Fuzzy Logistic Regression For Detecting Differential Dna Methylation Regions, 2020 Missouri University of Science and Technology
Fuzzy Logistic Regression For Detecting Differential Dna Methylation Regions, Tarek M. Bubaker Bennaser
Doctoral Dissertations
“Epigenetics is the study of changes in gene activity or function that are not related to a change in the DNA sequence. DNA methylation is one of the main types of epigenetic modifications, that occur when a methyl chemical group attaches to a cytosine on the DNA sequence. Although the sequence does not change, the addition of a methyl group can change the way genes are expressed and produce different phenotypes. DNA methylation is involved in many biological processes and has important implications in the fields of biomedicine and agriculture.
Statistical methods have been developed to compare DNA methylation at …
Statistical Analysis Of Fnirs Data: Consideration Of Spatial Varying Coefficient Model Of Prefrontal Cortex Activity Changes During Speech Motor Learning In Apraxia Of Speech, 2020 Old Dominion University
Statistical Analysis Of Fnirs Data: Consideration Of Spatial Varying Coefficient Model Of Prefrontal Cortex Activity Changes During Speech Motor Learning In Apraxia Of Speech, Rachel Johnson, Jennifer Matthews, Norou Diawara, Rachel Carroll
Communication Disorders & Special Education Faculty Publications
Apraxia of speech is an impairment in the planning and programming of speech typically accompanied by aphasia (language impairment) secondary to a left hemisphere stroke. It is unknown if the structural and functional connections to the damaged area implicate the integrity of the cognitive functions of the prefrontal cortex (PFC). The present study examines the feasibility of measuring hemodynamic activity in the PFC in response to the structure of practice and during treatment. This multiple-baseline single case-design study involving two individuals with chronic acquired apraxia of speech measured the hemodynamic changes in PFC activity during treatment across the intervention period …
Spatial And Temporal Genetic Structure Of Winter-Run Steelhead (Oncorhynchus Mykiss) Returning To The Mad River, California, 2020 Humboldt State University
Spatial And Temporal Genetic Structure Of Winter-Run Steelhead (Oncorhynchus Mykiss) Returning To The Mad River, California, Steven R. Fong
Cal Poly Humboldt theses and projects
Distinct populations of steelhead in the wild are in decline. The propagation of steelhead in hatcheries has been used to boost population numbers for recreational fisheries and for use in conservation. However, hatchery breeding practices of steelhead can result in changes in genetic structure. I investigated the genetic structure of winter-run steelhead (Oncorhynchus mykiss) returning to the Mad River, California, where a hatchery has been used enhance production for recreational fisheries since 1971. Genetic variability in Mad River steelhead was evaluated using 96 single nucleotide polymorphisms (SNPs) among 4203 individuals, including the Mad River and nearby locations, and …
Joint Simulation Of Continuous And Categorical Variables For Mineral Resource Modeling And Recoverable Reserves Calculation, 2020 Michigan Technological University
Joint Simulation Of Continuous And Categorical Variables For Mineral Resource Modeling And Recoverable Reserves Calculation, Sentle Augustinus Hlajoane
Dissertations, Master's Theses and Master's Reports
Spatial variability and uncertainty of continuous variables (grade) and categorical variables (rock-types) in mineral evaluation significantly impact the economics of mining projects. The conventional approach of simulating grades using deterministic rock- types is problematic since spatial variability, and uncertainty of grades at rock-type contacts are not well captured in deposits where the grade changes gradually between rock-types. Therefore, jointly simulating these variables can improve confidence (reduce uncertainty) in a resource model. Also, resource classification and recoverable reserve calculation can significantly improve the understanding of the deposit and its economic viability. This research utilized the Plural-Gaussian geostatistical simulation to jointly simulate …
Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks, 2020 University of Kentucky
Orthogonal Recurrent Neural Networks And Batch Normalization In Deep Neural Networks, Kyle Eric Helfrich
Theses and Dissertations--Mathematics
