Physical Sciences and Mathematics Commons^™

Application Of The Tridiagonal Representation Approach And The J- Matrix Method Of Scattering In Theoretical Physics, Tunde Joseph Osunmusanmi

Dissertations

This dissertation is about the application of the Tridiagonal Representation Approach (TRA) in handling linear phenomenons, and for the first time, the J-matrix method of scattering in handling nonlinear phenomenons. The TRA is an algebraic method for solving linear ordinary differential equations of the second order. The advantage of the method in being algebraic is reinforced by the analytic power of orthogonal polynomials and special functions. On the computational side, it is favored as being reliant on powerful numerical techniques that deal with tridiagonal matrices such as Gauss quadrature and continued fraction. In the method, the solution of the differential …

Design And Synthesis Of Coke-Resistant Nickel-Based Catalysts For Efficient Methane Reforming And Hydrogen Production, Abdulmuizz Adamson

Dissertations

Ni-based catalysts are widely employed in methane reforming and cracking processes, however, their industrial application remains constrained by rapid deactivation caused by carbon deposition (coking). This thesis investigates the role of iron on the catalytic performance and coking resistance of the catalysts with focus on the mode of incorporation, oxidation state, reaction environment, and concentration of iron present as dopant.

The results show that doping the γ-Al₂O₃ support of Ni catalysts with well-dispersed iron ions in the support matrix significantly enhances catalyst stability during methane partial oxidation (POM) and combined dry reforming and partial oxidation (DR-POM). At 700°C, catalysts containing …

How Principals Who Use Artificial Intelligence For Innovation Create Cognitive Equity While Principals Who Use Ai For Efficiency Create Cognitive Debt, Jethro Jones

Dissertations

This dissertation in practice examined whether a targeted professional learning   intervention could shift school leaders’ use of generative artificial intelligence (AI) from efficiency-oriented tasks toward innovation-oriented strategic problem solving. AI is typically adopted to accelerate existing routines, which can deepen “cognitive debt” by reinforcing ineffective practices rather than improving systems. This study advanced a “cognitive equity” frame, positioning AI as a tool that can expand principals’ cognitive capacity to address complex problems and lead adaptive change. Using a quasi-experimental, single-group design, the study evaluated a free, full-day AI for Innovation workshop, which emphasized foundational understanding of how AI tools work …

Math Anxiety, Math Self-Concept And Math Self-Efficacy: A Study Of The Jingle-Jangle Fallacies, Marsha Natasha Durrant-Walker

Dissertations

Problem

The overlap and lack of clear distinction among the constructs of math anxiety, math self-concept, and math self-efficacy presents issues for research and practice. The literature reveals that math anxiety is closely linked to math self-concept (Klee et al., 2022). Additionally, math self-concept and math self-efficacy often overlap and are not easily distinguishable (Kranzler & Pajares, 1997; Pajares & Miller, 1994; Pajares & Urdan, 1996). Each of these constructs has been shown to play a critical role in student math achievement (Timmerman et al., 2016). -- When constructs are not defined or measured distinctly, inconsistencies may emerge in research …

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

Semi-Supervised Learning For Annotation And Representation Of Single-Cell Rna Sequencing And Spatial Transcriptomics Data, Haoran Liu

Dissertations

Semi-supervised learning has emerged as a powerful paradigm for analyzing single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data, where full annotation is often costly or impractical. scRNA-seq technologies measure the expression of thousands of genes across tens of thousands of cells, whereas ST additionally captures the spatial coordinates of gene expression within intact tissue sections. Annotation is a key step in both scRNA-seq and ST analysis pipelines, aiming to identify cell types, spatial domains, and latent biological structures. However, most existing annotation approaches rely on separate clustering methods that are typically fully unsupervised and fail to leverage side information …

Beyond Words: A Systematic Multimodal Framework For Text, Images, And Extreme Helpfulness In Online Reviews, Alvaro J. Aguado Marin

Dissertations

Online product reviews have become increasingly multimodal, combining text with media-rich elements such as images. However, academic research has largely examined textual features in isolation, overlooking how visual content and its interaction with text shape perceived helpfulness. This dissertation addresses that gap by developing and empirically validating a comprehensive framework capturing how textual, visual, and contextual features collectively influence review evaluation. Grounded in the Elaboration Likelihood Model (ELM) and extended through the Text-Image Elaboration Likelihood Model (TI-ELM), the framework advances understanding of how consumers process content from both user- and business-generated sources. It also lays the foundation for examining emerging …

