Physical Sciences and Mathematics Commons^™

Glioma Segmentation In Mri Using A 3d Hybrid U-Net With Adaptive Self-Attention And Multi-Modal Fusion, Evan P. Savaria

Computer Science Theses & Dissertations

Accurate glioma segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, radiation targeting, and longitudinal monitoring. MRI modalities such as T1, T1Gd, T2, and FLAIR provide complementary information for identifying clinically important tumor sub-regions, including enhancing tumor (ET), tumor core (TC), and whole tumor (WT). However, many existing segmentation methods do not fully preserve modality-specific information, process all modalities and slices uniformly, and often rely on a single shared fusion strategy for all sub-regions. These limitations can reduce segmentation accuracy, increase computational cost, and limit clinical interpretability.

This dissertation addresses these challenges through three …

Simulations Of Inverse Compton Scattering With Radiation Reaction, Elizabeth Breen-Lee

Physics Theses & Dissertations

Inverse Compton scattering (ICS) is a versatile mechanism for producing bright, tunable, and quasi-monochromatic x-ray and gamma-ray radiation through the interaction of relativistic electrons with intense laser pulses. These sources have applications spanning medicine, archaeological studies, materials science, and fundamental research, motivating continued efforts to improve both their performance and the accuracy of theoretical models used to describe them. As nextgeneration laser and accelerator facilities continue to reach increasingly intense regimes, efficient and accurate simulation techniques have become essential for the design and optimization of ICS sources. This dissertation presents the development of analytical and computational methods for modeling inverse …

Parameter Dependent Chen-Fliess Series And Their Nonrecursive Interconnections, Natalie T. Pham

Electrical & Computer Engineering Theses & Dissertations

In control theory, a Chen-Fliess functional series is a weighted sum of iterated integrals constructed from a given set of input functions. Such series can be used to represent nonlinear input-output systems. In applications, they have been employed to characterize interconnected nonlinear systems, to solve system inversion and tracking problems, and to design predictive and adaptive controllers.

Distributed parameter systems exhibit spatial dependence along with temporal dependence. Such systems are typically represented in terms of partial differential equations. In control theory, there appears to be no existing method for representing the input-output map of a distributed system via a Chen-Fliess …

A Longitudinal Analysis Of Hospital Consumer Evaluation In Virginia, Tulay Akmandor Inac

Health Services Research Dissertations

Suboptimal patient experience signals a need to improve clinical efficacy, patient safety, and healthcare quality. The COVID-19 pandemic intensified hospital resource and staffing demands, reducing hospitals’ capacity to invest in patient experience improvement. Prior studies remain limited because many lack longitudinal structure and organized theoretical models, making temporal inference difficult and weakening analyses of patient experience disparities. To address these limitations, this dissertation incorporates three studies examining patient experience and pandemic-related effects.

In state-level markets comparable to Virginia, limited research has examined longitudinal patient experience trends across regional hospital systems using HCAHPS scores. The first study uses descriptive trend analysis …

Energy Transition In Africa: A Comparative Analysis Of Five African Countries, Boluwatife G. Ayankojo

Graduate Program in International Studies Theses & Dissertations

Global energy transition remains a critical challenge for reducing carbon emissions and protecting the environment, with solar energy adoption driving improvements in both energy efficiency and conservation. This research focuses on Africa's transition from fossil fuels to solar energy. As population growth and blackouts increase, African countries must transition from unreliable, centralized national grids to decentralized, low-carbon alternatives that ensure quality household electricity. While some countries have advanced in this transition to sustainable solar energy, others lag behind. The limited access to electricity hinders the African continent's healthcare, education, economic growth, and mortality rates. This research establishes the importance of …

Atomic-Scale Investigation Of Functional Molecular Systems And Graphene Nanoribbons Using Scanning Probe Microscopy, A.M. Shashika D. Wijerathna

Physics Theses & Dissertations

Emerging molecular electronics, molecular devices, and carbon-based nanotechnologies increasingly rely on the precise control of molecular structures and interfaces at the atomic scale. Advancing these systems requires experimental techniques capable of resolving atomic structures, quantifying molecular behavior, and revealing the mechanisms governing nanoscale assembly. Scanning probe microscopy uniquely provides these capabilities by combining atomic-resolution imaging with local spectroscopy and manipulation. This dissertation employs ultra-high-vacuum low-temperature scanning tunneling microscopy (UHV-LT-STM) and qPlus atomic force microscopy (qPlus AFM) to address three complementary challenges: (i) atomic-scale characterization of complex supramolecular architectures, (ii) quantitative investigation of mechanically induced conformational switching of individual molecules, and …

