Digital Commons Network^™

Unified Deep Learning Techniques For Spatial Detection And Temporal Forecasting Across Visual Domains, John Olawale Olamofe

All Dissertations

This dissertation proposed a unified deep learning framework for spatial detection and temporal forecasting across visual domains, designed to address limited supervision, class imbalance, and scale variability. The framework was structured around four complementary principles: Domain-Aware Input Rebalancing, Representation-Centric Learning, Diversity-Driven Robustness, and Transfer Across Scale and Modality, which together enabled robust visual representation learning across heterogeneous data sources.

Domain-Aware Input Rebalancing mitigates non-uniform and sparse data distributions by actively reshaping inputs before learning via class-aware augmentation, sampling, resolution manipulation, and super-resolution. This principle underpins object detection in overhead satellite imagery (xView), dense urban aerial scenes (CADOT), temporally sparse NDVI …

The Bridge To Becoming An Educator: An Analysis Of Educator Preparation Programs And Their Effects On Stakeholders, Myltazaire Kynshasha Crayton

All Dissertations

This qualitative study examined how national, state, and institutional policies impact teacher certification processes and influence the experiences of stakeholders in educator preparation programs (EPPs), including administrators, professors, and teacher candidates. Guided by Astin’s Input-Environment-Output (I-E-O) model, this research examined how inputs such as policy mandates, institutional culture, and candidate demographics interact with educational environments to produce diverse outcomes in teacher preparation, certification, and retention. Data were collected through semistructured interviews, questionnaires, document analysis, and policy review across two university EPPs, one in Texas and one in Arkansas. Findings revealed that inconsistencies in policy interpretation, financial and academic barriers, and …

R Software Tutorials For Introductory Statistics Courses, Erica M. Hurst

All Graduate Reports and Creative Projects, Fall 2023 to Present

This project focused on creating online resources for using the statistical software package R in an introductory statistics course at Utah State University. The goal was to make R more accessible, user-friendly, and engaging for learners. To do this, existing written materials were moved to a new section of an online website called Stats Stuff. Then interactive features were added such as step-by-step video tutorials, real datasets, and an embedded coding tool. These resources have been in use for the past four years and have helped students build confidence and enhance their statistical learning experience.

Discovering Strategic Behaviors In The Floor Using Reinforcement Learning, Kevin Parks

All Graduate Reports and Creative Projects, Fall 2023 to Present

This project investigates how a reinforcement learning (RL) agent can develop territorial strategies in a highly stochastic, grid-based approximation of the television game show The Floor. I design a custom Gymnasium-compatible environment that models the show’s core mechanics on a 10×10 board, including probabilistic duels governed by player skill, adjacency-constrained attacks, chain-attack rules, and a Randomizer mechanism for selecting new initiating players. A Maskable Proximal Policy Optimization (Maskable PPO) agent is trained under several reward configurations and evaluated against stochastic non learning opponents as well as random and “always pass” baselines.

Across experiments, the best-performing configuration achieves a win …

Open Space Management Planning: A Framework And Case Study From Eagle Mountain City, Utah, Nathan K. Shumway

All Graduate Reports and Creative Projects, Fall 2023 to Present

Open Space Management Plans (OSMPs) are increasingly used by municipalities to guide the long-term stewardship of natural lands; however, little formal guidance exists regarding how these plans are structured, developed, or organized in practice. While numerous OSMPs are publicly available, their compositional logic, procedural workflows, and thematic priorities remain largely undocumented within academic literature. This study addresses that gap through a qualitative document analysis of fifteen publicly accessible OSMPs from the western United States, published between 2015 and 2024.

Using manual matrix-based coding in Microsoft Excel, each plan was analyzed through three lenses: structural composition, procedural development, and thematic content. …

Sql Query Optimization - Human Vs. Chatgpt, Hailey Dennis

All Graduate Reports and Creative Projects, Fall 2023 to Present

Large Language Models (LLMs) such as ChatGPT have become ubiquitous tools for working professionals in the software industry. Many engineers are finding new ways to increase productivity by offloading tasks onto LLMs, while others are finding it difficult to trust code produced artificially, even after review. Taking a look at both perspectives, this study aims to compare a human’s ability to optimize SQL queries to that of an LLM and assess the experience using both methods.

