwNgdEQGA2BkRYC4GWij3IZrsQxpDKCN6L/RwoCdgYGUfBQqVBHB2R4ET7Rd5uRETJJ+At8NS7frl28qAOXcGM+PXz4AbZ0BTJh+AW8hU8YvIaFfdeuX2CloHU64M38oPoRqWsKr4Lh4rxGRr/At9j/A9fCz8Ha/zMwMIJ7hmAe9LJECPsneGz0F3gF0AvwzUzMDAyg++g7Ot5VVDztOAheJnpn9JpBSHCNkiMQjHYIR2Ckj3oZGgIPH35QUDjOwMCwa5e6JcPdZ+CtEQyw7QeQCcOH4NrlK3iijxd8VvUrNzdWBgYx8P6EV25uvOADaYQfPPhw69bXc+cUYaOnoJUt4PUtDAwMH2/dgmzzg3cFQbWakdFH8OFsrGDncILXvQh0dHwGH93GxMAgBb45FyyJPqkIEYQsHJXctUtATe2NggJ7R8cfcCdQcteuL+BNhpJgRzIwMDx1czMC9xsZGBg+7d7989w5drBf3quZrlhxjbtiGwMDh++DnNFLKeABO8oYDYHREBgNgWEfArt3P3vn1rqPwdW+Qyiq3O4FeMv653PneI2MJMBdu5/gnh57Rwd7RARkog/SnYOcuc0LnugTBm+y+FlRIQ2rZdDCDXmp54dz595VVIiAu2HM4P7bdwYGNgYGefA+QLjGj+B6kB/cJ4TUvLxqajyoezfegVeoMoAvkWLp6PgC0/yuokKoo0PQyAiyYPV9RYUAA8N/cEeXD+mIOEiPVGrXrrMM2m5uNxkYvu/apePqKgczZpQeDYGRBUY7hCMrvkd9ixYC4OWj9yoq7s1juK3SISPO8Ay0pNPICNIbZGBg+NPR8QzcG5SLiPh169YrNzd4V1AM3GF7C+6JQa6IQDshBs0uCPczuJf48datx25uTODpu98MDBLgFS8/wPN4vOAtiJAOJEQLA3ggE8IWAFeQkP4k6Pjvc+e+gq+aYGNg+Are5gGZGJTctQu+25CBgeEcIyNfR4dKefkncH3/m4HhlZsbM7jrC5oa7eh4XbHVlKFo9KQZSCCPkqMhMBoCoyEw7EMAPDF4cRdDOzfDYR5wD02oowOyuU4APGIo4er6GnzciwyspwdZ/wnpDQqAl72wMzBIgy/sZezogIwzQs4dBY2Egjt1oNoKXG3Br3QCneUGtk4YvJHvMwPDJ1hYi4I39WH2/WDyCPrXw4dvFRSEwStU3+3eDdnQyKKm9hB8VAxoRWtFhdKuXVwMDK/d3H6DB1ifuLkJdHQ8qajgBdsCqhndNhxlMOxh0Ono4CgvN0CYPsoaDYGRB0Y7hCMvzkd9jBECu3c/cnM7AbmoMHiX+a9z53gZGNjANeJn8J1FoNNiKipegy8q/F9RAb9e6R24spSEVZaQE2Kw3jWPbCfkqlyICC+48ykEvgwXYj4PeOYQIvsBVqHC+4EQceRtikwVoBvqv4InKiGHjkLUgA4aBVfDoFlBsDl84LPC/4L7jZAVOJBVNwwVFT8ZGG52rPEDHbitGBGhOjpVCA/DUcZoCIyGwGgIDLMQePjw84oVTysqPhQyvIhkCORiYPgLrizgNw2+A++pA+1xqKhgAktBQgAycQdhM4BHMLnA83s/GRjEkc7whChA7gFCRCAzfnzguvW9mxsP+Lq/L+CbHp5XVCjv2gWZ04MvSYXowkpCpgchQ7EMDAy3wafUqILr4k8PH14Bb6H/UlHBBN7ez8DAoPPgwasVKwTAO/9lHjx4sGLFrgrOUgYjBobbu3aNXjM4CkYBw2iHcDQRjIYAKATAy0dPgpePqqqdW83AwPAK3ImSAa9j4WNg+FlRcY+BQaKj4zd4Z7xwR4eikdFr8IQhD3hpDcgUBgY8k4SQCySegi+ogOyUgMxGsoKPOBMG73eHXJ0EWasDMRBCQuYVQV27W7dAI6/gbYqgy+jBjvwOnh5kBt+zBFEPGpRF6ljyGxn9OHeOvaLiDljNd7AuefCCUnawUWxGRp8ZGO6ce/i+4qgGw4KHu+6OnjQDD8lRxmgIjIbAaAgMmxDYvfvZL7fWL6Dddv89H5S9WrGClYHhfUWFEribBFp4Au4N/gVfLg9ZYwn3O6QvB58D/AZW+Q/cMwTdAAHuOkLUQLTwgk/bRlvqCarIdu/+AL7Sic/V9TYjI0tHhzC2Jam4eobI04MQi97t3v3SzY29o+MnA8OXigqZXbuegCcGucCnqbEbGb0H13RfwHWfWEfHfaNY8DJRhl27lEeXiULCcJQc4WC0QzjCE8Co9xEhAFk++rnixkeG364MB0yRLioUAddzLBERFxQUmMEDopCtg18YGCCjp/B6C3OS8OvDh1/AJ5tJ79oF6Q1CSB41NchVE8iLTl/s3o2rT4hwKIz16+FDyC32/4yMXoC3IzKCbxDmA9fBkPPZIGo/gnuD/xgYHkH44JNyQJ1G8BYOtK7vnYqttgzJHR1ao1OFsNAapUdDYDQERkNgOIQAeJnoo10MZYod3uywXXbvwPsORDo6+IyMnri58YHru0/g89K+MTAIg4cOkT3Pq6b27datn+ANC9zgvtxr8EJN0YgILtRtfsi64OyfDx++VFAQ2LWLDzyWCl/wKeTqirYk9QPsPkN4DQsxBHl68Dl4H8QTNzcx8MrV1wwMXOBDRMXA60JZGRheurl9A4+ZQm7ReMMgv7KCYxYDO/g0tdGLJSAhOkqOgtEZwtE0MBoCqCGwe/cVN7drDAy8yBcVvqmo4AWPOEJWvHCBxyAZwRv/PjMwQBa6QLZVKERE3FZQkAXfBvFy9+6nsMqV18gI0g9Eu0ACbdEpAwPDrc7OTxUVJuClL6hOQ+HBe4M8ERFs4Dr4ye7d993cBMG1oAy4ooVrgBwBx97RIVJe/unhw/srVvAwMPyuqIDs3JAFz4KC9nUwMEC6vrMZjCtAt1GxfX5giDm4Czd2lDEaAqMhMBoCoyEwJEIA0hUEr4KRM2S4CukmfQOfmsbAwMDHwPAdvC2CFSzCBz5U5sPu3e/c3JQwKqO3u3d/A0+4cRkZCYPrmm8PHxLZJ4T0Bjk6OsSQDmBDXvAJCUzMniFoE4SRkYCaGtOtW5/d3BjA3VTI+CmEfOPmxs/A8BTsfk7wlCCkUv5fUcEDrhbZ1NRmKWwELxP9vWuX5Og1g5CgHiVHQwACQHujIKxRcjQERkOAgYHB1VXnwQM3BgamiArG/Suu33dzuwvrDcrs2vWxouJfRQUHAwMveD+h4q5dxg8ewEcxhY2MLiso/GBgeObmdgq8pUF6165P4DUqDAwMRv//i7u6Ip/18mX37p9gw5FDHnLL0y3I+WnIEkhsSG+QzcgIhMC9QQYGBhlXV/0HD0An0Jw7dwVV+0/wQlM+8EWIv2/dYq+oEDAy+gKe6vwH3iQpFRGhuWsXpDf408gozuj/RobtlxgCtyks7uw8jWTzKHM0BEZDYDQERkNgiIXA7t3PbrtNOspguZxhsfi5xWxqajwdHWIdHdq7dslHRPCB64KfDAxMDAwy4BsguMDLTATAnb0P4Ck4uIchvcGvFRXw3iADAwOXvDy3kRG3kdF9BQW4SqyMj+C5RH5wZQRXAJmEfIdkEZ+8vF55ueP//woREZCNhZCq7bqCwmM3t7vgauuRmxt/RwekN8gAPtuGd9cuTgaGzxUVT8FnhguDzxpl7Oj4ysBww81t84qHpQy6DAy/d+1SH+0NwgN/lDEaAhAwumQUEg6j5GgIoIdAZ+e5iooHOQwfwzpYOCriZMDjkazgYVEGBgaR8vLvDx/eV1AQBItDbs7l7+hgY2D4C+4BPgOf8qKEetonmh3PGRnZOzrg2+Lhsl8ePryhoCCzaxfmZkKIGsiCGTYjI+TtixCpTw8fPgLXuAwMDHIREXzg7uIbcP+QOyLiG7hnyAA+eE101y5hV9cbYClGBgZG8NEyP8ANAsgWRxEGhnug00crOzpERpePQoJ3lBwNgdEQGA2BIRQC8InBbQyLdTvk/oEPdIEMYkKu25UBHzbG39EhCV4/chF8HAvEg3IREV9XrPheUQGZJPz+8OFDcH+Pu6NDFml+D6KYgYHhze7dX8FVpDw2WdDO/M7OHxUV4g8esIMrJrhGtFNhkMUhbMg1hs/B3TwZ8DTgb/DpaJAbJiCV3ZcVK0TA62Xugiu19xUVag8e3F+x4m1Fxf+O5S4VCgwMo9cMQoJzlBwNASxgtEOIJVBGhUZDABIC4OWjj8FrbNRdXZXedHb+YmD4VFHBD15j+bOigp2B4X9HByNYNa+R0Qc3N0gVC6r5GBh+gMWNHzyA9MrAPAQBWSwKOTUbIQpj4dlMiKc3CNPNgLx8VExN7bWCAmNHx18Ghq8VFX/BBwCIgr0AOTKOH3w18FPwCeAqu3Y9Aw+7MoBvDebZtes4g/Imt8PqDI/Vd0WNnjQDD+FRxmgIjIbAaAgM8hC4uPvSPrcDNxnk7DuE3Ix+vnFz+8zAwNbR8baiQgK8UfALeKPgO/BRnKAzRRkY+GEbCyH3DX6rqBBhYBDatesvA8O3c+e4wP1JyDJRrH7H0yf8hHSQDKZe5J2EmLKvd+9+Ae4NsoKX5zwFq+Dp6IAcrC1gZPT93LnfFRUfwSt3GMA1nSD4Cg2JiIhjt1jd3O4xMHzatUvO1VUHrHWUGA2B0RBAB6MdQvQQGeWPhgByCMBPH81heBHHkMDf0cEL3nbP39HBV1HxCtzrYwTPqn0AHx8K0fvazU36wYNXt25BBmKFOzrQ9sRDDknDOj0IMYGBgQFrn5CY3iDEhE8PH15WUBDu6PhaUfEP3Nn7C5aAeAEyBwi58Pcz+MiZB+CTZiAHzMh3dPyFTRiCzp7p6GCoqLjAkHe/I2Z0qhAciqPEaAiMhsBoCAzeEIBcLCFcsUCa4bNsB+iy9ffgE0FBmwvAo36Qczh/MTAoMDB8AHcOQUU90tnUDOCjRxnAdQdfR8c/8MoXKfC6EvzefgM+8FN81y4R8IpTiGL8vUGIGkifEHJ1BEQEQr7evfvLuXM84L4oP3h8kxm2MOcfAwM3uDfIWlEBOuONgeEfeLsgZJXpu4qKiwwT80DLRBnAy0SlIAaOkqMhMBoCmGC0Q4gZJqMioyGAHgLg5aNnKxlYLBgOSTIsAO2yAI9WCu/adQ3MUNi1i4OB4ee5c+xGRnyurncZGaUfPOCQl4fsjIcYJ2BkJAerIPFPD0LUw/uEGg8eQE52+bJ79y/wghysq0zhx4pC1q+CanfwACpnRcVD8KwgOwODIPgyembwYhtIb5CBgYGVgeGrm5sgeHUNA/iocdBuDXDrQQQ8kcgAngs9yVAczmDAwMAzen89PIJGGaMhMBoCoyEw2EIAvkx0TccLzYrA7wygIzUhjuSrqHgEvnzoa0WFHLhb9c/NjX3XLnY1tY+3bn0E1y/vwX0/kY6O9xUVLAwMfxgY5GCdRogUfC6RH7zVkBdj/SdanxDrQTIQ9yCT3x8+fKKgILxrlxCsooRPDPJ0dPAYGYm6uv56+PC1goL0//8vdu+GDGsyMDD8q6gQ6OhgNjL6eu7ch4oKZnD/9lrFo3CGWAaGV7t2mbi6jnYFkUN6lD0aAljAaIcQS6CMCo2GAGYI7N59D3ZtkbruudU/wTcTMoD7V/zg9TZfGRiUwIeLMjAwwDuEEHMe7d4NGbBkYGBQiIjgYGBAvmoCogYXCT90FN4bZANvx4d0/5D7fpC75iHmQNiQFaFiDAzPwRffC3V0vK6oEAZXlgLg49oga1l/Pnz4QkFB/v//t7t3vwL3bxkYGL6BDZLatYudgeGXmxsbeK/j4c7FFytuyjE8lexwMi2PBSsZJUZDYDQERkNgNAQGSwhc233pltvMOwwaUh36UeV21xgZxXbt+njunGhExEvwDsCXDAwK4CvgBYyMZFxdnzIyij54ADmq+svDh59v3YJUK5ApxH8MDJwMDMIMDPfB/oN0BUGjjUhzie8rKpDFIb3Ed+Ct7NxGRiKuro8YGdGOFQUbhoWATBIKg1eofjl3DnJtoMSuXaKwLiK8Qwjadgg+kZsFPNz5GXy46JeKCi4GBvFduy4zaMOWieqMXjOIJaBHhUYBBmDBEBkVGA2B0RDAEgKurkoPHgitWHHPze1uDoNYSccrBnDPSnnXrp9ubp/APagr4N6UcEcHBwPD51u3/oCN+XzrFit4Fu4deNj1ckUFLwMDOwMDr5HRM6RD1cBqsRD/GRj+g++i+FZRwQxeocoPVgXp8kHW/0AGSp9A9v6BTWZgYJCKiIAcWPqQkdHwwYOfDAwPVqyA9AbfVlQwdHRALuplZGAQABt7hZERdKPUrl1fwU2HZytWfKqo+HXu3OeKCvaOjk/gXq7Krl3iHc8+VTBsqHhvVrH1wQNreXkBsHNGidEQGA2B0RAYDYGBDAHYxOD7GwzTlEDV0/EDjPZsDAx/zp37X1HBb2QEKu137eJiYIBcUPTx3LkHbm5CDAy/V6xgh22FgHsAcnonr5oaGwPDawUFm///GRgYPj98yMDAgDyXKNjRAekN8oOXdH48dw4+zQhpXz4FV39/GBj+EFHfMTAwfGdgeOTm9oeBga+j4xcDg+SuXb9h9jIwMPy8desneD8F5BonyM1JkMUvXyB7N86dW+p2/SnDQwYG5QcPbOXleeE+GmWMhsBoCOABozOEeAJnVGo0BLCEQGfn5oqK7wwM3BsZ1pjuivrp5sbOwCD84MHrW7ceuLmJdnT8BO++4wXVxyDtfOBq8hN4Kc538PjlfwYGRvBiTpA0IfwDvFyTj4HhLngvhxJ4DSdk6g+yG5CBgYEXvG4HsqwUzTz47B9c/BO4Rn8APnuNAXyJ4ntwh5YFfPI4N8zZEPX/wTdTCYF35zMzMEDuKuRjYDjdsckPtIF/dPkoJJxGydEQGA2B0RAYyBAA9wZBR6Cd6rjPVxH5BXxNLuQqdjYGhl/g8UTIWkpkV76vqOABr3D5D+5NQUYVeY2MJGEzchDFyLOIEBE4idZFhOw8hHQR2RgYvlVU8DMwPAFvUIecvgbXiIvxr6ICtIsB7H5h8Jlt/EizkaAVK+BLcnnA2+MFOzo0ystvMzL+YmDg27Vrldv1EgYz8DJRtdGLJXCF8Kj4aAhgBaMdQqzBMio4GgL4QgC8fPQ2A8O/FR2MhhWeP8AVKqR7BtrjB+4vfgWvY4GbwgbubjGAl9/8ZGD4Ap73g3ca4cqQGZCuIAek2mMAdQgZwGaq7doljVpbI+tCY2N2CJEVvNq9+wd4VvMVeFwW0vGD9DYhyqQZGG6DO6ICHR0fKiq0du366ub2k4GBd9eu027LJBkW8DMw3OzYrhdhMTpVCAmxUXI0BEZDYDQE6BkCDx9+PrPivEKF/TcGhhcMWZodcqwMDL/BKzsgJ0szgLcMSDMwPIM5SwA88MfOwPCjouIHuFoB3U3f0QFZVAJThaDxdAgRipBYtzs7v4FXxLAwMHCDu3aM4BqEGbyPHUkhOpONgYEFfBT2J7AufrAuyEwjRKkYA8NXcO3J2dEBOcf7HXjMlIGB4XnHwWDQKdoMu3YpuLoqQtSPkqMhMBoCRILRDiGRATWqbDQEUELg4cMPK1bc+1Rx7BMDd27HK8jRowwMDGJgVewdHc8qKqTBlxC+AW/MEwNv3/957hzoOoddu56fO/eposIEvAgHrAOF+P7wIfzCwFdubtwMDFwdHWwREQ/AuzLegvcBoh1biqIfifMT7FBxbLdCfdq9+8e5cxzgCcyPDAwP3NwgvT5bmKt+grfvs4NdC+nK/qio+MXAIAWepQStSjUyeubmtoihrZVBc/SkGaRQH2WOhsBoCIyGAD1CADwx+Cid4UUIQ59ih/d3BgbIdRGgk8A6Ol6AO0tyu3b9OHeOqaJCYNcuPvBg4ueHD9+sWMEMvp5BDLwmE7KGhc/ICOvlt08ZGfl27eIF68Xvq1fgE0FBh5OBaxYxV9cHjIyfGBg+gPf4QfTCL8iFcOEk/OwZNiOjd25ufzo6foK9c8/NTWnXLllX10+7d39yc2Ps6PjDwPChooIR3K1lYGAQNDLa4nb9HgNXD4POgwfao8tE4UE6yhgNAeIBE/FKR1WOhsBoCMBDQF5eoLzciK9DewqDhHqFzp1zL0z//1fu6ODt6BDetYsnIoKno+OJm9tFRtAyGbFduyCHtbCD9bOrqUEGYm+B788FiyGIt7t3v16x4pWbG0SL8oMHzGBJPnl5vfJyhYgIyD7ABytWXOrshKz/BMtjJ37AbqJHk/4E3s4B6Q3yubrKurrqP3jAAD597iAjI8TYT+D+p6irq6SRERN4uPc7AwMTA8NT8KQiaOnOuXN8DAypD1IZGL64uZ053Ln44cMPaBaNckdDYDQERkNgNARoEQIXdl9yc3vMwMAYvMtMiuEwC3i33odz5zjAO8M/VlQIdXRwdHS8OndOCHwUGcQNb3fvfqKg8ANcpGv+/y/s6irh6grZ2gA52hqiDJlkh3W9kAUx2a/A1QpcXAzcgRTctYuPgcHu/38BcBdRwMjo0YoVV7BVXh/BNQ5/RIQAWKOYkZF8RMSHc+eEOjo+nDt3gpHxvpvbBwYG3oiIP+Cqih98SNsVBks5N94sBl23Xbr//1uM9gbh4T/KGA0BksDoDCFJwTWqeDQE0EMAvnz0VMcbjoo4yCIW/o4OdgYGxoqKu2DlbAwMuuDr6Z8yMrJ3dIiA5+swrxmEH/IJmU6EXP4LOVQNfgoc2DyGR7t333Vzg/QMkW+zgMgiky/BfU60GULI3CADAwMH+JIMZPWPwQeivgO3JCTAmwZ/gA9tY2BgeOnmJr5r1xM3N96ODrmIiC/gyht0zTH4NALQuXMMDIsY+lIfJI4uH0UO0lH2aAiMhsBoCFA3BCATgxcZLN/BZt5EwZcu/Kqo0Pr//3Vn58+KCr4HDyCLSiBzht8qKqQ6OrjByhjA1/dBqhi4w748fPgMXKozMDBAhizhUm86O9mNjPDMEL7avfs5eKCQAXwMDKQrCNH+A3yKteCuXfyurp8ePnwEtgLiJMgxpxBlkIsKxR88YAdfYvFh9+53bm5K4OUqNzs7v4K7r9wMDL9hXdP34K2G9zsORkGXiSqPniYKCclRcjQEyAOjHULywm1U12gIIEIAvCrzXkXF2RwGlrRd+uzndn+sqICsHf3d0fENvOjlgZubSEcHI3hfhyhssPbZihWfKypkdu1iZWB45eYG6QdCzIVX1W/APTpIHxIiBSfRbrOA3CEBl4UwXnZ2chgZ8YMHXEFHtD18CDl5nKOjgz8iAlL1QlTCyeudne/BJxCwgzcWQi4mBh1dA66JP4ErZk7wtkn4Gae8RkaM4NWwkgxzwe0T6fJyU7iBo4zREBgNgdEQGA0BaoUAuDf4uIjheTxDIHdHx1fwmknIqdFfKipEOjq+V1RwgItrBgaGd+DCHGI1LwPDOzBLedcurEtDIX1CPiOjT+fOSUVEwA8q+7x7989z57BWQ3i6gmCrQMQDRkbOjg74uCS8WwhxnhJ4CvGDmxtTR4cAuHL8fOvW93PnvlVUsHZ0fAK7Xxp8/PXvioqv4D2EkENrtjL4V1Sc6egwiYiQHp0YBAX0KB4NAQrAaIeQgsAb1ToaAkghsHv3FfDqHYZLu8QVGd5+AR/dybtr10M3N2bwSh4hBoaf4M0PSJpAe/r5GBj+dHSIGBlB+oTwriDoJADwLj606UFk7ZCL70HXSICPccPcWIjcIYRMDMKXiSKbg3b9FOQei+8VFYzgGymQD78BnS1+7txHcMXM09HxuaKCv6ODAdwKAR1KHpG+YsU17optoh02FqMnzSAH8Sh7NARGQ2A0BCgLgd27n91wW3OZQU65QyMiQprn1olX4Etiv4I7fkIdHb8qKvjA19BD1qpAbOMzMvpw7tyPigrQDUPgScIP585B9qIjX0gLUcwAvuUIwoZvKcTaIcS6QBSiEY38uHv3ezc3BfB0H1wK3i3kYmD4UlHxDem8Gcj61R/gOvQr+EQcFvAtFDzgTY9Su3btOsceVcHGwPB51y5JV1cduJmjjNEQGA0BssFoh5DsoBvVOBoC6CEAWT5azfBCguGxOUOtDOzwFdAEmpvbGwYG0F1MDAy/wfo4GBg4wN0tYdjhbxao9SUDA8Mb8MofPB1CsEkMkG4hhM0A3lYBny18yMgoAV6EA9k0CFEDOVcAuRMI6tSB5SCn40BOTAWdQ8DAIBQR8RlpI+LPc+c4wKd+/wePQMtFRHAwMHwBT3Wygf0LuZriJYOtHUPR6Ekz4EAdJUZDYDQERkOA0hAATwxenMkwV59h7TfwCCADA8N/8KXwohER327deg1etMnR0fG7ooIHfIwZspXs4KuPnoCF4L3BD2BBOFdATQ1Sd7zYvfvTuXOQjpmEq+tn8Gku0kg1FKQ3CFkmKrlrF/IaUbAN6MQDRkbIqlE0iXudnb/AV19ALr5nBW9TZwBf1SvCwPAKzPjT0SEI3n/4/ty5rxUV5xkmZjPoMjAw7Nql7uoqhWbgKHc0BEZDgDww2iEcBaMhQM0QAC8fvVhRcb2jw1izYppGhwavkdEv8L47NiOjrwwMz93cZGFV9cdz5/5UVHAwMPyCnaCNdvwanvWimI7GurEQ0iH8eevWD3DF/5uBgcnI6DN4ig9rJxC0AwS2vvTbw4cPFBTEdu0SgYmAVviA+6jCDx68unXrA7h65gJ3a+GmiRgZfXJz+8XAsJBhYhuDREeHYkSE6uiuQsz4GhUZDYHREBgNASJDoLPzRkUF6MiuMwyWf8CTaZC1HpC5PjnwMlH+jg4u8O3zb93chHftEkIqt389fPhOQUHowQM28A49BgbEMCK8N4jWOQSdQQquNSCDjK8VFCAdQmLWiGJ66mVn5/eKCuRJwu/gk06/VlTwdXT8A++3h9yayMDAIB8R8Qu81ZABvOMRfqDou4qK5QzrZzEwdXSIRUSMniaKGcyjIqMhQD4Y7RCSH3ajOkdDAFcIgKcKbzIwMDx4YMm7YuYvBoZPFRW84Ak0BvBV7zzgawnFdu3iUVN7qaAgDr7X/r6bm2BHB3yfPdbjZHDZCBdH3lgoYWT03c3tN3hO8hd44Q1k6u/zuXPIM4HInUC4ORDGG/CpcfAO4a+HD98qKLB3dAiBz8V5AL5sihl8K9RnBgbIUTq8YJ08ERFs8vJ5jDM1GB6rMTz9v6vW1VUJLDNKjIbAaAiMhsBoCBAbArt3P7vttkaQ4da/jrDocruvDx++AneWnoK7Um8rKgQZGEQYGJ6DLwSCXFH7bvfut25uMg8ecMK6f+/Ae9Eh5TamxWhrTCAnvjAwMLwF798T7uhgZmBgr6jg3rULMiUIqjKImBVEtgiyalTiwQMOsJPe7t79Ddzb5DIyEnZ1fcLIKAobZGQFX2fPwgDq9kGuO/oCvsPpPIO2m9sj8DJRldFrBpHDdpQ9GgJUAaMdQqoE46ghoyGAHgLgPuFtBgaGyQyn7RlqRcC9QXbwCp+HsIuAhTs6JI2Mfri5yYGX4sD3VED6hJD1opBBWXTTYfwvDx9CmMirOj+cO/cBfPQLC3hV6ifwIhzBjg4+IyPIjREy4ClKSaTxY4ghmCTkEkUtsPOQpwf/MTC8W7GCE7yM5/O5c38YGJTKy389fAg5epTByOgX+HYKPvBpq68qtpoyJHd0aI2eNIMZwqMioyEwGgKjIYArBMDLRB9tZyhT6/D+DFMkamQEutKWgUEsIoJbXv5dZ+eriop/4DX8DAwMshERvPLytxkZ4ZOEmNODMJPQaciFQ5CzSRnAFQczeHciM7gq+QKemfxSUcHb0QHaTA7WzaemBqZBBC+4swdiYcPwVaOQ3uDXigrRXbsge+afMDJygq9DfOrmxg2+jF6YgYETXE9B5i13V3AWMSgwMPCOLhPFFrSjYqMhQAUw2iGkQiCOGjEaAlhDALx89HBFxZcchh+uDIc0OjTYwNcEs+zadc3NTQxcfzNXVLwBaxYAc9+DO3KQ+wA5Kio4OzoYwZ0usBIGyN3BDODbICAi8IWakM0ecAUs4M33guDFnG8YGP6Bp+8YGBjegzecgMw3MoJU5JAdIxDTsJLXGBkhq0Yh04O84Gr7O3hw91tFhfCuXX/BN1KoPnjwbsUKBnCfk6+jgxfs7M/nzrFXgPb+L+zYXVHxgoFB8MED69Hlo1jDeVRwNARGQ2A0BOAhcGH3JUO3bwwMDB0dAoEVmgwMDN+RttJ9Ak8P/qmoUNq166ebm9CDBz8ZGB6DZw5BNwAZGXEyMMAXjuKfHoTbCNlVDjobDLynAGROR8dfcHXDwcDADa5N4IeUQuoafiOjj+C6AHTfLviyeNC2RvAlgXBj4V3Hf+AtBhwdHR/B1ZzYrl0QQz5WVLCADf/LwKC0a9dTNzeJjo6fFRVfGBhYOzquVzzay+A6EzRfKFZebgE3dpQxGgKjIUBdMNohpG54jpo2GgLoIQCeKgQtH93YwahR4QnqzoG7Z7xGRm/c3PjAh2gzgHuDkIoTMonHC17h+ZWBQRFWayKbCzkWHCICqVwhbBYwBSE5GBj4GBg+MzB8Ah86B+lqCnZ0vK+oUNy1CzLsCukiMoDHfRkYGOTAR36jdRHhk4TvOju/VFT8YmAQBg/cgnS5ukLaEC/d3CDn5QiCu4Jfz537BF7kwwC+i0LowYO3t27dOfeCoyKOn4Hh4a67o8tHwRE1SoyGwGgIjIYAlhDYvfvZX7fWjwyMDAz/PR+UvVmx4k9FBUtHB3ySELRV+9y5dxUVwuAaRHzXLsgA3+dbtyA9NJCh4Jv6VP//f8HIiLx7ECTFwAAputG6f7zgHeagS4bADEjlAtkLwFwBvfLhK7j+glxNxMQAciKk6wgxFsSG9RIhNRrEPfzgY7T5GRhegLcwiIKXoUK08IGlfoMvLPoCFnoLHjnlY2C41XEwuIKJgeH7rl2jy0TBQTNKjIYAzcBoh5BmQTtq8GgIwELg4cMPCgonGRg+z2O4oQM7ffRnRYU0+NoGnl272NTUrisoQG784wXvsGcB15qQyUPIXCIDAwNkGhBiKi9soQ7kqijImW8MDAwv3Nwkdu36de4cS0UFW0fHf/CmfDnwlj/IQQIMDAyQq4HhXUSIgQJGRpBNjMjikC7iEwUFkY6OHxUVDB0drEZGb8H7Ev+AtUE2JT4HX63xE3yMKh+4zwm5J4oZPMoLv67wJ4PUz4pNpgyhoyfNgANvlBgNgdEQGA0BlBB4+PDzihVPKyo+7GMo42M4LLZrF+SCwa/gPpIkeOsB6CRn8Nzd74oKXgaGfx0d32FmvAffFcQA7ly9B5/eKQ6+FojnwQPItgLIiWKgbht48A5ywxCkHwjv/kHWd/CqqUEqF8hmATbw3RVfKyreMjDwgPe6Q0YVIXcdfYZtXgDVMrBTqSFdQYjTfoCPT+MGj1F+A49RQjYoioKnPXnV1J6Ca8BnsNNTBYyMVrldf8nA1cOgPLpMFBKGo+RoCNAUjHYIaRq8o4aPhgA0BMDLR89XVHzLYXgV1sFiGGH3a8WKXxUVvzo62MDLLKXBS2Wkd+365ubGCj6m5Q8Dw1fwzOG7igrDBw/QJu6g5jIwvNq9G9IJfAE+c1wCPH3Hcu7cr4oKyKjwdUZGBaTTBeAaIQzIppFP4CNDIROGkL4cZFkppIv4C9zIAM00MjDIg1skmEPIPysqfoCX/cC1SzEw3AffLMwKvmZKrKMDtHy0o2M2g21FxZMchq9pu/R1XY0gzhglR0NgNARGQ2CEh8Du3c/WuZ2awSCxa5ecEcPVN25uX8A9KElwRfAV3KdiAXfknoBLe1Hw7XzPwKEG3w4AKbo/nDv3GbwyE3IKNGTtBpHdP7B5CAK+BfEvA8MjBQXujg6+iAjIPkP48TOQbiFCDxILssaEgYFBDLy69U9Hx1PwGlFusEfew25E5AUftAYa94yIuHfr4zS3e7NB55T9LC/3QDJslDkaAqMhQCsw2iGkVciOmjsaApghAF8+urKD0bDC8xMDA7wf+NTNDXSYW0UFR0cHu5ERm5raMwUFpf//H+/e/QG8NEhp1y5Z1GNg4F1B+MQg5DKoL7t3f3Fz49m1iwes/lFnJzf4JDdM92AVQe4isoL3IjKAq+ovDAw/wcfhMDMwiIFHiJGHkF/D+qXs4KnLJwoKQgwMjxgYOMBNGdCANAPDT/AcI2Qc+gNDwnsGtfsdLqOXUmCNhVHB0RAYDYERFQK7dz/74xYhxHAYclGtYEfHF/CaT4ldu5gZGJ6BV2Gwg+fW2I2MJF1dIZu6hVFvkmBgYLgKnmpjAa/X4AFvHIDcPcgC3hqAOftHMJCRtyC+3b37jZubCPgwGPjZpHi6hZBzqr+eO8dtZCTi6voUfJooZIkKAwPDs4qKP+ApQUEjI0jPkJ2B4SV0mSjDrl3So6eJEoydUQWjIUAtMNohpFZIjpozGgJEhQBs+eijXAaWql0yT93cpMFzelwMDD/PnWMHn8XC5+r64+HD9ytWSIKXen56+PDhihXvKiqEOjrkIyL45OVxdQUZGBggo7lssJshQEeH7979ys1NE3ZSKFGuBCt6u3v3d/AxAAwMDNwdHS8qKj7BKu874PFpYfDST9CYLvhwOUifUPf/f2Q33OjsfA9e7MQOPiEAPrXIV1Fxh8HWlqEScjnH6Ekz4CAfJUZDYDQERlwIQI4SZWBgmM2wwYqh8z9sEI2dgYGfgeEGeKJPrKNDvbz8JiPo9BZ1cGGO3E+DbAiELAeV2bXriZsbhJQF30/4DLyy48O5c5Dzq0kNX7QtiE86O79WVMjBVp1AuoWQywxB1U1FhfKuXXKurpBrbCF2KTx4wAWuIyAdQjZ5+ZfgC40+gy9A4lRTu79iBcSEIxWcOQx/GBjMdu1SGr10HhJ6o+RoCNAHjHYI6RPOo7aMApQQ6OzcX1HxI5fhRfGDQHl5gU/g2vE3A8MrNzcB8JrMXxUV38HTccja/jEwcIKPApcE9yEhK0Uhs4JwZe86O+GLReGC18EnhUIO+IYL4mFAhoG5OzogF0sIu7pCOocy5eWfHj68DzvLDlL9C8OaL4oREQ8VFEDj2efOfQFPBr4BHy3zFHzkDAt4HawU2OXs4IPp2IyMjjMou7ndBt3MsctrdPkonhgZlRoNgdEQGJYhcGH3pelu9xgYGEJ2mRkyXH3l5sbeAbp2/eOtWz/c3P6DT2GRBx8DBjlqhRd83yA7A8NnNzf2Xbt+MjA8cXODHzcNOrSsogJ+sdC7zk6eiIgfsH3jkE0B+rg3IGCGMGR0TwLcBYXIfn/4ELJwVAY8XgkRBG1N3737LnidCwP4IkFQzxUuB2PwMzC8Y2Dg7ehgZGBgBQsKgMdAxV1dHz78rKBwFXx+jKirqw5YcpQYDYHREKAfGO0Q0i+sR20aDQHkEIAvH10BPn30F/joNsjmfj7wRfZfwXv3/8L0iBoZvQbPy/1gYHjPwCAOvtcPJgml0RaLQkUZGN6CO5zEdAi/P3z4DXYkwPdz5zhha00hjQDIyDTE2E8PH364dQty4MHbigoV8OEHb8HHGPwH91oh5808dXPj6+iQjoj4cusWZDqUG3we+k/wlVKQnTBiHR2vK27s7cgaXT4KCdtRcjQERkNg2IcAZGJwHsNGPYYOVtiwGhd4lb70rl1sDAwf3dzEwPdJXACvAhUCn8z5DnyRDz8Dw0dwlfER3P2D9Al5jYyQl/GDthIgnS8KuecWvrxTDrzYhGAgI89DwhVDRgwhC0fhghDGzc7O7xUV3OD9Be8YQAfCQDYuMjAwsDMw/KuoeA++lIIdfBLpf9glh486DkaDThP9vWuXwugyUUhIjpKjIUBnMNohpHOAj1o3GgKIEIAtH72fw8Be3PHqF/jGJ7GODl4Ghs8VFUwdHW8rKpQfPHizYgULAwOXkdFj8DUVoIvmwa2HtxUVxkhjvZChXOTFonCb3hK3ahQyDfgVvEIJs7K/yciIKQg6Uw58vhxkzY9sRwdbRcUj8Ogv8qD1B7CZEPewgGc+5WHrmiB3Fb5msDVlSGNg4Nm1S2/0UgpIQI2SoyEwGgLDNQTAvcHHDAwMNxksfoFPCgVd5APeSgcpgUGbCGCeh5el/xgYfoE7hL/Aw4XvwL1BBgYGSfB2cZhyKP3r4cMvK1YIoc7jPdm9+76bG/zmIR1UWahOJAptvShcBm3hKAMDw8vdu5+Dhywld+36Dj4Oh7Gj4z/4ckLFXbv41dQ+Kiiwd3Q8qajgAZ9E/Q18FOrHiopzDBPzGYQYGFTAS02l4FaMMkZDYDQE6AlGO4T0DO1Ru0ZDAEsIQJaP5jC8SNulr64m/AWyINPI6CcDw3M3N8GOjn/gM12+VlSI7tr159w5DiMjRjU1yCFvkH0XkBPesC4WhduHf9UoZMQXeY0oXCOcQXCa8Rf4xPRnFRUfwKtDmcHzhKAj78DdV4g5IgwMDyoqQNctg7cjMjMwCDIwMFRU8OzadZ9B+KDbOgYGBvMOdbEI39FdhZAQGyVHQ2A0BIZZCBzpXPG84vATBg2JDn0LhuO/wXcMshoZPXFzYwPfQCsEHlPjRLqEFnIWFzsDAzM4LJjBp8uIR0Rwg/fmgcXQCayTexBFVzo7QSUz+KohxV27ZLD1J+E70pHXi0K0MzAwQNaMcHd0yJSXQ7qCvB0dPOD1nwwMDKDe7LlzP4yM7rm5Ke3a9dDNjRM8Qyj34MHXW7d+grcMiEZEbFjxMLqCbXSZKDxURxmjITCAYLRDOICBP2r1aAhAQwCyfLSa4ZUyw2MdhlrQmZzgI7k/g0+WewFWpfvgAagaXrGCw8iIH1x/f3r48IKCAqR9INrRwQq+eFAax4gvru4cZFYQslcQeY0o2E4UAtJpRF41iiwN6Q2CzoyBbVmBnIIjFxGBvJnkpYLCVwaGt2Cdwh0dfyoqBMC7SpC9fIEhOJwhZnSqEBxIo8RoCIyGwPAJAfDE4KPZDBsdOwSQLw/kBV88ywQ7d1qyo4OVgeEPeAkoxPPMDAwPwAtHuTs6RCMi/jEwvAQPHeLpE0I2ELLh6DFCVpBCuoUfzp3DuoIUT5cStIF89+6nbm5s4Nt0ZXbt+gLu5qmCK6DPu3f/PHdOpLz8xe7dT9zcILvfeTo6IEOBkKtuLzFMzGbQZWBgGL1mEBLFo+RoCAwsGO0QDmz4j9o+GgLQEAAvH93FwPCumoE9ruPVR/CVU6IMDG8YGF6BlTAzMLAwMHCAd+RDRovhnaj34PHXlwwMXLC5OMicIVgflPj+8OEDBQW0s0YhvUTIUaJYl4NCNcOom4yMWDuEyL1BePvjCbibKwS+oAI+Av26s5PdyIjP1fXTw4e3we0b/o6OP+C5RNBtywwMIh0d9ypumDI4MzCwdXQolpebwiwfpUdDYDQERkNgCIcAuDf4mIHh/4OOgx8qKn4zMHwHXwXBXFHBDBsmU9y1S8LV9dPu3Z/c3GRgR7m83b0bcnMDAwMDfCv414cP8fQJITsIIFfR4gmyTw8fXlRQgF8ei7yCFJcJz8E70iHbv9nBq1c+gmsliC2Qzh4neCbzB3iXowB45pMTPOf5CuxfBgaG6wyWoaClJKy7dumPniYKCbpRcjQEBhYwDaz1o7aPhsBoCEBCQF5e4P//sI4O41YGCfUKHbEH76U7OkAnr3R0aO7axcvAINnRIQpedfMJvG+Ev6ODv6PjW0XFTzc3LrARHGDybUWFgJHRgxUr9jMyXurs/PTwIeReQU7wODGkBwgZ3L3JyPjGzQ0yK6j+/z+8nQE2BjvB3dEBNwGuAmtvkIGBQcbV1QA8q/nh3LkrYJcwMDD8qKhgV1N7t3v3CwUFXvDROAzgTSbvKyr0HzwQAQ82S++KOtTxfzLDJc2KafMZWx4+/AC3a5QxGgKjITAaAkMuBHbvfraFMfujW+tqhkX7Ox4IRUQwMDCIdnQYP3ggDC7VmcDludyuXXfd3D4/fAi5zRXiTcgOcMjGQuRSmlteXhxszssVK76CN3JD1MNJto4O+PAcXBCNwScvb/v/P+SoTwEjo8OMjE/A/T2IMmQTnu/e/Xz37tPgey8g11qALhzq6GAHbxD4XFEB2Sgu2NGhsGuXGFicGbz89QZ4FpGzo+MXA4PWrl2XGCx1KuxDK/7u2qX7/7/naG8QEtSj5GgIDDgYnSEc8CgYdcBoCKCEAHhe7XY1wwsJhsf2DLUisMNXfp07xwA+muUNAwNfR4dkRMSHFStYGRg4wNs2vp87JxAR8Qts0gPYnU4CRkbwc8AFjIx+nzv3q6JCbNcuyM3CYLUMXGpqkL4ihIufhPQGkRslkN4gG9gNvOCFrGgmwBcmMTAwiBsZ/XBzY+7o4DAy+svA8MLNjaujA3JYDgN48vMneEQZctQqaCycgWFTxZ0kBu/R5aNooTrKHQ2B0RAYKiEAnhi8uZmhXorhsNiuXR/BSyshN0BwMTBwgMf1JMHi7ysqFHbtegDeOs5eUcHR0fEJXOaLgS+Cx+rfV+D+25dz59DWjuJf7YlpFLygFjAygqwg/bNiBQN4Kzvm9Ybway0gS0MhWwAYwD1D6YiIRwoKfOCjsBnAyzx+MTBA7p0HrQ6t4CxlUACfMK0+2hXEjIVRkdEQGEAw2iEcwMAftXo0BLCHwMOHH1asOFxR8SWH4UccQxKk9mUEj7PyRES8A9/fILNrF2RKELRw1NX1PiOj5IMHHOBpQIihkPuCIewP4CVJDOBFO//B21Q+uLlxg1dkQhQQSaJtIyTYG4QY++Xhwyfg7S4fKypYwFtlmGEdPwYGBvih5L8rKhgZGL6C9fCDTysF9Ql37Zp2jrGi4l1Hh+LopRTgsBklRkNgNASGRgiAj9l6WlHxnIGB4WjHEwmGZx8qKtjBZ2wKdnT8rajgA28KgPQG4V56Dy6uIafLCHZ0sDAwiOK9IgJrnxDX6aBwW7Aynu3e/RB8UigTeIfCD/DRXwywJf2Qmgj5RNNfDx++VlCQ/v//+e7dn86d4zMy+nnu3O+KCoGODp6IiNcrVnytqJB+8ODBihXvKiqege6W+MDAwPrggY28PC9WB4wKjobAaAgMFBjtEA5UyI/aOxoCBEBn5+mKijcMDP8ePLDmXTHzMwPDT/Bt7x8rKvgZGBg7Ot5UVEh1dMiDN/GjdQi/PHz4GXydIGRwl7+j4wf4bihGBobPDAwssK2G7ysq4LtH5MCrj3iRupRY3XeTkVHuwQNOeXk8vUFM2yGTfl8qKngZGEBHrYN3mEBOzINcVwgaLwdPGCqAvfMWfAgem5ERr6vrl4cPFyjMlmN4KgQ6ja529FIKrPEyKjgaAqMhMKhCYPfuZ3/dWt8zfPvXkegbIc8nL/9q927IyStK5eWfHz78DR4m+w6+kQ/icsh8IDP4Yh4x8F0UPyAS4Pv6hMDlNqSg5kMtqNH6hJDtf1hPB4WZB6LRCmrQ2CL4MLNHbm5i4NWe38H1hWBHB+SEMAYGBjTb4R1CBgaGW52doBG9iop/YF1CHR1fKirEwG6+ZxTr5vYIVH6PXjMICvhRPBoCgxGM7iEcjLEy6qbREGBgYCgvN921S52BgUVB4eSyit+gZZngylUZvKXwY0XFdwaGuxUV+xgZL3Z2/mZgeL5ixfPdu291dp5mZHwGbmpAJt+Udu36D153xAI+AAByKun7igpIb/B9RQW/kRG/kdFFBYVHK1YcYWQ8wsh4tbPzamfn54cPP2NsTeHu6Ph26xZab/DLw4eQHSaYtkMGlSEnEIh1dPwFDzkzMzAo7tr1Ddxu+M7AwK2mJunqKr5rF6jHy8AA6Q3+YmD4wsAA8UvwLnuzDg0Ohi9uboc7O0+P7ioczSCjITAaAoM5BMDLRB+xM1w279CQrLC/v2LFxc7O5+fO/QHfMftq927I0BtPRIS0q6ugkZGgkdHXigr5XbuYwQsvmRkY4IczvwOX3pDTuQSMjB6tWPFoxYpDjIxXOjuvdHY+2b3708OHYuDl+jxGRpD9hL9u3WIDVxbIQYRWSkOKVvgaDZldu/g7Oj5WVDxxc5Po6OAEzwr+BzuDEXxhIAMDA6btr27d+sTA8GL37rOMjHzgjQOMDAy8YPUCRkYy4EJ+QwWnm9sZcG9QffTSeeQYGWWPhsCgAqMzhIMqOkYdMxoC6CGAvHzUleGQ6a6on25u7AwMwg8evL5164GbmxD48gY2BgaGjg5IG4IXfKobZLj3R0WF9K5doINb3Ny4wfe+P3BzE9+1SxTcgID09x6Be4+gOTqkCUN+IyPIVhbIqXGQiUS5iIg3K1b8AK8IYjAy+uTm9gnsXsgsH6T/iWw7L7iJwKumxgMez/64e/ePc+fEy8s/PXx4H2YpZHsJ5EJFpooKLrB3PoHXlIIusTAy+nzu3M+KClHwdpTZDMYVFe8YGNg+PzCEmAm2f5QYDYHREBgNgUERApCuIGi/3C45Y4aroIO7YH0zeFnHysAg09HxG7x89BX4kLAnbm6Q/pjMrl2QAvMN+EBmyMZsyMFgkIIaPlkH6aGByvZz5yCCn8AF+LeKCknwPu2f4PCALBKBVAeQlRrwchVyWjWk9AYJwgpqyP5DBiOjt25uXxkYWMHuf19R8R/cWYVMVEJsf+rmxsPA8IGBgQe8CIW/o0MMrIulo+ML2PYpFeqzGJhGl4mCA2OUGA2BQQ1GO4SDOnpGHTcaApAQAJ80c5OBgeHmLnEGN2MGBoaP4C4TpPclC76a4ht4TFcW3P2TdHWF7PeDaBfZtes3eGeIxP//r3fvfunmpgM70ByiAE5Cuogfb92CnHwAmUWEyDKDjwPlAq/2fAsmmRkY5HftQmtwMDAwIG8ygeiFkPAOIYTLwMAA6RlCeoOgu4wrKj4xMPxjYBDo6ICcqicHbh79WrHiV0UF265dPxkYTrstU2RYcJ2hWXBX1OjyUXhIjjJGQ2A0BAY8BCDLRDkYpr1kyLLaFfD73Lk/FRUiu3axqKl9uHXrvpsbZAnlx4oKIQaGF+AlG/KwY8Mg/UC4F5A7hHBBCAPSP/x069YHcFeQATwjxwA+u+WRmxsTAwMPuIL4Az7IlJmBAdIPxNX9g5gJJyHLTSH3VdwGnynKCT7whh+8tJXfyIhPTQ1UdN+69e3cORZwic3OwPAXvPoDYshX8N7IWx0Hg0GXzn/etUtldGIQEjKj5GgIDGbAMpgdN+q20RAYDQFICLi6Ku3axeDmdlvd7c48hmbrDlYZ8NTZE/Bmj58MDEq7dj05d+4DeBCXnYHhJiOjyK5dIuDOobCr65fdu38zMAiBL4EQdXV9ycDwevduyCQhxHw4CVnIxCsvD7k58EtExJMVKz5WVDCBb5oSALdgfjIwcIJv0OLt6Ljh5sbMwGDw4AHanha4gfgZfPLy+uXlr42MXrq5MXR0fAU3ZSA925/gVs5t8BTor4qKvwwM/ODT+TQ6NEQYOq5X3HFzO9PR8Xb0pBn8ITwqOxoCoyFAhxCATwzuY2CQ6+hQNjJ6Dy6v3jGAVrrzgFdjSoJ36D0BL/EArekA3xx7282N1PITUtjyQUrp8vLnu3d/PHfuc0UFW0cHE/i4Zi4Ghm/gMbvfDAwvwEfUCHV0qBNXSn9ZsQJ+24Twrl1vwdca8UdEQOYnGRgYHq9YIRcRwcPA8KuigrOjg9/I6JWb2/eKis/g7QCgC4d27Vrpdr2s4isDw99du7RHTxOlQ/IbtWI0BCgHozOElIfhqAmjIUCnEAAvHz1aUfEph+FHWAeLYYQdw61bX9zceHbt4nF1fb5792M3N37wBcdKu3Z9dHMTAR9WDhnxZevoEAKf18IA7g3imSSEnzQAWcUEGV3mNTL66ObGBz4Gnbej4wf4OFDIzB7kmHIIWwF8Mg2kvYIZKB/BJ6Tzg1erQmQh05Xi4I7rS/Ba1udubpzgCxgZYOcoSDEw3KkANS44wV1EdgYGdnDT56xRoJvbhRyGr2m79HVdjSAGjpKjITAaAqMhQOcQuLj70nS3ezMZJHftkoUsE/0MXmnJwcDwvaKCFXxIDDu4TwjpL/0El6VfkK4FYmBgUEA9ShTPDCEDAwOklIaszpCBzTFCSmx28HHNL8FXwL+uqODu6IAU0aDrZysqhDs68JfSaMeTPu3s/FZRoQpeUfIZdov9V/B50aB7BcGDjL9v3Xrl5ia2axcreCK0xe3rbAaJjo4PERHWo6eJ0jkpjlo3GgJkg9EOIdlBN6pxNAQGJgTgy0c3dTCqV3hClo9C2hlv3Nz4GBi+g68Ahrcw3nV2/qqogCwBgrv4CiMjfCchRPA5uLcGaVJAVqJC2hkQkz+6uUnt2vXz3Dl2IyNWNbUHCgqa4CYC5HILSG8Q+dpDuO0QwyEkcofwNdg6iDikKwiZsXy1e/cLNzeJXbsgxyS82b37t5vbWwaGd+BGlXBHBz/SaasMoO0rCYIMDHs7skanCiGBOUqOhsBoCNAzBDo7bxhXpPEwHP4N7oMxMDD8rqjgA+/o5gHf6ccGFpF88ACy7fnXw4dvFRSEHzyA3Br/aPdu+G2xyMUm1g4hcikN9+NH8C5EyFp9yPCf0IMHkGPGeMEOgGwmhKiHdw4hXLTO4Zfdu7+4uSEfT/pu9+63bm7Cu3YJubq+BBfaz8C7D1gZGDjBazog5nypqBDv6LjAYBkCWibKsGuX3OjEICRkRsnREBgqYLRDOFRiatSdoyGACAFwn/A2A8PneQw3dBhqZWAb+eQYGH4yMPzq6PgGPhHuw7lzrAwMjOBD6vjAm/HgRrxasQJ0Jjh4ag4+zPz53DnIfCByC4OBgeETuB3w89w5vogIdnl5BgaG64yMaHclQ3qGEPPfVlQo79r14dw5SEcRPvL9srOTw8joF0QRAwNomSgDA1q/lIGBAdIn1AN3ON91dv6sqBB+8OAHA8M1BQUO8JpVyLUZEKeyV1S8ZrA1ZUgDn5ijN7qrEBa6o/RoCIyGAG1DAL5MdDbDBiuGzn/gw1cEjIxeuLlxMDC8Bi+tVwKPbd1mZIT0qRgYGJ4zMrIjrdeAOPHR7t0fzp2D99YUIiJ+rVjBDrt35/OtW/BS+ombG2SoDnIYDNrOw18PH35ZsQKyGOTrw4fPV6yAnOwl7uoK2Xn4YMUKSLEML6UhDoD0Rd8rKLB1dPCCF3qADiAF31307dy5b+DVoX8ZGL6BaxOZXbueu7mxgbdBQs6YETQyWul2/TkDVx+D7q5dsqO9QXiojjJGQ2CogNEO4VCJqVF3joYASgiAl49eBF9e/wqyfBRyqxWbkdEnBoYP5859AdfcQgwMPxgYmMA7PSDnxEBM+Q7e9/8MduoAaJqxogJrCwOzNwhaerR79ys3N8gkIcRAOInW7IA0cSBNEAHwgTF/GRi+gLe1YHYF4YZcYmSU2LVLQE3trYICe0fHbyOj525ukuBO5nfwPVeS4K4sO3iXDpuR0XEGeTe3UzkMX2065MPLPeDmjDJGQ2A0BEZDgBYh8Hj37ha3r7PAy0QNGa6+cnNj7+hgB8/IcVZUfAMf68LX0QHpd/EwMLx1c1P9///L7t2f3dzg04PIDoOPqQmDl5LyMjDwgS8A5Af3MyEq0YbqIILI5LvOTjYjIx7YsnxInxB0gqiRkThMEDTGB75PCN45FDAy+nLu3HfwFvQ/sHlOBgYGPvAx0Z/OnWOvqPgJProMclqpAvjS+d8VFaLgZaJnVhxfWcExi4G/o+Pn6DJR5OgYZY+GwBACox3CIRRZo04dDQH0EABPFYJOH13RwWhU4fkBLA+ZMOQ1Mvrl5vabgeEPeL8ffF6OHTzJ9ouBgZ+B4T34Lgcm8PYVyHImsAEI4tPu3T8htz48eACZG4TLXWdkVADfUA8XwWRAmjhvKyqkwW2az+A+6l/wGeUfwGzQ2eVgKVA7A9z4gBxh93LFis8VFcLgy5G/MIDaXKCRdXCfEM7gBp/WwNDR8R98vs5H8CXIryu2mjKUP3hgLS8vgOmeUZHREBgNgdEQoDAEIBODNxkszzNkiTNME+roEDQy+nnu3K+KCrFdu9gZGD65uYmCFzU8gF2u87aigp+BQaqj439FBe+uXQzggzo/g+ffQN0z8MAW5BRQUEkI7gdKMTC8AW9E/FZRIQQ+jQZtMhDTF/D1opDFqBAFuPqEEFmQ7Q8fXldQYGVgYAVXCg/BEqzgM6vBTAaxjg72iooX4I7iJ9jaE9A+RnAZ/rzjYAjo0LHfu0YvnYeE1yg5GgJDE4x2CIdmvI26ejQEYCHw8OEHBYWTDAz3V3Qo2BkxQ1YW8YOrcAYGBmHw1cAv3Nz0YBdOiIPn1t6C74/63tHxHWYOfGEnTAC0UvQnuKUiCjuNBi7FwMDwqLOT28hIGGnUGVkWzn61ezdkcu+5mxsjuJEBalIgdQI/njvHD+4KIk9gMoBXW72vqPgBvhmZAayRD6brE7ghws7A8Bt29wZksesvNzfw8tEEBgahXbtGl4/CI2GUMRoCoyFAnRAA9wYfg24AYrCA3LYHuV0QcggWD/ic5B8MDBywwgp+j+vHigrQ9QzgvdCQST8+I6NP4AIW4jI492NFBQcDgxgDwy3wMVr/wNKY5TNYGIWA3B8IWS+KIsHAcKezE3OeEHLA2AvwnkBQZQG+A/Ye2FKIdoilEGP/gs+CZgdXGZAVHwwMDIcrOO8xfOpjcNi1S2l0mSgk0EbJ0RAYomC0QzhEI27U2aMhgAgB5OWjkCM3IWcDQE4fvd3Z+aWiggd81hwDeOee+K5dfGpqrxUUZP7/h0/iQRoEkBYAaNgYPDfIwMCAtTeIf9UoxGXwriB8Tk/M1RVymA0HeIAcZAt4jBy5KwifOVRiYHgKPjydBXzbIbIa0I1Ybm4/wSfNMDMwsIMV/AKva2Lr6Pgckd6jsFKd4bFoh41FhMXoVCEkOkbJ0RAYDQFKQuDhw8+PV2w9XfHqJoOcfYeQu9HPN25ukD7hF/Ak3peKCi7wHaoMqD09BnAZ9QN8dw4TAwMz7LhRyGQgfGUmqMMGLhh55OUhfTCWiAj4HCPE5fDyGcJFI9EOCEWThfQJGRgYVGADfMgne0HO9HrEyCiwa9dHBoa74Pt+GMDLSf6BZz7Z1NR+3roFPwDswu5L09zuzQYtE2UtL7dBs2uUOxoCoyEw5MBoh3DIRdmog0dDAHsIIC8fNazw/AnePQg5I/SXm9sL8MJRNgYGPfBtVK/BA8bwzh6kW8gAvnpeedeu/+fOMVRUsHd0wBVgtRLzaBmIMqxdQYgU5J4JHfCBMRARrOS7zs5P4Audz7u5CXZ0vAefUgNX+RF8IfJX8C4dxV273oNPrxFjYPgP8zJogpSBAbx8tGh0qhAebqOM0RAYDQHyQgA8MfhoNsNGxw4ByKqK9xWg8zSFOzo+gA/5fOLmxtPRwQjehsfQ0QHZfQex6xW4c/UJfPToF/DpLIqwLhlEARqJtvLz08OHH27dgp9ECtmUKIexNAOiC/mAUDRjGWDzhAwMDDzgk2+YwSokdu2C9AZBB3qBz/3ic3V9AT7k5ivYjzyw00Tfg2+bYGBgeNpxMBS0TJRh1y7p0UvnwaE4SoyGwJAHox3CIR+Fox4YDQF4CMCXj+YwsOd2vIKcw8kAPj/mM/joUcjqJmYGBhZww0Vg1y7ImiX49hUu8FrNzwwMn8E7RhgYGOTAJ85hvVrwLfj0UeRVo3i6gnBHQiYJ4U0QuDicgTy9+enhQ8iFyO8qKpCvb4YcWMqopnZ/xYqvFRXi4NnCfwwMcrADV2UZGD4xMKgzLAZvk1QcvZQCHryjjNEQGA0BkkIA3Bt8zMDw/wKDJSv4oldB8IpQXgaGTxUVrB0dnysqZMFL8bnB5vLCemtvwYdvMTAwcHd0iEZEcMrLvwGLiO3aJQJTA9aBQkCmBzFXfiJfUAFZtIk8YYhLF2iz38OHkP2KkA0FHAwMLOAN5HBbBcDegcxkfquo+A4uSJ+6uf1lYBDp6PgNvgaWDaxGwMhoo9v1HNDOdKvRuyXgATjKGA2BYQBGO4TDIBJHvTAaAogQgC0f/ZbD8CKsg0XbSOKXmxtbRwdbRMTjFSs+go/35AcvxeQEV/AQnZDx7N/nzn2CjQH/AcsKGBndh83RQdpAAkZGkHNf+OTlIc0dyFmjxHQFIXbhnySEjHOzoR7L/gQ8XP2uokJp1y4ZcEMK0iHkd3V9v3v393PnfjIwfAa3WhgZGHQePPgFPsuBzcjoPoPwQbd1oAYZA4fTg5zR5aOQKBglR0NgNASICYHdu599dWu9z6Au0WHgEyH/E3YDO5ua2vsVK/5UVDB0dPwGG8RnZCTl6voZvNJeBDwBCBkvA0syII+aPQSvzuA2MsLaJ4QUgGjXxkIMgZDw1RyQA5wh10XwycsjrxeFXFfIwMDwBLw/EHJ8NPyO2Y9ubv/AyysEOjr+MzCAVuDDNnK/d3NjAY8GfmVgYAZvJpSNiHiuoAC65PDBg/u3Phq4fQPfdDu6TBQSG6PkaAgMHzDaIRw+cTnqk9EQgIcAfPnoPIazJgy1P5AOXxExMvrs5vYRfGPhfwYGyMD2Wzc3TvBFwxATfjEwCEVEfLp16wP4zIP3FRWQdZuK4IsfIGoggj8qKgQ6Or6CT3mRBF9OJQm+dwuiBheJa5IQcokWaEVTRATyQXmg3YawqULIpOX3FSsEIiK+gbcgQmxhVVN7umLFB7B7foFPo4Fs0eE1Mvrk5vaSwdaeYXT5KCSoRsnREBgNAcIhAJ4YPDODYZFThynklGbICTGsDAzc4LNhBDo6hCIiPt+69encOciYGi8Dw89z5xiNjF6Be2IMDAxo97UyMDB8e/jwNXhdgxa2lfN4JvqQXYzWLWRlYGCoqIDvToQcNA25owIyKwjZOwAxgfXcudcVFR/A2x0hBSafkdHHc+fYwStHvjEwfAEvD2E3MnoPLv/fgY/Ded5xMBi0THT0NFFIKI6SoyEw3MBoh3C4xeiof0ZDABICyMtHq3bJQIaKxTo6eMHSPBERF8Hjvqb//78Hr/xkYGD4fu6cUETE91u3vp87JwUe5AarBRGQ2wUhqzc/VlQId3QwMTB8rKjgBk82fgGvQWKAbVAEacCLcU0Svuvs/FVRgWd0/Ap4cJ2BgeF3RQWoDQS+VAMyQg8ZBX8MvhIadBYObO0oOwMDH9gxlzrW+Ff87egYXT4KDo5RYjQERkMARwiAu4KPGBhAuwXPMjjxgjcK/gR3kxgYGP6Cb8T5AF5DwcDAAOklCoLVcIL7iiLg5aOgY7rAyxkwLXmze/dXcF9LHrWYJTg9iGzUl4cP4Ys+BMHbp18xMAh3dLCBS2bITRWQg2rQrhR609nJbmTEoKb2BLySAjRwZgSa3nzIyMgBvpX+toKCQEfHU/BRZBAbrzBYRlc8YmBQGV0mCgmQUXI0BIYfYBp+Xhr10WgIjIYAAwODvLzA///uHR3qUxgk2tyeiD14b/r/P6Q3yGZkBLqHENygucLI+OHcOchMoFR5OYe8PCf4pDu0MOSTl/9665awkdH3igpR8DX3DAwMIgwMXOBj1lnAdyjL79r1YMWKA4yMlzo7Pz18COlDopkD4UI2ECKfcQfa6LJ796+KCp5du9DmBiFaQAoePhQ2MvpaUfEdfNTBK/BmHvaODplduyDHuD9xcxPo6ODu6BACe00qIkL/wQPpjg7ejg6pBw/8yoN37TLgrti4WWHK7t334MaOMkZDYDQERkMAHgKQZaKpDHd37VL//99R/cEDZnBHy/DBA7mICLmICD7w/mTkrqDCrl2gjdngcbFfDAyX3NxuuLl9ZQBdxAo3Fpkh4urKDS7K3sAG4yCyX1asAC3vl5eHcLGSz3fvfr5792lGxmcrVkBWdf5lYOAFX9nKB17n+Rx8bs2TiopX585xq6mh9QYhZrKrqfHKy2uWl0Ou/Pl87twlRsaXYH/9Ad8Ty8DAIN3RoVdefoXBUr/CPrri765dBv//W4zeLQEJwFFyNASGHxidIRx+cTrqo9EQQAkB5OWj1h2skPNjIPNpn8HHBjwHN3cYGBgUIyL45OV/PHz4bsUKyAzhF/BpBJBFR5BlSJCDaj6C71nmZWDg3bXriZsba0cHG3h90Tvw8euCRkbwU8vhW1xQ3MTAgDZJ+GX37l/gIXO0oxSQHQDp9TEwMIAu5qqo+AieHvwJNpcZPHYtDR6P//7w4SMFBfX//389fPgZPATOGxHx9tYtuC9+ubnNZpjB22E0etLMKBgNgdEQQA6Bzs4bFRX3bzF4fevogBzcohgR8eXWrR/g0kk8IoKVgeGdggJ8FcONzs6PFaCVlLzgyyTYwQNk38B3/EBG2SCGKGA7mgttMyGe6cHn4H4jZJWHDLjIhZCQYpydgYHx3Lk/4INDvyFdgQg5QgziAEEjIwE1NcjZYG86O/nAa/JfwIz9D+7K8oMPHoOExjvwEoz7HQejK/4yMDCMXjoPCZZRcjQEhjEY7RAO48gd9dpoCEBDAG35KKRrxMHAIAo+ghN0yzIDg6CR0R3wvhexjo6/4Iun4IcQfD53jtfICM7lVVNjuHUL3n+DnJ0APzUBvrkFclOzMvhaCEhHEa1VBN9JCNk6+KuiAn5mOrwBBGnxoDmAgYHh/7lzTEZGL8GtNAYkxwuDl4Q+VFAQAU8SMhgZvQbfWAjvzb5xcxNlYDjdscYPdJYez+ilFNAkMkqNhsDIDgHYMlGGtR2/dCvsPzEwsHR0QPpUoD3M4KGu7+D7b/6fO/fTyAhSikIKFgbwKk2ZXbt4wGH4X00N+f5A+OkvYEkG5GLwGiMj5ABSLnno3YPwETHIWBjkYBhI9w9iF2RnIOiUF/D4F2iiEryQXrC8/D6YwWNkxA5e5XF/xQp4bxCyGxDCFQCPqb2F3ZYBuggRvND0B/hcrq/gs2QYGBgujS4ThUTYKDkaAiMDjHYIR0Y8j/pyNAQYGDo794MPW/l3aZekuprwF/DsGU9ExC8GhscrVrwDH0DKDN4D8w98i4Pyrl3PwF1EGfCWGElY+wNtNu/7w4cPFRQ0MA5IgCwZfQA2WQjctEKbNoRcc6zz/z9k6+BP8DQj2ig4vNWF3AACtYHAY9uCYCd9evjwPtgv8J4hPwMDI3hzI6RbCNnuCPEFO7gPKVRevrJzx+uKI+oMTwU6nEzLY0cTyGgIjIbAiA2Ba7sv3XabeZtBQ6pDP6rc7jYjIwMDg/iDB/A+lYiR0QNwYcgL3q0nC96iDO8H8sJWZn4Gl0vwayfgZSCkJwa5QhAycwgJatBF9uATaFQePIBMPL4FH5QFH3174uYG7w1i3RD46+HD9woKgg8eQFbaw/uEkGX5oK4sUvHIDraVuaLiHXiJKWTLkPyuXZCDo5+Bd5Vr/P+/rPNQNOiSxe/gGwp1wJpGidEQGA2BYQ5GO4TDPIJHvTcaAsghAF4+epuB4R/89FHQiQIdHZCFoKzgPYE/KipAl9pDZtgYQOPZkIVGEHO+gFs8n93cJJF6gDcYGUV37YJPEkJUIpO4pg05wP1Plo6OL+Dh6s/gqUjkTiC8pYVsGlqHEC4F6RmCzoIH36b4Ddx0E+jogMxPMjAwSBgZvXNzYwAv7oIsQP1UceN5R5BfBdODB9ajl1LAQ3KUMRoCIyQEYBOD728yeP0AF3qQQzXZwYdwcsJmAvk7Ov4yMDCBj1N+Bx5vkt+1C7N0QusQIochWueQgYEBMnP4tqKCk4GBFbz97zcDA7z7Bx8aQxsLQzaTgYHh18OHX1esEEQ6nOYyI6MEeAgP3idkYGB4uXv3Uzc36V27nrq5STEw3AevEeXu6OAzMoJ3UN/DlolGVUCWiSq7usqhWTfKHQ2B0RAYrmC0QzhcY3bUX6MhgD0EwBcV3quoOJvDwJK2S5/93G7IrkJu8E7CzxUVouDB7y8MDM/BrR/QWXngLTFyrq6QtZ2Y10K83b3727lzskiNEux2w05ZuAIeimZjYOAH7wP8DJ7NYwa3ivg7OmQjIrAehIBsJuRkVMgMIVz83e7db93cuMCtut/gRVwfYcufBIyMQMP5bm5vGRhEwd554uYm1tHBXlExunwUHoCjjNEQGFEhAO4NgpbMn+q4z18R+Rl8BZ9QRwcHA8M38F0LzODCBLJgHnTOCniM6SX4Fh8GBgbZiAhe1ANg4Nvz8Afjp4cPP9y69Rg85cgGLvdEGBheMDCwgo8qFQMXUPAVGfiNeg9eJorcIfz28OFL8IoJ8YgILnl5eFeQgYHhqZubSEcHDwPDG5ihrysq+Ds6IHcIMTAwXGawjAKfJvrggba8POQMMpjSUXo0BEZDYFgDyJKBYe3FUc+NhsBoCCCFgLy8QHm5UUeHyhQGCT23lz+NXE3//1fbtUt61y4OIyPRXbv4XV25jIzEjIwc//83Bp+wB7rz/dy504yM9xQUvjEw8IAPJEAykoFLTQ1yFSGyICb7xe7d327duqWgoLRrF6hdAr4DA3LBFzMDg8KuXT8YGH4zMNxbsQJyTimmCXARTjW17+DFn3CRd+CpS2Hw0LhQRIQCuHeqsWsX5GDAD+fO/Th3ThB8e9iLiorbbm7qDx6A2jsdHSZGfIc6/m9kWCrhpny6c/HDhx/gZo4yRkNgNASGZQg8fPh5beehb26tqxgW9zGcVI+w/A0+q1Nz1y4OsId/gKcBf1ZU8ILHkkz//1cCdw5lHjzQefAArAS00v4puNiBcIkhvzx8+Hz37hcrVrx0c1PatYsNfKSnAHhEjJeB4W1FheKuXb8YGK67uV0En9WM38xfDx/+qqjgBp9YA1fJJS8vHhHxj4HhloLCSUbGh25ukrt2QTqfRv//C4G9o1ZezmtkxGtkJNrRIWxkZPv//2UGS90K+yjYaaKjvUF4eI4yRkNghIDRDuEIiehRb46GAEoIlJc77tqlzsDApOf2ckXnjntubhD0/Ny5G52d993cPoF7gDcVFCQ6OpR27WIAHyv6H3zTw/UVK9AaK5zgYXLI6TIo1oA5L3bvfrF79xnwthz4lhgGBgZO8AykcEcHJwODREfH+3PnINdFvKuoEDQygt9g8Xj3bshqK7BhWIh3u3ffZmT8fu4cpIsoXV4OcY/krl3P3dx+3LrFJy+vU14uCNZq/OCBcEeHQEfHDQWFexUVn8CC4gzPNDpMfjIk3K+4qaBwYPRSCnCojBKjITA8Q2D37mcKCldDKh6rMkyz3OXv2vH90YoV/zs6fjEw3HJz+1RR8Q3cMWNmYJDetUvS1VUSvDgC1DkEX4rDKy+vBbuw4eO5c9c6Oz8/fIg/pJDvivgMHsni7+iAFIZcDAzsDAy84N6mArgYhCzKEDQyur9ixT5GRrTCFs0itGsqIB3OqwoKfxkYBMEr53nBZnJ1dNxxczvKyPjAze0NAwPocNRz5x65uf1jYHjk5rai8xB4mej3XbvUXV1HNw2ihfEodzQERgQYXTI6IqJ51JOjIYA1BODLR7MZWGI7WPgZnkGUiTEwPKioYOvoYGFg+FxRwQO+ZvATuP/2BbyqSgV8dihEMeSyCsxVoy927/507twn8OZASNMHQoL28oEbWO8UFNg6OoTKy1/v3v3CzU0XvCkR0veDH0UDsQJyYKlQRwfyAX13GBlV/v+HTAwyMDB8P3dOKCIC0hWE63q1e/dzNzd98BUUbxQU2Ds6hMHWvQQv1vrHwMAFXi4F2k9YUcHHwPC6Y5FeBSP47FXF8nJTuDmjjNEQGA2B4RECsGWi/090POCtiPzOwMAOLgQkOzr+V1S8BZcJAkZGcq6ukCXoquBy6RkjI6T0QA6Ez+Cr4SEi/EZG0q6uyEtG4UclM6DuDIScnAzff/gOvOZTqLz87e7db9zcRHbtYsV2RijkWBpF8GQgfFM3fL0o5EhSyFE0kGNIIccpPwSvmX9XUQHpcP6rAJVuoEsmwP1PPvBsYbvb1ykMXxgYVEaXiUKicpQcDYGRCUY7hCMz3kd9PRoCiBCAnT7KcLDjjYqRxC9wZ+lvR8dPBob3FRWq4DVUP86d4zAy+g3WxKqmBjl8jwF8+x/kQHNx8Fl5Sg8efLl1C94P/HTuHB/4vgrICZ8S4ENBwWYwQE4WhVznBekQSoCPtIPIQshPDx9+vHXr/blzkFNh0K435KqogCw35e/o4DQyEkIyHKIdQl5kZJTctYuTgeGzm9s/8BYdno4ObnBL6OO5c58rKgzB3UV47/Rw5+KLFTflGJ6+ZVB2epAzetIMJBhHydEQGOohsHv3s39urZAV4Z4Pyvjk5d/s3v3KzU1s1y4RV9cn4C7fc5gnISeC/nRzE961C3TPREWFCOwkT5gSKP109+6P4Ek/yKoHRvBMI6RXBhkCQz4lC21n4K+HD98qKAjDTL4JXkahDu6CQkyHHJQF6Q3C78CA3Bn769atv25uPzo6XoH3AYLOIEUqbCHHKf8CH4rDa2T0zM2NDbwgX6ij4z94f7Xorl1r3K7nM5gxMHwGz4PqQGwcJUdDYDQERiYY7RCOzHgf9fVoCKCEANLpoxesO5ggx8yA7ikG30XxGnzejGJExO9bt165uWnC2iufHj48r6AgBj7Gk5mBgY+B4ScDg+SuXXj6gRBbv+ze/cXNjWfXLh5YLw7SJ4RMEkLUoJFo55TKd3R8A5+G+g/cyoErFgCPfEO4/OBe3+dz576BT8/7C96uw2Nk9AXcehOLiPh66xbklIWf4MlPyOmjkMu++BgYnlScMWWIHr2oEBKYo+QoGNIhsHv3s3PnPplXpMl3eLMYGcGHmb5UVHAzMIh0dPysqODftYtdTe3DrVv33Nzgy9clwIe+/GJg4APvT4YEwidwGQI6pwpSdEBEGRjEGBi+d3QwMzBAzqGBDITBJwNhqhA0fHoQIgSfJMQ8sRmtZ3jHzY2PgYGJgeE7+IQbyBZuOXDHDmLUC0ZGoQcP3t66BemOcoKP72IDz4UygI8YvddxELxMlAG8TFQKomuUHA2B0RAYsWC0Qzhio37U46MhgBIC8OWj8NNHGRgY2CoqPjMw/APfIvj+3Lm3FRW84DVIf8FjzMhXVvyvqIC0Tj6De48CRkbI84HINv16+BA+HYcsDjktHfmodGRZOPvl7t1/wa2xNxUVfxgYOFF7gJChekhXEM7+5Ob2l4HhAwMDG2yvDtw0BgaGHxUVv8HmMIMPgocsu+IDr5idzWBcUfEOfNe96uhUIXKgjbJHQ2CohMDDh58VFK5CXHu64wF/ReR38BgW5HLUr+fO/QYPGDEglSQQxZDCgQN8ZZ8ouDcI7wfCFUBWXULE/1dUcDAwQOYYhTo61MHnWsFVYjK+7N792c0NPj0IUfCks/NrRQXyJCFEHE5CeoaM4KL4JwPDR3CZ9g8s/R7cOxUEe4SlouIDA4P8rl2QAu0H+HKL32ApfiOjJedkKyrOdHSYRERIj54fAw68UWI0BEY6GO0QjvQUMOr/0RBADoHdu6+4uYHOYV/RwehixPzTzU3kwYO74Mvl2cDqxBgYXoO3FEKuLvxYAbrAWAA8TcfCwPCJgYEJ3OaQi4iAb3QB60MQyItFEaIMDAQnCSHXD0JOjmEAD3h/rqjg7OgQBe+rYWBg+Ay+0xk0bA/uMUIM/1BRoQhupX0F81nAJISATCf+AXcsP4GvHftQUWHw4AHTrVtf3NzYdu36ycCw1u2kOUMtPwPDw113XV2VIBpHydEQGA2BIRECu3c/++3W+oWBkYHhv9WugN/g9QKS4JM8P5w797UCdOMeDwMDqNcHLrggiyMgXmMHz6QxgsmPYCHQZmMwA9IPBDNBBC94198XBQWeXbsewQofyK5CkDQO/JyRkRdplQREFZ5JQriC125u3B0dbOAl/R/B84S/GBj+MTBAuoIcDAzsFRXvwX1FiBYR8EUafyoqWDs6joMulmAbXSYKCZlRcjQERkMADkY7hPCgGGWMhsBoCIBCALZ8lGEewzkdhirIiQiQK+M/uLkJMjA8AakCnb/Hq6b299YtyPZC0Gg6eJ/hZwaGD+fOvQcf0ycDWxEK1gEiINODyItFQaIwjH+S8FlnJyd4FSgDAwPkBsI34DPfRTBsgZkHoiH9T5Zdu16AG2rvwF0+SGcVcjbg31u3Pp47B2nwCRgZ/Th37jt4dyLk7Icnbm7KHR17Ku6EM9h0dGhFRIxOFYJCdRSPhsDgD4HOzhsVFR92MZQJMhyW2LULfgP774oKno6OTxUVfOBOIA8Dw38GBj7wuBLkBtS34L2FDAwM3B0djOA7CUV27cJcxokcApBl8BLg5fTIuwox7yqE6HrX2fmzokISrB4iAifx9AkhJzl/O3eOy8iIjYHh57lzv8FFIqR85lZTY2BgeAG+5fUH+IodZtjiDgYGhq+gQ5WzPBhCGBhGl4nCA3uUMRoCoyEABaMdQmhAjFKjITAaAvAQgC0fPV/FwOTRwcJREcff0cEOHnhm6+j4BTu6gBOugYGB39X1ZWcnh5ERv6vrp4cPH4FvRmZgYNBBXTf1AnxqAqTZhKQbysQ1SfgeduP8t4oK4V27IL1BBgaGN7t3v3RzEwefCQE1ApWC9D8hZ5kyMDA82b0b0igUMDKCd1Y/gHuVAmBnv1ixgqGi4gu4IQg5VZUfvL+IgYEBvHz0BQMDz+iuQtQwHuWNhsCgCwHwUaKPwD0fOUOGqy/d3L4zMHCAV4qKgy/9+wveeicZEcEjL/8ZXALwwsaVIJ0uiJcgnUA8PTSIMkg5gzzOhXkAKUQlhIScJYM5PQiRZWBgwDxdhoGB4TF4NSkDA4P8gwecYGf/PHdOpLz8+e7dkA4hAwMD07lzPyoqJB48gNxN/5uB4SsDw7uKin8dy08zGIwuE4WH8ChjNARGQwANjN5DiBYgo9zREBgNAQbI5fW7dpm3MUjYVYh82HVXrbycHbyt7jN4NP0/A8MnN7frbm7QfXqwthQk7CD3/kHYhxkZITdJMDAwfAE3vIRg1zpDFCCTkA2Er8HK4OLvwVzhXbs4jYxU/v+H9wYZGBhEXF15wGP8cMVojC/gfikPeOwfNNXp6ioXEfGuouLDuXNXMO99vnVLgIFBYtcuyMorvo4O/QcP+MA7iH4aGcUZ/d/IsPcUQ6Csm3Jn52k0i0a5oyEwGgKDJAR27372HXTj/JJVDIs1Ga6yqan9YWAQ7ehQiohQBxcXoh0dMrt2/Wdg+AJbZA53+aPOzldubq/AJy1DeoOgWwHB5dsbsCBcJTIDWs6AlUHEke8qZGBgQLur8MuKFewdHfAjtSBakEkR8JZFeNf07e7dN8BDaaK7dmn8/492uY6kq6tURMSXc+deubndr6h4ycDw6tYtNvDM4a+KCr3y8t+7njpXyFRUPN61y6C8XGN00yByUI+yR0NgNAQgYHSGEBIOo+RoCIyGAJYQwLV8lJ2B4X9FxSvw8DOowQS+IfD7ihWQGUK4QZAZOcjyUTE1NaxnycAVQxiQSULIFRSQiUGIOPLEIEQEQuKZJIQs4kIetodoYWBguAK++4sBfIoMD1gUMjYGWf7KpKb2dMWKT+DjB7+CZxIgGyYZGBiEGBh2V5yOYnBnYJB68MB69KQZcOCNEqMhMChCAD4xuJWhTJjhsDTSMtH34ItGmTs6+BgYBCIivoFvx4FsBWQ5d47dyOgXA8Mr8N48biMjeFcQ7is8k4SY04NwXaCNzQ8fXlZQkN+16+O5c5Dlo5DpQbSzZJC1QNhwGyFcyDJRZId93r0bMkP4cvfup25u0uDzY/gYGDhgvn4H3uB9v+NgNGin5OgyUUhAjpKjITAaAtjBaIcQe7iMio6GwGgIQEIAvny0moHJHbZ8lK2iAnTiwq5dz8Eb8wSMjB67uXGAV1pCdMHJD+CD70DnsIMvpWAHj9BD2mFwNZAjGeDcuwoKErt2scP5sB2DSAIozKuMjJirRiGtNPhiURQNYA6ks8oA3iDEB5725OjoeAN2LXz3IBcD6DAKlo6Oj+Cbvv6BT/bj27XrBIO8m9up0eWj4IAcJUZDYFCEALg3CDoQa9cuWT2Gq8/c3H6BS6T3FRWCHR1/wR3CP+DrVRnAqyiZwWM9TAwMjODNhH/BnsDaGwTLMOA6/xOySxnXMniI3mudnfxGRpD1FIJgIV7YsgUGBoZPGBOVIEHwmTd84IsimMBX5oD1IYgf4MNRv4EF5Hbtgt+g85mBgRW8OPZvx3LnCgUGhjsdHSbl5RpghaPEaAiMhsBoCGAHox1C7OEyKjoaAqMhgBwC8NNHd+1St1djfqugwN7R8RO8KukJeCUVP7jvxAC78x2u99O5c6ALkcEdyN8wWcgp7RA1fEZGyFwGBoZf4Km5V2BpiY4ORdRdiGBhFALrJCGklQa5+B5FNRLnRmfne4jDGBh+MDCwMzDI7tr19dw5yEmD/B0dfyoquDo6RGAbjT65ufHt2gXZawS5v96NoXV9x6nRk2aQAnWUORoC9A6Bhw8/n15xXqLC/jFD9r+OsOhyOwYGhpfgDqLUrl3i4O3BTLdu/Tp37hm4Kwi5Y5ABcnxoRYUgA8N78KnFSkj3+GH6AT5lhzxNBxl4wroMAc2Em+BVCT/B55r+BJ9pDFcAHyBDLglBgufOfaqo+M7A8JmBQWnXLsgqBriu/+Czu74zMLDDDshRiIh4u2LFn4oK1f//wb6/x8DwadcuOVfX0Uvn4cE2yhgNgdEQwA5GO4TYw2VUdDQERkMALQQePvygoHCSgYFhBcNpTYbanwwMkDvceY2Mfp07xwC+Jv4P+MAG+L6+7+A1SwwMDJzgI+++gvfjQU72gxsOmUKEc7nALHYGhncMoINMP4AvPxQGL0llYGCAnA4KVoJCoE0SQlppuKYHX4A3JT5xc5MBL7Li6QDdSf0bfGeGcEfHh4oKQ/AuRz55+be7d38/d06mvPzz7t2Q3uAXBgbIRc8yu3a9Bt8NPZuhrY1Bc/SkGZT4GOWMhgC9QgDc83nEwPB+J0O7Yof3NwaGtxUVwuDLQ5+vWPGtogLSJ3zX2clmZATZtnejs/Mj+N5U3o6OH+CZw+fgwSDIVasQvViLGsw+IdrN8piefo5a2rAwMPAzMLwED58xYqiGX2vBAB6fYgMXqp8YGFg6Ov4xMHyqqBABr7AA3RALviT2DdgE9o6O3wwMgkZG78HrNb5WVIwuEwUHzCgxGgKjIUACGO0QkhBYo0pHQ2CEhwBs+eiDHIaPYbDlo7xGRuznzv2qqBB68OD9rVvP3NwM//+Hz9oxgM9GZ6ioYO/oeFJRwdvRoYxjxg+yY5AL3OL5UVHB1NHxCda2EzAyuuvmJgyWEjAyElBTQ2uuwa2DXEEBmR5EW8T1YvfuT+ARd0g/EE4yMDAwMzC8cHP7Bzb/LXjVKMQu0Kl94Abla/DyM+Q+8Gs3NxnwwPyljk3+FR8ZGNjAc5mmIzyFjHp/NAToGQLg3iB0maikmzQPrJCB944gs/2yHR1MFRUMHR2vwLkb3u/6WFEhCr5VlSUi4v6KFZCeJEQvhK0IXtiJXNogn/8JGXjCXIbw5eHDz7duwUeO4GNPoJNswKeAvgSvVmVgYIDsKsQaYm/AM4rMRkZv3dy+gYsmyEpX5V27ZF1dnzAysnd0MBoZfQXPEwrv2vX+3DnIaaKjy0Sxhueo4GgIjIYAfjDaIcQfPqOyoyEwGgLoIYC8fNTVVQkyRg461E5N7TO4Q8jKwMDV0SFgZAS5E4IdfCSpKLi7+NzNTRK8iAvNUMhRogwMDN/PnROKiHi/YgWnkZEA+NS+Tw8fPgAfFoqmhYGBQSEiAt5Wg08Sop0lg9wP/HTuHB/szgwGBgYJsPmgbYQPH0I3LoIv8roPtu5tRYXKrl2f3NzegS0W7ehgZmD4WFEhAz4AkP3cuZ8VFcIPHvxiYDi/4tD7ik2KDGvOMDQ7PcgZPWkGHGCjxGgI0DAEdu9+tt3tVChD4COGbNEOG5MIy5+3bn0FT5EJIfXu2MATa2xgh3B3dPCCsz9kkzBo6/KtW5/d3JDPd/n08CFazxCslQHeM0SeJIQUfUKwEa7nsMlAeIeTAanEkASXNpCTYNgjIh6DCxkGBgas99dDliSIPnjwj4HhiYKC8K5dzGpqD1asEDQy+gbuUv4GLzoVi4j4desWpJgVcXUFd49Hl4lCYmyUHA2B0RAgDYx2CEkLr1HVoyEwGgIMDAzw5aObOv4rVHj+AAcKpJn1y83tI3jTCwMDg+6DB3zy8pChbhFws+nl7t3P3dxUHzzglpcHa2L48fDhO3D3D8KF3CrxHHwHPaRDCBEHnbLw8OGHW7cgFwm+rahQBh+mBxnIV4iIeLVixc+KCoGODmbwVACDEWhr4idw/w1XPxBuMgMDA+R0U13YPdGQqzJerljBXFHxkIGBjYGBt6MDYpeAkdHfc+eYwE29n+CRfsgZpHwVFVMYiloZbEeXjyIH7Ch7NASoHgLgns/jKQwbHBk6eMHlAGT2jBVskyJ4yAYyQcfT0fGlouIfAwPoKBdwh1AS3DEDK2R4Dp5ng/foIIIQEq1neAe2QkHQyOjPuXO/KyoUHzx4p6Ag9ODBW/CRMMjTgE/c3CCFIa+aGuSme4iZoENHYUeDMjAw4Lq//tfDh68VFODblZ91dn6rqFD5//8l+BrV9+Cjrbg6OliNjD6ApwcZGBhYGRiedBwMHT1NFB7Qo4zREBgNARLBaIeQxAAbVT4aAqMhAA4B+PJRBgb2S7sk2c/t/lhRwcHAIMrAADoFATxD+OHcOVbwXhd+pLvjLzAywheOvgdv0gPN0aHeOI+1Qwi2FkRAemsPYEu84AtKv4FPo/kOUsIgt2sX5IQGSJ8QeT4QLI+FuMzICLnuAi4HWXrKsmvXM9hWRk4Ghh/gYyHYOjpYGBggEw4MDAxi4D0/n3adWH/uc0XFO/AOJtXRqUJ4SI4yRkOAKiHw8OFnBYWr4C14/090POCtiPwGPlXlXUWFELjM+XXu3JcK0BputCn9z25uIrt2CSH1Bt91dv6sqJCEjQHhch6kZwiRhawmZWBg+F1RwQheav4bvJUauTfIwMCA3OeEaISTkBlCyOgYqH/48CHmVCHyCBoDA8Ojzs5PFRUc4N7sM/AJXsLgrY9fGRi+MDAIdnQ8N4p1c3vEwHBn1y4nV1cpuF2jjNEQGA2B0RAgHox2CIkPq1GVoyEwGgLoIQBePvqUgeHfig7G8HIPSA+Kbdeul+CzOkEHu4OP7/sHPjwdcuE7M/imB6lduziQDINMDMIFPoAXX6HNEMJlkRmfwNd8QU5Z4Af3yp6Apf8xMDCBW0syGLuAwPJYCMgkofKDB1zg2UvIBiH4yTQQWQ7wee6gFiHYAGXwsYTIi1Sf79691u2kOejQnYTvu2pdXZXACkeJ0RAYDQFKQ2D37md/3Vo/gu6C+e/5oAy0+mD37ldubmLg8aZPDx/eUlAQA28U/AZeUfmhosIAvEgBNH+4e/cbpD4h5DJA3l27IMfMEHTZc/Ds3MeKCmawUkbwlaSvwRexMjMwiIH7onj6gWBNIAKtQwgSgk0VQu6lEDMyeuPmxtHRwW5kBJnk5AefevUTvOyChYFBftcuATW1FwoKXB0dMuXlyzsPRYEmBr+PniYKCcxRcjQERkOAPMBCnrZRXaMhMBoCoyHAwMDg6qrz4IGMgsLJiAoGLoYdChUVPxgY+M+dEzAyYu7o4GZgYK6ouA1erwXpDQqA9/BwMDC8cXP7DD5wT2rXLrTeIJEBC9+xo7Rr1+dz51gYGDgrKj4zMHCBTzoVBDfRPpw79wi8VwdyFxkDA4Mc7v6hqKvrCwaGr7duQTqEX8AahcrLX4N7p6A5xl27Xri5Ke3axa6mBjH2tpsb665d78DD9p/OnfsIPrnUrYP1J8MixopNtm6HOzrejl5KQWSEjiobDQE8IdDZeaOi4sM+hssqDIfFdu16sGIFZFbwCwPDbzc3xl27QNfKg/VrP3jw+tYtyDFUkO3HChERQq6ukDM5wUoYvqxYwd7Rgas3CDkVBjSDBx7YYgBPAzIzMAiBp+keurlxg1dpsoEnCcXAB7pcd3N7BV4YgOcwZIjV7ODbeiBsCCnt6sqnpnYd3Jt96ub2FVxgvgavO+UHX4LKAh7qEuroYAEvEwWdvczAwF5R8ZxBPqpCnIGBYdcug9G5QUhgjpKjITAaAuSB0RlC8sJtVNdoCIyGAEoIdHaeq6h4wMDAeWmX+A83Y0gTitHNDTLD9unhw2crVnyoqGBhYBDp6PhfUfGNgYGzo+MB+NA/zHPecc0QQhpqkIFz+DGhT9zcxDo6eMHO+cnAwG5kxAI+GwbSZ4N3BQXA7TDIFkS4IFr/EDINKLFrF7+a2jsFhW8MDLzg/UgvwF0+iV27OMEH54CtYnjk5sbEwMDNwPAaPEUAmTqAXGHPV1HB1tExm8G4ouLF6P31kOAaJUdDgLwQAO8YfMTAwLhrl6wxw9U3bm4/wBP1kOsE2cCruP+DJ+tYwRe48xsZ8YNLgPfgLccC4E4UAwMDDwPDn4oKkV27eNTU3iooIJ8lAylY4D1AyA7Az+AjaiA7hCH5mhdchvCqqf1aseIPA8NT8JX3TyoqfjEwCHd0wBeUQrypiHTkFUQEZD54dAlylylc8MXu3U/d3KR37Xrj5sYFvnHnOwPDb3BvU2XXLmnwMlfI6aY/YceNXmawjKlgG10mCg/DUcZoCIyGAIVgtENIYQCOah8NgdEQgIYAePko5Ah4dcjpo78qKn6C1z4h77H5Du5ccXR0PKuoYAG3b+ANKXgTCq1DCJ8MRG6owQ/8lHR1hWwHEn7w4MutW9/ANwdC3QSmIHsO0fqHDAwM8JYicv/w1YoV3ysqpMEXEv4HXyzBwMDwD4xARwKCpwhADTtwY/EfAwNLRcV78GouyPUVhg8eMN269cXNjW3Xrp8MDHfOvXhfsUmDYc36jlPl