Towards More Realistic And Practical Graph Backdoor Attacks, Jiawei Chen

Computer Science Theses & Dissertations

Graph Neural Networks (GNNs) have demonstrated remarkable performance on graph-based learning tasks and are increasingly deployed in security-critical applications. However, recent studies have shown that they are highly vulnerable to graph backdoor attacks (GBAs), where adversaries implant malicious triggers to induce targeted misclassification during inference. Despite their effectiveness, existing GBAs are often developed under unrealistic assumptions, such as focusing exclusively on simple homogeneous graphs or assuming the adversary possesses privileged access to target nodes during inference. This dissertation aims to systematically investigate and design graph backdoor attacks under significantly more realistic graph settings and adversarial constraints.

First, we investigate the …

Exploring The Determinants Of User Discontinuance In Ai-Driven Usage-Based Insurance, Wenzhuo Li

Theses and Dissertations in Business Administration

While interest in algorithmic decision-making continues to grow, limited research has examined the post-adoption phase. This study examines how users evaluate their post-adoption experiences with algorithmic decision-making in the context of usage-based insurance (UBI), focusing on how expectation disconfirmation shapes satisfaction and the intention to discontinue use. It explores two key questions: What factors influence users’ discontinuance intention toward AI-based UBI systems? And how do specific algorithmic characteristics alter how users form these post-adoption evaluations? To investigate these questions, this study develops a comprehensive theoretical model that integrates the Expectation Confirmation Model and Reactance Theory, incorporating additional factors such as …

Proxy Interfaces For Enhancing Non-Visual Web Usability, Md Javedul Ferdous

Computer Science Theses & Dissertations

People with blindness or visual impairments (BVI) face significant barriers when navigating the web, even when digital content is technically accessible. These challenges arise not from a lack of access, but from a mismatch between vision-centric user interfaces and the linear, audio-based interaction paradigms of screen readers. Sighted users rely on spatial layout, grouping, and visual cues to interpret structure quickly, while blind users must explore elements sequentially, often without sufficient context. As a result, they expend greater cognitive effort, become disoriented more easily, and struggle with tasks that are usually trivial for sighted users. Although accessibility initiatives have focused …

Macroscopic Classical And Quantum Models Of Inverse Compton Scattering, Emerson Penn Rogers

Physics Theses & Dissertations

Inverse Compton sources — in which a relativistic electron beam scatters a laser pulse to produce tunable, collimated, high-energy radiation—have emerged as among the most promising compact radiation sources, with applications ranging from nuclear photonics to medical and nanoscale imaging and metrology. The most viable current tabletop configuration couples laser-wakefield acceleration with inverse Compton scattering, producing GeV-scale electron beams over millimeter distances. As laser intensities increase and electron energies grow, the interaction enters the radiation reaction regime, where the energy radiated by the electron becomes a significant fraction of its kinetic energy. Predicting the scattered electron energy spectrum — the …

The Role Of Phenotypic Plasticity And Bet-Hedging In Coping With Unpredictable Temperature Fluctuations In The Marine Annelid Ophryotrocha Labronica, Gabrielle Dawn Newton

Biological Sciences Theses & Dissertations

Coastal ecosystems are increasingly exposed to unpredictable temperature fluctuations, potentially favoring strategies that buffer populations against environmental uncertainty. This study examines how phenotypic plasticity and diversified bet-hedging contribute to variation in life-history traits under differing thermal predictability in the marine annelid Ophryotrocha labronica. After three generations at 24 °C, broods were reared for three generations under four thermal regimes: constant 24 °C (C24), constant 27 °C (C27), predictable fluctuations (Fp), and unpredictable fluctuations (Fu). Survival was assessed before and after acclimation to evaluate selective pressure among regimes. Age at maturity, fecundity, and growth rate were used to quantify plasticity (trait …

Modeling And Generating Crash Avoidance Behaviors In Safety-Critical Vehicle–Pedestrian Interactions Using Deep Reinforcement Learning, Qingwen Pu

