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Uji Kuat Tekan Green Concrete Dari Pemanfaatan Limbah Beton Dan Abu Sekam Padi Samuel Bryant Agus Jaya; Didik Ariyanto; Djoko Suwarno; Budi Setiyadi
G-SMART Vol 5, No 1: Juni 2021
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v5i1.2477

Green Concrete adalah beton yang memperhatikan lingkungan. Hal itu, dilakukan melalui inovasi teknologi beton. Pemanfaatan dan penggunaan limbah beton/bahan daur ulang menjadi salah satu solusi terhadap permasalahan lingkungan. Bahan daur ulang diperoleh dengan memanfaatkan limbah beton bangunan yang sudah hancur. Pemanfaatan abu sekam padi untuk mengurangi penggunaan semen dalam campuran beton. Tujuan penelitian ini untuk mengetahui kuat tekan beton dengan mengganti 60% agregat kerikil alam dengan agregat kasar limbah beton dan mengganti komposisi semen dengan abu sekam padi sebesar 0% (A60), 8% (A60:AS8) dan 10% (A60:AS10). Pengujian sampel silinder beton dilakukan pada umur 7, 14 dan 28 hari dengan total sampel 38 buah. Metode penelitian yang dilakukan meliputi studi literatur, persiapan alat dan material (persiapan material diantaranya penghancuran limbah beton dan pembakaran abu sekam padi), perencanaan mix design, pembuatan sampel beton dan pembahasan. Hasil kuat tekan beton A60 sebesar 23,638 MPa, beton A60:AS8 sebesar 24,638 MPa, beton A60:AS10 sebesar 23,968 MPa. Beton A60:AS8 mengalami peningkatan kuat tekan sebesar 2,9% dari beton A60. Hasil kuat tekan optimum terdapat pada beton A60:AS8 dengan kuat tekan sebesar 24,345 MPa.

Pengaruh Matos terhadap Peningkatan CBR (California Bearing Ratio)dan Sifat Kedap Airpada Tanah Sekitar Rawa Pening Erwin Harris Saputra; Lie Sanders Deckcrealy; Djoko Suwarno; Budi Setiyadi
G-SMART Vol 2, No 1: Juni 2018
Publisher : Universitas Katolik Soegijapranata Semarang

Rawa Pening soil contains peat has low level of California Bearing Ratio (CBR) and high permeability so it is less suitable for embankment. Above the embankment functioned as a road body. This study adds cement and matos (normal) to increase soil bearing capacity. (50%), 50% G 50% Cement (C) 8%, TSRP 50% + G 50% + C 8% + Matos (M) 2%, 4% and 6%. CBR soaked, normal results obtained CBR 2.99% while the CBR requirement of 5.44%. Addition of C 8% adds CBR value to 9,25%, while addition of M 2% increase CBR value equal to 8,9%, M 4% yield CBR become 16% and M 6% get CBR equal to 16%. Low compaction, normal soil undergoes seepage after a day, compaction is undergone seepage after 3 days and standard compaction does not undergo seepage for 7 days. The most optimal result, with stabilization of C 8% and M 4%. Addition of M 2% is not effective or equal to 8% cement. The addition of M 6% equals M 4%.

Potensi Laju Erosi Das Waduk Randugunting Menggunakan Metode USLE Rigel Putra Samudera; Muhammad Genta; Budi Santosa; Djoko Suwarno
G-SMART Vol 5, No 1: Juni 2021
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v5i1.2892

DAS merupakan daerah sebagai daerah tangkapan hujan yang rentan terhadap erosi yang dapat mengakibatkan permaslaahan pada sedimentasi Waduk. Besarnya sedimentasi yang terus meningkat dapat mempengaruhi kinerja waduk. Tujuan penelitian ini adalah menadapatkan nilai laju erosi menggunakan metode USLE (Universal Soil Loss Equation) dan sedimentasi pada DAS, serta membuat peta kriteria sebaran erosi DAS Waduk Randuguting dari tahun 2010 hingga 2019 dibantu dengan sistem informasi geografis (SIG) berupa Arc-Gis. Pada metode USLE dibutuhkan beberapa parameter seperti faktor erosivitas hujan (R), faktor erodibilitas tanah (K), faktor panjang dan kemiringan lereng (LS), dan faktor penggunaan lahan dan pengolahan tanah (CP).Hasil dari penelitian ini didapatkannya nilai laju erosi DAS Waduk Randugunting tertinggi pada tahun 2014 sebesar 153.848 ton/ha dengan klasifikasi tingkat bahaya erosi sedang, dan terendah pada tahun 2012 sebesar 58.672 ton/ha dengan klasifikasi tingkat bahaya erosi ringan. Selain itu, didapatkan pula peta kriteria berupa peta sebaran erosi DAS Waduk Randugunting dari tahun 2010 hingga 2019. Berdasarkan nilai laju erosi didapatkan sediment yield sebesar 7705.682 ton/ha dan didapatkanpula umur layanan Waduk Randugunting yaitu 35 tahun.

Analisis Ketersediaan Air Waduk Jatiluhur Sebagai Dasar Penerapan Pola Operasi Pembangkit Listrik Tenaga Air Rumboko Kalbuardhi; Djoko Suwarno
G-SMART Vol 2, No 2: Desember 2018
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gs.v2i2.1573

Analysis of the availability of water in a reservoir or mathematical approach required Jatiluhur physical, in order to predict discharge mainstay as the operating pattern of HYDROPOWER that was later used as a guide the management of the reservoir itself as initial predictions. With data-daily rainfall data of the 3 IE rain Rain Station station I, II and III, the discharge outflow, as well as the relative humidity-temperature reservoirs during the 2014 to 2016, then to estimate the availability of specialty discharge data processed with the method developed by Dr. f. j. Mock in 1973. This research is a descriptive quantitative research using secondary data obtained from relevant agencies namely Jasa Tirta II. This study aims to learn the specialty processed with debit method Mock with 15 precipitation process data daily range in year 2014 – 2016, produces a result with mainstay debit results Jatiluhur Reservoir Water Availability IE 814,399,963.00 m3/year, with 0.063341% RMSE with debit data field and serve as the basis of the operating pattern of Jatiluhur. The results of the mock model parameters where the average value of open land of outcrops (m) 39.17%, the value of the coefficient Infiltration (i) of 0.5, the Soil Moisture Capacity (SMC) of 200 mm, Flow Coefficient (K) amounted to 0.7, and Initial Storage of 100 mm.

Pengaruh Bandara Internasional Jenderal Ahmad Yani Semarang Terhadap Kinerja Akses Jalan Di Sekitar Bandara Aloysius H G Daika; Tua Ebenezer Tampubolon; Djoko Setijowarno; Djoko Suwarno
G-SMART Vol 4, No 1: Juni 2020
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v4i1.1911

Semarang merupakan salah satu kota yang besar di Indonesia dengan jumlah penduduk yang mencapai lebih dari satu juta jiwa. Dengan kepadatan penduduk yang jumlahnya cukup besar tentu berdampak terhadap transportasi dan lalu lintas, baik transportasi darat, laut, maupun udara. Pada tahun 2018 kemarin, Pemerintah Kota Semarang telah selesai membangun terminal baru Bandara Jenderal Ahmad Yani Semarang yang telah diresmikan juga oleh Bapak Presiden Republik Indonesia dan mulai beroperasi bulan Juni tahun 2018 yang lalu. Oleh karena itu, kami sebagai penulis mecoba untuk menganalisis kinerja akses jalan yang ada di sekitar Bandara Jenderal Ahmad Yani Semarang sebelum dan sesudah bandara yang baru beroperasi. Ada empat jalan yang menjadi objek penelitian yaitu Jalan Madukoro, Jalan Yos Sudarso, Jalan Puri Anajasmoro Raya, serta Jalan Puri Eksekutif. Metode yang digunakan pada penelitian ini adalah penelitian langsung di lapangan, setelah mendapatkan data volume lalu lintas yang diperoleh kemudian dianalisis berdasarkan peraturan Departemen Pekerjaan Umum tentang Manual Kapasitas Jalan Indonesia (1997). Berdasarkan hasil analisis yang telah dibahas, maka didapat hasil sebagai berikut: nilai derajat kejenuhan (DS) pada masa sekarang untuk masing- masing ruas jalan adalah 0,63 di Jalan Madukoro termasuk dalam tingkat pelayanan kategori C; 0,67 di Jalan Yos Sudarso termasuk dalam tingkat pelayanan kategori C; 0,71 di Jalan Puri Anjasmoro Raya termasuk dalam tingkat pelayanan kategori C; 0,08 di Jalan Puri Eksekutif termasuk dalam tingkat pelayanan kategori A. Kemudian kinerja akses jalan yang berada di sekitar Bandara Jenderal Ahmad Yani Semarang masih berada dalam kondisi arus yang stabil, karena nilai derajat kejenuhan (DS) di masing-masing akses jalan yang menjadi objek penelitian kurang dari satu. Akses jalan yang memiliki tingkat pelayanan terbaik adalah Jalan Puri Eksekutif dengan tingkat pelayanan kategori A, artinya kondisi arus bebas karena volume lalu lintas yang rendah. Pengemudi dapat memilih kecepatan yang diinginkan tanpa hambatan.

Analisis Terhadap Kualitas Air Sungai Kaligarang sebagai Sumber Air Baku PDAM Natasha Cindy Kardia Etnovanese; Tiyas Matilda Aprillia; Djoko Suwarno; Budi Setiyadi
G-SMART Vol 3, No 1: Juni 2019
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gs.v3i1.1772

The usage of water should be wisely. Water which most used by the community is river water. Kaligarang river water is used for irrigation, to wash the clothes, cleanse sewage. and as the basic source of PDAM Tirta Moedal Semarang. This observation method is literature study, collect data by interview and take the water sample, and analysis the water quality. The observation of water quality is done to search for the cause of Kaligarang river pollution so that we know how to neutralize the river basic source in PDAM Tirta Moedal Semarang. The observation is done in the draught season because at this time the content of pollution is higher. The water sample is taken from the river junction, back of PDAM TM intake, and at the estuary. The quality criteria of water is based on PPRI number 82, 2001. The result shows that water at every spot has parameter which higher. Has high value parameter which is more than the limitation value which is different and the pollutant is much in the muara. The penetrate used by PDAM Tirta Moedal is chlorin and tawas, and the usage doesn not influent the cost of production nor distribution but it causes the production process stop, so that the people should have water containers at their homes.

Pengaruh Kuat Geser Matos dan Semen Terhadap Tanah Ekspansif Nikodemus Budi Prayitno; Daniel Hartanto; Djoko Suwarno
G-SMART Vol 2, No 2: Desember 2018
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gs.v2i2.1635

The development of development in Indonesia is very advanced and requires extensive land to build. A lot of development has been carried out to increase the pace of development but has faced several obstacles. The constraints faced are often land conditions that do not support. One of the soil conditions is expansive soil. This expansive soil condition must be corrected so as not to damage the building built on the land. One way to improve this is to add additional ingredients to improve the expansive soil. The added ingredients used are matos and cement.This research is also useful to determine expansive soil shear strength with the addition of matos and cement. The method of mixing matos is using cement and water. If the matos are not mixed with cement, the results obtained cannot be maximized. When it is mixed homogeneously the expansive soil experiences better mixing than without the addition of matos and cement. Shear strength also increased significantly when mixed with matos and cement.

Kajian Potensi Sedimentasi Pada Waduk Jatibarang Dengan Pemodelan SWAT (Soil and Water Assesment Tool) Han Lois Herlambang; Montana Raisya Putri; Budi Santosa; Djoko Suwarno
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.3130

