diff --git a/PhotoThermalWaveguides.ipynb b/PhotoThermalWaveguides.ipynb new file mode 100644 index 00000000..fd102248 --- /dev/null +++ b/PhotoThermalWaveguides.ipynb @@ -0,0 +1,2196 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Photo-thermal optical control in silicon waveguides\n", + "\n", + "This example demonstrates the usage of the Tidy3D to calculate absorbed optical power, its resultant heating, and the corresponding optical heat perturbation on the silicon-on-insulator (SOI) platform.\n", + "

\n", + "Our setup is based on [All-optic control using a photo-thermal heater in Si photonics](https://opg.optica.org/oe/fulltext.cfm?uri=oe-30-23-41874) by L. Li, T. Tamanuki, and T. Baba. We build two parallel silicon rib waveguides separated by a gap of varying length. One waveguide (the \"control\" waveguide) has a triangular-shaped heavily doped region. This waveguide then absorbs an optical \"control\" signal, and the absorbed optical power heats both waveguides. The second waveguide (the \"signal\" waveguide) then has its refractive index perturbed by the change in temperature, and we analyze the resulting phase change of a signal sent through this waveguide.\n", + "

\n", + "This analysis is done for three gap lengths: 0.5 μm, 1.0 μm, and 1.5 μm. We first create the perturbation mediums with Tidy3D heat and charge perturbations in order to capture the properties of doped silicon. We then record the absorbed power and permittivity of the control waveguide, plot the resulting temperature change along the propagation (z) direction and along the y direction, and finally plot the heat-perturbed refractive index along z as well as the siginal waveguide's phase change.\n", + "

\n", + "Thus our workflow will be: Control optical simulation $\\rightarrow$ Heat simulation $\\rightarrow$ Signal optical simulation\n", + "\n", + "\"Illustration" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tidy3d as td\n", + "import tidy3d.web as web" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we describe some parameters that will remain constant throughout our simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "wvl0 = 1.55\n", + "freq0 = td.C_0 / wvl0\n", + "\n", + "length = 30\n", + "wg_width = 0.45\n", + "wg_height = 0.11\n", + "slab_height = 0.11" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perturbation Medium Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because perturbing loss can result in numerical instabilities in our simulations, Tidy3D adds a warning about this when we define perturbation mediums. Since our perturbations will be small enough for stability, we will turn off Tidy3D warnings with the following command." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "td.config.logging_level = \"ERROR\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we define the [`MultiPhysicsMediums`](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.0/api/_autosummary/tidy3d.components.material.multi_physics.MultiPhysicsMedium.html#tidy3d.components.material.multi_physics.MultiPhysicsMedium) of our simulation. To capture the absorption of doped silicon, we will perturb our silicon according to Tidy3D's charge perturbation. Here we will use the [`Nedeljkovic-Soref-Mashanovich`](https://docs.flexcompute.com/projects/tidy3d/en/v2.9.0/api/_autosummary/tidy3d.NedeljkovicSorefMashanovich.html) model with Li et. al.'s doping concentration." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "ref_temp = 300\n", + "doping_concentration = 2.273e20\n", + "Si_heat_coeff = 0.00018\n", + "\n", + "NSM = td.NedeljkovicSorefMashanovich(ref_freq=freq0)\n", + "\n", + "# for maximum generality, we include charge in our index perturbations\n", + "Si = td.MultiPhysicsMedium(\n", + " name=\"Si\",\n", + " optical=td.PerturbationMedium.from_nk(\n", + " n=3.5,\n", + " k=0,\n", + " freq=freq0,\n", + " perturbation_spec=td.IndexPerturbation(\n", + " delta_n=td.ParameterPerturbation(\n", + " heat=td.LinearHeatPerturbation(temperature_ref=ref_temp, coeff=Si_heat_coeff),\n", + " charge=NSM.delta_n().charge,\n", + " ),\n", + " delta_k=td.ParameterPerturbation(charge=NSM.delta_k().charge),\n", + " freq=freq0,\n", + " ),\n", + " ),\n", + " heat=td.SolidSpec(\n", + " conductivity=148e-6, # W / (um * K)\n", + " capacity=0.711, # J / (kg * K)\n", + " ),\n", + ")\n", + "\n", + "Si_doped = td.MultiPhysicsMedium(\n", + " name=\"Si doped\",\n", + " optical=td.PerturbationMedium.from_nk(\n", + " n=3.5 + NSM.delta_n().charge.sample(doping_concentration, 0),\n", + " k=0 + NSM.delta_k().charge.sample(doping_concentration, 0),\n", + " freq=freq0,\n", + " perturbation_spec=Si.optical.perturbation_spec,\n", + " ),\n", + " heat=Si.heat,\n", + ")\n", + "\n", + "SiO2 = td.MultiPhysicsMedium(\n", + " name=\"SiO2\",\n", + " optical=td.PerturbationMedium(\n", + " permittivity=1.44**2,\n", + " perturbation_spec=td.IndexPerturbation(\n", + " delta_n=td.ParameterPerturbation(\n", + " heat=td.LinearHeatPerturbation(temperature_ref=ref_temp, coeff=0.000012)\n", + " ),\n", + " freq=freq0,\n", + " ),\n", + " ),\n", + " heat=td.SolidSpec(\n", + " conductivity=1.38e-6, # W / (um * K)\n", + " capacity=709, # J / (kg * K)\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## First Optical Simulation\n", + "\n", + "Here we create a function that takes in a gap length and returns a Tidy3D simulation with the corresponding two-waveguide structure. The simulations returned will be our first optical simulations - that is, a simulation where we launch a TE mode and record the field and permittivity along the control waveguide, as this will be used to compute the absorbed power. We also add a field monitor along the propagation direction to visualize the optical power being absorbed along the waveguide." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def make_sim(gap):\n", + " z_min, z_max = -length / 2.0, length / 2.0\n", + " sim_buffer = 4\n", + "\n", + " # Waveguides (above slab) in y; separated in x\n", + " wg_signal_x_min, wg_signal_x_max = gap / 2.0, gap / 2.0 + wg_width\n", + " wg_control_x_min, wg_control_x_max = -gap / 2.0 - wg_width, -gap / 2.0\n", + "\n", + " wg_signal_box = td.Box.from_bounds(\n", + " rmin=(wg_signal_x_min, 0.0, -td.inf), rmax=(wg_signal_x_max, wg_height, td.inf)\n", + " )\n", + " wg_control_box = td.Box.from_bounds(\n", + " rmin=(wg_control_x_min, 0.0, -td.inf), rmax=(wg_control_x_max, wg_height, td.inf)\n", + " )\n", + "\n", + " # Slab (below guides)\n", + " slab_y_min, slab_y_max = -slab_height, 0.0\n", + " slab_x_min = wg_control_x_min\n", + " slab_x_max = wg_signal_x_max + 0.5\n", + " slab_box = td.Box.from_bounds(\n", + " rmin=(slab_x_min, slab_y_min, -td.inf), rmax=(slab_x_max, slab_y_max, td.inf)\n", + " )\n", + "\n", + " # Triangular prism: hypotenuse aligned with z (length) direction\n", + " tri_vertices = [(wg_control_x_min, z_max), (wg_control_x_min, z_min), (wg_control_x_max, z_min)]\n", + " tri_geom = td.PolySlab(vertices=tri_vertices, axis=1, slab_bounds=(0.0, wg_height))\n", + "\n", + " slab = td.Structure(geometry=slab_box, medium=Si, name=\"slab\")\n", + " wg_signal = td.Structure(geometry=wg_signal_box, medium=Si, name=\"wg_signal\")\n", + " wg_control = td.Structure(geometry=wg_control_box, medium=Si, name=\"wg_control\")\n", + " tri = td.Structure(geometry=tri_geom, medium=Si_doped, name=\"doped triangle\")\n", + "\n", + " slab_width = slab_x_max - slab_x_min\n", + " margin_x = max(0.5 * wvl0 + 0.1, 0.5)\n", + "\n", + " sim_center = (0.5 * (slab_x_max + slab_x_min), 0.5 * (wg_height + slab_y_min), 0.0)\n", + " sim_size = (slab_width + 2 * margin_x, wvl0 + (wg_height - slab_y_min), length + sim_buffer)\n", + "\n", + " tri_min, tri_max = tri.geometry.bounds\n", + " tri_center = tuple(0.5 * (a + b) for a, b in zip(tri_min, tri_max))\n", + " tri_size = tuple(a - b for a, b in zip(tri_max, tri_min))\n", + "\n", + " tri_monitor = td.FieldMonitor(\n", + " center=tri_center,\n", + " size=tri_size,\n", + " fields=[\"Ex\", \"Ey\", \"Ez\", \"Hx\", \"Hy\", \"Hz\"],\n", + " freqs=[freq0],\n", + " name=\"field\",\n", + " )\n", + "\n", + " perm_monitor = td.PermittivityMonitor(\n", + " center=tri_center, size=tri_size, freqs=[freq0], name=\"tri_permittivity\"\n", + " )\n", + "\n", + " plane_monitor = td.FieldMonitor(\n", + " center=(sim_center[0], wg_height / 2, sim_center[2]),\n", + " size=(sim_size[0], 0, sim_size[2]),\n", + " fields=[\"Ex\", \"Ey\", \"Ez\", \"Hx\", \"Hy\", \"Hz\"],\n", + " freqs=[freq0],\n", + " name=\"midplane_field\",\n", + " )\n", + "\n", + " clearance = min(0.05, 0.25 * gap) if gap > 0 else 0.01\n", + " clearance = max(clearance, 1e-3)\n", + " buffer_x = max(0.2, 0.5 * wg_width)\n", + " buffer_y = max(0.1, 0.2 * wg_height)\n", + " extra_x = max(0.3, 0.5 * wg_width)\n", + " extra_y = max(0.5, 1.5 * wg_height)\n", + "\n", + " source_x_min = wg_control_x_min - (buffer_x + extra_x)\n", + " source_x_max = wg_signal_x_min - clearance\n", + " if source_x_max <= source_x_min:\n", + " mid = 0.5 * (wg_control_x_max + wg_signal_x_min)\n", + " source_x_min = min(wg_control_x_min - (0.1 * wg_width + extra_x), mid - 0.05)\n", + " source_x_max = max(mid, wg_signal_x_min - 1e-3)\n", + "\n", + " source_y_min = -buffer_y - extra_y\n", + " source_y_max = wg_height + buffer_y + extra_y\n", + "\n", + " mode_center = (\n", + " 0.5 * (source_x_min + source_x_max),\n", + " 0.5 * (source_y_min + source_y_max),\n", + " z_max + 0.1,\n", + " )\n", + " mode_size = (source_x_max - source_x_min, source_y_max - source_y_min, 0.0)\n", + "\n", + " mode_source = td.ModeSource(\n", + " center=mode_center,\n", + " size=mode_size,\n", + " direction=\"-\",\n", + " source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n", + " mode_spec=td.ModeSpec(num_modes=1),\n", + " )\n", + "\n", + " simulation = td.Simulation(\n", + " center=sim_center,\n", + " size=sim_size,\n", + " structures=[slab, wg_signal, wg_control, tri],\n", + " sources=[mode_source],\n", + " monitors=[tri_monitor, perm_monitor, plane_monitor],\n", + " grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=wvl0),\n", + " medium=SiO2.optical,\n", + " run_time=5e-13,\n", + " )\n", + "\n", + " return simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "sim_0p5 = make_sim(0.5)\n", + "sim_1p0 = make_sim(1.0)\n", + "sim_1p5 = make_sim(1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize a constructed simulation to ensure the setup is correct." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "sim_0p5.plot(y=wg_height / 2, ax=ax[0], monitor_alpha=0.1)\n", + "sim_0p5.plot(z=sim_0p5.center[0], ax=ax[1], monitor_alpha=0.1)\n", + "ax[0].set_aspect(0.1)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We run the three constructed simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
19:14:54 EDT Created task 'sim_gap_0.5' with resource_id                        \n",
+       "             'fdve-d71b8ec3-9673-44cc-bc85-c32915b0a103' and task_type 'FDTD'.  \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m19:14:54 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_gap_0.5'\u001b[0m with resource_id \n", + "\u001b[2;36m \u001b[0m\u001b[32m'fdve-d71b8ec3-9673-44cc-bc85-c32915b0a103'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
             View task using web UI at                                          \n",
+       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-d71b8ec3-967\n",
+       "             3-44cc-bc85-c32915b0a103'.                                         \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \n", + "\u001b[2;36m \u001b[0m\u001b]8;id=782284;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d71b8ec3-9673-44cc-bc85-c32915b0a103\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=730075;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d71b8ec3-9673-44cc-bc85-c32915b0a103\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=782284;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d71b8ec3-9673-44cc-bc85-c32915b0a103\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=246125;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d71b8ec3-9673-44cc-bc85-c32915b0a103\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=782284;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d71b8ec3-9673-44cc-bc85-c32915b0a103\u001b\\\u001b[32m-d71b8ec3-967\u001b[0m\u001b]8;;\u001b\\\n", + "\u001b[2;36m \u001b[0m\u001b]8;id=782284;https://tidy3d.simulation.cloud/workbench?taskId=fdve-d71b8ec3-9673-44cc-bc85-c32915b0a103\u001b\\\u001b[32m3-44cc-bc85-c32915b0a103'\u001b[0m\u001b]8;;\u001b\\. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
             Task folder: 'default'.                                            \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=429157;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "072e6d19118740829b6ad48ce507bf44", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:14:56 EDT Estimated FlexCredit cost: 0.061. Minimum cost depends on task     \n",
+       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
+       "             FlexCredit cost after a simulation run.                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:14:56 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.061\u001b[0m. Minimum cost depends on task     \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             status = success                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3c19a8af17b4473c8eb3198ca56f5183",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:14:59 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:14:59 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Created task 'sim_gap_1.0' with resource_id                        \n",
+       "             'fdve-48052617-df61-4991-b8c6-5dbd3d2caafe' and task_type 'FDTD'.  \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_gap_1.0'\u001b[0m with resource_id                        \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-48052617-df61-4991-b8c6-5dbd3d2caafe'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             View task using web UI at                                          \n",
+       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-48052617-df6\n",
+       "             1-4991-b8c6-5dbd3d2caafe'.                                         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=590265;https://tidy3d.simulation.cloud/workbench?taskId=fdve-48052617-df61-4991-b8c6-5dbd3d2caafe\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=844092;https://tidy3d.simulation.cloud/workbench?taskId=fdve-48052617-df61-4991-b8c6-5dbd3d2caafe\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=590265;https://tidy3d.simulation.cloud/workbench?taskId=fdve-48052617-df61-4991-b8c6-5dbd3d2caafe\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=448536;https://tidy3d.simulation.cloud/workbench?taskId=fdve-48052617-df61-4991-b8c6-5dbd3d2caafe\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=590265;https://tidy3d.simulation.cloud/workbench?taskId=fdve-48052617-df61-4991-b8c6-5dbd3d2caafe\u001b\\\u001b[32m-48052617-df6\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=590265;https://tidy3d.simulation.cloud/workbench?taskId=fdve-48052617-df61-4991-b8c6-5dbd3d2caafe\u001b\\\u001b[32m1-4991-b8c6-5dbd3d2caafe'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Task folder: 'default'.                                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=325104;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f4012e6b375943cc90f5cec7c1685e8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:00 EDT Estimated FlexCredit cost: 0.070. Minimum cost depends on task     \n",
+       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
+       "             FlexCredit cost after a simulation run.                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:00 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.070\u001b[0m. Minimum cost depends on task     \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:01 EDT status = success                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:01 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6aad0d1bff524adf9fe2360769aa6b1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:02 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:02 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:03 EDT Created task 'sim_gap_1.5' with resource_id                        \n",
