Design Live Load Factor Calibration for Michigan Highway Bridges

In this study, a reliability-based calibration of live load factors for bridge design specific to the State of Michigan was conducted. Two years of high frequency WIM data from 20 representative state-wide sites were analyzed, and load effects were generated for bridge spans from 6 to 122 m (20 to 400 ft), considering simple and continuous moments and shears, as well as single lane and two lane effects. Seventy-five year statistics for maximum live load were then estimated with probabilistic projection. Bridge girders considered for the calibration included composite steel, prestressed concrete, side-by-side and spread box beams, as well as special long span structural members. In some cases, it was found that Michigan load effects are greater than those previously assumed, often requiring higher load factors than in current use. Moreover, significant variation in the required load factor was found, potentially resulting significant inconsistencies in reliability if a single load factor is used for the design of all bridge types and load effects considered

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

The Mammographic Density of a Mass Is a Significant Predictor of Breast Cancer

Our study shows that, in contrast to previous research, breast mass density is significantly associated with malignancy, even after controlling for other predictive variables