Civil & Environmental Engineering Theses & Dissertations

Traffic crashes between vehicles and pedestrians arise from complex, split-second interactions in which both parties make rapid evasive decisions. Four fundamental gaps persist in existing research. Surrogate safety measures assume linear trajectories, failing to capture the curved movements of turning vehicles and crossing pedestrians at intersections. Single-agent modeling treats one party as a fixed obstacle, ignoring the joint decision-making that governs near-miss outcomes. The effect of vehicle type on pedestrian avoidance behavior—whether pedestrians respond differently to automated vehicles (AVs) than to human-driven vehicles (HDVs)—remains poorly understood. Finally, automated driving system development is hampered by a severe scarcity of large-scale, behaviorally …

Multi-Particle Reactions In Lattice Field Theories: From Euclidean To Quantum Computations, Marco Antonio Carrillo Bernal

Physics Theses & Dissertations

Amplitudes of multi-particle processes provide a probe of the dynamics described by relativistic quantum field theories. In the context of quantum chromodynamics, these amplitudes encode the way in which quarks and gluons are bound together by the strong nuclear force to form hadrons and govern their interactions. The non-perturbative nature of the strong interactions complicates the study of hadronic amplitudes from first principles. The most effective way of studying hadronic reactions non-perturbatively is the numerical framework known as lattice quantum chromodynamics. This method relies on theoretical formalisms that establish relations between numerical observables, such as finite-volume energy levels and matrix …

Quantitative Surface-Enhanced Raman Spectroscopy For Antiretroviral Drug Monitoring: Novel Quality Index And Distributional Approaches In Aqueous And Plasma Matrices, Marguerite Rose Butler

Chemistry & Biochemistry Theses & Dissertations

Surface-enhanced Raman spectroscopy (SERS) offers high sensitivity for molecular detection but is limited for quantitative applications by spatial heterogeneity in signal enhancement on dried colloidal substrates. This Dissertation addresses these limitations through the development and evaluation of novel quantitative methods applied to the antiretroviral drugs tenofovir and emtricitabine.

A quality index (Qi) metric was developed to rank individual SERS spectra within each well based on analyte peak prominence relative to local baseline. SERS spectra were acquired from evaporated silver colloid deposits on aluminum well plates using a custom raster-scanning Raman system. Calibration curves were constructed using the top-ranked spectra (Top-Qi …

Computational And Ai Tools For Understanding Telomere-Associated Cancer Mechanisms, Eleni Adam

Computer Science Theses & Dissertations

Telomeres are the protective caps of the human chromosomes and are critical for genome stability. Dysfunctional telomeres caused by their erosion with age and cell proliferation as well as by defects in their maintenance is a major early event leading to genome changes and cancer. Subtelomeres possess the critical role of regulating adjacent telomeres. Due to their complex repeat structure and high variance from one person to another, these areas have not been analyzed in detail. We present a set of computational and machine learning tools to aid in the understanding of subtelomere structure and its rearrangements in cancer.

Initially, …

Variability In Nutrient And Enterococcus Concentrations In Tidal Floodwater Across Watersheds With Different Land Uses, Alyssa Bucci

OES Theses and Dissertations

Tidal flooding is an increasingly pressing hazard in coastal Virginia due to sea level rise and land subsidence. The Hampton Roads region of Virginia has the second-highest rate of relative sea level rise in the United States and experiences frequent tidal flooding, but the water quality impacts of this flooding on the Chesapeake Bay are not being accounted for. High concentrations of dissolved nutrients and Enterococcus bacteria (a fecal indicator) have been observed in tidal floodwater of the Lafayette River, a tributary of the James River and the lower Chesapeake Bay. This study investigates variability in dissolved nutrient and Enterococcus …

Long-Term Spatiotemporal Change In Coral Reef Fish Populations And Communities In The Philippines, John Christopher Whalen

Biological Sciences Theses & Dissertations

The Visayas Region of the Philippines is a global epicenter of marine fish biodiversity. Hypotheses attribute this peak of species richness within the Coral Triangle to processes that occur at geological time scales. However, changes in biodiversity may occur on ecological time scales, allowing for the assessment of the potential impact of anthropogenic activities. Recent declines in genetic, species, and ecosystem diversity have been observed in the tropics, including the Philippines. Within the Philippines the Visayas Region is considered the epicenter of species adversity due to anthropogenic stressors such as habitat degradation, overfishing, and harvesting individuals for the aquarium trade. …

Nanostructured Negative Electron Affinity Gallium Arsenide Photocathodes With Enhanced Quantum Efficiency And Extended Charge Lifetime For Advanced Photoinjectors, Md Aziz Ar Rahman

