Cross Flow Filtration Modeling Using Analytical and Numerical Solutions Along with Implementation as a Web-Based Calculator

In this thesis, we present a 1D model for the complex phenomenon of cross-flow filtration. We begin by developing the governing equations and providing analytical solutions that can be found using Laplace transforms for both the simple clean filter case, and the more complex filter with fouling in the form of cake, and depth plugging of the filter media. A walk through of the set up and implementation of a simple web-based application is given, and the code to produce the web application for this model is included in an appendix. The web application acts as a calculator, accepting model parameters and returning graphical output on the client side while the numerical solution is calculated on the server side. Lastly technique of a least-squares finite element approach is applied to the governing equations to obtain approximate solutions now under the assumption that viscosity is not constant, but varies linearly with respect to time. The major contribution from this thesis is the development of a web-based application for simulation of cross flow filtration, set in a framework that can be applied for a wide variety of modeling problems. This thesis is a significant step towards the long term goal to combine multiple disciplines including fluid dynamics, mathematics, and computer science, to produce an effective and robust modeling tool

Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis

BACKGROUND: It has been reported in the quantitative trait locus (QTL) literature that when testing for QTL location and effect, the statistical power supporting methodologies based on two markers and their estimated genetic map is higher than for the genetic map independent methodologies known as single marker analyses. Close examination of these reports reveals that the two marker approaches are more powerful than single marker analyses only in certain cases. Simulation studies are a commonly used tool to determine the behavior of test statistics under known conditions. We conducted a simulation study to assess the general behavior of an intersection test and a two marker test under a variety of conditions. The study was designed to reveal whether two marker tests are always more powerful than intersection tests, or whether there are cases when an intersection test may outperform the two marker approach. We present a reanalysis of a data set from a QTL study of ovariole number in Drosophila melanogaster. RESULTS: Our simulation study results show that there are situations where the single marker intersection test equals or outperforms the two marker test. The intersection test and the two marker test identify overlapping regions in the reanalysis of the Drosophila melanogaster data. The region identified is consistent with a regression based interval mapping analysis. CONCLUSION: We find that the intersection test is appropriate for analysis of QTL data. This approach has the advantage of simplicity and for certain situations supplies equivalent or more powerful results than a comparable two marker test

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

This paper investigates using machine learning to rapidly develop empirical models suitable for system-level aircraft noise studies. In particular, machine learning is used to train a neural network to predict the noise spectra produced by a round jet near a surface over a range of surface lengths, surface standoff distances, jet Mach numbers, and observer angles. These spectra include two sources, jet-mixing noise and jet-surface interaction (JSI) noise, with different scale factors as well as surface shielding and reflection effects to create a multi- dimensional problem. A second model is then trained using data from three rectangular nozzles to include nozzle aspect ratio in the spectral prediction. The training and validation data are from an extensive jet-surface interaction noise database acquired at the NASA Glenn Research Center's Aero-Acoustic Propulsion Laboratory. Although the number of training and validation points is small compared a typical machine learning application, the results of this investigation show that this approach is viable if the underlying data are well behaved

