Physical Sciences and Mathematics Commons^™

Unraveling The Origins Of Grb190114c: An Investigation Of Progenitor Models Through Observational Analysis, Nusrin Habeeb

Dissertations

Gamma-ray bursts (GRBs) are among the most energetic and violent events in the universe, characterized by sudden and intense emission of gamma rays lasting from a fraction of a second to several minutes. The primary focus of this thesis is to study gamma-ray bursts (GRBs), with a specific emphasis on GRB190114C. GRB190114C is a long-duration GRB that was detected on January 14, 2019, by the Fermi Gamma-ray Burst Monitor (GBM) and the Swift Burst Alert Telescope (BAT). It had a T₉₀ duration of 116 s and a redshift of z = 0.4245, which corresponds to a luminosity distance of …

Nonparametric Tests For Replicated Latin Squares, Joseph Yang

Dissertations

Two classes of nonparametric procedures for a replicated Latin square design that test for both general and increasing alternatives are developed. The two classes of procedures are similar in the sense that both transform the data so that existing well-known tests for randomized complete block designs can be utilized. On the other hand, the two classes differ in the way that the data is transformed - one class essentially aggregates the data while the other class aligns the data. Within these contexts, the exact distributions and asymptotic distributions are discussed, when applicable. The exact distributions are easily computed using the …

Evaluating The Performance Of Estimators In Sem And Irt With Ordinal Variables, Bo Klauth

Dissertations

In conducting confirmatory factor analysis with ordered response items, the literature suggests that when the number of responses is five and item skewness (IS) is approximately normal, researchers can employ maximum likelihood with robust standard errors (MLR). However, MLR can yield biased factor loadings (FL) and FL standard errors (FLSE) when the variables are ordinal. Other estimators are available. Unweighted least squares and weighted least squares with adjusted mean and variance (ULSMV and WLSMV) are known as the estimators for CFA with ordinal variables (CFA-OV). Another estimator, marginal maximum likelihood (MML), is used in the item response theory (IRT), specifically …

Machine Learning And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane

Dissertations

High-throughput technologies such as DNA microarrays and RNA-seq are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed into Gene Co-expression Networks (GCNs). GCNs are analyzed to discover gene modules. GCN construction and analysis is a well-studied topic, for nearly two decades. While new types of sequencing and the corresponding data are now available, the software package WGCNA and its most recent variants are still widely used, contributing to biological discovery.

The discovery of biologically significant modules of genes from raw expression data is …

Trustworthy Machine Learning Through The Lens Of Privacy And Security, Thi Kim Phung Lai

Dissertations

Nowadays, machine learning (ML) becomes ubiquitous and it is transforming society. However, there are still many incidents caused by ML-based systems when ML is deployed in real-world scenarios. Therefore, to allow wide adoption of ML in the real world, especially in critical applications such as healthcare, finance, etc., it is crucial to develop ML models that are not only accurate but also trustworthy (e.g., explainable, privacy-preserving, secure, and robust). Achieving trustworthy ML with different machine learning paradigms (e.g., deep learning, centralized learning, federated learning, etc.), and application domains (e.g., computer vision, natural language, human study, malware systems, etc.) is challenging, …

Ai Approaches To Understand Human Deceptions, Perceptions, And Perspectives In Social Media, Chih-Yuan Li

Dissertations

Social media platforms have created virtual space for sharing user generated information, connecting, and interacting among users. However, there are research and societal challenges: 1) The users are generating and sharing the disinformation 2) It is difficult to understand citizens' perceptions or opinions expressed on wide variety of topics; and 3) There are overloaded information and echo chamber problems without overall understanding of the different perspectives taken by different people or groups.

This dissertation addresses these three research challenges with advanced AI and Machine Learning approaches. To address the fake news, as deceptions on the facts, this dissertation presents Machine …

Mapping Programs To Equations, Hessamaldin Mohammadi

Dissertations

Extracting the function of a program from a static analysis of its source code is a valuable capability in software engineering; at a time when there is increasing talk of using AI (Artificial Intelligence) to generate software from natural language specifications, it becomes increasingly important to determine the exact function of software as written, to figure out what AI has understood the natural language specification to mean. For all its criticality, the ability to derive the domain-to-range function of a program has proved to be an elusive goal, due primarily to the difficulty of deriving the function of iterative statements. …

Importance Of Vegetation In Tsunami Mitigation: Evidence From Large Eddy Simulations With Fluid-Structure Interactions, Abhishek Mukherjee

Dissertations

Communities worldwide are increasingly interested in nature-based solutions like coastal forests for the mitigation of coastal risks. Still, it remains unclear how much protective benefit vegetation provides, particularly in the limit of highly energetic flows after tsunami impact. The present thesis, using a three-dimensional incompressible computational fluid dynamics model with a fluid-structure interaction approach, aims to quantify how energy reflection and dissipation vary with different degrees of rigidity and vegetation density of a coastal forest.

