Physical Sciences and Mathematics Commons^™

Functional Generalized Linear Mixed Models, Harmony Luce

Dissertations

With the advancements in data collection technologies, researchers in various fields such as epidemiology, chemometrics, and environmental science face the challenges of obtaining useful information from more detailed, complex, and intricately-structured data. Since the existing methods often are not suitable for such data, new statistical methods are developed to accommodate the complicated data structures.

As a part of such efforts, this dissertation proposes Functional Generalized Linear Mixed Model (FGLMM), which extends classical generalized linear mixed models to include functional covariates. Functional Data Analysis (FDA) is a rapidly developing area of statistics for data which can be naturally viewed as smooth …

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 …

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 …

Blockchain-Enabled Ehr Sharing In Healthcare Federation: Sharding And Interblockchain Communication, Faiza Hashim

Dissertations

Electronic Health Records (EHRs) are crucial components of the healthcare system, facilitating accurate and efficient diagnosis. Blockchain technology has emerged as a promising solution to improve EHRs sharing among medical practitioners while ensuring privacy and security. By leveraging its decentralized, distributed, immutable, and secure architecture, blockchain has the potential to revolutionize the healthcare system. However, due to security concerns, blockchain networks in healthcare typically operate in private or consortium modes, resulting in isolated networks within a federation. Scalability remains a significant challenge for blockchain networks, as the number of participating nodes increases within each network of the federation. Consensus mechanisms …

Synthesis, Characterization, And Photocatalytic Activity Of Photoactive Mofs And M-Hof Under Simulated Visible Light Irradiation, Lamia Ali Siddig

Dissertations

The increasing global energy demand has resulted in environmental issues, leading to a shift in research towards sustainable and renewable energy sources. Among these, solar energy is the most abundant natural resource available. One of the most profitable ways to utilize sunlight is through chemical transformation using photocatalysts. In this regard, we reported the synthesis of different stable porous materials, such as metal-organic frameworks (MOFs) and metal hydrogen-bonded organic frameworks (M-HOFs). The MOF photocatalysts are bismuth-gallate (Bi-gallate), a mixed ligand manganese-based MOF (Mn^II₃(tp)_6/2(bpy)₂. (dmf)) and a new hexagonal layer manganese MOF compound named …

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 …

Bismuth And Lanthanides In Mixed Metal Oxides To Metal– Organic Frameworks: Synthesis, Cherectarization, And Photocatalytic Performance, Reem H. Alzard

Dissertations

Known as rare–earth elements (REE), lanthanides (Ln) found in f–blocks of the periodic table, have gained much interest because of their unique characteristics including magnetism, photoluminescence and catalysis. Having a diverse range of coordination geometries, lanthanides can result in different complexes and materials. Herein, three classes of materials containing selected lanthanides such as lanthanide bismuth mixed metal oxides (Ln–Bi oxides), 1D lanthanide coordination polymers (Ln–CPs) and lanthanide metal–organic frameworks (Ln–MOFs) were investigated. Simple route sol– gel method was used to prepare Ln–Bi oxides while Ln–CPs and MOFs were prepared by solvothermal condition. The materials were characterized using multiple analytical, spectroscopic …

Stream-Evolving Bot Detection Framework Using Graph-Based And Feature-Based Approaches For Identifying Social Bots On Twitter, Eiman Alothali

Dissertations

This dissertation focuses on the problem of evolving social bots in online social networks, particularly Twitter. Such accounts spread misinformation and inflate social network content to mislead the masses. The main objective of this dissertation is to propose a stream-based evolving bot detection framework (SEBD), which was constructed using both graph- and feature-based models. It was built using Python, a real-time streaming engine (Apache Kafka version 3.2), and our pretrained model (bot multi-view graph attention network (Bot-MGAT)). The feature-based model was used to identify predictive features for bot detection and evaluate the SEBD predictions. The graph-based model was used to …

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 - …

Large-Scale And Meso-Scale Ion Dynamics Of The Near-Earth Space Environment, Matthew Cooper