Sedimentasi menjadi salah satu permasalahan dalam pengelolaan waduk. Sedimen yang mengendap pada waduk akan mempengaruhi kapasitas tampungan mati dan umur waduk tersebut. Tujuan dari penelitian yaitu menghitung besaran potensi sedimentasi dan hubungan volume sedimentasi dengan umur perkiraan Waduk Jatibarang. Hasil pemodelan Soil and Water Assesment Tool (SWAT) menunjukan potensi volume sedimentasi dari Mei 2014 sampai Desember 2019 mencapai 1.361.811,915 m3 dan Maret 2034 diprediksi tampungan mati pada Waduk Jatibarang akan penuh. Hubungan volume sedimen (x) dengan umur perkiraan Waduk Jatibarang (y) berdasarkan grafik regresi yaitu y = -0,1215x3 + 81,822x2 + 71490.img 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ONvn8nmq57bFPNt03/pdfPKR0/r/7cyAX1RvSri1TLxLFPQZ9wZ+XkYkIEMU82QNPZ3aOJlKMqEjnQADI3sxqJI6tJeGpDOensb0kadHP8Gao/DhLTMScs49aiPn/Y4SvX9aZrssk9c+nnob+7p1MGfXm4ulZnq0Mysh9Mwd5ajXn5Q7MhyXfX2NU/ur+nl22L8cuXvrz/NiO17x22zsjAM4NnItNaIH2XYNvNNJK3Otq31/VGGrA0bkaP5v75tTf8lTODf/v4B3NXtN9Ig/e3vHwzrsM96DUM1gEOdX/ePj/zNMnnVOcO3NNpn/K3/tp9nnvhn95AM3VDrqC6wXLaOG0T2A/uBDJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMtF0GQjFy9bT673O5/73MX9XeljaBW8wArqk5hXnz5mLrhp/ibO1XsOuLK3Dk8HaUl+/D3Jl345ddz8fDD/wJq1amIDc3Hzm5+Vi5cjX+8LsHcfdt/415s36F8vK9OHJkO3ZnXI66s1/C9s3XY96c2aiubpoBrAwVaUTZoiLFFsOYVGaXkdZrndjiNOv8pQOMyFq3uanq4rlURmEAM81zP1e9vFP5M+pcBp2nYeqvrQ5NTAPYrqeoj7G/bj76MxYNo9NnXrrqZzXPnae1Sf/h2R7TJPXQ2l5ekO3Wy/P3W/WvzXR26Kfta6+HaZh7GrHuPOS+zrb56RtnOU4DWEWJ+3tBwegve4S01gyXoe9ZN3MHkde9943HHvNvawhoPUMAOSvfRNLcHahyrDf+NCOAtx5AWa1ngiatFAawivwV5qiIBBbrIvFpCwbw4089TQP4aNu94VM3L1yyD8kAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyMC5z0AoRq5Iq3/PdT4Cmb+q7S1pAreYAVxYWIrpU0cg98j3AEThwK6LcfDABuze/iaefPQ7+P63L8aoV+/CoYPzcOzoNhzN2oFDB2Yjcegd+P53LsZTj38bu3a8joP7N+DgzotkHvlHL8aMaa8gP/94+LwV04TzGZQeBp9plPki4ewRbz4jzGNfVVNZTogGsNi3Ufu5DUBVDX3pz3jzNgAddQ9WEz9ma+MN4EBtawYDONh260IH/G3U2ceQ//Y5+8WfYe5+McGPuW22xce+UVFnOS4DWCSz6eAz562mWua2e7hre/7+2yvyCo8BbNSqMts3B3BJ5Rmrqk35IYze+x982Ir8NSKB75HrImECh2oAHz4a2vDKYvjm8ZPew5tJ41xf1xDQHmnEfmIYaXXxC24ZWh2Dy/PcvzGjDuxjMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAaaykCoBnBTy2sr+wdj/qq2tJQJ3GIGcOahY5g1/WWcLL4AqItCQfZX8cXmCVj+yTO48ccX46fXXI4VC67De/+5Bu+O74J3x3XBtIk/wrJF1+L6ay7DzT+9CJ8ueQZbN01A0bEOQH0UKo6fj1nT++Lgfn9DvTbGynEaUF4mrtc6r7ICpJOGmIdR5pWNvs6PSacncf92tsmdQqyRRpwzKlSt1yNLZR0cBrBnpLRHOZ77inSGVqFHAAdqmztPjxqZ5qWzPcHqEaCPPQtreKURMetjI9h+acgA1rX1zNMPW3aD1mf2Oo1io2Vmf3Rz62lsN/S6S4tydubvWTdTtnAawFZPVJcgNy8PxytPW6sa80M3f9Wcv2Kd+C1eFhHGcLhN4NAN4PDdAAlzV70EI36rixuX4dOYWlJLMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAaakwEawG7eQjF/VV+1hAncYgZwSclxJE+fhG2bf4GaE19F/pHzsXrl75A89bf4ywMX4mfXXoj77r4E//PoJUieeieSp9yFfzxyCX5393fx02svwiMPXYBZU3+Ltat+h+Lsb+JU+VexM+MGTJ/2DoqLwxgBbA6X7DPLvAy+QKajbh2ZZlcwpqq+W6DfrTUCOMhhpo1IUS9z0NDKp7v/IY2dBqnzb0s+M+pUz9Papv/wY0r7MyLthmWwLOgFBv7tNHLt5al9TbY0Y97QwUNbD2PXs20e6URprvL9pFM1U2a+t0GsDGRfPZ35++1PkzHXHMAz9/mKNn8FHgLalRxnK4txvLwG9e5NQa3xMn/VjpE0gWkAuy/G6gLLJbUhA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMhMaAPqRzML/bg74v9nqpUQFQT3f/v0bt11hNW8wAFmbI2jVbMPP9f6Cy9JvI3P19ZKy/BAs/6o4tG17DzKkPYNzo2zFnxkM4sG8J9uxaiFnvP4R3Rt+OmVP/gE0bRmLBvO7YtvE7OHbwu6gq/SY+mPEoUlI2AI2ybYSB5ouyVGaNNKNs6/0YfH7MxZ1Jep6mSdftHuhmmGFw2dep8n3LLzBOM/fketN4sxuaZhkeJrMvLz9t8CWQvzxNQS8D0CNaV2YQjCayDT7zz1cFox1625xGqErrNghNDWx6+bTX81R52JfussX2oPUIpt32AuVfIn+dC7nSzMu23iN/g1P7cMqKq7u8dLCt89M2P8auLEvf35GuZP4YfJKnNVDW19fH9mPCNPa1Y8yVv3qZQEsjcjfaPAa7taIaNwdwGbKOlGu5nEZlcdMigB957AkZDasif7XM5U/dBBZpw/UJ1QDODONctJGKAA5nHRt7oeR+od0UUi/qRQbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGWhZBlrMAK6vr8f+/Ycx/f3BKC38HkrzLsC+bd/Dtk2/wPZtn+LUqRPIykrDls1LkbElHVs2pSJjyyJkHVkjt+3c/il2bPkFsvZfjMrj30BJ3ncxY9pgHDhwGPWNCtvzGYRqGFO51I0u5dKYBpzcrhutphGm7283G40yxmWYBmw3NVewzxxTRXgtLZPP2s/DMDTNWN+csV45OctX9bAbiLI8vX1mVm6DTpl4Rj62Njekidzu1X63CRu8ASwq6uxPYcS78/RSR6yzzNNu90C1JxQ97HPgeujiUbBX/97lZ+hkvX6CN2EQO/vF0kvnVbDjr0+d6x3GrqqysxzVVmVSO+vmbIO7nfpLEu522MrV2Lcb20aqRhvAGzbii62+OYBzT5xSxTZqGfNibznUc6AhnpUJLNKG6xOqARzOC3CkDOBw1pF5tewNB/Wn/mSADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkIHmYaBFDeCammp8smgJZk7vgWOZVyEn8yLs3/5tbF3fGVs2jMeGtJVYuWwiVix9GyuWjsPqz95HxubPsWXDBGxOuxpH9p2PvKPfQkHODzA7uQeWLl6O2tpTEOZy6/woA7h11u7crxX1P/f7uH23sHEGcG5Yhp2IjAEcnrrxhqJ5biioM3UmA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMtA4GWswArqurk05NdXUlVq1ah/ffG4HM3T/B8ZwLcXDHD7Bv2wXYnNYFm9Kexca0/ti8fgC2bIjBFxu6Yu/2C5C559s4UXgeSosuxtzZfbBkySrU1FTJPFXerc8KogHZon3iGIq4RevCwqlABBRonAEcnouRbgCPn/ReWExl3iiEp2+oI3UkA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMtC8GWswAFlG6wqg1gnXrsXr1Jsyf0xO1J7+GUye+iaLsi5Cb+V84tOs87N9xPrZvvBAbUy9C5t4LUHDsApQVXoTqE/+FjZ/fiblzFqC62mf+MgI4As5SW8pSDF3sZzhjNZxzW2oO60oFglWgJQ3g1anpePypp/HHR/6GjVu20wAO4/zKvDFrXzdm7G/2NxkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADTWWgxQxgZWioaN3i4uOY8u5IrFx6Jz5P+R02b7gDRdnfQ1XpV1Fe9HWcLDoPVWVfR2nht7Bjyy1Y+FEMPpk/FG+PfhHLli4FIAzls614+GfRYkYAq36P7NLQWZ+LWc2TG9lymTsVaFkFWtIAburFiPvzhoYMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABshAeBhocQNY2SWnTtVg+vSp+Gjux1iTshEfzVuAOTN7Yd/OO7Bzyx3Y/UUX5GXdgE+XPoYZ06di44ZtOLDvCNatXY/t27erbLikAlSACrRbBWgAh+fCyBsM6kgGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA22ZgRY3gNVwzWfPnsXxkhLUnqoB6s/IIZ3T0rbgP++MxbsT38XECeMxYdzr+HDuIhQWFAE4K2J+ZeRvvWn3iOGkVX7t1gFiw6kAFWi3CtAA5g1JW74hYd3JLxkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZCA8DLS4AdxunRo2nApQASoQZgVoAIfnwsgbDOpIBsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANtmQEawGE2YJgdFaACVKClFKABzBuStnxDwrqTXzJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyEB4GAjJAD5yLL+lfA2WSwWoABWgAg0oUFZeAV4cw3NxpI7UkQyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAG2ioDIRnA2bli7l1+qAAVoAJUoDUqUFFZTQP4KG9I2uoNCetNdskAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIAPhYSAkA7iwuLQ1eh6sExWgAlSACgCorT1NA5gGMBkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkIF2zkBIBnD5yUqaLFSAClABKtCKFcjKLgjrhV0M/Z95NDeseYo3uCKRJ98MC8+bYdSROpIBMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbaNgMhGcCnz5xpxbYHq0YFqAAVoAJFJWVhN2uPiDelssJ3scvMyoXMs52/gcUbqPAxRS2pJRkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZMDHQNAGcH7RcTorVIAKUAEq0MoVOBXxYaCbEg3clH19Fy5exKkFGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQAf8MBGUAiyFAz5w928ptD1aPClABKkAFhALFx0+EPQrYdSE9lgcRySsjg9VSRfSKaGG1TkYO50e+PqpsLqk1GSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAy0cwaCMoArq2roqlABKkAFqEAbUaC+vh45+cW8wLfzC7zLtKcePCbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSAD7YKBBg3gEycr24jlwWpSASpABaiAUuDs2Tpk5xW1iwsZjU7/w3xQG2pDBsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZaH8M+DWAs7LzUV19SnkJXFIBKkAFqEAbU6Curg55BSU0gflGGxkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkIF2xICnAZxfdBxnznDO3zbm9bC6VIAKUAFPBU5WVOFYTiEv7u3o4s43+trfG33sc/Y5GSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIAOKgaisY/lymFBh+paeOImamlpPA4ErqQAVoAJUoG0rUFVdg8LiMuTkFeHIsXwawjSEyQAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJyDDES1bTuDtacCVIAKUAEqQAWoABWgAlSAClABKkAFqAAVoAJUgApQASpABagAFaACVIAKUAGlAA1gpQSXVIAKUAEqQAWoABWgAlSAClABKkAFqAAVoAJUgApQASpABagAFaACVIAKUIE2rgAN4Dbegaw+FaACVIAKUAEqQAWoABWgAlSAClABKkAFqAAVoAJUgApQASpABagAFaACVEApQANYKXpc2TQAACAASURBVMElFaACVIAKUAEqQAWoABWgAlSAClABKkAFqAAVoAJUgApQASpABagAFaACVKCNK0ADuI13IKtPBagAFaACVIAKUAEqQAWoABWgAlSAClABKkAFqAAVoAJUgApQASpABagAFVAK0ABWSnBJBagAFaACVIAKUAEqQAWoABWgAlSAClABKkAFqAAVoAJUgApQASpABagAFWjjCtAAbuMdyOoDdXX1qD19BjWnalFVXYOKqmqUV1TySw3IABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZCAgA+J5sniuLJ4vi+fM4nkzP1TgXFbgxPp0HBkxDNsfvh/pna/gt5k0EHpnJQ5H+aYNzYIXDeBmkZmFhFuBs2fPorrmFE5WVgW8eNMIphFOBsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIAOhMCCeO4vnz+I5ND9U4FxR4PiKZdj2kM/03fbAvch++y2UrfkMFRmbUX1gH2oL8nGm9Di/TdRA6Cj0rMjYJPU99tZIbL3/N5bZvv3PD0D0RyQ/NIAjqS7zDrsCdfX18m2sUC7WTMubOzJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZaAwDVTU1EM+l+aECbVWB00WF2PWPv0nzccNPr8aR4UNQue0LmrxNNHkbY5QLo/3wK7HYcP2PZH/sfuoxnC4siAhaNIAjIiszDbcC9fX1qDl1itG+HK6l9TFwkjfOjblx5j7khgyQATJABsgAGSADZIAMkAEyQAbIABkgA+2UgTb6PE08nxbPqfmhAm1JgbLPVmLTL34izcZDL7+EmsxDNH5bwPh1msXVmQdxsH9v2S+bbr0BZatTwo4VDeCwS8oMw62AuKZWVnNeX94Qt9MbYpr+rc/0Z5+wT8gAGSADZIAMkAEyQAbIABkgA2SADJABMtAuGRDPqflp3wroLwGcPnsWO4sKMHVHBv6xaA5+OXMSLn/nNXz9jSFyKf4W68V2ke702TOWeHo+1sow/8j9zzvSYNz2h9+iavdOGr+twPh1GsFVO7dj6x/ukf2UN3VyWAmgARxWOZlZuBUQQ2twnl8anzS/yQAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA62BgYqqag4JHW4joA3mN3P3NvxxfjIuTBqOqNGvIOqNIYh6PQ5Ro+IQNTLWWIq/xfrRr8h0f5w/E7N2b2+W1mYOHihNxT3P/i9q8/No/rZC81eZwbV5udjd/UnZX0eGDg4bHzSAwyYlMwq3AmfP1uFkJW9qWsNNDetADskAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIAMGA+K59dm6unA/Emd+rVQBPVp38aG9uDN5IqJGxSLq7WGIGjkIXxk1GF8aFSe/HUbGQn3VOrFdpJPpR8Xh7lnvYsXh/VZr9fytlU34kT12tDQTsxKG0vhtxcavMoDVUszNnN75CmSPS2pC7/t2pQHs04K/WpkCFZVV7XIYEd5E8UaaDJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEy0LoZEJHA/LQfBWrPnsXLa5ajw+uDEfVmvDR7v2KaviLiV5m+MvpXRAA71gkz2Er/Zjw6jIrDy6uXI9wvEhz/dLk0EQ/0ep7mbxsyf5UJfKDPC7L/SlNWNfngogHcZAmZQSQUqKquofnLeUTIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZKDVMiCeYzfHp/7UKdi+tervGrketr/FOt8Xzn3F31Z6I5/maENbLKPOjPIurKrAo/NnIWr0UGn0fnlUnFxKk1f7rZu/+m9pDptDQ4vf1v6jX8H/LJqD0poqKU9TI4Erd+/E+p90xs5H/4TTxUU0gNugASz6beff/4wNP7sGVfv2NumwoQHcJPm4cyQUqD19ptVe0PnWXet+6y4c/fPu5PcgvuHIi3mc+7ywj9nHZIAMkAEyQAbIABkgA2SADJABMkAGyAAZaN8MiOfZkfwI47fwvp+jqNt1KBDfX12LgruvQ/7dxu/8u6+Vv31/m9tEml+Jbca3QCx/dR3EUuwv8hJ5irxFGS39OXr0GOYvXAyxbC0fYQCLCN3HF81B1JhX8bU3hviMX0eEr274ev1WEcJiKbaLpcgvasxwPLl4riynKQZwfV0dvvjNXdjyy644dewozd82aP6qKGDRf1t+1RVb7/9Nkw6FFjeA9+7zjXPepJa04M7lJ0+2YOmNL7qqqgp79x2QX/G7oU8waRrKI5jtnPe3fd8wtfQNMw1g8tfSDLJ8MkgGyAAZIANkgAyQATJABsgAGSADZIAMkIHwMfB5ejrEN1KaiufZkfwIc7bgV9eg+PZOKLitIwq7XoXC2zqi4PZOKDTX5Xe9EgW3dULhHT9CwZ1Xo+COq1F4549kmvzbrkJB1yt9+5h5iLxknr+6psUN4NTP0/HPZ/5tfcXfLf1RZuwr61YhavQr0qz9kmbeRr02CPIr1qnf+tK5XvtbmcAiP2kCj34F8anGkL+q3FDbX/jBTGvoYGUkcnm8RYzw6j07Ib5N0f/4yhWyP4s+mhcqClb6FjWAJ02ZJg9osWyrn63bduDTVSnYtbtpodjN1f7i4hLMnD0XL/WPtU6m6sQq1oltIo3zI064Hy/4xLk67H/X1NZG7EIcqQs88w3fzVhr0JIG8LnVn62BKdaBTJEBMkAGyAAZIANkgAyQATJABsgAGSADZKDlGEgYMRIjEl+L6HNn8Vw7Uh9pAItI3ds7If/2Tsi7raOxvP2H8nfebZ2Qf9c1yLuzE/K7/AB5v7hYfvNv/T7y7rgKeb+8Gvl3Xo282zsh97aO5v5GHtJA7nZdixvA/47pZfMrhFfRkh9lwn58YJec81fM32szf00j2CvS11onzOAA6XQTWOQv5hYW5YmPKj9YDepqa7H59pux68m/Ncl0bIphyX0Nsznv5Rex/8ar5TdvQK8m9ceux/+KzXfcAtG/jfm0mAGszF9hPibPntOYureKffbtPyAN4LZgAgudldnb0FIMtaA+6u2bSBvAYjiF5rgRyi8sapZymqMtLCP8N440gMOvKTmlpmSADJABMkAGyAAZIANkgAyQATJABsgAGSADLcHA4axj6P7Ms/IrfkeyDqGadur5e0NL3QAWBm7+bR2R2/Uq5HS9CnldO8pv7i0/QO7Pv4Pcn16A3J+dj9wbxPdbyL3h28i96XvIveVS5Ha5Evldr5L7ijzEt7UYwF5+RUO6RHK76MvK07X42ZS30eGteHzl9cFyyGZl2qqI3w6vDYLx9YgCVhG/iQONffXoYNMcFvmJr8z/rXhZXoWYn7m+PqTm5YwfK6NFyzdtaJLhSBO3aRHDlZvWW+avMoGbEglctna17NfcdyeExINK3CIGsG7+tuXoXyXizt17Wr0JnDR2vGX+9uk/CMs/XWUbS1+Mqy/WiW3qZPvulPehzF+xLtIGcHNE/4qL/OikMRG90EfyJoJ5R/5GuekGcBbmdL8J19w/ButKIl/flmYiM/kpdOzUGbFpLdfWorQxuO/am/D03Czt2E5HbKfO6BgXueF9mkv7zLnP4mfXPoDRace19gXQ+8gsPNGpM55I1vUIkL5CbDuMJcN7Ibr/VGwqbSht47anj4tBdI/xSJflNS6P5tF8I8b2iEH0uI3B6d3q29OatW5jddswHtE9YjB2Qxurd6MZPYyF8TGIjl+Mg43Oo3FalWyZin49emHYssPt+jhskg4mr0MWNZ+GBxcNRXSPoViY2bh+b55zPOtGnckAGSADZIAMkAEy0N4YmDZ9hmUAi9+RbH+kooB1AzjvdmH4XoW8LsLIvRIi+jf35u8j58ffQM4tVyDvj/cg/+m/y2/eX+9H7p0/Rs715yPnJ99E3s0/kOnzTCM4XwwZLYaQbuYIYOE5iAA0/as8CX2pbxe/16WtV5ZMsyzHbUlH1BtDLPNXRvMmDjSiel8biKjXBqJDQn985dX+iHq1H6LENqfJK/4W64f3w1df7Y8OCS8bf6t8zKUwgb88Kk6WN+GLjSG374tf34FtD97Xhs3f/dj15BVIj1vdhttwHOE2gIUhv+3392Drvb8KmQmxQ7MbwOea+atUb80msK55Q9HWYp5fYfTqJ1r1O9IGcEVlVUQvwOLiPmHiJHnBX7kqJcJlrcdb5ptlQ5fnBSjLTDdwAQ4088PVSN7stOW8aQCH9h8hGsCh6dWYYyNiBnDecgzrEYNhy/JpANvOvy1hAGdgcp8Y9J6SEeB6EXnWGsNnc+2TsywB0T0SsCSvBXVodQZwpLlp2wawnZky7Fw5CSPiB6CneMFDfF8YgGFT0pDleuklH3tXz8CI+L5W2p6xIzFtXXazH58tawCHfi4MrwEcSp85zwtlyFo9Hv0CvHSUs205xg7XeRiMEe978WDkbaRXTPRC7+FTkXq0rNmZaK5zLstxMsW/yQQZIANkgAyQgbbKgBgJ8vmYFywDWPyO5OiQ4vl2JD66ASxMW2HgShP4zs7I/fklyLnxEhT+319xYtY0VKenoXrrVuObno6KTz7G8cRY5P/l18jt2hE5t14u9xWRwPm3N38EsO5VKN8hlGVzBRSW1VTj1vffQYfRQ/GVUYOl6Sujf83I3ajEQfh64gC88vlnWHBgD7ovnoeoV3obw0RrRrDcZ+hLeOmzJVh4cC/6pCw1zGKVj4ggNoeJ/ooYBjppKO6Y8R+Un6oJGqXTxUUySvToyIQwmaersbXzFdg4fb+f/IztW9c2LVrWHm18bhjAok22IaBfftGPhsFrl5U4TPbv6ZLioJlQCZvVANYP7uY6UFVDm2PZGk1gPYJXRPgG+9H7Sp2AI2kA19XVR/wByu69+62Lfd9+L0f0Yl9e4TOAuz/zJlYW+btRDJcBXIaM+W8iLmYSPrcZGf7K5Xp/N65NN4Dbl7atwQD27stzJwLYu30BOAsyAnjvzMGI7jEGqS7zI0DeTTi/MALYv64l68YgusdgJO/3nyZkDprQV81TVhm2LhqDYX2mBhEVfgDJsTGITkpDScTbdRipE0diUKJH1GsrM4Ajz03LGcDeDDaFmcNYkpiA0RNnY8m6NKSuW47kpKGGwTvc0deZizHkhQEYMc5Mu2wGRvQ3TOMRK8ULM+E+TkNpVwhlNzkCuKUN4BD6zOqTMkijNr6XYfL7M4C3TJV93zN+DKYtWit5mJZomsFOHioqcXCReAElBj3jx2Ph6jSkLJpkMpHAaGdL+xDY5D4ROI9Q//Cfm6kpNSUDZIAMnCsMiCAgMfzz4MHx8it+RzowSDznDvdHN4DzbrvKiPrt2gm5t34fOT//Dgoe/z0qU1OhihY1UF9Rl9rjZSibkYz8B7og5/r/Qq6YS/iOH8rhoAtv64iCu5tvDmDlNTRlGW59vfJbn3MUX3vrFRmV2+E1Y5hmGQGsfo8YiP8aMQDrjmXJ3XcU5eP8kYPQYcQAI0JYDvschw6v9sPlScOQffKETDf/wB5EDeuLDioC2DSChQks5hgWcwF//Y14fFGQ61Utz3UFs2dKg/BE+romm42GKdu2DODMuNYXOSyGfW7K0M+6OX7i81TZv4VzZ3v2f6CVzWYA64biuWj+KpFbmwmshnQWQ0AH+9FNY/1EHEkD+FTt6Yj/RzhhxEjLABYX+zlzP4xgmYax+6+BsXjpmWfRIykVBZ4PO8JlAGfio4FiLou3aQB76hz8jTsN4OC1Ev8ZoAEcml7N8h+ooAzgSEcOunWhAezWxOAhH0uGxyB6+HLkNPH81Sx8ha2Oprnoz6DRy9kyFb179MXkLf40DOd60/TyGva4VRnAzcFNazOAw8/MwbliyOIYjF6nRXKW5iPH+WJMURrG9onUcNghtEs/Lhr63eYNYO/j2rPPhBZHV2HEC4ZR3zP+LdOg9TPtwJZ5mLzFbeZ75r1/Ngb1iEHvcRvt9/KlezBNvJjida5oqG+4PYL/B/Pmpm1dH9kG9hcZIANkgAyQgXAyIIxfZfpaZvCQVyJ6PyKec4f7oxvAYu7fvNt/iLzbfojs676C3HuuQ3nydNTV1KKuthanMg+ictVSVK5egVOHDuDsyZM4U1OLk0sWo+CPXZFz7VeNYaBvNw1gNQR0zalwV9szP91vaOxvz4zDvHLkhlREjTYNYDNC1zCAB1kG7zdHDMDSzP04ffYsas+exV/mvY+o+BeNoZxfG2Qsh7yI55Z9JGsn0szcvQ1Rw/vKeYNt+cn5go25gEW5b23+POgWZcYPwuauN4XJ/BWRqTSAdQO2NfzefNsvcHhoXNBMqITNYgC3F/NXidpaTGAxr686iRYXl6jqBVz6M39FPpE0gKtrTkX0wvt5errN/BUXfjHkh5gTOJw3Fb68TGM3KQUbxvVE92d6Yuwm7eGi9eCHBrBPs9Zxg0sDOLR+oAEcml7NwnsQBnDkIwfdutAAdmsieTDNBZsBZV0j/OxzTmwP1vQqQ2pSDKJjZ2Nvs7S7jRjAzcJNWzWAQ2BGRPv2iEHD8+SaWvSZiq1h5zDYYyHE88E5agCX++uzzMUYETsGyRuyUVLRSE2PLpZTI/SessP6/8HOmQP8zmtcsro9jt4QIodhP15YfrPcS7LfrHMA9eYxRwbIABloGwyIIZ0ztm6T35WfpWDuvA/l9z8T35XPg9Wwz/pw0GKbSif2UfuHY3ho8Zw73B/dAM67rSPyhQF8+49w7EcdUPD4b1C9fTvq64HTBcU4/mpf5P7kfOR1uQSFjz+AsokTcHLRQhT986/IvekHyL31UjkUtBhKOv+2jmAEsHdv/W7Oe+jwxhAZkdthZJxh+upGcOIgfCPhZaQcPYx9JUUorKzAjF3b8KVXevvM3cSB+Mar/bDyyCEcOF6MY+Un8OG+XYga9pIxVLSe30gjylhEAItyfzvnPe+Keazd91x37P6fx2gAlwY/rHJrMHVDqcPuJ/+OfT3+z6P3A6+KuAHc3sxfJXdrMIGFYSuM27j44apaAZeBzN9IG8CV1dUR/U9W33795QVfvPUlIoHV32JO4MjczCkDeD3Ki9bjrZhn0T1mEjJc/5kOZACX4cjaqRg6sCf+Zc4n/K+YWIyavhlHtHwOzI91mdvC4O6etL7Bth3ZNAOj9Px7xWPCJuecxXnIWPw24mKizXKi8dLrC5CR630TmL11Cd4a0seqc/fn+iBu1n6zLpouWhuMPvDapq0r2oWPEvvIOsTN14370OrXUH833QDOwvQnO6Njp3isc7UxC6smPI9Hfn0TOnYSaW5Ct0cHYHJalquvDGP1KUw/4tbZa5u+LjNlDB65oTM6Xptg1CEtXpYXm1aJoh0fIvbJrrhGlH9tVzzUfxa2lbjLKD+yBu9EP4ZuIh+R9oZ78XTSGmQ62hSUARzIkPTcpg3fXLITc+Kews3Xinpcj5sfHoDpO47b9TLb90SyrqOWh1bnoh1T8cS1nXHNw2Ow0Wp3PjbOjcfT95u6+CtHy0fnKHvzh4jt/oBZx8645pY/o2/yThR5pvco6/5nMX2fRx+Y+/vXOB8bkwf4eLrhz3huwhpk7puFJzp1hl0PPX+vyEGvh+XmOhndlI3U90ein4qu6pOAyX7mwyw5mobJ2ryKYsjMJbuL4d8AzsfWZeMxrI8asrMXBiUtxlZtrteclSNllJ7bpNkh584V5uBOT70rUa5FbRp10+dwNOqm92d5hTbsadEeLBmXgN6q3bFjsHB3sZ0/PX1pNlKnaOn7JGDssj32qDFHPTdN6YvoPlOxybG+vCJfDpE8yCxbzFc6YmYGcvyYH1lbZmO0Pmdp/wR7hJumQ86W2RjR39T7haGYpiLhjqbBGga1Ry/0S1qMnR5TGAgdpyUOtXSJfqGvMZeqow36fKBZ66ZiiGzLJHy6yIi6lPOwqvlYxXLcRoe2lSi3zVWtcSy0fn8khmjcGHNyamnk0K2irKGew7Tq9RMMGIya88Nq9bK40zQs2C3mD/WxJJj10qq8ogxZ6xzzyfYZitEzM5Dl0MvJ3sI3hPEUg2GL3HPPenNTjL2rp2KY6tsevdA7fiSWWEOLa2wHKtvapp8DnDya86VaaQ3dfZqWYe/K8Wafx6Bn/zFYst94EU5qZw3R2wtDxDy8jnzUcau0N/J1901IzDjLEH/7OZ7s54RKlJduNCKAHcOQR/xYCGTieh0D8ZOQqs6dfvc1mYz1nXO957MNwEveDixM8p0DevY35kj29b/9OHTp6dUXwa4Lqs+8rmlB1Kl0LUaLY3+ibz52/9cuHz+RGRo8iPoGqxnTua8t1ISakAEyQAbIABk4JxgQZu1bo9/2fi5qPksVz0j157/it3xuqm13/i3SNMUIFs+5w/2xGcAyAvhHyLu9E3I6RaHo6d+j5uABnD17FqdLy1A2/g1k/+IqHLvsazj2/Sjk/vxbyP/tdci9+TLk3nSpHP5ZRhGLOYBpAPvtqmsmvYmoUXHSqFVz9KohoOUycSC+nvAyUo8dwaojBxG/bhWqz5xGx3GJiBreD2I+36hhfXDTlLdxtq4OL65aLI3i+ft3G0NAa3MAy/yUASxM4VFx+MnkJL91c27Y8cffY3/Pf7eoAWwNw3xwJjZ2vkIOWZwulk/ORIGHMVsw/SlfGjnf8GrsetI5lLM5L7C//Na+bstDltf5dWRq5cl6afuHd95ib8O5ctN65A3oJb/idyhGr7+0+3s8ix1/ftDZ9Q3+HVEDWMw5qyJQm7oMZf7aBlsdZIJ9+w/g01UpYfmKaNzm/ohhn4XuybPnNHfRIZd3srIqYjceYqhndSHfsnWbLMdrXVgfTqk5gE0TtmDt2+jxzLN4afouRzv9GcB5WP26iBx+Fv8a+DY++iwVqz9bgLHDDAO0+5AFOGDerJYc2ozVa5fgrV5iCOhXMXltKlaL706nkWt/kHRgvjEEyovDxhv5LxZmcDQGzM/U6liMz5NEPaIxIGkBVq5NxaLpb2LAc8LQfhuf24yBYmyYaBjt/+r1KiYvFvVIwUdT3sSAiZvNPDVD13Wz7bVNrUvBytdF+4yvr46h1M/efn/9HTEDOHcN+v7aMF2fiJuFpSlrsDQ5Ac/df700Np9IPqDproZWboQBvGwqHpLmsmZCKwM4eRaeuOUx9J3yIValfIh3oh+QRvA10Z8g29Yfhnl6zf3PY2jyCqxKWYHpcX+WabsNT7cZm/7NSU1vT5PX3O65TZm3szD9ya54pP9UzElZgzkTnsd9wgi+9nnM119ACNYAFsao2P/X8Vhnmb9K65usclbNnYinZV89hTkeBryNHbP+PxNG/tw1hq7d7zX6dK5uSFeivCQDox8W/X0THoqeKNu0atksvNP/KYxO0/Sy9YWqX2cIA99X9gHM6S5eJLge95l5KZ66PfkU7gtkAHtGDno9LFfmzwwsHDcA/RInmXMfKlOnLyZv0+tUiYINk9BPPDx/YSjGmvMqGvNsJmBYvDBvnMNxCmO4L6J79MKQcYuRsi4NS2aOMUyjPuORbp1jDuMDsb9Ypw3NapgN7nr4dNIM4LmLMewFj3J69MWELfoIDcr0mIeFw3uhX+IMOWfoEmGAS1PQOeejPb2aIzJ1mTBkDYOl98QM77lrS9Ok2TBopv34LxfRa2JY6B4xmu7GnJO9hyfIeihjTLRVzU+p+kiVradRhtroiZPQT5g1y8Q8lqovByN5izHE7SDZD2uxcJyYIzoG0YmrHENTm+3tPxITZB/70jrbqcygD1YbQ6gahu94rMvMkHNujpXzqgrjSMzJmobU3e4hWf3NVW2YMgMwYqLBjWiLGKY1us8kbHIxEpwBnLNb1GMGRoh8+o8354pNw6ZMkw9lAEuWBmDE+8utuUNl21xzh+YjNUnw7Zs7NHX1YkxINIzdaFd6xdJapIioZ8mbR3SqJzdl2DTRPJaSjLlr1TylYzeo41Tl72G06y8yWOcg/RzQF9LkW5aGQG1QfT52YoI1X6rv2BmDlG0iwrIvhinthhv66BGX8vh1mJclYWDGdl4w21gQVASnOk/5OfYjeSw4dLDakLkKr4shqbVzpzjuxyaOwZJMs7/97GucL3znNsVJdA8/7XO+mJEpzqWibHUMrMXCieL82BfDhvuPlrXqbvGluAxtGVyfeV3TgihHDjcfA93Q3Spe0vHzEkn57tmu83G42sl8guivJrJEjakxGSADZIAMkIFzhwGnqTtt+gw59Z8YEVI8C9bNXPFbrBPbxPNhkVY9bxRL3SxuLCMVlVUhPxtvaAdpAN99nYzWze16JfLuMIeAvvrLKPzjrahY9Zmc/7e+vg6nMjNx8uOPUfrmayjp9Xfk338dsn98HnJ+/F/IveVy5Mjho69EbpcrkX97JxQ28xDQe/fth/gKz6KxntH8hYshvpH0XC5MGiajfm3mrx6xKyKARwzA2uwsbMo9ip9MfBNn6uoQ8+lCRA15EV9+fQiiBr+ANzasRVlNDTqNH4miygrMP7BbDgEt5vtVxq++VOVdMiahISys7WJ44EOx/cNiNBoGZOhDQEuj9cmnsNFm+Br5OE1gw5TVjVrN6I1b7WvH2texcfp+39/m0NTpeprS47DMZ834PVNq5qnXxzSMI2kC1x7LwsG7bsL+G6+WX/Hbn6kbyvpDA17Clrtutfo82B8RNYD16N/GHsxqv5aYN1iP4m2qESzyau7PiJFvypNoJIduDlebGntBbWg/cVEXw3yoC7mXATwi8TXNVAnXzY8yLlUUbrFpYMZi5iG9DDPdQJ+hK9qUvfxVWec4mxlr7HdksbHtpekqqlasD3UO4F2YJgzjfh9YRrKhZRmyc31GiFGPWMzc51sn0x36AAPE3MYTDUNdrCv47E1Z5x5J6wNEuzl18dDCFrms9IlF3JAZyNDq5tMpuPoZ7dPL8/4dGQM4H/Ojr0fHa5/yiPTMwhyxrdOfMVmLAtUjep1199pmrHsMTzz5x/QNaQAAIABJREFUAPouO2AzactNg9Rd/nEs7e8uu7wiHXOmZDhMYZXWbr5G1ADudD2cxnjRsgHSiH5oimaYBWMAl6QjVpi6v+6D+ZrOQtvMhVOx1Gn07jCM9DuSfFFAzn6Qfx/5BJOXOYzeip2Y/HBndLxzDDZaDyeVfvciNs0RwWyl8WbSS+Pshc/jGg99yisOYPqTok/9RwB7Rw56PSxX6zzmPtxtGnq2aDgzGtdm3BptKtgwHr2lmWU3gI3I3qH4wIwMtDTOnCeHZbWZQmaZg2buMc7ZRUakli2Nl5amCSIe4LvKyTOjvWzDC5smmYy8PGy7PhjGQwz6qTrI8nzph8w94DB6800jbzCSrShMXz/nLEuAMF2WqIg9s/4q4nnYInv5ujHsM3f3IFkYqbHzcNDW/jLk5GnnbqVDn/FItYz1SggDQxqnPWJgaSvzKTbr7jRPM7Bw5g6HKazSjsQKrS2GGTgYQ+IHYPTqww5tFF92JiwGZB38z1W9adFsW5S42E/1j27eKENyoTLFNI28t5n96TWvp9LQZZSp9g9A8m6tf83IdXc/ViJrmRHVbjf/VdlDMWy4aJ/Wf1q9PbkpNVkel2ZjVkSS51j9bebvNPRk3l7bVB+Jc0Cao8/LsPN94yUBffhyQ1MxL6qdR9U3wtQevVqPot+DZDGPqvPlEE/zUtWncczY2CrNxs7VkyT7g97f4WDT14flFcr8HYAJG/R6izTNcCx46qA0cxq2er19L7/4zhWVKN9mzKc9dp3jZQs1x7HthQ8vJvKxIlH0l0fZwhiW53nnOcN/dL16ycFYBupXEYUdbJ8JHYJhxaGX9eLNGKRqL5GUm6awaw7gimwskVp4vKShHa827rjecX5y9gH/Ji9kgAyQATJABshA4xlY9MkS6/lv334vY/de/bmpd74ijUirnhuLPMLVB+F6Zq7yqa85hYJu10mzNu82EbnbCXldOyLnxguR2+UylMT2xelj2ag7W4e6unrUnzmDulO1OF1cjMrUdShJeAX5v70ROTdejNwulyP3tqvk/i0RAazaJJYZX2xttAmsfCMxqmkkPl9/fTCiXhskh4DWDVrrd+IgGQG85ugRHCkrwdeG9cWSQwdwqKQYHRJeRlRCf5w3Kg6l1VX4z9ZN8u9TZ07jw/1iCOi+nkNAi7zFENCiXFF+sB8R+ZqVMDQsRmOTDGBH9K3My4wItkxX+fdT2HXQGT1rmsUOc9dpksrIYd3U9WcAS7PXXY5hVHtHJTvLaszfFSmfWuavMoHFusbkpe9zZFi8jHaWY70HCwaAiBrAoh5i+GF1MAoTV73hEeoyhDaFNWlpaRka89XN4/UbN4W1TsFm1pYigMN1cXXm43wDzMsAFhf5latSwnaBN+rgYXTmrsBQEcE6UDddlcGpG8D7Ma2fMGdnYI/ngyJze68Z2G5tb6QB3GuqlofzZsgsx2bIqjR5WDpMN5DN8mMmYYNVJ5VWX3roYqX32maueyYei/SIT7lPKPXT6xD4d0QM4H2GmWgzLa12V6Lc3H7flJ0Wh4bpF2oEcGd4GpamQarnr44VYagKszA2JbAuIr1XnbzMSZW3tfSM8jXL89xmRgDfPxXbdJ3E75IV6CsinOPWWFopg9s+5LGKIk6HZYp6GvD+2q3v7y+N//Xr4rQIbFFvs52e/eNso+Nvt8YHDIP54ane54jNY3CHPwPYM3JQtMPrYblaNwYp+sNwWT+36WjMKxwD3QyyGKgwh522mTwHDOPH04wyTQaHqWlEYgmT0Yx2dER7+srT+sY0UHpP8TbzjfkdB+MDyyA0TQ/PYZnTMFYYHDaTLVB6w2AVkcOD5jrNXLP9NhNd1NtcbzOl3e3xmTpmX/Sf4X8YbNFnpg5281rka+7fw93PxvyWMfBFkGr1cHDqZaYqM9Bbe8WXf9Mn5LmqPYaG9aqX4sR7m9mfAQxgz/asGy8jdseuUxo10I+qn/vrw5ebZXu8FKDqbPHh5EYZwG+sDfASlpm/5zHntU31kfslBVkfVaZ2PKg+H73aYV6rtLb2Glp5zrHqaXyq+jSWGaWvMDDFdygmbxHzxao+cy+NaNm+GOsyf91pVR95caV08WTH8/xr5u+lg7lu2DKHietsh2tfc25kL7YrKmGcX8cj1crHgwlzBAn7iws+LeQLRh7RskZ0vRnpryL+Xcs9jpcMRL6h95nRDw2zovrLWOYjVY5G4dXX6iUAMZT5SIydOAkTJo7HCPHiTdIYvB7UHNI+jezlcj31IANkgAyQATJABshAOBkQz3eVmSsCggKZwGKbHjQU7mfDwT6/DzadHgEszd8uV0FEAufe3gm5N12MvDs7onhAL1R8loLTpcdRX1uL+rNnUQ9IU/hMSSlOzpuD/Ae6IOfH34SYR1gMIZ3ftSMKu16Fgl9dh/ra8M9dHEz7/h3Ty/KPlI8UyvKl/rHBFBNymsveec2IAH7NO1I3yowAXp11GMXVVYga2gdPLpgty/n1zEn40svP4Z+L5uBMfT3umT4B542MRX19Pebt2xk4Atgs7/LxI4Ou86Zbf46DL7/UZJPRZzg2MgLY07y15+Vl4BrlmhG7HnkYEcPasNIOo9krAthrnSjHGHpajz52GtFN+1tGAN95o2UCH7zzxrD0y8H+fbC5y41BM6ESRtwArqqqspnAkXojQzWoNSxz8/KtYaOF+Xv69OkWqZaaA3jwK682unzRf83xEUNjhPOCL/ISF3J10VdLfwaweNtLHw6k6XXxMjMroYZd9kX2mun0CGDTKNaja5312TDOGO55qWWKhmoA++oih5hOz3Q/AFWGdYA5Mbo/8zY+Fw8KS1MwSqQbp4Z69ncD662L0T6vbR76qAeTodRP7RPEMhIGsIjW7NjpVxi92Z8u6RgqhibWhmL2MlsVB17b3CahVpZpAHuavJ7Rs2Lf49izeQWmT0hAbPRjuM+aH9c+FHHAcpXeniavWT/Pbcp81UxelVeFue3JWb75iD3boPIQw0gb0deTnXMHW3lWojx3J9bNnYqhwwfg6fsf8M19rJejp7f9zse2tA8xeXgC+nZ/APdZczz7DHzDaL8eQ23DOGt9ZMvPvt6lce4neK6TH7Nf5OOpqZGnZ+SgLNvrYbm5zmHCGhy60/vMWXv9FbeueRTzVhlD7UojRhkyzqXD6DGj1MQwyIN69MXYdc6IPI+yTRPEZUgpzc3tPpNTmR7OSEqRt7nNZqCY65LWus+jsgyVn2PYXRlV1heTtzjqrIwybQ5KpaFcepqcIpLYHGZ4gzPS1sxftdMyKFW5AfpZ7WMNIaz2KcPBbWux8P1JGJs4FP2sOWftZrEyvXzaqv3F0s2QrZ3qpYHhyz1MISOfkswdSFk0AxPGjcSQ/gPQU7GkGZxeZpwqx3ubVx+b9VZ6uDT0GeyWMW/yHShCfdNEwbseNR2gbMWrP26sSFUxbPgMpOzO9+DRD4t+OTX7yMO0NTQ0t2vHg6Gp/kKF6nNVtvu48uwHU2tLT1nHpjKTj53KdJTDJRvM9Bw+23v+ZrMP7ZHxqj1qGeFjwUMHwzBPwMKjqg5+lq59zVEa1HHiudSjd1Wf+c5dxugE/ofd9+xLxW6jliH2mVVGQ6xomuXtQPJwMVz/AExwRkZb+TnmqlfznptRz3ZOtbyt/blOnXe5JAtkgAyQATJABshApBkQz36VsSuWh7OOuZ45i3V6mkBGcWPqG5EhoGtPSZNWmLXCuM299UrkdREG8NXI63olcn78DRzr/GXk//4nOP7ysygb8xbKP/4INQcPou50rTSCz5ysxPH4fsj+6beRd/MPkHtbRxlJXNjlShTcfR2EydwSn6YawH36D4pItX8xdZwxB/CoWKhhmWX0rzKEEwfimyMGIOVoJoqrqvC11wbhopGxOHayHB/u3o6ol59DWnYWdhUXIuqVPrjs7QTZD/P27pIGcAd/cwCPMuYAFuUH+9l2/2+w99l/hsVoNMxYu2nrM4aVOWpst6J6/UXhyiGZ7Xn5j8B1G8DWPMGaKexl4LrNXm1I6c66cax+R84AFlrJOYBffhF5L78of7v1UzoGv9z7zP9g2x/uDRYJK13EDWBRUnsygVuL+St0F2Pgq7dliotLrE4P9ofYJzqmNyL1Fo1ej8rqatfFuDEXWH2fhBEjXQbw4CGvQAz5rA/vocxhMe+Dvn/TfnuZmeImLxMfDdHNWw+D89ACObyyb55b983hgfmx6P5MLD6yhpMO3QAuryjDkbWTjPl8hXn7XCzeWrzLFzlk1uPFpCXGnMJqbmHbcpcxTHAQdTb09KeLaKPXNq91ph6h1C+EB3CRMIC9DFs7X1mY/mRndNTMxkD7eG3zWmeVoQxgL/PRyzzd9wleEMMld7oeN9//LPoOT8A7ySswJ+4BI1pYy8co124KW+Uq3QMYkt5mpTJvRfSuk/8QDeBrr5dDRt/cf419WGwr3+PYmGTMb9zxhnvxSHQ8hg4Xcw5PxNMi0ljrE3ddKlG0eQweEuZ9p5vQ7dHnETs8Qc4F/E53sc5nAAfsH6suzrYaf7s0DqSnyMvvdn8Rp6Icr4flbnPHp4E7vcvgdbTLtd00MvuNE/Oo+osMc0eEqeGRo4cvR5ajDF/9NC2VaecyMc00ru1u08OXr7lNM7wsU1gzHX3pRRle+ZmReF5Rvh4Gry0/z+1lyFo31Zg7WZg6Yg7mZXt853Ohk6udSqMA/ey1jzX3qIiGS4CYU3jaorVYMs6Y11Y3ewObQW6GbO30nKvaV+cVbxhzx0a/MADDkiZhwvuLkbJsvDFPs9YXgergvc2rj/2xourj09cygjz7SUsv524e6phb1IsVfZ8A3MhjIR9bF41BPzk/q5jHWMyvnK2dRwPl77UtABuyPPd2b01FG7zyN9rmuY/JnqWnXp5tJAFNn4DMaOlkXubfathi29DHvm1DethfarAx2hzHgocOrnOp3h79t2tfsw8SZwQ452bgoDXig7vPPPtKK7Oh7Tb9tP1CWh+oz6w8Gzi/mOkKds825jOOnYTUhgx1K2+NJTmkdgBGvPbhOu2cpGlJXagLGSADZIAMkAEyEEYGxPy+6nmvV8CPWKe2i7Qh3Y8GUU/xnDvcH3sEcEfkyQjgjsjpchlyf/495HbphLz7rjeM4O9E4Vin85B729Uo7NkdlatTUHfmDOrrzuLE1MnIEabxjRcgt8sVyL/9hyjsahrALRAB3JqHgP7LxzPR4Y0h+PKowXYDWM3dmzhIGsAiArig4iS++eYriIqNwcSMdBwqO45fznoXRVWVePmzpYgaGI3Lxr0G4QDP22cYwF5zAAujWZb3xhCI8oP97PrH37DjLw+F0QB2m7E2E9NjGGe3CavMTbtZHHQEsHPoaHN+3+AMYH/zAqs6tb3l9j/9AXueeixYJKx0zWIAi9Lagwncmsxf1cPiDRhhAr89boJaFfRSzSEcqbdo9IpU15wK68VWv9CrC7pYDh4cD2EM9+3X37rQq+3izS+vm4LG3QQEMi6N+XO7v56CAmV66hHARxcgzjG/rrMOGRN7ovszb2Kl9ZCuMQaweuhShiPpC/DWwGipiRV5bNaj4ajeSpQHUWejDQF0UVrYhpwOkD6U+gVxc6Y0jogBPPepBiKAMzD6zs7o2H+FZVIGMgy9tnmtU21SQyTHasatc5tv+OR8zBHm5Z3xWGVFmBusuIxIa1joVmwAx63BnuSn5Hy5DyVlWPpa7VfDJU/R5hSWvHgYzS6OzH570j0UszEEtGYAN8iAOh7dS5fupsHrNaS3bJc/A9hv5KAo0+thudvcsXTzSG+YEu5hhNU+LtPiqDlfpL9IV5feop5GBFvvPsL88x+FpsqUSy8TU8tbGcoTrEhct+nhy8/cFooBrCKd9XbmLZdzZXoO4aqMcds8wxoXAY3FMmRtWIyx8SKaLQa26FO/OgToZ9c+5lDeYh5hba5foY9h/NiNkMBmkBdzvnZ6z1VtbDci2UUEuGMIXFOb6NZgAJt82/pA405oZkTN68dMIPYqUR6IG1ve+di7eoYxRG2PGPjmRA6Uv9e2AGzI8syI0uGLrZcx/Pe5V/5Gf3ru4zIvRdrGM+M7hn2MqXWp40QktmO0AdE+sw89j1MVoR7pY8FDB3fkuLtNsm2ufTMwQbwgovWX0sB76e4zo6/sc13r+3r2ZUWY5gDWGPfbZ1aawKyIOqu56QdNyfA7yoDeNq/fRnv1KH4/fWHVi9u9dOQ6ckEGyAAZIANkgAyEmwE1FLR4DqzyFs989ee+6tlwuId+FuWJ59zh/tgMYBEFfFsn5HbtiNyfX4S8uzqjeFBfVCxdiuMJ8cjr9mPk3