Costly information and the CRA

Community Reinvestment Act of 1977

A market-based approach to CRA

Community Reinvestment Act of 1977

Procedure to Approximately Estimate the Uncertainty of Material Ratio Parameters due to Inhomogeneity of Surface Roughness

Roughness parameters that characterize contacting surfaces with regard to friction and wear are commonly stated without uncertainties, or with an uncertainty only taking into account a very limited amount of aspects such as repeatability of reproducibility (homogeneity) of the specimen. This makes it difficult to discriminate between different values of single roughness parameters. Therefore uncertainty assessment methods are required that take all relevant aspects into account. In the literature this is scarcely performed and examples specific for parameters used in friction and wear are not yet given. We propose a procedure to derive the uncertainty from a single profile employing a statistical method that is based on the statistical moments of the amplitude distribution and the autocorrelation length of the profile. To show the possibilities and the limitations of this method we compare the uncertainty derived from a single profile with that derived from a high statistics experiment.Comment: submitted to Meas. Sci. Technol., 12 figure

Strategies for Motivating Band Students Experiencing Difficulty in Skills Acquisition

The purpose of this research was to establish a consensus on the type of music learning environment that affords the band student the greatest level of individual success. Both cooperative and competitive environments were examined. This project also investigated alternative methods for helping the low ability band student enjoy success in an instrumental music curriculum. Finally, the research identified teaching strategies for aiding the low ability student to be successful in a competitive learning environment. The research results do not conclusively reach a consensus about the teaching environment that affords band students the greatest amount of success. The majority of the band director respondents utilized a cooperative learning environment, with some competitive elements. This environment utilized many of the teaching-learning methods needed for success by the low ability band student. The needs of the low ability band student proposed in the research for certain instructional environments and strategies were supported by the responses of music educators to a questionnaire. It was discovered that while the low ability band student could enjoy group success, individual success was also important. It was determined that the band director could foster individual success by encouraging all students to do their best and to use teaching strategies, such as mastery learning, to aid the low ability band student. Specific strategies were suggested by the research and supported in the questionnaire results, which aid the low ability band student. Data indicated that providing students with extra help, including the use of peer tutors, helped students perform on the same level as their peers. Providing instruction in small units aided the low ability band students in mastering difficult passages of music. Seating the high and low ability music students next to each other enabled students to work together in class

A Predictive Modeling Approach for Assessing Seismic Soil Liquefaction Potential Using CPT Data

Soil liquefaction, or loss of strength due to excess pore water pressures generated during dynamic loading, is a main cause of damage during earthquakes. When a soil liquefies (referred to as triggering), it may lose its ability to support overlying structures, deform vertically or laterally, or cause buoyant uplift of buried utilities. Empirical liquefaction models, used to predict liquefaction potential based upon in-situ soil index property measurements and anticipated level of seismic loading, are the standard of practice for assessing liquefaction triggering. However, many current models do not incorporate predictor variable uncertainty or do so in a limited fashion. Additionally, past model creation and validation lacks the same rigor found in predictive modeling in other fields. This study examines the details of creating and validating an empirical liquefaction model, using the existing worldwide cone penetration test liquefaction database. Our study implements a logistic regression within a Bayesian measurement error framework to incorporate uncertainty in predictor variables and allow for a probabilistic interpretation of model parameters. Our model is built using a hierarchal approach account for intra-event correlation in loading variables and differences in event sample sizes that mirrors the random/mixed effects models used in ground motion prediction equation development. The model is tested using an independent set of case histories from recent New Zealand earthquakes, and performance metrics are reported. We found that a Bayesian measurement error model considering two predictor variables, qc,1 and CSR, decreases model uncertainty while maintaining predictive utility for new data. Two forms of model uncertainty were considered – the spread of probabilities predicted by mean values of regression coefficients (apparent uncertainty) and the standard deviations of the predictive distributions from fully probabilistic inference. Additionally, we found models considering friction ratio as a predictor variable performed worse than the two variable case and will require more data or informative priors to be adequately estimated