Physical Sciences and Mathematics Commons^™

Bridging The Gap Between Network Science And Network Systems To Identify And Mitigate Cyber Risk: Identify And Mitigate Backdoor Attacks On Graph Neural Networks And On Complex Systems, Sabah Ettahri

Electrical & Computer Engineering Projects for D. Eng. Degree

This doctoral project aims to bridge the gap between graph theory and network science to identify and mitigate cyber risk, represented as a CY-Triangular Network that connects different networks. The CY-Triangular Framework is a cybersecurity system that integrates graph theory and network science through an interoperable learning approach. The objective of this project is to bridge the gap between two domains: network science and network systems. Accordingly, it examines one representative network from each field, focuses on a complex system network, and explores Graph Neural Networks (GNNs). The connection between these domains lies in graph theory. This research demonstrates that …

Interactions Between Black, Brown, Blue And Green Carbon From Terrestrial To Marine Ecosystems: A Critical Review, Allison White, Richard Kline, Md. Saydur Rahman

School of Earth, Environmental, & Marine Sciences Faculty Publications

Black and brown carbon are byproducts of the incomplete combustion of biomass or fossil fuels. These light-absorbing aerosols influence climate by altering the radiative balance through the absorption and scattering of sunlight, exacerbating global climate change. Their subsequent deposition on terrestrial and marine environments affects surface albedo, snow and ice melt, and biogeochemical processes, leading to further environmental consequences. In contrast, green and blue carbon refer to carbon stored in terrestrial vegetation and marine ecosystems, respectively. These ecosystems play a crucial role in mitigating climate change by sequestering atmospheric carbon; however, they are increasingly threatened by climatic disturbances, anthropogenic activities, …

Leptinotarsa Texana Schaeffer And Gargaphia Arizonica Drake & Carvalho As Potential Biocontrol Agents For The Noxious Weed Solanum Elaeagnifolium Cav, Samikshya Subedi, Stephanie L. Kasper, Alexis Racelis, Greg Lefoe, Rupesh R. Kariyat

School of Earth, Environmental, & Marine Sciences Faculty Publications

Silverleaf nightshade (Solanum elaeagnifolium Cav.; SLN) is a perennial forb native to the southern United States, Mexico and South America that has become a serious agricultural weed across the world. Biological control has emerged as a significant alternative for the management of (SLN) due to the challenges and high costs associated with chemical and mechanical controls. In this study, we used a combination of field and laboratory studies to (1) explore the fundamental and realized host ranges of two North American insects, Leptinotarsa texana Schaeffer and Gargaphia arizonica Drake & Carvalho and (2) assess their suitability as potential biological …

Long-Term Sedimentation Rates And Patterns Of Alluvial Ridge Basins (Esteros) Of The Rio Grande Delta (Tx, United States Of America), Kynan Joseph Rutan

Theses and Dissertations

Little is known about the century-scale sedimentation rates and patterns of Alluvial Ridge basins within river deltas, especially in the Rio Grande Delta (RGD). To better constrain this, 22 cores were collected in the Bahia Grande Complex (BGC) and analyzed for grain size, organic content, age dates, and elemental composition.

Results show that grain size and sedimentation rates decrease from an artificial inlet to the basin interior, with elevated sedimentation in the south. Also, human activities, such as the construction of the Brownsville shipping channel in the 1930s, altered sedimentation dynamics in the basin by blocking natural inlets and leading …

On Sobolev Spaces And The Existence Of Weak Solutions To Boundary Value Problems, Skye X. Paul

Master's Theses

Many boundary value problems that arise in mathematical models have close connections to second order elliptic partial differential equations. This thesis introduces the idea of weak derivatives and Sobolev Spaces to generalize possible solutions. Using functional analysis centered around the Lax-Milgram theorem, we show the existence of these generalized solutions to boundary value problems including Laplace's Equation, 2nd order linear ODEs, and ultimately a general second order elliptic PDE. The work cumulates with recovering a number of central theorems of functional analysis in the context of Sobolev Spaces, creating a new perspective on the solvability of these boundary value problems.

Exploring Halogen Bonding Interactions In Cocrystals And Deep Eutectic Solvents, Madhushi Bandara

All Dissertations

Halogen bonding is a noncovalent intermolecular interaction analogous to hydrogen bonding that occurs between an electrophile and a nucleophile. This is often observed in compounds containing heavy halogens such as iodine and bromine. When these halogens are covalently bonded to another atom or molecule, preferably to an electron-withdrawing moiety, an electrophilic region called a sigma hole is created on the halogen atom on the extension of the covalent bond.

