Digital Commons Network^™

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 …

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 …

Exploring Hybrid Cohort-Based Doctoral Program Features That Support Nontraditional Doctoral Students’ Persistence: A Preregistered Case Study., Glenda Winborn

Graduate Theses and Dissertations

With the rise of hybrid learning models, non-traditional learners have greater access to doctoral education. However, attrition rates from doctoral programs remain high, ranging from 40% to 50% across all doctoral programs and exceeding these levels in online non-traditional programs. Numerous causes of attrition or persistence in doctoral studies have been researched and can affect the student, the academic institution, and the broader academic society. This qualitative case study will explore adult and lifelong learning doctoral students' perceptions of the programmatic and instructional support resources that have helped improve persistence in their program. Two of the important programmatic features of …

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 …

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 …

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 …

Geochemical Analysis For Potential Critical Mineral Resources In Carbon Fly Ash, Korei D. Patterson Teer

Graduate Theses and Dissertations

The growing demand for critical minerals, coupled with the increasing supply chain vulnerabilities, has intensified the need for alternative domestic resources of critical minerals beyond traditional mining. Many of these critical minerals are essential for advanced technologies, energy storage, and national security, yet the United States remains heavily dependent on foreign imports, particularly from China. This study evaluates the economic potential of critical mineral recovery from Carbon Fly Ash (CFA), a byproduct of coal combustion, to determine its viability as a secondary source of critical minerals. A geochemical and mineralogical assessment was conducted on CFA samples from various storage sites …

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 …

Geospatial Modeling Of Winter-Related Property Damage In Arkansas, Braedyn Mcbroom

Graduate Theses and Dissertations

Storm Data, a monthly publication by the National Weather Service with records of weather events, reports on property damage caused by natural hazards. Since 1996, the database has included winter-related events. While the property damage data is considered incomplete, no other unique, public datasets report winter-related property damage at the county level. From 1996 to 2024, Arkansas ranked #1 in winter-related property damage. To understand the factors that are driving property damage and potentially reduce Arkansas’ vulnerability to it, three groups separated by temporal periods, i.e., “2000” (01/1996 - 06/2005), “2010” (07/2005 - 06/2015), and “2020” (07/2015 - 06/2024) …

An Instructional Remodel: How Adopting A Unifying Model-Of-Instruction Impacted Teacher Self-Efficacy And Teacher Development And Evaluation Efforts In A Midwestern High School, Charles D. Patterson Carpenter

Graduate Theses and Dissertations

This mixed methods study sought to determine the effect of adopting a unifying model-of-instruction on teacher self-efficacy and the development and evaluation processes of administrators. By assessing the level of teacher self-efficacy (Tschannen-Moran & Hoy, 2001) in the nascent stages of the instructional model’s implementation, then reassessing it after a multiple-month sequence of professional learning, the researcher provides a judgment on statistical significance concerning the two measurements and conjectures the extent to which the instructional model impacted any differences in self-efficacy levels. Using two separate “before and after” interviews with purposefully selected administrators, the researcher explored how the unification of …

Real Time Adaptive Control Of A Pid Via Genetic Algorithm Machine Learning Systems, Cemre Tas

Graduate Theses and Dissertations

The most common control method that is utilized by all industries across the world is the proportional-integrative-derivative controller (PID) due the relatively low cost and complexity of the system. However, there are draw-backs with PIDs, it is not adaptative to a changing system, so it works on nominal systems, and it starts breaking down when a system begins to have a non-linear response. The method chosen to overcome both is the utilization of machine learning with the use of genetic algorithms.

This method allows any PID system to be capable of adapting in real-time, while not adding significant additional cost …

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 …

Quantum Oscillation Studies Of The Nodal Line Semimetal Ni3in2s2-Xsex, M. M. Sharma, Santosh Karki Chhetri, Gokul Acharya, David Graf, Dinesh Upreti, Sagar Dahal, Md Rafique Un Nabi, Sumaya Rahman, Joshua Sakon, Hugh O.H. Churchhill, Jin Hu

Physics Faculty Publications and Presentations

Ternary shandite compounds with the general formula T3M2X2 (T = Ni, Co, Rh or Pd; M = Sn, In, or Pb; and X = S or Se) have emerged as a large pool of topological semimetals. This family of compounds hosts different topological phases for various combinations of T, M and X. This paper reports the observation of quantum oscillations under the high magnetic fields in Ni3In2S2-xSex single crystals. Angular dependence of oscillation frequency suggests an evolution of the Fermi surface from three-dimensional to two-dimensional on Se substitution for S in Ni3In2S2. The effective mass obtained for each composition by …

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 …

Effects Of Sensor Separation On Flux Calculations In The Eddy Covariance Method., Maria D. Vergara Murillo

Biological and Agricultural Engineering Undergraduate Honors Theses

Rice production contributes approximately 8% of global anthropogenic methane emissions. Methane (CH₄) is a powerful greenhouse gas with a global warming potential 28 times greater than carbon dioxide (CO₂) which makes it important to monitor for understanding and mitigating its contribution to climate change. The Eddy Covariance (EC) method is used to monitor greenhouse gas emissions such as CO₂, CH₄, etc. As part of the EC method, the instruments on the EC towers are used to record the gas concentrations in the atmosphere; these concentrations are then run in the EddyPro software …

Characteristics Of Patterned Surface Dielectric Barrier Discharge Devices For Ionic Wind Propulsion, Ethan J. Graef

