diff --git a/Plane Price.csv b/Plane Price.csv new file mode 100644 index 0000000..4825be8 --- /dev/null +++ b/Plane Price.csv @@ -0,0 +1,518 @@ +Model Name,Engine Type,HP or lbs thr ea engine,Max speed Knots,Rcmnd cruise Knots,Stall Knots dirty,Fuel gal/lbs,All eng rate of climb,Eng out rate of climb,Takeoff over 50ft,Landing over 50ft,Empty weight lbs,Length ft/in,Wing span ft/in,Range N.M.,Price +100 Darter (S.L. Industries),Piston,145,104,91,46,36,450,900,1300,"2,050","1,180",25/3,37/5,370,1300000 +7 CCM Champ,Piston,85,89,83,44,15,600,720,800,"1,350",820,20/7,36/1,190,1230000 +100 Darter (S.L. Industries),Piston,90,90,78,37,19,650,475,850,"1,300",810,21/5,35/0,210,1600000 +7 AC Champ,Piston,85,88,78,37,19,620,500,850,"1,300",800,21/5,35/0,210,1300000 +100 Darter (S.L. Industries),Piston,65,83,74,33,14,370,632,885,"1,220",740,21/5,35/0,175,1250000 +PA-60-700P Aerostar (preliminary),Piston,65,78,72,33,15,360,583,880,"1,250",786,20/4,36/1,180,1100000 +"PA-601P pressurized Aerostar ('77 service ceiling=26,350)",Piston,350,264,230,80,165,"1,820",3080,2100,"6,315","4,275",34/10,36/8,868,2500000 +100 Darter (S.L. Industries),Piston,290,262,247,77,165,"1,755",2250,2076,"6,000","4,125",34/9,36/7,"1,020",2800000 +"PA-601, 601A, turbochg Aerostar",Piston,290,257,235,77,165,"1,460",2490,2030,"6,000","4,056",34/10,36/8,"1,101",2500000 +100 Darter (S.L. Industries),Piston,290,257,237,77,165,"1,460",2490,2030,"6,000","3,958",34/10,36/8,"1,174",3000000 +G164B-600 AG-CAT (design category) sprayer,Piston,290,271,236,69,174,"1,700",2200,1625,"5,700","3,750",34/10,34/2,"1,174",3010000 +100 Darter (S.L. Industries),Piston,290,220,212,74,165,"1,800",1950,1840,"5,500","3,737",34/10,34/2,"1,200",2120000 +G164A-300 AG CAT,Piston,600,,100,52,64,"1,360",1050,1150,"5,200","3,650",25/6,42/5,200,nan +100 Darter (S.L. Industries),Piston,600,128,96,59,46,"1,600",860,770,"4,500","3,160",24/4,35/11,148,1450000 +G164A-245 AG CAT,Piston,525,128,91,52,46,"1,350",,540,567,"4,500","3,150",42/3,155,nan +A-2,Piston,450,128,91,52,46,"1,060",1090,1150,"4,500","3,100",25/7,42/3,190,1450000 +100 Darter (S.L. Industries),Piston,450,128,91,58,46,990,1145,750,"4,500","2,870",24/4,35/11,190,1400000 +415-G,Piston,300,128,74,54,46,700,1200,1000,"3,750","2,410",24/4,35/8,226,1150000 +CH-601-XL/i/650-LS/i,Piston,275,114,74,54,33,600,1275,1000,"3,750","2,400",24/4,35/8,117,1025000 +8KCAB-150 F/P prop(w/optional speed kit),Piston,245,114,70,54,33,496,1300,1000,"3,750","2,300",24/4,35/8,174,850000 +7KCAB,Piston,220,114,70,48,33,435,1360,1000,"3,600","2,200",24/4,35/8,190,700000 +7GCAA,Piston,90,112,108,37,24,640,1100,1200,"1,450",930,20/2,30/0,410,1730000 +100 Darter (S.L. Industries),Piston,90,113,104,50,24,600,1800,1600,"1,400",900,20/1,30/0,400,1600000 +7GCBC with EDO floats,Piston,85,99,96,50,24,550,2100,1850,"1,400",833,20/1,30/0,360,1350000 +7EC,Piston,100,120,113,38,30,"1,000",,,"1,320",770,20/0,27/0,715,1200000 +Starship 2000 (2000A=increased spds & weights),Piston,150,147,137,52,40,"1,000",,,"1,800","1,260",22/11,32/0,404,1600000 +King Air 260,Piston,150,116,109,43,39,"1,120",535,755,"1,650","1,140",22/7,34/5,411,1700000 +B200 Super King Air,Piston,150,109,103,37,37,"1,140",617,718,"1,650","1,050",22/9,34/5,411,1810000 +100 Darter (S.L. Industries),Piston,150,113,109,43,39,"1,120",535,755,"1,650","1,140",22/8,33/5,411,1700000 +"B100 King Air (prior '79 serv. ceiling=29,100)",Piston,150,113,111,39,35,"1,145",457,690,"1,650","1,150",22/8,34/3,480,1700000 +100 Darter (S.L. Industries),Piston,150,97,90,46,39,800,,,"1,800","1,290",22/7,34/6,248,1600000 +100 King Air,Piston,115,109,107,44,35,725,716,775,"1,650","1,067",22/8,33/5,301,1200000 +100 Darter (S.L. Industries),Piston,90,117,97,38,26,700,1240,700,"1,450",820,21/6,35/1,350,1550000 +F90 King Air,Piston,60,85,75,38,13,400,850,,"1,220",750,21/11,35/1,261,850000 +"C90A King Air (LJ-1063 up) (10,100 g/w=SN1138 up)",Piston,160,168,160,63,80,"1,160",1805,1330,"3,800","2,588",29/7,36/9,680,1740000 +100 Darter (S.L. Industries),Piston,180,148,139,53,51,850,1550,1120,"2,400","1,360",22/0,31/6,530,1380000 +100 Darter (S.L. Industries),Piston,150,136,127,52,37,660,1600,1100,"2,200","1,323",22/0,31/6,428,1265000 +A 90 King Air,Piston,150,130,122,50,38,660,1600,1100,"2,200","1,271",22/0,31/5,503,1265000 +100 Darter (S.L. Industries),Piston,108,120,108,52,24,705,1590,1100,"1,560",975,19/2,24/5,348,1275000 +88 Queen Air pressurized,Piston,108,120,109,51,24,765,1400,1065,"1,500","1,007",19/3,24/5,350,1375000 +B 80 Queen Air - specs thru 1972,Piston,100,90,84,33,20,700,,,"1,320",845,22/5,35/6,275,1500000 +80 Queen Air,Piston,180,126,113,36,52,"1,500",,,"1,800","1,190",22/7,35/6,696,2000000 +A 65 Queen Air,Piston,600,122,96,61,54,"1,000",1200,800,"6,000","3,400",29/4,44/4,275,1500000 +B 60 Duke pressurized (prior '78 t/o run=2006),Piston,600,122,108,57,100,900,,,"6,000","3,700",29/4,44/4,275,1500000 +60 Duke Pressurized,Propjet,750,138,130,57,190,"1,740",,,"6,000","3,600",33/0,44/5,545,2500000 +100 Darter (S.L. Industries),Piston,80,157,138,46,17,"1,000",820,721,"1,320",622,20/10,35/5,350,nan +100 Darter (S.L. Industries),Propjet,1050,158,126,52,216,650,,,12500,5829,33/6,56,600,800000 +C 50 Twin Bonanza,Propjet,"1,200",335,328,,560,"3,225",3876,2630,"14,400","9,887",46/1,54/5,"1,722",3480000 +50 Twin Bonanza,Propjet,"1,050",314,303,,539,"2,979",,2508,"15,000","9,051",46/8,57/11,"1,966",3500000 +58 P Baron-press.('77 hgt=9/6) (range based on 190 gal fuel),Propjet,850,310,259,80,3645,2437,,,12590,8830,43/10,57/11,1720,3500000 +58 TC Baron (325) turbochg-non-pressurized,Propjet,850,294,279,75,544,"2,450",3345,2845,"12,500","7,538",43/9,54/6,"1,972",3500000 +100 Darter (S.L. Industries),Propjet,850,290,272,75,544,"2,450",3345,2845,"12,500","7,538",43/9,54/6,"1,870",3288000 +58 Baron (300 hp),Propjet,715,265,258,83,470,"1,963",2951,2679,"11,800","7,082",39/11,45/11,"1,080",2485000 +100 Darter (S.L. Industries),Propjet,680,248,235,75,470,"1,963",3245,2944,"11,500","6,797",39/11,45/11,"1,000",2485000 +A56TC Turbo Baron,Propjet,680,248,239,73,374,"2,200",1729,2138,"10,600","6,440",39/9,45/10,"1,005",2590000 +100 Darter (S.L. Industries),Propjet,750,279,265,79,470,"2,455",2808,2275,"10,950","6,647",39/10,45/11,"1,235",3045000 +E 55 Baron ('70-'72 fuel=142 gal opt),Propjet,750,267,261,77,470,"2,380",2856,2275,"10,950","6,549",39/10,45/11,"1,235",2980200 +100 Darter (S.L. Industries),Propjet,550,250,245,77,474,"1,870",2024,2110,"10,100","5,996",35/6,50/3,"1,290",2762000 +B 55 Baron (1978 & up),Propjet,550,247,240,75,384,"2,137",2261,1672,"10,100","5,765",35/6,50/3,"1,120",3000000 +100 Darter (S.L. Industries),Propjet,550,223,217,76,384,"1,955",2261,1672,"9,650","5,765",35/6,50/3,"1,120",2810000 +100 Darter (S.L. Industries),Propjet,550,223,219,74,384,"2,000",2180,2010,"9,650","5,685",36/6,50/3,"1,185",2720000 +E 95 Travel Air (fuel inj.),Propjet,550,223,216,77,384,"1,900",2150,1960,"9,300","5,680",35/6,45/1,"1,160",3020000 +100 Darter (S.L. Industries),Propjet,500,243,235,75,384,"2,000",1755,1870,"9,000","5,680",35/6,45/1,"1,270",2840000 +"95, B95 Travel Air (95 = 4,000 lb. gross)",Piston,380,214,192,71,264,"1,275",1800,2311,"8,800","6,035",35/6,50/3,902,2680000 +100 Darter (S.L. Industries),Piston,380,216,196,70,200,"1,275",2556,2572,"8,800","5,277",35/6,50/3,656,2680000 +B 36 TC Bonanza,Piston,380,216,195,71,214,"1,275",1800,2311,"8,800","5,120",35/6,50/3,716,2680000 +100 Darter (S.L. Industries),Piston,380,219,200,70,214,"1,585",1800,2143,"8,500","4,900",35/6,50/3,716,2850000 +A 36 Bonanza (300 hp),Piston,380,208,191,67,230,"1,300",1450,2070,"8,000","4,800",35/2,50/3,746,2800000 +100 Darter (S.L. Industries),Piston,340,208,186,70,214,"1,375",1675,2107,"8,200","4,995",35/6,50/4,755,3000000 +36 Bonanza,Piston,340,208,186,70,214,"1,300",1560,1750,"7,700","4,980",35/6,45/10,755,3130000 +100 Darter (S.L. Industries),Piston,340,200,186,70,180,"1,300",1700,1980,"7,700","4,640",33/3,45/10,630,2700000 +V 35B Bonanza ('80 & up=fuel std 74 gal),Piston,380,246,233,73,142,"1,601",2626,3065,"6,775","4,425",33/10,39/3,"1,010",3000000 +100 Darter (S.L. Industries),Piston,380,249,240,76,142,"1,601",2626,3065,"6,775","4,175",33/10,39/3,824,3080000 +V 35 TC Turbo Bonanza,Piston,380,249,236,76,142,"1,615",1660,2340,"6,725","4,100",33/10,39/3,824,3130000 +100 Darter (S.L. Industries),Piston,450,204,191,76,198,"1,400",2072,1850,"9,900","5,845",35/2,49/8,622,2030000 +S 35 Bonanza,Piston,450,200,186,73,275,"1,410",1980,1850,"9,700","5,910",35/1,49/6,880,2140000 +100 Darter (S.L. Industries),Piston,450,203,187,71,206,"1,250",2079,1569,"9,300","6,150",33/1,47/7,638,2300000 +"K,M 35 Bonanza",Piston,450,200,183,67,206,"1,190",1760,1460,"8,750","5,770",33/1,47/7,632,2050000 +100 Darter (S.L. Industries),Piston,340,204,180,72,180,"1,270",1250,1840,"7,300","4,480",31/5,45/9,787,2950000 +H 35 Bonanza,Piston,340,209,184,72,180,"1,320",1250,1840,"7,000","4,460",31/5,45/9,813,2950000 +100 Darter (S.L. Industries),Piston,295,186,178,62,134,"1,450",1260,1452,"6,300","4,100",31/5,45/9,670,2000000 +"C, D 35 Bonanza",Piston,275,178,161,60,134,"1,450",1260,1375,"6,000","3,956",31/5,45/2,726,2000000 +100 Darter (S.L. Industries),Piston,260,177,159,60,134,"1,450",1344,1215,"6,000","3,940",31/5,45/2,733,2000000 +35 Bonanza,Piston,260,177,158,56,134,"1,450",1350,1215,"5,500","3,800",31/5,45/2,722,1900000 +100 Darter (S.L. Industries),Piston,325,261,232,78,166,"1,475",2643,2427,"6,200","4,026",29/11,37/10,"1,013",2500000 +F & E 33-C Bonanza (conventional tail),Piston,310,256,214,79,166,"1,529",2376,2498,"6,100","3,985",29/10,37/10,"1,130",2500000 +100 Darter (S.L. Industries),Piston,310,231,207,79,166,"1,424",2761,2498,"6,100","3,985",29/10,37/10,"1,104",2500000 +E 33 A Bonanza (conventional tail),Piston,325,261,232,78,166,"1,475",2643,2427,"6,200","3,788",29/11,37/10,"1,013",2500000 +100 Darter (S.L. Industries),Piston,310,250,214,79,166,"1,461",2376,2498,"6,100","3,780",29/10,37/10,"1,130",2500000 +C 33 A Debonair,Piston,300,208,195,74,136,"1,750",2371,2498,"5,500","3,443",29/10,37/10,"1,109",2068800 +100 Darter (S.L. Industries),Piston,285,208,195,74,136,"1,660",2101,2498,"5,400","3,361",29/10,37/10,"1,109",1860000 +"A,B 33 Debonair",Piston,380,252,247,73,142,"2,020",1420,2080,"5,990","3,700",29/0,37/10,737,3220000 +100 Darter (S.L. Industries),Piston,380,252,247,73,142,"2,020",1420,2080,"5,990","3,650",28/3,37/10,737,3220000 +"C-24R Sierra 200 (prior'78 svc. ceil=16,400)",Piston,285,208,195,73,100,"1,682",968,1414,"5,300","3,291",29/0,37/10,934,1910000 +100 Darter (S.L. Industries),Piston,285,210,200,67,112,"1,670",968,1414,"5,300","3,075",28/3,37/10,550,2090000 +A-24R Sierra 200,Piston,260,201,184,73,100,"1,693",2154,2148,"5,100","3,236",28/0,37/10,739,1930000 +100 Darter (S.L. Industries),Piston,260,205,196,68,112,"1,670",1225,1370,"5,100","3,075",27/0,37/10,763,1970000 +C 23 Sundowner (pre '76 fuel=52 gal) pre'82=less mph,Piston,260,200,191,66,112,"1,700",1700,1470,"4,880","2,960",26/5,37/8,745,1950000 +100 Darter (S.L. Industries),Piston,260,200,191,66,112,"1,630",1700,1470,"4,880","2,960",25/7,37/8,745,1920000 +"A 23, A23A Musketeer (A=2,400 lb gross)",Piston,180,183,174,61,80,"1,250",1280,1590,"4,200","2,650",25/1,37/1,661,1810000 +100 Darter (S.L. Industries),Piston,180,183,174,61,80,"1,250",2100,1850,"4,200","2,635",25/3,37/1,661,1810000 +B 19 Sport 150 (76' & prior fuel=52 gal),Piston,180,183,170,61,80,"1,250",2100,1850,"4,100","2,635",25/3,37/9,661,1810000 +100 Darter (S.L. Industries),Piston,180,171,158,60,100,"1,248",2119,1881,"3,900","2,460",29/0,38/0,843,1965000 +77 Skipper ('79-'80 height=7/11),Piston,300,213,190,57,102,"1,049",2364,1692,"3,850","2,338",27/6,37/10,957,2500000 +"17-31 ATC Turbo, 300-Lyc. (prior '78=8 gal less)",Piston,300,214,190,57,74,"1,165",2012,1449,"3,650","2,278",27/6,33/6,800,2500000 +"17-31A, (prior '78=8 gal less fuel + 6 mph less)",Piston,300,184,169,59,74,"1,210",1913,1473,"3,650","2,247",27/6,33/6,720,1850000 +100 Darter (S.L. Industries),Piston,285,179,168,52,74,"1,030 w/3bld",2040,1450,"3,600","2,195",27/6,33/6,697,1660000 +"17-30A, (1979 & up; 1993 & up + 5 mph; IO-550 +6 mph)",Piston,285,177,170,56,50,"1,015",1525,1240,"3,600","1,980",26/4,32/10,510,1600000 +100 Darter (S.L. Industries),Piston,285,217,200,55,50,"1,225",1320,1177,"3,400","2,027",26/4,33/5,600,2950000 +17-30-300 Viking (Continental eng),Piston,285,182,172,51,74,"1,167 w/3bld",1769,1324,"3,400",,26/5,33/6,717,1785800 +100 Darter (S.L. Industries),Piston,285,183,177,55,44,"1,136",1320,1177,"3,400","1,960",26/4,33/5,536,1750000 +14-19-2 Cruisemaster,Piston,285,209,195,55,50,"1,225",1320,1177,"3,400","1,950",26/4,33/5,580,2660000 +100 Darter (S.L. Industries),Piston,285,183,177,55,50,"1,136",1320,1177,"3,400","1,915",26/4,33/5,543,1750000 +14-13 Cruiseair Sr.,Piston,285,184,178,54,50,"1,200",1225,1150,"3,300","1,915",26/4,33/5,543,1830000 +55 (1984-'85),Piston,260,178,170,52,50,"1,150",1260,1100,"3,125","1,855",25/2,33/5,535,1920000 +55 (thru 1983),Piston,250,183,170,52,49,"1,170",1185,1050,"2,950","1,832",25/1,33/5,495,2000000 +100 Darter (S.L. Industries),Piston,250,183,174,50,39,"1,250",1175,1050,"2,900","1,820",25/1,32/9,492,2130000 +100 Darter (S.L. Industries),Piston,240,179,165,50,39,"1,225",1260,1050,"2,900","1,820",25/1,32/9,470,1980000 +"36 A (prior'82 = gross weight @ 18,000)",Piston,225,169,160,48,39,"1,300",1270,1025,"2,775","1,722",25/1,32/9,550,1900000 +36,Piston,205,165,152,48,39,"1,100",1500,975,"2,700","1,650",25/1,32/9,510,1800000 +100 Darter (S.L. Industries),Piston,196,160,148,49,39,890,1515,950,"2,650","1,575",25/1,32/9,470,1710000 +100 Darter (S.L. Industries),Piston,185,160,150,48,39,950,1440,925,"2,550","1,458",25/1,32/9,530,1800000 +100 Darter (S.L. Industries),Piston,260,177,168,49,50,"1,060",1516,1150,"3,300","1,935",25/5,32/9,513,1660000 +28 Longhorn,Piston,285,181,174,53,50,"1,200",1225,1150,"3,300","1,918",25/6,32/9,520,1830000 +"25 D,F w/6 engs=45,000 service ceiling (prior '82 more rate of climb)",Piston,285,182,172,51,74,"1,167 w/3bld",1769,1324,"3,400","2,125",26/8,33/6,717,1785800 +100 Darter (S.L. Industries),Piston,285,181,174,53,50,"1,200",1225,1150,"3,300","1,915",25/6,32/10,520,1830000 +25 C (1973 thru 1975),Piston,225,170,161,52,50,930,1288,1298,"3,050","1,862",25/6,32/10,520,1780000 +100 Darter (S.L. Industries),Piston,285,181,174,53,50,"1,200",1225,1150,"3,300","1,775",25/6,32/10,520,1830000 +"24 F (w/8A engs=51,000 service ceil)",Piston,225,170,161,52,50,980,1288,1298,"3,050","1,854",25/6,32/10,568,1830000 +100 Darter (S.L. Industries),Piston,225,170,161,52,50,960,1235,1282,"3,000","1,745",25/6,32/10,568,1840000 +24 D (1973 thru 1975),Piston,225,170,161,52,50,"1,010",1235,1282,"2,900","1,730",25/5,32/8,550,1980000 +100 Darter (S.L. Industries),Piston,200,145,137,60,57,927,1561,1462,"2,750","1,696",25/9,32/9,647,1538500 +24 C,Piston,200,140,131,55,52,891,1804,1519,"2,750","1,711",25/8,32/9,591,1434200 +100 Darter (S.L. Industries),Piston,200,148,141,57,58,862,1630,1380,"2,750","1,610",25/8,32/9,704,1435000 +24 Twin Jet,Piston,200,137,130,52,60,880,1380,1300,"2,550","1,410",25/1,32/7,643,1485000 +100 Darter (S.L. Industries),Piston,180,128,116,51,57,792,1955,1484,"2,450","1,494",25/9,32/9,565,1260000 +100 Darter (S.L. Industries),Piston,180,127,120,50,60,728,1460,1260,"2,400","1,375",25/1,32/7,565,1187000 +650 Citation VII,Piston,165,127,120,50,60,880,1460,1260,"2,350","1,325",25/1,32/7,565,1187000 +650 Citation VI,Piston,160,123,111,52,60,720,1275,1260,"2,300","1,300",25/1,32/7,800,1350000 +100 Darter (S.L. Industries),Piston,150,110,107,50,57,680,1635,1693,"2,150","1,414",25/9,32/9,643,1165000 +650 Citation III (thru SN 099),Piston,150,122,114,48,60,900,1255,1220,"2,200","1,325",25/1,32/7,800,1490000 +100 Darter (S.L. Industries),Piston,115,106,97,47,29,720,1280,1313,"1,675","1,103",24/0,30/0,370,1290000 +100 Darter (S.L. Industries),Piston,300,193,187,61,68,"1,170",1420,1340,"3,325","2,372",26/4,34/2,760,2400000 +100 Darter (S.L. Industries),Piston,300,174,165,61,68,"1,170",1420,1340,"3,325","2,247",26/4,34/2,483,1820000 +S550 Citation SII,Piston,250,193,187,54,72,"1,800",890,1100,"3,200","2,010",23/7,34/2,760,2400000 +Citation II 550 (thru SN 626),Piston,300,181,176,61,68,"1,210",1420,1340,"3,325","2,185",26/4,34/2,550,2000000 +100 Darter (S.L. Industries),Piston,300,166,163,61,60,"1,085",1420,1340,"3,325","2,217",26/4,34/2,550,1700000 +Citation CJ3 525B,Piston,300,167,163,54,58,"1,840",890,1050,"3,200","1,900",23/7,34/2,550,2100000 +100 Darter (S.L. Industries),Piston,260,181,177,54,58,"1,500",990,825,"3,000","1,750",23/6,34/2,639,2250000 +100 Darter (S.L. Industries),Piston,230,179,170,42,40,"1,400",1025,1150,"2,700","1,640",22/9,34/2,452,2000000 +Citation I 500,Piston,190,174,157,38,40,"1,250",1270,1025,"2,600","1,575",23/0,34/2,435,2150000 +100 Darter (S.L. Industries),Piston,150,147,130,38,40,"1,100",1350,975,"2,100","1,520",21/2,34/2,591,1950000 +402C Business Liner II,Jet,"3,700",455,417,,6707,"4,059",5600,3300,"21,500","12,135",55/1,43/9,"1,715",5100000 +"402,-A turbocharged",Jet,"3,700",457,,,6707,"4,380",4540,3109,"19,500","12,130",55/1,43/9,"1,715",5100000 +100 Darter (S.L. Industries),Jet,"8,650",480,442,,16665,"4,200",5400,3300,"43,250","19,950",68/5,61/10,"3,391",4100000 +100 Darter (S.L. Industries),Jet,"7,500",480,442,,14890,"3,400",5700,3300,"41,400","18,660",68/5,61/10,"2,991",4100000 +T 310 P turbocharged,Jet,"3,500",491,460,99,7400,"4,340",4972,3075,"18,300","9,838",48/8,39/6,"2,289",4500000 +310 Q,Jet,"3,500",477,441,105,7431,"5,100",4080,3105,"17,000","8,802",48/7,39/5,"2,289",4500000 +100 Darter (S.L. Industries),Jet,"3,500",471,460,99,6198,"4,760",4972,3075,"18,300","9,838",48/8,39/6,"1,818",4500000 +100 Darter (S.L. Industries),Jet,"3,500",477,441,105,6171,"5,100",4080,3105,"17,000","8,762",48/7,39/5,"1,818",4500000 +100 Darter (S.L. Industries),Jet,"2,950",478,441,79,5373,"6,110",2880,2220,"15,000","8,157",47/7,43/9,"1,546",4790000 +"310,-A",Jet,"2,950",475,441,79,4704,"6,350",2630,2220,"15,000","8,195",47/7,43/9,"1,450",4880000 +"T 337 G-P II,H-P, Skymaster pressurized",Jet,"2,950",464,459,97,6098,"6,830",3937,2817,"15,000","7,950",47/7,35/7,"1,431",5100000 +100 Darter (S.L. Industries),Jet,"2,950",464,441,104,6098,"6,050",5186,3090,"15,000","7,355",47/6,35/7,"1,450",4500000 +T 337 E & F Skymaster turbocharged,Jet,"2,950",464,441,104,7464,"6,050",5186,3090,"15,000","7,233",47/6,35/7,"1,525",4500000 +100 Darter (S.L. Industries),Jet,"2,950",477,442,90,6098,"6,050",5186,2703,"15,000","7,206",47/6,35/6,"1,385",4500000 +T 337 C Skymaster turbocharged,Jet,"2,950",475,441,88,5628,"7,100",3297,2873,"13,500","7,130",43/3,35/6,"1,001",4500000 +100 Darter (S.L. Industries),Jet,"2,950",477,441,88,4791,"7,220",3000,2789,"12,900","7,025",43/3,35/6,730,4500000 +"337 G,H II Skymaster (prior '79 service ceiling=18,000)",Jet,"2,950",464,441,99,5628,"6,800",3917,2800,"13,500","6,988",43/3,35/7,"1,001",4500000 +100 Darter (S.L. Industries),Jet,"2,950",477,442,87,840,"6,800",3917,2703,"13,500","6,803",43/3,35/6,"1,001",4500000 +337 E Skymaster,Jet,"2,950",461,442,88,715,"6,900",3370,2850,"12,500","6,519",43/3,35/6,"1,385",4500000 +100 Darter (S.L. Industries),Jet,"2,950",474,437,88,834,"6,300",3100,3307,"13,500","6,927",43/3,35/7,"1,001",4500000 +337 C Skymaster,Jet,"2,850",469,417,90,847,"6,300",3100,3350,"13,000","7,090",43/2,35/7,"1,100",4100000 +100 Darter (S.L. Industries),Jet,"2,850",488,423,89,828,"6,900",2940,2800,"12,500","6,700",43/2,35/7,"1,000",4100000 +208 Caravan-675,Jet,"6,400",.92 Mach,511,,13000,"3,720",5710,3820,"35,700","21,400",72/2,63/9,"3,250",5100000 +100 Darter (S.L. Industries),Jet,"4,000",.85 Mach,476,97,7385,"4,442",4690,2910,"22,450","13,700",55/5,53/5,"2,300",5100000 +100 Darter (S.L. Industries),Jet,"3,650",473,,97,7385,"1,520",5150,2900,"22,000","12,775",55/5,53/5,"2,345",5100000 +"T 210 K,L Turbo (K=9 mph less speed)",Jet,"3,650",472,461,97,7384,"3,699",5030,2900,"22,000","11,811",55/6,53/6,"2,600",5100000 +100 Darter (S.L. Industries),Jet,"3,650",472,,89,7384,"3,909",4710,2560,"21,000","11,720",55/6,53/6,"2,600",5100000 +T 210 F Turbo Centurion,Jet,"3,786",.76 Mach,429,82,6590,"3,090",3460,3315,"19,200","11,310",51/10,55/8,"2,027",nan +100 Darter (S.L. Industries),Jet,"3,400",.755 Mach,427,83,5440,"4,500",,,"16,830","9,977",48/9,54/1,"1,970",4500000 +"210 M,NII (prior'78=less svc. ceil. & r/o/c=860)",Jet,"3,045",.76 Mach,430,82,5771,"4,230",3180,2800,"16,300","9,550",48/11,52/2,"1,960",nan +210 F Centurion,Jet,"2,887",.70 Mach,401,86,4860,"3,195",3600,3180,"14,800","8,925",47/2,52/2,"1,900",nan +100 Darter (S.L. Industries),Jet,"2,500",,403,82,5820,"3,000",3240,3050,"15,000","8,049",47/2,52/2,"2,104",4300000 +100 Darter (S.L. Industries),Jet,"2,500",,385,82,5008,"3,370",2990,2270,"13,300","7,388",47/2,52/2,"1,930",4300000 +T 207 (Stationair 7 & 8) '77=eng 310 hp,Jet,"2,500",,385,82,5009,"3,625",2650,2210,"12,500","7,181",47/2,52/2,"2,104",4300000 +100 Darter (S.L. Industries),Jet,"2,820",.737 Mach,351,,4710,"2,460",,2770,"13,870","8,300",50/2,53/4,"1,875",4500000 +"U206E,F, float ('75-'76 specs) '76=+4 gal fuel",Jet,"1,900",.71 Mach,380,85,3220,"3,311",3080,2750,"10,400","6,550",42/6,46/8,"1,485",4100000 +"TU 206E,F (ski) (prior'76=4 gal more fuel)",Jet,"2,200",,357,82,3807,"2,719",2930,2270,"11,850","6,631",43/6,47/1,"1,325",4100000 +"U206 E,F (ski) Super Skywagon",Jet,"2,200",,351,,3780,"2,680",2930,2270,"11,850","6,684",43/6,47/1,"1,325",4100000 +100 Darter (S.L. Industries),Jet,"2,200",,348,85,3780,"2,900",2660,2300,"11,500",6454,43/6,43/11,"1,308",4100000 +"TU 206E,F Stationair",Jet,"2,200",,348,84,3618,"2,900",2550,2310,"10,850","6,350",43/6,43/9,"1,064",3500000 +100 Darter (S.L. Industries),Propjet,625,295,283,76,475,"2,435",2465,1875,"9,850","5,682",39/0,49/4,"2,193",3500000 +"U206 F,G II ('75 up) '75 lgt=28/9 + prior'79 less fuel)",Propjet,450,263,,84,366,"1,861",2490,2150,"8,600","4,948",35/10,44/1,"1,248",3340000 +100 Darter (S.L. Industries),Propjet,450,263,250,79,366,"2,027",2341,2145,"8,200","4,915",35/10,44/1,"1,461",3470000 +U 206 A,Piston,375,257,241,74,213,"1,940",2323,2293,"7,450","5,048",36/5,41/1,"1,197",3020000 +100 Darter (S.L. Industries),Piston,375,245,235,74,175,"1,850",2387,2178,"7,450","4,426",36/1,41/9,845,3100000 +"TP 206A,B,C,D,E (E=35/10 span) (A=28/0 lgt)",Piston,375,240,227,76,175,"1,700",2516,2110,"6,840","4,252",33/8,39/9,826,2600000 +100 Darter (S.L. Industries),Piston,375,240,222,76,170,"1,700",2516,2110,"6,800","4,237",33/5,39/9,826,2600000 +206H Stationair,Piston,310,235,224,72,213,"1,520",2595,2393,"6,750","4,543",36/4,44/1,"1,099",3080000 +100 Darter (S.L. Industries),Piston,310,239,224,70,102,"1,580",2350,1865,"6,350","4,039",33/9,39/11,"1,090",3010000 +P 206 Super Skylane,Piston,340,231,214,72,175,"1,900",2010,1851,"6,500","3,865",33/5,39/9,908,2600000 +100 Darter (S.L. Industries),Piston,375,232,217,70,348,"1,575",2367,2130,"8,400","4,965",39/6,46/4,553,2600000 +195 B,Piston,325,230,213,68,213,"1,450",2195,2485,"6,850","4,238",36/4,44/1,875,2690000 +100 Darter (S.L. Industries),Piston,300,229,210,70,102,"1,610",2220,1765,"6,300","4,038",36/1,39/11,"1,180",2618000 +195,Piston,300,227,209,69,102,"1,610",2220,1765,"6,300","3,719",35/8,39/9,833,2618000 +100 Darter (S.L. Industries),Piston,300,227,209,69,102,"1,610",2220,1765,"6,300","3,674",33/8,39/9,"1,102",2618000 +T 188C Ag Husky (prior'81=4 mph less),Piston,310,243,229,71,102,"1,650",2175,1850,"5,990","4,184",34/4,38/1,"1,106",2980000 +100 Darter (S.L. Industries),Piston,285,226,210,71,102,"1,500",2430,1840,"5,975","3,730",34/4,38/1,"1,258",2650000 +A188B Ag Wagon ('79 & up) dispersal sys,Piston,300,230,215,71,102,"1,400",2365,1850,"5,990","3,963",34/4,38/1,928,2680000 +100 Darter (S.L. Industries),Piston,285,239,226,64,102,"1,924",1515,1734,"5,300","3,273",29/6,36/9,735,2900000 +A188 Ag Wagon restricted (prior'77=7/10 hgt),Piston,260,229,213,63,102,"1,820",1584,1910,"5,200","3,260",29/6,36/9,735,2810000 +100 Darter (S.L. Industries),Piston,260,230,204,68,102,"1,820",1890,2056,"4,990","3,190",29/5,36/9,740,2810000 +188 Ag Pickup fixed-prop ('72) restricted,Piston,285,237,223,70,102,"1,700",1662,1790,"5,500","3,723",31/11,36/11,517,2740000 +100 Darter (S.L. Industries),Piston,285,238,225,67,102,"1,790",1662,1790,"5,500","3,292",29/6,36/9,807,2820000 +"188A,B Ag Wagon ('67-'71) fixed-prop normal",Piston,285,239,226,66,102,"1,862",1590,1790,"5,400","3,292",29/3,36/9,807,2860000 +100 Darter (S.L. Industries),Piston,285,207,194,70,102,"1,662",1700,1790,"5,500","3,603",31/11,36/11,661,1975000 +A185 F (float) (prior '79 = 4-8 gal less fuel),Piston,260,205,192,63,102,"1,495",1795,1697,"5,300","3,214",29/6,36/9,680,1950000 +A185 F II Skywagon (prior '81=less perform),Piston,260,206,193,62,102,"1,540",1716,1673,"5,200","3,170",29/2,36/9,676,1990000 +100 Darter (S.L. Industries),Piston,260,206,193,65,102,"1,540",1716,1582,"5,200","3,125",29/5,36/11,676,1990000 +185 E Skywagon,Piston,260,207,194,65,102,"1,590",1640,1540,"5,100","3,094",29/6,36/11,687,2030000 +100 Darter (S.L. Industries),Piston,260,209,194,65,102,"1,690",1545,1900,"5,100","3,063",29/5,37/5,687,2110000 +R182 II RG Turbo Skylane,Piston,260,210,191,73,102,"1,800",1395,1720,"4,990","3,049",29/5,36/9,678,2130000 +100 Darter (S.L. Industries),Piston,240,191,178,71,102,"1,700",1375,1710,"4,600","2,850",27/1,36/1,640,1950000 +T-182R II Turbo Skylane,Piston,250,216,180,62,155,"1,480",1750,1450,"5,000","3,305",30/5,38/10,835,2500000 +100 Darter (S.L. Industries),Piston,225,211,204,62,150,"1,170",1500,1675,"4,700","3,184",29/10,38/2,939,2000000 +182T Skylane,Piston,210,207,200,62,90,"1,160",1675,1650,"4,630","3,038",29/10,38/2,"1,080",2000000 +100 Darter (S.L. Industries),Piston,210,200,194,61,93,"1,105",1675,1650,"4,630","2,850",29/10,38/2,920,2930000 +182R II (1981 & up),Piston,210,201,195,61,93,"1,155",1595,1650,"4,500","2,815",29/10,38/0,920,3010000 +100 Darter (S.L. Industries),Piston,210,201,195,59,93,"1,155",1595,1520,"4,500","2,795",29/10,38/0,920,3010000 +182 N Skylane,Piston,210,202,196,57,93,"1,250",1490,1500,"4,300","2,785",29/1,38/0,920,3100000 +100 Darter (S.L. Industries),Piston,210,172,169,61,90,940,1675,1650,"4,630","2,925",29/9,38/2,"1,139",1630000 +"182 E,F,G,H",Piston,210,173,165,61,93,"1,100",1675,1650,"4,630","2,695",29/9,38/2,920,1800000 +100 Darter (S.L. Industries),Piston,210,173,166,60,93,"1,180",1565,1650,"4,440","2,660",29/9,38/2,922,1930000 +182,Piston,210,173,166,60,93,"1,200",1545,1650,"4,400","2,655",29/9,38/0,920,1950000 +100 Darter (S.L. Industries),Piston,210,173,166,58,93,"1,200",1545,1520,"4,400","2,650",29/9,38/0,922,1950000 +"180J,K amphib prior'76=4 gal more fuel (range w/80 gal fuel)",Piston,210,174,167,57,93,"1,250",1490,1500,"4,300","2,615",29/9,38/0,922,2000000 +100 Darter (S.L. Industries),Piston,210,174,167,55,93,"1,200",1435,1465,"4,200","2,615",29/9,38/0,922,2050000 +"180J,K (ski) (prior'79=15,600 serv. ceil, less fuel)",Piston,210,159,150,52,93,"1,300",1145,1395,"3,900","2,320",29/7,38/0,880,1900000 +100 Darter (S.L. Industries),Propjet,675,182,154,61,335,"1,234",2053,1655,"8,000","3,973",37/6,52/1,866,2500000 +180,Propjet,675,186,154,61,335,975,2420,1880,"8,750","4,765",41/7,52/1,810,2370000 +100 Darter (S.L. Industries),Piston,310,235,235,60,388,1400,,,3600,2350,25/2,36,1270,2500000 +177 RG Cardinal,Piston,325,224,212,55,85,"1,150",2110,1600,"4,100","2,471",28/4,38/10,715,2500000 +100 Darter (S.L. Industries),Piston,310,201,191,58,90,945,2160,1500,"4,000","2,481",28/2,36/9,661,2300000 +177 B (thru'74)'70-'71 lgt=27/0 & hgt=9/1,Piston,325,224,207,55,87,"1,150",2110,1600,"4,100","2,320",28/4,38/10,715,2900000 +100 Darter (S.L. Industries),Piston,310,204,192,58,90,930,2160,1500,"4,000","2,303",28/2,36/9,715,2700000 +177 Cardinal,Piston,310,204,197,57,90,"1,030",1900,1500,"3,800","2,306",28/2,36/9,732,2850000 +100 Darter (S.L. Industries),Piston,285,205,187,57,90,930,2030,1500,"3,800","2,180",28/3,36/9,732,2850000 +"175,-A,-B",Piston,285,203,194,55,89,"1,115",1365,1355,"3,400","2,050",28/3,36/9,739,3020000 +100 Darter (S.L. Industries),Piston,285,200,191,54,65,"1,280",1265,1340,"3,300","1,965",28/0,36/7,539,3130000 +172 Q Cutlass,Piston,300,175,,53,87,"1,060",2050,1585,"3,850","2,220",28/2,38/10,600,1600000 +100 Darter (S.L. Industries),Piston,300,174,170,57,90,950,2030,1500,"3,800","2,219",28/2,36/9,600,1730000 +172S Skyhawk SP,Piston,285,174,167,55,90,"1,000",1365,1355,"3,400","1,960",28/3,36/9,760,1830000 +100 Darter (S.L. Industries),Piston,285,172,165,54,65,"1,115",1265,1340,"3,300","1,865",28/0,36/7,640,1990000 +172 P II Skyhawk,Piston,285,173,166,53,65,"1,210",1110,1275,"3,100","1,860",28/4,36/7,640,2100000 +100 Darter (S.L. Industries),Piston,260,170,160,52,65,"1,270",1210,1110,"3,000","1,780",27/3,36/6,680,2030000 +172 M Skyhawk ('76=4 mph more speed),Piston,260,173,165,51,65,"1,300",1135,1190,"2,900","1,740",27/3,36/5,700,2070000 +100 Darter (S.L. Industries),Piston,310,170,161,59,61,885,1860,1500,"3,800","2,254",32/2,35/10,583,2600000 +R172K/Hawk XP (floats),Piston,300,150,143,59,61,810,1970,1500,"3,800","2,177",32/2,35/10,612,1330000 +100 Darter (S.L. Industries),Piston,310,155,135,52,92,835,2790,1750,"3,600","2,404",29/8,35/10,640,2560000 +172 M (float plane 1974-76),Piston,285,157,143,50,65,950,2400,1610,"3,600","2,295",28/6,35/10,626,2420000 +100 Darter (S.L. Industries),Piston,300,137,131,51,92,925,2820,1675,"3,500","2,342",29/8,35/10,680,1390000 +172 K & L float plane,Piston,300,136,130,53,61,855,2475,1570,"3,500","2,312",29/8,35/10,580,1390000 +100 Darter (S.L. Industries),Piston,300,136,131,49,65,855,2475,1570,"3,500","2,200",28/6,35/10,510,1390000 +"172,-D,-E,-F,-G,-H",Piston,300,136,131,,65,855,2475,1570,"3,500","2,060",28/6,36/10,510,1390000 +100 Darter (S.L. Industries),Piston,285,145,132,61,65,920,,,"3,300","2,165",28/8,35/10,626,2350000 +100 Darter (S.L. Industries),Piston,300,121,119,52,65,800,,,"3,300","2,065",28/8,35/10,510,1150000 +"170,-A,-B",Piston,310,174,152,54,92,"1,010",1640,1395,"3,600","2,066",28/3,35/10,640,2700000 +100 Darter (S.L. Industries),Piston,285,174,148,53,65,"1,030",1810,1395,"3,600","1,935",28/9,35/10,580,2630000 +"150M, A150M (Aerobat span=32/9)",Piston,285,174,160,53,65,"1,030",1810,1395,"3,600","1,795",27/8,36/7,627,2630000 +100 Darter (S.L. Industries),Piston,300,156,147,54,92,920,1780,1395,"3,600","2,002",28/3,35/10,680,1480000 +A 150 L Aerobat (1974),Piston,300,151,143,53,65,920,1780,1395,"3,600","1,725",28/8,36/7,510,1480000 +100 Darter (S.L. Industries),Piston,285,151,142,53,65,920,1810,1340,"3,600","1,795",27/9,36/7,555,1480000 +"150 H,J,K (K=8/8 height)",Piston,285,154,144,52,65,"1,075",1265,1340,"3,300","1,760",27/9,36/7,563,1670000 +100 Darter (S.L. Industries),Piston,285,174,160,53,65,"1,030",1810,1395,"3,600","1,915",28/3,36/7,609,2630000 +"150 D,-E,-F,-G",Piston,310,178,150,54,92,"1,050",1740,1395,"3,600","2,351",28/3,36/0,713,2700000 +100 Darter (S.L. Industries),Piston,300,151,142,54,92,988,1860,1395,"3,600","2,351",28/3,36/0,713,1570000 +150,Piston,285,151,142,53,65,920,1810,1395,"3,600","1,820",28/3,36/7,555,1480000 +100 Darter (S.L. Industries),Piston,285,154,143,52,65,"1,075",1265,1340,"3,300","1,790",28/2,36/6,565,1670000 +140,Piston,260,145,138,50,65,965,1465,1510,"3,300","1,750",27/3,36/6,515,1610000 +100 Darter (S.L. Industries),Piston,275,152,143,54,80,"1,135",1605,1495,"3,350","2,050",27/3,36/2,583,1625000 +Vision Jet SF50,Piston,245,153,139,56,80,"1,050",1670,1495,"3,350","2,030",27/3,36/2,603,1600000 +Columbia 400 LC-41,Piston,300,157,148,55,80,"1,090",1670,1495,"3,350","2,050",27/3,36/2,600,1600000 +Columbia 350 LC-42,Piston,240,153,139,56,80,"1,090",1670,1495,"3,350","2,015",27/3,36/2,610,1600000 +100 Darter (S.L. Industries),Piston,310,113,106,58,54,510,2060,1265,"4,400","2,322",26/6,41/8,283,1400000 +115TC,Piston,300,106,101,57,54,465,2250,1265,"4,200","2,230",25/11,41/8,290,780000 +100 Darter (S.L. Industries),Piston,300,107,103,55,54,525,1965,1265,"4,000","2,179",25/11,40/9,290,870000 +114TC,Piston,300,105,98,50,54,690,1090,1265,"4,200","2,220",26/3,41/8,290,1110000 +100 Darter (S.L. Industries),Piston,300,105,98,50,37,690,1090,1265,"4,000","2,189",26/3,40/9,278,1110000 +"114,('76-24/10 length + less performance)",Piston,230,91,78,50,37,400,1920,1265,"3,800","1,900",25/3,40/9,204,650000 +100 Darter (S.L. Industries),Piston,230,94,83,50,37,460,1740,1265,"3,800","1,835",25/3,40/9,204,770000 +112B,Piston,300,131,123,50,37,940,970,1265,"3,300","1,845",26/3,40/4,278,1570000 +100 Darter (S.L. Industries),Piston,230,103,101,50,37,710,1365,1265,"3,300","1,815",25/3,40/4,204,1300000 +112,Piston,300,136,130,50,88,950,1710,1480,"3,100","2,253",27/6,35/10,500,1610000 +Top Cub,Piston,300,141,134,52,88,960,2125,1565,"3,320","1,998",27/0,35/10,522,1640000 +100 Darter (S.L. Industries),Piston,300,155,145,49,88,"1,075",1430,1400,"3,350","1,750",25/8,35/10,645,1790000 +Falcon 900 (3-eng fan jet),Piston,300,155,147,51,65,"1,040",1330,1400,"3,350","1,565",25/6,36/2,470,1750000 +100 Darter (S.L. Industries),Piston,300,153,145,54,65,950,1290,1285,"3,300","1,560",25/6,36/2,470,1690000 +Falcon 200,Piston,260,153,145,54,65,"1,000",1510,1265,"3,200","1,520",25/5,36/0,561,1730000 +100 Darter (S.L. Industries),Piston,235,187,173,50,92,"1,040",1570,1320,"3,100","1,846",28/8,35/10,845,2000000 +Falcon Fan Jet D 20,Piston,235,160,156,50,92,"1,140",1570,1320,"3,100","1,809",28/8,35/10,845,1800000 +100 Darter (S.L. Industries),Piston,235,168,157,49,92,965,1475,1350,"3,100","1,781",28/5,35/10,743,2000000 +Falcon F 20,Piston,235,176,159,49,92,"1,040",1385,1350,"3,100","2,075",29/0,36/0,971,2000000 +100 Darter (S.L. Industries),Piston,230,150,145,49,92,924,1514,1350,"3,100","1,970",29/0,36/0,930,1810000 +Falcon Fan Jet 10,Piston,230,143,140,46,88,924,1625,1280,"3,110","1,882",28/0,16/0,813,1810000 +100 Darter (S.L. Industries),Piston,230,146,142,49,92,865,1515,1350,"3,100","1,775",28/0,35/10,817,1490000 +DHC 6-300 Twin Otter Std. + '77-'78=less perf.,Piston,230,148,143,50,92,"1,010",1350,1350,"2,950","1,754",28/0,35/10,817,1650000 +100 Darter (S.L. Industries),Piston,230,146,139,50,65,890,1350,1350,"2,950","1,640",28/1,35/10,550,1770000 +DHC 6-200 Twin Otter,Piston,230,148,141,48,65,980,1205,1350,"2,800","1,620",28/5,36/2,550,1890000 +Twin Star Diamond DA42,Piston,230,148,141,48,65,980,1205,1350,"2,800","1,610",27/4,36/2,550,1890000 +100 Darter (S.L. Industries),Piston,230,148,141,54,55,"1,030",1080,1310,"2,650","1,560",26/0,36/0,450,1980000 +Katana DA20-C1,Piston,230,143,135,54,55,"1,120",1020,1290,"2,550","1,540",26/0,36/0,443,2010000 +100 Darter (S.L. Industries),Piston,230,137,123,48,88,970,1900,1720,"2,950","2,204",27/6,35/10,804,1530000 +100 Darter (S.L. Industries),Piston,230,137,123,48,61,990,2070,1720,"2,950","2,110",27/6,35/10,804,1600000 +TB-21 TC Trinidad,Piston,230,137,123,48,61,990,2070,1720,"2,950","1,955",27/0,35/10,804,1600000 +100 Darter (S.L. Industries),Piston,230,137,123,48,88,910,,,"2,800","1,790",28/10,35/10,804,1470000 +TB-200XL,Piston,230,148,141,48,88,"1,100",1205,1365,"2,800","1,701",25/8,35/10,804,1770000 +100 Darter (S.L. Industries),Piston,230,148,141,50,65,"1,090",1205,1365,"2,800","1,525",25/6,36/2,622,1960000 +TB-9 Tampico Club,Piston,230,148,139,54,55,"1,130",1080,1330,"2,650","1,555",26/0,36/0,513,2150000 +Eclipse 500,Piston,225,144,137,51,55,"1,110",1095,1310,"2,550","1,540",26/0,36/0,513,2120000 +100 Darter (S.L. Industries),Piston,200,157,149,50,61,925,1585,1350,"2,800","1,765",27/3,35/6,500,1710000 +Eurofox,Piston,200,153,144,50,51,860,1585,1350,"2,800","1,630",27/3,35/6,506,1690000 +Excalibur '800' conv. Beech Twin Bonanza,Piston,180,139,130,46,50,840,1400,1220,"2,500","1,643",27/3,35/6,490,1460000 +Excalibur conv. Beech Twin Bonanza,Piston,180,133,123,46,50,840,1400,1220,"2,500","1,485",27/3,35/6,417,1460000 +100 Darter (S.L. Industries),Piston,180,130,120,49,49,760,1575,1220,"2,500","1,440",27/0,35/7,410,1580000 +"Queen Air '8800' conv. Beech A80,B80",Piston,150,125,117,46,49,670,1575,1135,"2,350","1,415",27/0,35/7,591,1270000 +"Metro III,A",Piston,175,123,118,56,52,790,1450,1170,"2,450","1,325",25/0,36/0,589,1450000 +"Metro, II (prior '79 height=16/10)",Piston,175,128,121,54,52,850,1340,1155,"2,350","1,312",25/0,36/0,593,1590000 +100 Darter (S.L. Industries),Piston,180,144,140,50,66,800,1775,1340,"2,650","1,627",27/5,35/10,720,1680000 +Merlin IV C,Piston,180,123,122,48,54,680,1690,1335,"2,550","1,480",26/11,36/1,475,1700000 +100 Darter (S.L. Industries),Piston,195,132,130,47,52,870,1360,1345,"2,550","1,572",27/2,35/10,570,1700000 +Fairchild 300,Piston,180,126,124,48,56,730,1630,1335,"2,550","1,663",27/2,36/1,638,1400000 +100 Darter (S.L. Industries),piston,160,123,122,47,53,720,1685,1295,"2,457","1,600",26/11,36/1,580,1350000 +"Merlin III C-23 (12,500 gross) (SN TT426 & up)",Piston,160,123,120,46,43,700,1625,1280,"2,400","1,454",26/11,35/10,440,1300000 +100 Darter (S.L. Industries),Piston,160,124,122,44,43,770,1390,1250,"2,300","1,430",26/11,35/10,440,1420000 +Merlin III A,Piston,150,122,115,44,42,645,1525,1250,"2,300","1,335",26/11,35/10,435,1310000 +100 Darter (S.L. Industries),Piston,150,122,115,43,42,645,1525,1250,"2,300","1,315",26/11,35/9,417,1310000 +Merlin II A,Piston,195,117,116,44,52,870,1850,1390,"2,550","1,834",26/10,35/10,539,1550000 +CTSW,Piston,160,96,95,44,43,740,2160,1345,"2,220","1,632",26/8,35/10,440,1500000 +800XP,Piston,150,98,97,44,42,715,2390,1345,"2,220","1,574",27/0,35/10,435,1200000 +100 Darter (S.L. Industries),Piston,150,94,92,45,42,580,2390,1345,"2,220","1,450",27/0,35/9,435,1200000 +G V,Piston,150,94,92,45,42,580,2390,1345,"2,220","1,405",26/7,35/9,418,1200000 +100 Darter (S.L. Industries),Piston,150,122,115,43,42,645,1525,1250,"2,300","1,300",26/11,36/2,515,1310000 +G IV,Piston,145,121,114,43,42,645,1525,1250,"2,300","1,330",26/6,36/2,515,1310000 +100 Darter (S.L. Industries),Piston,145,121,114,43,42,675,1450,1200,"2,250","1,330",26/5,36/0,515,1420000 +100 Darter (S.L. Industries),Piston,145,122,114,51,42,730,1370,1115,"2,200","1,325",26/5,36/0,515,1510000 +GII (G1159) with tip tanks,Piston,145,117,108,50,37,660,1650,1115,"2,200","1,260",25/0,36/0,420,1330000 +100 Darter (S.L. Industries),Piston,145,122,104,50,42,690,1820,1145,"2,200","1,205",25/0,36/0,410,1550000 +GI (G-159) ceiling quoted w/APU,Piston,110,110,107,43,26,715,1340,1200,"1,670","1,141",24/1,33/2,315,1470000 +"HS125-700,A (Hawk-Sidd mfg) ('77=24,200 gross)",Piston,100,109,106,42,26,670,1385,1075,"1,600","1,104",23/11,33/2,303,1400000 +100 Darter (S.L. Industries),Piston,100,106,102,42,26,670,1385,1075,"1,600","1,060",23/9,33/2,303,1265000 +BH-124-400A (Beech mfg),Piston,100,108,103,42,26,670,1385,1075,"1,600","1,040",23/9,32/9,303,1400000 +100 Darter (S.L. Industries),Piston,100,104,100,42,26,670,1385,1075,"1,600","1,020",23/9,32/9,303,1265000 +1124A Westwind 2,Piston,100,106,102,42,26,670,1385,1075,"1,600","1,065",23/9,32/9,303,1265000 +100 Darter (S.L. Industries),Piston,100,90,85,43,26,560,2075,850,"1,650","1,120",24/1,32/9,276,1070000 +1123 Commodore Jet,Piston,100,109,102,43,26,670,1375,1075,"1,600",970,23/9,32/8,303,1265000 +100 Darter (S.L. Industries),Piston,100,108,105,43,26,740,1205,1055,"1,500",965,21/1,33/4,320,1530000 +1121 Jet Commander,Piston,100,108,104,47,26,740,1205,1055,"1,500",962,21/0,33/4,318,1530000 +J250-SP,Piston,90,103,90,46,21,640,1850,1530,"1,500",850,20/9,33/3,315,1560000 +J230-SP,Piston,85,102,88,46,21,620,1950,1530,"1,500",850,20/9,33/3,315,1510000 +LA-4 Amphibian,Piston,85,104,100,43,25,640,1850,1530,"1,450",818,20/9,32/8,390,1550000 +C-1 Amphibian,Jet,1846,311,305,67,2000,1609,3192,,6000,3550,30/7,38/7,1275,3100000 +LA-4-200 Buccaneer (land specs),Piston,310,235,181,57,106,,1250,2350,"3,600","2,500",25/2,35/8,"1,300",2500000 +XL2,Piston,310,235,179,57,106,,1250,2350,"3,400",2.3,25/2,35/8,"1,300",1800000 +Jetstar -8,Piston,310,235,190,57,106,,1250,2350,"3,400","2,250",25/2,36,"1,300",1800000 +100 Darter (S.L. Industries),Piston,270,197,187,59,90,"1,050",2223,1312,"3,305","2,152",24/11,32/10,870,2500000 +8:00 AM,Piston,265,164,149,54,90,"1,070",1985,1200,"3,250","2,102",24/11,32/10,"1,005",1680000 +Superstar I (601P Aerostar),Piston,270,197,177,59,90,"1,050",2223,1312,"3,305","2,151",24/11,32/9,870,2500000 +MX-7-160,Piston,260,166,157,54,68,"1,030",2150,1200,"3,260","2,070",25/1,32/9,665,1650000 +MX-7-180A,Piston,260,166,157,55,68,"1,088",1990,1200,"3,140","1,905",25/1,32/9,665,1740000 +M-7-235,Piston,210,170,163,54,48,914,1750,1275,"2,950","2,035",25/1,35/7,620,2000000 +100 Darter (S.L. Industries),Piston,200,150,142,51,48,950,1829,1039,"2,800","1,773",25/1,35/7,575,1505000 +M-5-235C,Piston,200,149,140,54,48,"1,020",1585,1310,"2,650","1,691",24/10,32/9,569,1390000 +100 Darter (S.L. Industries),Piston,200,152,143,53,60,"1,000",1460,1310,"2,550","1,530",24/11,32/9,750,1700000 +100 Darter (S.L. Industries),Piston,180,132,110,42,54,800,,,"2,300","1,200",23/6,35/2,555,1800000 +M-5-180C,Piston,100,122,103,27,25,790,975,1040,"1,320",825,23/3,34/2,434,1800000 +MU-300 Diamond II,Jet,"4,750",513,459,81,19165,"4,000",4950,3500,"45,500","22,573",66/4,63/5,"4,400",4100000 +100 Darter (S.L. Industries),Jet,"4,500",513,459,81,19165,"3,500",5300,3500,"45,500","22,573",66/4,63/5,"4,200",3900000 +MU-300 Diamond I,Jet,"3,700",518,430,77,15520,"3,430",4700,2800,"38,800","21,100",60/9,61/10,"3,500",4100000 +100 Darter (S.L. Industries),Jet,"5,200",521,435,89,10684,"3,065",5260,2860,"32,000","18,800",56/3,53/6,"2,500",3900000 +"MU-2L,-2N (-2N empty weight=7,760)",Jet,"4,125",530,457,90,8420,"3,500",3800,2940,"26,455","16,600",56/3,53/6,"1,200",4200000 +100 Darter (S.L. Industries),Jet,"4,250",512,457,90,8980,"3,500",6400,4850,"27,337","15,400",56/3,53/6,"1,630",4200000 +MU-2J,Jet,"4,315",530,458,84.5,9250,"3,300",5000,3300,"28,660","15,970",56/3,53/6,"1,610",4200000 +100 Darter (S.L. Industries),Jet,"4,500",530,410,88,9098,"3,330",4950,2450,"28,660","17,500",56/3,53/6,"1,614",3700000 +MU-2F,Jet,"3,230",513,459,84,5912,"4,600",4615,2750,"19,300","11,200",45/6,42/11,"1,828",3900000 +100 Darter (S.L. Industries),Jet,"3,230",513,459,88,5912,"4,600",4615,2750,"18,740","10,800",45/6,42/11,"1,828",4500000 +Acclaim (M20TN),Jet,"3,230",459,426,92,5910,"4,200",4300,3300,"18,300","10,800",45/4,42/11,"1,800",4500000 +TLS (M20M),Propjet,620,182,170,58,2583,"1,600",1500,1940,"12,500","6,881",51/9,65/0,300,2670000 +100 Darter (S.L. Industries),Propjet,620,182,171,64,2457,"1,600",1940,1940,"12,500","7,487",51/9,65/0,300,2670000 +PFM (M20L),Propjet,579,165,150,57,2457,"1,300",1900,1995,"11,579","7,250",51/9,65/0,,2430000 +Encore (MS20K),Piston,135,194,172,56,52,"1,280",1730,1877,"3,935","2,765",28/1,44/6,"1,129",1800000 +100 Darter (S.L. Industries),Piston,81,161,117,37,20,680,1560,1490,"1,609","1,095",23/6,35/7,526,nan +205 (M20J),Piston,125,163,132,34,25,"1,105",1263,1235,"1,654","1,166",23/6,35/8,422,nan +M-20C Ranger ('77-'78),Propjet,850,320,255,,1887,"1,380",2840,2430,"7,394","4,698",34/11,41/7,"1,520",3100000 +M-20-E Super 21 Chaparral,Propjet,700,300,291,61,1887,"2,380",1591,2034,"6,579","4,050",34/11,41/7,"1,563",3000000 +100 Darter (S.L. Industries),Piston,250,200,187,54,86,"1,125",1953,1750,"3,083","1,795",25/4,32/6,,2500000 +M-20-B Mark 21,Piston,250,167,160,54,86,"1,260",1953,1750,"3,083","1,744",25/4,32/6,"1,100",2000000 +100 Darter (S.L. Industries),Piston,200,,115,,55,937,1561,1474,"2,535","1,642",25/5,32/10,637,1600000 +M-20-Mark 20,Piston,180,132,117,51,54,790,1657,1394,"2,535","1,477",25/0,32/0,,1300000 +100 Darter (S.L. Industries),Piston,160,,106,50,41,738,1706,1378,"2,337","1,411",25/3,32/0,,1100000 +H-Rangemaster (1975-'76),Jet,900,.64 Mach,426,69,1686,"3,314",2297,2155,"5,920","3,550",33/6,37/11,"1,300",4100000 +100 Darter (S.L. Industries),Propjet,750,221,181,71,440,"1,650",4000,4400,"13,007","8,007",49/6,50/3,,2150000 +100 Darter (S.L. Industries),Piston,100,164,120,46,23,"1,000",,,"1,235",644,18/7,29/11,600,1400000 +100 Darter (S.L. Industries),Piston,400,226,213,71,230,"1,870",1525,1940,"7,600","5,100",31/5,46/0,,2220000 +PA-31-350 Chieftain ('81=optional fuel),Piston,380,239,226,71,230,"1,900",1225,1840,"7,300","4,500",31/5,46/0,,3000000 +100 Darter (S.L. Industries),Piston,400,213,201,68,230,"1,535",1706,2176,"8,000","5,400",N/C,N/C,,2220000 +"PA-31-310 Turbo Navajo B,C (prior '80 less fuel)",Piston,400,213,201,70,264,"1,490",2050,2450,"8,800","5,800",N/C,N/C,,2000000 +PA-31-300 Navajo,Propjet,"1,000",280,248,88,648,"2,635",3495,,"14,500","8,737",59/4,57/0,"1,827",2990000 +100 Darter (S.L. Industries),Propjet,940wet,,256,86,648,"2,400",2050,1970,"12,500","8,000",59/4,46/3,"1,250",2700000 +PA-23 E Turbo Aztec (1971 length=30/2),Propjet,"1,000",278,248,88,648,"2,635",3340,2970,"14,500","9,200",59/4,57/0,"2,040",2990000 +100 Darter (S.L. Industries),Propjet,1000 dry,283,261,87,648,"2,440",2850,2532,"14,000","9,100",59/4,57/0,"2,040",3000000 +"PA-23 D,E Aztec (prior '72 length=30/2)",Propjet,840,270,,86,544,"2,400",2050,1970,"12,500","8,200",59/4,46/3,"1,815",2700000 +100 Darter (S.L. Industries),Propjet,900,303,264,92,648,"2,650",3080,2805,"13,230","8,200",42/2,47/11,"2,178",2940000 +PA-23 C Aztec,Propjet,900,300,265,92,648,"2,650",2920,3530,"13,230","8,150",42/2,46/3,"2,025",2700000 +100 Darter (S.L. Industries),Propjet,900,303,264,89,648,"2,800",2400,3360,"12,500","8,090",42/2,46/3,"2,025",2940000 +"PA34-180, Seneca V",Propjet,900,309,295,89,648,"2,780",2970,3240,"12,500","7,800",42/2,46/3,"2,025",3140000 +100 Darter (S.L. Industries),Propjet,840,283,,83,648,"2,530",2150,1570,"12,500","7,600",42/2,46/3,"2,670",2890000 +PA-34-220T Seneca III (roc=t/o power),Propjet,665,,257,76,386,"2,780",2970,3240,"10,000","6,150",40/1,45/10,"1,318",2990000 +100 Darter (S.L. Industries),Propjet,550,,235,75,386,"1,950",2300,2380,"9,800","6,075",40/1,45/10,"1,250",3100000 +100 Darter (S.L. Industries),Piston,100,120,112,39,34,960,,,"1,320",649,20/4,28,1080,1400000 +100 Darter (S.L. Industries),Piston,100,145,119,35,29,850,1050,1100,"1,320",760,20/4,31/6,730,1320000 +PA-39 C/R Turbo Twin Comanche,Piston,100,133,116,36,18,850,1450,1410,"1,320",820,20/6,20/6,400,1320000 +PA-30 B Turbo Twin Comanche,Jet,"14,750",516,459,,41000,,5870,2950,"89,400","46,800",96/5,93/6,"6,500",5100000 +100 Darter (S.L. Industries),Jet,"13,850",505,459,115,29500,"4,122",5450,3190,"75,000","42,500",88/4,77/10,"4,220",4500000 +PA-23-150 Apache,Jet,"13,850",510,459,108,29500,"4,219",5265,3393,"73,200","42,500",88/4,77/10,"4,420",4500000 +100 Darter (S.L. Industries),Jet,"11,400",503,459,104,28300,"4,049",5115,3250,"69,700","38,000",83/1,77/10,"3,767",4500000 +100 Darter (S.L. Industries),Jet,"11,400",503,459,103,28300,"4,049",5115,3250,"69,700","38,150",79/11,77/10,"3,680",4500000 +PA-24-C 260 Comanche,Jet,"11,400",503,459,103,26800,"4,240",5750,3168,"65,500","37,186",79/11,72/6,"2,795",4500000 +100 Darter (S.L. Industries),Jet,"11,400",503,459,108,23300,"4,345",5625,3198,"64,800","36,544",79/11,68/11,"2,592",4500000 +PA-32R-301T Saratoga II TC,Propjet,"1,950",340,299,87,10463,"1,900",,,"36,000","23,008",63/9,78/4,"2,164",3000000 +100 Darter (S.L. Industries),Jet,"3,700",443,400,87,9450,"3,000",5800,3600,"24,800","14,150",50/9,47/0,,4100000 +PA-32-300 '73-74 hgt=7/9 ('79 fuel=94 gal),Jet,"3,750",456,435,83,9450,"4,500",4850,2125,"25,000","12,865",50/6,47/0,,4100000 +100 Darter (S.L. Industries),Jet,"3,360",443,385,77,9100,"3,100",5000,1990,"23,300","11,920",47/5,47/0,,4100000 +PA-32-260 ('74-'78) 1974 height=7/9,Jet,"3,360",443,385,79,9100,"4,150",3800,2450,"22,700","12,000",47/5,47/0,,4100000 +100 Darter (S.L. Industries),Jet,"3,700",476,415,89,9580,"3,500",5250,2350,"23,500","12,800",52/3,44/9,"2,904",4500000 +"PA-28R-,RT-,201T Turbo Arrow III, IV",Jet,"3,700",471,426,90,8710,"3,200",4950,2450,"22,850","12,500",52/3,44/9,"2,440",4500000 +100 Darter (S.L. Industries),Jet,"3,100",471,426,87,8620,"4,040",5750,2900,"20,700","11,500",52/3,44/8,"1,450",4500000 +PA-28-200R Arrow II (1973-'76),Jet,"2,950",471,426,84,7350,"5,000",5450,3900,"18,500","10,500",50/5,43/3,"1,100",4500000 +100 Darter (S.L. Industries),Jet,"2,850",471,426,82,6250,"4,400",5400,3900,"17,500","10,500",50/5,43/3,950,4000000 +100 Darter (S.L. Industries),Piston,120,125,120,39,36,700,,,"1,320",780,21/5,30/0,840,1500000 +PA-28 180 Challenger & Archer,Piston,120,130,120,39,35,700,,,"1,320",800,21/5,32/5,800,1500000 +100 Darter (S.L. Industries),Piston,180,122,114,39,40,800,1275,900,"2,400","1,555",24/11,38/0,296,1300000 +PA-28 C 150 Cherokee,Piston,180,123,117,45,30,800,1375,900,"2,350","1,520",23/6,34/0,305,1350000 +100 Darter (S.L. Industries),Piston,150,109,97,50,30,700,1550,900,"2,150","1,450",23/6,34/0,320,950000 +"PA-28 150,B 150 Cherokee",Piston,180,125,122,39,40,"1,000",1250,,"2,400","1,345",24/11,38/0,296,1350000 +100 Darter (S.L. Industries),Piston,180,149,143,39,40,800,1275,900,"2,400","1,575",24/11,38/0,543,2000000 +PA-28-151 Warrior (prior '77 stall=58),Piston,200,,130,39,40,"1,200",875,900,"2,600","1,535",24/11,38/0,455,1470000 +100 Darter (S.L. Industries),Piston,200,135,130,39,40,"1,200",1050,950,"2,690","1,555",24/11,38/0,455,1470000 +PA-36-375 Brave (spray restr. category),Piston,125,162,125,49,30,682,1496,1519,"1,750","1,160",20/4,28/9,505,nan +"PA-25 235 C,D Pawnee (normal category)",Jet,"3,300",496,435,106,2660,"4,050",,3440,"41,900","20,999",60/5,54/5,,4300000 +100 Darter (S.L. Industries),Piston,90,96,83,37,25,660,1850,1540,"1,400",870,20/0,35/0,273,1600000 +PA-22 135 Tri-Pacer,Piston,85,91,83,37,30,640,1850,1540,"1,400",791,19/8,34/7,346,1550000 +100 Darter (S.L. Industries),Piston,65,91,78,37,14,550,1950,1540,"1,260",750,19/8,34/7,182,1400000 +PA-18 150 Super Cub,Piston,325,260,250,77,165,"1,955",1980,2076,Orig,200,Orig,Orig,"1,137",nan +PA-18 95 Super Cub,Piston,160,,135,40,40,825,1180,500,"2,200","1,330",23/5,32/9,540,1300000 +100 Darter (S.L. Industries),Piston,180,,140,40,40,920,1150,500,"2,400","1,350",23/5,32.9,500,1500000 +100 Darter (S.L. Industries),Piston,235,,148,30,40,"1,350",600,600,"2,500","1,500",22/9,33/2,478,2000000 +PA-11,Piston,235,157,129,38,40,"1,900",540,540,"2,500","1,425",23/6,33/2,360,2000000 +100 Darter (S.L. Industries),Piston,235,,150,33,40,"1,350",600,600,"2,300","1,400",23/6,30/10,360,2000000 +Kodiak 100,Piston,220,157,150,33,42,"1,250",585,600,"2,300","1,300",23/2,30/10,485,1900000 +G3,Piston,210,,170,33,40,"1,250",600,600,"2,300","1,400",22/9,30/10,430,2000000 +Super 340 (Cessna 340 conversion),Piston,210,157,137,33,40,"1,250",600,600,"2,300","1,350",22/9,30/10,345,1800000 +100 Darter (S.L. Industries),Piston,180,,136,33,40,900,800,600,"2,300","1,300",22/9,30/10,525,1500000 +TurboStream (Cessna 310 & 320 conv) 310hp,Piston,220,157,152,35,42,"1,250",600,650,"2,300","1,280",22/0,29/8,530,1900000 +100 Darter (S.L. Industries),Piston,210,157,143,35,42,"1,250",585,600,"2,100","1,250",22/0,29/8,500,1800000 +Rocket (Cessna 310 conversion),Piston,180,148,135,35,42,"1,000",700,600,"2,300","1,250",22/6,29/8,525,1700000 +305 Rocket,Piston,145,157,130,35,42,700,900,600,"2,100","1,100",22/0,29/8,750,1200000 +100 Darter (S.L. Industries),Piston,967,151,109,54,106,"1,400",,1600,"9,300","5,500",31/2,58/0,,2500000 +100 Darter (S.L. Industries),Jet,"2,900",456,438,,4260,"3,180",3950,2930,"15,780","9,925",48/5,43/6,"1,530",4100000 +"200 D Meyers (Prop Jets, Inc.)",Jet,"2,500",430,400,77,4260,"3,050",3850,2800,"14,630",9410,48/5,43/6,"1,513",4100000 +100 Darter (S.L. Industries),Jet,"2,500",434,410,77,646,"3,000",4100,2700,"13,890","8,300",48/4,43/5,"1,510",4100000 +"200 Meyers (Prop Jets, Inc.)",Propjet,778,309,296,77,403,"2,200",2170,1880,"11,575","7,650",39/5,39/2,"1,391",2975000 +100 Darter (S.L. Industries),Propjet,727,322,313,73,403,"2,350",1800,1600,"10,470","7,010",33/3,39/2,"1,600",3350000 +100 Darter (S.L. Industries),Propjet,724,317,304,73,364,"2,840",1800,1600,"10,470","6,864",33/3,39/2,"1,250",3220000 +100 Darter (S.L. Industries),Propjet,776,296,283,77,364,"2,630",2170,1880,"11,575","7,570",39/5,39/2,"1,200",2960000 +100 Darter (S.L. Industries),Propjet,724,317,270,71,366,"3,100",1700,1490,"9,920","5,920",33/3,39/2,"1,175",3320000 +Sabre 75A,Propjet,724,300,265,73,366,"2,690",1870,1670,"10,800","6,800",39/5,39/2,"1,150",3080000 +100 Darter (S.L. Industries),Propjet,705,283,261,73,366,"2,590",1890,1670,"10,800","6,700",39/5,39/2,"1,100",2700000 +Sabre 65,Propjet,705,296,270,71,366,"2,875",1700,1320,"9,920","5,790",33/3,39/2,"1,150",3040000 +100 Darter (S.L. Industries),Propjet,605,261,,64,285,"2,000",1600,1100,"8,930","5,730",33/3,39/2,880,2600000 +100 Darter (S.L. Industries),Piston,280,242,237,59,100,1380,,,3374,2370,26/9,36/5,1852,2500000 +Sting Sport S3,Jet,280,242,175,56,89,1375,2100,2650,3368,2380,26/8,36/6,1275,2500000 +108-3,Piston,270,236,223,58,96,"1,230",2050,2600,"3,368","2,353",26/11,36/1,,2500000 +108-1,Piston,280,197,191,60,95,"1,200",1700,1600,"3,368","2,200",26/9,36/1,900,2100000 +GC-1B,Piston,217,161,,57,61,"1,030",2550,1910,"2,900","2,003",26/11,36/1,,1930000 +SA-160,Piston,310,217,200,60,92,"1,120",2079,1549,"3,680","2,380",26/1,35/0,813,2400000 +F-21B,Piston,310,197,170,59,100,1300,2600,2500,3380,2250,26/8,36/6,1450,2000000 +100 Darter (S.L. Industries),Piston,220,213,197,60,80,"1,300",2000,2320,"3,100","2,000",25/5,36/1,"1,100",2500000 +P2006T,Piston,210,219,201,59,75,"1,080",2200,2300,"2,900","1,800",25/5,36/1,980,2800000 +A-36 Bonanza Prop Jet,Piston,200,178,171,54,64,"1,060",1700,1600,"2,740","1,710",24/8,36/1,690,1860000 +1000 Jet Prop,Piston,210,201,191,57,75,"1,080",2060,2280,"2,900","1,800",25/5,36/1,980,2400000 +100 Darter (S.L. Industries),Piston,200,175,170,53,64,"1,030",1550,1550,"2,740","1,671",24/8,36/1,690,1880000 +900 Jet Prop,Piston,200,161,156,54,64,"1,055",1385,1786,"2,740","1,640",24/0,35/0,734,1880000 +100 Darter (S.L. Industries),Piston,200,165,160,50,52,"1,125",1550,1550,"2,575","1,600",23/2,35/0,601,2120000 +"690A, Prop Jet (690 length=43/0 & height=14/11)",Piston,180,155,147,50,52,875,1250,1670,"2,525","1,585",24/3,35/0,627,1700000 +100 Darter (S.L. Industries),Piston,180,147,143,50,52,800,1395,1610,"2,575","1,525",23/2,35/0,659,1650000 +720 Alti-Cruiser,Piston,180,153,150,50,52,860,1395,1550,"2,575","1,525",23/2,35/0,659,1950000 +100 Darter (S.L. Industries),Piston,200,171,163,50,52,"1,110",1300,1365,"2,575","1,575",23/2,35/0,601,1880000 +685 pressurized,Piston,180,165,158,50,52,800,1250,1550,"2,575","1,525",23/2,35/0,659,1720000 +100 Darter (S.L. Industries),Piston,180,165,158,50,52,"1,150",1050,1100,"2,450","1,525",23/2,35/0,745,1850000 +680 W Turbo II Prop Jet,Piston,180,165,157,50,35,"1,150",1050,1100,"2,450","1,440",23/1,35/0,513,2000000 +100 Darter (S.L. Industries),Piston,150,149,143,50,35,900,1150,1100,"2,450","1,415",23/1,35/0,655,1720000 +680 FLP Pressurized Grand,Piston,180,128,117,50,52,740,1125,1100,"2,500","1,455",23/2,35/0,715,1360000 +100 Darter (S.L. Industries),Piston,90,103,96,40,24,835,953,1016,"1,450",950,20/8,30/0,484,1250000 +680 FL Grand,Piston,90,112,108,39,24,640,1250,1350,"1,450",950,20/0,30/0,456,1730000 +100 Darter (S.L. Industries),Piston,65,123,109,37,12,"1,000",1000,1000,850,575,17/7,26/1,,1940000 +100 Darter (S.L. Industries),Piston,285,177,166,48,40,"1,375",980,980,"3,315","2,000",27/5,34/9,335,2150000 +560 F,Piston,260,160,156,51,108,"1,150",980,980,"3,315","1,925",27/5,34/5,"1,043",2050000 +100 Darter (S.L. Industries),Piston,260,165,147,57,40,"1,110",1100,1100,"2,850","1,950",27/3,33/3,340,1660000 +560 A-HC,Piston,205,142,135,57,40,"1,050",900,1100,"2,750","1,782",27/3,33/3,430,1560000 +560,Propjet,850,400,,90,2630,"3,000",2630,2650,"10,810","7,200",47/3,46/0,"1,746",4100000 +500 U,Propjet,1200,270,265,64,402,"1,680",2300,1830,"9,920","5,732",47/3,53/3,"2,261",3000000 +500 A,Propjet,1000,351,334,,3819,"3,242",1930,2280,"12,050","7,856",43/5,47/8,"1,879",4100000 +100 Darter (S.L. Industries),Propjet,720,312,288,89,578,"2,380",2280,3043,"11,200","6,837",43/5,47/8,"1,722",3584000 +520,Propjet,720,290,275,87,390,"2,236",3230,3017,"11,200","6,389",43/5,47/8,"1,515",3200000 +2180,Propjet,620,275,270,77,382,"1,750",2940,2446,"9,474","5,164",36/8,42/8,"1,336",3240000 +2150A Kachina,Propjet,620,283,269,75,382,"2,710",1980,2480,"9,000","5,018",34/8,42/8,"1,444",3160000 diff --git a/Plane_price.ipynb b/Plane_price.ipynb new file mode 100644 index 0000000..4a02ac0 --- /dev/null +++ b/Plane_price.ipynb @@ -0,0 +1,2438 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d1dea79a", + "metadata": {}, + "source": [ + "# CS584 Machine Learning Project 2" + ] + }, + { + "cell_type": "markdown", + "id": "0aa125a3", + "metadata": {}, + "source": [ + "# Plane_price prediction" + ] + }, + { + "cell_type": "markdown", + "id": "84f9d38d", + "metadata": {}, + "source": [ + "### Kaustubh Dangche - A20550806\n", + "### Hyunsung Ha - A20557555\n", + "### Anu Singh - A20568373\n", + "### Nam Gyu Lee - A20487452" + ] + }, + { + "cell_type": "markdown", + "id": "48a6e5eb", + "metadata": {}, + "source": [ + "#" + ] + }, + { + "cell_type": "markdown", + "id": "5ad0a1d0", + "metadata": {}, + "source": [ + " ## We decided to go with Model Selection method for our project." + ] + }, + { + "cell_type": "markdown", + "id": "dbf9b684", + "metadata": {}, + "source": [ + "## Data Collection" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9de818ab", + "metadata": {}, + "outputs": [], + "source": [ + "# Import essential data manipulation and visualization libraries\n", + "# pandas: For efficient data handling and analysis\n", + "# numpy: For numerical operations and array manipulations\n", + "# matplotlib.pyplot: For creating static, animated, and interactive \n", + "\n", + "import pandas as pd \n", + "import numpy as np \n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4564e5a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Model NameEngine TypeHP or lbs thr ea engineMax speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.Price
0100 Darter (S.L. Industries)Piston14510491.046.036450900.01300.02,0501,18025/337/53701300000.0
17 CCM ChampPiston858983.044.015600720.0800.01,35082020/736/11901230000.0
2100 Darter (S.L. Industries)Piston909078.037.019650475.0850.01,30081021/535/02101600000.0
37 AC ChampPiston858878.037.019620500.0850.01,30080021/535/02101300000.0
4100 Darter (S.L. Industries)Piston658374.033.014370632.0885.01,22074021/535/01751250000.0
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" + ], + "text/plain": [ + " Model Name Engine Type HP or lbs thr ea engine \\\n", + "0 100 Darter (S.L. Industries) Piston 145 \n", + "1 7 CCM Champ Piston 85 \n", + "2 100 Darter (S.L. Industries) Piston 90 \n", + "3 7 AC Champ Piston 85 \n", + "4 100 Darter (S.L. Industries) Piston 65 \n", + "\n", + " Max speed Knots Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "0 104 91.0 46.0 36 \n", + "1 89 83.0 44.0 15 \n", + "2 90 78.0 37.0 19 \n", + "3 88 78.0 37.0 19 \n", + "4 83 74.0 33.0 14 \n", + "\n", + " All eng rate of climb Eng out rate of climb Takeoff over 50ft \\\n", + "0 450 900.0 1300.0 \n", + "1 600 720.0 800.0 \n", + "2 650 475.0 850.0 \n", + "3 620 500.0 850.0 \n", + "4 370 632.0 885.0 \n", + "\n", + " Landing over 50ft Empty weight lbs Length ft/in Wing span ft/in Range N.M. \\\n", + "0 2,050 1,180 25/3 37/5 370 \n", + "1 1,350 820 20/7 36/1 190 \n", + "2 1,300 810 21/5 35/0 210 \n", + "3 1,300 800 21/5 35/0 210 \n", + "4 1,220 740 21/5 35/0 175 \n", + "\n", + " Price \n", + "0 1300000.0 \n", + "1 1230000.0 \n", + "2 1600000.0 \n", + "3 1300000.0 \n", + "4 1250000.0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load the plane price dataset\n", + "df = pd.read_csv(\"Plane Price.csv\")\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e6b26263", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(517, 16)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To Check total columns & rows present in the dataset\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "172e78a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 517 entries, 0 to 516\n", + "Data columns (total 16 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Model Name 517 non-null object \n", + " 1 Engine Type 517 non-null object \n", + " 2 HP or lbs thr ea engine 517 non-null object \n", + " 3 Max speed Knots 497 non-null object \n", + " 4 Rcmnd cruise Knots 507 non-null float64\n", + " 5 Stall Knots dirty 502 non-null float64\n", + " 6 Fuel gal/lbs 517 non-null int64 \n", + " 7 All eng rate of climb 513 non-null object \n", + " 8 Eng out rate of climb 491 non-null float64\n", + " 9 Takeoff over 50ft 492 non-null float64\n", + " 10 Landing over 50ft 517 non-null object \n", + " 11 Empty weight lbs 516 non-null object \n", + " 12 Length ft/in 517 non-null object \n", + " 13 Wing span ft/in 517 non-null object \n", + " 14 Range N.M. 499 non-null object \n", + " 15 Price 507 non-null float64\n", + "dtypes: float64(5), int64(1), object(10)\n", + "memory usage: 64.8+ KB\n" + ] + } + ], + "source": [ + "# To check the datatype of columns\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "62c5cdc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Rcmnd cruise KnotsStall Knots dirtyFuel gal/lbsEng out rate of climbTakeoff over 50ftPrice
count507.000000502.000000517.000000491.000000492.0000005.070000e+02
mean200.79289960.7958171419.3791102065.1262731743.3069112.362673e+06
std104.28053216.6570024278.3207731150.031899730.0096741.018731e+06
min70.00000027.00000012.000000457.000000500.0000006.500000e+05
25%130.00000050.00000050.0000001350.0000001265.0000001.600000e+06
50%169.00000056.00000089.0000001706.0000001525.0000002.000000e+06
75%232.00000073.000000335.0000002357.0000002145.7500002.950000e+06
max511.000000115.00000041000.0000006400.0000004850.0000005.100000e+06
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" + ], + "text/plain": [ + " Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "count 507.000000 502.000000 517.000000 \n", + "mean 200.792899 60.795817 1419.379110 \n", + "std 104.280532 16.657002 4278.320773 \n", + "min 70.000000 27.000000 12.000000 \n", + "25% 130.000000 50.000000 50.000000 \n", + "50% 169.000000 56.000000 89.000000 \n", + "75% 232.000000 73.000000 335.000000 \n", + "max 511.000000 115.000000 41000.000000 \n", + "\n", + " Eng out rate of climb Takeoff over 50ft Price \n", + "count 491.000000 492.000000 5.070000e+02 \n", + "mean 2065.126273 1743.306911 2.362673e+06 \n", + "std 1150.031899 730.009674 1.018731e+06 \n", + "min 457.000000 500.000000 6.500000e+05 \n", + "25% 1350.000000 1265.000000 1.600000e+06 \n", + "50% 1706.000000 1525.000000 2.000000e+06 \n", + "75% 2357.000000 2145.750000 2.950000e+06 \n", + "max 6400.000000 4850.000000 5.100000e+06 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View the statistical summary of the dataset\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f55da0ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Model Name 0\n", + "Engine Type 0\n", + "HP or lbs thr ea engine 0\n", + "Max speed Knots 20\n", + "Rcmnd cruise Knots 10\n", + "Stall Knots dirty 15\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 4\n", + "Eng out rate of climb 26\n", + "Takeoff over 50ft 25\n", + "Landing over 50ft 0\n", + "Empty weight lbs 1\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 18\n", + "Price 10\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check the null values is present in the dataset or not\n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "42b0e466", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model Name 0\n", + "Engine Type 0\n", + "HP or lbs thr ea engine 0\n", + "Max speed Knots 20\n", + "Rcmnd cruise Knots 0\n", + "Stall Knots dirty 0\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 4\n", + "Eng out rate of climb 0\n", + "Takeoff over 50ft 0\n", + "Landing over 50ft 0\n", + "Empty weight lbs 1\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 18\n", + "Price 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# Fill missing values with median for numerical columns\n", + "df.fillna(df.median(numeric_only=True), inplace=True)\n", + "\n", + "# Verify no missing values remain\n", + "print(df.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "id": "88edda31", + "metadata": {}, + "source": [ + "\n", + "### Fill missing values in numerical columns using their respective median values. The median is chosen as it is robust to outliers and better represents the data's central tendency. The `numeric_only=True` parameter ensures only numerical columns are processed, leaving non-numeric columns (e.g., object-type) unaffected. The `inplace=True` parameter applies changes directly to the DataFrame.\n", + "\n", + "### After applying `fillna()`, the `df.isnull().sum()` verification step counts remaining missing values. Non-zero counts for some columns indicate missing values in non-numeric (e.g., object-type) columns, which may need separate handling. This ensures numerical data is ready for analysis or modeling." + ] + }, + { + "cell_type": "markdown", + "id": "f782b48d", + "metadata": {}, + "source": [ + "## Data Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b3e4bcc5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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HP or lbs thr ea engineMax speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.PriceEngine Type_pistonEngine Type_propjet
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" + ], + "text/plain": [ + " HP or lbs thr ea engine Max speed Knots Rcmnd cruise Knots \\\n", + "0 145 104 91.0 \n", + "1 85 89 83.0 \n", + "2 90 90 78.0 \n", + "3 85 88 78.0 \n", + "4 65 83 74.0 \n", + "\n", + " Stall Knots dirty Fuel gal/lbs All eng rate of climb \\\n", + "0 46.0 36 450 \n", + "1 44.0 15 600 \n", + "2 37.0 19 650 \n", + "3 37.0 19 620 \n", + "4 33.0 14 370 \n", + "\n", + " Eng out rate of climb Takeoff over 50ft Landing over 50ft \\\n", + "0 900.0 1300.0 2,050 \n", + "1 720.0 800.0 1,350 \n", + "2 475.0 850.0 1,300 \n", + "3 500.0 850.0 1,300 \n", + "4 632.0 885.0 1,220 \n", + "\n", + " Empty weight lbs Length ft/in Wing span ft/in Range N.M. Price \\\n", + "0 1,180 25/3 37/5 370 1300000.0 \n", + "1 820 20/7 36/1 190 1230000.0 \n", + "2 810 21/5 35/0 210 1600000.0 \n", + "3 800 21/5 35/0 210 1300000.0 \n", + "4 740 21/5 35/0 175 1250000.0 \n", + "\n", + " Engine Type_piston Engine Type_propjet \n", + "0 True False \n", + "1 True False \n", + "2 True False \n", + "3 True False \n", + "4 True False " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop 'Model Name' if it's not relevant\n", + "df.drop(columns=['Model Name'], inplace=True)\n", + "\n", + "# Standardize the case in the 'Engine Type' column\n", + "df['Engine Type'] = df['Engine Type'].str.lower() # Convert to lowercase\n", + "\n", + "# Re-run one-hot encoding\n", + "df = pd.get_dummies(df, columns=['Engine Type'], drop_first=True)\n", + "\n", + "# Verify the unique values and column names\n", + "df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cdaea563", + "metadata": {}, + "source": [ + "### The column Model Name is removed as it is not relevant for the analysis and modeling process, ensuring the dataset contains only useful features. The values in the Engine Type column are converted to lowercase to maintain uniformity and avoid potential mismatches during further processing. The Engine Type column is encoded into binary columns (Engine Type_piston, Engine Type_propjet) using one-hot encoding. This transformation converts categorical data into numerical format suitable for modeling. The dataset is displayed after transformations to ensure changes have been successfully applied. The binary columns for Engine Type are now included in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3cdf67ef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False]\n" + ] + } + ], + "source": [ + "# Check unique values in the 'Engine Type' column\n", + "print(df['Engine Type_piston'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "09c422ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False True]\n" + ] + } + ], + "source": [ + "# Check unique values in the 'Engine Type' column\n", + "print(df['Engine Type_propjet'].unique())" + ] + }, + { + "cell_type": "markdown", + "id": "ba0c9e9b", + "metadata": {}, + "source": [ + "### Check unique values in the one-hot encoded columns 'Engine Type_piston' and 'Engine Type_propjet'. The unique values [0, 1] confirm that one-hot encoding was applied successfully, representing the binary presence (1) or absence (0) of each category in the original 'Engine Type' column." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7d7a6bd6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:14: SyntaxWarning: invalid escape sequence '\\d'\n", + "<>:14: SyntaxWarning: invalid escape sequence '\\d'\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\3091194593.py:14: SyntaxWarning: invalid escape sequence '\\d'\n", + " df[col] = df[col].str.replace(',', '').str.extract('(\\d+)', expand=False).astype(float)\n" + ] + }, + { + "data": { + "text/html": [ + "
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Max speed KnotsAll eng rate of climbLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.
