Physical Sciences and Mathematics Commons^™

Hdra-Fusion: Hybrid Detection With Routed Architecture For Manipulation-Aware Ai Face Forgery Detection, Omar Ebeid

Theses and Dissertations

With the rapid advancements in artificial intelligence-based image generation and manipulation tools, it is extremely difficult to detect if an image is genuine or artificially crafted. Despite extensive research in this area, existing image detection systems suffer from three major problems: suboptimal cross-dataset generalization due to shortcut learning of dataset-specific patterns, unreliable probability estimates due to domain shift, particularly in cross-manipulation evaluation settings, and an inability to detect images manipulated by multiple types of manipulations within a single detection framework. To address these limitations, we propose HDRA-Fusion (Hybrid Detection with Routed Architecture), a framework built on the conclusion that different …

Hybrid Machine Learning For Zero-Day Malware Detection: An Adaptive Static-Dynamic Analysis Approach, Garrett Farmer

Theses and Dissertations

In an era of swiftly evolving cyber threats, zero-day malware continues to be one of the most challenging classes of attacks to detect and mitigate. Traditional signature-based methods often fail to detect novel malicious code, leaving institutions vulnerable to unknown exploits. This thesis proposes a machine learning (ML)-based framework that is designed to detect unknown malware variants. By combining both static and dynamic techniques, such as file structure exam ination and sandbox-based runtime analysis, this approach aims to successfully capture malicious characteristics. The proposed custom pipeline addresses the computational overhead that is associ ated with deep inspections, outlining a staged …

Optimisation Of Photosensitive Recording Materials For Broadband Holographic Optical Elements, Michael Murray

Doctoral

The introduction of broadband (white) LED outdoor lighting has led to significant energy savings. However, this has come at the cost of increased light pollution which has negative impacts both on ecological systems and human health. This light pollution is largely the result of the lack of control measures for the directionality of the light emitted by outdoor LED lighting. The lack of directionality also results in higher energy consumption in order to compensate for the light scattered to the atmosphere and sufficiently illuminate the target area. In this thesis holographic optical elements (HOEs) are proposed as a complementary technology …

The Trouble Of Energy: Theoretical Foundations Of Loop Quantum Gravity, Hallie Gift

Senior Honors Theses

While general relativity and quantum mechanics have proved to be successful theories with elegant explanations of special cases, an underlying theory is needed to harmonize their copious discrepancies. Popular theories to reconcile the two are broadly termed “quantum gravity,” which refers to theories that introduce a discretized component of gravity either by directly suggesting gravitational quanta or attempting to discretize spacetime geometry. Loop quantum gravity falls under the latter category; it respects the background independence intrinsic to general relativity while attempting to provide an explanation of underlying quantum behavior via Planck-scale-level geometrical discretization. This thesis aims to provide a primer …

Analyzing The Writing Style Of Generative Ai When Prompted With Writing Samples, Samuel Mcdowell

Senior Honors Theses

Authorship attribution is an important topic in today’s world of Large Language Models (LLMs). It is the technology that helps to verify the author of a written work. This study explores whether LLMs can successfully mimic an individual’s writing style if they are given a text sample. A dataset of human-written texts was collected and used to prompt several LLMs to generate new texts that attempt to replicate the original author’s stylistic characteristics. The generated texts were then tested with modern authorship attribution models to determine whether they would be identified as being written by the original author. The results …

Volume 17, Christian O’Neill, Kyara Greene, Savva Sidorov, Laura Bisaillon, Luke Clemmer, Hannah Gordon, Kitt Benson, Taylor Blount, Rachel Danzitz, Nicholas Duellman, Chase Gionis, Hima Fernando, Seth Franzyshen, Onyx Gonzalez, Bryan Lin, Samantha Start, Ysabel Wells, Maggie Duncan

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Amorette Barber, Director, Office of Student Research

From the Editor Dr. Hannah Dudley-Shotwell

Cover Artist’s Statement Maggie Duncan

On Mentoring Dr. Yulia Uryadova

Ukrainian Resistance in the Face of Russification: Nestor Makhno and Anarchism

by Christian O’Neill

Life Vest by Kyara Greene

Isolation and 16S rRNA Identification of Bacteria from Fire Department Connection Pipe by Savva Sidorov

