MIME-Version: 1.0 X-Document-Type: Workbook Content-Type: multipart/related; boundary="----=_NextPart_01C8E39C.74EC5DB0" This document is a Single File Web Page, also known as a Web Archive file. If you are seeing this message, your browser or editor doesn't support Web Archive files. Please download a browser that supports Web Archive, such as Windows® Internet Explorer®. ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" <body> <p>This page uses frames, but your browser doesn't support them.</p> </body> ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/stylesheet.css Content-Transfer-Encoding: quoted-printable Content-Type: text/css; charset="us-ascii" tr {mso-height-source:auto;} col {mso-width-source:auto;} br {mso-data-placement:same-cell;} .style18 {mso-number-format:"_\(\0022$\0022* \#\,\#\#0\.00_\)\;_\(\0022$\0022* \\\(= \#\,\#\#0\.00\\\)\;_\(\0022$\0022* \0022-\0022??_\)\;_\(\@_\)"; mso-style-name:Currency; mso-style-id:4;} .style65 {color:blue; font-size:11.0pt; font-weight:400; font-style:normal; text-decoration:underline; text-underline-style:single; font-family:Calibri, sans-serif; mso-font-charset:0; mso-style-name:Hyperlink; mso-style-id:8;} a:link {color:blue; font-size:11.0pt; font-weight:400; font-style:normal; text-decoration:underline; text-underline-style:single; font-family:Calibri, sans-serif; mso-font-charset:0;} a:visited {color:purple; font-size:11.0pt; font-weight:400; font-style:normal; text-decoration:underline; text-underline-style:single; font-family:Calibri, sans-serif; mso-font-charset:0;} .style0 {mso-number-format:General; text-align:general; vertical-align:bottom; white-space:nowrap; mso-rotate:0; mso-background-source:auto; mso-pattern:auto; color:black; font-size:11.0pt; font-weight:400; font-style:normal; text-decoration:none; font-family:Calibri, sans-serif; mso-font-charset:0; border:none; mso-protection:locked visible; mso-style-name:Normal; mso-style-id:0;} .style20 {mso-number-format:0%; mso-style-name:Percent; mso-style-id:5;} .font1 {color:black; font-size:11.0pt; font-weight:400; font-style:normal; text-decoration:none; font-family:Calibri, sans-serif; mso-font-charset:0;} .font9 {color:black; font-size:10.0pt; font-weight:400; font-style:normal; text-decoration:none; font-family:Calibri, sans-serif; mso-font-charset:0;} .font10 {color:black; font-size:10.0pt; font-weight:400; font-style:normal; text-decoration:none; font-family:Calibri, sans-serif; mso-font-charset:0;} td {mso-style-parent:style0; padding:0px; mso-ignore:padding; color:black; font-size:11.0pt; font-weight:400; font-style:normal; text-decoration:none; font-family:Calibri, sans-serif; mso-font-charset:0; mso-number-format:General; text-align:general; vertical-align:bottom; border:none; mso-background-source:auto; mso-pattern:auto; mso-protection:locked visible; white-space:nowrap; mso-rotate:0;} .xl65 {mso-style-parent:style0; mso-number-format:"\[$-F800\]dddd\\\,\\ mmmm\\ dd\\\,\\ yyyy";} .xl66 {mso-style-parent:style0; font-size:14.0pt; font-weight:700; font-family:Arial, sans-serif; mso-font-charset:0;} .xl67 {mso-style-parent:style65; color:blue; text-decoration:underline; text-underline-style:single;} .xl68 {mso-style-parent:style0; color:black; font-size:10.0pt; text-align:center;} .xl69 {mso-style-parent:style0; font-size:8.9pt; font-weight:700; font-family:Verdana, sans-serif; mso-font-charset:0; vertical-align:top; white-space:normal;} .xl70 {mso-style-parent:style0; font-size:8.65pt; font-family:Arial, sans-serif; mso-font-charset:0; white-space:normal;} .xl71 {mso-style-parent:style0; font-size:8.65pt; font-family:Arial, sans-serif; mso-font-charset:0; text-align:center; white-space:normal;} .xl72 {mso-style-parent:style0; font-size:8.65pt; font-family:Arial, sans-serif; mso-font-charset:0; mso-number-format:"Medium Date"; text-align:right;} .xl73 {mso-style-parent:style0; font-size:8.65pt; font-family:Arial, sans-serif; mso-font-charset:0; text-align:right; white-space:normal;} .xl74 {mso-style-parent:style0; font-size:8.65pt; font-family:Arial, sans-serif; mso-font-charset:0; mso-number-format:"\#\,\#\#0"; text-align:right; white-space:normal;} .xl75 {mso-style-parent:style0; border-top:none; border-right:none; border-bottom:1.0pt solid windowtext; border-left:none;} .xl76 {mso-style-parent:style0; font-style:italic; text-align:center-across; border-top:1.0pt solid windowtext; border-right:none; border-bottom:.5pt solid windowtext; border-left:none;} .xl77 {mso-style-parent:style0; font-style:italic; text-align:left; border-top:1.0pt solid windowtext; border-right:none; border-bottom:.5pt solid windowtext; border-left:none;} .xl78 {mso-style-parent:style0; text-align:left;} .xl79 {mso-style-parent:style0; font-style:italic; text-align:left; vertical-align:top; border-top:1.0pt solid windowtext; border-right:none; border-bottom:.5pt solid windowtext; border-left:none;} .xl80 {mso-style-parent:style0; text-align:left; vertical-align:top;} .xl81 {mso-style-parent:style0; mso-number-format:Fixed;} .xl82 {mso-style-parent:style18; mso-number-format:"_\(\0022$\0022* \#\,\#\#0\.00_\)\;_\(\0022$\0022* \\\(\= #\,\#\#0\.00\\\)\;_\(\0022$\0022* \0022-\0022??_\)\;_\(\@_\)";} .xl83 {mso-style-parent:style20; mso-number-format:Percent;} .xl84 {mso-style-parent:style0; text-align:right;} .xl85 {mso-style-parent:style18; mso-number-format:"_\(\0022$\0022* \#\,\#\#0\.00_\)\;_\(\0022$\0022* \\\(\= #\,\#\#0\.00\\\)\;_\(\0022$\0022* \0022-\0022??_\)\;_\(\@_\)"; text-align:right;} .xl86 {mso-style-parent:style0; color:black; font-size:10.0pt; text-align:left;} .xl87 {mso-style-parent:style0; color:black; font-size:10.0pt; text-align:left; vertical-align:top;} .xl88 {mso-style-parent:style20; mso-number-format:Percent; text-align:right;} .xl89 {mso-style-parent:style20; color:black; font-size:10.0pt; mso-number-format:"0\.000%"; text-align:right;} .xl90 {mso-style-parent:style20; mso-number-format:"0\.000%"; text-align:right;} .xl91 {mso-style-parent:style0; mso-number-format:"0\.000%";} .xl92 {mso-style-parent:style0; color:black; font-size:10.0pt; mso-number-format:"0\.000%"; text-align:left;} .xl93 {mso-style-parent:style20; color:black; font-size:10.0pt; mso-number-format:"0\.000%"; text-align:right; vertical-align:top;} .xl94 {mso-style-parent:style0; mso-number-format:Fixed; text-align:right;} .xl95 {mso-style-parent:style20; font-weight:700; text-decoration:underline; text-underline-style:single; mso-number-format:0%; text-align:right;} .xl96 {mso-style-parent:style20; mso-number-format:"0\.000%";} .xl97 {mso-style-parent:style20; mso-number-format:"0\.00000%";} .xl98 {mso-style-parent:style0; mso-number-format:"0\.00000%";} .xl99 {mso-style-parent:style18; mso-number-format:"_\(\0022$\0022* \#\,\#\#0\.00000_\)\;_\(\0022$\0022* \\= \(\#\,\#\#0\.00000\\\)\;_\(\0022$\0022* \0022-\0022??_\)\;_\(\@_\)";} .xl100 {mso-style-parent:style0; font-weight:700; font-style:italic; text-decoration:underline; text-underline-style:single; mso-number-format:"0\.00000%";} .xl101 {mso-style-parent:style0; font-weight:700; font-style:italic; text-decoration:underline; text-underline-style:single;} .xl102 {mso-style-parent:style65; color:blue; text-decoration:underline; text-underline-style:single; mso-number-format:"\[$-F800\]dddd\\\,\\ mmmm\\ dd\\\,\\ yyyy";} .xl103 {mso-style-parent:style0; font-size:8.65pt; font-family:Arial, sans-serif; mso-font-charset:0; mso-number-format:"\[$-F800\]dddd\\\,\\ mmmm\\ dd\\\,\\ yyyy"; text-align:center; white-space:normal;} .xl104 {mso-style-parent:style0; font-size:8.65pt; font-family:Arial, sans-serif; mso-font-charset:0; mso-number-format:"\[$-F800\]dddd\\\,\\ mmmm\\ dd\\\,\\ yyyy"; text-align:right;} .xl105 {mso-style-parent:style0; mso-number-format:"0\.0000"; text-align:right;} .xl106 {mso-style-parent:style0; color:black; font-size:8.0pt; font-family:Arial, sans-serif; mso-font-charset:0;} ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/tabstrip.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii"

Regression

oil

html<= /a>

Oil1<= /a>

------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/sheet001.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii"

SUMMARY OUTPUT

Table 1

=

Regression Statistics

Multiple R

96.40%

R Square

92.94%

Adjusted R Square

92.88%

Standard Error

2.78892967

Observ= ations

130

<= /td>

ANOVA

Table 2

df

SS

MS

F

Significance

Regression

1

13103.76

13103.75819

1684.693

1.5553E-75

Residual

128

995.6005

7.778128702

Total<= /td>

129

14099.36

<= /td>

Table 3

Coefficients

Standard

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.

Upper 95.

Intercept

-7245.095698

178.136

-40.6717147

4.61E-75

-7597.56816

-6892.62323

-7597.57

-6892.62

Date

0.184903278

0.004505

41.04501064

1.56E-75

0.17598958

0.19381697

0.17599

0.193817

<= /td>

Table 5

Forecasted comp= ared to Actual ETN values using the last day observed

RESIDUAL OUTPUT

Table 4

PROBABILITY OUT= PUT

Clomun J

K

L

M

N

Observation

Predicted Open

Residuals

Standard Res

Percentile

Open

Line19

Coefficients

1

83.91553893

-1.13554

-0.40874677

0.38461538

50.62

Line20

Intercept=3Da

-7245.095698

2

83.73063565

1.109364

0.399325011

1.15384615

50.72

Line21

Date=3Db

0.184903278

3

82.99102254

3.528977

1.270285066

1.92307692

51.01

Line22

Today=3DX

Tuesday, July 08, 2008

4

82.80611926

1.503881

0.541334499

2.69230769

51.52

Line23

Today Julian

39637.00

=3DL22

5

82.62121598

3.098784

1.115433324

3.46153846

51.78

Line24

Date*Today

7329.01

=3DK26*K28

6

82.4363127

3.143687

1.131596636

4.23076923

52.03

Line25

Forecasted=3DY

$ &nbs= p; &= nbsp; &nbs= p; 83.92

=3DSUM(L25:L29)

7

81.88160287

2.478397

0.892119855

5

52.26

Line26

Actual

$ &nbs= p; &= nbsp; &nbs= p; 82.78

=3Doil!B3

8

81.69669959

0.4033

0.145171368

5.76923077

52.38

Line27

Difference

$ &nbs= p; &= nbsp; &nbs= p; (1.14)