Manual query optimization is a tedious task that relies heavily on statistics, heuristics, and good intuition. The SQL developer must search for the optimal …

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

Undergraduate Honors Capstone Projects

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

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

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

Undergraduate Honors Capstone Projects

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

Inter-Government Communication– Transportation Options For Utahns, Andrew Nicholas Barber

Undergraduate Honors Capstone Projects

As a growing Western state, Utah has ambitions for building more infrastructure, especially public transportation. The way it organizes its communication strategy for public transportation, however, represents a microcosm in a larger debate between political scientists and economists. Professionals have backed one of two models for inter-government communication: the decentralized or “fiscal federalism” model and the centralized model, and pros and cons have been theorized for both. Decentralization, on paper, is more democratic, can better represent the needs of its constituents, and can better navigate pesky bureaucratic red tape, but is less efficient. Centralization, on the other hand, is more …

Ngc - Torch Test Ablation Stand Improvements And Upgrades, Sarah Nelson

Undergraduate Honors Capstone Projects

This capstone project is sponsored by Northrop Grumman (NGC). The goal of the project is to redesign an ablation torch testing system to improve usability, safety, and versatility. Northrop Grumman relies on their ablation torch testing system to conduct quantitative evaluations of materials and high stress and high temperature conditions. The system provides essential data that supports product development, safety validation, and compliance with performance standards. By improving usability, safety, and versatility, NGC will be able to streamline testing operations, increase accuracy of results, and reduce operator risk. The system must allow testing using both a bent and straight torch …

Prediabetes Prediction Before Disease Onset Using Multimodal Health Data: A Machine Learning Approach, Luisa Veronica Gracia Mazuca

Open Access Theses & Dissertations

Prediabetes is a critical health condition that increases the risk of developing type 2 diabetes. The hemoglobin A1c (HbA1c) test diagnoses patients with prediabetes, but the disease has already caused metabolic alterations. Early detection is essential for timely interventions, and machine learning models offer a promising approach to identify prediabetic individuals through the analysis of biomarkers such as cytokines. We compared four classifiers-logistic regression, decision tree, random forest, and k-nearest neighbors - using cytokines (TNF-α, MCP-1, IL-1β, IL-6, IFN-γ), age, BMI, and waist-to-hip ratio (WHR). Models were evaluated using stratified 5-fold cross-validation and ROC-AUC. K-Nearest Neighbors (k-NN) achieved the highest …

Provenance Analysis Of Advanced Persistent Threats In Operational Technology Environments, Laura Aminta Guevara

Open Access Theses & Dissertations

Live vulnerability testing and penetration assessments of critical infrastructure are costly, complex operationally and, in many cases, unsafe to conduct directly against live systems, whether these are commissioned in-house and through third-party providers. For Operational Technology environments specifically, these constraints are further aggravated by the limited vendor support and inconsistent security patching available that characterize many industrial communication protocols. This significantly restricts the scope and feasibility of conventional penetration testing. At the same time, the increasing overlapping of OT systems with enterprise networks and the internet has expanded the attack surface of ICS infrastructure, making more pressing the need for …

The Effect Of Temperature And Hydroperiod On Rotifer Hatching Diversity And Phenology In Ephemeral Playas, Brent Samuel Hogue

Open Access Theses & Dissertations

As climate continues to change, weather events such as heat waves and precipitation are expected to become more extreme, and organisms living in these ephemeral habitats will experience unprecedented conditions that may test their limits. However, some organisms that live in these habitats are adapted for resiliency in the face of fluctuating conditions. The focal taxa of this study are rotifers, which have the ability to produce diapausing eggs, that are stored in sediment egg banks. Diapausing eggs will hatch once suitable conditions occur in the habitat, while the rest remain stored in the sediment. This strategy assists in the …

Exploratory Proxy Occupational Health Risk Assessment On Pm2.5 Exposure Among Outdoor Personnel At International Ports Of Entry, Josdell Maria Guerra Ruiz