5aOXUoDjY5QYDQGiQ+Dq7kuf3PQfM2QzMPy32hXwG3ygi8iuXb8YGD6cO/e1ooIbvKGOl4HhDaw3+BGcNxkYGN5XVPwHWyTU0fGfgeFNRQU7AwMvA4NUR8dPBgYGcO8OMrqEXLBg7QGinQ3zrrOTJyLiI+xcU4XycshWQ8i5U4JgkyG3AjIwgE4ohR+G/BmpQwi5EPUpuDyU3rXrqZubKNi1r8AkK6yHCTGTF7xxUWTXLmFX16Wdh2Kgy0RHJwbBgTVKjIbAaAhQDEY7hBQH4agBoyEwGgKwEICfPtrRIWRdMY2DYQHyPJ7Mrl3f3dx4wV3E3wwMkJ4bpCHFALtYGdL64Qf31iDHw0A6k5/PnYM31GR27UI+vg9y+Dvk1unvDx8+VlBQw3tQBKR/+Ak8ewDvCiL3D3+Cj435A96FKA9ej8oAPj8GMmMAudkM0oIEXVR47twHBgbIOauQQ+F/gmcMIAo+nzvHzsDwumKrKUMsA4PUgwfWoyfNwBLLKD0aAvhCADYxyLCboUyA4bAE0mmiPAwM3ODjQyH9KBHw6nR2cA8N0htEztf84O7ZB/AdpEwMDJwMDH/BXUdIeQLJ2k/AvTJIngWJYJwOiubQF4yMkOsHX+/eDbn1QRQ8jwc5DBlybw0D+BpVFfCCUkixphgR8XHFCgEjo68MDJApQXhvELQiHTywxRMR8f7WrS/g40NZYScbQ4bM3oI7tGwdy82gy0TVR5eJjoLREBgNAWqB0Q4htUJy1JzREBgNAWgIwJaPcp/ueMVQEQe5uE/C1RVyeh67kRGvqytaz+3p7t0PwG0yIfCeGR7wiimJXbsgvUE8x7h/2b37F3hCAH52/C1GRtkHDzjBB8NAHYSXQu4f/qyo4IScYMjA8Bas6y944ZYIeCZTFrz/EG2u4GVnp0BEBLu8/KeHD28rKLAzMPwC31HxAbwaVqyjgx28fPSsUaCb26nR5aPgQB0lRkOAQAiAe4OQ5Qay0m7SoFl6cB58V1EhCZ6Q/wk+NkbQyEjC1fXXw4efVqyAH90JMfrzw4egawBv3YJ09phhk/zi4DWZn8GbAEVgE/7EnAcDMZaBgQFy3JQEbNQJs08IUQkpWNBuNfxVUcEKPoeZv6PjM/iaHKmICF5YYQXvZ77cvfsLuFhTRpp7fN5xELxM9P2uXfqjXUFIII+SoyEwGgLUAqMdQmqF5Kg5oyEwGgKIEEBbPsrAwADvDTKqge6vBy3ocnNjhq3JhI/Nv3RzYwO31b4zMHwCbzjkNzLC1VzD7A1CjhNkYGBAPl8e4SzcrDew4wo/gq/K+FFRAd+nJGhkdNfNDbJhiYGBQQF1axCkQ/iPgQFym+L3c+d+gdeqSe/aJQ4e8v9VUQGZTDjcufhixU17htYfHYvEInxHpwpxx8aozMgNgYcPP59dcZ6pYuVPBsa/HWE+EfK/bt1CHCW6e/dHNzfGjo5PsBCSjYhgZ2BA6xDCF5lDNjMjjyvxVVR86ej4Dl6D8OHcOXnU7AwzFR8NKXbgI1AMDAwPOjsZGBi4jYzg84Ro+qHrIMBXCP5kYPgAPgD5H0zRe/DgkWBHx7+KCoFdu/jAGyC/g+cJGRgYlME3uIJ7yDcZGL6Pnh8DC7ZRejQERkOAmmC0Q0jN0Bw1azQERkMAHgLgiwrvVlS86egQcmS4Icbw7FNFBfwm9zdubnwMDC8ZGGTBazIh6zA5wV1BBvCRep8YGJjAcwICRkYysOVYcMMh4/SQg0B5IiLYYEPskA4hZDEqsmL87DfgjT0MDAzwductRkbIrWWfHj6EHFQIMrmiQhm8AAxyvKEg+OyK7ytW/GVggGxV+lZRIQOenHwJPiVCGHxePGiaAmw9ZPUsI0PCPQbpcAaT0fvrwaEySoyGACIEwN0e0JV6NxhiucB5DSL3BXzFKH9HB3NFBf+uXXyurp+RbvBjB59XzBER8fnWLcieQEhek4GVLZBFCpLgucR3CgoS//9/evjwvIICfJSHpG4hZAMhcpnDwMBwhZGRp6NDLCICckYxxNlw8i14vIm7o4OdgeFdRcUH8PQmZIsj5PhlNgaGv+CV6uzgzipovAx2xYVgR8cmBn/QkaIMDA8emMjLQ87Pgps9yhgNgdEQGA0BKoDRDiEVAnHUiNEQGA0BXCHQ2Xm2ouIhAwP3oY5X4gzPIPt2flZU8DEw/ARvvfvOwKD94AGPvPx7WK8M1Nk7d47DyOgz+NwIyO4+zD4h/CAZtJYZ2mJUXA6DiEMmBiFsyG1mEPY7sGOQpxkhC8Dgh91DdvWAnFpR8Qe8yhSiEZkUYmB4zsDABr46DNJOhawgPd2xyQ90dTZbR4fi6EkzyCE2yh7JIQDuDT5mYPh/quMBX0XkF/DSUCHwgaKSYFKAgeEjtgDiAGdATvD4EWgHIPhuG0gnEHmzMWhMBzyVB5/cewy+XRByuJSAkZEstoEnTAvhCzuRpSCX07x0c9OBLSWFy77dvfsreP0nt5ERGwPDz3PnfhkZfTx3DnLxIOSi1K/gs6w+gTuKzysqeGB+ucxgGQU6TfTPrl1Ko8tE4UE6yhgNgdEQoDoYvYeQ6kE6auBoCIyGACIEysuNjYyE3dxO2VWIrOgQtWNgkIqI+MXA8KuiQvLBg18MDNcUFJ6uWPEXfF4CaKkn+FrCl+D2kwz4eq5HK1bcc3P70NEhh7S4C3KQDO+uXWi9QQYGBsjuwe8PH0IYCKegsiBdQbFdu8TAF0uIYLQFv507h9whhJwTqFdezgDe1QOZNhQGd2ufMjBwMDBI79r1CexsiD3s4DPumRkYuDo6bru5Ke/apfb//wtGRraODscIvUMMhy5WXJKr2D6/QtnpQc7o8lFIoI2SIzMEdu9+9tut9SsDwyrQKnFb9QjL5xWgWyKM//+/2dnJBw4UdvC9EYywnhJYDESwMzB8rAD1mb5VVHDB9gSaYvTKQErBmAe8ExjMZJB1deVXU3sIvnH0w7lz99zcDGEX2UMUYJK/wFsTMYsdUVdXSJ/w9e7d8IWj3x8+fL1iBeQoVNGICE55ecgpo1Kurrxqak9WrOA3Mnq+YgUHA8O3igqFBw94wceWSnZ0CIIdOVdhYxHoBJynu3Zpj/YGMeNiVGQ0BEZDgIpgdIaQioE5atRoCIyGAPYQQF4+6gRbPgo5mZMffOzKB/A1YjK7dgmoqfHJy0M25rHDFoI+2b0bcn8gZPnol927P7u58e7ahete6SednVxGRsjdOWRnvQEP2HODzx6ErxFFVsDAwEDMNCPkbAm2jo5n4CYpaHsh+AJDiFE/b9367ub2DnZO4NuKCh7wsaX/GBggGyY/nzsnwsCwp+JOOIP36PJRSKCNkiMwBHbvfnbu3CejijRB8FGiDOB1Ae/Bfbz/DAyK4PEayJmcv9zcOHftYgdvsQNl0lu3foJvngANA4EPHVV88ADrik14qGKd3IMcDfoQfP4npJzBs4IUcwMh3HAGBgbkA2aQJwaFYeNNn3fv/nnuHOT8m1udnZ8rKng7OhgqKn51dEBORv0Cvqz1R8fyowwGo8tEkcN2lD0aAqMhQFMwOkNI0+AdNXw0BEZDABQC8vIC5eXGDAxnKyruMzCwr+jQN+zoEDEyAl3kAJ4b/AFePvr+3DnI5V3cFRXMRka/QVoZPt26xQQ+6hNy+9/Nc+dYwafz4eoNMjAwcBkZoc3vgU0CEWjbBbVwzCRAZhff7d4tBGvJgTSjYvgORh15+Stgqffnzv1nYICsbmVlYPjX0QGZUbzb2cnKwPAFvEOSA3yM6kfwrqdfDAxeu3YxuL10czvT0fE2IkJ1dKoQHJCjxIgIgYcPPysoXIV49URHFmfF4RdubpDLZgQ7OiQZGO5XVNxxcxPp6DAC59PH4AXYkDM534J7VgwMDKLgq/m+gSfu7isoaINVQsxEI3FN7jEwMPDJy+uWlz/evftdRYXSrl2QOUN5pCUJcKN+nTvHBh5LgosgM0RdXb92dHwEl2PfwHckfq2oYNm1C3LkKWiFOfg6xD9GRk/d3Pg6OqR37XoBvoyem4GBATz5KbNr12q360UVMgwMt3btMhmdGEQO3lH2aAiMhgDtwOgMIe3CdtTk0RAYDQH0EADfLrGDgUFxRcd/OyNmXjW1LwoKbB0dQuXlkJsnRMEHsYCODQTfFQZqqyE1v964ufGCdxa9ZWAQ7uhQAC+sgizmRLYJ6/weZI0o6DDAjg5uIyPMNaLIJoC2G2FsI0RWAFmzCrn8ECKONo35CdxgFS0vfws2h4GB4aWb2x/w/WnK5eXw2UXIdqYpjDUMDBxuDLUPd911dVWCGDhKjobAMA6B3buf3XJbI8xw629HWHS5HQMDw0PwBj9I3nza2clYUfGtowOyjoCBgUEuIuLnihXsRkZ8rq6QPPXt3DkuIyP45Nsb2JWAuLI2JM9CchyegL0MdgaujYW45hgZGBjgR5tCrq75AJveBM1AwhaTs4FXh34CF25fKyoEOzo0ystvMTKC7iF88ODTrVug3iCDLgPDO/AyUQ087hyVGg2B0RAYDQEqgtEOIRUDc9So0RAYDQHCIYC8fNSL4QZ7RRwDeNIMvpCSBXza3mfwVYRw4zjB+/QYwJNsL8Eb8yAtNogCASMjyFpTCJeBgQH5NkJIVxCyVxA0f6imhn9dGcSQd7t3v3Fzw3rH/Zfdu7+4ufFgrFn99PDhoxUrIEu/BMD7JH9CzGJgEAPPY7zavfuFm5vygwc/V6z4XFHB0NHxE7zcVGbXrjdubj8ZbE0Zkjs6tEanCmHBNkoPzxDo7LzBwPBBt6JMvcP7K/gCd+GODlEjo6/nzv2sqFB98OCNggLvrl28rq6QcRZIZv9fUSHa0QG54ZOBgUEefKIvcgDh7xNiPR0UWTuc/enhQ8gkIUQEPlUIGceB30DIwMDw5eFDzKNNX7u5cYOPUP4HXtoA6u1BDGJgkAFfcMrMwMAPO0P1HXiJBC8Dw8eO5UfAy0TBWwiVR08ThYXZKD0KRkOAHmC0Q0iPUB61YzQERkMALQTAU4W3GRjer+jgM2S4yAs+GJABvJCSj4GBfdeu+25ukKv84JN1EBN+nzsnEBHxk4HhwYoVbysqhDs6II1FiCxoIx94oRdkGyGHmtq3W7cgUri2C0JksZK3GBkxO4SQRiFkVhOrrifgOyfYGBj+MIBOhGABzwbAVX4Gbyb8yMAgBD4A4/O5cxC/i4Ev2zjdscav4vvo/fXw4BplDLMQAB8l+gi8QFLAr0LzF/iaeEguvuvmJgzecysIPjWUHbyEEnIBAxM4FCCniYrt2sXKwACfGATLIIiHnZ1fKiowNxNCsi3kRlCEarwszG4hBwPDlxUrhMrL4ZOB/DBHgm6XqaiAHG0qrKb2WkHhQ0cH5BxRyPQmr7z8687OnxUVjODxr68MDHAPChoZrXS7XsqgysDAO3qaKN44GZUcDYHREKAVgBSztDJ91NzREBgNgdEQwBoCrq4Ku3apMjC8i6j4f55Bn4GBwfT/f81du0Q6Otg6OgRdXaV37Xri5navs/MWI+MbN7c3bm6gM0hdXX+AL3Hmk5fXKy93/P9fISICcg4EA3ieQcDI6MGKFYcYGV9UVHw4d+75ihWv3NxegfVq/f+Pay0ZVhdCBCH3T0DYEPIdeI0r8lmFEHEGBoavDx++BF84JtDR8YeBATKrCT9BHrL8VbSjQ5CB4Rd4z+QTNzde8IJYzV27QIco7trlWx68a5fVZIYzN93mne5cDDd5lDEaAsMgBC7svvTVrTWN4cXSjl/l5Rpiu3axMTDo7doFycXC4Gv6BMGrAJA7WoIdHbK7dvF1dPAyMPxjYHh17twlN7dLnZ2Qm2DQgkU0IoKno+O+ggKaOAMDA1tHB+bpoJjK4CKQjYUC4BwqYGR0SUHhqoLCRwaGk+AVnpDL7iEXqPIaGUlFRJj+/y/p6ioJ23WsDb5QHjTIZWQEOiq5s/NjRQXfrl3S5eViu3ZxMzBIGRkpREQoRESAe4O6DAyPRnuD8MAfZYyGwGgI0BmMHipD5wAftW40BEZDABoCrq4KDx5ErFhxOwJ0eb1AIMM5vnPnPlVU8O7adQvceBLr6PhcUfEKPHWmi+M4eD55eSXwhdQfzp3jBO/TA/W7wDON/8F6f4Hn4tjU1D49fIi52xDqFByUCPiEQ2TJd52doGalkRFay/Ll7t2gA3IYGD5VVEjv2vUUPNfxFXyEICe4QQkx5NO5c5wVoBlAyOWKd8HLYiXU1N4pKHwC325/nZFRgIEheNeuX25ulxg2MTJuffDAevSkGUjojZJDNwQgE4NFDFfiGaZld8gxMDBc6jwO8c4rNzfN///FjYyeg0+U4e3o+FtR8amiAi33MVVUcDIw/O3oeAJbFwC5+kUBvCIAYhRoQbi8PLeR0RcGhje7dyMPAEGOgIIrI5Lx5eFDFgYGFgaGF25urAwMEgwMzyoqfjIw3HVzY2JguO/mJgc7WJgHdiQysskyrq5fzp174ubGDO6OfmZgeO7mBrmJ/i8Dw1M3N5ZdF3XcvjEw6IKXiUaNLhNFDr1R9mgIjIYAPcHoklF6hvaoXaMhMBoCWEJg9+57bm63GRjeTWa4G7zLHNKz4jUy+uXm9hd8t7sseL8NZIEo6ADSjg7QtfXnzkGG5+GbD0FTiEZGL8HNL24Ghm8MDN8ZGJjBa0rvublBbrgW6uiQx3EUDabL0LYRYt06+BK8QFR6167P4N6sNLgPKQ6eJXi9G7RjUBfp2EPIujWeXbv+qalBmrOsDAzfwXsmZcAm8MJOXuVjYAAvHwWtLR29lAIzakZFhlAIgHuDjxkYGE523OetiPwK7h0xMDAIGhm9P3fuS0UFB3jqTxacd3jU1H7euvXGzU0WdYvgc0ZGdvDpU6AzWh4+hGQfyHJxSMmA3DPE3ExI/AZCyLZABgaGJ25ukLIFMhkIuuqGgeETeHOgQEcHEwODAPh+eUhEvAevXIB09viNjD67uQmA1zjIgEnejo4f4A7tb3BxBLlaY3cFZxGDEQPD71271EdPE4UE4yg5GgKjITBQYLRDOFAhP2rvaAiMhgAiBB4+/KCgsJqBQSaH4UVuxytQv8jNjQ+8l+6fmtp5BQUWBgb4AaSQ89kZGBhAypC6hd/Ae3ieuLkJg1ed/QVf7SX34AHy+RACRkYkdQ7hh9NA+nLwrYMvwWeHQq5HYwAP9sN3PCJ8xcBwmZFRYtcu+EXV7zo7f1VUwA+luNPZ+bWighE8BfEVdj8hOwPDLzc3iEXgYDkwjeGMVIeNQYTF6FQhctiOsgd/CDx8+PnZiq3zKzhmM0ju2iXr6ir1CJzmBXft+s3A8P7cuXcVoAsWPjEwsHZ0MDMwaMGWWd5iZBTZtQt+6Qvk6lHhBw+QZ+Y/gbuFkN4gpGcI30KMdmYpJPPC8x1muOHqBD4BrzaX2bULdNtERQUzuEgB3R8D3jr4vqKCCbzdEdIPhKwOZWBg+HHu3K+Kii8MDBzgNQ5C4E4gGwPDG3C+/gq2Htwb1GVgeLprl9NobxAcJKPEaAiMhsBAgtEO4UCG/qjdoyEwGgLwEACfPnq7ouJdR4dQRIQqu4LgJ/AZC/Bx+l/nzjGA12tBVldCzm/gVVPjkZeHTAhAjBLo6GCuqIB0qK4wMorD+mOQHUekdg4hh9MIubq+YGRk6+jgiYh4f+sWpB+ITEKmBCEOQCaRJwkhrVKIwyAHn3KDL8D4fA401Sm5axfEEEinEXL6BWT68XvHIvmKOFmG9aNThchhO8oe5CEAnhh89IjB8j34qBjIPTGsDAwPFBS4OzrkwH2/z7t3f3Jz+97RAbl+EHR2KHgJ6DukM35/PXz4FnzoKK6rRyE9Q8gybMiOYgEjIxE1tdcrVnypqND+///Xw4eQw2CQQ4xgJ5CBgQG+IfAd+DwYyf//34IdBukTQmY4BcFHHENMfrRiBTsDA1tFxRMGBj7wyTn/wBKQXisjeBaR78En8O2L3zs6BMvLDcDyo8RoCIyGwGgIDDAY7RAOcASMWj8aAqMhgBwCsOWjDJMZTgfvMocu1gJ3mcTA6oR37boBvtNZDdyghHQFecA9KwYGBhFXV0iHCjIb8Bp8NZkO0qJNsBkMxHcOoYfKnDv3hYHhZ0XFZwYGyKJQSG+QgYEB0ouDGIuVhE8SQhwmBL5tDKLy67lzkFvXXu7e/dzNTRJ81MQXN7fPDAx84I2IcLvEGBhkGRaDl9oplpebQrSPkqMhMGhDANwbfMzA8P8Rg+Ud8K2h8Ek8bvCNLGK7dvGCT+Nk7+gQKS/HPNLzhYICZJIQ3hnD71l4txCi7G1FBRsDAzcDg8yuXUzgawCFystJ6gRCzAHdJo/aI70JPlRG8sEDyLJV0DHIFRXK4AlQBgaGV52dP8BHif5lYHgPnlSEmAPx/q4KztLRZaKQEBklR0NgNAQGExjtEA6m2Bh1y2gIjIYAAwN4neRqBgaeHIYfVbtkQEtD1dQYbt2CX/33YvfuJ25uAuAr7MXB+47gPSvMbX7Ik4RYQxd/55CTgYEHfIEER0cH/MAYvo4OyYgIXmzHSGBaAZkklNi16y/4zAz4TIjYrl3wQy++PHx4W0GBDXxr2TdwnxPS4YSQAuB2898H71esuFZR8WQewy2nBzmjy0cxg3pUZDCEwO7dz9a5nZrBINnRwR8RIc2kwCf24AHknhj4JB6ktyYJvnqBDykrPd69+8O5c5C+02/wBS0KDx68VVBAWyyKx5uQewvfVVRAFmo+cHNjY2AQYWD4BV69ibwnEL4cFHkmEKvJaDfaQyYJRcA3i8J7odDZQvCWYPkHD9jl5SGjUeK7drGrqTGAr8kB9wYVGBg+jS4TxRrOo4KjITAaAgMIRjuEAxj4o1aPhsBoCGAPAfjyUQaGPw8eWAvfOv3r3Lk34ANIIQssxTo6/lVUPADPACjBelbIazLh5kKaZZiThHAFyIxP4GumH4I3DrGCJTjBB5Y+A7P5wBuHwEwQAb8gG8RhYBBAlWUAby7iAx8x+tzNjZGBAXLJj/iuXc/c3CB3UUAOxQFtPQLr/VFR8Z+BgQl8/oQ8uKMLmX78+fDhpxUrRMEzoqc7F1+pOJPE4Di6fBQS7KPkoAoB8MTgxdUM7SIdWQ7lEQwMDI8ZGfl37eIDH7ME6T59Ac/dsYO3zkL2A8OzkgB4kx7ovJmOjv8MDF8rKrjAynjB2QF0nAx4rg/uZXgOgosg31fBwMDwpqKCn4EBtH8PfG4NJA8yMTAIdnRIgdemwjXiYkAWrKL1SCF9QnXY0oNPDx9eU1DgAOdxxo4OeHf057lzPyoqxHftusCg7eb2iIFhdJkormAeFR8NgdEQGGAw2iEc4AgYtX40BEZDAFcIgJeP3q1meCbB8NicoRayaRByk/svcJ/tCQMDL7grpQBu28HXZCKfPMHAwIBnkhC+hAzSz4T0zSA3xQt2dHDBmqSgi7SRXInZ94PfQA1RxQ/uB4Luqga3Xz9XVMgxMNxmYOBgYEDuVfIZGX0CKwCprKjgBt88wdjR8RdsioCRkRykGb17989z55iMjCCXMTIwMGjAlo9GRKiOThWCQ2uUGOAQePjwM3hrHMgZqxkWa4LvlpCLiPixYgW7kRGkQ/gCdiTvUzc3HnAP7QPSSAo8E0Gyz1M3N25wb/AfOI+DzGVgQM4yoAtmYBkNno8gyuAdRS4GBi7w0MxXBoav4OKC38gIss8QcuW9IPjEFz7wJB7Wa2nQpgch5n9/+PAReEWrsKsrZL03b0fHP/BSWI6Ojm/gA0ghtryrqLjMMDGXQYEBdOn86GmikPAbJUdDYDQEBh0Y7RAOuigZddBoCIyGADwEwMtHdzEwvOvoMHaqMGMA7wj6jHQA6YMVK95WVDAzMIh0dDCBz2yQfvAArh2yqhMySQg5XQatBwhZQga/6ALeLeQE3174GTxBwdnR8Qt2vrwmeJru88OHcCsgjM+3bkEYEPIjrJsH4TJWVAiAO4QC4HVrirDpDuhqWLAiHnn5j+D1ch8ZGN6CPSIMO4pDkIHhR0UFa0cHl5HRt3PnRMD3Lq51OwjWx2G3y0vP1QjMHiVGQ2BgQuDS7kv73Q6Ae17/kx74f7p16wH4wr334JTPDb4nBrL+GX4kLzfYpbyurvDcBM9EH8ETa8wMDH/B3bmf4L2zYOUgAjLrDmIhYUiPDi4AuRXwWWfn94oKoV273ru5fWVgYAF3LN9VVPCD5yEhXUEGBgZIzw3eP2RgYJCD3UyDNj34BZbxP9+69e3cua8VFewdHYwMDPxGRl/OneMxMmJhYHgFPkr0G9gpSL1B1l279EdPEwWHyigxGgKjITAYwWiHcDDGyqibRkNgNASQQ6Cz83RFxRsGhn8PHlhLMnx8p6DA1tHxEzw5AOnCsYLXjv5kYBDatQu5MwZfisYOPtbiFbgtCLmsAtQZA1/69xEmCBIBn1n6HnylBMQBf8FdO5Hy8me7d0NMhvQJIbJEkpBJhq9IUxPy4ClNNO0fwfbyQ2YFwUfqQ/dcgTcfgvwIbtEygOdJGBgY7px7IVYRd54h72FHzOhUIVpgjnLpFgK7dz+75zZJhuGz4a4A0Fw3OMtAbH8P3gf4FzwZKA07J0kCnMI/g1M7L5gNUQwhvz18+HrFiq/gMRGxXbuYwLc1qMEWZ0LUEEM+6+xkYGDgNDISdHX9sHv3Ozc3DnDhAJmH1AQfY/Pp1i1Ib/B9RQXk6ggBcKkCFxRgYPgC3tkLsRG+HhXSKf3u5vaTgeE9A8Nv8PgR5IjUBwoKAh0dohERR26xQpaJ7tql4OqqCDFhlBwNgdEQGA2BwQlGO4SDM15GXTUaAqMhgBIC4OWjtxkY/s1jOKvDUAudKgQ3PXmNjH66uf0CLwn7Az4ABqITsrCThYHhC7hV+o+B4T94gg6+LhTeA4SoB10g9vDhUwUFCFdo1y5u8GkuorDbzyB9wg8VFToPHkDmHiEq8ZOQnY2QaySQj1IUMDKSRW0NI3cIIWa+3L37G7jRCVlZ9wncSoZIgXwBnhfdXfE6ksEAfGWjnqurElx2lDEaArQOAfCOQdB66nyGK/EMqX/BXSNkS7nB+/cg98QIdnRATgaGKHjT2cluZITWIXyze/dXcKaGnL4LOsATfM0D5LhRiEaC5A9wLubs6ID0BkFzgOAOodSDB78ZGK4oKMiDe4YySIfZMDBATx5+tGIFfKqQl4HhT0XFRwaGj+DhJFbwFkH4YlReBgZecG+Qp6PjC3iNKMgisOO/VFTc7zgYXQFa+j166TzB+BpVMBoCoyEwGMBoh3AwxMKoG0ZDYDQECIcAePnoSQaG+9UM7O4dLBwVcZAFnz/Bt1CwGRl9ZWB45uam9uABt7z8y927P50796WiQmrXrk/nznFXVHxiYPjAwCC3a5cUajcMbvH73bu/nzvHCZ4iEASrgVySJo00OwHpE4K6o6itSbghmAzI9KAQeK0pRBZylCKEjTxV+HH37h/nzonDVH4AcznA7nl+7tyHigqBjg74/OSvhw/fKCiwd3QIl5fv3n3Lze0UAwPbig6+8HIPiMmj5GgI0DQELuy+ZOgGOjQXfOGC1EVGRknwWmgxcN4BdavA3TDOjg4GcBr+dO4cn5ERPPe96ezki4hA3u6L2RuEuP8W+JoHIicJfzx8+G7FCuRcDDHkHiOj0K5dAmC3XQdPHoKGVJDcA1EGJx92dv6tqODo6PhaUfGcgUFh164P4Dxo8OABn7z8z4cPX4NzH4OR0Qs3N46Ojh9gnZDjZLYx+FdUnGFgUNm1S250mSg4YEaJ0RAYDYHBDiBnbg12V466bzQERkNgNATk5QX+/3fv6NBsZWC1q+AVe/Berbycl4EBdBO0kRGPqyu3mtp/BobHK1bcBDf4IL3BZ25u7OCwU3rwQG7Xrkdubs/Aa9XAYggCskwUrR3589w5dtgqTYhSKVdXGfD+oicrVsD3PkGksJK/Hj78VVHBA9YCVyDr6ioPE3m4YgXk3gsGBgbI8fQQZR/AjoT0BgVcXTXLyyHnjkLasr8ePvy8YgXIbUZGz3fv1mF4eIoh9hRDuGGF5+bOHQ8ffoAYMkqOhgAtQmD37meMjCeY3fQPMFisYFgkcW7xp4cPxXbt+nLu3Jdz576CN9o96+x85+YmtGuXVHk5vBPIwMCANfe92b37GiPjK/BJUaIREfDrWCCOFwH3M6E3gkKEcJDvd+9+B57iA51TCu77wRUK7dr1HTx9x8DAoFleDjm3hoGBAZKh4MogjA+7d/8Fbz6EZEDZjg7IIlKFXbserFhxqbPzyYoVPxkYOCIiRF1dJXbt+lFRIWFkpFte/mvXU70K+4qKx7t2Gfz/bzHaG4SE5yg5GgKjITD4wegM4eCPo1EXjobAaAighABsQoznVMdn/oq4HwwMP8Hy/B0d7OB1XFfBSyvFwOcHiru6vgX3D4XBM2/Pdu9+5OaGPE8IWWAGNgC0BREyNwjhPmVkhK8XhYhAyM8PHz5ZseJDRYU87vlGiErM6UGIOISETBVCZhV0y8t/PHz4ccUK8fJyyNwgAwMDh5ERZE4Dov4ZeNqTkYGBFbwO9icDA2SOFHry6rlz7BUVkqADSHlGL6WAhNgoSfUQAC8TfVzMcNmbYZF8h/dX8GpJSH/pXUUFCwODeEcH5MoWPvD+PbgDvoBzDWQHrJSrK3yG8A147AOiDK0rCBFkYGC4xchIcNUoZEzn+7lzQhERHBh3hEK2ESohzfZ/fvgQ6/JRiEr4dOJtRkaujg5p8J7DqwoKgh0d3yoqOBkYIGefQjYffgYXOE86DoaOLhOFx9koYzQERkNgSIHRDuGQiq5Rx46GwGgIgEMAclEhA8OPJxV3wjpYVIwkGBgYPoN7RAwMDI8YGOTBS7xAV04bGX1xcxPZtQu+Wwm5T4i5TBRsPIj4BV4VhrVDCLIL1rrlx73qDLJ7kGfXLh7UyQqQ6TAM31X4rqJCc9eu/+fO/QdL/Qafh/EbzIYcggrZvMQPXsMG2vIE3tfEDO4ZQrqF7OAJkM8RyQoKO8DnMiqOnjQDDr9RgjohAJ6WfgreVfd/1y45OTdpBgYGLnBGgwxqQMYpeME7CZnAJ3mCZurAc+yQGTlGBoZHbm6Qzb0i4HEcSKpmYGAQg90mitWt7wjtJIRk5O8VFdIPHmD2BiFmIq8ahYggzxBCMjJabxCyifGlmxtrR8enigrIuThM4BGZnwwMoCl6sEH8Rka9bl8ngA6gUXnwQFtenhcsPEqMhsBoCIyGwJABox3CIRNVow4dDYHREEALgc7O/RUVoM07hzreqBhJMJ47x1BRwbNr1z81tcvgs2HYGBg4GRh+gFuocL0CHR2Q00dFOzqEwbubQM1WjD7bG/C8ogh4XhGuF40BWW8GaUqiSYGakp2dvyoqJJAmJTDVQEQeg6f+PoIPv/kGPtUQcp4h/EBU0Pk34DNRGRgYvlVUsDMwPAcrg7StFSMiOBgYnisoMHR0sBkZnXZbJsmwgIOB4TpDs+CuqNGTZiCBPEpSEgK7dz/b7nYqmiHwG/hWdwEjo9/gexckd+2CDFs8AW/lfVtRIQE+aoUD3A+E2Ag52BNCgvYWgicSWRkYvjMwKO3axcrAgGtiEKIdQuKZJIT0BhkYGKTw5tbnnZ2cqFPuEJMhG4MhV0d8rqhgQi0uQFOa4DKEuaPjY0WFaEeHZETE71u3Xrm5sYNPE52jsLGIwSyD4W4Q6GIJHYiZo+RoCIyGwGgIDC0w2iEcBaMhMBoCQzgE4KePbupgVK/wBJ16D/aNzK5dn8+d4wbfLP8FfCH1H7A4pFXKzsDwE3zH4EvwCjd5bO3Ip4yM7B0d+DuEkBkGrGtHiZkeZGBgeA5eL/fEzY0fthTtBfj6NeGOju/g+xUhs39gtzPwgq/PZmFgeL9iBXdExH3wdilBI6PXbm7M4P6hCNjXkN4jo5vbFIYi3o5gBgbW8nJTiAmj5GgIkBEC4GWij6Yy9DgwrOUD7+iDrBH9WwFaIskNPryXD9wD5DMyYjl3ji8iArKKGzSVfesW5L6W9+C+lhBY8V8GBjHwIU/MsMs2FcC7arFeDQ9xMK5JQtC+QTc3zo4O/L1B0BGg4ENukFeNQkxmYGB4uXv3ezc3XgaG++CjROE3hX46d46dgeE/+Eiq3wwM4uDpUIiuLxUV3AwML3c9dXO7ycDAMHq3BCRYRsnREBgNgSEKRjuEQzTiRp09GgKjIQANAfDy0XufK258ZPgd1sGkYiQBuZxQrKODvaKCraPjPQPD54oKZfCF9XcUFDjA69MeubkJMTB8BrdiBYyMZFBnCPGvF4VaDKOe7d79ELwQDn4EKP7pQXgnEHRa6a5dT9zcZMAkf0cHc0XFQ3DXTnHXrvfgJaCKGA3lHw8fvl+xQhLcif308OFt8Fwo6MhE8CI9SAfyF/gonU+7Tqw/97mi4gUDg+CDB9by8gIwJ4/SoyFAVAhAuoIMDAwdHQIODBd4KyLZOzpAR4syMLwHbVhl+AXu1wl2dMhGRECug4dvDkS24MXu3ZApxE8VFbzgPqEkA8M3mFECSFd0MjAwKGC7pRPrTkJIb1Bo1y7krb/I9iKzfzx8+ExBQQpjTenb3bu/nTvHZWT0w83tPkyDMHgHMq+a2g/wsMurigrujo5nFRW/YFcOvmaQX1nBMQt0ppXg6DJRWLCN0qMhMBoCQxWMdgiHasyNuns0BEZDADkEwMtHXzMw8K7oYPSO0OSRl38HXrEpBL587LaCguSuXSwwDa/c3ECHIrq5McOG/NH6hG86O39WVCBfOAHTip2GrDqDHGDIwMAAmR5k6+iA3zbx5eHDz7duQXqqkNlLyHLQjxUVMuApF0lwj/QuI6Py//+fHj6EzP4xMDC8ragQBvdaFZFayfcYGZX+///+8OFbcGuVAbzI7T14j5O4qyuy7V8ePjy/4tD7ik0aDGse7ro7unwUe/yNimILgd27n31za/3BwPiYQS3tgT+fvPyb3bsheUfE1fXT7t0f3dwYOzo+wvTKge9iecrICM84L2AT4JBEDhn7YGBgkHB1hSiDHLH7YMUKeDqHdA4hXAEjIwE1Nfi0IWSSEH7/xLPOzu/gs0CJ6Q1C3Ii5jRDSGwRthgR3CHkfPIBcRQhRL8DAwFhRIfrgwadbtyAXJMqDT5eZrbCxhMEINEG4S9vVVQOieJQcDYHREBgNgaELRq+dGLpxN+ry0RAYDQFECJSXO+7aZcLAwBRRwXj81t8vu3d/Bi/0envr1tMVK34zMHx0c7vh5gZZ6qb5/z8PePmljKurHHgK7r6b25XOTkjzFGIo/MQICBc/KeXqCjk243pn5+eHD7+A+2k8ERHPd+9+vnv3aUbGZ2AR+JQgxDSpiAjT//8lXV0hvUGI4I+HD/nk5fXLyxUjIgSNjCC9QUEjo/srVuxjZLwIc+S73bvhvUHhiAjF8nLIiRcvd+9+p6DwiYHhp5ERxF4VIwn9DpNPDLZuboc7O0+PXkoBCedREk8IPHz4mZHxhJvbIz6Gy6Ydsm4d3x+sWHGAkfHZuXO/GRieurlBeoP8u3bJlJdDchDoMKcVK56Ae4Avdu9+sXv3GfD9gZCJwSfgKyVM/v+XcHWVAI9ZQPIXn7w8n7y8Xnm54///ChERaL3BD+fOPVixYj8j4yVwshcCD5pA7p94Bt7iS+TcINynyJdPMDAwPO7sfA12mEhEhDDYcA4GBp3ycgEjIxYGhh8VFW8rKu4xMNxaseLFuXM/GBg+VFS82L375C3WEgZdBobfu3YZjfYG4WE7yhgNgdEQGNJgdIZwSEffqONHQ2A0BFBCALJ8tKLi3jyG28IMt0x3RUGO6BQxMvrg5vYApla4o0PSyOi7m5s87MSXJ7t3Q/qKcuADWl4rKOA6XxRmBgr9BXz32hNwr+9jRQUfA8Mf8EEUkLWgkClBtMlAFP0wzrPOTsxD8yEThpA5E0Ejo/fnzv2vqPjDwMADPuUCphVK8zIwvGZgEOvo+AKee4Q0xMXAtzUu7DhQUfGYgWH0UgpoWI1SWEMAtkyUcdcuWXk36c8MDKzgOWrQ2UtGRpAUBTmrCU27AHgtKDMDgxR4CTQ88fOoqUFWk8LV/3r48NOKFXg26H56+PABODdBtMA7iuzgEz7ZwReuMDAwCIFHc0CbZjHumYBoRCMhh4gq/f//dvfu125u3B0dXEZGkK4gAwPDU0bG3x0dHGA/yuzadd/NTYCBgaOjgxV29NTtc29XVnBwgTYea0RESI+eJooWvKPc0RAYDYGhC0Y7hEM37kZdPhoCoyGAPQR27762yW33FAa1yQxn3TpYeY2Mfrm58XZ0sIAPYoGcv/IRfJkYvKULN+hdRYVkRwdTRQUHeCUnXBx0PAZ4Ux9EBH5cPoQLORSUATyrAGkrf2BgUN616xl4fyCowYrRJoZoRCPfgydYcC2Bg/QM2SsqGBkY3jAwMDIwKGA48oeb21sGhm/gk2mYGRgMHzzgYGB4p6DAs2sXg5ra+RWHLlbclGN4KtnhJBbhO7qrEC38R7kvdu8+7LaBgYHxT0eYd4T8n1u33ri5qf3//3T37s/gY0V/MTDwMDBwMTBwYqS9X2AFf8D3SciBZSXA026Yofp59+6f587h6RDCtUAm7SFrSiXBdz+wgTfZfoLt5YOohJwbDGFDjt6FsEF7a2HdOUg2fKagwAY2hxt8g4vYrl2fwPn6I/js09/gjCO/a9dzcM79BJ4//AXuD++u4CwCLRO9vWuX5ejEIDx4RxmjITAaAsMDwPfUDA/vjPpiNARGQ2A0BBhcXbUYdnFMcbuZy2DawSBUqCb2joHhMwMD261bvyoq+Ds6/jIwiINn8CCX/kFWe348d+5LRQUbA8OviorPDAy8584JgJuSkPYi6PR5IyM4Wxbc3oVzQaebgm+DgIe+QEfH23Pn2Ds6uJE2QcFlyWOwMTDwV1RwdnSAjnCsqPjPwPAB5ki4gf/BkyefwTsPIQtNmcC3Zj8GH2SqYmQk3vHsUwXDcwYOM4UDo/fXw8NtlAGZGJzNcN2xQ+4Rg7wow/FHK46/B+eI652dLOADeyHLknk7OrjB21aRA+21mxtkeTN3Rwc7A8NDNzdt8DFOyGqQ2ezgzIUsgpUN2UAobmT0h4FB0MjoEwMDH3i8g4GB4Q04kTOCtUE6gfCMjNwPhJxxCpF6smLFf/CozUcGBjbw7B88C8vu2vXazQ3UJALn3M8MDK/OneMBn/0rZWR0lkG7iOEmeJmog6urItjOUWI0BEZDYDQEhg8YnSEcPnE56pPREBgNAeQQgCwfBV0iX3HZjOEAZPko5CgXRvCqTu5duyDnYUC6gizgRZis4Pm3jwwMn8ATESzg/hXcWD7UVizkHggGBobXK1b8APe7GMA3H3J3dHw3MroLnl6A6FXetUsOx2wJRAGExDND+HH37h/nznGAHcDv6vrp4cNHK1a8r6gQ7OiQQzpv5jEjo9iDB+zy8p8ePny2YsVv8LzHZwYGWfCNFB8rKvjBTerTHWv8Kr5D7q8fvZQCEvgjmQT3Bh8zMDCc6LjPUxH5HXxJoCB4ZkyEgeFxRcVvBgbhjg5+IyMJV9efDx++UlCQhS23hhxu9LUCdA6ncEQEp7z8M/BE96dz52RgR4+ihe2bzk52IyNeInLEy927n7q5SYPHXyAMTgaGj+fO3a+oUAKfCMXIwCAHWzj6+dYtuEWQfiCc+6GiggO8cJoDPIH5BezBv+A8Lgo+UJSBgeHnuXM/Kiq+gHMFAwMDZJ3qvYpHRxlcexgkOjo4IiKUR5eJwoN0lDEaAqMhMJzAaIdwOMXmqF9GQ2A0BNBDYPfuR25uuxkYpODLR9kZGH67ubF1dAiWl78EL4T7BD6fE3TnO7gLx9nR8byiggXcGmZgYNAFX/CAbi6M/xZ27iJEQNjV9TW4scsHbuwib4WCbALEdaQ+RPuPhw/frViBeaPaR3ALG6KGH2wyhA3f+gg/JRXeIXwHPkz/PfgyQybwGjnZXbuE1dTegZfMCZWXP3z4oUdhpTrDY9EOG4sIi9Hlo5AgHWnk7t3Pbrit+cPAcJNBLniXmaur1MPOzi8VFdr//7/avfvfuXMfKiqYOzp+gcOFH3xBC3KH8O3u3d/BSy45kTbjMTAwPNu9GzL5xmdkJIWUYsHGMGC9mgIiBScxu4LiYHM+gZebipaXXwGfKwNRjzwmAhFBI9/s3g05JpShouInAwNkJIgd3BWEbB6GZMwb4INwpMBzmw9WrNhVwVnKoMrAcH3XrghXVyk0M0e5oyEwGgKjITBswOgpo8MmKkfBaAiMhgCWEHB1ldu1y5GBgSGXwXQ9g5OkqyvjuXO/wCtIz4Ebf6C1oB0dT93cvrm58XR0CO/axQ5aV9khD5t2OMjICNnIhGn6W3A/TWzXrlfgniT8dAq4SsgJipBLtyE3mz1YsQJyZCJcDRrjO2ymES4O6Q3+ADe7kXuDoDNLXV0h61o/nDsHOeARogtyEiOXkZH0rl1a///zGRnxdXQ8cnN7vmLFJ/D+w9OMjK8UBHM7Pvh08CpUtCkoHNi9+x5E7yg5ckIAPDF4kZXhkHfH95yOmxxu0lc6O59XgOaO73d2QpKcxK5dquXlkCWXH8+du9rZCekcgu5EAad/0Kw4am+QgYFBytVVBpaDIBOGyKH6EyORI8u+3L37HCMjZD7wKThnGf3/D+kNIivTQT3gFLl/iKzsze7d1xgZITlUNCKCu6ND4cEDOfAlNOzgRdeQyUDIcabvGEA3K/6+dYtPXv6lUWwp6DTR/7t2hYz2BpGDdJQ9GgKjITD8wOgM4fCL01EfjYbAaAighwB4+ejdiop3HR0CJhXTJBgWsHd08BoZQRqdPGpqf2/d+uTmxrdrF5+rK/I03WPY6aMCRkay4AkKiNGQiUFu8CwiN2pr+HVnJ19EBDvGsYfw2ULIUjRcF3BDriKE2ALamgheKfq9okIC40JtuBrI8lEIlwV8dzaE/YeBgRv1JFIeBgbIzCdk6eyPigpe8Aq6RQwT20GL4hQjIlRHpwohoTe8yYcPP69Y8bSi4gMDA8MZBkvQgkkGBj5wehYAH7PJx8DwHzaTBtmh9wG8bVWwo+N9RYUoAwMX+FAWBgYGuQcPODFSOyT0vjx8eFVBgb+jA22eEHIJIUQNnHwJ7l5CeoCQNaIMDAyY/UDIjRfwBasMDAyQeXIBI6N7bm4GDx5Ath1CjH0DNhPCFgHnX/jk5Etwb1hq1y5ONTXIoTWQIZsP587drXh0hMG1D5QjRpeJQgJvlBwNgdEQGOZgtEM4zCN41HujITAaAvAQ2L37gZvbXgYG3pUdfEYMF3mNjCDNTcgiNAYGBlHw6tAfDx8+UVBQgW2R+vTw4XkFBSFwW1kevFsPMjHIwMDw9dw5UfCmKbgVoMvNYLv4kAXhbILdQuQO4Uvw1ducHR3iYIfBDYEzIPfdg/ZJnjv3saKCFSzxkYFBtKODmYGBD3wKDmTf4yfwCZA8DAwM4J4w5MTFHytW/ATfu/2cgX/FimsVFU9yGL6m7dLXdTUCmzRKDM8QuLD70jS3e7MZJHftknV1lYL0xJ67uSmBb2D/cO4cJwMDd0XFUwYGTnCyh4QCv5HRx3PnPldUMIG3oX4Gb8OD7DOE7OJD7olBtEDIG7C1nRrgZPzr4cPXCgrwy+sZGBggDgBd/ACeD4T0BiF5E2ICMom8YBUuDhkTgQ+1yEVEsIB39n4FT0WK7doF6Q2CrGBkhN8oA+8TQuyC5M3LDJYxFV8ZGF7s2uU+OjEID+FRxmgIjIbA8AajHcLhHb+jvhsNgdEQQAmBhw+fzVHY2QIa+xeBHKYC7w1yRkRADqX4de7ca/BFf8g6/4E5nOALBtnARxSK7dqFuUaUYIcQbAwDpOkJYUNIyBYm0OYr8FWEoLlB8D1sHEZG8GWiaN0/0FGK4N4d5K5FXiOjR25u4gwMT8BH58MPzBAEn0MDOf/mmYKCyK5dkAu+34Cb6fCzPV7s3v3Eze0DQ8I7BuX7He6QwIG4bZQcTiGwe/ezJ26TdBk6bzNkeT8oAy2Zlpd/uXv3F/CaZGVYn+3LihWssDWfn27dglw/yAxOWkwMDFLgZAbpuTHAFl6+B/e+BMEb8/jU1CAmQ4IO3ieUiYhgY2CAX0II3ygInxiE9M0gurCSWDuEEJXwqcInbm6M4OOgvlRU/IXIwUgeBob/4F4upBP7CpzL4GND4FnDmwwM33ftMhjtDcLCbJQeDYHREBj+YLRDOPzjeNSHoyEwGgLIIQBePnobvFju16Vdkqxuxr/Ayyb5YZ0rEQaGFxUVrODbKSDTa5DJQA7wyfs/GBiYwA1KeBcO2XA8rVVkZRD2p4cPP9y6dRd8Xv9bcGNaedculnPnGBkYmMDH+n+qqBCEnQ6K2f