The first study of this dissertation presents a comprehensive characterization of eight newly synthesized triiodide salts using single-crystal X-ray diffraction, powder X-ray diffraction, thermal analysis (DSC and TGA), and Raman spectroscopy. …

Examining The Roles Of Embodiment And Theory Of Mind In Shaping User Perceptions Of Llm-Driven Conversational Agents, Elizabeth A. Schlesener

All Dissertations

Large Language Models (LLMs) have advanced conversational agents, enabling natural, human-like interactions in domains such as education, programming, and workplace collaboration. Yet, user distrust persists over privacy, accuracy, and bias. As developers work to mitigate these issues and human-AI collaboration expands, reinforcing trust in LLM-driven systems is essential. To address this problem, this dissertation explores the role of anthropomorphic form in LLM-driven conversational agents and its impact on user perception.

According to the familiarity thesis, humans attribute human-like characteristics to nonhuman entities — a process known as anthropomorphism — to better comprehend unfamiliar phenomena, based on the assumption that they …

Ai In Consideration Of Her: Accounting For Gendered Workplace Dynamics In The Design And Evaluation Of Human-Centered Ai Integration In Everyday Workplaces, Kelsea S. Schulenberg

All Dissertations

Rapid advancements in the technical capabilities and availability of generative Artificial Intelligence (AI) systems, such as OpenAI's ChatGPT, have drawn widespread attention to the opportunities and challenges associated with AI integration into everyday workplaces (i.e., office-type work). Following calls for organizations to consider the ethical and workplace-specific impacts of generative AI's use before integrating it into the workplace, this dissertation addresses three critical gaps in Human-Centered Computing (HCC) and AI workplace integration research. First, this dissertation unpacks the underdeveloped links between women's representation - or lack thereof - in AI-related fields and how their experiences with gendered workplace dynamics in …

Quantum Entanglement As A Resource For Coordinating Navigation, Aamir Ahmad

Theses and Dissertations

John Clauser, Michael Horne, Abner Shimony, and Richard Holt (CHSH) originally formulated the CHSH game as an experiment to establish entanglement as a quantum mechanical phenomenon that could not be predicted by classical theories. I will apply the quantum strategy used in the CHSH game to demonstrate that it also establishes a structure that employs entanglement as a resource to enable coordination without communication in the context of navigation.

Grokking Applied To Chaotic Iterates Of The Logistic Map, Felix Donkoh

Electronic Theses and Dissertations

This thesis investigates grokking, the delayed transition from memorization to generalization in neural networks trained on deterministic chaotic data. Using an integer–arithmetic discretization of the logistic map, yn+1 =( a yn(p − yn))/ p 2 , bounded aperiodic sequences were generated across control parameters α ranging from 3.0 to 4.0. Transformer-based models displayed characteristic grokking curves. In periodic and chaotic regimes, validation accuracy rose suddenly after long plateaus, while at the Feigenbaum boundary (α ≈ 3.57) generalization failed completely. Increasing data diversity restored learning in chaotic domains, and explicit α–conditioning enabled a single network to generalize across all regimes. A …

An Income Subsystem As A Discrete Stochastic Leslie System: A Simulation-Based Approach, Fahd Nii Okantah Cobblah

Electronic Theses and Dissertations

This thesis formulates the household-income engine of an integrated population sim- ulator as a Discrete Stochastic Leslie System (DSLS). The nonnegative state vector nt ∈ Rk + aggregates income, savings, debt, employment, and transfers. (Here, the subscript + denotes the positive cone, i.e., vectors with nonnegative components). Annual evolution is linear in state, stochastic in coefficients: nt+1 = Ttnt + εt, with Tt : Rk + → Rk + cone-preserving. Exogenous macro drivers (inflation, employment, tax, salary inflation, mortgage) are forecast via ARIMA; forecasts multiply entries of Tt, preserving linearity in expectation while introducing realistic temporal correlation. The discrete-event implemented …

Application Of Graph Neural Networks On Phase Space Graphs For Cybersecurity, Parker H. Cole

Graduate Theses and Dissertations (2019 - present)

Non-linear phase space analysis may be used to represent time-series data as graph data with transitions between states in the time domain. By studying these transitions, we can predict anomalies within the system. Previous research has demonstrated success in learning from phase graphs for malware and seizure detection. These solutions either require extracting global features or converting the graph into an image for convolutional neural networks (CNNs), which adds a layer of complexity and limits the size and potential expressiveness of a graph. To sidestep current limitations, this study proposed Graph Neural Networks (GNNs) for analyzing phase graphs. GNNs do …