+       "             'fdve-3946d53e-f0a4-4478-8d79-b6154b53ac0b' and task_type 'FDTD'.  \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:03 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'sim_gap_1.5'\u001b[0m with resource_id                        \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-3946d53e-f0a4-4478-8d79-b6154b53ac0b'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             View task using web UI at                                          \n",
+       "             'https://tidy3d.simulation.cloud/workbench?taskId=fdve-3946d53e-f0a\n",
+       "             4-4478-8d79-b6154b53ac0b'.                                         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=272812;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3946d53e-f0a4-4478-8d79-b6154b53ac0b\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=475661;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3946d53e-f0a4-4478-8d79-b6154b53ac0b\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=272812;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3946d53e-f0a4-4478-8d79-b6154b53ac0b\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=378243;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3946d53e-f0a4-4478-8d79-b6154b53ac0b\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=272812;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3946d53e-f0a4-4478-8d79-b6154b53ac0b\u001b\\\u001b[32m-3946d53e-f0a\u001b[0m\u001b]8;;\u001b\\\n",
+       "\u001b[2;36m             \u001b[0m\u001b]8;id=272812;https://tidy3d.simulation.cloud/workbench?taskId=fdve-3946d53e-f0a4-4478-8d79-b6154b53ac0b\u001b\\\u001b[32m4-4478-8d79-b6154b53ac0b'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Task folder: 'default'.                                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=154096;https://tidy3d.simulation.cloud/folders/folder-86acd7be-dbf5-477e-9c86-3e20787acc03\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10ac98ef9ab448869bad0734380302ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:04 EDT Estimated FlexCredit cost: 0.079. Minimum cost depends on task     \n",
+       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
+       "             FlexCredit cost after a simulation run.                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:04 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.079\u001b[0m. Minimum cost depends on task     \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:05 EDT status = success                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:05 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1da044e8480545688660a9195cf863f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:06 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:06 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sims = {\"0.5\": sim_0p5, \"1.0\": sim_1p0, \"1.5\": sim_1p5}\n",
+    "run_results = {}\n",
+    "for label, sim in sims.items():\n",
+    "    result = web.run(sim, task_name=f\"sim_gap_{label}\")\n",
+    "    run_results[label] = result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will visualize the mode getting absorbed as it propagates through the control waveguide for each simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(15, 10))\n",
+    "\n",
+    "for ax, label in zip(axes, [\"0.5\", \"1.0\", \"1.5\"]):\n",
+    "    run_results[label].plot_field(\"midplane_field\", field_name=\"S\", val=\"abs\", f=freq0, ax=ax)\n",
+    "    ax.set_title(f\"Midplane |S| (gap {label})\")\n",
+    "    ax.set_aspect(0.4)\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Heat Simulation\n",
+    "\n",
+    "We will now use the recorded field and permittivity along the control waveguide to compute the absorbed power. The absorbed power is given by the formula\n",
+    "
$P = \\frac{1}{2}\\omega|E|^2$ $im(\\epsilon)$

\n",
+    "\n",
+    "We construct a function that creates a heat source as a [`SpatialDataArray`](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.SpatialDataArray.html), using the coordinates of the field monitor and interpolating the permittivity monitor data to these points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def absorbed_power(field_data, permittivity_data, freq):\n",
+    "    \"\"\"Return 0.5 * 2π * freq * |E|^2 * imag(permittivity) on the field grid.\"\"\"\n",
+    "    if not isinstance(field_data, td.FieldData):\n",
+    "        raise TypeError(\"field_data must be a tidy3d.FieldData.\")\n",
+    "    if not isinstance(permittivity_data, td.PermittivityData):\n",
+    "        raise TypeError(\"permittivity_data must be a tidy3d.PermittivityData.\")\n",
+    "    missing = [comp for comp in (\"Ex\", \"Ey\", \"Ez\") if comp not in dir(field_data)]\n",
+    "    if missing:\n",
+    "        raise KeyError(f\"Missing field components in field_data: {missing}\")\n",
+    "    if (\n",
+    "        permittivity_data.eps_xx != permittivity_data.eps_yy\n",
+    "        or permittivity_data.eps_yy != permittivity_data.eps_zz\n",
+    "    ):\n",
+    "        raise ValueError(\"Permittivity_data must be isotropic.\")\n",
+    "\n",
+    "    coords = field_data.Ex.coords\n",
+    "    coords_dict = {dim: coords.get(dim) for dim in \"xyz\"}\n",
+    "\n",
+    "    # perm_interp = permittivity_data.eps_xx.interp(field_data.Ex.coords, method=\"nearest\")\n",
+    "    perm_interp = permittivity_data.eps_xx.interp(**coords_dict)\n",
+    "\n",
+    "    E = np.abs(field_data.Ex) ** 2 + np.abs(field_data.Ey) ** 2 + np.abs(field_data.Ez) ** 2\n",
+    "    perm_imag = np.imag(perm_interp).sel(f=freq, method=\"nearest\") * td.EPSILON_0\n",
+    "    absorption = 0.5 * 2 * np.pi * freq * E.sel(f=freq, method=\"nearest\") * perm_imag\n",
+    "\n",
+    "    return td.SpatialDataArray(absorption, coords=coords_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heat_rate_0p5 = absorbed_power(\n",
+    "    run_results[\"0.5\"][\"field\"], run_results[\"0.5\"][\"tri_permittivity\"], freq0\n",
+    ")\n",
+    "heat_rate_1p0 = absorbed_power(\n",
+    "    run_results[\"1.0\"][\"field\"], run_results[\"1.0\"][\"tri_permittivity\"], freq0\n",
+    ")\n",
+    "heat_rate_1p5 = absorbed_power(\n",
+    "    run_results[\"1.5\"][\"field\"], run_results[\"1.5\"][\"tri_permittivity\"], freq0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the absorbed power data we calculated, we create the heat source by assigning it to the optical waveguide structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heat_source_0p5 = td.HeatSource(structures=[\"wg_control\"], rate=heat_rate_0p5)\n",
+    "heat_source_1p0 = td.HeatSource(structures=[\"wg_control\"], rate=heat_rate_1p0)\n",
+    "heat_source_1p5 = td.HeatSource(structures=[\"wg_control\"], rate=heat_rate_1p5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we will construct a temperature monitor around the signal waveguide so we can heat-perturb the signal structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_temp_monitor(sim):\n",
+    "    rmin, rmax = sim.structures[-1].geometry.bounds\n",
+    "    size_z = rmax[2] - rmin[2]\n",
+    "    temp_monitor = td.TemperatureMonitor(\n",
+    "        center=sim.structures[1].geometry.center,\n",
+    "        size=(sim.sources[0].size[0], sim.sources[0].size[1], size_z),\n",
+    "        name=\"temp\",\n",
+    "        unstructured=True,\n",
+    "        conformal=True,\n",
+    "    )\n",
+    "    return temp_monitor\n",
+    "\n",
+    "\n",
+    "temp_monitor_0p5 = make_temp_monitor(sims[\"0.5\"])\n",
+    "temp_monitor_1p0 = make_temp_monitor(sims[\"1.0\"])\n",
+    "temp_monitor_1p5 = make_temp_monitor(sims[\"1.5\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also define the discretization for our heat simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# parameters for meshing\n",
+    "dl_min = slab_height / 3\n",
+    "dl_max = 3 * dl_min\n",
+    "\n",
+    "grid_spec = td.DistanceUnstructuredGrid(\n",
+    "    dl_interface=dl_min,\n",
+    "    dl_bulk=dl_max,\n",
+    "    distance_interface=3 * dl_min,\n",
+    "    distance_bulk=slab_height,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define boundary specs for our heat simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boundaries = td.HeatBoundarySpec(\n",
+    "    placement=td.SimulationBoundary(surfaces=[\"y-\", \"y+\", \"x-\", \"x+\"]),\n",
+    "    condition=td.TemperatureBC(temperature=ref_temp),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now combine the structures, computed heat source, temperature monitor, discretization, and boundary conditions into Tidy3D Heat simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heat_sim_0p5 = td.HeatChargeSimulation(\n",
+    "    center=sims[\"0.5\"].center,\n",
+    "    size=[sims[\"0.5\"].size[0] * 1.5, sims[\"0.5\"].size[1] * 1.5, sims[\"0.5\"].size[2]],\n",
+    "    medium=SiO2,\n",
+    "    structures=sims[\"0.5\"].structures,\n",
+    "    sources=[heat_source_0p5],\n",
+    "    monitors=[temp_monitor_0p5],\n",
+    "    grid_spec=grid_spec,\n",
+    "    boundary_spec=[boundaries],\n",
+    ")\n",
+    "\n",
+    "heat_sim_1p0 = td.HeatChargeSimulation(\n",
+    "    center=sims[\"1.0\"].center,\n",
+    "    size=[sims[\"1.0\"].size[0] * 1.5, sims[\"1.0\"].size[1] * 1.5, sims[\"1.0\"].size[2]],\n",
+    "    medium=SiO2,\n",
+    "    structures=sims[\"1.0\"].structures,\n",
+    "    sources=[heat_source_1p0],\n",
+    "    monitors=[temp_monitor_1p0],\n",
+    "    grid_spec=grid_spec,\n",
+    "    boundary_spec=[boundaries],\n",
+    ")\n",
+    "\n",
+    "heat_sim_1p5 = td.HeatChargeSimulation(\n",
+    "    center=sims[\"1.5\"].center,\n",
+    "    size=[sims[\"1.5\"].size[0] * 1.5, sims[\"1.5\"].size[1] * 1.5, sims[\"1.5\"].size[2]],\n",
+    "    medium=SiO2,\n",
+    "    structures=sims[\"1.5\"].structures,\n",
+    "    sources=[heat_source_1p5],\n",
+    "    monitors=[temp_monitor_1p5],\n",
+    "    grid_spec=grid_spec,\n",
+    "    boundary_spec=[boundaries],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We run the heat simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
19:15:08 EDT Created task 'heat_sim_0p5' with resource_id                       \n",
+       "             'hec-c34e3746-9d1b-49ea-956c-dda7a70a1188' and task_type           \n",
+       "             'HEAT_CHARGE'.                                                     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:08 EDT\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'heat_sim_0p5'\u001b[0m with resource_id                       \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-c34e3746-9d1b-49ea-956c-dda7a70a1188'\u001b[0m and task_type           \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:09 EDT Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "             HeatCharge simulations is subject to change in the future.         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:09 EDT\u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "\u001b[2;36m             \u001b[0mHeatCharge simulations is subject to change in the future.         \n"
+      ]
+     },
+     "metadata": {},
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+    {
+     "data": {
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+      "text/plain": [
+       "Output()"
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+     "output_type": "display_data"
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+    {
+     "data": {
+      "text/html": [
+       "
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+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:10 EDT Estimated FlexCredit cost: 0.025. Minimum cost depends on task     \n",
+       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
+       "             FlexCredit cost after a simulation run.                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:10 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task     \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:11 EDT status = success                                                   \n",
+       "
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+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:11 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
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+     "output_type": "display_data"
+    },
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+       "Output()"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
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+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:12 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
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+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:12 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Created task 'heat_sim_1p0' with resource_id                       \n",
+       "             'hec-c0ae4c25-1af3-407e-9e03-64e90745a224' and task_type           \n",
+       "             'HEAT_CHARGE'.                                                     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'heat_sim_1p0'\u001b[0m with resource_id                       \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-c0ae4c25-1af3-407e-9e03-64e90745a224'\u001b[0m and task_type           \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                     \n"
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "             HeatCharge simulations is subject to change in the future.         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "\u001b[2;36m             \u001b[0mHeatCharge simulations is subject to change in the future.         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
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+       "Output()"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+     },
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+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:14 EDT Estimated FlexCredit cost: 0.025. Minimum cost depends on task     \n",
+       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
+       "             FlexCredit cost after a simulation run.                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:14 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task     \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:15 EDT status = success                                                   \n",
+       "
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+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:15 EDT\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
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+     "output_type": "display_data"
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+       "Output()"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+     },
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+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:16 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
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+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:16 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Created task 'heat_sim_1p5' with resource_id                       \n",
+       "             'hec-6cec850c-2297-4bc4-ba35-d91a68c10cc5' and task_type           \n",
+       "             'HEAT_CHARGE'.                                                     \n",
+       "
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+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'heat_sim_1p5'\u001b[0m with resource_id                       \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'hec-6cec850c-2297-4bc4-ba35-d91a68c10cc5'\u001b[0m and task_type           \n",
+       "\u001b[2;36m             \u001b[0m\u001b[32m'HEAT_CHARGE'\u001b[0m.                                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Tidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "             HeatCharge simulations is subject to change in the future.         \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTidy3D's HeatCharge solver is currently in the beta stage. Cost of \n",
+       "\u001b[2;36m             \u001b[0mHeatCharge simulations is subject to change in the future.         \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+       "Output()"
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
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+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:18 EDT Estimated FlexCredit cost: 0.025. Minimum cost depends on task     \n",
+       "             execution details. Use 'web.real_cost(task_id)' to get the billed  \n",