Computational Design Of Nanoporous Materials For The Adsorption Of Per- And Polyfluoroalkyl Substances, Daniel D. Mottern

Dissertations

Per- and polyfluoroalkyl substances (PFAS) are a large family of chemicals that have seen wide usage due to their fluorinated carbon backbone. The presence of strong C-F bonds in the backbone lends PFAS molecules high thermal and chemical stability, as well as strong hydrophobicity and lipophobicity. This combination of properties has led to heavy use of PFAS as surfactants, non-stick coatings, and aqueous foam forming films and flame retardants. However, these properties bring their own consequences. The high chemical and thermal stability of PFAS renders them persistent, with the C-F bonds resisting naturally occurring forms of degradation. Existing forms of …

Computational Methods For Single-Cell And Multi-Omic Data Integration And Regulatory Network Inference, Jianlan Ren

Dissertations

Single-cell and multi-omic technologies have transformed the dissection of cellular heterogeneity and regulatory dynamics in health and disease. However, the high dimensionality, technical variability, and biological complexity of these datasets present significant challenges for integration, annotation, and interpretation. In this dissertation, a suite of computational approaches is introduced to address key problems in single-cell and multi-omic data analysis through model-based innovations and applied statistical frameworks.

First, a constrained deep learning framework for single-cell data integration, label transfer, and clustering is proposed. By incorporating biologically motivated constraints into the training process, robust performance is achieved across simulated and benchmark datasets spanning …

Data-Driven Analysis And Atomistic Simulations Of Next-Generation Materials For Energy Conversion And Storage, Yuliang Shi

Dissertations

Metal-organic frameworks (MOFs), with their modular architectures and tunable properties, represent an especially rich domain for accelerated material design and discovery for a range of diverse applications. Within this class of multifunctional materials, two-dimensional (2D) electrically conductive MOFs (EC MOFs) are of particular interest, as their 7r-stacked layered structures combine permanent porosity with electronic conductivity, enabling potential breakthroughs in energy storage, energy conversion, and quantum sensing. But the discovery and design of new EC MOFs based on expensive experimental screening is increasingly impractical due to the infinite chemical space. Furthermore, the practical implementation of EC MOFs for specific tasks depends …

Upcycling Commodity Polymers To Advanced Materials For Energy And Environmental Sustainability, Anthony Griffin-Espinoza

Dissertations

Synthetic polymers play an essential role in nearly every aspect of our lives. Extending beyond single-use packaging, polymeric material design has progressed to attain tailorable architectures granting exceptional performance across advanced applications, including carbon-fiber reinforced polymer composites for aerospace, conductive materials for soft electronics, and drug carriers for biomedicine. While highly promising, intricately designed polymers needed to achieve excellent performance often have limited processability, complex synthetic methods, and expensive precursors. Furthermore, due to a lack of recyclability, commodity polymer waste streams result in both environmental impacts and a substantial loss of economic value. This dissertation focuses on developing robust strategies …

Privacy Preserving-Based Artificial Intelligence For Precision Agriculture, Partha P. Sengupta

Dissertations

The research work finds a solution to precision agriculture of cotton cultivation using artificial intelligence (AI) models. Two sets of model performance based on the application are selected namely a low resource and a high resource setting. This is because using drone surveys to capture images identifying the classes of stressed and unstressed cotton plantation requires limited model architecture and CPU based computation. Thus, traditional AI models were selected for low resource settings. Again, for high computation intensive models like transfer learning-convolution neural network (CNN) based architectures were grouped into high resource settings. There was another issue of class imbalance …

The Malonate Template: The Key To Unlocking Novel Amino Acids, Thomas Owens

Dissertations

The “Malonate Template” provides access to various amino acids, all from a common intermediate. Herein, demonstrates the use of the malonate template to establish a synthetic route to both 2-thiohistidine and 2-thio-3-N-methyl-histidine from a benzyl methyl malonate derived intermediate. Furthermore, efforts exploiting the applicability of the malonate template for the of both 2-thiohistidine and 2-thio-3-N-methyl-histidine as a suitable alternative to methods previously described by Erdelmeier are described.