Despite the recent success of various machine learning techniques, there are still numerous obstacles that must be overcome. One obstacle is known as the vanishing/exploding gradient problem. This problem refers to gradients that either become zero or unbounded. This is a well known problem that commonly occurs in Recurrent Neural Networks (RNNs). In this work we describe how this problem can be mitigated, establish three different architectures that are designed to avoid this issue, and derive update schemes for each architecture. Another portion of this work focuses on the often used technique of batch normalization. Although found to be successful …
Stochastic Technique For Solutions Of Non-Linear Fin Equation Arising In Thermal Equilibrium Model, 2020 University of Gujrat
Stochastic Technique For Solutions Of Non-Linear Fin Equation Arising In Thermal Equilibrium Model, Iftikhar Ahmad, Hina Qureshi, Muhammad Bilal, Muhammad Usman
Mathematics Faculty Publications
In this study, a stochastic numerical technique is used to investigate the numerical solution of heat transfer temperature distribution system using feed forward artificial neural networks. Mathematical model of fin equation is formulated with the help of artificial neural networks. The effect of the heat on a rectangular fin with thermal conductivity and temperature de-pendent internal heat generation is calculated through neural networks optimization with optimizers like active set technique, interior point technique, pattern search, genetic algorithm and a hybrid approach of pattern search - interior point technique, genetic algorithm - active set technique, genetic algorithm - interior point technique, …
The Effect Of Time And Temperature On The Quality Of Latent Fingerprints On Incandescent Lightbulbs, Varying Donors Age And Sex, 2020 Virginia Commonwealth University
The Effect Of Time And Temperature On The Quality Of Latent Fingerprints On Incandescent Lightbulbs, Varying Donors Age And Sex, Kinaysha M. Collazo Maldonado
Master of Science in Forensic Science Directed Research Projects
Fingerprints are used as a means of identification, but there are no established methodologies to determine time since deposition of latent fingerprints by visual means alone. This research considered the influence of age and sex on the quality of recovered latent prints from lit and unlit lightbulbs from 1 to 10 days, using accumulated degree hours (ADH) to account for both heat and time simultaneously. Two male and two female donors (one of each aged40 years) were used. A thermal imaging camera was used to monitor the lightbulbs top and middle regions, which were significantly different (p≤0.05) for the experimental …
Predicting Student Success In Arcadia University’S Math Courses, 2020 Arcadia University
Predicting Student Success In Arcadia University’S Math Courses, Chutong Wu, Tong Zhu, Yijin Qiu
Capstone Showcase
This project examines the relative efficacy of Arcadia’s math placement test and math SAT scores in predicting student success, and explores whether SAT scores alone might suffice for certain courses.
Aggregate Loss Model With Poisson-Tweedie Loss Frequency, 2020 Wilfrid Laurier University
Aggregate Loss Model With Poisson-Tweedie Loss Frequency, Si Chen
Theses and Dissertations (Comprehensive)
The aggregate loss model has applications in various areas such as financial risk management and actuarial science. The aggregate loss is the summation of all random losses occurred in a period, and it is governed by both the loss severity and the loss frequency. While the impact of the loss severity on aggregate loss is well studied, less focus is paid on the influence of loss frequency on aggregate loss, which motivates our study. In this thesis, we enrich the aggregate loss framework by introducing the Poisson-Tweedie distribution as a candidate for modelling loss frequency, prove the closedness of Poisson-Tweedie …
Interpretations Of Bicoherence In Space & Lab Plasma Dynamics, 2020 West Virginia University
Interpretations Of Bicoherence In Space & Lab Plasma Dynamics, Gregory Allen Riggs
Graduate Theses, Dissertations, and Problem Reports
