Digital Commons Network^™

Elucidating The Mechanisms Of Collagenase H From Hathewaya (Clostridium) Histolytica In Collagen Degradation And Developing A Cross-Linked Platform For Efficient Capture And Release Of Adeno-Associated Virus Serotype 2 (Aav2), Adjoa Otubea Bonsu

Graduate Theses and Dissertations

In biological systems, Hathewaya (Clostridium) histolytica secretes collagenases that degrade structural collagen in host tissues and cause tissue necrosis. Despite their detrimental effects, these enzymes have significant therapeutic potential by effectively digesting various collagen subtypes to treat connective tissue disorders. The bacterium produces two homologous collagenases, ColG and ColH, with a catalytic module crucial for hydrolyzing collagen and binding domains, including collagen-binding domains (CBDs) and polycystic kidney disease-like domains (PKDs), for substrate interaction.

Although the crystal structure of the catalytic module of collagenase G (ColG) has been determined, shedding light on its chew-and-digest mechanism, the structure of collagenase H (ColH) …

The Reality Of Reputation: Exploring Proxy Measures And Predictive Modeling In U.S. News & World Report College Rankings, Aubree Diane Hughart-Thomas

Graduate Theses and Dissertations

Since 1983, U.S. News & World Report (USNWR) has published annual college rankings that shape institutional decision-making, student choices, and public perceptions of higher education. While widely utilized, these rankings have been criticized for their reliance on subjective reputation metrics, methodological opacity, and emphasis on factors correlated with institutional wealth rather than student outcomes. This study examines whether publicly available data from sources such as the Integrated Postsecondary Education Data System (IPEDS) and the U.S. Department of Education’s College Scorecard can serve as proxies for the factors used in the USNWR National Universities 2025 Best Colleges rankings. Using …

Meeting The Challenge: The Relationship Between Students’ Educational Gains And Teacher Full-Time Versus Part-Time Status, Dorissa Kaufman

Graduate Theses and Dissertations

This study investigates the effect of instructor status—full-time versus part-time—on student Educational Functioning Levels (EFLs) in adult education centers across Arkansas from 2018 to 2023. Given that funding for these centers is directly tied to student performance, determining the most effective type of instructor is essential. The research examines programs like GED, English Language Learners (ELL), Integrated Education and Training (IET), and Workforce Alliance for Growth in the Economy (WAGE), addressing challenges such as a high number of adults without high school diplomas. Employing a regression model for analyzing EFL gains, the study seeks to provide actionable insights for hiring …

Investigating The Impact Of Gear Up Arkansas On College Readiness And Post-Secondary Enrollment In The Delta, Erika Mcmahan

Graduate Theses and Dissertations

This study evaluates the impact of the Gaining Early Awareness and Readiness for Undergraduate Programs (GEAR UP) Arkansas initiative on improving college readiness and postsecondary enrollment among low-socioeconomic status (SES) students in the Arkansas Delta, a region marked by systemic educational inequities and persistent poverty. Framed by Opportunity Gap Theory, this quantitative, quasi-experimental research addresses two questions: (1) How does GEAR UP participation influence postsecondary enrollment compared to nonparticipants? (2) Do participants achieve higher ACT scores than nonparticipants? Using data from 491 students across high-poverty schools, the study employs logistic regression to assess enrollment rates and multiple regression to analyze …

Comparison Of Airborne Lidar-Derived Elevation Data In Fayetteville, Arkansas, Usa, Angelica M. Otting

Graduate Theses and Dissertations

Light detection and ranging (lidar) laser scanners are prominent remote sensing tools to produce high resolution three-dimensional (3D) imagery of the Earth’s surface. These laser scanners combined with global navigation satellite systems (GNSS) and real-time kinematic (RTK) reference stations can generate some of the most accurate ground surface imagery and elevation data for terrain mapping and related applications. Lidar aerial survey is an important tool in industries such as architecture, civil engineering, forestry, geology, geography, and agriculture where digital terrain models (DTMs) can be used to examine the geographical landscape and urban industry. Currently, there are three different common laser …

