TEAMS WHITE OPAL Alpha Sigma Alpha Sigma Phi Epsilon SILVER QUARTZ Sigma Sigma Sigma Delta Chi Phi Lambda Phi Cardinal Key PURPLE AMETHYST Alpha Phi Omega Phi Kappa Tau Prim Roses EMERALD GREEN AMBER ORANGE Delta Zeta Tau Lambda Sigma Lambda Chi Alpha Alpha Gamma Rho MAP Phi Delta RUBY RED YELLOW TOPAZ Delta Phi Epsilon Sigma Kappa Tau Kappa Epsilon Phi Sigma Kappa Delta Sigma Pi Sigma Tau Gamma BLUE SAPPHIRE BLACK ONYX Alpha Gamma Delta Alpha Sigma Gamma Pi Kappa Phi ABC Beta Theta Pi Alpha Kappa Lambda
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Methodology TVA sigma measurements. • Sigma-Y (replaced in 1989). • Average of 12 5-minute WD Standard Deviations • Hourly Sigma-Theta. • Sigma-Theta based on 720 WD readings. • 15-minute Sigma-Theta. • RMS of 4 15-minute Sigma-Thetas. • 15-minute Sigma-Thetas based on 180 WD readings each. Annual average of 15-minute Sigma-Thetas. • Did not include cases with WS < 5 mph. • Sorted into 10 degree WD sectors. Used 25 degrees as arbitrary cut-off in analysis. BFN Sigmas 3
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Student Affairs Greek House Renovations •Kappa Delta (new construction) •Kappa Sigma (renovation & addition) •Sigma Phi Epsilon (renovation) Kappa Sigma Organizations that received national recognition in 2010-11 National Residence Hall Honorary (regional chapter of the year) Panhellenic Council Chi Omega Sorority Zeta Tau Alpha Sorority Kappa Delta Delta Sigma Phi Fraternity Kappa Alpha Fraternity Kappa Sigma Fraternity Pi Kappa Alpha Fraternity Sigma Chi Fraternity Sigma Phi Epsilon
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Control Limits The control limits for the x-chart are: UCL–x = =x + A2R and LCLx–= x=- A2R Where = X = central line of the chart, which can be either the average of past sample means or a target value set for the process. A2 = constant to provide three-sigma limits for the sample mean. The control limits for the R-chart are UCLR = D4R and LCLR = D3R where R = average of several past R values and the central line of the chart. D3,D4 = constants that provide 3 standard deviations (three-sigma) limits for a given sample size. © 2007 Pearson Education
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Lean Six Sigma – What is it? • Lean Six Sigma is a comprehensive and flexible system for achieving, sustaining and maximizing organizational success. • The Lean Six Sigma approach is driven by: Improve Processes Process Flow Teamwork Variation & Defects Speed • To insure an organizational transformation, Lean Six Sigma also focuses on the culture of an organization. Delight Customers Quality – Closely understanding customer needs – Disciplined use of facts, data & statistical analysis – Diligent attention to managing, improving & reinventing organizational processes Lean Six Sigma Data and Facts Source: What Is Lean Six Sigma, George, Rowlands & Kastle 2
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References • • • • • • • David N. Card, “Sorting out Six Sigma and the CMM”, IEEE Software, May June 2000, pp. 11-13 Jack Horgan, “Six Sigma and CMM”, September 13 - 17, 2004, http://www10.edacafe.com/nbc/articles/view_weekly.php?articleid=2 09200 M. Murugappan and G. Keeni, “Blending CMM and Six Sigma to Meet Business Goals”, IEEE Software, March/April 2003 “IT Perspective: Balancing Six Sigma and the Capability Maturity Model (CMM ®/CMMI SM )”, http://www.gartner.com/4_decision_tools/measurement/measure_it_art icles/2002_10/six_sig.jsp SEI Website, http://www.sei.cmm.edu/ Six Sigma, http://www.isixsigma.com/ Thomas Pyzdek, "DMAIC and DMADV", Pyzdek Consulting, Inc. Six Sigma Handbook, http://www.pyzdek.com/DMAICDMADV.htm
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E. Coli Promoters • • • • • • E. coli has five sigma factors: Sigma 70: Regulate expression of most genes. Sigma 32: Regulate expression of heat shock proteins. Sigma 28: Regulate expression of flagellar operon (involved in cell motion). Sigma 38: Regulate gene expression against external stresses. Sigma 54: Regulate gene expression for nitrogen metabolism.