=3DL31-L30

9

81.51179631

0.168204

0.060546329

6.53846154

52.58

Line28

%Difference

-1%

=3DL32/L31

10

81.32689304

0.403107

0.145101736

7.30769231

52.85

11

81.14198976

-0.29199

-0.10510416

8.07692308

53.1

12

80.58727992

0.38272

0.137763305

8.84615385

53.15

Table 6

E= xcess of Monthly Growth over Annual Interest

13

80.40237664

1.597623

0.57507794

9.61538462

53.26

Clomun J

K

L

M

N

14

80.21747337

0.352527

0.126894922

10.3846154

53.31

Line 38

Open

$ &nbs= p; &= nbsp; 50.62000

15

80.03257009

-0.03257

-0.01172388

11.1538462

53.31

Line 39

Date

X-Coefficient

$ &nbs= p; &= nbsp; &nbs= p; 0.18490

16

79.84766681

3.332333

1.19950131

11.9230769

53.4

Line 40

Daily growth ra= te(X Coef) =3D

0.36528%

=3DB18/G25

17

79.29295698

1.387043

0.499277782

12.6923077

54.06

Line 41

Annual Interest rate=3D

5.000%

18

79.1080537

0.981946

0.353459816

13.4615385

54.14

Line 42

Days in Year=3D

360

days

19

78.92315042

1.26685

0.456013143

14.2307692

54.16

Line 43

Daily Interest rate =3D

0.01389%

=3DK41/K42

20

78.73824714

2.761753

0.994116129

15

54.33

Line 44

Daily Growth%Gain =3D

0.35139%

=3DL40-L43

21

78.55334386

3.006656

1.08227112

15.7692308

54.37

Line 45

Days in a week=3D

6

days

22

77.99863403

2.321366

0.835595172

16.5384615

54.62

Line 46

Daily Growth%Gain =3D

2.10833%

=3DL44*K45

23

77.81373075

-3.80373

-1.36918482

17.3076923

54.65

Line 47

Week in a Month=3D

4

days

24

77.62882747

-3.66883

-1.32062525

18.0769231

55.22

Line 48

Monthly Growth%= Gain =3D

8.43332%

=3DL46*K47

25

77.44392419

-1.63392

-0.58814473

18.8461538

55.23

Line 49

Annual Interest= rate=3D

5.000%

26

77.25902092

-0.69902

-0.25161845

19.6153846

56.08

Line 50

Excess of Month= ly Growth over Annual Interest

3.43332%

=3DL48-L49

27

76.70431108

-0.40431

-0.14553517

20.3846154

56.5

28

76.5194078

0.920592

0.331374891

21.1538462

56.62

29

76.33450452

-0.2045

-0.07361312

21.9230769

57.1

30

76.14960125

2.030399

0.730859078

22.6923077

57.25

31

75.40998813

4.700012

1.691808732

23.4615385

57.4

32

75.22508486

4.924915

1.772764555

24.2307692

57.57

33

75.04018158

2.569818

0.925027717

25

57.78

34

74.8552783

1.974722

0.710817656

25.7692308

57.96

35

74.67037502

1.339625

0.482209259

26.5384615

58.03

36

74.11566519

2.064335

0.743074648

27.3076923

58.03

37

73.93076191

1.629238

0.58645793

28.0769231

58.14

38

73.74585863

1.224141

0.440639964

28.8461538

58.18

39

73.56095535

0.599045

0.215631151

29.6153846

59.06

40

73.37605207

1.423948

0.512562011

30.3846154

59.09

41

72.82134224

2.558658

0.921010343

31.1538462

59.27

42

72.63643896

1.203561

0.433231901

31.9230769

59.36

43

72.45153568

0.608464

0.219021839

32.6923077

59.56

44

72.26663241

-0.28663

-0.10317574

33.4615385

59.88

45

72.08172913

-1.40173

-0.50456417

34.2307692

59.93

46

71.52701929

-3.85702

-1.38836648

35

60.09

47

71.34211601

-3.94212

-1.41899776

35.7692308

60.19

48

71.15721274

-1.70721

-0.61452556

36.5384615

60.56

49

70.97230946

-1.16231

-0.41838305

37.3076923

60.7

50

70.78740618

0.392594

0.141317442

38.0769231

60.82

51

70.23269635

0.227304

0.081819859

38.8461538

61.29

52

70.04779307

-0.69779

-0.25117647

39.6153846

61.54

53

69.86288979

0.23711

0.085349811

40.3846154

61.64

54

69.67798651

-0.12799

-0.04606982

41.1538462

61.87

55

69.49308323

-0.12308

-0.04430484

41.9230769

62.5

56

68.9383734

-1.32837

-0.47815916

42.6923077

62.65

57

68.75347012

-0.16347

-0.05884244

43.4615385

62.77

58

68.56856684

-0.86857

-0.31264793

44.2307692

62.88

59

68.38366356

-0.88366

-0.31808212

45

62.96

60

68.19876029

-2.26876

-0.81665931

45.7692308

63.11

61

67.64405045

-2.63405

-0.94814858

46.5384615

63.12

62

67.45914717

-1.23915

-0.44604143

47.3076923

63.5

63

67.27424389

-2.54424

-0.91582195

48.0769231

63.99

64

67.08934062

-2.77934

-1.00044699

48.8461538

64.06

65

66.90443734

-2.84444

-1.0238791

49.6153846

64.31

66

66.3497275

-3.69973

-1.33174798

50.3846154

64.51

67

66.16482423

-4.62482

-1.66474431

51.1538462

64.73

68

65.97992095

-6.04992

-2.17771984

51.9230769

64.99

69

65.79501767

-6.43502

-2.31633864

52.6923077

65.01

70

65.61011439

-2.73011

-0.9827276

53.4615385

65.93

71

65.05540456

-1.9454

-0.7002647

54.2307692

66.22

72

64.87050128

-1.7505

-0.63010763

55

67.4

73

64.685598

-3.8656

-1.39145446

55.7692308

67.5

74

64.50069472

-4.62069

-1.66325786

56.5384615

67.61

75

64.31579144

-4.12579

-1.48511327

57.3076923

67.67

76

63.57617833

-4.30618

-1.55004504

58.0769231

67.7

77

63.39127505

-0.62128

-0.22363317

58.8461538

68.59

78

63.20637177

-0.24637

-0.08868359

59.6153846

69.35

79

63.0214685

-0.52147

-0.18770696

60.3846154

69.37

80

62.46675866

2.043241

0.735481875

61.1538462

69.45

81

62.28185538

2.708145

0.974819392

61.9230769

69.55

82

62.09695211

1.893048

0.681418483

62.6923077

69.81

83

61.91204883

1.587951

0.571596357

63.4615385

70.1

84

61.72714555

0.142854

0.051421659

64.2307692

70.46

85

61.17243571

0.467564

0.16830369

65

70.68

86

60.98753244

0.302468

0.108875739

65.7692308

71.18

87

60.80262916

-1.24263

-0.4472948

66.5384615

71.98

88

60.61772588

-0.05773

-0.02077892

67.3076923

73.06

89

60.4328226

0.267177

0.096172748

68.0769231

73.84

90

59.87811277

0.211887

0.076270588

68.8461538

73.96

91

59.69320949

-0.63321

-0.22792907

69.6153846

74.01

92

59.50830621

-0.41831

-0.15057283

70.3846154

74.16

93

59.32340293

-1.1834

-0.42597582

71.1538462

74.8

94

59.13849965

-1.1785

-0.42421085

71.9230769

74.97

95

58.58378982

-0.55379

-0.1993413

72.6923077

75.38

96

58.39888654

-0.36889

-0.13278381

73.4615385

75.56

97

58.21398326

-0.43398

-0.15621592

74.2307692

75.81

98

58.02907999

-0.45908

-0.1652497

75

76.01

99

57.28946687

-0.78947

-0.28417523

75.7692308

76.13

100

57.10456359

-1.88456

-0.67836449

76.5384615

76.18

101

56.91966032

-2.58966

-0.93216997

77.3076923

76.3

102

56.73475704

-2.08476

-0.7504258

78.0769231

76.56

103

56.54985376

-2.40985

-0.86744709

78.8461538

76.83

104

55.99514393

-3.61514

-1.30130141

79.6153846

77.44

105

55.81024065

-5.09024

-1.83227486

80.3846154

77.61

106

55.62533737

-3.59534

-1.29417187

81.1538462

78.18

107

55.44043409

-3.66043

-1.31760398

81.9230769

80

108

55.25553081

-2.99553

-1.07826647

82.6923077

80.09

109

54.70082098

-1.39082

-0.50063769

83.4615385

80.11

110

54.5159177

-1.41592

-0.50967146

84.2307692

80.15

111

54.33101442

-0.27101

-0.09755392

85

80.19

112

54.14611114

-0.99611

-0.35855857

85.7692308

80.32

113

53.96120787

-1.38121

-0.49717737

86.5384615

80.57

114

53.40649803

-0.1465

-0.0527332

87.3076923

80.68

115

53.22159475

-1.70159

-0.61250332

88.0769231

80.85

116

53.03669148

-2.02669

-0.72952461

88.8461538

80.97

117

52.8517882

-2.23179

-0.8033509

89.6153846

81.5

118

52.11217508

0.737825

0.265586273

90.3846154

81.56

119

51.92727181

1.472728

0.530120878

91.1538462

81.68

120

51.74236853

1.567631

0.56428211

91.9230769

81.73

121

51.55746525

2.602535

0.936804234

92.6923077

82

122

51.37256197

3.247438

1.168942584

93.4615385

82.1

123

50.81785214

3.552148

1.278625447

94.2307692

82.78

124

50.63294886

4.597051

1.654747155

95

83.18

125

50.44804558

5.631954

2.027269279

95.7692308

84.31

126

50.2631423

6.356858

2.2882043

96.5384615

84.36

127

50.07823902

7.321761

2.635529336

97.3076923

84.84

128

49.52352919

7.726471

2.781208039

98.0769231

85.58

129

49.33862591

8.841374

3.182526835

98.8461538

85.72

130

49.15372263

7.946277

2.860329254

99.6153846

86.52

------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/image001.png Content-Transfer-Encoding: base64 Content-Type: image/png iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAYAAACNbyblAAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAAAlwSFlz AAAXEQAAFxEByibzPwAAACdJREFUGFdjYMAF2tra2oH4PxDPh6uBCoAE/yMLHgcJtLa23sdpHABu wBXnNHZUpgAAAABJRU5ErkJggk== ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/image002.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhAwADAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAD AAMAggAAAHd3d4aGhoCAgISEhIiIiI6OjqWlpQMGCAJ0AAYkADs= ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/image003.png Content-Transfer-Encoding: base64 Content-Type: image/png iVBORw0KGgoAAAANSUhEUgAABu4AAAERCAYAAACU6FEZAAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAAAlwSFlz AAAXEQAAFxEByibzPwAAjt5JREFUeF7t3fvTZVd933n9F5mZqrn9ECeT1NSkkkzGJFUmFU+cGPWj GwhJSINBvtCpCSRAJobQI3RBQpcGhGSD6TBREVt0iBVbNOBg1C21Lqi5qw0C0VwMjexABxfuClQo inLtOZ/T+j69nvWstdd37cs5e5/zPlVPtfQ8+5yz9mutvc7e63PW2pdcwgMBBBBAAAEEEEAAAQQQ QAABBBBAAAEEEEAAAQQQQAABBKYhcMcdd7x98dPwgwFtgDZAG6AN0AZoA7QB2gBtgDZAG6AN0AZo A7QB2gBtgDZAG6AN0AZoA7QB2gBtYOVt4IHd1BD8leMTkhIU0wZoA7QB2gBtgDZAG6AN0AZoA7QB 2gBtgDZAG6AN0AZoA7QB2gBtgDZAG6AN7LaBfcFdwwMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA AQQQQAABBFYmYBPsCO5WRs4bIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIILBfgOCOVoEA AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDABAQI7iZQCRQBAQQQQAABBBBAAAEEEEAAAQQQ QAABBBBAAAEEEEAAAYI72gACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACExAguJtAJVAE BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBAjuaAMIIIAAAggggAACCCCAAAIIIIAAAggg gAACCCCAAAIITECA4G4ClUAREEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEECC4ow0ggAAC CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMAEBgrsJVAJFQAABBBBAAAEEEEAAAQQQQAABBBBA AAEEEEAAAQQQQIDgjjaAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwAQECO4mUAkUAQEE EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGCO9oAAggggAACCCCAAAIIIIAAAggggAACCCCA AAIIIIAAAhMQILibQCVQBAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQI7mgDCCCAAAII IIAAAggggAACCCCAAAIIIIAAAggggAACCExAgOBuApVAERBAAAEEEEAAAQQQQAABBBBAAAEEEEAA AQQQQAABBBAguKMNIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDABAYK7CVQCRUAAAQQQ QAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECA4I42gAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAA AggggMAEBAjuJlAJFAEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABgjvaAAIIIIAAAggg gAACCCCAAAIIIIAAAggggAACCCCAAAITECC4m0AlUAQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB BBBAAAEECO5oAwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghMQIDgbgKVQBEQQAABBBBA AAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQILijDSCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC CCAwAQGCuwlUAkVAAAEEEEAAAQQQ6C5w+vTp5vDhw7s/R48e7f5iPLNVAGsayDYInD17tjl37lzv XdXrhH3TiRMner/mKl9gisf7FMu0yjrhvRBAAAEEEEAAAQS2Q4Dgbjvqmb1EAAEEEEAAAQRmI6DB 7jNnzjQa5NYgrf6/7aGB8UsuuWTPz2x2dmYFxXpmFUZxqwSOHTu2px85dOhQ1fPjjdWHzblvmuLx PsUy9WokPBkBBBBAAAEEEEAAgYQAwR3NAgEEEEAAAQQQQGDtAgrnjhw5sm+QOxz01t9TIR4Due3V d/78+WZnZ6fVNg4XDh48mHzRKVtrP1XueF/i/5eF9kMhjQJiHvUCXmvZm/UQM9jqS1r3jFTbKX1x oO0dCO4u6HiOS4Wkmi2tL2u0PabcB9W1NrZGAAEEEEAAAQQQQCAvQHBH60AAAQQQQAABBBBYq0Bq ILYtfImXwtzkgVyFBvbTtZL0/FKYlfp76v2mbH3q1KlO+6kgb11LGA5Rv13bRZ/ndbVW+D7lB8Hd 3toZ4nhXEFfb/yjoy4XqQ5Spaxuc6/HadX95HgIIIIAAAggggMD6BAju1mfPOyOAAAIIIIAAAlst oBk4tTPBbAA4hFvnQO6YFRjvl/6/y2NbgrvU7KaawKDvsoi1dTNU/da+7xDb97FetXPN/qZmhvWZ KciMu2YZitcch+G2qdl36+rv53y81hwDbIsAAggggAACCCAwDQGCu2nUA6VAAAEEEEAAAQS2TqBt +TQN7mugVD+p7cLl69Y1kDt2hQ01UNwluFOgmnpM2ToVEKjtWDvSv6WgeJWh0lD1O3Y7TL2+x1qW ucBmXTMcS1YK6TSjV3Wj2YF9l1IluEsHdzXHpZZlDR/r6oPmfLyW2j1/RwABBBBAAAEEEJieAMHd 9OqEEiGAAAIIIIAAAhsvoMHx1KC+BkfjgVphaEA9vAdeuM26BnLHrqShBorj4E6BSrjkW+q/U3Wg /Z2ydSokyQVEmsmTC467zmysbQ9D1W/t+w6xvdda7SgV4OWC4SHKNqXXILhLB3ep4zK3/OpUlkae 8/E6pWOCsiCAAAIIIIAAAgj4BAjufE5shQACCCCAAAIIIDCQgAbzU6Gd5/5XCpni7TxhkoVTuUCq tGt63hj3N9Jr5pbiG2qgOA7u+gRTq7Au1UXu794wKXx+LkAOZ3S2ladPuxiqftV++rbvWvMa69zx 7jkWh9i3sI76LHvZZmTljF9/KsFd13bqOd5LbaemreRmcobv0bdMXfvxoY7Xkhd/RwABBBBAAAEE EEBAAgR3tAMEEEAAAQQQQACBlQqkwpI+M3ByA7karE69l2ZaeZbA06yscJZfHDbqdUpL/unvNrNL M48srIgHqG2JRpXLlnaMl3XU/4fLPnqCTlXsKoK7NmvNpCk9ZC0Tz7a516oJCMLXSM0Ia7Pt0y6G ql/VaS50VDs5duxYibzX32utU0uU5sLRofYtPPbiY1f1m3r/cCamylw6xuScOk6tX/AEd2E59Vrx DLOwotR+rD8pla9PO7X37BuS6XVq2oqCz9SXOkKD2jKpb1I95WbY6vf6eypIHup47XWw8WQEEEAA AQQQQACBrRQguNvKamenEUAAAQQQQACB9QmkBvFLAVhbaVMDuRq0Lt3PLBduaPC49NxwcLntvmhx 2dpCidwgd+4+Yfq9Z9bS2MGdx7otjIiNNJDe5VETEMRhSCks0PZDtItUGWvrV+227Tn2N7XhsWaY 1VqnQpNU2x1i37TPbffPDH3iduYNhTzvoX4htT9d3zPXP8SvN0Q7tdf0erQdr7VtpXQs1pQpt/xm 6j10vKgvCx9DHK9d+jKegwACCCCAAAIIIIAAwR1tAAEEEEAAAQQQQGBlArkZFX0ChtRArifY0Dap WTddXi8XTMWvlZslpbLkBuZrg524MscO7rzWuXA29fwu7aE2IAidPDPChmgXfYMAb7A1dnhXY51a KjM1w3aofUvNoMy10a4hWs17xO/d9T1z/YPn9UrHqLf/sn6q5gOjb1uJ39Mb3NUea2YUzsaufQ3P Fylq7NgWAQQQQAABBBBAYHsFCO62t+7ZcwQQQAABBBBAYOUCGhQtzaioLVQpUNEge24GXWrAOnw9 W7pOMzEUgKn8ufAtVe5S2UILPV/vUxpkb5sxlCpDHNzFS26qjFoSULNTSgPPpf3R33OhRm451NT+ eu8xF+5vTUAQO6WWRI2DxiHaRZ/6jetRbrK2oEF1lzLoc0/D3LFYY51qD/FxN9S+pfqXcBlEBcJq 5zYjzxN6xdvkZnHpNWVdmu3X5T3tOZ6lN4dop/Z+3pCsrc+uaSup4yOe0ewpU6o92fGi8uhHbTD1 uRDO+O1zvNZ+jrE9AggggAACCCCAAAKhAMEd7QEBBBBAAAEEEEBgZQJjBAu5MCm8p5x2MDWjJxUm 2T2n4mXTQqTUftTM3rNl2SwMTL1XvF9dA5jcIHYuIKxZ1tJeQ2ULQ7/ce6b2MzV4XgoQUw22JiCI n+957pDtQu9fW7/eJUVT+9LFszaMUfksFGkLRlLH3FD7Fu9727KrqVmdnlAoFczFx0zbEo2xq+c9 7Tme4G7IdlpTtlx78Rxb9tyUbbyksadMqW1S/VpuWdEx++OVfdjyRggggAACCCCAAAKzFiC4m3X1 UXgEEEAAAQQQQGBeAqsK7nL3nUsNDHcRTAVTqaUgUwPI3nuP1QY7uf2oDe5sZkrq9VL7kwsUU3Wt mW3xI57Vkrv3YKmeagKC+LX6PDd8LW+70HNq6je13KTCodwjDmXbQuiSa+rvtUsIWnl0/MWB2ZD7 FpcrN8szt8+lUCi11G8uHMzN1orfu/Se4fae4M5Tn952WlO23PuWji3Vv6xyMxXj0LlUplR7agtw U/WU6qdqjldPHbANAggggAACCCCAAAJtAgR3tA8EEEAAAQQQQACBlQmsKrjL7VBq1l2X2Uh9Br7b Apew3EMNFMdhg0JNvXZu+VALWUrLiNp2OevUAHouUB2iAZYCgrb36PPc8HW97ULPqanfVLjQtpxo HNylQuU+5rXBnYKTXLsfct9Sr6X39gaXpVAo9fpttqn+JnYvvWe4/aYEd97lgL1fhgiNUnVU6nPj 8nhmhXadAd3nuOO5CCCAAAIIIIAAAtsjQHC3PXXNniKAAAIIIIAAAmsXWHdw513iMg5jwiUAbRlA TzhSMyhfGtAfa6BYA925EK92tku8D0Pfz3Cs8K1LcKfgrGu7qA3uUuWzAFbtIv6J3bvOYsx51wZ3 Kk+uDEPvW64t6/fh/e5S+1Y6Xmv7D0/QVnrPvsFdn3ZaU7Yh20pbeymVqbaOVO7UfRjX1R+v/UOa AiCAAAIIIIAAAghMQoDgbhLVQCEQQAABBBBAAIHtEEgNqtYuZ1caUNWgb80gcmrmkmapabk078wQ bddldkhbrdfMyOrbelJLAGqf4plKpUHzuByeAfG+Zbfndwnf7Lmp/UrN0hqqXeh9a+q3S1AWtt22 +xZ28U+VR++hY8mColSAlgrvht633BKVHo9S+64NhdYV3A3VTksenrZTW796z7bZpKUypfrtttdL HYupz5Ca49XjwjYIIIAAAggggAACCLQJENzRPhBAAAEEEEAAAQRWJpC731qX5SrbQpfcDnkG3j0D /6lAb87BnbxSIVu8T6VB89i9dvs+DbFPcJcKmeJ7sQ3ZLlJhQduMytrwI26fpaUCa9091rljPXYd Y9/03rl7pplNatnWUnv19B+h5TqCuyHbacnD025SBqobmyWqoE3bqI16PgdKZUr9neDOU1NsgwAC CCCAAAIIIDAlAYK7KdUGZUEAAQQQQAABBLZAIBV69QkWSgO5pYH0cFBXA8epEMcGl8+cObOcDaLy bspSmW0BaN/gbg5LZaaCjngW6NDtYojgTm3QZriV/h26W/EEd3pPzcKL24COpdIxOdS+6Xhtmzlb 276nHtwN3U5r+tZcG/O2FW8bLZUpdV9BgjuvLtshgAACCCCAAAIITEWA4G4qNUE5EEAAAQQQQACB LRFIzezSDIyuj9JAbikkCAd1U4PMqSUTU7OJ5j7jLuVYG2yE1goR4tBmrPv06X27BgSpmVnx0pJD twuVt2bpvdrAqOux5H2e1zrVBtQmwplVq9g3vV8q0In7nVJf4m0H5ph6z9i49J7h9qXX85ZvFf2X ldvbVrxtr+TlNQjfL+6nPLMxx+zLvBZshwACCCCAAAIIILC5AgR3m1u37BkCCCCAAAIIIDBJgdRs NQ2ceu7DZfduCnesNJAbblsKCVKzc1KIqxj4jvcrNZg8VAXnApZ4pkrKOnXfMpUrZZ2r43DGWNd9 6hIQpELkOFhSeYZuF3rNmvrVzDHPDM+udrXPq7EutZlV7lupHkt9Seq4zwU46qtSs3dj69R7xsuJ 6jme1yvtn733Kvove6+atuJph6U6SrWneJZn+D6pGbepfqrmePXsB9sggAACCCCAAAIIINAmQHBH +0AAAQQQQAABBBBYuUBqQFvBhAZHU4PWCpbCZffCQKk0kBvuXCm4877WOpbKTAVKnorTPqVmDYbP 9c6CTPmoXHF4lwsZNKgeP+LlFLsGlN6AQG1JHrl7oKWCyKHbhQxSr9l2j684uNMxlDpWYl/PfcM8 7ah0HKVmnOo5nqVIh9o3ebQti5haujPcL089p5Z/jY8vlSHXx8XWqXYbB006bjyv5ym/3n8V/Zft p/e49LZBzz566sjeL9UPpNpQ7fHq3R+2QwABBBBAAAEEEEAgJUBwR7tAAAEEEEAAAQQQWLlAalZE ONiqwVQNlOqnNLDqGchtG0QuLZUZz75IDf6r7EMvlZmaPSMLvY/KEN+HLVWJ4cwabW/36tNr6Ef/ nwsEUvcdzAV32v+wbKmB89xyqKltu4RNqYAgbEe52XXh++dCQ8/swZp2obqqrd/U66vu7H5wek25 2T0Y7fXHWNKvNoxJtbEw7Bpq3+x1UmG13i8uRxyQefqS3D3z7Nhqu6ee2lr8SAWbdjypPKV2G77e 0O3U41H68KhtK6XX85Qpdyzq93afUpUr1S5zx0vt8VraD/6OAAIIIIAAAggggECbAMEd7QMBBBBA AAEEEEBgLQKp+zWlQpzU78KZRp6BXNvB0ow7va63DJ6lC2vK5h3QD9+3NOMqtSSeZ/9yS1rG+5Ob tZZ6j9RsO+1zatu2WVO5xpqqW8++2jZtAdfQ7UL7kAts2uo3F7K27ecUgjvP/eWG2LdSaBY7eZaC jdtbTVvwLJWp1/fue+n1aso2dv/V1ufmZmd6Poi8farXNHTQc3JfGuhyvHr2h20QQAABBBBAAAEE EEgJENzRLhBAAAEEEEAAAQTWJpCaBVMKW+JQyTuQq50sBXfaxhMo2uy1sKxDz7hTWWpm26QqMXfv ujbj3D3r9PqxtcK4tll49j5t9y8casZd7t6Jpfak8LG0lOjQ7cLqqrZ+FczUhKXa9ykEd7l2GAbP Q+xbTXjb53j1hDiqp1SQljpOSzOQVY9qKynH+PWm1H/l+txVBHe17cnqq+3DsPZ4XdsHK2+MAAII IIAAAgggMHsBgrvZVyE7gAACCCCAAAIIzFtAg9EabG4LJBSUKfxJzcSKB6rblpCMw53cDAttl5ux oVk9KnP8WqmlJWvKlgvecsGYN5BRKOCZiaRtSjPd4v2x4CW37Jwt5djWQuPXjJcv9LZu1Ykn1NLg u+xU5tKMxfi9h2oX9roqc5f6VdlL+6q/y7bLsqMl81TQVAo/U/uZOmb67lvp+ar/XDuvOV5lkKuD MKgOw562vin3enpOGHSF/VLu9YZqpzUeuTbTpa3U9BelJYNzfZMF+rbEb6nN6+9dj1fPa7MNAggg gAACCCCAAAKhAMEd7QEBBBBAAAEEEEBgMgJ2jy6FAHbvrtpwZcid0QC/yqLBXw1AjxGCeMprLiqP frqa6LnaD7vHnd3vyVMGzzZ6fb12eN81z/Ns/7rul+c9htxm6HbRtX7lZeZyV1vV/6+rnQ5p3Hff 9PywrY/lovcJ+4i+BnaPQtVnKUgvvdfQ7bT0flP+e9we1Da69jddj9cp+1A2BBBAAAEEEEAAgWkJ ENxNqz4oDQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwJYKENxtacWz2wgggAACCCCAAAII IIAAAggggAACCCCAAAIIIIAAAtMSILibVn1QGgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA gS0VILjb0opntxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKYlQHA3rfqgNAgggAACCCCA AAIIIIAAAggggAACCCCAAAIIIIAAAlsqQHC3pRXPbiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggg gAACCExLgOBuWvVBaRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBLZUgOBuSyue3UYAAQQQ QAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEJiWAMHdtOqD0iCAAAIIIIAAAggggAACCCCAAAIIIIAA AggggAACCGypAMHdllY8u40AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDAtAYK7adUHpUEA AQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEENhSAYK7La14dhsBBBBAAAEEEEAAAQQQQAABBBBA AAEEEEAAAQQQQGBaAgR306oPSoMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIILClAgR3W1rx 7DYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMC0BAjuplUflAYBBBBAAAEEEEAAAQQQQAAB BBBAAAEEEEAAAQQQQGBLBQjutrTi2W0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFpCRDc Tas+KA0CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMCWChDcbWnFs9sIIIAAAggggAACCCCA AAIIIIAAAggggAACCCCAAALTEiC4m1Z9UBoEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEt FSC429KKZ7cRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSmJUBwN636oDQIIIAAAggggAAC CCCAAAIIIIAAAggggAACCCCAAAJbKkBwt6UVz24jgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAA AghMS4Dgblr1QWkQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQS2VIDgbksrnt1GAAEEEEAA AQQQQAABBBBAAAEEEEAAAQQQQAABBBCYlgDB3bTqg9IggAACCCCAAAIIIIAAAggggAACCCCAAAII IIAAAghsqQDB3ZZWPLuNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAwLQGCu2nVB6VBAAEE EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDYUgGCuy2t+NRunz17tjl37lyryPnz55vTp083J06c WP7LAwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYBgBgrthHGf9KgrrDh061FxyySXN4cOH s/uioE7bhD87OzsEeLOufQqPAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACUxEguJtKTaypHMeO HdsN7NqCO83G098V8GnWnR76nYI7/djv1rQbvC0CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg MHsBgrvZV2H3HbDgzZa8bAvujhw5sgzu4oDOZuGdOnWqe0F4JgIIIIAAAggggAACCCCAAAIIIIAA AggggAACCCCAQENwRyPYFWgL7nJ/U5BXWmITYgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA gbIAwV3ZaGu2yAVwugee/nb06NGkBcHd1jQRdhQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRG FCC4GxF3bi+dC+Ds/nYnTpzIBnd6Lg8EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHuAgR3 3e027pmrDu4efPDB3bVarSHav/fdd9/G+bJDCCCwWQIfOP6N5lPPfX+zdoq9QQABBBBAAAEEEEAA AQQQQAABBBBAAIG1ChDcrZV/Wm/eJ7g7ePDgoDujhskDAQQQmKrA+/7gTHPN208219/zBOHdVCuJ ciGAAAIIIIAAAggggAACCCCAAAIIIDBDAYK7GVbaWEUu3ePuyJEjybce4x53BHdj1TKviwACfQUs tLv8lhONfgjv+oryfAQQQAABBBBAAAEEEEAAAQQQQAABBBAwAYI72sKuQFsAp78dOnRon9a5c+ca /e3o0aODShLcDcrJiyGAwEACcWhHeDcQLC+DAAIIIIAAAggggAACCCCAAAIIIIAAAksBgjsagiu4 02w7BXQK6sKHAjv9/uzZs4NKEtwNysmLIYDAAAK50I7wbgBcXgIBBBBAAAEEEEAAAQQQQAABBBBA AAEElgIEd1veEE6cONHYjwI43avO/j8M6RTM2d9Pnz69DOq03RjLZFrD3PKqYfcRQGAiAj/68U+b UmhHeDeRyqIYCCCAAAIIIIAAAggggAACCCCAAAIIzFyA4G7mFdin+LbMpcK31M+pU6f2vLwCu52d nT3bDr1Epr0hM+761CzPRQCBoQQU2l3z9pPNZS/cz84CurZ/uefdUPq8DgIIIIAAAggggAACCCCA AAIIIIAAAtsnQHC3fXXee48V+A29NGZcKIK73tXECyCAQE+Bi6Hd8WbnrcebAzcfbzzBnba59q7H m0899/2eJeDpCCCAAAIIIIAAAggggAACCCCAAAIIILBtAgR321bjM9lfgruZVBTFRGBDBRTaveb+ pxcz7S6Gdd7wTs/Rz2e+RnC3oc2D3UIAAQQQQAABBBBAAAEEEEAAAQQQQGA0AYK70Wh54T4CBHd9 9HguAgj0EUiFdjbTrhTeKbA78NZHlsHdDfc80Xzzu/+lc1F+8MOfdH4uT0QAAQQQQAABBBBAAAEE EEAAAQQQQACBeQoQ3M2z3ja+1AR3G1/F7CACkxRoC+1K4V0Y2tm2XcM7BX7X3Pl4874/ODNJJwqF AAIIIIAAAggggAACCCCAAAIIIIAAAuMIENyN48qr9hQguOsJyNMRQKBawBPa5cK7VGjXNbxTaKfA T8+/5u0nCe+qa5InIIAAAggggAACCCCAAAIIIIAAAgggMF8Bgrv51t1Gl5zgbqOrl51DYHICNaFd HN61hXa14V0Y2tlzCe8m11woEAIIIIAAAggggAACCCCAAAIIIIAAAqMJENyNRssL9xEguOujx3MR QKBW4M0PfL7Zufn4cpZbzY+ec+lNF+5pV3peadnMVGhHeFdbk2yPAAIIIIAAAggggAACCCCAAAII IIDAvAUI7uZdfxtbeoK7ja1adgyBSQp878//a3Pju55yBXAWpl1284nm0rcer3pOLrxrC+0I7ybZ ZCgUAggggAACCCCAAAIIIIAAAggggAACowgQ3I3Cyov2FSC46yvI8xFAoFagJry7YjEz78Bitp1n pl04Ey8V3HlCuzC8e+/Hvlq7a2yPAAIIIIAAAggggAACCCCAAAIIIIAAAjMRILibSUVtWzEJ7rat xtlfBKYh4Anvrrr10eaN7/9s856PfrXR/edKS2S23eeuJrTbvbfeIjB802JpTx4IIIAAAggggAAC CCCAAAIIIIAAAgggsHkCBHebV6cbsUcEdxtRjewEArMUaAvvLLT7yU//Yrlv7/uDM67wLrdE5nV3 Pl49a08Bnmb7HfmPZ2bpS6ERQAABBBBAAAEEEEAAAQQQQAABBBBAIC9AcEfrmKQAwd0kq4VCIbA1 AqnwLg7tDKMU3uVCOz2/y4y7A4v76unn2rsebz766ee3pk7YUQQQQAABBBBAAAEEEEAAAQQQQAAB BLZBgOBuG2p5hvtIcDfDSqPICGyYQBje5UK7UnjXFtrZc2vCOwvtbNlMwrsNa3TsDgIIIIAAAggg gAACCCCAAAIIIIDA1gsQ3G19E5gmAMHdNOuFUiGwbQIK7171jieX97Sz5TFzBvHMO09oVxPexaFd HN6d/OJ3i2XctvpjfxFAAAEEEEAAAQQQQAABBBBAAAEEEJibAMHd3GpsS8pLcLclFc1uIjADgR/8 8CfuQMzCu5rQzhPe5UI7C++uvO3R5qrFjydgnAE5RUQAAQQQQAABBBBAAAEEEEAAAQQQQGBrBQju trbqp73jBHfTrh9KhwACeYEPHP/G8t51XR6pZTNLoZ2FdwduPtFcfvNxwrsu8DwHAQQQQAABBBBA AAEEEEAAAQQQQACBiQjMJrg7e/bsHjL9/5EjR5rDhw83p0+fnggnxRhKgOBuKEleBwEE5ibw1T85 3+zccrxRIOcN7Sy8u/Sm482Vt54gvJtbpVNeBBBAAAEEEEAAAQQQQAABBBBAAAEEXhCYRXCngO6S Sy5pLLzTv/r/8OfEiRNU6gYJENxtUGWyKwggUC3w8KnvNJe+9fgyuLNQrvRvGPJddSvLZlaj8wQE EEAAAQQQQAABBBBAAAEEEEAAAQQmIDCL4E4B3cGDB3e5NMvOQrtDhw4t/3tnZ2cCnBRhKAGCu6Ek eR0EEJirwPFnvttcf88TruAuNTOP8G6uNU+5EUAAAQQQQAABBBCYrsBPfvoXzeuPfLrR/b15IIAA AggggMA4ArMJ7hTW6XH+/Pnd0M6WyLTwLl5OcxwyXnUVAgR3q1DmPRBAYOoCn3ru+8Xwrm05TcK7 qdcw5UMAAQQQQAABBBBAYD4CCu3e+P7PNrrOuObtJ/eFd7p+6Xq/7/koUFIEEEAAAQTGF5hNcKcZ dWfOnFne1y6eYWcz8Ajuxm8wq3oHgrtVSfM+CCAwdYG28M5zD7zLbj7R/NP3fKrRRTYPBBBAAAEE EEAAAQQQQKCLQBja2TL+YXhn1y03LFYNIbzrIsxzEEAAAQQQuCgwi+DOwrrcPe20jKb+ptl4PDZD gOBuM+qRvUAAgWEElhfBd+9dNtMT2tkF9c7iXnlPP3dumMLwKggggAACCCCAAAIIILBVAqnQLgzv bnnwj/asFEJ4t1XNg51FAAEEEBhBYBbBnfbblsNUQGfLZur3mmUX/24EJ15yxQIEdysG5+0QQGDy AmF4VxPaHbj5keayW443XDxPvoopIAIIIIAAAggggAACkxNoC+12vyh48/FG1yj2//qX64/JVSUF QgABBBCYkcBsgjuZnjt3bvkTPjTLjiUyZ9TinEUluHNCsRkCCGyVgMK7q+84ubyfhMK48MI49d8W 2tnfuHjequbCziKAAAIIIIAAAggg0EvAE9rZtUbqy4Vcf/Ti58kIIIAAAlssMKvgbovraet2neBu 66qcHUYAAafA9/78vzY/+vFPm9fc/3RreBeHdoR3TmA2QwABBBBAAAEEEEAAgeU9st/4/s82V936 aPELg4R3NBgEEEAAAQSGFZhccKcZdTs7O8vlL2t+9BwemyNAcLc5dcmeIIDAOAJt4V0utCO8G6cu eFUEEEAAAQQQQAABBDZN4K2/c7q58tYT7tBuN7xbLJupe2yzbOamtQj2BwEEEEBglQKTC+7snnU1 oZ1tu0o43mtcAYK7cX15dQQQ2AyBVHhXCu3sAvq6u0423/zuf9kMCPYCAQQQQAABBBBAAAEEBhV4 /vs/am5811OuJfrtGkPL+R946yPJsI9lMwetHl4MgZUI/OCHP1nJ+/AmCCCwX2BywZ3uWXf48OHq nyNHjlC/GyRAcLdBlcmuIIDAqAL/+fyPlxfTly1uAO8N7ezC+srbHm209CYPBBBAAAEEEEAAAQQQ QCAW0LWCN7xrC+10/XH9PU80umc3DwQQmIfARz/9fHP17Y9x3M6juijlBgpMLrjbQGN2qYMAwV0H NJ6CAAJbK/DvHv/WC+Hd3iVpwuVp4v/W8jUK+973B2d6uT39lXO9ns+TEUAAAQQQQAABBBBAYLoC nvCO0G669TeXkum69NYPnp5LcTe+nArtrr3r8eXsWUL3ja9udnCiAgR3E62YbS8Wwd22twD2HwEE agXCE+u2wE5/U2h3YHHvCf33NW8/2Tm804XVy3s8v3Yf2R4BBBBAAAEEEEAAAQRWL9AW3hHarb4+ Nu0dFdrpuvRlb3uU8G6AytUtNXSPytTqOp4v3qbGFgjvBqgYXgKBSoHZBHdaCrN037vKfWfzCQsQ 3E24cigaAghMVsAT3oWhnQV8XcI7hXa6sOob/k0Wk4IhgAACCCCAAAIIIIDArkAqvFNot/PCFwJT Xx5ksH/9DUjLk77mvqcbhTlTfFhoZ+2H8K5fLameX3P/08sv62qZ2zC8k7W+eNs2s7FtTIHjuV/d 8GwEagVmEdwdPXq0GNop1OOxOQIEd5tTl+wJAgisVqDtRDsV2nUJ78LQrsvzVyvCuyGAAAIIIIAA AggggMAQAmF4p9BOAcFvfOS55WypOLhjkH8I8X6vodBO9WB1FYZ3P/npXzTPPX++3xv0fHYc2hHe 9QO10E71LUv9a+FdaJ0LRz1fBOa47ldHPBuBGoFZBHc7OzvL4E6z7k6cONEcPHhw+aP/tpl4p0+z DnJNxU99W4K7qdcQ5UMAgSkLpE6420K7mvAtFdqlnv/N7/6XKRNRNgQQQGDWAhqIu2ExEJdaAmnW O0bhEUAAgTUIKMDgkRaQTexj4Z1COwuC4gCGwf31tygL7exaLQzvVKdvfP9nm1cs7mGm7dbxyIV2 hHfdaiMO7cJ6v/qOxxr9hOF6HN55Qjt7Psd3tzriWQjUCswiuLMlMm3nDh8+3OjHHocOHWoU7vHY HAGCu82pS/YEAQTWI6AT75e+7cLJuSe084R3baFd+PzXvufTiwuDk43KwAMBBBBAYFiBiwNx+5dA GvadeDUEEEBg8wXUp1535+N8ESJR1RbuKOBJhXfx0osWxDCov/7jJg7twhDn1+77ZPP6I59prrr1 wm0P1lFfpdBum8O7D3/qO9XLmuZCu7DeD7z1keUMvFR4VxPaEd6t//imBNsjMNvgTjPu7HHs2LHl jLyzZ89uT81t+J4S3G14BbN7CCAwusAPfviT5qrbHm10gn6g5b4TqXtRXLMYvNDzw4cntLPX0n0u DizW1L928Q1OwrvRq5o3QACBLRJIfXs+vn/JFnGwqwgggEAvAb4Ikeez0E7hjn5S4V3q2R84/o21 zeDq1Rgm+GTVwUNPfbu6ZLnQLg5xwuvAVYZ33tBuG8M72WhmXDibtdQASqFdKbx76eK+9W33qUyN F9jvXr74su5U751YcuPvCMxBYBbBnS2VaaC2POapU6eaM2fOLGfbEdzNobn5y0hw57diSwQQQCAn oOUqr71z//0m2k6+tfRavMxlTWhnr63gjvCOtokAAggMJ9D27XnCu+GceSUEENgOAb4I4Qvt7Ny+ JrzbjhY07l5acPqy2x9rdC3mfZRCu3WHdw984uvNyxP3Q2y7PtXfFC6992Nf9TLMcrsw0EzdkzC1 U97QrhTe6Qu/V9x2Yt99KtvqZZVh7ywrlEIjMIDALIK7o0ePLoM53dNOD93PzpbPtH9ZKnOA1jCh lyC4m1BlUBQEEJi1gEI4hXGliyH9PRXa6R4W1y1mznmeH26ji41Lb7qwFAcz72bdhCg8AghMQKA0 