HAecjp9FTldrkb55P+gvq5OzgtcNnmCZQDn3CoM4B+hsMsVzRoBLKYGzfhiG2bOnmsFr6kgtmCXYuRT8RUBcJH6vPPFBkSNHoqvjBJzAWvDQKvfiQPxjYQBWJV1CLkV5fj6G0MQ9Upv/GxyEkprqpF78gTyK8rx/TEJcnjo749NxNm6OszZu8NvBLAoR5aXNBSi/GA/+1+MxsYbr8fp4yVhM4G9jFZlAnuZuNIAdgzNLNM75+J1/m0au2dMwzddRft6GsAqstcewetlPgeqv2pHW1meLi7Chp9ejQO9ewaLhJWu2QxgUeK5bAK3RvNXaC6G3FYnzuWfrrI6vqEfavhosa94EyfSn1O1p60LsrowN2WpLuLK3FVLf0NAq+1izuCmlOvbN4BxWVGJ7dOFAd0Tb61NwVg5L7A+B/A2TIgJYg5g2xzBx8zIYnNI5kY9aMrEzIEiOnm8OY+vWQ/bXMP+bkobqrPaz0w3ZAGOOOtoRvR2D9YArgilfqr8hpeRMIDLTZOxoTmA9e3GsNHeQwavi7veFl0quAufAew/+nZj0q8aFwFsDll8zXCPtyrT4mWErs+AFn3kvw7WNj0y1yuK2ZbHcayLu1dGND+RbDd6/epm1jlgBLA/o7UiH/Oj7RHADTLgPB60v10GcIU5ZHj3D40IfC2tYCF77rOyn+yaNhQ56PWwPJD5406ftUgMQ+wxpLGsXwYm93GaLKZ55Hd4Wffx6htm2iw/Vp8/1Z1enpNNE8Q9961Ib+pim//WbXr4zu3mNi8D2E87rLmRtTlR984cjOgeY5BqvcSj112ZNN5DHxvmZwzskZH6/uL3YXwQL/SehE2KD1MHPULXaFeAfnbt418bo29CMYCzsXC4kwmzHX7nqja2u14mUG00o/F0A9ht8Pu0MvLRh7wV20z9bX1s7uPSw5eXirD29YvJt1eUt6yv1xzB/vUVfRWYG60uSg9zyHSf4ec2bC22Pcxzn+Gqm9RaOWbErX5s+TMAwxMB3HhmrHYqbbRlw2ai1mZrP/99FdZjwWTOx1UlFNPe861rdXXta86tbjvfaemttql1Hm3cMkm+XBLQFA/rHMCqLvZlw33mvkbZGDCjiIfNPeAxVLq9LNt+No3M48k5H7ctTbB5MZ1/nakNtSEDZIAMkAEyQAZCZ2Da9BkyyEU95130yRI55LMI/lm0eKl89iu2iefBIm24NRbPucP9EfPzinl6xRDQ+bd1lMM353W5AjnXno+SF/4Xp3NyUHf6DM4UFaNq/ecomzQGJSOHo3zOTJw6nCkN4LrqGpQmxCH7+ouRd8v3zSGgzQjgZpwDWHkVTVmGW1+v/LYW5klTV0TlimhdaxhoNXSzHAL6ZaRmH8GJU9X42qg4RCUOlObu4oN7ZJYTMtZL8zcq4WX8YMwIue7j/bsRNewlOIeAVuV8aVQcvvFGPLYW5npVy3Nd7qTxSO98BcrWfBY2A/hMqRG5m/7kTBQok1YspYF7BfThn4WRahjAV8AycEVap6kr8/EycdU6fX+zfGUIW/P3imGc7Qawt9nrv/7OukfCCK7esxPiG468S1d9Kvs3b+q7nv0faGWzGsCiIueiCdxazV/V8ZOmTLNMYPFmTaCP6J93p7xvpRf7Nsenrq4+bBdbcVFXhq5z2ZABLNKHZ96HwAZwecUuTOv3LLrHvIqhYqlHAFeI+XnFEM/PwjdXsO9m58jiV+W2ocvzbJp9niTMWz0q2LeP+0amGNmO6E5hhKx8XdRpEjLMh1dGpLF3PbLXLsBKbbi8PdLUfhZxs/YHeJhmlvFMLGZaw1eLehZjdZKIan4WwRvAlQilfm4NvPWJiAFcYQ7xfO1TmL7PWW4W5kSLOWqfx3y9T3ZMxX1intc4x9DF+2bhCTE0sTa8sGibXyNT9GVIQ0Cb5qvTXBTlyqGO7dG+bnPS2T7x905Mvt+MKi7Rtx8whr7u1Bl2szLcBrAoU5QljPOb8PRcnwls1N85P7MyjBsYAto0gO9IyrAdi0Vp8ejm6qMDmPxwZ3T0ZEDXxP3bS2PDjL8XsWnHbWWXl6QjVg7f7dC0wchBr4flAYxBr0g8swxhNh10PAA/KM1ht9mnjJJhiw7b21FRiZx1i5GinWPKTXPPMkLMvwPPz+kbmlfMtbpEz0+LAOs9bqN23vIwPaz2mNts5qC5TkRaLtOH261EedFGjBXGd5/xSLfMXsMM7z3Fzo1+jpLRr2KO4w2OOY5Vfj10A7gYOY5oXHE+T0kS5U7FVlV3v+ZlgH527WO21TkvrxqS1TFcrn8z0ODcX9S4/7mq9f0SsMTW7sOmoRwD3QAu3z1bDgvde1yafUjszHkQw/tGu8wqUw8vQ9+lh3a8usw2X1S0F99Zy0ZKI80XnSvyCsReA9yU5iPHYkzVaw+S+8cg2hre2OSix1B8kKnSiGUxUseZQ2pr0dM+AzgGg6bssOsnzgHSwLcb6P773H/bPPfx0FMcI41l5uDKGViyv8x1nilX7IZs4jXPseB+saAS5eYLEuK84ozE188jXvsq81ic8wrUucFcluxfjCXWSAj+eDRf5hHntCKdIV9ErfuYsqez1dFRB31b0/rM65rmq4e89vSZ6ntBJkA99Dr5fuebx8xgJO/25evbznXUggyQATJABsgAGSADLcmAiPwVzxjFdH9iKkD5vFE8czS/Yp0yicV0geGuq3jOHe6PFQHc5UrkizmAu14lh3DO/sm3Ufi/f0XV+g2oO1sn55gVZZ+tOYXTJyugqlJXV4eKJUtR8KdfIue685DXtaOcSzi/61UovK1js0YABzJ+k2fPgYgQFstA6cKtr1d+J2pq0PX98eggo4DjIOb/lSawigAeMQjfSByA6bu2YlNejs8AHtoH982Zij0lRbhl6lhEDesLYQBf8naCXPf2lnRpEkcpI/m1QZa5/JVRceiQNBS/nDkJFSEMyV2bny8NwqzEYWExHH2mpWbMWnPp2s1XlVZF4RpmrJpr9wpsnL7fo07OfJ/CroPmOs3wtQxks2yRV0CzV6bT6+cs5wq4DG3d3A7TbzH37/4br5bfvAG9PNof2hDUR4bHy/6tLSjwQjXgumY3gEVtziUTuLWbv6r3k8aOt06aYk7fFSs/s4ZIEP2xd98BzF+4WM75q06uzWX+qjpWVFY1+YIrhvIQb3OpC3pjluImoOkX/oYM4EqUb52EHurmw2EAG3MFG0My/2vg2/jos1Ss/mwBxg7rg3+J4aGT1rse3GUvNm5ueiTOwMq1SzB21uYA7RD1i8aLw8Ybea9dgmmJxrDYNtO5aD3G9jJukKy0i2dglBwuWp+DWNzYivmNVZ3fxLTFqVi9NgUfTXkTAyb66lKyabzRbjHn8PwU2S4x/HSPpLeREKIBXB5S/YK7+Y6MAVyJ8n0f4ukbhAHYFU/EzcLSlDVYmpyA5x6+CR07eRh5FVmYIw3L63Ff9ETMSVmDOROex303PIXYuMciaAAfx7rhRrSsVe6UAXjkhnvxXLQotyEDOAtzut+Ejjc8izlHfJpnzhXDMHfGNfc/j3fmrsGquRPx3P034Ym4eDzSLAZwJcpLdmKy1FTT+8iHhrF9w2OITV6BVctmYWj0A/hZ9POuOYCL0sbgvmuvx31J6eZQ0qqPbsIjsk9XYPpw0UfP4znHHMDynKIz0F/MM7xGlvdO/6cw2hye212GMvftuvuM3pvwiJnXHNlP1+O5pDHyJQHdVG84ctDrYXkAY9DLAJbDAIsoYGPu0QmL1iJ19WJMSBwgjcjJXsOsFm3EBGFQ9YhBv8RJWLg6DanLZmO0HMJYN5Z80ZQ7rQf1yrTymVklW6ZKs6/flAyfoWsaSa+PG4/eLwzF6JlizuG1WDhuKHqKurqMDP9GlWXQeRnAb4zH2D69MCRpNpasS0PKovHmnLx2I9eICB6M5P2+48N1zbGM3l4YMm4xUtalYcnMMRjyQi+8/v5UaVxaRrg0DXv59Fu3HMlviAjjGNiMR7/mZYB+du1Thk0TDaOwX+IM2U6jXn0xItEoU48w9jT2rP6rhIpm7v3GbKSsW44Jc4UpHmiuakOzki2T0NvkbNoyHzO9E0dikFjvNDGlUSkYM+v8/kj0eyEBY8eJOuucGfkbBnwMBk1cjtTVszFtpfmCgksPrQ/Nbb5+EduESWoMx90zfrzBt3lMCPbcJpx/9hrkRkbw+viTx95wY6h0/UUCSzsxT7R5jIohw3uPG4/XvbQTkeSxYzA6VmNsmRhe2miXjTFrKHC3ptaxY+sbQz9PTjz1bDwzRhlaG1YvxrQkf+cAs1/z0uQLHD2HL3e91CJesoj8seB7gcXOlTBbJxnzXb8wACMmGucIce4cmzgGS5S576mhzuQYJIvjR2NSP3799ZkaOlnMNW6cT5cjWWo5EpOnOOe11o4R7dh3nfM8tjWqz6x8vK5pqi7mtj7GHOYi8sP9nYdN1gsmh7EkMQGjxTzj8tw+yRxevS9eV+cGq1xVBpfB9DHTkBMyQAbIABkgA2QgUgx4PQ8WZrD4em0LZz3E8+1IfGwRwGII6C5XIq/rVci59XLkiGGc//EATkxMQtXa1ajZuw+1BUWoPX4Ctdm5qNmxA+XzZqPw8d8j98bvIOfmS6SBnN/YOxHDAAAgAElEQVS1o4wmLry9Ewq6XYf6CAxd7aWF8h6cS+copIHmB/bKNxLrRm/6HB3eekUOy2xFACcOkmawjPZNHIjvvDEE330j3oj+Fabua4Pw5dcG4dK3XkEHEREs1iUOxJfkuqG48PUhRlqxTcwnbC5F/l8WUcRvDME7GetDbs72B+/D9j/e32SzURm6oS6VARzqfuda+spN6y3zV5nATY0E3v7A77DjT78PmQmxQ4sYwKJg3QQW5mRb/WzdtgOfrkrB+o2bcPp0+Id3CKcu+rDOzhOs82+Rtrk/NadqAxiWwd0Uqbe3vC7moawTcwg37eIfhAFcUYYN48yoV5cBLNqbh4zFbyOul2Gqivr/q9+rmLz2mM/YsD1wOobVE2OlQdxdmLvTdwVoQx4+n/IqBsSovKPRY6CfvEtFvvF48Tn1plw0XhzyNhZttUcgG3qZdbbyfRb/6hWPt9Zm2upyZO0kxKk0z/VE3MRUHAl1DmDV9pDq1zBHETOARX1zMzAn7lncd4uIRDXM4IeiJ2KVZpTauCvZien9/4ybZeTt9bj54QGYvuO4Z7Rv+CKAhUZZWJX0LLoJw7qTs1y7EemOTvU2gMsrjmNb8gA8ZLb9mlv+jL7JO1HkOYxyJCKAzb63ImR9JnDRjlno+3BXaVB3vOFePJ20BplqCGltqGkvc1aYyr4+ugnduo+R/emcA9jqV5MBQ1uDgfu6x2OpyYBXGW6NzbbkZmhli356Hu+kZKHcpWkDkYPyWPJ6WB7AGPRjAAtTJGvdVAzrY5hD0S/0xbBxy7GzSEXuecyzWZqN1CkJ6PeCYQRH9+iFfsPHY8k23/yuRqRwX0zY4ojgU/PrmlHHgQxgYWz4q5vVP1IL/yacZYh4GcDC2DqahsnD+xrGco9e6C3asVuP4jXn0HVG0Krzmb7M24GFSYPNvGLQM3Ykpq3LhohYFJGrPkMoH+nvj8QQpbkoN95IW6Ln59e8DNDPnvsY/dXb7K+esWOwcHdxI+YAFhyLvEwTTvS7mPc44FzVJvsVzr4cjNGL9qDAcxhjEYm9x6GlXmcPs1KmH2D14+jVJoueeph18jTbxLZ8bF02HsNMw1QY86ovbf0TkL0guCndgxXjAh9HinPXcTAlDVme0ccaGy4NTR51xprBAG40M+I8YztOYtCzfwLGLtqILFfktNmnAQ1gkSbCx4LQ1i9XlSg5moZpib5zhDjfDkmch62qPX73zcfWRWMaPmd4MmFok7NtsXwpQL7w06MXBiXOQOpRFfX+/+zdf3Ac533neWxd6q7usld7f+RqWVu3myinxNbFia1sdiklyyTmyXcX1bk20q1Ku77j3WqlxdE3cY6SYJGiZNIUCJkiaVpEGKFkk5YpC4yWEBnRFrQSTZEOTVKBGYExcSFN2SQsSiAsAysKkECIoKHP1TMzz8zTz3T3dM/0DH7MW1WsGcxMdz/9er7dgPozz9Mhx5RXJ7YWYx9r6bPSdsJ+pxX7tfT7y/7OCXt092FU3/d+R5n9PXzOPbfbdfMY26el/sEJJ2qAGqAGqAFqgBpoXA2Y2R/da8Bdjz4WmO3RzPxoXnM/Y2eMzKJfpq9ebcgl9dII4JsKoa0ZAXxp6T/RpZt+VSO//Ut664Zf0Fsf/4e69Ok/0E//tF1jXQ/rP23ZqLFH1ujtf3enLv3uR/XWx35RI7/zDzVy8/XFALkwmng+jAC+74G1oW4r/2RVaVCbm1+EfjjjFz/88ENdnr6iG772lUIIvOUL+ZG6pSC4GPa2PbpG+X/2Z/vYtVptjxUC4XwIvOlBtT26Wm2PFkNh87niqGKzzl8w69+2Xr+5a3t+9K/Zfpr/Lj7+5fwo0XePHZ2TEJgAuDCqN+sA+N0Tx/L9+uafP56mHEqfnbMA2LTAhMAmaBwbGy81aKE9uXLlin58/sK8D3+tq7E2UyiYk6p70jTPzQnVvDdX/TH7Yf3TQJtf2Fn8y2Ya6Mb9MZPFHySsI7x/MguA79ijM1zoCgT/1Fx4zTXaperIwcVep3GhXSb7HhcYe31evGdq1Xt3xrWrOPV1cOpgbztxy8/r96rdq3qx7GfK/ciibuZ1v6f0COwLNdPo3yGsv576ZFnqhxqgBqgBaoAaoAaogWbXgB3la2aI/M6hw5HXpcx7dhZJs0xW7Uwb2tlr9tUezehcM0rXjNbNTwF906/o0j//ZZlRvJeW/mO99TtL9NbH/1u99Zv/QG99/O/rrd82/35Rb934X+mtj//XGvn4L+mt3/lHeuumX9aIGflrppC+qXA/YXNfYXN/YTPKuBn/uTOV2nzi0ce+HLpp87r9jH1s1qylti/7fjikv7flCzL35jX3ArahbX70rhPg2p/zoW/+9cK00eXXzRTSD5eme3bXY9Zr1t+2+WHtP/f/5S3s9kNhQl6cGR/TX//Gr+n0//ZpAuCMpnKudXRyYAro1f9vXf1x+rb/VX/9Wx/RtcuXQ3q9+ktzGgBXbx6faKRAYernc/l59ecq9PX3b/qDDzL7hZvVL27W01p/rNYfABfvd/vAy8VpglvLj+NlvvV3gpGDgTBlvrU/g/bMowA4P7VwXfedfF+F6bzXLM77Tla9V3UG9bAA6z2Lulm052Zqhr+bF+AxvWiPR/qC45EaoAaoAWqAGqAG5kENmNG95hYf5jaB1f7uMp8xnzXLVPtskvfNde1G/ZefAtoJgE0IfMmEwGZE8M2/qku/9+u69HtmZO8/0qVP/ANd+s2/r5GP/Zca+a1f1KV/+ksavflXdOn3fi1/39/ycmbZ6/Khcn4K6CYFwMbo6LET+YGBZnCg+Wd+Dvsv6efCls3iNRvCrj96SG1f+aL+863rgiFwfrRvIRQujPJ1RvyasNiOBraP+dcKr9uRxCb8Nes169/wvUP5Ztvtpt2HN/98e3606PgLB+oKHWsJPhkBHLyvr5n2ud6pn8cO7M/351tP1j6DMgFw2qOIzzdUwJzckvxC5TOteRG8Gf1edwB8slu/e931umtvefraZrSbbXBMUAMRNTCPAuB6+2j8XJ8eMfdpDUxBHbHf8+B/vOvdX5anb6kBaoAaoAaoAWqAGqAGqAFqgBqgBtLUQC2zOtayTFibag3tklxsz48A/v1f15i5X68Jfc2o3Zt+pTAi+KZf0U+X/hPlp3L+3f9eb//ur+vtf/ERvb3so3p72Uf09u/9Wv5zb9/0yzKjfc0oYvPZ/Mjfm36lsM7f/3WZaab5r1JgdnZWP5+d1b/+1rNq+7OufFhrw1vzaP+5I32jntvPusvnw98/26j/84Xn9KE+lNlerf/Nzszob/7FP9ff/N4/0/TwhaaHwLUExywTDI6tx5XzP9LJpTfqtT/8XZl+rfU/AuBa5ViuYQJZ3As47Jcwr/EHY5IaqDcA/lfmvrmf7NYA4Qtf5qAG5kcNLMAA+Effekyf7+xWz+4XdPh7x3X0yAv6xuPFe+X+aZcOnOd8nuR8zmeoE2qAGqAGqAFqgBqgBqgBaoAaoAaogWbUQKPu/WsvwJtw9u3/+eP62R9+ND8V9OgffFSjf/ARjf7+R5R//vsf0aXi80v2vT/4iC6Zz/zBR/XT/Hvm54/ml/mpff0PP5pfp1k3AbDVDj7aQHb0vUn979/6D2rb9sX8NND/2ebydM5/z3keG/6aaZ6LobFZPv/ZbV/Uv33hOY1PvZ/fsN1esBXJf3rvbwf16g2/qtP/6l9qZuxnhMBzPB20DXTTPF792U/1t5/+X/Tqb1yv9/9uKHnnh3ySADgEhZfmXuD9qSvzIzggwKEfUtbApx/Yo78d54/rZvxxzTaos0Q1sAAD4PG/O6SeL23Q5//0T/RZM+L3//kTfe6+DXp091/p7CX6PVG/pzx3s07qihqgBqgBaoAaoAaoAWqAGqAGqAFqoJYaMNexm/GfCWhL/67a59P5e/eWXv9gWh9G/vug+FnzGfvcrofRv0n60IzQ3XDslcI00F9enx/9+wvm3sBOsGtH99og2I76NT+bz5nP5z/z5fX59Txy7BVlPXp87IVv5acO/tHq+wmAF2AAfO5P/u98//2ngy8lKcvYzxAAx/Lw5lwJfPih9N77U4SPXMCmBqgBaoAaoAaoAWqAGqAGqAFqgBqgBqgBaoAaoAaoAWqAGph3NWCuX5vr2Py3+AXckPboxQv6H/9iV2EE7/ZH1PbYWv3C5i/kA14T8trQ1zyanwvB7xfyn2vLf/6h/PJHLw6X4Nz1l16s48nFr2zNh4jDj6wnBF5AIfCFDQ/n++3NP/tKHb1fXpQAuGzBs3kmMPvhh5p8n2+e1fLNM5ahbqgBaoAaoAaoAWqAGqAGqAFqgBqgBqgBaoAaoAaoAWqAGmhMDUy+977M9Wv+a12Bv/i7H+hf/mWv/pvHO9X2lS+qbes6tW15WG12emfzaH42r3/li/nPmc//xZkfNAXtJ10b8mHi2fa7dPWnowTB8zgIvnppRGf+3YpCaN/1SGb1QQCcGSUraoSA+ebLe0wHPe++3cYfjo35wxFXXKkBaoAaoAaoAWqAGqAGqAFqgBqgBqgBaoAaoAaogfldA+Z6ddYjNhtxbZ11NkbA9L3t/5mfX9PQz36qp06/lr9H8LLer+q/+/NN+i+2rss/mp//j2/t1TeGXst/znze/OeuozGtLKz17ef26tWPXqe/vfUWTZ07Swg8D0Pg98/+nU79T3+Y76ef7dubaTkQAGfKycoaJTA1PU0IyjQv1AA1QA1QA9QANUANUAPUADVADVAD1AA1QA1QA9QANUANUANzVgNTV6YbdQmc9SLQEIHJwb/R9//pxzTw8Rv0k00b9cEbwwTB8yAInh6+oOGNGzTw8Y/q+//s45ocfC3z/icAzpyUFTZKYObaNZmpNfgGHAbzqgYm6Y951R+cIzhHUgPUADVADVAD1AA1QA1QA9QANUANUAPUADUwv2tgAV5PM9elZ2YKozcbdf2b9SLQKIFr77yjnzzaqb/+2K/pr3/rI7rwyBd0+buvEATPQRB8+fB3dGH92kJffOzX9MbmR/Xzdy83pOsJgBvCykobJWBuq3Dlgw/m9x8w/IFJ/1AD1AA1QA1QA9QANUANUAPUADVADVAD1AA1QA1QA9QANbAoauDK9Afidr+NuuLPepspMDM+ng9/B278jfz9Zgc+8T/ozL//t3rzzx7Xz577D5p49Zg+uPgTguEMguHpnwznPY3rm91f0Zl7/q/8KOwT1//j/Ijs4Y1flOmPRv5HANxIXdbdMIGf/3xWTAvNyFNGnlID1AA1QA1QA9QANUANUAPUADVADVAD1AA1QA1QA9QANdCIGjDXn811aP5DYNEJzFzVu391RD9+sEPfv+m382GwCSb51ziDkzf/tn689vN69+h39eHMTFNKigC4KcxspJEC1679XOZbWO9PXdF7709p8v2pRfHNskb80cI6+WOYGqAGqAFqgBqgBqgBaoAaoAaoAWqAGqAGqAFqgBqgBqiBcg2Y68nmurK5vmxmn7z285838nI260ZgfgnMzurqT0c1+drfaOzbz+utJ/9cb3x5E//qNBj56p9r/IUDedeZn47qw9nmf5mEAHh+HWq0BgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEKhZgAC4ZjoWRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOaXAAHw/OoPWoMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjULEAAXDMdCyKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALzS4AAeH71B61BAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEahYgAK6ZjgURQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB+SXQ9vqPfyL+YUANUAPUADVADVAD1AA1QA1QA9QANUANUAPUADVADVAD1AA1QA1QA9QANUANUAPUwMKvAUYAz69AntYggAACCCCAAAIINFig66/e1tjUNf7VYWAM+Q8BBBBAAAEEEEAAAQQQQAABBBBAIL3AxMSEGv2PADh9v7AEAggggAACCCCAwAIWIACuP/wmAF7ABwBNRwABBBBAAAEEEEAAAQQQQACBORVodPhr1k8APKddzMYRQAABBBBAAAEEmi1AAEwA3OyaY3sIIIAAAggggAACCCCAAAIIIICAFSAAthI8IoAAAggggAACCCCQkQABMAFwRqXEahBAAAEEEEAAAQQQQAABBBBAAIHUAgTAqclYAAEEEEAAAQQQQACBeAECYALg+ArhXQQQQAABBBBAAAEEEEAAAQQQQKBxAgTAjbNlzQgggAACCCCAAAItKkAATADcoqXPbiOAAAIIIIAAAggggAACCCCAwDwQIACeB51AExBAAAEEEEAAAQQWl4AJgPlXv8Hiqgr2BgEEEEAAAQQQQAABBBBAAAEEEGiOAAFwc5zZCgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINBwAQLghhOzAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKA5AgTAzXFmKwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDDBQiAG07MBhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHmCKQNgP/4j/9YaZdpa86usBUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgtQXShLkm/LX/0ixHANzaNcbeI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAkwSSBrk2+HUfky5LANykzmQzCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQ2gJJQlw39PWfJ1meALi1a4y9RwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBJglUC3D9wDfs52rrIABuUmeyGQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaG2BuPA2LOyNei1uPQTArV1j7D0CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCDRJIC64zeo9AuAmdSabQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB1hbIKuSNWw8BcGvXGHuPAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJNEogLbrN6jwC4SZ3JZhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoLUFsgp549ZDANzaNcbeI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAkwTigtus3iMAblJnshkEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGhtgaxC3rj1EAC3do2x9wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0CSBuOA2q/cIgJvUmWwGAQQQQAABBBBAAIG0Apde7FSuvV3t7T0adBce6Vdnrl3tHTs1OOW+kfT5kHab5dvb1RNYcdLl+dxCFhh7uTPf9+3r+nWpaTsyo7FXd6vzvlxh2+05reodatrWF8+GpjT49Q61t+fU+WJtvTc3/b94eqDanozuuV1tbW1qW7IxeN6utuCrGwvLtX1G+0adDw/v093Xt2nJJzfqxHvO60me1rNskvU37DMX1LviuoLHIycatpVGrLjm/m9EY1gnAggggAACCCCAwLwVyCrkjVsPAfC87X4ahgACCCCAAAIIINBQgTO7tSofrhaCUBOGhv2by4D00gvri20KBsDne1eV2lpb+wbVU9zf2pZvaM80eeUTOrI5vO+D9RDsgyY3MtPNleqqiQHwxOFNpZq1rp0vjmW6X2Erm3l7UP1Pdqrjc8E+zt3XofWb+zSUNlAL20gzX3vviLbZc1WN/TcX/d9MotTbmn5F95vANv/vfr0ynXoNwQVskHtHr9wcN/ihkJ/e7NXt+TZslBt5num+udi2Nm181V3usvofXK6br/dfL38metnyZ+bns1H13lHskwUWAKvW/p+fHUGrEEAAAQQQQAABBBokEBfcZvUeAXCDOo/VIoAAAggggAACCMxzgdF+rbdBSszjnAak3+8phGYPPx8YqTnzg93qWNmu3OrdGvqgFudLev7hQiA2p/tXS9MbsMzgk8Fw0AaUwcfFEwDL1lWNAWL6LriovgeN8Sr1HJ/QTPoV1LTE1GBPlS95LMQ+vaSDXTm1r8xp08u1jQBufv/X1H1NW2j64P1qa7tRN9+0JB+03n+wzgS41gAwIgCePrlVy5e06bo/2qrBQNNsSHq7et8M54peNvzz8+nVE18oBMC3P31hPjWreltq7f/qa+YTCCCAAAIIIIAAAotIIKuQN249BMCLqGDYFQQQQAABBBBAAIEUAjYAjgrBZmc0NTGhqWalVWFNHywGwFFtDFsm0WuX1L+uEMjtPpNogUX9IRsAR4bhH0xpYmKqacFlw7FtXW0+oomGb0zSB8fUbb5ksXKnmjbp8ztHtGml2eZa7Xz1oqacL0rMvHdRZ7+zR5u+0swpsJsBnXAbze7/hM2am49d1r672tT2ia06cfghLTEjcD//igI5a9qG1RoARgTA0ZuvHgBHLzv/3znxSDEA3pNqHPXc71it/T/3LacFCCCAAAIIIIAAAk0UiAtus3qPALiJHcqmEEAAAQQQQAABBOaRQLUAuMlNvfTdbnUU78u76XBxalwb1DQsAF6v/gV2bb0R3VI1AG7ERudynbaunmzSDaDDjrXZGc008MsVdsrpjr3n51J6fm672f0/PxUKrSqGrsu/ekGafkUPLTGh493aV8/s5LUGgBkFwNN1pdfzp7MIgOdPX9ASBBBAAAEEEEAAgewFsgp549ZDAJx9v7FGBBBAAAEEEEAAgYUgYEMpb3rluKaff269OjpyWpsPlWY09uoebXowV7q3aa6jU7uPXtRUxEomfnhQPV0dypmRifkRkTl1dPXo4Jkj6rGvtberNBLVBjVeAHzxQKc6Vnco1+mPYCy2ad0q5UrTWue0avV6bXr5otMqOwK4GABPnNVB9z6puVVa392noXFnkQyezrw9oD2b15b3/3Md6nzyoM5WDEM9r751Her43Fr1mfxuZkwDvZu01t7HdaVx261jFyulp4b2aL0J0nOdOpgw3E4VAE8cU/fqDq3Kdar/bWnqR/3qtjWQW6WOnmPB/i/ariqG++3GdvMeDbxdmX5OHO1Wx+pVynUfy482LtRLuS9z961X93NDGpuN6Qyzva9v0vr7nLq8b732DDnbs3WVD4Ara6ZaHcds3XnL1lj09Nr5Ok/qOTWkPevMPuXUmWDqY3uf21XfSDHmuNSWwtTQYz/oU7dzLMX5T108pr6eTVpv+q907LXLWPZ856wm/D4rbatbx8zo5LA6CTsGJwa00xwb9+XK5wmrXjxO3L439daxbpMO/sR+SFJT+t/ZXv5p+Pky1Mb9vONv+r7yXOZvJ93PF75+q9rabtXOHxWWG9x8Y34a6NuTjDodPaHeR+7W8o8Vpo5uu/5m3f1Ivy4c3li4Z2/UPYAvX1B/d06333Rd4XNLbtCtn92pwaHwewBfePpuLf/Uct2wwt5T+IQ2lu5ZXLxPrvuzc8/cymU9n2ujeuXr9+tu25a263TzHTntOBx+8jzz1du1/JM36FYTmEsaPbxDuU/fUBg53bZEN3zybm198ULlCOr3Lqj/6w8pd0fhnsWF+y23acnHluvuR/bpzGWvXZJqDoAvn9G+zY5vW5uuu+l27TgZnoxfHtqnrZ+9VTfkw//4NgVaWXP/n9G+R+7O37s573D9zbr9szv0Sji5ZPbHrbO2QhuX37VVJ0LcAm3kBwQQQAABBBBAAIF5KxAX3Gb1HgHwvO1+GoYAAggggAACCCDQUAEbALcnvw/opQNrC8Htjud18MurSsFv8F6x7Vr1zbMVTb/0Qmfk5/PLdz2vi840tfkVjDyvtSZM8gLg83s7CuvyXo/bht+mgR4TzK3Vnhd3a60TPgf2ZeUmHXmnYldqemHseHf0/VgrwtryPYq7//Kgtt0XFSKuUnAK62DouP6FZPdITRUAa1A9xYCv54V+dXp2nd8pp9lTP+qreL/ku7JDu3/ghLJG1d6b9+E96v9msdacMLG0bMTUzWPHe/L3hi59zll25w+cbrN1VUMdO2up8jTYF2FtKnzRIZmnDXTz6/HqPrQhNuRc2am+H5b7JPSz9kU7VXX7WvU8GXO8mi9eeIGuraGw/cy/5n9ZY3agWEcpj8FSG50vipj2z15Sf2fC46Qp/W9RTdvGdGxH9Pky13UwcI9zs2Sac5mzpZRPB7X1E21qu2ufSjnaqa260YSpf7RTcXeenX51Y/6+vDbIdB+XLCkGwmFTSQ/v093Xh4S2ZptLlhSC1E8Ft33hq8uDgfL0K7rfDXy95zduK4/sr1jWFRrujW6LCU3v2ldhcOHp2wtteXCn9n3u5sJzb/vG4manDflN2lHRIZ/N2y35jPZ59zG220oUxhf36/LBhyL6ZYk2nnR33jy/rBOPLi+G1yF9smS5Nr4aHhrX2v/TZ3fqM8Wg2a2ZgsFybfVD6vdOaONNIW3LO+bUXypcf9/4GQEEEEAAAQQQQGC+C2QV8sathwB4vlcB7UMAAQQQQAABBBBojEBcAJy//2/lPV9njneXQtzcut06dn5MM8UgaGb8rPpKAUxxVJ9tud3Wyg71fOe8Joq538zURQ183QZ9a9XnDtI1y9rlvMCrFIa5r08c0SYT+K3q1pGL5babbQw+t0093w2GYKXAamWHNvUO6OJ7xUbNzujS0XKQuPY5p1E2VHOCxfDAy5taeqQYlOY6tWfwYum+yqZtpWAoEGrO6Fi3DbNyWv+NYzo/Xm7f2BknWAdyz1QAACAASURBVC2Oli1Qn1ffartcuzpfTDaPq7VI9vlgsLn26wO65Af3pjFTg+oxwXWxz8fsZz4Y09m9xXDxvt066waJ5/vUYW07tqkvyqq9ozAy2tZXfnM9hYA9t167zT1vbY2Nn9exb/QER0PbumpvV26dGYE+Vv78O2fV11UcPbyyWwO23Qrud3i/F+39qaXt9tx6LbU9uN4oz9KXHoxPZ7+q9uzsefU9WK6Fjk17NDBSRClt238ypJ020L8v6K8PLmnwLzpLo3uDX6iY0LGvbdKe75wtH0dm1R+M6fx37Bcf/C8rOLW6skPbnhvUxVKnTeniUbtcu4LTWDuBeTnnk53yelX3kfJ6NKOpi4Pq+0qPjriHv+2PJvW/DXNzXXs0WDo3mbYdU3fxyx2bDjsNTHku83sx6c/T+Xv+LtFDh92Q74J2/pEJ3G7U1lMRa3pzXynEu+6uHToxXF5+emxQvTYYdUbi5tc0PVgK85Z88iHtO3u5PFL2vQt65dFbC2GkN3J4dE8xdPVel+xI4I06EdHUyGVL+7BEtz76ii68V1zB9GUN7snp5mJQe/MjJ8ptlDR98P5S6HvdHVvV7+yD2fedK4rhd9v9eqXMossvblSue58Gh5191rQun92nh4oBpxtcm9bYticNgE0om2/39bdr6+ELmr5W2KfpsTPq37zRC5inNfhoMcC+KafeU+V2TQ+/oo2fLu7Hks+od9jDLdmZkDxF/9swd8lyPbT/jC5bH2P+1c8U+v6mrXIOaw1uK45If2Kw/Pm8W7+2/vsdgc96reRHBBBAAAEEEEAAgXkuEBfcZvUeAfA8LwKahwACCCCAAAIIINAgAScECQ+zQkYG2wD04ed10Q3ubBPfOajOfIAXDOhseOWODrWLSBM62FUIqioCSNtGPzizI0Xd13+wsxBOdx2UE6WUN+M9s6Fnz/e9N4o/Dn2jOGLPDVjt/tuQMvLRDYAndOTLZv+K0zn7m5s9rz350HZTIKSy7Vv7l04A7Sw78Z1iiLq6T+5dXi+9XAjpcuv6dLYUXjoLhjy124ocMRzo6wkd2VwMFru96Z6ddZ/vLfh1H6+cplqa0rG8iRcK2v5u79ZAWFY5O6Tdqwrb7j7ufOCDQfWY11d2qn/EaUTUU7udqOnPQ+s4GNSGHzNFl1QBcDJPjR5UZ35q7/XqO+Pse9Q+mtcnhrT74WKbirWaW92tvsGx/BTblYvafezQnuKUwP5nLh1YXzjOAuG4/yn35/KXGYL1ZbfVru5Xw/cn9BhUeXR8aap4SUNfL+xn+DnGbY/zxZJm9P/EEW0zofqDfTofOI6KbfrRnsKXHtwvgKQ8l3l7l/DHy9p3lxl1+1AgqDQLX95/dz7kXPLgK4Hws7Diab3yYCEcXLKit2KEbP4zdhSxFwAXpptuU9tNG3XCBq5uay/3K2eCVz/o/d5DhdDVfz1BAKzQZcv74Ae8tjkX9hQDSWd67Px7diTvbcFRynY5je3T3fnweHlpWu3SexFPSqGyt3+pAuDpE9poRnOHBbZh2/3RTt1q2hn1eRvWmumWA3VQtkvb/2e6TeC8RPcftMmv27Bp9X/O/+LBqPatMK/dEP1lBHcVPEcAAQQQQAABBBBYUAJZhbxx6yEAXlAlQWMRQAABBBBAAAEEMhOwIVhkiBkSANtl3OA10KByqOOGMzZgdF9zF7MjeoMBkRPU+NuzQaw7EtK2rd2MmI0YlepstFqbStMR+9t21pHoqR3NF7We2Rkd21EIr1yfSBO70dL+hvST/UzCR2sRHmq6YXZhhfbzUeG5dLYQ1K7s0WBY6KUZXdxbGPkd7HM7ujN6n+y23eXsyPTE97u1dlF9EhEyJuSs/FiV7dl9ivasXGXiV2YndPHobq2394+2QfC6PRqq+KbEWGkaZbcWA9v6YEDdxVHCSdtr98/tM7NO+3rktuxxHuin8jnGndbbHi/t+RHgl0ozEwTabn+o0h/KsP/tyGR/321TVJrS2ql5276E57LSutI8KQaAN252x1sWV1AKMUOm2C1Nv7xcOypn+i+s4M3ivXwDAfAF7fxUYSrf3IthAaBZtDii1wtCZUPXFfsUvE1s9RHAocvaoLntbu2LHEpfnB67rU2f2ets1e6b38aS/ah67yjs58ZXSy/GP7H7560zTQBsQ+TQ/gzZuh1ZG/d5+0WAtiUbyyNta+7/oueS6NHaF75aGOntjng+8UjxywafvD8/SjlkV3gJAQQQQAABBBBAYIEKxAW3Wb1HALxAi4NmI4AAAggggAACCNQpUAoZnODBXeXMTOUoQbtMIJBxFwoPZ2zQE5hOubSYHQ3aLjfQyb8dtT0bDAVGWk5p8En3Pps5dWzeo2M/qUi58qu2baoaPrkhc6nNKZ7YtkYG7eURmm5bbKAVGRxZm5U7NZSiOWEftRapA+CQ7Ci/ftu2BPsc3L/kAbA7WtyO/nT9wvaz9JptX8o6Li2f9kmV7Vn/xO1Pu33zeTO1+at7tKmjXG/t9/Vo0BugXb0t5ZA42HdmIzMaO3NQfT2btH51h1aZEctODfifr7ote+wEjkF7jvG+mGCnHLfby5mp3Y/pYtjhX6U/TADcv67Q9opzUkp7u4+uQ/hz9zyc7lyWskn5jw9uLkytW3Ef1uLUx/b1u/d7N1k9ubEwVa8bCvoNsCGpew/g0X36TH7dn9E+J08NLloMdL17AJdC3ECgbJZMEQC7y9rA9baIEczFRg0+WpwG2V3W7psX1pb3wwbAYffcNV9qGtS+rz+k3B3Ltfym60rTSee9vXWmCYBtW5OFzraNbXroe+WWVzwr9dnt6rX3J661/62bV1+2ztxHNwDWcG9punHzmSUf+4w27jmh0eL01hVt5gUEEEAAAQQQQACBBSOQVcgbtx4C4AVTDjQUAQQQQAABBBBAIFMBG4K0u8FDlS3YZaoGZ8Fwxo6C8+8HG7gHsH8/WNMUe09Yf3s2GAoEwCbkmtBQ7/rSfUpt0NLxlSO65I1EtcFMZOgWtY0qRBVv2/XYYMp7zHV0qGN14d8eJ8lNHAD7NhUNqP6CtfADutKSae1snXj7avsjd195n7cddofgJQ+A3X6z7XdfK7U97IltX6SdDQCDdRy2qkSvVdle6vYn2mjUh2Y09upOrbX3+t0xEPiiR5K2DPQUwtFAvUxVTjdt+9s+Bj6fZgRw4DiP6ZuJIe1ZV7x/s609c3/hw5eCGFX6oxwA19//1tMaBB5X5krHfsfqPcEvcqQ4lwV3LsFP06/ooSWFUapu8Bb6/I+8qY5teOqFlYGt2rDP/Yx9rS16BGhkoGu36Qax+Q3WGQC77QvsQOEHG8C2udu1+xG5rA1XndA0v7ppDW4r3uM4KgT11nnhq8vzAXEgEA1pp3npxCNpRh3bNrYpPjC2vs6+2L7w2hpoVpiRfS1i36+7abmWf6rw7/5ved8QGN6n+z9ZDOPt8tffrh2nokaSB1rDDwgggAACCCCAAALzVCAuuM3qPQLgedr5NAsBBBBAAAEEEECgwQI2BGlCAKzZS6WpZQMBiBPS7PSHIprdt+GpH9TZ1wPBkOM1cVHHejepwxmBuKpnQO5gRxvMRIaG1bbhbC72qV1Pz0Dsx/w351UA7DWuqp2trah7rHrrK/9YXwAcuC9weaWVz2z7/LoqfTImZCx9JsWTKtur6pliU0k/OvX9Hq3KH3/BL4BUb0v5Hrxlb+f4frhH/d8/r7GJqVKwbNfZ8AC4uPMTPzmmPZs7nC+CrFLP952jv0p/NCIATjpddkX/JTiXVSxT5QU7te/yr16I/mRpqt8bg/dftQGgP0rXXZMN+9yQ0L7Wdn/FPYfLi9rA0QuJ7TbdIDa/UMTnyytU6Ohhu76EI4Bvf9pxsvvh7pu7Pdlw1QlNzcDfvcV7Ci+5VQ/teUVnRi9r2o5ite3x1mlD3TQBcPj9dQMNNK0pTVOdbATwQzphV2HbWmv/VzG3mwl7HH21Vxv/zQ2FEej5IPh29Q6HfZLXEEAAAQQQQAABBBaCQFYhb9x6CIAXQiXQRgQQQAABBBBAAIHsBWwI0owA2LR+4pi6czmt6nBG6H2uQ51P9mvw7Znw/bPhqR/U2dc3H1HYDK+llc1O6OwL24pB1yYdKX3YBnztShUA2+3a4Dry0Rk5aEcxr+rR4AelllV9shAC4E2HS6DB/Snd19RxCH4i4qfaAmBr1d51ML4e7FZtP/p1Zd8vTQHstr9cM6FfYnBrwf9igj3WIgJxG5BGepbaleUTa92p/rfL67VtiQws3zmozvy+Oja2xh/s03lvtLhZc2md3pThUa+XWmP7KeBp+2GVdp8pfTL8ycRZ9X+5OC28e66w621C/5/f25GfBnvVk4OlQDy8sVVejTyXVVmu4u0L2vlHZrTocu38UcWbzgvTeuXB4v1X3eD11FbdmA/f4u6fe0IPmc+4gWbpvrteoOxsUbqg3ttM2yIC4M/2KzghtQ2AQ+5VbNdrA0t32dI+BENau0jh0TotCY6SrSkAtvc/vjXc3LbRdXZG9SYJgEujle/a5xkF96rw02X1f7YwYniJt03309OHHyqErW4/luxS9n/pCwVx5u7WY56PvqKNny7U5o3bvJNKzGK8hQACCCCAAAIIIDC/BOKC26zeIwCeX31OaxBAAAEEEEAAAQSaJWBDqTQB8MjzWmvCn6rBiR+sFkcIphwFW3UEcGQ7XEQ7YtEJrEoBn99OZ7mwkMi+5oZ9oc/dbV1U34OFKXNXPTmgiZCAzNlq6emlA2vzwZE/arL0gYj+u/TdbepY2a5cV3/FtNelZb0nNoiL3Fbqz89oYEfx/q+dz+tiRL7vrVaaHVBP3jM4KtX9XGhbS6Fkuzpf8Kb7dRe2z20/RtaPDRnd+ii/VnMAHHGshe6Tbat9HD2ibeb+vblO9Y/YF+t4nDqmbcbau4d0fFumNNBTDFTdsN16BoLacttsQO8H3HYq6apfwgj0U7kfIpcrb1oKO2fZ9gbW6y4Uto3ya6n6/2Jf4ZzZbkYhR3xhwt107POQc9nsJR35SiFk7vjGUPWQ2QZ4cSM4i22YPnh/4R61Sx5yRu0OausnCuHhjV84ofBJeEMCYF3WvrsKy5lg2BlT6+xxlQDYDSLzS9m2xITKNlwNLGuXa1PUPph9X2JC7E9sVSBiHO7V7X647eyBO7q2PL2yDaq9YNsuZ0NlN6ROGQBrbJ/uzgfzS/SZPeG6dnPm0Y4Cb2uLGkVrA/A2Be8DXd3OTOVd8QUAOV8oWNGrC3b0s9uoNM+/91ChNt0Ae8pOAZ9T58sJfg+k2R6fRQABBBBAAAEEEMhcIKuQN249BMCZdxsrRAABBBBAAAEEEFgQAhEBYrntM5qamNCEM5VrZCBbWsgGFG5wJunt/sKowfu6dezihKaSjoS1Qc2q3Tpb2kbE1NA/2K31ZjTxxSnN2JB1ZkoXj3YXRgCv7NGgfT1JAPz9nnwAGx12uw2q8vzM7uIo5Hbl1u3WsfNjmqoSisYHcSoHW4FQ8ax2ryoGr+3t8gO3qFZW3Za3YKLPv3NEm+x9Zju2qW/wYvV+r1qTkg0N/bD60gudhf5qb9fanoM6+3Z5CmKv+dFTi5c+GFHHpfdTPrF1HOir8jqSeJ79ZjF4NaGtO5q1vJrAs5lXe7S+u69Qa87xNjM1oYuDfYUwub1dq74ZOLJKo3XbH96pAXMsFdc6M35W/cWgsb19rfrOO5uz9b1qk54/M1FeZmpM54/u1vriVOzBPisHqpFBbugxGL7c0DfXq+eFQV18z7ZYMvcYP7aj6OaG07Y/YgLg5x8uHEeRbXN2v9rTct/ltP4bx3Q+rjbNytKcy+y+VPnihG3j4OYb88FZopGTpVGbbcp9qzz2tjSdcdsSLf/CPp0p3cZ7WpeHT2jn524uhHOB0FWaPrlRN+dDyjZdd9cOnXizHB9Pj55R/6P2HrleUBoa4po9mtYrny+OZF2xU4Oldti9VXkKaK8tkftw7bIG99yv5fl7JN+sja+W25hfa2Rb7DZtiO3eX9eGpjcq9/SgLtvw89q0Rs/2a+sd14V62SmgE/WVpAt7itNMty3RrQ/u0+Co13bbRPM4PaiNNxUD+evv1o5XR0th/vSbr5TatMSEte5y7nTWKftfY/3KFe89veST92vnqxd0OaaJJkzf9/m7tePFMxp9r9yI6bFB7VxRGAHsjo62XzTJf0Ej8tgur4dnCCCAAAIIIIAAAnMrEBfcZvUeAfDc9jFbRwABBBBAAAEEEJgrgVLYVg4Mw0e2OaMxbdgQeXHVTivrBcA6rz33xW0np1WrO9XzwpDGSiGtE/T6wVlIO8ZeLAeAYfvR+aI7Iig8RHK7onQxOXJf3U9Xfz52vCc/MjesbYXXejTg7HvVUNAaBGyGtNOGru3tCgZu0W2suq3AomOl+zlXW//Uj/rU6dyHuXLf1+p5dzRrqSadmgtsu9xvldue0tA3C6OmK7fTrnZ3+mVrF9m3UXUcaEzyH+z2An1lF0/mOfR15/iJbLddp1QOHZ3l8iFh+eewUeK2FnI5Z6r2wHKrtO2weyyZbTr3AA581owwXqttXy70S8deNzUu92VUyBp+DIYtVzYM7fuVneofLdtU/yJLxv0/O6ZjPYVRuqHtM2bO7AhpzmUzx7tLX3xo90ZzO3tceDr9ih7KB3DetMYVH7QvXC5NFdwWGJ06qn4b8hYD3bbS4xItf3SjcuZnf/SspjXYbUPeYvhYWs6Ewhv1UH4KaG9K55jQtTRNsbOeNnd0c+Sy0xrcFt2WtiXLdf+3/OgzOlC2Ymb068ZiW8ojgJ17ALvtzD9folvvzelW89xttzMCuM0d5VreUMizKvvk3393uF85GwJXtKtN192xQ4NO8FreYK39L02f3am7r6/s+3L9uCOSB7WxGBiX33eWvWmjBp0A2U63nj/GOvsV9n2A8j7wDAEEEEAAAQQQQGCuBbIKeePWQwA8173M9hFAAAEEEEAAAQTmRsCO2vMDm4qfnTDOBlmRAVREcDI7ofMvm5G4OeU+Vw6gQsOQzn7ZeKkchDhtMHHTC+srRudO/OB5dXd1qOM+J7hamVNHV4/6f+BfCg4LkYLdELaN4Cdq+Gnioo71btJ69z7I7e3K3deh9d1HSvtt1myDuMqgs7hd2xdeqHjpxU7lin3Yfbw8GjK6tRM6srnQJ5HbCixctkv0+ZkxDb7Qo87Vq0rtyvf75zrU0dWns87oVNUVABcaOfGTY+rr7gzWgbnX9DeHyvcHtnZp6zjgkOIHuz2vrwprSOg50l8O07uPlUbZRrViYrBPPZvXBx1srW3eqf7BsdB12Lrr+f6YBr7RqQ57vK7Mae3mPRqIul/3zEUdebJTq2zgn7+/90GdNbMe2/13Qs58aLyuUHfpAuCwc8yEhv6yW52rO8rbN8dA8R7jQ+Oekm1Ps/q/uHlTm3tMn1jT/HFqvvyyXt3ftWc9KdW57IMh7TZTg6/s0DZnHd4e538sTencdr8zpXPYJ8uvXXj69sLo1DYvlNW0zuzfqLs/eUNhquS263TzHTntOGySdhuCeiN5i6u9/L0dyt1xs64rBqA3fPJubdx/Rpc1qt47TMAXvE9s6f623ihe28rRw1t1903FUbRLbtDyB8v3Cq627OUf9WvHZ2/VDXZk6seW6+5HenXC/cKA3ZB5jAyU7YfsvrsjgAvvXXhxY7Cdd23UviEzstou85BO2NWY0a8rimFn4gC4sLDZp52fv1vLrUlbm5aY/dp2ovL+wNdGdWKP0y7bjy9eqPxsqW3mSe39L7vNT9kaKOynaePyu3Y4oe4F9X8hp9s/tbzUPyYIvu6m25Xr7tcFP5wePVg4R+bWq+9Mkt99gR3iBwQQQAABBBBAAIEmC8QFt1m9RwDc5E5lcwgggAACCCCAAAKtJjClwSdXqf2+Hg1E3AJz5r0xnX1hW3GaZPf+ua1mlcX+2mmgO3XwnSzWxzrmi4Ad1dv5nYgDKYOGlgLgwI1PM1gxq0AAAQQQQAABBBBAAAEEEECgKJBVyBu3HgJgyg0BBBBAAAEEEEAAgUYKnO9TR6LpiO1ISALgurrjYp/WmpGFOwZCR3jWtW4WnkOBi+p70Iz07NaAO2o64xYRAGcMyuoQQAABBBBAAAEEEEAAAQQqBOKC26zeIwCuYOcFBBBAAAEEEEAAAQQyFChOt7rqycH4QHJqQD2r2tW+areGnHvhZtiSFljVTGG09cpNOsLo30XV3zODPfkR8psON270rwGzAXCy6cMXFTE7gwACCCCAAAIIIIAAAggg0CSBrELeuPUQADepM9kMAggggAACCCCAQIsK2BGp7e1a+/VjOv/2VCAInnnvos5+Z7fWF+8d2uiAazH3wlQ+JFylnsGpxbybrbdvU4Pqua9d5ksUje7ZgZ4094Nuva5gjxFAAAEEEEAAAQQQQAABBOoXiAtus3qPALj+fmINCCCAAAIIIIAAAgjEClx6oVPtZlriuH8rO7Tt8KXY9fBmnMCMzj+3TT3Hx+I+xHsLUGDmfJ+29RzTWMNHxttp2Nu1/gWOxQVYKjQZAQQQQAABBBBAAAEEEFgQAlmFvHHrIQBeEKVAIxFAAAEEEEAAAQQWusDEDw9q5+b16rgv5wTBOa1a3anu547pYmNntl3ofLQfgSYIEAA3AZlNIIAAAggggAACCCCAAAItLxAX3Gb1HgFwy5cZAAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