From adc925801f7c5cd7231248b6891713969100753c Mon Sep 17 00:00:00 2001 From: masoudabedinifar <140504378+masoudabedinifar@users.noreply.github.com> Date: Wed, 17 Jul 2024 08:59:21 +0200 Subject: [PATCH 01/22] Declaration of Helsinki --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index f38425e8..fea27246 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -68,6 +68,6 @@ NGMT is a community effort, and any contribution is welcomed. The project is hos # Acknowledgements The authors would like to thank every person who provided data which has been used in the development and validation of the algorithms in the NGMT toolbox. -The authors declare no competing interests. +The data collection have been performed in accordance with the Declaration of Helsinki. The authors declare no competing interests. # References From 8f60c7b70988f37a3a1ff59d00dae7ca4ba27bac Mon Sep 17 00:00:00 2001 From: masoudabedinifar <140504378+masoudabedinifar@users.noreply.github.com> Date: Wed, 17 Jul 2024 09:04:31 +0200 Subject: [PATCH 02/22] data used for each module --- README.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 36ae2c45..fe23e910 100644 --- a/README.md +++ b/README.md @@ -11,13 +11,15 @@ Welcome to the NeuroGeriatricsMotionToolbox (NGMT). We are a Python based toolbox for processing motion data. -The toolbox is aimed at motion researchers who want to use python based open source software to process their data. -We have implemented validated algorithms in modules to process motion data, such as: - - Gait sequence detection (GSD) - - Inital contact detection (ICD) - - Physical activity monitoring (PAM) - - Postural transition detection (SSD) - - More to follow ... +The toolbox is aimed at motion researchers who want to use Python-based open-source software to process their data. We have implemented validated algorithms in modules to process motion data, as shown in the table below: + +| Module | Description | Data | +|--------------------------------|------------------------------------------------|----------------------------------------| +| Gait sequence detection (GSD) | Detects gaits | 3D accelerations from the lower back | +| Initial contact detection (ICD)| Detects initial contact during gait | 3D accelerations from the lower back | +| Postural transition detection (SSD) | Detects sit-to-stand and stand-to-sit movements | 3D accelerations and gyroscope from the lower back | +| Physical activity monitoring (PAM) | Monitors physical activity levels | 3D accelerations from the wrist | +| More to follow... | Additional modules to be added | | The idea is that various motion data can be loaded into our dedicated dataclass which rely on principles from the [Motion-BIDS](https://bids-specification.readthedocs.io/en/latest/modality-specific-files/motion.html) standard. From 75636bf3af23cab1da17591d4b0fdd8bde5304ac Mon Sep 17 00:00:00 2001 From: masoudabedinifar <140504378+masoudabedinifar@users.noreply.github.com> Date: Wed, 17 Jul 2024 09:06:38 +0200 Subject: [PATCH 03/22] Contributing --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index fe23e910..4947ee72 100644 --- a/README.md +++ b/README.md @@ -110,7 +110,6 @@ classDiagram In the examples you find a [tutorial (the basics of NGMT)](https://neurogeriatricskiel.github.io/NGMT/examples/00_tutorial_basics/) that explains the basics of the dataclass and how to work with them. - ## Installation The toolbox has been released on [pypi](https://pypi.org/project/ngmt/) and can be installed via pip: ```bash @@ -118,6 +117,10 @@ pip install ngmt ``` It requires Python 3.10 or higher. +## Contributing +We welcome contributions to NGMT! Please refer to our [contributing guide](https://neurogeriatricskiel.github.io/NGMT/contributing) for more details. + + ## Authors [Masoud Abedinifar](https://github.com/masoudabedinifar), [Julius Welzel](https://github.com/JuliusWelzel), [Walter Maetzler](mailto:w.maetzler@neurologie.uni-kiel.de), [Clint Hansen](mailto:c.hansen@neurologie.uni-kiel.de) & [Robbin Romijnders](https://github.com/rmndrs89) From a62e73108f44192dade141ce92d335053f51e768 Mon Sep 17 00:00:00 2001 From: masoudabedinifar <140504378+masoudabedinifar@users.noreply.github.com> Date: Wed, 17 Jul 2024 09:12:39 +0200 Subject: [PATCH 04/22] summary revision --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index fea27246..36f00769 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -38,7 +38,7 @@ output:
# Summary -The NeuroGeriatrics Motion Toolbox (NGMT) is an open-source Python-based toolbox designed for processing human motion data, following open-science practices. NGMT offers a range of algorithms for the processing of motion data in neuroscience and biomechanics and currently includes implementations for gait sequence detection, initial contact detection, physical activity monitoring, sit to stand and stand to sit detection algorithms. These algorithms aid in identifying patterns in human motion data on different time scales. Some of the toolbox algorithms have been developed and validated in clinical cohorts, allowing extracted patters to be used in a clinical context. The modular design of NGMT allows the toolbox to be easily extended to incorporate relevant algorithms which will be developed in the research community. The toolbox is designed to be user-friendly and is accompanied by a comprehensive documentation and practical examples, while the underlying data structures build on the Motion BIDS specification [@jeung:2023]. The NGMT toolbox is intended to be used by researchers and clinicians to analyze human motion data from various recording modalities and to promote the utilization of open-source software in the field of human motion analysis. +The NeuroGeriatrics Motion Toolbox (NGMT) is an open-source Python-based toolbox designed for processing human motion data, following open-science practices. NGMT offers a range of algorithms for the processing of motion data in neuroscience and biomechanics and currently includes implementations for gait sequence detection, initial contact detection, physical activity monitoring, sit to stand and stand to sit detection algorithms. These algorithms aid in identifying patterns in human motion data on different time scales. The NGMT is versatile in accepting motion data from various recording modalities, including IMUs that provide acceleration data from specific body locations such as the pelvis or wrist. This flexibility allows researchers to analyze data captured using different hardware setups, ensuring broad applicability across studies. Some of the toolbox algorithms have been developed and validated in clinical cohorts, allowing extracted patters to be used in a clinical context. The modular design of NGMT allows the toolbox to be easily extended to incorporate relevant algorithms which will be developed in the research community. The toolbox is designed to be user-friendly and is accompanied by a comprehensive documentation and practical examples, while the underlying data structures build on the Motion BIDS specification [@jeung:2023]. The NGMT toolbox is intended to be used by researchers and clinicians to analyze human motion data from various recording modalities and to promote the utilization of open-source software in the field of human motion analysis. # Statement of need Physical mobility is an essential aspect of health, since impairment of mobility is associated with reduced quality of life, falls, hospitalization, mortality, and other adverse events in many chronic conditions. Traditional mobility measures include patient-reported outcomes, objective clinical assessments, and subjective clinical assessments. These measures are associated with the perception and capacity aspects of health that frequently fail to show any relevant effect on daily function at an individual level [@maetzler:2021]. To complement both patient-reported (perception) and clinical (capacity) assessment approaches, digital health technology (DHT), including body-worn or wearable devices, offers a new dimension of measuring daily function, that is, performance [@warmerdam:2020; @fasano:2020; @maetzler:2021]. DHT allows an objective impression of how patients function in everyday life and their ability to routinely perform everyday activities [@hansen:2018; @buckley:2019; @celik:2021]. Nonetheless, due to several persisting challenges in this field, current tools and techniques are still in their infancy [@micoamigo:2023]. Many studies often used proprietary software to clinically relevant features of mobility. The development of easy-to-use and open-source software is imperative for transparent features extraction in research and clinical settings. The NeuroGeriatrics Motion Toolbox (NGMT) addresses this gap by providing software for human mobility analysis, to be used by motion researchers and clinicians, while promoting open-source practices. The conceptual framework builds on FAIR data principles to encourage the use of open source software as well as facilitate data sharing and reproducibility in the field of human motion analysis. From 66176ffa3bf350fdb48501102bdf303c567ea519 Mon Sep 17 00:00:00 2001 From: masoudabedinifar <140504378+masoudabedinifar@users.noreply.github.com> Date: Wed, 17 Jul 2024 09:15:10 +0200 Subject: [PATCH 05/22] Statement of need revision --- paper/paper.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 36f00769..3fb7f930 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -41,7 +41,17 @@ output: The NeuroGeriatrics Motion Toolbox (NGMT) is an open-source Python-based toolbox designed for processing human motion data, following open-science practices. NGMT offers a range of algorithms for the processing of motion data in neuroscience and biomechanics and currently includes implementations for gait sequence detection, initial contact detection, physical activity monitoring, sit to stand and stand to sit detection algorithms. These algorithms aid in identifying patterns in human motion data on different time scales. The NGMT is versatile in accepting motion data from various recording modalities, including IMUs that provide acceleration data from specific body locations such as the pelvis or wrist. This flexibility allows researchers to analyze data captured using different hardware setups, ensuring broad applicability across studies. Some of the toolbox algorithms have been developed and validated in clinical cohorts, allowing extracted patters to be used in a clinical context. The modular design of NGMT allows the toolbox to be easily extended to incorporate relevant algorithms which will be developed in the research community. The toolbox is designed to be user-friendly and is accompanied by a comprehensive documentation and practical examples, while the underlying data structures build on the Motion BIDS specification [@jeung:2023]. The NGMT toolbox is intended to be used by researchers and clinicians to analyze human motion data from various recording modalities and to promote the utilization of open-source software in the field of human motion analysis. # Statement of need -Physical mobility is an essential aspect of health, since impairment of mobility is associated with reduced quality of life, falls, hospitalization, mortality, and other adverse events in many chronic conditions. Traditional mobility measures include patient-reported outcomes, objective clinical assessments, and subjective clinical assessments. These measures are associated with the perception and capacity aspects of health that frequently fail to show any relevant effect on daily function at an individual level [@maetzler:2021]. To complement both patient-reported (perception) and clinical (capacity) assessment approaches, digital health technology (DHT), including body-worn or wearable devices, offers a new dimension of measuring daily function, that is, performance [@warmerdam:2020; @fasano:2020; @maetzler:2021]. DHT allows an objective impression of how patients function in everyday life and their ability to routinely perform everyday activities [@hansen:2018; @buckley:2019; @celik:2021]. Nonetheless, due to several persisting challenges in this field, current tools and techniques are still in their infancy [@micoamigo:2023]. Many studies often used proprietary software to clinically relevant features of mobility. The development of easy-to-use and open-source software is imperative for transparent features extraction in research and clinical settings. The NeuroGeriatrics Motion Toolbox (NGMT) addresses this gap by providing software for human mobility analysis, to be used by motion researchers and clinicians, while promoting open-source practices. The conceptual framework builds on FAIR data principles to encourage the use of open source software as well as facilitate data sharing and reproducibility in the field of human motion analysis. +Physical mobility is an essential aspect of health, since impairment of mobility is associated with reduced quality of life, falls, hospitalization, mortality, and other adverse events in many chronic conditions. Traditional mobility measures include patient-reported outcomes, objective clinical assessments, and subjective clinical assessments. These measures are associated with the perception and capacity aspects of health that frequently fail to show any relevant effect on daily function at an individual level [@maetzler:2021].To complement both patient-reported (perception) and clinical (capacity) assessment approaches, digital health technology (DHT) introduces a new paradigm for assessing daily function through wearable devices, providing objective insights to an individual's functional performance in everyday life activities [@warmerdam:2020; @fasano:2020; @maetzler:2021; @hansen:2018; @buckley:2019; @celik:2021]. DHT allows an objective impression of how patients function in everyday life and their ability to routinely perform everyday activities [@hansen:2018; @buckley:2019; @celik:2021]. Nonetheless, due to several persisting challenges in this field, current tools and techniques are still in their infancy [@micoamigo:2023]. Many studies often used proprietary software to clinically relevant features of mobility. The development of easy-to-use and open-source software is imperative for transparent features extraction in research and clinical settings. The NeuroGeriatrics Motion Toolbox (NGMT) addresses this gap by providing software for human mobility analysis, to be used by motion researchers and clinicians, while promoting open-source practices. The conceptual framework builds on FAIR data principles to encourage the use of open source software as well as facilitate data sharing and reproducibility in the field of human motion analysis. The NGMT comprises several modules, each serving distinct purposes in human motion analysis: + +1. Gait Sequence Detection (GSD): Identifies walking bouts to analyze gait patterns and abnormalities, crucial for neurological and biomechanical assessments. + +2. Initial Contact Detection (ICD): Pinpoints the moment of initial foot contact during walking, aiding in understanding gait dynamics and stability. + +3. Physical Activity Monitoring (PAM): Quantifies the intensity and duration of physical activities, supporting assessments of overall physical fitness and activity levels. + +4. Sit-to-stand and stand-to-sit Detection (SSD): Detects transitions between sitting and standing positions, essential for evaluating functional mobility and independence in daily activities. + +These modules are pivotal because they enable researchers and clinicians to extract meaningful insights from motion data captured in various environments and conditions. These modules are designed to process data from wearable devices, which offer distinct advantages over vision-based approaches. wearable devices such as IMUs provide continuous monitoring capabilities, enabling users to wear them throughout the day in diverse settings without logistical constraints posed by camera-based systems. # State of the field With the growing availability of digital health data, open-source implementations of relevant algorithms are increasingly becoming available. From the Mobilise-D consortium, the recommended algorithms for assessing real-world gait were released, but these algorithms were developed in MATLAB, that is not free to use [@mobilised:2023]. Likewise, an algorithm for the estimation of gait quality was released, but it is also only available in MATLAB [@gaitqualitycomposite:2016]. Alternatively, open-source, Python packages are available, for example to detect gait and extract gait features from a low back-worn inertial measurement unit (IMU) [@czech:2019], or from two feet-worn IMUs [@kuederle:2024]. NGMT builds forth on these toolboxes by providing a module software package that goes beyond the analysis of merely gait, and extends these analyses by additionally allowing for the analysis of general physical activity and other daily life-relevant movements, such as sit-to-stand and stand-to-sit transitions [@pham:2017] as well as turns [@pham:2018]. 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pelvis_ACC_xpelvis_ACC_ypelvis_ACC_zpelvis_ANGVEL_xpelvis_ANGVEL_ypelvis_ANGVEL_z
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40.918973-0.053218-0.3979470.608625-0.614677-0.349559
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" - ], - "text/plain": [ - " pelvis_ACC_x pelvis_ACC_y pelvis_ACC_z pelvis_ANGVEL_x \\\n", - "0 0.920901 -0.047850 -0.400888 0.000000 \n", - "1 0.919441 -0.051282 -0.392583 0.000000 \n", - "2 0.922828 -0.047359 -0.392093 -0.172905 \n", - "3 0.926741 -0.048830 -0.384279 0.262815 \n", - "4 0.918973 -0.053218 -0.397947 0.608625 \n", - "... ... ... ... ... \n", - "2903 0.966803 -0.027822 -0.279782 -0.089911 \n", - "2904 0.957517 -0.035152 -0.285636 0.525631 \n", - "2905 0.960437 -0.034171 -0.291979 0.871441 \n", - "2906 0.962890 -0.036623 -0.299794 1.051262 \n", - "2907 0.963883 -0.038584 -0.294921 1.134256 \n", - "\n", - " pelvis_ANGVEL_y pelvis_ANGVEL_z \n", - "0 -0.614677 0.436291 \n", - "1 -0.700049 0.176093 \n", - "2 -0.261807 -0.262826 \n", - "3 -0.261807 0.000000 \n", - "4 -0.614677 -0.349559 \n", - "... ... ... \n", - "2903 -1.309034 0.000000 \n", - "2904 -0.438242 -0.436291 \n", - "2905 -0.961855 0.086733 \n", - "2906 -0.700049 0.176093 \n", - "2907 -0.347179 -0.525652 \n", - "\n", - "[2908 rows x 6 columns]" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = recording.data[tracksys].loc[:,[f\"pelvis_{ch_type}_{ch_comp}\"\n", - " for ch_type in [\"ACC\", \"ANGVEL\"]\n", - " for ch_comp in [\"x\", \"y\", \"z\"]]]\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/robbin/.cache/pypoetry/virtualenvs/ngmt-CBrNr8GT-py3.10/lib/python3.10/site-packages/pywt/_multilevel.py:43: UserWarning: Level value of 10 is too high: all coefficients will experience boundary effects.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "from ngmt.modules.ssd import PhamSittoStandStandtoSitDetection\n", - "pts = PhamSittoStandStandtoSitDetection()\n", - "pts = pts.detect(\n", - " data=data, sampling_freq_Hz=fs\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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onsetdurationevent_typepostural transition anglemaximum flexion velocitymaximum extension velocitytracking_systemstracked_points
03.5054.560stand to sit71.5654276186imuLowerBack
19.5001.855sit to stand23.4096533618imuLowerBack
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" - ], - "text/plain": [ - " onset duration event_type postural transition angle \\\n", - "0 3.505 4.560 stand to sit 71.565427 \n", - "1 9.500 1.855 sit to stand 23.409653 \n", - "\n", - " maximum flexion velocity maximum extension velocity tracking_systems \\\n", - "0 6 186 imu \n", - "1 36 18 imu \n", - "\n", - " tracked_points \n", - "0 LowerBack \n", - "1 LowerBack " - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pts.postural_transitions_" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ngmt-CBrNr8GT-py3.10", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/paper/paper.ipynb b/paper/paper.ipynb deleted file mode 100644 index 90dbd30f..00000000 --- a/paper/paper.ipynb +++ /dev/null @@ -1,431 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d208afe9", - "metadata": {}, - "source": [ - "# Figure 1" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "ec888b0a", - "metadata": {}, - "outputs": [], - "source": [ - "# Import libraries\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from ngmt.datasets import mobilised\n", - "from ngmt.modules.gsd import ParaschivIonescuGaitSequenceDetection\n", - "from ngmt.modules.icd import ParaschivIonescuInitialContactDetection\n", - "from ngmt.config import cfg_colors" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "38d75f76", - "metadata": {}, - "outputs": [], - "source": [ - "# Read the data\n", - "file_path = (\n", - " # r\"C:\\Users\\Project\\Desktop\\Gait_Sequence\\Mobilise-D dataset_1-18-2023\\CHF\\data.mat\"\n", - " \"/mnt/neurogeriatrics_data/Mobilise-D/rawdata/sub-4005/Free-living/data.mat\"\n", - ")\n", - "\n", - "# Define tracking system and tracked points\n", - "tracking_sys = \"SU\"\n", - "tracked_points = {tracking_sys: \"LowerBack\"}\n", - "\n", - "# Load recording data\n", - "recording = mobilised.load_recording(\n", - " file_name=file_path, tracking_systems=[tracking_sys], tracked_points=tracked_points\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "5e360e6f", - "metadata": {}, - "outputs": [], - "source": [ - "# Extract lower back acceleration data\n", - "acc_data = recording.data[tracking_sys][\n", - " [f\"{tracked_points[tracking_sys][0]}_ACCEL_{x}\" for x in [\"x\", \"y\", \"z\"]]\n", - "]\n", - "acc_data" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "80e592dc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "100.0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Get sampling frequency\n", - "sampling_frequency = recording.channels[tracking_sys][\n", - " recording.channels[tracking_sys][\"name\"] == f\"{tracked_points[tracking_sys][0]}_ACCEL_x\"\n", - "][\"sampling_frequency\"].values[0]\n", - "sampling_frequency" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "93989f12", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "12 gait sequence(s) detected.\n" - ] - }, - { - "data": { - "text/html": [ - "
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onsetdurationevent_typetracking_systemstracked_points
01322.3006.100gait sequenceSULowerBack
11334.2755.075gait sequenceSULowerBack
21712.5505.125gait sequenceSULowerBack
32458.55011.000gait sequenceSULowerBack
42483.9507.175gait sequenceSULowerBack
52578.8007.650gait sequenceSULowerBack
62600.2504.625gait sequenceSULowerBack
72665.72522.275gait sequenceSULowerBack
82692.175583.125gait sequenceSULowerBack
93345.92510.225gait sequenceSULowerBack
103360.3506.050gait sequenceSULowerBack
113472.0005.050gait sequenceSULowerBack
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" - ], - "text/plain": [ - " onset duration event_type tracking_systems tracked_points\n", - "0 1322.300 6.100 gait sequence SU LowerBack\n", - "1 1334.275 5.075 gait sequence SU LowerBack\n", - "2 1712.550 5.125 gait sequence SU LowerBack\n", - "3 2458.550 11.000 gait sequence SU LowerBack\n", - "4 2483.950 7.175 gait sequence SU LowerBack\n", - "5 2578.800 7.650 gait sequence SU LowerBack\n", - "6 2600.250 4.625 gait sequence SU LowerBack\n", - "7 2665.725 22.275 gait sequence SU LowerBack\n", - "8 2692.175 583.125 gait sequence SU LowerBack\n", - "9 3345.925 10.225 gait sequence SU LowerBack\n", - "10 3360.350 6.050 gait sequence SU LowerBack\n", - "11 3472.000 5.050 gait sequence SU LowerBack" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Gait sequence detection\n", - "gsd = ParaschivIonescuGaitSequenceDetection(target_sampling_freq_Hz=40)\n", - "gsd = gsd.detect(\n", - " data=acc_data, sampling_freq_Hz=sampling_frequency, plot_results=False\n", - ")\n", - "gait_sequences = gsd.gait_sequences_\n", - "gsd.gait_sequences_" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "608e85a2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.axvspan(\n", - " first_gait_sequence[\"onset\"],\n", - " first_gait_sequence[\"onset\"] + first_gait_sequence[\"duration\"],\n", - " alpha=0.2,\n", - " color=\"gray\",\n", - " label=\"Gait duration\",\n", - ")\n", - "ax.plot(acc_data.iloc[1315*100:1350*100,:])\n", - "ax.set_xlabel(\"time (samples)\")\n", - "ax.set_ylabel(\"acceleration (g)\")\n", - "ax.set_xlim((131500, 135000))\n", - "ax.grid(which=\"both\", axis=\"both\", c=\"tab:gray\", alpha=0.2)\n", - "ax.spines[[\"top\", \"right\"]].set_visible(False)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "31d0df89", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "86 gait sequence(s) detected.\n" - ] - }, - { - "data": { - "image/png": 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6Ojrw9ttvY+PGjTh27Bi0Wq1X4ejq6mrk5OR0u79A2bt3L7+ovfTSS31ud9lll+HTTz/FwYMHYbVaIZPJPLY577zzvLZ1FawOxkkULn766Se899572LFjB6qrq6HX6z22qa6ujthjnDlzBm+++Sa2bt2K06dPo6OjAw6HI+B9jxw5Emq12utn3G/V9XfavXs3ANZhcdFFF3ltKxaLMW3aNKxcuTLg7xIMNpsN9fX1Xj+zWCwe7wl1TgXL1q1beeHwQLn55pvx2GOPobKyEj/88INb+z179uDYsWMAOp3xHNx8eezYMb/f2Wg0AvA9p/VkDAXKFVdcgX/9619Yv349Zs2ahYULF2Lq1KkBCd8TBEEQvYOcUgRBEERE4HA4sGzZMgCeixqORYsWYdWqVfj000/xyiuvuC1QuCfcRUVFAR/T9al4MO04R5bD4fC5YO2Kt0pUXWlpaYHZbAbARjL0Zr/dOcBOnjyJSy65xM2RkJCQgJSUFD4ihftu3pwbvaGhoYH/21+US79+/QCwjoGWlhZkZ2d7bJOUlOS1rVTaeYvT2wptgeC6GE5PT3f77MEHH8Tzzz/Pv5ZIJEhNTYVcLgcA6HQ66PX6XvVzOI/xxRdf4Prrr+fHJsA6iZVKJUQiESwWC1pbW/3u29fvBHT+Vjabze19bpxkZGRAoVD4bM+Nk2Bx/Z1aWlqQm5vrsc3w4cPdooLKy8sxYMAAr/sT8pzqCwoLCzF16lSUlJTgo48+cnNKcVFS5557LoYPH+7WjpsvTSZTt1UWAd9zWk/GUKBceOGFeO655/Doo4/im2++wTfffAOAHVuXXnopFi5c6FYFkSAIgggdlL5HEARBRAT/+9//+MXc7bff7lG6WyQS8SlIOp0On376qVt713SRQOlJG6AzAio7OxsMq8/Y7b+ukV/+9gsAX3/9dUD7ffzxx73uSyKR+D3WLbfcgurqavTv3x+fffYZmpubodfr0dDQgLq6Opw9e5bf1luqDuHOgQMHALApRq6Otm+//ZZ3Fv3xj3/EoUOHYDab0dLSgrq6OtTV1eGvf/0rgJ73cziP0dzcjMWLF8NsNuPiiy9GSUkJDAYD2traUF9fj7q6Onz22Wc9sltoXKMc9+/f3+v9xcM5xT0wWLNmDR/ZZLPZ+BTbrmnXQOe8tmDBgoDmtPLy8r75Ml24//77UVZWhpdffhlz585FVlYWqqursWzZMlx88cW49tpr+8TBTRAEEW+QU4ogCIKICDgtpZ5uz6WF+Er98IZrKklP2jU1NYU04iE9PZ1/4h+MPcFSVVXFpzuuWrUK8+bN89DUCae2imsUl7+UL+4zqVTqU/MnEjCZTPj+++8BAOeffz6USiX/2SeffAIAmDFjBt544w2MGjXKw2HY274O5zE2bdqE9vZ2pKamYsOGDZg6dSpUKlVI9t0d3DhpamrymirH4ersCYbp06fzjun169f3aB8cQp9TfcW8efOgUqnQ3t6OL7/8EgCwefNmNDQ0QCaT8bpQrvRkbhaKvLw83Hvvvfjiiy9QX1+PgwcP4vbbbwfAOuLeeustgS0kCIKIPcgpRRAEQQhOY2Mjvyhcs2YNOjo6fP779ddfAbAaUidOnOD3wWlMbdiwIeDjFhYW8lEtwbTj9Jrsdju+/vrrgNt1h0wmw6RJk4K2J1iqqqr4v32JoX/33Xc+27sKTvck4mPChAn8PrZs2eJzO86GsWPHetWTihRef/11NDU1AQAWL17s9hnX1776mWEY3qHlDa6f/PVzb4/hD27fw4YN8ypID/gfK71h4sSJANhInJ9++snrNg6HAyUlJT3af25uLi+m/9FHH6GsrKxH+wF6f075o7fnWyhJSkrC3LlzAXSm7HH/z5o1CxkZGR5tuPlyz549qK2t7RtDXQjkHPLF6NGj8e677/Lf4dtvvw2pbQRBEAQ5pQiCIIgI4KOPPoLVakVycjJ+97vfQa1W+/znqlniGi112223AQCOHDkS1NNsrt3SpUuxb9++gNoMGTKE11N55JFH0NbW5nf7YMR377zzTgBshMqmTZtCtl9XXKv6cWlnrnR0dODpp5/22V6j0fB/a7XaoI+fkpLCV+H697//7VVD5sCBA/j8888BwGv0RaTw6aef4uGHHwbAVn686aab3D7n+tpbPwPA22+/jTNnzvjcP9fX/vq5t8fwB7fvkydPetUD2r9/Pz7++OMe7bs7xowZg+LiYgDAM8884yGsDrDVM3sjEP/0008jMTERer0ec+fO5fWPgqW355Q/enu+hRouhW/z5s0oLS3lI6Z8aQFee+21SElJgdVqxd/+9je/ziGHwxHy7xjIOeSql+YNLjrQWwVIgiAIonfQzEoQBEEIDudcuvLKK3lhZn9ce+21AIAPP/yQF7adPn06rrvuOgDAn//8Zzz00ENui9WmpiYsXbqUd0Jx3HfffRgyZAjMZjMuueQSvPvuu2hvb+c/P336NJ588km88MILbu1ee+01qNVqnDx5Eueffz6+/PJLt0X72bNn8dFHH+GSSy7Bgw8+GHBf3HTTTbj00kvBMAyuuuoqPP30024LZb1ej61bt+JPf/oTBg4cGPB+XSkuLkZhYSEA4NZbb8WePXv4z7Zv345p06b5FVofOnQo/zstXbq0RxEITz/9NGQyGU6dOoUZM2bg0KFDANhF6aZNm3DFFVfAZrNh0KBBuOuuu4Lefzipq6vD2rVrMXv2bCxYsABWqxX5+fnYuHGjm8A6AF4H7euvv8ZTTz3Fp3tqtVr885//xN133+0hjO7KqFGjALBOSl9par09hj8uv/xyiMVitLS04MYbb+RtsFgs+PTTT3H55Zf7FaDuLc888wwAtlrcDTfcwJ/TJpMJb7/9Nv785z8jJSWlx/sfPnw4VqxYAblcjoMHD2LMmDF4+umnceTIEbdx3d7ejm+++QZ333231/309pzyRyjOt1By2WWXIScnBzabDTfccAOMRiNSU1N9Vj5NSUnBf/7zHwBsquns2bOxc+dO3snocDhw7NgxvPjiixg5ciQ2btwYUnu5c+inn37C8ePHvW4zd+5c3Hrrrfj666/dnFctLS14+umn+YjO2bNnh9Q2giAIAgBDEARBEAKyfft2BgADgNmwYUNAbQ4ePMi3WbduHf++Xq9nrr76av4zAIxGo2GSk5P512PHjvXY3+nTp5kRI0bw24jFYiYtLY1JSEjg3/vLX/7i0e7nn39mcnJy+G0kEgmTnp7OqFQqNxtuv/12t3ZlZWX8Z2VlZR77bWtrY3772996fI+UlBRGJBLx70mlUo+2RUVFDADmgw8+8NuHGzZsYKRSKb+vhIQE/vsmJiYy3333Hf/Z1q1bPdrfdtttbm0LCwuZoqIi5v/+7//4bT744AMGAFNUVOTVhk8++YSRy+Vu31GpVPKvCwoKmKNHj3q027p1K7+NP/zZ7w/X30ej0TDZ2dlMdnY2k5mZ6WYv95vffPPNTHNzs9d9WSwW5qKLLuK3F4lETGpqKiMWixkAzOzZs5lHH32UAcBMnTrVo/3Jkyf5PhGLxUx2djZTVFTEFBUVMVVVVSE5Rnc8+OCDbt85OTmZkclkDABmwIABzMqVK33+HkuWLOn2uN39no888ojb8VNTU/mxe9FFFzEPPfRQj78bx/bt25lhw4Z5/Lbp6emMRqNxez8pKYl56qmnGKPR6LaP3pxT3c0JgZxv3eHaz6mpqfy49vXv3//+t899/e1vf3Prk7vuuqvb47/11ltu549CoWDS09P5scT9W7FihVu73o6hlpYWJjMzk/88IyODP4e2b9/OMAzDTJ061WO+7fq7z5s3j7Hb7d1+T4IgCCI4KFKKIAiCEBQuSio5ORmXX355QG1Gjx7Np/W4pvAlJCTg888/x8aNG3HVVVchLy8PJpMJUqkUY8aMwT333IN33nnHY38DBw7Evn378Oabb2LatGlITU1FR0cHUlJSMHnyZDz11FN89TJXLrjgApw8eRIvvPACpkyZgpSUFGi1WkgkEhQXF+Omm27CypUr+SiBQNFoNNiwYQM2bdqEBQsWoLCwEGazGQaDAfn5+bj88svx7LPPumlqBctvf/tb/Pjjj5g9ezZSUlJgs9mQkZGBW265BXv27MEll1zit/0bb7yBxx9/HKNHjwYAVFZWoqKigtdWCoQFCxbgyJEjuOuuuzBo0CCYzWZIpVKMGzcOTzzxBA4fPsz/zkLR3t6O+vp61NfXo62tDRqNBsXFxViwYAFeeuklVFZW4sMPP/QpxC6TybB582YsWbIEQ4cOhUwmA8MwmDRpEt566y2sX7/eb6XEIUOGYOvWrZgzZw4yMzPR3NyMiooKVFRU8FGCvT1Gd/zrX//Chx9+iEmTJkGlUsFqtWLw4MF4+OGHsW/fPuTl5fV434Hw9NNPY+PGjbj44ouh0WhgNptRXFyMf/3rX9iyZUtA0ZXdcf755+Po0aP4/PPPsXjxYgwfPhwajQZtbW0Qi8UoLi7GjTfeiOXLl6O2thaPPvqom6A90Ptzyh+hON9caW1t5ce1r386nc5n+66per5S91z5/e9/jxMnTuC+++7D2LFjoVAooNVqoVarMXHiRNx999349ttvQ56um5qaih9//BHXXXcd8vPz0dbWxp9DXHTra6+9hueeew5XXHEFhgwZAoZhYDQakZeXhzlz5uDzzz/HZ599Rul7BEEQYUDEMFFak5YgCIIgCIIgCIIgCIKIWsjdTxAEQRAEQRAEQRAEQfQ5MeWU0ul0WLJkCWbOnIm0tDSIRCIsW7YsoLbLli2DSCTy+q+uri68hhMEQRAEQRAEQRAEQcQZ0u43iR6amprw5JNPorCwEGPHjkVJSUnQ+3jyyScxYMAAt/d6U9WFIAiCIAiCIAiCIAiC8CSmnFK5ubmora1FTk4Odu/ejXPPPTfofcyaNQsTJ04Mg3UEQRAEQRAEQRAEQRAER0yl7ykUCuTk5PR6Px0dHbDb7SGwiCAIgiAIgiAIgiAIgvBGTDmlQsH06dOh0WiQkJCAOXPmoLS0VGiTCIIgCIIgCIIgCIIgYo6YSt/rDQkJCVi8eDHvlNqzZw9eeukl/OY3v8HevXtRUFDgs63ZbIbZbOZfOxwOtLS0ID09HSKRqC/MJwiCIAiCIAiCIAiCiAgYhkFHRwfy8vIgFvuOhyKnlJP58+dj/vz5/Ou5c+dixowZmDJlCp555hm8/fbbPts+++yzeOKJJ/rCTIIgCIIgCIIgCIIgiKigqqoK/fr18/k5OaX8cOGFF+K8887Dd99953e7hx56CH/729/4121tbSgsLERVVRU0Gk24zYx6du/eTeLy3aC36JH3Yh4AYPu87UhSJglsUWRjNIpw7rnsxLdrVzVUKkZgi2KXeO3rsrIyj0qthH/idawECvVP3xDP/Sz0vBXPfR/rCD22iN5hs9ngcDhQWFgImUwmtDlu0Doxumlvb0dBQQGSkvyvXckp1Q0FBQU4ceKE320UCgUUCoXH+xqNhpxSATBx4kTqp26QWCSAkv07JSUFGhX1lz8UChEAto9SUlKRkEA3vuEiXvtaqVRCpVIJbUZUEa9jJVCof/qGeO5noeeteO77WEfosUX0DpvNBovFAo1GE3FOKVonxgbdSRqR0Hk3nDlzBpmZmUKbEdPU19cLbQJBEERQaLVaoU0gCIIICpq3iHBBY4sIF7ROjA/i0ilVW1uL48ePw2q18u81NjZ6bLdp0ybs2bMHM2fO7Evz4o6WlhahTSAIgggKnU4ntAkEQRBBQfMWES5obBHhgtaJ8UHMpe+9/vrr0Gq1qKmpAQBs2LAB1dXVAIC7774bycnJeOihh7B8+XKUlZWhf//+AIDf/OY3GD9+PCZOnIjk5GTs3bsX77//PgoKCvDwww8L9XXiAqk05oYhITAyGYNnntGisbEJMlmC0ObENFxfc3/HC/4qiBDeidexEig0b/UN8TwOhZ634rnvYx2hxxYRu9A6MT4QMQwTU1eF/v37o6KiwutnnBNq8eLFHk6pRx99FF999RXKyspgMBiQm5uL2bNnY8mSJcjOzg7Khvb2diQnJ6OtrY1yYImQoLfooX5WDQA4dtMx0pQiCIIgCIIgCKLXcJpSRUVFEacpRUQ3gfpFYs6tXV5eDoZhvP7jHFDLli1zew0ATz/9NPbt2wetVguLxYKKigq8+eabQTukiOD59ddfhTaBiFFKS0uFNoGIUWhsEeGCxhYRLmhsEeGCxhYRLmidGB9QPBwhODEWrEdEAHY7sHOnHNXVyRg4EJBIhLYoduH6GgDOO89CfU34hMaKf2je6htoHAoH9T1BEMFC68T4gJxShOBQdUMi1JjNIlx7bQaADPz2t7VUdjqMdPY1UFoaP31NqdnBE69jJVBo3uob4nkcCj1vxXPfxzpCjy0idqF1YnwQc+l7RPSRmpoqtAkEQRBBkZiYKLQJBEEQQUHzFhEuaGwR4YLWifEBOaUIwTl58qTQJhAEQQRFbW2t0CYQBEEEBc1bRLigsUWEC1onxgfklCIIgiAIgiAIgiAIgiD6HNKUIgRn2LBhQptAEAQRFHl5eUKbQBAEERQ0b0UeDMPA4XBEvZhzdnY2bDab0GYQPcRms8Fut8NkMsFutwttjhsDBgyAyWQS2oy4RCKRQCqVQiQShf1Y5JQiBKelpQUpKSlCm0EQBBEwOp2ONDQIgogqaN6KHKxWK0wmE6xWKxiG6ZNFXzix2+3klIpiGIYBwzCoqqqKuLFos9nQ3NwstBlxi0KhQEpKClJTU8M6NsgpRQhOY2MjBg4cKLQZBEEQAdPe3o7s7GyhzSAIgggYmrciA7PZDJ1OB4VCgfT0dKhUKojF0a2oYrVaIZPJhDaD6AUMw0Amk0XcWNTr9eRMFwCGYWCz2dDW1ob6+npYLBbk5OSE7XjklCIEJ9ImPyL6kUoZPPpoG5qamiGVKoU2J6bh+pr7O16ItCeJ0UC8jpVAoXmrb4jncSj0vBXPfc9htVqh0+mQnJyM7OxswX+TUCESiaBQKIQ2g+ghXKSUXC6PuHWZ3W6HUknXRKFISkpCa2sr6urqoFKpkJycHJbjiJhoT2KOQNrb25GcnIy2tjZoNBqhzSFiAL1FD/WzagDAsZuOQaOicUUQBEEQBBFNdHR0QCQSoaioKGYcUkT0E8lOKSIyqKiogFgsRkFBQVDtAvWL0KgjBGf37t1Cm0DEKKdPnxbaBCJGobFFhAsaW0S4oLElLAzDwGq1IikpKeYcUmazWWgTiBhFr9cLbQIBQK1Ww2AwwOFwhGX/5JQiBCfSqjwQ0Y/dDuzfL8PRo4mg4RVeuL7ev18WV30drotyLBOvYyVQaN7qG+J5HAo9b8Vz3wPgq+ypVCqhTSGIqIGSuiIDpVIJh8MRtoIGpClFCE56errQJhAxhtkswuzZmQAyUVpai4QEuqCFi86+Rlz1dVJSktAmRB3xOlYCheatviGex6HQ81Y89z0AvspeLKZHxeJ3IiIDqZTcFZEAd45TpBQRs2RlZQltAkEQRFCQXiBBENEGzVtEuJBIJEKbQMQoVNUxMgh3yjE5pQjBOXbsmNAmEARBBMXZs2eFNoEgCCIoYn3e2r3pAD56dA3stjjMDRQYq9UqtAlEjGI0GoU2gegDKB6OIAiCIAiCIIiopeJINV67830AwISZozHywmECW0QQBEEECkVKEYIzZMgQoU0gCIIIipycHKFNIAiCCIpYnbdsFhvevXcl/9qgpciKvoZSrIhwoVQqhTaB6APIKUUITnt7u9AmEARBBAWFkxMEEW3E6rx1+MfjqDpWw7826k0CWhOfCF3ZkYhdqEp7fEBOKUJw6uvrhTaBIAgiKNra2oQ2gSAIIihidd7qaNG7vTZ1kFOqr4kWx8GPP/4IhULB/9u+fbvQJhHdQHpl8QFpShEEEXNIpQz+9rcONDc3QyqVC21OTMP1Nfc3QfiCxop/aN7qG2gcCke4+t6kN7u9NnZ5TRAcK1as8Hg9efJkgawhCIJDxDAMXZFDTHt7O5KTk9HW1kbld4mQoLfooX5WDQA4dtMxaFQ0rgiCIAiCIL564zt8+uwG/vUVv78YCx69UkCLvGOz2dDe3o7CwkLSyREAo9GIgoICdHR0QK1WQ6fTITU1FRUVFVAoFEKbJygMw4BhGMjlcojFlEhFeGIymVBWVoYBAwYENX8F6hehUUcIzt69e4U2gYhRysrKhDaBiFFobBHhgsYWES5idWyZDF0ipXSUvtfXWCwWoU3oli+//BIdHWyk3ksvvQQAaG1txVdffSWkWUQ36PX67jcioh5yShGCQ7nCRKhxOIATJ6QoLZWDtDfDC9fXJ05I46qvbTab0CZEHfE6VgKF5q2+IZ7HodDzVrj63mxgHSJSuQQAYNJR+l5fEw2JN1zq3ujRo7Fo0SIMHTrU7X0iMomGsUX0HnJKEYKTmpoqtAlEjGEyiXDxxVm45ZZzYTKJhDYnpuH6+uKLs+KqrxMTE4U2IeqI17ESKDRv9Q3xPA6FnrfC1fecplRKVjIAipQSgkhP+aqtrcX3338PALj++usBADfccAMAYPPmzWhsbAxoPx0dHXj55ZcxY8YMFBYWQq1WIyMjA5MmTcK9996Lbdu2+WzrcDiwevVqLFiwAIMHD0ZycjKSk5MxcuRILFq0CGvXrg36Qf1zzz3Hi7a/8MILPrfbu3cv1Go1FAoFLr/88qiqliiVkgR2PBDZMwgRF+Tl5QltAkEQRFCkpaUJbQJBEERQxOq8ZXFGSiVnsXolRqq+1+dEuuNg1apVsNvtEIvFuO666wAA1113HUQiEaxWK1avXt3tPrZs2YLhw4fj73//O0pKSlBfXw+r1YqOjg4cOHAAb731FqZPn+61bXl5Oc4//3wsXLgQ69atQ1VVFUwmE0wmE06dOoVPPvkE119/vV+nljfuv/9+XHjhhQCAJ554AgcOHPDYxmAwYNGiRbBarUhJScF7770X8U5EV2QymdAmEH1A9IxIImY5cuSI0CYQBEEERVVVldAmEARBBEWszlucplSK0ynVtRofEX4iXVPq448/BgBMmTIF+fn5AIABAwbwlfdWrlzpt31JSQnmzJmDpqYmSCQSLFy4EJ9++il27NiBkpISvPXWW5g7d65XB0p9fT2mT5/OO4ymTZuG9957Dz///DN++eUXrFy5ErfffnuPnMZisRjvv/8+kpOTYbFYsHDhQhiNRrdt7r//fpw8eRIA8Nprr6GgoCDo4whJ1+9DxCaR7dYmCIIgCIIgiD6kpVYLxuFAen5sRhbFGpymVEo2RUoRnhw4cACHDh0C0Jmyx8FFJ+3duxfHjh1DcXGxR3uTyYRbbrkFNpsNCQkJWLduHaZOneq2zeTJk3Hrrbd6dfzefffdqKmpAQA888wzuO+++9w+nzhxIubNm4fnnnuuRzq7RUVFeOWVV7B48WIcP34cf//73/HKK68AADZu3IilS5fy33X+/PlB758g+gKKlCIEZ9CgQUKbQBAEERTZ2dlCm0AQRBhw2B14/IoX8Njl/4bFFFuFWGJ13jI7I6P49D3SlOpzIjl9jxMyV6lUuOqqq9w+mzdvHuRyudt23tpzTqUnn3zSwyHlStcopBMnTmD9+vUAgDlz5ng4pFxRq9U91tm9/vrrsWDBAgDA22+/jW+++Qb19fX4/e9/D6DTcRWNKBQKoU0g+oDInUGIuMFgMAhtAkEQRFBEeqoCQRA9w9hhQlsjWza+tVaL7AGZAlsUOmJ13uIipVKzWaHzaK++xzAMzIbo+g42mx1SqSTodooEBUSi8BUcsNlsvF7U7NmzodFo3D5PS0vDzJkzsX79enzyySd46qmnPPSWNm3aBIAtFHDbbbcFdfyvv/6arx53zz339PRrBMSrr76K7du3o7KyEnfeeSeKi4vR2NjoluIXjUSTKDvRc8gpRQhObW0tCgsLhTaDIAgiYFpbW5GRkSG0GQRBhBjXKJvW+raYckrF6rxlNroLnVvNVtisdkhlwTtJIgGzwYxr0oNzfkQrnze/B2WiMmz737x5M+rr6wF0Vt3ryg033ID169ejuroaJSUluPjii90+57SgJkyYgISEhKCOz7WVyWQ477zzgjU/KDgR8xkzZqC+vp7/3vfddx8vhh6NWK1WipaKA8gpRRBEzCGVMvj973VobW3t0ZM7InC4vub+Jghf0FjxD81bfUN349Dk6pSqa+szu+KBcM0BnLA5J3QOsL+jOjUxZMcgohNOwDw9PR0zZszwus0VV1yBlJQUaLVarFy50sMp1dTUBADIyckJ+vhc27S0ND5NMJxMmTIFN954Iz766CMAQHFxMf7xj3+E/bgE0VvIKUUIzsSJE4U2gYgx5HLgscfa4XCIEEVVb6MSrq/jDdLCC554HSuBQvNW39DdODS6pH5p62PLKSX0vBWuOYBL30vQqCBXymAxWWGMYqeUIkGBz5vfE9qMPkGREL4ImLa2NmzcuBEA0NzcDLVa3W2bdevW4dVXX0ViYnSOnaqqKmzYsIF/XV5ejlOnTnkVcI8WovW3IIKDnFKE4Bw+fBhjx44V2gwiBqmsrET//v2FNoOIQWhsEeGCxpawuFZuizWnVCyOLYfDAYszfU+RqIBSrWCdUlFcgU8kEoU1pS0cWCyWPokECoY1a9bAZApuHOh0Oqxbtw433ngj/15GRgaqq6tRV1cXtA1cumxLS0vY+8jhcOC2226DVquFTCaDVCqF0WjE4sWL8fPPP0Mmk4Xt2OHEaDQGnTZJRB/klCIEJ9gLBkF0h8MBnD0rQWWlGIWFoKiDMML1NQDk59vjpq97UrY53onXsRIoNG/1Dd2NQ3dNqdiK7BN63grHHGAxdn4nRYIcKrUS7U06PqWP6Bs4Me9Igkvdy83NxfPPP9/t9g899BCqq6uxYsUKN6fUuHHjUF1djT179sBgMATlIBk3bhw+/vhjWK1W7NixA1OmTAn+iwTISy+9hB9++AEA8Oijj0Kj0eCvf/0r9u/fj8cffxzPPPNM2I4dTkjoPD4gpxQhONFaDYKIXEwmEc4/PxtANkpLa5GQEHk3S7FCZ18jrvqantoFT7yOlUCheatv6G4cukbYxJqmlNDzVjjmAM75JBKJIFfKoExiI4yiOVIqGulasU5oysrKsG3bNgDA3LlzMX/+/G7b7Ny5E6+//jpKSkpw9uxZ5OfnA2Cr9m3cuBEGgwFLly4NqorerFmz8OCDD4JhGLz22mthc0odOHAATzzxBADgggsuwP333w+JRIJvvvkG//vf//DSSy9h1qxZUSl4LpGQxmI8EFkzCBGXUOU9giCijVisYEUQhLvQuTbGnFKxOG9xelKKBDlEIhFUzrQ319+RCD9SaWTFOaxcuZKP3rr66qsDasNt53A4sGrVKv79G264gXdQLVmyBD/++KPPfVRXV7u9Hjp0KK688koAwPr16/Hiiy/6bKvX69Ha2hqQra6YTCYsWrQIFosFGo0G77//Pu/Ieeedd5CRkQGHw4Fbb70V7e3RF/0ZaWmhRHggpxQhOIcOHRLaBIIgiKCorKwU2gSCIMKAa/qetr4tItOSekoszltmAxsppUhgF658pJSO0vf6EovFIrQJbnCpe1lZWQFHB02ePBm5ublu7QFAqVTi/fffh1QqhcFgwKxZs3DnnXdiw4YN2LdvH3bs2IHly5fj+uuv9yoo/uqrryIvLw8A8PDDD2PGjBlYuXIldu/ejT179mDt2rW45557MHjwYBw8eDDo7/rQQw/h2LFjAICXX37ZTTcuJycHb731FgCgoqIC9957b9D7Fxqj0Si0CUQfEFlubYIgCIIgCIIQCNcIG4vJCkObEYkplK4bqXRGSrFV3FRq9n+KlIpftm3bhjNnzgAA5syZE3BqoVgsxpw5c/Df//4XR48exd69ezFhwgQAwLRp0/DFF19g4cKFaG1txfLly7F8+fKA9pudnY0tW7Zg3rx5OHLkCEpKSlBSUtKj79aVb7/9lnc6XXPNNbjppps8tpkzZw5uvfVWvP/++1i5ciVmz56Na665JiTHJ4hQQZFShODEWiUYgiBin6ysLKFNIAgiDBg73CNsWmOoAl8szlucphQfKaUmTSkhiKT0vRUrVvB/X3XVVUG1dd3edT8AcPnll+P48eN46qmnMHnyZKSnp0MikUCj0WD8+PG4++678csvv3jd78CBA7Fr1y4sXboUs2bNQm5uLmQyGVQqFYYMGYKbbroJa9asCUrzqbm5GXfccQcYhkF+fj7eeOMNn9u+8MILGDx4MADgT3/6E86ePRvwcYRGoVAIbQLRB0TODELELTabTWgTCIIggoLmLYKITYxdImy09W3oNyxXIGtCSyzOW3z6XiIXKeXUlNKTU6oviaQ01zfffBNvvvlmj9pOnz4dZrPv1M+UlBQ88MADeOCBB4Let0Qiwc0334ybb765R7Z1JT09HeXl5QFtm5iYiCNHjoTkuH1NJI0tInxQpBQhOF1FAQmCICKdlpYWoU0gCCIMdI2w0dZHnzCwL2Jx3jLrO4XOAUDFV98jTam+xG63C20CEaNEml4ZER4oUoogiJhDImGwaJEebW1aSCQioc2Jabi+5v4mCF/QWPEPzVt9Q3fjkIuUStCoYGg3orVO25fmxTThmAPMRtb5pHRqSimdEVNdI94IgiCIyIWcUoTgcCKCBBEqFArgn/9sg81mjyidg1iE6+t4Y8CAAUKbEHXE61gJFJq3+obuxiEnkJ0zMAtn9legraGjr0wLO0LPW+GYA0w+IqVI6LxvkcvlQptAxCgJCVRoIh6g9D1CcI4fPy60CUSMUlNTI7QJRIxCY4sIFzS2hMWkYyNv0vNTAcRWxE0sji1fmlIGEjrvU6xWq9AmEDGKyUTncjxATilCcAwGg9AmEDEGwwDNzWLU1ztA+ojhhevr5mZxXPW1PyFUwjvxOlYCheatvqG7cchpSqXmJLu9jgWEnrfCMQd01ZRKSFYBAAztxtAcgAgIEqMmwoXD4RDaBKIPoPhwQnDUarXQJhAxhtEowpgxOQByUFpai4QEulkKF519jbjqa6VSKbQJUUe8jpVAoXmrb/A3DhmG4SOjeKdUDEVKCT1vhWMO4DSlFCrWKZWYwqb66LX0wLMvEYlIB48IDxKJRGgTiD6AIqUIwRk0aJDQJhAEQQRFTk6O0CYQBBFiLCYrHHb2qXxqbgqAznS+WCAW5y1OU4oTOE9MZp1ShjYDRe/0ITKZTGgTiBhFoVAIbQLRB5BTihCcAwcOCG0CQRBEUJSXlwttAkEQIYZL1ROJREjJ0rDvxVCkVCzOWxZOUyrBPVLKarbBYiKdo77CYrEIbQIRo5DMS3xATimCIAiCIAgi7uEqtqmSlHwVt1jSlIpFzAanppQzUkqZqIBYwi5vKIWPIAgiOiCnFCE4hYWFQptAEAQRFBkZGUKbQBBEiDE6U/WUagVfxc0UQ5FSsThvmQzumlIikQiJnNh5Gzml+gqplGSKifAgl8uFNoHoA8gpRRAEQRAEQcQ9JmdUlFLdGSll0pup+lMEw0VKcZpSAJCYkggA0FGkFEEQRFRATilCcCorK4U2gSAIIiiampqENoEgiBDD6Uep1Eoo1Z2V6sz62NDLicV5ixOiV7g6pfhIKaMgNsUjNptNaBOIGIX0yuIDirUkCCLmkEgYXHutAe3t7ZBIqPpOOOH6mvubIHxBY8U/NG/1Df7GIacfpUpSQqaQQiIVw25zwNhh4iOniJ4TjjnA0M46nhI0Kv49Tuxcp9WH5BgEQRBEeCGnFCE4Y8eOFdoEIsZQKID//EcLq9VKZYrDDNfX8Ub//v2FNiHqiNexEig0b/UN/saha6SUSCSCUq2EXmuImQp8Qs9boZ4DGIaBscOLUyqZdUpRpFTfQbo/RLhISEgQ2gSiD6D0PUJwTp8+LbQJRIxSV1cntAlEjEJjiwgXNLaEg6++p2ZTwWKtAl+sjS2L0QK7jdX78h4pRZpSfYXVahXaBCJGMZvNQptA9AHklCIER6fTCW0CEWMwDGAwiNDaagFDWTBhhetrg0EUV31tMsXGIrUvidexEig0b/UN/sYhFxGldDqj+Ap8+tg434Wet0I9B+idkVBiiRiKhM5Inc5IKXJK9RUMTVpEmLDb7UKbQPQBlL5HCA6FZRKhxmgUYciQXAC5KC2tRUIC3SyFi86+Rlz1tUKh6H4jwo14HSuBQvNW3+BvHBo72CfyqkSnUyrGIqWEnrdCPQe46kmJRCL+/YQUNmqKIqX6Dtf+J4hQIhZTDE08QL8yITjDhw8X2gSCIIigyMvLE9oEgiBCTNdIKaWzolusaErF2rzFOQtdU/cAQJ2SCAAwkFOqzyAdPCJcKJVUZCIeIKcUITh79+4V2gSCIIigKCsrE9oEgiBCjMlF6BzojJQyxUikVKzNW1x6nkrjvmhNSKZIqb7GYrEIbQIRoxgMdB7HA+SUIgiCIAiCIOIeLvKGc0Ypnc4po46EdiMRLn0v0SNSijSlCIIgoglyShGC069fP6FNIAiCCIq0tDShTSBiGKvZhpcW/RcfPrJGaFPiCqOP6numGEnfi7V5y1VTypUEp9A5J4ROhB+JRCK0CQHx448/QqFQ8P+2b98utElEN8jl8u43IqIeckoRgiOVkt4+QRDRBc1bRDg5ufMUDmw5ii3Lf0JHC1Wo7StMzogoVYxqSsXavGXwoSmVyDulDHA4HH1uVzwSLULnK1as8PuaiDyiZWwRvYOcUoTglJeXC20CQRBEUDQ0NAhtAhHDHP7pBP/3qT3lwhkSZ/BC5+rYrL4Xa/OWwRkJpfJwSrGvGQfDOxqJ8GKz2YQ2oVuMRiPWrl0LAFCr1QCAzz//HGYzjZFIhn6f+CC2HpkQBEEAEIsZzJ5thE6ng1hMZdXDCdfX3N8E4QsaK/5xnbeO/Xycf7901xmMv2yUgJbFFv7GIa8pxTmleE2p2HBKCU2o5wBf6XtylRwyhQxWsxV6rcHjcyI++fLLL9HR0QEAeOmll3DnnXeitbUVX331Fa6++mqBrSOI+IacUoTgjB49WmgTiBhDqQTeeacVZrMZCoVCaHNiGq6v443CwkKhTYg64nWsBArXP7Vl9fj7RWf590spUiqk+BqHdpsdFiNbQYyLkOIjpWIk2kboeSvUc4AvpxQAJKYkQFvfBn2bAZlID9kxCe9Eg+4Pl6o3evRoLFq0CC+88AJOnjyJFStWkFMqglGpyKkcD1D6HiE4lZWVQptAxChNTU1Cm0DEKDS2iHCx8+s9AICU7GQAQNn+StgskZ8aE+24pnlxWlJcpFSsCJ3H2rxl5JxSyd6cUux7ei1V4OsLIj19r7a2Ft9//z0A4PrrrwcA3HDDDQCAzZs3o7GxMaD9dHR04OWXX8aMGTNQWFgItVqNjIwMTJo0Cffeey+2bdvms63D4cDq1auxYMECDB48GMnJyUhOTsbIkSOxaNEirF27FlarNeDv1NTUhKSkJCgUCvzpT3/qdvuNGzfyAu9r1kRPEQ2LxSK0CUQfQE4pQnDa2tqENoGIUQwGuhklwgONLSJcHPj2GABg2o2TkZSWCKvZiorD1QJbFftwKXoyhQxSOZtIoIyx9L1Ym7f8RUpR6mXfEumC8qtWrYLdbodYLMZ1110HALjuuusgEolgtVqxevXqbvexZcsWDB8+HH//+99RUlKC+vp6WK1WdHR04MCBA3jrrbcwffp0r23Ly8tx/vnnY+HChVi3bh2qqqpgMplgMplw6tQpfPLJJ7j++uv9OrW6kpGRgd/97ncAgDVr1sBk8j/WP/zwQwBsFU6uXTRgt9uFNoHoA8gpRQiOUqkU2gQixjAYRMjPz8O0aVNhMFDVjnDC9XV+fl5c9bVMJhPahKgjXsdKILQ3daDk0wPIz8/DO7++CAeUuOCaczF44gAAwOm95cIaGEP4GodcNJQqqTPlW6Vm/zbFiNC50PNWqOcA3imV5OmU4hyKJn1spF5GOpFeIe3jjz8GAEyZMgX5+fkAgAEDBmDy5MkAgJUrV/ptX1JSgjlz5qCpqQkSiQQLFy7Ep59+ih07dqCkpARvvfUW5s6d6/Ucq6+vx/Tp03HgwAEAwLRp0/Dee+/h559/xi+//IKVK1fi9ttvR1paWtDf65ZbbgEAaLVafPnllz63a2xsxKZNmwCwzrhokrYQi8ldEQ+QphQhOKNGkYArQRDRhdDaLERs8fHjX+DnLw4DmAUAGDW9GFlFGeg3LBf7Nh9GXVlspV1FIpxulDKx80FZp6ZUbDilYm3eMvhJ31MmsBpHZnJK9QmRrCl14MABHDp0CEBnyh4HF520d+9eHDt2DMXFxR7tTSYTbrnlFthsNiQkJGDdunWYOnWq2zaTJ0/GrbfeiqqqKo/2d999N2pqagAAzzzzDO677z63zydOnIh58+bhueeeCyp9DwAuueQSFBUVoaKiAsuXL8eCBQu8bvfxxx/z+168eHFQxxAa0pSKD8j1SAjO7t27hTaBIAgiKE6fPi20CUSMwDAMDv90wu29SxZeAADILGQFmhsqyCkVbvjKe0mdTiku2sZmscNqjmzNnECItXnL0M7+Zt7S9/hIqSgUqWcYBgarNar+tep0PWrHMOGvxMoJnKtUKlx11VVun82bN493qHHbeWvPOZWefPJJD4eUKwUFBW6vT5w4gfXr1wMA5syZ4+GQckWtViM1NbWbb+OOWCzGokWLAABbt2716hQDOlP3xo0bh7FjxwZ1DKHR6/VCm0D0ARQpRRAEQRAEIRA1pfXoaNZBqkgEnOvn4t8MAQBkFWUAIKdUX8BFQ3FaRF3/NupMkCnUfW4X4R2LyQqrmY388OqUcorVmwzR55Qy2myY8N7bQpvRJ+y97fdICGNaqc1m4/WiZs+eDY1G4/Z5WloaZs6cifXr1+OTTz7BU0895ZEuxqW9JSYm4rbbbgvq+F9//TXveLvnnnt6+jX8smjRIjz99NNwOBxYsWIFHnroIbfP9+7di8OHD/PbEkQkQpFShODk5uYKbQJBEERQBPs0kyB8cXzHKQDA4AlF/HucPktWERsp1VTdAoc9soWEox0uUkrpEiklloihcKaBcZXeoplYmreMHezvIRKJoFR76uMoOKdUFEZKEaFj8+bNqK+vB9BZda8rXEpfdXU1SkpKPD7ntKAmTJiAhISEoI7PtZXJZDjvvPOCahso/fr1w2WXXQagMyLKleXLlwMAFAqFzz6IZITWwiP6BoqUIgQn2AmeIAhCaCJZP4OILo5vZ51SQ84dBGx3/yw1JwVSuQQ2ix0ttVpk9AteCJcIDF7ovIuDI0GjgtlggSEGxM5jad5yTbf0JoTMi9RHoaaUSirF3tt+L7QZQWG32yGRSIJup5KGdynKCZinp6djxowZXre54oorkJKSAq1Wi5UrV+Liiy92+7ypiY1UzcnJCfr4XNu0tLSwnn+33nor/ve//+HMmTP46aefcNFFFwEAzGYzHyk2Z86cqHRMk9B5fEBOKUJwTp8+jYyMDKHNIAiCCJj6+nqPNACCCBaGYXDCGSk19LyBHp+LJWJk9EtH3ZkGNFY0k1MqjHDpe0q1e0VglUaF1ro2PjInmomleUvf5hQ595K6BwCKhOh1SolEorCmtIUDs8MBRYTZ3NbWho0bNwIAmpuboVZ3n367bt06vPrqq0hMTAy3eSFl9uzZyM7ORn19PT788EPeKbV+/Xq0trYCiN7UPbPZTNFScQC5HgmCiDnEYgYXX2zC+ec3QywOv4hmPMP19cUXm6ivCb+EYqyUfLwdm5eWhNYwAWmoaEZbYwdkCikGjy/wOm9xYuf1pCsVEnyNQ5MXTSkASHCm8xnaot8pJTShvF7wlfd8OKW4lD6qvhe/rFmzBiZTcBGOOp0O69atc3uPe3BeV1cXtA1c25aWFlgslqDbB4pMJsONN94IAPj888+h0+kAdKbuFRYW4pJLLgnb8Qmit1CkFCE4I0eOFNoEIsZQKoGPPmqByWSCUqnsvgHRY7i+jje6Vtghuqe3Y6WjRYdlD64GwzCYMHNMTEQNtdZpAQBpealISpZ5nbdI7Dy0+BqHxg7WeeFafQ/odHoYYiBSSuh5K5TXC84p1fX34o/l1JQyklOqT4jE1FAudS83NxfPP/98t9s/9NBDqK6uxooVK3gHD8BWrKuursaePXtgMBiCkh0ZN24cPv74Y1itVuzYsQNTpkwJ/osEyC233IKXXnoJer0en3/+OS655BJs2bIFAHDTTTdFbRqcSuXd8UzEFuSUIgSnpqYGQ4cOFdoMIgZpaWlBXl6e0GYQMQiNrb6ndFcZX8Wo8sjZmHBKdTSxT7M1GZ1pJV3HFid23ljZ3LfGxRnequ8BbPoe0KlhFM3E0rzFCc+rfEVKJbK/I0VK9Q02my2iUqzKysqwbds2AMDcuXMxf/78btvs3LkTr7/+OkpKSnD27Fnk5+cDYFPjNm7cCIPBgKVLlwZVRW/WrFl48MEHwTAMXnvttbA6pYYOHYoLL7wQP//8Mz788EPU1tbC4XBAJBJh4cKFYTtuuLFarT3SKyOii+h0mRIxBZfrTBChRq/XC20CEaPQ2Op7Tvx6mv+78uhZAS0JHe0trFMqKa3TKdV1bFGkVN/gKpztCp++FwPV92Jp3uKciAk+I6XYyB0uLZMILw5HZFUHXblyJf8Q4+qrrw6oDbedw+HAqlWr+PdvuOEG3kG1ZMkS/Pjjjz73UV1d7fZ66NChuPLKKwGw+k4vvviiz7Z6vb7Xa6JbbrkFAPDzzz/jzTffBABMnToVAwYM6NV+hcRmswltAtEHkFOKEJxIerJCxAYGgwiDB+dg5swLYTCIhDYnpuH6evDgnLjqa2mYKwbFIr0dK6W/nuH/rooVp1RTBwBAk5Hkc97iNaXKGmG32QWxM5bwNQ47hc7dq++pkpzpezHglBJ63grl9cLY7t2JyMEJ1kej0Hk0IhJF1vWfS93LysrChRdeGFCbyZMnIzc31609ACiVSrz//vuQSqUwGAyYNWsW7rzzTmzYsAH79u3Djh07sHz5clx//fUoLi722O+rr77KRyg+/PDDmDFjBlauXIndu3djz549WLt2Le655x4MHjwYBw8e7NX3vuaaa5CcnAyALWwARK/AOUekjS0iPNBdNSE4EyZMENoEIgYxGsnn3lfEY19H81NHIenpWDEbLSg/VMW/rjxaEyqTBKWjmY1c4dL3vPVP7uBsaDLUaG/SYe//DuHc2eP60sSYxFs/+xQ6T46d9L1ImLdCdb3g0y19OKWiufpeNBJJmlLbtm3DmTPsQ4w5c+YErKUkFosxZ84c/Pe//8XRo0exd+9efo0ybdo0fPHFF1i4cCFaW1uxfPlyXkS8O7Kzs7FlyxbMmzcPR44cQUlJCUpKSnr03bpDpVJhwYIFeOeddwAAycnJuOqqq8JyrL4i2iohEj0j/lYSRMSxc+dOoU0gCIIIitLSUqFNiCvO7K2A3eZAYjIrMNtQ0cQvSqOZ9mY2Uso1fa8rUpkEU6+fDADYsvznPrErHjHpWOeF0kf1PWMMRErF0rzFCc93dSJyqLjqewZLxKWWxSJmc+Q4/1asWMH/HaxDxnV71/0AwOWXX47jx4/jqaeewuTJk5Geng6JRAKNRoPx48fj7rvvxi+//OJ1vwMHDsSuXbuwdOlSzJo1C7m5uZDJZFCpVBgyZAhuuukmrFmzJuCoLn+4irRfe+21US8UzlUSJGIbipQiCIIgCCKiKd1TBgAYNXUYTuw8A219G6qP12LIROEjP3pDRzMndJ7kd7vpN1+AjW98h2PbSnH2ZB3yh+b0hXlxhS9NqVhK34slTNzv5VPovDMN02yw+HReEbHHm2++yespBcv06dP9OthSUlLwwAMP4IEHHgh63xKJBDfffDNuvvnmHtkWKEeOHOH/Xrx4cViPRRChgiKlCMHJzs4W2gSCIIig4DQbiL6h5mQdAKBoZD8UjmC1OWJB7Ly92bP6njfS81Ix/rJRAIBfN+4Lu13xBsMwPqvvcel7hhhI34uleYv7PXwJncuUMojErBYNFwVHhA+qjhY5cGmFI0eOxLnnniuwNb2HtIfjA3JKEYKj0WiENoEgCCIooj0cPtqoPdMAAMgZlIWCYtYpdfZEnZAmhYSOpu7T9zi4qLDaU/VhtSkesZiscNjZFK+uQuexlL4XS/OWr8g2