Physics Theses & Dissertations

This dissertation presents a comprehensive experimental and computational investigation of nanostructured negative electron affinity (NEA) gallium arsenide (GaAs) photocathodes. This work was undertaken to achieve higher quantum efficiency (QE) and longer charge lifetime than conventional flat NEA GaAs electron sources commonly used in photoinjectors. Finite-Difference Time-Domain and charge-transport simulations of three types of nanostructure geometries reveal substantial QE enhancement over flat GaAs via Mie resonance mode excitation. Consequently, the truncated nanocone array (TNCA) photocathodes with heights of 400, 700, and 1000 nm were fabricated and NEA activated via Cs-NF₃ deposition. In a low-voltage ultra-high vacuum chamber with a base pressure …

Enhancing Stem Education With Modeling, Simulation, And Ai Technologies: From Virtual Laboratories To Intelligent Teaching Assistants, Yiyang Li

Electrical & Computer Engineering Theses & Dissertations

Rapid advancements in modeling and simulation (M&S) and artificial intelligence (AI) present new opportunities to enhance various aspects of STEM education, from virtual laboratories that simulate physical lab environments in software to intelligent teaching assistants that provide on-demand, curriculum-aligned instructional support. Virtual laboratories offer a potential solution to the access and scalability challenges of laboratory courses by allowing students to conduct experiments without physical equipment or geographical constraints. AI-powered teaching assistants, particularly those grounded in course-specific materials, can help mitigate the instructional support gap that arises when students work independently in digital learning environments. This dissertation presents three-phase research into …

Experimental Investigation Of The Discharge Modes Of Nanosecond Pulsed Plasmas At Atmospheric Pressure, Md Ziaur Rahman

Electrical & Computer Engineering Theses & Dissertations

The generation of repeatable and stable nanosecond pulsed atmospheric pressure plasmas is important to non-thermal plasma applications in various fields including medicine, material processing, food processing, and plasma ignition for combustion. This dissertation investigates the atmospheric pressure plasma initiation and formation under 10 – 200 ns pulsed power for electrode configurations applicable for transient plasma ignition (TPI) for combustion. The impacts of pulsed power parameters, gas condition, and electrode geometry on the discharge initiation and modes (i.e., streamer, transient spark and spark) are systematically evaluated for applying TPI for combustion. We evaluated the discharge modes driven by both longer and …

Interpretable Sparse Modeling Of Longitudinal Signals Via Critical-Range Rectification And Anytime Rule Compression, Jason Orender

Computer Science Theses & Dissertations

High-dimensional longitudinal data arise in clinical monitoring, industrial control systems, and other sensor-driven domains where outcomes are often governed by threshold-and-lag behavior. Traditional longitudinal workflows frequently depend on expert guessing to nominate candidate variables, lag windows, and threshold hypotheses, followed by repeated hypothesis testing over a limited set of manually specified relationships. While such approaches can be useful in narrow settings, they are often less robust in high-dimensional regimes because important interactions may be missed, multicollinearity can destabilize inference, and the resulting process can be labor-intensive and difficult to scale. This dissertation develops an end-to-end framework for interpretable sparse longitudinal …

Convergence Theory For Deep And Multi-Grade Neural Architectures, Lei Huang

Mathematics & Statistics Theses & Dissertations

This dissertation studies two complementary notions of convergence arising in modern neural network models: the convergence of recursively constructed neural network architectures and the convergence of optimization algorithms used for training neural-network-based image restoration models. The first part develops a convergence theory for deep neural networks (DNNs) viewed as recursively generated sequences of functions. Within an Lp framework motivated by statistical learning, sufficient conditions are established under which increasing-depth neural network sequences converge to well-defined limiting functions. The analysis covers both bounded-width and unbounded-width architectures, establishes explicit convergence rates, and motivates a network initialization strategy derived from the convergence conditions. …

Tagged J/Ψ Photoproduction Near Threshold, Mariana Tenorio Pita

Physics Theses & Dissertations

J/ψ production (heavy quarkonium in general) is sensitive to the gluonic content of the nucleon. Recent theoretical studies have suggested that near-threshold J/ψ production is sensitive to the gravitational form factors (GFFs) of the nucleon, which parametrize the matrix elements of the QCD energy-momentum tensor (EMT). The energy-momentum tensor of the nucleon encodes its mechanical properties through the gravitational form factors, which give access to quantities such as the mass radius and pressure distribution of the proton. Previous J/ψ electroproduction measurements were either performed near-threshold at very low Q² (in the quasi-real photoproduction regime) or far above threshold at …