Dissertations

The mechanisms whereby low energy (order of lOs to 100s of eV) solar wind and ionosphere sourced particles are accelerated to high energy (order of 10s to 100s keV) in the Earth's magnetosphere are a trending research topic due to impacts on radiation belt populations, coupling to the high-latitude ionosphere, and energy budgets for collision-less plasma in dipole fields. Ultra-low frequency (ULF, order of 1-100 mHz) Alfven waves observed in the nightside inner magnetosphere during geomagnetic activity are one such acceleration mechanism. Several source processes for these waves, including the micro-scale drift-mirror mode, have been previously suggested but observational evidence …

Design, Synthesis, Biological And Computational Studies Of Flavonoid Acetamide Derivatives, Daniel Kasungi Isika

Dissertations

Flavonoid compounds (FC) have extensive biological applications through potent antimicrobial, anti-inflammatory, anticancer, antidiabetic, and antioxidant properties. These applications are nevertheless limited by low bioavailability, poor aqueous solubility, enzymatic degradation, rapid metabolism, and fast body clearance. FC show unique structure-activity relationships (SARs) essential in assessing and mitigating the prevalent challenges impeding their applications. These challenges can be addressed by structural modification of the FC through functionalization of the phenolic rings, modification of the ketone group, and modification of the phenolic hydroxyl groups with bioisosteres.

The objectives of this work are to (i) design and synthesize novel flavonoid acetamide derivatives (FADs) and …

Topics On Asymmetric Classification, Subhrasish Chakraborty

Dissertations

Asymmetric classification refers to a situation where the cost of misclassifying one class is significantly higher than the cost of misclassifying the other class. This problem is common in many real-world scenarios, such as medical diagnosis or fraud detection. In this dissertation two of the common types of asymmetric classification problems have been dealt with — imbalanced classification and ordinal classification. An example of imbalanced classification is to detect fraudulent credit card transactions where the distribution of the normal and fraud transactions are extremely skewed. On the other hand, ordinal classification, also known as ordinal regression, is widely used in …

Domain Decomposition Methods For Linear And Non-Linear Elliptic Problems, Tadanaga Takahashi

Dissertations

The primary purpose of this dissertation is to expand upon the circle of domain decomposition methods (DDM) which are algorithms that reformulate a boundary value problem in terms of multiple localized problems on subdomains. The first project involves expanding upon DDMs in a relatively mature field: the Helmholtz equation for wave scattering applications. The proposed method is an adaptation of a continuous cross-point Finite Element Non-overlapping DDM algorithm. The usual unbounded computational domain is truncated and then the near-field wave pattern is solved with a parallelized finite element method. Several improvements over the standard transmission operator are discussed in this …

Molecular Simulations Of Mechanical Effects Of Adsorption In Gas And Liquid Phases, Alina Emelianova

Dissertations

Fluids adsorbing in nanoporous solids cause high pressures that affect the solids and the properties of the fluids themselves. This work explores the mechanical response of solids to the adsorption of gas and liquid. Chapter 1 outlines the current achievements in investigating the mechanical response of adsorbents to the adsorption process, specifically, the computational approaches to predict adsorption-induced deformation. Chapter 2 introduces the theoretical and computational methods employed in this work. Chapters 3-7 address the design and application of the computational techniques for such prediction in different systems: mesoporous silica, microporous zeolites, and metal-organic frameworks, and Chapter 8 focuses on …

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 …

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 …

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 …

Spectral Multistep Methods For The Scalable Simulation Of Time-Dependent Phenomena, Bailey Rester

Dissertations

Krylov subspace spectral (KSS) methods are high-order accurate, one-step explicit time-stepping methods for partial differential equations (PDEs) that also possess stability characteristic of implicit methods. Unlike other time-stepping approaches, KSS methods compute each Fourier coefficient of the solution from an individualized approximation of the solution operator of the PDE, using techniques developed by Golub and Meurant for approximating bilinear forms involving matrix functions. As a result, KSS methods scale effectively to higher spatial resolution.

This dissertation will present spectral multistep methods, explicit and implicit, designed through the combination of KSS methods and Adams methods. This combination allows spectral multistep methods …

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 …