Feedback Strategies In The Market With Uncertainties, Mustapha Nyenye Issah

Graduate Theses and Dissertations (2019 - present)

This paper explores how established firms use strategic advertising to deter new competitors in uncertain markets. Specifically, it models a situation where market demand evolves unpredictably - captured by the CKLS stochastic process, and the incumbent firm may be either strong or weak, a fact hidden from potential entrants. For a company already in the market, advertising is not just about driving immediate sales, it is a strategic tool to project an image of strength and deter potential new competitors. On the other side, a business thinking about entering that market faces a high-stakes, irreversible decision. It will typically hold …

Taxonomy Of Endophytic Fungi Associated With Vallisneria Neotropicalis, Md. Arafat Rashid

Graduate Theses and Dissertations (2019 - present)

This study presents the first comprehensive taxonomic and ecological investigation of endophytic fungi (EF) associated with the submerged aquatic macrophyte Vallisneria neotropicalis in the southern United States. Over a 12 month period, from April 2023 to March 2024, leaf samples were collected from two distinct sites in Mobile Bay, Alabama a disturbed, brackish Causeway location and a cleaner, less impacted site at Meaher State Park. Using culture dependent methods, a total of 257 fungal endophytes were isolated from 1,200 leaf segments. All isolates belonged to the phylum Ascomycota, distributed across 3 classes, 6 orders, 10 families, and 19 taxa. The …

Human Capital, Immigration, And Growth: A State-Level Dynamic Panel Study, William R. Cooper

Graduate Theses and Dissertations (2019 - present)

This study examines whether who immigrates, rather than how many, matters for state economic growth in the United States. It integrates a policy-relevant proxy for skill (H-1B approvals) into an augmented Solow framework that separates immigration's quantity channel from its human capital channel and estimates dynamic effects in a balanced quarterly panel of 50 states (2010 to 2023 ). The empirical strategy estimates a two-step difference GMM Arellano-Bond model that reinforces identification using a double/debiased machine learning (DML) variant that orthogonalizes high-dimensional nuisance components via cross-fitting. This design targets the distinct roles of immigrant headcount versus skill in per capita …

Modeling Flood Risks For Small-Scale Coastal Watershed Across Shared Socioeconomic Pathway Scenarios, Sebastian Loschner

Graduate Theses and Dissertations (2019 - present)

Coastal flooding, driven by the mixture of natural processes and human activities such as precipitation, waves, sea level rise, and urbanization, remains a growing concern. While recent flood-risk studies use IPCC's Shared Socioeconomic Pathways with climate projections to assess future flood exposure, smaller coastal watersheds, < 120,000 acres, remain insufficiently studied. Projected sea level rise is expected to intensify these impacts in smaller systems, potentially causing more severe flooding than reported for larger watersheds. To address this gap, this study used the SRH-2D model integrated with Aquaveo's Surface-water Modeling System to simulate flooding in the lower Fish River basin, Alabama. A coastal watershed sensitive to both riverine and tidal influences. Simulations tested different sea levels based on SSP scenarios corresponding to low, moderate, and high emissions for the years 2050 and 2100. Results indicate that the midcentury scenarios remained even in inundation extent and volume, between 9.9-10.4% compared to the 2020 baseline, the latter scenarios had a wider range in results of between 25.9-50.3% compared to the same baseline. Across scenarios, the most vulnerable and inundated areas of the smaller coastal watershed were the narrow points where upstream flow convene with downstream backwater. These findings highlight the need for futuristic location-specific flood mitigation strategies.

Short-Term Preeclampsia Prediction: Cutoff Variations For Sflt-1/Plgf In U.S. Patients With Or Without Hypertensive Disorders, Yaxin Li, Kristen Cagino, Jim Yee, Caroline Andy, Dajana Borova, Ayush Shah, Isla Racine, Tracy Grossman, Zhen Zhao

Student Papers, Posters & Projects

BACKGROUND: Preeclampsia (PE) is a complex disorder with significant maternal and fetal risks. The soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) ratio shows promise as a diagnostic tool, but its adoption in the U.S. remains limited due to the lack of accessible testing platforms, U.S.-based studies, and evidence-based implementation guidelines.

PATIENTS/MATERIALS AND METHODS: We conducted a cohort study to evaluate the sFlt-1/PlGF ratio for predicting PE within two weeks among pregnant individuals ≥18 years, ≥20 weeks gestation. Serum samples were obtained from routine prenatal visits or triage evaluations. sFlt-1/PlGF ratios were measured using Roche Elecsys assays, and …

Coherent Span-Valued 2d Tqfts, Sophia Marx, Rajan Amit Mehta

Mathematics Sciences: Faculty Publications

We consider commutative Frobenius pseudomonoids in the bicategory of spans, and we show that they are in correspondence with 2-Segal cosymmetric sets. Such a structure can be interpreted as a coherent 2-dimensional topological quantum field theory taking values in the bicategory of spans. We also describe a construction that produces a 2-Segal cosymmetric set from any partial monoid equipped with a distinguished element.

Algebraic Approach To Data Processing: Techniques And Applications, Julio Urenda

Open Access Theses & Dissertations

In many areas of human knowledge, symmetries and invariances play an important role. In fundamental physics, starting with Relativity Theory, new physical theories have been formulated in terms of invariances and of the corresponding transformation groups – i.e., in terms what a mathematician would call an algebraic approach. In engineering, devices like wind tunnels, which are based on scale-invariance, enable us to test smaller-scale models of the actual designs. In biological sciences, symmetries and invariances are extremely important in analyzing the shape and functioning of living beings, from mammals to viruses. Invariance and symmetry – in the form of fairness …

Understanding Outer Solar System Ice Composition Through Impact Processes: Simulations And Hydrates, Jill Rebecca Mcdonald

Graduate Theses and Dissertations

This work concerns solar system ices, particularly water ice and clathrate hydrates. Water ice is abundant as a mineral in the outer parts of the solar system, providing source material for the formation of clathrates. We explored constituents through a combination of numerical modeling and laboratory experiments. Understanding the differences and similarities between these materials can shed light on planetary habitability, composition, and evolution. Chapter one contains relevant background information. This chapter introduces the ices that will be studied and gives insight into the locations of interest within our solar system. It also explains the mechanics of impact cratering processes …

Privacy Protection In Cloud-Based Biometric Systems, Yatish Reddy Dubasi

Graduate Theses and Dissertations

The widespread adoption of server-based biometric authentication systems, often hosted in the cloud, has introduced significant privacy risks. While these systems offer convenience, they require storing sensitive biometric templates on remote servers, creating a high-value target for adversaries. Unlike passwords, compromised biometric data is immutable and cannot be reissued, leading to an irreversible loss of privacy. This threat is exacerbated by template inversion attacks, which can reconstruct a user's original biometric trait (e.g., a face image) from its stored feature vector. This dissertation addresses these critical privacy challenges by designing, implementing, and evaluating a suite of novel frameworks for privacy-preserving …

Multi-Hop Hybrid Graph Neural Network, James Arthur

Open Access Theses & Dissertations

Graph-structured data appear across diverse domains, such as social networks, citation graphs, biological systems, and knowledge bases. Graph Neural Networks (GNNs) have emerged as a powerful framework for learning on such data, yet existing architectures face significant challenges. Graph Convolutional Networks (GCNs) suffer from over-smoothing as depth increases, Graph Attention Networks (GATs) introduce computational and statistical instabilities, and naïve multi-hop propagation inflates memory and computation while failing to adapt to topology. These limitations motivate the development of a new framework that is both expressive and scalable. This dissertation proposes the Multi-Hop Hybrid Graph Neural Network (MHHGNN), a novel architecture that …

Advancing Effective Connectivity Analysis: Robust And Sparse Group Dynamic Causal Modeling Via Extended Parametric Empirical Bayes, Godfred Arhin

Open Access Theses & Dissertations

Dynamic Causal Modeling (DCM) provides a principled framework for estimating effective connectivity in neuroimaging data, with Parametric Empirical Bayes (PEB) enabling hierarchical inference across sessions and subjects. However, standard PEB assumes Gaussian distributions at all hierarchical levels and employs conventional shrinkage priors that do not enforce true sparsity, limiting robustness to outlying subjects and reducing sensitivity to parsimonious connectivity structures. This thesis introduces a robust, sparsity-inducing hierarchical extension to DCM-PEB that addresses these limitations through two key innovations. First, a Student-t group-level likelihood replaces the conventional Gaussian likelihood, automatically downweighting outlying subject-level parameters using adaptive precision weights while retaining computational …

Microgrid Assessment And Ml-Based Power System Faults Detection Leveraging Real-Time Co-Simulation, Diego Normando Gandara Mendez

Open Access Theses & Dissertations

The rapid growth of distributed energy resources (DERs) and the increasing reliance on data-driven decision making have reshaped the operational challenges of modern electric power systems. As microgrids become more prominent in distribution networks, utilities require methods that unify planning, control, and real-time situational awareness to ensure resilient operation under faulted or uncertain conditions. The goal of this MSEE thesis is to design and validate a latency-aware ML framework for rapid, reliable fault detection in distribution grids. To achieve the goal of the thesis, there are three specific objectives. Objective 1 evaluates optimized microgrid configurations under varying DER levels and …

