Two-measured variable method for wall interference assessment/correction

An iterative method for wall interference assessment and/or correction is presented for transonic flow conditions in wind tunnels equipped with two component velocity measurements on a single interface. The iterative method does not require modeling of the test article and tunnel wall boundary conditions. Analytical proof for the convergence and stability of the iterative method is shown in the subsonic flow regime. The numerical solutions are given for both 2-D and axisymmetrical cases at transonic speeds with the application of global Mach number correction

A wall interference assessment/correction system

A Wall Signature method, the Hackett method, has been selected to be adapted for the 12-ft Wind Tunnel wall interference assessment/correction (WIAC) system in the present phase. This method uses limited measurements of the static pressure at the wall, in conjunction with the solid wall boundary condition, to determine the strength and distribution of singularities representing the test article. The singularities are used in turn for estimating wall interferences at the model location. The Wall Signature method will be formulated for application to the unique geometry of the 12-ft Tunnel. The development and implementation of a working prototype will be completed, delivered and documented with a software manual. The WIAC code will be validated by conducting numerically simulated experiments rather than actual wind tunnel experiments. The simulations will be used to generate both free-air and confined wind-tunnel flow fields for each of the test articles over a range of test configurations. Specifically, the pressure signature at the test section wall will be computed for the tunnel case to provide the simulated 'measured' data. These data will serve as the input for the WIAC method-Wall Signature method. The performance of the WIAC method then may be evaluated by comparing the corrected parameters with those for the free-air simulation. Each set of wind tunnel/test article numerical simulations provides data to validate the WIAC method. A numerical wind tunnel test simulation is initiated to validate the WIAC methods developed in the project. In the present reported period, the blockage correction has been developed and implemented for a rectangular tunnel as well as the 12-ft Pressure Tunnel. An improved wall interference assessment and correction method for three-dimensional wind tunnel testing is presented in the appendix

A wall interference assessment/correction system

A Wall Signature method originally developed by Hackett has been selected to be adapted for the Ames 12-ft Wind Tunnel WIAC system in the project. This method uses limited measurements of the static pressure at the wall, in conjunction with the solid wall boundary condition, to determine the strength and distribution of singularities representing the test article. The singularities are used in turn for estimating blockage wall interference. The lifting interference will be treated separately by representing in a horseshoe vortex system for the model's lifting effects. The development and implementation of a working prototype will be completed, delivered and documented with a software manual. The WIAC code will be validated by conducting numerically simulated experiments rather than actual wind tunnel experiments. The simulations will be used to generate both free-air and confined wind-tunnel flow fields for each of the test articles over a range of test configurations. Specifically, the pressure signature at the test section wall will be computed for the tunnel case to provide the simulated 'measured' data. These data will serve as the input for the WIAC method--Wall Signature method. The performance of the WIAC method then may be evaluated by comparing the corrected data with those of the free-air simulation

Measuring productivity and efficiency: a Kalman filter approach

In the Kalman filter setting, one can model the inefficiency term of the standard stochastic frontier composed error as an unobserved state. In this study a panel data version of the local level model is used for estimating time-varying efficiencies of firms. We apply the Kalman filter to estimate average efficiencies of U.S. airlines and find that the technical efficiency of these carriers did not improve during the period 1999-2009. During this period the industry incurred substantial losses, and the efficiency gains from reorganized networks, code-sharing arrangements, and other best business practices apparently had already been realized

Evidence from Identified Particles for Active Quark and Gluon Degrees of Freedom

Measurements of intermediate pT (1.5 < pT < 5.0 GeV/c) identified particle distributions in heavy ion collisions at SPS and RHIC energies display striking dependencies on the number of constituent quarks in the corresponding hadron. One finds that elliptic flow at intermediate pT follows a constituent quark scaling law as predicted by models of hadron formation through coalescence. In addition, baryon production is also found to increase with event multiplicity much faster than meson production. The rate of increase is similar for all baryons, and seemingly independent of mass. This indicates that the number of constituent quarks determines the multiplicity dependence of identified hadron production at intermediate pT. We review these measurements and interpret the experimental findings.Comment: 8 pages, 5 figures, proceedings for SQM2006 conference in Los Angele