The application of bicoherence analysis to plasma research, particularly in non-linear, coupled-wave regimes, has thus far been significantly belied by poor resolution in time, and/or outright destruction of frequency information. Though the typical power spectrum cloaks the phase-coherency between frequencies, Fourier transforms of higher-order convolutions provide an n-dimensional spectrum which is adept at elucidating n-wave phase coherence. As such, this investigation focuses on the utility of the normalized bispectrum for detection of wave-wave coupling in general, with emphasis on distinct implications within the scope of non-linear plasma physics. Interpretations of bicoherent features are given for time series from …
Modeling The Galactic Compact Binary Neutron Star Population And Studying The Double Pulsar System, 2020 West Virginia University
Modeling The Galactic Compact Binary Neutron Star Population And Studying The Double Pulsar System, Nihan Pol
Graduate Theses, Dissertations, and Problem Reports
Binary neutron star (BNS) systems consisting of at least one neutron star provide an avenue for testing a broad range of physical phenomena ranging from tests of General Relativity to probing magnetospheric physics to understanding the behavior of matter in the densest environments in the Universe. Ultra-compact BNS systems with orbital periods less than few tens of minutes emit gravitational waves with frequencies ~mHz and are detectable by the planned space-based Laser Interferometer Space Antenna (LISA), while merging BNS systems produce a chirping gravitational wave signal that can be detected by the ground-based Laser Interferometer Gravitational-Wave Observatory (LIGO). Thus, BNS …
Process Based Analysis Of Fluvial Stratigraphic Record: Middle Pennsylvanian Allegheny Formation, North-Central Wv, 2020 West Virginia University
Process Based Analysis Of Fluvial Stratigraphic Record: Middle Pennsylvanian Allegheny Formation, North-Central Wv, Oluwasegun O. Abatan
Graduate Theses, Dissertations, and Problem Reports
Fluvial deposits represent some of the best hydrocarbon reservoirs, but the quality of fluvial reservoirs varies depending on the reservoir architecture, which is controlled by allogenic and autogenic processes. Allogenic controls, including paleoclimate, tectonics, and glacio-eustasy, have long been debated as dominant controls in the deposition of fluvial strata. However, recent research has questioned the validity of this cyclicity and may indicate major influence from autogenic controls. To further investigate allogenic controls on stratal order, I analyzed the facies architecture, geomorphology, paleohydrology, and the stratigraphic framework of the Middle Pennsylvanian Allegheny Formation (MPAF), a fluvial depositional system in the Appalachian …
Evaluating The Accuracy Of Firearm Examiner Conclusions Using Cartridge Case Reproductions, 2020 West Virginia University
Evaluating The Accuracy Of Firearm Examiner Conclusions Using Cartridge Case Reproductions, Eric Freeman Law
Graduate Theses, Dissertations, and Problem Reports
The forensic science pattern comparison areas, including fingerprints, footwear, and firearms, have been criticized for their subjective nature. While much research has attempted to move these disciplines to more objective methods, a majority of examiners are still coming to conclusions based on their own training and experience. To compare accuracy between examiners, a method called double-casting was used in this study to create plastic cartridge case reproductions. In the first part of this study, double-cast accuracy was evaluated using two automated comparison systems to quantify the similarity. It was determined that the double-casting method used here produces accurate reproductions with …
Sex And Age Differences In Prevalence And Risk Factors For Prediabetes In Mexican-Americans, 2020 The University of Texas Rio Grande Valley
Sex And Age Differences In Prevalence And Risk Factors For Prediabetes In Mexican-Americans, Kristina Vatcheva, Belinda M. Reininger, Susan P. Fisher-Hoch, Joseph B. Mccormick
School of Mathematical and Statistical Sciences Faculty Publications and Presentations
AIMS:
Over 1/3 of Americans have prediabetes, while 9.4% have type 2 diabetes. The aim of our study was to estimate the prevalence of prediabetes in Mexican Americans, with known 28.2% prevalence of type 2 diabetes, by age and sex and to identify critical socio-demographic and clinical factors associated with prediabetes.
METHODS:
Data were collected between 2004 and 2017 from the Cameron County Hispanic Cohort in Texas. Weighted crude and sex- and age- stratified prevalences were calculated. Survey weighted logistic regression analyses were conducted to identify risk factors for prediabetes.