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Gaussian Smoothing (cont’d) Gaussian(sigma, hSize, h) float sigma, *h; int hSize; { int i; float cst, tssq, x, sum; cst = 1./(sigma*sqrt(2.0*PI)) ; tssq = 1./(2*sigma*sigma) ; for(i=0; i
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Example Non-Parametric DE in R  Demo Rcode: setwd("C:\\Users\\C. Eick\\Desktop") a<-read.csv("c8.csv") require("spatstat") require("ppp") d<-data.frame(a=a[,1],b=a[,2],c=a[,3]) plot(d$a,d$b) w <- owin(poly=list (list(x=c(0,530,701,640,0),y=c(0,42,20,400,420)), list(x=c(320,430,310), y=c(215,200,190)),list(x=c(10,70,170,20), y=c(200,220,170, 175))) ) z<-ppp(d[,1],d[,2],window=w, marks=factor(d[,3])) plot(z) summary(z) q<-quadratcount(z, nx=12,ny=10) plot(q) den<-density(z, sigma=80) plot(den) den<-density(z, sigma=30) plot(den) den<-density(z, sigma=15) plot(den) den<-density(z, sigma=12) plot(den) den<-density(z, sigma=10) plot(den) den<-density(z, sigma=4) plot(den)  documentation for function ‘density’: http://127.0.0.1:28030/library/spatstat/html/density.ppp.html Han/Eick: Clustering II 15
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Part 22: Stochastic Frontier [21/83] ML – Cost Function +---------------------------------------------+ | Maximum Likelihood Estimates | | Log likelihood function 66.86502 | | Variances: Sigma-squared(v)= .01185 | | Sigma-squared(u)= .02233 | | Sigma(v) = .10884 | | Sigma(u) = .14944 | | Sigma = Sqr[(s^2(u)+s^2(v)]= .18488 | +---------------------------------------------+ |Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X| +---------+--------------+----------------+--------+---------+----------+ Primary Index Equation for Model Constant -7.49421176 .32997411 -22.712 .0000 LOGQ .41097893 .03599288 11.418 .0000 8.17947153 LOGPL_PF .26058898 .06554430 3.976 .0001 5.58088278 LOGPK_PF .05531289 .06001748 .922 .3567 .88666047 LOGQSQ .06058236 .00493666 12.272 .0000 35.1125267 Variance parameters for compound error Lambda 1.37311716 .29711056 4.622 .0000 Sigma .18487506 .00110120 167.884 .0000
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Federal Maximum Exposure Limits Limits for Exposures Occupational Dose limit (US - NRC) Occupational Exposure Limits for Minors (10%) Occupational Exposure Limits for Fetus Public dose limits due to licensed activities (NRC) Occupational Limits (eye) Occupational Limits (skin) Occupational Limits (extremities) Exposure 5 rem/year 0.5 rem/year 0.5 rem/9 months 0.1 rem/year 15 rem/year 50 rem/year 50 rem/year ALARA: The above limits are the Maximum Permissible Doses allowed by regulation. However, all doses should be maintained As Low As Reasonable Achievable (ALARA). Federal CFR Part 150
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6. Examples of Control Mechanisms a. Alternative Sigma Factors Sorenson, MK, Ray, SS, Darst, SA (2004) Crystal structure of the flagellar sigma/anti-sigma complex s28 /FlgM reveals an intact sigma factor in an inactive conformation. Molecular Cell 14:127-138. Gruber, TM, Gross, CA (2003) Multiple sigma subunits and the partitioning of bacterial transcription space. Annu. Rev. Microbiol 57:441-66 b.Increasing the Initial Binding of RNA Polymerase Holoenzyme to DNA Lawson CL, Swigon D, Murakami KS, Darst SA, Berman HM, Ebright RH. (2004) Catabolite activator protein: DNA binding and transcription activation. Curr Opin Struct Biol. 14:10-20. c.Increasing the Rate of Isomerization of RNA Polymerase *Dove, S.L., Huang, F.W., and Hochschild, A. (2000) Mechanism for a transcriptional activator that works at the isomerization step. Proc Natl Acad Sci USA 97: 13215-13220. Jain, D. Nickels, B.E., Sun, L., Hochschild, A., and Darst, S.A. (2004) Structure of a ternary transcription activation complex. Mol Cell 13: 45-53. Hawley and McClure (1982) Mechanism of Activation of Transcription from the l PRM promoter. JMB 157: 493-525 d. DNA looping **Oehler, S., Eismann, E.R., Kramer, H. and Mueller-Hill, B. (1990) The three operators of the lac operon cooperate in repression. EMBO 9:973-979. Vilar, J.M.G. and Leibler, S. (2003) DNA looping and physical constraints on transcription regulation. J Mol Biol 331:981-989. Dodd, I.B., Shearwin, K.E., Perkins, A.J., Burr, T., Hochschild, A., and Egan, J.B. (2004) Cooperativity in long-range gene regulation by the l cI repressor. Genes Dev. 18:344-354. e. The dynamics of lac Repressor binding to its operator Elf, J., Li, G.W., and Xie, X.S. (2007). Probing transcription factor dynamics at the single-molecule level in a living cell. Science 316, 1191–1194. Li, G.W., Berg, O.G., and Elf, J. (2009). Effects of macromolecular crowding and DNA looping on gene regulation kinetics. Nat.
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Joining Tables with SELECT Backus Naur Form (BNF) Notation [} [ FROM { | } [WHERE [{ }...]] [GROUP BY ] [HAVING [{ }...]] [ORDER BY ] [LIMIT [,] ] ] ::= {
,
[{,
}...]} | {
[INNER | CROSS ] JOIN
[]} | {
STRAIGHT_JOIN
} | {
LEFT [OUTER] JOIN
[]} | {
RIGHT [OUTER] JOIN
[]} | {
NATURAL [{LEFT | RIGHT} [OUTER]] JOIN
}
::=
[[AS] ] [{USE | IGNORE | FORCE} INDEX [{, }...]] ::= ON [{ }...] | USING ( [{, }...])
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texture force_table; __constant__ unsigned int exclusions[]; __shared__ atom jatom[]; atom iatom; // per-thread atom, stored in registers float4 iforce; // per-thread force, stored in registers for ( int j = 0; j < jatom_count; ++j ) { float dx = jatom[j].x - iatom.x; float dy = jatom[j].y - iatom.y; float dz = jatom[j].z - iatom.z; float r2 = dx*dx + dy*dy + dz*dz; if ( r2 < cutoff2 ) { float4 ft = texfetch(force_table, 1.f/sqrt(r2)); Force Interpolation bool excluded = false; int indexdiff = iatom.index - jatom[j].index; Exclusions if ( abs(indexdiff) <= (int) jatom[j].excl_maxdiff ) { indexdiff += jatom[j].excl_index; excluded = ((exclusions[indexdiff>>5] & (1<<(indexdiff&31))) != 0); } float f = iatom.half_sigma + jatom[j].half_sigma; // sigma f *= f*f; // sigma^3 Parameters f *= f; // sigma^6 f *= ( f * ft.x + ft.y ); // sigma^12 * fi.x - sigma^6 * fi.y f *= iatom.sqrt_epsilon * jatom[j].sqrt_epsilon; float qq = iatom.charge * jatom[j].charge; if ( excluded ) { f = qq * ft.w; } // PME correction else { f += qq * ft.z; } // Coulomb iforce.x += dx * f; iforce.y += dy * f; iforce.z += dz * f; Accumulation iforce.w += 1.f; // interaction count or energy NIH Resource for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC } http://www.ks.uiuc.edu/ Stone et al., J. Comp. Chem. 28:2618-2640, 2007. } Nonbonded Forces CUDA Code