DpFIxDsYzQZySoWbQHWbiPDy008/8bIod9xxh8DWhAZyeMYHNIMQghNLGgcEQcQHdXXR7xCJFhiGQd1p1imVOygLqbkpAIC2xnYBreo9DrsDulYDgO4jpQBW8BwAap19QYQOvfN3kEjFbmlfgEv6XgxESsXSvNXplPLtaFMksOLbRoqUCjtWq1VoE+KWiooKnDx5El9++SVuu+02AEBOTk7MpO6ZTNE/9xLdQ5pSBEHEHCIRgzEjtLBYLBCJGKHNiWlEIgaTJ5v5vwnCFz0dK9r6dpj0ZoglYmQVZaDqeC2ATj2maEXXqgfDsP2gTk2Excr2j8Fg9No/uYOyAAB1pxvgcDgoMqGHeBuHOi1bBVGdmuhRfpxP32s3gmEYKk/eC0J5veiu+h7Aida3wUwV+IgY5rLLLkNFRYXbey+//HJMRUYSsQ85pQjBKS4uFtoEIsbQN7cg5+RfIVNKIRU/C5rqwodKBaxZ0yy0GX1Ofn6+0CZEHT0dK7Wn2XS1zII0SOVSaJypbh0t0e2U4vSk1KmJkEglUEnZ/jEYDFCpEjy2zyhIh0QmgcVkRWutFun5aX1tckzgbRxykVKJKZ79nuCMxLFb7bCarJCr5OE3MkwIPW+F8nrBpVP6dUolsr+VSU+RFuGGdH+EJykpCSNHjsSDDz6IK664QmhzQgY51+IDWqkRgtPQ0EC6UkRI2bl+Hxx2B8x6Cxorm5A3hCpVEaGlvb0dCQmeC1gi9HDpajnOSKGk9EQAQHtTdDulOpq8i5z7GltSmQRZRRmoPVWPmlMN5JQKIVyklDenlCJRDpFIBIZhYOgwRbVTKlbmLZvVDouJTRfzV1VPmch+ZtJb+sSueMZut1P0pkCcPHlSaBPCitVqJV2pOIBmD0JwmpvjL8qCCC87N+zl/6470yigJUSs0tHRIbQJcYOrnhQAaDKSAAB6rQF2m10wu3pLuzPSq6vIub+x5ZrCR4QOvZaNlFKnJnp8JhaLodKwzg1DlIudx8q8ZdJ1Rj75i5RSOPXBXLcnwoPD4RDaBCJGsdlsQptA9AHklCIEh7zfRCipO9OAMwcb8QPzHn5g3kPFiRahTYppDAYRRo/OxujR2TAY4kdrhZ4IB09PxwofKTWQFfrmdH8YhoGuVR8WW/uCdmflPc7JxvXPlVf+xmf/cE4pLqWRCB5v45AbR94ipYDOFL5or8An9LwVqusF5xyUq+SQSH3fQ6o4pxRV3yOIqIV0/OIDSt8jBGfixIlCm0DEELs2HQAAWMGmhNaXU6RUuGlpiT/H8qBBg4Q2ISrpyVipO+MeKSWWiJGYkgBdqx4dzTokZ0Zn+re39D22f3z3EVXgCw1dxyEfKeXDKcVF40R7Bb5ImLdCcb3gRM4TNL6jpABAqeYipcgpFW4UCkX3GxFED0hM9IxgJWIPetRLCM6uXbuENoGIIc6eqHV73VDeJJAlRCxz6tQpoU2IC2wWG5qq2WjHnIGZ/PucI6ejOYojpXyk7/mDc8zVlNbxlfuI3sNFSnlL3wMAlcYZKdXhHinFMAzqyxqjJnUpVuYtY7uz8p4fPSmgM32Pqu+FH7OZ+jiSMXaYUH28lnfARxN6ffRe54nAIacUITjRcjNHRAetdW1ur8kpFVk0Vjbj6av+g1837hPalF5BDoG+oaVWC8bBQKaQITmrMyKKc+S0N0evRk6Hs/peUnrgTqn8obmQKWTQ1rfjh1U7wmVa3KHT+q6+BwAJTqeUoc3dKfXrhn144KKn8eHDn4XXwBARK/MWFymlSvJflUvpdEoZySlFxDltTR2wWWxorGyG2RBdwv+xMm8R/iGnFCE4mZmZ3W9EEAGirW/3eG0mPYmI4fuPfkHprjK88ftlaD4bvXpfVDG0b2ip0QIA0nKT3XQlOB2m9uborcDHReckpQWemqBKUuLq+9lS36ue+AINFeR0DwX+hM4BIMFH+t7O9axzfeuKbTi2rTSMFoaGWJm3OE0pVZL/lDElRUr1GaQPG9nYzJ1i4dF23ZDJZEKbQPQB5JQiBCctjcpaE6FDW9/m8V49RUtFDK11Wv7v5Q9/FrVPwNTqwKNbiJ7TXNMKAEjLS3V7PymddR5wukzRSHfi2r6Yecc0DD1vEEx6M9Y8/1U4TIs7dFr/vwUXkeOavme32d0cUcsf+hRWc2RXiYqVecsUcKQU60w0kVMq7Agtok/4xmF3wGbpnJvsVjvs1uipXEsOz/iAZhBCcE6cOCG0CUSMYNSZvN58ckLJhPDUnekUnj+w5ShqT0VnFbGamhqhTYgLWmq1AIC0vBS39/n0vZbodUp1F53jC7FEjJufugYA8Ov6fTh7si7ktsUbnULnPiKlkj3T98oOVMHQbkRCsgrJmUmoPd2ALct/Cr+xvSBW5i0uYo0ToPeFMlEOgJxSfYHVahXaBMIHXLqeVCHlq1XabdEjnWIyRXeBCSIwyClFEETMwEVJqZLkGDvWgmxNNQBWiJYIDyIRg7FjLRg71gKRyH/UE8MwvINQrmIXCzVR6pQigieYscLR4iNSikvf64jq9D13HSOuf4YNa++2fwpH5GPirDFgGAZf/uebsNsaS3QdhwzDuDgIvUdKcVX5XEWCj/zEPlAbccFQzHvwtwCAL//zP3REsaM03PRkDvCGMUCnFPc5F5VIEPGIxcg6pRQqOSRSdulvt0VPpBQRH5BTihCcoUOHCm0CESNo61g9qfScBGza1IT7b/oSEpHFQ/ycCB0qFbBpUxM2bWqCyn8mBdoaO2DsMEEkFmH01OEAgIaK5rDb6LroDBW5ubkh3V88EMxY4eA0pdI90vc4ofPodABYjBZYzWxkARedw/XP+vV1AfXPnHtnAAB+3bgfZmN0CdcKSddxyP4WbGpLoo9IKbVT96vDxblx2OmUGjVlGC68dhIKR+bD0G7E1//dGuZv0HOEnrd6Mgd4g0uj7K76Xv4w9vuePVnHL8yJ8EC6P5ELd31QJMghlnGRUtHjlFIq/Z/nRGxATilCcFpbW4U2gYgRWp2RUqnZrJirQs3eJLU1Rm+Frlii7jQbJZVRkIb8YTkAgIby8EexrVyyFnePewQ/fbozZPukEsV9Ay903iV9T+N0SkVrpBQXJSWRiqFUu4s1Bzq2CkfkIzElAYyDokF7A1d5TyqXQJEg97pNkjPFkouCcjgcOLOvHABQ/JshEEvEuHTxRQCAisPVYba458TKvMVFSnFVEX2R0S8NKdka2K12lB+q6gvT4haqpB25cOl7crdIqej5vez26HGgET2HnFKE4DQ20s00ERq49L2U7GQAgETFTnHRGk0Ra9Q6nVK5A7OQ3Z+tutkXVWB+Wr0TdpsDS//2MQ5uPRaSfba3t3e/EdFrOKHz9K6aUlykVFN0OpxdhbVdqwoCgY8tkUiE3EFZABC12myRQKfgfKLHb8GhdmqY6VrYbdubdLBZ7BCJRcgsTAcA/v/ms5H7oC1W5i3OKaXsJlJKJBJh8IT+AIDS3eVhtiq+IcdBZGKz2HhRc9YpFX2RUqRXFh+QU4oQHF83gQQRLFykVEJaOs47Lwv/ePU62Bk52ilSKmwYjSKcd14WzjsvC0aj/3O57gy7cM4ZlIWsInYB1xeVEV2FpD97dkPYj0d4J5ixAgBmg5lPu/TQlHI6pfRaQ1TdXHPoWz1Fzrn+WbDgvID6BwByB2cD6HT4Et3TdRxyY8xfFcQkLn3P6ZTitM5SsjT8Io9LMW2ubonaqqLhJtg5wBcGPlKq+7SewRMHAABO7Snr8fEIIlrh5jelWgmxWBSVTilaJ8YHUqENIIhJkyYJbQIRI2jr2afAKVlJqK6WAlADEEVtNEU0wDBw9jX7tz/4SKlBWchyRko1n22FzWKDVB6+y5GryK22ITT6YkOGDAnJfuKJYMYKADQ7U/eUaoVHmo46NRFiiRgOuwPNZ1uRVZQRanPDimukFEdn/0gD6h+AjToEKFIqGLqOQ28Owq5w1R4tRgvMRotLVchOZ2lqbgq7jckKXauebxNJCD1vBTsH+MLEC513L0w1xOmUKt1dBoZhaIEbJhQKRfcbEX2Ovs3d6R6N6XuJicFVqCWiE4qUIgRnz549QptAxAid6Xspbu8b2o2wmCj8V2jq+PS9bCRnJkGRIAfjYNBU3RK2Y1pMVrdy4MaO0JQGP336dEj2Q/jGV+U9ABBLxBh8Tn8AwIEtR/vSrJDQWXmvdzfbfKTUGYqU6imcg1DtJ1JKqVZA4hQI1rXqO7XOnI4oAJArZUjOZKtCRmoKX6zMW4YAhc4BoGhUAaRyCTqadWjog8jceMVsDs21Ndz8+OOPUCgU/L/t27cLbVLYsJptsBitgAhITGYduFyklMMaPZFSsaKFR/iHnFKE4NhsNqFNIGIELn0v2Sl0DoCvNEJluoWFYRheHyizKB0ikYiPbgmnSDOnAcNhNVths/R+ziFR1/Djq/IexzkzxwAA9nxzsK9MChl6zhGS6tsREgg5Tk2putMNlDLWQ3QBpO+JRCI+kkrXovcpwJ+enwYgcp1SsTJvGYNI35MppCgozgcAVB45G1a7iMhnxYoVfl/HElyUlEqthFjCLvk70/eiZy6ga1t8QE4pQnDS0tKENoGIARiGgbauM32PQ+PUAiFdKWHRaw2wWdgncylZrNMwuz/rlGqoaA7bcTlnpCajM5XGNXKqp6jVkZeaE2twC/uuC3+OCTNGAwBO7DztlqIZDXA6H/6icwIhqygDEqkYZoMFrbWhSU2NN7ixo+4mao2rwKdr1aOl1vvYTM936kpFqFMqFuYthmECFjrnyChg7zNb67ThMivuEYsjf0lpNBqxdu1aAJ3nwueffx41UV7BYnVmCLieJ53pe9ETKSWVktpQPBD5MwgR82RnZwttAlrqWnHH6L9h5TOfC20K0UMMbUZYzewFODkzmX8/yZlO0Ua6UoLSWscumJPSEnn9KD5Sqjx8kVKcUyo5U8OXeze0G3u935SUlF7vg/BP9YlaAECOUzepK1lFGSgozoPD7sAjl/4LLy38b9TcaPOOED86RoEglUmQWcieR7WnSVeqJ7Q1sA8zNJlJfrdTu4id85pSLul7AJCez77mokIjjViYtywmKxx2Nsqjq9acL1Jz2HuCFnLchg2JRCK0Cd3y5ZdfoqODvRd86aWXAACtra346quvhDQrbHDXQy71GADEUonL59ERLSWTyYQ2gegDyClFCM6xY6Ep0d4bfvxsB8qPVGHZY5/g9IFyoc0hegCXuqdOTYRM0flUReMUm6VIKWHROhd+yVmdqZWc2Hk4dT46mlmnVFJaIlRJ7NNCo87U6/1WV1f3eh+Ef8oPVQEABowu8LnNpN+NB8AWOTjw/dGoEfwOJGUsUHIHO8XOqQJfj2hzXhuSu3FKccLlHS06F00p99RSvgJfhEZKxcK8ZXQ+VBCJRfyDhu7gnIcUKRU+rNbI1+3kUvVGjx6NRYsWYejQoW7vxxp2p26U1MURJRIBYme0lCNKHuIYjb1/kEhEPuSUIggAtWc6FzJv/XUZ5S9HIZ0i5xqIRMDQoVb076/n07YCqcDncDhQdrAKVnPodM5a69rw+OwX8cOq2BTT5Pp66FAr/BU14n6f1OzOKLas/ukAgIaKPnBKpat5UVwu9YPoO07+ehrVx2sCGisAG0nUVMUK4BeOyve53ay7LsYdL9+IzCJ2LDVFqDOgK51Oqc5IKdd5K5gCYVwabGNV+NJgY4mucxbnME9x0SL0Bhcp1d7UwUd+dk3fS3Om77VEyTjsawK9XviDe6igSlIGXEkvNScFANBSR5FS8UptbS2+//57AMD1118PALjhhhsAAJs3b0ZjY2AR2x0dHXj55ZcxY8YMFBYWQq1WIyMjA5MmTcK9996Lbdu2+WzrcDiwevVqLFiwAIMHD0ZycjKSk5MxcuRILFq0CGvXrg3auffUU0+5Cbe7/hs4pj+GnTsYyeka/r0PP/zQRVcqOpxSRHxASZqE4AwePFhoE1BxrPPp4YGSIziy7QRGXTBcQIuIYOEWCanZyVCpGGzd2oiOjg5sepUN7w8kfe/HT3bigwc+Qd7QHPzxjUUoKM7rtV2/btyHsgOV0Na3Ycp158dcOWqur7uDj5RyWfhlc5FSlc1w2B28EGco6eDTpNS8roIpBJFSOTk5vd5HvNBY1Yx/zX8dSrUSm/c85RbJ6IuKw+ycnFWUgcRk39FEMoUUF147CXu+OYjGimY0h7GSYyjxJnTuOm+pVP6jdlzRZLDnFOeAJfzTdc7i0veSM/07pZKcTqmzJ+r4+Soly70NL3Qeoel7Qs9bgV4v/GFodzqlAtSTAoC0XPZhCEVKhY9IT7FatWoV7HY7xGIxrrvuOgDAddddhyeeeAJWqxWrV6/Gn//8Z7/72LJlCxYuXIimJvcHaVarFQcOHMCBAwfw1ltvedWoKi8vx/z583HgwAGPz06dOoVTp07hk08+webNmzF16tRefNPukUjFsCJ60veUysDP9d5iMpjRXNOCzH4ZkCsje0zHGhQpRQiOTif8jXTFETZNRJPOLgTOHKgQ0hyiB/CRUjmdkTgmk4lPyQgkfe/03nIAQM3JOjw77zVejLg3VB2rAcA6zbiFdjyirfOMlErLTYFULoHdag/bIq4zUqozfY9b1PQGk4mirQLl8A/HYbc5oNcacGp3WUBtyg+x50rR6H4Bbc8JTHPRVZGOrpUTOvfUlAp2bCWlcxE8wl9Low2r2cbP810dTF3hhM4rjrBjMyVb4+FIz+jHjkNtfXtII25DRaTPW7pWfbeR6iaXSKlA4SKltHVtFAkfJiK9suPHH38MAJgyZQry89no2wEDBmDy5MkAgJUrV/ptX1JSgjlz5qCpqQkSiQQLFy7Ep59+ih07dqCkpARvvfUW5s6d69U5V19fj+nTp/MOqWnTpuG9997Dzz//jF9++QUrV67E7bff3qPCT3fddRf27t3r8W/njl+xYdUmrP/kK4wdO5bfvqCgIOoipez2vrOz8mg19FoDGqvCF8FPeIcipQjBqaurQ1FRkWDH17fp0XSWXchcMHcSvn5vC6pP1ghmD9EzWrnKey5OD61Wi6T0wNP36ss6n+Dq2ww4/NMJnOfUrOkplUc7S1Dv+/Yw+vvRx4llvGlKiSViZBako/Z0AxoqmpBZkB7y43a0sBEpSWlqJIRQU0qr1SIzM7PX+4kHDv94gv/7YMkxFF8wpNs2gehJuZLRj72Zj4b0PYZhvEZKcQQ7tjQZrOOdE/UnAqe9iZ2XpHJJt/peaqemFOf4TMtL9dwmNRFypQwWkxUttVo+tTJSiNR5q+5MA1Y9uQ77vzuCK++dgavvu8LntlyhimCcUtx9gdVsg65Vz+uDEaHDbrdHbJW0AwcO4NChQwA6U/Y4rr/+emzbtg179+7FsWPHUFxc7NHeZDLhlltugc1mQ0JCAtatW+cRzTR58mTceuutqKqq8mh/9913o6aGXVc888wzuO+++9w+nzhxIubNm4fnnnsu6PS9rKwsZGV5FgMx6cxIkTTghdef551hDz74IKZPn44W50NATnMq0rFarVAoFGE/jquTzm6PbCdrLEKRUkTcU3GMdRqk56Vi+CQ2lZCcUtFHp2aRBkajCNOnZ2Lx4olQaNib0UCiCOqcTqnh57Pj4FBJ70T47TY7ak7W8a/3fXu4V/uLRLi+nj49E0aj79REPpKtSzRClnPRFi6xc1dNKaVzEWMiTak+w2F34NgvpQAAOyPHk+9d2e1YAYDyg+yNfaCRUpxTKhrS98wGC2wW9ubXVVPKdd7qrn9c4RbYgTjeCfc5q66iszpnd6nVXPoeR9fKewAgEon4ByGc45HoxNv1wuFw4N83voX93x0BABz4/qj/fTgfKiQkBVZ5D2DTfLnfhaucGNEwDMAYouyfsYftwh+5xgmZq1QqXHXVVW6fzZs3D3K53G07b+05p9KTTz7pN72uoMD9QcqJEyewfv16AMCcOXM8HFKuqNVqpKZ6Ort7gs1mxxcb1+Ld5e/wx37iiScAgK+AbLVEh1Oqr9C3dWZHhENOgvBPZLq0ibhi0qRJgh6/8qgzTWRkAfoNYzWEqk6QUyra6BQ6TwbDACdPygDIkJTGRhF0pyll7DDx2iKX3nIRju84hUMlx8AwTI91oOrLGmE12yBTyGCz2FBxqBottVqvi5lopbOvO+8tT+w8DW1DOyb9dhzfd1ykVKpLeiXAagYBYXRKtXKRUomdQuchiJSKBC28aKD8UBX0bQYoEuQwGsTQmnOhPel/HWI2Wnjx+6JRAabvRVGkFOeskMolbtXDXOcthmkPeH9cMYeOZn2v5qt4wXXO0jrTurtL3QM6I6U4hkwc4HW7BI0KzWdboW+LvIpRQs9b3q4XFYfPuqXdnj1RB7vNzqcYdYUrVKEMIlIKYJ2IHc06tNa1oWhkYPOKcBghbRX23jhYpADQA8UDW+qvAHpfhdTn/m02rF69GgAwe/ZsaDTu53paWhpmzpyJ9evX45NPPsFTTz0FsdjdIbFp0yYAQGJiIm677bagjv/111/zKaP33HNPT79G0Gzbtg2P/fMRAGy1wWXLlvHXBpmCPQdt5sivmAiw/d4X6FweJERLFFksQW5AQnD2798v6PErOKdUcT/0G5oLAKgvb4QlgMnaarGi4miV20RGCEOri1PKFU0mu5DQtej95s9zUVLJmUkYe8lIyJUyaOvbeU2onlB5lG1bNCofhSNZDYOyA5U93l80YLfZ8fLid/DmH5bh5UXvwNBuBMMwvNMwucvijxM7rw93pFSamk/3CEX1vfLy8l7vIx448hObujfyomEoGOm7ip4rnP6bTCGDOjWwm1FOy6etoR0WU2TfaHMppYkpiSFxIGmcESBWsxUmvafALuGbtibPtGJfuKZaJmhUuGjBeV6344T5DW09WKGHmUictw7/wEYkj7t0JOQqOaxmq1sqfVe4+TshSKcU90CkNRoipYiQsXnzZtTXsxW2uap7XeFS+qqrq1FSUuLxOZf+NmHCBCQkBOdA49rKZDKcd573OSPUVFZW4pbbF8FqtSIjIwOff/65m2OnM1Iq8nTvvGE0ht/Bb7fZ3SKlbNbo6JtYgiKlCMGxWCyCHr/iqDNNZEQ/pGanIEGjgqHdiJpTdeg/0r+eyUt3vI3vPvoRAHDxDRfioRV/Cbu9hCcOh4OPckrt4pRKSlVDJBaBcTDoaNZ5OK046s40AACyB2ZBrpSh+DdDcOD7ozhUchyFIwJbTHelypkaWlCcB5PejIrD1ag9Vd+jfUUL9eVN/KLhwPdH8cWLX+PKe2fw6UopWV0ipZzpe/XlvavI5A2HwwGdl0gpQwicUjYb3bAEQtkBdn4ddv4gaMo6gAAyWNtdxOkDddqoUxMhV8lhMVrQUtOKnIGeGhuRgq9U1p6iSFDw372jWRdUVbJ4p62BdYAG4pRy1SGafNVEn/2ckMymlXHaR5FEJM5bh39gHddjphejo0WP03vLUXm0BnlDvFcKNHY4NaWCHOepzgjl1tq2nhvbZ6icEUTRg9lsgUIh735DDwJPw+wJnIB5eno6ZsyY4XWbK664AikpKdBqtVi5ciUuvvhit8+5ans9qV7JtU1LS+PTBMOJTqfD1VdfjabmJshkMixbutxDt1cqlwIigHEwsFvtkMi8RyVGCn0hot9Y3QzGwUAsFcNhc8Bus1PkcR9DkVKE4KSkpAh6/OqTtQCAguH5EIlEKAgwha/pbDO+//hn/vX3H/+MAyVHwmco4RM2CsoBkUiE5Cz3UupiiZivwMelkHmDezKb43SSjLhoKACgdPeZHttV5YyUKijOQ+7gbABA7emGHu8vGqg+7n7enNx1hl+EJ6YkQKZwfxbCCQE3VDSHvCqSoc0IxsHuU52mhsqpQWIKQfpeX4WTRzuNlc0AgJyBWXw0U3dwgt2cBkwgiEQifv/NEZ7C18pVoszx7iDvCRq+oAOJnQcDp8PFXSP8oUiQI3tAJkRiEWb/8WKf23FOqVBUbw01kTZvGXUm/ho7amoxCkew91+uBUK6wlVPVWmCc2Zw51tLnbYHlvYxIhEgSoiqf2JJYg/bhm/R39bWho0bNwIAmpuboVaroVAoPP5pNBpotVoAwLp166DXR2f2A8MwWLx4MS/q/sTfn8IFv7nAYzuRyCVaKgKrhHYlHAL6VrMVLbWtsNvsMHQY+WtB3iCn45EB7DYSO/dFa0Mb1r32NczG0EVnk1OKEJx+/YTL7Xc4HHzZzxxnGlG/oexN0dluxM43vbsFDrsDo6cU43d/YJ++vP/Ix1RuWAC41D1NZpJXHQruKTjnHPEGFynFRVhwmhO9Sd876xQ57zc8D3mDnE6pGI+Uqj7GOnmHT2a1S86eqEVzjRaAZxQbwApUi8QiWIwWPtotECzG7iMsOedGgkYFqUwS0vS9npRujjcYhkFDJTu/ZhWmI71fYNUVuZRLTXr3jgJX+Ap8ES52zkVqhNIpxTnwqAJfcGgbOE2p7n8LkUiEx768Fy/tfBzp+b7Pfy59LxI1pSJt3jq2rRR2mwNZRRnI7p/Bp7lXHvHtlOI0AYONlOK0HDmnMBFaIrHy3po1a2AyBXe91+l0WLdundt7GRnsw7O6ujovLfzDtW1paQl7Zshjjz2GDRs2AABuu/l2XDNnns8oKE5XyhoFulIymSzk+2yuaUXT2RY0VDbx68DkTA0SklQQS1n3iD/Jj3hn2aOr8MZf3sdnL2wI2T7JKUUIzuHDwlUka6nTwma1QywRIz2PvVnjnFJVfpxSNqsNm5Z+BwD43e9n4MZHr4FCJcfR7Sfx+csbw2844Ya2jtOT8p6CkcI7pXw7PerOsJFS2QNZ52RBMTsOmqpaepSGYbPY0Owsu5s9IBO5g1lnV+3phph2XFY5I6UmzBgNZaICVrMNJ3acAgAke/l9pHIpMgtYZ8XZk93f8Bl1Jvxr/uu4d+ISVByu9rstFzXCVc1SqRX8PnqLt7LPhDu6Vj1MOvYpWka/NKTnBxYpxf9uQURKAS5i55HulOIqheakhGyfnZFSVIEvGDj9sq4Rtr5ISlN3W6iC15SKwPS9SJu3uNS9UVOHAQAKR3APg/w4pTq4SKngnFJc6r6/h1NEzxFaisMbXOpebm4uPvroo27/cQ/Ju1bhGzduHABgz549MBiCi4Dk2lqtVuzYsaN3X8gPq1atwr///W8AwIwZM3D/3Q8CACRS70t9Lmo9GnSlwqEpZdKz80hHiw5mg8VtHSh1PtyOV10pnVaPyuNn/epzHt/F3tfv/e5gyI5LTikirmmoYB0RGflpfIQNl75X7Sd979BPx9Bc04qUTA0uvHoS0nNTcdM/rgUA/Pe+D/HZi6HzHBPd01rvriclEgH9+tmQnW2CSNT5FNxX+p7NaveIlFKnJvKLj64paYHQfLYVjIOBXClDcmYSsvqzaR+GdiPaGmNn4cj1db9+NohEnU6pwuJ89BvOFg7YtnY3APDOp670H8Nqt53pRgTeZrHhP7e8i2PbSqFvM+Djx7/w6+BrrGJTxzhnCB8pFYGLxVikoYLt/5TsZMhVcqTnp0CJBijRALPBd8h3Z6RUcKlGmZxTqirCnVI+0ve6zlvBkMRX4KNIqe5wnbPaG9lrQqj0vQA2MhNwLy9OsHS9XnAi56OnFgMACopzIRKJoK1v9+lg7RQ6Dy59L5HT+orACDYi9JSVlWHbtm0AgLlz52L+/Pnd/ps7dy4AoKSkBGfPdjpGZ8+eDQAwGAxYunRpUHbMmjWL1yV67bXXQvDNPNm5cyfuuusuAMDw4cPx4fIP+WP6jJTqQfqeTqvni0NEMw67w8PhkpqTAqmzryQytm/itQKftqENJp0J+jbvaaxWi5WvXH98ZylMfu7ngoGcUoTgDBw4ULBj1zvLjmcXZfLv5Tsr8HFaU944tqMUADDu4lGQydmw0gUPXImFj88HALz/8Eo0VIWnmhjhibZL5T2VisHOnQ0oKSmHSsV0Rkr5cEr9/OlOGDtM0GSokTOgcywU8PoWwTulGpxaOplF6RCJRJArZbxTJpZS+Li+3rmzAbAb0eh0RPQrzkWBUyCeW4SPv3yU130MGFsIACjb798ptf+7Izi+/RSUagVkChmO7ziFvf875HP7BmdFvyxnaq7Sme4Rikip7OzsXu8j1mlyOgWzithxn5GjwuUp9+FC0Z+gb2722Y7XlEoLLlIq23nuclGPkUqrU9Oma9GFrvNWMGicfdVOTqlu4fp5+/Y66FvYcRiI0Hmg8ELnEeiUEnrecr1edDQ0or68CRKpGMW/GQKAFe3PKGSdyzWl3q+TPRU6j+QItlgg0tL3Vq5cyT+0uvrqqwNqw23ncDiwatUq/v0bbrgB+fns/cySJUvw448/+txHdbV7BPfQoUNx5ZVXAgDWr1+PF1980WdbvV6P1tbgNBGrqqowf/58mM1mpKWlYe3atUhMYB/oiCVin0LdwabvGTrYAlD15Y0w6nt/DxUMCoUipPszm9ioPrFEDLFUDJlChlSXFG7OORWv6Xucw87X9685XQ+b02FntdhwdPvJkByXnFKE4ASb7x1K6p2RUllFGfx7+UNYp1R7cwfam70/qTu2kz0Bi88byr8nEolw8z+uxdhpI2Gz2vHp81+Gy2yiC51OKfeFBRdOzqWNedMsspis+PI//wMA/O7Pl/HijwD4qnv+RFd90ejU0sks7BxbeUM4sfPYcUq5cvYEm36Xkq1BUpqaF60F2CilkRcO89puIOeU6iZSiovCmjR7HGbeOQ0A8NmzG/gLZ8XhamxeWsK/5p1SzvObi2AwdvT+qU4kpipEGrxjtrAzQi4Q3Sc+UiojOE2pHGfqbV1ZZKfIdid03pOxleTsK4qUChxdix52q50tkBHkWPNHYkrkOj8iad46/ONxAMCgcwbwUaxAp75nXZl353JP0/c4Z6FJb+YXVEToiLQ5l0vdy8rKwoUXXhhQm8mTJyM3N9etPQAolUq8//77kEqlMBgMmDVrFu68805s2LAB+/btw44dO7B8+XJcf/31KC4u9tjvq6++irw89n7o4YcfxowZM7By5Urs3r0be/bswdq1a3HPPfdg8ODBOHgwuHSoxYsX81pXjzzyCEwmEw4dPIyTp07iVFkpjhw54vaPE3Tn0vdsFhu6++lsVhvqyjqL9PT1dSbUY8tsYOdBZaICA0YVomhEP4glnS4RCZ++F3/zhN1uh82Z0ulL6L3quPua6GCIinxFllubiEtqampQUFAgyLG59L2sgk7HgSpRicyCdDRWNaPqRA1G/sZ9Ic0wDI7vZHNph5832GOfNz56DQ6UHMGmpVtw/cNXIz03MB2V3sAwDL59/0cY2o244g+XQK4MvShgJNM1fY9/v7UVGRkZfjWltn+xGy21WqTmJGPaTe5VSrhIn+oeiJ1zEUNZLgvy3EHZ2P/dEdSeis0KfFyaY7/h7M0X59QDgHGXjvSovMdRNLofRCIRWmq10Da0+0yl4SoX5g7OxvSbLsD3H/2C2tMN2L5uDzIL0vHCTW/DYrRAIpPgkkUXoaGCc0qxvwH3ZN1qtsJmsbk5ILt+j60rtqH/mAKcM3MM78xyhRtb8cDaFzbhqze/w6Tfjsc1D8zmHUvdwZ0DXZ1SlUfO+k2x4x4GBKsplVWUAZFIBEObER0tel5nKZKwmKx8VTZfTqmejC1eU4qcUgHDOUaTszQ+54KewKfvaSPPKRVJ89ahEtYpNXrqcLf3swdk4tAPx/mKuF3hnVJBRkq5zuPGDmPQkZiEf+x2e8RES23btg1nzrBVHefMmQOxOLAYDLFYjDlz5uC///0vjh49ir1792LChAkAgGnTpuGLL77AwoUL0draiuXLl2P58uUB7Tc7OxtbtmzBvHnzcOTIEZSUlKCkpKRH360rrjpx//d//9ft9u+++y4WLlwIiUwCkVgExsE474e8p/nptHrUlTfCYWP1dx12BzpadcgsSPcZhRVqLBYL5HJ5yPbHyQcoEhReiyPxkVJx6JSyuqQ1+oqUqnRq/qVkaqBtbMf+ktBoQ1OkFBHXcCl2rul7QKfYebUXsfO68gZoG9oglUkwePwAj8/HTR+FEb8ZBqvZim/e/z4MVnvyv6UlWLlkLb548Ws8fdV/eIHteIGPlHIu8oxG4IorMnDXXeNhNLoInTd4CpxWHGYn199cPdHDmceJnVcdr4XDHlxpWG9RIrzYeQyl73F9fcUVGThziP1eBU6nVL/hefxNy7mzx/nch0qtRK4zisxftBTXb7mDs6FKUmL2Hy8FAHy85Av8+4a3+Ip8X7+zFQ67A/XOSCkurUup7gwBN+l9R0tteO1bfLfsJyz928f414LXfX95J42Vzdjw+rc4tacs4p4W9xaGYVCychtsFju2rd2N/9zybsBtG/nKe+wi2GgEPt59F35lnkVtmW+x4Y5mVscg2EUjq1vFPgTgNOIiDW6ukilkfEQNR9d5Kxj46nvklOoWrp9v+WMx7IwcGQWhrUjXWX0v8tL3hIbr+1mzMnBkWwUAYNQU9wd/3HztzSnlsDv4uVvl5WGBPyRSCZSJ7DWAcwwTsYmrUPlVV10VVFvX7bsKnl9++eU4fvw4nnrqKUyePBnp6emQSCTQaDQYP3487r77bvzyyy9e9ztw4EDs2rULS5cuxaxZs5CbmwuZTAaVSoUhQ4bgpptuwpo1awKO6goFrtFS3jAbLag5XQeHzQ5FghwFw/Mglkpgt9ph6Ai9091mtaGjRQedVh/W1DneKaXy7ujidLhscZi+ZzF2OqV8rXu4SCmu8vzxnaeg03rXnwqGyHBpE3HNOeecI9ixO9P3PJ1S+7Yc8ip2zkVJDRrXH3Kl54QmEokw67ZLcHTbCfzw6Tbc+Mg1YbC8k0M/HMcnT7GpgnKVHBWHqvHaHe/j0XX38t7+WIdPh3FGSjGMCAcOyAHIwTB6Pq2vrbEDDMO4Pd1pqWUdeN4WJjkDMiFTyGAxWtBY2czfLAdCY6W7ng7Q6aBqOhs7TsPOvgbOV7BOo37FbPi7MlGBGXdMRX15E8ZMH+F3PwPHFqLmZB3K9ldi/GWe2lMOhwN1XKTUINa5d+niC/G/d7fywvGjpw5H2cEqNFY048fVO6FrZS+SXL9LpBLIVXJYjBYY2o1Qp3oX0nYVoq86WgOHw+HxpJXTwtu7+RDevXcln6oz5NwB+L8Pf++WjhLNnD1Ri7bGDsgUMjjsdlQdq0F9eROy+3cfbeGqqwawY6W6KRtANhqr1/psx0X7BBspBQA5g7LQVN2CutMNGHqucHqFvnBN3ev6lLnrvAUE7uDkUh2p+l73uPZzAUTICLAqZKDwgtrtRo/rjdAIqeEJuF8v0mGGUilF0ah+btv4c0q56gEGGykFsCl8Jr05IlMro51Q6/70hjfffBNvvvlmj9pOnz4dZrPvh1YpKSl44IEH8MADDwS9b4lEgptvvhk333xzj2zzxsmTnno+rXVtaGtoR1KGGul5vuc3qVwGi9EKq9nq9tAOYB9I1Vc0AgyQkJyAvEHZEIvFSEpNRFtjO3QtOiRqEnzsuWfUlTXw56YqScUXnkpMDK7oiT8YhoHZ+QBTkeB9zEriOFKK09sC/ERKOZ1SF1w1CT+u2Y6Ko9XYsXEPLr1pSq+OHVORUjqdDkuWLMHMmTORlpYGkUiEZcuWBdxeq9XizjvvRGZmJhITEzF9+nTs3bs3fAYTAIAjR0KTixosDMPw6T1dI6W4ibDKS6TU8Z2syPnwSUN87vuCuedCKpOg7FAlKo6GrwRzR4sO7967AoyDwZTrzsc/t/wdickJKDtQiXUvfxO240YSdpudL+vdVVOKIzmTfd9utfOOCo6WGi0AIM3LhVssESN7ALv4DibywnVsuWpKcTcHzWdbYy6iBugsDsBFSgHA9f+4Cve+f4fP1D0OTuy8/JD386WlRguLyQqJTMI7mRQJCvzlvdtx1f/NwmNf3ou/fXQXLlnEPmX87Fm2AmZyZpLb4iVB073YuWuEg8Pu8Fqtqbq6Gi21Wrx+1wcwtBuROygLcpUcpbvK8Nqd7/l88hhtHP2ZveEddv4gDD6HjQzlKmb5w2a1o9npfHVNYeVoPus9fc9sMPMRb5qMHjileLHzyIyU4pxSKT5S93oKV6mwo0UXk3NLOAl1pBSXJsY4GJh0oalKFCq6ijALTd7QHI/0Ge4cri9vgsPh/qTe5Jy3ZQppt9cUb3RWRiSnVKiJJL2yeIdzJnhLTXOFO4e8VeDraNbBpDNBJBYhuyiDfzDHRYIa/USb9wSGYdzuy4wdRl7TyRhs6LAfrGYrGAcDkVjkU+pEymtKxcZ9XDC4p+95RkrZbXYYO4yQSCUoLM7HhVefBwD4+YudvT52TDmlmpqa8OSTT+LYsWMYO3ZsUG0dDgdmz56Njz/+GH/+85/x/PPPo6GhAdOmTUNpaWmYLCYA4YTOdVo9H36aVej+1L+fswLfWS8V+Er3snnqwyZ56klxJKWqMXHGOABAyeptoTDXK8v+/inaGjuQNzQHNz91DTIL07H4ObYC4MbXv42LND4u+kksEfuMrJDKpUhKYxdtXXWlWmq1AIC03BSvbXMGslE5vvQtvKHXGnjdC9cFT6rzGBajJSbTBwxaI8QSMXIHB1/hqd9w5zlXWuf1cy51L3tAptuN1qAJ/TH3rzMx+JwBEIvFuGTRhZBIxZ1RUkXu5zbnoOJ+H290/W28RZ9YLBbs+fog7FY7BowtwNPf/R0Pf34PFAlyHPnpJP73bonXfTMM47HQChaT3txnjocjTqfUyAuHYvQ0Vvvl0A/Hu23XfLYVjIOBTCHzWtnMl1OKi5KSKaR8qk0wcOdrpFbg4yrv+dKT6ilcqqPNYvc7tgNhzzcH8c27W0NhVlQQqEZaoMhVcn6xF2kpfJHmOHDVHeTIKEiDRCqG1WzlnbgchnZ2bCt7ECUFUAW+cELO8MiBcyZIpP6X+f6cUtpG9txLy03lq4wDndFFFqMlaFkLf1hMLs4iZ1qdoZ2dP3t7z+QKJ3KuUMl9RrFKZGy/2G32uBvXlm4ipbixlTMgCzK5jHdK7f5mf6+rMsaUUyo3Nxe1tbWoqKjAv//976DarlmzBtu2bcOyZcuwZMkS/OlPf0JJSQkkEgmWLFkSJosJANBoQleKORi41L3kjCQou4Rw9nNGSp09VQe73f2krHKm9BWNcA8578rU+b8BAHz/8U8e+wgFh388jt2bDkAiFeP3r97MT+KTfjseQ88bBMbB4NcN+0J+3EiD15PK0vgVs0zmxc47b3ItJiuvweLbKeW/EpA3uNS95CyNW866XCnjoz9i1WGYMzCzR0L7+UNzAABNVS18vr8rnFMqz5m654vkTA3GXz6af53VxSnFLWZM/iKlnE4pkZi9YWlv8tTpUalU2L3pAADg/LnnQCqTYMCYAlz3GFv6ecd6zyhbhmHw1JX/wWOXP99jvYT9W47gDyP+jjXPbexR+2CwWe04vp1NVx5x4VCMmspWFTr2S2m3kWAtziipjH6pXm/8dC0Gr7pe3PmYlKbuUdoTd77WRmikFDf/hNopJVfJ+WtA12jQYHnv/1Zh1RPr0FjVHArTIp5QO6UANt0FiDznh0oVnA5TuCkszvN4TyKVIKOAja7s+jCIi6RI6GF6NF8ZMcKchbFAoGLiRPhxBBwpxd6rWc1Wt/fNRgt7fRbBozKpTC7l09vMxtBFS7mKj/O6fE6nlEQSOikSk8txfMFFSoHxncIWizAOBhaXscA4PB+icv2R63wAOGhsf+QMyILZaMHub/b36vgxNYMoFArk5OT0qO2aNWuQnZ2Nq6++mn8vMzMT8+fPx5dffuk3v5joHf379xfkuFx50656UgAbOSVTyGA1W/k0LADQt+l5sez8Ibl+93/B3HOhTklEzel6bPtydwgtZ1OKVj+9HgBwyaKLPDQZzp8zHgCwc308OKXYyKeUbP+LPO5zbUNnpFSrM0pKrpJ7iA5zdEZeBL7I5RZzmV7Sljgh5uYY0pVypd9wz0VGICSlqflIt5pSTyH4GpfKe90x7YbJ/N9dtY84rSfuiXtXbFY77yzhjuVNPFopUuHEztMAgImzOiNzz509DmKJGJVHzrrNHQA7Vk/vLUf18Vo+bTQY7DY7PnlyHRx2BzYv/aHXzofuKD9YBZPejMSUBBSOzEfRqHwkpath0ptxak+537ac0zXNj14PV/3MlY5e6EkBrKYUADSUN4b0KW6ocNWUCjVcNGhvxoXZaOGje7ylrcYi6eFwSvEV+CLL+ZGV5d+p39cUeImUAlxS+Lo6pZxOPmUPnVLc7xIvY7sviZTKe4RrpFRg6Xt2q3tEUFsTe5+cmJwIqczzd+WimP0VjAmU9uYO1J6p5x3OCpW8M6KxjdXlC6VeWXd6UgD7QJIXO48RKYZAsFqsANP5QBbwTOHjnVID2PtjkUiEC68KTQpfTDmlesO+ffswYcIED0//pEmTYDAYvArJCY3FbMWub/bx5bOjlYMHDwpy3GM72LTMQWOKPD6TSCTIH8w6OF0r8FU7U4vSclK6FfhTqVW48k8zAQCrn/sipCGgO77ci8qjZ5GgUeHKe2d4fD7xirEQiUUoO1DpsTCONVoDjDzorMDX6ZTiHAPpeSk+ozJ6kg7UWOEUOffmlHLqSvXEKRENFHh58h0oXLSUtxQ+vvLeoO6dUiOnDOOjH3K7RFZxUVwWk9WjHeD+BJ1bGHmbY7/75EcwDIMBYwvdIi2S0tQYfv4gAGwalCuuCyzXcRgov6zZhVqnc85ismLrCu9VfkLFqb1lAIAh5w6EWCyGWCzGiAuHAgBO/Hrab1vO4ZSR73vB79Up1cI6VDQ9dEql56VCppDBZrFH5NxXeYQVCOXmlVDCCfdzfdgTXFNVvUUsxiLpIRY6BzrFzgNN39u/5Qievuo/+OjRNTi9rwIndp4OS/9XVFSEfJ+9wVv6HtApdt41QtnQwUVK9SziKyHI34UInEhLDY1nOIHu7tL3xBIxxBJ2G5szhY9hOh8OdY2S4uCyS3rrlGIYBg1Vzeho0aGtsZ3ftypRCbFEDLvNDpPBDIMhdOdrd5X3ODhnnC2OxM45B5RUJuUdmo4umT6dkVKd9+JcCt+OjXvcIq2ChZxSTmpra5Gb6xn5wr1XU+MpeM1hNpvR3t7u9i8cVBytwtr/fIUXb38Lz1z/Mm4e+Cc8fMU/8dicf8Vdzmsw1J6px+8n3I8HZzyF/y3byvfVoZ+OAgDGTB3ptR23+HVNAznrdFDlD/UfJcUx955ZUKjkOLHrNA7+cLTH36ErP3/GeqNn3jndawWx5EwNRlzACrFvfOO7kOZjRxp8+l6XSKm0NDuSkztvktLyUgCw6WEcnJ5Uqo/UPaDz5rj5bCsvwNwdfNUxL06ptLzYi5RKS7NDKWFvYjhtqJ7AOaVqTno6pTinIJee5Q+xWIw/vLEIv/3zpThn5hi3z6Ry/yWQdc7IhoRkFZIzuYpm7pFSDMNg30a2QMOk347z2Mc5zsip3V+7O6VcF1iuaaSBsvH1bwEAQyexFbS+/eCnsD7FO72XXcAOntDpuB88ob/zs3K/bbnx3XXBn5Zmh0rGOk1cz0WO3lTeA9ib7AHjWNH84zv8O876Gl2rnncqcv3Yla7zVjCouUipXjmlOsc6p70Ri6QkWyFDO5IyErtdnPSEYLSLPnxkDV5e9A5Kd5Xhu2U/4cnfvYR/XvMq3r//k5DbFQkka9i+T81N9hmhnDeEvf86sOWo26KQiwLknH7BwkdKRVhaJUGECoeD4dc53UVKAS66Us57CX2bAXabHRKZhJ/HuqJMdMog6M0wdBh7vMYw6c18qiGHIkEOkVjEO56NITxXbVYb77Dr1iklFz5Symax8emGfQEXXS6WiCF2OjR9RUrluDilis8fgrTcVBjajdi35VCPj0+xlk6MRqPX8EClUsl/7otnn30WTzzxhMf7u3fvRmJiIiZMmIBjx47BaDQiKSkJAwYM4KODioqK4HA4UFXFVpwaN24cTp06BZ1Oh8TERAwdOhSbv/gOG1/6Hqd3eX/CdXT7Sax+Yy3m/f5K7NmzBwCQl5cHpVKJM2dYUe5Ro0ahuroaWq0Wcrkc48aNw6+//goAyMnJgVqtxqlTrHZIcXEx6uvr0dLSAqlUinPOOQe//vorGIZBZmYmUlNT+cixYcOGoaWlBY2NjRCLxTj33HOxe/du2O12pKenIysrC8eOsZWahgwZgvb2dtTXsxEP5513Hvbu3QuTyYSTJ08iLy+Pr8Q3aNAgGAwG1NayQuMTJ07E4cOHYTKZkJycjMLCQhw6xA78/v37w2az8VVlJkyYgOPHj8NgMECtVmPzaz/h9P5yAMDebw/CYNIjY3gyTuxiFyyKHAl27tyJhIQEDB8+nK+4mJTJ3uDv27Yf2eekYPTo0di/jT1mQroCdrsdu3ezaXm5ublISEjA6dPsPkeOHImamhq0trZizOXF2PXlAXz+9nqYVDpkZ2dDo9HwAvrFxcVoaGhAc3MzJBIJJk6ciF27dsHhcCAzMxNpaWnYf2Q//3tXnqnC0V/YtudfOQGnT5+Gw+GAWq1GSkoK3w/nzzsHR346iR8+3o7aijo8uOLPqKqugs1mQ2JiItLS0vhxl52dDYvFgtZWdiE5cOBAVFdXw2KxQKVSISsri3/CmpmZCYfDgeZm1vEyYMAA1NTUwGw2Q6lUIicnB+XlbH+np6dDLBajsbGRH+8NDQ0wGo2Qy+Xo168fP0bT0tIglUrR0MAu2goLC9HU1ASDwQCZTIbCwkK+f1NSUqBUKlFXV4eKk+x3kCaKUFpaColEgoEDB2Lt2p9htVqh02XA4UiA2PnAp+rYWdTV1aGjowOlh9kxL1NLUFpaiqSkJGg0Gpw9e5b/XY02PZRqBUw6M+rLm2BVmGC32z36OycnByaTCVqtFhXHWZvEiQxKS0uRkJCAjIwMVFZWwiFnL3C1ZXX8GAimv/v374+6ujqYTCYoFArk5eWhrKws6P5OTU2FXC7nz8eCggK0tLRAr9dDKpWif//+/Jzg2t8A0K9fP2i1Wuh0OojFYuzdbcWdw+6Hw+GAKkMOnU7Hn7t5eXnQ6XRob2+HSCTC4MGD+THbtb/TClIAACf3nUZpaSmGDBmCsrIyGHVG3omjc7SjtLTUY8wOGjQIlZWVsFqtSEhIQMGoXDAaK8oqypCVlQWbzYaWlhYYzexc3ljfiNLSUo8x21HNfi5PlMEqYp/41FawvxXX399//hPqShshV8ow7ooR/O/IjdnUoaxD5dTuMhzYfRAJySqkpqai6uRZ/jxuOtuCmpoavr8HDBjA7yc5ORkqlYrv7/z8fFScrEJ9eRNEEhH+8t7teGDKU2hraMfu7/dh9EUj+Acnubm50Ov1/MORIUOG+JwjXMcsAAwePBjl5eX8HME5nhRZMrS3t8NisUCeyd6ond5bgfLyclitVq9jtq6CPY+tEjNsNhs/R2zaVImSd3fiu/fNKD10GhNaRrqNWW0Da4tdbEVFRUWP5oisoWk4ufM0Dv14FPnnZniMWW6OcO3vhIQEfszm5+ejvb0dHR0d/Jg9deoUGIbxOkcYDAa0tbXx/X3mzBmvc0RLKfubpPVLRm1TDQYlu4/ZjIwMft6yWvPR3MyOWSCwOQJS9uZR29SOqqqqHs0RnLgte440uV2zezNHDBo0iO9vjUaDxMTEHs0ROTk5MBqNbv1dVlYW0HXNdY74z6NbseKBL5CSmYXS0lK3OQLwf13LyGDHVFMTG4nnbU62idh5vrGmCW1tbT7HbMMRLbYs/wkisQi/mT8RzVWtKNtfCbPegjMHKoOaI7gxy/U3N2Y1Gg3UajVqamqc10RdyOaIYO8jzp6twKOLP8P/XvsJ/YYO5b9P1/6eMGs0Pn12PWpP1WPjfzdj2k2T0djYiPKTlQAAsUqM0tLSoO8jOKdU/dlGNDY2+hyz4ZojRCIR0tLSYLVaIRKJIBaLIZFIYLWy1xqZTAaHw8HrkCoUClgsFraYi1gMqVTKRyRJpVIwDBPwtgBgs7HjUi6Xw2q1gmEYiEQiyGQyv9vabDY4HA6IRCLI5XJe0kQikUAkEsFms4FhWGeIv23FYrHbd7Xb7bxDQ6FQ+N22a79w2/ZlH7puG2wf+trWtQ+D6W9f2xr17D2MSCwCAwYWs8Vvf3POB4vRAolCzGcfJKYkQK/XQyQSITExEXq9HgzDQCqVQqbkBNKtqD5RA2Wigr9/S0xMhNHIOqqkUilkMhm/hlYoFGAYhv/uRm0Xh4sIsDqssOlskCfIAa0eHVod0tQpcDgcMJvNsNvtkEgkUCgUfASVXM6KlnP9lJCQwG8rFouhVCphMBhg1jv7QimDwci2ValUsFqtsNlsEIvFUKlU0Ov1gDN5wmK2QKfTeWzL9Qv3mUwmg0Qi4Yt3KZVK2Gw2t21d+1Aqlbpta7fb+TGsVquh1+vRUNYMq8mKfsPz4EDnGHU4HPy2wfR3QkICTCYTHA6HRx8qFAp+rDBgIJGIYQVg0BkgkrKf6/V6/iGBLEmMnTvZAIkxY8ZgxNQh+PmTX7F+6dc45/Ixbv4IztbuIKeUE5VK5VU3ihsw/sQhH3roIfztb3/jX7e3t6OgoAATJ07kRbzHjHF/Wn/eeee5vc7L60x5GTmSjdwx6k346u3v8P7DH8NkMEMsFuGcGeMwbOIgaNKTkJabisM/H8O6177Gzx/twoI/Xe2x38zMzqiCYcOG+bUhPb0zqqOr+PikSZN8tk1JScHAgQP51xMnTvS5bVpampuG1IQJE3D27Fnk5+d7tamwsJD/u2tFxa7bcvsAgNGjWaFji8mCRz9lRe/HTR+J/VuPYO2/v8afX70VDrsDmQXpuOiyC9xSt7j91uxsxmb8AJFZwr9namVP2NHnjYJEIvGwgbtZBYChQ9k0F9MNDuz68gDqjjW5be/6t0ajweDBndX8zj33XLf9nnvuucB37N/VB+rBOBgUFOche0AmsuEeOTJkyBD+fwkjwQcPfoKTv5Rh01vfY849l3vd1pv9RUVFfrdNS+tMy3H9nbxtm5KSwv/dr18/v9smJ3dGPLn+pt62TUpKgsO4BQBQMLif2+dDhgxBS0sLb+c5U8ZhHTaj9lQDsrOzkZOTg1/MrOZW/2GFHm051Go18gbn4Mz+CtSdacC5s8f5tCkpKQmZmZnQNbGTfPGE4RgyZJDbtq1jdfgOv0DXbHBrG0x/FxQU+N02mP52Pddd5yFv2yYldYZyu86Jp/aUwWFzICktEcPHD4VIJHJrm5iYiOzszqcqgwZ19onrcWzOoERtTQf/3oABA1B9nHW4JCSrMOac0W7nq+uY7apP19X+9PR0pGXsAgCoE5O8/ub7K1nHeEpGMvoPKcTP2AWH2X1fBzecAABMue585BW5R4bl5+cjPz8fuYO/Ru2pethbRBgykW2rPdu54Ne3GoLqb205e+PTf1Q/qFMTMeScgdj/3RF01BqRmJjoMWYD6W/uGK7XiQEDBrDHq29D89lWiEQiXHTFZF6L64IZ5+Mj+VroWvVQMYnoPyTT6347Glh7R04ohlQqdZsjsgtZ57Jdx/Djmhuz3zezNzlFQ4v4cyLYOcL2W+Dnj3bh1K4KDB48mB8vXa/jXffr+johIcFNo9J1fu66rVqtdtPqcb0eum679stNAIDi84fy73kbsy0tLfx3dL0udzdH5BXl4ghKYWw39XiOqNi+g/9bIVFCo9GEZI7wtm1P5gjuGK79zY1ZX8fxNkeU/cA6DPIH57lt79rf3V3XUlM7owC79ndOv2wcwUnAJkJycrLXMatr1ePFR94FAMz+4yW49u+/A8AWyrjvN0+itbYNubm5bvOdv/4OZMy2tLRArVaHZI7wZVN39xE/NrIP8waP7+/Wtmt/X/v332H5Q59h81s/Ysai6RgyZAh+drAPDfP65/i8ZgO+54jqFPYiI3ZI+O/U0zHbkznCZrOhvb0dMpnM7UG469+cM4RDLneP6Oj6AD2YbV0Fo4PZViaTdbut3W7nHS7+tu36XYPZ1vW7BrNtKPswUvrb17ZS52cSKevY666/VWoljO0m6FoNSEhW8Y6btOwUyJWdNicmumdlKNVKtmCMiI14MraakFnAnvsJCe4RVmq1e+Qz1xfNlawDW5GggNlghlwp589HqdiMlppWWIxWyOVy3mHkb7+ufeFtW0sHezxlgsKtbVchdbVaDXOHFfpWAxx2ptttfb3uqrPWtQ+7buv6W8mlclidMhPGDhPSc92jzl23DbS/GYaBvsWI9uYOFAzLg1gsdttWJGLHh0wuY/M4AcikMr4vVUoVGAf7/uhJI5CY3Pl9fnfbTPz8ya84/tNpgHFf4zbWByZ/Qul7TrjKfV1xfZLnC4VCwd+4db2BC5b9Ww/j85c34pkb/oMbCn6PN+/9ACaDGeMvGY3lp17HP796GIueWICr7rkCU6+djBseuQbKBAVO7DqNHz/b3uPjCgn3RC4cbPtyF3RaPTIL0vHEugeRlpuKurIGvPbn9wAAY6aO8K0lNIC96XVL33Nq3fQLMH0PAMZOGwEAKDtUidaG4FN2unLgW3bRPPGKsd1sCVww71zc8vx1AIB1L32NisPh62uh8JW+B4CPLgLYdEyxRAxDu5EXG27hxJidqX2+4FLGuoquesNus6O5mt2vV6HzGEzf40SvB50zoEcV0zjyhzkr8FW28GKUANDAaXQVZfRq/4BL+p6XEsgA6ywC2KeEXKVEV6FzbX0bDpUcA0TAjDum+TwOl2J3cucZ/r26XmhKle5y6jtNZPfLaXdVHfOdWt4bTu9jo3Dyh+bwDimADfUvGsUuwE/5SOFjGKYzfa+fp14PV1mrqdqzuptrNc2eMmhCEaRyCbT1bQGds30F16eDfKTucbjOW8GQmMremOpaPIX5A8VNUyqElZUiDW5O8ad51hsSU7oX1N799UG0N+mQOygLc/86i38/NScZIpEIVrO1V/pg3ujp2AolVcdYh2BhsXc9KY6p109Gen4q9G0Gfq7hxieXWh0srgLKRGjhoncIYbFbOZHzwJb4SWlqSBVS2K121J5ir5cqtdLNIeWNgqF5GDSuP68t1FrfFpQOntVi5VPE8wZnIzUnBVkFnQ5thUoOsVQMxsHA0BEaTSkDJ6buR+Scg9eUCjJ9z2q2hkRSxzXFuKfpxg6HAw2VTehoZe8JtA3taK3Twm61o8NLQZTO9D0Rn/rpWn2Q64vElAQ3hxQAjJ06AqnZyWhv7sCvm9yLbJ3aVx6QveSUcjJu3Djs3bvXIy+WS+viol7CyWcvrMf9lzyBt/9vOUo++QU6rR65A7Nx9+u349lvHkFOf09h1NSsZFx73xwAwJt/XQZ9W3irMUUbm5eXAAAuXzgNCUkq3PL09QCA+gp24h1z0QifbTmnFFelj2EYXvQ8GKdUSmYyBo5lnxYe2Ho4uC/ghaPb2HDyQJxSAPCbqydi4hVjYbc5sMGpSRNL8CXWszsXsUYjMG9eOv7yl7HgMm9lCimvD3X2BOtsbq11tvWjKQV06kpxejD+aKnRwmF3QKaQIiXbc2HNaey01rXFRKlZoxF44rULsZt5HEVjBnXfwA9J6WqoUxPBMIybrlR9OXu+ZhVl+GoaMDK5u35CVzgBXHVKAjTpTk0pF6Hzs0670vul+LVn2HlsX3AV+hwOBxrKO4W3uaqRgVK6i3VuDTmXjVLgqlZVHQ2vU2rgeM9CEIOcGlNn9nlPKe9o0cNiYtNTUnNS+Pe58/If/5kMOyNHY6WnphTnrPN27gSKXCXH4AlsP/306c6IEDV2OBx8fw0+p7/XbbzNW8GQlOZ0ovZGU8rFARurmlJGI/Cf1bOwm3kcOcMKu2/QA/jqe37G3tmT7HVo7CUjeF0XgHWcJ2exc08sPbwAAL3Ogc8P3I7dzOPIHOTfKSWRSjBgLPv7cNdszinFzc3BEsjvQhDRDHdfGYieFMCm+WU6HxSBYSCRSZDRz/OBqrd2EqkESalqqFNYB0WghbcYhuELnajUSsjkMmT2S+fPT4Ct6qZSs69DcS2yWmx8IRu1Dy07V3hNqSCEztua2lF2qBIttb2ft/XtnXOUSWfqUTXhjhYdtA1tqK9ogtloQWN15z2oN41cTtRc4qop5XJcq5W9b87s53nvK5FKcOlNUwAA33zwvdtnx3aWBmRvXDqlamtrcfz4cbccx3nz5qG+vh5r167l32tqasJnn32G3/3udyEtR+mNhspGfPj4pwCASVeMx42PXoOXfngSH5x4BXP+OMMjXNCV6/4+F/lDctFS24p3H1wZVjvDwfjx48OyX7vdzouLT7vuAgDAjMXT8NDKv2DA6EJkF2Xi/N+d47M955TSafXQafXQNrTB0G6ESCRC7qAcn+28MX76KADolQAch91iR9GofrwodHeIRCLMvHM6AODozydjSvTcarbxCzDXSCmGEWH7dgUOHEgBw3RG1vDV3ZyOBa5sfXo3kVJcu+oTntGUXWmoZCf9jIJ0j1BpANBkJkEik4BxMD2qwCYEu77aj58+3en14swwIlS2FEGLkRgwrn+vjiMSiTBgjDMKxxl9BYCvopbdv/dOqe6EzvW80HkCktKdN1ou4s81ziqABcP9L6g4p1TF4SqY9GZo69vdKv61BfHbGztMqHKmMHKRUoUj2Eip6uM1PbpZ6Q7OCTbIi1OKE+n2FSnFLaSTs5LcFtvcebnvoAaACLpWPV8GmqOtnnNK+a+m2R3FXKGH17/Dwxc/yxc1EIpTu8tgaDdCkSBHv2HeH2r4mrcCJSkkQueu1fdi1Cmls6BWPxhajMSgcwZ036AHqDiRXp3vyAHuOpQ3xPNanpbLVWkNrVOqa+pdX1N3pgktzAhoMRIZBd3P59y5ws1/bZxTqseRUiR0Hi66pqcRwhCsUwpgxcWz+mcgLT8F/UcVQKVWdt/IBY2zSl97i67bKCGGYaBtbEdHiw4Qwa8DLMEZpW3qsMDai6puANDhdJgFEgUGBB8pxTAMf5/R2tDWq/syhmFg6HDOUSL2tVFv8t/ICx3OqGmHzc6uTVx+GrNXp1Sn0Lm/SKnMAu8RxjNuYdeaO7/ai5a6zmvXyV2nArI35pxSr7/+Op5++mm8//77AIANGzbg6aefxtNPP80LYz700EMoLi7mRQgB1il1/vnn45ZbbsGTTz6JN998E9OmTYPdbvcqYt5bGIbBpqVbsOKpNbBZbXj9nvdhMpgx6sLheHrDQ1j85HUYfVGxX2cUh1wpx1/eugMA8NU73+L7j38Kub3B0FqvxU9rdwZcFpITTQ811SdqYDZaoExU8GHiIpEIF19/Id458CJWlL2JtBzfpaBViUqkZLELo7qyBlQ7n2pmF2VArpD5bOeN8ZewGle7/rc/JNExly6+KKg0pgFjC6FUK6DXGviS5IFw9mQd9n/HRned/PU0Nr/3Q1gWwD2Fi5KSKaQ+q/i4wt3gVp+oRd2ZBui1BojEIqT385/CwaVKnT1R1+33b+RSzbyk7gFsTn+aMzKrudozUiTSOPD9Ubx+1wdY+reP8Y+Zz6PujHu0WLPLE6H+o/t1bR40Q85lnS6luzvT3uqdEUahjJTqzimlTkngb7T0WgN/3taeYr9/Uo5n1UtXMvqlIS0vBXabA6f3lvNpZCIxe95qg0jlPbW3HIyDQWZhOlJz2Dkpu38m5EoZLCYrH0kWKvRtBt4pOPKiYR6f9x/NOg7Pnqjz6uRuPsuOay5V1RtcaXbXc8BisvIRDL1J3wOAixdeiCkLzkNylgba+nZ8/fb33TcKA9XHa1BTWocPH1kDADj3t+OCWiwEA1eJlQvV7wmukVKBVhuNNs4c6Izw66rTESpU/GLK90KCiwb1Vs2Xi6gNdaSUv0rSfUHV8c4HO94e2nSFq+bKR0o1OtP3fJSq744EDZe+Z6Cq1SEmUDFjIrxw1dIksuCW+KokJdSpiT2SSEjQqCCWSmC32v06fO02O6pO1KDR+fA2PTfNrwMsMSURIrEIFqMFFUerYTH17JpkNpg7HdoBzh1cpJTD7gho3aNvM/COM4fN0avrsNlghsPmgFgi5iOgg3Wk26y2TscWAKPzb84JaDFZPO7f7LxTSgKJhB0/Dpfqe7xTykukFAAUjSjA8POGwGF3YMsK1hfBMAyOx2uk1AsvvIDHHnsMb731FgBg7dq1eOyxx/DYY4/xFUG8IZFIsGnTJixYsACvvvoq7r//fmRkZOD777/3EAjvLQzDYOmDK/DynW9j+ZLVuGP037B9/W5IpBLc/frtPZoQxl88Gjc8fDUA4OU7/8unp/UFjdXNaG1og8VsxbJ/fIKFg/6MJ+e9gKfmv8hXqfCHXh+elEMuh3Xg2P4B3fx4g9eVKmtA5TFWj6m7CAlvjJk2Epr0JDRWNePbD3/okS0cKo0S5105Iag2UpkEwyezkQO/btyPr974Do2V/rUlHA4HXrz5bby8+F1UHavB89e/hZVL1uKbd7b22PZQ01jl1AXplxbQecNpFp09UYefPnVWjZhW3O1ToayiDMiVMljN3TsAGpz9yunmeIN7ytDQzW8gNB0tOrx33yoArD7B2RN1WLlkrds2Z/ZV8n8HkqffHUM5p5RTQwnojJQKhVNKypU//n/2zju8qbIN4/fJXt170MGGlr0R2UOWbBQXKIgLRdwLPxC3n34quEChiKjgQLayZO+9Zxele6Zpmn2+P07OadImadKmTdq8v+visibnnLw9efsm5z73cz92MqVUpeaW44Ey7gsaTdNcK/KcW4xTKjC69i82rFvq+vFUTpSKS2LWj/KiCqcF6hvHzaV7PatcDjw+D7HtGbE0080lfBf3X4PJaEJ0mwibuWihLYLBF/Kh1+pRnF1a4/li84V0cIz9C35WCC60EKXKChiXlFAs4ESruuIfosDsTx/A4/97EACwd+1hXD9+q1EzplLPZuCtkR/j9SEf4PaVbMgDZbjvzQkN9noK1ilV4nppklatRUWZGuWFzb98z9KF2VBI5MxaaO/utmW2YXSbiBrPszmHmZfv4OMZX2Hv2sNuGZethj6NCet4cpYqUSoXlSoNNyedvbCsDpv1ZTSYmq3o6imIyOcdsMIBX9h4vcx4PB78gmov4SvJK4VGpQHFoxAcHcTdoLWHSCxEXIdYCCUCmIwml6sLaJpGXkYBMi5nQa/Vg+JRUAQpat8R5k6H5puIBn3tbin2RiNfyDf/v7LOfxOaCmadliokkJuF9Oqu8tpQlVQANKyujSgehcAwf6Y0j4aVex8ATEZmvPacUuzPQRGBdl/3HrNb6p+Uf5kojlu5nCBYG81OlEpPT+faklb/x3ZdSUlJsfp/lqCgIHz//fcoLCxERUUF9u7dW6OTnDvYuOxvrP/vJgDMG886cJ7/di5adq5ZKuEsjyyejo7920Gj1mLbil1uGWtt7PppPx5p9QweaPEEZneYj7Xv/gGNOeju6OZT+PChL5F2wXbuCEv1LgHu4uYZ5qK2dT1KirhcqdQ8ZLLhnHUQpaRyCWa8PgkA8OPi9U67yFgsF4W7pvaCWOq6TTppAJOLtvWrXVj/wWZ8N3+NwwUz9UwGd5c289Id7g7AX//722vcUgVOCECWWDqlDv52HABw9319HO0CgPk7jWH3veK4hI8dU3i8A1HKLK5YZgx5GzRNI+XVdSjLVyK6bSQWb38ZFI/C+X+vIPNyldvu5olUB0dxnZbd4sDj81CcU4qiO8Uw6KuC48MboXyPDcCVB8jA4/OgMIdHsyV82TcYUcpWyU11WnZl1vOMS1mcmNW6eyJ4fB5omkZZgXMf1KxrjA1PZ2lhLuG7fdl596MznN/DlD13Hmo7c48v4HMCYXXnHAAUmtcNRyHSrIvKMleKzdkKCPevd6A9S/LAdkjsEgedRo/3Jn+JN4Z90GDh8NXZ8cM+rlMNADy4aDL8Qxrm8w4A/MxftlVOlFBYQtM03pv8JV4d+B43TwHb9v7mwK1TabVvVE+kCkaUsueUyjY3TQmKDLDKUWFhnVIHfzuOSweuY/NS9+RBSiSuleW4mzsuilIRCWEQihlH6C1zubBQLIREUbcbICKpiAuAriglJXzuxF1rNqF+GHTM9YJQ1DCOXHuw7kVVSYXNqAeTyYRS83eeyMRwhEY7dzNZLBUhwOycLi9WWX2m1kZpfhl3s0seIENsmyjOAeQMtTnrWYyGKodYTOtIUBQFrVrrspDEwmaeCsVC7mavVq116XOdDTIPigwEzKeZ/V7LXkNWD6a3LN+zlSnFuvDYJkC2GHxff4ilImRczsLV4zdx+Yjz1VDNTpTydgqzi7HqLcZ5MPfjh/HWrwsQFhuC2R88iHseG1qvY/P5fEx+bgwAYOeP+5xyKbmKXqdH2oUMZN3IwbJnf8BHjyyFQW+EQW9EbnoBAsP88da6F/D2by+CoijsXXcYc7u8hJS3f7X7x1S9ja67uHnWLEp1q3uGAhsun5OWj8yrjFMqrkPdSpTGPzUSoTHBKLhdhF8/2ODSvttXVpWejJg9sE6vX70M58aJNJz6+zz+Xv4v0i/crrH9qe3nuZ+vHb/F/axV67D358NecVes4DZzQRsW51wHpYjEMITGBkNXqUNpnhKKIDm6jUh2at8W5ju2tV3QsrZkWw4TbhysKJXhvaLUoT9O4OT28+AL+Xjyy4fRokM0eo3tCgDY/l2VW+7yEedqxZ1FLBMjPpkRfq8fT0VRVrE5OF5Yr/BrltqCzlWlVd33AMDP/EWrvFiFynINVzKa1Kt9ra/FuqIyL91B2jnmbyyhcyx3l9+ZXCmD3siFjls6pYCG6cBnMplw/l9GlOpiR5QCgKhW5rXRRvg/13nPgVOKFRjZsGfAPZ33qkNRFKa9Pg58AQ88Pg8GnRF//neb245vj7ICJY5vOQsAeOWXp/HBv6/jrqm9GvQ1WQHVaDCh0kHZWHWK7pQg42IWyotUVg7C5uiUYv6eMmvfsJ5IzO5b9o53dbg8KTvZkKyDgL0AK8wqtsr7qiuRka7lYboTmqZdXqt4fB6i2zJOsssHmYubgDC/OgsgFEVBxnbgU7ruKCTYRyh0LdaC0DCwAgp7A66xkMglEMvEoGnapluqvFgFk8EIgUjABaM7S0CIP/hCPowGo1UAuCN0Wj0KsthqihDEtInisv6chbuJWUvYOeuIFUqEkMgl3He8ktxSl16PxfI9FEmEoHgUaBPtdK6W0WBEpYoRyfxD/KAwd8pjxyWWmoWuajeeWFGKX4tTyj/YvlNVHiDHgCnMzf5/Vv2Ly4evOTVmgIhSjYpBb8DSZ76HurwS7fu0wZQXxuHuKX3xc+a3uP/ViW55jX4TesEvWIGCrCKc3lX/UG1Lti7fiUdazcPcLi/h0XbPYeNXfwMApr04Hl8cfg9zPnwIy89/ikHT+uHuKX3x8a63cdek3gCAte/+gU9nf4Nj207XKFk5e/asW8cJMF9+bp2pvyjFtjrNTc+vckrV0sbYHmKpGHM/eQQAsPbd33Fu7yWn9stNz8eaxeu5//dz0npaneg2EYhuGwmhWIj2/RghcOnjK/HLO3/hP6P/i++eW8NlwdA0jVN/V4lSVw5Z1wOvfv03LBz5MW6a7zgb9EaoSioaXagqNLuSwpx0SvEFfLzy6zOIM3cuGzSjr9Mf3JwAUMudXtb54UiUYi/IvdUplZuajzVvMRk4k14cjfhkRogd8yQjnB/beArFOaUozilFnhMdCV2FzZW6djyVc26ExdsOjncVZ4POWVHK31zPryxUcWMJCPdHfnGezf0tYedZ0Z0SpJ5lLoRbdonjxDVnrOi3L9+BVq2DLEBa4wKWPX6mG51SaeduQ1mogkQh5kopbRFp7kiZl1qzHM4ZUSrRnEuVeq5KIKjqvFe/kPPqJA1oh+XXP8F7u14FxaNw+p8LnNDXUOz75SiMeiNado1H0t3tnHLW1ReRVASR+Q6oKx34Us/aPhfV76Ie/O04fnr7jybdLOPsrovQNYLYxpaE2ws6v8PlSdmeF7b+diz/VupKenp6vY9RV/IzilCW77qwxjqcLx9iRCm/eroNqzrwEaeUO9Hpmp+I3dQwGozc93C2jKwxCQhjvtuUFSihLCzH7WvZyLh8G9pKHYrNAk1geIDLonJlZSV37cOGd9eGqlgF0ExWVlAdv1M4G3auMTuipHJm3Wdfr6JMDW2l6yXTnFNKJABFUZyzSWPx2UXTNAw6g83rLnV5JUADIokQIokQEQlhiOsQw4lTYhnrlLItSvH4PM5RZjmnWKeUXy3dT+95lLlW+PfXgzjtQoOvxpVRfRhlUTkWTf4EFw5cAY/Pw/yvH3fLBVZ1RGIhhj1wN/5ath2/f7YZ3Yd3ciosvTZO7jiHz59cDoDJStBrDUjsFIfHP3oI3Yd3BgB07NvWap+uQ5LRdUgy/vxiK75ZkIJ/Uv7FPyn/omO/tnhtzXOc4GMPo8GIzCtZiGoVCYmLWTX5mYUoL6kAX8BHfFILl/a1JNYcQHrt+E1O+a+rUwoAhtx/F07tOId/Uv7Fx7OWYfWNpdyiZwudVo937/vMLZ1iKIrC2xsXQKfRQVepxyt3vwuT0QT/UAWUhSoc/vMkTmw9i4jEMIilIi5cGqhy9LTv1xoxbSNxYP1x3L6SjXcnfgFZgJS7kG/dMxFPfz3TYcCxOykw59HYEqWkUpPNxToiIRQLNz6P1HOZDi+6qxPrhCilVlZy2UNOiVJe6JTSVuqwdO5KaFRatO3TCmOfGsY9l9glDu36tMK1Y7ew96fDCE9kfg8BTwuhi+H/jkga0BY7vt+Hw3+cQPp55kKM7fhWX9hucAY7mVJs0LY8gHVKmUWpAiWM5rtlrEuoNmT+UoS2CEbh7WLotXqIpCJEtY7gnEBsuZoj2C54bXok1vjMYIXS4uxSqEoquKDr+nBy61kAQOchHR0KtpGsU8pG+R7bMczWhbVUynypie/MrKNZV7Khq9RBJBVxzjF3OqVYBCIBottE4q4pvXDwt+PYsmwn5v8wx+2vAzBus32/HAEADJs1wKV97a1bzuIXLEfRHR1UxSqnu1WmnbUtdmjVOlw6cA0FmUUYOKMvVixguvv2HNMF7fu65nCmaRpGgwkCD1woWbL3JyabSSTQN+hFG1tepqvUwWgw1gi350LO7YiVtv520s5mouuwJDePtG5UqjQ4tf08QqKDkNg1jsvQcgTrdBLwdFZdOWuDzc5j3aYBdey8x8Ku7exaTyA0F9jSPb6Q75FySv9gBQqziqDX6pGbXvXdIPNKFmgTDb6QX+cmBX7BCpTml6HC3KSgtt+v3JwP6hdcd2clG3Zuz1nPwt58YG9GiCQiKILkUJVUoCi7BNEudmznnFLmdVIsE0NToWVuFJmNJyV5pQDNnJfq19Ps2sa6QvkCvtVnENt9sLrzqkqUoqq2pwGTiQZF0dwNKfZmrT06D+qIuA4xyLxyx6XrV+KUagRomsanc77BhQNXIPOXYtGfL9fLvVMb454cAaFIgNM7z+PbBavr3K2AhaZpzqlzz6ND8EfhKmzX/oJvTn3MCVKOmDx/LN7d/BrueXQIZP5SXD5yHc/2fZ2zVcZEx+Dm2TQU3ikCTdOoKKvA5m/+waPt52Nul5cwI/YJ/LTkd5e+qN84zVzIxSfFutwpz5IOfdtAHiDjBKnAMH/416IQ18YzSx9DYJg/8jMLcWTzKYfbLn/pR1w7cQsKJzrLOYPUT4KAMH+ExYXgiS8fxsQF9+DjgwuxaNtLaN+vNfRaA7Ku5nAuAjZslSW6TQQeeW8a/nd8EQZM6828X6VVX+xunkzDf+75xCqXpDZomoZaWVmnC7FCNui8WntSmYzGzZu5OHHiCmSymscVSUVo37c1eC7UlrNfjAsyiuyWZLDB634hCofh6WweT3lxhde1pv7tg83IupqDgDA/PP3VzBrnaPijdwMA/l17COf3XAaf0uKTp1Nw82auzXNdFzoP7YjWPROhVeuQdu42hGIhJi4Y5ZZjCxyU71nOZ9YpxQqeeemF3LyOah2BkBDn3HmsmwkA4pNjwBfwuXwEZzrwXTcHvrfpVfMzgxW9APeU8NE0zZWc9R7X1eG2rDCXW80pp9Pouays6hfW7N/lzZu5aNE6EP6hChgNJs7pVeWUcr8oxTL2aUZkPbvrksuhqc5y40QaCm8XQyIXcyWvzlDbuuUMVWHnrjil7ItSH8/4GqteXYd9Px/hHncl04Nl9Ru/4cn2ryDl9fU2w/Ebg4LMIlzcfw18Souj+664dc2qjkRetf7b+ry4Y86UshVyDjCfIewFESuepbnBKeXsulUb3y9YixUL1uLD+5bhjaEfOBUafukgc+6/eOEnl8599bLluoacs0j9zS42F0pcCbUjEBCfg6fxVOkeC4/PQ2TLcCiC5JDIxQgMDwBfwOc+MyITwuvUfVYkEkEiF4PiUTAZTbVe1+q1emjN6259rp/Ym62OyuZoEw2NuXxPYvG9P9jc2VVVUmHVBa82TCYT9z6ycROWuVLaSi1TFmhePsuLVVbZVcx1tPXN1epUlSVWOa1omuZif3h8HigexYl5JoORc0kBsJmDaAmPx8MDb0zh/t/ZJkVElGoE9vx8EIc3noBAyMen/y5Gv/HuD0+3JL5jC7ycMg8A8Ney7ZgS+hjee+BznNlzwekL/wqlGjtW78XnT3yH9x/8ApePXIdIIsSj782ASCx0WXXuM7YHXvzhaSw/9ylado5HWWE5PnpkKTKuZOGj+7/CU91fwYwWT2K0eAYmhzyKL5/5HjmpeeDxeVCVVmD1f9bhyOaTTr/eyX/OAajp3nIVoUiIvuN6cP/foo6le5ZI5RIuP2zLt/9wj1eUWV9E7F13iCuRfGHFU/V+3er0ndAdk14cDalCgsTOLfDa+nlYsuMVvPzzU3h2+WN46quZWJAy12of9gJdESTH4/97EB8feAvv7X4NS8+9h48PvIW4pBiUF1fg22fX1Gp3BYBt3+7B3DYv46mOr2HxuE9RlO18+2s2Fwqw70pypxvRP0SBUHPHsKtHbLc3Lcgwh5w7cEkBzN0UNijQm9xSt69kY/fqgwCAxz9/CEGRNS3P3Ud1RlBkAJSFKhzbdAZAVYi+u+DxeJj5/jSu88k9TwxBiIPQbFdgL/RszU+NSsvdKWK/yES1ZrOT8pBlbkse3Src6bnF5koBjNMMAHdeHV2cV5ZrkPL6epzewVif2/S07epjRS93hJ2nns1EYVYxxDKR3ZBzlsiWzHkpulNidUHKuqTEMhEn7NmCoiguCJ4VRUrN3cgCGsApxRLdJhKteyTAZDTh0O8nGuQ12OP2Gte1Tk0p6rNusW65cidFKZPRhLTztsUOSzFl2zdVuYb2Olc64uS2c9BrDfh3zSG8c+9nVk7cxmLfL0dA0zSS7m7rtIusrgjFAm6tqS5KadVa7m8/qrVtUYrH43EXNXdNZr4zpp7LrHeJvDs+E8/uuoiT28+Dx+dBohCj6E4Jjvzl+AabyWTiYgA6uvh50apbvNXFlaOQXWfgSiuJKEVoZlQXMzyBIkCO6FaRiOsQi/C4UES1ioBALEBIdJBdkaQ2KIoRSFix315ZNAv7+Sf1kzqsRqkNTpRycD2jrdSCNtHg8XkQSapMEBKZmCtnzM8oYELanVi/2fwqilflVpJYlNux8QiKIDkn0LOmDoBxsRr1RlA8yu7NcYGFC4qN1KFpmhO6eHweKIqqCjs3mLjteHyeU58jg+/rj+jWjEOsXW/nnNVElGpgrhy7gS+fXgEAeGjhtAZ1SFky5P67sGD5kwiLDYFGrcXeXw/hleHv4MnuL2PPzwcctiI/s+cCHk9+AZ88+hW2rtiFvb8eAgCMnTsCwZH1K8uKiA/Dwt9ehEQuxrm9lzAnaQGuHrkJoUgAHp8Ho8EIk4lGfMdYPPW/WdhQnIKJz44GAKx66xcYjUac2nkO8+96E798sAF6XU312mQy4cgm5qKg/8Te9RovAPSfUBVOG9e+7qV7lox9YgQoisLpXRdweNMJLJ76X0wMmoX5d72Jv1f9i32/HcGns78BANz/2iT0uqebW17XERRFIa5jDJIHtkfPMV3Qd0J3xLaPsrLZVy+Ti0gMQ2y7KPiHKBCRGIYFKXMhD5Qh/fxtrHptncPuTbmp+fj9oy1cS9K0c7exeNxnTrus2A5fEoXY7sVvQYF72793Hc6UTliGwFuSz2Zc1SJKAVV3DrxBlMq8fAdbv9qF755bA5PRhJ5juqDTINtB3gIhHyPnDOb+P7RFcI072e4grmMMZr4/HQOm9ca4Z4a77biOgs7Zu0tCsYDL5ok2XzTm3MzH7cuMGykuKdbpuWXplErszIhSEQnmPKY0+8fY+/Nh/LvmEIx6I7qOSLbplAKAFmYHX6YbnFInzC6priOSaxVT/EIUkAVImZbLFgJDkUXpXm03L1iRjs00Yp1Ldc1/cJa77+sLADiw7qjbc/B0lToc38KItXdNqVuweX3WLbYtt8rJTKnsm3nQqnUQy0Q1yj8tQ1ot16nqWVO1oavUobyIyQHxC1GgJLcMH923zCU3V30x6AxcSeWQB+9qlNfkLqCqiR+55r97eaAMfg7KIEY/OQSdBrXH1NeYoP7yIhUKs4rtbu8M9f1M1Gn0WPPWHwCAex4fjAnP3wPA3GnSwd/S7cvZUJVUQCIXc2K0s/D4PCQPqmrUUtfyHxaZH/u+eJdLualjMLguVvsyYrEYYrEYS5Yscdsx9ZxTyrNl0pbI/KRITI5DSHTdbyxqtdblcWyIty0sg9b96hlpwH1f1Ortrm+sS0mqkNT4zhMSHQSegAedRo+c1Dzkplk7y00mE4pzS62cWJZuN/Z4IqkIoBgBSWUuSwyJDkZIFPM9q7Jcg7ICJYxGE/LSmTXeL1hhVzyyFLzYWArLrursftw2RiO3nbMVJnwBH88um4O4DjFcxlRtEFGqAdm5Zh9eG7UE6vJKdBmchPtendCorz9mzjCszfgGy459gHFPjIBELkbquQx88NCXeKzDfOSkVV3856Tm4dqJm/j2hRS8MvwdFGQVITIhDNNfnoAH35yCaS+Ox8zF090yrtg2UXjx+6cgD5BBKBYiNikK3537LzaXr8Evt7/FL1nfYcWFzzB5/ljI/KR4ZNF0KALlSL94G6+OWII3xryPy0euY+WbP+Op7q/gwoErVse/euwGinNLIfOXouuQ+ucv9BzVlVPL6xpyXp3IhHD0GdsdAPCfiR/j4J/HAACXj1zHp7O/xrv3fQaNWoveY7ph1jv3ueU16wKPx+PcQQAQWovYEhwViMc+uR8AcHD9cbw57EPsX3e0hiuFpmn8vHgDjHojOg3ugI8PvIXY9lEoy1fi66dXO3UnvsAi5Lz6B4FGAzz8cDBeey0ZGjfeDO0xmilXPbPzok1hl+u854RVNZwVJjwYdq4sUuHdSZ9j4ciPsf6Dzbh9JRsiiRD3L3S8Vt0zdzD+s+UFvLf7NSz6+w3MnhuJhx8Oduu5BoAhD/XH4/970Km8Emdha/RtOaWq1+EDVY6g4pxS7oKQzXJyBmunFJNvx5W+2chjYmFLdcY/OwILVj1u1/LeoqN7OvAxpXuMmNLbiZIziqIQmVjz9+BCzm3kyrF/l+xcYS9Ob53OAE3TnCjVkE4pAOgzvhtEUhFybuXbFZjrysUD11BZrkFITBDa9W3l0r7uWLdYkUNZ5FwYLCsIJnaOw0trn0JYfAjunT+SGY+du9GuduVjxXqpnwTv7nwVYXEhKLpTghNbz7l0nPpw6p8LUBaqEBjhjw6DOlnNw4ZCahY/qp9HtuTVnkuKZejDA/DS2qcQEOaPGHPYd9bVHIf7NDR71hxEYVYxgqMCMfGFezDo/r4QSUXIupqDq4ftd2I99Adzo7BVr3Z49LEwl8+9pXPTP6x+64PUXHpCnFK+h8FgwIYNG/DUU0+he/fuiI2NhVwuR1hYGDp06IBp06bh008/RVpamqeHWidYl42nyvfs4a58K3ZNrVRpoC6vtFnGV6nSQFepA8WjHIr+ziAQCQAKAG0/7JwViWx19hMIBYhrH8PcaKOYUjvLoHZlkQqFWUXIz6y6DmCvfyzdbjwez6okPCgiAGKpCEKxkHPU5mcWIvNyFvRaPQQiAcJiHV+vsWXhhmqiFOuSAgC+2SllsnRK8Zx/L3uO7IIfLn2O5Ltq71YNEFGqQVk27weolZXoNLADlmx+rV4WwrpCURTa9WqN+d/MxdqMbzDrnfsRGOaP7Ft5+O9jX2PPLwfxeOcX8EjreZjX53X88flWAIwravn5T/H4Rw9h1pL7MfeTRyAPqH+ILsvg++7CXyWrsa3yZ3x9/CO0aBcDkUSE0JgQhEYHWy1gfkEKzHh9EgDg3N5LMBlN6DO2OwLD/JFxOQsvDHobi6Z8gmsnmC9Eh/5ivvz0GdsdQlH9w5dlflIMe/BuCIR8pzK0nOWF75/CyFmDIZGJEd0qAh/8/RZmv/8AF1g3du4ILN7wSp3qr91JqIU7KqxF7Xc6eo7ughd+fALBUYEoyCzCDy/+gjeHf4SjG0/jxNazOLrxNL6c8wPO7b4MvoCHBxdPQkRiGF766SkoguTIvHQHv3+8pdbXYfOkbLmSTCYKe/ZIcPRoCEwm94U9tuvTCvJAGcqLK3DjRM0vLc6W7wFAeDyzjac68JUXq/DRfctw40QaBCI+uo1MxuSXxmDRtpdq7WbI4/HQsms8YttFgS8UYc8eCfbskbj1XDcU7Ae9raBztbkbkzyg6ssFY5Gu+mITEhMEeaAM8fHO3e0PjQ3G3ff1Qf8pvRBh7ljHCl1lBeV2M8UyLjLleO36OBY2WCfWnWs5Dh2wtZF6JgNFd0qY0r0hHZzaJ9KGuFaUxYhSwTaCmtm/S3autOwaB4GIj/yMQqx563fO3ePu7nvVkfpJMGrOIADAT2//YZXHUF8u7mPaH3cdnuRyqZQ71i02mL+80LkuZxkXsgAACZ1jkdi5Bf576G3cPb2Pw31cdUqxLqvwhFAEhvtzDjJ7ZdCuYDKZ8M/3e7kyV4BpOHHq7/OcCxcA9vzIlCUPur8feHxBo6xZrJjO5o2w5LCilJMNE4Cqkl+lk++rPZxdt2xRqdJgy7JdAIAJC+6BWMa4lO+ayryf+349YnO/68dvYcf3+wAAA2f0r9O5t3TuiqT1+17Hui3URJRyKyKR66XKjcnmzZvRuXNn3H///Vi5ciUuXbqEgoICGAwGKJVKpKamYtOmTXjjjTfQvn17TJw4EZcuOdcl293s27ePc1Lt27fP6f08nSnVUMhkzI1CVpgxaA3IupaN9Iu3kZOaZ+XyKStgbm75BStqXD8tWrSIKwV0BoqiHJbw6XUGTtz2C7Z9jSySiBDWIpSrNMrLLOTGy36WqssruRBxg7kCqPp7GN0yAtGtI5HQKQ5hLapufAdHBXJlgnqtHhSPuWFY27WjgBOlmN/LUpRiYRulGQ1GTrxyJYvXVZrXrPUygqMCcf8Lk3HvM6MglrrvTn9d8Q/2w4NvTcHQBwZgbucXcX7fZZzfdxkAMzlDooOhCJJj1jv3W+UoNTRpaWno2NFxfsnUF8ejVdcEZF65g7AWIbhrYm+Ul6jww2trse373Ti04TiObDyBV1Y/i91r9wMA7ppQ/9I9lvnfPI4nP3WvMBcUHoCXVz6D+d/MBZ/PA1/AR8+RXTD9lQkoyStDSFTjdLCrDVY8kfpJHGbEWNJlaEd8sPcN7FlzCNu/3Y3c1Hx888xqq234Qj4eemcyoloxIlxQZABmfzoDXzz2PXanHMSkF0Y7dMgUZDKuleoh5w0JX8BHtxHJOPjbcZz6+zza97Ouk863cG/VBlvC5UoovDv5dclGJtA83B+v/zaPex+aO46CzlmBqHqIY1SrCCgLmbtbLcwiUH5+PmJjay/npSgKcz59wOoxqZ8EgRH+KM1TIudWPlp1s75Q1FRoudK++GTHrxEWHwKJnOnMkn0jzyUXlyXHzV33uo1I5koXa6PK8VVVEsSW71k6LO2hCJLjgUWT8eMbv3FZZnFJMVAEuaexgyPufW4kjm46jYKMIvz53214cNFktxz34j7GuZs80Lk7g+6mqiW3c+IFW/YZ17FqnrHtou3hslPKLLyzJcvt+7UG/gdcPXrTqS5Kjti8dCf+/GQb+EI+3tv1KgLDA/D+lC9x+0o2WvdIwPyVj0NVrMLVIzdB8SgMerBfnV/LVdgOfDXK91hRqqXzopRfiGsOOHs4u27ZYvu3e1BepEJEYhgGTKv6fjVgai/8u+YQzuy4yHXSZNFUaLF8wVrQNI0B03uj8xDH3/XsERDmj97ju+H68Vu1CvW1wQada4go5VYMBgOEQvd14XUnH3zwARYvXsyVYA0aNAijR49Gp06dEBISArVajdzcXBw4cADbtm1DRkYGtm/fjpiYGHz11VcNMia2JM2dNFdRSqvVQiqVgs/nQSwTW90YKS9Wcc4gg86A8hJmjQwMd8/NLaFIAL1Gz5TYVXNDqcyvJVFIajVBBEcFQllUDoPOgMoKDeT+Mu7GCW2ioVFpIPOXcd9Nq+eCCUQCKGy8rxRFITwuFBK5BDw+BalC6lSXW9Yow4pNRhuiVFWmFBOtA7g3q7fGmBrsyAT8cPlzBAYGenoYNYhqGYHH3n8AXz+/ChRF4YE3J2PKgnHwC6qfzbGulJfX/uWZx+Ohx4gu6DGiC/eYf7AfFix/EpPmj8UPr6/F0S2n8OHDXwJgfsc+bhTWBEIBBAEN8+dSvTsgj8fzGkEKqHJK2SqTc4RELsaYJ4di8AP9sPHzv3Fh31VIFRIIxULIA2WY8Pwoq7wdgLkgDo8PRX5GIc7/exm9x9nP0sq+yXQvYsWdxqLHPZ05UeqBRZOqulMYTVx5V1h87aJUy65snk4mtGot112jMVAWqXB0IxNMO++7R31GkAIsuo64IEpFtgrHtWO3AABx5nK5ysr65ZFEJoajNE+JXBuiVOblO6BpGoERAbV2muLxeGjTMxEX9l3Fhb1X6iRK0TSN45uZ0r1etXTds/odWrLCqnPle7YY9sgAFGUVY+vXu9FnQnc88u7UBv3SwyKSijDzvWn470PfYnfKAQx56C4uP6yu5GcUIi+9EHwBDx36t3HTSF2D7VxYWlB7Z0GappFlFqXYMlAAta5F9rqP2oN1SrHh4q26J0AoFjDzPzW/zuvPxf1XseG/2wEwuRirXlkHGlWlrDdPpePD6UvRthfTJKDbiGSERAdBrbZ3RPfCOnKqn69s842ISBecUmzX3/J6ilJ1Xbeyb+Ri69eMS2rqq2OtLnpadU9ASEwQiu6U4PzeK+g5uup72q9L/kJBRhFCYoLqLfw+/fVMAPUvBapySpFMKXfCOj28jZSUFCxatAgAEBERgTVr1mDQoEE2t50yZQo+/fRTrF+/Hm+//XYjjrL+GPRGJqiasgiybiawXeEA5oZXeZEKQZGB0FXqkJOah7ICJYIjA1FWqARoRiSSuOk7NeOUqrTZgY8NVHfm+pkpwRNDpTNAW6E1i1JVN3gqlJWQ+csswuqdF3gpinI5a48t36ueKWXllOIypUw2n3c3pHyvAWmML9Z1ZcK8e/D62vlYevR9zHrnfo8JUgAglTpuLVkbCUktsPC3F9GhL3MRIA+QYcnm19y2IPk67fq0AsWjariCnEXmL8WMtyfh/d2vY+HGBXht/Tw8u/yxGoIUwCysPc25TSdryXrJuGguO+nknvB5Z0ke2A4iqQhFd0qQbi59AZhQYKPeCL6Ah+CowFqPE5EYhtAWwTDqjbh6xH4WR0Ow75cjMOiMSOwSx12w+QpCLlOqZqmbPVHKUqxgc+XqW6rAXpDm2MiVYud2fLJzGXZdRyQDYLLO6kLqmQwUZ5dCIhej82DnSveAqjLE3Fv53B1oTpSyUb5nj+lv3Itvr36Ep7+aWSNsuyHpNLgDuo5IhtFgwq/v/FXv413cfxUA0LpHIpd90dhwTqn82m/2FGYVQ62shEDEt5rj1cujgqMDuU6YABw2sLAFm5vH3kAQSYRo1S0BAOq89tE0jXXvbQJN0+g+qhMEIj6uHbuF68duQSwT4amvZiIg3B93ruXi358OAwCGPNQ4AecsEhtd3miaRp7ZWehK+R7nlKpn+V5d1i1dpQ4/vPwrDDojugzriF7VMucoiuJuILHitqqkAr99uJk793M+e6DWNuK14UrZjSNkfiRTqiFwV26QO7l9+zbmz58PAPD398eePXvsClIsfD4fM2bMwIkTJzB69OjGGKZbMGgtyr68762oF5bX03J/GSITwyGWMs05RFIRTEYTSvPLUGZeHwPrmTtnCVe+Vy3ywWg0QWMu/Xf2ewtbfqhRa63Cw4EqkZx9HYG4YX1DVU4pB+V7gqryPVeDzuuC96omhAaFx+Nh6IwBaNerbkKDO+nQwfmLIHuIxEIs/utVTH95Aj7a+TbiOzSuUNGcadUtHl+dfx8PLJrUKK/Xw3yX9dyuS1aZIJaU5pWhNE8JikfVuVyproikInQezJTmnPq7SjjLN+dJhcYGO7VoUxTFlfhc3H/N4bZqZSV2rtyPpY//gL/+9zd0Ll4UWmIymvDvGqaj5vBZd9f5OE0VrnzPQaaULKB6+V7VxSMbXF7XEpjqx7QVdp7JiVLOvUa3EUxDhxsn06xCNJ3l/L9MyVmnwR2cLt0DwGVkVZSpoSqpYDrJZLsuSgGw27q4oZmxcAL4Qj7O7bmMSwcd/x3WxiXz33Gync6VjUFAGHO3VFlYXqtzIfMSk1sW3SbSqtyDx7Nubd1tRCcsv/4JJi5gOq3Vt3wPAHeT4/Khqlwpo8GIi/uv4tclG7Ht2z0oMOcG2uLW6XRkXroDoViI2f+dgamvjoNIKkK/iT3w9qYF6DuhO2Z9UNWcJSw+xKqDW2NgyylVklsGTYUWPD7P6nzUBptrV+5kV0V7uLpuKYtU+GjG17h5Mg0SuRgz359mU3zoPb4rAODMzksozVdi0bhPufype+YORse72tZr3O6ELd8jopR78cZMqS+++AIac6L+4sWL0bq189c8gYGBGDdunM3nSkpKsHr1asyaNQtdunRBcHAwFAoF4uLiMHbsWHz//ffQ6Ryvk7a676Wnp0MsFmPkyJHcYyNHjuS2Zf/9+OOPNY7HBWSL7TtsVCoVPvnkEwwcOBCRkZHw8/NDy5Ytcf/992Pbtm0Oxzt48GBQFIXBgwcDAO7cuYMXXngBrVu3hlQqRUhICEaNGoXt27c7PI7RaERKSgpGjRqFyMhIiEQiBAQEoE2bNhg2bBjef/99XL582WoficT6+8Fff/2FadOmIT4+Hu17tEHPId0waPggfP7VZ1CpVTVEopSUFFAUhcWLF3OPsSK35b/09PQa42VvZFZ3SrEuJ76QX6PUzh4Sc2m8Rq2F3nx9w97w0VZoYdAbuK7yjt5HdyCw45TiW2VKVQWdGwwkU4rgA5w+fRp9+jgOVnWGoPAAPP7RQ24YEaE6zmZJuYOW3eIQFBmAktwyXD50HV2H1eygmG6+aI9qHdGoZW8s3e/pjJPbz+PU3+cx9ZWxAKpKVGrrUGhJ8sB22Lv2MOewsIVaWYn3Jn2BrGtM16WT28/jyIZTWJDyOOdUcYVrR2+h6E4J5AEy9B5vvzyyuSK0KN+rnmdjzynVokMMKIqCPFDGlWampqaiTZu6l2hxIeG3bDilzGJBfJJzF5AhMcGI6xiDzMt3cG7PZQyY6lqe3oV9zPzrNNg1MUUsFXFlOzm38hGREAq91gCKohDkhFvQG4hsGY6hD92Fnav2489PtqHjXW3rfMc/2/xesqW5noAt9zQZTVCVqOEfYt8FnXmZmWe2XKtimYi7KaAIkkEkEUJszvjTulC+Z9AbubLm8IQqEabT4Pb4639/49Tf55F9Mw+qkgqsfm09t84BwLp3NyJ5UHtMe20cEjq1sDrurpQDAIC+E7tDESTH6CeGYvQT1m2nu4/qhD4TuuPYxtMY+ejARnevs5lSGosgfVaEDmsR7FLuC9dVsZ5OKVfWrbO7L2HlS7+grKAcsgAp5n8/ByExtrPiErvEIaZdJO5cy8V/H/wGBRlFCAj3x8PvTEGPMe5rEOMOpDYcbIT6o9VqIRZ7T4UCTdP4+eefAQB+fn6YOXOm247dp08fZGRk1Hg8Ly8PeXl52LVrF1asWIGNGzciMjLSba/rCFY0Edpx2Jw9exaTJk1CdrZ1p947d+5gw4YN2LBhAyZMmICff/6ZCxa3x6FDhzBx4kQUFlY16tFoNNixYwd27NiBTz75BC+99FKN/VQqFcaMGYMDBw5Yj12vh1KpxM2bN7Fnzx6cPn0av//+O/e8Wq2GQqFASUkJpk6dij179ljtr9VqcenKRVy6chG//PkLNm/ehL59+zr8HZzFXtC5rpI53yKJ82Ise71i0Bq4BisSuYQRozR6qEoqAJoRqpzJhaoPjrrvcdvYckq50H3PVYhTikAgeBU8Ho8LQ71uzvGpDtcxqtqFSmPRdXgS+AIesq/ncnfzs28yOSGu5NJ0vKstKB6F7Bt53AWiJSajCV8/lYKsazkICPPDvfNHIjDCH7mp+fjovq8cOgnscWIb04a9xz2drNwQvgJ7IUjTNIwGaydJlShl/YUsODoQC1bPxYtrnnDbhW2k2WWUm1pg1TnGaDDiznXmwpx1ZTlDN3MJ37ndl2vZ0hpVSQVSzzJfrjsNct21atmB7851JuctJDaowb9QuZNx84ZDKBbi5ql0nN/j2vljoWkaxXecD3lvKARCPtcFqCzfca7U7cs186RYLMV+9q6zRM58+XbFKVV0pwQmowlCsZDLuwKYHKIuwzrCqDfio/u+4oR3eYAMA6b3Rof+bUBRFC7uu4pPH/7Wyh2qLFLhhDmYf9gjAxy+/tzPH8LCjc9jxGzHJTsNAduow7K7Y85N1/OkgCqx0dlMKV2lDgWZrn8+AExHqFWvrsP/Zi5HWUE5ottE4K0Nzzss4acoCmOeHAagKtNr/LMj0GtcV6+LspD6k/I9X+DSpUsoKmL+Bu666y7I5e4rDTcajejduzcWLVqEDRs24PDhw9i7dy9SUlI4l9PZs2fx8MMPu3TcmJgYnD59GsuXL+ceW758OU6fPm317957762xryOn1J07d3DPPfcgOzsbFEVh5syZ2LJlC44cOYKVK1eic2dGON64cSMeffRRh2PMycnBxIkTwePx8OGHH+LgwYM4fvw4PvvsMy5H+fXXX7fZuXDRokWcIDVu3Dj88ssvOHToEE6dOoXt27fj/fffR//+/W3eGNJqtRg+fDj27NkDPp+Phx9+GL/88guOHj2KAwcO4O23/oPgoGAUFhZgzJgxVqLhxIkTceHCBTz11FPcYxcuXKjxLyam5ncuVuQz6o1W2VasU8qVbqB8AZ97f1hXu0gihNjsUGczqkQSYYOXw1bvvuc46NzEdXcmTilCs8bWIkDwbVp2i8e+X44g7VymzefTL9wGYD9PSiajcedONoqKiiCTOe9cchZ5gAzRbSJx+0o27lzLQViLENy5xlyQx7R1/q6YPFCGDv3b4PLB63h34ud46quZnLgAMALShX1XIZKKsGD1E0js3AIjHh2I96cuRc7NPHw+awUW//2y0wKAyWTCqb8ZUarnmC61bO0c7LluKljeRTToDFbnzp5TCmA6SloSHFw/4SEsLgRSPwkqyzX45Z2/uND8/IxCGHRGiKQilzpLdujfBhu/+IcTmJzl0sHroE00ottGIjg60MXfgglsv7T/GnJv5XMd31p2td123lvnSmBEAIbNGoC/v/sXu1IOoIsNd2ZtqMsquTItZzLlbOGudSsgzB/lxRUoK1A6LG/OvGLfKSWy6MDHilJsF2GN2nmnFOsgDYsPsRInKIrCQ+9MweWDN1CaVwYAGDSjH6a/MZ57vfyMQnx031cozCrGoT9OcJlQRzechEFnRELnFkjs4tiVJhDy0bpHotVjjTUP2VwxjarqfLHCbUzbKJeOxZbvKYtUVg7PynINirJLENuOOR5N09i5cj82L90BZaEKT389E33u7c4dp7Z1i6ZpLJ//E5fpOOrxwZj6ylinynr7TuiO3z/agpLcMvgFyzHw/ppOBW9YAzinlEpT7+6PhCrY9vHewoULF7ifu3Vzryv877//tuk47NevH2bMmIHVq1dj7ty52L9/P/bs2YOhQ4faOEpNhEIhkpKSrBxICQkJSEqq/TOpSpSqeWn/0ksvoaSEuWnyzTffWAlP3bt3x9SpUzF+/Hjs27cP69evx6xZs+zmaV2/fh3x8fE4dOiQ1fVbr1690KtXLwwcOBAGgwHLly/HF198YbXv+vXrAQBTp07Fb7/9VuPY99xzD15//XUUFxdbPS4SibB48WKcPn0agYGB2LVrF3r0sG5mNWDAADw251H069cPOTk5eOONN7B27VoATClmYGAgwsOrbgYkJyfDGfh8PvhCPox6I3QaPaRyZp6zopTYBacUwNys0Gv13PcFkUQIo4EPoAKVKuY7aEOX7gFVmVK0ibYbZM46pfQ6PROij4bNy/au2xcEn8Qb69AJnoUtf0k7f9tmLkqGk5k7AkHD6e5RZkcU23mMdbe4IkoBwBNfPowO/dtAq9Yh5bV1Vq4ZNvvpnrmDkdiZcYX5h/rh1V+fgV+wHFnXcrhtnOHW6QyU5ikh9ZOg44DGzVfxFixLZqp34LOXKWXzOPWcW3wBHzPfnwYA2PHDPhz87TiAqovW6DYRLn34x5lD0QtvF6Oi1Pn2Yhf3mvOk6piDVJWNVYDUM4wgVr2bYFOgj7mUNfVsJhfa7gpF5iwtvxCFS7lctqjv3AoIZ1w1jsLOS3LLUJBRBIqibJfvSW2IUjLXnVKsW8uWUBceH4rZn85At5HJePPP5/DYJ/dbZYGEx4di1BzG4bT9u3+5tfGA+W9l4H31L/tvSLhQW4tyR+7mRTvXPifY8j2j3oizuy5h5cu/4tclG/Fiv8V4c9iH2Pr1bgDAkQ2nsPY/f0JZyNyFX/ufPzmxHah9bh3fchYnt58HX8DDyz8/hQf+M8np+SwQCbjcsXufG2k1h7wJmTlTymQ0uZyP1ljQNI0KXUWT+qfWq+u0X13WW2ewFHZCQ+3nt5lMJly6dMnuP72+ZrZpbSWwM2fORJcuzI2/TZs21fE3cB6apqu6tlUTpbKzs7Fx40YATD6VLSeUWCzGd999x60Py5Ytc/h6S5cutWkoGDBgABfHUr1EDwByc5n17+67HeeZVhfPKyoq8NVXXwEAlixZUkOQYomPj8fChQsBAL/99hsqKuqXwcfCluhZOnaryvdcE5BYB63lsbm1krZ+vYaEx+dxeVZGvcGOKMWzGhdFUVZNT9wNcUoRPE5aWpqVek0gxLSNhEgiRGW5BrmpBVYlceXFKq7DV22ZO/n5+QgICGiQMUaZ85xybuWjslyD4uxSAExosCsEhvvjxTVPYn6PhSjNU+LSgWvoNLgDsm/m4crhG6B4FAY/0M9qn6DIAEx5ZSxSXluPPz/dhr4Tu3MXLfY4veMCfvtwCwCg67Aku7kDzR2+gA+KR4E20TXCzh05parjjrnVb1JPZF3LwZZlu3By2zncPb2PhSjl2jySBzB5VwUZRci4lOVUsDBN01V5UnUUpSJbMmWI2TdyoTaXw7RsgqJUbPto8AU8qEoqUHSnxOUSvLp0HbRHfecW14GvwH753gWzGJnYJc5m5yCxlVOKKWdlM6V0LlzIK83lZvayrfpN7IF+E21fYADAwBn98Nf//kFeWgHO7r6EkJggZF66A4GIb+UA8kbYTCnLMrGqmxeuOaVEEiEkCjE0Ki2Wz//JSmgCgPXvbwJoGrtWMxeCox4fjLO7LiEvrQAbPtuOBxdNBuB4blWUqrHmTca9MP7ZkVwjDlcY/GB/9BzTpVG7aLqKSCoCj8+DyWhCZbmmxkWiN6DWqxH8qefKgBuT4heLIRe5f76oVFWlro5K95RKJbp3t7+WXLt2DQkJCXafp2kaeXl5UCqVVgJWdHQ0zp07Z+XYaigM5u8yPD6Pc7ew7N+/nys7c1Sal5CQgGHDhuGff/7B3r17YTQabbrfAgMDMXbsWLvH6dGjB44ePYrU1NQaz0VFRSEzMxPr1q3DnDlzas2uYtm9ezfKyhhH7dSpUx1uO3DgQABMTtWpU6e4/68PYqkQleWVXM6iyWTiMrxcvQnlH+oHnUYPZVE5KB4PErmYy3ViaaxoDYFIAL1GD4PeyAlulqHt1Uv1xDIxNKjZJMhdEKcUgUDwOvgCPuLNpXnVy5HSzXlSkS3D7bZd12iAuXOD8J//dISmgWIjLIOqs28wQkJAuH+dvowLxQL0ncBcmLGOmb1rmVbaXYYl2QyXHTSjH+I6xkBdVok//+u4a8rVozfxxWPfI/t6LiRyMUbNHeLyGO3Bnuu5c4Ma7Fy7G8uwc0tcEaXcBZufxmaKZbPlPW2czyZjSUhm3HTs30ht3Lmei5LcMgjFQrTr08rl1wOA+E4twBfykXMrH2X5SvAFPCTYcTB681wRSYSIMZdApZ+/7fL+nCgVXXdRyl3rliNR6ufFG/D10yk4vYO5ULIXbu/IKaVxIeiczUDycxC47giJXIxBM5gysAPrjmH/L0cBMB0B6yp8NNY85Mr3KpgXURaWo7y4AhRFIboOf9/sOWTXqf6Te2LWh/dhzFNMltP6DzajOLsUwdGBmPrKWDy4mBGi9v96lMsDccTWr3ehvLgC0W0jMf7ZES6Pj8XR++INawBFURZh55W1bE1oqigUVWuOuxwzlmzbtg0TJ05EWFgY4uPj0alTJ3Tv3p37x3ahs3RsNRSOSvcss5169erl8Djs82q12qaoBDAuMUcubtblVF5e06nLhs0fPnwYiYmJmDdvHjZs2ICCggKH4zpz5gz3c1RUlM3Oeew/y7I81plVX6o7pdjOebZEwNrg8/mIiA9Dqy4JaNkpDnwBHyKxdYaUsLFEKXN8ha5Sxwlulp2QeTyelTOKzZVssPE06NEJBCfo1KmTp4dA8EJado3HjRNpSDubadVNLMOcJxVvJ08KAEwmClu3SgFIYTLlgPOeupEoy4DnG67nSVVnwLRe2L36AE79fQEVpWoc3XgaAGq4pFh4fB4eXDwZH0xbin9/OowhD91lswzHaDDip4V/AAB6jeuKWR9Md+td7KpzDXz+eSka4ly7G4FYAJ1GX6ObCnuxJ3eifC8uzj0d1tjMn+LsUqhKKpB9gwlCZgUSV4jvFIsTW89yfyO1cdHskmrft1WdS878QxS4e3ofTkSN7RBt91jePlcSOrVA5qU7SL9w2+XMNXc4pdy1bnHlewXWFwU6jR7/rNhr9RgrilbHVqYU6yjRVjrvlGLDXOsqSgHAgOl9sO3bPTi3+xJ353bwg7bXRWdorHnIni82U4ot3QuNC65TaZt/iB8KMpjgZnmgDHO/eAgURYGmaYTEBOHP/25DRaka9705ASKpCJ0Gt4c8UIaKUjXSzmWidY9Eu+tWcXYpdvywHwBw35v3utQZ0BW8ZQ2Q+ktQUab22rBzmVCG4heLa9/Qi6hrPpdM2DDdnUNCqnL5HAlDgYGB0GqthfY5c+ZgzZo1NrenaRpPPfUUVq1a5dQ4KisbXvhkXTsCG6KUZT5TbVUpERFVYnn1XCeW2txNrGBlK3pj4cKFuHPnDlatWoX8/Hx89dVXXFleUlISpkyZgqefftpqHAC4PCxXUaudjzJwBPudRqfRg6bpqjwoqajOmXSWLiSKR0EkEXKfraJGyJQCmPFXlmtQYs51FEqENUQ2voDP3cCVyiUodb++y0FEKYLHuX37Ntq18818G4J92LDk1LPWYeesC8SeG6OxiDSX75UVlOPmyXQAQIyLJVeWJHaJQ3TbSGRfz8XqN9ajLF8Jmb/UYVlV+36t0WtcV5zYchZr//MnXls/r8YH5L8/HcbtK9mQB8gw8/1pXl1W0VjYckqZTCbuAsUZp1RhYaFbmjTI/KUIbRGMwtvFyLiUhZxb5i6OdZhLbDlrxqWanRxtcd5cwpVsxy3jLOOeGY79vx6FyWhqknlSLAmdW2D/r0fr5pTKdl/5Xn2pckpZi1KqEutvk/JAGZffVx3WFcUX8DjHDyukaF0IOlcWMmOorbzYETFtI9GyazxSz2bAaDChXZ9WSLrb+78zWAZqA0BWHUv3WCyFvfjkWG6tpygKw2fdjf6Te6I4p5QLPefxeOjQvw1ObjuHSwevo3WPRLvr1pavdkKv1aNdn1Y1mjo0R9j3Ru2lohRFUQ1S0taQ6PV6CIXe09GX7SgHMJ3w3EVKSgonSHXp0gXPPvssevXqhZiYGMhkMq7k7bHHHuOCthsaR533LPF0qL9QKMQPP/yAF198Eb/88gv27NmDkydPQqfTcRlen332GX766SdMmDCB28+yLPL06dNOz7PYWPdcJ7DldHqtHrev3rEKKXcXIqkI2kod+AK+y+6ruqIIlKMsX8mJmrZKmY0WpYX1zcusDVK+R/A4paWlnh4CwQthuyplXs6yqrdOv2juvGcO/vYUUj8J1+L85Hamo52r4bWWUBTFtTc/tomxKncdnlTrHev735oAoViIq0du4uTWc1bP5aUVMFkjACa/PKZeF4bNCfacWmZKVZZruMBVqV/topS77sABVd3PTv99AXqtAUKxEGEudN5jiTeHnefeyq+1xEpbqcP1Y7cAAJ0GdXD5tSwJiwvB0IeZzmjdR3WuZWvvJaGTufzxYpbL4bvuKN9zF1VOKevyveqiVKdB7e22d2Y77ckDZdyFDBd0XqFz+vyUFzGvaS9Tylnuvq/KLTv9jfEev7hyBonCuvteVee9un1OWJ5DW3mKMn8pJ0ixdBzAZMtdPngdgO11S1upw+E/TwIAJiwY1STObX2Rmm88kPI992HLGeNJkpKSOLfUoUOH3PaZvXLlSgBAq1atsG/fPjz88MNo3749/Pz8rDKY7DmNGgJWVLBVvmcZGp6Xl+fwOJbP17fDsCM6duyIJUuW4NChQygrK8POnTvx6KOPgs/nQ6VSYcaMGcjJyeG2DwwM5H4OCwtDcnKyU/8s96sPfAEfPHPot6ZCC1CMgBMY7r7MWlbwaazSPYAR5y2/A0jlNSNRLD/rG7LzHkBEKYIXIBZ7X8gkwfOExQVD6ieBQWfkMpsqStVc+UJtIeeNAeuWYrud1ad8DwAGTOtt5dJxpoQoNDYYY59mMkV+fXcjV/NuNBix/PmfoFXr0L5fawx95K56ja05IbDhlGJL94RioVMh8O68I8yKUkf+OgUAiGodblcscERAmD9CYoJA0zS+mfcj59CwxdUjN6HXGhAcFVinfJvqPPjOZHx+8p06B6Z7Ay06MGHn5UUqFOeUurRvsVmUCvYmp1S+baeUf6gCE1+4B9PfGG/3GKwAZemsZIPOaZrmMjVqwx3lewDTFKDjgLYY8+RQtO6RWK9jNRZSc9C5pkILk8lUf1EqtOocxiU559JMMotSN0+lQVupg1AoBE3TuHLoBnJTmc6xp7afR2W5BqEtgtGhv+OuYs0FmR+bKeWdTqmmiLeJmRRFYcaMGQCYMHN75XiucvnyZQDAuHHjIJXavoFF03S93FmunktHTqmkpCTu5xMnTjg8zsmTjDgtk8nQsmVLl8ZQVyQSCYYPH46VK1fik08+AcCUPG7ZsoXbpmvXrtzPhw4533G6OnWdoxRFQSCs+l4YEReGuA6xkMjcd/3qFySHUCJEQIif245ZGzweD/KAqnJMiQ1RSmz+HRvjpjYRpQgex9JiSyCw8Hg87mI901yOlHGJKd0LiwuBPLBhcghcISyuKrMgrmMMWnVPqNfxmFBfJitFLBM5fYE/5ulhCI4ORGFWMbZ/9y8A4I+Pt+LmqXRI/SSY+/mDDX6Hoylhq3yPCzl3Ik8KcF+mFAC06MjkSrHiZnW3gys89M4UCMVCnN15ET+++bvd7c6Yg667DE9yy8UEj8dDUGTDdLpsLEQSIaLMnT5vX3auBBIADHojl8ngDeV7geGMKKUqqbByA7KiVGSrCEx6YbTNBgosNkUpC+u+tlKHktwyfDh9GU5sOWv3OFzQeXD9SpGkCgle/fUZ3PfWhNo39hIsSyG0FTrkpzNhvpbdZF2hevmeM0QkhiE4KhAGnRE3TqQiJjoGa976Ax/etwyLxn6KwqxiHFjHhMcPmNbbZz4nqoLOiSjlLkSihm9j7yrz58+HRMK81wsXLkRaWlq9j2kwMGuqo/D0TZs2WTl9XIUdM4AaeVfVMRlNMBkZl5rQhrN+4MCBnIMrJSXF7nEyMzOxe/duAMDgwYNtdt5raIYNG8b9bJkDNmbMGC7L6ssvv3TZycziynmtjmXVgn+o+4UjkUSExOQ47qZSY6EIZD6bKR5lM+swqlUEQmKCEREf1uBj8Y1PH4JXU5tyT/Bd2C/eGRcZMYrLk+rk2dI9Fku31nPfz3ZLHfg9cwejVfcE3PvcSKfrt8VSEe43X6xtWbYT277Zja1fM18uZn8yw+HFpy/ChoFaBp2ry1zrvHfr1i23jccyoF4oFmL0k0PrfKzuozph/g+zATDCk62uWyaTCad3XAQA9BhFGk1YwmZ5Zd/Md3qf0rwy0CYaAhHfys3iKeSBMk4QKbhdxD1eXsxcRCmcEPRtiVI8Po+7E6+p0OL0jgu4cvgG9vxk+861TqPnykjr65RqijChsczX7MpyDXf+/cPqdkHjb76DLpIIuUYbtUFRFJIGMvlb53Zfxvev/YTdqw9wY3p/ype4fOgGKIrC3dP71GlcTRGpPxGl3I2rF/mNQVxcHD777DMAQFlZGYYOHVqr04amaYexIq1btwbAdN+zVaJ369YtPP/883UeMwBERla5Ke11wWNhS/f4Ir5VpzSW6OhoLp/pn3/+sekY0+l0eOKJJ7jspnnz5tV57PYoLi7G5s2bHQpKO3bs4H5OTKxyxAqFQm5Mhw8fxoIFCxyWi+bl5eH777+v8XhUVNUNP1e/w4XGBEPmL0VchxivcwXWB3mgDH7BCoTGBNucPyKxECFRQXVy77sKCTonEAheC9thL90sSqWeybB63NPcNbUXCrOK0HdCDyvXVH0IjAjA25sWuLxf7/HdsGv1QVw/dgvr3mNypEY8OhC9xnV1y7iaE1z5ntaGU8pJUcqdhLYIRmhsMEryyrAg5XGbXRRdIenudlzXrdSzmWjT07rcKfVMJsrylZD6SXymXMdZWBdLzk3H2RuWsHlSQVGBXuE0oSgKUa3DkXbuNrJv5HK/k8osijhjw0+6ux3C4kNqlBBL5CLotXpo1YxTCqhy+FWHLd3jC/ke+bvyNBRFQSJnuryV5JZygbF1bTbBClGteyS4dIHQbUQyDqw7hlN/n4fK3Dpp6qtjsWXZLm7u3jN3CEJjfefmRVXQOcmUau7Mnj0b2dnZePfdd5GdnY2hQ4di8ODBGDt2LJKTkxEcHAyj0Yjc3FycPXsWv//+O1eix+fzazjAHnzwQbz++uvIzs7GwIED8dJLLyEpKQkajQZ79+7F0qVLodVq0a1bN5w5c6ZOY46Li0NsbCyysrLwv//9DzExMWjbti3nXoqIiICfHyNS6zW1h5x/8skn+Pfff1FSUoK5c+fi8OHDmDp1KoKCgnDt2jX873//w7lzTCbptGnTMHr06DqN2xFKpRL33nsvEhISMHnyZPTp0wfx8fEQCATIycnB5s2bOSEpJiYG48aNs9r/nXfewb59+3Ds2DF88cUX2Lt3Lx5//HF07doVcrkcJSUluHTpEnbt2oXt27ejU6dOmDNnjtUx+vfvz/28YMECvPnmm4iKiuJEpoSEBAgEtqURiUyM2LbR7jwlXgGPx0NUy/pHOLgDIkoRPI7lHQECwRLWiZR5iQk7v3TwGgCgQz/HF9JSKY0bN3JQWFgIqdQ9YpEtZP5SzHh7UoMd3xUoisJDiydjyYT/geLxcO9zIzDmqWG171hP2HPN/twUENlySrkoSrkrQBNgvhS8vXkB9FqDWy4MeXweku5uh+Obz+Divqs1RKnT/5wHAHQe2rHBWr/boinMFbZ8L9sFUarwNnO3PKye7507162oVhFIO3cbObeqHF9s+Z7CiVK6uI4x+O+ht2s8LpaJUV5cYRalSgE4EKW40j2FV91Zbsx5qAiSoaJMjdxUpnRPKBbaLJFwhsQucXj992eddkmxJA9sB6FYWBXGHxOEsc8MR4uOMdi1cj9GzB7UaB33vGUNqAo6J04pd+GJci9nWbhwITp37oxXX30VaWlp2Lt3L/bu3Wt3e4qiMGLECHz44YeIjrYWIp599lns3r0bu3btwo0bN/DEE09YPS+VSrFy5Ups3769zqIUALzyyit47rnnkJ6ejqlTp1o9t2LFCjzyyCMAHIecs8TGxuLvv//GpEmTkJ2djZUrV3KB7ZZMmDDBYYmfO0hPT+fca7aIiorCxo0boVBU3TwRCoUQi8XYuXMnZs2ahT///BPnzp1z6Ojy969ZBte6dWtMnz4d69evx44dO6ycWQCQlpaGhIQE138pglsgohTB48jlTavtLaHxiGodAaFYAI1Ki2MbT0NdVgl5gP025iwUBchkNIKCRPCia6EGJz45Fh/tfxNimbjOd+NdhT3XTQlHQefOZkpZZhO4A3fnCHCi1IFrmPRi1V1Po8HIdXfsPrJxS/eawlxhQ99zbuSBpmmnxBS2RC60Rf2EJHeuW1E2HF+sc6k+a4OI7cCn1nJOKdZ9Ux1WlKpv5z1305jz0C/UD3nphci+yYScK4Lql4XYvm9rl/cRy8RIurstzu66BADoP6UXeDweug5LQtdhSbXs7V68ZQ0gQefuxxtcoo6YMGECxo4di40bN2Lnzp04evQoCgoKUFpaCplMhuDgYCQnJ6Nv376YOnWqVfmYJUKhEBs3bsR3332HtWvX4sqVK6BpGtHR0Rg6dCjmzZuH9u3bY/v27fUa7xNPPIGIiAisWLEC58+fR3FxMZdnZYmjkHNLunbtigsXLuCbb77Bpk2bcP36dajVaoSGhqJ37954+OGHMWbMmAbLBouPj8fx48exbds2HD58GBkZGcjLy4NKpUJgYCA6duyI8ePHY+7cuTUEJXZu+fn54Y8//sDBgwexevVqHDhwANnZ2aisrIS/vz9atWqF3r17Y+zYsRg5cqTNcfz000/o2bMnfv/9d1y7dg3l5eVe1znSVyGiFMHj3Lp1C6GhoZ4eBsELEQj5iG0fhbRzt7F52U4AQMe72zpdupCbm8tZnH0Fkh9VOzZFKRczpbx9biWbc2RSz2SgokzNdVg5uf08CrOK4ReiQPeRyZ4colcSmRgGiqJQUaZGeZHKqUDTwizGKRXawj1/e+6YW6ybJuempVOKcTTVJ3Sc7TZkWb6nUWlh0BshEFo7JZSFbOc9373xxApy2TcYcbAxOhjZotvITpwoddeUnh4Zgzch4YLOSfmeu9Dr9V7fTVsgEGDKlCmYMmVKvY/zzDPP4JlnnrG7zffff28z14jFmQyuiRMnYuLEiQ63YR3ftkLOq6NQKPDyyy/j5ZdfrvEcTdMO854cOcssWbRoERYtWlTjcYqi0KtXL/Tq1cup41ii1WqtOh4PGDAAAwYMcPk4ACMq2jsHBM/i3bI2gUDweToN7gCg6o6/Mx3ptFrg+ecD8cEH7eCF2ZvNCvZcP/98YJM516zNXe8lmVINQWhsMKLbRMBkNOHAumMAmC+df5u7Mw57ZIDTQfruoinMFZFUxIlLrJBQGwWZjFMqrJ5OKXeuW5xT6lYed6GhKql/Jzwx55TSodTccRAA1GU1S/hYZ5anhBh7NOY8ZEVN9vOrvk6putJzdGdEtQpH0rC2iGrlufwQb1kDZFz5npcuRASCM9AW5XsSx04pAsHbIaIUweN07Ng4WQaEpsm9z41Cy67x3P93GtSh1n2MRgq//SbDP/9Ewmj0ofo9D8Ce699+kzWZc806pWyJUnInRanYWO8I23fE6CeYLn6bl+6EWlmJa8duIfVsBoRiAYbNrNtdxvrQVOZKtIWg4wzuckq5c92KSAgDxaNQWa5BWb4SgEX3vXqU77GilLKwnHMXAoCqWq4UTdNQsplSXla+15jzkO3GmJfOtDeXN1JZdXUUQXJ8uO9NPPvtYx55fRZvWQPYv4GyAqXHxtDcsHSyEBoHvc4A0ADFo2o4VZsTUmnzuFlIcAwRpQgeJzc319NDIHgxQrEAzy5/DNFtItB7fDcERwd6ekiEJo6t8r2ygnIAzl80OmoZ7S3cNbUXolpHQFVSgc1f7sCGT5mMiwHTejtVluarRJlzpZxxShkNRhRnlwKov1PKnQjFAoSbO4KyJXxc0Hl9RCk5U56Tl1Zg9Th7bAC4cz0XTye/jq1f7QLgfaJUY8K6xExGJrOksbL+7NEU1q3GIDye+dsozi6FTqP38GiaB0aj0dND8Dn0GjbkvHkLgno9+Rv1BYgoRfA4xcXFnh4CwcsJjg7EB/++gWe+meXpoRCaAWz2gmX3PdYVE9kyzKljqFQq9w/MzfAFfEx9dSwAYNu3e3D1yE0IRHyMf3aEh0fm3bDlTXdu1H7DpDinFCajCUKxAAHh3iX0cZ0Eb+VBrzVAo2JKleolSplLPnNS860et+zAt3PlPisXlX+Id52XxqS6+OtpUaoprFuNgX+oHyQKMWia5spvCfWDhEU3PqygKmrmpXu2At4JzQ8iShE8DrH8EgiExkRgzpQymMv3tJU6FGUx7dLZ0q3a8Ob215b0HN0FY54axv3/oBn9SRh+LbTowLQAz7qSXeu2hZnMTZWQmGCv6z7FzuWMi1mck4niUU53mLSFhHVKpVo7pVhRSqfR49hm6zbo9cmwauqw5Xssnj4XTWXdamgoikJEInMDorrrj0BoKrCiVHPPk3KmCy6h6eNd36AIPkn37t09PQQCgeBDCKuV7+Wm5oOmacgDZU6XGrVs2bLBxudupr8xHvc8MQSJXeJw73zbbZIJVcS2jwLFo1BWUI7SfMeZMwVZjMvCXZ333En7/m0AABf2XuFCxxVB8nqJZ2ymFJujxaIqrUDauUzsXn0A6rJKBEcHom3vluAL+Yjv5P35aw2Ff7X1RBHoWVGqKa1bDU2kWZTKJaKUW/D2znvNEb1GB6D5O6Xkct+9seFL1N4/kkBoYI4dO4Y+ffp4ehgEAsFHEFQr32Mzd6LbRDh9R+7GjRto06ZNwwzQzVAUhRkLJ3p6GE0GsVSEyJbhyLmZh8xLdxAY7m93W9YpFeaFolSHfq0hFAtRnF2Ka0dvAah/+RgrSlXn5LZz+GXxX9z/D5jWGxMX3AONSgt5oGc6znkDNcv3PHsumtK61dAQp5R70Wq1RJhqRGgTzTVrEUmbtyilUqmgUPhuNqGvQJxSBAKBQPApqjulss3ZQZ5slU7wLuKSYgAAmZeyHG5XwHbei/U+UUokFaHjXYwAceiPEwDqXz4mlllfdMrM3SpvnEjjHpMHyDBoRj/wBXyfFqQARgS0FLo9nSlFqIKIUoSmDCtI8fg88AWkLJfQ9CFOKYLHCQ8P9/QQCM0MqZTG+fO5KCgogFTqPR2xmiPsuWZ/bgqwmVKcU+pWlVPKWQICAtw/sGZOU5or8R1jcGzjaWRevuNwu5ybTEB+WFz915mGWLc6D+2Ic3suI+1cJoD6iyIhMUFW/x/bPgrXj6dy/z/llbEY98xw8Pjee8+zMechj8+DIliO8iJz+WSwZ+/2e3rd8qY1IJKIUm6F5JU1LjofKd0DSPawr+C93xoIPoOnvyQRmh8UBYSEmBAbKwbJR2xY2HMdEmJqMueaLd9jg85Zp5SzIecAIJP5tgOkLjSluVLllLIvShXnlHJiT5te9c/qaYh1q/OQDlb/X19RqtuIZAx6oB/3/3EdY6yej24d4dWCFND489Ayp87T5XueXre8aQ1gnVLFOaXQVuo8O5hmgLc1emjucCHnzbx0DyCCp69AVhCCx7lx44anh0BopuTk5Hh6CAQvxLJ8z2Q0cXfKo1wQpcjcat6wolRuagG0aq3NbU79fR4A0LpnIoKjAt322u6cW+HxoRj7zHC06BCN2HZRuGtKr3odj8fn4dGP7sMTXz6MiS/cg+RB7a2ej2xFnM/VYcPOeXweV+7oKci6VYUiSM51osxPL/TwaJo+er3e00PwKfRmUUokbv6ilEaj8fQQCI0AKd8jEAjNDq0WWLw4AGVlrfHZZwDJ3mw42HMNAP/5T1mTONeWQef5GYXQaw0QioVemQvUnGhKcyUgzB8B4f4oy1fixsk0JA9sX2Obk1vPAQB6je3iltdsqHVr+uvjMf318e45GJjg/P6TewIAbpysypKieBQiEsLc9joNRWPPQzbsXB4o8/nW5t60BlAUhcjEcKSezUDauUy06BDtucEQCC7CZkoJfaB8j+AbEKcUweO0b1/zyz6BUB+MRgqrV8vx118xMBp9+yKgoWHP9erV8iZzri2dUhf2XgUAJHaNc6nsKCYmpvaNCFY0tbnS455OAICtX++u8VxZgRLXjjEd7XqNcY8o1RTXLctytNAWwRCKvf9eZ2PPQ9Yp5Q0h555et7xtDeg2MhkAsH35vzCZTFbP0bR35955GyT3p/GgTTTXqEXoA04piUTi6SEQGgEiShE8TmEhsU0TCITGgws61xpwcjvjdmEFCGdRKpVuHxfBuxj79HDwhXxcPngd10+kWj13/t8roGkaiV1aICTGdx128sAqoSWqJSndswXrlKpv50N3QNYta4bPuhsyfymyr+fi9N8XuMe3f7cHz3Z5E1lXsz04uqaF0Wj09BB8BrZJC9N5r/lfyhsMBk8PgdAINP+ZTPB6iChFIBAaE7Z8rzSvjHO79Lins0vHKC8vd/u4CN5FaGwwBkzrDQDYsmyn1XOX9l8DAHQa3KHGfr6EPKAqI4nkSdkmJJbpWBjkxtyxukLWLWtk/lIMf/RuAMA/3+/lHv91yUaUF1dg89KddvYkVKe604zQcLB5Ur5SukdEKd+AiFIEj0M6dhAIhMaELd8ryS0DbaIRlxSDsBYhLh3D17NhfIUxTw4FwDijSvMZlwlN07h0kBGlkga089jYvAG+gA+pH1NaEUVEKZv0HtsVDy6ejKmvjPX0UMi6ZYOeo5ny2zxz2LnJWCWuiKQij4yJQHCEXmsWpZpAubQ7IOuWb0DUAILH6dWrft2ACAQCwRWq31101SUFAK1bt3bXcAheTGTLcLTqngDaROPoX6cAALevZENZqIJIKkLrHgmeHaAXwGYmRbeO9PBIvBORVISRswchPD7U00Mh65YN/IKZ+asqVoGmaeRnVLn35QEye7sRqiH25s4VzQwu5NwH8qQAQC73fOkzoeEhohTB45w4ccLTQyAQCD5EQqdY9B7fDa17JKDvhO4YNnOAy8e4efNmA4yM4I3cNYXpNHfoD+az6vLB6wCA9n1bcaWgvsyM/0zCuHnD0bZPS08PhVALZN2qCRvWbzSYoFFpkXUth3tOU0Fa0TuLVqv19BB8BrZ8T+QjTqmKigpPD4HQCPjGbCZ4NaQOnUAgNCZ8AR/PfDOrXscgnZl8hz73dsfaRRuQeekOfnlnA079wwQiJw307dI9lm4jktFtRLKnh0FwArJu1UQkFUEkFUFXqUN5sQp3rudyz6mVlbXuryqpsCpjJRAaGs4p5SOZUmTd8g2IU4rgcUJDPW9pJzQvJBIaR4/mYfPmi5BIyIdZQ8Ke66NH83zqXPv5+Xl6CE2OpjpXFEFyDHuEcdP9vXwvCjKK4BeiQJ/x3d36OmTdahya6jx0B55et7z13LNuKVVJBe5YOKXUZY5FKbWyEi8PWIKFoz6GQe/b3edIPqxriMViiMViLFmyxKX9DDoDI9JQcLtT99133wWfz/e6DCeBoPE8NAkJCaAoCrNmzWq01yQwEKcUweMQUYrgbng8oEULI0JCJCDfkxoW9lz7Gv7+/p4eQpOjKc+VBxZNQpteidj85U4kdonD1NfGcVlK7oKsW41DU56H9cXT65a3nnu/YAWKs0tRXlyBO9csnFLljsv38tILoS6rhLqsEie2nEG/ST3dOi6j0dhkqglMJhP0er1L+/B4PPD5/AYaUU0MBgM2b96MHTt24NixY8jPz0dJSQlkMhlCQ0ORnJyMvn37YvLkyUhMTGy0cbmCr+VJAY0rShE8B3mXCR7n6tWr6NOnj6eHQWiG3LlzB23atPH0MAjNEDK3fAuKotB7XDf0HtetwV+LzC1CQ0Hmlm1Yp1RZgRI5qfnc42plJa4du4W9Px/BjLcnwj9EAZPRhE1f7kBUq3AogqoCmLcv/xd9J/Zwm8vEaDQiOzsbOp3OLcdraPR6PYRC14QSkUiE6OjoRhGmNm/ejFdffRW3bt2q8ZxSqYRSqURqaio2bdqEN954A6NHj8Z7772HpKSkBh9bdfbt24eRI0cCAHbs2IFBgwZxz+k0bOc93xGlNBoNFIq63wQaPHgw9u3bh0GDBmHv3r3uGxjBrRBRikAgNDt0OuCjj/xRUtISH34IiEhX5waDPdcA8OqrSnKuCXYhc8UxZN1qHMg89Bzeeu4V5g58N0+lw2hRhqdWVmLr17twbvdltO6RgGGPDMDB345jw6fbAQBPW2QTZlzIwrWjt9C+n3s6HJpMJuh0OvD5/EZ1E9UViqJcEqWMRiN0Oh1MJlOD/34ffPABFi9ezGUTDRo0CKNHj0anTp0QEhICtVqN3NxcHDhwANu2bUNGRga2b9+OmJgYfPXVVw0yproGw+u15pBzCbmEbwjS09M9PQSfhcxogschd+0I7sZgoPDttwoACrz7bg5EIu/JrmhuVJ1r4MUXy33mXEdFRXl6CE0OX50rzkLWrcbBl+ehp9ctbz33fmbH063T6QCYHDlVSQXUZZUozVMCAMrymf/u/fkIt19ZXpnVca4cueE2UYqFz+c3ifKlumQRGY0NX8qZkpKCRYsWAQAiIiKwZs0aK+eRJVOmTMGnn36K9evX4+23327wsdUFXyzfk0hIEwFfgKQWEDxOWVlZ7RsRCASCF6FWqz09BAKBQHAJsm7Zhi3DYzvvxSfHAGBcKYVZxQAAZWE5CjKLOOEKgFWpHwCU5Pju91lvzL66ffs25s+fD4DJU9uzZ49dQYqFz+djxowZOHHiBEaPHt0Yw3QJ1inlS6JUY4iXBM9DRCmCx8nPz699IwKBQPAiiJhOIBCaGmTdso0imBGlaBPj3GrRMYZz/VSUMkKeslCFfb8etdov9xbz/ZUVtUpySxtjuF6JN4pSX3zxBTQaJqx+8eLFaN3aeRdbYGAgxo0bZ/O5kpISrF69GrNmzUKXLl0QHBwMhUKBuLg4jB07Ft9//32tWWC2uu+lp6dDLBZzeVIAMHLkSG5bsViMNt1a4s/Nf0Aodt09l5WVheeeew7t2rWDv78/EhISMHnyZOzevbvWfdPT00FRFCiKQkpKisNtHXWwS0lJ4Y6Tnp4OrVaLzz//HH379kVoaCgoiuKcbQCg0+mwceNGzJs3D7169UJQUBCEQiFCQkLQp08fLFq0CIWFhTbHMWvWLFAUhX379gFgsrrY12b/JSQkOD12SzZv3oypU6ciNjYWYrEYISEh6NevHz788EOoVCq7+1X//U0mE5YvX47+/fsjKCgIcrkcnTt3xnvvvedzNxG83w9KIBAIBAKBQCAQCA2AZWA5AITHh0LqJ4FaWck9VlZYDo3aOgcoxyxKxXWMxuVDN1CcU9rgYyU4B03T+PnnnwEAfn5+mDlzptuO3adPH2RkZNR4PC8vD3l5edi1axdWrFiBjRs3IjIy0m2vy8IT8EDxXCuVPHjwICZNmgSlUsk9lpOTg61bt2Lr1q1466233D3MWiksLMSkSZNw9uxZu9vMnTsXq1evrvF4cXExjh8/juPHj2PZsmXYuHEj7rrrrgYcLYNGo8EDDzyADRs21BjP0aNHcfToUSxduhRbt25F165dHR5LrVZj5MiRNUTBCxcu4MKFC9i0aRP27NkDuVxu5wjNCyJKETwO6bxHIBCaGiQLj0AgNDXIumUbv2Dri76wFiGQ+UutRKnywnKohdaB3CW5jPMstgMjSvly+Z6rnfcamkuXLqGoqAgAcNddd7n1wt5oNKJ3794YM2YMunTpgoiICOh0OqSnp+Pnn3/Gjh07cPbsWTz88MPYuXOn08eNiYnB6dOncfLkScydOxcAsHz5cvTs2RMAUFGmRmmuEgmt4l0ab2ZmJidI8Xg8zJ49G5MnT0ZAQAAuXLiATz75BO+++y66d+/u0nHry+zZs3HhwgU88sgjuO+++xAZGYnMzEyIxWJuG4PBgJYtW2LSpEno3bs34uLiIBAIkJGRgV27dmHlypUoKirCpEmTcPHiRYSHh3P7vvfee3jppZfw6KOP4uTJk+jZsydWrVplNQaRi90WZs6cyQlSXbp0wYsvvogOHTqguLgYv/76K1JSUpCdnY1hw4bh/PnziImJsXusxx9/HEePHsXMmTMxffp07vf/+OOPceTIERw/fhzvvvsuPvjgA5fG2FQhvDJCngABAABJREFUohTB45w+fbrRF0ICgUCoD6mpqWjZsqWnh0EgEAhOQ9Yt21R3SoXFBUPqZx2uXFZYDphz2eOSYpB56Q73XIsO0QAY0UCr1kIsE8PX0Ov1XiVMXbhwgfu5W7dubj3233//bVPg7devH2bMmIHVq1dj7ty52L9/P/bs2YOhQ4c6dVyhUIikpCSrcrSEhAQkJSUBAIpzSqFUlMM/1M+l8b766qucQyolJQX33Xcf91yPHj0wZcoUDB06FKdPn3bpuPXl/Pnz+P777zF79mzuserXg4sXL0ZERAQUCoXV4z179sSUKVPw9NNPo3///igoKMDSpUutyiFjYmIQExPDCZJyuRzJycl1Hu/WrVuxfv16AMCwYcOwbds2K1Fr5MiR6NevH+bOnYvi4mK88MILWLdund3jHT58GGvWrMFDDz1k9fuPHj0aPXv2xMWLF7FixQosWbKkSTQ7qC8kU4rgcfR6vaeHQCAQCC5BgjcJBEJTg6xbtrF0SlEUhZCYYMj8pVbbaFRaaCqY8r345Fir58JiQyCRM0IU654ieBZLYSc0NNTudiaTCZcuXbL7z9Y1Sm2Ow5kzZ6JLly4AgE2bNtXxN6iJVs3kVAklzot/ubm52LhxIwBgzJgxVoIUi5+fH7766iv3DNIFhg4daiVI2aJVq1YOn+/UqRPmzJkDAPjrr7/cNTSbsOdIKBRi1apVNl1Wjz/+OIYPHw4A+PPPP5GTk2P3eJMnT7YSpFjEYjHmzZsHACgqKsLly5fdMXyvp/nLbgSvJzg42NNDIDQzJBIae/bko6CgABJJkKeH06xhzzX7s69Q/a4doXZ8da44C1m3GgdfnoeeXre89dwrgqvOS2CEP0QSIWQBUpvbygNlCImx/vuUB8kQFBWInJt5KM4pRWTLcJv7NmfYYHhvwTJs2lHpnlKpdFitce3atRph2JbQNI28vDwolUorASs6Ohrnzp2zcmzVB9pEQ2vONGMFUGfYt28fJ0Y7ytXq1asXOnbs2KgCyIMPPujUdpYuoZKSEhQXF0Oj0YCmmTUkMDAQAHD58uUGc+wZDAYuMH3kyJFo0aKF3W0ff/xx7Nq1CwaDAXv37sWMGTNsbufo9+/Rowf3c2pqKjp37lzHkTcdiChF8DgNEQJI8G14PKBdOwPi4iTgET9og8Kea1+D/RJEcB5fnSvOQtatxsGX56Gn1y1vPfdiqQhCsRB6rR5hcSEAUMMpxRISHQT/EGtxTxEkR3BUAHJu5qHoTgkKs4oRGutbN1z5fH7tGzUilgJsRUWF24+/bds2LF++HAcPHkR5ebnd7ex1hnMVrVoH0ABfyHep897Fixe5ny2FDlv07NmzUUUpZ4WWq1ev4ssvv8T27duRm5trdzuTyYSSkhKrXCl3kZqaynXDqy0L2fJ5y/Nfnfbt29t9ztKw4Wh+NSfI1x6Cx/EVWyKh8cnKyvL0EAjNFDK3CA0FmVuEhoLMLfuwJXyhLRyLUsHRgfCrLkoFMk4pAFj9+nq82Hcxjm0+03CD9UIMBu8SG0NCQrifHQlDgYGB0Gq1Vv8efvhhu9vTNI0nn3wSkyZNwvbt22sVDCorKx0+7yyaCg0A11xSANMVjqU2saYhxBxHBAXV7gj+4Ycf0KtXL6xatcqhIMXirvNdHVfOo6XZwnK/6shkMrvP8SzuTPlK2TURpQgEQrNDpwM+/dQPq1bFQ6fz9GiaN+y5/vRTP3KuCQ4hc8UxZN1qHMg89BzefO7ZsPOwFoxDwZ4oFRQVCD+Lcj+RRAiRVIRgsyil1zLizJkd7inbItQNSxfO2bNn3XbclJQUroNbly5d8P333+PcuXMoLCyEWq3mhC1nS9Ochc0zc1WUssTbSixrc9ddvXoVTz75JAwGA8LDw/HJJ5/g1KlTKCoqgk6nA03ToGkaP/zwA7cPW9LXkHjbeWwukPI9gsepLcSOQHAVg4HCZ5/5AfDDW2/lQCTynuyK5kbVuQaeekrlM+ealB27jq/OFWch61bj4Mvz0NPrljef+6DIAGRevsPlQVmKUsHRgSjOLgUAhEQHWgWjywMZtwMrSrGknbvdsAP2MrytO1hSUhJCQkJQVFSEQ4cOQa1WO3SmOMvKlSsBMNcu+/btg1RqW7x05JBxFdpEQ1vBqLgShaSWra2xdCPl5eU5zELKz8+3+5ylc8dkMjl8TXeVS6akpMBgMIDP52Pfvn12y93cea7tYVlOl5eX53BbS0cXyU12HuKUInichqj1JhAIhIZEo9F4eggEAoHgEmTdss+018djyitj0XMM0zXNUpSK6xjD/RwcFWRVvseKUkHVRKnc1HxUlKkbcMTeRW1CRWNDURQXMK1UKrFmzRq3HJeNHBk3bpxdQYqm6Xq5s6ycODRQkFkEmqZdzpMCgOTkZO7nU6dOOdzW0fN+fn7czyUlJXa3Ky4uRlFRkQsjtM+lS5cAMK43R/lLJ0+edHgcdzibWrZsyYmax44dc7jt8ePHuZ8tzz/BMUSUIngcZ2qECQQCwZsoLS319BAIBALBJci6ZZ8WHaJx73MjIZIwnbssRakWHaK5n4OjA7lSP6Cq7C8oIoB7TChmjpF+3nfcUt4mSgHA/PnzIZEwzqKFCxciLS2t3sdks7Mc3VDftGkTcnJy6vwa7JgBIOtmNtTKSlAUxYXwu8KgQYO4MjlHwtzJkyc5EcgWQUFBXKMERyLQr7/+6rYSOvZcW3ZSrE5OTg42bdrk8Djs+dRqtXUei0AgwKBBgwAAO3fudJjP9/3333P7DB48uM6v6WsQUYpAIBAIBAKBQCAQzMgCGFGKL+QjqlUE93hwVCD4Aj7nkFKY/xvdJhItOkQjeVB7dB/FuCNSz2a49Jq0iUZluQa0yXvKGpsycXFx+OyzzwAAZWVlGDp0KA4dOuRwH5qmHYq3rVu3BsB037NVNnbr1i08//zzdR4zYF1mm56RDopHISw+pE55UlFRURg/fjwAYMuWLfj9999rbKNSqTBv3rxajzVw4EAAwMaNG3Hr1q0az1+7dg0LFy50eYz2aNOmDQDmnB4+fLjG82q1Gg888ECt4eZRUVEAmA569RHMnnnmGQCATqfD7Nmzodfra2yzcuVK7NixAwAwefJk7rUJtUNEKYLH6dWrl6eHQCAQCC5BsvAIBEJTg6xbzsNmRIXHh8I/rKp0iS3T8zeX8MkDGaeUUCzAuztfxUs/PYmWXeMBAKlnM51+PbWyElnXcpCXVoCcW/kwGppWxy2hUOjpIdhk9uzZeOuttwAA2dnZGDp0KEaNGoUvv/wSe/bswdmzZ3Hq1Cls3boV7733Hrp3747NmzcDYIK4RSKR1fHYAPPs7GwMHDgQKSkpOHHiBA4cOIAlS5agX79+KC4uRrdu3eo85ri4OERFMu68lF9W4nzaGdzOycTNmzdx8+bNWjv+Veejjz7iyu8eeeQRPPfcc9i7dy9Onz6N1atXo2/fvjh79iy6d+/u8DhPP/00AKbD3eDBg/HDDz/g9OnT2L9/P/7zn/+gT58+CA4ORlhYWB1+65qwXRBNJhPGjh2L999/H/v378fx48fxzTffoGvXrti7dy/uuusuh8fp378/ACYz64UXXsCpU6e4c5mR4bxwPHbsWEybNg0AsGPHDvTt2xdr167FqVOnsGvXLsyZMwdz5swBwGRJsYIowTm8K5WO4JOcP38eXbt29fQwCAQCwWkyMzORkJDg6WEQCASC05B1y3kiW4Zj/g9zEBYXAr6QKX8Kigzgyvv8QhTIuZUPRZB1eDZFUWjZNQ6A86KU0WBCfkYhYDZx6Cp1yL1VAJPY+0ri7GEwGLwu7Jxl4cKF6Ny5M1599VWkpaVh79692Lt3r93tKYrCiBEj8OGHHyI6OtrquWeffRa7d+/Grl27cOPGDTzxxBNWz0ulUqxcuRLbt2/HmTNn6jZgGnji0Sex6IO3kXk7E1OnTrV6esWKFXjkkUecPlxCQgL++OMPTJkyBeXl5fjuu+/w3XffWW3zxhtvgKIonD592u5xRo0aheeeew5ffvklsrKyOAGGJS4uDps2bcLo0aOdHpsjevXqhcWLF+M///kPSktL8eabb9bY5sUXX0RycrJDB9z999+PDz74AKmpqfj888/x+eefc8/Fx8cjPT3d6TH9+OOPMBgM2LBhA06fPo2HHnqoxjbR0dHYunUrYmJibByBYA/vXD0IPkV9anwJBALBE9iybRMIBII3Q9Yt1+g+qhP386wPpyMyMZz7/+pOKUvik2NBURRK88pQVqBEQJi/w9dRl6kBGhBJhQhrEYK89EJoKjVQqSogjPROB1J1XJ1bRmPjOsEmTJiAsWPHYuPGjdi5cyeOHj2KgoIClJaWQiaTITg4GMnJyejbty+mTp2KxMREm8cRCoXYuHEjvvvuO6xduxZXrlwBTdOIjo7G0KFDMW/ePLRv3x7bt2+v81g1FVrMmPwAQkNC8dfff+L8+fMoLi7mMpbqwqBBg3DmzBl8/PHH+Oeff5CTk4OgoCB0794dTz/9NEaMGIElS5bUepwvvvgCffv2xbfffouzZ89Cr9cjLi4OkyZNwksvvYSQENdzrxzx9ttvIykpCcuXL8eJEydQUVGB8PBw9O7dG08++SRGjBiBlJQUh8dQKBQ4fPgwPvjgA+zYsQMZGRlQq+vWhEAikeDPP//E5s2bkZKSgqNHj6KwsBByuRxt27bFxIkTMW/ePCgUitoPRrCCot2VRkbgUCqVCAgIQFlZGfz9HX8QEZga5Hbt2nl6GF5Nha4Cig+YBe7KQ1fgLyXzyhFGI3DhghAFBQUYOjQQ5oxHQgPAnmsA6NRJ7zPn+s6dO+QumIv46lxxFrJuNQ6+PA89vW41p3N/Ye8VbPlqFx77+H5EJNYsV3p14HvITc3HS2ufQqdBTOcwg8EApVKJuLg4qzDr3NR8aFRaBEUFIiDMD1q1DlnXs1FUWgRFiAximajG8b0No9HIBWo7i0gkQnR0tMv7NXeKs0ugLFRBESxHaGxwo7wmTdOgaRoikQg8nnel+2g0Gqu/F4Jn0Gg0SEtLQ2Jiokvvh7O6CHFKETxOixYtPD0EQjODzwe6dtVDq5U26S+9TQH2XPsaoaGhnh5Ck8NX54qzkHWrcfDleejpdas5nftOgzug0+AOdp+PS4pBbmo+Mi/d4UQpWxgNRmhUTMWA3ByuLpaJEBQRCAAQ8AWIjouwt7vXQNM0KIpyaR8ej0cEKRtUmueDzE9ay5a+gbfmlRHci3dJoQSf5MKFC54eAqGZkpnpfMgogeAKZG4RGgoytwgNBZlbjUdcEuNIy7xkv3V8SW4Zsm/kAWCEKIGoyisQGO4PgVAA2kBDrzZAKBR69T+apl3ehwhSNTEZTdBrGOFWLPd+h1xjUFt3PULzgIhSBAKh2aHTAd98I8evv8ZCp/P0aJo37Ln+5hs5OdcEh5C54hiybjUOZB56Dl8693EdmIDszCvZNp9Xl1WiLF8Jo57JVvIL9bN6nsfnwT+Eeaw0X9mAIyV4E1o184chEAvAFxDRjuA7kPI9gsexFyZIINQVg4HCu+8GAAjAiy/mQCQi0XkNRdW5BmbOVPvMuQ4PD699I4IVvjpXnIWsW42DL89DT69bvnTu45JiAQA5N/Ogq9RBJLV2vZTklgEA/EP9EBjhDx6/pk8gIMwPysJy6Cp10Gn0XOc/b8RbO+81NTRqpnRPIhN7eCTeg1hMzoUvQJxSBI+ja+63ywgEQrOjPl1wCAQCwROQdavxCIzwh1+IArSJRta1HKvn1MpK6LV68AQ8u4IUwLilpAoJt483Q/pmuQdtBSNKkdK9Ksjc8g2IKEXwOHfu3PH0EAgEAsEliouLPT0EAoFAcAmybjUeFEUhriNTwvf38n9RlF3CPadWagAAAaF+dgUpFqk/I0pVerkoZTQaPT2EJou6rBIalQYmo4kr35PIiTuIhZgXfAPitSQQCAQCgUAgEAgEN5I8sD0uHbiOY5vO4NqxW/jkyEKYTDS0Wi344EMWIKv1GDI/KYpQAq1aB6PBBL6A+Ancha5Sj+Ic5tzy+DxEtY6AQNi4OU6l+UqUmks5WXh8HoRi7y3VJBAaArKyETxO9+7dPT0EAoFAcImWLVt6eggEAoHgEmTdalxGPzkUL/z4BCgehdI8JVTFFUxnNZoJshaKa/cG8IV8Lo+qstx73VJNLfenslyDnFt50Ki0oE00jHojCm8XNeoYVCUVnCBF8SgAgFAsREhsUKOOw9uRyWoXbwlNHyJKETzOlStXPD0EAoFAcImsLPttvgkEAsEbIetW40JRFLoM7YigSCbcvSRPCZ2GKUWS+UudPo7MXMJXUea9olRDlFjRNI3SfCWyruYg51a+W7KFjAYTCjKLkJdWANpEQ6IQI7JlGCgeBY1KC2WRyg0jdwxtoqEsUqHwNlNOGxDmh7iOMYhtH4WYdpGQO+Gg8yU0Go2nh0BoBIgoRfA4lZXe+yFLIBAItiAZBwQCoalB1i3PENoiBABQkl0CnUYPwDVRSh4oB8A4pYwG78xuaogw6qI7JSjNLYNBZ4C2QgtlYd0FI9pEQ1VSgTvXc1BRqgYA+IUqEJEYBolCgqCoQABAaV4ZTEaTO4ZvE2VhOTIuZaH4DpMx5heiQFBUICgeBYGIpOrYwmRquPeD4D2Q2U/wOH5+fp4eAqGZIRbT+O23QhQUFEAsDvD0cJo17Llmf/YVpFLnLygIDL46V5yFrFuNgy/PQ0+vW7567kNjgnAdwM3T6egekwyKoiCROV/uJhQLIJaJoFXroCpRIyDM+74383ju9TkY9EaoSioAAIogOVPqllcGRZAMfIETuU80UKnSgDbRqFRpoCqpAG1i5pxQIkRYi2CuLBIA/IMVKC9UQa/VQ1lYjsAI96/BukodirNLATBlmX4hCgSG+7v9dZobfH7j5nwRPAMRpQgeJzEx0dNDIDQz+Hygf38ddDopyGdZw8Kea18jPDzc00NocvjqXHEWsm41Dr48Dz29bvnquQ+JDQYAXDt+C93HJ0MoEgCUa8dQBMvNolSFV4pSAoF7LynL8pUADUgUEoS2CIZeq4dWzYg6YXEhte6fn1kIdbVyR4FYAEWQHAFhfqCoam8ABQRFBiA/oxBl+eXQ64wIigxwW/C50WBCYRZTricPlDn1OxAYmlpeGaFukPI9gsc5f/68p4dAaKZkZGR4egiEZgqZW4SGgswtQkNB5pZnCDWLUuXm8jOBEwHn1ZEHyEDxKOg1jDjjbbizNNRkNEFVzLikAiMYJ1FIDBP+XVGqRmW544yh4uxSRpCiALFcDFmAFJGtwhHbLgqB4f41BSkzsgAppH4S0DSNipIK5GcUuuX3KS9SIetqNnSVevAEPARHkyBzV1Cr1Z4eAqERIKIUgUBoduj1QEqKDBs2REOv9/RomjfsuU5JkZFzTXAImSuOIetW40Dmoefw1XMfWq2bmjNd96rD4/MgC2DKL9mytuaKWqkBTdMQSoSQyBmXjEgqgr/ZIVaQWYSyfKXNHCu9him/A4CwuBBEtQpHeHwod5zaiEgMQ2TLcFA8Cjq1DhqVtk6/Q3F2CW5fzUbB7SIU3SkBbaIhkokQkRAGvoBcfhMI1SHlewSPEx8f7+khEJoZej2FN98MBBCIefNyIBT6TnZFY1N1roHp0yt95lyHhYV5eghNDl+dK85C1q3GwZfnoafXLV8996xTikVYx0BrvyAFKkrUqChVI9gcju0tuLN8T61knDHyAOsMtKCIAGhUWugqdSjJLYNGrUNEQqjVNmUFjCAlC5DWuYudRCGGIkiO8iIVygqUkChc+7spzSvjQtkrdMzvEhDuz3VhJLgGKd/zDYgoRfA4pKsCgUBoapB1i0AgNDXIuuUZqpdr1bXLmkQhhkAkgEFngFpZCXlg3UQXb4Y20Vx5XvUOhRSPQnTrCKhKK1CUVYJKZSWKs0sRHB0IADDqjVCVMi6ygLD6BYgHhPmhvEiFynINKlUaSBUS2+OlaahK1FCVVIDiUaCNJq680i9UAV2lHlKFuEGC032FhujsSPA+iH+Q4HFu377t6SEQCASCSxQVFXl6CAQCgeASZN3yDCKJEAHmLmsURYFfj/BsRRAjRLElat6CwWBwy3E05o55fCHfqjseB8V04wuNY9xnysJyrstewe0iJhxdLoZYZmNfFxCIBPALUQBgygWNemONbZRFKmRdzUFRVjG0FVpoyjWMIGUOTQ+JDkJUq3AiSNUTd+aVEbwX4pQiEAgEAoFAIBAIhAYiNCYIyuJy8IX18wP4hShQll8OrVoHjUoDiR0Hj9FgAo9HcSV+amUlNCoNjAYTJAox/IIULncAbAxUpUy5W3WXVHXkATJoQ7VQFqpQlFUCvpAPbYUWFI/inFP1JTgqENoKLXQaPQqyihGZWFXGV5avREluGQCAL+TDP9QPPD4F2kRDHigDX0BaqBIIrkBEKYLH6dq1q6eHQCAQCC6RkJDg6SEQCASCS5B1y3OEtghmRKl6ihV8AR9+IXIoC1UoyVMiUia2ypbSaw0oySmFWsl0n2NLBQ3aKidTRakapblK8EV8BIT51Tl7yRKRqH7OJAAw6I2oKGNEKb9gea3bB0YGoqKsEgadAQadARSPQkRimG2HVR2geBTC4kORfT0XmnINVCUVUATJodfoUZJXZh5DAALC/Ox29CPUH5ms+ZWpEmpCyvcIHufmzZueHgKBQCC4RG5urqeHQCAQCC5B1i3P0a5vawCAUCys97ECwvwBCtBWaHH7ajaK7pRAVaKGsrAc2TdyGUEKAGhGjDJoGcHGL0SBwAh/8AQ8GA1G6NQ6FGWVwGSsf9aY3g3tFJWF5Uz5nULslLDE41EIaxECoUQI/1AFYtpGOt1lz1mEYgECzQHlxdmlKC9WIS+9EKABqb8UgeH+ePfddyEWi90SyN22bVuIxWLMmTOn3seyRXp6OjfWH3/8sUFew91otXXrgEhoWhCnFMHjqFQqTw+BQCAQXEKj0Xh6CAQCgeASZN3yHMMeGYCeYzvDSNU/e4kv5CM8PhTF2aUw6AwoL1KhvKjqu7REIUFITCB4PB70WgNMJhPEMjH4AsaLEBDmD51Gj8KsYug1epQVlNe7M1x9w6gryzXc7+BKSLlEIUZM20ibz+3btw8jR44EALz11ltYuHBhncYWEOoHdZkaWrOIBzAOtNDYoFr2JLgDo7Fmnheh+UFEKYLHkctrt+gSCK4gEtFYvboIBQUFEInq14GF4Bj2XLM/+wqkRbHr+OpccRaybjUOvjwPPb1u+fK5B5gcJKVS6ZZjyfylkPpJUVleiUqVBnqNHiajCfJAOQLC/LjtbIWqUzwKYpkIQZEByE8vhLKwHP6hinqVFrpavlZRqoamQguxTAytWssJUhK5GFI/2zlZHoMCIluGozSvDGWF5ZAqJAiLCwGP71zB0YgRI7B//34MHDgQO3fubODBNj/4/IbJ5xo8eDD27duHQYMGYe/evQ3yGu5i1qxZWL16NeLj45Genu7p4TQIRJQieJy2bdt6egiEZoZAAAwfroXBIIOArHINCnuufY3o6GhPD6HJ4atzxVnIutU4+PI89PS65cvnviGgKEacqi0U3B4yfylEMhF0ah1UxRVch8C6IBQ6V5ZoMtHISyuAtoKZB5YOL78QhdtCyt0NxaMQFBWIwMiAGgLcwoUL6+zCqs7169fdcpzmhKfFdELjQDKlCB7nzJkznh4CoZmSlpbm6SEQmilkbhEaCjK3CA0FmVuE6viHKAAA5cUV3GOV5RoY9a6VTOl0Oqe2K84u4brkKYLlEEqEkCjEiGwVjpCYIK8PDPf28TVH1Gq1p4dAaATIvTgCgdDs0OuBP/+UIi8vAgkJgJM38Ah1gD3XADB5ciU51wS7kLniGLJuNQ5kHnoOcu69D3mADEV3SmDQGaBRaWEympCfUQixTISo1hFufS1VcQVUZvErIjHM7aHkBAKh6UKcUgSPExsb6+khEJoZej2FF14IwkcftYdeT+5qNSTsuX7hhSCfOtchISGeHkKTw1fnirOQdatx8OV56Ol1y5fPvbdC8SgogphsV2VROZTmcjqtWge91vlQdoGNmmOT0YSS3FJk38hDzq18FGYVAwACwv29QpDat28f14lu3759AIDff/8do0aNQkxMDAICApCcnIzXX38dxcXFdo+zZMkSm9335syZA7FYjP379wMA9u/fz23H/qseYVJb972cnBx8++23uP/++9GxY0cEBQXBz88PiYmJmDJlCn777TeYTPXvpugs27Ztw0MPPYSWLVtCLpdDIpFwY0lJSbHrcjKZTPjpp58wZswYREZGQiQSISwsDEOGDMHXX39t5bwTiaw7MS5atAgURXGuNY1Gg08++QTdu3eHn58f/Pz80Lt3byxbtgwGQ805PGvWLFAUxb3n+/bt447H/ktISLDap6KiAuvWrcOcOXPQtWtXBAQEQCgUIiwsDIMGDcJ///tfpxt3lZeX49NPP8XQoUO5393f3x/dunXDs88+i0OHDtX4XVevXg0AyMjIqDFWW+69PXv2YMaMGUhMTIRUKoVMJkN8fDz69u2Ll156CXv27HFqrI0JcUoRPE5DBdgRCARCQ8HjkXs6BAKhaUHWLYIt/EIUKC9SQV1WafV4RWkFAiNc78pn0BlQmqdERZkatMk60D4g3L/enf4aApPJhEcffRQ///yz1eM3btzAZ599ho0bN2LPnj2IjLTd6a8xMBqNaNmypU3RKTs7G9nZ2diyZQtWrVqF9evXQ6FQNNhYioqKcN9992H37t01nktPT0d6ejr+/PNPAIwIZElxcTHuvfdeK/EFAAoLC7F3717s3bsXy5Ytw/bt2xEfH++wZDIvLw/33HMPzp49a/X4iRMncOLECezYsQN//fVXvde+sWPHciJW9THv378f+/fvx9dff41t27ahffv2do+za9cuzJgxA4WFhVaP6/V6nD17FmfPnsWyZcvq1c1ywYIF+Pzzz2s8npmZiczMTBw7dgwpKSk1xuBpiChF8DgZGRkeXeQJBALBVQoKChAYGOjpYRAIBILTkHWLYAuRRAi/UAXKCxmnB8WjQJtoqErVEMvEEElF4AscX9QbDAbw+XzotQbk3sqH0cBkUgklQgSE+YE20RBJRRDLRA6P4ykWL16MI0eO4N5778VDDz2EuLg45OXl4dtvv8X27dtx69YtvPzyy1izZo1Lx1ywYAEef/xxnDp1Cj169MCKFSustnE2IB4AJ1QMHjwYo0aNQnJyMsLCwlBeXo60tDSsXLkSR48exe7duzF//nz88MMPTh/bFdRqNYYMGYILFy4AAHr06IG5c+ciOTkZYrEYt2/fxv79+7Fu3boa+xqNRowbNw5HjhwBAAwaNAjz5s1DYmIisrOzsXLlSvz111+4cuUKhg0bxolN9s7T5MmTcfnyZTz33HMYP348goODce3aNSxZsgRXrlzB5s2bsWLFCjzxxBPcPu+99x5eeuklPProozh58iR69uyJVatWWR23ujvLYDCgU6dOuPfee9GzZ09ER0eDpmlkZGRgw4YNWL9+PdLS0jBx4kScPXsWEknNDpL//vsvRo8ezf2tPPzww5gwYQLi4uKg0Whw+fJlbN++HZs3b+b2efrppzF16lS89dZb2LhxI6Kjo/HPP//YfW+2bNnCCVKdO3fGU089hQ4dOiAgIAClpaW4dOkSdu3ahePHj9s9hqcgohSBQCAQCAQCgUAg+ChBEQFQl1XCqDciJCYYRVnFMGgNyEsrgEAsQHSbSPB4jksu9VoDclMZQUokESIkNghimefL9JzhyJEjWLx4MV577TWrx0eNGoVx48Zh165d+PPPP/HZZ58hLCzMqWPGxMQgJiYGcjlTHimXy5GUlFTnMfL5fFy4cAGtW7eu8dzAgQMxc+ZMvPPOO3jvvfewdu1avPbaa2jTpk2dX88eb731FidIPfPMM1i6dKmVm6lHjx6YOHEiPvzwQ5SUlFjt++2333KC1COPPIKUlBRu3x49emD8+PF488038f777+PWrVtYsmSJw86GrBtq8ODB3GPdu3fHqFGj0LFjR+Tl5eHrr7+2EqVsvS/JyckOf+dVq1bZPJd9+vTB9OnTMXv2bIwaNQrXrl3D2rVrMXv2bKvtNBoNHnroIRgMBshkMmzdutVqzADQv39/zJkzB7dv3+YeCw8PR3h4OHczQSgUOhzr+vXrAQDx8fE4dOhQDbfc4MGD8cwzzzgsR/UUxMdL8DidO3f29BAIBALBJeLj4z09BAKBQHAJsm41LSoq7P/TaJzftrKy9m0rNTz4RYRDER4CRZAM/mF+4PF50Gh5UJYacft6CVQqGmUlBuRmqZB1qxS5t8uh0+gBACY9jdQL+ShXmqA3iqCICIeBFnPHrx4tVH1MnqZ79+549dVXazxOURTmz58PgHHLHD16tLGHZjUWW4KUJW+++SZCQ0NB0zS2bNni9jGUlpbiu+++A8CISF988YXd8jqRSISICOuw/K+++goAEBYWhmXLltncd/HixVwJ3IoVKxzGvDz77LM1xB0ACA4OxqOPPgoAuHDhAsrKymr/5RxQm7g3fPhw3HvvvQCAv/76q8bzP/74I7KzswEA77//vs0xs7Ro0aLO48zNzQXAzGdH5ZvBwcF1fo2GgjilCB4nPT0dHTp08PQwCAQCwWny8/NJkwYCgdCkIOtW0yI42H6p2z33mLBxY1WIc2ysEGq1bXFg4EATdu6s2rZtWyEKC21tK0KPHhIcPmxAUGQAgiID0Ka1AJm3q3sYZACA1ok6bF1/G1I/CSpVGkx+qAVuptkec3w8jevX9dz/DxsmwOHDzgepNzT33XefXXGle/fu3M9paWmNNaRaMZlMyM3NRXl5uVWgd0xMDAoLCzk3kzvZs2cPF17+3HPPuZQLnJ2djStXrgAApk+fDj8/P5vbCQQCPProo3j11VdRUlKCY8eO2RVxHnzwQbuv16NHDwBM2WNaWhq6du3q9Fhro6CgAKWlpdBqtdxjrIPu3LlzNbZnBUK5XI7HH3/cbeOoTlRUFAAmVP/WrVto1apVg72WuyGiFMHjKJVKTw+BQCAQXKLS227zEggEQi2QdYvgKpSDkj2KzzxXWc7Ytnj8pttRsV27dnafs3SVONthraGgaRq//PILUlJScPz4cYd/0w0RZH3mzBnu57vvvtulfS9evMj93KdPH4fbWj5/4cIFu6KUo1Bxy/etvLzcyVHa59ChQ/jyyy+xa9cuh+Vvts47e9569OgBmUxW77HY45FHHsGPP/6IoqIiJCcnY8KECRg1ahTuvvvuWl12noaIUgSPYysMjkCoDyIRjW+/LUZhYQFEItt3YgjugT3X7M++QvUQTELt+OpccRaybjUOvjwPPb1u+fK5rwvFxTq7z1U3qGRl6W1vCKB64zFLx1Jt2549qwfbCEyn0YMv4IEvYF6cogABFYGS3DLwxXwcO0GDpm2PuboJafdu73FJAXAoFFh2bjMajY0xHJtoNBpMnz7dYdB19e3djaXgwrpynMVSyAkPD3e4rWUDrNLSUrvbNdb7tmjRIixevNipbW0Jhex5c/WcucqwYcOwbNkyvPzyy6isrMS6deu4wPmYmBiMGzcOTz31FLp06dKg46gLRJQieJz6hP4RCLYQCIDx4zUwGmU1vrgR3At7rn0NUgLjOr46V5yFrFuNgy/PQ0+vW7587uuCOYfZo9taXvPL5bY6oIkQkehc8LclUqnLu/g8H374ISdIDRw4EE8++SS6du2KyMhISKVSToQZNmwYDh48yHXr80bslUrawpUOhQ3B7t27OUGqZcuWeOmllzBgwADExcVBLpdDIGDklLfffhtLlizx5FABMOHz06ZNw88//4ydO3fi0KFDKCsrw507d/Ddd99h+fLleOONN/Duu+96eqhWkKBzgsc5deqUp4dAaKakpqZ6egiEZgqZW4SGgswtQkNB5hahobDM1iG4H5qmsWrVKgDAgAED8M8//2DKlClo1aoV5HK5lSuoITurhYaGcj/n5OS4tK9lOV1eXp7DbdnAbgBclzxPsWLFCgBAUFAQjh49iqeeegqdOnVCQEAAJ0gBjs87e95cPWd1JTw8HM8//zy2bt2K4uJinDp1Cm+99RYCAwNB0zTee+89bNy4sVHG4ixElCIQCM0OgwHYvFmCvXtDYfAuh3izgz3XmzdLyLkmOITMFceQdatxIPPQczSlc68zGvH8/j1Ydfli7RsTCA5wxRVkj+LiYk6omTx5spUIZYlKpcL169fr/Xr2sAx9379/v0v7Jicncz8fO3bM4bbHjx/nfu7YsaNLr+Mszr4vly5dAgAMGTKECzO3xcmTJ+0+x563kydPckHxrlCfOcTj8dC9e3csWbIEu3fv5h5fv359nY/ZEBBRiuBxoqOjPT0EQjNDp6Pw5JPBWLQoCTpd0w3ebAqw5/rJJ4N96lwHBQV5eghNDl+dK85C1q3GwZfnoafXraZ07m+UluBkfh7W3bjq6aE0CVzpwuZriMViAPVzk1l213MkaqxcudJqW3czZMgQzrm0dOlSl7KaoqOjuW7r69evtxsabzQakZKSAoBZs3r37l2/QduBzTSu7X1hz2dFRYXdbc6cOeNQaBs/fjwA5r1bvny5q0N1eqy10b17d+5zoCGC8OsDEaUIHocEnRMIhKaGpwODCQQCwVXIuuU8agMTBq7S61Hp7bYuL8AdbqDmChtunZaWVuecp7CwMAQGBgIA1q1bZ1OcOHnypNNh3HUlMDAQTzzxBAAmfuX555+3+zvp9Xrk5+dbPfbMM88AAAoKCvDcc8/Z3G/x4sW4fPkyAODxxx9vsOtE9n1JTU11+L60adMGAHDw4EHcvHmzxvMFBQV4+OGHHb7WQw89hJiYGADAm2++iX379tndNisry+5Y8/PzHXYSXLduncOOjCdPnkRJSQkAIDEx0eGYGxsiShE8Dsk4IBAITY3a8hAIBALB2yDrlvOoLYSogkrXy218jYZ05zR1+vbtC4ARFF5++WWcPn0aN2/exM2bN5GRkeHUMXg8Hu6//34AwIULFzBkyBCsW7cOp06dwp49e/DKK69g2LBhkEgknIjSUCxZsgSdOnUCACxbtgy9evXCihUrcPToUZw+fRqbNm3Cyy+/jMTERGzbts1q3yeffBL9+vUDAKxatQrDhg3DH3/8gdOnT2Pr1q2YMmUKFxbeqlUrLFy4sMHyyvr37w+AeV9eeOEFnDp1yub78sgjjwBgnFKDBg3C0qVLcfjwYRw+fBj//e9/0aVLF1y+fJn7vWwhkUiwZs0aCAQCqNVqDB8+HI899hg2bdqE06dP48iRI1i1ahWmTZuGVq1a2R2ryWTCk08+iaNHj3JjtRTKXn31VURHR2PWrFlYuXIlDh48iDNnzmDXrl1YtGgRRo0aBYBxNs6ZM6f+J9GNkO57BAKBQCAQCAQCgWBGrddzPxdUqhHn5+/B0TQt9CYTyvU6BIklIP4pYPr06fj444+RlpaGpUuXYunSpdxz8fHxTmdAvfPOOzhy5AjOnTuHU6dOcWIJS3BwMH799Ve88847uHHjhlt/B0tkMhn27NmDKVOmYP/+/Th16hTmzp3r1L58Ph9btmzBvffei0OHDmHPnj3Ys2dPje06dOiA7du3Q6FQ2C3zqy/3338/PvjgA6SmpuLzzz/H559/zj0XHx+P9PR0AMDUqVPx6KOPYtWqVcjOzq7h8OLz+fjf//6HkpISHDlyxO7rDRkyBFu2bMGMGTNQUlKCVatWceH1tTF06FD07dsXR48exc8//4yff/7Z6nlLp1dpaSlWr16N1atX2zyWWCzGt99+i549ezr12o0FcUoRPI5l8B2BQCA0BVq0aOHpIRAIBIJLkHXLeSydUvkOymEIDJaloXdU5ShQq1Gq1XhwRN6DQqHAvn37MG/ePLRv3x4ymaxOxwkICMDevXuxaNEiJCcnQyKRQKFQoH379liwYAFOnDiBu+++282jt01oaCj27duHP//8E1OnTkVsbCzEYjEkEglatmyJadOmYe3atZgxY0aNfYODg7F//378+OOPuOeeexAREQGhUIiQkBAMHjwYy5Ytw9mzZxEfHw8AkEqlDfI7KBQKHD58GPPnz0eHDh0cvi8rV67EmjVrcPfdd8PPzw9isRjx8fF4+OGHuWM4w6hRo5Camor3338f/fv3R0hICPh8Pvz9/dG9e3c8//zzViHvLDweDzt27MBbb72FLl26QKFQ2CyZ/ffff/HFF19gypQp6NSpE8LCwiAQCODv749u3brhpZdewuXLlzFr1iynz1NjQdF1LW4l2EWpVCIgIABlZWXw9yd3Vmrj2rVraNeunaeH4dVU6Cqg+EABALjy0BX4S8m8coRaTaFNG6b++saNHMhkZJlrKHz1XGdnZ5MmDS7iq3PFWcj5aRx8+Tx7et1qrHNvomnw6plxtObqJXx38TwA4Inkzni4fVK9x2UwGKBUKhEXF9fs8lT1ej2EQiEq9HpkqZjMG4lAgHjiMGsS0DQNmqYhEonsdvbzFBqNptn9vTRFNBoN0tLSkJiY6NL74awu4l2zjuCTlJaWenoIBAKB4BKOurAQCASCN+IL69bN0hJM2LIBK8yCUl1R64lTyhVMJhMAoEhTda40BgP05scJhLpC8sp8A7dkShkMBpSWliIoKIi0BCW4DOkGQ3A3QiGNzz4rQWFhIYRCuaeH06xhzzX7s68gEJBIRlfx1bniLGTdahx8eR56et1qjHP/6K6/QQNYffUSHk/uXOfjsN33AKBA7TtB5zqTCYWVaviJRPATOv/9nKIoaAwGVBoMoCgKQh4POqMR5XodgsXE5UKoO97m3CI0DHX6dLp27Rr++usv7N69G6dPn+ZaCwJAUFAQevTogaFDh2LChAlo37692wZLaJ507drV00MgNDOEQuC++ypB0zKQLsUNC3uufY2EhARPD6HJ4atzxVnIutU4+PI89PS61dDn/mJRIVipS1zPm+TWmVK+IUpVGgzIUpXDRNOo0OshDxA6XQYpEomQo2aceH4iESR8AfLVFSjXEVGKUD8aKlOK4F24JD1u2bIFw4cPR8eOHfHGG29g165dKC4u5upQaZpGcXExdu7ciTfeeANJSUkYMWIEtm7d2lDjJzQDbAW6EQjuwLJNKoHgTsjcIjQUZG4RGormPrd+unqZ+zmsnheyVk4pHxGl8ivVMJmjhk00jVKd1ul91RoNynU6AECgSAw/kRAAU8KnMxnrNB4TTUOp08G3/IyE6vhC2THBSafUpUuX8Nxzz2Hv3r2gaRpdunTBiBEj0L9/fyQlJSEkJAT+/v4oKytDUVERLl68iMOHD2PXrl3YvXs39uzZgyFDhuCLL75AUlL9gwIJBALBEQYDsHevGNnZwUhMBEilVcPBnmsAGDxYS841wS5krjiGrFuNA5mHnqOhz32qsoz7WWesX5aRZaZUiVYLvckIIa95R5RojYx4FCyRolhTiRKNBjKBABJ+7W+UymAATdMQ8/mQmt9YuTn4vEyrc1kkpAFkqcpRaTBAY5QgXFq3jnUEAqFp4NTHQdeuXSEUCvHMM89g9uzZ6NKli83tQkJCEBISgrZt22Ly5MkAgLNnz+KHH37ADz/8gG7dukFnVtEJBJbIyEhPD4HQzNDpKMycGQIgBFOn5kAgIPfZGoqqc810U/KVcx0YGOjpITQ5fHWuOAtZtxoHX56Hnl63Gvrcl1tcY+iMdXPnsFg6pQCgsLISUXJFvY7pzRhMJrAN2UMkEpTptDCYTMhQKiHm8xEpV0BipySSBlBuZES8QItSvQCxmBGldFqESqVwpSq5qLISleYSylKtFiESKfikrtknEQqFnh4CoRFwqnxv1qxZuHHjBr788ku7gpQ9unbtiqVLl+LatWuYOXNmnQZJaN4oFM33Q55AIDRPSHtiAoHQ1GjO65aJpqHSV4lS2nqKUhXVRKnm3oFPZ+6SJ+TxwaMoxCoUkAuZTCmt0YgMZRk0Rttd0Eq1jIAl4PEQIBZzjyuEIvApCkaTCWq93ua+tjDSNNfFj8/jgaZplGg19fjtCE0Z0kTNN3BKlFqxYgViYmLq9UItWrTAihUr6nUMQvOkuWccEAiE5kdubq6nh0AgEAgu0ZzXLbVBb5U9VNccI+545vI91h2Ur27euTass0zEZy4NJXwBYhV+aBkQCJnZqVJYTZgz0iYUaTScgBQisXZDUQD8RIxIpdQ7XynDutTEfD4izGV7pRrn860IzQuNhgiSvgDpsUggEAgEAoFAIBCaLOXV4kGMNA2Dqe65UqwwEqPwA8DkSpVoNNh9OwP6egpe3gj7O4mquVL4FIVImRwAUKHXQ2MWr4w0jQylEoWVahhNJggoa5cUi79IBABQuRBYrjGX7UkFQihEIlAUBSNtcqkkU2M0okSrgb4ec4BAIDQeRJQieJwOHTp4eggEAoHgErGxsZ4eAoFAILhEc163VObyMLmgKn+mrm4pmqa5PKMosyBTptXi24vn8J9jh/Fv1u16jtb7YIPhbYW5C3k8TlzKU1dAbTAgS1UOvckEIY+HMJkMcX5+NjOjpIL/s3fe4XFVd/r/nOkz6l3ucrexsY0BGwglQIAEQhISIB0SCGSTbDabTULKbja9t2VDwoYkP9gUNgUSagKEjg3YGGyMjbsty7Z612j6zPn9cYtG0oymaOSRZs7neXiQNVczR1dnztzz3vf7fm3YLBZiUjKcZgmf3xSlbAhG3Gr+NEWp494hjg4O0Onz0Tw4wFAGLi3F9MM9yU6aiplBVn0vfvOb36R1nMPhoLa2lvXr11NdXZ3NSymKgI6ODsrLy/M9DIVCoUib/v5+daGkUChmFIW8bhlOqRq3i+EhTfwIRqN4bJmHJIdiUaJ66PesEl2UCgU54fUCmOVqhYQh4Dksif0KNW43Q+EwgUiEY0ODAFr2VFkZDouVcDgMSbJ/yhwO+gIBhkIhSlOEVksw3VhGFz+XzYY/EiEYjQCOCX/eGw7r4pfAbrEQjkVp9XqZV1aOR7XanJGEw2GVKzUNMBohTBVZvTs/9KEPITLogGCxWLjyyiv56U9/OulsKkXh0dvbm+8hKBQKRUZ49c2JQqFQzBQKed0y3DAVDic2MUwkw3KveIw8KcAsXRsMhegPadk22YaoCyGQUhKbhiVlRpnb2PI9A4fFSlN5OW3DwwQiEUodDupcbhy6s2qi36nMrotS4RA1MXdS4Qu00j0pJVaLBbtlJN/KeCwVfXr+UJXLSZ3bQ/vwMIOhIG3DXprKK1QHvxlIJI2/u2LqMd7jlgnev5MhK1HquuuuY3BwkPvuuw8hBGvWrKGpqQkhBM3Nzbz66qsAXHnllXi9XrZv3859993Hjh07ePnll6mqqsrpLxFPMBjkP//zP/ntb39LX18fa9as4Zvf/CaXXHLJhD/31a9+la997Wvjvu90OlXA2hRjU3cuFDnGbpd861v9dHV1Y7d78j2cgsY418bXxcJUfSgXMsU6V9JFrVsnh2Keh/let6by3BtOqVK7A4fVQiQSM0vSMsXIk3JbbVTpHQsHgkH6g1rYdjCSnShlsVgQQuD3+/F4ps97PBKLmS4I2wRzxGGxsqCsnKiUGYk7bpsNj92OLxymY3iYeWVlSY81S/esI3sDo3wvkEIMDEaj5t+uyulCAA0eD/5ohHA0SodvmNklquP3TCMTI4xi6ggEAlgslinbt2f1rLfeeitnnXUW5557LrfffjurVq0a9fjrr7/Oxz72Mfbu3cuLL76IEILrrruOhx56iJ/85Cd8/etfz8ngE/GhD32Ie+65h3/9139l6dKl3HXXXVx++eU89dRTnHvuuSl//vbbb6e0dGTBUnbBqef000/P9xAUBYbdDh/6kA+YPhd9hcrIuS4uFi9enO8hzDiKda6ki1q3Tg7FPA/zvW5N5bk3nFJlDjtOqxVfJJJ1ppRPF0Y8dhsVeve4/lCAwaD2GoFods4NIQR2u52hoSGqq6unzWbbr/8+dqs1YS7UWBIJUs4EIefxNHpKODI4gC8SpmVokDKHA4fFSklcOV9MSvqCmhHAYx/ZojqsVtNlFoxGcSbZmw2GNNGwzOEwXVYWIZhdUsLRwUGGQiEG7SEzH0sxMyjRS2gV+cXr9eLxeKbs5kZWz/rVr36Vrq4uHnrooXGCFMApp5zCAw88QHd3N1/5yleoqKjgV7/6FSUlJTz44IOTHnQytm7dyh/+8Ae+853v8IMf/ICbb76ZJ598kgULFnDLLbek9RxXX301H/jAB8z/3vve907ZeBUaW7duzfcQFAXKgQMH8j0ERYGi5pZiqlBzSzFVFPLcGgppDpkyu8MsQcu2zM6nB3K7bXYqdAHjuNdLTO8fl+3zArhcLoLBIB0dHVOe0TIRkViM494h2n3DtA8PA6ND4jMlqLvIkmG3WKh3a4K7PxKh0+fjuHeIE14vvkiYnoCfdt8wkVgMm8ViioEGI26p5IKg4bIqsY8WnVxWG7V6llqHb1h15JthDOvzU5E/+vr68Pl8U5oBnZVT6q9//SsXXHDBhAOrqKjgggsu4P777+fWW2+lrq6O0047je3bt2c92FTcc889WK1Wbr75ZvN7LpeLG2+8kS996UscO3aMefPmTfgcUkoGBwcpKyubNncwCp18figrCpNoFLZscXD8eAWLFiXN3lTkAONcA2zcGFLnWpEUNVcmRq1bJwc1D/PHVJ57bziufE/POco6U8pwStlsVOgOoHghajKilN1up7S0lIGBAQKBAKWlpVPqPkhGu294VEaTw2qj3GLJOrIkHA6nvJ53AXNdbobCIULRKL5IhKFwmCHfaNGhylNCaIzIZYvFkOEww34/zlji1/EFAiAl1mh03O9RgmAgJglFw7T299HgUe6bsRh5Z/ku8x1LIBBQlUt5QEpJJBJhYGCAoaEhqqqqqKiomLLXy0qUam9vT2vCCiHo6Ogw/z1nzhy2bNmSzUumxfbt21m2bNk4sWzDhg0A7NixI6UotWjRIrxeLyUlJbzjHe/gRz/6EQ0NDVM2ZgXU1dXlewiKAiMYFFxzTS1Qy1vf2obHo4TPqWLkXMOBA8VzrlXH0Mwp1rmSLmrdOjkU8zzM97o1lefeyJQqczjM8q7sRSnNKeWx2cc5diD78j0Dp9OJRReAent76enpOak3wn3hMIOhEEJoLiKJpNzh5FhPT9bPGY1GMxYOItGoWa7ntFqJSc1R1e0YGHesPxJhIBikz2LBm6CDZCgapTcQwCIEwt2f8HxGYjF6An6khC67nVK7HYsyIACaACGlxGazTTtTRiQSUfnDecTpdNLQ0DClmeCQpSjV0NDA008/jdfrHZW/FI/X6+WZZ56hvr7e/F5PT8+U/kJtbW3MmjVr3PeN77W2tib92aqqKv75n/+Zs88+G6fTyXPPPcfPfvYztm7dyrZt2yb8IA8Gg6Nsq4ODg5P4LYqPqZ7kCoVCkWtUxoFCoZhpFPK6NZIpNVK+l4tMKbfNht1iGVXyNRmnlIHdbsdut5vulJNVNeAPh/nKthcZCof5wPKVXN60KCfPOzw8nNX8KotEsAiBPYWg5R328tWXXsBhsfCriy8bF8j+0JFD3L1/L2fWN/Dp085I+jzPH+nh7v17ARDArJISzqxv5JL5C6h2jRe7ioVIJEI4HGb27NnY7dmXcU4FAwMDU+rQUSTHarWeNKEyK1Hq7W9/O7fddhvveMc7uP3221m6dOmoxw8ePMjHPvYxent7R2Uy7dmzZ0pDFv1+f8KgPZfeOcPv9yf92U996lOj/v2ud72LDRs28P73v5+f//znfOELX0j6s9/5zncSdu7btm0bJSUlrF+/nj179uD3+ykrK2PhwoXs3LkTgAULFhCLxTh27BgA69at4+DBg6Zba9myZWbJ49y5c7FarRw9ehSANWvW0NzczODgIC6Xi1WrVvHyyy8DMHv2bFwuF4cPHwZg9erVHD9+nP7+fhwOB+vWrTOznBobGyktLeXgwYMArFy5ko6ODnp7e7HZbJx++uls3boVKSV1dXVUVVWxf/9+AJYvX05vby9dXV1YLBbOPPNMtm3bRjQapaamhvr6evbs2QPA0qVLGRwcNN1zGzdu5JVXXqGzs5NFixYxe/Zsdu/eDWhhnD6fj7a2NgDOOOMMdu3aRSAQoKKigvnz5/Paa68B0NTURCQS4fjx4wCsX7+evXv34vP5KC0tZfHixWZHyPnz5wPQ0tICwNq1azl06JAZ3rZixQpeeeUV83zbbDaam5sBOPXUU2lpaWFgYACXy8Xq1avZtm0boAmfHo+HQ4cOAbBq1SpaW1vp6+vDbrezfv160yXY0NBAeXm5me2wcuVKOjs76enpwWq1csYZZ/DSSy8Ri8Woq6ujurqaHbt3mPOqu7ubjkCHeU4PHTpELBajtLSUyspK8zw0NjYSCATo7+8HYMmSJTQ3NxOJRCgpKaG6utqcdw0NDYRCIfr6+gDNMXj8+HFCoRBut5v6+npz3tXV1RGLxejR76gtXLiQ1tZWgsEgLpeLxsZG85zV1NRgsVjo6uoy53tnZyd+vx+Hw8HcuXPNOVpdXY3NZqOzs9P8W3V3d+Pz+bDb7cyfP988v5WVlbhcLtrb282/VX9/P16vF6vVSmPjyDrT1dVFTY3LnEtz5sxhcHCQoaEhhBAsWbKEgwcPIqWkrKyM8vJyTpw4Yf5dfT4fAwMD5vk+fPgw0Wg05flevHgxLS0thMNhPB4PtbW15ryrr68nEonQ29ub8fluamqivb2dQCCA0+lk9uzZHDlyJOPzXVVVhcPhMN+P8+bNo7e3l+HhYWw2G01NTeaaMNH5DgZtgCb8Hzx4kIaGUkpKSszzPXv2bLxeL4ODg+b5Nubs2PPd2NiI3+8fdb6PHDmS1pzN5HxPNGdra2vN99lE59vr9TJ//vy052yq871w4UJzTaioqMDtdpvnO37OWiwWFi9ebM7Z8vJySktLzZsus2bNYnh42Lw5Mp3WiP7+8Ki54nbH8rZGLFq0aNT59ng8eV8jPJ4R1/DAwAB+f7gg1ghjzhrnu7y8PK9rxOBgdNw8nIo1ItM5ezLWCK/Xy9KlS/O2Rhw8eMw89729vZw40Z3yfKc7Z7v038cSDhPVbxR7A0FOnDiR8RpxrFv7e0X9AQ4ePEipzUaf7sQC8IVCtLe3z8jriL91trFvcIBGl4tlMe2GfS7WCK/Xy8qVK6dsjbD7A/QGAwRiMU74fdDdM2qNeOLIYVr9PmqdLnp7e5OuEZfNnY+/f4AHO9to8fs44fexrbuLO/fu5hcXXYq1f+CkXUfA9Fkj3G43ra2t9Pb2smLFCvr6+ujq6kIIwYYNG3j55ZeJRCJUV1fT0NBg7u2WLFmC1+s1X2fDhg3s2LGDUChEZWUlc+fOZdeuXeYcDQQC5vXK6aefzu7duwkEApSXl9PU1DRqfxyNRjl+/Dh9fX1cdNFF7N+/n+HhYUpLS1myZAk7duwwz4PFYhm1Pz5y5AhDQ0O43W5Wrlxp7u3mzJmDw+Ewz/+pp57KsWPH6O/vx+l0smbNGl566SXz/VhSUmKe/1NOOYX29nZ6e3vH7e3q6+upqKgwz/+KFSvo7u6mu7vb3B8be7va2lpqa2vZu3evOUcHBgbMOWDsj8PhMNXV1TQ2NvL666+ba8Lw8LB5vs8880x27txJMBiksrKSefPmmfvjhQsXEgqFzLUoX3pEWM/oS4WQWUjzvb29bNiwgcOHD2O1Wlm/fj1NTU0IIWhububll18mGo2ycOFCtm7dSk1NDS+99BIbN27ky1/+ckIBJxesXr2ahoYGnnjiiVHff/3111m1ahX/8z//w0c/+tGMnnPWrFmsWrWKxx9/POkxiZxS8+bNY2BgIO9W6ZnAli1b2LhxY76HMa0ZDg1T+h3NlbjnA3sod6t5NRE+n2DpUu3Ct9jKM042xXquDxw4MO6GjGJiinWupIs6PyeHYj7P+V63pvLcv//Rhzk6NMh/n38Rv9v3Ols72vn3MzbyliycQHe+votfv/4ab1u4mFtO38D1//gbhwZGSspWVlXzy4svy9nYTxaBSISr//4A/cFg1ucmGSdjbv3z00+wo7uTL52xcZTDS0rJlQ/9lf5gkP+58BJW19Sm9Xy9AT/bu7r47d7dHBzoZ0lFJXdcdKnptCsmIpEIoVCIBQsWTDunlNonzmwGBwepqKhIqYtk5ZSqrq5m06ZNfPzjH+f+++/npZdeMlVF0LKk3va2t3H77bdTU1MDaEpebIq7HcyaNctUA+OJV9ozxVChJ8LpdKZshapIzvLly/M9BIVCociIbD5PFAqFIp8U8ro1OlNK294Es9x3xGdKAZSPyZXKRflePrj/8EH6g0FmlZRwyfymnD73yZhby6uq2NHdyb6+3lGi1OHBAfqDQZxWK8sq048EqXa5uXjefNbV1XH9Y3/n4EA/v937OjeuOnUqhq/IErVPLA6yTg1rbGzkL3/5Cy0tLTz33HOm9XTOnDmcd955LFiwIGeDTJd169bx1FNPMTg4OEqJM6x169aty+j5pJQ0Nzdz2mmn5XKYijH09vZSWVmZ72EoFApF2hiWZoVCoZgpFPK6ZXTfK7M7cFi1vKFJd9+za9uksWHngRkoSgWjEe7er5VcXbdi1bhMpslyMubW8qpqAPb2jTYLbGnXzAen1dVn5XKqcbn51Lr1fG3rCzx45BAfPmW1CkCfRqh9YnEw6RVp/vz5vP/97+fzn/88n//85/nABz6QF0EK4OqrryYajXLHHXeY3wsGg9x5551s3LjR7LzX0tJi1nEaGLXT8dx+++10dXXx5je/eWoHXuQkOvcKhUIxnVENLRQKxUyjUNetYDRCSHdFlTkcOC1W/ftZilLh0U6pCudYp9Tkuu9NFQPBIP/xwiYuue/PXPLXP3Pn67vMxx46cpieQIAGj4c3L2jK+WufjLm1QhelDvT3j/rbbu3QRKkNDeObXaXLBXPmUWq30x3ws7Nb7UumE2qfWBwUVH/FjRs3cs011/DFL36Rzs5OlixZwv/+7//S3NzMr3/9a/O46667jmeeeWZUp4sFCxbw7ne/m1NPPRWXy8WmTZv4wx/+wLp16zLOoVJkhiXHd2sUCptN8h//MUB3dw82myvfwylojHNtfF0sTLeWyTOBYp0r6aLWrZNDMc/DfK9bU3Xuh0KaiGQVAo/NNtJ9b7JOKVtip9R0LN9rHfbyyWeeoMPnM7/369dfo9zh4I1z53HnHk2gev/yU7Bbcp+ZdDLm1tzSMurdHjr9Pra0t3H+nLkEIhFTRNo4CVHKYbVy/uy5/O3oEZ443sK6uvrUP6Q4Kah9YnGQlij1yiuvsH79+km/WK6eZyJ+85vf8OUvf5nf/va39PX1sWbNGh566CHOP//8CX/u/e9/P88//zz33nsvgUCABQsWcMstt/Dv//7veDyeKR1zsXPmmWfmewiKAsPhgI99bBhQG7upZuRcFxdLlizJ9xBmHMU6V9JFrVsnh2Keh/let6bq3A/ppXuldjtCCJyGKBXLTjwySgFLHQ4AKvT/GwQi00+U+vnOHXT4fMwtLeVLZ5zFK50d/Or11/jJjpf5/b499AeDLK6o5K05DDeP52TMLYsQXDR3Pn84sJfHjx3l/Dlz2d7VSSgWo8HjYX5Z2aSe/6J58/nb0SM8dbyFT61dn/MSR0V2qH1icZDWu+3MM8/kPe95z7iSt3TZvXs311577UmZVC6Xix/84Ae0tbURCATYunUrl102ukPG008/zdimg7/85S/ZvXs3g4ODhEIhDhw4wHe/+13KJrnAKVKzbdu2fA9BUaAYbVwVilyj5pZiqlBzSzFVFOrcMkLOS+2aeOSwTM4pZYam6883tnwvImNEprh5UyYc6O/j6RPHEMC3zz6PNbV1XL9yFe9bthKATr8Pl9XK1zaeM2Wd5U7W3LpkvhYRs6n1BL5wmIeatdfd0DBr0m6tM+obqXQ46Q8GeVHPqVLkH7VPLA7SEqX+5V/+hb/85S+sWrWKc889l1/96lccOXJkwp85fPgwv/jFLzj77LNZs2YN9913H5/61KdyMmhFYRGdhjZoxcwmGoUdO+y8/noJanpNLca53rHDXlTneqq7yRYixTpX0kWtWyeHYp6H+V63purcmyHnuqPJEF6yLbMbGvN8Y8v3IHvBayowsqMumjufRRWVgFZO9/E16/j5G9/EG+fM4+tnvYGm8oopG8PJmlvLKquYV1pGKBble69s5ZkTx7EKwTVLJt+hzWax8JamhQD85dD+ST+fIjeofWJxkFb53k9+8hNuvvlmPve5z/H3v/+dF154AYC6ujpWrlxJTU0N5eXlDA4O0tPTw549e0aFkl1++eV8//vfZ+XKlVPzWyhmNDU1NfkegqLACAYFV1xRB9Rx4EAbHk9x5YacTEbONUV1rpWLNnOKda6ki1q3Tg7FPA/zvW5N1bkf1oPJS/Rg8smW7w0mcUp5bDb8kQgSCEQjeOz2yQw7J/gjETa3nQDg+pWrxj2+praONbV1Uz6OkzW3hBC8bdFifrZzB08cawHg7YuWsKgiN4LbOxYt5Q/797K1o51jQ0PMU5/1eUftE4uDtIPOV65cyUMPPcSBAwe47bbbuP/++2lpaaGzszPh8fPnz+cd73gHn/jEJ1i6dGnOBqwoPOrrVZigQqGYWZSXl+d7CAqFQpERhbpuGY4olx5MPhJ0nrl7JxSNms9XrjulFldUsqKqmlXVNTzcfJhANMrB/n529R7k3UtXUJJHcWpvXy9RKalzu1k4hU6oVJzMufXupSuwW6z8YterlNjs3HDKqTl77jmlpZzVOJsX2lv588F9/NtpZ+TsuRXZofaJxUHG3feWLl3Krbfeyq233srhw4fZvn07HR0dDAwMUFlZSX19PevXr2fhwoVTMV5FAbJnzx42btyY72EoFApF2pw4cULdcFEoFDOKQl23AoYopYtRDj2gOpsSO6N0T4ApNjmtVn51sZZP+8SxFgLRKP+26WkAjg0N8ZWN50xm+JPiNb3z3Oqa2rx2VzyZc8siBFcvWcYVTYuISZlzUfDapct5ob2V+w8f5B2LlpglkYr8oPaJxUHGolQ8ixYtYtGiqenioFAoFAqFQqFQKBQTEYhEAHDadFFqEplS8aHplgQiz9ig8H8cO5pXUWpXTzcAp9ZMfYnedMNtm9Q2NilnNjRy3uy5PNd6nB9v38ZPL7g4r4KfQlEMqF6XirxTiHftFApFYdPY2JjvISgUCkVGFOq6ZZbvWTWRYjKZUmbnPb10bywu29R0r8uGmJTs6jVEqdq8jqXQ5tan1q7HabWyo7uLbZ0d+R5OUaP2icWBEqUUeWdwcDDfQ1AoFIqM8Pv9+R6CQqFQZEShrluBqOaUGinfMzKlsi/fSypKWce7c/oCgYxfJxe0DA0yGArhtFpZWlmVlzEYFNrcaiwp4S0LtCiah5oP5Xk0xY3aJxYHSpRS5J2ODnUHQqFQzCwGBgbyPQSFQqHIiEJdt4xMKad1TND5ZJxSSXKKnNbxTqm9fb0Zv04uMEr3VlbVYLPkd0tXiHPrrQsXA/DcieMMhoJ5Hk3xovaJxcHUFOMqFApFHrHZJP/2b0P09PRgsyW+26nIDca5Nr5WKJKh5srEqHXr5KDmYf6YqnMf1DOljNI65yQypQZTlO8lE6XOnjU749eKJxCJEM0wtPvgQD8AK6qqJ/XaisQsr6xicUUlhwb6+UfLUd61ZFm+h6RQFCxKlFLkHdVRQZFrHA74zGeGALWxm2pGznVxoTIOMqdY50q6qHXr5FDM8zDf69ZUnfukTqloLOPnMsr3yh