Open Access Theses & Dissertations

Despite growing public health concerns on PM2.5 exposure and its respiratory health effects, no documented evidence has been found on the application of proxy methods to perform occupational health risk assessments for PM2.5 exposure among outdoor personnel. The purpose of this research study was to conduct a descriptive and predictive occupational health risk assessment for cancer and non-cancer respiratory health outcomes related to PM2.5 exposure among outdoor personnel at targeted international ports of entry (IPOE). A total of 61 samples were collected, for 24 hours, at two targeted IPOEs (every sixth day) over 12-month period. Samples included measurements on PM2.5 …

Numerical Methods For Modeling Darcy-Forchheimer Flow In Heterogeneous Porous Media, Nate Mcnair

Open Access Theses & Dissertations

Classic approaches to modeling fluid flow in porous media rely on Darcy's Law, which assumes a linear relationship between volumetric flow rate and the pressure gradient. However, in recent years, applications such as enhanced geothermal systems have highlighted the need to model nonlinear flow behavior, since experimental and observational data show that, once flow velocity exceeds a certain threshold, the relationship between velocity and macroscopic pressure gradient becomes nonlinear. This requires alternative models that more accurately capture the physical behavior of these systems.The Darcy-Forchheimer model provides one such alternative by introducing a nonlinear velocity-pressure relationship. However, this nonlinearity creates additional …

Zero-Knowledge Proofs For Verifiable Machine Unlearning, Bidur Niroula

Open Access Theses & Dissertations

Machine unlearning aims to remove the influence of deleted data from a trained machine learning model. This is important because privacy regulations such as GDPR, CCPA, and PIPEDA give individuals the right to request the deletion of their personal data. Since unlearning can be expensive, a dishonest server may skip it or return an incorrect model and this motivates verifiable machine unlearning. Existing cryptographic approaches prove that the server executed a specific training or unlearning algorithm on the committed dataset, so the proof is tied to the algorithm's trajectory. Empirical verifiers avoid this cost but are known to be circumventable …

Best Linear Unbiased Prediction Based On Optimally Selected Order Statistics Using Compound Design, Foster Nkansah

Open Access Theses & Dissertations

Any life-testing experiments with censoring generate ordered failure data, which are used to estimate model parameters of the underlying lifetime distribution. However, predicting unobserved failure times is an important goal post inference. Balakrishnan and Bhattacharya [2021] showed that joint best linear unbiased predictors (BLUPs) are more efficient than their marginal counterparts. In this thesis, we introduce a compound optimal design to compute joint BLUPs based on a suitably chosen subset of the observed data. It is shown that the linear predictors of the parameters from any location-scale distribution, based on a few optimally chosen ranks, obtained from the compound optimal …

Log Anomaly Detection With Parameter-Efficient Tiny Language Models: From Centralized Fine-Tuning To Privacy-Preserving Federated Learning, Isaiah Thompson Thompson Ocansey

Open Access Theses & Dissertations

System logs are a primary source of information for identifying faults, misconfigurations, and security incidents in computing infrastructure. As these logs grow in volume and complexity, manual inspection becomes impractical, and automated detection methods are needed. This thesis investigates how compact language models can be applied to the task of log anomaly detection under two operational conditions: when log data is centralized and when it is located at different data sites.

In the first part, we propose LogTinyLLM, which applies parameter-efficient fine-tuning through Low-Rank Adaptation and adapter-based techniques to adapt tiny language models for identifying contextual anomalies in log sequences. …

Contamination-Aware Boosting For Cancer Prediction, Francis Opoku

Open Access Theses & Dissertations

Modern cancer research increasingly relies on machine learning methods to model complex relationships between high-dimensional genomic predictors and clinically relevant outcomes. Gradient boosting methods, particularly XGBoost, have become widely used due to their strong predictive performance and ability to capture nonlinear effects and interactions. However, these methods are sensitive to contamination in the data, especially in the response variable, where even a small fraction of aberrant observations can disproportionately influence model fitting and degrade generalization.

In this thesis, we propose a contamination-aware extension of XGBoost, termed CA-XGBoost, which improves robustness by explicitly modeling residuals through a latent mixture framework. This …

An Ordinal Categorical Data Analysis Using The Stereotype Model Within A Bayesian Framework With A Logistic-Normal Prior, Nathaniel A. Sakyi

Open Access Theses & Dissertations

Ordinal categorical data are pervasive in applied research, yet their analysis is often compromised by modeling strategies that implicitly assume equidistant category spacing or impose restrictive structural constraints. Metric regression models and latent-variable threshold approaches routinely misrepresent ordinal information, leading to biased inference, distorted uncertainty quantification, and loss of structural insight. This paper develops a Bayesian framework for ordinal regression that explicitly accommodates \textbf{unequal spacing among ordered response categories} while preserving ordinal structure and interpretability.