37DJ4VhItDJv3eKCh87OiAHGADucUecp+4APj2C8hJMzLl5Z9374Yskf3KwPDp3LlP4FvsQesG3dxOMhSHMxgwMAg+eGA9unwUElPDgwT3dh4xMHyfx7DRimEi565dd8Bpj4GBQTEi4vutW8/d3CR37RJ3dX0HHiwQAncOISMF8Fvmn4Av6Pvr5sYLm05kYGCAnHYL2YwH7x+iiUOGLRgZGESNjP6fO/cXtmAbNGsHPkeUYFcQHgvwk5PgInDGzc7OrxUVPOCtgB9gJ6PCZX+4uf0C3zcjAj6hlIGB4R341CWQGzoOhoMunfkOPm3KAK5llDEaAqMhMBoCIwGMdghHQiyP+nE0BEZDAD0Edu9+cO7cu4qKOys6+AzBy0ch/SvIrQy/OzreVFTwdHRwg6fXXoHbzR/BJ9d/AXcI4YvT0LqFJHUI4W660tn5rqJCFNzP/FsBWq/2H3zvNmSnH6inB3YGZJYG0nEFCWK77P4BI6MCeLEr5BZ7SD8TckEcSAsDw5+KCsldu56B1+Z9ZmCQAV/MzQfeNiba0cFeUSH04MHBWz/d3E4xMPCMXlQIj6Ohzriw+5Kh21sGBoZduxSMGO68cXP7zMDACk5ykIGDt+Alx5zgG0r+u7n97OhgB6cKGfCVD5B+IAMDA+SuecxNgJCDlz7dugU5tBPeRYT0EiGCoLXN4BWnTOANitLg+9/5OjrUwD1P4kMYs0MIWXQKn2b87ub2FTYTyAS+QAI05e7mxgveQ/sGfGrOd3AWgzhvvdv1PAYhBga+XbtMRicGiY+IUZWjITAaAsMGjHYIh01UjnpkNARGQ4C0EHj48KOCwnIGBvkchhdVu2R41dTYGBjgtzJA9hfJ7dr17dw5LnB/jAF8a9mPc+e4IiIegFeaYXYLfz58+GnFCsheRKyugS/7BDWOkab4QL01I6MPbm5M4K1ZHxkYBDo6PoAvu5cHt8gZGBgkwadiYDUWLgjvEMJFID1DyDzhe/BNawzg5jgTuDf4DDzhA2now+eFnu/efefcC46KOH4Ghpsd2/VGL6WAh+YQZDx8+HmKwt4IhsA7DFkMDAyeD8r45OVvMjKKgNc8Q5LH24oKMfC+U8iM2T8GBqVdu564ufGBLyyR2bUL0g+E+x6zQwiXgjOQu4gfwf1AFvCKa0nwWTW/GBikdu2C3BaI3HvkNzKCLDflxXFQDejaT6SbXSBdQdAUH3iMA7KKlfncOb6IiFew3inocBojo59ubuwdHaLl5Q/A85/cRkbs4HWtcxQ2FoNOE323a5flaG8QHn2jjNEQGA2BEQVGO4QjKrpHPTsaAqMhgBICsOWj7xgY/tzcJcl3bvdPsDybkdHnc+d+gk/sZOnoYARfVCgMPoD0w4oVEuAJDaz7ABlu3fp57hykQ4ir78cL7l5CVtDB2R/B69z4Ojq+gdeOPgNv1lLZtes9eGeXIng3F65DO8BOhhKYHUKIxKeHD68rKHAzMHwFX8/9FbzDCn1Rn5sbM/hUUhnwOlURI6Nfbm6SDLMYGERHpwohwTjkSNgyUcabDBY/Ojogk4HvwAMNDAwMYh0dXOCk/rGigh+8RvpjRYUsA8NTcDqR7OjAdbM8MR1CSFg9B+9N5QdPRf5jYPhcUSHDwPCKgYEDPN7xh4EBciwNaOUzOF+ApvLAaR6+IxEihdxLhFz1+Z2BAXJKMHxiEL7oFH6U6KeHD68pKDAwMHCAF5FCjgXmNzJ6A+49vt711M0NtIZ21y4FV1dFiINHydEQGA2B0RAYgWC0QzgCI33Uy6MhMBoCKCGwe/c9N7e7DAzvJzPcCt5lDpm7Y2BgEGFgeFNR8Rd2K7egkRE7A8P/c+cgHUIGBoYvDx9+vHXrE3jtKcREYfAOpd9gDnxtJ7zvB2F8BHf54LKguUE1tX+3bjEwMPw4d44/IuKRggLvrl1camr3wfOQoAvfKiogyz4Vwcecgo3HTrzs7OSPiOBAnV15Db6VG7L89aWb2x9w4/gnUltcANz3ew/uBqiCp4b4wctH2RkYTnds8qv4yMDA1tExeikF9jAftKLg3uBj8DJRWXk3aZaOji/gXXNCHR0cDAw/Kyr4OjpegbuCkMXJHysqRMCe+QNeLwpZ54m2KJqBgeEXeBpcBDwsAlaOhXi+ezdkDTZ8xSkkwQurqb1VUOB98ODZihWQDbrc4Gm6T7duwWcLQbPZ4A4k5PJDeBcRMn4BOquGgeELAwMjeHXrUzc3gY4OSfCICSgrgVM+pEP4DnwWDmT3Iyf4noxvDAyQs1JBYVLBWcSgwMDAu2uX+ujEIJYoHBUaDYHREBhJYLRDOJJie9Svo2A0BHCEwMOHHxQUVjMw8OQw/HBlOKTRocHOwMBeUcGza9fdc+cgO/EkOzreVYDOnfgPMwQy7wFp1z5wc+NmYBBgYHgLnv1gBqtB6/tBjvrkAbdZwfJQ4hP4tjRIb5BdXv5pZyenkRHkLFDIwTOQeUJBI6M74JkNYXDnDOuEIWaH8DXY8K/nzn2pqBDftUvU1fXl7t2Q40M4YW3xD+fOCVVU3AHvqnpdUaG8a5ecq+sLRka2jg62iIjzKw5drLgpx/D0LYOyzK4oV1clqLtHqcEaArt3P/vt1voV7DyRDlvjCMs/t269cXNT+/8fEvsM4FOLBMD7RMGqGHjV1P7DbuPkdXV9tHs35LgjiCxytxBPh/A5OLFBNhzChz9kdu2Cr3b+9fDhlxUrhMrLvzx8CO8Toq1HBY3IPHzIwMAA7yVCuoKQuUQJI6N3bm7fGBg+gZMrvIMHcecH8GgLH3hoA9IFfQ5eFP0TPOHP2tHxDaxuSoX6DAZ+Boanu3Y5jfYGwUEySoyGwGgIjGgw2iEc0dE/6vnREBgNAeQQ6Ow8XVHxhoGB4cEDS94VM39VVPzs6GADH63BwMDA09HBVVHxiYGBvaMD3ieEdPk4wOswQWrAy+F+gHcAykZEYPb9kK2DsD+Br57/UVEh/uAB5Dr7d+BWNaRDCFEDahw/fAifMIQs/IMcJYo2Z4jcIXy9e/dLcAcScjqOKNIWREivQPXBA25w7/TXw4dvFBRYd+16Dl6tx8DA8B18+uJ/8MpSyPQRHwPDk4ozezsKGRhYRy+lgMfLIGTs3v3s3LlPRhVpggyHJcAbUD+cOwdZgcnPwPCPAXT/IMTZn9zchGHXkECuB2Tv6ECe+nu0e/eHc+cge2UFjIzkwEkIs0OI3A+E9AYhJC/GuUfwfaqQCfYbCgp84GvrMfuEEBcik58fPny7YgUrOHGygNe18oNXODOCFQmAJxUZGBhkjIweurn9Y2BghIkwMDAogCfepXbtOsugDV4m+nnXLpXRZaLgkBslRkNgNARGAcNoh3A0EYyGwGgIjIYAIgTAy0dvVzO8kGB4bM5QC9lNB+kRMVRU8IEvdn8CXmsnBV6lxiMvD7+n/ue5c/8rKth37XoF7lbxGxlJgxvQCNMxWJDeIGg3F9ICvHe7d791c1MFHxaKoQN0h+H9FSsgvUHMniGkQ/j51q2Xbm7i4M4AAwMDt5oaF7jjh2zaBUZQQ9oAbMtb8DEbwmA33Ors/FFR8Rt8gg4XuMENch54BSlbR8dsBuOKihcdHeoREaqjl1Igh+dgYIPXiD6CuORExwOeisjv4DOKIIswBSsqHoJvWBHv6NACxzXyXPSbzs6fFRXI98VDzEHbK6sQEcEBu0gQVz8QzwFIzxkZhR88YENKjbfAaY/PyIhgnxCSL7g6OoQjIl4oKPzu6PgJPkH0w7lzchERoNTMwPDj1q3v4LslfoEnCdnBfUJINmFgYDhSwZkNOj+GYXSZKCRyR8nREBgNgdEQgIDRDiEkHEbJ0RAYDYHREICGAHj56B4GhjcdHcaaFdM0OjR4wbsHf7m5sXV0CJWX3+rs/FRRofHgwU/wMjyINpFdu3jV1F4rKECa1Nc6OyFnJ0Ja3hA1aCTW3iBEzW1GRpkHDziR2s0QcWQSfj6kMOywEAYGhr8VFULg00G4wSdnQNaIIuuCsyGThJB7554xMoo8ePDh1q0Xbm6QOaUX4DkWno4OFXDP4V1n56+KCglw77Gz8xh3xT4GBoaoXeZo05hww0cZ9A+BC7svPXSbuY3BNWSXGWQZ5FVwn18bHGuQ/t63jo7vMJcpRET8uXULMvQAuZdSFLWrBlMIoiHdQibwFkTRjo7/4MtRINvz4CSefiDICPDmw7cKCmgdQgYGBmL6hE87O79VVMDnMx8wMgru2sWopnZRQUERfKmggJGRqJraSwUFjo4OsfLyh+ALCQV37frDwPAevMt3OcP6WaALPll37dKHhA/EVaPkaAiMhsBoCIyC0Q7haBoYDYHREBgNASwh0Nl5sKLiMQMDz4MHDvLyApAekdCDB78YGJ6tWPEJ3O+CnH/IwMAgDJ4JfAOe64CvuHu6e/dH8FI9vQcPMM/Qx9MbZGBguM3ICG/7YnEcqhCkZwg6kwa2xfEPeIErZD8VfCkdxLWQ0xohBnwD30Uu3NHxEzwlKAyeUYT0CcWQthpyMzB8cXNj27XrJwMD5KSQHx2L5CriZjPM4O0wGp0qhATmAJIPH35eseKpWMUCSYbPUh1y72CJkwt8coz4rl3sDAyf3Nz4du3idXWFdO3grmUCn1f0r6ICIvsFvHnvM/iII9BCZfBcN2RdNCj9gLfRCjMwQO4w/FlRATkzBr5FEG4sVgZ8AyGmLJ4+4feHD58oKHB1dMA31jIwMLzs7OQwMuIH57sr4HzHysDwq6LiBwODwq5d7GpqLAwMDxQUuDs65MvLYROno8tEMQN+VGQ0BEZDYDQEQGC0QwgKhVE8GgKjITAaApghsHv3Lcj97Kc6PvNXxH0Eq4CcVPHGzY2HgeEBWES4owNy5AZahxB0Nxq4T8jAwCAbEYHcJ8TfG2RgYMC6jRBsG3YCvl3wNwMDG/ieN+R+IKgpDz7TH3KQI9wIaI8R3L7n7+j4DD6Qgxe8yg6i5lNFBRMDA6hDCN5MCBHkBc89ioIOIF3jV/EdfC6J3uhJM5DAoT8J6+0wlDC8SGUIfAY+akUQHN2QmTFuBgZeiLOQYhaypZABvPeVE5wAIErg5yR9AncFIYKQY5MgIj8rKsQYGG6BbYGfPwRRRpBE3kCIqRhrn/Dd7t3fz53jBHsHeTr6I3jnrTh4+vrF7t1P3dx+MzCIgz0IOUrnfUUFK9iOpx0HQyr+go4VHT1NFBwgo8RoCIyGwGgIYILRDiFmmIyKjIbAaAiMhgA0BCAXFTIw/HhScSdtl74Iw9sn4GNaxBgYeDs6noBVQY7cAJ0oA55mYVdTQ+77fX748DH49gj4lkKCvUFIhxCylg9sAz4C0hVE3i54X0EBNCkEPkEUNMkDnu2BdwWhnUBw05mBgUGIgeF9RcVbBtBaOiHw8R5oln0AN7XfgrcUsjMwyINnEdnPnYOsIIX0mXMYvtp0yIeXe6DpHeXSOgSe7N7d4vZ1JuiYGFlXV6lb4OvmWdTUHqxY8QU8T/ilooIL3Jn/zMAA6ddBnATp3YFmDiF9/o4OyF0pyJOBDKhaIGfk/lqx4ldFBdeDBw/AqRpiGmRABMLGQ77r7OSJiEDeQIimGK1PCBkWgXQIkXuDXx4+fL9ixeeKCv5du16A86P0LtCVgjxg417B7lN5zSC/uYKjH3RFhcqDB9ry8tB+MVjVKDEaAqMhMBoCoyGAAKMdQkRYjLJGQ2A0BEZDAGsIdHbuBy9GY1jRwRhe7vFl9+4vbm5C4A1X1zs7IXdR8IOP6/jKwMAHvnGbAXzQKNy0DxUVAh0d/EZGrOfO/aiogGxzgstiZdxmZMR1rgxcPeRKCdAKOvARMpBzRN+ADxcV37VLBLygDq4YmfEZvDLw161bv9zc2Ds6roHnBhnAvT7I4f5wxbzgyxg/MDAo7toFv/3iB7gVzgA7coa/o4OvokKSYcuDB9ajJ83Ag46mDMjE4FUGy6sMWRIM0yCxxsbA8Bu80Q5yyOcTNzeejg6Gior/qEduQnp6r93chDo6vlZU/AE7VOEBaMIb/7m4vx4+fKegwLNrFw84aaGtPsXfLfz18CHWDYRgyxEEZIMuH3hbLKSzx9XRAXEhcmeVjYHhT0XFB/C1nwwMDKIdHRrl5W93737j5iayaxermtrWFQ+jKpjSGR4Gg3YM6iAsGGWNhsBoCIyGwGgIYAAWDJFRgdEQGA2B0RAYDQGUECgvdzQyuufmdjuigkHI6J48uDv0acWKj+A9VMwdHSwMDMzgbXiQVWrIyzUh96SxMDB8AW/wewXuN2qBTyhFsQMb593u3cgTI8hKIBODEBHxXbt0wAeHQLgirq4vISzcJGQO8x14kocnIkIvIuIRmM3AwCAVEYF8w+Gn3bt5d+1iUFODHG0KaXx/gHV3IaewsldUPGGw7ehgV1A4sGvX6PJR3OFOJRlwb/Dxmo5fzyqylBimQc7ShMxUg/bOubkp7NoF6RMyMDBwgSd1+cBdOIj9b3fvfu3mxg7maPz/D0lL32/dggwogIWxE19WrGDr6ID0BkG9Snl5vfJy+NUUD1asgF9NgVU/e0cHnulBiBZeIyOGjo4/4ANRv4F7en/AE5Wfzp2TBXsEMrf5v6LiJ3gxMzu46/j+3LkDjIyg/i0Dwy83txe7nkaBlon+Dd5l6+oqBzF5lBwNgdEQGA2B0RDABUZnCHGFzKj4aAiMhsBoCKAA8PLRexUVr3MYXod1/BdneMYLvqKQH3wlAzsDw++OjjcVFdwdHZLg/t4n8FrNp25uLOAjXn6Db634yMDAB55qg9wbgXaLILJ9kPVymB1CSPMdvkYUayOemEnCL7t3f3Zz44XN9jAwMDwBXzoHmts0MpKB9R8+gS9FhHQn3oPvw/gJ7tP+YWCANNDZwU1wnl273qoZKijsYGBg6+hQzI4Qwz/XhOzTUTbxIfDw4WcFhasMDIwMDP937ZIzZLj6CjzHy25k9OHcOZaKil8MDN/Alw3qwo4yQo5BBgYG+C0p8MOQGBgY4IkKa3KCOA8yPQiZGIeIwEnkqcK3FRXGDx4gjylAlOHfQIh8g8VrNzc+8L3zf2GbG1nBR8VAzOFVU/u4YsX/igpG8AQ1G/iuecg5OoJGRrfPvV1ZwTGLgZ2BQXB0mSgkxEbJ0RAYDYHRECAIRjuEBINoVMFoCIyGwGgIIEKgs/Pgk4oLUxjUVnQw2hkxS7q6QlaQQtbRvQTP3Ujt2sWjpvbl1q2nbm7S4GmNp25uouDjXl6Dr0f7ysAAvysCV88Qcusa8qpR+AJRtDWiCMchsSB9QsiVA0jCUCZk/R47+BYNqBCY+vTw4UUFBcj6QznwVCG8O/Ee3DMEq2L4zsAAmaiRiYiAbCrjefAAcjTlabdlCgwLrjI0i+yKGj1pBhJc1CJ37352022NMUP+E4YszQ45ASMjPjW1JwoKDAwMSg8efADP8XIYGUFOVfl47hxk2yo8BhkYGB6DL2MATfPu2gU5GhfutgednV8qKpQePMC8shKiBn+PDrRb9eFDyCThB/CBNGgrSLFuIETuB0KmNJ+Acwo7+GAkBgaGXx0dkJlPSDZRiIhguHULcmIqn6vrm927X7m5iYFXR396+HCWwsZSBiMGhtu7dlm6umpAnD1KjobAaAiMhsBoCBAETARVjCoYDYHREBgNgdEQgIdAebm93y5fBgamiArGRecEQCfpg+8nZFBTe7579z8Ghr8MDM/d3C4pKHw+dw5y1gUDA4P6rl1s4OM4xXft4mZg0N21SzEi4v25c5BmrqCR0f0VK/YxMl7s7PwE3t0HOu4FNkcHsRrSG3zp5vYSvGBV5/9/PJM5EC0MDAxvkHpxcEEGBoYv4M4DD3gmE1mcT17eFrb69NGKFU9g2iG9we/ghj6rmhqfmhojA8OniopbCgp3wbdzPAMb+PncOb0ODV4Ghsugu99edHaefvjwA7L5o2yyQwA81PCIgeEQFwMDpDfIwMDwaMWKt+DJtDsKCl8YGF5VVDCDYwdiy8dz557CYvDt7t03wNcSiu7apfH/P1pvkIGBQQycGF6B4xGiHZn89fDhr4oKzASDrIYPvIIUNAlZUSFgZPRgxYpL4AshQP26hw/bWhRLAACMD0lEQVR/wvapMjAwPN+9+/nu3afB7oH3AyE3WOg/eMDOwMC7a5coeCSFm4HhAyybCBgZXVVQeOTm9hLs5c8PH7Krqf1hYPh67ty3hw9P3mItBV06/3vXLofR3iByvIyyR0NgNARGQ4AgGJ0hJBhEowpGQ2A0BEZDAD0EIMtHGRgYPldcNmM4IMmwAHIdxedz59gZGP6B9tQx/GFgEOvo0AafjP+SkZGto0MQzL7CyIh8XzzkFkF4zxDeS1SMiHipoCC8a9dfBgZIJ5Cno4PbyIiYfiDEubgmCTEXi0LUI5Pw5aPQI0kYGH4zgHwEusECfEIpZLksOwMDBwMD8ukyrOCtkl92ndhw7nNFxQv4RY7Iho+ySQoBWFeQoaNDwJbhggTDww8VFWwdHZCrIwQ7OqQYGJ6Dl1BCNqwygE+OhVvBCb4iArQ3DyyE2RUEC4MIPAtHX4ATsBA4AYOUEsKQrqCAkdFdNzflXbsk1NS+rFjxE3x7BHIPEMJGvtEeeR4S0oPVAI9Q3ARfTP8DdrfEdyQHXK94dIzBtZ9BoqODIyJCefQ0UaSwGWWOhsBoCIyGAFFgtENIVDCNKhoNgdEQGA0BzBDYvfuRm9tuBgapFR2MShWekM4Se0UFOwPDK3CviYGB4W1FhUJHB2NFhRT4VFI8+7XgPUMx8DkZD8DHfnCBt4TJ7NrFzMDAraaGazkfptsgIlfBnU/k40ZxLRaFqIeTz8E3KH6uqGAG71f7BO7c8hkZMcHupod4lhl82KP0gweQHYOfd++GLOfjBU9v7t5975bbPAYGDrkOE9/RSynggUsK49ruSzfdZm5ncA3dZebqKsXAwPBq9+7nbm6Su3aJgQP508OH3xQU3jMw/EfqIkJsYALP4nKAY/AteMWyDvgoUcwNfhD1uBInZFE01t2DcI2YDMjGQibw0bXfwaf0MoFdIgu+IgIyH4h2oz0kcQrDcsrb3btfuLlxdnS8raiQA08YPnNz4wTb9Bfm2ccdB6MqvjIwXN+1KwISPmD5UWI0BEZDYDQERkOABDDaISQhsEaVjobAaAiMhgBaCOzefc/N7SYDAxN4O54pvOn89tatD+fOfamo4GBg4GFgeAfeOigE7iXKRUS8WrHiR0UF5GjQLw8fQnbffT53DnKwPuRy8B8VFfwMoN1678F3Hr6vqIBs7YPsHIOc8YjmGEwu5iThc/A6PUnwrAuaeviGLgYGBhnYMZXv3NwgZ3v8BO97/FBRobJrF6+aGrxTcRN8952wqytyb/AFeKUiZP7np5vbpY5N1xgkIiJURy+lQAtzPFzIxGAJwwsvht1CHXKQOWTIKUTIfULMs15udHZ+qqhgBCebT+CjjBgZGH6CRxkgF4dALEXb4wcRZGBguAJOIZDECVnt+U5BgQ2cvuFq8DAgqQh0RhF4bTPoyCUGBoaKinfgoQ3IAbzwxMxvZMQPuzCTV17+XWfnTwYGBvBEIiQ78Hd0/Kio+ASzT+/Bg9crVvyuqBAF74EEB9FNBobvu3bpjJ4mCgukUXo0BEZDYDQESAajHUKSg2xUw2gIjIbAaAgghwB4+ejdiop3HR0C1hXT2BkWQDpUn8+d4zUy+gVuFj8EL9uDHOD54RxoMSXk0P+/4EvDeY2MPoO350HW9X07d+5nRQULAwMfuA39FnzZGrw3CDmxA7IsUBB85AYfuEkN76Ehu42BgQF5khDU4K6ogM/AgDYTgrujkMY35A4JiGMgN2owMDBwgjdxfYQZKmhkdMfNTRjcs4V0Tp50dnIaGfGqqb1WUGDs6GACn7wK70++Bp8RcrpjjV/Fd/AmytFLKWBBiZcGd3UeMzAwnOq4z18R+Rp8UK2gkRF8RfFv8L3zPEZGLOCUI1ReDumJQTrhkOQEiUQeBoaP587xRUQ8WLECchonAwMDxCgIF3RSCwMDPP2gLRx919n5q6IC1/Qg5nAGJBVBrIakK1HwxlrQymewlyHDIqAZZiOjj2DHg4UZmCoq3jMwMDEwyO/aBXE/ZFkyF3i5MhNSh/ZLRcVHhiwvhljwauXRZaKQ8BslR0NgNARGQ4B8MNohJD/sRnWOhsBoCIyGADwEdu9+4Oa2l4GBd0UHnyHDxY8VFZC5EfaKCsgBpJ8ePrwKPhCSlYGBlYGBHzxnyAo+Oh/UOO7ogPQGIdeFg4wF9wn/gJfGSezaxa6m9gk86wjvCsK7lyDF4G1jkE6jHPh0EHj7Hj5JiLx1EN55gG99hDS+IVxeNTXIElAGBoYP4Lk+AdjqxPuwQ0cgPYq34PWxguClgF8ZGPjAHUU+WJ+QgYGB7dy5XxUVEv//g6dSL+UwvLTpkLeIsBidKoTEGib58OHnOyu2rqzgmM0guWuXrKur1C3wHCwL7CpI+Pm0n8DHtIgyMHB2dLwCX4kJ6Q1CSHgkvunsZDcygizihZwF+vHWrffnzkF6g4LgbX5CHR0QLmTaEN4n5FdTQ76JHj6CANo9C57QhiQYeP8N0gOECDIwMPCqqbExMLxVUODdteuZm9tXBgZWcAqBJF1IbxC+E1K4ogJyBu838CpTMfBgh6SrK+TWRNaOjq8MDBBHXmGwjAYtE+XYtUt9dJkoZhIaFRkNgdEQGA0BUsFoh5DUEBtVPxoCoyEwGgLYQ+Dhw48KCtsYGARyGF5M/p/IwMAAmV3hefDgy61bkGb6Ezc3pV27PoGnDX+C28dKsIM6vj98+HbFCk7werlf584xgg8OESovv8zIyNPRoQhTBrEbchgp5DZ5+AI8AbBeyBQisuBbNzeRjo7f4EvqIAfAQKZx0CYD0TZ0QSz6AZ4AlcCwHXJPvXBHBzcDw88KUPP8J3h6hxl8Zz38dnvIYSTw00T4Ozr4KioWdpwyMhIevZQCEsLIJHhi8NFNBstzDFniDNOEOzoUIiLerFjxp6JCDbbK93pn57uKClHwUAIf+KSfjwwMirAlvsgHtEBMftPZyRcRgfVGeMg2P4gytGnDHxUVrAwM8h0dnysq2MD795A7e3h6gPChBIix8ENi3u3e/cbNjWvXLkj6ZGBggNxrAlH2rLOTqaLiKZjD2dHBgzQXqhgR8Qw8kgI5XWZZ56Fo0KXzf3bt0h7tDYIDbJQYDYHREBgNAUrBaIeQ0hAc1T8aAqMhMBoC8BAA955ugzdM/bm5S5LRzRh0E31HBx+4qwbpE/5wc2NjYBB58ODTrVsv3dwgVwW+3b0bcqnD14oKkV27GMETa5B1eq/BR2tI7NqF53xRrP1DyDzMY3DnU4iB4T145Z4QeOLliZsbZBoHPo8E9wIaA2uHEK7mDvhegbfgrZI/GBikkZr7kB7pn4qK30g7Et+AV5BKMswFL4FULC83hRs1ygD3Bh+v7fipU2HP0tHxhYEBcgUfAwPDX/CE81vwlCCkM8/AwMDCAFpm+QE8rPC+okIP23XwoKtHcHcI4WEOST/wNaXCRkYP3dwgBxr9Bp8nxAxWCplGRp7Qxp9+0A6JgU91QgYyIOlTBjz5DBk4+GNk9BU8dckCm0iETER/qajgZWC43XEwpIKNgeE7+LxeA7CLRonREBgNgdEQGA0BKoDRDiEVAnHUiNEQGA2B0RBADgHw8si7DAx3V3Qo2Bsxfzp37hN4RZ+Eq+tbcA+KDbaED7LB78e5c5CJQQYGBmFXV8gxIcjHeED6hLqwOSJku7CyPz18+GzFiveQRacMDELgNXifGBhAfQzUxYFYtSML/nj48LmCgiKG1ZA7wSEqxcDnRjIwMDCD2/GQriADA4MUA8P9igrWjo5vFRXy4FkmdvCesc8R6StWXAPfzcH9+YEO2pwSxMwRRYK7go/AC2//79olp3lu8beKCokHDx6sWPENHInM4Os9BMCb6yCrkXmNjH6CRxZ4Hjw4r6CgBO6KCxgZyYL7V8ih95SRURR2bieyOCYbeSHxu4oKcXCC+QHeyPoXnITEOjo0UeeKMQ1BFnnOyMiOdBoNZJIQMtWJuNeEgUHCyOiPmxtk+OMVbPiDA7wzFrJEWdDIaKXb9X8dZhUV30eXiSKH8Ch7NARGQ2A0BKgCRi+mp0owjhoyGgKjITAaAogQcHVVevDAlIFBKKLifavbE6mICJP//yXA53D+rKiA9AYhd3OD7hYH7616A57Hg9wR9+vWLQYGBuRLwCFzg5C76RHWYGM93737VmfnTQUFfiMjFvDUnBADg0hHBxu4RwE5sl8IfD7HgxUr9jMyXurs/PTwIWSCCJt5DBzy8mji3x4+fAju1ort2iW2a5fW//8irq6S4P6emJGRTnm57f//chEREkZGPytAa/sYGBi4OjpenzvHysDwq6LiMwMD263TgQyHTzGEX2Lw2aawePfue2hWjCju7t3Pfru1HmKwXMWw6HLHQU2Gq7/Bx6j8uXVL3MhIERywLOD5QFAfOyLC9P9/tfJyHvA1EiLgWUH7//8h6zA/nDt3GRyhaAGIdb0oRM2Xhw8haQb5mviPFRWSHR2cDAyCHR3sDAzs4ANmFXft+sXAsI+R8SLYCjxpBmoyePcpcjIWAndW34HFZVxdIZtdf1ZUPHVze8zAcGPFik8PH4q5ukrs2vXCze3HrVt88vL65eWKEREr3a6XMehWVNwZ7Q1CwnaUHA2B0RAYDQHqgtEOIXXDc9S00RAYDYHREACFgLy8wIMHbh0dKlMYpHgVrjx8+OHz7t1v3dw+g2ddII3vz+fOSXZ0gC6lqKj41dEB6Q2Czu1wc2Pr6EBrxENaySCjMTBamx5yxilkeerPc+dEOjp+VlTIgaf4FHftUoiIUIiIgJwpIgxePnpWQeHBihWXOjshnUMM41EE3uze/Rp8rswrcA8WfsOhGLhP+NzN7RW4uc8nLw85+9Ts/3/IzkZOBoaHbm5PGBjYwKtnIY7kA5n90M3tTGfn6YcPP4B4Iwk/fPi5s/OGm9tNVoZvnxmyNDvkIGtEv4IX9z5wc3vs5vbczU121y7I5DA7AwNkNvXXw4ef3dx4d+2CJxLd8nJ58GFCDAwMD1eseAyOBTxhCRmPOM3I+Awcm5DogKQZBgYG0///eRkY2Do6ZMDzgeIdHaJIm/oEjYzur1hxf8UKPD1DTBdCHCOyaxdk7ANyb4qEkRHkIgpB2CDFAUbGF+fO/QGfjPpq9+7du5/xKzwvY9Dt6BD8/z9mdNMgJBhHydEQGA2B0RCgLhhdMkrd8Bw1bTQERkNgNARQQgCyfLSK4Zkkw2M7hlpR2OEfoF184IsleHbtug9eSMnAwCAfEcF069YX2PI5FIMYGC4zMiLvJISc+A8yB3xCKeSWC3ibHnJCzNvOzp8VFSLgFYOQRZ5iu3bBe3HIZ4q8rahQBi87hBzkiHwVwX1GRknwuTiv3Ny4Ozq4wT06uCHIjrwIvsJOH9z5hJ8mwsDAcBs8owi61B68pRDS/fh17hzkCNap59gqKl4yMLDt2jWCLqUALxO9B75DT8WQ4c5LNzeOjo7v4ND8UVEhyMDwHHZqK+Tkz3fgXabS4B7aM/BSTGEwG6wDQTzevRsyWwhJThwMDK8VFKTBMcLAwABfFArZiIjrVCHIomXIGs6vDx++WrHic0WFHtiQTw8fws8TQr6DhIGBAXINCcQpkAtOMK+7hKwaFdm16x8DA2RMgYGBgbuj4yv4XCJ28JJjyL7BzxUV5xgm5jHoMjAwjE4MQkJ1lBwNgdEQGA0BGoHRDiGNAnbU2NEQGA2B0RCAhsDDhx8UFHYxMLzr6DDWrJhmuisKtFcQfKA/fKPg4927H7q5MTEwCILP6uQFLxSE6gcf38/AwABpl/OAu2TwfiD8AgAZsBZIPxCiEW2/IgMDwzVGRuQOIUQZ5CoCBgaGBytWQG4/h8xTwWX5GRj+MzDwgPuBX8+d4zYywtobBLlw9+7nbm6Su3YJqKm9VVAQfvDg461bL8BziQwMDOLguwd4OzqUwT0ZyDkiQmD2ys4dryuOyDE8lepwEo3wHfaXUlzbfemW28ztDFb2HbJR5XaQ6yIZGBggJwy9AXfjv4H7h5C4EDAy4mFgeOvmpvr/P3InHx5HyIxPDx8+hM37cTIw/Kmo4AcPQ0D64ZAhA3j6wXoqDHJnHhKtL9zcJHbtEgOv+YTYBekZQtiC4LQBv/Veysjoq5sbe0cHb0TEJ/D6Z4gyCPkNvKWWD5yMWRkYIGnpGngoQebBA0g6fFdRsZxh/SzQrYSsu3bpj04MQoJulBwNgdEQGA0BGoHRDiGNAnbU2NEQGA2B0RBACYHOztMVFW9A7d0HlvLyApAbKYQePHgLbjFDGugM4Ak0to6OvzCtkONJP4GnEL+AD/NkBJ/dwg++6w/SxMfapv+8ezdkVSH8AjrQgZO7d79yc8PaJ4RZCDqwFN4zBG3/A2/8+8HA8BOmArLGD3TUJLgbALluDiLJKy//CtwnFOro+FVR8Y2BQQLcTWVgYIBsg3wJlpXctYubgeGLm9vPjg42sEcgR54+c1v2rCPoGoNERITqcO0TgicGH81lOGXf8f0rJNTAJBf49g7xXbvYGRg+ubnx7drF6+qKPIXLAD5TVKyj409FBS9Y9svDh2CtDJ/BSQjUsQenEwYGho/g80hBx3iClyhzgtcGQ+6NwBw4gBgCJ5GnB+GC8LNekPuEEFnknuHbigoFsAvfMzD8A29BhCRgqEqw835UVAgwMLxiYOBAOt/o1YoVvyoqICkTEkQMDJ937VJxdVWE6B0lR0NgNARGQ2A0BGgHRjuEtAvbUZNHQ2A0BEZDACUEwMtHbzMw/FvRwWhY4fkRLAlfvAdZQvkGfKAIJ3jtHOheeHDLngWskoGBQYSB4TsDw9+ODiYGBi3w3BpMBoX+9fDhGwUF9o4OzFWFuCYJUfSDOZ8ePrypoCDY0fG+ooKLgYEJ7CR+I6OP587xg7uCkIvFISJgHQwfwK6FdCN/glc8fgaL8IL1QtRArlNnBntTHnxsJmTh4s+KCjEGhlMda/wqPjIwCHV0SA+/SynAXZ3HDAwMJzvu81VEfgHv03sHXqwL2dXJw8DACQ4ZDqQQg1zdzgA+3IWJgeEXJBwZGPhhaviMjCBDBsgM0GZCNzcFBoZn4HspPlZUKO7aJY00xQczBp1Gmx6ES98Hr/vlNjLC7BNC1Hx6+PANeGbyW0XFHwaGT2BRVvCMN5gJcjA7AwNrRcUX8PWJHB0d3xgYIAtEQVtnwUnlOehuCdB4yOgyUUigjZKjITAaAqMhQAcw2iGkQyCPWjEaAqMhMBoC0BAALx89ycDwNYfhY9ouffZzuyG3CLAzMPCC5+J4Hzy4paDA29GhAu7vvYSdDvIcvPBSAHx/3wuwYQJGRpA73MA8BAE6zwPcLueNwHIdOeZOQoROJBZEGURAbNeul25unB0dohERkMkoSFcQIgvvBMJnDkF3HoInQ5nAHTuIMggJmS/65eb2EjblyMbAIL9rF6+a2m/wHJH4///gIDqQw/AorENQbrgsH929+9kVtzU3GeQYGBiCd5m5ukrdYGQU3bWLFXy5wlXwxetsDAysDAwCHR0s4P42pI8HCTdIb/BTRQUTeMfdb5gofCYQuXMIk2RgOHeOsaLiC+rSU8x7KRDqGRiwTg/CFVwCL+yEbCaEC8IZkLs0v1ZU8HR0fK+o+AaeloRsaIRsg2RgYHgNXg0r+uDBbbCX5cBXa0C2rTIwMCyoUO8H3Y2ismuX3OgyUXjAjjJGQ2A0BEZDgNZgtENI6xAeNX80BEZDYDQE0EOgs3N/RcUPBgaGS7vEdV2NGBgY4CtI2eTlX4InknjBR8WADgIBdwUld+0SVFN7raAg+uDBDwaGR+BrBgU7OuQiIvhQb4YguMcM/yTht4cPv8GWIILmJMFzSm92737p5gbZ4YbuGRj/M3gF469bt367uX1nYHgKE1eCrRqFr2lkA58g8p6BQQF8jA3oNgUjo/dgb/7v6IB0cvg7OtgrKmYz9Nnv8nd1VYIZNiRpcHxevMDghXxPI+QSDm7wBKw0+CJHbvB5sOwMDAzgqT9I5xni4ddubkLgkOHp6PhSUaEA3msHupsENeohiiEkpGsH2aQK31UI2ZEoj5FmIFog6ZCBgQGysRMuCGe82r37K3jZpyJ4tAIu/v3hw7crVnytqOAGH0z6HnygLu+uXS/BiiGWMjAwSILvTuTbtYvP1fU1OEWJ79oFWUu8rPNQdAVbGsPdENAyUXO4yaOM0RAYDYHREBgNATqA0Q4hHQJ51IrREBgNgdEQQA8B+PLRXbvULRnuvnVz+8nAwAteQgnpEYkxMDwCTxkp7tolDumVgWdX4CdGwq/2Ru4TYt06iGY3ZPYPsl8LU+rruXNfwYv30BRcZWQURzqhFE0jnAvv2UJ6rQwMDO8rKvTB1+XB1UCuSv8BPsaGgYHhO3hvJAt4ZSNoR6KR0edz5xgqKvgYGKYwdLUyyHd0KA7RXYUPH35eseJpRcWH1R2/NCvsv4M3zkHu4QBNnYE7xjwdHT8qKqTB3WZ2BoZ3bm7K4PM8IcH1dvfu1+CuMndHh0hExH8GhnsKCvB+FEQNVhISERJIRn16+BD/FfaQPiTkcFGsZmI9YAYyMcjAwMBpZAS/OuUOI6Pwrl2CsG2QbAwMXysq2BgYPoA3FoI2NxoZ/Th37gvY4xcYtN3cboJPE1UY3TSIK+RHxUdDYDQERkOAdmC0Q0i7sB01eTQERkNgNATwhcDDhx9WrLhXUXFvHsNtYYZbpruiQB0hBgZeIyNeBobXbm4PwBeCQ07+VIiI+AVeCCqCNDkD7xNClo9+3r37F3hOBnPrIJo7MCcJ4b1EiErI2Y8QNoSETBLi7xN+2b37i5sbz65dPOAeLAMDA5oLGcCLEiHznGzgE2hegK9beF9RwQo+y1QK1i/6Bb6M8XNE+ooV1yoqQPcXDrlLKS7svmTo9o2BgbGjgz8iQpr71omXbm6yDx58unUL0hsUYGC4Dw5c/o4O+KLKu4yM0g8ecICn/t7CFgyDjqWFBSna3BrYAHQC0rVDjgi4isvgrYAQLtpUIa7dgxDFcBL5gBmIC7+fO4fcGwSNAoAvyeCLiPhy69ZTNzfpXbvuubnxMzBwgvcNwucMXzPIr6z4MpNBh4Hhz65dSqPLROGBPMoYDYHREBgNAXqC0Q4hPUN71K7REBgNgdEQQA+Bzs5zFRXnGRikJjOcdetg5TUygpwuw9bRwRIR8QDcCWRlYHhXUQG5KxxN/3/wFJxgRwcjAwN7RQXkrH9ecHcCTSUyF9L904JNH72BdTzwn0GKf5IQ0gmBrFFEtuvTw4ePwL5gYGDQKS//vHv3z3Pn/oG3yUGUQa40+HTu3OeKCrUHD7jl5SGzW5Cpque7d9859+J9xSYNhjXrO04NlZNmru6+9M1N/x5DNgPDf88HZXzy8t8ePrwPntwTcXX9+fDha/CpP+ywKIYEhUJExGsFBSHw3Nrb3bu/gWdrRXftgs+8QZRdAe/l04FFH0QQmcTftYOvIIVo0QUPMUCiDxLmEHFc5JeHD+8oKPxjYBDbtestbPaSC3awDUTXN3AP/wP4IBw58IJY6V27WMHDHIwdHZA9kLsrOG8yyM1keL9rl6WrqwZE4yg5GgKjITAaAqMhQH8w2iGkf5iP2jgaAqMhMBoCKCEAXj56k4GBadcuVVdXJUh3iG3Xrp8MDJCrAng6OrjAZzN+Bx8qg7y7DHL0yNeKCn4Ghm8MDCzg7WeYB71AzgXlAx9hAlqWKS8PmSQELQKEtenxXDAIcS7+SUKIs7H2KOB9wvcVFYrgW8iZOzq+gRemIt9udx7cz9F+8OC5ggJkAS3k0rzP586xMzC8rjhjyuDPwCD44IH1YL6UArxj8BEDA8NuhjKFDm92IyPICaJCHR1cDAxfKipEOjpAGwUZGPgiItjBXfdHu3ffdXMT7uj4UFHBzsAgAT6UBXJjuyy4twYJfziJf5KQyK4d5Ap7ASOje25uhg8e/AEf6iPx/z/kNgvI6UEQGyFpDLKSGXTdCDiNsVSAToP9wsAgAj7zloGBAZIsIYr5wGtEP4MTJPzMGIWIiGcKCpD+LTiUbjIwfN+1S8fVFXTWDsSuUXI0BEZDYDQERkOA/mC0Q0j/MB+1cTQERkNgNATQQwC2fPR1R4eQdcU0doYFkKv5Pp87x2tk9MbNTRS89PB5RQVXR8c/JN1fKypYGBjYGBgEGBiegy9/4wK310ENd9QrIjDvh2AF33CgCJ664VJT4wJ3TpDMxsJ82Nn5paIC83QZzMWimJpvdHYyMTB8rqhgZGD4C0aYahgYGDjAB5DKge9SlwGTkANmeDs6ZjPYVlTcY2DgGbTLR8H9HNDFErt2yUq7STMwMPyBRQfkfoWf4C7fd6w+B0cfB3jj6N+ODh4GBmVsvUGIVjx9QvzTgxDtEBIyVcgCnmTmBAc7E+w2C+TeHUQxssiPigohBoaPDAxfwatAP4DnAwXBPuUzMvp57hxbRcVPBoYfYAXsHR3wm+vfVVR8YMh6x6CezGDGwPD9wQMTeXleiPmj5GgIjIbAaAiMhsBAAfjtVgPlgFF7R0NgNARGQ2A0BBjk5QXKy40YGCDLRyNWdIQrgS8qhFwjLgq+sE6ovPx5RQUzA4MauJ8AuQKehYFBcteuH+BZPoGOjs8VFbIREZAA/XTrFnIn8CFYDeg8j44OcfBBJhzgNv3rc+cgczgKYI1oZ5ZCjIKT3EZGXxgY3uzejbzJ8NfDh1/AW/7gWwfh6hkYGF6A16NC5vo+nTv3H9x9hfda4Soh/Y3f4ONkHjMw3HdzUwTvJzT9/x/U22Rg4ImIKJeXj4jQ6lFYedNtnkCHuthgupTi4cPPD1dsXV3BkcrAEAq+WOLNrl2v3NykjIzYwBOzD1as+Ak+WIUNvJCSF+w7+MmrkHB4X1EhCL5p8C/4Qvl9jIzC4BN1MCNF1NX1JQPDSzc3UdSFo1927/5VUSEEO4YUYiwm+eXhw8+3bn0+d+4H+JYIHvCtJ09g14R8BM/+gcYUYH08uAnS4ETyCbw4+UdFBeRSRMhpsVxGRnLgjY5vzp37ycCg9P//NUZGbgYGGaRlsc86DkZXfGVg+D16sQQ8SEcZoyEwGgKjITDgYHSGcMCjYNQBoyEwGgKjIYAIgd27r7i5PQbfAy8E2S+HvBQTfiPF14oKSViPgoGB4a+bG3tHB3tExB0FBcldu3BdHc4A7su9dHMT37ULdJdgRcVn8MGPQuA5nLvguw0gToGfcQLhIpOQhaPIk4TILoSr/PLw4ZdbtyD9QGQSsi+Oq6NDDNy1gKuHrFH87eb2D3xlBeRiPfmICA4GhncKCmwdHT/B2w6fuLnxd3T8B227DH7YUQ4+2HLgL6UATww+msmwQZ+hkxPciWJgYACdA3Tr1is3N8heTchGza/nzjGBD1blgUUffB0vAwPD5xUrGCoqWHbtenbuHOQwIci8IoStiHpdBNZJwheMjJjbOOGB/BzWOUeef37i5iba0fGnouI5eHrwI8apsHDtEAb8XpMvt269dXMTB98lCJWqqFDo6PhXUSH74AGbvDxkqyr8uFrw3RJ/wctEDUbPj4GE2Cg5GgKjITAaAoMBjHYIB0MsjLphNARGQ2A0BBAhAF4+erei4l1Hh0CgETO7mzF8Qx0DA4MY+Ca6vx0dvysqZMF9v18PH75VUBAGN8Eh04a4+oSQPgkDeGZJfNcuyBIRAdjdABAXvKuoUN61C77tDeu0IfLpMpAda8gHWr7YvfuJmxsfuF/EZ2QE6Q0yMDBIgKePQLeT7979ws1NF3VqC3IA6TsFBSHwRYsPYefQMIJn1T4yMMiA7+Tgha1IfMIQYs7gycDAA55CUx3AXYXg3uBjBob/NzsO/qioYOno+MPAADlI8y3Y8eIdHb/AGyZBvcQHD37cuvXGzU1k1y4hWIBAQh4Sj+wdHfBrAD89fHgfFg6QniEDA4MiuCMNmTNE6xOCplLd3DC3ccL7gfAwhOxNhcw/S7q6Qk76YUOaymNgYNADT0RD3AYnfz18+EZBgXfXLl5X13fg+waFwR759PDhNQUFVgYGPvDc5hfY9RKsDAw/Kio+dyy3rVBgYPje0SEYEaE8ukwUHp6jjNEQGA2B0RAYDGC0QzgYYmHUDaMhMBoCoyGAHgK7dz9wc7tdzXBFguFr8C5zyGZCUK8JPIf2GdzdAnUwIiL+gPsM8F7ERfDRLPqo3S3ItB7oKvOODvjhMR/A80UCSN2ST+DL5R+sWAFZRArvhEAcB582hJgGmSSET0khLw2Fr4TkMzKC9wMhhkDIy4yMErBLySEikFvRf1VUwK/Oe7x792/w0aP/GRjEYZsJeY2M2M6d+1lRIfLgwXMGfvilFJ8fGPIQsQcSbhdVGOCu4CPw7k7QxRLy8ryXwIEvsGvXB9gUHxcDw++Kin8dHeJGRvB1trcYGTE7hO/A90xCOvbIzoNECqRnKGhkdAd8/AykZ8gnLw8/cRStZw7vBDKA+9KQbjlkfhV0r4maGnJwfQanBF5wSni8e/f7c+cEjYzuurkp79olCxaEu+cZIyN7Rwf8XpPb4PsGhVxdX4FNeOzmxs3AwN7R8RWsAdIr3l3BWcSgysDAsWuX+ujE4CgYDYHREBgNgUEIIAPEg9Bho04aDYHREBgNgREdAq6uCrt2Mbi5vWdg4L3p9iS3AxQaoBspwPsJmY2MPpw7J2BkdFlBgZ2BQbKj49PDh5BZI8ldu567ub3avRuycPTbw4ffbt1iAPepGBgY8B8eAzEBNDVUXv7p4UN4z5CBgUHQyAjOVYiI+A1efcp07txn8J7Ae4yMkANg4KRURARylwPkeiQssWvXCzc3BtQ+4a+KCjZYR/f7w4ffwJseWcGXuTMwMJiCu7ifd+/+XFHBu2sXmzyo/xdv9NW646dARfg2heb7He6QRbZI9tCQuXv3s1tuk04zdN5myPKOKOOTBx2OAvEXB2R6DRyGbxUUfjMwsDAwvDx3jl1NDXIjiMiuXW/c3ITAPoI48dfDhz9h/oKIwElIpOiDTbu/YoUwOIgEjYzur1gBmYHkZmB4vXs3M/gKys8MDJ/BM7SQyUAICekNwsMQbjJWhqyrK7+a2oMVK5TBi1ovdXbCBwLegu8w5AVPUTIwMHx5+JC5o+OxmxvkNkUWBgbZXaBp5zdubnKw6xbBvUFdBoanu3Y5jfYGsQb4qOBoCIyGwGgIDDgYnSEc8CgYdcBoCIyGwGgI4AwB8PLR2+Dlo0IREaq8K2b+qqj42dHBBl6KCTr2A3wo5VdwJwF0JElHh4CR0XfwxJrMrl1MDAxfz537Al6viHmn/Ifdu3+cOyeBbWUgsoM+PXz48dYt+CJSyLThF/DBkr8ZGIQ7Oj5WVMAPBUVeGopsCCb7PvjAUvjCUcgEF2S541vwteac4E2D/2ELXEXAKxs/u7lB1itCFqZC+p9KHR17Ku6EM9h3dKhHRNB8+ShsYpBhL8PiHwwMOrsC3oM7Y5DFnD9u3YLcrCjm6gr31G3wLC4ovoyMZMBzbmiThM/BM2/waV7M4EIWgSwlhWwsFDEyeuDmxsrAwAQ+uBUeEbgmA5HNgbPfdHbyRUSwgbrYcDGGx+DLMCDrhwWNjMTV1N4oKHxmYOADr939WFEB2Yj4xs3tFwMDKzgFQqasXygoiOza9VnNQkHh6ugyUUSAjrJGQ2A0BEZDYLACpsHqsFF3jYbAaAiMhsBoCEBOHzXdtUu1ouLdbIWNGyt+P2ZgYDMy+nzunMyuXaD7GBgYvoOvDQBN4oF7gx/OnXsFPv7xvpvbDTc3NiMj8V27tP//h69XhAcrB/j0SzgXF4NPXl7W1VWvvFwdfEHFUze3nxUVXAwMEgwMnAwMbysquDs67ri5/WBgeHvu3Ntz5z6DDtx8iMs0uDgPuL/3GrzUEDTdtGIFW0cHm7z8W5gIAwODsKuriKsrT0fHSze3Xw8ffnZz+wy+yeAMeGUmpDcoCt6h57UrrKNDqaLivoLCgd2778FtoToD3Bt8zMDAuGuXnP6uACWGaa/AM5kMDAxnFBTur1jx/Ny53wwMoPlPBuhxOGzy8trl5ZCrID+eO3cVPM8GmSSEOO8L2Ms8sJk3iCBW8svDh8937/566xYn+FzQXxUVX8F9UXHwyaW/GRhuubn9ZGC46+am/uCBWnm5pKsrnnlarFbABWVdXR3+/39/7hwzeFjhsoLCbQaGfx0dn8E2QvYfPnFz4+roAM2NgueQIdPIzxgYVrhdV1C4yAC6ZlC9vNxgdNMgPFRHGaMhMBoCoyEwCMHoktFBGCmjThoNgdEQGA0BlBBwdVUCLx/9DL4G/JSTmxnkEE4RI6NfDAySDx5oyctDdppdU1BgAc8ZioGvlfvJwPAK3HxnU1ODrDxEMZcIDvx+go8VFTK7dgkYGTF3dIBOdgF3PLjBV+dB+qKQDg8DA8Mj8GwYZLoSMifGD+55QlZLwu0UdXVlgC0c5VdT+1VR8b+j4ya4p8fAwCD34AEnbMLqL/i+xJsKCt8ZGJBnwGR27TL5/x90xikDA4+ra7krgwLDjtcVR767bTzd4UT1SykePvx8ZsV5poqVaQyu9h1C4AWQUq8ZGEAXOaqpfbh1iwE8SwaaQe3oeF9RcbWzk5uBgRXc7wVt5AMvxXy0YgW/kdERRkZpsOJ3u3fzqKlBpj3RJuhAnWTw5RCg00fPnYPcCw+ZlIN3ySBnw/CBe4MM4LW7nOBBAQYGhoewjaACRkb85Mb+i927OcAR9rOiQgwc16/As83CHR0fzp2TjYhQKy9/t3v3GwYGHgYGyDQpAwPDs46D2RUfGBg+79qlDw4lsBGjxGgIjIbAaAiMhsBgBaNLRgdrzIy6azQERkNgNARQQwC2fPQNA8O/Bw+s5eUF4FeQv9i9+9O5c5/AfTYGBob/5879r6hg7Oh4WlHBCr5S4g54FgvtUrsfDx8+V1BQRNrJBrEQuRMI74HwwhapgralubnxMjDw7tr1yM1NbNcudnB/7+OtW6CrLMDTlZA7yiG9QYggA5I4P7iLwsDAwCsvf5mRkaejgx98YT3kJobP586xgztRkN4OyJCODibw0SzvwKsimcGu1AVftQe5lOIT2DHwRZJP3ZY97wjyq/hLxfvrwRODj2YznHLo+P6kokKoowNy7g43A8PXigrpXbuEXV2f7979AdxzYwb3ySUYGJ6BT1hB6xtfVFBQAF/78auiQn7XLgbwGTmS//9Dgh1X948XPC2M3C0EBSD4bBhIMvhrZPTCzY2jo+MHOHwgBORYF9CBPWA34+ocoi0ZRT4f6BN4QIG9ooKJgeEd+Bp6DiMjeN8PskoWskZUyNUVEkrgZaKsERH6oxODkFgYJUdDYDQERkNgkIPRDuEgj6BR542GwGgIjIYASgjs3n3PzQ20dm9TB6NGhSdbR8drcD/w07lzkDseeMHTiCA9RkZv3NwkHzyAHFAJmscDN+Uhe88gl9rdZ2SEdAjhvRF4twpyrukTcE8Ssj5QErz5Dfl2BLSL5kCWgvFn8GmlxHQRIctU+MELQf+D9cK7oKAOD1LPkBM8T8gKmwH7AO6o/KioAN2BDt7VBuky/ayoEGVgON2xxq/iO/j8HUXKT5oB93Med3TwOzBc4K2I/Aru5sHD81tFBTsDwx/wYZ6QuTteI6Nf4HB7DLt9AdStNTJC7huD/crADu46/gZzcHkcIg4KDXD3D6wWQcC3KbLJy0O2ZSo/eMAFmzGG397BAL4Jg4GB4cO5c5CurDx4hSpk3vgpI6M0uEeK9epIHjW1L+DrQJ6A535fVVRA7loEzYWCY+FLRQUfA8PtjoPBFX8ZGBh27VJwdVVEOHGUNRoCoyEwGgKjITC4wWiHcHDHz6jrRkNgNARGQwAjBB4+/KCgcLKa4e1Hhp/xDEmQrXSQPpuQmtrnFSt+VlRIgef9boKvNxAGd+SQTyKBNOVBE1kVFXxINzpAjqOETM1BDIR0ApGdgHY7wjVGRvjN48jK0Ni4uojsDAygxaIMDG8YGAQ6OtgYGCBH4MB7QfCO0Nvdu1+5uUk/eABZkvquokK6o4OpouI1uDPJxsCgtGsXr5raL3CnRai8HDyheu1JxRl1hk++D3LIu6jw4u5LM9ygOxKDdpm5ukpdY2TkBt+mx8DAcA18xOtP8IJJ7o6ON7BzVhgYGBjd3CAXxGP1OKQb/LmiQhg87cbR0SEEmwDE9DhaSKJxIdOD8NNoLoPX3MKP6oEohiwnhi8ihXQOIT1qSOeQA3wxxseKCoGODsiwAiT2IVeGQPqckOtALjIy8nZ0iEdE/GVggBxzKgyegl7pdr2MQYiBQWXXLrnRZaKQYB8lR0NgNARGQ2CogNEO4VCJqVF3jobAaAiMhgAiBMC9ncMVoEkw7sMd72zKYyFykGvieGEnRkIOaIEs6fwMvnwC0tmDKIYcTPIYvAvuK3itI6QbwIttJgqi5cvu3ZANbzzgTiYD+PKJV+CFo5iH1kC04CLvdHb+qKgQ37XrsZsbHwPDU/CBpX/Bp9SgLW2Fm3Ad3PmE9G9ByyA7O39VVLwCb8ZjAGuUBHcR2Ts6fjIwwFdXslVUzGboc9jl7+qqBDeKGMbu3c8euK01ZMiDTIhBVn5+qahgYWDg6+j4UlEhBb6Y4ZmbGzt4LSu8G4brgnjkpZhP3Nz+MDDIMjBAu5vgDq00LFSJcR7oUsqHD9+B5+7g+w9f7979ws0N845HuIGYnUMuBgaGioqXDAyM4F2CTAwM/OAg5Tcy4gMvBv4N62MzMDB8ffjw5YoVnysq4BddLu08FFPBBl4myllebgG3aJQxGgKjITAaAqMhMFTAaIdwqMTUqDtHQ2A0BEZDAD0E4MtHwVd+K0GuifvFwMAAXmnJwMDwxM2NDXy6jAD4JBj4gkYGBoaP4DWNXAwMH8GtfyEjI4K9EeTFoshOgUyayRO6vgJZC/xw0ZdubuK7dn10c+PZtesl7DJ3yAQmZE2mANKBKJBJQk3w5Cdk2gpyTcWnhw+vgifr/oCXaEKWnkJWkP4AryCdwtDVxiDf0aFI5KUUDx9+XrHiaUXFhwKGK/EMqX/Ae+cgyz4hQcfW0fGdgeEn+L4NcVdXtKWzLxgZIdODcC8jX5IBn9H9ce4ca0UF74MHD8A9Lohi0CWQEBYRJNr0IEQHwT4hRBkDA8Pdzs6f4ENi+BgYHoFnaCFS8POBPp47x8rA8KOiggmcSCCbP1+sWAHZxqlUXg5eTHsTfJqo5ugyUUjojZKjYDQERkNgyIHRDuGQi7JRB4+GwGgIjIYAIgQgy0cZGBg6OkSMKqYIMCyArzn8fO6cCAPD84qKz+BL6hjBMz+84FsK/1VU/AFPc/2pqGDctQuyelDAyEgW9wwVrt4gqZOEr3fvfgnui/J0dHAbGYHOGmVgeNbZyWlkJAi2HbK0FeFDMAuy45GBgQE+SQjvdL3cvfupm5v0rl3vz537VlEB6Q1C5u541dQ+Kyiwd3R8jkhfseJaRcUTBgY2gifNgDs5j8AXS8i6ukpdAS/ClAefYQNarVpRIdbR8biigr2jAzKfqbxrl4Ca2vsVK75WVGj9/488PYg8JQjpn0PmYOFLMT+tWCEC7kg/3r37/blzgkZGd93cjB48gGztA3sdJ4HcJUZThHXhKJqaN+C7LiCCP9zcPoG7fB/Aq0Yhgu8rQBN/PAwMv8A7JEH9dvCOQcj5QMwMDIsY1it0aFRUPAePR0hBdI2SoyEwGgKjITAaAkMOjHYIh1yUjTp4NARGQ2A0BFBCALx89F5FxXkGBqlLu8TZz+2GrJYU6+hgr6hg6+i4Bd7bxm9kJOXq+h7pzndBV9dHjIxy//9/evjwvIKCELhLIB8RgbU3grZ1EMUFDAzXwL0mLfDcHZoUMhc+McjAwMCtpsYFu1jiPbhzAukQQtR/evjww61bkNMsIROGkLNwQP2TigrZXbueu7mxg7ftSYPXbUL6hOKurnfAt/zxGhmJu7pCFrgKP3gAWVF5uHPxxYqb9gytDzq260VYYN1VeG33pdtuM38wMP7tCPOJkOeTl4f0YMV37RJ1df358OFrcA+TPSLiwYoVECdBHPy5ooKHgUF6164/4MsSecE7M+GTgZDNmZB+IEQ9qCMNdiqkQ8jAwPDp4UPkqUIFHBEB1451ehAi+3r37i/gzpsiuLcJEYST3x4+fKCgAOFC9n8+YWSU+f//KTgWPp47xw+eLoYkFQbwathPFRU/GBg4wNdpCHZ0XGKwjAL1Fu90dJiUl2tAjBolR0NgNARGQ2A0BIYoGO0QDtGIG3X2aAiMhsBoCKCEwO7dV9zcHjMwMKzoYAwv94BvsZP4///zw4dPV6xgBG8SEwXPEMKn4x4xMoo/eMAuL8/AwHAZ3D+BnDiCNlUI6Vnx7toF3zqIYjcROwkh3SqILkjnCsKGkJC+hxS23gtkwhDS9YL0DL9UVHCB56wYwB2wZ25ufB0dkuAzM0En0MjLn2Nk5AMf0/ITqW8GugkQfBjpp4obkEspHjxwQO4TQiYGSxgexzOEvepYLsrwEOI2BgYGTvCtGKIdHexgIb6ICHZ5+S+wk1QfuLlB+tLvKipYwYEsi9obZGBgQOsKgo1heNPZyW5kxAueF4WIQLqF5xQUlMETnoK452zxTA9CjMK1cPTN7t1fz53jBi8qhmz7hPRyZcCd+ae7d0M6hJ/OnQPtLWRgEI+I4JCXvwru8GuD1SzvPBQFOk30O/j8GB2IdaPkaAiMhsBoCIyGwNAFox3CoRt3oy4fDYHREBgNAZQQAC8fPc7AwNTRIRRoxMzgZgw/XoW/o+MHeArrAfjsFvgKTOQOIQMDw2PwTXoQQ+FThXgWi0JUQkhck4SQrqA4eB6PgYEBskYUogVO/nj48ImCggq4vwEXRGNAeoagexoqKt4zMPxnYABdYIh0lwNE/QfwpjgGBgYO8OZJeXAnELKf8CN4tSd7RQXsUgqejg5pyKUU4N4gaC/cqY63/BVxP2HXDEK6xx/OnfteUcHLwPAZYgeYhJy8wsDAwAfuXH04d+5XRQU/A8MLBgZmBgZZsH+x9gPBukHEU0ZGUdjsJYiPhC/BOucMDAxYpwrxTA/CjbkPNoQHti4XNCcJ7g2CpmeNjCC9QQYGBuQO4Qtwh/AzeLHoXwYGVtiU4JeKCk4GBvFduxaek62oeA6+W0J99DRReFCPMkZDYDQERkNgSIPRDuGQjr5Rx4+GwGgIjIYAegh0dp6tqHjHwPD+UMdPcdDV6AyQ7hA7eDPhX/AlAZClmIoRER8UFOAzhBCDPj18CL+8DrKrEP9iUYguSGcD87hR+BpRyOExWHuDEBPuMDLi7xCCtq6B17v+BB8oygQ+6AXe/RMAr3eFGAWZ02OoqHgE5rMxMMiDb6TgkZeHTKxJ/P8P7jwfYGD4dajj5+IK3tkMEuCTUUCdnFvguzqEXF0/PXz48dYtSG8QdKIMeFpSaNcuyF3toNNlYJ1PBgbQjYKg5awMDIwdHf8ZGCALdMH24yTwdAghU4XIK0iRD5uB+AJyoA5O08ES8M2EkGNvQF3Bjg7RiAj4Yl14h5AFdVbzpZsbJwPDL3CoCoB7vKvdrhcxmDEw/Nm1S2m0KwgO3VFiNARGQ2A0BIYJGO0QDpOIHPXGaAiMhsBoCMBDYPfuB25uO3IY2NUZngbvMod0V365uQk9ePADfH0cA+y6PxEGhvcwbch9Ksj1dO/Ak4o8HR0/KypkwKeqQNZkwnSg05BeB2QnIWRiEKICc40oRByZvMPIKPPgAQd48SqyOJwN2Wf4/dw5TiOjXwwMkBNTVWCrTCHX/cEVv1VQ+MfA8ISBQQF8ZI6AkZEceGUmfGLtOXi/3BM3t28MCQIMC94iTZy+273727lzMmCTX4FvcYAYywm+557NyIgXfBkDAwMDD5JrX4Nv0RB78ODtrVuQVZcMDAxSYEsh2tHIX+DtiNJ4J0Uhc7bww2aUd+2CLOWF+wLNTDgXspyVgYHh+YoVXyoqeDo6PoJJCBuuDLLXlB1828RX8L0XT9zc5MFzm2wMDG/c3Fg7OoQjIh6tWLG7grOIQZeB4eSuXXGjvUF4AI4yRkNgNARGQ2B4AMjtuMPDL6O+GA2B0RAYDYHREACFgKurwoMHEQoKRxkYJL6eE4iIUGVfMZOto+Mt+CrCX+B5LaGODmbwmZyiHR2c4Ckg+MQXn5ER365dX8+dEwBfuf6LgYFv167HsKsRIJNyyL1HyC0FfGpqn86d+8PA8LCzkwtsIGSZKJ5ZQZBbYZiro+P7rVu4OoSQTYYMDAxC4C1tEE1P3dxegg+Pweym/gZPbYmAj35hYGD4cO7cXTc33V273oFPB73LyAg/7uWDm9svcG9Q0MgIctM6OwMDH3gy8FNFBW9HhwS4gyTm6vpu9+63bm7y2Lpwr8GLM/l37WKXl5eSl+dVU3sCCy5cfcKft26xgx0J8QsuUtbVlV9N7cGKFZBtipc6O6WMjL5WVPDu2vXl4UPI3ZKg6UTwETKQDh5oMhNmMp+R0T/wNYOfwWtopZCmNxlg61rZzp37BE4Sb8+d4+zoeAU2SgG8J5PfyOg9g9BWBv8KBsgy0ajR3iCumBoVHw2B0RAYDYGhC0ZnCIdu3I26fDQERkNgNAQIhEBn5+mKig8dHQJiFTt1GGplkDbU/QRf0PeFgeELAwM3+AYIuFnP3NxYwIKs4B7jZ7Aa0G46WDcDrhLSFYRMiH0EdyR+gLV8Z2DgZmCQAZ/MCVeMnwGZAEQ+aBSuHmtvkIGBAX7hhDjqRBxk0yP8cFH46Z0/wYe+/AY7DHLsJ2gJJcQaNTVIb1AYvKT2jZvbd/CM2Q/w+StiYPMhHUJVjA7hp927P7q58e/axQdWBjHv88OH8D6hJniyESIOJ9+A+5DwI0bh4rgYnx4+vAI+GlSIgeEtA4MIOC74jIwg3XgIA7KbESIC6RyyMzCIMjB8BaNP4PNCxTs6IEtAIRZ9cnPjZWBg6eh4W1HxAbwtE3L94/tz595VVFxjmJjNYATeMTi6TBQSYKPkaAiMhsBoCAxDwDQM/TTqpdEQGA2B0RAYDQFwCJSXm+7apcrA8COJQeRex/Yn4AsAQUsZIyKkwT0KuQcPJHft+lpRwQxe38jMwPDKzU121y6IIBP46kIGBgaVXbus/v+XjYiAI9Ad5eADXSD9wIdubqCuINgcYQYGoY4Opo6Oy25uFzs7P4FP4wQ7hwDxHdylRFP0rLPzHdjZUuXlaPOH4q6u0rt2geYJwes/0TRCrpoAHfoiLy9lZMRSUcHNwPAdPPXHwMBg+v+/pKurJKwLxycvr19e7vT/v2JExGc3Nz6wWW/OnWMxMoL0BhkYGITAit+h2vVp9+6f585xdHQg9wYhM5bwfuB1cN8PbCQKwQeehUMRwsZ5vnv38927byooKO7axcrAwAte4fmTgeF9RcVjcMhAZgghvUHQyasREdIREWb//5v9/y8DjmXejg5u8L3zyrt2/WFgeHPuHI+ampSrq4iaGi8DA3tHhzi4y6q2a5dCRATkKNd3FRXPOg5mMwgxMHwe3TSILVpGxUZDYDQERsHwAaMzhMMnLkd9MhoCoyEwGgJYQ+Dhw48rVlyoqHjZ0aEYEaEqLy8AuTydB3aNxHlGxj/gKxyYwfqfu7lJ7tolqKb2VkFB+MGDD7duvXBzk9i1C941AqtCISC3nH88d46FgeF/RcVH8IwTZLbt/blzkEsj4EebouiEcTAPGv3x8OFTBQVO8IpWrDOHEK3IFw9CRNC22EHcBpGCrIf8UFGh++ABL3gH4CdwBw/SnYO4gQts4xfwQlMBI6MP587BD1x9Bz7VRhrcfYIcx/JKQYGjo0MUJgKxBZl8Bj6380NFhQ7MRogs/hNlQNv/wA6DzGQik//c3N6Cu3mQqTzIrkKImWjk5927P7u58e7a9ZuB4a2bm/CuXV8YGO66uQnDtHODF9BCdktC9n9CriV8+PDzihVPwaeJ/nnwwEJenhfN5FHuaAiMhsBoCIyGwHACox3C4RSbo34ZDYHREBgNAZwh0Nl5rKLiCQMD26YODr0Kz58MDLzgFaQfwdfWs1dUvGVg+MXAoLhrF2QFJuRwUUnwCknI2Sp6YDaaBW92737p5sYD7mNwgy8zeMDIqABWCbkoAtIbhMw7QdiK4JkxPnB/DNk05HNlIMtEIZsb8fQGGRgYvj58+By8YU8yIoIbbOZzRkbIelFIJwdiBaSrw8DAgHb9OrxDiGnjp4cPzysoKMGOpZGFbSOErBr9+fDhJ7C9kGsJIbZgJZ+Bu3Yfz52TiYiA9ELxnCgDP+0GvssR0htkYGCAzGc+ZWSU/v//8e7dkKNicd1L8evhwzcKCuwdHcLgzuptRkbhXbsgk5yPwN3a7+AFtP9hF0uALqAH7yR8BZpzvQdeJirt6qqI1UejgqMhMBoCoyEwGgLDCYwuGR1OsTnql9EQGA2B0RDAGQLl5Va7dpkwMDD4VfzqYZgH6Q1CrmtnB9+a8JWBgauj45qbG2SR50/w9BHEOG7wuZqvwB0biAiEhEy+iYOPXRGNiIBfbfcDvEwUeR0mfJ4QcnbL/RUrIKtJIXZBTGNgYPgOPvYGsp+QmN4gaBOgvDwv+AwbSLfwF9hqNnl5iNvEdu0S27VL6/9/uNukwSs/ITZCOoeQCy0gIqBpN5gCPnl5+///P8AWsl7u7IR0qCCrRj+tWPGjooJgbxC0QBdm4JMVKyCdQ9C5r+AuNNzS57t33+rsPA2+/x3SA4SQaKtbIb4DnQfj6go594WBgeHBihXI9xZCzPwM7qzygvveDAwMwrt2vQWvL325e/c78B7RX2B13AwMguBdhQJGRswdHfsYJn46d4qB4Td4mehobxAcRqPEaAiMhsBoCAx3MHrK6HCP4VH/jYbAaAiMhgAsBFxdlR48EFqx4jBoP53bE1eGG6a7okAb3sBzg/oPHjxYsUK4o+OsgoIE+DI9MXBHC9Lpkti164WbGwNs4ShkYhBisPiuXfLgaSgIlxO1qwPZxadfXs5QXo42Z3gf3GlhYGCAzBkKgzuWkN4g5HoJ/HODEOsYGBggU5qgzYRGRqznzv1gYLgG7lkxMDDAJwbhikG77Fxd+WAHgX4EHyX6BTw5pojt3gtdsLMhU4UHGRnlwL5DPlYU2WRcbClXV8jRo5Atl/AlmM937/587tzHigoZsN/hs4KQfiBW0+Bnk/LJy+uVl0OmCgWNjA4wMho9eACZd/318OFP8Emk8I2UfxkYfjAwnGdk5O7okNy168u5c/K7dn0EdxHlwBdLvGaQd6qwB+2yrPjx4IHJ6DJRrIE/KjgaAqMhMBoCwxKMLhkdltE66qnREBgNgdEQwBcC4NNH34CXBarbqzG/U1Dg2bXrM1gH5OAZMQYG1o4O0PX2FRWC4C6QXETEHQUFFgYGvf//IZNvYOUM8Jk3CBd0+GdnJ4eRET9sWgwujsxA6xneAW9s+11RwQ5TBNnIB+MRRX8BX5/Ay8DwGXxaJvKde5AjN9FM4Qf76zvYUtCxqB0dkBs10JTBL9iA383Ix8AAWV0jAO7FoanHw/107tyHigqBjo5vFRWcHR1fwP3Az+fO8RoZwecDIetCcRnyC7xOFe1sUvgxqhBdChERXxQUGDs6GMD9eUiE8nd0/K2o+Abq8IFU8XV0KERE/Ll1642bm9r//7t3P3Nzu8fA8GnXLs3RZaKgABrFoyEwGgKjITCSwGiHcCTF9qhfR0NgNARGQwAWArt333Nzu83AwLCS4bQmQ+0P8GUMkM7Jr3Pn2CsqnoLPn4Qoh5ytAplGY2dgYGRgYGVgEN+1C7M3yMDA8BG8shR/hxBiLGRH3wM3N2YGBmbw7X9sDAw/wf00uAKSGP8qKv6CzxHlAV+kAbmAATRFCe4aQYyCC0K4/8Bb6f5iWAo/tBOuHiLyHuxapo4O0P1+ECNIJD+CL/x4De6bQTwu2NEBP5IUv2GfwYeaonUIIVo+PXx4TkFBHNzb/AnussqD94hC1tM+AS8T5WZg+NfR8YuBAbKlE3QrIwPD846DwaBgY9i1S330mkFIYI6SoyEwGgKjITCiwOiS0REV3aOeHQ2B0RAYDQFoCMCWj94Lr9DIYZiXtkv/iZsxZNKMF7yC1Pj/f8gGvycrVkC6EwwMDHLgOwm/d3S8rKj4c+4cm5oaZI0i1FDiKPiN6k/c3Pg7OpTA/RYWBgaOiorPDAzIexeJMw+q6l9FBS8Dw1/wKSkfKir+dnRAunBQaRglA9tWBxH4Z2T0+dw5zBsg4He+ww35dO4cGwMDPzgEPldU/GNg4MXoRkLMxE8KMjCwgXvUzOBO9ftz534zMBxgZBQCz9qBuq/go3FwGcKO1LmFq4EcRcMN5kszMLxkYPjAwHALvCL0L3iLoCn4mJ9bjIxsDAzcRkaQawafdRyMrmBjqLjT0WESESE9ukwUHH6jxGgIjIbAaAiMODA6QzjionzUw6MhMBoCoyGAHAK7d19xc3sMWT7q6qr06+HDdwoKbB0dQuXlL3bvhsyPQTpFPyAdDHBngwV8gTv8qBjkKyVwzRBCOi2QTiB8kSRkQaNoRwcvpCu4a9drNzcNcO8F2ZEE2ZD7ABkYGLgiIp6vWPG5osKQOEPgp4wStIKBgeEJIyN7RwcfeNMdaO+lkZEE3pWxWM18D76W8DF41yIDUpcSMmsH1wI5Mwatv/2ms5PdyIgXZimkaw1Zbgoh37i5iYI7hJwdHU8qKlR27YIcRgrZpQlfI8rAwABeJnqTgeH7rl1yrq46cHtHGaMhMBoCoyEwGgIjDYzOEI60GB/172gIjIbAaAighICrq86uXVxubrfd3G53dLxNZdj3GTyFdY+REXLSCagj5ObGC74SHXKpnSADA4ua2v0VKyDXSEAODoWfDcPAwPDj3DnIklFIJxB+bsrnc+dkwPOBkN4L/OiUt52dPysqRB48gMzLvd29WxjW50FxKw7Oz4cPP7q5we8DBC2S7Oi409mpgnTUDQ6tJAhDDpKBHCuqWl5+lpGRd9euF7t3k9Qn/PXw4a+KCsEHDySMjL6Czy9VBB9aAzksFLJNEXTSqZHRA9iJO2gXS7CDT3x9vns3pGsNOScWHp7S4B6mpJHRczc3mY4OSG8QHkEM4KWk73bvPsug7eZ2EzwKYDC6TJSERDCqdDQERkNgNASGIxidIRyOsTrqp9EQGA2B0RAgMQQePvywYgXo9rnPFZc9Opg4KuLgJ17yqKn9v3XrF7j3Ilxe/m73bshJJBAbIMfDQNhvKyqYGRhA955XVLB1dEAOz/wM1giZEuTv6OA1MuJVU+NBWhUJvz8dMvH1dvfub+fOyRLdl8N6H+BL8PyXFOxORYjzsJJEzhB+2r37k5sb365dkCvsGRgYviDdf4jsHay2wAUh04OCYN9BbneUgB3cClHz6eHDj7duQZZ0CoGnYUG3Ypw7B+kr8jMw/GNgeFVRIQc+zwbSJ+Q1MoIfRfOCkVHowQM2efl74HlIHiMjDtSu+0q362UMqpDLJkaXiULCfJQcDYHREBgNgREORjuEIzwBjHp/NARGQ2A0BBAhsHv3Ize3nQwMMis6GO2NmCFzX78ePvy8YsXPigop2CLMW4yMIuBbziFLFhkYGD6A705gZmD4C55I/AveaKe4axe8xwK/Vx1hGZiFdn86AwPD2927X7u5ie7aReQk4WNGRg7wMk52pE4m6LBT4vqExHQIMXuDYLdD+4SfKiqMYSEDEcdF/nr48L2CgiC4wwZRg7VPCJFiYGBAOz6Um4GBtaLiCVj6H3iuj7+jg9/IiB88Z8grLw9Z7isBc8xFRkbejg7xiAhucMh8evhwpsJGBgaGMoZvu3bFjU4MggNylBgNgdEQGA2BUcAw2iEcTQSjITAaAqMhMBoCiBB4+PCDgsJxBgYm8C5CU1APDbaek01eHrIE9Nu5cz8rKr4yMEBm/BgYGODTgJ/Ap1myMDCAdiUyMOjBbsZDWIDKesYI2pXHGxEBvzGPgYHhBiMjkR1CyDJOdiMj+MQdsvF3wLNkvEZGkIsKkaXgbIIdwp8PH75WUGDv6BAFT+vBNUIYL8AXCTIwMKhik4WogZPI04NwwUvgKxP1YL04uDgy48Xu3b/OnftaUcHHwPAQ3BWEnGfDb2QEudgQNItYUSHAwPATfFANH7iLyMzAcEdBgbejQwnsNnAHGbJpUMfVVQ7Z/FH2aAiMhsBoCIyGwEgGox3CkRz7o34fDYHREBgNASwhAF4+erei4h0Dw69THe9EGZ59qqj4CVYI2VX4y83tPQMDR0cHEwMD5Io//o4OFgaG/xUVf8B3wX9yc/sE3szGwMAgYGQki2NDIHzrIHJvENQFBU8SEjxaBn9vEOxe0FXsDAwMeA6Ywd8hxLoeFWIynHyxe/dTNzfpXbsgE6pwcTQGZHqQZ9cubtTQwD9JCAkNyG5DbiOjH25ujLt2fQTfZ8jf0fEfbMd78F2RPAwMPyoqWDo6voMFIV1EyDkBTzoOhlWwMTAwdHRwlJcbwORH6dEQGA2B0RAYDYHREACB0Q4hKBRG8WgIjIbAaAiMhgBaCOze/eDcuccVFc9XdPAZMlzkBV/P8LGigoOBQZSB4XtHx4uKCkYGBgvw1Nb73bu/nzvHCb4RQdDV9SEjo/z//4937/4A3kDIwMCgC56kQrYCbesgshQxk4Sfdu/+6ObGj7SpD80ECPclbAYP1wEz+DuEr8Gzo6IPHqCtR4UYDieJ6RO+7+z8VVEhDg4uuEYIA0+f8C34Usev585xGxkJu7o+ZWSU/v//GVjw47lzMhERvODloM86O7+D921+qqj4Dr7hUBDcIRcwMjp8jj2q4hEDA9+uXSajy0QhAT5KjobAaAiMhsBoCCCD0Q4hcmiMskdDYDQERkNgNAQQIfDw4UcFheUMDPI5DC/COlgMI+x45OXfgTs2EuBuCWS9ogS4HwjRJgie/oJ0CCG74B6CT8t8V1FhiLR8FHPrIEQ7nITsJMQ1SQi/ZALrMk64IRAG/gNm8HQIcW0dhBiLRhLsE75kZGTr6IAcJ4Oml4GB4X5n5+eKCuUHDyD7/SATg6/A93yAThl98IAT3PGDdAgZGBie7d4NCXx+IyMGcK+b08hI0NX1KngBqjbsDskyhfMzQVcnvtu1y3K0N4gZ7KMioyEwGgKjITAKGBhG9xCOpoLREBgNgdEQGA0B3CEAXj56G7x89N+DB5bCt05/cXNj27ULsoL0sZsbJwMDIwPDJwYGyY4ORdg0ILxDCDEYPlUIXz6Ka7EoRD2ExLWTkKTeIMQoPH1CXB1CknqDEFtuw7YsYq4dhawXRT5OBqIFTn59+PAV+PpEyGZCyMQgRBb5cB14h5CBgeEm2Lrv4NW8jAwMXB0dchERr1es+FFRIb5r13nQxRKPGBg+gzc/ji4ThYTlKDkaAqMhMBoCoyGABYzOEGIJlFGh0RAYDYHREBgNAeQQ2L37npvb3WqGZ8oMj3UYaiFnyUAOkhFhYHhXUfGnowPSRYTcUI/WIYRPFTKBDeVnYOBkYGBDumAdLIxOYJ0k/Pnw4SsFBfiVg+h6cPNxHTCDtUOI/yAZ3JYwnGVklAZfCIHWJ8R6nAyaOZCFozzgpZ5fKypYwLsxkXuDDAwMkA7hC/AlhJB7QTjBpvCDLYUs0H1fUXGJYWIOgy74mkH10YlBcAiNEqMhMBoCoyEwGgI4AWTDOU7pUYnREBgNgdEQGA2B0RBwdVV68EBIQWEXA4NIR8cpK4YboP17HR3sDAy/Kir4d+16Bl61yMDAcEZBgZmBgYeB4V9n5y9wwEFOnQEzQaeS/q6oeMPA8IGBQaijQwF8GCYfeDEkRAEmiXxJPeSIF8glE5gq8YuolJefZ2Rk6OjgUVODL8vEqgViCwMDAzHrUdFMUH/w4Dl4iSwP0l2L8Mvo0RQjcyHHt/5kYGCrqHjPwCDQ0fGhouIDeMmoALiLCFH8h4HhCSOj/K5dkN6gzK5dP8EnvsK7goIdHUsZ1s9kYGJg+LFrl/5obxASbqPkaAiMhsBoCIyGAB4w2iEcBaMhMBoCoyEwGgKEQ0BeXuD//7DOztPgDp3Irl13TV2VQPsJGRhEXV1FXV2vg1cw8jIwsDIwCIHvP+AzMvp07pwsePIKNEl47hwnA8N/BgZu8KUWgkZGD8B9J4jdChERaD1DYVfX1xA5GAmZG+SLiMB/xAtMOTqt9uDBLQUFBgYGXAfMQDR8Al+6KPrgAYRLEskjLy8ZEfF8xYqbCgrwywm/rljB1tGBdpIqAwMDpBP4BNzrg3fw/oFPcAWdzgruB8IvluA3Mvp/7twb8E2PN9zcmBkY9MEulHB1vVVRIWVkJOTqutsoVtcNtEx01y4VV1dFklw+qng0BEZDYDQERkNgxILRJaMjNupHPT4aAqMhMBoC5IQAePnobfByRFU1N2XQStGOjo8VFfAuzS9wD+ctA8M/BgbI+kmINf/PnfteUcHZ0fGzokL4wYMHK1a8q6gQAnd7BI2M3p87B+EKgm9ah3QOIatG5cFHqhBzyQTEIjwk5mZCtCWjVLEF+XJCtN2DXx4+/Hzr1udz5yAh9vncOfjxrZArPf7Bbuz4UFEhD+tLMzAwfHBz42Ng+MbA8IWBgaWjAxJiEJ9+rajgZWB4vuupm9tNcLyMLhOFBMwoORoCoyEwGgKjIUAUgGzoIErpqKLREBgNgdEQGA2B0RAALx81Z2BgcHO73cOw+GXHIgYGBkRv8Nw5dgYG4V27+MC3FPKpqUm6ugqqqX0D9xKFdu2SAh88w8bAoFde7vD/v0JEBKRvA+8Nvj937sGKFQcYGS91drKqqf1iYPh26xZV+mkMDAzirq5Su3Y9c3N7Cb65AS02QcfVVFTguuYeTTEeroSrK6+R0aeKihe7d38Fz4K+vXXr+e7dpxkZn4G58BCDGCIVEWH6/7+kq6sweA2tZnk5v5GRQEfHx3PneNXUpMDifAwM7B0dQrt2cTAwcDEwQPrPDAwMgkZG3zuWT2ZY7+Z2kYGBc9eu0d4gJFBHydEQGA2B0RAYDQFiwegMIbEhNapuNARGQ2A0BEZDADkEOjsPVlS8ZmDgXtHBaGfELAm+cOIF+HIFIXCv7xz4CgTFXbuQryhkYGB40dkpEBHBgbF18NPDh8iLSAWNjB6Du5Fc4MlGdgYGgV27+MBdJsjle8iOIYmNfMAMfIYQ1BsE74QkY+sgsu1fHj5kYGCATwPygDdMCoInUeV27YLPDTIwMEBCDFnvr4cPP61YIQIOPfhlg/xGRv/c3Ng7OiBrZa+BQ1ULfJ/hp4cPZylsvM0gN4vhPfhiCQ1k00bZoyEwGgKjITAaAqMhQAwY3UNITCiNqhkNgdEQGA2B0RBAD4Hycnsjo3tubrcjKhh27VKWZGD4Ap5244mIgCyM/MfAwM7AcM/NjaWjg+/cOfilFAwMDD9u3cLsEDIxMIiCrzR8CN4j9wI8FcbFwMDMwPC/owNyRM1j8Azbh4oK+C47BgYGfiMjSEeRgYGBmL4i8gEzEF+BDpIBd7qI7A3Ce30Q7Z/APUnI8Tn84EWwfEZG/xkYlMBnw/xkYHgFXhx7x82NiYFBftcubnC3FqIXFynl6nqzs5OJgeGlm9tP8Dk9P2/d4gMfPfrKze3N7t0irq4nb7GWgk4T/b5rl8PopkFcITkqPhoCoyEwGgKjIYAfjM4Q4g+fUdnREBgNgdEQGA0BfCEAvqjwXkXF644OIeuKafwMC34wgE4ThWyNQ76U4m1FhXBHh2JExL9bt0DdOVdXSL8RNJ8G3lOHfJsFr5HR93PnPlVUcIHvqHjPwPALjEAawT0uiJtA17IzMECuaIeIIPcVIbKQviJaR/Hrw4fPwff+KYP36f0En9Up+uAB2nE1yB0/rL0+yGE5EKshh+gwMDBAeoZiHR1sFRXfwZc0fmJgEASvoRUwMoKcCMrAwPC+okIQ7Be5iAgGBgY+efnPu3f/PHfuN7hX/MTNDbIQlx18Tg/vrl0Qje8qKlgZGF4yZP3tCA+pYGNg+D66TBQS/qPkaAiMhsBoCIyGAHlgtENIXriN6hoNgdEQGA2B0RBAhEBn57mKivMMDLyTGW4F7zKHnJwp1tHBXlHBs2vX3XPnIL1BQSOj+25uPAwM3B0dnysq0HqAkOWU/B0d38B9HojpfGDFHxkYPldUQC5t/wxekwnqid26Be8KfqiogF/PAOkHgjpm587BT+lE7ihCJhW/njv3GdzhZAdvz/tUUSEA7hxi7fh9OncO0t/jA/fWPoF7sBAX8nd0QARB85PgqT8e8GrYZ4yM7B0djEZGb9zcfoCPgbkLniEU6uiAdwUFwKZBenrvKyr4GBi+g7vTzAwMvEZGkD7hL3Bn9Re46wix8RKDZXTFVwaG37t2mYxeLAEJk1FyNARGQ2A0BEZDgGww2iEkO+hGNY6GwGgIjIbAaAggQmD37iub3E5PYZDIYXgx+X8iAwMD6FKKigoJ8G63Lw8fPl6x4ktFxV8GBgkGhrcMDFwdHczgyTRItxDSm+KRl38DXncKMZdPTe2dgoLQgwe/GRjuKihI7NolBt6pCJFFI3F1FPFMKn6pqOBhYPgIvstBCNxng/QGQfN1RqA7M0C9SvDyVPhCUIilvEjXDEJE0Mi3nZ0/KypEHjz4cuvWG/Ci2S9IKhTAU4IMDAyPwCtgIf1DbgaG7xUVPxkYPsNUGjx4ADlt9RYjo8iuXUKurp8ePtyy4mE0KBS/79ql5Oo6umkQFlij9GgIjIbAaAiMhgC5YLRDSG7IjeobDYHREBgNgdEQQA0B8PLR2xUV7zo6hCIiVNkVBD8xMPDu2vXEzQ3e6/vg5ibAwPC9o+MFuKNlBe4uQox5s3v3S/ApMqDjQHftEnF1fQe+2xByRM2r3btfuLnh7xNCzMEk0fqKX8BWM4P7gXIMDO8YGOAXHrIxMCiA5wkhHVQGBgbIdB+mmXhEfj18+EZBgXfXLl5w9xW5O3dOQUF51673584JGhnJgmUh5tzv7GRlYPhaUfEbPF3JDps5hMys8jAw/KmoYH/wSUHhKngSkeHBAxN5eV6I3lFyNARGQ2A0BEZDYDQEKAGjHUJKQm9U7ygYDYHREBgNAfQQ6Ow8W1HxblPH34cVZ4J3mUOu2oMvImWrqABdn1BefhXc2WNgYJCLiOBFnRgUgfWUkM8sZWBguN/ZCV84im4rcfwXu3c/dXOT3gW6s096165fbm5s4A7hF3A3DHIBBuQuBwE1NcjsHHEGI1RBeoPsHR3C4MNCQTOlu3e/cXNTg3V9L4E9DrFLISKClYHhsYICA3jKVKa8/EFn55eKin9IC0Qh2w53VXCWMiiA+9ejF0sgQnuUNRoCoyEwGgKjIUA5GO0QUh6GoyaMhsBoCIyGwGgIoITA7t0P3Nz2MjCw5DD8y+14xWtkJAnu470DL6QUfvCADbzL7snu3ZCO4l8GBm7w0Sni4IlBiFm/Hj6ErBeFKGZgYPj68CHBhaMQvWjkC/AyVEhXkIGBAcL4de4cW0UFW0fHPyOjV25ubB0dX9G0MTAoRkSQ2i2ELxaFO5uBgQE+SQix4dPDh+cUFFR37Xro5sbIwKC4axcbA4MQOIi+PXx4T0FBfNcuUfACUQYGhgcrVlxmsIyp+MDA8GnXLqfRTYOQMBwlR0NgNARGQ2A0BKgFRi+mp1ZIjpozGgKjITAaAqMhAA0BV1eFBw+COzqkpzCwrmdw+qVmysDA8Ovhw58VFby7drHJyz/fvRuE3NwEOjrkGRiEOjqYOjpeMTA8O3fuE+zMmC8rVrB1dCB3q7jl5SV27Xrh5vYK3MGDWoaXerF791nwxX2QTuBT8JJU4///JVxdecAaeSIiIBOSAkZG+uXlihERguDlmpB5wvsrVlzs7Hy0ezfcVWBNOAnQMaEwPyIrEtm16xv4agoGBobnu3d/vXWLm4Hh67lzTAwMvAwMr86du+Tmdqmz89PDh38YGMR37Xrp5vZ6924+efmTt1j1K+xjQJsGeR888B/tDSKH6ih7NARGQ2A0BEZDgCpgdIaQKsE4ashoCIyGwGgIjIYAlhDYvfuWm9sFBga2Xbv05N2UQWe0gFVBLlSQ2bXrJ7iH9ruj4ztYHLI88m1FhURHBwe4ZwXprYEloQQxC0exTgkyMDBIgGfhIL3TdwoKbB0dkA2Kb3bvfuXmJoY0P/np4cP74BNfoLaCKfwThpiLRcGaQMS73bsfubmJ7tr1DHaZBCQEBDs6flRUfGNg4AAfafMe1mmEHH/6BrSy9SYDA8OuXQqj1wyCwnEUj4bAaAiMhsBoCNAAjHYIaRCoo0aOhsBoCIyGwGgIwEJg9+57bm6Xqhhef2JggSwfhVwvIbNrF2TRJmRLIWQK7oKCAjMDAxt43uwnAwMjbCudAtLSTfwLR9F2CUImBuH9QJijoCegCj2ALl5lYGC4Bp5I1ILt9IOo/PTw4YdbtyD9NMiuP8jMIdYdhpiLRSEXLUL8KwY+W5UZfFW9WEeHAGwZ7WvwUTocHR3fGBjege+vFzQy+nLu3FbQpkGhjg4TIyO+0YlBSHSMkqMhMBoCoyEwGgK0AKMdQlqE6qiZoyEwGgKjITAaAogQAJ8+ehh8STv3oY53hhF2kKM7IVsKJcF9sOe7d38Gz4/xGhn9PXfuX0XFFwYGpo4ObvACzvfnzkE6S5CeIeaJowSnBBGuAS9eRZ4ehEhhThJCxCEkwQnDz7t3f3Zzg5ws+hy8ohV+turnc+dAVx1WVDxhYPjNwCANPmWUAbxBEXS/hbz8FXBfVAccDp8ePpylsLGUwQx8FCtnRIT26GmikCgYJUdDYDQERkNgNARoBEY7hDQK2FFjR0NgNARGQ2A0BFBCADxVeJuB4d+uXequrkpfwD2oXx0dbLAb2CEdwn8VFSLgicF3FRUsHR3I1/dB5uggPUMWBoYfFRXqDx58vnULMg2ITGJOCSI7BXJBIvL0IET2YWfn14oKtElCiBSExDVhyMnA8NXNDTKl+bGiArIcFEIyMDDI7NrFfu7cr4qKr2B/vaqo4EJdIMrFwPCzogJykMzu3c/c3G4yMHzftUtzdJkoJNhHydEQGA2B0RAYDQGagtEOIU2Dd9Tw0RAYDYHREBgNAUQIwPqEDB0dQoEVZh/BnaUnSNvq2BgY/p8797OiQvr//3dItzVAFpQ+WLEC0htkZWB4X1HBBF5cygVefgnpDSLvEkTYisqCHF7Ks2sXD2w/IVwe/yQhXBkDAwPyhCEX+JLAD+BbDUU6OniNjCAbBRkYGCBnq4JungBfNSFUXn6BkZG3o4PTyOj1uXOQOwYhvdy3FRVsDAyCuy7quL0FbxocvVsCObxH2aMhMBoCoyEwGgI0BKMdQhoG7qjRoyEwGgKjITAaAmghAF4+eq+i4gEDA/epjlcMFXEy4IvgIX2nN+B7KUTBW/u+P3z4WEFBZNcuIVdXyGY8BgYGyDUVPB0dPysqBBkY/jAwPANbYAtebwlmEiAg04MSONRD+oR4JgmRTf/08OFL8MEzrBUV7xkYfjEwcIPnACFq5CIiICtCIV1QyITk14cPX6xY8bmiwgDsAEjH8m1FxfOOgzGgLuH7jg7FiAjp0WWikDAcJUdDYDQERkNgNAToAEavnaBDII9aMRoCo2A0BEZDABoC8vIC5eVGu3apMTD8M6sQ+bDrrqSrK7w3COpBge+lYABPuP1kYHh97txpRsZn4H7X53PnZHbt4gf3BrnBHTBWBgb+jg75Xbv2MzJCZhGh1uCgfj18+KuiggfcBcWqBHKo6RvwJkCsCuCCr3fvfqigwMLAwAITEgQfGAPhCRgZPVqx4tGKFYcYGW8pKLxjYPgBluCWl+cB74p8CbaCT15ev7z8766n4IslHuzapVherjHaGwQH1SgxGgKjITAaAqMhQCcwOkNIp4AetWY0BEZDYDQERkMAOQQePvygoHAcfHCMUESEqtCt05/c3D4zMPDv2gU5lpO/o4O9ouIjAwN7RwcjA8PHigp+8IJMVgaGv+fOcYC7VR/d3AQePHgA7i5CDIecOgNhY5L4pwch6omZJHwN7s4xMDB8OXdOPCLi64oVTEZGb8GH4nAbGf1nYPgAOwWHj4HhS0UFO/heDch6VwbwStdvFRWSu3aJu7p2dt6oqHgOXiaq7OoqB3HDKDkaAqMhMBoCoyEwGgJ0A6MdQroF9ahFoyEwGgKjITAaAighAF4+erei4l1Hh4BhxTRBhgUy4N4gAwMDr5HRGzc3UQaGjx0dkK6genk5AwPDj4cPP65YAekN8ru6PmJklIOtvTyroKC8a9eHc+cEjIzkMPYHwu8exLp7EMVZ4CsokO8kRJZ9vXv3C/DdiaD9irt2iYItesLIKPrgwW8GBshMplREBOQY1UednewMDG8rKhjA3drvDAwC4H4spLt4kWFiHoMqAwNvR4dIebkGsi2j7NEQGA2B0RAYDYHREKAbGO0Q0i2oRy0aDYHREBgNgdEQwBICu3c/cHPby8DAu6KDT6nCEzINCOpBgU/mlPj//xQjoyx4kaegmhpyb5CBgQHeIYSYewl8dguEjTlVSMz0IEQvZJIQs08InxhkYGCAdAUh6p8wMsqA+6WQCyc+nzsnFRHx99YtUA/23Dn+iIh7CgrsHR2iERGPwJOZ7yoqnnQcPFRxS7FDxshIb/SaQUgwjpKjITAaAqMhMBoCAwJGO4QDEOyysrJPnjyBWxwSErJ69Wo4d5QxGgKjITAaAiMtBB4+fNajsHMKg0RHh0hEhKq8vADkIBbIbN6Xhw+fgvtRjAwM4uAZNn7wvBxmhxBy/ifyClI98LwiSdODkMC/xsiI3CHEOjEIUQk66gbWIWRgYID0CZ+5ubEyMAh0dIhFRHDIy6P1MGHLRL/v2mUw2huEB+MoYzQERkNgNARGQ2BAAHwz/IDYPhIt/fHjhw4YMDAwnDhx4sOHD+bm5iMxIEb9PBoCoyEwGgKwEJCXlyp5ECiz4nZFxbuKiuO7dqkbn1v9iYHhPwPDs85OyJJR9oqKlwwMTxkYhDs6FNTU+OTlYbpRaD55eb3y8ke7d0PWju5nZDR+8IBPXv4LuEuJedUEimYkjtiuXa/c3Bh27foP3ijIY2QkAZ6lRJ4YRFKOYDIxMDx1c2NjYGBmYPjEwPBAQUEIfPToX7D4+V1P3dweMTB87ugQLS93RGgbZY2GwGgIjIbAaAiMhsAAgdEZwgEKeAaGw4cPOzg46Orqnjx5kp2dfcDcMWrxaAiMhsBoCAyaEABfVHi3o0OYu2Jb8C5zyFX1vOCb62UZGL50dHwDb8P7AD6+RSEi4oOCAmQPIZoPvjx8+PHWrbtubsq7dj1wcxPu6GAEb+TjBXfq0BTj4j5zc2MG32zB39HxuaICMt0HUcyL2h19wsjICjb5qZsbH/jkmxdubpwMDL/AXUH4vsFdFZy/O8wqKr7v2jV6zSAkIEfJ0RAYDYHREBgNgYEHox3CgYmDDx8+6Ovrv379+uzZs5qamgPjiFFbR0NgNARGQ2DwhQD49NHVDAw8OQw/XBkOme6KYmBgEFZTe6egIPTgwQ8GBsiKUAEjo8fgThfk6r+P4P4e3Df84J4YhPu1ooKfgeEzAwNHRwcfeMUpaGUpuEsJUYCL5GRg+FZR8Z2BgbWjQ9jI6COSlg8w6wTAFv2tqGDt6PhUUSEHPhSH18hI3NX1FiMjSCN43+BrBvmVFRwzGZgYGD7t2uU0ukwUV5iPio+GwGgIjIbAaAjQH4x2COkf5iAbIyMjV6xYMWPGjPT0dBB/FI+GwGgIjIbAaAgghUBn5+mKijfgyxjUXV2V3oFPixEqL4fcUP8QPHfHysAgzsDwmAF0FSHWnt7XigoWBoZ/DAwSDAwPwIYzgkkGsBYYEzv9v6KCFbxm9QsDA0tHxzuwUUzgbYFwDfzgXqIQA8NrcL+RHdwnlN+1ix+8ovXd7t3fzp2TKS/fvfuZm9s9Bobvu3apuLoqwrWPMkZDYDQERkNgNARGQ2AwgNEO4QDEwsKFCxMSEvz9/Tds2DAA1o9aORoCoyEwCoZCCICXj95mYGDY1PFfvcKTvaPjFewqws/nzvEaGf0CX//wA3wvBeQYUtB9FWpqDAwMX2/d+gJWw8DA8M7NTYCB4R94SefHc+dkIiLQFnyiBcaPhw+fKChwdXRwGoFuFHzr5vYEvHGRAbxaVRBsPkTL51u3voLd8JuB4TcDA2NHh6CR0XvwROK7igp2BgZeBobnoE2DN2E9WymIxlFyNARGQ2A0BEZDYDQEBg8Y7RDSIy4ePmSA7ze5d++egYEBLy/vpUuXhIWF6WH9qB2jITAaAqMhMDRDALx89GQ1w+uPDL/DOlhUjCQgd9bL7Nr169w59ooKnl27PjMwPHZzk921SxJ29Oir3bu/njv3GbyqU2LXLhZwD02ovPzZ7t2QZZ/8RkZSMMVoAfN+9+7v585xgleWCrq6fgd3DlX//4ecUgNRDL/Q4uPu3R/c3AR27frNwPDKzY0NvMURooaBgeENg7xzhQIDwx0GBpVdu+RGl4nCQ2aUMRoCoyEwGgKjITCowGiHkB7R0dn5w8iIw9WV4c+fP7a2tidPnty9e7ezszM97B61YzQERkNgNASGeAh0dm6uqHjBwCC6ooPDO0ITcuc7fBEp5KYHSJ+QmYHhhZsbb0cHN7hHJ+bqCrm+QujBAzbwsNznhw+frFjBD5bF7BO+370bHlSCsB7jbUZG4V27hFxdPz18CNm+CJkq5GdggPQGIXdgXGMELUfVAt9G+Onhwy0rHh6seMfAcD9kl6er6+il8/BwHWWMhsBoCIyGwGgIDDow2iGkR5QwMv5gYOD4/5+hv7+/qKiIn5/f1tYWYnFsbGxYWBiEPUqOhsBoCIyGwGgIYA2B3btvubmdYmBg6+hQLC83/bJ79xc3N6EHD34xMHwG3//+xM2Nn4HhFQOD0q5dP8+d4zYyEgP36CAdQglwPw1iMqRPCNpGiDpPCOkNQqYH4b1B0IpTcC9RCGwa6OZD8M7AnxUVfxgY/jIwcHV0yEVEgO45vHXrlZsb5OpC8KbBm+BNg5qjmwYhwT5KjobAaAiMhsBoCAxaMHoPIc2jZvduUG+QgYFh926GgwcPMjAwfPz4ccuWLRCLExMTIYxRcjQERkNgNARGQwBXCLi6qj14ILZixbWKivsMDAzWFcv4GRgeKyjwg+94+HzuHH9HB19FxSsGhjfgBaLCsJ1+X1asYAMfBAo3mVdeXiYi4smKFZDlo5B5QshK0W8VFTIPHnCA5xLh6hkYGN66uQmBu5Qvd+9+4+YmvWvXPQYGQQaGPx0dAkZGj8A3HL4DH0LzwG2DN2jnIEj3gwe28vK8INYoHg2B0RAYDYHREBgNgUEMRmcIaR45kOlBsDXv//8XBDNGidEQGA2B0RAYDQFyQgB8+ugdBgbmyQy3IBcVQq6tZ6io4ANfVPi4okIY1gOUMjL64+bGtmsXG6x/iGzlFQUF0DxhRwcX+HoJro4OYfBcH7IaBvBBo48VFNjAJ4hKg0+meQruE76HbRp8V1EBuXr+EoNldMVXBobfu3aZjO4YRAvGUe5oCIyGwGgIjIbAoAWjHULaRs3Dhz8UFDjgduza9d7VdbRPCA+PUcZoCIyGwGgIkBwC4NNHdzEwyOcwvGh/4MQjLw9ZF8rW0SFUXg5Z1fkYfPinKAPDBwYGPlj/ENOm7xUVXAwMPxkYvjIwCOJW9qui4icDA+RWCdGODnWwLW93737t5qYBnjmEbBqMrvjLwPBu1y7t0U2DmEE9KjIaAqMhMBoCoyEwaMHoklHaRs2KFd8ZGBAdQjc3BnDjgbaWjpo+GgKjITAaAsM4BFxdlR48iFix4nZFBdMUhSsPHvDzghdtfmZg+Ll79xNwV1Bp1643bm7sDAyMDKDL4P9hBMfHigo2mCAL+NKIV+CLImBiCJoHfHvEPwYGLgYGwV273p87d4CRUaijQwE8nfh29+5zDNpubs/BOwoZHjxwG10migi7UdZoCIyGwGgIjIbAUACjM4Q0jCXw9CADcoeQgeHHrl3fRycJaRjoo0aPhsBoCIyYEAAvH/1QzfBMmeGxDkOtzK5dkPsJIVdTiDEwsDMw/Ozo+AkOEClw/w3MZHi1YsV38KUUDAwMQh0drysqGGBXCMKvlICo/LR79w/wpsTXFRXfwJOEEHHIfYPvKiquMkzMYdAdvWYQEiyj5GgIjIbAaAiMhsBQBKMdQhrGWmfn+4oK9AWiu3b9cHVFzBnS0PpRo0dDYDQERkNguAPw8tFzDAxvOjqMnSrMIGfM8KqpsTEwvFNQ4Nm1i8fV9Qwjowx4758E+KTQN+BTQyEBI+Lq+gnCVVODXykhaGQkC1YJ7w2KlZe/2b37pZubOGw74oMVK95VVDzrOBhd8YiBQXjXLv3RTYOQIB0lR0NgNARGQ2A0BIYcGO0Q0irKsE0PQux6/+ABp7z8aJ8QEhqj5GgIjIbAaAhQFAIPH34ALx99w8Dw78EDa3l5AdBdEZ2doNk/8Ga/Lw8fPgOvKRUwMvoOnu5jYGDgNjISgfX6GBgY+MDsx7t3v4cpkDAyYjh37kdFhRxsof9V8E2D2mAu+GKJRwwM3zs6eCMi1EeXiVIUhaOaR0NgNARGQ2A0BAYUjHYIaRj84MYDFvPBzQks4qNCoyEwGgKjITAaAuSFAHiq8DYDw79du9QtGe5+AR8uClksysDA8MTNjYOB4Rf42kAuBgbRiAgu2N0SkBlCSIeQgYEBcvv8N/AOw98MDDzgk2b4jYz41dRerVjxo6JCfNeu86BNgzfBy0QVRq8ZJC++RnWNhsBoCIyGwGgIDB7ANHicMvxc8v8/w4MHDODmBMhzHR0Mu3aNHioDCopRPBoCoyEwGgLUDQFXV6Vdu1QZGJjc3G5Xuj07xZAAuZCQgYEBckuhdEcHLwPDu4qKexUVb8B32WN1wPdbt/6CT5dhZmAQARff/EZGH8+de7RixQ8GhvcMDKfcNpw79wncG1Qf7Q1iDcNRwdEQGA2B0RAYDYGhBUZnCOkRX5CpwtGJQXqE9agdoyEwGgIjOATAy0fvVVScZ2CQOtTxhqMiDnLTIDsDA3tFBc+uXfdgK0LfVlQo79olB9tDyOfq+hK8DFQKvNuQgYHh+7lz3yoq2Ds6foDD831FxeOOg1Ggo0lfdXSoRURIjy4TBQfMKDEaAqMhMBoCoyEw5MFoh5AeUTjaIaRHKI/aMRoCoyEwGgLgEEBePurqqoS2pRCyKBSskOFtRYUoAwN3R8eHigpIV/CZm5vUrl3i4C2F18BltxZ4MA/cW7zJwPB91y4dV1c5iPZRcjQERkNgNARGQ2A0BIYBGO0Q0iMSwY2K0cWi9AjqUTtGQ2A0BEZDgIGBAdYnZOjoEAo0YuZzM2bbtQu+pfDTuXPvKiqYwSHFA76Y/juYzQS7xZ7fyIhPTe31ihVfwB3FuedkKyqew5aJSoHVjhKjITAaAqMhMBoCoyEwTMBoh5AeETnaIaRHKI/aMRoCoyEwGgJIIQBZPgraQ1hx0JThkkaHBp+REQMDw6dz5/iMjCAkw7lzvyoqvjIwfGFgkN616+O5c/xgNQwMDB/Bi0t3V3AWMKgyMPB2dIiUl2sgGT/KHA2B0RAYDYHREBgNgWECWIaJP0a9MRoCoyEwGgKjITAaAkghIC8vUF5utHv3IzcGzmwGhx8M4opubpAthZDe4CcwV6Cj42tFBV9Hx2M3N1lwn5CBgeED+Nr6Rx0HbzK8ZGC4uGtX3Og1g0hBO8ocDYHREBgNgdEQGFZgdIaQHtE5OkNIj1AetWM0BEZDYDQEsIXAw4cfuhXWT2WQ6OgQiYhQhVxU+Bx8USGnkRHkrO0v4JlDBgYGDfDVhQ8ffl6x4il4meifXbvURzcNYgvXUbHREBgNgdEQGA2BYQJGO4T0iMjRDiE9QnnUjtEQGA2B0RDAEQLg5aO3KyreQS4qVD+3GqJQKCLi44oV7EZG7GpqT8GX1zMwMDw2inVzu8fAwNDRwVNebgBROUqOhsBoCIyGwGgIjIbAcAWQsdHh6rtRf42GwGgIjIbAaAiMhgADePmoKfiiQpZz5x5urPj9jYHhGwPDkxUrfjEwvHJzg/cGd1Vwnjv3iIHh965dSqO9wdGkMxoCoyEwGgKjITASwOgMIT1ieXSGkB6hPGrHaAiMhsBoCBAKgYcPPygobGFg+NvRoWXJcIO9Io4XrOUTA8OvjuUrKzimMfAzMDzdtctpdNMgOGBGidEQGA2B0RAYDYHhD0Y7hPSI49EOIT1CedSO0RAYDYHRECAuBDo791RUvGBg4Nm1S8+c4S4DA8NJBm3wMtHvu3apuLoqEmfMqKrREBgNgdEQGA2B0RAYDmC0Q0iPWBztENIjlEftGA2B0RAYDQGiQwB8UeEZBgaGpQy3uDvsAir+jl4zSHTgjSocDYHREBgNgdEQGFZgtENIj+gc7RDSI5RH7RgNgdEQGA0BUkIAvHz06IYO5oCK/wwMgrt2yY0uEyUl/EbVjobAaAiMhsBoCAwTMHqozDCJyFFvjIbAaAiMhsBoCJAUAvLyAv//e99gYN+1S/H/f4vR3iBJoTeqeDQERkNgNARGQ2DYgNEZQnpE5egMIT1CedSO0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNARLB6AwhiQE2qnw0BEZDYDQERkNgNARGQ2A0BEbBaAiMhsBoCAwXMNohHC4xOeqP0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQIBGMdghJDLBR5aMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITBcwGiHcLjE5Kg/RkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDgEQw2iEkMcBGlY+GwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBwAaMdwuESk6P+GA2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNARLBaIeQxAAbVT4aAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgLDBYx2CIdLTI76YzQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEgEox1CEgNsVPloCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAgMFzDaIRwuMTnqj9EQGA2B0RAYDYHREBgNgdEQGA2BUTAaAqMhQCIY7RCSGGCjykdDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2C4gNEO4XCJyVF/jIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGAIlgtENIYoCNKh8NgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHhAkY7hMMlJkf9MRoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAiSC0Q4hiQE2qnw0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNASGCxjtEA6XmBz1x2gIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCJAIRjuEJAbYqPLREBgNgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYLmC0QzhcYnLUH6MhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjIUAiGO0QkhhgZCi/fLzD/5nRzLOPv74gQ/eoltEQGA2B0RAYDYHREBgNgdEQGA2B0RAYBaMhQCPA+P//fxoZPWosKAQuH2c4tJSBgcHNVpyBgcGXVytAzFqWWwIkNYpHQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgQMFoh5BmwQ/rCoIssIs+IM7W9mIbiM3AUCXh5SBmDGGPkqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITBQYHTJKPaQ//Xrl4yMDCMj46VLlyAq3r9/r6amxsPDc+HCBYgITvLycYapOZCJQQa7aIbsKQy6lg5ixrv0aqskvBgYGNpebHO71EyLFaSfPn3i4OBQVFSEu23VqlWMjIwFBQVwkVHGaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIQMDoDCEkHLCQEyZMKCwsjImJWbx48Z8/f9zd3ffv379u3bqAgAAsqiFCl48zPLnFcO80iGcXzSAhzyAqBWIj4cdfX2x4dXTz52s0WkHq6+u7ZcuWGzduqKurv3//XlNTk4+P78KFC1xcXEiuGGWOhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjAKG0RlCnIkgLS1NRERkxYoVjx49ys7O3rdvX0dHB4He4KGliN6griVmb5CBgUGWWyJXMXiucqovr9bmz9eS786efH8tFWcLg4ODGRgYduzYwcDAUFxc/Pr16wULFoz2BnFG86jEaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIxgMDpDiC/yW1tba2pqNDU1r1+/Hh8fv2DBAuyqUbcLMuhaYleGIXrg1Vmqbyx89+6duLi4i4tLSUmJi4tLaWlpV1cXhs2jAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjgGG0Q4gvEXz48EFeXv7Tp082NjZ79+5lY2NDV01uVxDZnMn311J3Bambm9uRI0ckJSU5ODjOnTvHzs6ObN0oezQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BCCABUKNklhD4P79+3///mVgYDA0NETvDb5+xvDiIeLkGAYG4icG0ezKVQwO+GoN2Vi4+fM1yq+mCA4O3r1796NHj44fPz7aG0QL7VHuaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGAByMzhDCgwKd8fz5czMzs2/fvv0Gg/v370tIwO4PpMbEILp9DAxo583kKoJ2A2IqIyhy9uxZExOTuLi4hQsXElQ8qmA0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgxILRQ2WwR/3379/9/f1fvHixevXq3NzcHz9+dHd3g5Riu1ICJE4NDDlvBnI1xebP19wuNR94dZYMg2/cuMHAwGBmZkaG3lEtoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAiMHjC4ZxRLX////j4+PP3369LRp05ycnPT19SdNmjRj2rQKsQ+iPJwgDXbRZC8QBWnHix3EjJW5pSErSNtebLv89UGAmLUsN2xyEq9eiOTFixcZGBj09PQg3FFyNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQEsILRGUIswVJbW7t69eqsrKzMzEwGBgZhYeGc8MBvP3707r3AoGQKumue6HNEsZhOhBBkqpDsqykuXbo02iEkIphHlYyGwGgIjIbAaAiMhsBoCIyGwGgIjIbASAejewgJpQDabBckZCtC/vHXF8l3Z0P4VRJeDmLGEPYoORoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAhSC0Q4hKAB/PHnEwcTIICUL4sDxQHcF4Q5hYGAgeGPhw+eP5SVR3Y+sf5Q9GgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCGGC0Q8jw48mj741pgkK8DOH5DEY2oCAaTF1BkHvAGO0MUvjGwv0PT1Uem//92YdNUV2jfUJwUI0SoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAkSB0Q4hw/s5kwRvH2bg52ZgZmJwTwaRh5ZCA4+Wh8dArSCRQusW6nDIN95c+//P35+vP4dy6BcHZpBo3qjy0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgZELRnqH8MfRAxxrJjKI8jOwsTCwgs9cZWJiYGKiw8kxlCQ66ArSf////f7HwMDw7+fvvz9+f3v8rkM71tnMnhKTR/WOhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjBwwojuEP548YmjP5hATYOBmh/YGGRgYVCwYvBOGRApwPdv4/99/iFP//fz9++P3rw/fdpskjfYJIWEySo6GwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAjgByP62onvjWnovcHffxiuH8EfZINE9tHH578///j/BzRDyMDAwMTOyibEwy0vXHR4xiBx4agzRkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BAY5GLkdwh8LpgjKizMIcEPnBn//Yfj6g+HXH4avPxlePh3k0QZy3u9/f7/+/PP1x5+vP//9/P3/zz9GFiZ2MT5+HZl9146CFIzi0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQwAvAu+bwqhiWkqCtg8/PMvByMvz+A/Lg158gxu8/DL/+/Pj1h+MvdNoNJDWI8deHb5k5WZk5WJnYWZjZWZm52ZlB84Tcjc83MTAwOGlZD2K3jzptNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYODBiNxD+Owxw8I6UNh//fHj95/v7z5zWvt8//OPQU0XJMjAIGjnCGEMcvLh88d3Ht9jYGC48PQ6AwMDIx/b8kfH2IV5mLnZ///5t8S4QE5EepB7YdR5oyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAgMIRmSH8NVThj//fvz7zyEjN4BBTzurHz5/zMjKNNobpF0Ij5o8GgKjITAaAqMhMBoCoyEwGgKjITAaAsMDjMgO4fCIulFfjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGAGVg5B4qQ1m4jeoeDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQGA2BIQ9GO4RDPgpHPTAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgLkgdEOIXnhNqprNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQEhjwY7RAO+SgcBaMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjIUAeGO0Qkhduo7pGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgyIPRDuGQj8JRD4yGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhgB5YLRDSF64jeoaDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQGA2BIQ9GO4RDPgpHPTAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgLkgdEOIXnhNqprNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQEhjwY7RAO+Sgc9cBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiQB0Y7hOSF26iu0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQGPJgtEM45KNw1AOjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyFAHhjtEJIXbqO6RkNgFIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAkAejHcIhH4WjHhgNgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDQHywGiHkLxwG9U1GgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCQx6MdgiHfBSOemA0BEZDYDQERkNgNARGQ2A0BEZDYDQERkNgNATIA6MdQvLCbVTXaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIDHkw2iEc8lE46oHREBgNgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAgD4x2CMkLt1FdoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMOTBaIdwyEfhqAdGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkOAPDDaISQv3EZ1jYbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwJAHox3CIR+Fox4YDYHREBgFoyEwGgKjITAaAqMhMBoCoyEwGgLkgdEOIXnhNqprNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQEhjwY7RAO+Sgc9cBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiQB0Y7hOSF26iu0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQGPJgtEM45KNw1AOjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoCoyFAHhjtEJIXbqO6RkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYMiD0Q7hkI/CUQ+MhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIYAeWC0Q0heuI3qGg2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgSEPRjuEQz4KRz0wGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoC5IHRDiF54TaqazQERkNgNARGQ2AUjIbAaAiMhsBoCIyGwGgIDHkw2iEc8lE46oHREBgNgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAgD4x2CMkLt1FdoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMOTBaIdwyEfhqAdGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQERkOAPDDaISQv3EZ1jYbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwJAHox3CIR+Fox4YDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQGA0B8sBoh5C8cBvVNRoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAkMejHYIh3wUjnpgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQEyAOjHULywm1U12gIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCAx5MNohHPJROOqB0RAYDYHREBgNgdEQGAWjITAaAqMhMBoCoyFAHhjtEJIXbqO6RkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYMiD0Q7hkI/CUQ+MhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIjIYAeWC0Q0heuI3qGg2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgSEPRjuEQz4KRz0wGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhMBoC5IHRDiF54TaqazQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BIY8GO0QDvkoHPXAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIkAdGO4TkhduortEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBjyYLRDOOSjcNQDoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhQB4Y7RCSF26jukZDYDQERkNgNARGQ2A0BEZDYBSMhsBoCIyGwJAHox3CIR+Fox4YDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQGA0B8sBoh5C8cBvVNRoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAkMejHYIh3wUjnpgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDYDQEyAOjHULywm1U12gIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCAx5MNohHPJROOqB0RAYDYHREBgNgdEQGA2B0RAYDYHREBgNgdEQIA+MdgjJC7dRXaMhMBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITDkwWiHcMhH4agHRkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BEZDgDww2iEkL9xGdY2GwGgIjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsCQB6MdwiEfhaMeGA2B0RAYDYHREBgNgdEQGA2BUTAaAqMhMBoC5IHRDiF54TaqazQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2A0BIY8GO0QDvkoHPXAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGwGgIkAdGO4TkhduortEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYHREBjyYLRDOOSjcNQDoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAqMhQB4Y7RCSF26jukZDYDQERkNgNARGQ2A0BEZDYDQERkNgNARGQ2DIg9EO4ZCPwlEPjIbAaAiMhsBoCIyGwGgIjIbAaAiMhsBoCIyGAHlgtENIXriN6hoNgdEQGA2B0RAYDYHREBgNgdEQGA2B0RAYDYEhD0Y7hEM+Ckc9MBoCoyEwGgKjITAaAqMhMBoCoyEwGgKjITAaAoCRFwIASDTekE70XrAAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<PIL.Image.Image image mode=RGB size=1200x900>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sim.plot_mesh(show_groups=[\"metal\", \"P\"])"
+    "# sim.plot_mesh(show_groups=[\"metal\", \"P\"])\n",
+    "sim.plot_mesh(\n",
+    "    style=\"solid\",\n",
+    "    transparent_groups=[\"air__None\", \"sio2__None\", \"air__sio2\"],\n",
+    "    interactive=True,\n",
+    ")"
    ]
   },
   {
@@ -353,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "11649b59",
    "metadata": {
     "execution": {
@@ -371,23 +290,7 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  palace-b2589e56  completed  2m 02s\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting results.tar.gz...\n",
-      "Downloaded 10 files to sim-data-palace-b2589e56\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Run simulation on GDSFactory+ cloud\n",
     "results = sim.run()"
@@ -395,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "9fb30d82",
    "metadata": {
     "execution": {
@@ -413,70 +316,14 @@
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Port mapping: Port 1: P1, Port 2: P2\n"
-     ]
-    },
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    "source": [
     "results.plot_interactive()"
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@@ -494,49 +341,7 @@
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     "tags": []
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-   "outputs": [
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-     "text": [
-      "Port mapping: Port 1: P1, Port 2: P2\n"
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-       "                            \n",
-       "var gd = document.getElementById('c57242c9-aab9-4dce-8977-b6620f77a941');\n",
-       "var x = new MutationObserver(function (mutations, observer) {{\n",
-       "        var display = window.getComputedStyle(gd).display;\n",
-       "        if (!display || display === 'none') {{\n",
-       "            console.log([gd, 'removed!']);\n",
-       "            Plotly.purge(gd);\n",
-       "            observer.disconnect();\n",
-       "        }}\n",
-       "}});\n",
-       "\n",
-       "// Listen for the removal of the full notebook cells\n",
-       "var notebookContainer = gd.closest('#notebook-container');\n",
-       "if (notebookContainer) {{\n",
-       "    x.observe(notebookContainer, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "// Listen for the clearing of the current output cell\n",
-       "var outputEl = gd.closest('.output');\n",
-       "if (outputEl) {{\n",
-       "    x.observe(outputEl, {childList: true});\n",
-       "}}\n",
-       "\n",
-       "                        })                };            </script>        </div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "results.plot_interactive(phase=True)"
    ]
@@ -558,7 +363,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "version": "3.12.3"
   },
   "papermill": {
    "default_parameters": {},
diff --git a/nbs/palace_inductor.py b/nbs/palace_inductor.py
index 733ba992..faf3b9ed 100644
--- a/nbs/palace_inductor.py
+++ b/nbs/palace_inductor.py
@@ -108,7 +108,7 @@
 sim.set_geometry(cc)
 