The method presented incorporates modifications to the biomimetic pathway described by Erdelmeier for the synthesis of 2-thiohisidine. By exploiting the biomimetic formation of 2-thiohisitidne’s mechanism via mass spectrometry, we …

A Line In The Sand: Investigating Late Pleistocene-Holocene Stratigraphy And Large-Scale Morphodynamic Evolution Along The Mississippi Coast, Carrie Miller

Dissertations

The imprints of past geomorphic evolution are embedded in the geologic record, providing valuable insights into the mechanisms that induce changes within coastal systems and the magnitude of those changes. Large-scale paleo morphodynamic evolution often occurs due to cyclonic activity, variations in sediment supply, and relative sea-level rise. The landscape of the U.S. Gulf Coast reflects a combination of erosional, depositional, hydrological, and climatic factors that remain of ongoing interest to geoscientists seeking a holistic understanding. Low- lying coastal plains, such as those along the Mississippi and Louisiana coasts, are particularly susceptible to future sea-level rise. Projected sea-level rise over …

An Investigation Of Paleoclimate Through A Sedimentological Lens In The Gulf Of Mexico And Western North Pacific, Sarah Monica

Dissertations

This dissertation explores the use of sediment cores as proxy data for the reconstruction of paleoclimatic and environmental conditions. Human-caused climate change is leading to dramatic shifts in the global climate. As the instrumental record of climate is relatively short compared to the amount of time Earth has experienced weather, this work aims to extend our knowledge of climate beyond the instrumental record, thereby improving our holistic understanding of the climate system. In Chapter one, sediment cores from the central Texas inner shelf are used to produce a record of intense tropical cyclone (TC) activity over a ~4500-year period. X-Ray …

Molecular Design And Engineering Of Luminophores For Aggregation Induced Electrogenerated Chemiluminescence, Jesy Alka Motchaalangaram

Dissertations

Most conventional luminophores produce intense emissions in solutions but suffer from weak emissions or quenching when aggregated in poor solvents due to intermolecular interactions, such as π-π stacking. This phenomenon is known as aggregation caused by quenching (ACQ), limits their applications in their solid state. In contrast, aggregation induced emission (AIE) is a phenomenon in which luminophores are weak- or non-emissive in solution but emit intensively in their aggregated or solid states. AIE has enabled significant advancements in various real-world applications and has inspired new areas of research. The combination of AIE with electrogenerated chemiluminescence (ECL) has resulted in a …

Problems In Extremal Graph Theory And Spectral Graph Theory, Fareeha Jamal

Dissertations

Spectral graph theory is a subfield of algebraic graph theory that studies the matrices associated with graphs. It lives at the nexus of Linear Algebra and Combinatorics. Many intriguing results in the domains of Matrix Theory and Combinatorics have come from studying the eigenvalues of graph matrices; in fact, several open problems in both areas have been resolved. Beyond its theoretical appeal, spectral graph theory has found meaningful applications in theoretical chemistry, particularly in the mathematical classification of chemical graphs. These classifications underpin quantitative structure–property relationships (QSPRs), facilitating the prediction of physicochemical properties such as enthalpy of vaporization, molar refractivity, …

Trustworthy Federated Learning Framework For Secure, Efficient, And Quality-Aware Distributed Ai, Asadullah Tariq

Dissertations

Federated Learning (FL) emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL and its application in various areas increased, addressing trustworthiness issues in its various aspects became crucial. In the FL process, clients contribute updates computed on their local datasets, which the server aggregates to iteratively refine the global model. However, not all client data may be relevant to the learning objective, and incorporating updates from irrelevant data can harm the model's performance. The selection of training samples significantly impacts model …

Ai-Enabled Multi-Layer Security Operations: A Combined Siem, Ids, And Threat Intelligence Model For Adaptive Cyber Defense, Mohamad Khayat