The proposed approach builds on the stereotype regression model, which embeds ordinal categories into a latent one-dimensional continuum through estimable score parameters. While the stereotype …

Daughters Of The Road, Kristen Marie Sche

Open Access Theses & Dissertations

The beloved Road Novel has long been a tale submerged in the coming-of-age story following men as they search for themselves along the American highways. The genre can vary from journeys of families seeking refuge or tackling a task to men seeking answers to something missing in their lives. However eclectic the genre, it lacks in the pilgrimage of girls finding what the road can bring to them in a literary fictional context. This thesis provides a novel written in the popular genre while exploring themes of sisterhood, grief, and inclusion through the context of a hybrid form of narration …

Penalty Approach To Constrained Optimization Problems In Water Reservoir And Energy Generation Management, Edwin Horacio Trejo

Open Access Theses & Dissertations

The growing demand for resilient and sustainable energy generation has driven interest in Hybrid Floating Photovoltaic-Hydropower (HFPVH) systems. Operating these systems effectively requires making water release decisions that satisfy physical and regulatory constraints while maximizing energy production. Prior work by Vega (2024) developed a Dynamic Outlier Filter Long Short-Term Memory (DOF-LSTM) architecture for forecasting reservoir release patterns. That predictive work is valid and addresses an important component of the HFPVH decision pipeline. However, the constraint system in that work operates externally to the learning process, and the behavior of penalty-based constraint enforcement had not been studied independently in this context. …

Molecular Mechanisms Of Pfas Induced Protein Dysfunction: Implications For Neurodegenerative Risk, Daisy Lee Wilson

Open Access Theses & Dissertations

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with widespread human exposure and well-established links to adverse health outcomes. However, despite increasing epidemiological and experimental evidence, the molecular mechanisms underlying PFAS-induced neurotoxicity remain incompletely defined. This dissertation investigates PFAS toxicity across biological scales, integrating protein biophysics with neuronal cell models to elucidate pathways relevant to neurodegeneration. At the molecular level, we demonstrate that PFAS disrupts the structure and function of globular proteins, including the impairment of α-lactalbumin's calcium-binding capacity and of β-lactoglobulin's retinol binding, through concentration-dependent conformational destabilization. By applying these findings to a neuronal model, we show that …

Comparative Case Studies Of How General Chemistry Ii Students Explain Entropically-Driven Phenomena, Raymundo Aragonez

Open Access Theses & Dissertations

Understanding how students conceptualize entropy remains a central challenge in chemistry education. Often referred to as a "driving force" of chemical reactions, the dispersal of energy as a process proceeds is indicative of a process's spontaneity or thermodynamic favorability. Students frequently default to discussing phenomena in deterministic terms, potentially reflecting the language choice and heuristics selected by the instructor such as "disorder". In this study, assessment prompts about entropy from a convenience sample of general chemistry textbooks were characterized as to their potential to elicit three-dimensional learning, and students' responses to assessment prompts from a curriculum aligned with three-dimensional learning …

Automated Arctic Coastal Boundary Extraction From Multispectral Satellite Imagery, Harshavardhini Bagavathyraj

Open Access Theses & Dissertations

Arctic coastlines are changing rapidly as permafrost thaw and coastal erosion accelerate under a warming climate, creating a need for accurate and scalable methods to monitor landscape change. Two geomorphically important indicators of this change are the shoreline, which represents the land--water boundary, and the bluff edge, which marks the transition from vegetated tundra to the coastal slope or cliff. However, delineating these features from high-resolution imagery remains challenging because Arctic scenes are spatially extensive and visually complex. In this study, we address these challenges by developing deep learning methods for automated shoreline and bluff-edge extraction from high-resolution satellite imagery …

Computational Studies Of Dna-Wrapped Carbon Nanotubes: From Enantioselective Dna Binding To Molecular Recognition Of Small-Molecule Analytes, Sayantani Chakraborty