0AyBrELeuPUQADejJ9kGAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi0vEBccJvVewTALV9mACCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQDMEsgp549ZDANyMnmQbCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQ8gJxwW1W7xEAt3yZAYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAs0QyCrkjVsPAXAzepJtIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAywvEBbdZvUcA3PJlBgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCDRDIKuQN249BMDN6Em2gQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACLS8QF9xm9R4BcMuXGQAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINAMgaxC3rj1EAA3oyfZBgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIItLxAXHCb1XsEwC1fZgAggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAzBLIKeePWQwDcjJ5kGwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0PICccFtVu8RALd8mQGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALNEMgq5I1bDwFwM3qSbSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQMsLxAW3Wb1HANzyZQYAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0QyCrkDduPQTAzehJtoEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAi0vEBfcZvUeAXDLlxkACCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQDIGsQt649RAAN6Mn2QYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLS8QFxwm9V7BMAtX2YAIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAMwSyCnnj1kMA3IyeZBsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINDyAnHBbVbvEQC3fJkBgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACzRDIKuSNWw8BcDN6km0ggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDLC8QFt1m9RwDc8mUGAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIINEMgq5A3bj0EwM3oSbaBAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAItLxAX3Gb1HgFwy5cZAAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0AyBrELeuPUQADejJ9kGAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi0vEBccJvVewTALV9mACCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQDMEsgp549ZDANyMnmQbCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQ8gJxwW1W7xEAt3yZAYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAs0QyCrkjVsPAXAzepJtIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAywvEBbdZvUcA3PJlBgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCDRDIKuQN249BMDN6Em2gQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACLS8QF9xm9R4BcMuXGQAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINAMgaxC3rj1EAA3oyfZBgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIItLxAXHCb1XsEwC1fZgAggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAzBLIKeePWQwDcjJ5kGwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0PICccFtVu8RALd8mQGAAAIIIIAAAgggvw+IFwAAIABJREFUgAACCCCAAAIIIIAAAggggAACCCCAAALNEMgq5I1bDwFwM3qSbSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQMsLxAW3Wb1HANzyZQYAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0QyCrkDduPQTAzehJtoEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAi0vEBfcZvUeAXDLlxkACCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQDIGsQt649RAAN6Mn2QYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLS8QFxwm9V7BMAtX2YAIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAMwSyCnnj1kMA3IyeZBsIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINDyAnHBbVbvEQC3fJkBgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACzRDIKuSNWw8BcDN6km0ggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDLC8QFt1m9RwDc8mUGAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIINEMgq5A3bj0EwM3oSbaBAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAItLxAX3Gb1HgFwy5cZAAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0AyBrELeuPUQADejJ9kGAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi0vEBccJvVewTALV9mACCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQDMEsgp549ZDANyMnmQbCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQ8gJxwW1W7xEAt3yZAYAAAggggAACrSFwWnpgafnfM6dbY7fZSwQSCXB8JGLiQwgggAACCCCAAAIIIIAAAgggULdAViFv3HoIgOvuJlaAAAIIIIBAiwlcGZGO9km7Vktdd5YDRRMurrlV6loh7eqVBs5JV1rMZl7vLgHXvO4eGjfHAnN0fLxxUtq/pXDeXOt8QcOcT9feWTyf9kiHjktvjEuzc8zE5hFAAAEEEEAAAQQQQAABBBBAoG6BuOA2q/cIgOvuJlaAAAIIIIBAiwiMn5R2rAgGvu6I0qjnXeukgWHpWos4NXU3x6UtXmg0FNWAOQq4oprD6wjMK4EmHx+v9Uqdy9KfTx+4RereLg2NEAbPq/qZT43xavmBe6Wfzaf2LYC2vLal8thcs0W6ugDa3vJNpP5bvgQAQAABBBBAAAEEFohAViFv3HoIgBdIMdBMBBBAAAEE5kxgdlzam6u8GBoV+Ea9vvYe6dTknO1GwzZ86F7HZnvDNhO+4lYLgL0Lu4fGw1l4FYHUAl5tNWqK9NkR6Ws1fJGm4rxKqJe6ixfUAl49pjrXecsSAKfs+avSUxFfzjhJApwScw4+Tv3PATqbRAABBBBAAAEEEKhBIC64zeo9AuAaOoZFEEAAAQQQaBmBK8PS4yEXQs2o3qPnpMnJ4Ci0K5PSG6el/i3SwyHLpbqIvUCUCYCb2FHehd3FWE9N1GRTroBXW40IgE34232L84URM3r/FumJXun1kcop89+359PtUpe/HAGw23uL77lXj6nOdd6yBMDpymPyiHeMOrNsdL+Ubl18eg4EqP85QGeTCCCAAAIIIIAAAjUIZBXyxq2HALiGjmERBBBAAAEEWkLAhBV++LvmHum1FKMuh/qC05ymuoi9QJQJgJvYUd6F3cVYT03UZFOugFdbjQiAD7uzBSyVNnRJI24bqjx/d1jae6+0xgRSBMBVtBb42149pjrXectSK+lq4ehqJwC+R3rM/TLbaunddKvj080WoP6bLc72EEAAAQQQQAABBGoTiAtus3qPALi2vmEpBBBAAAEEFr/At+9xLoIuldbcK43WsNtmCulni+tKdRG7hm3NxSJzGgCn2WHvomgjAq40zanps94+LMZ6qsmFheoX8Gor6+Nj9py03hlJ+MA90hs1tnrynPRkF/d1rZFvYSzm1SPnuiZ1m3dbBTPi178fcH+ab200qdlsBgEEEEAAAQQQQAABBBacQFYhb9x6CIAXXFnQYAQQQAABBJog8LMDwfD3gTuloTrvfXeqRzqcYvRwE3Yzk00QAGfCmGwlhCLJnPhUegGvtrIOgF9/KnhO3XU8fRNZooUEvHokAG5O34/0BY/To+Y2F6eLo+6LX+D4Ul9z2sJWEEAAAQQQQAABBBBAYFELxAW3Wb1HALyoS4idQwABBBBAoEaBvbcFL4I+d67GFdWw2Pi5wj2Eu1YEL7puWCHt6pNezyhEnr0qDR2Qnsg59yteJnXmpP3HpcmEbV8MAfDVEelQj7TFNb9F6lotHTotXUto0fCPZRyKNKvWanEZPS3t3yL5x8HaOwv98qw5FkYa1zdXJ6XXT0r9PVL3Cqnz1uA5wdy71rRtV690ajibdlyblAb6Cttba0fLZlCHk8PFc8qdzj6Y9eakZw9Ib5iD3autrAPgoe3OtpdKWa+/lhpr1DJvnpSeXResmYdXSE/0SK8nPbHGNM70p3++WnOrtKV4b/pazlezk9Kp4u+DDc79lu16D51M/jshpunJ3/LqcS4C4Kx+R4bt9BvHpV2rnd+9SyVTI91bpIGMzidh26322rdXOMfpunKfB/4muk16vdqKqrxvzu/mGHFrzZzbTQ2b37l1ft9O9lwa+PvGzORSPE6yqmczLX2/+T3lnFvNfnRvl4bq+FttNObvwCe2S0fP1W8U1UX5fdru/T1UrE9zDjO/72ajFo553dT80+uCVg8U12v6fb/5XTRe27pjNstbCCCAAAIIIIAAAvNXIKuQN249BMDzt/9pGQIIIIAAAnMjcPVkMHh9oEn3vLsyLH3NvfhqA6CQx84uaTjJFVLvIroNXV57Knjh2VyE8/+tWSEdi5rq0Zsm0l826mf/Ir4/0tp93wQSFWFY8b6h7nXVxAF0iIXZxqEur79DLNbmpDPVghvPZMuB5PVbbR/88CzKN/D69ujtN6LWsuhL0+J3TkpbnAAqsE8hfWOC2D0no/e1lnf8fanahqWSqZEk9wf3121qPl+HW6RS6Bu2n8Vt/DDJcV/caTP9/N5c5bEdtj+d3rnHnitq8Qtbxq/hrNfvbtPf1pD7Ztxz7xzhno/8xQLHbPF+xOMJa7e7T7rir9D7OaxOriXsz7WrE/5+KG7z1FPVay9fM7dIe08mC2jCfOwuRn0h4WtHJL/vwmq14rWQc90zzjEUey72+tzWZd2/I+3Oeo8jL0md7j11nXa6+2V+/37NvWd2E+557U/T7o7S90fw763xS3GTpxOe32+RdnUFz12JjuOryX6n23ruP1e9nsNq2UxDv8u7TYjbf/b5lt7qx7pbIma9Sf8ONL/7nuiV3L+H7LoS179dQFKavwvMOebH1f4mKq57+ECymrdmJqR/OepvT6e9PEUAAQQQQAABBBBY8AJxwW1W7xEAL/gyYQcQQAABBBDIWMC/AO1eBM14U6XVvXNc2hBxIdheFKt4vFM6GXblr7TWylF9T/WluLho2hM19bUXdla0LWJf/EAlLOQwzY8NJLwL4YGLsyFBQInDu9C/Y0u6i5KRFnYDnkls6GCXKT5W2we/JhN5R1g0qtay6MvRA9XD+LB9T2Pt0Yf+6O9L2DZDX1smHapy4dpf9/6XEgYixWMq6b3IzcX8xxMETaH70YARumd6gmHO+qeqBy+hnZPgRf94SRQcmfV65wj/fOVuOnDMrpCeXh3cvyhX+/rjB+L336+TXdsThrRp6uSq9G03ZIw4b9s228fHEwTYAZ/iObvaFxLMcez3nd1m7GPIuS5xAOb1eWa/I91iKT4/5Y2Cj90nvy+833shq6/7pR+607Qvk15zv2wyLHU5bVqzPb5+wxozUuP53TpVO45nR6QnnZG4drlqjzb0D2uzeS1QyznpcG+6Y7HasW63O9yXbr12v8Lan7j+ixsfPVLb36DVbo1S0/HcgN8/1phHBBBAAAEEEEAAgXklkFXIG7ceAuB51eU0BgEEEEAAgXkg8Jw3/fOxhKMcam361dOVF962PFWY3ta9/jo+HDKaLyqgtY3xLm7bC4bmsWtdYapJdySa2caz3qiWDWFBjRd2uuuNe+4HKn7I8R9PJgiovQvhgYuzIUGApfDDnVI7l0k7eiu9R05XtmXNOund0gq9J55JmlCy2j7UdBE1xKKRtVZvX5rRZ4EvQdwiPXNAGjH3oHSo3zdTMx8vTB9qR8ymsXZWFfnU3ZeH75X6XypMTekej2aKaFMjz/gjbO+R3ohcs+Suu1SDJlhZJm0z04aOlKf2NFPQmm34ocYTR2I2IMkEIX742xlyvF8z6y9ONfqwFxaHhQrxW41/9+rxyoB07+lg38avIfm7/vFSLTgqrdk7X/rnq9Ln/FDICcbMSHAzvey4UyxXx6WBkFG2cbcWiKyT4nFhpkq1Uz2bOjHTq/oj50PP3c5OHPbCXzPqtN9v+6Q01Ff5ZZlqoVbgnHav9FqCka/zIQB2j8m6fkc6zuapP4LWjN70z2+mHydHpKO9Upc/C4L3e89bfSY/PuOcA9ZsKZ+H7Mr73b8N/IDYfiji8d0j3pd7ir933To2i06OF6Yi3+ZPub9Uij2OJ6VdXvhrZkox0xW7f+NcMcdir1fPVb64E6hl51g3I1X9PjS/F8zxEvhdtkyq9ndk2Jefov4OPOR9GSTsXJ0mAH7X/wLiMulrfYXfefYcY/rG/H24f12wH+O+kOT3uTm/mNuLuOdG2+dnXpJ23Vted9g+RZQWLyOAAAIIIIAAAggsXIG44Dar9wiAF2590HIEEEAAAQQaIDApPeFc4Hsgg3vdVWvlc+5Fy2XSt4fjlzCjRNY4bdzQU3mhtrQGL9AwF7erTlV7VXrGa1PchdfAxdGQ0LHUlpAnkSGHs3/uBfn8c+9CeOLth1iYC6zVBlEH1r9Uipz6soEBcInO24e4gKq0jPOkkbVWb18eW+cEhMukgQRfvMjfo9OMan/J2ckMnpp9MbWRvzdugvW95o3si7t4HeZkgoq46TQrAl3vGPCb6Ad73QfKYaH/2dLPXm3F7UNpmZRPAvcRLR7jYSFbytVWfHwuAmATbhw6F+88edILhmJuL1BRJyaUORDzBRQT/E965+6l0tGI48gPZzZskd6pkCy/YEbvPuX9Xjgcc/L0z5sV5/GQc3zgixxePaY91yUOwLztNOJ3pP/lFlMrpyL6pSwu/Uf3yyVVjnl3uVqe+7e+CPs9N9LnnKOXSolnRzF/Vznhsgm/q82UYPbhtS3B7cX9HRI45y2Tnqvy5ZJr3pdkTODtBsWuYUUtF4/FuC70A91AbbsrL35hZ4t7PCyrPgWyvX2Fqdewc3Xi+h8PfllozT3Vb3fhj+SOqoPAlynvkar8aZtXyd+7+SnJfDmI/xBAAAEEEEAAAQQWvUBWIW/cegiAF30ZsYMIIIAAAgikERiRHnMvxDX4ouu7LwUvcFYb2Wd35aR3YTTqIr8/6nVLX3xAYdfvhw9xoXTg4mhWAXDx3nb+6CDbPvcx8fa9C/1PHnfXEvN8RPqSUxNhI6PyS8/zALjRtebXTCnwSdiX7gXrB1LWUUzvNeetSanbqZGuvujN+k5PJ7ynqh9qnnRGmLpbM6O81zttif2CiLugd3yEhQrux2t5fiVktoNSnSyVHl4hdW+R9h+QzgxLkxH7WG3bvlVccBRYl2cQFzoGzjsrpB8HVhT9g3/u7o+YMtyvk/0J77l65XjwC0JfiqjFQBh/p/R6dJNL78wOB8+FcdN4B3ycelx7T/gowNJG7JMUfWEXcR/d80lc+Nb035HLpLjg3N2HgGGD/xYJ1OVt0g/dhtjn3nnugXVSXAhqF/OPx2cThnv+clHHsR9eRwWStj320f+dGPV3VKAfbpNeS7LTktwR1Q+EjKi27QjYRwS69rP+o7mn9P4Qz6T1HwjZl0lRv1f87R51p7wP+yJLHX8P+dviZwQQQAABBBBAAIFFKxAX3Gb1HgHwoi0fdgwBBBBAAIFaBLyLzo0OovwLi6EXXcP2wwsloy7y+xe3E4c6p4Mhggmpov4L7EPK4M4POUwYlGRUrtuWxNv3+jaxhaTAxc5lEVNR1nHBs9Z9iAuoXCPzPLCNqAv8/kLm54S1Vm9fuhesG33che1mva99e4XzZY6Y48B3StqH496XRV6OCA4DYUKaaVrrOD7S2Jl7UHe6owGdcNANg0vPb5G2rCtMqxw5/brXgKTBkbdY7fcAThHOmdGgbkAfFU7WWidmnwLhbkjbZr3ze3eKEfR+YBS4T6wDGjjfmD6+Rdp7MtkXkPKr8eox6XFim+CeT6KMw7aT+PeCZxj5O9K7d27k72rbcOcxYBjSj85H63vqBbsm2I/6L/C7MGaEeWn5q9Iu5xiP/AJVaYHyk6THcWD2iLAwsrzK4DOvbVFfwKu1HwKjknPSaHDrhZ88+7iRyGGLR72WqP69GW/SnAf8Kf0rpriu4++hqH3idQQQQAABBBBAAIFFJ5BVyBu3HgLgRVc27BACCCCAAAL1CHgXnRsaRHkX3+IuuobtUiBwujdiKmNvfxJf3E5x8S5wcTQm+ArbBz/kSDrKzV1X4u3XaiHJTOdYCqSWRkzPmMLMbb95Xus+JA5FmlBr9fbls24omHAKaN9xLn9O2oe+U+I+TFK/XqCxZnuKe+wmWX9GwPnpS7dI9h7O7rEV+zzB1KumiUmDo4rd8Qzi+ibQ3ynDuaedQMz8jnHvcW3bVHOdSDrlTUl+yq60+Ojfj7YivPE+7/7oBz9RozkDPilGSJe2laIvSss4TxIFYObz3nay/h35hjdtclxNOc3PPw0Ypqwxf11xP/sjYeNm/Jg8EvxdGBuuS/LrJbFv0uPYO+clHf1rPdyQdk2PfTX4WGs/JDkP+Z5p2x9safmnJPXv903S0b/5rZipo53zWMV5wHv/gYRTQJf3gGcIIIAAAggggAACLSAQF9xm9R4BcAsUEruIAAIIIIBAcgHvYnBDA2BvW5EjiCJa71/kD50e0dtG4ouvKcLMwMXROgPgNBfHLUvi7ddqYTaUZNkUZrbt9rHWfUjs5bW/EbVWT2BlHAIjC4sjBp85II1Mhgdk1q5Zj+PD0qnj0v4e6dktUteKwr8NtwQDkXyAGXMc1Ozk9WHoseyNLo0a0RZqlmT9oQvW/qK5h/MbJ6X9RU/33uZxQbC5T+UPY6aHThK8hLbaM4g7vgLHbMpwLsmyNdeJpGrLBrYfNTIxFKgwI4B7m4SoADCwjZQ++U2n6IuwpiYJwMK2E3pchW0g4fk+MDr1tmRTbdvN1W1oV1Tlsf8e5xx2T37Sh+glvMDVBHsRkxHk11HPlw0SHcfeOS9qZoSoHQpsI2JK61r7IbDupeEzh5zpceyXJp8ePGp/7OtJ6j/QN2nPA2aKaycADvtdE6irpZK59/X+49J4zLnbtp9HBBBAAAEEEEAAgZYQyCrkjVsPAXBLlBI7iQACCCCAQFKBkHsAjyddNuXn/Cldn0t4j0e7GX9UamhY4V1Ez/ritmlL4OJoTPBl2+0+Vgsq3M9GPU+8/VotzIa9i8yPh02ZmjAQCNuPWvchtM9DNtCMWqu3L2dHpC3OBeVAAHiL1LVaerZPOnVOer9JF5DHT0vPrK5hpGrMcVCzU4L69UeTVYzKCqmN0ksJ1l/6bAOfvD8pvXFaOton7VotPeyODHfqY01MqJgkeAndBc8g7vgKHLMxbQnbjj9N95mQD9VcJyGjLv06CIy2jwi9QppUeskfwVx6w3lSj09+NSn6wtls6akbTkWF1GHbyfp3ZKKZOkqtDj6p2zC4uvCfvCn+Y62Ka/C/rBM3YjhJrYc3LNlIfv+cF/i94ZwvEr0ecRzX2g9JzkOBLwhEhMRRPnGvJ6l/f9uJjCJMw+rmysngdPfu+tfcWpja397v/UrczvAeAggggAACCCCAwGIViAtus3qPAHixVg/7hQACCCCAQE0C/rR1K6Q3alpR9YXqucBv1p5oee8ietYXt007AhdHY4KvMJFE+xC2oPNa4u3XamG2lSTcTfIZp93u01r3IS6gctdfr3OS5ZN8xm1T2PMr56THIgI/9+Kxef7wPVL/6RT3FA3bYMRrs+PSs+6ouIiL3n6bSj/HHAc1OyWo35rXbRwSrD+Cq+Evj56WvubeY7nYH09F3Js8SfAS2mjPIO74ChyzEcFR6DYSTm3byL50w6FaZrlIsnw9Pnm3FH0R5uy2MSycKi3jbSfr35FuO9Ja121Y2snoJ4FRoEnu6SvJv4e0uX1F2DTmZqv17EOS49g/Tkrn4LTnbPP5iOO41n1I0v7AupscAPvbrscu6hgbPSJtSNgXG1ZLA8PRtRRdxbyDAAIIIIAAAgggsEAFsgp549ZDALxAi4NmI4AAAggg0DCBwOiopVKa+yOmaZR/4TIubAhbb6LlG3xx27QrcBExJviqeR/CFnReS7z9Wi3MtpKEu0k+47TbfVrrPiStmUS14jbIe55k+SSf8VYb+qOZFvi1XulLtwanxoy6ON25XXondE01vnhVeubOym2bEUtPbJf6X5JOnSxMTT05KU0WRyMn7cOanRLUb83rNlQJ1l+jaGaLBYzNlKIR9+xMEryENsoziDu+Am2JCI5Ct0EAHMUSfD1FXwQXLPzkBq9R4VT+k952Wi0A3ntb5bku6lwb+fpt0g/DOsH/+6AJx0lkG5OEkBHtq/VYT3IeCqx7EQbApiyuTUpHt0uht0sI6ZfuPokRwREHFC8jgAACCCCAAAKLSyAuuM3qPQLgxVUz7A0CCCCAAAL1CwSmLFwqhd3brP6tSM2YlrfmUCdFmBm4gLlYA+DTknt/0tB76KYw8+snsaEXVsQFVO42mlFrdYWPbmOd51eL0wEf6pWeyEVPB7whZgSas7pET/3pTc19Cw+dqz4qKWkf1uzk9X1YUOWvO9X9MBOsPxFgAz/kjzyMGrGXJHgJbaZnEHd8Bfo7IjgK3YYfikXce9Pvy7i2+Nvxl/XrIPAlJ6aAlhsahh1Xvm/+54TnezeINiOAo0bKhm2jnhoLW5//WsXxFBLGuTZxz6NG4wf2IaLW/XbZn5Mcx/7vtjTHid1OtcfAPqQ41pO0//C9wQB+qFpjEr7v1l3UFyAC206xXwmbEPqxK5PS6yel/h6pe0X07RUa9Xd3aKN4EQEEEEAAAQQQQGCuBLIKeePWQwA8V73LdhFAAAEEEJivAhX3lFstvduIxnphw67j6TZyanuCC4feNrK+uG1aHLg4ukgD4KvHg9ah92tOGAiE9XJiQ68/E1/s