EKc+V+HdZ772/eam335m35JmE9gFioAAAghMRmCOX4TQoP7rj3x6tP5d5/z6CWfaxef/Uwjv7n34 y/u+ZDiZhjVQQeI6iO9Flnub0rlCXJ86d9DqLKuceWf3R4yXbGy71rRznE2+r29qFmIpvKsN7YYM 7wjtBjrYeRkECgKzCO7Onz+/vKedBXfaJwvzFNwptFOYx2NzBAjuNqcu2RMEEFi/gCe8S4V2VnLP 8+PQLl5Dn/Bu/e2AEiCAwPwE1P/+8iKQe8Xd5S9gaIBHP1fcety9pNn8RCgxAggg0E+gFG5MMSSw Qf2xgjMLU179rieb1/3Wp3fvfZYKU8Yqg6dWNfPsqsXMq7brFs/rTHmbXHBaCu+ee/5884qeX7Zc 1b3LasK7KR6PQ7eftqVDS+Fdl5VxLrv5xHJlnFSQq/t9Xlc45yS0G7oF8HoI5AVmEdzlin/u3Dnu a7ehrZvgbkMrlt1CAIG1CbSFb56LX294Z9/cTH2LkvBubdXPGyOAwAwF1O9es1ju+LLFzOX4G/G5 b6ZbH3zlrScI72ZY5xQZAQTGFSiFduGMlHUuQRzeazoe1B86OAtDlOVnyOJe1aXZUEOXwVPrcUDh uX7xvO6Utmmb7ai22RbelZ6bO284sAhxdhIhztjhjCe82/bQLuyP2u55VxPetYV21ue19ZNjt4sp HY+UBYEpCMw6uJsCIGUYR4DgbhxXXhUBBLZbIBW+1Vz0lsK7ttBu92JxcXP7h0+d3VcR4QDFdtcS e48AAtsooP71xnc+tVymTA/9f/jN+dRyVvllpS4sVXzlor994/s/O9qyattYT+wzAgjMV8Ab2q07 vNMg/Kve8eTy8yA3E2eo4CwVnrjO5xch0lBl8LSoXDBRcx3jeZ91buMN3oYM73KhnY6B5Uyv+54e laQtvOsT2r35gc8vj52pP9pm2qVmw/UN70qh3eNf+u4uWaq/JLSbeouifJsoMIvg7uDBg/vuacc9 7jaxOV7cJ4K7za5f9g4BBNYnYOGbLoa6XOzmwjvvRf6FpdxOND/88U93EZYXLYtlOfTaPBBAAIFt Ewj7ZbtPXerepN7wTssfaXBGgz6rHFjdtnpjfxFAYD4Cz3//R801bz9Zfc/myxfnrQrRFKqUHhro /vV/8znXtrnXsoBK/f3Vdzy2/Ml9SaNr/273siuFJvGy98llM297tHngE18v0fT6e2k2UZfrmV4F GuHJ3tDO6mCI8K4Y2t3/dKN7qHkeOo/5wPFveDbdt00uPO4649Xai473KYd3NaHdEDPvSqHdu37/ y8s+MjQLwztCu07Nmych0FtgFsFdHNLl/r+3Bi8wGQGCu8lUBQVBAIENFNDFldav7xqUxeFdTWin QYBwCbfwomUTLrw3sLmwSwggMKJAqj+1e9WlBklL4V0Y2tnzuw7ujrjbvDQCCCCwcoH3fPSrzVWL oCk/W/nCFx7CH+9gtQ1w1yxTrMDiQ098a9chDqg859e1/buFJL+0CCN/6R1PtC6JWXr/2vfuUuGl 0M7qas7XELWh3RDhXSm0U/v4f5wz9u08pk9QFi/X2je0M6M+ZerSXr3P6Vrn2q9SW08dMzpWr1z0 ffESuDar0UI7vX4qvHv5HScb9XE8EEBg9QKzCO5Onz7dnDhxYt/P0aNHlzPxdnZ2mjNnpj8NevXV O993JLibb91RcgQQ2A6BcIbIznIW3f4bXIcDH/HFvy4gbrjnyebl0befSxcj26HLXiKAwDYIdJ3B nAvvUqEd4d02tCT2EQEESgI2UH754v6fWvnBE97VhnY1/a0FFToP1pfYcgFVKTyrmVm97152iy/T 1Z6/1+xjqU5Kf/eGdnMP7zz3ekvOdiwsh50Lhzyh3SvufmLPFy3Dugpn4cXnMX2CMnMYKrTbxPDO e50cHjsWsOu2FFpq0475VGiXM/POvCwd06m/KxDUkqxjvkeXcvEcBKYiMIvgrg1LgZ7CuyNHjkzF lHIMIEBwNwAiL4EAAgiMLGD3ZPr45/600eBGbhAkN+iQW7LDe1Ey8u7x8ggggMBoAn3vGRqHd22h 3SoHWkcD44URQACBjgJxgHHF8v5d7eFd19DO09/uC2oUJr6wvHHbTOu2kE0z/W767WeyQn3uZZf6 8t3Y90+tDe3awrv3fuyru/eP7diERn9abXjnne0Yt/1r73q8ufNDX2z0b+oeapppp9Au145/8yPP Nb967yeXIUvuPKZveGf3+q1BL7WXPmWqKUfttjUz72qvj5cmtz+2517HqjeFdzs3H28UkIYz7eL2 sAozm6m8vJ9ixdKstc5sj8CcBWYf3Anfls48f/78nOuCsgcCBHc0BwQQQGBeAqkbWOsCoPRN4bbw 7tmzf958+FPfmRcEpUUAAQQKAqXQLryXSds9htS/XnrT8cYT2qWWP6KiEEAAgU0XyA2Mt4V3fUO7 tvAuF9CoH9dP7ZfgPLPNhriX3Y7Ktxjs9wZGfdtV1/t/xbO1FF689G2PLkOKLoFQ3/2oeb43vKut AzsGNLvzo59+flkk/RuGdzqfiEO7uB1rqVmFOTZT6/og4FtH6GO2pdBuE2be1YZ2ZvPQU9/ed89N hXdvfuDzraHdKszisQPCu5regm23SWD2wZ2WyLTg7uzZs9tUdxu9rwR3G1297BwCCGyoQOoE3HNj +7abZb/09kcnfWPxDa1KdgsBBEYS8IZ2nvBOg7yXLn7aZmqsYvBlJCpeFgEEEOgkoIHpOxazirQ0 nGaGKehIBWKp8G6o0C4MPf7pez7VvOP3nl0GRwqQcjPnuoZ3bYP6pff0fMlu1aGdVXpNeGdhUhjO hYFO6u+dGtfITyrVV21oZ8VVePe5r/3ZntJbeNcW2lk71rGyE8xSLd1vd1VfFvKGdlM/F2qbedc1 tGtrqjXH1hgz79q+8MvMu5E7GV5+dgKzCO50L7vDhw/v+zl48OBuaKfwjhl3s2t/2QIT3G1OXbIn CCCwXQIf/9yfvLD8kL45XL53xu7g9GKJoNQ3jQ/c/Ehz9R2PEd5tVzNibxGYnIAGgl/73k8tl4bq +tCA3HWJ5alyMyzC8E4z68LtrrxNA9GPNr9236nsoPTUB6q6OvI8BBAYTuC558/vm5Ex3Kuv/pV2 l4JbfKnhqkU/mQvtwkDCls0cOrTbc467mK125WLmV+mecqXwTkvchefLfUO70pdELlu836UrnGkX txhPwFAK7cJ9nPPMu66hXdtRqPDuFYvzknB5zOysz+habd3hnZZAvWpxTJXOoeK/67pyrBVdvvit H1Tdq01hqkI7PVLh3RihnZY7VRhX4zZkeJcL7cLjlPBu9Z+dvON0BWYR3NmMurZ/jx07Nl1lSlYt QHBXTcYTEEAAgUkI6KJDA8kaZC4NToQXDLmBClsKbsgLhklAUQgEENgV0KwIG7jow/KhJ77Veyms VEBnA8Ea3O07iFL77XD1k3H/aP2hQsTS/VHoO/u0KJ6LwOoEdDzXLOWn7dU39XloAFWD9mPfr6xP GWuee7GvvvBFB0+woO1s5p08Sg8FnTKrGfRelmURely6+EKblpssPbftnFhfirPnDxXa5cK7ly1C kZt+55nlvafW2UbawjtvaDf38G6M0E5tvRSixG01XiXFc4yNdR7y/J/9qLliEc5r5YHSMRXW/xWL +0H+5/M/Lh3q1X+X5XV3P+6+V5tCUy1fGh5bdk6ne1b2Pd9M7YA+Y665s0P/tVhK9ZX3PDnIZ46+ IFGqL5bNrG5+PGGDBWYR3OVm3GkW3pEjR5rTp09vcBVt564R3G1nvbPXCCCwGQKlgeT4ZL3t28Wa cWcB4FgXfpuhzl4gME8BDSK86h1P9h4U1MCeBkD6fJs+FdDFA8Hqv/oMptisOw2IlgYu2kK7sLZz fS595jyPCUq9fQK2hK63/7Lt+8xKCAfsxwoFVlmTqb66JrzTfdD0xYrSo/Yc1/r5A4vgTktO2rKT pf4/PjeOA5LS59ADn/j68jOx9D7x3618+owyD9kO8eWakm3b31PhXW1oN9fwTkHTGMHp01851ymE jmd9lsI73UvP7q3Xpw2kznuuXCyDeyEUL4d34X3X+/SdqX0I+9M4dNLxc+M7n9pjEN5jMO5/dazd dvSPeq3w0ObcZcl272dT2/tWh8SLsHDoehqq/fE6CKxSYBbB3SpBeK+8gJYiVUh64sSJ0cNSgjta IgIIIDBvAe/Ahje0s4ttBqLn3S4oPQKhQHgvlz4Dx+GAXmogT0silWampAZ9r7/n8ez9iEqDpqma tv3VoNfLFvfvLIV38eBYW/8X97n0lRxrCKxXQMdkKuyIfx8OonruwxVv32VgMzWA2qcPXq90s+zf 5ZBb6aEULFjA57X0nuPGoZ39vye8C8+PVX6FN7Z/3s8fzzKTYXCXCu3WXbfh+5c+62tmtHuOtSns u84b7nno2VGCUx03bfeAzIW+apvxvXVzx9hQoZ36LAv/UsdfKbyLyzfkjK5Uf2qvr5l91jeZRRja mfGq+19veDfUcdJ5pvKi71PoyQOBbRYguNvm2q/YdwV28VKlOzs7owV4BHcVlcOmCCCAwEQFSgMb taHdVMO7ex/+sutb2kNX01DLCw5dLl4PAY9AGNr1GbgofQtff9f9TNoGZNsGfcNZv/EglnfwVB7x /mowpC28U/D2y+/65G645wnirM99+R0nR7svqN5DAzA8EJi7gAZPPTOsuuynHYvxLJn496nB07aB 0tz23sBJ+9I262HVg8ddbOPnhAPjbbPL2sK7LoP4pXPcXGjnCe/i0E71a/tZ87kjK394t5gNqC+V OGceDlF3XV4jN7tex7IFsN5ZhkOFEl32YyrP8bbj3facCO1223R0/8UhQzstr6jXe/jU2WzYmAvv csd+l+Pe6s2+lNHWn8aBu5xeevtj2fvyrbr/LYV3Qx4fte3M2tRQbWgqxxvlQKCLwOSCu3Pnzu0L iDz3uNM2PMYROHv27LJODh061GjWnR76nYI7/djvhnx3grshNXktBBBAYH0CuRN1LUmk+xKkLq7b BsqnFt7ZgMiqBzqGWl5wfS2Dd+4qoMA2fujie+hliLqWT88rhcqp0K5LeFe6740CO/20zaYozdRY LlcZLNnbJbzL7W8uvAtDOg1Garkz7avnoT735Be/69m0ehvrz3V/J8+9oKrfgCcgsCIBm/Ewxmd3 eN4TDsTGv/+n7/lUk7vXT2rAtG2Q1TsA7VmqbNWDx32q/OQffW95D1AFTp6wJjWA77VLlbM0GG3L Y+bKlpp5F860Ti25p3ZQ+yiFd3Yc6PNmrDC7tsxt26fuZ/va93x62Q50D8DczMu4HvrU/ZD7s+7X KrVjT2gXLkOp7YcKXOI+S/cavrzleI/Du9Js2y5tQOeNv3rvJ5tbHvyjbB8eLssa3pfywjnlhWVz U/3Cqvvf3OfKkKGdtW9vOyO0W3ePwPtPTWBywZ2FRN6wLtxuaribUh7dR1DOcUBns/BOnTo1+K4S 3A1OygsigAACaxPILeGmgTMNUIcXLp7QTtuPMdhWCxQPhKyqTEMtL1i7v2y/OgG1LS0NEy/vqAE1 3Q9ObcAedtGdGiTRdl0G+frsaSlUbgvtasK70kCkhXXhIF5qELRteTVv39Q2A6K0v3F4l5pZp/vQ rPqhcmtwNDfgosCB8G7VtcL7DSEQL1M25Gd3amBSA7Fv+NefbV6/+NF/7w7oLgZvFTrlQp1w4LQ0 M6Ltywlm5gntavrgIeqiz2sotFsO4msgvGUWUCqssYH0LoP2cZmtznWvrb2fGfnB+XC7MLwLZ1oP UbawrLnPzCHbf5/67PPceAlNT3gnXwV9b/2dZ/q89cY8txSqtB1jqwrtdgPEluBr2Re+cM+7UmgX hmveWcvhl73i5czb+vI5hXdjhHa5c8mc2VDB78YcoOzIVgtMLrhTbegeavGPZnYpPFKIZH87evTo 8nf625kzvm+hbnVtd9x5GR8+fHjfsxXk5f7W8a12n0Zw11eQ5yOAAALTEsgt4RaGd3MO7ezCY+wB kKGWF5xW66A0qcG1eNDO7t3SNpgbXuhaW6ldVqtPbZRC5X/1gc8319550vVt+LZvHXtCu/jbzvYN fHP1Lq/mDe+uW8xCCwNVOZZCu7CMWjazZmZdn3oqPdfKbeXJDeiF4Z3q5PVHPj3KfXhK5eXvCHgF UvcWGurLQK0D35mQTgPMpfDuurtONtfceWHmcOknF/bUhHZzCO+W+3P3E3uDMmd4Z0HZkMGY6v51 v/Xp3ft+lWba7flcWZRbbSCeae0NErxtX9ut6wtnNWWs3Ta3XHZbeGeh3VDH/iNf+NPaYk9y+1wf pvYc39MuPH+Rp51jDRW4lPqs0jFWuuddKtAvHXOpFRrabvsQvkcqRJzizLsxQztr9KWQeKg2NMmD jEIh0EFgksFdvB/Hjh1bBkQK6uKHZntZoNdh/3lKQcCWLk3Z66kEdzQhBBBAAAGvQG4JNw2kXb1Y 8/+Vh58oDuiPHYx59qUUGoxVxqGWF/TsI9u0C2hA4TX3Pb1vRlxft7ht2cCivhGudhUOlOh4ue7u x/cN5OqC94Mn/7i58V1P7R5PqwjvSqGyQrvLtIzV4ie3RFA8kJIK70rHX2pgO/42+HKA6dbFQJT+ dQyGh9tcupwhs/d5qWPeG9rFdRrPsuzbpmqfH5dbg8k33PPknplCoYfCu1t+5/Ry0HnVSzzV7hvb b7ZA7v6LOqa++K0fLJcTVv+YO+b7fHaXBiJtFkgqpPOEdwohdm55xNVfpQKpX/83n2uuXPR5tf2d wvvPfe3PJtVw2gb0SzPvxgjtwsFohXeahbOzqC+PtQ36p2Zaj/VZsK4l3sdoRKXlslPhXRjaDfGl u+WXqhZLcs9hiVFPHcR9mT7jX/++z+w5Bw3PW3Su+ZmvfX9xnnDhHnRDLNteCu28M++8oZq9npZW //CnvpNkar0X8gvhe+mYv3S5jOvefrgtvNOy5Ku8p7Bmdmu1j/hLaJ52U7tN7jNzqDZUWx62R2DK ArMI7mw5zNy91Ep/n3IFTL1stnSpZjmmHmY/9H4w425oUV4PAQQQmLaALohKA919BtWG2ntvaDBk WXURrIviMIhJXRwyaD5ULZdDOw1kDPltfb1jrm1dGGTaPwgYh1Fhm9hZDAzEA4djhnelUPmaxSy7 MOwqfVM63BcNHP/Hz/3JbqXYzMPSAEn893hGb2rwrvSaqQH2XGj3S4efrA4Gh25TtUdErh5Lg187 wbfx6Ydq1dl+CIHc/RdtsPWlixmtL71979LcqeO9y2e3J7TbHejuOPNOYdCFQV/flw3iAeiaMlpZ pziA6hnQz4V3urfyVYsfWZZm1nRtk3J+5Jn/VDxfk7Hq6Kq3PbZnpl3X9619ns435h40ec7HU1/a yd0PscuxH56PdHl+bb2tavtUfxqfe8Uzs4a617LnGN8zYzW3bGZhNnPc/6fCc/N23Qu5EN61falA XwiLZzRuw3Lk8efSFD9zVnXM8T4ItAnMKrjTPdXih80IU4CkkInHsAJjBncPPvhgYw0w/vftb397 9m+55/D7OzC7AwOOA9rA3NvATbcfbl5688f2D0699Q+b/+uW311rP/crt/xOc8XNf+j6FvXygnCA Mr/utvcvbsL+h4tllPRte8eA3SLgufbmh5u33XHnWq3m3g5z5f8Xt723ufLmj++Z+Xb1zR9pbrvj 7l7epbaVu09HW3iXCseuvPkPmrfcfm91WfWc1PP0u19/27vTx2zwreJU4OUJ73S8ySauD/UFOr5K QdvFb2WnB7y99z/JzpbJHOM6/m645T9UlXGoPqPrsZfte4N7SLUt6bdnUIp+qPoY61pvU33eKj+D 9F763Lv8hS84qI9WX61+Wf2zhV2l2Vi7/UnFZ3f83p4+KTfDLvf7MDj3LCme6zdrynrFwvCGm/99 o35hKm0s/vxts95X1y/UqfbnFbf8Xu/P7JJJqT+1OtJn6Gtu+beTMS7t11T+XjpnCtuGfc5rxmp8 n7F9baji2E+eh1Q8fyqWuXKov3jz7fftaZu2zzLVtdrQ/cM/u+19e86xPf2pttl3PvnCOcg1wedC 22vl+kzZxJ8jpX4ndZ7Uen/ARMBon2FTbyNDlO/i59LHG13zhq+p/vHGW47SP1aOcd53333DhgK8 2toFZhHcHTx4cLkkoy2Xafe40xKadu87/S03I2/tyjMugCe4U/0M/WDG3dCivB4CCAwtQD81tOjF 14tnfUzhW6xaOkXfjvZeRNp2+ob3ez/2VReWvtGpbx/aI7z/34ULU9+37XMzXn7ww5+4ysFGaYHc t4D7zpLyfGt8GRwtBkpSg0614V3tzDt9i1rPiZ+n379isVTncuaaYwm32vCu7dvPqiHvzLvScXPF Yvm40rHtnWkXtxxvGVW/6+znSrOddwPQim+UM/Nue3tS9ZU6ftVHeB9dz6my919c3P/slYlZr97w 7qrbHm3u+t0vFYv/wCe+3mjb2nMDlSM18yf+fWq2a1ufVuo3PTPvNOvhTQ98bnnPTc30X8WyaSVo talXLD6HapytrtfVt+b61VIdlSy2/e/ec6ZUeOeZsdrWXqyfavtsX1d7W1W70L6/6h1PjtIv1M62 C89NbMZaeO7h6e/6zrTbt7pDdJ5UG9ptw0y7uK2qnuIlme3aQ7Ok5z47eFXHpr1P1/OpVZeT9/ML zCK4O3PmzG5wZwFe/K9CPB7DC9iMxiNHjiRfnHvcDW/OKyKAwDwEOCkat55swEPB1xRO2D3LpMQX bzU3+LbXf+P7P7sM73L34imFEFYGhREPPfXt3UqSpy60p2A5bssZ59VLgwldw7vaAahVh3d24Wzt ysI7C+3CQNEzIOYN77wDm6VgrHS86O9XLwanNUit90wNCl+hZUeje5LUDMyVyjiX0M4T3imICL9t Tng3Tn805VcN+8qaLwl0OacqDcrmBkw94Z2Wf/23j3yzSF0qQ6pPKfXjnmMt1bd5+822MltoZ/1h zXlMEavjBvrSkc4FL71p/3JypSBPz/l//+0XOr5z/6el7hmqz30e3QU8n6mp8/HU/e5y7Sf3Ga9+ yvP+NecI3SXW98wxvwhYOt9OBWWp0M502vq7Up/55gc+n/yCRanfOfDWxXnjC+dD8RKY9tzU+fA2 hnapVhxfe2z68TT0kdzlfGroMvB6wwrMIrjTLiu8O3To0L4ATzPucvdfG5Zqe19N4Zzs44eFekeP Hh0ch85mcFJeEAEEBhagnxoYNPFyz3//R839x74y/hs530EXqr9239Oue8zUDHaFoaAGum981yeb axbfeM9dGJbCiB3dK2ERNNjgUDhwxMWPs7KDzbyDCLXhne7bphkNpQGA/QNQi9mXi6VT49/HoUlp 8Lc0qB5fONvrXXfXyUY/Xb/NnipnuMxRaSAlrsHcIFrpOAn/Hg9W275Z8PSej351N9jrcgxN9dv5 3pl2pW+ULwPczP1kCO/q+5y5PiPVV5b6GdvX2nMqb2DWJbyz53gHUb1lCb/okAsWSv12+LywD6vt N1NlzvWD8fnMmIP2uUFcmxXtCV13DRd90v/9m6fWfjhZP6vPe0K7YarDE57tBiQvrFbg+XJReHxd d+fj+2YNa6lInQN4ztu03a+8+5N7VtIYZu83/1W8591hf9B2rpHq7zx9ZpdzJF2DHViEdjrec+f4 KuvLbn+suXLxr7Ul7+fNptd+7tqjy7n3plvl9q/2fGpbnea037MJ7kJULd+oHwVHPMYX0Gw7hXex twK7se4tSGczfr3yDggg0E+Afqqf39yebRd9v3bfJ4vhXdfQru1bmPsGzjPLZtoFo7bXRem7fv/L y6WuwgELLn78rc87eBAOEL3m/qcbhbGlR81gb26wNh6Y8gQsu6HUYlDp3oe/nCxm7sL54oDo/mVb 25bstOeF7XN/WU8sZ1V0GdjcN4i3mHF65eKnJvyOB63jQSCVS4MwXWetpgYa130sluo55xfXYy60 s+erLm767WdKh8Ra/6760UxnHt0E2vpKT3hXc05V23fWhHfxtt7BVE+ZWmfavXB/Ph0zqeUx2/oy hVpd+s2wzLnQLvyM0bnEZ762WAZ1EWh0eb8uLSvVR3nCu2sXZXzb0dNd3nKU5ygA+NAT3xrltbf1 RT3hnY65V7/zyeaX3733PLgUvKWOe71fzb119R76rLzmzpOEdx0aaen8+/pgOWTPF4TC/s4T2lmR 68K7C6GdtZ/UPlhZf7i4TtAqKzo/8n7OdGCc1VNK56TrPmeeC2bN+dRc9mnbyznL4G7bK23V+2/3 udO97E6fPr0MTTXLcaxlMrV/dDarrmXeDwEEagXop2rF5rV9GLyEF3saBGgL7/qGdn3Cu1QoouW+ UjdK5+Kn3B6fe/58c/XtJ5uXaHmuRb2XBnr2DHC+86nyGyy28Az2jhHatdV/6cK5a3jXFtot75O3 GOz4jY8853JLbaRBNd0LQyHBV//k/HJARAMk3tBb2+3e2+mOk8vnh/ec1Hs+/ZV+XxoMBxqncgx6 6zsXvpZCOz3PE9x0rvgBnmj1ovqPwzv9f8292gYozuxeojTA6mkD3nOq2j5zt7/STPTF52Hqyw32 +1wg5B1UbStbW2inL3u8ehGKLfvIwn0k9/dnJ5ZL/XZ9qMyvP/Lp1uWCw8827Yd+aga+u5atrW9q C+9Sx3HXMvC8aQu0hXfh+XjNcvep412zTH9lsRpGzbmgjpvlcb84h/xn7/sM4V2HppT7bLE6im8z UHoL6+9qv3jgCe90Tnfzg880v/5vPteo3PYI9yEOGFWeePvSPmzq373nolM5d55yPXjPp6a8D5Rt r8Akg7vDhw8vQyG7b539f+7+dvq9lszkMZ6AAjsZh3UwxhKZtgd0NuPVJa+MAALDCNBPDeM4xVfR QIDNmkoNxOXCu6FCuy7hXftMpr33nbLX38aLH1183/PQs64BlCefPddcvgx+5Ld/hlkuyKsdNPQO RKeWfswNBrcN/g4R2tWGd6XQ7vLFN477hHbWj2gGoYUsKdfS8pkW3mmmahzaDdVXLQcaFzNkus7c G6oc4et4B0xqZ9p5Apsx9qfmNeOB3/D4tXuNTj14rNnfobf1hHbWX7Q5es+pPve1P+u4xPDx5T3S 4n7blu4tzeLqE961nTOEM7RvvPep5orElw1ynzVaErtPaGdtoeZeq+HM6jHDO0+flKqz2s/foY8H Xm/1AqnwLnU+7gnv2o5zPf/qmz/iDu/CY8UzI2z1cvN4x/gzJq4j1ctY52uhUFt4V7qe0j68YvHF oNQXwuZRC+OW0tPfh5+DJe9xSzv9V/eeT01/TyhhmI+oXhd5zIWHJXnrJLJwSIGdHm2BXfi3dZZ5 W95by2Vqxt3YDzqbsYV5fQQQ6Cvwm7/5m31fguevWUAXUnf97pf2lMIGAGyg7fVHPpOctRMPxA0d 2nnDu2VAsxiM1NIsbTPCckHONl382EX3FYuQqHTxHA8UeJaBtNCny3J7pfBO9Rt/09tmPuQGolPt YcjQzhvelUI7DVyPdc8kub7utz7dXLa858h44WttV/bIF/609imjb18aOLnytsXsgXD50cxs3rDd TT3wys3W0OD/nR/64nIGpid0Gr1yJvoGNaFdybHmnOqDJ/+4ufI2332mdme9JO4LarNFS6Gdlb1L eBfe+zQMDnL3RPWGaEMFVN73C4/rscO7Ul8UliWsu6FMJnq4UawWgbAvbzsfbwvvPMf3u3/jt5Zf 7CvNvEudMxLedW/C9lnjqaPu71J+Ziq8815HaRWPVQSM5b2Y1hY1/T3hna/uas6nfK/IVusWmOSM O1uGUf/qoZl3CvHafnQfNh6bI0Bwtzl1yZ4ggAACUxSwi0DdU8tmvsSDuLnZTOGyUbZspu79ogu6 0sPzjd84cMktRecN7cKlwrZl2Uw533/sK7sXyfHFdtsASm4wuhTe9R00zIV3Cj80q0KDA2Hb0+DR yS9+b3lvDPu9ZkC86YHP7wkcvDMsP3D8G8vlz9oC4NTfFCoqnEsFiJcvwp0rFoFP6nm5gevSMVTz d5n+0jueXAbbpYG2uIyy+OK3flDzdrPfNjeAYgNTNgNNx88b/vVnm9cvflLLkc5xpl1c/wcWbTdu 11MPIlfZANVWrl4sKVvbX2h7HVtdBzCtL9eyuFc4wrvc5/iVizasz8NLNZs6sYRmbr+8A8fWn8f3 PLVzgLZ7oZbCtL6fNWE78dwnLN3vX/wixJAz77oM4qoeX7pY1rrLl2ZWeczwXuMKqC3rnL50Pp46 D/ce19qD0nl827ki4V33NqBz83AJyu6v1O+Z4fWEN7Tr946b/Wx5esLw1Dl67ZKnmy3J3m2ywCSD u00GZ998AgR3Pie2QgABBBCoF4iDGV14aUA2DEbCgETLJOYG8Wzm3R9/74eugtz577+0WAorHWS0 DYDGM+a0nNaBmx4pzrTbPxidDlm0dN8UZwG5UKONbFDFZtY9/2c/Wg7kxMFNOICiQdaHnvr2clAg DML2haiZZTOHGkiNw7swKAhng4aDvlbmcPDUAhZvaGeEpQHjfe0pcz8mmwWhMqcGYlcR2tk+leo0 ddxt82BMXF+xhdqW7omlttoWNk/5vnDesILwrr0H1pKyWq6xJrzTcmFdB1/jL2BokL4tvMuFdur7 X3P/qea6u5+oLn9NSKXjI7xfrmnqd6nfh9q5vnioz5o+4V1q9nKNS65VyeTlHcNgnRONNXO7y3kI z1mPwHs/9tXqL9HVhHbhMZwKG0pf8FJfSXi3nrYx5Lvqs0jn51Na8nzI/Vv1a5XCcEK7VdcI7zc1 gdkEd/HyjPp/zbLTLDzdf43HZgkQ3G1WfbI3CCCAwFQEcoP4peX8SuFd27fnw30vLYmYnJkULUmn QYaHT51trl8MOtYMmOYGMTcppIgv/jSrQgO7udlWe2YOLbbLzQ4LneOBmaEHUq2NXH/347v3a7M2 FN5/MWxXateaMRc+LLyrrV9veJdbflUDuDfc8+SeAY0wDFplaNclvKv1mkrfNmQ5rL40q6k0MNUW Ng9ZpqFeyxva7c5UHmjm3QOf+LprQHmo/Rz6deLZpxai6R5x+vF8Fl31tseaq257rFNwl7u/UC68 Uz+TKlc4aF4b6g8RTtXUS9wXD/1ZE5bFe1y0LTk8hE/8pRNPu9L7MtuupmWxrQSW54v3Pd2pP7Ln a9UNO7/0hHbWngnv5t8GPauszH8vV7cH3vBuiM+Z1e0V74TAMAKzCO4U0Oledhbe6d/4vne2rOYw LLzKugUI7tZdA7w/AgggsHkCpUG6PuGdnnvH4p5InkdNeBcvkxl+M7hmSaltDO12Z02W7sUV/L2t DaTCu5ffOc6AodrI89//UbI5lWZqhE/SYGYpeEm9SSm8awvt9NzUMnjWXr0ht+dYqtmmdPyrfgnt Loqqvu59+Msu4raw2fUCK9ronoeebRRGesKAcJu+M+90TLx8ES6UlnBbEUP2bVTn1925/wsD1h/Y slRxiJYLyUJDhXYvXfzod7UzXHKhnb1+HN7ZlwN+76mze5YNTg2We/oFW95zHctymf2YoZ01iFJ4 57lP6BBLDNeEd6twWfdxyftPV+A/n//xchUNfUlAX/DzLsm9ji8wTVeRkiFwQaAU3hHa0VK2VWAW wZ1CuoMHD+7WkWbZWXB36NCh5X/v7Oxsax1u5H4T3G1ktbJTCCCAwNoEvINzXcK7LhfgnvDuysUA Z7gMWWqw0xPeDRna6aLqxnc+Nblvt5cu9nL3CUyFe97w7vLFjI4+92pa28HgfONseLcYpErdE8pz Qa32WhM8Oovq3qytHyC0czMmN2wLm/u98nDPfu7588vlrWqCu7ZZFJ573oXHkV5rquGdfZaojOF+ heXXMa7lMVNLD7eFd2FoZ/be8K4U2sXhXfx5HN6X8Y3v/2zySwWl8wNP3zZcK93/SqqDVc0oy4V3 3tBuqHDTE94R2o3Z6nhtr4DCO30hZOeW/LL68Re/1vUFJu8+sR0C6xLIXc+t+3N4XR68LwISmE1w p7BOj/Pnz++GdrZEpoV38XKaVPF8BQju5lt3lBwBBBCYmkBpUC4exG0Lbi58q/bismBdQjvzaQvv bGbAw6e+s5wx0DbI2RbeDR3a2T09pjRgVgrtvDPvwnDPE95ds5iZ0vVeTVM7RnLlicM7u3COB3fn dEGd6g8I7ebSIvuX0/Nlh90+I3NPS2/4lAq/pxjepUwU3t390JeWX04IPyN3WmYwp8K7VGjn9fOG dmF4d/Xtj+37ckB4X8ZcC8qdJ8ypb+t/dFx4hbh/X3VoZ/vRFt5N6RxkKHdeZ74C7vPQxWcKod18 65mSr0YgPp628XN4NdK8y1wEZhPcaUbdmTNnlve1i2fY2Qw8gru5NLtyOQnuykZsgQACCCBQFjj+ zH9ahl41Myy0bSq4iWde9Ant2sK7eDkvDV6VAqLUwKvK9yv3PtW8ejFDLly+p0tIkRqUmMLAmXew xBPexXXeFt55Z4uUW+j0t7DwIb5wtsHdOV5Qh4P0XY6H6dcaJWwT8IR3pfsVlfqAtuVmpxTetVno iyqppd9yS+Wqnw3Du7bQrhTe1YZ29np9+qM4vOvzWnM/AtW/axbRdXedXP60nUON6ZQK76Zw7jH3 +qX8wwuUzkeHuGYYvtS8IgLTFLDj6eo7HmuGmsk9zT2lVAiUBWYR3FlYF97XLrynnZbR1N80G4/H ZggQ3G1GPbIXCCCAwLoFug7+aWAyXA5wjNAuFd71uWF9OAAbDhCEBl1CirbBCM8A2g9++JOqZpC6 P1rqBUqDJLmBxtSymbmQLvX70oB91c7OZGNdNKcunDW4O9cLag3Sa8C5yz0AZ1JtFLNFwDNTOXe/ olIfULpHpAVc61420xNg5mZblcI73e/J7mlX+uJM12WgU6/b5TMubCYW3o0ZRs3lwNQ9LtVG2trJ KpzC8M5zzjEXX8q5eQK581JCu82ra/ZofAEdTx/+1HfGfyPeAYGJC8wiuJOhLYepgM6WzdTvNcsu /t3EzSmeQ4DgzoHEJggggAACLoHa8C4V2oUzD8a4ALdlM3P34HHt6GIjG2CLl+KRwave8WR1SOEJ xzSQ9vCps81Nv/3M8v3Dh/5fS0p6w53l9oswpTTDUO/huU9gamA3Du5Ky2KGfy8N2Hvrie2mIfD8 9380jYJQirUI5GYq52aaqT8p9QGe0C5cinOo8O6hp76dvH9bDtYT2lk5u4Z3l950cVnpVYR3fUM7 s3riS99r3v/xr62lTU71TVPtZRWhnXkovFMQvKp7/U21HijX9AXi8+b4mkF/v+NDX1zr/X6nr0gJ EUAAAQRMYDbBnQp87ty55U/40Cw7lsjcvAZNcLd5dcoeIYAAAusU8IZ3qdBOA6tv+cDnGw0KjhHa mYuCKO9sszZLDbBpYCB+1M5884R2FwegTzSXL5ZV032RLLwLB/o8A3zh9qXB8dBMYadmKpYGhpez XKL7M5VCu/A5r1jsmydQXGc7570RQKBd4Ivf+sGeLxjEM5XV37/r97+8795uQ4d2Q4Z3y2VrF/d2 837xoya06xretc3IS/fVx5dfLok/A71lHSq04/jJC9R+ptsrfewzzw9ybqPzOB4IzEHAzp91jhl+ kS73e+2Tjq8bF0vb087nUMOUEQEEEFidwKyCu9Wx8E7rFiC4W3cN8P4IIIDA5gkUwzuFP4tgJzeg qsHRbbmpfE1od3Fg98K9ARXePf7sd5f/hgO0beFdanB26PCO0G7zjmn2CIEaAQXv1939+J4vGNiA qfqrcPZbPHPO0x/VzLYbKrize03q9TxLLesLHJoF7fmiQ7zNSxIz6PRllnhm3UsXX3K54jbflyk8 y4aWwjtCu5qjoN+2uVn0Or+6btGu4hlxOiZefsdJd6jcr3Q8G4HpCMQz69pm4lkfN6X7n05HkpIg gAAC2y0wq+Du1KlTje53p6Uy9d/20Iw77m+3WQ2Z4G6z6pO9QQABBKYikAvvNCj75LPnloNLGvzM XTynZrJNZd+GLMdr7nt6aVA7uKv7Gl22GEDOLTWXCu/aBmU9g+Xa79KymapTzUi58oWZeZctZgde uvhp2z8955o7TzbXLgYjmWk3ZOvitRAYTiB3/8X4HezeZXbMh7ODLbyLZzpYEOfth/Q6NeFd30Ha MLSz/fKEdzVl3P1ixuJLGZpFF/aZ8b1f9Tfr48P7krX1s16D3OcEod1wx5L3leLZ++F5VXgPurCd edql9/3ZDoG5CbTd+05fGLn+7otfdPP2iXMzoLwIIIAAAt0EZhHcKZQ7ePDg8l529mP3udPSmfqd 7oHHY3MECO42py7ZEwQQQGBqAnF4Fw7KWgCkJbu2ebmaeJDbE+DFy07m7osUhnelmRSe5emsfeXC Oxsw/OFi+VAFswrw/tVi6VMNluTCyXCQMb5v39TaM+VBYMoC6ks+cPwboxTRgoHSUry5/iwO71KF VNlrg3tPMNZ3cDYV2o0V3qWWvWwL7cyxFN7VGsSfF4R2oxxWVS+a+jKUwrs3PfC5fcvNEt5V0bLx hgiUVrCwvjT+UsRQ9z/dEEZ2AwEEENhagVkEd5plFwZ2+m8L7lRzFuptbS1u4I4T3G1gpbJLCCCA wIQEbLBJy6bFg7IKgGrvBzehXRusKDXhXe5ecW3h3d0PPbtvOc1cQOid8RKHd/FAof7+0FPfXhrl Zl8yuDhYE+KFtlzA+pBSsNaFKQ7Hcu9R6sc84d0Q5RtyULYttBs6vMuFdvGs6px/LryrDe2sDiy8 u2qxHKcceKxPoG358QOLZcd1XhB/pvP5ur764p1XL1AK7cIlk7VixZCfE6vfW94RAQQQQGAMgVkE dwrqdnZ2Gs2u0yMO7hTi6XdaMpPHZggQ3G1GPbIXCCCAwJQFNOj0xW/9YMpFXHvZSoPeGmTIhXYX 732n5TP3D+Bp4Ddeeq1tZl91eLe4x5Jm2Cmsyz3igUcGFdfe5CjAhgjEfceQ4V1uRlv4HgqMfuPY c436jdKM4VWGd10DK2sWmoVx1W0X78VaWu631Ae2zQ5M9dFW/rcsZi1rxlu4PGau6cbhXV8DhXf3 PvzlDTlS5rkbxXsGL84NCO/mWbeUehgBb2hHeDeMN6+CAAIIbKrAbIK7cIYdwd2mNseL+0Vwt/l1 zB4igAACCMxDoC28K4V2pfDu0puOV91L7+rFMpee2ZAK6x74xNdbQzvTtwHIK249UQz65lFjlBKB 9Qrk+owhwrvSMpR6jzc9sAiV7jiZDQ5SYdcqwru+gZXKuOxzl/cS9d2D1PNlhJSpHONZ0XH5NePt 5Yvt9PzSw8K7vgal9+Hv4wt4QruLn/3MvBu/RniHqQnUhnaEd1OrQcqDAAIITEdgNsGdZtzZIw7u 9Df9zmbkTYeXknQVILjrKsfzEEAAAQQQGF4gNRDvDe1S9+/QIEXu2/i5GSS6b44Gf8d4aCDynsXS nW2z88Z4X14TgU0TKM3S7RPelUI76zvCmWI1/cyY4Z0Crj73LLrpd55pLl/MUtY+pu4vl+s3vUFZ aJu6D2nudZ7+yoUVcTwP9d/X3/3EVt8/1uM05W1qQjvCuynXJGUbUyB3z+XS7O/LFkvMplaiUP/7 ynuebBQI8kAAAQQQ2C6BWQR3do873cvuxIkTy5DO/tvubxcGezVVOPXlNadevhrrmm2nEtxtq39N XbHtuAJzaoNzKuu4tcarI7CZAuGAfN/Q7to7H29+9d5PNrrHYGkgQ39XaPfAhz+1cbD0mxtXpVu9 Q6XQzo71LuGdN7TbDQuWM9MuBF3e8G7MLwd86IlvdQ6sUve0C8O7X3jD0WQ/6g3trNGacTyDTktT 3vjOpzqXPzwoVvHlCPrVcboh1d2r3vHkoq35ZnuGn+0XZonuXeK1tn2Os1e86tACHH8XRGvDu1xo 51mOeOg65PUQ2HaBTerHNmlftrVdrjy4U6M5f/580lu/TzUqzaSzWXUK7VI/p06dqq5DvZdeS2Hg FB9TKp/K0nbAq46GnPE4heDOQuKpdHQqR41xqc48bV6vIQf9lBx0/J4+fXq5rf5te3i21b7aPqT+ zfUjnv1a5zal/QrrbWptsM1tSv2VymnO66zrKb6359izctcc/559rXm9mnLWbqvzBeun5tqPeLzH 2kYD89ctQrcb732q2fn1DzUaMI5//vG//A97ZoXEwZxCO808+UdvPNr8/K+9q7nypodbwzsL7aZw zpb7XKr5fLa6mVMfP2R7qukLarb1lLHm9Wr7lppzoE3sh7yhXZfw7gPHv9Eo7POE/HvCgsUMNX3J IBfeqa/6e686vPy57M3/frQZvZ62mdsmFdqFy6r9/D9/cHk9q30I971rKDLEfV+HuAbxmNnxHPa/ XftVz3VWTf9RU/51XGdZ+dTP1fRHtcd57j64Xdunx3WbtlHb916rr8JlSteDU7gW9IZ3v/imY83P HXzfsh//+//kyJ6+PPdFF0+f5anzmn6N8yKPKNuEArXnA7Xbj6G9Sf1Y13Oi0rhf7ecO/VW/lrrS 4M4OgKNHjyZLfejQoeWFR2rQXx+8L3rRi/aFdppx1yW0UwGmdECmQKZSPiuH6iZXd/Hypf2aZdMQ 3F0UVNu3YyO812ObsbazgLsUuKVeR+9ps1nDoDz3/jpm40BdYXvqWPZumwvp7fdei75tcejnh3XT to963zE+aIfeH3u9qfRXVh5zHmt/5/i63mMvd/yrH+rymEJ/onLr8yt1zJW+aNBlnzf9OZp9EZ4b xK5//R8d3F3KLRfa6b5Lf/tlF877FN7lZt7ZDJgp9DFt+9zlM2lOffwQbbqmL6jZ1lO22tfz9pd6 75ptN7Uf6jKYXzOLQH2OlrGsD+723gsunHmnfiruu3Tu2/Wh/mHoL4O0hXZm8Q/f8MHlfvzdV92z 67POUOTMmTO7rmN/vtrxFH4Jt7Zf9Vxn1fYfpTZU+3o1fUzNtl37o5rjPTU7f53ts1Q3c/p76pqy Tx82xL5P4VxtateCpfDu7x88su+z6L/5Ky9aBnip0M7TZ3nqcu79kGcf2Wa9AuF1k2eFvHD8scsX Iofa203qx2rPidoMa/sMvRb91TCtcqXBnYqsAzZ3QmEXT1oaM37oQsiCI0vh+14cTemATFXnVMoX D1SlOlGCu2EOyPhVjh07tmz3dmLuGRi0CzZ7TpfgTh28nq+L77jDVZnCh7UPDerbManf6VjXT3ic 1mybm5lmJl0D+3Fqyv+q8X5ZKGv9WvgtoyE/aP0l7LblVPqrqV2sddMc/lk1x57anT6Hra/XMWz9 SZcZ6lPoT6xfDL98ov2yC4S+5xPD19j0X9Ha1K/8+jv3zbz7xTd/eDmjTvcyCgfabaadQjv9/iVv PnZhpsti2a3rF0tmxuFduGzdFPqY8AtocZ/dpQ3NqY8fokXW9AU123rKVvN6Nf1lzbab3A91DdZq lqasfY8DN+8N7cL7bf3Nl75leX79c796/3IZXt17Tee8+qzrciyrDQ59LeQJ7bRPmvGs9/7ZV96z 7EvXHYroc9ZWyun6hR/PMa1tVFfxeUlNv+q9zqrpPzxlr3m9mj6mZtu+/ZEnvCO087SGbttY6Bpe D/ftw7qVZO+zpnCuNsVrwVx49wuvv9B//8yLb1ieE1ufruDuv/tf/m5z7+9+bg+wt8/y1OUm9EOe /WSb9QlYf2DX221f5rEv/YRjY+sq+Sb1YzXnRCXvmj5Dr0V/VRL1/33lwZ3dry6+KAq/nZdK4+3k MnewdwknpnRApqpsKuULB6pyF6VDX6wy4+7CjNBw1prXWM/Rcdank04NWtgxGoeHuWPajtnwgqJm 21w3ZgFC14EVf/e4mi3bZob1qcPVlP7iu0ylv5rixdqq6yL1fn2PPatfz5cH4vefQn9i+x+XbU7H 2BTaUViG8JgP7z2lQePX3P9086Mf/7QJB9rj0C6eOROHd/GA/hT6mKHLsG3tr6YvqNnWc2zUvF5N f9ll203th2qDtZrQzsy875EL7azf+UuLQdH/8W/v7IZ2njZU2sZ7nl56HQ3w3vTbzzQve9uFLziU fiy404w73Ufsl97xxCD3oiuVM/d3XYcoVLBgYdXn695+teY6q6b/8LjVvF6XPiZ+/bZrsj79UXt4 t1imdrFU7RDLt3pMt22bti/Dr9Ni6POkPvsytdVX4vBOX2L7h6+88CWSyw99ZM+x8o9e9/8tfx+O o9T0WR63TemHPPvKNusRsP5AAY76rLZxBH3WKeDzfoaPuUeb1I8N6VnTZ9BfDdtCVx7cpU4ctUuW xlrD0jf97Vt0+t2tt966/PBKnVz+4i/+4u70ciX0qRkBej078VWnofezICLcXuWzDkW/t28HhLMP 4jLotWy73PuHr6Vt4gDSWz69tz7Aw6URVLYwuLS/pw4s+1tNMwo7LrsIi4PStotV7bt9c8Iu5sKy WXIf/s6CO/tbrXlbPdprhXWSWgI07OTkFtZxLigutYWwXOE3EDz14RkQsA9FO3b0nC6hdq48qTLk ymWzZMMP6JptU2UIQ2SP2Ry28QZ3pf7I2lY8I1b/r/cI+5xSO5xzf2V1nnMt9Z96fs3nQI27p/+O 3z/sJ/r07UMde6kZ8V2Ps1X2J7nPLvNd53IcXf3W/bz4okbh3cvueGw3tLPyffT4J5v/7SX/ZDkD 70XX3748X9MSdRrM0zJA+u/wnniaefeSN/2H5rVvvmu5bds5m+eY6nPcxMa1F3Klc4Ka84y2fnsu /VDNucUQ23qOEfohj5J/G2+w1iW0s1KU3qMU2qnv+Ut/5WeXsxk00670KJ032LWK9Vc6/9CPfa7U HJ+2rWbb/b0b9vaXKrf6yr9x2RuWfaNmZPwfr3hb8/Ov+0CwVObx5o+/98PlLpXKPWTfaIZ2na9r 7NR1dridXR+UrrFq+76ug1Se66y4rXR5Tlt7m2N/lArvFDrf/OAzzRvf/9nGZtiveyZo6Tif299t ZZtSubv0P3rN0nWntpny+JXKl7oWHNND71k679sN7257dHl82DEfzrDW8piaaVfqX0p/L7WNmvOs muvImm25PutaS/N4XmocOTVGbeOG6nfaPsNL48pSqRnH2YZ+LPYc49zP0xd5tunSqud43tRlP1ce 3NlBGQ/+KdjRh2s4MG/LbKgy9POX//Jf3l2+JLUmuxql/T5ezs9eS3+3cMN+FwZ31rBtmQ9ta6+Z mgmoctugUu79bWDQwkL9Gy8dUlM+C7/0fuG+WCdoF0qxgRqIwqfatc/DDtfqL36N3IFogZ0MQx/t Q3hBG3+jyIK71LfJPOaperTgVu0srN/w9+FBZK8Rvp95q7zxQG+Xcqks3mVkSp1d+IFnJ9xDBnfW rsJjVwZ6D8+9D2u2zXVmdixu0iC7J7gL21auP8qd5KQGmuPjI2yHc++vrO2kXLXfpf4zPHY8nwO1 7l3e3+qna98+5LE31P1qVt2fhMtHxZ89uf6ry0nVNj0n1bc88oU/Xc60Cx9/+Injy8+Jv3P1oeVA s4I6LQmkQWgtk6m/acaIfStfg3v//V//e7ufLaVzttIx1fW4SdVlTXBXc07gOc9o67fn0A/ljo1U XzDEtp5jkX7Io1S/TSlY6xPaWWly7+EJ7TQL6GevvWnZx+ictm1GmOe8IQzI7BvlYXBXe3zaefX/ sOgH/9d/fLG/VB+pPlR/1/1BFdrp/+13L/7ld+6Gdp5yD9k3Wr3IM7xezs0KSl2n5a6xavu+VQV3 NX2X5yiac38UhncK7RRC6GEhxRW3nlh+eed7f/5fPRRs4xCw60FPH5YaD/BcH5bGwaY8fiXC3LXg WB6e8z47Lh74xNebP/nT7+4ZR9Fx8/JFaKcvwulRGvsp/d3RjPZtMud+qMv+8pxxBcJ+xsYjUmPU Nv7UNvnAM65cO46j7belH7PJHEOf+3nPheiv+h1rKw/uVFz7UAuLroq0AE0Hz9/6W39r301a7QLL DloL9OLgIF6aI3dPLHudVHAX3q8r7ADCQcvchUH8/qVpwTXlS11cpmYxpi6U7KCqvTdYfGJn+x1a pA5E2y5+P3u9MABSecMASw0zVV6veRi6he3MTuDisCy1/GL4GqF7qvy15QqDS+8hXOrstA9hoNr1 wjVXHgs4w3ovDWLaMarXrNk2VYbUDD6v3ZS38wZ38SxV2Xr6o7YLs1Q7nHt/ZXWdcvX2n7ljP9X3 1QzI1b5/rn7iL06U+vYux56eo33Tj31mh5+VfY+pdfQnOl7s5NzaR+rioe++bcvzwy9a6b/DHzNQ iHf56+7fnR1iM+sspEsFdxqMVv924J+/b88gX+qczXtMDXVO5Nnn8JwxnvEenx92Oc9I9Qtz6Idq zi2G2NZzHNIPeZS6bZML1oYI7axE8XsokFPw37a05A33PNHc/dCXljPt2lYDsffw9jHaPneeXnN8 2rZ/5W++uDnw5t9rrlqEIFfddmG5TOsbdV++cB9/5sXXL9/7Y//xE7uV5S33UH2jvXHumjz+wp3t p85nvNdY3r6v6/VP6TorPhJS/Ue3o+XCs+beHym8u+7Ox3dDO7NQeHfPQ88S2vVpHJnnevqwLv2P Zxxs6uNXIhsquPN4eMeCwqpMXZ89/ZVzu5uE4yipJlDbZ3ma4Nz7Ic8+ss3qBOI2rs/81GQY/c7G hlPHUs24cs04zjb1Y+E16ZDnft5zIfqrfsfdWoK7eGkqG3S0k/p3v/vdu6Gdtn344YeTId5f+2t/ rXn1q1+9T8A6CBvUznUQbQPaKlPpg1Wvm5otlXv/3CyhmvKlqju1H6mZSV3vNZB6fR3sYaebOhDb ZvfZiabtT1xeNcxUeb3m1mHH9Vj6fdih5bZVmeM6qy1XbXjaNiCgv9kxlCp/PHDYpcuwNhCvS10T CNRsmyqj9RtDzfjp4jDGczzBnac/6nJhlmqH1rbn2l9ZHXnva9D3c6DG3dt/505O9fwufXuXY09t I/xyjM1qGuIYWGd/YieXtm9DhpFD2MzpNawew3aScn3VW44s21I82Jybcfc//e87zX+7mEWiJYQ0 4GePUjtu267LcdN2vJb2ufacIO7jU+cZbf3CHPqhNk/PvTNz/UbXY4Z+qKuc/3lxsDZkaGelsPdQ IPfwqe801y/+zQV32kbbhw8dO/aFjvBLpG17meuLugZ3D334D3bfzo7lf/Ca31juh4JIC+/UN2p2 XXzPsOtv/f1lH1v6PBv7etHOG8LrjvDLDrG7yjxG37eK4I7+KH2ExMeXv7dgy64CpT6s5vwgN/aR 6jumPn4lz6GCO891uPe8L6zn0nntqoM7zou6HoU8LycQt3E7T4hv4RKeD6T6rJpxZfqxveeDKc+h r4s913FDB3fb1l+tJbiLD2BbGsMO+I9//OPLC5Drrrtu+StbdsMG7vU3O+jDJUnsngLxt7JzjaRt wDYOO1Lb6nU972+hin0LMX7tmvLJQ99M1P7rgLMl1OILttRU5NJMGm+Hq+2sA7CLxNQ+tB2cdiJl 4UA8ZVcNM56Fp/f1mudOUmt+33bhF58I9i2X5+O+zVMfZnGH2fXCNS6L2psFtfE3eD0nnDY7qGbb lEduuR2P3ZS38QR3nv6oy4VZKtSde39ldZ1z9fSfQ1jm2nuf99e+denb+x57el87wfMu7Zs75tbV n8jN+hB5qBxD7dOU+5cxyxYOyOq/w5/wfe14esm/+Hf7BtRTM+70Wffil/+zPaGdXq/PMdXluEnZ efd5iHOCuA9r+0yv6bO855Ft7zeEZ1tfUHMe0qWN0w91Uev2HAvWxgjtrER6DwsMUvfbUtCVCu3C PdJ1lQV48RfEPJ/bdo2SGrwoHZ//4NfetTtLyb7oEC8frPBO/YrdH9RCvdfc/3Tz3Ne+mQzuPOUe 4lg2R50fpG7HkBrgr7nGqu37ul7/eAeVavouz1FDf+RRYpuSQK4PK/U/YeBfs+3Ux6/kNVRw57kO 9573hfXY9/rM22eV2o6dF65jvIfrM0/tzHeb3Bh6eK4Unzuk+qGacWX6sXJwN8S5X+25EP1Vv+N4 LcGdihyGSDpYw2UTn3322eUFyA033LDcO/umvx2Edj8abaOLAf0+9WMhQ82JRe2B7nl/+zC0+yVZ gGdVV1M+WxZT7xvfNy488dJrhxdKNhjfZbZS7qQifP2+wZ2V1y743vCGCzdej8vrrfOagE7vndq+ 5qKyb7k8h3GunViIbe3BjgU7WbXfe94jtY0tk5eagWWdfnzPylTbrtk2LoftY9zGu+7TlJ43teBu 7v2V1W3K1dt/1nwO1Gzb9/1t32r79j7HXnis2JdiUoGv95haV3+SWqJbZba+hSUzvTV4cbvSgINt acfIG+/7eKN734SzReLg7qpbLwxO33X3PfsKlHo/7zE11DmRd5+HOCcYK7jzmpUGv2v7obhC2/qC Ptt6WjL9kEdpuG0UqmlpylU94vCuFNpZuVLf3vUeL3qN3Hl67lh68nNfWT5H/aD6RpXz5258x777 flpIp211z7swtNNyxFPoG205e5Ux9xPOWqm5xppacFfTd3naPP2RR4ltPAKpPqzmOqVm26mPX8lr 1cGdd1zQ6rLv9dmQA+H0Q54jjG1qBVLnJ+GqanYMlL5AMIXgruZ8sO81Uug8Vj/Wt4y150L0V7VH z97t1xbc2XJVdrCGS7bZAa773H3hC19YXgBoFp4d5PrXtnnta1+7/O/csm7a3dxMnSFm3HWZfRDu e235UvuSG0iyAUldKJldl+aSe33rvDTgabMdw9dXveV8Uh1QuITqtddeu6z3+NH2muG2Ywd38bdH +5bLUy+l4K7tYjm1lrTnPT0dcm7f7di2oN0GNVJtIrVtWL7cchyefZj6NlMM7kKzufVXVvaUq7f/ rLlwrdm27/vbvnXp22uO09wxY3101+BuXf1J6f6YQ55ITr2/GbJ83hArPEZu/eDpPeFdGNwptNPy mJceOJCctZF6P+8xpf3uctzEXt59HuKcIP7cqx28Vtn7mJWCuz6enr7A7Gu29bRvz+vV9JfebemH PLUz3DYW3nlDO3tn1Wd4zlzTx9QEdwozr3zL7+4Gdwrjdt56vPnZX7onGdzp71om83/+OzvNlYt7 3mmmnUK7vsf5UH2jnR/EXyJUPxL+zZzb+pe+fV+p78q1Ms+5gKf/qGnFntfz9jE111n0RzW1NK9t 4z6s5jqlZtua8bWafrTPuUVcU2MNeOdmEXUZF6w5vuP98/RZntZLP+RRYpsuAqljxX6nc4PUGHWq H6oZV6YfK8+463vu5+kz6K+6HDH556wtuLPQJx4UDi9A2oKI1N9yu5l6D21rvy8l/LmLotzrlqoo vh9ZTflSH9D2evFsJDspt5l+YYASltGWDcuVu22gyk6IUks15vYr9+2icMruX/2rf3XPLEwrm9d8 yOAudjOPcJZZ33KV2oxdjHnWD/ZcFJfqXK9hs2tS958Iy5vbd/sgDgf5a7a197C+Itd+PXZT3mao 4M6c4vvWWT14+rmU09z6K9uHVNjr7T9rTvhq3Pu+v+2bt2/vepy23d9Q+xD/fer9iXmlLqjtb7lZ w1PuO9ZdNm+IFR9PYXhnwd0/fuO/272n3ZTPibz7XHtO4DnPaBuMnks/VHNuUbst/dC6e4Tpvb/C u9w9t1JfQEmd33s/t9vO0+Pj05YP/dsvO7QnuFM496JX5oO7v3HZhdVILn/L7zd//L0f7oKnrmdr yu09p2g7xtruP6OC6u/hFzKtP4s/e1N1UPulhbGCO+91kY0bxLcXiI8Q7+vVXDt5t+W8aHr9VW2J vH1YzflBzfXPlM/V2q4FV+1RqlfvMZt6nVJwx3lRSZ+/jy2Qu26ycRq14fgaKNUP1Ywr04/5gjvv uV/Xc5f4efRX/Y62tQV34ZIa8WwgO8Brgzs9Tz8auA4DDht41gmy/ls/uXvD1RzoVk51PDoJSL2/ Bjjjv9uJup3Q15TPLnz0HL2f3tfuyRAHd2oa1snJMhXAWFjWFgi1DVSFdRW/hv1N5TMfK69+lxoc tv1TeVPLenrMtd9DBne2rKv8Qu/whLlvuXKHsfbDfmQSLsHQNsu0zcBT59Zu1FbDMth/h++d2nfz z7WJ8JjIbWsmFmyVLoD7dYXre/ZQwV24zrTaaZd+bu79lSzDb3fHA0Le/rPmc6DGve/7h6201LfH LbrmOLUTK+u3rS3p97HpXPoTO85Ufvus1n4Nsfzn+nqP9b6zN8RKHU8W3llw96t3f2T3nnZTPify 7nPtOYHnPKNtMHou/VDNuUXNtvRD6+0L5vbudnzqc0HXjHbtaNdT4fm993M7DKfsesHOW8Pj86PH P9lc9i8fXN6r7mdefMO+4M76xJ9//Qf33RP051/3geX2P/Pi65uX/qujzSc/+0fZ69macve9Xkzd KyVuEzbrzq7trD+z8422a6wxgzvvdRb90dyO8s0ub00fVnN+UHP9M9VztdK14Fge3vO+PtdnNr7j GRvivGiz+4C57F3uuik8B4i/hJDqh2rGlenHfMGd59wvbmc150L0V8MepWsL7rQbNmCW+qa7DrgX vehFywuU+++/P3sfu/C+cRb02T3xQqowcAnTXv23ZyZKrtPR78OwyZYnCL85YAOGVj5tH4do3vLZ wHq4r7kZd9p/+1ZRbqnE1DcbcycUqWBQ29qMnlQ9qmyxjzxyoZMtj6AZd7mHx3zI4E4neGEdWhCZ ciq1hZpvgdoJVy7AjmdWxeWx90rNjkkNwIfPj9tsXIb4veP2m/r2jL1+zbaek85hu8TVv9pQwZ0d 7zbw1KWf02vMub+yzxQLueO27+0/a074atz7vn/8mWafN95W6z32Uv222lXqM8DzGTKV/sQ+q8L+ LPV57PXc9u28IVbueFJ493O//M7led43vvmtPZxTPSfy7rN2puZcxXOeUTp/GNqs9H6ec8z4GKnp C2q2pR/a9t6ofv/VvsPzJfVDOoeIB5C8n9sqgT47w9cMz0F0fP78L/zi7v3fFNxphp3eV2Gd3fvT grv/859/sDlw8/F94d3f/ydHlktm2ueYfUEuvp6tKbfnWG47xnLXG2GtxKut2HPkUrrGGiu4q7nO oj+qP8Z4xrgC3j6s5jpl3dc/4VhB2zVOW39UuhYc08Nz3pdqFd7rs5o+i/OicY8/Xt0nkLtuskk8 ug6PH7l+yDuuTD/myzc85359ruPor3zHiHertQZ33kJ6ttPBr46hNCvHs43n/eJtPO+v9y7NkvKW T69Tei2V0Q6Y3DKDnoGZLh45H+9rqWGWHh7z0mvU/F3v5zFfdblq9kHbjlnn8okHPXLlq9m2dh+3 fXtvP9LmNNf+ytM3erbp0oa87kO8f6lvbyt/zbFXagdz7E9q9r9LO+A5foGHnvr27ky71LOGPqZK x80Y7bnmnMB7nlHqu0vnwnZ+6DmnKfUlbV/U8beEfluOUW9Wopr+Yqxt++nw7DYBOz5LSjWf26nz 4OeeP9/ofnu/8IajzUvefGxfIGfBXfiv7nkXh3eX3XJ8eX+75772zeI1b81xvuq+MT5mh+j7SnW4 qr/TH61KmveRgLcP07bec6paWe/revvRvv2R93285a71qDnvC1+75hyiVCb6oZIQf5+rQE2fV7OP 3v7A27/MoR8rlbHGr8+29FdlvY0J7sq7up1b2AyD3OCMvhmq5Uum9vAEd1Mr81zKM9U6n4sf5RxP YK791Xgi+VcuWa2qTPQnq5LmfYYQKB03tOc65ZJn3at135p6627HM8cX+MlP/6J55eEnm8sWM+xS AV3b7w689ZHd51lo96Mf/3TwQpeO5aGPsTEHaQbHqXzBoa0q357NEZi9wKr7o9mDJXaAfmgTa5V9 mpPAHPqxUhlX5U1/VZYmuCsbzXoLHQSpKcjaKS13qOUEpvgguBunVqZc5+PsMa86J4G59lfrMG6z WlV56E9WJc37DCVAHzOU5IXXoR8a1pNX21wBhW2aKafwzRveHbh5sZznYtadth8ztCsdy2N81m9q cDeG1eYeFewZAmkBztX6tQz6oX5+PBuBIQTm0I9xHTdETa/mNQjuVuO8lnfRLDutyW83Al9LITq+ KcFdRziehsBMBebcX62aHKtVi/N+myDAcTNsLeI5rCevtvkCNeHdKkO7dRzLuldN2z3PN781sIcI IJASWEd/RE0ggAACQwrMoR+bQxmHrJO5vxbB3dxrcEPLT3C3oRXLbiGAAAIIIIAAAgggsIUCrvDu 1kebK16YmTf2TLstrAJ2GQEEEEAAAQQQQACB2QgQ3M2mqraroAR321Xf7C0CCCCAAAIIIIAAApsu 0BbeXXvX481HP/18c+sHTzdX3fbocnnNMe5pt+nG7B8CCCCAAAIIIIAAApsgQHC3CbW4gftAcLeB lcouIYAAAggggAACCCCw5QKp8M5CO6N54BNfJ7Tb8nbC7iOAAAIIIIAAAghstwDB3XbX/2T3nuBu slVDwRBAAAEEEEAAAQQQQKCHQBjexaFdj5flqQgggAACCCCAAAIIILAhAgR3G1KRm7YbBHebVqPs DwIIIIAAAggggAACCJiAwrsb3/nUcnlMHggggAACCCCAAAIIIIBAKEBwR3uYpADB3SSrhUIhgAAC CCCAAAIIIIAAAggggAACCCCAAAIIIIDAiAIEdyPi8tLdBQjuutvxTAQQQAABBBBAAAEEEEAAAQQQ QAABBBBAAAEEEJinAMHdPOtt40tNcLfxVcwOIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAQCRA cEeTmKQAwd0kq4VCIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAwIgCBHcj4vLS3QUI7rrb8UwE EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCYpwDB3TzrbeNLTXC38VXMDiKAAAIIIIAAAggggAAC CCCAAAIIIIAAAggggEAkQHBHk5ikAMHdJKuFQiGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMCI AgR3I+LO6aXPnj3bnDt3rrXI58+fb06fPt2cOHFi+e+YD4K7MXV5bQQQQAABBBBAAAEEEEAAAQQQ QAABBBBAAAEEEJiiAMHdFGtlhWVSWHfo0KHmkksuaQ4fPpx9ZwV12ib82dnZGS3AI7hbYSPgrRBA AAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQGASAgR3k6iG9RTi2LFju4FdW3Cn2Xj6uwI+zbrTQ79T cKcf+92Qe0FwN6Qmr4UAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJzECC4m0MtjVBGC95sycu2 4O7IkSPL4C4O6GwW3qlTpwYvIcHd4KS8IAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCExcgOBu 4hW0quK1BXe5vynIKy2x2bX8BHdd5XgeAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIzFWA4G6u NTdwuXMBnO6Bp78dPXo0+Y4EdwNXBC+HAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACWytAcLe1 Vb93x3MBnN3f7sSJE9ngTs8d+sGMu6FFeT0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCYugDB 3dRraEXlI7hbETRvgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghkBAjuNrhp2DKXCuXsp3bJ S8+Mu4MHD3ZSfPDBBxtrgPG/d999d6fX5EkIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwFwF CO7mWnOOcp8/f77REpfhz+nTp5PPLN3j7siRI1XPcxSPTRBAAAEEEEAAAQQQQAABBBBAAAEEEEAA AQQQQAABBAIBgjuaw1IgF9zZ3w4dOrRPymb05WbxQYsAAggggAACCCCAAAIIIIAAAggggAACCCCA AAIIIOAXILjzW230lm3BnWbb6e8K6sKHAjv9Xstp8kAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA AAEEEOgnQHDXz2/Wzw6X0FQAp3vV2e/CkM7uc6e/a6lN/b+2awv7Zg1D4RFAAAEEEEAAAQQQQAAB BBBAAAEEEEAAAQQQQACBNQgQ3K0BfQpvactcKnxL/Zw6dWpPMRXY7ezs7NmWJTKnUJOUAQEEEEAA AQQQQAABBBBAAAEEEEAAAQQQQAABBDZFgOBuU2pyRfuhwI+lMVeEzdsggAACCCCAAAIIIIAAAggg gAACCCCAAAIIIIDAVgkQ3G1VdbOzCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACUxUguJtq zVAuBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBrRIguNuq6mZnEUAAAQQQQAABBBBAYNsE tNR9/HP+/PltY2B/EUAAAQQQQAABBBBAAIFZCBDczaKaKCQCCCCAAAIIIIAAAgggUC9w4sSJ5pJL Lkn+HDx4sNHf+zwsEOzzGjwXAQQQQAABBBBAAAEEEEDgogDBHa0BAQQQQAABBBBAAAEEENhQgTC4 O3ToUHP48OFmZ2dnT5Cn33d5KLSzUFD/zQMBBBBAAAEEEEAAAQQQQKC/AMFdf0NeAQEEEEAAAQQQ QAABBBCYpEAY3IXh2rlz5xoFdha8HT16tLr8BHfVZDwBAQQQQAABBBBAAAEEECgKENwVidgAAQQQ QAABBBBAAAEEEJinQC64s70JZ9/Z73T/OwV54d/032G4d+zYsT3Bn83mC5feVDh45MiRPbP79P/c X2+ebYlSI4AAAggggAACCCCAwGoECO5W48y7IIAAAggggAACCCCAAAIrFygFdwrgbNbdmTNnluXT cpq5++IpeGvbRs/VQ6GdBX/6N1yiU/fW44EAAggggAACCCCAAAIIIJAWILijZSCAAAIIIIAAAggg gAACGypQCu4U1llIZ7Pl9G84c0404cw5/b9mzZ06dWr3ufpvLZ1ps+ks/AtDOoV59l7angcCCCCA AAIIIIAAAggggMB+AYI7WgUCCCCAAAIIIIAAAgggsKECpeAuvE9dHNaFJOHr2O/b7nEXh4H2HAv0 bObehrKzWwgggAACCCCAAAIIIIBAZwGCu850PBEBBBBAAAEEEEAAAQQQmLZAKbiLZ83Z3pw+fXo5 y05Bm340c87CuFJwFwZ6uSU3bUnNaetROgQQQAABBBBAAAEEEEBg9QIEd6s35x0RQAABBBBAAAEE EEAAgZUIlIK7o0eP7gZyCtz0CH+nwK5PcGfPtwDQ/mWpzJVUP2+CAAIIIIAAAggggAACMxQguJth pVFkBBBAAAEEEEAAAQQQQMAj0Bbchfecs3vR5e5DV7NUpu5zl1sq01NmtkEAAQQQQAABBBBAAAEE tlmA4G6ba599RwABBBBAAAEEEEAAgY0WCAM3zXLTrDr96Pc7Ozu7AZuWxtQjXObyzJkzuzZaNrNt qcz4/ni2tKYFgiGywkEeCCCAAAIIIIAAAggggAACaQGCO1oGAggggAACCCCAAAIIILChAmFwl7vf 3LFjx/bsvQV6+ld/O3To0G5op9cIH/Fr2r3rwnvn6XXie+XZspwbys5uIYAAAggggAACCCCAAAKd BQjuOtPxRAQQQAABBBBAAAEEEEBg2gJhgBaGbArjdC+71Ow3zb4LZ+Ppv/W7eMad9jz8vf6u17SH 3jt8HXu+3lvLafJAAAEEEEAAAQQQQAABBBDYL0BwR6tAAAEEEEAAAQQQQAABBBDYJ2DLanpotG0u jFM4WPNanvdjGwQQQAABBBBAAAEEEEBgUwUI7ja1ZtkvBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAA AQQQQACBWQkQ3M2quigsAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDApgoQ3G1qzbJfCCCA AAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsxIguJtVdVFYBBBAAAEEEEAAAQQQQAABBBBAAAEE EEAAAQQQQACBTRUguNvUmmW/EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEZiVAcDer6qKw CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACmypAcLepNct+IYAAAggggAACCCCAAAIIIIAA AggggAACCCCAAAIIzEqA4G5W1UVhEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEENlWA4G5T a5b9QgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQmJUAwd2sqovCIoAAAggggAACCCCAAAII IIAAAggggAACCCCAAAIIbKoAwd2m1iz7hQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMCsB grtZVReFRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ2FQBgrtNrVn2CwEEEEAAAQQQQAAB BBBAAAEEEEAAAQQQQAABBBBAYFYCBHezqi4KiwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg sKkCBHebWrPsFwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwKwECO5mVV0UFgEEEEAAAQQQ QAABBBBAAAEEEEAAAQQQQAABBBBAYFMFCO42tWbZLwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE EEAAgVkJENzNqrooLAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwKYKENxtas2yXwgggAAC CCCAAAIIIIAAAggggAACCCCAAAIIIIAAArMSILibVXVRWAQQQAABBBBAAAEEEEAAAQQQQAABBBBA AAEEEEAAgU0VILjb1JplvxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGYlQHA3q+qisAgg gAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAApsqQHC3qTXLfiGAAAIIIIAAAggggAACCCCAAAII IIAAAggggAACCMxKgOBuVtVFYRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBDZVgOBuU2uW /UIAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEJiVAMHdrKqLwiKAAAIIIIAAAggggAACCCCA AAIIIIAAAggggAACCGyqAMHdptYs+4UAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIDArAYK7 WVUXhUUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEENhUAYK7Ta1Z9gsBBBBAAAEEEEAAAQQQ QAABBBBAAAEEEEAAAQQQQGBWAgR3s6ouCosAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIILCp Atngzv7Av3c0GGBAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtgDZAG6AN0AZoA7QB2gBtgDaw qjZwiT0Wb3hqVW/K+9DAaQO0AdoAbYA2QBugDdAGaAO0AdoAbYA2QBugDdAGaAO0AdoAbYA2QBug DdAGaAMX28Dtt9/+rd3gjv9AAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH1Cvz/ABX2 xUBG9O0AAAAASUVORK5CYIJ= ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/image004.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhbwSuAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAABv BK4AhwAAAAAAAAAAMwAAZgAAmQAAzAAA/wAzAAAzMwAzZgAzmQAzzAAz/wBmAABmMwBmZgBmmQBm zABm/wCZAACZMwCZZgCZmQCZzACZ/wDMAADMMwDMZgDMmQDMzADM/wD/AAD/MwD/ZgD/mQD/zAD/ /zMAADMAMzMAZjMAmTMAzDMA/zMzADMzMzMzZjMzmTMzzDMz/zNmADNmMzNmZjNmmTNmzDNm/zOZ ADOZMzOZZjOZmTOZzDOZ/zPMADPMMzPMZjPMmTPMzDPM/zP/ADP/MzP/ZjP/mTP/zDP//2YAAGYA M2YAZmYAmWYAzGYA/2YzAGYzM2YzZmYzmWYzzGYz/2ZmAGZmM2ZmZmZmmWZmzGZm/2aZAGaZM2aZ ZmaZmWaZzGaZ/2bMAGbMM2bMZmbMmWbMzGbM/2b/AGb/M2b/Zmb/mWb/zGb//5kAAJkAM5kAZpkA mZkAzJkA/5kzAJkzM5kzZpkzmZkzzJkz/5lmAJlmM5lmZplmmZlmzJlm/5mZAJmZM5mZZpmZmZmZ zJmZ/5nMAJnMM5nMZpnMmZnMzJnM/5n/AJn/M5n/Zpn/mZn/zJn//8wAAMwAM8wAZswAmcwAzMwA /8wzAMwzM8wzZswzmcwzzMwz/8xmAMxmM8xmZsxmmcxmzMxm/8yZAMyZM8yZZsyZmcyZzMyZ/8zM AMzMM8zMZszMmczMzMzM/8z/AMz/M8z/Zsz/mcz/zMz///8AAP8AM/8AZv8Amf8AzP8A//8zAP8z M/8zZv8zmf8zzP8z//9mAP9mM/9mZv9mmf9mzP9m//+ZAP+ZM/+ZZv+Zmf+ZzP+Z///MAP/MM//M Zv/Mmf/MzP/M////AP//M///Zv//mf//zP///wECAwECAwECAwECAwECAwECAwECAwECAwECAwEC AwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwEC AwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwj/AAFcuQJAIEGDBQcmPKgQocOGEBlKXEjx 4USLFSNmvKgRo8eOIDmK3Ejy40iTJUOmPKkSpcuWMFnKXEnz5UybNWPmvKkTp8+eQHkK3Un051Cj RYMmPaoUqdOmUJlKXUr16VSrVaNmvaoV68OCgsIKaiWWbFizY8uqPbs2Ldu3buOinduWLly7cuvq vbs3L9+/fgPjHdyXMGDDggsrPrw4MePHjiMjntyYMmTLkitrvrw5M+fPnkNjHt2ZNGjTokurPr06 NevXrmOjnt2aNmzbsmvrvi1WEEFB2IILH068uPHjyJMrX868ufPn0KNLn069uvXr2LNr3869u/fv 4MOL/x9Pvrz58+jTq1/Pvr174YLAvp9Pv779+/jz69/Pv7///wAGKOCABL4XHwDAFajgggw26OCD EEYo4YQUVmjhhdP5hmArGHbo4YcghijiiCSWaOKJFrbyG4ostujiizDGKOOMNNaYXnxXJGjjjjz2 6OOPQAYp5JDhHagjkUgmqeSSTDbp5JP7adgKh1BWaeWVWGap5ZY+angkl2CGKeaYZJZppnpGnqnm mmy26eabWqYJ55x01mnnnXha6CWVefbp55+ABioodyoiOOihiCaq6KJ14vglo5BGKumklHYpX6WY ZqrpppxOKCWfnYY6Iaj3kQqlqdih+pyqorYaoZeuxv9KIKlXsMBCfoLYyqqNvj06XK2+TteKrcEu l+sVu8qqrIByLuusfcOyEIC0Y2Ez7LTJetfKtNxSK9wVAQRQrLHSlstCWPOp2G0AOWInSLgssAqv duCKe5yt3A4EqrQBPOtvgc3+K3B69YZr8K3Y8KutvscVbPC034abLXLvPnzwuMYNe4V2FVsMsXXb hovctQi7G66vHVu8cXD1ThycbysPLHN5e85ss3gd56iitCxj213Iwb5ra63ARmzvdPXqjO/Jz9XL cbg61zovyNUiN7XJRxNXcby+GZygws4J7fLNZENXKMZlp72q18ORFRzYU44cd3F88lmxquCWXFzL wY3/DR/Tf0tM99zDiZ0x4cyFHHPIAZA6paqIJzd3udE9XlzFMRcOODZb99z44Mblrfbo1zlK+unS 8RssyWRxe2RYB8f767nbsrDxQPDWejm7FJ/MeNXW+oYc379CDV/BtgunLtTJ9y31tMAnh/lw/FLZ eu7EPS8uh+pmLrXtISNcu47XWp/rw8BX7KviyhvMIdjf8stugtHmnjnq+FN8af78I1e9//CyGP0e Jj/rSQt5jHsYrT4WugA+LGbTM476tDa1ji0NYSkzWN8OZrD7HSeC1oIX9/h1QaPZSlpUgp/DCPgy 422Qd5zjoAtjmDXNZY1xnrOex5iWQZH174eFI0jk/4DIPxweZ3ni6hoDaRe8q8nvXLer17keJTTb 2e5IBctRx6iUtIYJjjgatBYTlwccsqgOWW/7IvGkBzXudTGG52oh766FRrKM0GckGwvyXghBwA1k gD6kIcpm+Eb4qY5D30Nk3rRIRCLCqpFADF9yVCec/xlnhpS0Id52GLN66QiExPJiDdOot7ZtTmim rOEMRybDacVslZTDxior6cMJeg6Dm2OfBL8IQhsiy4x+FFwvLSk6SDpyf8bMnySPWC4+wa9vj3Ni INs3SuWNZUoFS9Dd4BPL5axROGFU3pRA6DRqIists9ylxxZ3wLQsU4qEYx02vjk1XYqxhty71ucE mf+xHcIwYbXcHA1XZrhk9i9gBj0d41j1TOLp84F962bgXMavlX3zmWwc5TI5Jz+2tVB2cuSksdpI xhfuUHxP1BHY1kgyOS6OkB0FnC0pCK+hgYpv34zgNxN6uprxFHXuO2LenOnD3+VImv0iDtCao0tP ctNn3hQoDXEJPSV2cprhE4sWs2VLeZ5Pi70h3/mgd0+EWTI49ZzhUucJtbBYcqa+tJZxwAZXXRb0 pwpdEV5PJ0UAJjWHbN2nLP8pUTm67I1r7GVGj6TPFAZyrXC0m1SZSkjj2TOjK1vpF8sqx09adobb HKg6i8U3uBKvmHslnelSOzoLxs2MZSVqUh2KVLr/WVap8czkFtN4tGhtcp9T6uAdS0bOzQ62lBMD IQ7l2Ta5Dmde8kzaHT8myeAybaNvnWwI/1kcuIWTuRP0G2tjhdDx3sxhS0tqQwWXNNy5sKVK7SBI Nzi05121gwfj4tVoakUCynYg2QycFW8J4FpxFZY8zB3MPretAgMOfgdDb0RrClF95kh+2tQuXLOn xg4+z7Ou9KB5BfapEadtrBHmrWxpKV8GhrN4L74nAb03v5QaTcSBJSATqek1tSKwiRYr5QdXCc95 xlR2D20jZ00qsW6i+MJHQ7HteKnhdLJYeStsHounaWIS67XLZCMLgNOHLmuCCmZlBJ4dP+gbU4n5 /4+Oq5aYo8c5OheuN0PsG8y4F705g+rNZWbOWbQW6DfrzJoAPnOgg8ewNSNam2ruFYcGHTw7V5pV ZlHqmFEVFjSCeWDl/bSoR03qmYW61KhOtao55dNVu/rVsF7U2WJN61rbGk+rvbWud81rMZ2618AO trCFVOJhG/vYyKbRI5PN7GY7O0S/fra0p01tZiGz2tjOtrbv0+pte/vb4CbPrMNN7nKbu3QCQdu5 183uc0e73fCOt7eLLe9621vby763vvfd7Hfz+98Ap7W/CxQ5Rwf84AjvU7cf5JuSqejC4k24xCeu pXFHyCzExSCOKc7xjscp3RUaViWp1GaPm/zkWP8a+KxK5ultoepxEUe5zGdOInpfvGQgzdXLH6du TSXiCzGnudDJlu+bf4t+Qqabs1rxBRp8wRVDj/oxDUUhkb/MomjreaWY7nSnB13qYF+WygUkNU8X OFtfV9TPnV6Dpj897HBv7W/Sjis72i3Pl5PVINzedbdDPe6At5nFYUT3Qf287Yj/QttpkIjAOx7U IJeR1iG1975bvul/L87bH8/5TY09RIUH1NoV7/bFk57xmm975jvPeknZ3EWTVxTX+U57y6/+C3xf fet3n6iiuyj0f2J64k0//LYLR/GJz4Luec98QH0eRLGXNe0H4YrLY+H4tcd887fv/Gv/PlbC/8L/ IIKjCBq0Hfe0EM4giN901XP//bieu+RlxfTxC6f6uCeOK8qffRq4AvjwF4BNMngvAoCBkn7E8XMI mIDDR3ptZ4ACGIFDkmvWYWkAczqJcHl9B4ES2IE98nxaczvaNSuos36ld4Ic6IFlsnwJ93rPES8a kysMhzqtwH8a+AWNp4KJwnRfoDwS53tNM2Ay6CAp6HMNmHgsqIN9En6DoAg1kIMIB4JtAy6TtnHM kj+0kIH9pwhJqIR2MnuWpwgt6H0tUoSbwoNsV3r254VLeHpu+IRRKH9msy7lYoUBEn0Dg3saqAhm yIZM4gr9x3d8GHAEyFREY0VFQ4REpHhpiHhr/+iHdTJ7R6h4XagmrVCJXEKBL4KHA0MLN+h/R+QK mAiJFReIoPghiZAIC/gerLJ2q0gmUjg7VsSJpdJIgHiCTqd8xjEINPCIpEgmgGh+i1cDNTCK75GF 18F1vsgeTNd4TJd5weh+ZuKCYXNCQzODkKSHtbd6raCFNKAIr5hMffhpPIh/p8gftFB+ObiMzFF5 TQeFzOh27sgho9d1xmglQOgcyQMci7Yg4xgqt3iEYhgcvOiA4sdTP8eORQSP55EIA/l3gDiQ/WGC vYh7DLkco7d4CvkzGeOAxOh2GemA9wglsfg2tzMlSUcgtDgzNjh91lKQl9eDyTR6MhlJTSeR5v9R eSOpHtRXHPt3gxtZHPXId6ooHhapVKYYiDXpa2ToHCoSWCtZH/8oKonAfjnoiR/pdHpofJAUkjj4 Q+GHetWRiswhimuXBcUoHf8nHlVZAzjpChSJeyd4kcdRfeeXhkGJHcgHdPfnhto4jOyXlyknhxWI dwMSlSzZd49ojtm3lP2DBf1XlPgDhto3HfxHl8MBlxq4k9iAf2ZYj6v3ifLYHJ7okuGhjU4HhajZ mC3pmGJSiKtSFoYpIFPZKlX5lfpHjEfIlbQQjmRDll+wA414eoIpM+GXeGKZmcmRCINwm6mJHLTw hg6Ik8rBf9LIHYfngFDoCjWwA5PYdr5pHNz/SXrsCJecaRzR6Z0GySfr14BaCXVVuQPFmXKRhzQq g40JpQgKWZpAiQ1Od56x0pKi2YsAKiuM2XeOWX/615neSHsXGZ0wmX3zeaB8uR3uaHvCwZ8SqlSD sJ9Ox46tUJB0d6F815MEmX1eoH2K4JpjUpLQhJK1KR4xKjCukAXSqZVupzbll5VfYKM0gJZu56Ms ejPuOIy+GH7wuHe8yKM+CocZKoxBOpfNwX9C+oDb4Zw92n7waJd/6XbcExxaWpfl6YYFqn5R+p/E YYLReZc1oIvNAYhXQo3SoTsOgpj9Q5ka2JxkQ6ED2nSks5paiZSKGRwN+omZp6E3iJnGAaja/1ej 4fkctEChy+gKWJCFEvoFkDmobxqhXmcdq4kqawioxliaXoCPXyYdMIdKijhqgPid7Tekm1KJWuiq xfcFj/ovcIl7DXikBnl+Jkir0JgF6jmJinocunp6jcedbVqmw3GbbVes2PBzNop4PJp40Boca2qQ aFodwbmtyBGMvUqdPomW3QmrSOKi30NWdVpqJHqD5gonyVKsrtB4Pwer7Up7raChlTonL5ccK6ot SimoSWmoTxqIWMCc0oGaUHegt+oc+CeYiJqU1+qf/TeKIeqLzMkhuJctfJp/33p5pfokLmots5ku JeuDpdaWZ3p6XIkn0Wmrx6Gkx3Gbe3d+M/9relspjbQApA0LjOjXrC1LHDv6rtHRqrhIeroXkmm4 tCyblsrjl6/as8nRrckKpea3edZBlsvRnm7XpTgKs9+KDUV6glgrnqSXCIA4r0iLDVIbrTj7s3V5 taW3A0QLJAvXHO6Ut7gyZoKWavcak9TRjZw5sQwCgGC4qF1bfgvYsa7Jp/wHKtFJmjPqIHDJHD/H Ic0Zol2HgDSpf4xZt0wlsbs4sLlXtn3Tf3o5kJ+IHowKuFO7dwMbnpqblH7TmsqBpzc5uRcCmyMl UviBZPM1MqrWlj83rB5JuPrndm0brfIJIWsZrQtbodFBC2sIrue3isc6jG/nrL1Kt0Dbq8n/ypl/ 54SDsLwsoqx5Ca5j+5H/55FLaZ17mSon2ICKGpLISbeUmBxGS6DcMQjGm3jmix1cW3zlqhx7Sau4 p3sv6569arrKobJliYvz+SOa6JQcxY9xVCo4x1Wr1oRguqGVg6/tOJoNgn+p2IvzWH7QQQPeO5RO d30f/IkCmn05yKcFWo5dpwgZayORiqAjQ7ruGhyMCzKimxx/i6nW0rNcx4X9a4poW5cpeK9yacBA zHfEkalVvJP/Gro5HMA8gq4sl5LtITwmKTcwl09oTLJpbDls/FpuvMZvrMZy3MZwXMdzHMd0fMd2 nMd8jMd+vMd/rMdzLE6AXMiC3Mf5FA25/6q9h4zHnXm0UMfGcnWJYzt+X2rIiNzImFyVidAK17DG 14ANO8p+S6t6fDzJtyi3uVh6bRcNSpulVgvLLPt0IWp6TdqDlxzIHBKNpMyXmfzLuhzMmCzMk6zJ cgWX3IN8TWrJr/XJZ1qt7WfLTdeL0QqkSzsIw7zGN1oDPzoIn3zI1IuLNIAFcRPKmXy5xgzMmAvN P2p66cfGt+nJ2WzMKbp2qofN0aDHa9rL/Ax0nrymTdrPV0sL6kyy+fzLztyprRAN81zQv/wqQvR1 oXQt+EHGsrQ6dpS3Gb3RGt3RHP3RHh3SID3SIl3SJH3SJp3SKL3SKl3SbMHSMN3SrWAKdv/ECiHq dl4w0zFNFoHo0azAqW5HzjI91Ds9Fj/ddYMQ0kA9oInQ0YOQ04JQxUA3Fkfcp0Bn01GtgU1d1KRL 1F5d1GDt1EMd1AbL0VV8g1B909bXCli90ymK1iEK0xfaoTwX1nbtTljcp3kboV8t0m29dznd1h79 1oHYoZaXsYJQqKR71Xe90k04Fina142ttwx3qpXTUbprLJ2ENpkNr/kBiBOsef8btISKttlrkPCJ vOERkIonrqKMwA0ItsLhhE/Hva4q29YyvwZ52ubni0aLeK6tHNbbwEhIIVUprqiSra7qdKnHwLTa vJlJejZ6lAm7m6BLHNHJ3PRB3MRtfzz/iNrHGJyTSCVqOBwH7JFHuAOqjR2Ht94cyXBN+Rydph9W lzB9u3SdvRxpN8OOOa99+o2Vx8TsYcO617pJCcPk99+0x6IuvOA3Wa8daY/SsdSBGHr5rX853DZu dtZPbN5n3bURHqHU5zdDLCyDSB+4G8SifIMXjn2Xxyea2eHC4a7eWK+hzd6CyKwVCCGxSHJ1nR9/ lMG36y8tvh1/Z4K4N91CybRe+4Y3buRvK4zLp41N7rVQiKW9/Jd0K7s4GJdUPpCPKnzBrd9Qy7RK bh23yD1erB3OSYzw+CXZXeZlvox7CaXHiqNX+8NNfo9Y3n5P3h/7+3Q7y6aherRW6h4v//ufJ56h wV3nuFmV9ufeWVutkp4dRZ4dd6vfjZNl+YFNLmOnO3jp1UHQuf0F/K1UM6zgjDeONeiYrQuOiKvq mZfqSZm+uc3iicOsKb7g2KGYiqce6biH+aR5H854uucFW/6h1qLY5tqxnVqdKj4qIDnjIJ6heS3C 7yF+MYfUdeMKa34dDf6NmQ3qrGjZYTNOdifqINNsL2u/pOeaBbnc3E3N3jGvWZjnJ3qExYp8h6d8 5Il4tyec84576jnBA+yG5RGMdAuIh/eR17HAiuedW44etJDeuN023I14//uOxWF/1Dscv4qbyFGz DeytxmHb8WshgAi5iseO4t3Al/7tL//77d0xnru6JBW8KvCh7juebLRw7VpNKlWdCEBvmoTCmkX/ jcuxeWAb6efo4v2HzRR744Aq9eXBg8QRnNedHEnvpz5J82Eb7g4crUlpq0WPvP59kFtLu8jRujoO 6MURucZa4Q3C85LKJC4qPE61qscG0NGMi8S4fKMX0GKYytIJokQszbFcpX/uk3NvtdeJjNHh6GCv lrsowNwsy9q7itTrtNw6fhAqiV3bdMsYlnL5dKSHrY5+nnraHK+8eMXKtVf79hTSrbNsJanspDgf 0dQBg0kkxldobPkqpAOahBeqCFggkRGr7ELpsaha7NL7HXq4ok1X+VDP83pZ6/fnjcz/Wnn1+t+S SfYaiIC+uIGoCh1/K4rKcaG0XyGrieBVUpqNPyP5iLcnxFEPgv2oc97u6Z2ymr8AgU3gQFdfanxp ZXDQQIaJdhxcyFDiRIIGLR7EeFGjK4odPX4c+IVjQY4gTTL8IvLkSpYtXaJ8qBEiw0EZa5R8WfMg jZg2ZX6hERHbzxo7EnWklRLny5eJEhnEePSkzqVMrV7FavXLw4NZvX4FG1aiK6lizZ5FO1AQAACC sl5hIVCQ27R1KdK1m1fvXr59+aakAVRwYMJVKQ4yPDCpQFdCaQ5OqYhjokGtJKpkSIsw5M2dEfqt axl0X8CQhbZS5JmGK1oeHYfkHFs1/1DE2J56/gJSNGjAiV37Hh2cZUos2FxJFp5c+XLmWNe2bR59 4G7p1a1fXw5V8EGRmO0mqrHzSxbat3MPHLSDBnXj4QG7D/z+IHvs9fe2kpqyVc0vjl2J36mrjiyS SCfBAIspMPJogM+i87BJKMDXqvtCqkRaK6g1+zakyTjayHqQQxFHJJEhQa5oi76WWmHBrVbwKvGu GGekkUYvgFLkqRDzKmi2zmorbTWJNBMMC9kSUbFGJS/TUKDbkDQOQsokasUV1Zq87ECGrJTNNCI3 29G8HesbRCnilqyuR8jGRLNNN81qBUUYXxIkABbiOvHNJN/ks8+0+mMMtIQC84lQwf/KNPSmsbLo yhVXIlTKT/ueAi6kgzQ0r4YJKQJPOyxhwkhAxooq1L2ICgpvh00TYvM6Awnd4YtPJdULVZlEpTVX Xe8C4Io5XWoxTgjjcvPXXY9FVrfkzPOxSyEzCxOoWZMNjsviPioti4I4U2RPggibdqjYQvxyMEXK 3JGnViEcMUjIrqX2rEG7zCJee998zliWrriCRWzg0vNegQeuFqgdYj1oQY0Unu+kKgkerVMGZR2w wZ0YvWjTiZ4KV7uEgVL0W4RoEZC+xtrUTkGgMF4XYpb+E0zhWCd2ueYNTwSgFW+no4hFO+809kWv BNnZI/Z21vekpE1ScU/LjtZZIrz/nmZXXnafXrolomnUebeulZ0I6omy5mveZj274iqqO0qbrzm/ /ujtqBmCumiruNwsC1rYc9ezg1oI7CTggqShBcHKUqyso13CumkqdZubZ7ojl2ssZ4GCsjmhrSLb 6LtVw6IwgZDumcrI4W7Tcc7V9mhrkIJ+PWyxp6PcJJw7F1YuYrER5M4WO/L3rbZfAlggX8F+KXc6 fzeR+bH5ZUG03vmli0W46OIXertZihMuFnwN3qvjrep3tBP5lT574KHHCy5+B8q+/H+1b+4KAVhA YIaMxmsBi4xcGZ9L3BdAukVPL90zIITY15HzJdB647Me87r3PrPAzCa4wkZSfGIT/xogAGjAK5rH vsCCFhykUgwMAEvmMqyxOa+BSTqf/CIove+Nb3rO81CiLBIU6cSPfJ2jCAFb4gry/A1/LBBS95KE wPapz3jZE032ErikOsHIekxh0fYmIr/nfYSJ06FgzxaoQCfOD31PhMv28rWSBLIgAOmTHM/i5DXk je5fVmTJndqWJ7pJ7XFhC1tcFgdIufTOioYE3hOH5aLytVGOXBRLAvmoxcVBsGorCaDqPEfIu5Rv kossHQt5R6w8ncgy7wte75pjBUH4zwu0MNJsFuLAlhxvkNgQZCh1uRIa8syUoVTlHRVoGeYZ0JY4 /Mp+VKOITymzS/lbCB3VsrsBcf+mBQmo0CUxGUY7AtIyfARjJnWny2I+TZC/myMuRUNNCMkGOc0B 584WR8taArGPxglMC5D4BQS0zVfsvGcrUjhKuaSNj8cLJjiVdCJqTu+ef+TZ7rQokClKRIgQfaPx ghnKVBLrfRREqD+H9zq2APGg4EvjsL5XPeoJkmi9E82LwEfGKPoKldP7ZkyN9c9TVu9833TR5s7n IqKhr3uja9EZyUgXoiGTmy8daRDTFr7oKY+AChULBJNavs0J7UUwVeITT0TU7+W0kB0xoEzlV1Su uo6p1yukr9zavuqxE5kTMSDzVKlVUV40OOWjWCJaMNgvAO4LWFhpW79nPOnRR4j/3XNRixDJO5iu kLIQ+qlcWGRZ3vWMneFj4FR316/wHW98FQWLBkFWA0UgZYev3cFgpTZZuHaVLljAAgJoULj8CaAF rrhGZl23OSoVVS0v0qspvQpQYUotqls06PCquru8MpI9SQnMbTQmHKy6FahjhZ9NyVq+tarIlj7F bGaXypgvzOBGX9goaD2SVzja0KAetWd9xoqXpI4OipqlHlJt+k24/uu4FDGgZVdYygPj1WvMpRJ9 C6rOCR/UpHKyW57m6MYABIBfdnJLVU3btgAQzV9xcospuco70kqQxS9urkSiV8qphlRnLjXo1qbr lgCj843IHaYwAcaeTKbNrwwJ/+lAyipdGKE2LA78HSqJdWJ0rtioLqaxRsXoIqSC8pfUhVDUElxV Cys5pmlFsbeCB8EUyjDBBWZOi1gRtSsgwHA02Gc/b/y0ngoznRbtF0sVGGITE2uxMp3wmYdVYwJ3 EtAgeR840Ufdtn3PyWDBj+iMlgjVDGIQKQ0zMb8JZooO684zEABQDu3f5l76eG3UsSKB/GckP5eb CG60QErM2UO7r2mehlC3rBNgiq7zlKLt6Z7tOOIgbzGyXUakLacKzqrubYoQLmCzBUrhUR77rjSS qyfnUmlzLprFop61mPtMa4a8ecKmtW4gs/06Qa41hbsL5hgdJqc8XvZOPgOxzv/C3eV0CiuLN55f FOE4umAak20hPqpVeaxs8KEYl11+sVVlqrO97rR9uQ7thCk6biUPD6uRrCvB22bw0crP5VNT5Zrn 6zUNT7neTxuewHmccuXFOKMT9BbzZPjGofv31skZ4DcRsJUsICB0CJAKF4+q4ef+q4bJPurLuT2+ TDYcfin+VRYLqOYpS7dfw7spnNFSk9aChBZIGoxQHArGFK+cq9GjBRJjpduDzADmlW62RUWudUmK Nohvw7bIqU3ykZPx0iOaO9dPqXCJTyfLPUdy3RNd9oQjmrGjG+iwdtbGXhadxUenUZ6MiXBJh1jJ MP6X15J88s9zXfNUR7AVE1//bN4T1N4FRT0De2XPueh4LlkMltAg2MjXX+9FNU1bAqW98dhzVsY4 z/K7zzfylwaZjyuk6vxe2mdn624uZV2fWtDeVDN/vS6F37zKd9Nj98/xjIFOGkKLf3MFlrrUXxU7 zsK/dmMp6tio8tMyFoK/3DnA5KgoQdAfVaMBxGo/FaOLuIAk9wuv5yMm+Jk8xYuu89O2uGAuSLI4 ozG59SutomKk/GKcE6KSQQiKqvix40MnUmuFVMMzwtgnRNsvUmI8qdGjxbo4LVur6Do8i/KWX4I9 PEG7Duw9rvkd0msu5uM9Cyy1IzsmVvs+Iws04oK/1/sILuIr+OvCNlmws3ux/9YrOMP7P1MqJm5z MBjLk9NitwScjt2TobUiQTg6qZVYI0AkQu9xI9+ZGpsTPRaSry7juugrt/Xqps9LOFGiMMjCQ1Fi QDf0F6zKQDy0qX8To3lTJ7HjtkXMqpRTxD1ywzysQojrv9PbEwnLHUSMvf4rswBSLxC8pE/KvFqr MFVsLlMcjau6goLIAgGggUSQn7waPKWyKBghIFKUsPkxQkaiRDPaIuo4wfWhDtKTRoS7ROYgi1mZ Li2bJGLpoPDQrZTIH44wLZwDPvMKqur6vAUswanpRLoZqeChstfbqG8jkSiLrA+kMMIDwXwUpjPk RyTsItgTxku0PWp8Mb3aPf8Robq0uq/2c6SCO6cELKM5BCnNg8VnlDePIKCIbERceitARJHa+QgP M54OK8Q72bWloiBSox4T2z9Y/B2YSqk4wbcmkx6YTLjlC72hirc/FKhxqzG5Ij+1mJ6oisiB8DCb Mj+hsykms4swtDARLLjii65+ibKzW7k70cCRzEn+S6fsiTnwyagwYpHPainsIzZc9DAm3Cqc46lF 2r44o473sRIE4LG5VCIivCJJjDG+3JpdGzI3Y0rxa8vdmCz4SayYHCuQw6XKHCXaykrzc8brCLem 3KyRO7YZmIEAkECDoAEB8ILto57iwaUUxLhxSiz8Gicm9C+gISbNjCegacT/vjSkYgLL1MtKAjs/ 8hsxnirMsnzCQqKucXNCkZMs11kq6buCmqQ19/mlzTojyLK5x0STmySlShu4Nuypf6IpiuSv9PlO 16yrqJoeJvww8CPDxDIn4Wop8wMvpeE37pFMbcRN8ItEyZypr7I8uDm++Vu/yaGdA6WbQ5IhyYma rrG+/aSPrzo+Bk1MDMUL3KQsPLKL04mjBI3E4jvQuSFRT+xQ6kxRaaKsueFAPuvF4qLONJvN9MrQ QqK5Gs1R5qAcGF3RrwHA3bg2F7U8y+K4qdFHBDWxSLxGhis+rIlS2MNRfIQRE1U/6tQvzhpShrMM Vxis3doMjMAC0pKeJlXR/9HRxsnj0gbVpiqF0gy9quJb0SuFJCxNvQo1sbXZxT1Nr8gpUi/NPBhV U6h8mx6l00eM0+HqUQOlu4mqDxh1UTydVDTt0kTEVOIapgkl0qNJ1ELKUNDiUJ3iUSuaKUAsKTfJ zqxwQb6oOpsBo/kTFIR8HUglmD/T1L96VZPYVVgVCER5rc2wGwfSUgQLjWL1VTiRoVYNC2RNVrfJ OWa9Q0yy1WZN1dRh1k16VhHJVUHJ1m31IiVdDmdlGnDtCDFZrc9gGhCNm2rlJXOVF3EVDneFV84x 0hXJVnr9itvR11z51noF2ICFKIHtE2ANisDoV4JV2IVlWLSIE+gA14Rt2P+JpdiKjY704CFHsdiN 5diOZdXhM9d/9diRJdmSvYpByByTVdmV7dhA3FaJZdmYldmZpdmatVmXwRmXtBmRvdme9dmfBdqg FVrluB1zhdmhRdqkVdqlZdqhddln5dmmldqppdqqtVqBfdpkPdqr5dqu9dqvBduFwjBJ2U8WWyml Cdu0Vdu1Zdu25ZCHjdrqOE4li7c6ctu7xdu81du9ZYq1OLI24asobB2+JdzCNdzDZdus5RO90kq7 RdzHhdzIlVybzdmtDQ4A1EBwKjEQ4rjJ9dzPBd3QndiidRNi47bW41ydFd3VZd3Wdd1dUdw3Cdwi fN3atd3bxd0+iV00eZH/u3yio8rd4BXe4SVe6eBXP0ExCCrb4mXe5nXe502m/oTe6aXe6rVegfHb uL3e7e1YPuXe753Z3QXf8Y3ZmQRI3rFc8lVfZKnc9XVflr1OFsuoq6HKGn3f+4Va6cXf/d1Ys6Qo PClEHrMTf/og/jXgexHfA1ZgePVfq+O4OPkx3y0rgUrfBbbg6EjgC9bgmmng+A2zOrEMmBSopDrf DTbhEjneE1bhmoFNszSk63SLzcWGqqS9FbbhDYHbG9bhewFFGJ5hg8oooiTCHSbimwHZIkbiXLnO QvSnEnOjELOTUbK0Ck7iKnaJDLbiLB6RS02vzjqrP9XiMAaN9hXjMjbj/zNuWf1F4zVm4zYGVyx2 4ziW4zkm22ul4zvG4zxGw7HV4z724z/GjhwG5EEm5EL2i+w15ERW5EXeVztm5EeG5Eh+npyhYkm2 5EuuYtLF5E3m5D6G404G5VDO4k8W5VI2ZRtO4VNW5VXWYUFm5VeGZQ1G5Fim5VreX1K25VzW5eIl 41325V92Xk0G5mEmZtzF5WJG5mQm3BcpKTp15meG5miW5mmm5mq25mvG5mzW5m3m5m725m8G53AW 53Em53I253NG53RW53Vm53Z253eG53iW53mm53q253vGZ2puCxTZ55LiZ5zp54AG6IH+54L254MW aINOaIQmaIZW6IZe6GWIhuiJfuiKduiLlmiLzmiMpmiO1uiO3uiQBumR/uiS9uiTFmmTTmmUJmmW VumWXumYhumZfumadumblmmbzmmcpmme1ume3umgBuqh/umi9umjFmqjTmqkJmqmVuqmXmqRBoCA AAA7 ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/image005.png Content-Transfer-Encoding: base64 Content-Type: image/png iVBORw0KGgoAAAANSUhEUgAAA4sAAAFCCAYAAABYa0g2AAAAAXNSR0IArs4c6QAAAARnQU1BAACx jwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAAAlwSFlz AAAXEQAAFxEByibzPwAAXMVJREFUeF7tvb3OFTuXrs2J9SGgfRocBMkOer1Si6ilTkh2tEROTkxK SvSJkIwzeL73ZvVgGS+7bFcN/1VdU0LAnFX+ucawPe7yT716xQcCEIAABCAAAQhAAAIQgAAEIJAi 8Mcff/zr339e+AMDfAAfwAfwAXwAH8AH8AF8AB/ABx7vA//vl27EGR7vDDwo4GEJPoAP4AP4AD6A D+AD+AA+gA/88oF/iMUXPhCAAAQgAAEIQAACEIAABCDwWAI2kYhYfKwLUHEIQAACEIAABCAAAQhA AAL/JIBYxCsgAAEIQAACEIAABCAAAQhA4B8EEIs4BQQgAAEIQAACEIAABCAAAQggFvEBCEAAAhCA AAQgAAEIQAACECgTYGaxzIgrIAABCEAAAhCAAAQgAAEIPI4AYvFxJqfCEIAABCAAAQhAAAIQgAAE ygQQi2VGXAEBCEAAAhCAAAQgAAEIQOBxBBCLjzM5FYYABCAAAQhAAAIQgAAEIFAmgFgsM+IKCEAA AhCAAAQgAAEIQAACjyOAWHycyakwBCAAAQhAAAIQgAAEIACBMgHEYpkRV0AAAhCAAAQgAAEIQAAC EHgcAcTi40xOhSEAAQhAAAIQgAAEIAABCJQJIBbLjLgCAhCAAAQgAAEIQAACEIDA4wggFh9ncioM AQhAAAIQgAAEIAABCECgTACxWGbEFRCAAAQgAAEIQAACEIAABB5HALH4OJNTYQhAAAIQgAAEIAAB