In this study, tree trunks are represented as cylinders, and the elastic modulus of hardwood trees such as pine or oak is used to characterize …

Continuum Modeling Of Active Nematics Via Data-Driven Equation Discovery, Connor Robertson

Dissertations

Data-driven modeling seeks to extract a parsimonious model for a physical system directly from measurement data. One of the most interpretable of these methods is Sparse Identification of Nonlinear Dynamics (SINDy), which selects a relatively sparse linear combination of model terms from a large set of (possibly nonlinear) candidates via optimization. This technique has shown promise for synthetic data generated by numerical simulations but the application of the techniques to real data is less developed. This dissertation applies SINDy to video data from a bio-inspired system of mictrotubule-motor protein assemblies, an example of nonequilibrium dynamics that has posed a significant …

Coronal Magnetometry And Energy Release In Solar Flares, Yuqian Wei

Dissertations

As the most energetic explosive events in the solar system and a major driver for space weather, solar flares need to be thoroughly understood. However, where and how the free magnetic energy stored in the corona is released to power the solar flares remains not well understood. This lack of understanding is, in part, due to the paucity of coronal magnetic field measurements and the lack of comprehensive understanding of nonthermal particles produced by solar flares. This dissertation focuses on studies that utilize microwave imaging spectroscopy observations made by the Expanded Owens Valley Solar Array (EOVSA) to diagnose the nonthermal …

Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi

Dissertations

Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.

Chapter 1 provides background information on …

V-Shaped Temperature Dependences And Pressure Dependence Of Elementary Reactions Of Hydroxyl Radicals With Several Organophosphorus Compounds, Xiaokai Zhang

Dissertations

Organophosphorus compounds have brought increasing attention since they are widely used as flame-retardants, which can take effect in combustion via reactions with reactive radicals. These reactions are influenced by variables such as temperature and pressure, resulting in a temperature and pressure dependent rate constant. Studying this reaction kinetics has great importance in both combustion reaction and atmospheric environment.

This study is focused on kinetics of several elementary reactions of combustion importance. The kinetics of hydroxyl radicals were studied using pulsed laser photolysis coupled to transient UV-vis absorption spectroscopy over the 295 - 837 K temperature range and the 1 - …

Utilizing New Technologies To Measure Therapy Effectiveness For Mental And Physical Health, Jonathan Ossie

Dissertations

Mental health is quickly becoming a major policy concern, with recent data reporting increasing and disproportionately worse mental health outcomes, including anxiety, depression, increased substance abuse, and elevated suicidal ideation. One specific population that is especially high risk for these issues is the military community because military conflict, deployment stressors, and combat exposure contribute to the risk of mental health problems.

Although several pharmacological approaches have been employed to combat this epidemic, their efficacy is mixed at best, which has led to novel nonpharmacological approaches. One such approach is Operation Surf, a nonprofit that provides nature-based programs advocating the restorative …

Special Education: Inclusion And Exclusion In The K-12 U.S. Educational System, Erik Brault

Dissertations

The U.S. Department of Education defines students with disabilities as those having a physical or mental impairment that substantially limits one or more life activities. Previous research has found that students with disabilities placed in inclusive environments perform better academically and socially compared to students with disabilities who are placed in segregated environments. Yet, we know that inclusion in K-12 general education classrooms across the country is not consistently implemented.

The purpose of this study was to better understand the effects, if any, of general education high school teachers’ personal and professional experiences and knowledge on their attitudes toward educating …

Connecting Social And Ecological Systems In Small-Scale Fisheries In The Philippines, Sara Eisler Marriott

Dissertations

Nearly 50% of all marine fish capture in the Philippines is from artisanal fisheries, most of which is un- or under-reported. As in many emerging nations around the world, the Philippines cannot fully address overfishing by managing only half of the catch that comes from commercial fisheries. Marine reserves are a popular governance strategy for conservation and of growing interest for fisheries management. Many marine reserves in the Philippines, however, are not considered effective. In 2014, Rare, an international NGO, implemented a community-based management program to increase the effectiveness of the marine reserves, and while it found biomass increased, there …

Origin And Structure Of The First Sharp Diffraction Peak Of Amorphous Solids, Devilal Dahal