3OhI8nKt/b09eT8evEE5UxrvvH3/FFwvziwkuZU1qa1s8d0kWpxZWVk3r9XJDvuTUVCCG4cuFi/mvHy/z54D7evmhJ3h1pxYjaJxYH6p2lyDuvvPJKvoegKFCOHDmS7yEoChQ1txRThZpbiqmiUOdWTjOlsijf2zfJ8r3eQIDWYS/9wSD//sJzaTm8pJQjolR5xaRePxcU6ty6vGkhlQ4nx71eHjma+Hfs9Pn46pbnkz6umBxqn1gcKFFKkXfC4XC+h6AoMGIx2LfPxoEDDmKZ3yhVZIBxrvftsxXVuY7o5SKK9CnWuZIuat06ORTzPMz3ujVV5z4YMZxSRvmetr0JZpMplSLo3BnXfa/W5QagJxDAF8n+WrbD5zO/PjjQz//u2Z3yZ3oCfgZDIaxCsGAaiFL5nltThcdm5wMrTgHgzj27aBv2Apoo2OX3sb+vl3997kkeP3aUb770Yk6EqRNeL3/cvzcrUbUQUfvE4kCV7ynyTlVVfjuGKAqPQEBw0UX1QD0HDrTh8ajckKli5FxTVOe6pKQk30OYcRTrXEkXtW6dHIp5HuZ73Zqqc286pWzatsZpnYxTStsAl9lTd9+bXVJKIBrBGw7TPjzMoorKjF8PoNPvG/XvvxzazwdWrMRjS54vdUgPFp9bWpbQvXWyyffcmkquWryEPx7YS4fPx/sefZj5ZeUMhUKj/m4Oi5VQLMp3tm1hV083N646lSqnK+PX8oXDvPuRBwEosdvNsPViRu0TiwPllFLkndmzJxcOqVAoFCeb6moVLKtQKGYWhbpuGZlSZvme/v+olEQytGQNZRB0XuF0MLtEy39qHR4GNCFsV083UZn+63bqTqkL585jbmkp3nCYvzdP7LgxSvcWVeTfJQWFO7dAyyr78XkXcnpdA+FYjEMD/XT6fViFoNzhYGVVNXe+6c1cuXAxUSm57/BBPvnMEwSycI/996sjpWqv6d0Vix21TywOJuWU2rJlC48//jgnTpwgEAgkPEYIwa9//evJvIyiwNm9e7cKsVMoFDOKY8eOFWSwq0KhKFwKdd0K6k4pM+jcMiIchWLRjMKpB8NBID2nVLnDiUUI9vf3mWVdd+3ZzW/27uZLZ2zk8qZFab2mIUo1ekpYV1vPT3a8zJ8OTBysfXiwH4DF5ZVpvcZUU6hzy2BheQX/df6FHB4coMfvx2axsLK6BrdtZD58/vQNXDa/ia9s2Uzz4CA/eOUlrly4mGVVVUldb8eGhvjJjm2sqaljXV09DzUfNh9r1edUsaP2icVBVqJUKBTive99L/fddx+g1dUmQ4lSCoVCoVAoFAqFYioIRBI7pUDrwOfJYLdjlu8lcUrFP3eFw2mKV226U+pgfx8AzYMDab+mUQZW7/ZwedMi/t/rr3Fi2MsDRw7xzsWJhZ7p1HmvWBBCsLiiksUTlGmuq6vny2eezaefe4pHW5p5tKWZWpebfz9zI2c2zBp17CudHfzHi5sYDIXY2tFOo0crgVxZVc2evl6OZDCHFIqZTlai1De+8Q3++te/UlJSwgc/+EFWrlxJeXl5rsemKBIWL1b10gqFYmbR0NCQ7yEoFApFRhTqujU2U8oiBHaLhXAspruonGk/V6ryPdeY8j3DOdXm01wtHbrA1BtMXEGSCCPovN7jwW2zceOqNfx4+zZ+tXsnb5o3n3LH6PH7IxGODAwCTCiQnEwKdW5lwxkNjXxy7XoePHKI/mCA7oCfTz/3NF84fQNvXbiYcCzK/7z2Kn86sA8JeGw2fJEI7b5hHBYrX9l4Du995CH6g0H6goGssqkKCbVPLA6yEqX+7//+D4/Hw5YtWzjllFNyPSZFkeHz+VIfpFAoFNOIkL5xUSgUiplCoa5bwejo7nuglfCFYzFC0fSznYLRKCG9Y195MlEqrlyrwuGkRu/AZ2RKGaV4fYFg2q9rOKUa3B4A3rZwMfcdOsDhwQH+b/9ePrp67ajjd3Z3EZExGjweZnmmR8B4oc6tbLl26XKuXbqcQCTCf+14mYeaD/O9l7dy3DvE7t4etnd1AnDlwsXcvGoNNz7xKJ1+H1cvWcrc0jJmlZTSOuzlyMAAVfXFLUqpfWJxkFXQ+fHjx3nDG96gBClFTmhra8v3EBQKhSIj+vr68j0EhUKhyIhCXLcisRhhPcw8Pu/JYdW2OIbIlA6GS8qCSJoBFC98lTuczNK7zrUNe/FHIgyFtefoS9MpFY5F6Q34Ac0pBWCzWLh+5WoAnjreMi4mZVtnOwBn1DcihEjrdaaaQpxbucBls/H50zfwjkVLkMDv9u1he1cnHpuN75xzHp8/fQNVLhfff8P53HDKaj58yqmAlmEFqBI+1D6xWMjKKVVVVVXQXRYUCsXMxmaT/NM/eenr68Nmy3+r5ELGONfG1wpFMtRcmRi1bp0c1DzMH1Nx7kPREdHJFfe+0ULPg6MeT4UhKJU67FiSiD2juu85HKYo5YtEzDwpgP5gek6pbr8fCdgtFirjyrTObpyF3WLhuNdL89CgKVJAvCilSuZmAkII/u20MziluoYt7W2EYzFuXHXqqNLLJZVVLKmsMv+9sLyczW0nlCilKBqyEqXe9KY3sXnzZqSU00ahV8xczjjjjHwPQVFgOBzw5S8PEosJMmi6o8gC41wXGyrjIHOKda6ki1q3Tg7FPA/zvW5Nxbk38qRgdNc9wykVzECUGg5rIecl9sQuKRjtxqpwOnFabdS4XPQEArza3WU+1hcMpLVPMsr96tyeUUKYx27njPpGXmhv5bkTx01Rqi8Y4EB/PwCnTyNRKt9za7pjEYLLmxal3ZGxSf97ZxKYX6iofWJxkNVlzze+8Q16e3v56le/muPhKIqRXbt25XsIigKlpaUl30NQFChqbimmCjW3FFNFIc6tQHSk8168AGQIVJmIUoarymlJ7lR0jum+BzDLUwowSpQKx2IMR8IpX7MjrvPeWM6bPQeA51qPm997qUNzSS2uqKBaz7OaDhTi3MonhijVMjSU55HkH7VPLA6ycko9++yzfPjDH+ab3/wmjzzyCFdccQXz58/HkuTW3nXXXTepQSoKm0Ag/Q4lCkU6xGJw4oSVlhYL8+ejXAdTiHGuAebMiRbNuQ6HU282FKMp1rmSLmrdOjkU8zzM97o1Fec+EDFEqdFbGrsuLBl5U+lgHGubYGDx3feMDn1zSkvZ1dvNq92do47tCwQptScOTDcwnFINnvGi1Btmz0G88hJ7+no5PDBAU3k5d+/bA8A5s+ak8RudPPI9twoNI/S+LxggEotNOCcLHbVPLA6yEqU+9KEPIYRASslLL73Etm3bJjxeiVKKiaioqEh9kEKRAYGA4KyzGoAGDhxow+NRuSFTxci5pqjOtSfBBkIxMcU6V9JFrVsnh2Keh/let6bi3Af18j3nmBw2o3wvnEHQuSFK2SdwSpXoIlOFw2EKBafW1PJoSzO+SGTUsX3BAPPKyiZ8zXaf1rWvPsHfpsbl5vw5c3nmxHF+/fpOLpgzj4MD/ZTY7Lxn6Yq0f6+TQb7nVqFR7nRgFYKolPQHA9QmcNIVC2qfWBxkJUpdd911KktKkTPmz5+f7yEoFApFRtTW1uZ7CAqFQpERhbhujZTvjd7SGIJRZk4p7bkMQSsRc0tLufGUU5lfVm5+74yGxoTHptOBr3VYC36fU1Ka8PEbT1nDsyeO88yJ42zVS/c+sGIlFU5nyuc+mRTi3MonVqEF3/cE/PQEiluUUvvE4iArUequu+7K8TAUxcxrr73Gxo0b8z0MhUKhSJuWlhaWLl2a72EoFApF2hTiuhXQ3UnxWU8wkimV6/I9IQQfPmX1qO/NKSml0VNiup7cVhv+aIS+NDrwGaLU7CSi1KKKCi6d38SjLc34IxFWVtVwzZLlaf0+J5NCnFv5RgvQ99Nb5OVrap9YHGQlSikUCoVCoVAoFApFPgnm1CmlHeuYoHwvEUIIzqhv4KHmwwAsraxiZ08X/SnEhKiM0TasCVlzShOLUgCfWnc6c0vLOKW6hjMbGkd16VMULjUuN9BHT8Cf76EoFFNOTlLTpJR0d3fT3d1NLIPFX6EAaGpqyvcQFAqFIiPq6+vzPQSFQqHIiEJctwJ6ppRrbKaUIUpl0H3PONaeRah0fAnfsqoqIHX5XqfPR1RK7BYLte7knfTKHQ4+fMpqNjbOmraCVCHOrXxT7XIB0FPkTim1TywOJiVKPfHEE7z5zW+mtLSUhoYGGhoaKCsr4y1veQtPPPFErsaoKHAiY4IhFQqFYrqj1i2FQjHTKMR1y8iUcubAKRUyg84z3x6dWd9IpcPJ4opKMx8qVfmeUbo3q6QEq5jZ3dUKcW7lG0OU6i1yp5SaW8VB1ivg17/+dS699FIee+wx/H4/UkqklPj9fh599FEuvfRSvvnNb+ZyrIoC5fjx4/kegkKhUGREb29vvoegUKTFH/fv5a49u/I9DMU0oBDXraC+YXWNzZSyZp4pFTFEqQmCzpNR4XTy20sv5+dvfBNVupiQyinVqpfuJcuTmkkU4tzKN7UuzT1X7E4ptU8sDrLKlHr88cf56le/isPh4Oabb+bGG29k8eLFABw+fJhf//rX3HHHHXzlK1/hnHPO4aKLLsrpoBUKhWIirFbJ9dcPMzDQj9U6Pa3uhYJxro2vFYpkqLkyMVOxbnnDIX66czsA71y8lHLH9OrYlQ/UPMwfU3Huze57ttFbGrvplEq/fC8UM8r3MsuUMjDEqCq9M14qUeqEdwhI3nlPUdyMlO8Vt1NKURxkJUr993//N0II7r//fi677LJRj61Zs4Zbb72VK664gre85S3ceuutSpRSTMj69evzPQRFgeF0wre/PUAkEsVmU/0cphLjXBcbCxcuzPcQZhzFOlfSZSrWrZahIfPrwVBIiVIU9zzM97o1FefeyJQa233Pnk3QeTT78r14qpyamNAfSFW+VzhOqXzPrUKkWndKFXv3PbVPLA6yWnW3bNnCOeecM06QiufSSy/lnHPO4YUXXsh6cIriYO/evfkegqJAaW1tzfcQFAWKmluKqSKXc6tlaND82hsO5+x5FTOTQly3ghGj+14SUSqaefe9SYtSusOlPxSc0Kl1Qs+Umqjz3kyhEOdWvqk1M6WKW5RS+8TiIKtVt7+/nwULFqQ8bsGCBQwMFOfdKEX6+Hy+fA9BUWBICT09Fjo6YkhVnTGlGOe6p8dSVOc6mCLAVjGeYp0r6TIV69bROFFqOBzKzZPOcIp5HuZ73ZqKc29237OOLd/TM6VkJqKUJiA5sizfM6hwOE1hq8efWFCQUtLq1USpQnBK5XtuFSKGuOmPRvAV8U0FtU8sDrLyh9fW1qalWu7du5fa2tpsXkJRRJQWwB0ixfTC7xesWdMINHLgQBseT5HtPE4iI+eaojrXLv1iUZE+xTpX0mUq1q1jceV7yimlUczzMN/r1lSc+6DRfc+WzCmVfqZUeBJB5/FYhKDW7aZteJhOv4/GkpJRj0spufXVVxgKh3BYrAUhSuV7bhUiHpsdt82GPxKhJxDAY7fne0h5Qe0Ti4OsVt03vOENbN++nbvvvjvpMb///e955ZVXOPfcc7MenKI4MELyFQqFYqbQ2NiY7yEoFCkZ7ZRSolSxU4jrVjKnlC2LTKmRoPPJiVIA9W4PAF3+8S6PPx3Yxz0H9wPwb6edPi6kfSZSiHNrOlBj5koVb9i52icWB1mtup/73OcQQnDddddx7bXX8vDDD/P666/z+uuv89BDD3H11Vdz/fXXY7Va+exnP5vrMSsKjFdffTXfQ1AoFIqMaG5uzvcQFIoJicoYx73KKaUYoRDXrUCSTCmH/u9MRKmImSk1ufI9gDpTlBotJvgjEX6793UA/mXtet66sDA23IU4t6YDNUYHvhSdHAsZtU8sDrKS5s8880xuv/12PvGJT3DPPfdw7733jnpcSonNZuNnP/sZZ555Zk4GqlAoFAqFQqFIj/bh4VEbcuWUUhQiQaP7nm1sppThlEq/fC+Uo+57AHVuzeEy1il13+ED9IeCzCkp5Z2Ll076dRSFjeGU6k7guFMoComsV92bbrqJV155hRtuuIFFixbhdDpxOp0sWrSIG2+8kVdeeYWbbropl2NVFCjz58/P9xAUCoUiI1ReomK6E1+6B+BVQedFTyGuW4Foiu57GTilctV9D0acUp1xYkIkFuMP+7VM3g+uOMUsMSwECnFuTQeMMtCOIg77VvvE4mBSRcyrV6/mV7/6Va7GolAoFAqFQqHIAS1xIeegyvcUhYmRKeVMJkpFM+++Z7dOvnzPzJTyjZTv9QYC9AQCWIXgzQsWTvo1FIVPvWe8uKlQFCKFI9ErZiwtLS35HoJCoVBkRHd3d76HoFBMiOGUqnA4AK18T0pJTBZPtznFaApx3TLKUktsozuTGblQoQzK9wynlCOH5XvxYkKfngtU5XQVlEsKCnNuTQcaPMoppfaJxcHMb/egUCgUY7BaJddc42NwcBCrVW3AphLjXBtfKxTJUHNlYnK9brXootTK6hpebG/DGw7xr88+RX8owK8vfnPBbYrTRc3D/DEV534opJWlluniq4HhlIpkUr6nu6py8d4wHC49AT9RGcMqLKYoVel0Tvr5FcVBg6cEKG5RSlEcpCVK3XDDDQgh+Pa3v01DQwM33HBD2i8ghODXv/511gNUFD5r167N9xAUBYbTCf/1X/2Ew2HsdnvqH1BkjXGui42mpqZ8D2HGUaxzJV1yvW4ZotQpuijV4fNxTO/G1+HzMae0dNKvMRMp5nmY73Ur1+c+HIuamVLJRKlQJqKUNJxSky/fq3a5sApBVEr6AkFq3W76gkFAc0oVGvmeW4VKg14G2hvwE45Fc9IZcqah9onFQVqi1F133YUQgs9//vM0NDRw1113pf0CSpRSpOLQoUOsWrUq38NQFCDt7e3Mmzcv38NQFCBqbimmilzMrcFQ0NwAr6yqAeCE12s+bhViUs+vmJkU2rrlDY3kpJWMEXKNXKjMnFJ6plQOnFJWYaHa5aLL76fL76PW7abfKN9zFZ5TqtDm1nSh0unEYbEQisXo8vuZXVJ8NxPUPrE4SEuUuvPOOwGYNWvWqH8rFLnAG3ehrFDkAinB7xf09YWYOxfU/mvqMM41gNsti+ZcBwKBfA9hxlGscyVdcrluGSHn9W4PtXq2TYyRcqlMOpIVGsU8D/O9buX63A/pHSVLbHasYrSQNOKUSj9TKpTD7nugdeDr8vvp9PtYSQ19gcJ1SuV7bhUqQgjqPSUc9w7R6fMVpSil9onFQVqi1PXXXz/hvxWKyeDR6+4Vilzh9wuWLp0FzOLAgTY8HpUbMlWMnGuK6lw7VSZIxhTrXEmXXK5bRunevLIyShOUAhazKFXM8zDf61auz32yPCnILlPKODYX3fdAE4Vfp4cuv9aBr7+AM6XyPbcKmQa3h+PeITp8w/keSl5Q+8TioDhTLhXTihUrVuR7CAqFQpERs2fPzvcQFIqkGJ33FpSVU2Ifv2GPZOAeURQOhbZuGU6pMsd44dXI3glHYxwZHOD+wwdTdp4Mx3JXvgcjndPahjWnR38BZ0oV2tyaThR7Bz61TywOslp1rVYrN954Y8rjbrrpJmw21eBPMTGvvPJKvoegUCgUGXHkyJF8D0GhSIpRvje/rJwS+/jrsGJ2ShUzhbZuGU6p0gTCa3z53n9tf5kfvPISL3e2T/h8oWhuy/fmlZYBmA0GRrrvFZ4oVWhzazphdODr9BenKKX2icVBVquulBKZ4m5D/LEKhUKhUCgUipNDi+mUKsMqLHjG3CCMqGszRQHgDWtB52WJRCnrSPleT1ArnzPE2mSY5Xs56nA2r6xs1OsazQcKsXxPMXXUF7lTSlEcTGn5ns/nU+3YFSmZO3duvoegUCgUGVFdXZ3vISgUCYnEYhzXnRnzysqB8U4SVb5XnBTaupVOplQoFsMfiQCpnSZGKLrDmiunlPb+axv2EonFRrrvFaBTqtDm1nSiwW2IUsWZKaX2icXBlIlS/f39bNq0yezYp1AkQ5V4KhSKmYZatxTTFW84TFR3QtW6tM57JWNuEKryveKk0NYtI1MqUZh/vNvJG9IcVZ0TOE1iUprvm1w5pWrdblxWK1EpOTzQTyCqiV5VrsJzShXa3JpONJZo5Xttw8Mpc9EKETW3ioO0/8qLFi0a9e977rmHp59+OuGxkUiE9vZ2otEoH/3oRyc1QEXh09zcTENDQ76HoVAoFGnT2dlJRUVFvoehUIxjWN+ou602bLpbZOymPZOOZIrEfOXFzbT5hrntgotx5Khb21RTaOuWWb6XwCnliMuFGo5ox01U/hQv1OYqU8oiBHNLyzg40M/Onm59XFbc1sLbZBfa3JpOzC4pxWGx4o9GaB32MlfPKisW1D6xOEh7VWxubja/FkLg9Xrxer1Jj3c4HLzjHe/g29/+9qQGqFAoFJlisUiuuMKP1+vFYim+u0onE+NcG18XAu3DwzzbepzLmxYmDNBVZEchzpVckqt1y9iox7ujlFNqhFzMw0gsxhPHWwB4rvUEF8+bn7PxFTK5XgPM8r0E67QtgbA0UfleOK6kNVeiFGjNBg4O9PNadxeguaSEEDl7fkXhY7NYWFRRwd6+Xg709xWdKKUoDtIWpYyuClJKFi1axNVXX80PfvCDhMc6HA7q6uqU3U6RFqeeemq+h6AoMFwuuOOOPoLBIE4VKDqlGOe6kPjl7p082tLM/+3fw+8vuwKPbXxpyPz5ahOaKYU4V3JJrtYtr1HS5EguShWzUyoX89AoGwPY29czY0SpfK9buV4DJsqUslksWBDEGBG/uvw+ojKGVYwXncLR3DulYKQD36uGKFWAeVKQ/7lV6CytrGJvXy8H+/u5cG5xnWu1TywO0l51FyxYwIIFC2hqauL666/nzW9+s/m9sf/NmjVLCVKKtGlpacn3EBQFSnd3d76HoJiBbO/qBKDL7+dbL72Y8Bg1txRTxWTnluGUKrWNbNTHB50XryiVCwb0Lmowsl7MBApt3TLEwUROKRjvlopKSV8gkPBYI+TcJiw5dTIZHfi6A5pDrFBFqUKbW9ONJRWVABwYKL4bO2qfWBxkpRzdeeeduR6HoogZGBjI9xAUBYpPtc9VZEEoroxjU+sJYlJiGbNJUXNLMVVMdm4NJyjfG5spVczle7kgXpTa19fLYChIuWP6u3ILbd0yRakETinQuuiFxnSabPf5qNW7mcVjCLX2HHXeM5g3ptSqskDd24U2t6YbSyqrADjY35/fgeQBtU8sDqas+55CkS4uV2HeNVLkD59PMGfObN74xgvw+VR2w1RinOs5c2YXxLn2RyL0x204o1KabbzjsSfo9qSYmEKbK7kmV+tWovK9sxpnU+/2YNPLlopZlMrFPOwPjawRkpnjlsr3upXrNWCiTCnILFcqpL8nHDks3QNoKq8Y9ZxVBSpK5XtuFTqGU6rT72Mwbv0pBtQ+sTiYVI3dtm3buOeee9i3bx+Dg4PIBG0qhRA88cQTk3kZRYGzevXqfA9BoVAoAGj3DQOas8RptdITCNDl91Ptco86TuVnKKYrZvle3Eb9tLp6/nLF2/naluf5x7GjRGXxilK5IN4pBZoodcGceXkaTfoU0roVk9J0BcYLsPE4LOO7InYmcfSEo3r5XoKfmQwldju3X3gJDzcf5ujQIG9pWpT6h2YghTS3piMldjuzSkpoGx7mYH8/6+uLpxud2icWB1mLUp/97Gf5yU9+YgpRQohRopTxb9VhQpGKbdu2sXHjxnwPQ6FQKGgf1kSpWSUlWITFFKWWV40+7tChQyxdujQPI1QoJsYs30sQ0G8EOBezUyoXDIxxKnRN0NVtOlFI69ZwOGxGmCdzSiUKLE8qSk2RUwpgeVU1y6uqc/6804lCmlvTlaUVVZooNdBXVKKU2icWB1mtvH/+85/58Y9/zJw5c/jFL37BpZdeCsCjjz7Kbbfdxtlnn42Uki984Qs8+eSTOR2wQqFQKBRTRZvulGr0lFKnu6O6Z8iGU6GAeKfUeFHKqkSpnGCU+DZ4tGyiwVBoosMVU4CRJ+W0WnFYE7ubEolSHf7hhMeGpyhTSqHIFYsqKgA4MjiY55EoFLknq5X3jjvuwGq18sQTT3DTTTcxa9YsAC655BI+/vGPs3nzZv793/+dH//4x1TobyCFIhnG/FEoFIp80z7sBaDR46HWrYlSnX7/uOOqqqrGfU+hmA6MZEqNd48Ym3TVfW9yGE6puXqI9dAMEaUKad3y6ud8bGfJeOIFJqNuo9M3fj0HCOuB6ImELEVqCmluTVcWluuiVJEFf6t9YnGQ1cq7fft2Nm7cOKFN82tf+xqzZs3im9/8ZtaDUxQHHs/4LigKhUKRDwyn1KySEur0Dk2JnFKOJN2eFIp8M5FTSpXv5QbDKWV0VjNcO9OdQlq3RjrvJQ/YtokRB5XRoS8UiyQ81nRK5ThTqlgopLk1XWkyRKnBgYQ5zoWK2icWB1mJUkNDQ6MC7YyFyOv1jjyxxcLGjRvZvHnzJIeoKHQOHTqU7yEoFAoFMJIp1egppV53SnUlcEp1dHSc1HEpFOkyUaaUTTmlcsLAGFFqppTvFdK6NRTS5nmyPCkAR5xTyjguGku8mR8RpZRTKhsKaW5NV+aVlmEVguFIOOF1SaGi9onFQVZB53V1dfT395v/rq2tBaC5uXlUQv7w8DCDqu5VoVCcZCwWyUUXBfD5hrFYiuduUj4wzrXx9Uwn3inVH9Q+IrsCKlMqFxTaXMk1uVq3TKeUKt9LSC7mYf+Y8j1/JEIkFjNFP0VicrkGGOHy1RO0i4//e5TozsFIEodJKKrK9xTTG4fVytzSMo4ODXJkcIB65SBSFBBZiVJNTU0cPXrU/Pdpp52GlJK7776bb3/72wC0t7fzzDPPsGDBgtyMVFGwrFq1Kt9DUBQYLhf89re9BAIBXBNcsComj3GuCwF/JGKW5TR6SszNSXeCO5Lz5k3/9u/TjUKaK1NBrtYtM1MqQfmeTajyvVzMQ8MpZYhSoJWTVTmn9+dNvtetXK4B7cYNBE9p0mMccaV4I06pxHM/osr3JkW+51axsLC8whSlNjYWR9aS2icWB1ndDrj44ovZu3cvzc3NALzlLW+hurqa733ve1xzzTV85jOfYePGjQwPD/Oud70rl+NVFCCtra35HoKiQOntVRtgRfoYm5wSm50yh8PMlPKGw/gjo3NI1NxSTBWTnVtm+V4iUUo5pSZNKBrFp68HVS6XKf7NhLDzQlq3WodHXK3JiHc9lerZU9FkTin9PeFQ3feyopDm1nRmYVyuVLGg9onFQVZOqfe85z20trZy7NgxmpqaKCkp4c477+Q973kP9957r3nc6aefzhe/+MWcDVZRmPT19eV7CIoCZXg4cetnhSIRnT6tHMRo815it+O22fBHInT5fcwvKzePVXNLMVVMZm4Fo1HTBZUoa0cFnU+eQb10zyoEpXY7ZXYH3nB4RuRKFdK61aZ3Sk1XlDKdUjLx3DfeE6oEMzsKaW5NZ5qKUJRS+8TiICtRauXKlfzyl78c9b0rr7ySAwcO8OCDD9Lb28vKlSu58sorsVqVDVYxMfYEd3MVisng8wnWrGlAygZee60Tj0fl10wVxrkG2LmzY0af6049oyQ+p6HO7aZlaIhuv3+UKGWzZfXxWdQU0lyZCnKxbhmlewJwJ5ijplMqyca8GJjsPDRKfMsdDixCUOZw0OYbnhFOqXyvW7lcA8zyvZLk5Xt2a4JMqSSCbDimZUo5VPleVuR7bhULC8u165CWoeLJbFb7xOIgpyvI7Nmz+ehHP5rLp1QUAevXr8/3EBQFiN+v7naeLArlXBtOqXp3nCjl8tAyNGSG6hosXLjwpI6tUCiUuTJVTPb8xJfuWYQY97gq39OYzHke0J1SFU4nAGV6oPxQePqLUtNh3crFGjAYCpmB/o2eiZxSCTKlkpTvhaOq+95kmA5zqxgw5rs3HMYXCeNJ0GW10FD7xOIgq5XXYrGoCaLIGVu2bMn3EBQKhYIOv3bnPd4p1VCifX3c6x117IEDB07ewBSKNDE77yUo3QNVvpcL+oOa+FTp0EUp/VzPBKdUoaxbRuleldOZ0BFoMKp8z2EEnScRpYygc1XhkRWFMremOx49VgASN2EpRNQ+sTjISpQqKSnhlFNOyfVYFAqFQqHIG2amVJxTanllNQB7+1SIq2L6M6y7dRKFnMOIU0qJUtmTzCk1EzKlCoV0SvdgTNC53Qg6Tzz3Q3r5nl0op5RielPndgPFI0opioOsVt6lS5fS2dmZ67EoipSGhoZ8D0GhUCgSZkqtrK4BYE9vDzKu7KOiouLkDk6hSIMh0ymVWJQyNulRJUpljRF0Xq47pcodM8cpVSjrVqsRcj5B6R6MFqVKdEdbJIlTKmI6pZQolQ2FMrdmArUuXZQKFIcopfaJxUFWK+8HPvABnnvuOQ4dOpTr8SiKkPLy8tQHKRQKxRQipYzLlBrZ6CypqMQmLPSHgrT5RroLufU7lQrFdGJYle9NOf5IBACPXkJjlu/NgEypQlm32oYNp1QqUSo+U0oTamNIYglypUL6e0IFnWdHocytmUBtkTml1D6xOMhKlPrXf/1XLrvsMi666CLuvvtuAoFArselKCJUHbpCocg3Q+EQgahWvlHvGbm4dlitLKmsBGBP70gJX3t7+0kdn0KRDkb3vWROKatQotRkMUQpI9dlJpXvFcq61a6LUo0ZlO8ZfycgoSgV1td/mwo6z4pCmVszgVqX5uYuFlFK7ROLg6y67y1ZsgQpJceOHeODH/wgH/zgB6mvr0+okgshlKNKoVCcVISQnH12EJ/PjxCq7fxUYpxr4+uZSofukqp0OHFaR380rqyuYW9fL3t6e7h43vx8DK8gKJS5MlXkYt3yhka67yXCrrrvTXoeJhOlZkL5Xr7J1RpguFZnpyrfs8ZnSo2IUpFYbJz4ZAadK1FKMc0xnFJdAV+KIxWKmUNWolRzc7P5tZGx0dHRkfBYkaAlsUIRz8qVK/M9BEWB4XbDPff04PP5cMeFVityj3GuZzpm6Z5n/HxZWVXNX4E9fSO/55w5c07W0AqGQpkrU0Uu1q2xpWVjUeV7k5+Hxjl26eJ1+Qwq38v3upWrNcBwiNSmeJ/El+/FuwejiZxSRvme6r6XFfmeW8VEsZXvqX1icZCVKHXkyJFcj0NRxHR2dqp6YcWUMDg4iCeByKBQjMUMOU+wyVlRpYWd7+/rQ0qJEELNLcWUMZm5FYjqgkkSUcpwh0SSdCBTpGZcptQMKt8rhHUrHIuaAmC1yzXhsYYIa7dYcNpGxKZEHfjCMVW+NxkKYW7NFOqKLOhc7ROLg6xEqQULFuR6HIoipqenhyVLluR7GIoCZGhoiMbGxnwPQzEDmMgpNa+sFAH4oxH6ggGqXW41txRTxmTmVlDPxXFZU4hSReyUmiwj5Xua82Ymle8VwrrVH9TK/6xCmJ0Pk2GIUm6bDQsjlRvRBB34TKeUEqWyohDm1kzBcEr1+P3mjbJCRu0TiwO18iryjlVZpRU5xucTnHpqA29/+zn4fIX9YZ1vjHN96qkNM/pcT+SUsluspljVqgfsWtTGJWMKZa5MFblYt0ynVJLPVVW+N/l56I9quV1GppQhjIRiUYL6+Z+u5HvdysUa0Kc3V6p0OrGk2IzHi1JCCKz68YmcguGoypSaDPmeW8VEje6UCsViU+LQ/O7mZ/n0o38jGJke65naJxYHWTmlDAYHB/nd737H888/T1dXFxdffDG33HILAPv376e5uZnzzz8fVwp7raK4OeOMM/I9BEUB0ttrBdQH2clAO9czm9ZhLwCNSVqMzy4ppcPno3XYy+qaWhYvXnwyh1cwFMJcmUomu24FIppTypmqfC8WY3dPN95wmI2Ns7J+vZnKZOahXz/HRomkx2bHgiCGZCgUwume1KX1lDId1q3JrgG9QUOUSr23MPKh3Lpz0CosRGU0oVMqpJfvxedQKdJnOsytYsFhtVLpcNIfCtLl91HhdObsuTuHh7nj5ZcAaCgp4QvnXpCz584WtU8sDrKWtR977DEWLVrEJz/5Se6++24ef/xx9u7daz6+b98+3vKWt/DAAw/kZKCKwuWll17K9xAUCkWRY4hSc5O0GJ+tf/+EVzvu4MGDJ2dgCkUGBNN0SkViMT6/+Vlu2fwMg6HgSRtfITA2U8oiBB679vVweHo4C5JRCOuW4ZSqTkOUKndom3Uje8pm0ZxSiTKlIqr73qQohLk1k6hxT02u1La2E+bXv3xlG1tPHM/p82eD2icWB1mtvHv27OGqq65iYGCAj33sY/zxj380u/AZXHbZZXg8Hu6///6cDFRRuMSKuIxAoVDkH38kQo++0ZldWpbwGEOUMsSrsZ95CsV0IJAiU8rYcAeiUfpDQaJSzoiA7umEPzK6fC/+a/80L98rhHWrT8+UShVyDrC2to7Pn76BT5+mOS2swhBlEzmljO57SpTKhkKYWzOJWtfUdODb2qqJUk6rDQn8duf2nD5/Nqh9YnGQ1cr77W9/m0AgwB//+Eduu+02rrnmmnHHOBwO1q1bx6uvvjrpQSoKm7q6unwPQaFQFDGG0FTucCQNzp1TOlqUUp1gFNMRI1PKmcQpZU3gAjHC0RWpkVLGBZ0nEKWmSQZLMgph3eqNy5RKhUUIrly4mIXlFQBYJ3BKjXTfU+V72VAIc2smUeeeGlHqJV2Ues/qUwHY0d6e0+fPBrVPLA6yEqWeeuop1q5dyzvf+c4Jj5s7dy5tbW1ZDUxRPFRXV+d7CAqFoog57h0CYE6S0j0Y75QqLU1+rEKRL4Jj8o7Gkqg0KaREqbQJxaIYfhB3nBvNcKYFprkoVQjrVp+eKZWOU2osNt0pFU3g6gmr8r1JUQhzayZROwXle/3BIAf7egG4Yd3pCODE0CBdvuGcvUY2qH1icZDVytvV1cWyZctSHheJRBgezu9EVkx/9u3bl+8hKBSKAqbDN8zjx44mLS8wcqJmT3BRbYhSXX4/wWiU1tbW3A9UoZgk6Xbfi0c5pdLHFyc6OW0j53imlO8VwrplilJpZEqNxXRKJSgHMrrvOZQolRWFMLdmErV6p+BcOqVe6+0BYFlNLfMqKlhaXQPAzjy7pdQ+sTjIqkVIRUUFJ06cSHnc4cOHqa+vz+YlFAqFImuEkKxdGyIQCCCEyjmYSoxzbXw9HfnmSy+yvasTfyTClQvHdwgaCTlPnCcFUOFw4LHZ8EUitOnHKzJjJsyVfJKLdcvIlHImyZSyKafUpOahUZ7nslrNfCLt3zPDKZVvcrEGjJTvZSFKGZlSCZ1Sevc91X5eMQMwMqW6cuiUOjQ0AMDps2YDsKaxkf29PezoaOPiRaq7omJqyUqUWr9+Pc8++ywtLS3Mnz8/4TG7du3i1Vdf5aqrrprUABWFTzquO4UiE9xu+NvfuvF6vbjdylI+lRjnerrSG/Czo6sTgPsOH2RpRSXPtp7g+pWnmBt3s3xvAqeUEILZJaUcHOinddjLmlmzpn7wBcZ0nyv5ZrLrlpQyrnwvSaaUsCCA+C15MFZcotRk5qEhOo0tj5wpmVKz8rxu5WINyCTofCxWMYFTSpXvTYp8z61io3YKMqWM91ajfi20rmEW97y+m1fz7JRS+8TiIKuV9yMf+QiBQID3vve9tCeYqN3d3XzkIx9BSslHPvKRSQ9SUdj09fXlewiKAkWVDys2tZ4wN+D7+nr5yJOP8Zu9u3nq+DHzmBO682miTCmIDzsfVnNLMWVkO7fCsRgxfbYn674H4zfdxeaUmgy+BCHn8f+e7qLUTF+3YlLSr5fvVWXhlDKcgipTKvfM9Lk10zCCzvsCASI56k5niFK1nhIA1jVqQuOrHe3E8thdUe0Ti4OsVt6rr76aa665hhdeeIHFixdz6aWXArB582be9ra3sWjRIrZu3cr73vc+LrvsspwOWFF4dHV15XsIigJlcHAw30NQ5JlnW48D4BjTUcnIkQrHonQM+wCYU5q8fA/iws69XjW3FFNC67CX1t7erH42ECcuJeu+B+NL+FSmVPoYopNnjChlOKcC0zxTaqavW0OhkCkoVblSd98bi+GUGruJl1KaotTYzwpFesz0uTXTqHQ6sQpBDGnmrE0W43mMvKplNbU4rTaGQkGODvTn5DWyQe0Ti4OsbwfcfffdfPGLXwTg8ccfB+DAgQM89NBDhEIhPvOZz3DXXXflZJCKwkboFwkKRa7w+wUbN9bz7ndvxO9X82sqMc71xo310+5cD4fDvNzZAcCn1q0f9Zhx8dU2PEwMictqpSZFOcjYDny54OjgIJ985gl2dhf+Rdd0nivTgT/sPsTZZzVw1bVncqTbl/HPB3VBxCYsCbOjDMY6QYpNlJrMPDREKbfVPur7bl0EnO5OqXwz2TWgV1+3y+wO7FmIR9YkTqlwnEg10XtHoZguWIXFLGHNVQlff8hwSmmilM1iYWmNFna+rzt/pfdqn1gcZJUpBWC1WvnWt77FZz/7WZ566ikOHz5MLBZj3rx5XHzxxSrgXJE2GzZsyPcQFAWGlHD8uA2wkUfHcVEwcq5Jea4jsRiRWCxpu/pcs7O7i3AsxpySUt62cDHzSsvY2tHG7/btMS/iduvdZhZVVKa88DFEqRPDXpYuXZqTMf7xwF62d3Vy78H9rKmty8lzTlcymSvFRIdvmDt27eTvB44T6T0PgP/c8lfuuOzCcWViExFIkSdlYBuzmS+28r3JzEOzu+GYc+y22fXHp/e5zNW6lS2TXQP69JDzbFxSADYjU0qOdkrFO6ccViVKZUO+51YxUuvy0OX30+X3s3KSzyWlHFe+B7CitpZdnR3s7e7izUvy8zdW+8TiYNI7g6qqKt75znfmYiyKIuXll1/m9NNPz/cwFArFFPMvzzzJvv5erluxivcuW4Ejh12OtnW0I5Esq6ymwqltWA70azkEp1TXIIRgfX2Dngmzx+xYs71Lc1Ktr0t9IyXeKXXw4EGWLFky6XFv10PYj+lh64riotvv58OPP8JgKET8Jdmh/n4ePXqEdyxOfxNgCCbJOu8Z2Cyjxddic0pNhpFMqdFOKUOkmu7d9w4dOsTixTO3i5bhcK3OIk8KRrrvjXVKhZRTatLM9Lk1E6l1u6EPunPQgc8XiZjvA8MpBbC8RrtZtq8nf04ptU8sDk7O7WqFYgIi0/wirtCIScnrvT0sraxMuXlRKHJFXyDAzh6tRO2Xu3cyHA7z8TXrcvLcm1qP84XnnwO00qT/ufASlldVc1DPQFhSWWkeO7ZjjSEKnVbXkPJ1Gks8WBAEo1H6Q6FJj7vb7zPFqGNDQ0gplU29yHjgyEEGQyHml5Xx2bPO4cq4x05kWCZqiEuuFGLvuKDzAu6+JxjAxbNI3IRYTYzGMY/7AHfaz+ePhIHxmVJu/bPUN82vZ2I5CkTOFwP6umvceMgUqyVxplRYfw9YhTCFK0VmzPS5NRPJZQc+Q/D12O247SOi+/KaWiC/opTaJxYHae1IW1paJvUi8+fPn9TPKwqb6urqfA+hqHjqeAtf2fI871m6gn9ee1raP3fvwf281NHO1856w4QhugpFInb3jr6g2dLRysdZN+nnlVLyv3teB7SA2lAsyh/27+UrG88xnVJLK6rM4+M71pzwemkbHsYqBKfqF14TYbdYqfd4aPcN481BiccruiAG4I9G6An4zYBRReETicV44PAhAD68cjUrqmtGPZ7pRsNw6aQu3yueTKky8Qs84m8AxGQ5XfI3CGyA1lXKzQPAu9N+Pn+S7ntm0Pk03zyVlk7cYXS6M6SLUmV2R1Y/bzXL9xJnSqnOe9kz0+fWTKTOZYhSmWcQjqXXKN0bcw2yvFa7Njra34cvHMZjt4/72alG7ROLg7REqaampqzv3gohlMKpmJCGhtQOBUXuODakOTMyDWv+04F9nBj2srunm/X1M/dv9vt9r/OPlqP0BgJ88YyNnD1rdr6HVBTs7tGymzY0NLK1o53mwUGC0eikBc7tXZ3s6evBYbHy7XPO5bObnuGp48f4yKohjusupHinlNGxJiolTx4/CsDyquq0L7Rml5TQ7htmKIPNy+GBAUCyqKJy1Pe3x4lSAC1DQ9S6Pezt7WEoHOLMhllpv4Zi5rG57QTdAT+VTicXzJlHJDj68a5MRSldXErlgB3nlCpYUSqCi00AxGQZFjFIOf+NQwwCdwNgF3sJZ/CMpvA35hy7Z0j3vcq4tXAmYopSjuxEKUOQjY51SkUNUUrdcMuWmT63ZiKmUyoH5XtGXluNZ7QoVecpocbtocfv40BvD2sbGhP9+JSi9onFQVpX1fPnz8/6v3nz5k3176CY4ezZsyffQygqhsLaRV2mXYKMu+mDOShbyheDoSC3v/YqBwf66Q0GePzY0XwPqWgwnFJvnDOPCoeDqJQcyaDFcNuwly3tbcgxd7j/cGAvAJc3LeSsxtmcWlNLRMb40fZtSKDG5aLaNVKeYxUWavR/P9ai/f1PSyNPysDIldrb3jbhceFYlHAsSl8gwHX/+BvX/ePv4zb/hihllP60eIcIRCJ86tmn+OymZ+j0Tf7up2L68sQxzYV+RdOihPlqmW40jO57qcr3bKI4nFIOXsMiBonJcvrkNwBwiydxiN3mMRYGMnpOXxKnlPEenu7d944fP57vIUwK4/ole6eUNvcj4zKltPeAckplz0yfWzORWlfuyveMzpaJ3NqGW2pfnroEq31icZCWU6q5uXmKh6FQKE4W3rB2XzjTO7rGRZtxUTidEQKWLQsTCoWIN3m2DI0Okz46NHiSR1Z4GOfa+DoRkViMPb29AKyuqWVpZRXbOjvY3983rmQpEVJKPrf5GZoHB3nfspV87NS1CCHwhcO81NEOwDsXLwPg6iXLeK2nm6369xePcSeBdnex0+/jyKC2Ic1GlOoKBZMe4w2HuPHxR7EIwVsXLhr1/WqrdhEZikZNJ9d5c+bwWMtRjg0NsrWjnWE9t+bQQD/1npldztc27OXvR4/wnqUrEMKRcq4UE8bf/1Q9SNZ4L/kCQRDQ4/dnlDMWMDOlUgWdF0emlFNsBiDI2YRZh19ehls8SkQuYvmyPqx0Y6OV5O/k8aQq35vuolS+SefzYiIm65Qyy/diibvv2VXnPcUMwhCQOv2+SWdSGplSNYlEqZpanj/Wwt485kopCh+VcqzIO7noYKVIH68uKmUayBoynVKZXMLnB7db8tRTXQwNDeF2l5nfNzaB1U4XvcEALUODKlx6khjneiKODA7gj0bw2GwsKC9nWWW1KUqlw+7eHpoHNQHx7v17qHQ6ed/ylWztaCccizGnpJSF5eUAvHHuPNYfrjfzmhaVV457vtpRzinBGl0USIfZem7GwAT9zH+z53UzpPqOXTvN78dvWAf095FVCFbX1GmilHfI/D5Ay9DgjC8v/dH2bbzY3kY4FuOjq9emnCvFRLtvGIBZJVr7beO91NXXx1VPRPBHtXW6JM3SUqO0zJkiU2qsG6QwnVISJ5ooFZDnAjAgP4dPXkHYvZKnnuqkwaLFyvtifmSaYefJRKmZUr7X2HjyS2/iSefzYiJMp9Rky/eSdN9T5XvZk++5VYzMKS3FbrHgDYc57vUyr6ws9Q8loc/IlPKMXwsX6Df32ofy0yVY7ROLA3VLQJF3vN7Mso0Uk8Mb0u5SZnJHV0pJSM9cGJpB5XsBvUbewMjTOqtxFhYEvkiEnjHHKHLP671antTKqhqswsKyKi14PF1R6u9HjwDQoLuGfrN3N8FohOfbTgDwhllzTGHRKiz854azzZ9dVTPeiWWEnUNmeVIAc3SnVKsv8bp1wjvEnw/uM/8dv/mJF4L79QvAcoeDBfqF5JGBATa1njCPmelOvr5gwHSyPXtClXbE4wuHzVLoRk/J6AcjEUr1OZlJWUYwS6dUIYpSVo5iE21I6SDEGfp3bYRZA9iRlBGTZfqxE5fixmOITslEqenulBr7mTjTyF3Q+dhMKVW+N1lm+tyaiTitVlbpbvPtXR2Teq7eQHKnVJV+zdSfp7+x2icWB5NafQ8dOsQtt9zCueeey/Lly7nlllvMx7Zs2cIdd9zBwEBm9fqK4qO9vT3fQ8g7B3p6+N7mZ9nWemJcZs5k6fT5aInb3Bp3GjPpEhSVkhhS//lMYmHzS39//6h/G06phRUVpjuhZYZv/GcCxjk2SumWVWqi1KGB/nGbg7EEo1Ge0LO/vnD6Bho9JXjDYZ48fozn21sBeMPs0W6iWreH3156OZ9au57z58wd95y1caJUJqV7APPKyrAKQV8oxAnv+LuGP39tB+FYjDPrG1lUXjHqsVFOKV2UqnS6mFequbzafMNmeS3M/Ln51PFjpih3dGiQo4Mz+/fJJYZLqszuGOeE6u/vN918XRl0VTIEk1TNA8a6QQox6NzJSwCEWJvUBRXVO/BZSf8aJGn5ntXovhfN+Wd4Lhn7mTjTmKxTymrRRKlITHXfyzUzfW7NVE6r00LAd3R3pjhyYozyvdoEkQGVLhcAvTkIVM8GtU8sDrJeff/3f/+X1atX88Mf/pDnn3+egwcP0t09Umvq8/n42Mc+xl//+tecDFShKFS2tZ7g6j//H794+SWuvecPfPLvD+XsonZ3TzfX/v1BPvz4I2bZXTZB5/GZIzOhfM/vF1x4YR0f+tAZ+P0jpXnHdBFhXmk588s0IaAlgbCgSB/jXF94Yd2ocx2P0elxjl76Nre0DLfNRjAa1TvTJWdrRxvecJh6t4fT6xt5a5OW0fSzndvpDwYptdtZWzteWFpYXsE1S5ebwbbxxAd5ZipKldodrNNf79nW0e6fVzo7eObEcSwIPrl2PZ8/fYN5LIwWgvv191GFw0mt222GrwOcWqO3YJ7hotQ/WpqBEXfC44dbU86VYqFteHTpHoxetyos2nslk7DzQMTovpci6Nwy+twXolPKIV4GICjPGPeYcZ5Pe+Md+HwurLSm/byG29GTxCkVQ5qlYIrxpPN5MRGTd0oZ5XtjnFL638yhyvcUM4x1dVr8wPauzkntHYzyvUROKaNZTH+eRClFcZCVKPXiiy/ykY98BIfDwfe//322bNky7o1wwQUXUFFRwYMPPpiTgSoKlw0bNuR7CHnjxOAgH7r/XoZCQZoqq7BZLPzt4H52d03ujgfA0cFBPrPpaSIyRjAaNTN5jPK9QDRCLM0PMKN0D2ZG+Z6UsH+/nebmEoxfUUoZJ0qVMV8vmWpR7o1JYZzr/fvtJJtOJ3TrtRESbhGC9frdvc1x5WqJ2KG/F86ZNRuLEFzetAgLwix/u2rx0nHlSKkwXCiZ5kkZGO6r505oY5dSMhgKceurrwDwtkWLWVRRwaqaWm5748WmyOSPJnJKObEIwc/f+Ca+uvEcvnvOeXz77PMA7SJxJojAiej2+3mtpxsBXLdiFQCbWltTzpViwXBKxZfuxa9b2XRVGum+V+xB5yEc7NC/Gi9KGed5z/7ZSARWkX75nnEzZ+w5dsXleE3nEr58Z7Ok83mRjEgsZoqCk86UGueU0sv3VNB51uR7bhUrq6prsVssdPn9ZpZlNkzolNLd5X16842TTTHvE4uJrFbf73//+0gpefjhh/nsZz/LmWeeOf6JLRbWrVvH66+/PulBKgqbHTt25HsIeeN7m5/FFw6zvnEWD7/3g1y6SPtQv2/v5Nuf/vngvlGlQK3DXmJSMqx/T5L+HfL48o7BGSBKJaInEMAfiWBBMLu0hHmmU0qJUlOJlNJ0Ss0tHQnhPG/2HACea5tYlHpN7/ZiCDv1Hg/vXLKUGpeLL56xkZtXrcl4TKuqa2gqL+eKpkUZ5UkZnKuP/bWeLq752wO89cG/8KF//J1DA/2U2R3cuOrUUccbLopEmVKVTiegucjeNG8B586eS5XLRb1+t3Jsx8iZwv5+rdtiU3mF2YHwQF9vPoc0rUgkSsVjOOcyckoZmVJFHnTuYDcWESAqq4iwKOXxGWVKJSnfswqL6bLJpDT+ZDOTu2nHX8+UZrFuw4hrM5LEKaXK97JnJs+tmYzLZuMUPVfqlc7scqVC0aj5+VHlGl/ubDilwrEY3jzsAYp5n1hMZLX6bt68mQ0bNnDuuedOeFxjYyNtbel/2CuKk9AMFTkmy7bWEzx0YB8C+MaFb8Jtt3PVilMAeGD/HrNFcTZIKXm+TStJqNFrwVuHvfgjETMbCsAfSS8fKv5OulH+N9Mw8qQaS0qwW6zM1wWSmbrpnyn0BAIEolEsCBpLRu7AvWHWHASwr6+XDn2DPpZAJMI+XchYUzviaPrU2vXc/9aruKJpUVadEz12O7+79ApuOT27u28NnhKa3B4kWg7UQChEp99Hg8fDredfSJXTNer4RCHI8eV7iTCcfDO1hO/QQD8ASyoqqXd7cFqto9aeYsDGYSrEN3Hzt3GPGeV7jSUpRKkMnFIjmVITO6XGZ0oVVrmZQ2wDDJdU6vUhk/K9kUyp8aKIWxcD/dO4A19kGgtmqTBc2h6bLWN3rIEZdJ40U0qV72XLTJ5bM50z67XOh5tSOM+TMRy3F0jU7dVtt+PSr2PyEXZerPvEYiOrVb2/v5/58+enPM7v96uJpEhJZWVlvodw0pFS8r3NzwJw7apTWann2py/oIlql5tun49NLUezfv7DgwN0+n04rVbe2rQY0DZBYwUlfyRzp1Suy/dC0Sjff3krP9+5fUpLleJL9wAWlGtOqfbh4YIM+p0uGGHgDR7PqAv+KpeL1br7KdmF1J6+XqJSUutyj3KUZCNE5Zqr5y+k3u3h+hWruPX8C/nkmtP41cWXsayqetyxZrv4hEHniUWpBbqTb6aGgx/Ss8IWVVQihGBWEkdQoeLicWrETbjF45SJ/wZGr22GEDsriShlhPFnVL4XMbrvpcqU0i79jHdRcBqLKNngYhMAQZme6GyjDdIQTKWUpuCUyI3mSvA+n26UJJlvM4HJhpxDXPneGKdUSHXfmzQzeW7NdC6YOw+Alzrb8WZx49inuxDdVivWJO+BqjyGnRfjPrEYyWr1ramp4ejR1BvmgwcP0tjYmM1LKIqIuXPHd8cqdJ452szLba04rTY+fdY55vftVitvW7ESgO9seoZglhe3z+slUafXN7CwQusA1jrsHWe7TdspFSfaeMPhlB3TUuGLhHmspZkjgwM813qcB44c4u79e3nvIw9NWccxwyk1T3egVDtduHT3RmcGHa4UmWFkHMzWQ87jOW+29t6///DBhHPqte4uAE6trZ0WQlQ8b166jL9c8XZuWr2G0+sbefeyFeMcUgaGq2KUU0rPb6hIIko16Z37DMfRTCPeKQUwq2T83z/f2NhPCb/ByXM4eJFK8QWcPJ2T5y4RdyOEtm5aRAAn20Y93paqfE+fS5mV7xmCSYpMKf29ZLj0Cql8z8pRbOIoUtoIcnZaPyNEEEHqz4B4x7A7gRvN+N50zpSqrh4vms8UJhtyDnFOKam67+WamTy3ZjoLyytYUFZOOBYzqyQywYj1GFuWHI9R1tefwY2SXFGM+8RiJKvV96yzzmLbtm3s3r076TGbN29m9+7dKUv8FIpdu3ZN+WtIKZG+e4gN/QAp83vBKKXkxy9uBuCDa9ZSP2az9skzz6LW4+FAbw+ffvRv/N+unQwGM7PLGh9K5zTONsOlW4e9ozIZYCSDJBXBMaWERlh6prQMDfLzndt518MP8PWtL/DpZ5/iiWMtgHaxOBAK8ddDB7J67lQYrdWNrB4hBHX6150+JUpNFUbIeXyelMFbFy6izO7g8OAADx05PO7xnT26KJVFGPlUc+zYsbSPNTarvjgReEB3BVYlEaWW646rvX0907rFfCJC0agpLi82RanpdRe9XHyfWsvNlFn+H1WWL1Nt+QIu8SIV4rtYmGyjiQA2mrWv5AUAuMSz5qP+SMTMFEuVKdUTSD9Y1hCXUnXfMzbexp3vQirfc/EMAEHOQJJaCJVSe/9ZSH0zJN5Z7JzAKTWdRalM1q3pRi6cUkb3vbHxCKYopYLOs2Ymz61C4I1zNLfU08cz/zsMm11Fk2e1mWHneXBKnYx9oiL/ZLX6fuITnyAajfKud70rYfjYnj17uOGGGxBC8PGPf3yyY1QoJoWMdiH7P44c/BIM/xKCT+R1PC8cP8auzg48djsfTZBpU+V2860LLwHgkUMH+Pcn/8FVf/x92s/f7htmlx4OffasEVGqy++nZ0wteDZOKcg8V8oXDvNfO17mA4/+jbv37zV/vjvg59nW4wC8b7nmEHvmxPFJb8KFgLlzIzQ0BDAMNkYZjFEWA1poNqCcUpPAONdz50ZIZGYynFJzEjhlyh1ObjhlNQC/3L3TtJCDJt6+3tsDjIScz1Q8iTKlgtp7IFmm1JKKSmzCwkAoZIZizxSODg0SlZIyu4M6/f02q6QEBJTWDSedKyePEG4eAyAoNxKT5UhpJSrrsYgAZeL2ST27nYMIESMqqxmWVwHg5AVA+/sbf89Su33UBjt+3apwafMiHIulnVEUSLv7niaoGIJoRMYm7X6dLhjiX1Cen/SY+DVLoomCgtTZgsb5dVgsprgRj1mmW2DlkLkk1efFROTEKWVJ5pTSrnEcKlNKMUN5o17Ct6WjLeP13Lj2KpnAKWWEnffmwSmlKA6yEqUuvvhi/u3f/o39+/dz+umns2zZMoQQPProo6xZs4ZTTz2VAwcO8LnPfY6zzjor12NWFBiLFqXujpMtMvAPZPdbRglRMjA+dPZk8oddOwF4x4pTqEnQehXgksVLuO0tV/Le1WuYX15BewZtXh8+chgJrK+rp8FTQoXDQYl+9+Ngf9+oY7PJlILMOvC1DA3ykScf5Z6D+4khOatxFt86+1w+cspIh7Jal5vrV6zCbbXR6fexd5JdutxuyZYtnTz9dDNut3bx2WWIUnGdRQzXVJcSpbLGONdbtnSa5zoewyk1J0H5HsBVi5cyu6SU/mCQTXGd+FqHhxkMhbAJi+m2mU40NDSkfexYB4WU0syUSla+57BaWaznKOyZYV3rjNK9xRUVZtnl7JJSLI4I5//oz0nnysnCziGEiBCTFfTJ79Ip/0yn/At98ltIacEtnsJOcid46uffD0CY5YQ5lZiswCIGcaCt/e3DiUv34tetqtKRbm7GXElFIM1MKYd1tFMKCsMtZaUFuziIlBYCvCHpccZ5fvHFDvxWbR62eVOXvBiZXcmC5GdC+V4m69ZUkOrzYiJMUWoymVLCyJQaI0rp8z/bAHVF/udWsbOoogKHxUowGjU/Y9LFCDr3TCBKVeqfF/kIOp/KfaJi+pD16vvDH/6QX/ziFzQ2NnLw4EGklLS1tbFr1y6qq6v56U9/yne/+91cjlVRoASmaIGTMoIc+BLIQbCtQpR/S3sg+DQylh8Rotvn41G9PO29Y9rGj+Xypcv41kWX8Lf3X8/Zc0caC4y9mIonKmM83KyVQV25UAs4F0Iwu1Tb/OzrH725TdcpFR5jdU837LzdN8zNTz5Gy9AQ9W4PPz73jfzw3DdywZx5XLV4qbnpOm/OXFw2G2fNmgXAMydyYwOPb7RgZLMYJXswIkp1qPK9KaPNyJRKkilks1i4ZN4CYLTtfG+f5pJaUlmJI8UmOx9k0sRjrINiOBI2W5InCzoHWGmU8PXOVFGq0vyeEXTe5ktfYJ8q7OwFIMwKtLhvJ5IKIizFz2UAlIg/Zf/8Yp/2/HI5YCXEWgBsHAFGnFLJSvdCoRBCCHNupCtKmeV7KTKl3jhnHhsaGnnnoqXjfnYm8ddDB7j5ycfM7DmPeBiAIBuQVKT8+T8c2MuRQe3z9Je7NvFYS/OExwcmCDnXvm+8z6fvuZzJzYfM8r0cOKWSle85lCiVNTN5bhUCVmFhXpl2nZVp117DKTVR+V6V7nrOR9D5VO0TFdOLSa2+N910E8ePH2fbtm386U9/4g9/+AObN2+mtbWVT3ziE7kao6LAaW3NPJQvLcI7QA6AqETU/AncV4N1Hkg/BJ+amtdMwV/27CYci7GmoZFV9endVfLY7XzjwovNfz92rDnpsVva2+j0+6hwODhfry+HEUFg/zinVHp3dOMDXoG0u+Q9cPgQ3nCYxRWV/OriS9nQOMt8rMLp5IMrTqHUbuftuoBm1MQ/c+J4Ws+fir4+7ff1hcPm71rjHnEH1KnyvSklFI3Sr8+VhiSuQBixnb/Y3mbmLu3RhZgVCbrZTQeMuZUOhijl0+egITK4rbakrgsY+d0NgW6msLWjDRjJxYKRoPP+YHBUtlY+sIt4UWo0PnkNAE6ew0pbVs9vQxOlIiwHIIrW8MUqOgCtEypAY5KcLWNuVTi1zXd/muvtSPnexCJuU3kFPz7vQtbW1ZvOkZnUgTQmJXfv28OPtm/j9d4ePrf5GQ71d+LmEQD88sqUz7Grp5v/ee1V+kOa8FfpDKQUfwPRiZ1Shlg1nZ1Smaxb041cOKWsplNqrChldN+bfjdAZgozeW4VCvP1rr0tQ6nLkeNJxylV7c5f0PmU7RMV04pJ3xIQQrB+/Xquvvpqrr32Ws4++2xsKe7SKRQnAxnUAk9xnosQdq2MxPUW/bHH8jKmRw5qLqlr9RyddJlVWm5+/ZgeDJ6ITa1a+dOb5i0YFXY7O25DGE+6WSXjM6VSbyqjMsbfj2quretXrjLr0eP58CmreeTtV7OksgqAsxpnYxWCY94hWjMoWRyL3w+XX17LRz96Gn7/SOleic0+6k5QgyrfmzTGub788lrGXqv06QH9NmGZ8O72kopK5pSUEopFebFdEwKMEs7pKkplwthMqT79fTiRSwpGfvd9fX3ExjgkHz16hK9s2cyPtm/j8DTq0HdooJ9DAwPYLRbOnT3SMafM4aBEujnxw/dz5RX14+bKycR0Ssnl4x6LsIigPBMhYnjEPRk/t8CHDa07cZhlAESldgPCCFDv0J1Ss8Y4pcauW+V63li65dIj5XvpX4MZnxMzxSn1SmcH73v0IX7+2g4A6txuvOEwfz98FxYxQFTWEmTjhM/h98MH3jmXlh+8D2tUEwwrHKnFUqMbbjLRz5Ogy6ZiNBN9XqQiF0HnRnleNDZ6PQ2p7nuKAmCBLkpl6pQaDhtB5xOV7+Uv6FxRHKjVV5F3Tj/99Kl54qAWeCqcF5jfEg494ywyNR3eJqLP7+dV3UFwYVP29dF7+nqSBh/v6NY2PWc2NI76vtFefizpXjyP3bCkU773Ukc7XX4/5Q4H586ak9brlNjtrKquNX8+W6QUvPqqg337ypFS0B3QRKc692hhzPi36r6XPca5fvVVB1KOTq7t1S3X1S6XmS2UCCEEF8R1jonKmFlqurK6ZopGPjkyyThwjcmaSZUnZdBUXoHbZmM4EubH27eZd/cP9Pfx7W1beOJYC389dIDvv/xSNr/ClGCUQJ3VOJvyMZvHBk8poWON7N3lHjdXTg5BrLRhRRP2EzmlAIbltQC4+VtaAdjx2DiEEJKorCWGNnej1ANgRXdK+RI7pcauW5W6KDX2ZkIiYlKajtZEneGSYeRLjXXDTkcGQ0H+c8tmjnu9lNrtfPzUdfzmksuZW1rKm+e+CoCftwATi3JSCroOVhM61ki9ezYAlY6g6WRMhuGUSib6GWJVYBqLUvnOZpno8yIVI0HnyUuMUmEViYPOI6r73qTJ99xSxDulMizfS8cplceg8ynbJyqmFVmtvrfddhtWq5UHH3ww6TEPPvggVquVX/ziF1kPTlEc7N6dfaBsMmS0AyJ7AAHO80YesOrZTJEW5EnuNrT52FEksKymllllZZN6ricTuKV6A35ahoYQwJra+lGPra8b/W8jkDXdi+dsyvceOqK5pC6d35RRJtAGXVCbjCg1FsMpVTNGlKrXnQoDoRBB1TEp5/TEiVKpuFAv4XuhrZWD/f34IxFcVqt552+6cfy4VmJqZwc2Pdg6GWamlP5+M8qxknXeM7BZLPzL2vUI4L7DB7npicf4W/Nhvv/yVqJSsq62DguCXb3dnPBmJp4YlIpfUi/eRrX4GE6ey+o5DGJS8o8WzSV06fwF4x4f6wzKBYJ+Svg/04WUHEm1+Ax1lvfqolEDMRK78EKcQVg2YRF+3Dyc0XhsaLloERaa3xsRpbQxJgs6H0tFBplS8TcOnBmst0Yp2kxwSv185w76g0Gaysv5yxVv533LV1LmcPC1DQs4t/EEMQkvd0/skgLojnPGVrq0v02FI5DyJo1RHplM9DMbGkzjzxJj3ZqJ5MIpZYhSYzOlQqp8b9LM5LlVKGQrSg2nkSlVaZTvTTLfyRcOZ9xheyr2iYrpR1ai1P33309dXR1XXHFF0mMuv/xyamtr+etf/5r14BTFwZQE2AUe1f5vX42wxG08rLMBOxCCWO5Ej3R45mgzABcsaJr0cz15fLwotUMPe11UUTnOoTCrpJQ5cUHTdR7twyXVnWGDUDTGp1a9xH1vupcSW8i8OExGy9CgGVZuBK6ni+Hy2tbZPu7CMVu6/UbI+WhRqsxuN+9ud/qUJTnXGIGY6YhSK6qqafB48Ecj/PTV7QAsq6yett2QQqEAZeLn1Fj+lWrxaSB56Y97TFnPQJrle6C9f7521htw22zs1x1Se/p6KbHZ+erGczhD73j0aIqQ5kRYOa4JOmIQh9hDpfg6VjJ/HoPXe3vo9Pvw2Gyck8AdGd9kIBdY6KJOXEeZ5ReUiV9PeKyNgzjELvPfQU6b4GiBT3dLlYh7gfRFBqswRKmR0sUoDfpjPQSjPnr1stZZSTKlDAzRciCNmwDxNxgKsXxvX18vD+lNPG5ZvwGPzY6DVygXP2JDzQMAbGqfy33NqV2vu3tHMtpsFs1FXJlO+V4Kp1SmN3vywUwOo/aGtL9P6aSCzifuvqfK97JnJs+tQmF+qXbDuy8YTDv7FeJFqdROqT6/P2NRCbTy5+9tfpa1//NTvvXcMxn9rAo6Lw6yWn337t3L6tWrsUyweFutVk499VT27NmT9eAUxUF5eW6dEFKGkMO/AkC43zXqMSFsYNU3C5Gj6T1ftA0p01/cE49J8py+aTxvftOknssiBHv7esflyOzo0u7CnzbGFWVwRlxJX51L2xwGMsiUeveivayu7mZtdSfbOzu5Y9dOXuns4G/Nh7nn4P5RAtLd+/YggXNmzR7VgSsdVlRXU2q34w2HzVyhyWJ03qsdk2slhKDeo3Klpgpj812TIE9sLEIIM+jeKEO9YuH0LQdoqn/G7NBmEcPYJhBzPGOCzo2LxbHicTIumjufP7/lbXxwxSmsq61nRVU1nz9jA7VuD5fp68ljLc0ZXyiWiLsRIkZQrtdzlMJUiO+RiQgTzwttWhjpWY2zE7p1qtMQ4dInRpX4HBah3RF28vyER7vEEwAE5Fn0xr7HkJy4GYufNxGVVVhFF062pj0qwykVlSONJiQVSKmHlvs1N4HHZkvZRcx0SqWxufDF5R1ZJiiVHYtZvpehKPXo0SP8JK6kdKr5/T7tWvKSeQtYU1uHm4eoEp/FIx7ELR4H4A+HV7KlvS3l+2B3z4goFUO7WVPhCKZ2ShnnWHdKCbwIBszHZ0L3Pbc79Vo8XclF9z2bWb43et5GVKbUpJnJc6tQ8Njt5s3XTMLO0ynfq9RvLoZiUbNbXybc8vij/OLll4hKybNHj2T0s7neJ06ElJIDPT3jcjyLiccPH+SaP/8fR/pPbvOCrFbfrq4uGhsbUx7X2NhIZ2cqS72i2GlqasrtE/rv11xQlnoYI0oBYNNL+KITi1JSSqT3dmTXBci+m7K6M2Cwt6ebzuFh3DYbZ85OL18pGWc1aB3s7jt8cNT3X9U38mtr6xL+3Olx3f5q9Q+tdDOlojJIrUsTbSocQdp8w/xm727+5dkn+fa2LfzXjpe5XQ+ebRka5BHdFXbdilXp/VJxWIWF9XXaWHfq7q/JYjilahM4Ner173UoUSrnmJlSztROKcDMlQJoKivnzTlwFU4VNeXNo/5t1zuuJcIo3wvFokRiMYb1912yO/6C/nHlaJVOJx9dvZbb3ngxv7r4Mi6aq61j58+Zh9tq47jXy4EMLiCsHMWN5ij1yhsZkLcQkyU4xB6cbEr7eeJ5vl1rtHD2rNkJH0/HMZcuDl7FLpqRUruMsYghLHpm03hiuHkSAL98CyE2IklVQu0gyPkAOMWLaY/LiiY6xTulQJhuqaGQJlo1ekomzFkDqNBFy3TK94wbDO4MG804LdmV733jpRe599ABnpig8UauOOEd4unj2nl7//KVONlEheWHCBEjIrUy0YhsYHPHIjr9Pg4PDkz0dKOcUhJts5OOU2qku6ENiFAjPk6duA7BsP59w3U2fZ1S9fWJb1pNd6SU+PQw5pLJZErpolOy8r1MogYUo5mpc6vQyKaEbyToPPl7y2O3m++PbMLOn9X3BZD5503O94kT8I3nnuay39/FvXuKt2Twnx5+gJfbWrn+vsybvUyGrESpsrKytNoztra24pmgDbhCAbBz586cPZeMdiG9/w2AKLkBIRLcmbcu0I+d+GJaen+I9P5E+0foRQhtznpcm3SX1Ma583BOsjvlFQu0rJJHjh4xL6KNjlcWBOtqE18YGEIPjGwO0xWlXJY+LPr+6UMr5/PxU9fxpnkLqHG5WKI7of54YB+/eO1VPv3cU0RkjNPrG1hdU5vNr2i6qzLtIJKMZOV7MCJKdamw85zTm0GmFMDqmlrz73HT6jVm++6pxs6rppiQLtGwJsBEpCYO2UXyXClX3Hs+EI0wrN/x99gTrQWSGvEv1IoPY6EnweOjcdtsrNPdka+mKeIKfFSKryBElKDcSJhVxKjDz1u18YoX0nqeeLr8Pg709yOAsxpnJTymKg3HXLq4xD8A8HO52UXPwWsJj7WzC6voJCY9BDkr7dcISu1YJy8C6dyUiGLjhP7VvDGPaH+jUFR7fGzIeSIqM3BK+U0XT2afL5MNOj+SQgDKhP/ds5ur//YAH33yMW584lE+8NjD/OeLm/nMpqeJITmrcRbLKiNUiO8DMCyvolveRXfs1/TK21hbq4mhhmPPQErJc63HeejIIf566AAH+0ccuJk5pYzyPSsOtmMTLVjEgNlt0XRKRaavU+ro0fQc4tMNfyRCTH8Plk5B0HlYOaUmzUydW4XGPL2E71gWTqmSCT4/hBBmnps3w1LNoWCQgeBICV6molYu94kTcWJwkLt2vALAn3Ynvp4oBgyX2PHBwaSuOCkl/iwccxOR1e547dq1bNq0iWPHjjFv3ryExxw7doznn3+es85K/wJQoZgMUvqR/f8EsQ6wLgT3exIeJ6zztUubCcr3pO8eGP6l9g/bKojsRnp/Bo43pLy7nQjjDsFkS/cATqutZ05JKSeGvdyxayf/vOY07tZLGy6YO5eqJAJApdPJB5av5LjXyyl6R7N0RSmPbcSBsazCypyKlaMe//nOHdy9fw+/3fc6oH0ofnXDORn/bgZNulW3eRIbnurqKFH9bkyX3n0vURmZUb7XqZxSWVNdnXgT1pNBphRopak/PPcCTgx7OW/23NQ/kAOstFAtPk2UuXTL36T9c06btrENcB6l/H7CsHOHxYJVCKJSEohEzLuSiZxSFjqwCU0wd8oXTKFoIk6tqeWF9lZ29nRxzdLlSY8T9OPgNUrE77GLZqKyhgF5i/l4QJ5NifijLsJEgfRdA8/rQsAp1TVUJXHGVTmdWEp8E5SXxdDEn1SvG8SFlknhl5cihQs7+3CI1wjIN4072qmLbEHeAKRfQhjkNKR0YBWd2OQRIkxcTmqlEyHCSGk3RSgDwylFrAOYmzT0PX7dqjC776XeABhr+UTlF4mYbNB5Op0B0+HvR4/wy93axiO+u2zzoHZjwmOz8ZFVaygTt2ERg4TlMobkxwFBBC23cGPjLJ5vb+XF9lY+sOIUQMu1+/rWF9jWOeKii0kbzrIAJXZ7nFMqkLIkxQw6t9pwi7+b37fSRphTzJLVdMvii5VknxcT4dX/NlYhMgryH4uRURiNKVFKUZjM1vNjk3XpTkQ6mVIAJXYHPX5/xuV7J8bcYPaGQoSi0WnnTPzvrSM35BwZZDMWElJKXDabWa7+6MEDXLXylHHH3fXqdr757FP895vfyhXLkl93ZkJWZ/x973sfTz75JO985zt58MEHx5Xytbe38653vYtwOMz73ve+nAxUUbgsWDC+S5OM+YAowqIp/jJ8ADn0dRBlCMeZ4Hk/hLYhg89CrBvhuhTpfwDCr4GoRFTdgbAkcenZ9NdL4pSS4d3Iwa8AIEo/Ce5rkV0XQ/hlCO8Ex9qMfj9/OMxLrdrd8fMTdKTKFIsQvH/5Sr7/ykvcc3A/W9rbaB32AvD+5eMXjnj+6dR1AGxtb9PGlqYoVWobubNsEYPjTAMfPXUNs0tL2dreRiAa4Zb1G5KKY+nQVK6Fzx4dGkRKiV0coVz8hAgLGJSfASYWBj0eyWuvddDf34/LXUGPX7tDU5vAKWWEL3cqp1RWGOc6EX1BwymVvkNmUUUlizLMIZsMDnYhRAwbLQg5lEZZF0AIh027CxmU51Iqfo+dQ2hh5+Pv4gshcNtseMNhfJERp1SiMhQ7B8yvneIF/DK1KLWmto411Z28f+HjlPMMYdYR5CwEISx0Y2GAEvGHUUHfMemiX36VGDXm98KsJiZLsIgB7HIvYdIvv93WqTWOOLsxcekewNxqJwu+fTsWBE73tYw2a4eoFR9BYqdH/oKJLk9cPI9FDBOVDYRZjUX2UyLuwU7iu6lOXgIgKDek/fsYrxRkPS5exMkLaYhSRsj5HMYKa1FZDwJsohOYS0MCUSp+3fJ4KilnpPuelHLCGyKmUyrDC+lsMqXiS9l7cxBA2zw4wPdf1nK7rl2ynDW1ddgtFixCcHCgnzq3mzfMmkOFY8gUIwfk5xj7Xjtn1mx+suNldnZ30+X3Uef28L2XX2JbZwdOq5WVVdX0BAJceepirn29G5vFgkD7OzisMSBATMqkoqkh3JXao6M6VVrRPk9dM6CTYV1d4vL+k8VEnxcTYYhSpXZ7VjcGDaxJMqXCqvvepMn33FJoGJ8tGYlSulMqVfm3R3dKDWfolDqh31w4pbaOvT3dxKRkIBCgLg3HMCTeJ+aabp+Pv8SV7B3PoQt4JtEfCIxq1nHPnt0JRak/7n4NCXxn8zO8adHiSVcBQZai1PXXX8+dd97J5s2bWbx4MVdccQUrVqwAtBD0v/3tb/h8Ps4++2xuuOGGSQ9SUZhIGYHQZuyxFmTkArDOA6Lgvx859G3toKo7wL4eOfjvEN6h/VzwcRj+NcRGMldk4AH9Kzui6mcI2wQLmF6+R+QoUsYQcSVCUoaRA18EwuC8GEo+gRAWpPM8CD4B4VczFqW2njhOKBpldlkZi6oStyDPlLctWkKJ3c6Ptm/jmN4K/vT6Blak+fxmi/o07+iW2fvNrwXjLcFWYeEdi5bwjkVL0nq+VMwrLcOCwBsOEwi9QIPr61hEAAevEZKnE+DCtJ4nFovRHwwQkTEEUJNAKDPKxZRTKvf0BIyg89xlCeUamxgRgWwcJczqlD9jRSuTk9JBmOXEZCkW4cUmm4mwNOHPGKKUPxIxLwBLEuQ32MVIVpyDl4Egqdw9Z9Y+z8UX3Y/dEgMOAf9IeJyUgiizCXIuw/JqYozdRNgIsgE3T+EULxCW6YtShqNl+QRrUIXTiQVBDEl/MDjKuejgtTiH2IsEOTfp87jEYwD4uQSwmH8zuziCkANIKsxjLfSZ5zTE6Wn/PgZBeTYu8SJOsYlh+f4Jj7XpJaBjS/cAYmYHPm1DvqyyKunzxHTXRqXulIrIGP5IBM8EZUtZZ0pl0X0vvvSpLzh5UeoXu14lHIuxsWEW/7z2tFGiUHw+mZu/I0SUkFyd8H02q6SUtbV1vNrdxSNHj7CxYRab205gQXD7G9/EsgRzU+JGShtCRKh0BAlMcJ6Nc7yyYi8WMfJ5YRWtIEcypaazUyqWo462J5sRIT/7kHPALAmPjBWlVPe9STNT51ah0ai7/9MVpaL65wtMnCkFIzfShjN0Sh0f0gSeeRWVtHmH6AsE6A340xaloidB6N/U0kxUSmrcHnr8PlqHBonEYtO2A/RU0TrG1fbi8Ra6fT5q4+KYTgwNsr+nWz9+iD/ufo3r1k7U0Tg9sjrTVquVhx9+mKuuugq/388999zDt771Lb71rW9xzz334PP5ePvb387DDz+MLQfKmaJwkOFdxHpvJNb1JmTXeci+m6iyfAPZ/SZk51nIzo3IwS+CHAI5hOy7ETn4JU2QEh5E6b+BqNQFKSu4rwbPBzDuSouKb2tOqomwztaPD4wStgBN7Irs1dxW5d8cEaxsmtgiI4cy/p2fa9HKBM+b3zSpO3xjuXjeAv70liv50hkbeceiJXxufYrfOw4j+yJdp1RFnChlITc5TxPhsFqZXapnfVjvxCICRKXm6CgTP0eQXj16T08PbcPaB3Od25PwLqgq35safJGwOb9yGXCda+KdSRN10IvHqoeQayVZFsIs058redi54aLwRyLmXf9ETilb3Hg0IXZHirEco9ryU+yWGI8cW8iOvisJSe0mkZQ2InI2ETmPYflOuuSf6Za/Z0h+LIEgpRGUZwPgZMuErxtPJBYz8yuaJuiSYxUWqlya0DLWYeMUIx3u4kujxiLoN7vh+eUlAMSoJiyb9HG/Mup4h/7vsFxMjMxvCgQ5FyktOMQerEycmWIVhlNqfOmpUc7X4Bqk0uE0c8AS0aN3h3PZbKZo1J8iV8rovpe5KJW5uyf+2L5JOqV2dnfxXKsmHP3L2vUTlHZG8YgHAfDJtyV9viuaNDfbw82H+fXrmjPw4nnzEwpSGoKY7o6scATN85gIIytqjkcTT2NSm+tWNJegUe4RnMaZUj1xnQdnEvFOqclgOqVU+V7Omalzq9CYpQs93X6/6QCciPh9wESZUjAiCvvC2Tml5paXU6nfjOr3p58rdfx4Zpmf2WDErLzrlFU4rFaiUtKWQS5XoWCUWq5paGR1XT0SeOLI6L3vU0cOAyONIX669UUGcuCazloxKi8v595772Xnzp088sgjZsDd/PnzefOb38zatZm5SRSZI2UIAo8jw69q4oooQ3iuQdhPzffQEiKH/x9y6HuMqv0SVXgDVZQ6j4HUc4tEJaLkRqQRLu6/V/t2yUcRpf8E7ndq33NegLBrlkLp+QDEBhGOdSnHIYQDaZ2jle9FDoFVKz+VMoL03aUdU/5FhHWkrEXYFus5VNmIUs1AbvKkxlJqd3B50yIub5q4rGQsY1vUp6LSMWJjtSRwSk0FC8vL8Yc7qLJrLodeeStVfBabaMctH8LHNUl/1u+HD36wBp/Pwz/fqgkFs5LckTGcUoOhEIFIJOOg4GLHONcAv/1tD0aFpLFZdVmtKe++5Y8oNkacSTbRnFaetUV3SkV1YSfMKpy8glO8lLTczh0nBPsmEKUMkSwsF2IXRygVv6NPrkaSeP6WiZ8jRJR9Ayv55xfO4/KmRXzpjM/opYhuMv2YN9xENg4iSK+csW14mIiM4bRaE5alGfj9cOgn78QfidB6RidLK0cec+gldgBOXsBCz6jSQgM3TyJElLBcTpQRR2yIM7HTjEO8RECOOCkdYtuo3ytTYtQQZCMuXsAtHsUrb056rPG3i8rxTqkoWtOHOrePC+bOS3j3NX7duvdeH263livV6fcxEAyaWSGJCGQpSjksmZfvxR/bEwikLC1MRiAS4cfbtb/PFQsXsWACQdPBy1hFBzFZRoALkh534dz5/GTHyxz3ejnu9SKA61aOdvyNXbOkpxzo03KlImEgcbmxIcZVObTNd5D1uHkaK1qemss2/Z1S+SbZ50UqTHfpJEUpM1MqWdD5NMu4USgypcrpwmGxEopF6fT5mFM68We40dXSJkRKUdZwkWYadH5cF6XmlJVT7XZzpL9v0jc0JsszzUf4yZbn+daFb2JlXb25V7twwUIeP3yQw319HBscYF5FxcRPNE05NjDA9vZWrli63Ow6mg6tuhA3p6yclbV17Orq5IF9e9l87Cj9/gDLa2t5Rc8Q/fgZG3lg/x4O9/Xx3c3P8p2LL53UmCe9+1qzZg1r1qyZ7NMoxqCVtm0BbGBfirBUayJUeA+EdyCjRyHwDz00Ne7nAg9A9W+njTAlYz4IPoqMHIHh/9G+6XorwvMeQIB9La4wCDva7yasYFuJEFaQHwb/PUj/Q2ApgRKtFFRY66H0Y6NeR9gyE2Wwr4NoCzL0EsL5Bu17oa0Q69WcWK4xG0ubFqRKNDNRqm1oiAO9PViE4A3z5mc2xilkpEtQJK0NRZVzxB0lpsQpFUZbjkbG0VRWQaOzBSEkYbmMKHPxyWspF/+NSzyDTyYXpaQUvPCCE3DS5tWcUo1JNsuldjtumw1/JEKn32e201Wkx8i51r42VJ2RzntuLHRRKu7CJ68kwop8DXUcVk5gESMXRuk7pbR113C/BOR5lIrf4mALAr8uBo0mXghOdtdf0I9VaILXoPw0VdyCQ7xGNZ+mR/6UsWV8DrbjEi8gpZXW4E3APv7RcpTrVqxibooL0WTEqCEi52ETx3DI1wiSumGB0SVzflnZBE4XbX707dU68/X6j5nft9CFXRxGSkGEJuziCC75BD6uHfccZtc93SVlEJRnUiL+rLuoJNpaEtJD27XHs8Uv34xLvICbx/ByI4mC2G3sxSF2IaWFIOvHPR6KVYEFKhwhLp7TMO5xGL1uSekHJBVOB51+X8pA8WwzpbIp34s/NhSL0h8KJg23T4aUku++vJWDA/1UOp3ceMrEZbNus2TzTUxUzuq22Xhr0yL+fHA/VU4nN5xyKgvLR28qxq5Z8U6pidzDhthUbtfKFkJyPW7xtO6cjMSV70WzFuqmmoULF+b19ZN9XqQi106pyJhSM6P7pEM5pbIm33NLoSGEoLHEQ8vQEO2+4ZSilCH4etLIaytxGE6pTMv34p1S2mdFJh34Tjtt8qVhY7nr1e3s7Gjna88+xZfPv5Aev58Su53TZs1mXnmFJkoN9MM02rtlwgX/+ysA3DY7lyxOP1rFcErNLivjksVL+PGLm3nh+EgG86ZjI47xSxYv4ay583jPvX/kj7tf46oVp7BhTvZNigpu9Q0Gg3z+859n9uzZuN1uNm7cyD/+kThfYywnTpzg2muvpbKykvLyct7+9rdz+PDhKR7xeLQucv+M7Pswsu+DyM6ziHVsQHasQfZegxz6Fvh+pwlSlnrwfABR9gWwnwHSh+y7Cel/CCnzW98tZQjZdwNy4PMjglTJR7BU/hjh2IBwnIkQDvbv348QDoRjLcK+WhOk0BxNwvM+LDV3Y6n6JUKk3zUpFcKhB96GRkpGZEAvGXFdihBjLnysuugV60HG+tN+HUN5X9vQSMU0KmEy7qZL0tuM1DinzillpY16cTWV4j9GfX9BeTlvmt0MQEBq+TIBPWfGzm4spGcV79DL8pI5pYQQpluqS5Xw5YxePWumzuWgUnwDj3iYCvFdtA5r0wPD2RKT2nszbVFKaOV7MV2UirCUiJyll9ttTfgzbt0tNhAKmnfpx2ZK2XXXVkTOIcwaeuWtxGQ5drEfN0+Me06P+AsAft7KiprT2NDQSDgW47ZXt6f1eyQjhHajySFeTev4o3peRCaCbnz5nhFEHmYFfnm5/trjfwc7O3GIPUhpI8BFY8a8Vu+U102p+H+U8H94uA+r6CMqawmRvXs7yNnEZDlW0Y2TTQmPKRV3AxDgTcRoHPf4/v4Qwaj22XZaXfrCkdGBbyBF+Z4/W6eULqSE0ijzMBh7bPtw+oG6Bs+2HufxY0exCsE3zzqXWneSxiSAYBiXHizul5elfO6Pr1nHry6+jL9c8XauWpw44y2emNGBzxlIq3yv1KaJUmFWIaUDIWJY6RwlCIamab5Oa2trvoeQFRM1h8gEazKnlJ4pVWz5Mblkps6tQsS4CduWxtps5EMlyrgci/H+y1SUao1zSlXp9si+DMr39u9P3t04G6SU7NI7sm5rPcE3nn0KgHPmzcdhtTJXv5FxbHDq40qmgu64xk0HejMrqzWcUrPLyllWXcMCvfGQw2rlS+dewNuXr8RhtbKuYRYramrZMGcu79LdyA/u3zupcWe1+j7yyCNcdNFFPPnkk0mPeeKJJ7jooovSFoRyxYc+9CF+/OMf8/73v59bb70Vq9XK5ZdfzqZNiS8kDbxeLxdeeCHPPPMMX/rSl/ja177G9u3bueCCC05qnbSUEtn3zxB8EnCCVVccZT8Q01w8zguh5CZExQ8QdU9iKf9PRMkNiKo7wLYSYr3IgX9D9n0IGetN/mJT/XsMfgPCr4AoBedFiLLPI0o/N+7Y4SwuaCeNY6P2//CrmggowxB4FADhunzc4cJSAhbtDn8mJXyb9Dypc3PQdS+XxF88+xOUGlhpoUbcjJsHgTCVjpG/Ua5FKY/4MxYxgEtsHiU0rawc5g0NWh251spdEwFCciVCyKSbw7G0650JkzmlIC7sXHXgyxk9gQACyXVLX8QhtK5odtGMk815HtkIRsi5EaptFd0Jg/zHYpbvSSMXSJglRS7xTMKfMcSCbv1CTDBeQDBL9/QQ5wjL8Uqtg61H/Il4V4GFbvNc+uQ7EELL5LEKwaa2E+zty37tD8m1+ngSd7Mbi+GUaipL3+beawZkBykR/6d9Jc8ihObydbAbwRAe7kGgiV6l4v8B4OfyBPlQTlN4KhW/pczyC8otPwfAKz8ITCYg2YGPdwBQJn4FjF4zrbTgEs8CMCzfm/AZOnx+ugLaxbjD0pf2K1c4NVFqMIUodTKDzsceO1Ggbrffz3seeYifvvqK2bVPSslv974OwPuWrZwwXwvAxdMIESQiFxAhdetpu8XKiqrqtDupGSWqlY4g/kjyzVYgGqHUHsRp1T5ToswiinZdYKVtVHvz4DQt4QumcNxNV0acUpMLOrcl7b6n/duhuu9lzUydW4WIcb3bkcY1rSEwedL47PDo77/hDDKlhkMhenVX1Jzycqr0TKlMnFK53id2DHvpibsJvU3vkP52vYP5/ApDlJqZHfie0/OxAEocmQn5RtD5nLIyhBB8eN16yp1Ofnzp5Xxk/Rn85LLL2flPn+TP17zHdNZdqEfIbG+bnDCdlSh15513snXrVs48M7kdfsOGDWzZsoW77ror27FlzNatW/nDH/7Ad77zHX7wgx9w88038+STT7JgwQJuueWWCX/25z//OQcOHOChhx7illtu4dOf/jSPPfYYbW1t/OhHPzpJvwEQfglCzwEORPWdWOqeRNRvR9Tcj6jbhKjfgqXqF1jKPodwvx0hRj6ghaUUUf1/iNJ/BeGB0IvInquR0dyKalJKpO9epP9+TcxJ9PjQ98H/R0AgKn+Mpep/ECU3JrSGlpYmz8mYMqzzwdIAhCG0A/wPasKfpRoMF9VYjBK+NEWpaCxm2hzPX9A02RHnFIsQZqnB0LjacEm5+C/sYj/l4lacbMEiwMgFFSKI1hFs8ggGcfM3899GuLKTpzmz8rO4bFH29VfTHZxjHhOU5wPgEs+RDqmcUgB1Hu1DsjODOzeKiRGxw/z9sj/x9vnaHaiw1OzDpeJ3pFuyMdUYzqiQXENU1unfmzjMGuKDzkfCwoNSE6WcPE+i94eRN9Otz8eSBFZ5QySLyBF3h58riEm3Lug9b35f60QW0zuRaWUTTeUVnDtbe6+8MImLA0PcsbMPQeqL2pZBTcjLxCll5EmUit9hE8eIyhp8vJMIS5DSiUUMUim+TLnlNqrEl3GyCafYgZR2vPIDCZ8zoP8NYrLCdL9F5Cz8jL/RkCnD8t3EZDk2cQwP9416zCPu01//HPNvMZZOv48uvyZ+W0hfMDQ68PUHJ94EZOuUMkSpTDKlMhGldnR3ctw7xB8P7OOO3ZrIua2zg719vbitgvcvr8CiB4UnJoJH3AOAX15KfIl3rjCcUimDzqMR5pVocz0qK5F4iMSJUjaLxcxkCUzTsHPXNHJsZ0Imbo6JMJ1ScUHnUkozENpuVU6pbJmpc6sQMZ1SPm/KY+PL91JhCBzDGWRKGeVg5U4n5U6XKUr1j8mU6vX7iCZxmOZ6n2i4pGaVllHlclHmcPLTN7+Vy5dqTWtMp9TAzBSlnjo6UuWVaf7XSPme9rl43drT2H7zJ8xzA5prKj6nav0s7XNwb093xi66eLJafbdt28a6desoK0tep1pWVsZpp53G1q2JSxmmgnvuuQer1crNN48EkbpcLm688UZeeOEFjh07NuHPnnnmmaOEthUrVnDxxRfzpz/9aUrHHY/03q594bka4TgD0Fw6wr4SYa1PWe8rLB5E6ccR1X/WXFbR40jf73M7SP9fkINfRA58Dtl1KTL0EjL4DLG+TxIb+AKy5yrw/VobT/lXEM43Tvh0S5akX+uaK4QQpltK+n6PHPq69n3P9QiR5KJeF6XS6cDnDYX42MMP0B8IUO50srZhVm4GnkMWllcC8Ju9u0d938nzOIXWsUqICFUWrazuxHAZUmpLRi7cUjYOUiF+OCrTxylexM5rVIpvYREBtnXN54bnLudonIU2oLumHLzKWMdCIjqGDVEq+Ydag1v7AFcd+HKDwM875/+KJRX9BKJuhmI30Sd/gJQO7GJfyi5mJwsrmmMlRi0RmoD0SvgMUcoo3wOt9Cwqa5N2zDPC3rv1u4OJQ84P6s81siZKykxRpVL8BxXiGzjYQon4MzC+E9lZjbMB2NLelvL3SEaMBqKyASFi2Nk94bFSSpr18r0FE1wTjKU3EEQwRAmaS2pQ/ovuWLER1nPHnGIHAA6x01yHfFw56rzH4+cKemI/o0v+nl55G355KQPyy8Dkg/YlJbrjCsott+nlxmEEPtxoLlufvCrpz3f5fXQFMhelyjMs38s0U8pwhmTilBorYE10Nz7+psdv977O7p5u/nRgH/NKBnn+bb9jietD1FveQ7n4MTA++NbN37CLI8RkGT4SNxGYLDGpfTaU2UMTZkoFI1HmlWifRYZDynRKCe395jRzpaanU6qxcXxp6XShPxjkJ9u3caB/vJNweILmEJlgZkrFOaWiUpq3SVT3veyZznOr2GjUb8K2D6e+ph0RfNN3SmUiPJwYGindA6gyMqX0m8BSSn720hY2/Op/+PzjjyZ8jlzvE1/TRalz5s3n8Q/ewAs3fpQrlo24cOeVz1ynVDQW47mjI9fYmYhSwUjELP2bE3eTMZX20FhaxqzSUmJSsrNjoptME5PV6tvW1sb8+amDv+bNm0dbW/YXxpmyfft2li1bRvmYDi4bNmjOlx07diT8uVgsxs6dOznjjDPGPbZhwwYOHTrE0EloCylDW7Vuc1gRno9M6rmEfSmi9DPaP/x/SuhoGvf6sSFk6BUtnDzZMZEW5NA39RdxQ+wEsvd6ZN9NEHwU/H+ByOuADVH+dYTnfSlfN9nfZaoxc6WCj4H0geMsKEneWckMU09DlPr844/y+JFDOKxWvn3RJdMyp+Bf161HAI8cbebFdsNVEaNM3AGAT76VqKwyj2/zlRKR2qbTMsmwcw9/oUbcZJa8ePWSFwdbqRL/jhBhAvI8bt3zITr8JRwZGnm9KHOJSRdChM2uRxMRikWxMJIblYg6vcZdle/lhjJxGw3uLtp9Hu499h2GeT8xqsyyNDsnP6svERb6AYhRRRRNzLGKiT9QBT4swijfqR/1SJCzAXCKF8b9nCFKGRv4sXf8BX6saDdOIozOwfHKDxGQ5yGExC2eoNryeSxikJBcQYA3jjp2Q4O2MXi9tydlyddEhFgJgI0jEx7XFwzgDYcRwLxMnFIhP3b2IkSEiJxNkPPjXnsk9NpwPAEE5BsYkh+d4FmFlvNDKRGWMCC/RJhT0h5TKnxcxbB8N1JacYlNuHgWF09gEcNE5JwJO/x1xolSVpGBU0ov3xtIM+g8nRKMeJxZZEqNc0oND9MXDPCFzc/yzInRN//GOnG3dLSxo6uTT63aRpndi5Ta+8AjHqBGfBwrI9eMAq9ZsumVH0IyNV2QJJooVW4PTrjZGuWUMkQpqYXWG80PDFEwE5HvZNLc3JzvISTl569t595DB/jOti1mqadB7kSp8U6pcJw7I92ST8V4pvPcKjZGyvdSl735DKdUBplSwxmIUi0D/cCI0FPpHl2+9/Vnn+JHL2wiJiWPHDqQ8DlyvU/c3andWFxdX0+V2z3OJbagohKbxUKP3zeqFG4msLOjnYHgyA2eTESpvd1aNIXbZjMD6dPlNP2G6Pb27F36We2UHQ5HWiKN1+vFchI3421tbcyaNd6RYnwvWQhfb28vwWAwq58FrY56cHBw1H+ZIsO7kX16Rzn3VQhb9un1Jq5LwFIDsU49oyrR6+5Fen+GHP4tsvvNyN73IDs3Euu/ReuYh16OF9pKrP9fkd1vATkM9tMQdZvB9RZMt4r7akTppxDl30DUb9I77E1j3FeC5wNgXw+OsxAVPzRD1hNi062L4Z1ad8Qk7Onq5O8H9yOA3151NZcvTZ2BkQ9W1dRyzRJtbH/cvw8AB9uwiaPEpIch+TF65c/xyg/ydFsTt+85jYh+RzlV7o6FXsrFD6kW/4RtjABRwu8pt/w3QkgC8g30xn6AV95EVFZhEQEsYpCwXM6A/BILyjRRrHnU3QoLUTRRfKJSK7c7hsOp/Z3qPO4JhcF6j54ppZxSWeF2x3C7jQv7IG60pgGf3XoRpY6RTmOmG0k0n9wBJkRiMZ1SlUSlJuZYJywlGsmTikTdSEaXhAalJkq5eIGxJYq1+oXYca/23imx27Gx13Qi2TiMEJKorBmXlyQpo19+g+7YLwlJTbAJyRX0yR8wNiupwVPCwvIKYkhemsQdK/M9JlomPO6QfsE5p7TUFDgmwuWOIRxhev0B7OwB0IWjkTtxYbnK/HpYvo/B2CcYit1Ev/waE3Vem3psDMmPmflSdrELt9Dmuk++nYkuqbp8frr1TKmJnFJudwyXa0TQMDKlUjmlDGeOK8ug84ycUmMErN5ggOfbWtnUdoK79uwa9diQnj1i08WAh44cZpaniysXaK7AHnkbvbEfEpVV2MVhqsXHzM+MEvE7rKKfiJyHj7dn9HulIn7NihmilCO5U0pKSTAaZe4YUcoQygz3sFGmG5jAcVXsjP680DjuHeJRffO3v7+PV/XNkUGuuu/ZLOMzpcJx81k5pRSFgCFKdfp9xOTEcQnDYW2tSkfwLTGdUukLHc39/QA0VVYCxGVKBdjT3cVv4hqzRGKxlOPNBbu6tJsIq+sTd8Itczr54Jp1AHzt2SczKm/PN6+PXTvTFKXC0Sj/8dTjAFywYGHG3WPXNWqfidsnYUbK7OpFZ+nSpWzevBmfz4fHk9h94PP52Lx5M4sWLcp6cJni9/txOsdfsBp1zv4keTHG97P5WYDvfOc7fO1rXxv3/W3btlFSUsL69evZs2cPfr+fsrIyFi5cyM6dWrbCggULiMUCVMduxGEbImZbz/7j72Lo/7N33mFyFOfW/1VPDpvzrqRVjigiIUDknI1JNiYY44wvBofr68S9Dtc2tq+Ns/mwMdHGJicRTRQgQEgIoZzDKmzOk6fr+6O6e2Z2Z2Znk7QSe56HB+1Op+2pruo6dc55N76Dz+dj6tSpvP++emDHjBmDzWZjlyHLmzNnDjt37qSjowO3282sWbNYuXIlANXV1bjdbsKtx1Nd8BTxjl+xa7eHplYXTqeTefPm8cGqp5hR+QMctoTnOK47sWlhCD2OHnySA52XUFXWBOFXrW1iYjbrdlxDZOs6pk39H8LhmbS2QWdkAYsWLeK9994jHt9CSUkJ5eXlbNigJh5Tpkyho6OD+nrVGSxevJhVq1YRCoXYvHkz1dXVrFunJmiTJk0iEAhYSruFCxeydu1aQqEQBQUFjBs3jg8//BCA8ePHE4vFqKtTodgLFixg48aNBAIB/H4/kyZN4oMPVBUpU+G3e7eaaM2d+y227dlGV1cXXm8906cXsWrVKut+2+12a/Vn9lEzcOh+bFobWzc8xMRpV/Dee+8Birz0er1s27aN2zarv/ekymr0PXtZdaCBBQsW8M47Ki+poqKC/Px8tmxRKwIzZsygoaGB5uZmbDYbCxcuZMWKFei6TllZGcXFxaxet9q6/01NTdSH6q17um3bNnRdx+/3U1hYaN2HyspKQqEQbcaAMHnyZHbu3EksFsPn81FcXMwcm50HgQ+aGthXX09t3n3gh4B+Dh9s24dTl/i9Z/Pld/1EdZ1gaAMeHwS69rFzv3r2J0yYwL59+wiHw7jdbmqrGiiy3YJDUwRPofw67+/8JqFoBdMn7CHP+RcA9rRegua/ge3bdwDbsI89mRLPk+xtPZ1dTRczZqxGobGCubGhHl3X2bZNqdRc4yoo8mymrXkVdS2VjBkzhra2Nrq6urDZbEycOJFnn13GG4313LkvRrkrcb9ramro6Oigs7MTIQSTJ08m3Khy1+q7uwgEAuzdu9f6XgOBAO2Gr3zKlCls376deDze5/2eNGkSu3fvJhqN4vV6KS0ttdpdeXk5sViMlhY1OZ04cSJ1dXVEIhE8Hg/l5eXWc15WVoau61bBhfHjx3PgwAFCoRAul4vq6mp27FAEcklJCZqm0dioBqXa2loaGhoIBoM4nU7GjBljVRQtKirC6XRaz+PYsWNpaWmhu7sbu93O+PHj2bpVTRwLCwtxu90cOHDAejbM+61pGlu3SrZs2cLevVBe3EJlmU57xMXbDdV8eoqkvr6ejo4Oaor8TCyHUPdatuzdQl5eHvn5+db9rqysJBgMptzvHTt2pLRZ04ZdUVFBJBKhtbW13/d7woQJHNi/jcpaNViHoz6amiUzqkHG9tLa0UpTU1Pa+z2hZj9oEIoW0dzZjN1up8FYdasdN4sClwObVk993RuUVS2x2qzbIA3iUuLQ4nx52guUaivQpY1Vu37FlHE7AWjvrqahqwGPx2Pd70SbBU27iWmTJJu2xdH1A+Tnq37OXDipqqpidl4BOzraeX7zJk4fWzugPmJMaR7+EogGN7Flz5Ze99tss2/WqXY6Ob/QesZ6ttnkPuKZF7fz2ZVvEwRC3SvIy4OOQC1b6rZYbXZ/UzEF1XaE0OmOn8GW7Wq1t7i4PeV+jxs3jqamJgKBAA6Hg3Hjxln3O1ubNfsI83oLCgrwer3WeJOuj9i6dStSSvLy8hBFU/F5QIu+gcOh2smGHeOJxLZk7CP2dbbTUKD6zGDXbrbs25K2zT777DIikQjR6Biam2N0Narjt4XD7Nq1K2Mf0Wnkc7TWNxCtqMq5j9BcRlXI7m527dqVUx+xy3heXTYb4Xicxq5O9hjf9ba2NtZt2sTEceNoa2ujzniOjquqYtm+vTQEA3zzmNXYhKSpcx4b9mlUV8/gQOAnjC24lTz3bor4Dhv2Xk159cMgoDH0Wbbu3jGkfcRrr9VZfUTQ10HhGGXf29vYwJYtW3qNayXl5cSlZKxfLTp2dhewde8Win1dFIyBaKSRLTu3oBlV3Lbt3o27tY3i4uKc22xfffKECRNS2mz6PqITTdOYNGmS1Wbz8/OtPiISidDV1UV3d7e1gDrY94hM9zvduLZ37y6efXYzZWVlhEI6e/eqdvOv1ibiUiJQdP6/Nm/E19ZutdkOQ1XR3tBIpKIq47jW1/12l5YCSh3V2NiI2+1m0x41Tgig4cABuru7B91HJI9rH5X3iEgkQigUGvB7xKRJk6z7nZ+fj8/ns+53dXU1XV1ddHR0WPfbbLOH8j0iuY+orKy05gulRjvL9B6RfL+Ho48wq3/GpWTtpk14bLaMfcS+RnVeu5Ts3r2bxsZGpk+fTmtrK42NjQghOOaYY1i5ciV7mtS2HaGQNaeZPHkyXV1d1vd6zDHHsHr1aiKRCIWFhWw17oFsbaOxsZGw0bYbOzv5vzeXIYFjikpY0dpMJB7nxTffoNDhpLa2lng8Tl1dHaFQiEgkwubNm+nu7sbv9zN58mRLQTV27Fg0TUuZH+/YsYPOzk48Hg8zZsyw5nau4mIaursRQNeOXQQKitizZw9tbW24XC7mzJnDihUrOMHm5HGXi+2trfxi6VOcWVHFzJkzOXDgAC0tLTgcjpS5XXl5OQUFBVYbnj59Ok1NTTQ1NaFpGosWLbLmdqWlpZSWlrJx40arjba3t1ttwJwfR6NRiouLqaysZP369VYb7e7utu73okWLWLNmDeFwmMLCQtYbi5E+m53ueIzG9nbrGqfNPortm7cQDYd78REvdXWwrrEBv93ORflFhMNhtm7dSldXV058RLGRe/lu3W4i0SjvG/e7urqaaI7KOiF7amRzwI9+9CN+8IMfcN1113HnnXf2YtOklHzuc5/j7rvv5vvf/35awmY4cNRRR1FRUcFLL6WWzl6/fj2zZs3i9ttv54tf7C39b2pqoqysjB/96EfccsstKZ/96U9/4itf+QobN25k2rT0ipdwOJxSdaKjo4OxY8fS3t7ey0qYDjLwELLje6BVIEqfRWhDF+gm4/XI5ktAbwTcRpD3YoT7VGTnbRDfAbbxYKsAxzyE/ysQ3YTs/gOEk6tIOZWCy3slwjF0dgiAAwcOHDZedL3tPyH0BHivQ8v/bq/PNzQ1cv4/7kUAz171aaaWlA7Jebsj3fh/ptrFhqs3kO/J3SaTDoI2JAVICR9f+gRNoSB/OXU6p5R+HSEkS/f/kq8t28ZFEyfx9fkLOemRfwLw/qXvk2d7l3b9W2nDg52soEh8HyHCROUUII5DbCcuCwlyEV4eRBMhuuWldMobe+wtEQSRJIjutc1NfOmVFynzeHjs/Iut3/u4nzztrwTlmbTp383I6N/x/kru3baZc2rH8/1Fx2W8H13RCOc88QgAL158eb/DgkeRgJsXKdR+worGSq585WM8feEllgXJyXsUa98kJsfSJO87pNdpo44y7Wp06aZBPoeD9ZRoNxCXZTTKhzLu52EpBdov6YosoMv+616fF4rv4BbL6dQ/TzdXWb/f0tbKZ/79HACfmrSOHx2dqB7Zrv8XDrEWr1hKl7yaLjk4+/b7jQ3c+NpL+OwOnrzwYlz9zBkCsLOJUu2LxGURjfKxjNvd8vYbvFK3hy/PnstV03IbG858/CGCsSgbLvsXDq2dZv2PRJmVso0TRfhH6G2rP9TQaKBcu8L6OSon0iz/lnF7XUpOe/RBTq7azv874Xkicjot8vaM27e1tVForCqb7abE7eaJCzJnVl353NPs6erkjyefztw+qtkl490D+/n6G68yuaCQu888N6d9nt6xjVtXvkttXj67Ojvw2OxcOGESD25Vits/n3IGs0tVEYDvL3+DV/fu4atz5/P/1q4hHI/x9kX3UeoO0qz/lqgRqg+q8EWJuAG7qLN+F5aLaJW/YDgCzk04+IAS7Sa2dxTwp83f55sLehfx6YxEOPfJR3junH8xOb+NFv1XRDgaBx9Sot1ITNbQJP/OF19+gXUtzdx6/ImcUD0EivchRnLbGiloCAS4/NkniUvJdxcu5qfvvYOG4LELPkaJoaq4/Nkn2d/dze2nnslRg3ivag4F+djTjyOAZZep2IAD3d1c9uyTODUbL19yRfYDjCIjRmLb+qhCSskpj/6LuJQ8ct5FVGSpPv1/q1bw+PatfHraTK6aNIXa2locGVRT7+6t45OP/IsJhUW8dO31OV3LKXf/ld0d7TxwyRUsHjOWpkCAY/76Z+tzmxA8f/V1XP3oQxzo7uLxT1zFnIrUOeFQzhOf2ryRm55byqyycp668pqs2/5pxTv83/I3OG/yVP5w3oVDcv7hxqceeZC39+7hlPETeHXnDhbXjOGBSz/Bvs4OzrzvLuZUVHL/xy9PCSrf1dbG2X+/m0g8zm/OPo+Lps3o93nDsRiL/vpnuiIR/nnpJzimJjH+tbW3U1RY2CcvMiCd6le/+lWqq6u55557WLRoEX/60594+eWXefnll/nTn/7EokWLuPvuu6msrOSmm24ayCkGhKqqqrQZVslMezoUFxfjcrkGtC8ohVV+fn7Kf7lCSh3Z/VcAhO+6ISWkAIStAlHyMNhnASHQ90HoMWTbVxUhpVUgiu9DK74PLe8bCOFGOOciCu9A5P8EcIFtHKLkQbSCHw85IQVYTOvhAOE+Q/0j/FKvzAOA37+jsmTOmzJ1yAipoYaTd6jQLsbHfQghWFhRgUAyzv1nhJAE9GP4yXv70JE8vWN7D0+6atsiTaaUh2cpEt9GiDAheSzN8g+0yl8RlZOxiTb84l40ESIsF9Epv5zmykQKIQVQa2TUNAaDdCXJhWPUqj3kTj71/FJufC3997G9Ra0KVnmzP1d+h9PKYmkctfANCqY1b3N7MXahUeBM2MtM+56NvQxVBceBIjlPCiBOhfH7ZiDzqo5NqJWszkD6NhWRKlfIIT5M+X1yptmkfHVuKZXVRwX8q9U1s0rhYDC3tIxKr4/uWJRlRqnj/iLOWABsojXt825is7HCPLWwOOM2PVHu8VLt7cKhtSOlLSXY3USEhTkTUs2h4EGR/JvQKbeyhAAiZK5EDCoPKiZ1msxMqT6Czk2FAkC+8fy0hcNp+zgTg62+1z/7nlqFrzCU8sF4LKXK04bWxN9nZkoVutzMKCqhxttFqTuILm1WoL0JST6t8mdE5SR06SUmx9Ihb2Q4CSl13r7te+F4DE3ovTKlzMp9Zs6imSkVGqF2j+S2NdzQaMLLw0afmhmPbttMXErml5Vz3viJjM/PR0ey1VBUwNBlSpk2UglWn2HaUZ2jlfcGhYPZtkaRHUIIy+raV/5Twvrdt/3efP5yDTqPxOPUGZmwE4rUu1bPrKLLZh7FxKJiqoxCKfvSxAMN5Tzx7Tqlkjt2zNg+tzUtaWYw+uGAzc1KmXZ0leItzEqJ79TVEYzFeGdvHXetXmVtL6W0LIpLxo7jwqnTex80B7jsds6brGJuHt2QWiBnW2v2dx4TA+qBCwsLWbp0KTU1NaxatYobb7yRM888kzPPPJMbb7yRVatWUVNTw9KlSykuzv1FdbCYN28emzdv7pXpZMrW5s2bl3Y/TdOYPXu2ZcXque/EiROzVhocFMIvKnJI5IHnE8NyCmGrQpQ8jChZiii6G5wngVYF3msRJQ8hbL09tUIIhPdyRPlyROlzw0JGHZZwngA4Ib4HYhtTPtrYuIdtje8ggP84JrMq51DDaUyWnUJlfywsr+SqyeuYlLcZKV38v42nWdlKManzyNak4EFhvICL1EHDySoKtJ8jRJygPJ02+SPAhU4RzfIPBOS5xGUpHfpXaZW3kqtzOM/ppNRYKU2uwBcz8m5s7KKuq4P3GxvYamTbAIRCcM01xTzzy1PRozYmFPQdkGuSBqNh5/2Dea+vuaaYUCgRjL2lo4hSjydFxaZTgi79CKFjJ3M11IOB5Dwp9f9ipHQihI6NzC/XZqZUOFqU9nMzpNvJeiCRW5LvdFoEQKFTWa1CRri3k3ewG5X3Ygw+g04TgrPHjQfguV3Zg8pNdEUjbGlrZYeR3ybxEpdK7ZLpu+qMRNjbrciIqYXp70cyzLay8XfnMcuvXlJiTGIwOVHP7NzOx55+nAe3bBrwMQaCSJKyKyyPybqt2Z9GjcIRKlOqN8Fk3p9vf/sozGrZBUb1vbiUVunudBhoptTAgs7VuQpdLqt63/ak0tkbWxMkhJkpledwMru0lLkl6uU+KtN/73HG0izvpEE+Q5O8z8o2G0r07LP05KDzDPc4FI9T6+/AZYujS7dFYktroaYLiOMazZQCYrh5iVJxPfnaHygWNyNQ7aPnvQ/FYjyxXVmTrjDyN8fnqfF6lzGZlVJaE+vBZkrZtMR4ZOZKmUHno3lSoziSYOY/9UlKxVTf78lBUe11msfMLadoT3sbupT4HA7KDLWWXdPIM8Y1p83GV435UrUxz97fNbyFxd4xinEcW9M3KXVUuVId7+lot6oFjmQ0BQK0hIIIsNRm5sLQppYma7tfv/0mdca73vrGBl7duQOHpvGDk0/rd5ZUMj4+XXEFz2zZTChpLH1vb12mXVIw4B547ty5bNy4kdtuu42zzz6b6dOnM336dM466yxuu+02NmzYkJEEGi5cdtllxONx7rjjDut34XCYu+66i8WLFzN2rGqAu3fvtnycyfuuWLEihZjatGkTL7/8MpdffvmwXK+UcWTX79UP3quGXCWVDCFsqiKf63i04r+ilb+Glv99hC27HFJofoQYXivTnDlzhvX4A8Uf3n2b8/9xLw3diRVgofnApSaSMpCwH8nISkoCl/PsOQ/x2xN3Mm2EqqQAa8Jtrl4uKi/m5lmq3f9188n8ca2arJ82Rk0GHjIsGRoCUlaFdexsRBDAJ+4HICjPpl1+n9TwZTcd8r9olA8T4BKgf9Vtag3l4Y6ksPM4NUhpx6FFqPKq7+f1pE5P1wUvv+ym+cNa0AUT8nMgpUbDzgcE816//LIbXRfY2QnAlvZii1BMQCTCzo3tDhV6KqVAWBNNs5JWOthQSqm8gilpP48xGSldaKLDqqYHiugvM4jPQqdSiUXkInSZjyZCCKETkkssBcZgcU7tBADePXAgK9HaEYnw+w9WccGTj/GZfz/HNS88w4r6/cbfYhYUUJkaHzQ28I9NG7h3wzpCsZhVur3K67MCubPBbCsHVtcws0CRUj3VMv2BLiU/fU8tOv2/tR8M+DgDgRnGrks3EWZn3dZUX9pECQBCxNKqz8z78/bbJei6ejF02+24DeKoPZx+IiClHLBSakBB50Zukstmp8itvvd9SePk+pbeSqk8p5NF5ZXMK1bPT0wcuoWunn2WqZRy2nSievqJRygWY0aheqmPMQFzHNNREykhJIKuEV99r7a2dliPb2czZeJyCrUfo4kOpBTYxR6KxPeAeK97//DWzXRGI1T7/BxvrOyPMxTSuw1SKhSPEzdUTUNVfQ8gZlTgS5BSo5X3BoPhbluj6B9MArerD1LKJOJzWdDwW0Hn0azKXROJkPOiFLKj2Cj8cs2ceZZCqspvKqV6j41DNU+s7+pie2srAlhUU9Pn9vkuN+ONBbe1h4FaylRJjSsotEhAM+h8U1OClArFYpZaarmhHDth3HgmFZcM6vyLasZQk5dPVzTCc0aWHMCqHCvyDWpZwOv1ctNNN/HMM8+wbt061q1bx7PPPstNN92Ez5fZvzpcWLx4MZdffjnf+c53+Na3vsUdd9zBaaedxs6dO/nFL35hbXfttdcyY0aqX/KGG25g0qRJnH/++fzyl7/kN7/5DWeeeSYVFRV84xvfGJ4LDj0Lsc0g8hC+zw7POQ4DmGF/IwnrGuq57e032dDUyGMb16d8JnxG3kvwCWRsN3rnb9BbrqLEpV7Ez6t6Hhl4+GBfcs7QjEm1DdVBVXvWUegKUx/08ss1tdiFxo1z5vO9RcdS4HRaL4NOm2atCnvFU5SKqynVvkSpuBaXWIWUGp3yeobaajG9SKktVxsBiwp2umKKUJ2crybGr+9Lz8SXucOM9fdN+JqEwSgpNXAIgtiFGny2dBRR4uldUlZN6A59Bb6eSimAOH1X4DNJqabWTJMju0W0OEmtRFZhkVIh69xhlMpGlz465M39+huyYWxeHvNKy9CR/HX9ml6fSyl5pW43Vz+/lH9t2URM6tZK6T0bVJ9nKRLFbta3NPOV117iTx+u5o51a/jtB6vYaEiypxb1rZLqiVojMDomxw/kzwPg3fqE5b4mh2d8KBHiROKylCAX0bMCYk80GCusRe58dKn60L4sfMnoqwJfVNetfnqg9r3+VBcyCReXzUaRSz3jydOTuq5Oi4xKVkotKK/g4gmq7UflyFFfSzxIqcYtG+lLqIficWYUqoWcWIrd1I4u1XOt0WkRiKZybaTBDNIdLvjF37CJVuKyiC75aZrlHejSh1Osxc1rKdtua2vlzvVKuf3pGbMswqjWmKSaSilTlWETIic1RzbYRBqllNGeR5VSg8Nwt61R9A9eu2Hfy6KwhX4qpQyiS5cyJzXoDmPhanyPrLEvHr2IsyZN5iuLFlu/qzLI6P1p7HtDNU80VVKzysrJd/V+P00HUy11OFj4thhqqKklJZYazSSlNjcrQcJ/LDoWgIfXryMQjbLCmDstTsqAGig0Ibh8pnIL/PadtwjHYkgpeS/HGIlh64Gbm5u57bbbmD07+wriUOPee+/l5ptv5r777uOrX/0q0WiUp59+mpNOOinrfnl5ebz66qucdNJJ/O///i+33HILc+fO5bXXXqOsrGzIr1PKGLLrdwAI32cRWt9KjiMVnWk6oEMJKSU/fv1V6yX75R3bUz4XzgXgPBaIIpvOhO4/IdB5dOdUntm7RB2j40fIWG5yxYMNc1KtiXYgglu8DEC3fjJfOmoBfzn9LD4xdToum405pYm277TZiCeVqjfJB5tQnWCI09FJX151MFhYroiClQ311sqMlJK1rWoSes44gU0ItrW3sberq9f+L5//d/zav/s8z6h9b/DQDEVNVzSPlrAnjVIqQUIccqWUaAOUbc+EpZQSmUgpaZG6Hd2ZF15MC59DpPrqTTVekStsnDufbnkZMVlLu/wWOkM71nxp9jwAnt25wyKQAN7av5erXljKLW+/SUs4RG1ePr864RT+fvb52IXG6qYG1jU3EZcJpdRj25SNd3x+PgJ4asc2HjAqjU7rR56UiWpvajbPQPDw1s3WvwPRg0sC6JTTKB+mU97Q57aNRp9S5vFYfajWH1LKeLlsD6cnpZIJEHc/J+09lVK5ZHOZBJZT0yxSyoTPmAhtbmshLnVrlT7P6QCiFDl2AhCl/0GqwweNqEEs2UTvMQTUyvJ0U90nJ6V8lsiVasdlkILmRG+kIVsl6cFC0IaLFQC0yN/QJT9DjCl0SxUe7hP3kWxp/sX7K4jqOidU1XCeoeyERJakqZQy25DX7hiUtQSUdchE3FBKJZOsoxg4hrNtjaL/yFUpFYznrpTyJCkVNzQ1smp/dgVMgpRKXbj65FFzuP38j1GY9I6Yzb43VPPEZ4x3hsU55EmZmGPMQQ4HUmqzUV1zakkpPqf6rsLxGC3BAPuN+dFn5x/NuPwCOiNhnti0gRUGYXTMEJBSANfPP5pyn49d7W38bfVKNjY30Zxj3zCkpJSUkmeffZbLLruMmpoavvnNb1olDA8W3G43v/zlL9m/fz+hUIh3332Xs88+O2WbV199Na3scMyYMTz00EO0t7fT2dnJU089xeTJgw+dTYvgYxDfCaIIvNcOzzkOE3g8vSeuhxJv1+3h3X111sv6yv37aAulPlDC9xXjXxIpSvj2inP51run4ir8LjgXAyFk5/8e3AvPCTIlL8fGflyoKmAFngv51LQZTEkaPJLDi52ajTDH0SWvpEt+kjb9+zTqDxCW89BlAV3y6mG54jmlpTg1jcZgkD3GYPX87p2sb1XqhFOq3cw1yLO39qdn493ilT7PM2rfGzwcqDDKvQG1slSa5tmOoQY+G4eWtLWUUrLQ+l1cZldKCTrRhFJ6SC0zAWtau3oqpUzis8BSSuURYzpN8h7CnDyAvyI7jiop5axxtUjgd6tXIaVkV0cHt7z9Jrs7O/Ha7Xx6+iz+dsY5LK6sotzr5axxyoLxwOaNxIywc03u4iWjbPq3j15sZb+0hsOUe7ycO35C2vNnQ5VXKVLiAySyW0JB3jmQUEo1h4KW8mGkwexTyjxeiwTtK/w5GSYp1ZZBKRUwVqydmpYy6c4F5kQ8LiWrGxs494lHeGrHtqz7JIKhbRQl2TYFMNsohb6rs5PuJKIwz+nEzjaEiKJLP3FGVmW6uDTyTkR/lVKJXCmNDkspFR6hSim7w0FrKJST9aa/cPMqQsSJyqnESVi5AlyCLr04xA6cvG39fltbG167nf86+pgUssm07zWHQnRGItakerB5UqBW8s0zxYz+IjjAPLZRpMLpzK4YHcXBRa6h5EFTKZVD+9eEsI572UMPcNlDD7C3o7fdzoRp35uQQ+Zkwr7Xm4Aainnic1u38MK2rdiE4OJ+VJebXa7eUT5syKygN9EcCPDqzh2ED1Gm4GrjnWhKcYmVKQZY5GGVP48Ct5ur5qiqt79e/gZtoRBeh4NZ/ajamw1+p5NvHX8ioKoXPrVpYx97JDAkpNSOHTu45ZZbGDduHBdccAGPPvookUiE+fPnc9tttw3FKY4oSBlBdv0RAOH/4rBmSR0O6GmlPNR4bfdOAC6cOp2pJaXoUvLarp0p2wjXYkTxQ4iSR3k1cC8P7xhHdV4ep02YhMj/H8AO4ZeR4bcO+vVng6AdIRK5JF6xFE2EiMmqtCvX05JsOYqkc9Elv0iX/BIhziBOFa3yNzTIR1JeQocSLpvdKgG9ov4A7xzYzx8+eJ/GkBqk8hwdLChTg8bGDBUenHwIZF+5NgmD0ep7A4ddqMns9k71faVTSpmTUTv7SF41P9jonSmVXIEv/YqYqTKMy0JqaiZmPHYEZU2yi91GALJCudeLTegUOCPGuYdfIfvFo+bistlY09zI0p3b+dGK5YTjcRaWV/D4+Rfz+aPmpCgEPjlV9QOv7d3Dni51b2wcICajTCooYFZxCV84ag6XTprCZ2fO5v6zz7Osr/1BoaEWMy2T/cWb+/chUQHrGoK4lLRlUBIdaph9SrnXSxyVY9Ef+6pl38uklIoNfFLtTMrRead+P92xKO/VZ3/5TlaWFCYppQqcLiYa+X27OtotC5/bZsOh2XALNR5GmMtwV9TrL3Rp5EppGfp/2UqlQaTGSH32dSvsvHPEV997tr2VC59+jMuffYp7Nqwjpus5E1QaBygQP6VUXEWh+A49q5R6hFIkB+UZKb+X5BHgYgC84umUz84fP5GiHtW4vA4HZcYkdHdnx5BV3jNh2gRNpZSVxzZIa+BHHWPGjCyi+aMOX65KKav956YU9DpSyce6pLzXZLQEA6wxLPaTivpWU1cbZHRDd5dlqTUx2HliIBrllldU//SFoxcxqzz3xbBZ5RU4NI19nZ1ZbWg/fO1lFt95O9c/+SjffumFQV3vQPBB/QE2NDXi1GwsGVuLw2az3gneM0ipacai0RUzZ1Pm9VkKpgWV1TiGUCl68fSZTCkuoTsa5a/v9y4ilwkDJqXC4TB///vfOe2005gyZQo//elP2btXfVnf+ta3WLt2Le+99x5f/epXB3qKIxfBR0HfB1o5eD91qK/mkGPVqlV9b3QQsdxQBCwZO47TxquXz54WPgDhnItwHMWKvephP3HceGyahrBPBveFAMjI2732O7hIZevNSbUJUyUV4RjSTRKSlVLZbR3D+zJnWvjuWLuGb7zxKm2RMDahOleNFiYbqzBb21uNPVInb5roxk7v7zAZplKq/gi07wm6yRe/olRcRZm4FA+P07v6V5gi8Z8Uim9bKqL+wmFUkPuwRX03JWlJqQqktCFEBI2mXp8fLAwkU8p8fnTK2b49c3uSFCZVrktkIZR7vBQ4w0nbDf+CRIXXx1XGquCtK99lU2sL+U4n3190rJUPkYyJBQUcX1WNBO7b3ISUdmxajEpPNxdNmIwQApfNztfmL+QzM4+ycisGgpawm4g+sMp7y4zCBifXjKXYmNQ2jlD7iJkpVebxEpVKZWM+K7mg0MqUChnPTOqzO9CQc0jY9wCL1OsrpDth37NZQecARW43tSYp1dlh5ElJPj5+Ozb24UYpVkPylH5f53DDfBZdWvo25LXtBKA+WIwklYQ1w8412i2CdyRmSsV0nSe3q3Z3INDNX9at4doXn+HsJx7me8uX9bU3ReIWPOIF7GIvbrGcPPFX61M7G3GKtUipEeK0XnsH5QUAOHnf+p0ALjNKiPdEctj5UCqlAOxGBT5TWTkYUncUCWQbE0dx8GGSUn1Vyutv++/53pBpZvDHFe/QHY0yq6yc2RV9Lz6VeDw4bTYkUJ9UOAMGP09csbeO5mCASp/fqvaXK/xOJ5fOUOr3372zPO02m5qbuOeD96150hObNvC+QQTt7ehgl6EYG07cv2Y1AOdNmUqJMZ/xG+pFUyk11VjgL3C7+f25F1gZe0Nl3TOhCcHVc+YBatzJeb/+nmjlypXccMMNVFVVce211/Lqq6+iaRoXXngh48ePB+DWW29l5syRE2I50iAD/wJA+K5HiNyC1kZxcNAeClkVFo4bM46TascDZGXHTT/uoupEJQfhNCpFxDb0+xpkbDd6+/+gN30MveUzyODTyNCLOe8vCJIvbqNMXEaFOBMvj1if9Sx1b+ZCRWV61Uey/epAIL2t4WBgoTGgdceiaAgunzyVq2eonDhFShUCsLOjg0g8jiZ3Wfu2RecB4GR11nOYSqmuaDRjWfCRBBevUSRupkxchoPMNmmNBorFf+AVT2EXe7GJZgq031AkvoWXf1o2Ii9P4hIrcIu3KRZfplDcgo/7yfzK0Rt2lFLq3cZCIL19D+xWjpD9EFr40iulFJFko5l0f7dmPD/xHLKfTDVFMhla4U2QUjHpY7jJXBOfmjrDat9j/XncevxJlGZRN11lqKWe2bmThpC6P3OLw5w/PrM6bCDY2+2nORjq936BWJT3DCn9idVjrHbWNAJVjlLKhFLK4yGGqtpoZ0vOx8g3XixPKX+Ycu0ySsQXcJFQ4Q5G6aEJgdOw/JlKrL4IlXRB5wBFLldSHpAKOz+rZgc/Pvo5SsQXsIs9SOkkzJJ+X+ewQyhiyecIpQl9l9T61ALTnu7eGWim4lETnRYpFe5nplRjMMD1/36Om157mUe3bRkWe93qxga64jEKnE6+ffQxeOx2dnd2EojFeG1vnaVsSwcvj+AQW9Cln079iwD4xL/wi9uxsR+/uBuAEGeg07v6cJxqInIuQiT+rsWVVdQYlp2eMNvRrs4OOgzbat4Q2cMspZRxj0OWfW80U2oURw5MEjdb0LmU0mr/uY4f/h7PYXeafmNvRwd/X6Mq4n5ryYloOWTBCSGyWvgGg5UGKXP82HFW7l9/cMOixdg1jTf27Eo7H1y6WVUpP33CRCvo+2vPP8PVjz3ESXf/hTPvv4u3DMHDcKA1GOSpzcomd41BBgH4Dev/GkP9PK040TcfUzOGn51+Fgura7hkxtBzNhdPm2ERo5OLcqvql9M309rayv3338+dd97Jhx+qShlSSqZPn87111/PtddeS3l5OSeeeCK7du3q42gfbcjoOoitAxzgufhQX86IQE0OZTkPFt7euweJkppW+P1WqN/+rk7aQsGUUD6AYDRqhd+lMM12Q2oa7R8pJWNbkS3Xgp5QkMjIm8hobkyzRgNF4ts4RGIi7OevhORp6BT1UkqZMCuijVTMKCrmxjnzCcfjnFU7nkqvz5rs22ihwuMlz+GkMxphZ0c7Ve61xPd/nhWNVfj9FwDLcYo1BOTlGc/hczjw2R10x6I0BoLU5g/NquxwwMWrFGk/sH4u4Gc0yb8CPVUnMQrFf+MQO4jLYjrkN7CzE7+4E5dYgUusIE/eSZCzrEmuLj3YxQHsHMAtlhHXywlxVtrreGbndrqiEerqdOxiN3laK7p0s6ZFER6ZbF0xxmCnzsiVWjC4mzEgxNFQkvNkpZSZ9yNEFCE7kD3sdTZh2Pcop7g4uxw9ykRcvINDbCdozMPKvd5E5T2jCtvBgNtu5w+nnM6WtlaOr6rus/T5nNIyZpeU8mFzE+tbPVR44Mqp/kErCbxeyd69+/DyCD4tyN7mSmyuAJX9rNb77oEDRHSdMX4/E/LzKfN42NgKTSNQKdUZjVgkTqnHS5SJSKlhE61oshmdxMuaeX+am5vxehO/L3C6OLNmBydXKmWrQ2yhkB9QL58CXINSSoFSS0V03aru16dSKiVTKomUcrsthUtDMEBDMMB5Y1U/rRkB4mGO7aU0Otgw73MKDFIq3xEhFI+nKMj84k4qC9S9X9UynzE9Hn1pKKUEHdYz0l/73st7drPZCAVe2VhPtc/HsZXV/TpGX3jVqDx1YvUYLpgwifll5by5fy8PbN5IYzDI5rYWji7vrWgQdOEXdwHQKb9MkPPRZBM+8Qh+8U98/AshJFJqdMnMGalBeQ4F3g/YsWUxpz1zOedPzUxOJkipTmKGza7KOzTKUpuhlDJX8IP9qD42iszoa0wcxcGFmSnUncW+F0mq3Oq22yEHMrynUqorjRLrpR3biOhxFlbXcOK48Tlfc4XPz672Nhq7UxfBBztPNImkhdUDO86Y/AIunTGLf637kFvfeI2HLr/SysGTUvL0FkVKXTh1OseOGcszWzaxu6Od3Ya1Mabr3PDMkzx6xaeYmIOVsb94dutmIvE4M0vLmFeZWDgxCURzoWVCj0rJl808issMEm2okedycfms2dy9ehVnTprESznsk5NSqqqqiptvvpk1a9bg8/m4/vrrefPNN1m/fj3f/OY3KS8fmnCsjwJk4CH1D/eZCG20A4eRFY5oMtnHj1VVp/JdLsbkq5ejjU29rUbvH9hPTNep8vupyUuaZNqnAQL0BmQ8t0BbqXcgWz6tCCn7NETh78H3BXUsrffKY2/oFIif4hDbicsiWvWfEpXT0EQQn7gXAM2YVEuZ+uhnI6XGZFjJPJgQQvCJqdO5dsYsKr1qAhs3JnOa6ECIKFMMtdSW9jbaQ2rFoCVcRUzMA8DJB/Sl+inzKtJxJIedC7rIF78HICjPJi5LsYs95Im/9drWy8M4xUZ06aNF/pEwS+jmKprlX+nQv0REzkKIKF6xFJtoJSaraZL3067fTECeC0C++C1aGjtbezjMz957h9998D7L9u3FgRqUu+Lj0aWG3+HIuLKdyNU5NEopjQ5jEiWsPBgFp0UW2dIEUZu2vriswN4HARCTplIqERrtdzi5aLx6loU4eKQUQLXPz8k1Y/skpEA9b/973AmcUzuePd3qOmcXD11ek00oIr8ukGdZ2/qDZUYJ4xOqxiCEsGyiI9G+Z15TodNlqGjcxI0AeXsGC1/PtlXihp8ufA2AbnkxcVmEEBEchtoqNMigZlPd056jfS9VKZUgwotdbvKdTooNompjy35OqVJjqpRqIhOUva1dIwOK8MhzhomkKMUi+HgAgP9eeQKbOub22tPsMzQ6Bmzf29Sm8hBNO8Xre4e2b4xL3TrmyTWq/dX487hiynRmGavnm1rT27ZdvGNkT44liBoXOuV/0Kr/mLBcZKmfQpyZNcA+xMno0sk4fytT8juzkm4T8p18e+5ybHIbe7s7jesdIlJKmPY947pH7XtDgr7GxFEcXPiM7yNbplQoKZDbPcBMqXRKqS0t6v1pUT9JoGJD9dzc4x18MPPEaDzOaiPb6uiqgZNbNy0+Dq/DwaoD+3kiKbx7XWMDO9tacdvtnD5hEuU+Pw9efiU/OuV0fnDyaTzzqWtZUFlFRzjM798dnkiXF7er98xzp0xLKRrR8x18XMHw55gm49tLTuIvF1zMZ+cvzGn7nEipiNHgxowZw1NPPcVf/vIXjjuuf57MUYCM74PQEwAIT2bFxkcNO3bs6Hujg4S369RKoklKAcwwKrttaGrstf0KY3K0sHpMSkcgNB/YxqsfcrTwye67QW8E23hE8b0I99loed9EK30KUfgHazun6GnViuDidfLE7bjEanTppkX+njDH0ymVzN7Lk2i0Wva9ZBIqLkutld50+K+jjwHgisnTcvo7DhYk+UipBl2NViYXGLlSba1oRo5POFRNlKlIqaGJjj4zjEyL00gmpfziLmyimZgcS7v8Oh3y6wB4eQgH6zQ1n1IAANUUSURBVKztbCSIqk75FcsyB8paFuCTtMg/0qz/npBcgi59dMivolNCkIvpkN8gIo9CE934xQO9rmNNc6NF8f3+g1VoUg3SDUH17FT7Mk8g4tKswJfZFjucMPOkVNWs1Bdpi+ykd2i+nT3GNmNoaEivOjQRY5Kxzw6SydALJ6j+JJUMG3kocXv4/qLjOGe8qqLiENlLP/cHJrm3tzuv34UFYrrOm0aVzRMNdaqpyGsKjTxSqsHIqDMJb4CoYeFzZLDw9Wxb4327KHKFaQz56ZRfIWoE6TtQY8uglVIGUdnvTCmbLSWk2sz2MtVSLrEKvyNKaySfZvkn2vWvEeakAV3jcMMcA02llAkbzaqinG7nH9tmpp246SnV99R30F/7nlmk45JJqm28uX8v3dEoOzKECPcXG1paaAmH8Gg2FlakhvyaBU02ZSgU4hJKJRbiRBLTBkGYE2mVv6RRv592/b/okDdnvQaJlwNBNQ6dVxvNGlw+v+g1PjdtDddMfokdRnWvoVogMytUxk2lVHxwz88oFPoaE0dxcOHPQSkVHEDl1p7PbbrqflsNUipX25aJYiMLqaXHAtNg5onrGxsIxWIUut1MGoSar9Kfx5cXLgbgF2++bo2Dd6xaAcCp4yfiM0igGaVlXD1nHtfOnc/00jK+dpxShWaLghkouiMRKwv5rImplWGTK/D5nU6K0uS8DiecNhunT5yUojzOhpxa4OzZs5FSUldXx2mnnca8efP43e9+R3Nz7iWNP+qQUiLbbwHZDY754Bwl9UYaWoIBi90/pjqx2jfdIqV6D7jvGuH+aUPiHNPV/3Ow8Em9AwL3ACDyvobQUiWWwpGoPFFo+wNgrkzEKBLfo0j7b3ziQQC65Oet1coIC4jKKQgRx8kKy76XXGmvL+ve/LJynr7wEm6cO7/Pv+PgQlh2KxstllJqQ0sLebRwxed/y69v+TKhkMMKsO4rw8iqwDdCw84FHXhR1Ys65FcBF2GOJyjPQgidAvFzBEGUau4XCBEhLBdaq9vpEGU2bfInNMilRDg26RO7ZcVw8To9qxeubjRCv6M2Vv/xVK76/PmEQk52dqvV76oslqyYVYHv0CilLMVTmuwT3SKleo5vEhtq4I8xjr4QYyxS2tBEN1qSbVajwzjPwV2xGigcNvW3DgWBGArBF75QxDVfuI5QyKkypfpJJH3Q1EBXNEqhy8VRJeq7MjOlRmLlTPOakq2sVti5SCWlzPvzP/8zk1BS1FaVR62CrmqqBhxEpUFKGQsUg60eZiml+mvf02xWCDtgWfnGG+riOUWKJN/QNpsYUwjyMYao6POgYN7nL3yhyLrPZtB5viOc8vebCxmd0XxApFQrNCFTSKn+K6W6o1H2GBkqV06dgc/uoDkU4pKlT3DNC8+wvb2tv39iL6wwMkVm5eX3UktOMwqamGqtVERx8S4AYZnebhdnDEHORdL3hGfF/gqu+PxveerWy1PaeE8UOdUix1FFjdR1maTUUCmlVBuMydHqe6M4cpEIOs9CSg2AkPX1UN90pVFKbW1RfUl/SaCSDEqpwcDM/V1QWZ1TtlU2fG7+0ZR5fRzo7uLVndt5ecd2nt68CU0IvrTwmIz7zSmvRAB7Ozto6BHiPli8vnsnET1ObUEhk3vc7+T8r9qCwhTxxEhETm8HH3zwAe+++y5f+MIXyMvLY82aNXzta1+jpqaGT3ziEzz//PPDEsp4RCH0NESWAU5EwU8R4tC/mI0UzJ49+1BfApBgsKcUl1CUFNA8o1TZUzc2piqlIvE47x9Q6oF0ElVh5ErJWOYQaguB+0B2gn0quM7OuqlD202e+BN2tlIofoxLrEBKF2E5n275cav0sokwitl3iXetyXFUTrc+zyVPqtDlGpGdWdwgpVIr8O2n0t3NI0+fy7JXp6DrwiLp+ppYl41wpZSXpxAiTFROJkJCDtsh/4O4LMYudlMqrqNI/BdO8SG69NAuv8lAy69HWIAu87GJVpysSfnsfYOUmlVYRmD1NJ559kTiuo11rYrEzZb/kfg+9tGT7DoYMNtBOquJSUr1tO9pNKKJEFLaiFPNuHF9EVMOYtQa/0pY+DRhklKH3habC2IoklHds8GN87ouWLrUw6NPn0Bct1EXyKMl28w0DZYZ/fSSqhprcllmrP71l+A6GGhMqrxnIoYipXqGnZv357XXytD1xDNb5FTbvdtYhpTSWlRw0oOUGkSmFMCk/FYePv0xFpZmD2FPtu85NJsVqGuSUuPy8tGEzhnVOwHY3jWyFjTM+7x0qce6z7pJSjkjPUgp1Q+0RRTxlG7F11RKCTqsEN3+ZEptbmtFohZFyr1ejjUyQcyA4kwKpv7gXcO+cqJRvCUZ04yMk7qurl75ME5Wo4lu4rI4ZTFrIGgNhXhzfx6PPH0uL70wP6WNp0LHyVoAStwhKjwBbEJQ4e1f9lwmWPY9fbT63lCi7zFxFAcT/hxIqYG0/WiPvq1ndb/WYNAilSb1Mz+pxJNeKTWYeaIZcj7QPKlkuOx2Lp6u+sF716zmllf+DcD18xYwu7wi4355LpdV+e79A/sHfR0mdCl5fKN6Dzhz4qRe87RkUmpcQeGQnXe4kDMzsnDhQm6//Xb279/PXXfdxZIlS4hEIjz00EOcd9551NbWsnHjxr4P9BHC67t2sL21BV0PI7tuA0D4b0DYJx3iKxtZ2LNnz6G+BCDBpvfsuEz73uaW5pTSlusa6gnFYhS53UwuTiNRNdVN0eyklJQSGXwcAOH7XE6EpU88Tqn2OdziNaTUaJP/Q6u8jU55E5D60hyWir138QZ2cQApNSLMsz6PyZEdcp4NyaqWiQUFzC4pZVJeG1qPd11TmWPrI8MoUXp96PJzhg4xvOIxALrl5SQTTZJ82uQPicsKbKIel1By4k55Azp9l+LNDDshTgDALV61ftsVjbDVKHH7iakJW6eUDj5sVhOHbEqpOOVIaUeIqFXRrn/QsbETB+sQ9J9AtAn1rMfo/ZJiEZ0ilZSyGyqpONWAnaY0GXM9kajAl5jkm0opOcLteybiVCGlQBMhy/Y4VNjXnUdrOHdSSkpp5UmdmKRmTSilRh4p1WAppZLte9ORUsMu9qWo6NJDx2dTeW0rmyqI6Lqxv8Am6tFotla7B5spde6YbcwraeDcMdnHrEhcjYMmQTMhX6n+ak2FVEkpC0sPUOwO0Rp20Rod+dWYTaVUniOVlLIZSqnWiCKRXWlJKfWZRqellAr3Qym1sVX1NdONCdwpY8amfL5/kJVvu6NR1hsq8Ilp1EAFLhdVBuGzuUeulEu8CUCY4xmsym1VYz2b2pInqelJbju70ESi+tbMwiaqfP6c7UV9wbLvmUqpUfvekCCXMXEUBw9mIHlXNJpRODIQlWBjj/6oZ6bUVqM/q87L66Wq6gtWplQPt8JA54lSykGHnPfEJTNmASqDeH9XJ7UFhdx8bN8VZRdUqQW+9/cPTRRCTNf55gvPWnlSF0yd3msbs/oeHPw8qYGg3z28x+Ph05/+NK+//jqbNm3iW9/6FhUVFdTV1Vl2viVLlnDHHXfQ3j40XvjDFf/x7NOccd9d3PvOLRCvU2HV3usO9WWNOLQZk9tDDZOUWlSdqp4YW1CAz+EgEo+zPWnFMpnESisJdRjMfnwHMp5l4h1bC/FdgBtcZ/Z5na2xLxKVE5FSEJaLaJG3GS+M6RFlJrr0ogk1+VNhpDXo0mN8fjiTUgmllE1o/Pak07h+Rm8yxMww6ssuZpFS4cylsQ8V3LyGTTQRl0WEOLXX51Fm0yTvpkO/kQ79SzTpdxDkwkGfNyRPBlItfB82NaEjqfH5WVSRIL2C+hj2BhQxkI2UApuVcdVfC5+gnWLxFcq06yjRvkKR+A79VfDYTaWU7P2Sosv0mVJmnpRp3QvkYPGMyHkAeMUzQMy4fkMpJUf+C4KCEx2lFh3KDLBwPI+umLNfSqnWcIj6QAABLExalSw1Vlc7IpE+rWcHG6Z9rzxJKSXxE2UqoJQo2WBjNzbRRTBmZ2NbMYFoFImXGOMBlSs1WPuRSS5VetVkI98Ryqp+NwkXk6D5/QmCVy48wBi/moBMLy7hmimKbHt5Xy1+58HNsRgI9CRSKjn8VxMGKRVWxFM2pZQmuvEYH4f6kSllBoybiqVTasbyg8XHc8F4RWof6B44KbWxtYXHt28hLiVj/H78evrv1Ty3SV4pyCTr3rFp9uof3muoZ0tHgpTqbZFWcPBhys+zipqGzLoHyUHnPZRSOeaejCI9chkTR3HwYCqlYlK3LNc9MRCVbc94i57qym2Gda+/eVKQrJRKPcdA54m72ttoDgZwarasSqb+YFpJKUcZx7JrGr85+7xeFQnTwayKt6qfSqmYrvPitq00Jd13GV7O1h1X85lx/8s1k9fxizPOZk5F7wXonva9kY5BLTtMmTKFW2+9lT179vD4449zwQUXoGkay5cv58tf/jJVVVV88pOfHKprPewwNr+ARWUNXFD9HAAR9+cR2qEthTwS4UrKpDhU6I5EWNegKkIt6lF6VBOCGWVqUvZuUkUc8989SSwTQisGu7FCHMlccUEGVUYQ7tNUQHofCOgX0iz/Rr18iVb5S6L0rgaUCjsRjlbnkna65HWAoFN+iW55CTFjcnQ4wsqUEmoQdNpsnD6m9+BrKaX6IEDyjVWFjhGnlJJ4harcGZAXA+lXnyQeAlxKgE8O2fca4Wh0mZdi4Vtt5KvNKytPySepD1aw35hAVWUJOofk7yR3okPQSbG4GafYgJROpLThFB/g4o1+/U3mOWNZ7Xupq742kZon5cjhJSTImcRlETZRj5uXgeRMqcPDvgcJRZmdXUN2zKCu7n1/lFI7jcDjKp8/RRWU53BYWT8tI8zC15AmUwoggrK0OcX7Wfd3GsUL1rWWE5M2Aoaly7RSOcRG63fZgqOzwWXcu0qPenYLXUFiUs+4vaWUMvarcf6EsZ4nKBD/Z2whOaVKrd6+sHfCYTHZT86USp7Amf1AU0h9nk4pZe4L4HWo9tcfpdSWNpOUUhZ0IQRnjK1lvvHeMVCl1Pb2dr7w0gv8+cMPAFhYXpmx35ptWEvWJBV0sbEXu9iHlHYiLBjQNSRjZUM9oXjiuU2uTJoMp1CkVCiu+siZhU1DWgXYZkipY7qZKaW+71Gl1OCQy5g4ioMHrz3xfWSy8A1EZfut41XxEzNIPRBJPfZA86Qgc9B5f+aJu9ra6DDeK0yV1OyKCstaPRT4woKF2ITgOyeczNzKqr53ABYY231YX9/LApkMGV6G7LodKcPoUvKtF5/ji0uf4PKHHqArEkFGVhBv+RxTfe8xq6iJ789/m0unpl/kPGLte9lgs9m46KKLePLJJ9mzZw8/+clPmDRpEqFQiIceemgoTnFY4ulLavj7qU9T7AqxtqWUh3aMfAn7ocCcOXMO9SWwun4/cSmpzsujJq+3reb0CWrF8tmtmwHl4zV9yovShZybcCkFk4y8lfZjKXUIPQOAcJ/fz6vO/fENyrMA6OYKS6ES5GN0yq/26zgjDXFpKqUSK66moiVlOytYey+QebJVYHTg7WmCGw8lHKzDKTYipYMgFx3ks9uNqksJC58Zcj7PmDSZWNdaYJUfruoj/8P6TvqwVCbDJx7GIXYQlyU0yb/QzacAyBN3YCqR+kY0Keg8nX0vfdC5VXlPKlIqt/wMFwF5mXHt/wBkEil1eNj3AKIoi6ajV+XPgSNu5G21hsKWYqEv7OpU9258jz5aCEGBa2Q+u42GcrC8JyllqOicZCelHEIVyljXpvrtgLGyHZXGd8IWAlH1u4GSUqb6p8qrAliLnOGsirOwVX1PQxBECDW594gXKRS3UCI+g8fWRCjuZHXzBGaXlA3oug4m+sqUajRIKWdaC5kdXar+zmtTJGRE13Nq11JKi8gf609t15UGsX9ggKTUsn116IaKVENw9rjxGfutuUZfvqa50bpuUyUVYTaSwS2o7u/uYl93l6VSArCzPe22ZhXZxsg5AMwsaqKmj0WO/sDMorOUUubEfDTofFAYzZQaWdCEwGsQMV0ZSKnQAJRSp02YyNuf/SI/PV05O3pmSm0z7HtpY036gGnfaw0Frcw3yG2e2NjdzU3PLeXUe+/k6kcV9zCUeVLJuGDqdDZ85WY+My93sn5CUTH5LhfheMwqqtUTMlaHbP0ysuvXyNYb+PXyl3l8k3oH2NXexh+X/51Yy5fRRJSX941jR/dEbCKG7PpV2uN9pJRS6VBZWcl3vvMdNm/ezCuvvMLVV1891Kc4bCDbv4NGlN2hY/jUqxfxt9Vr0UcD4XthxYoVh/oSWGuopOZVpGe8z5+iJgDv7K2jsbubdY0NtIdDeB0OZvWYmCdDOA1bXfit9HaIyDug14PIA9fJg/sjsiDMiTToj9AlPz9s5zgUSGRKJaxWtrSkVHKGUeYMlwLLvhceUcUbvOJRQClvdIr62HrokWzhC8bCVvnyeaWpk83n96jJbZHL1efKW0zmpl4zIQjiRWVqdcgbiVNLt/wkcVmIXezBxfIMe6Z+jzbqEUJHSpfVfpKRbAlN3tduVd5TeS/btqVf5e+JABchpQOH2ImN/Ydd9T2AqDwKSKh2hgKapkgpHUlHjnZZk5Qys4uSYakcwyNH5dgdjVph1cmZUqDstlLasIsD2Mgs5zefj7ruCuuYkBqWbv5uwEopW6pSqsgVspQ+j2zdzO8/WGW9u8SlbqmoXDZ7L6WjWyzDIXaqbcVZPHrBFVbm10iGxMyMimMnWS2klFINQa/xefp+zVRLuW0J5V8uVtLWcIjphfu5atI6yr2p359J7DcEAil5lrnibaMQy9fnL+S5iy9ldmlZxn5rckEhXrudrmiU7Ub0hlOY1r3F/T53T5jV/6YnWXrsRjtJRcR6HjT7BQCM8XUxcQg5fLtFSqk2PRp0PjTIdUwcxcGDqWbKqJQaoPW73Oe38qK6IxGe3LSBX761DCkl24z3w/6GnAMUGUVLJKowgolc5ok/XvYKT21W2dZrGxtoCQYSeVJVQ0tKAf3OuNOEYJqhSN3cnD5/TXb9Equ6emQZE22/A+C6eQsY7+/g2nG/xUYHq5vL+ePmK6ms/AUgIPQMMk2ld5OUcmo2KofQAj1cGFaJxMknn8w999wznKcY4YiC+1zKqv6C3eZnV3sbL+8Y7bRHItYZyo+ZGQimMfkFzK2oRJeS57Zt4f41qwE4uXZC9o7JeTTgAH0/xHf2+lgGFdmA+3yE6F8gYH+hJuAjr4LeYGDZ9yxSSsdOuhBBe04ZRgXGxDYmdWuwPtQQBHGjwmYD8mCrpBSSLXz7O98gLiUVXq9h0UuQAGtb1cyhL+seJFRKuWZKeXgWTXQQk9WEDeWWxEeI0wBwifd67ePkHcrExykVV+MXd6ImPOp8ypLW+3mwiE4RskLUBUFsosHYb2yvfbJBkmcFnjvYaGVKycPIvhdBBXvaxS4EnX1snRukGG8pE1tytPDtMux7tWnUrPnGsTqiI0cpZeZJ+R2OXpkTEm9SFb3VGY9h9mctEdUuE/a9SUipYRMtODRl//IOovqezx4h36nuncsWJxIL8PyuHdy2eiX/2rLJUkdG43rSfpr1PEXleLrkVXTqn6dFv5VG/R46+YalShnpkHjZ1K7yFWcVLLV+rxmkVH1IkVJOW/q/x1RaubRE7kc4h1ypA4EAvzn2JX549BtU2L6JIKGKKvG4sQlBXEqa+mlL7YiEWWfmvFZVW1Yer3MvXv6JILUsuV3TLEXb6sYGBN24DBVfhMylznPFs7t2ALA4KffEVKwmQy0aSHTpxmYbx4FAIQCzioauyILD+A5N0tAc6wf6/IxiFCMVvqSw83QYTOVWn0F4dUWj/Pj1V/jze++ysbnJyj4aCAli1zSK3KqKa89cqb6wo0eRhtd27rQIMjNk/FDDrMC30SClWoIBvvb8M6zYV4eMfAChZwGByPsuABeN28BRxe18d8kiHjv7NSo8ATa2FfOztVdx58euxOebA64zAJChF3qdr8SwQ44vKsI2RIUihhMj/woPZziORxT8Ep/LwyePUtLDnyx7jS3Nzfx6+Zv89p23WLGvfyG/RyIqKwdTHWxosN4sb59F9WSqpe5avYonDDnl9fOzSzeF8BjEFBB+PeUzqXdB6Hm1neeSAV33Rx2pVqs4Go0IEcHjjrBlyx6WL/8Qj0ethuaSYeS2262clJFSgc/F2wgRJiariTGt7x2GBYkqfC4jG2leqXpW8jw76Ng2l13rF9Osqxeg7CHnConvYz99W++kpRbrlp8gucJkRC4EwEnqSpqXhygS38Ym2rCLOvziPjz8OxFyniZPSp3Jgy7VQG5ad8yJty4LkIbCqbCwsM+/0UQUVRXFJd6yCg4cCsXbQCEpJGaEwjsYuIXP4wnTvnU+Hdvm4vSMTbx85hh2biml0pBSJqHcPoKUUpuNrKDqDCRtFGXptwtVXc/jkWzZsj+p34pY1SnbIoowCFhkuZu4QZDWeFSbTs4Q6Q9cNpulkjJR17WHn69MPFPvGWriZPWPU7NZz1OMKXTJz9PNVUQ41rBnjsxFEPM+b9my3xofAF7YdzoAswreQKMJQQBNKDJof8AgpbT0+VimUsomApbyLJRDrlRHeBfj/KpdO8WH5ItfW5/ZhEaFoZaq72fY+bv1B9CRTMgvsI7hYDXzan9OvnY7ReJ7WCvyBuaVqTb2QVMjeeLPxrgzltggi6FsaWvlw+YmbEJw8YzxbN/yBh3b5pLv2dFrW5tBwqoqp4J8t3pGip1DV2TBfE4CxkR9sNUrR6HQnzFxFAcHZti5uZjRE4Np+6YKpy0UpNnIgNrT3mYpD4s9A7P8mvslV+DLZZ7YGmhiTnEDM0sV8fP7FSrLd3ppGUUjRK3bUyl17wereWLTBn69/E1kWM0HcZ+P8F3H3sjxaAJumb8Krevn5Nn2IbUKKLqLey75nHWfhEuNW4Rf63W++ZXVfPeEk/npaX0X0RoJGCWlhhGi8JeW+uWGhcdQ5c9jV3sbZ//9bv6w4m1++85yPvHwv3hzTyI8Nj4AifbhDl8OE9jhRCAatRj2TEopgIunz6TE42VnWyuReJy5FZUsqOybfTc7DBl6NvWD0HNACGwTwNFXWPko0kGn1LDlxdBoslQ3uqjG67VRVOTEjLAwJ3B2kT2sudCVbXIbwsl7eHgajaFbuc0Gt3jFOPMpHMpJXkiqdjw1fyVOLW7lSTnFVnzeIPl5Y/n0jKM4trKKKyb3TZ7plCOlAyFi2LJYKgHsbMIu6pDSRYjUwTXCPMMGtc+a0Pi4h3ztjwghCcgLCEhVhdAl3sYmzEl0Zjl3Iuy82Th/asg5gNsgVHJBVCpSygw7j8gZg85oOdiIYlj4RMLC5+bflIpPUyS+jpuX+jyGQ9Th93Xj8WhIUUyJy8iPyEEpFYhGrdDw2vze1sc84wW5cwRlSr1jVNlZlKYqDkBUqmIEDrYCIAR4vdLqt2zsN1QjHmJGtcZA0op31LDw1frVeQacKaXZrMp7JpoDb7L07L/zsVql7l7ZoFQtZgi4TQjsmobNyIQz7biHA8z77PVKkgvn7uqeynuNldi1GF7xlKWS0qWPjogimtIFnUNCKSXosoLdgzkopZw9Ks25eQNIPA+mha+/Yedm2zvWCNfVqKdY/Bc2TU0eneIDCsTPSF4QmGssNHjECrxCFWBpl99gsOPO49tV+z65ZiylHg9ubwU+bxCb1p6iDAMs656pbNbFJADsojeBNVCYiqhALIYupUW0DrR65SgU+jMmjuLgwCRg+1RKDaDtm0qptqRFJTPk3KnZBjwemblSyWHnfc0Tg9Eo/zn7BR494zH+d9F7gGRnWysuW4yzJo4f0HUMB6b2IKWW16l3yzX1B5BhlT0sXKq69pN7zySuC44u2QzBfwGgFdzKzIoZqcpr10mAgNhaZLw+5XyaEHxuwcIRoxTrC6Ok1DAiudJevsvNr8861xraZ5dXcNwYNUn+/sv/prG7m1++tYyj/vx7vvLMUykM8ZGOQ+1D39jUiATKvD7KsnR8pV4vD17+SSsI/fMLFiFEDi9r7nMAAdFVyHjCWiZDyiIgPB/P7TijSANbki1vX5I1S02QDhxI2AOiUk3gHGzOesT8jGHnkhLxFYq1b1Kg/R/5In2w4FBCEMCFWu0JyVOH/XzZEGE+Yb2EPEeIU6p2Mbe0DI1G/OJvAGi22XzhqDn83wmnMMsYeLNDM1bD+67A5xGKzAlxfC8yR9mglL3MyXu4WE6edhcAnfr1dMhvEJTnGZ+vxMlaAOIyMykV75FVZhe9SankttUXTKWUEGrRYSjKqx9sRKS6xw7j/oHEL+7GLnbhEquM5yH7ooqdnQDEGA8ISynVnIM1abehkip2ua1nNBmJIgUjQymlS8m79WqCvThDVmGUKQDY2QokCAyzbdmTVCPm5KI7acU7KtX+E/PUi+hgMqXMkHMTi0vfYUJeO1dNVhl9G1tb6I5GrQm8Sc70pTw8nOCy2Xlsl7qnDj608qR0SiwyzpmBlJKodweNLit3KpcKfIUOlYHyduPJxGUFQoRxscr6vMKn+rsD/VRKbW4zc/8U0eQVTyFEmI7gRFr1nyKlDY94hULxY0C1qelFxYz3B/jhArVi3y0/TpR5/TpvT7SGQjxvWPcunqjGYIkP3SBZe+ap2UWyUgpiUqm0MoWiDwS+JPVIspptVCk1OPRnTBzFwYGplOoZRm5iIEHnJnxpxuGtRoB3sccz4HlNiamUSrLv9TVPrO+s4+wa1c/MyXuer8xcxVhfB8vO/zs3jP9PZNefkXLwC1aDzZo1lVL7Ojtp6O5itbF44NY6IWZkQjmPA+DlPYLb1i4ioJcDbvB9EeFa0uuYwlYKjtnqhzRqqeGAjK5Dhp5DRlYNaf7uKCl1ELF4zFj+3wUf40ennM7Dl1/J7ed/jEqfyppafOft/Pm9dwnHYzy7dTPn/eNeNmUIQhvF0KKvPKlkTCgs4ukrr+Gfl36C86ZMzen4wlYBjkXqB0MtJfVOiKgQUdxn9/+iR2EhmdiwGyqYQLiWm28u5Gc/m4YpeLIqiLGFbHYxywbUY3LrYB0OsQ0p1SDv4q2UgPXhgJP3ECJiWPcmD+u5+kIgpvPYTrVq/ZOFy1mYfwVl4hPEIh18+qY/8PmbvkZ/nVMxqypiNhuzbimMQvK0tFuEDQufW7xqZEdBt7yUbq4FBFGmocsCNNGNQ2xGSgdhjstyRvXiYKPe+L9BSsn+5UmZiDEOXSZWkbOde6QiglJzOvkQQSd2dhrqNQdSOtBEIG2RgWTEI3V85qZb+ezN3yEcVgQTpAaaZsLOLCHnkBR0PkKUUlvaWmkNh/HY7MwuTU/SxhmDLt1oIoSNOsJhUvqtZCuTNZGOJvous0+YVqAsfr4B2vecaex74/1qglnuDjLG7ycuJe83NhBJqrzXO6Pt8IB5n2++uTClz3LZ7HzQrALlHWyyrJNxSnuRcT1hKaVEtzXByyWXcIxPkS3tsZmEUBMOl3jT+rzKq47bH6VUTNfZ3amy3yYUFABRPKhFsLqWswhzPG3yR0jpwC1eI1/8Ght7KbbfxX2nLKXYHaIxNI5O+aWcz5kJ92xcRygeZ3pRMfPLyq17/5mbbyUcdvQipSyllFTjetTI47Ozg75I71xhKqW6o1FrUg6Zv9tRjOJwRZ+ZUoOy7/Ueb0xSajB2uWJvb1KqL0QDT+G06XRG1TvF1456jztOeI5idwg7rciu25Dt/zUoAkWGnkc2LEQG/jHgYxS43VZV1X98uIao4Y46tnwfAgn2qQhbKZF4nHWNDdy+cT5N3qfQKteg5X0j43FNdZUMvzrga8sVMl6PbL4C2fZVZMsnIXDXkB17lJQ6yDhj4mSunjMPh81GnsvFT08/y8ooqPL7+cHJpzGluITGQDdXPfIga+qP/JWHmTNnHtLzb8ghTyoZBW43x9T0b1VYeJRSQwaNANXw60AUbBMR9sHlNXzUYU6GbGKvNUEKxcby0ENenn++knhcrdbEGYsuPQgRtuxY6ZBvlJbvObl1i3+rY3MqETkDIfScLEuDgUuoTBdFYhxaNd19G9Zzz2ZVMa3I1aWCwIVOOFbF/Q+exYMP5Vv3OlfEre8uMynl4ENsogld+giTvgpUiJOQUsMlVuEQW9Glly55bdIWGmEWWT8FOS9t5T0TVmVA47rM9hJPUkqNGdOfPsBm5YHFZSkxQyFzOCFOLVE5ASGiuHkdF8sACHN0EuG7KesxZLyOex+8hH88eAzxeEIplUvQ+fb2NiB9nhQkBZ2PEFLKVEkdXV6BI0MOkWoXCQVnPC5S+i2bSFZKmZajZPueake1eR147dFeYeq5QmVKpSqlPHZ1Hr+jm6PLlf1wZcMBi5z55THPUiHOxyZajGs8fEgp8z4/9JA3pc9y2Wxs6SgiEnegiW5cQimWdEqTyLjsSilBlzXB6ytTStDJGK8ivnUxh7BU1XpdvIVJwFQa6u3sSikdD09QKP6HQvEdwpFnEETV9+r14eZ1bKKVuCzGU3g+AGGW0CZ/iJQaXvEspeIa/OJ+qrwtNIfc/G79JwFX1uvvCwe6u3nCsO598ag5CCGse3//g6cTi9st4tWE+XPMWGyKMwYp7WgiaC0SDBZWplQsZlks3TYb2qhifVDo35g4ioOBvmztgwn5d9nsvZ4ZM1h8MKRUibGvjO1BBh5ERtczc8aMrPsUSqXufHrfGeC9CoApBa3EdDsi71uAHUJLofsPA74u2X0XyE5kxw+RgUeQsn/FJ0xMMxap7v5AjS+aEBxXYbgFjIrtm5ubiMTjFLjc1BYU9n1Qp8p8JbICKYc5BiiyHKWuVW1Ldv0RqQ9NnMkoKXWIccr4Cay94atsuOEm3vjMF7h27nwevOyTzC6voCUU5PKHHuB37yynPccg2MMRh1ryu6FJrYbOLCvrY8tBwH0O4FCe3+g6ZNggM9ynD985PyIwV1Tt7LUUN6Z6KhUaMcwMl8yT5/SByVE8hlqnI346rx9QqhGPeG6QV58NEhdmSe5FfWw7vAjHYzy5YytbOor5oO0KAvJCmvU/0qD/kyb5twEf1yJ/soXPG6qBMCdgDoI9EWc87fIWpFRDWoDLrUBy628wyppLqdEtP5n1uqz8MXajKjqaapAEKdXW1pb1GD0RMTKZRgLBOFCEpKry4hYv4RYGKSVPtOyJDrEx6/49yeDifgSdf2D005msoQml1Miw771vLHZkypMyYRJLDrGl12fmcxGT1SkTaROSAqJ6IQCT8zpwDrC6jtNmo8rIlArGUicnPnsnU40A433d3UTicewizqlVWxFCEVdSaodVNclMcNtsxKXG7m41fpiLDlE5vm+llFSr3xpdeHLMlHKwDk3A9o4CitzVRJiLLn3YRCt2w2ZebkzQslXf8/AUBdptuMVruMVyJnn/j9Uf/xtPnvkwJdo3KBA/UdfDebS1JcjHMMfTKf8DUNbisFzIB22f57znL+flfemVFbkiEItyy9tvEtV1FpSVs7A8/XNgE8lKKZmkDjQtr3Zi1Br/GppcKa8jQfCGRkPOhwz9HRNHMfwo6ENBbCoFB9L+hRBWrpSJoBVyPhhSSimlzq+4G9nxfWTzxUTbPs8buzenVTrJ2E5KHFuI6YJdoSWIvG/TqavFnqDrMwjf5xD5P1Tbdv05NUZFD6C3fgW9/QdZVVQyvheipq1aIju+g6xfiAw+0++/75yx+3jyzIc5uUJFIZw3ZQonVqh3TGFY9z4wBCmzyytys0E6ZoLwgGyH2PBG4siIihTBew3Ypymiruv2ITn2KCk1AmDXNFx2u9XwCtxu7vv4ZZw1cTJRXec377zF4jtvZ/Ffb+dj/7yfN/fs4l9r1/CLN5fxdt0e9CH0cx4KtLQMrwWqL+w2VuAnFBUP2zmEVmzZ9GT3Xy3fr1U1YRQDhqW2YU9SSGr6VXtT0WFWu0qHdPY9FyvQRAdxWcxD2/L41ttuoroNh9g2pFkXybCxB5uoR0oHUQ5tEP5Le3bTHolQ4fVSmvclOuQ3iDILnUpgYOoMSEw80pUGN6FUAxAyVASZEOJUWuVPlW0vDekU5gRC8kQ65ReSJjzpYZJPdvag0YAQYaS0Eycxserq6sq0e1p0yyvp0G+gU36hX/uNJIRQ9kkn7+MQW5BSI8QSojIXpVTMyh8yYdn3+lBKhWIxNhorsHNL0y8eFGTMgjs02Net2sfEPlY5Y0bYuT1N1l0iU6rGUkEFetgwgrrq62YWdQw4wyO5+t6m9tRx0KFFyHMociUUixHR49Qa1eJMZFMdHk4wCadtneqeChFDSo1O/QzMt6zM1ff6r5SKxdVEZEtHEZVeH+AggqrU7EQVFCgyigFkIm4FXfiFsk8E5IV0yU/TFc3HadOZlN+AS6xCCJ2QXEK3vLJXvxXg47TqP6ZF/xWt8pfkez9BW8RHfSDQ7xyrZPzwneVsaG2mwOnkm1nyN5P7BEE7mggipSBOhfV7s/rfUI21JsEbjMYGFfQ8ilT0d0wcxfAjv4+sxcG2/3QWPoBi9yDsex4PdhGn1quKEunSTqHzfUJNN/BWnSKmdT3M8o1fY82u+60q5ssbaijwjkEIF/mVDyAK/0R+sbK8Ce/l4DwWiCnFEyofSnbcAuEXIfgPpaTKBLNQlWMheK8DrRSIIrt+3y9LoAwu5dKqPzGzqJkfH72Msb4Y/7mwgLH+TgIxO2266v9Nl9ScPha1TAjhSBTMiq7M+XoGhPA76pyu4xF5/6l+F/g7Uh/8XH6UlBqhyHe5+fP5F3Hb2ecxo7SMSDxOY6CbDxvqueaxh/nOyy9y+8p3+dSjD/KNF54d0qCxgw3HAC0HQ4FANGrlmdRksIUMFYT3SvWP0FKQnaBVjVbdGwKYMn+H2IEQUXTpQSf9xDWXyXOBq/cg7hSqkw9zIhta2+mIulh2QKl8TNJkqGGqpCLMRnJoy9k+sk2pOD4+cQo2MXTDRiop1bsPs7HbyC2yE2Fhn8eLcCyd8sa090vioU3+mADZVVLqutR3q4l2a3Ko8q8SL262fuaPSPwEuOKwVpTEqSIiZyOE+q4CXIqkMKGUypLXZmMfQqR+llBKZVc3rW9pJi4l5R6vVY2sJ/JGkFJKl5J6o1hJpTd7lUWTKHeyDqdR1ADAJ/6WRLJXp1QMS0ZHVPV/kwvaB3y9XnucMT5FNG1o600wFToVORGKxwjH40wtUC+funQTkUfRKb844HOPJJik1Mb2hNI2zPGE4kXWz5nse2amlEY3bmOCF+ojUyoYU+q/rqjfIrKiUtlUHEKF3prPSGc0QlTvrbzyiQewiTZiciwd8ia65Gf4r5Xf5uSnP8Uju75Eu/4NmvU/0yZ/gsSXpt8ShDmRCEcDAo/dzjRjge79puxVUTNhR0c7b+7fi11o/GLJyYzL8m6VnCllkrAq0y9hHbTCzsUQkVJJQefBQShFRpGK/o6Joxh+FFjVpDPY9+IDDzoHeimlTAzGvndUeQXTCttw2uJ0Rl1c8+q5hGI2TqveTSygiKN9B37O4sKlTLT9DBl6AoAX9k6gJk+9XwmtAOE+A5H0vip8xoJg8EFlNws+CKGnrM9l563IrjuQ4cQ4bH0WNAtTXYSW/11E6fMgvBDflsgH7gMyVods/zaaiCNx4XdEef7jHdQ4Xwfgxb3j2dCsxlqTlJqbIykFgONodZ7I8JFSMrYH9L2AHRwLwHmiEbIegcA/B338UVJqBEMIwcemzeDpK6/h39d8hic/eTWXz1QWkEqfnwunTseuaTyxaQNPbNpwiK924FiwYMEhO/feDvUinu9yke8aXH5Cn3AsBLsRji4KEUV/QojRQXywiFOFlIlV2BAnk6lrS2TfbCXT5Dmdfc/BegAicjbbDGXdS/uUpSA5lHYo4RKmde+YYTl+rtjd2cGm1hbsQuOCCZOG9NhxypFSoIkQGm29PjcJvwjzLCXCwYDEQ1wqYtP8fk1Ln4mJEycetOsZSWiX36FD/wqN+j10yq8ASsmjSx9CRKwKez1hZ1ev35kT7rZwKKvid7UxOZ5TWpZRcWGSyZ2RyCFXD7eEgkR1HQ1BmSc7KRVjgkH0RSkUP7J+7xcPIkTUUOiVp0ykk9EWUS+tk/IGnukw1b8MnyPGnq483m+u6PV5vkMpIBQppTOlQJ0rxKm0yD8Q4owBn3skwWmQSRvbEhOBgLwwpYpeJoukNIPO6bImeH0ppaJxFQoclQnSJopBSqGssPlOJzajzbemIW/N6qxd8tOYpPnOzk72BvKIaUsIcqF1TMit35pv5Gu+N8BM01eMMufHVFb2WYlVLUjEjX8nSNhkRK3cta0Dup6esILOk+x7A52UjyKBj+qYOJLRl609ZGaqDZSUSlOBD6C4j3EvG8YXFvGzk9SC5ZqWUlY1j+HuLaq6XKn2DjK6gUrxAABeexRiW9El/HtvLdXZxAXOJWCfCTKI7Pw1sus3AAj/TWCrBb0B2fV/yNbPpah+ZOgViK0D7OA+S+2j5YH7IvV54O85/V2y81YgDM7FaEV/VZcUeQSCjwDw1O7JbGlpIhCNssUIjM9VKQUgnIqUGlallGndc8xBaD6EEAjvdYC6D4OtcDhKSh0GEEIwsaiYo8or+PkZZ7PsM5/n1es+x2/POZ+bFiv/6f+8+jLbjEacDRuaGlm1fx9dI8TiAPDOO+8My3FzUY/VdarV5eFWSYH6HkX+f4P7XETx3xGOWcN+zo8GnOgkQupDMnM1QzV5diNEtFfVHxO9q3iFDQUIBPTp7DSIzJf3KYuXU2xAo+9nrz8QdODEVGcd2kpty/er1et5ZWUUDjlx67SsP+m+D7dYDmAFAB9MxAwSys1rxs/jUj7fsqV3BtBHAXGqCXA5cSPnRUHrM+w8HVllEsBxKTNWB4JEnlQm6x4kbAoS6MpQ/vpg4YChkirzerD3mfMk6JSfV/8SiYDSkDyWsJxvVECzW5X1epJSTWFFItX6B0pKxZiWp1aB/7ppLk2h3ivcfrsipYKxOJF4jCn56lymguVIgdtQetR1+wnI8wjK04mwkEhcfS9OzZaRFE1RSpmkVB+ZUhrqPkZlofW7hMW8DkEHmhAUuUxFYc9cKWnljkWNvERdSnZ1qPeaCfkF9EQu/dbiCjUhXFF/YEAE76t1qgrnqTXZq5VK6UCImNX324XKQjGrspowiwHY2INgYOHCybDy2Ubte0OKj+qYOJJR0Kd9T40nA1ZKZSKlBmHfA5hZqIoaeD0LeO26z2GLHQtAredDZMcP0USc3V0J1fnq5koaQz6q8zIr0YUQCLOCXfBfoDeDbRz4voAo/DW4zgatEohAUKmvpAwiO39s/LHXqSgW83hGoDrhF5Hx7AS+DC6F8AuADZH3fYRrMXiuBHSQ3QTjft44MIbNzc2sbahHl5IKn58Kvz/3m+aYB2gQr+vzegYKGTHm685jE790nw1aOeiNEBpczu4oKXUYoiYv35KQf/HoY1hQWUVnJMynHn2IO1au4F/rPuz1IiGl5NY3X+f8f9zLZQ89wJK/3cHK/ZnDhQ93vLlnFzP/9Dt+tfyNrNuZSqmDQUoBCOcxaIW/RTgOv+pbIxlmBb64rLBK16eHsKxZ9gzl6y25szGIO9iMEDHisogdnT5iUsdls9Ec8rO6WZFhLpYP0V+i4OYNhIgTlZN6TP4PPt46oEip46vShccPHplypTQacbAGgBCHgpRSJJQQamIZkXMO+jUcTjAnjpksNnbRWynltNmsyWCmldyYrrOuuQnITko5NJv1Yt0WDhPTh7kCTRYcCCgJfmUGq2FPRJlDSC5J+V27/AGt8jYCXAYkl7FPVd8cCKp7Uu1tIZP6MxvcvITX3kRTyMPDO6fRGnb32sbn6ASUHS0UjzPFsO/FGN/v841kmPa9cFynQ36LdnkLYOsz5Bx6KKWsoPPs34dDqPePqJ54/5DkWwUgTLVUcYYqlRotaCKElJrVj9YHugnF49iFRrWvHxOaJMwuLcVjs9MSDlnK4Fyxq6OD7R3t2IRgSXX2amxmyL+T1UBCkRyVqRWZdUqIy2KEkEOSK5VcyXIwQc+jGMVIh7nw0xWNph0TzWIMAyVlfRniVwYTdA5AVIWALxh3FpX+PAKRSbSE3XjtQYiuIqLbuerVC9ndpfrO5/eORwAVffR5wnUiuC9I/Oy/ESEcCMdstKLfI/w3ACADD6J33oZsPBPidaBVI3z/kXosxzTlgCGODPwr4zll4FFku0GGea9R+wEi/wdKpYXGnug5xKSNLS3NA7PuAULzq+BxgOj7/do3F0gpLaWUcCYqYQvhtAg62X33oOKERkmpwxx2TeOOCy9memkZjYFubn3zdb7z0gv8cYVqOG2hIN/+9/Ocdu/fuGOlKi9f4vHQGQnzny8+z/bWFlbu30v8EL7Al5eX971RPxCIRvnOSy8Qjse4/b132ZpFQba30yCl8g8OKTWK4YE5IQ5yDqDh8UjWrDnASy99iMcje2yrVm9tmUgpUyllePAdRqZQlFlsNarLTCssZkpRES9bFr4hJqXEKwCE5ClDetz+ojsaZbVRRey4yvTh8ea9XrPmQK97nQvM8PCeSik3ryKEJCKPMgLVDy7iMrHKH5XjiZBqoywo6K1C+CjDyn3JMGm0swuvJ8jGNU+ktBWTBG4LpyelNrW2EIrHyXc6GZ9G+ZEM89m97sVnueLZp3pU0Dx4MAOicyWlANrl9wi5fsSaNfvT9luZ7HsNoTwCMTt2LZ61YEB66PjFPwD42+Y5hON2OqO9bRcem0FKxWNE4kHG+5US53AlpTL1WSbpFIqnKpwiRpZTpjwpSASdayKA26YZx8lOSrmM+xqTqe26p4UvU5VKUyWlQsFV+9jcptRXtfn5aVV6ufRbDs3GfOO97J0D6RXFmfDaXjWuLiyvtNSLyUi+93aPiqNwilVAzPp7o8zstV/MILDsDF6Nk3iWYknV90ajFAaL0TFx5CEv6Rns7KEgjkvd6tsGbN9LypRyJRFbg8mUkjIMMaPwh0P1ERPKq3h9f+KdbGndfPYH8vjq8jN4qu5E/r51JhU+P44ccs1E3ndBq1GEUhJBBaifhUdlRXX/GfQGFbdScCtC6z02Wmqp4INprWsy/Aay47uADp7LEHn/ldhXCIT/K4jylejemwDY0pwgpWb3k5QCDLUUyMgHGTeRUiLjTUi9vX8EUnyHuh84wDk/9TPvJwAnxNb2IsSkjKN3/DynU4ySUkcAij1e7v/4ZVw3dz6nT1Ce7t++s5z71qzm2sce5sH1a9nV3oZD0/jFGWfz0rXXU+Hzs7OtlTPuu4vLH/on5/3jXt7Y3Xsl+2BgqAey3727nDpDARWXklvfeD3jthYpdZCUUqMYHnTJa2jVf0CXvAYAIaCkRGfMGBc93RZmNpBd1KU9lplNEzRCfZ0isXprrhpPKixkelExrxmDpJP3GYhKIR0EbZZ1L8Sp/dpXSskrdbvZkIOVNxesqD9AXErG+vMYm0EWbd7rkhK9173OBZZSSqROqN3iZQBC8rT+H3QIkGwhCchPAKl/nLePAOuPGqJGhSwH2+kdWq9jZzdCQGFJVUpbMSeumZRSpnVvTkkZWh8NzDxWRNdpCAZYunN4KmP2BVMpVdEPUkriJSoWUVIi0/ZbviTLUTK6ozo7OgsBsLO7X9fp4k3sYhcx3cc/tioiQCcxFu7sVP/22BQJFYrFcWv7sGuSYMydsaDESEemPiuhlEolpcyfnbbMr8x6UuZdgVMRh30ppbwGKSUpSvl9IuxcjT3FrvSklN0ipRILBmsNVeFRGbKccu23TAvfu/X9I6WWG8raE6vTL2Ik33sVrg5OVmFnO5oIoUsfsTTqYCtXSgw+V8p8lsLxuGUbHrXvDR6jY+LIg13T8BskbM9Fmu4ky7x/gAWnTPueU7NRmzSXG5RSKroBiIFWDJpS6E8sL+fl/WaEgpvfrFHxJ2tby/jaWzMJxR0syFHNL2yliLKX0Ur+0SvXV2j+BFGllSMKf4sofwPhOjbNkQD3maCVGda1F1M+krHdyLabUYTUJYj8n6TNERaaj0nFJWhC0B4O8frunQDMqeid79jn32ZV4MtCSnX8N7LxeGTDImTDAvTmK5Gh5/smqCzr3gKESFVUC60YPGbG1j2p+wXuhmBuIeijpNQRgmKPl/8++TT+cuHHuWzmLHQp+Z9XX2JtYwMlHg93XPAx3rr+i1w28yjyXW7+97REMKnX4WBLSzPXPfEI969ZfdCvfah96A+vU7LPbx1/InZN4+Wd29lsvKj1hGXfG1VKHdaQ5BHmFJKrowHs39/7hdq0RmRSSvnsDuxGxY62cNBSSkWYxVaTlCooZFphEevbSumIeNFEwLIeDBYu3kYInaicbFkNc0Fc6vxi1QpueftNvvTKi7y0Z/Ak81sH1KRnuKx7AHHZ275nYy9OsQEpNUKcMmznzoYo09Clh7isIJgmyDld2/ooI8Z4I7S+HY3U0sA26hEijJQOSxlnwioskCHncI1JSmWx7pnoqcx4YvvWQxJ6nmvlvUxI17a8DtW3RfR4ig2jOxplW0chkD5MPht8RlhsY/R8umLq3rnseUR11f+tbVXflVNT46SOxGdT/WZjuIqeRO3hDlMtEO6hcIrkYN8DJ1KqtpzvVG05u1Iqjteu2kkvUspQBTlQ5c+LMtj3bKL/pFSu/dbiStUvf9DUSHOvLKv06IhEWNesFkSOrex7zIgyA126sYk2vGKp9bt0U5OoNO/JUCilEu8JJtE3at8bPEbHxJGJ/AxjrJmb6rHbc8g+TA+TlCr1eVPCzYsGkSklw8opgGOhleHXvm8fL9RN4K7Ns9mnfY+9gd75pifWjs/5HJmyAQFE/vcRBb9BlC5FuM9FiPS5Weo4TvBcoa67R+C57L4DZAc45iPyf5T1nC67nXEFhQB0hMMUuz0c04f9OS2cRsxEdB1S9s7plJHVKk8rcZEQXYlsuxHZej1SD2Q8tExj3UuG8H5a/SP0HDKywtjnA2Tnr3O+/FFS6gjED08+nS8vPIb5lVXMKC3j/kuu4IyJkylJekE+fcIkln7qWpZd93neuv4LXDpDEVn//epL3HcIiKmhQmc4bIWBXj1nHouMh/r9DBL0UaXUkYlwGL773QJ+85vJ9HTwWEqpDKSUEIIyY5UnGFmPTTSjSzdRpltKqckFSiklEbxZryYELrFiSK7dqrpHhpWZDLhz3Vqe2qGCYuNS8sN3lvPmvoHnxulS8rbxknlclgmGea+/+92CXvc6F6Sz77lQWXAR5qFTnHa/4YakkCZ5D03yDkxrzCiywW1Nju3GZNqEGXLeHZ7Id79bktJW8g1lYkeaxqNLaSml5pX1bfM2X75N7O3u4q2k7MSoHu9lfxsO9DdTykS2fssMZ4ZUC193LMouI1fDJvb142w6DlTV3o74edZvC50enqubwJb2It5vHg+AU2uzPndoinTojB6a53IokKnPMu15kUxKKS27NcRUS+U71GQvm1JKowNNKMLUbkslpUxbpE3UI+imxCClmoJB/t/aD1jZoAh8074XkzXWdW5sVYTw7D6q3vWFMf48ZpeUEpfSGlcyYVt7G/+3agUPb92EjmR8Xj6VvvRtP/XeO4gaGZAeVGn2KOkLwFiZdWxnsKpkh2bDYUzCmw1SalQpNYojFQUZ1MidBimV58hMuvQFv7FvqddnqaPynK6cbHTJkDKC3vFzZPgtCD0PgHAnihY5NRt+l4+frD6el/YrgrpnOPsJ44Ymf1UID8JzHkLLzcUjvJ8AbBB9DxlVhV6kDEJIEe3C//WsxJaJqcUl1r+vmjMX10CIcttEEHlACGKpBL6UOrLzJ+oHz6WIirWI0mfA9xVlWYy8iWz7IlKGeh1W5UmZSqkMpJRjGnguAySy/b/QO36KbPkUEAXnyTld/igpdQTC43Dwn8efyCNXfIqln7qWaRleTmaUllGTn0++y80vzjib/1ikJsE/eu1l3hwClUWumD59+pAdq86oOlPkduN3OjnKyEVY21Dfa9twLEaDkf0xSkodWYjHBffc4+Pxx2uIx1NXJ0xblk00I0i/KmDabjzamwBEWERrSNIcCiGAiQUFTCgowKFpvLLfWKWO964i6WIZ+eL/cPMigq5crhwX7wEQlsf0sW0qXjbKcH9z/kLOHz8RHcmtK9+hNdx7gMkFm1pbaAmH8NrtzC3LrFIx7/U99/h63etckCCl6gGlAHEJdS/D8tBWHtQpR5L+xaSmJr095aOMGMo+3pOUshkKnlB8fK+2UphFKbWzo53OaAS3zcbUwqJen/dEspJlUblqV99+axk3vvYSv3r/PT7+9BNcsvSJjFZBUC9fHZEITcEAT2zfyj82behXaLqUMkFKZZiYZ0K2fsuuaTiNiXSyhS8QjbIvoKy16hnKDYIgwiBFNC3xjlDocvG1t8/g3Ocvp80gnmy0WupRDTMHKXOVo5GOTH2W2X4iuk5cJr5zM3clu1IqEXbud6j21dMGmAyNNgAV3utItUJI8ohL9Z3Y2WlV33t9bx33bVzPre+9i5Syl31vc2sLUV2nyOWiJkPgb3/6rY9PUpO/J7dvy/gM7Ons5KbXX+bx7Vv523qlUjdVVunQ896HjMqqZuXJSI+Qc2s/qtGlDyGivfqXgcAMOzcXMUeVUoPH6Jg4MmEppXqsdJhKqXTZb7nCb+xb5vVapNSArHvhlyBwJ7L1OohvBxzgSsRXTJ8+3Qoxf7tORW8srK6xFhImFRUfsnmcsFWCS6npZUBlNBJ6QamQbGPAuSin40wpUaSUQ9O4ana2gk1ZrkVo4JitfoiuSf0w/LKy9Qkfwv81FVBun4yWdxOi6B4QPkU8BR/vfeDYRtBbADc4Ml+bldcVr1O2PaLgOgNR8KOcrn+UlBoFoNQhXzv2eC6eNoO4lHzt+WfoPEghsU1N6a11A8Eeg5QaYwTiHlWuPLnrGhp6bbu/S71cu+32wVeKGMVhA/XCrya4NtLnSlX6lKqw0qWyncLyeEslVeP347U7cGg2JhcU8ma9Irm8ts3s7Uw+XowC8XO84mkKtZ9QLi4hX/wKCCOlZG1zE39Zu8aqLAbgYBOa6ECXvrRhr5nQHApS19WJAE4fW8vX5y9kYn4BreEwt72/MufjJOOt/Up1cUxFFY4+FAKDQZwypNQQIoqGIgqdfAhAmPQrMiMBHYb1dxQJxIxcqZ4V+MzKezHZeyXTfGFORxSZKqlZJaU52Qt2Gv0/wA+PPZ4TqmoQwPuNDTy2bQttkTBd0Sjb29vT7q9LyX+++RrnPfkIFy99gl+uWsGfPlzNnes/7PPcJjqjEUshUzHEGSvpws67Y1H2dquX9f6RUoo4k9KB05YY/wpdLmuLiFEVTqPNCoJ2aopcj8sjbyEnmXSKxBMkTCJTKjellM+u2nJ2pVQboEipvDR5LtazxA4r6Fw3str2B7rZ291pjV+3r2thR0c7H1rWvbKMVpH+9Fun1Iyl0OmiIRjgjTSq26ZgkK+/8UqvIgXHZiGleiLIhXToNxCTVcRkLVFmZ9hSsyrrung75+Nngqk8tJRSo6TUoDE6Jo5MmDmpHT0Wfszg87xBkFJnTJzEieNquXr2PMuNM5D5lIz2sOW6lqh8JwNNTU1U+tVCyDtGMYWJRcVMMBarThwildRAYQWeh55A6i3I4CPq955LFFGUA04y7Iefmj2X8gFWTgUs0kj2yJWS3Xeqf3ivQthSlefCOQ/h+5zaLpKmfw2/qv7vOi67nVHzI4p+D67TwXMFouDXiMI/IrTc3hdGSalRWBBC8LPTz2J8YRFNgYBVwW+4MbSklBoUxxqk1CzD8rGhqbHXSl+ydS+b13cURx7MrKZMFr5Kr49qbycVnjoj1+i4lDwpE1OLiqkP+tjSXoQmYGPzv63PHKxHE13o0ktM1iJEBK94Ch8PcOvKd/nSKy9yz8Z1fHf5MmuS6URZ9yIspGc+VjaYk/dJBYXkOZ24bDa+ZygfX63b0+tlJBeYgbXHDWOelIKdONXGv7bj5H2EiBKTVZbVciSis7PzUF/CiENUKqWUo0cFPtO+F0vzfWarvrfDIJlmFOVmFbtmurL+XDxxMvlOF7cuOYl/nnMhX5+/0FJ9AL0ycqSUBKJRnti+lbeTrN7jjZXX+zeu5/3G3gsb6bDfUN8WuVwp1YiGAuZEOjmgtjsaZa+hlNKop3fIfHpoJimF11JgARS6EqqdSLxQbSs6yXOoMdJtM0gpjmxSKlnllFumVEIp5bUroiNbplQyKeVLY59JELw7KE6Tz7K2aQuaCKBLuG9TG9e88Ax3rFUr49mse/3pt5w2GxdNVLa529d+YN2HQDTKmqZGvvnGq+zv7qbG5+f/nXompW4PFV4vc0r7U1FZI8AVNMkHaJL3IMlM5IYNVZVLvNWP46eHSfCaSqlRUmrwGB0TRyYSmVJDb98bk1/APRdfxsnjJ1hKprJ+2tYBiKe6c4Tr7JSfFSmljt9mEMnjCws5a9JknDYbH5+R3vZ70OBcDPaZIAPI1hsg8jaggefjOR9iUfUY3vv8l7nlpP4VOOoJYVTgI7LK+p2MrIboSsCB8F6bfkfHQmO/93qFnsvwa+rYrlNyOP9RaEV/Riv4X4Tngn7Nr0d74VGkwGW38/0TT+FzTz3GXatXcfnMo5iU5HNNh85wmI5ImAqff0BhedoAA/bSwbTvjTGqQIwvLMLvcNIVjbC1pZnpSWG5O9vaABg7Wsb2I4cYY3HyIXaxG7vcRr74Pzrll4miQgIrvD5Or1aDZJSjkBSyrV2Vq04mpRaWV/DE9q283zyWKQWt2FhFJH4NTpstKRvqONrl9/HI5yjQfo5P/J0PGy9FoxC/00FzKMQ/N2/k+plT8QhVvSMsc5P7mjDDoOcmte9pRcWMy8tjd2cn7zfWc3JN7gRPYzBgZZP0Z9V7oIhwFHbqcIo1lj1IqaRGLlk8SmT3RnLui6AbiQ+QVgB3nHG99slWfa8pqCaM5Tm+5J5YXcM/zj6fGn9ilbHG7+cSvyKkOsJhXqrbnUJK1XV18r3ly9jW3o5mtLevzl3AJZOmYNc0fvre2zyzcwd/XbeGP57SO/C+JxqDgX5dc39gWo4CSQqcQCzG/oA6lyZCCNmR0XKaDFMppeNDCIHHZicYj1mqHAAh8gwVo06FJ8L+AOQ5zHt35JFSNqHh0DSium6EnavJXO6ZUqrdeexhwE4ols2+1wpAS8hDcX4apZScAMJQSrncvT4/0KXywA4E/ER0u7GPzlh/HqeOydzX97ffumraDJbu3EZdVyc/WfE2UV3n7QP7iBiLfMUuN78+8VRq/H4eOOcCNNE3eTdQhFF2bqfYgCab0cn+bpoN5rNk/h3uYbrmjxJGx8SRiUJLjZw+6Hww9r1knDdlKttaW/jYtBn93zlmLGR5Pw3Ca1VyM6FpmkV6mVhUPYaZZeXceMxxAw5qHyoIISDvm8jW6yFqkEHeTyNs/bO0JofFDxjOhYAN4juQ8X0IWzUycL/6zHNRL5VUYr+5gAP0BojvAbt6X5N6K0RXq21cuWVDDRSjSqlR9MJpEyZyyvgJRHWdG59bSihDMGw4FuPu1as45q+3c+Jdf+GoP/+O77z0ArsNRUmuWLSofxPwbNhj2DJMpZQmBDPL0udKbW1Rga1T+iDdRnHkISZVZ2tjF17xBE6xAa94zPq80uvljJqdAITkEgC2tqtJxOSCRLbNKTVjuf3UMzl57MUALCjZw7J9ylLhwsxFUuRKkHMIy4VoIsrdJz/DjbPDfHOBavsPbNqILX4PdrGXuCzud8W51YaKo+cKtZmrs6L+QK99suGlPSqfam5pGSWDqKKSK6JSyY2dvI8LtQoe6Wem1sHG5MmTD/UljDjEqSYmxyJEFJeh+tNoQhMBpNRSqoSZyFZ9r9EgpUpzbINCCMbl5WPLIJc327JJdu3t6uTzL73ANmPc0JHMKy3jsslTrZfcaw311fqW5qwZQSbMY5cNw3PjS2PfC0SjRHQ7Yb0QyN3CZ9n3DMvZZ2fN5pJJUyx1GIDX4SKO6lOmFLQBUOhU5KEmCgf8d4xkmIRK8ncd7qdSyqOpNpDNvicMpVRz2JO2HHs0yb6X53RiMyb8pqLHIdQiycZ2pSL862ln8c9zLuAfZ59PVRbrR3/7LZ/DwZdmzwPgpbrdvL6vjoiuU+7xsqSqht+cdKpFAnvs9iFXByZDp4SIVBNeF8sHdazkwgFA1ns2itwwOiaOTOS70mdKWUqpISKl8l1uvnfiKVZsSq6QUkJc5cQJ75VoeV9DiNTnc9GiRVQlLTadO3mqNbc71ISUCeE6AZxKzYltDMJ/06G5Di0PHEYVvrDKxSWmFjGE+9zM+wl3Io8q/CIy9KIKPQ8vA3SwT0PYhtc5MaqUGkVa/Oy0s7jggXvZ2NTICX/7CzqSUo+XKSWlVPnzeGP3Tra2tljltjUhiMTj/GvdhzyzZTP/vPQKZuRQLQlgxYoVQ0ZMmUopk5QClSv17r461jbUc9nMo6zfb24eJaU+qogxCVA2I91Y8Xew1fq82iuZk6dsPCG5hJjU2WlYQ5OVUkIIo/T2AgCmFrTyo/dXccYYHw5NeeQjmG1b8OL+T7KwcD3j/B3cOOMumuPTeaComEn+dym2vQpAh/yqNbnJBZ2RiJV3layUAlhUUckj27YMgJRSypbTxx4cn37EUKg5xXoAdJlPmKMPyrkHiq1bt46+hPeCIMQJ+HkAl3iDkDw1SSVVQ7oqhqZ9L51SylQ0lQ1R5l+pcRwzR+bJHdvojEaYXFDIDxcvoS0SZkphIVrSin+Nz0+Ry0VrOMym1hbm9HjGeqLJuObSYcgpNCfSyUqpboOgiupluLQ2bNQTY2qfx0q27wF8cqoqOLKlrdXaxu9wEGUWdg4wp3gfD20vpMil7p0QR6bC2GWz0RWNptr39FwzpfzGdkEgL6t9T5dKiZrJvhdH9b020YpNtFPsdtMYDHJu7QRe2L2TWUUq42llUyUlbg/Tc3yPGUi/dfa48ezu6GB/oJsJ+QUsqapmUkHhIVHGhOXxOMUGXOItgvKCAR/H60id/owbLXYzaIyOiSMTGavvRYdWKTVg6PUgA4AdbOlVnitWrKC0NGHj/8ZxSw7SxfUPIv9HyK7fIHyfQWhDmynZLziXQPR9ZOQNVREvblTmtY3pY7+FEF2F7Py5+lkrB2nYcodZJQWjpNQoMqDC7+e2s8/nuicesTz3baEQWw1Lj4kit5ubj13Cp46aw/sH9vO/y15lTf0BPv3EIzx02ZXUFhb2eS69H5WNskFKSZ2REzUmP/GCMadCsfZv16XmB40qpT66MKuE2ahDw2n8ey8QAtyM832IQ9PZ0l6EdJXRFOwkqut47Xaq0lTUkhQS0ifg1nbwsdrn8EsllY3KaegUsba5iad2bGPpzu347Z/gzpNXcXTJBxRov+EPx0+g1qfUQV3xEwmL/nX8W9vbkKgcrJ4T4fllFdiEYG93F/u6u6jOYTW4rquTDa0taAhO7YflbzCIU01clmITKl8uyFmY1pmRip6e+1EohOUJ+MUDRhhxNClPanza7c0X5vZwqlIqLnWLlBoqgqfEsKaZaqY396uJ/VXTZlCbn086ClYRz2Us21fHh82NfZJSlrprWEgp9cpmZkpJKa1/x6gAtmAjNwLarDyq9yDAk9VAPoeTiJyNR7zErMK9wExLKWW35ZbzdbjBVPqkZkoZVUH7UkpJHwhwaureRnWdmK6nXcmXUpF/7RFv2uNKPMRkNXaxDwdbqfT6aAwGWVRRyYFAF0eXqO95ZVNlxkp76a+x//2WJgRfHGA1qKFGmCXkcadRpVaN1wOBL0kpVexyH/qJ+RGA0TFxZCI/gxq5YwgypYYEsW3q/7axvRRSJnRd54SxtZw9aQrHjhnDxBxzJg82hH0covDXh/oyEK4lyO4/QHg5yFZVCRCgD6WTcC5Edt9h/GRTVj4AxzyE7/rhu2ADo6TUKDLihHG1PHfVp+kIh/E7ndR3d/FhfT17Ozs4pmYMx40ZS5nXZ62WLayu4d6LL+XKRx5kQ1Mj1z7+MA9e9kkq/NlfmEpLM4dyBqNRDnR3EYnHKXK7s1YkaAkGCUSjCEgpDXpy7QTsmsbmlma2t7YwsaiYtlCQRqNsd1+ZWaM4/OB2S95+u57Gxkbc7t6Dl04xusxHEx0IzJV/HYfcQZQZ+G0q5P/f+2qZVd7Nzs6ESkrLsDocFwuAHVw+YRMAMVlGh7yRlQ0HuOn1V6ztzhs/m8qCS4nLz2AX+6n17SeuC/6wfgGa45NcPqV/q891RhXJ2jQrvT6Hg1nFpaxpbuS9+gNWaG02vGxY944ur6DI3fcLv3mvzX8PDIIIs/Gg7lNQnjfA4xw85OXlHepLGJGIMoO4LMYmWnDKVTiEUgzGqE3bVkz7XkSPE4rFrNLsbeEwcSnREBSlydQZCBJKqSB7u7rY2dGBTQgWV2Z/UZtdUqpIqaYmmJb9HJZ9bwDZEH31W1b1PYOIiuhx4tZEUGW/2URDTlnnoodSykQyQaKUUkrOPyW/Dq89iseu1D8OWxGHK7L1WebfH0pSo4UNxVPfmVJ5xnbd1u9C8Rh+rfekTxikVHcs8ztNlJnY2YdTrObr8y9lTVMjS6pqaA5socwTJBLX+LC1jNPH5E5KHep+a7DjRYwJxGQldnEAl1xJmIEpJpLte6MqqaHBoW5bo0iPjEqpIbbvDRhmnpR9YsZNSktLcdnt/Pn8izJuM4okOOaA8IFsg9BL6ndaibLoZYPzOHCeCLZKRN43IfQyaMXgOuWgKGNHSalRZMXkJMJmakkpJ44bn3X7fJebuz92KVc8/E92tbfxmScf5dErrsRtT89+Q29SantrCy9u38q/t29j1f59Ke/Xl86YxU9POxNHmpVF07pX4fPjSqqkUuB2c9yYsSzbvYsXtm3lSwuPYYuhkqrOy8N/qDvkUQw5NA3Gjo1TUuImvd1cEGUSLt5P+a2dbcSpsPIq/r13PCX+gBUkPi3L6kxQnoWHJ9nZ6eef26ezsOorzCkbw6oGVRFpRlExX5kzn3mGrbVDfp1CvkecSp7e92l+v76F2rydXD6lf1VE9hiE2dgML4RzS8tY09zI+pbmnEipd+qVbfHkmj5kvgbMez1YROQCPOIVInKmpWQbycjPH53IpIdGiJPx8Rh+cR8OFCkVlovSthWP3Y5daMSkTlskTKXRd5uKoyK3e8gyI6xMqVDQUknNKS3rUyVhqqPWNjchpcz6ctYUUiqZXHOwktFXv5VnkFJdBinVGVH/FwCaQUrlmCmliUTQeTKSM4F8DgcxxqFLHx57N8eVq3sW1TXc9sPXvpetz0qXKWWGYfedKaX6BJvoQEOgIwnF4vjTvP7YhHpfCcUz9yNhuRCP+DdO3mNK4eeYYpQ/P6ZMrV6vay0lHLdT48+dDDjU/dbgxwtBmCXYeQSXeJOwHCAplWTfqx3ty4cEh7ptjSI9EplSkZTxqzOqSKpDrRKURp5UX6TUKHKHEA6k42iIvI4MPad+qfVdtEgIF6L4zsQvvJcO0xWmx8hIBxvFEYUyn497L76MUq+XjU2N/HTZa1m337hxo/XvP7/3Dmfcdxc/f3MZKw1CyutwUOz2IIBHNqzji0ufIJomcHaPQUrVpBkYzzbKgT+/TU2QtrYoG+Kode/Ixt69ezN+lo74sItt+MWdaCLEjs4aPmgp50Cg2yJqzODw9MebRoN8gR99cDN/2zyHjW2qdPqmNtXWzqmdYBFSoCoJNcp/0STv4aiyU7EJwa7ODnYbJFOu2NOlzjMmw8RkhtHGN/Sw3qZDIBZlnZG1tqgi8986HAhyLh36V2mXtxzU8w4U2drWRx0BeQVSajjFWoQIE5VTrMqWPSGESORKJQWxJhRHQ2eDM5VSXdEor9QpReCSqr6r40wtLMKpabRFwuzpyl72vGkI7HuZ2pbfsFl0GVkgHUkr3RJlU8/dvpcadG6ip1IKbERRWYxnVO8EoD3iwj2MgdaHEhYppScHnRtKqT4zpVQfrNGB224orjLkStmF6rdjMrPKKWLk6jnYhKATO9soE1cwr/AuAFY2qz56TB9q9GQcCf1WWKowYbV4NLD4h2SlVO2owmdIcCS0rSMRZvW9mNRT8gjNRY1Db99TSilhy0xKJc8TR5EjHEYVxIhyftDPSoCHAqOk1CiGBWMLCvjVmSrl//4PP+ClHduybt8WCvL/Vr7LL996A4ATxtbyw1NOZ9lnPs/aL3+V975wA3+96OO47XZe3bmDn735eq9jfGhU15uchmg6c+JkBPBB/QE+OLDfUkql23YUhz8iEfjxj/P5858nkqaoFwAxmRgAo1IpiHziMTw8A8AL+y8FBCvqD7C/uxu70Jhf3ld4v2BKoVJTbW5rRUrJJoMMSqey0ikDHPgdTuYbhNUb+/r3Ymfa98ZmIKVmFqvz7mhvz1oNCuCDxkZiUqfS68spfwoS9/rHP87PeK9zg50AlxCn79WcUYxsxKkiyNnWz93yckBkbCtWrlTSL5uCA1ccZYLP7rBIhw+bVX7ZcX1Y90CREZOMqpvbslSXjcTj1t8wEDKtr37LVPUmlFKJle64RUr1s/qezGzfMyfuEaksfKdWKyKvPeI+rMu/Z+uz0iqlrEyp7K/MOkbVXzqsKnmZ+ly7UO0bkZkQ0SkjJmsRQuLmFQrF97CJBjShLOdv1atJRn8ypQ41hmK8iDAXXfqwiVbsZH+3zARvkpp+1L43iiMZbrvd6tfakhZ+RoJ9T8oYRJWbAEcf3vhR9AvCbt5Po1rvMFfOGwqMklKjGDacWDuez85XK33//cpLdPd4A2no7uKGpU/y5dUrWHDHn/j5m8sAuGHhYu79+GVcM2deSjbUqeMn8rtzzgfg7tWr+Nv7K1OCFZcbQeaL04Qzl/l8nDFRVVy75vGHecFQTI0qpY5MxGKC22/3869/jSUWSz95SlZKBeVZ1r+FkATlGfhcqu0uP6CqVswuLe1VRjodphapyeuWtlaaQkFaw2FsQjClj9D/E6uVXa4/pJQuJXsNUiqTUqrU46XU7UFHsrkPtdRKg9g9urwi50mnea9vv92f8V4fiaiqGiXPsqFbXoWULmKyihCnApnbihnEamZeRPX4sFSxE0JYFj5QxNG4HFUS1X6lKDoQ6M64jXnNTk0b0OpzX/2W37DvmVWTOqJmUK3LIqU00Y4g2Oe5tAxB55oQOA3voHm+KEppXOoOGucf+hD3g4lsfZYVdJ6SKWVU3+szU0q9r2h04E6TTZVAFLtmWi+zE0phFgJQoP0auzhATFbTqv+YOzZdyesH1LtOdT/se4e63xqa8cJuLV5o9K0ATofkiId0eYyj6D8OddsaRWYUGmrktrAitKN6nKCh4sxzHsKiMrH1ILtA5IN9RsbNpkyZchAv6giBY3rKj2JUKTWKjzq+fuwSxuTns7+rk1+8tYyYrvPc1i18/5V/c97f7+W5bVss5n5qSSnfXnJS1lKfZ0yczA0LFwPwv8te5ctLnyQaj9MRDrG+UeUsHDcmfcWwX511Houqa+iKRNjXqSby8ypHB9GPKmKMR0onUtoIcRq6VJPOoDyTDvl1Th0zlulJ6qZjKnJrK1ON3I+dHR2WGqM2Lz8lqyUdTBvR2uYmWo0Xh77QEAgQ0XXsQqPCmzlYOVcL33sNyvqzsLwip/N/lBEIBA71JYxoxBlDo7yHZvlnIDuZW+BKKKUe3baFUx99kH9s2gAMfRW7ZOXVgrLcyddKr+of6rN87wnrnndYlEQm0WWucJv2vXynE0me1YdpNPR5LIGyj/UMOgeVT+fUbFQalUZ7Wp27ooew1PUwI71SKp7yWSaYmVJCxChyKXVVOvueqVIDcNj6IKUMqxpAXJbTJn9CmBMJcQogKHA6+5UJc6T0WyaZqhntuL9oT1KMVHh7V9QdRf9xpLStIxGFRrEQc75ljiGCxOLDIUFY5bfiPAYhMvev7e3tB+mCjiDYxgNJY8NhoJQ6MkMBRjFi4HE4+OEpp/PZJx/jvjWreWzDeisPA2BmaRlXlFfxsRNOpCCHSl8AXz9uCcUeD794axkvbN/K31avZFJRMbqUTCgsyljtz+90ctfHLuXh9WuJS8mCqmqmloyG531UIfHQKn8KxNAppUX+Ck12WzkeNgHfnL+Qz7/8AhJYnGPGUrnHS4HTSXskwnM7VYBjtoB0E5U+H5MLCtna3saK+gOc1UdRAcDKt6nx+7OGQc8oKmbZvjo2GLbVdGgNh9hqWJOOHiWl+kR7ezvlfdo5P9rQye2ZMSvwvbGvzioqYIZLD6SKXTaUeBLjTH/aeZUxcd3fnVkpZYazD6XlMBn+HkHnHUn2PYA45WjswEY9cWqzHksYSqmemVIAvz/5DALRqKVg0yklFPfitql9umMfMVLKyJfqK1NK4kZKB0JEKXer7ygY651/qRmkVHfUjsee/b0nwtG06LchcRNlKqCu4djKKm7/UGNBP/vqI6XfkkZ+lxggKTXPKF7gszsyVtQdRf9wpLStIxFmrlSbMWaYCxo+x6Ft/9LIOxLOY7Nu19DQwIQJEw7GJR0xEMKOtE+B2Dr1i1FSahSjULa7W046ld+8/RadkTD5LheXzpjF0VU1nD5hIqtXrsyZkAJlL7h+/tEUut1888Xn+M3byzlh3DgAjs2gkjLhdTi4du78Qf09ozhyEDGsEQAxpvf6fHpxCf99zHG0RyJMzYFYAmURmlJYxHsN9bxlWP9yIaUAFpZXsrW9jVUN9TmRUnV9WPdMmEqp9a2ZSan36pVKalJBIcXDNKkexSjS4bQx43h6x3bere8d0j3UBE9PpVSuMFVDBwKZJ8HNZuW9IVZ3mUhkSvVWSgHEqcBhkFJ9wSRGelbfM4+Xqr4RtEbGUOXZDEAwfuQqS9xpSKlwjkopEOgUYKOJYncE0LIqpTqjTvzOvlUKEXq/s4zLy+fh8y7i/7d333FyV/X+x19nyva+6T0kJAECBEJCF0LRCAJKs6GAWFCKXgu266Wo4FXhXhW5/FQE9YJcRQXFglIiIBACoRNIgUB625at087vj9mZzWbbzGZnzs73+34+HjzYTNtPTj45yfnknM8pd7nLwaF93Sk1r66e/7fkVCaWezeXRVL2Pr63O/1nh7uje9ZGIPJs8gdFRzuLw9PC8/YoSo3+43sqSkleXLzgcN437wCe3LiBY6dOo6q4pwh15JFHDusz3zfvQH7/2qs8seFtHnozeXvDMVOmjUi8IimnZlAc2tuc7qJUyvwMd+QdPm4cd695jZU7hj5+A7Bhd6ooNfgRkFRRbEtbGy2RSL/HPVZ0FwQW5/nWvUKlHgcj54jxE/jqEYv59jPLCZkAh4wZk/49MJK37wHpnlKTyivShaZMTCxL/h7b0tbW61rtPe3IwY2Be0od32uNRLHW9llYxLt3pgXNNrD9f0ZKz+17me16ao5OTRelujxclEods96zmBTJsKcUJPtKBdlJfXEXUNpvo/NUQbA1WpS+UXE4hvOPB16Zt9I7pczuIXN9IAdpp/yI8kpueVFPUar7+F7UfZNzoi8AnRAYA6HZg750uOtEvzOhucnp0ZQn+3aNcuopJXlTU1LKu2fP6VWQAli5cuWwPs8Yw/dOWcpJM/ajLBxmfHkFx00b/MiCSD4s6L5JryIc5ouHHdGrN9VgDh0zjqAxbG5rHbShcsrbrS3AwDfvpVQVFaWPH61tauzzvLWWFdtVlMrGG2+84ToET3n3jP342Unv5Kcnv5NPzj80/fhI7zpaPH4C4UCAM2fOyup9qZ5t7bEYu7uPz+0t1VOqPkc7DSu7d9UksHTEYumdUqmFRcKmbuDru+Nsb4F0USqzm9va4j3/4NNlC+e2t2yldh617fFrnG50PuROqZ5iSW1xV/d7B94p1RoryvtOJ6/MWwm7bzulZOR5Jbe8aO+iVPrPjjzOP9ZabGI31nbPrZHnk/8PLxyyB+Nw14m+F16Q/H9odkHcmKudUuJcdIC/4GdiYmUlPzvzfcQSCQLGqDeAjApHT5jET056J1MrKrP6l6jycJg5NXWsatzFczu28+7pg5+hT11Pv191zZCfPbumli3tbaxpauzTh2T97hZ2dHRQFAhwSHevDRlcPN63V4zsm3ndx0yttVx8wHxCgcCIHy+YU1vHw+87P+v3lYRC1BYX09jVxda2VqqK+haac3Fj4J6KAkHCgQDRRILd0Ug/PaVSRamhju/FMCb53kSGO6U64zN63m0zv+2t0Ozdtwt6ekoNfXwPElQDUNNdlOqvp9Sex/fyuSgE78xbNn18b7fjSCTFK7nlRalG5817NTrP5/E92/xF6PwTYKDiCmwseQu6CR885Hv3ZZ3oZ6ZoAdTcAqE5rkPJiIpS4lxdXWa7SAYzWJNn8Z+SEsvDD29nx44dlJTU5v37G2M4sHuBna3Dx41LFqW2bxu0KNXU1ZU+LjQ7g6LUnJpaHtu8kdX97JRasW0LkNypNdQtgXtLjXXqa7+oGOLIpPSVaa4YY7jkoKH/ojpcw/0XwwllFTR2dbGlva3fHnONncl+HfVZ9Ejc01DzljGGinCYxq4uWqPRfntKAQSHuH1vz9vf+mt03p8IPXNRvMCLUoPlYU9RqudClkgWO6US3TfwVYeTc3PnIMf3dkeLKM/z8RnX89ZI/XmR2MdG5zLyXOeWDCy1U6rR0fE9m9gNnX9J/QjbfifQ/edkeP6Q7x+JdaJfmZJTXIeQMRWlxLkJE3RcSEZWIABz58aYNq2EQqtXLhw7njtfX8XT27YO2LsGeo7hTS6voCyDf23fvya5yF3b3F9RKrmzYtEwju6lxtpvampqXIdQcAo9VyaWl7OqcRdbB7iBr6G7iWxt8fB2SmUyb1WEi2js6mJ3JNJPT6lkUSrADiDGQH/FC6Ru3rPFA75mb+FAJa80jmFu9S7aYhMz/jmNRoPlYXl3j6f+ju9ltlMqWZSqKkoWpToGO74XLWJieX53Srmet0ZqDtBOqdHHdW7JwHpu30v+GZXaMVW1Dz3tshJ5EohDcAokGiGxx6U74YOGfLvWif5QYMs18aJXX33VdQjiURs3bnQdQtYOHTuO0mCInZ0dvN7PrqaUNd3FpVSxaSj7d/+FcX1LS+/rzuNxntuRLEqpn1TmCjG3ZN9M6O4r1V+/t1gikd65VDfMnVIpg+VWqjF2azTSZ6dUgjqsDWNMggA7B/wMM8jNewMpDYW46J+nsfRv55PAuw2iK/s7vhdPABk2OrfJolRFaOCdUsb07JQqC+W3KOWVeUs7pUYfr+SWF1Xv1VMq9f/UDqpcs12PJ78oPhGKjup5IjgdE6ge8v1aJ/qDilIi4jmRCNx4YyW33z6dSGTo148mxcEgi7v/VejxzQP/JW9tUxOQ2dE9gHGlZVQXFRG3lje7e1EBvLxrJ53xOHXFJczK8LP2lBrrG2+sLLixlvwq9FyZUJ7cndFfUaqxe5dU0Jh+b7fMRCbzVqrZeVNXF22xZOGkpy9IgDjJnnCD9ZVKLeQzPboHyVvpGiOlrG+toTRU2JvsB8vD8r2O78USCWI2WZTKZKeU7e4pVR5K7kbrd6eUTY7/7mj+G527NlJzQAI1OhfJVKqnVEcsRlc8nteilLUWIsmilCk6HlN8Qs+TGfSTEv9QUUqcmzUru1uQRIYSixluuqmSX/xiBrFY4TW/P27iZAAe37xpwNekjuHNznCnlDEmvatqzx1YK7Ylb+paNH7CsHrtpMb6ppsqC3Ksh0vbybNX6LmSuuXy1YZdxLsLFSmpflI1xcXDvnAjk3krtVNqyx5HCCv2KGzESeZl/0UpS635PPWBz3X/KLMm50CvQlRZgRelBsvDir2O77XvsdOpLDz0zzt1fC9VlGrrp0FvwiaPnLVGw3kfS9fz1kjNAbbXTin/9DIczVznlgysMhwm2P3nUlNXZ/oYX23xvu3qzUj8LYhvBMJQtBiKj08/ZTLoJwVaJ/qFilLiXNsA/TlE/OroiZMJYFjb3NRv/5pIPM76lhYAZmfRx+GguuSxm5U7ehasT2/vKUpJ5jq7ixDiHwvGjqUyXMSuzk6e39G7mXhPP6nc/iU/VYDa3Naa/vGeF33EGQdAkK193mvYTbHpuVo7m+N7JaGeXUKleT5ylk8V3TvRIokEXfE47d1FpaJAgHAmx/e6iyUlwe4jev1sB7I2+VxHrCSj5ukjySvzVmqnlDEJTHePNHHLK7nlRcaY9K6opq6u/B7fizyR/H/R4ZhAOSY4GULdxaiiRRl9hNaJ/qCilDi3dWvfvzyL+FlNcTGHjEkWkP6xYX2f5//+9nri1lIZLmJ8aea7HVI9o57ZtpWEtezq7GB1YwMAR4xTUSobTd3HJ8U/woEgJ06ZCsCDG97u9Vxqp9S+9pMayt47pfa+PSluk03Ig6bvn6t9d08l+rxmIEEToKi7+FXoO6UGUxYKk9q/0xaN0N59RDLT3k+J7uN7qaJUa79XmScLipHE8Bri7wvvzFvFWJv8NVFfqdHBO7nlTf0XpXK/U8pGVgBgio5MP2Zqb8HU3YnJ8Pie1on+oKKUiMgodNqM/QD405tvkLA9xxOWb93C91Ym/5A/Z/b+WR25O6h+DGWhEM2RCKubGnngrfVY4KC6esaU5n+BJFJoTpk6DYBlGzcQ2ePCgNRV27neKZXqKZXaKdXTTyopziQAQvQ9+rv37qkA2f3rc3EwWYwq9J5SgwkYs0dfqWj6+F0mN5wC2O7je+FAG0GT6HenVGrcYzbzf1CQvhK6gU8kYzVFyT+bNrbuJt79d8pc75RK9pN6JvmDcM+uKBOcgMlwl5T4h4pS4tyiRZqYRPZ20pRpVITDbG5r5dntyR0OnbEY//ns08St5V3TZvCxA7NrEhkKBFjYvSPq6a1b+PP6NwB4z0yd18+Wehz404Kx46gvKWV3NMJLu3akH2/ozM/xvcrunVKp44J7X+mdKkoF2dznvamiVNyOIWpnsNt+PKvvfWBdPRXhMFMrq7KOu5DsWZRK9ZQqz6CfFPQc3wOoLupidzSSXJjtIdB93CyRRU+vkeKlecvqBr5RxUu55UWpG/jW7062figLhXJ/fDi+ARLbSPaTOnTYH6N1oj+oKCXOvfjii65DEBl1SkIh3jltBgC/X7cGgN+seZ3tHe1MKCvnqoWLh9VQOXWE7561q3lrdwslwSAnTZk2YnH7xdtvvz30i8RzgibArOrkEa3t7T29bFK37+X8+N5ex/X2vukvRvKShAA7ga5ezwVNd3Gbk9ll7yDCkWTju8e9gz+c/t5h3y5YKFJHJFsjkfQNh5ke34MQCZvcwVNf3EG0uzdVr1cEuotSNvOeXiPFS/OWdkqNLl7KLS+q7S5KpW5fzsfRvZ5dUgdjzPC/n9aJ/qCilDjX1dU19ItEfOi9++2PAR7bvJHfr1vD/77+KgCfnH9IRteT9+fICRMx9Oy0WDJlmu+uJR8J0X57xYgfpHZDpX4PwZ47pXJ7HKJir9+re/eUslSTsGUYY/sc1+vZKTW8/nFBE/D00b2U1Bi3xqLpRuflWTR3T92AOL0iWSzZHd3zCF+CsEnmijUVIxBtdrw0b6V2SgW0U2pU8FJuedHE8uR8s6q7j2g+mpzbaHdRquiIffocrRP9wft/u5BRryaL28NEMlFcbPnzn3ewY8cOiotrXIczbPtVV3PO7Dncs3Y1Nz2X/MP9wLp6Tpk6fdifOam8gu8eewJPbN1MQ2cHFx+Y2ZW8A0mNdeprvygrUz+YbHklV1K7oRr2uG0qtVOqdh92SmUyb1XsdVyvumjvhYUhziQCrCXEJuL0zBWpRudxxg87Ri8YKg9TY9wWjdLRvVOqNMPjewAxphBmLXOrW3lwc/IGvrHdF1IY2jEm+T2Ng6KU63lrJOeA9A18KkqNCq5zSwY3rTJZxE3t3MxPP6mnADD7WJTSOtEfVJQS56ZOneo6BPGYYBAWLIjS1VVKnm/cHnGfOOgQ/rlpAzs6Ojigtp7vHnvCsI7t7enoiZM4euKkEYkvNdZ+M6b7dkTJnFdyJbVTqrGrb1Gqbh+ORGQyb1XuUZQqDYY4dVrfAnWcyYRZS3CvZufpnVL4+6bNofIwtVNqdySSXsBlt1NqCgAzq1I7pXq+l+nuJxWJBygK5v9yCdfz1kjOAan+XQGzGwq3xu0ZrnNLBjd9j16Ac6p38dWDHyHEeGLM2+fPtpEXILEFU7K058HYyxDfCKYUwov36fO1TvQHHd8T51566SXXIYhHeaHHQXk4zA/ecRKfW7CQH5ywJC9brmVoXsgtGZ69d0olrE1fsT0SPaUGy63akhICJIvSVx95DDOqqvu8JpZqdm56mp0b2pOLd7RTaiipolRbNEp71j2lIG6Tfb2mVzQD9LqBL3XUrDValFWha6R4ad6y2ik1qngpt7xoQnk54UBy2f/lQ5ZzQM1qxgQuBSIU8y8MnYN/wF5s7E1s5DmsjWIbP4FtuhIbfb3n+c6/Jr8oPhET2LdddFon+oN2SomI50QicNtt5ezcOYUvfxkKvS/vtMoqpo3SG69SYw1wySVtBT/WkjteyZXUbqhUUao50rXHFdvDL0plMm9VFRXxn8e+g+JgkMPH9V9citvJYCC0xw18qV1SCVuJJf8NtkeTofKwPNXoPBohmkh0P5Z5ASnVbH5yWRPQu6eUoS352bEiynzYy28k54CErQCjRucimQiaAFMqKnmzpZmqcE+PpnpzGWGzhnDwTLZzacafZxs+AontmIrPgm1KPhh9HsJzk0f3OpJFKVNy2gj+LMTLVJQS52bOnOk6BPGYWMzwrW9VA9V84QtbKCrS3v5c6RlruPDCdt+M9bhx41yHUHC8kiupvlGpRueNncm/4FcVFREKDH8Deqbz1lBHb+OpnVL9FKX8fnQPhs7DdKPzaDRdbCzPoqdUvLsoNaakmaJAvNdOqdTxvd2Odkq5nrdGcg5INTo3KkqNCq5zS4Y2vbKKN1uaKQ723AgaNsnbncsCDxDgwxl9jrURSGxPft36g57Hoy+DPR86/gCJTWDKoPgd+xy31on+oON74lwkEhn6RSIio0gsFnMdgjhS312Uau7qIpZI0NjVAexbP6mRlD6+xxYg1v21mpxnas+iVOr2vWyO7yWoI2FLCRjLlPKWfo/v7Y662SnlpXkr0b3jL9Bd6BO3vJRbXjW1u9n5pLLeR14TtoqA6aIy/LfMPiixs//Hoy9jd1+HbflK8scl78GYfe+dp3WiP6goJc5t2rRp6BeJiIwiDQ0NrkMQR6qLiwlgsCSP7u3oSBal6kvz37i6PwnGkrBVGBMjzMsYOigzvwcgxgy3wRWAiqLU7XuRnp5SWRWQTHq31IyKZlqjexalkn2mmiLFTnZKeWnesiR/vxkVpUYFL+WWV02vrKI8FKGmOLm7tynxNZoS36DFfgaA6vCfwGZwEUF8x14PdO8kjb0G7Xclvy7/DKbqGyMSt9aJ/qCilIiIiEiGgiZAdXGycNHQ2ZkuSo0dJUUpCNLJMQCUmMeoNLcQMhuI2zG02fMcxzb6VXQXi3ZHo7SlG51n1+0i1n0D3/TKll49pYImuWNtS3tFVkcCpS9LsnmyilIimZleWcXEsmRfu5gtp5N30snJdHIScVtDKLATE3lo6A/qPrqXVvIuMDVAHLBQdDSBys9hjC7mkcypKCXOHX744a5DEBHJyn777ec6BHEodcteY2cnOzuSi+KxJft2w9BI6rLHA1DKXykzfwKg2X4VS9/b+qS3np1SUdqiySNJ2e5qincXpWZWNrE70rPzIEByMbeprSKrI4EjxUvzVk9RqmMfPiUBWd46Jv3zUm551bTKKqaWJ4tSiV5HuYtoS5wBgOn81dAflOjeKVV8MqbmFkzV1RA+OP20KT1/pEIGtE70CxWlxLlVq1a5DkFEJCsbN250HYI4VFvc0+x8e/dOqTGjZqcUdHEECVtCwCQLZm32vURY6DiqwtDTU2q4x/cgaucCcMbUdT03U9HTcH5ze0VWN/qNFC/NW6mi1L70lKoy/8V4czoh3hipsHzLS7nlVeXhMJcfnLzsIrFXf8HW+BlYG8LEnsdGXxz0c2zq+F5gHKbkFEygBsLzk4+ZGig5dUTj1jrRH1SUEuc6OvblX7lERPJPjTf9LbVTqmHPnVKlo2enFBTTxVEAxO1YWu0nHcdTOFLFouROqWRRKtudUl0cS3N0BpVFEd47fVn68WD3TqnN7ZVOilJemrcSqZ5SpotUQ/9sBFlPKfdjTJxinhrh6PzHS7nlZbOqk/2k9r70IkEdrbHkTXm27c7BP6T7+J4Jjk0/ZEpOh8BYTMUVGFM0ghFrnegXOtAuzlV23wYhMlKKiy2//e1OduzYQXGxjqvkUmqsU1/7Reko2hVTKLyUK+mdUnv0lBq3jzkx0vNWm/0wAVpotRend5XI0HlYEU4uqCwQTSSA7HdKQYA32y9mQfXVnDH1eVp4igiHEzS7gOROKRfH91zPWyM5B+yZ04ZOLBVZvb/C/C/GJGMImTeSv+AybK5zSzKTuok1Ycf1ea4lehqV4Yeh62/YxDcwgQF+T6Vu3wvsUZQKz8GM+9eIxwtaJ/qFilLi3MyZM12HIB4TDMIxx0SIREoJBl1H422psfabceP6/oVOBuelXEntlNrZ2UFDZ7InzZh93Ck10vNWjP1ptDft+wd5zFB5WBwMMra0NF1shOwbnQPEzULue2s2Z01fS639Bi32SgA6Y0EaukooHcZn7ivX89bIzgFFyeNGJoahPauiVIBtlPBw+sch1o1QTP7lOrckM6ndmnvvlALoShyADczEJN6Ezr9B2bn9f8gex/fyQetEf9DxPXHuxRcHP7ssMlxvvfWW6xDEo5Rb/lZXktwVsLa5kQSWoDHUlozMTUPKLfdmVPbsVCsOBgkFsv/rcmVREV9++kT+tmEmxkSpND8GenZJBYwZsXgz5bXcGm6z8zBrMSZB3NYBEOJtwBsFc1e8llteFejeKdVfUQoMtuQsAGzH7wb+kNTte3sc38slrRP9QUUpEfGcaBTuuKOMP/xhEtHo0K+X4UuN9R13lGmsZVBeypXUUb23WloAqC8pJWj27a9UmrfyI5M8nFFVlf56OLukAMrDIWI2yHdeTPb2CpjkjjpXTc5Hg5GeA2x3X6lsm50HaEjGw1wStgJj4t2FKREvixNkR/dX/e9yssVnAQGIPouNre37vI1DInkMec/jeyL7SkUpcW769OmuQxCPiUYNX/96DT/4wf5Eo/n/12g/SY31179e46uxHjtWfxnLlpdy5cC6MYRMIN2GZiRu3tO8lR+Z5OH0qp6dUsMtIAVNgIpwmI1tVbTFenYlbG6vzLpx+khxPW+N9ByQSO+UyrYo1dj9/jpizALQDXz7yHVuydAC7MSYONYGSVA/wIvGQfHJANi2X/Z9PtEIxAEDgQE+Y4RpnegPKkqJc4nuRqIiIoVC85a/lYZCHDJmTPrH40bVzXuyr2ZW7rlTavgFpGMnTgbggY17FqUqKHVUlPLavGWHW5QyyZ1SCWqJsh8AYaO+UvvCa7nlRUG2AKmjewM3LjTlFyW/6LgXm2jCxneSaL6GxK4PYxsuSD4XqMeY/PTFU275g4pS4tyGDRtchyAikpVdu3a5DkEcWzx+YvrrkdgpJaPHnjul9qX3078dtpApFZU8tKl3Uao87OaeIa/NW6nje9n2lAp2H99L2DpiVjulRoLXcsuLQumi1MTBXxg+AkIHAJ3Y1luwzf8GHXdBdAXEu3+f5PHontaJ/qCilIiIiEiWFo+fkP56rIpSnlJT3NO0fmdHdgWPPVWEi/jKwsU8uX0ycZssbm1uq2D6HjuxZPiGvVOq+/henDqizAEgzEtZF7dECknQbAWGLkoZYzAVn07+oP0OiCwHU4qp+FLPi2xnjqIUv1JRSpxbsGCB6xBERLIyY8YM1yGIY7NraqntLl6M1fE9z9rZuW+FikPGjKUkVMvd6w7g1cZ6Xmwcx0lT3fRI8dq8NfxG56meUrXE2J+YnUTAdFLMEyMeo194Lbe8KH18zw6xUwowJUsxFV/o+XHlVzAVn8BU/jtgMKVn5irMPrRO9AcVpcS5tWv73u4gIjKabd261XUI4ljAGC458GAOHTOWoyZMch2OjLBg97G94d6+lxIwhiVTpnL1yuM58x/nUhGu5uD6MUO/MQe8Nm+lG52b4d2+l6AWMHRyKgAl5h8jGp+feC23vKinp9SEIV6ZZCo+ham+EVP5DSj9QPKx8o9ixj0J5Z/JWZx70zrRH1SUEudaW1tdhyAikpXOTm1dF3jvrP358YmnUFVU5DoUGWE/PvEU9q+p4TvHvGOfP2vJlGnpr0+ZOm2f+lTtC6/NWz3H97LZzdZJoLuIlaAOgA57CgDFPJ3eRSXZ8VpueVEw055SezClZ2DKP4LZY84ygbpeP841rRP9wU2nRZE9lJeXuw5BPKaoyPKLX+xix44dFBWpd0cupcY69bVfFO/Rc0Yy49dcyZTmrfzINA/n14/h9lPePSLf86C6eqZVVrKptZV3TZ85Ip85HK7nrZGeA6wtA5NdT6lgd9HJ2jCW5N8/40wlaucQNqspsivo5J37HJvfuM4tGUqEAMnfezEKa2ev1on+oKKUODdnzhzXIYjHhEJwyildxGJl7OPJCxlCaqz9ZtKkwvpL3Wjg11zJlOat/HCRh8YYfvCOk2jq6mJWdU1ev/eeXM9bIz32w+kp1XN0rw7o2e0RZQ5hVhMym0A186y5zi0ZXJBtGGNJ2BIs1UO/YRTROtEfdHxPnHvuuedchyAe9eabb7oOQTxKuSW5otzyprGlZexfU+s0Bq/lVmIYx/f2vHlvT3GbLKqkjjhJdryWW17T++iem+PDw6V1oj/o3+JExHOiUfj970vZtm08M2ZAOOw6Iu9KjTXA2Wd3aKxlQMqVwWneyg/loTsjPfY9PaWGs1Oqd4EwdaQpyOYsIohRbb5NnAm02k9l8T6R/BpOPymRfFJRSpybMmWK6xDEY6JRw+c/XwvU8vGPbyEc1l78XOkZazjjjE7fjHV9fb3rEAqOX3MlU5q38sPPeeh63hrpsR9Oo/PUTqm9i1KpxXo2RakinqfUPAJApz2VGPtl/F6vcZ1bMrigSd6OWIhFKa0T/UHH98S5YDDoOgQRkawEAvrjU0QKi9fmrVRPqax2SplUUWqv43upnVKmAcjsJrki80z661Lzx4xj8CKv5ZbXBNkOQNyOcxxJ9rRO9AfNIOLcW2+95ToEEZGs7Nixw3UIIiJZ8dq8ldoplU2j82Dq+J7tvVPKUknCVgIQynC3VDF7FKV4IKvimNd4Lbe8Jkjy1ydB4RWltE70BxWlRERERESkoCSGs1Oquyi1d6Pz5GOpI3xDNzsP0EDYrE2+z44nYDooYVnGcYjkU6C7KBVnrONIRPqnopQ4d8ghh7gOQUQkK9OnT3cdgohIVrw2b6V7SpkoEMvoPT07RvouzrNpdl7ESgCidhadnAhAyKzLKAYv8lpueUsinfeFWJTSOtEfVJQS59avX+86BBGRrGzfvt11CCIiWfHavJUqSkGmu6USBNgJ9L847+krlUFRyjwPQIQjiNmpAITYkEEM3uS13PKSAE0YE8PaAAkKryG91on+oKKUONfS0uI6BBGRrHR0ZH7bk4jIaOC9eSuEtWEgs6JUgEaMiXcvzvs5vmdTO6WGPr4XZBsAUTuTGFO6H9uYceRe473c8o5AendgHRByG8wwaJ3oD4WXmeI5JSUlrkMQjykqstx6awM7d+6gqKjSdTielhrr1Nd+UVRU5DqEguPXXMmU5q388HMeup63cjH2ljIMzQRoJzHEa4danKd6SoXYNOT3TfWmSlBHPF2U2gpEgXCm4XuG69ySgaVv3ivAo3ugdaJfqCglzh100EGuQxCPCYXgjDM6icfL0E2yuZUaa7+ZMmWK6xAKjl9zJVOat/LDz3noet7KxdgnKCNAM4ahd+oM1VenZ8fTFpI9qgZeJvUUpWpJUE/ClhAwnQTtFuJMy+4n4QGuc0sGNlgftUKgdaI/6PieOPfss8+6DkE86o033nAdgniUcktyRbklueLF3Eo3O8/g+N5Qi/MEY7G2CGNi6eN5/YsToLn7PXWASe+WCvn0CJ8Xc8srAqZwm5yD1ol+oaKUiHhOLAZ/+lMJy5aNIZbZhTwyTKmx/tOfSjTWMijlyuA0b+WH8tCdXIy9pRTIsKeUGeoYU4AYk4HB+0MFaMaYBNYaElQDEGPqkO8TcSG9Q9AWZlFK/EHH98S5SZMmuQ5BPCYSMVx6aR1Qx4c/vIVQyF99Q/KpZ6xhzRr/jHVtba3rEAqOX3MlU5q38sPPeeh63srF2PfslMri+J4dN+Br4kwlzJuE2EiEI/t9TYBGgO6CVKj7fd07pcwG8E9KpbnOLRlYzw7BgfN+NNM60R+0U0qcUwM7ESk0auoqIoXGi/NWorsoFRiB43uwR18ps2HA1/QUpXoKMTGb6kc1dJN0L/JibnlFYIheaqOd1on+oKKUOKdz6CJSaLZtG6zfiIjI6OPFeSubnVKZLM7jNnkMb7DeUHvevJd+X7qn1NtDxuFFXswtb0gM2eB/tNM60R9UlBIRERERkYKT7illhtoplSDITmDwxXmqp1SIwXZK9dy8t/f7gmYn0DVU2CJ5YWjBmCgACcY4jkZkYCpKiXPz5893HYKISFamTp3qOgQRkax4cd7K9Pa9AE0YE+1uTj7w4jze3bA8wHYGKi4FTer4Xs9OKUs1CZuMJcjWjOP3Ci/mlhcEUwVU29P/rNBonegPKkqJcxs36qYSESksDQ0NrkMQEcmKF+cta5M7pYbqKRVI95OqY7DFeYIaErYcYyyhAfpDpXZKxW3dHo8a4kwEIMiWDKP3Di/mlhekc5W6IV45emmd6A8qSolzTU1NrkMQEclKW1ub6xBERLLixXkrkWFPqZ7m5EMtzg2x7t1SwQGO8PXX6BxIF6VCPixKeTG3vKC//meFRutEfyjMfXziKbqxQ0ZaOGy56aZGdu7cSThc7jocT0uNdeprvwiF9MdntvyaK5nSvJUffs5D1/NWLsY+8+N7yaJJgoohPzPGbIp4jSLzMl32hH4+q/8CV5zk1fVBswX8lVrOc0v654WilNaJ/qAZRJxbsGCB6xDEY8JheP/7O7C2DGNcR+NtqbH2mxkzZrgOoeD4NVcypXkrP/ych67nrVyMfbrR+RBFKdNdlEoVsQYTsYdTZu6niJX9Pt9fo3OAuJ0IBoJsHiTa+wnQQheLibH/kLEUCte55R8WyPwPiKAp/KKU1on+oON74tzTTz/tOgTxqLVr17oOQTxKuSW5otySXPFibmW6Uyr1vM1gp1QXhwMQNuvSBagecQI0A32LUrFBe0p1Um2uozpwI5WBnzIm8AkqzE+GjKVQeDG3Rpsyfs9Y8z7K+B2ZbsXrv/9ZYdE60R9UlBIRz4nF4MEHi3nyyTpiMdfReFtqrB98sFhjLYNSrgxO81Z+KA/dycXYp4pSgaF6SplWoKcH1eCfWUPUzgagiOd6fw5NGJPA2gAJano917vRee+iQYX5JaXmEawN0WWP6H7sLsr4w5DxiATZTKX5H4KmiarAj6gy3wfiQ77PC8f3xB90fE+cmzBhgusQxGMiEcOFF9YD9Zx77hZCIZ81d8ijnrGGNWv8M9Y1NTWuQyg4fs2VTGneyg8/56HreSsXY5/IeqdUZv3aIiwkzFqKzLN02pPTjwfZ2P1964Fgr/fESf59NmDaMbYZmy5aWUp4BIBmexWdvJNy+ysqA7dRaX5Mhz0ZS1VGcY1WrnPL6yrNjzEmSsxOIshWysyfMUTotCcQZQ4JxvX7Pi8UpbRO9AftlBLnKiqG3kotIjKalJSUuA5BRCQrXpy3eveUGrjIlWp0bm1mRakumzzCV8TzvR4v4gUAIszv513FxO0YoPcNfCHeJGS2YG0RXRwPQBsXELUzMCZGMYV/PMmLuTVaBNlIifkX1gZotDfQZP8DawOUmn9QG/h3xpiPUsyjA7y38ItSWif6g4pS4pzOoYtIodm6davrEEREsuLFeSvdU8rEgeiArzPp2/eGPr4HEGM/AIJsBXrOGhaZ5wGI2AX9vi/eT1+pYh4HoIuF6SIaGLo4Jvm8eTKjmEYzL+bWaBEiuU6KMpc40+niRJrsN4nYQ4jZqQRMJ7WB/6CUv+z1zigB0wJAvICLUlon+oOKUiIiIiIiUnB6ijyDH+HL9vhegnqsDWFMggC7uh+NUsQrAEQ4tN/3xZkEQJAN6cdKzL8A6LLH9nptlz0aoHunlBqcSf/C5k0AYsxMP9bFsTTYH7LT3k6bfS8AVeZGingm/ZoAjQBYG8RSmb+ARYZBRSlx7oADDnAdgohIVqZMmeI6BBGRrHhz3gqSsMmjY4M1O08f38uwKAUB4t19eoJsAyDMaxjTRcJWE2d6v++K2rkAFJlXut+7kbB5HWt7dkalX8uBJGwVAbObMK9mGNfo5M3cGh1CdBel7Mx+n91tP0uHPQVj4tSYq9Ov7+knVUshL/m1TvSHws1Q8Yxt27a5DkFEJCtNTU2uQxARyYpX563efaX613N8L9OiFMQZD6SO8O3ZT+pQwPT7nlSvqTCvAHHKzB+7H1/cT1+fIF0cCUCxeSrjuEYjr+ZW/vXtixbiDaD3TqneDM32KiL2YAKmjVrzFQI0eKKfFGid6BcqSolzDQ0NrkMQEclKa2ur6xBERLLi1Xkrm6KUzbCnFOxZlOreKWVeBiBiDxnwPTH2I2FLCJg2wryW7vPTZt/X7+u77EKgp+BVqLyaW/kSZAvV5puMN0upNtfs8UwXQTYBgxWlAIpotN8kZicTNNuoMt9L75Qq5H5SoHWiX4RcByASCikNZWSFw5Zvf7uJHTt2Eg5n/hdQyV5qrFNf+0UgoH/TyZZfcyVTmrfyw8956HreytXYp5udD1KUCmTZUwogkSpKma1gIbxHw+mBhYhyIMWspNL8iIBpJWYnEWFxv6+OdvemCvMaho5ePbIKievcKmyWGnM1YbMagFKWsdvuIkE9Id7CGEvCVg2548lSQ5P9JvV8ihLzZHqHX6HvlNI60R/0qyzOLVy40HUI4jHhMFx0UTtk8S+iMjw9Y+0vs2bNch1CwfFrrmRK81Z++DkPXc9buRr7nqLUQD2lYhjTBWR5fM9OAJPcKWVoImh2dn/a4OMY5WCKWUmReQ2Adns2Ax1OiTOBuB1H0GwnbF8lQmH+ndh1bhWyIp4jbFZjbTFxxhEyGyhmOR2clu4PFWU/BjoyuqcY+9FqL6LS/DTdID1ux+cy/JzTOtEfVNYW555++mnXIYhHrVmzxnUI4lHKLckV5ZbkildzK9FdlAoMsFMqdXQPhn98L7VLKmYnD/kZETs//XWXPZx23jvIqw0RkscBi0zhHuHzam7lQ7n5PwDaeTednAT09BgLm6H6SfXVxvtpt2fQZRfRaj9IO2eNcMT5pXWiP2inlDhnrb+2z0vuxeOwfHkRGzdWs99+EAy6jsi7UmMNcOSREY21DEi5MjjNW/mhPHQnV2M/VE+pVLEqeUtf5kuf3kWpZNElyv5Dvi/KQcRtHZYSmu03hvyeEbuAUvNgwfeVkuyFWEuxWY61hnZ7HobdVJhfUMQKIEIRzwEQs9nsRAvRYr+Qk3hd0DrRH1SUEufGjh3rOgTxmK4uw3nnjQHG8J73bKGsTH+g5UrPWMOaNf4Z66qqKtchFBy/5kqmNG/lh5/z0PW8lauxH6qnlKG11+syFWcc1hqMiXQXCSBmZ2cUzw57F8njVsVDvj61UyrMqxjas45zNHCdW4WqwtwBQCcnEmcykCBuawmaRsrs77uP9QXp5DincbqkdaI/6PieOFdbW+s6BBGRrJSXZ96XRERkNPDqvJUuSpn+e0qZYTQ5TwqToB6AYrMSgChDF6WSSsikIAUQZyoxOxljopTwcJYxjg5eza1cCvE6JeZxrA3Qai/qfjRAJ0sAqArcCkAXR2GpcRLjaKB1oj+oKCXOrV692nUIIiJZ2bJli+sQRESy4tV5yw7RUyrQ3VMqmybnKXEm9PpxLOOiVDYM7fZMAMrMvUDh7d7zam7lSpAt1JjrAejkZOJMTz/Xai8kbnsKMR32XXmPbzTROtEfVJQSEREREZGClLCpnlID7ZRKFqWGcywuwoL013E7Pr1zaqR1sBRriwibtRTxTE6+h7gR4g1KuR+IpH9cZz5NyLxF3NbTaj/W6/WWanbbKwBI2Gq6OCrfIYvknXpKiXNz5851HYKISFYmTZrkOgQRkax4dd4auqfUcI/vQau9hE57AmFWE2UeyT5RI89STQcnUcbfqAt8qfvmtAuIcmhOvt9I82pu7asSHqHa3IAxEYrtE7Tb86g21xE0TUTtbBrtDSTo2zOpkyU0JsLdO/WK8h/4KKJ1oj+oKCXONTQ0UFNT4zoMEZGMtba2qoeGiBQUr85bPUWp1n6fD+zDTikwxNifWAa37u2r3fZSArRTzOMUmxUUmxVE7MF02HeToIYuFpJpn6p882puDY8lxOtUmDspMY+lHy0xT1BingAgamfTYP8LS+UAn2Ho4vg8xDr6aZ3oDzq+J87t2LHDdQgiIllpaWlxHYKISFa8Om/FmAZAmNVArM/zxqR6SlXkM6ysWWpostex095Juz0La8MUmZeoDnyX2sDXqDT/4zrEAXk1t7IVZCNjzEWMCVxKiXmsu4n5B2lI/CcJW0LCVtJhT6XBfn+QgpTsSetEf/DUTqmmpiauuuoq/vCHP9De3s7ixYu58cYbOfzww4d870UXXcQvfvGLPo/PnTuX1157LRfhSrdAQLVRGVmhkOXf/72ZnTt3EQqVuA7H01JjnfraL4zJzREOL/NrrmRK81Z++DkPXc9buRr7GLNJ2CoCpoWwXUWUg3s9vy89pVyIM5EW+2+08hHKuIciVlFkXqCMv9LKx7BUuQ6xD9e5NRoYWqk1XyNk3iZhS+jiGFrtR4kzA4Dt9j6SS++gyzALjtaJ/mCstZ74EzmRSHD88cfzwgsv8KUvfYkxY8Zwyy23sGHDBp599ln233/wbbcXXXQRd999Nz/72c96PV5dXc0ZZ5yRVSwtLS1UV1fT3NxMVdXo+4NDCk9bpI2KG5L/wrfqglVUlSqvRERERACqzbWUmkdotRfSai/e67nrKTV/pyVxKe18wFGE+8JSbz5O2Kwr4J+D18WpNV+l2DxN3I5hl72VBGNcB5WxWCxGJBJh+vTphMNh1+GIh2RaF/FM6fGee+7hiSee4I477uDqq6/msssuY9myZQSDQa6++uqMPiMUCnHBBRf0+i/bgpRk75lndMuI5Ma6detchyAepdySXFFuSa54Obci9giAfm+u69kpVag9jwzt9mwAys0fgLjbcPrh5dzKRKX5fxSbp7G2mEb77YIqSI12Wif6g6eKUuPHj+fss89OPzZ27FjOP/987rvvPrq6ujL6nHg8rnPReRaPj74/XKWwxePw/PNhXn21HKVXbqXG+vnnw74a60Qi4TqEguPXXMmU5q388HMeup63cjn2XSSLUmFW9Wl4XmjH9/rTwSkkbCVBs40wL7sOpw/XueVSKX+l3PwGgGb7FWLotriRpHWiP3imKPXcc89x+OGH9zl3unjxYtrb21m9evWQn9He3k5VVRXV1dXU1dVx2WWX0dra/00eMnLq6+tdhyAe09VlOP30sVx66eF0danPQS6lxvr008f6aqwrK9WgNFt+zZVMad7KDz/noet5K5djn2A8MTsZYxKE6d0LNkA7UNhFKSimk2MBKDGPOo6lL9e55UqYl6kyNwHQaj9KJ0scR+Q9Wif6g2eKUlu2bGHixIl9Hk89tnnz5kHfP3HiRK666ipuv/12fv3rX3PmmWdyyy23sHTpUmKxvjd57Kmrq4uWlpZe/0nmxo0b5zoEEZGsqF+giBQar89bMaYCEGRrr8cNnQBYCvsCgS77DgBKeBQYXTuTvJ5b/QmxhlrzVYyJ0mnfQau9yHVInqR1oj+Mytv3EokEkUgko9cWFxdjjKGjo4Pi4uI+z5eUJP8A6ujoGPRzbrjhhl4//sAHPsCcOXP4+te/zj333MMHPjBwU8EbbriBa6+9ts/jzzzzDOXl5Rx++OGsWrWKjo4OKisrmTlzJi+++CIA06dPJ5FIsGHDBgAWLFjA2rVraW1tpby8nDlz5vDcc88BMGXKFILBIG+99RYAhxxyCOvXr6elpYWSkhIOOuggnn32WQAmTZpESUkJb7zxBgDz589n48aNNDU1UVRUxIIFC3j66acBmDBhAhUVFaxduxaAAw44gG3bttHQ0EAoFGLhwoU8/fTTWGsZO3YstbW16Z1nc+fOpaGhgR07dhAIBFi0aBHPPPMM8Xic+vp6xo0bx6pVqwDYf//9aWlpYdu2bQAceeSRrFy5ku3bt7PffvsxadIkXnnlFQBmzZpFe3s7W7ZsAeCII47g5ZdfprOzk+rqaqZNm8ZLL70EwIwZM4jFYmzcuBGAww8/nNdee4329nYqKiqYNWsWL7zwAgDTpiWvDX777bcBOPTQQ1m3bh2tra2UlZUxb948Vq5cmR7vUCjE+vXrATj44IN5++23aW5upqSkhPnz56fPOU+cOJGysrL0mfqDDjqIzZs309jYSDgc5vDDD2f58uUAjB8/nqqqKtasWZMe7+3bt7Nr1y6CwSBHHHEEK1asIJFIMHbsWOrq6nj+lefTebVz5062dW5Lj+m6detIJBJUVFRQU1OTHocJEybQ2dlJU1MTALNnz2b9+vXEYjHKy8upq6tL59348eOJRCI0NjYCsN9++7Fx40YikQilpaWMGzcunXdjx44lkUiwa9cuAGbOnMnmzZvp6uqipKSECRMmpMesvr6eQCCQvs51+vTpbN++nY6ODoqKipgyZUo6R+vq6giFQmzfvj39a7Vz507a29sJh8NMmzYtPb41NTWUlJSwdevW9K9VU1MTra2tBINBJkyYlR6vHTt2UF9fks6lyZMn09LSwu7duzHGMHv2bNauXYu1lsrKSqqqqti0aVP617W9vZ3m5ub0eL/xxhvE4/Ehx3vWrFm8/fbbRKNRysrKGDNmTDrvxo0bRywWo6GhIevxnjFjBlu3bqWzs5Pi4mImTZrEm2++mfV419bWUlRUlP79OHXqVBoaGmhrayMUCjFjxoz0nDDYeHd1hYBk8X/t2rWMH19BeXl5erwnTZpEa2srLS0t6fFO5eze4z1hwgQ6Ojp6jfebb76ZUc5mM96D5eyYMWPSv88GG+/W1lamTZuWcc4ONd4zZ85MzwnV1dWUlpamx3vPnA0EAsyaNSuds1VVVVRUVKT/4WXixIm0tbWl/3FkNM0RTU3RXrlSWppwNkfst99+vca7rKzM+RxRVjaWlObmZjo6op6YI1I5mxrvqqoqp3NES0u8Tx7mYo7INmfzMUe0tray//77O5sj1q7dkB77hoYGNm3aOeR4Z5OzB02roqQUmhte461dc9PjXTOhlVAY4oli1qxbM2TOjtY5YsyYg6ksLiYU2EHX7mdo7Zo1auaI1tZWDjjgAE/MEZn8PWLOfglqAp8nEGijtWs2TXyRN9cnfy8X4hxRWlrK5s2b2bFjB/PmzaOxsZEdO3ZgjGHx4sU8++yzxGIx6urqGD9+fHptN3v2bFpbW9PfZ/HixTz//PNEIhFqamqYMmUKL7/8cjpHOzs7039fWbhwIa+88gqdnZ1UVVUxY8aMXuvjeDzOxo0baWxs5KSTTmL16tW0tbVRUVHB7Nmzef7559PjEAgEeq2P33zzTXbv3k1paSkHHHBAem03efJkioqK0uN/8MEHs2HDBpqamiguLuaQQw5hxYoV6VwqLy9Pj/+BBx7I1q1baWho6LO2GzduHNXV1enxnzdvHjt37mTnzp3p9XFqbTdmzBjGjBnDa6+9ls7R5ubmdA6k1sfRaJS6ujomTJjAq6++ms7Rtra29HgvWrSIF198ka6uLmpqapg6dWp6fTxz5kwikUj694arekQ0GiUTo/L2vWXLlrFkSWbbH1etWsW8efOoqKjg/e9/P7fddluv5//yl79w+umn87e//Y13vetdWcXR0dFBRUUFF198cZ9b+fbU1dXVq2dVS0sLU6dO1e17GVq+fDlHHnmk6zBGNd2+l532dsP++yf/4rtmzRbKykbdNOcZfh3rNWvWDHmrq/Tm11zJlMYnP/w8zq7nrVyPfTl3Uhn4KR32XTTbr6YfH2fOJGBa2Jm4nRgzR/R75lvqlsEOewrN9uvA6DiCOtzcKuZREtQQ5ZAcRDWyAuyimMcwxKgwvyJgmonYA2i038NS4Tq8fTKab9/TOrGwZXr73qjcKTVv3jxuv/32jF6bOp43ceLEdEV9T3tW2bNVWlpKfX19uio+kOLi4n53aUlmtLATkUIzYcIE1yGIiGTF6/NWnOQxnwDbez3uleN7AB32TEpYRql5kJidQhsXuQ4JGF5uFfMYtYH/IGFL2W7/AKPw18fQShm/o8g8TxEvYkxP0+2onUuj/W7BF6RGO60T/WFUFqUmTJjARRddlNV7FixYwGOPPUYikejV7Hz58uWUlZUxZ86crOPYvXs3O3fuZOzYsUO/WIatpaWFuro612GIiGQstf1ZRKRQeH3eijMegCDbej1qTLIlSIJSB1GNrAiHsdteSZX5AZWBO4gnpo+K5trZ5pZhN1XmvwEImA6K7Qq6OD5H0fUnRjFPUmz+Rac9lQgLAUsxTxBgJx28izCvUW2+Q8j09CiL2INIUEeCOnbbj2Px7u+n0ULrRH8YlUWp4Tj33HO55557+P3vf8+5554LJM/y/va3v+WMM87otZMpdTZ01qxk35nOzk6i0WifyfSb3/wm1lqWLl2ap5+FP23bto0ZM2a4DkNEJGPNzc1qvikiBcXr81ZPUWoHyUbgAQw97TW8sFMKoJ33EbRbKTf/R5X5T6J2JnFmjNjnG5opZjkBmunkZBKkCgIxII6hgzDruh+ZQoLxWedWhfkVQbMr/eMS8yhdNl9FKUutuYpik+wzVMrf6eB0wrxO2CR75lbanxAwbQDE7ATa7IeJsIB4dzN9yR+tE/3BU0Wpo446iosvvphXX32VMWPGcMsttxCPx/s0IT/55JMB0k0Ut27dymGHHcYHP/hB5s2bB8ADDzzAX/7yF5YuXcpZZ52V15+LiOybUMjy+c/vZteuXYRCRa7D8bTUWKe+FhmIcmVwmrfyQ3noTq7HPkE91gYwJkrANpKgPn10L8k7v692208QYjXF5jmquZEG+0OS/aVs93/ZX7Bexm8pN3f3KhZV2p/RwckkqKaM+wiY9l7vsdbQYj8HHJjFd4pRwj8AaLUfpML8mmKeAKJA7vsZJXdIrcTaYiIcSLF5jjL+BEDClmCpJGh2YG2IDk5jt/2kjuiJ5NiobHQ+XI2NjXzpS1/i3nvvpaOjg0WLFvH973+fI444otfrUtXWVFGqqamJK664gqeeeorNmzcTj8eZPXs2H/7wh/niF7+YdcO3TBt6iWRKjc5FREREBjfWnE/QbGdX4haiHEiQzYwNfIiELWG7/Zvr8EZUgO2MNRdgTISGxHeIcjA15huE2EiTvZooB2X5WR/GmORNWVG7HxAmbF7v9/UxOwkIEjLJ27qaEl+jk3dm9L2KeJa6wBdI2Cq2298y1nyAoGmkIfFdIiwe8v0hXqPC/C8xZtFh30mcyRl93yRLnbmCIvMyrfYDtNpPUcqfCZtVxOxsOjmBBOUUs5woc0l0777zutHc6FwKW0E3Oh+u2tpafvaznw16Ux70FKNSampq+NWvfpXDyGQwK1eu5PDDD3cdhnjQm2++ycyZhX3TjoxOyi3JFeWW5IofcivOOIJs7252fqCnmpzvLcE42nkf5fwfVeZHJKiiyKwCoI7P02i/TYQjhviUpHLzfxgTJWIPptHe0L0zyBK2L1Jq/k6AXXTY9xBhARDAUgZYKvkR5eb3VJvvYG1pRn2hSswjAHTyDqCYLo6njD9Sav5OxA5elAryFnXmKgKmBXicMu5hp/1fEtRm9PMM8wJF5mWsDdNuzwMMHbyHDvueXq/r4h0ZfZ7kntaJ/pD93k6RERaNRl2HIB6TSMDrr4dYs6aIRMJ1NN6WGuvXXw/5aqxjsZjrEAqOX3MlU5q38sPPeeh63srH2O/d7Lzn+J43b8lutR8iYcsJmU0UmVUkbDld9jCM6aLK3EiyD9TeEpTwd+rNJ6gyN1LCQ5Rxf/fnXbjHUTVDlENpsV+iyX6HLo7DUtFdkEo+v9teTrtdijEJasx1hFg9RMQxSngUgE6bbNDeYd8NQAn/xLB7kPd2UWu+QsC0ELVziNmpBEwbZeZ3mQwVkKDS3Jr8npxGgvoM3ycuaZ3oD57aKSWFqbY2s3/dEMlUZ6fhpJPGAeNYs2YLZWWeOaU86vSMNb4a6/LyctchFBy/5kqmNG/lh5/z0PW8lY+xj5P8/KDZDranKOWFm/f6Y6mmwd5ECU9gaKfDvosYUxjLBwmZLZTav9PBaUAXRbxCmFcoNX9PH7sLs4Yyk+ynFLHzu2+hy0aAFvtFop3bqC59jgruoMleP+Cri3mCgGkhbmuJcCgAUeYRtbMIm3WU2n/Qztn9vreUvxIyW4jbMTTY71LEi9Sa/6CMe2njQ3sUy/pXwoMUmddI2DJa7YVZ/jzFFa0T/UFFKXFu0qRJrkMQEcmKricWkULjh3krbseD6btTyovH91JizKXVzu31WJv9IFXmFirMHQRsM2Xmnl4NzBO2jHbOIcwqgmymkyW02fNJNkvPVojdXEaV/SQl5glCdh0xZvX7yjLzR4DuQllqGWrosKcTNj+k3NxNzE4mwpF9fpYV5tfdP7cPYamhi2OJ2amEzAbK7H208cE+3y/IWwTZRpBdVJofd7//w3vcKCijndaJ/qDje+LcK6+84joEEZGsbNiwwXUIIiJZ8cO8lTqSFSBZgPH68b2BtHMmcVtP0GynMvD/CJpdxG0dnfYdNCe+xA57D632Ehrt99lp76LVfgJL9bC/35tvWzo5AYAq80MC3UXBPQXZSLF5BmtNnx5OHZxK3I4haLZTF/gypdzX6/lS/kHQbCNua2nn9PQnttkPAVBu/pcAjenXF/EcdeYyxgYupC5wFdWB/yRgWonYg2jj3GH/PCX/tE70B+2UEhERERGRgme7j+mlilFeP743sBIa7I8o5U+EWU3EHkYb55HL4lyb/QglPE6ReYGxfIQm+3W6ugtVAKXmzwBEWEycib3ea6lkl/0pFdxGmbmfSvMzOu2J3YWyLirM7d3f4/29fg4dvJMy+wfCZjXVfIco8wjzKsVmRfJzbZAYUzF00sm7aLUfQctfkdFHvyvFuVmz+t/iKyIyWo0f749rokXEO/wwb/UUpbq6/+/943sDiTOJVvupvHyv8ePHE6OKXfZmqriFIvMCNVxLk/0PujiR5K/AMgDauxub7y1BLS323wizirBZRyU/ocV+nnLuIWi2E7fJ2wZ7C9JiL6feXEmxWU4xywGSu7E4k1b7UTU0L3BaJ/qDilLiXHt7u+sQRESyEolEXIcgIpIVP8xbqeKToaP7//48vpdvqdyKMZcGexPVfJdS8wDV3MR2ezwh1hMyW7C2qJ9+UXsKstt+hjrzBcrMnylmefoo5m77cfr7dYxyCM2Jz1NkXsISJmZnEuEIYszMwc9U8k3rRH9QTylxbsuWLa5DEBHJSmNj49AvEhEZRfwwb9nuokX6+J7x6/G9/OqdW0Ga7ZdI2HICpoUQayjmMQC6WJTezTaQCAtpSXyWhC0naHZijKXDnkQnpwz4ng7OpNl+nRZ7Fe2cp4KUh2id6A/aKSUinhMKWS69tJXGxkZCoaDrcDwtNdapr0UGolwZnOat/FAeupOPse99fM/6+vieWyEiHEYJj1PMCkrM4wB02eMyenc776PDnkSRfYUos0kwLpfBiohjxlqrP5FHWEtLC9XV1TQ3N1NVVeU6nFEvHo8TDOov4INpi7RRcUMFAKsuWEVVqfIqE4lEgkBAG0Jl5Cm3JFeUW5IrfsgtQxvjA8nb2bYmHqDK/Jgy80da7YW02osdR+dd/eVWKfdRHfgvYnZi99G9ANvt77HUuAlSBhSLxYhEIkyfPp1wOOw6nF60TixsmdZFvP0nkxSEl19+2XUI4lFvv/226xDEo5RbkivKLckVP+SW3aPnkKGz5/Y9q+N7udRfbkVYCEDIJI9fdbJEBSnJmtaJ/qCilDjX2dk59ItEspBIwIYNQd5+O0Ai4Toab0uN9YYNQV+NdTQadR1CwfFrrmRK81Z++DkPXc9b+Rn7ENYmd3rsWZTS8b3c6i+34kwmZicmv7Y1tNgr8h2WeIDWif6gnlLiXHV1tesQxGM6Ow1HHTUeGM+aNVsoK9Mp5VzpGWt8NdZlZWWuQyg4fs2VTGneyg8/56HreStfY28pwRDtVZTS7Xu51X9uGTrsaVTwK1rsVdolJcOidaI/qCglzk2bNs11CCIiWRkzZozrEEREsuKXeSt5hG937+N7un0vpwbKrTY+Qpv9IFpyynBpnegPOr4nzr300kuuQxARyYoferOIiLf4Zd7quYFPx/fyZfDcUkFKhk/rRH9QUUpERERERDwhVYDS8T0RkcKgopQ4N2PGDNchiIhkZdy4ca5DEBHJil/mrf6KUjq+l1t+yS3JP60T/UFFKXEuFou5DkFEJCuat0Sk0Phl3uqvKKXje7nll9yS/FNu+YOKUuLcxo0bXYcgIpKVhoYG1yGIiGTFL/OWju/ln19yS/JP60R/UOc5EfGcYNBy4YVtNDc3EQwa1+F4WmqsU1+LDES5MjjNW/mhPHQnX2PfU5TqwJgIoON7IiKjmbHW6k/kEdbS0kJ1dTXNzc1UVVW5DmfUi0ajhMNh12GMam2RNipuqABg1QWrqCpVXmUiFosRCqn2LiNPuSW5otySXPFLblWZGykzf6LVfoAKczcAWxN/Ax3hyxm/5JZXxWIxIpEI06dPH3VrMq0TC1umdREd3xPnXnvtNdchiEdt3rzZdQjiUcotyRXlluSKX3IrtVMqQNMej+r4Xi75Jbck/7RO9AcVpcS59vZ21yGIx1gLu3YF2LYtgfaC5lZqrHftCvhqrLu6ulyHUHD8miuZ0ryVH37OQ9fzVr7Gfu+iVMKWADoSm0uuc0u8S+tEf9A+S3GuoqLCdQjiMR0dhkMOmQBMYM2aLZSV+WzlkUc9Y42vxrqkRMdAsuXXXMmU5q388HMeup638jX21paA6SlK6ea93HOdW+JdWif6g3ZKiXOzZs1yHYKISFYmTJjgOgQRkaz4Zd7ae6eUilK555fckvzTOtEfVJQS51544QXXIYiIZGX9+vWuQxARyYpf5i0VpfLPL7kl+ad1oj+oKCUiIiIiIp6QLkqZzu4fl7oMR0REhqCilDg3bdo01yGIiGRlzJgxrkMQEcmKX+atvXdGJRjrKBL/8EtuSf5pnegPKkqJiIiIiIgn7F2UijPOUSQiIpIJFaXEubffftt1CCIiWdm5c6frEEREsuKXeatPUcqOdxSJf/gltyT/tE70h5DrAERERlowaDnvvHZaWloIBv1z3bcLqbFOfS0yEOXK4DRv5Yfy0J18jb12SomIFBZjrdWfyCOspaWF6upqmpubqaqqch3OqNfZ2UlJiW5GGUxbpI2KGyoAWHXBKqpKlVeZiEajhMNh12GIBym3JFeUW5IrfsmtIJsYG/hw+sc7E7cSY57DiLzPL7nlVbFYjEgkwvTp00fdr6PWiYUt07qIju+Jc+vWrXMdgnjU1q1bXYcgHqXcklxRbkmu+CW31Og8//ySW5J/Wif6g4pS4lxra6vrEMRjrIX2dkNjYwTtBc2t1Fi3txtfjXVnZ6frEAqOX3MlU5q38sPPeeh63srX2PctStXm7psJ4D63xLu0TvQH9ZQS58rKylyHIB7T0WHYf/+JwETWrNlCWZnPVh551DPW+Gqsi4uLXYdQcPyaK5nSvJUffs5D1/NWvsZ+z6JUwpajf4PPPde5Jd6ldaI/aJYW5+bN0zl/ESkskyZNch2CiEhW/DNvBdNfJah2GId/+Ce3JN+0TvQHFaXEuZUrV7oOQUQkK2+++abrEEREsuLHeUtFqfzwY25Jfmid6A8qSomIiIiIiOdYdFuxiMhop6KUODdlyhTXIYiIZKWurs51CCIiWfHjvJVQUSov/Jhbkh9aJ/qDilLiXCikfvsiUlg0b4lIofHjvNVlj3Ydgi/4MbckP5Rb/qCilDi3fv161yGIiGRl+/btrkMQEcmKn+atHYn/pSnxDTpZ4joUX/BTbkl+aZ3oDyo9iojnBAKW00/voLW1lUDAP9d9u5Aa69TXIgNRrgxO81Z+KA/dyefYx5lCHB37EREpBMZaqz+RR1hLSwvV1dU0NzdTVaWz7ENpb2+nrKzMdRijWlukjYobKgBYdcEqqkqVV5no6uqiuLjYdRjiQcotyRXlluSKcktyRblV2GKxGJFIhOnTpxMOh12H04vWiYUt07qIju+Jc2+//bbrEMSjdu7c6ToE8SjlluSKcktyRbkluaLcklzROtEfVJQS55qbm12HIB7V3t7uOgTxKOWW5IpyS3JFuSW5otySXNE60R9UlBLnSkpKXIcgHtPebpg8eRInnngC7e3GdTielhrryZMn+WqsR9v29kLg11zJlOat/PBzHrqet/w89l7nOrfEu7RO9AcVpcS5+fPnuw5BRCQr06ZNcx2CiEhWNG9Jrii3JFe0TvQHFaXEuWeeecZ1CCIiWVm3bp3rEEREsqJ5S3JFuSW5onWiP6goJSIiIiIiIiIieaeilDg3ceJE1yGIiGSltrbWdQgiIlnRvCW5otySXNE60R9UlBLnysrKXIcgIpKVoqIi1yGIiGRF85bkinJLckXrRH9QUUqc0zl0ESk027Ztcx2CiEhWNG9Jrii3JFe0TvSHkOsARERGWiBgOemkTtrb2wgErOtwPC011qmvRQaiXBmc5q38UB66o7EXEZH+GGut/lQYYS0tLVRXV9Pc3ExVVZXrcEa91tZWKioqXIcxqrVF2qi4ITlGqy5YRVWp8ioTnZ2dlJSUuA5DPEi5Jbmi3JJcUW5Jrii3ClssFiMSiTB9+nTC4bDrcHrROrGwZVoX0fE9cW7z5s2uQxCPamhocB2CeJRyS3JFuSW5otySXFFuSa5onegPKkqJc42Nja5DEI9qa2tzHYJ4lHJLckW5Jbmi3JJcUW5Jrmid6A8qSolzo22bqBS+9nbD7NkTWLr0ONrbjetwPC011rNnT/DVWIdCasmYLb/mSqY0b+WHn/PQ9bzl57H3Ote5Jd6ldaI/aAYR5w4//HDXIYgHdXSo5p4vfhzrmTNnug6hIPkxV7Kh8ckPv47zaJi3/Dr2Xjcacku8SetEf9CfDOLc8uXLXYcgIpKVNWvWuA5BRCQrmrckV5RbkitaJ/qDilIiIiIiIiIiIpJ3KkqJc+PHj3cdgohIVqqrq12HICKSFc1bkivKLckVrRP9QUUpca6qqsp1CCIiWSktLXUdgohIVjRvSa4otyRXtE70BxWlxDmdQxeRQrN161bXIYiIZEXzluSKcktyRetEf9DteyLiOcZYjj66i/b2DoyxrsPxtNRYp74WGYhyZXCat/JDeeiOxl5ERPpjrLX6U2GEtbS0UF1dTXNzs7YcZqClpUXjNIS2SBsVN1QAsOqCVVSVarwy0d7eTllZmeswxIOUW5Iryi3JFeWW5Ipyq7DFYjEikQjTp08nHA67DqcXrRMLW6Z1ER3fE+e2b9/uOgTxqJaWFtchiEcptyRXlFuSK8otyRXlluSK1on+oKKUOLdr1y7XIYhH7d6923UI4lHKLckV5ZbkinJLckW5JbmidaI/qCglzgWDQdchiMe0txsOPng8Z511DO3txnU4npYa64MPHu+rsQ4E9MdntvyaK5nSvJUffs5D1/OWn8fe61znlniX1on+oEbn4twRRxzhOgTxoIaGIKA/yPIhOdb+MmvWLNchFCQ/5ko2NG/lh1/zcDTMW34de68bDbkl3qR1oj+orC3OrVixwnUIIiJZWbt2resQRESyonlLckW5JbmidaI/qCglziUSCdchiIhkRRfXikih0bwluaLcklzROtEfVJQS58aOHes6BBGRrOh6YhEpNJq3JFeUW5IrWif6g4pS4lxdXZ3rEEREslJRUeE6BBGRrGjeklxRbkmuaJ3oDypKiXOvv/666xBERLKyefNm1yGIiGRF85bkinJLckXrRH/Q7Xsi4jnGWA49NEJnZyfGqM9BLqXGOvW1yECUK4PTvJUfykN3NPYiItIfY9WZbsS1tLRQXV1Nc3OzzlhnoLGxkdraWtdhjGptkTYqbkhujV51wSqqSpVXmWhtbdWWcskJ5ZbkinJLckW5Jbmi3CpssViMSCTC9OnTCYfDrsPpRevEwpZpXUTH98S5xsZG1yGIR7W1tbkOQTxKuSW5otySXFFuSa4otyRXtE70BxWlxLkdO3a4DkE8qqWlxXUI4lHKLckV5ZbkinJLckW5JbmidaI/qCglzhljXIcgHtPRYTjyyHG8//1H0tGh/Mql1FgfeeQ4jbUMSrkyOM1b+aE8dEdjLyLZ0jrRH9ToXJxbvHix6xDEY6yFjRtDQAh1zcutnrHGV2O9//77uw6h4Pg1VzKleSs//JyHructP4+917nOLfEurRP9QTulxLlnn33WdQgiIllZt26d6xBERLKieUtyRbkluaJ1oj+oKCXOxWIx1yGIiGQlkUi4DkFEJCuatyRXlFuSK1on+oOKUuJcXV2d6xBERLKiq69FpNBo3pJcUW5Jrmid6A8qSolz48ePdx2CiEhWampqXIcgIpIVzVuSK8otyRWtE/1BRSlxbtWqVa5DEBHJysaNG12HICKSFc1bkivKLckVrRP9QbfviYjnGANz5kSJRCLoJtncSo116muRgShXBqd5Kz+Uh+5o7EVEpD/GWl3KOtJaWlqorq6mubmZqqoq1+GMert27aK+vt51GKNaW6SNihuS5/VXXbCKqlLlVSZ2795NZWWl6zDEg5RbkivKLckV5ZbkinKrsMViMSKRCNOnTyccDrsOpxetEwtbpnURHd8T51pbW12HIB7V2dnpOgTxKOWW5IpyS3JFuSW5otySXNE60R9UlBLntm7d6joE8aimpibXIYhHKbckV5RbkivKLckV5ZbkitaJ/qCilIh4TkeHYcmSsVx00RF0dKhxRS6lxnrJkrEaaxmUcmVwmrfyQ3nojsZeRET6o0bn4tzixYtdhyAeYy2sXh0Gwljb4jocT+sZ6+TXfjF79mzXIRQcv+ZKpjRv5Yef89D1vOXnsfc617kl3qV1oj9op5Q49/zzz7sOQUQkK+vXr3cdgohIVjRvSa4otyRXtE70BxWlxLlIJOI6BBGRrMRiMdchiIhkRfOW5IpyS3JF60R/UFFKnKupqXEdgohIVsrLy12HICKSFc1bkivKLckVrRP9QUUpcW7KlCmuQxARyUpdXZ3rEEREsqJ5S3JFuSW5onWiP6goJc69/PLLrkMQEcnKhg0bXIcgIpIVzVuSK8otyRWtE/1Bt++JiOcYA1OmxIhGYxjdOp1TqbFOfS0yEOXK4DRv5Yfy0B2NvYiI9EdFKXFuv/32cx2CeExpqWX58u20tLRQWlrlOhxPS42134wfP951CAXHr7mSKc1b+eHnPHQ9b/l57L3OdW6Jd2md6A86vifOdXZ2ug5BPEo3dkiuKLckV5RbkivKLckV5ZbkitaJ/qCilDi3efNm1yGIRzU2NroOQTxKuSW5otySXFFuSa4otyRXtE70BxWlRMRzOjrgtNPG8KlPHUZHh+tovC011qedNkZjLYNSrgxO81Z+KA/d0diLiEh/1FNKnFu4cKHrEMRjrDW88EIRUIS1bYB1HZJn9Yx18mu/jLV6HGTPr7mSKc1b+eHnPHQ9b/l57L3OdW6Jd2md6A/aKSXOvfLKK65DEBHJysaNG12HICKSFc1bkivKLckVrRP9QUUpcU4N7ESk0Kipq4gUGs1bkivKLckVrRP9QUUpca6qSldfi0hhKS0tdR2CiEhWNG9Jrii3JFe0TvQHFaXEuRkzZrgOQUQkK+PGjXMdgohIVjRvSa4otyRXtE70BxWlxLkXX3zRdQgiIll56623XIcgIpIVzVuSK8otyRWtE/1Bt++JiCfV1cWJx+Ouw/CFujqNs2RGuTI4zVv5oTx0R2MvIiJ7U1FKnJs+fbrrEMRjysosL720jaamJsrKalyH42mpsfabsWPHug6h4Pg1VzKleSs//JyHructP4+917nOLfEurRP9Qcf3xDn9q7DkSiKRcB2CeJRyS3JFuSW5otySXFFuSa5onegPKkqJcxs3bnQdgnjUrl27XIcgHqXcklxRbkmuKLckV5RbkitaJ/qDilIi4jkdHXDuufV89rOH0tHhOhpvS431uefWa6xlUMqVwWneyg/loTsaexER6Y96Solzhx12mOsQxGOsNTz5ZDFQjLUdgHUdkmf1jHXya7+M9cyZM12HUHD8miuZ0ryVH37OQ9fzlp/H3utc55Z4l9aJ/qCdUuLc6tWrXYcgIpKVzZs3uw5BRCQrmrckV5RbkitaJ/qDilLiXFtbm+sQRESy0tXV5ToEEZGsaN6SXFFuSa5onegPKkqJcxUVFa5DEBHJSklJiesQRESyonlLckW5JbmidaI/qCglzs2ePdt1CCIiWZkwYYLrEEREsqJ5S3JFuSW5onWiP6goJc49//zzrkMQEcnK+vXrXYcgIpIVzVuSK8otyRWtE/1Bt++JiCeVliawVjf75ENpacJ1CFIglCuD07yVH8pDdzT2IiKyNxWlxLmpU6e6DkE8pqzMsnbtVhoaGigrq3Mdjqelxtpv6uvrXYdQcPyaK5nSvJUffs5D1/OWn8fe61znlniX1on+oON74lwgoDSU3FBuSa4otyRXlFuSK8otyRXlluSKcssf9Ksszr311luuQxCP2rFjh+sQxKOUW5Iryi3JFeWW5IpyS3JF60R/UFFKRDynsxM+8pE6vvKV+XR2uo7G21Jj/ZGP1GmsZVDKlcFp3soP5aE7GnsREemPekqJc4cccojrEMRjEgnDww+XACUkElsANQ7OlZ6xTn7tl7GePn266xAKjl9zJVOat/LDz3noet7y89h7nevcEu/SOtEfPLNTasuWLXzlK19hyZIlVFZWYoxh2bJlWX3Gpk2bOP/886mpqaGqqoqzzjqLN954IzcBS9qbb77pOgQRkaxs377ddQgiIlnRvCW5otySXNE60R88U5R6/fXX+c///E82bdrEwQcfnPX7W1tbWbJkCf/85z/52te+xrXXXstzzz3HCSecwK5du3IQsaTs3r3bdQgiIlnp6OhwHYKISFY0b0muKLckV7RO9AfPHN9buHAhu3btoq6ujnvuuYfzzjsvq/ffcsstrFmzhqeffppFixYB8O53v5v58+dz4403cv311+cibAFKS0tdhyAikpWioiLXIYiIZEXzluSKcktyRetEf/DMTqnKykrq6uqG/f577rmHRYsWpQtSAPPmzePkk0/mN7/5zUiEKAM44IADXIcgIpKVKVOmuA5BRCQrmrckV5RbkitaJ/qDZ4pS+yKRSPDiiy9yxBFH9Hlu8eLFrFu3TlsHc2jlypWuQxARyYr6DYpIodG8Jbmi3JJc0TrRHzxzfG9fNDQ00NXVxcSJE/s8l3ps8+bNzJ07t9/3d3V10dXVlf5xc3MzAC0tLTmI1nva2to0VkNoi7RB9/XJTU1NxDvjbgMa5To6DFAOQFNTI11duuEnV/w61rt376axsdF1GAXFr7mSKY1Pfvh5nF3PW34ee69znVuyb2KxGIlEgpaWFsLhsOtwetE6sbClfu2sHXy+H5VFqUQiQSQSyei1xcXFGGP26fulmvMVFxf3ea6kpKTXa/pzww03cO211/Z5fOrUqfsUl0h/jv7O0a5DKCh7nMiVHNNYS6aUK4PT+OSHxtkdjb2IiH/s3r2b6urqAZ8flUWpRx99lCVLlmT02lWrVjFv3rx9+n6pBmp77nZK6ezs7PWa/nz1q1/l85//fPrHiUSChoYG6uvr97lg5nUtLS1MnTqVDRs2UFVV5Toc8RDlluSKcktyRbkluaLcklxRbkmuKLcKn7WW3bt3M2nSpEFfNyqLUvPmzeP222/P6LX9HbnLVl1dHcXFxWzZsqXPc6nHBhvI4uLiPrusampq9jkuP6mqqtJkIzmh3JJcUW5Jrii3JFeUW5Iryi3JFeVWYRtsh1TKqCxKTZgwgYsuuihv3y8QCHDwwQfzzDPP9Hlu+fLl7LffflRWVuYtHhERERERERERr/Pl7Xtvv/02r732Wq/Hzj33XFasWNGrMPX666/z8MMPc9555+U7RBERERERERERTxuVO6WG61vf+hYAr7zyCgC/+tWvePzxxwH493//9/TrPvrRj/LPf/6zVxf4z3zmM/z0pz/l9NNP54tf/CLhcJibbrqJ8ePH84UvfCGPPwt/KS4u5uqrr+63ybzIvlBuSa4otyRXlFuSK8otyRXlluSKcss/jB3qfr4CMlhT8T1/mieeeGKfohTAxo0b+bd/+zf+/ve/k0gkOPHEE/mv//ovZs+enbOYRURERERERET8yFNFKRERERERERERKQy+7CklIiIiIiIiIiJuqSglIiIiIiIiIiJ5p6KUjArf/va3McYwf/5816FIAVu2bBnGmH7/e+qpp1yHJx6wcuVKzjzzTOrq6igrK2P+/Pn88Ic/dB2WFLCLLrpowHnLGMOmTZtchygFbM2aNXzgAx9gypQplJWVMW/ePK677jra29tdhyYF7tlnn2Xp0qVUVVVRWVnJO9/5Tp5//nnXYUkBaW1t5eqrr2bp0qXU1dVhjOGOO+7o97WrVq1i6dKlVFRUUFdXx0c+8hF27NiR34AlZzx1+54Upo0bN3L99ddTXl7uOhTxiCuvvJJFixb1ekwXFsi++vvf/84ZZ5zBYYcdxje+8Q0qKipYt24dGzdudB2aFLBPfepTnHLKKb0es9Zy6aWXMmPGDCZPnuwoMil0GzZsYPHixVRXV3P55ZdTV1fHk08+ydVXX82zzz7Lfffd5zpEKVArV67kuOOOY+rUqVx99dUkEgluueUWTjjhBJ5++mnmzp3rOkQpADt37uS6665j2rRpHHrooSxbtqzf123cuJF3vOMdVFdXc/3119Pa2sr3v/99XnrpJZ5++mmKioryG7iMOBWlxLkvfvGLHHXUUcTjcXbu3Ok6HPGA448/nnPPPdd1GOIhLS0tfPSjH+X000/nnnvuIRDQRmMZGUcffTRHH310r8cef/xx2tvb+fCHP+woKvGCX/3qVzQ1NfH4449z0EEHAfDJT36SRCLBL3/5SxobG6mtrXUcpRSib3zjG5SWlvLkk09SX18PwAUXXMCcOXP42te+xu9+9zvHEUohmDhxIlu2bGHChAk888wzff5BOeX666+nra2NZ599lmnTpgGwePFiTj31VO644w4++clP5jNsyQH9rVqcevTRR7nnnnv47//+b9ehiMfs3r2bWCzmOgzxiLvuuott27bx7W9/m0AgQFtbG4lEwnVY4lF33XUXxhg+9KEPuQ5FClhLSwsA48eP7/X4xIkTCQQC2l0gw/bYY49xyimnpAtSkMyrE044gfvvv5/W1laH0UmhKC4uZsKECUO+7ne/+x3vec970gUpgFNOOYU5c+bwm9/8JpchSp6oKCXOxONxrrjiCj7+8Y9z8MEHuw5HPOTiiy+mqqqKkpISlixZwjPPPOM6JClwDz74IFVVVWzatIm5c+dSUVFBVVUVn/70p+ns7HQdnnhINBrlN7/5DccccwwzZsxwHY4UsBNPPBGASy65hOeff54NGzbwf//3f/zP//wPV155pdomyLB1dXVRWlra5/GysjIikQgvv/yyg6jEizZt2sT27ds54ogj+jy3ePFinnvuOQdRyUjT8T1x5tZbb+Wtt97iwQcfdB2KeERRURHnnHMOp512GmPGjOHVV1/l+9//PscffzxPPPEEhx12mOsQpUCtWbOGWCzGWWedxSWXXMINN9zAsmXL+NGPfkRTUxO//vWvXYcoHvHAAw+wa9cuHd2TfbZ06VK++c1vcv311/PHP/4x/fjXv/51vvWtbzmMTArd3Llzeeqpp4jH4wSDQQAikQjLly8H0AUNMmK2bNkCJHfi7W3ixIk0NDTQ1dVFcXFxvkOTEaSilDixa9cu/uM//oNvfOMbjB071nU44hHHHHMMxxxzTPrHZ555Jueeey6HHHIIX/3qV/nb3/7mMDopZK2trbS3t3PppZemb9s7++yziUQi/L//9/+47rrr2H///R1HKV5w1113EQ6HOf/8812HIh4wY8YM3vGOd3DOOedQX1/Pn//8Z66//nomTJjA5Zdf7jo8KVCf+cxn+PSnP80ll1zCVVddRSKR4Fvf+la6gNDR0eE4QvGKVC71V3QqKSlJv0ZFqcKm43vixL//+79TV1fHFVdc4ToU8bjZs2dz1lln8cgjjxCPx12HIwUqdUzhgx/8YK/HUz1/nnzyybzHJN7T2trKfffdx7ve9a5evVpEhuPuu+/mk5/8JD/72c/4xCc+wdlnn81tt93GhRdeyJe//GV27drlOkQpUJdeeilf+9rXuOuuuzjooIM4+OCDWbduHVdddRUAFRUVjiMUr0j9/aurq6vPc6n2Cf0dJZXCoqKU5N2aNWv4yU9+wpVXXsnmzZtZv34969evp7Ozk2g0yvr162loaHAdpnjI1KlTiUQitLW1uQ5FCtSkSZOAvg2Dx40bB0BjY2PeYxLvuffee3XrnoyYW265hcMOO4wpU6b0evzMM8+kvb1dvVhkn3z7299m27ZtPPbYY7z44ousWLEifQHInDlzHEcnXpE6tpfahbenLVu2UFdXp11SHqCilOTdpk2bSCQSXHnllcycOTP93/Lly1m9ejUzZ87kuuuucx2meMgbb7xBSUmJ/uVOhm3hwoVA3z4ZmzdvBtAxZBkRd955JxUVFZx55pmuQxEP2LZtW787hKPRKIBuqJV9Vltby3HHHZe+sOjBBx9kypQpzJs3z3Fk4hWTJ09m7Nix/V5a9PTTT7NgwYL8ByUjTkUpybv58+fzhz/8oc9/Bx10ENOmTeMPf/gDl1xyieswpQDt2LGjz2MvvPACf/zjH3nnO99JIKApT4Yn1d/ntttu6/X4z372M0KhUPqWK5Hh2rFjBw8++CDve9/7KCsrcx2OeMCcOXN47rnnWL16da/Hf/3rXxMIBDjkkEMcRSZe9H//93+sWLGCz33uc/r7loyoc845h/vvv58NGzakH3vooYdYvXo15513nsPIZKQYa611HYQIJK8u3rlzp66RlWE76aSTKC0t5ZhjjmHcuHG8+uqr/OQnPyEcDvPkk09ywAEHuA5RCtgll1zCz3/+c84//3xOOOEEli1bxm9/+1u++tWvcv3117sOTwrczTffzBVXXMHf/vY33vWud7kORzzg0Ucf5aSTTqK+vp7LL7+c+vp67r//fv7617/y8Y9/nJ/+9KeuQ5QC9eijj3Ldddfxzne+k/r6ep566iluv/12Tj31VP70pz8RCukuLcnMzTffTFNTE5s3b+Z//ud/OPvss9O3ZV9xxRVUV1ezYcMGDjvsMGpqavjsZz9La2sr3/ve95gyZQorVqzQ8T0PUFFKRg0VpWRf/fCHP+TOO+9k7dq1tLS0MHbsWE4++WSuvvpqZs+e7To8KXDRaJTrr7+e22+/nc2bNzN9+nQuu+wyPve5z7kOTTzg6KOP5o033mDz5s3pK9ZF9tXTTz/NNddcw3PPPceuXbuYOXMmF154IVdddZUKBzJs69at4zOf+QwrV65k9+7d6bz6/Oc/T1FRkevwpIDMmDGDt956q9/n3nzzTWbMmAHAK6+8wuc//3kef/xxioqKOP3007nxxhv79PqUwqSilIiIiIiIiIiI5J0O/IqIiIiIiIiISN6pKCUiIiIiIiIiInmnopSIiIiIiIiIiOSdilIiIiIiIiIiIpJ3KkqJiIiIiIiIiEjeqSglIiIiIiIiIiJ5p6KUiIiIiIiIiIjknYpSIiIiIiIiIiKSdypKiYiIiIiIiIhI3qkoJSIiIs4ZY7L+78QTTwTgxBNPxBjDsmXLnP4cRsIPfvADjDH87ne/cx1KwVm2bFmvvBiuj3/844RCIV566aWRCUxEREQGFHIdgIiIiMiFF17Y57GtW7fywAMPDPj8vHnzch5XPu3YsYNrrrmGRYsWcc4557gOx7euueYa7rzzTq688koeeeQR1+GIiIh4mopSIiIi4twdd9zR57Fly5ali1L9PZ/yy1/+kvb2dqZNm5aj6PLj2muvpampiWuuucZ1KL42ZcoUPv7xj3PzzTfzxz/+kTPPPNN1SCIiIp6l43siIiJS0KZNm8a8efMoKytzHcqwNTU1cccddzB58mSWLl3qOhzfu+SSSwD47//+b7eBiIiIeJyKUiIiIlLQBuopddFFF2GM4Y477uD111/n/e9/P+PGjaO8vJxFixZx3333pV+7fPlyzjzzTMaOHUtpaSlHH300Dz300IDfs6OjgxtvvJGjjjqKmpoaSkpKmDt3LldddRW7du3K+udw++2309bWxkc+8hECgb5/Pevq6uJ73/seCxcupLKykqKiIiZMmMCiRYu46qqraGhoGLEYV69ezWc+8xnmzp1LWVkZVVVVHHjggXzmM5/h5Zdf7vP61157jYsvvpjp06dTXFxMXV0dJ598Mr/5zW/6/fxrrrkGYwzXXHMNO3bs4LLLLmPq1KkUFRUxdepUrrjiCpqamgaM75e//CWLFi2irKyMuro6li5dymOPPTbg6wEefPBBzjjjDMaPH084HKa2tpb999+fCy64gEcffbTP6xcsWMChhx7KI488wqpVqwb9bBERERk+Hd8TERERT1u5ciWXX345U6ZM4eSTT+att97iySef5H3vex+/+c1vCIVCnH/++cyfP5+TTz6Z1157jaeeeoqlS5fyyCOPcNxxx/X6vM2bN7N06VJeeukl6urqWLRoEZWVlaxcuZLvfe97/Pa3v2XZsmVMnz494xjvvfdeAE455ZQ+zyUSCU4//XQeeughqqqqOP7446mpqWHHjh2sWbOG733ve3zoQx+irq5un2O86667+NjHPkZXVxfTpk3jtNNOI5FI8MYbb3Drrbcybtw45s+fn379n//8Z84991w6OzuZO3cuZ599Ntu3b+ef//wnDz/8MA888AC33XZbvz/nDRs2cPjhhxONRjn22GPp7OzkX//6FzfffDPLly/nX//6F+FwuNd7PvvZz/LDH/6QQCDAcccdx6RJk3jxxRc58cQTueKKK/r9Pr/4xS+4+OKLAVi8eDFLliyho6ODjRs3cvfddzNmzBje8Y539HnfqaeeygsvvMC9997LAQcc0O9ni4iIyD6yIiIiIqPQI488YgE71F9XTjjhBAvYRx55pNfjF154Yfr93/rWt2wikUg/98Mf/tACdsqUKba2ttb+8pe/7PXez33ucxawp5xySq/HE4mEPfbYYy1gL7nkEtvS0pJ+LhqN2i984QsWsEuWLMn459ne3m6LiopsIBDo9Xkp//znPy1gDzvssH6fX7Fihd25c+c+x/jMM8/YcDhsjTH2hz/8oY3H472eX79+vX3mmWfSP966dautrq7ud3xXrFhha2trLWB/8pOf9Pqcq6++Ov3rctFFF9nOzs70c2+//badPHmyBexdd93V633333+/BWx5ebl99NFHez13/fXXpz/zhBNO6PXczJkzLWAfe+yxPmO3bds2u3Llyj6PW2vt73//ewvYk08+ud/nRUREZN+pKCUiIiKj0kgVpRYvXtyrYGJtsjhTV1dnAXveeef1+cydO3dawBYVFdlIJJJ+/K9//asF7IIFC2w0Gu3zvng8bufPn28B+9JLL2X081yxYoUF7LRp0/p9/je/+Y0F7JVXXpnR5w03xve+970WsFdccUVG3+eb3/ymBezChQv7ff773/++Bez+++/f6/FUUWrKlCm2ra2tz/u+853vWMB+7GMf6/X4KaecYgH75S9/ud/vt2DBgn6LUmVlZba6ujqjn9OeXn/9dQvY2trarN8rIiIimVFPKREREfG0d7/73Rhjej0WCoWYOXMmAKeddlqf99TX11NXV0ckEunVf+nPf/4zAOeccw6hUN8uCIFAIH0U7Iknnsgovm3btqW/Z38OP/xwgsEgP//5z/nxj3/Mli1bBv284cQYj8f5xz/+AcAnP/nJjOJO9fC68MIL+30+1Sx8zZo1bN68uc/zJ598cr/N6VNH5TZt2pR+LBaL8fjjjwNwwQUX9Pv9PvrRj/b7+OLFi2lubuajH/0ozz77LIlEYoCfUW+pX4/GxkYikUhG7xEREZHsqCglIiIinjZt2rR+H6+oqBj0+crKSgA6OzvTj73xxhsAfOMb38AY0+9/t9xyCwA7duzIKL7m5mYAqqqq+n1+1qxZ/Nd//RfRaJTLL7+cSZMmMWPGDD74wQ9y55139imYDCfGXbt20dbWBsDcuXMzijtVNEoV9/ZWU1OT7nO1cePGPs8PNO6pcdhz3Hft2pX+8UDfb6DHb7nlFvbbbz9+9atfccQRR1BTU8PJJ5/Mt7/9bd5+++1+37NnHMCgjddFRERk+NToXERERDytv9vssnl+T6ldNscddxyzZs0a9LUHHXRQRp9ZU1MDQEtLy4CvueKKKzj//PP54x//yOOPP87jjz/O3Xffzd13383VV1/NY489xsSJE3MWYy5kM+774oADDuD111/n73//Ow8//DBPPPEEjz32GA8//DDXXXcdt912W7+7r1LFQoDa2tq8xCoiIuI3KkqJiIiIZGjq1KkAnHXWWXzxi18ckc8cN24cQK9jgv0ZP348n/jEJ/jEJz4BwGuvvcbHPvYxnnzySb7yla/wi1/8Ytgx1tfXU1ZWRnt7O6+//nqvG/YGMnnyZF577bX0zqy9NTc309DQkH7tvqivr6e4uJiuri7Wr1/fbzFt/fr1A74/FApx2mmnpY9qtrS0cNNNN3HttdfyqU99ive9732Ul5f3ek/q16O2trbPLYAiIiIyMnR8T0RERCRD7373uwH47W9/i7V2RD7zoIMOoqioiI0bN7J79+6M3zdv3jy+/OUvA/D888/vU4zBYJBTTz0VgJ/+9KcZvefEE08ESBfD9vbzn/8cgP3333+fi1KhUIhjjz0WgDvvvLPf1/zqV7/K+POqqqq45pprqKmpob29ndWrV/d5zcsvvwzAwoULhxGxiIiIZEJFKREREZEMnXXWWSxatIinn36aiy++uN++UY2Njdx6663EYrGMPrO0tJSjjjqKRCLB8uXL+zz/8MMP85e//IVoNNrrcWst999/PwDTp0/f5xi//vWvEwqFuPnmm7nlllv6FLTeeustnn322fSPP/GJT1BVVcXKlSu5/vrre73+ueee41vf+hYAX/rSlzIah6F87nOfA+BHP/pRnyby3/3ud1m5cmWf97S3t3PTTTf1OwaPPfYYTU1NBINBpkyZ0uf51Pc46aSTRiB6ERER6Y+KUiIiIiIZCgQC3HvvvSxYsIBf/OIXzJw5k2OPPZYPfvCDnHPOORx22GGMHTuWT3/60xkXpQDe+973AqRvwNvTiy++yOmnn86YMWNYsmQJH/7whzn77LOZOXMmP/vZz6iurua6667b5xgXLVrEbbfdRjAY5LLLLmPmzJmcd9556ffMnDmTP/3pT+nXjx8/njvvvJOSkhK+/vWvc+CBB/KhD32IU045hcWLF9PQ0MDFF1+cPm64r8444wwuu+wyWltbOf7441myZAkf+tCHmD9/Pl/96lf57Gc/2+c9kUiEL3zhC0yYMIEFCxZw3nnn8aEPfYhjjjmGE044AUgW48aOHdvnvQ8++CCQLPKJiIhIbqgoJSIiIpKFSZMm8dRTT3HrrbeyePFiXn/9de655x4ef/xxAC699FIeeOABSkpKMv7Miy++mPLycv73f/+XeDze67kzzjiDa665hkWLFvHGG2/w+9//nmXLllFdXc1XvvIVXn75ZRYsWDAiMX70ox/l+eef55JLLiEQCPCnP/2Jhx56iEgkwmWXXcb555/f6/Xvec97WLlyJRdeeCGtra3cc889PPvssxx//PHcfffd6SN8I+Xmm2/m5z//OYcddhhPPfUUf/nLX5g4cSIPPfRQurC3p4qKCm699Vbe//7309XVxT/+8Q/uvfdetm/fztlnn81DDz3Etdde2+d9zz33HC+++CJLlizhwAMPHNGfg4iIiPQwdqQaIoiIiIjIsF1++eX8+Mc/5o9//CNnnHGG63B87YorruDmm2/mvvvu48wzz3QdjoiIiGepKCUiIiIyCuzYsYM5c+Ywe/ZsVqxY4Toc39qwYQNz5szhqKOO4pFHHnEdjoiIiKfp+J6IiIjIKDB27FiuueYannnmGe655x7X4fjWtddeSzQa5Qc/+IHrUERERDxPO6VERERERERERCTvtFNKRERERERERETyTkUpERERERERERHJOxWlREREREREREQk71SUEhERERERERGRvFNRSkRERERERERE8k5FKRERERERERERyTsVpUREREREREREJO9UlBIRERERERERkbxTUUpERERERERERPJORSkREREREREREcm7/w+xC4tWm0lvAwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Initial contact detection\n", - "icd = ParaschivIonescuInitialContactDetection(target_sampling_freq_Hz=40)\n", - "icd = icd.detect(\n", - " data=acceleration_data,\n", - " gait_sequences=gait_sequences,\n", - " sampling_freq_Hz=sampling_frequency,\n", - ")\n", - "initial_contacts = icd.initial_contacts_\n", - "\n", - "# Access the first detected gait sequence\n", - "first_gait_sequence = gait_sequences.iloc[0]\n", - "\n", - "# Plot setup\n", - "fig, ax = plt.subplots(figsize=(12, 8))\n", - "colors = cfg_colors[\"raw\"]\n", - "num_samples = len(acceleration_data)\n", - "time_seconds = np.arange(num_samples) / sampling_frequency\n", - "\n", - "# Initial contacts within the first gait sequence\n", - "ic_within_gait = initial_contacts[\n", - " initial_contacts[\"onset\"].between(\n", - " first_gait_sequence[\"onset\"],\n", - " first_gait_sequence[\"onset\"] + first_gait_sequence[\"duration\"],\n", - " )\n", - "]\n", - "\n", - "# Plot raw acceleration data\n", - "for i in range(3):\n", - " ax.plot(\n", - " time_seconds,\n", - " acceleration_data[f\"LowerBack_ACCEL_{chr(120 + i)}\"],\n", - " color=colors[i],\n", - " label=f\"Acc {i + 1}\",\n", - " )\n", - "\n", - "# Plot gait onset and duration\n", - "plt.axvline(first_gait_sequence[\"onset\"], color=\"green\", linestyle=\"-\")\n", - "ax.axvspan(\n", - " first_gait_sequence[\"onset\"],\n", - " first_gait_sequence[\"onset\"] + first_gait_sequence[\"duration\"],\n", - " alpha=0.2,\n", - " color=\"gray\",\n", - " label=\"Gait duration\",\n", - ")\n", - "\n", - "# Plot initial contacts within the first gait sequence\n", - "for ic_time in ic_within_gait[\"onset\"]:\n", - " ax.axvline(ic_time, color=\"blue\", linestyle=\"--\")\n", - "\n", - "# Customize plot\n", - "start_limit = first_gait_sequence[\"onset\"] - 1\n", - "end_limit = first_gait_sequence[\"onset\"] + first_gait_sequence[\"duration\"] + 1\n", - "ax.set_xlim(start_limit, end_limit)\n", - "ax.set_ylim(-1, 1.5)\n", - "ax.set_xlabel(\"Time (seconds)\", fontsize=16)\n", - "ax.set_ylabel(\"Acceleration (g)\", fontsize=16)\n", - "plt.xticks(fontsize=12)\n", - "plt.yticks(fontsize=12)\n", - "ax.grid(True, linestyle='--', linewidth=0.5)\n", - "ax.legend(fontsize=12)\n", - "plt.title(\"Acceleration Data and Gait Events\", fontsize=18)\n", - "ax.legend(\n", - " [\"Acc x\", \"Acc y\", \"Acc z\", \"Gait onset\", \"Gait duration\", \"Initial contacts\"],\n", - " fontsize=20,\n", - " loc=\"upper right\",\n", - ")\n", - "plt.tight_layout()\n", - "\n", - "# Save the figure as a PNG\n", - "plt.savefig('fig_2.png', dpi=300, bbox_inches='tight')\n", - "\n", - "# Save the figure as a PDF\n", - "plt.savefig('fig_2.pdf', format='pdf', dpi=300, bbox_inches='tight')\n", - "\n", - "# Show the plot\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a6e95c9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/paper/paper.md b/paper/paper.md index 3fb7f930..fdd246e0 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -54,7 +54,7 @@ Physical mobility is an essential aspect of health, since impairment of mobility These modules are pivotal because they enable researchers and clinicians to extract meaningful insights from motion data captured in various environments and conditions. These modules are designed to process data from wearable devices, which offer distinct advantages over vision-based approaches. wearable devices such as IMUs provide continuous monitoring capabilities, enabling users to wear them throughout the day in diverse settings without logistical constraints posed by camera-based systems. # State of the field -With the growing availability of digital health data, open-source implementations of relevant algorithms are increasingly becoming available. From the Mobilise-D consortium, the recommended algorithms for assessing real-world gait were released, but these algorithms were developed in MATLAB, that is not free to use [@mobilised:2023]. Likewise, an algorithm for the estimation of gait quality was released, but it is also only available in MATLAB [@gaitqualitycomposite:2016]. Alternatively, open-source, Python packages are available, for example to detect gait and extract gait features from a low back-worn inertial measurement unit (IMU) [@czech:2019], or from two feet-worn IMUs [@kuederle:2024]. NGMT builds forth on these toolboxes by providing a module software package that goes beyond the analysis of merely gait, and extends these analyses by additionally allowing for the analysis of general physical activity and other daily life-relevant movements, such as sit-to-stand and stand-to-sit transitions [@pham:2017] as well as turns [@pham:2018]. +With the growing availability of digital health data, open-source implementations of relevant algorithms are increasingly becoming available. From the Mobilise-D consortium, the recommended algorithms for assessing real-world gait were released, but these algorithms were developed in MATLAB, that is not free to use [@mobilised:2023]. Likewise, an algorithm for the estimation of gait quality was released, but it is also only available in MATLAB [@gaitqualitycomposite:2016]. Alternatively, open-source, Python packages are available, for example to detect gait and extract gait features from a low back-worn inertial measurement unit (IMU) [@czech:2019], or from two feet-worn IMUs [@kuederle:2024]. These advancements facilitate broader accessibility and usability across research and clinical applications. Additionally, innovative approaches like Mobile GaitLab focus on video input for predicting key gait parameters such as walking speed, cadence, knee flexion angle at maximum extension, and the Gait Deviation Index, leveraging open-source principles and designed to be accessible to non-computer science specialists [@kidzinski:2020; @mobile-gaitlab:2020]. Moreover, tools such as Sit2Stand and Sports2D contribute to this landscape by offering user-friendly platforms for assessing physical function through automated analysis of movements like sit-to-stand transitions and joint angles from smartphone videos (Sports2D) [@Boswell:2023; @Pagnon:2023]. NGMT builds forth on these toolboxes by providing a module software package that goes beyond the analysis of merely gait, and extends these analyses by additionally allowing for the analysis of general physical activity and other daily life-relevant movements, such as sit-to-stand and stand-to-sit transitions [@pham:2017] as well as turns [@pham:2018]. # Provided Functionality NGMT offers a comprehensive suite of algorithms for motion data processing in neuroscience and biomechanics. Currently, the toolbox includes implementations for gait sequence detection (GSD) and initial contact detection (ICD), whereas algorithms for postural transition analysis [@pham:2017] and turns [@pham:2018] are under current development. NGMT is built on principles from the Brain Imaging Data Structure (BIDS) [@gorgolewski:2016] and for the motion analysis data are organized similar to the Motion-BIDS specifications [@jeung:2023]. diff --git a/paper/references.bib b/paper/references.bib index 96e8cadf..56c262f2 100644 --- a/paper/references.bib +++ b/paper/references.bib @@ -1,3 +1,15 @@ +@article{Boswell:2023, + author = {Boswell, Melissa A and Kidzi{\'n}ski, {\L}ukasz and Hicks, Jennifer L and Uhlrich, Scott D and Falisse, Antoine and Delp, Scott L}, + title = {Smartphone videos of the sit-to-stand test predict osteoarthritis and health outcomes in a nationwide study}, + journal = {npj Digital Medicine}, + volume = {6}, + number = {1}, + pages = {32}, + year = {2023}, + publisher={Nature Publishing Group UK London}, + doi= {10.1038/s41746-023-00775-1} +} + @article{buckley:2019, author = {Buckley, Christopher and Alcock, Lisa and McArdle, Ríona and Rehman, Rana Zia Ur and Del Din, Silvia and Mazzà, Claudia and Yarnall, Alison J. and Rochester, Lynn}, title = {The {Role} of {Movement} {Analysis} in {Diagnosing} and {Monitoring} {Neurodegenerative} {Conditions}: {Insights} from {Gait} and {Postural} {Control}}, @@ -72,7 +84,19 @@ @article{jeung:2023 doi={10.31234/osf.io/w6z79} } -@ARTICLE{kuederle:2024, +@article{kidzinski:2020, + author = {Kidzi{\'n}ski, {\L}ukasz and Yang, Bryan and Hicks, Jennifer L and Rajagopal, Apoorva and Delp, Scott L and Schwartz, Michael H}, + title = {Deep neural networks enable quantitative movement analysis using single-camera videos}, + journal = {Nature communications}, + volume = {11}, + number = {1}, + pages = {4054}, + year = {2020}, + publisher={Nature Publishing Group UK London}, + doi = {10.1038/s41467-020-17807-z} +} + +@article{kuederle:2024, author = {Küderle, Arne and Ullrich, Martin and Roth, Nils and Ollenschläger, Malte and Ibrahim, Alzhraa A. and Moradi, Hamid and Richer, Robert and Seifer, Ann-Kristin and Zürl, Matthias and Sîmpetru, Raul C. and Herzer, Liv and Prossel, Dominik and Kluge, Felix and Eskofier, Bjoern M.