The Effectiveness of Planned Exercise in Reducing ADHD Symptoms in Children by Laura Bisaillon & Luke Clemmer

Linguistic Analysis on Confidence and Communication Strategies with Disparities Between Sign Fluency and Hearing Impairment by Hannah Gordon

Freedmen in Indian Territory by Kitt …

Analyzing The Evolution Of Science: Topological Cycles And Community Detection In Knowledge Networks, Frances C. Mcconnell

Mathematics, Statistics, and Computer Science Honors Projects

How scientific knowledge grows and organizes itself is a central question in the study of science. This thesis uses tools from topology and network science to detect and characterize knowledge gaps—places in a field’s literature where related concepts do not co-occur. We develop a metric to quantify the degree of interdisciplinarity of each gap, using the community structure of the underlying network as a proxy for subfields. Across a wide range of fields, gaps reliably span multiple subfields and evolve in recognizable temporal patterns, highlighting new insights into how scientific fields are structured and their stage of development.

Understanding Delays In Emergency Department Care: A National Analysis Of Wait Times, Gregory Forsberg

Mathematics, Statistics, and Computer Science Honors Projects

Emergency department (ED) wait times remain a persistent bottleneck in the United States healthcare system, impacting patient outcomes, hospital efficiency, and equitable access to care. This study analyzes nationally representative data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a complex, multi-stage probability sample. Using survey-weighted analyses and predictive modeling, we examine the effects of patient characteristics, triage acuity, and visit timing. Results indicate that operational and system-level factors, including hospital capacity, geographic region, and temporal variation, are among the most influential predictors of ED wait times

Utilizing Pine Needles To Compare Spatial Profiles Of Polycyclic Aromatic Hydrocarbons (Pahs) In Two Urban Areas, Autumn Jensen

Undergraduate Honors Capstone Projects

Polycyclic aromatic hydrocarbons (PAHs) are an important class of semivolatile air pollutants known for their adverse health impacts. This research used conifer needles as passive air samplers to create spatial maps of two Utah cities (Rose Park and Spanish Fork) to compare overall PAH exposure and identify any differences in their congener profiles or PAH “fingerprints”. Conifer needles were chosen due to their prevalence as a passive air sampler in previous research and their ubiquity in urban and suburban environments. Second year needles were collected and the concentrations are considered to be an annually averaged view of the PAH concentrations …

Pattern Dynamics And Stochasticity Of Brain Rhythms And Spike Trains In A Tauopathy Mouse Model Of Alzheimer’S Disease, Clarissa M. Hoffman

Dissertations and Theses (Open Access)

Systems neuroscience posits that every aspect of perceived physical reality, every aspect of animal and human behavior, and every cognitive phenomenon emerges from patterns of neuronal activity. While most researchers embrace this idea, there are major difficulties in describing and analyzing these complex neuronal dynamics—spike flows produced by cells ensembles, synchronized extracellular field oscillations, and other patterns—which limits our understanding of how the activity of individual neurons and the whole-animal cognition and behavior might be connected. In particular, we lack the approaches and even the semantics for connecting the individual cell outputs and the integrated results of their activity. Current …

Membrane Organization Of Rheb And Rhoa: Roles In Function And Allosteric Druggability, Chase M. Hutchins

Dissertations and Theses (Open Access)

Small GTPases of the Ras superfamily are membrane-bound molecular switches that regulate nearly all major cellular processes, and their dysregulation drives cancers, neurological disorders, and metabolic diseases. While anchored to cellular membranes, the catalytic domains of small GTPases undergo orientation dynamics, adopting distinct configurations that can occlude or expose effector-binding surfaces and modulate signaling output. These dynamics have been characterized in the Ras oncoproteins, but whether they extend across the superfamily and influence allosteric druggability has remained unknown.

In this dissertation, I address these questions through Rheb and RhoA, two small GTPases with complementary differences in subfamily lineage, membrane localization, …

Securing Cloud-Native Systems: From Vulnerability Analysis To External And Insider Threat Detection, Jiongchi Yu

Dissertations and Theses Collection (Open Access)

Cloud-native systems have become the backbone of modern software infrastructure. However, their dynamic resource orchestration and complex configurability introduce a large attack surface and intricate security challenges. Adversaries can externally exploit vulnerabilities in cloud components or perform insider movement within cloud environments to launch attacks. As these systems increasingly support critical services, security breaches can lead to severe operational and economic consequences.