Dynamics, Characteristics, And Sinking Velocities Of Small, Gel-Like Ocean Particles (24–1000 Μm): Insights From Shadowgraph Imaging And Turbulence Modeling, Huanqing Huang

OES Theses and Dissertations

Particle concentration and sinking velocity control the export flux and transfer efficiency of the biological carbon pump, and are therefore key parameters in ocean biogeochemical models. Gel-like particles form an important part of the particulate organic carbon pool, but their contribution is difficult to quantify because they are fragile and nearly transparent to conventional optical instruments. Here, a focused shadowgraph imaging system (FoSI) was applied to marine gel research to examine the ecological significance, distribution, and transport of gel-like particles along a transect from the Azores to the upwelling region off northwest Africa.

A new image processing protocol was developed …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

Advancing Imbalanced Classification Through Representation Learning, Morteza Mohammady Gharasuie

Computer Science Theses & Dissertations

Real-world datasets frequently exhibit severe class imbalance, where certain categories are significantly underrepresented relative to others, leading standard empirical risk minimization to bias learning toward majority classes and degrade performance on minority categories. This dissertation addresses imbalanced classification across both structured tabular data and long-tailed visual recognition by developing methods that improve representation learning and decision reliability under skewed distributions. For tabular data, it introduces Conditional Probability Representation (CPR), a target-based encoding framework that embeds feature–label relationships directly into the representation space, enhanced by a progressive feature upgrading mechanism for semi-supervised settings and a class-frequency–aware extension that improves robustness to …

Improving Fairness On Semantic Segmentation Using Large Language Models, Samuel E. Burggraf

Electrical & Computer Engineering Projects for D. Eng. Degree

As machine learning systems are increasingly integrated into critical decision-making processes, ensuring fairness in their design and implementation has become a significant concern. While fairness research has primarily focused on specific protected attributes, less attention has been given to spatial fairness, which can affect individuals at specific locations. If fairness is not addressed, models may systematically underperform in certain regions or across populations which can lead to unequal access to accurate predictions and potentially biased decision-making. Fairness considerations should extend across all machine learning applications to align with the National Institute of Standards and Technology (NIST) guidelines of fair and …

Temperature-Induced Uncertainty In Fixed-Context Retrieval-Augmented Generation, Steven Zeng, Murat Kuzlu

Cybersecurity Undergraduate Research Showcase

This study examines how decoding temperature affects output uncertainty in a fixed-context retrieval-augmented generation (RAG) system. We define uncertainty as the semantic dispersion among repeated answers under the same fixed retrieved context, with greater dispersion interpreted as higher uncertainty. To isolate this answer-generation variability from retrieval drift, each question was paired with a fixed retrieved context, and repeated generations differed only in temperature. The experiment used nine questions drawn from a machine-learning textbook corpus, with three questions each at easy, moderate, and hard difficulty. Each question was evaluated at five temperatures (0.0, 0.25, 0.5, 0.75, and 1.0) over 30 iterations, …

Phishing Restraint: University Simulated Phishing Campaigns, Alexander M. Abou Khir

Cybersecurity Undergraduate Research Showcase

Universities face heightened vulnerability to phishing attacks due to their open information-sharing culture and diverse user populations. This study examines how phishing exploits human factors within campus environments and evaluates three major training strategies: embedded phishing, microlearning, and role-based instruction to understand their individual and combined effectiveness. I explored studies that implement these strategies in pairs and use the strategies alone, identified trends in susceptibility reduction, behavioral reinforcement, and contextual relevance. I suggest that, while each method independently improves user awareness, multiple approaches offer stronger, more adaptable protection by addressing both psychological triggers and role-specific risks. The paper contributes a …

Hijacking The Prompt: A Survey Of Prompt Injection Attacks, Detection, And Defense In Large Language Models, Edward J. Griggs

Cybersecurity Undergraduate Research Showcase

Prompt injection attacks, ranked the number-one vulnerability in AI systems by OWASP's 2025 Top 10 for Large Language Model Applications, remain largely unsolved, and this survey examines why. As large language models (LLMs) are deployed across enterprise workflows, agentic systems, and consumer tools, their fundamental inability to distinguish trusted instructions from untrusted user data has created a persistent and expanding attack surface. This paper presents a structured taxonomy of prompt injection attack vectors, including direct injection, indirect injection, multimodal attacks, tool and agent exploitation, hybrid chained techniques, and autonomous propagating threats. These vectors are mapped across five impact categories (data …