CEAAAmUCiMUyI66AAAQgAAEIQAACEIAABCDwOAKIxceZnApDAAIQgAAEIAABCEAAAhAoE0Aslhlx BQQgAAEIQAACEIAABCAAgccRQCw+zuRUGAIQgAAEIAABCEAAAhCAQJkAYrHMiCsgAAEIQAACEIAA BCAAAQg8jgBi8XEmp8IQgAAEIAABCEAAAhCAAATKBBCLZUZcAQEIQAACEIAABCAAAQhA4HEEEIuP MzkVhgAEIAABCEAAAhCAAAQgUCaAWCwz4goInCbw5cuXl3fv3v368+HDh9NpcSMEILAGAdp12g4r c+lVtm/fvr18//49CaQlz6N01vD6MaVoYTamROQCAQggFvEBCDQQ0ID+9evXl0+fPr1oUNP/jz4S iq9evfrtT0N2XAoBCCxIgHadNsrKXLzL9vHjx9/69bdv3/4DSk2eNeks2AS6FamGWbfMSRgCEEgS QCziGBAoEJAgfP/+/T9EXygC9XtKODLwHcP98ePHy+vXrw/ZxmL7zZs3t/NZBZpxPeU7uRmLEEB8 rx5m8OlLwKNdy49jm8f/l221GkEPpnb4eHDpVU/vsqVsF48BNXnWpNOLiVe6nr5cw8yr3KQDAQjU EUAs1nHiqocSSA1cRwFevMz0zgOfAiP7c9Y9dH8pYE79fja/Fe+TEMgxqBHG8b2a9ebTl8DVdn1k 8yNfWP1BwFUuPa3mXbYakVeTZ006xsWjz/Vm7O3LNcy867Ay3151JV0ItBBALLbQ4trHENCMTuuM lw36IaSZA19PY8X10v/PfBCLLz+XNB8J5pL4Qyye8bxr91xt1yWbH/nDyrOMV7lcs8rx3d5lS82m xSsBavKsSUc18+pzvRl7+3INM+86rMy3R11JEwKtBBCLrcS4/hEEjpbVaGmYHVqTui5cijRr4Ott JK/A5YxYlIi/06cUbJXqi1gc7w1X23XK5upLrF/R30cPq7R8e8XPVS496+RdNglDrSRRutqGkJr1 rcmzJp2VxYy3L9cw6+EnXmNaj7KRJgRmE0AszrYA+S9HQAFA6sm+BpNUkKbBPtzTGF4za+DrDdVr YI3FooR4uNQq9e9VA+WzzEtiUb54NLuIWDxL/vx9V9t1yuYpG3/+/DnZF616qvJVLuctUr5zRtk8 8/Tqc8uk2q7w9mVPZi01WZVvSx24FgK9CCAWe5El3S0JSIikhKLEYOljB+GE19UMfCaIzoog3ddj L8vRUe5eA2ssFs8uZy3Z5uj3XvxqyxQHW6nZ6qPZxaticbTvlLi07INtuTaV71nb17Tro3rWBthK IzdzU+IY/n7mtQxn/KKGy6z+bkTZYpvU5FlrR68+tza/2uu8ffkqszN+q7quyrfWDlwHgZ4EEIs9 6ZL2dgRSs4qlZYBHlcwNfApSU3lJKNQcYqF9S0cntCqd0l43/W7CRDN6Jlbjwd+OhFe5bJlcvERO /w+X0NWIa3GbJRY9+JndlZaYaRbozCcVbKV8Izeb1CoWZWcd159baq3v9fvRw4tW3zEuufvi2Xmr k3wq3gcmn0m1K/lczQMXD9tfDWhbAmzVv3TI01l7hP7q4Rdn+ruadtPTZkd98VHZVCZrQ+r/Un1e jZ8cpXOmz9U9YdsO+/Zc/xSOJbq+ZgwK23RtH1TjyzXMvPz2DN8zfTz3QGB3AojF3S1I+V0JpPYJ lURXq1hUcFA6PEfBeurTevBO6t1flm48KCsIzy3B1T01yyXDoKEmcB8tFj35iUnMsOb00tiuKa65 oCrFtDZQU765ZY0pMSIfzR2m0uo7Rz5Xc5qiBa/xO+nicqvMudeNeNq+NaCtsXnLUmPVO/yctYel 0csvVM6a/i73IKS3za6UrcYHrl5zps9N2TI3nsj+qb6mZcxrefCh/EoPPmqYefntGb6uAQeJQWAT AojFTQxFMfsTyAXoNe+6y5UuNfDVviqi9r2NpfRygVhctpxQtMC0x8A6WiyescfR/rAU+1Z/SXFN CVHllSpLrVhstZ+lW3NwR8l3cmLx6L6wXhKBtdfmljJ72r4loE31DS0Bdm5p/JFYrLXHmYdALX5R 6pvC31MCpbfNastXW7bY1jV+cnRNa5uVr6T85eghVuoBTEsf1tuX4wcjxriVTcpvW9OoeQDaP1Ih BwiMJ4BYHM+cHBcloKC49NSzteilYEczf7lZxpQwCNOzAFpPxyW6VP6jmcGaQCYXPOnemhkgu792 6W4sFuPlrKqvlkjpabnHQO3JL/eUPCXyj/wmJxZzJ8XGgVyNWMylJf9T/voj30n5YirQLPl1WKYj UROXvSXdo2tTwa6n7WtEQKvNc7M5qXYXrxho4RaWa7RfqJwqe24m+6iP6t3fXSmb1aemj2255myf m2KcE4DxcvTW1REtYvGsL8fMvPz2LN/WOIDrIbA7AcTi7hak/G4EUoNebpaiNtNcEBfvI0k93U0J LtubdPSutVQ9WmYpbfmhCdBUXnG9znJqfXXG1VMgPfnJB1LiqlXU5sSi0k/tS41Z14jFlB+mWOaW /cU+kPPrku/Utoej/bhhucU6FRinhJen7UeKxdTe0nhZoac9eviFyhe2i1y7j/2st83Uds6WrcYH vK5RX9DS56ZEUGopamoW8mjJamrcaxGLZ325RmBf8dtWvrXjP9dB4C4EEIt3sST1uExglFjM7SNM DaRnKpUKxGqXUR3t+TqaIRolFhXcHe3DPMOr5ql17axPa6ClvI/EYm4JYij+S2KxdVlaKtCMD+9I BcE1vpO6L+U7uXqnAsLUioAzdpAtrrSdFt8rBdiqf3jwSWzj+IHEGXuM8otc35BiUHswVsj6is2u lM1LCNakc0bMxD6TmjFM7W9sWYKa67/C/tLDl0N79/DbM3xb2jvXQmB3AojF3S1I+d0IjBKLuQKn ZhdbZ6muBrw1JxN6DqzxPlEJQQVPpQOArs4wHjlNbfDp5XhHYlF5pJYWh0FuSSymxF/JznGa8Sx3 KsAtpZnym9x+JF0b+8DRQ4LUctYz9qm1fW2AnytD616pM3v7SvYY5Rc5Bqmg/8yDoB42qylbjQ94 XXOmz031G/H+43hWvnUJak4s9twL2sNvz/A9079wDwR2JYBY3NVylNudwGyxWLt8NKy4AiXbcxbu PSsJiNbAPYbdsiTqiqGOTis8I6Tjslzhd6Ve4b0lsViaXSzZ+oxfpZZ2hmWuCYJTfFrua/GxM2Lx iu1b6pHicFYs5mZMz5RnBb9o3SM+0malstUw97rmjJhJzbjHS7hLfUdNHzfal3v47Rm+NWy4BgJ3 IYBYvIslqcdlAqlBqPagllzmNcGC3Vs7CObeSXf0NLd2GWotxJZAvjbN3HW5U2qP9m0e5enF72q9 juwep330IKMU8KX2/5UO4Sn5bel3j/bQ4mO1YtHL9mfrf2Tzo/ar/I5sdqY8K/hF6aGEeM2yWals Ncy9rjkrZuKtDeF4llqCeuYBXKtYvOrLPfz2LF+vMYB0ILA6AcTi6haifMMI5A5dODOAWqFrgoUW sdhyetuZpWu1sFsC+do0j66rPcSklJcnv1Jetb+XZhYtndTS3JoleCkffKJY9LR9S7tO+UHK5grs la7+KCDWNbWnAJ8pzwp+USr3TJuVylb6PSVAUsuua9I5K2ZSWxtsKWosus4sAVa5RvtyD789y7e2 j+c6COxOALG4uwUpvyuB1NP90t6fowLUBgK5QTcM6iVaU4LBAksFAbo+9cR455nFXNCVO3QmZw9v fl6OVysWc7OLpZnFVMD4NLHobfuWdl0rFlv9OUz3THlW8IujpZ6zbbb7MlT5R2oJux0iFNfv7DhX OqyptZ8s+XIPv0UstlqJ659GALH4NItT30MCqRmsM5v+LZPSwBcWprQMNfV7ailmzWxT7VPvHKzR M4spjq3BtTc/r6ZUKxaVX+ngHwWAMZfaeof1iQPJmvf61fBoaQ8tPlZahlrLYETbyT0YavXnq2Kx lkkvv0gJGdncPrXl62GzUtlq+88af6+55oqYSY1pXktQZ/hyrV+0+O0VvjX9HtdAYHcCiMXdLUj5 XQmkBlEFojWnb9remrNBXEkspvZqpCrfI3iK84kDnLNLmGqMVzrgpSYNXePNz/IVb/tTW5bwuhax mPPPoyXHqYMujl5RkFr6F/t/bYBb8puj01A9xaK37c/W/0gIjRaLo/widyhPyu9DPxths7NlW0Es 1va5qfbcctJwqU8bPbPYw29T9qzlW+LD7xC4AwHE4h2sSB1cCeRmbxQgpt5BJTETHlMeLvFrCSpL YrE2rRnLUBX0n9nbqTqVDqrxmu315ieni4+nPxNgtIhF5Zl6H2dpf2pqSV2Oeyr9eNlqLctVxGJt eUe0HTEZHWDnOsgRfqE8YlGmfjTVz4avdhhhs7NlW0EstvS5pVdZnF2COsuXvf02Z88zY5prMEJi EFiEAGJxEUNQjHUIpJ5choNTeBBFKbCuDXhyg24YpJeexKfEi5Xbe89i6qm/WCgfCaiaU2TDGVBd b3svlYb+6P854X4muPHmJ96poKU1wGgVi6VDP1K2Tr1zzWbMba+r7kvxDpcGWitt8euwZbfc5zmz 6G37lnqkerZVxOIIv7A2EvYPqXYTL/cfYbOzZZshFq/0uTk7W/1b+6zQp2f4srffqj5X+K4TvVAS CPQhgFjsw5VUNyeQ2kRfejprv4ezjy1BZWlmMfcKiZpyeYvFkmBRmVKzsKFb5E6fLdWnZklwyv28 +eXEYunwmLhsrWJR9x/NLuaWM9bsd4zZ655UINni1yuIRW/bn62/sZgRYOe65J5+UZoFD/0tfmF8 b5tdKdsMsXilzz16AHpmNcRssaj8Pf1W6V3hu3m4Q/EhUCSAWCwi4oKnEjh6IXxO0FzZ21USi7JD jYi1WbrS0sSrAW9qeWiYZ8lvcnsRj8Ribo9RKS/73ZNfTiy2PqU/IxaPhHZu1lXBd0uArGtzgv+s 77Tc5zmzuFrbWUks9vQLiZSUzeM2nnsA5Nle43JcLVuNL3tdY/3XlT431/bPrNJYQSx6+q0H39ox iOsgsCMBxOKOVqPMwwgo8FfAchRkS5wp2EnNKMXBztHyzHi/VG5WR9flnqpqKY3KHKeVCghaypYC rnxygWBq6WIqDQVsqeU/cTCpa1pn7HJO4sUvJUCODo7JlSd+6l97+m7qYUbNLEFuuWm8JO+okZ31 nZb7Yr84Yhu3h9xDBS/bt9Qj5/exj5f27vawRxz0H83W2DLSlnLYw4aczym/kljpZbOrZavxAa9r jPmVPjf1gKJl32Nt/6U0R/qyR3/mwXdYUEJGEJhAALE4ATpZ7klAA7UEiwZCBTD6d2mpZc+aWlk0 WEpwtM5oeZXNuNiJoGeZ6H7Vw/Ys2l46r3LG6Xjxs/qfrXev+pXSVXlj3rvVoVTH3O9etj+b/8r3 9fQLcVf7tv6zhUNvm10pW0s9PK490+emVnLUPFzyKO+INDz99gzfEXUkDwjMIoBYnEWefCEAAQhA AAIQgMAAAqklvaVZ3QHFIgsIQGADAojFDYxEESEAAQhAAAIQgMBZAqklxmfT4j4IQOBZBBCLz7I3 tYUABCAAAQhA4EEEUid9ntlf/SBkVBUCEAgIIBZxBwhAAAIQgAAEIHBTAqlDxK4cQnNTTFQLAhDI EEAs4hoQgAAEIAABCEDghgRyryi6YVWpEgQg0IkAYrETWJKFAAQgAAEIQAACswno1U56nZH+6N86 BZkPBCAAgVoCiMVaUlwHAQhAAAIQgAAEIAABCEDgQQQQiw8yNlWFAAQgAAEIQAACEIAABCBQSwCx WEuK6yAAAQhAAAIQgAAEIAABCDyIAGLxQcamqhCAAAQgAAEIQAACEIAABGoJIBZrSXEdBCAAAQhA AAIQgAAEIACBBxFALD7I2FQVAhCAAAQgAAEIQAACEIBALQHEYi0proMABCAAAQhAAAIQgAAEIPAg AojFBxmbqkIAAhCAAAQgAAEIQAACEKglgFisJcV1EIAABCAAAQhAAAIQgAAEHkQAsfggY1NVCEAA AhCAAAQgAAEIQAACtQQQi7WkuA4CEIAABCAAAQhAAAIQgMCDCCAWH2RsqgoBCEAAAhCAAAQgAAEI QKCWAGKxlhTXQQACEIAABCAAAQhAAAIQeBABxOKDjE1VIQABCEAAAhCAAAQgAAEI1BJALNaS4joI QAACEIAABCAAAQhAAAIPIoBYfJCxqSoEIAABCEAAAhCAAAQgAIFaAojFWlJcBwEIQAACEIAABCAA AQhA4EEEEIsPMjZVhQAEIAABCEAAAhCAAAQgUEsAsVhLiusgAAEIQAACEIAABCAAAQg8iABi8UHG pqoQgAAEIAABCEAAAhCAAARqCSAWa0lxHQQgAAEIQAACEIAABCAAgQcRQCw2Gvvbt28v379/P7zr x48fL1++fHn59OnTz7+PPi3XNhaVyyEAAQhAAAIQgAAEIAABCJwmgFisRCeB+Pbt25dXr169vHv3 LnuXxKGuCf+8fv06KRpbrq0sJpdBAAIQgAAEIAABCEAAAhBwIYBYrMD48ePHXyLxSCxq1lG/S1Rq xlAffSexqD/2nX1fe21FEbkEAhCAAAQgAAEIQAACEICAKwHEYgGniT1bTnokFt+/f/9TLIaiUMnb DOLnz59/5dZyravFSQwCEIAABCAAAQhAAAIQgEAFAcRiBaTwkiOxmPtN4jH+reXaxiJyOQQgAAEI QAACEIAABCAAgcsEEIuNCHMiT3sa9duHDx+SKYb3tVzbWDwuhwAEIAABCEAAAhCAAAQg4EIAsdiI MScWbb+iTkBNfezAG/3Wcm1j8bgcAhCAAAQgAAEIQAACEICACwHEYiNGxGIjMC6HAAQgAAEIQAAC EIAABLYkgFhsNNsVsfjmzZufudXMLNq1jcV7+fPPP1/MqOHf//Vf/5X8PnUt3/0Bqz9gQDvAB/AB fAAfwAee6AOKJflAwAggFht9obRnUaecpj6pPYs11zYWL3u5DM0HAhCAAAQgAAEIQAACEIBALQHE Yi2p/72udBqq3rEYf1IH2tg7FmuubSxi8nLEogdF0oAABCAAAQhAAAIQgMBzCCAWG21d855FicPw oxNSdZ+Wn9rH3rNYc21jERGLHsBIAwIQgAAEIAABCEAAAg8ngFiscACdcGp/JPq0n9D+H4o924uo 3798+fJTHOq6lMBsubaiiMVLmFksIuICCEAAAhCAAAQgAAEIQCAggFgsuIMtIbVXX8R/f/78+bcU JBJfv379UyDan9y7F1uuveq1iMWrBLkfAhCAAAQgAAEIQAACzyKAWOxkb4nMcNnpUTYt154tLmLx LDnugwAEIAABCEAAAhCAwDMJIBYfYnfE4kMMTTUhAAEIQAACEIAABCDgRACx6ARy9WQQi6tbiPJB AAIQgAAEIAABCEBgLQKIxbXs0a00iMVuaEkYAhCAAAQgAAEIQAACtySAWLylWf9ZKcTiQwxNNSEA AQhAAAIQgEAjgf/zfz+96A8fCMQEEIsP8QnE4kMMTTUhAAEIQAACEHgkgZTgu/LdIyFS6X8QQCw+ xCkQiw8xNNWEAAQgAAEIQOBWBK4Iviv33goilTlNALF4Gt1eNyIW97IXpYUABCAAAQhA4D4Eroi2 Wffehz41uUIAsXiF3kb3IhY3MhZFhQAEIAABCECgicAsQXXnfJsMwMW3JbCNWPz48ePL69evX169 epX9c1srOVQMsegAkSQgAAEIQAACEPhF4M5Cibrh6BD4i8AWYvHDhw+HItEEJEbNE0As4h0QgAAE IAABCJwlgHh6+XlaaHxi6J2/O+sr3HcvAluIRRODmlmUcPz06VPyz71M41sbxKIvT1KDAAQgAAEI 7EbgzsKGuvmL2d38m/L2IbCVWPz69WsfCg9IFbH4ACNTRQhAAAIQmEoAweIvWGA6j+nUxkTmyxDY Qiy+efPm5zJUxOJ5v0EsnmfHnRCAAAQgcE8CCJF5QgT267O/Z6unVq0EthCLEokSi2/fvm2tH9f/ LwHEIq4AAQhAAAJ3JIDoWF90YKM9bXTH/oI6tRPYQix++fLlp1A8OglV+xn55AkgFvEOCEAAAhDY mYAJjicdMILI2lNk3cVuO/cXlN2PwBZi8Ugkhr/5YblfSojF+9mUGkEAAhBYmUCvgBmx+KwTOXv5 Udh2yCMtylfuXyjbOAJbiEWdgPru3bvDP+/fvx9HbcOcEIsbGo0iQwACEOhEYOfgGLGIWNzZf3cS qZ26H5LdjMAWYnEzpksWF7G4pFkoFAQg8HACBL3tywwRi4hF2k17uzkjUh/ePVP9/yWAWHyIKyAW H2JoqgkBCCxLgADXJ8BFLCIWaUs+bakkIJftTCnYUAJbicXPnz+/aLmplqTq3/b59u3by48fP4aC 2y0zxOJuFqO8EIDAbAIEpGMC0lbOiEXEYqvPlEQR6bFncfZ4s3L+W4hFCUF716IdaCPBqM/37995 rUaFhyEWKyBxCQQgsA0Bgrs1hdyIoByxiFik/Y9p/9sMCBS0K4EtxKJmE0ORqH+bWBQdE5JdSW2e OGJxcwNSfAgsQIAAbUyABudjzohFxCJtZExftMCwQxEWILCFWJQ41HsUNYuoTywWJRz1nZajzvwo /9Sf3BJZfa93SH769Onn3z0/iMWedEkbAmMJECiNCZTgvCZnxCJikbY5pm2OHdnIbVUC24jFcCZx RbEokZh7H6TEYPyROIyvlyDuJRoRi6s2Qcq1A4HWwIRglmC21WfCdsC9zCyaP+ALY0QRnNmzuEMs MquM24hFCSn7xGJRv+k7m3mcAdPEog7eiWcX45lFu/bt27e/DubRd6qH/vQ4rAexOMMryPMKgZ0H b8QiYnFn/11duNK+aF+0rzEi+soYzr33IbCFWLQ9i9qbqFk6CUP7t+1XDMXkDPOYAKxZCmv1iUWh zTaGJ7161QWx6EXyeekwKLcPygSzBLO0m/Z2UytSaV+0L9pXv/b1vCiHGpcIbCEWNWNos4e5pZ49 BFYJXvh7i1iMZ0YtHYnH3G8tZUldi1i8SnDv+xlYxwysxplglmCWNtevzdG+aF+0r37ta+9oh9L3 ILCFWFTFJRjtIJtQMGpmcbZQVPlqxaK96uPDhw9JeyIWe7i5b5oMUmMGqZ05E8wSzO7sv7UzfLOu o33RvmhfY8Zh3+iJ1HYlsI1YjGfxJM567O07a8jUATe2VDY1A5k69EbXmRA+W47cfbvPLDIwjBkY 4OzDmWCWYJa25NOWUoKU9kX7on31a1/e8Sfp7U9gK7EYvmrCXjexkmBUmb5+/fpzllH/tv2U4Umu Jip3FYt00GM6aDjvzZlglmCWNtyvDdO+aF+0r37ta39pQw28CWwjFlNLUG0WTgfGrPqxcttJrTVi USLz7OfPP/98MaOGf//rX//69b11suHvfPfHCwxgoDbh4QdKg/YFA/zgL1Hj3RZ6pOldRtLztztM xzFVLMkHAkZgC7Go/X25g23s+3D2biXz2umttq/S9izmBO6IPYs8kRvzRA7Oz+XMzAczH7T/fu2f 9kX7on31a18rxdCUZQ0CW4hFE4SacdPrJew9hvq3LfXUNSstSTXzmljU8lT7qKx6x2L8KR1+c8Vl wj2LdLJjOlk4P5czwSzBLO2/X/unfdG+aF/92teVWJN770lgK7EYCq7QHCYma95xONqMJmZDIWvv WbSlqVYmm0HtUQ/E4qffTM9AM2ageSpnglmC2af6fupAGu/vaF+0L9rXmDF8dMxMfmsS2EIsmuAq icWZiPUeyI8fP/464Eaznpo9lJDV9+HH9i2GM6U2A9lrOS1iEbFIgDUuwIL1ONbeQoT01u8raV+0 L8QiYnFmzP+0vLcQixKJuaWbNhuXO110lEElCCUYa98BKTEZX59796JHHRCL6wdABKn3sRHBLMEs wWy/YJb2RfuiffVrXx4xJ2nci8AWYjGcpSsddGO/x7N5o8ym5aYt74DUUtQey07j+iIW7yNEEJXr 25JglmCWYLZfMEv7on3Rvvq1r1HxMvnsQ2ALsVgrEMPrei3n3Me0v5cUsbi+wEAE3sdGBLMEswSz /YJZ2hfti/bVr33tGudS7n4EthCLWp4p8dfyx15V0Q/dXikjFu8jRBCV69uSYJZglmC2XzBL+6J9 0b76ta+9oltKO4LAFmJxBIi754FYXF9gIALvYyOCWYJZgtl+wSzti/ZF++rXvu4eD1O/dgLbiEV7 t6L9veI7Fdvxj7sDsXgfIYKoXN+WBLMEswSz/YJZ2hfti/bVr32Ni0zJaRcCS4tFHf5i7yRM7Vu0 V0/sAntmORGL6wsMROB9bEQwSzBLMNsvmKV90b5oX/3a18xYlbzXJLCsWJRQjF8tkTvopucrJ9Y0 W3upEIv3ESKIyvVtSTBLMEsw2y+YpX3Rvmhf/dpXe4TJHXcnsKxY1KyhiUP9W4JQ71K0P/GMo16v wSdPALG4vsBABN7HRgSzBLMEs/2CWdoX7Yv21a99EUtDICawpFjUrKIJxaNZQ+1bNFGpv/kgFhlA xgwgcD7mTDBLMEsb6dcX0b5oX7Svfu2LWBoCW4hFvfZCYrFGAOrAGxOWHHqDWGQAGTOAwBmxaL0N vkCbky+M9APEImJxpL89edUPsgkCIrDkzKKWmkoA6r2KNR8TixKOfNIEWIZ6nyWOTx64dgkQCGYJ Znfx1R37E9oX7Yv2NeYBDTE1BBCLD/IBxCJikQBrXIAF63GsdxQ7lPlaf0z7on0hFhGLDwrhp1d1 yZnFr1+/Vi8tbbl2Ou2JBUAsXgtOCO7g1xKgtlyLb+Fb+Eub+IFXGy/6GPqYs21mYthK1gsRWFIs io8tLX379u1Lbi+ilp3a6zVq9jcuxH14URCLDBZnBwue4LY/wYU1wSztpr3d1Ioa2hfti/bVr30N D1DJcHkCy4pF27cYikbtYbQ/4as1dI2u55MngFhELBJgjQuwYD2Oda3A4Lr79IG0L9oXYhGxSMw/ jsCyYlEI9NoME4tHfx+9XmMcyrVzQizeJ1Ai6F3flgSzBLMEs/2CWdoX7Yv21a99rR3NUroZBJYW iwKiPYnv379PikYtUf3y5csMbtvliVhcX2AgAu9jI4JZglmC2X7BLO2L9kX76te+tgtwKXB3AsuL xZCA9i5qnyLvU2z3C8TifYQIonJ9WxLMEswSzPYLZmlftC/aV7/21R5hcsfdCWwlFu9ujJ71Qyyu LzAQgfexEcEswSzBbL9glvZF+6J99WtfPWNR0t6TAGJxT7s1lxqxeB8hgqhc35YEswSzBLP9glna F+2L9tWvfTUHmNxwewKIxdub+K8KIhbXFxiIwPvYiGCWYJZgtl8wS/uifdG++rWvh4TFVLOBAGKx AdbOlyIW7yNEEJXr25JglmCWYLZfMEv7on3Rvvq1r51jXcrehwBisQ/X5VJFLK4vMBCB97ERwSzB LMFsv2CW9kX7on31a1/LBbAUaDoBxOJ0E4wpAGLxPkIEUbm+LQlmCWYJZvsFs7Qv2hftq1/7GhOV kstOBBCLk62l14DoXZGfPn3q+s5IxOL6AgMReB8bEcwSzBLM9gtmaV+0L9pXv/Y1OSwm+wUJIBYn GkUi8dWrV7/9ef36dRfRiFi8jxBBVK5vS4JZglmC2X7BLO2L9kX76te+JobFZL0oAcTiJMN8+/bt p0h8+/bti2YX9dF3Eov6Y995FQ+xuL7AQATex0YEswSzBLP9glnaF+2L9tWvfXnFnaRzHwKIxUm2 fP/+/U+xGItCm238/Pmza8kQi/cRIojK9W1JMEswSzDbL5ilfdG+aF/92pdr8ElityCAWJxkRgnF d+/e/SN3icfcb1eKilhcX2AgAu9jI4JZglmC2X7BLO2L9kX76te+rsSa3HtPAojFCXb9/v37T0H4 4cOHZO6IRQZCBsIxA2EvzgSztOFevsVDJXwL39p7fNipDU8IkclyQQKIxQlGsf2KOgE19bFDbzyL xszifWatdhponhrUIBYJ6J/q+yP6J9oX7Yv2NUYwe8ahpLUvAcTiBNv1FIt//vnnixk1/Ptf//pX 8vvUtXz3B6z+gAHtAB/AB/ABfAAfeKIP/Pd///eE6JgsVyWAWJxgmRqx+ObNG9eShTOLljDfvfwU hfFnpe9UtpXKQ1mwx5188k51oW3SNnfw5x3KSFtKtyXXoJTEtiKAWJxgLtuzqBNRc8tQU4ffXCkq nd+egQQD619ej//CoIcf9EgTX8VX8at8vw2bfce0KzEo9+5NALE4yX72jsU4+9LhN2eLSwCzZwDD wLrvwEqbW7/N0b5oXzam0l7HtFfa3L5t7mz8yX37E0AsTrKhvWdR4jD86IRUCUktVfX8MBCOGQi9 OTOw7juwevsC6fm3YdoX7Qux+HekMaKPoc3t2+Y8Y1LS2osAYnGSvWzfovYmfvny5ac41OmoPV6b Qee8d+c8YgAnD38hAtP1mdI37ts30r7Wb18pG9Hm9m1zk8Jlsl2AAGJxohEkEl+/fv1TINqf3LsX rxaTgZWBlSfoY5+g0+bWb3MErvsGrrSv9dsXYvFe7etqHMr9+xJALC5gOy1F9V52Glfrf/7nf/5R 0/A7lUHC9T/+4z8Or7MfS+mtft2s+rbmK44erC1fzWR7pBc6yVF6T8tXXFpt7GUP8v3bK2uZnmlf T/PpWfWt7WN6XEdbam9LZ9rcLN96Wr5e49IC4TJFmEQAsTgJ/GrZ2sE6muF8wmdWfcl3jHfN4myD sq0UGFPbv3KZVWfyHWPlp3EeQzWdy9NYU98x3jaL88zxYQxZculNALHYm/BG6asj+/Hjx0YlvlbU WfUl32t2q717FmcbmGe0pVl1Jt9ar7x23dM4X6N17e6nsaa+1/yl9u5ZnGeOS7VsuG5dAojFdW1D ySAAAQhAAAIQgAAEIAABCEwjgFichn69jLVvUk+9nvKZVV/yHeNhszirdrPyJt97+9bT7DvGmulc nsaa+o7xtlmcZ45LY8iSS08CiMWedDdK217l8ZQ9i7PqS75jGsUszjYgz9izOKvO5Htvn55l3zFU 80KRNtzfArN862n5zhyX+nsROYwggFgcQXmDPLS/6t27dy/v37/foLTXizirvuR73XY1KczirLLN ypt8azzj+jVwvs5w9RSw8RgLwXkM55nj0rgaklNPAojFnnRJGwIQgAAEIAABCEAAAhCAwKYEEIub Go5iQwACEIAABCAAAQhAAAIQ6EkAsdiT7mZpawnqhw8fNiv1+eLOqi/5nrdZy52zOKuMs/Im3xYP OX8tnM+z2+VObDzGUnAew3nmuDSuhuTUiwBisRfZzdKdteF7FqZZ9SXfMRafxVm1m5U3+d7bt55m 3zHWTOfyNNbUd4y3zeI8c1waQ5ZcehNALPYmvFH6nz9/fvny5ctGJb5W1Fn1Jd9rdqu9exZnlW9W 3uRb6x3XroPzNX473I2Nx1gJzmM4zxyXxtWQnHoRQCz2Iku6EIAABCAAAQhAAAIQgAAENiaAWNzY eN5F//79+88Zkd0/Oo479+Lbjx8//npFiK7x+Njx32Knf6/6GWVfy+fr16/deFgenz59egn/KM9Z H/lTjxcuq66pj+qq/T565Y3+iIP3R/4sv7Y87N1zb968+fmd9jh7taPasquer1+/rr286brRfUdT 4f59sWcbnulXrfUedX2PNsz48Lv1njg+9PAro0o7HtU7PDsfxOKz7f9b7RWEKRjs/ek5WGgZrQW0 +js8sOft27e//abfPcRFuA/B8sx14L3ZHqXvZV8FP0orJYwlIEL++rcEutdHXFN5xHn2OqhJ9bb3 kYZLtmPf8npfqRin/FQCLq6z/i8R5fXAQvVTeql84u/ExOtjgVXub9lW+XsHYDP6DmNmD7FSvhVy 9WzDs/yq5Cc9xwflPboNMz78bfE7jw+j/UpUZ44PpXa86u/qX+wBc68HnUpXeYyIA3s8oE7ZDrG4 qkdPKJdnIDJDTFjHqVkP5a8AzMSK1c1mTnWtAnxde/VjwYAFfBZIa2BcaQ+ol32tvnFHa+nbLJf+ L77i4TVjbelJMIityiDBrzyUn/5tYkL29fykHjYozzA/+YCV0WOmzwRMXA+JOJUnHIxM3HkJN+Wh P0o3J0CVv9XfQ6DHgXWNUPV4wDWr75Bdcw8/9H3M3asNz/SrmQ+bZrRhxoe/eq87jw8z/EpMZ7Zj z7F1VFqpvtYjBozLb2OiRwyQY6Ox1/zOa8w/sgNicZSXTsjHZmFsCVnpbwtyrxZ1lpiwjjMMsMLZ gjiYNZHhXV/V3zoLBbImXq/mE9+/mn1Vz1TH6yXKzV4p4WlLIo2RbCCh4yFglKb5tPKxGS2lbYIq zkfXeSyVTIkDEzapBxHeYqL2yatXviHneImx/d8G/PD3q21rdt+hNmKrHGx5sc0Uhw8EvDjP8quw LY1+2DSrDcfjIePD76119/Fhll+FAjwkOmJ8uNrfzrjfYrIwflBfm3ood7V89kDsajq5+20ixMZC xGIv0g9Jd9ZT+pxY7C0mcoGUNaj4KX2unK3ukUtH+alR21I+Ey9eywRXs6/N7sX8UoF4K+PcwGjp 2BKgMF3rUM/kFd9jvhXbzp7sxdebsL26DCX15NiCgZSQ8/LpVlHila84WnvVUt5UW2ktW439Z/Ud lm/KT+yBh/oN+92r7rP86kgsjhofRrdhxoe/WuBdx4dZY4OYzmzHNf2qxzX/5/9+emn5k8tT/WiP WUSPOrakYeOCPSxWu0IsthDk2iQBBbO1juQViOQGx96Dhe3jCkEoyLKlbHFwbeW8GtDXBMoqm83c ejbu1ezbU8Ck7Gu2tiXAoe1r7FLbbeTaRu57r7xNGMYzl7Y8tJcwbxX4dv3VtmT1sSW1qme8lMer nwrZzeo7SnWx5fLioAcQpetr/XmWXx2Jxd7jw6w2XNMXMD78tQf5ymfW+DDLr8RqZju+YquWe1uE oq49Eostq33CLSWKs+JxSGOUxdbhTJ+JeP2WGg9L6ep+5WXxovI+2s7kGU8e2YVlqC1eu+G1JpZq 9s55BSJHYrGnmIg7TuWlhqYOQrMU8VMlfeex36kmGAiDYJs58XCnmfbV4GxLMo1B6sAgm2W7OqNq dU0tLbXTOXuJxdyDhVybye0lOWNzG4jkrzb4iEH8EMgElsfTU2tLaj8lu6lMyrNlIK7lYHVX+tZ3 ePVTYRlm9x2lg7ZsBjs3k13LM7xuhl+VxGLP8WFWG2Z8+OuU6NyBcruPD7P8KnxQKrYjx4cz/c3Z e7zEoi1Dza1YCctncaP6SI03dm94WJ+NQ7YdReOxnZNgv8X9WU264Vkbahv6/9H5C4jFs57Fff8g YEshS2i8gjDrPEeLCdUv3CtoM4omlG1/mTVqO7m0xKX0e0swYGl5zcAovVn2TR0+Ej99aylfiXNo XxOHtjdQZYkfiBw9aa7JK75GecannMr2uT2UHqItDgiMeThLHdohXLJ4po7hPeF+X9sTGu4TFIvw YIeaB1JnymSvHrBB0dr4mbSO7pnRd6g8qQcdqXKGrL3qboHJSL+aOT7MaMOMD69+re656/gww69S D35GtmOvPqiUjpdYVD7Whx5tCcoJPRsf7OGpXZcac1Np1Kar9GpXA6pOiMWSB/F7E4HaJR4eIsYG xxliQlAUtCqQjd8BF54e5dnAbMbLg12TUYOLR9pX2YYziuG/U+VX5+f5+oxwH6gdApIKQvSdZ752 5HbJRuYPpdmiUjrx77ZpPnzYYX6s77xOnA3zlW1tBj53Mql+r/W/1jqH14tnKJKvpJW7d3TfoXKY X5VmcHWtvVfTs+6j/Wrm+DCjDTM+/DWzmOsj7jA+zPCrFcYHz34ol5anWFQeigvCV0LFsYPGmNRM nvVb9lDUxF9q3E0Jw9p0bZVObTzpGcse2ZNlqCO8/YF5zBQTJdwKjmoCs1I6/D6XgDpT7DjOBuId tuvawcy7hBqcewjjmnLSd9RQKl+z8vhQLj1X7ECA8WEHK5XL6C0WLUeNISYaw1Ux9gA6fnuAzUqa uMzNFJogVTrhA5LadG1Ztq18Kz2IRSyWfYgrIAABCEAAAhCAAAQgAIEbEuglFoXKZgvDZZ8SX/a6 s3Dbhf07XoaaEnMpIVmbrsqlPGwvZGm7FGLxhk4/o0q5p//2Li97epJaxjejvL3ztCfaPWZF1MD1 tMqYhvsHbM9X6SlRa/13sK/K6DUTtEN9ZUOvOtt+PfFbbRa1d1tS+rnT5NSeRi1/tTbZs76hz8QB ivdy5to+xpZr1V6/+3XWZsW7R1tjfEh7iFdfaW0olctK8Y5nfVceH7z6g55iUWW0GT8rr/5/dKCM XXdmZrEm3ZibbQPJxT6IRS9Pe3A6dsJfHGzYwR/xHiRNyfcYJEebQI3YgslweUF4QISdHuZVNjuJ MrevK/y+ZfPyUfl2sa/XwUkr1Nde0ZHyr9BWXnWO93fpaWOPBx05P5vVlsL2Ep5+G7dhXecppmbU V/a0E5KP+o/UKcBX+q/cUlD73g506CGSZzz0sb2ZqTEuxd9zvzPjQ95TvfrK2ePD6LFBRGePD1f6 n9p7vcRi6kG98QsPriuJs7NisTbdmIstS81NNCAWaz2J67IEcsf3SxQq6AoHbBvMvITMLLOkgsnw 6GP9Hr7nxmtG1U5aFcec4BZvC8A8Ar9d7OsVDMyuby6g1/exzb3qbIOZBSImJpRnr9NHre3OaEsW 8NkyoPAY8fhAAV2rMnqdOjujvmJth/aoT5BNZXMLEFRn/dv6jTNPplN98dEhM0eC1aNfnxXUW53j oMv8Sm3KZnXNJl4rIhgf+ovFmePDjLEhFIszxgePvqAmDQ+xGC43tVP6wz2LYZ9g16oPsP5Y39mq sbNisSZdxYhxvjYuhTFGuPokXt7a62EyB9zUeOum16QCVhuoU4GmV4A7C1fYIYRPx22gjgWavXbh anltkKpdYurFeZZ9bSYk3gCe+78FXlc5z6pvOCir47aZLFvaZMtYwk7ay8ZxgKv/h694MFF1lW18 /+y2FA6M4Ss84jZsoupq/WfV18qfe/1KWF+V0Y5896xval+OzbDKt8Pfr+ar+2cF9TmxqDaUeuDg 9SCC8eHdr60ZqTFi9/HB/Gr02BCOSxZ7jBofPPqB2jQ8xKLysmX14cMw2Sw34xi/nirue1uXoZq9 SunGDx50fbh6xk5Xzj3U83rAFdsHsVjrsRtelxqUTSweTcnXip7VkFjjjWd5ci+ytkDt6pOYVmGQ C1paec6y76xZiVn1tYFGnXPKVyyID9+31OoTOdvnfEU+Hr5C5Oi9Ua1+FdZ3lbZkA2hcHq+2tGLf YYIttJ/NtJ6xaXyPMc29pNrLh+N8Zz30yfmKCeK4nNbfXN2a0crRy6dn9ZdPGx/MvqPHhpRYNB/u PT549D+z0hCb2hjXrr3aB8R1rUlXZbwam3oyRix60lwsLROG8dN4BZapmUWvwXEWhtygnPvee1Cu 7VCM89WOYKZ9JcBrlyy3Bks5/5lZ31IdbEmk2pYeQpSur20jNT6qJ4nh08pauxyVYVZbsv3UYdnC J6nxIG98rralleprdbelZSGLGn+o9S1dZ9sP5LdiEH68fDgnwsLvRzzEPBKLPR+eto6rjA8tHvz3 tbPGh1I76TU2HInFkGCP8eGchbhrZwKIxZ2tV1F2exKtp8cWUEk8xgGlBQ1e+38qiuZ+SS5wzHXm ueVQrQWzQUoCqiQYbU26gjOPzyz7WgBfs2+uNJi2cJhVX/Ot0mEqNoudm81uqWttMGBpyhY2W9Sa T3z97LZkD7hUDrFUe1EfFvdPdmjArvW1dpTaw6y6xv20t1gMhantfTHh5NluU8Jw9ENMY2d7lvT/ o3ZtK09KfXrJ9xgfjgl5+tmM8WHW2DBzfCj5PL/fjwBi8X42/UeNrAMNX+WQWu8cLqPbFYsCrPBk K+tQc3uCvMRxuK/KXpMR7vVRmcIDNGpEVq0NZtnXlkGWyukZDCivWfVNBe+puod2LrEp/X5GHFyd ZbMyzWpL4Z5M66esvdj+Y7Ux4+xxWJTqPLu+5l+2l1p1j/uJ1MxryYdqf5eosYcN1oepDD0+M4P6 1NgXz6paP+P1UI/xIe9FdxgfZowNrWLRLOA1PvToF0hzXQKIxXVt41oyBQK2ByYMZm3Dea9Nsa6V qEhMHWFq4I9vtSf6pZmiiix/XaLA3mY6cpuPe70bbpZ9a9f+ew9QM+prvlUz0yA7eywHNT/15lfj 1zPbkoJrMZQQDH1MZYr7r5q61Fwzs77h/lM7MCnVj+k7z1c6pLioTwyXNdewO3PNjIc+NpsY/50q v4SiJ2vGh7yXePdvo8eHGWODaM4cH860ee7ZlwBicV/bUfKFCagTDwMS78Fw4apTNAh0J6BgsEa0 dy+IcwbqJ1aplx4g9n6IODqodzbX6eQYH06j40YIQGACAcTiBOhkCQEIQAACEIAABCAAAQhAYHUC iMXVLdS5fFrW5LUvo3NRLyevp7l6Uq5lVqOf3tss4+gZxl72zdXD3j1oy5trlgTXGtb2VMmGo+0X ltH8KNyTqn97LmmOmai+9lLg8D1MtlcmXq5Zy9TrOmPild5ROjPaUs++Y0ZbMr52hHuqDHYaa6+l 8yN85Uoed/dpsWF8uOIh/7x3xtigUqw+PvhSJrUZBBCLM6gPzDO3R8O+t8MkZgRg3hhsSVNKSMQv OtW+IM/9KDbwKh8FV+HBFOEeK+UbH8BzhcMM+9rpfrE4ssM34v2aehjhIe7ssBdLX747Unwrr5Qf xfX1OnDF/MJOKs7tgw2/99gnGfqjCYaUX4fXeR9SYfurR7WlWX3HrLYk24WHrsiHQr+N+yz93vNh yJU+sNe9u/u0uDA+fO/lHr+lO2tssHasMXbG+DAELpksQQCxuIQZ+hQiDq5rOpNep9/1qeHvqeZO jrRBXwGvzQbZAQ5ee3JSwZXyMjGu3xV4W74eM26z7Jt75YgGLNUzFHAmdDxEjNXXBIz5s9L2PF02 56tmO9lU+ak8dry+zSyG9vbyeTsBVHnmRLeYW95eYjUnjPV9XA7PwHpmW4oPbOrdd8xqSyZS5dN2 aI49QLM6W99o74nzOjnaq130TucuPl077tt1V7nO8umZ48OssUG2mjU+XPUT7t+LAGJxL3s1ldY6 z1AkxUvnLCAMv2/KZKGLc2JRHXkq0FFQ6hEAhZzDGVvrxOPg3Y7Gv4puln1TQZQFnynR5hV0xfbV /8PXLFjge5Vr6n4ThblXsIQ2Vrlkew/RZoFX7amz3qzVRmxGyZYY22md4UMB73zVRma0pZhz775j VltKvSg+nGmMfdf8v0fbGpWmzf7YEvnS3yYArpaP8eGvZZKpV8GIrXffYW141Pgwa2ywWUVxHT0+ XG0T3L8fAcTifjZrKrGJQS3nSs1KeHXUTYXqdHFOLKozTc3kpQKmM0UzhjHf3IvZbXDxWEI5w76p J8cWDKQGrZxdWlnn0lHe4asHTKh5LH21Mh61E1syGdbHXgvQWsf4+tb26cXa8k35qInh8L2sreXM cZnVlmb1HbPaUs5e1p/EbcfLr662hyv3z1qJMcunxYrx4S+h2nN8mDU2nBHad2jHV/oA7j1PALF4 nt02d9pSQAV3sWjyCvJWgHEU8PUUMTmGue+9O+zR9jVhGM8+yL9SM4teoryGm2b+wnfEeSx/lW8f vQzdlsWGbaCmrDVtppWdXX/1QUSpX7ClibK5Hn6Urq+p61Hw07stzeo7ZrWllD/bO9tSMxXG56pf 1fpBr+v0AK+2T9jdp40h48Pf3tRjfJg1NqhWs8aHXu2TdNclgFhc1zbuJbPZDgXTJp68BkT3wp5I 0AIadd62hM2+Sx3OYDN8V2egcoFUjm1uT8eJKv92y0j7Wl6asbYAUuIxDsQsUPFc7luz5Eb52lP1 q1x1vwXSqaWldiJpD7FoYkJBbslPVUaVxeN046N2E9bTZs9zs+it7Ge1pVl9h/jMaEuxSFX9ZUP5 jtp03F713c772c0PrR3X7HH2Ghtn+XTc9hgf/ibiOT7MGhtUm1njQ2u/zvX7E0As7m/DphrY6wc0 8CuYtj1fTYksevHRMqPUMlRbmuJRHQVX8SmnKk9uj5uHeEqVe6R9LfiwgxHCGb3wUIVwueIV1mdm 6zxnQqytmDi0vaepvThHT5tbGYR7yazNhnuM5XfhoTA1gXBNGVIiOHVfmHdNuqVrZrSlmX1HKBhH tSXlGe73tXzNd2y/tfzN7OuxB7dk+xG/1/b7XmJRdZrh04wPZW/yGh9mjQ2q4azxoUyXK+5EALF4 J2s21EWzamFw33Dr0peGM4rhv1OFVkDk9foMDTopQRrna08hex9DP8q+9sqBMKi0BxH6zuu0WXE0 dl4D/BlHDve+2EEvKbvrOy/fUjnlyza7kzvd0Pt9eObTpRlNlU951y7vK3Gf1ZZm9R3GY2RbsjwV aMp28Xs6ZYPwIYCXbUu2H/V7zeoE63M8yjTLp3NlZ3zwsOrvacwaG2aND/4ESXFlAojFla0zoGwK 5j0D+gFFJosGAti3AVbDpQr+akRUQ5LVlyrvUNjMFM/VhebCLQnIx2f5+ZbANis044O/wWaODfaA g/HB365PTxGx+HQPoP4QgAAEIAABCEAAAhCAAAQSBBCLD3ALPRnWk6bUDISd5Oi9hG0HrOIxY1bV O98V7atlmB4HraT8SPWV3ew9aeE+L9vTV7vMrMVPczN49v5BK0/NcuSWfEvX2lPkXjOM5q/hPkn9 u/dS6lS9rSzKe+aMl1cbtj3G8ueZ9Umx7u1XJb/u8fuqbTisq5dvWZqMD69+Hs5ke7DjJdcefray X92xHXvYjDTaCCAW23htd3W4+VkdZnhIQbgnxQLuGQHgLKieBxioDia8JRokvnMHjXjmO8u+uf1d 9r1t+PceqOx01dy+vfB7z31Wdupc3D7sIJu4PBLKnsG/vcsx9qu4DceHLF1pWwqA7ETZI96eB5/Y vr0Uu1RZPPeFtrDyasPxwTpi2Uv0p+o3w69aOHteO7sNMz58+7V8/k7jw2y/Uht5Ujv27BNIq54A YrGe1XZXWiemJ2p24IaCPg1aFuzYzJq9N63XKZ0rwvMK+FS3XFCt7+PA1yvfWfY9OjnySFR4+ICd 0ijRmBNjCrYtGPESMrnXnag8EmxhgG+C1kusph7qyIesjvpdbdoOrPKa2bT0lI/qJLvb62ZsZjEs g4d9cyfeWpsRU5vhtPLNWB3g1YatviYkrP2onl6n2ubsMsuvPPzkTBoz2zDjw1+ze3ccH2b6ldrB 09rxmbbPPdcJIBavM1w2hdQLW8OZqDiQtkBw2QoVCmYzIbYcsPS3BZtX62sBnzptm3mypYl2YmYo JrwCzVn2tfqGgXu8RNGCo/D7q5ytvrVLTL0425Pb+D1zJtZTQb1X3iHrcNbWRHPchu11HldZW1+Q e/VLmK/KpfJ4CPOcWFRbTT3IUpvzeMA1u+8wn9bf4Sst7EHfVXvG98/yK+96tKSXapMj2zDjw6df D3ruND7M8iv5/hPbcUub51o/AohFP5bLpZQLWK2jjmdmcoHachXLFGjWjJdxTi0fs0A6fNegl5CY aV/zIS17TM3wedUxNHVrmp7+nHp6bIFmSrx65W11jhnb0+S4KZjIu7qU8Yi1LXkK87Z3bl7tK3Lc JNRTM6apByZnyjCr78jVV/YOj+I3Me61tHmWX52xjdc9s9sw48Pflmzty2t8oDVNrz56ll+FDzFH jw819uCaexFALN7Lnr/VJvVicHtXnYKvOMi1zvNqoDkTqYLo2uV/rYNLrl6ldGyJrwI+BfOl62v5 zbavLbdUveJA3quOIYtWYWDXe/izCcN49kx1T80stpa11bdyfL0CoJRvWRltyWRYZq98j8RiT1Gu uszoO2q4yRbhO3Fr+7ejfiTnP739qrZv63Hdam3Y6sj44GPt1j7Xa3yY5VehWIwJ3rkd+3gLqbQS QCy2Etvo+rgTU2CigEgBrmaE4uVb9tLvjar4j6KaGK7Z7+MlaCzgKx0OZLNBuVmhVu6r2NdmleRP FtB7sQ2ZWH3FrzTDIj9QeTxPZLV6qp2YAJV4jIN3E9EeyyNzD3ByfHP7Z1p9y9pRammpnSrYUyxK INmy26P2ZTOpJX+oqf/MvqNmabVsazP6NfU5umaWX10t99X7Z7Zhxof7jg8z/Ept4ant+Go/wP3t BBCL7cy2uiPc/2Kby01I2b4nBSAmYDz2Hc0GZMu3SuXwFDSpADqVf7gZvVS+mt9Xsa+CdQtk7fUV 8R6/mvqUrgn33Fo+4b5ICbmQcc1Dg1Ke4e8WFISv60gd2hAuO25JP3WtfCs+5VRBQm4/oYdIVTnM t8y3bT+k6htzPZqJbKn/0XLQ1DLU2rZeW4ba9Lz6jpqZxbjsHjPlSnOWX9Xaotd1s9pwzYww48M1 q88cH2b41ZPb8TVP4e5WAojFVmIbXq8OVMFm/H4hBR3h4FQzmO1S/Zon9aqLV+CldBRA1sxwyBae rFeyr56eh0vmeviLbGuz4LnT9Xq+N9Re7xA+ZFE57EAl79M5zbdKLG1mrDSDUUonFscSvsY5teRY 18v3vV5jEc4ohv9OlVvl8crX0h/Zd5jNvPqhFtvO9KuWcva4dlYbZny49/gw2q8shkk9SEs9ZFI/ 7jk+9GibpLkmAcTimnYZWip1cDWD2NBCkZkbgRn2lWDyFk0pIAp4Q0ExI+h2M9TCCYkrfcTCBqJo EDhJgPHhJDhug8CDCCAWH2RsqgoBCEAAAhCAAAQgAAEIQKCWAGKxltRNr7NZmbvMyOTqYe89tKWC Ncs2epjc9vbFe9B65BWmSb69Cf+dPqzHsIbzvTmPqV06F3xrDH04j+GsXGaxHldDcupJALHYk+4i adt70SRQwoMpwv2KWss+WsB447HTMuM1+Xb4Rry/TfudRi+tCw/w8K7/UXrkO442rMewhvO9OY+p XToXfGsMfTiP4axcZrEeV0Ny6kkAsdiT7gJpx4LQXm5tJx3qdx0QYYeSzJpx80CVe22ARKHqGc46 2usNPA+aqa2DxKv3KZ01eZNvDSWfa2Dtw7GUCpxLhHx+n8XZp/TnUplVZ/I9Z6/Wu57GWXxm1bnV Nly/HgHE4no2cSuRPUmSILLlphKJ9sqM+DUZdjS+WwEGJ5Q6zt5mG1PizOv4+8HVJDsIQAACEIAA BCAAAQgMIYBYHIJ5TiYmhuKllrmXwtsLrnfdv5iaWTSxmDoO/8w7zuZYklwhAAEIQAACEIAABCAw ngBicTzzYTnmZs5y3+8unkwYxjOmmklNzSyauByxb1HMVY4eH5VftkuJfC0x1oxxz/cOpurU8+Ck FesbM5AtPF4dYocSKK0RfnrWP73qq/xXP6TKyuhh3xJv46oHeT3srzRVDzv4y/Z1a1uCvovfzVsq 7y6/G1f1y+GfWe+gY3zw85zVxwfPvnKX8cHPuqQ0iwBicRb5Afma+IuDr5xYzO35G1BUtywkjuyw Hqu3Ap54b6LtWVRQ5PHJvUg8XP6rcnmLKLOZBXmhUE7tV/UMhmYcnDSzvvITE98mwHN7T72WOIeH EsjGsu+KM/9e9d3hkCr5gWd9lVZKCMrH4kO55H9eH+sD4zxS/5+xt9urnmE6ajsprnGd4weOV8vC +PDlF8KeB+vNHB9Gjw0Cusv4cLX9cP98AojF+TboWgKJofiUU3UwqafiGkS9xFPXShUSN8EYPiVP BUCa6fMIvOMOuyb40jVXPxZYy2YKOK3e+tuCWbOzrtUg7WXfGQcnzayvbJULMvV9HOx7iQnzLQtE zLeU54xDknI+61XfXQ6p8qpvbjWHpS8728yXHULmNaNpe9fFPDdrqf7RDkPzFlBX+78z9xtD1UX1 Fn/bfiHO+nd4+NuZPOJ7GB9e/XzoIb49D9abOT7MGBtCsbj6+ODRjkhjLgHE4lz+3XPXYF9zwqmu U4fuOfPUvXIHGWjgsJmvUNjYciuvgCvssMPALl7eZINJ+P1VPqlltOGT1Ti4s6Doar6zDk6aVd/Q xvIlayP27k61m/jBQy8xIfYWcClfe1Bw1abx/TYDY+2l9LcF4VfLMeuQqln1zYlF8Uw92PF64GNt KbWXO2VDL3++6h9X7rf+L/egNOwvxUVt2kMgh/1lPC7Y/xkfrm/RmDU+mH1Hjw3huGTteNT4cKUd cu+eBBCLe9qNUi9GwAZ7zeKmntL3CLZyaVpZ4nJ47Um1fEcfnDSrvnI1yzs1E22BZSgYveyds5nY 62my8jSxqsDWa1/brNmQWYdUzapvzr42ExN3c177rFv906vvmNltH9XZHiyG5bOVGh5lZnx4+bmy JbWixutgvVnjw6yxISUWzVd7jw8ebYI09iKAWNzLXpR2YQK2B0gBfDyb2xqc1VRTT8jjwddmiG1/ ZJhObg9rTV7hNbm65L73CjRn1TcUizlWtsxXtlfw42XvGnbiYjN7srvX/jIFd7VpedV35iFVM+p7 JBZTs341/lDTnltFp13vsWy/pnw9rkn1H5aPLeNL9Ze1s6+lMjM+fEqKRS+fnjU+lPq+XmPDkVgM fbHX+FDyd36/FwHE4r3sebo2dqpWvL/xdIKL39izvvZEWgG8BRqlAeUMrjiwVl4KeCVYZMd4GZu+ 89grmROduTp6HZw0q77hoFxapm1Pz3NP0Vvt3BJIibPNYLTmk7reHjzU7I309O9Zh1TNqK/ZVwGd /m1/clsCbBbm6gyytSX5aSktcVFf0us0Zw9frUnD7JtaWmqnv/YUi6EwtSXkjA8vP/eOeoxLs8YH