Digital Commons Network^™

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 …

How A College Course Benefits Under-Resourced High Schoolers Interested In Veterinary Medicine, Gwendolyn R. Svenson, Olivia Arnold, Patricia Vigil, Naomi Nishi, Christianne Magee

Journal of Youth Development

The Veterinary Perspectives Institute (VPI) was designed to address the challenges faced by underrepresented youth who are interested in veterinary medicine. This study examines the VPI, a tuition-free program that introduces students from under-resourced high schools to veterinary medicine and One Health through a hands-on, transdisciplinary approach. The VPI integrates One Health concepts by linking human, animal, and environmental health in the context of veterinary medicine. The program’s aim is to foster college readiness and career exploration in veterinary and biomedical fields, while also addressing barriers to diversity in veterinary medicine. Through hands-on learning and case studies in One Health, …

A Comprehensive Performance Comparison Of Machine Learning And Federated Learning For Intrusion Detection In Vehicular Ad-Hoc Networks Using Can-Bus Data, Tim Leonhardt

Honors Theses

Federated Learning (FL) is a Machine Learning (ML) approach that decentralizes training across distributed devices, eliminating the need to centralize data. Unlike traditional ML, where models are trained on aggregated data, FL sends a global model to multiple nodes for local training, with updated parameters transmitted back to the server for aggregation. This process preserves data privacy, making FL ideal for sensitive applications like cybersecurity. However, FL introduces challenges such as data heterogeneity, communication overhead, and difficulties in achieving model convergence, which can impact performance.

This study investigates a fundamental assumption in ML and FL research: that the superior performance …

Mathematical Melodies: The Exploration Of Music Using Fourier Signal Analysis, Courtney Francois

Honors Theses

Upon initial inspection, mathematics and music appear to be distinct disciplines lacking any connections to one another. However, many complex intersections lie beneath the surface. This paper seeks to explore these connections through the analysis of the Fourier series and the Fourier transformation of musical sound signals. Orthogonal functions of various frequencies are used to approximate and recover sound signals. Following the examination of the basis of Fourier signal analysis, MATLAB is utilized to visualize the connectedness of mathematics and music through sound signals from varied musical instruments and by performing Fourier signal analysis to the sound signals. Then, the …

Privacy-Aware Ai-Based Agricultural Monitoring Using Internet Of Drones, Md Benozir Hossain

Honors Theses

Artificial Intelligence (AI) has become a vital tool for agricultural farming. AI-based image processing models utilizing different machine learning (ML) algorithms and deep learning (DL) offer advanced functionalities in disease detection, yield estimation, land use, etc. This thesis examines AI-driven techniques utilizing Convolutional Neural Networks (CNN) with the addition of Federated Learning (FL) to analyze satellite and drone images for agricultural insights, especially in detecting Cotton diseases. The AI models improve agricultural farming in many ways, such as using data to make critical decisions, reducing labor costs, pest infestations, etc. Moreover, these models allow farmers to minimize yield losses by …

Cellular Telemetry For Real-Time River Flow Velocity Data From Fluvial Acoustic Tomography Loggers, Joshua Lee Seymour

Honors Theses

Streamflow is a critical element in understanding watershed processes and the effects of land use on those processes because it is the primary medium through which water, sediment, nutrients, organic material, thermal energy, and aquatic species move. Fluvial Acoustic Tomography (FAT) systems offer accurate direct measurements of river section-averaged flow velocity by measuring reciprocal acoustic travel times between at least two acoustic nodes positioned on opposite riverbanks. However, similar to many in-situ sensors used in estuarine and riverine environments, FAT loggers require manual data retrieval, which is time-consuming, labor-intensive, and limits real-time access and increases maintenance costs. This project presents …

Engaging Scientific Sense-Making: An Analysis Of Participation Within Informal Science Interaction, Spencer Barbier

Honors Theses

While science learning in formal educational settings has been extensively examined using frameworks such as Bloom’s Taxonomy, its application to informal science educational environments remains underexplored. Informal science education—occurring outside traditional classrooms—presents unique challenges for evaluation due to its unstructured nature and diverse audiences. This action research study aimed to address this gap by developing and testing a framework that categorizes learner responses into high-order and low-order answers, based on Bloom’s six cognitive domains. The study was conducted at the Hattiesburg Zoo, where a docent volunteer presented two separate docent boxes, or mini science lessons, to members of the public. …

The Effect Of The Black Ink Red Ink Testing Method On Anxiety Levels, Test Performance, And Note-Taking Behavior Among College Students, Peyton Murphy

Honors Theses

Anxiety among students has emerged as a pressing concern due to its significant impact on academic performance and mental well-being (Chen, 2004; Davis, DiStefano, & Schutz, 2008) and is particularly evident in student performance within STEM-related courses (Magula, 2019; Vakili & Pourrazavy, 2017). Previous research has explored various factors influencing test anxiety, including testing methods. The present study examined a testing method called "Black Ink Red Ink" (BIRI), proposed by Rodgers and Rodgers (2011). The effects of the BIRI method were examined on (1) test anxiety, (2) test scores, and (3) note taking motivation and behaviors. Thirty-eight students in a …