Dissertations

Several explanations have been reported in the literature about the origin of extended-range oscillations (EROs) in the atomic pair-correlation function of amorphous materials. Although the radial ordering beyond the short-range order of about 5 Å has been extensively studied in amorphous materials, the exact nature of the radial ordering beyond a nanometer is still not resolved. This dissertation address this problem and explains the nature of the EROs by using high-quality models of amorphous silicon (a-Si) obtained from Monte Carlo and Molecular Dynamics simulations. The extended-range ordering in a-Si is examined through radial oscillations on the length …

The Influence Of Diamine Curing Additives On The Network Architecture Of Phthalonitrile Thermosetting Polymers, Tyler J. Richardson

Dissertations

Phthalonitrile monomers undergo diamine-promoted polymerization by a complex reaction mechanism involving two competitive cure pathways, forming two primary network architectures: linear polyisoindoline chains and branched triazine crosslinks. The influence of the diamine curing additive on the polymerization pathway, and the influence of the resulting network architectures on cured network properties, have not been adequately explored within the phthalonitrile field. Two structurally different diamine curing additives, bis[4-(3-aminophenoxy)phenyl] sulfone (mBAPS) and 1,3-phenylenebis((4-(4-aminophenoxy)phenyl)methanone) (AEK-134), were studied for the polymerization of resorcinol phenylphosphate phthalonitrile (RPPhPN), where the influence of diamine structure and concentration on the polymerization behavior, network architecture, and bulk thermal and thermomechanical …

Loss Scaling And Step Size In Deep Learning Optimizatio, Nora Alosily

Dissertations

Deep learning training consumes ever-increasing time and resources, and that is
due to the complexity of the model, the number of updates taken to reach good
results, and both the amount and dimensionality of the data. In this dissertation,
we will focus on making the process of training more efficient by focusing on the
step size to reduce the number of computations for parameters in each update.
We achieved our objective in two new ways: we use loss scaling as a proxy for
the learning rate, and we use learnable layer-wise optimizers. Although our work
is perhaps not the first …

Topological Data Analysis Of Weight Spaces In Convolutional Neural Networks, Adam Wagenknecht

Dissertations

Convolutional Neural Networks (CNNs) have become one of the most commonly used tools for performing image classification. Unfortunately, as with most machine learning algorithms, CNNs suffer from a lack of interpretability. CNNs are trained by using a training data set and a loss function to tune a set of parameters known as the layer weights. This tuning process is based on the classical method of gradient descent, but it relies on a strong stochastic component, which makes the weight behavior during training difficult to understand. However, since CNNs are governed largely by the weights that make up each of the …

Statistical Clustering Of Networks With Additional Information, Paul Atandoh

Dissertations

As the online market grows rapidly, many companies and researchers are interested in analyzing product review dataset which includes ratings and text review data. In the first project, we mainly focus on analyzing the text review data. In the current literature, it is common to use only text analysis tools to analyze review dataset. But in our work, we propose a method that utilizes both a text analysis method such as topic modeling and a statistical network model to build network among individuals and find interesting communities. We introduce a promising framework that incorporates topic modeling technique to define the …

Using Visual Imagery To Develop Multiplication Fact Strategies, Gina Kling

Dissertations

The learning of basic facts, or the sums and products of numbers 0–10 and their related differences and quotients, has always been a high priority for elementary school teachers. While memorization of basic facts has been a hallmark of elementary school, current recommendations focus on a more nuanced development of fluency with these facts. Fluency is characterized by the ability to demonstrate flexibility, accuracy, efficiency, and appropriate strategy use. Despite recommendations to focus on strategy use, there is insufficient information on instructional approaches that are effective for developing strategies, particularly for multiplication facts. Using visual imagery with dot patterns has …

Ab-Initio Investigation Of 2d Materials For Gas Sensing, Energy Storage And Spintronic Applications, Saba Khan

Dissertations

The field of Two Dimensional (2D) materials has been extensively studied since their discovery in 2004, owing to their remarkable combination of properties. My thesis focuses on exploring novel 2D materials such as Graphene Nanoribbon (GNR), holey carbon nitride C₂N, and MXenes for energy storage, gas sensing, and spintronic applications, utilizing state-of-the-art techniques that combine Density Functional Theory (DFT) and Non-Equilibrium Greens Functions (NEGF) formalism; namely Vienna Ab-initio Simulation Package (VASP) and Atomistic Toolkit (ATK) package.
Firstly, on the side of gas sensing, the burning of fossil fuels raises the level of toxic gas and contributes to global …

Metal Oxide Based Materials For High Performance Supercapacitors, Hammad Mueen Arbi