Contrastive Learning Techniques For Fraud Detection, Vinay Madanbhavi Shashidhar

Graduate Theses and Dissertations

Detecting fraud in computing platforms involves identifying malicious user sessions, often using deep learning models, but several challenges hinder effective deployment. Attackers can craft diverse malicious sessions that closely resemble normal ones, complicating the learning of robust decision boundaries. While supervised contrastive learning offers a promising solution through class-specific clustering, its potential remains underexplored. Real-world datasets typically contain few labeled malicious sessions and many normal ones, creating an open-set anomaly detection challenge. Costly expert annotation further limits labeled data, especially for smaller organizations, leading to Positive Unlabeled (PU) learning and noisy label learning issues. Organizations are increasingly turning to LLMs …

Adversarial Machine Learning: Methods For Attacks And Defenses, Minh Hao Van

Graduate Theses and Dissertations

With the rapid development of machine learning in real-world applications, enhancing security plays an important role. Adversarial machine learning focuses on understanding malicious actions from attackers and developing defensive techniques against such threats when deploying machine learning systems. An attack can occur in different scenarios, such as poisoning attacks during the training stage and evasion attacks during the testing stage. Although extensive research has explored defense strategies to deal with these harmful attacks, there is a need for further research into areas such as how to counteract malicious attacks with healthy noise or how to train an adaptive defense against …

Controls On Nutrient And Sediment Export In A Non-Perennial Agricultural Stream, Kathleen Cutting

Graduate Theses and Dissertations

Climate change is expected to increase flow variability and intermittency in streams, changing the magnitude and timing nutrient and sediment delivery. This may exacerbate water quality issues in agricultural landscapes, where nutrient and sediment inputs are often elevated. Elevated nutrients can drive eutrophication in downstream waterways, and excess sediment loss can decrease primary productivity. Non-perennial headwater streams are particularly vulnerable to the impacts of climate change and agriculture as they serve as key sites for nutrient cycling at the terrestrial-aquatic interface. Despite their importance, the interactive effects of agriculture and stream intermittency, as well as unpaved roads in rural watersheds …

Synthesis And X-Ray Characterization Of Nickel Phosphide Nanostructures For Water Oxidation, David Thompson

Graduate Theses and Dissertations

The pursuit of earth-abundant 3d transition metal catalysts for alkaline water electrolysis remains an active area of research. While NiFe layered double hydroxides (LDH) are currently the most active catalysts for the oxygen evolution reaction (OER), doping Ni-based catalysts with phosphorus to form Ni metal phosphides has emerged as a promising alternative. However, the complex crystalline and amorphous phases of NiP_x have hindered their characterization, and the OER mechanism and origin of activity remain unclear. This dissertation aims to elucidate the synthesis, OER performance, and in situ reconstruction of amorphous NiP_x-based nanomaterials, with a focus on in …

Constraining Neutron Star Kicks Through Gravitational Wave Detection, James Phillips

Graduate Theses and Dissertations

Neutron stars have, on average, much higher proper motions than those of hot and bright O and B type main sequence stars. Thus, it is widely suspected that asymmetries in core-collapse supernovae provide a substantial kick to these compact objects at birth. Gen- eral Relativity predicts that such supernova asymmetries will generate bursts of gravitational radiation, though no such burst has been yet detected. Here we argue that the polarization amplitudes of those gravitational waves can be used to calculate the kicks imparted on that nascent neutron star. We begin by reviewing the evidence for natal kicks, then we describe …

Real-Time Anomaly Detection In Ot Networks Using Gru-Based Autoencoders, Grant Austin Wilkins

Graduate Theses and Dissertations

Operational Technology (OT) networks, particularly those used in critical infrastructure, face increasing cyber threats that target network-level protocols and behaviors. While most anomaly detection research for OT systems has traditionally relied on sensor data, this thesis explores the viability of detecting malicious activity directly from network telemetry. We propose a sequence-to-sequence autoencoder model based on Gated Recurrent Units (GRUs) with multilevel attention, trained to reconstruct normal patterns of packet-level communication extracted from raw PCAP data. The developed feature engineering pipeline integrates general networking attributes such as IP and MAC addresses, ports, and transport protocols with OT-specific protocol information from Modbus …

A Robust Rf Fingerprinting Approach Using Physics-Informed Neural Networks, Jozef Dusenka

Graduate Theses and Dissertations

Radio frequency (RF) fingerprints, caused by unique imperfections in communication hardware, offer a promising solution for zero-trust security. However, existing RF fingerprinting techniques, which aim to extract these signatures from transmitters to uniquely identify devices, often struggle with robustness in the face of temporal and spatial variations in real-world, time-varying wireless environments. For example, a neural network trained on RF signals collected on Day 1 can experience a significant performance drop when tested with data from Day 2.