Pengaruh Kasus Pandemi Virus Covid-19 Terhadap Kinerja Trans Semarang Megawati Takary Putri; Joshua TH Panggabean; Djoko Setijowarno; Djoko Suwarno
G-SMART Vol 6, No 1: Juni 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i1.3480

Tugas akhir ini merupakan pengamatan pengaruh kasus pandemi virus covid-19 terhadap kinerja Trans Semarang studi kasus pada Koridor II Terminal Terboyo-Sisemut Ungaran. Metode yang digunakan untuk mendapatkan data primer adalah pengamatan atau survey lapangan dan penyebaran kuesioner. Sedangkan untuk data pembanding atau data sekunder diperoleh dari BLU UPTD Trans Semarang dan dari jurnal-jurnal atau sumber referensi lainnya. Hasil analisa waktu tempuh aktual rata-rata bus Trans Semarang rute Sisemut-Terboyo adalah 1 jam 22 menit dan rute Terboyo-Sisemut adalah 1 jam 25 menit. Selisih waktu kedatangan antar bus (Headway) rata-rata untuk rute Sisemut-Terboyo yaitu 6,62 menit sedangkan rute Terboyo-Sisemut 6,75 menit. Frekuensi kendaraan yang lewat dalam 1 jam berdasarkan rata rata headway aktual untuk rute Sisemut-Terboyo yaitu 9 kendaraan sedangkan rute Terboyo-Sisemut yaitu 10 kendaraan. Load factor tertinggi Bus Trans Semarang Koridor II per ruas halte untuk rute Sisemut-Terboyo adalah 105,88 sedangkan untuk rute Terboyo-Sisemut adalah 105,88. Tingkat kepuasan penumpang pada Bus Trans Semarang Koridor II di masa pandemi Covid-19 adalah sudah memuaskan karena kepuasan berada pada kuadran II pertahankan prestasi.