}, title={Gaitmap—An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking}, journal = {IEEE Open Journal of Engineering in Medicine and Biology}, @@ -193,6 +217,15 @@ @misc{gaitqualitycomposite:2016 url = {https://github.com/KimvanS/EstimateGaitQualityComposite} } +@misc{mobile-gaitlab:2020, + author = {Kidzi{\'n}ski, {\L}ukasz and Yang, Bryan and Hicks, Jennifer L and Rajagopal, Apoorva and Delp, Scott L and Schwartz, Michael H}, + title = {mobile-gaitlab}, + year = {2020}, + publisher = {GitHub}, + journal = {GitHub repository}, + url = {https://github.com/stanfordnmbl/mobile-gaitlab} +} + @misc{mobilised:2023, author = {Micó-Amigo, M. Encarna and Bonci, Tecla and Paraschiv-Ionescu, Anisoara and Ullrich, Martin and Kirk, Cameron and Soltani, Abolfazl and Küderle, Arne and Gazit, Eran and Salis, Francesca and Alcock, Lisa and Aminian, Kamiar and Becker, Clemens and Bertuletti, Stefano and Brown, Philip and Buckley, Ellen and Cantu, Alma and Carsin, Anne-Elie and Caruso, Marco and Caulfield, Brian and Cereatti, Andrea and Chiari, Lorenzo and D’Ascanio, Ilaria and Eskofier, Bjoern and Fernstad, Sara and Froehlich, Marcel and Garcia-Aymerich, Judith and Hansen, Clint and Hausdorff, Jeffrey M. and Hiden, Hugo and Hume, Emily and Keogh, Alison and Kluge, Felix and Koch, Sarah and Maetzler, Walter and Megaritis, Dimitrios and Mueller, Arne and Niessen, Martijn and Palmerini, Luca and Schwickert, Lars and Scott, Kirsty and Sharrack, Basil and Sillén, Henrik and Singleton, David and Vereijken, Beatrix and Vogiatzis, Ioannis and Yarnall, Alison J. and Rochester, Lynn and Mazzà, Claudia and Del Din, Silvia and {for the Mobilise-D consortium}}, title = {Mobilise-D Technical Validation Study Recommended Algorithms}, @@ -202,3 +235,13 @@ @misc{mobilised:2023 url = {https://github.com/mobilise-d/Mobilise-D-TVS-Recommended-Algorithms} } +@misc{Pagnon:2023, + author = {Pagnon, D.}, + title = {Sports2D - Angles from video}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + url = {https://github.com/davidpagnon/Sports2D}, + doi= {10.5281/zenodo.7903963} +} + From 32d62a3fe5792e3ccea451be69f2f8a1442defa0 Mon Sep 17 00:00:00 2001 From: masoudabedinifar <140504378+masoudabedinifar@users.noreply.github.com> Date: Wed, 17 Jul 2024 10:08:04 +0200 Subject: [PATCH 07/22] name changed to KMAT --- paper/paper.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index fdd246e0..93033214 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -1,5 +1,5 @@ --- -title: "NGMT: NeuroGeriatrics Motion Toolbox - An Open-Source Python Toolbox for Analyzing Neurological Motion Data from Various Recording Modalities" +title: "KMAT: Kiel Motion Analysis Toolbox - An Open-Source Python Toolbox for Analyzing Neurological Motion Data from Various Recording Modalities" tags: - Python - Motion capture @@ -38,10 +38,10 @@ output:
# Summary -The NeuroGeriatrics Motion Toolbox (NGMT) is an open-source Python-based toolbox designed for processing human motion data, following open-science practices. NGMT offers a range of algorithms for the processing of motion data in neuroscience and biomechanics and currently includes implementations for gait sequence detection, initial contact detection, physical activity monitoring, sit to stand and stand to sit detection algorithms. These algorithms aid in identifying patterns in human motion data on different time scales. The NGMT is versatile in accepting motion data from various recording modalities, including IMUs that provide acceleration data from specific body locations such as the pelvis or wrist. This flexibility allows researchers to analyze data captured using different hardware setups, ensuring broad applicability across studies. Some of the toolbox algorithms have been developed and validated in clinical cohorts, allowing extracted patters to be used in a clinical context. The modular design of NGMT allows the toolbox to be easily extended to incorporate relevant algorithms which will be developed in the research community. The toolbox is designed to be user-friendly and is accompanied by a comprehensive documentation and practical examples, while the underlying data structures build on the Motion BIDS specification [@jeung:2023]. The NGMT toolbox is intended to be used by researchers and clinicians to analyze human motion data from various recording modalities and to promote the utilization of open-source software in the field of human motion analysis. +The Kiel Motion Analysis Toolbox (KMAT) is an open-source Python-based toolbox designed for processing human motion data, following open-science practices. KMAT offers a range of algorithms for the processing of motion data in neuroscience and biomechanics and currently includes implementations for gait sequence detection, initial contact detection, physical activity monitoring, sit to stand and stand to sit detection algorithms. These algorithms aid in identifying patterns in human motion data on different time scales. The KMAT is versatile in accepting motion data from various recording modalities, including IMUs that provide acceleration data from specific body locations such as the pelvis or wrist. This flexibility allows researchers to analyze data captured using different hardware setups, ensuring broad applicability across studies. Some of the toolbox algorithms have been developed and validated in clinical cohorts, allowing extracted patters to be used in a clinical context. The modular design of KMAT allows the toolbox to be easily extended to incorporate relevant algorithms which will be developed in the research community. The toolbox is designed to be user-friendly and is accompanied by a comprehensive documentation and practical examples, while the underlying data structures build on the Motion BIDS specification [@jeung:2023]. The KMAT toolbox is intended to be used by researchers and clinicians to analyze human motion data from various recording modalities and to promote the utilization of open-source software in the field of human motion analysis. # Statement of need -Physical mobility is an essential aspect of health, since impairment of mobility is associated with reduced quality of life, falls, hospitalization, mortality, and other adverse events in many chronic conditions. Traditional mobility measures include patient-reported outcomes, objective clinical assessments, and subjective clinical assessments. These measures are associated with the perception and capacity aspects of health that frequently fail to show any relevant effect on daily function at an individual level [@maetzler:2021].To complement both patient-reported (perception) and clinical (capacity) assessment approaches, digital health technology (DHT) introduces a new paradigm for assessing daily function through wearable devices, providing objective insights to an individual's functional performance in everyday life activities [@warmerdam:2020; @fasano:2020; @maetzler:2021; @hansen:2018; @buckley:2019; @celik:2021]. DHT allows an objective impression of how patients function in everyday life and their ability to routinely perform everyday activities [@hansen:2018; @buckley:2019; @celik:2021]. Nonetheless, due to several persisting challenges in this field, current tools and techniques are still in their infancy [@micoamigo:2023]. Many studies often used proprietary software to clinically relevant features of mobility. The development of easy-to-use and open-source software is imperative for transparent features extraction in research and clinical settings. The NeuroGeriatrics Motion Toolbox (NGMT) addresses this gap by providing software for human mobility analysis, to be used by motion researchers and clinicians, while promoting open-source practices. The conceptual framework builds on FAIR data principles to encourage the use of open source software as well as facilitate data sharing and reproducibility in the field of human motion analysis. The NGMT comprises several modules, each serving distinct purposes in human motion analysis: +Physical mobility is an essential aspect of health, since impairment of mobility is associated with reduced quality of life, falls, hospitalization, mortality, and other adverse events in many chronic conditions. Traditional mobility measures include patient-reported outcomes, objective clinical assessments, and subjective clinical assessments. These measures are associated with the perception and capacity aspects of health that frequently fail to show any relevant effect on daily function at an individual level [@maetzler:2021].To complement both patient-reported (perception) and clinical (capacity) assessment approaches, digital health technology (DHT) introduces a new paradigm for assessing daily function through wearable devices, providing objective insights to an individual's functional performance in everyday life activities [@warmerdam:2020; @fasano:2020; @maetzler:2021; @hansen:2018; @buckley:2019; @celik:2021]. DHT allows an objective impression of how patients function in everyday life and their ability to routinely perform everyday activities [@hansen:2018; @buckley:2019; @celik:2021]. Nonetheless, due to several persisting challenges in this field, current tools and techniques are still in their infancy [@micoamigo:2023]. Many studies often used proprietary software to clinically relevant features of mobility. The development of easy-to-use and open-source software is imperative for transparent features extraction in research and clinical settings. The Kiel Motion Analysis Toolbox (KMAT) addresses this gap by providing software for human mobility analysis, to be used by motion researchers and clinicians, while promoting open-source practices. The conceptual framework builds on FAIR data principles to encourage the use of open source software as well as facilitate data sharing and reproducibility in the field of human motion analysis. The KMAT comprises several modules, each serving distinct purposes in human motion analysis: 1. Gait Sequence Detection (GSD): Identifies walking bouts to analyze gait patterns and abnormalities, crucial for neurological and biomechanical assessments. @@ -54,13 +54,13 @@ Physical mobility is an essential aspect of health, since impairment of mobility These modules are pivotal because they enable researchers and clinicians to extract meaningful insights from motion data captured in various environments and conditions. These modules are designed to process data from wearable devices, which offer distinct advantages over vision-based approaches. wearable devices such as IMUs provide continuous monitoring capabilities, enabling users to wear them throughout the day in diverse settings without logistical constraints posed by camera-based systems. # State of the field -With the growing availability of digital health data, open-source implementations of relevant algorithms are increasingly becoming available. From the Mobilise-D consortium, the recommended algorithms for assessing real-world gait were released, but these algorithms were developed in MATLAB, that is not free to use [@mobilised:2023]. Likewise, an algorithm for the estimation of gait quality was released, but it is also only available in MATLAB [@gaitqualitycomposite:2016]. Alternatively, open-source, Python packages are available, for example to detect gait and extract gait features from a low back-worn inertial measurement unit (IMU) [@czech:2019], or from two feet-worn IMUs [@kuederle:2024]. These advancements facilitate broader accessibility and usability across research and clinical applications. Additionally, innovative approaches like Mobile GaitLab focus on video input for predicting key gait parameters such as walking speed, cadence, knee flexion angle at maximum extension, and the Gait Deviation Index, leveraging open-source principles and designed to be accessible to non-computer science specialists [@kidzinski:2020; @mobile-gaitlab:2020]. Moreover, tools such as Sit2Stand and Sports2D contribute to this landscape by offering user-friendly platforms for assessing physical function through automated analysis of movements like sit-to-stand transitions and joint angles from smartphone videos (Sports2D) [@Boswell:2023; @Pagnon:2023]. NGMT builds forth on these toolboxes by providing a module software package that goes beyond the analysis of merely gait, and extends these analyses by additionally allowing for the analysis of general physical activity and other daily life-relevant movements, such as sit-to-stand and stand-to-sit transitions [@pham:2017] as well as turns [@pham:2018]. +With the growing availability of digital health data, open-source implementations of relevant algorithms are increasingly becoming available. From the Mobilise-D consortium, the recommended algorithms for assessing real-world gait were released, but these algorithms were developed in MATLAB, that is not free to use [@mobilised:2023]. Likewise, an algorithm for the estimation of gait quality was released, but it is also only available in MATLAB [@gaitqualitycomposite:2016]. Alternatively, open-source, Python packages are available, for example to detect gait and extract gait features from a low back-worn inertial measurement unit (IMU) [@czech:2019], or from two feet-worn IMUs [@kuederle:2024]. These advancements facilitate broader accessibility and usability across research and clinical applications. Additionally, innovative approaches like Mobile GaitLab focus on video input for predicting key gait parameters such as walking speed, cadence, knee flexion angle at maximum extension, and the Gait Deviation Index, leveraging open-source principles and designed to be accessible to non-computer science specialists [@kidzinski:2020; @mobile-gaitlab:2020]. Moreover, tools such as Sit2Stand and Sports2D contribute to this landscape by offering user-friendly platforms for assessing physical function through automated analysis of movements like sit-to-stand transitions and joint angles from smartphone videos (Sports2D) [@Boswell:2023; @Pagnon:2023]. KMAT builds forth on these toolboxes by providing a module software package that goes beyond the analysis of merely gait, and extends these analyses by additionally allowing for the analysis of general physical activity and other daily life-relevant movements, such as sit-to-stand and stand-to-sit transitions [@pham:2017] as well as turns [@pham:2018]. # Provided Functionality -NGMT offers a comprehensive suite of algorithms for motion data processing in neuroscience and biomechanics. Currently, the toolbox includes implementations for gait sequence detection (GSD) and initial contact detection (ICD), whereas algorithms for postural transition analysis [@pham:2017] and turns [@pham:2018] are under current development. NGMT is built on principles from the Brain Imaging Data Structure (BIDS) [@gorgolewski:2016] and for the motion analysis data are organized similar to the Motion-BIDS specifications [@jeung:2023]. +KMAT offers a comprehensive suite of algorithms for motion data processing in neuroscience and biomechanics. Currently, the toolbox includes implementations for gait sequence detection (GSD) and initial contact detection (ICD), whereas algorithms for postural transition analysis [@pham:2017] and turns [@pham:2018] are under current development. KMAT is built on principles from the Brain Imaging Data Structure (BIDS) [@gorgolewski:2016] and for the motion analysis data are organized similar to the Motion-BIDS specifications [@jeung:2023]. ## Dataclass -Supporting the data curation as specified in BIDS, data are organized in recordings, where recordings can be simultaneously collected with different tracking systems (e.g., an camera-based optical motion capture system and a set of IMUs). A tracking system is defined as a group of motion channels that share hardware properties (the recording device) and software properties (the recording duration and number of samples). Loading of a recording returns a `NGMTRecording` object, that holds both `data` and `channels`. Here, `data` are the actual time series data, where `channels` provide information (meta-data) on the time series type, component, the sampling frequency, and the units in which the time series (channel) are recorded. +Supporting the data curation as specified in BIDS, data are organized in recordings, where recordings can be simultaneously collected with different tracking systems (e.g., an camera-based optical motion capture system and a set of IMUs). A tracking system is defined as a group of motion channels that share hardware properties (the recording device) and software properties (the recording duration and number of samples). Loading of a recording returns a `KMATRecording` object, that holds both `data` and `channels`. Here, `data` are the actual time series data, where `channels` provide information (meta-data) on the time series type, component, the sampling frequency, and the units in which the time series (channel) are recorded. ## Modules The data can be passed to algorithms that are organized in different modules, such as GSD and ICD. For example, the accelerometer data from a lower back-worn IMU can be passed to the gait sequence detection algorithm [@paraschiv:2019;@paraschiv:2020]. Next, the data can be passed to the initial contact detection algorithm [@paraschiv:2019] to returns the timings of initial contacts within each gait sequence (Figure [1](example_data.png)). @@ -71,13 +71,13 @@ The data can be passed to algorithms that are organized in different modules, su
# Installation and usage -The NGMT package is implemented in Python (>=3.10) and is freely available under a Non-Profit Open Software License version 3.0. The stable version of the package can be installed from PyPI.org using `pip install ngmt`. Users and developers can also install the toolbox from source from GitHub. The documentation of the toolbox provides detailed instructions on [installation](https://neurogeriatricskiel.github.io/NGMT/#installation), [conceptual framework](https://neurogeriatricskiel.github.io/NGMT/#data-classes-conceptual-framework) and [tutorial notebooks](https://neurogeriatricskiel.github.io/NGMT/examples/) for basic usage and specific algorithms. +The KMAT package is implemented in Python (>=3.10) and is freely available under a Non-Profit Open Software License version 3.0. The stable version of the package can be installed from PyPI.org using `pip install kmat`. Users and developers can also install the toolbox from source from GitHub. The documentation of the toolbox provides detailed instructions on [installation](https://neurogeriatricskiel.github.io/KMAT/#installation), [conceptual framework](https://neurogeriatricskiel.github.io/KMAT/#data-classes-conceptual-framework) and [tutorial notebooks](https://neurogeriatricskiel.github.io/KMAT/examples/) for basic usage and specific algorithms. # How to contribute -NGMT is a community effort, and any contribution is welcomed. The project is hosted on [https://github.com/neurogeriatricskiel/NGMT](https://github.com/neurogeriatricskiel/NGMT). In case you want to add new algorithms, it is suggested to fork the project and, after finalizing the changes, to [create a pull request from a fork](https://docs.github.com/de/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork). +KMAT is a community effort, and any contribution is welcomed. The project is hosted on [https://github.com/neurogeriatricskiel/KMAT](https://github.com/neurogeriatricskiel/KMAT). In case you want to add new algorithms, it is suggested to fork the project and, after finalizing the changes, to [create a pull request from a fork](https://docs.github.com/de/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork). # Acknowledgements -The authors would like to thank every person who provided data which has been used in the development and validation of the algorithms in the NGMT toolbox. +The authors would like to thank every person who provided data which has been used in the development and validation of the algorithms in the KMAT toolbox. The data collection have been performed in accordance with the Declaration of Helsinki. The authors declare no competing interests. # References From 2a11cda3ac0e2ec15585a45c4931ae6f623cac42 Mon Sep 17 00:00:00 2001 From: masoudabedinifar <140504378+masoudabedinifar@users.noreply.github.com> Date: Wed, 17 Jul 2024 10:36:54 +0200 Subject: [PATCH 08/22] name changed to Kiel Motion Analysis Toolbox (KMAT) --- .github/workflows/test-and-lint.yml | 2 +- .gitignore | 4 +- CHANGELOG.md | 20 ++++----- README.md | 36 +++++++-------- docs/contributing.md | 8 ++-- docs/dataclass.md | 10 ++--- docs/datasets/keepcontrol.md | 2 +- docs/datasets/mobilised.md | 2 +- docs/examples/00_tutorial_basics.md | 10 ++--- ...3_tutorial_physical_activity_monitoring.md | 12 ++--- ...ial_sit_to_stand_stand_to_sit_detection.md | 10 ++--- docs/examples/basic_00_intro_ngmt.md | 8 ++-- docs/examples/basic_01_load_Data.md | 20 ++++----- docs/examples/basic_02_events.md | 8 ++-- docs/examples/index.md | 10 ++--- docs/examples/modules_01_gsd.md | 12 ++--- docs/examples/modules_02_icd.md | 18 ++++---- docs/index.md | 34 +++++++------- ...utorial_physical_activity_monitoring.ipynb | 14 +++--- ...ysical_activity_monitoring_with_load.ipynb | 18 ++++---- examples/basic_00_intro_ngmt.ipynb | 12 ++--- examples/basic_01_load_Data.ipynb | 24 +++++----- examples/basic_02_events.ipynb | 10 ++--- examples/modules_01_gsd.ipynb | 14 +++--- examples/modules_02_icd.ipynb | 20 ++++----- examples/modules_03_pam.ipynb | 18 ++++---- examples/modules_04_ssd.ipynb | 14 +++--- kmat/__init__.py | 7 +++ {ngmt => kmat}/config.py | 0 {ngmt => kmat}/datasets/fairpark.py | 8 ++-- {ngmt => kmat}/datasets/keepcontrol.py | 8 ++-- {ngmt => kmat}/datasets/mobilised.py | 8 ++-- {ngmt => kmat}/modules/__init__.py | 0 {ngmt => kmat}/modules/gsd/__init__.py | 0 {ngmt => kmat}/modules/gsd/_paraschiv.py | 4 +- {ngmt => kmat}/modules/icd/__init__.py | 0 {ngmt => kmat}/modules/icd/_paraschiv.py | 4 +- {ngmt => kmat}/modules/pam/__init__.py | 0 {ngmt => kmat}/modules/pam/_pam.py | 4 +- {ngmt => kmat}/modules/ssd/__init__.py | 0 {ngmt => kmat}/modules/ssd/_pham.py | 4 +- .../test/example_lower_back_acc.csv | 0 {ngmt => kmat}/test/scripts/test.py | 6 +-- {ngmt => kmat}/test/test_calc.py | 42 +++++++++--------- {ngmt => kmat}/test/test_modules.py | 10 ++--- {ngmt => kmat}/utils/FIR_2_3Hz_40.mat | Bin {ngmt => kmat}/utils/file_io.py | 0 {ngmt => kmat}/utils/importers.py | 8 ++-- {ngmt => kmat}/utils/matlab_loader.py | 0 {ngmt => kmat}/utils/ngmt_dataclass.py | 4 +- .../utils/orientation_estimation/__init__.py | 0 .../utils/orientation_estimation/_madgwick.py | 0 {ngmt => kmat}/utils/preprocessing.py | 6 +-- {ngmt => kmat}/utils/quaternion.py | 0 mkdocs.yml | 10 ++--- ngmt/__init__.py | 7 --- .../__pycache__/keepcontrol.cpython-311.pyc | Bin 3267 -> 0 bytes .../__pycache__/mobilised.cpython-311.pyc | Bin 1954 -> 0 bytes .../__pycache__/data_utils.cpython-311.pyc | Bin 1840 -> 0 bytes .../__pycache__/matlab_loader.cpython-311.pyc | Bin 11880 -> 0 bytes .../__pycache__/preprocessing.cpython-311.pyc | Bin 28092 -> 0 bytes paper/make_figures.py | 10 ++--- paper/paper.html | 16 +++---- pyproject.toml | 12 ++--- 64 files changed, 274 insertions(+), 274 deletions(-) create mode 100644 kmat/__init__.py rename {ngmt => kmat}/config.py (100%) rename {ngmt => kmat}/datasets/fairpark.py (95%) rename {ngmt => kmat}/datasets/keepcontrol.py (93%) rename {ngmt => kmat}/datasets/mobilised.py (96%) rename {ngmt => kmat}/modules/__init__.py (100%) rename {ngmt => kmat}/modules/gsd/__init__.py (100%) rename {ngmt => kmat}/modules/gsd/_paraschiv.py (99%) rename {ngmt => kmat}/modules/icd/__init__.py (100%) rename {ngmt => kmat}/modules/icd/_paraschiv.py (99%) rename {ngmt => kmat}/modules/pam/__init__.py (100%) rename {ngmt => kmat}/modules/pam/_pam.py (99%) rename {ngmt => kmat}/modules/ssd/__init__.py (100%) rename {ngmt => kmat}/modules/ssd/_pham.py (99%) rename {ngmt => kmat}/test/example_lower_back_acc.csv (100%) rename {ngmt => kmat}/test/scripts/test.py (95%) rename {ngmt => kmat}/test/test_calc.py (98%) rename {ngmt => kmat}/test/test_modules.py (98%) rename {ngmt => kmat}/utils/FIR_2_3Hz_40.mat (100%) rename {ngmt => kmat}/utils/file_io.py (100%) rename {ngmt => kmat}/utils/importers.py (96%) rename {ngmt => kmat}/utils/matlab_loader.py (100%) rename {ngmt => kmat}/utils/ngmt_dataclass.py (99%) rename {ngmt => kmat}/utils/orientation_estimation/__init__.py (100%) rename {ngmt => kmat}/utils/orientation_estimation/_madgwick.py (100%) rename {ngmt => kmat}/utils/preprocessing.py (99%) rename {ngmt => kmat}/utils/quaternion.py (100%) delete mode 100644 ngmt/__init__.py delete mode 100644 ngmt/datasets/__pycache__/keepcontrol.cpython-311.pyc delete mode 100644 ngmt/datasets/__pycache__/mobilised.cpython-311.pyc delete mode 100644 ngmt/utils/__pycache__/data_utils.cpython-311.pyc delete mode 100644 ngmt/utils/__pycache__/matlab_loader.cpython-311.pyc delete mode 100644 ngmt/utils/__pycache__/preprocessing.cpython-311.pyc diff --git a/.github/workflows/test-and-lint.yml b/.github/workflows/test-and-lint.yml index 93619de2..df7c46c1 100644 --- a/.github/workflows/test-and-lint.yml +++ b/.github/workflows/test-and-lint.yml @@ -32,7 +32,7 @@ jobs: poetry install - name: Testing with coverage run: | - poetry run pytest ngmt/test/ --cov=ngmt --cov-report=xml + poetry run pytest kmat/test/ --cov=kmat --cov-report=xml - name: Upload coverage to Codecov uses: codecov/codecov-action@v2 env: diff --git a/.gitignore b/.gitignore index 587b52cb..b0fb2561 100644 --- a/.gitignore +++ b/.gitignore @@ -1,12 +1,12 @@ # See: https://git-scm.com/docs/gitignore __pycache__/ projects/ -/ngmt.egg-info +/kmat.egg-info my_messy_code/mytestconde.py /my_messy_code/* -ngmt/examples_gait_sqeuence.py +kmat/examples_gait_sqeuence.py examples/data diff --git a/CHANGELOG.md b/CHANGELOG.md index 5a4e58b1..ba6c6cb7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,35 +6,35 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [0.0.4] -Forth release of NGMT for for JOSS publication. +Forth release of KAMT for for JOSS publication. ### Fixed -- Gait sequence detection with datetime [[#61]](https://github.com/neurogeriatricskiel/NGMT/pull/61) +- Gait sequence detection with datetime [[#61]](https://github.com/neurogeriatricskiel/KMAT/pull/61) ### Changed -- Reworked documentation [[#60]](https://github.com/neurogeriatricskiel/NGMT/pull/60) +- Reworked documentation [[#60]](https://github.com/neurogeriatricskiel/KMAT/pull/60) ## [0.0.3] - 2024-02-27 -Third unofficial release of NGMT for testing purposes. +Third unofficial release of KMAT for testing purposes. ### Added -- Pyarrow as dependency [[ADD]](https://github.com/neurogeriatricskiel/NGMT/commit/22e401a5519cc9adde37b5c752a361a07d8166ac) -- Testing coverage [[ADD]](https://github.com/neurogeriatricskiel/NGMT/commit/f6a919100e7a9d7319a4af77592a78bd6949bb69) +- Pyarrow as dependency [[ADD]](https://github.com/neurogeriatricskiel/KMAT/commit/22e401a5519cc9adde37b5c752a361a07d8166ac) +- Testing coverage [[ADD]](https://github.com/neurogeriatricskiel/KMAT/commit/f6a919100e7a9d7319a4af77592a78bd6949bb69) ### Fixed -- Existing algorithms adapted to new dataclass structure [[FIX]](https://github.com/neurogeriatricskiel/NGMT/commit/3adf7756d9998b36454dccc86d9e2283200d72ed) +- Existing algorithms adapted to new dataclass structure [[FIX]](https://github.com/neurogeriatricskiel/KMAT/commit/3adf7756d9998b36454dccc86d9e2283200d72ed) ## [0.0.2] - 2024-01-22 -Second unofficial release of NGMT for testing purposes. +Second unofficial release of KMAT for testing purposes. ### Added -- Physical acitivity monitoring algorithm [[#29]](https://github.com/neurogeriatricskiel/NGMT/commit/a8d9067cde00f0c9a0dba8b7fc623ba4eeb32d0a) +- Physical acitivity monitoring algorithm [[#29]](https://github.com/neurogeriatricskiel/KMAT/commit/a8d9067cde00f0c9a0dba8b7fc623ba4eeb32d0a) ## [0.0.1] - 2023-11-21 -This is the first unofficial release of NGMT. +This is the first unofficial release of KMAT. Therefore, we do not have a proper changelog for this release. ### Added diff --git a/README.md b/README.md index 4947ee72..a4559c14 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,15 @@ -[![codecov](https://codecov.io/gh/neurogeriatricskiel/NGMT/graph/badge.svg?token=L578RHZ699)](https://codecov.io/gh/neurogeriatricskiel/NGMT) -[![build docs](https://github.com/neurogeriatricskiel/NGMT/actions/workflows/mkdocs.yml/badge.svg)](https://github.com/neurogeriatricskiel/NGMT/actions/workflows/mkdocs.yml) +[![codecov](https://codecov.io/gh/neurogeriatricskiel/KMAT/graph/badge.svg?token=L578RHZ699)](https://codecov.io/gh/neurogeriatricskiel/KMAT) +[![build docs](https://github.com/neurogeriatricskiel/KMAT/actions/workflows/mkdocs.yml/badge.svg)](https://github.com/neurogeriatricskiel/KMAT/actions/workflows/mkdocs.yml) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) -![GitHub issues](https://img.shields.io/github/issues-raw/neurogeriatricskiel/NGMT) -![GitHub contributors](https://img.shields.io/github/contributors/neurogeriatricskiel/NGMT) -[![lint-and-test](https://github.com/neurogeriatricskiel/NGMT/actions/workflows/test-and-lint.yml/badge.svg)](https://github.com/neurogeriatricskiel/NGMT/actions/workflows/test-and-lint.yml) -![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ngmt) +![GitHub issues](https://img.shields.io/github/issues-raw/neurogeriatricskiel/KMAT) +![GitHub contributors](https://img.shields.io/github/contributors/neurogeriatricskiel/KMAT) +[![lint-and-test](https://github.com/neurogeriatricskiel/KMAT/actions/workflows/test-and-lint.yml/badge.svg)](https://github.com/neurogeriatricskiel/KMAT/actions/workflows/test-and-lint.yml) +![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kmat) -![NGMTLogo](ngmt_logo_transBG.png) +![KMATLogo](kmat_logo_transBG.png) -Welcome to the NeuroGeriatricsMotionToolbox (NGMT). We are a Python based toolbox for processing motion data. +Welcome to the Kiel Motion Analysis Toolbox (KMAT). We are a Python based toolbox for processing motion data. The toolbox is aimed at motion researchers who want to use Python-based open-source software to process their data. We have implemented validated algorithms in modules to process motion data, as shown in the table below: @@ -26,21 +26,21 @@ The idea is that various motion data can be loaded into our dedicated dataclass ## Data classes ### Data classes: conceptual framework -Motion data is recorded with many different systems and modalities, each with their own proprietary data format. NGMT deals with this by organizing both data and metadata in a [BIDS-like format](https://bids-specification.readthedocs.io/en/stable/modality-specific-files/motion.html). The BIDS format suggests that [motion recording data](https://bids-specification.readthedocs.io/en/stable/modality-specific-files/motion.html#motion-recording-data) from a single tracking system is organized in a single `*_tracksys-
" ] @@ -297,13 +297,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "That's it for this tutorial. You have learned how to load data and channel information into an `NGMTRecording` object and how to add Recording specific information to the `NGMTRecording` object. " + "That's it for this tutorial. You have learned how to load data and channel information into an `KMATRecording` object and how to add Recording specific information to the `KMATRecording` object. " ] } ], "metadata": { "kernelspec": { - "display_name": "ngmt-3JmP5GSQ-py3.10", + "display_name": "kmat-3JmP5GSQ-py3.10", "language": "python", "name": "python3" }, diff --git a/examples/basic_02_events.ipynb b/examples/basic_02_events.ipynb index 0ea91269..1627d48c 100644 --- a/examples/basic_02_events.ipynb +++ b/examples/basic_02_events.ipynb @@ -23,7 +23,7 @@ "metadata": {}, "source": [ "## Import libraries\n", - "The necessary libraries such as numpy, matplotlib.pyplot, dataset (mobilised), Paraschiv-Ionescu gait sequence detection, and Paraschiv-Ionescu initial contact detection algorithms are imported from their corresponding modules. Make sure that you have all the required libraries and modules installed before running this code. You also may need to install the 'ngmt' library and its dependencies if you haven't already." + "The necessary libraries such as numpy, matplotlib.pyplot, dataset (mobilised), Paraschiv-Ionescu gait sequence detection, and Paraschiv-Ionescu initial contact detection algorithms are imported from their corresponding modules. Make sure that you have all the required libraries and modules installed before running this code. You also may need to install the 'kmat' library and its dependencies if you haven't already." ] }, { @@ -32,8 +32,8 @@ "metadata": {}, "outputs": [], "source": [ - "from ngmt.datasets import mobilised\n", - "from ngmt.modules.gsd import ParaschivIonescuGaitSequenceDetection" + "from kmat.datasets import mobilised\n", + "from kmat.modules.gsd import ParaschivIonescuGaitSequenceDetection" ] }, { @@ -179,7 +179,7 @@ "metadata": {}, "source": [ "Add some information about the recording first which is necessary for the BIDS file name convention.\n", - "NGMT has some implemented check on the information to make sure that the file name is in the correct format." + "KMAT has some implemented check on the information to make sure that the file name is in the correct format." ] }, { @@ -229,7 +229,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv_ngmt", + "display_name": "venv_kmat", "language": "python", "name": "python3" }, diff --git a/examples/modules_01_gsd.ipynb b/examples/modules_01_gsd.ipynb index 6b02d247..76d8c9e3 100644 --- a/examples/modules_01_gsd.ipynb +++ b/examples/modules_01_gsd.ipynb @@ -26,7 +26,7 @@ "\n", "This example can be referenced by citing the package.\n", "\n", - "The example illustrates how the Paraschiv-Ionescu gait sequence detection algorithm is used to detect gait sequences using body acceleration recorded with a triaxial accelerometer worn or fixed on the lower back. The gait sequence detection algorithm is implemented using [`ngmt.modules.gsd._paraschiv`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/gsd/_paraschiv.py). This algorithm is based on the research of Paraschiv-Ionescu et al ['1'-'2'].\n", + "The example illustrates how the Paraschiv-Ionescu gait sequence detection algorithm is used to detect gait sequences using body acceleration recorded with a triaxial accelerometer worn or fixed on the lower back. The gait sequence detection algorithm is implemented using [`kmat.modules.gsd._paraschiv`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/gsd/_paraschiv.py). This algorithm is based on the research of Paraschiv-Ionescu et al ['1'-'2'].\n", "\n", "The algorithm detects gait sequences based on identified steps. It starts by loading the accelerometer data, which includes three columns corresponding to the acceleration signals across the x, y, and z axes, along with the sampling frequency of the data. To simplify the analysis, the norm of acceleration across the x, y, and z axes is computed. Next, the signal is resampled at a 40 Hz sampling frequency using interpolation. Smoothing is then applied through a Savitzky-Golay filter and a Finite Impulse Response (FIR) low-pass filter to remove noise and drifts from the signal. The continuous wavelet transform is applied to capture gait-related features, followed by additional smoothing using successive Gaussian-weighted filters. The processed data is then analyzed to detect gait sequences.\n", "\n", @@ -45,7 +45,7 @@ "metadata": {}, "source": [ "## Import libraries\n", - "The necessary libraries such as numpy, matplotlib.pyplot, dataset, and Paraschiv-Ionescu gait sequence detection algorithms are imported. Make sure that you have all the required libraries and modules installed before running this code. You also may need to install the 'ngmt' library and its dependencies if you haven't already." + "The necessary libraries such as numpy, matplotlib.pyplot, dataset, and Paraschiv-Ionescu gait sequence detection algorithms are imported. Make sure that you have all the required libraries and modules installed before running this code. You also may need to install the 'kmat' library and its dependencies if you haven't already." ] }, { @@ -59,9 +59,9 @@ "import os\n", "from pathlib import Path\n", "\n", - "from ngmt.datasets import mobilised\n", - "from ngmt.modules.gsd import ParaschivIonescuGaitSequenceDetection\n", - "from ngmt.config import cfg_colors" + "from kmat.datasets import mobilised\n", + "from kmat.modules.gsd import ParaschivIonescuGaitSequenceDetection\n", + "from kmat.config import cfg_colors" ] }, { @@ -245,7 +245,7 @@ "metadata": {}, "source": [ "## Applying Paraschiv-Ionescu Gait Sequence Detection Algorithm\n", - "Now, we are running Paraschiv-Ionescu gait sequence detection algorithm from gsd module [`NGMT.ngmt.modules.gsd._paraschiv.ParaschivIonescuGaitSequenceDetection`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/gsd/_paraschiv.py) to detect gait sequences.\n", + "Now, we are running Paraschiv-Ionescu gait sequence detection algorithm from gsd module [`KMAT.kmat.modules.gsd._paraschiv.ParaschivIonescuGaitSequenceDetection`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/gsd/_paraschiv.py) to detect gait sequences.\n", "\n", "In order to apply gait sequence detection algorithm, an instance of the ParaschivIonescuGaitSequenceDetection class is created using the constructor, `ParaschivIonescuGaitSequenceDetection()`. The `gsd` variable holds the instance, allowing us to access its methods. The inputs of the algorithm are as follows:\n", "\n", @@ -400,7 +400,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv_ngmt", + "display_name": "venv_kmat", "language": "python", "name": "python3" }, diff --git a/examples/modules_02_icd.ipynb b/examples/modules_02_icd.ipynb index e3af70a0..14c0fe9d 100644 --- a/examples/modules_02_icd.ipynb +++ b/examples/modules_02_icd.ipynb @@ -27,9 +27,9 @@ "\n", "This example can be referenced by citing the package.\n", "\n", - "The example illustrates how the Paraschiv initial contact detection algorithm is used to detect initial contacts using body acceleration recorded with a triaxial accelerometer worn or fixed on the lower back. The initial contact detection algorithm is implemented in the main module [`ngmt.modules.icd._paraschiv`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/icd/_paraschiv.py). This algorithm is based on the research of Paraschiv-Ionescu et al [`1`-`2`].\n", + "The example illustrates how the Paraschiv initial contact detection algorithm is used to detect initial contacts using body acceleration recorded with a triaxial accelerometer worn or fixed on the lower back. The initial contact detection algorithm is implemented in the main module [`kmat.modules.icd._paraschiv`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/icd/_paraschiv.py). This algorithm is based on the research of Paraschiv-Ionescu et al [`1`-`2`].