RESULTS:
The prevalence of prediabetes (32%) was slightly higher than …
Measuring Variability In Model Performance Measures, 2020 University of Kentucky
Measuring Variability In Model Performance Measures, Matthew Rutledge
Theses and Dissertations--Statistics
As data become increasingly available, statisticians are confronted with both larger sample sizes and larger numbers of predictors. While both of these factors are beneficial in building better predictive models and allowing for better inference, models can become difficult to interpret and often include variables of little practical significance. This dissertation provides methods that assist model builders to better understand and select from a collection of candidate models. We study the asymptotic distribution of AIC and propose a graphical tool to assist practitioners in comparing and contrasting candidate models. Real-world examples show how this graphic might be used and a …
Nonparametric Tests Of Lack Of Fit For Multivariate Data, 2020 University of Kentucky
Nonparametric Tests Of Lack Of Fit For Multivariate Data, Yan Xu
Theses and Dissertations--Statistics
A common problem in regression analysis (linear or nonlinear) is assessing the lack-of-fit. Existing methods make parametric or semi-parametric assumptions to model the conditional mean or covariance matrices. In this dissertation, we propose fully nonparametric methods that make only additive error assumptions. Our nonparametric approach relies on ideas from nonparametric smoothing to reduce the test of association (lack-of-fit) problem into a nonparametric multivariate analysis of variance. A major problem that arises in this approach is that the key assumptions of independence and constant covariance matrix among the groups will be violated. As a result, the standard asymptotic theory is not …
Statistical Intervals For Various Distributions Based On Different Inference Methods, 2020 University of Kentucky
Statistical Intervals For Various Distributions Based On Different Inference Methods, Yixuan Zou
Theses and Dissertations--Statistics
Statistical intervals (e.g., confidence, prediction, or tolerance) are widely used to quantify uncertainty, but complex settings can create challenges to obtain such intervals that possess the desired properties. My thesis will address diverse data settings and approaches that are shown empirically to have good performance. We first introduce a focused treatment on using a single-layer bootstrap calibration to improve the coverage probabilities of two-sided parametric tolerance intervals for non-normal distributions. We then turn to zero-inflated data, which are commonly found in, among other areas, pharmaceutical and quality control applications. However, the inference problem often becomes difficult in the presence of …
Algebraic And Geometric Properties Of Hierarchical Models, 2020 University of Kentucky
Algebraic And Geometric Properties Of Hierarchical Models, Aida Maraj
Theses and Dissertations--Mathematics
In this dissertation filtrations of ideals arising from hierarchical models in statistics related by a group action are are studied. These filtrations lead to ideals in polynomial rings in infinitely many variables, which require innovative tools. Regular languages and finite automata are used to prove and explicitly compute the rationality of some multivariate power series that record important quantitative information about the ideals. Some work regarding Markov bases for non-reducible models is shown, together with advances in the polyhedral geometry of binary hierarchical models.
Bayesian Kinetic Modeling For Tracer-Based Metabolomic Data, 2020 University of Kentucky
Bayesian Kinetic Modeling For Tracer-Based Metabolomic Data, Xu Zhang
Theses and Dissertations--Statistics
Kinetic modeling of the time dependence of metabolite concentrations including the unstable isotope labeled species is an important approach to simulate metabolic pathway dynamics. It is also essential for quantitative metabolic flux analysis using tracer data. However, as the metabolic networks are complex including extensive compartmentation and interconnections, the parameter estimation for enzymes that catalyze individual reactions needed for kinetic modeling is challenging. As the pa- rameter space is large and multi-dimensional while kinetic data are comparatively sparse, the estimation procedure (especially the point estimation methods) often en- counters multiple local maximum such that standard maximum likelihood methods may yield …
Moment Kernels For T-Central Subspace, 2020 University of Kentucky
Moment Kernels For T-Central Subspace, Weihang Ren
Theses and Dissertations--Statistics
The T-central subspace allows one to perform sufficient dimension reduction for any statistical functional of interest. We propose a general estimator using a third moment kernel to estimate the T-central subspace. In particular, in this dissertation we develop sufficient dimension reduction methods for the central mean subspace via the regression mean function and central subspace via Fourier transform, central quantile subspace via quantile estimator and central expectile subsapce via expectile estima- tor. Theoretical results are established and simulation studies show the advantages of our proposed methods.