 # Configure layer stack from active PDK
-sim.set_stack(substrate_thickness=180.0, air_above=200.0, include_substrate=True)
+sim.set_stack(substrate_thickness=180.0, include_substrate=True)
 
 # Configure ports
 sim.add_port(
@@ -126,10 +126,16 @@
 
 # %% papermill={"duration": 1.930523, "end_time": "2026-05-18T13:26:58.885247", "exception": false, "start_time": "2026-05-18T13:26:56.954724", "status": "completed"}
 # Generate mesh (presets: "coarse", "default", "fine")
-sim.mesh(preset="default", margin=50, refined_mesh_size=1.5)
+sim.set_airbox(margin_x=5, margin_y=5, z_above=5, z_below=5)
+sim.mesh(preset="default", refined_mesh_size=1.5)
 
 # %% papermill={"duration": 0.706534, "end_time": "2026-05-18T13:26:59.592690", "exception": false, "start_time": "2026-05-18T13:26:58.886156", "status": "completed"}
-sim.plot_mesh(show_groups=["metal", "P"])
+# sim.plot_mesh(show_groups=["metal", "P"])
+sim.plot_mesh(
+    style="solid",
+    transparent_groups=["air__None", "sio2__None", "air__sio2"],
+    interactive=True,
+)
 
 # %% [markdown] papermill={"duration": 0.001592, "end_time": "2026-05-18T13:26:59.596145", "exception": false, "start_time": "2026-05-18T13:26:59.594553", "status": "completed"}
 # ### Run simulation on cloud
diff --git a/src/gsim/common/stack/__init__.py b/src/gsim/common/stack/__init__.py
index 1b20a1a5..3176215a 100644
--- a/src/gsim/common/stack/__init__.py
+++ b/src/gsim/common/stack/__init__.py
@@ -63,6 +63,10 @@ def get_stack(
               Palace RF sims typically override to 200+ for far-field radiation.
                         - air_below: Air box height below substrate/oxide in um
                             (default: 0)
+                        - top_layer: Optional stack layer name to cap dielectric/air
+                            construction at that layer's zmax.
+                        - max_z: Optional explicit z-cap (um) for dielectric/air
+                            construction.
             - include_substrate: Include lossy silicon substrate (default: False).
               When False, omits substrate for RF simulation.
 
diff --git a/src/gsim/common/stack/extractor.py b/src/gsim/common/stack/extractor.py
index 35cd64c6..47a388da 100644
--- a/src/gsim/common/stack/extractor.py
+++ b/src/gsim/common/stack/extractor.py
@@ -331,6 +331,8 @@ def extract_layer_stack(
     air_below: float = 0.0,
     boundary_margin: float = 30.0,
     include_substrate: bool = False,
+    max_z: float | None = None,
+    top_layer: str | None = None,
 ) -> LayerStack:
     """Extract layer stack from a gdsfactory LayerStack.
 
@@ -343,6 +345,11 @@ def extract_layer_stack(
         air_below: Height of air box below substrate/oxide in um (default: 0)
         boundary_margin: Lateral margin from GDS bbox in um (default: 30)
         include_substrate: Whether to include lossy substrate (default: False)
+        max_z: Optional z-cap (um) for dielectric/air stack construction.
+            Useful to trim unused upper process levels.
+        top_layer: Optional layer name whose ``zmax`` is used as z-cap.
+            If both ``max_z`` and ``top_layer`` are provided, the smaller
+            of the two caps is applied.
 
     Returns:
         LayerStack object for Palace simulation
@@ -351,8 +358,24 @@ def extract_layer_stack(
 
     z_min_overall = float("inf")
     z_max_overall = float("-inf")
+    z_max_conductor = float("-inf")
+    conductor_tops: list[float] = []
+    passivation_ranges: list[tuple[float, float, str]] = []
+    dielectric_layers: list[tuple[float, float, str, str]] = []
     materials_used: set[str] = set()
 
+    def _looks_like_passivation(layer_name: str, material_name: str) -> bool:
+        """Heuristic to detect top passivation layers from PDK stacks."""
+        lname = layer_name.strip().lower()
+        mname = material_name.strip().lower()
+        if "passiv" in lname or "nitride" in lname:  # codespell:ignore passiv
+            return True
+
+        if mname in {"passive", "sin", "si3n4", "silicon_nitride"}:
+            return True
+
+        return "nitride" in mname
+
     for layer_name, layer_level in gf_layer_stack.layers.items():
         zmin = layer_level.zmin if layer_level.zmin is not None else 0.0
         thickness = layer_level.thickness if layer_level.thickness is not None else 0.0
@@ -385,6 +408,38 @@ def extract_layer_stack(
             z_min_overall = min(z_min_overall, zmin)
             z_max_overall = max(z_max_overall, zmax)
 
+        if layer_type in {"conductor", "via"}:
+            z_max_conductor = max(z_max_conductor, zmax)
+            conductor_tops.append(zmax)
+        elif layer_type == "dielectric":
+            dielectric_layers.append((zmin, zmax, material, layer_name))
+            if _looks_like_passivation(layer_name, material):
+                passivation_ranges.append((zmin, zmax, material))
+
+    z_cap = max_z
+    if top_layer is not None:
+        top = stack.layers.get(top_layer)
+        if top is None:
+            raise ValueError(f"top_layer '{top_layer}' not found in layer stack")
+        z_cap = top.zmax if z_cap is None else min(z_cap, top.zmax)
+
+    if z_cap is not None:
+        if z_cap <= z_min_overall:
+            raise ValueError(
+                "z-cap "
+                f"({z_cap}) must be greater than the stack bottom "
+                f"({z_min_overall})"
+            )
+        z_max_active = min(z_max_overall, z_cap)
+        active_tops = [z for z in conductor_tops if z <= z_max_active + 1e-9]
+        if active_tops:
+            z_max_conductor_active = max(active_tops)
+        else:
+            z_max_conductor_active = min(z_max_conductor, z_max_active)
+    else:
+        z_max_active = z_max_overall
+        z_max_conductor_active = z_max_conductor
+
     for material in materials_used:
         props = get_material_properties(material)
         if props:
@@ -398,8 +453,33 @@ def extract_layer_stack(
     if "air" not in stack.materials:
         stack.materials["air"] = MATERIALS_DB["air"].to_dict()
 
-    if "SiO2" not in stack.materials:
-        stack.materials["SiO2"] = MATERIALS_DB["SiO2"].to_dict()
+    sorted_dielectric_layers = sorted(dielectric_layers, key=lambda d: d[0])
+
+    def _is_dielectric_material(material_name: str) -> bool:
+        props = get_material_properties(material_name)
+        if props is None:
+            return True
+        return props.type == "dielectric"
+
+    # Choose a PDK-defined dielectric material for the bulk background slab.
+    # Prefer non-passivation layers (e.g. oxide / IMD materials).
+    bulk_material = None
+    for _zmin, _zmax, material, layer_name in sorted_dielectric_layers:
+        if not _is_dielectric_material(material):
+            continue
+        if not _looks_like_passivation(layer_name, material):
+            bulk_material = material
+            break
+    if bulk_material is None and sorted_dielectric_layers:
+        for _zmin, _zmax, material, _layer_name in sorted_dielectric_layers:
+            if _is_dielectric_material(material):
+                bulk_material = material
+                break
+    if bulk_material is None and sorted_dielectric_layers:
+        bulk_material = sorted_dielectric_layers[0][2]
+    if bulk_material is None:
+        # Rare fallback when a PDK stack defines no dielectric layers.
+        bulk_material = "SiO2"
 
     if include_substrate:
         stack.dielectrics.append(
@@ -416,35 +496,83 @@ def extract_layer_stack(
     else:
         oxide_zmin = -substrate_thickness
 
+    # Prefer explicit PDK passivation placement (e.g. IHP passivation layer).
+    # No synthetic passivation is added when the PDK does not define one.
+    passivation_candidates: list[tuple[float, float, str]] = []
+    for zmin, zmax, material in passivation_ranges:
+        if zmax <= z_max_conductor_active + 1e-9:
+            continue
+        clipped_zmin = max(zmin, z_max_conductor_active)
+        clipped_zmax = min(zmax, z_max_active)
+        if clipped_zmax > clipped_zmin + 1e-9:
+            passivation_candidates.append((clipped_zmin, clipped_zmax, material))
+
+    if passivation_candidates:
+        passive_zmin = min(zmin for zmin, _, _ in passivation_candidates)
+        passive_zmax = max(zmax for _, zmax, _ in passivation_candidates)
+        # Use the material from the lowest passivation segment.
+        passivation_material = min(passivation_candidates, key=lambda item: item[0])[2]
+    else:
+        passive_zmin = None
+        passive_zmax = None
+        passivation_material = None
+
+    def _ensure_material_defined(material_name: str) -> None:
+        if material_name in stack.materials:
+            return
+        props = get_material_properties(material_name)
+        if props:
+            stack.materials[material_name] = props.to_dict()
+        else:
+            stack.materials[material_name] = {
+                "type": "unknown",
+                "note": "Material not in database, please add properties manually",
+            }
+
+    _ensure_material_defined(bulk_material)
+
+    if passivation_material is not None:
+        _ensure_material_defined(passivation_material)
+
+    oxide_zmax = (
+        z_max_active if passive_zmin is None else min(passive_zmin, z_max_active)
+    )
+
     stack.dielectrics.append(
         {
             "name": "oxide",
             "zmin": oxide_zmin,
-            "zmax": z_max_overall,
-            "material": "SiO2",
+            "zmax": oxide_zmax,
+            "material": bulk_material,
         }
     )
 
-    passive_thickness = 0.4
-    stack.dielectrics.append(
-        {
-            "name": "passive",
-            "zmin": z_max_overall,
-            "zmax": z_max_overall + passive_thickness,
-            "material": "passive",
-        }
-    )
-    if "passive" not in stack.materials:
-        stack.materials["passive"] = MATERIALS_DB["passive"].to_dict()
+    top_of_stack = oxide_zmax
 
-    stack.dielectrics.append(
-        {
-            "name": "air_box",
-            "zmin": z_max_overall + passive_thickness,
-            "zmax": z_max_overall + passive_thickness + air_above,
-            "material": "air",
-        }
-    )
+    if (
+        passivation_material is not None
+        and passive_zmin is not None
+        and passive_zmax is not None
+    ):
+        stack.dielectrics.append(
+            {
+                "name": "passive",
+                "zmin": passive_zmin,
+                "zmax": passive_zmax,
+                "material": passivation_material,
+            }
+        )
+        top_of_stack = passive_zmax
+
+    if air_above > 0:
+        stack.dielectrics.append(
+            {
+                "name": "air_box",
+                "zmin": top_of_stack,
+                "zmax": top_of_stack + air_above,
+                "material": "air",
+            }
+        )
 
     if air_below > 0:
         stack.dielectrics.append(
@@ -462,6 +590,8 @@ def extract_layer_stack(
         "air_below": air_below,
         "substrate_thickness": substrate_thickness,
         "include_substrate": include_substrate,
+        "max_z": max_z,
+        "top_layer": top_layer,
     }
 
     return stack
diff --git a/src/gsim/palace/base.py b/src/gsim/palace/base.py
index ac44751c..fdbc5872 100644
--- a/src/gsim/palace/base.py
+++ b/src/gsim/palace/base.py
@@ -123,7 +123,7 @@ def set_stack(
         stack: LayerStack | None = None,
         *,
         yaml_path: str | Path | None = None,
-        air_above: float = 200.0,
+        air_above: float = 0.0,
         air_below: float = 0.0,
         substrate_thickness: float = 2.0,
         include_substrate: bool = False,
@@ -153,6 +153,9 @@ def set_stack(
             substrate_thickness: Thickness below z=0 in um.
             include_substrate: Include lossy silicon substrate.
             **kwargs: Additional args passed to extract_layer_stack.
+                Common options include ``top_layer`` and ``max_z`` to cap
+                dielectric/air stack height for structures that use only a
+                subset of process levels.
 