To address this challenge, we propose a novel, robust RF fingerprinting method based on Physics-Informed Neural Networks (PINNs). Rather than training the …

Insights From Automated Mineralogic Analysis Of Modern Sand, Quinten Jones

Graduate Theses and Dissertations

Methods for determining and categorizing the modal compositions of sand and sandstone have long been a subject of debate in the field of sedimentary geology. Point counting is the most commonly used technique for determining modal compositions from petrographic slides, which are then categorized based on relative proportions of quartz, feldspar, and rock (or lithic) fragments. However, this approach fails to adequately preserve relevant textural data, such as grain size and sorting, which play an important role in diagenesis and in influencing reservoir quality. Additionally, the categorical nature of point counting results in a lack of specificity and loss of …

Atomic Level Characterization For The Transport Cycle Conformational Pathways Of Multidrug Resistance Protein 1 (Mrp1), Samuel Wamwere Mwatha

Graduate Theses and Dissertations

The multidrug resistance-associated protein 1 (MRP1/ABCC1) is an ATP-binding cassette (ABC) transporter that mediates the cellular efflux of endogenous and xenobiotic substrates, including therapeutic drugs. Its overexpression is a major contributor to multidrug resistance in cancer, which tends to result in negative clinical results. Even with considerable progress made in structural biology, the intricate details of how membrane lipid composition affects the conformational changes and functional switches of MRP1 still lack explanation.

This thesis employs long-timescale, all-atom molecular dynamics (MD) simulations to study the influence of various lipids on MRP1 lipid bilayer environments. Both inward-facing and outward-facing conformations of MRP1 …

Koszul Cohomology Of Canonical Products, Alexander Scott Duncan

Graduate Theses and Dissertations

In this thesis, we give a complete classification of the Koszul cohomology groups K_p,₁(C, B, ω_C ⊗ B) on a smooth curve C of genus g ≥ 2 with B p-very ample. Such a classification in the case B = ω_C has been incomplete until [5]. The classification follows easily from our main result which precisely calculates K_p,1(C, B, B ω_C ⊗ B) in terms of h⁰(C, B). This result also handles the case B = ω_C. A straightforward …

Rotation Errors Due To Field Quantization For Simultaneously Driven Atoms, Hunter Lindemann

Graduate Theses and Dissertations

If we want to physically implement qubits by using two level atoms within a cavity, then certain single-qubit gates (such as the X-gate) can be performed by bathing the atoms in an electromagnetic field from a laser. If the average photon count n̄ of the field greatly exceeds the number of N qubits, then the slight fluctuations of the field's phase and amplitude are mostly negligible. However, such a strong field might require more energy than what is desirable for the setup. If a weaker field is used in which phase and amplitude fluctuations might be noticeable, then the …

Leveraging Machine Learning Models For Enhanced Landslide Prediction In Western North Carolina, Andrew Edmonds

Graduate Theses and Dissertations

Landslides pose significant hazards to human safety, infrastructure, and the environment, particularly in regions of high elevation that experience extended periods of heavy rainfall. This research focuses on preparing and evaluating landslide susceptibility maps (LSMs) for the Blue Ridge Mountains, a portion of the Appalachian Mountains in western North Carolina, utilizing three machine learning algorithms: Logistic Regression, Random Forest, and Gradient Boosting Regression. Sixteen landslide conditioning factors, reflecting topographic, geological, environmental, and anthropogenic influences, were identified for model input. The landslide inventory database, comprising 7,350 locations, was randomly divided into training (80%) and testing (20%) sets. The performance of each …

Perceiving School Success: A Phenomenological Study Of Asian Indian Parents, Scott Nozik

Graduate Theses and Dissertations

This phenomenological qualitative study aimed to explore how Asian Indian parents at East Ridge Elementary School perceived student success. The research sought to understand their experiences, their views on student success, and the factors they perceived as supports or barriers to achieving that success. Interviews with 14 Asian Indian families revealed four major themes: Positive School Experiences, Academic Programming, Holistic View of Education, and Parental Engagement. While communication emerged as a central element, it was not categorized as a theme but rather as a supporting factor. The study found that parents expressed satisfaction with the school’s cultural inclusivity and sense …

