A Users Guide to Performance Engineered Mixtures

Performance Engineered Mixtures is an initiative, spearheaded by the Federal Highway Administration and the National Concrete Pavement Technology Center, in cooperation with state Departments of Transportation and the concrete paving industry, to develop a comprehensive approach to modernizing the way concrete is specified, tested, and accepted. It focusses on three components: testing of six critical concrete properties, a robust approach to quality control, and the replacement of prescriptive specifications. Many new tests have been recently developed that provide the ability to test concrete properties more easily and quicker than in the past. This paper provides background of how this initiative began. It elaborates on each of the six properties of concrete that are the focus of PEM. The new tests that are integral to the PEM process are described. The effects PEM will have on the acceptance process and the quality control responsibilities are outlined. Finally, tables are included which list the properties and the tests that are associated with each property, as well as how each is applied to each step of the paving process.

Accuracy, Precision, And Agreement Statistical Tests For Bland-Altman Method

Background: Bland and Altman plot method is a widely cited graphical approach to assess equivalence of quantitative measurement techniques. Perhaps due to its graphical output, it has been widely applied, however often misinterpreted by lacking of inferential statistical support. To compare data sets obtained from two measurement techniques, researchers may apply Pearson’s correlation, ordinal least-square linear regression, or the Bland-Altman plot methods, failing to locate the weakness of each measurement technique. We aim to develop and distribute a statistical method in R in order to add robust and suitable inferential statistics of equivalence. Methods: Three nested tests based on structural regressions are proposed to assess the equivalence of structural means (accuracy), equivalence of structural variances (precision), and concordance with the structural bisector line (agreement in measurements of data pairs obtained from the same subject) to reach statistical support for the equivalence of measurement techniques. Graphical outputs illustrating these three tests were added to follow Bland and Altman’s principles of easy communication. Results: Statistical p-values and robust approach by bootstrapping with corresponding graphs provide objective, robust measures of equivalence. Five pairs of data sets were analyzed in order to criticize previously published articles that applied the Bland and Altman’s principles, thus showing the suitability of the present statistical approach. In one case it was demonstrated strict equivalence, three cases showed partial equivalence, and one case showed poor equivalence. Package containing open codes and data is available with installation instructions on SourceForge for free distribution. Conclusions: Statistical p-values and robust approach assess the equivalence of accuracy, precision, and agreement for measurement techniques. Decomposition in three tests helps the location of any disagreement as a means to fix a new technique.

MSA: The forgotten index for identifying inappropriate items before computing exploratory item factor analysis

Kaiser’s single-variable measure of sampling adequacy (MSA) is a very useful index for debugging inappropriate items before a factor analysis (FA) solution is fitted to an item-pool dataset for item selection purposes. For reasons discussed in the article, however, MSA is hardly used nowadays in this context. In our view, this is unfortunate. In the present proposal, we first discuss the foundation and rationale of MSA from a ‘modern’ FA view, as well as its usefulness in the item selection process. Second, we embed the index within a robust approach and propose improvements in the preliminary item selection process. Third, we implement the proposal in different statistical programs. Finally, we illustrate its use and advantages with an empirical example in personality measurement.

Robust clustering of functional directional data

A robust approach for clustering functional directional data is proposed. The proposal adapts “impartial trimming” techniques to this particular framework. Impartial trimming uses the dataset itself to tell us which appears to be the most outlying curves. A feasible algorithm is proposed for its practical implementation justified by some theoretical properties. A “warping” approach is also introduced which allows including controlled time warping in that robust clustering procedure to detect typical “templates”. The proposed methodology is illustrated in a real data analysis problem where it is applied to cluster aircraft trajectories.

Confirming a Drosophila Melanogaster Model of Huntingtin Aggregation

For decades, doctors, psychologists, and psychiatrists alike have struggled to treat the symptomatic effects of Huntington’s disease. Huntington’s disease is an autosomal dominant brain disease that results in the deterioration of a person’s physical and mental state. Once a person inherits the disease, they end up dying from it more often than not. At present, there are 41,000 Americans with symptomatic Huntington’s disease, and 200,000 more are currently at-risk of inheriting the disease. Given its 50/50 chance of inheritance, there seems to be no end in sight to this degenerative ailment. My research study, however, will show that with a more robust approach, finding a cure for this disease is possible. Ultimately, the aim of this project was to test an already established model in Drosophila melanogaster regarding the “huntingtin” protein responsible for Huntington’s disease. This was achieved by first demonstrating that the flies which were modified to produce huntingtin could, in fact, produce the protein. Secondly, an experimental process was created to configure a system through which the amount of protein produced by each fly could be quantified. This quantification was vital in creating a baseline that would allow for the identification of potential therapeutic treatments in the future. In short, by establishing a quantifiable model for huntingtin, this study will pave the way to new insights on huntingtin aggregation and the identification of possible treatments for Huntington’s disease in the future.