Dissertations

This dissertation presents a comprehensive framework for the evolution of Security Operation Centers (SOCs) through the integration of advanced artificial intelligence (AI), blockchain, and optimization techniques. Motivated by the increasing complexity of cyber threats and the limitations of traditional reactive SOC strategies, this work begins with a systematic literature review that identifies critical gaps in current SOC operations. Based on these insights, a reference architecture is proposed to guide the integration of intelligent components into SOC environments. To address the challenge of secure and trustworthy information sharing, a blockchain-based threat intelligence platform is developed, leveraging Byzantine Fault Tolerance and Zero-Knowledge …

The Influence Of Defects: Oxidation Of Non-Planar Transition Metal Model Catalysts, Maxwell Gillum

Dissertations

Metal-catalyzed oxidation reactions are a major application of heterogeneous catalysis and are a widely applied synthetic route for the production of chemicals and reagents essential to modern society. However, as the intricacies of heterogeneous surface catalysis are slowly being unraveled, minute details of the catalytic environment have been revealed to play outsized roles in the catalytic activity of the surface. The experiments herein further investigate the interplay between surface geometry and the formation oxygen-induced surface structures. Understanding how defects influence surface reactivity is a necessary step in gaining the ability to accurately model heterogeneous catalytic environments. Two different aspects of …

The Global Phase Space Of The Three-Vortex Interaction System And Its Application To Vortex-Dipole Scattering, Atul Anurag

Dissertations

This dissertation presents a global reduction of the classical three-vortex problem that is free from coordinate singularities, enabling a comprehensive analysis of the system's dynamics across all circulation regimes.

To achieve this, a two-step symplectic reduction procedure is developed. The first step introduces Jacobi coordinates adapted to the symplectic structure of the vortex system, and the second applies a Lie-Poisson reduction to the resulting system. This formulation eliminates the non-physical singularities associated with collinear vortex configurations and facilitates a global phase space analysis, including a detailed and novel investigation of bifurcations.

Within this reduced framework, all relative fixed points are …

Robust Ai Solutions For Financial Markets Through Generative Modeling, Dynamic Graph Learning, And Reinforcement-Based Portfolio Optimization, Jingyi Gu

Dissertations

Financial markets are inherently uncertain and dynamic, driven by complex factors such as macroeconomic signals, investor sentiment, and evolving inter-asset relationships. While machine learning has advanced financial modeling, existing approaches often fall short in addressing the real-world intricacies of finance. This dissertation confronts two critical challenges, human-driven stochasticity and risk-intensive decision-making under real-world trading constraints, while seizing a pivotal opportunity, the structural dynamics of evolving financial systems. These elements are foundational to advancing robust and practical financial intelligence.

To this end, this dissertation develops a unified framework for robust financial modeling and decision-making. The framework is architected as a progressive, …

Large-Scale Graph Algorithms And Applications With An Emphasis On Fintech Data, Fuhuan Li

Dissertations

Graph algorithms are essential analytical tools with applications spanning cybersecurity, biology, social media, and increasingly, financial technology (FinTech). The complex and interconnected nature of financial data, particularly in cryptocurrency networks, presents unique opportunities for graph-based analysis in fraud detection and anomaly identification.

This dissertation presents the design and implementation of scalable graph algorithms tailored for large-scale networks, with particular emphasis on FinTech applications. The primary contributions include: (1) novel cover-edge based triangle counting algorithms that significantly reduce computational overhead through breadth-first search preprocessing, achieving substantial speedups over traditional methods and dramatic communication reduction in distributed settings, (2) optimized parallel implementations …

Bodipy-Based Photocages For The Controlled Release Of Hydrogen Sulfide And Persulfides, Yanmei Li

Dissertations

Reactive sulfur species (RSS) such as hydrogen sulfide (H2S) and hydropersulfides (RSSH) are crucial mediators in a variety of physiological and pathological processes. Their regulatory roles span from maintaining cellular redox balance to influencing signaling pathways critical for homeostasis and stress responses. This dissertation investigates H2S and RSSH as biologically important molecules and explores the development of boron-dipyrromethene (BODIPY)-based photocages for its controlled, light-triggered release. Given H2S and RSSH's critical role in redox regulation and cellular protection, achieving spatiotemporal control over its delivery is essential for both therapeutic and research applications. BODIPY scaffolds offer favorable photophysical properties, including visible-light activation …