Changes in skeletal muscle gene expression following clenbuterol administration

BACKGROUND: Beta-adrenergic receptor agonists (BA) induce skeletal muscle hypertrophy, yet specific mechanisms that lead to this effect are not well understood. The objective of this research was to identify novel genes and physiological pathways that potentially facilitate BA induced skeletal muscle growth. The Affymetrix platform was utilized to identify gene expression changes in mouse skeletal muscle 24 hours and 10 days after administration of the BA clenbuterol. RESULTS: Administration of clenbuterol stimulated anabolic activity, as indicated by decreased blood urea nitrogen (BUN; P < 0.01) and increased body weight gain (P < 0.05) 24 hours or 10 days, respectively, after initiation of clenbuterol treatment. A total of 22,605 probesets were evaluated with 52 probesets defined as differentially expressed based on a false discovery rate of 10%. Differential mRNA abundance of four of these genes was validated in an independent experiment by quantitative PCR. Functional characterization of differentially expressed genes revealed several categories that participate in biological processes important to skeletal muscle growth, including regulators of transcription and translation, mediators of cell-signalling pathways, and genes involved in polyamine metabolism. CONCLUSION: Global evaluation of gene expression after administration of clenbuterol identified changes in gene expression and overrepresented functional categories of genes that may regulate BA-induced muscle hypertrophy. Changes in mRNA abundance of multiple genes associated with myogenic differentiation may indicate an important effect of BA on proliferation, differentiation, and/or recruitment of satellite cells into muscle fibers to promote muscle hypertrophy. Increased mRNA abundance of genes involved in the initiation of translation suggests that increased levels of protein synthesis often associated with BA administration may result from a general up-regulation of translational initiators. Additionally, numerous other genes and physiological pathways were identified that will be important targets for further investigations of the hypertrophic effect of BA on skeletal muscle

MEDPRAT-SEL: Medical Resource Set Selector Searching High-Dimensionality Space

NASA Human Research Program's (HRP) next generation risk model closes the loop on medical risk and resource evaluation, providing researchers with a tool to assess and choose capabilities scientifically. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) simulator (SIM) evaluates resources in terms of medical benefit, such as reduced crew quality time lost (QTL), vs. cost, in mass or volume. The MEDPRAT resource set selector (SEL) searches the space of resource benefits v. cost, closing a feedback loop around the simulator, thus selecting resource sets with best benefit for cost. We discuss the design and results of the resource set selector. NASA HRP's emphasis on scientific assessment of risk, PRA, has led to the Cross-cutting Computational Modeling Project (CCMP)

Evidence for an evolutionarily conserved interaction between cell wall biosynthesis and flowering in maize and sorghum

BACKGROUND: Factors that affect flowering vary among different plant species, and in the grasses in particular the exact mechanism behind this transition is not fully understood. The brown midrib (bm) mutants of maize (Zea mays L.), which have altered cell wall composition, have different flowering dynamics compared to their wild-type counterparts. This is indicative of a link between cell wall biogenesis and flowering. In order to test whether this relationship also exists in other grasses, the flowering dynamics in sorghum (Sorghum bicolor (L.) Moench) were investigated. Sorghum is evolutionarily closely related to maize, and a set of brown midrib (bmr) mutants similar to the maize bm mutants is available, making sorghum a suitable choice for study in this context. RESULTS: We compared the flowering time (time to half-bloom) of several different bmr sorghum lines and their wild-type counterparts. This revealed that the relationship between cell wall composition and flowering was conserved in sorghum. Specifically, the mutant bmr7 flowered significantly earlier than the corresponding wild-type control, whereas the mutants bmr2, bmr4, bmr6, bmr12, and bmr19 flowered later than their wild-type controls. CONCLUSION: The change in flowering dynamics in several of the brown midrib sorghum lines provides evidence for an evolutionarily conserved mechanism that links cell wall biosynthesis to flowering dynamics. The availability of the sorghum bmr mutants expands the germplasm available to investigate this relationship in further detail

Measuring Parent Perceptions of School Climate

Parents' attitudes about their children’s schools matter. Their views can shape their children’s attitudes about school, affect their levels of family-school engagement, and influence their residential and school enrollment decisions. This paper describes the development of a survey scale to assess parent perceptions of the climate of their child’s school. Our comprehensive scale development process incorporated feedback from academics and potential respondents from the outset of the design process to enhance scale quality. We conducted three studies with national samples of parents (n = 385; n = 253; n = 266) to gather evidence of scale score reliability and valid score inferences based on convergent/discriminant validity. Through confirmatory factor analysis we identified a theoretically grounded factor structure that fit the data well. Interestingly, we found no evidence that parental response patterns distinguish between academic and social elements of school climate. Furthermore, we found that parents of younger children, on average, had a more positive perception of the school’s climate than parents of older children. We conclude by discussing how researchers and Pre-K – 12 schools and districts can use the scale to aid school improvement efforts