Dissertations

Recent years have seen a healthy rise in the research and development of sustainable and renewable energy storage systems due to the pressing need to conserve natural resources and cut energy use. Due to the rapid growth in global population lately, there is a tremendous demand for energy to fulfill the ever-increasing needs. Innovative alternative energy sources and energy storage techniques are of tremendous interest for dealing with these current world issues. One promising solution for this is the recent research trend for achieving reliable and cost-effective high power and high energy density energy storage devices. On the basis of …

Development Of Innovative Multi-Drug Approaches To Counteract Illicit Drug Abuse In The Uae Population, Manal Ali Alhefeiti

Dissertations

The abuse of addictive substances is on the rise in the United Arab Emirates (UAE) population. Consequently, the UAE government spends about Dhs 5.5 billion annually on the rehabilitation of drug addicts. Blood, urine, and hair tests can reveal signs of sporadic or chronic drug use. Given the list of banned chemicals in the UAE, our main objective in this work was to develop a novel analytical method to identify and measure banned substances, especially prescription and over-the-counter drugs in the UAE. We developed and validated a rapid, sensitive and reliable liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) approach for the targeted …

Irregular Domination In Graphs, Caryn Mays

Dissertations

Domination in graphs has been a popular area of study due in large degree to its applications to modern society as well as the mathematical beauty of the topic. While this area evidently began with the work of Claude Berge in 1958 and Oystein Ore in 1962, domination did not become an active area of research until 1977 with the appearance of a survey paper by Ernest Cockayne and Stephen Hedetniemi. Since then, a large number of variations of domination have surfaced and provided numerous applications to different areas of science and real-life problems. Among these variations are domination parameters …

Zonality In Graphs, Andrew Bowling

Dissertations

Graph labeling and coloring are among the most popular areas of graph theory due to both the mathematical beauty of these subjects as well as their fascinating applications. While the topic of labeling vertices and edges of graphs has existed for over a century, it was not until 1966 when Alexander Rosa introduced a labeling, later called a graceful labeling, that brought the area of graph labeling to the forefront in graph theory. The subject of graph colorings, on the other hand, goes back to 1852 when the young British mathematician Francis Guthrie observed that the countries in a map …

Lax Integrability And Solution Methods For The Nonlinear Schrödinger Equation With External Potentials: Exact Solutions And Applications, Laila Yahya Al Sakkaf

Dissertations

The investigation of a nonlinear differential equation (NLDE) integrability is the foundation for understanding and predicting the behavior of systems that are governed by that NLDE. The existence of a Lax pair (LP) representation is a reliable indicator of the indicator of the integrability of an NLDE. If an NLDE admits an LP, it is considered integrable in the LP sense, and this has important implications for the behavior and solutions of the system. The LP plays a crucial role in many powerful analytical solution methods for solving NLDEs. One such method is the Darboux transformation (DT), which allows for …

High-Dimensional Variable Selection Via Knockoffs Using Gradient Boosting, Amr Essam Mohamed

Dissertations

As data continue to grow rapidly in size and complexity, efficient and effective statistical methods are needed to detect the important variables/features. Variable selection is one of the most crucial problems in statistical applications. This problem arises when one wants to model the relationship between the response and the predictors. The goal is to reduce the number of variables to a minimal set of explanatory variables that are truly associated with the response of interest to improve the model accuracy. Effectively choosing the true influential variables and controlling the False Discovery Rate (FDR) without sacrificing power has been a challenge …

Socially Aware Natural Language Processing With Commonsense Reasoning And Fairness In Intelligent Systems, Sirwe Saeedi

Dissertations

Although Artificial Intelligence (AI) promises to deliver ever more user-friendly consumer applications, recent mishaps involving fake information and biased treatment serve as vivid reminders of the pitfalls of AI. AI can harbor latent biases and flaws that can cause harm in diverse and unexpected ways. It is crucial to understand the reasons for, mechanisms behind, and circumstances under which AI can fail. For instance, a lack of commonsense reasoning can lead to biased or unfair decisions made by Machine Learning (ML) systems. For example, if an ML system is trained on data that is biased or unrepresentative of the real …

A Multiscale Linkage Between Riverscape And Fish Community Coevolution, Loren W. Stearman

Dissertations

Sediment dynamics are foundational to stream and watershed morphology. Yet aquatic ecologists have relied on an oversimplified model of sediment dynamics characterizing sediments as agents of stream bed burial, and which fails to describe many types of aquatic habitat evolution. In this dissertation I employ both fluvial geomorphic and ecological frameworks to gain a deeper understanding of how sediment dynamics shape stream morphology and fish community evolution at multiple scales. Using a paired historic and contemporary approach, I analyzed geomorphic evolution and fish community change in the Bayou Pierre, Mississippi, from the 1980s to recent. Patterns of erosion due to …