Reduced Order Models Of Hydrodynamically Interacting Flapping Wings, Jose Pabon

Dissertations

Fish schools exhibit a collective behavior and self-organization that is mediated by hydrodynamic interactions between individual fish. However, the long-time evolution of hydrodynamically interacting collectives is challenging to investigate due to the persistent influence of long-lived vortical structures, and the high-resolution requirements of direct numerical simulation at large Reynolds numbers. Reduced-order models have therefore played an important role in theoretical investigations of collectives of swimming bodies. The main results detailed herein are several new reduced-order models of swimmers that self-propel by flapping, i.e., by executing a prescribed periodic rigid body motion. The models are extensions of a discrete-time dynamical system …

Harnessing Graphs For Knowledge Representation In Natural Language Processing, Uras Varolgunes

Dissertations

This work proposes innovative methods for integrating domain-specific knowledge into natural language processing tasks through the use of graphs, aiming to enhance the performance of models across various domains, including finance and healthcare. Several novel approaches are proposed that fuse graph structures with modern deep learning techniques, addressing the challenges of missing word embeddings, label prediction, and graph representation learning for large language models.

First, a powerful embedding method built on top of the recent advances in latent graph learning is introduced to address the critical problem of word embedding imputation. Second, a graph-enhanced label attention model designed for medical …

Optimizing Hip And Knee Assistance For Walking And Sit-To-Stand Transitions: An Intrinsic Muscle Mechanics Based Predictive Approach, Neethan Ratnakumar

Dissertations

As the global population ages, the demand for wearable assistive technologies continues to rise, driven by their potential to enhance mobility and independence in older adults. Effectively designed controllers for lower-limb exoskeletons to assist sit-to-stand (STS) and walking are crucial for delivering efficient, safe, and comfortable assistance during daily activities. Traditionally, controller optimization involves biomechanical modeling and user-specific customization. Musculoskeletal simulations play a central role in this process by providing insights into human-exoskeleton interaction dynamics, thereby informing and refining control strategies.

This work presents a simulation-driven approach for developing exoskeleton controllers for walking and STS using two distinct methods: optimal …

Development And Implementation Of Novel Mass Spectrometric Methods For Metabolism Studies In Drug Discoveries, Huifang Yao

Dissertations

The field of drug discovery has evolved significantly over the past few decades, with a growing emphasis on understanding the metabolic pathways and biotransformation processes that influence the pharmacokinetics and pharmacodynamics of drug candidates. Novel mass spectrometric methods have emerged as powerful tools in this domain, facilitating the comprehensive analysis of metabolic profiles and enabling the identification of metabolites with implications for safety, efficacy, and overall drug development. However, challenges remain in quantifying metabolic flux—an essential aspect of understanding drug action—particularly due to the limitations of traditional isotope kinetic assays, which often involve lengthy sample preparation and the use of …

Multi-Messenger Diagnostics Of The Origin And Transport Of Solar Energetic Particles, Meiqi Wang

Dissertations

Impulsive solar energetic electron events (SEEs) are a common phenomenon originating from the Sun. They are characterized by rapid onset and brief duration, and are commonly associated with solar flares. Solar radio bursts, which offer a rich variety of diagnostic tools for solar activities, exhibits a close relationship with impulsive solar energetic electron events. Such diagnostics are particularly powerful when combined with multi-wavelength remote sensing and in situ observations. The dissertation focuses on combining state-of-the-art microwave imaging spectroscopy data obtained by the Expanded Owens Valley Solar Array (EOVSA) with other multi-messenger observations to investigate the origin of impulsive solar energetic …

Understanding Structure-Property Relationships Within Polyolefin-Derived Vitrimer Systems, Mikaela Sadri

Dissertations

Polyolefins are extremely ubiquitous materials due to their satisfactory material properties, ease of synthesis, and low cost. Unfortunately, their extensive use has led to a global plastic waste mismanagement problem. As such, notable efforts have recently focused on converting these commodity polymers into vitrimers, or dynamic networks, to extend their use-life and tailor their properties. However, the fundamental polymer physics of these emerging materials, remain largely underexplored, hindering their widespread implementation. To address this challenge and enable a more sustainable future, the overarching goal of my dissertation research is to understand the fundamental structure-property relationships within complex polyolefin-derived vitrimer systems. …