Rewriting The School Calendar: A Mixed-Methods Analysis Of Four-Day School Week Implementation In Arkansas, Kate Barnes

Graduate Theses and Dissertations

The four-day school week (4DSW) has gained traction across the United States, particularly in rural districts seeking innovative responses to mounting challenges. Despite its expanding adoption, key questions remain regarding the motivations for implementing 4DSW schedules and their implications for teacher well-being and student achievement. This dissertation examines the emergence and effects of the 4DSW in Arkansas, where adoption has increased rapidly in recent years, offering a unique context shaped by state-level policy and rural dynamics. Employing a mixed-methods, this dissertation investigates (1) why and how districts adopt the 4DSW, (2) whether the policy alleviates teacher burnout, and (3) how …

Survival Signature Estimation For All-Terminal Networks By Solving The Multi-Objective Bottleneck Spanning Tree Problem, Dewan Maisha Zaman

Graduate Theses and Dissertations

This research examines the problem of estimating the survival signature of all-terminal networks using Monte Carlo (MC) simulation. Following a recent similar result for twoterminal networks, we show that the work required within each MC replication corresponds to solving a multi-objective bottleneck spanning tree (MOBST) problem. We implement the resulting MC procedure using a “Blocks” algorithm from the literature to solve the MOBST in each replication by identifying its minimal set of non-dominated points. We compare this implementation against intuitive benchmark procedures for completing the work within an MC replication. We conduct numerical experiments to assess the efficacy of multi-objective …

Nonparametric Methods For Bayesian Community Detection In Complex Networks, Kedran Young

Graduate Theses and Dissertations

Network analysis is becoming an increasingly popular interdisciplinary area of study, with emerging interest in fields like sociology, biology, economics, and ecology. Within the niche of network analysis, capturing the community structure of a network is one important achievement that many statisticians have been working toward over recent decades. The most popular modeling technique for latent community detection is the Stochastic Block Model (SBM), which falls into the category of latent variable models and will serve as the baseline model throughout this thesis. SBM is widely regarded as the most effective community detection method as it detects latent community membership …

Application Of Ordinal Regression Models To Acquired Stress Resistance In Wild Strains Of Saccharomyces Cerevisiae, Carson Stacy

Graduate Theses and Dissertations

This thesis explores the application of ordinal regression to the analysis of semi-quantitative growth assays often used when comparing fitness for different strains of the model yeast Saccharomyces cerevisiae. For stress survival assays, yeast stress resistance is measured using an ordered survival score that ranges from 0 (no growth) to 4 (confluent growth). Traditional approaches to analyze this type of data either treats data as a nominal categorical variable or as a continuous numerical variable. These approaches risk loss of information or violation of testing assumptions. In contrast, cumulative logit ordinal regression uses the information contained in the order …

Comparison Of Rt-Qpcr And Rt-Ddpcr On Assessing Model Virus In Wastewater, Wafa Youssfi

Graduate Theses and Dissertations

There is an increasing demand for quantifying viral loads in diverse wastewater systems using polymerase chain reaction (PCR). This study evaluates the performance of two commonly used workflows: reverse transcription quantitative PCR (RT-qPCR) and reverse transcription droplet digital PCR (RT-ddPCR) in wastewater. We compared the two methods by measuring the viral ribonucleic acid (RNA) of a model virus Phi6 in samples collected from various treatment stages at the Westside Wastewater Treatment Facility in Fayetteville, AR. RNA was extracted from real and synthetic wastewater samples and analyzed in parallel using both RT-qPCR and RT-ddPCR. Findings reveal that both methods demonstrated similar …

Subsurface Characterization Using Electrical Resistivity Tomography (Ert) Integrated With Masw, P-Wave Refraction, And Boring Results, Mohammadyar Rahimi

Graduate Theses and Dissertations

Electrical resistivity tomography (ERT) has gained widespread application in geotechnical engineering as a non-invasive method for subsurface characterization. Despite its versatility, several challenges remain regarding its resolution capabilities, interpretive limitations, and integration with other investigative methods. This dissertation addresses three key applications of ERT, focusing on its effectiveness and limitations in resolving thin subsurface layers, detecting anomalies such as voids, and supporting environmental assessment in river restoration projects.