a8Ojx4ZasWh+5z0+1LQ5rrkPAcTifWx5qSbhMqxLCW1yc+/6mhi1mR7bZ+aNJ9y/ZoefWHBvB1hI QJh48diDozrMOjhpVn2tzjUzbeEe2av2bhGLlpfn7I8tdS3Vw1MsKq/Rh1SFgXyNKPKq79Hy19Re 81p7lOyl38P9zfaajHBPnR4uhb5c89CgJt+Z11j/YeJQ9bal3HH9jmYir9aB8eFvgp4H680aH1IP G1I+4jk2tIrFHuPD1XbA/fsQQCzuY6vuJdXgeIeAoBbUiPrqaWO4RLC2bC3XyWYK7OJ3okk0xIf7 tKR7dO3Mg5Nm1FcsrM6lmRhdK3vUCMuSPWw2xFMAlvKMf69dhuddxpGHVIV1Hl3fcEYx/HfKThI2 nq/PUH624iB3ivPod7S2+mfr9eFeX9vvmxLm+s6TdaqcjA/+B+vNGB9mjA02JsmHvfve1jbF9fcn gFi8v42p4QIEJEw9T2BtqZKC7hqB05Lmytc+rb4r24Ky7UNAAWcoVu8egKp+q/SLjA/j2gnjwzjW 5HQfAojF+9iSmkAAAhCAAAQgAAEIQAACEHAjgFh0Q7luQnqSpieX9q608GX1tleldunVurX8vWR6 aqw6x++1Km1CH1E/K9uIvMI8yLcvcZuVGT0jMytfta2a/X3e1Gfka21H/cfI2aie+eb81N4fauNF aommt01HpTejzrY/UePRSN+Zla/ZUvmrb0oxt9NneyxxnpWv6j0z7piZ96j2Sz7zCCAW57EfkrMd 153bjxJ+77HHakilDjKxl2uX6ut10MuZ+nodjtGat3e+NuDLbzTo5/a73iVf8bb3scX1DfeGyve8 TxWekW9uL519b4dJeIvVGfna/shUMG8nzIZ9itdetpn5pk5ctUNd4v5TDwVGCp3Wvq3mejstM35g 2LvO8SFGajcjHijNyle2CA9Pki+F423cV+ZO/q2xaXzNrHxnxh0z8z5jI+7ZkwBicU+7VZfaTsRU J5ob7NXZWOA3U0RVV+rgQjtMRvVQnTVg2nHzCrj1b6urBq0ZH2/xVFsHz3xTAbSd/Br72R3yFeNU kKO6hf4kEWE+6DUjMyPfo1M6jx7E1Ppi7rrZ+cYrLMx35e+2SsHs67EHOXfabe98c68r0HghfwvF jD1w3P1h4qw6m43t4Zq1H/HseaDcrHxNlKud2CFBqrPqb35tbUfXyt/i1zyd6Udm5auyzow7ZuZ9 xk7csycBxOKedqsqtQ2OtUtMPYP6qgI6X2SiMBXE2XJby1JMFBh5iGN7smfLtkp/W+d+tfqz8rUg RIO8PaW3pWt2umAYbHr51ax8ZSfLW7YNZ9bsYUzsR3Yk/1Ubr5BvvJTb/m8PDMLfPes7I9+4r1Rb TQWyXgFuTiz2zjfVJi3YTgkYrzZ81T+u3D+rzrGN9f/wFQ8mqq7ULXXvrHwt7ggfGoYzfnFfaeP2 1frPyndW3CFeM/O+ai/u34sAYnEvezWVtnWAzwUuTZlOvPiovraMLyyevcvtapFnz4aUltzGv1+t r3FOLaUyES4RZb+3+mGufLPyVXks73jW1Gb94jLbIH51udmsfFUfE4NaUptaleBl15jdjHxzfZ/a TmqGOBWYnmlXs/JNzbKZWEw9XNx9bJBtZtU5x068w9d42MNLr+W+s/LN9QvWruP6efnWavnauBHP yHvFHeG4lOp7esY8Z/o67tmbAGJxb/sdlr41oLHrrwa4s5AevUTZlgCFZfMapJSmREPtMi3PIHtG vqXy29IiBT8STaXra/2llE6vfI8G5VyZvHwrl37vfM0mtgRRtoxFU8ketXZNXTc63yPR1lM8zcrX hGE8yyM7p2YWW8eSK7bvde+sOtf0BRq7wvfx1o4lR6xm5Zsah+19sXr4ErcnK+fVuGOlfM0uveOO mTFPr3ZKumsSQCyuaReXUtngKEFRelqpjlqD1YzTDV0q++9EbEBKLS1V3eIBuGYwrS2b5V2zB8Uz yJ6Rr3ErnSxrs2652bdatnbdrHyVfy6gydkytz/qbJ3jQKp3vnE57Wm42pEFe55+nOMyKl+zr4Iv W2Z85G82c1zqV0v2npWvymVsNXNs/qW+M+4nTbh77Csr8ej9+4w6t4wzYm0zcFdZzMo3FuUqh8YA xRbytdiP9J1E5NXPrHxXiDtmxDxX7cX9exFALO5lr+bShnsFbN9euBdIHXV4gEaN2GkuxMAbbC+I iUPbO6bBKK7b0VO5M0W2JUWle72D7Bn5psR3qt6hb5W41Pw+K1+VTXnHp5wqEMrtkfUKrmflG9vD juK3g4ysrdXY7co1I/I9WkqeWoZa2+ZK9Z6Vr5XLxFP4OqXUsvZwWXmpTqv/PrrOLaLN2F2dZVM6 s/JV3uGeTPMnG39tn7fGZhsfPM4OWCHfGXHHzJhn9bZO+fwIIBb9WC6bkgYNe3qX29/W431Hs4CE +0DswJVUwKfvvI7At7qmlqylOHgEA2G6o/NV+cWvZmZFvuWxrEr1nZVvmHfJr+1Jc2nmtZROGDim /De+3zvfXPlUr3DJXG09rl7XO99wRjH8d6rcCni9+o5Z+Vq97PUdYfBuDwT0ncepr1dt733/yDpb u/Tu80tMZuVr5ZI4VN8vMROOTypX+BDRa2yYne/MuGNm3iU/5Pd7EEAs3sOO1bVQRx0GJ6MHsOqC OlyoutWIGYesSAICjyMgETFDSMzK93EGpsIQ6EhAY/OM8bl3vjPjjpl5d3QVkl6AAGJxASNQBAhA AAIQgAAEIAABCEAAAqsRQCyuZhHKAwEIQAACEIAABCAAAQhAYAECiMUFjEARIAABCEAAAhCAAAQg AAEIrEYAsbiaRSgPBCAAAQhAAAIQgAAEIACBBQggFhcwAkWAAAQgAAEIQAACEIAABCCwGgHE4moW oTwQgAAEIAABCEAAAhCAAAQWIIBYXMAIFAECEIAABCAAAQhAAAIQgMBqBBCLq1mE8kAAAhCAAAQg AAEIQAACEFiAAGJxASNQBAhAAAIQgAAEIAABCEAAAqsRQCyuZhHKAwEIQAACEIAABCAAAQhAYAEC iMUFjEARIAABCEAAAhCAAAQgAAEIrEYAsbiaRSgPBCAAAQhAAAIQgAAEIACBBQggFhcwAkWAAAQg AAEIQAACEIAABCCwGgHE4moWoTwQgAAEIAABCEAAAhCAAAQWIIBYXMAIFAECEIAABCAAAQhAAAIQ gMBqBBCLq1mE8kAAAhCAAAQgAAEIQAACEFiAAGJxASNQBAhAAAIQgAAEIAABCEAAAqsRQCyuZhHK AwEIQAACEIAABCAAAQhAYAECiMUFjEARIAABCEAAAhCAAAQgAAEIrEYAsbiaRSgPBCAAgQcS+PHj x8u3b99evn///sDaz6kyzOdwJ1cIQAACOxFALO5kLcoKAQhAoEBAYuvTp08//3z58uVFgmCHz7t3 715evXr18vr1a7fiSnzGf9wS3ywh+YP4irN9ejDfDAvFhQAEIACBAgHEIi4CAQhA4CYELPiXKAj/ fP36dfkahmX3KKyJo5iF/v/+/XuPLJZMw8RxXLiQQywW9RsfCEAAAhCAQIoAYhG/gAAEIHADAqHY evv27cvHjx9f9LeEgITT6p+eYlEclL5mLU006bu7fSQUrX76d/iRD4iB/AKxeDfLUx8IQAAC/Qgg FvuxJWUIQAACwwiYSAiXGSpzLUuNhcOwQjVk1FMshvUP89Ey3Tt9jsRiqp7ezO/EkrpAAAIQgMBf BBCLeAIEIACBGxDIicW4atrD+OHDh99m2TTjpu/Cj/6vNCWotIz1zZs3v/YU2uxU+L2uDWetlFYu DV0b55cTLhK7WjYaLqPU/0t7McNlqKFYVJktrXDGtTafsE5hHmF54rTEN57dPZNfzDtkGM4kq342 m2r56vfcnsV4GWpt2W7QbKgCBCAAAQgUCCAWcREIQAACNyAQLrE8ElO5fY3xXj67zpayxnv/YgFn v4ezdaU0UoethMJFosXqpb/DpaQSr0efFrHYkk+uTiZIw7RiZnbSq0d+lrYJ9JxdjXFKjKe+aynb DZoNVYAABCAAAcQiPgABCEDg/gRSB7qkRKOdlBoSCYWffR/vgZQY+vz5828zfBKS8fc5AahrNTMW z47ZjNyRmAmFocSMCSWVJ/fJicUwHxN49l1NPuH9ErASx+GsYXjCqKWvv1X/uK6t+YUMQxEtBko7 tI/+rXxr+IYCvYXF/VsVNYQABCAAAWYW8QEIQAACNyEggRDOMKZm+1JVDYVVSiyG99hMYzyzZ9+X ZguVlgRWXLaUWEwtF9X9du3RqaZhnWxJpi2ljZdjtuQTljP1TkhLK15mGzI8m1+Yhi2HDYXe0Z7F 2pnFlrLdpNlQDQhAAAIQOCCAWMQ9IAABCNyMgERjKIzidxdKrEloSUDoT3htSSya6IgP0kl9n9uH GIoam5WLrw2viZdz1uzPPHp1RijkWvM5OhQmVa/YtbzySwn8q2KxtWw3azZUBwIQgAAEEgQQi7gF BCAAgZsSCPcb2pLIcEZKInEHsWjlNHFrf9cuQ1Wd42WZZvJQINXk4ykWr+TXWyzWlO2mzYZqQQAC EIBAQACxiDtAAAIQuCmBeN9ebr9fyzJUj5nFmmWo2muXWxJZY67cnsX43tZ8jsRiyDc+Gdby9cqv h1hsLVuNHbgGAhCAAAT2JoBY3Nt+lB4CEIDAz4NM7PCTUJTEy0vDWTQdNGOf0gE3IeKzYjFc+hmW 6+gAFrsudfJpar9gWM5asah7WvI5EotKKzx4xsqoOuo++79HfiWxGL+qo3bPYkvZaHoQgAAEIHB/ AojF+9uYGkIAAjcnUNprFgqHUMzE7+ZLnYoZv4PvrFhM7TsMD6hJiZnwdE97dUa4bDZ8f2Js4hax 2JJPSSwe7ZW08nrklxKLYhBztr2ltWKxpWw3b1ZUDwIQgAAE/k0AsYgbQAACELgBAc3cxSehapYo 3tenJaDhdfb6BxMZhsJmG+PDcez71gNuYhEV35/LL3fCa/gqipT5wvxKs5C6vzafXDnDMijv2Bbx 6ahX88uJxXCJr2xq+abK7c38Bs2IKkAAAhCAQEQAsYhLQAACELgRAc1e6U9JINl1Pauems0K3/3X krfqM6LMnvnUlNczv5DnWc5hGr3K1mJ3roUABCAAgbkEEItz+ZM7BCAAgdsSKC3ZvG3FqRgEIAAB CEDgJgQQizcxJNWAAAQgsBoBxOJqFqE8EIAABCAAgTYCiMU2XlwNAQhAAAKVBLQvT4Ix9xqJymS4 DAIQgAAEIACBSQQQi5PAky0EIAABCEAAAhCAAAQgAIGVCSAWV7YOZYMABCAAAQhAAAIQgAAEIDCJ AGJxEniyhQAEIAABCEAAAhCAAAQgsDIBxOLK1qFsEIAABCAAAQhAAAIQgAAEJhFALE4CT7YQgAAE IAABCEAAAhCAAARWJoBYXNk6lA0CEIAABCAAAQhAAAIQgMAkAojFSeDJFgIQgAAEIAABCEAAAhCA wMoEEIsrW4eyQQACEIAABCAAAQhAAAIQmEQAsTgJPNlCAAIQgAAEIAABCEAAAhBYmQBicWXrUDYI QAACEIAABCAAAQhAAAKTCCAWJ4EnWwhAAAIQgAAEIAABCEAAAisTQCyubB3KBgEIQAACEIAABCAA AQhAYBIBxOIk8GQLAQhAAAIQgAAEIAABCEBgZQKIxZWtQ9kgAAEIQAACEIAABCAAAQhMIoBYnASe bCEAAQhAAAIQgAAEIAABCKxMALG4snUoGwQgAAEIQAACEIAABCAAgUkEEIuTwJMtBCAAAQhAAAIQ gAAEIACBlQkgFle2DmWDAAQgAAEIQAACEIAABCAwiQBicRJ4soUABCAAAQhAAAIQgAAEILAyAcTi ytahbBCAAAQgAAEIQAACEIAABCYRQCxOAk+2EIAABCAAAQhAAAIQgAAEViaAWFzZOpQNAhCAAAQg AAEIQAACEIDAJAKIxUngyRYCEIAABCAAAQhAAAIQgMDKBBCLK1uHskEAAhCAAAQgAAEIQAACEJhE ALE4CTzZQgACEIAABCAAAQhAAAIQWJkAYnFl61A2CEAAAhCAAAQgAAEIQAACkwggFieBJ1sIQAAC EIAABCAAAQhAAAIrE8iKRfuBv/94gQEM8AF8AB/AB/ABfAAfwAfwAXzgqT7wyj7/BvD5qRCoNx0A PoAP4AP4AD6AD+AD+AA+gA/gA3/7wH/+53/+f7/EIv+AAAQgAAEIQAACEIAABCAAAQiEBP5/4eLr D6+w4PsAAAAASUVORK5CYIJ= ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/image006.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhRALPAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAABE As8AhwAAAAAAAAAAMwAAZgAAmQAAzAAA/wAzAAAzMwAzZgAzmQAzzAAz/wBmAABmMwBmZgBmmQBm zABm/wCZAACZMwCZZgCZmQCZzACZ/wDMAADMMwDMZgDMmQDMzADM/wD/AAD/MwD/ZgD/mQD/zAD/ /zMAADMAMzMAZjMAmTMAzDMA/zMzADMzMzMzZjMzmTMzzDMz/zNmADNmMzNmZjNmmTNmzDNm/zOZ ADOZMzOZZjOZmTOZzDOZ/zPMADPMMzPMZjPMmTPMzDPM/zP/ADP/MzP/ZjP/mTP/zDP//2YAAGYA M2YAZmYAmWYAzGYA/2YzAGYzM2YzZmYzmWYzzGYz/2ZmAGZmM2ZmZmZmmWZmzGZm/2aZAGaZM2aZ ZmaZmWaZzGaZ/2bMAGbMM2bMZmbMmWbMzGbM/2b/AGb/M2b/Zmb/mWb/zGb//5kAAJkAM5kAZpkA mZkAzJkA/5kzAJkzM5kzZpkzmZkzzJkz/5lmAJlmM5lmZplmmZlmzJlm/5mZAJmZM5mZZpmZmZmZ zJmZ/5nMAJnMM5nMZpnMmZnMzJnM/5n/AJn/M5n/Zpn/mZn/zJn//8wAAMwAM8wAZswAmcwAzMwA /8wzAMwzM8wzZswzmcwzzMwz/8xmAMxmM8xmZsxmmcxmzMxm/8yZAMyZM8yZZsyZmcyZzMyZ/8zM AMzMM8zMZszMmczMzMzM/8z/AMz/M8z/Zsz/mcz/zMz///8AAP8AM/8AZv8Amf8AzP8A//8zAP8z M/8zZv8zmf8zzP8z//9mAP9mM/9mZv9mmf9mzP9m//+ZAP+ZM/+ZZv+Zmf+ZzP+Z///MAP/MM//M Zv/Mmf/MzP/M////AP//M///Zv//mf//zP///wECAwECAwECAwECAwECAwECAwECAwECAwECAwEC AwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwEC AwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwj/AAEIFHhloMGCBgkmVLgQYUKHBxcCgDiQ IsOHEi1OzMixYUeMHkOCHBlRZEmSFT+eXJnSZEuUF1nGfClzo8uZOG3C1FlTo0+VNIPm/HmTp1Cj Q4EmLUp0Z9OagqJKnUq1qtWrWLNq3cq1q9evYMOKHUu2rNmzaNOqXcu2rdu3cMMCEIStFba7dvHe rbs3L1+9gP8K9ku4r+HAhREfHrw4MWPFkB9Ldky5seXIlTFfnrw5M2fNoD+L9ky6s+nQpVGfHr06 NWvVsF/Ldp26VUG6e3Pr3s27t+/fwIMLH068uPHjyJMrX868ufPn0KNLn069unXjggTivs69u/fv 4MOL/x9Pvrz58+h5Z5+bvr379/Djy59Pv/57Qbft69/Pv7///wAGeJxt7Alo4IEIJqjgggwCt952 DUYo4YQUVmghdA9eqOGGHHbo4YH4FfjhiCSWaOKJ1BEIIYostujiiyZmCOOMNNZoI4Iy3qjjjjz2 WF6IK/oo5JBEFlmcikYmqeSSRubI5JNQRsmik1JWaeWVEwKJZW+2XRHklsnZxsKX17XCgpfK4XeF X+q1kpcgY+LVCpnWqQnmkflZCScLfF6x1xUBBECndWJeceaZflZXKJvY2DlfK4GyoJ6hlArCaJqB Dupgpr4FGkBeLAR6lyCRRkcpopbmBuindw5HJZNmev8q66icTicVl7LKeulzqwawZl+hJipfrMKq mmugvzJHqq/KrUpmrILWFSoLdq0qKV7JIgfpsWf+GeiuXHoJrpKvKgmtr1c422it0RGLawDT8klt db1Gu1epj+KrW6i+4ufpvMstq+lvAvs253b87hWVX9NiCq+X/Nq76ri7Taxnnk/apu9dvxYc2G4U L4YsrvPaFbJeJ1cM78bBcqncruPC3LJuGgOMjacpL6bwyMHtqq5gXDbcG6DFGretsKsmym/O69oc JZJQbsuqepmayeeK+MmLJrB0UeqmoQ+nmtu2TIsJb5xji63ymTzfDK9u/l5NmKHSop1uXYaeaXLe Rc//ySe8EG577b7fesusmHUTtufVuQksZrdj591v41XLCyGcwibMcZxmj7mmmGxafemywkrNceEK y6s3XnAiuzWU5RqZtG/L/v2v4bKijQ2gY+IcsacrQur0b1LLCqGnQwM+rckz3/V7pNshy6+k2y7r KcS578xt6syu7dfsR/ObaPH/vvlv9qcfu521uSarbqwlt2z97Rt7PHbbgleL+rm61nVsAFKKXZFm 1xvreQ5nm7PUnPRlQD75LVIOpJnr0kUxr7kJeXhBoMoASEB9Wetr+DoX44qXrvb1CjeQ+lzr7CU4 3ixtL/yiiwjRBrwHdm9+UQFeAt20Qu0Bznq4sRg2/zTXQn9Jr2uoG+LbdmO/Fu4OdUmbk7XwNj3I wQ5jTHphAfUVw48pEYDpKxkMuwey/2ULZJSz1+74lLx5YXBm8GMY6nTIF/jhxnp5+RmjPOYumvFO htbLYK30p0a3gbF22hufboQYRVp1733La1TziJZGFLbtfoeDFm5e6ES81YpUw8sYFpf0M6qp8WjJ m5r9xli0vqRLTdcbjhCDo7nZ4WuVpJPWEvuir+ppj0y59N8uc/M8OnYyfWxS1yo3ZixVssuJQiQi F4fptmut0ofty2PhcPnJJEZJgETq4hbVyEerrQyK3nQeGTclqgJKTpzB4R3z4NXHUuoyUczs413Y 1f9EsEUMhc1r5r9sdswvLnKb+BOiOSOmzULWsnBxvKe3zvhHg/KGfKFSm8VW6URQMs1H4BzSFMe5 nWBaS3LOLKREhRNRJv4LbFOLZzuf6KtbskuY1BMaL3cZq5LyTGooHR8z1SkoNzEKfgeN6bqitcoX 9upQqGuiU1EnzV3q1JGWyug4xXUpi9kTlTT9aI+0BLtIgSuYS5XhNKc2y3utEzgYpFkk0ypLerp1 ZYla5c/y2Tyw0vWL5mNhQLFl14syU4tEtQtanehBdBYSX5BcYuuQ9tYvXnOnrRwjq9C61LwWVkpQ g5L45qbYZ47MfkK87FXHltTBYXJws8QPmVaVxhD/sgyiq+2kL7Gqy8HxcaiAfZdraVo6mxY2l35N rVkdmahotrOTmhMoNdOYWWQu1bVaXJZYeRTSIUntTHtiKj85lb8cOvZSMTyjEqUC0y/ZUWPs0uBB CRqx8X3QiIPj6zrb5rGlWYqOk+WNPDtFTWitaYV3XBl4EZi/BTo2U/4MIm7tqs+k4QdYsXxXdcNK VHGJT3ueC6B2qjS/8v21s+k7X0qDNL+iFVOl0j1nXqS2q+g6kqfFHFxBdTveG+bqwzh1YTp3Otyl tu+XR05xpKL6sCRz2JCC9C36iKpUuF0yqTMuZrb4F8BRQildhxKbmbKVtRkv7mu+pRvIMBdKMPcp /2RZQxMF/3Q39Yxpj4YK3eJex7E6u1J3u3sd6IAlNzjN2FBfwty40pXZx4F3bN2y2p37sjhLqblR d4vz5VYnSRQiunGVwiQYg8ZnuF26z/IaHaU2XKTQturVsHYpjGNNne7S+tZRqiiu6zTiXfv6SgX9 NYa8LOxiN4lxxm4XsZPN7B5tt9kE6zW0p03tWNu62tjOdpHIqu1ueztJrv62uMd9o2uT+9zo5pC5 083udjeI2+6Ot7wnFO552/veAlo3vvfNb/Tou98AD7h34C3wghtcPPU+uMIXbitppwdmDI94tv/N q8t9elSvlLjGoU3w74BZYZ+LH94GtvGS3ynh3P9RLNLu6KdkXdjkMMc1xZ0DJxiC3DZvKnLMd76l mTfn5Tin1RDNR7wZu6kuRzcq0k2W9KYz/elLj7rSp+50qVed6lDHutWzfvWuc/3rWw+71sfudbGX nexgR7vZ0372trP97WuPu9rn7na5153uzw5Qx79jJpv3eXdZpt3BeDinwhPeUgosPOKNuvjEN57x iof84g9v+Mg7PvKUv/zkLS/5yj8+853XvOcxz3nQm770qP986kmv+taz/vWbd33sYT/62du+9rgX ve5Dz/vTyz73vV/95DeE8u68fHcsHzrGg0NynjvfRj5fjt+Ojh83zavmsgXO0Z/P/SFFP0yrflP/ nkFeapd1//zcXTaAto/+9teo+ABqvvvn36Hvu4f99J8/LRjTCkXk/T/2d3/5132JABhfUICD8AXY kAgK6Ao1oG7q9x/4N4Di5gqcYYF3YYGJoQgIqAjY4AoK2ApfsH80sH9fMAjYcIAfmAV1MYLEF4Es xWjlBx7yR4FRkggW6Ap24QoFuIB3oQgWiIMfaBcMiA20QAN38QV2oYIJuIAKSAsPmIJLiIAKWIRQ mIQW+AUe2IRF6IDq5nADcj6sVib/Z4M0Uhj7Z4JJiIJf0IBIKIJ2UYIpyIZu2IJxaIJsiIKu8IZK iA1IOIeAuId26IdrmIIFKIhwuCHRF0EPZ4Yt/4KCCxiEOXiIHogNkJgI+yeERWiFUVgDWViAm9iA UfgFn+iEpuiFKViKXCiKWGiIlliFrJiKrriKHxiFGrJ3xFFzD1eGjiiAKLN/2IAFmYiCg1CAtBCC fUgDWaiAbfiBfBiHyxiIDyiCFiiHJxiILEiNfoiHrugK2UiK2wiIKuiNLViN3DiO30iC53iIyIiB FwJ/PdMnj/YjvSggxxiEsHgXf9iHTJiPhCiFs+iP+ziFr2iKR5iEBEmLVwiQ/WiQA5mCW5iPCwmO WliQnNiKFamQtngh0RdegwUeE1iP1tGDtGAyd1GSdYGB+1iA4LiJNUCHRviM4XiNzXiQiWiNef/o jIOIk4Eokzx5jYiYjOeohz45lDp5k4UIlD6ZlHqIBYN4i2DIHLwofSJpHMd4F4NggYOQiURojE+4 AxboieJYh/eIDVlQis24h6CIjEY4igRZhCLYlhi5li0ol7KognD5hKNYiqFolxRJl124l67Yl6j4 lxbZgCx4l1R4igqoiDA4HFMplZFZcq6AgT2ogwu4f4rgBab4jyqIBR6oCEhYhJ5ZgKCJDZvZmUgI gqYZmpyZCDtAjERZjn6olQo4CLOpjcpoibJ5lOD4k7n5m0YZlOYojofolLq5jivYguponMspgs1J k4eYji8oIslBSVFhHjV4bsC4F+6YgXvRnR//+IQu2JKgeIgP6IDVmIXE2IDuuX9nCYhNCILjuX9i eY3z+Z7YcJ/t6Yx0GZf7iZCD6Y9uOaCdWaANeZEMuZgKyo8MqpcCmqAQypARaZCCmZH5iIpQaZ3H ASfYN4YGAzR1xFKTeSeYiYlGWICtgIGQiJkVqQgCmYQsGZox6oovyoKkuZIQiZo1qoIv2qMzaoo5 KqM76pL9aZNCOZYxuZNGiaR3KI7B+aQ0GaUzmZNBKaVWupRQ6ptxmJSHKJMbup37Yn3Scp2MlnNz 1jNCgoF+kYbhCZ5/MQh2UYzjqZV0eZV3GY3z+Yc1YBf8WZCCmKfySZYUyZ6AioRlSZH96YWJ/2qo 8/mAjTqotVininmodEmfE8mXbJmpBhqXnIqXm3qhgBmLhkmYonqYk1qptFiYqpihG2khuOgq1DKr INob1FNHHaNeIFOi/iGElviDc5qBoGiBBwmC0FiQTVisyWibllgDBegFX7qTRDqOyJmMRAqtqFmt XeqKXhCa2lqa2NCt2VqX4CquiuCUQ+qKp5ma6fqZrqmaArqurxmbvMmltYmswbmeBQmU0yicW+qF yfmv31ic0vmc/lqw5BiwCEudP8mOtKkh8Kh9T/VstmFqfucg8OEK+6excIqBHruxwOixSWiCCAiK LOgKO6CPJAuIXdiYNbCy19iyd/GyNoqCif+wm6t6oczahSwYqbh5ij1bqJJKjiCYhY7qnpQan/iJ tEVrlkc7nvXptEOrn3+6p//ZmA8JqvC6oKjqmQkpkVn7oH75tQ4ZoRXaoFl4thNKkWprl2HaHIan HIiSRwA1MDnznR/onbrRmEK4lR+Igv2XhHchhLsJowV5kMd4rIabrIi6rDx6uI2ruLf5hKvpuItL ueMpuZa4A0HqCtWqj9Mamt8IujZKo+Sqoz96uqH7uO0apIbbujv6ulg7rRYou6TLj/i4gC8JuZQq hz9bk5VLsDmprMILiMRbpT3Zu0ypk027j1nKnJ75vNAJrtQKvW8rfVLxf0YVJ68za0tXeIn/4CY6 KL6tkICtwIBukgXhe4IiKKfmOwh92go0sL5K2Ibl26fw6ybz274iqITvq4Tyu75yar//q78CzL8F HMD8y74JvL/sy8D+C8AOPMDu66z9e8E36yYPTL8YTAMaPMAcbL8ZvMAhrIQjvMEXLMIeTMIpbMLx i8IP7MIavL4HrMIzLII1bMI04L5eUL4A/MIUXL49bL6tAMQIPMQ//MEIHMFuYsQMjMRNrMQQ7MPn +8LhmwXuy8SJYMWtgMVUbL6JkAU37MUEbMJi7JgcajS5wot9tzVB5zJG9QWCEA37OwhYcA1eIMEa PMd17AV4rMftGw1YEL4J+Mf6+8GCTMhf/2DI8qvEg+zDjLzC7FvHi5zHh8y/lBzJSuzAlQzIk0y/ eByb14AFcnqz0UDEkjwIo1zKNHDKnqzKpHy+rYzKH7zKsuzKl3yCtmzKtNy+u4wFuNzIvvzIiQDM vXyNxGzMr+yHhKzMXhANKvyKBSjJzxzNCTjNGlzNOvyKcZjN0MzK3HyE3sy+N8vNezjO4Gy+5yyC 1ZzOSrjOX9DOsmzO1PzN5+uU5ludYkpM8aNzxXFhuth3aqq0i0mf9GmWK/uzBu2yMKuHSLufDQ21 B02zPurQUAvRNfu4Ex3RhjvR04qCHe2y0/qlO0uqRluvgYmRWmmzpAqfKw20Iyu1MYuYRv/ogkrL 0lB7jC49tTU9s0+LqS770zTggXk5sxF6tUbNtUUdoJXal0zNtkIq0nfZtp9K1Rdq1SqNqgt5vcmh i0NUq2smRTJEQYDGJSvah+sao4kbjKZrhZVrF2ldtsYajLU7uUs614Vr1056r5e7pDeZu4y7k4CN gnu9m0U4CJwbuwyblKYLoM4bu0AK2Vvro209u6XLupZN2Zh9u0Hq1KhL1FW4u4F9k+eIuaS9pYU9 nEX5r6s9pfYKnK+9vFcavd0IphwZlUeCKHyznWKtOLrqMruJn4DLlvcq3Kbo2I6K08g9i8qNtWjZ 3Pr43Mft3DaK1MXNktZtjdi9gMF9pFn/+7QHKahLq5PivaiNe9LjLYiRmt6QKrTsXdPovajp6d4F Pb+M6ZeaWpefitT7Pd34baAp3dSuOpeoyqoBeYqnmrMQmMb//GOfxXetcIwsCdiZPeGm2ITUS+Gg u9YtadeEyOEaTohzHZ9NGNgjzqwm3ockrtdvvZ8o7qy1La3V/YHfqqPYeq4yroLmWuPX6q2qy60+ DrvyOtmtiZqvmdlw/a6w2ZuzjbO3ma/32oT8SpuwDbAHa6UDi7zoSOXKmbBX3o0M2+XtqM+S+b28 mosufuALbZ9oybQueJ8sadETLd0h7dMZLbMYXdH3nedxvucU3ed4/ucs6+QIntWrGrQn//2zPAvf d7moJ+vei+qeFnjTgCrpMh3pUUvp+Rm1VeuG2X3UUX27ZNuuo465XKuRoK6QWYvVU63VV+3qX8jg xKF46CG+aF3ZoDvXcT2kul7XvJuIWODro225LC7YjzvsmovsfF3sf33hif2in6vlPu7Yq6sIOFrh kg27qavtoE3kRRrZFenZtCukuwu8TKqkqc3a5+7aTa7a6y69ScruWirvOW6l38rVx1Ev3st3xd2b eN7dKP3QwY2A0H3dAX+60r3UA9+p1O2j2Y2WDz/jN/vSexqhP42gF2/xQ6uzG8/oigqo7R3fIO/x rbqc613f/I2gKU/gRd3fLX+qfenyA/8u4IWuqhmamIa5qo15i4+5RQrkL2c+6wQJjn2d4ReO7R1e kF4rtYeN5Exf7Gsdn02/pFGP4piL11Z/145b4jA+jvO+3cRJvR7ouVRe7dH+2eMKuzvu7Wsv5Eo+ u6zJ1ka+gPSq0HyoryUO5TjL5Hev5Xrv7grLhsfJ5c7p5dEp+AZ7+GBuve/Y8y7kYv48cDJd0PpJ 0Cno5nau5wud+X2++XwOiHX++dcY+oI+0xdd+gWP+qhJ6AGe8wO+3jjNqOBI6YtelppO0017+xJt 0z9Ntb3P6UJt3Sof6k/9lgSa6hI5/Kh+6smP/BZqtrDe6gquiLjdM6XGRuRh60m+2R//fuubrazb b7vgT9fHfvXE/uvnr+yGzeyOe9iE7ZOA/eyiC72PnbrULo64Lvbcj/a2q//9z/8A8QVbIoHYaGDD 9iVRQkUDBRJEeDDhwi8NISaqMQjbIIG0Drb60sogrYQav3T8GHJkSZYesYEUSYPkF5MaXaWMObMm tpsvVcpkSZMnzpVChfaEaRChUZtEJRpdiFQlQqpVrV7FmlXrVq5drwoCAECQV6wsrli9woLsWrYx XbEcpBGkz4hvhcZ1SNegXY14Ic7dO3Gjxr8FadilSFgg4MOChRau63gh5MAKBVMGatlyosZ3US4V +YXvxs8J7Y7mOHTpabgCe9ISbbq1/2rYrD3zrIGttuzbrnLvjt07t6vgiEkPney6Y27eyenSYh7c +Vzoqy8vXm5980PX0Y1f7K59YWqIvsUfL+9dMPnwbN2/h09W0BWx8QUFuCKo1fwAIuP/R6iVVmCj 6C2IUlPKMQMFQvCpAvNqUDeVYjvQMAlDW/A4pYgTKYu3UkvNJQ6x8fC4EA8asUQQPxuxhg8ZrIGi qJxaSkaesPApphqx8WIhRXBMykEeG/oxxwQt84JIIC0ULElsioToyIWwINKLvKTEhsonrYzyKZG0 VITLHfpqysjGQCwTpLfOFOio3NQsaifz4MxMzix8WlMnwVy5k049LeMTzzj37PMLkv/q3BO7twBk tFGyWqFvLPsCoJTSsxy1DxsX11OOJ0M1Ne4kTz0laVPNbOoUVMc0UiRVUxPLiziEapgJ1vIKonXV WHGtlaVbZ+31MTZjVS829ni6E7gPFUNWQtv6cu1O4p49TtZpSUSt02tL7E05kriFdlTiSs0WubwY A+26K73kdN10LTvWpYRCG4873aKjF73SJmwo3mL7tbc63gDOS2BMD0YYIbDqAzC/K/xLeC0BRwTT 3g1Vqthdit9q9TiXksKCYwY/C1Jkj50y+UQzUyb5J5Z1QxnCHShSUi9EK3pyyYh2xFmRO7vkmUiL hex56KDzAtoxi4xWGmkmNcsQI5P/Ws4pKKoHPcqpP9P8aWvVgvRaqqqZ+vqnpeSkMSibloQ4Yrft C0vSt+fGyi24mJW1MjJ3jQxeZhkzzi/s+rZ18MpsxCzU6QxT/FzGJRvozHB7mpe3yfG1vFrMg7tc u3LNU3Y20IsT/TfS4Y3W2eaqpWHxe991XWDpHH99dchk/y7gYp1L7zzwcDvv2EBXF74guo+XL1Lk lxdQwSdH3tn5CqOHGsKnO8wwwspLnN4l2LC3HuYLsTWRRZfLF3/EYQeJcc+0NZtRUKIbcmXJNY8u Et2moXx6ISf5p57/apY0y4CJSxYizpSqNJAxDaZsa3qRA6UCQQfiZYJY41qe1PZA/6xFxX4dbBZI DvWnqBRqhEEpoU9IsjwWZgVSDGvh3MbCrXqJ61M0TIi2cBUqVI1KVaeyIbB01bG85QqIROSVrn6l qWD9LYlHHFbviGev0VGrd7sBV6ySFZsspiZQ2/qct8jXLVL9cHJiNNXkWkc7TcHOabNy4+/aWDnU FWxzNYSI7PLlLzfykY4Ey+O/9AW8GBaSKguTmyERNjGMCc1d38uSI/OIokY+r2CU/NLLRHS+jqms ZJb05Pm6F7MDaeRjomTQzBjyJBMeySSO1J9RJNm/VXaMgDSz5C1ryTTNzDKAq5Tj/JCWkZOZ6U9X u5kptbZBsWGwbGNDmzGZ+b5oBv/pbO4zkiJjiEhtegVSD/Om3hw4F8DdjY2d2Vvi1uNEwvnKcJ0p nM0ap074nTNYk0FnMelYLnmdTo2eE91BQtcbgfpTc6rj3EEH2rlrzc6La/wV7tRFnd3RTqLbeU5F f2ce21GxosL7qEeDJ9JutnA+MCxp3Viwn/xwRUAEks30sEQh6M00exaCJE01VFAMhe9i4FtR+lSi IuhtEqhFxaSqQNQ+QL3PRvUz0456lLOoNqlm1rQqVWMpQKrq0oDuEsxXCfilBSaigRaMGZo4OCw3 yS9sb4pNMkMYVxLOVYNQsSsIAyVCvSoqpcxT3l+x0lJszKcrY3lVtdAYKh2SS1f/1trhEF3FQ74x UYmuamJljRjPImaWMxGUIumEt0XWCK5ZC62WtAzqReW8pYs6dG0Yy/haGzqWoK47Ur6C2Ude7jFg 7KpjIHl7yXcBMjsDG+RFjVs7wdKNm82tCqT8Iwi1uHR7vsytprDLrk3Z8nqq6uRnINmY8IpvvJpM KnmR6im7ldeogekkU4kE11/2rFD1JdJ9Z7pd/PESl97Fby5pWTSwVs+WxBTVJu86NaEuOIcNdmZP riVXCdN1mp6634X5+pRqforDLJHKCqHrtueOGCErVVgAXLgfFmNBP4J4MYwF4YX99KgVLt5PjGFM 4xkn4sYxzvGOa+xjHMM4yDOu/7GAivziQQgZyTceRI7302QkPxkLUTayIKjMYxpDWcpadnKXr/xl Kl9ZxklOBBYE5AX9eIHKL6Zxmtfc5jejWc2JYHMr3HxmJMsZz3Tmc5zVPIg87znLNB7EoAtdZyQn uhWEbnMi+KwfR0Naz5IuNKVxTOgaCwLPndYzpbns6VGH+tGj/vSM2yzqTqc606cOMqdVbWpZw1jW r661lkud51zfetWwljGn22ZiTJ00kdCVboCqW7eXNG8sAhIJtK/xbGc3O9oCmra1C6ttVmCb2iL5 Nja63YpsQ3vb4Na2IK4hoHC3u9nuNje1TSHuasc73fXG97XRHY1803vf/R73uf8B3u9nCxzcreA3 vJ2N8P2k+94Jd3i8Ga7wg7M74gu3uL0lnnGCQ5vihfW4tqEdjY/rh+Po3njDNY5xlXec2Al74bGh i+LCXmorkvJPzq+tsAD1vNk/3/ZLfN685gU95wHaudGpcvSfF53pOgf605Ee9aFP3elVFzrVs/50 qW996l4HO9G7DjGua73sVw970seudp/jnO1Zd7vZed70rscd7dO1+tnxnnag43zoSff71gGP9aK/ vNhxMzx1X0JYrRTd7nh3fN55Hnm6T37vYrf83I2O7p5TvtqVV/rm+952yYf+8XPfu9A9j/rBw730 gZeb3Zc++qbP3vPRtv3lcd//ed2L/vaCp/3nVd/704s+7qCnNutH//vV+x7yvS+84RtVYhN/k/E3 b/vOm68f5Ge/+3DX/uVn73ytnx7z5D+/+VuP+fRLvv2Vfz/717/24P+9/sBvPtTz/3rCr9/++/8+ vXM/8QO5AFSY8EO9A/w+SeG+6JM+ADG2B8w66+o8FCO7CbzADBw/1UOIKziLC+y82TMLn8NADFQ8 Etw9DqQuuUnBFsQGmtNAEnwJFQtBFFQ9Fig4G7zAAAg8DqzBtLAKEJw9Dxy2Ily6Vlg2I0RBHAzC DZTBEowuq+iPJzRCJMzBCbRBJsRCKHyJE/TBEgQ3C9zAFmyFKQRD20sxNJTA/0aJuTXMFORxwK6I Q5caNq+QOTl8jznkijukQ/fQw8arwz50Dz6MmEDcwzw0RED0Q0JsPER0w4ShvkeUxEl8wkehxK1I REy8RMHKxE30xENCvE9sPJqjm+vzCg8cRJt7lGVbxU68CiR0xegiRbLQwrUwxcNSRTvMRTlkxbmB RfeYxVNkxMHaxT3sRV6MxarwQlFMRZRixihMxv9IQUvMQz/cQjysxraIRieUGGvMRonZRgAJx0qk QG/sxm9si2fMw8BSx3Z0x3eEx3iUR0OKxHb8xeO5Ra7Ix5s7RmMMx2DkRT8EJ7YASH0sxq1Axbbo R0wcSLpBQoEcxquoxeRJRf8/XEhMvMh5LKxQjMdxhI9x/MMVQ8dHAcmR9KZwDEkXMklshMOVFERw dElN9EOPZMYInEeaxMSca0gXishwIslYZLeGo8agK0cZs0PqIsL36EmngzGf5MZXPKSmhENX5D5G 2UahlMMXK0mWZCmcFMU2lMedvLmE5Mf8QMK0yEQWUMuebLyD/DqknEisgEuzEEu5JMK0iMsgvJT5 8EC3VJi+9MARHEsPnA+zyETqwkHrG8bAlJu6PKRLAUy/DML5iEi+hJS1xEjDhEC/rMq0AEKGNAu1 zMuvGEG8HEap7MvRnJu+PMq1qEo75EhRdBjKRMu2/MvC5Aq1uMwA8UuzpMv/o5zNwBzLxQM3x/zL aHs2yaTMFRSQjIQU4uxAQky2qtjHE8sPvsRBRmyptEjOsdQPzcxIw5rO6iwsBsRB1bxN5nxBjKy5 laLK4FzBrbDA6jIsrajPhiPP5UzMh0TIaLO5jHSPt8C9YbvO18S+rETPr4hNTzxLvsyPglS2Dpwu 6YTM6npOrRjI/dBM+8TBvsRN+5QUmzPOmjuk6Ly5EA3RRLxQEaXQr/BLSME5yXzBaKPB9cTQ2HuY Ea1PET3M7uzA4cQGwqrOC108sswKD43M/ixP3lRSoAtPSdHCEQ3SZ2tMr6wKjPgCGqiBGsjSpetH qRQ6A+3CnJNKATHSPWRH/9kk0Ba10SmVTxgjwm9qUqogTxazivBcKbIc0WQzMjcFUQn9UYQs0K5s yw9UtvfUDxmdDysci0W9UWX8zBs9s6S0z7OozyBNRNT0DyntRRhlSKu41LKA08YczhyDzqzQVEAt VOmC04M5CRrI0liFGErtQocpUhn7QCNDMbOcVbZcPGf8xGFjxLOszSVd1SAVTAwFSSXkScJk0rFU y+nqxGMj0kplgQDY0LYMTe58FKQ8RSZEzGkFVUVNC2yV0sKiSxE9xOh6URmtm7YJSRgdSDA9UrW0 udPUSzkMTPxw17XgjC/gUi69E+oUzPo0LC00LIN9mGM805tbUI18RZMr0f90NDygTEmWxNisvFhm i0lMicYXO8KOvcYR21iZdEqE4QhYVdmpWDpUNItEHYt5PQvC9FT1NFH5eNiaFFO3KdnCpNes3NmT 5djy9NWZtMqTDJCgNVmX0kqlZJ6N1VCz5MqfJTGlLUeRnBsshdVXlUu1OFOaMyxUhFfGa9hKBdZL vFC4tNr4uEwNTVYX2tbQ1Ef/2NaiVVW5ZEDPVNSXpQrn5Ndo/KbmfNuudU8PNcixkFtMREr8SNDo dJi17ESdLNvomi7TNMjA5M5MBKfLnNxP/VPOhdBD4tsT01zIdZsv2IGVhVWIMc+aG1CZdV0ffEiR kFpvSlNPbEr+xNSEIcz/pCRPsQzVqwCn+3zR5ZSx0N3RTR3W7EzIPX0YpIxF5n0YT1VWZPVPQeXL F+PMvYxZRryP2fxNhERRvgTSlprOuhFL8qwuwgreyZQxOO3N6108jITfu8Vb67PSq/CZk+Bf47G+ e+1L2p3Qv/TdpETMbHXYs6VE8WTRhLFQC43ffK1eInXODu1d5IVMOtVOnDtYFQXgxLTNIOVe+UzD l8jIzbXAEc1QutXHzqzMRuXRP23TPT3G9m1ZAf5LQY3MSMVbY93dI8XRIJ3bpB3dgxExlYRG6xLC AcVZBZ7EwJ1eR0WYzR1hDG1IOR3FM8Pi6q2K0BXhL5PSUA3XEBZdD35W/6qlzu9sKRum002lUyBN VYbsXlzUPB1dKS8c0om8R1R9XpMjTyFmVzlmMZYCUmXzYlPsXI20SWaM2r+NGBYcU4OMW329AmwF 4ZwMwk7cVwR+V3h1zn49whSU0raN2kzESoU5zcQFZDrk1q6IzPhsPNp8Y2j1zMZtz2v9Y6CbWG3F Zck0Ny9tYaiEWPR14k0sWY89SYmFyZaEx2M22jcETk5eJGU+x+Nx5mGWoZxl5PyVQ24+LKek5hXz SGgrWZaa3o80x6E0WYvD2FO25nRG2nJk53WdyWncwxW0W3esR7R9X6TMZ5bES3I108GN2Pct4h5G VmcdRVtGi9AM37J06P9h/KbYG9Y4tdc9nC577dEODE1NHkGkBGVGGWUiREzlfN/MVVLODWPMdE3R fGhBFc1ixWZILeZHLMMLvuSDoc8PHFLudNbffVkP5NdSpeJMfd7s1EVwizH2ZFcPflBwUt/UZLcN hs7fjbZlq+Cag0xvXjpcPd60xE4zZcS1fFBp7uIcnl8QJdakLk9CFt8gBFB5BMuaHMhz/citRuus mM9ndSGyPSwf/WH0TVqFluGg09FdzOPHNNXqPcFqFd7i7NsTTevA/gpJ4cGbPRhiBczQXdEq5mOd dGWl5uvBilGzjsqJldJbtGt43GdjNks0bpTCjdGUpmJcRFyvSEzuk2L/u6RcL3ZUqfTiWaRe9CW7 VtbrZ3VstMjRGPXgRIW2F81Tsc1lpTw2zobgvG7WxfbO53RrVD02WnVR0Z5RQSW7ZZzpjazpBZZp hHHQQzJlpoPtyVzvvi5X0x5tW9RoBeTQ1GxcA7VCTM66/DTcyabva7VvhRlBJCRoR3HAtW3ANrbd BXfR+pbM6VbuaNVvDE3X+Z7pRbbHa46hq/RVzrvaQbxmLUZnkRXmk/TIf34bK11bqATKjyw8iwXZ 89bL9LbpkP1weI5lXq1nXwTxkUXmNfzld/bxZo3xVxxyHAdVbaZE1vRZwLrc09bWRD1ouQyyOC1L S/5j1PTM00TFMcZI/7w8cKJkzHXkR/d0TVTES6628sL8Y5ai6DIXCVVesRi7Y0m2VwAF8ywn7lJ2 coWBcklcVG+lbAbvZ8pESLpM4bfm1uRWRn7FXOge241OtvN9xQ0309zsTjaG8EUd7rrJsuP1TsIu 8wAG9f9gYP9U0ZfVTPUdaMjWcP7Oz2Nb9dy9FE0nTQfdYhz3cNy9FIR1ca2I24BuS53s7rLwjxoF 0No9V6r9XcDmbMj0bBcNWfVF5Qjd72N/6/LMad720/i2jwy2X7im0+d1yxXdae+M7GBGi0QU0r5N SxY+sUH/1WKHrkPXQi+Gj59d9Z1kTSueaALn45iNXOEe5dwuTqPedv8/1Ws3JtEbpV5Jb1l0L+wi Ld8UjRiGP1XBbtkELdzh9WVRl9Eztfi+bdrdJm1VXe13bO0oB8Iphzn/HtbqrujcZmmm5XDSJMyX hlsKx9fOW/hWXlvMHeCbg1eqbsIDVb0lh4/QNNehF93l3c6ef0XhdOXexfouFnpBBO1Bj/lg1ffq K1p3juUzawuoH9pY9ko4f8oU51m0f3G7Jffoel9wLHa4f8Rgx3e2HfJxDnKKTXJNREm5V+dqTnyH RHyhXfyXVPzIn+m5/so1xnDeNWQJF9SKVEiLREl/l8h/tFsLf8xBjGu45vtN/8dwJP0ODGl09XzT x/ext+nzPPSXXwv/+qRSuxV8wvd9yXf8En/8pQX+jH3xxjf+4Cf+4Rd+bKZ9N0zIKIXzFa3c3//7 69c27JdG7V8ev79Eg0VR3jW53Vfi2FXc4T3zeEtkZXs2QNfyWc1N3EfVw0z3cPemYQ35yixXxn3x SnfFAQUIQVewtcJm8CBCbCyuFFw4MCHEVoIMCmIxEWLCK1cmCmRRECNCQa0+YnsIEptAkydXsmzp 8iXMk62uALgY8ybOnBINfpyZs6VGFgEcsrzCYuHRABpXCmzKUODKVkc3UlUZ0aJGhjNJYpRq1KJB hjKxinVZ8OzIpSfFCkxrUyZBglKZXtQ4823EjSNHQv250qJEhhuj/05FepQrRIspRQ4+WVFjU7CO q2q1mvio5LIgUwL26/kz6IOCANQMbdoz4pGnExYUiRhjRdVtWXokGOAgC6YDpdZ9LfphZrwZHzZV 6Jvn7sfCWX81HGA57rAX5zo2vHDoysa5DW7fbFkQdL/geX60nLBz4LggfZbc3hdkbeoKs0/kHfZ4 SeKDu4MUO9MufqsJCNpopQ14IE4BnqYgQY2ZlxFH8fEHkXa9ybTbQtLpdtBdmuUVklEB6hXXXfSh 9NF7sPkn14QJQcVeSfileJ+Afe1UUngUDTYebWFtByNG/j0EJIURftRiSG+lJKKSGSL4JIE05Qgl lVIx+FmJQGXVUv9HT215IXkN4jfTQoxVxJJEJM1021gRKWhVY+sZdYVSC73JFZEuzmnSgwdlJdFj A/5XkYVoptSnaA7dVZtjKCJHWIgbSbbeW2sWJdyUVGoqp4GbesrhplcihyiHqv2XYKaqoeThehFF lemCSyK4V6lcZhWngIHZJKqYZvWEa6shKXhjllG5GhWvnyorGmmwLusXrc9KO620yVJ7bbDYZqst t90W6Gy3Fwo1lbUvHQrsSzfGlKlIIbWkKkXDyqYWS+3+dyWKpCLUGqvHNoiuv5Ch+audgk73VYD3 xmTXTQzPaWtrBUd1a79dUVxuuJ4J1GnGOAG2F8OrQRYbwKyRuKr/sWexSNuchVqMVHkJt3wry1jJ KidSdB7215xJ0QtbfZAxulldVOX4ol6zrdbXx0giZFGZL2Xl0ZUUbzSji1DHxqOcghldr15kYdxx glKSjRPXfo4tp2gFlewjZnEzFbfck+no9pg/DvY2yvbFaLdCdeVYcHpFUb3qnQ0+6ljQatsNI985 Ie1neOwtBpSOkf8tCJt5IuRfAOVtSFGva03X+NlQfpv6upZpflN39iK6FVpYg0jrSE7ryCF4Fe9O +dBBXsRo8Mz9nrbwHMG6FXdAKY8cfoPBWPy+7lGeq1h85l3qznKSBB71T7fG5nyMo5RZ9KdzJ2JP R7L+5Orvm2WX/0gpDegUW1O2yyGp+y9OaUZCl52MTMpiYWIepdBCIzRVBSaQ0VfPDja3hy1wPQ5x CLhaFxSj5Eg+bfuLmyYomOJciH72u1DvXNYVxQhpbfKbDMdeKJPHRO1Aj1HTTfBlMmPtC17bQolZ 9jWl1IDLb+b6mUzg5UNXuUY0LgEf8laTlhEVBW3lIlYB12Mqo7jkS0BkYJwyKMP5xXCMZjwjGtOo xjWyEVvxayMc4yjHOdKxjmZ8ox3zqMc98rGPfnyi2f4oyEESspCGTN1MynjIRTKykY58JEzwCMlJ UrKSluyjJC+pyU1yspOp25gYPSnKUZKylDoJpClTqcpVstIxzf9qJSxjKUtPZnKWtrwlLv8Iylzy spe+nGMiQ/nLYRKzmMqqpTEhCR5hwkZ35Gnips5kxGSuEpnUXKRUMGMaodBGKUNxIdh214rnXJOV uywnJHf2PSV+sZ1oKQ/5oImb7SjlmaDaX7TkYpP6bDGetjGJ/9DZyWAK9JF0eotRhjKROQXgOUIR 4FcUWhI2NYcrR+FObrLpJMMQZFwROsrwEuoRzn2TcyMhJw2ZWVBBWnOlf5SKRGl1Uc6JRCgSAQtI x5kbOrXHIOSc50lrOhCTKoSc2ZSLV85XEIWOkzG3kUjomooNlJLPpZZsqVX9WBEBAoqbPFUIcBZK 0Z3eRigb/Nz/UOqpU40I8KI+3WedbKoQeg6EiyiJ6nM4ZxhwZtWO5+zrIp/jlaNyLiwYmohbL8pT puKle3d9imrcOk6AKiVNS31IPb9q0qY2VZ6AhSRBPztI8EBVJG0lK3fCOtHcodau6sFoQuqpHrfO dS9Sne12MhSiuwb1tnwVrRyxCtw5inQ3HJwpRQ8714ZS9Kl0opNFJ8S5OTXEJONsKEeGUjDZGhW7 47TNQgsz3EcKd7xxzCfudqgenqpKiQGVi5vY2UNhgSpM8FWPmpZoXkP+db+apK1/A2wxRQr4keoq MIJR8soEM7jBlCyvgyMsYb+icsIWvnAeQ4vhDXO4jRDuMIhD0sytD4u4xCb+VH9PrOIVL0vDLH4x jOG34BjTuMahIbGNc6xjHRF4xz7+cYN6DOQh1xjHRD5yiI2M5CVjOMVMfvKJXQzlKYtYyVS+sn8l 0qxlcrnLXv4ymMMs5jGTucxmPjOa06zmNbO5zW5+M5zjLOc507nOdr4znvPM5mbRpCZ93pif+Szo QBMa0Ib+M6IHfWhFJ7rQjV60oxkt6UhTGtKWfjSmJ31pTWe60p3etKc5LepQkxrUpv40qkd9alWn utStXrWrWS3rWNMa1rZ+NAACAgA7 ------=_NextPart_01C8E39C.74EC5DB0 Content-Location: file:///C:/50625AF3/7-10-8c_xls-mht_OilETNTradingRuleLinearPolyDailyOpenCloseRegressionForecasts_files/sheet002.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii"