+       "             FlexCredit cost after a simulation run.                            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:18 EDT\u001b[0m\u001b[2;36m \u001b[0mEstimated FlexCredit cost: \u001b[1;36m0.025\u001b[0m. Minimum cost depends on task     \n",
+       "\u001b[2;36m             \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed  \n",
+       "\u001b[2;36m             \u001b[0mFlexCredit cost after a simulation run.                            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             status = success                                                   \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "357d4f4f1c0045d99fcf07d7f63fe7ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:15:20 EDT loading simulation from simulation_data.hdf5                       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:15:20 EDT\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "heat_data_0p5 = web.run(heat_sim_0p5, task_name=\"heat_sim_0p5\")\n",
+    "heat_data_1p0 = web.run(heat_sim_1p0, task_name=\"heat_sim_1p0\")\n",
+    "heat_data_1p5 = web.run(heat_sim_1p5, task_name=\"heat_sim_1p5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To visualize our temperature distribution from the heat source, we can add an interactive plot to show a cross-section of the signal waveguide sliding along the propagation (z) axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "57546b4a1fe64bbeb4ef4e7efe90c8bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=4.0, description='x', max=15.0, min=-15.0, readout_format='.3f', step=…"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# adjust for different sims\n",
+    "temp_data = heat_data_0p5\n",
+    "\n",
+    "\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "zs = np.linspace(\n",
+    "    temp_data[\"temp\"].monitor.center[2] - temp_data[\"temp\"].monitor.size[2] / 2,\n",
+    "    temp_data[\"temp\"].monitor.center[2] + temp_data[\"temp\"].monitor.size[2] / 2,\n",
+    "    61,\n",
+    ")\n",
+    "\n",
+    "hmin = temp_data[\"temp\"].monitor.center[0] - temp_data[\"temp\"].monitor.size[0] / 2\n",
+    "hmax = temp_data[\"temp\"].monitor.center[0] + temp_data[\"temp\"].monitor.size[0] / 2\n",
+    "vmin = temp_data[\"temp\"].monitor.center[1] - temp_data[\"temp\"].monitor.size[1] / 2\n",
+    "vmax = temp_data[\"temp\"].monitor.center[1] + temp_data[\"temp\"].monitor.size[1] / 2\n",
+    "\n",
+    "field_slider = widgets.FloatSlider(\n",
+    "    value=4,\n",
+    "    min=zs[0],\n",
+    "    max=zs[-1],\n",
+    "    step=None,\n",
+    "    disabled=False,\n",
+    "    continuous_update=True,\n",
+    "    orientation=\"horizontal\",\n",
+    "    readout=True,\n",
+    "    readout_format=\".3f\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def update_wg_plot(x):\n",
+    "    if x not in zs:\n",
+    "        x = min(zs, key=lambda zz: abs(zz - x))\n",
+    "    fig, ax = plt.subplots(figsize=(6, 5))\n",
+    "    slice_temp = temp_data[\"temp\"].temperature.plane_slice(axis=2, pos=x)\n",
+    "    slice_temp.plot(\n",
+    "        cmap=\"jet\", grid=False, vmax=np.max(temp_data[\"temp\"].temperature.values), ax=ax\n",
+    "    )\n",
+    "    temp_data.simulation.plot_structures(\n",
+    "        z=x, fill=False, ax=ax, hlim=[hmin, hmax], vlim=[vmin, vmax]\n",
+    "    )\n",
+    "    ax.set_title(f\"Temperature at z = {x:.3f} µm\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "widgets.interactive(update_wg_plot, x=field_slider)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will also plot the temperature profile through the middle of the structure to show the distribution along the waveguide all at once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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H8Le//Y1ly5a1y9+ZMGECdXV13H777VRXV1NZWcnUqVM7zcvRzJ07N6+pjozaj5JzAao0+Cc/+Qknnngil112Ga7r8l//9V+MHDmSf/mXf+ny6xkMA4b+K1QxDER2LEXV3HHHHfKwww6T6XRaVldXy8mTJ8tvf/vbcv369flzxo0b12EZISAvuuiikn26XPE///M/8/vmzZsnKysr5Zo1a+Txxx8vKyoq5MiRI+U111zTrsywu9ckpZQPPvigPPjgg2UqlZJ77bWXvOmmm+TPfvazdmWkGzdulCeeeKKsrq6WQEnZ5UsvvSSnTp0qE4mEHDt2rPyv//qvTktRO7uO5uZmuWDBAjlx4kSZSCTksGHD5BFHHCG/+93vSs/zOnzOjq/7yCOPyIMPPlgmk0m5//77y3vvvbfkPH1NL7zwQoevc+utt8r9999fuq4rR44cKS+88EK5bdu2knO6U4oqpZRr1qyRc+fOlaNGjZKu68o99thDnnTSSfK+++7Ln3P99dfLT37yk7Kurk6m02m5//77y3//93/f5c8hm83Kf/mXf5GjR4+W6XRaHnnkkXLFihXtrk1KKX/zm9/ISZMmScdxyi5LzeVycsiQIbK2tlZmMpldnt8V3n33Xfn5z39e1tTUyKqqKnnSSSfJVatW9eh7GAx9jZDyI2TUGQy9wDnnnMN9991HS0tLf1/KoGGvvfbioIMO4ne/+11/X8rHmiAIGDNmDCeffHK7vA6DwdAek3NhMBgMu+CBBx7ggw8+YO7cuf19KQbDoMDkXBgMBkMnPPfcc7z66qtcd911HHrooRx99NH9fUkGw6DAeC4MBoOhE5YsWcKFF17IiBEjuPvuu/v7cgyGQYPJuTAYDAaDwdCjGM+FwWAwGAyGHsUYFwaDwWAwGHoUY1wYDAaDwWDoUYxxYTAYDAaDoUcxxoXBYDAYDIYexRgXBoPBYDAYehRjXBgMBoPBYOhRjHFhMBgMBoOhRzHGhcFgMBgMhh7FGBcGg8FgMBh6FGNcGAwGg8Fg6FGMcWEwGAwGg6FHMcaFwWAwGAyGHsUYFwaDwWAwGHoUY1wYDAaDwWDoUYxxYTAYDAaDoUcxxoXBYDAYDIYexRgXBoPBYDAYehRjXBgMBoPBYOhRjHFhMBgMBoOhR+l34yKXy3HFFVcwZswY0uk0U6dO5bHHHivruQsXLkQI0W5LpVK9fNUGQ3m0tLRwzTXXMGvWLOrr6xFCcOedd3bpNRobG5k/fz7Dhw+nsrKSY489lr/85S+9c8EGw0eguzq/8847O1zLhRBs3Lix9y7c0Gs4/X0B55xzDvfddx+XXnop++yzD3feeScnnHACTz75JEcddVRZr7FkyRKqqqryj23b7q3LNRi6xIcffsiiRYsYO3Ysn/jEJ3jqqae69PwoijjxxBN55ZVXuPzyyxk2bBi33XYbxxxzDC+99BL77LNP71y4wdAFuqtzzaJFixg/fnzJvrq6uu5foKHvkf3Ic889JwH5n//5n/l9mUxGTpgwQU6bNm2Xz7/mmmskID/44IPevEyD4SOTzWblhg0bpJRSvvDCCxKQS5cuLfv5v/zlLyUg77333vy+zZs3y7q6Onn22Wf39OUaDB+J7up86dKlEpAvvPBCL12hoa/p17DIfffdh23bzJ8/P78vlUpx3nnnsWLFCt59992yXkdKSVNTE1LKst973bp1nbruhBAsXLgw/1iHX958802+9KUvUVtby/Dhw7n66quRUvLuu+9y6qmnUlNTw6hRo/je975X1jVkMhkuueQShg0bRnV1Naeccgrvv/9+u/d/++23+frXv85+++1HOp1m6NChnHHGGaxbt67k9bRr8Y9//CMXXHABQ4cOpaamhrlz57Jt27ayfzaGniOZTDJq1KiP/Pz77ruPkSNH8k//9E/5fcOHD+fMM8/kN7/5DblcbqfP31FLmr322otzzjkn/1hr509/+hOXXHIJw4cPp66ujgsuuADP82hsbGTu3LkMGTKEIUOG8O1vf7us37coili4cCFjxoyhoqKCY489ljfeeKPd+2/dupXLLruMyZMnU1VVRU1NDbNnz+aVV14peb2nnnoKIQS//OUvufLKKxk1ahSVlZWccsopZa8Xhp6nuzovprm5mTAMu/Qco/OBR7+GRf7617+y7777UlNTU7L/k5/8JAAvv/wyDQ0Nu3ydvffem5aWFiorKznttNP43ve+x8iRI3v8er/whS9wwAEH8B//8R/8/ve/5/rrr6e+vp4f//jHHHfccdx0000sW7aMyy67jMMPP5zPfOYzO329c845h1/96ld8+ctf5lOf+hRPP/00J554YrvzXnjhBZ555hnOOuss9txzT9atW8eSJUs45phjeOONN6ioqCg5/+KLL6auro6FCxeycuVKlixZwttvv50XrGHw8Ne//pX/9//+H5ZVeh/wyU9+kjvuuIM333yTyZMn99j7feMb32DUqFFce+21PPvss9xxxx3U1dXxzDPPMHbsWG644QYeeugh/vM//5ODDjqIuXPn7vT1FixYwM0338zJJ5/MzJkzeeWVV5g5cybZbLbkvLfeeosHHniAM844g/Hjx7Np0yZ+/OMfc/TRR/PGG28wZsyYkvP//d//HSEEV1xxBZs3b2bx4sXMmDGDl19+mXQ63WM/D0Pfcuyxx9LS0kIikWDmzJl873vf65XQn9F5H9CfbpMDDzxQHnfcce32v/766xKQt99++06fv3jxYnnxxRfLZcuWyfvuu0/+8z//s3QcR+6zzz5y+/btO33u2rVrO3XdAfKaa67JP9bhl/nz5+f3BUEg99xzTymEkP/xH/+R379t2zaZTqflvHnzdvr+L730kgTkpZdeWrL/nHPOaff+bW1t7Z6/YsUKCci77747v0+7Fg877DDpeV5+/8033ywB+Zvf/Gan12ToXT6Ku7iyslJ+5Stfabf/97//vQTk8uXLd/r8HbWkGTduXIlGtXZmzpwpoyjK7582bZoUQsivfe1r+X1a+0cfffRO33vjxo3ScRx52mmnlexfuHChBEreP5vNyjAMS85bu3atTCaTctGiRfl9Tz75pATkHnvsIZuamvL7f/WrX0lA/uAHP9jpNRl6n48a/jvnnHPkXXfdJe+//3551VVXyYqKCjls2DD5zjvv7PL5RucDj34Ni2QyGZLJZLv9utojk8ns9Pn//M//zA9/+EO++MUvcvrpp7N48WLuuusuVq1axW233dbj1/vVr341/71t20yZMgUpJeedd15+f11dHfvttx9vvfXWTl9r+fLlAHz9618v2f+Nb3yj3bnFFqrv+2zZsoWJEydSV1fXYdXA/PnzcV03//jCCy/EcRweeuihXXxCw0Cju78jXeW8884r8W5NnTq1nca19nel8SeeeIIgCMrSeDKZzHtnwjBky5YtVFVVsd9++3Wo8blz51JdXZ1//PnPf57Ro0cbjQ9SzjzzTJYuXcrcuXM57bTTuO6663jkkUfYsmUL//7v/97j72d03vv0q3GRTqc7jBlrV9JHcft88YtfZNSoUTz++OPdvr4dGTt2bMnj2tpaUqkUw4YNa7d/VzkOb7/9NpZltcuMnjhxYrtzM5kM3/nOd2hoaCCZTDJs2DCGDx9OY2Mj27dvb3f+jm7EqqoqRo8e3S5HwzDw6Y3fkZ3RkcaBduHJcjUO7TVdX1/PkCFDSvZFUcT3v/999tlnnxKNv/rqq2VpXAjBxIkTjcY/Rhx11FFMnTq1z9ZyMDrvSfo152L06NG8//777fZv2LABoF38qVwaGhrYunXrR3qu3EnyTkclrp2Vve7sdbrKN77xDZYuXcqll17KtGnTqK2tRQjBWWedRRRFPfY+hoHH6NGj878PxXT3d6QzfXam547296TGb7jhBq6++mq+8pWvcN1111FfX49lWVx66aVG47sxDQ0NrFy58iM/3+i8/+hX4+KQQw7hySefpKmpqSSp87nnnssf7ypSStatW8ehhx5a1vnNzc0ljzdt2tTl9/wojBs3jiiKWLt2bYl1unr16nbn3nfffcybN6+kCiWbzdLY2Njha69atYpjjz02/7ilpYUNGzZwwgkn9NwHMPQJhxxyCP/3f/9HFEUlSZ3PPfccFRUV7Lvvvrt8jR01DrB58+Yevc6OGDduHKA0Xeyh27JlS7u7wfvuu49jjz2Wn/70pyX7Gxsb23kGQWm8GCklq1ev5uCDD+6pyzcMAN566y2GDx9e1rlG5wOLfg2LfP7znycMQ+644478vlwux9KlS5k6dWqJi+qdd97hH//4R8nzP/jgg3avuWTJEj744ANmzZpV1jU8+eSTJY/vv/9+oGet1Y6YOXMmQLvckB/+8IftzrVtu931/PCHP+y0XOuOO+7A9/384yVLlhAEAbNnz+7uZRt6kQ0bNvCPf/yj5P/u85//PJs2beLXv/51ft+HH37Ivffey8knn9xhPsaO7NjQ6OGHHyabzfa6xqdPn47jOCxZsqRk/6233tru3I40fu+993bo2QS4++67S/6Y3HfffWzYsMFofBDQkc47WssfeughXnrppbLXcqPzgUW/ei6mTp3KGWecwYIFC9i8eTMTJ07krrvuYt26de0su7lz5/L000+X/MeMGzeOL3zhC0yePJlUKsWf/vQnfvGLX3DIIYdwwQUXlHUNy5cvZ86cOXzmM5/hzTff5I477qCiooJHH32Uww8/nJNOOqlHP7PmsMMOyyehbtmyJV+K+uabbwKUJBuddNJJ/Pd//ze1tbVMmjSJFStW8PjjjzN06NAOX9vzPKZPn86ZZ57JypUrue222zjqqKM45ZRTeuWzGHbOrbfeSmNjI+vXrwfgt7/9Le+99x6gQl463rtgwQLuuusu1q5dy1577QUo4+JTn/oU5557Lm+88Ua+Q2cYhlx77bVlvf/f//53TjjhBE455RTWr1/PD37wA2pra3nmmWf4+c9/ztlnn93zHxoYOXIk//zP/8z3vvc9TjnlFGbNmsUrr7zCww8/zLBhw9ppfNGiRZx77rkcccQR/O1vf2PZsmXsvffeHb52fX09Rx11FOeeey6bNm1i8eLFTJw4kfPPP79XPoth13RH50cccQSHHnooU6ZMoba2lr/85S/87Gc/o6GhgSuvvLKs9zc6H2D0cXVKOzKZjLzsssvkqFGjZDKZlIcffniH5XVHH3203PFyv/rVr8pJkybJ6upq6bqunDhxorziiitKSnc6Q5ei3nDDDXLGjBkymUzK8ePHy/vuu09eeeWVsqKiQl577bVSys47gc6bN09WVlZ2eK0HHnjgLq+htbVVXnTRRbK+vl5WVVXJ0047Ta5cuVIC7cpbzz33XDls2DBZVVUlZ86cKf/xj390Wmb19NNPy/nz58shQ4bIqqoqOWfOHLlly5ZdXo+hdxg3bpwEOtzWrl2bP2/evHnt9kkp5datW+V5550nhw4dKisqKuTRRx9ddidDQH7rW9+SZ5xxhkyn03L06NHy1ltvlbfffrusqKiQX/3qV6WUnXdI7Kr2dyQIAnn11VfLUaNGyXQ6LY877jj597//XQ4dOrSk7C+bzcp/+Zd/kaNHj5bpdFoeeeSRcsWKFfLoo48uKQXUJXo///nP5YIFC+SIESNkOp2WJ554onz77bfL+pkYeofu6Pzf/u3f5CGHHCJra2ul67py7Nix8sILL5QbN24s672Nzgce/W5c9Bc763PRn/z1r3+VgPyf//mfLj/XtNA17Aid1P/3J9u2bZOAvP7667v8XL3oFrdDNxiMzgce/T4VdXemox4FixcvxrKsXXb3NBgGA51pHOCYY47p24sxGHoJo/P29PtU1N2Zm2++mZdeeoljjz0Wx3F4+OGHefjhh5k/f35Zbc8NhoHOL3/5y/yk46qqKv70pz/x85//nOOPP54jjzyyvy/PYOgRjM7bY4yLfuSII47gscce47rrrqOlpYWxY8eycOFC/u3f/q2/L81g6BEOPvhgHMfh5ptvpqmpKZ/8dv311/f3pRkMPYbReXuElL1cp2MwGAwGg2G3wuRcGAwGg8Fg6FGMcWEwGAwGg6FHMTkXOxBFEevXr6e6urqk+UlXkFLS3NzMmDFjSlo2F5PNZvE8rzuXmieRSOSnZBoMHdETut4Zu9K80buhN+htXXdGX67xXdX7+++/zxVXXMHDDz9MW1sbEydOZOnSpUyZMgWAc845h7vuuqvkOTNnzsxP6gbYunUr3/jGN/jtb3+LZVmcfvrp/OAHP6Cqqqrs6zDGxQ6sX7++xyo13n33Xfbcc892+7PZLMPTaVp65F1g1KhRrF271iy4hk7pSV3vjI40b/Ru6C36Sted0RdrfFf0vm3bNo488kiOPfZYHn74YYYPH86qVavaTWedNWsWS5cuzT/ecYzAnDlz2LBhA4899hi+73Puuecyf/587rnnnrKv2xgXO1BdXQ0o0RQPU+sKTU1NNDQ05F9rRzzPowX4JrDryRA7Jwd8f+NGPM8zi62hU3pC1ztjZ5o3ejf0Fr2t687oqzW+q3q/6aabaGhoKDEcioepaZLJJKNGjerwNf7+97+zfPlyXnjhhby344c//CEnnHAC3/3ud8uexGyMix3QrrWamppui3VXbrpKoLvLo/kPNJRDT+q6nPfpCKN3Q0/TV7re1ft3Rnc1r/Xe1NRUsj+ZTHY4tPDBBx9k5syZnHHGGTz99NPssccefP3rX283i+Spp55ixIgRDBkyhOOOO47rr78+P6tqxYoV1NXV5Q0LgBkzZmBZFs899xyf+9znyrp2k9DZj7g9tBkMgwGjd8PuRk/pvaGhgdra2vx24403dvh+b731FkuWLGGfffbhkUce4cILL+SSSy4pybGYNWsWd999N0888QQ33XQTTz/9NLNnz85P2d64cSMjRowoeV3Hcaivr2fjxo1lf3ZzI2AwGAwGwwBmx7BPR14LUAmuU6ZM4YYbbgDg0EMP5bXXXuP2229n3rx5AJx11ln58ydPnszBBx/MhAkTeOqpp5g+fXqPXbPxXPQjTg9tBsNgwOjdsLvRU3rXYR+9dWZcjB49mkmTJpXsO+CAA3jnnXc6vca9996bYcOGsXr1akAlkG7evLnknCAI2Lp1a6d5Gp19dkM/4dB9N2/QExdiMPQBRu+G3Y3uar6rej/yyCNZuXJlyb4333yTcePGdfqc9957jy1btjB69GgApk2bRmNjIy+99BKHHXYYAH/4wx+IooipU6eWfS3Gc2EwGAwGw8eAb37zmzz77LPccMMNrF69mnvuuYc77riDiy66CICWlhYuv/xynn32WdatW8cTTzzBqaeeysSJE5k5cyagPB2zZs3i/PPP5/nnn+fPf/4zF198MWeddVbZlSJgPBf9Sk+4ec1/oGGwYPRu2N3orua7+tzDDz+c+++/nwULFrBo0SLGjx/P4sWLmTNnDgC2bfPqq69y11130djYyJgxYzj++OO57rrrSkIty5Yt4+KLL2b69On5Jlq33HJLr167oQfpiex34yY2DBaM3g27G93V/EfR+0knncRJJ53U4bF0Os0jjzyyy9eor6/vUsOsjjDGRT9i7uQMuxNG74bdjb72XAwkTM6FwWAwGAyGHmUwG0aDnp7Invd74kIMhj7A6N2wu9FdzQ9mvQ8qz8Uf//hHTj75ZMaMGYMQggceeKDk+DnnnIMQomSbNWtW/1xsGZi6fwN8/HTdGUbvuxe7i653xu6s90FlXLS2tvKJT3yCH/3oR52eM2vWLDZs2JDffv7zn/fhFRoMXcfo2vBxxOh692ZQGUazZ89m9uzZOz1nZ9PeBho9kT1vZi0Mfj5uuu4Mo/fdi91F1zuju5ofzHofVMZFOexs2ltH5HI5crlc/rGePnfbMzC0FrIeuDZ4AfgRyAjqKsC1oKkNZAiODXb8/C0tcOze5V2rWWwN5dJjun4Fcg4EIaQtEEAkIfChPqX2SyDtQJUL2zNgW2BJ9TuQsCEKIePBX1ZAyztw1ddg3312/RmM3g070lO6/t06eD8CISFlgR8AUrnmQw+EBUQQRWrtjkKwbaVzIsiFEAVgCaV1KSGI4M2/wz7VUBFBlJM4To499gg54ojyPp8xLj4mzJo1i3/6p39i/PjxrFmzhiuvvJLZs2ezYsUKbNvu8Dk33ngj1157bbv9C/4AJFCFxhGQQyk1KtpArcQ+EMbfSyDbs5/LsHvTo7p+GWUJS5S2Q5R+A0qL6rWeifdrnUuU9gPgDeAD+M3PYfv67n9Ow+5FT+p6zgrUeh2htBsUfZ+Lv2rdhvGTOtO3LHqNNcBW4DUgJ9XdJFt57bV0dz/+x56PlXHxUaa9LViwgG9961v5x01NTTQ0NCiT0Ub9hLTYtHGhBQjgUThXi7fMTBZT928ohx7VtQOkKRgVPgXttlDQtUshVV0bI4KC0SGKjpd5e2X0biimx9frBEqfWiQStRbr9bv4BlFrGErXa32uiLcKoA2olPFNpANYbNsWUg67c5+LwXztu6R42ltnYk0mkx1PmEtRsIQjlMgslPC0FWxREKgXf98FP1ZPlOZ9rP8DDR3SLV0nUMaCFW82hUU3RcG4gMICq+/i9HOInzcGyKB+N8rA6N2wM7ql6yRKhx5Kq8W6hcLNYY6CiLR3QxvPxTjxcxJAJTAMaBTxiTbJZHl3kN3V/GDW+2C+9l2y47S3LpFE3eFpAWrXmRMf8+LH2lrWXjyLj/lP1dDfdEvXerXTWtZrZBDvj4rO1ca0jVqsg/h7va8ifo7pyW3oAbql6xRqXbZQHgaPgn61xiPUeq09bsVhFH2zKCn8HkTAaGAL0BYnY4TKnd1J1MZQxKD6M9jS0pKfOQ+wdu1aXn75Zerr66mvr+faa6/l9NNPZ9SoUaxZs4Zvf/vbJdPeuoS+w5NFjyMKnopE/L1HaQxPi7MMjJvYAH2s6woKutYhERelZZ1PofUsKCy4xTlGetN5GmWK0Oh996JPdZ1GaVWvy1rfSUpvAHNFz9FhkyA+16H0ZtIB3gcaUR46G/CVuzoITFhkVwyqa3/xxRc59thj84917G3evHksWbKkrGlvZVNNwTUWUrhD0/G44thzcfyuC5jseQP0sa5dlLa1y9hDJSDru7nihLbihDcoGNP6zrCGgrFS5lsbve8+9LmudVhE61EbE9qI1i+rDebidVuv6VDw6nnx4+HAB8A2QSFho7ywiKkWGSQcc8wxSLljcKxAOdPeyiaN+ukUu81SqIU4ouBatnf4WpyNbDCUQZ/quga1LroUtJykdBUr9lIUh0Wg4Oloo3CHZ1zEhg7oU11XSEhLsIVat20K3ocQ5XnQHrhij4X2NDsUvBrahgiAUcD2ov0AJMhmi10gho4YVMZFn5IidoNRmuxWRcHY0F4MbS0XZ9qXgXETG/qcKgmuhIxQOtVJnWHRVpwQp5OUtc51noZ2IQ8BNpT31kbvhl7DDuJ8CqmaVaRQBrCu9tPhEu2RKzZAikN9xeu5hypDTaKME0ArsNycCxMWMbRH381VoCza4gVXlzPpRVcbFhHKGIk6eL0OMNnzhj7HCiElCwZCVhQ8b8ULrU5oK+5tkaQQBhyGchU7qIW7DIzeDb1GvQdWXNIUCMhZBYNCl1pLlEe6OClZHwuLzknG36dRnr4PUTeV2/Xdo4XrmmqRXTGYr713qZVKFcU5FtqToROAdBmTFqeusS4zLGLu5Ax9jahrRVrVIGwQopAEp0v4NDq8p3ONtKsYlPZTqEz69RRi2bvA6N3Qa1g5SAWqnSxASwJcobwXxcmcuuFhcQ6d7mekbxqL8+nqUKWow1FVI56KqUhZXpKd8VwY2uPKuA+sKA2F6Fr/4ux6n0JmvaDsxdZg6Gts10NWtBC2JIGkWgGaLLUQJyk1jPUdno5PQ2njId2J1qwihn6mqjpLxsoRWrEYQ1vNbbCEag4XogxiKBjJOuSnc4v0zaJDIcG5kkKHz7xBIXEHc6ZlH2GWhc6obQJRDaGAVgEpUZpnUdwfQGcW61LVMo0Lkz1v6GtSySy+GxGlPaRjQWCBZ0Eolda1h0J38NTfJ4v26aTOoSi38Zry3tvo3dBbWI5PRUWG1uYUMrKQFmBLtTZXitIqKE1Y9FjfNOYo7V+0BZVX9ApxGEVAmKK5ubyETlMtYmhHsjIkF3rgO6p0L3KUFazj0i0UWiBrA0PX/ZuwiGGAUpPO0pLKghD4FkTYUGWhVmOUgaHv2nQ4sLi9vS7P84FWCvkaZWD0bugtko4PySwQkMkkwAkJcg7ISnAspWudR6QNaJ3sqXti+EXHtM51Z2ZQBnZOQkYSBOUl1pmwiKEdtpPBSrUSeSmIbGiLR5+6sYFRFZ+o43i6+1txUy2DYYAhhEeFux0h0jiuTZgSZLcICNOQkBDGIRLdNEjfwemQoF6c9cpRgTK+DYZ+JCHakKIKEhJh2YSBci8H2Vi41XGFVPGMnOIhlM4OX4srBXVyv+6PgUVlZZkDpHZjjHHRCZUVAZYbksUn8AVURuruzo9rqS2hDAmdQa9j0jt2gdsJJnve0NckCRBWgOX6tEQ5pEjj1uYIvAgZJsFPqBnsVaI01KcrQmTJixXmlJSB0buht0jjgWij1aoisnMEXrwoJz3lbQhsSFpxaESUJnQWN43Tetff6xbi41FTgEMASSJhqkV2xWC+9l4mJOm2YFXbtDbXEKYFSFsVOAexFyMhIScKiW9ZlBjLnMZrYtCGviYRt+QMhSDtRLRKiS2rEIR4bghOnGQROcp4CFAtj/Viq9vgA4wFNlL2KmL0bugtEvj4+FSIFiLLJZUWJNMBmZYQP2kTbq+EKAmOUOt2JEobH+oyVCjtgyFRFSfawHYE+Ba+X161iMm5MLSjUmzDd9II28Ye0kJjaCNDQEhIhOAJkC5YEWStQpaxrh4xGAYgDjkCqnAJEU6EsF1yTgvZbCWiIocMothtHKmMe9tSiXFCFDLufVTYpA21MJfZ58Jg6C2S5KggQwYfYaXICIlPFUEqg6QaUZcl8HxorlQe6CCuBNTdOnWYROfUgfo9GA2six8HxF2aRdmlqLszxrjohCSQEC14JLAsgZvIEQaSyLaRkY1IgPQiFSKJ3DgRTqiFtq289zAJboa+JkmWFC20EGJhkRUpHBsqKlsJ/BSRZSMlRETKoBCoxluBDcJSC7JuIKcz7E2fC0M/kySLR4CLjQekREAoA2zbwU22YdkVSCkJ3TaV5JkQKsQNyouhbwh1nyJtaGxHlaPqaqkuJDDrlzMJnYYSBCEOIUnRRCDTJFICKSMiaRP6FrYtydkOkefGYZG4K1xgqx73ZeDYKneuOzg7llcZDDshSUCIpJYcbdhU0EqrVYktIxJJl8DKIYRNFEX4WRtp2RBFUJmCXKS6H1YJ5bmzgFpK8zB2gtG7obdIEAJtREgsPDIkQQikU4VrQ4sfIYSlQtYyVCFuEScwW0WeuWIt26gy1GbgbZTWmym7AzN0X/ODWe8m5bUTqmglRRt1tFFptVKR9kml2kilsiTTPrYTkkx5WIkMuAGkIqgIoCoEtwvq62P22msvhBDttosuugiAbDbLRRddxNChQ6mqquL0009n06ZNJa/xzjvvcOKJJ1JRUcGIESO4/PLLCYKgo7czDDASZKikGUEOB48kHmmyJAhw3ZzSthtgOyGJihxOIoOV9MEJlL5TqAZzbgQ1sjTZ02DoJ9K0UUlEAg+bLEnAIqRSZEB4JNM53ISPZQWQzEEiC8lYx66ERKAeJ1Ear0DpWo9+SKEMkzSq95y5Ld8l5kfUCRYRAklISJIMbaICy3IBD2SItFxsRyABD4coEOCEIANIebt6eUAJ1OnjO7kXXniBMCw84bXXXuOzn/0sZ5xxBgDf/OY3+f3vf8+9995LbW0tF198Mf/0T//En//8ZwDCMOTEE09k1KhRPPPMM2zYsIG5c+fiui433HBD9z6ModdJ4WGRJSSBjUUQL8g5YZFwckTSwiPCcQRhYOE4Lr4f4AU5wI6romyoi2Czqxbbcgf19YPeDbsHDlkicjjY+CSQhAgCBIJKEdFmh1iV1SBCvFwaIovQk2DZyuus60wlyjunexdVoQwNPciyuFttOdfVTc0PZr0b46ITqmmilQosHBJIEnhYwkcisBybnJREkU0iIRH4yCggDF3CQBA55anB7QE3sVumS1ozfPjwksf/8R//wYQJEzj66KPZvn07P/3pT7nnnns47rjjAFi6dCkHHHAAzz77LJ/61Kd49NFHeeONN3j88ccZOXIkhxxyCNdddx1XXHEFCxcuJJEwt7EDGSdeGStpo4UENklcLARteFaaSNrYriDERkqbKIxIJIBIEAYQJlFhEkdCjQWhU361SD/o3bB7kCaHSnazEHHrzQAbB0EGm5SQtEkPNyGAFDKMEJavQt2BQPp2XJ5qqTYDjijtWKt1F1eRlDsVtbuaH8x6N2GRTkiQo5I2amjEJUOSHI5ackmIkLTlU2m3kUz6JFM5bDeH5bSRSHm4ZXouepKmpqaSLZfbdbMNz/P4n//5H77yla8ghOCll17C931mzJiRP2f//fdn7NixrFixAoAVK1YwefJkRo4cmT9n5syZNDU18frrr/f8BzP0KGky2HjYBNhEJMjh4pEkR5KQlMiRsDwSIqAi4ZNO5UgkfNxUjmRFK3bCw0r44IaqoqQ6Gtz1coaPBS4BVeRI0UqSDDW0Uhuv3WmyWCIgZfsk7QDL9rDsENsJse0AywmwU54KlaQ9qAwgHSoDekRE7J4utMK3wTcVgbvEeC46oZJWoIoM1SQISeIRERLhEGJhEyCFhSTCdi3lRo4soijEa82VVY3aY25ioKGhoWT/Nddcw8KFC3f63AceeIDGxkbOOeccADZu3EgikaCurq7kvJEjR7Jx48b8OcWGhT6ujxkGNknaSOCSowqBxCKgGRuJg0sWkGrKulDtOG0Evm0hUoIwFDiuRy4bIkMbz3ZVBUllee/dk3o3GIqpIEsOHxcLF59W6knikyMAclhxHYllJ0gm1QjgIBAEXgrbgTCUCMsh8OM5O8JWJaujgWGUdvLsAj0SFhmkGOOiEyxsqmnDxybCxiFHRBKJxMJGYBFi4xAihY2FgxCSyJFYKZ9MGe/h2uB203ekc0ffffddampq8vuTyV3XB/70pz9l9uzZjBkzpnsXYRg0pIgQRNi0YhESUkFAMz4pkspUxkbgkiFHgkhYOAQILIRtE5LDshJk1lbEY60pTErdBT2pd4OhlIgEWSJcJBZZcjgksIhw42M+SRIiIBIOEhfXhSjykKGDZUv8XIiI13Ps+PbwzYTKtaigYFyI8hM6u6v5wax3Y1x0Sg6opBaPVlqQBIR4+CQIsXGxYw+Gg0OGrHAIbQtHuoR2mT6zLrRO7pR4Ya+pqSkxLnbF22+/zeOPP86vf/3r/L5Ro0bheR6NjY0l3otNmzYxatSo/DnPP/98yWvpahJ9jmHg4pAhpBILC4cIgUUatai6hHGhqjIoIiQRDg4RGWFjIbBsiVXpIffaSvbD4VCRKrsUtSf1bjAUkyBHSGXcJE4bFSEJcnjYpPBxsBE4BFaIlBBIG9eJwI2IIkHgC5IVAb4XISOHKApUD6MonrWj8z6T4JWb1NldzQ9ivZuci06oIUeaZkScb2FjkSDExSNBgI2HS4iDj4skSUAFAUnhIcTANzeXLl3KiBEjOPHEE/P7DjvsMFzX5YknnsjvW7lyJe+88w7Tpk0DYNq0afztb39j8+bN+XMee+wxampqmDRpUt99AMNHooKAOppwyCEISNOWz8Fw8HHIkiSLQ0CaHEkyJPCpJEdC5LCtkITwqKjwoNJSK4jJuTD0M2ky1NJEBVksAtJkEPhU0EqaVhxy2PjYsa5dkVG5RU6IY2UQ0sNNegjLx3FzOIkswg0gFaiW+B6q50VsVCSN5neJMS46QeDjEGBhkyDCwkMQkcQnERsYLjnceEF2yGIhsQhxuzK5rCe2LhJFEUuXLmXevHk4Rf692tpazjvvPL71rW/x5JNP8tJLL3Huuecybdo0PvWpTwFw/PHHM2nSJL785S/zyiuv8Mgjj3DVVVdx0UUXlRWKMfQ3KnfIxSeJV2RItJHAJ0EU6z1QyctEcYWJMqxdslgiJGlJErbfNeOin/T+/vvv86UvfYmhQ4eSTqeZPHkyL774Yv74Oeec067vy6xZs0peY+vWrcyZM4eamhrq6uo477zzaGlp6frFGHqFBB5WnHGZIEcKjyRBnCEnqcDHwsfBxyIgiU+KDK7IkrQi0gkfx/FIJH1SFQFuwiNZ6UONA2mhDAsL1ftCgFduzn4/6H2gMIgvvXepjJOAcmTxSJPEI4jH5QlCJElCRGxIODhxnK+QWlwGDt037z6Ck+Txxx/nnXfe4Stf+Uq7Y9///vexLIvTTz+dXC7HzJkzue222/LHbdvmd7/7HRdeeCHTpk2jsrKSefPmsWjRou58CkMfkcJHEJCLjQaJIIkf97vwccgQYCFwCUgQYOEiCXCQSBwsfCSBFSLq2sCrLL/uvx/0vm3bNo488kiOPfZYHn74YYYPH86qVasYMmRIyXmzZs1i6dKl+cc7Gspz5sxhw4YNPPbYY/i+z7nnnsv8+fO55557PvJHMfQcNh4RAUmycZacJIOFg0OaLB4WLm48ADWIU5UlKTx8XISwSLvgRxZSqr72wvLByoBXASOB11DGha3aY5RFdzU/8J3gnWKMi07QtdI1tNKKRw43Xl5tIiIEgggPGyuurLZJ4CJJxPn2A5fjjz8eKTsOlKdSKX70ox/xox/9qNPnjxs3joceeqi3Ls/QiyTIERFQQVu8CPtkSSPQQ83cONwnycbzR1RNiUNEhI+tMvJFRG7bUNXqvrslIL3ITTfdRENDQ4nhMH78+HbnJZPJTnOG/v73v7N8+XJeeOEFpkyZAsAPf/hDTjjhBL773e+ahOgBQA1ZMmSxkbRiExJRjYdgOxnqsLBJkUEikGQJSBJgIwCBjNOcEzhWRCQdRJxcYY/IEK5Jq2YVaVTuhQuW8fnvEvMj6gTV8U3l46Tx47s6HwcPKw6JJPFw8NQMEiKSeLhkSJR7K9dPbmLD7ksFORxaSBCQjFv/pWmLE+BULlGCAIeAFAHpOBcjjU+SLBXkSJHDkjkY1grVUnUvLIce1Hu5fV0efPBBpkyZwhlnnMGIESM49NBD+clPftLuvKeeeooRI0aw3377ceGFF7Jly5b8sRUrVlBXV5c3LABmzJiBZVk899xzZX54Q+8SUUkmDlMHpMjEfVygihYcIlL4uPH3DkHcKlxpXfX1zKn9widheVgyQ9hWCRVC6U63BI9bvJTFbry+G+OiE2ppI0ULFhnsuHY6TRaXkCQBadX0OzY6AgQhScK47KlM48JCWS/d2cz/oKFLBCSQ2PFCK5CxoZHN5xApP4UXJ8CpPCMIceKW+KqRXEQ6nVO3cOWm2vSg3hsaGqitrc1vN954Y4dv+dZbb7FkyRL22WcfHnnkES688EIuueQS7rrrrvw5s2bN4u677+aJJ57gpptu4umnn2b27Nn5NvkbN25kxIgRJa/rOA719fWmt8sAIU2WGlqwCUmQwSIiTSsWMi69zmEBKSQOIQk9Twc/rioJsfNreIAlJBWJCDcVwbDYy+uRzy8qt0NntzU/iNf3QWwX9S4CwRDacIBmLFwSgKNaz5MERLzY2kR4cbonuEiicnMuDIY+poY22qgAAgIEHjk8BB4OaQKgDTsOmKjB6yERkIrnkajnuSSwGZLcQoZh/bKKlNvXJYoipkyZkp97c+ihh/Laa69x++23M2/ePADOOuus/PmTJ0/m4IMPZsKECTz11FNMnz69Fz+FoadI5D1sLYREBKSBgApayJAkiUNEDhsHCw9btUHExgYCPCwsQGATIgmJECRwq7fjv5mGVlSzuAxgdSGhczfGGBedIPCwSJImR0vcJlnm+wFIAiyiOEqdzmdiOLFZ0YWwSLkWcOcXajCUjSo/zRIiyVIbJypDZTwntQIbSYSPRYIACURxlxd1hiBHPLDPTYCUavx6OfSg3svt6zJ69Oh2JdIHHHAA//u//9vpc/bee2+GDRvG6tWrmT59OqNGjSopvQYIgoCtW7ea3i4DBBsPhyQuIZX4BGxFUkuaiIgEIbl4oJkgSYAkQOb//AkcQlIo54RNkiQRkcyS9fdU01JTKONiE5AGpysVUt3R/CBe341x0QkuGVyS+LikaMGPfb/K/hWATUSIhcinb+biRLiyEzqNcWHoY6y4rLoaGw+PCBew4hJqP544YsVleyJOVo+wCRFxQrODzxY/QbNfA1UhVJa5jPSD3o888khWrlxZsu/NN99k3LhxnT7nvffeY8uWLYwePRpQvV0aGxt56aWXOOywwwD4wx/+QBRFTJ06tWsXZOgVXAIs/Nj7QDyMLwcI2sgBSSrI0kKVagOOhY3Ei3suO3EzfJsAjwiJixQwqnYD6+19VCkqQDXQDE5XqkV2U+NiEEd0epcKAtzYcZZC4JDDivMp0mRwixKBBEHcnMUnhYc7wKtFDLsvFWRwYkOhggyCgARRHHtWyZqJWMcq/qxi01XxQCiXgApyjHYbSQQ5SIoBvQB+85vf5Nlnn+WGG25g9erV3HPPPdxxxx1cdNFFALS0tHD55Zfz7LPPsm7dOp544glOPfVUJk6cyMyZMwHl6Zg1axbnn38+zz//PH/+85+5+OKLOeuss0ylyAAhSRsp2rBpwyFHmiw2PpIo7ncRxImcrfEanYsTP/34e7WOW/G6b+EhZcTmLXtApYQtqGTOOMs/GMQlon2FMS46IUUbkKGGJirZToocKdpwCFHTUbNxUpAfL84BNiF2LNSy6G5ym94MhjKx8Ejj4dJGgogkWVL4JOJMe9VYK4NFGGtbNx6SuETx3aEKD3qRAympSvTKoR/0fvjhh3P//ffz85//nIMOOojrrruOxYsXM2fOHHVJts2rr77KKaecwr777st5553HYYcdxv/93/+V5HEsW7aM/fffn+nTp3PCCSdw1FFHcccdd3TtYgy9hhsbyVWxAZzEI0UON27+VkkrSdpIx0aFSszP4eDhEpEkjKsCvbiSJMSNfNzqFjUBuAZoQhnSXhfs6X5Y33fVNE5KyXe+8x1Gjx5NOp1mxowZrFq1quQ1eqJpnAmLdIKFZAhZtpDAAVwiQkIkPnY8j0FikSGMY3QhISF2SaBkF5iwiKGPSeCTpomAOiRt+HGdk0Dg4CFxcZTpEPe8EPGAMz+OXqtWWk2kGDK8iU3bq5Dl+oj7Se8nnXQSJ510UofH0uk0jzzyyC5fo76+3jTMGsCoLrIeCVIkCBAIMvmunWDHbeHsOKwXkkAgcQkRceciPx57JuNwoGVBgAvpAKpdZWC0AfHg1DIvrE/DIuU0jbv55pu55ZZbuOuuuxg/fjxXX301M2fO5I033iCVUnXlPdE0zhgXnWDFbWOtOJanSpnCuHVWAomO7UWAR4itJu3FucplYWP+Bwx9SoqQJF5+UJmSoIckIoGNj4ckEZ8jiXARhHEOhoxby4HAQoYWUqDcxuVg9G7oJRJkqCTkQxI4sW/CRZAmQ4jERhCQwsWPO3SqdVzV+zlxmaryy7n4+IAlLBzAFzbUorZNQBXEVcq7po81v6umcVJKFi9ezFVXXcWpp54KwN13383IkSN54IEHOOuss3qsaZwJi3SCqg7R7jUvvuPzSZGNBzqp75OEWPEAM5ssdtwfwGAYiKhkNp+KeKhTglzcHCtLKq7xT8a1/xV4pGmNczDUpvq7ZKmhkTBjQ2hDYNxnhv4lFc/EsQlI0kqSlngWTkA1WRw8KmnLN85SvS28WNdZ7HiwGbH2k2SR0qe28gOE5yujogmVc+FDVK7noo/ZVdO4tWvXsnHjRmbMmJHfV1tby9SpU1mxYgXQc03jzH1EJ6RpRZIkiueLJAjiPhYQ4ZCAuJWs8mbkSGIT4qsipvLepCdyJgaoyA0DExefFHa8kCaoog2JpI0qLHwSsS8OZNzrQn3vEQJWPI/BZjtpRtRuYmtrFZE9tLw3N3o39BJOPEkkiRd7nFviUWUpIgQuEVE8yEwl3AfxqAYbEf8jbnefi+dKWSJkj+RGNtY0QBbVYSD2RCQTZV5YdzUf672pqalkdzKZ7LC3i24a961vfYsrr7ySF154gUsuuYREIsG8efPyTd9GjhxZ8ryRI0fmj/VU0zhjXHRCComMZZakmgQ5LDw8nLjIyYptZUFAkhRhPG/Sxu7K4DLzP2DoQ6ppIUeaFFm2k4hDeyEpfJLkcONAoI1LNp6VozLtc3EpqsDGZYTYxPaoFrDL/4Nv9G7oJZJEqJJp1SDAjqeDKN2GZAhwsEiQw8NHYpPAj/u3qLOz8eptI1WTASF4y98bKxEQ7SnhPQHb1ft5XRnW1wOab2hoKHl8zTXXsHDhwnbnldM0rq8wv+qd4JABklQiaCZNAh/lt1DNV9JEBHHdf0BAGE+MlLTGkWmDYeDhEOHSRoSgCmhGInDj5LaAIG7xbRHFEyQhICKNQ0RIBicuz5ZkZBLbEkRVRu+G/kXgI8iRoI0IO+55lcEjjcCO8+YSJOKWWlH8LIswzr+wcREECFwSWPjkIrA2C3g3BdUB1Lqq1X2yC+2/e4hyO9Luqmmcbvq2adOmfB8X/fiQQw7Jn9MTTeNMzkUnuPhxjM4nTVtcjqfKTZ24HM+JS/UqyMa5GSEVcRvasjCDywx9jJRR3MWwOR57IEiRo5IW7NiRrIaYqX6zqjQ1iHOKZDwYKoeNxxCrkRoaSY0ss0TN6N3QS6RkjioypMjFyfcSB0malnjIZIZEnFdRvAmiuLeFRxI/LlXNkCCkQniknTYSe2yD9aLw1zIFmWyZF9ZDetcdafXWmXGxq6Zx48ePZ9SoUTzxxBP5401NTTz33HNMmzYNKG0ap/koTePMr2onqB6chWE2NkEcPlMD1nOkEVjYhHGoxCYgwkLEZVFlYBZLQx/jyBwCF6gkRYZMrGMLBxcfG48cILFIAX7cwVDERkcy9t5FCCI/ILctRbBd7dv1m2P0bugVHHxc6ZIQIQkkHkFc3UecwCnjoXsqoVnE9X4WKkfTQsQTR9zYg6cCLO4Yj2BLXE8qUVuuCyPX+1jz3/zmNzniiCO44YYbOPPMM3n++ee544478j1ZhBBceumlXH/99eyzzz75UtQxY8Zw2mmnAaVN426//XZ83/9ITePMr3onpMggSZIiQ4gTV49IIhwEAh8PgYUbR+jUtAbVvs0t17gwGPqYKuGTkC20iQpcBA4+Tlyo5xLgx1NTfRLxwivjglQ1ZSQbj64Ocah1s8gREcF6NfzJYOgv3CBHGpdWQpJk8LHwcPO6dfGRQDIuRoWIDBE2Vnzr6JDCJ4dNAiduHZ4gRRuWFamIeArlvbCgpqofP+xO0E3jFixYwKJFixg/fnxJ0ziAb3/727S2tjJ//nwaGxs56qijWL58eb7HBaimcRdffDHTp0/HsixOP/10brnlli5dizEuOiEhfSRZImrizm1BXHQqkcg4FU55KVRv+pAsNpIkQbkJnXocb3cwbWgNXcH38XGpoo0mIBnX+OeI4lmnOXwcrLgCKhFPjgzJ4ZEggSRAEBKwKRyCjARWqsyMTqN3Qy9R6YeEvoebyBJgkSBJLS2AoIUKnLiPshN7LyQWSSCMM+UEISGQjG8bHWxC6WHnIuxtFlZDQPSOA2kVHpHlJjF3V/MfQe87axoHynuxaNEiFi1a1Ok5PdE0zhgXnZAMMvhUYxHE+fF+XM5k45OIxZjAQ5DCJ4uNQxSHRvowLGJK8wxdwI1UOZ6FR4CDoIkPSZKI1ezHAxTUoLJMPGY9inMtQgIc7Hj6ZNKOSCQztEX15b250buhl3Bz4NohngyxhE+WIN8vuSKeierHa7lDoJrAxWJSf/stnHgFTxDgk0IIh5rUdqxERGRLqJDQJsDpQp+L7mp+EOvdGBed4DgWFbIZISQ+Fg5+nFGh2oC7WBBPjhTYcWQ6Ge81LmLDwKSiDdzWVrzKJK6I8GJDQvsnZDwpVQAWdr7lt6qI0n4L3Y02wrYDrLJ7IRsMvYMTgvQDKhIttApJGkFIXdxd1sMhIoWHRUiWJBYWSSQeNrrHtsRHYBHFfWCEiPhH9gCCahu7NiDMJlS1iIBMrl8/7qDAGBedYAU5othxlqaNNqqIkHgksOOKf9WAxcEjyrvWXCBRbimquZMz9DGWhEQAtghIxG2O02TIEWKTiJM9wUd7ZAXEMxhUNYkgiKtGGniHfwT7IR2jd0P/IrOQDCJwQwKZJS1scoRU0UwbFaRpJohX7hRhfnxDEPuaHUK8uPV3Lm6O6CIZY73FO4n9yLVWQw6oBBxIuGVemPFcGHYk0RZgD8mQFRXY8YTIEIEVj6zOxK7hLBE+Tn5OQw4fu1zjoic6FpoYtKELiBx4rsCOQgIhQQgqYvNYdaHNxo2EQtRkBSvOolfjzHRPjAj4gFqSFS2IdJntv43eDb2EHUAiC6HwsNIWCBubiAQBGWySeLQgcQFBFoEbZ2BE8TxVEYdD1GR1F4scLpHl0LqtCoSAatTo9QiicnXYXc0PYr2bPhedYFngyghbenG/tgCLAIcozrD34wU4Fw85Ewgikkgc00TLMEARbZBukziRD3i4cea8E/eysGIvhRo9rVrbu/gk4rI+Ea92EZLhNKnyVWH0buhnsoAHbkQ8slRp2kKPcrBIkUWSIUUUa1viEsVa9xHxKDOXECfuY5S2Q0aPXKesiVryt+Nll6LuxgyqH9Ef//hHTj75ZMaMGYMQggceeKDkeDlz6svF8UGEyphIxkJz4upnN076cQjjxllZUmRIkEHg45RrbpqmQgb6VtciB/Z2SOY87CgkKX1s2kjGpag2QRx1juIhZmqYmRUbGS65+KvPFupwRIAfdtzQpx1G77sVfalrAOGDlQM38EnIiASZ2O8mceLhZRUQN0AMsPAQRYa1LlsVcV2gRUCd/IAWvxaaLFWKGqFcG+WyG+t9UBkXra2tfOITn+BHP/pRh8f1nPrbb7+d5557jsrKSmbOnEk2W247tQJOABWZkITMYhEUTc8LcYhIkIvnjfj5yagukhQeLmVm++hxvN3Z+rgNraHn6UtdE4BIQWUWqnMtRIQkEfE0yRwpPBw8iBvICYLYqPZJksUmIBUb09vEMKTlkk62lvfeRu+7FX2q6zawm8Bpg5pcRDKeXO3SikuONDlSZGLvco5EPN3aiW8GVaPEHDZZEni4eDhIhogW6lLboYJCzkV1F6IV3dX8INb7oLKLZs+ezezZszs8Vs6c+q5geeD6kAg9hLCxRIDKn88RkIgTOCUuFtl8+amykcsOi/REDHoQi8+g6EtdE4LwVJ6YJSSJRJbQseNKJxm7h31kPK3BQsYVI1kkKgdDBUsk4+Q6GoMDCBybsjLPjN53K/pU1x4QKRU6ObCkj02KChHgx1NRBYIkWXySqPtqvZKH+Ii4d5EdV0upBOb3GIYfREp3gQRfgAS3XB12V/ODWO+DynOxM8qZU98RuVyOpqamkg3UAgxQ4XskgrbYwvVI4cX961WykEtAihwJ/DhckqHs9t8Gwy7oaV2TA3ywPRBJqJYeSemRpjlucR+VeOCS5LDjrp1K3zkEHs1UqMXaCQlaygyLGAwxPa5rD/DVTaEUYAdeHKIOCeLuyirUEeXDIok4j84C3HieTgKJgxc/9nBwqXGypCdsB0eoTp2eyu807JyPjXFRzpz6jrjxxhupra3Nb3q0rWgGK1CLsBOoDp3JEtdwBhsvrgyRJJBY5NCJQGVhYtCGXdDTusYHmoAI7FimCZmLw3xeXG4axQaFl0/wLCzMEpuIKlqx8JARyK7OWTB63+3pcV3nUEmdLWDnoG57gBv5OEgq4rBIBRlcMujBkxaqHFtNTA3ilogeNrm4Hb5K9oykgx2G0IzSXwLdGmPX7MZ6/9gYFx+VBQsWsH379vz27rvvqgNSGRg2kIzAkV4+38IhR4IoNjZUjDpBlhQeiTjTuCzMYmvoJTrVdS7etkGiBVwvIBmGWFJl0Cdi74ROXlbfe9j5OLRHBR4uGSx8UnYrtXVN5V2U0buhm3Sq60htApRnQUJlphlXtpBAkIjziNxY4/omUQ01U7kXdhwwSRBh4RMhqWI7Tpgl61fACPL6ay23idZurPePjXFRPKe+mE2bNu10Bn0ymWw3zhaAnHJ9iabYeyFDXKkSONX/dxhP2VNeDJVBr+J0ZnCZoafocV3Hdq9wwYrv8kTWx418XDLxKGrdIjyKDYyIVOxGFkgEUIWnJkhKn6aW2t746IaPMT2uay/e2oAALBvSIbhRhEWOCEgTxoayMpxVOaofh0AClAc6iyAkiaQCj/G8jWWF6i4zBFar9/igsad/Ih8/PjbGRTlz6rtEfIdnCbAjsGWAjYyt3gwuXhyD9mMDU4/zDbDLHVxm7uQMu6DHdR1BfBOHcFW3TteGZKQXWT82IgLS5LBjD4XAh7gs1SUbf21TsxpSbeW9t9G7IabHdR0WNhGByIAvIbBc0nGViIg9zzZhHPLwSRBBbEg7cYhbVUkpT3U1jViWAM+GtIS9gSoY37n9U8purPdBdektLS2sXr06/3jt2rW8/PLL1NfXM3bs2F3Oqe8SIbBNWcBRBKlaHz+SRJZACguHCIlPhMDGJ8AmgRUnEfXhlMiPjXm4+9Knuo4TOmlRxkUowU6DbUfYMiQhAsK4CkoCEgsr7ncREcbzddSI9RZSRH5I0o0oS8hG77sVfarrVmA7qlykHkQWnCRU5loJE7VUW21sJRnfDKqh6zaCBDnsuDWcH/ui1URgK64ITJISzchcBhrjkeQJaC23Wra7mh/Eeh9UxsWLL77Isccem3/8rW99C4B58+Zx5513ljWnvmwE6qfjgagFy/NJJiCIBFhqqE0Y9+x0iIjI4JOMp42YVGJD+fSprnNABkgDlkpadlohCCNsJ0DaQdzkO4wnMDgoSztAYqO7dkZYDKOJdTKB5fgM6po5Q6/Qp7rWjScCVMLyEMACWxJ7LSQ2fhzMU2GRIE5VtvDw43Vbxn2XU8h4BrAype1qi7ASlcxZOahHfvQZg8q4OOaYY5Cy8//WcubUl02Ach+nQDaDWwV+GGI7DpZUkyMRLgkiAkJS2Ag8QpLYXa0W6Q6m8/Kgp891XbQJu5C07IYe2ElswCfCw0ViYUN+HLsgR4hDApsMFVRbzThOFzvSdgej90FDn+rahzhtAnzV5t4NgKzEdbIEIklaZAlxyBHm+xTpBvcOkCTCwqMNCxEHTDYynCQ5LEdCrVD687vQ56K7mh/Eeh9UxkWf0gzUAC0qNOLkwBIRAQG+7WCJAGFH8fgbjxA3diXLuI1sGZjF1tDXZFCayaJcyTbIUDnq3ChESh8pZNwmS89FFUgcJA4W4BDRiqs8G8Ii5bahWhjuAqN3Q2+RpaANDxBgZUCEYFWLOInTipXsIfPD+Dyy6nQETr5/UYgkhcMwsRUvcrHcEBISqgVsUr00ymI3Ni4GcUSnl4k7vsXBOGwf7BAsP8LxfZKWJBmFcdtkHyfucZGK2yYPVN5//32+9KUvMXToUNLpNJMnT+bFF1/MHz/nnHMQQpRss2bNKnmNrVu3MmfOHGpqaqirq+O8886jpaWlrz+K4aOQQ2lafw1AtKhF2In0zIUoniWiPBaJ/AwGPz+roYKAT/I8tvCoTJZZimow9BZqDh9xfqbyYuTUpNRkxsMNcyQJSNAah/aUyQwSJ66OsslixaWqduzT2CxH4UQRTiKAVpGPh5TtudiNMZ6LzhCQryiVaqSv3armMoSuJAwChGXhhj6+HWDHSW4qLl2mm7iP2yFv27aNI488kmOPPZaHH36Y4cOHs2rVKoYMGVJy3qxZs1i6dGn+cTJZ2oFxzpw5bNiwgcceewzf9zn33HOZP38+99xzT7c+iqEPiN3GJFBlewnVqdNqAjsR4aQDpLSQQs35dXAgzqSPiABBiCRA4pOg0s6w3aop771N+29Db6ENizbU2p0FUQ2RBNsBW/pEspKECOPW3jkirNjQSMVFVHacTxSSwCaHT6tIkbQ8ZBBBUkJWgNOFnIvduP23MS46o4W814JQTdyTAqzYMk7nAnJJtfQm7CDuVG+hFuIyHUJ97Ca+6aabaGhoKDEcxo8f3+68ZDLZaa353//+d5YvX84LL7zAlClTAPjhD3/ICSecwHe/+13GjBnTtes39C0BKhzioFoZZ4Cq2HOBRAY+OBYCFwsfHwmIuJthhI1LFkkam7fEXngksANZno5NWMTQWwQoLVsow7kVGKIMZ7dJwlCBECEOFkkCIiLUzGuVuqzDfX7syfAR2LhUkMUTaWRLEuo9CJJgqxYFZWHCIoZ2BKjSJt2cJQARxHE8TxkZTjYg4QeIUM0dsYhIxiN8y6IHp0Tu2G8/l2vfQu7BBx9kypQpnHHGGYwYMYJDDz2Un/zkJ+3Oe+qppxgxYgT77bcfF154IVu2bMkfW7FiBXV1dXnDAmDGjBlYlsVzzz1X3uc29B8BymDWhrMHtIDMqryihB/ghD5ulEXEbcCd2HPh5pvGqZkMQ/kQiwhPlLmMmKmoht5C97kA9VfNjzcJlg+pjEd10IJNW6xl3aNIfa9bgjtkcQlx42ZbCbJksEiO2qLGrqcBAV65fRJ346moxrjoDOUBVhawID8YR8i4Y2cbJD1wfHBlgA1x90KVh9HXNDQ0lPTcv/HGG9ud89Zbb7FkyRL22WcfHnnkES688EIuueQS7rrrrvw5s2bN4u677+aJJ57gpptu4umnn2b27NmEofrN3bhxIyNGjCh5XcdxqK+v3+lMAMMAIYvy6ep8Ig+wVOMhWxvNMsKNQmypivWU4awsEj1bJEFIWnqkaEGUPX/aYOgl4vWZJlQ+UULtEy1guRDZIGwbR6qcOBuVtKzGNuTifX5+QJ8Vl1O1UsFwtuLYISIp1N+CJGw0aUa7xIRFOqMFJVgXFcerQFmRcVMUQSGc5gYegWWTEuCLqHxjsyfcxPHz33333UIrXNrnSQBEUcSUKVO44YYbADj00EN57bXXuP3225k3bx5AyajjyZMnc/DBBzNhwgSeeuoppk+f3s2LNfQ7WdTi24q6CwO1KLuqE630ARHgORauFeTNZFdE2OTIkoy7BlhU0kgkHWSFRVnV1z2od4OhBO2RS5L3NJNFrd9NkHBAWlncRCWuyBCQwMFFIPAQ2EgcXEICBDZJJAERleTI0kTOq0LUhUhHJVyMqSvzurqr+UGsd+O56AwH2Bp/7xdtgYpPi/juzwogHUiShLj5bPouhEV6YoN2/fY7Mi5Gjx7NpEmTSvYdcMABvPPOO51e4t57782wYcPynfZGjRrF5s2bS84JgoCtW7fudCaAYYAQUihH3UKhn0urqohyAkj4kPAjXM8jGeRwIuU2dolIx3d6KTw+ZAwhCYRfZmC4B/VuMJTgURjKlyXf4p5Q9XJJBpBqlbhBBiduEmfF/S7UMDPluShu/Z3CQ+LTKqvI5tJEgQXV6u2SiTKvazfWuzEuOiNAuY11PwAtXi3cOF4tcioHQ+Q8BJKUVClCA5EjjzySlStXlux78803GTduXKfPee+999iyZQujR48GYNq0aTQ2NvLSSy/lz/nDH/5AFEVMnTq1dy7c0HNk4q9qkhPxOIW8nq2cSl62Q7Ak2FJiBz62DBBxWMSJBz1lSWPJiGqnzNkiBkNvoUN9OlE5i9K0pXLlUN8i/BAhVampkzcw1ObiI/ITgSUSSZOsB+FQnWiMvSGqTiQyLTp3iTEuOiNDIZseCsaGrqGOB+RYofohpqXEzmRBRjhyYI5c/+Y3v8mzzz7LDTfcwOrVq7nnnnu44447uOiiiwA1C+Dyyy/n2WefZd26dTzxxBOceuqpTJw4kZkzZwLK0zFr1izOP/98nn/+ef785z9z8cUXc9ZZZ5lKkcFAiAr56a/agFaV1AgJlqeMC+FJCEOcUOVYJOLYtIj7YFTShIdNgjLnT/ex3g27EdtROo7X5nzHzqw6Jvy4kCQCBx8n38vFj/Mt1DAz1adIxVVcAvZmFVuCaj7ctocqRRUSLMiV2YR5d9b7IL70XkaLM240hEfBRWWR74Eh7LgLXDzm1wkCLK8P2yF34fmHH344999/PwsWLGDRokWMHz+exYsXM2fOHABs2+bVV1/lrrvuorGxkTFjxnD88cdz3XXXlYRZli1bxsUXX8z06dOxLIvTTz+dW265pZsfxNAn6OTkD4GRKK9cgkLYL6c0bUWQSkIowBdgSR+EQA19UoP7hrOJGprJleu7NTkXht5CogwM7bmoQGlbp6Fl1LJt2eAgkXhxv5YELgAWFjY2Ni4CF4+ABMPFFvZ0NuC4Pr5MwGhgPaTbR507ZjfOuRjEl97LtKHu7AKgioKB4aAWYaG+ChtsOzYyfAjDEGdgRkUAOOmkkzjppJM6PJZOp3nkkUd2+Rr19fWmYdZgRU9zjA0JchS8GFXkq6RE7KWzQjX8yZKSKPKxLHAIaKZalexZOWqcNvqhQMpgKCWBWp9bUAaGzi9y1Ah2HHUT6OITSAtHOERxt04ZV48ob0YOnxSVeKxnOBKBTRY/rISNAuwulKLuxpiwSGdYKHG2oeaMhBRyLXLx93GcT0iwsuBEKr5nlys8PY63O5v5HzR0BW00p+LvtetY6z3uDSBkHPbzwAmVRy6JRMgIm4hmqgAXV4Z8GA4r772N3g29he5HpEPXutfFDmu15YGIwlhOAVacdOTEPVysuBW4FecXVZKlhu2MHf4ubJP5ETp2uU20uqv5Qaz3QXzpvcw2lFAtCkaFRFnCeqpkjnwHT3xlWLgh5XsuTAza0NfoMJ/2vunEN92ESHvoipKVrQASXoTtB6QIsQiooYUasY1QOKTdTGfvVko/6X1X83SklHznO99h9OjRpNNpZsyYwapVq0pew8zTGeDohnDNFNZqrWnd2yVQQ/rsSHkodM+LQpWfmqejunUqw2MEm/iQOtrCVNwERp0gy/3LuRuv78a46AyBMh4khQU4gxJw8YIcV5GIeJ/ldcFzYTD0NbpxljaYBWoB06V7WZTO48VYGxu2hIQfYoUhiTBHhM8WhpKmlYQYuNUiep6O67o8/PDDvPHGG3zve98rmadz8803c8stt3D77bfz3HPPUVlZycyZM8lms/lz5syZw+uvv85jjz3G7373O/74xz8yf/78/vhIho7Q67Fer/XNX4589Yjw1Y2fHYZxBZRPQobYRNhxxYhEYsVJnRYh26mnVjbSkq2DUQGkQghhJ5PkDTGD2C7qZQKU2zhN4U5PezGgtNWsJF/yJIWKU5eFSXAz9DU6m34bKjQSoYwJO96v3cpZIBn3c4ln60QuiDDEcRxCaZETaSxCUlaZqfP9oPddzdORUrJ48WKuuuoqTj31VADuvvtuRo4cyQMPPMBZZ51l5ukMBrTRDMpg1muwNjJc8ka160tsGZATAtsOiFAxDhcfiY9K+YQInxYqSYmQken1NH44SrksnD6cLTKI13fjuegM7T4WFCak6jVUh0d0RYluSkQ8f6TcMiXTVMjQ18h403FoXSXSSmEMu3YpxzNIRBzqc0JIZiOE51Mtc+wh3yUQSSJR5grYg3ovZ5YO7Hqeztq1a9m4cSMzZszI76utrWXq1KmsWLECMPN0BgXaSxFP+s0b0brNfY58A0Q7BMcDNwyw4yROmwgn9mDoJ3k41NFCmgzrW/cELKW/ii784dyN13djXHSGDom0Uciz0EJVgyILCURavNotN4CrRQy7OVnU/AXir20UEt+K8i1KPBix9kXsoUtIieNlaZRDqKSZkHJv43qOcmbpwK7n6eh5OCNHjix53siRI/PHzDydQYBeg7Wm2yjkEEE+YdkKwI5DKJYEKwjUbKi4sVaCAAePJAFJctSwhUCo9E4xNAfVIQSw3evwKgxFDGKnSx/QjKqT1hMk9V2fjRKytipT8X5Q2cQmLGIYqIQozejF2EEtwDYFT5wsOicO+4mMWpTDCjXGertThyNCKmgt37ToQb2XM0sHypunY/gYoI1iQSHvIocKa2tDOVSeZRGBK0BmJW4yQACWZeHYqpmRagquvBij2QJS0lC5jn80HwrrbXBBljusz4RFDO3Q7WS127i4TbKuEtGhE0lpFn5fjeN1GNRuM0M/oKf9gtL09vh7l4KWdTJnUYhExM9NxIuzE2axCYkQ1NJY3nv3oN7LmaUDu56no+fhbNq0qeScTZs25Y+ZeTqDAH3z56BCfHotzqHyi4paCYj45s+Ovc6WlAgpQYIdzx2x41Hs78uhJIVPbaIZd88WqA/AgSEVZV5XdzU/iNd3Y1x0RgulJajFd3XFcWudSBS3T84bHuXQT6V5ht0YfUendasN4eJS1GIjOVf4XgBIcHKwt7eZMf4G1sqJuOVa0/2g913N0xk/fjyjRo3iiSeeyB9vamriueeeY9q0aYCZpzMo0MawLknVN4X6xi8O6SFUcrIIVO6FHYDlhdieT0Kqpi8i7nXhEFEvmvhQDiUQSSwXaLSgGVrLDYvsxuu7MS46Q5fp6QZaIaVT97IUkt60haytY5NzYRioeKgcIh2TdiidyaA1DqVGhi7FDtQdXzILfxEHMVpspoVyeyH3PbuapyOE4NJLL+X666/nwQcf5G9/+xtz585lzJgxnHbaaYCZpzMo0M3gfNSNXoTyxhV7M7TxERsbAnAkJHRvolAiAJcICw8Hn+1UkRateJHEe6sKfAsEVA5cyQ8YBrFd1MtYKEFaFDoZOhS8Ftrg0OgYnPZylENPZAMPYreZoR/QHgvdkVP3btGeuDRq1dXhEx0OjBdrEXfzDCyocVrYSiuVlHkb1w9639U8HYBvf/vbtLa2Mn/+fBobGznqqKNYvnw5qVQqf46ZpzPA0d4KbSRLCjlFO4b6fBCW6lVhSZBuHL0QEmRETgTYJIGISazkf+WZYDlQ5UHKgWQhxW6XdFfzg3h9N8ZFZ+gyVFClTdoroRdlfY5FQcS650W5yjMJnYY+RuoFdjuwB0o/ufixTkzWpdZpCgnMuirKArKwsbYGV0ZYwiVRrnHRT3rf2TwdUN6LRYsWsWjRok7PMfN0BjgeSsNjUOtyCzActYY3oob06UnXRVUkIta2iED4PtgOlhREIkRg8xYNZEQVTVEdTmWAXwm4su+SmAfx+m7CIp0RojwXuuS0OCSicy18SsMj2n1swiKGgUpxa2SPgndOey6KE5WLx1fnSp+7Z6aJEdn1ZEhiUW7qvMHQO0iUByKfUwTK46zXbt0+oMizIeLmcIX5OeBISQKJIETE7cCTbKeaRvwP05