The first study investigates the ability of ERT to resolve thin, discontinuous clay layers beneath levees, using the Melvin Price Reach of the Wood River Levee on the Mississippi River as a …

Survival Signature Estimation Using Optimization And Monte-Carlo Simulation For K ≥ 3 Classes Of Nodes On Two-Terminal Networks, Md Sazid Rahman

Graduate Theses and Dissertations

This research develops an efficient approach to estimating survival signatures for two-terminal networks with more than two classes of components. Recently, the survival signature has gained substantial attention in the literature on network reliability estimation due to its unique separability property, which enables passing the network topology information independent of the failure distribution of the components. Following recent results from the literature, estimating the two-terminal survival signature by Monte Carlo simulation entails solving a multi-objective maximum capacity path problem on a two-terminal network in each replication. We adapt a multi-objective Dijkstra’s algorithm from the literature to construct the set of …

Towards Multimodal Scene Graph Generation Approaches To Video Understanding, Trong-Thuan Nguyen

Graduate Theses and Dissertations

This thesis advances video understanding by enhancing Video Scene Graph Generation (VidSGG) through improved temporal modeling, the integration of long-range temporal dependencies via continuous updates to interaction histories, and the utilization of Large Language Models (LLMs) for scene graph reasoning. To this end, three novel datasets and corresponding approaches are introduced. First, the ASPIRe dataset incorporates interactivity annotations and leverages the Hierarchical Interlacement Graph (HIG) for hierarchical temporal modeling, providing deep insights into scene changes and effectively capturing intricate interactions. Next, the AeroEye dataset, focusing on drone videos, is paired with the Cyclic Graph Transformer (CYCLO), which establishes circular connectivity …

Administering Grant-Subsidized Workforce Training: A Critical Case Study, Kaleb Preston Futch

Graduate Theses and Dissertations

Grant monies are distributed for the purpose of funding workforce training programs aimed at affecting the skills gap in the United States. While research has shown that these grants are being awarded, there is not a well-established standard for designing and implementing the programs they fund. This study focuses on recording and classifying the lived experiences of grant staff working on a state funded workforce training program in the United States. This study will employ a qualitative case study approach designed to capture and codify, in the words of those most familiar with it, the aspects of the grant program …

Quantifying The Effects Of Urbanization On Stream Nutrient Loads And Decomposition, Caroline Anscombe

Graduate Theses and Dissertations

Urban stream syndrome (USS) refers to the common symptoms of degradation in urban streams globally. Two symptoms of USS are increased nutrient and salt concentrations in streams, affecting ecosystem structure and function. Excess nutrients negatively impact freshwater ecosystems, placing downstream habitats at risk of eutrophication and harmful algal blooms. Excess salt runoff, derived from road salt application, is toxic to freshwater organisms and affects leaf litter decomposition, the base of aquatic food webs. Here, I aimed to quantify the effects of nutrient pollution and salinization on urban water quality and ecosystem function. Despite the widespread consequences of nutrient pollution, most …

Exploring Latent Mediation Through Bayesian Regularization Methods Of Lasso, Ridge, Horseshoe, Spike-And-Slab, Ethan Harris

Graduate Theses and Dissertations

Regularization is a powerful tool to combat overfitting and drive sparsity in complex models. Regularization was initially applied in regression modeling but has been increasingly utilized in structural equation modeling where its utility in identifying the essential components has helped improve modeling. As structural equation models have increased in complexity both in the number of indicators but also the number of latent factors, researchers have begun to investigate how applying Bayesian regularization to these systems can further push the limits on modeling complex models with limited sample sizes. One area where research is limited is the application of Bayesian regularizations …

Achieving Flexible Fairness And Privacy In Federated Learning, Alycia N. Carey

Graduate Theses and Dissertations

Having access to large, high-quality datasets is crucial for training machine learning models that achieve satisfactory performance. Unfortunately, it is common that a single entity (e.g., mobile device or organization) does not have access to such datasets due to monetary or resource constraints. Traditional machine learning requires that all training data reside in a centralized location during the entire duration of model training, however, in many circumstances it is difficult or even impossible (e.g., due to governmental regulations) for multiple parties to combine their data to meet this constraint. Federated learning is a machine learning paradigm that facilitates the joint …