Despite extensive efforts in vulnerability detection and attack monitoring, existing approaches struggle to remain effective in cloud-native environments characterized by rapid evolution and inherent heterogeneity. In particular, they exhibit three fundamental limitations: (1) Insufficient understanding of defect patterns …

Enhancing Control Charting Schemes And Exploring New Assessment Metrics To Advance Quality Control And Cyber-Attack Detection In Manufacturing, Ahmad Al Majali

Dissertations

The increasing integration of digital technologies and industrial control systems in modern manufacturing has introduced new cybersecurity vulnerabilities within cyber–physical production environments. Malicious actors can exploit these vulnerabilities to induce subtle process deviations that degrade product quality while remaining undetected by conventional statistical monitoring tools. Such attacks can be deliberately engineered to manipulate process behavior through transient shifts that vary in magnitude, duration, and frequency. Despite extensive research on transient shifts caused by assignable causes in Statistical Process Control (SPC), limited attention has been given to intelligently designed cyber–physical attacks that exploit the structural characteristics and limitations of control charting …

Barriers To Climate Change And Sustainability Action: Nursing Education And Practice, Dawn Marie Smith

Dissertations

Climate change is one of the most pressing public health emergencies of our time and nurses can have a great impact in their current practice and in the education of future nurses (The Alliance of Nurses for Healthy Environments, n.d.; American Nurses Association, 2023; Health Care without Harm, 2025). Deaths due to rising temperatures, vector-borne illness, and food insecurity related to drought and extreme weather are on the rise (WHO, 2024). It has been estimated that globally over 250,000 additional deaths will be attributed to climate related effects between 2030 and 2050 (Watts et al., 2020; WHO, 2023).

A primary …

From The Hopf Fibration To Instantons: Geometry In Gauge Theory, Emily D. Wessman

Undergraduate Honors Capstone Projects

This paper explores the relationship between topology, differential geometry, and gauge theory through the study of Yang-Mills theory and its solutions, known as instantons. Beginning with the Hopf fibration, we show how principal fiber bundles encode topological information and appear in physical contexts such as electromagnetism. In particular, we consider how the fibration of S³ over CP¹ ≅ S² represents the Dirac magnetic monopole, and how the Chern number associated with the bundle is exactly the winding number for the monopole.

We then develop the framework of gauge theory, focusing on connections on principal SU(2) bundles …

Safe Control Design For Quadruped Locomotion In Unstructured Environments Using Linear Transfer Operators, Sriram Sundar Krishnamoorthy Shankara Narayanan

All Dissertations

Deploying quadruped robots in unstructured, obstacle-rich environments requires control and planning methods that remain safe and reliable despite complex terrain geometry, limited sensing, and inevitable modeling errors. This thesis develops operator-theoretic tools for safe control design of robotic systems using linear transfer operators, with a focus on quadruped locomotion in unstructured environments. The central goal is to develop a unified operator-theoretic framework for safe control design based on the Perron–Frobenius (P–F) and Koopman operators. In particular, the thesis leverages \emph{density functions} to develop safe navigation frameworks in the dual space of densities. In the operator-theoretic perspective, the P–F operator governs …

Monomial Quadratic Identities Of Hecke Eigenforms, Trevor Vilardi

All Dissertations

Duke and Ghate independently studied the question of when it is possible for the product of two eigenforms to be an eigenform. In this dissertation, we take up a generalization of that question, namely is it possible for the product of two eigenforms to be equal to a different product of two eigenforms? Under this formulation, the question becomes closer to one about unique factorization, i.e., how closely do eigenforms work like irreducible elements? Our conjecture is that there are only finitely many cases where the product of two eigenforms is equal to a different product of two eigenforms, and …

Learning Global Context For Sparse Activity Recognition In Lengthy Recordings With Limited Dataset Size, Zeyu Tang

All Dissertations

This dissertation describes methods to analyze lengthy recordings of data in order to detect sparsely occurring activities. The narrative below describes the progression of research that led to the development of these methods and their generalization into a unified framework. My research started with designing models for dietary monitoring, including detecting meals from day-long recordings and detecting intake gestures from meal-length recordings. Both tasks share some common characteristics: (a) the target event takes only a small portion of data recordings, and (b) there is global context within full-length data recordings that can help a model make better decisions. After finishing …

Generalizable Adaptation For Vision-Language Models, Niloufar Alipour Talemi

All Dissertations

Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs) have recently emerged as powerful frameworks for learning joint representations across visual and textual modalities. These models enable a wide range of applications, including visual recognition, multimodal reasoning, and visual question answering. However, adapting large pre-trained VLMs to downstream tasks while preserving their strong generalization ability remains a significant challenge, particularly under domain shifts or limited supervision. This dissertation focuses on developing methods for generalizable adaptation of VLMs, aiming to improve robustness, efficiency, and applicability across diverse tasks and environments.