Appendix A

iPath S&P GSCI Crude Oil Tot Ret Idx ETN (OIL)

http://= finance.yahoo.com/q/hp?s=3DOIL

Date

Open

High

Low

Close

Volume

Adj Close

LagDate-1

Open

LagDate-1

Adj Close

Tuesda= y, July 08, 2008

82.78

82.78

80.96

81.36

1228800

81.36

39636.00

82.78

Monday, July 07, 2008

81.36

Monday= , July 07, 2008

84.84

85.91

83.6

84.91

898800

84.91

Thursday, July 03, 2008

84.84

Thursday, July 03, 2008

84.91

1 year 1 Day La= g Adj Close on Date

Thurs= day, July 03, 2008

86.52

86.76

85.78

86.56

1095800

86.56

Wednesday, July 02, 2008

86.52

Wednesday, July 02, 2008

86.56

y =3D= 5E-11x⁶ - 1E-05x⁵ + 1.154x⁴ - 60847x³ + 2E+09x² - 3E+13x + 2E+= 17

Wednes= day, July 02, 2008

84.31

86.3

84.08

86.26

1121600

86.26

Tuesday, July 01, 2008

84.31

Tuesday, July 01, 2008

86.26

R&sup= 2; =3D 0.963 Not Forward 1 Day

Tuesd= ay, July 01, 2008

85.72

85.78

83.93

84.59

860800

84.59

Monday, June 30, 2008

85.72

Monday, June 30, 2008

84.59

y =3D= 5E-11x⁶ - 1E-05x⁵ + 1.154x⁴ - 60847x³ + 2E+09x² - 3E+13x + 2E+= 17

Monday= , June 30, 2008

85.58

85.58

83.35

84.16

837200

84.16

Friday, June 27, 2008

85.58

Friday, June 27, 2008

84.16

R&sup= 2; =3D 0.963 Forewared 1 day

Friday= , June 27, 2008

84.36

85.6

83.71

84.19

1195400

84.19

Thursday, June 26, 2008

84.36

Thursday, June 26, 2008

84.19

y =3D= 0.207x - 8119.

Thursd= ay, June 26, 2008

82.1

83.98

82

83.64

856500

83.64

######################

82.1

Wednesday, June 25, 2008

83.64

R² =3D 0.956

Wednes= day, June 25, 2008

81.68

81.68

79.02

80.49

918900

80.49

Tuesday, June 24, 2008

81.68

Tuesday, June 24, 2008

80.49

Table 0

Poly Linear Mod= els of Regressions of Open or Adj Close on Date of a Year or a Month

Tuesda= y, June 24, 2008

81.73

82.59

81.32

81.94

475500

81.94

Monday, June 23, 2008

81.73

Monday, June 23, 2008

81.94

Model 1

Monday= , June 23, 2008

80.85

82.62

80.83

81.98

460300

81.98

Friday, June 20, 2008

80.85

Friday, June 20, 2008

81.98

Chart 1

1 year 1 Day La= g Adj Close on Date

Friday= , June 20, 2008

80.97

82.16

80.27

80.79

879200

80.79

Thursday, June 19, 2008

80.97

Thursday, June 19, 2008

80.79

Thursd= ay, June 19, 2008

82

82

79

79.15

1170800

79.15

######################

82

Wednesday, June 18, 2008

79.15

Poly

Wedne= sday, June 18, 2008

80.57

82.07

79.14

81.8

692900

81.8

Tuesday, June 17, 2008

80.57

Tuesday, June 17, 2008

81.8

y =3D= 5E-11x⁶ - 1E-05x⁵ + 1.154x⁴ - 60847x³ + 2E+09x² - 3E+13x + 2E+= 18

Tuesda= y, June 17, 2008

80

81.16

79.86

80.24

651800

80.24

Monday, June 16, 2008

80

Monday, June 16, 2008

80.24

R&sup= 2; =3D 0.963 Not Forwarded 1 Day

Monday= , June 16, 2008

83.18

83.31

79.91

80.53

685000

80.53

Friday, June 13, 2008

83.18

Friday, June 13, 2008

80.53

Frida= y, June 13, 2008

80.68

81.54

80.2

80.96

1043200

80.96

Thursday, June 12, 2008

80.68

Thursday, June 12, 2008

80.96

y =3D 5E-11x⁶ - 1E-05x⁵ + 1.154x⁴ - 60847x³ + 2E+09x² - 3E+13x + 2E+= 18

Thursd= ay, June 12, 2008

80.09

82.52

79.12

82.18

1119400

82.18

######################

80.09

Wednesday, June 11, 2008

82.18

R² =3D 0.9= 63 Forewared 1 day

Wednes= day, June 11, 2008

80.19

82.92

80.1

82.06

1047800

82.06

Tuesday, June 10, 2008

80.19

Tuesday, June 10, 2008

82.06

Tuesda= y, June 10, 2008

81.5

82.41

78.55

79.19

1324300

79.19

Monday, June 09, 2008

81.5

Monday, June 09, 2008

79.19

Linear

Monday= , June 09, 2008

81.56

82.59

79.87

80.83

905900

80.83

Friday, June 06, 2008

81.56

Friday, June 06, 2008

80.83

y =3D 0.207x - = 8119.

Friday= , June 06, 2008

80.32

83.35

79.49

82.95

1713300

82.95

Thursday, June 05, 2008

80.32

Thursday, June 05, 2008

82.95

R² =3D 0.957

Thursd= ay, June 05, 2008

74.01

76.85

73.5

76.82

530900

76.82

######################

74.01

Wednesday, June 04, 2008

76.82

Wednes= day, June 04, 2008

73.96

74.72

72.98

73.14

551500

73.14

Tuesday, June 03, 2008

73.96

Tuesday, June 03, 2008

73.14

Comparison

Function

R Square

Tuesda= y, June 03, 2008

75.81

75.99

74.24

74.51

554000

74.51

Monday, June 02, 2008

75.81

Monday, June 02, 2008

74.51

Line 1

Poly

96.300%

Monday= , June 02, 2008

76.56

77.53

75.29

76.55

599700

76.55

Friday, May 30, 2008

76.56

Friday, May 30, 2008

76.55

Line 2

Linear

95.800%

Friday= , May 30, 2008

76.3

76.87

75

76.37

659300

76.37

Thursday, May 29, 2008

76.3

Thursday, May 29, 2008

76.37

Line 1-2

Difference

0.500%

=3DP25-P27

Thursd= ay, May 29, 2008

77.44

79.77

75.61

75.9

855300

75.9

######################

77.44

Wednesday, May 28, 2008

75.9

Wednes= day, May 28, 2008

76.13

78.86

75.86

78.38

775000

78.38

Tuesday, May 27, 2008

76.13

Tuesday, May 27, 2008

78.38

Model 2

Tuesda= y, May 27, 2008

78.18

78.41

76.81

77.05

785000

77.05

Friday, May 23, 2008

78.18

Friday, May 23, 2008

77.05

Chart 2

1 Year 1 Day La= g Open on Date

Frida= y, May 23, 2008

80.11

80.13

78.09

78.99

831800

78.99

Thursday, May 22, 2008

80.11

Thursday, May 22, 2008

78.99

y =3D 0.000x² - 10.01x + 194= 06

Thursd= ay, May 22, 2008

80.15

80.3

78

78.47

1007200

78.47

######################

80.15

Wednesday, May 21, 2008

78.47

R² =3D 0.960

Wednes= day, May 21, 2008

77.61

80.25

77.11

80.18

612600

80.18

Tuesday, May 20, 2008

77.61

Tuesday, May 20, 2008

80.18

y =3D 0.209x - = 8204.

Tuesda= y, May 20, 2008

76.83

77.46

76.78

77.36

202300

77.36

Monday, May 19, 2008

76.83

Monday, May 19, 2008

77.36

1 Year 1 Day La= g Open on Date

R² =3D 0.959

Monda= y, May 19, 2008

76.01

76.25

74.99

76.24

269300

76.24

Friday, May 16, 2008

76.01

Friday, May 16, 2008

76.24

y =3D 0.000x² - 10.01x + 19405

Friday= , May 16, 2008

76.18

76.21

75.06

75.79

685600

75.79

Thursday, May 15, 2008

76.18

Thursday, May 15, 2008

75.79

R² =3D 0.959

Comparison

Function

R Square

Thursd= ay, May 15, 2008

75.56

75.56

72.28

74.23

1070800

74.23

######################

75.56

Wednesday, May 14, 2008

74.23

y =3D 0.209x - 8204.

Line 1

Poly

96.100%

Wednes= day, May 14, 2008

74.97

75.15

74.1

74.11

1645700

74.11

Tuesday, May 13, 2008

74.97

Tuesday, May 13, 2008

74.11

R² =3D 0.958

Line 2

Linear

96.000%

Tuesda= y, May 13, 2008

74.16

75.88

74.06

75.35

585100

75.35

Monday, May 12, 2008

74.16

Monday, May 12, 2008

75.35

Line 1-2

Difference

0.100%

=3DP25-P27

Monday= , May 12, 2008

74.8

75.6

74.1

74.21

516700

74.21

Friday, May 09, 2008

74.8

Friday, May 09, 2008

74.21

Friday= , May 09, 2008

75.38

75.59

74.57

75.5

387800

75.5

Thursday, May 08, 2008

75.38

Thursday, May 08, 2008

75.5

Model 3

1 Year 1 Day La= g Open on Date

Thursd= ay, May 08, 2008

73.84

74.62

72.82

74.59

312000

74.59

######################

73.84

Wednesday, May 07, 2008

74.59

Chart 3

y =3D 7E-11x⁶ - 2E-05x⁵ + 1.659x⁴ - 87493x³ + 3E+09x² - 4E+13x + 3E+= 17

Wednes= day, May 07, 2008

73.06

74.12

72.17

74.03

334400

74.03

Tuesday, May 06, 2008

73.06

Tuesday, May 06, 2008

74.03

R² =3D 0.966 =

Tuesda= y, May 06, 2008

71.98

73.38

71.95

72.99

211100

72.99

Monday, May 05, 2008

71.98

Monday, May 05, 2008

72.99

y =3D 0.209x - 82=

0.967

Monday= , May 05, 2008

70.68

71.93

70.64

71.81

114000

71.81

Friday, May 02, 2008

70.68

Friday, May 02, 2008

71.81

Chart 5

R² =3D 0.957 =

0.958

Friday= , May 02, 2008

67.67

69.61

67.66

69.55

217000

69.55

Thursday, May 01, 2008

67.67

Thursday, May 01, 2008

69.55

Thursd= ay, May 01, 2008

67.4

67.52

66.02

67.12

280100

67.12

######################

67.4

Wednesday, April 30, 2008

67.12

Comparison

Function

R Square

Wednes= day, April 30, 2008

69.45

69.68

67.79

68.72

266000

68.72

Tuesday, April 29, 2008

69.45

Tuesday, April 29, 2008

68.72

Line 1

Poly

96.700%

Tuesda= y, April 29, 2008

69.81

70.15

68.84

68.94

151600

68.94

Monday, April 28, 2008

69.81

Monday, April 28, 2008

68.94

Line 2

Linear

95.800%

Monday= , April 28, 2008

71.18

71.44

70.76

71.06

174500

71.06

Friday, April 25, 2008

71.18

Friday, April 25, 2008

71.06

Line 1-2

Difference

0.900%

=3DP25-P27

Friday= , April 25, 2008

70.46

71.45

70.24

70.87

257300

70.87

Thursday, April 24, 2008

70.46

Thursday, April 24, 2008

70.87

1 Year 1 Day La= g Open on Date

Thursd= ay, April 24, 2008

69.35

70.42

68.32

69.31

244800

69.31

######################

69.35

Wednesday, April 23, 2008

69.31

y =3D 7E-11x⁶ - 2E-05x⁵ + 1.659x⁴ - 87493x³ + 3E+09x² - 4E+13x + 3E+= 17

Wednes= day, April 23, 2008

70.1

70.9

69.69

70.72

257700

70.72

Tuesday, April 22, 2008

70.1

Tuesday, April 22, 2008

70.72

R² =3D 0.966 =

Tuesda= y, April 22, 2008

69.55

71.1

69.48

70.46

227100

70.46

Monday, April 21, 2008

69.55

Monday, April 21, 2008

70.46

y =3D 0.209x - = 8203.

Model 4

1 Month Open on= Date

Monday= , April 21, 2008

69.37

69.87

68.8

69.83

129000

69.83

Friday, April 18, 2008

69.37

Friday, April 18, 2008

69.83

R² =3D 0.957 =

Chart 4

y =3D -6E-08x⁶ + 0.013x⁵ - 1308.x⁴ + 7E+07x³ - 2E+12x² + 3E+16x - 2E+= 20

Friday= , April 18, 2008

67.61

69.58

67.5

69.51

127700

69.51

Thursday, April 17, 2008

67.61

Thursday, April 17, 2008

69.51

R² =3D 0.823 =

Thursd= ay, April 17, 2008

68.59

68.73

68

68.42

86900

68.42

######################

68.59

Wednesday, April 16, 2008

68.42

1 Month Open on= Date

y =3D 0.162x - 63=

0.824

Wednes= day, April 16, 2008

67.7

68.46

66.86

68.4

212300

68.4

Tuesday, April 15, 2008

67.7

Tuesday, April 15, 2008

68.4

y =3D -6E-08x⁶ + 0.013x⁵ - 1308.x⁴ + 7E+07x³ - 2E+1<= span style=3D'display:none'>2x² + 3E+16x - 2E+20

R² =3D 0.570 =

0.571

Tuesda= y, April 15, 2008

67.5

67.8

67.34

67.8

149900

67.8

Monday, April 14, 2008

67.5

Monday, April 14, 2008

67.8

R² =3D 0.823 =

Monday= , April 14, 2008

65.93

66.54

65.68

66.45

205900

66.45

Friday, April 11, 2008

65.93

Friday, April 11, 2008

66.45

y =3D 0.162x - = 6351.