Tailoring The Morphology Of Block Copolymers Via Solvent-Vapor Annealing, Bayleigh Madelin Loving

Honors Theses

Triblock polystyrene-polyisobutylene-polystyrene (PS-PIB-PS) copolymers have widespread applications from thermoplastic elastomers to protective coatings for ballistic applications. The incompatibility between the soft PIB and hard PS phases leads to phase separation and subsequent formation of morphological domains like lamellae and cylinders. The structures are stabilized by physical crosslinks, which can greatly improve the toughness of the triblock systems by preventing chain pullout during tensile deformation. We tailored the morphologies (achieving different morphologies with the same block copolymer) by changing the annealing conditions of the block copolymers. Solvent vapor annealing was used to selectively activate the mobility of a particular block, causing …

A Spectroscopic Survey Of Atmospheres Of Super-Earths, Luke Brust

Honors Theses

Recent research has made it possible to use spectroscopy to analyze the composition of the atmospheres of exoplanets, planets that orbit other stars. Most projects thus far have focused on the atmospheres of gas giants, as it is less challenging to observe them with available equipment. This project studies the atmospheres of four Super- Earths, planets that have a mass greater than the Earth but smaller than Neptune. This is accomplished by processing the raw spectroscopic data from the Hubble Space Telescope and modeling the atmosphere using the program 𝝉-Rex3. This survey found clear results from two of the selected …

Globally Adaptive Exponential Integrators For Stiff Systems Of Odes, Anzhelika Vasilyeva

Honors Theses

This thesis introduces a novel method for solving systems of Ordinary Differential Equations (ODEs) resulting from the spatial discretization of Partial Differential Equations (PDEs). The proposed approach builds upon an existing technique that employs Krylov projection, which requires evaluating a matrix function at each timestep. The innovation of the new method lies in its reuse strategy, which shifts the perspective from direct matrix function evaluation to polynomial interpolation. Numerical experiments conducted on constant and variable coefficient heat equations, with both smooth and discontinuous initial data, demonstrate the computational time advantage of the new approach. The results indicate that this method …

Chemistry Education At The University Of Southern Mississippi Through Computation And Active Learning Methods, Sadie Pitre

Honors Theses

Education is one of the pillars of society. It evolves alongside scientific progress, shaping how students learn and engage with complex topics. The incorporation of new educational standards at The University of Southern Mississippi (USM) was implemented in two parts: through computational chemistry laboratory exercises for teaching laboratories and through the synthesis of organic molecules for research projects in an upper-level spectroscopy class. The goal of these projects is to improve learning of abstract and/or complex topics beyond the teaching methods traditionally utilized in the undergraduate laboratory and in the classroom.

Computational chemistry, which uses theoretical calculations to model molecular …

Evaluating Accuracy In Legal Research: A Comparative Analysis Of Traditional And Artificial Intelligence-Driven Methods, Lily Collins

Honors Theses

Legal professionals are challenged with performing accurate legal research to meet professional standards. The use of artificial intelligence (AI) presents a potential for enhanced efficiency. However, knowledge and process gaps exist, questioning the accuracy of results generated by AI. The uncertainty of accurate results poses a risk for poor outcomes for all stakeholders. This thesis aimed to quantitatively measure the accuracy of AI legal research in comparison to traditional legal research. Accuracy and relevance were measured by analyzing case precedent results using three legal research platforms, Westlaw, Nexis Uni, and Chat GPT. The three platforms represented traditional and AI-driven research, …

Synthetic Efforts Towards An Analog Of Ly426965, Macy Sproles

Honors Theses

The 5-hydroxytryptamine_1A (5-HT_1A) receptor is a key target in the treatment of various neuropsychiatric conditions, and the development of selective antagonists offers promising therapeutic potential.²⁴ This study aimed to develop a synthetic analog of LY426965, a selective 5-HT_1A receptor antagonist. The synthetic route was initiated from dimethyl-2-phenylmalonate, employing pig liver esterase (PLE) as an enantioselective biocatalyst to achieve regioselective hydrolysis of one ester moiety in the malonic diester intermediate.¹⁷ Through a series of stereochemically controlled transformations, a new intermediate was successfully synthesized. Although the target molecule was not obtained, a new compound was synthesized …

Smoking And Oral Health In The Tipu Maya: A Study Of Nicotine In Dental Calculus, Emily Byrd

Honors Theses

Tobacco smoking was a deeply rooted tradition in Maya society, often associated with ritual and daily life, and may have contributed to patterns of dental health. However, despite its cultural significance, direct biological evidence of tobacco use in ancient populations is rare. This study addresses that gap by analyzing nicotine residues preserved in dental calculus from the Tipu Maya, offering new insight into both cultural practices and potential health impacts.

Mass spectrometry identified molecular peaks corresponding to nicotine and its degradation products within the calculus samples.  Analysis revealed that over two-thirds of the individuals tested positive for nicotine, supporting the …