Matching Items (113)
Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Mammalian olfaction relies on active sniffing, which both shapes and is shaped by olfactory stimuli. Habituation to repeated exposure of an olfactory stimuli is believed to be mediated by decreased sniffing; however, this decrease may be reserved by exposure to novel odorants. Because of this, it may be possible to use sniffing itself as a measure of novelty, and thus as a measure of odorant similarity. Thus, I investigated the use of sniffing to measure habituation, cross-habituation, and odorant similarity. During habituation experiments, increases in sniff rate seen in response to odorant presentation decreased in magnitude between the first and second presentations, suggesting of habituation. Some of this reduction in sniff rate increases was revered by the presentation of a novel odorant in cross-habituations. However the effect sizes in cross-habituation experiments were low, and the variability high, forestalling the conclusion that sniffing accurately measured cross-habituation. I discuss improvements to the experimental protocol that may allow for cross-habituation to be more accurately measured using sniffing alone in future experiments.
ContributorsVigayavel, Nirmal (Author) / Smith, Brian (Thesis director) / Sanabria, Federico (Committee member) / Gerkin, Rick (Committee member) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Since its inception in the early 1990s, the concept of gene vaccines, particularly DNA vaccines, has enticed researchers across the board due to its simple design, flexible modification, and overall inexpensive cost of manufacturing. However, the past three decades have proven to be less fruitful than anticipated as scientists have yet to tackle the issue of inducing a strong enough response in humans and non-human primates to protect against foreign pathogens, an issue that has since been coined as the “simian barrier.” This appears to be a human/primate barrier as protective vaccines have been produced for other mammals. Despite millions of dollars in research along with some of the world’s brightest minds chipping in to resolve this, there has yet to be any truly viable solution to overcoming this barrier. With current research illustrating effective applications of RNA vaccines in humans, these studies may be uncovering the solution to the largely unsolved simian barrier dilemma. If vaccines using RNA, the transcribed version of DNA, are effective in humans, the problem may be inefficient transcription of the DNA. This may be attributable to a DNA promoter that has insufficient activity in primates. Additionally, with DNA vaccines being even cheaper and easier to manufacture than RNA vaccines, along with having no required cold chain for distribution, this concept remains more promising than RNA vaccines that are further along in clinical trials.
ContributorsWillis, Joshua Aaron (Author) / Johnston, Stephen (Thesis director) / Sykes, Kathryn (Committee member) / Shen, Luhui (Committee member) / Dean, W.P. Carey School of Business (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12