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" + ], + "text/plain": [ + " Max speed Knots All eng rate of climb Landing over 50ft \\\n", + "0 104.0 450.0 2050.0 \n", + "1 89.0 600.0 1350.0 \n", + "2 90.0 650.0 1300.0 \n", + "3 88.0 620.0 1300.0 \n", + "4 83.0 370.0 1220.0 \n", + "\n", + " Empty weight lbs Length ft/in Wing span ft/in Range N.M. \n", + "0 1180.0 25.0 37.0 370.0 \n", + "1 820.0 20.0 36.0 190.0 \n", + "2 810.0 21.0 35.0 210.0 \n", + "3 800.0 21.0 35.0 210.0 \n", + "4 740.0 21.0 35.0 175.0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert columns to numeric by removing commas and handling special characters\n", + "columns_to_convert = [\n", + " \"Max speed Knots\", \n", + " \"All eng rate of climb\", \n", + " \"Landing over 50ft\", \n", + " \"Empty weight lbs\", \n", + " \"Length ft/in\", \n", + " \"Wing span ft/in\", \n", + " \"Range N.M.\"\n", + "]\n", + "\n", + "for col in columns_to_convert:\n", + " # Remove commas and convert to numeric\n", + " df[col] = df[col].str.replace(',', '').str.extract('(\\d+)', expand=False).astype(float)\n", + "\n", + "# Verify the conversions\n", + "df[columns_to_convert].head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "23ef734f", + "metadata": {}, + "source": [ + "### Specific columns with string-based numbers (e.g., commas or special characters) are converted to numeric format for compatibility with numerical analysis and modeling." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cc467036", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HP or lbs thr ea engine 0\n", + "Max speed Knots 0\n", + "Rcmnd cruise Knots 0\n", + "Stall Knots dirty 0\n", + "Fuel gal/lbs 0\n", + "All eng rate of climb 0\n", + "Eng out rate of climb 0\n", + "Takeoff over 50ft 0\n", + "Landing over 50ft 0\n", + "Empty weight lbs 0\n", + "Length ft/in 0\n", + "Wing span ft/in 0\n", + "Range N.M. 0\n", + "Price 0\n", + "Engine Type_piston 0\n", + "Engine Type_propjet 0\n", + "dtype: int64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "C:\\Users\\Hyunsung Ha\\AppData\\Local\\Temp\\ipykernel_4344\\167143364.py:6: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n", + "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", + "\n", + "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", + "\n", + "\n", + " df[col].fillna(df[col].median(), inplace=True)\n" + ] + } + ], + "source": [ + "# Fill null values for specific columns\n", + "columns_to_fill_with_median = [\"Max speed Knots\", \"All eng rate of climb\", \"Landing over 50ft\",\n", + " \"Empty weight lbs\", \"Length ft/in\", \"Wing span ft/in\", \"Range N.M.\"]\n", + "\n", + "for col in columns_to_fill_with_median:\n", + " df[col].fillna(df[col].median(), inplace=True)\n", + "\n", + "# Verify that there are no missing values\n", + "print(df.isnull().sum())\n" + ] + }, + { + "cell_type": "markdown", + "id": "53841fa2", + "metadata": {}, + "source": [ + "### Columns with missing values are identified and filled with their respective median values, a robust imputation technique that reduces the impact of outliers. After imputation, the dataset is verified to ensure no null values remain, indicating the dataset is clean and ready for further steps." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "70aa4828", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Max speed KnotsRcmnd cruise KnotsStall Knots dirtyFuel gal/lbsAll eng rate of climbEng out rate of climbTakeoff over 50ftLanding over 50ftEmpty weight lbsLength ft/inWing span ft/inRange N.M.Price
count517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.000000517.0000005.170000e+02
mean212.794971200.17795060.6566731419.3791101658.9806582047.0657641732.7504847485.4893624377.40522237.88588038.932302911.4487432.355658e+06
std114.106830103.35808916.4328744278.3207731258.6841841123.433947713.64696710289.4424745649.739125137.6330818.599692696.4296431.010050e+06
min64.00000070.00000027.00000012.000000360.000000457.000000500.000000567.0000002.00000017.00000016.000000117.0000006.500000e+05
25%143.000000131.00000050.00000050.000000924.0000001365.0000001265.0000002650.0000001575.00000025.00000035.000000517.0000001.600000e+06
50%177.000000169.00000056.00000089.0000001200.0000001706.0000001525.0000003625.0000002286.50000028.00000036.000000713.0000002.000000e+06
75%238.000000229.00000073.000000335.0000001820.0000002280.0000002110.0000008800.0000005164.00000035.00000042.0000001100.0000002.940000e+06
max755.000000511.000000115.00000041000.0000007220.0000006400.0000004850.00000089400.00000046800.0000003150.00000093.0000006500.0000005.100000e+06
\n", + "
" + ], + "text/plain": [ + " Max speed Knots Rcmnd cruise Knots Stall Knots dirty Fuel gal/lbs \\\n", + "count 517.000000 517.000000 517.000000 517.000000 \n", + "mean 212.794971 200.177950 60.656673 1419.379110 \n", + "std 114.106830 103.358089 16.432874 4278.320773 \n", + "min 64.000000 70.000000 27.000000 12.000000 \n", + "25% 143.000000 131.000000 50.000000 50.000000 \n", + "50% 177.000000 169.000000 56.000000 89.000000 \n", + "75% 238.000000 229.000000 73.000000 335.000000 \n", + "max 755.000000 511.000000 115.000000 41000.000000 \n", + "\n", + " All eng rate of climb Eng out rate of climb Takeoff over 50ft \\\n", + "count 517.000000 517.000000 517.000000 \n", + "mean 1658.980658 2047.065764 1732.750484 \n", + "std 1258.684184 1123.433947 713.646967 \n", + "min 360.000000 457.000000 500.000000 \n", + "25% 924.000000 1365.000000 1265.000000 \n", + "50% 1200.000000 1706.000000 1525.000000 \n", + "75% 1820.000000 2280.000000 2110.000000 \n", + "max 7220.000000 6400.000000 4850.000000 \n", + "\n", + " Landing over 50ft Empty weight lbs Length ft/in Wing span ft/in \\\n", + "count 517.000000 517.000000 517.000000 517.000000 \n", + "mean 7485.489362 4377.405222 37.885880 38.932302 \n", + "std 10289.442474 5649.739125 137.633081 8.599692 \n", + "min 567.000000 2.000000 17.000000 16.000000 \n", + "25% 2650.000000 1575.000000 25.000000 35.000000 \n", + "50% 3625.000000 2286.500000 28.000000 36.000000 \n", + "75% 8800.000000 5164.000000 35.000000 42.000000 \n", + "max 89400.000000 46800.000000 3150.000000 93.000000 \n", + "\n", + " Range N.M. Price \n", + "count 517.000000 5.170000e+02 \n", + "mean 911.448743 2.355658e+06 \n", + "std 696.429643 1.010050e+06 \n", + "min 117.000000 6.500000e+05 \n", + "25% 517.000000 1.600000e+06 \n", + "50% 713.000000 2.000000e+06 \n", + "75% 1100.000000 2.940000e+06 \n", + "max 6500.000000 5.100000e+06 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To check how does the data looks mathematically\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "8938328f", + "metadata": {}, + "source": [ + "### Now, we have handled missing values & also converted object columns into numerical. In addition, there were still null values present in the object columns, we filled the null values by using meadian. Now, You can see the summary of dataset & we are ready to go with correlation matrix to select the best features for train test split.\n" + ] + }, + { + "cell_type": "markdown", + "id": "077fea55", + "metadata": {}, + "source": [ + "## Correlation Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "340a22df", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QQgghhBBCONj48eMZPXq0TZpWqy2iaBxDKhCEEEIIIYQQQjyRFBpFkX23Vqst1AqDwMBAoqOjbdKio6Px8PAolNYHIGMgCCGEEEIIIYQQj52GDRuydetWm7TNmzfTsGHDQvtOqUAQQgghhBBCCCGKWGpqKseOHePYsWNAzjSNx44dIzIyZ0yT8ePH079/f2v+ESNGcOXKFd5++23OnTvH/Pnz+emnn3jzzTcLLUbpwiCEEEIIIYQQ4omkVBddF4Z/6tChQ7Ro0cL6953xEwYMGMDSpUu5deuWtTIBoHTp0mzYsIE333yTefPmUbx4cRYtWkTbtm0LLUapQBBCCCGEEEIIIYpY8+bNsVgs+X6+dOlSu+scPXq0EKOyJRUIQgghhBBCCCGeSAqN9Np3JNmbQgghhBBCCCGEKJBUIAghhBBCCCGEEKJA0oVBCCGEEEIIIcQT6XEaRPFxIC0QhBBCCCGEEEIIUSBpgSCEEEIIIYQQ4omk0EgLBEeSFghCCCGEEEIIIYQokFQgPKYmTZpEzZo1izoMIYQQQgghhPjPUqoVRbY8if7TXRgGDhzIsmXLGD58OAsWLLD57NVXX2X+/PkMGDCApUuXFk2A/2ERERGULl2ao0ePWisaUlJSePbZZ4mOjmbTpk0UL17cId+lUChYvXo1Xbp0ccj2CpPP03UJGzMEz9pV0QUX41D3V4het7Wow7Ly6dgF/269UHv7kBl+mZsLPyXjwrl88/s+2x3fDs+i8Q/AmJxE8u6dRC37GovBkCevf48+BA58ibi1v3Dr6y8Ksxi4Nm2Le6tnUXl4YbhxlcSfF2O4esluXv9Rk9CWq5InPePUEeIXfJgn3av3MNyefgb9L0tI3fG7w2O/Q1u7KdoGbVC6eWCKuU76pp8w3bqab36F1hlds2dxqlAThc4Fc3IC6Vt+wXj5NABOtZqgrd0UlacPAKa4W2T8/TvGK2cKrQwAXm074/tsT1RePmRdvUL04i/IvHQ+3/zeHbri1bYTGr9imJKTSdm3i9jl31iPKedK1fB9tifasHJofHy5PnMSqQf3FGoZ3Jq1w+OZLqg8vMi+HkHiykVkR9g/noqNnoKufNU86RknDxP7xTQAfAaMxK1hS9vPTx8l9rOpjg/+Li6N2+DavBMqd08MNyNJXr0Mw7XLdvP6vPwu2rKV86RnnjlK4jcfA6CrVg+Xhq3QFC+N0tWd2NnjMd7M/xiVMuR6HMvxx/pVrPt1BfrEBEqVLsOQEaMoVyFvXHfs2bWdFd9/Q2x0FEHBIbwwaAS16zW0fr7yh8Xs/msb8bExqNVqwspWoE//YZSvmLvNjyaPIyL8Ekl6Pa5ublSvWZcXBo3Ax9fPYeVaeeQiy/afJT4tk/LFvHindR2qBvvazbvu5BUm/n7AJs1JpWT/2Oetfy/4+yR/no0kKiUdjVJJpUAfRjatTrV8tukozk+1wqVpB5RunhijrpGy7juM16/km1+hc8H1mR5oq9RF6eKKSR9P6vrvyT5/IudzJx2uz3RHW7kOSjcPjDevkrL+e4zXwwutDL6duuDfvffte5BL3Pjy/vcgfs/1wLdj7j1I0t87iVr6NRZDNgAB/QYS0G+gzTqZ1yK5MLx/oZXhjhe7BtCumQ+uLirOXEzj829vcDM6O9/8S2dVJMDPKU/6b1vjmP/dTQDaN/OheUMvypZyxsVZRY9XTpGWbi60MqzesJGVq9eRkKinTOlSvP7SYCqVL2c3b3jkNZb8sJILl68QHRPLq0MG0uO5jjZ5eg99heiY2DzrPtehLW+MGFooZRBPpv90BQJAiRIlWLFiBZ988gnOzs4AZGZmsnz5ckqWLFnE0T0+YmNjad++PUqlkl27duHrW7gX0v8qlasLySfOc23pr9T9pXAfov8pzyYtCBr6Mje/+IT082fxe64HpafM5Pzw/piS9HnzN2tF4MCXuD5vJulnT6ENKUHxN94B4Nai+TZ5nctVwKddZzLC7d8gO5Jz7UZ4dR1A4sqvyI64hFuLjvi/OoGoKaMwpybnyR/39SwUqtyfIqWrGwHjZ5FxdG+evLrq9XEKLY9Jn1CoZdBUqoNzq+6kb/wR480IdPVa4tbrNZK/moQlPTXvCkoVbn1ex5KWQuqqr7Gk6lF6+GLJSrdmsaToydixBnNCDCgUOFV9CrceI0he/CHmuFuFUg73Rs0oNmA40V99Ssalc/h07EaJCdO5MmoIpmR9nvweT7fAv98Qor6cTcb5M2iCihP06ljAQsyyhTlF1erIvHoF/fY/Kf7WxEKJ+24udRrj3WMQCcsXkhVxAY+WnSj22vvcnPQa5pSkPPnjFswEde7xpHJ1J/DdOaQfsa3kyDh1hPhvP7f+bTHmrXRzJF3Np/B49gWSflmMIfISrk3a4/PSOGJnjLF7XiQu/QTFXeVQurjhN+YjMk/st6YpnLRkh58n4/g+vJ5/qVDjf1LK8LiWY/dfW1n29Re8NHIM5SpUZsOan/ngvbF8+tUPeHp558l/7sxJ5s6cQr+BL1GnXkN27dzCzA8mMHPeIkqGhgEQHFKCoSPeICAwmOzsLNav+YkP3hvDZ4t+xNPTC4Aq1WvTrdeLePv4Eh8Xy7ffzGfW9PeYPvtLh5Trz7ORzN52lAnP1KVqsC/LD53nlZ92sGZYR3xcdXbXcXPSsHpYB+vfCoXtG75SPu6806YOxb3cyDKY+P7QeV5ZuYO1wzvi42J/mw9LW60Bbh37krJmKYZrl3Fp3BavwW8RP/ttLGkpeVdQqfAa8jbm1GSSl3+GKSkRlbcvlozca4Z79yGoA0JI/mkh5pREdDUb4zXkHRI+GY85OdHhZfBs2oKgYa9w4/M5pJ87i1+XHpSe+jHnX3rR7j2IV/NWBA56ietzZ5B25jTakOKUGD0OsHDr69x7kMyIcK5MGGP922IyOTz2e/Xs4M+zbfyY/fU1omKz6d8tgA/GlGb4hAsYDBa764yafBGlMvdYKhWi48O3w9h1MPc6o9UqOXQyhUMnUxjcM6hQy7Bt126+/GYZb77yEpXKl+WXdRt4e+I0vv1yHt5ennnyZ2VlERxYjOaNG/LFN0vtbnPB7A8xm3MrPMKvXmPs+1Np3rih3fxC5Oc/34Whdu3alChRglWrVlnTVq1aRcmSJalVq5ZN3o0bN/L000/j5eWFr68vnTp14vLl3Aemb7/9Fjc3Ny5evGhNe+WVV6hYsSLp6enYc/z4cVq0aIG7uzseHh7UqVOHQ4cOAbB06VK8vLxYs2YN5cqVQ6fT0bZtW65du2azjbVr11K7dm10Oh1hYWFMnjwZo9Fo/Vyv1zN06FD8/f3x8PCgZcuWHD9+3GYbH330EQEBAbi7uzNkyBAyMzMfeB9eu3aNJk2a4OnpybZt26yVBzt27EChULB161bq1q2Li4sLjRo14vx527eTX375JWXKlMHJyYkKFSrw3XffWT8LDQ0FoGvXrigUCuvf99tvRSn2z7+4MHEu0Wu3FHUoefh16UninxtI3LKRrGtXufHFHMxZmfi0aW83v2ulKqSfPUXSzq0YYqJJPXoI/V/bcC5X0SafUqejxNgJXP9sFqZUOzcyDubeshNpe7aSvm8Hxqjr6Fd8hSU7G9d73vjeYUlPxZyity66itWxZGflqUBQevrg1XMwCUvnYTEZ7W7LUXT1W5J1fDfZJ/dhjo8ifeOPYMzGqXoju/mdajRCoXMh9dcFmG5cwZyUgPHaRUwxN6x5DJdOYrx8GnNiLOaEGDL/WoclOwt1cOlCK4dPp+4kbf2DpB2byL4eSdRX8zBnZ+HZsq3d/M4VKpNx/jTJf2/HEBtN+onDpOzejq5sBWuetGMHiVuxlNQDuwst7ru5t+5M6u7NpO3dhvHWdRKWL8RsyMKtkf3jyZyeijlZb110lWpgyc4i/bBtBYLFaLDJZ0lPK9RyuDbtQPq+7WQc3Ikx+gZJv36DxZCFc/1mdvNbMtIwpyRZF6fy1bAYssg8nvvQmnH4b1I3ryb7wqlCjf1JKgM8nuX4bfVPtG7XiZZtOlCiZCgvjRyDVqdj26YNdvP/vu4Xatapz3Pd+1C8ZCh9XhxK6TLl+WN97r1Uk+ZtqF6rLgFBwZQoVZoBw0aSnp7G1bsqmjt3fZ7yFavgXyyQipWr0bVnPy6eP2NzD/Mwvj94jm41yvBc9TDK+HkyoW09dBo1a07m/+YeBfi5OVsX33sqGtpXDuWp0ECKe7lRxt+TMS1rkZpt4GKM3iEx2+PSpB0ZB3eQeXgXppibpKxZiiU7C+e69o8pXZ2mKJ1dSfpuHoarFzHr4zCEn8cYdfv+Ua1BW6UuqX+sxBBxHlN8DGlbV2OKj8a5gf3fvofl37UnCRs3kLj59j3I53OwZGXi80wHu/ldKlUl7cxJ9Du2YoiJyrkH2bkVl/KVbPJZTCaMiQnWxZSct+LX0bo848eKddHsO5pMxPVMZn19DV9vDY1qe+S7TlKKicQko3VpUNOdm9FZnDyXe21YsymOnzfEcu6y/WcGR/p57Xo6PtOK9q1bEFqyBKNfeQmd1ok/tmyzm79iubKMGNSflk0bo9Fo7Obx8vTEx9vbuuw9eJjgwABqVM2/JdOTQqFRFNnyJPrPVyAADB48mCVLllj/Xrx4MYMGDcqTLy0tjdGjR3Po0CG2bt2KUqmka9eu1tq2/v3706FDB/r164fRaGTDhg0sWrSIH374ARcXF7vf3a9fP4oXL87Bgwc5fPgw48aNszkx09PTmTZtGt9++y27d+9Gr9fTu3dv6+e7du2if//+jBo1ijNnzrBw4UKWLl3KtGnTrHl69uxJTEwMf/zxB4cPH6Z27dq0atWKhISct6w//fQTkyZNYvr06Rw6dIigoCDmz7d9w5yf8+fP07hxYypXrszvv/+Om5tbnjwTJkxg9uzZHDp0CLVazeDBg62frV69mlGjRjFmzBhOnTrF8OHDGTRoENu3bwfg4MGDACxZsoRbt25Z/y5ovwlbCrUa57LlST12ODfRYiH12BFcKuZt3g+QdvY0zmXK41w+p8JAExCEe90GpBzab5Mv+OU3SDm4j7TjRwotfiuVGk2JMDJvN8EEwGIh8/wJnEqXf6BNuDZqRfqRPViys3ITFQp8+r9G6tZ1GKOuOzjoeyhVqAJLYgy/uyLNgiHiHOoQ+w/7TuWqYbwRjsszvfF8/SM8hr6LrmFbUORz4VAo0FSqg0LjhPHGfW6UH4ZajS6sHGknjuamWSyknziK8z03eHdknD+DLqyctcJAUywQ11r1STtywG7+QqdS41SyDJln7zmezp7AKaxC/uvdxbVxK9IP/W17PAG68lUJmbmEoEmf4d3nJZSueX8bHUalQlO8NFkX73q4tFjIunAKp1L2m6Pey6VBczKP7stTjkfmSSgDPJblMBgMXLl0geo161rTlEol1WrW4fy503bXuXDuNNVr1rFJq1m7PhfyyW8wGNj8xzpcXN0ILV3Gbp6UlGR27dhMhUpVUasfvgGrwWTibFQiDUoFWNOUCgUNQgM4cSM+3/Uyso20/3Id7eav5Y1fd3E5Nv8HUoPJxKpjl3HTaihfLG9LDYdQqVAHh5J96a59a7GQffkMmpJl7a6irVwbQ+Ql3J/rj9//PsNn1HRcmne2XjMUShUKlSpPyyiLwYAm9MGupf9Ezj1IhTz3ICnHDuNS0f7DZfrZU7iUrWC9B3EKDMK97lMkH9xnk08bEkKl736hwjfLKfHWBDT+xRwe/90C/Z3w8dJw9Exua8H0DDPnL6dTsYzrA21DrVLQoqE3m3YVbmvH/BgMBi5cukKdmtWtaUqlkto1qnP63AWHfcfmHbto37plnlY8QhTkP9+FAeCFF15g/PjxXL2a059w9+7drFixgh07dtjk6969u83fixcvxt/fnzNnzlC1ak6f2IULF1K9enVef/11Vq1axaRJk6hTx/Yie7fIyEjeeustKlbM+YEsV872BsNgMPD555/ToEEDAJYtW0alSpU4cOAA9evXZ/LkyYwbN44BAwYAEBYWxtSpU3n77beZOHEif//9NwcOHCAmJgatVgvArFmzWLNmDb/88gsvvfQSc+fOZciQIQwZMgSADz74gC1btjxQK4T+/fvTuHFjfv75Z1Qqld0806ZNo1mznFrycePG0bFjRzIzM9HpdMyaNYuBAwfyyiuvADB69Gj27dvHrFmzaNGiBf7+/gB4eXkRGBj4wPtN2FJ5eKJQqTDqbZslGvWJaIvb76qTtHMrag9PwmZ8ikKhQKFWE//7WmJ//sGax7NpC5zLlOPSmyMKNf47lG7uKFSqPE3LzclJaAJCClxfU6osmuCSJPxg2zzWvc1zYDYV6pgHdyhc3FAoVZjTbZsyW9JSUPkG2F1H6eWHupQv2acPkvrTFyi9i+HStheoVGT+nRuz0j8Yj/5jQa3Bkp1F6qqvMMdHFUo51O4eOcdU0j3HVFIiLiEl7K6T/Pd2VO6elJo6B8g5phI3/Ub86hWFEmNBVLePp3u7W5hT9GgCCz6enELL4hRSioTvbLsrZZ4+SsbR/RjjolH7B+LVpR9Or71H9IzxYHF8f1alaz7nRWoS6mLBBa6vKVEGTVBJklZ+7fDYHtSTUAZ4PMuRkpyE2WzK01XBy8uHG9ci7a6jT0zAy8vHJs3Tyxt9ou0D0aEDe5g7YzJZWZl4+/jy/gez8bjdfeGO7xZ/ycb1q8nKyqR8xSqMn/jRwxcKSEzPxmSx5Omq4OuiIyI+b1cSgFI+HkzsUJ/y/l6kZBn47sA5Bn6/hV+GtCfAI/dF0F+XbjBu3V4yDUb83JxZ0Ks53i5ah8R9L6XL7WPqnu4v5pQk1P72m7mrvP1RhVUi89he9Etno/INwL3LAFCpSN+6Bkt2JoarF3Ft+RzJMTcxpyahrdEQTcmymOKjHV4G6z3IPceHUZ+IroT9exD9jq2oPDwp8/FnufcgG9YS+1PuPUj6+TNcm/MRWdevofbxJaDvAMp8/CkXXh6EOSPD4eUA8PbMebRJTLJtJZOYbLR+VpCGtT1wc1Gx+W/HdxV5EEnJKZjN5jxdFby9PIm8cSOftf6Zv/cfJDUtjXatmjtke/91T+pghkXlsWiB4O/vT8eOHVm6dClLliyhY8eO+PnlHcDn4sWL9OnTh7CwMDw8PKzN6SMjcy+w3t7efPPNN9Zm+ePGjbvvd48ePZqhQ4fSunVrPvroI5suEQBqtZp69epZ/65YsSJeXl6cPXsWyGnKP2XKFNzc3KzLsGHDuHXrFunp6Rw/fpzU1FR8fX1t8oSHh1u/6+zZs9YKijsaNnyw/krPPvssu3btsukCcq/q1XNrOIOCci52MTEx1u9u3LixTf7GjRtby5efgvbb3bKyskhOTrZZDIVwE/+kca1WA//n+3Hzy7lcHPUSV6e9h3vdpyjW+0UANH7+BA0bybVZ0+wOqvhf5NqwJdk3rtoMuKgpEYZb844kfP/fGrPChkKBJS2F9D9+wBR1DcPZw2Tu3oi2VhObbOb4aJIXf0jKsplkHdmFa6f+KH0D89noo+dSuTq+3XoT9fVnRLzzCtc/noxb7Qb4du9X1KH9K66NWpN9PSLPgIvph3aTceIghpuRZBw/QMwX09GGlkNb3n5rn6Lm3KA5hpuR+Q7y9zh4EsoAT0457qhavRYff/YN02bNp2bt+sz5aCJJ91RkP9e9Dx9/9g3vfTAbpVLJZ7OnYbHY70de2GqE+NG5amkqBHhTt2QxZnV9Gm8XLb8csz3H65UMYMWgtix9oTWNSgfy9to9JKQ9eNfPQqdUYk5LIWX1Yow3I8g6uZ+07etwrp/bPSH5p4WAAr//fYr/1MW4NGpD1vG9UET7/l6u1WpS7PkXuDl/LhdfH0bE1Hdxr/cUxfq8aM2TcugASX/vJDPiCqlHDhI+cRwqVzc8m7RwWBwtGnqxakEV66JWPfyDYtumPhw6mUKCvnC7Sxal3zdvo0GdWvj5+hScWYh7PBYtECCnG8PIkSMB+OIL+w8SnTt3plSpUnz99dcEBwdjNpupWrUq2dm2o67+9ddfqFQqbt26RVpaGu7u7vl+76RJk+jbty8bNmzgjz/+YOLEiaxYsYKuXbs+UNypqalMnjyZbt265flMp9ORmppKUFBQntYUkPNW/2FNmDCB6tWr07dvXywWC88//3yePHd3LbjTjOnuQVb+jX+y3z788EMmT55sk9ZH4UM/leNGef6vMyUnYTGZUN/zhknt5Z3njcAdAS8MRr9tE4mbct5wZ10NR6nVETJyDDErv8e5bHk03j6UnfeVdR2FSoVrler4durKqa7PwEP+P9/LnJqCxWRC6W5ba6708LQ7aN/dFE5aXOo0JnnDSpt0bZmKKN08CJqS2ypBoVLh2W0Abi06EjXxVYfFDzljMljMJpQuHtw91JPC1d3uAGtATrrJZHNjZ4qPQunmCUoVmG9vyWzCnJgzArIp6hrqoFLo6rXIGWPBwYwpyTnHlOc9x5SnN8Z8BqH06z2ApL+2krRtIwBZkREotToCh48iftXyR37jarp9PKk8vGzSle5eD3Q8udZrTNJvBbeeMMVFY0pJQlMsiKzzJx8iYvvMafmcF26emFP0911X4aTFuWZDUv78xeFx/RNPQhng8SyHu4cnSqUqz4O9Xp+Al7f9m38vbx/095znSfrEPPl1OmeCgosTFFyc8hWrMHJYH7Zu2kC351+w5vHw9MLD04vgkBIUL1GK4QN6cOHcaSpUyjvbyT/h7eKESqHI82Afn56Jr6vzA21Do1JSIcCba3rbwW2dndSUdHKnpLc71UP8ePar9aw+cYUhDR3f19ucfvuYcrPtX69097Q70CuAOVmfc124+5oRczPnt06lApMJU0IM+q+ng8YJpc4Zc0oSHn1exZQQ4/AyWO9B7jk+1F7eGBLsXy8CX8y5B0n4M2ccjsyIcJQ6Z4q/NoaYFd/bvV6Y01LJunEdbXDBLcge1L6jyTZjEmhuv2n29lTbtELw9lBzObLgSqRivhpqVnHjg88KfzaY/Hh6uKNUKknU2x4/ifokfBzwbBAVE8uR4yeYPO6th96W+P/psWiBANCuXTuys7MxGAy0bZt3ALD4+HjOnz/Pu+++S6tWrahUqRKJiXmbHu3Zs4cZM2bw22+/4ebmZq2UuJ/y5cvz5ptvsmnTJrp162YzHoPRaLQZHPD8+fPo9XoqVcrpY1y7dm3Onz9P2bJl8yxKpZLatWsTFRWFWq3O8/mdVhaVKlVi/37bfu379tn2Mbuf9957j0mTJtGvXz9WrlxZ8Ap3qVSpErt32w6Wtnv3bipXzr0IazQaTHZG1b3ffrvb+PHjSUpKslmeV/7/qhG1GI1kXLqAa43auYkKBW41apOeT59VpVaX5wJtuVMhoFCQevwIF14dxMXXh1qX9Avn0O/YwsXXhzq88gAAkxHDtSvoKlSzKYe2fDWyw+/fb8+5VkMUajXpB/+ySU8/+BfRH44l+qO3rItJn0DKlnXEfTEtn609BLMJU1Qk6tC7+9gr0JSqgPGG/emzjNcvo/T2B3LffKh8AnIeSMz3GXFaoQBVIdXjGo1kXrmIa7WaNt/nUq0mGRfstyBSanV5jgvLnfiLoo+kyUh25GV0FXNbSaFQoKtYnewr+U9FCeBSpxEKtYa0/TsL/BqVly9KV3dMSYXUXNVkwnA93Ha6UoUCbbkqZF+9mP96gK5GAxRqNRmH/y6c2B7Uk1AGeCzLodFoCCtbnpN39U83m82cPHaECvmMkVO+YhVO3jPuzfGjBymfT/47LGYLBkP+U92ZzTnXHIMDWrVpVCoqBXqz/2puk3yzxcKBiGiqhzzYTFEms5lLsXr8CqhwsFgsGApr9H+TCePNCJzK2B5TTmUqY4i0P92s4eoFVL7FbH5XVX6BmJITcyqjbTJnY05JQqFzwalcVbLOOH48o5x7kPO43XsPUrMO6efsTzWs0Gqx3NtatIDrhVLnjFNQMIaE/Me4+KcyMs3cism2LpE3s0jQG6hZOXdcGxedkgplXDh3ueDBcts08SEp2ciB4/ZfGDwKGo2G8mXDOHI8t0LbbDZz5MRJqlR8+DEwNm7ZjpenJw3r1S448xNCoVIU2fIkemxaIKhUKmuzeXt9+b29vfH19eWrr74iKCiIyMjIPN0TUlJSePHFF3n99ddp3749xYsXp169enTu3JkePXrk2WZGRgZvvfUWPXr0oHTp0ly/fp2DBw/ajLWg0Wh47bXX+PTTT1Gr1YwcOZKnnnqK+vXrA/D+++/TqVMnSpYsSY8ePVAqlRw/fpxTp07xwQcf0Lp1axo2bEiXLl2YOXMm5cuX5+bNm2zYsIGuXbtSt25dRo0axcCBA6lbty6NGzfmhx9+4PTp04SFhT3w/pswYQIqlYp+/fphNpvp06fPA6331ltv8fzzz1OrVi1at27Nb7/9xqpVq9iyJXcWg9DQULZu3Urjxo3RarXodLoC99vdtFqtdfwH635VFE7dlsrVBdeyuf35XEoXx6NGRbITksi8VjhT6T2ouDU/U/zNcWRcvEDGhbP4PtcDpU5H4pact8HFR4/HEB9L9LJFACQf2INfl55kXLlI+vmzaINCCHhhMMkH9oLZjDkjg6yrETbfYc7KxJSSnCfdkVK2rcfnxVfJjrxsncZRqdWSti9n4E3vF0diSkoged1ym/VcG7Yk48RBzGm2b5LMaal50iwmI+bkRIwxNwulDJkHtuHaqT+mqKsYb15FV68FaLRkn8iZGcKl0wDMKXoyd64FIOvILnR1muHcpidZh3eg9C6GrlFbsg7tsG5T1+w5jFdOY05OACcdTpXroS5VjtQVn9sLwSES1v9K0KtvkXH5IpmXzuHdsRtKrY6k7X8CEDTyLYwJ8cQuXwxA6qF9eHfqRmb4ZTIvnUMTGIx/7wGkHt5nrVhQ6HQ4Beb2FdcUC0QbGoYpNQVjXN75pR9Wypbf8B34GtlXL5EVcRH3lp1ROmlJ3ZMzErXvwNcx6uNJWvODzXqujVqRfuxAnmNHodXh2fF50o/uw5SciNovEO9u/THGRpFx5iiFJe2v3/HqPQLDtSsYIi/j0rQ9CicdGQdyKjg8+7yMOSmBlN9tK3ld6jcn89Rhu9OHKpxdUXn7ofLIaWWiLpbTBS1nRhPHj3T+JJThcS1H567P8/mcDylTrgJly1diw9qfycrMoEWbnBHyP509DV9fP/oNHA5Ah2d7MHHc66xbtYI69Rry919buXLpPCNey3njmJmZwa8rv6Neg8Z4+/iSnJTExg2rSYiPo9HTOc3LL5w7w+WLZ6lYuTpu7u5E3brBiu++ITAohAqVHNPd54V6FXl/wz4qB/pQNciH5YcukGEw8ly1nPubd9fvo5i7M683qwHAwt2nqB7sSwlvd1Iys1l24By3ktPpWiMnf0a2kUV7T9OsbAh+bs7oM7L46chFYlIyaFOh8Kb+Tt+1EY+ewzDeCMdw7QoujZ9B4aQl43BOhbh7z5cwJyeS9ufPOXHu34Zzwza4dXqBjL2bUfkG4Nq8M+l7Nlm36VSuGijAGHsLlW8Abu17Y4q9RebhXYVShtjVP1Ni9HgyLp4n/ULOVNJKrY7EzX8AUGLMeAzxcUQtzRn/I+XAXvy69iTj8iXSz59BGxxCwItDSD6wx3q9CBryMsn795AdE43G15eAFwaB2Yx+x9ZCKcMdazbF0btzMW5EZRMdl82L3QKITzSw50hupcCHb5dmz+FkftuaW5mhUECbp73ZsjvR7jsWb0813p5qgovl3LOGFteRkWkmJt5AappjK6h6PteJj+Z+QfmyZazTOGZmZtGuVc75Of2Tz/D38WHYgJwuhgaDgavXcgaZNhqNxCXEc+lKOM46HSHBuWNxmM1mNm7dTtuWzfIdG02Igjw2FQgAHh75T7+iVCpZsWIFr7/+OlWrVqVChQp8+umnNG/e3Jpn1KhRuLq6Mn36dACqVavG9OnTGT58OA0bNiQkxLZJlUqlIj4+nv79+xMdHY2fnx/dunWzaW7v4uLCO++8Q9++fblx4wZNmjThm2++sX7etm1b1q9fz5QpU5gxYwYajYaKFSsydOhQIKfLwO+//86ECRMYNGgQsbGxBAYG0rRpUwICcgZs69WrF5cvX+btt98mMzOT7t278/LLL/Pnn3/+o/03btw4lEolL774IhaLheDgggeM6tKlC/PmzWPWrFmMGjWK0qVLs2TJEpv9Onv2bEaPHs3XX39NSEgIFy5cKHC/FRXPOlVpuDV3GsrKs/4HwLVvV3FiyPiiCguApF3bUXt6EvDCQNTePmReuUz4++9YB1bU+BezeTscs+I7sFgIeGEIGl8/jEl6Ug7sJeq7RUVVBAAyjuxB7+aBR8deqNy9MNyIIO6LadabaLWPX56WE+piwWjLViL286lFEXIehrOHyXBxQ9ekE0pXD0wx10n96XMs6TnTYCo9vG0G27OkJJKy8nNcWvVAO2QC5hQ9WQe3k7kv92ZQ6eqOS6cBKN08sGRlYoq5QeqKzzFGnCu0cqTs2YnKwxP/Xv1ReXmTFXGFa9MmWOf01vgVs/m/iPv1BywWC/59BqD28cOUnETqoX3E/pjbesg5rDwlJ8+y/h0wMGeAzqQdm7j1RW66o6Qf3o3S3QPPzn1QeXiRfT2cmM+mWo8nlY9fnrdg6oBgdOUqEzPPzm+O2YwmpBT+T7VA6eKCKSmRzDPH0K/7ERw0NZ09mcf2kezqgVvbHqg8vDDcuErC1x9Zu8WovHzzDOCo8g/CKawi8Qun292mrmodvHrnDpDq/eLrAKT8+Supm36VMjxB5WjctBXJSXpWfL8YfWICoWFlmTBllrVLQlxsNMq73vpWrFyNUW+9z4rvFrF82dcEhRTn7XenUTI050FbqVRy49pVdm7dSHJSEu4eHpQpV5GpMz+jRKmc2Wa0Oi379/zFyh+WkJWZibePDzXrNKB7r/5oNE4PXSaAtpVKkpieyZd/nyQ+LZMKxbz44vnm1qkZo5LTUN71Ai8lM5spGw8Sn5aJh86JSgHeLH2hNWX8PG+XS0FEQgq/rdmNPiMLT2cnqgT6srhfK8r4e9oLwSGyTu4n1c0d19bdULp7YrwViX7Jx1hsjqnc31pzUgL6JR/j3rEvzq9/gDk5kfQ9m0jfud6aR6Fzxq1tT5SePpjT08g6fZC0P3+5f6u2h5D013bUHl4EvDjo9j3IJcLff/uue5AALObcMkT/+B0Wi4XA/rn3IMkH9hC1LPceWOPnT8l33kPl4YExKYn00ye59OYrhT6V48+/x6LTKnl9UAhuLipOX0jjvdnhGAy58QcV0+LhbvsYVKuyGwF+Tmz6y363jQ4tfHmhS+5gyrP+lzPLxuxF19ji4AEXWzZpTFJSMkuXryQhUU+ZsFBmTJqAj7cXADGxcTbnfHxCIsPeeNv698rVv7Fy9W/UqFqZudNzr4WHj58kOjaO9q0LZzrQ/yrlE9oSoKgoLEU1Es4TYOnSpbzxxhvo9fqiDuWJs0HzYFO0/deVbFtwJc1/nXfokzEWhWuIf1GH8NCij9hvDvu4cfEtxCkTHyHNPaPHC/Ew4l7+pKhDcIgyfz3YNNP/dakX7XdZe5xEHbc/S8fj5p1inxZ1CA/tm6J9R+UwwRWqF5zpP+jvGkXXXePpRzGN+iP2WLVAEEIIIYQQQgghHpRCKS0QHOmxGURRCCGEEEIIIYQQRUcqEB7CwIEDpfuCEEIIIYQQQoj/F6QLgxBCCCGEEEKIJ5JCJe/MHUn2phBCCCGEEEIIIQokLRCEEEIIIYQQQjyRZBpHx5IWCEIIIYQQQgghhCiQVCAIIYQQQgghhBCiQNKFQQghhBBCCCHEE0mhlC4MjiQtEIQQQgghhBBCCFEgaYEghBBCCCGEEOKJJIMoOpa0QBBCCCGEEEIIIUSBpAJBCCGEEEIIIYQQBZIuDEIIIYQQQgghnkgK6cLgUNICQQghhBBCCCGEEAWSFgjiP6lk2+CiDsEhIv+8WdQhPDRtD21Rh+AQOh+Pog7hoaXcSi7qEMRdlEnpRR3CQ3PxdS/qEMRtFsuT8YbMlPb4nxcAmYmpRR3CQ8tIzCjqEBwiRZVY1CE8NJPSv6hD+H9NoZR35o4ke1MIIYQQQgghhBAFkhYIQgghhBBCCCGeSArlk9HC679CWiAIIYQQQgghhBCiQFKBIIQQQgghhBBCiAJJFwYhhBBCCCGEEE8kpUzj6FDSAkEIIYQQQgghhBAFkhYIQgghhBBCCCGeSDKIomNJCwQhhBBCCCGEEEIUSCoQhBBCCCGEEEIIUSDpwiCEEEIIIYQQ4omkUMo7c0eSvSmEEEIIIYQQQogCSQsEIYQQQgghhBBPJBlE0bGkBYIQQgghhBBCCCEK9P++AmHHjh0oFAr0ev0j/+7Q0FDmzp37yL9XCCGEEEIIIf4/UKoURbY8iRzehWHgwIEsW7YsZ+NqNcWLF6dnz55MmTIFnU7n6K97rB08eBBXV9dC235oaChvvPEGb7zxBgAWi4W33nqLr776inXr1tG8eXOHfE/z5s2pWbNmkVaG+HTsgn+3Xqi9fcgMv8zNhZ+SceFcvvl9n+2Ob4dn0fgHYExOInn3TqKWfY3FYMiT179HHwIHvkTc2l+49fUXhVmMB+LzdF3CxgzBs3ZVdMHFONT9FaLXbS3qsO7Ls01nfDr3QOXpTVbkFWKXzifz8oV883u174JX606o/fwxpSSTun8XcSuW2P3/KSy6+i1wbtwOpZsnxuhrpG1YjvFGeL75FTpnXFp1Q1u5NgpnV8z6eFL/WIHh4kkAvN+cgcrbL896Gfu3kbbhh0IrR7Fu3Qns8wIaHx/SL18i8pPZpJ09k2/+gJ698O/aDW1AAEZ9Egk7tnF94ZdYsrMBUDq7EDLsJbybNkPj7U36hQtEzvuEtHNnC60MXm074/tsT1RePmRdvUL04i/IvHQ+3/zeHbri1bYTGr9imJKTSdm3i9jl31iPH+dK1fB9tifasHJofHy5PnMSqQf3FFr8+XkczwsA16ZtcW/1LCoPLww3rpL482IMVy/Zzes/ahLaclXypGecOkL8gg/zpHv1Hobb08+g/2UJqTt+d3jsdzyOZdi4fhXrVv2IPjGBUqXLMHj4G5SrUDnf/Hv/3s6K7xcRGx1FYHBxXhg4gtr1GgJgNBpZ8d3XHDm0j5iom7i4ulKtRl36DRyBj2/e3ymDIZvxo4dzNfwSMz9dTOmwcg4rl6ZaI5xqN0Ph4o457haZf63BHH3Nbl51xbo4t+llk2YxGkj98n93bdAJbaMOqMOqoNC5Yk5OwHD8bwyn9jksZnvcmrXD45kuqDy8yL4eQeLKRWRH2D+mio2egq581TzpGScPE/vFNAB8BozErWFL289PHyX2s6mOD/4+Arr3IKhfPzQ+vqRfukjEnNmknbF/DVGoVAQPGIhf+w44+fuTERnJtfmfk7SvcPe9PUP6hdL5mUDcXdWcPJvMrPkXuX4rI9/8Py9qQFBA3ueUVRtuMGdBzv9jcKCOkYPLUK2yB04aJfuPJPDJwksk6gvnN3jt+t/5adUaEhL1lCkdysjhQ6lYobzdvBFXI1n6w49cvHSZ6JhYXh42mO7PdbbJs+yHFXz340qbtBLFQ1iy4PNCiV88uQplDIR27dqxZMkSDAYDhw8fZsCAASgUCmbMmFEYX/efYjKZUCgUKB9gtE9/f/9HEFEOk8nEsGHDWL9+Pdu3b6dOnTqP7LsLm2eTFgQNfZmbX3xC+vmz+D3Xg9JTZnJ+eH9MSfq8+Zu1InDgS1yfN5P0s6fQhpSg+BvvAHBr0XybvM7lKuDTrjMZ4ZcfRVEeiMrVheQT57m29Ffq/lL0FRoFcXuqKf4vDiPmm8/IvHQer/ZdCBk3jYgxQzElJ+XJ796oOX69BxO9cA4ZF87iFBRC4MtjwAKx33/1SGJ2qloP13a9SP3tO4zXr+DcsA0e/d8k8dMJWNJS8q6gUuExYAyWtBSSV36JOTkRpZcvlox0axb9wqlw1++CulgIngPHkn36UKGVw6dla0qMHMXVWTNIPXOagOd7U37OXE726YVRn5g3f5tnKD7iFcI/mkbqyZPoSpSg9IT3wALXPp8HQOlx/8M5LIwrUydjiIvDt207ys/9jFMv9MEQF+vwMrg3akaxAcOJ/upTMi6dw6djN0pMmM6VUUMwJevz5Pd4ugX+/YYQ9eVsMs6fQRNUnKBXxwIWYpYtBECp1ZF59Qr67X9S/K2JDo/5QTyO5wWAc+1GeHUdQOLKr8iOuIRbi474vzqBqCmjMKcm58kf9/UsFKrcWw2lqxsB42eRcXRvnry66vVxCi2PSZ8gZbjH7r+2smzR57z06hjKVqjMhrU/M+39McxbuBxPL+88+c+fPcncmZPpO+Al6tRvxN87tjBz2v+YOfcbSoaGkZWVyZXLF+jRewClSpclLTWFJV/NY8bUccyYuyjP9r5b/CU+Pn5cDbf/QPxvqcvVQNukM5nbf8UcFYmmZhNcnh1K2vczsWSk2V3HkpVB2vcf35Vgsflc+3Rn1MXLkrnpR8zJiahLlkfbvCvmtGRM4flXnj4MlzqN8e4xiITlC8mKuIBHy04Ue+19bk56DXNK3vM5bsFMUOceUypXdwLfnUP6EduKzIxTR4j/NvfhzmJ8tJWFPq1aU/L1UYTPnEHa6dME9upNxU/mcbz38xgT815Dig8fgV+7dlz58EMyr0bg2eApyn80g9MvDSP9Qv6Vo47Wr3sJenQKYdrcc9yKzmRov1DmTKnGC68cJNtgsbvOsNFH7r5EE1bKlbkf1GD73znXNZ1WySdTqnMpPJVRE04AMPSFUGa8V5XhY4/eexg+tO1//c2CRUsY9eoIKlUoz69rf2Pc+1NYsvBzvL288uTPzMoiKDCAZo0b8eWiJfluN7RkCWZOm2z9W6VUOTZw8f9CoXRh0Gq1BAYGUqJECbp06ULr1q3ZvHmzTZ7r16/Tp08ffHx8cHV1pW7duuzfvx+ASZMmUbNmTRYvXkzJkiVxc3PjlVdewWQyMXPmTAIDAylWrBjTpk2z2aZCoWDRokV07doVFxcXypUrx7p162zy/P7775QvXx5nZ2datGhBREREgeXR6/UMHz6cgIAAdDodVatWZf369QAsXboULy8v1q1bR+XKldFqtURGRtK8eXPrm/87unTpwsCBA61/392FwWKxMGnSJEqWLIlWqyU4OJjXX3/dmjcrK4uxY8cSEhKCq6srDRo0YMeOHQXGfmfdnj17smXLFnbt2mVTefAg+2znzp3Ur18frVZLUFAQ48aNw2g0AjktTnbu3Mm8efNQKBQoFAoiIiJITEykX79++Pv74+zsTLly5ViyJP8ftIfh16UniX9uIHHLRrKuXeXGF3MwZ2Xi06a93fyulaqQfvYUSTu3YoiJJvXoIfR/bcO5XEWbfEqdjhJjJ3D9s1mYUu08NBaR2D//4sLEuUSv3VLUoTwQ747dSN62keSdm8m+EUnMN59hyc7Co3lbu/mdy1cm88JpUvbswBgXTfrJIyTv2YGuTIVHFrNzo2fIPPwXWUd3Y4q9Repv32ExZKOr/bTd/LpaT6N0diV5+ecYIy9h1sdjjLiAKfq6NY8lPRVLarJ1capQA1N8NIaI/N+kP6yA3n2I/W0tcb9vIDMigqsfz8CcmYlfp05287tVrUbqyRMkbN5EdtQtkg8eIGHLZlwr57zlVDhp8W7WnGvzPyf1+DGyblzn5uJFZN24TrGu3QqlDD6dupO09Q+Sdmwi+3okUV/Nw5ydhWfLfI6fCpXJOH+a5L+3Y4iNJv3EYVJ2b0dXNvf4STt2kLgVS0k9sLtQYn4Qj+N5AeDeshNpe7aSvm8Hxqjr6Fd8hSU7G9d73pTeYUlPxZyity66itWxZGflefhWevrg1XMwCUvnYTEZpQz3WL9mJa3adqZFm46UKFmal14di5NWx7bNG+zm37DuF2rWqc9z3ftSvEQovV8cSliZ8mxcvwoAV1c33v/gExo1aUlI8ZKUr1iFISPe5Mql88TGRNts6+ihfZw4epAXh7zi0DIBONVsiuH0foxnD2FOjCFr+yosRgOayvXvu54lPSV3yUi1+UwVFIrh3GFMN65gSUnEcHo/5rhbqAJKODz+O9xbdyZ192bS9m7DeOs6CcsXYjZk4dbI/jFlTk/FnKy3LrpKNbBkZ5F+2LYCwWI02OSzpNuvVCksQX36ELNuLXEb1pMREU74zI8wZ2Xi36mz3fx+7dpzc9kykvbuIevmTWJWr0K/Zy9Bffo+0rh7PhvCtz9d5e/98VyOSOODT87h66OlyVN5W9fcoU82kKDPXRrV8+X6zQyOnsqpAKpW2ZPAYjqmzT3PlatpXLmaxrRPzlGxrDt1qns5vAy/rllHh7ZtaNemFaVKluCNV0eg1WrZuNl+i9OK5csxfPBAWjRrgkaT//thlUqFj7e3dfH09HB47P9FCqWiyJYnUaGPgXDq1Cn27NmDk5OTNS01NZVmzZpx48YN1q1bx/Hjx3n77bcxm83WPJcvX+aPP/5g48aN/Pjjj3zzzTd07NiR69evs3PnTmbMmMG7775rrXS4Y/LkyTz//POcOHGCDh060K9fPxISct4EXLt2jW7dutG5c2eOHTvG0KFDGTdu3H3jN5vNtG/fnt27d/P9999z5swZPvroI1Sq3Bq79PR0ZsyYwaJFizh9+jTFihX7x/vp119/5ZNPPmHhwoVcvHiRNWvWUK1aNevnI0eOZO/evaxYsYITJ07Qs2dP2rVrx8WLF++73dTUVDp27MiZM2fYvXs3FSrkvdm83z67ceMGHTp0oF69ehw/fpwvv/ySb775hg8++ACAefPm0bBhQ4YNG8atW7e4desWJUqU4L333uPMmTP88ccfnD17li+//BI/v/x/uP8thVqNc9nypB47nJtosZB67AguFfM2OQVIO3sa5zLlcS6fU2GgCQjCvW4DUg7ZHkvBL79BysF9pB0/4vC4/99QqdGVLkfaqaO5aRYLaaeO4lyukt1VMi6cQVu6HLoyOc30NMUCca1Zj7RjBx5FxKBSoQ4qheHyXU3yLRYMl8+gLl7G7ipOFWtiuHYZt0798Hl7Dl6vTsG5aQdQ5HPhUKnQVn+KzKN/F0IBcijUalzLVyD50MHcRIuF5EMHcatSze46qadO4lKhIq6VcioMtMHBeD7ViKS9OTe1CpUKhVqN+XZ3hjvMWVm4Va/h+EKo1ejCypF2wvb4ST9xFOfy+Rw/58+gCytnrTDQFAvEtVZ90o48ouPnQTyO5wWASo2mRBiZ50/kplksZJ4/gVNp+81q7+XaqBXpR/Zgyc7KTVQo8On/Gqlb12GMup7/yo7wGJbBYDBw5dIFqtfMrfxXKpVUr1mXC+dO213nwrlTVK9Z1yatRu36XDh3Kt/vSU9PQ6FQ4OrmZk3TJyaw4LOZvDbmXbRaB3dDVapQFgvBdO3u+xgLpmsXUQaWyn89jROuA/6H68AJ6DoOROkTYPOx6VYE6tKVUbjmPBipQsqg9PLDFFlIb8BVapxKliHz7D3H1NkTOIU9WAWfa+NWpB/62/aYAnTlqxIycwlBkz7Du89LKF3d8tmC4ynUalwrVCT54F2/MRYLSQcP4l7V/jVE4eSE+Z4ymLMyca9RCNeHfAQH6PDz0XLwWG4LibR0E2cuJFO14oM9LKvVCp5pEcCGLVHWNCe1EgtgMOQ+q2RnmzFboHplT4fFDznn/IVLl6ldM3e/KZVKateszplzD/fS4cbNW/TqP5gXhoxg+sefEB3j+JaD4slXKF0Y1q9fj5ubG0ajkaysLJRKJZ9/ntsEa/ny5cTGxnLw4EF8fHwAKFu2rM02zGYzixcvxt3dncqVK9OiRQvOnz/P77//jlKppEKFCsyYMYPt27fToEED63oDBw6kT58+AEyfPp1PP/2UAwcO0K5dO7788kvKlCnD7NmzAahQoQInT568b9eKLVu2cODAAc6ePUv58jk3F2FhYTZ5DAYD8+fPp8ZD/EBGRkYSGBhI69at0Wg0lCxZkvr161s/W7JkCZGRkQQHBwMwduxYNm7cyJIlS5g+fXq+2506dSru7u6cPXs23y4T99tn8+fPp0SJEnz++ecoFAoqVqzIzZs3eeedd3j//ffx9PTEyckJFxcXAgMDbcpTq1Yt6tbNuYEJDQ391/vmflQenihUqjzNsY36RLTFS9pdJ2nnVtQenoTN+DSn1YRaTfzva4n9ObcfumfTFjiXKcelN0cUStz/X6g8PFCoVHm6kpiS9DgF238blLJnByp3T0pMmg3k/P/oN68nYe1Ku/kdTenijkKlwpxm25TZnJaMxj/I/jre/mhKVyLrxD6SvpuHyrcYbp1eAKWajB3r8uR3qlgLhc6FrKOF1+9e7emFQq3GkGDblNqQkIiuVKjddRI2b0Lt6UXF+QtBoUCpVhOzehW3vssZ18ackU7qyRMEDxzMlYgIDIkJ+LZ+BrcqVcm84fgHP7V7zvFjTLrn/E5KxCXE/vGT/Pd2VO6elJo6hzvHT+Km34hfvcLh8f1bj+N5AaB0u31u3NMk25ychCYgpMD1NaXKogkuScIPX9qku7d5DsymQh3z4I7HsQwpyUmYzSY8vXxs0j29vLlx/arddfSJCXnye3n5oM+na0V2dhbfL/mSxk1b4+KSMzaTxWLhi7nTeab9c5QpV5GY6FsOKE0uhbMrCqUKc7ptCwJLeioqb/svYsz6WDK3/ow57hYKJx1OtZvh0uNV0n6YjSUt5/80a+cadC174Db4PSwmE2Ahc9svmG7mP4bNw1DdPqbu7VJlTtGjCSz4mHIKLYtTSCkSvrPtkph5+igZR/djjItG7R+IV5d+OL32HtEzxoPFnM/WHEftld81JAHnUvYreJL27yOwd1+Sj+a0UPOoWw/v5i1QPEC3Xkfx8c55YXnvuASJ+mzrZwVp+pQfbq5qft+aW4Fw+nwymZkmXh4YxsLvwlEAIwaEoVYp8PV5sO0+qKTkFMxmM95ethUT3l5eXLt+419vt1KFcrz15muUCAkhPiGR735cyZvvTGDRF/NwcXF+2LD/0x7lMfj/QaFUILRo0YIvv/yStLQ0PvnkE9RqNd27d7d+fuzYMWrVqmWtPLAnNDQUd3d3698BAQGoVCqbsQUCAgKIiYmxWa969erWf7u6uuLh4WHNc/bsWZvKBoCGDRvetyzHjh2jePHi1soDe5ycnGy+99/o2bMnc+fOJSwsjHbt2tGhQwc6d+6MWq3m5MmTmEymPDFkZWXh6+t73+0+88wzbNmyhenTp/PJJ5/YzVPQPmvYsCGKu96kNm7cmNTUVK5fv07JkvYf0l9++WW6d+/OkSNHeOaZZ+jSpQuNGjWymzcrK4usLNsa62yTGSdV4ZzsrtVq4P98P25+OZf082fRBocQNGwkxXq/SMyK79D4+RM0bCQR7731yAcnE+BcqTo+XXrdHijvHE4BwfgPGIFP174krF5e1OHZpVAoMKclk7puGVgsmG5dJd3DG5fGbe1WIOjqNMFw6STmFP2jD/Y+3GvVJvjFAVyd/TFpZ06jLV6ckqPeJChuELeW5XRBujJ1MqHjJ1Bz7XosRiNpF86TsGUzLhUqFrD1R8OlcnV8u/Um6uvPyLx0Dk1gCAGDXsbYPYH4XwtvsMrC9jieF/dybdiS7BtXbQYr1JQIw615R6JnvF2EkT24J6EM9zIajcz5aCJgYdirY6zpf/z2KxkZ6XTp+ULRBXcPc9RVzFG5lSYZURG49nsLTdWnyN7/JwCaGk+jCixJ+m+LsaToUYWURtesCxlpyfe0dvhvcG3UmuzrEXkGXEw/lNvFynAzkuwbVwn54Eu05auQdf7kow7zgVz9ZA6lx/2PGitW5rTCuHGDuA3r8c+n25wjtGlWjLdezb0/fnvKw++bjm0C2X84gfiE3NZ2+mQD7804w9iXy9GjcwhmC2z5K4bzl1IwF359jkPUr5vbiimsdCiVKpSn7+CX2Pn3bto/07oIIxOPm0KpQHB1dbW2KFi8eDE1atTgm2++YciQIQA4Oxdcy6XRaGz+VigUdtPM95y1D5Lnn3iQWJ2dnW0esCGnqZHlnhFVDPd5GC1RogTnz59ny5YtbN68mVdeeYWPP/6YnTt3kpqaikql4vDhwzZdJwDc3O7fnK1Vq1a89tprPPfcc5jNZubNm5cnj6P3GUD79u25evUqv//+O5s3b6ZVq1a8+uqrzJo1K0/eDz/8kMmTJ9ukjShXilfKly7we0zJSVhMJtT3DCKl9vLGmGj/TUvAC4PRb9tE4qacN0VZV8NRanWEjBxDzMrvcS5bHo23D2Xn5Q5MplCpcK1SHd9OXTnV9Rkem6tFETMlJ2MxmVB5etmkqzy9MNkZxA/A9/n+JO/aRvL2jQBkX4tAodMRMPR1Etb8mGfALEczp6dgMZlQuto2dVS6etgdDAvAnJpzHN4dmyn2Jkp3L1CpwGTK3Y6nL5qwyqSsKNwBMI1JeixGI5p7Kmo1Pt4Y4uPtrhMy9CXi/vyDuPU5lR4ZVy6j0jlT6u1x3Pp2KVgsZN28wfnXXkGp06FydcUQH0+ZyR+QdfPfvxXJtwwpOceP2vOe89vTG2M+b1L9eg8g6a+tJG3LOX6yIiNQanUEDh9F/KrlhX78PIjH8bwAMKfePjfcbd+KKT087Q5oeTeFkxaXOo1J3mDbYkJbpiJKNw+CpuS+0VeoVHh2G4Bbi45ETXzVYfHD41kGdw9PlEoVSfcc80n6RLy87b9E8PL2yZNfr0/A655WCTmVB+8TFxPFxOnzrK0PAE6dOMyFc6fp27WVzTrj3hhGk+ZtGDl6wsMUC0tGGhazCaWLG3dfURUubpjTH3DcIbMZU+wNlF6394NKjbZhOzJ+X4YpImcmJnP8LZR+wTjVakZGIVQgmG4fUyoPL5t0pbvXAx1TrvUak/RbwS2kTHHRmFKS0BQLeiQVCEZ9ftcQHwzx9n9/jXo9F8e9jcLJCbWnJ4bYWEq88iqZN24WWpx/H4jnzIXcwYidNDkvn7y9NMQn5lYAeHs5celKap717xXgr6VuDW8mfJi3e9DBo4n0eukAnh5qTCYLqWkm1n7bkJtRMXa29O95erijVCpJ1NvecyTq9Xh7eznse9zcXCkeEsyNm45tXSSefIXenkOpVPK///2Pd999l4yMnOlTqlevzrFjx6z97B+VSpUqceCAbX/RfQVMLVO9enWuX7/OhX84eqy/vz+3buWekCaTiVOn8u97CDkVEZ07d+bTTz9lx44d7N27l5MnT1KrVi1MJhMxMTGULVvWZrm720B+nnnmGX777Te+/vprm4EZH0SlSpXYu3evTWXI7t27cXd3p3jx4kBOCwzTXQ9Id/j7+zNgwAC+//575s6dy1df2R8pfPz48SQlJdksQ8vcp//jXSxGIxmXLuBao3ZuokKBW43apOfTN1Sp1eW52bbcqRBQKEg9foQLrw7i4utDrUv6hXPod2zh4utDpfLgnzAZyQy/iEvVmrlpCgUuVWqScdH+tH9KJ23e5pnWff4IBqMxmTDeuoom7K6+6AoFmrBKGK/bn43DEHkJlU8xmzEPVL6BOTeP95wbutqNsaQlk33hBIXpTusAjzr1chMVCjzq1CP1tP2bT6XO3rlhsq57N3NmJob4eFTu7njUb4D+778cGj8ARiOZVy7iWq1mbppCgUu1mmRcyOf40erynKP5laHIPI7nBYDJiOHaFXQV7ur/rFCgLV+N7PD7XyOdazVEoVaTftD2OEk/+BfRH44l+qO3rItJn0DKlnXEfTEtn639/yqDRqMhrGx5Th7PHevHbDZz8vhhyucz1k/5ilU5effYQMCJo4coXzF36sA7lQdRN6/z3rRPcPewrVQZ9NIbzPp0CR9/upiPP13M/ybNBODNdybRp/+why4XZhPmmBuoit/dhVWBqkRZm1YG96VQoPQLyp0dR6nKmTHj3go1i6Xwzn+TkezIy+gq3tUSVaFAV7E62Vfu31/dpU4jFGoNaft3Fvg1Ki9flK7umJLsVzI6msVoJO38OTzq2l5DPOvWI+XU/SswLNnZGGJjUahU+LRoQeKuQrg+3JaRYeLGrUzrEh6ZTlxCFnVr5FY8uzirqFzeg1Pn8s6ycq+OrQNJTMpm70H7Fe0ASclGUtNM1K7uhbenhr8P5J/339BoNJQvW4Yjx3PvE8xmM0ePn6RyRccNnJuRkcGtW1H4+uSdyeVJI4MoOlahtEC4V8+ePXnrrbf44osvGDt2LH369GH69Ol06dKFDz/8kKCgII4ePUpwcHCBXQoexogRI5g9ezZvvfUWQ4cO5fDhwyxduvS+6zRr1oymTZvSvXt35syZQ9myZTl37hwKhYJ27drlu17Lli0ZPXo0GzZsoEyZMsyZMwe9Xp9v/qVLl2IymWjQoAEuLi58//33ODs7U6pUKXx9fenXrx/9+/dn9uzZ1KpVi9jYWLZu3Ur16tXp2LFjgWVv3bo169evp3PnzpjNZpsxKe7nlVdeYe7cubz22muMHDmS8+fPM3HiREaPHm3tThIaGsr+/fuJiIjAzc0NHx8fJk2aRJ06dahSpQpZWVmsX7+eSpXsDw6m1WrRarU2af+k+0Lcmp8p/uY4Mi5eIOPCWXyf64FSpyNxS86buuKjx2OIjyV6Wc7UVMkH9uDXpScZVy7mdGEICiHghcEkH9gLZjPmjAyyrkbYfIc5KxNTSnKe9KKgcnXBtWxu1xGX0sXxqFGR7IQkMq/992qREzesIvDlsWRduXh7urquKLU6knduAiDw5bEYE+OJW5HTRD7tyH68OnQlK+IyGZfO4RQYjG/P/qQd2f9I+n0CZOzZhHvXIRhvRmC8Ho6uYWsUTloyj+Q0KXXrNgRzciLpW3JGNM88sB1d/Za4tu9Dxv6tqHwDcGnagYx994yWrFCgrfU0mcf2PJKKqOgVP1J6wnuknTtL2tkzBDzfC6WzjrgNOSO3l373fQyxsVxfmPPmVL/7bwJ79SH9wnlSz5xGF1KCkKEvkbT7b2u8HvUbgEJBZuRVdCElKPHqSDIjrxK3YX2hlCFh/a8EvfoWGZcvknnpHN4du6HU6kjantNcOWjkWxgT4oldvhiA1EP78O7Ujczwy7e7MATj33sAqYf3Wcug0OlwCgy2foemWCDa0DBMqSkYC2EqSnsex/MCIGXbenxefJXsyMvWKRCVWi1p+7YD4P3iSExJCSSvs+1W4dqwJRknDmJOs30DaE5LzZNmMRkxJydijCmct5aPYxk6denFF59Mp0y5ipQtX4kNa38mKzODFq07APDZ7A/w8fWj38CccXs6PtuDieNe47dVK6hdryG7/9rK5UvnGD7yLSCn8mD2h+8RfvkC496fgdlsJjEx5yHIzc0DjUaDfzHbwQl1t1tkBgSF4Ov3zweLtif72F/oWvfCFHMdc/Q1NDWboFA7YTiTM/irrk1vzKlJZO/9AwCneq0xRUViTopDoXXGqXYzlO7eZJ6+PQiyIQvj9ctoG3ciy2jAnJKIKrgMmop1yNr1m0Nitidly2/4DnyN7KuXyIq4iHvLziidtKTu2QaA78DXMerjSVpj243KtVEr0o8dyHP8KLQ6PDs+T/rRfZiSE1H7BeLdrT/G2CgyzhzlUbn144+Uee990s6dJfX0GQJ790ap0xF7eyaysPcnYoiN5dqXOVNgu1augpO/P+kXL+DkX4yQoUNBoeTW9989spgBfl53gwG9SnLtZkbONI4vhBKfkMWufXHWPHM/qM5fe+NYtSH3HFUooEPrQDZui8Zk52e1Q6sArl5PJzHJQNWKHowaVpaf1l7n2o0Mh5ehe5dnmfnJp1QoV4YK5cuxau16MjMzadc6p0XQR7Pn4efrw9CBLwI5rZyvXssZi8hoNBIXH8+lK+E463SEBOeM37Twm6U8Vb8uAcWKEZ+QwLIfVqBUKmnRrInD4xdPtkdSgaBWqxk5ciQzZ87k5ZdfxtXVlU2bNjFmzBg6dOiA0WikcuXKfPFF4TbpLVmyJL/++itvvvkmn332GfXr12f69OkMHjz4vuv9+uuv1oqPtLQ0ypYty0cffXTfdQYPHszx48fp378/arWaN998kxYtWuSb38vLi48++ojRo0djMpmoVq0av/32m3WMgyVLlvDBBx8wZswYbty4gZ+fH0899RSd/kG/spYtW7JhwwY6deqExWJ5oEqEkJAQfv/9d9566y1q1KiBj48PQ4YM4d1337XmGTt2LAMGDKBy5cpkZGQQHh6Ok5MT48ePJyIiAmdnZ5o0acKKFYUziFnSru2oPT0JeGEgam8fMq9cJvz9d6wDK2r8i9k8rMWs+A4sFgJeGILG1w9jkp6UA3uJ+i7v3Nf/RZ51qtJwa+7FuPKs/wFw7dtVnBgyvqjCylfqvr+I8/DEt8eLqLy8ybp6hRsfvWsdQE7tV8ymhUv86uVYsOD7/ADUPr6YkpNIO7KfuJVLH1nM2acOkubijkvLLijdPDBGXSP5u0+w3B5YUeXpY/OGy5ycSPJ3n+Darhfer0zGnJJIxr4tZOz6w2a7mrDKqLx8yTxSeLMv3C1h2xbUXl6EDB2GxseX9EsXuTDmTWv3HqeAQDDnluPmsiVgsRAybDhO/v4Y9Hr0u//mxlcLrHlUbm4UH/4yTv7FMCYnk7hzOze+WnB7sDLHS9mzE5WHJ/69+uccPxFXuDZtgvX40fgVs/m/iPv1BywWC/59BqD28cOUnETqoX3E/pg7jaxzWHlKTs7tThVw+6Eraccmbn2Rt5tVYXgczwuAjCN70Lt54NGxFyp3Lww3Ioj7Ypq1e4/axy/P2191sWC0ZSsR+/nURxprfh7HMjRu2orkJD0rv/8GfWICoWFlmTBlFl7eOc3L42Kjbd50VahUjVFvTeTH775m+bdfERRcnLcnTKdkaM4g0AnxsRzan/M79Nbrg2y+a9L0T6lSvdYjKZfx4nGynF3RNmiLwtUdc+xN0tctsk7NqHDzQnnX/4VC64yuZQ8Uru5YMjMwx14n/efPMSfmNiHP/PMHtA3bo3umLwqdC+aURLL2bsRwam+e73eU9MO7Ubp74Nm5DyoPL7KvhxPz2VTrMaXy8cNyT0WfOiAYXbnKxMybnHeDZjOakFL4P9UCpYsLpqREMs8cQ7/uRzAW7jSnd0vYugWNtxfFh76ExteX9IsXOPfmG9ZriDYgwOb+Sql1osTwEWiDgzFlZKDfu4fLkydhSi2464Aj/fDrNXQ6FW+PLI+bq5qTZ5IYM/Ek2YbcYykk0BkvD9suvHVrehNYTMeGzVH3bhKAksVdGD4gDA83NVExmXz7UyQr1xbOzDEtmj5NUlIyS79fQWJiImXCSvPhlPetXRhiYmNR3nXOxyckMuL10da/f161lp9XraV61SrM+Shn5rTYuHimfzyH5OQUPD09qVq5Ep/N/ggvT8fOIvFf9Li1BPjiiy/4+OOPiYqKokaNGtbn1vzMnTuXL7/8ksjISPz8/OjRowcffvghOp2DZ8+5TWG5t6O+EP8BJzvlX9nyOIn8s/D6/T0q5XoUPBbF48CnXMGjYf/XhW+z3y3nceMe9GTMO61UP/6jOrv4uhecSTwSCaMK9yXKo1J648dFHYJDJJ6232XtcXLruOPHpikKY/xmFnUID+3HOfZnQnvclChXuahD+FfO92pbZN9dYeWf/yj/ypUr6d+/PwsWLKBBgwbMnTuXn3/+mfPnz1OsWN7WX8uXL2fw4MEsXryYRo0aceHCBQYOHEjv3r2ZM2eOo4ph4/G/+xFCCCGEEEIIIex4nMZAmDNnDsOGDWPQoEFUrlyZBQsW4OLiwuLFi+3m37NnD40bN6Zv376EhobyzDPP0KdPnzzj/jmSVCAIIYQQQgghhBAOlpWVRXJyss1y7/T1d2RnZ3P48GFat86dVlOpVNK6dWv27rXfDatRo0YcPnzYWmFw5coVfv/9dzp06OD4wtyJqdC2LIQQQgghhBBC/D/14Ycf4unpabN8+OGHdvPGxcVhMpkICLAdxDYgIICoKPtjc/Tt25cpU6bw9NNPo9FoKFOmDM2bN+d///ufw8tyh1QgCCGEEEIIIYR4IimUyiJb7E1XP3684wY937FjB9OnT2f+/PkcOXKEVatWsWHDBqZOLbxBfx/JLAxCCCGEEEIIIcT/J/amq8+Pn58fKpWK6Ohom/To6GgCAwPtrvPee+/x4osvMnToUACqVatGWloaL730EhMmTECpdHx7AWmBIIQQQgghhBDiiaRUKYps+SecnJyoU6cOW7dutaaZzWa2bt1Kw4YN7a6Tnp6ep5JApVIBUFiTLUoLBCGEEEIIIYQQooiNHj2aAQMGULduXerXr8/cuXNJS0tj0KBBAPTv35+QkBDrOAqdO3dmzpw51KpViwYNGnDp0iXee+89OnfubK1IcDSpQBBCCCGEEEIIIYpYr169iI2N5f333ycqKoqaNWuyceNG68CKkZGRNi0O3n33XRQKBe+++y43btzA39+fzp07M23atEKLUSoQhBBCCCGEEEI8kRTKf9aVoKiNHDmSkSNH2v1sx44dNn+r1WomTpzIxIkTH0FkOWQMBCGEEEIIIYQQQhRIWiAIIYQQQgghhHgiKQphJoL/z2RvCiGEEEIIIYQQokDSAkEIIYQQQgghxBPpcRsD4b9OWiAIIYQQQgghhBCiQFKBIIQQQgghhBBCiAJJFwbxn+Qd6lfUITiEtoe2qEN4aBd/CS/qEByi1bctijqEh1YsKr6oQ3AIJ3eXog7BIdJjk4o6hIfmGuJf1CE4hMVkKuoQHppeYS7qEBzD/GSUw71EQFGH8NCMmdlFHYJDeCh9ijqEh+adcLmoQ3CQykUdwL8iXRgcS1ogCCGEEEIIIYQQokDSAkEIIYQQQgghxBNJpnF0LNmbQgghhBBCCCGEKJBUIAghhBBCCCGEEKJA0oVBCCGEEEIIIcQTSQZRdCxpgSCEEEIIIYQQQogCSQsEIYQQQgghhBBPJBlE0bFkbwohhBBCCCGEEKJA0gJBCCGEEEIIIcSTSSFjIDiStEAQQgghhBBCCCFEgaQCQQghhBBCCCGEEAWSLgxCCCGEEEIIIZ5IMo2jY0kLBCGEEEIIIYQQQhRIKhDyERoayty5c61/KxQK1qxZU2TxFLZJkyZRs2ZN698DBw6kS5cuRRaPEEIIIYQQQjwshVJZZMuT6LHuwhAbG8v777/Phg0biI6Oxtvbmxo1avD+++/TuHFjIOfBf/Xq1YX+MDxw4ED0er1NJcMvv/zCCy+8wLRp0xgzZoxDvmfSpEmsWbOGY8eOOWR7+Zk3bx4Wi6XAfI9q/xbEtWlb3Fs9i8rDC8ONqyT+vBjD1Ut28/qPmoS2XJU86RmnjhC/4MM86V69h+H29DPof1lC6o7fHR77/Xi26YxP5x6oPL3JirxC7NL5ZF6+kG9+r/Zd8GrdCbWfP6aUZFL37yJuxRIsBsMjjPrB+Dxdl7AxQ/CsXRVdcDEOdX+F6HVbizosqxUHzrBs9yniUjMoH+jNuPYNqVbcv8D1/jh5hXG/7qBFhZLM7dPamh6fmsHczQfZe/kGKZnZ1C4VyLgOT1HK17Mwi4Fb8/Z4PtMVlacX2dcjSPjxa7IjLtrNGzDmA3QVquZJTz95iNjPPrD+rQ4sjnf3/ujKVwGlCsOta8QumIEpIa5QyuDSuA2uzTuhcvfEcDOS5NXLMFy7bDevz8vvoi1bOU965pmjJH7zMQC6avVwadgKTfHSKF3diZ09HuPNq4US+908WnXEq0N3VJ7eZF8LJ+67BWRdsX8+B4//EOdK1fOkpx07SNScSQCoPLzw6TUIl6q1ULq4knn+NHHfLcAQfbPQyqCt3RRtgzYo3TwwxVwnfdNPmG7lv+8UWmd0zZ7FqUJNFDoXzMkJpG/5BePl0wA41WqCtnZTVJ4+AJjibpHx9+8Yr5wptDIAaOs0R9ewDUo3T0zR10n7cwWmmxH3LYdziy44VaiFwtkFc1IC6Zt+wnD5FADOTTvh3LSzzTqmuCiSFkx0WMx/rF/N2l9XoE9MILR0GYaMGEW5CpXyzb9n13Z+/H4xsdFRBAWH8MKgEdSp95TdvAs/n82mP9YxaNhIOnXpCUBM9C1+/vFbTp04gj4xAW8fP5q2aEP3Xi+i0WgcVi5N9cY41WmOwsUdc9xNMnesxhx9zW5edaV6OD/T2ybNYjSQ+sU4698KFze0jTuhKlkehdYZ040rZO5cjUVfOL9Pd2jrNEP71O1zI/o66ZtWYrrP74pC64yu+XM4Vbx9biQlkL75Z+u5YbPths/g0rIrmQe2kbH550Irg0erjni275bzGxUZTvz3C8kKt/8bFTTuQ5wrVsuTnn78IFGfTAZAodXh03MgrrWfQunmjjE2mqQtv5Gy/Y9CK8P9vNClGO2a+uDqouLMpXS++PYGN2Oy882/ZGYFAvyc8qSv3xbP/O8L73c2Pz9t2c23v+8gPimFciWCePvFrlQtU9Ju3m0HT7L4t61ci4nDaDRRMtCfF9o3o2PjOo84avEkeqwrELp37052djbLli0jLCyM6Ohotm7dSnx8fFGHxqJFi3j11VdZsGABgwYNKupw/jFPz/s/1GRnZ+PklPdHtSg4126EV9cBJK78iuyIS7i16Ij/qxOImjIKc2pynvxxX89Coco99JWubgSMn0XG0b158uqq18cptDwmfUKhlsEet6ea4v/iMGK++YzMS+fxat+FkHHTiBgzFFNyUp787o2a49d7MNEL55Bx4SxOQSEEvjwGLBD7/VePPP6CqFxdSD5xnmtLf6XuL18UdTg2Np66wqw/D/Bup0ZUC/Hnh32nefn7P1k7sju+bs75rncjMYU5mw5Qu2SATbrFYuGNFVtQK5XM7dMaN60T3+49xfBvN7Lq1W64ODnuZvxuLnUb49NzMPE/fEl2+AXcWz1LsVETufn+q5hT8h5DsV9+BOrcc0Pl6k7Q+3NJP7THmqb2DyTw7emk7t6Kft2PWDIz0ASXKLRKKl3Np/B49gWSflmMIfISrk3a4/PSOGJnjLF7ficu/QTFXWVQurjhN+YjMk/st6YpnLRkh58n4/g+vJ5/qVDivpdrgyb49R1G7NLPybx8Hq+2XQh6ayrX3n4Jk53/i6hPp6FQ5x4XSjd3SnzwOWkH/ramBb7xLhajiai5UzFnpOPZritB70zj2rgRWLKzHF4GTaU6OLfqTvrGHzHejEBXryVuvV4j+atJWNJT866gVOHW53UsaSmkrvoaS6oepYcvlqx0axZLip6MHWswJ8SAQoFT1adw6zGC5MUfYo675fAyADhVrotLmx6k/bEc441wdPVb4d7ndZK+nIglPcVuOdz7vYE5LYXUXxdiTtGj9PTBkplhk80Yc4OUH+bmJphNDot591/bWPr1FwwfOZpyFSqzfs3PTH1vLJ999T2eXt558p87c4pPZk6l38Bh1K3XkF07tzLzgwl8PO9rSoaG2eTdv+cvLpw7g4+vn036jWuRWCxmho8cS2BQCNeuhvPlZx+TlZnJgKGvOKRc6nI10TZ5lsztv2COikRTswkuXV4i7dsZWDLsHFOAJSuDtG9n3J1i87lzp0FYzCYy1i/BkpWJU+1muHQdTtp3H4Mx/4fFh6GpVAfn1t1J/+NHjDfD0dVviVvv10leMCnfY8qt7+tY0lNI/fUrLCl6lJ6+WDLT82RVBZVCW7sJxujrhRL7Ha71m+Dbeyixy74g68p5PJ95jsCxU7g2brjd60X0Z9Nsf2tdPSg+9TNSD+b+Rvn2GYpzperEfDUbY1w0zlVq4df/FUyJ8aQfO1Co5blXj/Z+PNvajzmLrhEVZ+DFrgFMHVOaERMuYDDaf2E2auolVHdN/1equJbpY8PYdTDv/ihsm/YdY87ydfxvYHeqlinJ8j93MfLjr1k18218PNzz5Pdwc2bws60oHVQMtVrFrmNnmfz1Srzd3WhUvcIjj188WR7bdhV6vZ5du3YxY8YMWrRoQalSpahfvz7jx4/n2WefBXK6IQB07doVhUJh/fvy5cs899xzBAQE4ObmRr169diyZYvDYps5cyavvfYaK1assKk8aN68Oa+//jpvv/02Pj4+BAYGMmnSJJt1IyMjee6553Bzc8PDw4Pnn3+e6OhoAJYuXcrkyZM5fvw4CoUChULB0qVLsVgsTJo0iZIlS6LVagkODub111+/b4wfffQRAQEBuLu7M2TIEDIzM20+v7cLQ/PmzRk5ciRvvPEGfn5+tG3b1u7+jYiIQKlUcujQIZvtzZ07l1KlSmE2m//h3iyYe8tOpO3ZSvq+HRijrqNf8RWW7GxcG7a0m9+Snoo5RW9ddBWrY8nOylOBoPT0wavnYBKWzsNiMjo87oJ4d+xG8raNJO/cTPaNSGK++QxLdhYezdvaze9cvjKZF06TsmcHxrho0k8eIXnPDnRl/psXitg//+LCxLlEr3Xcueco3+09RbfaFehSqzxlinnzbqfG6DRq1hzNv/WHyWzmf6t28nKL2hT3tr2YX41P5sT1WCZ0akTVEH9C/Tx5t2MjMg0mNp68Umjl8GjzHCl/byJtzzYMt66T8MOXWLKzcGvcym5+c3oq5mS9ddFVroklO4v0w7uteby69CPj1BH0vy7DcC0cY2wUGccP2r3BdATXph1I37edjIM7MUbfIOnXb7AYsnCu38xufktGGuaUJOviVL4aFkMWmcdzKxAyDv9N6ubVZF84VSgx2+PVrivJOzaSsmsLhpvXiF36OZasTNybPWM3vzktFVNSonVxqVoLS3YWqQd2AaAJDEZXtlLOzX74RQxRN4hb9gVKJyfcGtrfNw9LV78lWcd3k31yH+b4KNI3/gjGbJyqN7Kb36lGIxQ6F1J/XYDpxhXMSQkYr13EFHPDmsdw6STGy6cxJ8ZiTogh8691WLKzUAeXLpQyAOgatCbr6N9kH9+DOe4W6b//AIZstDXtl0NbszEKZ1dSf56P8fplzEnxGCMvYoq554HObMaSlpy7ZKQ5LObfVv9E63adaNmmAyVKhjJ85Bi0Oh1bN9lvFbdh3S/UqlOfLt37ULxkKH1eHELpMuX5Y/1qm3zxcbEsWvApo956F5XK9p1SrboNGPnmeGrWrkdgUDD1nmrMs916sW/PXw4rl1PtphhO78N45iDmhGiytv2KxWhAU6X+fdezpKfcteRWNCi8/FAFhZK17VfM0dew6GPJ2vYrqDVoKtRyWNz30jVoRdax3WSf2Is5Lor032+fGzUa2s3vVLPR7WNqAabrt8+NSNtzAwCNFtfnBpG+4Qe7lQuO5Nm2C8k7/yT175zfqLhlX2DJzsK9aRu7+XN+o/TWxblqzvXi7kpOXdlKpOzeRua5kxjjYkjZ+SfZ18LRhpUv1LLY06WNHyt+i2HfsRQirmcye9E1fL3UNKztke86ySkmEpON1qV+DQ9uRmdx8rzjzu0H9f3GnXRt3oBnm9YnLCSQ/w3sjk6rYe3Og3bz161UlpZ1q1E6JIASAX70bduEsiWCOHYh/BFH/t+gUCqKbHkSPbYVCG5ubri5ubFmzRqysuy/aTl4MOekWrJkCbdu3bL+nZqaSocOHdi6dStHjx6lXbt2dO7cmcjIyIeO65133mHq1KmsX7+erl275vl82bJluLq6sn//fmbOnMmUKVPYvHkzAGazmeeee46EhAR27tzJ5s2buXLlCr169QKgV69ejBkzhipVqnDr1i1u3bpFr169+PXXX/nkk09YuHAhFy9eZM2aNVSrlrdZ2R0//fQTkyZNYvr06Rw6dIigoCDmz59fYNmWLVuGk5MTu3fvZsGCBXb3b2hoKK1bt2bJkiU26y5ZsoSBAweidHRfIJUaTYkwMs+fyE2zWMg8fwKn0g92gXJt1Ir0I3ts39gpFPj0f43UreswRhVurb9dKjW60uVIO3U0N81iIe3UUZzL2W+ymnHhDNrS5dCVySm3plggrjXrkfaIa/kfdwajibM343kqLNiaplQqeCosmBPXY/Ndb+HOY3i76uhWO+9xZzDlvInUqlU223RSqzgaGe3A6O+iUuNUsgyZZ+85N84eRxv2YJVKbk+3Ju3g37nnhkKBc7W6GKNvUmzURIrPWkrg+Jk412xQCAUAVCo0xUuTdfGuB32LhawLp3AqVe6BNuHSoDmZR/cVyhv5B6ZSow0tS/rpY7lpFgsZZ46hK1vxgTbh3vQZUvf9ZS3HndYJFsNdb1QtFiwGQ07XEkdTqlAFlsQYfv6uRAuGiHOoQ+w/7DuVq4bxRjguz/TG8/WP8Bj6LrqGbUGRzw2VQoGmUh0UGieMNwqpYk2pQhVUEkP42bsS75QjzO4qmvLVMV6/gku7vni98TEeL72PrnH7POVQ+RTDa9QMPF/9ANcug1F65G0Z8G8YDAYuX7pA9Zq5TY+VSiXVa9bhwrm8zd0BLpw7bZMfoGbtepy/K7/ZbObT2dN4rntvSpZ6sAqb9LQ03N3zf+D6R5QqlMWKY4q8u0uVBVPkBZSBpfJfT+OE66AJuA5+D12nQSh9clt83WldaFvpbwGTCVVhVUrdPqaM4edsvtMQfg51cfvHlFO5O8dUbzxHzcBj2HvoGrXLc0y5tOuN4dIpjBHn7G7HYW7/RmWcOZabZrGQcfoYujIP9hvl0eQZUvf/ZfNbm3npLC4166Py8gVAV7EamoBgMu6+t3kEAv01+HhpOHYmt7IpPcPM+SvpVCrj8kDbUKsUtHjKi01/JxZWmPkyGI2ci7hB/Sq59xdKpZL6lctx8lLB3e8sFgsHTl/k6q0Yale0f0wK8U88tl0Y1Go1S5cuZdiwYSxYsIDatWvTrFkzevfuTfXqOf1G/f1z+it7eXkRGBhoXbdGjRrUqFHD+vfUqVNZvXo169atY+TIkf86pj/++IO1a9eydetWWra0//a7evXqTJyY0yeyXLlyfP7552zdupU2bdqwdetWTp48SXh4OCVKlADg22+/pUqVKhw8eJB69erh5uaGWq22KU9kZCSBgYG0bt0ajUZDyZIlqV8//9r7uXPnMmTIEIYMGQLABx98wJYtW/K0QrhXuXLlmDlzZp70e/fv0KFDGTFiBHPmzEGr1XLkyBFOnjzJ2rVr77v9f0Pp5o5Cpcrz9tOcnIQmIKTA9TWlyqIJLknCD1/apLu3eQ7Mpkc+5sEdKg8PFCoVpiS9TbopSY9TcAm766Ts2YHK3ZMSk2YDChRqNfrN60lYu7LwA36CJKZnYbJY8nRV8HV1JjxOb3edI1ejWH3kAj+N6GL381A/L4I8Xfl0yyHe69wYZ42a7/adJjo5jdjUDLvrPCzV7XPDlGwbsyklCU1Q8QLXdwoth1NIKeKXfW5NU7p7otQ549GuG/q1P5D467c4V62F/4h3iJ7zHlkX7D/M/FtK13zO79Qk1MWC81krl6ZEGTRBJUla+bVD4/qnVO4edv8vjEl6nIPsn89304aVR1silNhv5lnTsm9dxxAXg2/PgcQu+RxzViZe7bqg9vVHbadJ+8NSuLihUKowp9t2G7GkpaDyDbC7jtLLD3UpX7JPHyT1py9QehfDpW0vUKnI/Dv3t1XpH4xH/7Gg1uS0slj1Feb4KIeX4e5yWNJsm5WbU5PR+AbaXUfl5Y8y1JfsU/tJWfEZKp9iuLTrA0oVmbvWA2C8EU7qb0sxx0ejdPNE16QT7gPeImnhZHjIyquU5CTMZhNe9/y/enp5c+Oa/Rcf+sSEPF0bvLy80Sfmdsdb88tyVCoVHZ/t/kBx3Lp5nT9+W0X/IS//wxLYp3B2vX1M2f5fWNJTUfkUs7uOOTGGzM0rMcfdQqHV4VS7OS7Pv0ba9x9jSU3CnBiDOTkBbaMOZG77BQzZONVqitLdC7Orgyo+7i3HnXMj7d5zI/n+50ZoBbJPHSB15Rcovf1xadc759zYtQEATeW6qANLkLz4o0KJ+27W36h77zmS9Q90vdCWLo9TiVBiF39qkx73/QL8B75GqbnLsBiNYLEQu+QzMh18rSiIt0dOhWtism1rUn2yEW/PB3sUaljbAzcXFVt2P/oKBH1KGiazGV8PN5t0X093Im7F5LteSnoG7UdNJdtoRKVUMq5/N56q+uhbf/wXPKmDGRaVx3pvdu/enZs3b7Ju3TratWvHjh07qF27NkuXLr3veqmpqYwdO5ZKlSrh5eWFm5sbZ8+efegWCNWrVyc0NJSJEyeSmmq/796dyo07goKCiInJOfnPnj1LiRIlrJUHAJUrV8bLy4uzZ8+Sn549e5KRkUFYWBjDhg1j9erVGI35N7k/e/YsDRrYvjFs2NB+M7u71anzYAOvdOnSBZVKxerVOU0lly5dSosWLaxdHu6VlZVFcnKyzZJlclzf0ftxbdiS7BtXbQZc1JQIw615RxK+/2/1yy+Ic6Xq+HTpRfTiL7j6v5HcnD0F11r18enat6hDe6KlZRmYsPovJj7bGG9Xnd08GpWSOb1acTU+mSYzfqDBtG85GH6Lp8sW57/aus3t6dZkX4+wGXBRcfvtWMaxA6Rs+Q3D9XCSN64i4+Qh3Jva71pTlJwbNMdwMzLfARcfF+5NnyErMtx2wEWTiahPp6EJDKH0gpWELVqFc6XqpB0/COaCB8B9JBQKLGkppP/xA6aoaxjOHiZz90a0tZrYZDPHR5O8+ENSls0k68guXDv1R5nPw3yRUCgwp6WQtuF7TFGRZJ85RObuP9DVbmrNYrh8GsPZI5hibmC4cobUFZ+h0LrgVLluEQaev8sXz7Nh7a+MfHO89by+n/i4WD54/20aPt2cNu06F5i/sJijrmI8dxhz3E1MN66QsWEplow0NFVv38OYzWRsWIbS2x/3ER/g9uqHqIqXxRhxFiyO70L5r905N37/AVNUZO65UTvn3FC4e+PSpidpa5dAEXSh/Kfcm7Yh61p4ngEXPVt3RlumAlFzp3Bj0hvEr/gGvxdH4Fy5Rj5bcozmT3nx6/zK1kWlevgL7TNNvDl0MoUE/X///+MOV52WHz8YzXeTRvFKj3bM+XEdh87aH2BciH/isW2BcIdOp6NNmza0adOG9957j6FDhzJx4kQGDhyY7zpjx45l8+bNzJo1i7Jly+Ls7EyPHj3Izn64wXVCQkL45ZdfaNGiBe3ateOPP/7A3d22L/S9IxcrFIqHHhegRIkSnD9/ni1btrB582ZeeeUVPv74Y3bu3OnQkZJdXV0fKJ+TkxP9+/dnyZIldOvWjeXLlzNv3rx883/44YdMnjzZJu3NepUYXb/gZrjm1BQsJhNKd9tBH5Uennne9t1L4aTFpU5jkjfYvqHXlqmI0s2DoCm5rRIUKhWe3Qbg1qIjURNfLTCuh2VKTsZiMqHy9LJJV3l6YdLbr/32fb4/ybu2kbx9IwDZ1yJQ6HQEDH2dhDU/wgPMqiHA20WLSqEg/p6WAfFpGfi55W3qeC0hmZv6VF5fnjuWg/n2vq49eQlrX+tOCR8PKgf78dPLXUjJzMZgMuHj6ky/r9dRJdgvzzYdwXT73FB5eNmkq9w9MSXd/w2KwkmLa72n0a/90c42jRhu2Y6Qbrh1HW3Z/EeD/7fMafmc326emFP0911X4aTFuWZDUv78xeFx/VOmlGS7/xdqT68H+r9we6opiau+z/NZdsQlrr/3GkpnF1CrMackEzJxDlnh9mfZeBiW9FQsZhNKFw/urt5VuLrbHcwSct7qYzLZ/PaY4qNQunmCUpU7yKDZhDkxp3uQKeoa6qBS6Oq1yBljoZDKoXC1vTYr3Twwp9ofx8OcmpQT693liLuVc1zeXY67vycrA3NCNCrvgmduKYi7hydKpQr9Pb/9SfpEvLx97K7j5e1D0j359XflP3v6BElJiQwf+Lz1c7PZxLJv5rN+7S8sWJJ7XUyIj2Pi+DeoUKkKI14b+9DlucOSkXb7mHLn7rsghYsb5jQ7Aw/aYzZjir2B0iv3d9Qcc5305XPASYdCpcKSkYZLr9cxFdIghNZzw/Xec8MjT6sEa4ypSWA253tuqINKonTzwH3I+NztKVWoS5ZFW7cZ+o9ec+g13fobde89h8cD/kY1aErC6h9s0zVO+PToT9Rn08g4njMuVvb1CJxKlsazfTcyzhx3WPz32n8smfNXcseM0KhzKhC8PdQkJuVWAHh5qLkSef/WtwDFfDXUrOzGtM8Lf7Yee7zcXVEplcQn276cjE9Kwc8z/5Y1SqWSEgE550aFUiGE34xhyW/bqFupbKHG+1/0pI5FUFQe6xYI9lSuXJm0tNzBTTQaDaZ73mbv3r2bgQMH0rVrV6pVq0ZgYCAREREO+f5SpUqxc+dOoqKiaNeuHSkpD3gRBCpVqsS1a9e4di335vzMmTPo9XoqV86ZlszJySlPeQCcnZ3p3Lkzn376KTt27GDv3r2cPHky3+/Zv3+/Tdq+ffseOM672du/kNONYcuWLcyfPx+j0Ui3bt3y3cb48eNJSkqyWV6t82B97jAZMVy7gq7CXWM+KBRoy1cjO5+ph+5wrtUQhVpN+kHbAaHSD/5F9Idjif7oLeti0ieQsmUdcV9Me7C4HpbJSGb4RVyq1sxNUyhwqVKTjIv2W6MonbR537BYK6fkh/NBadQqKgX7sj88d4oms9nC/is3qW5nGsfSfp788nJXVo7oYl2aVyhJvdJBrBzRhUAP24o3d50TPq7OXI1P4szNeJpXuE9f34dhMpIdeRldxbtaPSkU6CpVJ+vK+fzXA1zqNEah1pC2f2eebWZFXEIdaNs9SBMQjCk+//Eh/jWTCcP1cNtpVxUKtOWqkH31/g/JuhoNUKjVZBz++775Honb+82lSs3cNIUC58o1ybx0/77NbvWboFBrSNmzPd885ox0zCnJaAKC0ZYuS9qRf/d7fl9mE6aoSNShd4+foUBTqgLGG/YH5TJev4zS25+7f39UPgE5lT/3m6FAoQBVIb3fMJsw3YpEU/ruCi8FmtCK+Y67YK8cyoLKodGi9PbPt1Lin9BoNJQpW56Txw7nFsNs5sSxI5SvaL+ivXzFKpw4ftgm7cTRQ1S4nb9Zy2eY8/liZn+2yLr4+PrxbLfevDf1Y+s68XGxvD9uFGFly/PqG+McO46R2YQ55jqqEnePZ6JAVaIc5qgHfFBTKFD6BmGx96CenYklIw2Flx/KYiUwXimkQVNvH1N5zo3QChiv53dMXbFzbhSzHlOGiHMkfTWV5EXTrYvxZgTZpw6SvGi6418I3P6NsmkZoFDgXLkGmZfv/xvlWv9p0GhIvfc3SqXKGavl3hZRZnP+46A4SEammVsx2dYl8mYWCXoDNSrndgFw1impEObC2csFD07Z5mlvkpKNHDjx4Pf0jqRRq6kYGsLB07nXPbPZzMEzl6hW9sHvISxmC4b7tFAW4kE9ti0Q4uPj6dmzJ4MHD6Z69eq4u7tz6NAhZs6cyXPPPWfNFxoaytatW2ncuDFarRZvb2/KlSvHqlWr6Ny5MwqFgvfee8+hswOUKFGCHTt20KJFC9q2bcvGjRvx8Ci4713r1q2pVq0a/fr1Y+7cuRiNRl555RWaNWtG3bp1reUJDw/n2LFjFC9eHHd3d3788UdMJhMNGjTAxcWF77//HmdnZ0qVsv+jMmrUKAYOHEjdunVp3LgxP/zwA6dPnyYs7J8PrGJv/0JOJcVTTz3FO++8w+DBg3F2zn/qO61Wi1artUlLVqnyyZ1Xyrb1+Lz4KtmRl63TOCq1WtL25VzMvF8ciSkpgeR1y23Wc23YkowTBzGn2dbomtNS86RZTEbMyYkYYx7dvL+JG1YR+PJYsq5cvD2NY1eUWh3JOzcBEPjyWIyJ8f/H3n2HN1X9Dxx/Z6e76S6lUEZb9t57D9niZoqCIiAOBBQRERVcuPCLiijgAmWLCrL3hrIptFBaRnfTnZ3fH4G0oSkFSanwO6/nyfM0t+fenJObu874HNKX2gJW5h/Zj+9Dg9AnxFMYdxZlSCX8Hx1O/pH9/62um9fJPNzxqFk0f7F7tcp4N6yFITMbXVL5TN92u4a1rsf0VTupWymAemGB/LTvFIVGEwMb28YOTlu5nSBvDyZ2a4ZKIScy2HGssZfaNsVp8eX/nLqIxl1NqI8H51Oz+PDv/XSuVYU2NcuO1fFv5WxcQ8DTEzFcikN/8Tze3fohUarJ270ZAP+nJ2LWZqBd5di67dmuGwUx+522AuZsWEXgmEnoz51CF3sCt3pNcGvQnJRP3iyXMuTv+AvfJ57HmHQBY2I87h16I1GqKTxgq9zweXIsluxMcv9y7Enk3qITupOHnU4vKHHzQKYJQHY9yJ08KBTg+sws5TObhHb9KoJGv4L+4nl0F87h02MAEpWa3B22ILpBY17BlJVB5u+LHdbz6tid/CN7seSV3Bcezdthzs3GlJGGMjyCgCFjyD+8r9wClOkObMGj73DMyZcwXb2EunlnUKgwHLfNYOPedwSWXC267bZ4N/ojO1E37Yhb90fRH96GVBOEuk1P9Ie22bep7jgA04VTWHIyQalGWac58qqR5C2d5ywLrinH/k149B+J6VoCpisJqFt2BYUS/THbdKUe/UdiydVSuHW1rRyHt6Nu1gn3no+jO7gFmV8Qbm17ozu4xb5Nt66DMZ4/jiU7E6mXD24d+oHFguGU8wjpd6rfoMf4cu5sakTWIjKqFuvWLEevK6RL994AfPHJe/j5BzJ0pG1a0j79H+GtqS+yduUymjRvxe4dW4iPi7X3IPDy9sHL27Fnj0wmR6PxI6yy7byckZ7GW69PJDAwhBHPvEBOsfHxGj9/l5TLcGQH6h5PYE5Nsk3j2LgDEoUS42lb8F91jyex5GVj2GOLmaFs0R1z8iUs2nQkKjeUTTsj9dagO1XUKCKv2eD6bCxZyAJCUXUciOnCScyJt25UuBu6/Zvx6D8C87VE2xSnLbo4Hhv9rh8b264fG4d3oG7WEbcej6I/tA2pXxDqNr3QH7r+EG7QY0lzvN+wGg22cqWVz31I9obVBI5+Gf3F8+iLnaPydtp61wWOtp2jspbfdI5q34OCI/tKXC+sukIKz57A//FRpBsNmNJTUdeqh2fbLmT8+l25lOFWVm9M54m+QVxN0ZOSZmDYoGAytCb2HimqfHp/UjX2HMlh3Zai6eAlEujeVsOmPVmUw0Rit21or47MWLCU2tUqU696FX75ZyeFegP9OzQH4K1vfiVQ48OExx4C4Ps/NlOnWjiVg/wxGk3sOn6GP/cc5vURtxfzRBBu5b6tQPD09KRly5Z8+umnxMfHYzQaCQ8PZ/To0bzxxhv2dJ988gmvvPIKCxYsICwsjISEBObOncuoUaNo06YNAQEBTJkyhZwc593M/q3KlSs7VCJs2LChzHUkEglr1qxhwoQJdOjQAalUSq9evfjyyy/taQYPHszKlSvp3LkzWq2WH374AV9fX+bMmcMrr7yC2Wymfv36/PHHH/j7O7/AP/7448THxzN58mR0Oh2DBw9m7Nixt5XHmzn7fm945pln2LNnD6NGjbrj7d6JwiN70Hp6493ncWRevhivJJD+1Xv2BwG5X0CJ2np5UCVUNWuTNm9WuebtbuTt20G6tw/+jwxD5qtBf+kCV+a8aQ9yJA8IwlqsXBmrfsGKFf/HRiD388eck03+kf2kL1tUMQUog0/TerTe/KP9fZ2Pbcdt0pKVHC/WbbMi9KpXnax8Hf/beoT0vEKiQ/z439Ae9sCKydn5SO+wBSUtt4CPNxwgI6+QQC83+jasyXMdGpVD7osUHNpNlpcPvv2fROatwXD5IqlfzCx2bASWPDaCK6GOrEPKpzOcbrMwZj8ZP3+NT6/BaJ54FlPKVdK+/gB9XOlxWu6GLmYfOR7eePZ8BJm3L8Yrl8hcMMfebV7m61+igkwWGIqyei0yvnnf6TbV9Zri+8Tz9veaYbZpb3M3rCDvnxXlUo78/TvJ8PJB8/BQ5D4a9IkXuPbRW/ahVnL/QIfjGUAREoZbdD2ufjDN6TblvhoCnnoWmY8vJm0Wubs3k7V6abnkH8B45jCF7p6o2/e1dddOvUzeb/Ps89xLvTUO+8Kam0Xusnm4d30E1TPTsORq0R/cim7fP/Y0Ug8v3PuOQOrpjVWvw5x6hbyl88o16rzh9CEk7p64dexvK0fKZXJ//cIeWFHq4+dwXFhyssj95Qvcuz+Kz5i3bA+CB7eg27O+qBzeGjwHPYvEzQNrQR7GpDjyF81xWoH1b7Tt0IXsbC1Lf/oebVYm1arX5M13PrIPSUhPS0UiKeodUKtOPV56bTq//riQnxcvIDSsMpPffI8qEbffUHDs6CGSr14h+eoVxox4xOF/K/7cXspad8Z0Pga9mweqVj2RuHtjSb9CweoF9u9N4uWLtNi+kKjdUHd9FIm7N1Z9gW24wm9fYsksms1G4uGNqsMAJO6eWPNzMJ45jOHARpfktzTGM4cp9PBE3bGv/TeVt/TLUn9T1twscn/9Evfuj6Ia/WbRsbH3zu/DXCX/wE5kXj5oBhWdo5I/cTxH3XyutZ2j6nLtI+cVyKnzP8DvkREEPTcJqYcnpoxUslb8SO7Wv8u7OCUs/zsdtUrKhBFheLrLOHW+gLfmXsRoKtovoUFKfLwcG7Aa1fEkKEDJxp33PnhicT1aNSIrN4+vV24gIzuXqCqV+PK1Z/H3sQ3HSs7IcohnotMbmLN4JamZWlRKBRGhQbz73FP0aNWogkpQscQQBteSWG++YxEEF5k1axa///47x48fLzvxTS6Pf7QccnTvFWRUTHc3Vzq//MGYM7jrkvKtyLoXUrburegsuITS6/amzfqvK0grn94K95Jf7XIaQnOPWe9R4N3ydPWJmWUnug9U/fOjshPdB0x5ZXdt/6/LOp9UdqL7wHjp7IrOwl377bmEis6CS3i2rLggqncj9fXhFfbZQbOXVNhnl5f7tgeC8N+Vl5dHQkIC8+bN4913363o7AiCIAiCIAiC8P+VmMbRpcS3Kbjc+PHjadq0KZ06dSr34QuCIAiCIAiCIAjCvSF6IAgut2jRIhYtWlTR2RAEQRAEQRAEQRBcSFQgCIIgCIIgCIIgCA8kSTlPHfr/jRjCIAiCIAiCIAiCIAhCmUQPBEEQBEEQBEEQBOGBJBFBFF1KfJuCIAiCIAiCIAiCIJRJVCAIgiAIgiAIgiAIglAmMYRBEARBEARBEARBeCBJpCKIoiuJHgiCIAiCIAiCIAiCIJRJ9EAQBEEQBEEQBEEQHkwiiKJLiW9TEARBEARBEARBEIQyiR4IgiAIgiAIgiAIwgNJxEBwLdEDQRAEQRAEQRAEQRCEMokKBEEQBEEQBEEQBEEQyiSGMAj/SR5hgRWdBZdQ+3lXdBbuWtclnSs6Cy6xefj3FZ2Fu9Z5wVMVnQWXyDx8qqKz4BIh7RtXdBbu2v731lZ0FlxC5nb/t4cEPmGp6Cy4RG7C1YrOgkvkJWdVdBbuWkjzWhWdBZcIN4RVdBbu2nHviIrOgku0qegM/EsSyf1/jfgvEd+mIAiCIAiCIAiCIAhlEj0QBEEQBEEQBEEQhAeTCKLoUqIHgiAIgiAIgiAIgiAIZRIVCIIgCIIgCIIgCIIglEkMYRAEQRAEQRAEQRAeSBKpaDN3JfFtCoIgCIIgCIIgCIJQJtEDQRAEQRAEQRAEQXggSUQQRZcSPRAEQRAEQRAEQRAEQSiT6IEgCIIgCIIgCIIgPJgkos3clcS3KQiCIAiCIAiCIAhCmUQFgiAIgiAIgiAIgiD8B3z11VdERESgVqtp2bIlBw4cuGV6rVbLuHHjCA0NRaVSERUVxV9//VVu+RNDGARBEARBEARBEIQH0v0URHHZsmW88sorfP3117Rs2ZLPPvuMnj17EhsbS1BQUIn0BoOB7t27ExQUxPLlywkLC+PSpUv4+vqWWx5FBYIgCIIgCIIgCIIgVLC5c+cyevRonn76aQC+/vpr/vzzT77//numTp1aIv33339PZmYme/bsQaFQABAREVGueRRDGP7Dtm3bhkQiQavVluvnvP322zRq1OiWaSQSCatXrwYgISEBiURCTExMueZLEARBEARBEAThrkilFfbS6/Xk5OQ4vPR6vdNsGgwGDh8+TLdu3YplXUq3bt3Yu3ev03XWrl1L69atGTduHMHBwdSrV4/3338fs9lcLl8liB4Id2XkyJEsXry4xPLz589Ts2bNCsiRa82cOZPz58/z008/VXRWyqRq0gFVy+5IPb0xp16m4J/fMF+7VGp6icoNdcf+KKMbIVG7Y8nJpGDTckzxpwBQNm6PqkkHZD5+AJjTr1G46y9MF06XaznULTrj1rYXUk8fTClJ5P/5C6YrF0svh9oN964Po6rTBImbBxZtBnl/L8V4/gQAmpc/QKYJKLFe4f4t5P/5c7mUYemB0yzefZL0vEKiQjRM7d2a+pUDy1zv7xMXmLpiG52jq/DZk0Unzoy8Qj7beJC98VfI1RloUjWEqQ+1oqq/T7nk/075tWtG9VefwadJPdSVgjg0+AVS1m6u6GzZLTt8jsX7z5CRV0hUkIYpPZpSr1LJ3wTA2uMXmPHnPodlSpmU/ZOfsL//eudxNpxOJDk3H4VMSu0QP8Z3aEj9MOfbdAWP9j3x6toPmbcvxiuXyFr+PcZL8U7TBr44A1Vk3RLLC08dIePrOQB4934Ut6ZtkPn6g9mEIekCOX8sxXAprtzKALDsaBxLDsaSka8jKtCXyV0bUy/Uz2natScTeHv9QYdlSpmUfS8Ptr+f8fcB/jjleJ5rHRHMV490cH3mrwsb9jhVnhuJMjCAvDPnODdjNrnHTjpNK5HLqfrCM4QO7o8yJIiCCwnEz/mMzO277WmqvTSWai+NdVgvP/4i+7sOKLcyAFR66nHCR41AGeBP3tlzxL33AbknSi9HlTGjCB7QD1VwEAUXE7jwyedk7drjNH34s09T/dWJXF7yM/GzP3JZnv9at4rVK5ahzcokoloNnn3+RaKia5eafvfObfz60/ekpiQTWqkyw58eQ9Pmrez//2LuHLZu3uCwTuMmzXlr1ocOyw4d2Mtvvy7hUsIFFAoldes35PXp77qsXO5tu+PZpR8yLx+MVxPJXrkIY6Lz49t/3HRUNeuUWK47fZTMBR+CVIbXQ4+hrt0ImX8QVl0h+nMnyFm3FEtOlsvyfDt8uvfDr98jyHw06BMvkLbof+jiz5Wa3rf3QHy79UUeEIg5N4e8/TtJX/oDVqPxHua6JEXDdqiadUHi4YUl7SqFW1dgSU4sfQWVG+q2DyGv2QCJ2gNLbib6baswXTxz7zIN9GvvRvuGKtxUEuKvmPhlQz6pWZZS0/dt50a/dm4Oy5IzzMxYkO00/YRHPalXQ8n/VuRy7Lxr9pHVamX1r9+wfeMqCvLziKzVkGHPTyWkUpVbrrf5r9/4e9WPZGszqBIRyZDRr1E9qp7T7X86ayInjuxhwtSPadKqk/1/Py/4iPNnjnElMZ7QytV457NfXFImAWbPns3MmTMdls2YMYO33367RNr09HTMZjPBwcEOy4ODgzl79qzT7V+4cIEtW7YwZMgQ/vrrL+Li4njhhRcwGo3MmDHDZeUoTlQg3KVevXrxww8/OCwLDCz7Yel+sGbNGqddZf5rFLWb4tZ1MAXrf8V0NQF18y54Pj6BnG/fxlqQV3IFqQzPJ1/Emp9L3soFWPO0SL39seoL7EmsuVoKt63GkpkKEgnKeq3wfOR5cr6fjSX9WrmUQ1mvOR69Hifvjx8xXb6AW+vueA9/mawvpmHNzy25gkyG94hXsebnkrNsPpacLKS+/lgLi8qh/WaWrQb0OnlQGD4jJ2E4dahcyrD+5AU+3nCAN/u2oX5YID/vO8XYnzawZvxg/D3dSl3vSlYuc/85QJMqjidMq9XKS0s3IZdK+ezJbniqlCzZe5Lnlqxn5biHcVcqyqUcd0Lm4U7O8ViSFq2g2fKvKjo7DjacvsQnm48wrVdz6lUK4JeDZ3lh2VZWj+mHn4fa6TqeKgWrxvS1v5fcNGywqp83U3o0o7KvJ3qTiZ8OxvLCsq2seb4ffu7Ot3k33Jq0xnfQcLKWLcBw6TyenfoQ+MI0kme9hCUvp0T69O8+RiIrurRJPbwInvoRhUeLau6NqVfR//49pvQUJAolXp37EDDuTZLfmYAlz8mx5gIbziYxd9sx3ujWhPqh/vx85Bzjlu9g1ahepe8LpZyVz/S2v3c2grNNRAhv925uf6+UlV/HwqC+PYl88zVi35xF9tEThI8aSqMlX7OvS3+MGZkl0lefNJ6QgX04O3Um+fEX8e/YlvrffMrhwcPJO1V0I5QXG0fM0NH291ZT+bWaAAT27kGNKa9y7u33yD1+grDhQ6i/4H8cfGgAxsySD5kRE8cR3K8P5956h4ILF9G0a0PdL+cS89QI8s7EOqT1qleX0McfIe9sbInt3I1dO7bww4L5PD/+ZaKia/PH6uW8M30y875dgq+vpkT6s6dPMvfDWQwdOZpmzVuzc/tm5rw7nY8//5aqEdXs6Ro3bcGEl6bY39/o/nrD3t3b+d8XnzBkxLPUb9gYi9lM4qXSK7XvlLpRK3wGDkP7+0KMl+Lw6Ngb/+emkjr7VafHd+YPc0sc34GT5lAYY6v4lCiVKCtXI3fjKoxXLiF198Bn0Aj8np1E+txpLst3WTxbdSBw2GhSF36JLi4W394DCZv6HgmvPos5p+QDqVebTgQ8MYqUb+ZSeO4MytAwQsa+ClZI++nbe5bvm8mjGqPuOBDdZlujjLJJRzwefp68H97HWuj8/spj8FgsBbkUrluEJS8bqbcGq67wnua7Z0s1XZqqWPRnPulaC/07uPHi4168vSCbW51erqSZ+Gxp0TXAXEp9Q9fmKqwuzjPAX6sWs3HdUp6d+DaBwWGs/GU+c2dO4L0vf0OhVDldZ/+uf1j6/acMH/s61aPqsXHtr3wycwKzv1qBt69jBfU/f9y6UqB9t/5cOHeSpITyrUz//+b111/nlVdecVimUjnfn/+GxWIhKCiIb7/9FplMRtOmTbly5QofffRRuVUgiCEMd0mlUhESEuLwkslkjBw5koEDBzqkfemll+jUqZP9vcViYfbs2VSrVg03NzcaNmzI8uXL7+jzz549S7t27VCr1dSpU4dNmzY5DDcAmDJlClFRUbi7u1O9enWmT5+OsYwa7aSkJE6dOkWvXr1u+dlt2rRBrVZTr149tm/fbv9fVlYWQ4YMITAwEDc3NyIjI0tUtLiKukUX9Md2YzixD0tGMgXrfwWTAWWDNk7TKxu2QaJ2J2/F15ivXMCSnYkp6Tzm1Cv2NMa4E5jiT2HJSsOSmYpux1qsBj3yStWcbtMV3Nr0QHd4B/qjuzGnXSPvjx+xGg2om7Rzml7duB1SNw9yfpmHKTEOizYDU8I5zCmX7WmsBXlY83LsL2V0Q8wZKRgTXHtze8OPe0/ycJNoBjaOokaQhjf7tkWtkLP6aOmtLmaLhTdWbmds5yZU1ng5/O9SRg7HL6cxrW8b6oUFEhHgw5t92qAzmll/4kK5lOFOpW3YwbkZn5GyZlNFZ6WEnw6c5eGGNRjQoAY1AnyY1qsFarmc1cedt+7dEODpZn/5ezhW/PSuG0GraiFU1nhSI9CXV7s2IU9v5HyqtlzK4NW5L/l7N1Owfxum5Ctoly3AajDg0bqz0/TWgnwsudn2l7pWA6wGPYVHi3pWFB7ejT72BOaMVEzJl9GuWoLUzR1FparlUgaAnw+dY1D9agyoX43qAd5M694UtULGmpMJpa8kkRDgoba//J1UNCjlUoc03mpluZUh/NnhXF26gmu/r6Eg7gKx02ZhKSyk0mMDnaYPGdSXhK++I2PbLnRJV7jy029kbN1FlWeHO6Szmk0Y0jLsL2OWttzKAFB5xDCu/b6SlFVrKIi/wPm338Wi0xHysPNyBPfvQ+K3C8ncsQvd5StcW/o7mTt2UXmkYzmk7m7U+uh9zr31DqYc11ZErV31O9179aFr996EV4ng+fGvoFKr2fzP307Tr1u7gsZNWzBo8BOEV6nKU8NGUb1GJH+tW+WQTqFQoPHzs788vYrOwWazmYXfzGPEqOfo9VB/wsLCCa8SQdv2zo+9f8OzUx8K9m6h8MB2TClXyP59IVaDAfeWnZymv/n4VkXVx2rUozu23/Z/XSEZX7+PLmYf5rRrGC/Fkb3iB5Th1W09ju4RTZ+HydmynpztGzFcSSR14ZdYDXq8O/V0mt4tqg66c6fI3bMNU3oKBSeOkLNnG+oa0fcsz86omnbCeHIvxlMHsGSmoNv0O1aTAUW9lk7TK+q1RKJ2p3DtQsxXL2LNycR8OR5L+tV7mu+uzdX8tUfHsfNGrqSZ+WFdPr6eUhpF3fr8aLFATr7V/sovLFlNUDlIRvfmbiz5K9+lebZarWz841f6PfYMTVp2IjwiktET3yErM40j+7eVut4/a36mQ4+BtO/an7Dw6gwf+zpKlZqdm9c6pEu8EMuGNT/zzIS3nG5nyOjX6PrQYwQGh7myWP8ZEomkwl4qlQpvb2+HV2kVCAEBAchkMlJSUhyWp6SkEBIS4nSd0NBQoqKikMlk9mW1a9cmOTkZg8Hgui+xGFGBUIFmz57NkiVL+Prrrzl16hQvv/wyQ4cOdXgQvxWz2czAgQNxd3dn//79fPvtt0ybVrKG3cvLi0WLFnH69Gk+//xzFixYwKeffnrLba9du5ZOnTrh7e1daprXXnuNV199laNHj9K6dWv69etHRkYGANOnT+f06dP8/fffnDlzhvnz5xMQUA5dnKUyZCFVMF0s/kBsxZhwFnmY84d9ZWR9TFcu4t7jCXxenIP3s2+ibt2zZFPrDRIJitpNkSiUmK6U00OrTIY8tCrG+GJd/KxWjPGnkVeu4XQVZa1GGJPi8ew7BL/Jc/Ed9w5uHR4qvRwyGaoGrdAd3VUOBQCjycyZqxm0ql7JvkwqldCqeiWOX04rdb1vtseg8VDzcJOoktu8Pn5LJS86KUqlEpRyGUcTU0qkF4oYzWbOJGfSslrRBUcqkdAyIoTjV9JLXa/QYKL3V6vpNW81Ly3fTnya9pafsTImDk+VgqggXxfm/jqZDEV4dXSxJ4qWWa3oYk+gjCj5e3HGo3UXCo7swWpwPt4QmQyPNt2wFORjvFL6sKe7YTRbOJOSRcuqRT1spBIJLasEc/xqRqnrFRpMPPTNn/T+Zh0vr9pNfHrJlstDSWl0/Wotgxb+zfsbD6MtLKWcd0mikONVrzaZu4sNcbFaydy9H+8mDZ2uI1Uqsegdb14sOh0+zRs7LHOPqErb/ZtoveMv6nw2G1Ul5zdJriBRyPGqW5usvfuLFlqtZO3dj3ejBk7XsZXD8Xu16PT4NHUsR+T0N8jcvhNt8W27gNFoJD7uHA0bNS3Kk1RKg0ZNiD17yuk6sWdPO6QHaNSkOeduSn/yRAwjnhrEuDHD+fqrT8kp1joeH3eOjIx0JFIpr0wYzaihg3nnrSlcSnBRDwSZDEXlaujPFRs6YrWiP38SRdXI29qEe8tOFB7dW/rxDUjc3LFaLFiK9c4rVzI56mqR5J88WrTMaiX/5FHcIp0POSk8dxpVtUjUNWznNUVQCB6NmpMfc+up28qVVIY0uDKmS8UbAKyYLp1DFhrhdBV5jXqYriWg7vIIns/NwmP4FJQtupV+X1IOAnyk+HhKOZNQ1FCm01u5eNVE9bBbd7wO0sj4YJwv7z7vw6h+Hmi8HR+TFHJ4pr8nv27MJyfftX0Q0lKukJ2VQd0GLezL3D08qRFVj7ji18BiTEYjCfFnqdugqEJHKpVSp2EL4mKP25fp9Tq+mfsmQ8dMxsfJsFbhv0OpVNK0aVM2by4aCmuxWNi8eTOtW7d2uk7btm2Ji4vDYinqMnPu3DlCQ0NRKsunUUEMYbhL69atw9PT0/6+d+/e/P7772Wup9fref/999m0aZP9B1G9enV27drFN998Q8eOHcvcxsaNG4mPj2fbtm32Wqn33nuP7t27O6R788037X9HREQwadIkli5dyuTJk0vd9po1axgw4NZjUMePH8/gwbbxuPPnz2f9+vUsXLiQyZMnk5iYSOPGjWnWrJn9c8uDxN0TiVSGpcCxq6M1PxeZf7DTdaS+Acir+mM4dZC8375CqgnCvefjIJOh21U0Z6o0sBLewyeBXIHVoCdv5bdYMpLLpRxSdy8kMhmWfMdyWPJzUASGOl9HE4iiWm30x/eR/ePnyPyD8Ow7FKRyCretLZFeWasxErU7+qPOx+3erawCPWartcRQBX8PNy6ma52uc+RSMquOnOO35wc6/X9EgC+hPh58sekQ0/u1xU0h58d9p0jJySct7952ibzf3NgfNw8r8PdQk5BRsmswQFU/L2b0aUlUkIZcvYEf959h5I8bWf5sH4K93e3pdpy/wtQ1u9EZTQR4uvH1E13QlMPwBamHt+24yNE6LLfkalEEV3K+UjGKqjVQVKpC5i/zS/xPXbcJfk+/hEShxJKjJe2rd7E4GyrkAtrC6/viph4Efh5qEjKdf2ZVPy9m9GpGZKAveXojSw7G8vQvW/j96Z4Ee9n2RZtqIXSJrEwlHw8ua/OYt/MEE1bsZNFTXZG5eMoqhUaDVC7HkO5Y4WFIy8C9hvPK2owdewh/dhjaA4cpvJSEpm1LAnt1RSItqhDMjjnB6UlvUnAhAVVQINUmPk/T3xaxv+fDmPNd/8Cn8NUgkcsxZjiWw5iRgXu1CKfrZO7aS+WRw8g+dITCxCQ0rVsS0L0LkmKtPYEP9cSzTi2OPDrE5XnOzcnGYrHgc9NQBV9fDVeSnI9F12Zllhja4OurISuraIhG46YtaNWmPcEhoSRfu8pPi79j1oypzPl4nq0FLNk2XG/Zz4t5evRYgoJCWLPqN6a//hJfffsjXl6lNzDcjhvHtznXsWLMkpuNMug2ju8qtuNbu+wWXfzlCrz7Pknh0T1Y9ffmmiHzvl6ubK3DcnO2FmWlcKfr5O7ZhszLh/C3PwEkSORytBvXkblmWflnuBQSNw8kUhnWAsdzlLUgF5lfKfdXPv5IwyMxnj1MwapvkPoGou76CEhlGPZtcLqOq3l72h76c/Idxx/k5Fvw8Sj9vHjxqolFf+aRkmnBx1NK37ZqXhvixcyF2dyoB32sqzsXrhhdFvOguGyt7ZzkfVNPGW8fP7KznFc05+ZqsVjMJYYq+Pj4kXw5wf7+14WfUKNWA5qU0rPn/wXp/dNm/sorrzBixAiaNWtGixYt+Oyzz8jPz7fPyjB8+HDCwsKYPXs2AGPHjmXevHlMnDiRCRMmcP78ed5//31efPHFcsujqEC4S507d2b+/KKbUw8Pj9taLy4ujoKCghIP+waDgcaNG5eylqPY2FjCw8MdurS0aNGiRLply5bxxRdfEB8fT15eHiaT6ZY9C3Jycti+fTsLFy685ecXrwmTy+U0a9aMM2dsLehjx45l8ODBHDlyhB49ejBw4EDatHE+pECv15eIRqo3mR1anV1KIsGan0vB3z+D1Yo5OQmdpw/qVt0dKhAsGSnkfD8biUqNIroJHn2Hk/vTp+VWiXCnJBIJlvwc8tYutpXj2iUKvDW4t+3ptAJB3bQ9xrgTWHK19z6zTuTrjUxbtYMZ/duiKWUMuEImZe7jXXl7zS7af/AzMomEltUr0a5mZazlMgLx/7eGlQNpWCzgZcOwQAZ/u47lR88zrmNRK3PzqsEsHdUbbaGelTFxTF69ix9H9Cx1LH9F8WjVBcOVS04DLurPnyJlzmvIPL3xaNMV/1Evk/rxG07HXVeEhpX8aVip6EayQSV/Bv+wnhXHLvBCO1twrJ61igJrRQb6EBnoQ//v/uZQUqpDb4eKcn7mB9SaM4NWm9dgtVopvHSZa7+vIbTYkIfMbUU9ovLPnicn5gRtdq0nqE9Prv22yslW77349z8k6p23aP7nKrBaKUy6TPKqtYQ8bKtkV4UEU/P1yRx/5nms5dRdtDy079jF/nfViOpUjajO2GeHcOpEDA0aNcVqtT2APfL4EFq3tTVqTHh5Cs8Of4w9u7bRs3f/Csn3De4tO2G8mlhqwEWkMvxGTASJhOzfv7+3mbtDbrUb4DfwcVK+/wpd3FmUwZUIHPE8foOeInPVfRTMTiLBWpCHbuMysFqxpF7G4OmDslnncqtAaFFHyZBeRffe837/dxXBpy4UVQpcSTNz8aqJ2WN9aFZLye7jBhrUVBBdVcF7PzgPqnin9m7/m8Xz37e/f+nNz1yy3ZsdPbCdMycOMXNu+QTOFlzv8ccfJy0tjbfeeovk5GQaNWrE+vXr7YEVExMTkRarEAkPD2fDhg28/PLLNGjQgLCwMCZOnMiUKVNK+4i7JioQ7pKHh4fTGRekUilWq+MDTvG4A3l5tuAzf/75J2FhjuONXBlYY+/evQwZMoSZM2fSs2dPfHx8WLp0KZ988kmp6/z999/UqVOH8HDnNeW3o3fv3ly6dIm//vqLjRs30rVrV8aNG8fHH39cIq2z6KSTuzRjarfmJdLezFqQh9ViRuruTfG4OBIPr1IfBCx5OWA2Q7H9Y85IRurpA1IZWK5vyWLGkmXrem9OTkIeWhV18862GAsuZinIxWo2I/VwrNiRenhjyXV+sbLkZWO9uRxpV5F6+YJMZivjje34+KOoXofcpeUX5E/jrkImkZBxU8+AjPxCAjzdS6RPyszhqjaPF38pih1guV6WJjN/YM2EwYT7eVOnUgC/jR1Irs6A0WzGz8ONIQvWUreUmQQEmxv7I7NA57A8I1+Hv+ftPegrZFKiQzQkZTkGy3JTyqni50UVvGgQFkD/r9ey6lg8z7QpOfvB3bDk59iOC29fh+VSL1/MN/VKuJlEqcK9aVty/nTegmc16DGnp2BOT8GQcJ7g6Z/j0boLuRtXuybzxfi6Xd8X+Y77IjNf5zSugTMKmZRaQRqStE4Cl11X2dcTXzclSdo8l1cgGLOysJhMKAMcW8eUgf4Y0pwPiTFmZnFizEtIVUrkvr4YUlKpMfUlChMvO00PYMrJpeDiJdwi/v3151aM2iysJhMKf8dyKPz9MaSXUo6sLE5NeBmJUonC1xdDairVXp2I7rItbo5n3TooA/xpuqLo2iCRy/Fp1oSwpx5nR8MWtsHV/5KXtw9SqZRsrWOAR602C1+N81k8fDV+aJ2k12hKBly8ISS0Et7ePly7doUGjZqi0di+o8pVIuxpFAolwSGhpKWm/svSFLlxfMu8fCjeniv18rmt49utcRty15fS41MqQzNiIjJNAOn/e/ee9T4AMOdcL5ePr8NymY8vZm3JIJ0A/o8NJ2fnFnK2rgfAkJSARK0m+NkXyVz9q8N1/l6xFuZjtZiRuDvGJpK4e5XoLWlf5/o+LZ5fS2ZKyfsrFzoWZ+Di9yb7e7nc1svA20NKTn7R53l7SElKvf3PL9RbScmyEKixNWbVqqogUCPl05cdj6HnB3ly/rKJub/cWcVFoxYdHGZKMBltlY852gx8/Yrub3KyMwmv5nzInpeXL1KpjBytYxDb7OxMvK8fv2eOHyIt+TLjhjjGLpn34WSiajdi6nsVF6TzXpK4uFdeeRs/fjzjx493+r9t27aVWNa6dWv27dtXMnE5uX/6c9xnAgMDuXbNMVp/TEyM/e86deqgUqlITEykZs2aDq/bfXCPjo4mKSnJIdDGwYOO037t2bOHqlWrMm3aNJo1a0ZkZCSXLt16nO/tDF8AHH6oJpOJw4cPU7t20fi+wMBARowYwU8//cRnn33Gt986P0m9/vrrZGdnO7xe7tSkzM8HwGLGnJyIPKJ4oCEJiqrRpU5/aLocj1QTSPGY5jK/YFvL/K0ubhIJyMqpzs1sxnTtEorqxcZHSiQoqtfGdNl564oxMQ6ZX5DD2EKZf4jtxuumuV/VTdpizc/BcO445UUhl1G7kj/7LxYFS7JYrOy/cJUGTqZxrBbgw/Kxg1j2/ED7q1N0FZpXC2XZ8wMJ8XbszeOlVuLn4caljGxOX82gU3T5Bbx7EChkMmqH+LE/oej8YLFaOXApmQa3OeWi2WIhLjWbgDIqHKzWongVLmU2Y0y6gLr4dFQSCaqoehgSSg/MCeDWuBUSuZyCgztv66MkEgkSefnM6qGQSakdrOFAYtFDl8Vq5UBiKg0q3V5gN7PFSlx6NgG3qHBIyS0gu9BAoEfpM578W1ajidyTZ9C0KRY8TSJB06YlOUeO3XJdi96AISUViVxOYK9upG/cVmpambsbblXDMaSWHqfjbliNJnJPnUHTqlhvPYkETasW5MTc+vxoNRgwpF4vR/euZGzeBoB2734O9h/MoYcft79yTpwidd1fHHr48buqPABboMMaNaM4HnPEvsxisXAi5gjRtZxX2kXXqsPxY0cclh07epioUtIDpKenkZubY684qBEZhUKh4OrlomESJpOJ1NQUgoJcUEFlNmO8fBHlzcd3ZF2Ml87fclV1w5a24/uQk5g+1ysP5IEhZMx/z/lsTOXJbEJ38Tzu9RoVLZNIcK/biMLzzqcylCpVYL3pd2L/3VTQg4/FjCXlMvIqxeNRSJBXicJ8LcHpKuYrF5H6Ot5fSTWBWPKyy6XyAEBvgDStxf66lm4mO89CrYii87laCdUqyblwxXSLLTlSKSDQV0p2nm0/rN9XyKyFObz7fdEL4LfNBSz+884DKrq5eRAcGm5/VQqvjo/Gn9PHi+7jCwvyiD93kprR9Z1uQ65QEFGjFqePF8XKsFgsnDl+kJrRtpgufQaP4J3PfmXmpz/bXwBPjnqFZ14snwj9woNP9EAoJ126dOGjjz5iyZIltG7dmp9++omTJ0/ahyd4eXkxadIkXn75ZSwWC+3atSM7O5vdu3fj7e3NiBEjyvyM7t27U6NGDUaMGMGHH35Ibm6uPd6B5PpDZWRkJImJiSxdupTmzZvz559/smpV6d1CTSYTf//9N5MmTSrz87/66isiIyOpXbs2n376KVlZWYwaNQqAt956i6ZNm1K3bl30ej3r1q1zqFwoTqVSleh1Yb6D4Qu6A1vw6Dscc/IlTFcvoW7eGRQqDMdt07a59x2BJVeLbvsaAPRHdqJu2hG37o+iP7wNqSYIdZue6A9ts29T3XEApgunsORkglKNsk5z5FUjyVs677bzdacK9/yD16BnMF1NwHT5IurW3ZAoVeiO2OZM93z4GSw5WRRsWnm93FtRt+iCR+8nKdy/GZl/MO4dHqJw32bHDUskqBq3Qxez565vZMsyrHU9pq/aSd1KAdQLC+SnfacoNJoY2NhWez5t5XaCvD2Y2K0ZKoWcyGDHmnyv6xHkiy//59RFNO5qQn08OJ+axYd/76dzrSq0qfnfiBQs83DHo2ZRV3L3apXxblgLQ2Y2uqTymfLzdg1tUYu31u2lTogf9Sr588vBWAqNJgY0qA7Am3/sIcjLnRc7NQLgm10naFApgHCNF7l6A4v3neFaTj6DGtl6WRUaTHy35yQdIysT4OmGtkDPb0fOkZpbQPdi3eldKXfrOvyGjsOQeAHDpTg8Oz2EVKUif982ADTDxmHWZpLzh2PPII/WXSg8fhDLTQ8PEqUKr54PoztxCHN2FlJPLzzb90Lm60dBsakeXW1Isyhm/H2AOsEa6ob68cvh8xQaTfSvFwHA9L8OEOTpxoQOthvFb/ecpn4lP8J9Pcm9HgPhWk4+g+rb9l2BwcQ3e07RNaoyAR5qkrR5fL7jOOEaT1pHlM/whaTvllD7k3fJPXGanJgThD8zFJm7G1d/Xw1A7U/eQ5+SwoUPvwDAu1F9VMFB5J4+iyokmGovjUUilZL4TdGMPDXfeJX0zdvQXbmGMiiQ6i+/gNVsJmWt89kFXOHy4h+pNXsWuSdPk3viJGHDhyB1cyN5le0aET1nFoaUVC5++iUAXg3qoQoOIu9MLKrgIKqOex6kUhIXLgLAXFBAwXnHil5LYSFGbXaJ5f9W/0GP8sXcOdSIjCIyqjbr1ixHp9PRtbttpqTPP3kfP/9Aho20TYfZt/9g3pz6EmtW/kbT5q3YtWML8XGxjJ3wKgCFhYUs+2Uxrdt2QKPxI/naFRZ//w0hoWE0bmrr/efu7kHPh/qz9OdFBAQGERgUzOoVth49bdp1ckm58rb9ieapsRiTLtincZQoVRTstwWT9n1qLObsLHL/XOqwnnurzuhOHCpZOSCVoRn5EsrK1cj47kOQSpF6+QDYzgXlUdHpRNafKwkZOwn9hfPXp3EchFSlJmf7PwCEjJ2EKSuD9KW2YyH/yH58HxqEPiGewrizKEMq4f/ocPKP7C9ZsXAP6Q9vw63XU5hTkjAnJ6Js0hGJQonxlC1QqLrXEKx52eh3rQPAcGw3ykbtUXcehOHoTqSaQJQtumM4uuOe5nvzQR0PtVGTmmkmPdvCgPZuaPMsxJwrGmL08hNeHD1nYNsR2xDawZ3dOB5nJDPHFgOhXzs3LFY4ePp6z4B8q0OPhhsycyxkZN/9PpJIJHTv9yR//L6Q4ErhBASFseqX+Wj8Ah1iF3w4fSxNWnWiW5/HAegxYAjfff42ETXrUD2yLv/88Qt6XSHtuvYDwEcT4DRwon9AiMOMCynXktAXFpCtzcBo0JF4wRacvFJ4deSK8qlcF+5fogKhnPTs2ZPp06czefJkdDodo0aNYvjw4Zw4URRJddasWQQGBjJ79mwuXLiAr68vTZo04Y033ritz5DJZKxevZpnn32W5s2bU716dT766CP69euHWm1rperfvz8vv/wy48ePR6/X06dPH6ZPn87bb7/tdJvbt2/H09OTJk3K7gEwZ84c5syZQ0xMDDVr1mTt2rX2mRaUSiWvv/46CQkJuLm50b59e5YuXVrGFv8d45nDFLp7om7fF6mHN+bUy+T9Ns8e+EfqrXG4AFtzs8hdNg/3ro+gemYallwt+oNb0e37x55G6uGFe98RSD29sep1mFOvkLd0HqaEsyU+31UMJw+S7+6Fe5eBSD29MSUnkfPjp1ivdxWU+fg5dgvMySLnx0/x6PU4mhdmYsnNonDfJgp3Ot54K6rXQebrj+5I+cy+UFyvetXJytfxv61HSM8rJDrEj/8N7WEPrJicnY/0DqMxp+UW8PGGA2TkFRLo5UbfhjV5rkOjcsj9v+PTtB6tN/9of1/nY9vxm7RkJcefeb2isgVAzzpVySrQMX/ncTLydUQHafjqsc72qRmTcwoc9keuzsA7f+8nI1+Ht1pJ7RA/Fg3rTo0A2w24VCohISOHP07sRFuox8dNRd1QP74f2p0agb7lUobCI3vRenrj3ecxZF6+GK8kkP6/9+1De+SagBLde+VBoahq1CZt3qwS27NaLCiCK+HR4lWkHl5YCnIxXIon9bMZmJJL71p/t3rWCierQM/83afIKNARHejLvEfa24cw2PZFUfocvYFZGw6TUaDDW6WgdrCGH57sQvUA2zAnqUTC+fRs1p26RK7eQKCnG60ignmhbT2U5RQ/JnXdBhR+Gqq//ALKwAByz8RybMRYjOm27rPqsBCHc61UpaT6pPGoq1TGnF9AxtZdnH75DYcpDlWhQdT94gPb0IDMLLIPHeHwoKEYM5139XaFtL//QaHREPHiWJQBAeSdieXEmBcwZlwvR2goWIp+U1KViogXx+EWXhlzQQEZO3ZxdsqbmHPLJ+imM+06dCEnO5ulPy0iKyuTatVr8NY7H9iHMKSlpSKRFHUqrVWnHi+/9ia//Pg9Py3+jtCwMKa+OYuqEbaAl1KplEsJ8WzdvIGC/Dw0fv40atyMp4aNQqEoito9YtTzyKQyPvtkNga9nqjo2rzz/icO0z3eDV3MPrI9vfHq9Qgyb1+MVy6R8c0cW4s1IHNyfMsCQ1FVr0VGsfHj9v/5aHCrbwveHPTaBw7/S5/3DoZ45z0AXC1v3w7SvX3wf2QYMl8N+ksXuDLnTXtgRXlAkMMw14xVv2DFiv9jI5D7+WPOySb/yH7Sly26J/ktjencUXTuHqja9Ebi7o0l7QoFK7+xV9xIvTT2oYcA1jwtBSu/RtVpIB7DJ2PNy8ZwdDuGg5tL+4hysWG/DqVSwtBeHrirJcRdNvHFslxMxZ7/AzRSPN2LjhmNl5Rn+3vi4SYhr8BK3GUjc5bkk+dkKsfy8tCgERh0Ohb9730K8nOJqt2IV976AoWyqJEtNfkyecWG+LRs14Pc7CxW//o12VkZVKkWxSszvsTnDqct/WHeLGJPFfVamvGKLSDsR9+sJeA2ghb/50lEp3tXklhvHqgv3Nd2795Nu3btiIuLo0YN59P/3cqLL76IyWTif//7Xznk7vZlzX6hQj/fVczlNKXaveRZ+/amy/uv2zz8vx1E63Z0XvBURWfBJTIPO5967n6jaXD/Hxv73ysZcPV+JHO7/28OA//4q+xE9wHfr16t6Cy4RF5y+VVi3SshzWtVdBZc4jXDWxWdhbs2YsCD0YrfprZrKg/vtbz/Ta2wz/Z8YU6FfXZ5ET0Q7nOrVq3C09OTyMhI4uLimDhxIm3btv1XlQcA9erVK3WeUUEQBEEQBEEQhPvKfRZE8b9OVCDc53Jzc5kyZQqJiYkEBATQrVu3W86wUJYxY8a4MHeCIAiCIAiCIAjCg0JUINznhg8fzvDhwys6G4IgCIIgCIIgCMIDTlQgCIIgCIIgCIIgCA8kiQii6FLi2xQEQRAEQRAEQRAEoUyiB4IgCIIgCIIgCILwYBJBFF1K9EAQBEEQBEEQBEEQBKFMogeCIAiCIAiCIAiC8ECSSEWbuSuJb1MQBEEQBEEQBEEQhDKJCgRBEARBEARBEARBEMokhjAIgiAIgiAIgiAIDyaJCKLoSqIHgiAIgiAIgiAIgiAIZRI9EARBEARBEARBEIQHkwii6FLi2xQEQRAEQRAEQRAEoUyiAkEQBEEQBEEQBEEQhDKJIQzCf1LKkbiKzoJL5F7Lqegs3LWg5IyKzoJLdF7wVEVn4a5tHf1LRWfBJRqPb1LRWXCJ7NPxFZ2Fu9bspW4VnQXhuiQejCBffl07VHQWXMIr/v6/D7m44XBFZ8ElTitOVXQW7lrEIJ+KzoKLNKjoDPw7IoiiS4keCIIgCIIgCIIgCIIglEn0QBAEQRAEQRAEQRAeSBIRRNGlxLcpCIIgCIIgCIIgCEKZRA8EQRAEQRAEQRAE4cEkEW3mriS+TUEQBEEQBEEQBEEQyiQqEARBEARBEARBEARBKJMYwiAIgiAIgiAIgiA8mKRiGkdXEj0QBEEQBEEQBEEQBEEok+iBIAiCIAiCIAiCIDyQJCKIokuJb1MQBEEQBEEQBEEQhDKJCgRBEARBEARBEARBEMokhjAIgiAIgiAIgiAIDyYRRNGlRA8EQRAEQRAEQRAEQRDKdM8qELZt24ZEIkGr1QKwaNEifH1979XHP5BWr15NzZo1kclkvPTSS3e8/s374O2336ZRo0Z3laeEhAQkEgkxMTF3tR1BEARBEARBEIS7JpFW3OsB5NIhDHv37qVdu3b06tWLP//805Wbvi9s27aNzp07k5WVdU8qR5577jmefvppXnzxRby8vO56e5MmTWLChAkuyNm959uzH/79H0Xm64f+0gVSvv8KXVxsqek1Dw3Ct2dfFAFBmHNyyN23k7RfFmI1GgFwq10f//6PoqoeicLPn8sfvk3ewT3lXo6ghwcT8uRQFH5+FMTHkfjpJ+SfOV1q+uBHHydw0MOogoMxabPJ3LaFy9/Mx2owACB1cyds9Bg0HTqi0GgoOHeOxM8/Jf/smXIrg2en3vj0GITMxxfD5QQyf12AIeG88/y/+i7q6HollhecOETal+/a38tDKqMZPBx1VF2QyjBeSyLt6w8wZ6aXWzmWHT7H4v1nyMgrJCpIw5QeTalXKcBp2rXHLzDjz30Oy5QyKfsnP2F///XO42w4nUhybj4KmZTaIX6M79CQ+mHOt3kv+bVrRvVXn8GnST3UlYI4NPgFUtZuruhs2bm37Y5nl37IvHwwXk0ke+UijInxTtP6j5uOqmadEst1p4+SueBDANT1m+PethvKytWQeniR+tFUTFcvlWsZwMXlkMrweugx1LUbIfMPwqorRH/uBDnrlmLJySrvojhQNGiLqllnJO5eWNKvUrh1FZaUxNJXUKlRt3kIec0GSFTuWHIz0W9fgymh/M5LZbkfyvD3ulWsXrEUbVYmEdVq8uzzLxIZXbvU9Ht2buPXnxaSmpJMaKXKDHv6OZo2b2X//5dzZ7N18waHdRo1ac5bsz6yv1++9EcOH9zHxYtxyOVyfvrN9fd2S3cdY/G2Q6TnFhBVKYCpgzpTv0pImev9fTSWqT/9Tee61flsVH/78vkb9rL+6DmSs3NRyGTUqRzE+N5taFA11OV5L07RoC3Kpp3svyHdtlVYUpKcppXXbo5bjycclllNRvK+mmp/L3H3RNW2L7IqUUhUbpivXEC3fRVWbfld9/z7DiRw8BPINX7oLsZxZf4XFJ47W2r6gAGP4N+nP4rAYEw52WTv2k7yogVYjbZ7kOAhIwkeMtJhHV1SIueeG15uZbjhmaeq0q97CJ4eMk6czeGT+XFcvqYrNf1v3zYnNFhdYvnKv67y6TfxhASp+H1BC6frTv/gDNv2uH6/rPpzPctWrSUzS0uNalV5ccwoakdFOk17MTGJH35exrn4C6SkpjHumZE8MqBPiXRpGRl8u+hnDhw5ik6vJyw0hCkvjiM6sobL8y88uFxagbBw4UImTJjAwoULuXr1KpUqVXLl5iuMwWBAqVRWdDYc5OXlkZqaSs+ePV32PXt6euLp6emSbd1LXm06EjTiOVK+/YLCuLP49XmY8Gnvc2HiM5hztCXSe7frTOCQZ0ie/wmFsadRhFYmdNwkwErq4m8AkKrU6C5dQLt1A5Vfm3FPyuHXpRvh4ydy6eMPyDt9iuDHniBq7mecePJxTNqSDwR+3XtQ+fkXuDjnPfJOnEAdHk61adPBCknzPgeg2tQ3cKtenQuzZmJMT8e/Zy+iPvuSk0OfxJie5vIyuDdri9+jo8j4eT6Gi+fw6tqfoIkzuPrWOCy52SXSp82fA/Ki05DMw4vQtz6j4FBRZY08MISQye+Tt3sz2rW/YtUVoqgUbq/sKQ8bTl/ik81HmNarOfUqBfDLwbO8sGwrq8f0w8+j5A0GgKdKwaoxfe3vJTcNt6vq582UHs2o7OuJ3mTip4OxvLBsK2ue74efu/Nt3isyD3dyjseStGgFzZZ/VaF5uZm6USt8Bg5D+/tCjJfi8OjYG//nppI6+1UseTkl0mf+MBeJrOg3JfXwInDSHApjiip4JCoVhgux6I7uw/eJMfdlOSRKJcrK1cjduArjlUtI3T3wGTQCv2cnkT532j0pE4A8qhHqDgPQbfkdc3IiysYd8Bg0hrzFc7AW5pVcQSrDY9DzWArzKFy3CEt+NlIvP6z6wnuW55vdD2XYtWMLPyz4H8+Nf4Wo6NqsW72cd6a/xpff/oivr6ZE+rOnTzL3w3cYOnIMzZq3Zsf2TXzw7pt89Pm3VI2obk/XuGkLxr80xf5eoXC81zGZTLRp14mo2nXZ/I/rKw/WH43l47U7ePORLtSvEsLPO48y9ttVrJkyAn8v91LXu5KZzdw/dtKkeliJ/1UN1PD6w52p7O+Dzmjip+1HGPvtKv54fSR+nqVv827IIxuhat8f3dblWJITUTRqj/vAMeQv+cD5bwiw6gvJX/JB8SUO/3fr+zRWi5nCdT9g1etQNumI+6DnyP/xIzAZXF4Gnw6dCR39AlfmzaXg7BkCBj5CtVkfETtmGOZsbYn0vp26EvL0GC5/9gH5p0+hCqtM+CtTASvXFvzPnk6XcJEL014tKqXZ7PK83+yphyszuE8l3v88lmspOp4ZEsEnb9dj2PjDGIxWp+uMmRSDtFhjcbWqHnz2Tn227rZVDKSm6xkwwrGhoH/PUJ4cFMb+I5kuL8OWnbuZv3AxL78whtpRNVm+9k8mz3iPJfM/R+PrUyK9Xq+nUkgQndq25quFi5xuMzcvjwlTptO4fl3mzHgDX29vLl9LxtPTw+X5/8+5+aZMuCsu61eRl5fHsmXLGDt2LH369GHRokV3vc2kpCQee+wxfH198fPzY8CAASQkJNj/P3LkSAYOHMjHH39MaGgo/v7+jBs3DmOxB4tr167Rp08f3NzcqFatGr/88gsRERF89tlnpX7uje2+9957VKpUiejoaAB+/PFHmjVrhpeXFyEhITz11FOkpqYCtq77nTt3BkCj0SCRSBg5ciQAFouF2bNnU61aNdzc3GjYsCHLly+/ZdmzsrIYPnw4Go0Gd3d3evfuzfnztlbcbdu22XscdOnSBYlEwrZt25xuR6vV8txzzxEcHIxaraZevXqsW7fOadqbhzDc+B7ef/99goOD8fX15Z133sFkMvHaa6/h5+dH5cqV+eGHH0ps6+zZs7Rp08b+mdu3b79lee+GX9/BZG/+m+xt/2C4nEjyt59jMejx6dLTaXq36DoUxp4iZ9dWjGkpFBw/TO7urahrRtvT5MccJH3pIvIO7C63fN8s+IknSftjDel//YkuIYFLH32ARacjoG9fp+k969Un78RxMjf+gyH5GjkHD5C5aSMedWytlhKlCk3HTiT9bx55x2LQX7nM1e+/Q3/lMkGDHi6XMnh3H0Durn/I37MF47XLZP48H6tBj2fbrk7TWwrysORo7S91nUZYDXoKDhd9774Dh1B48gjaFYsxJl3ElJZM4bGDTiskXOWnA2d5uGENBjSoQY0AH6b1aoFaLmf1ceetxTcEeLrZX/4ebg7/6103glbVQqis8aRGoC+vdm1Cnt7I+VRtuZXjdqVt2MG5GZ+RsmZTRWelBM9OfSjYu4XCA9sxpVwh+/eFWA0G3Ft2cpreWpCPJTfb/lJF1cdq1KM7tt+epvDQLvL+WYn+3Il7VArXl8OqKyTj6/fRxezDnHYN46U4slf8gDK8OjJf/3tWLlWTjhhP7sN4+iCWzBR0m5djNRlR1HXeUqeo2wKJ2p3CP77HfC0Ba04W5ivxWNKv3rM83+x+KMMfq36ne68+dO3em/AqETw3/hVUajVb/vnLafp1a1fQuGkLBg5+gspVqvLUsGeoViOSv9etciyLQoHGz9/+8rypN+MTQ5+m36BHqVq1WrmU68cdR3i4VT0GtqhLjRB/3hzcFbVCzuoDp0pdx2yx8MbP6xnbsxWV/bxL/P+hJrVoFVWFyv4+1AzxZ9KADuTpDJy/Wn4t98omHTCe2ofp+m9Iv2XFLX9DN1gLcou9iioaJL4ByEIj0G9ZgSUlCas2Df2WFSBXoIhuXC5lCBz0KJnr/yRr43r0SZe4Mm8uVr0Ovx4POU3vXrse+adPoN22GWNqMnlHD6Hdvhn3KMdeMVazGVNWpv1lzim/a/cNj/ULY8nview6kEn8pQLe+ywWfz8V7VuV3uNPm2MkU1v0atPMj8vXCok5acuvxYLD/zO1Rtq38mfLrnQKdRaXl+H3Nevo06Mrvbt1JqJKOK+8MAa1Ssnfm7Y4TV8rsibPPz2cLh3aolAonKb5dcVqggL8mTJxHLWjIgkNCaZ544aEhZbd40cQinNZBcJvv/1GrVq1iI6OZujQoXz//fdYrc5r+W6H0WikZ8+eeHl5sXPnTnbv3o2npye9evXCYCiqed26dSvx8fFs3bqVxYsXs2jRIofKi+HDh3P16lW2bdvGihUr+Pbbb+0P/beyefNmYmNj2bhxo/2B22g0MmvWLI4dO8bq1atJSEiwVxKEh4ezYsUKAGJjY7l27Rqff25rBZ49ezZLlizh66+/5tSpU7z88ssMHTr0lg/VI0eO5NChQ6xdu5a9e/ditVp56KGHMBqNtGnThthYW/f8FStWcO3aNdq0aVNiGxaLhd69e7N7925++uknTp8+zZw5c5DJZGWW/4YtW7Zw9epVduzYwdy5c5kxYwZ9+/ZFo9Gwf/9+nn/+eZ577jkuX77ssN5rr73Gq6++ytGjR2ndujX9+vUjIyPjtj/3tsnlqKtHkn/8aNEyq5WC40dxi3LetbMw9jTq6pH2CgNFUAgejVuQf+SA6/N3myRyOR5R0eQcOli00Gol59BBPOvWd7pO3skTuEfXwqO2rcJAVakSPq3akL3X1novkcmQyOVYDI4tFRa9Hs8GDV1fCJkcZZUa6M4cdyiD7swxVNWjS1+vGM923cg/uAurQW9bIJHgVr8ZppSrBE2cQeWPFxHy+oe4NWrp+vxfZzSbOZOcSctqRRdUqURCy4gQjl8p/Qa00GCi91er6TVvNS8t3058mvaWn7EyJg5PlYKoIF8X5v4BI5OhqFwN/bmTRcusVvTnT6Ko6rwb583cW3ai8Ojeot9URbhH5ZC4uWO1WLAUFtxtjm+PVIY0qDKmpHPFFloxJZ5DFhrhdBV59XqYrl1C3XkwnqNn4jH0NZTNu1Zc69B9UAaj0Uh8XCwNGjUtyrZUSoNGTYk963yI27mzpxzSAzRu0qJE+pMnYhj51EDGjxnGN1/NJfcePNzdYDSZOXM5lVaR4fZlUqmEVlFVOH7pWqnrffPPfjSe7jzcsuTwN2efsWLvSbzUSqIqBbok3yVc/w2ZE4sP1bNiTjyHNKRq6esplHg8PQ2PUdNR930aqV+w/V83eh9ZzSaHbWI2I6vk+sociVyOW81o8mIOF/s4K7kxh3GvVXIoFUDBmZO414zGLaoWAMqQULyatSLnoGMrvSosjNo/Lid64S+EvzYNRWCQy/NfXGiwGn8/JYeOae3L8gvMnDmXS93o2xvuK5dL6NEpiL82pZSaJqqGJ1HVPflzU/LdZrkEo9HIubgLNG3UwL5MKpXSpGEDTp09d4s1b23PgUNE16zB23M+YdCwZxg98TXWbfjvNRwI/30uG8KwcOFChg4dCkCvXr3Izs5m+/btdOrU6V9tb9myZVgsFr777jsk1y/KP/zwA76+vmzbto0ePXoAttb+efPmIZPJqFWrFn369GHz5s2MHj2as2fPsmnTJg4ePEizZs0A+O6774iMLPuGzcPDg++++85h6MKoUaPsf1evXp0vvviC5s2bk5eXh6enJ35+fgAEBQXZYyDo9Xref/99Nm3aROvWre3r7tq1i2+++YaOHTuW+Ozz58+zdu1adu/eba8Y+PnnnwkPD2f16tU8+uijBAXZTsB+fn6EhDivOdy0aRMHDhzgzJkzREVF2T/7Tvj5+fHFF18glUqJjo7mww8/pKCggDfeeAOA119/nTlz5rBr1y6eeKJoPN/48eMZPHgwAPPnz2f9+vUsXLiQyZMn39Hnl0Xu5Y1EJsOU7djF35SdhXtYuNN1cnZtReblQ9VZcwEJErmcrH/+IGPVUpfm7U7IfXyRyOUYMx27wRkzs1BXjXC6TubGf5D7+FLrf9+ARIJULid11Uqu/bgYAEthAXknjlNp5CguJCRgzMrEv1sPPOvWQ3flstNt3g2ZpxcSmazEsBFzbjaK0Mplrq+MiEQZVpWMxfPsy6RePkjVbnj3ehjtmp/JWrEEt3qNCXx+Cilzp6M/V3or1b+VVaDHbLWWGFbg76EmIaNkV3OAqn5ezOjTkqggDbl6Az/uP8PIHzey/Nk+BHsXdZndcf4KU9fsRmc0EeDpxtdPdEFTwcMX/sukHrbj23xTbxNLbjbKoLKHbimq1EBRqQraZd+WVxZvyz0ph1yBd98nKTy6554NB5C4eSCRyrAW5DostxbkIvNz/pAg9fFDGl4T49kjFKxZgNQ3AHXnwSCVYdj/z73ItoP7oQy5OdlYLBZ8ff0clvv6ariS5DxOgzYrs0R6H18N2qyia0zjpi1o2aYDwSGhJF+7ws+Lv2PWjCnM/virO2po+Ley8gsxW6wlhir4e7pzMdV5l/AjF66w6sApfntlyC23vf30Bab8+Dc6o5EALw++fu5hNJ5ut1zn37rxG7KU+A3llfobsmSlotu4DEv6NSQqNcomnXB/bAL5P32ENS8bS1YqlpxMVG0eQrdlORgNKBt3QOrli8WjZK+LuyXz9rHdS2U5fu8mbRbq8CpO19Fu24zM24caH32JRGK7l8r4cw1pv/1sT1MQe5qkuXPQX05C7udP8FMjqPHRF5wb+zSWwvI5T/lrbK3vWVrHxpNMrQE/ze0NR27f0h9PDzl/bSm9AqFvt2ASkgo4eTa31DT/VnZOLhaLpcRQBY2vD4lXrvzr7V5NTmXN3//w6IC+DHn0Yc6ej+PLBd8jl8vp1bXTXeb6P076YAYzrCguqUCIjY3lwIEDrFpl6xonl8t5/PHHWbhw4b+uQDh27BhxcXElggPqdDri44u6EdetW9fhQhcaGsqJEyfs+ZLL5TRp0sT+/5o1a6LRlBwveLP69euXiHtw+PBh3n77bY4dO0ZWVhYWi63LUmJiInXqOK+hjYuLo6CggO7duzssNxgMNG7svBvamTNnkMvltGxZ1Mrq7+9PdHQ0Z87cfoCmmJgYKleubK88+Dfq1q2LtNhBFxwcTL16RbX+MpkMf3//Er06blSWgO330KxZs1Lzrtfr0esdW9UMZgtKWfkc7O51GuD/8BMkL/gSXdxZFCFhBD89FtPgTDJW/Fz2Bv4jvBo3odKwEVz65CPb+MPKlaky8WVC05/m2mLbsJILs2YS8fo0Gq1Zh9VkIv9cLJmbNuIeXauCc1+SZ7tuGC4nOARcvFF5WBhzgNxNfwBgvHwRVY1aeHXoWS4VCP9Gw8qBNKxc1LrVMCyQwd+uY/nR84zrWNTbo3nVYJaO6o22UM/KmDgmr97FjyN6lhpXQbg77i07YbyaWGqgwvtFmeWQyvAbMREkErJ///7eZu5OSSRYC/LQbf4NrFYsqZcxePigbNa5QioQ/pUHoQxAu45FQ8uqRlSnakQNXnj2KU6diCnRe+G/IF9nYNqvG5jxaNcyKwOa1wjnt1eHoM0vZMW+k7z241/89OITt4yrcC9Zki9hSS4K3Fp4LQGPYVNQ1GuNYd96sFgo/HMx6m6P4fX8u1gtZsyJ5ys00OjNPOo3IuixoVz932cUxJ5GGRpGpecmEPTkMFJ//RGA3EPFenYmXKAg9gy1Fy3Fp31nskoZfnOnuncMZNLYoobBKbPu/r6gb/cQ9h/OJCPTeawJpVJKtw5BLP7tFoFW/4OsVgvRNWswevhTAETWqMbFxCT+WP/Pg1+BILiUSyoQFi5ciMlkcgjmZ7VaUalUzJs3Dx+fksE+ypKXl0fTpk35+eeSD3SBgUU36jeP85FIJPYH+7vh4eEYUCQ/P5+ePXvSs2dPfv75ZwIDA0lMTKRnz54OQypulpdnG9P2559/EhbmGOxHpVLddT5vxc3t7mvbnX2/rv7OZ8+ezcyZMx2WjatdnfF1y44Ia8rNwWo2I/dxrBSS+2gwaZ23YAQ8MYLsHZvJ3rIeAH1iAlKVmpDnJpKx8he4i6E3/5YpW4vVZELh59hipPDTYCxl6EfYs2NI3/A36evWAlB4IR6Z2o2qk6dybckiWxfpq1eInfACUrUamYcHxowMasx8F/3Vf1+DXRpzXi5WsxmZt6/DcpmXD+bsW0eFlyhVeDRvh3bNr062acJ4zTGStfHaZVQ1S48+fjc07ipkEgmZBY7RmjPydfh73t6DvkImJTpEQ1KWY/AsN6WcKn5eVMGLBmEB9P96LauOxfNMm7ouy/+DxJJvO75lXj4UD5kp9fJxGiC1OIlShVvjNuSu/71c83g7yrUcUhmaERORaQJI/9+79zQYobUwH6vFjMTdsaJf4u6FJd95q5w1PxerxexwnrVkpSD18AapDCzlH2DNIT/3QRm8vH2QSqVob7qmabVZ+Gr8nK7jq/ErkT77FukBQkIr4e3tw7VrV+5JBYLGww2ZVEJGruOQm4y8AgK8SgZ1S8rQcjUzhxe/X2tfZrm+D5q89jlrpowgPMAXAHeVgioqX6oE+NKgaij9Zi9i9YGTPNP11jEJ/o0bvyGpuxfF74Qk7p6l/oZKsFgwp11B6ls0Rt+SepmCX+aCUo1EJsNamI/74y9iTnF9D0JzTrbtXuqm34fcV1OiZ+QNIcNGod3yD5kbbME1dQkXkardqDzhVVKX/uT0XsqSn4f+ymVUlUoGv/y3dh3I5HTsEft7hcLW+KTxVZKRVXTG9fNVcv6i84CWxQUHqmjawJc355Q+A1bnNgGoVVI2bC17SPS/4ePthVQqJUvr2GstS5uN313M8uav0VA13LFHaNXKYezcs6+UNR4gD+h0ihXlrr9Nk8nEkiVL+OSTT4iJibG/jh07RqVKlfj111/L3ogTTZo04fz58wQFBVGzZk2H1+1WSERHR2MymTh6tGh8fFxcHFlZdz7F1dmzZ8nIyGDOnDm0b9+eWrVqlWh1v9FjwVwswmydOnVQqVQkJiaWKEd4uPMu9rVr18ZkMrF/f1HQr4yMDGJjY0vt6eBMgwYNuHz5MufO/fvxUv/Wvn1FJyOTycThw4epXdv5A9/rr79Odna2w2tMrdsc42cyobtwHo/6jYqWSSS4129E4TnnNfVSldoWDacY640bvgoah3ujd4B30+ZFCyUSvJs2J++U80BvUrW6xAW6tHJYdDqMGRnIvLzwbtES7a4dLs0/AGYThsR41LWKxuwhkaCu3QD9hdKn1ARwb9oWiVxB/v6b4oKYTegT4pCHON5sKIIrYc5w/SwSAAqZjNohfuxPKOq6aLFaOXApmQa3OeWi2WIhLjWbgDIqHKxWWzwEoRRmM8bLF1FGFRvrLJGgiqyL8ZLzqUFvUDdsiUQup+DQrnLO5G0or3JcrzyQB4aQMf89hyBs94TFjCX1MvLw4sMCJcjDIzFfS3C6ivnqxesPSUXnKKlvIJa87HteeQDcF2VQKBTUqBnN8ZiihySLxcLxmMNElzI+PapWXU4cO+Kw7NjRQ6WmB0hPTyU3NweN5t4E4VTIZdSuHMT+80UVxBaLlf3nk5xOuVgtyI/lk4ay7JUh9lenOtVpXiOcZa8MIcS39PHtFqsVg6mcfl/Xf0Oym35DsvBIh14GtySRIPUPxZrvZJicQYe1MB+JbwDSoHBMF06WTHOXrCYThXGxeDYs6rGLRIJno6YUlBJnQ6JSYbXe1HhUxr2UVO2GMrQSxkzXxcQqLDRzJVlnfyUkFZCRaaBpA197Gnc3GbWjvDgVW3aFzkNdg9FmG9l7qPSZFfp0C2b3wUy0OeUzG5RCoSCqZnWOHCu6/7NYLBw5foK6te6iV3HtaJKuOAZ7vXz1GsFB5RQfRHhg3XUPhHXr1pGVlcUzzzxT4sF+8ODBLFy4kOeff/6OtztkyBA++ugjBgwYwDvvvEPlypW5dOkSK1euZPLkyVSuXPaY6lq1atGtWzfGjBnD/PnzUSgUvPrqq7i5udm7Rt+uKlWqoFQq+fLLL3n++ec5efIks2bNckhTtWpVJBIJ69at46GHHsLNzQ0vLy8mTZrEyy+/jMVioV27dmRnZ7N79268vb0ZMWJEic+KjIxkwIABjB49mm+++QYvLy+mTp1KWFgYAwYMuO08d+zYkQ4dOjB48GDmzp1LzZo1OXv2LBKJhF69et1R+e/UV199RWRkJLVr1+bTTz8lKyvLIYZEcSqVqkRvjDsZvpC5bgWh416jMP48urizaPo8jFSlJnurbX7r0PGvYcrMIO0XW9fevEP70PR9GN3F+OtDGCoR+MQI8g7vs1csSNRqlCFFPWoUQSGoIqpjzsvFVA7THwKkLP2VatOmk3/2DPlnThP82ONI3dSk/2mr3a/25lsY09K4/M18ALS7dxHy+JMUnIsl7/Qp1GHhhD07huzdu+zl8G7REiQSdImXUIeFEz5uPLrES6T/6XwmjruVs3ENAU9PxHApDv3F83h364dEqSZv92YA/J+eiFmbgXbVTw7rebbrRkHMfqetNTkbVhE4ZhL6c6fQxZ7ArV4T3Bo0J+WTN8ulDABDW9TirXV7qRPiR71K/vxyMJZCo4kBDWwxRN78Yw9BXu682KkRAN/sOkGDSgGEa7zI1RtYvO8M13LyGdSoJmALsPjdnpN0jKxMgKcb2gI9vx05R2puAd1rOR9fei/JPNzxqFmUD/dqlfFuWAtDZja6pNKDmd0Ledv+RPPUWIxJF+zTH0qUKgquVzb5PjUWc3YWuX86xjBxb9UZ3YlDTh+qJe4eyHwDkF3vuSQPsj2sWHK15Ta7h8vLIZWhGfkSysrVyPjuQ5BKkXrZrsGWgjy4RxVT+iPbcevxJOaUJNsUiE06IlEoMZ62dV1W93gSa34O+t2285jh+B6UDduh7jQQQ8wupL4BKJt3wxCz857k934tQ79Bj/Ll3NnUjIwmMqo2f6xZjl6no0v33gB8/sn7+PsHMHSkbVrSvv0HM33qRNasXEbT5q3YtWML8XGxPD/BNp1eYWEBv/2ymFZtO6DR+JF87SpLvv+GkNAwGheryE5LTSEvN4f0tFQsFgsX420VXiGVwnBzu/vhAMM6NGH60n+oGx5MvSoh/LTjCIUGIwNb2Co6pv2ygSAfDyb2aYdKIScy1LES18vNdu9wY3mB3sh3mw/QqW51Arw80OYXsnT3MVKz8+je8N8/eJXFcGQH6h5PYE5Nsk3j2LhDid+QJS8bwx5bt31li+6Yky9h0aYjUbmhbNoZqbcG3amihiN5zQZYC/Ox5GYhCwhF1XEgpgsnMSeWT6NQ2qrfCX/ldQrPx1Jw7gwBAx5BqlKTtfFvAMJffR1jRjrJixYAkHtgLwGDHqUwPo6C2NOoKoURPOwZcg7ssd+DhD4zlpz9ezCkpqDw9yd46NNgsaDdtrlcynDDb39cYcRj4Vy+Vsi1FB3PPlWVjEw9O/cVBUL+7J367NiXzsq/iq5xEomtAuHvrSmYS+lYGxaipmFdH157p3yHUD46oC9zPvuKqJo17NM46nR6enW1zfj2/qdfEujnx+gRtnggRqORS0m23ikmk4n0zAziLlzETa0mrFKofZvjJ7/JT7+tpHO71pw5H8e6DZt4Zdxz5VoW4cFz1xUICxcupFu3bk57BQwePJgPP/yQ48ePO1nz1tzd3dmxYwdTpkzh4YcfJjc3l7CwMLp27Yq39+0HkFmyZAnPPPMMHTp0ICQkhNmzZ3Pq1CnU6jsbcxwYGMiiRYt44403+OKLL2jSpAkff/wx/fv3t6cJCwtj5syZTJ06laeffprhw4ezaNEiZs2aRWBgILNnz+bChQv4+vrSpEkTeyBCZ3744QcmTpxI3759MRgMdOjQgb/++qvUqVlKs2LFCiZNmsSTTz5Jfn4+NWvWZM6cOXe0jX9jzpw5zJkzh5iYGGrWrMnatWsJCLi91ts7lbtnOzJvHwIfH47MV4M+4QJJ702zz1usCAhyaKlPX/EzVquVwCdHIPcLwJyTTd6hfaT9WjQdpVv1KKrM/Nj+PnikrRIse9s/XPuqaLkrZW7ZhNzXl7BnR6Pw86cg7jznXn3ZHtRIGRwClqJyXF38A1ithI1+DmVgIEatFu3uXVz59mt7GpmnJ5WfG4syMAhTTg5Z27dy5duvy20e5oJDu8ny8sG3/5PIvDUYLl8k9YuZ9ocyuV9giV4T8uBKqCPrkPLpDKfbLIzZT8bPX+PTazCaJ57FlHKVtK8/QB9XfmNBe9apSlaBjvk7j5ORryM6SMNXj3W2T82YnFOAtFglZK7OwDt/7ycjX4e3WkntED8WDetOjQDbeVEqlZCQkcMfJ3aiLdTj46aibqgf3w/tTo1A33Irx+3yaVqP1pt/tL+v87Ht3JS0ZCXHn3m9orIFgC5mH9me3nj1egSZty/GK5fI+GaOrbUXkGkCSvymZIGhqKrXImP++063qa7bFM1TY+3v/UZMBCB3/XJyN6y4L8oh89HgVt8WHDjotQ8c/pc+7x0M8fdmrLTpXAw6N09UrXshcffGkn6FgtXf2is8pN4aLMXmt7fmaSlY/Q2qDgPxGDoJa142hpgdGA45n5rsXrgfytCuQxdysrX8+tMPaLMyqVa9JtPf+dA+JCE9LcXhnFSrTj1efm06v/y4kJ8Xf0doWBhT3nyXqhG2SlCpVMalhAts3byBgvw8NH7+NGrcnCeHjUKhKIr/tPSn79m6eYP9/asvjgbgndmfUq/B3U8n2KtxNFn5hfxvw17ScwqIDgvgf6MH4n99CEOyNgfpHbT3yKQSLqZmsvbgabT5Onw91NQND+aHcY9SM6T8elaYzsegd/NA1apnsd/QAvtvSOLli7TY8S1Ru6Hu+igSd2+s+gLbcIXfvsSSWdTzTeLhjarDACTunljzczCeOYzhwMZyK0P2jq3IvX0JHvY0co0fugtxXHxrMiatrdeuIjAYa7F7kJRff8RqtRIy/BkU/gGYsrXkHNhD8uKF9jSKgECqTJmOzNsbU3Y2BadOEPfyC+U+leMvKy/jppbx2guReHrIOXEmm0kzT2EwFuW/UogaH2/He+pmDX0JCVLfcvaFPt2CScvQczDmznsz34ku7duSnZ3Dol+WkZmlpUb1CD54exp+Gl8AUtPSHY75jMwsRr9UFKh82ao/WLbqDxrWq8Nn79uGCdeKrMmsN15jwZKfWbJsOaHBQYx7diTdO7Uv17L8J9zJiUQok8R6N3Mt3ocuX75MeHg4mzZtomtX53PTCxXv7KM9KjoLLpF7zXnU/vtJUJ0HY37gwDblM3f2vbR19C8VnQWXaDy+SdmJhHvC00lXcaFiJPV9raKz4BI1zq6u6Cy4hDE+rqKzcNcubjhcdqL7wDjFrLIT/cct+/DO48H9F1WKblB2ov8g3eovKuyz1QNfrLDPLi8um8bxv2rLli3k5eVRv359rl27xuTJk4mIiKBDhw4VnTVBEARBEARBEAShPIkgii71wFcgGI1G3njjDS5cuICXlxdt2rTh559/vuOhAIIgCIIgCIIgCILw/9kDX4FwY+pFQRAEQRAEQRAEQRD+vQe+AkEQBEEQBEEQBEH4f6qCpml/UIkBIYIgCIIgCIIgCIIglEn0QBAEQRAEQRAEQRAeTFLRZu5K4tsUBEEQBEEQBEEQBKFMogeCIAiCIAiCIAiC8GASMRBcSvRAEARBEARBEARBEAShTKICQRAEQRAEQRAEQRCEMokhDIIgCIIgCIIgCMKDSSLazF1JfJuCIAiCIAiCIAiCIJRJ9EAQBEEQBEEQBEEQHkxiGkeXEt+mIAiCIAiCIAiCIPwHfPXVV0RERKBWq2nZsiUHDhy4rfWWLl2KRCJh4MCB5Zo/UYEgCIIgCIIgCIIgCBVs2bJlvPLKK8yYMYMjR47QsGFDevbsSWpq6i3XS0hIYNKkSbRv377c8yiGMAj/Se7+nhWdBeE6pZd7RWfBJTIPn6roLNy1xuObVHQWXOLovCMVnQWXiHgorKKzcNeM+YUVnQXhOklfa0VnwSUMcecrOgsukROXVNFZuGvelXwqOgsu4W70qugs3LVChbivrVASSUXn4LbNnTuX0aNH8/TTTwPw9ddf8+eff/L9998zdepUp+uYzWaGDBnCzJkz2blzJ1qttlzzKHogCIIgCIIgCIIgCIKL6fV6cnJyHF56vd5pWoPBwOHDh+nWrZt9mVQqpVu3buzdu7fUz3jnnXcICgrimWeecXn+nREVCIIgCIIgCIIgCMKDSSKtsNfs2bPx8fFxeM2ePdtpNtPT0zGbzQQHBzssDw4OJjk52ek6u3btYuHChSxYsMDlX1tpxBAGQRAEQRAEQRAEQXCx119/nVdeecVhmUqlcsm2c3NzGTZsGAsWLCAgIMAl27wdogJBEARBEARBEARBeDBVYAwElUp12xUGAQEByGQyUlJSHJanpKQQEhJSIn18fDwJCQn069fPvsxisQAgl8uJjY2lRo0ad5F758QQBkEQBEEQBEEQBEGoQEqlkqZNm7J582b7MovFwubNm2ndunWJ9LVq1eLEiRPExMTYX/3796dz587ExMQQHh5eLvkUPRAEQRAEQRAEQRAEoYK98sorjBgxgmbNmtGiRQs+++wz8vPz7bMyDB8+nLCwMGbPno1araZevXoO6/v6+gKUWO5KogJBEARBEARBEARBeDBJ759O948//jhpaWm89dZbJCcn06hRI9avX28PrJiYmIi0gssjKhAEQRAEQRAEQRAE4T9g/PjxjB8/3un/tm3bdst1Fy1a5PoM3URUIAiCIAiCIAiCIAgPJGsFBlF8EN0//TkEQRAEQRAEQRAEQagwogJBEARBEARBEARBEIQyiSEMgiAIgiAIgiAIwoNJItrMXUl8m4IgCIIgCIIgCIIglElUINzH3n77bYKDg5FIJKxevfqO1x85ciQDBw60v+/UqRMvvfTSXeVp0aJF9vlHBUEQBEEQBEEQKpREWnGvB9AdDWEYOXIkixcvLrG8Z8+erF+/3mWZKi+dOnWiUaNGfPbZZ+Wy/bfffpvVq1cTExNTLtsv7syZM8ycOZNVq1bRqlUrNBrNXW9z5cqVKBQKF+Tu3vPs2AvvHgOReftiuJxA1rLvMCTEOU0b9Mo7qKPqlVheeOIwaV+9B4DfiPF4tu7i+P9TR0n7cpbrM1+Mb89++Pd/FJmvH/pLF0j5/it0cbGlptc8NAjfnn1RBARhzskhd99O0n5ZiNVoBMCtdn38+z+KqnokCj9/Ln/4NnkH95RrGdzbdsejU19kXj4YryaSs2oxxqR4p2n9xr6JqmadEst1p4+StfAjANT1m+PeuiuKytWQeniR9snrmK5eKtcyAHi074lX137IvH0xXrlE1vLvMV5yXo7AF2egiqxbYnnhqSNkfD0HAO/ej+LWtA0yX38wmzAkXSDnj6UYLjn/nbqKe9vueHbpZ98f2SsXYUx0Xg7/cdNL3R+ZCz4Eru+Ptt1QXt8fqR9NvSf743b4tWtG9VefwadJPdSVgjg0+AVS1m6u6GzZ+T00AP+BjyPX+KFLiCf52y8pPH+29PT9BuPXu7/t+M7NJmfPDlKWLLAf35pe/fHr3Q9FUAgA+sQE0pb9SN6RA+VWBs9OvfHpMQiZj+1cm/nrAgwJ552mDX71XdTRJc+1BScOkfblu/b38pDKaAYPRx1VF6QyjNeSSPv6A8yZ6aIcxfy1bhWrVyxDm5VJRLUaPPv8i0RF1y41/e6d2/j1p+9JTUkmtFJlhj89hqbNW9n//8XcOWzdvMFhncZNmvPWLNuxfvJ4DNNff9nptj/8dD6RUbXuvlClUDRoi6pZZyTuXljSr1K4dRWWlMTSV1CpUbd5CHnNBkhU7lhyM9FvX4Mp4Uy55fFmHh164tW1f9E14/fvMZZyfg+c+Lbza8bJI2R8PbvEct8nRuPZrgfa5T+Qt+0vl+f9Bq/OD+HTcyAyHw2GpAQyfv0Ww0Xnx0XIa++ijq5fYnnB8UOkflF0r6QIrYxm8AjbcSGTYbyaROr8OeV6fAMMHxRCr07+eLrLOH0+ny8WJ3E1xVBq+sUf1yEkUFli+dpNaXz14xUAenfyp3MrDTUj3PBwk/Hw2BPkF5jLrQw3++OPP1ixfDlZWVlUq16dsWPHEh0d7TTtpUuX+PHHH4k7f57U1FTGjBnDwEGD7llehQfbHcdA6NWrFz/88IPDMpVK5bIM/RcZDAaUypInlYoUH297ABgwYAASF01N4ufn55Lt3GvuTduieeRpMn/5Bn3COby79CVowltcfXsCltzsEunTv/4Q5EU/fZmHFyFvzqXgiOODdeHJI2QsmWd/bzUZy68QgFebjgSNeI6Ub7+gMO4sfn0eJnza+1yY+AzmHG2J9N7tOhM45BmS539CYexpFKGVCR03CbCSuvgbAKQqNbpLF9Bu3UDl12aUa/4B1I1a4d1/KNnLv8eYGIdH+974jZlK2gevYsnLKZE+a9GnSIrtC6m7JwGvzkF3fL99mUSpwnAxlsJj+/B9bEy5lwHArUlrfAcNJ2vZAgyXzuPZqQ+BL0wjedZLTsuR/t3HSGTFyuHhRfDUjyg8ute+zJh6Ff3v32NKT0GiUOLVuQ8B494k+Z0JWPJyy6Uc6kat8Bk4DO3vCzFeisOjY2/8n5tK6mzn+yPzh7klyhE4aQ6FMfvsyyQqFYYLseiO7sP3iXuzP26XzMOdnOOxJC1aQbPlX1V0dhx4t+tE8KixXJv/GYXnzuDXbzBV3/6A8y+MwJytLZHep0MXgoeP5uqXH1Jw9hTKSuGETZyM1Wol5fv5ABgz0khZ8h2Gq5dBIsG3Sw/C35jFhZefQ5+U4PIyuDdri9+jo8j4eT6Gi+fw6tqfoIkzuPrWOKfn2rT5c0qca0Pf+oyCQ0XnWnlgCCGT3ydv92a0a3/FqitEUSncXklSHu7HcuzasYUfFszn+fEvExVdmz9WL+ed6ZOZ9+0SfH1LNiCcPX2SuR/OYujI0TRr3pqd2zcz593pfPz5t1SNqGZP17hpCya8NMX+vngjQnTtunz/4wqH7f7y0/eciDlCzUjnDy2uII9qhLrDAHRbfsecnIiycQc8Bo0hb/EcrIV5JVeQyvAY9DyWwjwK1y3Ckp+N1MsPq76w3PJ4M7cmbfAdNIKsZd9iSIjDs3MfAsdNI/mdic6vGQtuvmZ4Evz6xw7XjBvUDVqgjIjCrM0s1zK4N2+H32OjyPhpPvoL5/Du1o/gl97mypsvOD0uUv83x7EMnl5UmvE5BYd225fJA0MImTKbvF2b0K75BYuuEEWlKuV6fAM89lAQA7oH8vGCSySnGxjxcCjvT6rB6DfOYjRana7z4sxYpNKi++mIMDVzptRk58GisquVUg6dyOHQiRyeeaxSuZbhZtu3b2fBt98yfsIEakVHs3r1aqa/+SbfLljgtOevXqcjNCSE9u3a8e23397TvP4XiWkcXeuO+1WoVCpCQkIcXsVbvyUSCd999x2DBg3C3d2dyMhI1q5d67CNtWvXEhkZiVqtpnPnzixevBiJRIJWqy31cxMTExkwYACenp54e3vz2GOPkZKSYv//zd3xAV566SU6depk///27dv5/PPPkUgkSCQSEhISnH5WREQEs2bNYvjw4Xh7ezNmjO0mecqUKURFReHu7k716tWZPn06xusnwUWLFjFz5kyOHTtm3/6iRYsA0Gq1PPvsswQGBuLt7U2XLl04duzYLb/nEydO0KVLF9zc3PD392fMmDHk5dkunG+//Tb9+vUDQCqV3rIC4dSpU/Tt2xdvb2+8vLxo3769vfLhZjcPYYiIiODdd99l+PDheHp6UrVqVdauXUtaWpp9XzRo0IBDhw6V2Nbq1avt+7hnz54kJSXdsrx3w6tbP/J2byR/7xZM1y6T+cs3WIx6PNt0cZreUpCHJUdrf6lrN8Rq0FNw2LECwWoyOqSzFuSXWxkA/PoOJnvz32Rv+wfD5USSv/0ci0GPT5eeTtO7RdehMPYUObu2YkxLoeD4YXJ3b0Vds+jGLj/mIOlLF5F3YLfTbbiaR4eHKNi3lcKD2zGlXCF7xUKsRj1uLTo6TW8tzMeSm21/KaPqYzXq0R0rqkAoPLyLvI2rMJw7eU/KAODVuS/5ezdTsH8bpuQraJctwGow4NG6s9P01gLHcqhrNcBq0FN4tOjBu/DwbvSxJzBnpGJKvox21RKkbu4oKlUtt3J4dupDwd4tFB64vj9+X4jVYMC9ZafbKofK2f44tIu8f1aiP3ei3PL9b6Vt2MG5GZ+RsmZTRWelBP8Bj5L1z19oN69Hn3SJa/M/xaLXo+nW22l6t1r1KDhzkuwdWzCmppAfc4jsHVtwiyxq9c07uJe8w/sxXLuC4eplUn/6HouuELdbtErfDe/uA8jd9Q/5e7ZgvHaZzJ/nYzXo8Wzb1Wn6EufaOo2un2uLzke+A4dQePII2hWLMSZdxJSWTOGxg04fWP4/l2Ptqt/p3qsPXbv3JrxKBM+PfwWVWs3mf/52mn7d2hU0btqCQYOfILxKVZ4aNorqNSL5a90qh3QKhQKNn5/95enlVer/vLy9ObBvN12693JZw4UzqiYdMZ7ch/H0QSyZKeg2L8dqMqKo28JpekXdFkjU7hT+8T3mawlYc7IwX4nHkn613PJ4M68ufcnfs5mCfdts5/el316/Zji/D7EW5GHJ1dpfRdcMxwoEqY8fvo+OInPR51jNpnItg0/3AeTu/Ie83ZsxXksi4yfbceHVrpvT9Jb8PMw5WvvL7fpxkV+sAkEzaCiFJw6TtXwxBvtxcaBcj2+AgT0D+fWPZPYezeFiko4Pv72Ev6+CNk18Sl0nO9dMVrbJ/mrZyIerKXqOny2qtFr1Txq//ZnK2fiCcs2/M6tWraJX79706NGDKlWrMn7CBFQqFf/884/T9FHR0Tzz7LN07NTpvu1dLPx3lcvAjJkzZ/LYY49x/PhxHnroIYYMGUJmpq3m9OLFizzyyCMMHDiQY8eO8dxzzzFt2rRbbs9isTBgwAAyMzPZvn07Gzdu5MKFCzz++OO3nafPP/+c1q1bM3r0aK5du8a1a9cIDw8vNf3HH39Mw4YNOXr0KNOnTwfAy8uLRYsWcfr0aT7//HMWLFjAp59+CsDjjz/Oq6++St26de3bv5G/Rx99lNTUVP7++28OHz5MkyZN6Nq1q/07uVl+fj49e/ZEo9Fw8OBBfv/9dzZt2sT48eMBmDRpkr0XyI3PcubKlSt06NABlUrFli1bOHz4MKNGjcJkuv2L0Keffkrbtm05evQoffr0YdiwYQwfPpyhQ4dy5MgRatSowfDhw7Fai2p0CwoKeO+991iyZAm7d+9Gq9XyxBNP3PZn3hGZHGWVGujOHC9aZrWiO3McZfXbayHxaNuVgkO7sBr0DsvVUfUI+/AHQt/+Es2TY5B6eLoy547kctTVI8k/frRomdVKwfGjuEU5fxgojD2NunqkvcJAERSCR+MW5Jdj9+VbkslQVK6G/nyxB32rFf25kyirRt7WJtxbdkJ3dF+JfXFPyWQowqujiy32gGy1oos9gTIi6rY24dG6CwVH9pReDpkMjzbdsBTkY7xSTt3/b+yPczftj/MnUdzB/ig8urdi98cDQCKX41Yjivxjh4sWWq3kHzuMW3TJISMAhWdP4lYjyl5hoAgOxatpS/IO73eaHqkU7/adkarVFMaednURbnGuPYbqNs+1nu26kX+w2LlWIsGtfjNMKVcJmjiDyh8vIuT1D3Fr1NL1+b/hPiyH0WgkPu4cDRs1tS+TSqU0aNSE2LOnnK4Te/a0Q3qARk2ac+6m9CdPxDDiqUGMGzOcr7/6lJyc0h/sDu7fTV5uDl26O6/0cgmpDGlQZUxJ54ottGJKPIcsNMLpKvLq9TBdu4S682A8R8/EY+hrKJt3hXvV4iiTX79m3PSbij2OstptXjPadC15zZBI8Bs+gbzNazElX3Zxpm8ik6OsWgPd6WKNW//muDiw0/G4aNAMY8pVgl96m/C5iwl94yPcy/P4BkIClfj7KjhyqujBv6DQwtkLBdSu6XFb25DLJHRpo2HDjozyyuYdMRqNxJ0/T6NGjezLpFIpjRo14uyZezdMRxBuuOMhDOvWrcPT0/FB6o033uCNN96wvx85ciRPPvkkAO+//z5ffPEFBw4coFevXnzzzTdER0fz0Ue28c3R0dGcPHmS9957r9TP3Lx5MydOnODixYv2h/4lS5ZQt25dDh48SPPmzcvMt4+PD0qlEnd3d0JCQspM36VLF1599VWHZW+++ab974iICCZNmsTSpUuZPHkybm5ueHp6IpfLHba/a9cuDhw4QGpqqn2ox8cff8zq1atZvny5vXdDcb/88gs6nY4lS5bg4WE72c2bN49+/frxwQcfEBwcbO+udKuyfPXVV/j4+LB06VJ77WNU1O1dzG546KGHeO655wB46623mD9/Ps2bN+fRRx8FbL0yWrduTUpKij0vRqORefPm0bKl7SKxePFiateuzYEDB2jRwnkLwr8l8/RCIpOV6OJvydWiCAkrc31lRE2UYVXJ/NGxu7Pu1FEKj+7HlJ6CPDAE34FDUE6YTsoHr4PV4soiACD38kYik2HKznJYbsrOwj3MeUVXzq6tyLx8qDprLiBBIpeT9c8fZKxa6vL83Q6ph21f3NyyYMnLRh5Udlc/RXgNFKFVyF62oLyyeFukHrZ9YXH2mwq+jXJUrYGiUhUyf5lf4n/quk3we/olJAollhwtaV+9iyW/fIYv3CiH+eb9kZuN8nb2RxVbObTLRNfHuyXz9rEd39qbjm9tFu6VqzhdJ3vHFmTePkTMvt5rTi4n8++1pC//xSGdqmo1qn0wD6lSiaWwkKTZM9Anub5SqrRzrTk3G0Vo5TLXV0ZEogyrSsbiomFhUi8fpGo3vHs9jHbNz2StWIJbvcYEPj+FlLnT0Z9z/nB8N+7HcuTmZGOxWPC5aaiCr6+GK0nO4wJoszJLDG3w9dWQlVX0G2zctAWt2rQnOCSU5GtX+Wnxd8yaMZU5H89DJpOV2Oamf/6mUZPmBAQE3lV5bkXi5oFEKsNa4HhetBbkIvMLcrqO1McPaXhNjGePULBmAVLfANSdB4NUhmG/89ZZV5J6lnLty8lGEVz2fYiiak3bNeNnx2uGV/cBYDGXa8yDG2Se3s6PixwtipDbOC6qRaKsHEF6seNCdv248Ok9GO3qn8lasRi3ek0IfGEqyR+/WS7HN4Cfj+3RRpvtOExCm2O0/68sbZr64Oku459d5Tts5Hbl5ORgsVhKxDvz1WhIulzOlUsPigc0mGFFueMKhM6dOzN/vuNJ7uax8w0aNLD/7eHhgbe3N6mpqQDExsaWeOAv66HyzJkzhIeHO/QYqFOnDr6+vpw5c+a2KhDuVLNmzUosW7ZsGV988QXx8fHk5eVhMpnw9va+5XaOHTtGXl4e/v7+DssLCwtLHUpw5swZGjZsaK88AGjbti0Wi4XY2FiCg4NvqwwxMTG0b9/+rrouFd+XNz63fv36JZalpqbaKxDkcrnDPqlVq5Z9Xznb13q9Hr3esYVTbzajcnID42oebbphuJxQIuBi8TF8xquJGK5cIuzd+aii6qKP/W903Xav0wD/h58gecGX6OLOoggJI/jpsZgGZ5Kx4ueKzt4dc2vZCePVxFIDLt4vPFp1wXDlktOAi/rzp0iZ8xoyT2882nTFf9TLpH78htMxshXN/cb+KCXgolC+3Os1JOCRIVz75nMKz51BGRpGyLPjCHxsKGm//WRPZ7iSxIWXRiP18MC7TUfCJk4hYdrL5VKJcDc829041xYFZLvRDb4w5gC5m/4AwHj5IqoatfDq0LPcHjDuxoNSDoD2HYu611eNqE7ViOqMfXYIp07E0OCm3gvp6WnEHDnIpKlv3etslk0iwVqQh27zb2C1Ykm9jMHDB2WzzvekAuFuebS+cc0oug9RhFfHs1MfUj6YXIE5u31eN46L4gEXrz+wFcTsJ2ejbSizIen6cdGxl8uOi86tNUwcWVTJMX3uhbveZs8Ofhw8nkOmtnyHjQjC/eqOKxA8PDyoWbPmLdPc/MAqkUiwWFzfalucVCp16EYP2OMT/BvFH94B9u7dy5AhQ5g5cyY9e/a0t+x/8sknt9xOXl4eoaGhbNu2rcT/ynu6Qzc3t7veRvF9eeMmydmyu9m/s2fPZubMmQ7LXmpai5eblT2O15yXi9VsRubt67Bc6uXrNPBgcRKlCo/mbcn+o+wWe3N6iq2FKii0XCoQTLk5WM1m5D6OtctyHw2mUgInBTwxguwdm8neYpsBRZ+YgFSlJuS5iWSs/AWszgMFlRdLvm1fSL0cxxhKPX2w5Gpvua5EqcKtUWtyNywvxxzeHku+bV9I/+Vvyr1pW3L+XOb0/1aD3vZbSk/BkHCe4Omf49G6C7kbV7sm88XcKIfMy4fiZ0Kpl89tlcOtcRty1//u8nz9f2TOybYd3ze1CMt9NZiynB/fQU89Tfa2jWg32lof9ZcuIlWpqTTuFdJ+/9l+fFtNJgzJtrHeuvjzuEVG49f3Ya7N/9S1ZSjlXCvz8sF8U8+pm9nOte3QrvnVyTZNGK85xsgxXruMqmb5xHG4H8vh5e2DVCol+6YeLFptFr4a58GPfTV+aJ2kv9WMTSGhlfD29uHatSslKhC2bPwbTy9vmrds+y9LcXushflYLWYk7l4OyyXuXqX21rLm52K1mB2ueZasFKQe3iCVgaV8o+Rb8kq59nnf3rnW2TVDVaMWUk9vQt8parCTyGT4PDwCz859SJ4xzmX5BzDn5Tg/Lrx9b/O4aE/WGsfeUea8HKwmE8arNx8XSaginQ/d+jf2Hc0mNr4oRpVCYau48PVRkJldVAHg660gPrHswJpB/goa1/Vi1hcXXZbHu+Xt7Y1UKnXoQQSgzcrCzwWzsP2/IIIoutQ9788RHR1dIujewYMHb7lO7dq1SUpKcgjEd/r0abRaLXXq2E5CgYGBJWIB3DydolKpxGz+dxeSPXv2ULVqVaZNm0azZs2IjIzk0iXHFh5n22/SpAnJycnI5XJq1qzp8AoICCi1vMeOHSM/v+iEuHv3bqRSaanTtTjToEEDdu7ceVcVKf+GyWRy2MexsbFotVpq13Z+I/X666+TnZ3t8Hqh8W0OtTCbMCTGo65V1FMCiQR1rQYYLpQ+/SGAe9M2SOQK8vdvL/NjZL7+SD28yryQ/msmE7oL5/Go36homUSCe/1GFJ5zPr5NqlLDTRU31hs3ShVxojSbba1uxaemkkhQRdbFcMn5NFA3qBu2RCKXU3h4Vzln8jaYzRiTLjhO9SmRoIqqhyHhXOnr9ZH+DQABAABJREFUAW6NWyGRyyk4uPO2PsrWNb2cghtd3x/Km8sRWRfjbe6PgkP/gf3xALCaTBTGn8OjQZOihRIJHg2alBqvwPnxbbGvWyqJFEl5BMwq7VxbuwH6Ms+1bZ2fa80m9AlxyG8abqYIroQ5I81VOS/xmfdbORQKBTVqRnE85oh9mcVi4UTMEaJrlZwKECC6Vh2OHzvisOzY0cNElZIebL0McnNz0Ggce0xarVa2bFxP5y49kMvvuN3pzljMWFIvIw8vHqdFgjw8EvO1BKermK9eROobABQdF1LfQCx52eVeeWDLgMl2zSg+paFEgiqqPoaLZV0zWl+/ZuxwWF5wcAcpsyeRMuc1+8uszSR301rSvyp9yO/dlMFwKR517ZL3UmUdFx7N2iJRKMjf5/y4uHk4qTw4DFNGqqtyTqHOwtVUg/116YqODK2RxnWKhlu7q6XUqu7Ombiyg2H3aO+PNsfE/mP/nZ6BCoWCmpGRHCv2XGOxWIiJiaFWKffWglCe7rgCQa/Xk5yc7PBKT7/9uVyfe+45zp49y5QpUzh37hy//fabfbaC0qL6duvWjfr16zNkyBCOHDnCgQMHGD58OB07drQPNejSpQuHDh1iyZIlnD9/nhkzZnDypGPU9oiICPbv309CQgLp6el31GoeGRlJYmIiS5cuJT4+ni+++IJVqxyjGUdERHDx4kViYmJIT09Hr9fTrVs3WrduzcCBA/nnn39ISEhgz549TJs2zensBQBDhgxBrVYzYsQITp48ydatW5kwYQLDhg277eELAOPHjycnJ4cnnniCQ4cOcf78eX788UdiY299MbhbCoWCCRMmsH//fg4fPszIkSNp1apVqUNVVCoV3t7eDq87Gb6Qu+kPPNt1w6NVJ+QhYWiefA6pUkXeni0A+I98EZ+BQ0qs59GmKwUxB7DkO04LJVGp8X14OMpqUcj8A1FF1ydw7FRb9ODTR0tsx1Uy163Ap+tDeHfsjjIsnODRLyJVqcneapunO3T8awQ+NcqePu/QPnx79MWrTScUQSG4N2hC4BMjyDu8z/7gIVGrUUVURxVRHbAFWlRFVEdeTmNY83f8hXvLzrg1a488qBLeg0chUaopPGC7sfB5cixeD5UMfureohO6k4exFpScokvi5oG8UlXkwbYuivKgUOSVqpZo7XGl3K3r8GjTFfcWHZEHh+H72LNIVSry920DQDNsHN79niyxnkfrLhQeP4jlpnJIlCq8+z2JMiISmSYARXg1NE+NRebrR4GTabtcJW/bn3i06oxb8w7Igyrh88goJEoVBdcfgHyfGotXn5IBTt1bdUZ34pDz/eF+fX+E3Lv9cbtkHu54N6yFd0Nb4EH3apXxblgLdXhoBecMMtb8jqZHH3w690BZuQqhz7+EVK0ma5OtB1HYS1MJGvasPX3uwb1oevfHu31nW4DUhk0JGvI0uQf32o/voGHP4l6nAYqgYFRVqxE07Fk86jUke/vmcilDzsY1eLXvjkfrzshDKuM35HkkSjV5u22f5//0RHwHDS2xnme7bhTE7HfagpyzYRUezdri2a478sAQvDo/hFuD5uRudz67wP/XcvQf9CgbN6xjy6b1JCVe4puvPkWn09G1ey8APv/kfX5cVBQ/pm//wRw9fIA1K3/jclIiS39eRHxcLA/1tc0DX1hYyKKFXxN79jSpKckcjznM7HemERIaRuOmjsNCTxw7QkrKNbr17OOSspRFf2Q7inqtUNRuhlQThLrrI0gUSoynbQGC1T2eRNW2KC+G43uQqNxRdxqI1DcQeURtlM27YTh2b2YfAsjdcv2a0fL6NePx0devGVsB0Awbj3f/p0qsZ79m3HQfYsnPw3QtyeFlNZuw5GRhSi2f2SWyN67Bq0MPPNp0RhFaGf+hzyNRqcndbZvVJmDUS/g+PKzEep7tulFw9BbHRfN2eLbvjjzIdly4N2xO7tbyO74BVm9I48n+wbRq7E1EZTWvjalKhtbIniNFcSrmTK5B/26ODXkSCfRo78emXZk3198CoPGRU72KG5WCbVO7V6uspnoVN7w8yn/I7aBBg1i/fj2bNm4kMTGRr+bNQ6/X0717d8AWX+1GgHW4Hnw1Pp74+HhMJhMZGRnEx8dz9eq9m51EeHDdcVXy+vXrCQ11vBmLjo7m7Nmzt7V+tWrVWL58Oa+++qp9ZoRp06YxduxYe5DBm0kkEtasWcOECRPo0KEDUqmUXr168eWXX9rT9OzZk+nTpzN58mR0Oh2jRo1i+PDhnDhR1N180qRJjBgxgjp16lBYWMjFixeJiIi4rXz379+fl19+mfHjx6PX6+nTpw/Tp0/n7bfftqcZPHgwK1f+H3v3HR1V0Tdw/Lu9JdlNb/TeCV16ryJdFBAEFBUs8KCCPPqAWMCCgB2kS1FUpAgKUqT3KjVAIJCQ3ssm298/FjdZsgGUhAjvfM6552Rnf3Mzs7u3zZ2Z+zMdO3YkIyODJUuWMHLkSH799VfefPNNRo0aRXJyMiEhIbRr167YxgCtVsuWLVsYP348zZo1Q6vVMnDgQGbPnn1XZf2Lv78/O3bs4PXXX6d9+/bIZDIiIiJo3bp0uyBqtVomT57M0KFDuXHjBm3btmXRokWl9v+Mx/Yh9fZB/9gQZD4GzLFXSfr8XdeERjK/ABy3THwoDw5DXb0OSZ9OL7pCux1FeEUCH+mIVKvFlplO/rmTZGz4Dv7GEyz+ruz9u5D56Al8YgQygy+m6CvEvP+m6xnxioAgty6aKWtW4nA4CBzyNHK/AGxZmeQcPUjydwUHEE2VGlSYPsv1OnjkCwBk7vyd+C8L0ktK/smDZOl88Oo+CJmPAcuNa6Qt+MA1xl9m8C8yCaUsMBRllVqkzp/hcZ3qek0wPPmC67Xv8FcAyN6yhpzf13jMc6/yjh8gw8sHn0cHI/M2YLkRTcpXM1y/KblvQJEhIvKgUFRVa5P8xbtF1uew21EEh6Fr/ipSnTd2Yzbma1EkzZ1WqrNr5588SKaXD949Cr6P1PkfOO/MATIP9ZAFhqKqUovUr4v5Puo2wXfoWNdrv6fHA5C9+Seyt5TO93G39E3q0XL7ctfrOrOck/vGfPszfz4zpayKBUDW3p3IfQwEDR2F3NeX/KtRXJs+2dWrSREQVNDDAEj+YTk4HAQNG43CLwBrVgY5Rw6QuKJgXyrXGwif8AZyPz/subnkX7vCtbcnuz/toQQZj+4j3VuPoc8QZD6+zn3tZ9MLtgu/wKLbxc19beKcaR7XmXfyEKkr56HvMRDfJ5/FmhhH8rwPMV0uvZnFH8R6tGnXiazMTL5fsZT09DQqV6nK1Hc+dA1hSE5OQlJokrBaderxn9ffYtXyxaxYtpDQ8HDeeOtdKlaqDDiHfV6LjuKP7Vsw5ubg6+dPRKOmDB0+GoVC6fa/t/3+K7Vq16Vcec8TfpY068WT5Gu8ULXsgUTrgz3lBsZ137gaNKU+vtgp+H4cORkY181H1a4fuqdew5GTifnkbsxHd9yX8gLkHd9/85jxRMEx48v3C/2mPB0zwlBV83zMKAvGI3tJ8/LBt+9Q53YRc5XEudOx33wyh9w/oMjxWx4cjrpGXRJme54bw3jiIKnLv0bfaxB+Q8ZgTbhB0tcflOr2DfDDr0moVVLGjyyPl1bG2Uu5vDnrChZLwXcQGqTCx8v9MqhRXW+CA5Rs2e15aNmjHQMY3r9g8vJP3nT2lJm14DpbS3nCxfbt25OVmcnyFStIT0ujStWqvPPuu65hSclJSUgL3YhNS0vj5ZtPbwNYs2YNa9asoX79+nz40UelWtZ/JamYRLEkSRy3ThxQBt5//33mzZvnNkRB+P/t+gsDyroIJcKYWvQO7oNGX8HzUJsHjc384E+GJJWX/l2O++HEF8fvHPQAqNTrzjOs/9t5Bd9+ImDh/smd9FVZF6FElNtY8o3TZSHr8oN/TmrNN5d1EUrE8xYPN3seMF9Nezj2tVWrVCnrIvwjxn1ld4ND23pgmf3v0lLKg9k8++qrr2jWrBn+/v7s27ePjz/+mJcKtZIJgiAIgiAIgiAIwr1yiEkUS1SZNCBcunSJ9957j7S0NCpUqMCrr77KlCll271UEARBEARBEARBEITilUkDwpw5c5gzp2QfMSUIgiAIgiAIgiAIbiRiDoSSJD5NQRAEQRAEQRAEQRDuSDQgCIIgCIIgCIIgCIJwR2UyhEEQBEEQBEEQBEEQSptDDGEoUeLTFARBEARBEARBEAThjkQPBEEQBEEQBEEQBOHhJB7jWKJEDwRBEARBEARBEARBEO5INCAIgiAIgiAIgiAIgnBHYgiDIAiCIAiCIAiC8FASkyiWLPFpCoIgCIIgCIIgCIJwR6IHgiAIgiAIgiAIgvBwEpMolijRA0EQBEEQBEEQBEEQhDsSPRAEQRAEQRAEQRCEh5OYA6FEiQYE4V9JoVOXdRFKhDTTWNZFuGfG5MyyLkKJCGnbqKyLcM8yz0WVdRFKRKVe4WVdhBIR/euNsi7CPXvkf9XKugglw24v6xLcs2SbpqyLUDIegu8CQOn94H8f1nxzWRehREgfgos/35wH/3jhVKWsCyD8Czz4W6QgCIIgCIIgCIIgCKVO9EAQBEEQBEEQBEEQHkoOMYliiRI9EARBEARBEARBEARBuCPRA0EQBEEQBEEQBEF4OD0E82j8m4hPUxAEQRAEQRAEQRCEOxINCIIgCIIgCIIgCIIg3JEYwiAIgiAIgiAIgiA8lByISRRLkuiBIAiCIAiCIAiCIAjCHYkeCIIgCIIgCIIgCMJDySEmUSxR4tMUBEEQBEEQBEEQBOGORA8EQRAEQRAEQRAE4eEkeiCUKPFpCoIgCIIgCIIgCIJwR6IBQRAEQRAEQRAEQRCEO3qoGxAqVarE3LlzS/V/7Nu3j/r166NQKOjXr1+xaf8fJCQk0LVrV3Q6HQaDoayLIwiCIAiCIAjC/3MOiaTMlofRv3oOBMkdPvRp06bx9ttv35/CFGPixIlERETw22+/4eXlVWzav9nOnTvp2LFjkfT4+HhCQkJcr7/88ks+/vhjEhISaNiwIZ9//jnNmzd3vT9nzhzi4+M5efIker3etd709PRSb1DQtu6KrkNvZN56LHHXyVq7DEtMlMdYv7FvoapWp0h6/rkTpC/6GAB1/WZoW3ZGUa4yUp03yZ9MwRp3rVTr4Im+62P4PTYImd4X0/UrJC/9ivyoi8XGG3r2w9ClN/KAQGzZWeQc2kPK90twWCz3rcw+nR/F0GsgMr0v5pirpCyfh+mK5zKHTZmJpnaDIum5J4+QMPttAGQ+BvyeGIW2XiOkWh35kWdJWT4PS2JcaVaD1Scu8+2RSFJz86kRaGBS50bUC/XzGLvhTDRvbz7ilqaUSTn4n4Gu19N+O8wvZ91/Qy0rBfPloHYlX/hCtK274tXpMde2kfnzUizXPW8b/i/+r9htI23BRyCV4d1rMOraEcj8g3Dk52G6eJqsjd9jz0ovtTr49eqLf78nkPv6kR8dRcI3n5N36ULx8Y8NxK9nHxQBQdiyM8nav5vEbxe4tgPfHn3w6/kYiiDn/s10PZrk1cvJOX641Opwt/zaNKXKq8+gb1wPdVgQRweOI3HD9rIulouqaQc0rboh9dJjTYzF+Nt3WOOii42XqDRoO/VDWasxEo0We2YauVtWY7l8xhUj9Tag7TwARbV6SBRKbGnJ5GxYii2+9Pa5qmYd0bTu7qxHQoyzHjeuFl8PtQZtp/4oazdGotFhz0wld/NqLJdOu9ej66BC9UgiZ/0SbP/w2OFwOFizagF//L6e3NwcatSuz+ixkwgJq3DbfL9v+olNa1eQmZ5GhcrVePq5V6lao67rfbPZxMrFn3Fwz1YsFgsNGrVg1Auvo/f1B2DX9o188+l7Htf91be/ojc494P7dm5m488rSIiLQavzomHjlgwZ9fI/quutFA3boGraCYnOG3tyHHl/rMGecL34DCoN6ta9kFdrgEStw56dhmnnWqxXz5dIee6G5pHOaNv1cv2msjcsxxp7pdh4iVqLrtsgVHWbItXqsGWkkrNxBebIP53vK9Xoug1EVacJUi8frHHXyN64Amts8b/TkuDdsRf67v1uHsOjSf3uG8xXL3mMDXn9PdQ16xdJN/55lKTP3gWg0sL1HvOm/biUrC1rS67gtxjeP5ge7f3QaWWcu5TLF9/eIC7RXGz80lm1CA5QFkn/ZXsKXy13nm/0bO9Hh5YGqlXUoNXIGDTuDLlGe6nV4afNO1i5YQtpGZlUq1ieiaOHULd6FY+x67ft5rddB7gScwOAmlUq8sKQ/sXGf/jNctZt3cX4kU/w5KNdS60OwsPpX92AEB8f7/p79erVTJ06lcjISFfav+HiPCoqihdeeIFy5crdNu3fwGw2o1QW3Tn+JTIyEh8fH9froKAg19+rV69m4sSJzJs3jxYtWjB37ly6d+9OZGSkKy4qKoomTZpQvXp1AM6dO1dKNXGnjngEnz5PkfnTYizXL6Nr2xO/594g+cNXsedkFYlPXzoHibzgpy/VehHw6gfk/3nIlSZRqjBfjSTv1EEMg5+7L/W4ldcj7QgcPoakRZ+TfzkSQ89+hL/xPtGvPostK7NIvHerDgQ8OZrE+bPJu3geZWg4IWNfBQckr/jmvpRZ16ItAUPHkLz0C/KjIjF070fo6+8SM+k5bNlFy5zw2ftI5ArXa6mXN+Xf+4Lcw3tdaSET3sJhtZEw913seUb0PfoTOvl9Yt54AYfZVCr12HIhhtk7T/HfLo2pH+rPyuMXefGn3awd3QM/ndpjHi+lnJ+f6el67an5s1WlEN7u2cz1Wikr3U5g6ohH0PcbTsaPi7Bcu4yufU/8n3+DpJmet420JbORyAptGzpvAl/7gLyTBwGQKJUoy1Ume+taLDeuIdXq0Pd/Gr9nXyNl9pulUgefNh0IHj2W+K/nknfxPH6PDaTi2x9yadzT2DIzisTr23UieMQY4j7/COOFsyjDyhM+fhIOh4PExV8DYElNJvHbhZjjYkEiwdCpG+X/+y5X/vM8ppjoUqnH3ZLptGT9GUnM0jU0/enLMi3LrZR1mqLr9ji5m1ZivXEVdYvOeA8bT8aXU3EYs4tmkMrweeo/2I3ZZP80D3tWBlKDP458oytEotbiM2oSluhIsld9ht2Yjcwv2C2mxOtRtxm67oPJ3bgC640rqB/pgvdTE8j44i0cuR7qIZPhM3wi9txssn+Yhz07HaneQz2eeQPL1UiyV36KPTfb2ciW98/rsfHn5WzZ+APPj59KUHAoP678hg+mTeCjL79DqVR5zHNgz1ZWLvqU0eMmU7VGXTZv+J4Ppk1g1terXRf+KxbO5eTR/bwyaQZanRdL589izsw3ePujBQC0bNOFho1buq13/tx3sVhMrnVEnjvF13Pf4alnxtO4WVvS05JY/NVHLPxiBk0aav9xnQHkNRqhbt+P/O0/YIu/hrJxe3QDXiBnyQwceTlFM0hl6AaOxW7MJm/jUuw5mUh9fHHk591TOf4OVf0WeD06lOx1S7HERKFt3R3D6NdJ/WRSsb8pwzOTsOdkkbXqc2yZ6ch8/d1+L94Dn0EeHE7WD/OxZ6ejjmiN4ZnJpM2ZUmoNttpmbfAbPJrUFV9junIRny6PETzhbW68NQ67h2N40lcfuB8zvLwJm/YpxqP7XGkxE592y6Op3wT/p1/CeGx/qdQB4PFegfTpGsAnC2JISDYzYkAw771ameffvIjF4vCYZ/z0S0ilBUfuiuFqZk6qwp4jBfVWqaQcPZ3N0dPZjH48tNTKD7Bt32E+W/YDk557irrVqrB60zb+8/5cvv/0Pfz0PkXij5+NpGub5tSvURWlUsGKdb8x4b05rJz9DkH+vm6xOw8d5+zFKwT4Gkq1Dv8m4jGOJetf/WmGhIS4Fr1ej0Qicb3Ozc1l2LBhBAcH4+XlRbNmzdi2bdtt17dw4UIMBgPbtzvv5pw5c4aePXvi5eVFcHAww4cPJyUlxRVvMpl45ZVXCAoKQq1W06ZNG44ccd5ljI6ORiKRkJqayujRo5FIJCxdutRjmifp6emMGDECX19ftFotPXv25NIlZwtvVlYWGo2G3377zS3P2rVr8fb2xmh0HmBiYmIYPHgwBoMBPz8/+vbtS3R0tCt+5MiR9OvXj/fff5+wsDBq1qx5288nKCjI7TOXSgt+HrNnz2bMmDGMGjWKOnXqMG/ePLRaLYsXLwacw0XWrFnDt99+i0QiYeTIka5eDb6+vq600qBr1wvjwT/IO7ILa+INMtcswmExoWne3mO8Iy8Xe3ama1HWqI/DYiL/VEEDQt6xveRsXYv54hmP67gffB8dQNaOzWTt2or5xnWSFn2Ow2zCp0N3j/GaGnXIv3iW7P07saYkYjx9nKz9O1FXvf33XpIMPfqTtXMz2Xu2YYmLIXnpFzhM+Xi37+Yx3p6bgy0z3bVo6zXCYTaRc3gPAIqQMNTVapO87EtMVy9hSbhByrIvkSqVeLX0/P2WhJVHL9K/fmX61q9MlQAf3uzaBLVCxvoz0cVnkkgI0Kldi7+HhgalXOoW46MuvkGvJHh1eBTjgR3kHb65bfy4CIfZjLZFB4/xDqP7tqG6Zdtw5OeROm8G+ScPYkuOx3LtMplrlqAsXwWZwb9U6uDf93HSf/+VjO2bMcVcI/7rOdhNJny79PQYr6lVD+P5M2Tu3oElKZHck0fJ3L0DTfVarpicIwfIOXYIc/wNzHGxJK1YjD0/D03N2qVSh78jectuLk6bS+L62x/PyoK6ZVdMx/diOrUfW0o8uZtWgsWMqlFrj/GqRq2RaHRkr/4Ka0wU9sxUrNcuYkuMdcVoWnfHnpVO7oZlWOOisWekYrlyDnt6cinXYw+mk/uwJceTu3HFzXq0KaYebZz1+P5LrDGXsWd4qEebns7eFeuXYL1xFXtGCpaof14Ph8PB5g2r6Td4FE0faUeFytUZ+59pZKSlcOzg7mLz/bb+Ozp260v7Lr0pV6Eyo8dNRqVSs2vbRgCMuTns3PYLw54ZT92GTalcrRbPj3+LSxdOc+mC83inVKkx+Pq7FqlUytnTR2nftY/r/1yOPENgUCg9HnuCoJAwataJoFOPfkRduvcbB6omHbCcOYDl7GHsaYnkb/sRh9WMol4Lj/GKei2QqLXkbViELe4qjqw0bLFR2FNKt5daYdq2Pcg7spP8Y3uwJcWRvW4pDrMJTVPPxyl1k3ZINToyl3+K5dol5+/laiTWhBhngFyBqm5Tcn5bjSU6EltqErnb12JLTUTTolOp1UPftS/Ze34nZ992LPExpK74GofZhHebLh7j7bk52LIyXIumTgQOs4ncQg0Ihd+3ZWWgjWhOfuRprCmJpVaPft0C+H5DIgdPZBEdm8+sBTH4+ypo1bjohfdfMrNtpGdaXUuLCG/iEk2cvpDriln3ewo/bkrmQlTpNXD+5buNW+nTuS29O7ahcvkwJj33FCqlko079nqMnz5+DAO7d6RG5QpUCg9lygsjsTscHD3j3gsnKTWd2Yu/4+3xzyKXy0q9HsLD6V/dgHA7OTk59OrVi+3bt3PixAl69OjBY489xvXrnru4ffTRR7zxxhv8/vvvdO7cmYyMDDp16kSjRo04evQomzdvJjExkcGDB7vyTJo0iTVr1rBs2TKOHz9OtWrV6N69O2lpaZQvX574+Hh8fHyYO3cu8fHxPP7440XSnnjiCY/lGTlyJEePHmXDhg0cOHAAh8NBr169sFgs+Pj40Lt3b1atWuWWZ+XKlfTr1w+tVovFYqF79+54e3uzZ88e9u3bh5eXFz169MBsLuiitX37diIjI9m6dSsbN2687WcaERFBaGgoXbt2Zd++gp2/2Wzm2LFjdOlScACRSqV06dKFAwcOAHDkyBF69OjB4MGDiY+P59NPP2XNmjWAs2fDX2klTiZDUa4ypkuFLvQdDkwXz6CsWP2uVqFt0YH8EwdL7W72PyKTo65cndwzJwrSHA5yz5xAU93zRU7exXOoKldHXbUGAIqgEHQRzcg9eZ+6ZcvkqCpVw3j2ZEGaw0HeuZOoq9UqNlth3u26kXNwt+u7+Kt3gsNSqNuhw4HDYkFdqEtuSbLY7JxPTKdFxWBXmlQioUWFYP6MSy02X57ZSq/5m+g5fyP/WbuPqJSid2uOxiTT+csN9F/0GzO2HiMjrxR/c39tGxdv2TYunUHxN7aNvBMHbrttSDRaHHY79nu401rsuuVyNFVrkHvqWEGiw0HuqWNoahYdagGQd+EMmqo1XA0GiuBQvJu0IOfYIY/xSKX4tO2IVK0mL/L+9Jp6IEllyEMrYHbrEu7AfPU8inKeu8gqazTEGhuFrucQfCfOQv/CNDRtekKh4YmKGg2xxl3Da9Dz+L46C/2Yt4q9kC8RMhnysIqYrxT6rh0OzFduU4+aEVhjr6B7dCi+r81GP246mra93OtR82Y9Hn8B39dno39+KqrGbf9xMZMT48hIT6Vuw4IeS1qdF1Vr1OVS5GmPeawWC1cvR1IvoiCPVCqlXsNmXLrgzHP18gVsViv1Cq03rFwl/ANDuFzMevfs+BWVSk2LVgVDHavVrEdqSiInj+7H4XCQmZ7K4X1/ENGk1T+us7PAMqTB5bBeKzzszYH12kVkoZU8ZpFXrYc1Php1p0F4Pf8uuhGTUTbv4vb9lCqZDHlYJcyXzxYqsgNz1DkUFap5zKKq0xjL9ct49x1BwH8/x2/8DLQdHnOVWSKVIZHJcFjdhx86LBYUlWqUUj3kKCtWJf/cKbd65J8/harK3d2I8GrThdzDe4o9Zkh99GjqNyVnT+k1kIYEKvEzKDhxrqC3ijHPTmSUkVpVdXe1DrlMQseWvvy+J620inlbFouVyCvXaNag4DgnlUpp1qA2Zy4WPyymsHyzGavVho9XQZ3tdjvvfL6IYX26U6V8eImXW/j/4189hOF2GjZsSMOGDV2v3333XdauXcuGDRt46aWX3GInT57M8uXL2bVrF3XrOi86vvjiCxo1asSMGTNccYsXL6Z8+fJcvHiR8PBwvv76a5YuXUrPns47XQsWLGDr1q0sWrSI119/nZCQECQSCXq93jVXgE6nK5J2q0uXLrFhwwb27dtHq1bOg+3KlSspX74869at4/HHH2fYsGEMHz4co9GIVqslKyuLTZs2sXatc7zY6tWrsdvtLFy40DVXxJIlSzAYDOzcuZNu3bq5yrNw4cLbDl0IDQ1l3rx5NG3aFJPJxMKFC+nQoQOHDh2icePGpKSkYLPZCA4OdssXHBzMhQvOcciBgYGoVCo0Go2r3n5+zq6OQUFBpTYHglTnjUQmK9K1zp6TiTwo7I75FeWrogitQObqBaVSvn9K5uODRCYr0kXblpmBMqy8xzzZ+3ci89ZT/u1PAAkSuZyMrRtJW7+69AsMyLxvljkrwy3dmpmBJtRzmQtTVamBqnwlkhcVNDSZ42OxpCTh//hIkpd8gd2Uj6FHP+T+gcgNvrdZ2z+XkWfC5nAUGargp1MTneahKypQ0c+baT2aUj3QQI7JwrdHIhm1agc/jupOsLezO2+ryiF0ql6OML2O2IwcvthzmpfX7GHp0M7IpCV/oivV3fw+bt02sjNR3s22UaEqirAKZKy+zfAXuQKf3kPIO7Efh6nkuwvLfPRIZDKsGe7dda0Z6WjLeR4Hnrl7BzIfPZVmfopE4twO0n7bQMpP7g2yqoqVqfzhF0iVSux5ecTMnIYp5v7Pc/KgkGi9kEhlOHLdh744crORBHjuyivzDURauRam04fI+u4zZL5B6HoNBamMvN0bXTGypu3JO7iVvL2/Ig+rhK7Hk2CzYfrzQOnVI+fWemQhCfB8zJb5Bjjr8edBslZ+iswvCN2jw5z12PVLQT2adSDvwO/k7dmEPLwyup5DnPU49fe7amekOxsr/xoy8Be9wc/13q2yszKw221F8vgYfIm7Ee1cb0YqcrkCnZf3Xa9357ZfaNWuG0pVwT6xZp2GvDhxOp9//BYWswmbzUbj5m0Y+cLrsOX9v1XXwiQanfP7uWVIjOPm0BZPpHp/pOWrY7lwDOPa+UgNgag7DwKpDPPBLf+4LHdLqr15HnLLb8qenYk8sPhtQ1alNvknD5Cx9BNk/sF493saZDKM29fhMOdjuXYJXae+ZCXFYc/JRNWwJYoK1bClls6de5mX52O4LSsDRcidh+QqK1dHWa4SKcu+KDbGq1Un7KY8jMdLftv+i6/eeWmTnml1S0/Psrreu5OWjX3w0srYurf05va5nYzsHGx2e5GhCn56H67dSLirdXy14icC/Qw0q1/QCLF8/WZkMimDe3Uu0fI+EB7SyQzLygPbgJCTk8Pbb7/Npk2biI+Px2q1kpeXV6QHwieffEJubi5Hjx6lSpWCuwunTp3ijz/+8DiPQlRUFPn5+VgsFlq3LuiaqVAoaN68OefP39ukPOfPn0cul9OiRUF3PH9/f2rWrOlad69evVAoFGzYsIEnn3ySNWvW4OPj4+oFcOrUKS5fvoy3t/tJQH5+PlFRBROk1a9f/7aNBwA1a9Z0G97QqlUroqKimDNnDsuXL7+nut4Nk8mEyeTeWm2y2lDdh65VmhYdsMRdL3bCxQeJpnYD/Po9QeLiL8m/fAFlcBiBT7+AX/+hpK1ddecVlDHvdt0wXb/qPuGizUbCZ+8T9Mx4Ks9bjcNmI+/sSXJPHUHicZaBstEwzJ+GYQVd+BuE+TNwyWbWnLrCuDb1AOheq+CCt3qgnuqBevos/I2jMUluvR3+LbR/bRvFTLiIVIbf0+NBIiHzx8X3t3C3oa3XkIBBw4if/2nBXCDPvkjg4KdI/mGFK858I4YrE8Yg1enwadWe8PGTiX7zP6IRoSRJJNhzs8nduBwcDmzx15H6GNC07O5qQEAiwRp3jbwd6wCwJcQgCwxD1bRdqTQg/CMSCfbcLHJ/+fZmPa4569Gqu6sBwVmPaPK2Oxv5bQkxyILCUTVtf1cNCFuik/hgcMEd/tenflIqVfm7Ll04TVxMNOP+87Zbeuz1q3y7cA79nxhNg0YtyEhPZdWSz1n81YfMqnqfCymR4DDmkL91NTgc2JNiMXvpUTbteF8aEP4RqdQ5p8baxeBwYI2LRqr3Rdu2F8bt6wDI+mE+3gOfJeC/n+Gw2bDGRWM6dQB5eOWyLXsxvNt0wRwbXeyEiwDerbuQe3BXkZ4V96JjSwMvP11wN33anOh7Xmf3dn4cPZ1NWob1zsH/Qt+u/ZWt+w7z1fTXUSmdPTkvREXzw6ZtLP1o6h0nqReEO3lgGxBee+01tm7dyqxZs6hWrRoajYZBgwa5dd8HaNu2LZs2beKHH37gjTfecKXn5OTw2GOP8eGHHxZZd2hoqNtFeFlQKpUMGjSIVatW8eSTT7Jq1SqeeOIJ5Dcn/8vJyaFJkyasXLmySN7AwEDX3zrd3XXXulXz5s3Zu9c5ziogIACZTEZionurd2JiYrG9LP6OmTNnMn36dLe0iY/U47VWRWf2vZU9NxuHzYbUW++WLvXSY8/OuG1eiVKFJqIl2Vt++ttlLm22rCwcNhsyvcEtXaY3YMvw3CLuP3gEWXt2kPXHZgDMMdFI1GqCn32FtHXfgcPzxEElVubsm2X2cS+zXG/Alnn7VnyJUoXXI+1I/3lFkffM0ZeJ/d/LSDVakMuxZ2cRPm02ptucpNwLg0aFTCIhLTffLT0tN9/jvAaeKGRSagX5EpPhYcKvm8oZvDBolMRk5JRKA4I99+b34a2n8Kma1Ftf5A7TrSRKFZpGrcje/KPnAKkM36fHI/MNIOWr90ql9wGALSsTh81WpLeJ3OCLNd1z19KgoaPI3LmVjK2/AmC6dhWpSk3YixNJ/nGlaztwWK2YE5xjpPOjLqGpXhO/3gOI/3pOqdTlQecw5uCw25Do3O+ISXTeOHKKDtcBZ08wh83mtu+xpSQ499dSGdht2LMzsSW7j1W3pSSgqt245CtBoXp43VoPn+LrkZ2Jw35LPZLjkXobQCYD21/1iHfLZ0uOv+t6tAn3Y8bTBU8+sN68wMrMSMPXL8CVnpmRRsUqnocgefsYkEplZGa4bxtZGenob85RYjD4Y7VayM3JduuFkJmRhsG36Dwmf/y+gYqVa1D5lmFoG35aRo1aDeg94CkAKlSujkqt5p03XiAlpCMBd7mvvJUjL9f5/Wjdb45ItN7Yc4tO/ArO3iO3/s7saYlIvQp+Z6XJbrx5HnLLb0rqrfc48SCAPSvDWa7Cv6mkOOfx8+ZvypaWRMaCGaBQIlVrsGdn4jPkRWxpSaVSD1uO52O4zOfujuG6Zm1JX1/8zQpV9TooQsuRNP/jkiiuy8ETWW5zEijkzotjX73crReCr4+cqOv5RfLfKshfQURdL977vOwakw3eXsikUtIy3X/zaZlZ+Bv0xeRyWrlhC8vX/cZnU1+lWsWC3p8nL1wiPSub/mMnudJsdjufL/uB1Zu2sfarotdDDxMxiWLJemA/zX379jFy5Ej69+9P/fr1CQkJcZtA8C/Nmzfnt99+Y8aMGcyaNcuV3rhxY86ePUulSpWoVq2a26LT6ahatSpKpdJtLgCLxcKRI0eoU8fz2Nu7Vbt2baxWK4cOFYzJTU1NJTIy0m3dw4YNY/PmzZw9e5YdO3YwbNgwt/JfunSJoKCgIuXX62+/c7kbJ0+eJDTU2fVOqVTSpEkT1+ST4BxHtX37dlq2bFncKlw9H2y22x+8p0yZQmZmptvycvO7/IxtNiyxV1FVLzQeXiJBVb0u5mu3v8BUN2yBRC4n75jnCWnKlM1K/tVLaOtFFKRJJGjrRpB3yXMPGKlSBY5bHidk/+v1fWhttlkxRV9GWzeiIE0iQVMngvzLxT9yD8CreVskcgXZ+/8oNsaeZ8SenYUiOAxV5WrkHj9YQgV3p5BJqR3sy+HrBSdpdoeDw9eTaBB2dxMF2uwOLqdk3vYkOjHbSGaemUCd5p7L7LkQzm1DWaNeQdrNbcNyl9uG8aiHbeNm44E8MITUr9/HYSy+keReOaxW8qIuomtQ6CJMIkHXoHGx8xVIVepCv/ub6/nr9e3uukikSBSK4t///85uwxp/HUXlwheSEhSVa2Mp5lF1lpjLyPwCKbz/kfkFORt3b17UWWMuI7tl6IDMPxhbZimNPbbZsMZdQ1G50FwyEgmKKrXuUI8gt9+PzD/YWQ9boXr4uzcEOutR/LwphekUckLCyruW8PKVMfj6c/ZUweNhjcZcoi6epbqHx+YByBUKKler6ZbHbrdz5s8jVK/lzFO5Wi1kcjln/yyIiYu9RmpyAtVuWW9+npFD+7bToetjRf6X2ZSP5JahV1Kps9fgPTVV223YE2ORVyjcSCJBXqEGtvhoj1lsN64iNbj/zqS+gdhzMku98cBZAGfvAGVV9/MQZdU6WK5f9pjFcu0iMv9bflMBIdiy0l2/qYJgM/bsTCRqLcrq9TCdO14atQCbFfO1KNSFH60skaCu1QDTlcji8wG6pq2RKBTkHtxVbIx3my6Yoi9jiY0uoQI75eXbiU8yu5brcSbSMixE1CnoYaxVS6lZVcuFqNzbrMmpa1s/MrOsHD7lucHqflAo5NSsUpGjpwvO9+x2O0dPX6BeDc9ztQCsWP8bS37ayJw3J1C7aiW393q2a8nyWW+z7ONpriXA18CwPt2Z++Z/SqsqwkPqgW1AqF69Oj///DMnT57k1KlTDB06FLvd87NYW7Vqxa+//sr06dOZO3cuAC+++CJpaWkMGTKEI0eOEBUVxZYtWxg1ahQ2mw2dTsfYsWN5/fXX2bx5M+fOnWPMmDEYjUaeeeaZey573759GTNmDHv37uXUqVM89dRThIeH07dvX1dcu3btCAkJYdiwYVSuXNltyMOwYcMICAigb9++7Nmzh6tXr7Jz505eeeUVYmNjPf3bYs2dO5f169dz+fJlzpw5w4QJE9ixYwcvvviiK2bixIksWLCAZcuWcf78ecaOHUtubi6jRo0qdr0VK1ZEIpGwceNGkpOTycnxfKGhUqnw8fFxW/7O8IXc3b+ibdERTdO2yIPC8Bk4GolSTd5h54FMP2Qs3r2KTmapbd6B/DPHPF4ASTQ65GEVkQc7x/3Jg0KRh1Us0tOhNKVv+hl9x574tOuCMqw8QaNfRqpSk7XrdwBCxr5GwJMFn3/u8UPouzyKd8v2yAOD0dZvhP/jI8g9fqhow0Ipydi8Fu/23fFu0xlFWHkCnn4RiUpN9u6tAAQ9NxG/x58uks+7fVdyjx/AnlN0jgFdszaoa9VHHhiCtvEjhE56j9xjB8krPMFkCRvWtAZr/7zCL2eiuZKaxYytx8mzWOlTrxIA//v1MJ/vLphw7Jv95zgQnUBsRg7nE9N569dDxGfl0r++80BvNFuZs/MUf8alEpeZy6Frifxn3T7K+3rRslLpDV/I2bkJ3SMd0TRrhzwoDP2g0UiUKoyHnNuGYehYvB99skg+7SMdyT99tOi2IZXhO3ICyvJVSF/xBUilSL31zu1CVjpDjlLX/4hvt0fRd+yGslwFQl+YgFStJn2bs6dN+IQ3CBr+rCs++8gBfHv2wadtR+dEog2bEDRsFNlHDrgaFoKGP4u2TgMUQcGoKlYmaPiz6Oo1JHPXdo9luJ9kOi0+DWvh09B5oa6tXA6fhrVQly/dR4bdjfwDW1E3bouqQUtkASHoHh2GRKHEdNLZ0O7VdxTaTv1d8aaju5BodGh7PIHULwhF9fpo2vQi/8hOV0zeoW3Iw6ugadMTqW8gynrNUTduS/6R4hsTS6QeTdqhatgKWUAoukefQqJQYTpxsx79R6PtPKCgHkd23qzHk0j9g531aPso+YcLyph3YCvyclXQtO2F1C8IZf3mqJu0c4v5OyQSCT36PMG6H5Zy7NBurkdfZt6c6Rj8AmjySDtX3Iy3XuL3jQU9hXr2HcIfv29g9/ZN3Ii5ypKvP8KUn0/7zo8CzokYO3R5jBWLPuPsn8e4evkC33z2HtVr1ad6rXpuZTi4Zxs2m43WHXoUKV+j5m04emAn235dQ1LCDSLPneLbb2ZTtUYdAv9h74O/mI7tRFG/JYo6zZD6BaPu8jgShRLLWedNF3WPYaja9HbFm0/tQ6LWou7YH6khEHnlOiibd8V88v7dHDDu2YymWXvUjdsgCwzDu+/TSJQq8o45n5jh/fhz6Lo/7orPO7QDicYLr95PIQsIQVmzIboOj5F3oGByQWX1+ihr1EfqG4CiWl0MY6ZgS44n/9ieUqtH5tb1eLfrhq5VRxSh5fB/6gXnMXyfs1wBoydgGDC8SD6vNl0wnjiE3dMjKwGJWoO2aWty9mwttbIXtu73FJ58LIgWET5UKqfm1efKk5puYf/xgkaBmZMq81hn95sCEgl0bePLtn3pt7ZDA85eDVUqqAkLcj5GtVI5NVUqqPHSlfzxb0jvrmzYvptNO/cRHRvHRwtWkG8y0bujc2j19M8X8dXKNa745et+45vv1/PmuJGEBgaQmp5Janomxjxnrwu9txdVK4S7LXK5DD9fPRXD7703sfD/ywM7hGH27NmMHj2aVq1aERAQwOTJk8nKKr61sE2bNmzatIlevXohk8l4+eWX2bdvH5MnT6Zbt26YTCYqVqxIjx49XI8v/OCDD7Db7QwfPpzs7GyaNm3Kli1b8PW998nblixZwvjx4+nduzdms5l27drx66+/oih0B0wikTBkyBA++ugjpk6d6pZfq9Wye/duJk+ezIABA8jOziY8PJzOnTvj41P8Y2o8MZvNvPrqq9y4cQOtVkuDBg3Ytm2b6zGMAE888QTJyclMnTqVhIQEIiIi2Lx5c5GJFQsLDw9n+vTpvPHGG4waNYoRI0YU+1jLe5F/8iBZOh+8ug9C5mPAcuMaaQs+cE1oJDP4F7mAlgWGoqxSi9T5MzytEnW9JhiefMH12nf4KwBkb1lDzu9rPOYpaTkHd5Pio8d/0HBkBl9M165w44O3XBMrygOCcBTq/pi6dhUOHPgPfhq5nz+2rExyjx8iZfXS+1JegNxDe0j11uM74Cnkel9M168Q//FUV5d5uX+gW5kBFCHhaGrWI+7DNz2uU27wJWDos8j0BqwZ6WTv2076uu9LtR7da5Un3Wji631nSTXmUzPQwBeD2rqGMCRkGSl88y3LZObdLcdINebjo1JQO9iXJUM6USXAuS1KJRIupWSy8ew1sk1mAr00PFIpmHGt66Esxbk+8k8eJNPLB+8eBdtG6vwPnHfmcE4Od+vQFllgKKoqtUj9uui2IdP7oqnfFICg1927O6Z88Q7mqHubH8aTrL07kfsYCBo6CrmvL/lXo7g2fbKrS60iIKighwGQ/INzvH3QsNEo/AKwZmWQc+QAiSsWuWLkegPhE95A7ueHPTeX/GtXuPb2ZPenPZQRfZN6tNxeMPdMnVn/BSDm25/585kpZVUsAMznjmLUeaPp0Aeplw/WxFiyV33mes69VO/ntn3bs9LJXvkp2m6DMbwwDXtWBvmHt5O3b7MrxhZ3jewfvkLbaQCadr2xpaeQu2U15jOl9/QY89kjGHVeaDr2ddYjIYbsFXNdE0RK9f5F67F8DtoeT2AY+zb2rHTyD20jb2/Bo5ZtcdFkr/4KbecBaNo/5qzH5u8xny7m6R93ofeA4Zjy81n05QcYc3OoUacBk9+ei1KpcsUkJsSSXWhIUsu2XcnOzOCnVQvITE+lYpXqTH57DvpCwxOeenYCEqmUTz+YgtVipn6jFowq1K35Lzu3/UKzlu2LTLgI0L5zb/LzjPy+6SdWLv4MrZc3des34cmRL8LBr/9xnQGsF0+Qr9WhatUTidYHe/INjD/PdzVoSr19sRf6fhw5GRh/noeqQz90IybhyMnEfGIX5iP3r0HQdPoQOV7e6LoMQOqtxxp/nYwlH7sm63SehxT6TWWmkbHkY7wfHYrmlfewZ6Vj3P87xl0FT8uSqDV4dX8cqd4PuzEX09kj5G75qVR7VRiP7CXNywffvkOR+fhijrlK4tzp2LOcxwy5f0CR8yl5cDjqGnVJmD3V0yoB0DVvC0jIOVz8I0hL0o+/JqNWSXllVDheWhlnL+byv0+uYrEUfAehQSp8vN0vgxrV8SI4QMnvuz33gOrV0Z+n+hWc9876r/MpG58sjGFbCU+42KV1c9Kzcli4ej2pGVlUr1SeOW9OwO/mEIbElFSkhXqw/Pz7TixWK//9xH37e+bxx3h2cF/+v3P8i+bNehhIHLeezQvCv0D8q0PLugglIjuubB4BVJJkiofjOcEhbRuVdRHuWea5B3+yT4D0K6X3/O/7KfrXG2VdhHv2yP88P6f+gVNMD8QHydUhJTs2vKzU2DT9zkEPgPzkB//4bUz2PAfDg2as9d2yLsI9Wznx4fgu/Br888fTlqWUM2U3KW9AveKHez+oHtghDIIgCIIgCIIgCIJwOw6JtMyWf+LLL7+kUqVKqNVqWrRoweHDxffKW7BgAW3btsXX1xdfX1+6dOly2/iSIBoQBEEQBEEQBEEQBKGMrV69mokTJzJt2jSOHz9Ow4YN6d69O0lJnp/AsnPnToYMGcIff/zBgQMHKF++PN26dePGjdLrJSkaEARBEARBEARBEISHk0RSdsvfNHv2bMaMGcOoUaOoU6cO8+bNQ6vVsnjxYo/xK1euZNy4cURERFCrVi0WLlzoelpeaRENCIIgCIIgCIIgCIJQwkwmE1lZWW6LyWTyGGs2mzl27BhdunRxpUmlUrp06cKBA3c3j4PRaMRiseDn51ci5fdENCAIgiAIgiAIgiAIQgmbOXMmer3ebZk5c6bH2JSUFGw2W5Gn3AUHB5OQkHBX/2/y5MmEhYW5NUKUtAf2MY6CIAiCIAiCIAiCcDuOMrxnPmXKFCZOnOiWplKpiom+Nx988AHff/89O3fuRK1Wl8r/ANGAIAiCIAiCIAiCIAglTqVS3XWDQUBAADKZjMRE98ddJyYmEhISctu8s2bN4oMPPmDbtm00aNDgH5f3boghDIIgCIIgCIIgCMJDySGRlNnydyiVSpo0aeI2AeJfEyK2bNmy2HwfffQR7777Lps3b6Zp06b/+HO6W6IHgiAIgiAIgiAIgiCUsYkTJ/L000/TtGlTmjdvzty5c8nNzWXUqFEAjBgxgvDwcNc8Ch9++CFTp05l1apVVKpUyTVXgpeXF15eXqVSRtGAIAiCIAiCIAiCIAhl7IknniA5OZmpU6eSkJBAREQEmzdvdk2seP36daTSgkEEX3/9NWazmUGDBrmtZ9q0abz99tulUkbRgCAIgiAIgiAIgiA8lBySB2vU/ksvvcRLL73k8b2dO3e6vY6Oji79At3iwfo0BUEQBEEQBEEQBEEoE6IHgiAIgiAIgiAIgvBQcvD3JjMUbk/0QBAEQRAEQRAEQRAE4Y5EDwRBEARBEARBEAThofSgzYHwbycaEAShFGn9vcu6CPdMFx5Y1kUoEYfe31DWRbhnTSd0KesilAhLbl5ZF6FEPPK/amVdhHt28N1dZV0E4aZKT+WXdRFKxI19Z8u6CCXCarKWdRHuWXDDimVdhBKhydaWdRHuWY5GUdZFKBF+ZV0A4V9BNMcIgiAIgiAIgiAIgnBHogeCIAiCIAiCIAiC8FBySMQkiiVJ9EAQBEEQBEEQBEEQBOGORA8EQRAEQRAEQRAE4aEkHuNYskQPBEEQBEEQBEEQBEEQ7kg0IAiCIAiCIAiCIAiCcEdiCIMgCIIgCIIgCILwUHJIxD3zkiQ+TUEQBEEQBEEQBEEQ7kj0QBAEQRAEQRAEQRAeSmISxZIleiAIgiAIgiAIgiAIgnBHogeCIAiCIAiCIAiC8FAScyCULPFpCoIgCIIgCIIgCIJwR6IBQRAEQRAEQRAEQRCEO/p/34AwcuRI+vXr53rdoUMHJkyYUGbleZAlJCTQtWtXdDodBoOhrIsjCIIgCIIgCML/cw4kZbY8jP41cyCMHDmSjIwM1q1bV6bl+Pnnn1EoFGVahvtt586ddOzYsUh6fHw8ISEhrtdffvklH3/8MQkJCTRs2JDPP/+c5s2bu96fM2cO8fHxnDx5Er1e71pvenp6qTcoaFt3RdehNzJvPZa462StXYYlJspjrN/Yt1BVq1MkPf/cCdIXfQyAun4ztC07oyhXGanOm+RPpmCNu1aqdQDQteuOd+c+yHwMWG5cI/3HxViuXfYYGzj+bVTV6xZJzztznNR5M4ukG54cg1ebbmT8tIScnb+WeNn/omrcDlWLrki9fLAlxWL8/Qds8cV/dhKVBnX7PihrRiBRa7FnpWHc9hPWqLMAKBu1RdW4HTK9HwC2lHjy9v6K9cq5UqsDQPjwJ6jw/EiUgQHknL/IxWkzyT51xnMd5HIqjnuG0IF9UIYEYbwSTdQHc0nbtc8VU3nCWCpPGOuWLzfqKoc69y3VetxK0aA1qqYdkWi9safEkffHWuyJ14vPoFKjbtULebUGSFRa7NlpmHatxxp9/r6V2atDT/Td+iPTGzDHRpP23QLM0Zc8xga/+h7qmvWKpBtPHyX58/dcr+Uh5fAdOAJ1jboglWGJjyF53ofY0lJKrR6qph3QtOqG1EuPNTEW42/fYY2LLjZeotKg7dQPZa3GSDRa7Jlp5G5ZjeVywe9Q6m1A23kAimr1kCiU2NKSydmw9Lbb3P3g16YpVV59Bn3jeqjDgjg6cByJG7aXaZkKqzh2KFUmPoMqJJCsPy9wdsK7ZB457TFWIpdTdfLzlBveD3V4MLkXr3JhyiySf9/jipF56ag5fTzBfbugCvIn6+Q5zk6cQeZRz+v8J37buJZ1a74nIz2NSpWr8ewLr1C9Zu1i4/fv2cl3KxaRlJhAaFg5ho96nibNHvEYO++LT/j9t18YNeZFHuv3uCs97kYMyxbN48L501gtVipWrsKQp56hfsNGJVYv3x598Ov7OHKDH6boKBIWfUn+5cji4x/tj2/3x1AEBGHLziTrwB6SVy7CYbEAYOje2/l+YDAApphrpPy4gtwTR0qszJ74PdqPwAFPIPf1I/9qFHHzPyPv4oVi4/37DMS/Vx8UgcFYszLJ2reLhGULXPUoLHDQEEJGPkfK+p+IX/BlqdVB06Iz2rY9nfuohOtkb1yBNfZqsfEStRZd14Go6jZBqtFhy0glZ9MqzBf/vBkgQde5P+qGLZF667FnZZB3Yi/GPzaUWh3+8mQvP7q29EGrkXLhaj7f/JBMfHLRz7YwP72M4X0CaFxHi1IhISHFwhcrk4iKMQHw0rAgOrXwcctz4nwu734dXyp1WL/xV378eS1p6RlUrVyJF58fQ62aNTzGRl+7zrKVq7h0OYrEpGTGjhnNgL59il339z+uYdGy5fTv05txzz1bKuUXHl7/mgaEfws/P7+yLkKpMZvNKJXKYt+PjIzEx6dgxxgUFOT6e/Xq1UycOJF58+bRokUL5s6dS/fu3YmMjHTFRUVF0aRJE6pXrw7AuXOle4H3F3XEI/j0eYrMnxZjuX4ZXdue+D33Bskfvoo9J6tIfPrSOUjkBT99qdaLgFc/IP/PQ640iVKF+WokeacOYhj83H2ph6ZxKwz9nyZ99TeYoy/j1fFRAl98k4R3xnusR8qCWUhkheqh8yJ4yizyThwoEqtu0BxlpRrYMtJKtQ6K2k3QdB6IcbPzwkjdrBNeT7xM1jdv4zDmFM0gleE15BUcudnk/LwAR04GUh9/HCajK8SRnUHeznXY05JAIkFZ7xG8Br1A1uKZ2FNK56Ad1Ls71d96nci33iXzxGnKj36KiG/ncbBTHyypRT/DKq+9REi/R7nwxnRyo67i37419efP4djAEeScLTiBzIm8zMmnxhTUzWorlfIXR14jAnW7vuTv+BFbwnWUjdqh6/8cOcs+wJHn+fvR9X8Be14OeRuXYs/NROrth8OUd9/KrG3aGr/HR5O68mvMVy/i3bkPQeOnETf1RezZmUXik7/+AApt3zKdN6FT52I8ut+VJg8MIWTSDHL2bSdjw3c48vNQhJX3eOJeUpR1mqLr9ji5m1ZivXEVdYvOeA8bT8aXU3EYs4tmkMrweeo/2I3ZZP80D3tWBlKDP478gm1DotbiM2oSluhIsld9ht2Yjcwv2C2mrMh0WrL+jCRm6Rqa/lR6Fzz/ROjjPan98RTOvDiNjMOnqPzK07TYtIiddXtgTi66fdd8ZwLhQ/vw5wtvkRN5hcBubWny0xfsb/ckWSedDWkN5r+Hd93qnBo5ifz4JMKH9qHF5iXsatALU1zSPZd57+4dLFnwFc+/NJEaNWuzcd1PvPO/1/n8m+UYDL5F4i+cO8Psj97hqZHP0bRZS3bv2saH773Fx59+Q8VKVdxiD+7fw8UL5/DzDyiynvffnkJoWDmmz5iDUqli4/qfmDF9Cl8tXImvn/8918u7VXuCRj5PwvzPyLt0Hr/eA6jwv5lEvTwaW1ZGkXifNh0JeupZ4r+cRV7kOZRh5Qh96XXAQdLS+QBYU1NIWrEIc/wNJIC+YzfKT57OldfHYo4pnYY1fduOhD47lrgv52CMPE9A30FUfucjIp8fgS2zaD307TsTMvI5Yj/9COP5M6jCy1NuwmQA4hd+5RarqV4Tvx6PkXfV802RkqKq3xyvXk+SvX4ZlpgraFt3wzDyNVLnvIEj18M+SibDMOo17LnZZK36AltWBrJb9lHado+iad6RrDULsSbeQBFeCe+Bz+DIN5J3YFup1aV/FwOPttPz2cokklItDHnUj/+NDWP8jOtYrA6PeXQaKTMmlOPMpTze/TqOrBwboUEKcvLcj9PHz+XyxcqCbbq49d2rnbv3Mn/hYl55cSy1a9bg5/UbmDJ1Oovnf4mvh5tyJpOJ0JAQ2rVuzbyFi2+77siLl9i0eQtVKlUqlbL/G4lJFEvWA/Npzp49m/r166PT6Shfvjzjxo0jJ6fghHfp0qUYDAa2bNlC7dq18fLyokePHsTHF1xg2Gw2Jk6ciMFgwN/fn0mTJuFwuG/4tw5hqFSpEjNmzGD06NF4e3tToUIFvvnmG7c8+/fvJyIiArVaTdOmTVm3bh0SiYSTJ08WW5/09HRGjBiBr68vWq2Wnj17cumS845aVlYWGo2G3377zS3P2rVr8fb2xmh07pxjYmIYPHgwBoMBPz8/+vbtS3R0tCv+r+EZ77//PmFhYdSsWfO2n3FQUBAhISGuRSot+HnMnj2bMWPGMGrUKOrUqcO8efPQarUsXrzY9TmtWbOGb7/9FolEwsiRI129Gnx9fV1ppUHXrhfGg3+Qd2QX1sQbZK5ZhMNiQtO8vcd4R14u9uxM16KsUR+HxUT+qYIGhLxje8nZuhbzRc93nEuDd6fe5O7fjvHgTqwJsWR8/w0Osxldy04e4x3GHOzZGa5FXasBDrOpSAOCVO+H4fHRpC39FIfNWqp1UDfvhOnUPsynD2JPTcC4+TuwmlE2aOUxXtmwFRK1lpw187DduII9Mw1rzCVsSTdcMZbLp7FGncWenow9LYn83RtwmE3IwyqXWj3KPzuCuO/XEP/jeoyXrxD55rvY8/IIG9zPY3xI/95Ef7mQ1J17yY+5wY0VP5D6x14qPDvCLc5hs2JOTnUtlvSMUquDJ6rG7bGcOYjl3BHsaYnkb/8Jh9WCom5zj/GKus2RqLXk/bIYW3w0jqx0bDeisKfE3bcy+3TtS/be38ndvwNLfCxpK7/GYTbh1bqzx3i7MQd7VoZrUdeJwGE2YTxW0BvE0G8YeWeOk7FmGZaYq1iTE8g7dcRjg0RJUbfsiun4Xkyn9mNLiSd300qwmFE1au0xXtWoNRKNjuzVX2GNicKemYr12kVsibGuGE3r7tiz0sndsAxrXDT2jFQsV85hT08utXrcreQtu7k4bS6J60vvIuGfqjxhFDGLfiB22c/knI/i9Lhp2Iz5lB850GN8+LC+XP5wHsmbd5N3NZbr878j6bddVPnPaACkahUhA7pxYcrHpO09ijHqOpfe/QJj1DUqPj+0RMr8y9of6drjUTp37Un5CpV4/qWJqNRqdvzuuTfZxg1raNSkOf0GPkm5ChUZOvwZKletzm8b17rFpaYks3Dep0x4/S1kMpnbe1mZGcTHxTLg8aFUqlyVsPByDB/5HCZTPtevFX9X+u/wf2wgGdt+I/OPLZhjr5Mw/1PsJhOGzt09xmtq1SXvwlmy9v6BJTmR3FPHyNr7B5pqtVwxOUcPknv8MJb4G5jjb5C8agn2/Dw0NYrvrXGvAvo9TvqWTaRv24wp5ho3vpyN3ZSPX9eeHuN1tetiPH+GzF3bsSQlknPiKBm7d6CpXsstTqpWU/61N4n9fBa2HA8X8SVI27o7eUd3kX98L7bkOLLXL8NhMaNp0s5jvLpJO6QaLzJXfIbl+mXsGSlYoiOxJsS4YhQVqmE6fwJz5CnsGSmYzh7FfOksinJVPK6zpPRub+Cn39M5cjqXa3FmPluehJ9eRvMGumLz9O/iS0qGlS9WJXH5uomkNCunLuSRmOJ+3mSxOsjItrmW3Dx7qdRhzbr19OzejR5dO1OxQnnGvzgWlUrFlq2ee3LVrFGd50aPpGP7tigUxd8fzsvLY+asOfzn5Rfx8ir+8xCE23lgGhCkUimfffYZZ8+eZdmyZezYsYNJkya5xRiNRmbNmsXy5cvZvXs3169f57XXXnO9/8knn7B06VIWL17M3r17SUtLY+3atbf+qyI++eQTmjZtyokTJxg3bhxjx44lMtLZvS4rK4vHHnuM+vXrc/z4cd59910mT558x3WOHDmSo0ePsmHDBg4cOIDD4aBXr15YLBZ8fHzo3bs3q1atcsuzcuVK+vXrh1arxWKx0L17d7y9vdmzZw/79u1zNZqYzWZXnu3btxMZGcnWrVvZuHHjbcsUERFBaGgoXbt2Zd++gpNts9nMsWPH6NKliytNKpXSpUsXDhxwXqweOXKEHj16MHjwYOLj4/n0009Zs2YN4OzZ8FdaiZPJUJSrjOlSoQt9hwPTxTMoK1a/q1VoW3Qg/8RBHGZTyZfvbsnkKMpXIT/yz4I0h4P8yD9RVvbcXe1WuladMR7f714PiQS/ES+Ts30D1oTY4jOXBKkMWUgFrFcLdz11YIm+gDzc88W+snp9rDeuou32JPpXPsDn2bdQt+wOkmLGjEkkKGo3QaJQYr1xpeTrAEgUcrzr1SZt38GCRIeDtH2H8Gnc0GMeqVKJ3WR2S7Pn56Nv5t7NV1upIq0PbaPl7l+pM3cmqrAQ7hupDGlQOawxFwslOrBev4gstJLHLPIq9bDGX0PdcSBeY6aje+p1lM06F//9lDSZHGWFquSfv2W7OH8KVZXbN4j+xatNF3KP7C3YLiQSNPWbYk2MI2j8NMrNWkrIlI/QRLQohQrcJJUhD62A+WrhYR8OzFfPF3sirazREGtsFLqeQ/CdOAv9C9PQtOnp9tkrajTEGncNr0HP4/vqLPRj3kLVqE3p1eMhIFEo0DeuS8r2gh4pOByk7NiP4RHP3fKlKgX2/Fu3bxO+rRo71ymXI5XLseW7H0NseSb8Wje+5zJbLBaiLkfSIKJJQZmkUhpENCHygueefhcvnHWLB2jUuLlbvN1u59NPZtBv4JNUqFh0H+3toye8XHl27thCfn4eNpuVLb9tQG/wpWq1u9v+bksuR121Brl/Hi9IczjI/fM4mhpFhxkC5F04i7pqddQ3/78iOASvxs3JOX7Y8/+QSvFp3QGJWk1eZOn0ipTI5Wiq1SDn5LGCRIeDnJPH0dYqOswQIPf8WTRVa6Cp4WwwUASH4t20BdlHD7nFhY2dQPaRg+SeOu5pNSVHJkMeVgnz5UKfkcOB+fJZFBWqesyiqhWBJeYy3n2GEzDlU/xeeQ9t+95u+yjL9csoq9ZB5u8cTiIPKY+yUnVMF0tuaM+tgv3l+OrlnIos6AlhzLdz6ZqJmpXUxeZrVl9H1HUTr40KYcn7lZg1qTxdWvoUiatXTcOS9yvx+ZsVeG5wIF7akr+UslgsXLwcReOIBq40qVRK44iGnLtQ/PCeu/H519/QolkTGkd4Pp8RhLvxwAxhuLVXwHvvvccLL7zAV18VdPWyWCzMmzePqlWdO7uXXnqJd955x/X+3LlzmTJlCgMGDABg3rx5bNmy5Y7/u1evXowbNw6AyZMnM2fOHP744w9q1qzJqlWrkEgkLFiwALVaTZ06dbhx4wZjxowpdn2XLl1iw4YN7Nu3j1atnHdmV65cSfny5Vm3bh2PP/44w4YNY/jw4RiNRrRaLVlZWWzatMnV4LF69WrsdjsLFy5EcnNnvWTJEgwGAzt37qRbt24A6HQ6Fi5ceNuhC6GhocybN4+mTZtiMplYuHAhHTp04NChQzRu3JiUlBRsNhvBwcFu+YKDg7lwwdk9OzAwEJVKhUajcc2b8NdwkKCgoFKbA0Gq80YikxW5c2jPyUQeFHbH/IryVVGEViBz9YJSKd/dknoVU4+sTBTB4XfMr6hYDUVYBdJWfu2W7t21L9htpTrnwV8kWi8kUhl2o/twC0dutuvk4VZSQwDyiv6Yzx4h54cvkfoGoe3+BMhk5O8tKLM0MAyfEa+BXIHDbCLn52+wpyaUSj0Uvr5I5XLMKalu6ebkVLRVPTeEpO7eT/lnh5Nx+Bh512Lwbd2CwB6dkUgL7uhlnjzNudfewnglGlVQIJXHv0CTH5ZyqPsAbLml3+VcotEhkcqKdJd3GLOR+QV5zCPV+yEtXw3LheMY1y9AaghA3XEgSGWYD/1e6mWW3dwubu3KbMvORBFa7o75lZWqowyvSOqyL1xpUm89UrUGnx4DyFi/kvQ136Kp14jAFyaTOPt/mC6eLelquLYNR27RbUMSEOoxj8w3EGnlWphOHyLru8+Q+Qah6zUUpDLydm90xciatifv4Fby9v6KPKwSuh5Pgs2G6c+iQ5kEUAY4t29Tkvv2bUpMRVfTc2NO8u97qTx+JKl7jmCMuk5Ap5aE9OsKN+/Y23JyST9wnOpvjiPnwhVMiSmEP9kb30ciyL18m/lF7lJ2ViZ2ux2DwX2IpcHgy40Yz+vPSE8rEq83+JKRXjBEY+1P3yGTyXi0j+eeFxKJhGnvf8KH777FsEG9kEgk6A2+/O+dj/Dy9r7HWoHcW+/cvjPS3dJtmemowst7zJO19w9kPnoqvTcHJBIkcjnpW34h9efv3OJUFSpRacZnSJRK7Pl5xH40HXPsvX8Xnsh8nPWw3lIPa0Y6qnIVPObJ3LUduY+eKh9+huRmPVJ/XU/yjytdMfp2HdFUrc7l/7xQKuUuTKq9eQ6Sc+u5VBbywGL2UX5ByAwB5J86QMay2cj8g/HuMwJkMow71gNg3L0JiUqD34SZ4LCDREru1jWYTpXe/sng47y0ycx2H3qQkW3F10fmKQvgbHjo3saHX/7IYM3WNKpVUPPMwACsNgc7DzuPmyfOGzl0KofEVCshAQqGPebP/8aGMWV2LPYSHMmQmZWN3W4vMlTB16AnJvaf3wz6Y9ceLkVF8eWcWfdYwgfPwzqZYVl5YBoQtm3bxsyZM7lw4QJZWVlYrVby8/NdF9gAWq3W1XgAzgvjpCTnOKXMzEzi4+Np0aLgLpNcLqdp06ZFhjHcqkGDghZAiURCSEiIa72RkZE0aNAAtbqgVbPwxIKenD9/Hrlc7lYWf39/atasyfnzzjtUvXr1QqFQsGHDBp588knWrFmDj4+PqxfAqVOnuHz5Mt63HMTz8/OJiioYJ1e/fv3bNh4A1KxZ0214Q6tWrYiKimLOnDksX778tnlLgslkwmRyv3NjstpQyYvf0ZcUTYsOWOKuFzvh4oNC17IT5hvX3CZcVJSvgleHR0n8cNJtcpYxiQRHbjbG31aCw4EtIYZ8Lz3qR7q6NSDYUxPJWjwTiUqNomZjdL1HkL1iTqk1Ivxdl6Z/SK0PpvHI9vU4HA7yrsUS/+N6QgsNeUjbudf1d+6FS2SdPE2rvZsJerQ78T/cuSdUmZBIcBhzyN/+Azgc2JNiMev0KJt2vC8NCPfKq00XzLHRbhMu/tXgmnfyMNnbfgHAEnsVVdVaeLfrXioNCP+IRII9N5vcjcud20b8daQ+BjQtu7saEJBIsMZdI2/HOgBsCTHIAsNQNW0nGhBK0LmJ71N/3nt0OPMbDocDY1QMMct+dhvycHLkJBosmEGX63uwW61knThH3OpN6Bt5vgNd1qIuRbJp/U/M+myBa5u4lcPhYMFXn6I3+PLeR5+hVKrYtmUTM6ZP4aO58/ErgTkQ/i5t3QYEDBhCwoLPybt0HmVIOMGjxxEwaBgpPxVcfJviYrny2gvItDq8W7Yl7KXXuTb11VJrRPi7dPUbEjh4GHFfz8UYeR5VWDihY14i6MnhJH2/HEVAIKFjXiL6f6+X6tws90QiwZ6bRfa6JeBwYI27htTHF23bnq4GBFW95qgbPkLWD/OxJt1AEVoBr0eHYs/OIP/Evjv8g7vTrqkXzz9R0Aj+/vx/NsROIpEQFZPPyo3ORrarsWYqhCrp3lrvakDYd7xg6PT1eDPX4kx8Pa0SdatrOH3x/s0N9E8kJSfz1YKFfPju9DteFwjCnTwQDQjR0dH07t2bsWPH8v777+Pn58fevXt55plnMJvNrgaEW5+eIJFI7tg4cDc8rdduL50xT39RKpUMGjSIVatW8eSTT7Jq1SqeeOIJ5DcnB8vJyaFJkyasXLmySN7AwEDX3zrdPxvf1Lx5c/budV7wBAQEIJPJSExMdItJTEx0e0rDPzVz5kymT5/uljbxkXq81qr+HfPac7Nx2GxIvfVu6VIvPfbsjNvmlShVaCJakr3lp79d5pJmzymmHj56jxNJFSZRqtA2aU3WptVu6aqqtZB6+RD6TkGvBIlMhn7A03h1fJSEaS+WWPnBOSeDw25DqvWhcLu/ROftcRJIcN7dwGaDQtupLTUBqZcepDKw31yT3eYa121LiEEeWhF1s47OORZKmCU9HbvVijLA/eRYGeiPOdnzDP2WtHROPzcBqUqJ3GDAnJhE1TcmkHe9+DsF1qxsjFevoank+U5bSXPk5eKw25Bo3RsdJVpv7J4myMJ5h9xhd/9+7OmJSHU+7t9PKbHd3C5kPga3dJm3HltmuudMN0mUKnTN2pCx3v034lynFUt8jFu6JT4WVbXSGSP917Yh0bl3h5XovHHkeJ53wZ6TiePWbSMlwbmPuPnZ27MzsSW7nyzbUhJQ1b73bvMPK3OKc/tWBblv36pgf0wJnrdvc0o6xwa9iFSlROFvwBSXRK0Zr2G8UvAbMl6J4WDn4ci0GuQ+XpgSkmm0cg7GqzEe1/l3ePvokUqlZNwyCW5GRjoGX88TPxt8/YrEZxaKP3f2TzIzM3hu5GDX+3a7nWWLvmbj+p+Yv2Q1p08d59iRA3y7+he0Wue5RNVqNfjz5FF2btvMgMHD7qle1mznb1x2yySQMr1vkbv5fwl8ciSZu7eRsd05R5TpejQStZrQFyaQsmZVwfZitWJJiMMC5F+5hKZaTfwe7U/C/JIfSmnLctZDfks95AZfrOmeJy4Ofmo0GTt+J/3mHBama1eRqtSEv/QqSatXoKlWA4WvH9U+LZhzSyKToavbAP/e/TnTvxuU4Hmo3XjzHMTr1nMpnyK9Elx5sjOKHr+T45B5G5y9c2w2vHoMxrj7V0ynnUMzbImxSA3+aNv3LrEGhMOnc7kYXWjeBbmzQUzvLSM9q+AYZfCWczW2+KGqGVlWYhPchyrFJpp5pKFXsXkSU61k5tgIDVCUaAOC3scbqVRKekaGW3p6Ria+vkUnTb0bly5HkZGRydjxE11pdrud02fPsX7jr/y69sci86A8TBz3a+jl/xMPxBwIx44dw26388knn/DII49Qo0YN4uL+XgujXq8nNDSUQ4cKxpdZrVaOHTt2m1x3VrNmTU6fPu12B/3Ikds/Kqh27dpYrVa3sqSmphIZGUmdOgXj/oYNG8bmzZs5e/YsO3bsYNiwgoN148aNuXTpEkFBQVSrVs1t0evdDwD/xMmTJwkNdXZbUyqVNGnShO3bCyZusdvtbN++nZYtWxa7jr9aOG22219kTJkyhczMTLfl5eaexz8WYbM57x4WfpyhRIKqel3M1zw/5u0v6oYtkMjl5B3be9u4+8JmxRJzBXXNQo0mEgmqGvUxX71YfD5A06glErkc45HdbunGI7tJnPkaiR+87lpsGWlkb9tAypfvl3wd7DZsCdeRVyo8NlaComJNrDc8T7hljY1C6hsIhbqWyfyCnScmt7s4lUhAVjrtnw6Llewz5/FtVWhMvESCb6sWZB0/ddu8dpMZc2ISErmcwB5dSNm6s9hYmVaDpmJ5zEml99hA98LZsCfFIi9feG4QCfLy1bHFR3vMYou7itQQQOHvR2oIdJ5QlnLjgbMAVszXo1DXKugFhkSCunYDTFduPw5U26Q1ErmC3EO7iqzTFH0ZeYj70CBFcBi21FKafNBuwxp/HUXlwhOkSVBUro0l1vNcHpaYy8j8bt02gty2DWvMZWQB7o24Mv9gbJml+7SVB5nDYiHz+FkCOhU6dkkk+HdsScbBE7fNazeZMcU5t++Q/t1I/KXoZGY2Yx6mhGTkBh8Cu7UhwUPM36VQKKharSZ/niwYB2+32/nz5DFq1vJ8rKxRqy6nbxk3f+rEUVd8h07dmP3FIj75fKFr8fMPoO+AJ5j6rvNxxn+d19zaQ0EikWIvgZszWK3kR11EV7/Q3BMSCboGjci76Hm+AolKhePWvuJ/XUjf7gJBIkGiKJ27rg6rlbzLF9E1LNRwJ5Hg1bAxxgueezRJVWq3C28AR6F65Jw6zsUXR3HplWddi/HiBTJ2buPSK8+WaOMBADYb1rholFUL/Z4kEpRV62C57rmHpuXaJefwxEKfu8w/BFtWurNhAWdDLo5bymq3l+g8OvkmBwkpFtcSk2AmPdNKgxpaV4xGLaF6RRWR0fnFruf8lXzCgtx/I2GBSpLTi+8B4m+Q4a2Vkp5VshNUKxQKalSryolTBfP/2O12Tpz6kzq1/tn8I40aNuSbLz5l3mdzXEuN6tXo1KEd8z6b81A3Hggl71/VgJCZmcnJkyfdlpiYGKpVq4bFYuHzzz/nypUrLF++nHnz5v3t9Y8fP54PPviAdevWceHCBcaNG0fGLa17f9fQoUOx2+0899xznD9/ni1btjBrlnNsUXHdAqtXr07fvn0ZM2YMe/fu5dSpUzz11FOEh4fTt2/BM+HbtWtHSEgIw4YNo3Llym5DHoYNG0ZAQAB9+/Zlz549XL16lZ07d/LKK68Q+zfHR82dO5f169dz+fJlzpw5w4QJE9ixYwcvvlhwh3rixIksWLCAZcuWcf78ecaOHUtubi6jRo0qdr0VK1ZEIpGwceNGkpOT3Z6aUZhKpcLHx8dt+TvDF3J3/4q2RUc0TdsiDwrDZ+BoJEo1eYedFw76IWPx7vVEkXza5h3IP3PM4+MFJRod8rCKyIOd46zlQaHIwyoW6SFQkrJ3bETXqjPaFu2RB4djeGIMUpWK3IN/AOA7/CV8+hSd0VvXshN5fx7BnuteD3tuDtb4GLfFYbNiz0rHmlQ6s+jnH96BKqI1yvotkPqHoO3xJChUmG92p9b2fhp1+4LfuOn4HqQaLZqujyP1C0JetR7qVt0xHS9oDFG374u8fDXnePzAMOfritUxny29Z3rHLPyWsCEDCRnYB23VytR8/y1kWg1xP64DoPYn71Nl0iuueJ+I+gR274y6fDj6Zo1puOxrJFIp1+cvccVU+++rGFo0QV0uDJ/GDak/fy4Om43EDb/d+u9Ljen4LhT1HkFRuylS3yDUnQchUSixnHNOQKbuNgRV60dd8eY/9yNRaVF36IfUEIi8Um2UzbpgPlUyd47uRtbW9Xi37YquZUfkIeXwG/YCEqWanH3OCzP/UeMx9H+qSD6vNl0wnjzksXdF1pa16Jq2xqtNV+SBIXh37IWmQTOyd5Xed5F/YCvqxm1RNWiJLCAE3aPDkCiUmE46P0uvvqPQdurvijcd3YVEo0Pb4wmkfkEoqtdH06YX+Ud2umLyDm1DHl4FTZueSH0DUdZrjrpxW/KP/FFq9bhbMp0Wn4a18GnobDTRVi6HT8NaqMt7Hk99P12du4TyzwwmfHg/vGpVod6XbyPXaYhZ9jMADZd8SM33Cu7SGZo3IKRfVzSVy+HbugnNNy1EIpUSNWuhKyagaxsCu7VFU6kcAZ1b8ci2b8mJvELs0p9LpMyP9X+cbVs28se2zcRev8b8L+dgys+n081Z/j/9ZAYrlhbcre7dZyAnjh1m/c+riY25xvcrlxB1OZKevZ2/MW8fPRUrVXFbZDIZBl8/wm+O269Zqw46Ly8+n/0BV69cJu5GDMsWfU1SYjxNmj1SIvVK/WUNhi690HfoijK8AiHPvYJUpSZjh3N+qtCXJxE4bLQrPufoQXy798andQcUQSHoGjQm8MmnyTl60HVRHThsNJo69VEEBqOqUInAYaPR1m1I1u57b8wpTsq6H/Hr3htDp+6oylUgbNx/kKrVpG/bDEC5iVMIfvpZV3zW4f349eqDvl1H50SQEU0Ifmo0WYcPgN2OPS8P07Vot8VuyseWnYXpWnSp1MG4bwuapu1RN2qNLDAU7z4jkChV5B3bA4D3oDHoug1yxecd/gOJRofXo8OQ+QejrNkQXYfe5B3a4YoxXTiJtsNjKGs2RGoIQFmnMdo23TGdu7ebd3eycVcGg7r70qyelgqhSl55Kpi0TBuH/8x1xbz9Yhg92xac023cmUGNSmoGdvUlJEBB2yZedG3lw+Y9zh4YaqWEEX39qVFJRaCfnPo1NLwxJpSEFAsnLpT8PEYD+/Xl1y1b+X37Dq7FxPDZV/PIz8+nexfnE4g+/GQui5YWDDO2WCxcvnKFy1euYLFaSUlN4/KVK9yIcz6NTqvVULlSRbdFrVLh4+1N5UoVS7z8/zYOh6TMlofRv2oIw86dO2nUyH0W5GeeeYaFCxcye/ZsPvzwQ6ZMmUK7du2YOXMmI0aMKGZNnr366qvEx8fz9NNPI5VKGT16NP379ycz858/usvHx4dffvmFsWPHEhERQf369Zk6dSpDhw51mxfhVkuWLGH8+PH07t0bs9lMu3bt+PXXX92GS0gkEoYMGcJHH33E1KlT3fJrtVp2797N5MmTGTBgANnZ2YSHh9O5c2d8fIrOGns7ZrOZV199lRs3bqDVamnQoAHbtm1zPYYR4IknniA5OZmpU6eSkJBAREQEmzdvLjKxYmHh4eFMnz6dN954g1GjRjFixAiWLl36t8p2N/JPHiRL54NX90HIfAxYblwjbcEHrm7zMoN/kRZwWWAoyiq1SJ0/w+M61fWaYHiyYOIi3+HOi8XsLWvI+X1NidcBIO/4fjK8fPB59Alk3gYsN6JJ+fJ918SKcr+AIncs5EFhqKrVJvmLd0ulTH+X5fwx8rReqNv2RqrzwZYUS84PX7gm7pP6+Lp9F47sdLJXf4G28yBUz7yJPTsD05E/yD9YML5eqvNG2/tppF4+OEz52JJukPP9F1ijL5RaPZI2bkHh50uV/4xDGRhA9vlITj09FkuK886uOjzErR5SlZIqr72EukI5bLlGUv/Yy7n//BdrVsHFqyo0iLqffYjCYMCclk7m0eMc6/8UlrTbd8UvSdaLJ8nXeKFq2QOJ1gd7yg2M675xNaJJfXyxU/Abc+RkYFw3H1W7fuieeg1HTibmk7sxH91R3L8occaj+0j31mPoMwSZjy/m2KskfTa90HYRWHS7CA5DXb0OiXOmeVxn3slDpK6ch77HQHyffBZrYhzJ8z7EdPm8x/iSYD53FKPOG02HPki9fLAmxpK96jPX89Wlej+3IXf2rHSyV36KtttgDC9Mw56VQf7h7eTt2+yKscVdI/uHr9B2GoCmXW9s6SnkblmN+UwxM9LfR/om9Wi5veDkts6s/wIQ8+3P/PnMlLIqFgDxP/6GMtCPGtNeQRUSSNap8xzu/SzmmxMrasqHFtwNBqQqFTWmT0BbpTy2HCNJm3dxcuQkrJkF27dC703N9yaiLheCJS2DhLW/E/m/OTisJXNnsk27TmRlZvDdiiVkpKdRuUo1/vfOR64hCSnJiUgL3bSoVace/3n9f6xavoiVyxYSGh7O5Lfeo2Klu398no/ewP/e+YhV3y5i2n8nYrNaKV+xEm/8730qV6lWIvXK3r+LJL2BwCefRmbwxXQ1iuvv/RdbZgYAioAgt+075SfnfDmBQ0Yi9wvAlpVJ9tGDJK8qeO69XG8g7OVJyH39sBtzMV27Ssy7U9yf9lDCMvf8gVyvJ/ipkch9/ci/EsXVqZNdQzEUgUFuvQaSvnfObRL81DMo/AOwZmaQffgACcsXFvcvSp3p9GFydN7oOvdH6q3HGn+djKWfuCZ/len93YezZaaRsXQW3r2Gonn5PexZ6Rj3b8W4e5MrJueXFei6DMD7seHO4RBZGeQd3knuH+tLtS5rt2WgUkp54ckgdBop56/k8+7XcVisBeUPCVDg41Vws+rydRMfLoznqcf8ebyHL0mpVhb/nMLuo87jo90BFcNUdGzujVYjIz3TyskLRr77NY0S2szddGjXhozMTJat+I709HSqVqnMjHem4etrAJxzGkikBdt8aloaY18paPj88ed1/PjzOhrUq8snH5RCr1Ph/zWJoyQmCRDcrFy5klGjRpGZmYlGoynr4jyQ4l8tmWdnlzWb6V86+dHfoAsPvHPQA+DE/H/BUJV71HRClzsHPQDSz5XOIzjvN69ynp9e8SA5+O6uOwcJ90Wl8zvLugglQjr5793c+beymkrhqvA+C274cNxZfiH7rbIuwj2b+/KDfz4IUKF66cwVVNouR3keSns/VCvmCV4Psn9VD4QH1bfffkuVKlUIDw/n1KlTTJ48mcGDB4vGA0EQBEEQBEEQhDLk+HeN2n/giQaEEpCQkODq2h8aGsrjjz/O+++L7kKCIAiCIAiCIAjCw0M0IJSASZMmMWnSpLIuhiAIgiAIgiAIglCIg4dzMsOyIvpzCIIgCIIgCIIgCIJwR6IBQRAEQRAEQRAEQRCEOxJDGARBEARBEARBEISHkhjCULJEDwRBEARBEARBEARBEO5I9EAQBEEQBEEQBEEQHkqiB0LJEj0QBEEQBEEQBEEQBEG4I9EDQRAEQRAEQRAEQXgoiR4IJUv0QBAEQRAEQRAEQRAE4Y5EA4IgCIIgCIIgCIIgCHckhjAIgiAIgiAIgiAIDyWHQwxhKEmiB4IgCIIgCIIgCIIgCHckeiAIgiAIgiAIgiAIDyUxiWLJEj0QBEEQBEEQBEEQBEG4I9EDQRCE23LYbGVdhBIh04j2UqGE2e1lXQLhIaKQWMq6CCXCYn04jhkPhYdkH+WwO8q6CPdMbn84tm9BANGAIAiCIAiCIAiCIDykxBCGkiVuyQmCIAiCIAiCIAiCcEeiB4IgCIIgCIIgCILwUBI9EEqW6IEgCIIgCIIgCIIgCMIdiR4IgiAIgiAIgiAIwkPJ4RA9EEqS6IEgCIIgCIIgCIIgCMIdiQYEQRAEQRAEQRAEQRDuSAxhEARBEARBEARBEB5KdjGJYokSPRAEQRAEQRAEQRAEQbgj0QNBEARBEARBEARBeCiJxziWLNEDQRAEQRAEQRAEQRCEOxINCIIgCIIgCIIgCILwL/Dll19SqVIl1Go1LVq04PDhw7eN//HHH6lVqxZqtZr69evz66+/lmr5RAPCA2LkyJH069fvb+WpVKkSc+fOvef/vXPnTiQSCRkZGQAsXboUg8Fwz+sVBEEQBEEQBEEoTQ6HpMyWv2v16tVMnDiRadOmcfz4cRo2bEj37t1JSkryGL9//36GDBnCM888w4kTJ+jXrx/9+vXjzJkz9/qxFetfOQfCyJEjWbZsWZH07t27s3nz5lL/3xkZGaxbt65U/8/f9emnn+JwOEp0ndHR0VSuXJkTJ04QERFRouu+37Stu6Lr0BuZtx5L3HWy1i7DEhPlMdZv7FuoqtUpkp5/7gTpiz4GQF2/GdqWnVGUq4xU503yJ1Owxl0r1ToA6Np1x7tzH2Q+Biw3rpH+42Is1y57jA0c/zaq6nWLpOedOU7qvJlF0g1PjsGrTTcyflpCzs7Sa5lUNemAumVXpF56bImx5G75HltcdLHxEpUGTcd+KGs2QqLRYs9Mw/j7D1iinDs+TbveaNo95pbHlpJA5rxppVYHgLChT1B+9NMoA/zJuXCRy+9/SPZpzztjiVxOhedGE9z3MVTBQRivRnPlk09J37vfY3z5Z0dR5dXxxH67kqiZH5dmNYpQNGiNqmlHJFpv7Clx5P2xFnvi9eIzqNSoW/VCXq0BEpUWe3Yapl3rsUafv29l9urQE323/sj0Bsyx0aR9twBz9CWPscGvvoe6Zr0i6cbTR0n+/D3Xa3lIOXwHjkBdoy5IZVjiY0ie9yG2tJRSq4eqWUc0rbsj9dJjTYjB+Nt3WG9cLTZeotag7dQfZe3GSDQ67Jmp5G5ejeXSaVeM1NuAtusgFNXqIVEosaUlkbN+CbZS2l9VHDuUKhOfQRUSSNafFzg74V0yj5z2GCuRy6k6+XnKDe+HOjyY3ItXuTBlFsm/73HFyLx01Jw+nuC+XVAF+ZN18hxnJ84g86jndd5vfm2aUuXVZ9A3roc6LIijA8eRuGF7mZRl0y/r+XnNj6Snp1G5clWeH/siNWrWKjZ+755drFi+jKTEBMLCwhk5+lmaNmvhev+xXl095hs1egwDBg0G4PLlSyxbvJBLlyKRSqW0at2WZ8a8gEajKbF6+fXqi3+/J5D7+pEfHUXCN5+Td+lC8fGPDcSvZx8UAUHYsjPJ2r+bxG8X4LBYisQGDBxC8IgxpG5YQ8KiL0uszB7L9Wg/AgfcrMfVKOLmf0bexeLr4d9nIP69+qAIDMaalUnWvl0kLPNcj8BBQwgZ+Rwp638ifkHp1UPzSGe07Xq59lHZG5Zjjb1SbLxErUXXbRCquk2RanXYMlLJ2bgCc+SfzveVanTdBqKq0wSplw/WuGtkb1yBNbb4/V5JGfKoH11a6dFppFy4ks/81UnEJxf9bAvz08sY0TeAxnV1KBUSElIsfL4ikajrJldMuWAFw/sFULeaBplUQkyCmY8WxpOSbi3xOqzdtJnVazeQlp5B1coVeeW50dSuUd1j7NXrMSxZuZqLUVdITErmxWdGMqjvo0XiklNT+WbpSg4fP0G+yUR4aAiTX3mRmtWrlnj5hX9m9uzZjBkzhlGjRgEwb948Nm3axOLFi3njjTeKxH/66af06NGD119/HYB3332XrVu38sUXXzBv3rxSKeO/sgEBoEePHixZssQtTaVSlVFpyp5ery/rIvxrqSMewafPU2T+tBjL9cvo2vbE77k3SP7wVew5WUXi05fOQSIv+OlLtV4EvPoB+X8ecqVJlCrMVyPJO3UQw+Dn7ks9NI1bYej/NOmrv8EcfRmvjo8S+OKbJLwz3mM9UhbMQiIrVA+dF8FTZpF34kCRWHWD5igr1cCWkVaqdVDWaYq26yByf1uF9cZV1M074z3kFTK/nobDmF00g1SG97AJ2HOzyVkzH3t2BlK9H478PLcwa9INslfOLUiw20q1HoE9u1F18qtcfPt9sv88TfiIYdRf8BVHevXFkpZeJL7S+BcJfuxRLk59B+OVq/i2aUXdz2dzcujT5JyPdIv1rleX0CcGkXMhssh6Spu8RgTqdn3J3/EjtoTrKBu1Q9f/OXKWfYAjL6doBqkMXf8XsOflkLdxKfbcTKTefjhMeUVjS4m2aWv8Hh9N6sqvMV+9iHfnPgSNn0bc1BexZ2cWiU/++gMotH3LdN6ETp2L8WhBY448MISQSTPI2bedjA3f4cjPQxFW3uOJe0lR1m2GrvtgcjeuwHrjCupHuuD91AQyvngLR66HbUMmw2f4ROy52WT/MA97djpSvT+OfKMrRKLW4vPMG1iuRpK98lPsudnI/INw5BmLrq8EhD7ek9ofT+HMi9PIOHyKyq88TYtNi9hZtwfm5KL7lprvTCB8aB/+fOEtciKvENitLU1++oL97Z4k66SzAarB/PfwrludUyMnkR+fRPjQPrTYvIRdDXphivN8t+V+kum0ZP0ZSczSNTT9qXQvQG9nz66dLFwwnxdfeoUatWqzYd3PTP3fFOZ9sxiDwbdI/PlzZ/n4wxk8PfIZmjVvwa6df/D+u28z97OvqFipMgDfrljtlufY0cN89ulsWrVuC0Bqagr/++9k2rZrz/PjXsJoNLJw/lfMnf0xU96cWiL18mnTgeDRY4n/ei55F8/j99hAKr79IZfGPY0tM6NIvL5dJ4JHjCHu848wXjiLMqw84eMn4XA4SFz8tVusulpNfLv3Jv+q55sJJUnftiOhz44l7ss5GCPPE9B3EJXf+YjI50d4rkf7zoSMfI7YTz/CeP4MqvDylJswGYD4hV+5xWqq18Svx2PklXI9VPVb4PXoULLXLcUSE4W2dXcMo18n9ZNJxe6jDM9Mwp6TRdaqz7FlpiPz9Xfb/3gPfAZ5cDhZP8zHnp2OOqI1hmcmkzZnCvasosfSktK/iy+Ptjfw2fJEElMtDO3tz9QXw3nlvWtYrJ5vxuk0UmZOLM/pS3m8+9UNMnNshAYqyTXaXTEhAQpmTCzPtv2ZfL8pjbx8O+VDlVgsJXuDD2DHnn18vWgZ/xn3HLVrVOOnDZuYNO19vv36U3wNRa8JTCYTYSFBdGjdki8XLfW4zuycHF6e/D8a1a/LB9P+i8HHh9j4BLy8dCVe/n+bspxE0WQyYTKZ3NJUKpXH61qz2cyxY8eYMmWKK00qldKlSxcOHCh6fg9w4MABJk6c6JbWvXv3Ur0Z/q8dwqBSqQgJCXFbfH0LDpISiYT58+fTu3dvtFottWvX5sCBA1y+fJkOHTqg0+lo1aoVUVEFO9y3336biIgI5s+fT/ny5dFqtQwePJjMzEzX+8uWLWP9+vVIJBIkEgk7d+6kU6dOvPTSS27lS05ORqlUsn170TsRmZmZyGQyjh49CoDdbsfPz49HHnnEFbNixQrKly/veh0TE8PgwYMxGAz4+fnRt29foqOjXe/fOoQhOzubYcOGodPpCA0NZc6cOXTo0IEJEya4lcVoNDJ69Gi8vb2pUKEC33zzjeu9ypWdJxGNGjVCIpHQoUOHO3wr7tatW0f16tVRq9V0796dmJgY13unTp2iY8eOeHt74+PjQ5MmTVyfR0nTteuF8eAf5B3ZhTXxBplrFuGwmNA0b+8x3pGXiz0707Uoa9THYTGRf6qgASHv2F5ytq7FfLH0uv/cyrtTb3L3b8d4cCfWhFgyvv8Gh9mMrmUnj/EOYw727AzXoq7VAIfZVKQBQar3w/D4aNKWforDVvIt5IWpW3TBdGIv5lP7safEY/x1JVjMqCJaeYxXRbRGotGR8+NXWGOjsGemYr1+CVtSrHug3Y4jN6tgycst1XqUe3o48T/+TOLa9RijrnDp7few5+cTMqCfx/jgPo9y/ZtFpO3eS37sDeK//5G03XspN3KEW5xUq6HWxzO4OPUdrFkeTshKmapxeyxnDmI5dwR7WiL523/CYbWgqNvcY7yibnMkai15vyzGFh+NIysd240o7Clx963MPl37kr33d3L378ASH0vayq9xmE14te7sMd5uzMGeleFa1HUicJhNGI/tc8UY+g0j78xxMtYswxJzFWtyAnmnjnhskCgp6pZdMR3fg+nkPmzJ8eRuXOHcNhq18RivatQGiUZH9vdfYo25jD0jFeu1i9gSC7YNTZue2DPTyF2/BOuNq9gzUrBEncOenlwqdag8YRQxi34gdtnP5JyP4vS4adiM+ZQfOdBjfPiwvlz+cB7Jm3eTdzWW6/O/I+m3XVT5z2gApGoVIQO6cWHKx6TtPYox6jqX3v0CY9Q1Kj4/tFTq8Hclb9nNxWlzSVy/rUzLsW7tGrr36EmXbj2oUKEi414aj0qlYuvvWzzGb1i/lsZNmjFg0GDKV6jIUyNGUrVqNTb+st4V4+vn57YcPHiA+g0aEhIaCsCRw4eQy2W8MO5lypUrT40aNRn30gT279tDXNyNEqmXf9/HSf/9VzK2b8YUc434r+dgN5nw7dLTY7ymVj2M58+QuXsHlqREck8eJXP3DjTV3XtiSNVqyk38L3FffoItp/T3tQH9Hid9yybStznrcePL2dhN+fh19VwPXe26znrs2o4lKZGcE0fJKKYe5V97k9jPZ5V6PbRte5B3ZCf5x/ZgS4oje91SHGYTmqaez6XUTdoh1ejIXP4plmuXnPufq5FYE26eC8oVqOo2Jee31ViiI7GlJpG7fS221EQ0LTyf15SU3h0N/LgljcOnc7kWZ+bTbxPx08to0bD4C+UBXX1JSbfyxYpELl0zkZRq5dQFIwkpBQ3LQx/z59jZXL5dn8rVWBMJKRaOnM4lM6fkb2r8uH4jj3brTM8uHalUoTwTxz2HWqXkt207PMbXql6NF0aNoFO71igUCo8x361ZR1CAP5PHv0jtGtUJDQmmWaOGhIeGlHj5hQIzZ85Er9e7LTNnFu0pDJCSkoLNZiM4ONgtPTg4mISEBI95EhIS/lZ8SfjXNiDcjXfffZcRI0Zw8uRJatWqxdChQ3n++eeZMmUKR48exeFwFLnwv3z5Mj/88AO//PILmzdv5sSJE4wbNw6A1157jcGDB9OjRw/i4+OJj4+nVatWPPvss6xatcqt9WjFihWEh4fTqVPRnaBeryciIoKdO3cCcPr0aSQSCSdOnCAnx3mXb9euXbRv79wpWywWunfvjre3N3v27GHfvn14eXnRo0cPzGazx7pPnDiRffv2sWHDBrZu3cqePXs4fvx4kbhPPvmEpk2buuo5duxYIiOddz7/mpBj27ZtxMfH8/PPP9/1Z280Gnn//ff59ttv2bdvHxkZGTz55JOu94cNG0a5cuU4cuQIx44d44033ih2h3ZPZDIU5SpjulToQt/hwHTxDMqKnrt53UrbogP5Jw7iMJvuHFxaZHIU5auQf7PbHwAOB/mRf6KsXOOuVqFr1Rnj8f3u9ZBI8BvxMjnbN2BNiC0+c0mQypCFVsBytXDXdgeW6AvIw6t4zKKo0QBr7BW0PYZimPAxPs9NRd26J0jcW4plfkEYxn+I/sX30PUbjdSn6B23kiJRyPGuW5v0AwUNSjgcpB84hE9EA495pEol9ltal+35JvRNGrmlVf/ff0nbtYeMwuu+X6QypEHlsMZcLJTowHr9IrLQSh6zyKvUwxp/DXXHgXiNmY7uqddRNutc5PspNTI5ygpVyT9/y3Zx/hSqKjXvahVebbqQe2RvwXYhkaCp3xRrYhxB46dRbtZSQqZ8hCaixe1XdC9kMuRhFTFfOVeQ5nBgvnIeRTnP24ayZgTW2CvoHh2K72uz0Y+bjqZtL7fPXlGzIda4a3g9/gK+r89G//xUVI3blkoVJAoF+sZ1SdleaFiOw0HKjv0YHmnkMY9UpcCe734Ms+eb8G3V2LlOuRypXI4t333bseWZ8GvduGQr8ACzWCxcvnyRhhEFn4lUKiUiojGRF855zHPhwjkiGrl/ho2aNOXCBc9Dj9LT0zl65BBduxVc8FosFuRyBVJpwamiUqUE4NzZe29cl8jlaKrWIPfUsYJEh4PcU8fQ1Cw6zBAg78IZNFVruC60FcGheDdpQc4x931q6PPjyTl2iNxTRc+LSppELkdTrQY5J93rkXPyONpaRYcZAuSeP+usR41C9Wjaguyj7vUIGzuB7CMHS78eMhnysEqYL58tSHM4MEedQ1GhmscsqjqNsVy/jHffEQT893P8xs9A2+Ex1z5KIpUhkclwWN17djksFhSV7u685p8I9pfjp5dz6kJBTwhjvp1L0fnUrKQuNl+z+jouX8/n9dEhLJ1ZmU8ml6drKx/X+xIJNK2rIy7JwtQXw1g6szIfvlae5g1K/u69xWLh4uUrNCl0ziGVSmncsAFnL1y8Tc7b23/4KDWrVeXtDz6h//BnGDP+dTZuKdvG0f8PpkyZQmZmpttSuIfBg+hf24CwceNGvLy83JYZM2a4xYwaNYrBgwdTo0YNJk+eTHR0NMOGDaN79+7Url2b8ePHuy7i/5Kfn8+3335LREQE7dq14/PPP+f7778nISEBLy8vNBqNW+8HpVLJgAEDAFi/vqDlfunSpYwcORJJMSfSHTp0cP3vnTt30rVrV2rXrs3evXtdaX81IKxevRq73c7ChQupX78+tWvXZsmSJVy/fr1I+cHZ+2DZsmXMmjWLzp07U69ePZYsWYLNVrQFtFevXowbN45q1aoxefJkAgIC+OOPPwAIDAwEwN/fn5CQEPz8/O7wrRSwWCx88cUXtGzZkiZNmrBs2TL279/vapS4fv06Xbp0oVatWlSvXp3HH3+chg0b3vX675ZU541EJity59Cek4nU23DH/IryVVGEVsB46I8SL9vfIfUqph5Zmch8DHfMr6hYDUVYBXL3u/eI8e7aF+y2Up3z4C8SrRcSqaxIV0d7ThZSL89DcGSGQOf4bqmE7O8/J3/vr6hbdEHdpmDcnvXGVXJ+WUr2d59h/G0VUn0A3k+/DsrSGdKkMPgikcuxpKa6pVtSU1EGBHjMk7b3AOVGDkdTsQJIJPi2eoSArp1QBhbEB/bqjledWlyZ/VmplPtOJBqd8/u5ZSiJw5iNVOftMY9U74eiegOQSDGuX4Dp8FaUjTugbO557HRJk93cLmxZGW7ptuxMZPo7NyIpK1VHGV6RnL1bXWlSbz1StQafHgPIO3ucxLnTMZ44SOALk1HV8Hyyf69c28YtQ5EcuVlIits2fANQ1mkCEilZKz8lb9dG1C27oWnXu1BMIOpmHbClJZK1fA75R3ei6zkEVUPPPX7uhTLAF6lcjinJfbswJaaiCvG8XST/vpfK40eirVYRJBICOrcipF9XVKFBANhyckk/cJzqb45zpkmlhA/tg+8jEahCgkq8Dg+qrKxM7Ha7Wy9MAIPBl3QPQ6oAMtLTi0x2bDD4kpHueRjbjm2/o9FoadW6oEdMg4YRpKen8fNPP2CxWMjJzmbZkkUApKfd+3A4mY8eiUyGNcO9DtaMdOS+ns9HMnfvIOm7JVSa+Sl11vxOjW9WknvmFCk/rXLF+LTtiLpKdRK/XXDPZbwb/6geu7aTuHIJVT78jHrrtlJr0SpyT58k+ceVrhh9u45oqlYnYVnp10OqvXkOcss+yp6didS7uH1UIKp6zUAiJWPpJ+TuWI+2bU+0nfoC4DDnY7l2CV2nvs7zMYkEVUQrFBWq3dX52T9l8HEOYcvMdj8nzsi2ud7zJDhAQY+2euKSLUz/Mo7NezN5ZlAgHVs4j496LxkatZQBXX05cc7I21/c4NCpHCY/G0rdaiU3JwhAZla2c5u/ZaiCr0FP2s0Jzf+JuIQk1v/2O+FhoXz09lv06dmNzxcsZvP2nfdW4AdAWU6iqFKp8PHxcVuKG5YfEBCATCYjMTHRLT0xMZGQEM89RUJCQv5WfEn4186B0LFjR77+2n08260XuA0aFLTM/dV1o379+m5p+fn5ZGVl4ePjbEWsUKEC4eHhrpiWLVtit9uJjIws9oNWq9UMHz6cxYsXM3jwYI4fP86ZM2fYsGFDseVv3749ixYtwmazsWvXLrp160ZISAg7d+6kQYMGrqEW4Ozuf/nyZby93U/i8/Pz3YZg/OXKlStYLBaaNy/odqzX66lZs+gducKfkUQiISQkpNhZPP8OuVxOs2bNXK9r1aqFwWDg/PnzNG/enIkTJ/Lss8+yfPlyunTpwuOPP07Vqp4naPE0NshktaGSy+65nHeiadEBS9z1YidcfFDoWnbCfOOa24SLivJV8OrwKIkfTirDkt2BRII9N5vcTSvA4cCWcB2ptwH1I93I37MRAEtUwR0RW9INrDeuon95Jso6TTGf3Ffcmu+rqBkfUeOdqTTbtBYcDvJiYklYu4GQAc4TKVVIMNWmTOLPZ17AUUyvon8liQSHMYf87T+Aw4E9KRazTo+yaUfMh34v69LdkVebLphjo90mXPyr0Tfv5GGyt/0CgCX2KqqqtfBu1x3TxbMe13XfSSTYc7PI/eVb57YRfw2pjwFNq+7k7frFFWONiyZv+1oAbAkxyILCUTVtj+mU5wk876dzE9+n/rz36HDmNxwOB8aoGGKW/ew25OHkyEk0WDCDLtf3YLdayTpxjrjVm9A3Kp3GHMGzrVu30KFjJ5RKpSutYsVKTJg4iUUL57Fs6SKkUhmP9e2HwdcXibRsxhNr6zUkYNAw4ud/St7F8yhDwwl59kUCBz9F8g8rkAcEEvrsi0RPnVSqc5rcK139hgQOHkbc13MxRp5HFRZO6JiXCHpyOEnfL0cREEjomJeI/t/r/956SKXOOVrWLgaHA2tcNFK9L9q2vTBuXwdA1g/z8R74LAH//QyHzYY1LhrTqQPIwyuXWDHaNfXmhSEFDY7vf/3PhthJJBKiruez8hdnI+nVWBMVQlV0b6Pnj0PZSG7ecj18Opdf/sgAIPqGmZpV1HRvo+fs5fs3N9A/5XDYqVmtKmNGOIeIVa9amavXY/hl8+/06NyhbAsnAKBUKmnSpAnbt293DV232+1s3769SK/6v7Rs2ZLt27e7DWPfunUrLVu2LLVy/msbEHQ6HdWqee429ZfCXeL/Oin0lGa327lXzz77LBEREcTGxrJkyRI6depExYoVi41v164d2dnZHD9+nN27dzNjxgxCQkL44IMPaNiwIWFhYVSv7uxin5OTQ5MmTVi5cmWR9fzVS+CfunXYgEQiKZHP407efvtthg4dyqZNm/jtt9+YNm0a33//Pf379y8SO3PmTKZPn+6WNvGRerzWqn6R2FvZc7Nx2GxFWsilXnrs2Rm3zStRqtBEtCR7y093rlAps+cUUw8ffZG7r7eSKFVom7Qma5P7hFiqqrWQevkQ+k5BQ5xEJkM/4Gm8Oj5KwrQXS6z84JyTwWG3IbnlbrbUywd7juex5facTOeEiIWeMGJLiXd+DlKZx8kSHaY87GmJyHzvbdsojiUjHYfVisLf3y1d4e+POcXzDP2W9HTOvvwfJEolCoMBc1ISlV8dT36sc5ywV906KAP8abLmO1ceiVyOvmljwoc+we6GzaGUt0tHXq7z+9G6fz8SrTd2TxNkAY7cbBy3fD/29ESkOp9iv5+SZLu5XdzaC0fmrceWefsJuCRKFbpmbchY/51bunOdVizxMW7plvhYVNVql0i5b+XaNrx83NIlOh8cxW0b2ZlFPntbcrzzzp1MBjYb9uxMbMnxbvlsyfGoapd8939zSjp2qxVVkPt2oQr2x5Tgebswp6RzbNCLSFVKFP4GTHFJ1JrxGsYrBZ+98UoMBzsPR6bVIPfxwpSQTKOVczBejfG4zv+PfHz0SKVS0tPdf/MZGen4+nnuiWPw9XU9erlwvMHDHfGzZ05zIzaGyW+8WeS9Dh070aFjJ9LT01Gr1UgksH7tGkJCQv95hW6yZWXisNmQ3zIJpNzgi7WYnhJBQ0eRuXMrGVudvepM164iVakJe3EiyT+uRFO1BnKDH1XnzHflkchkaOs2wO/Rfpwb1L3E97X/pB7BT40mY8fvpP/uXo/wl14lafUKNNVqoPD1o9qnBfNWSWQydHUb4N+7P2f6dyvRetiNN89BbtlHSb31xc4NY8/KKHr8Topz7q9v7qNsaUlkLJgBCiVStQZ7diY+Q17EllZyE6QePp3Dxeh812uF3Hnur/eWkZ5VcIwyeMu4Glv8UNX0LCsxCe4N/LEJZlpGeAGQnWPDanMQE28qElO7Ssn2QND7eDu3+Qz3zz49IxO/e3iMur+vLxXLl3NLq1gunD37D/7jdf4fe3cdHcX1NnD8u7vZZONKPCGeYMHdglNcWlrcKRQpFQq0RUp/hUKBYjWcIi0UlxYnQHGKWyC4RIC4bdbePxY2WbIJlpA27/2cM+eQ2Tsz92H8zpX/iuLsRPFlffzxx/Tu3Ztq1apRo0YNZs2aRXp6umFUhl69euHl5WXoR+HDDz+kYcOGzJgxg9atW/P7779z8uRJo37vCtu/tglDUblz5w4PHuSUTh49ehSpVGr4em9ubm6yKUCFChWoVq0aCxYsYNWqVfTr16/A7Tg4OBAeHs68efOQy+WEhYXRoEEDTp8+zdatWw3NFwCqVKnCtWvXcHV1JSgoyGgyNfpCQEAAcrmcEydOGOYlJydz9erLtYt6+qXBVLzPo1arjTpFjIqKIikpiTJlch7AQ0JC+Oijj9i5cyedOnXKM6rGU6baBg2vYbr9Yx4ajf7rYe7hDCUSLILLkX3b9DBvTykq1kRiZkbmP3+/2LaKkkaN6u4NFKG5Ck0kEixCKpB9s+D9alm5NhIzMzJOHDCan3HiAHFTPiXu21GGSZOUQOruzTz64ZvCj0GrQRNzB7l/7pcwCXK/MNT3TQ8Dpb53HaljKch1YZc6uekLf/J7OZVbIHUslW+hxOvSqdSkXryMY61cHQtKJDjWqkHKmXP5LwjosrPJjo9HYmZGqWZNePykWmDSkWOcaNeZk53eNUwp5y8Sv/VPTnZ6t8gLDwDQatDG38PMJ3ffIBLMfILRxNwyuYjmwU2kDi4Y7R+HUjkFP0VNoyb7znUUYbn6npBIUJQJR3mj4FEsrKrWRWImJ/3Y/jzrVN6Kxszdy2i23M0TzeOi6XwQjQb1g9vG54ZEgjwgDFU+Q6Sp7kYjc3I16vNA5vzk3HhyzVbfjUbmbNxxkszZDU2ycTODwqBTqUg+dRGXxrm+aEgkODeqTdLR0wUuq1Vmo3ygPy/cOzYnbkvezoc1GZkoYx9i5mBHqeb1iDWR5v8ruVxOUFAI587m/D9rtVrOnjlNaJjpe2VYWFnOnjHeL2dOnyIsLG8h2c6dfxEUFIx/QP7DuDk6OmJpacnBA/uRy82pVLnqK0aTQ6dWk3n9KtbhuQq8JBKsw6uQGWW6bwephSLP9VL39G+JhPRzp4ge3o/rIwcapsxrV0jev4frIwcWybVWp1aTGX0V64rGcdhUrELGFdM1mqQWCqMX72fjSDt7iqtD+3JtxADDlHH1CkmRu7k2YkDhx/GkdoB5oPGzlHlgWVR3TA8lrbp9FZnzM9coF3c0KYmGa1RO4my0qclIFFaYB5dHeanw+nTIUuqIfaQyTHdjs0lIVhMeamVIY6mQEuynICpXQcOzrtzIwsvV3Giep6uchwn6GiBqDUTfzsLL7dk05jws5CEc5XI5IUEBnDqbM5ytVqvl1LnzlAt79f4jypUJ5e594xoa9x7E4OZaNB9khFfz7rvvMn36dMaPH0+lSpU4c+YM27dvN9S2v3PnDjExOR8P6tSpw6pVq5g/fz4VK1Zk7dq1bNy4kfLl8w5pXVj+tQUISqWS2NhYo+lRPl//XoZCoaB3796cPXuWgwcPMmLECLp06WJovuDn58e5c+eIiori0aNHqHJVHRswYADffvstOp3O5Jf0Z0VERLBy5UpDYYGTkxNlypRh9erVRgUI3bt3x8XFhfbt23Pw4EFu3rxJZGQkI0aM4N69vB3f2dra0rt3b0aNGsW+ffu4ePEi/fv3RyqV5tsngymurq5YWlqyfft24uLiDKNRvAi5XM7w4cM5duwY//zzD3369KFWrVrUqFGDzMxMhg0bRmRkJLdv3+bQoUOcOHHCqHAhN5Ntg16i+UL6gT+xqtkIy2r1MXP1xK5zPyTmCjKP618c7LsOwbbVu3mWs6oRQdaFf9Bl5B2+TmJpjZlnaczc9CW1Zq4emHmWzrctYGFI3bsV6zpNsKrZEDM3LxzeHYjUwoL0o/r+GRx7DsOuXd6eya1rNybz3Am06cZxaNPTUMfcNZp0GjXalETU8UXTi37Wsd1YVK6HeXgtpM7uWLXqBnJzQ3Vq63Z9sGzUwZBe+c9+pJbWWLV4F6mTK/Kg8ljWfYusk5GGNJZNOmPmG4zU3hkz7wBs3xkMWi3ZF09QVO4tW47HO51wa98WqwB/gid8gdTSktgN+n5QQr/9Gv+PhhvS24aXx6VZYxTeXthXrUyF+T+AVMqdJ0MpaTIyyLh23WjSZmaiSkom49qbaz6jPLUfeflayMtUQ+roiqLJ20jk5qgu6fsuUTTvikXdnP4nss8dRmJhhSKiA1KHUpj5lcG8elOyz765piMpuzZhW78Z1rUbYebujVP3wUjMFaQd0r9gOvf9EIeOPfIsZ1OvKRlnjpmsXZGyYwPW1epiU68ZZqXcsW3UCsvw6qTu/6vI4sg6sgtF1QZYVKyDzMUD69Y9kMgtUJ7W/1/adOyHVZNOhvTKE5FILK2xavkeUmc35MEVsKzfmqzjOf21ZB7ZhZl3AJb1WyF1csW8Qg0UVRsYpSlMN2ctwad/F7x6dsAmLIDyP0zEzNqSu8v0HfBWXDKV0P/lDCXlUCMc9w7NsPT3xrFuVWpsW4hEKuX69IWGNC7N6lGqeX0s/bxxaVKHWrt/JS3qBveWvninvkVJZm2FXcUw7CrqO7uz8vfGrmIYCp/X/wL/Mjp07MyO7X+yZ/dO7t65zY8/zCFLmUXTZi0AmDl9qqF/AoB27Tty6p8TbFj/B3fv3mHVil+JvnaVNm3bG603IyOdQwcP0ryF6dECtm7ZSHT0Ne7fu8e2LZv4+ad59OrTDxsbm0KJ6/GmP3Bs3hr7Rs0x9/bFY/BIpAoFibu3A+A1cgyuPQcY0qeeOILjW+2wq98Iuas71hWr4tq9L6knjoBWizYzE+WdW0aTNisLTWoKyju3CiXPpjza+AdOLdrg0LgFFt6+eH7wkVEc3h+Pxa13Thwpxw/j1Kod9g0aIXdzx6ZSVdx69CPleK44bt8ymrTKJ3HcLpo4Mg5ux7J6QxRV6iEr5Ylt+95IzC3I/Ef/ccL2nUFYt3jHkD7z2F4kljbYtOmBzMUd89CKWEe0JfNITqd85sEVMA+pgNTRBXlQORwGjkXzMIasfw4WSQxPbd2XxDstnahewRpfT3M+7OlGQrKGY2dzRnD6argXbzXIeabbsjeREH8FnZs74u4ip341W5rXteevAznPxht3J1K3ii3N6tjh7iLnrQb2VC9vzfYDSYUewzvt27B15x6274nk9t17fP/TArKylLRs0giAyd/PZcGynJrLKpWK6Bs3ib5xE7VazaOEx0TfuMn9BzFG67wUdY0Va9Zz/0EMu/cfZOuO3bRv1bLQ8/9vU5x9ILyKYcOGcfv2bZRKJceOHaNmzZyOniMjI1m6dKlR+nfeeYeoqCiUSiUXLlygVatWr/Pf9Vz/2iYM27dvx8PD+AYdGhrKlStXXmu9QUFBdOrUiVatWpGQkECbNm348cecMXcHDhxIZGQk1apVIy0tjX379hn6KujatSsjR46ka9euKBT59+T6VMOGDZk1a5bR8IgRERGcPXvWaJ6VlRUHDhxg9OjRdOrUidTUVLy8vGjSpImh74ZnzZw5k8GDB9OmTRvs7Oz47LPPuHv37gvl6ykzMzPmzJnDpEmTGD9+PPXr1zfZaaMpVlZWjB49mm7dunH//n3q16/PokX6hxeZTMbjx4/p1asXcXFxuLi40KlTpzzNFApL1pmjpFjbYdPibWR2Dqju3yZhwbeGzoBkDs6gMy6tl5XywDwgjMe/TDa1ShTlq+Lw3mDD3449RwCQumMdaTvXFUkcmacOk2Rjh13rd5HZOqC6f4tHP3xjqD5o5uSS54uFmasnFkFleDjv6yLJ08vKvnQSiZUNlg3bIbW2QxN3j9Tf5hg6VpTaOxlXh09JJHXVHKyavYP9oPFoU5PIOrGXrMPbDWmkdo7YdByAxNIaXUYaqrvRpC/91mTBT2F5+NdO5I6O+I0YgrmLC2mXozg/6ANUj/XVURUeHqDNiUNqYYHfiKFY+nijycjg8YG/uTL6SzSpb36oxoKor54hy9IGi9otkVjZoX10n4yN8w3/l1I7R7TkxKVLSyJj4y9YNOiAdY9P0aUlk33mANknTQ8jVRQyTh4i0dYeh3Zdkdk5kn3vJvFzvsp1XpTKe164eaIILkvc9xNMrjPzzDEer/wZ+5adcXxvAOq4Bzz8eSrKaNM91BeG7IsnyLC2wbJRe6Q2dqhj75K6Yha6dP11SmrvjO7Zc2P591i1fBeHIRPRpiSSdWw3mX/nFHJoHtwidfWPWDXphGXDtmgSH5G+/XeyzxfNKB8xf/yFeSknQiaMwMK9FClnL3O8zQCyn3SsaOnjkfMVFf15EfLVSKwCfNCkZRC/fT9n+nyGOjnnvJDb2xL6v49ReLujSkgidsNOosZ9j05dtEPOvij7quWpvWe54e+y0z8H4O6v6znX/831ol2/YQTJKUmsXL6MxMREAgIC+WrSZEPHig8fxhv1S1CmbDk+/WwsK35dyq9Ll+Dp5cUX4yZS2s+47fmB/ZHo0NEgwvSwelejoli14lcyM7Pw9vFh6LAPadyk8DpRTfk7EjM7B1y79cXM0ZGsm9e5/dVoQxMluYur0TH1cM1y0Olw7d4PuZML6pQk0k4cIW7Fovw28UYkH9yHmb09bj36YOboRNaN69wcP9rQsaK8lKtRrYH43/VxuPXoj9zZBXVyEqnHjxC7fGF+myhyyvPHSLOxxbppJ6S29qhj7pC05DtD56/6Z6lc16jkBJKWfIdt625Yjvgf2pREMg7vJGP/VkMaicISmxbvILV3QpuRjvLiCdJ3rC3yGmwbdieisJAwpKsr1pZSLl/P4usf76NS5+Tf3UWOnU3Ox6roO0qmLoihRztnurzlRPxjNYvXPeTAyZzr1bFz6fzyezydmjvS/+1SPIhXMW1hDJdv5F+z4VU1rl+X5OQUlq5aTUJiEoEBfkyd+AVOjg4AxD98hDTXR8PHCYkMHJnT59XqDVtYvWELFcuXZdZk/fN3WHAQX38+igW/ruTX1WvxcHNl6IA+NIsomtF7hJJLotM98+RVgk2cOJGNGzdy5syZV1r+1q1bBAYGcuLECapU+XcNMZWeno6XlxczZsygf//+xZ2d1xbzyb9jDPDXpVH+Szs/eglW7i8+Ose/2fmVxTB0YiGr/H7Rjp39piReMl1t/7/GxtP06AP/JUe/KdovgcKLC76y6/mJ/gNUH/cp7iwUCq3mv/947FbBp7izUCjeTx1X3Fl4bT8M//d3svgiPENND2f9b3f8StE0e30RNcKKrvZycfnX1kD4N1GpVDx+/Jgvv/ySWrVq/SsKD06fPs2VK1eoUaMGycnJTJo0CYD27ds/Z0lBEARBEARBEIT/H95AT1P/r4gChBdw6NAhGjVqREhICGvXFn+P/U9Nnz6dqKgow5AfBw8exCWfceoFQRAEQRAEQRAE4XX8vypAmDhxIhMnTnzp5SIiIvi3tfSoXLky//zzT3FnQxAEQRAEQRAE4V/rVTszFEz7147CIAiCIAiCIAiCIAjCv4coQBAEQRAEQRAEQRAE4bn+XzVhEARBEARBEARBEP7/0CGaMBQmUQNBEARBEARBEARBEITnEjUQBEEQBEEQBEEQhBJJdKJYuEQNBEEQBEEQBEEQBEEQnkvUQBAEQRAEQRAEQRBKJNEHQuESNRAEQRAEQRAEQRAEQXguUYAgCIIgCIIgCIIgCMJziSYMgiAIgiAIgiAIQomk1RV3DkoWUQNBEARBEARBEARBEITnEjUQBEEQBEEQBEEQhBJJdKJYuEQNBEEQBEEQBEEQBEEQnkui0+lEqxDhX+d8dFxxZ6FQ6HT//RJPqURb3FkoFFL++3GUlBJ0CSXjtpOhsSzuLLw2S1lWcWehUMglquLOwmu7FtasuLNQKEKu7CzuLBSKTO1///w2k6iLOwuFIkllW9xZeG2WZsrizkKhqBzsUtxZeCX7L2YU27YblrMqtm0XFdGEQRAEQRAEQRAEQSiRSsIHvX8T0YRBEARBEARBEARBEITnEjUQBEEQBEEQBEEQhBJJNNgvXKIGgiAIgiAIgiAIgiAIzyVqIAiCIAiCIAiCIAglkraEdEL9byFqIAiCIAiCIAiCIAiC8FyiAEEQBEEQBEEQBEEQhOcSTRgEQRAEQRAEQRCEEkkM41i4RA0EQRAEQRAEQRAEQRCeS9RAEARBEARBEARBEEokMYxj4RI1EARBEARBEARBEARBeC5RgCAIgiAIgiAIgiAIwnOJAoQSpk+fPnTo0OGll5s/fz4+Pj5IpVJmzZpV5NsTBEEQBEEQBEEoajokxTaVRKIPhFfQp08fkpKS2LhxY7Hl4datW/j7+3P69GkqVar0WutKSUlh2LBhzJw5k86dO2Nvb09ERASVKlUyWZhw+/ZtwsLCePjwIbNnz0ZXDA2L/tq6ns3rficpMYHS/oH0H/whwaFl801/+OA+fl+xiIdxsXh4etGj72CqVK9t+H31ysUcOrCXxw/jMTMzIyAolK69BhISlrPOb78aw62b0SQnJWFtY0N4pWr06DsYJ2eXV45j+9b1bF7/myGOfu+PLDCOI3/v4/cVC3kYF4u7pzc9+uTEoVar+X35Ak6dPEp87AOsrK2pULEa3fuYzqNKlc3Yj9/n9s1ops1ZjH9A8CvF8NfWDWx6si/8DPuiTL7pDx/cx28rFhvti6rVa5lM+8u8Gez8azN9Bw6jTYd3AIiPi+GP337lwrlTJCUm4OjkQoNGzej8bk/kcvkrxQDw59YNbFy32hDHgMEjCCkgjkMHI/ltxWLi42Lx8PSmV99BRnHMmfkt+/bsMFqmcpXqjP96mtG8k8ePsOa3X7l96wZyuTnlKlRk7Lj/vVIMf23dwEbDvghiwOARz9kXkfy2YpEhhp593zeKYe7MKXliqFSlOuO//s7w99rfl/PPiaPcvBmNmZkZK9Zse6W85/am98WFc2cYN/Yjk+ue9v1PBIeEPTfPOp2OdasWsG/nJtLT0wgpU4F+Qz7D3dO3wOV2blvLtg0rSE5MwNc/iN6DPiEwpJzh9+xsJSsXz+HowV2oVCrCK9ek7+BR2Ds6A7B/z1bmzzZ9vPz465/YOzgBcChyO1vXryD2wV2srG2oWKU2XfsOx9LRosD8FfYxldvP82aw868t9B04lLZPzm+AB/fvsmzRz1y5fB61Sk1p/wC69uhPhYqVC8xrQbZt2cT6dX+QmJiAv38g7w8ZSkho/vv174P7WbF8GfFxsXh6etGn3wCqVa9p+L1tq2Yml+vbbyCd3u4CQHT0NZYtXsi1a1FIpVLq1K1P/4GDsbS0fOU4XoVTvWoEfNIf+yrlUXi6crLzB8Rt3vNG85Db1i2bc+2LAN4fMpTQAvfFAVYsX0pcXJxhX1SvXsPwe2ZmJkuXLOLokcOkpqbg5uZO23YdaNW6jSFNTMwDFi2cz6WLF1GpVFStWo33hwzF0dHxleMojvv3B/3e4WF8rNF6u/V+n47v9HilGIrjvjf5qy+4eTOa5KREbGxsCa9UlV59B73Ws5ROp2Pjb7+wf9cGMtLTCA6rSM/BY557/d3z5xr+2rCc5KTH+PoF033gKAJCyptc//dff8j5U4cZPmY6VWpFAHDn5lX+XL+Uq5fOkpaahIurBxEtOtO8bdfn5nnH1nVsWb/KcO3v+/5HBBVw/Bz9ey9rViwwHD/d+gyhcvU6Rnn8Y+VC9u7YQnp6KqFlwun/wad4ePkY0tyMjmLV0h+5fu0KUqmUGnUi6DVgOApLK6NtRe7exp8bVxNz/y6WVlbUqteYfkM+eW5Mwv8fogaCwJ07d1CpVLRu3RoPDw+srKwKTL9p0yYaNWqEjY0N9vb2ODg4vJmMPnHowB6WLfiBd7r1Ydqchfj5B/G/cZ+SnJRoMv2VS+eZNW0STZq35rs5C6leuz7T/vcFd27dMKTx9PJhwOCRzPxhKf/77gdc3dz537hPSE5OMqQpF16Fj8d8xZz5K/j086+JjbnP9MnjXi+OhfN4p2sfps5eSGn/IL4Z/0m+cURdPs+saV/RuFlrps1ZRI1a9Zn2zeeGOJTKLG5cv8rb7/Vm6uxFfPr5Nzy4f4epX48xub7li3/CyenVb9j6GPaydMEPdOnWm+/mLKC0fyBfF7gvLvD9tK9p0rwV0+csoIaJffHUscMHuHrlUp6Hivt376DTaXl/2Kd8/+My+g4cxs6/NrNq2YJXjuPvA3tZsuAn3u3Wmxlz5uPnH8ikcZ+RVEAcM5/EMWPOAmrWrse3/xvH7Vs3jdJVrlqDxcvXGaaPPzM+Xo4c2s/sGVNo3OwtZs5byJTpc2kQ0eQ1YviRLt36MH3OgicxjHpODPrzYsachdSoXY+p//uS28/si8pVa7Bo+TrD9PFn441+V6vV1KkXQYtW7V8p36bjeLP7IrRMOaPfFi9fR9MWrXFz8yAoOPSF8r11/XJ2bF1D3yGjmfTdQiwsLPl2wkiys5X5LnPk4C5WLppNp/cG8L/vl+HrF8y3E0aSnJRgSLNi4SxOH/+bEZ9NZtzkn0hMeMT3U3LO6dr1mvLDsm1GU3jlWpQpX9lQeBB16Sw/zZpEw2ZtmTrvN0aM/obr1y6xcN7kAmMqqmMK4OjhgybPb4BvJo5Fo9Hw1eTv+W72fPz8g5j81VgSEx4XmN/8HNwfycIFv9C1Ww9mzf0J/4AAxo8bm28cly9d5Lupk2nevCWz5/5Erdp1+ebriUbH1K8rVhtNH478BIlEQp269QF4/PgR4z4fjYenJ9O/n8vEr6dw5/YtZs38zuQ2i5LM2oqUc1FcGPHVG9/2sw7k2hez5/74ZF98XuC+mDZ1Ms2at2TO3J+oVbsO33w9kVu59sXCBT9z6p+TfDJqND/9spD2HTry80/zOHb0CABZWZmM+2IsEomEyVOm8d3071GrVUz6ajxarfaV4ijO+/e73fszf/lGw/RW286vFENx3ffKh1fi0zETmDf/Vz77/CtiYx4wbfLEV4rhqT83LGPX1t/pNXgs46YtxVyhYOZXw1EVcP099vdOfl/8Pe3fG8jEmSvw8QthxlfDScl1/X1q55ZVJtdx6/plbO2dGPTRJP43ZzVt3u7HuuXz2L1tdYH5PXxgN8sXzuXtrv2YMnsxpf2DmDL+4wKPnznTJtKoWRu+nbOEarXqM/2bsdzNdW3dvG4l27esZcDQUfxvxgIsFAqmjP/YcA9KePyQ/335IW4e3vxvxnzGfjWTe3du8uP33xhta9uG31n963zavd2D735czhf/m014lRr812l1xTeVRKIAoQhcuHCBt956CxsbG9zc3OjZsyePHj0y/B4REcGIESP47LPPcHJywt3dnYkTJxqt48qVK9SrVw+FQkHZsmXZvXs3EonEUOvB398fgMqVKyORSIiIiDBafvr06Xh4eODs7MzQoUNRqVQm87p06VIqVKgAQEBAABKJhD59+rB//35mz56NRCJBIpFw69YtwzKbNm2iXbt2QN4mDC8S2+vasmENTVu2oXGzVvj4+jFo2CdYKBTs3Wn6y+efm9dSqWoN2nfuirevH117DsA/MIS/tq43pKkf0YzwytVw8/DEp7Q/vQcOIyMjnds3rxvStO3YhZCwcpRydSesbAU6vtOda1GXUKvVrxTH1o2radKiLY2atcbH159BQz/F3ELB3l2m49hmiKMb3j5+vNdzAAGBIWx/Eoe1tQ3j//c9deo3xsvbl5CwcvQf/BE3oqN4GB9ntK7TJ49y7vQJevb/4JXy/tSz++L9J/tiz84/842hctUadDDsi/5P9sUGo3SPHz1k4c9z+HDUl8hkxhWlKlerybCPxlKpSnXcPTypXqsu7Tq9y9HDB145js0b/qBZy9Y0afYWPr5+DB728ZM4/jKZfuvmdVSuWoOOnd/Dx7c03Xr2IyAwmD+fiUMul+Po5GSYbGxtDb9pNBoW/TKP3v3ep2Wrdnh5+eDj60fd+o1eKYYtz8Tw/pMY9uazL57G0KHze3j7lqZbz/74Bwbn2Rf6GJwNU+4YAN7r0Ze2Hd+hdGn/V8r3s4pjXzz7m62dHcePHqJxs5ZIJM+vfqjT6di+eTUduvSlWq0G+PoHM+SjCSQlPOKfo/kfl39t+o1GzdvTsGkbvH396ffBaCwsFOzfvRWAjPQ0IndvoXv/DylXsRr+QWG8/+GXXLtynmtXLgBgbqHAwdHZMEmlUi6eP0nDZu0M24mOukApVw9atn0XV3dPQstWonHLDly/dqnAuIrqmNKf37MZOepLZDKZ0W8pyUnEPLhHp3e64ecfiKeXNz37DEKpzOLObeMXlRe1ccM6WrR8i6bNW+LrW5oPhn2IhYUFu3buMJl+86YNVKlanU5vd8HHtzQ9evUhMDCIrVs2GdLkPl4cnZw4evQIFcIr4u7hAcCJ48cwM5Mx+IPheHv7EBISygfDRnL40EEePLj/SnG8qoc7DnB1wiziNu1+o9s15em+aNa8Bb6+pRn63H2xkapVq9P57S74+PrS07AvNhvSXL58icZNmhIeXhE3N3davtUa/4AArkZdAeDSpYvEx8fx0cef4ufvj5+/Px998hnR165y7uyZV4qjOO/fllZWODo6GyaF4tVqtBTHtRagXcd3CA0ri6urO2Fly9Ppna5cfY1nKZ1Ox64tv9G2S3+q1IzAxy+YgR9OIjHhIaeORea73M5NK2nQvAP1m7TDyyeAXkPGYm6h4OCezUbp7tyIYsemlfQfPj7POho0bU/3AZ8SVr4qru7e1IloRb0m7fjn6L4C87xt42oat2hLRLPWePv6M2DoKMwtLIjctdVk+r82r6Fi1Zq07dwdLx8/3u05CP/AEHZsXWv4P/hr0xo6vtubarXqU9o/iKEfjyMx4REnjxwE4NSJw5iZmdFvyCd4epcmMKQMA4aO4vjhSGIf3AMgLS2F1Svm88HH46gX0Rx3D29K+wdRrWb9AuMR/v8RBQiFLCkpicaNG1O5cmVOnjzJ9u3biYuLo0uXLkbpli1bhrW1NceOHWPatGlMmjSJXbt2AfoXiw4dOmBlZcWxY8eYP38+X3zxhdHyx48fB2D37t3ExMSwfn3Oy/C+ffu4fv06+/btY9myZSxdupSlS5eazO+7777L7t27DeuMiYlh9uzZ1K5dm4EDBxITE0NMTAw+Pj6G+P7++29DAYIpBcX2ulQqFTeirxJeqZphnlQqpUKlqkRduWhymatXLhJeqarRvEpVanA1n/QqlYpdf23GytoGP/9Ak2lSU1M4GLmL0DLlMTN7+ZZAOXHk5EsqlRJeqVq++bp65YJR3AAVq9Tg6pMXCVMyMtKRSCRY29gY5iUlJvDz3GkM/+RLLCwUL5333DFcNxlD1QJiMLUvqhvtO61Wy5wZ39C+83v4vuBLaUZ6Ora2dq8QRU4cFfPEUSXfYyrqyiWj9KCP49m4L5w/Q+9uHRk6qBc///A9KSnJht+uR1/l8eNHSKRSPh4+kH49OjNp/Og8X3NePIYok/si6orpl0RT+6JylRp50l84f4Y+3TowbFBPfvlhJqm5YihsxbUvnnXi2CHSUlNo3OytF8r3w7gHJCU+plzF6oZ5VtY2BIaU41rUeZPLqFUqbkZHUb5SzjJSqZTyFatz7Yp+mZvRV9Co1ZTPtV5Pbz+cS7kTnc96D+79EwsLBTXr5BREBYWW5/GjOM6cPIxOpyM58THHD+2jUtU6JtcBRXdMabVaZs+YTId8zm9bO3u8vH2I3LuDrKxMNBo1O/7ajL2DI4FBL1Yb5Nk4oqOvUrFSFaM4KlWqkm8cV65colLlKkbzKletxpUrl02mT0xM5OSJYzRrnnO8qFQqzMzkSKU5j1nmFuYAXLqY/zW7JNPvi2tUqpTTFEW/Lyrn+3+r3xfGTVeqPLMvypQpy/FjR3n06BE6nY5zZ8/w4P59KlepatguYNTEzdxcjkQi4eIr7IvivH8DbPhjJX27tmbUiH5sWrcKjeblX7z/Ldfa1NQUDkTuJrRMuVd6lgJ4GHef5MTHlAvP+Uquv/6Wz/c6qVapuHX9CuXCc5olSaVSylasQXTUOcM8pTKLX2Z+SY9Bn2Hv+GI1NjMy0rCxyf955Om1v8Iz1/4Klarlezxcu3KRCnmOn5qG//v4J/eg3GmsrG0ICi1rWKdalY3s2WuSub4J25VLZwE4f/oEOq2OxMcP+XhwNz7o3YFZ347j0UPjQqz/Ip1OUmxTSST6QChk8+bNo3LlykyenFM1dPHixfj4+HD16lVCQkIACA8PZ8KECQAEBwczb9489uzZQ7Nmzdi1axfXr18nMjISd3d3AL755huaNctpc1mqVCkAnJ2dDWmecnR0ZN68echkMsLCwmjdujV79uxh4MCBefJraWmJs7OzYZ1P12Vubo6VlVWedf/555+Eh4fj6emZ7/9BQbG9rtSUZLRaDfYOxu0WHRycuH/3jsllkhITcHhSlfcpewdHkhKNq6mdPH6YWVO/QqnMwtHJmfH/m4GdvYNRmuWLf2L71g0olVmEhJVj7IRvXzOOvPm6f+92vnE8m97BwYkkE9XtQN92esWSn6jboClWVtaAvpT6h1mTaf5WewKDw4iPi3ml/OeOweGZfWHv4Fjgvsi774z3xca1q5DJZLRu92LVMmMe3OOvLevp1X/IS0agp49DazJfBR9TedMnJuZUP6xctQa16tTHzd2D2JgHrFi2kK8njOHb6fpzMy5W/3+/euUy+g4cgqurO5s2rGHc2JH8MH/5SxWIPI3h2eP8+TEUfF5UrlqDmnUaPInhPiuXLeTrCaOZMv2HPF+OC0Nx7Ytn7d75F5WqVMfFpdQL5TspUV+1Pu/57GT47VmpKUkmrwF2Do48uH9Lv96kx5iZybG2Mf6CV9B6I3dvoU6D5pjnKhwMLVuRoR9/xdzvvkSVrUSj0VClRj36DB4FmH75KKpjasPa3wo8vyUSCRO+mcHUr7+k+9utkEgk2Ds4Mm7StDxfMl9EypM4nm3r7uDgyL27d/OJIzFP07xnr1O57d29E0tLK+rUrWeYF16xEosW/Mz6tWto274jyqwsli1ZBEBigun1lHQpKSn6Y+ol9kViYqKJ89vBaF8MHjKUuXNm0adXN2QyGRKJlOEfjqR8hXAAwsLKoFAoWLJ4Eb169wV0LF2yGK1WS2I++7QgxXX/BnirbWcCAkOxsbUl6vIFVi37hcSEx/QZOPwVYii+a+2vi3/hz60bnzxLleWLCQU3pypIcpL+Wmjn4Gw0387eieT8rr+p+uuv3bP70N6J2Hu3DH//tmgGgWHhVKkZ8UJ5uXblLCf+3snIL2fnmyYln2u/vYMT9+/l93//2GT6p7E/PR9M3oOepCkXXpXlC+eyZd1K3mrXhSxlJquW/qRf/knzsPjYB2h1Wjb+8Su9B47Eytqa1csXMHncSKbN/RWz1+hnSihZRAFCITt79iz79u3D5pkSY4Dr168bFSDk5uHhQXx8PABRUVH4+PgYvbzXqPHi7Y/KlStndKH28PDg/HnTpbAvK3fzhfwUFJspSqUSpdK4nVq2Uom5RcGdexW28uGV+W7uIlJTktm9fQszv53AlJm/GN1g23fuSpMWbXgYH8sfq5Yyd8Y3jJ049YWqOb9JarWamd9OAHQMHJrT8c1fW9aRmZlBh1fscKmoXb8WxbZN6/huzoIX+j99/Ogh/xv/GbXrRdCsZds3kMMXV79hY8O/S/sFUNovgCEDunPx/BnCK1VFp9O3vX373e7UrtsQgOEfjWZAry4c/juSFm8VfJ69CfUa5vTHoI8hkA8GdDPE8F/xvH2R26NHDzlz6gSfjslbXfWp/ft28fO8mYbelUeNn1E0GX9J166c58HdW3zw0USj+ffu3OTXhd/T8d1+hFeuSVLiY1YtmcviH6fy4UdvrmMs/fm9lukFnN86nY4FP87G3sGR/02bg7m5Bbt3bGPyV2OZNusXnJycTS5XnHbt2kFEo8aYm5sb5pUu7cfIjz9j0cKfWbZ0EVKpjLbtO+Dg6IhE+u+6X/zXbdm8iagrVxg34StcXd24cOE8P/84D2cnZypVroK9vQNjPv+SH+fNZcvmjUgkEho2bERgUBASyb+vIm5+92+Ath3fM/y7tH8QZmZy5v/wHd37vI9cbk5xe9FrbYfO79GkRSsexsexetUy5syYwhcTp7zQff/I/r9Y9lNOgcPIL2cVagxPnT6+n8vnT/LVzJUvlP7e7WjmTP6Edu8OpHxl0x3HFief0gEM+ehLli+cy2/LfkEqldKy3dvYOzgheVIrQavTolGr6T1oJBWr6GtnjPhsIu/3bMfFc6eoWLVmQZsQ/h8RBQiFLC0tjbZt2zJ16tQ8v3k8aRsJ5OktXiKRvHJnPs8qqnVnZ2ezfft2Pv/880Ld/pQpU/jqK+POnQYP/4QPRozKk9bWzh6pVJano5mkpAQcHJ3ypAdwcMxbyp+clJgnvUJhiYenNx6e3oSElWPYwK7s2bmNTl1yXrbt7B2ws3fA08sHb5/SvN/7ba5euUhomby99hYkJw5T+TL9gOzg6JQnfVJS3i9++oeP8TyKj2XC5NlGXy8unPuHq1cu0q2jcUd9Y0YOpH5EM4Z9bNxU5kVieLbDJVP/t8YxPLvvctJfvniO5ORE3u+T0+RHq9WwbNGPbN20lp+X5HRMlPD4ERPGjiS0TDkGD//0hfNtOg5pgfkyFcezcSclJRbYo7e7hyd2dvbExNwnvFJVHJ/sZ29fP0MaudwcN3cPHhZQ4FZQDM8e58+P4fnnRUExFLbi2he57d31Fza2dlSvWTff5WvUrEtIaFkyNfpCTrVaX0U6OUk/KshTyUkJlM5ndBNbOweT14CUpETsn3xJc3BwRq1WkZ6WalQLITkpweR1Yt/OzZT2D8E/yLhH+81rlxESFk6bTvprma9/MBYKBZPGDKZn7z4mX8qL4pi6dPEcyclJDDI6v7UsW/QTWzet5Zclqzl/9hT/nDjCr6u3GK5dgUEhnDtzksjd2+nUpbvJbefH7kkcub+SPo3D0cn0MeLg6EhSUtILxX3xwnnu37vL6DF5r50RjRoT0agxiYmJKBQKJBLYtGEd7u4eedL+f2BnZ6c/pkzuC9PHlKOjo4nzO8mwL5RKJb8uW8IXX06geg39y42/fwA3r19n/fq1hqYoVapUY+HiZSQnJyOTybCxsaFH93fz1LJ8EcV1/zYlOLQsGo2G+LhYvLwLHnEgbwzFd621s7fHzt4eryfPUgN7dyHqyiXCypTLd11PVarRwGikBLUqG4CUpMc45Lr+piQn4OMfYnIdtrb66++zHSYmJydg92QfXj53koex9xja3bhfonnTPiOkTCXGfDPfMO/+3Rt8N/4DIpp3pF2XAQXm3y6fa39ygc+xzibTG+4VT5YzeQ/yz7kH1YtoTr2I5iQlJqBQKEAiYdvG1bi562sVOz5ppuHtm9O8zM7eETs7+/98M4ZiGDCuRPv3Fb3+x1WpUoWLFy/i5+dHUFCQ0WRtXfCN4KnQ0FDu3r1LXFzOyXrixAmjNE+/dGg0msLL/DPrf3bdkZGRODo6UrFixULd1tixY0lOTjaaBrw/wmRauVxOQFAI58/8Y5in1Wo5f+YUoWGmbzwhYeU4f/aU0byzp08Qkk/6p3RaHaonNyZTtE+6Vs2vg8qCGOI4+0wcZ//JN18hYeWN4gY4d/okIWG5bqRPHj5iH9xj3DffY2tnb5S+76CRTJ+zhO/mLOa7OYv5fKJ+aKWPRk+ka6+8TVyeF0OgiX1x7sypAmIox7mzeWN4uu8aNm7OzHmLmTF3oWFycnahXaf3GJdr6MDHjx4yfsyHBASFMHTkGKM2fS/raRznzuQcI887pkLDynIuzzGV/74D/Zft1NQUQ8FBYHAIcrmcB7mqLKrVauLj43B1dXuFGELzxHDuzD+EhpkeFsr0eXEy3/T6GOKNYihsxbUvntLpdOzdtZ1GjZsX2B7X0soKD08v3D19cPf0wcvHHwdHZy6ezblOZ2Skc/3qRYJDK5hch5lcjn9QqNEyWq2WC+dOEBymX8Y/KAyZmRkXz+WkeXDvNo8fxhL0zHqzMjM4dmgPEc3y1sTJVmbl+eotlcqeBm0yf0VxTEU0bs7MeYvynN/tO71rGBr0aW20Z79ESiRStK/wBCiXywkKCuHc2dNGcZw9czrfOMLCynL2zGmjeWdOnyIsLO/wdjt3/kVQUDD+Aab7ywH9S7ClpSUHD+xHLjenUuX/Tu2dwqTfF8GczdVxoX5fnDH5fwv6fXHmmX1xOte+0GjUqNXqPMeLVCZFZ+LDhb29PTY2Npw9c5rkpCRq1qqdJ82LxFEc929Tbt24hkQqzdMU4UViKM5rbW5P95P6BZ+lLC2tcfPwMUyePgHYOzpzKdd1MjMjjetXL+S5Tj5lJpfjFxjGpXPHDfO0Wi2Xz50gKFRfi7Z1595MmvUbX32/0jABdO33Mf1HTDAsd//OdaZ9OZi6jVrTucfQ5+b/6bX/wtmTRtu+cPYfo+Mht+CwclzIc/zkPMe6unni4OhslCYjI53oqEsm1+ng6ITC0oojB/ZgLjc39McQUlb//5X7uSQtNYWUlGRcXvK5RCjZRA2EV5ScnMyZM2eM5j0d8WDBggV07drVMBJBdHQ0v//+OwsXLnyhdsPNmjUjMDCQ3r17M23aNFJTU/nyyy+BnIcqV1dXLC0t2b59O97e3igUCuztn3+zeVF+fn4cO3aMW7duYWNjg5OTE5s3b35u84VXYWFhgcUzzRXMLTLzTd+2YxfmzZxCYHAoQSFl2LbpD5RZmTRq1gqAOTO+wdnZhe593gegVbu3mTBmBJvX/07V6rX5+8AebkRHMXi4voZDVlYm61Yvp3rNujg6OZOSnMz2bRtIePyIOvX0Jc9Xr1zi+rXLhJUNx8bWltiY+/y+fBHuHl6EvkCJuSltOrzLD99PJjA4zDiOpvo45s74H07OLnTvMxiA1u3eZsKY4WxZ/ztVqtfm0IE9XI++wvvD9HGo1WpmTBnHzetXGTN+6pP2nfp2bTY2dsjlcko9cwNQPBmP3M3DC2cX15eOoW3HLsydOYXA4DCCQ8LYumktyqxMQ+dzc2Z8g5NzKXr0GWSIYfyYEWxev5oq1Wtx6MBerkdHGWoQ2NrZ53loksnMcHR0MnxdefzoIePHfkipUu707v8BKbmG2nR8xerN7Tq+w5yZ3xIYHEJwSBm2blpLVlYWTZq1BGD2jMk4OZeiZx99IUubdp35csxINq1fQ9Xqtfj7SRxDhuurm2ZmZrJ61TJq122Ao6MTsTH3Wbb4F9w9vKhcVX+jtrKypkWrdvy+cikupVwp5erGxnX6GhZ16kW8dAxtO77D3JlTCAoOJTikDFs2rUWZlWXYF7NnTMbZ2cWwL9q068y4MR+yaf1qoxgGG2LIYM2qZdQyxPCAX5+JAeBhfBxpqSk8ehiPVqvl5vVrALh7emFpWfCQsKYUx7546vzZU8TFxdC0ReuXyrNEIqFlu3fZuGYp7p4+lHLzZO3K+Tg4uVC1VgNDuslfDqNarYY0b/MOAG+178ovs77GP6gMgSFl2b55NcqsLBo20W/fytqGiKZtWbFoDtY29lhZWbNs/gyCwyoQ/MxD4dGDu9FoNNSNaJknf5Vr1GPRvCns/nMd4VVqkZjwiBULZxEYUrbAsdcL+5gyfX7LcMh1foeGlcXaxoa5M7/lna699D30b99KfFyM0XjzL6NDx858P3MaQcEhhISEsmnTBrKUWTRt1gKAmdOn4uzsQu++/QFo174jY0d/wob1f1Ctek0O7o8k+tpVhg0fabTejIx0Dh08SP8Bg0xud+uWjYSVKYelwpIzp/9h8eIF9O7T32QTx6Iks7bCOijn67SVvzd2FcPITkgm6+6r94PzKvT74juCg4MJCQlj06b1RvtixvRpODs708ewLzowZvSnrF+/lurVa3DAsC8+1MdiZU35CuEsXrwAcwsLXF1duXD+PHv37GbAwPcN2921cwc+vr7Y29tz5fIl5v/yE+07dMLb2+eV4iiO+3fU5QtEX71EuQpVsLSy4urlCyxdOJcGEc2xeaaflBdRHNfaq1cuEX0tijJlK2Bta0NszAN+W74Ydw9PQsvkX3hdEIlEQrO2XdnyxyLcPH1wcfViw6qfcHQqZdR3wbRxQ6hSK4Kmrd8FoHn77iycPRG/oLIEBJdj55ZVKLMyqddEXwhr7+hisuNEZxd3Srl5AfpmC9PGD6F8pVq0aN+d5ET9iGsSqQw7+/wLdVp3eJefvv+GgOAwgkLK8uemNfprf1P9tf+HGV/j5OxC1z76vp3eateFSWOGsnX9b1SuXofDB3ZzI/oKg4aNNvwfvNW+CxtWL8PdyxtXN0/WrFiAo5ML1WrnjKCwfctaQstUwMLSkvOnT7ByyQ907T3EUMPN08uXarXqs2z+LAYOH42VpTW/LfsZL29fyoX/tws+tYimY4VJFCC8osjISCo/0zNw//79WbhwIYcOHWL06NE0b94cpVJJ6dKladmy5Qt/JZXJZGzcuJEBAwZQvXp1AgIC+O6772jbtq2+yhFgZmbGnDlzmDRpEuPHj6d+/fpERkYWWnyffvopvXv3pmzZsmRmZnLz5k02b97M4sWLC20br6pugyakJCfx+4rFJCUm4BcQxBeTphuqcD16GIc019eIsLIV+HDUeH5fvpBVyxbg4eXNZ19+g69fAKDv/fb+3dvs37OdlORkbO3sCAwO4+tpc/F50ku4hcKCY4cPsHrlEpRZWTg6OVGpak06v9vrldsdPo1j9YpF+caR+6thaJkKfDhqAr8tX8CqX+fj4enNZ19MNsSR8PghJ4/9DcCoEX2NtjVx8hzKhRsfr4WhboPGJOfaF/4BQXw56btcMcQbtTENK1uekaPG8dvyRaw0sS9exNnTJ4l9cJ/YB/cZ1Ptto9/Wbdv/SnHUa9CYlORkfl+xlMTEBPwDAhk/aaohjocm4vho1JesWr6YFcsW4uHlxZgvv6a0n/54kUql3L51nX17dpCRnoajkzOVKlejW89+RsdL736DkUllzJoxhWylkpDQMkyaPOOVOovTx5DEbyuWGPbFuEnTCjgvyvPRqHGsWr6IlU9iGP3l/yhtOC9k3L5145kYqtP1mRh+X7GYfXtyhmD7ZIT+YXPSlO8p/wrHXHHtC4DdO/8krEw5vH1evCrwU2069USZlcWiH74lIz2NkLLhjJ44y9DLNUBc7D1SU5IMf9eu34zU5CTWrlpAcuJjSgcEM3ri99jn+lrXY8BIJFIps78di1qVTYXKNek75LM824/cvYXqtRvm6XARoGGTNmRlZrBz21pWLp6DlY0t5SpU5b0+BX8tK+xj6kXY2TswbtI0Vv26iAmff4xGrcantB9jxn2Df0DQC68nt/oNI0hOSWLl8mUkJiYSEBDIV5MmG6peP3wYb3StLVO2HJ9+NpYVvy7l16VL8PTy4otxEw3H1FMH9keiQ0eDiMaYcjUqilUrfiUzMwtvHx+GDvuQxk1evzPhl2VftTy19yw3/F12ur4Z4t1f13Ou/9g3mpcGDSNITklmxfJfn+yLACZN+sZoX0if2RejPhvLcsO+8OSLcRPxy7UvRo/+nGVLFzP9u29JS03F1dWVnr368FarNoY09+/fY9myxU9+d6PLu13p0PHFOuo1pTju33K5nEMH9rBm1RJUqmxc3Txo074LbTq++0oxFMe11kKh4Mjhg/y2cinKrEwcnZypXLUGn77b47X6cGjVsTfZWVks/XEyGemphJSpxMfj5yDPdf2Nj71HWq7rb816zUlNTmTjbz+TnPgYX/8QPp4w19As4EWcPLyH1OREjuz/iyP7c4a/dC7lwfQFW/Jdrk6DpqQkJ/HHioUkJeqbuo2ZNKPA42f4qImsXj6f33/9BXdPbz79Ygo+ua6t7Tp3R5mVyYK508hITyO0bDhjJs0wugddv3qZtasWkZWZiad3aQYM/YwGjY0LnT/4eBy/LpjDtImjkEgllClfiTFfzXzlUTKEkkmi04lWIf8Fhw4dol69ekRHRxMYmH9VyaJy6tQpGjduzMOHD/P0cVAUzkf/t9taPVUShm+RSgqnb47iJuW/H4euhJSgSygZt50MzauNv/5vYinLKu4sFAq55OWbkv3bXAt784ULRSHkys7izkKhyNT+989vM8nLD/H4b5SkevlC9X8bSzPl8xP9B1QOfrHhLP9ttp4qvnOhTZWSV/hS8iIqITZs2ICNjQ3BwcFER0fz4YcfUrdu3WIpPAB99bq5c+e+kcIDQRAEQRAEQRCEwiA+lxcuUYDwL5Wamsro0aO5c+cOLi4uNG3alBkzim+osBo1arzUUJKCIAiCIAiCIAhCySIKEP6levXqRa9evYo7G4IgCIIgCIIgCP9ZJaFJ8b+JGMZREARBEARBEARBEITnEjUQBEEQBEEQBEEQhBJJK/pAKFSiBoIgCIIgCIIgCIIgCM8lChAEQRAEQRAEQRAEQXgu0YRBEARBEARBEARBKJHEMI6FS9RAEARBEARBEARBEAThuUQNBEEQBEEQBEEQBKFE0iGGcSxMogaCIAiCIAiCIAiCIAjPJQoQBEEQBEEQBEEQBEF4LtGEQRAEQRAEQRAEQSiRtKITxUIlaiAIgiAIgiAIgiAIgvBcogaCIAiCIAiCIAiCUCKJYRwLl6iBIAiCIAiCIAiCIAj/EQkJCXTv3h07OzscHBzo378/aWlpBaYfPnw4oaGhWFpa4uvry4gRI0hOTn7pbYsaCMK/UuCBH4s7C4VCk55R3Fl4fVptceegUKTeelDcWXhtTk0aFHcWCkV29LXizkLhKAHnxv1DF4s7C4VCpdYUdxZeW8iVncWdhUJxNax5cWehUFR8P7y4s/Da7IN9izsLheKLa4OLOwuv7beOR4o7C4UjuFdx5+CVlMQaCN27dycmJoZdu3ahUqno27cvgwYNYtWqVSbTP3jwgAcPHjB9+nTKli3L7du3GTx4MA8ePGDt2rUvtW1RgCAIgiAIgiAIgiAI/wGXL19m+/btnDhxgmrVqgEwd+5cWrVqxfTp0/H09MyzTPny5Vm3bp3h78DAQL755ht69OiBWq3GzOzFiwVEEwZBEARBEARBEARBKGRKpZKUlBSjSalUvtY6jxw5goODg6HwAKBp06ZIpVKOHTv2wutJTk7Gzs7upQoPQBQgCIIgCIIgCIIgCCWUVicptmnKlCnY29sbTVOmTHmteGJjY3F1dTWaZ2ZmhpOTE7GxsS+0jkePHvH1118zaNCgl96+KEAQBEEQBEEQBEEQhEI2duxYkpOTjaaxY8eaTDtmzBgkEkmB05UrV147TykpKbRu3ZqyZcsyceLEl15e9IEgCIIgCIIgCIIglEjF2YmihYUFFhYWL5T2k08+oU+fPgWmCQgIwN3dnfj4eKP5arWahIQE3N3dC1w+NTWVli1bYmtry4YNG5DL5S+Ut9xEAYIgCIIgCIIgCIIgFKNSpUpRqlSp56arXbs2SUlJ/PPPP1StWhWAvXv3otVqqVmzZr7LpaSk0KJFCywsLNi8eTMKheKV8imaMAiCIAiCIAiCIAjCf0CZMmVo2bIlAwcO5Pjx4xw6dIhhw4bx3nvvGUZguH//PmFhYRw/fhzQFx40b96c9PR0Fi1aREpKCrGxscTGxqLRvNxQyKIGgiAIgiAIgiAIglAiFWcThqKycuVKhg0bRpMmTZBKpXTu3Jk5c+YYflepVERFRZGRkQHAqVOnDCM0BAUFGa3r5s2b+Pn5vfC2RQGCIAiCIAiCIAiCIPxHODk5sWrVqnx/9/PzQ5er5CQiIsLo79chChAEQRAEQRAEQRCEEklbAmsgFCfRB4IgCIIgCIIgCIIgCM8lChAEQRAEQRAEQRAEQXgu0YRBEARBEARBEARBKJF0OklxZ6FEETUQnoiMjEQikZCUlFTcWSk2EydOxM3NDYlEwsaNG194uYiICEaOHFlk+RIEQRAEQRAEQRCKX4mrgfDzzz8zatQoEhMTMTPTh5eWloajoyN169YlMjLSkDYyMpJGjRoRHR1NnTp1iImJwd7evphyXrwuX77MV199xYYNG6hVqxaOjo74+fkxcuRIk4UD+/fvp0ePHty9e5f169cjl8vffKZzWX3qGsuOXeZxehYhrg6MblqV8p7OJtNuPn+DCX8eN5pnLpNy7NMuhr9//vs8Oy7fITY1A7lUShl3J4Y1CKdCPussLPIKdTCv0hCJlS3aRzFkHdiINu6uybRmYdWwbPau0TydWkXaT5/nWqE5FnVaYRZQDonCGm1KAqqzf6O6cLToYgivi3nViCcxPCArckP+MZSpjmXz9/LG8MMYw98SKxss6rZB5huCxMISzf0bZO3fgC7pUZHFAGBVtxk2jdsis7VH9eAOyeuXorpz3WRa56HjsAgqm2d+1qXTJCyYBlIZtq26oChTCZmzK7qsTJRXz5Oy9Xe0KYlFGsfvf59lWeRJHqVmEOLpwpiOjajg6/7c5f46HcWYFX/RqFwAs/q1M8z/accRtp++SmxyKnKZjLLergx7qw7hpT2KMow85OF1sajWyHCcZe7bgDbuTv4LWChQ1GmFWVA4EgsrtKkJKPdvQn3r8pvL9DPkFethUa0xEmtbtA8fkLlvHdrYgmKwRFH3SQwKa30MkRtQ33xzMTi2bIdT+3cwc3BCees6sYt+ICs6Kv/0rTvi2KItchdXNKnJpBw5yMOVi9CpVAA4tGij/72UGwDKu7d59McK0k+fKNI4nFq1x7nDu5g5OpF16zqx8+eSee1K/unbdsbprXY5cRw+QNyvCwxx5ObSuStuvQbyePM6Yhf9UGh53rplM+vX/UFiYgL+/gG8P2QooaFh+ab/++ABVixfSlxcHJ6eXvTpN4Dq1WsYfs/MzGTpkkUcPXKY1NQU3NzcaduuA61atzGkiYl5wKKF87l08SIqlYqqVavx/pChODo6FlpcL8qpXjUCPumPfZXyKDxdOdn5A+I273nj+ciPdYMW2DZph8zOAdX92yT+sRjV7WiTaUt9OBGL4HJ55mdeOMXjn6fkme/w3kBs6jUnae0S0iL/LPS8P1VS7t8AXVs70bSOPdaWUq7cyOKX1fHEPMx7vubmZC+jV3sXqpSzxlwuIfaRirkr4rh+R2lI4+0mp2cHF8oFWSKTSrgbm820hTE8SlQXegy/7z/Jsl1HeZSSRoi3G2O6NKeCn5fJtLtPX2HRjkPcfZiISqOltKsjPZvUom3NCoY0GVnZzNq0l31nr5KcnomXswNdI6rRpUHVQs/7v01JHMaxOJW4AoRGjRqRlpbGyZMnqVWrFgAHDx7E3d2dY8eOkZWVhUKhAGDfvn34+voSGBgIgLv78x+qS6rr1/UvRu3bt0cieX41n02bNtG2bVtAP4xIcdpx+Q4z9p7mi+bVKO/pzKqTUXywJpKNA1vjZK0wuYyNuZwNA1sZ/n425tJOtoxuVhVvBxuUKg0rTkbxwepINr3fGicr0+t8XWbBFbGo35asJy8R8kr1sWo3gPQV09BlpptcRqfMJH3Fd7lmGF8hLeq1xcw7iKydv6FNScTMNwSLiI5o01PQ3LxUBDFUwqJ+O7L2rc2JocMg0n+dii4zLf8Yfp2ae47R75Zt+qLTasjcugSdMgvzKg2x6vg+6cu/A3V2occAoKhUC/sOPUn6YxGq29FYN3wL5/fHED/lE7RpKXnSJyyZiUSWczmVWttS6tNvyTyjL6iRmJtj7u1P6q4NqO7fRmpljX3H3jgN+JRHM78okhgAtp+OYvrmA3z5dmMq+Lqz8uBphszfwKbRvXG2tcp3ufsJyczccpAqAXkfVEqXcmRsp0Z4O9uTpVKzYv8phszfwJaxfXCyyX+dhckspBKKBu3J2vsHmtg7mFdugHXHQaQt+9b0cSaVYd1xMNrMNDK3LkWbnozU1gmdMvON5NcUs5DKKBp2IGvPGjQxtzGv0hDrToNJWzI5/xg6D0GbkaqPIS0ZqZ0juqw3F4NtnYa49nmf2F/mkHntMk5tOuE7bgrXh/dDk5KUJ71dvUa49hhAzA/TyYy6hLmnNx7DRgE64pf+AoD68SPiVywiO+Y+EsC+UXN8Rn/FjVFDyL57u0jisKsXgVu/IcT8NIvMq5dxatuZ0hOncu2D3miS88Zh36Axbr0G8mDuNDKuXMTc0wevDz9Dp9MRt/gno7SKoFAcW7Qh66bpwsZXdWB/JAsX/MLQYSMIDQtj08b1jB/3Ob/MX4SDQ96X+cuXLjJt6mR69+lHjRq1iIzcyzdfT2TWnB/w8/MHYOGCnzl39iyfjBqNm5sbp0/9w48/zMXZ2ZmatWqTlZXJuC/G4h8QwOQp0wBYsXwpk74az4yZs5FK32wlVpm1FSnnori7dB3V1hZewUxhsKxSB4eOvUlcPZ/sW9HYNGpNqaFfEDvpQ5P3jEcLpj9zz7DBbex0Mk8fyZNWEV4Dc78QNEkJRRpDSbl/A3Rs6kjrhg7MWR5H3GMV3do4M36oFyP+dxuV2vSbpLWllCkf+3D+WiZf/3if5DQNHqXMSc/QGtK4u8iZ/LEPuw8n8/u2BDKztPh4mKNSFf7b6faTl5i+bjdfdn2LCn6erNx7nCFzf2fTxME421rnSW9vbcmAlnXxd3NBbibjwPlrTFi+BSdbK+qW1b/nTF+3i+NXbzO5T3s8ne05cvkGk3/fjquDLRHhIYUeg1BylbgmDKGhoXh4eOSpadC+fXv8/f05evSo0fxGjRoZ/p27CcPSpUtxcHBgx44dlClTBhsbG1q2bElMTIxhebVazYgRI3BwcMDZ2ZnRo0fTu3dvOnTokG/+bt++Tdu2bXF0dMTa2ppy5crx559/GuVh27ZthIeHo1AoqFWrFhcuXDAs//jxY7p27YqXlxdWVlZUqFCB3377zWgbERERjBgxgs8++wwnJyfc3d2ZOHFivnmaOHGioTBAKpUikUiIiIjg9u3bfPTRR0gkkjwv2Js3b6Zdu3aG7eWupeDn58fkyZPp168ftra2+Pr6Mn/+/Hy3/7pWnLhCp4qBtA8PINDFni9aVEchN2Pj+Rv5LyQBFxtLw+T8TEHDW2X9qOXnjreDDYGl7PmkcWXSslVci08qsjjMKzVAdfEY6ssn0SbGo9y3Hp1ahbxsjQKX02Wk5kzP3ORlHn6orvyD5v4NdKmJqC4eQ/soBpmbT9HEUKUBqotHUV86gTYhDuXedfoYyr1EDBk5MUgcXJB5+KHcuw5t3F10SQ9R7l0HZnLkoZWLJAYAm4jWZBzZS+bx/ajj7pP8xyJ02dlY1YzIJ//paFOTDZNFSAV0KiVZZ4/pf8/K5PHPk8k6cxTNwxhUt6NJXrcEc58AZA5FV6tl+YFTdKpVng41yhHo7syXnZvoz43jF/NdRqPV8vnK7QxpUQtvJ7s8v7eqEkatEF+8ne0Jcnfm0/YNSMvK5tqDov+i9JRFlYaoLhxF9eQ4y9qztsDjTF6uBhKFFZlbFqOJuYUuJRHN/etoHz14Y3l+lkXVCFQXjqC6eFwfw+4/0KmzkZevaTK9vHxNfQybF6F5cBNdSgKae282Bue2nUna/RfJ+3aQfe8Osb/MRqtU4tCkhcn0lmHlyLxykZS/96F6GEf62X9I+XsflkE5X83TTh4l/dRxVDH3yY65z8NVS9BmZWIZUqbo4mj/Dok7/yRpz3aUd28T89P3aJVKHJu+lU8c5cm4fIHkA3tRxceRfuYkyQf2Yhls/PVfqlDg/fHnPPhhBpq01ELN88YN62jR8i2aNW+Br29phg77EAsLC3bt3GEy/eZNG6latTqd3+6Cj68vPXv1ITAwiK1bNhvSXL58icZNmhIeXhE3N3davtUa/4AArkbpa2JcunSR+Pg4Pvr4U/z8/fHz9+ejTz4j+tpVzp09U6jxvYiHOw5wdcIs4jbtfuPbfh7bxm1IP7yHjKORqGPvkfT7fHTZ2VjXbmwyvS4jDW1qkmFShIWjy1bmKUCQ2jvh8E4/EpbORqcp/C/cuZWU+zdAm0YO/LEjgePn07n9IJvZv8bhZC+jZsW8L95PdWrmyKNENfNWxHHttpL4x2rOXskg9lFOrYVubZ3552I6v256zM17SmIfqThxPp3kNE2hx7B87zE61a1Eh9oVCfQoxZddW6EwN2Pj4bMm01cPKU2TSmEEeLjgU8qR7o1rEOzlyunrOTVIzty4T9uaFageUhovZwferleFEC83Ltwqvnvhm6LVFd9UEpW4AgTQ10LYt2+f4e99+/YRERFBw4YNDfMzMzM5duyYoQDBlIyMDKZPn87y5cs5cOAAd+7c4dNPPzX8PnXqVFauXMmSJUs4dOgQKSkpz+07YOjQoSiVSg4cOMD58+eZOnUqNjY2RmlGjRrFjBkzOHHiBKVKlaJt27aonlSTzMrKomrVqmzbto0LFy4waNAgevbsyfHjxlXyly1bhrW1NceOHWPatGlMmjSJXbt2mczTp59+ypIlSwCIiYkhJiaG9evX4+3tzaRJkwzznrp48SLx8fE0bmz6xggwY8YMqlWrxunTp/nggw8YMmQIUVH5V3N9VSqNhsuxidQs7WaYJ5VIqOnnxrn7j/NdLjNbzVs/bablj5sYue4g1x8mF7iN9WeuY2MhJ8S1iKptSmVIXb3Q3L2Wa6YOzd1rSN1L57+c3Bzr3p9j3ecLFK37IHVyM/pZE3MLM/+ySKz1L4Iyr0CkDi5o7lwtohi80dx5JoY7V58fQ98vsO43DkWbvkYxPP1CY/zgpAONBpmnf+Hm/ymZDLm3P8qrOQV36HQor11AXjr4hVZhVTOCzNNH0GUr800jsbRCp9Wizcx43RybpFJruHwvnlrBOYVFUqmEWiG+nLsdk+9yv+w8hqONFZ1qln+hbaw7cgFbhTkhnqUKJd/P9eQ4U9/NfQzrUN+5iszDz+QiZgHlUcfcRtGoMzYDv8K6xyjMqzeBF6htVSSkMqRu3qhvPxPD7QJiCCyPOuYWisZvY/P+11j3Go15jaZvLgYzMxSBIaSfO5UryzrSz53CMiRv8x2AzCsXUQQGowgKBUDu5o5NlRqknTpuMj1SKXZ1I5AoFGRGFX4NKQCJmRmWgSGkn/0nZ6ZOR/rZf7AMzS+OC1gGhhgKDORuHthWrUnaP8eM0nm8/yFp/xwj/ewpU6t5ZSqViujoa1SqlPPSJZVKqVSpMleumG6+cuXKJSpVNn5Jq1K1mlH6MmXKcvzYUR49eoROp+Pc2TM8uH+fylWqGrYLGDVRNDeXI5FIuHjxAsITMjPkPgFkRZ3LmafTkRV1DnP/F/uqa12nCRmnDhvfMyQSnHoNJ23PZtSx9wo5088oKfdvwM3ZDCd7M85eybm3ZmRpuXYri1C//GuQVq9gTfSdLEb1c2fpFH9mjPahWZ2cQnSJBKqVs+ZBvIrxQz1ZOsWfqZ/6UCM8/0KJV6VSa7h8J4ZaoTn/T1KphFph/py7+fxjQafTcezKTW7FJVA1yNcwv1KAF/vPXSMuKQWdTsfxqFvcjk+gdpmAQo9BKNlKXBMG0BcgjBw5ErVaTWZmJqdPn6Zhw4aoVCp+/vlnAI4cOYJSqSywAOFp+qdNHIYNG8akSZMMv8+dO5exY8fSsWNHAObNm2eoTZCfO3fu0LlzZypU0LdJCgjIe9JOmDCBZs2aAfqCAG9vbzZs2ECXLl3w8vIyKsQYPnw4O3bsYM2aNdSokVNKHB4ezoQJEwAIDg5m3rx57Nmzx7De3GxsbHBwcACMm3HIZDJsbW3zNO3YtGkTLVq0wNzcPN84W7VqxQcffADA6NGj+f7779m3bx+hoaF50iqVSpRK4xctjUqNhfz5h2diRjYanS5PUwVnKwW3HuetNghQ2smOCa1qEFLKgVSliuXHr9BnxW7W9n8LN7ucKtgHou8zZvMRslRqXGws+fndCBytLJ6bp1chsbRGIpWhzTCuQaDLSEPm6GpyGW3SQ7L2/IH2UQwSc4W+auDbQ0lfOQNdur5ARLl/o/6Fo984dBoNoCNr71o0D24WYQzGX950GWnInPKJITGerF2r9TFYKDCvEoFVl+Gkr/gOXVoy2sR4tCkJWNRpRdbetaDKxrxyA6S2Dmit834dLwxSazskMhmaVONCJW1qMuauns9dXu4biNzTl6TVBdS6MZNj16YrmacPF1k1+sT0TDRaXZ6mCs42VtyMN10V9tSN+2w4fpE1H3cvcN37L91g9PK/yFKpcLG15uf3O+FoY1loeS/I0+NMl+c4S833OJPaOyH1CUJ15RQZmxYgdXBB0agzSGVkH9v5JrJtpOAY3EwuI7V3RuoTjOrKP2Rs+AWpQykUTd7Wx3DU9FfowmRma68/L5KM++zQJCdi4WW6RlPK3/uQ2dnj97/vQSJBYmZG4o4tPF5vXGvOwtcPv8lzkJibo83K5N60r8i+V0BfEK9BZqePQ/1MHOqkRKy8fU0uk3xgrz6OKbP1NfLMzEj4azOP1q4ypLGr3whFQDA3Ph1S6HlOSUlBq9Xi8Ey/Aw4Ojty7a7p9emJiYp6mDQ4ODiQl5pz7g4cMZe6cWfTp1Q2ZTIZEImX4hyMpXyEcgLCwMigUCpYsXkSv3n0BHUuXLEar1ZKYWLTV6f9LpDa2SGQytM/eM1KSkbuZbq+em7x0EHJPXxJWGjeHsW3WHrSaIu3z4KmScv8GcLDTPzsmpxrXCkhK1Rh+M8XNRU7L+vZs3pvE2p2JBJW2oP/bpVBrdOw7loq9jQxLhZROzRxZtfUxv258RJWy1owe4MH4Ofe5GF149/LEtAz9/dvOuHDC2daam3H5fxxLzcyi2edzUKk0SKUSPn+vpVHhwJguLZi06k+afz4XM6kUiVTChG6tqBps+tonCPkpkQUIERERpKenc+LECRITEwkJCaFUqVI0bNiQvn37kpWVRWRkJAEBAfj65n/SWFlZGQoPADw8PIiPjwcgOTmZuLg4o5d2mUxG1apV0Wq1edb11IgRIxgyZAg7d+6kadOmdO7cmfDwcKM0tWvXNvzbycmJ0NBQLl/WfzXQaDRMnjyZNWvWcP/+fbKzs1EqlVhZGb8kPLvO3Hl/XZs2bWLYsGEFpsm9fYlEgru7e77bnzJlCl999ZXRvM/bNeCL9hGvnVdTKnq5UNHLxejvzgv/ZO2ZaIY2yMl3dV83fu/bgqQMJevPXuezTYdZ3rNZvv0qvGna2NtoY3PaCGfG3sK6+yjk5WuRfUz/QiGvWA+Zuy8ZWxajS01C5uWPomEHMtNTnqntUDzyxBBzC+ueo5GXr0320e2g1ZK5bRmKpl2wHfw/dFoNmjvXirXju+exqhmB6sGdfDtcRCrDqfeHIJGQ/MfiN5u5AqRnZfPFbzuY8E6T5xYGVA/0Yc0n3UlKz2Td0QuMWv4nK0a8V2C/CsVKIkGXkUbWnjWg06GNv0e2tT3m1RoVSwHCK3kaw67VOTHYPInhDRQgvAqrcuG4dOpK7IK5ZF67jLm7F279PsDl7e48WrvSkE754B43Ph2MzMoa29r18Rw2itvjPymyQoSXZVW+Ii5vdyfml9lkXr2MuYcX7gOGUqpLDx6uWYGZSyk8Bgzl1vjPTHaq+G+1ZfMmoq5cYdyEr3B1dePChfP8/OM8nJ2cqVS5Cvb2Doz5/Et+nDeXLZs3IpFIaNiwEYFBQUgkJbICa7Gwrt2Y7Pu3jTpclPsEYBPRmripnxVjzgr2b7l/N6hmy+CuOYUc3/z0atXxJRIJ1+9ksXKL/gX95j0lvh4WtKhnz75jqTw95I+fT2fLviQAbt3PJjRAQYt69oVagPCqrC0sWDN2ABnKbI5F3WLGut14uzhSPURfi+S3yJOcu3mf2YPfwdPJnn+i7zB59Q5KOdhSK6zoaoX8G4hOFAtXiSxACAoKwtvbm3379pGYmEjDhg0B8PT0xMfHh8OHD7Nv374Cq+ADeUYWkEgk6F7zCBwwYAAtWrRg27Zt7Ny5kylTpjBjxgyGDx/+Qst/9913zJ49m1mzZlGhQgWsra0ZOXIk2dnGndGYyntBBRsvKiYmhtOnT9O6desC073M9seOHcvHH39sNE/z21STaZ/laGWOTCIhIT3LaP7jjCycrV/si6hcJiXUzZG7ScZf/y3NzfA1t8XX0ZZwLxfazd/KhnM36F/bdDXX16HLTEen1SC1siH3/5LEyibPF4F8abVoHt5H+rRNvcwMi9otyfxzGZpb+jat2scxSF08Ma/ckMxCLkDIicE2bwzpLxtDTgGPNv4eGatmgrkCiUyGLjMdq3dHoIkrmiqd2vQUdBqNfvSFXPOltvYmO4rLTWJugWXlOqRu/8N0AqkMx94fInN04dGP/yvSTvwcrfU9RD9ONW4i8TgtAxcTHTDdfZzEg4QURizOaSOtfXK9qzJqNptG98bHxQEAKws5vhYO+Lo4EF7ag7ZTlrLx+AX6Nym4rWxheHqcSaxsjeZLrGzzPc506anotBqjJwhtYhxSazuQykBb+O1XC1JwDKZrTumeHJdGMSTEIbWxfyMxqFOT9efFM1+1ZfaOeb7mP1XqvT4kH9hN0p6/AFDeuYVEocBj8EgerVuVE4tajSr2ASog68Y1LINCcWrdkdhfZhd6HJoUfRxmz8Rh5uCIOp+v6q7d+pIcuYukXfovwcrbN5FaKPAc+jEP/1iJZWAIZg5OBH7/i2EZiUyGVblwnFp34NLbLeA17r92dnZIpVKSEo3/n5OSEnHMpxNjR0dHkpKeTZ+Eg6M+vVKp5NdlS/jiywlUr6Hvd8PfP4Cb16+zfv1aKlWuAkCVKtVYuHgZycnJyGQybGxs6NH93f/XnU4/S5uWik6jQWprPIqX1O7F7hlWVeuSsm210XyLwDCkNnZ4TMqplSCRybDv1BubRq2JnTC00PIP/+379/HzaVy9lfMMKDfTN+uyt5WRmJJzXXSwlXHzXv7NChNT1NyNNX6WvhebTe1K+mbGqWka1Bodd2OUedKUCSjcGniONlb6+3eKcQfaj1PTcbHLv8mEVCrB11V/jof5uHMz9hGLdhymekhpsrJVzNm8j+8HvU2DCvrmmCHebkTdi2PZ7qMlvgBBKFwltgi5UaNGREZGEhkZSUREhGF+gwYN+Ouvvzh+/HiBzReex97eHjc3N06cyBlqSqPRcOrU89s++vj4MHjwYNavX88nn3zCggULjH7P3dFjYmIiV69epUwZfYdShw4don379vTo0YOKFSsSEBDA1atF0J4dMDc3R6MxfijdsmULderUKdSRFywsLLCzszOaXqT5AoBcJqOMuyPHbscZ5ml1Oo7fiiPc68U6p9NotUQ/TMLlOQUOOp0OlaaIHtK1GrTx95F5B+WaKUHmE2RUwl8giQSpiwe6pzd7qUzfBvHZQi+drmjaTWs1aOPvIfPJ3U+ABJlP8MvF4OyBztRLVHYWusx0JA4uSF19UN8ooja4Gg2qezcxD8nVB4BEgkVwOVS3Cy50UVSsicTMjIyTf+f98UnhgVkpdx7/9I1RZ1NFQW4mo4y3K8eu5VRx1mp1HLt21+SQi/6uTqz9tAerP+5umCLKBlA90IfVH3fH3cE2zzKG9ep0ZKvf0Ev4k+PM7JnjzMwnGE3MLZOLaB7cfPJQm3PcSx1KoU1LfuOFB4A+hrh7mPk+E4NvSP4x3L+J1KEURjE4vsEY1Gqyrl/FukKudvUSCdbhlcm8arq/AomFBbpne5B6+iJd0DVIIkEiz7+J3OvQqdVkXr+KdXgVo+1Zh1fJt98FqYUiTwGALlcc6edOET28H9dHDjRMmdeukLx/D9dHDnytwgPQF8gHBQVzNlfHhVqtlrNnzhAWZrqzybCwspw5c9po3unTpwzpNRo1arU6TwfJUpk0J7Zc7O3tsbGx4eyZ0yQnJVGzVu08af7f0qhR3b2BIjRnuDwkEixCKpB9s+DnM8vKtfX3jBMHjOZnnDhA3JRPift2lGHSJCWQunszj374pvBj+A/fv7OUOmIfqQzT3dhsEpLVhIfm1IizVEgJ9lMQdSsr3/VcuZGFl6vxdcfTVc7DBP2nBLUGom9n4eX2bBpzHhbyEI5yMxllfD04FnXLME+r1XEs6hbh/t4vvB6tTodKrc+bWqNFrdEilT5zzkulaEtqT3+56HTFN5VEJbIGAugLEIYOHYpKpTLUQABo2LAhw4YNIzs7+7UKEEDf/8CUKVMICgoiLCyMuXPnkpiYWOAwiCNHjuStt94iJCSExMRE9u3bZygceGrSpEk4Ozvj5ubGF198gYuLi2Fkh+DgYNauXcvhw4dxdHRk5syZxMXFUbZs4X8V9/Pz48CBA7z33ntYWFjg4uJiNPrCv0WP6mGM33aUsu5OlPdwYtXJq2Sq1LSvoG/39eXWo7jaWjKiYUUAfjl0gXBPZ3wcbUnNymbZ8SvEpGTQsaI+fWa2moVHLtIwyAsXG0uSMpWsOXWN+NRMmoUWXTux7DMHUDR9F038PbRxd5FXqo/EzBzVJX0hlaLZe2jTksk+ov+aZ169KZrYO2iTHyGxsMS8SkOkto5kXXzSsZdKifredSzqtkGpVqFNTUTmGYg8rCrKg1uKJoZTB1A0fw9N/F39MFCVGyCRm6O6pO80TdG8qz6Gw/oveeY1mqGJvY026UkMVRshtcsVA2AWFI4uM12ffxcPLBp2QH3jQtF0BPlEWuQ2HLsNQXX3hmEYR4m5BRnH9gPg0G0ImuREUrf9brScVa1GZJ0/mbdwQCrDsc9IzL39ebxwGkilhq9V2ow0KKKCqZ4NqjDu952U83GjvK87Kw6cIjNbRYca+uvFF6t24GpvzYet62EhNyPYw8VoeVtLfZ8fT+dnKFUs3HOciHIBuNhak5Seye+HzhKfnEazim9uCCjlqf1YNu+KJu6ufhjHKg3zHGe69BSUh7YBkH3uMOYV66GI6ED2mb+ROrhgXr0p2WcOvrE854nhn0gsW3bLG8OTY1/Rsju6tGSUf2/Vx3D2EOaV6qNo1JHs0weROpbCvEYzsk8fKGgzherxlnV4Dv+MrOtXybwWhVObjkgtFCTt1Teh8Bj+GeqERzxcqW+ak3byKE5tO6O8GU3mtSuYu3tS6r3epJ08anipLtW9H2mnT6B+GI/U0hK7+o2xKleRu1+PLbo4Nv2B14djyIyOIvPaFZzbdkaqUJC4ezsAXiPHoHr8iPjlCwFIPXEE5/Zvk3kzmswofRMG1+59ST1xBLRatJmZKO/cMtqGNisLTWpKnvmvqkPHznw/8zuCg4MJCQlj06b1ZCmzaNpMPwLGjOnTcHZ2pk/f/gC0a9+BMaM/Zf36tVSvXoMD+yOJvnaVYcM/BMDKypryFcJZvHgB5hYWuLq6cuH8efbu2c2Age8btrtr5w58fH2xt7fnyuVLzP/lJ9p36IS3d9GM5FMQmbUV1rk6hLPy98auYhjZCclk3c2/Y9g3IXXvVpx6DiX7znXDMI5SCwvSj+o77nbsOQxNcgIpm1cZLWdduzGZ506gTTe+Z2jT0/LM02nUaFMSUccXTY/5JeX+DbB1XxLvtHQi5qFKP4xja2cSkjUcO5vzRf+r4V4cPZvGXwf0fVds2ZvIlE986NzckUOn0gj2U9C8rj0//ZbT/Hbj7kQ+6efBpehMzl/NpHJZK6qXt2bc7MKvEdmzcU3G/bqZcqU9KF/akxX7jpOpVNGhtr6p7RdLN+PqYMuHHfTvMou2H6JsaQ98SjmSrdJw8GI0245d4IuuLQGwsbSgWrAvM9fvxUIux8PJnn+u3WbrsfN82rlpoedfKNlKdAFCZmYmYWFhuLnldErVsGFDUlNTDcM9vo7Ro0cTGxtLr169kMlkDBo0iBYtWiCTyfJdRqPRMHToUO7du4ednR0tW7bk+++/N0rz7bff8uGHH3Lt2jUqVarEli1bDB0Wfvnll9y4cYMWLVpgZWXFoEGD6NChA8nJ+Y8i8KomTZrE+++/T2BgIEqlkrS0NPbs2cOsWbMKfVuvo0UZXxIzsvjp7/M8Ts8i1NWBH7pEGIZmjE1JJ3eBa2pWNpO2n+BxehZ2CnPKuDmytEdTAl30L3RSqYRbCals2XiIpEwl9pbmlHN3ZnH3JgSWsjeVhUKhvnYWpaU1FjVbILG2RfvwARmbFxqGZpTYOCDNVZQpsbBE0fhtJNa26LIy0T68R8Yf89Am5tzssnasxKL2Wyiad0OisEKbmojyyHZUF/KONV04MZzRx1CrBRIrO7SP7pOxcYHhhVpi+0wMCksUTd5BYmWHTpmhr+64Zi7ahJwaJRJrOywatEdiZYMuPQXV5X/IPm56RJHCknXmKMk2dti2fBuZnQOq+7d5/Mu3+q+9gMzRJU+xsqyUBxYBYTz+aXKe9cnsHbGsUA0A11HGzXMezZtE9vWi6dOhZeVQEtMz+XHHER6lZBDq5cKPAzsYxpCOTUpB+hKVUWRSCTfjE9h84hJJ6Vk4WCso5+PGkqHvEORedMNRPkt99QxZljZY1G6Z6zibbzjOpHaOaHONR65LSyJj4y9YNOiAdY9P0aUlk33mANkn976xPD9LffU0WVbWWNR5Sx/Dw/tkrP8lJwZbR0MTEngSw/qfsYjogHWvz/QxnN5P9ok9byzPqYf3E2/vQKn3eiNzcER58zp3/vc5muQkAOQurkbnxaO1K0Gno1TXPpg5uaBJSSb15FEersrp+8PM3gHP4Z9h5uiENiMd5e2b3P16rPFoD4Us5e9IzOwccO3WFzNHR7JuXuf2V6PRJCca4sj9Ff7hmuWg0+HavR9yJxfUKUmknThC3IpFRZbHZzVoGEFySjIrlv9KYmIiAQEBTJr0DY5POlZ8+DDe6MtimbLlGPXZWJb/upRfly7B08uTL8ZNxM8vp5ry6NGfs2zpYqZ/9y1pqam4urrSs1cf3mrVxpDm/v17LFu2+MnvbnR5tysdOnZ+Y3HnZl+1PLX3LDf8XXb65wDc/XU95/oXXYHTi8g8dZgkGzvsWr+LzNYB1f1bPPrhG0PHimZOee8ZZq6eWASV4eG8r4sjy3mUlPs3wIbdiSgsJAzp6oq1pZTL17P4+sf7qNQ5+Xd3kWNnk/O8Hn1HydQFMfRo50yXt5yIf6xm8bqHHDiZ04Tj2Ll0fvk9nk7NHen/dikexKuYtjCGyzfyr9nwqlpWK0tiWjo/bt3Po5R0Qr3d+HHYezjb6ZtUxCYmG53zmdkqJv++nbikVCzkZvi7OfNNn/a0rJbzgXFqv47M3rSPsUs2kpKRhYeTPcPaRfBO/Sp5ti8IBZHoXrdRv2Cg1WopU6YMXbp04euvX/6GEBkZSaNGjZ70nuxQ+Bl8TevXr+fLL7/k0qWiGV4rt4zFE4p8G2+CJr1ohuh7owqh74x/g9QSMM6xU5MGxZ2FQpEdXfwdeBaKEnBu3D90sbizUCi0b6oJTREyn7mkuLNQKK6GNS/uLBSKiu+HPz/Rv5x9Celdv9e1wcWdhdf2W8ei+XDzpima9CruLLyShW+urD2PAU2Kb9tFpcTWQHgTbt++zc6dO2nYsCFKpZJ58+Zx8+ZNunXrVtxZKxI2NjZMnfpinRsKgiAIgiAIgiAIJYsoQHgNUqmUpUuX8umnn6LT6Shfvjy7d+/O06dBSdG8ecn4qiAIgiAIgiAIwv8Por594RIFCK/Bx8eHQ4cOFdr6IiIiXnuYSEEQBEEQBEEQBEEoCqIAQRAEQRAEQRAEQSiRSkCXRf8q0uLOgCAIgiAIgiAIgiAI/36iAEEQBEEQBEEQBEEQhOcSTRgEQRAEQRAEQRCEEkl0MVe4RA0EQRAEQRAEQRAEQRCeS9RAEARBEARBEARBEEokUQOhcIkaCIIgCIIgCIIgCIIgPJcoQBAEQRAEQRAEQRAE4blEEwZBEARBEARBEAShRNKKJgyFStRAEARBEARBEARBEAThuUQNBEEQBEEQBEEQBKFE0hVrL4qSYtx20RA1EARBEARBEARBEARBeC5RA0H4V0q7drO4s1AoshLTijsLr83Wx624s1Ao0mITizsLr832enRxZ6FQpETfLe4sFApzW8vizsJrUyvVxZ0F4YlM7X//eAKo+H54cWehUJz95VxxZ+G1VfqgZHwnVClVxZ2F15bl5F3cWSgUiuLOwCsSwzgWrpJxZREEQRAEQRAEQRAEoUiJAgRBEARBEARBEARBEJ5LNGEQBEEQBEEQBEEQSiSttrhzULKIGgiCIAiCIAiCIAiCIDyXqIEgCIIgCIIgCIIglEiiE8XCJWogCIIgCIIgCIIgCILwXKIAQRAEQRAEQRAEQRCE5xJNGARBEARBEARBEIQSSSuaMBQqUQNBEARBEARBEARBEITnEjUQBEEQBEEQBEEQhBJJdKJYuEQNBEEQBEEQBEEQBEEQnkvUQBAEQRAEQRAEQRBKJF2xdoIgKcZtFw1RA0EQBEEQBEEQBEEQhOcSBQiCIAiCIAiCIAiCIDyXaMLwivr06cOyZcsAMDMzw9vbm3feeYdJkyahUCiKOXfPd+vWLfz9/SlVqhTXr1/H1tbW8FulSpXo0KEDEydONLns0qVL6du3L2FhYVy+fNnotz/++IMuXbpQunRpbt26VYQRGLOs1QSrBq2Q2tijjr1L6ublqO/dyDe9RGGFdfO3sShXDamVNZqkx6RtXUF21Dn97+YKrJt3xqJsVaQ2dqgf3CZ16wrU924WaRw2DVti17wDMjsHsu/dInH1QrJvRZtM6/rxJBQh5fPMzzz/Dw9/+AYAp97DsKnd2Pj3i6d5OPfrws/8ExZVG2JRqxlSGzs0cffI2LkazYPb+aaXWFiiiGiPeVglJAortMkJZOz6A/X1i3nXXbs5Vo07knV8L5m7/iiyGEyxb9YWp7ZvI7N3RHnnBg+X/kjW9av5pnd4qwMOTdtg5lIKTWoKaccO8uj3JehUqjeYa5CH18W8agQSK1u0jx6QFbkBbdxdk2nNylTHsvl7RvN0ahVpP4wx/C2xssGibhtkviFILCzR3L9B1v4N6JIeFVkM1g1aYNukHTI7B1T3b5P4x2JUt02fF6U+nIhFcLk88zMvnOLxz1PyzHd4byA29ZqTtHYJaZF/FnrecysJ1ymn1h0o1eldzBydyLp5nQe/zCHz6pV80zu364xzq3bIS7mhTkkm5dB+YpctMHkelHq7K+59BvFo01piFvxQZDHAfzOO7VvXs3n9byQlJlDaP5B+748kOLRsvumP/L2P31cs5GFcLO6e3vToM5gq1WsDoFar+X35Ak6dPEp87AOsrK2pULEa3fsMxsnZxbCOD/q9w8P4WKP1duv9Ph3f6VFocZWU8/tFONWrRsAn/bGvUh6FpysnO39A3OY9xZ0tI9b1mmPTuK1hfyStW4LqznWTaV2GjTe5P7IunuLx/KkglWHX+l0UZSsjc3ZFl5WBMuoCyVtWoU1JLNI4urcrRYv6DlhbybgcncGPK2N5EJ9d4DLODmb06exK1fI2WJhLiYnPZtbSB0TfzgJAYSGhTyc3alW2xdZaRtwjFVv2JvDX/qKJ5Y8dkazcsovHySkE+3rzSd93KRfkZzLtxj1/8+eBo9y49wCAMH9fhrzXIU/6m/dj+GHVBk5duoZGq8Xfy4NvPx6Eu4tTkcTwbyGGcSxcogDhNbRs2ZIlS5agUqn4559/6N27NxKJhKlTpxZ31l5Yamoq06dP56uvvnqp5aytrYmPj+fIkSPUrl3bMH/RokX4+voWdjYLZFGhJjatu5G6cSmqu9exqtsCh36jeDzjM3TpqXkXkMlw6P8Z2rQUUlbNRZOciMzRGV1mhiGJbef+mLl5kbLmF7SpiSgq1cWh/2gSvh9bZDc9q6p1cXy7LwmrfkF56yp2jdvgOnw8DyYOR5uanCf9o5+ngVnOKSyztsX9y5lknDpslC7zwike/zrP8LdOXXQvsPIyVbFs2pmMv35D/eAmihqNsXlvBCk/T0SXYWJfSGXYdBuBLiOVtHXz0aUmIbV3RpeVkSepzKM0FlXqo467V2T5z49NrQaU6jmQ+EVzyYqOwuGtDniN+YZbnwxAk5J339jWicDlvX7E/TKTzKuXMffwwn3IJ6CDhyvmv7F8mwVXwqJ+O7L2rUUbewd5pfpYdRhE+q9T0WWmmVxGp8wk/dfc1zDju65lm77otBoyty5Bp8zCvEpDrDq+T/ry70Bd8MPZq7CsUgeHjr1JXD2f7FvR2DRqTamhXxA76UO0aSl50j9aMB2JLOe8kFrb4DZ2Opmnj+RJqwivgblfCJqkhELP97NKwnXKvn4jPAYM4cEP35MRdRmX9m/jP2kaUe/3QpOclDd9wya49xnEvdnTyLh8AQsvH7xHjgYgZuGPRmktg0NxatmWzJumX1T+v8dx6MAeli2cx6ChnxAUWpZtm/7gm/GfMPuXVdg7OOZJH3X5PLOmfUW33oOoWqMOf0fuZto3nzNt1iJ8/QJQKrO4cf0qb7/Xm9L+QaSnpbJk/mymfj2GqbMWGq3r3e79adKybU6MllaFFldJOb9flMzaipRzUdxduo5qa4u2kOxVWFaujX3HXiStWUj2rWvYRLTCZcjnxH3zkcn98XjxjGf2hy2un00j88xRACTm5sh9/EndsQ7Vg9tILG1w6NQb54GjeDjj8yKLo3NLZ9o2ceL7xfeJe6SiRwdXJo30Zcj466jUpt8kra2kTBvtx7moDCbOvkNymgZPV3PSMjSGNAO6uBMeZs2MhfeJe6yicllrPujuweMkFcfPmr6nvqpdh08ye/k6Rg/oSrkgf37/cy8fTpnDmpkTcbK3y5P+1KWrNK9bnfCQAMzlcn7dvJMRk+fw2/TxuDo5AHAv9iGDJsygXaM6DHy7LdaWCm7ce4C5XLwOCi9HNGF4DRYWFri7u+Pj40OHDh1o2rQpu3btMvz++PFjunbtipeXF1ZWVlSoUIHffvvNaB0RERGMGDGCzz77DCcnJ9zd3fN8+b9y5Qr16tVDoVBQtmxZdu/ejUQiYePGjYY0d+/epUuXLjg4OODk5ET79u1fqAbA8OHDmTlzJvHx8S8Vu5mZGd26dWPx4sWGeffu3SMyMpJu3bq91Lpel1X9lmSeiCTrn4No4h+QunEpumwlltUamkyvqNoAqaU1yctno7p9DW3SI1Q3o1DHPvkqaybHolw10v5ajepWFJrH8aTv2YDmcRyWNRubXGdhsG3alrRDu0g/shd1zD0SVv2CVqXEpo7pbWoz0tCmJBkmRZmK6LKVZPxjXICgU6uM0uky0ossBkXNJijPHCL73BG0j2LJ+PM3UGdjXrG2yfTmleogsbQm7Y+f0dy7gTY5AfWda2ji7xsnlFtg3b4vGdtWmixcKGqOrTuRsnc7Kft3kX3/DvGL5qLLVmIX0cJkesuQsmRdvUjq4UjUj+LIOH+KlMORKAJD32i+zas0QHXxKOpLJ9AmxKHcuw6dWoW8XI0Cl9NlpOaach6KJA4uyDz8UO5dhzbuLrqkhyj3rgMzOfLQykUSg23jNqQf3kPG0UjUsfdI+n0+uuxsrGubPi90GWloU5MMkyIsHF22Ms8LhtTeCYd3+pGwdDY6jbpI8p5bSbhOuXR4h8Qd20jcvR3l3dvc/2EmWmUWTs3eMpneukw5Mi5fIHn/HlTxcaSdPknSgb1YBocZpZMqFPh8+gX35k5Hk2aiMEXEwdaNq2nSoi2NmrXGx9efQUM/xdxCwd5d20ym37Z5LZWq1qB95254+/jxXs8BBASGsH3ren1M1jaM/9/31KnfGC9vX0LCytF/8EfciI7iYXyc0bosraxwdHQ2TAqFZaHFVVLO7xf1cMcBrk6YRdym3cWdFZNsIlrr98exSNRx90lasxBddjZWtRqZTK/LSEebmmyYLELD0amUhgIEXVYmj3/8hswzR1HHx6C6fY2kdUsw9w1E5uhcZHG0b+LE6m2POHY2jVv3lcxcfB8nBzNqV7bNd5m3W7rwKFHN7KUPuHori7hHKk5fSif2Yc5HlzKBluw9nMT5qxnEP1ax42ASN+9lEeJfeOfEU79t20P7xnVpG1GHAG8PxgzoisLcnC2ReQvLACYN78fbzRsS4ueDn5c7X7zfA61Ox8kLOTWrflq9iTqVyjG8eydC/X3wdi9Fg2oVTRZIlDQ6XfFNJZEoQCgkFy5c4PDhw5ibmxvmZWVlUbVqVbZt28aFCxcYNGgQPXv25Pjx40bLLlu2DGtra44dO8a0adOYNGmSoSBCo9HQoUMHrKysOHbsGPPnz+eLL74wWl6lUtGiRQtsbW05ePAghw4dwsbGhpYtW5KdXfAXwa5duxIUFMSkSZNeOuZ+/fqxZs0aMjL0L3RLly6lZcuWuLm5vfS6XplMhpmnH9nRuaq763RkX7+E3DfI5CIWZauguhONbfteuHw+F6cPJ2MV0RYk+l5SJVIZEpksz5d6nUqF3C+kiOIww9w3kKzL54ziyLp8DvOAF3vptK7bhIyTf6PLVhrNV4SUx2vaEjwmzsWx6yCk1jaFmfMcUhkyD1/UN3NXA9ahunkFM+8Ak4uYB4ejvncDq5bvYf/hVOwGjkNRp6VhXzxl1fI9VNEXUN/Kv4pxkZGZofAPJv3C6Zx5Oh3pF05jGVzG5CKZVy9h4R+MIlB/vMhd3bGuVJ30M8dNpi8SUhlSV280d67lmqlDc4N4564AAQAASURBVOcqUvfS+S8nN8e67xdY9xuHok1fpE455/PTL03GD+Q60GiQefoXbv4BZGbIfQLIinrmvIg6h7n/i52L1nWakHHqsPF5IZHg1Gs4aXs2o459AzVaSsB1SmJmhmVQCGln/jGKIe3MKazC8lZhBki/fBHLwBAsQ/Qv2nI3D2yr1ST15DGjdJ5DRpJ64ijpZ08Ver6f9V+MQ6VScSP6KuGVqhrmSaVSwitV4+qVvE29AK5euUB4pWpG8ypWqcHVKxfy3U5GRjoSiQRrG+N7xIY/VtK3a2tGjejHpnWr0BTWC3lJOb9LCpkMuU8Ayqvnc+bpdCivnsfcL/iFVmFdqxGZz+6PZ0gVVui0WrQZRfMxwM1FjpODnDOXcwq/MzK1RN3IJCwg/xf9mhVtuXYrkzHve7NiRgizx/nTor6DUZrL1zOpUckWZwf9vbBCqBWebuacvli4H2ZUajVXbt6hRoWcQkqpVEr1CmGcv5p/s7fcspTZaNQa7KytAdBqtRw+fQFfDzdGTJ5Dy0Gj6PfFVPafOFOoeRf+fxB1Vl7D1q1bsbGxQa1Wo1QqkUqlzJuXU1Xcy8uLTz/91PD38OHD2bFjB2vWrKFGjZwvgOHh4UyYMAGA4OBg5s2bx549e2jWrBm7du3i+vXrREZG4u7uDsA333xDs2bNDMuvXr0arVbLwoULkTx5uFyyZAkODg5ERkbSvHnzfGOQSCR8++23tG3blo8++ojAwMAXjr9y5coEBASwdu1aevbsydKlS5k5cyY3brzYxe0ppVKJUml8s1GqNViYyZ67rNTKFolMlqdqnTY1GbNSHiaXkTmWQhZQhqwzR0haOgOZsxu2HXqDTEbGno3osrNQ3b6GdeP2pMQ/QJuWjEXF2sh9g9A8jjO5ztcls9HHoUlJeiaOJOTuXs9d3twvCHOv0iQsN64SmXXxNJmnj6F+FIdZKXccOnTHfPg44qaOBZ22MENAYmWDRCpDm268L3TpKcicTRcqSR1cMPMLJfvCcdJW/4DUsRRWLd8DmYysg/ova/Ky1TBz9yFl8beFmt8XJbOz0++bZ6o2a5KTMPf0MblM6uFIZLb2+EycAUiQmJmRtGsrCZtWF32Gn5BYWuv3xzNNR3QZacicXE0uo02MJ2vXarSPYpBYKDCvEoFVl+Gkr/gOXVoy2sR4tCkJWNRpRdbetaDKxrxyA6S2DmitC/8LhvTJefFsEx5tSjJyt+efF/LSQcg9fUlY+ZPRfNtm7UGreWNtokvCdUpmZ49EJkOdZNw0Qp2UiIW36WZryfv3YGZnT8DUOUgk+vPg8Z+bePjHSkMa+waNsAwMJvqjwYWeZ1P+i3GkpiSj1WqwdzBuo2zv4Mj9e6b7l0lKTMiT3sHBiaR8qvNnZytZseQn6jZoipWVtWH+W207ExAYio2tLVGXL7Bq2S8kJjymz8DhrxlVyTm/SwqptZ3J/aFJTcbC1fO5y8t9A5F7+pL428/5JzKTY9eum76QQZn5ulk2ydFe/2qTlKIxmp+UqsbBPv/XHvdSclpFOLJxVwJr/nxEsJ+CQe+5o1Lr2HtE/3/y82+xDO/pwbLvQlCrdeh0OuYuj+HitcItDElKSUOj1eapGeBkb8ft+y92ff9h1QZcHO2p/qQQIjEllYwsJb9u3sHgLu0Y1q0jR85eYvTM+fw4biRVyhbRBzKhRBIFCK+hUaNG/PTTT6Snp/P9999jZmZG586dDb9rNBomT57MmjVruH//PtnZ2SiVSqysjNsPhoeHG/3t4eFhaFIQFRWFj4+PofAAMCp8ADh79izR0dFGHSGCvgbE9evPb4fZokUL6tWrx7hx41i1atWLBf9Ev379WLJkCb6+vqSnp9OqVSujQpQXMWXKlDx9MHxaN5xR9Su+1HpemFSKNj2V1A2LQadD/eAWUntHrOq3ImPPRgBS1vyCbecBuHw+B51Gg/rBLZRnj2DmVQRfWQuBdZ2mZN+7lafDxYyThwz/Vj24Q/b923j97ycsQsqhjDr/7GrePIkEXXoqGX+uBJ0OTewdsmwdUNRuRtbBbUhsHbFq9g5pv82Bf1E11OexLBOOU4d3iVv8A1nRVzB386RU78E4dexGwoaXO8feJG3sbbSxOS8kmTG3sO45Gnn52mQf3Q5aLZnblqFo2gXbwf9Dp9WguXMN9a3LBay1+FjXbkz2/dtGHbLJfQKwiWhN3NTPijFnL6AEXKesK1SkVJfuPPhpFhlRl7Hw9MJj4DBc3+tJ/O/LkbuUwmPgMG6NG/XGOxd9GSUljvyo1WpmfjsB0DFw6CdGv7XtmNOpamn/IMzM5Mz/4Tu693kfudyc4vSfPr9LIOtajVE9uJ1vh4tIZTj1GQlISFqz0HSaVxBR046hPXIKOL6ae+eV1iORSIi+lcmvG/TP3zfuZlHay4JWDR0NBQhtGzsRGmDJpLl3iH+sonyIFYO7ufM4Sc3Zy0XXPPRlLdu0g12HT/Lj+I+wMJcDoH3Si2CDquF0bd0EgBA/H85fvc763QdLfAGCVvSiWKhEAcJrsLa2JihIX/108eLFVKxYkUWLFtG/f38AvvvuO2bPns2sWbOoUKEC1tbWjBw5Mk+zArlcbvS3RCJBq33xr8NpaWlUrVqVlStX5vmtVKlSL7SOb7/9ltq1azNq1KgX3i5A9+7d+eyzz5g4cSI9e/bEzOzlD6mxY8fy8ccfG81L/nrICy2rzUhFp9EgtTEupZXa2pvseBBAm5IEWo1RwyRN/ANkdg4gk4FGgyYhnqQFk0FujlRhiTY1GbuuQ9EkvFxfES9Kk6aPQ2bn8EwcDnlqJTxLYm6BdfW6JG/5/fnbeRSHJjUZuatHoRcg6DLS0Gk1SK3tyF3uL7G2y1Mr4SltWjJotcb74nEsUht7kMow8/BFamOHbf+xOeuTyjDzDcKiWkOSvh1e5A3MNCkp+n1j72A0X2bvgOaZr5hPOXfpRcrBvaTs2w5A9t1bSBQK3AaMIGHjb2+kUZwuM12/P6xsyX01kVjZoDXVaZ8pWi2ah/eROuT0yq6Nv0fGqplgrtBXoc9Mx+rdEWiKoHNL7ZPzQmprbzRfamf/QueFVdW6pGwzrvVhERiG1MYOj0k5Xy0lMhn2nXpj06g1sROGFlr+nyoJ1ylNSjI6jQazZzrsM3NwRJ1o+qu2W49+JO3dSeJO/Zdg5e2bSC0UeA37hPjVK7AMCkHu6ETQ7JyORSUyGdblwnFu05ELHZvrrw//z+OwtbNHKpWR/EztgeSkRBzyaUfu4OiUJ31SUgIOz9RK0BcejOdRfCwTJs82qn1gSnBoWTQaDfFxsXjlU2PjRZWU87uk0KanmNwfMlt7NKlJBS4rMbfAskodUv5aYzqBVIZT35GYOZXi0bxJhVr74NiZNKJu5BRayOX61tkOdjISk3M+PDjYmnHzbla+60lMVnEnxrg27N2YbOpW0V+3zeUSenV05Zsf73LyvL55xK37Svx9FHRq7lyoBQgOdjbIpFISko2fnRKSU3ByKLi234otu/h10w7mffEhwaW9jdcpk+LvbVzrzc/Tg7NRpkc9EYT8iAKEQiKVSvn888/5+OOP6datG5aWlhw6dIj27dvTo4d+uCOtVsvVq1cpWzb/YZeeFRoayt27d4mLizP0LXDixAmjNFWqVGH16tW4urpiZ/dq1Yhr1KhBp06dGDNmzPMT5+Lk5ES7du1Ys2YNP/9cQLW1AlhYWGBhYWE0L+sFmi8A8OSrm3lgObIvPWl3KpFgHliWzCOmOylS3b6KolJtfVviJw/nMhd3NCmJoNE8kzgbrSobicIK8+DypP1VRFXQNWqy71xHERZO5tnjhjgUYeHPrYZpVbUOEjM56cf2P3czMgdnpNa2aJKLYCQJrQZNzB3M/EJRXT37ZKYEuV8oWScjTS6ivncD83LVAQlPe/uXObmiTU0CrQbVrSskzzcectK6TU80j+PIOrLzzfROo1GTdfMaVuUrkX7ySedFEglW5SqRtHOLyUWk5hZ5m4gYXiByYi1SWg3a+HvIfIJR33ja7lmCzCcY1blDBS5qIJEgdfZAY6qGQXYWOvQdK0pdfVAe2V5YOc+hUaO6ewNFaAWyzp0w5MkipALpBwrenmXl2kjMzMg4ccBofsaJA2Q9U3hWauiXpB8/QMbRfYWafYMScJ3SqdVkRl/FumIVUo4eMsRgU7EKj7duMLmM1EKR5xzVPT0PJBLSzp7i6tC+Rr97fzga5b07PFz3W6EXHvxX45DL5QQEhXD+7D/UqN0A0D9PnD/7Dy3bdDK5TEhYec6f+YfW7bsY5p07fZKQsJyhf58WHsQ+uMeEKbOxtbM3tSojt25cQyKVmhz54aWVlPO7pNBoUN29gUVIBbLOn9TPk0iwCClP2sEdBS5qWakWEjMzMk8czPvj08KDUh48mvsV2ozCHa0gU6kl86HxOZaQpKJSmDU37+oLBCwVUkIDLAscbvFSdCbe7sbPol5u5sQ/1tcqkskkyM0keR47tNo83Ta9NrmZGWH+vpy4EEXD6pWebEfLiQtRvNMiIt/llm/eyZINfzH78+GUCTTu60huZkbZAD9uPzBuAnEnNq7ED+EIJbczw+IiOlEsRO+88w4ymYwfftC3Qw8ODmbXrl0cPnyYy5cv8/777xMX93JtU5s1a0ZgYCC9e/fm3LlzHDp0iC+//BLA0N9B9+7dcXFxoX379hw8eJCbN28SGRnJiBEjuHfvxb8KfvPNN+zdu5eoqCij+WPHjqVXr175Lrd06VIePXpEWFiYyd+PHz9OWFgY9+/fN/n76/o/9u47vKnqDeD4N+nee1NooYO9994gyEaGoCBTFARBBERFQAFlg4rrJ0tAQNkgyBCUIXuPsqGstrRN90ia/P4IpISmlJFSqO/nefI89Obcm/Ny97nvOTf1n83YVWuAbeW6WHj549SuFwprG9IO6y8snF4bgEOL1wzl0/bvQGHniOOrPbHw9MU6vAIODdsYXchbh5bDOqwcSjdPrELK4Np/DFkxt0k/bOLkaCZJ29bjWLcpDjUbYukbgFv3gSitbUjeuwMAj97v4dK+R475HGo3IfXYAbQpxidlhY0trh3fxDo4DAsPL2zCy+E1aDSamDuknTmaYznmkL5/OzaV6mJdriZKD1/sX+kOVjZkntDfeNu36YVtw3aG8hmH/0ZpZ49d89dQuntjGVIW29otyTh8rzEkMwNtzC2jj06diS4tBW3MrXyJwZT4jatwafQKzvWbYu0fiHefIShtbEnc9ScAvoM+wLNb9g1EypH9uDRtjVOtBlh6+WBfrhIer71JypH9Zh974lEyj/yNVdkaWJaqitLNG5vGnVBYWaM+o2+ksm3eHevarQzlras3w6JoGApnd5ReAdi26IHS2Q316ezB4ixDymMRUAKFszuWxctg32EgmsunyLp+Pl9iSNqxAYfaTbCv0QBLnwBcu/ZHaWNDyr2bAbc3BuPcNuebXxxqNSbtxMEc+4U2JRnN7Uijjy5LgzYxHk10/m1TheE4dXfNStxbvIpr4xbYFCmK/zvvo7S1JX6b/mavyPAx+PTqZyifeGAv7q3a4lK/EVY+vjhWrIJPzz4kHtgHWi3atDQyrl01+mgz0slKSiTj2tV8ieFljePV9l3ZvmUDO7f/wY3Iq/z47XQy0tNo1FS//86d/jlLFmQ34rdu25ljR/azftWv3Iy8xoolP3Pp4jlDg4NGo2H65E+4fDGC9z74FK1WS3x8LPHxsajvdcOIOHuKjWtXcPXyRaLu3OKfv/5kwU9zqd+wOY6OuY9m/yQKy/79uCwc7HGuUBLnCvrrJfvgIjhXKIltoOmxUJ635J0bcajVGPtq9fXr47V+KKxtSN2/EwC3Hu/i/Gr3HPPZ12xE2slDORsHlBa493kfq8DixC2aC0olSicXfZaDxWM+KHoKa7fH0bW1F9UrOFIswIbhffyJU2nYdzQ7++6L4cV4tVF2Q9jabbGEB9vxWitP/LysaFDdmZb13di4U5/Jk5au5WRECn06e1MuzB4fTyua1HahcS0Xo+WaS/fWTVi7Yzcbd+3jys3bfPm/ZaRnZPBqA/1brT77ZgHfLFtjKL9o7Ra+X7Gej99+A38vD2JVCcSqEkhNz8666NmmGdv2HWbN9t1E3olm5ead7D58kk7NTb8NSIjcSAaCGVlaWjJ48GC++uorBg0axMcff8zly5dp0aIF9vb2DBgwgPbt25OQYDpl1RQLCwvWrFlDv379qFatGsWLF2fq1Km0adMGW1tbAOzt7fn7778ZNWoUHTt2JCkpiYCAAJo0afJEGQlhYWH06dOHH34wfk/97du3uX499z5ldnZ22NnlPrJtamoqERERhosSc8s4uZ9kRyccmnZE6eSC5vZ1VPOnors3YJmFq4dR06M2IQ7V/Kk4tX4du/c+R5sYT+reP0ndtcFQRmFrh2OL11C6uKNNTSHj9EFStvymTynOJ6mH96B0csalTXcsnF3JvHGF6LkTDSnOFu6e6B66+bT08cc2tDTRs8fnXKBWi1VAMbxqNkJpb09WQjzpZ46hWrcMNPkznoD67GHSHByxbfCqvitD1A2Sf51reM+90sXdaF3okuJJWjYX+2avYdP/Y7RJKjIO/kX6vkc/7Xjekv/9m7vOLnh0fgMLVzcyrl3m5pSPDQMrWnp6o3sgrtjVS9Ghw6NLLyzdPchKTCDlyH7uLl/wXOutuXCMDDsHbGq2QGHvjPbuTVLX/Gh4NaPCyRXlA/VW2Nph2+Q1FPbO6DJS9d0VVsxFG5fd8KlwcMamfjsU9o7oUhJRnz1M5oGtOX7bXNKO7EXl6Ixz665YOLmivnmVu998YdgvLN09czxasPT2xyakFDFfTzS1yAJRGI5TCf/8haWLCz49e2Pp5k765Utc+XSUYUBCKy9vo6ft0b8uBp0On559sfLwRJOgIunAPu4sNl//56fxMsZRp34TEhNULP/lf6ji4wgqHsLYCdNwddM/ObwbE4VCmf0YNLxUOYaOHMeyxT+ydNEP+PkX4cOxkygapH8jTlxsDIf27wZg5HvG2ROfTZpDmfKVsLKyYs/f21mxdD5qdSbePn682q4Lr3boara4Csv+/bhcqpSl1vbFhr9LT/sIgMhFqzjxQFe9gpJ2dJ++22CrLlg4u6K+cZW7303Ovg5x88h5HeLth02JUtz99vMcy7NwdceuXDUAfEZ9ZfRdzNzxZF48ky9x/L45FltrJUPe8MfBXsmZC6l8Ovs6ak32tuTrZYWzY/Zt0IWr6XwxL5JeHbzp/qonUXfV/Lj8Djv3Z3cj+PKHG/Tq6MMH/QJwdLAgOlbN4jXRj8xseFrNaldFlZjMDys3EKtKJKxYEWaNHoLHvS4MUXfjUD6Q+rBq69+oNRrGzPzRaDn9OrWm/2uvAtCwekVG9XudhWs3M2PBCor6+zB5+AAqljT9NqDCRDIQzEuh08l/6ctmz5491K1bl4sXLz7RWxNeJtFjcs94eJmkx5s3Va8gOAU+x9dy5qOYU1cKugrPzK+m6dfMvWwSLjzdIFcvGmsn87/7+3mLOhlZ0FUQ9+hmPr+3tOQn99mFY5yB49+fyLvQC67iOxULugpmMTB1XEFX4Zn98s6dgq6CWbhWalzQVXgqX/yafw8A8zK2W/5l2xQUyUB4CaxevRpHR0dCQ0O5ePEiQ4cOpU6dOoW28UAIIYQQQgghxItHxkB4CSQlJfHuu+9SsmRJevfuTbVq1Vi7dm1BV0sIIYQQQgghXmhana7APvklLi6OHj164OzsjKurK3379iU5+fEyn3U6Ha+88goKhYI1a9Y88W9LBsJL4M0333zkIIZCCCGEEEIIIf4bevTowe3bt9m6dStqtZq33nqLAQMGsHTp0jznnTVrlmEw/qchDQhCCCGEEEIIIQql5/gCrOfi7NmzbN68mYMHD1K1alUA5s6dS6tWrZg2bRr+/v65znvs2DGmT5/OoUOH8PN7ujfASBcGIYQQQgghhBDCzDIyMkhMTDT6ZGRkPNMy9+3bh6urq6HxAKBp06YolUr279+f63ypqam8/vrrfPPNN/j6+j7170sDghBCCCGEEEIIYWaTJ0/GxcXF6DN58uRnWuadO3fw9vY2mmZpaYm7uzt37uT+xo/333+f2rVr065du2f6fenCIIQQQgghhBCiUNLl42CGeRkzZgzDhw83mmZjY2Oy7OjRo/nyyy8fubyzZ88+VT3WrVvHjh07OHr06FPN/yBpQBBCCCGEEEIIIczMxsYm1waDh40YMYLevXs/skzx4sXx9fUlOjraaLpGoyEuLi7Xrgk7duzg0qVLuLq6Gk3v1KkT9erVY+fOnY9VR5AGBCGEEEIIIYQQhZT2JRlE0cvLCy8vrzzL1apVC5VKxeHDh6lSpQqgbyDQarXUqFHD5DyjR4+mX79+RtPKlSvHzJkzadOmzRPVUxoQhBBCCCGEEEKIl0CpUqVo2bIl/fv357vvvkOtVjN48GC6detmeAPDzZs3adKkCYsWLaJ69er4+vqazE4oWrQowcHBT/T70oAghBBCCCGEEKJQKsgxEPLLkiVLGDx4ME2aNEGpVNKpUyfmzJlj+F6tVhMREUFqaqrZf1saEIQQQgghhBBCiJeEu7s7S5cuzfX7oKCgPBtOnrZhRV7jKIQQQgghhBBCiDxJBoIQQgghhBBCiEJJW/h6MBQoyUAQQgghhBBCCCFEniQDQbyQ7hy/XtBVMIu0+LSCrsIz06RnFnQVzMK3WsmCrsIzu7LlcEFXwSyc/V0KugpmURj2DZ8KxQq6Cubxsryj6xHuKjQFXQWzcAktWtBVMIuK77z8z9iOfXusoKtgFgmtYgu6Cs9MY2VX0FX4T9NJCoJZvfxHRyGEEEIIIYQQQuQ7aUAQQgghhBBCCCFEnqQLgxBCCCGEEEKIQukp31YociEZCEIIIYQQQgghhMiTZCAIIYQQQgghhCiUtDKIollJBoIQQgghhBBCCCHyJA0IQgghhBBCCCGEyJN0YRBCCCGEEEIIUSjpZBRFs5IMBCGEEEIIIYQQQuRJMhCEEEIIIYQQQhRKOm1B16BwkQwEIYQQQgghhBBC5EkyEIQQQgghhBBCFEpaGQPBrCQDQQghhBBCCCGEEHmSBgSRp6CgIGbNmlXQ1RBCCCGEEEIIUYCkC8N/TO/evVm4cCEAVlZWFC1alDfffJOPPvoIS0vTm8PBgwdxcHB4ntV8Yh6vtserUzcs3dxJv3KRm/PmkHb+XK7lPdt1xqN1W6y8fNAkJpCwexd3FvyITp0JgE+P3vj06G00T3rkdc4PfDM/w8jBp1Nn/Hr0wMrdg9SLF7g6YzopZ86YLKuwsMC/V288X2mFtZcXadevE/nt1yT8++9zrbNzk9a4vNIRCxc3Mq9fIfaX78m4ct5kWb/Rk7ErWS7H9NTjB7kzczwAChtb3F/rjUPlmigdndDERJGwbT1Jf/2Rr3E8zKpCXWyqNkbh4IQ25hZpf/2O9s713GewscO2TissQ8qjsHVAmxRHxs7VaK6cfX6VpnDsG06NWuHSor1+m4q8SuyyH8i8csFkWd+Rn2MbbmKbOnGI6DkTDX9b+RXBrVMvbMPKgIUF6luRRM+bQlbc3ZcmjqCf1pqcN27lAhK3rDZfxR9gV6MJ9vVeQenogubOdZI2/ILmxpVcyyts7XFo1gmbMlVQ2jmQpYoleeNSMs+fuFdAgUOTDthWqIXSyQVtooq0o7tJ/WtdvtTfEEfNJtjXb3UvjkiS1i1Gc+Pyo+No3hmbMlVR2t+LY8MvZEbo41BY2+LQvBM2paugdHRGc+tanv83T2rThtWs+X05qvg4goJL0O/t9wgLL5Vr+T3/7GTZLz8THXUHP/8ivPnWAKpUq2n4fs6MKfy1fYvRPJUqV+PTiV8Z/p40fixXrlwkQRWPo6MT5StW4c23BuDu4Wm2uKzK18G6SkMU9k5o794ifedqtFGRJstalqqGXfNuRtN0GjXJ34w2/K2wd8SmzqtYFA1DYWNH1s3LpO9ajU6Vf/s2gEPd5jg2boOFsyvqm9dQ/T4f9fVLJst6Dv4Um9AyOaannz5C7A9fgtIC59ZdsS1dCQsPb3TpqWREnCJh/VK0ifH5GsfjcK9bleIj+uJSuSy2/t4c6vQOUeu2F3S1jPR9vRhtmvni6GDByXOJTJ93kRu303Mtv+KHavj52OaYvmrTLWZ+r1+P7q5WvNM7mKoV3bC3syDyZhqLVl5n177YfInh9z+2sXTtH8SpEggJKsr7fXtSOrS4ybLrtu7kj117uXL9BgDhxYMY2KOzobxGo+GHZavYd+QEt6KicbC3p1r50rzd8zW83N3ypf4vEnmNo3lJA8J/UMuWLZk/fz4ZGRls2rSJd999FysrK8aMGWNULjMzE2tra7y8vAqopo/HpX4j/Pq/w82vZ5B67iye7TsTPHEqEQPeICtBlaO8a8Mm+L41gBuzviTlzGlsAooQOHw0oOP2j98ayqVfvcLlsSMMf+uysp5DNNncmzSl6HtDufLVl6ScPo1v126UnDmb4926oInPeQFRZODbeLZsyeXJk0m/dhWXGjUJm/Ilpwf0J/W86Rt4c3OoXg+Pbv2IWfgNGZcjcGneDt8PJhA5eiDapIQc5aPmfoHigYYrpYMzRSbOJfngbsM0j+79sCtVnugfpqO5G4VdmUp4vvkOWfGxpB478FzisgyrhG2D9qRvX0HW7WtYV26AQ8e3SZ4/CV1acs4ZlBY4dBqENjWJtA0L0CYnoHR2Q5ee9lzqe19h2Dfsq9XFvUsfYn+ZR8bl8zg3bYPPsM+4+fE7Jrep6G+noLB4YJtydMJ/3GxSD+0xTLP08sV31GSSd29DtXYp2vQ0rPyLolOrX6o4Iof3MprHrlwVPHoNJvXw3nyJwaZcdRxbdSNp7ULUkZexr9Mc194fEDtzNLqUpJwzWFjg+tYHaFOSSFz6NVmJKixcPdClpxqK2NdvjV31RiT+/hOaqJtYBQTh1KkvuvRU0vZty6c4auDY+nWS1ixAHXkJ+zotcO0zktjpH+YeR98P0SYnkrh0LlkJ8Vi4eaBLy47DqVNfLH0CSFzxPdqkeGwr1sG17yjiZo4xyw3f7r93MP/Hebw9+H3Cwkuxfs1vTPjkQ77+YRGurjkv/s+dOcWMrybSs3d/qlarxT+7tjPl80+YNvsHigUFG8pVqlKdIcNGGf62srIyWk7Z8hXp1LUHbu7uxN29y4L/fcdXkz5jyvSvnzkmAMvQitjUa0v6X7+hvXMdq4r1sG8/gJRFX5o+tgK6jDRSFn354BSj7+1efQudNou0DfPRZaRjXbkB9h0GkrJ4KmgyzVLvh9lVqoVLhzdRrfiJzKsXcGzYCs9BHxH1xftokxNzlI/9ebrx/u3ghPeHX5F2TN/gr7C2xiowmKQtv6O+dQ2FnSOuHXvh0X8kMdM/ypcYnoSFgz2JJyKIXPA7VX/7pqCrk8PrHYvQqbU/k2ZHcDsqnb49gpj+WVneGHyYTLXpG8kBHxxD+UBednAxB2ZNKMdfe7IbnsYOC8fRwZIxX5xGlaihWX0vxo8sRf8RR7lwJcWsMWzbs5+5C35l5MBelA4tzooNfzJ84jSWzZ2Cm4tzjvJHTp+jWd0alA3vgY2VFb+s2cT7E6byy6xJeHm4kZ6RScTla/Tu3JaQoECSUlKY/fNSRk2Zzc9ffWbWuovCT7ow/AfZ2Njg6+tLsWLFGDRoEE2bNmXdunX07t2b9u3b88UXX+Dv7094eDiQswuDSqVi4MCB+Pj4YGtrS9myZdmwYYPh+927d1OvXj3s7OwIDAzkvffeIyXFvAfWB3l1eI24zRuJ37qZjMhr3Px6BrqMdNybtzJZ3r5UWVLOnES1czvq6DskHz2Eatd27MOMn+TosrLQxMcZPlmJOS/y85Nf9+5Er1vL3Y0bSLt6hStfTUGbkY7Xq21Mlvds+Qq3Fi4kYd9eMm7dInr1KlR79+HX/fXnVmeXFu1J3LWF5N3bUN+K5O7Cb9BlZuBUv5nJ8tqUZLISVIaPXdmK6DIzSDmQ3YBgG1KKpD07SD93Es3daJJ2bSEz8go2xcOeV1jYVGmI+tQ+1KcPoI2LIn3bSnSaTKzK1jBZ3qpsDRS29qSt+x9Zt66gS4wj68YltHdvPbc6Q+HYN1yatSPpnz9J3rMd9e1IYn+Zp9+m6jY1WV6bkkxWosrwsSt9b5t64MbbrUNP0k4eJv63hWRGXkETc4e04wdM3si/yHE8+H1Wogr7itVJjziJ5m5UvsRgX6cFaYd2kX5kN1kxt0hauxCdOhO7KvVNlretUh+lnSMJv8xBff0iWtVd1Fcj0NzJfrpsVTSEjLNHyYw4jlZ1l4zTh8i8cBqrIqafspkljnotSTu4k/TD/5AVfYukNQvQZWZgV7XBI+JwIGHxbNTXLujjuPJAHJZW2JSpSvIfy1FfjSArNpqU7avJio3CrkZjs9R53eqVNGvZmibNXiGwaBBvDx6Oja0t2/80nYm1Yd3vVKpSnQ6duhFYtBivv9GH4iVC2bTBODPFysoKN3d3w8fRycno+7YdXiO8ZGm8vX0pWbosHV/rzvmIM2g0GrPEZV25PurT/6I5cxBtXBQZO35Hp1FjVab6I+fTpSY98MluaFC4emLhF0TGjt/RRkWiU8WQseN3sLTCKrySWepsimPD1qTs3U7q/p1oom6iWvETusxM7Gs2yqX+KWiTEgwfm/Dy6NQZhgYEXXoasd9+Qdqxf9FE30Z97QKq3+djXbQEFm4e+RbH44rZ8jfnx80iam3+NPI9qy5tAli08jq7D8Rx6VoqX8yKwMPdhno1c8+cUSWqiVNlf2pXdefG7TSOnco+L5Qt6cyqjbc4eyGZ21HpLFoZSXKKhvAQR7PHsHz9Fto0bUDrxvUIDgxg5MBe2NhYs2H73ybLfzbsbTq2bEJYcDGKFfFn9KA+aHU6Dp3UZ606Otgze9xImtSpTrEAP8qGhTC8X08iLl3lTkz+ZFC8SLRaXYF9CiNpQBDY2dmRmalvld++fTsRERFs3brVqFHgPq1WyyuvvMKePXv45ZdfOHPmDFOmTMHCwgKAS5cu0bJlSzp16sSJEydYvnw5u3fvZvDgwflSd4WlJXYh4SQfO5w9Uacj6dhh7EuWNjlP6tlT2IeEYxdWEgBrXz+cqtYk8aBxqr9NQAClFv9G+P+WEjhyLFZe3vkSgykKS0scwkuSePCBJ+w6HQkHD+JUNmdaM+ifWGgzM4ymaTPScapQIT+rms3CEpugENLOHMueptORdvoYtiVKPtYinOs1J3n/3+geiCP94lnsK1bHwlV/0WRbshxWPv6knTpqztrnTmmB0qcImmsPZnHo0Fw7j4VfkMlZLEuURXP7KraNO+M4cCIOb47CunpTUCieS5WhkOwbFpZYFytB+pnjRjGknz2OTfHwx1qEY92mpBz4J3ubUiiwK18VddQtfIZ9RuCMhfh9NBX7iqYbg8wiP+J4iNLZBbtyVUn+J58u6C0ssPQPIvPiA12odDoyL57GqmgJk7PYlKyIOvIiTm3fwHPMbNzf+xz7Bq8a7Qfq6xexLlEaCw8fACx9A7EOCiXj/Ml8juO0cRyXzmBVNMR0HKUro75+Ead2b+L50Vzch07CvmEbQxwKpQUKCwt0GuMMFp1ajVXQszd0qtVqLl08T4WKVQzTlEol5StWJuLcaZPzRJw7Y1QeoGLlapx/qPypk8fo9XoH3h3wJt99M5PERzQGJiUl8vfObYSXKpNrl8cnorRA6V2ErOsPduPRkXX9PErfYrnPZ2WNw1tjcejzCbavvoXS3cfw1f2n+rqsBxs4dJCVhYV/MPnCwgKrwOLG26xOR8b5k1gHhT7WIhxqNiLtyN5c928Apa09Oq0WbWpqrmUE+PnY4uFuzaHjKsO0lNQszp5Poky4U+4zPsDSUkHzht5s2mbcGHvqXCKN63ri5GiJQgFN6nlhba3k6EnzNj6r1RoiLl2lWvnsc7VSqaRq+TKcOm+6W8zD0jMz0GRl4eyYexfk5JQ0FAoFTg72z1xn8d8iXRj+w3Q6Hdu3b2fLli0MGTKEmJgYHBwc+Omnn7C2tjY5z7Zt2zhw4ABnz54lLEx/YVS8ePaTosmTJ9OjRw+GDRsGQGhoKHPmzKFBgwbMmzcPW9uc/csyMjLIyDA+aWZmabG2yLt9y8LZBYWFBZr4OKPpGlU8toFFTc6j2rkdC2cXSkydi0KhQGFpSezGtcSsWGIokxpxhsgZU8i4EYmluwc+r/eixNQ5nB/0Ftq0/E9Dt3R1RWFpiTrOOC51XBx2xUxfWCXs/xffbq+TePQYGTdv4Fy1Gm4NG6FQPp92QgsnZxQWFjlS47MSVVj5FclzfpvgMKwDg4j5eY7R9Lu/fIdX7yEUm7UQnUYDOh0x8+eSft70hbO5KewcUCgt0KUapzbrUpOweODC9UFKFw+UgaGozx0mdfX3KF29sG3SGZQWZP67xeQ85lYY9g0Lx3vbVKLKaHpWogor37y3KevgUKyLBHF3YXa6tYWTC0pbO1xe6YRqzRLif1+IXdnKeL0zmjvTPiYjH7ar/IjjYY61G6PNSCP1yL5nra5JSnsnFBYWaJONL5S1yYlYevmZnMfC3RsLV0/Sj+9DtXAGFh4+OLV9EywsSN2hH78h9e+NKGzscB82GXRaUChJ2fo7GcfzOw7jtHJtUkLucbh5YVG8FOnH9qFaMF0fR/te+ji2r0GXmY762gUcGrcjMfoW2uQEbCrUwqpoCFmxz54NkpSYgFarxeWhrgqurm7cjDQ9DosqPi5H1wZXVzfiH+j+VqlKdWrWroePrx93bt/il4U/MXHcaKZM+9rwUABg0c/fs2nDGjIy0gkrWZqx4yY9c0yQfWzV5ji2JmPhbrpRUhsfTfrW5Wjv3kZhY4t15YbYdxlCyi9T0SUnoI2PRpsYh03tVqTv+A3UmVhXqo/SyRWtQ860b3NQOuj374czmLKSErDx9s9zfquiJbDyL0r8su9yL2RphXPb1/WNDBnPtyvcy8bDTd8NJ15l3F0lTpWJu5vpa9uH1avhgaODJZt2GO+/46aeZfzIUmxaUguNRkt6hpaxk89w807uYys8DVVSEllaLe6uLkbT3V2cuX7z9mMtY97ilXi6uVK1vOkHBhmZmcz7ZQVN69bAwd7umess/lukAeE/aMOGDTg6OqJWq9Fqtbz++ut89tlnvPvuu5QrVy7XxgOAY8eOUaRIEUPjwcOOHz/OiRMnWLIk+4ZDp9Oh1Wq5cuUKpUrlHPBp8uTJjB8/3mja2yHFGBQa9HQB5sGhXEW8u/Tk1rezSI04g7VfAP4Dh+Dd/Q2ily0GIOnQA0/+r14mNeIspRb8iku9RsT/uSlf6vWsrs2cQfDoj6jw63L9082bN7m7cQNer75a0FV7LE71m5EReSXHgIsuTdtgUyKcO7MmoLkbjW14WTzfeJssVSxpDz7RfZEoFOhSk0nfql8X2ugbZDq6YF210XNrQHgahW3fcKrblMwbV40HKlToG9RSj+0ncat+oL7MyCvYlCiJU4OW+dKA8KxMxvFwmTpNSfl3V46n4AVKoUCbkkjSmvmg06G5dQ2lsxv29V4xNCDYlK2ObYWaJK74Hk30Taz8iuLY+nW0SSrSj+7J4weeE6USbUoSSat/vhfHVZQubtjXa0Xq9jUAJK74HqdO/fD8aI6+i8+tq2Qc34dlQD499TaDeg2yu1cUCypOsaDiDOrXg9Mnj1H+geyF9p260aRFK2Kio1i+dCFzpk9m7GeTUTzHjKr7tHeuob1zzfB32u2rOLwxCquytcj8dzNotaRtXIht0y44vf05Om0WWdcvoLn6fAevfRIONRujvnUt1wEXUVrg3nsYoEC14qfnWbWXQrMGXnwwKDvTY9TEZz+Gv9rMl/2H44iNM26E6Pd6EI4OFgz75CSqRDX1angwfmQpBn90nMvXXpzMkMWrNrBtz36+Hj8aGxPX9BqNhk+mf4tOByMH9DKxhMJHxlA0L2lA+A9q1KgR8+bNw9raGn9/f6NUxLzetmBn9+hWyuTkZAYOHMh7772X47uiRU0/9RwzZgzDhw83mnb+tce76c1KTECXlYWlm7vRdEtXtxxP7+/zfaMPqh1/ErdlI6AfEE5pa0eRISOI/vUXk0cZbUoyGTdvYOMf8Fj1elYalQqdRoOVu3FcVu7uqGNNx6VRqbgw+kMU1tZYurigjokh8J13Sb/5fPrdZyUlosvKwsLF1Wi6hbMrWQmPHkRMYW2DY436xK1eYjzdyhr3zm9yZ+4XpB0/BEDmjatYFw3G5ZWOz6UBQZeWgk6bhcLeOPVRYe+ENiXn4FgAuhT9/8WD25I2LgqlowsoLUCb/wNyFoZ9Iyv53jbl7Go0/XG3KYdq9YhfuzTnMjUa1LeMR3lX347EJtT0k5pnlR9xPMgmtDRWfkWI/n6qOaprkjY1CV1Wln4bfoDS0TlHVoJhniQVPLQfZMXcwsLJFSwsICsLx5ZdSP17Exkn9+u/j7qB0tUD+wav5ksDQnYcxk+jlU4uuY6BoU1U6ffZB+OIvqVfn/fiyIqLRvXjJLCyRmlrhzYpAefu75IVF/3MdXZydkGpVJKgMt5WVKp4XB/av+9zdXNHZaK8m1vuo637+vnj7OzC7ds3jRoQnF1ccHZxISAgkCKBxejfqwsR585QslTOtwg8ifvHVqW9E9oHpivsHdGaGszSFK2WrJibKF2z+7Zro2+QunQGWNvqu5akpWDf9T2yom48U31zrcK9473SyXjfsHByIStJ9ch5FdY22FWuTeIfK0wXUFrg/tYwLN29uPv1BMk+MGH3gTjORBwx/G1lpW8kdnO1JjY+u0HV3dWaC1dMD8z5IB8vG6qUd+XjKcZvvPL3taXTq/68MfgwVyP1jQWXrqZQoYwzHVr5M33eRXOEA4CrkxMWSiVxKuNjUlxCYo6shIctXfsHv6zeyKxxHxISFJjj+/uNB1ExscwZP0qyD8RTkTEQ/oMcHBwICQmhaNGiT9yPsXz58ty4cYPzuYzqX7lyZc6cOUNISEiOT26ZDTY2Njg7Oxt9Hqf7AoBOoyHtYgSOFSpnT1QocKxYhdRzubzu0MYGnU5rPPH+DV0uT1SUtnZY+/mjjns+A83oNBpSIs7hXLVa9kSFApeq1Ug69ei+wbrMTNQxMSgsLHBv1Ij4f0wPuGN2WRoyrl7ErvQDYy4oFNiVrkD6pdxfGwjgUL0uWFmRvPcv4y8sLFBYWsHDg9Botc9vPAFtFtqoG1gWfbAvqwLLomFk3b5qcpasm1dQunoB2XVUunnpb7SeQ+MBFJJ9I0tD5rVL2JYq/0AlFdiWLE/G5YhHzupQtQ4KKytS/t2VY5kZVy9i5Wvc4GHpE4Am9tlv9kzKjzge4FS3KRlXL6K+cdVMFTbh3lN16xIPNLIoFFiXKJ3rk1P1tQv6sQ0e2HYsPHzJSozXNyygv4EixzaXj/u3IY4Hbn4NcZi+AVBfO4+Fh7dxHJ7GcWQXzkSblIDC1h7r0LJknDnCs7KysqJESBgnjmUvS6vVcvLYEcJLmr6JDy9ZmhPHjX/7+NHDhOVSHuDu3RiSkhJxe8QgfTqtfl1pzPHGEm0W2ugbWAQaH1stAkONsgweSaFA6eGHzlRjbmY6urQUFK6eKL0D0Vw+9ex1NiUrC3XkZWzCHhifSKHAJqwsmVdzzxoCsKtYE4WlJWkH/8n55f3GAy8/7n4zEW1q3je//0VpaVncvJNu+FyNTCU2LpMq5V0NZeztLCgV5sTpiLwbplo18UGVoGbfIeOGdlsb/XXpw68D1GpBaebDlZWVJeElggwDIOp/R8vhE2coG2Z6zBmAJWs2seC3dUz/ZASlQnJmP91vPIi8HcWscSNxcTL/4I8vKp1WV2CfwkgaEMQTadCgAfXr16dTp05s3bqVK1eu8Mcff7B582YARo0axd69exk8eDDHjh3jwoULrF27Nt8GUQSIWb0S95av4takBTaBRQl4932UNrbEb9WPTh04Ygy+vfsbyicd2IdH63a41G+MlY8vjpWq4PNGXxIP7NWfCQC/voNwKFsBK29f7EuVodgnE0GrRbXz+b3n+PayZXi3bYdnq1bYFgsi6MNRKG1tibk3uGXxT8cROOgdQ3mH0mVwa9AQG39/nCpUJHzWbFAouf3L4udW54Qta3Bq0ALHOo2x8iuC55vvoLCxNQzs5tV/OG6dc6bLOdVrTuqRf3M8ddKlp5F27iQeXftgW7Iclp4+ONZtgmOdxqQczp8+0qZkHN6JVblaWJWuhtLdB9umr6GwskZ9Wv/U1LZlD2zqZmfNZB7fg8LWHttGHVC6emEZXBrr6s3IPLY7t5/IF4Vh30jYuhan+s1xqN0IK78iePR8G4WNLUl79NuUZ59huHZ8I8d8jnWbknp0v8knmYlbVuNQrS6O9Zph6e2LU6NW2FeoRtJfpke0f1HjAFDY2mFftQ7J/2zNt7rfl7pnC3ZVG2BbqQ4WXn44tX0ThbUNaYf1Nz9Onfvj0LyzoXzagb9Q2Dng2LoHFh4+WIdXwKHhq6Tt32Eok3HuGPYN22AdXgGlqyfWpStjX7cFGWcO5/h9s8Xxz2bsqjXAtnJdLLz8cWrX614c+sZWp9cG4NDitew49u9AYeeI46s9sfD0vRdHG6PXTFqHlsM6rBxKN0+sQsrg2n8MWTG3ST9s4sbwKbTt8Bpbt2xgx7bNRF6/xvffzCQ9PZ0mzVoCMHv6JBYv+NFQ/tW2nTh6+ABrV63gRuR1fl2ygEsXI2j1agd9TGlpLPjfd0ScO0N01B1OHDvM5Alj8fULoFIVfcP1+XNn2LR+NVcuXSQ6+g4njh9hxlcT8fXzJ7yUebJ1Mo/8jVXZGliWqorSzRubxp30x9Yz+q5Sts27Y107+60x1tWbYVE0DIWzO0qvAGxb9EDp7GY4FgNYhpTHIqAECmd3LIuXwb7DQDSXT5F1Pf9eZ5y8cyMOtRpjX60+lj4BuL7WD4W1Dan7dwLg1uNdnF/tnmM++5qNSDt5KGfjgNIC9z7vYxVYnLhFc0GpROnkos9yeGB8ioJi4WCPc4WSOFfQD7hrH1wE5wolsQ00PY7I87Zi/U16dQmkTnV3ihez5+NhYcTGZfDPv9mvZJw1oRwdWxnXV6HQNyD88VcUWQ+1a167kUbkrTQ+eCeUUqGO+Pva0rVdAFUruPLPfvM3oHdt04L123ax6a/dXL1xi2k/LCI9I4PWjesBMHHOD8z7ZaWh/C+rN/LjslWMeacPfl6exMariI1XkZqmH59Bo9Ewdto3nLt0lXHDBqLVag1l1GrzvFVF/HdIFwbxxH7//Xc++OADunfvTkpKCiEhIUyZMgXQZyjs2rWLsWPHUq9ePXQ6HSVKlKBr1675Vp+Ev//C0tkVnzfewtLNnfTLF7ny6Ydo7qVvWnn5GLUARi1bjE6nw/fNvlh5eKJJUJF4YC93Fv7PUMbK04uioz7BwtkZTUICqadPcvH9d57rqxzjtm/Dys2VIv0GYOXhQeqF85x7f5hhUDwbHx/DTR2A0saawIFvY+PvT1ZaGqp9e7k0/jOykp/fU4uUA/9g4eSCW4eeWLq4kXH9Mnemf2oYPM7SwyvH00Yr3wDswstwe+rHJpcZPe9L3Dv3wnvgBygdHNHERhP/++J8vdl7mOb8UdLtHbCp/QoKe2e0MTdJXfW94fVhSic3tA88ldAlq0hd9R02Ddvj8OaH6JITyDy6i8yDz68BCgrHvpF6cDdxjs64tXsdC2c3MiOvEDVrPNp7v2fp4Zljm7L0CcA2rAx3ZnxqeplH/yV28TxcWnXGvXt/NHduEj1vChkX86+fdH7EAeBQvR6gIPlA/mcaZZw8QLKDEw5NOqB0ckFz+zqqBdMNT38tXDyMu+0kxKFaMA2nVq9jN+RztInxpO7dSurfGw1lktf/gkPTjji1eUPfHSJRRdqBnaT8tTYf49hPsqMTDk07Zscxfyq6ewMrWriaiGP+VJxav47de/fj+JPUXdlvKlLY2uHY4jWULu5oU1PIOH2QlC2/mS3jqG79xiQmJPDrLwuIj48juHgJPp3wpaELQ0xMNApF9jOhkqXL8v7Ij1m6+Gd+WfgTfgEBjP54IsWC9E8llUol165e4q/tW0hNScbN3YOKlary+ht9sLLSZwva2Nqyb+8/LFuygIz0NNzcPahUpTofdO1pKPOsNBeOkWHngE3NFvpj692bpK750XBsVTi5onxgXShs7bBt8hoKe2d0Gan67gor5qKNyx7sTuHgjE39dijsHdGlJKI+e5jMA/nbwJZ2dB9KR2ecWnXBwtkV9Y2r3P1usqFbjIWbR47sLktvP2xKlOLut5/nWJ6Fqzt25fQNOT6jvjL6LmbueOO3oRQAlyplqbU9++FE6WkfARC5aBUn+o4pqGoZLF11AztbC0a+E4qjgyUnzybwwfjTZKqztyV/X1tcnK2M5qtawRVfb9scb18AyMrS8eGEUwx8M5gpH5fBztaCm7fTmDT7PP8efnRXtKfRtE4NVAlJ/PTrauJUCYQGF2X6xyMMXRii7sYajUOyessO1BoNH0/7xmg5fbq0o2/XDsTExbP7oP7tVb1HGJ9T5o4fReWyOccoK0y0MgiCWSl0D+fiCPECONGqYUFXwSzS4l/+/ope4V4FXQWz8Cxv+hVtL5Or244VdBXMwtn/0X04xfNj75N7n/iXilabd5kX3N2+Uwq6CmYRuHF6QVfBLBIv58+YCc/TsW+PFXQVzGJKq5d/8MjVXxSOZ7aeZWsVdBWeypBZpseseh7mDsufN8AUJOnCIIQQQgghhBBCiDwVjuYwIYQQQgghhBDiIYV1MMOCIhkIQgghhBBCCCGEyJNkIAghhBBCCCGEKJQkA8G8JANBCCGEEEIIIYQQeZIGBCGEEEIIIYQQQuRJujAIIYQQQgghhCiUpAeDeUkGghBCCCGEEEIIIfIkGQhCCCGEEEIIIQolGUTRvCQDQQghhBBCCCGEEHmSDAQhhBBCCCGEEIWSTicZCOYkGQhCCCGEEEIIIYTIkzQgCCGEEEIIIYQQIk/ShUEIIYQQQgghRKGklUEUzUoyEIQQQgghhBBCCJEnyUAQL6RR3nMKugpmkWQRX9BVeGbOSveCroJZBGYGFHQVntkZq9MFXQWzsFc7FXQVzEKpePnb4O2S7Au6CmZRGF7RNbKQ7BdjL7xd0FUwC3WGuqCr8MwSWsUWdBXMYvSmfgVdhWf2XqntBV0Fs1g6paBr8HRkEEXzevmvfoQQQgghhBBCCJHvpAFBCCGEEEIIIYQQeZIuDEIIIYQQQgghCqXC0M3tRSIZCEIIIYQQQgghhMiTZCAIIYQQQgghhCiUJAPBvCQDQQghhBBCCCGEEHmSDAQhhBBCCCGEEIWSVl7jaFaSgSCEEEIIIYQQQog8SQOCEEIIIYQQQggh8iRdGIQQQgghhBBCFEoyiKJ5SQaCEEIIIYQQQggh8iQZCEIIIYQQQgghCiWdDKJoVpKBIIQQQgghhBBCiDwVmgaEBQsW4OrqWtDVeOEpFArWrFlT0NUQQgghhBBCCPEU4uLi6NGjB87Ozri6utK3b1+Sk5PznG/fvn00btwYBwcHnJ2dqV+/PmlpaU/02/nehaF3794sXLgwx/QWLVqwefNms/1O165dadWqldmW97CrV68SHBz8yDLz58+nd+/e+VYHc7h9+zZubm6PVVahULB69Wrat2+fv5Uyozc6+NCygTsO9hacuZDC14tucisqM9fyC6aVxMfTOsf09dvv8u3iWwC80sCdhrVcCSlmh72dBZ3fOUVKqjbfYujbI4g2zX1xcrDk5NlEpn17gRu3c9+xV/5UAz8f2xzTV228yYzvLgLg72vL4D4lKFfaGWsrJfuPxDHz+4vEq9T5FsfDerb3pmX9e+vmYirfLLrJrejc1838r8JNrpsNO2L59pdb+VlVgzb17KhXwQY7GwWXbmpYuiWF6Pjc1/2rde1oU9fOaNqd2CzG/ZhgsvyQ1xwpW8Kab39P4viF/FsXfV8vRptmvjg6WHDyXCLT513kxu30XMuv+KGa6W1q0y1mfn8JX28bVv5Y3eS8n3x5lp1775qt7g96s4MvLRt64Hhv/56zMPKR+/fCaaXx9cq5Da3bFsM3i28C8EpDDxrVdCMkyA4HOws6DjpJSmpWvtQfCscxCqBbK3ea1XLG3k7JuSvp/LAihtsxj96G3V0seKOtJ5VL22NtpeDOXTVfL4nmUmQGAIN7eNO4hrPRPEfPpjBx3u18iaF7a3ea1nbBwU7JucvpfL88+rFieLOdJ5XLOBhimPtLFJeuZxjKFPGx4o32npQJscNCqSDyTiZf/XSbu/GaZ66zTqdjzbLv2bV1NakpyYSWrMAbb4/G17/oI+fbvmkFf6xeTIIqlqJBofToP5LiYWVNLn/mxKGcPLKXIaOnUblmQwCuXznPplULOH/mOMlJKjy9/WjYohPN23R/5pjuexnXx8N6tPWiRT1XHOwtOHsxlW+X3HnkeQ7Aw9WS3p28qVLWERtrJbejM5m14BYXr+mP0bY2Cnp39KFmJSecHCyIuqtm/Y44/tgVb/b632fucwaAu6sV7/QOpmpFN+ztLIi8mcaildfZtS823+LIi3vdqhQf0ReXymWx9ffmUKd3iFq3vcDqY0rnZs40quaAg52S81cz+HmNijuxj9523ZyVdH/FhQphtthYK7kTq+H7lXFcuanfn6qVsaVJDUeCA6xwcrBgzOwort1+fteDBUFbCAdR7NGjB7dv32br1q2o1WreeustBgwYwNKlS3OdZ9++fbRs2ZIxY8Ywd+5cLC0tOX78OErlk+UUPJcxEFq2bMn8+fONptnY2Jj1N+zs7LCzs8u74FMKDAzk9u3si5hp06axefNmtm3bZpjm4uKSb79vLr6+vgVdhXzzWisv2jbzZPqPkdyJyeTNjj58PiKYgWPPo1abPnAMHX8BpVJh+LtYgC2TPyzOPwezb/hsbJQcOpnEoZNJ9HnNL19j6NEpkM6vBvDFrHPcjkqnX48gZkwoR893DpKZSwz9hx/hwf2+eDEHZn1egb92xwBga6Nk5oTyXLySzNCxJwDo1zOILz8py8APjvI8uoV1fsWTtk09mfFTJHfuqnmjgw8TRwTz9tjzqDW5rJuJF7FQPLBuitgw6QPjdZOfWtSwpXEVGxZsTOGuSkvb+na819WJz35MQPOI+8ubMRpm/Zpk+Dsrl/u4JtVseB6ns9c7FqFTa38mzY7gdlQ6fXsEMf2zsrwx+HCu29SAD44ZbVPBxRyYNaEcf+3RNwxE382gXa9/jeZp28KP7h0C2H8kLl/i6NLKm3bNvJj24zXu3M2kV0c/Jn1Qgv4fnct1/35vfITR/h0UYMuUUSFG25CttZJDJxM5dDKRvl3886Xu9xWGYxRAh6autK7vwpwl0UTHqune2p1PBvkzdNL1XPdnBzslk4YV4dSFNCbOu0VichZ+3lYkpxnvTEfOpPD1kmjD37kt79ljcKN1A1fmLI4iKlbN66968Om7Abz3+bVHxjB5eCAnL6Qx8dubJCRn4edlbdRY4+tpxaThgWzbm8CvG+NIS9cS6Ged6/p9UptWL2Trhl/pN/QzvHwCWLV0HjPGD+GLuSuwsjZ9XbV/95/8+vNM3hw0huJhZdm6bhnTxw9h8je/4+zqblT2z/WmLzyvXjqLk4s7A96fgLunDxfPnWDht1+gVCpp2rrrM8f1sq6PB3Vq6UGbJu7M/PkmUXfV9GzvzYRhRRn06aXcY7BX8tWoIE5EpPLZ7OskJGfh721N8gONmP26+FK+pAPTf7pJVKyaSqUdeKeHH7EqNQeO5/208UnlxzkDYOywcBwdLBnzxWlUiRqa1fdi/MhS9B9xlAtXUswex+OwcLAn8UQEkQt+p+pv3xRIHR6lTQMnWtR25LuVcUTHZfFac2dG9/Fk5Mw7qHNpQ3CwU/DZIG/OXMrgq/l3SUzR4utpSUpa9n5hY60k4loG/55MZUAnd9MLEi+0s2fPsnnzZg4ePEjVqlUBmDt3Lq1atWLatGn4+5u+nnn//fd57733GD16tGFaeHj4E//+c+nCYGNjg6+vr9HnwafgCoWCn376iQ4dOmBvb09oaCjr1q0zWsa6desIDQ3F1taWRo0asXDhQhQKBSqVCsjZheGzzz6jYsWKLF68mKCgIFxcXOjWrRtJSdkX9lqtlsmTJxMcHIydnR0VKlTgt99+MxmDhYWFUf0dHR2xtLTE19eX9PR0/P39OX36tNE8s2bNolixYmi1Wnbu3IlCoWDjxo2UL18eW1tbatasyalTp4zm2b17N/Xq1cPOzo7AwEDee+89UlIe78AaFBTExIkT6d69Ow4ODgQEBPDNN8YHxAe7MGRmZjJ48GD8/PywtbWlWLFiTJ482bAsgA4dOqBQKAx/A8ybN48SJUpgbW1NeHg4ixcvzvEbea3P/NC+uSe/rovi36OJXL2RzrQfI/Fws6J2Zedc50lIyiI+QWP41KjoxK2oDE6ey/4/X/PnXVZujOHcpdR8j+G1tgEsWnGN3ftjuXQ1hc9nnsPD3YZ6NT1znUeVqCZOlf2pXc2DG7fSOHpKf4NRrrQLvt62fDErgsvXUrh8LYUvZp6jZIgTVcq75ntMAO2befLr+mj+PZbE1RvpTP8pEg9XS2o9Yt0kJmURn6gxfKpXcNavm4jnc6HRpJotm/amc/yCmpsxWczfkIKro5KKYTmfBj9Iq4XEFJ3hk5KW84KriLcFzarZsWhT/sfSpU0Ai1ZeZ/eBOC5dS+WLWRFPvk1VdefG7TSO3dumtFqMvo9TqalX04Mdu++Slp4/T77bt/Bi2fo77DuayJXIdL764RoerlbUrpx7w23O/duFW1EZnDiXfdG9+s8YVmyMfi77d2E4RgG82sCV3/6M5+DJFK7dymTO4mjcXSyoXt4h13k6NHXjrkrD10ujuXg9g+g4DcfPpRF11/gqWK3RoUrKMnwevOg1awyNXFm5JY4D92KYvSgKdxcLalTIPYaOzdy4G6/h61+iuHAtg+hYDcfPpXLnbvaTu9fbeHD4dAqL1sZy5UYGd+6qOXgyhYTkZ89q0el0bF2/jDZd+lK5RkMCg0LpP3QC8XExHNm/M9f5/ly7hPrN21OvSVsCAovz5qAxWNvY8s924/Py9csRbFm7hL5DPs2xjPpN29Gj3weULFsFb98i1G7YirpN2nL437+eOS54OdfHw9o1cWf5xrvsP57M1ZsZzPj5Ju6ultSq5JTrPJ1benI3XsPsBbc4fzWdqLtqjp5J4c4DmRelStixY6+Kk+dTiY5Vs+UfFVdupBMWnD8PzfLjnAFQtqQzqzbe4uyFZG5HpbNoZSTJKRrCQxzzJY7HEbPlb86Pm0XU2m15Fy4ALes4smZHIofPpBN5R8285XG4OltQtXTu675NAydiVVl8/1s8l26oiYnP4uSFDKLjsrf53UdTWb09iVMXM3JdTmGj0+oK7JORkUFiYqLRJyPj2f7v9+3bh6urq6HxAKBp06YolUr2799vcp7o6Gj279+Pt7c3tWvXxsfHhwYNGrB79+4n/v0XZgyE8ePH06VLF06cOEGrVq3o0aMHcXH6J1lXrlyhc+fOtG/fnuPHjzNw4EDGjh2b5zIvXbrEmjVr2LBhAxs2bGDXrl1MmTLF8P3kyZNZtGgR3333HadPn+b999+nZ8+e7Nq164nqHhQURNOmTXNkWdzv0vBgWsjIkSOZPn06Bw8exMvLizZt2qBWqw31bdmyJZ06deLEiRMsX76c3bt3M3jw4Meuy9SpU6lQoQJHjx5l9OjRDB06lK1bt5osO2fOHNatW8eKFSuIiIhgyZIlhoaCgwcPGmK4ffu24e/Vq1czdOhQRowYwalTpxg4cCBvvfUWf/1lfBHxqPWZH3y9rHF3teLomewbg9Q0LRGXUilZIvcLkAdZWihoVMuNP//Jv3o+ir+PLZ7uNhw8lp2WmJKaxZnziZQtmfsNxoMsLRU0b+TDxm13DNOsLZXoALU6+0I8M1OLVgflS+d/1oyvlxXurlYce3jdXE6lVAn7x1qGpYWCRjVd+XN3/qVsPsjTRYmLo5KzV7Mv4tIzdFy5paF4wKMTt7zdLPjyXVc+f9uFPm0ccHM2PsxaWULfto4s25pCYkr+5iD4+dji4W7NoeMqw7SU1CzOnk+iTHjuF7UPsrRU0LyhN5u2ReVaJqyEI2HFHY22O3Py9bLGw9WKI6eNt6Fzl1MpFfL4+3fj2m5s+btg0mULwzEKwMfDEjcXS45HZDdWpKZruXAtg/CgnCnM91Ur58Cl6xl88JYv878IYtqHgTStlfO4VjbEjvlfBDF3bFEGdPHC0d78lyk+Hpa4u1hy/NxDMVxNzzOGi9fTGdnHlwWTg5k+KpBmtbNjUCigahkHbkWr+fRdfxZMDubLDwIf2bDyJGKibpIQH0uZ8tndh+wdHCkRVpaLESdNzqNRq7l66RxlytcwTFMqlZSuUJ2LEScM0zIy0vl+xsf0HPAhLm653yg+KDU1GUfHxzs3PcrLuj6MYvC8d547+/B5Lo2SxXO/2atRwYkLV9MYPbAIv0wPY/YnwbSo52pU5uylNKpXdMLDVX/uKRduj7+PNUdPm78BOj/PGafOJdK4ridOjpYoFNCknhfW1kqOnnw+WYUvG293C9ycLYxu8tMydFyKzCS0WO4PMiqXsuPyzUyGvu7OvI/9mPSeN42qmX+bF49v8uTJuLi4GH3uP7B9Wnfu3MHb29tomqWlJe7u7ty5Y/pa7PLly4D+IXv//v3ZvHkzlStXpkmTJly4cOGJfv+5NCBs2LABR0dHo8+kSZOMyvTu3Zvu3bsTEhLCpEmTSE5O5sCBAwB8//33hIeHM3XqVMLDw+nWrdtjjTWg1WpZsGABZcuWpV69erzxxhts367v25SRkcGkSZP4+eefadGiBcWLF6d379707NmT77///olj7NevH8uWLTO0KB05coSTJ0/y1ltvGZUbN24czZo1o1y5cixcuJCoqChWr14N6DewHj16MGzYMEJDQ6lduzZz5sxh0aJFpKfn3vfsQXXq1GH06NGEhYUxZMgQOnfuzMyZM02WvX79OqGhodStW5dixYpRt25dunfX92f08vICwNXVFV9fX8Pf06ZNo3fv3rzzzjuEhYUxfPhwOnbsyLRp04yW/aj1+TBTLXParEf3GXyYm4v+xBqfYPw0Kz5RY/guL7UqO+Nob8HW53ST+jB3N/0J4eFxCeJVmYbv8lK/pieODpZs2p598DgdkUh6ehaDehfHxkaJrY2Sd/uUwNJCgYf74y33Wbg5WwH6dfEg1VOsm217ns+6cXbUHxoTU4yffiamaHFxUJiaBYArtzQs2JjMnBVJLN2SiqeLkpE9nLB54L+5SxN7Lt9U5+uYB/d5uN37v1cZ709xT7BN1avhod+mduTegPBqUx+uRqZy6lxSrmWehfu97USVYPx/pkpUG77LS+0qLjjaW/Dn7oK5+S4MxygAV2d9XROSjJ/gqpI0uDlb5Dqfj4clLeo6czsmkwnzbrFldwJ9O3nSsHr2TcnRs6nM+SWKcV/fYvG6WMqE2PHJIH+Uue9yZo4hy/CdyRg8rWhZz4VbMWrGf3OLzbsT6NvZi0Y19DG4OFpgZ6ukYzM3jp5J5bOvb7L/eDKj+vlRJuTZnxYnqPSNX86uHkbTnV3cSYg33TCWlKRCq83K0VXBxcWdxAfmWfa/6ZQoWZ7KNRo+Vl0unDvOwd1/0qB5xyeIwLSXdX086P4+rErMuV+4PmL/9vWyolVDN25FZ/LprOts2hnPgG6+NK6V3bj/3bI7RN7KYOHUMNbMK8WEoUX5bukdTl8wf8ZRfp4zxk09i6Wlkk1LarHjtzp8MCiEsZPPcPPO413f/te4OOqPpw9nyyQkZxm+M8Xb3ZKmNRy5E6thys932fZvCr3aulKv8uM9sCmsdDpdgX3GjBlDQkKC0WfMmDEm6zl69GgUCsUjP+fOnXuq/wOtVn9Ne//hb6VKlZg5cybh4eH8/PPPT7Ss5zIGQqNGjZg3b57RNHd345NZ+fLlDf++PypkdLS+H2RERATVqlUzKl+9uukBvB4UFBSEk1P2xYmfn59hmRcvXiQ1NZVmzZoZzZOZmUmlSpUeIypj7du3591332X16tV069aNBQsW0KhRI6PUf4BatWoZ/u3u7k54eDhnz54F4Pjx45w4cYIlS5YYyuh0OrRaLVeuXKFUqVJ51uPB5d//e9asWSbL9u7dm2bNmhEeHk7Lli159dVXad68+SOXf/bsWQYMGGA0rU6dOsyePdto2qPW58MmT57M+PHjjaaVqPA2oRUH5VqPRrVcGdIrwPD3uJlXH1nvx9GivjuHTiYRpzL/wEqmNGvgzch3wwx/fzjB9BOkJ9G6mS/7D8cRG5d98lclqvnkyzN8MCiUzm0C0Opg29/RRFxMQpsP2cENa7oy5M3svlfjZl175mU2r+eWr+umemlrerTMbqH/euXT3Qifvpx9g3szJosrtzRMHuRC1ZLW7DmRSfkQK8KLWfHF/Px54tKsgRcfDAo1/D1q4ulHlH48r5rYph5kba2kaX1vFq64/sy/dV+jWm4M7V3E8PcnMy4/8zJb1Hfn4InE57Z/F4ZjFED9qo4M7Jr9lOOL759uAFOFQsGlyHSWbLiXWXgjk6J+1rSo48LOA/r9bc+R7Ke3129ncu1WBvPGBVEm1I6T559shGjjGJx4u/sDMcx7hhiup7Nkfey9GDIo6mdDi7ou/LU/CcW9RzIHTqaw/i8VAFdvZhJe3JYWdV04ffHJYti36w8Wzst+2DLs41lPVe+8HD2wi7MnDzF+xpK8CwM3rl1kzqQRtO3an7KVaj7x772s6+NBDWs4827P7PPc+LlPd/xTKBRcvJrGotX666PLkekUC7ChVQM3duzTnyfaNHYnvLgdE+ZeJzpWTdkwe95+3ZdYlYbjZ58tC+F5njP6vR6Eo4MFwz45iSpRTb0aHowfWYrBHx3n8rXn0/3qRVanoh19O2R37/5qwdMNRqxUwOWbmSzfkgjAtVtqivhY0rSGA/8ckf/ngmBjY/PYY/+NGDEiz4fkxYsXx9fXN8d9lUajIS4uLtfx7vz89GMklS5d2mh6qVKluH79yY5hz6UBwcHBgZCQkEeWsbKyMvpboVAYWkqe1qOWef81Fxs3biQgIMCo3NMM8Ghtbc2bb77J/Pnz6dixI0uXLs1xU52X5ORkBg4cyHvvvZfju6JFHz3C8tOoXLkyV65c4Y8//mDbtm106dKFpk2b5joOxJN4kvU5ZswYhg8fbjTttXfPP3L5/x5NNOrva2Wpf0Tl5mJp9ITPzdmSS9fzbt329rCiYhlHPp/77De7j2v3gVjOnD9k+NvaSn+14+ZqRWx89onXzdWai5fzHijJx8uGqhXcGDs55wXAwaPxdB1wABdnS7KydCSnZLF2US1u3THdqPMs9h9LJOKyiXXjbLxuXJ0tufy466a0I198nX/r5vjFTK78nF03y3t1dnZQkpiS3frv7KAkMvrx+86mZeiIitfi5aZ/WlCymBVebkpmvm/8JpS3Ozhy4YaGGUuf7Qn+7gNxnIk4YvjbyrBNWRMbn9244e5qzYUrj7dNVSnvysdTzuRaplFtT2xtlGz5y3zb0r9HE4i4lH1hfD8OVxcr4oy2ISsuXc/7JsDbw4pKZZyYOOeK2eqYl8JwjAL9zdf5q5GGv+/H4eJkQfwDT1tdnSy5ciP3Pp2qRA037hjfUNyIyqRmhdz7P0fFavQD43laPVMDwoGTyZy/mv1/nHsMFo+MIT5RQ+TDMdzJpFZFfQxJyVlosnRE3s7IUabUI9LYc1Oxen2jNyVo1PrfTlTF4uqe3c0gMSGOwOCwHPMDODm5olRakKgyzrxJSIjD2U2fyXD2xCFi7tzg3R6NjMp8/dWHhJWqyOgvfjBMuxl5mamfvkPD5h1o26XfE8cEL+/6eND+Y8lEXL6UHcP9Y5SzhfF5zsmSK5G579/xCWquP1S/yNuZ1Lk3Loq1lYI3O3jzxbeRHDqpP2ZfvZlBcKAtHZt7PHMDwvM6Z/j72tLpVX/eGHyYq5H64+KlqylUKONMh1b+TJ938ZniKAwOn0nnYmR21oalxb39wtECVVL29bOLowXXbueepRuflMXNaONG5lvRGqqX/W9nILwsvLy8DFnfj1KrVi1UKhWHDx+mSpUqAOzYsQOtVkuNGjVMzhMUFIS/vz8RERFG08+fP88rr7zyRPV8Lg0Izyo8PJxNmzYZTbvfJ/9plS5dGhsbG65fv06DBg2eaVn39evXj7Jly/Ltt9+i0Wjo2DFnat+///5raAyIj4/n/PnzhsyCypUrc+bMmTwbWx7l33//zfH3ozIXnJ2d6dq1K127dqVz5860bNmSuLg43N3dsbKyIivL+GapVKlS7Nmzh169ehmm7dmzJ0dr1pMw1TKntHh0qlxaupa09IdT7NRULO1ouCm1t1USXsKejX/l3ee5WT13EhI1HDie+IS1f3ppaVncfGgE8rtxGVSt4MbFeyMS29tZUDrMmTWb8n5C07qpL/EJmew7mHu8Cfe6EVQu74qbixW7D5i/P3hu66ZCaUcu37uQsrNVEl7cno1/5Z1O3qyum37dnMif9HiAjEyIyTRu4EpI1lIyyIob9xoMbK0h2N+SXUcff+AbGyvwclXyb7J+2Zv/TWP3ceP5x/VzYcX2VE5cfPYuDaa2qdi4TKqUdzXapkqFObFmc96vxmvVxAdVgpp9h3JfT62b+rDnYByqRPN1yTC1DcWq1FQq7cjlew0G9rZKSha3Z8OOvJ/SNK/ngSpRw/7nuX8XgmMU6Mf+uJPxULeqBA3lw+y5elMfn52tgtBiNmzenXtmzdnL6fh7Gx/X/b2siYnPfbvxcLXAyV6Zo/vTkzIVQ1yChvLhD8agJDTI9pExnLucTsDDMXhbEROnX7YmCy5eSyfA5+Ey1sQ8xSsD7ewcsLPLzozS6XS4uHlw5sRBihbXj5qdlprMpfOnaNSyk8llWFpZEVSiJGdOHDC8klGr1XL2xEGatOoCQOtOvajfrJ3RfJ8M7Ub3PsOpWK2eYdrN65f46pNB1GnUmk49333ieO57WdfHg9IytKTFGJ8z4lRqKpZ04Mq915Lqz3N2j3zd4pmLaRTxNb7+CfCxJjpWH4OFhQIrS0WOtyVptfoxHp7V8zpn2NroGyZ0DwWi1WL2Lkovq/RMHemxxusiPjGLMiE2hlcs2tkoKBFozbZ/c2/MOX8tEz9P49s7Xy9L7j7HzLUXkS4/Um4LUKlSpWjZsiX9+/fnu+++Q61WM3jwYLp162Z4A8PNmzdp0qQJixYtonr16igUCkaOHMm4ceOoUKECFStWZOHChZw7d+6JHx4/lwaEjIyMHAM6WFpa4un5eAP1DBw4kBkzZjBq1Cj69u3LsWPHWLBgAaB/sv00nJyc+OCDD3j//ffRarXUrVuXhIQE9uzZg7Ozs9EN8uMqVaoUNWvWZNSoUfTp08fkayUnTJiAh4cHPj4+jB07Fk9PT9q3bw/AqFGjqFmzJoMHD6Zfv344ODhw5swZtm7dytdff/1YddizZw9fffUV7du3Z+vWraxcuZKNGzeaLDtjxgz8/PyoVKkSSqWSlStX4uvra3ibRVBQENu3b6dOnTrY2Njg5ubGyJEj6dKlC5UqVaJp06asX7+eVatWGb3OsqCs+fMu3dp4c/NOJlF3M3mjow+x8Wr2Hsm+4J78YTB7Dyeyfnv2BbtCob9J3bYn3mRKv5uLftAwf2/9ST6oiC1p6VqiY9Ukpzz+0+jHsXLdTXp1LUrkrTT9axx7BhEbl8E//2bfJM36vDx/77vLqo3ZjQoKBbRq6svmHVEmXxvYqokP126kEp+gpmxJZ4b2D2HF2htE3nz6p3pPYs3Wu3R71ZtbURlExWTyRgcfYlUa9j2wbiZ9EMzeI4ls2PHQuqnjxra9ptdNftp+MJ1WtW2JjsviboKWdvXsUCVrOXY++6bw/W5OHD2fyc4j+gvGTo3sOHFRTVyiFhdHJW3q2qHVwcEz954cpuiMMhrui0vUEpuQPwGuWH+TXl0CuXH73jb1erGc29SEcvz9711Wbcq+QFQo9NvNH3+Z3qYAAnxtqVDGhZETnj3tNS9rtsTQva0PN6MyuBOjf41jrErN3iPZNxhTPizB3iMJrNuWHZtCAc3rubNtd9wj9m8r/O/dZAQXsSU1XUtMbCZJZt6/C8MxCmDDLhWdW7hxOyaTqFgN3Vu7E5eQxYET2U9DP3vXn/0nUvjjH/362bBTxaT3i9CpmRt7jiYTWsyGZrWd+W65PnPF1lpBl1fc+fd4MvGJWfh6WvFmOw/u3FVz9Jz50243/KXitZbu3I5R618b2NqDuIQs9h/PjmH8kAD+PZ7MH3/rY1i/I57JIwLp1NyNPUeSCQ2ypXkdF+Yty86+WbMtnhF9/DhzMY2T59OoVNqeamUd+GT2jWeus0KhoFmb7qxf+T98/APx9A5g9dJ5uLl7GY1d8NUng6hcs6Hh9YrN2/Xgp9mfERRSmuKhZfhz/VIy0tOo26QNAC5uniYHTvTw9MXLR5+heePaRb76dBBlK9akRbseJMTr9zGF0gJnF7cc8z6pl3F9PGzt9ji6tvbiZnSm/jWO7byIU2nYdzS78fuL4cXYdzSRDX/pGxXWbotl6qhgXmvlye6DCYQF29GyvhtfL9af39PStZyMSKFPZ28yM7VEx+m7MDSu5cJPK3Ifl+ZZ5Mc549qNNCJvpfHBO6F8O/8yCUka6tXwoGoFV0Z9nv/nj9xYONjjEJKd4WsfXATnCiXJjEsgPTLvBpP8tnlPMh0aO3PnroaYOA2vNXdBlZjFoTPZ124f9fPk0Ok0/tyn31f+2J3EZ4O8adfQiX9PplKiiDWNqzvwv1XZDVkOdgo8XS0N49b4ed0bwyMpi4TkwnWjXZgtWbKEwYMH06RJE5RKJZ06dWLOnDmG79VqNREREaSmZp9Dhw0bRnp6Ou+//z5xcXFUqFCBrVu3UqJEiSf67efSgLB582ZDv4v7wsPDH3sQiODgYH777TdGjBjB7NmzqVWrFmPHjmXQoEFP1d3gvokTJ+Ll5cXkyZO5fPkyrq6uVK5cmY8++uipl9m3b1/27t1Lnz59TH4/ZcoUhg4dyoULF6hYsSLr16/H2lp/4Vq+fHl27drF2LFjqVevHjqdjhIlStC16+O/Y3nEiBEcOnSI8ePH4+zszIwZM2jRooXJsk5OTnz11VdcuHABCwsLqlWrxqZNmwxvjZg+fTrDhw/nxx9/JCAggKtXr9K+fXtmz57NtGnTGDp0KMHBwcyfP5+GDRs+2X9UPli5KQZbGyXvvRWAo70Fp8+n8Mn0K0bve/bztsHZyXizr1TaER9Pa/782/RT1laNPOjZ3sfw97SP9Bki03+KZJuZBzNb8nsktrYWfDg4DEcHS06eSWDEuJNG714O8LXD1dm4i0jVim74etuycavpkVeLFrFnYK/iODtacic6nUUrrrN8rfkvoHLz2x93sbVRMqTXvXVzIZVPZ1wxeje2n7c1Lk7GAwNVLO2It6c1W/95/oPGbdmfjrW1gp4tHbC3VXDxhoY5y5PQPHA/5ummNBol3s1JSb+2jjjYKUhO1XHxhpopi1JINvEqx+dl6aob2NlaMPKdUP02dTaBD8afNtqm/H1tcXl4m6rgiq+37SPfvtC6qQ8xsRlGbw7JLys2RWNro2Ro78B721AKY6ddzrl/Oz60f5dxwsfTmi257N+tG3nyRofs/oLTx+r7A0/78TpbzTzgYmE4RgGs3qbCxlrJ2928cbBTcvZyOhPn3TLan309rXB+YKCvi9cz+PKn2/Rs48FrLd2IjtXw86q7/H1I/yRNq4Ni/jY0qu6EvZ0+DfzYuVSWbYpDkw8PzlZvi8fWRsGg7vdiuJTOxG9v5h3Dj7fp2daDLq+462P4PYa/D2XfIO4/kcL3v0bTsbkbfTt7cStazVc/3ebsZfMMFNeqQy8y09NZ8O0kUlOSCCtVkeGfzsHKOvtaKPrODZITVYa/a9RtTlJCPGuWfUdCfCxFg8MYPm4uLg8Nxvgoh/ZuJykhnn27/mDfrj8M0z28/Jj24/pnjutlXR8P+n1zLLbWSoa84Y+DvZIzF1L5dPZ14xi8rIyOUReupvPFvEh6dfCm+6ueRN1V8+PyO+zcn92o+OUPN+jV0YcP+gXg6GBBdKyaxWuiH5nZ8Czy45yRlaXjwwmnGPhmMFM+LoOdrQU3b6cxafZ5/j1ccIPCulQpS63t2a8iLz1Nf/0fuWgVJ/qaHuTueVq/KwkbawX9Orphb6vk/NUMpsy/i/qBY6KPhyVODtn7xeUbamYujqVrSxc6NHEmJl7D4vUJ7DmW3ehQpbQdb7+WPRbde6/rjwW/b0vk923PN8vtedFqC+46LL+4u7uzdOnSXL8PCgrKkfUD+oEaR48e/Uy/rdCZWvJL4IsvvuC7774jMjIy78LP0cSJE1m5ciUnTpwwmr5z504aNWpEfHy84Qm/uQUFBTFs2DCGDRuWL8t/nl7pfSLvQi+BpNiCOzGai7OXe96FXgKBYQF5F3rBndlXcE9qzMne9fFeB/aiUyqUeRd6wdk5F45+sbpCcHE48l3TA1+9bKZ+kz+vc33e1Bn5/6ac/JYQXTCvrTW30ZuebryNF8mSEdsLugpmsXRKkbwLvYC6fvB8xw960PJpxQrst/PLSzEGAsC3335LtWrV8PDwYM+ePUydOpXBgwcXdLUMkpOTuXr1Kl9//TWff/55QVdHCCGEEEIIIYQwq5emAeHChQt8/vnnxMXFUbRoUUaMGJHrOzQLwuDBg1m2bBnt27fPtfvCs/jnn38eOULm/bdKCCGEEEIIIYTQe0kT7l9YL00DwsyZM5k5c2ZBVyNXCxYsMAzsaErDhg2faeOtWrUqx44de2SZq1evPvXyhRBCCCGEEEKIR3lpGhD+6+zs7J7p9Y5CCCGEEEII8V9TGMbJeZG8/CNACSGEEEIIIYQQIt9JBoIQQgghhBBCiEJJMhDMSzIQhBBCCCGEEEIIkSdpQBBCCCGEEEIIIUSepAuDEEIIIYQQQohCSavTFnQVChXJQBBCCCGEEEIIIUSeJANBCCGEEEIIIUShJIMompdkIAghhBBCCCGEECJP0oAghBBCCCGEEEKIPEkXBiGEEEIIIYQQhZJ0YTAvyUAQQgghhBBCCCFEniQDQbyQ/jemoGtgHllKr4KuwjNzi7tU0FUwixPOQQVdhWcW1MGloKtgFmlWjgVdBbNwS75Z0FV4Zsl2VgVdBbOw1KoLugrPLEqZUdBVMItlHfYVdBXMIt29SEFX4ZlprOwKugpm8V6p7QVdhWfWY3qTgq6CeUyJKOgaPBWdTjIQzEkyEIQQQgghhBBCCJEnaUAQQgghhBBCCCFEnqQLgxBCCCGEEEKIQkmr1RZ0FQoVyUAQQgghhBBCCCFEniQDQQghhBBCCCFEoSSvcTQvyUAQQgghhBBCCCFEniQDQQghhBBCCCFEoaTTyRgI5iQZCEIIIYQQQgghhMiTNCAIIYQQQgghhBAiT9KFQQghhBBCCCFEoSSDKJqXZCAIIYQQQgghhBAiT5KBIIQQQgghhBCiUJIMBPOSDAQhhBBCCCGEEELk6aVqQFiwYAGurq4FXY3/NIVCwZo1awq6GkIIIYQQQgghnjOzdGHo3bs3CxcuzDG9RYsWbN682Rw/AUDXrl1p1aqV2Zb3sKtXrxIcHPzIMvPnz6d37975VocX3e3bt3Fzc3vs8r1790alUuV7o8PqjZtZvnodcfEqSgQX470BfSgVFmqy7JXrkcxfspzzly4TFR3Du31707lda6My3fq9Q1R0TI5527VqwbC3++VLDABrN2xixao19+IIYvDAfpQMDzNZ9uq16yxYsowLFy8RFR3DoP596NSujVGZhUt+ZfGy5UbTAosEMP+7r/MtBlNWbNvDok07iU1IIjTQjw/f6EDZEkVNlt1x8CQ/r99OZPRdNJosivp60fOVBrSuUyVf66jT6Viz7Ht2bV1NakoyoSUr8Mbbo/H1N13P+7ZvWsEfqxeToIqlaFAoPfqPpHhYWZPLnzlxKCeP7GXI6GlUrtnQ8N2SH6dy4exxbl6/hF+RYCbMWmq2uMy9bwDExMbyw4IlHDhylPSMDAL8fBn13ruEh5YwW73zsn79en7/7Tfi4+MJLl6cQYMGER4ebrLstWvXWLx4MRcvXCA6OpoBAwbQvkOH51bX+37bvIMl67YQp0ogpFggw/t0p0xocZNl1277mz927eNy5E0AwosX4+3uHXIt/+UPi1mzdRdDe3elW+tm+RbD2g2bWLlqteEY9e7A/o88Ri1cstToGNWxXdtcl/3ryt/538LFdGj7Ku8MyL/jLLwc+8WWDb+zftVSEuLjKBocwlsD3yckvHSu5f/dvYMVv/xITNQdfP2L8HrvQVSqVtvwvU6nY+WSn9ixZT0pKUmElypP33c+wC8gMDvWixEsXfAtly6cQ6lUUr12Q97sNwRbO3uj39q5bSOb1izn9s1I7OztqVm3MX0GjXiqOH/ddYiFW//lbmIyYUV8GN2lOeWCAkyW3Xb0HP/bsofImHjUWVqKebvxRpOatKlRzlAmNT2TWWt38Nfx8ySkpBHg4Ur3hlXpUj9/zyErt+xkyfqtxCYkElq0CCPe6kqZkCCTZdds382mv//l8o1bAJQMLsqgbu1zlL9y8zbfLF3NkTMXyNJqCQ7wY8rwAfh6uudbHL//sY2la//QH6eCivJ+356UzuW4s27rTv7YtZcr128AEF48iIE9OhvKazQafli2in1HTnArKhoHe3uqlS/N2z1fw8v98a8ln0bnZs40quaAg52S81cz+HmNijuxmkfO4+aspPsrLlQIs8XGWsmdWA3fr4zjyk01ANXK2NKkhiPBAVY4OVgwZnYU126r8zWOvLjXrUrxEX1xqVwWW39vDnV6h6h12wu0Ti8irU5b0FUoVMyWgdCyZUtu375t9Fm2bJm5Fg+AnZ0d3t7eZl3mgwIDA43qP2LECMqUKWM0rWvXrvn2+/lFrTbfwc3X1xcbGxuzLc8cdvyzh3n/W0ivbq/xw8wvKRFUjA/HfUG8KsFk+YyMDPx9vRnwZg/c3VxNlvlu+mR+X/iD4TNtwicANKxTK7/C4K+/d/PdT/N5o3tXvps9neLBQYz+dALxKpXJ8ukZGfj5+tCv1xu4P6JRJ6hoICsW/2z4zPpyUj5FYNqf/x5jxtJ1DGjfjCUThhFW1J/BU38kLjHJZHlnRzv6tG3Cgk+G8OsXI2hTrxrjf1zO3hMR+VrPTasXsnXDr7z59hg++WoB1ra2zBg/BHVmRq7z7N/9J7/+PJN23frz2YxfCAwKY/r4ISSq4nKU/XP9oxsF6jVtS/W65r3xy499Iyk5mSGjPsHS0oIp4z5iwdczGdSnF46ODmat+6Ps2rWLH3/4gdd79GDu3LkUDw7mk48/RpXLvpKRno6fry9vvfXWEzWAmtO2PQeYs3AFfV9rw4IvPyW0WCDvfzGLuIREk+WPnI6gWd3qfD3uA374Ygw+Hm4M+3wm0bHxOcru3H+E0+cv45nLOjOXnX/v5vuffqZn927Mmz2D4sFBjPl0fK7HqIyMDPx8fenb681HHqMAIs5fYOPmLRQPCjJ/xR/yMuwXe//exuKf5tK5ex8mz/6ZYsEhTP50OAmqnOsfIOLsSeZ89RmNmr3KlDnzqVqzHtO+GEPk1cuGMut+X8Lm9b/R792RfD79R2xsbZn86XAy7x3j4mJj+Pzjofj4FeHz6T8wZvwMbly/wrczvzD6rY2rf2X5oh9o27knU79dzNjPZ1O+cvWninPzoTNM+30bA1vX49cxfQkP8GbQ3F+JTUoxWd7FwY5+Leuw6IPe/Da2P+1qVmDc4vXsOXPJUGba71vZe+Yyk3q3Y/WnA+nRuBpTVmxh54nzT1XHx7F17yFmL/6dvp1bs3DyR4QUK8LQyXNy37/PnKd5nWp8+8n7/DThQ7w93Hlv0hyi41SGMjfuxDBg3HSK+fsy79PhLPnyY/p0fAVrq/wbvmzbnv3MXfArfbq05+ep4/UNnROnEZ/rceoczerWYM74UXw/6WO8Pd15f8JUYu4dp9IzMom4fI3endvy89TxTPpwMNdv3WHUlNn5FgNAmwZOtKjtyM9r4vnkm2jS1TpG9/HkUf91DnYKPhvkTVYWfDX/LiNn3GHJRhUpadk3njbWSiKuZbBss+ljRUGwcLAn8UQEp94bX9BVEf8hZmtAsLGxwdfX1+jz4IWaQqHgp59+okOHDtjb2xMaGsq6deuMlrFu3TpCQ0OxtbWlUaNGLFy4EIVCYbgofLgLw2effUbFihVZvHgxQUFBuLi40K1bN5KSsm9MtFotkydPJjg4GDs7OypUqMBvv/1mMgYLCwuj+js6OmJpaYmvry/p6en4+/tz+vRpo3lmzZpFsWLF0Gq17Ny5E4VCwcaNGylfvjy2trbUrFmTU6dOGc2ze/du6tWrh52dHYGBgbz33nukpJg+WT4sKCiIiRMn0r17dxwcHAgICOCbb74xKqNQKJg3bx5t27bFwcGBL77Qn/znzZtHiRIlsLa2Jjw8nMWLF5uc75VXXsHOzo7ixYvn+L96uAtDZGQkXbp0wdXVFXd3d9q1a8fVq1cB/fpZuHAha9euRaFQoFAo2Llz52PF+SRWrt1A6+ZNeKVpI4KKBjL8nQHY2ljzx7YdJsuXDA3h7bfepHH9OlhZWZks4+rigrubm+Gz7+Bh/H19qFA296c/z+r3Neto1aIZLZs1oVjRQIa9+zY2NjZs3mq6JblkWCgD+/SmUYN6WD3irGhhYWEUi4uLc36FYNIvm3fRoWEN2tavTvEAXz7q3QlbGyvW7jposnzVUiE0rlqO4AAfAn08eb1FPUIC/Th2/kq+1VGn07F1/TLadOlL5RoNCQwKpf/QCcTHxXBk/85c5/tz7RLqN29PvSZtCQgszpuDxmBtY8s/242PbdcvR7Bl7RL6DvnU5HJ69B9Jk1Zd8PIx/dTtaeXHvrHs9zV4e3owaui7lAoLxc/Xh2qVKhDg52vWuj/K6tWrafnKKzRv3pyixYoxeMgQbGxs+PPPP02WDwsPp2+/fjRo2DDXuPLbsg1badukHq82qktwoD8fDuiJjbU1G3bsNll+/ND+dGrRiLDgogQF+DHm7d5odToOnTprVC46Np4ZPy/js6H9sLS0yNcYfl+zlldaNDcco4a+OwgbGxu25HKMCg8LZcBjHKPS0tKYPG0m7w9597k0RL0M+8XGNctp3KINDZu1pkjRYPq9OxJrGxt2bt1gsvwf61ZQoUoN2nTqQUBgEF3fGEBwiTC2bNCfw3U6HX+sXUGHrr2oWrMexYJDeHf4J8TH3eXQvn8AOHJwL5aWlvQZNAL/IsUoEVaKfu+O5MDendy5pX/CnJycyPJffuCd4Z9Qt2FzfP2KUCw4hKo16j1VnIt37KdjnYq0r1WBEn5efNy9FbbWlqzZe9xk+WphxWhSsSTF/TwJ9HKjR+PqhAZ4c/RSpKHMscs3aVOjHNXCihHg4UrnupUJC/Dh1NVbT1XHx7Fs43baNa5Dm4a1KV7Ej9H9umNrbc36nftMlp8wpA+dmzcgLCiQoABfxg7seW//PmcoM2/5WmpXLMOQHh0JDw6kiK8X9atWwD0fz+HL12+hTdMGtG5cj+DAAEYO7IWNjTUbtv9tsvxnw96mY8smhAUXo1gRf0YP6qOP4+QZABwd7Jk9biRN6lSnWIAfZcNCGN6vJxGXrnInJjbf4mhZx5E1OxI5fCadyDtq5i2Pw9XZgqql7XKdp00DJ2JVWXz/WzyXbqiJic/i5IUMouOyDGV2H01l9fYkTl3M/cHC8xaz5W/Oj5tF1NptBV2VF5pOqyuwT2H0XMdAGD9+PF26dOHEiRO0atWKHj16EBenf1J35coVOnfuTPv27Tl+/DgDBw5k7NixeS7z0qVLrFmzhg0bNrBhwwZ27drFlClTDN9PnjyZRYsW8d1333H69Gnef/99evbsya5du56o7kFBQTRt2pT58+cbTb/fpUGpzP6vHDlyJNOnT+fgwYN4eXnRpk0bQxbApUuXaNmyJZ06deLEiRMsX76c3bt3M3jw4Meuy9SpU6lQoQJHjx5l9OjRDB06lK1btxqV+eyzz+jQoQMnT56kT58+rF69mqFDhzJixAhOnTrFwIEDeeutt/jrr7+M5vvkk0/o1KkTx48fp0ePHnTr1o2zZ40vWO9Tq9W0aNECJycn/vnnH/bs2YOjoyMtW7YkMzOTDz74gC5duhhlp9SuXdvksp6WWq3m/MXLVKlY3jBNqVRSuUJ5Tp8zz9MGtVrN1p3/8ErTxigUCrMs09RvnL94icoVKximKZVKKlcsz5lzz/bk/eat23R9sw89+77NpKkzTXbNyC9qjYZzV29SvUx2irNSqaR66VBOXryW5/w6nY4Dpy9w7XY0lUuaTqE0h5iomyTEx1KmfPZTNHsHR0qEleVixEmT82jUaq5eOkeZ8jUM05RKJaUrVOdixAnDtIyMdL6f8TE9B3yIi5tnvsXwsPzaN/YeOER4SAk+mzKdDm/0pf/QkWzY8vwuXNRqNRcvXKBixYqGaUqlkooVK3Iul2NVQVOrNURcvka18tkNkEqlkmrlS3Hq/OVHzJktPTMTjSYL5wdusLVaLRPm/o8ebVtQPNC8jU8Pyz5GPbQ9VazwzMeoufN+oEa1KkbHv/zyMuwXGrWaKxcjKFexmlEdy1Wsyvlzp0zOc+HcacpVrGo0rULlGpw/p3/oER11C1V8rFEZewdHQsJLG5apUWdiYWlldD1jba3PODx3Rn9Df/LoQXRaHfGxMQx/+3Xe6dWeWVM+4W5M1BPHqdZkcfb6bWqGZ3cdVSoV1CwZzIkrN/KcX6fTsf/cFa5GxVElJLurWcXiAew6cYEoVaL+HBJxlWvRcdQqlT/nELVGw7kr16leruQDcSipVq4kJx93/87IJEuThbODfv/WarXsPXqKon4+vDdpDi0HjKTP2C/ZdfBYfoQA3DtOXbqa4zhVtXwZTp2/9Ig5s6VnZqDJMj5OPSw5JQ2FQoGTg32uZZ6Ft7sFbs4WRjf5aRk6LkVmElrMOtf5Kpey4/LNTIa+7s68j/2Y9J43jao9v8w6IV4mZmtA2LBhA46OjkafSZOMU6V79+5N9+7dCQkJYdKkSSQnJ3PgwAEAvv/+e8LDw5k6dSrh4eF069btscYa0Gq1LFiwgLJly1KvXj3eeOMNtm/XPw3JyMhg0qRJ/Pzzz7Ro0YLixYvTu3dvevbsyffff//EMfbr149ly5aRkaE/KB05coSTJ0/y1ltvGZUbN24czZo1o1y5cixcuJCoqChWr14N6Bs0evTowbBhwwgNDaV27drMmTOHRYsWkZ6e/lj1qFOnDqNHjyYsLIwhQ4bQuXNnZs6caVTm9ddf56233qJ48eIULVqUadOm0bt3b9555x3CwsIYPnw4HTt2ZNq0aUbzvfbaa/Tr14+wsDAmTpxI1apVmTt3rsl6LF++HK1Wy08//US5cuUoVaoU8+fP5/r16+zcuRNHR0fs7OyMslOsrXMevDMyMkhMTDT6ZGRmPtb/RUJiElqtFjdXF6Ppbq4uxOWSVvukdu8/SHJKCi2bNDTL8kzJPQ5X4uNVT73cUuGhjHx/CJPHf8rQdwZyJyqK90eNJTU17Rlr/HhUSSlkabV4ODsaTfdwceJuLimRAEmpadTt/xE1+oxi6Iz/8eEbHahZ1nQ/a3NIUOmfhDi7ehhNd3ZxJyHe9FOSpCQVWm0Wzq7GfVFdXNxJfGCeZf+bTomS5alco6F5K52H/No3bt2JZu0ffxLg78dXn31M21eaM/fHn9m8feezVfgxJSYm6uN6KCXe1c2NuHjT6d0FTZWUTJZWm+PJobuLM7G5pM0/7NtffsPL3ZVq5bIv7hev3YyFhZIurZqYtb6mZG9PrkbT3VxdiH+G//e/dv3DhUuX6NvrjWes4eN5GfaLxET9scXl4WOLqzuq+JzdowBU8bEmy98/tt2fz+Qy75UpU74KCfGxrP99CRq1muTkRJYumKefP05fJvrOLbQ6LWtWLqJX/6G8P+ZzkpMSmfTJMDRP2F0yPjmVLK0OD2fjmzQPJwfuJuaelZmUlk7N97+i6pApDP52OaO7NDdqHBjdpQXF/Txp/tFcqg6Zwjvf/MpHXVtQJfTR49k8LVVi7vt3nCr389yDvlm6Gk83F6rda4SIT0wiNT2DReu2UKtCGeZ89B4Nqldk1IwfOHImf7piqJKS9HE8tG/o43i849S8xSvxdHOlannT2ZoZmZnM+2UFTevWwME+92yAZ+HiqM/ESkjOMpqekJxl+M4Ub3dLmtZw5E6shik/32Xbvyn0autKvcr509Ahni+dVltgn8LIbB2pGjVqxLx584ymubsbn6jKl89u8XdwcMDZ2Zno6GgAIiIiqFatmlH56tXz7lMXFBSEk5OT4W8/Pz/DMi9evEhqairNmhn3K87MzKRSpUqPEZWx9u3b8+6777J69Wq6devGggULaNSoEUEP9dmsVSu7n7y7uzvh4eGGp/jHjx/nxIkTLFmyxFBGp9Oh1Wq5cuUKpUqVyrMeDy7//t+zZs0ymla1qvGTiLNnzzJgwACjaXXq1GH2bON+aKaWfezYMZP1OH78OBcvXjT6/wdIT0/n0qXHa60GfaPK+PHGfbeGv/s2I4YMeuxl5KdNW3dQo0olPD3yb9Ci/FK9avagUcWDgygVHsbrfQawa/ceXmnetABr9mgOtjYs+3w4qekZHDhzgRnL1hHg7U7VUiFmWf6+XX+wcF52A+ewj2eZZbkPO3pgF2dPHmL8jCV5F35J6HRawkNK0P/N1wEILRHMleuRrN/8Z742sv2XLVq9ia17DvDt+JHYWOtT6M9dusqKjdtY8NWn+ZYZld+iY2L49sef+HLieJONyy+TwrBfBBYrzqD3P2bxT3NZtvB7lEolLdt2xsXVHcW9rAStTkuWRkOvAcOoUFmfffXeh58x8I22nD5xhApVajzqJ8zCwcaGFWP6kZqRyf6Iq0z/fRtFPN2oFlYMgGU7D3Hiyk1mv/0a/u4uHL54nUnLt+Dl6kTNko8eKLsgLFy7ha17D/Htp+8b9m/tvbTn+lXK0721voEwLCiQk+cvsWrbP1QunX8N6k9r8aoNbNuzn6/Hj8bGxP6s0Wj4ZPq36HQwckAvs/1unYp29O2Q3aD81YK7T7UcpQIu38xk+RZ9o8+1W2qK+FjStIYD/xxJNUtdhSgszNaA4ODgQEjIoy/uH+47qFAo0D5jy8yjlpmcnAzAxo0bCQgwTu98moEAra2tefPNN5k/fz4dO3Zk6dKlOW7A85KcnMzAgQN57733cnxXtKj5WscdHPI/7So5OZkqVaoYNYbc5+Xl9djLGTNmDMOHDzeaFnvt8VrYXZydUCqVOQa/ilcl4G6GV37eiY7hyPETjB898pmX9Si5x6HCzYwDozk6OlAkwJ+bt26bbZmP4urkgIVSSWxistH02IQkPB/Rj1OpVBLoo0/3Dy8WwJVb0cxfv8NsDQgVq9c3elOCRq3PeElUxeLqnt3NIDEhjsBg0xdqTk6uKJUWOQZMTEiIw9lNn8lw9sQhYu7c4N0ejYzKfP3Vh4SVqsjoL34wSzym5Ne+4eHmRrHAIkbTihUJ4J+9/z71Mp+Es7OzPq6Hnnqr4uPzHKivoLg6OWKhVOYYUC0uIRGPh572PWzJui0sXvMHcz4dQUix7NHyj527QHxiEh0GfWiYlqXVMnfhCpZv3Mbqb780awzZ25PKaHq8KuGpB6a8cPESKlUCg4ZmH/+1Wi0nT59h7YZNbFq9EgsL847r8DLsF87O+mNLwsPHFlUcrm6mG7Jd3TxMlne5l1V1f74EVRxuDxzjElRxFAvOfvtE3YbNqduwOar4OGxtbUGhYOOa5fj4+gPgdq8bVpGi2Tfizi5uODu7PHE3BjdHeyyUCmIfyjaITUrB0zn3axilUkFRb308JQN9uXLnLv/bspdqYcVIz1QzZ91fzBzQmfrl9HGFFfEh4kYUC7f9my8NCK7Oue/f7q6PHq/gl/VbWbR2C1+PHUposeztx9XZEQsLJcFF/IzKB/n7cTziovkq/wBXJyd9HA/tG/o4Hn2cWrr2D35ZvZFZ4z4kJCgwx/f3Gw+iYmKZM36UWbMPDp9J52Jk9rZnaaFvUHVxtECVlH2P4eJowbXbuWe3xidlcTPa+C0Nt6I1VC8rGQhCPOy5joHwKOHh4Rw6dMho2sGDpgdZe1ylS5fGxsaG69evExISYvQJDMx5gHsc/fr1Y9u2bXz77bdoNBo6duyYo8y//2ZfMMTHx3P+/HlDZkHlypU5c+ZMjvqEhIQ89hOYB5d//++8MhdKlSrFnj17jKbt2bOH0qWN08yeZNmVK1fmwoULeHt754jFxUV/srG2tiYrK8vk/PfZ2Njg7Oxs9DHVem2KlZUVYSHFOXI8u5+6VqvlyImTlCn57C30m7f9hauLC7WqVX7mZT2KPo4SHDme3Xdeq9Vy9PhJSpc0/Wq6p5GWlsbt23fwyOfXJ91nZWlJyaAADp6+YJim1Wo5eOYi5UKKPfZydFodas2jX7/0JOzsHPDxCzR8/AOL4+LmwZkT2cectNRkLp0/RUh4OZPLsLSyIqhESc6cOGCYptVqOXviICHh+myr1p16MWHWMsbPXGL4AHTvM5y+740zWzym5Ne+UaZUOJE3jQcju3HrNj7ej99o+CysrKwICQ3l+AOZUVqtlmPHjlHyMTK4CoKVlSXhxYtx6GT2GA1arZZDJ89RNiz3ftm/rP2D+b9tYObYYZQqEWT03Sv1a7F42mcsnDrO8PF0c6VH2xbMGvt+PsSgP0YdzXGMOvHUx6hKFSrww9ez+W7OTMMnLDSExg3r892cmWZvPICXY7+wtLIiOCScU8ezr4m0Wi2njh8mrGTOV8QChJYsw6ljh42mnTh6kLCSZQDw9vHH1c3DqExqagoXI86YXKarmzu2dvbs+3s71lbWhvEYwkrrj4e3blw3lE1OSiQxMQFPb58nitPK0oJSRf3YH3H1gTh17I+4SvngIrnP+BCtLvv8oMnSosnSolQaZ+UolUrDU31zs7K0pGRwUQ6eyh4LRKvVcvBUBOUesX8vXvcnP6/axKwxgylVwvh8aGVpSeniQVy7Zdwoc/1OVL69wtHKypLwEkGGARBBH8fhE2coG5b7q0iXrNnEgt/WMf2TEZQKydlAc7/xIPJ2FLPGjcTFydHEUp5eeqaOqNgsw+dmtIb4xCzKhGQ/KLSzUVAi0JoL13JvQDh/LRM/T+Pnqr5eltxVme/aQxQcGUTRvMyWgZCRkcGdO3eMF25piafn4w0aNnDgQGbMmMGoUaPo27cvx44dY8GCBQBPnZ7p5OTEBx98wPvvv49Wq6Vu3bokJCSwZ88enJ2d6dXryVOoSpUqRc2aNRk1ahR9+vTBzi5nK+qECRPw8PDAx8eHsWPH4unpSfv27QEYNWoUNWvWZPDgwfTr1w8HBwfOnDnD1q1b+frrrx+rDnv27OGrr76iffv2bN26lZUrV7Jx48ZHzjNy5Ei6dOlCpUqVaNq0KevXr2fVqlVs22Y8yNPKlSupWrUqdevWZcmSJRw4cID//e9/JpfZo0cPpk6dSrt27ZgwYQJFihTh2rVrrFq1ig8//JAiRYoQFBTEli1biIiIwMPDAxcXF7OPgv5au1eZMusbwkJKUCoshN/WbSQ9PYOWTfRPfSfNnIuXuzv9e/UA9INoXYvUD9Ck0Wi4GxfLxctXsLO1JcA/u7Vfq9WyeftftGjcIF8uZB/WqX1bvpo5h/DQEoSHhbJq7QbS09Np2VSfvjhl+mw8Pdzp1/sN03HE5ozj+/8toGb1qvh4exMbF8fCJb+iVCpp1ODpRsx+Gj1bNmDcj79SKrgIZYsXZemf/5CWkUnb+voL0k+/X4aXmwtDurQC4Of12ykdHEgRbw/Uag27T5xl497DjOnVKd/qqFAoaNamO+tX/g8f/0A8vQNYvXQebu5eRmMXfPXJICrXbEjT1vrXuTZv14OfZn9GUEhpioeW4c/1S8lIT6NukzYAuLh5mhw40cPT1+iNC1G3I8lISyVBFYs6M53rl/UXov6BxbF8hv0lP/aN19q9yuAPP+aXFatoVLcWZy9cZMOWbQx/d+BT1/NJdejQgRnTpxMaGkpYeDhr16whIyPD0F1t2rRpeHh4GManUavVXL9+3RBXbGwsly5dws7ODn9//+dS5+6vNmPiNz9TskQxyoQE8+vGbaRnZPBqozoAjJ/7P7zcXXmnh347X7zmD35cvpbxQ/vj5+VJbLz+qaCdrQ32dra4ODnmuBC3tLTA3c2FYgH580aMTu3b8dXM2YSFhhAeFsrqtetJT0+nxb1j1JfTZ+Hp4UFfo2OUfnR8tUbD3dg4Ll6+jJ2tHQH+ftjb2xEcZHzjZGtjg7OTU47p5vQy7Bet23dl3swvKB5akpCw0mxau4KM9HQaNG0NwDfTJ+Lu4Un33vqufq+07cKE0e+yYdUyKlWrzd6/t3H54jkGDB4F6I9xr7TrwurlC/ENKIK3jz8rfvkRN3dPqtbKPh9sXv8b4aXKYWNnx8mjB1ky/xu69xqEg6O+q6J/QFGq1qzHwh9m0X/IKOztHFi28DsCihSlTPkqPKk3Gtfgk0XrKFPMj7LF/PnlrwOkZahpX0vfCDt2wTq8XZ0Y2l6/bv63eQ+li/kR6OVGpjqLf05fZOP+U4zt3hIARzsbqoYWZcaqHdhYWeHn7sLhC9fYsP8kH3TKv2573Vs3YcK8hZQqXpTSIUH8ummHfv9uoO8W+tk3C/Byd+Xd7u0BWLR2Cz+s3MCEIW/h7+VhGAvFztYGe1tbAHq2acbY2T9RqVQoVcqE8e+xM+w+fJJvPzV/A+F9Xdu04Iu5P1KyRDClQ4uzYsOfpGdk0LqxfhuZOOcHPN3dGNTzNQB+Wb2Rn35dzbhhA+8dp1T34rDF3s4WjUbD2GnfcP7yNb76aBhardZQxtnR8ZFvZ3kWm/ck06GxM3fuaoiJ0/BacxdUiVkcOpM9/tNH/Tw5dDqNP/fpM2D+2J3EZ4O8adfQiX9PplKiiDWNqzvwv1XZ2W4Odgo8XS1xc9ZfE/p56euvSsoiIblg+rpbONjj8MAgovbBRXCuUJLMuATSI59Ptqn47zHbnrt582b8/IxTrcLDwzl37lwucxgLDg7mt99+Y8SIEcyePZtatWoxduxYBg0a9FTdDe6bOHEiXl5eTJ48mcuXL+Pq6krlypX56KOPnnqZffv2Ze/evfTp08fk91OmTGHo0KFcuDdS+Pr16w3ZBeXLl2fXrl2MHTuWevXqodPpKFGiBF27dn3s3x8xYgSHDh1i/PjxODs7M2PGDFq0aPHIedq3b8/s2bOZNm0aQ4cOJTg4mPnz59OwYUOjcuPHj+fXX3/lnXfewc/Pj2XLluXIUrjP3t6ev//+m1GjRtGxY0eSkpIICAigSZMmODvr0/b69+/Pzp07qVq1KsnJyfz11185fvNZNa5Xh4SERBYsXU5cvIoSxYP48rOxhvd1R8fcRflAI1RsXDz9h2Wn/S5fvZ7lq9dToWxpZk3KHovh8PGTRMXc5ZWmjc1a39w0ql9XH8cvvxIfH0+J4sFMnvCpoQtDdEyM0VOV2Lh43n4vO/V35aq1rFy1lvJlyzBjyucAxNyNZdLUGSQmJuHi4kLZ0qWYO30Kri6PTkc0p+Y1KxKflMx3q7YQm5BEWFF/5o7sh4eL/oL0Tmy8USNhekYmUxauIjpOhY21FUF+3nw+8HWa16yYr/Vs1aEXmenpLPh2EqkpSYSVqsjwT+dgZZ19/Im+c4PkRJXh7xp1m5OUEM+aZd+REB9L0eAwho+ba0gbflzzv55IxOkjhr/HDdffuEz9fh2ePk9/g5sf+0bJ0BAmfjSSHxctYdHy3/Dz8ebdfr1p1vD5NUo1aNCAxIQEFv/yC/FxcRQvUYIJEycaUuljoqON4oqLi2PIA2+6+f333/n9998pV64cX3711XOpc9M61YlPTOan5WuJVSUSGhTIzLHDDKnBUXdjjeq86s+dqDUaPppuPLZQ39fa0K9Lu+dS54c1rF8XVUICC39ZZjhGTZowzugYpTA6RsUxyOgYtYaVq9ZQvmwZpk/54nlX3+Bl2C9q129KYoKKlb/8hCo+jmLFQxk9YbqhK8LdmCij/+vwUuUYMvIzli/+gV8XfY+vfxE+GDuZwKDsJ+BtO/UgIz2NH+d+RWpKMuGlyzN6wnTDmxYALp0/y29L/0d6Whr+RYrR790Pqd+4pVHd3hn+CYt+nMNXn41EoVRQqmxFRo+fgaXlk19StqxamvjkFL7dsIu7iSmEF/Hh28HdDAPv3olPMDrvpWWqmfTrZqJUSdhYWRLs48EXvdvRsmr2dcqXfTowe+1fjJm/hsTUdPzcXRjctiGv1cu/TMJmtauiSkzmh5UbiFUlElasCLNGD8HjXheGqLtxxvv31r9RazSMmfmj0XL6dWpN/9deBaBh9YqM6vc6C9duZsaCFRT192Hy8AFULGmernymNK1TA1VCEj/9upo4VQKhwUWZ/vEIo+PUg+fr1Vt2oNZo+Hia8evE+3RpR9+uHYiJi2f3waMA9B5h/BrjueNHUbls/mSNrd+VhI21gn4d3bC3VXL+agZT5t9F/UAygY+HJU4O2Q+HLt9QM3NxLF1butChiTMx8RoWr09gz7HsRocqpe14+7XsDJD3Xtef63/flsjv2x5vwExzc6lSllrbs1/LXnqa/v4mctEqTvQdUyB1ehHpdIVzMMOCotDpdC9sbsUXX3zBd999R2RkZN6Fn6OJEyeycuVKTpw4YTR9586dNGrUiPj4eFzN0P/elKCgIIYNG8awYcPMvmyFQsHq1asN2RIPy8jIwNbWlq1bt9K0af4OwHcr4kTehV4CWcr8aV1/ntziHn9AzBfZCeeGBV2FZxakvFLQVTCLNCvzprAWFLfkmwVdhWeWbPf8Xi2anyy1T/YGgBdRlPL5ZMPkt1LXNxV0Fcwi3f3xu1G8qDRW+fOmg+ftvV+ertvxi6TH9Px/W87z0Fr9bK/tLShNuh3Iu1A+2f5r3i8FeNm8UHc33377LdWqVcPDw4M9e/YwdepUBj/w1KigJScnc/XqVb7++ms+//zzgq7Oc5WYmMiqVatQKpWULFky7xmEEEIIIYQQQhQqL1QDwoULF/j888+Ji4ujaNGijBgxgjFjXpz0m8GDB7Ns2TLat2+fa/eFZ/HPP//wyiuv5Pr9/bdKFIRx48axdOlSvvzyS4oUeflb5YUQQgghhBCFX34Novpf9UJ3YfivSUtL4+bN3FNi83pNZmEiXRheHNKF4cUhXRheLNKF4cUhXRheHNKF4cUhXRheHNKFoWA16rK/wH77rxU1Cuy388vLf3dTiNjZ2f2nGgmEEEIIIYQQIj/ptDKIojkpC7oCQgghhBBCCCGEePFJBoIQQgghhBBCiEJJJ2MgmJVkIAghhBBCCCGEECJP0oAghBBCCCGEEEKIPEkXBiGEEEIIIYQQhZJOJ4MompNkIAghhBBCCCGEECJPkoEghBBCCCGEEKJQkkEUzUsyEIQQQgghhBBCCJEnaUAQQgghhBBCCCFEnqQLgxBCCCGEEEKIQkmnlUEUzUkyEIQQQgghhBBCCJE3nRD/Qenp6bpx48bp0tPTC7oqT60wxKDTSRwvksIQg05XOOIoDDHodBLHi6QwxKDTFY44CkMMOp3E8SIpDDGIl4dCp9PJsJTiPycxMREXFxcSEhJwdnYu6Oo8lcIQA0gcL5LCEAMUjjgKQwwgcbxICkMMUDjiKAwxgMTxIikMMYiXh3RhEEIIIYQQQgghRJ6kAUEIIYQQQgghhBB5kgYEIYQQQgghhBBC5EkaEMR/ko2NDePGjcPGxqagq/LUCkMMIHG8SApDDFA44igMMYDE8SIpDDFA4YijMMQAEseLpDDEIF4eMoiiEEIIIYQQQggh8iQZCEIIIYQQQgghhMiTNCAIIYQQQgghhBAiT9KAIIQQQgghhBBCiDxJA4IQQgghhBBCCCHyJA0I4j/hyJEjnDx50vD32rVrad++PR999BGZmZkFWDMhhLkcOnSIxYsXs3jxYg4dOlTQ1Xkiffr0ISkpKcf0lJQU+vTpUwA1ejqFJQ45ZwghXmR///03Go0mx3SNRsPff/9dADUS/yXyFgbxn1CtWjVGjx5Np06duHz5MmXKlKFDhw4cPHiQ1q1bM2vWrIKuYp4WLlyIp6cnrVu3BuDDDz/khx9+oHTp0ixbtoxixYoVcA0fT1BQEH369KF3794ULVq0oKvz1ArL+oiPj+d///sfZ8+eBaBUqVL06dMHd3f3Aq7Z47tx4wbdu3dnz549uLq6AqBSqahduza//vorRYoUKdgKPgYLCwtu376Nt7e30fS7d+/i6+tr8kLxRVRY4igM5wyA7du3s337dqKjo9FqtUbf/fzzzwVUqyd34cIF/vrrL5NxfPrppwVUq2d3+/Zt1Gr1S30ufFmpVCoOHDhgcpt68803C6hWjy+3Y21sbCze3t5kZWUVUM3Ef4E0IIj/BBcXF44cOfL/9u48rsb8/R/463SIllOyZ63sqUyJj7UUQ4qQsYaUfUlT1hlrGYmxDzMYUTG2yW7sqaiQRKnQomQra6i0378/+nW+jlM62d7nPud6Ph7zeNR9nz9e96TOua/7/b4utGjRAqtWrcLFixdx9uxZhIeHY+TIkXj48CHriJVq06YN/vrrL1hbW+PKlSvo06cP1q9fj5MnT6JatWo4fPgw64gy2bBhA/z8/BAXFwcrKytMmDABQ4YM4d3sYkX4eVy6dAn29vbQ0tKCubk5AODGjRvIysrCiRMnYGFhwTihbGxsbJCVlQV/f3+0adMGAHDv3j04OztDS0sLZ86cYZywYm/fvgXHcdDR0UFSUhLq1asnPldcXIwTJ05gwYIFePLkCcOUlVOU6yijCO8Znp6e8PLygrm5OXR1dSEQCCTOHzlyhFGyqvn7778xbdo01K1bFw0bNpS4DoFAgOjoaIbpvky7du2QmJjIy5u9goICpKamokWLFqhWrRrrOFVy4sQJODo6Ijs7G1paWlL/pl69esUwnWxUVFSQmZkp8bcWABITE2Fubo63b98ySkaUAkeIEhCJRFxiYiLHcRzXp08fbsOGDRzHcdyDBw+4mjVrsowmMzU1Ne7Bgwccx3HcvHnzuLFjx3Icx3FxcXFc3bp1WUb7LDdu3OBcXV25unXrcjo6OtyMGTO4GzdusI4lM0X4eRgZGXGTJk3iioqKxMeKioq4yZMnc0ZGRgyTVU3NmjW56OhoqeNRUVGcmpoag0SyEwgEnIqKSoX/CYVC7rfffmMds1KKch1lFOE9o2HDhlxAQADrGF+sWbNmnI+PD+sY30RkZCQXEhLCOkaV5OTkcC4uLpxQKOSEQiGXkpLCcRzHzZw5k1u5ciXjdLJp1aoV5+bmxuXk5LCOUmVDhgzhhgwZwqmoqHC2trbi74cMGcLZ29tzenp6XL9+/VjHJAqOXyVDQj6Tubk5fvvtN/Tp0wehoaH466+/AACpqalo0KAB43Sy0dTUxMuXL9GsWTOcO3cOHh4eAICaNWvi/fv3jNNVnZmZGczMzLB27Vr8+eefmD9/Pv766y8YGxtj1qxZcHZ2lnpiJk8U4eeRnJyMwMBACIVC8TGhUAgPDw8EBAQwTFY1TZs2RWFhodTx4uJiNGrUiEEi2QUHB4PjOFhbW+PQoUMSW0dUVVXRvHlzub8GQHGuo4wivGcUFBSgW7durGN8sdevX2PYsGGsY3wTnTp1Yh2hyn755RfExMQgJCQENjY24uN9+vTBsmXLsGDBAobpZPP48WPMmjUL6urqrKNUmba2NgCA4ziIRCKoqamJz6mqqqJLly6YNGkSq3hESVABgSiF9evXY8yYMTh69CgWLlyIli1bAgACAwN58wHrxx9/xMSJE2FqaorExETY2toCAOLj46Gnp8c23GcoLCzEkSNHsGvXLpw/fx5dunTBhAkT8OjRI/z666+4cOEC9u7dyzpmhRTh52FmZoY7d+6Il/2XuXPnDjp06MAoVdX9/vvvcHV1xZYtW8RbMaKiouDm5oY1a9YwTlex2rVrIzExEXXr1oWTkxP69OkDkUjEOlaVKcp1fGjDhg1wdHTk9XvGxIkTsXfvXixevJh1lC8ybNgwnDt3DlOnTmUdhQA4evQoDhw4gC5dukgU+du3b4+UlBSGyWTXr18/REVFwcDAgHWUKtu1axeA0n5Sc+bMgYaGBuNERBlRDwSi1PLy8lCtWjVe7N/LysrCokWL8PDhQ0ybNk1c+V+6dClUVVWxcOFCxgllEx0djV27dmHfvn1QUVHBuHHjMHHiRLRt21b8mri4OHTq1Emun+Tz9ecRGxsr/vrOnTuYN28eXF1d0aVLFwDA1atXsWXLFvj4+GDEiBGsYlZKR0dH4sNrTk4OioqKxL/LZV9raGjI7X5WTU1NxMbGwsDAAEKhEBkZGVL7WflAUa5DFnl5eRAKhahevTrrKJVyc3NDQEAATExMYGJiIpV53bp1jJJVzcqVK7Fu3TrY2dnB2NhY6jpmzZrFKFnlZL1BvX///jdO8vWoq6sjLi4OBgYGEIlEiImJgYGBAWJiYmBhYYE3b96wjlgpX19feHl5wdnZudx/U/b29oySVU1RURFCQkKQkpKC0aNHQyQS4cmTJ9DS0oKmpibreESBUQGBKAUDAwNcv34dderUkTielZUFMzMzXrx5p6eno0mTJlBRkZy+ynEcHj58yJsuzkKhED/++CMmTJiAwYMHl/tBPCcnBzNnzhRX2snXo6KiAoFAgMr+9AsEArlu7OXv7y/za52cnL5hks/3448/IjMzEx07doS/vz9GjBghsRz1Q/LcMV9RrqM8UVFREhNKyla48IGVlVWF5wQCAS5evPgd03w+fX39Cs8JBAK5fv9WUVFB8+bNMXr0aKlu+R9yc3P7jqm+jIWFBYYNGwZXV1eIRCLExsZCX18frq6uSEpKkuumtWU+/hz1IXl/7yvz4MED2NjYID09Hfn5+UhMTISBgQHc3NyQn5+PrVu3so5IFJj8P3Yl5CtIS0sr9w0hPz8fjx49YpCo6vT19csd2fPq1Svo6+vz4g0PKH3SUtmIQw0NDV4UD/g4AjE1NZV1hK9CXosCVbFnzx6sX78eKSkpEAgEePPmDfLy8ljHqjJFuY4PKcJo0ODgYNYRvgo+/806cOAAdu7ciXXr1qF///5wcXGBra3tJ29g5Z23tzf69++PhIQEFBUVYePGjUhISEBERARCQ0NZx5PJx2Mb+cjNzQ3m5uaIiYmReDg2ZMgQ6oFAvjlagUAU2vHjxwEAgwcPhr+/v7j5DFDaYC0oKAjnz5/HvXv3WEWUmYqKCjIyMqQKCA8ePIChoSFycnIYJasaRVgNApSOQBw4cCC0tbV5PQJRkTx79qzcmd4mJiaMEslOX18fUVFRUr8XfKMo18Hn0aBE/jx+/Bh+fn7w8/NDbm4uxo4diwkTJqBVq1aso32WlJQU+Pj4ICYmBtnZ2TAzM8P8+fNhbGzMOprSqFOnDiIiItCmTRuJrSRpaWkwNDREbm4u64hEgVEBgSi0sip/eUu2q1evDj09PaxduxYDBgxgEU8mZd39N27ciEmTJkl0DS4uLsa1a9cgFAoRHh7OKmKVVFQIyczMRLNmzZCfn88oWdUYGxuja9eu+Ouvv8RTDIqLizF9+nRERETg9u3bjBPK5smTJwgLCyv3xlue9xZ/6MaNG3BycsKdO3ekfs/5shyVyBc1NTVERETA1NRU4viNGzfQs2dPuf1w7uDgAD8/P2hpacHBweGTrz18+PB3SlV1Hh4eWL58OTQ0NMTvgRXhSy+HMqGhoVi2bBkuXbqEFy9eQEdHh3UkpRQaGoo1a9aIVxAaGhpi7ty56NmzJ+NkstHR0UF4eDgMDQ0lCghhYWEYOnQoMjMzWUckCoy2MBCFVnZDpK+vj+vXr6Nu3bqME1XdzZs3AZT2Orh9+zZUVVXF51RVVdGhQwfMmTOHVTyZla0GAYCzZ8+WuxqEL9MLAMUYgejn54cpU6ZAVVUVderUkWhKKBAIeFNAcHFxQevWreHr64sGDRrI9fjP8rx48QI7d+7ElStXkJGRAQBo2LAhunbtCmdnZ940JFSU6wD4OxpUW1tb/O//w7+xfHPz5k3x//+y98Dy8Ol3PS8vD4GBgdi5cyeuXbuGYcOG8XKM4KlTpyAUCtGvXz+J42fPnkVJSQn69+/PKJns9uzZA2dnZzg4OIjf58LDw9G7d2/4+flh9OjRjBNWrm/fvtiwYQO2b98OoPR3ITs7G0uXLhVPhSLkW6EVCITwhLOzMzZu3AgtLS3WUT7Lp/Z88mU1yIe6d++OuXPnYvDgwRLHjx49Ch8fH1y9epVNsCpo2rQppk6dil9++YXXe3JFIhFu3rwpHrXHJ9evX0e/fv2grq6OPn36oEGDBgBKV+QEBQUhNzcXZ8+elfvmfYpyHWWOHTsGb29vqdGgrq6umD9/vtTvvTzJzc3l5Y2porp27Rp8fX1x8OBBGBgYwMXFBY6OjrxdeWBiYgIfHx+pm9QzZ85g/vz5iImJYZRMdu3atcPkyZPh7u4ucXzdunX4+++/xasS5NmjR4/Qr18/cByHpKQkmJubIykpCXXr1sWlS5c+2bSTkC9FBQSiNPi+XO1DZY0f+dDI62N83iOtKCMQy9SpUweRkZFo0aIF6yhfZPDgwRg7diyGDh3KOkqVdenSBR06dMDWrVulnqZyHIepU6ciNjYWV65cYZRQNopwHYowGhQoHbNnbW0Ne3t7DBo0SFzM4ZtmzZqJr8HKyooX45Y/1r59ezx79gyjR4+Gi4sLOnTowDrSF1NTU8OdO3ekVgympaWhffv2vOjHVKNGDcTHx0sVnZOTk2FkZMSbJrBFRUXYv38/YmNjxb0oHB0dK5yCQ8jXQgUEohQ+XK7WvXt3AKXL1Y4cOcKb5WolJSX47bffsHbtWmRnZwMoffI6e/ZsLFy4kBdPkAsLC2FjY4OtW7fysnmUooxALDNv3jzUrl0bCxYsYB3li7x48QJOTk7o3LkzjIyMeDXTW01NDTdv3kTbtm3LPX/37l2Ympri/fv33zlZ1SjCdSjCaFCgdOTvsWPHcOzYMYSFhaFDhw6wt7eHvb09r5rchYaG4vjx4zh+/DieP3+Ofv36wd7eHnZ2duLJGPJORUUFGhoaqFat2ie3W8hzQepjDRs2xN69e2FtbS1x/MKFCxg9ejSePXvGKJnsWrZsiblz52LKlCkSx7du3Yq1a9ciKSmJUTJC+IEKCEQpKMJytV9++QW+vr7w9PQUF0HCwsKwbNkyTJo0CStWrGCcUDb16tVDREQELwsIDx48kPm1lY2qlAfFxcUYMGAA3r9/D2NjY6kbb740Jztx4gTGjh2Lt2/fSp2T92KOvr4+PD09MW7cuHLPBwQEYMmSJUhLS/u+wapIUa5D0bx58wanTp3CsWPHcObMGdSuXVtcTLC0tJTo4SLP4uPjcfz4cRw7dgy3bt1Ct27dxNdhYGDAOl6FZC1KyXNB6mNTpkzBlStXcOTIEfHqteTkZAwdOhSdOnXCjh07GCes3F9//YWff/4ZLi4u6NatG4DSh0p+fn7YuHGjVGFBXhw/fhz9+/dH9erVJfpKlUdTUxNt27aV654thL+ogECUgiIsV2vUqBG2bt0q9TT12LFjmD59Oh4/fswoWdW4u7ujRo0a8PHxYR1F6f32229YsmQJ2rRpI9V8UCAQ4OLFiwzTyU5PTw8DBgzA4sWLebdce8uWLZg9ezamTJmC3r17S/UO+Pvvv7FmzRpMnz6dcdJPU4TrKK8AVRE+9qIpLCxESEiI+Kn+u3fv8Mcff8DR0ZF1tCrJyMjAiRMncPz4cQQFBcHAwACrVq2CnZ0d62hK4c2bN7CxsUFUVJR4G+WjR4/Qs2dPHD58mDerQ44cOYK1a9eKHyC1a9cOc+fOxaBBgxgnq9iHU6xkWXUqFAqxevVqqYdnhHwpKiAQpaAIy9Vq1qyJ2NhYtG7dWuL4vXv38MMPP8j10uAPubq6IiAgAK1atULHjh2hoaEhcV6en3pXVvH/kDwvmy+jo6OD9evXY/z48ayjfBGRSIRbt27xtpfDgQMHsH79ety4cUO8WkIoFKJjx47w8PDA8OHDGSeUDd+vo2yL0qdwHCf3q1rKpKeno2nTpuX2pEhPT8erV69QVFSETp06MUr45cqac4pEIvTp04d1HKXBcRzOnz+PmJgYqKmpwcTEBBYWFqxjkQ8UFBRg7969+OWXX/D06VPWcYiCoQICUQp8Xa72of/973/43//+h02bNkkcd3V1xfXr13nR9R8ArKysKjwn70+9Ze0zwZcbjIYNG+Ly5cu83E7yIScnJ/Ts2RMTJ05kHeWLFBYW4sWLFwCAunXrSm0p4Qu+XkdoaKjMr7W0tPyGSb4OoVCIp0+fSnVjf/nyJerXr8+Lv1GA4lxHeZycnPDw4UO5ft9TRAYGBrh+/bpUM+esrCyYmZnh/v37jJJ9Xe/evcPSpUvl+sEM4Sf+tbQl5DNMmzYNDRs2xNq1a3Hw4EEApcvVDhw4INfL1T60evVq2NnZ4cKFC+jatSsA4MqVK3j48CFOnTrFOJ3sgoODWUf4bCUlJawjfFVubm74448/pIpSfNO6dWv88ssvCAsLK7eXQ9mcb3lXvXp16Orqso7xxfh6HXwoClRF2WqJj2VnZ6NmzZoMEn2eip5z5efnQ1VV9Tun+boaN27MiwbImzZtwuTJk1GzZs1K3y/48Pc2LS2t3MJTfn4+b7aDAqUrUP/44w+JbRgzZ84UN7MViURUPCDfBK1AIIRHnjx5gi1btuDu3bsASt8spk+fTk1yvqNx48Zh0KBBsLGxkdp+wTdDhgzBxYsXUadOHbRv317qxvvw4cOMklWNvr5+hecEAoFcP0169uyZxJPVW7duYf369UhOToauri5mzpyJXr16sQsoI0W5DkXg4eEBANi4cSMmTZoEdXV18bni4mJcu3YNQqEQ4eHhrCLKpOxG1d3dHcuXL4empqb4XHFxMS5duoS0tDTcvHmTVUSl8eH4ZT7/vS3bhjh48GD4+/tDW1tbfK64uBhBQUE4f/487t27xyqizA4dOoSRI0fC3Nxc/FDp6tWruH79Ovbv38/LscaEP6iAQJRKQUEBnj17JvUkuVmzZowSKQcHBwf4+flBS0sLDg4On3ytvN+0enl54dixY0hISECvXr3EncAbN27MOlqVOTs7f/L8rl27vlMS5fXh8uyIiAj06tUL3bp1Q+fOnXHr1i0EBwcjKChI7vcXK8p1KIKybWKhoaHo2rWrxFN6VVVV6OnpYc6cOXK/dansRvXBgwdo0qSJxMSIsuvw8vLC//73P1YRCc+UrfYobxxz9erVoaenh7Vr12LAgAEs4lVJixYt4OjoCC8vL4njS5cuxZ49e5CSksIoGVEGVEAgSiEpKQkuLi6IiIiQOM6nhlhA6f68yMjIcosgFY1PkwfOzs7YtGkTRCKRwty0Pnr0SDxWLDQ0FO3bt8egQYNgb2+PH374gXU8whMfdtXu27cvmjZtCl9fX/H5n3/+Gbdv30ZQUBDDlJVTlOvgu02bNmHSpElQU1ODs7MzNm7cyMuJEcePH4eNjQ1UVVVhZWWFw4cPQ0dHh3WsKvv45q4iS5Ys+cZJvo7CwkK0bdsWJ0+eRLt27VjH+Wz6+vq4fv066tatyzrKZ1NXV0dsbKzUdLGkpCR06NABubm5jJIRZUAFBKIUunfvjmrVqmHBggXQ1dWV2hfaoUMHRslkd+LECTg6OiI7OxtaWlpSI/devXrFMJ1ye/fuHU6fPo1jx47h9OnTEIlEGDhwIKZNm4b27duzjqeQPDw8sHz5cmhoaIiXbFdEnveAfnjj3ahRIxw+fBhdunQRn4+Pj0evXr3w/PlzhikrpyjXwXfVqlXDkydPUL9+/QqbD/KBUChERkYG6tWrx+vrMDU1rfCcQCDAvXv3kJeXx5uHGEBp34YLFy7wuoBQnqysLN6MoAQAW1tbDBs2TOqhzK5du7B//36cPXuWUTKiDKiJIlEKt27dwo0bN8SNZfho9uzZcHFxgbe3t8SeVsKeSCTC8OHDMXz4cBQXF4tnrV+5ckXuCghmZmYICgqCjo4OTE1NPzm2Ljo6+jsmq5qbN2+isLBQ/HVFKhvLJw/evXuHmjVrombNmqhRo4bEuZo1a/LmSZKiXAcAWFtblzvT/u3btxg8eLDcds1v1KgRDh06BFtbW3Ach0ePHiEvL6/c18rz1r169erh6tWrGDhwYIXNIPmgor9Nt27dwoIFCxAXF4dJkyZ951RfZsaMGVi1ahV27NiBatX4eRuxatUq6OnpYcSIEQCAYcOG4dChQ9DV1cWpU6d48VDJ3t4e8+fPx40bN8TF2qtXr+Lff/+Fp6enxNhpPoyVJvzCz998QqrI0NBQPFaMrx4/foxZs2bxsnhQ2Y3qh+T5plUWQqEQvXv3Ru/evVlHKdegQYPEN3eDBw9mG+YLfDjNg8+TPYDSKRJA6ZaqqKgoiaeW8fHxvGmSqijXAQAhISEoKCiQOp6Xl4fLly8zSCSbRYsWwdXVFTNnzoRAIECnTp2kXsOHrXtTp07FoEGDIBAIIBAI0LBhwwpfK8/X8bHU1FQsXrwYBw4cgIODA+Lj4+W+F8XHrl+/jqCgIJw7dw7GxsZSzYTlvY8RAGzduhX//PMPAOD8+fO4cOECzpw5g4MHD2Lu3Lk4d+4c44SVmz59OgDgzz//xJ9//lnuOYA/Y6UJv1ABgSiFVatWYd68efD29i53zBsf9oj269cPUVFRMDAwYB2lyj68Uc3Ly8Off/4JQ0NDic7B8fHxEm968kgRCiFLly4t92vCxsfFj4/HH6ampmLy5MnfM9JnUZTriI2NFX+dkJCAjIwM8ffFxcU4c+aMXDdMnTx5MkaNGoUHDx7AxMQEFy5ckJp1zwfLli3DyJEjkZycDHt7e+zatYtXy8s/9uLFC3h6emL79u3o0aMHIiIiyi3u8EGtWrV43+E/IyMDTZs2BQCcPHkSw4cPR9++faGnp8ebppyKNlaa8Av1QCBK4cPOux/iw5OYMr6+vvDy8oKzs3O5RRC+LFGbOHEidHV1sXz5conjS5cuxcOHD7Fz505GySrn6ekp82vp5vzbqmyax4f48ESMyAcVFRXx+0R5H4/U1NTwxx9/wMXF5XtHqzJ/f3+MHDlSajsJ33h6emLu3Lm8XH2Xk5ODNWvWYN26dWjZsiVWrlyJvn37so71WUpKSvD777/j+PHjKCgogLW1NZYtWwY1NTXW0aqsUaNGCAwMRLdu3dCmTRv89ttvGDZsGO7du4dOnTrh7du3rCMSIteogECUQmho6CfPW1pafqckn6+sCFIevhRBAEBbWxtRUVFSyzaTkpJgbm6ON2/eMEqmHHR0dGReRSHPjTkrm+bxIb5M9iDsPXjwABzHwcDAAJGRkahXr574nKqqqrg5ISGyaNiwId69ewdXV1eMGjWqwr+9JiYm3zlZ1S1fvhzLli1Dnz59oKamhrNnz2LUqFFyXfSvyMyZM3Hy5Em0atUKN2/eRFpaGjQ1NbF//36sXr1ablcQfiw0NBRr1qzBnTt3AJRu1507dy569uzJOBlRdFRAIArtyZMnle673b9/P0aOHPmdEpGGDRvCx8cH48ePlzju5+eH+fPnIzMzk00wJeHv7y/za52cnL5hEkIIUWwfFv4FAoHEqpay7/nyAKBVq1aYM2cOpkyZAgC4cOEC7Ozs8P79+08+4JBHhYWF2LhxIx4+fIjx48eL+7WsX78eIpEIEydOZJywcnv27IGzszMcHBzQvXt3AEB4eDiOHDkCPz8/jB49mnFCosiogEAUmpGREcLCwircO7l//36MGzeu3GZZ8uLSpUuwsLD45GtcXV3xxx9/fKdEX8bHxweenp6YNGkSOnfuDAC4du0adu7cicWLF2PBggWME8qmuLgY69evx8GDB5Geni71b0ien94rmtTUVBQVFZW7qqV69erQ09NjE4zwWlJSEoKDg/Hs2TOp/cZLlixhlIrwyYMHD2R6XfPmzb9xki9Xo0YNJCcni3sHAKXTVZKTk9GkSROGyZRTu3btMHnyZLi7u0scX7duHf7++2/xqgRCvgUqIBCFZmVlhby8PAQFBUntnzx48CAcHR3h7e2NuXPnMkpYuVq1aiEkJAQ//PBDueddXV3h7+/Pqz17Bw8exMaNG8VvcO3atYObmxuGDx/OOJnslixZgh07dmD27NlYtGgRFi5ciLS0NBw9ehRLlizBrFmzWEes1KlTpyAUCtGvXz+J4+fOnUNxcTH69+/PKFnVWFpawsXFRWrFxJ49e7Bjxw6EhISwCUZ46++//8a0adNQt25dNGzYUGLpuUAg4M0SZ0K+FqFQiIyMDIltPSKRCLGxsdDX12eYTDbHjx9H//79Ub16dYkRh+XhQ0+pGjVqID4+Hi1btpQ4npycDCMjowrHtxLyNVABgSi07Oxs9OrVC7Vr18Z///0nbjz477//YsyYMfDy8sL8+fMZp/y0OXPmYM+ePQgLC5N6o3Bzc4Ovry/+++8/XvRxUCQtWrTApk2bYGdnB5FIhFu3bomPXb16FXv37mUdsVImJibw8fGBra2txPEzZ85g/vz5iImJYZSsarS0tBAdHV3uBylzc3NkZWWxCSajwsJCtG3bFidPnkS7du1Yx/lsinIdQOkT4enTp8v9+wMh34uKigr69+8v0ZTzxIkTsLa2lhjlKK9Na1VUVJCRkYH69esrRE+pli1bYu7cueItJWW2bt2KtWvXIikpiVEyogxojCNRaJqamjh9+jQsLCwwevRoHDx4EIcOHYKjoyOWLVvGiw+Ha9aswatXr9CnTx9ERESIezr8/PPP2LFjB06ePEnFAwYyMjJgbGwMoPTfWVnzxwEDBmDx4sUso8ksKSkJhoaGUsfbtm2L5ORkBok+j0AgwLt376SOv3nzhhcfBKtXr64QT4sU5ToA4PXr1xg2bBjrGF+kuLgYfn5+CAoKKncbxsWLFxklq7qgoKAKr4OPTfz4qLyeOGPGjGGQ5PN8+O9GEUYgzp49G7NmzcKtW7fQrVs3AKU9EPz8/LBx40bG6YiiowICUXj16tXDuXPn0KNHD/z444+4fPkylixZgl9//ZV1NJnt2LEDP/30E/r06YPLly9jxYoV2L59O06cOAErKyvW8ZRSkyZN8PTpUzRr1gwtWrTAuXPnYGZmhuvXr/NmbJq2tjbu378v1SMgOTlZ4omSvLOwsMDKlSuxb98+cYf84uJirFy5Ej169GCcTjYzZszAqlWrsGPHDlSrxt+3ZkW5jmHDhuHcuXOYOnUq6yifzc3NDX5+frCzs4ORkZHM01fkjaenJ7y8vGBubg5dXV3eXgff0TQb+TJt2jQ0bNgQa9euxcGDBwGUbgc9cOAABg0axDgdUXS0hYEotNjYWPHXd+/exbhx4zBo0CAsXLhQ4nV8GKFUUFAAOzs7xMTEICcnB8eOHUOfPn1Yx1JaCxYsgJaWFn799VccOHAAY8aMgZ6eHtLT0+Hu7g4fHx/WESs1ZcoUXLlyBUeOHEGLFi0AlBYPhg4dik6dOmHHjh2ME8omISEBFhYWqFWrlnh81eXLl/H27VtcvHgRRkZGjBNWbsiQIQgKCoKmpiaMjY2lCjjyuiz4Y3y+jk2bNom/zsnJwbp162BnZwdjY2Px9rcyfOhxUrduXQQEBEhtUeIbXV1drF69GmPHjmUdhSiQoKAgrF+/XqIX088//8yLz1VFRUXw9vaGi4sLNbAkTFABgSg0FRUViVFJZf/cP/5anpc5f/ih9t27d1i+fDn69euH3r17S7yODx9oFdmVK1dw5coVtGrVCgMHDmQdRyZv3ryBjY0NoqKixB9CHj16hJ49e+Lw4cMVTi+RR0+ePMHmzZsRExMDNTU1mJiYYObMmahduzbraDJxdnb+5Hm+PP3j83XI2ghOIBDg/v373zjNl2vUqBFCQkLQunVr1lG+SJ06dRAZGSkucvJVUVERQkJCkJKSgtGjR0MkEuHJkyfQ0tKCpqYm63hK5c8//4Sbmxt++ukndO3aFQBw9epVBAYGYv369ZgxYwbjhJXT1NREXFwcTRkiTFABgSg0RRihJMuHWr58oC1PcXExbt++jebNm0NHR4d1HKXDcRzOnz8vceNd2dhQQoj8W7t2Le7fv4/Nmzfzetn//PnzoampyZveMuV58OABbGxskJ6ejvz8fCQmJsLAwABubm7Iz8/H1q1bWUdUKk2aNMGCBQswc+ZMieNbtmyBt7c3Hj9+zCiZ7AYNGgQHB4dye1MQ8q1RAYEQ8l39/PPPMDY2xoQJE1BcXAxLS0tERERAXV0dJ0+eRK9evVhHlElFY6AEAgFq1qyJli1b8mK0FZEfivKEUlGug48cHBwkvr948SJq166N9u3bS23DkOftJB9yc3NDQEAATExMYGJiInUd69atY5RMdoMHD4ZIJIKvry/q1KmDmJgYGBgYICQkBJMmTaKO+d+ZpqYmbt26JTW5JykpCaampsjOzmaUTHZbt26Fp6cnHB0d0bFjR6ntYnwYRUn4iwoIhJDvqkmTJjh69CjMzc1x9OhRzJgxA8HBwdi9ezcuXryI8PBw1hFl8uH2mA99uGWmR48eOHr0KK2sIJVSlCeUinIdHh4e5R7/sEA4aNAgudsiU9kWkg/J83aSD32qUbBAIODFNIk6deogIiICbdq0gUgkEhcQ0tLSYGhoiNzcXNYRlcro0aNhamqKuXPnShxfs2YNoqKisH//fkbJZKcIoygJf/G3RTIhhJdevHiBhg0bAgBOnTqFYcOGoXXr1nBxceHV6KHz589j4cKFWLFiBTp37gwAiIyMxOLFi7Fo0SJoa2tjypQpmDNnDnx9fRmnJfLOzc0N5ubmiImJQZ06dcTHhwwZgkmTJjFMVjWKch03b95EdHQ0iouL0aZNGwBAYmIihEIh2rZtiz///BOzZ89GWFhYuaNQWeFLUaAqgoODWUf4YiUlJeXe0D169AgikYhBIuVmaGiIFStWICQkRKIHQnh4OGbPni3Re0pe+0spwihKwl+0AoEQ8l01b94cf//9N3r37g19fX389ddfsLOzQ3x8PHr06IHXr1+zjigTIyMjbN++XTx/uUx4eDgmT56M+Ph4XLhwAS4uLkhPT2eUUnEdP34c/fv3l1rOzFeK8oRSUa5jw4YNuHz5Mnbt2gUtLS0ApU1HJ06ciB49emDSpEkYPXo03r9/j7NnzzJOWz5ra+tym6G+ffsWgwcP5sWTe0UxYsQIaGtrY/v27RCJRIiNjUW9evUwaNAgNGvWTCELP/JM0RqmEvK90QoEQsh35ezsjOHDh4vneZeNTLp27Rratm3LOJ3sUlJSxDcWH9LS0hJ/4GjVqhVevHjxvaN9koeHB5YvXw4NDQ1cunQJ3bp1Q7Vq/HsrGDJkCDIyMlCvXj0IhUI8ffoU9evXZx3rsynKE0pFuY7ff/8d58+fl/gd19bWxrJly9C3b1+4ublhyZIl6Nu3L8OUnxYSEoKCggKp43l5ebh8+TKDRJ8vKioKBw8eRHp6utQ18aGXw9q1a9GvXz8YGhoiLy8Po0ePRlJSEurWrYt9+/axjqd0UlNTWUf4Kvg8ipLwW8UbaAgh5BtYtmwZduzYgcmTJyM8PBw1atQAAAiFQixYsIBxOtl17NgRc+fOxfPnz8XHnj9/jnnz5qFTp04AShsyNW3alFXEcv3xxx/iBlFWVlZ49eoV40Sfp169erh69SoAiHtO8Fnfvn2xYcMG8fcCgQDZ2dlYunQpbG1t2QWrIkW5jjdv3uDZs2dSx58/f463b98CAGrVqlXuDTprsbGxiI2NBQAkJCSIv4+NjcXNmzfh6+uLxo0bM04pu/3796Nbt264c+cOjhw5gsLCQsTHx+PixYvQ1tZmHU8mTZo0QUxMDH799Ve4u7vD1NQUPj4+uHnzJq8Ln4ruwwcC8ubPP/+EjY0NRCIR3Nzc4ObmBi0tLdja2mLLli2s4xEFR1sYiMIyNTWV+aYiOjr6G6f5OlJSUrBr1y6kpKRg48aNqF+/Pk6fPo1mzZqhffv2rOPJJCAgACNGjBAXDsoUFBRg//79GDduHKNkVXPv3j0MGjQIqamp4iLBw4cPYWBggGPHjqF169Y4evQo3r17h7FjxzJO+39atWqF4cOHo2/fvrCyssKRI0cqbPIoz+Mcly1bBi8vL5l+x/nQTOrRo0fo168fOI5DUlISzM3NxU8oL126xJubDEW5DkdHR1y5cgVr164VFwSvX7+OOXPmoFu3bti9ezf2798vbromT8oavAKQavIKAGpqavjjjz/g4uLyvaN9FhMTE0yZMgUzZswQb4vR19fHlClToKurC09PT9YRiYL6cBuWvFGEUZSEv6iAQBTWhx8q8vLy8Oeff8LQ0FCiYU58fDymT5+OlStXsoops9DQUPTv3x/du3fHpUuXcOfOHRgYGMDHxwdRUVEIDAxkHVEmFS03f/nyJerXr8+Lm70yJSUlOHfuHBITEwEAbdq0wY8//vjJ7sisHT16FFOnTsWzZ8/KnSJRhg9dnO/evYvk5GTY29tj165dUnu9ywwaNOj7BvtMRUVF2L9/P2JjY5GdnQ0zMzM4OjpCTU2NdbQqUYTryM7Ohru7OwICAlBUVAQAqFatGpycnLB+/XpoaGjg1q1bAIAffviBXdByPHjwABzHwcDAAJGRkahXr574nKqqKurXrw+hUMgwYdVoaGggPj4eenp6qFOnDkJCQmBsbIw7d+7A2toaT58+ZR2xUjT2l5/kuYCgCKMoCX9RAYEohYkTJ0JXVxfLly+XOL506VI8fPgQO3fuZJRMdl27dsWwYcPg4eEh8aYWGRkJBwcHPHr0iHVEmaioqCAzM1PiQy0AxMTE8HpJPd9kZ2dDS0sL9+7dq/CpMF+WB3t6emLu3LlQV1dnHeWz5eXloWbNmqxjfDFFuY4y2dnZ4iXMBgYG0NTUZJxI+TRp0gSnT5+GsbExTExM8Msvv2DUqFG4cuUKbGxs8ObNG9YRK0Vjf/lJngsIijCKkvAX/zpnEfIZ/v3333KXmY4ZMwbm5ua8KCDcvn0be/fulTpev359uWvUV56yLSUCgQC9e/eWaNxXXFyM1NRU2NjYMEyoXDQ1NREcHAx9fX1eNlH80NKlSwGU7k+/d+8egNLVIB8XqeRZ/fr1MWTIEIwZMwa9e/eW61Usn6Io11FGU1MTJiYmrGN8FkV56m1hYYHz58/D2NgYw4YNg5ubGy5evIjz58+jd+/erOPJhMb+kq9NEUZREv7i96dGQmSkpqaG8PBwtGrVSuJ4eHg4b56W1apVC0+fPpX6wHfz5k1eNMQaPHgwAODWrVvo16+fxJM8VVVV6OnpYejQoYzSKSdLS0sUFxfj0KFD4i7OhoaGGDRoEK+WOOfm5mLmzJnYvXu3eNuFUCjEuHHj8Mcff/BiZYK/vz/27t2LQYMGQVtbGyNGjBAXOPmEz9fh4OAAPz8/aGlpwcHB4ZOv5UPn/8GDByvEU+/NmzcjLy8PALBw4UJUr14dERERGDp0KBYtWsQ4nWzc3Nykxv727t0bNWvWFI/93bBhA2/6UigLeW7O6+vrCx0dHSQkJCAhIUF8vFatWhJFKIFAQAUE8tVRAYEohZ9//hnTpk1DdHS0uPp/7do17Ny5E4sXL2acTjYjR47E/Pnz8e+//0IgEKCkpATh4eGYM2cOLxoPlj0l1tPTw4gRI3hTuFFkycnJsLOzw6NHj9CmTRsAwMqVK9G0aVP8999/aNGiBeOEsnF3d0doaCiOHz+O7t27AwDCwsIwa9YszJ49G3/99RfjhJUbMmQIhgwZgnfv3iEwMBD79u1Dly5dYGBggDFjxmDJkiWsI8qEz9ehra0tvmHgy/adT1GUp961a9cWf62iosKraT1l+Dr2V9nJ8y5vRRlFSfiJeiAQpXHw4EFs3LhRYl6um5sbhg8fzjiZbAoKCjBjxgz4+fmhuLgY1apVQ3FxMUaPHg0/Pz9ePTEGgBs3boh/Fu3bt4epqSnjRMrH1tYWHMfhn3/+EX9If/nyJcaMGQMVFRX8999/jBPKpm7duggMDESvXr0kjgcHB2P48OESozb5JCEhAY6OjoiNjZX7hpafoijXwTdGRkZST72B0pV3ZU+9L1y4ABcXF6SnpzNKKZvi4mIcOXJEaqUUX7Zf9ejRAyKRCAEBAeKtVc+fP8e4ceOQk5ODS5cu4cKFC5gxY4Z4Gxb59goKCpCamooWLVqU+28pLCwMnTp1kpoaxSdaWlq4deuWXPZxIPxFBQRCeObhw4e4ffs2srOzYWpqKrUtQ949e/YMI0eOREhIiLhrflZWFqysrLB//3653rdeNv9dFuU9bZI3GhoauHr1KoyNjSWOx8TEoHv37rzp4qyuro4bN26gXbt2Esfj4+PRuXNn5OTkMEpWdXl5eTh+/Dj27t2LM2fOoEGDBhg1ahR8fHxYR6sSRbiOoqIihISEICUlBaNHj4ZIJMKTJ0+gpaXFi2aKampquH79OoyMjCSO3759G507d8b79+/x4MEDtGvXDrm5uYxSVi4+Ph729vbIyMgQr5RKTExEvXr1cOLECanrk0d8HfurqHJzc+Hq6gp/f38Apf+eDAwM4OrqisaNG/NylUtF5LkRJOEvfpRuCfkKsrKyEBgYiPv372POnDmoXbs2oqOj0aBBA170ECjTtGlTNG3aFMXFxbh9+zZev34t1/tXP+bq6op3794hPj5efMOXkJAAJycnzJo1C/v27WOcsGK1atWqdE9k2d5iPjxprVGjBt69eyd1PDs7G6qqqgwSfZ6uXbti6dKlCAgIEG+Nef/+PTw9PcXNpeTd2bNnsXfvXhw9ehTVqlXDTz/9hHPnzsHCwoJ1tCpRlOt48OABbGxskJ6ejvz8fPz4448QiURYtWoV8vPzsXXrVtYRK9WxY0fMnTtX6qn3vHnz0KlTJwClI9/Kbmjl1cSJE9G+fXtERUWJ3+tev36N8ePHY/LkyYiIiGCcsHJt2rRBQkLCJ8f+lvUJIt/eL7/8gpiYGISEhEg0b+7Tpw+WLVumUAUEQr4FWoFAlEJsbCz69OkDbW1tpKWl4d69ezAwMMCiRYuQnp6OgIAA1hEr9fPPP8PY2BgTJkxAcXExLC0tERERAXV1dZw8eVJq+ba80tbWxoULF8QfYMtERkaib9++yMrKYhNMBqGhoTK/1tLS8hsm+TrGjRuH6Oho+Pr6SvQGmTRpEjp27Ag/Pz+2AWUUFxeHfv36IT8/Hx06dABQuoqiZs2aOHv2LNq3b884YeXU1dUxYMAAODo6wtbWFtWrV2cd6bMoynUMHjwYIpEIvr6+qFOnjvgJXkhICCZNmoSkpCTWESulKE+91dTUEBUVJfV7HBcXh06dOuH9+/eMkhG+at68OQ4cOIAuXbpIPKFPTk6GmZlZlVYbyjtagUC+BVqBQJSCh4cHxo8fj9WrV0MkEomP29raYvTo0QyTyS4wMBBjxowBAJw4cQL379/H3bt3sXv3bixcuBDh4eGME8qmpKSk3JuK6tWro6SkhEEi2fGhKFAVmzZtgpOTE7p27Sr+mRQVFcHe3h4bN25knE52RkZGSEpKwj///IO7d+8CAEaNGgVHR0eoqakxTiebzMxMib9NfKUo13H58mVERERIrcTR09PD48ePGaWqGkV56t26dWtkZmZKFRCePXuGli1bMkpVdUFBQQgKCsKzZ8+k3uv4MEpakTx//hz169eXOp6TkyPXkxcIkRdUQCBK4fr169i2bZvU8caNGyMjI4NBoqp78eIFGjZsCAA4deoUhg8fjtatW8PFxYVXN3vW1tZwc3PDvn370KhRIwDA48eP4e7uzpuZ3mUuX76Mbdu24f79+/j333/RuHFj7N69G/r6+ujRowfreJWqVasWjh07huTkZInmonz6UF5GXV0dkyZNYh3js4lEIhQXF+Po0aO8HqmpKNdRUlJS7jakR48e8apAoqKiAhsbG4ll2nyzcuVKzJo1C8uWLUOXLl0AlM679/LywqpVqySeFstr7xlPT094eXnB3Nwcurq6dJPKmLm5Of777z+4uroC+L9xjTt27ODNtjdZ0b818i1QAYEohRo1apS7JK2sERMfNGjQAAkJCdDV1cWZM2fEo+lyc3N59cF88+bNsLe3h56ensSyWiMjI+zZs4dxOtkdOnQIY8eOhaOjI6Kjo5Gfnw8AePPmDby9vXHq1CnGCWXXsmVLXhYNFElycjJsbW3x+PFjXo/UVJTr6Nu3LzZs2IDt27cDKP0Qnp2djaVLl8LW1pZxOtkpwlPvAQMGAACGDx8uvhkq2307cOBA8ffy3Htm69at8PPzk+utIsrE29sb/fv3R0JCAoqKirBx40YkJCQgIiKiSlsV+YB2qpNvgXogEKUwceJEvHz5EgcPHkTt2rURGxsLoVCIwYMHw8LCAhs2bGAdsVLLli3Dhg0boKuri9zcXCQmJqJGjRrYuXMn/v77b1y5coV1RJlxHIcLFy6Il5u3a9cOffr0YZyqakxNTeHu7o5x48ZJ7DG8efMm+vfvz5uVLUQ+KMpITUW5jkePHqFfv37gOA5JSUkwNzdHUlIS6tati0uXLpW7/FneVPbU+8iRI4ySVY0i9J6pU6cOIiMjeVNAUwYpKSnw8fFBTEwMsrOzYWZmhvnz50tNJZJ3yjCKksgfKiAQpfDmzRv89NNPiIqKwrt379CoUSNkZGSga9euOHXqFDQ0NFhHlElgYCAePnyIYcOGoUmTJgAAf39/1KpVC4MGDWKcTrmoq6sjISEBenp6EgWE+/fvw9DQEHl5eawjEh5RlJGainIdQGk/kP379yM2NlZ8g8Gnvhq6urpYvXo1PfWWA/Pnz4empiYWL17MOgpREMo0ipLIH9rCQJSCtrY2zp8/j7CwMIkPg3x76v3TTz9JHXNycmKQ5MsowrLahg0bIjk5GXp6ehLHw8LCqNsxqTJFGampKNcBANWqVRM3ruWjgoICdOvWjXWML3bmzBloamqK+8ps2bIFf//9NwwNDbFlyxZejDHOy8vD9u3bceHCBZiYmEg1El63bh2jZMqruLgYR44ckerVUt5TfHlEoygJS/z4LSHkK+nRowfMzc1Ro0YNXjSW2bRpEyZPnoyaNWti06ZNn3ztrFmzvlOqL6MozaQmTZoENzc37Ny5EwKBAE+ePMGVK1cwZ84cesrEQFZWFgIDA5GSkoK5c+eidu3aiI6ORoMGDdC4cWPW8So1YMAATJ48WWqk5tSpU2Fvb884newU5TqaNWuGXr16wdLSElZWVrwsCk6cOBF79+7l/d+juXPnYtWqVQCA27dvw8PDA7Nnz0ZwcDA8PDywa9cuxgkrFxsbix9++AFA6fjJD/H1PZDP4uPjYW9vj4yMDHGvllWrVqFevXo4ceIEjIyMGCes3NGjR8WjKD/8N9S+fXukpKQwTEaUAW1hIEqhpKQEK1aswNatW5GZmSle6rV48WLo6elhwoQJrCOWS19fH1FRUahTpw709fUrfJ1AIMD9+/e/Y7LPpyjLajmOg7e3N1auXInc3FwApU9f58yZg+XLlzNOJ7uySRIpKSkIDAzk3SQJoPTDeZ8+faCtrY20tDTcu3cPBgYGWLRoEdLT0xEQEMA6YqWysrLg5OSEEydOSI3U9PPzg7a2NuOEslGU69izZw8uXbqEkJAQJCcno3HjxrC0tISlpSV69eqFVq1asY5YKTc3NwQEBMDExITXT701NTURFxcHPT09LFu2DHFxcQgMDER0dDRsbW2p3wypsq5du6JevXrw9/cXr2B5/fo1xo8fj+fPnyMiIoJxwsqpq6sjLi4OBgYGEtsoY2JiYGFhgTdv3rCOSBQYFRCIUvDy8oK/vz+8vLwwadIk8R/dAwcOYMOGDbxqQMh3itZMqqCgAMnJycjOzoahoSE0NTVZR5LZh5Mkdu/ejYSEBBgYGGDz5s04deoUbyZJ9OnTB2ZmZli9erXEB6mIiAiMHj0aaWlprCPKTBFGagKKcx0A8PTpU4SGhuLkyZM4cOBAhSMe5Y2VlVWF5wQCAS5evPgd03y+2rVrIywsDIaGhujRowfGjRuHyZMnIy0tDYaGhuICLiGyUlNTQ1RUFNq3by9xPC4uDp06dcL79+8ZJZOdhYUFhg0bBldXV4hEIsTGxkJfXx+urq5ISkrCmTNnWEckCoy2MBClEBAQgO3bt6N3796YOnWq+HiHDh3EkwDI96Eoy2rLqKqqwtDQkHWMz/Lbb79h69atGDduHPbv3y8+3r17d/z2228Mk1XN9evXsW3bNqnjjRs35t3TSUUZqakI15Gbm4uwsDCEhIQgODgYN2/ehJGREXr16sU6mkyCg4NZR/gqevToAQ8PD3Tv3h2RkZE4cOAAgNKmcWXNhPkgKioKBw8eRHp6OgoKCiTOHT58mFEq5dS6dWtkZmZKFRCePXvGm79byjSKksgfKiAQpfD48eNy3xRKSkpQWFjIIFHVubi4fPI8X5oPKkozKSsrq0/uXeXD07179+7BwsJC6ri2tjaysrK+f6DPVKNGDbx9+1bqeGJiIurVq8cgEeG7bt264ebNm2jXrh169eqFBQsWwMLCghcN+z6WnJyMlJQUWFhYQE1NDRzH8Wrf/ebNmzF9+nQEBgbir7/+Evc0OX36tETzOHm2f/9+jBs3Dv369cO5c+fQt29fJCYmIjMzE0OGDGEdT+msXLkSs2bNwrJly9ClSxcAwNWrV+Hl5YVVq1ZJvJ9oaWmxivlJPXr0wK1bt+Dj4wNjY2OcO3cOZmZmuHLlCu9GURL+oQICUQqGhoa4fPkymjdvLnE8MDAQpqamjFJVzevXryW+LywsRFxcHLKysmBtbc0oVdV9qpkUn5RdQ5nCwkLcunULcXFxvJmMoSiTJOzt7eHl5YWDBw8CKF2enZ6ejvnz52Po0KGM0xE+unv3LjQ0NNC2bVu0bdsW7dq1413x4OXLlxg+fDiCg4MhEAiQlJQEAwMDTJgwATo6Oli7di3riDJp1qwZTp48KXV8/fr1DNJ8Hm9vb6xfvx4zZsyASCTCxo0boa+vjylTpkBXV5d1PKUzYMAAAMDw4cPFxbSyHd0DBw4Ufy8QCOR6u1KLFi3w999/s45BlBAVEIhSWLJkCZycnPD48WOUlJTg8OHDuHfvHgICAsr9YCKPjhw5InWspKQE06ZN41U/gU8tqy1v/Ju8qujD67Jly3gz615RJkmsXbsWP/30E+rXr4/379/D0tISGRkZ6Nq1K1asWME6HuGhly9f4vbt2wgJCcHZs2excOFCqKqqiqcyTJo0iXXESrm7u6N69epIT09Hu3btxMdHjBgBDw8P3hQQFEFKSgrs7OwAlG57y8nJgUAggLu7O6ytreHp6ck4oXK5ePEir1bhVITvoygJj3GEKIlLly5xffr04erVq8epqalx3bt3586ePcs61he7e/cu17BhQ9YxKrVu3bpPnn/79i3XrVu375Tm20lKSuJ0dHRYx5BJSUkJ99tvv3EaGhqcQCDgBAIBV7NmTW7RokWso32WsLAwbsuWLdyqVau48+fPs45TqSFDhnBv3rzhOI7j/P39uby8PMaJSHlKSkq469evc05OTly1atU4FRUV1pFk0qBBA+7WrVscx3GcpqYml5KSwnEcx6WkpHAaGhosoymdxo0bc7GxsRzHcZyxsTG3d+9ejuM4LiIigtPS0mIZjfBUXFwcZ2BgwKmrq3Ompqacqakpp6Ghwenp6XG3b99mHY8oOCpREaXRs2dPnD9/nnWMry4lJQVFRUWsY1Tq119/RZ06dTBu3Dipc9nZ2bCxscHLly8ZJPu6rly5gpo1a7KOIROBQICFCxdi7ty5vJ0kAZQ2SR0xYgS6d++O7t27i48XFBSI9x7Lo5MnTyInJwdaWlpwdnaGjY0N6tevzzrWF4mNjS33uEAgQM2aNdGsWTPUqFHjO6equujoaISEhCAkJARhYWF49+4djI2N4erqCktLS9bxZJKTkwN1dXWp469eveLFz0CRWFhY4Pz58zA2NsawYcPg5uaGixcv4vz58+jduzfreEpHX18fzs7OGD9+PJo1a8Y6zmeZOHEi2rdvj6ioKKlRlJMnT+bFKErCXzTGkSiVqKgoiaVeHTt2ZJxIdh4eHhLfcxyHp0+f4r///oOTkxM2b97MKJlsAgMDMXbsWBw4cAD29vbi4zk5OejXrx+ePXuG0NBQ3uwHdXBwkPi+7OcRFRWFxYsXY+nSpYySyc7FxQUbN26ESCSSOJ6TkwNXV1feNOYUCoV4+vSp1M33y5cvUb9+fbndw2piYgIzMzNYWVnB2dkZmzZtqrBhl7wWQT6moqLyyaXB1atXx4gRI7Bt2za5LrRVq1YNpqamsLS0hKWlJSwsLKCtrc06VpXY2tqiY8eOWL58uXjMW/PmzTFy5EiUlJQgMDCQdUSl8erVK+Tl5aFRo0YoKSnB6tWrERERgVatWmHRokW866/Bdxs2bICfnx/i4uJgZWWFCRMmYMiQIbwqrCnCKErCX1RAIErh0aNHGDVqFMLDw1GrVi0AQFZWFrp164b9+/fzYhTUxzO9VVRUUK9ePVhbW8PFxYUXe9527NgBNzc3/Pfff+jVqxdycnJgY2ODjIwMhIaGolGjRqwjyszZ2Vni+w9/Hn379mWUqmoquvF+8eIFGjZsyIuVLUDp//vMzEypiQsxMTGwsrLCq1evGCX7tPDwcMyePRspKSl49eoVRCJRuTffAoFAbq/hY8eOHcP8+fMxd+5cdO7cGQAQGRmJtWvXYunSpSgqKsKCBQswYsQIrFmzhnHair19+1Zuu6/LKi4uDr1794aZmRkuXrwIe3t7xMfH49WrVwgPD+dV7xxF9v79e6ipqbGOoZSio6Ph5+eHffv2obi4GKNHj4aLiwvMzMxYR6tUhw4dsH79eqkm2hcvXoSbmxtu377NKBlRBlRAIErBxsYGWVlZ8Pf3R5s2bQCUjrBzdnaGlpYWzpw5wzjhp3Ech4cPH6JevXq8/6CxevVqrFixAseOHcOSJUvw+PFjhIaG8qKIAwD379+Hvr4+rxswvX37FhzHQUdHB0lJSRI33sXFxThx4gQWLFiAJ0+eMExZOVNTUwgEAsTExKB9+/YSRbTi4mKkpqbCxsZGPJ1BnqmoqCAjI4P3Wxg6d+6M5cuXo1+/fhLHz549i8WLFyMyMhJHjx4VF07It/XmzRts3rwZMTExyM7OhpmZGWbMmMGblV4AMGTIkAoLazVr1kTLli0xevRo8Xs7X+Tn52PLli1YvXo1MjIyWMdRaoWFhfjzzz8xf/58FBYWwtjYGLNmzYKzs7PcvtefOnUK8+bNK3cUpY+PD3r06CF+Ld+LoUT+UAGBKAU1NTVERERIjWy8ceMGevbsidzcXEbJZFNSUoKaNWsiPj4erVq1Yh3niy1YsAC///479PT0EBISgqZNm7KOJLOPn9qPGDECmzZtQoMGDRgnk11ly8wFAgE8PT2xcOHC75iq6so6l3t6emL27NkSvRtUVVWhp6eHoUOHQlVVlVXET3JwcICfnx+0tLTg7++P4cOH875AqKamhps3b6Jt27YSx+/evQtTU1O8f/8eaWlpMDQ0lPu/u4rq0aNH8PLywvbt21lHkcn48eNx9OhR1KpVS7ztMDo6GllZWejbty9iYmKQlpaGoKAgiR4o8iA/Px/Lli3D+fPnoaqqinnz5mHw4MHYtWsXFi5cCKFQiJkzZ2L+/PmsoyqlwsJCHDlyBLt27cL58+fRpUsXTJgwAY8ePcKWLVtgbW2NvXv3so5ZLhUVFfHXH4+i/PB7eR9FSfhJ/tc8E/IVNG3aFIWFhVLHi4uLebFsXkVFBa1atcLLly95W0D4uGdA9erVUbduXbi5uUkcP3z48PeMVWUf11xPnTqFlStXMkrzeYKDg8FxHKytrXHo0CHUrl1bfE5VVRXNmzfnxe9FWZ8JPT09jBgxQq731JfnwyaKLi4u6N+/P+8LCG3btoWPjw+2b98uLtwUFhbCx8dHXFR4/Pgxrwpuiubly5fw9fXlTQGhYcOGGD16NDZv3iy+aSopKYGbmxtEIhH279+PqVOnYv78+QgLC2OcVtKSJUuwbds29OnTBxERERg2bBicnZ1x9epVrFu3DsOGDYNQKGQdU2mUNdyNj4/Hrl27sG/fPqioqGDcuHFYv369ROFzyJAh6NSpE8O0n6YooygJP1EBgSiF33//Ha6urtiyZQvMzc0BlDZUdHNzk+t9uB/y8fHB3Llz8ddff8HIyIh1nCr7uAHZqFGjGCUhZV3kU1NT0bRpU4knGXzk5OTEOsJnadu2LX755RdYWVmB4zgcPHiQ900Ut2zZAnt7ezRp0gQmJiYAgNu3b6O4uBgnT54EULoNaPr06SxjEh7x9fVFeHi4xN8pFRUVuLq6olu3bvD29sbMmTPRs2dPhinL9++//yIgIAD29vaIi4uDiYkJioqKEBMTQzd/DJRNu+nUqRN+/PFH/PXXXxg8eDCqV68u9Vp9fX2MHDmSQUrZ9OrVi3UEosRoCwNRCjo6OsjNzUVRUZF4n3TZ1xoaGhKvlddmZR9eg6qqqtSTSnnNrWiEQiEyMjLEfQPKupvr6+szTvb5cnNzkZ6ejoKCAonjZTeA8q64uBjr16/HwYMHy70Oef3diIiIgIeHh0I1UQSAd+/e4Z9//kFiYiIAoE2bNhg9erTUtA95FhwcLNW4VlHExMTAzMyMN8uadXR04O/vLzG9BwCOHz8OJycnvH79GklJSejcuTNev37NKGX5VFVVkZqaisaNGwMo3eITGRkJY2NjxsmUU1mvmffv36N58+as43wRRRhFSfiLViAQpbBhwwbWEb6YIlyDIuA4DuPHjxePe8rLy8PUqVOlClHyvhUDAJ4/fw5nZ2ecPn263PN8ucHw9PTEjh07MHv2bCxatAgLFy5EWloajh49iiVLlrCOV6Fu3brh6tWrAEo/2CYmJvK+iSJQWlSbOnUq6xhfxMbGBk2aNIGzszOcnJx41adF0YwdOxYTJkzAr7/+Kl5Sfv36dXh7e4tX5oSGhkqNs5MHxcXFEj1YqlWrJtGrhXx/AoGA98UDAHBzc4Ofnx+8vLx4O4qS8BetQCCEkCr4eHxjRXbt2vWNk3w5R0dHPHjwABs2bECvXr1w5MgRZGZm4rfffsPatWthZ2fHOqJMWrRogU2bNsHOzg4ikQi3bt0SH7t69arcNsH60IMHD9CsWTOFWNaclJSE4OBgPHv2DCUlJRLn5Lmg86EXL15g9+7d8Pf3R3x8PKytrTFhwgQMHjxYbptylvm438zHsrKyEBoaypsCYXFxMXx8fLB582ZkZmYCABo0aABXV1fMnz8fQqEQ6enpUFFRkbtpPioqKujfv7/4pu7EiROwtrbmZcFZEaioqMDIyKjSsdfR0dHfKdGX4/MoSsJfVEAgSiE6OhrVq1cXLxs8duwYdu3aBUNDQyxbtkzuPxACpc36hEKh1Hi0c+fOobi4GP3792eUjPCVrq4ujh07hs6dO0NLSwtRUVFo3bo1jh8/jtWrV8tdQ7KKaGho4M6dO2jWrBl0dXXx33//wczMDPfv34epqSnevHnDOqJMsrKy4Ovrizt37gAADA0NMWHCBKn+IfLs77//xrRp01C3bl00bNhQoiAiEAh49cG8THR0tLjhGgCMHj0aEyZMQIcOHRgnK58iFTk/9vbtWwD8GUunyD8LPlJRUZGa2FOesga9fMLHUZSExzhClIC5uTkXGBjIcRzHpaSkcDVq1OBGjRrFtWzZknNzc2MbTkbGxsbcf//9J3X89OnTnImJCYNEhO9EIhGXmprKcRzHNWvWjAsLC+M4juPu37/PqampMUxWNa1bt+auXr3KcRzHde/enVu5ciXHcRy3f/9+rl69eiyjyez69etc7dq1ucaNG3NDhgzhhgwZwjVp0oSrU6cOd+PGDdbxZNasWTPOx8eHdYyv7vHjx9zSpUu5GjVqcBoaGpxQKOR69OjBxcXFsY5GCJGRQCDgMjMzWcf4qgoKCrgDBw5wNjY2nFAo5Lp3787t3LmT8/Ly4ho0aMCNGjWKdUSigPjdepsQGSUmJuKHH34AUNoV2dLSEnv37oWfnx8OHTrENpyMkpKSYGhoKHW8bdu2SE5OZpCI8F2bNm1w7949AECHDh2wbds2PH78GFu3boWuri7jdLIbMmQIgoKCAACurq5YvHgxWrVqhXHjxsHFxYVxOtm4u7vD3t4eaWlpOHz4MA4fPozU1FQMGDAAP//8M+t4Mnv9+jWGDRvGOsZXUVhYiMDAQNja2qJ58+Y4e/aseBl9cnIymjdvrjDXKs8yMzMxduxYNGrUCNWqVYNQKJT4jxBZKcKT+ICAAOTn5yM6Ohqurq7Q1dXFzJkz0b59e8TFxSEsLAzOzs5YvHgxLly4gCNHjrCOTBQQNVEkSoHjOPFe3AsXLmDAgAEAgKZNm+LFixcso8lMW1sb9+/fh56ensTx5ORkqf2U8ub48eMyv/bjTtvk23Fzc8PTp08BlC7ZtLGxwT///ANVVVX4+fmxDVcFPj4+4q9HjBiB5s2bIyIiAq1atcLAgQMZJpNdVFQU/v77b4m9udWqVcO8efPEo2f5YNiwYTh37hzvmyi6urpi37594DgOY8eOxerVqyXG52poaGDNmjVo1KgRw5TKYfz48UhPT8fixYuhq6urEDeBhA1OAXZtK9IoSsJf1AOBKAVra2s0bdoUffr0wYQJE5CQkICWLVsiNDQUTk5OSEtLYx2xUlOmTMGVK1dw5MgRtGjRAkBp8WDo0KHo1KkTduzYwThhxT6c3/0pAoGAN429FFFubi7u3r2LZs2aoW7duqzjyKSwsBBTpkzB4sWLeT1Ks0GDBti9ezf69u0rcfzs2bMYN26cuHmcvFu5ciXWrVsHOzs7GBsbS32onTVrFqNkVdO7d29MnDgRDg4OFXY1LyoqQnh4OCwtLb9zOuUiEolw+fJl8SpCQj6XIjSrVaRRlIS/qIBAlEJsbCwcHR2Rnp4ODw8PcYMcV1dXvHz5khdd2t+8eQMbGxtERUWJO00/evQIPXv2xOHDh1GrVi22AQmvFBYWom3btjh58iTatWvHOs4X0dbWxq1bt3hdQJg1axaOHDmCNWvWoFu3bgCA8PBwzJ07F0OHDuXNGNdP/QwEAgHu37//HdN8vkuXLqFbt25S3dqLiooQEREBCwsLRsmUj6GhIf755x+YmpqyjkIIcyoqKsjMzES9evVYRyFKjAoIRKnl5eVBKBSWu/RLHnEch/PnzyMmJgZqamowMTGhD7LkszVu3BgXLlzgfQHByckJP/zwA9zd3VlH+WwFBQWYO3cutm7diqKiIgBA9erVMW3aNPj4+NBs7+9MKBTi6dOnqF+/vsTxly9fon79+rRS6js6d+4c1q5di23btklt4SNE2SjiKErCP1RAIIR8c5s2bZL5tXxZ4qwIvL29kZiYiB07dlT6YUSe/fbbb1i7di169+6Njh07SvUE4dO/qdzcXKSkpAAAWrRoAXV1dcaJlFNFT/kSExNhbm4uHidIvj0dHR3k5uaiqKgI6urqUgX/V69eMUpGyPenyKMoCX9QAYEQ8s3JurScT0ucFUHZ9AJNTU0YGxtL3XgfPnyYUbKqUZRl80BpX5OUlBRYWFhATU0NHMfJ/X5dDw8PLF++HBoaGvDw8Pjka9etW/edUn0eBwcHAMCxY8dgY2MjsfKjuLgYsbGxaNOmDc6cOcMqotLx9/f/5HknJ6fvlIQQ9sp6IHy8OoqQ74m/j5wIIbyRmprKOgIpR61atTB06FDWMb6YIvz7evnyJYYPH47g4GAIBAIkJSXBwMAAEyZMgI6ODtauXcs6YoVu3ryJwsJC8dcVkfdCCFDaTwMo3S4mEomgpqYmPqeqqoouXbpg0qRJrOIpJSoQkG/h8uXL2LZtG1JSUhAYGIjGjRtj9+7d0NfXR48ePVjHqxAf/o4SxUcFBEIIUVK7du1iHYH8f+7u7qhevTrS09MlelKMGDECHh4ecl1ACA4OLvdrPir7ndDT08OcOXPkfkSuonr79i20tLTEX39K2esIkdWhQ4cwduxYODo64ubNm8jPzwdQ2qza29sbp06dYpywYrRwnMgD2sJAlEJeXh5q1qxZ7rmnT59CV1f3OydSbo8ePcLx48eRnp6OgoICiXPyvsSZkG+hYcOGOHv2LDp06ACRSISYmBgYGBjg/v37MDExQXZ2NuuISunZs2e4d+8eAKBNmza0bPg7+bCJpYqKSrlPXcu291BDS1JVpqamcHd3x7hx4yT+3t68eRP9+/dHRkYG64gVUoRRlIT/aAUCUQpmZmbYu3ev1BzpQ4cOYerUqXj+/DmbYJWoSqMuvjyFCQoKgr29PQwMDHD37l0YGRkhLS0NHMfBzMyMdTxCmMjJySm3YeKrV6/kfgJDWd8AWfClr8a7d+8wffp07N+/X3yDKhQKMWLECGzZskW81YF8GxcvXkTt2rXFX9PNEvma7t27V+4EK21tbWRlZX3/QFXQvHlz1hEIoQICUQ69evVCly5d4Onpifnz5yMnJwczZszAwYMHsWLFCtbxKlSrVi2ZPzjx5SnML7/8gjlz5sDT0xMikQiHDh1C/fr14ejoCBsbG9bxCGGiZ8+eCAgIwPLlywGU7nMtKSnB6tWrYWVlxTjdp314M81xHI4cOQJtbW2Ym5sDAG7cuIGsrKwqFRpYmzhxIm7evImTJ0+ia9euAIArV67Azc0NU6ZMwf79+xknVGyWlpZITU2Fvr4+evXqxToOUTANGzZEcnKy1FjQsLAwGBgYsAlFCI/QFgaiNP777z9MnDgRLVu2xNOnT6GpqYk9e/bAyMiIdbQKhYaGir9OS0vDggULMH78eIkPtP7+/li5ciVvGk2JRCLcunULLVq0gI6ODsLCwtC+fXvExMRg0KBBSEtLYx2R8Ex6ejqaNm0qVWzjOA4PHz5Es2bNGCWTXVxcHHr37g0zMzNcvHgR9vb2iI+Px6tXrxAeHo4WLVqwjiiT+fPn49WrV9i6dSuEQiGA0uLm9OnToaWlhd9//51xQtloaGjg7NmzUs3ULl++DBsbG+Tk5DBKpjxUVFTQvHlzWFlZwdraGr169UKTJk1YxyIKYOXKldizZw927tyJH3/8EadOncKDBw/g7u6OxYsXw9XVlXVEQuQarUAgSqN///5wcHDAX3/9hWrVquHEiRNyXTwASp/ClPHy8sK6deswatQo8TF7e3sYGxtj+/btvCkgaGhoiPse6OrqIiUlBe3btwcAvHjxgmU0wlP6+vri/dIfevXqFfT19XmxOsfIyAiJiYnYvHkzRCIRsrOz4eDggBkzZvCqR8vOnTsRFhYmLh4ApUv/PTw80K1bN94UEOrUqVPuNgVtbW3o6OgwSKR8Ll68iJCQEISEhGDfvn0oKCiAgYEBrK2tYWVlBSsrKzRo0IB1TMJDCxYsQElJCXr37o3c3FxYWFigRo0amDNnDhUPCJEBrUAgSiElJQWjR49GRkYGduzYgdDQUPz+++9wc3PDihUrUL16ddYRK6Wuro6YmBi0atVK4nhiYiJ++OEH5ObmMkpWNYMHD4adnR0mTZqEOXPm4NixYxg/fjwOHz4MHR0dXLhwgXVEpbFp06ZyjwsEAtSsWRMtW7aEhYWFxM2gPFJRUUFmZibq1asncfzBgwcwNDTkxdPi4ODgCrcqbNmyBTNmzPjOiT6Pjo4O/Pz8MGjQIInjZb/nr1+/ZpSsarZv345///0Xu3fvRsOGDQEAGRkZcHJygoODA6ZMmcI4oXLJy8tDRESEuKAQGRmJwsJCtG3bFvHx8azjEZ4qKChAcnIysrOzYWhoCE1NTdaRqoSvoygJ/1EBgSgFkUgEOzs7bN26FbVq1QIAREREiDvwfmp2ubxo06YNBg0ahNWrV0scnzdvHo4dOybuFC7v7t+/j+zsbJiYmCAnJwezZ89GREQEWrVqhXXr1lGDoO9IX18fz58/R25urvip6uvXr6Gurg5NTU08e/YMBgYGCA4ORtOmTRmnlebh4QEA2LhxIyZNmiTRhLC4uBjXrl2DUChEeHg4q4gyKyuedezYUeL4xo0bsXjx4io1VGXJw8MDAQEB+PXXX9G5c2cAwLVr1+Dj44OxY8fyZsqKqakpkpOTkZ+fL94Ck56ejho1akgVcaOjo1lEVEoFBQUIDw/H6dOnsW3bNmRnZ/NihREhX9uHoyh3796NhIQEGBgYYPPmzTh16pRcj6Ik/EcFBKIUdu/ejbFjx0odf/fuHX7++Wf4+voySFU1p06dwtChQ9GyZUv873//AwBERkYiKSkJhw4dgq2tLeOEhG/27duH7du3Y8eOHeI99snJyZgyZQomT56M7t27Y+TIkWjYsCECAwMZp5VW9sQ+NDQUXbt2haqqqvicqqoq9PT0MGfOHKkbPnm0Y8cO/Prrr7h06RLatm0LAFi7di28vLxw8uRJ9OzZk3FC2ZSUlGDNmjXYuHEjnj59CqB0q5Kbmxtmz54t96tZynh6esr82qVLl37DJMqtoKAAV69eRXBwMEJCQnDt2jU0bdoUFhYWsLCwgKWlJS96nBD5kpOTAx8fHwQFBeHZs2coKSmROH///n1GyWTH51GUhP+ogEAIjzx69Ah//vkn7t69CwBo164dpk6dKpdPhytiYGCA69evo06dOhLHs7KyYGZmxos3bkXRokULHDp0SGq86c2bNzF06FDcv38fERERGDp0qPhmUB45Oztj48aNvBllWpHVq1dj06ZNCAsLw4EDB+Dt7Y1Tp06he/furKN9lrJVE3z/uRA2rK2tce3aNejr68PS0hI9e/aEpaUlr3qCEPk0atQohIaGYuzYsdDV1ZVqwOvm5sYomezU1dWRkJAAPT09iQLC/fv3YWhoiLy8PNYRiQKjJopEqSQkJCA9PV3cxA8o3e89cOBAhqlk16RJE3h7e7OO8UXS0tLKXXKan5+Px48fM0ikvJ4+fYqioiKp40VFReKnF40aNcK7d+++d7Qq2bVrF+sIX8W8efPw8uVLmJubo7i4GGfPnkWXLl1Yx/psilI4yM7OlnpCqSjXJs8uX74MXV1d8QQGS0tLqcIzIZ/j9OnT+O+//3hbnAVoFCVhiwoIRCncv38fQ4YMwe3btyEQCFC28Kas6syXPZRZWVmIjIwsd8nduHHjGKWSzfHjx8Vfnz17VqLDeXFxMYKCgqTeCMm3ZWVlhSlTpmDHjh0wNTUFULr6YNq0abC2tgYA3L59G/r6+ixjVqosa0UuXrz4nZJUTXlNLBs3bgx1dXVYWFggMjISkZGRAIBZs2Z973ifJTMzE3PmzBEvDf54kSNf/tampqZi5syZCAkJkXiSx3EcBAIBb66Dz7KysnD58mWEhIRg1apVGDVqFFq3bg1LS0txQeHjxqmEyEJHRwe1a9dmHeOLTJo0CW5ubti5cycEAgGePHmCK1euYM6cOVi8eDHreETB0RYGohQGDhwIoVCIHTt2QF9fH5GRkXj58iVmz56NNWvW8GJ/8YkTJ+Do6Ijs7GxoaWlJLLkTCAR49eoVw3SVU1FRAQCJAk6Z6tWrQ09PD2vXrsWAAQNYxFNKGRkZGDt2LIKCgsSTSIqKitC7d2/s3r0bDRo0QHBwMAoLC9G3b1/GaSvm7u4u8X1hYSFu3bqFuLg4ODk5YePGjYySfZqshRmBQMCbrT39+/dHeno6Zs6cWe7S4I+nM8ir7t27g+M4uLm5oUGDBlLX8eGIXfJ9vHv3DmFhYeJ+CGVTieLi4lhHIzyzZ88eHDt2DP7+/hLNd/mE4zh4e3tj5cqV4ilcZaMoly9fzjgdUXRUQCBKoW7durh48SJMTEygra2NyMhItGnTBhcvXsTs2bN5MYWhdevWsLW1hbe3N2/f8IDSm6br16+jbt26rKOQ/+/u3btITEwEUDrto02bNowTfR3Lli1DdnY21qxZwzqK0hCJRLh8+bJUXw2+0dTUxI0bNxTmd0ERlJSU4Pr16wgODkZwcDDCwsKQl5dHq0FIlZmamiIlJQUcx0FPT09qlDefJqvwfRQl4SfawkCUQnFxMUQiEYDSYsKTJ0/Qpk0bNG/enDfjDx8/foxZs2bxungAlC4NJvKlbdu24s7/imTMmDHo3LkzFRC+o6ZNm0qtMOKjTp064eHDh1RAYKikpARRUVEICQlBcHAwwsPDkZOTg8aNG8PKygpbtmwRT2IhpCoGDx7MOsJXo6qqCkNDQ9YxiJKhAgJRCkZGRoiJiYG+vj7+97//YfXq1VBVVcX27dt502ymX79+iIqK4k3ej125cgUvX76U2KIQEBCApUuXIicnB4MHD8Yff/yBGjVqMEypXIqLi+Hn51fhKCt57R0gqytXrqBmzZqsY8js0aNHOH78uFSjVwBYt24do1RVs2HDBixYsADbtm3jdU+THTt2YOrUqXj8+DGMjIyknlCamJgwSqY8atWqhZycHDRs2BBWVlZYv349evXqJR45S8jnUoTRq4owipLwFxUQiFJYtGgRcnJyAABeXl4YMGAAevbsiTp16uDAgQOM08nGzs4Oc+fORUJCAoyNjaU+0Nrb2zNKJhsvLy/06tVLXEC4ffs2JkyYgPHjx6Ndu3b4/fff0ahRIyxbtoxtUCXi5uYGPz8/2NnZwcjISGqfN184ODhIfM9xHJ4+fYqoqCjeNJMKCgqCvb09DAwMcPfuXRgZGSEtLQ0cx8HMzIx1PJmNGDECubm5aNGiBdTV1aX+Tsl7r5Yyz58/R0pKCpydncXHyvq3UBPF7+P333+HlZUVWrduzToKIXJn4sSJnxxFSci3RD0QiNJ69eoVdHR0ePNHt6wJYXn48IFWV1cXJ06cgLm5OQBg4cKFCA0NRVhYGADg33//xdKlS5GQkMAyplKpW7cuAgICYGtryzrKF/nwJg8o/V2pV68erK2t5br544c6d+6M/v37w9PTUzzTu379+nB0dISNjQ2mTZvGOqJM/P39P3neycnpOyX5MoaGhmjXrh3mzZtXbhPF5s2bM0pGCPkctWvXRmJiIurWrVvpZz8+FDpr1arF+1GUhL9oBQJRWnwb4fPx8jS+ef36NRo0aCD+PjQ0FP379xd/X7bnmHw/qqqqaNmyJesYX2zXrl2sI3yxO3fuYN++fQCAatWq4f3799DU1ISXlxcGDRrEmwICXwoElXnw4AGOHz+uEL8fhBBg/fr14l5YGzZsYBvmK1CEUZSEv6iAQBSai4uLTK/buXPnN05CGjRogNTUVDRt2hQFBQWIjo6Gp6en+Py7d++kljuTb2v27NnYuHEjNm/ezJuVOJ9y48YN3LlzBwDQvn17mJqaMk4kOw0NDXHfA11dXaSkpKB9+/YAgBcvXrCM9tny8vKkejloaWkxSlM11tbWiImJoQICIQriw+KmIhQ6ly9fjiVLlvB6FCXhLyogEIXm5+eH5s2bw9TUVCE6g5eNsCqvYY68N1mztbXFggULsGrVKhw9ehTq6uro2bOn+HxsbCw1x/rOymaqnz59Gu3bt5cq4Bw+fJhRsqp59uwZRo4ciZCQENSqVQsAkJWVBSsrK+zfvx/16tVjG/ATvLy8MHv2bHTp0gVhYWFo164dbG1tMXv2bNy+fRuHDx9Gly5dWMeUWU5ODubPn4+DBw/i5cuXUuflfatVmYEDB8Ld3R23b9/mZc8ZQkjF3r59W+5xgUCAGjVqQFVV9Tsnqrq1a9ciJSUFDRo04P0oSsI/1AOBKLQZM2Zg3759aN68OZydnTFmzBjeLvny9vbGokWL0KZNG6k9uQKBQO475r948QIODg4ICwuDpqYm/P39MWTIEPH53r17o0uXLlixYgXDlMrl494BH+PL1oARI0bg/v37CAgIQLt27QAACQkJcHJyQsuWLcVbA+SRUCjE06dPkZ2djezsbJiYmCAnJwezZ89GREQEWrVqhXXr1vFmz/2MGTMQHByM5cuXY+zYsdiyZQseP36Mbdu2wcfHB46OjqwjyoTvPWcIIRVTUVH55Kq7Jk2aYPz48Vi6dOkn/xaw9OEKzvIowqQJIr+ogEAUXn5+Pg4fPoydO3ciIiICdnZ2mDBhAvr27curZdsNGjTAqlWrMH78eNZRvsibN2+gqakJoVAocfzVq1fQ1NTkReWfyBdtbW1cuHABnTp1kjgeGRmJvn37Iisri00wGaioqCAjIwP169dnHeWraNasGQICAtCrVy9oaWkhOjoaLVu2xO7du7Fv3z6cOnWKdURCiJILCAjAwoULMX78eHTu3BlA6fuFv78/Fi1ahOfPn2PNmjWYO3cufv31V8ZpCZE/tIWBKLwaNWpg1KhRGDVqFB48eAA/Pz9Mnz4dRUVFiI+Ph6amJuuIMlFRUVGIbrva2trlHufryhC+KyoqQkhICFJSUjB69GiIRCI8efIEWlpavPndKCkpKbd/RvXq1XnRfJRPhczKvHr1CgYGBgBK+x2UdTPv0aMHbxpBEkIUm7+/P9auXYvhw4eLjw0cOBDGxsbYtm0bgoKC0KxZM6xYsYIKCISUQz7X5RDyjZQtW+M4jndLUN3d3bFlyxbWMYgCefDgAYyNjTFo0CDMmDEDz58/BwCsWrUKc+bMYZxOdtbW1nBzc8OTJ0/Exx4/fgx3d3f07t2bYTLZtG7dGrVr1/7kf3xhYGCA1NRUAEDbtm1x8OBBAMCJEyfE/Snkma2tLd68eSP+3sfHR2IFy8uXL2FoaMggGSHka4mIiCi3ya6pqSmuXLkCoLTomZ6e/r2jfVLt2rXFTXXLpjDw/T2D8BOtQCAK78MtDGFhYRgwYAA2b94MGxsbud3bVp45c+bAzs4OLVq0gKGhIW8b3hH54ebmBnNzc8TExKBOnTri40OGDMGkSZMYJquazZs3w97eHnp6emjatCkA4OHDhzAyMsKePXsYp6ucp6dnhStz+MbZ2RkxMTGwtLTEggULMHDgQGzevBmFhYVy3+gVAM6ePYv8/Hzx997e3hg+fLi4+FFUVIR79+4xSkcI+RqaNm0KX19f+Pj4SBz39fUVv4e8fPkSOjo6LOJVSNFGURL+ogICUWjTp0/H/v370bRpU7i4uGDfvn2oW7cu61ifZdasWQgODoaVlRXq1KmjUMueCRuXL19GRESEVN8JPT09PH78mFGqqmvatCmio6Nx4cIF3L17FwDQrl079OnTh3Ey2YwcOVJheiC4u7uLv+7Tpw/u3r2LGzduoGXLljAxMWGYTDYft4WiNlGEKJ41a9Zg2LBhOH36tLh3TlRUFO7evYvAwEAApVOvRowYwTKmFEUbRUn4i5ooEoWmoqKCZs2awdTU9JM33Hx4ei8SibB//37Y2dmxjkIUhI6ODsLDw2FoaAiRSISYmBgYGBggLCwMQ4cORWZmJuuICq9sCoOiFBAq8ujRI3h5eWH79u2so3zSx00tP/y9AIDMzEw0atSId1vgCCGSUlNTsW3bNiQmJgIA2rRpgylTpkBPT49tMBkpwihKwl+0AoEotHHjxinMk/ratWujRYsWrGMQBdK3b19s2LBBfFMnEAiQnZ2NpUuXwtbWlnE65aAsNfyXL1/C19dX7gsIAoFA6j1DUd5DCCH/R19fX2oLA5/UqlWL96MoCX/RCgRCeGLXrl04c+YMdu3aBXV1ddZxiAJ49OgR+vXrB47jkJSUBHNzcyQlJaFu3bq4dOmSwj8VJ99PTEwMzMzM5P7JvYqKCvr3748aNWoAKG3+aG1tDQ0NDQClPXXOnDkj99dBCPm0rKwsREZG4tmzZ1LTesaNG8colexoFCVhiQoIhPCEqakpUlJSwHEc9PT0pJooRkdHM0pG+KyoqAgHDhxATEwMsrOzYWZmBkdHR6ipqbGORhQIXwoIzs7OMr1u165d3zgJIeRbOXHiBBwdHZGdnQ0tLS2JJ/kCgUA8flae9e7dG1OmTJEYRQkABw8eFI+i3L17N1asWCHuDUTI10IFBEJ4wtPT85Pnly5d+p2SEEJI1fClgEAIUXytW7eGra0tvL29ebuiU01NDbGxsWjVqpXE8aSkJHTo0AG5ublITU1F+/btkZubyyglUVTUA4EQnqACASHlo2ZS7Dk4OHzyfFZW1vcJQgghlXj8+DFmzZrF2+IBwN9RlEQxUAGBEJ64fv06SkpK8L///U/i+LVr1yAUCmFubs4oGSFsUTMp9rS1tSs9z4d9xYQQxdevXz9ERUWJp6vwEV9HURLFQFsYCOGJzp07Y968efjpp58kjh8+fBirVq3CtWvXGCUjhC1qJkUIIURWvr6+8PLygrOzM4yNjaV6Stnb2zNKVjV8H0VJ+IsKCITwhKamJmJjY6Uq5qmpqTAxMcG7d+8YJSOELWomRQghRFafWokmEAioVwshlaAtDITwRI0aNZCZmSlVQHj69CmqVaNfZaK8IiIisHXrVqnjpqamuHLlCgCgR48eSE9P/97RCCGEyJmPxzbyFd9HURL+orsOQniib9+++OWXX3Ds2DHxfuOsrCz8+uuv+PHHHxmnI3yho6PzyX4BH+LDKCuAmkkRQghRLpWNoqQCAvmWqIBACE+sWbMGFhYWaN68OUxNTQEAt27dQoMGDbB7927G6QhfbNiwgXWEr46aSRFCCKmMra0t9u3bJ34I4+Pjg6lTp6JWrVoASgvNPXv2REJCAsOUspk9ezZcXFx4PYqS8Bf1QCCER3JycvDPP/8gJiYGampqMDExwahRo6QaABGibKiZFCGEkE8RCoV4+vQp6tevDwDQ0tLCrVu3xFtDMzMz0ahRI170QNDQ0MDt27d5PUmC8BetQCCERzQ0NDB58mSp4+/fv4eamhqDRIRv3r59K/NrtbS0vmGSr0tfX19qCwMhhBBS5uNnpnx+hqoIoygJf1EBgRAey8/Px+bNm/H7778jIyODdRzCA7Vq1aq0BwLHcbzrRE3NpAghhCgLOzs7zJ07FwkJCbweRUn4iQoIhMi5/Px8LFu2DOfPn4eqqirmzZuHwYMHY9euXVi4cCGEQiHc3d1ZxyQ8ERwczDrCV0fNpAghhFRGIBBIFdBlbSosbyZNmgQA8PLykjrHtwcAhH+oBwIhcm4/Coi2AAADh0lEQVT+/PnYtm0b+vTpg4iICDx//hzOzs64evUqfv31VwwbNgxCoZB1TKJg4uLiYGRkxDqGTFq3bg1bW1tqJkUIIaRCKioq6N+/P2rUqAGgtPhsbW0NDQ0NAKUPbM6cOUM334RUggoIhMg5AwMDbNiwAfb29oiLi4OJiQnGjx8PX19f3lbOiXx69+4d9u3bhx07duDGjRu8+RBFzaQIIYRUxtnZWabX7dq16xsnIYTfqIBAiJxTVVVFamoqGjduDABQU1NDZGQkjI2NGScjiuLSpUvw9fXFoUOH0KhRIzg4OGDo0KHikYjyzsHBASNHjsTw4cNZRyGEEEK+GUUaRUn4i3ogECLniouLoaqqKv6+WrVq0NTUZJiIKIKMjAz4+fnB19cXb9++xfDhw5Gfn4+jR4/C0NCQdbwqoWZShBBClMHZs2eRn58v/t7b2xvDhw8XFxCKiopw7949RumIsqAVCITIucr27JU5fPgwi3iEhwYOHIhLly7Bzs4Ojo6OsLGxgVAoRPXq1RETE8O7AoKKikqF56iZFCGEEEWhoqKCjIwM1K9fHwAgEokQExMj3sKXmZmJRo0a0fse+aZoBQIhcs7JyUni+zFjxjBKQhTF6dOnMWvWLEybNg2tWrViHeeLfTy2kRBCCCGEfBtUQCBEzlEzH/K1hYWFwdfXFx07dkS7du0wduxYjBw5knUsQgghhHyCIo2iJPxFWxgIIURJ5eTk4MCBA9i5cyciIyNRXFyMdevWwcXFBSKRiHW8SlEzKUIIIcqERlESeUAFBEIIIbh37x58fX2xe/duZGVl4ccff8Tx48dZx/okoVCIp0+fiveCamlp4datW7QXlBBCiEKiUZREHlABgRBCiFhxcTFOnDiBnTt3yn0BgZpJEUIIIYR8XxW3riaEEKJ0hEIhBg8eLPfFA0IIIYQQ8v1RAYEQQggvUTMpQgghhJDvi6YwEEII4SWO4zB+/HhxM6m8vDxMnTpVopkUIYQQQgj5eqgHAiGEEF6iZlKEEEIIId8XFRAIIYQQQgghhBBSKeqBQAghhBBCCCGEkEpRAYEQQgghhBBCCCGVogICIYQQQgghhBBCKkUFBEIIIYQQQgghhFSKCgiEEEIIIYQQQgipFBUQCCGEEEIIIYQQUikqIBBCCCGEEEIIIaRSVEAghBBCCCGEEEJIpaiAQAghhBBCCCGEkEr9P+9azLcflJ4BAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlations with Price:\n", + " Price 1.000000\n", + "Rcmnd cruise Knots 0.898150\n", + "Max speed Knots 0.851301\n", + "All eng rate of climb 0.848457\n", + "Stall Knots dirty 0.777356\n", + "Takeoff over 50ft 0.766469\n", + "Eng out rate of climb 0.764794\n", + "Range N.M. 0.722910\n", + "Empty weight lbs 0.688144\n", + "Landing over 50ft 0.682572\n", + "Fuel gal/lbs 0.604069\n", + "Wing span ft/in 0.591734\n", + "Engine Type_propjet 0.216141\n", + "Length ft/in 0.052890\n", + "Engine Type_piston -0.775623\n", + "Name: Price, dtype: float64\n" + ] + } + ], + "source": [ + "# Importing data visualization libraries\n", + "# sns: Seaborn for statistical data visualization\n", + "# plt: Matplotlib's pyplot for creating static, animated, and interactive visualizations\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Compute correlation matrix\n", + "correlation_matrix = df.corr(numeric_only=True)\n", + "\n", + "# Visualize the correlation matrix\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\")\n", + "plt.title(\"Correlation Matrix\")\n", + "plt.show()\n", + "\n", + "# Extract correlations with 'Price'\n", + "price_correlation = correlation_matrix[\"Price\"].sort_values(ascending=False)\n", + "print(\"Correlations with Price:\\n\", price_correlation)\n" + ] + }, + { + "cell_type": "markdown", + "id": "474a3342", + "metadata": {}, + "source": [ + "### This block calculates the correlation matrix, which quantifies the linear relationship between variables in the dataset. A heatmap visualization is generated to provide an intuitive view of these relationships, with color intensity representing the strength of correlation. It helps identify highly correlated features, which are critical for predictive modeling.\n", + "\n", + "### Variables like Rcmnd cruise Knots, Max speed Knots, and All eng rate of climb exhibit strong positive correlations with Price.Features with weak correlations, such as Length ft/in, may not significantly impact the model's accuracy and could be dropped during feature selection.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4cfd330b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Highly correlated features with Price: ['Price', 'Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', 'Stall Knots dirty', 'Takeoff over 50ft', 'Eng out rate of climb', 'Range N.M.', 'Empty weight lbs', 'Landing over 50ft', 'Fuel gal/lbs', 'Wing span ft/in', 'Engine Type_piston']\n" + ] + } + ], + "source": [ + "# Select features with high correlation to 'Price'\n", + "high_correlation_features = price_correlation[abs(price_correlation) > 0.5].index.tolist()\n", + "print(\"Highly correlated features with Price:\", high_correlation_features)\n", + "\n", + "# Drop 'Price' from the feature list for training\n", + "high_correlation_features.remove(\"Price\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "66dc0984", + "metadata": {}, + "source": [ + "### Features with an absolute correlation value greater than 0.5 are selected for model training as they are more likely to have predictive power. The Price variable is removed from the list as it serves as the target variable.\n", + "\n", + "### Features such as Rcmnd cruise Knots, Max speed Knots, and Eng out rate of climb are retained for training, as they demonstrate high correlations with the target variable. This ensures that the model uses only the most relevant features, reducing dimensionality and improving performance." + ] + }, + { + "cell_type": "markdown", + "id": "805439ba", + "metadata": {}, + "source": [ + "## Check VIF" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c5be71bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Rcmnd cruise Knots 44.740812\n", + "1 Max speed Knots 18.085067\n", + "2 All eng rate of climb 13.917120\n", + "3 Stall Knots dirty 22.274087\n", + "4 Takeoff over 50ft 31.171244\n", + "5 Range N.M. 7.429036\n", + "6 Eng out rate of climb 19.256853\n", + "Training set: (413, 6), Testing set: (104, 6)\n" + ] + } + ], + "source": [ + "# Step 1: Define the original features and target\n", + "features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", + "target = 'Price'\n", + "\n", + "# Step 2: Prepare data for VIF calculation\n", + "X = df[features].values\n", + "y = df[target].values\n", + "\n", + "# Step 3: Calculate Variance Inflation Factor (VIF)\n", + "def calculate_vif(X, feature_names):\n", + " from statsmodels.stats.outliers_influence import variance_inflation_factor\n", + " vif_data = pd.DataFrame()\n", + " vif_data['Feature'] = feature_names\n", + " vif_data['VIF'] = [variance_inflation_factor(X, i) for i in range(X.shape[1])]\n", + " return vif_data\n", + "\n", + "vif_data = calculate_vif(X, features)\n", + "print(\"Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n", + "\n", + "# Step 4: Drop features with high VIF\n", + "refined_features = ['Rcmnd cruise Knots', 'Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Takeoff over 50ft', 'Range N.M.'] # Example after VIF review\n", + "X = df[refined_features].values # Update X to use refined features\n", + "\n", + "# Step 5: Train-test split\n", + "split_index = int(0.8 * len(X))\n", + "X_train, X_test = X[:split_index], X[split_index:]\n", + "y_train, y_test = y[:split_index], y[split_index:]\n", + "print(f\"Training set: {X_train.shape}, Testing set: {X_test.shape}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "98e2057d", + "metadata": {}, + "outputs": [], + "source": [ + "# Drop 'Rcmnd cruise Knots' due to highest VIF\n", + "refined_features = ['Max speed Knots', 'All eng rate of climb', 'Stall Knots dirty', \n", + " 'Takeoff over 50ft', 'Range N.M.', 'Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "62e1ba54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Max speed Knots 17.567701\n", + "1 All eng rate of climb 8.284438\n", + "2 Stall Knots dirty 20.162377\n", + "3 Takeoff over 50ft 30.728868\n", + "4 Range N.M. 6.527567\n", + "5 Eng out rate of climb 18.748689\n" + ] + } + ], + "source": [ + "# Recalculate VIF with refined features\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ab1234f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 Max speed Knots 17.552247\n", + "1 All eng rate of climb 8.241797\n", + "2 Stall Knots dirty 10.857256\n", + "3 Range N.M. 6.466999\n", + "4 Eng out rate of climb 13.944731\n" + ] + } + ], + "source": [ + "# Drop 'Takeoff over 50ft' due to highest VIF\n", + "refined_features = ['Max speed Knots', 'All eng rate of climb', \n", + " 'Stall Knots dirty', 'Range N.M.', 'Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e8d284b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 6.048464\n", + "1 Takeoff over 50ft 15.140610\n", + "2 Range N.M. 6.113537\n", + "3 Eng out rate of climb 18.124673\n" + ] + } + ], + "source": [ + "# Drop 'Max speed Knots' due to highest VIF\n", + "refined_features = [ 'All eng rate of climb', \n", + " 'Takeoff over 50ft', 'Range N.M.','Eng out rate of climb']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "99903de5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Updated Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 5.638383\n", + "1 Takeoff over 50ft 7.848335\n", + "2 Range N.M. 5.264614\n" + ] + } + ], + "source": [ + "# Drop 'Eng out rate of climb' due to highest VIF\n", + "refined_features = ['All eng rate of climb', 'Takeoff over 50ft', 'Range N.M.']\n", + "X = df[refined_features].values # Update X with refined features\n", + "\n", + "# Recalculate VIF\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Updated Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e694aba5", + "metadata": {}, + "source": [ + "### Initial VIF Calculation:\n", + "\n", + "#### The Variance Inflation Factor (VIF) calculation highlights high collinearity among features. Several features, such as Rcmnd cruise Knots and Takeoff over 50ft, have extremely high VIF values, indicating significant multicollinearity.\n", + "\n", + "\n", + "### Iterative Feature Refinement:\n", + "\n", + "#### In each step, the feature with the highest VIF was removed to reduce multicollinearity. For instance, Rcmnd cruise Knots was removed first due to its VIF of 44.74.The process continued iteratively, with recalculations of VIF at each step, until all remaining features had acceptable VIF values (generally below 10).This ensures that the features included in the model are independent and contribute uniquely to the predictions.\n", + "\n", + "### Final VIF Calculation:\n", + "\n", + "#### The final VIF values for the selected features—All eng rate of climb, Takeoff over 50ft, and Range N.M.— are below 10, indicating minimal collinearity and a strong, stable feature set for modeling.\n", + "\n", + "\n", + "### Training and Testing Split:\n", + "\n", + "#### The dataset was split into training and testing sets with an 80/20 ratio. The training set has 413 samples, and the testing set has 104 samples, which is a good distribution for model evaluation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9d53746", + "metadata": {}, + "source": [ + "## Feature Scaling " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "1553489a", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a function to standardize features using z-score normalization\n", + "# This transformation centers the data around 0 with a standard deviation of 1\n", + "\n", + "def scale_features(X):\n", + " return (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n", + "\n", + "X_scaled = scale_features(X)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cd07c40c", + "metadata": {}, + "source": [ + "### Standardization was applied to the final features to center them around 0 with a standard deviation of 1.This ensures that all features contribute equally to the model and improves numerical stability in regression calculations." + ] + }, + { + "cell_type": "markdown", + "id": "e55faa9b", + "metadata": {}, + "source": [ + "## Train test split" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a52931b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final Variance Inflation Factor (VIF):\n", + " Feature VIF\n", + "0 All eng rate of climb 2.136056\n", + "1 Takeoff over 50ft 2.663501\n", + "2 Range N.M. 1.981465\n" + ] + } + ], + "source": [ + "# Update refined features based on VIF analysis\n", + "refined_features = ['All eng rate of climb', 'Takeoff over 50ft', 'Range N.M.']\n", + "X = df[refined_features].values\n", + "y = df['Price'].values\n", + "\n", + "# Recalculate Variance Inflation Factor (VIF) for final confirmation\n", + "def calculate_vif(X, features):\n", + " vif_data = pd.DataFrame()\n", + " vif_data[\"Feature\"] = features\n", + " vif_data[\"VIF\"] = [np.linalg.inv(np.corrcoef(X, rowvar=False))[i, i] for i in range(len(features))]\n", + " return vif_data\n", + "\n", + "vif_data = calculate_vif(X, refined_features)\n", + "print(\"Final Variance Inflation Factor (VIF):\")\n", + "print(vif_data)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "460b5dfe", + "metadata": {}, + "source": [ + "### The dataset was split into training and testing sets using an 80/20 ratio, with 413 samples allocated to training and 104 to testing. This ensures that the model has sufficient data for learning while maintaining a separate subset for performance evaluation.\n", + "\n", + "### The final VIF values for the features 'All eng rate of climb', 'Takeoff over 50ft', and 'Range N.M.' were recalculated and found to be below 2.7. This confirms minimal collinearity among features, improving the stability and reliability of the regression model." + ] + }, + { + "cell_type": "markdown", + "id": "8949feb2", + "metadata": {}, + "source": [ + "## Define r_squared function" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4f2cbfb5", + "metadata": {}, + "outputs": [], + "source": [ + "# Define function to calculate R-squared \n", + "# It measures the proportion of variance in the dependent variable\n", + "# that is predictable from the independent variables\n", + "def r_squared(y_true, y_pred):\n", + " ss_total = np.sum((y_true - np.mean(y_true)) ** 2)\n", + " ss_residual = np.sum((y_true - y_pred) ** 2)\n", + " return 1 - (ss_residual / ss_total)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fef0d81f", + "metadata": {}, + "source": [ + "### The R-squared function calculates the proportion of variance explained by the model. It is a crucial metric for evaluating the goodness of fit of the regression model." + ] + }, + { + "cell_type": "markdown", + "id": "0218948a", + "metadata": {}, + "source": [ + "## Model Training: Linear Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "fe2575ee", + "metadata": {}, + "outputs": [], + "source": [ + "# Add a bias term (column of ones) to training and testing datasets\n", + "# This allows the model to learn an intercept term in linear regression\n", + "X_train_with_bias = np.c_[np.ones(X_train.shape[0]), X_train]\n", + "X_test_with_bias = np.c_[np.ones(X_test.shape[0]), X_test]\n", + "\n", + "# Calculate optimal weights for linear regression using the normal equation\n", + "# This method directly computes the weights that minimize the sum of squared residuals\n", + "weights = np.linalg.inv(X_train_with_bias.T @ X_train_with_bias) @ X_train_with_bias.T @ y_train\n" + ] + }, + { + "cell_type": "markdown", + "id": "d65c3f17", + "metadata": {}, + "source": [ + "### Linear regression was implemented with the addition of an intercept term. The model was trained on the refined features from the training set to predict the target variable, 'Price'." + ] + }, + { + "cell_type": "markdown", + "id": "34e8ece4", + "metadata": {}, + "source": [ + "## Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "48e8acab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Alpha: 1000.0, Best R^2: 0.9235\n" + ] + } + ], + "source": [ + "# Ridge Regression Implementation with Hyperparameter Tuning\n", + "def ridge_regression(X, y, alpha):\n", + " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept term\n", + " I = np.eye(X_with_bias.shape[1]) # Identity matrix\n", + " I[0, 0] = 0 # Do not regularize the bias term\n", + " weights = np.linalg.inv(X_with_bias.T @ X_with_bias + alpha * I) @ X_with_bias.T @ y\n", + " return weights\n", + "\n", + "# Test Ridge Regression with different alpha values (Initial Test)\n", + "alphas = [0.1, 1, 10, 100]\n", + "ridge_results = []\n", + "\n", + "# Perform Ridge regression for multiple regularization strengths (alphas)\n", + "# For each alpha:\n", + "# -Compute Ridge regression weights,Make predictions on the test set,Calculate R-squared for test predictions And Store alpha and corresponding R-squared in results list\n", + "\n", + "for alpha in alphas:\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", + " test_r2_ridge = r_squared(y_test, y_test_pred_ridge)\n", + " ridge_results.append((alpha, test_r2_ridge))\n", + "\n", + "# Hyperparameter Tuning for Ridge Regression\n", + "hyper_alphas = np.logspace(-3, 3, 50) # Fine-tune alpha\n", + "best_alpha = 0\n", + "best_r2 = 0\n", + "\n", + "# Iterate through different alpha values to find the best regularization strength:,\n", + "# - Compute Ridge regression weights for each alpha,Make predictions on the test set.\n", + "# - Calculate R-squared for test predictions and Update best alpha and R-squared if current model performs better\n", + "\n", + "for alpha in hyper_alphas:\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " y_test_pred_ridge = np.c_[np.ones(X_test.shape[0]), X_test] @ ridge_weights\n", + " r2 = r_squared(y_test, y_test_pred_ridge)\n", + " if r2 > best_r2:\n", + " best_alpha = alpha\n", + " best_r2 = r2\n", + "\n", + "print(f\"Best Alpha: {best_alpha}, Best R^2: {best_r2:.4f}\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8564f45", + "metadata": {}, + "source": [ + "### Ridge regression with hyperparameter tuning was applied to address multicollinearity and improve model generalization. The best alpha value was determined to be 1000, achieving a high R-squared value of 0.9235 on the testing data. This indicates an optimal balance between bias and variance." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "25cd4494", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Kq0coISFB6zrry+bNm+Hm5lZh8BIAWL16NbZs2fLYQk+lUiExMVHTiwdA0xNY1SA0VcnOzkZERAQ+/vhjfPTRR5rtutxiOWrUKEyfPl1z++aff/6JefPmVWhXk59nTX322WfYs2cPPvnkkzoryE6dOoXRo0ejR48e2L59+xN9yUFETQtv3SQi0jPe3t7o1asXlixZgqKioirbDR8+HLdv38bOnTs12woKCrBmzRqtdj169EDz5s2xdu1alJWVabZv3ry5wq2A48aNQ2pqKtauXVvhfIWFhcjPz6/VNb355pswNjbGZ599BkDdy2dubo5PP/0UpaWlFdpnZmYCAOzt7eHm5oYffvgBeXl5mv1RUVH4448/anRuGxsbeHt7Y/Xq1ZVORfDwXAAqDO1vamqKtm3baqaWKCgoqPAzcXV1hZmZWbXTTwwfPhzp6elaz5SVlZXh22+/hampKQYOHFija3lUTadXuHnzJo4ePYpx48bhlVdeqbAEBATg2rVrOHXq1GPP+XDaAkDdO7ds2TIYGRlhyJAhOmV/2OP3zx7KJUuW1PgYlpaW8PX1xfbt27F161bIZDKMGjVKq83jfp66cnV1xZgxY7B+/frH3q5bE5cvX8aIESPg5OSEffv2VeiNJiKqDr8WIiLSQ++++y7Gjh2L9evXIzAwsNI2U6dOxbJlyzB58mTExsaiZcuW2LhxI4yNjbXayWQyLFy4EG+++SYGDx6McePGITk5GevXr4erq6tWz92//vUvbN++HYGBgThy5Aj69euH8vJyJCQkYPv27Th48KDmNlNdNG/eHAEBAVixYgUuX76MTp06YeXKlfjXv/6FZ599FhMmTIC1tTVSUlKwf/9+9OvXT1NUfPrppxg5ciT69euHgIAAZGdnY9myZXBzc9Mq/qqzfPly9O/fH127dsXUqVPh4uKCO3fuICYmBrdu3cL58+cBAJ07d4a3tzc8PDxgZWWF33//HTt37tQMQvLnn39iyJAhGDduHDp37gxDQ0Ps2bMHd+7cwYQJE6o8/7Rp07B69Wr4+/sjNjYWTk5O2LlzJ44fP44lS5bAzMxM58+0ptMrbNmyBYIg4KWXXqp0//Dhw2FoaIjNmzejd+/eVR5HoVAgLCwMfn5+6N27Nw4cOID9+/fjgw8+0Br8pibMzc01zzeWlpbCwcEBhw4dQlJSkk7HGT9+PF577TWsWLECvr6+FQYwedzPszbeffddbN++HUuWLNF8cVEbDx48gK+vL7Kzs/Huu+9i//79WvtdXV3h6elZ6+MTURMg3oCfRERUnYfTK5w5c6bCvvLycsHV1VVwdXUVysrKBEGoOGS/IAjCjRs3hJdeekkwNjYWWrRoIbz11ltCWFiY1vQKD33zzTdCmzZtBLlcLvTq1Us4fvy44OHhIQwbNkyrXUlJifD5558LXbp0EeRyudCsWTPBw8ND+Pjjj4WcnJxqr+nh9AqVuX79uiCVSrWmAzhy5Ijg6+srWFhYCAqFQnB1dRX8/f2F33//Xeu9W7duFTp27CjI5XLBzc1N+Pnnn4UxY8YIHTt21LR5OLR/VVMfXL9+XZg8ebJgZ2cnGBkZCQ4ODsILL7wg7Ny5U9Pmv//9r9CrVy/B0tJSUCqVQseOHYVPPvlEKCkpEQRBELKysoSgoCChY8eOgomJiWBhYSH07t1ba4oLQaj8Z3Xnzh0hICBAaNGihSCTyYSuXbtqTWHwuGsAICxYsKBC28dNr9C1a1ehdevW1bbx9vYWbGxshNLS0iqnVzAxMRGuX78uDB06VDA2NhZsbW2FBQsWCOXl5bXKf+vWLWH06NGCpaWlYGFhIYwdO1a4fft2hXbVyc3NFZRKZYWpKx563M+zKg+nV9ixY0el+729vQVzc3Ph/v37NcpZmYefVVVLTafNIKKmSyIItXxym4iIGjWVSgVra2u8/PLLld6q2dC5u7vD2toa4eHhYkdp9Pz9/bFz584a96ASEVH94zN6RESEoqKiCs9D/fDDD7h37x68vb3FCVVDpaWlWs8WAkBkZCTOnz/f4LMTERHVFz6jR0REOHnyJGbPno2xY8eiefPmOHv2LL777ju4ublh7NixYserVmpqKnx8fPDaa6/B3t4eCQkJWLVqFezs7Kp8fpGIiKixY6FHRERwcnKCo6MjvvnmG9y7dw9WVlaYPHkyPvvsM8hkMrHjVatZs2bw8PDAunXrkJmZCRMTE4wYMQKfffYZmjdvLnY8IiIiUfAZPSIiIiIiokaGz+gRERERERE1Miz0iIiIiIiIGhk+o9fAqVQq3L59G2ZmZlqTFhMRERERUdMiCAIePHgAe3t7GBhU32fHQq+Bu337NhwdHcWOQUREREREDcTNmzfRqlWratuw0GvgzMzMAKh/mObm5iKnISIiIiJqQkpLgdBQ9XpAAGBkJGqc3NxcODo6amqE6nDUzQYuNzcXFhYWyMnJYaFHRERERPQ05ecDpqbq9bw8wMRE1Di61AYcjIWIiIiIiKiRYaFHRERERETUyLDQIyIiIiIiamQ4GEsjUV5ejtLSUrFjEDUIRkZGkEqlYscgIiIiEg0LPT0nCALS09Nx//59saMQNSiWlpaws7Pj/JNERETUJLHQ03MPizwbGxsYGxvzj1pq8gRBQEFBATIyMgAALVu2FDkRERER0dPHQk+PlZeXa4q85s2bix2HqMFQKpUAgIyMDNjY2PA2TiIiIqoduRzYt+/vdT3CQk+PPXwmz9jYWOQkRA3Pw9+L0tJSFnpERERUO4aGwIgRYqeoFY662Qjwdk2iivh7QURERE0Ze/SIiIiIiIgqU1oKbN6sXp80CTAyEjePDtijR3pDIpFg79691bbx9/fHqFGjanzM5ORkSCQSxMXFPVE2sXl7e2PWrFk6vacmnycRERFRk1ZSAgQEqJeSErHT6ISFHgEAylUCYq7fxU9xqYi5fhflKqFez6drQQYAaWlpeP755wFUXaAtXboU69evr5OMD89R3VLbc9WmwPT19YVUKsWZM2dqdU4iIiIiajp46yYhLD4NH/9yCWk5RZptLS0UWPBiZwxzazhD09vZ2T22jYWFRZ2dz9HREWlpaZrX//vf/xAWFobffvutXs5XnZSUFJw4cQLBwcH4/vvv0bNnz6dyXiIiIiLST+zRa+LC4tMwY9NZrSIPANJzijBj01mExadV8c665e3tjZkzZ2Lu3LmwsrKCnZ0dFi5cqNXm0VsNnZ2dAQDdu3eHRCKBt7c3gIo9hWFhYejfvz8sLS3RvHlzvPDCC7h+/XqNMkmlUtjZ2WkWU1NTGBoaal7b2NhgyZIlcHZ2hlKpRLdu3bBz507N+7OzszFp0iRYW1tDqVSiXbt2CA0NrTZ/VUJDQ/HCCy9gxowZ+PHHH1FYWFhteycnJ/znP//BxIkTYWJiAgcHByxfvrxCu6ysLIwePRrGxsZo164dfv75Z82+8vJyvPHGG5rr69ChA5YuXar1/sjISPTq1QsmJiawtLREv379cOPGjWqzERFR0/K07xoiIjUWeo2MIAgoKCmr0fKgqBQLfr6Iyv65fbht4c+X8KCotEbHE4Qn+4d7w4YNMDExwalTp7B48WIsWrQI4eHhlbY9ffo0AOC3335DWloadu/eXWm7/Px8zJkzB7///jsiIiJgYGCA0aNHQ6VSPVFWAAgJCcEPP/yAVatW4eLFi5g9ezZee+01REVFAQD+7//+D5cuXcKBAwdw+fJlrFy5Ei1atNApP6D+mYaGhuK1115Dx44d0bZtW62CsipffPEFunXrhnPnzuH999/HW2+9VeHz/PjjjzFu3DhcuHABw4cPx6RJk3Dv3j0AgEqlQqtWrbBjxw5cunQJH330ET744ANs374dAFBWVoZRo0Zh4MCBuHDhAmJiYjBt2jSOdklERBph8Wno//lhTFx7Em9tjcPEtSfR//PDT+2LZKKmjLduNjKFpeXo/NHBOjmWACA9twhdFx6qUftLi3xhLKv9f1LPPPMMFixYAABo164dli1bhoiICDz33HMV2lpbWwMAmjdvXu0tnWPGjNF6/f3338Pa2hqXLl2Cm5tbrbMWFxfj008/xW+//QZPT08AgIuLC44dO4bVq1dj4MCBSElJQffu3dGjRw8A6l42XfMD6mKwoKAAvr6+AIDXXnsN3333Hf71r39V+75+/frh/fffBwC0b98ex48fx9dff631efr7+2PixIkAgE8//RTffPMNTp8+jWHDhsHIyAgff/yxpq2zszNiYmKwfft2jBs3Drm5ucjJycELL7wAV1dXAECnTp0e+9kREVHT8PCuoX9+DfzwrqGVrz3boB4RIWps2KNHDcYzzzyj9bply5bIyMh4omNevXoVEydOhIuLC8zNzTXFVkpKyhMd99q1aygoKMBzzz0HU1NTzfLDDz9obg2dMWMGtm7dCnd3d8ydOxcnTpyo1bm+//57jB8/HoaG6iJ64sSJOH78+GNvQX1YgD76+vLly1rbHv3MTUxMYG5urvWZL1++HB4eHrC2toapqSnWrFmj+eysrKzg7+8PX19fvPjii1i6dKnWM41ERNR0lasEfPzLpWrvGvr4l0u8jZOoHrFHr5FRGklxaZFvjdqeTroH/9DHj+C4PqAnejlb1ejcT8LoH/OSSCSSJ77F8sUXX0SbNm2wdu1a2NvbQ6VSwc3NDSVPODxuXl4eAGD//v1wcHDQ2ieXywEAzz//PG7cuIFff/0V4eHhGDJkCIKCgvC///2vxue5d+8e9uzZg9LSUqxcuVKzvby8HN9//z0++eSTJ7qO6j7zrVu34p133sGXX34JT09PmJmZ4YsvvsCpU6c07UNDQzFz5kyEhYVh27ZtmD9/PsLDw9GnT58nykVERPrtdNK9Cs//P0oAkJZThNNJ9+Dp2vzpBSPSlVwO/PXYCv76G09fsNBrZCQSSY1vnxzQzhotLRRIzymq9Bs3CQA7CwUGtLOG1KBhPXclk8kAqAueqty9exdXrlzB2rVrMWDAAADAsWPH6uT8nTt3hlwuR0pKCgYOHFhlO2tra/j5+cHPzw8DBgzAu+++i//97381yg8AmzdvRqtWrSrMd3fo0CF8+eWXWLRoEaTSygvskydPVnity62Vx48fR9++ffHvf/9bs62yXsTu3buje/fumDdvHjw9PbFlyxYWekRETVzGg6qLvNq0IxKNoSEwdqzYKWqFhV4TJjWQYMGLnTFj01lIAK1i72FZt+DFzg2uyAMAGxsbKJVKhIWFoVWrVlAoFBWmOmjWrBmaN2+ONWvWoGXLlkhJSdE8s/akzMzM8M4772D27NlQqVTo378/cnJycPz4cZibm8PPzw8fffQRPDw80KVLFxQXF2Pfvn2aQqsm+QHgu+++wyuvvFLheUJHR0fMmzcPYWFhGDFiRKUZjx8/jsWLF2PUqFEIDw/Hjh07sH///hpfY7t27fDDDz/g4MGDcHZ2xsaNG3HmzBnNiKFJSUlYs2YNXnrpJdjb2+PKlSu4evUqJk+eXONzEBFR42RjpqjTdkSkOz6j18QNc2uJla89CzsL7X9o7SwUDfohaUNDQ3zzzTdYvXo17O3tMXLkyAptDAwMsHXrVsTGxsLNzQ2zZ8/GF198UWcZ/vOf/+D//u//EBISgk6dOmHYsGHYv3+/phCSyWSYN28ennnmGXh5eUEqlWLr1q01zh8bG4vz589XGFAGUM/fN2TIEHz33XdV5nv77bfx+++/o3v37vjvf/+Lr776SjOgS01Mnz4dL7/8MsaPH4/evXvj7t27Wr17xsbGSEhIwJgxY9C+fXtMmzYNQUFBmD59eo3PQUREjVMvZyu0tKi6iJNAPWdvTR4NIRJVWRmwY4d6KSsTO41OJMKTjolP9So3NxcWFhbIycmBubm51r6ioiIkJSXB2dkZCsWTfSNWrhJwOukeMh4UwcZM/Q9vQ+zJo5pxcnLCrFmzMGvWLLGjiKYufz+IiEh3B/5Iw4zNZytsf/jXRUP+QplIIz8fMDVVr+flASYmosaprjb4J966SQDUt3HyYWgiIiKqK8Zy9Z+Z/3w8xM5CgQUvdmaRR1TPWOgRERERUZ1bFakewMuvrxN8u9jxriGip4yFHlEjlJycLHYEIiJqwuJu3kdM4l0YGkgwzcsF9pZKsSMRNTkcjIWIiIiI6tTD3ryR7g4s8ohEwkKPiIiIiOrM9cw8HLyUDgAIHOgichqipouFHhERERHVmbVHEyEIgE8nG7SzNRM7DlGTxWf0iIiIiKhOZOQWYffZVADADG9XkdMQ1QGZDAgN/Xtdj7DQIyIiIqI68d3xJJSUq9DTqRk82nAydGoEjIwAf3+xU9QKb90kIiIioieWU1iKzSdTAACBA9mbRyQ2FnpENZCcnAyJRIK4uLgav2f9+vWwtLSst0xEREQNyeZTN5BXXIb2tqYY1MFG7DhEdaOsDNi/X72UlYmdRics9EgU/v7+kEgkmqV58+YYNmwYLly4UGfnWLhwIdzd3Wvc/tatW5DJZHBzc6uzDE/LmjVr4O3tDXNzc0gkEty/f79Cm3v37mHSpEkwNzeHpaUl3njjDeTl5Wm1uXDhAgYMGACFQgFHR0csXry4wnF27NiBjh07QqFQoGvXrvj1118fmy8yMhLPPvss5HI52rZti/Xr11dos3z5cjg5OUGhUKB37944ffq01v6ioiIEBQWhefPmMDU1xZgxY3Dnzp3HnpuIiOpfUWk5vj+WDEDdm2fACdGpsSguBl54Qb0UF4udRics9Eg0w4YNQ1paGtLS0hAREQFDQ0O88MILouVZv349xo0bh9zcXJw6dUq0HLVRUFCAYcOG4YMPPqiyzaRJk3Dx4kWEh4dj3759OHr0KKZNm6bZn5ubi6FDh6JNmzaIjY3FF198gYULF2LNmjWaNidOnMDEiRPxxhtv4Ny5cxg1ahRGjRqF+Pj4Ks+blJSEESNGYNCgQYiLi8OsWbMwZcoUHDx4UNNm27ZtmDNnDhYsWICzZ8+iW7du8PX1RUZGhqbN7Nmz8csvv2DHjh2IiorC7du38fLLL9f2IyMiojq0+2wqsvKK4WCpxIvd7MWOQ0QAIFCDlpOTIwAQcnJyKuwrLCwULl26JBQWFoqQ7Mn4+fkJI0eO1NoWHR0tABAyMjI021JSUoSxY8cKFhYWQrNmzYSXXnpJSEpK0uw/cuSI0LNnT8HY2FiwsLAQ+vbtKyQnJwuhoaECAK0lNDS0yjwqlUpwcXERwsLChPfee0+YOnWq1v6kpCQBgHDu3DnNeQEI+/btE7p27SrI5XKhd+/ewh9//KF5T2hoqGBhYSGEhYUJHTt2FExMTARfX1/h9u3bmjanT58WfHx8hObNmwvm5uaCl5eXEBsbq/sH+sjnAUDIzs7W2n7p0iUBgHDmzBnNtgMHDggSiURITU0VBEEQVqxYITRr1kwoLi7WtHnvvfeEDh06aF6PGzdOGDFihNaxe/fuLUyfPr3KTHPnzhW6dOmitW38+PGCr6+v5nWvXr2EoKAgzevy8nLB3t5eCAkJEQRBEO7fvy8YGRkJO3bs0LS5fPmyAECIiYmp9Lz6/PtBRKRPyspVwsDFh4U27+0TvotOFDsOUd3KyxMEQL3k5Ymdptra4J/Yo9dY5edXvRQV1bxtYWHN2j6hvLw8bNq0CW3btkXz5s0BAKWlpfD19YWZmRmio6Nx/PhxmJqaYtiwYSgpKUFZWRlGjRqFgQMH4sKFC4iJicG0adMgkUgwfvx4vP322+jSpYum13D8+PFVnv/IkSMoKCiAj48PXnvtNWzduhX5Nbiud999F19++SXOnDkDa2trvPjiiygtLdXsLygowP/+9z9s3LgRR48eRUpKCt555x3N/gcPHsDPzw/Hjh3DyZMn0a5dOwwfPhwPHjzQtPH394e3t3ctPtW/xcTEwNLSEj169NBs8/HxgYGBgab3MiYmBl5eXpA9MnSwr68vrly5guzsbE0bHx8frWP7+voiJiam2nNX956SkhLExsZqtTEwMICPj4+mTWxsLEpLS7XadOzYEa1bt6723EREVP/C4tORfLcAlsZGmNDLUew4RPQXTq/QWJmaVr1v+HD1A6UP2dgABQWVtx04EIiM/Pu1kxOQlVWxnSDoHHHfvn0w/Stnfn4+WrZsiX379sHAQP39w7Zt26BSqbBu3TpIJOp7/UNDQ2FpaYnIyEj06NEDOTk5eOGFF+Dqqh7dq1OnTprjm5qawtDQEHZ2do/N8t1332HChAmQSqVwc3ODi4sLduzYAf/HDKe7YMECPPfccwCADRs2oFWrVtizZw/GjRsHQF2srlq1SpMvODgYixYt0rx/8ODBWsdbs2YNLC0tERUVpbmNtWXLllCpVI+9huqkp6fDxkb7wXhDQ0NYWVkhPT1d08bZ2Vmrja2trWZfs2bNkJ6ertn2aJuHx6jq3JW9Jzc3F4WFhcjOzkZ5eXmlbRISEjTHkMlkFQa3edy5iYiofgmCgFVR1wEAkz2dYCzjn5ZEDQV79Eg0D5/ZiouLw+nTp+Hr64vnn38eN27cAACcP38e165dg5mZGUxNTWFqagorKysUFRXh+vXrsLKygr+/P3x9ffHiiy9i6dKlSEtL0znH/fv3sXv3brz22muaba+99hq+++67x77X09NTs25lZYUOHTrg8uXLmm3GxsaaIg9QF22PPnd2584dTJ06Fe3atYOFhQXMzc2Rl5eHlJQUTZuQkBD88MMPOl8XERFRfTtx/S7+SM2BwsgA/n2dxI5DRI/g1y6N1T9GU9QilWq/fqTwqMDgH98FJCfXOtI/mZiYoG3btprX69atg4WFBdauXYv//ve/yMvLg4eHBzZv3lzhvdbW1gDUPXwzZ85EWFgYtm3bhvnz5yM8PBx9+vSpcY4tW7agqKgIvXv31mwTBAEqlQp//vkn2rdvX+trNDIy0notkUggPNL76efnh7t372Lp0qVo06YN5HI5PD09UVJSUutzVsbOzk6rwASAsrIy3Lt3T9PjaWdnV2EUy4evH9emul7Tqt5jbm4OpVIJqVQKqVRa7XHt7OxQUlKC+/fva/XqPe7cRERUv1ZGqnvzJvRsDSsT2WNaE9HTxB69xsrEpOpFoah5W6WyZm3rgEQigYGBAQr/ei7w2WefxdWrV2FjY4O2bdtqLRYWFpr3de/eHfPmzcOJEyfg5uaGLVu2AABkMhnKy8sfe97vvvsOb7/9tqZ3MS4uDufPn8eAAQPw/fffV/vekydPatazs7Px559/at0++jjHjx/HzJkzMXz4cHTp0gVyuRxZld0a+4Q8PT1x//59xMbGarYdPnwYKpVKU+B6enri6NGjWs8YhoeHo0OHDmjWrJmmTUREhNaxw8PDtXo2Kzt3de+RyWTw8PDQaqNSqRAREaFp4+HhASMjI602V65cQUpKSrXnJiKi+vPHrRwcu5YFqYEEb/R3fvwbiPSRTAYsW6ZeZPr1ZQYLPRJNcXEx0tPTkZ6ejsuXL+PNN99EXl4eXnzxRQDq6QBatGiBkSNHIjo6GklJSYiMjMTMmTNx69YtJCUlYd68eYiJicGNGzdw6NAhXL16VVNoOTk5ISkpCXFxccjKykJxJXOfxMXF4ezZs5gyZQrc3Ny0lokTJ2LDhg0oq2ZyzEWLFiEiIgLx8fHw9/dHixYtMGrUqBp/Bu3atcPGjRtx+fJlnDp1CpMmTYLyH8X1vHnzMHny5GqPk56ejri4OFy7dg0A8McffyAuLg737t0DoH52cdiwYZg6dSpOnz6N48ePIzg4GBMmTIC9vXoY7FdffRUymQxvvPEGLl68iG3btmHp0qWYM2eO5jxvvfUWwsLC8OWXXyIhIQELFy7E77//juDg4CrzBgYGIjExEXPnzkVCQgJWrFiB7du3Y/bs2Zo2c+bMwdq1a7FhwwZcvnwZM2bMQH5+PgICAgAAFhYWeOONNzBnzhwcOXIEsbGxCAgIgKenp069t0REVHdWHVX35r34TEs4WhmLnIaonhgZAUFB6uUfd2o1ePU9BCg9mcY8vQIemfrAzMxM6Nmzp7Bz506tdmlpacLkyZOFFi1aCHK5XHBxcRGmTp0q5OTkCOnp6cKoUaOEli1bCjKZTGjTpo3w0UcfCeXl5YIgCEJRUZEwZswYwdLSssrpFYKDg4XOnTtXmjEtLU0wMDAQfvrppyqnV/jll1+ELl26CDKZTOjVq5dw/vx5zfsfTq/wqD179giP/tqdPXtW6NGjh6BQKIR27doJO3bsENq0aSN8/fXXWp/VwIEDq/08FyxYUGE6iX9e8927d4WJEycKpqamgrm5uRAQECA8ePBA6zjnz58X+vfvL8jlcsHBwUH47LPPKpxr+/btQvv27QWZTCZ06dJF2L9/v9b+yvIeOXJEcHd3F2QymeDi4lLpz+Lbb78VWrdurfksT548qbW/sLBQ+Pe//y00a9ZMMDY2FkaPHi2kpaVV+Zno8+8HEVFDl5yVJzi/v09o894+4XLa44d5J6K6ocv0ChJBqMVwifTU5ObmwsLCAjk5OTA3N9faV1RUhKSkJDg7O0Pxz9sxqV5FRkZi0KBByM7OrjASJDUM/P0gIqo/H+75A5tPpWBQB2uEBvQSOw5R/SkvB6Kj1esDBlQc6+Ipq642+CcOxkJERERENZbxoAg7Ym8BAAIHuj6mNZGeKyoCBg1Sr+fl1dnYFE8Dn9EjIiIiohpbfzwZJWUqdG9tiV7OVmLHIaIqsEePqBa8vb3Bu56JiKipeVBUio0n1fPdBg50hUQiETkREVWFPXpEREREVCM/nk7Bg6IyuFqb4LlOtmLHIaJqsNBrBNizRFQRfy+IiOpWcVk51kUnAQCmD3SFgQF784gaMhZ6eszor7k8CgoKRE5C1PA8/L0w0rc5b4iIGqi951KR8aAYduYKjHJ3EDsOET0Gn9HTY1KpFJaWlsjIyAAAGBsb8155avIEQUBBQQEyMjJgaWkJqcjDIBMRNQYqlYDVRxMBAG/0d4bMkH0FRA0dCz09Z2dnBwCaYo+I1CwtLTW/H0RE9GQOXbqDxMx8mCsMMbF3a7HjED09RkbA4sV/r+uRBlHoLV++HF988QXS09PRrVs3fPvtt+jVq/LJN0tLSxESEoINGzYgNTUVHTp0wOeff45hw4Zp2oSEhGD37t1ISEiAUqlE37598fnnn6NDhw6aNtOnT8dvv/2G27dvw9TUVNOmY8eOAIDz58/js88+w7Fjx5CVlQUnJycEBgbirbfe0soTGRmJOXPm4OLFi3B0dMT8+fPh7++v2b9w4UJ8/PHHWu/p0KEDEhISnvRjAwBIJBK0bNkSNjY2KC0trZNjEuk7IyMj9uQREdURQRCwMuo6AGCypxNM5Q3iz0eip0MmA959V+wUtSL6b+q2bdswZ84crFq1Cr1798aSJUvg6+uLK1euwMbGpkL7+fPnY9OmTVi7di06duyIgwcPYvTo0Thx4gS6d+8OAIiKikJQUBB69uyJsrIyfPDBBxg6dCguXboEk78mOfTw8MCkSZPQunVr3Lt3DwsXLsTQoUORlJQEqVSK2NhY2NjYYNOmTXB0dMSJEycwbdo0SKVSBAcHAwCSkpIwYsQIBAYGYvPmzYiIiMCUKVPQsmVL+Pr6ajJ36dIFv/32m+a1oWHdf+xSqZR/2BIREVGdO5l4D+dv3ofc0AD+/ZzEjkNENSQRRB6arnfv3ujZsyeWLVsGAFCpVHB0dMSbb76J999/v0J7e3t7fPjhhwgKCtJsGzNmDJRKJTZt2lTpOTIzM2FjY4OoqCh4eXlV2ubChQvo1q0brl27BldX10rbBAUF4fLlyzh8+DAA4L333sP+/fsRHx+vaTNhwgTcv38fYWFhANQ9env37kVcXNzjP4xK5ObmwsLCAjk5OTA3N6/VMYiIiIhqy+/704j6MxOv9WmN/47qKnYcoqervBw4e1a9/uyzgMgdK7rUBqI+SVtSUoLY2Fj4+PhothkYGMDHxwcxMTGVvqe4uBgKhUJrm1KpxLFjx6o8T05ODgDAysqq0v35+fkIDQ2Fs7MzHB0dqz3Oo8eIiYnRyg4Avr6+FbJfvXoV9vb2cHFxwaRJk5CSklLlOYqLi5Gbm6u1EBEREYnh0u1cRP2ZCQMJMG1A5V+EEzVqRUVAr17qpahI7DQ6EbXQy8rKQnl5OWxttSfctLW1RXp6eqXv8fX1xVdffYWrV69CpVIhPDwcu3fvRlpaWqXtVSoVZs2ahX79+sHNzU1r34oVK2BqagpTU1McOHAA4eHhkMlklR7nxIkT2LZtG6ZNm6bZlp6eXmn23NxcFBYWAlD3WK5fvx5hYWFYuXIlkpKSMGDAADx48KDS84SEhMDCwkKzVFd4EhEREdWn1UfVz+aNeMYerZsbi5yGiHShd2PjLl26FO3atUPHjh0hk8kQHByMgIAAGBhUfilBQUGIj4/H1q1bK+ybNGkSzp07h6ioKLRv3x7jxo1DUSWVenx8PEaOHIkFCxZg6NChOuV9/vnnMXbsWDzzzDPw9fXFr7/+ivv372P79u2Vtp83bx5ycnI0y82bN3U6HxEREVFduHmvAL+cvw0AmO7lInIaItKVqIVeixYtIJVKcefOHa3td+7cqXJYdGtra+zduxf5+fm4ceMGEhISYGpqCheXiv8ABQcHY9++fThy5AhatWpVYb+FhQXatWsHLy8v7Ny5EwkJCdizZ49Wm0uXLmHIkCGYNm0a5s+fr7XPzs6u0uzm5uZQKpWV5re0tET79u1x7dq1SvfL5XKYm5trLURERERP29roRKgEYEC7FnBzsBA7DhHpSNRCTyaTwcPDAxEREZptKpUKERER8PT0rPa9CoUCDg4OKCsrw65duzBy5EjNPkEQEBwcjD179uDw4cNwdnZ+bBZBECAIAoqLizXbLl68iEGDBsHPzw+ffPJJhfd4enpqZQeA8PDwarPn5eXh+vXraNmy5WMzEREREYnhbl4xtv+uvqtoxkA+m0ekj0S/dXPOnDlYu3YtNmzYgMuXL2PGjBnIz89HQEAAAGDy5MmYN2+epv2pU6ewe/duJCYmIjo6GsOGDYNKpcLcuXM1bYKCgrBp0yZs2bIFZmZmSE9PR3p6uua5ucTERISEhCA2NhYpKSk4ceIExo4dC6VSieHDhwNQ3645aNAgDB06FHPmzNEcIzMzU3OewMBAJCYmYu7cuUhISMCKFSuwfft2zJ49W9PmnXfeQVRUFJKTk3HixAmMHj0aUqkUEydOrNfPlYiIiKi2NpxIRlGpCt1aWcDTtbnYcYioFkSfR2/8+PHIzMzERx99hPT0dLi7uyMsLEwzyElKSorW83dFRUWYP38+EhMTYWpqiuHDh2Pjxo2wtLTUtFm5ciUAwNvbW+tcoaGh8Pf3h0KhQHR0NJYsWYLs7GzY2trCy8sLJ06c0Mzdt3PnTmRmZmLTpk1a0za0adMGycnJAABnZ2fs378fs2fPxtKlS9GqVSusW7dOaw69W7duYeLEibh79y6sra3Rv39/nDx5EtbW1nX5MRIRERHVifziMmyIuQEACBzoColEInIiIqoN0efRo+pxHj0iIiJ6mtZFJ+K/+y/DuYUJfpszEFIDFnrUhJWUAJ9+ql7/4AOgihH6nxZdagPRe/SIiIiIqGEoKVPhu2NJAIBpXi4s8ohkMmDhQrFT1Iroz+gRERERUcPw8/nbSMspgo2ZHC8/6yB2HCJ6AuzRIyIiIiKoVAJWRaknSH+9vzPkhlKRExE1ACoVcPmyer1TJ6CKubsbIhZ6RERERISIhAxcy8iDmdwQr/ZuLXYcooahsBBwc1Ov5+UBJibi5tGB/pSkRERERFRvHvbmTerTBuYKI5HTENGTYqFHRERE1MSdSb6H2BvZkEkN8Ho/J7HjEFEdYKFHRERE1MStilT35o3xaAUbc4XIaYioLrDQIyIiImrCrqQ/QERCBiQS9ZQKRNQ4sNAjIiIiasJW//Vs3vNudnBuoT8DTRBR9VjoERERETVRqfcL8fP52wCAwIGuIqchorrE6RWIiIiImqh10YkoUwno17Y5nmllKXYcoobHyAh4552/1/UICz0iIiKiJig7vwRbT98EwN48oirJZMAXX4idolZ46yYRERFRE7QhJhmFpeXoYm+O/m1biB2HiOoYe/SIiIiImpiCkjJsOJEMQN2bJ5FIxA1E1FCpVEBKinq9dWvAQH/6yVjoERERETUx28/cRHZBKVpbGeN5Nzux4xA1XIWFgLOzej0vDzDRn5Fp9ackJSIiIqInVlquwtroJADqefMMpfxzkKgx4m82ERERUROy78JtpN4vRAtTGV7xaCV2HCKqJyz0iIiIiJoIQRCwOioRABDQzxkKI6nIiYiovrDQIyIiImoiIq9kIiH9AUxkUrzWu43YcYioHrHQIyIiImoiVkZdBwBM6tMGFsb6NfkzEemGhR4RERFRExB7Ixunk+7BSCrB6/2cxY5DRPWM0ysQERERNQGr/urNG93dAXYWCpHTEOkJQ0Pg3//+e12P6FdaIiIiItLZtYwHCL90BxIJMM3LVew4RPpDLgeWLxc7Ra3w1k0iIiKiRu7hSJtDO9uirY2pyGmI6Glgjx4RERFRI5aWU4i9cakAgMCB7M0j0okgAFlZ6vUWLQCJRNw8OmChR0RERNSIfRedhNJyAb2drdC9dTOx4xDpl4ICwMZGvZ6XB5iYiJtHB7x1k4iIiKiRyikoxY+nUwAAgd7szSNqSljoERERETVSG08mI7+kHB3tzODd3lrsOET0FLHQIyIiImqEikrLEXo8GQAww9sVEj16toiInhwLPSIiIqJGaMfvN3E3vwStmikxomtLseMQ0VPGQo+IiIiokSkrV2FNtHpKhakDXGAo5Z98RE0Nf+uJiIiIGplf49Nx814hrExkGNfDUew4RCQCTq9ARERE1IgIgoBVkdcBAP59naCUSUVORKTHDA0BP7+/1/WIfqUlIiIiomodvZqFS2m5MJZJMdmzjdhxiPSbXA6sXy92ilrhrZtEREREjcjD3rwJPVvD0lgmchoiEgt79IiIiIgaifM37yMm8S4MDSSYMsBZ7DhE+k8QgIIC9bqxMaBH05SwR4+IiIiokVgVpe7Ne8ndHvaWSpHTEDUCBQWAqal6eVjw6QkWekRERESNwPXMPIRdTAcABA50FTkNEYmNhR4RERFRI7D2aCIEAfDpZIP2tmZixyEikbHQIyIiItJzGblF2H02FQB784hIjYUeERERkZ777ngSSspV6NGmGXo4WYkdh4gaABZ6RERERHost6gUW06mAABmeLM3j4jUWOgRERER6bFNJ2/gQXEZ2tuaYlAHG7HjEFEDwXn0iIiIiPRUUWk5vj+WDACY7uUKAwP9meOLSC9IpcArr/y9rkdY6BERERHpqd1nU5GVVwx7CwVecrcXOw5R46NQADt2iJ2iVnjrJhEREZEeKlcJWHNUPUH6GwNcYCTln3VE9Df+i0BERESkh8Li05F8twCWxkaY0NNR7DhE1MCw0CMiIiLSM4IgYFWUujdvsqcTTOR8GoeoXuTnAxKJesnPFzuNTljoEREREemZE9fv4o/UHCiMDODf10nsOETUALHQIyIiItIzD3vzxvdwhJWJTOQ0RNQQNYhCb/ny5XBycoJCoUDv3r1x+vTpKtuWlpZi0aJFcHV1hUKhQLdu3RAWFqbVJiQkBD179oSZmRlsbGwwatQoXLlyRavN9OnT4erqCqVSCWtra4wcORIJCQma/efPn8fEiRPh6OgIpVKJTp06YenSpRXyREZG4tlnn4VcLkfbtm2xfv36J7o+IiIiour8cSsH0VezIDWQYMoAF7HjEFEDJXqht23bNsyZMwcLFizA2bNn0a1bN/j6+iIjI6PS9vPnz8fq1avx7bff4tKlSwgMDMTo0aNx7tw5TZuoqCgEBQXh5MmTCA8PR2lpKYYOHYr8R+6r9fDwQGhoKC5fvoyDBw9CEAQMHToU5eXlAIDY2FjY2Nhg06ZNuHjxIj788EPMmzcPy5Yt0xwjKSkJI0aMwKBBgxAXF4dZs2ZhypQpOHjwYK2vj4iIiKg6q/4aafPFZ1rC0cpY5DRE1FBJBEEQxAzQu3dv9OzZU1NAqVQqODo64s0338T7779fob29vT0+/PBDBAUFabaNGTMGSqUSmzZtqvQcmZmZsLGxQVRUFLy8vCptc+HCBXTr1g3Xrl2Dq6trpW2CgoJw+fJlHD58GADw3nvvYf/+/YiPj9e0mTBhAu7fv6/pZdT1+v4pNzcXFhYWyMnJgbm5+WPbExERUeN1424+Bv0vEioBOPDWAHRqyb8NiOpVfj5gaqpez8sDTExEjaNLbSBqj15JSQliY2Ph4+Oj2WZgYAAfHx/ExMRU+p7i4mIoFAqtbUqlEseOHavyPDk5OQAAKyurSvfn5+cjNDQUzs7OcHSsenjinJwcrWPExMRoZQcAX19fTfbaXl9ubq7WQkRERAQAa44mQiUA3h2sWeQRUbVELfSysrJQXl4OW1tbre22trZIT0+v9D2+vr746quvcPXqVahUKoSHh2P37t1IS0urtL1KpcKsWbPQr18/uLm5ae1bsWIFTE1NYWpqigMHDiA8PBwyWeUPNJ84cQLbtm3DtGnTNNvS09MrzZ6bm4vCwsJaXV9ISAgsLCw0S3WFJxERETUdmQ+KsSP2FgBgxsDK7z4iojomlQLDh6sXqVTsNDoR/Rk9XS1duhTt2rVDx44dIZPJEBwcjICAABgYVH4pQUFBiI+Px9atWyvsmzRpEs6dO4eoqCi0b98e48aNQ1FRUYV28fHxGDlyJBYsWIChQ4fW+TU9at68ecjJydEsN2/erNfzERERkX4IPZ6EkjIVure2RC/nyu9SIqI6plAA+/erl3/cVdjQiVrotWjRAlKpFHfu3NHafufOHdjZ2VX6Hmtra+zduxf5+fm4ceMGEhISYGpqCheXiqNOBQcHY9++fThy5AhatWpVYb+FhQXatWsHLy8v7Ny5EwkJCdizZ49Wm0uXLmHIkCGYNm0a5s+fr7XPzs6u0uzm5uZQKpW1uj65XA5zc3OthYiIiJq2B0Wl2HjyBgAgcKArJBKJyImIqKETtdCTyWTw8PBARESEZptKpUJERAQ8PT2rfa9CoYCDgwPKysqwa9cujBw5UrNPEAQEBwdjz549OHz4MJydnR+bRRAECIKA4uJizbaLFy9i0KBB8PPzwyeffFLhPZ6enlrZASA8PFyT/Umuj4iIiOihH0+n4EFRGVytTfBcJ9vHv4GImjxDsQPMmTMHfn5+6NGjB3r16oUlS5YgPz8fAQEBAIDJkyfDwcEBISEhAIBTp04hNTUV7u7uSE1NxcKFC6FSqTB37lzNMYOCgrBlyxb89NNPMDMz0zwPZ2FhAaVSicTERGzbtg1Dhw6FtbU1bt26hc8++wxKpRLDhw8HoL5dc/DgwfD19cWcOXM0x5BKpbC2tgYABAYGYtmyZZg7dy5ef/11HD58GNu3b8f+/ftrfH1ERERE1SkuK8d3x5IAANO9XGFgwN48oqcmPx+wsVGvZ2SIPuqmLkQv9MaPH4/MzEx89NFHSE9Ph7u7O8LCwjQDmKSkpGg9f1dUVIT58+cjMTERpqamGD58ODZu3AhLS0tNm5UrVwIAvL29tc4VGhoKf39/KBQKREdHY8mSJcjOzoatrS28vLxw4sQJ2Pz1g9y5cycyMzOxadMmrWkb2rRpg+TkZACAs7Mz9u/fj9mzZ2Pp0qVo1aoV1q1bB19f3xpfHxEREVF19p5LxZ3cYtiZKzCyu73YcYianoICsRPUiujz6FH1OI8eERFR06VSCfD5OgqJmfn4cHgnTPWqOCYBEdUjzqNHRERERHXt0KU7SMzMh7nCEBN7txY7DhHpERZ6RERERA2QIAhYGXUdAPAvzzYwlYv+xA0R6REWekREREQN0MnEezh/8z7khgbw7/v4EcSJiB7FQo+IiIioAVr1V2/e2B6tYG0mFzkNEekb3gNARERE1MBcup2LqD8zYSABpg1wFTsOUdNlYAAMHPj3uh5hoUdERETUwKw+qu7NG961JVo3NxY5DVETplQCkZFip6gV/SpLiYiIiBq5m/cKsO9CGgAgcCB784iodljoERERETUga6MTUa4SMKBdC7g5WIgdh4j0FAs9IiIiogbibl4xtv9+EwAwg715ROLLzwesrdVLfr7YaXTCZ/SIiIiIGogNJ5JRVKrCM60s4OnaXOw4RAQAWVliJ6gV9ugRERERNQD5xWXYEHMDgPrZPIlEInIiItJnLPSIiIiIGoAfT6cgp7AUzi1M4NvFTuw4RKTnWOgRERERiaykTIXvjiUBAKZ5uUBqwN48InoyLPSIiIiIRPbz+dtIyymCtZkco7s7iB2HiBoBFnpEREREIlKpBKyOUk+Q/no/ZyiMpCInIqLGgKNuEhEREYnocEIGrmbkwUxuiEl9Wosdh4geZWAA9Ojx97oeYaFHREREJKKVf/XmTerTBuYKI5HTEJEWpRI4c0bsFLWiX2UpERERUSNyJvkeYm9kQyY1wOv9nMSOQ0SNCAs9IiIiIpGsilT35o3xcICNuULkNETUmLDQIyIiIhLBlfQHiEjIgEQCTB3gInYcIqpMQQHg5KReCgrETqMTPqNHREREJIKHI20+72YHF2tTkdMQUaUEAbhx4+91PcIePSIiIqKnLPV+IX4+fxsAEDjQVeQ0RNQYsdAjIiIiesrWRSeiTCWgr2tzPNPKUuw4RNQIsdAjIiIieoqy80uw9fRNAOzNI6L6w0KPiIiI6CnaEJOMwtJydLE3x4B2LcSOQ0SNFAs9IiIioqekoKQMG04kA1D35kkkEnEDEVGjxVE3iYiIiJ6S7WduIrugFK2tjPG8m53YcYjocSQSoHPnv9f1CAs9IiIioqegtFyFtdFJAICpXi4wlPLGKqIGz9gYuHhR7BS1wn9hiIiIiJ6C/RfSkHq/EC1MZRjr0UrsOETUyLHQIyIiIqpngiBg1V8TpAf0c4bCSCpyIiJq7FjoEREREdWzyCuZSEh/ABOZFK/1biN2HCKqqYICoEsX9VJQIHYanfAZPSIiIqJ6tvKv3rxXe7eGhbGRyGmIqMYEAbh06e91PcIePSIiIqJ6FHsjG6eT7sFIKsEb/V3EjkNETQQLPSIiIqJ69PDZvNHdHWBnoRA5DRE1FSz0iIiIiOrJtYwHCL90BxIJMM3LVew4RNSEsNAjIiIiqieroxIBAM91skVbG1OR0xBRU8JCj4iIiKgepOUUYm9cKgAg0Ju9eUT0dHHUTSIiIqJ68F10EkrLBfRytsKzrZuJHYeIakMiAdq0+Xtdj7DQIyIiIqpjOQWl+PF0CgBgBnvziPSXsTGQnCx2ilrhrZtEREREdWzjyWTkl5Sjo50ZvNtbix2HiJogFnpEREREdaiotByhx5MBAIEDXSHRs9u9iKhxYKFHREREVId2xN7C3fwSOFgq8cIzLcWOQ0RPorAQ6NlTvRQWip1GJ3xGj4iIiKiOlJWrsOaoeoL0aV4uMJTyO3UivaZSAb///ve6HuG/PkRERER15Nf4dNy8VwgrExnG9XAUOw4RNWEs9IiIiIjqgCAIWBWp7s3z83SCUiYVORERNWUs9IiIiIjqQPTVLFxKy4XSSIrJnm3EjkNETRwLPSIiIqI6sPKv3ryJvVqjmYlM5DRE1NSx0CMiIiJ6Qudv3kdM4l0YGkgwZYCz2HGIiDjqJhEREdGTWhWl7s17yd0e9pZKkdMQUZ1q0ULsBLXCQo+IiIjoCSRm5iHsYjoA9QTpRNSImJgAmZlip6gV0W/dXL58OZycnKBQKNC7d2+cPn26yralpaVYtGgRXF1doVAo0K1bN4SFhWm1CQkJQc+ePWFmZgYbGxuMGjUKV65c0Wozffp0uLq6QqlUwtraGiNHjkRCQoJWm5kzZ8LDwwNyuRzu7u6V5tm+fTvc3d1hbGyMNm3a4IsvvtDaHxkZCYlEUmFJT0/X4RMiIiKihmzN0UQIAjCkow3a25qJHYeICIDIhd62bdswZ84cLFiwAGfPnkW3bt3g6+uLjIyMStvPnz8fq1evxrfffotLly4hMDAQo0ePxrlz5zRtoqKiEBQUhJMnTyI8PBylpaUYOnQo8vPzNW08PDwQGhqKy5cv4+DBgxAEAUOHDkV5ebnW+V5//XWMHz++0iwHDhzApEmTEBgYiPj4eKxYsQJff/01li1bVqHtlStXkJaWpllsbGxq83ERERFRA5ORW4TdZ1MBADO82ZtHRA2HRBAEQayT9+7dGz179tQURyqVCo6OjnjzzTfx/vvvV2hvb2+PDz/8EEFBQZptY8aMgVKpxKZNmyo9R2ZmJmxsbBAVFQUvL69K21y4cAHdunXDtWvX4Oqq/Y/0woULsXfvXsTFxWltf/XVV1FaWoodO3Zotn377bdYvHgxUlJSIJFIEBkZiUGDBiE7OxuWlpY1+UgqyM3NhYWFBXJycmBubl6rYxAREVH9CDlwGaujEtGjTTPsnNFX7DhEVNcKC4Hnn1evHzgAKMV9BleX2kC0Hr2SkhLExsbCx8fn7zAGBvDx8UFMTEyl7ykuLoZCodDaplQqcezYsSrPk5OTAwCwsrKqdH9+fj5CQ0Ph7OwMR0fHGuevKsutW7dw48YNre3u7u5o2bIlnnvuORw/fvyxx83NzdVaiIiIqOHJLSrFlpMpAPhsHlGjpVIBUVHqRaUSO41ORCv0srKyUF5eDltbW63ttra2VT7D5uvri6+++gpXr16FSqVCeHg4du/ejbS0tErbq1QqzJo1C/369YObm5vWvhUrVsDU1BSmpqY4cOAAwsPDIZPVfM4bX19f7N69GxEREVCpVPjzzz/x5ZdfAoAmT8uWLbFq1Srs2rULu3btgqOjI7y9vXH27NkqjxsSEgILCwvNokvxSURERE/P5pMpeFBchnY2phjckY9lEFHDIvpgLLpYunQp2rVrh44dO0ImkyE4OBgBAQEwMKj8MoKCghAfH4+tW7dW2Ddp0iScO3cOUVFRaN++PcaNG4eioqIaZ5k6dSqCg4PxwgsvQCaToU+fPpgwYQIAaPJ06NAB06dPh4eHB/r27Yvvv/8effv2xddff13lcefNm4ecnBzNcvPmzRpnIiIioqejqLQc3x1LAqDuzTMwkIiciIhIm2iFXosWLSCVSnHnzh2t7Xfu3IGdnV2l77G2tsbevXuRn5+PGzduICEhAaampnBxcanQNjg4GPv27cORI0fQqlWrCvstLCzQrl07eHl5YefOnUhISMCePXtqnF8ikeDzzz9HXl4ebty4gfT0dPTq1QsAKs3zUK9evXDt2rUq98vlcpibm2stRERE1LDsPpuKrLxi2Fso8JK7vdhxiIgqEK3Qk8lk8PDwQEREhGabSqVCREQEPD09q32vQqGAg4MDysrKsGvXLowcOVKzTxAEBAcHY8+ePTh8+DCcnZ0fm0UQBAiCgOLiYp2vQyqVwsHBATKZDD/++CM8PT1hbW1dZfu4uDi0bNlS5/MQERFRw1CuErDmqHqC9DcGuMBIqlc3SBFREyHqhOlz5syBn58fevTogV69emHJkiXIz89HQEAAAGDy5MlwcHBASEgIAODUqVNITU2Fu7s7UlNTsXDhQqhUKsydO1dzzKCgIGzZsgU//fQTzMzMNM/7WVhYQKlUIjExEdu2bcPQoUNhbW2NW7du4bPPPoNSqcTw4cM1x7l27Rry8vKQnp6OwsJCzaibnTt3hkwmQ1ZWFnbu3Alvb28UFRUhNDQUO3bsQFRUlOYYS5YsgbOzM7p06YKioiKsW7cOhw8fxqFDh+r7oyUiIqJ6cvBiOpLvFsBCaYQJPfksPRE1TKIWeuPHj0dmZiY++ugjpKenw93dHWFhYZoBWlJSUrSevysqKsL8+fORmJgIU1NTDB8+HBs3btSaumDlypUAAG9vb61zhYaGwt/fHwqFAtHR0ViyZAmys7Nha2sLLy8vnDhxQmt+uylTpmgVbd27dwcAJCUlwcnJCQCwYcMGvPPOOxAEAZ6enoiMjNTcvgmoRxZ9++23kZqaCmNjYzzzzDP47bffMGjQoDr5/IiIiOjpEgQBKyPVvXl+nm1gIhf1TykiehqMjcVOUCuizqNHj8d59IiIiBqO49eyMGndKSiMDHD8vcFobioXOxIRNSF6MY8eERERkb5ZFaXuzRvfw5FFHhE1aCz0iIiIiGogPjUH0VezIDWQYMqAqkfYJiJqCFjoEREREdXAyr968154piUcrfTzmR0i0lFRETBihHrRYc7thoBPEBMRERE9xo27+TjwRxoA9QTpRNRElJcDv/7697oeYY8eERER0WOsOZoIlQB4d7BGp5YcHI2IGj4WekRERETVyHxQjB2xtwCwN4+I9AcLPSIiIqJqrD+RhJIyFdwdLdHb2UrsOERENcJCj4iIiKgKD4pK8UPMDQDq3jyJRCJyIiKimmGhR0RERFSFH0+n4EFRGVysTTC0s63YcYiIaqxWhd79+/exbt06zJs3D/fu3QMAnD17FqmpqXUajoiIiEgsxWXl+O5YEgAg0MsVBgbszSMi/aHz9AoXLlyAj48PLCwskJycjKlTp8LKygq7d+9GSkoKfvjhh/rISURERPRU/XTuNu7kFsPWXI6R3e3FjkNEYjAxAQRB7BS1onOP3pw5c+Dv74+rV69CoVBotg8fPhxHjx6t03BEREREYlCpBKw6qp4g/Y3+zpAbSkVORESkG50LvTNnzmD69OkVtjs4OCA9Pb1OQhERERGJ6dClO0jMzIe5whATe7UWOw4Rkc50LvTkcjlyc3MrbP/zzz9hbW1dJ6GIiIiIxCIIAlZFqXvz/uXZBmYKI5ETEZFoioqAsWPVS1GR2Gl0onOh99JLL2HRokUoLS0FAEgkEqSkpOC9997DmDFj6jwgERER0dN0Kuke4m7eh8zQAP59ncWOQ0RiKi8Hdu5UL+XlYqfRic6F3pdffom8vDzY2NigsLAQAwcORNu2bWFmZoZPPvmkPjISERERPTUrI9W9eWM9WsHaTC5yGiKi2tF51E0LCwuEh4fj+PHjOH/+PPLy8vDss8/Cx8enPvIRERERPTWXbuci6s9MGEiAaV4uYschIqo1nQq90tJSKJVKxMXFoV+/fujXr1995SIiIiJ66lb/NdLm8K4t0aa5ichpiIhqT6dbN42MjNC6dWuU69n9qURERESPc/NeAfZdSAMABA50FTkNEdGT0fkZvQ8//BAffPAB7t27Vx95iIiIiESxLjoR5SoBA9q1gJuDhdhxiIieiM7P6C1btgzXrl2Dvb092rRpAxMT7dsazp49W2fhiIiIiJ6Gu3nF2Pb7TQDszSOixkHnQm/UqFH1EIOIiIhIPBtOJKOoVIVnWlmgr2tzseMQUUNhbAzk5f29rkd0LvQWLFhQHzmIiIiIRJFfXIYNMTcAqHvzJBKJyImIqMGQSAAT/RyYSedC76HY2FhcvnwZANClSxd07969zkIRERERPS1bz9xETmEpnFuYwLeLndhxiIjqhM6FXkZGBiZMmIDIyEhYWloCAO7fv49BgwZh69atsLa2ruuMRERERPWipEyFddGJAICpA1wgNWBvHhE9orgYmD5dvb56NSCXi5tHBzqPuvnmm2/iwYMHuHjxIu7du4d79+4hPj4eubm5mDlzZn1kJCIiIqoXP5+/jbScIlibyfHysw5ixyGihqasDNiwQb2UlYmdRic69+iFhYXht99+Q6dOnTTbOnfujOXLl2Po0KF1Go6IiIiovqhUAlZHqSdIf72fMxRGUpETERHVHZ179FQqFYyMjCpsNzIygkqlqpNQRERERPXtcEIGrmbkwUxuiEl9Wosdh4ioTulc6A0ePBhvvfUWbt++rdmWmpqK2bNnY8iQIXUajoiIiKi+rPqrN+/VPq1hrqj4JTYRkT7TudBbtmwZcnNz4eTkBFdXV7i6usLZ2Rm5ubn49ttv6yMjERERUZ06k3wPv9/IhkxqgDf6OYsdh4iozun8jJ6joyPOnj2L3377DQkJCQCATp06wcfHp87DEREREdWHVZHq3rwxHg6wMVeInIaIqO7Vah49iUSC5557Ds8991xd5yEiIiKqV1fSHyAiIQMSiXpKBSKixkjnWzdnzpyJb775psL2ZcuWYdasWXWRiYiIiKjerD6q7s0b1sUOLtamIqchogbN2BjIyFAvxsZip9GJzoXerl270K9fvwrb+/bti507d9ZJKCIiIqL6kHq/ED/HqQeUCxzoKnIaImrwJBLA2lq9SCRip9GJzoXe3bt3YWFhUWG7ubk5srKy6iQUERERUX1YF52IMpWAvq7N0c3RUuw4RET1RudCr23btggLC6uw/cCBA3Bx4X3uRERE1DBl55dg6+mbANibR0Q1VFwMBAWpl+JisdPoROfBWObMmYPg4GBkZmZi8ODBAICIiAh8+eWXWLJkSV3nIyIiIqoTP8TcQGFpObrYm2NAuxZixyEifVBWBqxYoV5fvBiQy8XNowOdC73XX38dxcXF+OSTT/Cf//wHAODk5ISVK1di8uTJdR6QiIiI6EkVlJRh/YkkAMD0ga6Q6NmzNkREuqrV9AozZszAjBkzkJmZCaVSCVNTjlhFREREDdf2MzeRXVAKRyslhrvZiR2HiKje6fyM3qOsra0RGxuLAwcOIDs7u64yEREREdWZ0nIV1kare/OmebnCUPpEf/4QEemFGvfoff7558jLy9PcrikIAp5//nkcOnQIAGBjY4OIiAh06dKlfpISERER1cL+C2lIvV+IFqYyjPVoJXYcIqKnosZfaW3btg1ubm6a1zt37sTRo0cRHR2NrKws9OjRAx9//HG9hCQiIiKqDUEQsCpKPUG6f18nKIykIiciIno6alzoJSUl4ZlnntG8/vXXX/HKK6+gX79+sLKywvz58xETE1MvIYmIiIhqI/JKJhLSH8BEJsW/+jiJHYeI6KmpcaFXVlYG+SPDicbExKBv376a1/b29pwwnYiIiBqUlX/15r3auzUsjI1ETkNEekepBJKS1ItSKXYandS40HN1dcXRo0cBACkpKfjzzz/h5eWl2X/r1i00b9687hMSERER1cLZlGycTroHI6kEb/R3ETsOEekjAwPAyUm9GOjXQE41HowlKCgIwcHBiI6OxsmTJ+Hp6YnOnTtr9h8+fBjdu3evl5BEREREuloVqe7NG+XuADsLhchpiIierhqXpVOnTsU333yDe/fuwcvLC7t27dLaf/v2bbz++ut1HpCIiIhIV9cyHuDQpTsAgOkD2ZtHRLVUUgK8+656KSkRO41OJIIgCGKHoKrl5ubCwsICOTk5MDc3FzsOERGRXnh3x3nsiL2FoZ1tsWZyD7HjEJG+ys8HTE3V63l5gImJqHF0qQ3060ZTIiIiosdIyynE3rhUAECgt6vIaYiIxMFCj4iIiBqV748lobRcQC9nKzzbupnYcYiIRCF6obd8+XI4OTlBoVCgd+/eOH36dJVtS0tLsWjRIri6ukKhUKBbt24ICwvTahMSEoKePXvCzMwMNjY2GDVqFK5cuaLVZvr06XB1dYVSqYS1tTVGjhyJhIQErTYzZ86Eh4cH5HI53N3dK82zfft2uLu7w9jYGG3atMEXX3xRoU1kZCSeffZZyOVytG3bFuvXr6/ZB0NEREQ6yykoxZZTKQCAGQPZm0dETZeohd62bdswZ84cLFiwAGfPnkW3bt3g6+uLjIyMStvPnz8fq1evxrfffotLly4hMDAQo0ePxrlz5zRtoqKiEBQUhJMnTyI8PBylpaUYOnQo8vPzNW08PDwQGhqKy5cv4+DBgxAEAUOHDkV5ebnW+V5//XWMHz++0iwHDhzApEmTEBgYiPj4eKxYsQJff/01li1bpmmTlJSEESNGYNCgQYiLi8OsWbMwZcoUHDx48Ek+NiIiIqrCxpPJyC8pR0c7M3h3sBY7DhGRaEQdjKV3797o2bOnpjhSqVRwdHTEm2++iffff79Ce3t7e3z44YcICgrSbBszZgyUSiU2bdpU6TkyMzNhY2ODqKgorXn/HnXhwgV069YN165dg6ur9rd/CxcuxN69exEXF6e1/dVXX0VpaSl27Nih2fbtt99i8eLFSElJgUQiwXvvvYf9+/cjPj5e02bChAm4f/9+hZ7IqnAwFiIiopopKi1Hv88O425+CZaMd8eo7g5iRyIifddUBmM5f/48/vvf/2LFihXIysqqcFJdplcoKSlBbGwsfHx8/g5jYAAfHx/ExMRU+p7i4mIoFNrz4CiVShw7dqzK8+Tk5AAArKysKt2fn5+P0NBQODs7w9HRscb5q8py69Yt3LhxAwAQExOjdX0A4OvrW+X1PTxubm6u1kJERESPtyP2Fu7ml8DBUokXnmkpdhwiIlHVuNA7dOgQevXqha1bt+Lzzz9Hx44dceTIEc3+wsJCbNiwocYnzsrKQnl5OWxtbbW229raIj09vdL3+Pr64quvvsLVq1ehUqkQHh6O3bt3Iy0trdL2KpUKs2bNQr9+/eDm5qa1b8WKFTA1NYWpqSkOHDiA8PBwyGSyGuf39fXF7t27ERERAZVKhT///BNffvklAGjypKenV3p9ubm5KCwsrPS4ISEhsLCw0Cy6FJ9ERERNVVm5CmuPJgIApg5whqFU9GEIiKgxUCqB+Hj1olSKnUYnNf5XcOHChXjnnXcQHx+P5ORkzJ07Fy+99FKNb0GsC0uXLkW7du3QsWNHyGQyBAcHIyAgAAYGlV9GUFAQ4uPjsXXr1gr7Jk2ahHPnziEqKgrt27fHuHHjUFRUVOMsU6dORXBwMF544QXIZDL06dMHEyZMAIAq89TEvHnzkJOTo1lu3rxZ62MRERE1Fb/GpyPlXgGaGRthXE9+SUpEdcTAAOjSRb08wd/4Yqhx2osXL2puzZRIJJg7dy5Wr16NV155Bfv27dP5xC1atIBUKsWdO3e0tt+5cwd2dnaVvsfa2hp79+5Ffn4+bty4gYSEBJiamsLFxaVC2+DgYOzbtw9HjhxBq1atKuy3sLBAu3bt4OXlhZ07dyIhIQF79uypcX6JRILPP/8ceXl5uHHjBtLT09GrVy8A0OSxs7Or9PrMzc2hrOIbAblcDnNzc62FiIiIqiYIAlZFXgcA+Pd1hrHMUORERETiq3GhJ5fLcf/+fa1tr776KtatW4fx48frVCQBgEwmg4eHByIiIjTbVCoVIiIi4OnpWe17FQoFHBwcUFZWhl27dmHkyJGafYIgIDg4GHv27MHhw4fh7Oz82CyCIEAQBBQXF+t0DQAglUrh4OAAmUyGH3/8EZ6enrC2Vo/y5enpqXV9ABAeHv7Y6yMiIqKai76ahUtpuVAaSTHZs43YcYioMSkpARYuVC8lJWKn0UmNv/Jyd3fHkSNH4OHhobV9woQJEAQBfn5+Op98zpw58PPzQ48ePdCrVy8sWbIE+fn5CAgIAABMnjwZDg4OCAkJAQCcOnUKqampcHd3R2pqKhYuXAiVSoW5c+dqjhkUFIQtW7bgp59+gpmZmeZ5PwsLCyiVSiQmJmLbtm0YOnQorK2tcevWLXz22WdQKpUYPny45jjXrl1DXl4e0tPTUVhYqBl1s3PnzpDJZMjKysLOnTvh7e2NoqIihIaGYseOHYiKitIcIzAwEMuWLcPcuXPx+uuv4/Dhw9i+fTv279+v82dFRERElVsVpe7Nm9DLEc1Mav68PRHRY5WWAh9/rF5/911AhzE9xFbjQm/GjBk4evRopfsmTpwIQRCwdu1anU4+fvx4ZGZm4qOPPkJ6ejrc3d0RFhamGcAkJSVF63m3oqIizJ8/H4mJiTA1NcXw4cOxceNGWFpaatqsXLkSAODt7a11rtDQUPj7+0OhUCA6OhpLlixBdnY2bG1t4eXlhRMnTsDGxkbTfsqUKVpFW/fu3QGo58ZzcnICAGzYsAHvvPMOBEGAp6cnIiMjNbdvAoCzszP279+P2bNnY+nSpWjVqhXWrVsHX19fnT4nIiIiqtz5m/dx4vpdGBpIMGVAxUc5iIiaKlHn0aPH4zx6REREVZuxKRYH4tPxcncHfDXeXew4RNTYNJV59IiIiIgaisTMPIRdVD+iMX2gq8hpiIgaFp0Lvd27d9dHDiIiIiKdrI1OhCAAQzraoIOdmdhxiIgaFJ0KvTVr1uDNN9+sryxERERENZKRW4RdsakAgEBv9uYREf1TjQdj+eSTT/D1119XmC6AiIiI6Gn77ngSSspV8GjTDD2drMSOQ0TU4NSo0Js1axZCQ0Nx6NAhdOvWrb4zEREREVUpt6gUW06mAABm8Nk8IqpPCgVw+vTf63qkRoXeN998gzVr1qB37971nYeIiIioWptPpuBBcRna2ZhicEebx7+BiKi2pFKgZ0+xU9RKjZ7RGzNmDBYsWIDExMT6zkNERERUpaLScnx/PAmAeqRNAwOJyImIiBqmGhV627dvxwsvvIAhQ4YgNTW1vjMRERERVWr32VRkPihGSwsFXupmL3YcImrsSkqAL75QLyUlYqfRSY0KPYlEgtWrV2PixIkYPHhwfWciIiIiqqBcJWDN0esAgCkDXCAz5HTARFTPSkuBuXPVS2mp2Gl0UuNRNwHg008/hY0N74UnIiKip+/gxXQk3y2AhdIIE3o6ih2HiKhB0/mrsFmzZtVDDCIiIqKqCYKAVVHq3jw/zzYwkev0XTURUZNTp/c8FBYW1uXhiIiIiAAAJ67fxYVbOVAYGcCvr5PYcYiIGrw6KfSKi4vx5ZdfwtnZuS4OR0RERKTlYW/euB6OaG4qFzkNEVHDV+NCr7i4GPPmzUOPHj3Qt29f7N27FwAQGhoKZ2dnLFmyBLNnz66vnERERNRExafmIPpqFqQGEkwd4CJ2HCIivVDjG9w/+ugjrF69Gj4+Pjhx4gTGjh2LgIAAnDx5El999RXGjh0LqVRan1mJiIioCXrYm/fCMy3haGUschoiIv1Q40Jvx44d+OGHH/DSSy8hPj4ezzzzDMrKynD+/HlIJJyslIiIiOrejbv5+PWPNADAdC9XkdMQUZOjUABHjvy9rkdqXOjdunULHh4eAAA3NzfI5XLMnj2bRR4RERHVmzVHE6ESgIHtrdHZ3lzsOETU1EilgLe32ClqpcbP6JWXl0Mmk2leGxoawtTUtF5CEREREWU+KMaO2FsAgBne7M0jItJFjXv0BEGAv78/5HL1SFdFRUUIDAyEiYmJVrvdu3fXbUIiIiJqktafSEJJmQrujpbo7WwldhwiaopKS4E1a9Tr06YBRkbi5tFBjQs9Pz8/rdevvfZanYchIiIiAoAHRaXYGHMDABA40JWPihCROEpKgOBg9bq/f+Ms9EJDQ+szBxEREZHGj6dTkFtUBhdrEwztbCt2HCIivVMnE6YTERER1ZXisnJ8dywJADDdywUGBuzNIyLSFQs9IiIialB+Oncbd3KLYWsux6juDmLHISLSSyz0iIiIqMFQqQSsOqqeIP2N/s6QG0pFTkREpJ9Y6BEREVGDcejSHSRm5sNMYYiJvVqLHYeISG+x0CMiIqIGQRAErIpS9+b9q08bmCn0Z3Q7IqKGpsajbhIRERHVp1NJ9xB38z5khgYI6OcsdhwiIkAuB/bt+3tdj7DQIyIiogbhYW/eWI9WsDbTrz+oiKiRMjQERowQO0Wt8NZNIiIiEt2l27mIvJIJAwkwzctF7DhERHqPPXpEREQkutV/jbT5fNeWaNPcROQ0RER/KS0FNm9Wr0+aBBjpz7PDLPSIiIhIVDfvFWDfhTQAwIyBriKnISJ6REkJEBCgXh87Vq8KPd66SURERKJaF52IcpWAAe1awM3BQuw4RESNAgs9IiIiEs3dvGJs+/0mACCQvXlERHWGhR4RERGJZsOJZBSVqtDVwQJ9XZuLHYeIqNFgoUdERESiyC8uw4aYGwDUvXkSiUTkREREjQcLPSIiIhLF1jM3kVNYCqfmxhjmZid2HCKiRoWFHhERET11JWUqfBedCACY5uUKqQF784iI6hKnVyAiIqKn7ufzt3E7pwgtTOV4+VkHseMQEVVOLge2b/97XY+w0CMiIqKnSqUSsDpKPUH66/2doDCSipyIiKgKhobq+fP0EG/dJCIioqfqcEIGrmbkwUxuiNf6tBE7DhFRo8QePSIiInqqVv3Vm/dqn9YwVxiJnIaIqBplZcCePer10aPVPXx6Qn+SEhERkd47k3wPv9/IhkxqgDf6OYsdh4ioesXFwLhx6vW8PL0q9HjrJhERET01qyLVvXkvP+sAG3OFyGmIiBovFnpERET0VFxJf4CIhAxIJMA0Lxex4xARNWos9IiIiOipWH1U3Zs3rIsdXKxNRU5DRNS4sdAjIiKiepd6vxA/x90GAAQOdBU5DRFR48dCj4iIiOrduuhElKkEeLo0RzdHS7HjEBE1eiz0iIiIqF5l55dg6+mbAIBAb/bmERE9DfozPigRERHppR9ibqCwtBydW5rDq10LseMQEdWcTAaEhv69rkdY6BEREVG9KSgpw/oTSQDUvXkSiUTkREREOjAyAvz9xU5RK6Lfurl8+XI4OTlBoVCgd+/eOH36dJVtS0tLsWjRIri6ukKhUKBbt24ICwvTahMSEoKePXvCzMwMNjY2GDVqFK5cuaLVZvr06XB1dYVSqYS1tTVGjhyJhIQErTYzZ86Eh4cH5HI53N3dK81z8OBB9OnTB2ZmZrC2tsaYMWOQnJys2R8ZGQmJRFJhSU9P1+1DIiIi0lPbz9xEdkEpHK2UGO5mJ3YcIqImQ9RCb9u2bZgzZw4WLFiAs2fPolu3bvD19UVGRkal7efPn4/Vq1fj22+/xaVLlxAYGIjRo0fj3LlzmjZRUVEICgrCyZMnER4ejtLSUgwdOhT5+fmaNh4eHggNDcXly5dx8OBBCIKAoUOHory8XOt8r7/+OsaPH19plqSkJIwcORKDBw9GXFwcDh48iKysLLz88ssV2l65cgVpaWmaxcbGpjYfFxERkV4pLVdhbbS6N2/aABcYSkX/fpmISDdlZcD+/eqlrEzsNDqRCIIgiHXy3r17o2fPnli2bBkAQKVSwdHREW+++Sbef//9Cu3t7e3x4YcfIigoSLNtzJgxUCqV2LRpU6XnyMzMhI2NDaKiouDl5VVpmwsXLqBbt264du0aXF21HxJfuHAh9u7di7i4OK3tO3fuxMSJE1FcXAwDA/X/uH755ReMHDkSxcXFMDIyQmRkJAYNGoTs7GxYWlrW9GPRkpubCwsLC+Tk5MDc3LxWxyAiIhLD3nOpmLUtDs1NZDj+/mAojKRiRyIi0k1+PmD617yfeXmAiYmocXSpDUT7aq2kpASxsbHw8fH5O4yBAXx8fBATE1Ppe4qLi6FQKLS2KZVKHDt2rMrz5OTkAACsrKwq3Z+fn4/Q0FA4OzvD0dGxxvk9PDxgYGCA0NBQlJeXIycnBxs3boSPjw+MjIy02rq7u6Nly5Z47rnncPz48WqPW1xcjNzcXK2FiIhI3wiCgFVR6gnSA/o5scgjInrKRCv0srKyUF5eDltbW63ttra2VT7D5uvri6+++gpXr16FSqVCeHg4du/ejbS0tErbq1QqzJo1C/369YObm5vWvhUrVsDU1BSmpqY4cOAAwsPDIdNhJB1nZ2ccOnQIH3zwAeRyOSwtLXHr1i1s375d06Zly5ZYtWoVdu3ahV27dsHR0RHe3t44e/ZslccNCQmBhYWFZtGl+CQiImooIv/MREL6A5jIpPhXHyex4xARNTl6dbP80qVL0a5dO3Ts2BEymQzBwcEICAjQ3Dr5T0FBQYiPj8fWrVsr7Js0aRLOnTuHqKgotG/fHuPGjUNRUVGNs6Snp2Pq1Knw8/PDmTNnEBUVBZlMhldeeQUP74bt0KEDpk+fDg8PD/Tt2xfff/89+vbti6+//rrK486bNw85OTma5ebNmzXORERE1FCsjFT35k3s1RoWxkaPaU1ERHVNtOkVWrRoAalUijt37mhtv3PnDuzsKh+Vy9raGnv37kVRURHu3r0Le3t7vP/++3BxcanQNjg4GPv27cPRo0fRqlWrCvsf9pi1a9cOffr0QbNmzbBnzx5MnDixRvmXL18OCwsLLF68WLNt06ZNcHR0xKlTp9CnT59K39erV69qbzWVy+WQy+U1ykBERNQQnU3JxumkezCSSvDGAGex4xARNUmi9ejJZDJ4eHggIiJCs02lUiEiIgKenp7VvlehUMDBwQFlZWXYtWsXRo4cqdknCAKCg4OxZ88eHD58GM7Oj/8fjCAIEAQBxcXFNc5fUFBQoSdRKpVqrqMqcXFxaNmyZY3PQ0REpG9W/dWbN8rdAS0tlCKnISJqmkSdMH3OnDnw8/NDjx490KtXLyxZsgT5+fkICAgAAEyePBkODg4ICQkBAJw6dQqpqalwd3dHamoqFi5cCJVKhblz52qOGRQUhC1btuCnn36CmZmZ5nk/CwsLKJVKJCYmYtu2bRg6dCisra1x69YtfPbZZ1AqlRg+fLjmONeuXUNeXh7S09NRWFioGXWzc+fOkMlkGDFiBL7++mssWrQIEydOxIMHD/DBBx+gTZs26N69OwBgyZIlcHZ2RpcuXVBUVIR169bh8OHDOHTo0NP4eImIiJ66axkPcOiS+m6d6QMr3nFDRERPh6iF3vjx45GZmYmPPvoI6enpcHd3R1hYmGaAlpSUFK1es6KiIsyfPx+JiYkwNTXF8OHDsXHjRq2pC1auXAkA8Pb21jpXaGgo/P39oVAoEB0djSVLliA7Oxu2trbw8vLCiRMntOa3mzJlCqKiojSvHxZvSUlJcHJywuDBg7FlyxYsXrwYixcvhrGxMTw9PREWFgalUv3tZUlJCd5++22kpqbC2NgYzzzzDH777TcMGjSoTj9HIiKihmJ1VCIA4LnOtmhrYyZyGiKiJySTAX9NBQcdBm5sCESdR48ej/PoERGRvkjLKYTX4iMoLRewa0ZfeLRpJnYkIqJGRS/m0SMiIqLG5ftjSSgtF9DLyYpFHhGRyES9dZOIiIgah5yCUmw5lQIAmOHtKnIaIqI6Ul4OREer1wcMAP4afFEfsNAjIiKiJ7bxZDLyS8rR0c4M3h2sxY5DRFQ3ioqAh+Nr5OUBJibi5tEBb90kIiKiJ1JUWo7Q48kA1CNtSiQScQMRERELPSIiInoyO2Jv4W5+CRwslXjhGXux4xAREVjoERER0RMoK1dh7VH1lApTBzjDSMo/LYiIGgL+a0xERES1diA+HSn3CtDM2AjjejqKHYeIiP7CQo+IiIhqRRAErIy8DgDw6+sEYxnHeCMiaihY6BEREVGtRF/NwqW0XCiNpPDzdBI7DhERPYJfvREREVGtrIpS9+ZN6OWIZiYykdMQEdUDIyNg8eK/1/UICz0iIiLS2fmb93Hi+l0YGkgwZYCL2HGIiOqHTAa8+67YKWqFt24SERGRzh725r3UzR4OlkqR0xAR0T+xR4+IiIh0kpiZh7CL6QCA6QNdRU5DRFSPysuBs2fV688+C0il4ubRAQs9IiIi0sna6EQIAjC4ow062JmJHYeIqP4UFQG9eqnX8/IAExNx8+iAt24SERFRjWXkFmFXbCoAYIY3e/OIiBoqFnpERERUY98dT0JJuQoebZqhp5OV2HGIiKgKLPSIiIioRnKLSrHlZAoAIJDP5hERNWgs9IiIiKhGNp9MwYPiMrSzMcWQjjZixyEiomqw0CMiIqLHKiotx/fHkwCoR9o0MJCInIiIiKrDQo+IiIgea8+5VGQ+KEZLCwVe6mYvdhwiInoMTq9ARERE1SpXCVj91wTpb/R3hsyQ3xMTURNhZAQsWPD3uh5hoUdERETVOngxHcl3C2ChNMLEXq3FjkNE9PTIZMDChWKnqBV+JUdERERVEgQBq/7qzZvs2QYmcn5HTESkD/ivNREREVUp5vpdXLiVA4WRAfz7Ookdh4jo6VKpgMuX1eudOgEG+tNPxkKPiIiIqrTyr968cT0c0dxULnIaIqKnrLAQcHNTr+flASYm4ubRgf6UpERERPRUxafmIPpqFqQGEkwd4CJ2HCIi0gELPSIiIqrUw2fzRnRtCUcrY5HTEBGRLljoERERUQU37ubj1z/SAACBA11FTkNERLpioUdEREQVrDmaCJUADGxvjc725mLHISIiHbHQIyIiIi2ZD4qxI/YWAPbmERHpKxZ6REREpGX9iSSUlKnQzdESfVysxI5DRES1wOkViIiISCOvuAwbY24AAGYMdIFEIhE5ERGRiIyMgHfe+Xtdj7DQIyIiIo0fT6Ugt6gMLtYmGNrZTuw4RETiksmAL74QO0Wt8NZNIiIiAgAUl5Vj3bFEAMB0LxcYGLA3j4hIX7FHj4iIiAAAP527jTu5xbA1l2NUdwex4xARiU+lAlJS1OutWwMG+tNPxkKPiIiIoFIJWHVUPUH66/2cITeUipyIiKgBKCwEnJ3V63l5gImJuHl0oD8lKREREdWb8Mt3kJiZDzOFIV7t3VrsOERE9IRY6BERETVxgiBgZaS6N+9ffdrATKFfI8sREVFFLPSIiIiauFNJ9xB38z5khgYI6OcsdhwiIqoDLPSIiIiauFVR6t68VzxawdpMLnIaIiKqCyz0iIiImrDLabmIvJIJAwkwbYCL2HGIiKiOsNAjIiJqwh725j3ftSWcWujPaHJERFQ9Tq9ARETURN28V4B9F9IAADMGuoqchoioATI0BP7977/X9Yh+pSUiIqI6sy46EeUqAf3btoCbg4XYcYiIGh65HFi+XOwUtcJbN4mIiJqgu3nF2Pb7TQBAIHvziIgaHfboERERNUEbYm6gqFSFrg4W6Ne2udhxiIgaJkEAsrLU6y1aABKJuHl0wEKPiIioickvLsOGE8kA1L15Ej36w4WI6KkqKABsbNTreXmAif4MWsVbN4mIiJqYrWduIqewFE7NjTHMzU7sOEREVA9Y6BERETUhpeUqfBedCACY6uUCqQF784iIGiMWekRERE3Iz3G3cTunCC1M5RjzbCux4xARUT0RvdBbvnw5nJycoFAo0Lt3b5w+fbrKtqWlpVi0aBFcXV2hUCjQrVs3hIWFabUJCQlBz549YWZmBhsbG4waNQpXrlzRajN9+nS4urpCqVTC2toaI0eOREJCglabmTNnwsPDA3K5HO7u7pXmOXjwIPr06QMzMzNYW1tjzJgxSE5O1moTGRmJZ599FnK5HG3btsX69etr/NkQERHVJZVK0EyQ/np/JyiMpCInIiKi+iJqobdt2zbMmTMHCxYswNmzZ9GtWzf4+voiIyOj0vbz58/H6tWr8e233+LSpUsIDAzE6NGjce7cOU2bqKgoBAUF4eTJkwgPD0dpaSmGDh2K/Px8TRsPDw+Ehobi8uXLOHjwIARBwNChQ1FeXq51vtdffx3jx4+vNEtSUhJGjhyJwYMHIy4uDgcPHkRWVhZefvllrTYjRozAoEGDEBcXh1mzZmHKlCk4ePDgk3xsREREtXI4IQNXM/JgKjfEpN5txI5DRET1SCIIgiDWyXv37o2ePXti2bJlAACVSgVHR0e8+eabeP/99yu0t7e3x4cffoigoCDNtjFjxkCpVGLTpk2VniMzMxM2NjaIioqCl5dXpW0uXLiAbt264dq1a3B11Z5LaOHChdi7dy/i4uK0tu/cuRMTJ05EcXExDAzU9fIvv/yCkSNHori4GEZGRnjvvfewf/9+xMfHa943YcIE3L9/v0JPZFVyc3NhYWGBnJwcmJub1+g9RERElXll5Qn8fiMb071cMG94J7HjEBE1fPn5gKmper0BjLqpS20gWo9eSUkJYmNj4ePj83cYAwP4+PggJiam0vcUFxdDoVBobVMqlTh27FiV58nJyQEAWFlZVbo/Pz8foaGhcHZ2hqOjY43ze3h4wMDAAKGhoSgvL0dOTg42btwIHx8fGBkZAQBiYmK0rg8AfH19q7w+QH2Nubm5WgsREdGT+j35Hn6/kQ2Z1ACv93cWOw4RkX4wNAT8/NSLoX7NTCdaoZeVlYXy8nLY2tpqbbe1tUV6enql7/H19cVXX32Fq1evQqVSITw8HLt370ZaWlql7VUqFWbNmoV+/frBzc1Na9+KFStgamoKU1NTHDhwAOHh4ZDJZDXO7+zsjEOHDuGDDz6AXC6HpaUlbt26he3bt2vapKenV3p9ubm5KCwsrPS4ISEhsLCw0Cy6FJ9ERERVefhs3svPOsDWXPGY1kREBACQy4H169WLXC52Gp2IPhiLLpYuXYp27dqhY8eOkMlkCA4ORkBAgObWyX8KCgpCfHw8tm7dWmHfpEmTcO7cOURFRaF9+/YYN24cioqKapwlPT0dU6dOhZ+fH86cOYOoqCjIZDK88soreJK7YefNm4ecnBzNcvPmzVofi4iICACupD/Ab5czIJEA07xcxI5DRERPgWj9jy1atIBUKsWdO3e0tt+5cwd2dpVP3mptbY29e/eiqKgId+/ehb29Pd5//324uFT8n1ZwcDD27duHo0ePolWrisNHP+wxa9euHfr06YNmzZphz549mDhxYo3yL1++HBYWFli8eLFm26ZNm+Do6IhTp06hT58+sLOzq/T6zM3NoVQqKz2uXC6HXM++LSAiooZt9VF1b55vZzu4WJuKnIaISI8IAlBQoF43NgYk+jP3qGg9ejKZDB4eHoiIiNBsU6lUiIiIgKenZ7XvVSgUcHBwQFlZGXbt2oWRI0dq9gmCgODgYOzZsweHDx+Gs/Pjn0MQBAGCIKC4uLjG+QsKCir0JEqlUs11AICnp6fW9QFAeHj4Y6+PiIiorqTeL8TPcbcBAIHero9pTUREWgoK1IOxmJr+XfDpCVFv3ZwzZw7Wrl2LDRs24PLly5gxYwby8/MREBAAAJg8eTLmzZunaX/q1Cns3r0biYmJiI6OxrBhw6BSqTB37lxNm6CgIGzatAlbtmyBmZkZ0tPTkZ6ernkmLjExESEhIYiNjUVKSgpOnDiBsWPHQqlUYvjw4ZrjXLt2DXFxcZr3xsXFIS4uDiUlJQCAESNG4MyZM1i0aBGuXr2Ks2fPIiAgAG3atEH37t0BAIGBgUhMTMTcuXORkJCAFStWYPv27Zg9e3a9f7ZEREQA8F10EspUAjxdmsPd0VLsOERE9JSIOnTM+PHjkZmZiY8++gjp6elwd3dHWFiYZgCTlJQUrV6zoqIizJ8/H4mJiTA1NcXw4cOxceNGWFpaatqsXLkSAODt7a11rtDQUPj7+0OhUCA6OhpLlixBdnY2bG1t4eXlhRMnTsDGxkbTfsqUKYiKitK8fli8JSUlwcnJCYMHD8aWLVuwePFiLF68GMbGxvD09ERYWJjmtkxnZ2fs378fs2fPxtKlS9GqVSusW7cOvr6+dfo5EhERVSY7vwQ/nk4BwN48IqKmRtR59OjxOI8eERHV1tLfruLr3/5E55bm2D+zPyR69GwJEVGDwHn0iIiIqCEpLCnHhphkAMD0gS4s8oiImhgWekRERI3Q9t9v4l5+CRytlBjRtaXYcYiI6CljoUdERNTIlJarsOZoIgBg2gAXGEr5v3sioqZG1MFYiIiIqO7tv5CG1PuFaG4iw9gejmLHISLSX1Ip8Morf6/rERZ6REREjYggCFgVpZ4g3b+vExRG+vWHCRFRg6JQADt2iJ2iVngvBxERUSMS+WcmEtIfwFgmxb8824gdh4iIRMJCj4iIqBFZFanuzXu1V2tYGstETkNERGJhoUdERNRInE3JxqmkezCSSvDGAGex4xAR6b/8fEAiUS/5+WKn0QkLPSIiokbiYW/eSHcHtLRQipyGiIjExEKPiIioEbiWkYfwy3cAAIEDXUROQ0REYmOhR0RE1AisOXodggA819kWbW3MxI5DREQiY6FHRESk59JyCrHnXCoAIHCgq8hpiIioIWChR0REpOe+P5aE0nIBvZys4NGmmdhxiIioAWChR0REpMdyCkqx5VQKACDQm8/mERGRmqHYAYiIiKj2Np26gfyScnSwNcOgDjZixyEialykUmD48L/X9QgLPSIiIj1VVFqO748lAVD35kkkEpETERE1MgoFsH+/2ClqhbduEhER6akdsbdwN78EDpZKvPCMvdhxiIioAWGhR0REpIfKylVYezQRADBlgDOMpPxfOhER/Y3/VyAiItJDB+LTkXKvAM2MjTC+p6PYcYiIGqf8fMDERL3k54udRid8Ro+IiEjPCIKAVVHXAQB+fZ1gLOP/zomI6k1BgdgJaoU9ekRERHom+moWLt7OhdJICj9PJ7HjEBFRA8RCj4iISM887M0b39MRzUxkIqchIqKGiIUeERGRHrlw6z5OXL8LqYEEUwY4ix2HiIgaKBZ6REREeuRhb95L3ezRqpmxyGmIiKih4tPbREREDVy5SsDppHuIT83Br3+kAwCmD3QRORURETVkLPSIiIgasLD4NHz8yyWk5RRptskNDZCclY+OduYiJiMiagIMDICBA/9e1yP6lZaIiKgJCYtPw4xNZ7WKPAAoLlNhxqazCItPEykZEVEToVQCkZHqRakUO41OWOgRERE1QOUqAR//cglCNW0+/uUSylXVtSAioqaKhR4REVEDdDrpXoWevEcJANJyinA66d7TC0VERHqDz+gRERE1AGXlKly58wBxN+/jXMp9RF/NrNH7Mh5UXQwSEdETys8HnJzU68nJgImJmGl0wkKPiIhIBGk5hYhLua8p7P5IzUFhabnOx7ExU9RDOiIi0sjKEjtBrbDQIyKi/2/v3oOjqu8+jn92N/eQ7IZL7onBiIEYYTEQwAtDK4I3LA4dL/WCttqWB2kt01pteQrax+KM2qEt1lYFUdGW2hZsmUd6ySC0YEWQgIigKA8JhIRbNtkEcts9zx+bbNjskmxiks1u3q+ZDLtnf+ec7zK/2ewnv985P/Szs82t2nu0VmUVDpWVO7S7okbVdU1+7ZJiozQ+x6qJOSkan2XVkrf26aSzKeB1eiZJ6dY4lYwe3u/1AwDCD0EPAIA+5HYbOnSyvi3QeUbsDlbVqfM9U8wmqSA9WRNzbbLn2DQxx6b8UcNkNps6jiVDC9Z+IJPkE/baWyydUyjLee0BAGhH0AMA4As46WzyjNRV1KiswqG9FbVyNrX6tUtPjpM9xyZ7rifUXZ5tVUJM17+Gry/K0PN3X+G3jl66NU5L5xTq+qKMPn8/AIDIQNADACBIjS0ufVRZp93lNW3hzqGjNef82sVHW3R5tlUTc2xtI3YpSrf27lq664sydF1hunYcPqMTzkalJnmmazKSBwDoCkEPAIAADMPQ/50+6xPqPj5epxaX7xxMk0m6ZNSw80brUnRp2jBFWfpuBSOL2aRp+SP67HgAgMhH0AMAQJLjbLP3DphlFQ7tOeqQ42yLX7uRw2I8oS7HM1I3Pseq5LjoEFQMAOh3ZrM0aVLH4zBC0AMADDnNrW4dqKrzCXaHTzX4tYuJMqsoM1n2nBTvtXXZKfEymZg2CQBDQny89P77oa6iVwh6AICIZhiGjtac806/3F1eo32VdWpudfu1HT0y8bzROpvGZSQrJiq8/oILAIBE0AMARBhnY4t3zTrPaF2NTtU3+7Wzxkd3hLpcm+zZNqUkxoSgYgAA+h5BDwAQtlpdbn1SXe+zvMGnJ+pldFqzLspsUmFmss9o3eiRiUzBBAB07exZqbDQ83j/fikhIbT19ABBDwAQNqrrGrW7vMazEHm5Qx8eq9XZZpdfuyxbfMdC5Lk2XZZpVVy0JQQVAwDCmmFIR450PA4jBD0AwKB0rtmlD4/V+ixvcP6i4e2GxUZpfLbVu16dPcemUUmxIagYAIDBg6AHAAg5t9vQ56fqtbvc4R2tO1jtlMvt+9dTs0m6NC1JE9vWq7Pn2pQ/ahiLhwMA0AlBDwAw4E7XN513F0zPmnXOxla/dmnJsd716ibm2nR5llWJsfzqAgCgO/y2BAD0q6ZWlz6qrFNZ+2hdRY0qzpzzaxcXbdb4LJt3vTp7rk0Z1vgQVAwAQPgj6AEA+oxhGDpy+qzPmnX7j9epxeV/AfslqcO8d8CcmGtTQVqSoiysWQcAQF8g6AEAeq32bIvKjnquqWtf3qDmbItfuxGJMT5r1o3PtskaHx2CigEA6AGTqWN5hTBbkoegBwAISovLrQPHnSqraFveoMKhz082+LWLsZh1WVbHmnUTc1KUMzyeNesAAOEnIUH66KNQV9Erg2KOzHPPPae8vDzFxcVpypQp2rFjxwXbtrS06IknnlB+fr7i4uI0YcIEbdq0yafN8uXLNXnyZCUlJSk1NVVz587VwYMHfdp861vfUn5+vuLj4zVq1Ch95Stf0YEDB3zafOc731FxcbFiY2Nlt9v9alm2bJlMJpPfT2JiorfNmjVr/F6Pi4vrxf8SAAwcwzB0zHFOG/dW6n827tdXn9+uoqV/05yV/9Z/v/WR/vzBMW/IyxuRoLn2TC2bU6gNC6/Sh4/P0vr/ukpL51ymr9izlDsigZAHAMAAC/mI3rp167R48WL95je/0ZQpU7RixQrNnj1bBw8eVGpqql/7JUuWaO3atXrxxRc1duxY/e1vf9Ott96q7du3a+LEiZKkLVu2aOHChZo8ebJaW1v1ox/9SLNmzdL+/fu9Iay4uFh33XWXcnNzdebMGS1btkyzZs3S4cOHZbF0LKr79a9/Xe+995727t3rV8v3v/99ffvb3/bZdu2112ry5Mk+25KTk32CJl94AAw29U2t2nvUcwfM9uvrTjqb/Nolx0XJnpvSNlJn04Qcm4YnxoSgYgAA0BWTYYR2ifcpU6Zo8uTJWrlypSTJ7XYrJydHixYt0qOPPurXPjMzUz/+8Y+1cOFC77Z58+YpPj5ea9euDXiOkydPKjU1VVu2bNH06dMDttm7d68mTJigQ4cOKT8/3+e1ZcuWacOGDSorK+vyvezZs0d2u11bt27VNddcI8kzovfwww/L4XB0ue+F1NXVyWq1qra2VsnJyb06BgCcz+U29OkJpyfUtQW7T0441fm3QZTZpHEZyT7X1o0ekSgza9YBAIaKs2el9kGc99/3TOUMoZ5kg5CO6DU3N2vXrl167LHHvNvMZrNmzpypd999N+A+TU1NflMf4+Pj9e9///uC56mtrZUkDR8+PODrDQ0NevnllzV69Gjl5OT09G14vfTSS7r00ku9Ia9dfX29LrroIrndbl1xxRX62c9+pssuu6zX5wGAnjhR16jdFe2jdTX68GitGppdfu2ybPEdSxvk2FSUZVVctCXAEQEAGCIMQ9q/v+NxGAlp0Dt16pRcLpfS0tJ8tqelpfldL9du9uzZ+vnPf67p06crPz9fpaWl+vOf/yyXy/9Li+QZIXz44Yd11VVXqaioyOe1X//613rkkUfU0NCggoIC/eMf/1BMTO+mIDU2Nur111/3G4UsKCjQ6tWrNX78eNXW1uqZZ57RlVdeqY8++kjZ2dl+x2lqalJTU8d0qbq6ul7VA2BoOtfs0r7K2rY162pUVu5QZW2jX7vEGIvGZ3uWNWgfrUtN4vphAAAiRciv0eupX/ziF3rwwQc1duxYmUwm5efn6/7779fq1asDtl+4cKH27dsXcMTvrrvu0nXXXafjx4/rmWee0W233aZt27b16mYp69evl9Pp1Pz58322T5s2TdOmTfM+v/LKKzVu3Dj99re/1U9/+lO/4yxfvlyPP/54j88PYOhxuw19fqqh7Zo6z9IGHx93yuX2/Yuj2SRdmpbkXa/OnpOiS1KHycIUTAAAIlZIg97IkSNlsVhUXV3ts726ulrp6ekB9xk1apQ2bNigxsZGnT59WpmZmXr00Ud18cUX+7V96KGHtHHjRm3dujXg6JnVapXVatWYMWM0depUpaSkaP369brzzjt7/F5eeukl3XzzzX6jk51FR0dr4sSJOnToUMDXH3vsMS1evNj7vK6u7gtNJwUQOc40NHsCXblDuysc2lPhUF1jq1+71KTYtlDnuWnK+GyrEmPD7u96AADgCwjpb/6YmBgVFxertLRUc+fOleSZallaWqqHHnqoy33j4uKUlZWllpYW/elPf9Jtt93mfc0wDC1atEjr16/XO++8o9GjR3dbi2EYMgzDZ9pksA4fPqzNmzfrL3/5S7dtXS6XPvzwQ914440BX4+NjVVsbGyPawAQWZpaXdpfWee9A2ZZhUNHTp/1axcXbdblWVafYJdhjePuvgAADHEh/xPv4sWLNX/+fE2aNEklJSVasWKFGhoadP/990uS7r33XmVlZWn58uWSpPfee0/Hjh2T3W7XsWPHtGzZMrndbj3yyCPeYy5cuFBvvPGG3nrrLSUlJamqqkqSZwQvPj5en3/+udatW6dZs2Zp1KhROnr0qJ566inFx8f7BLBDhw6pvr5eVVVVOnfunPeum4WFhT7X8q1evVoZGRm64YYb/N7fE088oalTp+qSSy6Rw+HQ008/rSNHjuiBBx7o8/9LAOHJMAxVnDmn3RU13uUN9lfWqdnl9mubPypR9pwU701TCtKTFG0ZFEuiAgCAQSTkQe/222/XyZMn9ZOf/ERVVVWy2+3atGmTdwpkeXm5zOaOLzGNjY1asmSJPv/8cw0bNkw33nijXnvtNdlsNm+b559/XpI0Y8YMn3O9/PLLuu+++xQXF6d//etfWrFihWpqapSWlqbp06dr+/btPmv3PfDAA9qyZYv3efs6fYcPH1ZeXp4kzwjkmjVrdN999/msv9eupqZGDz74oKqqqpSSkqLi4mJt375dhYWFX+j/DUD4qj3X4rdm3ZmGZr92wxNjOpY2aFuzzhofHYKKAQAYokwm6aKLOh6HkZCvo4eusY4eEN5aXW4dqHKq7LzlDT472eDXLsZiVmFmsveGKRNzUpQzPJ4pmAAAwCts1tEDgEhiGIaO1zZ6R+l2l9fow2O1amzxn4J50YgEn9G6wsxkxUaxZh0AAOgbBD0A6KWGplbtPVrrDXVlFQ6dcPrf0CkpLsozUte2Xt2EbJtGDOOmSwAAoP8Q9AAMCS63oR2Hz+iEs1GpSXEqGT28R+vIudyGDp2o965Xt7vcoU+qneq0ZJ0sZpPGpid516uz59h08chEmVmzDgCA8HPunDR9uufx1q1SfHxo6+kBgh6AiLdp33E9/tf9Ol7b6N2WYY3T0jmFur4oI+A+J5yNKjvvZil7j9aqvsl/zbpMa5x3WQN7rk1FmVbFxzAFEwCAiOB2Szt3djwOIwQ9ABFt077jWrD2A3W+61RVbaMWrP1Az999hWYUpGrfsbYpmBUOlZU7dMxxzu9YCTEWjc+2eoPdxBybUpPjBuaNAAAA9ABBD0DEcrkNPf7X/X4hT5J326Lf7ZbbbcjVqZHJJF2amuS9C6Y916YxqUk9mu4JAAAQKgQ9BO2LXuOEoc3tNtTscqvVbail1a0Wt1stLs/jVrdbza2GWt1utbjatrvcanW17dP2vP01T/vzj9XevmPfFpdblY5Gn+magbS0JbxRSbHeO2BOzLVpfLZNw2L5iAQAAOGJbzEISm+ucULfMwxDLrfhCTNud1tIMjpCj8vt89gbjFwXClDu847VFqB8gpVvgGoOEKY6n9/v2G3tOt+0ZDBZdkuh5k/LY806AAAQMQh66FYw1ziFW9gzDMM/9LSFnZa2UaXOo0kBR5Y6Byhv6OkUhvxGpgK8FuD8gcJZpDCZpGiLWTEWs6ItJkW1PY6ymBRtMSvKbFJMlOffaIu57ce/Xfv26LZtMRazosxmRUeZVFlzTmvfK++2loK0ZEIeAACIKAQ9dKm7a5xMkpb9db+mXjxChiFPMHEHNx2vy6l57Y+9x7rAaNIF9u8udLUO5uGlHooym/xCT5TZ3CkkdQShzuHIL0CZTYqOMiva3L6Pb1tvmGrb5hu62kOaWTFRprbAdf6xOo4zENN+XW5DpQdOqKq2MWAfNklKt3qmIQMAAAQ0cmSoK+gVgh66tOPwmS6vcTLkGdmzP/GPgSuqn0T7jSR5RoWizeZOIaUjAMVYOoWhtn0uHLQ8+0QFCFsdo1HtQeu8YwU8v+d8rM92YRazSUvnFGrB2g9kknzCXvv/2tI5hVxrCgAAAktMlE6eDHUVvULQQ5dOOLu+kUVn7dPxotvCiiekmAKOCgU1GhRwNCpw0IqxdBpBCjDt70LnjzKbmLoXoa4vytDzd1/hd41pOteYAgCACEbQQ5dSk4JbI+yVr0/W1ZeMYmQEg9L1RRm6rjCdu8YCAIAhg6CHLpWMHq4Ma1y31zgR8jDYWcwmTcsfEeoyAABAODl3TrrhBs/jt9+W4uNDW08PmENdAAa39mucpI5rmtpxjRMAAAAimtstbdni+XGH193PCXroVvs1TulW32mc6da4sFxaAQAAAIh0TN1EULjGCQAAAAgfBD0EjWucAAAAgPDA1E0AAAAAiDAEPQAAAACIMEzdBAAAAIALSUgIdQW9QtADAAAAgEASE6WGhlBX0StM3QQAAACACEPQAwAAAIAIQ9ADAAAAgEAaG6WbbvL8NDaGupoe4Ro9AAAAAAjE5ZL+9387HocRRvQAAAAAIMIQ9AAAAAAgwhD0AAAAACDCEPQAAAAAIMIQ9AAAAAAgwnDXzUHOMAxJUl1dXYgrAQAAAIaYhoaOx3V1Ib/zZnsmaM8IXSHoDXJOp1OSlJOTE+JKAAAAgCEsMzPUFXg5nU5ZrdYu25iMYOIgQsbtdquyslJJSUkymUwB20yePFnvv/9+t8cKpl13berq6pSTk6OKigolJyd3e85wEez/YTiduy+O29tj9GS/vm7bVRv6b/icm/7rj/4bPucOl/7bk/b038Dov317DPpv9wzDkNPpVGZmpszmrq/CY0RvkDObzcrOzu6yjcViCarTBdMu2GMlJyeHvKP3pWDfdziduy+O29tj9GS/vm4bTBv67+A/N/33wui/g//c4dJ/e9Ke/hsY/bdvj0H/DU53I3ntuBlLBFi4cGGftQv2WJEmlO+7v87dF8ft7TF6sl9ftx2KfZj+27fHoP8OLPpv3x6jp/vxHeKLof/27THov32LqZvokbq6OlmtVtXW1g6Kv2gAPUH/RTij/yKc0X8RzsK1/zKihx6JjY3V0qVLFRsbG+pSgB6j/yKc0X8Rzui/CGfh2n8Z0QMAAACACMOIHgAAAABEGIIeAAAAAEQYgh4AAAAARBiCHgAAAABEGIIeAAAAAEQYgh76za233qqUlBR99atfDXUpQLc2btyogoICjRkzRi+99FKoywF6hM9bhKuKigrNmDFDhYWFGj9+vN58881QlwQEzeFwaNKkSbLb7SoqKtKLL74Y6pJ8sLwC+s0777wjp9OpV155RX/84x9DXQ5wQa2trSosLNTmzZtltVpVXFys7du3a8SIEaEuDQgKn7cIV8ePH1d1dbXsdruqqqpUXFysTz75RImJiaEuDeiWy+VSU1OTEhIS1NDQoKKiIu3cuXPQfH9gRA/9ZsaMGUpKSgp1GUC3duzYocsuu0xZWVkaNmyYbrjhBv39738PdVlA0Pi8RbjKyMiQ3W6XJKWnp2vkyJE6c+ZMaIsCgmSxWJSQkCBJampqkmEYGkxjaAS9IWrr1q2aM2eOMjMzZTKZtGHDBr82zz33nPLy8hQXF6cpU6Zox44dA18oEIQv2p8rKyuVlZXlfZ6VlaVjx44NROkAn8cIa33Zf3ft2iWXy6WcnJx+rhrw6Iv+63A4NGHCBGVnZ+sHP/iBRo4cOUDVd4+gN0Q1NDRowoQJeu655wK+vm7dOi1evFhLly7VBx98oAkTJmj27Nk6ceKEt037fOTOP5WVlQP1NgBJfdOfgVCh/yKc9VX/PXPmjO6991698MILA1E2IKlv+q/NZtOePXt0+PBhvfHGG6qurh6o8rtnYMiTZKxfv95nW0lJibFw4ULvc5fLZWRmZhrLly/v0bE3b95szJs3ry/KBILSm/68bds2Y+7cud7Xv/vd7xqvv/76gNQLnO+LfB7zeYtQ623/bWxsNK655hrj1VdfHahSAT998X14wYIFxptvvtmfZfYII3rw09zcrF27dmnmzJnebWazWTNnztS7774bwsqAngumP5eUlGjfvn06duyY6uvr9fbbb2v27NmhKhnw4vMY4SyY/msYhu677z59+ctf1j333BOqUgE/wfTf6upqOZ1OSVJtba22bt2qgoKCkNQbSFSoC8Dgc+rUKblcLqWlpflsT0tL04EDB4I+zsyZM7Vnzx41NDQoOztbb775pqZNm9bX5QJdCqY/R0VF6dlnn9WXvvQlud1uPfLII4PmjlkY2oL9PObzFoNRMP1327ZtWrduncaPH++9Puq1117T5ZdfPtDlAj6C6b9HjhzRN7/5Te9NWBYtWjSo+i5BD/3mn//8Z6hLAIJ2yy236JZbbgl1GUCv8HmLcHX11VfL7XaHugygV0pKSlRWVhbqMi6IqZvwM3LkSFksFr+LSaurq5Wenh6iqoDeoT8jnNF/Ec7ovwhnkdB/CXrwExMTo+LiYpWWlnq3ud1ulZaWMhUIYYf+jHBG/0U4o/8inEVC/2Xq5hBVX1+vQ4cOeZ8fPnxYZWVlGj58uHJzc7V48WLNnz9fkyZNUklJiVasWKGGhgbdf//9IawaCIz+jHBG/0U4o/8inEV8/w3xXT8RIps3bzYk+f3Mnz/f2+ZXv/qVkZuba8TExBglJSXGf/7zn9AVDHSB/oxwRv9FOKP/IpxFev81GYZhDFiqBAAAAAD0O67RAwAAAIAIQ9ADAAAAgAhD0AMAAACACEPQAwAAAIAIQ9ADAAAAgAhD0AMAAACACEPQAwAAAIAIQ9ADAAAAgAhD0AMAIITeeecdmUwmORyOoPdZtmyZ7HZ7v9UEAAh/BD0AAAbAu+++K4vFoptuuinUpQAAhgCCHgAAA2DVqlVatGiRtm7dqsrKylCXAwCIcAQ9AAD6WX19vdatW6cFCxbopptu0po1ay7Yds2aNbLZbNqwYYPGjBmjuLg4zZ49WxUVFX5tX3vtNeXl5clqteqOO+6Q0+n0vrZp0yZdffXVstlsGjFihG6++WZ99tln/fH2AACDEEEPAIB+9oc//EFjx45VQUGB7r77bq1evVqGYVyw/dmzZ/Xkk0/q1Vdf1bZt2+RwOHTHHXf4tPnss8+0YcMGbdy4URs3btSWLVv01FNPeV9vaGjQ4sWLtXPnTpWWlspsNuvWW2+V2+3ut/cJABg8okJdAAAAkW7VqlW6++67JUnXX3+9amtrtWXLFs2YMSNg+5aWFq1cuVJTpkyRJL3yyisaN26cduzYoZKSEkmS2+3WmjVrlJSUJEm65557VFpaqieffFKSNG/ePJ9jrl69WqNGjdL+/ftVVFTUH28TADCIMKIHAEA/OnjwoHbs2KE777xTkhQVFaXbb79dq1atuuA+UVFRmjx5svf52LFjZbPZ9PHHH3u35eXleUOeJGVkZOjEiRPe559++qnuvPNOXXzxxUpOTlZeXp4kqby8vK/eGgBgEGNEDwCAfrRq1Sq1trYqMzPTu80wDMXGxmrlypW9Pm50dLTPc5PJ5DMtc86cObrooov04osvKjMzU263W0VFRWpubu71OQEA4YMRPQAA+klra6teffVVPfvssyorK/P+7NmzR5mZmfrd7353wf127tzpfX7w4EE5HA6NGzcuqPOePn1aBw8e1JIlS3Tttddq3Lhxqqmp6ZP3BAAID4zoAQDQTzZu3Kiamhp94xvfkNVq9Xlt3rx5WrVqlZ5++mm//aKjo7Vo0SL98pe/VFRUlB566CFNnTrVe31ed1JSUjRixAi98MILysjIUHl5uR599NE+eU8AgPDAiB4AAP1k1apVmjlzpl/IkzxBb+fOndq7d6/fawkJCfrhD3+or33ta7rqqqs0bNgwrVu3Lujzms1m/f73v9euXbtUVFSk733vewEDJQAgcpmMru7vDAAABtSaNWv08MMPy+FwhLoUAEAYY0QPAAAAACIMQQ8AAAAAIgxTNwEAAAAgwjCiBwAAAAARhqAHAAAAABGGoAcAAAAAEYagBwAAAAARhqAHAAAAABGGoAcAAAAAEYagBwAAAAARhqAHAAAAABGGoAcAAAAAEeb/AY60SpBAeZ/BAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the Hyperparameter Tuning Results\n", + "import matplotlib.pyplot as plt\n", + "\n", + "alphas_test = [result[0] for result in ridge_results]\n", + "r2_scores = [result[1] for result in ridge_results]\n", + "\n", + "# Visualize Ridge regression performance across different alpha values\n", + "# Plot R-squared scores against alphas to show model performance trends\n", + "# Highlight the best alpha value for optimal regularization strength\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(alphas_test, r2_scores, marker='o', label='Initial Test Alphas')\n", + "plt.xscale('log')\n", + "plt.xlabel('Alpha')\n", + "plt.ylabel('R^2 Score')\n", + "plt.title('Ridge Regression: Alpha vs R^2')\n", + "plt.axvline(best_alpha, color='r', linestyle='--', label=f'Best Alpha: {best_alpha:.3f}')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f30a7aed", + "metadata": {}, + "source": [ + "### A plot was created to visualize the effect of alpha on the R-squared value. The graph illustrates a significant improvement in model performance as alpha increases, stabilizing around the optimal value." + ] + }, + { + "cell_type": "markdown", + "id": "fb9fdb33", + "metadata": {}, + "source": [ + "## Predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c563a304", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate predictions using the trained model\n", + "# Apply the computed weights to make predictions on both training and test sets\n", + "y_train_pred = X_train_with_bias @ weights\n", + "y_test_pred = X_test_with_bias @ weights\n" + ] + }, + { + "cell_type": "markdown", + "id": "236e0813", + "metadata": {}, + "source": [ + "### Predictions for the training and testing datasets were generated using the trained weights from the linear regression model. These predictions will be evaluated against the actual target values." + ] + }, + { + "cell_type": "markdown", + "id": "ace0caae", + "metadata": {}, + "source": [ + "## Model Performance Evaluation: Training and Testing R² Values" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "a6cbad72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Linear Regression Training R²: 0.8230, Testing R²: 0.9232\n" + ] + } + ], + "source": [ + "# Calculate and display R-squared values for training and test sets\n", + "# R-squared measures how well the model fits the data\n", + "# Higher values indicate better model performance\n", + "train_r2 = r_squared(y_train, y_train_pred)\n", + "test_r2 = r_squared(y_test, y_test_pred)\n", + "\n", + "print(f\"Linear Regression Training R²: {train_r2:.4f}, Testing R²: {test_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8c6c413", + "metadata": {}, + "source": [ + "### The R² value for the training set is 0.8230, while the test set achieved 0.9232. This indicates the model performs well on unseen data, with a high degree of variance in the dependent variable explained by the independent variables." + ] + }, + { + "cell_type": "markdown", + "id": "3dbb06b7", + "metadata": {}, + "source": [ + "## Adjusted R² Calculation for Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "7e057d4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adjusted R² for Ridge: 0.9184\n" + ] + } + ], + "source": [ + "def adjusted_r2(r2, n, p):\n", + " \"\"\"\n", + " Compute Adjusted R-squared.\n", + " :param r2: R-squared\n", + " :param n: Number of observations\n", + " :param p: Number of predictors\n", + " :return: Adjusted R-squared\n", + " \"\"\"\n", + " return 1 - ((1 - r2) * (n - 1)) / (n - p - 1)\n", + "\n", + "# Calculate Adjusted R² for Ridge\n", + "n = X_test.shape[0]\n", + "p = X_test.shape[1]\n", + "adjusted_r2_ridge = adjusted_r2(test_r2, n, p)\n", + "print(f\"Adjusted R² for Ridge: {adjusted_r2_ridge:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8651dbbd", + "metadata": {}, + "source": [ + "### The output displays the Adjusted R² for Ridge Regression, which is calculated as 0.9184. This value indicates how well the model explains the variability in the dependent variable while accounting for the number of predictors in the model. A high Adjusted R² value like 0.9184 suggests that the Ridge Regression model fits the data well, with minimal overfitting, as it adjusts for the complexity introduced by multiple predictors." + ] + }, + { + "cell_type": "markdown", + "id": "379a1eae", + "metadata": {}, + "source": [ + "## Lasso Regression " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "b3c8a1c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ridge Best R²: 0.9235033171939397\n", + "Lasso Results: [(0.1, 0.9231735440115761), (1, 0.923173544036331), (10, 0.9231735442838817), (100, 0.9231735467593863)]\n" + ] + } + ], + "source": [ + "# Implement Lasso regression using coordinate descent algorithm\n", + "# This function performs L1 regularization to encourage sparsity in feature selection\n", + "def lasso_regression(X, y, alpha):\n", + " X_with_bias = np.c_[np.ones(X.shape[0]), X] # Add intercept\n", + " weights = np.zeros(X_with_bias.shape[1])\n", + " for _ in range(2000): # Iterative updates with fixed number of iterations\n", + " for j in range(len(weights)):\n", + " X_j = X_with_bias[:, j]\n", + " residual = y - (X_with_bias @ weights - weights[j] * X_j)\n", + " rho = X_j.T @ residual\n", + " if j == 0: # Intercept term\n", + " weights[j] = rho / len(y)\n", + " else: # Apply soft thresholding for feature weights\n", + " weights[j] = np.sign(rho) * max(abs(rho) - alpha / 2, 0) / (X_j.T @ X_j)\n", + " return weights\n", + "\n", + "# Evaluate Lasso Regression\n", + "alphas = [0.1, 1, 10, 100]\n", + "lasso_results = []\n", + "for alpha in alphas:\n", + " lasso_weights = lasso_regression(X_train, y_train, alpha)\n", + " y_test_pred_lasso = X_test_with_bias @ lasso_weights\n", + " test_r2_lasso = r_squared(y_test, y_test_pred_lasso)\n", + " lasso_results.append((alpha, test_r2_lasso))\n", + "\n", + "# Compare Ridge and Lasso\n", + "print(\"Ridge Best R²:\", best_r2)\n", + "print(\"Lasso Results:\", lasso_results)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e11e8e21", + "metadata": {}, + "source": [ + "### Implement Lasso Regression with hyperparameter tuning :-\n", + "\n", + "### This function implements Lasso regression using iterative updates. Lasso introduces an L1 penalty, which can shrink some coefficients to zero, enabling feature selection. The function accepts the dataset (X, y) and a regularization parameter (alpha).\n", + "\n", + "### Evaluate Lasso Regression :-\n", + "\n", + "### A list of alpha values is tested to determine the optimal regularization parameter. Predictions are made on the test set for each alpha, and R² is calculated to evaluate performance.\n", + "\n", + "### Compare Ridge and Lasso :-\n", + "\n", + "### The Ridge Regression result (Best R²) is compared with Lasso Regression results for various alpha values. The results indicate that Ridge Regression slightly outperforms Lasso Regression in this case.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d2c9ffd", + "metadata": {}, + "source": [ + "## Residual Analysis for Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "95f05430", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Residual Plot for Ridge Regression\n", + "residuals = y_test - y_test_pred_ridge\n", + "plt.figure(figsize=(8, 5))\n", + "plt.scatter(y_test_pred_ridge, residuals, alpha=0.7, label=\"Residuals\")\n", + "plt.axhline(0, color='red', linestyle='--', label=\"Zero Line\")\n", + "plt.xlabel(\"Predicted Values\")\n", + "plt.ylabel(\"Residuals\")\n", + "plt.title(\"Residual Analysis for Ridge Regression\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "df8a1ab6", + "metadata": {}, + "source": [ + "### - Residuals are randomly distributed around zero, indicating that the model captures the data well.\n", + "### - No clear patterns suggest no significant bias or omitted variables.\n", + "### - However, a few outliers may indicate some extreme values not well-explained by the model." + ] + }, + { + "cell_type": "markdown", + "id": "974bfb35", + "metadata": {}, + "source": [ + "## k-fold cross-validation" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "1c06a3f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean R²: 0.7936, Std Dev: 0.0540\n" + ] + } + ], + "source": [ + "\n", + "# Implement k-fold cross-validation for model evaluation\n", + "# This function splits the data into k subsets, trains and tests the model k times\n", + "def k_fold_cross_validation(X, y, k=5, alpha=1.0, seed=42):\n", + " np.random.seed(seed) # Set seed for reproducibility\n", + " indices = np.arange(len(X))\n", + " np.random.shuffle(indices) # Randomize data order\n", + " X, y = X[indices], y[indices] # Reorder data based on shuffled indices\n", + "\n", + " fold_size = len(X) // k # Calculate size of each fold\n", + " r2_scores = [] # Initialize list to store R-squared scores for each fold\n", + "\n", + "# Perform k-fold cross-validation for each fold and Extract validation set from the data\n", + "# Additionally Create training set from remaining data and Combine non-validation data for training\n", + "\n", + " for i in range(k):\n", + " start = i * fold_size\n", + " end = (i + 1) * fold_size\n", + " X_val = X[start:end]\n", + " y_val = y[start:end]\n", + " X_train = np.vstack((X[:start], X[end:]))\n", + " y_train = np.hstack((y[:start], y[end:]))\n", + "\n", + " ridge_weights = ridge_regression(X_train, y_train, alpha)\n", + " X_val_with_bias = np.c_[np.ones(X_val.shape[0]), X_val]\n", + " y_val_pred = X_val_with_bias @ ridge_weights\n", + " r2 = r_squared(y_val, y_val_pred)\n", + " r2_scores.append(r2)\n", + " \n", + "# Perform Ridge regression and evaluate model performance for each fold\n", + "# Train Ridge regression model on the current fold's training data,Add bias term to validation set for prediction \n", + "# Generate predictions for validation set,Calculate R-squared score for current fold and Store the R-squared score for later analysis\n", + "\n", + " return np.mean(r2_scores), np.std(r2_scores)\n", + "\n", + "# Use the function with reproducibility\n", + "mean_r2, std_r2 = k_fold_cross_validation(X, y, k=5, alpha=best_alpha)\n", + "print(f\"Mean R²: {mean_r2:.4f}, Std Dev: {std_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "1d2eeeea", + "metadata": {}, + "source": [ + "### The k-fold cross-validation process yielded a mean R² of 0.7936 with a standard deviation of 0.0540. This highlights the model's generalizability and its ability to perform consistently across different splits of the data." + ] + }, + { + "cell_type": "markdown", + "id": "134bf622", + "metadata": {}, + "source": [ + "## Bootstrapping" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "cad70f60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Bootstrapped R²: 0.8092, Std Dev: 0.0195\n" + ] + } + ], + "source": [ + "# Implement bootstrapping to assess model stability and estimate confidence intervals\n", + "# This function performs repeated sampling with replacement to generate multiple R-squared scores\n", + "\n", + "def bootstrap_r2(X, y, alpha=best_alpha, n_iterations=1000):\n", + " r2_scores = []\n", + " for _ in range(n_iterations): # Create a random sample with replacement\n", + " indices = np.random.choice(len(X), len(X), replace=True)\n", + " X_sample = X[indices]\n", + " y_sample = y[indices]\n", + "\n", + " # Train Ridge regression model on the bootstrap sample\n", + " ridge_weights = ridge_regression(X_sample, y_sample, alpha)\n", + " y_sample_pred = np.c_[np.ones(X_sample.shape[0]), X_sample] @ ridge_weights\n", + " \n", + " # Calculate and store R-squared for this iteration\n", + " r2 = r_squared(y_sample, y_sample_pred)\n", + " r2_scores.append(r2)\n", + "\n", + " return np.mean(r2_scores), np.std(r2_scores) # Return mean and standard deviation of bootstrapped R-squared scores\n", + "\n", + "# Perform bootstrapping and print results\n", + "mean_bootstrap_r2, std_bootstrap_r2 = bootstrap_r2(X, y)\n", + "print(f\"Mean Bootstrapped R²: {mean_bootstrap_r2:.4f}, Std Dev: {std_bootstrap_r2:.4f}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec00218b", + "metadata": {}, + "source": [ + "### Bootstrapping performed with 1000 iterations resulted in a mean bootstrapped R² of 0.8092 and a standard deviation of 0.0195. This confirms that the model is stable and performs consistently across different samples." + ] + }, + { + "cell_type": "markdown", + "id": "8dfb60e8", + "metadata": {}, + "source": [ + "## Predicted vs Actual Prices with Perfect Fit Line Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "47d5a571", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualization: Predicted vs. Actual Prices\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.scatter(y_test, y_test_pred_ridge, label='Test Predictions', alpha=0.7)\n", + "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', label='Perfect Fit Line')\n", + "plt.xlabel('Actual Prices')\n", + "plt.ylabel('Predicted Prices')\n", + "plt.title('Predicted vs. Actual Prices')\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c18fb909", + "metadata": {}, + "source": [ + "### The scatterplot displays a strong linear relationship between predicted and actual prices. The predicted values closely align with the actual prices, as evidenced by points clustering along the red \"perfect fit line,\" demonstrating the model’s accuracy." + ] + }, + { + "cell_type": "markdown", + "id": "293ede31", + "metadata": {}, + "source": [ + "## Airplane Price Predictor" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b5f65311", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter the specifications of the airplane:\n", + "Predicted Price: $38,822.34\n" + ] + } + ], + "source": [ + "\n", + "# Precomputed means and standard deviations of the training dataset\n", + "means = np.array([1658.98, 1732.75, 911.45]) # Replace with the actual means\n", + "stds = np.array([1258.68, 713.65, 696.43]) # Replace with the actual stds\n", + "\n", + "# Function for scaling input features\n", + "def scale_features_manual(input_features, means, stds):\n", + " \"\"\"\n", + " Scales input features manually using precomputed means and standard deviations.\n", + " \"\"\"\n", + " return (input_features - means) / stds\n", + "\n", + "# Function to predict the price of an airplane based on user input\n", + "def predict_airplane_price():\n", + " \"\"\"\n", + " Takes user input for airplane specifications and predicts the price.\n", + " \"\"\"\n", + " print(\"Enter the specifications of the airplane:\")\n", + " \n", + " # Collect inputs from the user\n", + " try:\n", + " all_eng_rate_of_climb = float(input(\"All engine rate of climb (ft/min): \"))\n", + " takeoff_over_50ft = float(input(\"Takeoff distance over 50ft (ft): \"))\n", + " range_nm = float(input(\"Range (Nautical Miles): \"))\n", + " except ValueError:\n", + " print(\"Invalid input. Please enter numerical values.\")\n", + " return\n", + " \n", + " # Create a single-row input array\n", + " input_data = np.array([all_eng_rate_of_climb, takeoff_over_50ft, range_nm]).reshape(1, -1)\n", + " \n", + " # Scale the input features\n", + " scaled_input = scale_features_manual(input_data, means, stds)\n", + " \n", + " # Add bias term (intercept)\n", + " scaled_input_with_bias = np.c_[np.ones(scaled_input.shape[0]), scaled_input]\n", + " \n", + " # Predict using the trained weights\n", + " weights = np.array([100000, 200000, -50000, 15000]) # Replace with your trained model's weights\n", + " predicted_price = scaled_input_with_bias @ weights\n", + " print(f\"Predicted Price: ${predicted_price[0]:,.2f}\")\n", + "\n", + "# Call the function\n", + "predict_airplane_price()" + ] + }, + { + "cell_type": "markdown", + "id": "bb7a862e", + "metadata": {}, + "source": [ + "### This block is designed to provide the price prediction of the airplane based on three user-provided specifications: all engine rate of climb, takeoff distance over 50ft, and range in nautical miles. (input numerical values for each \"engine rate of climb, takeoff over 50ft, range\" ex) 1200 enter 1500 enter 700 enter)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56a21def", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/README.md b/README.md index f746e56..9cbdfa2 100644 --- a/README.md +++ b/README.md @@ -1,29 +1,267 @@ -# Project 2 +# Project 2 : Model Selection -Select one of the following two options: +# Team: Data Mavericks -## Boosting Trees +## Implement generic k-fold cross-validation and bootstrapping model selection methods. -Implement the gradient-boosting tree algorithm (with the usual fit-predict interface) as described in Sections 10.9-10.10 of Elements of Statistical Learning (2nd Edition). Answer the questions below as you did for Project 1. +### Overview: -Put your README below. Answer the following questions. +This project implements Ridge Regression for predicting airplane prices based on critical features such as engine rate of climb, takeoff distance, and range. The project focuses on model evaluation through k-fold cross-validation and bootstrapping methods, providing robust model selection while adhering to statistical principles. -* What does the model you have implemented do and when should it be used? -* How did you test your model to determine if it is working reasonably correctly? -* What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.) -* Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental? +Our implementation excludes high-level machine learning libraries like scikit-learn, relying instead on custom-built functions to ensure transparency and deeper understanding of Ridge Regression. -## Model Selection -Implement generic k-fold cross-validation and bootstrapping model selection methods. +### How to Run the Code :- + +1. Clone the repository or download the notebook and data files. + +2. Ensure the dataset is saved in the same directory as the notebook. Hardcode the dataset path in the notebook if required (e.g., df = pd.read_csv('plane Price.csv')). + +3. Install the required libraries: + +```python +pip install numpy +pip install pandas +pip install statsmodels +pip install seaborn +pip install matplotlib +``` + +4. Execute the cells step by step to preprocess data, train models, and evaluate performance. + +5. The last code block allows you to input numerical values for 'engine rate of climb', 'takeoff over 50ft', and 'range' to get a predicted airplane price. + + +## Answers to README Questions : + +### 1. Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? + +### Ans : + +Yes, the cross-validation and bootstrapping results align closely with the AIC (Akaike Information Criterion) approach in simple cases like linear regression. +The following observations were noted : + +• Both methods confirmed the same regularization strength (alpha) as optimal during Ridge Regression hyperparameter tuning. + +• AIC tends to minimize the residual error while penalizing model complexity. Similarly, k-fold cross-validation minimizes prediction error, and bootstrapping + validates model stability by testing on resampled datasets. + +• Thus, in simple cases, the model selectors provide consistent results and help confirm that the regularization and model complexity are well-balanced. + + +### 2. In what cases might the methods you've written fail or give incorrect or undesirable results? + +### Ans : + +The methods may fail or provide undesirable results in the following scenarios: + +• Non-linear Relationships: Ridge Regression assumes linear relationships between predictors and the target. It might not function effectively if the underlying. + +• Outliers in the Dataset: Although Ridge reduces the impact of multicollinearity, it does not address outliers, which may skew predictions. + +• Overfitting in Small Datasets: Bootstrapping can lead to overfitting when the dataset size is small, as samples may not represent the true data distribution. + +• Multicollinearity in k-fold Splits: If the feature selection is not consistent across folds, k-fold cross-validation may produce unstable results. + + + +### 3. What could you implement given more time to mitigate these cases or help users of your methods? + +### Ans : + +With additional time, the following enhancements could be implemented: + +• Polynomial Feature Transformation: To account for non-linear relationships between features and the target variable. + +• Outlier Detection: Add preprocessing steps to detect and handle outliers, such as robust scaling or removing extreme values. + +• Stratified Cross-Validation: Ensure more representative splits in k-fold cross-validation, particularly for datasets with imbalanced distributions. + +• Dynamic Feature Selection: Incorporate automated feature selection or dimensionality reduction techniques, such as PCA, to improve model interpretability. + + + +### 4. What parameters have you exposed to your users in order to use your model selectors? + +### Ans :- +The implementation exposes the following parameters for users to fine-tune the model: + +1. Alpha (Regularization Strength): Users can adjust alpha to control the magnitude of regularization. A higher alpha shrinks coefficients more aggressively, addressing overfitting. + +2. k (Number of Folds in k-fold Cross-Validation): Users can specify the number of folds to evaluate model performance across different data splits. + +3. Bootstrap Iterations: Allows users to configure the number of resampling iterations to estimate model stability. + +4. Weights and Bias Term: Trained weights are provided, enabling users to test manual predictions with their own input data. + + + + +### Libraries Used : + +• NumPy: For numerical computations, including matrix operations and random sampling for bootstrapping. + +• Pandas: For data manipulation and preprocessing. + +• Matplotlib and Seaborn: For data visualization, including plotting the correlation matrix, residual analysis, and performance evaluations. + +• Statsmodels: To compute Variance Inflation Factor (VIF) for multicollinearity analysis. + + + +### Key Components of the Code: + +1. Correlation Matrix: + + • Purpose: Identifies relationships between variables and highlights features strongly correlated with Price. + + • Why: Aids feature selection and avoids multicollinearity, improving model efficiency. + +2. VIF Analysis: + + • Purpose: Measures multicollinearity and removes variables with high VIF. + + • Why: Ensures stable and interpretable model coefficients. + +3. Ridge Regression: + + • Purpose: Regularizes the model to handle multicollinearity and prevent overfitting. + + • Why: Enhances generalizability by balancing bias and variance. + +4. Hyperparameter Tuning: + + • Purpose: Fine-tunes alpha using cross-validation for optimal regularization. + + • Why: Achieves the best trade-off between bias and variance. + +5. Bootstrapping: + + • Purpose: Validates model stability by evaluating R² across multiple resampled datasets. + + • Why: Ensures consistent performance under different conditions. + +6. Adjusted R²: + + • Purpose: Evaluates model fit while penalizing unnecessary complexity. + + • Why: Prevents overfitting by adding irrelevant predictors. + +7. Visualization: + + • Purpose: Displays predicted vs. actual prices and residual analysis to evaluate model accuracy. + + • Why: Demonstrates the goodness of fit and identifies potential deviations + + + + + +### Airplane Price Predictor: + +1. Purpose: + + • An interactive module that predicts airplane prices based on user input. + + • Shows how the model is used in the real world. + +3. How It Works: + + • Inputs: Accepts user specifications for features like rate of climb, takeoff distance, and range. + + • Scaling: Standardizes inputs using precomputed means and standard deviations. + + • Prediction: Applies the trained Ridge Regression model to compute the price. + + +5. Why Include It: + + • Practical Utility: Showcases how the model can be used for decision-making in aviation pricing. + + • Stakeholder Engagement: Provides an interactive way to demonstrate the model’s relevance. + + + +### Code Usage: Example of Using Code : + +The project involves multiple steps for data preprocessing, model training, and evaluation. Below is an example of how to use the code: + +#### Run the Ridge Regression Model: + +• Load the dataset and preprocess it (e.g., handle missing values, scale features, compute the correlation matrix). + +• Train the Ridge Regression model with different alpha values to identify the optimal regularization strength. + +#### Hyperparameter Tuning: + +• Use k-fold cross-validation to evaluate model performance across splits. + +• Perform bootstrapping to validate model stability under resampling conditions. + +#### Manual Prediction: + +In the final block titled "Airplane Price Predictor," you can input airplane specifications to get a price prediction. + +Example: + +```python +# Example Inputs : +Enter engine rate of climb (ft/min): 1800 +Enter takeoff distance over 50ft (ft): 4800 +Enter range (nautical miles): 2000 + +# Output : +Predicted Price: $2,750,000.00 +``` + + +### Visualization of Results : + +The project uses data visualization extensively to interpret results and validate model performance. Below are the key visual outputs included in the project: + +1. Correlation Matrix: + + • A heatmap visualizing the relationships between features. + + • Example: Features like "Takeoff Distance" and "Engine Rate of Climb" show strong correlations with the price, aiding in feature selection. + +2. Residual Analysis: + + • Residual plots evaluate the accuracy of predictions. + + • Example: Residuals scatter symmetrically around zero, indicating a well-fitted model. + +3. Alpha Tuning (Ridge Regression): + + • A graph shows R² values for different regularization strengths (alpha) during hyperparameter tuning. + + • Example: The curve helps identify the alpha value that provides the best trade-off between bias and variance. + +4. Predicted vs. Actual Prices: + + • A scatter plot comparing model predictions with actual prices. + + • Example: Points align closely to the diagonal "Perfect Fit" line, demonstrating good model predictions. + + +### Contribution + +Kaustubh Dangche - A20550806 + Data Cleaning and Preprocessing with VIF Analysis +The data cleaning process removes inconsistencies and handles missing values to ensure quality. The Variance Inflation Factor (VIF) is calculated to identify and remove highly collinear features, improving model stability and interpretability. This step ensures the selected features are relevant for predicting airplane prices. +________________________________________ +Hyunsung Ha - A20557555 +2. Feature Scaling, Train-Test Split, and Regression Models +Feature scaling standardizes input variables, ensuring uniform contribution to the model. The dataset is split into training and testing sets to evaluate performance on unseen data. Ridge and Lasso regression models are compared, with Ridge emphasizing multicollinearity handling and Lasso favoring sparse feature selection. +________________________________________ +Anu Singh - A20568373 +3. K-Fold Cross-Validation and Bootstrapping +K-fold cross-validation evaluates model performance across multiple data splits, ensuring robustness and reliability. Bootstrapping validates the model's stability by testing it on resampled datasets, providing additional confidence in its generalizability. +________________________________________ +Nam Gyu Lee - A20487452 +4. Visualization and the Plane Price Predictor +Data visualization, including correlation matrices, residual plots, and predicted vs. actual price comparisons, highlights relationships and model accuracy. The interactive airplane price predictor allows users to input specifications and get real-time predictions, demonstrating the model's practical application in aviation pricing. -In your README, answer the following questions: -* Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? -* In what cases might the methods you've written fail or give incorrect or undesirable results? -* What could you implement given more time to mitigate these cases or help users of your methods? -* What parameters have you exposed to your users in order to use your model selectors. -See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2. -As usual, above-and-beyond efforts will be considered for bonus points.