First, this work introduces novel prompt learning strategies for adapting CLIP-style …

Decision Making For Large-Scale Problems Under Uncertainty And Conflict, Benjamin J. Hamlin

All Dissertations

Large-scale decision-making problems appear in many areas including long-range forecasting such as energy generation forecasting. Many such problems are subject to conflicting objectives and uncertain data, and can be modeled as linear optimization problems. We study novel theoretical results and algorithms for large-scale linear decision problems under conflict and uncertainty. First, we propose a parametric Benders decomposition algorithm for solving large-scale linear optimization problems with multiple objectives or deterministically uncertain objectives. Second, we extend the parametric Benders decomposition to a multi-stage setting, developing a parametric stochastic dual dynamic programming algorithm, which enables decision-making when conflicts and uncertainty have planning impacts …

The Impact Of Label Material On Perceived Value And Environmental-Friendliness Of Beverages In Returnable Glass Bottles Versus Actual Environmental Impact Via Life Cycle Assessment, Toni Sharp

All Dissertations

With guidance from the EPA to reduce virgin packaging use and alternatively increase reuse, the concept of reusable packaging has re-emerged as a potential sustainable alternative to single-use and recyclable packaging. For businesses, an understanding of the potential environmental benefits of switching from single-use packaging to reusable packaging, along with consumer willingness to pay for sustainable packaging are paramount. Existing literature on willingness to pay (WTP) for ethical or sustainable products typically relies on consumers’ stated preferences found through surveys using hypothetical products and prices. It has been revealed that sustainability claims printed on the label or products in glass …

Parameterized Polynomial Systems: Monodromy, Sparse Polynomials, And Solutions, Julianne Barnhart

All Dissertations

The lift of a loop in the base space of a branched cover to the cover induces a permutation of points in a fibre. The monodromy group of the branched cover is the permutation group generated by all such permutations. When loops are restricted to a particular subset of the base space, the corresponding permutation group induced by these loops is the restricted monodromy group. Monodromy groups encode structure and symmetries of many enumerative problems. We describe the relationship between the restricted monodromy group and the monodromy group of the original branched cover. Our main result is a local-to-global property: …

Integrating Incentive Design And Spatial Prioritization For Climate-Smart Forestry Adoption In South Carolina, United States, Miah Maye Pormon

All Dissertations

Forests provide essential ecosystem services, including carbon sequestration, water regulation, timber production, and habitat provision. However, increasing development pressures and land-use changes threaten forest persistence and long-run ecosystem service provision. Climate-smart forestry (CSF) practices, such as improved forest management and extended rotation, offer opportunities to enhance carbon storage, forest resilience, and economic livelihoods. The effectiveness of these practices depends on forest owners’ participation and the strategic allocation of financial resources or incentives. This dissertation develops an integrated framework that combines behavioral economic analysis and mapping to improve the design of current incentive programs. The first chapter employs the Contingent Valuation …

X-Raying The Material Surrounding Active Supermassive Black Holes, Isaiah S. Cox

All Dissertations

It is important to understand the nature of the gas and dust immediately surrounding the supermassive black hole (SMBH) in active galactic nuclei (AGN), because this material feeds the accretion disk and couples the activities going on at the AGN scale to the host galaxy. However, there remain large uncertainties on basic properties about this material such as its location and distribution. This is primarily due to the difficulties in resolving spatial information on these scales at extragalactic distances.