Mechanical Engineering Undergraduate Honors Theses

Electroaerodynamic (EAD) or ionic wind propulsion is a growing area of research for small noiseless low speed aircraft. Previous flight demonstrations and experimentation have revealed that traditional methods of EAD propulsion utilizing corona discharge incur a large drag penalty due to their collector and emitter geometries. Recent publications detail flexible and geometrically conformable surface dielectric barrier discharge devices (SDBD) that could increase thrust and decrease drag as an ion source for an EAD propulsion system. This thesis explores the effects of different electrode sizes and geometries on total SDBD power consumption. SDBDs of a hexagonal, triangular, and square electrode geometry …

Icylib: A Scalable Solution For Reproducible Image Classification Workflows, Leo Williams

Data Science Undergraduate Honors Theses

With the rapid expansion of e-commerce over time, ensuring the diversity and quality of product images has become a critical challenge infeasible for human completion. In conjunction with Walmart Global Tech for the Team 1 Data Science Practicum Project, image classification models were trained to assess product image sets, but training and deploying such models often involves repetitive code and inefficient processes. This thesis presents a reusable modeling library, named IcyLib, designed to streamline the training, validation, and testing of image classification models as well as dataset importation using PyTorch. IcyLib provides a structured yet flexible approach for model implementation, …

Extending Simulation-Enhanced Bayesian Optimization Of System Designs: A Computational Study, Luke Kim

Data Science Undergraduate Honors Theses

This honors thesis builds off work initially accepted for publication in the Proceedings of the 2025 IISE Annual Conference & Expo, which introduced “Simulation-Enhanced Bayesian Optimization” (SEBO)—a hybrid testing optimization approach that combined the usage of unbiased but costly physical experiments with the usage of cheaper but potentially biased computer experiments to optimize engineered systems. The original study established the SEBO methodology and demonstrated its effectiveness on a multimodal, two-dimensional benchmark function. Expanding on the work performed, we conduct a broader evaluation of the SEBO framework through parameter testing and experimentation under a variety of additional benchmark functions. This investigation …

Enhancing Product Image Classification: Utilizing Machine Learning Models For Retail Applications, Avery A. Thompson

Data Science Undergraduate Honors Theses

The expansion of e-commerce has continued at a blinding pace since the COVID-19 pandemic, and retailers are constantly looking for new ways to retain customers. Ensuring that diverse and well-classified images are on product pages has been a paramount method for retailers to ensure retention as they increase product engagement and sales and enhance user experience. Managing and labeling these vast catalogs of images by hand is becoming increasingly infeasible, so some online retailers have started to turn to automated classification models to assist them. Accuracy in these classification models is integral, as a good image classification model can improve …

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 …

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 …

The Effect Of Language On Student Achievement Across Countries, Ryotaro Hiraki

International and Global Studies Undergraduate Honors Theses

Does language matter in achieving higher scores in school? Do international standardized tests measure students speaking different languages equally? We study whether a language students speak affects their academic achievement. Understanding this research might also help construct a format for standardized testing. It might also help to build a curriculum for bilingual students and facilitate foreign language learning in schools. We analyze the role of institutional factors and the role of linguistics in student’s performance. To do so, we use PISA 2018 data and linear regression models for both the whole sample of students in each country and first and …

Securing Distributed Energy Resources: A Secure Gateway For Modbus To Solid Communication Using A Raspberry Pi, Donna R. Thakadipuram

Electrical Engineering and Computer Science Undergraduate Honors Theses

As distributed energy resources (DERs) such as solar panels, wind turbines, and battery storage systems become more common, securing their communications has become increasingly important. Many of these systems still rely on legacy communication protocols such as Modbus, which were not designed with cybersecurity in mind. This project addresses this challenge by developing a secure communication gateway that allows Modbus RTU devices to interface with decentralized Solid pods, which are personal data storage units that give users control over their information. This system is built on a Raspberry Pi 4, and it translates telemetry data from Modbus into a Solid-compatible …

Analyzing Unmanned Aircraft System (Uas) Incidents From Nasa Asrs Data Using Unsupervised Machine Learning, Kacey Haws

Electrical Engineering and Computer Science Undergraduate Honors Theses

The NASA Aviation Safety Reporting System (ASRS) assembles voluntarily submitted aviation safety incident reports in their database to act on the information provided. This database allows the government, companies, and citizens to submit incident or situational reports to its database to discern recurring issues in the National Aviation System (NAS) so that the proper officials can act [1]. The narratives provided in these reports are text-based, resulting in large amounts of data to process. Previous work in the University of Arkansas Aerospace Systems Engineering and Transportation Laboratory (ASYST) lab involved parsing unmanned aircraft system (UAS) incident reports manually. While these …

Defend: A 1m Dataset Foundation Model For Tobacco Analysis, Matthew J. Shepard

Electrical Engineering and Computer Science Undergraduate Honors Theses

The study of tobacco imagery and marketing is a complex challenge that involves extremely large datasets. It also demands a detailed analysis of the so- cial context and specific types of tobacco being marketed. Despite major recent advances in computer vision and foundation model technology, this still poses a substantial challenge. Through the DEFEND model, we aspire to address these obstacles by integrating features such as multimodal learning, hierarchical under- standing, and feature extraction to develop a foundation model designed to handle the unique challenges of tobacco image analysis. One of the core elements of DE- FEND is the Tobacco …

Managing Graphical Fidelity With Stylized Shaders For Independent Game Development, Benjamin A. Edens

Electrical Engineering and Computer Science Undergraduate Honors Theses

This paper outlines work performed by the author within the Unity3D game engine to gain preliminary experience with technical art implementation and suggests design choices that could be useful to other students or independent game developers to manage complexity within their games while maintaining visual appeal. The final product of the discussed project is a small game consisting of an outdoor urban city environment as well as an interior aquarium environment. This paper begins with the author’s motivations and goals for the project before describing the implementation of specific aspects of technical art, including 3D modeling, rigging, animation, level design, …