Open Access Theses & Dissertations

Single-walled carbon nanotubes (SWCNTs) exhibit chirality-dependent optoelectronic properties and near-infrared fluorescence that make them promising platforms for biosensing. When functionalized with single-stranded DNA (ssDNA), SWCNTs form conjugates capable of selective molecular recognition. However, the molecular mechanisms governing chirality recognition and analyte-induced optical modulation remain incompletely understood. This dissertation investigates these mechanisms through an integrated computational approach combining molecular dynamics simulations, enhanced sampling techniques, and machine learning. First, atomistic molecular dynamics (MD) and replica exchange MD simulations were used to investigate how short ssDNA sequences interact with enantiomers of (7,5) SWCNTs. Analyses of base stacking, nucleotide orientation, sugar-phosphate positioning, and contact …

Additive Manufacturing Of Spatially Variant Actuating Structures From Shape Memory Polymers., Katia Lizbeth Delgado Ramos

Open Access Theses & Dissertations

This dissertation studies the structure-processing-function relationships of additively manufactured shape memory polymer blends, with emphasis on mechanical anisotropy. Chemical exposure, thermally activated recovery, self-healing behavior, and 4D printed functional response. Two polyester-based blends were studied: a binary blend composed of polycaprolactone and thermoplastic polyurethane (PCL/TPU) blend, and a ternary blend composed of polycaprolactone/thermoplastic polyurethane/ polylactic acid (PCL/TPU/PLA) blend. The materials were melt-compounded using a twin-extruder to produce filament for fused filament fabrication (FFF). Specimens were fabricated using four different raster orientations, 0°,90°, 0/90°, and 45°, to investigate the influence of processing architecture and composition on the mechanical performance, recovery behavior, …

Hvsr-Based Resonance Analysis And Fault Zone Characterization In The El Paso Region, Alexandro Dominguez

Open Access Theses & Dissertations

Horizontal to Vertical Spectral Ratio (HVSR) analysis offers an efficient method for characterizing near-surface structure, resolving sediment thickness, and identifying fault-controlled basin geometry in regions with strong impedance contrasts. This study applies HVSR techniques to continuous ambient-noise recordings collected across the El Paso region, a tectonically active segment of the southern Rio Grande Rift where accurate subsurface mapping is essential for seismic hazard assessment. Ambient-noise windows were processed, spectrally smoothed, and stacked to obtain stable HVSR curves, from which fundamental resonance frequencies and corresponding sediment-thickness estimates were derived. Spatial patterns in these resonance frequencies reveal pronounced basin asymmetry, abrupt lateral …

From Covid-19 To Influenza: Transforming Public Health Surveillance Through Freeze-Drying Innovations, Rui Dong

Open Access Theses & Dissertations

Public health surveillance increasingly relies on wastewater-based epidemiology (WBE) as a non-invasive, community-level early warning system for infectious diseases like COVID-19 and influenza. However, detecting minute viral concentrations in complex wastewater matrices remains a significant analytical challenge. Traditional concentrating methods, such as ultrafiltration, are limited by high performance variability and significant viral signal loss due to necessary pretreatment steps, such as pre-clarification, which inadvertently discard solid-associated viral fractions. To address these limitations, this dissertation introduces and evaluates freeze-drying as a novel, pretreatment-free concentrating method for wastewater viral surveillance. By sublimating water, freeze-drying uniquely retains both liquid and solid-phase viruses within …

A Diagnostic Test For Confounding Under Collapsibility In Generalized Linear Models (Glms), Samuel Dela Gadah

Open Access Theses & Dissertations

This thesis develops and validates a diagnostic framework for assessing potential confounding in generalized linear models (GLMs) with collapsible link functions. Under collapsibility and effect homogeneity, the absence of confounding implies equality between marginal and covariate-adjusted treatment effects on the link scale, which motivates the change-in-estimate parameter δ = γ − γ(c) as the target of a formal hypothesis test. Two complementary inferential routes are developed: a nonparametric bootstrap and an independent estimating equation (IEE) procedure based on a stacked-data construction that yields a closed-form sandwich variance and is implementable in standard generalized estimating equation software. A backward elimination procedure …