AGN emit a tremendous amount of radiation all across the electromagnetic spectrum, including X-rays. The X-rays come from a very small …

Advancing Apparel Education Through 3d Simulation In Vstitcher: Confidence, Competence, And Career Readiness In Higher Education, Jonah Diniakos Graham

Graduate Theses and Dissertations

The rapid adoption of 3D garment simulation in the apparel industry has created new demands of higher education to better prepare students with both technical proficiency and confidence within digital design platforms to better enable them for many career avenues. This study examines the integration of Browzwear’s VStitcher in an undergraduate apparel production course, addressing the need to assess both competence and self-confidence in digital garment construction while also fostering broader career readiness competencies. Guided by a combination of Self-Determination Theory (SDT) and Situated Expectancy-Value Theory (SEVT), the study emphasizes the role of autonomy, competence, and relatedness in the shaping …

Quantifying The Short-Term Hydrogeologic Responses Of Highway Construction In Hot Springs, Arkansas, Billy Jack Summerford

Graduate Theses and Dissertations

In late 2020, excavation for the Highway 5 Bypass in Hot Springs, Arkansas exposed seepage faces along roadcut walls. Groundwater levels at nearby wells decreased by up to 20.9 ft and have remained ~20 ft below pre-construction values on average. This study quantifies changes in storage in the unconfined aquifer within the Hot Springs thermal recharge zone associated with the seepage faces using a numerical groundwater model calibrated to pre-construction head at well ARb4. Model scenarios are used to evaluate whether construction-induced seepage or drought best explains the decrease from late 2020 to early 2021. The numerical groundwater simulation represented …

Advancement In Electrochemical Generation-Collection: Snapshot Redox Cycling Voltammetry For Rapid Analysis At Chip-Based, Individually Addressable Microband Electrode Arrays, Blake Aleck Sterling

Graduate Theses and Dissertations

Snapshot redox cycling voltammetry (Snapshot RCV) is introduced as a rapid electrochemical technique that reconstructs the current-potential profile of a redox active species using a microband electrode array. By simultaneously biasing individual generator electrodes alternating in the array to different voltages across the analyte’s potential range while individually holding collector electrodes at a reversing potential, “spatial” voltammetry is achieved in as little as 1 s with the temporal speed of chronoamperometry, rapidly capturing the steady-state redox cycling (RC) profiles. During RC (a mode of generation-collection), the analyte is oxidized (or reduced) at generator electrodes and converted back at neighboring collector …

Explainability In Deep Learning For Density Regression, Dalton James Oxford

Graduate Theses and Dissertations

Classical statistical methods focus on explainability and inferential power. Machine learning and deep learning can handle non-linear, high-dimensional data better than traditional methods. In modeling, a clear understanding and interpretation are essential to decision-making. Recent work in quantile regression and extreme modeling has begun to use deep learning due to its performance on high-dimensional, non-linear data. Semi-Parametric Quantile Regression (SPQR) is a nonparametric spline-based approach to quantile regression that estimates the conditional PDF and CDF of the response. Semi-Parametric Quantile Regression for Extremes (SPQRx) is a recent extension of SPQR that provides two features: out-of-sample estimation and accurate extreme-tailed estimation. …

Toward Causal Generative Modeling: From Representation Learning To Controllable Generation, Aneesh Komanduri

Graduate Theses and Dissertations

The hallmark of human intelligence is causal reasoning, the ability to infer relationships between causes and effects through observation and intervention. While modern deep learning has excelled at identifying statistical patterns, current generative models often struggle to capture the underlying structural causal mechanisms of the data-generating process, leaving them vulnerable to shortcut learning and spurious associations. To achieve true generalizability and interpretability, artificial intelligence must transition from simple association to higher-level causal reasoning to be capable of scheduling and planning in the real world. This dissertation develops fundamental methodologies for causal generative modeling by integrating Pearl’s Structural Causal Model (SCM) …

Deciphering Chemomechanical Couplings In Proteins Using Molecular Dynamics And Enhanced Sampling Techniques, Matthew Brownd

Graduate Theses and Dissertations

Proteins function through a complex interplay between chemical interactions and mechanical motions across multiple spatial and temporal scales. Understanding how ligand binding, conformational dynamics, and structural flexibility collectively regulate protein function remains one of the central challenges in molecular biophysics. This dissertation presents a computational investigation into chemomechanical coupling in three distinct classes of biomolecular systems using all-atom molecular dynamics (MD) simulations and enhanced sampling methods, with particular emphasis on free-energy calculations and long-timescale conformational analysis. The first part of this work focuses on ligand binding in hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, which regulate rhythmic electrical activity in the heart …