Comparison

Function

R Square

Friday= , April 11, 2008

65.01

65.74

64.82

65.67

116500

65.67

Thursday, April 10, 2008

65.01

Thursday, April 10, 2008

65.67

R² =3D 0.570 =

Line 1

Poly

82.400%

Thursd= ay, April 10, 2008

66.22

66.22

64.69

65.54

76100

65.54

######################

66.22

Wednesday, April 09, 2008

65.54

Line 2

Linear

57.100%

Wednes= day, April 09, 2008

64.73

66.5

64.55

65.91

127100

65.91

Tuesday, April 08, 2008

64.73

Tuesday, April 08, 2008

65.91

Line 1-2

Difference

25.300%

Tuesda= y, April 08, 2008

64.31

64.93

63.96

64.66

64800

64.66

Monday, April 07, 2008

64.31

Monday, April 07, 2008

64.66

Monday= , April 07, 2008

64.06

65.01

64.06

64.73

82400

64.73

Friday, April 04, 2008

64.06

Friday, April 04, 2008

64.73

0.253

=3DP25-P27

Friday= , April 04, 2008

62.65

63.1

62.25

63.04

94300

63.04

Thursday, April 03, 2008

62.65

Thursday, April 03, 2008

63.04

Thursd= ay, April 03, 2008

61.54

63.22

61.39

61.95

191600

61.95

######################

61.54

Wednesday, April 02, 2008

61.95

Wednes= day, April 02, 2008

59.93

62.38

59.36

61.95

97800

61.95

Tuesday, April 01, 2008

59.93

Tuesday, April 01, 2008

61.95

Tuesda= y, April 01, 2008

59.36

60.84

59.18

60.01

176200

60.01

Monday, March 31, 2008

59.36

Monday, March 31, 2008

60.01

Monday= , March 31, 2008

62.88

63.35

59.59

60.43

221700

60.43

Friday, March 28, 2008

62.88

Friday, March 28, 2008

60.43

Friday= , March 28, 2008

63.11

63.12

62.27

62.45

138600

62.45

Thursday, March 27, 2008

63.11

Thursday, March 27, 2008

62.45

Thursd= ay, March 27, 2008

63.12

64.26

62.8

63.69

115600

63.69

######################

63.12

Wednesday, March 26, 2008

63.69

Wednes= day, March 26, 2008

60.82

63.16

60.77

62.89

217700

62.89

Tuesday, March 25, 2008

60.82

Tuesday, March 25, 2008

62.89

Tuesda= y, March 25, 2008

59.88

60.48

58.86

60.48

121000

60.48

Monday, March 24, 2008

59.88

Monday, March 24, 2008

60.48

Monday= , March 24, 2008

60.19

60.74

59.33

59.71

291500

59.71

Thursday, March 20, 2008

60.19

Thursday, March 20, 2008

59.71

Thursd= ay, March 20, 2008

59.27

60.73

59.04

60.25

419200

60.25

######################

59.27

Wednesday, March 19, 2008

60.25

Wednes= day, March 19, 2008

62.77

63.24

60.68

60.72

298800

60.72

Tuesday, March 18, 2008

62.77

Tuesday, March 18, 2008

60.72

Tuesda= y, March 18, 2008

62.96

64.64

62.59

63.96

183600

63.96

Monday, March 17, 2008

62.96

Monday, March 17, 2008

63.96

Monday= , March 17, 2008

62.5

63.68

60.49

62.09

473300

62.09

Friday, March 14, 2008

62.5

Friday, March 14, 2008

62.09

Friday= , March 14, 2008

64.51

65.16

63.89

64.56

158100

64.56

Thursday, March 13, 2008

64.51

Thursday, March 13, 2008

64.56

Thursd= ay, March 13, 2008

64.99

65.19

63.96

64.74

126300

64.74

######################

64.99

Wednesday, March 12, 2008

64.74

Wednes= day, March 12, 2008

63.99

64.63

62.88

64.57

178400

64.57

Tuesday, March 11, 2008

63.99

Tuesday, March 11, 2008

64.57

Tuesda= y, March 11, 2008

63.5

63.95

62.64

63.75

108100

63.75

Monday, March 10, 2008

63.5

Monday, March 10, 2008

63.75

Monday= , March 10, 2008

61.87

63.44

61.23

63.42

188100

63.42

Friday, March 07, 2008

61.87

Friday, March 07, 2008

63.42

Friday= , March 07, 2008

61.64

62.41

61.06

62.35

224300

62.35

Thursday, March 06, 2008

61.64

Thursday, March 06, 2008

62.35

Thursd= ay, March 06, 2008

61.29

62.04

60.43

61.89

262300

61.89

######################

61.29

Wednesday, March 05, 2008

61.89

Wednes= day, March 05, 2008

59.56

61.5

59.47

61.28

208400

61.28

Tuesday, March 04, 2008

59.56

Tuesday, March 04, 2008

61.28

Tuesda= y, March 04, 2008

60.56

60.61

58.07

58.77

208400

58.77

Monday, March 03, 2008

60.56

Monday, March 03, 2008

58.77

Monday= , March 03, 2008

60.7

61

59.64

60.22

131900

60.22

Friday, February 29, 2008

60.7

Friday, February 29, 2008

60.22

Friday= , February 29, 2008

60.09

60.25

59.53

59.71

92200

59.71

######################

60.09

Thursday, February 28, 2008

59.71

Thursd= ay, February 28, 2008

59.06

60.4

58.91

60.19

167600

60.19

######################

59.06

Wednesday, February 27, 2008

60.19

Wednes= day, February 27, 2008

59.09

59.42

58.46

58.52

138400

58.52

######################

59.09

Tuesday, February 26, 2008

58.52

Tuesda= y, February 26, 2008

58.14

59.36

58.01

59.32

193400

59.32

######################

58.14

Monday, February 25, 2008

59.32

Monday= , February 25, 2008

57.96

58.47

57.42

58.23

115200

58.23

Friday, February 22, 2008

57.96

Friday, February 22, 2008

58.23

Friday= , February 22, 2008

58.03

58.25

57.13

58.02

134800

58.02

######################

58.03

Thursday, February 21, 2008

58.02

Thursd= ay, February 21, 2008

58.03

58.6

56.91

57.18

181500

57.18

######################

58.03

Wednesday, February 20, 2008

57.18

Wednes= day, February 20, 2008

57.78

59.09

57.56

58.55

155500

58.55

######################

57.78

Tuesday, February 19, 2008

58.55

Tuesda= y, February 19, 2008

57.57

58.54

57.24

58.1

117600

58.1

Friday, February 15, 2008

57.57

Friday, February 15, 2008

58.1

Friday= , February 15, 2008

56.5

56.61

55.53

56.06

40300

56.06

######################

56.5

Thursday, February 14, 2008

56.06

Thursd= ay, February 14, 2008

55.22

56.04

55.22

55.9

38600

55.9

######################

55.22

Wednesday, February 13, 2008

55.9

Wednes= day, February 13, 2008

54.33

55.05

54.08

54.78

26100

54.78

######################

54.33

Tuesday, February 12, 2008

54.78

Tuesda= y, February 12, 2008

54.65

55.13

54.2

54.34

41400

54.34

######################

54.65

Monday, February 11, 2008

54.34

Monday= , February 11, 2008

54.14

55.5

53.35

54.89

176800

54.89

Friday, February 08, 2008

54.14

Friday, February 08, 2008

54.89

Friday= , February 08, 2008

52.38

53.95

52.26

53.69

57600

53.69

######################

52.38

Thursday, February 07, 2008

53.69

Thursd= ay, February 07, 2008

50.72

51.77

50.7

51.77

134600

51.77

######################

50.72

Wednesday, February 06, 2008

51.77

Wednes= day, February 06, 2008

52.03

52.05

50.85

51.05

144200

51.05

######################

52.03

Tuesday, February 05, 2008

51.05

Tuesda= y, February 05, 2008

51.78

52.12

51.36

51.71

85100

51.71

######################

51.78

Monday, February 04, 2008

51.71

Monday= , February 04, 2008

52.26

53.11

52.03

52.66

176400

52.66

Friday, February 01, 2008

52.26

Friday, February 01, 2008

52.66

Friday= , February 01, 2008

53.31

53.38

51.89

52.16

221400

52.16

######################

53.31

Thursday, January 31, 2008

52.16

Thursd= ay, January 31, 2008

53.1

54.09

52.59

53.68

66900

53.68

######################

53.1

Wednesday, January 30, 2008

53.68

Wednes= day, January 30, 2008

54.06

54.24

53.5

53.93

49100

53.93

Tuesda= y, January 29, 2008

53.15

53.83

53.04

53.8

20500

53.8

Monday= , January 28, 2008

52.58

53.53

52.06

53.4

53700

53.4

Friday= , January 25, 2008

53.26

53.53

52.59

53.12

80600

53.12

Thursd= ay, January 24, 2008

51.52

52.57

51.11

52.45

104400

52.45

Wednes= day, January 23, 2008

51.01

51.67

50.78

51.38

147900

51.38

Tuesda= y, January 22, 2008

50.62

52.39

50.62

52.2

78400

52.2

Friday= , January 18, 2008

52.85

53.12

52.32

52.64

66200

52.64

Thursd= ay, January 17, 2008

53.4

53.57

52.14

52.38

110100

52.38

Wednes= day, January 16, 2008

53.31

53.31

52.22

52.93

111600

52.93

Tuesda= y, January 15, 2008

54.16

54.16

53.3

53.74

68800

53.74

Monday= , January 14, 2008

54.62

55.14

54.27

55

47200

55

Friday= , January 11, 2008

54.37

54.55

53.79

54.1

62800

54.1

Thursd= ay, January 10, 2008

55.23

55.23

54.4

54.81

86900

54.81

Wednes= day, January 09, 2008

56.08

57.02

55.7

55.83

25300

55.83

Tuesda= y, January 08, 2008

56.62

56.84

56.1

56.1

60700

56.1

Monday= , January 07, 2008

57.4

57.4

55.16

55.56

59200

55.56

Friday= , January 04, 2008

57.25

57.43

56.7

57.04

76200

57.04

Thursd= ay, January 03, 2008

58.18

58.33

57.47

57.79

163300

57.79

Wednes= day, January 02, 2008

57.1

58.13

57.03

57.91

120300

57.91

Monday= , December 31, 2007

56.24

56.4

55.26

56.15

311700

56.15

Friday= , December 28, 2007

56.75

57

55.93

56.11

31100

56.11

Thursd= ay, December 27, 2007

56.45

57

56

56.41

88200

56.41

Wednes= day, December 26, 2007

55.18

56.19

55.18

56.01

164000

56.01

Monday= , December 24, 2007

54.03

54.54

53.93

54.54

9100

54.54

Friday= , December 21, 2007

53.75

54.59

53.39

54.33

26300

54.33

Thursd= ay, December 20, 2007

53.05

53.54

52.98

53.09

22800

53.09

Wednes= day, December 19, 2007

52.85

53.65

52.66

53.09

81600

53.09

Tuesda= y, December 18, 2007

52.81

54.28

51.98

52.53

92200

52.53

Monday= , December 17, 2007

53.65

53.65

52.28

52.98

17600

52.98

Friday= , December 14, 2007

53.5

53.65

52.89

53.3

24500

53.3

Thursd= ay, December 13, 2007

54.91

54.91

53.58

54.02

60700

54.02

Wednes= day, December 12, 2007

53

55.27

52.9

54.64

79500

54.64

Tuesda= y, December 11, 2007

51.87

52.54

51.54

51.83

43900

51.83

Monday= , December 10, 2007

51.8

52.16

50.65

51.27

35900

51.27

Friday= , December 07, 2007

51.64

51.64

50.7

51.33

76400

51.33

Thursd= ay, December 06, 2007

50.7

52.71

50.66

52.56

92000

52.56

Wednes= day, December 05, 2007

52.11

52.48

50.48

50.79

166300

50.79

Tuesda= y, December 04, 2007

51.24

51.37

50.84

51.32

78800

51.32

Monday= , December 03, 2007

51.08

52.23

50.71

52.16

74700

52.16

Friday= , November 30, 2007

51.85

52.8

51.37

51.53

109000

51.53

Thursd= ay, November 29, 2007

53.33

54.23

52.57

52.94

103900

52.94

Wednes= day, November 28, 2007

55.12

55.16

52.5

53.19

142100

53.19

Tuesda= y, November 27, 2007

55.25

55.45

54.78

54.94

75500

54.94

Monday= , November 26, 2007

57.66

57.66

56.09

56.55

46300

56.55

Friday= , November 23, 2007

56.53

57.04

56.42

56.77

21400

56.77

Wednes= day, November 21, 2007

56.04

57.45

56

56.48

148000

56.48

Tuesda= y, November 20, 2007

55.75

57.26

55.5

57.13

66400

57.13

Monday= , November 19, 2007

54.85

55.08

54.14

55.02

47700

55.02

Friday= , November 16, 2007

54.36

54.65

54.17

54.48

32800

54.48

Thursd= ay, November 15, 2007

53.43

53.7

52.61

53.48

43600

53.48

Wednes= day, November 14, 2007

52.28

53.98

52.28

53.66

47500

53.66

Tuesda= y, November 13, 2007

53.69

53.73

51.72

52.42

200200

52.42

Monday= , November 12, 2007

54.47

54.47

53.75

53.81

132100

53.81

Friday= , November 09, 2007

54.6

55.6

54.54

55.25

91700

55.25

Thursd= ay, November 08, 2007

56.01

56.05

54.69

55.39

161300

55.39

Wednes= day, November 07, 2007

56.06

56.36

54.35

55.2

407200

55.2

Tuesda= y, November 06, 2007

55.3

55.73

55

55.56

92200

55.56

Monday= , November 05, 2007

54.04

55.11

53.94

54.45

79000

54.45

Friday= , November 02, 2007

54.2

55.11

53.99

55.07

84700

55.07

Thursd= ay, November 01, 2007

54.04

54.18

52.93

53.29

232500

53.29

Wednes= day, October 31, 2007

52.55

54.45

52.24

54.28

389600

54.28

Tuesda= y, October 30, 2007

52.92

53.06

51.54

51.59

86500

51.59

Monday= , October 29, 2007

52.97

53.75

52.7

53.68

80000

53.68

Friday= , October 26, 2007

52.8

52.8

52

52.66

73000

52.66

Thursd= ay, October 25, 2007

50.85

51.92

50.57

51.82

53300

51.82

Wednes= day, October 24, 2007

48.91

50.28

48.68

50.28

106500

50.28

Tuesda= y, October 23, 2007

49.51

49.51

48.75

48.89

44400

48.89

Monday= , October 22, 2007

49.26

49.81

48.8

49.17

152700

49.17

Friday= , October 19, 2007

50

50.16

49.47

49.71

272200

49.71

Thursd= ay, October 18, 2007

49.83

50.58

49.6

50.44

116200

50.44

Wednes= day, October 17, 2007

49.85

50.19

49.09

49.29

272700

49.29

Tuesda= y, October 16, 2007

49.25

49.79

48.83

49.45

130400

49.45

Monday= , October 15, 2007

48.11

48.83

47.92

48.83

48000

48.83

Friday= , October 12, 2007

47

47.52

46.91

47.36

27200

47.36

Thursd= ay, October 11, 2007

46.5

47.32

46.49

47

23400

47

Wednes= day, October 10, 2007

45.36

46.16

45.33

46.13

19900

46.13

Tuesda= y, October 09, 2007

44.94

45.87

44.94

45.46

26500

45.46

Monday= , October 08, 2007

45.45

45.45

44.47

44.76

37800

44.76

Friday= , October 05, 2007

46.04

46.22

45.67

45.96

16200

45.96

Thursd= ay, October 04, 2007

45.16

46.35

44.95

46.26

98300

46.26

Wednes= day, October 03, 2007

45.5

45.79

45.17

45.2

100600

45.2

Tuesda= y, October 02, 2007

45.01

45.5

44.76

45.5

74500

45.5

Monday= , October 01, 2007

46

46.01

45.22

45.51

72100

45.51

Friday= , September 28, 2007

47.36

47.4

46.14

46.32

62900

46.32

Thursd= ay, September 27, 2007

46.26

47.06

46.09

46.99

111400

46.99

Wednes= day, September 26, 2007

45.5

46.18

44.5

45.62

84900

45.62

Tuesda= y, September 25, 2007

44.91

45.2

44.78

45.03

23400

45.03

Monday= , September 24, 2007

45.97

46.2

45.6

45.62

31300

45.62

Friday= , September 21, 2007

46.25

46.63

45.65

46.07

64700

46.07

Thursd= ay, September 20, 2007

45.7

46.36

45.49

46.26

30500

46.26

Wednes= day, September 19, 2007

45.75

45.95

45.23

45.58

59200

45.58

Tuesda= y, September 18, 2007

44.9

45.88

44.69

45.72

88600

45.72

Monday= , September 17, 2007

44.12

44.95

44

44.9

49500

44.9

Friday= , September 14, 2007

44.45

44.74

44.06

44.23

59100

44.23

Thursd= ay, September 13, 2007

44.44

44.6

44.19

44.53

48400

44.53

Wednes= day, September 12, 2007

43.76

44.5

43.65

44.42

62800

44.42

Tuesda= y, September 11, 2007

43

43.61

42.89

43.56

47600

43.56

Monday= , September 10, 2007

42.05

43.63

42.05

43.6

49200

43.6

Friday= , September 07, 2007

42.4

42.76

42.13

42.63

40700

42.63

Thursd= ay, September 06, 2007

42.6

43

41.97

42.52

52900

42.52

Wednes= day, September 05, 2007

41.75

42.22

41.62

42.18

26000

42.18

Tuesda= y, September 04, 2007

41.21

41.84

41

41.73

26600

41.73

Friday= , August 31, 2007

41.27

41.33

41.05

41.13

51100

41.13

Thursd= ay, August 30, 2007

40.8

41

40.49

40.84

23700

40.84

Wednes= day, August 29, 2007

40.25

40.98

40.25

40.88

47700

40.88

Tuesda= y, August 28, 2007

40.09

40.17

39.62

39.8

38700

39.8

Monday= , August 27, 2007

39.35

40.05

39.07

40.04

34900

40.04

Friday= , August 24, 2007

38.83

39.6

38.83

39.55

32000

39.55

Thursd= ay, August 23, 2007

38.8

38.86

38.46

38.81

38900

38.81

Wednes= day, August 22, 2007

38.95

39.02

38.17

38.5

121900

38.5

Tuesda= y, August 21, 2007

39.35

39.72

38.35

38.64

148200

38.64

Monday= , August 20, 2007

39.29

39.5

38.71

39.34

92700

39.34

Friday= , August 17, 2007

39.85

40.11

39.61

39.79

80000

39.79

Thursd= ay, August 16, 2007

39.7

39.81

38.9

39.5

61900

39.5

Wednes= day, August 15, 2007

40.33

41.12

40.2

40.65

88300

40.65

Tuesda= y, August 14, 2007

40.06

40.06

39.59

40

26900

40

Monday= , August 13, 2007

40.2

40.4

39.4

39.66

25000

39.66

Friday= , August 10, 2007

38.85

39.63

38.78

39.62

39400

39.62

Thursd= ay, August 09, 2007

38.8

39.87

38.8

39.6

51900

39.6

Wednes= day, August 08, 2007

39.9

40.42

39.86

39.94

60600

39.94

Tuesda= y, August 07, 2007

39.7

40.16

39.37

40.01

74600

40.01

Monday= , August 06, 2007

41

41

39.61

39.84

85900

39.84

Friday= , August 03, 2007

42.6

42.6

41.54

41.63

102200

41.63

Thursd= ay, August 02, 2007

42.25

42.78

42.25

42.5

116400

42.5

Wednes= day, August 01, 2007

42.9

43.57

42.13

42.33

119500

42.33

Tuesda= y, July 31, 2007

42.58

43.25

42.4

43

54500

43

Monday= , July 30, 2007

42.7

42.7

42.06

42.41

49700

42.41

Friday= , July 27, 2007

41.66

42.6

41.63

42.54

50500

42.54

Thursd= ay, July 26, 2007

42.51

42.64

41.24

41.3

159600

41.3

Wednes= day, July 25, 2007

40.8

42.05

40.64

42

66000

42

Tuesda= y, July 24, 2007

40.6

40.86

40.28

40.56

47600

40.56

Monday= , July 23, 2007

41.34

41.44

41.15

41.39

37900

41.39

Friday= , July 20, 2007

42.05

42.15

41.74

41.94

51600

41.94

Thursd= ay, July 19, 2007

41.83

42.05

41.47

42.05

38300

42.05

Wednes= day, July 18, 2007

41.17

41.64

41.01

41.55

60500

41.55

Tuesda= y, July 17, 2007

41.35

41.55

40.66

40.91

51400

40.91

Monday= , July 16, 2007

40.9

41.12

40.72

40.95

34200

40.95

Friday= , July 13, 2007

40.6

40.99

40.51

40.92

37200

40.92

Thursd= ay, July 12, 2007

40.63

40.76

39.84

40.21

73200

40.21

Wednes= day, July 11, 2007

40.2

40.38

40.04

40.16

16500

40.16

Tuesda= y, July 10, 2007

39.81

40.41

39.81

40.22

51800

40.22

Monday= , July 09, 2007

40.15

40.35

39.76

39.88

26100

39.88

Friday= , July 06, 2007

40.22

40.4

40.01

40.21

46700

40.21

Thursd= ay, July 05, 2007

39.93

40.02

39.2

39.74

88300

39.74

Tuesda= y, July 03, 2007

39.25

39.37

39.05

39.29

41000

39.29

Monday= , July 02, 2007

38.74

39.39

38.56

39.34

90600

39.34

Friday= , June 29, 2007

38.8

39.25

38.76

38.98

69300

38.98

Thursd= ay, June 28, 2007

38.6

38.95

38.45

38.5

76600

38.5

Wednes= day, June 27, 2007

37.2

38.43

37.11

38.14

79600

38.14

Tuesda= y, June 26, 2007

37.95

37.95

37.42

37.55

56900

37.55

Monday= , June 25, 2007

37.44

38.37

37.42

38.2

51000

38.2

Friday= , June 22, 2007

38.2

38.4

38

38.23

187100

38.23

Thursd= ay, June 21, 2007

38.45

38.53

37.74

37.94

45100

37.94

Wednes= day, June 20, 2007

38.35

38.43

37.43

37.97

247600

37.97

Tuesda= y, June 19, 2007

38.34

38.77

38.06

38.35

104200

38.35

Monday= , June 18, 2007

37.8

38.45

37.65

38.33

53300

38.33

Friday= , June 15, 2007

37.72

37.96

37.53

37.89

55700

37.89

Thursd= ay, June 14, 2007

37.2

37.73

37.2

37.58

139700

37.58

Wednes= day, June 13, 2007

36.29

37.03

36.26

36.82

98400

36.82

Tuesda= y, June 12, 2007

36.5

36.6

36.22

36.29

81200

36.29

Monday= , June 11, 2007

36.3

36.77

36.06

36.65

86300

36.65

Friday= , June 08, 2007

36.5

36.91

35.97

36.06

251000

36.06

Thursd= ay, June 07, 2007

36.88

37.57

36.75

37.3

114200

37.3

Wednes= day, June 06, 2007

36.57

36.92

36.42

36.78

135300

36.78

Tuesda= y, June 05, 2007

36.56

36.86

36.42

36.59

38200

36.59

Monday= , June 04, 2007

36.3

37.06

36.24

36.81

131500

36.81

Friday= , June 01, 2007

35.89

36.38

35.76

36.23

74000

36.23

Thursd= ay, May 31, 2007

35.15

35.79

34.81

35.79

210000

35.79

Wednes= day, May 30, 2007

35.4

35.57

35.28

35.34

63600

35.34

Tuesda= y, May 29, 2007

35.8

35.8

34.85

35.31

218700

35.31

Friday= , May 25, 2007

36.1

36.34

35.8

36.31

87200

36.31

Thursd= ay, May 24, 2007

36.5

36.62

35.57

35.8

98500

35.8

Wednes= day, May 23, 2007

36.5

36.81

36.24

36.64

98400

36.64

Tuesda= y, May 22, 2007

37.05

37.05

36.43

36.55

96000

36.55

Monday= , May 21, 2007

36.8

37.35

36.38

37.19

244700

37.19

Friday= , May 18, 2007

36.8

36.97

36.55

36.65

159500

36.65

Thursd= ay, May 17, 2007

35.6

36.7

35.6

36.7

149300

36.7

Wednes= day, May 16, 2007

35.65

35.67

35.17

35.53

198300

35.53

Tuesda= y, May 15, 2007

35.5

35.98

35.47

35.89

114500

35.89

Monday= , May 14, 2007

35.85

35.85

35.39

35.5

68200

35.5

Friday= , May 11, 2007

35.58

35.71

35.29

35.62

103700

35.62

Thursd= ay, May 10, 2007

35.55

35.62

35.02

35.35

69700

35.35

Wednes= day, May 09, 2007

35.56

35.63

34.7

35.1

119500

35.1

Tuesda= y, May 08, 2007

35.23

35.69

35

35.55

93800

35.55

Monday= , May 07, 2007

35

35.32

34.78

35.23

128100

35.23

Friday= , May 04, 2007

36.15

36.19

35.25

35.34

408900

35.34

Thursd= ay, May 03, 2007

36.15

36.32

35.87

36.09

232400

36.09

Wednes= day, May 02, 2007

36.75

36.75

36.05

36.32

154100

36.32

Tuesda= y, May 01, 2007

37.4

37.79

36.72

36.88

158800

36.88

Monday= , April 30, 2007

37.8

38

37.45

37.5

103000

37.5

Friday= , April 27, 2007

37.05

38.07

37

37.87

148700

37.87

Thursd= ay, April 26, 2007

37.4

37.92

37.04

37.25

206300

37.25

Wednes= day, April 25, 2007

37.23

37.61

36.91

37.57

144800

37.57

Tuesda= y, April 24, 2007

37.5

37.79

36.64

36.87

202700

36.87

Monday= , April 23, 2007

36.75

37.6

36.56

37.47

161200

37.47

Friday= , April 20, 2007

36.12

36.64

36.05

36.55

62500

36.55

Thursd= ay, April 19, 2007

36.51

36.52

35.97

36.03

127500

36.03

Wednes= day, April 18, 2007

36.59

36.88

36.24

36.69

188300

36.69

Tuesda= y, April 17, 2007

37.65

37.79

36.74

36.79

175400

36.79

Monday= , April 16, 2007

37.7

37.8

37.11

37.45

138000

37.45

Friday= , April 13, 2007

38.05

38.1

37.62

37.75

115800

37.75

Thursd= ay, April 12, 2007

37.35

37.85

37.15

37.77

72000

37.77

Wednes= day, April 11, 2007

36.73

37.2

36.65

36.81

154800

36.81

Tuesda= y, April 10, 2007

36.8

37.05

36.71

36.82

112200

36.82

Monday= , April 09, 2007

37.88

38.02

36.52

36.75

355400

36.75

Thursd= ay, April 05, 2007

38.41

38.47

37.87

37.99

130600

37.99

Wednes= day, April 04, 2007

38.05

38.42

37.82

38.28

175700

38.28

Tuesda= y, April 03, 2007

38.75

38.75

38.04

38.32

227800

38.32

Monday= , April 02, 2007

39.12

39.6

38.8

39.08

123100

39.08

Friday= , March 30, 2007

39.15

39.6

38.9

39

220900

39

Thursd= ay, March 29, 2007

38.49

39.42

38.3

39.24

389400

39.24

Wednes= day, March 28, 2007

38.13

38.63

37.79

37.95

245600

37.95

Tuesda= y, March 27, 2007

37.09

37.44

36.98

37.38

98800

37.38

Monday= , March 26, 2007

37.56

37.56

37.02

37.37

158800

37.37

Friday= , March 23, 2007

36.98

37.15

36.7

36.92

172700

36.92

Thursd= ay, March 22, 2007

36.14

36.66

36.04

36.62

148200

36.62

Wednes= day, March 21, 2007

35.35

35.83

35.14

35.59

120300

35.59

Tuesda= y, March 20, 2007

35.5

35.67

35.11

35.39

146500

35.39

Monday= , March 19, 2007

35.5

35.73

35.25

35.56

216600

35.56

Friday= , March 16, 2007

35.87

36.13

34.94

35.32

154800

35.32

Thursd= ay, March 15, 2007

36.1

36.14

35.47

35.51

125600

35.51

Wednes= day, March 14, 2007

35.81

36

35.5

35.88

179900

35.88

Tuesda= y, March 13, 2007

36.25

36.65

35.6

35.78

129600

35.78

Monday= , March 12, 2007

36.38

36.41

35.67

36.07

123500

36.07

Friday= , March 09, 2007

37.57

37.57

36.66

36.71

190000

36.71

Thursd= ay, March 08, 2007

37.88

37.88

37.32

37.57

115400

37.57

Wednes= day, March 07, 2007

37.2

37.81

37.03

37.68

141500

37.68

Tuesda= y, March 06, 2007

36.83

37.03

36.52

36.95

86600

36.95

Monday= , March 05, 2007

36.82

36.99

36.35

36.53

112000

36.53

Friday= , March 02, 2007

37.85

37.98

37.39

37.52

100300

37.52

Thursd= ay, March 01, 2007

37.4

38

37.08

37.65

160400

37.65

Wednes= day, February 28, 2007

37.2

37.76

36.78

37.62

187200

37.62

Tuesda= y, February 27, 2007

36.91

37.85

36.76

36.8

192400

36.8

Monday= , February 26, 2007

37.35

37.58

37

37.46

93100

37.46

Friday= , February 23, 2007

37.38

37.6

36.96

37.12

360200

37.12

Thursd= ay, February 22, 2007

36.65

37.29

36.43

36.99

148300

36.99

Wednes= day, February 21, 2007

35.88

36.88

35.51

36.6

159100

36.6

Tuesda= y, February 20, 2007

35.4

35.84

35.18

35.78

110000

35.78

Friday= , February 16, 2007

35.84

36.45

35.62

36.34

146400

36.34

Thursd= ay, February 15, 2007

35.7

36

34.76

35.57

218500

35.57

Wednes= day, February 14, 2007

36.12

36.35

35.3

35.56

168600

35.56

Tuesda= y, February 13, 2007

36.7

36.7

35.74

36.3

101000

36.3

Monday= , February 12, 2007

36.27

36.38

35.38

35.6

96100

35.6

Friday= , February 09, 2007

36.82

37.34

36.58

36.75

214300

36.75

Thursd= ay, February 08, 2007

35.7

36.81

35.42

36.69

185400

36.69

Wednes= day, February 07, 2007

36.46

36.75

35.23

35.53

230600

35.53

Tuesda= y, February 06, 2007

36.61

36.69

36

36.34

429500

36.34

Monday= , February 05, 2007

36.6

36.85

35.98

36.19

657700

36.19

Friday= , February 02, 2007

35.6

36.45

35.16

36.35

261700

36.35

Thursd= ay, February 01, 2007

35.61

36.17

35.15

35.27

252800

35.27

Wednes= day, January 31, 2007

34.85

35.81

34.36

35.65

324700

35.65

Tuesda= y, January 30, 2007

33.4

35.08

33.4

34.89

327000

34.89

Monday= , January 29, 2007

33.95

34.03

33.14

33.14

176400

33.14

Friday= , January 26, 2007

33.65

34.12

33.65

34.07

200800

34.07

Thursd= ay, January 25, 2007

34.15

34.28

33.31

33.37

198600

33.37

Wednes= day, January 24, 2007

33.5

34.08

33.01

33.95

443500

33.95

Tuesda= y, January 23, 2007

32.8

33.85

32.64

33.67

373000

33.67

Monday= , January 22, 2007

33.15

33.55

32.07

32.35

520900

32.35

Friday= , January 19, 2007

32.01

32.86

32

32.82

333500

32.82

Thursd= ay, January 18, 2007

32.85

32.96

31.39

31.78

797600

31.78

Wednes= day, January 17, 2007

31.75

32.77

31.6

32.61

1242200

32.61

Tuesda= y, January 16, 2007

32.6

32.7

31.62

32.03

356100

32.03

Friday= , January 12, 2007

32.53

33.2

32.32

32.96

325200

32.96

Thursd= ay, January 11, 2007

33.44

34.13

32.47

32.7

327200

32.7

Wednes= day, January 10, 2007

34.37

34.42

33.44

33.44

204900

33.44

Tuesda= y, January 09, 2007

34.05

35.13

34

34.89

190500

34.89

Monday= , January 08, 2007

36.05

36.12

34.5

35.1

242500

35.1

Friday= , January 05, 2007

34.86

35.22

34.5

35.13

138500

35.13

Thursd= ay, January 04, 2007

35.65

35.89

34.7

34.88

225200

34.88

Wednes= day, January 03, 2007

37.57

37.57

36.13

36.38

259400

36.38

Friday= , December 29, 2006

37.57

38.26

37.34

38.06

139900

38.06

Thursd= ay, December 28, 2006

37.85

37.93

37.51

37.74

121300

37.74

Wednes= day, December 27, 2006

37.94

38.11

37.64

37.71

135000

37.71

Tuesda= y, December 26, 2006

39.15

39.15

37.77

38

175000

38

Friday= , December 22, 2006

39.2

39.22

38.79

38.91

40600

38.91

Thursd= ay, December 21, 2006

39.6

39.6

38.9

39.12

27700

39.12

Wednes= day, December 20, 2006

39.67

39.97

39.49

39.63

143300

39.63

Tuesda= y, December 19, 2006

38.93

39.88

38.91

39.47

73400

39.47

Monday= , December 18, 2006

39.58

39.6

39

39.1

71000

39.1

Friday= , December 15, 2006

39.65

39.96

39.31

39.86

51300

39.86

Thursd= ay, December 14, 2006

39.25

39.56

39.16

39.45

65800

39.45

Wednes= day, December 13, 2006

38.45

38.93

38.4

38.77

86200

38.77

Tuesda= y, December 12, 2006

38.93

39.17

38.38

38.5

106100

38.5

Monday= , December 11, 2006

38.95

39.3

38.75

38.76

182200

38.76

Friday= , December 08, 2006

40.05

40.19

39.23

39.31

172600

39.31

Thursd= ay, December 07, 2006

39.23

39.6

38.97

39.58

117300

39.58

Wednes= day, December 06, 2006

39.4

39.84

39.18

39.4

103300

39.4

Tuesda= y, December 05, 2006

39.79

39.9

38.94

39.57

222300

39.57

Monday= , December 04, 2006

39.77

39.77

39.23

39.5

129500

39.5

Friday= , December 01, 2006

39.61

40.23

39.43

40.18

274800

40.18

Thursd= ay, November 30, 2006

39.77

40.25

39.65

39.83

226800

39.83

Wednes= day, November 29, 2006

38.78

39.52

38.71

39.43

158600

39.43

Tuesda= y, November 28, 2006

38.42

38.6

38.17

38.48

138700

38.48

Monday= , November 27, 2006

37.75

38.18

37.65

38.12

98400

38.12

Friday= , November 24, 2006

37.77

37.96

37.67

37.75

36400

37.75

Wednes= day, November 22, 2006

38

38.08

36.9

37.46

167300

37.46

Tuesda= y, November 21, 2006

37.45

38.06

37.39

37.95

73200

37.95

Monday= , November 20, 2006

36.9

37.24

36.63

37.2

151300

37.2

Friday= , November 17, 2006

36.8

37.25

36.75

37.16

133300

37.16

Thursd= ay, November 16, 2006

38.6

38.72

36.85

37.01

354900

37.01

Wednes= day, November 15, 2006

38.17

38.7

38.13

38.32

219700

38.32

Tuesda= y, November 14, 2006

38.27

38.52

37.97

38.05

93800

38.05

Monday= , November 13, 2006

38.44

38.63

38.08

38.2

59400

38.2

Friday= , November 10, 2006

39.55

39.55

38.8

38.9

56300

38.9

Thursd= ay, November 09, 2006

39.55

39.79

39.44

39.78

59700

39.78

Wednes= day, November 08, 2006

38.5

39.11

38.44

39

67700

39

Tuesda= y, November 07, 2006

39.05

39.12

38.3

38.42

15900

38.42

Monday= , November 06, 2006

38.38

39.28

37.58

39.01

94100

39.01

Friday= , November 03, 2006

37.99

38.49

37.92

38.42

46700

38.42

Thursd= ay, November 02, 2006

37.7

38.25

37.44

37.6

37000

37.6

Wednes= day, November 01, 2006

38.05

38.57

37.62

38.03

103900

38.03

Tuesda= y, October 31, 2006

37.55

38.12

37.08

38.05

72700

38.05

Monday= , October 30, 2006

38.8

38.82

37.88

37.92

74500

37.92

Friday= , October 27, 2006

39.37

39.46

38.92

39.38

31100

39.38

Thursd= ay, October 26, 2006

39.96

39.98

39.01

39.17

106100

39.17

Wednes= day, October 25, 2006

38.65

39.9

38.6

39.9

74200

39.9

Tuesda= y, October 24, 2006

38.05

38.64

37.98

38.52

20800

38.52

Monday= , October 23, 2006

38.01

38.22

37.75

38.22

59200

38.22

Friday= , October 20, 2006

39.45

39.45

38.36

38.5

165600

38.5

Thursd= ay, October 19, 2006

38.7

39.48

38.42

39.37

41900

39.37

Wednes= day, October 18, 2006

39.22

39.63

38.3

38.54

37300

38.54

Tuesda= y, October 17, 2006

40.15

40.15

39.27

39.37

24700

39.37

Monday= , October 16, 2006

39.05

39.92

38.82

39.85

55600

39.85

Friday= , October 13, 2006

39.15

39.52

39.09

39.15

28200

39.15

Thursd= ay, October 12, 2006

38.5

38.89

38.45

38.82

27700

38.82

Wednes= day, October 11, 2006

39.12

39.28

38.36

38.36

31300

38.36

Tuesda= y, October 10, 2006

39.6

39.73

39.02

39.09

36600

39.09

Monday= , October 09, 2006

40.3

40.73

39.92

39.98

28400

39.98

Friday= , October 06, 2006

39.89

40.03

39.25

39.9

74800

39.9

Thursd= ay, October 05, 2006

40.55

40.55

39.54

40.05

82100

40.05

Wednes= day, October 04, 2006

39.31

39.7

38.5

39.65

141300

39.65

Tuesda= y, October 03, 2006

39.93

39.96

38.99

39

95100

39

Monday= , October 02, 2006

41.6

41.73

40.53

40.55

26200

40.55

Friday= , September 29, 2006

41.5

41.93

41

41.88

95400

41.88

Thursd= ay, September 28, 2006

41.95

42.59

41.55

41.55

218100

41.55

Wednes= day, September 27, 2006

40.83

41.97

40

41.9

86500

41.9

Tuesda= y, September 26, 2006

40.7

41.14

40.29

40.68

142500

40.68

Monday= , September 25, 2006

39.85

41.21

39.66

40.76

147500

40.76

Friday= , September 22, 2006

41.19

41.27

40.07

40.08

54700

40.08

Thursd= ay, September 21, 2006

40.57

41

40.38

41

41600

41

Wednes= day, September 20, 2006

40.85

41.33

40.34

40.4

104800

40.4

Tuesda= y, September 19, 2006

43

43.2

41.27

41.32

102900

41.32

Monday= , September 18, 2006

42.75

43.2

42.23

42.79

120900

42.79

Friday= , September 15, 2006

42.33

42.63

41.91

42.53

66800

42.53

Thursd= ay, September 14, 2006

43.19

43.26

42.43

42.64

68800

42.64

Wednes= day, September 13, 2006

43.28

43.6

42.91

43.2

47600

43.2

Tuesda= y, September 12, 2006

44.18

44.28

43.01

43.05

70800

43.05

Monday= , September 11, 2006

44.14

44.64

43.79

44.1

158400

44.1

Friday= , September 08, 2006

45.36

45.36

44.59

44.73

155500

44.73

Thursd= ay, September 07, 2006

45.62

45.65

45.01

45.26

454200

45.26

Wednes= day, September 06, 2006

45.89

46.27

45.52

45.64

200800

45.64

Tuesda= y, September 05, 2006

46.19

46.44

46.03

46.44

151100

46.44

Friday= , September 01, 2006

47.38

47.49

46.55

46.7

102400

46.7

Thursd= ay, August 31, 2006

47.11

47.41

46.89

47.41

49700

47.41

Wednes= day, August 30, 2006

47.08

47.43

46.36

47.42

74400

47.42

Tuesda= y, August 29, 2006

47.03

47.28

46.82

46.99

37700

46.99

Monday= , August 28, 2006

48.02

48.02

47.33

47.48

22000

47.48

Friday= , August 25, 2006

49.46

49.62

48.8

48.84

201300

48.84

Thursd= ay, August 24, 2006

48.24

48.9

48.13

48.9

109200

48.9

Wednes= day, August 23, 2006

48.83

48.86

47.93

48.33

137900

48.33

Tuesda= y, August 22, 2006

49.35

49.43

49

49.23

27800

49.23

Monday= , August 21, 2006

49.02

49.4

48.7

49.32

145500

49.32

Friday= , August 18, 2006

48.54

48.56

47.85

48.44

149200

48.44

Thursd= ay, August 17, 2006

48.55

48.58

48

48.36

152200

48.36

Wednes= day, August 16, 2006

49.72

49.79

49.11

49.19

184900

49.19

------=_NextPart_01C8E39C.74EC5DB0 Content-Location: fi