Recombination Models

We review the current status of recombination and coalescence models that have been successfully applied to describe hadronization in heavy ion collisions at RHIC energies. Basic concepts as well as actual implementations of the idea are discussed. We try to evaluate where we stand in our understanding at the moment and what remains to be done in the future.Comment: Plenary Talk at Quark Matter 2004, submitted to J. Phys. G, 8 pages, 3 figure

Mammography Facility Characteristics Associated With Interpretive Accuracy of Screening Mammography

BackgroundAlthough interpretive performance varies substantially among radiologists, such variation has not been examined among mammography facilities. Understanding sources of facility variation could become a foundation for improving interpretive performance.MethodsIn this cross-sectional study conducted between 1996 and 2002, we surveyed 53 facilities to evaluate associations between facility structure, interpretive process characteristics, and interpretive performance of screening mammography (ie, sensitivity, specificity, positive predictive value [PPV1], and the likelihood of cancer among women who were referred for biopsy [PPV2]). Measures of interpretive performance were ascertained prospectively from mammography interpretations and cancer data collected by the Breast Cancer Surveillance Consortium. Logistic regression and receiver operating characteristic (ROC) curve analyses estimated the association between facility characteristics and mammography interpretive performance or accuracy (area under the ROC curve [AUC]). All P values were two-sided.ResultsOf the 53 eligible facilities, data on 44 could be analyzed. These 44 facilities accounted for 484 463 screening mammograms performed on 237 669 women, of whom 2686 were diagnosed with breast cancer during follow-up. Among the 44 facilities, mean sensitivity was 79.6% (95% confidence interval [CI] = 74.3% to 84.9%), mean specificity was 90.2% (95% CI = 88.3% to 92.0%), mean PPV1 was 4.1% (95% CI = 3.5% to 4.7%), and mean PPV2 was 38.8% (95% CI = 32.6% to 45.0%). The facilities varied statistically significantly in specificity (P &lt; .001), PPV1 (P &lt; .001), and PPV2 (P = .002) but not in sensitivity (P = .99). AUC was higher among facilities that offered screening mammograms alone vs those that offered screening and diagnostic mammograms (0.943 vs 0.911, P = .006), had a breast imaging specialist interpreting mammograms vs not (0.932 vs 0.905, P = .004), did not perform double reading vs independent double reading vs consensus double reading (0.925 vs 0.915 vs 0.887, P = .034), or conducted audit reviews two or more times per year vs annually vs at an unknown frequency (0.929 vs 0.904 vs 0.900, P = .018).ConclusionMammography interpretive performance varies statistically significantly by facility

Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer

The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming (ILP) and conditional probabilities, and validate this association in an independent dataset. We ran our ILP algorithm on 62,219 mammographic abnormalities. We set the Aleph ILP system to generate 10,000 rules per malignant finding with a recall >5% and precision >25%. Aleph reported the best rule for each malignant finding. A total of 80 unique rules were learned. A radiologist reviewed all rules and identified potentially interesting rules. High breast mass density appeared in 24% of the learned rules. We confirmed each interesting rule by calculating the probability of malignancy given each mammographic descriptor. High mass density was the fifth highest ranked predictor. To validate the association between mass density and malignancy in an independent dataset, we collected data from 180 consecutive breast biopsies performed between 2005 and 2007. We created a logistic model with benign or malignant outcome as the dependent variable while controlling for potentially confounding factors. We calculated odds ratios based on dichomotized variables. In our logistic regression model, the independent predictors high breast mass density (OR 6.6, CI 2.5–17.6), irregular mass shape (OR 10.0, CI 3.4–29.5), spiculated mass margin (OR 20.4, CI 1.9–222.8), and subject age (β = 0.09, p < 0.0001) significantly predicted malignancy. Both ILP and conditional probabilities show that high breast mass density is an important adjunct predictor of malignancy, and this association is confirmed in an independent data set of prospectively collected mammographic findings