Analisis Pengaruh Lingkungan Asam Terhadap Beton Bertulang Ditinjau Dari Corrosion Rate Tulangan Iqbaal Rizky Ananto; Fiona Indah Yurisaputri Martanni; Djoko Suwarno; Widija Suseno
G-SMART Vol 6, No 2: Desember 2022
Publisher : Universitas Katolik Soegijapranata Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/gsmart.v6i2.4331

Jumlah penduduk di dunia meningkat setiap tahunnya. Pada provinsi Jawa Tengah jumlah penduduk pada tahun 2020 mencapai 36.516.035 juta jiwa. Perkembangan jumlah penduduk menyebabkan kebutuhan manusia meningkat. Guna memenuhi kebutuhan tersebut,maka aktivitas industri dan transportasi dilakukan. Aktivitas tersebut menghasilkan gas emisi yang kemudian menjadi polutan. Zat polutan tersebut akan berkumpul di udara dan menyebabkan terjadinya hujan asam. Hujan asam merupakan proses turunnya air yang bersifat asam dan memiliki pH di bawah 5,6. Kota Semarang dari tahun 2018 hingga tahun 2020 memiliki rata-rata pH air hujan sebesar 5,37. Dengan demikian dapat dikatakan bahwa hujan yang terjadi di kota Semarang adalah hujan asam. Penelitian ini dilakukan untuk mengetahui pengaruh hujan asam terhadap sifat mekanis beton melalui uji kuat tekan beton dan durabilitas beton bertulang melalui uji laju korosi.Hasil uji kuat tekan beton yang di-curing dan direndam menggunakan air PDAM pada umur 28 hari adalah sebesar 30,93 MPa, sedangkan beton yang di-curing dan direndam menggunakan air pH 5±0,5 pada umur 28 hari adalah sebesar 23,66 MPa.Hasil uji laju korosi pada beton yang di-curing dan direndam menggunakan air PDAM adalah sebesar 0,218 mm/tahun, sedangkan beton yang di-curing dan direndam menggunakan air pH 5±0,5 memiliki nilai laju korosi sebesar 0,343 mm/tahun.

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