Lamda: A Longitudinal Android Malware Dataset For Benchmarking Concept Drift Detection And Adaptation, Md Ahsanul Haque

Open Access Theses & Dissertations

Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in the Android ecosystem, adversarial development of new malware families, and the continuous emergence of both benign and malicious applications. Prior studies have shown that such concept drift—distributional shifts in benign and malicious samples—leads to significant degradation in detection performance over time. Despite the practical importance of this issue, existing datasets are often outdated and limited in temporal scope, diversity of malware families, and sample scale, making them insufficient for …

Using Uranium And Strontium Isotopes To Identify Water Flow Paths And Solute Sources In Agricultural Upper Snake-Rock Watershed In Idaho: Understanding Agrohydrology Processes Of Dryland Critical Zone, Jennifer Herrera

Open Access Theses & Dissertations

Irrigation in agricultural systems alters hydrological cycles by redistributing surface water and groundwater, modifying Critical Zone elemental cycles, and impacting water quality and availability. Here, I focus on understanding agrohydrologic processes in Dryland Critical Zones and the impacts of land-use changes and climate variability in the extensively irrigated agricultural Upper Snake-Rock Watershed in semi-arid south-central Idaho. The Snake River originates in Wyoming and flows across southern and central Idaho. The Snake River supplies irrigation water to the Kimberly and Twin Falls areas of Idaho, the focus of this study, which is characterized by intensive agricultural activity. With the increasing pressure …

Exploiting The In-Distribution Embedding Space With Deep Learning And Gaussian Discriminant Analysis For An Out-Of-Distribution Malware Attach Detection, Tosin Olusola Ige

Open Access Theses & Dissertations

State-of-the-art machine and deep learning models generally perform well on previously seen data, albeit with wrong close world assumption that all real-world data are from previously seen train and validation samples, hence there poor performance when exposed to data which deviates from previously seen training and validation set. This is clearly evident in the domain of cybersecurity where the world continues to experience several high profile malware attacks despite advancement in state-of-the-art research. The reason being that the constant evolvement of innovation in the development of tools and method deployed to carry out various attacks had given hackers and other …

Phishibl: A Systematic Evaluation Of Instance-Based Learning Model For Predicting Phishing Susceptibility, Shova Kuikel

Open Access Theses & Dissertations

Despite enormous efforts to develop defenses against phishing attacks, humans still struggle to detect phishing emails given the constantly evolving attacker strategies. This thesis aims to test the predictive capabilities of a cognitive model that represents the individual susceptibility to phishing emails. While training programs aim to raise awareness, most remain outdated and ineffective against evolving attack strategies. Recent advances in Machine Learning, Artificial Intelligence, and Large Language Models (LLMs) offer new defenses, yet understanding human decision processes remains crucial, as effective systems must emulate how people evaluate unfamiliar emails based on prior experience. This research introduces a cognitive model …

Lorentz Invariance And Quantum Coordination: A Neo-Aristotelian Framework For Entanglement And Relativity, Angel Rafael Sosa Muniz

Open Access Theses & Dissertations

The phenomenon of quantum entanglement has been at the center of heated debates among physicists and philosophers since the dawn of the quantum era. The early discussions initiated by Einstein, Podolsky, and Rosen—regarding potential violations of the Principle of Relativity by entangled particles, expressed in the so-called “EPR Paradox”—ultimately culminated in the demonstration of Bell’s theorem and its violations. Since then, philosophers of science and physicists have developed multiple proposals attempting to account for the ontological status of quantum formalism and its possible tension with relativistic principles. This thesis contributes to these efforts by advancing a Neo-Aristotelian ontological framework inspired …

Reservoir Characterization Of The Clarksville Formation In Red River County, Texas, Matthew Allen Parker

Electronic Theses and Dissertations

The Upper Jurassic Clarksville Formation in northern Texas and southern Oklahoma is a productive but poorly understood sandstone–conglomerate unit with unresolved questions regarding its depositional environment and sediment provenance. This study evaluates the depositional setting, spatial distribution, and diagenetic controls of the Clarksville Formation using integrated core and well log analyses in Red River County, Texas. Three cores (Lennox Unit #1, Reeder Oil Unit #2, and Reeder Oil Unit #7) were analyzed for lithology, mineralogy, porosity, permeability, and fluid saturation, and correlated with 137 well logs to map formation thickness and regional trends.

Four lithofacies were identified based on core …