\n", "\n", - "The algorithm takes accelerometer data as input, specifically the vertical acceleration component, and processes each specified gait sequence independently. The algorithm requires the start and duration of each gait sequence, in the format provided by the Paraschiv-Ionescu gait sequence detection algorithm ([`ngmt.modules.gsd._paraschiv`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/gsd/_paraschiv.py)). The sampling frequency of the accelerometer data is also required as another input. Detected gait sequence information is provided as a DataFrame, which consists of the onset and duration of the gait sequences. For each gait sequence, the algorithm applies the Signal Decomposition algorithm for initial contacts. The algorithm handles multiple gait sequences and ensures uniform output by padding the initial contacts lists with NaN values to match the length of the sequence with the maximum number of initial contacts detected among all sequences. Finally, initial contacts information is provided as a DataFrame with columns `onset`, `event_type`, `tracking_systems`, and `tracked_points`.\n", + "The algorithm takes accelerometer data as input, specifically the vertical acceleration component, and processes each specified gait sequence independently. The algorithm requires the start and duration of each gait sequence, in the format provided by the Paraschiv-Ionescu gait sequence detection algorithm ([`kmat.modules.gsd._paraschiv`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/gsd/_paraschiv.py)). The sampling frequency of the accelerometer data is also required as another input. Detected gait sequence information is provided as a DataFrame, which consists of the onset and duration of the gait sequences. For each gait sequence, the algorithm applies the Signal Decomposition algorithm for initial contacts. The algorithm handles multiple gait sequences and ensures uniform output by padding the initial contacts lists with NaN values to match the length of the sequence with the maximum number of initial contacts detected among all sequences. Finally, initial contacts information is provided as a DataFrame with columns `onset`, `event_type`, `tracking_systems`, and `tracked_points`.\n", "\n", "#### References\n", "[`1`] Paraschiv-Ionescu et al. (2019). Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions. Journal of NeuroEngineering and Rehabilitation, 16(1), 24. https://doi.org/10.1186/s12984-019-0494-z\n", @@ -42,7 +42,7 @@ "metadata": {}, "source": [ "## Import Libraries\n", - "The necessary libraries such as numpy, matplotlib.pyplot, dataset (mobilised), Paraschiv-Ionescu gait sequence detection, and Paraschiv-Ionescu initial contact detection algorithms are imported from their corresponding modules. Make sure that you have all the required libraries and modules installed before running this code. You also may need to install the `ngmt` library and its dependencies if you haven't already." + "The necessary libraries such as numpy, matplotlib.pyplot, dataset (mobilised), Paraschiv-Ionescu gait sequence detection, and Paraschiv-Ionescu initial contact detection algorithms are imported from their corresponding modules. Make sure that you have all the required libraries and modules installed before running this code. You also may need to install the `kmat` library and its dependencies if you haven't already." ] }, { @@ -56,10 +56,10 @@ "import os\n", "from pathlib import Path\n", "\n", - "from ngmt.datasets import mobilised\n", - "from ngmt.modules.gsd import ParaschivIonescuGaitSequenceDetection\n", - "from ngmt.modules.icd import ParaschivIonescuInitialContactDetection\n", - "from ngmt.config import cfg_colors" + "from kmat.datasets import mobilised\n", + "from kmat.modules.gsd import ParaschivIonescuGaitSequenceDetection\n", + "from kmat.modules.icd import ParaschivIonescuInitialContactDetection\n", + "from kmat.config import cfg_colors" ] }, { @@ -243,12 +243,12 @@ "metadata": {}, "source": [ "## Applying Paraschiv-Ionescu Initial Contact Detection Algorithm\n", - "Now, we are running Paraschiv-Ionescu initial contact detection algorithm from icd module [`NGMT.ngmt.modules.icd._paraschiv.ParaschivIonescuInitialContactDetection`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/icd/_paraschiv.py) to detect initial contacts throughout the detected gait sequences. For this purpose, we have to first apply Paraschiv-Ionescu gait sequences detection algorithm to identify gait sequences using acceleration data. The gait sequences are detected by Paraschiv gait sequence detection ([`NGMT.ngmt.modules.gsd._paraschiv.ParaschivIonescuGaitSequenceDetection`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/gsd/_paraschiv.py)).\n", + "Now, we are running Paraschiv-Ionescu initial contact detection algorithm from icd module [`KMAT.kmat.modules.icd._paraschiv.ParaschivIonescuInitialContactDetection`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/icd/_paraschiv.py) to detect initial contacts throughout the detected gait sequences. For this purpose, we have to first apply Paraschiv-Ionescu gait sequences detection algorithm to identify gait sequences using acceleration data. The gait sequences are detected by Paraschiv gait sequence detection ([`KMAT.kmat.modules.gsd._paraschiv.ParaschivIonescuGaitSequenceDetection`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/gsd/_paraschiv.py)).\n", "\n", "Then, in order to apply Paraschiv-Ionescu initial contact detection algorithm, an instance of the ParaschivIonescuInitialContactDetection class is created using the constructor, `ParaschivIonescuInitialContactDetection()`. The `icd` variable holds the instance, allowing us to access its methods. The inputs of Paraschiv-Ionescu initial contact detection algorithm are as follows:\n", "\n", "- **Input Data:** `data` consist of accelerometer data (N, 3) for the x, y, and z axes in pandas Dataframe format.\n", - "- **Gait Sequences:** `gait_sequences`, consist of gait sequences detected by Paraschiv gait sequence detection ([`NGMT.ngmt.modules.gsd._paraschiv.ParaschivIonescuGaitSequenceDetection`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/gsd/_paraschiv.py)).\n", + "- **Gait Sequences:** `gait_sequences`, consist of gait sequences detected by Paraschiv gait sequence detection ([`KMAT.kmat.modules.gsd._paraschiv.ParaschivIonescuGaitSequenceDetection`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/gsd/_paraschiv.py)).\n", "- **Sampling Frequency:** `sampling_freq_Hz` is the sampling frequency of the data, defined in Hz, with a default value of 100 Hz.\n", "\n" ] @@ -418,7 +418,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv_ngmt", + "display_name": "venv_kmat", "language": "python", "name": "python3" }, diff --git a/examples/modules_03_pam.ipynb b/examples/modules_03_pam.ipynb index c0899377..8129f3e3 100644 --- a/examples/modules_03_pam.ipynb +++ b/examples/modules_03_pam.ipynb @@ -27,7 +27,7 @@ "\n", "This example serves as a reference on how to use the physical activity monitoring algorithm. This example can be cited by referencing the package.\n", "\n", - "The example illustrates how the physical activity monitoring algorithm determines the intensity level of sedentary, light, moderate, and vigorous physical activities using body acceleration recorded with a triaxial accelerometer worn on the wrist. The physical activity monitoring algorithm is implemented in the main module [`ngmt.modules.pam._pam`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/pam/_pam.py).\n", + "The example illustrates how the physical activity monitoring algorithm determines the intensity level of sedentary, light, moderate, and vigorous physical activities using body acceleration recorded with a triaxial accelerometer worn on the wrist. The physical activity monitoring algorithm is implemented in the main module [`kmat.modules.pam._pam`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/pam/_pam.py).\n", "\n", "The algorithm determines the intensity level of physical activities based on the following steps:\n", "\n", @@ -50,7 +50,7 @@ "metadata": {}, "source": [ "## Import Libraries\n", - "The necessary libraries such as pandas, physical activity monitoring and fairpark data loader are imported. Make sure that you have all the required libraries and modules installed before running this code. You may also need to install the `ngmt` library and its dependencies if you haven't already.\n" + "The necessary libraries such as pandas, physical activity monitoring and fairpark data loader are imported. Make sure that you have all the required libraries and modules installed before running this code. You may also need to install the `kmat` library and its dependencies if you haven't already.\n" ] }, { @@ -65,9 +65,9 @@ "pd.options.mode.chained_assignment = None\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", - "from ngmt.modules.pam import PhysicalActivityMonitoring\n", - "from ngmt.utils.ngmt_dataclass import NGMTRecording # Import the NGMTRecording class\n", - "from ngmt.config import cfg_colors" + "from kmat.modules.pam import PhysicalActivityMonitoring\n", + "from kmat.utils.kmat_dataclass import KMATRecording # Import the KMATRecording class\n", + "from kmat.config import cfg_colors" ] }, { @@ -143,7 +143,7 @@ " \"sampling_frequency\": [fs] * n_channels,\n", "}\n", "\n", - "recording = NGMTRecording(\n", + "recording = KMATRecording(\n", " data={\"lb_imu\": acc_data}, channels={\"lb_imu\": pd.DataFrame(channels_dict)}\n", ")" ] @@ -153,7 +153,7 @@ "metadata": {}, "source": [ "## Apply Physical Activity Monitoring Algorithm\n", - "Now, we are running the physical activity monitoring algorithm from the main module [`ngmt.modules.pam._pam`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/pam/_pam.py). The inputs of the algorithm are as follows:\n", + "Now, we are running the physical activity monitoring algorithm from the main module [`kmat.modules.pam._pam`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/pam/_pam.py). The inputs of the algorithm are as follows:\n", "\n", "- **Input Data:** `data` Includes data with a time index along with accelerometer data (N, 3) for x, y, and z axes in pandas Dataframe format.\n", "- **Acceleration Unit:** `acceleration_unit` is the unit of the acceleration data.\n", @@ -178,7 +178,7 @@ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[11], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m pam \u001b[38;5;241m=\u001b[39m PhysicalActivityMonitoring()\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# Call phyisical activity monitoring using pam.detect\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m pam \u001b[38;5;241m=\u001b[39m \u001b[43mpam\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetect\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrecording\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlb_imu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43macceleration_unit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrecording\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchannels\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlb_imu\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43munits\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43msampling_freq_Hz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mthresholds_mg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msedentary_threshold\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m45\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlight_threshold\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmoderate_threshold\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m400\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mepoch_duration_sec\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 16\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 18\u001b[0m \u001b[38;5;66;03m# Phyisical activity information are stored in physical_activities_ attribute of pam\u001b[39;00m\n\u001b[0;32m 19\u001b[0m physical_activities \u001b[38;5;241m=\u001b[39m pam\u001b[38;5;241m.\u001b[39mphysical_activities_\n", - "File \u001b[1;32m~\\Desktop\\kiel\\NGMT\\ngmt\\modules\\pam\\_pam.py:109\u001b[0m, in \u001b[0;36mPhysicalActivityMonitoring.detect\u001b[1;34m(self, data, acceleration_unit, sampling_freq_Hz, thresholds_mg, epoch_duration_sec, plot)\u001b[0m\n\u001b[0;32m 107\u001b[0m \u001b[38;5;66;03m# check if index column is datetime\u001b[39;00m\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data\u001b[38;5;241m.\u001b[39mindex, pd\u001b[38;5;241m.\u001b[39mDatetimeIndex):\n\u001b[1;32m--> 109\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndex column must be a datetime index.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 111\u001b[0m \u001b[38;5;66;03m# check if index column in named timestamp\u001b[39;00m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[1;32m~\\Desktop\\kiel\\KMAT\\kmat\\modules\\pam\\_pam.py:109\u001b[0m, in \u001b[0;36mPhysicalActivityMonitoring.detect\u001b[1;34m(self, data, acceleration_unit, sampling_freq_Hz, thresholds_mg, epoch_duration_sec, plot)\u001b[0m\n\u001b[0;32m 107\u001b[0m \u001b[38;5;66;03m# check if index column is datetime\u001b[39;00m\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data\u001b[38;5;241m.\u001b[39mindex, pd\u001b[38;5;241m.\u001b[39mDatetimeIndex):\n\u001b[1;32m--> 109\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndex column must be a datetime index.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 111\u001b[0m \u001b[38;5;66;03m# check if index column in named timestamp\u001b[39;00m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimestamp\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "\u001b[1;31mValueError\u001b[0m: Index column must be a datetime index." ] } @@ -234,7 +234,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv_ngmt", + "display_name": "venv_kmat", "language": "python", "name": "python3" }, diff --git a/examples/modules_04_ssd.ipynb b/examples/modules_04_ssd.ipynb index 3d6cdb75..b856ecbe 100644 --- a/examples/modules_04_ssd.ipynb +++ b/examples/modules_04_ssd.ipynb @@ -26,7 +26,7 @@ "\n", "This example can be referenced by citing the package.\n", "\n", - "The example illustrates how to use PhamSittoStandStandtoSitDetection algorithm to detect sit to stand and stand to sit movements using body acceleration and gyro data recorded with a lower back IMU sensor. The sit to stand and stand to sit detection algorithm is implemented using [`ngmt.modules.ssd._pham`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/ssd/_pham.py). This algorithm is based on the research of Pham et al [1].\n", + "The example illustrates how to use PhamSittoStandStandtoSitDetection algorithm to detect sit to stand and stand to sit movements using body acceleration and gyro data recorded with a lower back IMU sensor. The sit to stand and stand to sit detection algorithm is implemented using [`kmat.modules.ssd._pham`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/ssd/_pham.py). This algorithm is based on the research of Pham et al [1].\n", "\n", "This algorithm aims to detect postural transitions (e.g., sit-to-stand or stand-to-sit movements) using accelerometer and gyroscope data collected from a lower back inertial measurement unit (IMU) sensor. This algorithm is designed to be robust in detecting sit-to-stand and stand-to-sit transitions using inertial sensor data and provides detailed information about these transitions.\n", "\n", @@ -47,7 +47,7 @@ "metadata": {}, "source": [ "## Import libraries\n", - "The necessary libraries such as numpy, matplotlib.pyplot, dataset and PhamSittoStandStandtoSitDetection sit to stand and stand to sit detection algortihm are imported. Make sure that you have all the required libraries and modules installed before running this code. You also may need to install the 'ngmt' library and its dependencies if you haven't already." + "The necessary libraries such as numpy, matplotlib.pyplot, dataset and PhamSittoStandStandtoSitDetection sit to stand and stand to sit detection algortihm are imported. Make sure that you have all the required libraries and modules installed before running this code. You also may need to install the 'kmat' library and its dependencies if you haven't already." ] }, { @@ -62,8 +62,8 @@ "import os\n", "from pathlib import Path\n", "\n", - "from ngmt.datasets import keepcontrol\n", - "from ngmt.modules.ssd import PhamSittoStandStandtoSitDetection" + "from kmat.datasets import keepcontrol\n", + "from kmat.modules.ssd import PhamSittoStandStandtoSitDetection" ] }, { @@ -109,7 +109,7 @@ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[6], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# The 'keepcontrol.load_recording' function is used to load the data from the specified file_path\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m recording \u001b[38;5;241m=\u001b[39m \u001b[43mkeepcontrol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_recording\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mfile_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracking_systems\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mtracking_sys\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtracked_points\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtracked_points\u001b[49m\n\u001b[0;32m 4\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\Desktop\\kiel\\NGMT\\ngmt\\datasets\\keepcontrol.py:53\u001b[0m, in \u001b[0;36mload_recording\u001b[1;34m(file_name, tracking_systems, tracked_points)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;66;03m# From the file_name, extract the tracking system\u001b[39;00m\n\u001b[0;32m 52\u001b[0m search_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_tracksys-\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 53\u001b[0m idx_from \u001b[38;5;241m=\u001b[39m \u001b[43mfile_name\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind\u001b[49m(search_str) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlen\u001b[39m(search_str)\n\u001b[0;32m 54\u001b[0m idx_to \u001b[38;5;241m=\u001b[39m idx_from \u001b[38;5;241m+\u001b[39m file_name[idx_from:]\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 55\u001b[0m current_tracksys \u001b[38;5;241m=\u001b[39m file_name[idx_from:idx_to]\n", + "File \u001b[1;32m~\\Desktop\\kiel\\KMAT\\kmat\\datasets\\keepcontrol.py:53\u001b[0m, in \u001b[0;36mload_recording\u001b[1;34m(file_name, tracking_systems, tracked_points)\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[38;5;66;03m# From the file_name, extract the tracking system\u001b[39;00m\n\u001b[0;32m 52\u001b[0m search_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_tracksys-\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 53\u001b[0m idx_from \u001b[38;5;241m=\u001b[39m \u001b[43mfile_name\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind\u001b[49m(search_str) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlen\u001b[39m(search_str)\n\u001b[0;32m 54\u001b[0m idx_to \u001b[38;5;241m=\u001b[39m idx_from \u001b[38;5;241m+\u001b[39m file_name[idx_from:]\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 55\u001b[0m current_tracksys \u001b[38;5;241m=\u001b[39m file_name[idx_from:idx_to]\n", "\u001b[1;31mAttributeError\u001b[0m: 'WindowsPath' object has no attribute 'find'" ] } @@ -284,7 +284,7 @@ "metadata": {}, "source": [ "## Applying Pham sit to stand and stand to sit detection algorithm\n", - "Now, we are running Pham sit to stand and stand to sit detection algorithm from pham module [`NGMT.ngmt.modules.ssd._pham.SittoStandStandtoSitDetection`](https://github.com/neurogeriatricskiel/NGMT/tree/main/ngmt/modules/ssd/_pham.py) to detect sit to stand and stand to sit.\n", + "Now, we are running Pham sit to stand and stand to sit detection algorithm from pham module [`KMAT.kmat.modules.ssd._pham.SittoStandStandtoSitDetection`](https://github.com/neurogeriatricskiel/KMAT/tree/main/kmat/modules/ssd/_pham.py) to detect sit to stand and stand to sit.\n", "\n", "The following code first prepares the input data by combining acceleration and gyro data into a single DataFrame called `input_data`.\n", "\n", @@ -459,7 +459,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv_ngmt", + "display_name": "venv_kmat", "language": "python", "name": "python3" }, diff --git a/kmat/__init__.py b/kmat/__init__.py new file mode 100644 index 00000000..9794221f --- /dev/null +++ b/kmat/__init__.py @@ -0,0 +1,7 @@ +# kmat/__init__.py + +""" +Kiel Motion Analysis Toolbox +""" + +__version__ = "0.0.1" diff --git a/ngmt/config.py b/kmat/config.py similarity index 100% rename from ngmt/config.py rename to kmat/config.py diff --git a/ngmt/datasets/fairpark.py b/kmat/datasets/fairpark.py similarity index 95% rename from ngmt/datasets/fairpark.py rename to kmat/datasets/fairpark.py index 98f5cfa1..c32e1b25 100644 --- a/ngmt/datasets/fairpark.py +++ b/kmat/datasets/fairpark.py @@ -5,7 +5,7 @@ import os import pathlib import polars as pl -from ngmt.utils.ngmt_dataclass import NGMTRecording +from kmat.utils.kmat_dataclass import KMATRecording SAMPLING_FREQ_HZ: float = 100.0 # sampling frequency @@ -19,7 +19,7 @@ def load_recording( tracked_points: str | list[str] | dict[str, str] | dict[str, list[str]] = { "imu": ["LARM"] }, -) -> NGMTRecording: +) -> KMATRecording: """ Load a recording from the FAIRPARK II validation study. @@ -32,7 +32,7 @@ def load_recording( If a dictionary is provided, it should map each tracking system to either a single tracked point or a list of tracked points. Returns: - NGMTRecording : An instance of the NGMTRecording dataclass containing the loaded data and channels. + KMATRecording : An instance of the KMATRecording dataclass containing the loaded data and channels. """ # Put tracking systems in a list if isinstance(tracking_systems, str): @@ -90,6 +90,6 @@ def load_recording( "sampling_frequency": [SAMPLING_FREQ_HZ for _ in range(len(df.columns))], } - return NGMTRecording( + return KMATRecording( data={"imu": df}, channels={"imu": pd.DataFrame(channels_dict)} ) diff --git a/ngmt/datasets/keepcontrol.py b/kmat/datasets/keepcontrol.py similarity index 93% rename from ngmt/datasets/keepcontrol.py rename to kmat/datasets/keepcontrol.py index df0c206e..a71a463f 100644 --- a/ngmt/datasets/keepcontrol.py +++ b/kmat/datasets/keepcontrol.py @@ -2,8 +2,8 @@ import pandas as pd import pathlib import os -from ngmt.utils.ngmt_dataclass import NGMTRecording -from ngmt.utils.ngmt_dataclass import REQUIRED_COLUMNS +from kmat.utils.kmat_dataclass import KMATRecording +from kmat.utils.kmat_dataclass import REQUIRED_COLUMNS def load_recording( @@ -23,7 +23,7 @@ def load_recording( If a dictionary is provided, it should map each tracking system to either a single tracked point or a list of tracked points. Returns: - NGMTRecording : An instance of the NGMTRecording dataclass containing the loaded data and channels. + KMATRecording : An instance of the KMATRecording dataclass containing the loaded data and channels. """ # Put tracking systems in a list if isinstance(tracking_systems, str): @@ -85,4 +85,4 @@ def load_recording( ] data_dict[tracksys] = df_data channels_dict[tracksys] = df_channels[col_names] - return NGMTRecording(data=data_dict, channels=channels_dict) + return KMATRecording(data=data_dict, channels=channels_dict) diff --git a/ngmt/datasets/mobilised.py b/kmat/datasets/mobilised.py similarity index 96% rename from ngmt/datasets/mobilised.py rename to kmat/datasets/mobilised.py index 025ec89a..ed8fc4a1 100644 --- a/ngmt/datasets/mobilised.py +++ b/kmat/datasets/mobilised.py @@ -1,8 +1,8 @@ import numpy as np import pandas as pd import pathlib -from ngmt.utils import matlab_loader -from ngmt.utils.ngmt_dataclass import NGMTRecording +from kmat.utils import matlab_loader +from kmat.utils.kmat_dataclass import KMATRecording # See: https://bids-specification.readthedocs.io/en/stable/modality-specific-files/motion.html#restricted-keyword-list-for-channel-type @@ -47,7 +47,7 @@ def load_recording( If a dictionary is provided, it should map each tracking system to either a single tracked point or a list of tracked points. Returns: - NGMTRecording : An instance of the NGMTRecording dataclass containing the loaded data and channels. + KMATRecording : An instance of the KMATRecording dataclass containing the loaded data and channels. """ # Put tracking systems into a list if isinstance(tracking_systems, str): @@ -133,4 +133,4 @@ def load_recording( channel_data[tracksys] = pd.DataFrame(channel_data[tracksys]) - return NGMTRecording(data=recording_data, channels=channel_data) + return KMATRecording(data=recording_data, channels=channel_data) diff --git a/ngmt/modules/__init__.py b/kmat/modules/__init__.py similarity index 100% rename from ngmt/modules/__init__.py rename to kmat/modules/__init__.py diff --git a/ngmt/modules/gsd/__init__.py b/kmat/modules/gsd/__init__.py similarity index 100% rename from ngmt/modules/gsd/__init__.py rename to kmat/modules/gsd/__init__.py diff --git a/ngmt/modules/gsd/_paraschiv.py b/kmat/modules/gsd/_paraschiv.py similarity index 99% rename from ngmt/modules/gsd/_paraschiv.py rename to kmat/modules/gsd/_paraschiv.py index 2b17038b..f5275933 100644 --- a/ngmt/modules/gsd/_paraschiv.py +++ b/kmat/modules/gsd/_paraschiv.py @@ -4,8 +4,8 @@ import matplotlib.pyplot as plt import scipy.signal from typing import Optional -from ngmt.utils import preprocessing -from ngmt.config import cfg_colors +from kmat.utils import preprocessing +from kmat.config import cfg_colors class ParaschivIonescuGaitSequenceDetection: diff --git a/ngmt/modules/icd/__init__.py b/kmat/modules/icd/__init__.py similarity index 100% rename from ngmt/modules/icd/__init__.py rename to kmat/modules/icd/__init__.py diff --git a/ngmt/modules/icd/_paraschiv.py b/kmat/modules/icd/_paraschiv.py similarity index 99% rename from ngmt/modules/icd/_paraschiv.py rename to kmat/modules/icd/_paraschiv.py index 8b332c00..82fc963a 100644 --- a/ngmt/modules/icd/_paraschiv.py +++ b/kmat/modules/icd/_paraschiv.py @@ -5,8 +5,8 @@ import matplotlib.pyplot as plt import scipy.io import scipy.signal -from ngmt.utils import preprocessing -from ngmt.config import cfg_colors +from kmat.utils import preprocessing +from kmat.config import cfg_colors class ParaschivIonescuInitialContactDetection: diff --git a/ngmt/modules/pam/__init__.py b/kmat/modules/pam/__init__.py similarity index 100% rename from ngmt/modules/pam/__init__.py rename to kmat/modules/pam/__init__.py diff --git a/ngmt/modules/pam/_pam.py b/kmat/modules/pam/_pam.py similarity index 99% rename from ngmt/modules/pam/_pam.py rename to kmat/modules/pam/_pam.py index f5772a1f..666c4068 100644 --- a/ngmt/modules/pam/_pam.py +++ b/kmat/modules/pam/_pam.py @@ -1,8 +1,8 @@ import pandas as pd import numpy as np import matplotlib.pyplot as plt -from ngmt.config import cfg_colors -from ngmt.utils import preprocessing +from kmat.config import cfg_colors +from kmat.utils import preprocessing class PhysicalActivityMonitoring: diff --git a/ngmt/modules/ssd/__init__.py b/kmat/modules/ssd/__init__.py similarity index 100% rename from ngmt/modules/ssd/__init__.py rename to kmat/modules/ssd/__init__.py diff --git a/ngmt/modules/ssd/_pham.py b/kmat/modules/ssd/_pham.py similarity index 99% rename from ngmt/modules/ssd/_pham.py rename to kmat/modules/ssd/_pham.py index a11e8b0f..b65c3461 100644 --- a/ngmt/modules/ssd/_pham.py +++ b/kmat/modules/ssd/_pham.py @@ -2,8 +2,8 @@ import numpy as np import pandas as pd import scipy.signal -from ngmt.utils import preprocessing -from ngmt.config import cfg_colors +from kmat.utils import preprocessing +from kmat.config import cfg_colors class PhamSittoStandStandtoSitDetection: diff --git a/ngmt/test/example_lower_back_acc.csv b/kmat/test/example_lower_back_acc.csv similarity index 100% rename from ngmt/test/example_lower_back_acc.csv rename to kmat/test/example_lower_back_acc.csv diff --git a/ngmt/test/scripts/test.py b/kmat/test/scripts/test.py similarity index 95% rename from ngmt/test/scripts/test.py rename to kmat/test/scripts/test.py index 39837726..e4768544 100644 --- a/ngmt/test/scripts/test.py +++ b/kmat/test/scripts/test.py @@ -1,6 +1,6 @@ -from ngmt.datasets import mobilised -from ngmt.modules.gsd import ParaschivIonescuGaitSequenceDetection -from ngmt.modules.icd import ParaschivIonescuInitialContactDetection +from kmat.datasets import mobilised +from kmat.modules.gsd import ParaschivIonescuGaitSequenceDetection +from kmat.modules.icd import ParaschivIonescuInitialContactDetection import numpy as np import pandas as pd import matplotlib.pyplot as plt diff --git a/ngmt/test/test_calc.py b/kmat/test/test_calc.py similarity index 98% rename from ngmt/test/test_calc.py rename to kmat/test/test_calc.py index 9465f6f3..5ff3fb6e 100644 --- a/ngmt/test/test_calc.py +++ b/kmat/test/test_calc.py @@ -1,12 +1,12 @@ # Introduction and explanation regarding the test suite """ -This code is a test suite for various signal processing and analysis functions which exist in the NGMT toolbox. +This code is a test suite for various signal processing and analysis functions which exist in the KMAT toolbox. It employs pytest, a Python testing framework, to verify the correctness of these functions. Here's a brief explanation of the code structure: 1. Import necessary libraries, pytest and the functions to be tested. 2. Generate a random input signal for testing purposes. -3. Define a series of test functions, each targeting a specific function from the 'ngmt.utils.preprocessing' module. +3. Define a series of test functions, each targeting a specific function from the 'kmat.utils.preprocessing' module. 4. Inside each test function, we validate the correctness of the corresponding function and its inputs. 5. We make use of 'assert' statements to check that the functions return expected results. 6. The code is organized for clarity and maintainability. @@ -17,7 +17,7 @@ 2. Run this script, and pytest will execute all the test functions. 3. Any failures in tests will be reported as failed with red color, and also the number of passed tests will be represented with green color. -By running these tests, the reliability and correctness of the signal processing functions in the 'ngmt.utils.preprocessing' module will be ensured. +By running these tests, the reliability and correctness of the signal processing functions in the 'kmat.utils.preprocessing' module will be ensured. """ # Import necessary libraries and functions to be tested. @@ -26,7 +26,7 @@ import warnings import numpy.testing as npt import pytest -from ngmt.utils.preprocessing import ( +from kmat.utils.preprocessing import ( resample_interpolate, lowpass_filter, highpass_filter, @@ -51,7 +51,7 @@ pham_plot_results, process_postural_transitions_stationary_periods, ) -from ngmt.utils.quaternion import ( +from kmat.utils.quaternion import ( quatinv, quatnormalize, quatnorm, @@ -140,7 +140,7 @@ def test_resample_interpolate(): # Test function for the 'lowpass_filter_savgol' function def test_lowpass_filter_savgol(): """ - Test for lowpass_filter_savgol function in the 'ngmt.utils.preprocessing' module. + Test for lowpass_filter_savgol function in the 'kmat.utils.preprocessing' module. """ # Test with inputs test_signal = random_input_signal @@ -238,7 +238,7 @@ def test_lowpass_filter(): # Test function for the 'lowpass_filter_fir' function def test_lowpass_filter_fir(): """ - Test for lowpass_filter_fir function in the 'ngmt.utils.preprocessing' module. + Test for lowpass_filter_fir function in the 'kmat.utils.preprocessing' module. """ # Test with inputs test_signal = random_input_signal @@ -279,7 +279,7 @@ def test_lowpass_filter_fir(): # Additional test cases for Savitzky-Golay filter def test_lowpass_filter_savgol_specific(): """ - Test specific parameters for Savitzky-Golay filter in the 'ngmt.utils.preprocessing' module. + Test specific parameters for Savitzky-Golay filter in the 'kmat.utils.preprocessing' module. """ # Test with specific parameters test_signal = np.ones(100) @@ -303,7 +303,7 @@ def test_lowpass_filter_savgol_specific(): # Additional test cases for Butterworth filter def test_lowpass_filter_butter_specific(): """ - Test specific parameters for Butterworth filter in the 'ngmt.utils.preprocessing' module. + Test specific parameters for Butterworth filter in the 'kmat.utils.preprocessing' module. """ # Test with specific parameters test_signal = np.ones(100) @@ -328,7 +328,7 @@ def test_lowpass_filter_butter_specific(): # Test function for the 'highpass_filter_iir' function def test_highpass_filter_iir(): - """Test for highpass_filter_iir function in the 'ngmt.utils.preprocessing' module.""" + """Test for highpass_filter_iir function in the 'kmat.utils.preprocessing' module.""" # Test with inputs test_signal = np.random.rand(100) sampling_frequency = 40 @@ -390,7 +390,7 @@ def test_iir_highpass_filter_invalid_input(): # Test function for the '_iir_highpass_filter' function: case 2 def test_iir_highpass_filter(): - """Test for _iir_highpass_filter function in the 'ngmt.utils.preprocessing' module.""" + """Test for _iir_highpass_filter function in the 'kmat.utils.preprocessing' module.""" # Test with inputs test_signal = np.random.rand(100) sampling_frequency = 40 @@ -469,7 +469,7 @@ def test_highpass_filter(): # Test function for the 'apply_continuous_wavelet_transform' function: case 1 def test_apply_continuous_wavelet_transform(): """ - Test for apply_continuous_wavelet_transform function in the 'ngmt.utils.preprocessing' module. + Test for apply_continuous_wavelet_transform function in the 'kmat.utils.preprocessing' module. """ # Test with inputs test_signal = random_input_signal @@ -655,7 +655,7 @@ def test_apply_continuous_wavelet_transform_zero_sampling_frequency(): # Test function for the 'apply_successive_gaussian_filters' function: case 1 def test_apply_successive_gaussian_filters(): """ - Test for apply_successive_gaussian_filters function in the 'ngmt.utils.preprocessing' module. + Test for apply_successive_gaussian_filters function in the 'kmat.utils.preprocessing' module. """ # Test with inputs test_signal = random_input_signal @@ -775,7 +775,7 @@ def test_apply_successive_gaussian_filters_large_input(): # Test function for the 'calculate_envelope_activity' function: case 1 def test_calculate_envelope_activity(): """ - Test for calculate_envelope_activity function in the 'ngmt.utils.preprocessing' module. + Test for calculate_envelope_activity function in the 'kmat.utils.preprocessing' module. """ # Test with inputs test_signal = random_input_signal @@ -832,7 +832,7 @@ def test_calculate_envelope_activity_invalid_duration(): # Test function for the 'find_consecutive_groups' function: case 1 def test_find_consecutive_groups(): """ - Test for find_consecutive_groups function in the 'ngmt.utils.preprocessing' module. + Test for find_consecutive_groups function in the 'kmat.utils.preprocessing' module. """ # Test with inputs test_signal = np.array([0, 1, 1, 0, 2, 2, 2, 0, 0, 3, 3]) @@ -963,7 +963,7 @@ def test_find_consecutive_groups_large_input(): # Test function for the 'find_local_min_max' function: case 1 def test_find_local_min_max(): """ - Test for find_local_min_max function in the 'ngmt.utils.preprocessing' module. + Test for find_local_min_max function in the 'kmat.utils.preprocessing' module. """ # Test with inputs test_signal = random_input_signal @@ -1073,7 +1073,7 @@ def test_identify_pulse_trains_single_element(): # Test function for the 'identify_pulse_trains' function: case 4 def test_identify_pulse_trains(): """ - Test for convert_pulse_train_to_array function in the 'ngmt.utils.preprocessing' module. + Test for convert_pulse_train_to_array function in the 'kmat.utils.preprocessing' module. """ # Test with inputs test_signal = random_input_signal @@ -1118,7 +1118,7 @@ def test_identify_pulse_trains_empty_signal(): # Test function for the 'convert_pulse_train_to_array' function: case 1 def test_convert_pulse_train_to_array(): """ - Test for convert_pulse_train_to_array function in the 'ngmt.utils.preprocessing' module. + Test for convert_pulse_train_to_array function in the 'kmat.utils.preprocessing' module. """ # Test with a list of pulse train dictionaries pulse_train_list = [ @@ -1280,7 +1280,7 @@ def test_convert_pulse_train_to_array_invalid_key_names(): # Test function for the 'find_interval_intersection' function: case 1 def test_find_interval_intersection(): """ - Test for organize_and_pack_results function in the 'ngmt.utils.preprocessing' module. + Test for organize_and_pack_results function in the 'kmat.utils.preprocessing' module. """ # Test case 1: Basic case with one intersection set_a = np.array([[1, 5], [7, 10]]) @@ -1575,7 +1575,7 @@ def test_classify_physical_activity_negative_epoch_duration(): # Test function for wavelet_decomposition function def test_wavelet_decomposition(): """ - Test for wavelet_decomposition function in the 'ngmt.utils.preprocessing' module. + Test for wavelet_decomposition function in the 'kmat.utils.preprocessing' module. """ # Generate a random input signal input_signal = np.random.randn(1000) @@ -1599,7 +1599,7 @@ def test_wavelet_decomposition(): # Test function for moving_var function def test_moving_var(): """ - Test for moving_var function in the 'ngmt.utils.preprocessing' module. + Test for moving_var function in the 'kmat.utils.preprocessing' module. """ # Generate a random input signal input_signal = np.random.randn(1000) diff --git a/ngmt/test/test_modules.py b/kmat/test/test_modules.py similarity index 98% rename from ngmt/test/test_modules.py rename to kmat/test/test_modules.py index 121c3949..0a63335c 100644 --- a/ngmt/test/test_modules.py +++ b/kmat/test/test_modules.py @@ -1,6 +1,6 @@ # Introduction and explanation regarding the test suite """ -This code is a test suite for various signal processing and analysis functions which exist in the NGMT toolbox. +This code is a test suite for various signal processing and analysis functions which exist in the KMAT toolbox. It employs pytest, a Python testing framework, to verify the correctness of these functions. Here's a brief explanation of the code structure: @@ -24,10 +24,10 @@ import pytest import numpy as np import pandas as pd -from ngmt.modules.gsd import ParaschivIonescuGaitSequenceDetection -from ngmt.modules.icd import ParaschivIonescuInitialContactDetection -from ngmt.modules.pam import PhysicalActivityMonitoring -from ngmt.modules.ssd import PhamSittoStandStandtoSitDetection +from kmat.modules.gsd import ParaschivIonescuGaitSequenceDetection +from kmat.modules.icd import ParaschivIonescuInitialContactDetection +from kmat.modules.pam import PhysicalActivityMonitoring +from kmat.modules.ssd import PhamSittoStandStandtoSitDetection ## Module test # Test for gait sequence detection algorithm diff --git a/ngmt/utils/FIR_2_3Hz_40.mat b/kmat/utils/FIR_2_3Hz_40.mat similarity index 100% rename from ngmt/utils/FIR_2_3Hz_40.mat rename to kmat/utils/FIR_2_3Hz_40.mat diff --git a/ngmt/utils/file_io.py b/kmat/utils/file_io.py similarity index 100% rename from ngmt/utils/file_io.py rename to kmat/utils/file_io.py diff --git a/ngmt/utils/importers.py b/kmat/utils/importers.py similarity index 96% rename from ngmt/utils/importers.py rename to kmat/utils/importers.py index d906dc37..d86f3140 100644 --- a/ngmt/utils/importers.py +++ b/kmat/utils/importers.py @@ -1,8 +1,8 @@ import actipy import h5py import numpy as np -from ngmt.utils.ngmt_dataclass import NGMTRecording -from ngmt.utils.file_io import get_unit_from_type +from kmat.utils.kmat_dataclass import KMATRecording +from kmat.utils.file_io import get_unit_from_type import pandas as pd from pathlib import Path @@ -10,7 +10,7 @@ def import_axivity(file_path: str, tracked_point: str): """ Imports Axivity data from the specified file path and - return the data and channel formatted to be used in a NGMTRecording object. + return the data and channel formatted to be used in a KMATRecording object. Args: file_path (str or Path): The path to the Axivity data file. @@ -48,7 +48,7 @@ def import_axivity(file_path: str, tracked_point: str): accel_col_names = [col for col in data.columns if col[-1] in ["x", "y", "z"]] n_channels = len(accel_col_names) - # Create the column names for the NGMTRecording object + # Create the column names for the KMATRecording object col_names = [f"{tracked_point}_{s}_{x}" for s in ["ACCEL"] for x in ["x", "y", "z"]] # Create the channel dictionary following the BIDS naming conventions diff --git a/ngmt/utils/matlab_loader.py b/kmat/utils/matlab_loader.py similarity index 100% rename from ngmt/utils/matlab_loader.py rename to kmat/utils/matlab_loader.py diff --git a/ngmt/utils/ngmt_dataclass.py b/kmat/utils/ngmt_dataclass.py similarity index 99% rename from ngmt/utils/ngmt_dataclass.py rename to kmat/utils/ngmt_dataclass.py index f8604381..f906cac4 100644 --- a/ngmt/utils/ngmt_dataclass.py +++ b/kmat/utils/ngmt_dataclass.py @@ -45,8 +45,8 @@ @dataclass(kw_only=True) -class NGMTRecording: - """Dataclass to hold any data and associated infos for a NGMT recording. +class KMATRecording: + """Dataclass to hold any data and associated infos for a KMAT recording. Attributes: data (dict): The data is stored as a pandas DataFrame for each unique tracking system. diff --git a/ngmt/utils/orientation_estimation/__init__.py b/kmat/utils/orientation_estimation/__init__.py similarity index 100% rename from ngmt/utils/orientation_estimation/__init__.py rename to kmat/utils/orientation_estimation/__init__.py diff --git a/ngmt/utils/orientation_estimation/_madgwick.py b/kmat/utils/orientation_estimation/_madgwick.py similarity index 100% rename from ngmt/utils/orientation_estimation/_madgwick.py rename to kmat/utils/orientation_estimation/_madgwick.py diff --git a/ngmt/utils/preprocessing.py b/kmat/utils/preprocessing.py similarity index 99% rename from ngmt/utils/preprocessing.py rename to kmat/utils/preprocessing.py index 534d60c9..ced6c10e 100644 --- a/ngmt/utils/preprocessing.py +++ b/kmat/utils/preprocessing.py @@ -9,13 +9,13 @@ import scipy.integrate import scipy.ndimage import pywt -from ngmt.utils import quaternion -from ngmt.config import cfg_colors +from kmat.utils import quaternion +from kmat.config import cfg_colors # use the importlib.resources package to access the FIR_2_3Hz_40.mat file with pkg_resources.path( - "ngmt.utils", "FIR_2_3Hz_40.mat" + "kmat.utils", "FIR_2_3Hz_40.mat" ) as mat_filter_coefficients_file: pass diff --git a/ngmt/utils/quaternion.py b/kmat/utils/quaternion.py similarity index 100% rename from ngmt/utils/quaternion.py rename to kmat/utils/quaternion.py diff --git a/mkdocs.yml b/mkdocs.yml index 07871e69..284fe09c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,4 +1,4 @@ -site_name: NeuroGeriatricsMotionToolbox +site_name: Kiel Motion Analysis Toolbox extra_css: - 'style.css' @@ -10,11 +10,11 @@ plugins: default_handler: python handlers: python: - paths: [ngmt] + paths: [kmat] theme: name: material - logo: ngmt_logo_transBG.png + logo: kmat_logo_transBG.png language: en font: text: "Roboto" @@ -40,8 +40,8 @@ nav: - Home: index.md - Examples: - examples/index.md - - Tutorial basics: 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zgur>)4#*cChNYEH?|BJ0ln`x^-i;L^*!>%qn+v7kpl)BE-v4sxe8GufpD;cEf2-a)<}XVSOsrnVm_UT39uGfwv!C~K2ET6Z?iNti;nFO1NWW~PrNC< zH3^&1G|Xo(B<*E}Gn_?7D-s~uX7r2d-D}mmk)-W5rKq_>mL{CBhR#)+tdg-Oj1>|% z{W-AG8T-9d)c){LGD5rBNEOX<7dE;M{iIs#eoMR)5GNjrT~kuml-xBXI`(aJ9{l|F z7oDfpI!~=0dTRao`P(SwZRZDpdk9f-M-i924~T+XZnqmU=NA1Gv({U^&n^1t)z_>Y&n^1t gq^;LV4-DsXwiavEbBlhuVm)iM?tE^^eP#sxKg^T~;Q#;t diff --git a/paper/make_figures.py b/paper/make_figures.py index c9b4aa6a..fee8d1d6 100644 --- a/paper/make_figures.py +++ b/paper/make_figures.py @@ -1,15 +1,15 @@ import numpy as np import matplotlib.pyplot as plt -from ngmt.datasets import mobilised -from ngmt.modules.pam import PhysicalActivityMonitoring -from ngmt.modules.gsd import ParaschivIonescuGaitSequenceDetection -from ngmt.modules.icd import ParaschivIonescuInitialContactDetection +from kmat.datasets import mobilised +from kmat.modules.pam import PhysicalActivityMonitoring +from kmat.modules.gsd import ParaschivIonescuGaitSequenceDetection +from kmat.modules.icd import ParaschivIonescuInitialContactDetection plt.rcParams.update( {f"axes.spines.{which}": False for which in ["top", "right", "bottom", "left"]} ) -# from ngmt.config import cfg_colors +# from kmat.config import cfg_colors FILE_PATH = "/mnt/neurogeriatrics_data/Mobilise-D/rawdata/sub-4005/Free-living/data.mat" TRACKSYS = "SU" diff --git a/paper/paper.html b/paper/paper.html index ec87e509..184d413a 100644 --- a/paper/paper.html +++ b/paper/paper.html @@ -8,7 +8,7 @@ -NGMT: NeuroGeriatric Motion Toolbox - An Open-Source Python Toolbox for Analyzing Neurological Motion Data from Various Recording Modalities +KMAT: Kiel Motion Analysis Toolbox - An Open-Source Python Toolbox for Analyzing Neurological Motion Data from Various Recording Modalities

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