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texture force_table; __constant__ unsigned int exclusions[]; __shared__ atom jatom[]; atom iatom; // per-thread atom, stored in registers float4 iforce; // per-thread force, stored in registers for ( int j = 0; j < jatom_count; ++j ) { float dx = jatom[j].x - iatom.x; float dy = jatom[j].y - iatom.y; float dz = jatom[j].z - iatom.z; float r2 = dx*dx + dy*dy + dz*dz; if ( r2 < cutoff2 ) { float4 ft = texfetch(force_table, 1.f/sqrt(r2)); Force Interpolation bool excluded = false; int indexdiff = iatom.index - jatom[j].index; Exclusions if ( abs(indexdiff) <= (int) jatom[j].excl_maxdiff ) { indexdiff += jatom[j].excl_index; excluded = ((exclusions[indexdiff>>5] & (1<<(indexdiff&31))) != 0); } float f = iatom.half_sigma + jatom[j].half_sigma; // sigma f *= f*f; // sigma^3 Parameters f *= f; // sigma^6 f *= ( f * ft.x + ft.y ); // sigma^12 * fi.x - sigma^6 * fi.y f *= iatom.sqrt_epsilon * jatom[j].sqrt_epsilon; float qq = iatom.charge * jatom[j].charge; if ( excluded ) { f = qq * ft.w; } // PME correction else { f += qq * ft.z; } // Coulomb iforce.x += dx * f; iforce.y += dy * f; iforce.z += dz * f; Accumulation iforce.w += 1.f; // interaction count or energy NIH Resource for Macromolecular Modeling and Bioinformatics Beckman Institute, UIUC } http://www.ks.uiuc.edu/ Stone et al., J. Comp. Chem. 28:2618-2640, 2007. } Nonbonded Forces CUDA Code
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Scenario One. Soils Ground W ater Ability Drift depth Flood Data Drainage Network W ater Table Landsat Classification Slope Percentage of Input - Strength Layer Soil Soil Ground Ground Ground Ground Ground Ground Class Nonhydric Hydric W ater Ability Low W ater Ability … W ater Ability … W ater Ability … W ater Ability High W ater Ability surface water Drift Drift Drift Drift Drift Drift Drift Value Count - 30m 0 546761 36 458394 0 1 2 4 5 6 1004942 1107345 1088580 698424 6634767 197938 Depth Depth Depth Depth Depth Depth Depth Deep … … … … … Shallow 0 1 3 5 7 9 11 21430 52158 130518 293099 376286 113922 9426 Flood Data Flood Data Non-Flood Flood Zone 0 8 844259 153237 20 83261 Drainage Network Layer Class Value Count 30m W ater Table Low End 0 390 W ater Table … 1 18370 W ater Table … 2 50080 W ater Table … 3 47163 W ater Table … 4 126689 W ater Table … 5 113951 W ater Table Mid Range 6 52256 W ater Table … 7 178577 W ater Table … 8 146845 W ater Table … 9 20465 W ater Table … 10 10122 W ater Table … 11 137026 W ater Table High End 12 93470 Classification Classification Classification Classification Coastal Forest Prairie W ater Slope Data High Slope Slope Data Low Slope Slope Data No Slope 35.25 35.75 35.5 35.35 -2 -1 0 553 8083 4770 273 1252634 808130 190524 Scenario One Output Table 27 5 8 6 15 10 27 1
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Six Sigma Personnel • Champion – Manager/Director/Executive who makes sure the resources are in place for a Six Sigma project • Master Black Belts – Quality experts in an organization – Responsible for strategic implementation – Teach/Mentor other Black and Green Belts • Black Belts – Six Sigma team leaders responsible for implementing process improvement projects within the business • Green Belts – Employee of an organization that has some training in Six Sigma and may lead a Six Sigma project, but only as part of their job
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