ASkLDImi7Mu2QQ20W9THEDFh2fTlBYmHWYRJf06SEzutNhORjPhaGPkaCMBH0Hpwc8afexNiYSKF0n43P070KcpxH68GFyGBVd6XNh9G7oLfSa2wbUUjCOW4GhKKMjTem8kZTq3SKjQq9EEfhYrqoYAck7NJCUgmorg1WRJdpqg+uQKDdcYTwXhna0QBx2K82c182GdFayFquOS2vruBzMlEhDX6MrRbIUsuj1LBHtsQiLjlH02C9szYkUDXIjIQKfRHnvbfRu6CVk8TgGnUME6o+z9sZpB1uxvkMVEgE1tsEBkBGWVK6OMWxghNhAE5U4aQscCwJo7rghbHvMVFRDO4rdx8X9LIpHrxcbFtrg6EqfC4Ohj4k8iIq7zzZTCIkUl6Lq73V3Wm2UxBp3/ZDa3HaSBFTS2vcfxGAoItT61ZVQOpytK0Z8Ct1oi2foFPUncnxwsyFOEGDJgIRUmfmjeR8vSmFHWWgTkIC028cfsEwWLlyIEKJk23///fPHjznmmHbHv/a1r5W8xjvvvMOJJ55IRUUFI0aM4PLLLycIuv5HbRA7XXoXqRfWJIWs+Rwq6U0PMdPWsb77K+5+WA7GTWzoY5qaobYSZVQkUB4Mi1LjmfiY1rhObi7q2PlBVTVb3aEIQjLllqIavRt6CV8bxbr0NBN/TcVfi8N8+u+knkFiq+dZcXjE8kN11+1aNFNNhM0w60PeS42BVASRTVu5f2v7ISxy4IEH8vjjjxdewil9kfPPP78kebmiotARLAxDTjzxREaNGsUzzzzDhg0bmDt3Lq7r5rvc9uKl7ybYKIFWoUSoLdzi2SLFrmT9k9Rlq+VgFltDH1OZVoto3mepS6314ptAaV9XSOlqTO29CIAKsHMBw+wPSNJCoqtNtLqD0buhA2RxD4sMan2uQOVcVKLWam1MaCM6zrtAgBBg5SBMgm1DGKk4SihdHBFii5AoSoJj559TFv1gXDiOs9POsRUVFZ0ef/TRR3njjTd4/PHHGTlyJIcccgjXXXcdV1xxBQsXLiSRKDMEigmLdE6EGoLTghKpLjvVmfO6SqS4o2Fx0pvBMADJeSBtkDkK4b02SkN/ktJFeMdhfAEM2Zrj3dYxZIIa/AHcRMuwe+CHEGTj3AvtcQsp7dnSUc5FUcjPisCNc5FcIAgk9XxIJCWtUQXCiiAdQhaSA7j/xKpVqxgzZgx77703c+bMybe61yxbtoxhw4Zx0EEHsWDBAtra2vLHVqxYweTJk0sG+c2cOZOmpiZef/31Ll2HuQ/ojAzK4g1RRkY1SrQu5PPXtDjb4nMzKKu2XOPCNNEy9DFSgvTVnRptwCiUhvWQvuJ24DoZLnYb59vcWxBWCvZNruZl+xCS5eZcGL0beomMD7mMak1PE6rHhUMhpJ2l4MHQBog2PhzyiZ2hBW4AoZQkEj6u41NHI9tzNcjAzntAys656KEmWk1NTSW7k8lkh/N0pk6dyp133sl+++3Hhg0buPbaa/n0pz/Na6+9RnV1NV/84hcZN24cY8aM4dVXX+WKK65g5cqV/PrXvwbUBOCOJgTrY13BGBed4HkQ1akMYqDUI6GbaUG+X33eTSYo37gwbmJDHxNGYGm9WqjGQ0MoLTct9lroRVgnOMf5GbnIokVWkhFJ3HJb0hq9G3qJCAiL29RrSQoKnuZWClOrdRVHVLSJ+NcgUL8jyUCScHN4doJ0IksUxpa1hKivGiXGz21oaCjZfc0117Bw4cJ2p8+ePTv//cEHH8zUqVMZN24cv/rVrzjvvPOYP39+/vjkyZMZPXo006dPZ82aNUyYMKEbF9rppRvaIdRwm5LSU515XHQHBxR6W+iWs13xXHT3f8DcyRm6QHMI2xuhNonSs86eL26eZVPQsda4ztOIjeehmzN8YDczpuE9nnWOLO/Njd4NvYgsvsnTa7Ieu75jvoXu61JsUMfrvR17p50I1lh7k5OVRJbArWjFz6QgsgnLNS66q/lY7++++y41NTX53Z1NAd6Ruro69t13X1avXt3hcT14b/Xq1UyYMIFRo0bx/PPPl5yjJwZ3dQKwybnohNZWCsZE8RRJPdAsLNqvuxfqMlTTsNAwQLGAdBKErmrShkWxd0IW7QuKzgsKj9uGuFQP8Wmy6hhS7sh1g6GXaETlWkqt4VYKiZ05CjN0ivu46FwMrflINdUqTtYPpfoTOUauJ7TSUGmBC14ftxuoqakp2co1LlpaWlizZg2jR4/u8PjLL78MkD8+bdo0/va3v7F58+b8OY899hg1NTVMmjSpS9dsPBedIIpL76BQEaJrp22UCIv7WqQoWMHlYNzEhj4mRHXXlEG8kLoUjAdtSGsdayMjjknnS7AFuC0+zYkUoWUjynXVGb0begkbCALwWyGpZ+JoAzqgNPFe98PQhoTOn4gNDuGpgWbZJBwQreRv9n40RlUIS6rn2pAsV4d9XC1y2WWXcfLJJzNu3DjWr1/PNddcg23bnH322axZs4Z77rmHE044gaFDh/Lqq6/yzW9+k8985jMcfPDBABx//PFMmjSJL3/5y9x8881s3LiRq666iosuuqhsg+YjXvrug2ODbFK956mmYO0W1/5DwQ23Y3lqOZgEN0Mf0wJsb4NkBmyd8KYN6OLGcTqJE0qbDbnqOdn6BNVuliQ5amkp782N3g29hATCUA0vlcn45lAbzMV5cDu2Eig2nrVxYavKkcoAPtG2kvdqxmBZEqcyB3YlSBurXJ9/H09Ffe+99zj77LPZsmULw4cP56ijjuLZZ59l+PDhZLNZHn/8cRYvXkxraysNDQ2cfvrpXHXVVYW3s21+97vfceGFFzJt2jQqKyuZN2/eTof6dYYxLjqhOQcjh4BIxwaGvnPTNc7FsTodr9Z9AkxYxDBAEcSznTJQUU2htFoPNi2enaNzLHR8GvJdaa0PfbbvkcZyoZV0X34Eg6EdERDGjbBoQq3TrahkZb02J1Fa1k3h9I2gzqHTeXWC/B/1MBK8L/dkuNjCWtFMMCpJdnXNgM0n+MUvftHpsYaGBp5++uldvsa4ceN46KGHun0txrjohBBo3Q41NZTOENGLcAWFxVfH7vSibKpFDAMUAVQIcATIDIhGlJZ1TFrnFLkUKki07ovcyDXbI+woxM0HtsvA6N3QS/hALoJMBioScU6RHsO+B0rHOlG5eFyDrhih6GuoQiO+bbEpOYKkzCGxadtcT9jsQqj6apTFbjy4bBBfeu+SAbwcSA+ES8ElXCxILTB9l1fcmKUcTPa8oY9pjr8GIbjVKMMiR0GHutNhkoIxsWPzoRAiAZlkNbW0kcrPuN4FRu+GXkIr0NIeCY1ExQL1pFRdWl1U+VRiRMvCsTCIyIokq8S+5EiTk0nCtrRquNWVsEgPVIsMRgaqd6ffSQPVFUXT9oqn6hUPvSkWZHF7cINhACIBX0IUQrCVgrGsF12ta4/CvBy97VC+J/yQMIpoNh06DQMAD1QPCj31N02hWqS4/TcU1uriEHZxVVQIQghqRROTon9QwXZqR25UZSKuWeLLwXguOiFC3d1ZrWDXU3ANRxQy7HUFiV589V1eV2qgTYKboQ/xgfeAdADpCgqeCt2/BQoGhp4sWazx2AjJDXP5wBlGQLL8RcTo3dBLaFsh54Ojdaxz4HTipm5+qA1q3fdChwCh8LsAJCPJiMYtNA2vIiMrSCYDkA644A7QhM6BhDEuOiEDZLKxwavniGjDoThBCAqJQLpxiylFNQxQtN0rLFVul+9kWHxHV7zwyg6OASIKqLA9EvhUldv+2+jd0EvoCF6rD9U6vzgX76xBrdltFMpOtfaLq0qK+1/Euk9In1rRwhAaETIEL4TAJuirydeDWO8mLNIJLajpeMh4/HpxOV5xeZPeVzzIzFSLGAYwNUDChlAnKkNB13px1QZGcShQh0tCcBslta0fkBUurVT16fUbDDuSQeVu6q7eUjc1rKK01FR74DTFlX+auN2ABDZUDaGFSrZTo1p21kgTEymTQWwX9S4BKrwmhBrnK3Qpqj6oE4e026qj6Xu7wtzJGfqYFuIp61llPNtDQGRRui3u3aIXUK1p3Q8Adcz2ofb9Vobu+yGu8CgLo3dDL6Ht3hYgysVeuRBVjlqLyr/QRkbx3BwoGBu61UAc4g6kzfZEPdXRdiLLJWxJqOMVcR+NcjCei48HCxcuRAhRsu2///4f6bX0aAUp4z4XxRnzGu210G60Hd3Iu8Lpoc3wsaYnda1z3dokOEnULZ9F6ZjqHTWtta/3x31f3txzHyRO+dUiRu+GHegpbUuU/aCXZ7mdgudC51nE49Tz+XHFnjrt0dAjHQDhhRDmyFgWNjmsnMS1vfz7lcVurPePlXEBcOCBB7Jhw4b89qc//ekjvY7uoxLtWAOtY3V64Q2L9uukuHKt2n7g/fff50tf+hJDhw4lnU4zefJkXnzxxfxxKSXf+c53GD16NOl0mhkzZrBq1aqS19i6dStz5syhpqaGuro6zjvvPFpayuzSaPhI9JSute0bosqs83qGUo+bXoj1AlzcoTbeP/LDzQTCQQ5kwRsGPD2hbQ8lWYnKJxKgrI1GSvWrN7/oe61xbWSjjksJW6tGxUaLQ0tYg9+aBhty5fa52I0ZxHZRxziO0+XpbR3hAx8CYwSEbSrcls+cD+LvE5Q2z9I/zTI9F9IC2c1sYNkF83Dbtm0ceeSRHHvssTz88MMMHz6cVatWMWTIkPw5N998M7fccgt33XUX48eP5+qrr2bmzJm88cYbpFIpAObMmcOGDRt47LHH8H2fc889l/nz53PPPfd078MYOqWndK2dDw6QDcHVuRXFA8qg0HAICv1dEuSTlwMLRlhbcPDJlFmK2td6NwwOekLb2kaojb+3KykkIPtFJ+oOysXdlaGwruuqwCTYEaTCNlqcaoaxGYmdv3G0y7Snu6v5waz3j51xsWrVKsaMGUMqlWLatGnceOONjB07tsuvk2++KcHSTbR0WROUtkjWC2+xF6MMQkdt3aErz7/ppptoaGhg6dKl+X3jx4/Pfy+lZPHixVx11VWceuqpANx9992MHDmSBx54gLPOOou///3vLF++nBdeeIEpU6YA8MMf/pATTjiB7373u4wZM6Z7H8jQIT2l6ySFtCAvGw8wcynoWfdsKY5HFz+OczCcAOSHIewZ4YpMWe/d13o3DA56Qtu68CNCJSrbLagGcSHKpZGMN22FWLTr21LyPRBisY1a6thGKC3G1b3F3zfXEvmuatZVBt3V/GDW+yC2i9ozdepU7rzzTpYvX86SJUtYu3Ytn/70p2lubu70OblcjqamppJNI4F0Kq4WKc6l2DHvQlvBOlmozIROLbzubkC7z5DLtY+DP/jgg0yZMoUzzjiDESNGcOihh/KTn/wkf3zt2rVs3LiRGTNm5PfV1tYydepUVqxYAcCKFSuoq6vLGxYAM2bMwLIsnnvuufI+uKFL9IauLSCMikIjxeE/ucOmj3mF7wMB2bEOGVyGlzlyvSf1bvh40FVtd6Zr/bf+QyCXo9RjUVzdpxvDaW+dfrLc4VwJVhjhWiGVsontVr0KAUrVRKul2BuyE3ZnvX+sjIvZs2dzxhlncPDBBzNz5kweeughGhsb+dWvftXpc2688UZqa2vzW0NDA6BKoiNUzoXUC+wOlm1JhQiULsx9TENDQ8nnuPHGG9ud89Zbb7FkyRL22WcfHnnkES688EIuueQS7rrrLgA2btwIwMiRI0ueN3LkyPyxjRs3MmLEiJLjjuNQX1+fP8fQs/SkrjOojHoPVS0S5ij1SuwYky6+QyvKKwr3hmSNYJhspo2KHv7Eht2Frmq7M107qCheBWruh9R9iIqnWOv7reI8In+Hx8VzRwQc/v5rVIZNVMg2WmUaSRIiqEn0yo/jY8Ugtot2TV1dHfvuuy+rV6/u9JwFCxbwrW99K/+4qampxMAAIIrLUXeYDJnPu9A5GA6l4313QWALgnKDd52+hlrx3333XWpqavL7k8n2cfAoipgyZQo33HADAIceeiivvfYat99+O/PmzevWdRj6ju7oWifDtwIjLFQ+hQ59aDdxsc79ou+tou0DQbbVpbmuiqDMNoI9qXfDx5NdabszXWdQms4BCRcCDxLFsZIEhYmnO94g6nw57cmIXXuWhDVD9+AtZ38qaaEq1Yqo9JB+quzP013ND2a9f6w8FzvS0tLCmjVrGD16dKfnJJNJampqSjZQ5czVwJZs7GbTWfXasCjuA6AXYb2VSeg4PbIB7T5DR8bF6NGjmTRpUsm+Aw44gHfeeQcgn1S1adOmknM2bdqUPzZq1Cg2b95ccjwIArZu3dojCYeGXdMdXVdQsIUDXY7aQqnLuHiwU7HBUXR3Z2+XJCoypGlSiW5l0JN6N3w82ZW2O9O1TyEtrjEHUqCm9Olx6vpOsXjdLg6b6GqRoscihMCyGMUmUmTJtlnIJhtS5RcE7s56/1gZF5dddhlPP/0069at45lnnuFzn/sctm1z9tlnd/m1dEVphIpNA6W10RR9r+N0eo0doGVKRx55JCtXrizZ9+abbzJu3DhAJXeOGjWKJ554In+8qamJ5557jmnTpgEwbdo0Ghsbeemll/Ln/OEPfyCKIqZOndoHn2L3oyd17aN+6euAtiD2xmnvhV10ku6LpRfj4hbgIUT1UCnVsAYxUAVvGPD0lLYF6oYwh6rySFRRyLvQ+RW6eVFx4n2xt0K3FihqKHfw2tfIiIhGhhKIZBxLZEC3GxgoDF6zqAPee+89zj77bLZs2cLw4cM56qijePbZZxk+fHiXX0vXTSeBVJJCRQi07/AmKExF7YK5Fto2YTfdxKFd3Khg53zzm9/kiCOO4IYbbuDMM8/k+eef54477uCOO+4A1BTASy+9lOuvv5599tknX4o6ZswYTjvtNEB5OmbNmsX555/P7bffju/7XHzxxZx11lmmUqSX6EldQyEv00K1SS5RYFFCW7vkZKvwNWqxWSX3ZjN70FpmzkVf690w8OkpbXvAZmAI8fKcBbsGhJ4pUk9Jh9n8lF8oGBs60dMmH97OpGuwohQbxWhE5GLJLFGmohAi3wXd1fxg1vvHyrj4xS9+0WOvpUv79fgFtzhWp5OEijt0Fo+mLlN4ETZhN03gqAvxuMMPP5z777+fBQsWsGjRIsaPH8/ixYuZM2dO/pxvf/vbtLa2Mn/+fBobGznqqKNYvnx5vscFwLJly7j44ouZPn06lmVx+umnc8stt3Trcxg6pyd1rdViA0kbsq1QMZTSDp06Nq31rD0aRbFpS4S0JqpJk6OikJ20U/pa74aBT09pWzuOW4gjIEH8jR67Hk/zzSdw6mZwGr2ea3nGQyjXjG0gLVr5f7zIu3IvUkN82qrLn/DQXc0PZr1/rIyLnqSNQu10vhS1uBpEu8a0aIuPD2Av8UknncRJJ53U6XEhBIsWLWLRokWdnlNfX28aZg1iim/mXN0zWecLaZdwcaKn1nxR0qfjwYhwI+uteobxYd9/CINhB1Iombaiqvyc4rVYh0WKp6JqA1r/FSxO7pQQ2lAdtlFlNZEkS9ptJpPbI5470kcfahBjjItO0Dp0iaejZlA/LZ/CaHWHgkGhBzt1wUgNsAm6eScXDGLL1tD3bAca4u9dJ07ojCiszPruzaagc93Fs6gFuGiF0ZmNvOwczGZRngvb6N3QW2jJVqDWbM9Xjd4sNz7gUchm1olHOlmZoq/Fa3gIQQDSFawRE3nvw72QdhICSJYZ/u6u5gez3o1x0Ql1FIY82bpkDwqLrrZ8ib/qrQtaCLEJu5lTG5r57oYukEIl0Q8B2nxIZ8FNUboS6EQ3fWenta09GpGKZadXetRP2YZHeaV5Ru+G3kJHpjPAXgISiThZ2Yp31lDI0LeKnlB8w6jD3LG3zgJSTR6NqTo2ZUfiujlENkRGNtlycy66qfnBrPePVbVITyIp3Li1ZuJGWoJCEobORNaeix2TPQ2GAUiIMjAiUK5fL5590EYhfwhKPXDam6FDgPGqsaVqCH4YUUlrn1y7wdAZWqI+yqiwHQrtv3fMg9Nhvh2bIGqNx4a0ADaPHIEjBK7rEVpJZGjDEPOHsxyM56ITJKqV7DCg1YPqHZsLFSd2FmcZd4GeuZMzNVGGrpFFOSAsAX6OwtgxrWdtROu7Oe2pg/zCHI2Cd/dtwBUCq8xJfUbvht4ibhxLAyqk7ecg4cXeCx0WyaI8GMVrub3Di0DeyMgCFWSoZjtDnW3qntG3wOtCn4tuey4Gr96NAdYJ24AqYCTx5Mgdy5egJDaXz7vQyZ9loITX/c1gKBfdrDBAeeMcXWYtKcSgdSiko3kj+vF6qHx3K5EEu8xlpD/0vnDhQoQQJdv++++fP37MMce0O/61r32t5DXeeecdTjzxRCoqKhgxYgSXX345QVDm6GNDn6ENjBDIZGPDohnlwdDa1blF2nAu7nmhbxpjj3RSwvCNm2mhglSUxc8JNckyjKsHy2B3Xt+N56ITtHesGXCKs+ZzqJ9a8T69ENsUXMcGwwAkgZJvGshJyGUhVZxRDwVXsSza9KIba9+SMHrLVt4ZO27Ax4UPPPBAHn/88fxjZ4euh+eff35JdVRFRaFvRxiGnHjiiYwaNYpnnnmGDRs2MHfuXFzXzbfRN/Q/OlKdBZpQ3ZUB5ZbT5dUeqpSknoIBrcPZxcZC3OciCCGqdknLLG9EE6hKZGgU1SAsvMGbZ9lnGOOiE6ooRD4CKB1cVmxYFDdg6eJP07iJDX1NK4Uq0xZgiBa41nGxnbCjtIqqpCIgO76CDdYeeKTLeu/+0rvjODttTV9RUdHp8UcffZQ33niDxx9/nJEjR3LIIYdw3XXXccUVV7Bw4UISCTPBaiBQ3AWgDmX8hm3gDEEZFdW0rwzZod13SQdmoV7Dc20Cy2EvsYGmqhE0vTeMCFn+yHUTFjHsSICOuUFFsWdKGxC6aVpxhYge6lQmIXZcqvTRt8HsNjP0PTp3U1fmOQ7IFMqa1l4KXWIN7RfkeK3z9oR0ugkvhEwXqkX6Q++rVq1izJgx7L333syZMyc/S0ezbNkyhg0bxkEHHcSCBQtoays0BVuxYgWTJ08umRQ8c+ZMmpqaeP3117t8LYbeo4lCUV8iAXZc2ZQvP62itG9LtMPX4n5F8WyRqg0tvM9YAuGQSmcQjlRejTKddd3V/GBe343nohNs1A+nBaiE9lnFWoR20RMs1Kpdds6FY0rzDH2Ki6rMyw/xtUC2UeibXFxqHVFYIbSHI87+dJpAbkqw957rWcm2st67J/Xe1NRUsj+ZTHY4rG/q1Knceeed7LfffmzYsIFrr72WT3/607z22mtUV1fzxS9+kXHjxjFmzBheffVVrrjiClauXMmvf/1rADZu3FhiWAD5xxs3buzWZzH0HAIlzSxFk9UlCD0GGJToXUqrRaB0VkjRcioENNVWk4gyJEWGlrYKZI2AUHTBc9E9zQ/m9d0YF52g5zaFxIlBxQltOy7C2tgoniZpMAxAtDxT8ddEQhkY+e6cusV98eK7oxtZgL0dKp0WAiEZy7t9dv2ahoaGksfXXHMNCxcubHfe7Nmz898ffPDBTJ06lXHjxvGrX/2K8847j/nz5+ePT548mdGjRzN9+nTWrFnDhAkTeu36DT2LXp4riQ1nS+VeWsXtv3XvluKS02KN5ydVql2RgA0Vo6i2WmmQ6/hjcATRh8r10To4x330Kca46ASddVxL3KGz2IDQXgztufBQGXLFmfZlEGJ12+1lckcNXUFSyHGTgO+B64LQScpQqufiChGtfQlUgG8naZJVrGFiWe/dk3p/99138+O2gQ69Fh1RV1fHvvvuy+rVqzs8rif7rl69mgkTJjBq1Cief/75knM2bdoEsNM8DkPfU4Hyyg0Dsh5U16GM5uJqkeJEZd1CoLgleBFSgO1KEtKniToyQTWkJSQlbWUWC3VX84N5fTc5F52gK5Y8VCvZfHanjtnp8j0odHzrYgaxKUU19DU+pTMYWtvAslCJGMXVTzuiF+R4URUpCHNQKQKsMqc29qTea2pqSrZyjYuWlhbWrFnD6NGjOzz+8ssvA+SPT5s2jb/97W9s3rw5f85jjz1GTU0NkyZNKus9Db2Pnpejc46REGYo7TKrPc3F63exp1nfNMYl2ULAkLYPgCxvRAdhuxISEnzYo7K869qd13djXHSCR6mBkW9/XBwGKQ6T6Bayg1cLht0AC9hKweGGVO5f9ACz4nj0jiE+nVdkQS4Jb48cR2TZyAG8jFx22WU8/fTTrFu3jmeeeYbPfe5z2LbN2WefzZo1a7juuut46aWXWLduHQ8++CBz587lM5/5DAcffDAAxx9/PJMmTeLLX/4yr7zyCo888ghXXXUVF110UdkGjaH3CVBzc3SEz3UgytE+YbM4LFKc3FlMfK6MIKxMUCE8DrBeI5Vsw8YHR5rBZWVgwiKdUI0qgW5CuZFFsUGhh+HoNVVnf+p4dZnojODuYFr5GLqCbv+tb+Qsu+hAsTFRPGqdov3xgpxwYP8P17KpoZZ0mSPX+0Pv7733HmeffTZbtmxh+PDhHHXUUTz77LMMHz6cbDbL448/zuLFi2ltbaWhoYHTTz+dq666Kv9827b53e9+x4UXXsi0adOorKxk3rx5O50abOgftANZV53aCQrrtL45LK7u29Gw0Ot5CCTVVNWhzVtZXzOagDSVQTMfJBpAWkRlGhfd1fxgXt+NcdEJ2mumG7nJ2E2WP1icdazFq63kMm/kIpxuu70ikz1q6AIehWTlAJTnArC0500bz7qEr9iILurrEgTQHKXxZDW2KG+l7Q+9/+IXv+j0WENDA08//fQuX2PcuHE89NBDXXpfQ99io2QrUOGRrAeVwyk0PMygGmDok4o70BYPnSzqShta4LZJZLVNtdiGtG1kNoKExC8zGaK7mh/M6/vA9Wf2MyHwAWp9TRdbu8WuteJkt47agxsMA4wUhQnqDlBZEd/hFXvcdApF8eqgXR0AEqwWCF2JK7KDutGP4eOBQ8HTnIDCoEmdJ6STOrPxE4rXbv14hyoSW8Kod7bQQhWhdAhCBzcRQiu45i/nLjF/CjtBd4aNgJS+eyu2bItPtCjkW3Sh/XdPJOwM5mxiQ9+ToxAWAQijeBHYcXHVX4vlGZHvc2E3Q9WHWaJRgqzVlZHrRu+Gnqd4WU6ghvLJCIS2ohMUqkOg8Aug5+lQ9DWuDgwFfDC2mhTNvMEkmkU1fmSBFGUbF93V/GDWuzEuOkGvr5WArz0TFoURkroMtVjVOrGzi0NtusNgFp+h73Ep3LzVotzHiRYQ9ZR2nS3OoNcp+PpOMF5YrYzPdlGP28Vqke5g9G7oiB37YklUNZOVRv2V06Hs4id0RFEY0BKwqXoEAUnSNOP7aYS0kJVS/U0og93ZuDDOnU7YgrrDc4FcFDfSgoLVoa1gHbsrdiWbTGLDAMZFxaWbiB0TDqULr0NB55rivIsYbxgIQnyzjBj6Ge08BrU0pxPKOMj/dd4xCV/ru7jzsn6BeNZOkLKpy7WygQaa5RBsP0LmLAglmcHbOLPPMJ6LTqhF9QHYjnJWSEDs2Lmwo8E30AXPRU80FTKWjKF8XAqe4EqU56JCN7/QFGfRFy+8CUo8Gu+NbSAlW8mK8or+jd4NvYmDkmkTUJWFlPbG6VBIcYfO4jBgcZVIUejP8UIqG1vYa8jr5MQXqK/9kOZtVdBiU1PmX87uN9EavHo3xkUnFI8MqdWrsbZ0tau4uGzPoctei54pzRu84jP0PRnU4juEeJaTVWQ079inpaOGWvGCHFTAcLazUWSp6NNSVKN3Q3u0fIfGjwUQtYI9jNJ1ufgGsLj5YbGREaD6vkTQOjJFKBLUyCwtIotlQ+QWVQ7ugu6Xog5evRvjohN0++8UkNM9AKB9PLo4AU5vZSd0OoTd/C8YzDE5Q9/jojwWutrUslHTInXf5OKk5WLNQ2FwWQROAKn1bdhjJaJMV53Ru6E30WWotUBFUrXvLmlbX0HB+6ZDf1A6V6RofRcSKt/2ccdZhBWC9c17EvkuNFt4ZedcdE/zg1nvJljaCRGqLNoH2oqNx5BCcpu2iLWBoTscmso8wwBFd+bMEDcbikAUl6J21AMASpu+xDdiFZsDNshhfEh9n1y7wdAZFsojl0XZv46tNqCQkKx7txSHtYt7FBWfH0DWgba6JBVRK6PkerzQhkBANThmjd8lxnPRCbpyyQGqi70VUBoS0YL8CLNFoh7Ino8GsdvM0PdYKEeFCzQDw+soHcin7960y7h4emRRvDpKQuteaeqsJobxYVnvbfRu6E3qUOE+D9jeCsPSYOlEZe2qk0Wb1ndH7b8TynORHZqgKVlBWnqMSb3L224lSLvstb67mh/MejfGRSd4qITOscSDnYpjdsV3dvCRx6z3TGne4BWfoe8JUboeSlF6RYCyODS6v4VemLW+tdfCBssBS0iCCCKrPA0avRt6Cz17z6bQXVlqD3Px4LId+7dYFPLldhjBbgPpLRlyo9MIESGFA7YFUua73O6K7peiDl69m7BIJ3got/E2irq96c5aWpC6XbIWrV6MjcvMMEAJUHd3AGkbguJVUqA0nIgf7zh3Qd+KhBDVgJdI4CLwMQO8DP1PJcqOcIGkC041hVC1j+ogV9xNGdp7oYsMDNuGNtfBx6VONuIkfKzw/2/v3YPkrM47/8977Z6em9B1pFjIQbaRhSTLxrGYbOJlgaDFChuvVdktGwNxURCzghgR81OoUizZhEtI1uBaCy/lJeBaoEhI2bsVzAYkYiBrSUElokUGhzVabMlGI3HTjDQz3e/198c55+3TPdOatzU9N835UF3T/b5vX0Y8c/o5z+X7JOBb2Z/IdGP79u1YllVzW7FiRXa+XC6zadMm5s2bR0dHBxs3buTYsWM1r3H48GE2bNhAqVRi4cKF3HbbbURR81NOTOSiAXpdpquq6NU8EXWBwkJON6NWFnwMIuwWVM+bhmtDftRohRLCufBLjGyj1qNzevGycqQ9sPsg8lJO2e30cJw8GHs3TBQqk1cGuuTjbFiZy8haC1XQWX/T1nXLguW/OsY/LmijkhYoWBXSEIit3PGE8dr8mdj7BRdcwK5du7LHrlv9mt+8eTM//OEPefLJJ+nu7uamm27ic5/7HD/+8Y8BiOOYDRs20NPTw+7duzl69CjXXHMNnudx1113NfU5jHPRgHMQdqakZGtQXofeJaI07NvIPcquNdXzMzdsZph81PBeDxgMocuSa64+i0GZpHKq62XA5UL8TjIfjzD3bBFj74aJQlcKGEQ4z/iIdr+CPFlEpP/0FIiuXaTXYiDkv987t53+tAPbSkhim9T2IYH+SVrjz8TeXdelp6dnxPH+/n4eeughHn/8cS655BIAHn74YT760Y+yd+9eLrroIp599llee+01du3axaJFi1i7di133HEHW7ZsYfv27fh+/piNSYs0IET4CT5Ux+vq4kJ6nUWkHTNrn2EaoxbhE0CUynW1iDB0XSRLORaa3HfmZFiQ2ND5Tj8+MSFtk/gbGAwjUY1MmXPRLltRbTLFTRzIJFkcqqlthbpeOiNOAu+lc7Gw+X/pciJLpv/slDm6ou0kMDAwUHOrVCoNr/3Zz37GkiVLOO+887jqqqs4fPgwAPv37ycMQy677LLs2hUrVnDuueeyZ88eAPbs2cPq1atZtGhRds369esZGBjg1VdfbeozG+eiAVrtWrXmQv1r6cqcqtZC6c828S+qin3GezMY8qLs2gc8C2xdPEtvr9Yr66G66MriOBuY1zVAlFoM5cxAG3s3TBQqjd2FtHEHLF0qoICouVBrta7WqTvSqm0V4ZyU24r0cJQLeBXXLkMUQYUmdC5aY+9Lly6lu7s7u919992jvt+6det45JFH+Pu//3u+853v8Oabb/Lbv/3bnDx5kr6+PnzfZ86cOTXPWbRoEX19fQD09fXVOBbqvDrXDCYt0oAiwsZUqZql90Irj7fe61U/c65/rameNzloQ37aEHNz5iCn/bpUR6XqQnBqFoOKZugLdSq68bBsYmxO1rSaNMbYu2EiURouc4AoBF9JeauwRkleqNszVD1upcRsVR/HnkvZKvB+2k2YuDhORFxwc+tcjL9bRNj7kSNH6Orqyo4XCqMXUV9xxRXZ/TVr1rBu3TqWLVvG3/zN39DWNrkRRhO5aIBqa+rXD+r9/2qNU4ari2eZbhHDNEVNn1ZCQyPqhvRFV7+vnGm5HbGAk8Nt2GnMB2huR2MwtJoYIQzXDXTZkKiea72eIqBa7anf9EF96htRrunu0CDv0c2QVSJJ2nBLIVQm/2uzq6ur5tbIuahnzpw5fOQjH+GNN96gp6eHIAg4ceJEzTXHjh3LajR6enpGdI+ox6PVcZwO41w0QM1x6kS0NaX1krH1UyLr2pjyEONk2vNnejNhYkMzKHNtRxQqx6eotqDWrwaqW0TZvQody2vL75V4z55POWcrqrF3w0ShAhQVyCLMlhK9sBELuS0v0DtDlMZFffefC6kNhSRiXvoOThLzTv8iwsESuKMU+TdgvDY/Xns/deoUhw4dYvHixVx44YV4nsdzzz2XnX/99dc5fPgwvb29APT29nLw4EGOH692gO3cuZOuri5WrlzZ1HubtMhp0FNxll7AqdIi9fNG1EKcE1M9b5hsXETB/AmgSykU6nUWyq5VSkQXG4JqO7YNx+fMpUw77zEn13sbezdMJB1UW1BtS9TKWQVq54moOVG6bIAurKXqM+T5brtMmxVyMu0kDS2SEy4Esg4vB5PdLfLVr36VK6+8kmXLlvHWW2+xbds2HMfh85//PN3d3Vx33XXceuutzJ07l66uLm6++WZ6e3u56KKLALj88stZuXIlV199Nffeey99fX1s3bqVTZs25Y6WKEzkogFqvQ2Q9RbqgNrlqVSIcjRc7fg0TYuMJbBy8cUXjzj/5S9/ueY1WiWwYpgaIqr+QZyA3U5tnllPiSgnQ9lzTFYIlwLB4iIOEUHNvHaDYfJR5RKDQNETzoWlDupTrHVBRKj9BlTruYxo2Db0+XOJkwSbFL80DF4C9vRtCvzlL3/J5z//ec4//3z+w3/4D8ybN4+9e/eyYMECAO677z5+93d/l40bN/LpT3+anp4evv/972fPdxyHp556Csdx6O3t5Ytf/CLXXHMN3/jGN5r+LCZy0YB2qjZYrkBJFbl5iAVW7erURYF2LHcPtN2CArfm5uadTmAF4Prrr68xpFKplN1vpcCKYWpwEZHiOVAdG61UZ9WuTV+I9am/Sr3ThWAOdEXvM8fvb0LnYvLt3TA7UN0ibcgyOGWvymFQJqou0Lv89JoLTf4bC87/xZsc/PBq+pNOgrAoXtiWk7JzMF6bb9ben3jiidOeLxaL7Nixgx07djS8ZtmyZTz99NNNve9oGOeiAWXEIuwCRb3/XzdCr+74KCpvp6M11fPNPb+RwIqiVCo1PN9KgRXD1NCGqGkDGdpVEbh2RnY/1XdEafnpNJXyyJTozMQDTs9U2LthdqB84U60zj7pCGeynXrxvS4OpyZU6npG0tZ/vuQDJHbMXOsEsW1DWwKnHE6U832u8XeLzFx7N2mRBoQI26wArqft8qC6yOr6F6rLp4khZq3s+88rstJIYEXx2GOPMX/+fFatWsXtt9/O0FD1i6OVAiuGqaMMtNlQKEi7LlHrSKj8tLJxPUInbT5a6tCRDtJOP2nO3ZXRuTBMFGqv14GYLeZ1UJvGVmqcuh6RbtejTP+NgXKhnTaG+H/xeeJvxRFv8mv5uq9ntb0b56IBcxAR4hRwVdhY1VbouTugpli+CZ2LVpJHZOV0AisAX/jCF3j00Uf50Y9+xO23385//+//nS9+8YvZ81spsGKYGiJEkEJFHuhAOMa6alwBsSDr0yLVDk/+HdjvpDiDAQk2J+mc/F/EYNBQWT01aDK1EHatlDmVE61SffqEVPVY3WQANnJg/sD7JEmBwHZwvADLioTOy3QtuphGmLRIAyJ5m6f+hfQ+aLWzUzf1WIXhwnzvodqUxoPybPOIrJxOYOW6667jhhtuyM6vXr2axYsXc+mll3Lo0CGWL18+rs9pmB54iDU3TYXQUMFHLMC6kFB9y3W93dvg2Cm2hZgYyUCu926lvRsMOg7VcQ2WLXfNKvKmOxX1m0Sorb2AbOPoORBbDkesc/kY/8y/9K8iLXtN1lyMz+Znsr0b56IBCXJT50EcgqMbnvJ6671g1SmSu+aiFa15Qs1Lias0gy6wMhrr1q0D4I033mD58uX09PTw0ksv1VxzpgIrhqnBRogNLWmD9m6ZFtEF4fS21HptF6d6zA1S2uxBQtoYyBm5aKW9Gww6PlVRWVvfEBblzwKi6F4NLtMjF3q7tUVWmG9Z8C+lJbxtzyNJPTwSKqkFFWjLacbjb0WdufZu0iIN6EQswmEkBzspz1ZZsWpB1fN16tw0bUWtRxdYGY0DBw4AZOdbKbBimBpchG07jlQxVKJYav3TIxRqR2dTjcrJcLAdQhpbpNlMa4Nh6lCOxVwHfFVDVEQau7wopaphodAF4pTDISN5gQP9xYV0M0SZds6Zf0z0bwOxSYuMiVkVGuAhU8+ujFrUh4d1g1TGq4eWczDZ1fOnE1g5dOgQjz/+OJ/5zGeYN28er7zyCps3b+bTn/40a9asAVorsGKYGhJgAOhJwepG2GsJEVPWevxr8tFqp6e1qVoplPuKHO/+Ndo4leu9TbeIYaJwkfIBNlROQrETEalQipw2wtnQBRB151mLyiln2rFh5eC/8GrHRzlJB2HowIAFYTMiWrO3W8Q4Fw1QE3WTFKyAkUaoijyVk6GiGk1EsVrT958/+KQEVt59910WLFjAb/3Wb2UCK+VymV27dnH//fczODjI0qVL2bhxI1u3bs2erwRWbrzxRnp7e2lvb+faa689I4EVw9TgA/MREbmimnqqFsosrkx18VUrhL77cyD14K2PfIAF/Io3OC/Xe0+2vRtmD6rLNJuIWp/iK1Fdn/ViZT1FUndzbPi/7ctxLSikQ3hFNUq1Gfnv8epczFx7N85FAypAtyML3hKqxlgv++1SO7isSQnwyeR0AitLly7lhRdeGPM1WiWwYpgasnb/GLGz0yNtyn6Vk6GoL2J2YPjX4JfpB4itAufUjvczGCYdCzFssrMgB/Ipm1UKtKpWDmqjzx6167ZXPWZZcE7wPl2Fd6jQRniyCIUUPIsTwWT+djOTmesWnYYdO3bwwQ9+kGKxyLp160YUIeYh1e5Y7fK+PpJX7exUONmhqmCY01Ed7xAndTPMDlph1w7QXoTOTrC6qC60RYSzoXLQyo49ec7RHjtQOg6dAwO4acgRluV6b2PvhtFohV0XgHOAULWeqixtiHAw1CawjaqjoWxdRZ91dU5btKIO2e2c5BwiPCrlDuhIq6+Vg9ls72edc/HXf/3X3HrrrWzbto2XX36Zj33sY6xfv76mCDEPBcD3IEkgq1lTToWek1aRMt0GmhxqM96b4eynVXatFJGddpGfJqY6ArjIyJ2csmtlZlKIqNzlsNL6KYfTZbzDObne29i7oZ5W2bX6vg8iqs6wTzUV4lCNYNjUpgNVRE7bGKaWmC0SuQW6rZN0Je9TLpfgpA0JdOQUI57N9n5GzsUll1zC17/+9RHH33//fS655JJxf6jx8M1vfpPrr7+eL33pS6xcuZL/+l//K6VSib/6q79q6nVcRM7NtqlWHCuHQp+/oBulOuaN9oqG6c5sseuiSnuU5c8haoc2qQiFPobd0R5bEJcskqJNZPu4eYVdDFPCbLDrrPbYAquEiMIlkHVJFxBRCxWVc+UxJRinp7zbgKJIi3jhIMOpg2dHWFYCoZDuPOt25RPAGblFzz//PAcPHuSf//mfeeyxx2hvF3mDIAhy5e0niiAI2L9/P7fffnt2zLZtLrvsMvbs2TPqcyqVSo1U9sCAEATqAqIE3DaqQ0b0QiCo7uyUB6wXEeUgaUH1fDKDw2bTjdlg1wXktEhdMEtvN9WL3HSHWbd5H0r9EUfeLtL2awO4Vj5FIWPvU8NssOsiomazs5vqpk91hxQQEboOeV+ltJV96zVHPqRyDY8cwLUpMcTxZAFhbEMlhYqVuxV1vDY/k+39jB2wXbt20dfXx0UXXcTPf/7zFn6kM+edd94hjuNRJaobyVPffffdNbLZS5cuBWTUTIXO9DSI0rzQi95cqrk8PZQ8BmbWwvTjbLfrGCmcFSBy0SWETavBZVrR5oifyrm2RDV+F/10R0OkORPQxt6njrPdrl2guyBLIWyEE1FCpK2Vw5xoj3VhOLRj0mlIbTEAtZNBHGuYY+liSqVBaE9gOJO7GJPZbO9n7FwsXryYF154gdWrV/Mbv/EbPP/88y38WJPH7bffTn9/f3Y7cuQIIOysrQCe0pRVkQu1k9NDxyotokuC50C1KY3vZgJ0reRst+sOR6T6LDVTRCkWtlGr46KblV5npNKANqQFh9BNKOR2Loy9TxVnu117CKfZKYLlIULPqvi+iLBzNbhMOcmqAEmTGEhtiH0ICzDs27xrz4PUpyt8F8eLwU2g3PzI9dlo72eUFrHkiNBCocDjjz/On/3Zn/Fv/+2/ZcuWLS39cM0yf/58HMfJJKkVx44dayhPXSgURhWASgBXxdrUoqtQDgXULrwqtDxzFVtnNbPBrm1dDEstsi4ikqFyz6oQTkUslEOtnI5UmPhr8z7KL61l9GME1KYzs8GuCwjZAEulsZV9KzEtqHWi69uu1TouazASC3wShmKX/+usYknxPX7eHkDFhS6YY0x+TM7ILUrr5Mm2bt3KY489xn/+z/+5JR/qTPF9nwsvvJDnnnsuO5YkCc899xy9vb1NvVaAqBi29GiEkvfW+/7VwlvfR50D05o3vZgNdu05YCfUisCpnZtaXPVIXH3dhVx8bR8++O4veZPzSWnL9d7G3qeG2WDXDmKmSKoX3PtU5e0jhGOhNotaW7V6nMox6zGQOBC7Fovp44h1LseZRxw64vXaoBLl+1yz2d7PyLl48803WbBgQc2xjRs38k//9E9NV/m2mltvvZXvfve7fO973+OnP/0pN954I4ODg3zpS19q6nUWekAMaUS1oliXi9VFhZRHrGte5MC05k0vZoNdu2o3p/f5d1Ad8KSrFaooh3I62qmxe5cK8zhOGydzvfdU2Pv27duxLKvmtmLFiux8uVxm06ZNzJs3j46ODjZu3DhiJ3348GE2bNhAqVRi4cKF3HbbbURRzm+XacBssGsbkRaxbGAOVbnviGrHSIpQR6xPZav1vQCxIwo5U8cmcRyO2B/kw7yOR0oUOeL6k1KoKwezeX0/o0++bNnoojkXXHABF1xwwbg+0Hj5j//xP/L222/zta99jb6+PtauXcvf//3fjygaGou2goyczaFW5lvl8JSzoTxklYNzEQZtmHHMBrvO7FhF2pQGgHKM1U1vQ1XPUQJFshgujByK6SAVK1/kYqq44IIL2LVrV/bYdavL3ubNm/nhD3/Ik08+SXd3NzfddBOf+9zn+PGPfwxAHMds2LCBnp4edu/ezdGjR7nmmmvwPI+77rpr0n+XM2E22LXnigGTlotIi9TrtPiMrCtSN8Sx1AbHgtC1SICK49FjH+MfuZgKBYJKJ7yLcFAMYzJz3aLTcNNNN3HTTTeN6zWCELGjg9oCIKVhofJ1uh69imLk1LkwrXmGZmiFXTsuQt+ik9p0iL4Ya6HiGg0AtThLoa2fdHwEN02IrHw2OFX27rruqDn8/v5+HnroIR5//PFM7+Hhhx/mox/9KHv37uWiiy7i2Wef5bXXXmPXrl0sWrSItWvXcscdd7Blyxa2b9+O7+dUUzI0pBV27XuQxpAGMnqhHIgOhGOh2lB1pVklGCfTKbEFoQ2kKaHjYFk2CRbnpa/zj8O9QCLGoSZW7sHXphXVMAKvoEUtuqgd4lRnmDWCLLoOxhiY1jzDZGN70q5LCBuub0VVzka9RLIuj+zASR8WtA3Rbp+ikrPmopX2PjAwUHPTtQ/q+dnPfsaSJUs477zzuOqqqzh8+DAA+/fvJwxDLrvssuzaFStWcO6552Y6C3v27GH16tU1O+n169czMDDAq6++muv3Nkw8vrTTVHU/xYgIRoiIzikfUNn9KA514ELiQmBZRG6BIcuhTBshHj3Ft4X+RSye3+xU1Nm4vhvnogFeO1hqIJmS/1Yeb5GqF2xrx2BaDy4zGChRdRKGqDoNqk1PLbrKgdYjdmn1fqcNhUqZgA6SKVhGli5dWqN3cPfdd4963bp163jkkUf4+7//e77zne/w5ptv8tu//ducPHmSvr4+fN9nzpw5Nc/RdRb6+vpG1WFQ5wzTBFdor9gloJtq4aaKPqsaC5XaU5END1LpRNsOJL5D6nkkloUDvMc5nGAu79lz8fxBIQFqBGlzYb4GG5CF1nRFNz0NAqOnSpqIkkY42OP0TGdyNbFhClDKhSoq0U51AJ+eAlFROPUc1S2lFmkLKrFPJXVxrXyrbSvt/ciRI3R1dWXHR2tPBLjiiiuy+2vWrGHdunUsW7aMv/mbv6GtbXrXihjy4xTAOoeqDavR6spp1mss1BpeqjoWqS0KOZXOVoJLgktIgXaG6UwHsOwUq6dC+qsiThMdgeOx+Zm8vhvnohFl4DzE4qsKOPX2JX2xrW/ly1nQKcJe4/tfMJPDZobJJ5P+7kJU1avwbsJIm1bX6s60JfLTqQ0/Ly6jyKnc791Ke+/q6qpxLvIyZ84cPvKRj/DGG2/wO7/zOwRBwIkTJ2qiF7rOQk9Pz4gpnaqbpJEWg2HysZQjoVIhqtZCOcpQjTDLDWEqnQ3lWMQuhK5HbHmEWMQ4zOEtdvNJjg8upPLOXFIcKOZvRR2vzc/k9d2kRRpgdSGcBJ+qQar7DlXdepUG0XuozeAyw3RFLbgq2KDy0SoXrS/IajFWlfbSsYg9eKvUzpDjE1kzawE8deoUhw4dYvHixVx44YV4nlejs/D6669z+PDhTGeht7eXgwcP1kzp3LlzJ11dXaxcuXLSP79hdCxLZvs6qBZvdiLsdw7CptX6rEWcEx8iT6py2g6R5VCxIMYnwuIIH2C+dZwF/rvYbgSBDcOQ5Ky5mM2YyEUDbDkZL2stVY6Epd3XpWR1QaLc8t/jL9iZSQu7YRqgFtkSIztFVApQj9Cpwk7pWKiw80mvHc+28IBz6M/11lNh71/96le58sorWbZsGW+99Rbbtm3DcRw+//nP093dzXXXXcett97K3Llz6erq4uabb6a3t5eLLroIgMsvv5yVK1dy9dVXc++999LX18fWrVvZtGlTw1SMYQpwqXZABcA58lhMrdihtHsVtUjk2p24YDkusSUEMmy52Ed0UyJiAJ/wqA0DQAhuk0X7Z8pMXt+Nc3E6ClR3dmpX10atUqc6prpHpPhWHoxzYZh0dOE3D5iHWJSVTdfXEGlKnXEB4iJEKfTEb/Ne2kGf9Wu8T770xFTY+y9/+Us+//nP8+6777JgwQJ+67d+i71792aiUvfddx+2bbNx40YqlQrr16/ngQceyJ7vOA5PPfUUN954I729vbS3t3PttdfyjW98Y1y/h6HFqCJ7C5HyC+T9gryvhOK0aaixK2ouQhtiLELLkjUOFiEpCS4f4if8A+sZ9Objl8oEngdtdu6Qv3EuDCMpIULHnfK+ruSmhLN0XQC9d9rMFjFMV1TaQ9e50JU59Tk5qvjTEuFjdU3sguNYLLTep8R7+NNYNe6JJ5447flisciOHTvYsWNHw2uWLVvG008/3eqPZmglUmGTbqrznVS3SJGqWqcPqQepK+w4ciF0HBLHJrQdLFyGsYkpkGDxKz5EnPosTX/Or4pLILKhIsS2DKfHOBeNiKjth653JvShT8rhUBLKOf9VjYiWYdJRthohFmIVcdNnjCj7lg5FaovFOHVkJb3vkVoW3QzgASfTfJELY++GCUMfRKYcDT3KHJGNYE+lQx1bEDs2qecSYRFZHikWFj6ptNUQmxQYtoqk75akhGf+movZLKJlnItGFBBer5oZohZbfew0VEti9Sr7JobaWKYV1TCZ6Om9NkRXlBKJU9otqohTE9TKnAvfJrUsIqBo9fNuuoihNF//tbF3w4Sh6uNiqm3UytY1TaJUzQ9xIfYsQtsmwSaxLFIcEjwCLFJSAsRskII1TDktweJB+FmncExyRqfHa/Mz2d6Nc9EIH9GG2o0IIatwsZKSVTs+Zcg+tQWfOYhxsE0rqmEyUeambFql+lRUTk1FlbadyvqLwIGKZxO5DonlEtsOHZykaL1NMc3nTRt7N0wYar0uIVIi9cP5ZGo7tiDwIXItYt8ntW1CPGLLIcQhkcPCQjwSHIZo5710ES6BdLSF/Pfxk/k+1nhtfibbu2lFbUQXwigjqt6wXhBUP0FS95Rnrj0YznaUI6zCum1UC5A1gSyVDsFXEQsLCh7YDpHlUqaNfem/wk4DkjhnBbPBMFHotW8q3ecgInMlhG2rlIgDtm2R2DYVyyGyfBJsYjxiXBnBcIhw6UrfISDi7coCCBLoSCCGNrMtHxPzT9QItXtrp7auQi3AuuR3veZFTp2LuAWKhTPZszVMASoXrYtmtSOiccquVUROtqImHsS2TQWbxCoQWxYWMR4hQ8ylZOUbE2ns3TBhKIdZdQerKLLaABYgLYr0XuSINEiCRSprKiJsYixSrOxniMsw3czlJJFVgGMeDNiQQmmS1viZbO/GuWiEjfCAdelvteCq+6qASNVkuNVK5DyYxdYw6dRPQgWxMMdUi990SeSiKF4LHY/U8oisNKuk72SYAtCXLsn11sbeDROGSzVCUUSkRdQ6nVJNa7dB6HkklktoWQT4JLgyQO0Q4hPhk8hKicXWEf4lXYFdTiBqE2pdAXg5zdA4F4aRqCiFcizUfX0CquoikQ5IKneCiRlsY5iu6HbdRbUwWRunnlXT+xDbovsusV0SLEJZSZ+QMoxLSsKppHkZboOhpahaIb1+yENE5GQdRuqJgF1iuSSWiL2J+SGunCniZRGMEJsIj1/RQzlNOdHXJfQy5Dia2Ch0jolxLhrRhjDYOVQNVykY6jfpUKRSHjmwIcgZMjPV84ZJp0R1Xk5KraS9FIZLXQg7RBFn6tikaUoMRJZNjIMlF1+fk5xKPZam/w9YPeZbG3s3TBj6cD3V8aQpzqZtIi1Sbreo4JHiZEWbYMsaC1FnkeKRYBHhEtLJL/kIC897h1+90Q1HxGwRN6fOhekWMYxEtePpWha6d6xqMnxILDFFtWxDWoIoZ32bqkweD4n5X2hoBl3TQtVZKInvAiRFIS4UO5DaFontENsWoeUT4RLgkOKJcK9lE9JBX/kDuUrDjb0bJgzdWVbpa7V2p5A4UOmAxBPOcSL/CFThpqiz8EjwSEBG56CN92nnPcL0A9Xi0LchzNmKOl6bn8n2PnM/+USjj09Xo9eV5LdbvZ96kLSLqXppCULbJnJSquX4BsM0wkLEhudSW1lfhNSXY6dtSB2H1LaJbZvAEru8AJeEAjE2EQ4nmYuFhZ2zoNNgmDD0dLVeqKxFlhNHyHqXZeQixibEIcAjwSZEaF2U5SIf4zOHfuZZx0XX4BKgD/E3ZJb3MTHORSOU9HEbIjWi8njquCx8S2wIihAVLKw0FX3TngOMveDGLQgTz+SCH8MU0ImIWKhBe0oszoGwIGS+EyC0rcyxiGxHho+9bIcHkKYpPsNiW5jDDI29GyaMImJYmcUIXaK0E0igYttEjkdsiXbTQDoWKTYRFhW5yKu21FCGqW0sbCXJWRI/rJxpkfHa/Ey2d+NcNGI+1U6QdmpTItI7Ti1R/JZ4gG3LqIVLaOd1LuwWLLZGqsTQBMoxdhFOhQzvpu0ivZfaoogzdlwqlk9sucRAIAWGEkRjSYLDKbqoRA7RcJt4rTEw9m6YMJRZtVPbEdWNKE52IPF8EsslwpHORYEEjxT1Je4TZlENh5SYE3QTYzP0bhecBIaBtJmpqOOz+Zls78a5aIRDVhkMVB0LWfyW+sK5iD21y3Mo2zZYxbxDUQ2GqUHpWlhU26htIYkc+Z7QtLB8EsshRUyKTOQOL5Qrd4TLcWshOJ6R4jNMPW1UCzldMhtPY5G6jn0Lm5TQcgnxiWWEIgECPJkisaVTIXQuAhzm8zYB7Th+AO/JkesBVExH4JgY56IRow0qK4n7aUE6F7aQk7Vtm1NOkdjyZCV9PqLsxc+cmVxNbJgCVGu1jQgjF0StED6Evk3kOFRwSCyRAgnwSUllu54o5KxInYtz0zf5ubUUCvnkv429GyYMH9FarVAt17YoUi4XbYYtT7aY2pnEd4xFgk+MKOIEhxAIKBDhcpAVvFlZSMEvVx1y8utcjNfmZ7K9G+eiES7VqXpdZBXIqfSMExk+rnhQ8dpILYsEVyq85SPGxRr3rAXzv9DQBGoEtVLiVA60JZQLI8sjtfxs5xbjkpDKinqRmxb3HWwiBmkntPPZoLF3w4Sh2k7VJlCmRuKSSF3HnkdAUXaDeDJCIZTkYmxioCLTIbFMjQzhU8Dmg4VjHB/4MLSlULIgBTt3zcX4bH4m27sJaDZCKRWmVNU3fdmmZ0PkwXDRJvZdIkuEiSNSytiEOQ0iyYz5zG/NjOTdvn07lmXV3FasWJGdL5fLbNq0iXnz5tHR0cHGjRs5duxYzWscPnyYDRs2UCqVWLhwIbfddhtRlHMMrGHqUVE4dYuFo1zuhMj1CHGIM2fCJ5F6AGKn55NQIMHmHebyBucTJkWCoXxTUSfb3g2zCKXQ6SHW7DYxY8zyob/NJXYcsEJikAPKLFG4jEsgizcjCkT4BLhUpJBRiZP004VlWTCUwgn5+jmdi/Ha/Hjt/Z577sGyLG655Zbs2MUXXzzie+DLX/5yzfNasc7PXLdoolGKhW2IkdNFiAqQyFG9Fc8G16VsWbKVyaOCS0yRaBpXXVxwwQXs2rUre+y6VRPYvHkzP/zhD3nyySfp7u7mpptu4nOf+xw//vGPAYjjmA0bNtDT08Pu3bs5evQo11xzDZ7ncdddd03672I4Q5SGiwdpNyRtkLoWARYxImpRwSeSjkWKTYBLik+I6L1/l7kc4iMM2HPwkpwrrcEwUagIXDs1ysoVCxzbph+XkJKsr3AzqW81CRWQjrTScRHpk1+lv4abRBSTQXjLQhZp5NYymkr27dvHgw8+yJo1a0acu/766/nGN76RPS6VStn9Vq3zxrlohFqA5XyRUC3GFgSeQ+QViIDIEuG1U1III8Emr38XtyAH3Wyrkuu69PT0jDje39/PQw89xOOPP84ll1wCwMMPP8xHP/pR9u7dy0UXXcSzzz7La6+9xq5du1i0aBFr167ljjvuYMuWLWzfvh3fz7eDNUwhsno+CyEXRO1QYNskjk9qOYRYWbQixslSIQAxFhEWNmVsKvjpEHPc94BFY771VNi7YZagDdqjg0xAK+yCwHJIrTbAlQ6zK1MfInciogRkaUBRX2RjIab/+vYww247hCIlgg1OE62o47H5M7X3U6dOcdVVV/Hd736XP/uzPxtxvlQqjfo9ALRsnTdpkUYEZNNN0wLEBRE+rrgQ2Y6oLLbE4luWseZEyiLnTYuMN0Ssbs3ws5/9jCVLlnDeeedx1VVXcfjwYQD2799PGIZcdtll2bUrVqzg3HPPZc+ePQDs2bOH1atXs2hR9Ytk/fr1DAwM8Oqrrzb1OQxThFLm7BZV9ADlEkRegdhyCKT9RtK5iKRgVoRHiEtZVtp3kHAh/4eTyRyODSzO9dZTYe+GWYKuyCkL7uN2IIVyKpTjYlLpMKvOJ5UGtIlwSeQQs0TWY6SAZ1V4l0WUB4vgW+I74RSEOXeQU2XvmzZtYsOGDTXruc5jjz3G/PnzWbVqFbfffjtDQ0PZuVat8yZy0YhOREqkGyiKOQux6xK6PpFTkCpuQqe+TAcpNkN4xHgEU5AWGRgYqHlcKBQoFAo1x9atW8cjjzzC+eefz9GjR/n617/Ob//2b/OTn/yEvr4+fN9nzpw5Nc9ZtGgRfX19APT19dUYnDqvzhlmAEWydr3Uh3IbhJ5DRMqQ5cvUh3CUY7nTi7BIsKlQIMWjgo1LSB/z6Rh+nzlWP+IPxmCYIkoIp1l1dLQjCjpdm9AtUaGNCIdh2mU9ETLd5xPhkUpd/FT+V5H2X05t3CRgaKAoNpwx4OXvFmkVedZ3xRNPPMHLL7/Mvn37Rj3/hS98gWXLlrFkyRJeeeUVtmzZwuuvv873v/99oHXrvHEuGlEAOkUaJHEg9D1it5AVAKnOkEQapGplEvm6fETYsv3pzFEiK0uXLq05vm3bNrZv315z7Iorrsjur1mzhnXr1rFs2TL+5m/+hra2NgyzABXRdMWAMqsIFdshsDpIceUQdTEBVVTPF0iJZS2GIwvihO2fYC4Vt0gQ5wuAttLeDYYaVORCTam2oOLDoN+F6AZJGc4Kk5WuRUFGKSwibMo4pBQoYxFTkBHoIo4V4HVGBHI4JakoFs3DeG2+mfUd4MiRI3zlK19h586dFIvFUV/zhhtuyO6vXr2axYsXc+mll3Lo0CGWL19+xp+1HuNcNELTA0jbIHI8EUazPAL8zKmo4FOhRCx3dwEWkcqnjEGcxfHOHNWqdOTIEbq6qo3ejbxanTlz5vCRj3yEN954g9/5nd8hCAJOnDhRE704duxYlpvr6enhpZdeqnkN1U3SKH9nmGZI+fq0BJQgiSG1HFLLktXpPgl2tuCK1mqPsqy/EE610AnwgL7+c4mtybd3g6EGpTpri+L7AKiUxALuknCSEhZCdTOSqpkiOmdlznJMIRs0lsrR6zYRVmLRFgwTlOcIxyVuRv57fDbf7Pq+f/9+jh8/zic+8Ynqa8QxL774It/+9repVCo4Tq2zs27dOgDeeOMNli9f3rJ13mwDToNagMs+BJZHYHmyit6VlcZFqfYmFuIKHpGsrJ9surq6am55nItTp05x6NAhFi9ezIUXXojneTz33HPZ+ddff53Dhw/T29sLQG9vLwcPHuT48ePZNTt37qSrq4uVK1e2/pcytB4PiETXU+IKDYDUFotuWdp2gkMFj4A2KhRlbtqTraii3ugwi2innwXdR4kDUwdhmGLUeIaSqI+L2yC2XVIrZgifCjYVXMp0EuIzjEuAL7tHPDnxF8JsUqpNQoEP8wZvVZZSsUpiKuogEOXuRG0Zedf3Sy+9lIMHD3LgwIHs9slPfpKrrrqKAwcOjHAsAA4cOADA4sWidqpV67zZBjRCyslWihB5FuUsXOxK43OIKBCSMkiRRBpwgpu75mKyq+e/+tWvcuWVV7Js2TLeeusttm3bhuM4fP7zn6e7u5vrrruOW2+9lblz59LV1cXNN99Mb28vF110EQCXX345K1eu5Oqrr+bee++lr6+PrVu3smnTplzOjGEaIDtFLF8Uvg8WigzbbVToIMIjkiOnQ9lOksrJkaIIzmUYi5Q25nGSn+NRtlxwEvLYsekWMUwYclZOWhTD94aLNmXLpZ92AtpJKMoUSCRrLoRtp3Itj7GIKZJQIAUCmV/5OR+g35lHpIosUsDJ71xMdrdIZ2cnq1atqjnW3t7OvHnzWLVqFYcOHeLxxx/nM5/5DPPmzeOVV15h8+bNfPrTn85aVlu1zhvnogFqkFNQcoh9EbWIZI4uyWSRxSQ9NYZahJTJ2vbGImnBYtuMyMovf/lLPv/5z/Puu++yYMECfuu3fou9e/eyYMECAO677z5s22bjxo1UKhXWr1/PAw88kD3fcRyeeuopbrzxRnp7e2lvb+faa6+t6Zc2THPmCG2LKIaTcwvEtivrKCxCadORTIVEsqYoxJaTI1VdUcox5uFgYdsxgV0gm4B2Gibb3g2zCB9RwFmEsF1NbUhQue1hufFT6rJivbalY1H9G0iyDimPCDiYfoyiV6Y/lH2uqSWE53LWXIzX5ltt777vs2vXLu6//34GBwdZunQpGzduZOvWrdk1rVrnjXPRCBvKBRj2PIasdmI5bETlnYURiv7/FItQysiKY9OTJ5544rTni8UiO3bsYMeOHQ2vWbZsGU8//XSrP5phkkhlq15SQo6ZLgBtsshNaVvYlCkR4mDLvHQg21OVTgAklCkQpiWi/gIsHp7i38wwqykI205dkesPbJvQ8oGYIamIGFKkLFN8aRatEGlsUUNXFdVSNRfF9CRJeQlOkBA5FsiOTSunczEdeP7557P7S5cu5YUXXhjzOa1Y541z0YCkA4a7fVLbA0vUU6TSy42yfJ1YaIdpk/LfLikuQY5x6yCG0thmJ2eYTKQ4XGxBZBVILaQ2i09Fdjul0klWoeGYJOsWCfBI8SgRcZwesYKU8721sXfDhFGEpF2IHYaOTdkuUaFABSEbENDGMFUhLWT0YpgCqYxcRDL6rLoAAwo4lkXR7yceWgyFVPzhpBDn7hYZn83PZHs3zkUDQh8sxyEhpiLbT21Z6FOWRW4ikmGRSoNNZL46IJ+CWXV4zpkzk43PMPnEDlRsOOl5BJbLKTqJpXiQaL/z5GNb2pboIhnCyZxrpQmQYJHEFgT5ReOMvRsmBE+IwgWOxVChnQrtDFIipSA7QSwSEkKQhckiWoEWpYjlzxQYll0jiVXEsS3SEz4MyEqLodN8jjrGa/Mz2d6Nc9GAoQ4bL7UJbIdhGTYWtRWiR7oiQ8ZqyE0glQtFamS6JkYMs53Uh3KXi+26BJYIA4dSm0UUKRczxcIIH0jlgivqjQIcLCzeTzsoMEwQe7hzTErEMLWkBYhcCF1XFCBbDpAwiEdIGxE+Ia7s6HOyrqdUdpGkMkUoCpqrA8PsdJhyMBfSBEIynQvTZjk2xrlohOtQtkoMUCSW4TW9er5CEVv2SIv8tCvPucQEud4ilrvB8TCTPVvD5BP7YNs2ZXwq+Ng4VHAoU5QOspupcZLVEFnE2U8hJjBktTOUdmC7NtEJB3I41MbeDRNF5INVgsgtYFkppygQybqhCkWGaJNOhk2EL1N/qs7IyrRbkiwtKNLc76bzePfkuWA5Qv3zJJA01y0yHpufyfZunIsGnLLbcShgyVSIMEhX1ln4UkjLJSKRLU6eHOPrEZAvIWcWW8NkM1SwcewigdWGjcUQFsO0UaEkRbPsbJ5IjAXyZ1mOo7Zk1K6LihCRi0NIPMhRZ2Ts3TBRpD6ktiiwH6BNtplaRPhE2NikRKRydo6w8wSkYJwt0yYOAZYsYhby4I5tc053H8cKnUL62wUssHOGLoxzYRhBjIdjIcNmQkAokkVtKmwmeqQ9kN0iQqu+QJjTr40ThzQZ52I7zucbZheR4xFaPhXL4iSiC6pCmyxQ9rHwCLNF2cKGzOlQhW4iXWLRznukybLcpfPG3g0ThWUj9SkKuDhS6NDFkh19Q5Tk5rAj2wiGmcR3KtWVXSk3AOBQkfUXiWVD2YF+RD4kyS//PV6bn8n2bpyLBoSWIxdakYcLpCqnCKMpES2hZFiR4bZEa0c1GKYjgeVJu+4iJaWMR4JNLPXuy3I6ZCoLOsNMA8CXYloOKWIh72cuYTgHkrE1LgyGieRkARy/g7IlCpJFdNlliCKJTOUJfyCgTLssvhf1c8JpRrtObCiT1OWjyas8896/g2PAMCIf4kM5nLJfdcZgnIsGDOOR0kY/Qr1QTYQMpUKn8nSFBkC1RVUVveUhjhySaHyeaTrO5xtmF4nlU7RSTmARyYiFCBlbWWFymjkVvnSYka2pBRmmFWHkzngAO0ygkrNbxNi7YYJICwVSy5bSAO0kWAzSKbubABKG8Ulo05xnG7AJ5HpekTUXoRSRS3D4Z+s38DrKDNuIyavvIkS08o5cH6fNz2R7N85FA1JcTtEuW5ZsWWOhpqHaDMrwmVB6U0VAvtzl5ZT/jlysaHz/C9JxPt8wuwgtUUcR0CYjcmJeTowjo3OixVoNdRJRDTUvRzw3wmKQIn3OUiq2A16+bZyxd8NEEVguPgUCbAIpY5/KVHVFFi2HeFRQdRC23BQ60okmG9qnZujElk0UWZQrLlkZ3SmgUn04FuO1+Zls7zP3k08ww3g42AxnoTUnG2ij1AzFYz1f50tJWYNhelKmTc5asKVQkJt1Q0UyLBzJlupAOtai/79EJBfrEIfhtMhQ2oZ9EuzIpEUMU0vZcrEtJ4u2pViU8ThJJxXaKNNGSFHWYjhZq3UsneUUambopEr23vEpFgKCUEp/y3bUgZNT/RtPf4xz0QAXm5O0yyKgIhUKxHKGSCSLNhMcYtJsSmogRYfC3GkRG2vcYWLTcW1ohhQLm0GKhBSkTkuJWMojhzItEskUoCjkTIlBjqSGCF80roZlht05JGUPcrRfG3s3TBQhRU7QTkCRMj5DtEv9CmQ3CJkdK6E4pLMManiZK6ekymL9xOXkqSJDJ7sBS0xELQJHYeG8fJ9rvDY/k+195n7yUfjgBz+IZVk1t3vuueeMXmuAdmIptJLgUJZRiSALsxWo4FGWVfbDFOXjNiKKud4jjpyW3AxnN62061OUOEUHFh4xRVKZf45lnYUQGyrILiix4FZwCSloEskixPzW8CKS/+vAqZzdUcbeDRqttGvRKl2QA/c8qW/RRkAbFQoEsitKpEYKUjzOlTZvS+da2LsqXq5YDnFqE8cJdKbCuUgBCyr5JjzMans/q5wLgG984xscPXo0u918881n9DoijOZTpphNyatIRyKWmhdC06JAGR9kni6RfdQGQytplV2nFKhkSrIJZZnuUAJaSjxIOBuODBt7hJmwkHAyAgoUPQsWh9A+M9Ii99xzD5Zlccstt2THLr744hFfcF/+8pdrnnf48GE2bNhAqVRi4cKF3HbbbUSRSX62glbZtYo6qMmmCUUsPCn6bckCTotYpkJSuXEM5RoeZkMprUxuIMUmHixCfxckttCJkw7GO++39J/hrOSsS4t0dnbS09PTglfyCBG1F5E0wFQWvEWyfS/VUiIiZ+dCJps8NlHkYIWmet4wNq2y6ySbDlmkTLtU3vSoSLsN5IA+VWMUSmdZdEvZpFIqPLZcXLeCG8VElXzlbVNp7/v27ePBBx9kzZo1I85df/31NeOkS6VSdj+OYzZs2EBPTw+7d+/m6NGjXHPNNXiex1133XVGn8VQpVV2HeLhSTuNZLo6IKWCwxAuEUVZa2FLMTil36LqilxEobMnNYsKBKlLKa0QUCGtFKENkf0L4dycH3m8Nj+T1/ezbot9zz33MG/ePD7+8Y/zF3/xF2PuMCqVCgMDAzU3EJMihyhRpoNBKY1ckb3TgRzwJIrjPDlRskBAiWG5M8xDGrsk47yl8VnnHxpGoVV2nWbhYEdOh3Q5IYs8K1LjQoWLAxzZSeKS4hHhUpYL8OFkERXbI4mL4Hi5foepsvdTp05x1VVX8d3vfpdzzjlnxPlSqURPT0926+rqys49++yzvPbaazz66KOsXbuWK664gjvuuIMdO3YQBPlk/g2NaZVdV2SUuUyJk7KAU3RElXBxGMKnLNPYoUxrh9KRCKWDIWzbJ6Yo21ShYvmklpvJfqvteJwzWDdem5/J6/tZ5Vz80R/9EU888QQ/+tGP+MM//EPuuusu/r//7/877XPuvvtuuru7s9vSpUsBOEEHCSUpsCKMTjgRRekZO7J9T7SnijSKIxftwmT8uoZZQivteoACMT7D+ATYDMvaC1VHEcr+f+E8FzNnQ2lgIO09ttrwnIQksMTCO8nUf8FUTpME37RpExs2bOCyyy4b9fxjjz3G/PnzWbVqFbfffjtDQ9Wxl3v27GH16tUsWrQoO7Z+/XoGBgZ49dVXW/cLzUJaaddijbblYDJRfG8RybS1K9MhJanV4klpcEdGOnxZR+fLyJxsZ00d/MIwDCVkynHyllehczYz7d2iP/mTP+HP//zPT3vNT3/6U1asWMGtt96aHVuzZg2+7/OHf/iH3H333RQKo3/h33777TXPGxgYYOnSpaR4DMl0yKAcsZ7KjpBEFgHZpITSwRAtqgVEaC2fzgWRI27jYQaHzWYzU2XXarKvyEdTY8+iC6ogW/FsmfqzZHV9kRgHSKlgMyd9h1/0nwsnPCjn/KVbaO/qS0Wxbds2tm/fPuLyJ554gpdffpl9+/aN+nJf+MIXWLZsGUuWLOGVV15hy5YtvP7663z/+98HoK+vr8axALLHfX194/tdzkKmyq5DHGxpy+ASYnOSbiJpy0GmwimK8tOs7dqWSp0Q4WJlrdgOkeUwOFwUnsQQws5PAgE4eUWYx2vzM3h9n/bOxR//8R/zB3/wB6e95rzzzhv1+Lp164iiiJ///Oecf/75o15TKBRGNWShV1GUHnE7gZwSmWS90MJgg+w6If2NLBrKhXEuZi1TZdeprKGIcOinnYgSCamsJVIiWhZK76IinRA1V6SCQ0pRXFIA7AjSfGmRVtr7kSNHatIXo/2uR44c4Stf+Qo7d+6kWBy9g+uGG27I7q9evZrFixdz6aWXcujQIZYvXz6+zzoLmSq7DigSU6Qix6qriHKKxQBtRBSo4FKmXUaWbbnG27IzCulUaAWfCYSDXTDgZgPLKAHvQe4SZuNcTF8WLFjAggULzui5Bw4cwLZtFi5c2PRzByiBXFxF/lnNXLCyoiDR82/J9iU1OdUiyhu5MMxapsquyxSo0MaQ7HoapkAoF+VQFrWpCZGi1sInJiWU7aqJLJgbjl3CUwVos8TMhUmmq6urxrkYjf3793P8+HE+8YlPZMfiOObFF1/k29/+NpVKBcepXbzXrVsHwBtvvMHy5cvp6enhpZdeqrnm2LFjAC0qHD+7mCq7ruBjUSShiCNFtFIShmkjpkCKn03FqUbkxPAy4YSkmfpyrEY82MCgjfwqQC740HaW1RNMENPeucjLnj17+Kd/+if+zb/5N3R2drJnzx42b97MF7/4xVGLuMYipICDR0SJgKKsMBbhsjIFOZZXLbpKl15U2FfyanTGFkTjHHIWmyFpZzOttushXGwsKfpWlA6DTUWqdaaoEdRiiJmYiupQoShHVYt2vUpSJLTawLLAzWmDk2zvl156KQcPHqw59qUvfYkVK1awZcuWEY4FiC84gMWLFwPQ29vLnXfeyfHjx7MvvZ07d9LV1cXKlSvP8JcwtNquIxyKMvUxhMOQ1LKISeWYhpQImzIWFU02IJEDzZTKsiXbtMEiTB2hyjlkiahFP6JbZAjCvPvH8dr8DF7fzxrnolAo8MQTT7B9+3YqlQq//uu/zubNm2vyc80QSREW0TOdyLyzI2eJiN1dKIuIypSkLn1C2lRaRN7Gg2m3P6tptV3HlLDxpLiQaN0bpEQi0yJCAM7K9C5SKXFfkZL3yv6tFKJfOXDCaaLmgkm1987OTlatWlVzrL29nXnz5rFq1SoOHTrE448/zmc+8xnmzZvHK6+8wubNm/n0pz+dtaxefvnlrFy5kquvvpp7772Xvr4+tm7dyqZNmxrWBRjGptV2XaHAEEUC2ihLReWKnA0V4jNImyb3rddbqEGTltQnEjV0YFGO2iAJ4N2iqLmYCxwFYnDzhi7Ga/MzeH0/a5yLT3ziE+zdu7dlryfSIG7WelrBB1xiEtmq5yEm6glBIYdUtjQVCPJm5IxzYRiD1tu1xZCskBdFml4m+R3ITpJUho0DmRZJiGQuWqRGLGws36Vz7gD9R9rFvIU8TDN7932fXbt2cf/99zM4OMjSpUvZuHEjW7duza5xHIennnqKG2+8kd7eXtrb27n22mtrdDEMzdNquxajGYQ0QBlXOhhCNXmIDtkhojaInqwhsuUka1dmPDy5SXSxsIhJoOBDTwo/tyBGfGOmImCXC+NcGOoJKeFgMUyJSMohW1hSe76QhdPUmPUYKxuCE5t/VsM0pUyJmDYSKSOUEpJIVcNYFm0iB/apRTaSEYtUVrUFWMSpTTSYT+Z+OvH8889n95cuXcoLL7ww5nOWLVvG008/PYGfyjBeKtg4ONjYJJSkQmcKMsWRyFq4gEIm9y2GT6rJv1amyplgiflkXgG7/RTJkc6qk1ABAtOKmgfzLdiAVObglAR4dUJkIXMmIlw5rkmE2IJMEjynuznNdnKG2YBoJx2mQIzFMB0ADMvpqAFt0mn2iLFAFr6lWRhZ2nri43RHULGmbVrEMHuIKJLiMSgFsFJi2Tki1u4yPhXaCaWGhYUtW7AtGbFIM8n7RCowY9k4Jy2SPks4FSCidDE4Ji0yJsa5aMApfCLOkWE0SxMQEkZZwZOhNCGJXMYhpA3Rippzqk1E/pDy6V7DYMhJJZuCqtIfHmXaQY6oTmX0LcqmQ4oiuTIFWWHvkOJz4lQ7A/1dolMk7ypi7N0wQQgNFocUV44AES2ng1LUMJZRCRGZ8LMizlCz8woOiXROktQmTSAc7IBORErkOMLJsEEKg47NeG1+Btu7cS4aEFDElg1HZQqE+ESZgTrSIIUIUUSJEBtbagiEeQs6DYZJJpGNejE2g/iUM6VZR87EURX0bmbjCcjBTlXtgAQLuwBJCkaQ1jDVBLJe4hQFEhldjrK6OF+KZCFVPEVETg2gFCltkRYBmzgRdh/GMipnW+Kb8iQiy5KCl1PaZTZjnIsGhHKhFYOcClIa1ifCIsHP8tMiSiZmNVgIBzckp+XF8jYejKSGoQlOUcCiU0baOmQqxM+iFSK8bGVRCtGyl8iaDDGsL8amrSvlZF9MYqXwTt5WVIy9GyYEsT4XZNoaQqlHVHUe3EwUMZE2LhQ7LTmUMiFKXUhtgsgCXIJAdJFgSxu3EFGIsAnnYrw2P4Pt3TgXDRAqbiUG5SyRSNOdT6TTIWot0qy9KUkd4tQlSfLKf2Ny0IZJJcTHwyGkXRa1WaSkhLQRyJRfmrWiyrHTQFm2oqZYpKlHGEF0vAgDFjnn9Bl7N0wYqk06xqGCRYUSQ7RRocAwDjElORtKqCrbkNVYxLikaUKcugRBgTRxiWNLdIX82knSY53wC8QXvXQwTLfI2BjnogFVgSwxETLUdOfF+Gmw5MIbpR5h6hEnKRYuUWhiZobpSUwhcxzKuIS0MYQjlQoLcmqqJVtVrczGYzk3hzQlSqE8XIDIlhLgU/xLGWY9MR4V2hmiQIJPRAEHi0iqcw5nKRIhIyCcaOFgJKnQtwgDhyQWGhdxZImppMcLwqmYC7xbfb9wvLVDswDjXDRgmDZSSsRYDOISyAK4iAKJrJ5PgSC1iFKPJPWI45QksYmCnFs5s5MzTDIpDqcoyRBxKtpKpW1XMmfaykayOyB2hKlYhJMEoriA7QODIQz4QmAoD8beDROE0iWKcLBkNE7UUgohOFE75MhOP+FMRKlFnAp7J4U4dkliizS1SGJHrOO+C6EFC4BXETUYMRTz1hmZyIWhHtWCmsiJkKGUjE3lHJEEhzi1iROLMPIIA4c0AfCJwiDfm5jF1jDJDFAAXCr4hBTkTq5IhEVIUToajrR1hwo2SZqSpjZR7BBHDkliCVnksitSInlnixh7N0wQYkSDLZU5fUKKDGbtqT6RFNgKsAjSAqSyIwRbOBOJRZK4xJFHElvEsQ2hDf0xBDaULVHZLDq5ifPWQhjnwlDPsDTMQLYtiZHroi86SUU3SJxYBKFNkvhUhmzAw7Jj4or5ZzVMV3wCCgTSeVDpEaFMaGfS9WpeDqlLnCYEFZ80dUliSBKHJAbmJPALG2aelpbhLEOoJrcT4wE+yPk5iYw2R3ImToQPqZgrEic2QeCQJh5pEhOUHdLUJo0ciB2oOPCunC9ik01EZUh2SRlOi/kWbEAshVZUaC2SIlpR4pOmNlgQxQ5BYBMMeViWKzzgigNh3sFljN8zncHVxIbJJ8KiIusuhqQEuI0jxYVEeiRJIUyFNoBlWUSRTRjagE0wLCanpmkqFt8y5JV1MfZumCiGpV5FgEcop1nHshDfIsmKPcPYJowd0tQSUbjIFVG50CZNXNKKD4kLiVCq5QLgaeAYZH3ZQJS35mK8Nj+D7d04Fw1QoisR7cRYVJICaSJU25LEFmmQ1CIObNLYJ01k+XDoQJjzn9WEiQ2TTEgbkZy9kMjWvEgKCAX4kHokqU2Y+KQpchG2CCseaerImy0WPSeBNid/5MLYu2GCSPEJ5FyQVGpcCIGshDK+2CCmDnHSRhS5pLFHktjEkaiRS0khcCAQBZ2ASPf9UtRjEICcbwYO5J5ZN4vTIqbOuwFl2ijTwTA25dQlDB0qlQJh4FIedgkrRcpDBaLAg9iGoABDPpR94WDkIWrR7Qy55557sCyLW265JTt28cUXY1lWze3LX/5yzfMOHz7Mhg0bKJVKLFy4kNtuu40omsF/BbOIIVwimZcOsKhgc4ouKrQRJx7DoUcY+8SxRZrahBWHMPSJI5+k7JPGrrDvwIdjKQymYleXhym2d8PZSxmHRI5ZP0mRMp1YOJyiSES7UKZN2wgjCCseSWIRRRZhxSENPQg9qIhpqSSWqLOI5LCyeYiuKC1yMdRsEfMstHcTuWhAWY2cTsUMhqDiYdsuYQhRRYTSktgSC23ZF0YXO8IwK3mboKeOffv28eCDD2ajpXWuv/76mqmPpVIpux/HMRs2bKCnp4fdu3dz9OhRrrnmGjzP46677pqUz244c4Zol3/0PgE2oZT+jlOLKHKIY5co8YgjWUGfuMShJXZ0kSNEhBIH3rOgA5GPnv7mbjjLCaRsd0wRR6pwipk4YiSD6gCJI5ew4pKmrrDdyIbUlZPIbJnis4SdV4C3gX5xiDLZHB3bbMvHxDgXDRDdIG0AVGKXKCwK/ZSKJ3JycSoiFpE00ATRsgT5vU2p9jYuzuD5p06d4qqrruK73/0uf/ZnfzbifKlUoqenZ9TnPvvss7z22mvs2rWLRYsWsXbtWu644w62bNnC9u3b8X0jfT6dSXEYlqk+MWdBzBuJsQlCnzj2RMFb7JIkkMQWSWRD6ggZ5CFH5oFl9XwFaMv55lNk74aznxA3i1wM4VDBlwWcDlEqCjuD0CcKLdLEBcsWDnNqQSy7nyq2uA/CtoWYkXAubITOhajuz6/QOV6bn8H2bvyvBiSJQ5BCJXEYGiwQDduEg20i/ZE4EPoQeRC5wiDLltL+hpydqJk07HhvTbJp0yY2bNjAZZddNur5xx57jPnz57Nq1Spuv/12hrQY4J49e1i9ejWLFi3Kjq1fv56BgQFeffXV5j+MYZKxsLGJ8KlQoEyJMHUox0Xi2CEKPKLQJQot4sgmCTyxCJcdGLblVEipyukhnIv3cr71FNm74ewnxCWQA8rETyGWFac2UWJTrngksU1YlumPYVe0mga2cCoqttgclhG1FgHifj/C5gezN4KkiZHrs9jeTeSiAUOUSJM2hoZSwnI7RD7E0l0tIzzdVDoVw4jFNiDzbCebgboxfYVCgcIoVUdPPPEEL7/8Mvv27Rv1db7whS+wbNkylixZwiuvvMKWLVt4/fXX+f73vw9AX19fjWMBZI/7+vpa8asYJpAyRQIcAlwqtJOmUkwosqmUbdLIw7JS0Y4XeSIyBzJCh0j7qdzzTzDbE8O0IMInkoWbIW0k2AzjEcQFKmGRFIfykCeK7UOh1lnt5NA2hok8pr7UQ0TBcozocC0Ag+Cab84xMf9EDbCwiOKEsNJJmjjC203tqvOQalXEibwpT3Oy2pTUawBLly6tObxt2za2b99ec+zIkSN85StfYefOnRSLo5f433DDDdn91atXs3jxYi699FIOHTrE8uXLx/lhDVONmnaayF1dgk0Q+ASVNtLAhdgnTVIRnYtlui+1hHORIOw9pho2rpAVuY2JaUU1TBDCplNCOek0xCVI2kktF8tOCYYtEYULZRpb1VDIGSKZLQtNcEGFaiTaRzgZ0n6bilyYVlSDTrnsMxx2kFRsOavXEdGJWPNy9Z8xVWcjb1qkha15R44coaurKzs8WtRi//79HD9+nE984hPZsTiOefHFF/n2t79NpVLBcWo7XdatWwfAG2+8wfLly+np6eGll16quebYMdEu0KhOwzB9iOUUyAoFwtQhij2SVBQoE3piIlNoCcdCORUxIjqnBjeBcCgWAkfJvwCaVlTDBFGRmi2RHMYXp8J5Vqm+yql2iP3q+mxZVQc50W5QjVyECHs/CQzIm8wQm8FlY2OciwYEgUdi+VDxRRdIAEI5CxkelhcmCA9YhdNgSgyiq6urxrkYjUsvvZSDBw/WHPvSl77EihUr2LJlywjHAuDAgQMALF68GIDe3l7uvPNOjh8/zsKFCwHYuXMnXV1drFy5sgW/iWEiGaZEmSJh7BElRYaHXJLIJ6zI9lIlIBShRemoRivUgvs2cALhcM/gojPD2UFIQSpxugxTEgqzFZskcgnKLkSptGNHOMzKqdBrG5RToUThYqAT0RXlIByLFEhTwtBIdI6FcS4aEA4Xoc0TC2lCtb1URSfUbk39VJ5vxLSdtdDZ2cmqVatqjrW3tzNv3jxWrVrFoUOHePzxx/nMZz7DvHnzeOWVV9i8eTOf/vSns5bVyy+/nJUrV3L11Vdz77330tfXx9atW9m0adOo0RLD9CLEIUwKRIkr5ylYhIENQ8XabieVAoHqwqt2ehGimLMfsRCbwWWGKSbEJpLTTqM4JbVt0qQopeo9CIoi1adHlpXzrBxpleJWNl6mWtgZA91knSOWlTNcN4sjF6YcqxGRVw0PB3bVq9WNTT0OqVYYq51ervdo0a1F+L7Prl27uPzyy1mxYgV//Md/zMaNG/m7v/u77BrHcXjqqadwHIfe3l6++MUvcs0119ToYhimL0HqguWSJCJcnMYOlNsAtzqgSTnPeuqvot3UItyJcCyaDRFPE3s3nD1EFIWEfepgWR7lsk8cOQTDNumwB5YjbDfSNCwCqk5zvfOsohrvINb7CJEesVNI0+YHl02RvY8mlFgul9m0aRPz5s2jo6ODjRs3ZqltRSuEEk3kohEVRxRwBo5YQFWoTP0P14va1H21KOetuZgGPP/889n9pUuX8sILL4z5nGXLlvH0009P4KcyTBRB6pJGUB50CYZ9kYcOVXGyVtymbFy/r0cwAqpOhUmLGKaYQE6vDlKXOHYYOlkkDoqiy6/syGiyTPepwk2bqvOcIrsAqU2RxFTFsyxkeiXCcfJWMU8djYQSN2/ezA9/+EOefPJJuru7uemmm/jc5z7Hj3/8Y6B1QokmctGIsi2qipV3G1E1Qt3BKFPtg1Y7u7wOnjLi8dxmcDWxYfJJkiJh5BEGbYAPFanVUpYCQmoh1Xd2qkWvrD0OgT7gffJHLoy9GyaIGI80sYTkd38bcVgka6UOkZLeVO0oQTgcFWrtS9m9qslYiLDvE/JcDJDiujlHPIzX5s/Q3nWhxHPOOSc73t/fz0MPPcQ3v/lNLrnkEi688EIefvhhdu/ezd69e4GqUOKjjz7K2rVrueKKK7jjjjvYsWMHQZB/52yci0YkUmNe5d1UW5J6rFIgysFQuzyVu8uDCRMbJpko9CgPuaSBL4SDErvWvusjF6F2UxE6feFrA9rzvnmLbgZDHXFiE6UW5YpFHBUQM0Kkw5zIrznlPOhpkPqfIJwJFYV+m2o7qpXKHtSESqXJmotJtvdGQon79+8nDMOa4ytWrODcc89lz549QOuEEk1apBEVq7qY1gusQO055VgMItw1EyY2TFOiyCIKO0U6JErglJI/ppqLBhkCpra4TXc63lcviNjVGQxTSCUuEJbbqAyVxPTq2IJTTjVCoSJuamOoHAm1fo+mU5TK5/YjHOgBwBXt2YXC5O7L84okwumFEvv6+vB9nzlz5tQcX7RoUSaC2CqhRONcNGKY6iQ81Z6kOxe68Ep9CCuvcxEiWpzGg3FkDE1QHmgD30Fo27vCiVZ2rAtiqSp6ZdMqVKwW4jaqEbu866yxd8MEEUU2wVAbceCLrpABT6Y2rKpzDLVpPl3nItbO6doXxxE2W/M9nhIEOWsuxmvz0t7ziCRCPqHEycI4F41QzoPeCaKO6W2oekW9alttpuZivDlkk4M2NEPggOdWoxVQ1a/Q5evV2qmnSdS1AWLB/DXglxh7N0w5QaVI2uYIZzmRc0ISC05RtWuV+lORZt25qHc6lE07CHXOEKGcFYFY6HMWGo3X5uVz84gkwthCic888wxBEHDixIma6MWxY8cyEcRWCSUa56IRqvhH7eb0m65tEVHNz6mUSN7dVStyyCYHbWiGxJVRCqu2lVo5zXqYGKoLrq4NoP4GfsGUKdIaDDphYIs20bhANgdHl95WNRW6jad199V55XSEiDV9WD6uVMPVo+gNjs54bV4+N49IIowtlLh06VI8z+O5555j48aNALz++uscPnyY3t5eoHVCica5aESZqrOgOxlKAlkVbqq8tPKMwSyAhulLIBUKVVW8WoBVUafeXq0WZHVMTwOC2NW5VO3eYJgqKg5EJSlZj9QoomrLqp6oXo1Tj0LrBcv1TnYFGawQJ6ZrK+pYQokA1113Hbfeeitz586lq6uLm2++md7eXi666CKgdUKJxrloRL3RKS+2XsdChdyUBLhD/p2cGeRkmGyGLCha1QhbhVpROOVQ6KkQvWtEnYPa8dR5MPZumCjCNiGUlQAnrar9BggbVQXJqrZI7+6rL+jUox3zEPNzQoQOjPQ2cst/j9fmJ8De77vvPmzbZuPGjVQqFdavX88DDzyQnVdCiTfeeCO9vb20t7dz7bXXNi2UaJyLRkRUK+YHEUZqUysRC9VWVH3oTTNh4vEWuJkoiaEZ1HRTFaXQ08f1KUA91adfryJ5qjU7b+TC2LthoogtEbU4ZVWdYBVV1idXq0g01Ebl1LlUe55yRADCFAL1R5HiujlrLsZr8y2wd10oEaBYLLJjxw527NjR8DmtEEo0zkUjVNhYOQp6n3+9FgDUFseZBdAwXVELrYpQKCEhleLT889qwdULlx2qu8AT8jneZP4CBsMohHKMuk3VMR6m2oaqNoD6XCj1t6AcZnVOdzBAFIX6ypkQoekkMV+dY2H+hRqhPE6LqjIh1Bb96C2oSnhFLch5UKmW8WBa8wzNECHa6tSuLNVuanHVHWjd1pVtJ9rzXera9E6DsXfDRDFsibSFPi9EORZQdRr0rqik7lx9YSeIThEfhOy3+qMokKY5d5DjtfkZbO/GuWiEarfTay70ENpoMxjUNXnzZKY1zzDZRIidmMo76ymOClWbV9daVKMWytYT4F3ElMgBqhoCY2Hs3TBRRIgvcT0Vou4r+1R2rNdb6KkRdR6qDnUZEZlz1RPEbtP3/Xyfq0WtqDMR41w0QhVuqlY9XQMAqnamcnh614hJiximK/VhX7WA1ivQ6tN9VYgZ7ZgeyTP2bphqQsS3mXKWVRRZX7frdS5UylvXJ7Korv0JYmjlIPCeyrmINsLh4Rn8rT9JGOeiEfXV88qBsLT7NrWeqS7Ikvc9TPW8YTJR0x2V3Q5TTevp6oT6gD7doVALcyeiil4VO+fB2LtholAt0kr0UO9wihBOQqw91ttM9eLP+vTIoLw/pCqfhVfiODlzgdOwW2SyMM5FI9ROTS2wuh693q6ndnyh9jivMSkHZTyYXaOhGSqIGgllp3obtR6VU3UV9Y6FWohPyvvNFHMaezdMFBWEJL1Kc6j6IdXRpBco685wfQRO/6kiFwAdKbyvTgRYVk7nYrw2P4Pt3UxFbYSek1Y/9d5pFWZTfdRR3TUGw3REj0roaT/9mNKv0J0JvVg5Rawc79KcMz3F3HPPPViWxS233JIdK5fLbNq0iXnz5tHR0cHGjRszqWPF4cOH2bBhA6VSiYULF3LbbbcRRTPkl54tKLsepLbQXo/G6V1Pav3WBbPU34L+N3ICYecVwHJR4Y04ztmKOosxkYtGqLVDORlqca0vBtLDXmrnlzdMrOaRjIcZXE1smAKUYNYwI3dr9Qsr1O4E9e4RB1iAmC0yJ+d7T6G979u3jwcffJA1a9bUHN+8eTM//OEPefLJJ+nu7uamm27ic5/7HD/+8Y8BMZdhw4YN9PT0sHv3bo4ePco111yD53ncdddd4/xlDC1DFXDqTrNK/+ltpvomUOm46C3XuuZFAiwC3pP30+pgnThuYnDZeGx+Bq/vJnLRCNXKpHQAdGnkek9YV3fTK47HIm7RzWDIi4pMQHX3porb9OJk3Z7DumPqGkd7jTxMkb2fOnWKq666iu9+97ucc8452fH+/n4eeughvvnNb3LJJZdw4YUX8vDDD7N792727t0LwLPPPstrr73Go48+ytq1a7niiiu444472LFjB0GQdxdhmHDq6+KU7epaFyoap65TKRM98qwreAaIuiLka2r92O3tOb86Z/H6bpyLRugOhbIpdUyXPdZllPWWvTxELboZDHlRXU66CqeKZtTv/HR7Vjs9tQMcptrSOphTCnmK7H3Tpk1s2LCByy67rOb4/v37CcOw5viKFSs499xz2bNnDwB79uxh9erVLFq0KLtm/fr1DAwM8Oqrrzb/YQwTwzC19qrWa/VYaRXpRcn1dUR6LR1UHZQBIFYFdbpARg5m8fpu0iKN0Ees1xcB6ff1qEV9msRgmG7o6T21s9NVO+tniqi1NNCOq8J5j2r4eZIZGBioeVwoFEYdqvTEE0/w8ssvs2/fvhHn+vr68H2/ZvQ0wKJFi+jr68uu0R0LdV6dM0wTdPEsVaQ82jwc5TSjHdfTJ3qbdgK0q2tVp4gI44Xh9BxcNp0wzkUjlB2pkHB9YZDuwOqer9rh5cG05hkmmzJVPQDdZvUKe1WwqSsZqtyxmrlgA32pLIqbpCFO6jWApUuX1hzetm0b27dvrzl25MgRvvKVr7Bz506KxeI439gwrQkRzq7aCKoohh690KMVat3U20+juuMxokDU0Rd7cd/K61CbVlTDCAapDm2q92ah6v3qEQt1PK8xtaJYZwYX/BimAOU86ItufcRNVyvUHWql8RIi6i08wEqhkHMFbKG9HzlyhK6uruzwaFGL/fv3c/z4cT7xiU9kx+I45sUXX+Tb3/42zzzzDEEQcOLEiZroxbFjx+jp6QGgp6eHl156qeZ1VTeJusYwDUiotpzqTrPuHNc7EHqkY7RifZUCjxBD0bIQn42dt6BgvDY/g9f3GVNzceedd/Kbv/mblEqlEWFMRUtbxtROTu+NVjlnfXqkHt1QA51MxMyQkymxaz3XrBdzqvoLNelUL3zTu0mylEoqF+HJN/iurq6a22jOxaWXXsrBgwc5cOBAdvvkJz/JVVddld33PI/nnnsue87rr7/O4cOH6e3tBaC3t5eDBw9y/Pjx7JqdO3fS1dXFypUrJ/4XnaFMul3Xpz/09VsX2NIjF3q9hf44rLsGZM1FtVjDpEXGZsZELoIg4Pd///fp7e3loYceGnG+5S1jw1Rzycr4lDcMI8NsytlQBXF5UK1Q42EGh80MU2DXKr2hukT0jhGl3lmvVKgXu0E1imHboj0vzbnQTrK9d3Z2smrVqppj7e3tzJs3Lzt+3XXXceuttzJ37ly6urq4+eab6e3t5aKLLgLg8ssvZ+XKlVx99dXce++99PX1sXXrVjZt2jSqQ2MQTLpdK3E4VUOkT/nV6zHUY4Uesaiv11R2PwS1fdgpjpMzLzJem5/B6/uMcS6+/vWvA/DII4+Mel61jO3atYtFixaxdu1a7rjjDrZs2cL27dvzD5pRqF5+tbhWqC68Sva7fgeo/8yDapsaD6Z4dEYz6XatHAiV2qivG7KpXUcT7T7UVrC/LV8vrxFOQ3u/7777sG2bjRs3UqlUWL9+PQ888EB23nEcnnrqKW688UZ6e3tpb2/n2muv5Rvf+EZrP8hZxqTbtYpcqDSGilLoBfdQmxZRToVylvU1XNn8EOLvJEP8UaQ5y4zGbfMzeH2fMWmRsWh5y5hKhZyi2getPGLlHSuj1Xd3KpdtMLSAltt1ot2GqLZQq1Cw0sGo17cI666LgGIqI3kzZo/C888/z/333589LhaL7Nixg/fee4/BwUG+//3vj6ilWLZsGU8//TRDQ0O8/fbb/OVf/iWuO3N+5+lIy+1ar33TnWK9Xk6PaNRHntX6rkc6VNShrN5AvSB4nlHoHIuz5i/kTFvGKpUKlUpVBShrcVPhY12xsP7xaLs5FVLOwzTcyRmmFy23a2Vz+gKqF3SqXZx+rV7cqdtbFl4eJtdSYuzdIJmQ9VrZ7zDVdXiI2i4+5SiLusyREWc9/Z0C5wDH1MXV8cHDw5MUrZvB9j6lkYs/+ZM/wbKs097+5V/+ZUI/w9133013d3d2y1rc9N5+FY3QQ226qJDuWKjn5UGF78Zzm8HGd7Yyre16mKpolip0U+Jw9XMX1E5P7zBRBXJqAc/tSWPsfYYzre1aj8DpjoRetFz1DarH9BoNfc6OWsdPpuCpNik7u9B1c351jtfmZ7C9T2nk4o//+I/5gz/4g9Nec9555+V6rTNtGbv99tu59dZbs8cDAwPCYOsjEfUtQXoran1vtEmLzGqmtV3rFfFQtW+1CKrcczUCXFtNr9bUbDemX2g4m5nWdg21QnApVQdY73TS6zB0Ea16/SK9/dqxIIwRvdfiK9NkxcZmSv+JFixYwIIFC1ryWr29vdx5550cP36chQsXAvlaxhop+9WouKmWJt3wlEGqhVeds8nvbeoh6DPFODLTjmlt17oDrOeaVYRCFSsrG1eLsf7Tohpuzl4kx+x1Y+8zmmlt12p9Vk6E3g2lR9+UY51qt/qZUTWRaAvKKv+tFvcUK6+K1nhtfgbb+4zxvw4fPsx7773H4cOHieOYAwcOAPChD32Ijo6O1reM6ToWKv1haefqi4HqO0by0IqQ1wwOmxmmwK6VFgvU2rjuKKvoharNgKpToRwPlSKpaSUZA2Pvs4ZJt+shalVl1Zqspxb0uiL1N6AXfOoOhopcvA9YlhTRUt53RBRNks3PYHufMc7F1772Nb73ve9ljz/+8Y8D8KMf/YiLL7649S1jeoW8vmPTVWDVNepxqj3XYMjBlNi1S3Uyqqq/ULYOI1MjepuqWpw9hK5AuQ04cWafxXDWMul2rdupcoj1Ak2l7aLWc/UcfUOod5HosgNZbVG1GjS3/PcsxkrT3B27s4KBgQG6u7thRT84XbVFnSpMXJ+LrncqogGodNPf318jUTziPX6jH9yR55siGoB9jd/LYADN5q6Qdq3y0Xprnq7lUi+frGxb7erSFN5NYSgA3qK/fz7d3aPbobF3w0SR2dZ/6Ae6qvUVekpEpbb1rqf6CAWMrlnUD5xIgWPAuwhtgn727FlBb++yiV/jZ7C9z5jIxaSjvGC9U0Q5r+o8jKxK1q8ZC91ROVNMlMTQDCoXrWxPRTCUg6Hbk74A6+18IFOEek4lB8beDROFilaodF29TosexVCFyzC6Y6ELIRbVH4leeCTE1XIxXpufwfZ+1ohotRy9CEgtxvouTy+K0xXddP2LsYhbdDtD7rnnHizL4pZbbsmOlctlNm3axLx58+jo6GDjxo1ZFbeipTMBDJOL8gVUa54uqlW/q9MFs3TVwxB4z4KhFDHhr4nitim0d8NZjD4/RK3dah1WdqvXyNW3eyZ1z1frfSapESD24uIPJQxzGuIstnfjXDRCGZt+0wvhlFGqcFtCa3Zmk8S+fft48MEHWbNmTc3xzZs383d/93c8+eSTvPDCC7z11lt87nOfy86rmQBBELB7926+973v8cgjj/C1r31tsn8Fw5mQpe6oXVD1BVhpXOgh5tGK4RimOj7YYJhClCOhJO31bih9E6h3/umORn0EWv0cAlFg5KOHOVzXFF2MhVkVGhFSK7CiRyj0W/3shWaod17O9NYkp06d4qqrruK73/0u55xzTna8v7+fhx56iG9+85tccsklXHjhhTz88MPs3r2bvXv3AtWZAI8++ihr167liiuu4I477mDHjh0EQV71MMOUoQSyFCo3XT+LQZe31wvhskr8CFHRWaKptMgU2LthFhAhInFDVNPYyo51rSI9zVfvYNdHNELkxF914iSqsjl3K+ostnfjXDSiXo1TqbmNtqs7U6Zosd20aRMbNmzgsssuqzm+f/9+wjCsOb5ixQrOPfdc9uzZA0zATADD5KIWTZUWGWakMqe+26tPA6qoXeAgtokWInqRA+NcGCYK5QTrdqrsVnccVLRZ3zBCbRpQrfmZE67+KKrFHJVKTkOcxfZuCjoboRuZMjqYtmmPTGNf0khs5oknnuDll19m3759I8719fXh+z5z5sypOb5o0aJM7/9MZwIYpglq0azP6apFGO2xEoWrDxfXvJhNLgEtg2EiUREK/b5qs66vh1PXqeJmfWZUvbNBjHCeA6CICI1YlMtnEqqeXRjnohH6YCfVnjcmejIvB8rYx4N8fiaBK9m2bRvbt2+vOXbkyBG+8pWvsHPnTorF4jjf2DAjKSMCDmrnBtUIhbLFiOqiq6dK9DZVO9VEtHKmw1po7wZDDSHVegtVVwG10Ta9zqJe46Je20XZu+tAlCIc6AglEtPZ2US3yHhsdgbbu3EuGqF3iYx5oX6Rsu4c1BTHnSHS+I4cOVLTBz1a1GL//v0cP36cT3ziE9WPEMe8+OKLfPvb3+aZZ54hCAJOnDhRE704duxYpvd/pjMBDNMEtVip3V0jGXu0+/p5dZwYIQIQIqvexqaF9m4w1KC+xHV71cW0YOTsp/q2U73gU4lnRQnQjrB1UF55U/Lf47H5GWzvpuaiEfVh4hHooha6vFsrtmfN09XVVXMbzbm49NJLOXjwIAcOHMhun/zkJ7nqqquy+57n8dxzz2XPef311zl8+DC9vb2AmAlw8OBBjh8/nl2TZyaAYZqgT46MqE5E1YvZVKRCFXKOcCwUattn9iiGKUYF0JQd68uyqrHQ7bm+oDOmVho/QjoWeqGGhfjKTBj/kJyzH7MqNGLUYs10lJ8qaqFibU1ELkTh8fhowo/p7Oxk1apVNcfa29uZN29edvy6667j1ltvZe7cuXR1dXHzzTfT29vLRRddBND6mQCGyUXt3PTKeb11T49kqHEKo+68YqoFGZXRLhjJJNu7YRahp+3qIxe6c6zbuW7v9REMkPNEEoR9K+17ZcQ5wxHjtfkZbO/GuWjEiP+p9fmR+p5UtdDqVUFjMA0X2/vuuw/bttm4cSOVSoX169fzwAMPZOdbPhPAMLmojZjagOm7N2Xi9YvsqLhUizL8fO89De3dcJagHAkVTNZbUJUd1yt26iSjHANk4YW8qTfRq0fHwDgXhtOTlQ5rj+u1YvWS+pkjq/b888/XPC4Wi+zYsYMdO3Y0fM6yZct4+umnJ/iTGSYEvT1PN1e9uA1yRH3VyNQaGUODYWrQ93dK0l4FkfVAs4pI60MoG36Bq3Vfn4gqvgcsa5q2DU4jjHNxWvQVV49KqJixRdX49Oq30xZrVAkxOznD5KJXy9c7Gfp6edq1U/0NqOhFTmfa2LthoqjPTus1c2rfV78fHNM/0PMnqq/VBVKGh3M6F+O1+Rls78a5aIhuiSpmPNrM9dG2fzmdi1wGPgbGgTY0Q31jU30ZUUP0C/U4cxNpQGPvholCb6NWS7AeUNZbTHOjO89F4H2U9+J5OQs6x2vzM9jejXPREFXIA7WiFyAaquvdY33VbkLnYrxFxzPY+AxTgNpJ6YvvqCinQRd50as/T1KVrTX2bphi9JkhuoOhluUzylQnVGVs9TdI8s8WGa/Nz2B7N85FQ4ao/vOo/iblxeqrcn2P3tS0ohoMuVDKhac10foohar4hKr9+8DbmG52w7RAH6Ou7wNHS/mdFj29rUehh6imwmNc19j9WBjnoiH6olrfvxTXndd79nIpbwnMTs4w2TT0fetjxvpFuvKQOm4BnVQnReV8b2PvholACV/p49ZzN+/VO9PKnkPEYL4hhEKnmmYJcdxEt4iJXBhq0SvfIu2+Hi5OtON6L1NO50L3ts+UGWx8himgoR+gJ6gV9Yutfl+lRppIZBt7N0wUarnWy4By1TvoNq6v4UooS+m4qBYUpXGRMzo9XpufwfZunIuGqJoLvbZCCanonq6eIlHHZ04rqmG2o5zn0SIX9eX1utqQ6pRqYraIwTBRKAFNPaMxKrpNqzSHPtFM/6kkbFWPa7WIw3FyzhaZxRjnoiERonBThYSVGkqsnYeRbaqqmToHKtMyHmawZ2uYSvQaoXqb1ieU6RKG9TOqlbBQE3MWjL0bJoLR9ngjqI8w695IfTq7flCJXikaE8c5IxfjtfkZbO+mKqUhai61XndRP92p3rGAnLG4Kuk4bwZD0ygbDahGH3T5QnVf2XdUdz7SntukiJaxd8NEcNrCTT06p9fQ6SIv+tqu37cRI9ddxGZTvF7umgv19pNk79/5zndYs2ZNNmOqt7eX//W//ld2/uKLL8ayrJrbl7/85ZrXOHz4MBs2bKBUKrFw4UJuu+02oihnql/DRC4aokJiuoOhU6/YqbfsNf8/wmCYHOo1kPWKeHWsWhVfexxqQ8b9nCb+bDBMHg3NsN6x0IvwRyvO0HXwAQaoSg+oHu6YMJyenu4HPvAB7rnnHj784Q+Tpinf+973+L3f+z3++Z//mQsuuACA66+/vmZcQ6lUyu7HccyGDRvo6elh9+7dHD16lGuuuQbP87jrrrua+izGuWiI2q2p+0r+tT7vXF/sVh9eMximE2rHpoeELe24vmWqry1Stu1Qu5qbCZGG6YYeSdYnlIXa8Xpbj+qeq/9NDKMrM5fL07PO6Morr6x5fOedd/Kd73yHvXv3Zs5FqVSip6dn1Oc/++yzvPbaa+zatYtFixaxdu1a7rjjDrZs2cL27dvx/ZxzhDBpkdOgvF0V/o0QFcPqfqKd03eDTVTPGwyTjh6tCKna9Ok0ktVN11iOgC6aqjEyGCYcva6ivuNPHzRZX8Spd/2hPb9ENcoxjHLEzzlncidADwwM1NwqlbFTkXEc88QTTzA4OEhvb292/LHHHmP+/PmsWrWK22+/naGhoezcnj17WL16NYsWLcqOrV+/noGBAV599dWmPrOJXDQkoOp76dVC9RELGBmpMKFiw3QlQAhg6QuwCvnq4WK91VpR73QYJ9owndCjFfqI3/oWktF+pqNcq2oslONdQNRfOJOeFlm6dGnN423btrF9+/ZRrz148CC9vb2Uy2U6Ojr4wQ9+wMqVKwH4whe+wLJly1iyZAmvvPIKW7Zs4fXXX+f73/8+AH19fTWOBZA97uvra+ozG+eiIcq7hWq7EtSGzNCOqQW5/pzBMJ1QEQu1sOoCWSrlodt6/U0tvMOImiT1HINhKtGjb3pUQi/ARztXv0ara/UhlAFChValyKvFzpXK5Ka+jxw5QldXV/a4UGgcOTn//PM5cOAA/f39/O3f/i3XXnstL7zwAitXruSGG27Irlu9ejWLFy/m0ksv5dChQyxfvryln9k4Fw2JEJ6qHl7TIxm6k1HfrpfX8NRCPx5MSNrQDHrqTo8+6I6D7kyoxVZXA0oRg5xUwXM1rHp6jL0bJor6wk0Y6VjoaRL9eVBr9+pvQtcT1ydgNyP/PV6bF89V3R958H2fD33oQwBceOGF7Nu3j29961s8+OCDI65dt24dAG+88QbLly+np6eHl156qeaaY8eOATSs02iEqbloiN5yp3u76r56rLfujSZGZDBMN/RFV7dhPSetL7qhdn1Ud40qdDYYphJ9XdY3enrEIaw7r3ePqJ+6EnOMSCHqKXFx3/dnTrQuSZKGNRoHDhwAYPHixQD09vZy8OBBjh8/nl2zc+dOurq6stRKXkzkoiHKuOq9X1VzQd1xPayWt+aivu3pTDCdKYZmqCDmJOipD30x1qNzek2GXlWvIhrK2c5rg8beDROFkg7Q0x8wshhZd5zrbUk/p9ZxFZXTu0MS0jRvzcV4bb65595+++1cccUVnHvuuZw8eZLHH3+c559/nmeeeYZDhw7x+OOP85nPfIZ58+bxyiuvsHnzZj796U+zZs0aAC6//HJWrlzJ1Vdfzb333ktfXx9bt25l06ZNp03FjIZxLhoSUu1vVsap90Yr6hdfvYU1z3uYMLFhMql3CPSUXr0ip0JPAerORTO2DsbeDRPHaGkNNbJBdwx0+6m38XoHOkFELoYRacD3UbbvOHnbr1uTFsnL8ePHueaaazh69Cjd3d2sWbOGZ555ht/5nd/hyJEj7Nq1i/vvv5/BwUGWLl3Kxo0b2bp1a/Z8x3F46qmnuPHGG+nt7aW9vZ1rr722RhcjL8a5aEj9mHU9TAy1uWh1rsmhNmYnZ5h09Kp53X7r7bbewaivM1LXq6LOPBh7N0wUeko6qXvcSLcF7bFec6RQrzWyLTtJpmfk4qGHHmp4bunSpbzwwgtjvsayZct4+umnm3rf0TDJ0obUTzvV89EhYlENqC1+0+f9GgzTEbXYKVvW5ycohzqsu0aXSlaLdYhoy1MO9fRkOskhGyaSGBFh0KXrdbuO6+7rUQ69lg7tHFT/Jk4h9uIisue6RjhuLEzkoiG6B6xCwPrgMn0Xpyt36oOgxqLZsHKj1zAY8lJfS1TfWq13iNTv9PRUioWw+2YK2ybf3qeTHLJhIhlroKRap/X6Cx1l4/VjHAKqNl5NGca5a/bHa/Mzd303zkVD6lXb9BoMlSpRhlhfYZ93J2dy0IbJpr4iHka2peq9/vp96o7ri24eJt/ep5McsmEiqY5Dr21LTeuO1bei6o50fbpEbRSVMmeZ5jsBJ7fmYjph0iINUSIqSuIbqsam2lPVTz3U1oq8ssEwUdRPQNXTI/VaF7F2nb4wJ4iFtsKZLbjjZybKIRsmErUZVJN69e4Q3eb1iEZ9a7X+HL091afWAYlxZk4n6pRhIhcN0ZUHR1N0g9pd3Gi7vDzvYQrcDJOJWixh5BwR3W51tdnRuqPUF3B9Nf5Y790ae5+JcsiGiUR3itXPgJEpPl2zQu+Q0gf5xVTXfo+RInEpUTRZa/zMXd+Nc9GQeiVDOH0+Wr/fjOGZmgvDZKLXBNW34qm6okbV9brzrGaRNFPM2Tp7n4lyyIaJpN6uk7rjDrXF9noXYH0noL7uF4AT8r6Hst/cMhezuObCpEVOi258una9Hk6O6m6mW8Qw3akPC+v56kZpEt3u1UJbYWRkY3JQ3R/qdjrnQskhX3jhhdx999187GMf41vf+tao1+pyyCAkj5X8seJM5ZANE4kehavvhqLumN79lFBr7/XOxom642Ztz4txLhpSYfRR6hXtmF40pJyLRimU0ah3TM70ZjDkRS2kqpW63r6juvv6wlvvXOuORx6mh71PlRyyYSJRNlk/rkHv+qvv6KvXL9Kfo2rqdEVmUB2D+VtRp97epwqTFmmIHhJWRmiNck7l7NT5Zjxb0y1imGz0RbVRpbzeCQW1uzq9QwqqC3AeJt/ep5McsmEiqZ8rUm/XjVqu1XP1c+qYQzV9okf7LE6dmiwV5pm7vhvnoiF6REI91hfdemejXtnNYJiO1Pf+q8UTqotwvdOhpz2Suutd8hd0Tj7TSQ7ZMJHoAm+jpThGOz5abZFeV6RqkNS5aiS7s9O0II+FcS4aUq8HUK/epoS19FkMzUYvTLeIYbIZrcCsvm5C3+kpgTio/RtQrdr1zsdY7z259j6d5JANE4nSIoLaNViPVIwWsVPPHW3d1juhhqlGM5qJTptuEcMIdC8YGnvAevhYPc8odBqmO6Pt3PRK+tHmjOhOdf25PBh7N0wU9Y7vaKk+tPsWI9Mg9anA+q6qaoFzc7NFZme3iHEuGqKkvpXRhYgK+dGG28BUVMwbDGeGbrO6k6EWXT09okctdLtPEcvHMM3t5AyGiaBeBqC+sL4+UmHXnbMaPEefjK3LfxubHwvjXDREj0AoB0PPLdcbl966l9dTNWkRw2SjRxoaLcAq3acfr79foRomnr5pEcNsob7tVI82NEqF1DvW9ZE8CyE4NzTiObbdbLfImTJz7X3GtKLeeeed/OZv/ialUok5c+aMek39dEPLsnjiiSfO8B3r2+0i7X59yFhvYWqGsEU3w0xl8u1aX1TrIxj6QtioAE7ZuU1V56LZ2SLG3s92Jt+uoWof9e2l9cWeo03+rU99IM+XqTrR1S/6JGm2Q2r22fuMcS6CIOD3f//3ufHGG0973cMPP8zRo0ez22c/+9kzfMf6nmiozePFdTcY2aY3FpPb92/GT08/Jt+u1aKqdnf1hcr11Nu7jk01ZZiH6aFzYZh4psauFWrd1qN09Q51PbrjrO4niKiFniYUeF7eoP/stfcZkxb5+te/DsAjjzxy2uvmzJnTIuU8feHVd3A2tTs5qPWUm6men1zM+Onpx+TbtUK3U31hHq3eor7tWv08k2idYTYwNXY9WmvpaGuyLoxV73zotq4m/+pFmeJcGM7cL/3JYsZELvKyadMm5s+fz6c+9Sn+6q/+ijS/CHwduirhaCqFo6m9NVM5D1WjHc8tv5FfeeWVfOYzn+HDH/4wH/nIR7jzzjvp6Ohg79692TVq/LS66fMb1PjpRx99lLVr13LFFVdwxx13sGPHDoJg+modnA20zq71wjfdORhN9ltHXa92U2WqjnYz+efJs3fD9Kd1dq3Xx9Wv1SO7PUYKY+l1FinVcQ++dl21ndWyJsvmZ669z5jIRR6+8Y1vcMkll1AqlXj22Wf5T//pP3Hq1Cn+6I/+qOFzKpVKjRTwwMCAvFdffdxo96aT1+AUU1fgFscxTz755Kjjpx999FF6enq48sor+dM//dMsetFo/PSNN97Iq6++ysc//vHx/SqGUWmtXetpPhh9caXufHVRrZ2WqhbgvJiCTkOV1q/Xevparx2CkWqc1J3X7+trfohwou2aawuFZtMiZ8rMtfcpdS7+5E/+hD//8z8/7TU//elPWbFiRa7X+9M//dPs/sc//nEGBwf5i7/4i9Ma6913352F8HRuuGENpZLPqVMx8+aVSNOUMExYuLCdwcGQwcFh4jghTS1838WyRAXx++9XuPLKc/l3/+6eXJ+5VVT/yASFQmFUeWIzfnrimc52/Yd/uIxTp3wsC3xfBC6Vbc+f72HbNnPnFkkSOHGiQqUS09FhkSQpQZDgecLWgyDmn/7pF3z4w+1ce+3qXL+HYWYzne36L/9yOW+8kRCGUCo5xLGITLiuS5IkJIlFFCXYdkJ7u0ux6OD7DuVyzNBQiO+7pKkoTo7jCMdJCQL41a8GKJViPvjBEpaV0NaWMnduGx/84Dm5fsfZjJWeeRxq3Lz99tu8++67p73mvPPOw/erUquPPPIIt9xyCydOnBjz9X/4wx/yu7/7u5TL5YZzAEbzhJcuXUp/f39NSqAZBgYG6O7ubvga6jz8d6A04nxzDAFXjzi6bds2tm/fPuJ4EAQcPnw4bNZLngAACOBJREFUGz/93/7bf8vGT9fzD//wD1x66aW88cYbLF++nBtuuIFf/OIXPPPMM9V3Hxqivb2dp59+miuuuGKcv8vZwdlq16fjdDY/EfY+Ub+HoTGz0a4bMXlr/My19ymNXCxYsIAFCxZM2OsfOHCAc84557QDhhrt8CeHVrQaiecfOXKkxvga/U5q/DTAhRdeyL59+/jWt77Fgw8+OOJaffz08uXL6enp4aWXXqq5xoyfHomx60a0zt4Nk4+x6zNhvDY/c+19xtRcHD58mPfee4/Dhw8Tx3E2GvlDH/oQHR0d/N3f/R3Hjh3joosuolgssnPnTu666y6++tWvTu0HnyRUe2mzNDt++s477+T48eMsXLgQMOOnx4uxa8PZiLFrw4xxLr72ta/xve99L3usigd/9KMfcfHFF+N5Hjt27GDz5s2kacqHPvQhvvnNb3L99ddP1UfOweQWuJnx09OPs9OuG2EKOmcLs8uuT8fsLeic0pqL6chYubRWvEY1H/cA0Da+D8ww8J9yfd7rrruO5557rmb89JYtW7Lx01/84hf5yU9+ko2f/vf//t+zdevWmtf9xS9+wY033sjzzz+fjZ++5557cN0Z46fOSlph12f6+lNl74azn4m26zN939bZ/My1d/ONMIsw46cNBoPBMBkY52JKMWFiw2zC2LthtjF70yLGuZhSQsb/v2DmVhMbZhvG3g2zjfHa/My1d+NcTClmJ2eYTRh7N8w2Zm/k4qybLWIwGAwGg2FqMZGLKUWftjee1zAYZgLG3g2zjfHa/My1d+NcTCkmTGyYTRh7N8w2TFrEYDAYDAaDoSWYyMWUEgJOC17DYJgJGHs3zDbGa/Mz196NczGlmDCxYTZh7N0w25i9aRHjXNSh1NAHBgbO+DXUc8dWVh99YFhztOI1DGc7rbDr05HP5o29G1rLRNt1IyZvjZ+59m6cizpOnjwJCDnsVryW0Jevxfd9enp66Ou7b9zvAWLcue/7LXktw9lJK+16rPept3lj74aJYrLs+nTvP9Fr/Ey1dzO4rI4kSXjrrbfo7Ozk5MmTLF26lCNHjow5NGZgYCC7Vj13yZIl2PboNbPlcpkgCFrymX3fp1gstuS1DGcnul1bllVjr83YdqNr0zQ9rc0bezdMBOOx62aZqjV+ptq7cS5OQzMT96ZqOp/BcCYY2zacjUykrZq/g+YwragGg8FgMBhainEuDAaDwWAwtBTjXJyGQqHAtm3bKBQKLb3WYJhqjG0bzkYm0lbN30FzmJoLg8FgMBgMLcVELgwGg8FgMLQU41wYDAaDwWBoKca5MBgMBoPB0FKMc3EaduzYwQc/+EGKxSLr1q3jpZdeGnHNiy++yJVXXsmSJUuwLIv/8T/+x+R/UIOhCfLYNRjbNsw88tp2Xu6++25+4zd+g87OThYuXMhnP/tZXn/99RZ92rMb41w04K//+q+59dZb2bZtGy+//DIf+9jHWL9+PcePH6+5bnBwkI997GPs2LFjij6pwZCfvHYNxrYNM4tmbDsvL7zwAps2bWLv3r3s3LmTMAy5/PLLGRwcbOEnP0tJDaPyqU99Kt20aVP2OI7jdMmSJendd9/d8DlA+oMf/GASPp3BcGaciV2nqbFtw/TnTG27GY4fP54C6QsvvNCy1zxbMZGLUQiCgP3793PZZZdlx2zb5rLLLmPPnj1T+MkMhjPH2LXhbGWybLu/vx+AuXPntuw1z1aMczEK77zzDnEcs2jRoprjixYtoq+vb4o+lcEwPoxdG85WJsO2kyThlltu4V/9q3/FqlWrWvKaZzNm5LrBYDAYDGOwadMmfvKTn/C///f/nuqPMiMwzsUozJ8/H8dxOHbsWM3xY8eO0dPTM0WfymAYH8auDWcrE23bN910E0899RQvvvgiH/jAB8b9erMBkxYZBd/3ufDCC3nuueeyY0mS8Nxzz9Hb2zuFn8xgOHOMXRvOVibKttM05aabbuIHP/gB//AP/8Cv//qvt+LjzgpM5KIBt956K9deey2f/OQn+dSnPsX999/P4OAgX/rSl2quO3XqFG+88Ub2+M033+TAgQPMnTuXc889d7I/tsFwWvLaNRjbNswsmrHtvGzatInHH3+c//k//yednZ1Z/UZ3dzdtbW2t+uhnJ1PdrjKd+S//5b+k5557bur7fvqpT30q3bt374hrfvSjH6XAiNu11147+R/YYMhBHrtOU2PbhplHXtvOy2j2D6QPP/xwaz7wWYyZimowGAwGg6GlmJoLg8FgMBgMLcU4FwaDwWAwGFqKcS4MBoPBYDC0FONcGAwGg8FgaCnGuTAYDAaDwdBSjHNhMBgMBoOhpRjnwmAwGAwGQ0sxzoXBYDAYDIaWYpwLg8FgMBgMLcU4FwaDwWAwGFqKcS4MBoPBYDC0FONc5OTtt9+mp6eHu+66Kzu2e/dufN+vGfNrMMxUjI0bznaMjU8eZnBZEzz99NN89rOfZffu3Zx//vmsXbuW3/u93+Ob3/zmVH80g6ElGBs3nO0YG58cjHPRJJs2bWLXrl188pOf5ODBg+zbt49CoTDVH8tgaBnGxg1nO8bGJx7jXDTJ8PAwq1at4siRI+zfv5/Vq1dP9UcyGFqKsXHD2Y6x8YnH1Fw0yaFDh3jrrbdIkoSf//znU/1xDIaWY2zccLZjbHziMZGLJgiCgE996lOsXbuW888/n/vvv5+DBw+ycOHCqf5oBkNLMDZuONsxNj45GOeiCW677Tb+9m//lv/zf/4PHR0d/Ot//a/p7u7mqaeemuqPZjC0BGPjhrMdY+OTRGrIxY9+9KPUdd30H//xH7Njb775ZtrV1ZU+8MADU/jJDIbWYGzccLZjbHzyMJELg8FgMBgMLcUUdBoMBoPBYGgpxrkwGAwGg8HQUoxzYTAYDAaDoaUY58JgMBgMBkNLMc6FwWAwGAyGlmKcC4PBYDAYDC3FOBcGg8FgMBhainEuDAaDwWAwtBTjXBgMBoPBYGgpxrkwGAwGg8HQUoxzYTAYDAaDoaUY58JgMBgMBkNL+f8BiLmqfCq0vmgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 3, figsize=(6, 5))\n",
+    "heat_data_0p5[\"temp\"].temperature.plane_slice(axis=1, pos=0).plot(grid=False, cmap=\"jet\", ax=ax[0])\n",
+    "heat_data_1p0[\"temp\"].temperature.plane_slice(axis=1, pos=0).plot(grid=False, cmap=\"jet\", ax=ax[1])\n",
+    "heat_data_1p5[\"temp\"].temperature.plane_slice(axis=1, pos=0).plot(grid=False, cmap=\"jet\", ax=ax[2])\n",
+    "\n",
+    "ax[0].set_title(\"0.5 µm gap\")\n",
+    "ax[1].set_title(\"1.0 µm gap\")\n",
+    "ax[2].set_title(\"1.5 µm gap\")\n",
+    "\n",
+    "fig.suptitle(\"Temperature profiles at y=0\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From this temperature distribution, we see that the middle section should have more of a perturbation. To verify this, we will visualize the change in refractive index along the propagation direction of the signal waveguide."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "heat_line_0p5 = heat_data_0p5[\"temp\"].temperature.line_slice(\n",
+    "    axis=2, pos=sims[\"0.5\"].structures[1].geometry.center\n",
+    ")\n",
+    "heat_line_1p0 = heat_data_1p0[\"temp\"].temperature.line_slice(\n",
+    "    axis=2, pos=sims[\"1.0\"].structures[1].geometry.center\n",
+    ")\n",
+    "heat_line_1p5 = heat_data_1p5[\"temp\"].temperature.line_slice(\n",
+    "    axis=2, pos=sims[\"1.5\"].structures[1].geometry.center\n",
+    ")\n",
+    "\n",
+    "plt.plot(\n",
+    "    heat_line_0p5.z,\n",
+    "    np.squeeze(heat_line_0p5.data) * Si_heat_coeff,\n",
+    "    color=\"blue\",\n",
+    "    label=\"0.5 µm gap\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    heat_line_1p0.z,\n",
+    "    np.squeeze(heat_line_1p0.data) * Si_heat_coeff,\n",
+    "    color=\"mediumblue\",\n",
+    "    label=\"1.0 µm gap\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    heat_line_1p5.z,\n",
+    "    np.squeeze(heat_line_1p5.data) * Si_heat_coeff,\n",
+    "    color=\"darkblue\",\n",
+    "    label=\"1.5 µm gap\",\n",
+    ")\n",
+    "\n",
+    "plt.xlabel(\"z (µm)\")\n",
+    "plt.ylabel(\"Δn\")\n",
+    "plt.title(\"Refractive Index Change in Signal Waveguide vs. z\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final Optical Simulation\n",
+    "\n",
+    "We are now in a position to see the effect on the signal waveguide from the the control signal through the control waveguide. We create a function that modifies our first optical simulations into ones that launch and measure the TE mode through the signal waveguide. Then, using the temperature data of the previous heat simulations, we create heat-perturbed copies to compute the phase changes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_sim_signal_source(sim):\n",
+    "    new_source = sim.sources[0].updated_copy(\n",
+    "        center=(\n",
+    "            sim.structures[1].geometry.center[0],\n",
+    "            sim.sources[0].center[1],\n",
+    "            sim.sources[0].center[2],\n",
+    "        )\n",
+    "    )\n",
+    "    new_monitor = td.ModeMonitor(\n",
+    "        center=(\n",
+    "            sim.structures[1].geometry.center[0],\n",
+    "            sim.structures[1].geometry.center[1],\n",
+    "            sim.center[2] - sim.size[2] / 2 + 1,\n",
+    "        ),\n",
+    "        size=new_source.size,\n",
+    "        name=\"mode\",\n",
+    "        freqs=[freq0],\n",
+    "        mode_spec=new_source.mode_spec,\n",
+    "    )\n",
+    "\n",
+    "    new_sim = sim.updated_copy(sources=[new_source], monitors=[new_monitor], medium=SiO2.optical)\n",
+    "    return new_sim\n",
+    "\n",
+    "\n",
+    "sims_opt, sims_opt_perturbed = {}, {}\n",
+    "\n",
+    "sims_opt[\"0.5\"] = update_sim_signal_source(sims[\"0.5\"])\n",
+    "sims_opt[\"1.0\"] = update_sim_signal_source(sims[\"1.0\"])\n",
+    "sims_opt[\"1.5\"] = update_sim_signal_source(sims[\"1.5\"])\n",
+    "\n",
+    "sims_opt_perturbed[\"0.5\"] = sims_opt[\"0.5\"].perturbed_mediums_copy(\n",
+    "    temperature=heat_data_0p5[\"temp\"].temperature\n",
+    ")\n",
+    "sims_opt_perturbed[\"1.0\"] = sims_opt[\"1.0\"].perturbed_mediums_copy(\n",
+    "    temperature=heat_data_1p0[\"temp\"].temperature\n",
+    ")\n",
+    "sims_opt_perturbed[\"1.5\"] = sims_opt[\"1.5\"].perturbed_mediums_copy(\n",
+    "    temperature=heat_data_1p5[\"temp\"].temperature\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We create batches for the perturbed and unperturbed simulations and run them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3efafa04ea9145598c56bfe07e882273",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:16:00 EDT Started working on Batch containing 3 tasks.                       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:16:00 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m3\u001b[0m tasks.                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:16:02 EDT Maximum FlexCredit cost: 0.221 for the whole batch.                \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:16:02 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.221\u001b[0m for the whole batch.                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Use 'Batch.real_cost()' to get the billed FlexCredit cost after the\n",
+       "             Batch has completed.                                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
+       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47c548ea9df443689607e1664a6965d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:16:05 EDT Batch complete.                                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:16:05 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e56170248ee14902bf3505e4ecd4ebff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "222011187098422793584925f44608e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:18:19 EDT Started working on Batch containing 3 tasks.                       \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:18:19 EDT\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m3\u001b[0m tasks.                       \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:20:21 EDT Maximum FlexCredit cost: 0.221 for the whole batch.                \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:20:21 EDT\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.221\u001b[0m for the whole batch.                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
             Use 'Batch.real_cost()' to get the billed FlexCredit cost after the\n",
+       "             Batch has completed.                                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
+       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87afe200a67548a5b42417a07be28e91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
19:20:23 EDT Batch complete.                                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m19:20:23 EDT\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80ec3c6b3ba940148ec85bdcb3dde4f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "opt_unperturbed_batch = td.web.Batch(simulations=sims_opt)\n",
+    "opt_perturbed_batch = td.web.Batch(simulations=sims_opt_perturbed)\n",
+    "\n",
+    "# Run all the simulations and get the results.\n",
+    "opt_unperturbed = opt_unperturbed_batch.run()\n",
+    "opt_perturbed = opt_perturbed_batch.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now compute the phase changes between the heated and unheated simulations for each gap, and plot the phase change vs gap length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "phase_0p5_unp = np.angle(opt_unperturbed[\"0.5\"][\"mode\"].amps.sel(direction=\"-\").values)\n",
+    "phase_0p5_p = np.angle(opt_perturbed[\"0.5\"][\"mode\"].amps.sel(direction=\"-\").values)\n",
+    "\n",
+    "phase_1p0_unp = np.angle(opt_unperturbed[\"1.0\"][\"mode\"].amps.sel(direction=\"-\").values)\n",
+    "phase_1p0_p = np.angle(opt_perturbed[\"1.0\"][\"mode\"].amps.sel(direction=\"-\").values)\n",
+    "\n",
+    "phase_1p5_unp = np.angle(opt_unperturbed[\"1.5\"][\"mode\"].amps.sel(direction=\"-\").values)\n",
+    "phase_1p5_p = np.angle(opt_perturbed[\"1.5\"][\"mode\"].amps.sel(direction=\"-\").values)\n",
+    "\n",
+    "phase_shift_0p5 = np.squeeze(phase_0p5_p - phase_0p5_unp)\n",
+    "phase_shift_1p0 = np.squeeze(phase_1p0_p - phase_1p0_unp)\n",
+    "phase_shift_1p5 = np.squeeze(phase_1p5_p - phase_1p5_unp)\n",
+    "\n",
+    "phase_shifts = [phase_shift_0p5, phase_shift_1p0, phase_shift_1p5]\n",
+    "\n",
+    "plt.plot([0.5, 1.0, 1.5], phase_shifts, marker=\"o\", linestyle=\"-\", linewidth=2, color=\"tab:blue\")\n",
+    "plt.xticks([0.5, 1.0, 1.5])\n",
+    "plt.xlabel(\"Gap (µm)\")\n",
+    "plt.ylabel(\"Phase Shift (rad)\")\n",
+    "plt.title(\"Phase Shift vs. Gap\")\n",
+    "plt.xlim(0.4, 1.6)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "applications": [
+   "Active photonic integrated circuit components"
+  ],
+  "description": "This example demonstrates the usage of the Tidy3D to calculate absorbed optical power, its resultant heating, and the corresponding optical heat perturbation on the silicon-on-insulator (SOI) platform.",
+  "feature_image": "./img/photothermal.png",
+  "features": [
+   "Heat",
+   "Mode analysis",
+   "Perturbation medium"
+  ],
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "keywords": "silicon, SOI, Tidy3D, heat, doped",
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  },
+  "title": "Photo-thermal optical control in silicon waveguides"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/case_studies/pic_active.rst b/docs/case_studies/pic_active.rst
index 15844c30..beefe34e 100644
--- a/docs/case_studies/pic_active.rst
+++ b/docs/case_studies/pic_active.rst
@@ -15,3 +15,4 @@ At the moment, Tidy3D’s heat solver can be used with the FDTD solver to model
     ../../HeatDissipationSOI
     ../../TransientThermoOpticShifter
     ../../CPOHeat
+    ../../PhotoThermalWaveguides