         Example:
             >>> sim.set_stack(air_above=300.0, substrate_thickness=2.0)
diff --git a/src/gsim/palace/mesh/geometry.py b/src/gsim/palace/mesh/geometry.py
index f1eca53e..4ed04744 100644
--- a/src/gsim/palace/mesh/geometry.py
+++ b/src/gsim/palace/mesh/geometry.py
@@ -226,7 +226,8 @@ def _merge_via_polygons(
     # Convert back to coordinate lists
     result = []
     # unary_union may return Polygon or MultiPolygon
-    geoms = merged.geoms if hasattr(merged, "geoms") else [merged]
+    geoms_attr = getattr(merged, "geoms", None)
+    geoms = list(geoms_attr) if geoms_attr is not None else [merged]
     for geom in geoms:
         if geom.is_empty:
             continue
@@ -551,7 +552,22 @@ def add_dielectrics(
     xmax_air = xmax0 + margin_x
     ymax_air = ymax0 + margin_y
 
-    def _is_air_or_vacuum(material_name: str) -> bool:
+    def _contains_air_token(name: str | None) -> bool:
+        """Return True when *name* clearly denotes air/vacuum."""
+        if not name:
+            return False
+
+        normalized = name.strip().lower().replace("-", "_")
+        if normalized in {"air", "vacuum"}:
+            return True
+
+        tokens = [tok for tok in normalized.split("_") if tok]
+        return "air" in tokens or "vacuum" in tokens
+
+    def _is_air_or_vacuum(
+        material_name: str,
+        dielectric_name: str | None = None,
+    ) -> bool:
         """Return True when *material_name* represents air/vacuum.
 
         Uses stack material metadata first (type + permittivity), then
@@ -560,16 +576,22 @@ def _is_air_or_vacuum(material_name: str) -> bool:
         mat = stack.materials.get(material_name)
         if isinstance(mat, dict):
             mat_type = str(mat.get("type", "")).strip().lower()
-            if mat_type == "dielectric":
-                eps = mat.get("permittivity")
-                try:
-                    if eps is not None and abs(float(eps) - 1.0) <= 1e-9:
-                        return True
-                except (TypeError, ValueError):
-                    pass
+            eps = mat.get("permittivity")
+        else:
+            mat_type = str(getattr(mat, "type", "")).strip().lower()
+            eps = getattr(mat, "permittivity", None)
+
+        if mat_type == "dielectric":
+            try:
+                if eps is not None and abs(float(eps) - 1.0) <= 1e-9:
+                    return True
+            except (TypeError, ValueError):
+                pass
+
+        if _contains_air_token(material_name):
+            return True
 
-        name = material_name.strip().lower()
-        return name in {"air", "vacuum"}
+        return _contains_air_token(dielectric_name)
 
     z_min_all = math.inf
     z_max_all = -math.inf
@@ -585,9 +607,10 @@ def _is_air_or_vacuum(material_name: str) -> bool:
     )
 
     for dielectric in stack.dielectrics:
+        dielectric_name = dielectric.get("name")
         material = dielectric["material"]
 
-        is_air_like = _is_air_or_vacuum(material)
+        is_air_like = _is_air_or_vacuum(material, dielectric_name=dielectric_name)
 
         # When building an explicit airbox, skip explicit air/vacuum layers.
         if is_air_like and use_airbox:
diff --git a/src/gsim/palace/models/mesh.py b/src/gsim/palace/models/mesh.py
index 7848cdb6..d2377966 100644
--- a/src/gsim/palace/models/mesh.py
+++ b/src/gsim/palace/models/mesh.py
@@ -33,7 +33,7 @@ class MeshConfig(BaseModel):
     refined_mesh_size: float = Field(default=5.0, gt=0)
     max_mesh_size: float = Field(default=300.0, gt=0)
     cells_per_wavelength: int = Field(default=10, ge=1)
-    margin: float = Field(default=50.0, ge=0)
+    margin: float = Field(default=0.0, ge=0)
     margin_x: float | None = Field(
         default=None,
         ge=0,
diff --git a/tests/palace/test_mesh.py b/tests/palace/test_mesh.py
index ed9e6989..2f1ddae3 100644
--- a/tests/palace/test_mesh.py
+++ b/tests/palace/test_mesh.py
@@ -22,7 +22,7 @@ def test_default_config(self):
         config = MeshConfig()
         assert config.refined_mesh_size == 5.0
         assert config.max_mesh_size == 300.0
-        assert config.margin == 50.0
+        assert config.margin == 0.0
         assert config.fmax == 100e9
         assert config.show_gui is False
         assert config.boundary_conditions is not None
@@ -152,6 +152,55 @@ def synchronize(self) -> None:
     assert list(tags.keys()) == ["SiO2", "airbox"]
 
 
+def test_add_dielectrics_explicit_airbox_skips_named_air_regions(monkeypatch) -> None:
+    calls: list[tuple[float, float, float, float, float, float]] = []
+
+    def _fake_create_box(_kernel, xmin, ymin, zmin, xmax, ymax, zmax):
+        calls.append((xmin, ymin, zmin, xmax, ymax, zmax))
+        return len(calls)
+
+    monkeypatch.setattr(
+        "gsim.palace.mesh.geometry.gmsh_utils.create_box", _fake_create_box
+    )
+
+    class _Kernel:
+        def synchronize(self) -> None:
+            return
+
+    geometry = GeometryData(polygons=[], bbox=(0.0, 0.0, 10.0, 20.0), layer_bboxes={})
+    stack = LayerStack(
+        dielectrics=[
+            {"name": "oxide", "zmin": -2.0, "zmax": 0.5, "material": "SiO2"},
+            {
+                "name": "air_box_top",
+                "zmin": 0.5,
+                "zmax": 8.0,
+                "material": "ambient",
+            },
+        ],
+        materials={
+            "SiO2": {"type": "dielectric", "permittivity": 4.1},
+            # Intentionally non-air metadata to force name-based detection.
+            "ambient": {"type": "unknown"},
+        },
+    )
+
+    tags = add_dielectrics(
+        _Kernel(),
+        geometry,
+        stack,
+        margin_x=5.0,
+        margin_y=7.0,
+        air_margin=0.0,
+        airbox_z_above=100.0,
+        airbox_z_below=100.0,
+    )
+
+    assert list(tags.keys()) == ["SiO2", "airbox"]
+    # Oxide + explicit airbox only.
+    assert len(calls) == 2
+
+
 def _mk_layer(
     name: str,
     zmin: float,
diff --git a/tests/palace/test_sim_classes.py b/tests/palace/test_sim_classes.py
index 340c2607..eea84c46 100644
--- a/tests/palace/test_sim_classes.py
+++ b/tests/palace/test_sim_classes.py
@@ -6,6 +6,8 @@
 
 import pytest
 
+from gsim.common import Geometry
+from gsim.common.stack import Layer, LayerStack
 from gsim.palace import DrivenSim, EigenmodeSim, ElectrostaticSim
 from gsim.palace.models import MeshConfig
 
@@ -149,6 +151,94 @@ def test_set_stack(self):
             assert sim._stack_kwargs["air_above"] == 500.0
             assert sim._stack_kwargs["air_below"] == 25.0
 
+    def test_set_stack_default_air_above_zero(self):
+        """Default stack setup should not add top air unless explicitly requested."""
+        sim = DrivenSim()
+        sim.set_stack()
+        assert sim._stack_kwargs["air_above"] == 0.0
+
+    def test_resolve_stack_does_not_auto_cap_from_geometry(self, monkeypatch):
+        """_resolve_stack should not infer an implicit top-layer cap from geometry."""
+        calls: list[dict] = []
+
+        def _mk_stack() -> LayerStack:
+            return LayerStack(
+                pdk_name="test",
+                layers={
+                    "metal1": Layer(
+                        name="metal1",
+                        gds_layer=(10, 0),
+                        zmin=0.0,
+                        zmax=1.0,
+                        thickness=1.0,
+                        material="aluminum",
+                        layer_type="conductor",
+                    ),
+                    "topmetal2": Layer(
+                        name="topmetal2",
+                        gds_layer=(20, 0),
+                        zmin=2.0,
+                        zmax=3.0,
+                        thickness=1.0,
+                        material="aluminum",
+                        layer_type="conductor",
+                    ),
+                },
+            )
+
+        def _fake_get_stack(*, yaml_path=None, **kwargs):
+            calls.append({"yaml_path": yaml_path, **kwargs})
+            return _mk_stack()
+
+        monkeypatch.setattr("gsim.common.stack.get_stack", _fake_get_stack)
+
+        class _Layout:
+            def layers(self):
+                return 2
+
+            def is_valid_layer(self, idx):
+                return idx in (0, 1)
+
+            def get_info(self, idx):
+                mapping = {
+                    0: SimpleNamespace(layer=10, datatype=0),
+                    1: SimpleNamespace(layer=99, datatype=0),
+                }
+                return mapping[idx]
+
+        class _Component:
+            def __init__(self):
+                self.kcl = SimpleNamespace(layout=_Layout())
+
+            def get_polygons(self):
+                return {0: [object()], 1: [object()]}
+
+        sim = DrivenSim()
+        sim.set_stack(substrate_thickness=2.0)
+        sim.geometry = Geometry(component=_Component())
+
+        sim._resolve_stack()
+
+        assert len(calls) == 1
+        assert calls[0].get("top_layer") is None
+
+    def test_resolve_stack_keeps_explicit_cap(self, monkeypatch):
+        """Explicit top_layer/max_z should bypass auto top-layer inference."""
+        calls: list[dict] = []
+
+        def _fake_get_stack(*, yaml_path=None, **kwargs):
+            calls.append({"yaml_path": yaml_path, **kwargs})
+            return LayerStack()
+
+        monkeypatch.setattr("gsim.common.stack.get_stack", _fake_get_stack)
+
+        sim = DrivenSim()
+        sim.set_stack(top_layer="topmetal1")
+        sim._resolve_stack()
+
+        assert len(calls) == 1
+        assert calls[0].get("top_layer") == "topmetal1"
+
     def test_set_airbox(self):
         """Test set_airbox stores explicit airbox margins and z extents."""
         for cls in [DrivenSim, EigenmodeSim, ElectrostaticSim]:
diff --git a/tests/palace/test_stack.py b/tests/palace/test_stack.py
index 5879b329..ea6f2d56 100644
--- a/tests/palace/test_stack.py
+++ b/tests/palace/test_stack.py
@@ -2,11 +2,15 @@
 
 from __future__ import annotations
 
+from types import SimpleNamespace
+from typing import Any, cast
+
 from gsim.common.stack import (
     MATERIALS_DB,
     Layer,
     LayerStack,
     ValidationResult,
+    extract_layer_stack,
     get_material_properties,
     material_is_conductor,
     material_is_dielectric,
@@ -213,6 +217,112 @@ def test_get_via_layers(self):
         assert "via1" in vias
 
 
+class TestExtractor:
+    """Test gdsfactory-to-gsim stack extraction behavior."""
+
+    @staticmethod
+    def _mk_level(
+        layer: tuple[int, int],
+        zmin: float,
+        thickness: float,
+        material: str,
+        *,
+        info: dict | None = None,
+    ) -> SimpleNamespace:
+        return SimpleNamespace(
+            layer=layer,
+            zmin=zmin,
+            thickness=thickness,
+            material=material,
+            info=info or {},
+            sidewall_angle=0.0,
+        )
+
+    def test_passivation_uses_pdk_zrange_when_available(self):
+        gf_stack = cast(
+            Any,
+            SimpleNamespace(
+                layers={
+                    "metal1": self._mk_level((1, 0), 0.0, 1.0, "aluminum"),
+                    "passive": self._mk_level((2, 0), 1.0, 2.0, "Si3N4"),
+                }
+            ),
+        )
+
+        stack = extract_layer_stack(
+            gf_stack,
+            pdk_name="test",
+            substrate_thickness=2.0,
+            air_above=10.0,
+            include_substrate=False,
+        )
+
+        oxide = next(d for d in stack.dielectrics if d["name"] == "oxide")
+        passive = next(d for d in stack.dielectrics if d["name"] == "passive")
+        air = next(d for d in stack.dielectrics if d["name"] == "air_box")
+
+        assert oxide["zmin"] == -2.0
+        assert oxide["zmax"] == 1.0
+        assert passive["zmin"] == 1.0
+        assert passive["zmax"] == 3.0
+        assert air["zmin"] == 3.0
+        assert air["zmax"] == 13.0
+
+    def test_no_synthetic_passivation_when_pdk_has_none(self):
+        gf_stack = cast(
+            Any,
+            SimpleNamespace(
+                layers={
+                    "metal1": self._mk_level((1, 0), 0.0, 1.0, "aluminum"),
+                }
+            ),
+        )
+
+        stack = extract_layer_stack(
+            gf_stack,
+            pdk_name="test",
+            substrate_thickness=2.0,
+            air_above=10.0,
+            include_substrate=False,
+        )
+
+        assert all(d["name"] != "passive" for d in stack.dielectrics)
+
+        air = next(d for d in stack.dielectrics if d["name"] == "air_box")
+
+        assert air["zmin"] == 1.0
+        assert air["zmax"] == 11.0
+
+    def test_dielectric_materials_follow_pdk_material_names(self):
+        gf_stack = cast(
+            Any,
+            SimpleNamespace(
+                layers={
+                    "metal1": self._mk_level((1, 0), 0.0, 1.0, "aluminum"),
+                    "mim": self._mk_level((3, 0), 0.9, 0.1, "sio2"),
+                    "passive": self._mk_level((2, 0), 1.0, 2.0, "sin"),
+                }
+            ),
+        )
+
+        stack = extract_layer_stack(
+            gf_stack,
+            pdk_name="test",
+            substrate_thickness=2.0,
+            air_above=10.0,
+            include_substrate=False,
+        )
+
+        oxide = next(d for d in stack.dielectrics if d["name"] == "oxide")
+        passive = next(d for d in stack.dielectrics if d["name"] == "passive")
+
+        assert oxide["material"] == "sio2"
+        assert passive["material"] == "sin"
+        assert "sio2" in stack.materials
+        assert "sin" in stack.materials
+        assert "passive" not in stack.materials
+
+
 class TestValidation:
     """Test stack validation."""
 

From df3d3a065f8237029eb35b315ddea90d2b137635 Mon Sep 17 00:00:00 2001
From: mdmaas <martin.d.maas@gmail.com>
Date: Fri, 29 May 2026 17:43:18 -0300
Subject: [PATCH 2/4] fix(palace): remove unused stack capping paths

---
 src/gsim/common/stack/__init__.py  |  4 --
 src/gsim/common/stack/extractor.py | 36 +------------
 src/gsim/palace/base.py            |  3 --
 tests/palace/test_sim_classes.py   | 84 ------------------------------
 4 files changed, 2 insertions(+), 125 deletions(-)

diff --git a/src/gsim/common/stack/__init__.py b/src/gsim/common/stack/__init__.py
index 3176215a..1b20a1a5 100644
--- a/src/gsim/common/stack/__init__.py
+++ b/src/gsim/common/stack/__init__.py
@@ -63,10 +63,6 @@ def get_stack(
               Palace RF sims typically override to 200+ for far-field radiation.
                         - air_below: Air box height below substrate/oxide in um
                             (default: 0)
-                        - top_layer: Optional stack layer name to cap dielectric/air
-                            construction at that layer's zmax.
-                        - max_z: Optional explicit z-cap (um) for dielectric/air
-                            construction.
             - include_substrate: Include lossy silicon substrate (default: False).
               When False, omits substrate for RF simulation.
 
diff --git a/src/gsim/common/stack/extractor.py b/src/gsim/common/stack/extractor.py
index 47a388da..ebe38a3d 100644
--- a/src/gsim/common/stack/extractor.py
+++ b/src/gsim/common/stack/extractor.py
@@ -331,8 +331,6 @@ def extract_layer_stack(
     air_below: float = 0.0,
     boundary_margin: float = 30.0,
     include_substrate: bool = False,
-    max_z: float | None = None,
-    top_layer: str | None = None,
 ) -> LayerStack:
     """Extract layer stack from a gdsfactory LayerStack.
 
@@ -345,11 +343,6 @@ def extract_layer_stack(
         air_below: Height of air box below substrate/oxide in um (default: 0)
         boundary_margin: Lateral margin from GDS bbox in um (default: 30)
         include_substrate: Whether to include lossy substrate (default: False)
-        max_z: Optional z-cap (um) for dielectric/air stack construction.
-            Useful to trim unused upper process levels.
-        top_layer: Optional layer name whose ``zmax`` is used as z-cap.
-            If both ``max_z`` and ``top_layer`` are provided, the smaller
-            of the two caps is applied.
 
     Returns:
         LayerStack object for Palace simulation
@@ -359,7 +352,6 @@ def extract_layer_stack(
     z_min_overall = float("inf")
     z_max_overall = float("-inf")
     z_max_conductor = float("-inf")
-    conductor_tops: list[float] = []
     passivation_ranges: list[tuple[float, float, str]] = []
     dielectric_layers: list[tuple[float, float, str, str]] = []
     materials_used: set[str] = set()
@@ -410,35 +402,13 @@ def _looks_like_passivation(layer_name: str, material_name: str) -> bool:
 
         if layer_type in {"conductor", "via"}:
             z_max_conductor = max(z_max_conductor, zmax)
-            conductor_tops.append(zmax)
         elif layer_type == "dielectric":
             dielectric_layers.append((zmin, zmax, material, layer_name))
             if _looks_like_passivation(layer_name, material):
                 passivation_ranges.append((zmin, zmax, material))
 
-    z_cap = max_z
-    if top_layer is not None:
-        top = stack.layers.get(top_layer)
-        if top is None:
-            raise ValueError(f"top_layer '{top_layer}' not found in layer stack")
-        z_cap = top.zmax if z_cap is None else min(z_cap, top.zmax)
-
-    if z_cap is not None:
-        if z_cap <= z_min_overall:
-            raise ValueError(
-                "z-cap "
-                f"({z_cap}) must be greater than the stack bottom "
-                f"({z_min_overall})"
-            )
-        z_max_active = min(z_max_overall, z_cap)
-        active_tops = [z for z in conductor_tops if z <= z_max_active + 1e-9]
-        if active_tops:
-            z_max_conductor_active = max(active_tops)
-        else:
-            z_max_conductor_active = min(z_max_conductor, z_max_active)
-    else:
-        z_max_active = z_max_overall
-        z_max_conductor_active = z_max_conductor
+    z_max_active = z_max_overall
+    z_max_conductor_active = z_max_conductor
 
     for material in materials_used:
         props = get_material_properties(material)
@@ -590,8 +560,6 @@ def _ensure_material_defined(material_name: str) -> None:
         "air_below": air_below,
         "substrate_thickness": substrate_thickness,
         "include_substrate": include_substrate,
-        "max_z": max_z,
-        "top_layer": top_layer,
     }
 
     return stack
diff --git a/src/gsim/palace/base.py b/src/gsim/palace/base.py
index fdbc5872..7984ac5b 100644
--- a/src/gsim/palace/base.py
+++ b/src/gsim/palace/base.py
@@ -153,9 +153,6 @@ def set_stack(
             substrate_thickness: Thickness below z=0 in um.
             include_substrate: Include lossy silicon substrate.
             **kwargs: Additional args passed to extract_layer_stack.
-                Common options include ``top_layer`` and ``max_z`` to cap
-                dielectric/air stack height for structures that use only a
-                subset of process levels.
 
         Example:
             >>> sim.set_stack(air_above=300.0, substrate_thickness=2.0)
diff --git a/tests/palace/test_sim_classes.py b/tests/palace/test_sim_classes.py
index eea84c46..63208bf2 100644
--- a/tests/palace/test_sim_classes.py
+++ b/tests/palace/test_sim_classes.py
@@ -6,8 +6,6 @@
 
 import pytest
 
-from gsim.common import Geometry
-from gsim.common.stack import Layer, LayerStack
 from gsim.palace import DrivenSim, EigenmodeSim, ElectrostaticSim
 from gsim.palace.models import MeshConfig
 
@@ -157,88 +155,6 @@ def test_set_stack_default_air_above_zero(self):
         sim.set_stack()
         assert sim._stack_kwargs["air_above"] == 0.0
 
-    def test_resolve_stack_does_not_auto_cap_from_geometry(self, monkeypatch):
-        """_resolve_stack should not infer an implicit top-layer cap from geometry."""
-        calls: list[dict] = []
-
-        def _mk_stack() -> LayerStack:
-            return LayerStack(
-                pdk_name="test",
-                layers={
-                    "metal1": Layer(
-                        name="metal1",
-                        gds_layer=(10, 0),
-                        zmin=0.0,
-                        zmax=1.0,
-                        thickness=1.0,
-                        material="aluminum",
-                        layer_type="conductor",
-                    ),
-                    "topmetal2": Layer(
-                        name="topmetal2",
-                        gds_layer=(20, 0),
-                        zmin=2.0,
-                        zmax=3.0,
-                        thickness=1.0,
-                        material="aluminum",
-                        layer_type="conductor",
-                    ),
-                },
-            )
-
-        def _fake_get_stack(*, yaml_path=None, **kwargs):
-            calls.append({"yaml_path": yaml_path, **kwargs})
-            return _mk_stack()
-
-        monkeypatch.setattr("gsim.common.stack.get_stack", _fake_get_stack)
-
-        class _Layout:
-            def layers(self):
-                return 2
-
-            def is_valid_layer(self, idx):
-                return idx in (0, 1)
-
-            def get_info(self, idx):
-                mapping = {
-                    0: SimpleNamespace(layer=10, datatype=0),
-                    1: SimpleNamespace(layer=99, datatype=0),
-                }
-                return mapping[idx]
-
-        class _Component:
-            def __init__(self):
-                self.kcl = SimpleNamespace(layout=_Layout())
-
-            def get_polygons(self):
-                return {0: [object()], 1: [object()]}
-
-        sim = DrivenSim()
-        sim.set_stack(substrate_thickness=2.0)
-        sim.geometry = Geometry(component=_Component())
-
-        sim._resolve_stack()
-
-        assert len(calls) == 1
-        assert calls[0].get("top_layer") is None
-
-    def test_resolve_stack_keeps_explicit_cap(self, monkeypatch):
-        """Explicit top_layer/max_z should bypass auto top-layer inference."""
-        calls: list[dict] = []
-
-        def _fake_get_stack(*, yaml_path=None, **kwargs):
-            calls.append({"yaml_path": yaml_path, **kwargs})
-            return LayerStack()
-
-        monkeypatch.setattr("gsim.common.stack.get_stack", _fake_get_stack)
-
-        sim = DrivenSim()
-        sim.set_stack(top_layer="topmetal1")
-        sim._resolve_stack()
-
-        assert len(calls) == 1
-        assert calls[0].get("top_layer") == "topmetal1"
-
     def test_set_airbox(self):
         """Test set_airbox stores explicit airbox margins and z extents."""
         for cls in [DrivenSim, EigenmodeSim, ElectrostaticSim]:

From c849ba833f69faa92677ed8cb800c8efd1870814 Mon Sep 17 00:00:00 2001
From: mdmaas <martin.d.maas@gmail.com>
Date: Fri, 29 May 2026 17:45:13 -0300
Subject: [PATCH 3/4] chore(stack): clean get_stack docstring formatting

---
 src/gsim/common/stack/__init__.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/gsim/common/stack/__init__.py b/src/gsim/common/stack/__init__.py
index 1b20a1a5..62b65171 100644
--- a/src/gsim/common/stack/__init__.py
+++ b/src/gsim/common/stack/__init__.py
@@ -61,8 +61,7 @@ def get_stack(
             - substrate_thickness: Thickness below z=0 in um (default: 2.0)
             - air_above: Air box height above top metal in um (default: 5).
               Palace RF sims typically override to 200+ for far-field radiation.
-                        - air_below: Air box height below substrate/oxide in um
-                            (default: 0)
+            - air_below: Air box height below substrate/oxide in um (default: 0)
             - include_substrate: Include lossy silicon substrate (default: False).
               When False, omits substrate for RF simulation.
 

From 6e30bc604f09c1a92c99bb0fb5221e4ab8d8d3a8 Mon Sep 17 00:00:00 2001
From: mdmaas <martin.d.maas@gmail.com>
Date: Tue, 2 Jun 2026 05:47:49 -0300
Subject: [PATCH 4/4] fixed pre-commit and tests

---
 src/gsim/common/stack/extractor.py            | 120 ++++++++++++------
 src/gsim/palace/base.py                       |  10 ++
 .../test_branch_line_coupler_mesh.yml         |  10 +-
 .../test_cpw_via_transition_mesh.yml          |  10 +-
 .../test_cpw_waveport_mesh.yml                |  10 +-
 .../test_microstrip_mesh.yml                  |  10 +-
 6 files changed, 109 insertions(+), 61 deletions(-)

diff --git a/src/gsim/common/stack/extractor.py b/src/gsim/common/stack/extractor.py
index 07e9a923..c1934fca 100644
--- a/src/gsim/common/stack/extractor.py
+++ b/src/gsim/common/stack/extractor.py
@@ -325,6 +325,8 @@ def extract_layer_stack(
     gf_layer_stack: GfLayerStack,
     pdk_name: str = "unknown",
     substrate_thickness: float = 2.0,
+    air_above: float = 0.0,
+    air_below: float = 0.0,
     boundary_margin: float = 30.0,
     include_substrate: bool = False,
     add_oxide_dielectric: bool = True,
@@ -336,6 +338,8 @@ def extract_layer_stack(
         gf_layer_stack: gdsfactory LayerStack object
         pdk_name: Name of the PDK (for documentation)
         substrate_thickness: Thickness of substrate in um (default: 2.0)
+        air_above: Height of air box above top dielectric in um (default: 0.0)
+        air_below: Height of air box below substrate/oxide in um (default: 0.0)
         boundary_margin: Lateral margin from GDS bbox in um (default: 30)
         include_substrate: Whether to include lossy substrate (default: False)
         add_oxide_dielectric: Add synthetic oxide background dielectric region.
@@ -375,6 +379,15 @@ def _looks_like_passivation(layer_name: str, material_name: str) -> bool:
 
         return "nitride" in mname
 
+    # First pass: discover substrate GDS layers independent of ordering.
+    for layer_name, layer_level in gf_layer_stack.layers.items():
+        material = layer_level.material or "unknown"
+        gds_layer = get_gds_layer_tuple(layer_level) or (0, 0)
+        info = getattr(layer_level, "info", None) or {}
+        layer_type = info.get("layer_type") or classify_layer_type(layer_name, material)
+        if layer_type == "substrate":
+            _substrate_gds_layers.add(gds_layer[0])
+
     for layer_name, layer_level in gf_layer_stack.layers.items():
         zmin = layer_level.zmin if layer_level.zmin is not None else 0.0
         thickness = layer_level.thickness if layer_level.thickness is not None else 0.0
@@ -387,7 +400,6 @@ def _looks_like_passivation(layer_name: str, material_name: str) -> bool:
         sidewall_angle = getattr(layer_level, "sidewall_angle", 0.0) or 0.0
 
         if layer_type == "substrate" and not include_substrate:
-            _substrate_gds_layers.add(gds_layer[0])
             continue
 
         layer = Layer(
@@ -415,13 +427,20 @@ def _looks_like_passivation(layer_name: str, material_name: str) -> bool:
             if _looks_like_passivation(layer_name, material):
                 passivation_ranges.append((zmin, zmax, material))
 
+            # Detect patterned dielectric layers: dielectric layers whose GDS
+            # layer is NOT a substrate placeholder (e.g., 999).
+            if gds_layer[0] not in _substrate_gds_layers:
+                has_patterned_dielectrics = True
+
+    if z_min_overall == float("inf"):
+        z_min_overall = 0.0
+    if z_max_overall == float("-inf"):
+        z_max_overall = 0.0
+    if z_max_conductor == float("-inf"):
+        z_max_conductor = z_max_overall
+
     z_max_active = z_max_overall
     z_max_conductor_active = z_max_conductor
-        # Detect patterned dielectric layers: dielectric layers whose GDS
-        # layer is NOT a substrate placeholder (999) — these carry actual
-        # polygon geometry (e.g. waveguide cores).
-        if layer_type == "dielectric" and gds_layer[0] not in _substrate_gds_layers:
-            has_patterned_dielectrics = True
 
     for material in materials_used:
         props = get_material_properties(material)
@@ -442,7 +461,17 @@ def _is_dielectric_material(material_name: str) -> bool:
         props = get_material_properties(material_name)
         if props is None:
             return True
-        return props.type == "dielectric"
+        conductivity = props.conductivity
+        if isinstance(conductivity, list):
+            conductivity = conductivity[0] if conductivity else None
+
+        if conductivity is not None and conductivity >= 1e4:
+            return False
+
+        if props.permittivity is not None:
+            return True
+
+        return bool(props.dispersion_models)
 
     # Choose a PDK-defined dielectric material for the bulk background slab.
     # Prefer non-passivation layers (e.g. oxide / IMD materials).
@@ -506,6 +535,7 @@ def _is_dielectric_material(material_name: str) -> bool:
 
             # Substrate silicon: from extended bottom to BOX bottom (or 0)
             if box_zmin is not None:
+                box_zmax_value = box_zmax if box_zmax is not None else 0.0
                 stack.dielectrics.append(
                     {
                         "name": "substrate",
@@ -519,11 +549,11 @@ def _is_dielectric_material(material_name: str) -> bool:
                     {
                         "name": "box",
                         "zmin": box_zmin,
-                        "zmax": box_zmax,
+                        "zmax": box_zmax_value,
                         "material": "SiO2",
                     }
                 )
-                oxide_zmin = box_zmax
+                oxide_zmin = box_zmax_value
             else:
                 # No BOX layer detected: use legacy behavior
                 stack.dielectrics.append(
@@ -548,21 +578,6 @@ def _is_dielectric_material(material_name: str) -> bool:
             oxide_zmin = 0.0
         if "silicon" not in stack.materials:
             stack.materials["silicon"] = MATERIALS_DB["silicon"].to_dict()
-    else:
-        oxide_zmin = 0.0
-
-    if add_oxide_dielectric:
-        stack.dielectrics.append(
-            {
-                "name": "oxide",
-                "zmin": oxide_zmin,
-                "zmax": z_max_overall,
-                "material": "SiO2",
-            }
-        )
-        if "silicon" not in stack.materials:
-            stack.materials["silicon"] = MATERIALS_DB["silicon"].to_dict()
-        oxide_zmin = 0.0
     else:
         oxide_zmin = -substrate_thickness
 
@@ -608,21 +623,25 @@ def _ensure_material_defined(material_name: str) -> None:
         z_max_active if passive_zmin is None else min(passive_zmin, z_max_active)
     )
 
-    stack.dielectrics.append(
-        {
-            "name": "oxide",
-            "zmin": oxide_zmin,
-            "zmax": oxide_zmax,
-            "material": bulk_material,
-        }
-    )
+    top_of_stack = z_max_active
 
-    top_of_stack = oxide_zmax
+    if add_oxide_dielectric and oxide_zmax > oxide_zmin + 1e-9:
+        stack.dielectrics.append(
+            {
+                "name": "oxide",
+                "zmin": oxide_zmin,
+                "zmax": oxide_zmax,
+                "material": bulk_material,
+            }
+        )
+        top_of_stack = oxide_zmax
 
+    added_passivation = False
     if (
         passivation_material is not None
         and passive_zmin is not None
         and passive_zmax is not None
+        and passive_zmax > passive_zmin + 1e-9
     ):
         stack.dielectrics.append(
             {
@@ -633,6 +652,26 @@ def _ensure_material_defined(material_name: str) -> None:
             }
         )
         top_of_stack = passive_zmax
+        added_passivation = True
+
+    # Preserve default synthetic cap for patterned dielectric stacks only.
+    if (
+        not added_passivation
+        and add_passivation_dielectric
+        and has_patterned_dielectrics
+    ):
+        passive_thickness = 0.4
+        stack.dielectrics.append(
+            {
+                "name": "passive",
+                "zmin": z_max_active,
+                "zmax": z_max_active + passive_thickness,
+                "material": "passive",
+            }
+        )
+        if "passive" not in stack.materials:
+            stack.materials["passive"] = MATERIALS_DB["passive"].to_dict()
+        top_of_stack = z_max_active + passive_thickness
 
     if air_above > 0:
         stack.dielectrics.append(
@@ -644,21 +683,20 @@ def _ensure_material_defined(material_name: str) -> None:
             }
         )
 
-    if add_passivation_dielectric:
-        passive_thickness = 0.4
+    if air_below > 0:
         stack.dielectrics.append(
             {
-                "name": "passive",
-                "zmin": z_max_overall,
-                "zmax": z_max_overall + passive_thickness,
-                "material": "passive",
+                "name": "air_box_bottom",
+                "zmin": -substrate_thickness - air_below,
+                "zmax": -substrate_thickness,
+                "material": "air",
             }
         )
-        if "passive" not in stack.materials:
-            stack.materials["passive"] = MATERIALS_DB["passive"].to_dict()
 
     stack.simulation = {
         "boundary_margin": boundary_margin,
+        "air_above": air_above,
+        "air_below": air_below,
         "substrate_thickness": substrate_thickness,
         "include_substrate": include_substrate,
         "add_oxide_dielectric": add_oxide_dielectric,
diff --git a/src/gsim/palace/base.py b/src/gsim/palace/base.py
index d62401fb..d229d352 100644
--- a/src/gsim/palace/base.py
+++ b/src/gsim/palace/base.py
@@ -174,12 +174,22 @@ def set_stack(
                 "margin_above=..., margin_below=...)."
             )
 
+        legacy_air_kwargs: dict[str, float] = {}
+        if air_above is None and air_below is None:
+            # Keep backwards-compatible defaults for direct stack extraction,
+            # while still treating explicit air_* inputs as deprecated.
+            legacy_air_kwargs = {
+                "air_above": 0.0,
+                "air_below": 0.0,
+            }
+
         self._stack_kwargs = {
             "yaml_path": yaml_path,
             "substrate_thickness": substrate_thickness,
             "include_substrate": include_substrate,
             "add_oxide_dielectric": add_oxide_dielectric,
             "add_passivation_dielectric": add_passivation_dielectric,
+            **legacy_air_kwargs,
             **kwargs,
         }
         # Stack will be resolved lazily during mesh() or simulate()
diff --git a/tests/palace/test_mesh_regression/test_branch_line_coupler_mesh.yml b/tests/palace/test_mesh_regression/test_branch_line_coupler_mesh.yml
index 93cd400e..1579b064 100644
--- a/tests/palace/test_mesh_regression/test_branch_line_coupler_mesh.yml
+++ b/tests/palace/test_mesh_regression/test_branch_line_coupler_mesh.yml
@@ -13,11 +13,11 @@ groups:
   - P3
   - P4
   volumes:
-  - SiO2
   - air
-  - passive
+  - si
+  - sin
 mesh:
-  elements: 29432
+  elements: 29451
   invalid_elements: 0
-  nodes: 4034
-  tetrahedra: 19752
+  nodes: 3997
+  tetrahedra: 19603
diff --git a/tests/palace/test_mesh_regression/test_cpw_via_transition_mesh.yml b/tests/palace/test_mesh_regression/test_cpw_via_transition_mesh.yml
index 7f1b1c71..cef13217 100644
--- a/tests/palace/test_mesh_regression/test_cpw_via_transition_mesh.yml
+++ b/tests/palace/test_mesh_regression/test_cpw_via_transition_mesh.yml
@@ -13,13 +13,13 @@ groups:
   - P1
   - P2
   volumes:
-  - SiO2
   - air
-  - passive
+  - si
+  - sin
   - topvia1
   - topvia2
 mesh:
-  elements: 17003
+  elements: 16363
   invalid_elements: 0
-  nodes: 2258
-  tetrahedra: 10785
+  nodes: 2162
+  tetrahedra: 10351
diff --git a/tests/palace/test_mesh_regression/test_cpw_waveport_mesh.yml b/tests/palace/test_mesh_regression/test_cpw_waveport_mesh.yml
index 29662553..75d21a43 100644
--- a/tests/palace/test_mesh_regression/test_cpw_waveport_mesh.yml
+++ b/tests/palace/test_mesh_regression/test_cpw_waveport_mesh.yml
@@ -9,11 +9,11 @@ groups:
   - P1
   - P2
   volumes:
-  - SiO2
   - air
-  - passive
+  - si
+  - sin
 mesh:
-  elements: 10690
+  elements: 9282
   invalid_elements: 0
-  nodes: 1502
-  tetrahedra: 6844
+  nodes: 1307
+  tetrahedra: 5902
diff --git a/tests/palace/test_mesh_regression/test_microstrip_mesh.yml b/tests/palace/test_mesh_regression/test_microstrip_mesh.yml
index ab54331a..b9afce86 100644
--- a/tests/palace/test_mesh_regression/test_microstrip_mesh.yml
+++ b/tests/palace/test_mesh_regression/test_microstrip_mesh.yml
@@ -11,11 +11,11 @@ groups:
   - P1
   - P2
   volumes:
-  - SiO2
   - air
-  - passive
+  - si
+  - sin
 mesh:
-  elements: 7742
+  elements: 7606
   invalid_elements: 0
-  nodes: 1139
-  tetrahedra: 4688
+  nodes: 1109
+  tetrahedra: 4560